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PMC4552518	ORIGINAL RESEARCH Unchanged content of oxidative enzymes in fast-twitch muscle ﬁbers and _VO2 kinetics after intensiﬁed training in trained cyclists Peter M. Christensen1,2, Thomas P. Gunnarsson1, Martin Thomassen1, Daryl P. Wilkerson3, Jens Jung Nielsen1 & Jens Bangsbo1 1 Department of Nutrition, Exercise and Sports, Section of Integrated Physiology, University of Copenhagen, Copenhagen, Denmark 2 Team Danmark (Danish Elite Sport Organization), Copenhagen, Denmark 3 Sport and Health Sciences, St Luke’s Campus, University of Exeter, Exeter, UK Keywords _VO2 kinetics, high intensity training, interval training, type I ﬁbers, type II ﬁbers. Correspondence Jens Bangsbo, Department of Nutrition, Exercise and Sports, Section of Integrated Physiology, University of Copenhagen, August Krogh Building, Universitetsparken 13, 2100 KBH Ø, Denmark. Tel: +45 35 32 16 23 Fax: +45 35 32 16 00 E-mail: jbangsbo@nexs.ku.dk Funding Information The study was supported by Team Danmark (Danish Elite Sport Organization). Received: 8 May 2015; Accepted: 19 May 2015 doi: 10.14814/phy2.12428 Physiol Rep, 3 (7), 2015, e12428, doi: 10.14814/phy2.12428 Abstract The present study examined if high intensity training (HIT) could increase the expression of oxidative enzymes in fast-twitch muscle ﬁbers causing a fas- ter oxygen uptake ( _VO2) response during intense (INT), but not moderate (MOD), exercise and reduce the _VO2 slow component and muscle metabolic perturbation during INT. Pulmonary _VO2 kinetics was determined in eight trained male cyclists ( _VO2-max: 59 4 (means SD) mL min1 kg1) dur- ing MOD (205 12 W ~65% _VO2-max) and INT (286 17 W ~85% _VO2- max) exercise before and after a 7-week HIT period (30-sec sprints and 4-min intervals) with a 50% reduction in volume. Both before and after HIT the content in fast-twitch ﬁbers of CS (P < 0.05) and COX-4 (P < 0.01) was lower, whereas PFK was higher (P < 0.001) than in slow-twitch ﬁbers. Con- tent of CS, COX-4, and PFK in homogenate and fast-twitch ﬁbers was unchanged with HIT. Maximal activity (lmol g DW1 min1) of CS (56 8 post-HIT vs. 59 10 pre-HIT), HAD (27 6 vs. 29 3) and PFK (340 69 vs. 318 105) and the capillary to ﬁber ratio (2.30 0.16 vs. 2.38 0.20) was unaltered following HIT. _VO2 kinetics was unchanged with HIT and the speed of the primary response did not differ between MOD and INT. Muscle creatine phosphate was lower (42 15 vs. 66 17 mmol kg DW1) and muscle lactate was higher (40 18 vs. 14 5 mmol kg DW1) at 6 min of INT (P < 0.05) after compared to before HIT. A period of inten- siﬁed training with a volume reduction did not increase the content of oxida- tive enzymes in fast-twitch ﬁbers, and did not change _VO2 kinetics. Introduction It has been known for decades that endurance training results in a faster increase in the pulmonary oxygen uptake ( _VO2) response in the initial phase of exercise (Hickson et al. 1978). During constant load exercise at intensities above the gas exchange threshold (GET) _VO2 continues to rise at a slow rate, with training reported to reduce the magnitude of this ‘ _VO2 slow component’ (Jones et al. 2011). These alterations may be due to ele- vated muscle oxidative enzyme capacity and greater oxy- gen delivery (Jones and Poole 2005) as have been found in training studies using untrained subjects with a maxi- mal _VO2 ( _VO2-max) of ~50 mL min1 kg1 (Saltin et al. 1976; Phillips et al. 1995; Shoemaker et al. 1996; Krustrup et al. 2004a). In the study of Krustrup et al. subjects per- formed high intensity training (1 min intervals) and it was reported that post-training leg _VO2 kinetics was fas- ter during intense but not moderate exercise when com- pared to pretraining, respectively (Krustrup et al. 2004a). This may have been caused by adaptations in fast-twitch (FT) muscle ﬁbers recruited during training, since these ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 1 Physiological Reports ISSN 2051-817X ﬁbers have been reported to have lower oxidative maxi- mal enzyme activity than slow-twitch (ST) muscle ﬁbers (Essen et al. 1975; Essen-Gustavsson and Henriksson 1984; Schantz and Henriksson 1987). In support for training causing ﬁber type-speciﬁc adaptations, maximal activity of oxidative enzymes in FT muscle ﬁbers can reach the same level as ST ﬁbers in highly trained athletes ( _VO2-max: ~70 mL min1 kg1) (Jansson and Kaijser 1977; Chi et al. 1983) and a training study with untrained subjects observed an increase in maximal activity in pools of FT ﬁbers following intense training (Henriksson and Reitman 1976). Training studies using untrained subjects typically result in several adaptations. Therefore, it cannot be determined if one adaptation (e.g., increase in oxidative enzyme capacity) is of importance for the faster rise in _VO2 at the onset of exercise and the alterations in the _VO2 slow component when other adaptations also occur in response to training (e.g., increase in bulk oxygen delivery or reduced mean transit time at the capillary level due to increased capillary density). Instead, studying the effect of an altered training regime of trained subjects could potentially provide insight into what factors are important to improve the _VO2 kinetics since fewer mus- cular and vascular adaptations are expected to occur. Fur- thermore, just a few studies have been conducted with trained athletes and some (Dufour et al. 2006; Christen- sen et al. 2011), but not all studies (Norris and Petersen 1998; Demarle et al. 2001), reported that _VO2 kinetics remained unaltered. However, comparison is difﬁcult due to various designs used ranging from high-volume train- ing for 8 weeks at moderate intensity at the start of the season in cyclists (Norris and Petersen 1998), 6 weeks in runners encompassing both moderate and intense bouts (~90% _VO2-max in hypoxia or normoxia) (Dufour et al. 2006), to intense training (<5 min) either with a reduc- tion in volume in soccer players for 2 weeks at the end of the season (Christensen et al. 2011) or a maintained training volume in runners for 8 weeks (Demarle et al. 2001). Recruitment of FT ﬁbers has been implicated in the development of the _VO2 slow component (Barstow et al. 1996; Jones et al. 2011). Thus, it may be that an increased content of oxidative enzymes in FT ﬁbers could reduce the _VO2 slow component in trained athletes since a high proportion of FT ﬁbers has been associated with a large slow component (Barstow et al. 1996) and since FT ﬁbers appear to have a lower maximal oxidative enzyme activity than ST ﬁbers (Essen et al. 1975; Essen-Gustavsson and Henriksson 1984; Schantz and Henriksson 1987) unless vigorous training has been performed (Jansson and Kaij- ser 1977; Chi et al. 1983). Moreover, since oxidative enzymes are thought to be implicated in the rate of the rise in _VO2 at the onset of exercise (Jones and Poole 2005), it may be that intense training targeting both ST and FT ﬁbers is essential for eliciting faster _VO2 kinetics in trained athletes. This argument is supported by an aug- mented signaling for cascades involved in the synthesis of oxidative enzymes in well-trained cyclists ( _VO2-max: 68 mL min1 kg1) following repeated 30-sec sprinting compared with less intense exercise (Psilander et al. 2010). Also, in less trained subjects repeated 30-sec sprinting activated signal cascades involved in the synthe- sis of oxidative enzymes to a similar extent as long dura- tion low intensity exercise (Little et al. 2010, 2011). The response of these signaling cascades increases in an inten- sity-dependent manner (Egan et al. 2010; Nordsborg et al. 2010), which could be related to recruitment of more muscle ﬁbers. In runners subjected to intense training in combination with a marked lowering of low- and moder- ate intensity training (~25–50%) maximal oxidative enzyme activity in muscle homogenate was maintained (Bangsbo et al. 2009; Iaia et al. 2009) which could be due to adaptations in FT ﬁbers since detraining is known to lower activity of oxidative enzymes (Chi et al. 1983; Christensen et al. 2011). Thus, repeated 30-sec sprinting separated by ~4 min of rest may be an effective training regime to cause increases in oxidative enzymes in FT ﬁbers. The majority of studies in untrained subjects (Jacobs et al. 1987; MacDougall et al. 1998; Burgomaster et al. 2005, 2008; Gibala et al. 2006) did not evaluate ﬁber type-speciﬁc adaptations in oxidative enzymes, but recent studies do suggest that repeated 20–30 sec sprints can increase markers of oxidative capacity in both ﬁber types in untrained subjects (Shepherd et al. 2013; Scribbans et al. 2014). However, such evaluation has not been per- formed on trained individuals following intense training with 30-sec sprints (Bangsbo et al. 2009; Iaia et al. 2009; Christensen et al. 2011; Gunnarsson et al. 2012). Oxida- tive capacity is higher in ST than FT ﬁbers (Essen et al. 1975; Essen-Gustavsson and Henriksson 1984; Schantz and Henriksson 1987, Shepherd), which means that the potential for improvements likely is greater in the FT ﬁbers supported by similar capacity in the two ﬁber types in highly trained subjects (Jansson and Kaijser 1977; Chi et al. 1983). A faster _VO2 response is associated with reductions in the anaerobic contribution to the total energy turnover at the onset of exercise. This has been determined from muscle metabolites and blood lactate either as a result of performing prior exercise (Bangsbo et al. 2001; Krustrup et al. 2001) or performing exercise training either with low intensity and high volume (Phillips et al. 1995; Green et al. 2009) or high intensity and low volume (Burgomas- ter et al. 2006). However, limited knowledge exists about muscle anaerobic energy turnover after a period of high 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 2 ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. Metabolic and Muscle Adaptations to HIT P. M. Christensen et al. intensity training of trained subjects. Venous lactate when running at intensities below _VO2-max was unchanged after 4 weeks of repeated 30-sec sprint training in moder- ately trained runners ( _VO2-max: 55 mL min1 kg1) (Iaia et al. 2009) and after 6–9 weeks of combined aero- bic high intensity training and repeated 30-sec sprinting in trained runners ( _VO2-max: 61 mL min1 kg1) (Bangsbo et al. 2009), with both studies encompassing a reduction in weekly training distance. Changes in blood lactate likely reﬂect changes at the muscular level (Green et al. 2009) but neither of the studies evaluated changes in muscle lactate and creatine phosphate during exercise. Following 3 weeks with high-intensity training (8 9 5 min) added to the normal training, cyclists ( _VO2- max: 65 mL min1 kg1) had lower muscle lactate but unchanged creatine phosphate content directly after intense exercise (Clark et al. 2004). Therefore, it is presently unknown if high intensity training encompass- ing repeated 30-sec sprint intervals, in combination with reduction in volume, changes muscle anaerobic energy turnover during submaximal exercise in trained subjects. Thus, the aims of the present study, using a training intervention consisting of repeated 30-sec sprinting and aerobic high intensity training for a group of trained cyclists, were to examine (1) whether intensiﬁed training increases the amount of oxidative enzymes in FT muscle ﬁbers as well as speeds _VO2 kinetics and reduces the _VO2 slow component during intense exercise; (2) if the anaer- obic energy turnover during intense exercise was reduced by training. We hypothesized that the intensiﬁed training would increase the oxidative capacity of FT ﬁbers result- ing in faster _VO2 kinetics during intense exercise, but not during moderate intensity exercise, as well as reduce the _VO2 slow component and anaerobic energy turnover dur- ing intense exercise. Methods Subjects Eight trained male cyclists with an average (SD) age, weight, and _VO2-max of 33 8 years, 81 8 kg, and 4.8 0.3 L min1 or 59 4 mL min1 kg1, respec- tively, were recruited for the study. Prior to participating in the study, the subjects had trained/competed ~three to ﬁve times each week for at least 3 years. The study proce- dures were approved by the local ethical committee of the capital region of Copenhagen (Region Hovedstaden) and all subjects received written and oral information about the study procedures and gave their written informed consent to participate in the study in accordance with the Helsinki declaration. Experimental design The subjects carried out a 7-week high-intensity training (HIT) intervention (see later) from October to December just after the season had ﬁnished, hence subjects were expected to be ﬁt and in a physical stable condition. Both before and after HIT the subjects carried out two main experiments (EXP1 & EXP2) to evaluate changes in the met- abolic response and _VO2 kinetics during submaximal exer- cise (< _VO2-max). The subjects and training intervention were the same as in a study focusing on adaptations of ion transport proteins, ion kinetics, and performance during repeated high intensity exercise (Gunnarsson et al. 2013). Training Subjects performed four supervised training sessions per week on their own bikes on public roads. Training was performed as 12 9 30-sec uphill (~6% gradient) maximal sprints interspersed with 4–5 min low intensity recovery (SPR; 2.59 week1) resulting in a 1:8–10 work rest ratio, and 5 9 ~4 min aerobic high intensity intervals separated by ~2 min of rest (AEH; 1.59 week1) on a ﬂat 2.5 km course with a work rest ratio of 2:1. In a training week day 1 was recovery, AEH was performed on day 2, SPR on day 3, recovery on day 4, SPR on day 5, recovery on day 6, and ﬁnally SPR or AEH on day 7 in alternate weeks. To ensure maximal effort during training drafting was not allowed and both SPR (1 vs. 1) and AEH (mass start) was performed in a competitive manner with the objective of ﬁnishing ﬁrst. Heart rate (HR) was measured in 5-sec intervals during training (Polar Team Edition, Finland). Peak-HR during each SPR interval was 90 4% of HR- max and average-HR during each AEH interval was 89 2% of HR-max. The physiological response from one of the subjects during a SPR and an AEH training ses- sion is shown in Fig. 1. Weekly volume was ~240 min during HIT (15 min SET [~6%], 30 min AEH [~12%], 135 min low intensity recovery between intervals [56%] and ~60 min moderate intensity [25%] as transport to and from training). This amounted to a ~50% reduction of the training volume being 472 153 min week1 before HIT (0–60% HR-max [29%], 60–70% HR-max [24%], 70–80% HR-max [19%], 80–90% HR-max [21%], 90–95% HR-max [6%], 95–100% HR-max [1%]). Exercise testing All testing was performed on a mechanically braked ergometer bike (Monark 839E, Varberg, Sweden) with the subjects using their own pedals and speciﬁc geometric setup which was maintained throughout the study. Sub- jects were instructed not to perform any training the day ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 3 P. M. Christensen et al. Metabolic and Muscle Adaptations to HIT before a testing session, and maintain the same food intake and abstain from intake of caffeine on days of test- ing. During the ﬁrst visit to the laboratory during the com- petitive season subjects performed an incremental test starting out at 100 W with increments of 25 W min1 until exhaustion. Pulmonary _VO2 was measured breath by breath (Oxycon Pro, Viasys Healthcare, CareFusion, Rolle, Switzerland) and VO2-max and HR-max was determined as the highest value over a 30-sec period. In addition incre- mental test peak power output was calculated as: iPPO ¼ Power output (W) at the last completed stage þ sec at the stage leading to task failure 60 sec1 25 W: iPPO was 409 24 W. Before and after HIT changes in pulmonary _VO2 kinetics was investigated using repeated exercise transitions. Subjects performed both moderate (MOD: 50% iPPO, 205 12 W) and intense (INT: 70% iPPO, 286 17 W) exercise with 2 min at 20 W pre- ceding all intervals. The absolute exercise intensity was maintained throughout the study. Both prior to and fol- lowing HIT subjects performed four transitions with MOD each lasting 6 min (EXP1, EXP2, and two addi- tional transitions on separate days) and three transitions with INT each lasting 6 min (EXP1, EXP2, and an addi- tional transition on a separate testing day) as well as two transitions with INT lasting 3 min (EXP1, EXP2). Pul- monary _VO2 was measured breath by breath. To deter- mine _VO2 kinetics, errant breaths, deﬁned as any value lying more than 4 SDs away from the local mean (e.g., due to swallowing and coughing) were initially removed. Then the _VO2 responses in each intensity domain were linearly interpolated to give 1-sec values, and then aver- aged. The initial cardiodynamic component was ignored by eliminating the ﬁrst 20 sec of data after the onset of exercise. MOD was modeled via a mono exponential function: _VO2ðtÞ ¼ Baseline þ APð1 eðtTdP=sPÞÞ INT was modeled via a bi-exponential function: _VO2ðtÞ ¼Baseline þ APð1 eðtTdP=sPÞÞ þ ASð1 eðtTdS=sSÞÞ with _VO2 (t) being _VO2 to a given time (sec). _VO2 base- line was calculated as average _VO2 from 30 to 90 sec of 0 4 8 12 16 20 1 2 3 4 5 0 4 8 12 16 20 1 2 3 4 5 6 7 8 9 10 11 12 Venous lactate (mmol/L) 500 650 800 950 Power (W) 300 325 350 375 400 0 1000 2000 3000 4000 5000 6000 6 12 18 24 30 Power (W) Time (min) INTERVAL # (12 x 30 sec; 5 min recovery) INTERVAL # (5 x 4 min; 2 min recovery) VO2 (mL/min) Venous lactate (mmol/L) A B C D Figure 1. The physiological response during a training session with 12 9 30-sec sprint intervals separated by 5 min of recovery (A & B) and a session with 5 9 4 min intervals separated by 2 min of recovery (C & D) for one subject having a _VO2-max of 5.2 L min1 (hatched line). Peak power (dotted bars), mean power (gray bars), and pulmonary _VO2 (full line) is shown for each interval (top) together with lactate (bottom) from an antecubital vein before (open bars) and after (full bars) intervals. Each of the two training sessions was performed indoor using the bike ergometer and _VO2-system described in the methods section. 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 4 ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. Metabolic and Muscle Adaptations to HIT P. M. Christensen et al. the 120 sec baseline cycling at 20 W. AP, TdP, and sP are the amplitude, time delay, and time constant, respectively, for the primary (P) response. AS, TdS, and sS are the truncated amplitude, time delay, and time constant, respectively, for the slow component (S). An iterative process was used to determine the best ﬁt of the curve. The relative VO2 slow component was determined as the ratio between AS and average VO2 during the last 2 min of exercise. Mechanical gross efﬁciency (GE) was calculated for both MOD and INT during the last 2 min of exercise using the formula GE ¼ external bike load ðkJ min1Þ energy turnover ðkJ min1Þ 100% Energy turnover was estimated as _VO2 (L min1) 9 energetic value of oxygen (kJ L1) with the lat- ter being calculated from the measured respiratory exchange ratio (RER) thus taking into account the differ- ent energy yield from oxidation of carbohydrate and fat. In the event of RER exceeding 1.0 a value of 1.0 was used in the calculation. Main experiments Two experimental days (EXP1 & EXP2) were performed both prior to and after HIT to quantify muscle metabo- lites during INT. Subjects arrived to the laboratory in the morning after consuming a light breakfast. On both EXP1 and EXP2 a biopsy at rest was collected from m. vastus lateralis under local anesthesia using a Bergstrom needle with suction. One part was frozen immediately in liquid nitrogen within ~10 sec for analysis of metabolites, enzyme activity, and protein content. Another part of the biopsy was embedded in tissue tec (Sakura Finetek, Netherlands) for histochemical analysis. Initially, 6 min of MOD was performed followed by rest for 30 min. To evaluate changes in muscle metabolism in response to the HIT-period a muscle biopsy was obtained following 6 min of INT on EXP1 and following 3 min of INT on EXP2. Following 60 min of rest, 3 min of INT was performed on EXP1 and following 30 min of rest, 6 min of INT was performed on EXP2. Muscle analysis All muscle samples were stored at 80°C until analyzed. Muscle metabolites The muscle biopsies taken at rest and after 3 and 6 min of INT were analyzed for levels of lactate and creatine phosphate (CP) using ﬂuorometric methods (Lowry and Passonneau 1972). Maximal enzyme activity In part of the muscle biopsy obtained at rest maximal enzyme activity of citrate synthase (CS), 3-Hydroxyacyl CoA dehydrogenase (HAD), phosphofructokinase (PFK), and lactate dehydrogenase (LDH) was quantiﬁed in mus- cle homogenates after freeze drying and removal of fat and connective tissue using ﬂuorometric methods (Flu- oroscan Ascent, Thermo Scientiﬁc, Waltham, MA) (Lowry and Passonneau 1972). Protein expression in muscle homogenate lysates Approximately 3 mg freeze dried muscle tissue was split in two for double protein determination to increase mea- suring sensitivity and then homogenized and centrifuged to exclude non dissolved structures, as previously described (Bangsbo et al. 2009). Total protein concentra- tions were determined in each sample using BSA stan- dards (Pierce, IL) and the lysates were then diluted in 69 Laemmli buffer and ddH2O to reach equal protein con- centration before protein expression of CS, cytochrome c oxidase complex 4 (COX-4) and PFK were determined by western blotting. For subsequent analysis the average value was calculated from the two samples. Protein expression in segments of human single muscle ﬁbers The determination of ﬁber type-speciﬁc changes in pro- tein expression for CS, COX-4, and PFK were performed as previously described Thomassen et al. (2013) with minor changes. After freeze drying the muscle tissue sam- ples (7–10 mg dry weight, n = 16) for 48 h, segments of single ﬁbers were dissected under a microscope and stored in single microfuge tubes. The average size of the segments collected were roughly determined by measuring the lengths of the ﬁber under a microscope (1.4 0.3 mm, mean SD, n = 398). Before SDS-PAGE 18 lL 69 Laemmli buffer (0.7 mL 0.5 mol L1 Tris-base, 3 mL glycerol, 0.93 g DTT, 1 g SDS and 1.2 mg brom- ophenol blue) diluted (1:3, v:v) in ddH2O was added to each ﬁber and incubated for 1 h at room temperature. In order to have equal number of ﬁbers in the different groups, all single ﬁber segments were ﬁrst ﬁber typed before the analysis of the protein of interest. About 5 lL of the samples were loaded on a 26 well Tris-Tricine 4– 15% Criterion gels (Bio-Rad Laboratories, Solna, Sweden) and by western blotting characterized as either slow twitch (ST) or fast twitch (FT) muscle ﬁbers by use of ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 5 P. M. Christensen et al. Metabolic and Muscle Adaptations to HIT antibodies speciﬁc for myosin heavy chain (MHC) type I (ST ﬁbers) and MHC type II (FT ﬁbers) as well as the FT-speciﬁc SERCA1 protein (Thomassen et al. 2013). Antibodies used were ST ﬁbers: 0.5 lg∙mL1, mouse monoclonal IgM, A4.840, Developmental Studies Hybrid- oma Bank (DSHB), University of Iowa, USA; FT ﬁbers: 2 lg∙mL1, mouse monoclonal IgG, A4.74, DSHB, both developed by Dr Blau, and SERCA1: 0.1 lg∙mL1, mouse monoclonal, MA3-912, Thermo Scientiﬁc. From this preﬁ- ber typing, 160 ﬁbers were selected (1.5 0.3 mm) to the ﬁnal analyses, including 20 from each subject with ﬁve ﬁbers from each of the four groups: ST pre, ST post, FT pre, and FT post. The remaining 13 lL of the given selected ﬁber segments were then loaded onto additional gels. Western blotting For both segments of single muscle ﬁbers and muscle homogenate lysates proteins were separated by SDS-PAGE (55 mA per gel and maximum 150 V) for ~120 min and then semi-dry transferred to a PVDF membrane (Milli- pore A/S, Copenhagen, Denmark) for 120 min at 70 mA per gel and maximum 25 V. After protein transfer, gels including single ﬁbers were incubated for 1 h in Coomas- sie stain, including 0.3% Coomassie Brilliant Blue R (Sigma-Aldrich, Copenhagen, Denmark), 40% ethanol (96%), 10% Acetic acid (Merck, Copenhagen, Denmark) and 49.7% ddH2O. Gels were then destained in ddH2O overnight and imaged using a ChemiDoc MP Imaging System (Bio-Rad Laboratories). These Coomassie-stained posttransferred gels were used to determine the amount of protein in each lane, based on the MHC (~200 kDa) bands (Murphy et al. 2006). Membranes were blocked in Tris-buffered saline including 0.1% Tween-20 (TBST) with either 2% skimmed milk or 3% BSA for 1 h and then incubated with primary antibodies over night. After 2 washes in TBST, horseradish peroxidase-conjugated sec- ondary antibody (DAKO, Glostrup, Denmark) diluted 1 to 5000 in TBST with addition of either 2% skimmed milk or 3% BSA was added, following which the membranes were washed in TBST (3 9 15 min). Bands were visual- ized using chemiluminescent detection (single ﬁber analy- sis using Super Signal West Femto Maximum Sensitivity Substrate, Thermo Scientiﬁc, – muscle lysates using ECL, Millipore) and images were collected on a ChemiDoc MP Imaging System. For further analyses, the membrane was kept in TBST and re-incubated in a new primary antibody overnight, giving the opportunity to determine the expres- sion of several proteins with different molecular weights on the same segment of ﬁbers (Thomassen et al. 2013). The membranes for single ﬁber analyses were divided into four pieces by cutting over 250 kDa, right below the 150 kDa and 75 kDa, above the 25 kDa and below the 10 kDa markers (All Blue and Dual Color, Bio-Rad Labo- ratories). The ﬁrst and upper part was used to conﬁrm the predetermined ﬁber type (ST and FT 200 kDa), the second part used for PFK (85 kDa) and SERCA1 (100 kDa), the third for CS (48 kDa) and Actin (42 kDa), and the fourth and lower part used for COX-4 (14 kDa). Antibody details: Other antibodies used for protein expression determination were: PFK: 0.2 lg∙mL1, mouse monoclonal, Sc166722; Santa Cruz Biotechnology, Dallas, TX; CS: 0.33 lg∙mL1, rabbit polyclonal, ab96600; Abcam, Cambridge, UK; COX-4: 0.2 lg∙mL1, mouse monoclonal, Sc58648; Santa Cruz Biotechnology. Data treatment In total, 160 segments of human skeletal muscle single ﬁbers from vastus lateralis were used for the ﬁnal protein expression determination in ST and FT ﬁbers. On each gel 5 ST and 5 FT ﬁbers from a resting pre-HIT muscle and 5 ST and 5 FT ﬁbers from a resting post-HIT muscle from the same individual were loaded. Given the small size of segments of individual ﬁbers, it was not possible also to determine the total protein concentration in each sample prior to sample loading. Consequently, different amounts of protein were loaded in each well. In order to compare the speciﬁc protein expression between ﬁber samples, MHC on the post-transferred gel was quantiﬁed and it was deemed that an equal proportion of total pro- tein was transferred to the membrane independent of total amount loaded. Thus, the signal on the posttrans- ferred Coomassie stained gel was used for normalization of the densities of the protein of interest, as previously demonstrated as a reliable measure (Murphy et al. 2006; Thomassen et al. 2013). The signal intensity for each protein of interest was ﬁrst normalized to the mean intensity of all single human ﬁber bands for that protein on the gel. Afterward data were normalized to the total amount of protein in each sample determined by Coomassie staining of the remaining MHC. In order to compare ﬁbers loaded on different gels single values were normalized to the mean of ST pre-HIT in the single ﬁber analysis and pre-HIT in the muscle homoge- nate analysis. Finally, in order to have a normal distribu- tion of the data, the ratios were log transformed before the statistical analysis. For clarity the graphical presentation of the results are displayed as ratios relative to ST pre-HIT for single ﬁber data and from the backtransformed log val- ues relative to pre-HIT for muscle homogenate. Capillary density Capillarization was analyzed using ﬂuorescence micros- copy. Transverse sections of the muscle biopsies were cut 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 6 ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. Metabolic and Muscle Adaptations to HIT P. M. Christensen et al. at a thickness of 8 lm and placed onto glass slides and ﬁrst treated with Biotinylated Ulex Europaeus Agglutinin I (Vector Laboratories, Burlingame, CA) and later with Streptavidin 1:200 (Dako, Glostrup, Denmark). Subse- quently pictures were taken of the muscle samples and analyzed on a computer (ImageJ) for capillary to ﬁber ratio. Statistics Before and after HIT changes in _VO2 kinetics, capillary to ﬁber ratio, muscle metabolites at rest and after 3 and 6 min of exercise as well as enzyme activity and protein level in homogenates were examined with a Student’s paired t-test. Also a paired t-test was used to evaluate if s of the primary phase differed between MOD and INT both pre- and post-HIT. Changes in segments of single muscle ﬁbers were evaluated using a two-way ANOVA General Linear Model (ST vs. FT ﬁbers and pre vs. post- HIT as factors). The ﬁve ST ﬁbers were averaged to yield one value for each subject both pre- and post-HIT and the same approach was made for the ﬁve FT ﬁbers. If sig- niﬁcant main effects or an interaction were observed, then a student Newman-Keuls post hoc analysis was performed to identify the speciﬁc differences in protein expression within ﬁber types. Correlations between _VO2 kinetics (s of the primary response during MOD and INT and the relative VO2 slow component) and maximal aerobic enzyme activity and the capillary to ﬁber ratio was evalu- ated using a one-tailed test with the a-priory hypothesis that fast _VO2 kinetics and a minor _VO2 slow component would be associated with a high aerobic enzyme activity and capillary to ﬁber ratio. Results Muscle adaptations After HIT no overall effect of the intervention was observed for the amount of CS (P = 0.12; Fig. 2A), COX- 4 (P = 0.20; Fig. 2B) and PFK (P = 0.70; Fig. 2C) in seg- ments of ST and FT ﬁbers. An overall effect was found for ﬁber type for CS (P = 0.01), COX-4 (P = 0.007) and PFK (P < 0.001) since protein content in ST ﬁbers was higher than FT ﬁbers for CS (P = 0.016 pre-HIT and P = 0.007 post-HIT) and COX-4 (P = 0.012 pre-HIT and P = 0.01 post-HIT) and lower for PFK (P < 0.001 pre and post-HIT). Before HIT the content in ST ﬁbers of CS and COX-4 was on average 53% and 41% higher than in FT ﬁbers whereas PFK was ~389% higher in FT ﬁbers with respective values after HIT being 34%, 15% and ~241%. In muscle homogenates both CS (P = 0.07; Fig. 2A) and COX-4 (P = 0.10; Fig. 2B) tended to be lower after compared to before HIT with an average decrease in protein content of 16% and 11%, respectively. PFK remained unchanged (P = 0.45; Fig. 2C) with an average decrease of 3%. Representative Western blots of the proteins investigated are displayed in Fig. 3. After HIT the maximal enzyme activity was not changed relative to before the intervention for CS (56 8 vs. 59 10 lmol g DW1 min1; P = 0.10), HAD (27 6 vs. 29 3 lmol g DW1 min1; P = 0.41), LDH (131 27 vs. 113 27 lmol g DW1 min1; P = 0.14) and PFK (340 69 vs. 318 105 lmol g DW1 min1; P = 0.49). Following HIT no changes relative to before the inter- vention were observed in the capillary to ﬁber ratio (2.30 0.16 vs. 2.38 0.20; P = 0.13) (Fig. 4A). _VO2 kinetics No signiﬁcant differences following HIT were observed in the _VO2 response during MOD (Fig. 5) and in all modeling parameters (Table 1) as s was unchanged (P = 0.67) together with absolute _VO2 (P = 0.39), RER (P = 0.63) and GE (P = 0.39) averaged over the last 2 min of exercise. The _VO2 response during INT was also unaffected by HIT (Fig. 5) as were all modeling parameters (Table 1) including s of the primary response (P = 0.32), the abso- lute (P = 0.26) and relative (P = 0.25) size of the _VO2 slow component of the secondary response together with absolute _VO2 (P = 0.52), RER (P = 0.13) and GE (P = 0.46) averaged over the last 2 min of exercise. s during MOD and INT was not different neither pre (P = 0.12) nor post-HIT (P = 0.22). Correlations Maximal activity of CS (r2 = 0.002–0.18; P > 0.05) and HAD (r2 = 0.0001–0.16; P > 0.05) did not correlate with s of the primary response during MOD and INT both before and after HIT. Neither did CS correlate with the relative size of the _VO2 slow component (r2 = 0.38 and 0.30 before and after HIT; P > 0.05) as was the case for HAD (r2 = 0.12 and 0.30 before and after HIT; P > 0.05). The capillary to ﬁber ratio was associated with s during MOD before (r2 = 0.90; P < 0.001) but not after HIT (r2 = 0.03; P > 0.05) and no association was present during INT (r2 = 0.10–0.14; P > 0.05). An association between the cap- illary to ﬁber ratio and the relative size of the _VO2 slow component (Fig. 4B) was present following HIT (r2 = 0.39; P < 0.05) but not before HIT (r2 = 0.38; P > 0.05). Muscle metabolites Muscle CP (n = 7) was not changed by HIT at rest (~90 mmol g DW1 min1; P = 0.60) and after 3 min of ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 7 P. M. Christensen et al. Metabolic and Muscle Adaptations to HIT INT (~60 mmol g DW1 min1; P = 0.41), but after HIT it was lower at 6 min of exercise than before HIT (42 15 vs. 66 17 mmol kg DW1; P = 0.011) (Fig. 6A). Muscle lactate (n = 7) was not changed by HIT at rest (~7 mmol g DW1 min1; P = 0.18) and after 3 min of INT (~17 mmol g DW1 min1; P = 0.95), but at 6 min muscle lactate was higher after compared to before HIT (40.2 18.4 vs. 14.1 5.5 mmol kg DW1; P < 0.012) (Fig. 6B). Discussion The major ﬁndings in the present study were that a per- iod of reduced and intensiﬁed training performed by trained cyclists did not elevate the protein content of oxi- dative enzymes in FT ﬁbers. Furthermore, no components of pulmonary _VO2 kinetics were changed, whereas higher muscle lactate accumulation and lower level of CP were observed during intense cycling after compared to before the intervention period. Contrary to our hypothesis the content of CS and COX-4 in the FT ﬁbers was not elevated after the inter- vention period in the form of volume reduced and inten- siﬁed training. The high exercise intensity used in HIT, in particular the repeated sprint training, was chosen in order to activate all FT ﬁbers with this type of exercise being a potent stimulator of the signal cascades leading to adaptations of the muscular oxidative system (Psilander 0 0,5 1 1,5 2 1.5 0 2 4 6 8 10 0,5 1 1,5 0 0,5 1 HOM COX-4 signal intensity (relative to PRE) 0 1 1,5 PFK signal intensity (relative to PRE) 0 0,5 1 CS signal intensity (relative to PRE) 1.5 1.0 0.5 0 1.0 0.5 0 1.0 0.5 0 1.5 A B C # CS signal intensity (relative to ST PRE) COX-4 signal intensity (relative to ST PRE) 0.5 0 1.0 PFK signal intensity (relative to ST PRE) ST PRE ST POST FT PRE FT POST HOM # # # # # 0.5 2.7 0 1.0 0 100 200 300 400 500 PRE POST PFK maximal activity (µmol/g DW/min) (µmol/g DW/min) 0 20 40 60 80 CS maximal activity D E F H G Figure 2. Enzyme content following 7 weeks of high intensity and reduced volume training in trained cyclists (n = 8). The average content (thick lines) of CS (A), COX-4 (B), and PFK (C) before (PRE) and after (POST) the intervention are shown in slow-twitch (ST) and fast-twitch (FT) ﬁbers together with individual values (average of ﬁve ﬁbers SEM at each time point) which have been normalized to ST PRE for all enzymes. Average and individual protein content measured in homogenates (HOM) are also shown for CS (D), COX-4 (E), and PFK (F) as well as maximal activity for CS (G) and PFK (H). For clarity the graphs A-F display ratio data but the statistical analysis was based on log data. #P < 0.05, ##P < 0.01, ###P < 0.001; signiﬁcant difference between ST and FT ﬁbers. 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 8 ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. Metabolic and Muscle Adaptations to HIT P. M. Christensen et al. et al. 2010; Little et al. 2011). Thus, it was unexpected that no change in oxidative enzyme content was present in FT ﬁbers after the training period. One explanation may be that the cyclists during their normal training and competition prior to the intervention period, routinely engaged most of their muscle ﬁbers, and that “extra” FT ﬁbers activated during the training in the intervention period were only ﬁbers from the highest order motor units. Then, the FT ﬁbers in “lower” order motor units were recruited less due to the reduction in training vol- ume from ~470 to ~240 min week1. In support of this, it has been shown that, even at 65% (Scribbans et al. 2014) and 80% (Krustrup et al. 2004b) of _VO2-max, a signiﬁcant number of FT ﬁbers are recruited. In the pres- ent study a considerable part of the training prior to HIT was performed near 80% _VO2-max (~28% of total 0 5 10 15 20 25 30 Relative VO2 slow component (% of end exercise VO2) Capillary to fibre ratio PRE POST Capillary to fibre ratio 1.9 2.1 2.3 2.5 2.7 1.9 2.1 2.3 2.5 2.7 A B Figure 4. Muscle capillary to ﬁber ratio (A) before (PRE; open bars) and after (POST; closed bars) 7 weeks of high intensity and reduced volume training in trained cyclists (n = 8) with insert picture showing staining of capillaries in a representative subject. Values are means SD. Association between the capillary to ﬁber ratio and the relative _VO2 slow component (B) before (open symbols, r2 = 0.38; P > 0.05) and after (closed symbols, r2 = 0.39; P < 0.05) 7 weeks of high intensity and reduced volume training in trained cyclists (n = 8). MHCI 200 kDa PRE POST ST ST FT FT 200 kDa MHC Coomassie CS 200 kDa MHCII SERCA1 PFK COX-4 100 kDa 85 kDa 48 kDa 14 kDa 14 kDa 85 kDa 48 kDa CS PFK COX-4 PRE POST HOM Figure 3. Representative western blots of the proteins investigated in slow-twitch (ST), fast-twitch (FT), and muscle homogenate (HOM) before (PRE) and after (POST) 7 weeks of high intensity and reduced volume training in trained cyclists. See methods section for details. ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 9 P. M. Christensen et al. Metabolic and Muscle Adaptations to HIT training time or ~130 min week1 was carried out with a heart rate above 80% of HR-max). Furthermore, detrain- ing in trained athletes is known to reduce content and maximal activity of oxidative enzymes (Chi et al. 1983; Christensen et al. 2011). Thus, it may be that the protein content of oxidative enzymes was reduced in the motor units controlling FT ﬁbers lower in the ﬁber hierarchy due to the reduction in training. This may also explain the overall tendency for a drop in protein content and maximal activity of CS and COX-4 measured in muscle homogenates. The single ﬁber data of CS and COX-4 showed a large variation in oxidative enzyme protein content within each ﬁber type as evidenced by the high standard error for many of the cyclists (Fig. 2A and B). This is in agreement with the pioneering work by Lowry et al. (1978) showing a large range in maximal enzyme activity in single ﬁbers, but in that study a separation between ST and FT ﬁbers was not made. The observed difference in the content of muscle oxidative enzymes between individual ﬁbers sup- ports the proposed concept that motor units have a range from very fast to very slow _VO2 kinetics as well as differ- ent steady-state values (Koppo et al. 2004). Studies using trained subjects (Jansson and Kaijser 1977; Chi et al. 1983) and a longitudinal study on untrained subjects (Henriksson and Reitman 1976) have shown that the maximal activity of oxidative enzymes in FT ﬁbers can be as high as in ST ﬁbers. This was not the case in the pres- ent study, which may be due to a higher aerobic training status ( _VO2-max: ~70 mL min1 kg1) of the subjects in the studies showing similar levels in FT and ST ﬁbers (Jansson and Kaijser 1977; Chi et al. 1983). Nevertheless, the combination of repeated 30-sec sprinting (2.5 9 week1) and aerobic high intensity training (1.5 9 week1) in the present study with a reduced train- ing volume did not increase the oxidative enzyme content in FT ﬁbers in already trained athletes with a _VO2-max of ~60 mL min1 kg1. On the other hand, it has been Table 1. Pulmonary _VO2 kinetics modeling parameters during moderate (MOD) and intense (INT) cycling before (PRE) and after (POST) 7 weeks of high intensity and reduced volume training in trained cyclists (n = 8). Changes in pre and post were evaluated with a paired t-test. PRE POST MOD Baseline (mL min1) 865 96 849 60 TdP (sec) 17.5 3.1 18.3 2.9 sP (sec) 15.9 2.4 15.4 2.5 AP (mL min1) 2209 119 2249 144 _VO2 4–6 min (mL min1) 3083 172 3110 165 RER 4–6 min 0.95 0.02 0.95 0.01 GE 4–6 min (%) 19.1 0.7 18.9 0.7 Cadence (rounds min1) 92 9 92 8 INT Baseline (mL min1) 870 65 824 53 TdP (sec) 15.2 1.5 15.3 2.3 sP (sec) 18.2 2.5 17.2 2.9 AP (mL min1) 2931 240 2990 186 TdS (sec) 103 30 95 56 sS (sec) 141 60 181 111 AS (mL min1) 479 264 539 257 _VO2 4–6 min (mL min1) 4153 234 4180 184 RER 4–6 min 1.03 0.04 1.01 0.02 GE 4–6 min (%) 19.6 0.9 19.5 0.7 Cadence (rounds min1) 93 6 94 6 Values are means SD. Baseline ( _VO2 before onset of exercise). Time delay (Td), time constant (s), amplitude (A) for the primary response (P), and the slow component (S). Absolute oxygen uptake ( _VO2), respiratory exchange ratio (RER), and gross efﬁciency (GE) in the last 2 min of exercise from 4–6 min. 0 1500 3000 4500 –120 –60 0 60 120 180 240 300 360 Time (sec) 0 1500 3000 4500 –120 –60 0 60 120 180 240 300 360 VO2 (mL/min) Time (sec) A B 0 1500 3000 4500 –120 –60 0 60 120 180 240 300 360 Time (sec) C Figure 5. Pulmonary oxygen uptake ( _VO2) following 7 weeks of high intensity and reduced volume training in trained cyclists (n = 8) during moderate (MOD; A) and intense (INT; B) cycling with the modeled responses shown (C) before (PRE; open symbols and hatched lines) and after (POST; ﬁlled symbols and solid lines) the intervention. The arrow in panel C indicates the onset of the _VO2 slow component during INT (103 and 95 sec on average PRE and POST HIT) being superimposed on the primary _VO2 response. 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 10 ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. Metabolic and Muscle Adaptations to HIT P. M. Christensen et al. shown that adding two weekly aerobic high intensity ses- sions (6 9 ~3 min with ~1.5 min recovery), at the speed eliciting task failure in an incremental test, increased the content of LDH in the FT ﬁbers after 6 weeks of training with a maintained training volume in well-trained run- ners ( _VO2-max: 67 mL min1 kg1) (Kohn et al. 2011). Therefore, it seems plausible that the training reduction in the present study can explain the lack of increase in oxidative protein content in FT ﬁbers. Alternatively, train- ing with repeated sprints may be less potent than aerobic high intensity training (3–5 min intervals at ~90–100% _VO2-max) to increase protein content of oxidative enzymes in FT ﬁbers in trained individuals, since the for- mer type of training dominated in the present study. Based on changes in blood and muscle lactate in trained individuals it does appear that different types of high intensity training can yield different outcomes. Accordingly, aerobic high intensity training (< _VO2-max; e.g., 3–5 min intervals) and a maintained training volume appears to lower muscle (Clark et al. 2004) and blood lactate (Acevedo and Goldfarb 1989; Kohn et al. 2011) during intense exercise (< _VO2-max), whereas training interventions with repeated 30-sec sprints in combination with a volume reduction does not lower blood lactate (Bangsbo et al. 2009; Iaia et al. 2009) and in the present study muscle lactate was higher after training. Future studies are needed to evaluate if the outcome on oxidative adaptations in FT ﬁbers differs between different types of high intensity training which appears to be the case with regards to lactate levels. The role of the total training vol- ume is considered of interest, thus either adding SET on top of the normal training or having SET substitute less intense training seems relevant. Pulmonary _VO2 kinetics was not changed with HIT (Fig. 5 and Table 1). This ﬁnding differs from the faster _VO2 kinetics observed after a period of intense training in untrained subjects at both the muscular (Krustrup et al. 2004a) and pulmonary level (Bailey et al. 2009) with the latter study also reporting a reduced _VO2 slow com- ponent. Despite the tendencies for a reduction in the con- tent of CS and COX-4 as well as the maximal activity of CS in the present study, _VO2 kinetics remained unchanged. These ﬁndings suggest that the level of oxida- tive enzymes does not limit the muscle oxygen utilization in the initial part of exercise, nor the development of the _VO2 slow component. It should, however, be considered that the content and maximal activity of the oxidative enzymes was not signiﬁcantly reduced, and it may be that endurance athletes have an excessive oxidative enzyme capacity allowing a modest drop without having an effect on _VO2 kinetics. The CS activity measured in muscle homogenate was ~60 lmol g DW1 min1, which is about twice as high as in untrained subjects (Krustrup et al. 2004a; Burgomaster et al. 2005, 2008) and some- what higher than previous observations in trained endur- ance athletes with a similar _VO2-max as in the present study (Yeo et al. 2008; Bangsbo et al. 2009). In a study of trained soccer players 2 weeks without training resulted in a decrease in activity and content of oxidative enzymes, which was associated with slower _VO2 kinetics (Christen- sen et al. 2011). Thus, it cannot be excluded that the level of oxidative enzymes under some circumstances may become limiting for _VO2 kinetics in trained individuals. Unlike previous ﬁndings in trained individuals (Koppo et al. 2004) the speed of the primary _VO2 response was not signiﬁcantly different between MOD (s~15.7 sec) and INT (s~17.7 sec) although ﬁve of eight subjects had larger s in INT than in MOD both pre- and post-HIT. Thus, despite an expected larger recruitment of FT ﬁbers in INT relative to MOD (Krustrup et al. 2004b) and the fact that FT ﬁbers had markedly lower content of oxidative enzymes than ST ﬁbers in the present study (Fig. 2A and B), the _VO2 kinetics of the primary response in INT was not slower than MOD. This in turn suggests that at least in ST ﬁbers there is an excess capacity of oxidative enzymes that does not impact on the speed of the VO2 response. Maximal oxidative enzyme activity of CS and HAD were poor predictors of fast _VO2 kinetics and the relative size of the _VO2 slow component showing that in trained individuals these enzymes appear to be of minor importance and other muscular variables needs to investi- gated. Of interest was the association between the 0 10 20 30 40 50 60 70 Muscle lactate (mmol/kg DW) 0 20 40 60 80 100 120 Creatine phosphate (mmol/kg DW) A B * * PRE POST Rest PRE POST 3 min PRE POST 6 min Figure 6. Muscle creatine phosphate (A) and lactate (B) at rest and after 3 and 6 min of intense cycling before (PRE; open bars) and after (POST; closed bars) 7 weeks of high intensity and reduced volume training in trained cyclists (n = 7). Values are meansSD. *P < 0.05; signiﬁcant difference between PRE- and POST-values. ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 11 P. M. Christensen et al. Metabolic and Muscle Adaptations to HIT capillary to ﬁber ratio and the relative size of the _VO2 slow component after the HIT intervention and the marked tendency for an association before HIT (Fig. 4). However, the association was mainly due to one subject having a low capillary to ﬁber ratio. Nevertheless, during intense exercise eliciting a slow component – likely due to recruitment of FT ﬁbers (Jones et al. 2011) – a high capil- lary to ﬁber ratio could be speculated to lower blood mean transit time optimizing conditions for diffusion of oxygen which is supported by the ﬁnding of a reduced _VO2 slow component during exercise inhaling an hyper- oxic gas (Wilkerson et al. 2006). The decrease in muscle CP and the increase in mus- cle lactate from 3 to 6 min in INT (~85% _VO2-max) were larger after the HIT period (Fig. 6). These ﬁndings suggest a larger anaerobic energy turnover in the last phase of INT. The activity of PFK was not elevated with the intervention period, neither when expressed as maximal activity or content, so it cannot explain the apparently greater rate of glycolysis (Spriet et al. 2000). The larger drop in CP indicates a higher accumulation of muscle ADP, which may have elevated the rate of glycolysis leading to a greater lactate production, but a larger ADP concentration would also be expected to stimulate the respiration but that was not the case in light of the unchanged _VO2 response. Alternatively, the tendency for a lower content and maximal activity of muscle oxidative enzymes (CS and COX-4) after the HIT period may have reduced the mitochondrial utili- zation of the produced pyruvate (Brooks 2000), thereby enhancing the lactate production catalyzed by LDH (Spriet et al. 2000). It is, however, unclear, why such changes did not occur in the ﬁrst phase (0–3 min) of INT (Fig. 6B). Nevertheless, it appears that the anaero- bic energy ﬂux during the last phase (3–6 min) of INT was higher after the intervention period, and thus, total energy turnover, as pulmonary VO2 was unaltered. Alternatively, this ﬁnding may reﬂect a greater imbal- ance between muscle CP and creatine and a change in the ratio between muscle lactate and pyruvate during exercise due to altered regulation. In moderately trained subjects (VO2-max: 49 mL min1 kg1) repeated sprint training, as used in the present study, resulted in lower muscle lactate and ATP concentrations after exercise at an intensity corresponding to 90% of _VO2-max. How- ever, there were major differences between the present study and the one by Burgomaster and co-workers including a lower training status, and an increase in training volume and a higher maximal aerobic enzyme activity after the training period (Burgomaster et al. 2006) which may in part explain the different change in muscle lactate accumulation during intense exercise. The ﬁndings in the present study are in contrast to observations in a study also using well-trained cyclists ( _VO2-max: ~65 mL O2 min1 kg1) who for a 3-week period added aerobic high intensity training (8 9 5 min ~85% _VO2-max) three times weekly to their normal training volume. Following the training period muscle lactate accumulation during intense exercise (~85% _VO2-max) was reduced and during more moderate exercise (65% _VO2-max) fat and carbohydrate oxidation was larger and lower, respectively (Clark et al. 2004). Content and maximal activity of oxidative enzymes were not reported, but was unchanged in trained cyclists in another study using the same type of training (Yeo et al. 2008). Taken together these ﬁndings suggest that the reduced amount of training in the present study is the major cause of the elevated anaerobic energy production during the intense submaximal work. Furthermore, the present study shows that anaerobic metabolism can be altered without a change in _VO2 kinetics. Such dissociation between anaerobic and aero- bic metabolism has also been reported in the exercise transient in studies using hyperoxia where CP utiliza- tion has been observed to be reduced (Vanhatalo et al. 2010) and the primary _VO2 response appears to be unaffected (Wilkerson et al. 2006). Likewise, during steady-state conditions with moderate exercise higher CP and lower muscle lactate have been observed in hy- peroxia (Stellingwerff et al. 2006) despite the _VO2 response being similar in both the exercise transient and in the stable phase of exercise (Wilkerson et al. 2006). Further studies are needed to evaluate how dif- ferent training regimes inﬂuence anaerobic energy turn- over during submaximal exercise. The functional signiﬁcance of the training period in the present study has been reported previously with regard to high intensity exercise performance in the form of improved performance in a repeated sprint test (6 9 20 sec) and in an exhaustive test lasting ~4 min with the latter test being preceded by a 2-min preload with high intensity in which muscle lactate at the end of the preload also was elevated after HIT (Gunnarsson et al. 2013). This indicates that the apparently larger anaerobic muscle perturbation with HIT does not lower perfor- mance during intense exercise. Each subject had ﬁve ST and FT ﬁbers analyzed both pre and post-HIT (total of 20 ﬁbers) and the average value of the ﬁve ﬁbers for each time point (pre- and post-HIT) was used for further analysis. Such a low num- ber may seem limiting, but a statistical difference was present between ST and FT ﬁbers for all enzymes investi- gated in line with previous reports using pooled groups of ST and FT ﬁbers to determine maximal enzyme activ- ity (Essen et al. 1975; Essen-Gustavsson and Henriksson 1984; Schantz and Henriksson 1987) or content (Thomas- 2015 \| Vol. 3 \| Iss. 7 \| e12428 Page 12 ª 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society. Metabolic and Muscle Adaptations to HIT P. M. Christensen et al. sen et al. 2013). Furthermore, the tendencies for a drop in homogenate protein content and maximal activity for CS and COX-4 and unchanged PFK levels were mirrored by the single ﬁber data. In addition the same three sub- jects who had a higher CS content in FT ﬁbers after HIT also had elevated content of COX-4 (Symbols □, ■ and ▲on Fig. 2). Taken together this suggests that the method is sensitive enough to detect possible changes in response to a training intervention. In summary, the present study showed that the amount of CS and COX-4 in FT ﬁbers and muscle homogenate as well as maximal activity of CS was not changed after a 7-week period of intensiﬁed training with a reduced volume in already trained cyclists. Further- more, no change in pulmonary _VO2 kinetics was observed during moderate (~65% _VO2-max) and intense (~85% _VO2-max) submaximal exercise. During intense cycling muscle CP levels were reduced and muscle lactate accumulation was elevated to a greater extent in the later part of exercise (3–6 min) following the training inter- vention without an altered _VO2. This could be inter- preted to reﬂect a larger total energy turnover following the training intervention due to a greater anaerobic energy contribution to exercise or an altered regulation of anaerobic muscle metabolism. Acknowledgments Lars Ropke, Rasmus Vilsgaard, Lars Nybo, Nikolai Nords- borg for assistance during the experiments, Rafael Casuso and Karina Olsen for assistance with muscle analysis and the subjects for their great effort and determination dur- ing training and testing. Conﬂict of Interest No conﬂict of interest is reported. References Acevedo, E. O., and A. H. Goldfarb. 1989. Increased training intensity effects on plasma lactate, ventilatory threshold, and endurance. Med. Sci. Sports Exerc. 21:563–568. Bailey, S. J., D. P. Wilkerson, F. J. Dimenna, and A. M. Jones. 2009. Inﬂuence of repeated sprint training on pulmonary O2 uptake and muscle deoxygenation kinetics in humans. J. Appl. Physiol. 106:1875–1887. Bangsbo, J., P. Krustrup, J. Gonzalez-Alonso, and B. Saltin. 2001. ATP production and efﬁciency of human skeletal muscle during intense exercise: effect of previous exercise. Am. J. Physiol. Endocrinol. Metab. 280:E956–E964. Bangsbo, J., T. P. Gunnarsson, J. Wendell, L. 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Metabolic and Muscle Adaptations to HIT	Unchanged content of oxidative enzymes in fast-twitch muscle fibers and V˙O2 kinetics after intensified training in trained cyclists.	[]	Christensen, Peter M,Gunnarsson, Thomas P,Thomassen, Martin,Wilkerson, Daryl P,Nielsen, Jens Jung,Bangsbo, Jens	eng
PMC8639140	1 https://doi.org/10.11606/s1518-8787.2021055003903 Original Article Rev Saude Publica. 2021;55:97 Running away from the jab: factors associated with COVID-19 vaccine hesitancy in Brazil Marco Antonio Catussi PaschoalottoI , Eduardo Polena Pacheco Araújo CostaI , Sara Valente de AlmeidaI,II , Joana CimaIII , Joana Gomes da CostaIV , João Vasco SantosV,VI,VII , Pedro Pita BarrosI , Claudia Souza PassadorVIII , João Luiz PassadorVIII I Universidade NOVA de Lisboa. Nova School of Business and Economics. Carcavelos, Portugal II Imperial College London. Faculty of Health Sciences. London, England III Universidade do Minho. Núcleo de Investigação em Políticas Económicas e Empresariais. Braga, Portugal IV Universidade do Porto. Faculdade de Economia e Gestão. Porto, Portugal V Universidade do Porto. Faculdade de Medicina. MEDCIDS – Departamento Medicina da Comunidade, Informação e Decisão em Saúde. Porto, Portugal VI Universidade do Porto. Faculdade de Medicina. Centro de Investigação em Tecnologias e Serviços de Saúde. Porto, Portugal VII ARS Norte. ACES Grande Porto VIII - Espinho/Gaia. Unidade de Saúde Pública. Vila Nova de Gaia, Portugal VIII Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto. Ribeirão Preto, São Paulo, Brasil ABSTRACT OBJECTIVE: To investigate how sociodemographic conditions, political factors, organizational confidence, and non-pharmaceutical interventions compliance affect the COVID-19 vaccine hesitancy in Brazil. METHODS: Data collection took place between November 25th, 2020 and January 11th, 2021 using a nationwide online survey. Subsequently, the researches performed a descriptive analysis on the main variables and used logistic regression models to investigate the factors associated with COVID-19 vaccine hesitancy. RESULTS: Less concern over vaccine side effects could improve the willingness to be vaccinated (probability changed by 7.7 pp; p < 0.10). The current vaccine distrust espoused by the Brazilian president is associated with vaccine hesitancy, among his voter base. Lower performance perception (“Very Bad” with 10.7 pp; p < 0.01) or higher political opposition (left-oriented) regarding the current presidency is associated with the willingness to be vaccinated. Higher compliance with non-pharmaceutical interventions (NPIs) is usually positively associated with the willingness to take the COVID-19 vaccine (+1 score to NPI compliance index is associated with higher willingness to be vaccinated by 1.4 pp, p < 0.05). CONCLUSION: Willingness to be vaccinated is strongly associated with political leaning, perceived federal government performance, vaccine side effects, and compliance with non-pharmaceutical interventions (NPIs). DESCRIPTORS: COVID-19 Vaccines. Vaccination Refusal. Socioeconomic Factors. Political Activism. Health Knowledge, Attitudes, Practice. Correspondence: Marco Antonio Catussi Paschoalotto Travessa do Hospital, 18 - 2° andar 1150-187 Lisboa, Portugal E-mail: marcocatussi@gmail.com Received: May 27, 2021 Approved: Jul 19, 2021 How to cite: Paschoalotto MAC, Costa EPPA, Valente-de-Almeida S, Cima J, Gomes-da-Costa J, Santos JV, et al. Running away from the jab: Factors Associated with COVID-19 Vaccine Hesitancy in Brazil. Rev Saude Publica. 2021;55:97. https://doi.org/10.11606/s1518- 8787.2021055003903 Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are credited. http://www.rsp.fsp.usp.br/ 2 Factors associated with COVID-19 vaccine hesitancy Paschoalotto MAC et al. https://doi.org/10.11606/s1518-8787.2021055003903 INTRODUCTION By July 2021, the COVID-19 pandemic had already resulted in more than 186 million cases and 4 million deaths worldwide, with Brazil ranking third place in the number of cases and second in the number of deaths1. In a global effort to contain the spread of the new virus, countries adopted several non-pharmacological interventions (NPI) such as social distancing2–4 and face mask use5. But despite the importance of such measures, the solution to the pandemic rests on the success of vaccination programs6,7. After the extraordinary efforts made to rapidly research and develop effective COVID-19 vaccines and their recent rollout, researchers and the media have pointed to a growing concern regarding public confidence in the vaccination process. In fact, “anti-vaccine movements” can foster vaccine hesitancy, reducing the population’s willingness to be vaccinated6–13. Several surveys have been used to characterize behaviours concerning vaccine hesitancy and NPI compliance10–18. According to the existing literature, sociodemographic conditions (e.g., education, age, or job occupation)10,18–20 and political and organizational trust aspects5,8–10,12,13, can affect people’s willingness to be vaccinated. One of the countries with the highest number of COVID-19 cases and deaths1, Brazil has a population of diverse sociodemographic backgrounds20–23 and is governed by a president with a long history of questioning scientific findings, including vaccine efficacy and safety24–26. Reducing vaccine hesitancy will largely determine Brazil’s – and other low-middle income countries (LMICs) – success in controlling the current pandemic. Given this context, this study investigates the factors associated with COVID-19 vaccine hesitancy in Brazil. Using a nationwide online survey, we analyse how sociodemographic conditions, political factors, organizational confidence, and non-pharmaceutical interventions compliance influence the population’s willingness to be vaccinated. METHODS This study was approved by the Research Ethics Committee at NOVA School of Business and Economics (Portugal) on November 23rd, 2020, via letter sent by the Scientific Council’s president. Regarding Brazilian ethical standards, the research complied with the National Health Council Resolution 466/12a. In its first page, the online survey highlighted the research characteristics and information, anonymity assurance, data protection, and a consent form. Data Data collection took place between November 25th, 2020 and January 11th, 2021, period before the second COVID-19 wave, considered the deadliest so far, and before the first COVID-19 vaccine (Coronavac – Sinovac/Butantã) was introduced. Using an online survey built on Qualtrics software and disseminated on different social networks (Facebook, Instagram, WhatsApp, and email groups), we sought to collect a diversified base of responses from all Brazilian regions and different social sectors. Table 1 describes the survey data and compares it to the national averages. Our sample comprised 1,623 valid responsesb, collected from almost all Brazilian states and capitals, but mainly from São Paulo (67%). While not representative of the Brazilian population, the study sample comes close to some sociodemographic characteristics, such as gender, age, residence area, number of households and professional situation27. a Ministério da Saúde (BR), Conselho Nacional de Saúde. Resolução Nº 466 de 12 de dezembro de 2012. Aprova diretrizes e normas regulamentadoras de pesquisas envolvendo seres humanos. Diário Oficial da União. 13 jun 2013; Seção 1: 59. Available from: https://conselho.saude.gov. br/resolucoes/2012/Reso466.pdf b These responses include all completed and submitted responses recorded after validation testing. Participants were given the option not to disclose their political preferences, perception of vaccine side effects, perception of the federal government, and compliance levels. 3 Factors associated with COVID-19 vaccine hesitancy Paschoalotto MAC et al. https://doi.org/10.11606/s1518-8787.2021055003903 Table 1. Sample characteristics (Survey Data) x National characteristics (National Data). Variable Survey Data National Data States and municipalities (number)a States 24 27 Capitals 20 27 Municipalities 263 5,570 Gender (%) Male 37.9 48.2 Female 61.7 51.8 Other/No answer 0.4 Age (%) ≤ 18 years 0.9 < 19 years 33.1 19 to 25 years 30.6 20 to 24 years 9.0 26 to 32 years 20.9 25 to 34 years 17.1 33 to 45 years 27.4 35 to 44 years 14.0 46 to 64 years 18.4 45 to 64 years 19.2 65 to 79 years 1.9 65 to 79 years 6.0 ≥ 80 years 0.1 ≥ 80 years 1.6 Education (%) Elementary school 0.5 55.8 High school 14.4 30.1 University – Bachelor 40.5 14.1 University – MBAs and specializations 20.8 University – Master’s 14.1 University – Doctorate 9.7 Residence area (%) Urban 97.4 84.4 Rural 2.6 15.6 Households (%; average number) One/Live alone 7.9 30.9 Two 33.3 Three 26.9 30.4 Four 20.7 22.8 Five 7.2 10.0 More than five 4.0 5.9 Professional situation (%) Retired 2.8 Out of the workforce 37.2 Student 21.2 Unemployed 6.5 Unoccupied 6.6 Public server 17.2 Occupied 39.1 Worker – Own business 10.9 Worker – SME enterprises 15.4 Worker – Big enterprises 22.1 Other/No answer 3.9 Other 17.1 a Sample comprising 88.9% of the Brazilian states, 74.7% of the Brazilian capitals, and 4.7% of the Brazilian municipalities. More than 75% of the Brazilian municipalities are characterized as “small” (< 25,000 inhabitants), reducing the likelihood of achieving a substantial representativeness for them (31). 4 Factors associated with COVID-19 vaccine hesitancy Paschoalotto MAC et al. https://doi.org/10.11606/s1518-8787.2021055003903 Beyond sociodemographic conditions, we also have collected data regarding political factors, organizational confidence, NPI compliance, perception of vaccine side effects and vaccine hesitancy (Appendix 1). Respondents were asked to disclose their political leaning on a scale of 1 (Far Left) to 7 (Far Right) and to qualitatively evaluate (Very Bad, Bad, Good and Very Good) their perception of several institutions’ performance concerning the COVID-19 pandemic, including the Federal Government. Regarding NPI compliance – mandatory mask use, social distancing (1,5 meters), respiratory etiquette, hand washing and staying at home –, respondents were asked to disclose their agreement level using a 5-point scale (disagree – agree), and their compliance level (never, rarely, frequently, and always) (Appendix 1). By means of a Principal Content Analysis (PCA), we used these questions to create a composite indicator labelled as “Compliance Index,” which represents 47.68% of the explanatory power of the total variables. Each measure contributed to the compliance index with different weights: mandatory mask use – 19.86%; social distancing (1.5 meters) – 21.84%; respiratory etiquette – 16.87%; hand washing – 20.49%; and staying at home, if possible – 20.94%. As for vaccines, respondents were asked about their perception of vaccine side effects and willingness to be vaccinated (no, maybe, and yes). Data Analysis We performed a set of bivariate analyses to understand the association between key variables – NPI Compliance Index, Age (years), Gender, Schooling level, Vaccine side effect, Political leaning and Government performance (Federal) – and willingness to take the COVID-19 vaccine. Subsequently, we used logistic regression models to estimate COVID-19 vaccine hesitancy. Using willingness to be vaccinated (measured by no/maybe (0), and yes (1)) as the dependent variable, the first model considers the baseline sociodemographic conditions as independent variables; the second model, in turn, includes political leaning, organizational confidence, non-pharmaceutical interventions compliance, and vaccine side effects as independent variables. Results are presented as Odds Ratios (OR), which indicate the odds of a dependent variable occurring in the presence or absence of the reference group, and as marginal effects (dy/dx), which tells us, in percentage points (pp), how a dependent variable changes when an explanatory variable changes, ceteris paribus. RESULTS Descriptive Statistics Regarding the willingness to take the COVID-19 vaccine, 70% of the sample showed to be willing to take the COVID-19 shot, while almost 30% exhibited some degree of hesitancy (“not” or “maybe”) (Figure A). Such willingness to be vaccinated assumes that a vaccine is available for a given individual. Plots 1B to 1H show the association between willingness to be vaccinated and the independent variables. Divided into tertiles, the NPI Compliance Index (Figure B) ranges from lower compliance (1) to higher compliance (3) levels, suggesting a possible association between this variable and willingness to be vaccine, with a higher percentage of “Yes” at the level “3”, than at the level “1.” Such findings may reflect the population’s level of concern: more concerned individuals are willing to be vaccinated and show higher compliance with sanitary measures. As for the association between age and willingness to be vaccinated (Figure C), younger (less than 25 years) and older (more than 65 years) individuals showed higher levels of hesitancy, 5 Factors associated with COVID-19 vaccine hesitancy Paschoalotto MAC et al. https://doi.org/10.11606/s1518-8787.2021055003903 Figure. Bivariate analysis plots (except for 1A), respectively: Willingness to be vaccinated (A); Willingness to be vaccinated and NPI Compliance Index (B); Willingness to be vaccinated and Age (C); Willingness to be vaccinated and Gender (D); Willingness to be vaccinated and Schooling level (E); Willingness to be vaccinated and Vaccine side effects (F); Willingness to be vaccinated and Political leaning (G); Willingness to be vaccinated and Federal Government performance (H). A B C D E F G H 100 80 60 40 20 0 Willingness to COVID-19 Vaccine No Maybe 1 2 3 NPI Compliance Index Gender Age (years) Female Male 1 2 3 4 5 6 7 Political position Government performance Very bad Bad Average Good Very good University High school Elementary school Schooling Disagree Partially disagree Neutral Partially agree Agree Yes Vaccine side effect <18 yrs 19–25 yrs 26–32 yrs 33–45 yrs 46–65 yrs >65 yrs 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 6 Factors associated with COVID-19 vaccine hesitancy Paschoalotto MAC et al. https://doi.org/10.11606/s1518-8787.2021055003903 while those between 26 and 65 years old were less hesitant. In our sample, women showed greater hesitancy regarding the COVID-19 vaccine than men (Figure D). As expected, the analysis found a strong association between schooling level and vaccine hesitancy (Figure E): individuals with only elementary schooling show vaccine hesitancy levels up to four times higher than those with higher schooling levels. Moreover, individuals more concerned with vaccine side effects show greater hesitancy in their willingness to be vaccinated (Figure F). In our sample, right-wing individuals – generally more favorable to the current government – show higher levels of vaccine hesitancy than left-wing individuals. Together with the previous findings, this suggests that distrust in government is associated with higher compliance and vaccine acceptance, possibly due to high levels of concern (Figure G). We observed a similar inverse relationship between perception of government and willingness to be vaccinated (Figure H): respondents who scored government action as “Very bad” showed and 86% willingness to be vaccinated; among those who scored the government action as “Very good”, in turn, this willingness drops to 38%. Logistic Regression Models In this study, we performed two regression models to estimate the factors associated with the willingness to take the COVID-19 vaccine. While model 1 includes only sociodemographic characteristics, model 2 considers the participants’ opinion on vaccine side effects, political leaning, perception of federal government performance and the compliance indexc. This section focuses on the marginal effects analysis, but full results are shown below (Table 2). In both models, age group does not seem to explain willingness to be vaccinated. Being retired is associated with the probability of taking the COVID-19 vaccine by 17.9 pp (p < 0.01) and by 14.5 pp (p < 0.05) in the first and second model, respectively, being the only professional situation with significant impact on the dependent variable – relative to being unemployed (baseline group). Although we found a positive impact associated with being male in the first model, this loses significance once we control for opinion on vaccine effects and compliance index. We observed similar results regarding educational variables such as Master’s and PhD programs. The second model shows a negative and statistically significant association between fear of vaccine side effects and willingness to be vaccinated. Respondents who answered having no concern over vaccine side effects show higher levels of willingness to be vaccinated, with their probability changing by 7.7 pp (p < 0.10). On the other hand, individuals with high levels of concern about side effects have lower willingness to be vaccinated, varying by 34.4 pp (p < 0.01). Regarding political leaning, results show an association between being left-oriented and willingness to take the vaccine. Rating the government’s performance as “very bad” affects the probability of agreeing to be vaccinated by 10.7 pp (p < 0.01). The compliance index, which gives us an indicator of the participants’ overall level of compliance with all preventive measures, is in turn positively associated with willingness to vaccinate. An extra score on the compliance index means a 1.4 pp (p < 0.05) change in the probability of agreeing to vaccinate. c Compliance Index explained in detail in the methods section. 7 Factors associated with COVID-19 vaccine hesitancy Paschoalotto MAC et al. https://doi.org/10.11606/s1518-8787.2021055003903 Table 2. Logit models analyzing the explanatory capacity of the independent variables concerning the willingness to be vaccinated. (1) (1) (2) (2) OR dydx OR dydx Compliance Index 1.123b 0.014b Age (years) (baseline group ≤ 18) 19–25 0.912 -0.018 0.951 -0.006 26–32 1.014 0.003 0.841 -0.020 33–45 1.002 0.0004 0.877 -0.015 46–64 0.785 -0.048 0.597 -0.063 ≥ 65 0.447 -0.174 0.619 -0.058 Gender (baseline group: Female) Male 1.324b 0.054b 1.218 0.023 Professional situation (baseline group: Unemployed) Retired 2.93b 0.179c 3.867a 0.145b Student 1.391 0.066 1.012 0.002 Other 0.834 -0.039 1.080 0.010 Public server 1.424 0.070 1.405 0.042 Worker – Big enterprises 1.178 0.034 1.829 0.072 Worker – SME enterprises 1.122 0.024 1.266 0.029 Worker – Own business 0.936 -0.014 1.225 0.025 Schooling level (baseline group: Elementary school) High school 2.940 0.246 2.856 0.127 University – Bachelor 1.960 0.161 1.218 0.027 University – MBAs and specializations 2.827 0.238 1.700 0.069 University – Master 4.747a 0.328 2.692 0.121 University – PhD 5.103a 0.338∗ 2.049 0.091 Vaccine side effects (baseline group: do not disagree or agree) Fully disagree 3.454a 0.077b Partially disagree 2.346a 0.060b Partially agree 0.503b -0.077c Fully agree 0.108c -0.344c Political leaning (baseline group: Center) 1- Far left 0.896 -0.014 2 1.869b 0.072b 3 1.553a 0.053a 5 0.690 -0.050 6 0.476c -0.104b 7 - Far right 0.388b -0.136b Federal government - Performance (baseline group: Fair) Very bad 2.355c 0.107c Bad 1.337 0.039 Good 0.699 -0.052 Very good 0.532 -0.095 N 1,623 1,623 1,261 1,261 a, b, c: indicate significance at 10%, 5% and 1% level, respectively. Note: We also ran ordered logit models, which presented the same significative results. 8 Factors associated with COVID-19 vaccine hesitancy Paschoalotto MAC et al. https://doi.org/10.11606/s1518-8787.2021055003903 DISCUSSION This study investigated the association between social characteristics, political factors, and organizational performance and vaccine hesitancy in Brazil, contributing to understanding vaccine hesitancy factors in a LMIC context. Our main finding suggests a negative association between positive perception of the federal government’s performance and willingness to be vaccinated, similar to previous studies on the likelihood of getting vaccinated in Brazil26. It also corroborates a North-American study, conducted during the Trump Administration, which suggested higher vaccine hesitancy among Trump supporters18. This phenomenon can be explained by the current Brazilian president’s negationist remarks regarding the COVID-19 pandemic and his position against compliance with NPIs and being vaccinated – a political scenario similar to the Trump administration24,25,28. Regarding political leaning, our results show that espousing far-right ideology is positively associated with vaccine hesitancy, while being centre-left is associated with vaccine acceptance. This finding corroborates other studies on anti-vaccine movements and ideological isolation11–13,26) and reinforces the importance of political leadership in promoting compliance and public trust during crisis. The NPI compliance index also provided interesting results, showing a positive association with willingness to be vaccinated. Such index is an innovative approach already used in previous studies4,10,18 and our results are in agreement with the literature5,11,13. We found a similar association regarding vaccine side effects, with more concerned individuals showing a positive association with willingness to be vaccinated. Such results highlight the importance of public communication about NPIs and vaccines. This research has two major limitations. First, the method of data collection prevented us from obtaining a representative sample, particularly regarding the vulnerable population, which was underrepresented. Research shows that the most vulnerable individuals (with low schooling levels and high poverty levels) may express least willingness to be vaccinated10,18–20. If we transpose this scenario to the Brazilian context, then our vaccine hesitancy estimates should be interpreted as a lower bound. Like previous studies with convenient sampling methods17,18, however, the present study can still be used to derive significant policies. Even if the sample is not representative of the entire population, it can be for particular groups. Second, some respondents were not comfortable disclosing their political leanings, thus reducing the number of observations available in the second model. If such respondents are not distributed randomly, then the results may be biased. Overall, the study contributes to a better understanding of vaccine hesitancy factors in a low-to-middle income country. Vaccine hesitancy is associated with multiple factors, such as NPIs compliance, sociodemographic and employment characteristics, political leaning, and public perception of government performance. Willingness to be vaccinated in Brazil is strongly associated with political leaning, perceived federal government performance, vaccine side effects, and compliance with non-pharmaceutical interventions. We found a strong association between vaccine hesitancy and being right-wing and positive perception of government performance. 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The association of age, gender, and education with potential acceptance of a COVID-19 vaccine: a country-level analysis. J Health Commun. 2020;25(10):799-807. https://doi.org/10.1080/10810730.2020.1868630 28. Troiano G, Nardi A. Vaccine hesitancy in the era of COVID-19. Public Health. 2021;194:245-51. https://doi.org/10.1016/j.puhe.2021.02.025 29. Paul E, Steptoe A, Fancourt D. Attitudes towards vaccines and intention to vaccinate against COVID-19: implications for public health communications. Lancet Reg Health Eur. 2021;1:100012. https://doi.org/10.1016/j.lanepe.2020.100012 30. Figueiredo A, Simas C, Karafillakis E, Paterson P, Larson HJ. Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study. Lancet. 2020;26;396(10255):898-908. https://doi.org/10.1016/S0140-6736(20)31558-0 Funding: Eduardo Costa was funded by Fundação para a Ciência e a Tecnologia (FCT) under PhD grant number BD128545/2017. Joana Gomes da Costa was funded by Fundação para a Ciência e a Tecnologia (FCT) under PhD grant number SFRH/BD/140727/2018. The remaining authors have no financial relationships relevant to this article to disclose. Authors’ Contribution: Study design and planning: MACP, EPPAC, SVA, JC, JGC, JVS, PPB, CSP, JLP. Data collection, analysis and interpretation: MACP, EPPAC, SVA, JC, JGC, JVS, PPB, CSP, JLP. Manuscript drafting or review: MACP, EPPAC, SVA, JC, JGC, JVS, PPB, CSP, JLP. Approval of the final version: MACP, EPPAC, SVA, JC, JGC, JVS, PPB, CSP, JLP. Public responsibility for the content of the article: MACP, EPPAC, SVA, JC, JGC, JVS, PPB, CSP, JLP. Conflict of Interests: The authors declare no conflict of interest.	Running away from the jab: factors associated with COVID-19 vaccine hesitancy in Brazil.	11-26-2021	Paschoalotto, Marco Antonio Catussi,Costa, Eduardo Polena Pacheco Araújo,Almeida, Sara Valente de,Cima, Joana,Costa, Joana Gomes da,Santos, João Vasco,Barros, Pedro Pita,Passador, Claudia Souza,Passador, João Luiz	eng
PMC6211760	RESEARCH ARTICLE Cardiorespiratory and metabolic responses and reference equation validation to predict peak oxygen uptake for the incremental shuttle waking test in adolescent boys Andreza L. Gomes1☯, Vanessa A. Mendonc¸a1☯, Tatiane dos Santos Silva1‡, Crislaine K. V. Pires1‡, Liliana P. Lima1‡, Alcilene M. Silva1‡, Ana Cristina R. Camargos1,2‡, Camila D. C. Neves1‡, Ana C. R. Lacerda1‡, He´rcules R. LeiteID1☯* 1 Programa de Po´s-Graduac¸ão em Reabilitac¸ão e Desempenho Funcional, Departamento de Fisioterapia, Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM), Campus JK, Alto da Jacuba, Diamantina, Minas Gerais, Brazil, 2 Escola de Educac¸ão Fı´sica, Fisioterapia e Terapia Ocupacional (EEFFTO), Departamento de Fisioterapia, Universidade Federal de Minas Gerais (UFMG), Diamantina, Minas Gerais, Brazil ☯ These authors contributed equally to this work. ‡ These authors also contributed equally to this work. * hercules.leite@ufvjm.edu.br Abstract Background Previous studies speculated that the Incremental Shuttle Walking Test (ISWT) is a maximal test in children and adolescents, however comparison between ISWT with cardiopulmonary exercise test has not yet performed. Furthermore, there is no regression equation available in the current literature to predict oxygen peak consumption (VO2 peak) in this population. This study aimed to assesses and correlate the cardiorespiratory responses of the ISWT with the cardiopulmonary exercise (CEPT) and to develop and validate a regression equa- tion to predict VO2 peak in healthy sedentary adolescent boys. Methods Forty-one participants were included in the study. In the first stage, the VO2 peak, respira- tory exchange ratio (R peak), heart rate max (HR max) and percentage of predicted HR max (% predicted HR max) were evaluated in CEPT and ISWT (n = 26). Second, an equation was developed (n = 29) to predict VO2 peak. In both phases, the VO2 peak, respiratory exchange ratio R and hearth rate (HR) were evaluated. In the third stage, the validation equation was performed by another 12 participants. Results Similar results in VO2 peak (P>0.05), R peak (P>0.05) and predicted maximum HR (P>0.05) were obtained between the ISWT and CEPT. Both tests showed moderate signifi- cant correlations of VO2 peak (r = 0.44, P = 0.002) e R peak (r = -0.53, P < 0.01), as well as PLOS ONE \| https://doi.org/10.1371/journal.pone.0206867 November 1, 2018 1 / 11 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Gomes AL, Mendonc¸a VA, Santos Silva Td, Pires CKV, P. Lima L, Silva AM, et al. (2018) Cardiorespiratory and metabolic responses and reference equation validation to predict peak oxygen uptake for the incremental shuttle waking test in adolescent boys. PLoS ONE 13(11): e0206867. https://doi.org/10.1371/journal. pone.0206867 Editor: Gustavo Batista Menezes, UFMG, BRAZIL Received: August 17, 2018 Accepted: October 19, 2018 Published: November 1, 2018 Copyright: © 2018 Gomes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its supporting information files. Funding: The authors are grateful to Brazilian agencies CNPq, CAPES and FAPEMIG for financial support. Competing interests: The authors have declared that no competing interests exist. the agreement of these measurements by Bland-Altman analysis (VO2 peak, bias = -0.13; R peak, bias = 0.0). Distance walked was the variable that explained 42.5% (R2 = 0.425, p = 0.0001) of the variance in VO2 peak. The equation was VO2 peak (predicted) = 20.94 + (0.02 x distance walked). The results obtained by the equation were not significantly differ- ent compared to the values obtained by the gas analyzer and the Bland-Altman analysis showed agreement (bias = 1.6). Conclusion The ISWT produced maximal cardiorespiratory responses comparable to the CEPT, and the developed equation showed viability for the prediction of VO2 peak in healthy sedentary adolescent boys. Introduction Assessment of functional capacity or cardiorespiratory fitness (CRF) is defined as the ability to perform a moderate to high intensity exercise involving large muscle groups over a period of time [1,2]. It is an important component of health related physical fitness, which reflects the functional capacities of the respiratory, cardiovascular and musculoskeletal systems [1]. The CRF assessment has been widely used in clinical practice and research aiming to provide parameters for physical activity prescription and to evaluate reduced exercise tolerance in sev- eral health conditions [3–5]. The performance of a cardiopulmonary exercise testing (CEPT) followed by the measure- ment of peak oxygen consumption (VO2 peak) through the direct analysis of exhaled gases is the most commonly reported procedure in the literature for the evaluation of CRF [6]. How- ever, this measurement is often infeasible because its require high-cost equipment, specialized laboratory and trained professionals [7]. Thus, field test and prediction equation to indirect estimate VO2 peak in clinical practice has been widely implemented [2]. Among the field test, we highlight the Incremental Shuttle Walking Test (ISWT) developed by Sing et al., [8] which comprises as a simple incremental walk test with pace dictated by external stimulus which asses CRF based on distance walked. Despite being developed initially for individuals with chronic obstructive pulmonary disease [8], it has been used recently in different health condi- tions and age groups [5,9,10]. Some studies have used ISWT to asses CRF in children and adolescents with asthma [11], scoliosis [5] and very low premature newborn [10]. However, the application and intensity of this test in a healthy population is scarce. Lanza et al., [9] developed an equation to predict dis- tance walked and also showed that the ISWT is a maximal test in a children and adolescent population determined indirectly by means of the maximum heart rate (HR max) achieved at the end of the test. On the other hand, Coelho et al. [12] demonstrated that a healthy control group of children and adolescents showed submaximal values of HR max in the ISWT. How- ever, these authors fail to confirm the cardiorespiratory responses with the completion and comparison with the CEPT. Taken together, there is a gap in the current literature regarding the ISWT intensity valida- tion, as well as an equation to predict VO2 peak in the adolescent population. Thus, the present study aims to evaluate and correlate the cardiorespiratory outcomes during the ISWT and a CEPT, in order to classify the intensity of ISWT, and to develop and validate an equation to Incremental shuttle walking test in healthy sedentary adolescent boys PLOS ONE \| https://doi.org/10.1371/journal.pone.0206867 November 1, 2018 2 / 11 predict VO2 peak in healthy adolescent boys. We postulate that the ISWT would promote maximal cardiorespiratory responses in agreement with the CEPT, and the regression equa- tion would be feasible for predicting VO2 peak in healthy adolescent boys. Materials and methods This was a cross-sectional study that included 41 healthy adolescent boys. They were recruited by convenience from private and public schools. The protocol began in July 2016 and ended in November 2017. All measurements were obtained in the physiology of exercise laboratory (UFVJM) by trained investigators. The parents were asked to report the health history of the subject (i.e., prematurity birth and current physical activity engagement and comorbidities). Thus, the inclusion criteria were as follows: male boys, ages 12–18 years old, absence of chronic or acute diseases, physical activity engagement less than three times a week, preterm birth and parents sign the consent form. The volunteers were excluded if they were unable to understand the test. To meet the objectives, this study was divided into three stages. The first stage aimed to evaluate the intensity of the ISWT; the second stage aimed to develop a regression for the prediction of VO2 peak; and the third stage to validate the prediction equation. This study fol- lowed the declaration of Helsinki. The Ethics and Research Committee of Universidade Fed- eral dos Vales do Jequitinhonha e Mucuri (UFVJM), Brazil, approved this study (Protocol: 52980816.4.0000.5108). The following protocol description reproduces information already reported elsewhere [2]. First stage procedures In the first stage, 26 volunteers went to the laboratory on three consecutive days at the same time period each day. On the first day, the body composition was assessed (weight, height, BMI) and familiarization was performed. The weight and height were measured on an anthro- pometric mechanical scale, the BMI was calculated as the weight divided by height squared [3]. Familiarization consisted of testing that would be performed on consecutive days to reduce the effect of learning. On the second and third days, the ISWT and CEPT were applied. The testing order was randomized and balanced. The subjects were instructed to avoid physi- cal activity and any intake of caffeine in the 24 hours prior to testing, to get at least 8 hours of sleep the night before, to eat a light meal and to ingest 500 ml of water in two hours before the tests. On the days of testing subjects were asked about their compliance with the recommenda- tions above and about possible complications or changes in their daily routines [2]. To perform the ISWT, the participants were instructed to walk a distance of 10 meters around a marking between two cones, placed 0.5m from each endpoint [8]. The walking speed at which the participant should walk (or run) was dictated by a sound played from a CD that was originally generated by a microcomputer. Each minute the walking speed increased by 0.17m/s. The test was finished when the volunteer was not able to maintain the required speed (more than 0.5m from the cone), at the request of the volunteer, or for some other reported symptom (dyspnea, dizziness, vertigo, angina). The original protocol consisted of 12 levels (1020m); however, as suggested by the literature, we used a protocol of 15 levels (1500m) to evaluate healthy participants, in order to prevent the ceiling effect [13]. Additionally, during the testing, the laps were recorded to calculate the distance and gait speed reached at the last full level. The ISWT were performed twice with at least 30 min of rest between them. The best test (i.e., the longest distance walked) was considered for analysis. The maximum difference between the tests should be 40 m [14]. A third test was performed when the difference was greater than this. A trained professional conducted the tests. Before and after the test, heart Incremental shuttle walking test in healthy sedentary adolescent boys PLOS ONE \| https://doi.org/10.1371/journal.pone.0206867 November 1, 2018 3 / 11 rate (HR, measured by a heart rate monitor) and blood pressure (measured by a mercury sphygmomanometer cuff and a stethoscope) were measured. The CEPT was performed on a treadmill using a protocol based on the progression of the ISWT. This protocol consisted of 1-minute stages, with speed increasing every minute without increasing the incline of the treadmill. The initial speed was 0.5 m/s, and it increased by 0.17 m/s at each stage. Before, during and after the test, heart rate and blood pressure were mea- sured as described above. The criteria for stopping the test was as follows systolic blood pressure (SBP) greater than 210 mmHg; diastolic blood pressure greater than 120 mmHg; sus- tained decrease in SBP, angina dyspnea, cyanosis; nausea, dizziness or by the request of the volunteer [15]. Second stage procedures In the second stage 29 volunteers went to the laboratory at two different days. On the first day, the body composition measurements were obtained as described in the first stage. On the sec- ond day, the participants went to the laboratory for two ISWT with an interval of 30 minutes between them. Completion of two ISWTs with this interval had been suggested to reduce the effects of the learning test [13,16]. For the data analysis, the results of the test in which the volunteer obtained the greatest distance covered were used. As with the first stage, the entire procedure took place during a single day shift: the subjects were instructed to follow all the rec- ommendations for the practice of physical tests, and prior to completion of the tests. Cardiorespiratory and metabolic responses During the tests of the two stages of this study, the exhaled gases were collected using a gas analyzer via the portable telemetry system (k4b2, Cosmed, Rome, Italy). Among other vari- ables, oxygen uptake (VO2), respiratory quotient (R) and HR breath-by-breath were moni- tored. The absolute VO2 peak rate (mL/min) was expressed as relative rate defined as VO2 peak (mL/kg/min) and R peak the highest value of these measures at peak effort [17] and maxi- mum heart rate (HRmax) as the highest HR value recorded during the test [2]. The maximum predicted HR was calculated as 208 (0.7 age) [18]. Validation of the reference equation To validate the equation, a different group of healthy males, composed of 12 individuals, was selected according to the same inclusion criteria of the study. This group completed the ISWT as described in the preceding stages. Likewise, the VO2 peak was predicted by the reference equation. Statistical analysis The statistical analysis was performed using the statistical packages SPSS 22.0 (Inc., USA) and GraphPad Prism 4 (Inc., USA). In the first stage, the normality of data was checked by the Sha- piro–Wilk test and the differences among measured variables were determined by paired-t- test for variables with normal distribution or the Wilcoxon test for variables with non-normal distribution. Pearson’s coefficient of correlation was performed to study the correlation between variables and the agreement between tests was assessed by Bland-Altman analysis. The sample size was calculated based on the study by Neves et al [2] and was identified at least 10 participants. In the second stage, the normality of data was checked by Kolmogorov-Smir- nov test and for compiling the reference equation, the linear multiple regression analysis was performed to identify the predictors of the dependent variable. Multicollinearity was measured Incremental shuttle walking test in healthy sedentary adolescent boys PLOS ONE \| https://doi.org/10.1371/journal.pone.0206867 November 1, 2018 4 / 11 by variance inflation factors (VIF). In this stage, the sample size was estimated on GPower Software version 3.1 and based on the relationship between the numbers of variables to be included in the multiple regression analysis and the minimum number of observations required, indicating at least 29 participants in order to develop a linear model containing up to three variables. At the end of the regression analysis, the paired t-test was utilized to compare the means of the results obtained by the reference equation with the measured values of VO2 peak obtained using the gas analyzer. Moreover, the validation of the reference equation was evaluated in an additional group of 12 volunteers: the values of VO2 peak obtained by the ref- erence equation were compared with the measured values of VO2 peak obtained by the gas analyzer using the paired t-test. The level of statistical significance was P<0.05. Results A total of 336 subjects were screened, but 186 did not return the baseline questionnaire. From the 150 eligible participants, 28 reported any chronic, acute illness or reported premature birth, 49 decline and 32 subjects were excluded for other reasons. The final sample was 41 male adolescents. First stage The general characteristics of the participants of first and second stage and their performance on ISWT are showed in Table 1. The cardiorespiratory responses obtained at the end of the ISWT and CEPT are presented in Table 2. Similar results in VO2 peak, R peak, and predicted HRmax were found. Moderate and significant correlations in VO2 peak (r = 0.44, P = 0.02) and R peak (r = -0.53, P<0.01) were found between the tests. The Bland-Altman analysis also showed agreement between the results for VO2 peak (bias = -0.13) and R peak (bias = 0.00) on the ISWT and CEPT (Fig 1A and 1B). Table 2. Comparison between the results of cardiorespiratory variables at the end of the test, obtained in ISWT and CEPT. Outcome Tests Comparison between tests ISWT (n = 26) CEPT (n = 26) P-value VO2 peak (mL/kg/min) 44.02 (8.2) 44.2 (6.2) 0.93a R peak 1.1 (0.2) 1.1 (0.1) 0.28b HR max (% predicted) 98.8 (6.3) 96.6 (3.5) 0.63b The data is presented as mean (SD). ISWT = incremental shuttle walking test; CEPT = cardiopulmonary exercise testing; VO2 = oxygen uptake; R = respiratory exchange ratio; HR = heart rate. aPaired-t test, bWilcoxon test. https://doi.org/10.1371/journal.pone.0206867.t002 Table 1. Characteristics of participants of the first, second and third stage. Characteristics of the participants (n = 41) First phase (n = 26) Second phase (n = 29) Third phase (n = 12) Age (yr) 14.2 (1.8) 14.3 (1.8) 14.3 (1.9) BMI (kg/m2) 19.5 (2.4) 19.5 (2.4) 20.5 (2.8) Gait speed (m/s) 2.2 (0.3) 2.2 (0.3) 2.2 (0.4) Distance walked (m) 923.9 (249.4) 938.0 (250.2) 915 (309.5) The data is presented as mean (SD). BMI = body mass index. https://doi.org/10.1371/journal.pone.0206867.t001 Incremental shuttle walking test in healthy sedentary adolescent boys PLOS ONE \| https://doi.org/10.1371/journal.pone.0206867 November 1, 2018 5 / 11 Fig 1. Agreement between VO2 (mL/kg/min) peak and R peak obtained in the ISWT and CEPT. (A) Bland-Altman plot of the difference between the VO2 peak of the ISWT and CEPT plotted against the mean VO2 peak of the ISWT and CEPT; (B) Difference R peak of the ISWT and CEPT plotted against the mean R peak of the ISWT and CEPT. ISWT = Incremental Shuttle Walking Test; CEPT = cardiopulmonary exercise testing; VO2 = oxygen uptake; R = respiratory exchange ratio. https://doi.org/10.1371/journal.pone.0206867.g001 Incremental shuttle walking test in healthy sedentary adolescent boys PLOS ONE \| https://doi.org/10.1371/journal.pone.0206867 November 1, 2018 6 / 11 Second stage The characteristics of the participants of the second stage are showed in Table 1. Considering the best ISWT, age, BMI and distance walked were the demographic, anthropometric and physical performance variables selected for the preparation of the reference equation, respec- tively. The univariate analysis showed that the VO2 peak correlated significantly with age (r = 0.38, p = 0.04), and distance (r = 0.67, p = 0.0001). There was no significant correlation with BMI (r = -0.24, p = 0.22). A model of stepwise linear multiple regressions showed that dis- tance walked explained 42.5% (R2 adjusted = 0.425, p = 0.0001) of the variance in VO2 peak. The 95% Confidence Interval for unstandardized coefficients were the constants (11.12 to 30.77) and distance (0.01 to 0.03). The reference equation for the VO2 peak in the ISWT was: VO2 peakðpredictedÞ ¼ 20:94 þ ð0:02 x distance walkedÞ Validation of the reference equation The characteristics of the volunteers who attended in the equation validation stage were pres- ent in Table 1. The results obtained by the equation of VO2 peak with the values obtained by the gas analyzer, showed no significant difference between them (VO2 peak [predicted] = 39.24 ± 6.1 mL/kg/min; VO2 peak [gas analyzer] = 40.87 ± 5.4 mL/kg/min, P = 0.1776). It was possible to verify the agreement between these measures by the Bland-Altman method, in which a bias of 1.6 was showed, representing a difference of 4.4% in the VO2 peak (Fig 2). Fur- thermore, there was no statistically significant difference between the participants of equation elaboration and validation for age (p = 0.7978), weight (p = 0.5498), height (p = 0.0650), BMI (p = 0.2480), distance walked (p = 0.9213) and walking speed (p = 0.0.6212). Discussion The present study describes the comparison of CRF between the ISWT with CEPT in healthy sedentary adolescent boys. In the ISWT, the adolescent boys reached values of HRmax > 90% and R peak > 1.1, thus classifying the ISWT as a maximal effort test [19,20]. Furthermore, results showed a moderate and significant correlations as well as agreement between VO2 peak and R peak by both tests. Our results are corroborated by the results of Lanza et al. [9]. These authors showed that ISWT is a maximal test in children and adolescent by registering higher Fig 2. Bland-Altman agreement of VO2 peak in the validation of the reference equation. https://doi.org/10.1371/journal.pone.0206867.g002 Incremental shuttle walking test in healthy sedentary adolescent boys PLOS ONE \| https://doi.org/10.1371/journal.pone.0206867 November 1, 2018 7 / 11 HR values (>90%) at end of the test. Our research group also showed previously in healthy men [2] and women (data not published) that cardiorespiratory outcomes (VO2 peak and R peak) collected during ISWT are comparable to CEPT test, as well as both tests showed agree- ment and high correlations between VO2 and R peak between ISTW and CEPT [2]. In the other hand, previous studies showed lower HR max in healthy control children at end of the test compared to our data, such as 69% [11] and 55% [10]. However, it’s important to high- light that our participants were allowed to run which can explain the higher HRmax found. To the best of our knowledge there are no studies evaluating CRF between ISWT and CEPT in healthy sedentary adolescent boys. Taken together, our data support that ISWT can be consid- ered as a valid measure to assess CRF in this population as a maximal effort test. Additionally, our study is the first one to develop an equation to predict VO2 peak in the ISWT in this population. Despite of have including anthropometric variables in the multivari- ate analysis, only distance walked explained the variance of VO2 (43%) peak in our population. Distance walked as one of the major determinant of VO2 peak was also observed in previous studies that developed reference equations for the prediction of VO2 peak during the applica- tion of the ISWT in healthy adults [21,22] and during the six-minute walking test in obese ado- lescents [23]. Although the age was significantly correlated to VO2 in the linear analysis, this correlation was not strong the sufficient for explained the variance of VO2 peak. Similar result was observed by Tsiaras et al. (2010), which shown that the addiction of age did not further improve the prediction accuracy of the equation for prediction of VO2 peak from a maximal treadmill test in 12–18 year-old active male adolescents [24]. This absence of influence of age seems to be related to the stabilization of aerobic performance in youth when compared to childhood. In fact, previous studies showed that the performance of adolescents improved lin- early with increase of age, it increased up to 12–13 years, and after (aged 14–19 years) tended to achieve a plateau [25,26]. As with age, BMI did not influence the prediction of VO2 peak. The probable reason for this seems to be related to homogeneity of sample of present study. It is noted that participants of present study showed normal BMI. Thus, given that the CRF is lower in adolescents who are overweight than in those of normal weight, the normal body composition did not was correlated to VO2 peak [25,27]. Finally, it’s important to highlight that distance walked is a feasible variable in clinical practice and have to take into account when developing a regression equation [28]. Although the prediction equation proposed in the present study might be explained by moderate variance, the VO2 peak data collected by the gas analyzer and the developed equation showed agreement. Moreover, the reference values from the current literature that classify CRF (i.e. very week to excellent) vary approximately 7mL/kg/min among the age ranges. Thus, the variation found in the present study (4%) is less likely to change the individuals CRF classi- fication [17]. Finally, the VO2 peak mean reached by the male adolescents in our study (~ 44.0 mL/kg/min) was smaller than previous study reporting VO2 reference for trained men with age ranging from 15 to 24 (53.3 mL/kg/min) [17] or 10–14 years old ( 52.3 mL/kg/min) [6], which classifies our population as sedentary [17]. The results pointed here raise important advancing scientific knowledge regarding the level of ISW in healthy sedentary adolescent boys. The results found in this study contribute to the process of measurement of peak VO2 becomes more accessible to clinical practice so that the prescription and elaboration of exercise programs happen in a more informed and assertive way, as well as ISWT can be used as a maximal effort test in replacement of submaximal field test available (e.g. six minute walking test) [29]. Moreover, clinicians should considerer ISWT instead of other field test (e.g. 9-minute walk / run test, 1-mile walk / run test and the 20 m Shuttle Run Test) [30–32] because these tests are influenced by external factors Incremental shuttle walking test in healthy sedentary adolescent boys PLOS ONE \| https://doi.org/10.1371/journal.pone.0206867 November 1, 2018 8 / 11 (e.g. motivation and self-paced) which can lead to great variability and compromising the application in randomized controlled trials. Lastly, our prediction equation could be used in clinical studies aiming to investigate CRF in disable adolescent boys population avoiding to use control groups for comparing theirs results [5,9,10]. However, due to restrictions of fund- ing and time, no further experiments such as assessing girls and children with age under 12 years old were conducted. Further studies are necessary to address this population. Conclusion In a conclusive way, the VO2 peak values found in our study allow us to affirm that the ISWT was in fact a maximum intensity test in healthy sedentary adolescent boys assessed by direct gas analyzer. Furthermore, the regression equation was feasible and might be useful for clini- cians for predicting VO2 peak in this population. Author Contributions Conceptualization: Andreza L. Gomes, Vanessa A. Mendonc¸a, Alcilene M. Silva, Ana Cristina R. Camargos, Camila D. C. Neves, Ana C. R. Lacerda, He´rcules R. Leite. Data curation: Tatiane dos Santos Silva, Crislaine K. V. 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PMC9653753	Citation: Jost, Z.; Tomczyk, M.; Chroboczek, M.; Calder, P.C.; Laskowski, R. Improved Oxygen Uptake Efﬁciency Parameters Are Not Correlated with VO2peak or Running Economy and Are Not Affected by Omega-3 Fatty Acid Supplementation in Endurance Runners. Int. J. Environ. Res. Public Health 2022, 19, 14043. https://doi. org/10.3390/ijerph192114043 Academic Editors: Nicolas Berger and Russ Best Received: 14 October 2022 Accepted: 25 October 2022 Published: 28 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article Improved Oxygen Uptake Efﬁciency Parameters Are Not Correlated with VO2peak or Running Economy and Are Not Affected by Omega-3 Fatty Acid Supplementation in Endurance Runners Zbigniew Jost 1,* , Maja Tomczyk 1, Maciej Chroboczek 2, Philip C. Calder 3,4 and Radosław Laskowski 2,* 1 Department of Biochemistry, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland 2 Department of Physiology, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland 3 Faculty of Medicine, School of Human Development and Health, University of Southampton, Southampton SO16 6YD, UK 4 NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust and University of Southampton, Southampton SO16 6YD, UK * Correspondence: zbigniew.jost@awf.gda.pl (Z.J.); radoslaw.laskowski@awf.gda.pl (R.L.) Abstract: Peak oxygen uptake (VO2peak) is one of the most reliable parameters of exercise capacity; however, maximum effort is required to achieve this. Therefore, alternative, and repeatable sub- maximal parameters, such as running economy (RE), are needed. Thus, we evaluated the suitability of oxygen uptake efﬁciency (OUE), oxygen uptake efﬁciency plateau (OUEP) and oxygen uptake efﬁciency at the ventilatory anaerobic threshold (OUE@VAT) as alternatives for VO2peak and RE. Moreover, we evaluated how these parameters are affected by endurance training and supplementa- tion with omega-3 fatty acids. A total of 26 amateur male runners completed a 12-week endurance program combined with omega-3 fatty acid supplementation or medium-chain triglycerides as a placebo. Before and after the intervention, the participants were subjected to a treadmill test to determine VO2peak, RE, OUE, OUEP and OUE@VAT. Blood was collected at the same timepoints to determine eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in erythrocytes. OUE corre- lated moderately or weakly with VO2peak (R2 = 0.338, p = 0.002) and (R2 = 0.226, p = 0.014) before and after the intervention, respectively. There was a weak or no correlation between OUEP, OUE@VAT, VO2peak and RE despite steeper OUE, increased OUEP and OUE@VAT values in all participants. OUE parameters cannot be treated as alternative parameters for VO2peak or RE and did not show changes following supplementation with omega-3 fatty acids in male amateur endurance runners. Keywords: peak oxygen uptake; oxygen uptake efﬁciency plateau; running economy; omega-3 fatty acids; endurance runners 1. Introduction There are many cardiopulmonary exercise tests (CPETs) that aim to assess parameters related to human performance, such as peak oxygen uptake (VO2peak) or maximal oxygen uptake (VO2max). VO2max is considered the best indicator of potential in endurance events, being a ‘gold standard’ measurement of integrated cardiopulmonary-muscle oxidative function [1–3]. Although heart rate (HR), respiratory exchange ratio (RER), and minute ventilation (Ve) are considered cardiovascular, respiratory, and pulmonary parameters, respectively, their comprehensive function is often difﬁcult to evaluate. Therefore, there is a need to identify alternative validated and reliable parameters for assessing cardiorespira- tory ﬁtness. Sun and co-authors [4] determined the relationship between oxygen uptake (VO2) and Ve, called oxygen uptake efﬁciency (OUE). They noted that OUE increases linearly with time during early exercise, but becomes non-linear as Ve increases faster than VO2. Int. J. Environ. Res. Public Health 2022, 19, 14043. https://doi.org/10.3390/ijerph192114043 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 14043 2 of 10 This curvilinear relationship during an exercise test is not as appropriate for assessing aerobic capacity as VO2peak. Thus, the authors described other physiological parameters that can be determined from respiratory gases during CPET, i.e., oxygen uptake efﬁciency at the ventilatory anaerobic threshold (OUE@VAT) and oxygen uptake efﬁciency plateau (OUEP) in healthy subjects. It was observed that both OUE@VAT and OUEP are simple measurements that do not require maximum effort. Moreover, they are also easy to visualise, recognise and calculate [4], making them potentially robust parameters for assessing physical ﬁtness. It is worth noting that there is still scarce evidence of improvements in OUE parameters after physical training and no evidence of improvements after supplementation with bioactive compounds such as the omega-3 fatty acids (eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)). Although some studies show no improvements in cardiopulmonary-muscle oxida- tive function following supplementation with ﬁsh oil containing omega-3 fatty acids [5,6], several studies do indicate a positive effect. For example, long-term EPA and DHA supple- mentation may contribute to the improvement in VO2max [7] or to the reduction in the cost of aerobic exercise in trained cyclists [8,9]. Moreover, our recent study showed that 12-week supplementation with omega-3 fatty acids improved running economy (RE) in amateur runners [10]. These studies focus mainly on VO2max and RE, and other submaximal oxygen kinetics parameters need to be further explored. The aim of our study was to determine whether OUE, OUEP and OUE@VAT can be considered as a robust measurements of endurance capacity. Moreover, we verify if those parameters are sensitive to changes after omega-3 fatty acid supplementation. The main hypothesis of this research was that OUE will be sensitive to changes in VO2peak. We also hypothesised that OUE@VAT and OUEP can be used as non-invasive, submaximal parameters of oxygen kinetics replacing VO2peak and RE. We also evaluated whether twelve- week of endurance training combined with omega-3 fatty acid supplementation can alter these parameters in male amateur endurance runners. 2. Materials and Methods 2.1. Participants A total of 26 male amateur runners (37 ± 3 years old; 77 ± 9 kg body weight; VO2peak: 54.2 ± 6 mLkg−1min−1) completed the 12-week experimental study as previously de- scribed [10], which tested the effect of supplementation with omega-3 fatty acids on exercise capacity in male amateur endurance runners. Participants were not taking medication and all were in good health, as conﬁrmed by a medical check. The study was approved by the Bioethical Committee of Regional Medical Society in Gda´nsk (NKBBN/628/2019) and conducted according to the Declaration of Helsinki (2013). All participants provided their written informed consent prior participating in the study. Detailed participant characteris- tics and project design are shown in Table 1 and Figure 1, respectively. Table 1. Characteristics of participants. Variable MCT (n = 12) Mean ± SD OMEGA (n = 14) Mean ± SD Age [y] 37 ± 4 37 ± 3 Body mass [kg] 78.0 ± 8 76.3 ± 11 Height [cm] 180 ± 4 181 ± 7 EPA [% of total erythrocyte fatty acids] Pre 1.2 ± 0.3 1.1 ± 0.4 Post 1.2 ± 0.3 4.9 ± 1.1 ,ˆ DHA [% of total erythrocyte fatty acids] Pre 4.4 ± 1.1 4.7 ± 1.0 Post 4.5 ± 0.8 6.7 ± 0.8 ,ˆ Int. J. Environ. Res. Public Health 2022, 19, 14043 3 of 10 Table 1. Cont. Variable MCT (n = 12) Mean ± SD OMEGA (n = 14) Mean ± SD HRmax [beatsmin−1] Pre 186 ± 9 190 ± 9 Post 184 ± 7 189 ± 9 VO2peak [mLkg−1min−1] Pre 54.7 ± 6.8 53.6 ± 4.4 Post 56.4 ± 5.9 56.0 ± 3.7 RE [mLkg−1min−1] Pre 47.7 ± 3.3 47.6 ± 1.8 Post 48.7 ± 2.9 46.5 ± 2.4 ˆ EPA—eicosapentaenoic acid; DHA—docosahexaenoic acid; HRmax—maximal heart rate; RE—running economy; data are presented as mean ± SD; * p < 0.05 for post vs. pre value ˆ p < 0.05 for MCT vs. OMEGA. Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 3 of 10 Post 1.2 ± 0.3 4.9 ± 1.1 ,^ DHA [% of total erythrocyte fatty acids] Pre 4.4 ± 1.1 4.7 ± 1.0 Post 4.5 ± 0.8 6.7 ± 0.8 ,^ HRmax [beatsmin−1] Pre 186 ± 9 190 ± 9 Post 184 ± 7 189 ± 9 VO2peak [mLkg−1min−1] Pre 54.7 ± 6.8 53.6 ± 4.4 Post 56.4 ± 5.9 56.0 ± 3.7 RE [mLkg−1min−1] Pre 47.7 ± 3.3 47.6 ± 1.8 Post 48.7 ± 2.9 46.5 ± 2.4 ^ EPA—eicosapentaenoic acid; DHA—docosahexaenoic acid; HRmax—maximal heart rate; RE—run- ning economy; data are presented as mean ± SD; * p < 0.05 for post vs. pre value ^ p < 0.05 for MCT vs. OMEGA. Figure 1. Procedure design. 2.2. Supplementation Participants were randomly assigned to one of two groups with the final character- istics as follows: OMEGA (37 ± 3 yr; 76.3 ± 11 kg body weight; VO2peak: 53.6 ± 4 mLkg−1min−1) or medium-chain triglycerides (MCT) as placebo (37 ± 4 yr; 78 ± 8 kg body weight; VO2peak: 54.7 ± 7 mLkg−1min−1). The division of participants into two groups was performed to check the difference in the response of OUE parameters to supplementation. Hence, participants supplemented four capsules per day, providing a total of 2234 mg of EPA + 916 mg of DHA (OMEGA group) or 4000 mg of MCT (MCT group). The capsules were provided in coded, identical-looking packages to avoid a potential recognition. To maintain the quality of supplements consisting of omega-3 fatty acids and their respective dosages, materials adhering to the International Fish Oil Standard (IFOS) were used. 2.3. Treadmill Exercise Testing Exercise tests were conducted under controlled environmental conditions (18–20 °C and humidity 40–45%) and were performed at similar time of day ± 2 h. Before carrying out the exercise tests, the participants performed a familiarization trial. The participants were informed to refrain from strenuous exercise for 24 h and from caffeine or alcohol consumption for 12 h prior to the tests. Before and after twelve weeks of the training pro- gram, participants undertook a ramp exercise test to volitional exhaustion on a treadmill (h/p Cosmos, Saturn, Nussdorf-Traunstein, Germany). First, participants stood on the treadmill for 2 min to make sure the measuring equipment was ready and to measure the resting parameters. Thereafter, runners walked for 5 min at 5 km/h speed and with a 1.5% inclination as a warm-up prior to starting the test. Every next stage lasted 3 min, and the Figure 1. Procedure design. 2.2. Supplementation Participants were randomly assigned to one of two groups with the final characteristics as follows: OMEGA (37 ± 3 years; 76.3 ± 11 kg body weight; VO2peak: 53.6 ± 4 mLkg−1min−1) or medium-chain triglycerides (MCT) as placebo (37 ± 4 years; 78 ± 8 kg body weight; VO2peak: 54.7 ± 7 mLkg−1min−1). The division of participants into two groups was per- formed to check the difference in the response of OUE parameters to supplementation. Hence, participants supplemented four capsules per day, providing a total of 2234 mg of EPA + 916 mg of DHA (OMEGA group) or 4000 mg of MCT (MCT group). The capsules were provided in coded, identical-looking packages to avoid a potential recognition. To maintain the quality of supplements consisting of omega-3 fatty acids and their respective dosages, materials adhering to the International Fish Oil Standard (IFOS) were used. 2.3. Treadmill Exercise Testing Exercise tests were conducted under controlled environmental conditions (18–20 ◦C and humidity 40–45%) and were performed at similar time of day ± 2 h. Before carrying out the exercise tests, the participants performed a familiarization trial. The participants were informed to refrain from strenuous exercise for 24 h and from caffeine or alcohol consumption for 12 h prior to the tests. Before and after twelve weeks of the training program, participants undertook a ramp exercise test to volitional exhaustion on a treadmill (h/p Cosmos, Saturn, Nussdorf-Traunstein, Germany). First, participants stood on the treadmill for 2 min to make sure the measuring equipment was ready and to measure the resting parameters. Thereafter, runners walked for 5 min at 5 km/h speed and with a 1.5% inclination as a warm-up prior to starting the test. Every next stage lasted 3 min, and the treadmill belt was accelerated starting from 8 km/h by 1 km/h per stage up to 12 km/h. Int. J. Environ. Res. Public Health 2022, 19, 14043 4 of 10 Then, the inclination of the treadmill was increased to 5%, 10% and 15% at 12 km/h speed until volitional exhaustion, despite strong verbal encouragement. During both tests, heart rate (HR) was monitored (Polar RS400, Kempele, Finland). RE was measured as an oxygen cost from last 50 s of each stage to 12 km/h speed and was expressed as mLkg−1min−1 [11]. 2.4. Respiratory Gas Measurements During both laboratory tests, the exhaled air was continuously measured using a breath-by-breath analyser (Oxycon Pro, Jaeger, Hoechberg, Germany). Before the tests, the analyser was calibrated in accordance with the manufacturer’s instructions. All measure- ments were averaged to 10 s intervals and included: oxygen uptake (VO2), carbon dioxide output (VCO2), minute ventilation (Ve), end-tidal pressure of oxygen (PETO2) and end-tidal pressure of carbon dioxide (PETCO2). 2.5. Determination of Oxygen Uptake Efﬁciency and Ventilatory Thresholds The OUE was individually determined for each participant by calculating the regres- sion slope from the linear relationship of absolute VO2 (mLmin−1) plotted as a linear function of Ve (Lmin−1) (VO2 = Ve + b), as previously described by Sun et al. [4]. Af- ter calculating the OUE individually for each participant from the formula, the OUE was correlated with the true VO2peak and normalized, and the original OUE values (“b”) were compared for the slope of the linear regression of the oxygen uptake efﬁciency. OUEP was calculated as the 90 s average of the highest consecutive measurements of VO2 (mLmin−1)/Ve (Lmin−1) and OUE at the ventilatory anaerobic threshold (OUE@VAT), as the 60 s average of consecutive measurements at and immediately before the VAT ac- cordingly to Sun et al. [4]. First, ventilatory threshold (VT1) was determined as increase in both the ventilatory equivalent of oxygen (Ve/VO2) and end-tidal pressure of oxygen (PETO2) with no concomitant increase in the ventilatory equivalent of carbon dioxide (Ve/VCO2) [12]. The ventilatory anaerobic threshold (VAT) was measured by the V-slope method [13]. Peak oxygen uptake (VO2peak) was obtained as the last 30 s oxygen uptake mean value recorded during the test [14]. 2.6. Training Program All participants underwent 12 weeks of an endurance training program. The partici- pants performed endurance training of varying intensity three times a week according to Costa et al. [15] with slight modiﬁcations. Additionally, participants performed training once a week, which aimed to strengthen the central stabilization muscles and to reduce the risk of injury [16]. The training intensity was distributed among 3 heart-rate zones (Z1-Z2-Z3). They were determined according to the ﬁrst ventilatory threshold (VT1), ven- tilatory anaerobic threshold (VAT) and the corresponding values of the heart rate [Z1: ≤HR@VT1 + 5 bpm; Z2: (>HR@VT1 + 5 bpm) to (≤HR@VAT-5 bpm); Z3: >HR@VAT-5 bpm]. Average training times spent in every mesocycle were (~80%-15%-5%) in zones (Z1-Z2-Z3), respectively. In the last week, the training volume was reduced to reduce the accumulated fatigue. All trainings were monitored by Polar M430 (Kempele, Finland) wrist watches and H9 heart-rate chest sensor and the supervision over the participants was carried out by a certiﬁed track and ﬁeld coach. 2.7. Erythrocyte Fatty Acid Analysis Sample collection and fatty acid determination were outlined elsewhere [10]. In brief, blood samples were collected into 4 mL sodium citrate vacutainer tubes and centrifuged at 4 ◦C (4000× g for 10 min). After centrifugation, plasma was collected with a disposable Pasteur pipette, transferred into separate Eppendorf probes and stored in a −80 ◦C freezer until further analysis. Erythrocyte lipids were extracted into chloroform:methanol and fatty acid methyl esters (representing the erythrocyte fatty acids) were formed by heating the lipid extract with methanolic sulphuric acid. The fatty acid methyl esters were separated Int. J. Environ. Res. Public Health 2022, 19, 14043 5 of 10 by gas chromatography on a Hewlett Packard 6890 gas chromatograph ﬁtted with a BPX-70 column using the settings and run conditions described by Fisk et al. [17]. Fatty acid methyl esters were identiﬁed by comparison with runtimes of authentic standards and data were expressed as weight % of total fatty acids. 2.8. Statistical Analysis Statistical analysis was performed using GraphPad Prism 7 (San Diego, CA, USA). Arithmetic means, standard deviation (SD), and signiﬁcance levels of differences between means were calculated. Two-way analysis of variance (ANOVA), with repeated measures, was used to investigate the signiﬁcance of differences between groups and time. Signiﬁcant main effects were further analyzed using the Bonferroni corrected post hoc test. Correlations between variables were evaluated using the Pearson and Spearman correlations coefﬁcients. All analyses used a signiﬁcance level of p < 0.05. 3. Results 3.1. Predicted VO2peak from OUE Equation Predicted VO2peak calculated from the OUE formula both before and after the supple- mentation intervention was moderately correlated with peak oxygen uptake (R2 = 0.338, p = 0.002; Figure 2A) for all participants before the study. Moreover, the results without grouping also showed a correlation after 12 weeks of intervention (R2 = 0.226, p = 0.014; Figure 2B), but the correlation was weak. Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 5 of 10 fatty acid methyl esters (representing the erythrocyte fatty acids) were formed by heating the lipid extract with methanolic sulphuric acid. The fatty acid methyl esters were sepa- rated by gas chromatography on a Hewlett Packard 6890 gas chromatograph fitted with a BPX-70 column using the settings and run conditions described by Fisk et al. [17]. Fatty acid methyl esters were identified by comparison with runtimes of authentic standards and data were expressed as weight % of total fatty acids. 2.8. Statistical Analysis Statistical analysis was performed using GraphPad Prism 7 (San Diego, CA, USA). Arithmetic means, standard deviation (SD), and significance levels of differences between means were calculated. Two-way analysis of variance (ANOVA), with repeated measures, was used to investigate the significance of differences between groups and time. Signifi- cant main effects were further analyzed using the Bonferroni corrected post hoc test. Cor- relations between variables were evaluated using the Pearson and Spearman correlations coefficients. All analyses used a significance level of p < 0.05. 3. Results 3.1. Predicted VO2peak from OUE Equation Predicted VO2peak calculated from the OUE formula both before and after the supple- mentation intervention was moderately correlated with peak oxygen uptake (R2 = 0.338, p = 0.002; Figure 2A) for all participants before the study. Moreover, the results without grouping also showed a correlation after 12 weeks of intervention (R2 = 0.226, p = 0.014; Figure 2B), but the correlation was weak. Figure 2. The linear relationship between VO2peak and predicted VO2peak before (A) and after (B) twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). 3.2. Oxygen Uptake Efficiency Plateau Pre-intervention OUEP values weakly correlated with VO2peak (R2 = 0.247, p = 0.01; Figure 3A). After twelve weeks of intervention, no correlation was found between these two indicators (R2 = 0.077, p = 0.17, Figure 3B). Figure 2. The linear relationship between VO2peak and predicted VO2peak before (A) and af- ter (B) twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). 3.2. Oxygen Uptake Efﬁciency Plateau Pre-intervention OUEP values weakly correlated with VO2peak (R2 = 0.247, p = 0.01; Figure 3A). After twelve weeks of intervention, no correlation was found between these two indicators (R2 = 0.077, p = 0.17, Figure 3B). Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 6 of 10 Figure 3. The linear relationship between VO2peak and OUEP before (A) and after (B) twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). 3.3. Oxygen Uptake Efficiency at the Ventilatory Anaerobic Threshold OUE@VAT poorly correlated with the peak oxygen uptake (VO2peak) before the study (R2 = 0.179, p = 0.031, Figure 4A) and there was no correlation after the 12-week interven- tion (R2 = 0.082, p = 0.154, Figure 4B) in all participants. Figure 3. The linear relationship between VO2peak and OUEP before (A) and after (B) twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). Int. J. Environ. Res. Public Health 2022, 19, 14043 6 of 10 3.3. Oxygen Uptake Efﬁciency at the Ventilatory Anaerobic Threshold OUE@VAT poorly correlated with the peak oxygen uptake (VO2peak) before the study (R2 = 0.179, p = 0.031, Figure 4A) and there was no correlation after the 12-week intervention (R2 = 0.082, p = 0.154, Figure 4B) in all participants. Figure 3. The linear relationship between VO2peak and OUEP before (A) and after (B) twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). 3.3. Oxygen Uptake Efficiency at the Ventilatory Anaerobic Threshold OUE@VAT poorly correlated with the peak oxygen uptake (VO2peak) before the study (R2 = 0.179, p = 0.031, Figure 4A) and there was no correlation after the 12-week interven- tion (R2 = 0.082, p = 0.154, Figure 4B) in all participants. Figure 4. The linear relationship between VO2peak and OUE@VAT before (A) and after (B) twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). 3.4. Correlation between OUEP, OUE@VAT and RE The changes observed in RE (presented as VO2 delta [%] at 12 km/h) were not corre- lated with the change in OUEP (R2 = 0.018, p = 0.511; Figure 5A). Similar results were ob- served in the correlation between RE and OUE@VAT (r = 0.079, p = 0.699; Figure 5B) in all participants. Figure 5. Correlation between changes in RE and OUEP (A) and OUE@VAT (B) after twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). Figure 4. The linear relationship between VO2peak and OUE@VAT before (A) and after (B) twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). 3.4. Correlation between OUEP, OUE@VAT and RE The changes observed in RE (presented as VO2 delta [%] at 12 km/h) were not correlated with the change in OUEP (R2 = 0.018, p = 0.511; Figure 5A). Similar results were observed in the correlation between RE and OUE@VAT (r = 0.079, p = 0.699; Figure 5B) in all participants. Figure 3. The linear relationship between VO2peak and OUEP before (A) and after (B) twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). 3.3. Oxygen Uptake Efficiency at the Ventilatory Anaerobic Threshold OUE@VAT poorly correlated with the peak oxygen uptake (VO2peak) before the study (R2 = 0.179, p = 0.031, Figure 4A) and there was no correlation after the 12-week interven- tion (R2 = 0.082, p = 0.154, Figure 4B) in all participants. Figure 4. The linear relationship between VO2peak and OUE@VAT before (A) and after (B) twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). 3.4. Correlation between OUEP, OUE@VAT and RE The changes observed in RE (presented as VO2 delta [%] at 12 km/h) were not corre- lated with the change in OUEP (R2 = 0.018, p = 0.511; Figure 5A). Similar results were ob- served in the correlation between RE and OUE@VAT (r = 0.079, p = 0.699; Figure 5B) in all participants. Figure 5. Correlation between changes in RE and OUEP (A) and OUE@VAT (B) after twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). Figure 5. Correlation between changes in RE and OUEP (A) and OUE@VAT (B) after twelve weeks of combined endurance training and supplementation (OMEGA and MCT groups; n = 26). 3.5. Omega-3 Fatty Acids Supplementation Baseline levels of EPA and DHA did not differ between the groups (OMEGA group: 1.1% EPA, 4.7% DHA; MCT group: 1.2% EPA, 4.4% DHA, both p > 0.999). Post-intervention values of EPA and DHA increased in OMEGA group (4.9% EPA, 6.7% DHA, both p < 0.001). Changes were not observed in MCT group (1.2% EPA, p > 0.999; 4.7% DHA, p = 0.551). All results are provided in Table 1. 3.5.1. Oxygen Uptake Efﬁciency At the end of the 12-week supplementation period, there was an increase in the slope of oxygen uptake efﬁciency in the OMEGA group from 35.4 ± 3.3 to 37.6 ± 3.0 and in the MCT group from 35.5 ± 3.7 to 37.2 ± 3.1; (both p < 0.001). OUE increased when groups were combined from 35.5 ± 3.4 to 37.4 ± 3.0; (p < 0.001, Table 2). Int. J. Environ. Res. Public Health 2022, 19, 14043 7 of 10 Table 2. Comparison of effects omega-3 fatty acid supplementation with placebo controlled on cardiorespiratory ﬁtness (CRF) parameters. Variable MCT (n = 12) Mean ± SD OMEGA (n = 14) Mean ± SD ALL (n = 26) Mean ± SD Pre Post Pre Post Pre Post OUE [mLL−1] 35.5 ± 3.7 37.2 ± 3.1 35.4 ± 3.3 37.6 ± 3.1 * 35.5 ± 3.4 37.4 ± 3.0 *** OUEP [mLL−1] 41.8 ± 5.2 42.9 ± 3.8 41.3 ± 4.6 43.6 ± 4.0 41.6 ± 4.8 43.2 ± 3.9 ** OUE@VAT [mLL−1] 33.2 ± 3.8 35.4 ± 3.5 * 32.7 ± 3.6 35.9 ± 4.7 * 32.9 ± 3.7 35.7 ± 4.1 *** Ve [Lmin−1] 93.8 ± 11.6 90.7 ± 9.3 * 92.9 ± 20.4 87.4 ± 20.2 * 93.3 ± 16.4 88.9 ± 15.7 * OUE—oxygen uptake efﬁciency; OUEP—oxygen uptake efﬁciency plateau; OUE@VAT—oxygen uptake efﬁciency at the ventilatory anaerobic threshold; Ve—minute ventilation; * p < 0.05 for post to pre value; p < 0.01 for post to pre value; * p < 0.001 for post to pre value; data are presented as mean ± standard deviation (SD). 3.5.2. Oxygen Uptake Efﬁciency Plateau Oxygen uptake efficiency plateau values increased in the OMEGA group from 41.3 ± 4.6 to 43.6 ± 4.0; (p = 0.017). There were no changes in the MCT group (p = 0.2). Moreover, the analysis of the two groups together (regardless of the supplementation that was undertaken) showed that OUEP increased from 41.6 ± 4.8 to 43.2 ± 3.9; (p = 0.007, Table 2). 3.5.3. Oxygen Uptake at Ventilatory Anaerobic Threshold There was an increase in OUE@VAT in the OMEGA group from 32.7 ± 3.6 to 35.9 ± 4.7; (p = 0.012) and in the MCT group from 33.2 ± 3.8 to 35.4 ± 3.5; (p = 0.003). The results, regardless of the supplementation undertaken, showed that OUE@VAT increased from 32.9 ± 3.7 to 35.7 ± 4.1; (p < 0.001, Table 2). 4. Discussion This is the ﬁrst study to report the correlations between OUE, OUEP, OUE@VAT and VO2peak as well as OUEP and OUE@VAT and RE. They were analyzed in terms of reliability and repeatability, and whether they could be non-invasive substitute measurements for VO2peak and RE. Additionally, we investigated whether these parameters were altered following supplementation with omega-3 fatty acids. The true VO2max value is mainly achievable during a laboratory progressive exercise test to exhaustion where large muscle groups are involved. Simultaneously, the observed kinetics of oxygen supply/utilization in the muscles must be without signiﬁcant changes: the so-called plateau [18]. It is known that this phenomenon occurs when a high intensity is met, and the primary criteria for achieving this parameter (VO2max) during CPET are: (1) reaching a VO2 plateau or (2) levelling-off the oxygen uptake (VO2) [19–21]. Thus, in Sun and co-authors’ study, OUE, OUEP and OUE@VAT comprehensively reﬂected cardio- vascular functions as an alternative for parameters assessing CRF without the need for maximum effort [4]. A steeper OUE (VO2/Ve) and higher values of OUEP and OUE@VAT show more efﬁcient oxygen uptake and utilization in the working skeletal muscles. OUE showed an improvement, but, for both groups, this occurred after 12 weeks of interven- tion. Hence, it is believed that the increase in slope/higher OUE values was the result of endurance training. Moreover, in our study, weak or no correlation was observed between OUEP and peak oxygen uptake. In a study by Bongers et al. [22], in which 214 children participated, OUEP was weak-to-moderately correlated with VO2peak (r = 0.646), which is inconsistent with our results. However, children and adults respond differently to ex- ercise, which might explain this difference. Another study also conﬁrms that OUEP does not accurately predict VO2max in male adolescents and should not replace VO2max, when assessing CRF [19]. In our study, OUE@VAT also demonstrated no correlation with VO2peak before and after 12 weeks of intervention. In contrast to our results, one study revealed that ventilatory anaerobic threshold (VAT) strongly correlated with VO2peak (r = 0.831) [23]. However, there is a difference between the compared parameters, because OUE@VAT is the Int. J. Environ. Res. Public Health 2022, 19, 14043 8 of 10 60-s average of consecutive measurements at and immediately before the VAT. On the other hand, VAT is a single measurement and is not free from intra-observer and inter-observer variability [24]. Hence, both OUEP and OUE@VAT may be more stable measurements than VAT; however, the results of our study did not conﬁrm this. Endurance capacity also has a stable predictor in the form of RE [25]. However, as earlier authors suggest, an accurate measurement of RE can be carried out with the use of invasive lactate measurement, which is one of the disturbances in VO2 steady-state indicators [26,27]. Therefore, in this study, an attempt was made to replace RE with OUEP and OUE@VAT and to check whether they can be a solid, non-invasive predictor of RE in recreational runners. Despite the increase in the efﬁciency of oxygen uptake in all participants, the linear regression did not show any correlation between OUEP, OUE@VAT and RE. Hence, the RE measurement should not be replaced with OUEP and OUE@VAT, as they are not related. The assessment of adaptive changes following supplementation with omega-3 fatty acids is also not fully known. The health-promoting effects of n-3 PUFA supplementation are well-established [28–30]. These effects are related to the incorporation of EPA and DHA into the erythrocyte cell membrane [31], skeletal muscles [32] and heart [33]. Furthermore, the systemic response to supplementation with omega-3 fatty acids as exempliﬁed by maximum oxygen uptake [7], exercise economy [9,10] or anaerobic endurance capacity [34] is well-known. Nevertheless, in our study, for the ﬁrst time, an attempt was made to link the effect of supplemental EPA + DHA to changes in OUEP and OUE@VAT. However, the OUE parameters increased in both groups. Therefore, changes in OUEP and OUE@VAT following 12 weeks of intervention are dictated by adaptation to endurance training rather than changes caused by EPA and DHA supplementation. Limitations and Future Perspectives Despite some valuable information coming from this study, there are some limitations. First, the small number of participants could distort the estimate of correlations between the variables. Second, this study was conducted in male runners only; therefore, these ﬁndings cannot be generalized and extrapolated to females. Future studies should include a larger number of participants and include females. 5. Conclusions In conclusion, the results obtained in this study do not support the use of OUEP and OUE@VAT as an alternative parameter to VO2peak and RE. Additionally, the 12-week supplementation of omega-3 fatty acids at a dose of 2234 mg of EPA and 916 mg of DHA daily did not reveal changes in OUEP and OUE@VAT. Hence, the suitability of using OUEP and OUE@VAT as alternative, non-invasive CRF parameters for VO2peak and RE can be questioned. Author Contributions: Conceptualization, Z.J.; methodology, Z.J. and M.C.; software, Z.J., M.T. and M.C.; validation, R.L.; formal analysis, Z.J. and M.C.; investigation, Z.J., M.T. and M.C.; resources, M.T.; data curation, M.C.; writing—original draft preparation, Z.J., M.T., P.C.C. and R.L.; writing— review and editing, Z.J., M.T., M.C., P.C.C. and R.L.; visualization, Z.J. and P.C.C.; supervision, P.C.C. and R.L.; project administration, M.T.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by National Science Center (Poland), grant number 2018/31/N/N Z7/02962. Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and approved by the Bioethical Committee of Regional Medical Society in Gda´nsk (NKBBN/628/2019). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Int. J. Environ. Res. Public Health 2022, 19, 14043 9 of 10 Data Availability Statement: The data presented in this study are available on request from the corresponding author. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Levine, B.D. What Do We Know, and What Do We Still Need to Know? J. Physiol. 2008, 586, 25–34. [CrossRef] [PubMed] 2. Taylor, H.L.; Buskirk, E.; Henschel, A. Maximal Oxygen Intake as an Objective Measure of Cardio-Respiratory Performance. J. Appl. Physiol. 1955, 8, 73–80. [CrossRef] 3. Poole, D.C.; Jones, A.M. Measurement of the Maximum Oxygen Uptake Vo2max: Vo2peak Is No Longer Acceptable. J. Appl. Physiol. 2017, 122, 997–1002. [CrossRef] 4. Sun, X.-G.; Hansen, J.E.; Stringer, W.W. Oxygen Uptake Efﬁciency Plateau: Physiology and Reference Values. Eur. J. Appl. Physiol. 2012, 112, 919–928. [CrossRef] 5. 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Bongers, B.C.; Hulzebos, E.H.; Helbing, W.A.; ten Harkel, A.D.; van Brussel, M.; Takken, T. Response Proﬁles of Oxygen Uptake Efﬁciency during Exercise in Healthy Children. Eur. J. Prev. Cardiol. 2016, 23, 865–873. [CrossRef] [PubMed] 23. Mourot, L.; Perrey, S.; Tordi, N.; Rouillon, J.D. Evaluation of Fitness Level by the Oxygen Uptake Efﬁciency Slope after a Short-Term Intermittent Endurance Training. Int. J. Sports Med. 2004, 25, 85–91. [CrossRef] 24. Yeh, M.P.; Gardner, R.M.; Adams, T.D.; Yanowitz, F.G.; Crapo, R.O. “Anaerobic Threshold”: Problems of Determination and Validation. J. Appl. Physiol. 1983, 55, 1178–1186. [CrossRef] [PubMed] 25. Saunders, P.U.; Pyne, D.B.; Telford, R.D.; Hawley, J.A. Factors Affecting Running Economy in Trained Distance Runners. Sport. Med. 2004, 34, 465–485. [CrossRef] 26. Hoff, J.; Støren, Ø.; Finstad, A.; Wang, E.; Helgerud, J. Increased Blood Lactate Level Deteriorates Running Economy in World Class Endurance Athletes. J. Strength Cond. Res. 2016, 30, 1373–1378. [CrossRef] [PubMed] Int. J. Environ. Res. Public Health 2022, 19, 14043 10 of 10 27. Jones, A.M. The Physiology of the World Record Holder for the Women’s Marathon. Int. J. Sports Sci. Coach. 2006, 1, 101–116. [CrossRef] 28. Calder, P.C. N–3 Fatty Acids and Cardiovascular Disease: Evidence Explained and Mechanisms Explored. Clin. Sci. 2004, 107, 1–11. [CrossRef] [PubMed] 29. Wang, C.; Harris, W.S.; Chung, M.; Lichtenstein, A.H.; Balk, E.M.; Kupelnick, B.; Jordan, H.S.; Lau, J. N−3 Fatty Acids from Fish or Fish-Oil Supplements, but Not α-Linolenic Acid, Beneﬁt Cardiovascular Disease Outcomes in Primary- and Secondary-Prevention Studies: A Systematic Review. Am. J. Clin. Nutr. 2006, 84, 5–17. [CrossRef] [PubMed] 30. Calder, P.C. Very Long-Chain n-3 Fatty Acids and Human Health: Fact, Fiction and the Future. Proc. Nutr. Soc. 2018, 77, 52–72. [CrossRef] [PubMed] 31. Katan, M.B.; Deslypere, J.P.; van Birgelen, A.P.; Penders, M.; Zegwaard, M. Kinetics of the Incorporation of Dietary Fatty Acids into Serum Cholesteryl Esters, Erythrocyte Membranes, and Adipose Tissue: An 18-Month Controlled Study. J. Lipid. Res. 1997, 38, 2012–2022. [CrossRef] 32. McGlory, C.; Galloway, S.D.R.; Hamilton, D.L.; McClintock, C.; Breen, L.; Dick, J.R.; Bell, J.G.; Tipton, K.D. Temporal Changes in Human Skeletal Muscle and Blood Lipid Composition with Fish Oil Supplementation. Prostaglandins Leukot. Essent. Fat. Acids. 2014, 90, 199–206. [CrossRef] [PubMed] 33. Harris, W.S.; von Schacky, C. The Omega-3 Index: A New Risk Factor for Death from Coronary Heart Disease? Prev. Med. 2004, 39, 212–220. [CrossRef] [PubMed] 34. Gravina, L.; Brown, F.F.; Alexander, L.; Dick, J.; Bell, G.; Witard, O.C.; Galloway, S.D.R. N-3 Fatty Acid Supplementation During 4 Weeks of Training Leads to Improved Anaerobic Endurance Capacity, but Not Maximal Strength, Speed, or Power in Soccer Players. Int. J. Sport Nutr. Exerc. Metab. 2017, 27, 305–313. [CrossRef] [PubMed]	Improved Oxygen Uptake Efficiency Parameters Are Not Correlated with VO<sub>2peak</sub> or Running Economy and Are Not Affected by Omega-3 Fatty Acid Supplementation in Endurance Runners.	10-28-2022	Jost, Zbigniew,Tomczyk, Maja,Chroboczek, Maciej,Calder, Philip C,Laskowski, Radosław	eng
PMC9794057	1 S8 Table. Consensus decision. Results of the consensus decision of the steering committee, sorted by level of agreement. Member 1 Member 2 Member 3 Member 4 Member 5 Level of agreement (%) Recovery speeda Yes Yes Yes Yes Yes 100 Weight/ BMI No Yes Yes No Yes 60 Tendon stiffness Yes No Yes No Yes 60 Heat resistance capacity Yes Yes No Yes No 60 Altitude training sensitivity Yes Yes No Yes No 60 Angiogenesis No Yes No Yes No 40 Muscle fibre transformation capacity No Yes No Yes No 40 Healing function of soft tissue No Yes No No No 20 Risk of joint injuries No Yes No No No 20 Risk of upper respiratory tract infections No Yes No No No 20 Emotion regulation No No No Yes No 20 Self-control No No No Yes No 20 Resilience No Yes No No No 20 a100% level of agreement and the factor therefore was included in the consensus report. Yes = Factor should be included in consensus report. No = Factor should not be included in consensus report.	Factors associated with high-level endurance performance: An expert consensus derived via the Delphi technique.	12-27-2022	Konopka, Magdalena J,Zeegers, Maurice P,Solberg, Paul A,Delhaije, Louis,Meeusen, Romain,Ruigrok, Geert,Rietjens, Gerard,Sperlich, Billy	eng
PMC9209328	Hutchinson MJ, Goosey-Tolfrey VL, Rethinking aerobic exercise intensity prescription in adults with spinal cord injury: time to end the use of “moderate to vigorous” intensity? Supplementary Material 1: Dynamic model with lagged independent variable for RPE and %V̇ O2peak 𝑥𝑥 = % V̇ O2peak 𝑦𝑦 = RPE Z1 = 1, when i = 1 (i = measurement occasion) Z2 = 1, when i > 1 TETRA = 1, if Group = TETRA 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛽𝛽1𝑖𝑖𝑍𝑍1𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝑖𝑖𝑍𝑍1. 𝑥𝑥𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝑖𝑖𝑍𝑍2𝑖𝑖𝑖𝑖 + 𝛽𝛽4𝑖𝑖𝑍𝑍2. 𝑥𝑥𝑖𝑖𝑖𝑖 + 𝛽𝛽5𝑖𝑖𝑍𝑍2. 𝑥𝑥𝑖𝑖−1𝑖𝑖 + 𝛽𝛽6𝑖𝑖𝑍𝑍2. 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖 + 𝑢𝑢0𝑖𝑖 𝛽𝛽1𝑖𝑖 = 𝛽𝛽1 + 𝑒𝑒1𝑖𝑖𝑖𝑖 𝛽𝛽2𝑖𝑖 = 𝛽𝛽2 + 𝑢𝑢2𝑖𝑖 𝛽𝛽3𝑖𝑖 = 𝛽𝛽3 + 𝑒𝑒3𝑖𝑖𝑖𝑖 𝛽𝛽4𝑖𝑖 = 𝛽𝛽4 + 𝑢𝑢4𝑖𝑖 𝛽𝛽5𝑖𝑖 = 𝛽𝛽5 + 𝑢𝑢5𝑖𝑖 𝛽𝛽6𝑖𝑖 = 𝛽𝛽6 + 𝑒𝑒6𝑖𝑖𝑖𝑖 ⎝ ⎜ ⎛ 𝑢𝑢0𝑖𝑖 𝑢𝑢2𝑖𝑖 𝑢𝑢4𝑖𝑖 𝑢𝑢5𝑖𝑖 ⎠ ⎟ ⎞ ~𝑁𝑁(0, Ω𝑢𝑢): Ω𝑢𝑢 = ⎣ ⎢ ⎢ ⎢ ⎡ 𝜎𝜎𝑢𝑢0 2 𝜎𝜎𝑢𝑢02 𝜎𝜎𝑢𝑢2 2 𝜎𝜎𝑢𝑢04 0 𝜎𝜎𝑢𝑢4 2 𝜎𝜎𝑢𝑢05 0 𝜎𝜎𝑢𝑢45 𝜎𝜎𝑢𝑢5 2 ⎦ ⎥ ⎥ ⎥ ⎤ ቌ 𝑒𝑒1𝑖𝑖𝑖𝑖 𝑒𝑒3𝑖𝑖𝑖𝑖 𝑒𝑒6𝑖𝑖𝑖𝑖 ቍ ~𝑁𝑁(0, Ω𝑒𝑒): Ω𝑒𝑒 = ቎ 𝜎𝜎𝑒𝑒1 2 0 𝜎𝜎𝑒𝑒3 2 0 𝜎𝜎𝑒𝑒36 𝜎𝜎𝑒𝑒6 2 ቏ Hutchinson MJ, Goosey-Tolfrey VL, Rethinking aerobic exercise intensity prescription in adults with spinal cord injury: time to end the use of “moderate to vigorous” intensity? 𝑦𝑦𝑖𝑖𝑖𝑖 = 5.133𝑍𝑍1𝑖𝑖𝑖𝑖 + 0.074𝑍𝑍1. 𝑥𝑥𝑖𝑖𝑖𝑖 + 3.411𝑍𝑍2𝑖𝑖𝑖𝑖 + 0.093𝑍𝑍2. 𝑥𝑥𝑖𝑖𝑖𝑖 + 0.074𝑍𝑍2. 𝑥𝑥𝑖𝑖−1𝑖𝑖 − 1.081𝑍𝑍2. 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖 + 𝑢𝑢0𝑖𝑖 −2𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑙𝑙𝑙𝑙ℎ𝑙𝑙𝑙𝑙𝑜𝑜 = 2744.893 ⎝ ⎜ ⎛ 𝑢𝑢0𝑖𝑖 𝑢𝑢2𝑖𝑖 𝑢𝑢4𝑖𝑖 𝑢𝑢5𝑖𝑖 ⎠ ⎟ ⎞ ~𝑁𝑁(0, Ω𝑢𝑢): Ω𝑢𝑢 = ൦ 3.524 (0.459) −0.047 (0.009) 0.001 (0.000) 0.000 0 0.000 0.000 0 0.000 0.000 ൪ ቌ 𝑒𝑒1𝑖𝑖𝑖𝑖 𝑒𝑒3𝑖𝑖𝑖𝑖 𝑒𝑒6𝑖𝑖𝑖𝑖 ቍ ~𝑁𝑁(0, Ω𝑒𝑒): Ω𝑒𝑒 = ቎ 0.768 (0.381) 0 1.044 (0.069) 0 0.212 (0.126) 0.000 ቏ Level 1 variance: 𝑍𝑍1 = 𝜎𝜎𝑒𝑒1 2 = 0.768 𝑍𝑍2 (𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇) = 𝜎𝜎𝑒𝑒3 2 = 1.044 𝑍𝑍3 = 𝜎𝜎𝑒𝑒3 2 + 2𝜎𝜎36 + 𝜎𝜎𝑒𝑒6 2 = 1.469 Level 2 variance 𝑍𝑍1 = 𝜎𝜎𝑢𝑢0 2 + 2𝜎𝜎𝑢𝑢02 + 𝜎𝜎𝑢𝑢2 2 = 3.431 𝑍𝑍2 = 𝜎𝜎𝑢𝑢0 2 + 2𝜎𝜎𝑢𝑢04 + 𝜎𝜎𝑢𝑢4 2 + 2𝜎𝜎𝑢𝑢05 + 2𝜎𝜎𝑢𝑢45 + 𝜎𝜎𝑢𝑢5 2 = 3.524 Coefficient Value Standard error P 𝛽𝛽1 5.113 0.473 < 0.0005 𝛽𝛽2 0.074 0.013 < 0.0005 𝛽𝛽3 3.411 0.242 < 0.0005 𝛽𝛽4 0.093 0.009 < 0.0005 𝛽𝛽5 0.074 0.010 < 0.0005 𝛽𝛽6 -1.081 0.419 0.009 Hutchinson MJ, Goosey-Tolfrey VL, Rethinking aerobic exercise intensity prescription in adults with spinal cord injury: time to end the use of “moderate to vigorous” intensity? Supplementary Material 2: Dynamic model with lagged independent variable for RPE and %HRpeak 𝑥𝑥 = % HRpeak 𝑦𝑦 = RPE Z1 = 1, when i = 1 (i = measurement occasion) Z2 = 1, when i > 1 PARA = 1, if Group = PARA 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛽𝛽1𝑖𝑖𝑍𝑍1𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝑍𝑍1. 𝑥𝑥𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝑖𝑖𝑍𝑍2𝑖𝑖𝑖𝑖 + 𝛽𝛽4𝑍𝑍2. 𝑥𝑥𝑖𝑖𝑖𝑖 + 𝛽𝛽5𝑍𝑍2. 𝑥𝑥𝑖𝑖−1𝑖𝑖 + 𝛽𝛽6𝑖𝑖𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇 + 𝑢𝑢0𝑖𝑖 𝛽𝛽1𝑖𝑖 = 𝛽𝛽1 + 𝑒𝑒1𝑖𝑖𝑖𝑖 𝛽𝛽3𝑖𝑖 = 𝛽𝛽3 + 𝑒𝑒3𝑖𝑖𝑖𝑖 ൫𝑢𝑢0𝑖𝑖൯~𝑁𝑁(0, Ω𝑢𝑢): Ω𝑢𝑢 = [𝜎𝜎𝑢𝑢0 2 ] ൬𝑒𝑒1𝑖𝑖𝑖𝑖 𝑒𝑒3𝑖𝑖𝑖𝑖൰ ~𝑁𝑁(0, Ω𝑒𝑒): Ω𝑒𝑒 = ቈ𝜎𝜎𝑒𝑒1 2 0 𝜎𝜎𝑒𝑒3 2 ቉ 𝑦𝑦𝑖𝑖𝑖𝑖 = −1.375𝑍𝑍1𝑖𝑖𝑖𝑖 + 0.160𝑍𝑍1. 𝑥𝑥𝑖𝑖𝑖𝑖 − 3.044𝑍𝑍2𝑖𝑖𝑖𝑖 + 0.168𝑍𝑍2. 𝑥𝑥𝑖𝑖𝑖𝑖 + 0.044𝑍𝑍2. 𝑥𝑥𝑖𝑖−1𝑖𝑖 + 0.707𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇 + 𝑢𝑢0𝑖𝑖 −2𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑙𝑙𝑙𝑙ℎ𝑙𝑙𝑙𝑙𝑜𝑜 = 2727.763 ൫𝑢𝑢0𝑖𝑖൯~𝑁𝑁(0, Ω𝑢𝑢): Ω𝑢𝑢 = [2.929 (0.387)] ൬𝑒𝑒1𝑖𝑖𝑖𝑖 𝑒𝑒3𝑖𝑖𝑖𝑖൰ ~𝑁𝑁(0, Ω𝑒𝑒): Ω𝑒𝑒 = ൤1.471 (0.209) 0 1.182 (0.073)൨ Coefficient Value Standard error P 𝛽𝛽1 -1.375 0.814 0.091 𝛽𝛽2 0.160 0.014 < 0.0005 𝛽𝛽3 -3.044 0.355 < 0.0005 𝛽𝛽4 0.168 0.010 < 0.0005 𝛽𝛽5 0.044 0.011 < 0.0005 𝛽𝛽6 0.707 0.324 0.029	Rethinking aerobic exercise intensity prescription in adults with spinal cord injury: time to end the use of "moderate to vigorous" intensity?	12-08-2021	Hutchinson, Michael J,Goosey-Tolfrey, Victoria L	eng
PMC6992743	1 Scientific RepoRtS \| (2020) 10:1523 \| https://doi.org/10.1038/s41598-020-58492-8 www.nature.com/scientificreports estimation of energy consumed by middle-aged recreational marathoners during a marathon using accelerometry-based devices carlos Hernando1,2, Carla Hernando3, Ignacio Martinez-navarro4,5, Eladio collado-Boira6, nayara panizo6 & Barbara Hernando7 As long-distance races have substantially increased in popularity over the last few years, the improvement of training programs has become a matter of concern to runners, coaches and health professionals. triaxial accelerometers have been proposed as a one of the most accurate tools to evaluate physical activity during free-living conditions. In this study, eighty-eight recreational marathon runners, aged 30–45 years, completed a marathon wearing a GENEActiv accelerometer on their non-dominant wrist. energy consumed by each runner during the marathon was estimated based on both running speed and accelerometer output data, by applying the previously established GENEActiv cut-points for discriminating the six relative-intensity activity levels. Since accelerometry allowed to perform an individualized estimation of energy consumption, higher interpersonal differences in the number of calories consumed by a runner were observed after applying the accelerometry-based approach as compared to the speed-based method. Therefore, pacing analyses should include information of effort intensity distribution in order to adjust race pacing appropriately to achieve the marathon goal time. Several biomechanical and physiological parameters (maximum oxygen uptake, energy cost of running and running economy) were also inferred from accelerometer output data, which is of great value for coaches and doctors. Running a marathon has rapidly become one of the most popular activities nowadays as shown by the number of amateur participants with hundreds of marathons worldwide1,2. It is well-known that running a marathon is one of the most challenging endurance competitions3,4. As a result of recent research focused on improving training programs, which aimed to avoid soreness and prevent energy deficit during ultraendurance races5, the number of runners crossing the finish (ultra)marathon line has significantly raised over the past few years6,7. For example, a total of 3,388 runners more finished the Valencia Fundación Trinidad Alfonso EDP Marathon in 2018 as com- pared to the 2016 edition (19,246 versus 15,858 finishers, respectively)8. In their way towards the improvement of marathon time, recreational runners are surrounded by a wide range of professionals in order to achieve their objectives9,10. Consequently, many studies has been focused on develop- ing different methodologies to evaluate factors affecting running performance, such as the pacing strategy2,11, the energy consumption12–14, the maximal oxygen uptake V ( O ) 2 max 15, the fraction of VO2 max  maintained (F)15, the running speed16, the energy cost of running (Cr)17, and physical, biomechanical, metabolic, psychological and social factors18. Among all these factors, changes of running speed over race sections have been widely studied in order to explain the running success of more efficient pacers – runners who are able to maintain their initial running pace for more kilometers2. These more efficient pacers may avoid an excessive energy consumption while running the first part of the marathon5. 1Sport Service, Jaume I University, Castellon, Spain. 2Department of Education and Specific Didactics, Jaume I University, Castellon, Spain. 3Department of Mathematics, Carlos III University of Madrid, Madrid, Spain. 4Department of Physical Education and Sport, University of Valencia, Valencia, Spain. 5Sports Health Unit, Vithas- Nisa 9 de Octubre Hospital, Valencia, Spain. 6Faculty of Health Sciences, Jaume I University, Castellon, Spain. 7Department of Medicine, Jaume I University, Castellon, Spain. email: hernando@uji.es open 2 Scientific RepoRtS \| (2020) 10:1523 \| https://doi.org/10.1038/s41598-020-58492-8 www.nature.com/scientificreports www.nature.com/scientificreports/ Therefore, measuring the energy expended by an individual while performing a specific activity has recently been targeted by researchers. Ainsworth and colleagues published The Compendium of Physical Activities in 1993 (which was reviewed in 2000 and 2011), allowing to directly extrapolate the energy expenditure in Metabolic Equivalent Task (METs), and thus in kilocalories (kcal), for running activities according to speed12,13,19. Since the Compendium did not take into account interpersonal differences, the use of accelerometry-based devices has been proposed to evaluate free-living physical activities performed by an individual, in terms of dura- tion, frequency and intensity14,20,21. Therefore, using the cut-points recommended for a specific population and/ or activity, accelerometer output data can be applied to indirectly measure the energy expended by an individual in METs22–24. In this regard, our research group aimed to monitor middle-aged recreational marathoners during a marathon using accelerometry-based devices. For this purpose, we previously established the GENEActiv cut-points that dsicriminate the six relative-intensity activity levels in recreational marathoners25. This lab-based study was essen- tial in order to delineate specific GENEActiv cut-points for a specific population who presents higher relative level of fitness than the standard adult population. At this point, the main goal of the current study was to apply the GENEActiv cut-points previously established for estimating the energy consumed by middle-aged recreational marathoners during a marathon race (a free-living condition). Accelerometer output data allowed us to analyze the effort distribution that runners followed to achieve their marathon time, by means of the time running at each one of the six related-intensity levels (sedentary, light, moderate, vigorous, very vigorous and extremely vig- orous activity) during the marathon. This information may be extremely valuable for both athletes and coaches. Knowing the intensity, duration and energy cost of an activity is useful for designing training sessions because it allows to objectively quantify and monitor training load. Energy consumption was also estimated based on run- ning speed12, and results were compared with those obtained after using accelerometer data. Results A detailed description of individuals included in this study is summarized in Table 1. The accelerometer output data allowed us to analyse the effort distribution that runners followed to achieve their marathon time, by means of the time running at each one of the six related-intensity levels (sedentary, light, moderate, vigorous, very vigorous and extremely vigorous activity) during the marathon. Values established for delineating the six-relative intensity levels of physical activity are detailed in Table 2. For all individuals, we estimated the energy cost of running a marathon, presenting the caloric consumption for each one of the 9 marathon sections as well as for the full marathon distance (Tables 3 and 4). The calories consumed by each runner were calculated based on both accelerometer data (Table 3), as previously described by our research group25, and running speed (Table 4), following the methodology proposed by Ainsworth and cols12. The aim of applying also the speed-based method12 in the estimation of energy consumption was to compare the results obtained with accelerometer devices25. Note that a gold standard method for energy quantification in long distance races has not been defined yet. Except for the last race section, a higher number of calories was estimated to be consumed by a runner when the accelerometry-based method was applied, as compared to the caloric consumption estimated by using the Variable Subjects (N = 88) Physiological characteristics* age 38.68 ± 3.61 BMI 22.91 ± 1.62 Weight 69.96 ± 8.91 Heigh 174.44 ± 8.66 % body fat 14.74 ± 4.38 VO2 max (ml·kg−1·min−1) 54.41 ± 5.66 maximum METs 15.55 ± 1.62 Training indicators* years of running 6.43 ± 2.78 sessions per week 4.90 ± 0.84 kilometers per week 63.45 ± 13.06 hours per week 7.44 ± 2.70 History as marathoner* marathons finished 3.36 ± 3.02 marathon per year 1.10 ± 0.63 Work intensity# high intensity 7.95% medium intensity 30.68% low intensity 61.36% Levels of study# school graduate 4.60% high school graduate 6.90% professional certificate 17.24% undergraduate degree 71.26% Table 1. Population description. Abbreviations: N, number of samples; BMI, body mass index; SD, standard deviation. Values are presented as mean ± SD. #Values are presented as percentage. 3 Scientific RepoRtS \| (2020) 10:1523 \| https://doi.org/10.1038/s41598-020-58492-8 www.nature.com/scientificreports www.nature.com/scientificreports/ speed method (Table 4). It is worth highlighting that a greater variation of calories consumed per each individual was observed after using accelerometry for energy cost estimation, rather than running speed (shown by higher standard deviation values). The reason of this difference is due to the fact that the accelerometer-based method takes into account the variability across individuals in terms of energy consumption, while speed-based method tends to standardize values for all subjects26. Although no significant differences between energy consumption and marathon time were observed (Fig. 1), correlation analysis showed that the accelerometry-based method tended to increase the number of calories con- sumed by the runner with marathon time (ρ = 0.179, p = 0.094). However, the Ainsworth’s method seemed to present a negative correlation between the caloric consumption and marathon time (Fig. 1). This correlation was also no significant (ρ = −0.137, p = 0.202). For a better comparison between methods, the energy consumed by runners was expressed as a relative rate in kil- ocalories per kilogram of body mass either per minute12,26 or per kilometer17,27, and as the number of times consum- ing his/her Basal Metabolic Rate (BMR)26,28 (Table 4). The results of this comparison denoted statistically significant differences in the energy estimated to be consumed by runners after applying the accelerometry- and speed-based method. That was observed in each one of the 9 race sections as well as in the full marathon distance (Table 4). Accelerometer output data allowed us to know the physical effort distribution of runners during the mara- thon, in terms of physical activity intensity. That is, we were able to identify and quantify when a runner is racing at each one of the six relative-intensity activity levels (sedentary, light, moderate, vigorous, very vigorous and extremely vigorous)25. Therefore, following the values established in Table 2, the percentage of VO2 max  produced per each runner was estimated, and this allowed then to calculate the energy of cost running above standing (Crnet)28 (Table 5). A negative correlation between the relative energy consumed and the marathon time was observed when energy consumption was expressed as kilocalories per kilogram of body mass per minute. This negative correla- tion was enlarged when the speed-based method was applied (ρ = −0.976, p = 1.12 × 10−58), in comparison with the accelerometry-based method (ρ = −0.307, p = 0.004) (Fig. 2). When the relative rate of energy consumption was expressed per distance (kcal·kg−1·km−1), the energy expended by runners was positively correlated with the marathon time after using accelerometry (ρ = 0.402, p = 1.01 × 10−4). No significant correlation was observed between energy consumption (expressed as a relative rate per kilogram of body weight per kilometre) and time when speed-based method was applied (ρ = −0.200, p = 0.062). Discussion In this study, we aimed to estimate the energy consumed by middle-aged recreational marathoners during a marathon race (a free-living condition) using accelerometry-based devices25. In our opinion, the application of accelerometers should be useful to minimize the interpersonal differences in energy consumption caused by physiological and biomechanical parameters and, therefore, to perform an individualized estimation of energy consumption. Up to now, the viability of accelerometers to measure VO2 in combination with other devices, such as pulsom- eters or global positioning system (GPS) devices, has been analysed under laboratory conditions29–31. Accelerometers have also been used to monitor athletes and infer their physical activity level24,32,33. However, accelerometry-based devices had not been applied so far for estimating the energy consumed by a runner in a marathon race, under normal race conditions, yet. By applying the GENEActiv cut-points for discriminating the six relative-intensity activity levels in recreational marathoners (previously established in a lab-based study by our research group25), we were able to know the amount of time that a runner was running at a specific relative-intensity level (sedentary, light, moderate, vigorous, very vigorous and extremely vigorous activity) Relative-intensity levels of physical activity# Reference values established for each intensity level by Hernando et al.25 Values used for energy consumption estimation VO2  (ml·kg−1·min−1) METs  %VO2max VO2  (ml·kg−1·min−1) METs* Sedentary X < 10% VO 5 45 2  < . METs < 1.56 8.26% 4.5 1.29 Ligth 10% ≤ X < 25%  . ≤ < . V 5 45 O 13 63 2 1.56 ≤ METs < 3.90 17.5% 9.54 2.73 Moderate 25% ≤ X < 45% V 13 63 O 24 54 2 . ≤ < . 3.9 ≤ METs < 7.01 35.0% 19.10 5.45 Vigorous 45% ≤ X < 65% . ≤ < . V 24 54 O 35 44 2 7.01 ≤ METs < 10.13 55.0% 29.99 8.57 Very Vigorous 65% ≤ X < 85% V 35 44 O 46 35 2 . ≤ < . 10.13 ≤ METs < 13.24 75.0% 40.90 11.69 Extremely Vigorous X ≥ 85% VO 46 35 2  ≥ . METs ≥ 13.24 92.5% 50.44 14.41 Table 2. Values established for delineating the six-relative intensity levels of physical activity. Abbreviations: N, number of individuals; VO2 max, maximum oxygen consumption; VO2  , oxygen consumption; MET, metabolic equivalent task. Each minute of the cardiopulmonary test was classified into one of the six intensity categories of physical activity relative to an individual’s level of cardiorespiratory V ( O ) 2max . 1 MET = 3.5 ml·kg−1·min−1. 1 MET = 1 kcal·h−1. #X denotes the percentage of a person’s aerobic capacity V ( O 2max)  used to classify each one of the six relative-intensity categories. 4 Scientific RepoRtS \| (2020) 10:1523 \| https://doi.org/10.1038/s41598-020-58492-8 www.nature.com/scientificreports www.nature.com/scientificreports/ during the marathon. Accordingly, the energy consumed by the runner along the race sections and the full mar- athon distance was estimated. Differences in the estimation of runners’ energy consumption were observed between the speed- and accelerometry-based methods. These differences lie in the ability of the accelerometer output data to determine the physical effort distribution of each runner during the marathon, in terms of physical activity intensity34–36. Therefore, accelerometers are able to perform an individualized estimation of energy consumption. Note that several physiological and biomechanical factors that are unique to the individual have been shown to affect the running efficiency among runners at the same steady-state speed16,27,37. This fact pointed up that estimating the energy consumption of a runner based uniquely on his/her running speed might be insufficient and that it might be advisable to apply a correction factor for adjusting for individual differences when estimating the energy cost of, at least, moderate/vigorous physical activities26. The speed-based approach, proposed by Ainsworth and cols12, analyse the marathon pace of a runner without taking into account the runner’s effort to race at this speed. Fewer interpersonal differences in the number of calories consumed by a runner were then observed with the speed-based method as compared to the accelerometry-based approach. For example, two individuals racing at identical speed and having equal body mass are estimated to present the same energy cost after applying the Race section Time spend at each relative-intensity level (minutes) Energy consumed according to the time spend at each relative-intensity level (kcal) S L M V VV EV Total S L M V VV EV Total 0–5 km 0.01 ± 0.11 0.00 ± 0.00 1.17 ± 4.87 1.30 ± 4.03 9.82 ± 10.65 14.81 ± 11.53 27.10 ± 3.35 0.02 ± 0.15 0.00 ± 0.00 6.76 ± 26.60 13.94 ± 45.35 136.71 ± 148.23 244.83 ± 191.55 402.26 ± 76.44 5–10 km 0.00 ± 0.00 0.00 ± 0.00 1.42 ± 4.31 1.63 ± 4.00 8.67 ± 8.94 12.86 ± 10.32 24.58 ± 2.23 0.00 ± 0.00 0.00 ± 0.00 8.28 ± 23.95 17.16 ± 44.77 119.77 ± 123.75 214.47 ± 173.67 359.68 ± 73.47 10–15 km 0.00 ± 0.00 0.00 ± 0.00 1.25 ± 3.66 1.84 ± 4.30 8.56 ± 8.94 13.09 ± 10.24 24.74 ± 2.32 0.00 ± 0.00 0.00 ± 0.00 7.84 ± 20.59 19.07 ± 45.53 118.40 ± 126.80 216.93 ± 171.78 362.25 ± 69.49 15-HM 0.01 ± 0.11 0.00 ± 0.00 1.88 ± 4.90 2.23 ± 4.44 9.74 ± 10.07 16.16 ± 12.47 30.01 ± 2.87 0.01 ± 0.12 0.00 ± 0.00 11.62 ± 28.85 23.10 ± 48.98 135.17 ± 141.08 267.77 ± 208.66 437.67 ± 87.00 HM-25km 0.00 ± 0.00 0.01 ± 0.11 0.51 ± 2.09 1.23 ± 3.48 6.06 ± 7.41 11.72 ± 8.45 19.52 ± 1.77 0.00 ± 0.00 0.03 ± 0.29 3.02 ± 12.38 12.57 ± 37.79 84.05 ± 102.70 195.15 ± 143.59 294.83 ± 57.25 25–30 km 0.00 ± 0.00 0.01 ± 0.11 1.13 ± 2.94 1.91 ± 3.84 8.33 ± 8.41 14.11 ± 10.23 25.49 ± 2.51 0.00 ± 0.00 0.04 ± 0.33 6.85 ± 17.57 19.15 ± 38.97 115.57 ± 118.10 235.14 ± 172.56 376.75 ± 72.58 30–35 km 0.00 ± 0.00 0.06 ± 0.38 1.53 ± 4.75 1.81 ± 3.95 8.06 ± 8.74 15.06 ± 11.00 26.51 ± 3.45 0.00 ± 0.00 0.20 ± 1.40 10.00 ± 31.54 18.34 ± 40.36 110.92 ± 121.80 250.91 ± 186.13 390.38 ± 77.84 35–40 km 0.00 ± 0.00 0.09 ± 0.58 2.08 ± 5.38 1.64 ± 3.28 8.22 ± 8.66 15.11 ± 10.51 27.14 ± 3.89 0.00 ± 0.00 0.33 ± 2.21 13.50 ± 36.03 16.04 ± 31.78 114.47 ± 120.83 251.14 ± 175.75 395.48 ± 72.99 40-M 0.02 ± 0.21 0.02 ± 0.15 0.67 ± 2.22 0.39 ± 0.84 2.55 ± 3.30 6.24 ± 4.10 9.89 ± 1.76 0.03 ± 0.31 0.07 ± 0.47 4.23 ± 13.93 3.79 ± 8.19 35.79 ± 8.19 104.43 ± 70.04 148.35 ± 37.76 Marathon 0.05 ± 0.34 0.19 ± 0.92 11.6 ± 25.32 13.95 ± 27.75 69.99 ± 66.19 119.16 ± 82.86 214.98 ± 20.78 0.06 ± 0.47 0.67 ± 3.48 72.10 ± 160.10 143.17 ± 301.99 970.84 ± 938.15 1980.78 ± 1386.54 3167.63 ± 584.12 Table 3. Evaluation of effort distribution and estimation of calories consumed by runners based on accelerometry data. Abbreviations: S, Sedentary; L, Light; M, Moderate; V, Vigorous; VV, Very Vigorous; EV, Extremely Vigorous; HM, Half marathon; M, marathon; SD, standard deviation. Values are presented as mean ± SD. Race section Running speed (m·min−1) Absolute energy (kcal) Energy relative to body mass per time (kcal·kg−1·min−1) Energy relative to body mass per distance (kcal·kg−1·km−1) Number of BMR Accelerometry Running speed Accelerometry Running speed* Adjusted p-value¥ Accelerometry Running speed* Adjusted p-value¥ Accelerometry Running speed* Adjusted p-value¥ 0–5 km 187.27 ± 23.06 402.26 ± 76.44 352.30 ± 44.85 0.214 ± 0.031 0.189 ± 0.023 6.27 × 10-12 1.154 ± 0.195 1.008 ± 0.026 1.09 × 10-11 12.82 ± 1.84 11.30 ± 1.40 6.27 × 10-12 5–10 km 205.06 ± 18.43 359.68 ± 73.47 354.24 ± 47.26 0.210 ± 0.034 0.208 ± 0.019 0.149 1.030 ± 0.176 1.012 ± 0.023 0.495 12.59 ± 2.03 12.43 ± 1.11 0.149 10–15 km 203.85 ± 18.88 362.25 ± 69.49 355.00 ± 46.26 0.211 ± 0.032 0.207 ± 0.018 0.062 1.040 ± 0.171 1.015 ± 0.025 0.169 12.63 ± 1.93 12.38 ± 1.10 0.062 15-HM 204.94 ± 18.82 437.67 ± 87.00 427.90 ± 54.89 0.210 ± 0.033 0.206 ± 0.020 0.093 1.030 ± 0.177 1.003 ± 0.020 0.358 12.57 ± 2.00 12.31 ± 1.17 0.088 HM-25km 201.49 ± 18.00 294.83 ± 57.25 273.44 ± 35.86 0.217 ± 0.030 0.202 ± 0.022 1.05 × 10-5 1.055 ± 0.164 1.001 ± 0.026 2.46 × 10-3 12.99 ± 1.78 12.10 ± 1.33 1.05 × 10-5 25–30 km 198.01 ± 19.10 376.75 ± 72.58 353.60 ± 47.10 0.213 ± 0.030 0.200 ± 0.020 7.85 × 10-5 1.080 ± 0.170 1.010 ± 0.024 3.16 × 10-4 12.73 ± 1.82 11.98 ± 1.21 7.85 × 10-5 30–35 km 191.43 ± 22.58 390.38 ± 77.84 351.74 ± 46.67 0.213 ± 0.032 0.193 ± 0.025 2.06 × 10-7 1.119 ± 0.186 1.006 ± 0.040 8.18 × 10-7 12.73 ± 1.89 11.55 ± 1.49 2.06 × 10-7 35–40 km 187.65 ± 24.50 395.48 ± 72.99 353.24 ± 46.98 0.211 ± 0.032 0.190 ± 0.026 7.21 × 10-7 1.134 ± 0.174 1.010 ± 0.039 2.37 × 10-10 12.65 ± 1.91 11.36 ± 1.56 7.21 × 10-7 40-M 229.14 ± 42.02 148.35 ± 37.76 153.73 ± 20.93 0.215 ± 0.034 0.229 ± 0.038 0.202 0.964 ± 0.210 1.000 ± 0.039 1.000 12.90 ± 2.03 13.69 ± 2.26 0.209 Marathon 198.06 ± 18.78 3167.63 ± 584.12 2951.45 ± 394.20 0.212 ± 0.030 0.198 ± 0.021 3.48 × 10-5 1.076 ± 0.163 0.999 ± 0.023 8.75 × 10-5 12.70 ± 1.77 11.86 ± 1.23 3.48 × 10-5 Table 4. Comparison between accelerometry- and speed-based approaches in the estimation of energy consumption. Abbreviations: BMR, Basal metabolic rate; HM, Half marathon; M, Marathon; SD, standard deviation; p, p-value. Values are presented as mean ± SD. Bold indicates significant results (p-value < 0.05). The values are estimated based on running speed, and following the methodology proposed by Ainsworth et al. (2000)12. ¥P-values were corrected for multiple comparisons by applying the Benjamini-Hochberg procedure for decreasing the False Discovery Rate. 5 Scientific RepoRtS \| (2020) 10:1523 \| https://doi.org/10.1038/s41598-020-58492-8 www.nature.com/scientificreports www.nature.com/scientificreports/ speed-based method, although their physical efforts are completely different according to accelerometry data. Nevertheless, note that, as in the speed-based methods, accelerometry is not able to perform an absolute quantifi- cation of the energy consumed by a runner and it is necessary, therefore, to combine different approaches, as well as to explore other technologies, in future work. In this regard, accelerometer data collected for each runner was thoroughly analyzed in order to compare effort distribution between the fastest and the slowest runner of our dataset (Table 6). Note that the fastest runner was almost running at very vigorous intensity level, showing a good control of physical effort along the full mar- athon distance. In contrast, the effort distribution of the slowest runner was far from being well-balanced2,38,39. In fact, the accelerometer data revealed a considerable decay of the intensity level at which the slowest runner performed after completing 30 km (running at a moderate intensity from an extremely vigorous level). This was a consequence of the high physical effort sustained by the runner from the beginning of the marathon line, which reveals the importance of controlling effort distribution in a marathon race. In short, our results suggest that future pacing analyses should include information of effort intensity distribution in order to adjust race pacing appropriately to achieve the marathon goal time. Thanks to accelerometer output data, we were also able to estimate the percentage of VO2 max produced per each runner, and afterwards the energy of cost running above standing (Crnet)28, at each of the 9 marathon sec- tions as well as at the full marathon distance. These physiological parameters seem to explain up to 87% of the long distance race performance27. In addition, the accelerometry-based approach also allowed us to extrapolate the running economy of each runner, which is considered an important physiological measure for long distance runners37,40. It is thought that a variety of biomechanical characteristics are likely to contribute to having interper- sonal differences in the running efficiency, such as the running technique, the elastic power of the muscle-tendon unit, or the amount of ground contact and vertical oscillation when running41. As results shown, the fastest runner seemed to present a better efficiency of movement than that presented by the slowest runner. That is, the energy demanded for a given running velocity was lower by the fastest runner as compared to the slowest runner. In fact, the average energy cost of marathon running was 3.31 J·kg−1·m−1 for the fastest runner (whose average speed was 237.05 m·min−1), while it was 4.59 J·kg−1·m−1 for the slowest runner Figure 1. Plot showing the linear correlation between the calories estimated to be consumed by each runner and the marathon time. Energy consumption was estimated by using both accelerometry (solid line) and running speed (dashed line). Each individual is represented by a specific point: filled circles are used when accelerometry was applied for energy consumption estimation, and filled triangles when speed-based method was used. Abbreviations: ρ, Spearman’s rank correlation coefficient; p, p-value. Race section Percentage of maximum oxygen consumption V (% O 2 max)  Oxygen uptake relative to body mass per minute (ml·kg−1·min−1) Energy cost of running above standing (J·kg−1·m−1) 0–5 km 82% ± 11.78 44.87 ± 6.43 4.54 ± 0.83 5–10 km 81% ± 13.05 44.07 ± 7.12 4.05 ± 0.76 10–15 km 81% ± 12.41 44.19 ± 6.77 4.09 ± 0.73 15-HM 81% ± 12.83 44.00 ± 7.00 4.04 ± 0.76 HM-25km 83% ± 11.46 45.45 ± 6.25 4.26 ± 0.72 25–30 km 82% ± 11.69 44.54 ± 6.38 4.25 ± 0.73 30–35 km 82% ± 12.11 44.55 ± 6.60 4.40 ± 0.79 35–40 km 81% ± 12.27 44.28 ± 6.70 4.44 ± 0.74 40-M 83% ± 13.06 45.15 ± 7.12 3.79 ± 0.86 Marathon 81% ± 11.38 44.43 ± 6.21 4.23 ± 0.70 Table 5. Estimation of the percentage of VO2 max  , the oxygen uptake relative to body mass per minute and the energy cost of running above standing based on accelerometry data. Abbreviations: HM, Half marathon; M, Marathon; VO2 max  , maximum oxygen consumption. *Energy cost of running above standing = (  −  V V ( O O ) 2 2standing (running speed)−1) · 20.9. 6 Scientific RepoRtS \| (2020) 10:1523 \| https://doi.org/10.1038/s41598-020-58492-8 www.nature.com/scientificreports www.nature.com/scientificreports/ (whose average speed was 152.88 m·min−1). Apart from physiological parameters, these differences may be also resulted from biomechanical efficiency, which is influenced by anthropometric parameters, kinematic character- istics and running style37. This suggests that the design of training sessions for the slowest runner by his coach should focus on improv- ing his running style and muscle strength, and subsequently his performance. The useful information offered by accelerometers (distribution of physical effort in free-living conditions and inference of physiological parameters as Crnet or % VO2 max) should become more and more important as race distance increase42. Application of accel- erometers to monitor ultratrail runners may be useful not only for adjusting race strategy, which is crucial for achieving performance goals2,27,43,44, but also to monitor training sessions and recovery time. Indeed, both long-term data collection and wrist watch-like format are valuable characteristics of accelerometers since data can be continuously collected for a long period of time (more than a week) without causing any physical discomfort to ultraendurance runners45. However, values of all physiological parameters analyzed in this study were merely estimations based on accel- erometer data, and were not directly measured46. It is quite difficult, if not impossible, to perform a direct meas- urement of VO2  on a marathon race, an extremely demanding free-living condition. This makes difficult to find a gold standard method for quantifying calories consumed by an individual when she/he is performing a physical activity. That is the reason why indirect measurement methods (such as heart-rate recording devices14,47, pedom- eters48,49 and accelerometers14,34,36, or their combination29,30,50) are normally applied. Another limitation of our study is related to the protocol followed to estimate energy consumption according to the range of % VO2 max  delimiting each relative-intensity activity level. Estimations can present a maximum error of 10%, since the median value of the % VO2 max range was used for energy calculations (as shown in Table 2). Having said that, our results indicate that accelerometry-based method allows to both identify the individual’s levels of physical activity intensity during the marathon race and estimate an individualized energy consumption. In summary, overall the results in this study lead us to believe that GENEActiv. accelerometer is an accu- rate tool for estimating the energy consumption of middle-recreational marathoners running a marathon, an extremely demanding free-living physical activity. Accelerometer-derived data was useful to evaluate the effort intensity distribution along the race, by means of the time running at each six related-intensity levels (sedentary, light, moderate, vigorous, very vigorous and extremely vigorous activity), and subsequently to estimate the energy consumption. Therefore, accelerometers may be extremely useful for both athletes and coaches who need to evaluate the race strategy to achieve marathon final time, but also to monitor training sessions and assess perfor- mance level progression needed to reach a goal. Several physiological and biomechanical parameters that can be inferred from accelerometer output data may also support coaches to design specific training sessions according to runner’s characteristics. Furthermore, the ability to perform an objective assessment of a runner’s fitness level, as well as energy consumption, in the context of free-living movement indicates that accelerometry-based devices may be of great value to sport medical professionals. Since accelerometry-based data is thought to be valuable for monitoring runners along ultra-trail races, future studies determining cut-off points for quantifying energy consumption would help in the race strategy in terms of food and fluid intake on race day (a key factor for performance success). Note that these future studies must take into account that biomechanics and physiology of downhill and uphill running, as well as the energy cost of running, may differ. Methods Sample set. A total of 95 recreational marathon runners (80 males and 15 females) aged between 30 and 45 years lined up at the start of the Valencia Fundación Trinidad Alfonso EDP 2016 Marathon (20th November, 2016). From all of them, eighty-eight participants crossed the finish line (74 males and 14 females). Non-finishers Figure 2. Plot showing the linear correlation between the energy estimated to be consumed by each runner relative to his/her body mass per minute and the marathon time. Energy consumption was estimated by using both accelerometry (solid line) and running speed (dashed line). Each individual is represented by a specific point: filled circles are used when accelerometry was applied for energy consumption estimation, and filled triangles when speed-based method was used. Abbreviations: ρ, Spearman’s rank correlation coefficient; p, p-value. 7 Scientific RepoRtS \| (2020) 10:1523 \| https://doi.org/10.1038/s41598-020-58492-8 www.nature.com/scientificreports www.nature.com/scientificreports/ were discarded from further analyses. The entire process of sampling (contact approach and criteria for inclusion and exclusion of volunteers) has been previously described25. ethics statement. All individuals included in the current study were fully informed and gave their writ- ten consent to participate. The research was conducted according to the Declaration of Helsinki, and it was approved by the Research Ethics Committee of the University Jaume I of Castellon. This study is enrolled in the ClinicalTrails.gov database, with the code number NCT03155633 (www.clinicaltrials.gov). Data collection and analysis. Four weeks before the marathon, we made an appointment with all partic- ipants in order to collect anthropometric data, demographics, medical information, training program and com- petition history. Indeed, all individuals completed a cardiopulmonary test. Details of data collection, processing and analysis have been previously described25. Population description according to data collected is also available in our previous work25. All participants were weighed one hour before the start of the marathon, wearing racing clothes and flats, by using a Seca 770 scale (Seca Hamburg, Germany). BMI was then calculated (height·mass−2). For this research, all the participants underwent the same testing under the same experimental conditions. Participants completed the Valencia Fundación Trinidad Alfonso EDP 2016 Marathon, which was held in November with a mean dry temperature of 15.6 °C and a mean relative humidity of 50%. The race course altitude varied from 1 to 27 m above sea level. During the race, participants wore a GENEActiv accelerometer (Activinsights Ltd., Kimbolton, Cambridgeshire, United Kingdom). The accelerometer was worn on the non-dominant wrist as a watch. Accelerometers were adjusted to record acceleration data at a rate of 85.7 Hz. Devices were calibrated by the man- ufacturer prior to use. Processing of acceleration data has been previously explained in detail25. Data analysis. The marathon race was divided into 9 sections as follow: 6 sections of 5 km (0–5 km, 5–10 km, 10–15 km, 25–30 km, 30–35 km and 35–40 km), 1 section of 6.0975 km (15–21.0975 km), 1 section of 3.9025 km (21.0975–25 km) and 1 section of 2.195 km (40–42.195 km). All data analyses were performed for each one of the nine marathon sections and for the whole marathon distance. Statistical analyses were done using the IBM SPSS Statistics v.23 software, and p-values lower than 0.05 were considered as statistically significant. Supplementary information includes raw data used in this study. Fastest runner: Marathon time of 178 min, body mass of 69.2 kg, and BMI of 21.36 kg·m−2 Race section Time running at each relative- intensity level (min) Energy consumption Running speed (m·min−1) V % O2max Crnet (J·kg−1·m−1) S L M V VV EV Total Absolute (kcal) Relative to time (kcal·kg−1 ·min−1) Relative to distance (kcal·kg−1 ·km−1) 0–5 km 0 0 0 0 21 0 21.00 283.70 0.20 0.82 238.10 75.00% 3.29 5–10 km 0 0 0 0 21 0 21.00 283.70 0.20 0.82 238.10 75.00% 3.29 10–15 km 0 0 0 1 20 0 21.00 280.09 0.19 0.81 238.10 74.05% 3.25 15-HM 0 0 0 3 22 0 25.00 326.92 0.19 0.77 243.90 72.60% 3.11 HM-25km 0 0 0 0 16 0 16.00 216.15 0.20 0.80 243.91 75.00% 3.22 25–30 km 0 0 0 0 22 0 22.00 297.21 0.20 0.86 227.27 75.00% 3.45 30–35 km 0 0 0 1 21 0 22.00 293.60 0.19 0.85 227.27 74.09% 3.41 35–40 km 0 0 0 0 18 3 21.00 293.13 0.20 0.85 238.10 77.50% 3.40 40-M 0 0 0 0 7 2 9.00 127.87 0.21 0.84 243.89 78.89% 3.38 Marathon 0 0 0 5 168 5 178.00 2402.37 0.20 0.82 237.05 74.93% 3.31 Slowest runner: Marathon time of 276 min, body mass of 74.9 kg, and BMI of 23.38 kg·m−2 0–5 0 0 0 0 24 5 29.00 441.06 0.20 1.18 172.41 78.02% 4.73 5–10 0 0 0 0 17 11 28.00 446.85 0.21 1.19 178.57 81.88% 4.79 10–15 0 0 1 0 7 20 28.00 469.66 0.22 1.25 178.57 86.07% 5.04 15-HM 0 0 0 0 4 31 35.00 617.25 0.24 1.35 174.21 90.50% 5.43 HM-25 0 0 0 0 0 22 22.00 396.54 0.24 1.36 177.39 92.50% 5.45 25–30 0 0 2 4 13 12 31.00 462.89 0.20 1.24 161.29 76.61% 4.97 30–35 0 0 41 1 1 0 43.00 304.84 0.09 0.81 116.28 36.40% 3.27 35–40 0 0 43 0 1 0 44.00 307.75 0.09 0.82 113.64 35.91% 3.30 40-M 0 0 11 0 0 5 16.00 165.11 0.14 1.00 137.19 52.97% 4.04 Marathon 0 0 98 5 67 106 276.00 3611.95 0.17 1.14 152.88 67.16% 4.59 Table 6. Comparison of effort distribution according to accelerometer output data between the fastest and the slowest runner of our dataset. Abbreviations: S, Sedentary; L, Light; M, Moderate; V, Vigorous; VV, Very Vigorous; EV, Extremely Vigorous; HM, Half marathon; M, marathon; VO2 max  , maximum oxygen consumption; Crnet, energy cost of running above standing. 8 Scientific RepoRtS \| (2020) 10:1523 \| https://doi.org/10.1038/s41598-020-58492-8 www.nature.com/scientificreports www.nature.com/scientificreports/ Firstly, accelerometer-derived data was used to determine the distribution of exercise intensity of runners along the marathon with the aim to estimate the calories consumed per each runner. The intensity levels of phys- ical activity were established following the cut-off points delineated by Hernando and cols25. For calculating the energy cost, we used the median value of the range of % VO2 max delimiting each intensity category (Table 2), except for the sedentary category where the standing oxygen cost (4.5 mlO2·kg−1·min−1) was applied as reference value28. As unit of measurement, we considered that one MET is equal to 3.5ml O2·kg−1·min−1, and one MET is equal to one kcal·kg−1·h−1. These equivalencies were applied in accordance with the determinations proposed by Ainsworth and cols12, and taking into account that all volunteers included in the study reported similar BMI (between 22.17 and 23.44 kg·m−2) and, therefore, differences in the percentage of fatty component among partic- ipants were absence26,46,51. Accelerometers were also used to estimate the percentage of VO2 max produced per each runner. Briefly, the time racing at a specific intensity level was multiplied by its corresponding % VO2 max (Table 2). A weighted aver- age relative to the total time spent at each section, as well as at the full marathon distance, was then performed. Then, the VO2net of each runner was calculated by subtracting the VO2standing to the percentage of VO2 max esti- mated17,28. Together with the running speed measured, the VO2net was finally used to calculate the energy of cost running above standing (Crnet), following the methodology proposed by di Prampero and cols17. Next, the average running speed was used to calculate the caloric consumption of runners, following the methodology proposed by Ainsworth and cols12. The split-times in minutes were recorded for each one of the marathon sections electronically, and the average running speed of all sections and the whole marathon distance was calculated. Then, the running speed was associated with a specific MET value, which can be directly used to calculate the number of calories consumed by a runner12,19. Finally, the relative values of energy consumption estimated by the two models were compared. As the energy consumption depends on the person’s body mass, the energy cost of each runner is presented as: (i) the calories consumed per kilogram of body weight per minute (kcal·kg−1·min−1), in order to obtain the effort intensity;12,19,26 (ii) the calories consumed per kilogram of body weight per kilometer (kcal·kg−1·km−1), to infer the running effi- ciency of runners;18,27 and (iii) as the number of Basal Metabolic Rate (BMR) consumed, used as an indicator of the effort intensity degree above the basal metabolism26,28. The Kolgomorov-Smirnov test was used for testing data normality. Since variables were not normally dis- tributed, all statistical analyses were performed by applying non-parametric statistical tests. The Mann-Whitney U test was used to compare the energy consumption values estimated by using the accelerometer-derived data and the relative running speed. Then, P-values were corrected for multiple comparisons by applying the Benjamini-Hochberg procedure for decreasing the False Discovery Rate.The Sperman’s correlation test was applied to analyze linear association between two continuous variables. Data availability All data generated or analysed during this study are included in this published article (and its Supplementary Information File). Any other relevant data can be obtained from the corresponding author upon reasonable request. Received: 13 February 2019; Accepted: 15 January 2020; Published: xx xx xxxx References 1. Ahmadyar, B., Rüst, C. A., Rosemann, T. & Knechtle, B. Participation and performance trends in elderly marathoners in four of the world’s largest marathons during 2004–2011. SpringerPlus 4, 465 (2015). 2. Aschmann, A., Knechtle, B., Onywera, V. O. & Nikolaidis, P. T. Pacing Strategies in the New York City Marathon - Does Nationality of Finishers Matter? \| Request PDF. Asian J. Sports Med. june, (2018). 3. 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Authors are also grateful to all the stuff involved in the organization of the Valencia Fundación Trinidad Alfonso EDP 2016 Marathon, and all marathoners and volunteers participating in this study. Author contributions C.H. and B.H. contributed to conception and design of the study, article drafting, and critical revision of the article. C.H. and C.H. contributed to data curation, analysis and interpretation. C.H., I.M.-N., E.C.-B. and N.P. contributed to data collection and critical revision of the article. C.H., I.M.-N. and E.C.-B. contributed to funding acquisition. 10 Scientific RepoRtS \| (2020) 10:1523 \| https://doi.org/10.1038/s41598-020-58492-8 www.nature.com/scientificreports www.nature.com/scientificreports/ competing interests The authors declare no competing interests. Additional information Supplementary information is available for this paper at https://doi.org/10.1038/s41598-020-58492-8. Correspondence and requests for materials should be addressed to C.H. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2020	Estimation of energy consumed by middle-aged recreational marathoners during a marathon using accelerometry-based devices.	01-30-2020	Hernando, Carlos,Hernando, Carla,Martinez-Navarro, Ignacio,Collado-Boira, Eladio,Panizo, Nayara,Hernando, Barbara	eng
PMC9192635	1 Vol.:(0123456789) Scientific Reports \| (2022) 12:9749 \| https://doi.org/10.1038/s41598-022-13965-w www.nature.com/scientificreports Differences in stress response between two altitudes assessed by salivary cortisol levels within circadian rhythms in long‑distance runners Katsuhiko Tsunekawa, Kazumi Ushiki, Larasati Martha, Asuka Nakazawa, Rika Hasegawa, Risa Shimizu, Nozomi Shimoda, Akihiro Yoshida, Kiyomi Nakajima, Takao Kimura & Masami Murakami There are conflicting reports regarding the efficacy of cortisol as a stress marker in altitude training due to the influence of the circadian rhythm. This study aimed to verify whether the automated measurement of salivary cortisol concentration via sequential sampling could detect the differences in exercise stress between two altitudes. We enrolled 12 elite female long‑distance runners living near sea level. For the first higher‑altitude camp, the runners lived at 1800 m and trained at 1700 m for 7 days. For the second lower‑altitude camp, they lived at 1550 m and trained at 1300 m for 7 days. Their saliva was sequentially collected on the last 2 days during each camp which involved different intensity exercises in the morning and afternoon. The salivary cortisol concentrations were measured using electrochemiluminescence immunoassay. Before dinner, the basal salivary cortisol concentrations were significantly higher in the higher‑altitude camp. The rate of change in the salivary cortisol concentration during the morning exercise was significantly higher in the higher‑altitude camp than in lower‑altitude camp (p = 0.028) despite the same exercise programs and intensities. Salivary cortisol level measurements during the athletes’ circadian rhythms could detect the differences in acclimatization and exercise stress between two altitudes. Abbreviations ACTH Adrenocorticotropic hormone ECLIA Electrochemiluminescence immunoassay ELISA Enzyme-linked immunosorbent assay RPE Rating of perceived exertion SpO2 Oxygen saturation VO2 max Maximal oxygen consumption Elite athletes in various sports often train at high altitudes to improve their performance when they return to lower altitudes. At high altitudes with low atmospheric pressure and low oxygen concentrations, the amount of red blood cells and their oxygen-carrying capacity are enhanced by an increase in circulating erythropoietin concentration due to hypoxia-inducible factors1. However, a hypoxia during high-altitude training causes exces- sive stress and decreases an athletes’ performance, resulting in poor acclimatization2. Therefore, monitoring of physical and psychological stress during exercise training at high-altitude camps may help assess maladaptation. Cortisol is an important biomarker that is secreted into the circulating plasma from the adrenal cortex via the hypothalamus–pituitary axis as an acute response to stress including exercise3. Serum cortisol concentra- tions are increased by moderate- to high-intensity exercise, but not by low-intensity exercise at less than 40% of athletes’ maximal oxygen consumptions (VO2 max)4,5. However, there have been conflicting reports regarding the efficacy of cortisol as a stress marker during high-altitude training6–11, which may be due to the influence of the OPEN Department of Clinical Laboratory Medicine, Gunma University Graduate School of Medicine, 3-39-22 Showa-machi Maebashi, Gunma 371-8511, Japan. email: ktsune@gunma-u.ac.jp 2 Vol:.(1234567890) Scientific Reports \| (2022) 12:9749 \| https://doi.org/10.1038/s41598-022-13965-w www.nature.com/scientificreports/ circadian rhythm. Changes in the serum cortisol concentrations resulting from exercise are greater in the evening than in the morning because of cortisol circadian rhythm12. Thus, to accurately evaluate exercise-induced stress using cortisol, it should be measured throughout day. To achieve this, continuous sampling and more efficient cortisol measurement are needed. Serum cortisol concentrations are measured as total hormone conjugated to corticosteroid-binding globulin, whereas salivary cortisol concentrations are measured as free hormones inde- pendent of salivary flow rates13. Saliva is also advantageous to its ease of collection in the absence of medical professional staff and without the stress of venipuncture14. Conventionally, the salivary cortisol concentration has been manually measured using an enzyme-linked immunosorbent assay (ELISA), which this makes it difficult to measure a large number of samples compared with automated methods. Recently, the automated electrochemi- luminescence immunoassay (ECLIA) used for serum cortisol concentration measurement was applied to saliva, and the salivary cortisol concentrations measured by ECLIA showed a significantly positive correlation with those measured by liquid chromatography-tandem mass spectrometry15 and with conventional ELISA16. Moreover, we reported that sequential saliva collection and automated ECLIA-based salivary cortisol measurements could detect the exercise-induced stress within the circadian rhythm in female long-distance runners16. In this study, the differences in the rate of change in salivary cortisol concentrations resulting from various exercise intensities could be compared at the same time on different days, even in the early morning. Training at altitudes of 500–2000 m, defined as low altitude, causes less stress on athletes than training at moderate (2000–3000 m) and high (3000–5500 m) altitudes17. In contrast, VO2 max of endurance-trained athletes decreased significantly beginning at 300 m above sea level and continued to decrease linearly by approximately 7% for every 1000 m ascended18,19. While these reports suggest that there may be differences in athletes’ stress responses at different altitudes, even at low altitudes, few biomarkers have ever detected this difference. We hypothesized that the automated measurement of salivary cortisol concentrations throughout the athletes’ cir- cadian rhythms via sequential saliva sampling would enable the assessment of the differences in stress induced by training camps at different altitudes, including those at low altitudes. If proven, this method can help in the prevention of excessive stress, and foster the development of exercise programs in several altitude environments for athletes. The present study expands on our previous study by verifying whether cortisol concentration meas- urement via continuous saliva collection during the circadian rhythms could adequately detect the differences in acclimatization and stress responses resulting from exercise at different altitudes in female long-distance runners. Methods Participants. This study was conducted in accordance to the Declaration of Helsinki, and the protocol was approved by the ethics committee of the Gunma University Graduate School of Medicine (Approval number HS2018-140). All the study participants provided written informed consent before being included in the study. We enrolled 12 Japanese elite female long-distance runners. All of them lived in the same dormitory before the first training camp and stayed in the same hotels during camps. Their living conditions, such as wakeup time, meal time, bedtime, and meal content, were standardized before and during the training camps16. Figure 1A presents the schedules of the pre-camp and the two training camps. This altitude training was a study without invasive interventions, because it was usually conducted to improve the condition of the runners. The runners lived and trained near sea level (150 m) before the first training camp. Then, for the first training camp simulat- ing the higher-altitude camp at low altitudes, they lived at 1800 m and trained at 1700 m twice a day, morning and afternoon, for 7 days. Afterwards, for the second training camp simulating the lower-altitude camp at low altitudes, they lived at 1550 m and trained at 1300 m twice a day, morning and afternoon, for 7 days. They then returned to near sea level. We sequentially collected saliva from these runners on the last 2 days during each camp which involved different exercise intensities in the morning and afternoon, modified as previously described16. Figure 1B details the relation between runner’s altitudes and saliva collection times during 2 consecutive days during each training camp. On both days, saliva samples were collected at eight time points: upon waking (05:00), before morning exercise (06:00), after morning exercise (07:00), before breakfast (08:00), before lunch (12:00), before afternoon exercise (15:00), after afternoon exercise (16:00), and before dinner (18:00), as described previously16. On each training day at both camps, no differences were observed in the meteorological conditions; the temperature was around 20 °C and the relative humidity was 50–60% with fine weather. The runners were subjected to the following exercise program in the higher-altitude camp: 40-min fixed running in the morn- ing and 50-min fixed running in the afternoon on day 1 (Higher-day 1); 8000-m fixed-distance running in the morning and uphill interval training with 8 sets of 200-m fast uphill running and light jogging in the evening on day 2 (Higher-day 2). The runners were subjected to the following exercise program in the lower-altitude camp: 50-min fixed running in the morning and 60-min fixed running in the afternoon on day 1 (Lower-day 1); 8000-m fixed-distance running in the morning and uphill interval training with 5 sets of 200-m fast uphill running and light jogging in the evening on day 2 (Lower-day 2). The runners drank enough water to prevent dehydration during these trainings. Physical examinations. The participants were weighed, and their body mass indexes were calculated as the weight divided by the squared height (kg/m2). After conducting interviews, no runners were found to use any medications or supplements. The runners used the Apple Watch Series 3 (Apple Japan Inc., Tokyo, Japan) during the camps. This allowed for the measurement of maximum pulse rate during each exercise and resting pulse rate at awakening and before dinner. The distance and duration of running during each exercise session was measured, and the running velocity was calculated as the distance divided by the duration (m/min)15. The Borg Rating of Perceived Exertion (RPE) scale20 was utilized to measure the runner’s subjective exertion, breath- lessness and fatigue after exercise. The runner’s RPE was scored using a scale ranging from 6 to 20 and used in the analysis as a Borg scale score. 3 Vol.:(0123456789) Scientific Reports \| (2022) 12:9749 \| https://doi.org/10.1038/s41598-022-13965-w www.nature.com/scientificreports/ Saliva collections and measurements of salivary cortisol concentrations. Sample collections and salivary cortisol concentration measurements were performed according to a previous study16. The runners were not allowed to brush their teeth, chew gum, or consume any food or drink except water, 15 min before the sample collection. Saliva samples were collected using Salivette® cotton swabs (Sarstedt, Nümbrecht, Germany), centrifuged (1,500 × g) at 4 °C for 10 min, then immediately stored at − 80 °C until analysis. ECLIA measure- ments of salivary cortisol concentrations were performed using the Elecsys Cortisol II on the Cobas 8000 sys- tem (Roche Diagnostics K.K, Tokyo, Japan)15,16. The intra- and inter-assay coefficients of variation for salivary cortisol were 4.1% and 4.6%, respectively. The rate of change in the salivary cortisol concentration by exercise was calculated as the salivary cortisol concentration after exercise divided by the salivary cortisol concentration before exercise (%)16. Statistical analysis. The results of each measurement are expressed as the median values and correspond- ing 25th–75th percentile ranges. The Wilcoxon signed-rank test was utilized to identify statistically significant differences in variables between two different time points. A p value of < 0.05 was considered statistically signifi- cant. All statistical analyses were performed using SPSS Statistics, version 26.0 (IBM Corp., Armonk, NY, USA). Ethics approval and consent to participate. Written informed consent was obtained from all par- ticipants. This study was approved by the ethics committee of Gunma University Graduate School of Medicine (Approval number HS2018-140). All measurements were carried out by trained athletes and in accordance with the Declaration of Helsinki. Results Running intensity of each exercise program. Table 1 presents the characteristics of the participating female long-distance runners, whereas Table 2 presents the different running intensities of each exercise pro- gram during the two training camps. Because the exercise programs on days 1 and 2 were similar between the two camps, the exercise intensities of each program were compared. During the morning exercise on day 1, at the higher-altitude camp, the running velocity was significantly higher (p = 0.015) and the running distance and Figure 1. The study design of the first and second training camps of 12 female long-distance runners. The altitudes at which the runners lived and trained during the two training camps and pre- and post-camp (A). The schema of altitudes and saliva sampling time in runners on the last 2 days during each training camps (B). The downwards arrows denote the saliva sampling from the runners, and the tips of the arrows denote the altitudes at the time of sampling at both camps. Higher-day 1, day 1 at the higher-altitude camp; Higher-day 2, day 2 at the higher-altitude camp; Lower-day 1, day 1 at the lower-altitude camp; Lower-day 2, day 2 at the lower-altitude camp. 4 Vol:.(1234567890) Scientific Reports \| (2022) 12:9749 \| https://doi.org/10.1038/s41598-022-13965-w www.nature.com/scientificreports/ Borg scale scores were significantly lower (running distance, p = 0.029; Borg scale score, p = 0.047) than those at the lower-altitude camp. During the afternoon exercise on day 1, the running velocity was significantly higher at the higher-altitude camp (p = 0.003), but no differences were observed in other parameters between the two camps. During the morning exercise on day 2, the maximum pulse rate was significantly lower at the higher-alti- tude camp (p = 0.029), but no differences were observed in other parameters between the two camps. However, during the afternoon exercise on day 2, the running distance, Borg scale score, and maximum pulse rate were significantly higher at the higher-altitude camp (running distance, p = 0.002; Borg scale score, p = 0.005; maxi- mum pulse rate, p = 0.036). When comparing the exercise intensities between the morning and afternoon on day 1, the running distance, running velocity, and Borg scale score were significantly higher during the afternoon exercise at the higher-altitude camp (running distance, p = 0.002; running velocity, p = 0.002; Borg scale score, p = 0.010), whereas the running distance was significantly higher during the afternoon exercise at the lower- altitude camp (p = 0.004). On day 2, the running distance and Borg scale score were significantly higher and the running velocity was significant lower during the afternoon exercise compared with those during the morning exercise at the higher-altitude camp (running distance, p = 0.002; Borg scale score, p = 0.004; running velocity, p = 0.002), whereas the running distance and velocity were significantly lower during the afternoon exercise at the lower-altitude camp (running distance, p = 0.002; running velocity, p = 0.003). Changes in the salivary cortisol concentrations in response to exercise within the circadian rhythms in each camp. Figure 2 presents the changes in the salivary cortisol concentrations in response to exercise during the last 2 days at both camps. The salivary cortisol concentrations peaked after waking and promptly decreased on both days at both camps. Within these circadian rhythms, the salivary cortisol concentra- tions significantly decreased after the morning exercise on both days at both camps but significantly increased after the afternoon exercise on 2 days at only the higher-altitude camp. These concentrations reached their low- est levels before dinner on both days at both camps. Table 3 presents that the differences in the resting pulse rates and salivary cortisol concentrations between the higher- and lower-altitude camps. The resting pulse rate before dinner was significantly higher on day 2 at the higher-altitude camp than on day 2 at the lower-altitude Table 1. Characteristics of female long-distance runners. Data are expressed as median (25th–75th percentile). Characteristics Value Number 12 Age (year) 23.5 (19.5–26.0) Height (cm) 160.0 (155.5–164.5) Weight (kg) 45.5 (41.0–47.5) Body mass index (kg/m2) 17.4 (17.0–17.9) Table 2. Running intensities of the exercise programs performed by female long-distance runners during the two camps. Data are expressed as median (25th–75th percentile). p < 0.05 and p < 0.01 comparing variables between the higher- and lower-altitude camps using the Wilcoxon signed-rank test. pday1 morning exercise vs. afternoon exercise on day 1 using the Wilcoxon signed-rank test. pday2 morning exercise vs. afternoon exercise on day 2 using the Wilcoxon signed-rank test. Altitude camp Day 1 pday1 Day 2 pday2 Morning exercise Afternoon exercise Morning exercise Afternoon exercise Exercise program Higher 40-min fixed running 50-min fixed running 8000-m fixed running Uphill interval training Lower 50-min fixed running 60-min fixed running 8000-m fixed running Uphill interval training Running distance (m) Higher 9700 (9250– 10,150) 12,790 (12,300– 13,450) 0.002 8000 (8000–8000) 12,000 (9800– 14,000)** 0.002 Lower 11,585 (10,700– 12,000) 13,145 (12,445– 14,000) 0.004 8000 (8000–8000) 4700 (4200–5250) 0.002 Running velocity (m/min) Higher 242.5 (224.8– 247.7)* 252.0 (246.0– 263.0)* 0.002 235.3 (228.6– 238.9) 178.0 (153.9– 215.4) 0.002 Lower 224.2 (205.0– 236.8) 219.1 (207.5– 233.3) 0.477 235.3 (228.6– 236.5) 168.0 (125.0– 213.8) 0.003 Borg scale score Higher 13.0 (12.0–15.0)* 15.0 (13.0–15.5) 0.010 13.0 (12.0–15.0) 16.0 (14.0–17.5)** 0.004 Lower 14.0 (13.0–15.5) 13.5 (12.0–15.0) 0.119 13.0 (12.0–15.0) 13.0 (12.5–15.0) 0.524 Maximum pulse rate (beat/min) Higher 175 (159–189) 182 (159–201) 0.182 170 (152–184)* 179 (165–192)* 0.167 Lower 168 (153–198) 168 (158–181) 0.937 196 (164–210) 163 (154–187) 0.050 5 Vol.:(0123456789) Scientific Reports \| (2022) 12:9749 \| https://doi.org/10.1038/s41598-022-13965-w www.nature.com/scientificreports/ Figure 2. Changes in salivary cortisol concentrations in response to each exercise within the circadian rhythm on 2 consecutive days during the higher-altitude camp (A) and lower-altitude camp (B). The white box plots denote the cortisol concentration at the higher-altitude camp, whereas the gray box plots represent those at the lower-altitude camp. The gray dot squares denote the time of exercise at each camp. The significant differences between two time points of each exercise for runners were analyzed using the Wilcoxon signed-rank test. Higher-day 1, day 1 at the higher-altitude camp; Higher-day 2, day 2 at the higher-altitude camp; Lower-day 1, day 1 at the lower-altitude camp; Lower-day 2, day 2 at the lower-altitude camp. Table 3. Comparison of the variables between the higher- and lower-altitude camps in runners. Data are expressed as median (25th–75th percentile). p < 0.05 and p < 0.01 comparing variables with day 1 at the lower-altitude camp using the Wilcoxon signed-rank test. † p < 0.05 and ††p < 0.01 comparing variables with day 2 at the lower-altitude camp using the Wilcoxon signed-rank test. Higher-altitude camp Lower-altitude camp Day 1 Day 2 Day 1 Day 2 Resting pulse rate at awakening (beat/min) 50 (46–55) 49 (44–52) 48 (44–52) 50 (42–57) Resting pulse rate before dinner (beat/min) 65 (57–70) 65 (57–71)† 56 (52–68) 54 (48–63) Salivary cortisol at awakening (μg/dL) 0.41 (0.37–0.45)† 0.36 (0.31–0.42)† 0.40 (0.33–0.43) 0.43 (0.39–0.50) Salivary cortisol at peak(μg/dL) 0.57 (0.44–0.61) 0.51 (0.45–0.61) 0.49 (0.43–0.59) 0.55 (0.42–0.64) Salivary cortisol before dinner (μg/dL) 0.15 (0.09–0.18) †† 0.11 (0.09–0.25)* †† 0.08 (0.06–0.11) 0.05 (0.05–0.07) 6 Vol:.(1234567890) Scientific Reports \| (2022) 12:9749 \| https://doi.org/10.1038/s41598-022-13965-w www.nature.com/scientificreports/ camp (p = 0.021). The salivary cortisol concentrations upon waking were significantly lower on both days at the higher-altitude camp than on day 2 at the lower-altitude camp (Higher-day 1 vs. Lower-day 2, p = 0.038; Higher- day 2 vs. Lower-day 2, p = 0.025). The concentrations before dinner were also significantly higher on both days at the higher-altitude camp than those on both days at the lower-altitude camp (Higher-day 1 vs. Lower-day 1, p = 0.026; Higher-day 1 vs. Lower-day 2, p = 0.005; Higher-day 2 vs. Lower-day 1, p = 0.012; Higher-day 2 vs. Lower-day 2, p = 0.003). Rate of change in the salivary cortisol concentrations resulting from exercise in each camp. Figure 3 presents the comparison of the rate of change in the salivary cortisol concentrations after each exercise at both camps. The rates of change in salivary cortisol concentrations were significantly lower dur- ing the morning exercise than during the afternoon exercise on both days at each camp (Higher-day 1, p = 0.002; Higher-day 2, p = 0.002; Lower-day 1, p = 0.005; Lower-day 2, p = 0.008; Fig. 3A,B). After the morning exercise, the rate of change in the salivary cortisol concentrations was significantly higher on day 2 at the higher-altitude Figure 3. Comparison of the rate of change in the salivary cortisol concentration resulting from exercise between the morning and afternoon time points on days 1 and 2 at the higher-altitude camp (A), lower-altitude camp (B), between the morning time points on days 1 and 2 at the higher- and lower-altitude camps (C), and between the afternoon time points on days 1 and 2 at the higher- and lower-altitude camps (D). The white box plots denote the rates of change in the cortisol concentration at the higher-altitude camp, whereas the gray box plots represent those at the lower-altitude camp. The significant differences between two time points of exercise for runners were analyzed using the Wilcoxon signed-rank test. Higher-day 1, day 1 at the higher-altitude camp; Higher-day 2, day 2 at the higher -altitude camp; Lower-day 1, day 1 at the lower-altitude camp; Lower-day 2, day 2 at the lower-altitude camp. 7 Vol.:(0123456789) Scientific Reports \| (2022) 12:9749 \| https://doi.org/10.1038/s41598-022-13965-w www.nature.com/scientificreports/ camp than on day 2 at the lower-altitude camp (p = 0.028; Fig. 3C). After the afternoon exercise, the rate of change in the salivary cortisol concentrations was significantly higher on both days at the higher-altitude camp than on both days at the lower-altitude camp (Higher-day 1 vs. Lower-day 1, p = 0.012; Higher-day 2 vs. Lower- day 2, p = 0.003; Fig. 3D). Discussion In this study, we demonstrated whether the stress responses of runners in training camps at different altitudes could be evaluated via sequential saliva collection and automated salivary cortisol measurement. These methods were able to detect the basal levels and exercise-induced changes in the salivary cortisol within the runners’ cir- cadian rhythms at each altitude camp. The basal salivary cortisol concentrations before dinner were significantly higher at the higher-altitude camp than at lower-altitude camp. The rate of change in the salivary cortisol concen- trations during the afternoon exercise on days 1 and 2 and the indicators of exercise intensity were significantly higher at the higher-altitude camp than at the lower-altitude camp. Moreover, the rate of change in the salivary cortisol concentrations during the morning exercise on day 2 was significantly higher at the higher-altitude camp than at lower-altitude camp; no differences were observed in the exercise programs and intensities, such as the running distances, velocities, and Borg scale scores. There have been contradictory reports with regard to the effects of altitude training on cortisol secretions. In male elite climbers, the resting serum cortisol and plasma adrenocorticotropic hormone (ACTH) levels taken at 07:00–07:30 did not change at 5200 m extreme altitude camp compared with those at sea level6. In male and female elite skiers, no significant differences were observed in resting salivary cortisol concentrations taken at 07:00–08:00 between a control group training and living at an altitude of 1200 m and another group train- ing at 1200 m but living at a simulated altitudes of 2500 m, 3000 m, and 3500 m for 6 days in hypoxic rooms7. In contrast, the basal concentrations of serum cortisol in the morning after 3–4 days at 4350 m were elevated compared with those at sea level in healthy men, but this was not statistically significant8. In the present study, a significant difference was observed in the basal salivary cortisol concentrations before dinner between the two camps with an altitude difference of approximately 300 m, even at the relatively low altitudes of 1300–1800 m. These differences may be due to the fact that the lowest cortisol levels were compared in the evening, whereas the previous studies compared the concentrations in the morning6–8. Another reason may be that the athletes were acclimatized to these altitudes when transitioning from the first higher-altitude camp to the second lower- altitude camp. Overtraining syndrome causes a reduced cortisol response to exercise and changes in the circa- dian rhythms of cortisol, including low resting levels and peak loss after waking21,22. In the present study, the runners’ salivary cortisol concentrations were lower in the evening, but peaked after waking and increased after high-intensity exercise. The low levels of salivary cortisol in the evenings indicated that the runners were not suffering from overtraining syndrome but rather were able to adapt to the higher altitudes. Although further analysis under higher-altitude conditions is required, it may be more useful to evaluate the cortisol levels in the evening rather than in the morning using serum or saliva for assessing the acclimatization to the several altitude environments among athletes. Regarding the acute response to exercise, the increase in cortisol levels after interval training at 09:00 exhibited an insignificant trend toward higher values at an altitude of 1800 m when compared with that observed at near sea levels in highly trained endurance athletes9. Another study found that the serum cortisol levels significantly increased after resistance training at 70% of the maximum strength under 13% hypoxic conditions from 08:00 to 11:30 but not under normoxic conditions in healthy male subjects10. Conversely, the serum cortisol levels did not change after resistance training at 50% of the maximum strength under hypoxic and normoxic conditions11. In the present study, the rates of change in the salivary cortisol concentration after the morning exercise were significantly lower than those after the afternoon exercise on both days at each altitude camp due to the influence of the circadian rhythm, which was validated by previous reports12,16. In contrast , the comparison of the rates of change in cortisol concentration during exercise at the same time on different days, whether in the morning or in the afternoon, was effective in evaluating the stress responses, as previously described16. In the present study, we were able to detect the difference in the rates of change in the salivary cortisol concentration during exercise with the same program at different altitudes, even in the morning. Moreover, we could detect the increase in stress due to the differences in altitude and exercise intensity, more clearly in the afternoon, by assessing the rate of change in salivary cortisol concentrations. It was revealed that an increase in the training altitude of approximately 400 m at the low altitude of 1300–1700 m with high-intensity exercise resulted in an increase in cortisol secretion in both morning and afternoon. This result validates the previous studies, where the serum cortisol concentrations were acutely elevated by high-intensity exercise with higher VO2 max 4,5. However, we did not measure the oxygen saturation (SpO2) or VO2 max as the oxygen tolerability of runners for different exercise intensities. Future studies should evaluate the relationships between the changes in salivary cortisol concentra- tions and these oxygen tolerability markers in response to endurance exercises at different altitudes. Addition- ally, the range of the runners’ salivary cortisol concentrations was broad, especially after afternoon exercise on day 2 at the higher-altitude camp, shown in Fig. 2. This broad range suggested individual differences in stress responses induced by the altitude training in the runners. Therefore, the cortisol concentration measurement technique described in this study which uses continuous saliva collection during the circadian rhythms would be more useful as a personalized conditioning tool to detect the differences in stress responses under various environments for an individual athlete, rather than as a statistical analysis tool for a large number of athletes. This study has several limitations. First, the sample size was relatively small. We focused on enrolling well- trained female runners with standardized meal and sleep times during the two consecutive camps. Second, the sequential saliva collections and measurements of salivary cortisol concentration were not performed at sea level. The temperatures were around 20 °C during both altitude camps, but the temperature during the same period 8 Vol:.(1234567890) Scientific Reports \| (2022) 12:9749 \| https://doi.org/10.1038/s41598-022-13965-w www.nature.com/scientificreports/ was as high as 30–40 °C at near sea level where the runners lived and trained. Previous research found that the salivary cortisol levels detected in a maximal progressive test using a cycle ergometer were significantly higher under the hot conditions (40 °C) than under normal conditions (22 °C) in nine young healthy men23. Therefore, it was impossible in this study to collect saliva at near sea level without the stress resulting from high temperatures. Future study should utilize hypoxic rooms, in which the environmental conditions, including temperature, are standardized. Additionally, more runners should be enrolled, specifically males. Conclusions Measurement of the salivary cortisol levels within the circadian rhythm led to the detection of the changes in the stress response due to the same intensity exercise at different altitudes, even in the morning. Additionally, evening resting salivary cortisol levels can be used to assess athletes’ acclimatization to high altitudes. The combination of sequential saliva collection and automated cortisol measurements may be useful for assessing adaptation dis- orders and excessive exercise stress, and also may help develop adequate altitude training programs for athletes. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Received: 3 February 2022; Accepted: 31 May 2022 References 1. Wang, G. L. et al. Hypoxia-inducible factor 1 is a basic-helix-loop-helix-PAS heterodimer regulated by cellular O2 tension. Proc. Natl. 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Urhausen, A. & Kindermann, W. Diagnosis of overtraining: What tools do we have?. Sports Med. 32, 95–102 (2002). 22. Cadegiani, F. A. & Kater, C. E. Novel causes and consequences of overtraining syndrome: The EROS-DISRUPTORS study. BMC Sports Sci. Med. Rehabil. 11, 21 (2019). 23. Silva, R. P. M. et al. The influence of a hot environment on physiological stress responses in exercise until exhaustion. PLoS ONE 14, e0209510 (2019). Acknowledgements We are grateful to Kenichi Morikawa, Mai Murata, Mayumi Nishiyama, and Tomoyuki Aoki for providing tech- nical assistance and helpful discussions. Author contributions K.T. participated in the collection and analysis of data and manuscript writing, reviewing and editing. K.U., L.M., A.N., R.H., R.S., N.S., A.Y., N.K., and T.K. participated in data collection and analysis. M.M. participated in con- ception of the study, supervision, and manuscript editing. All authors read and approved the final manuscript. Funding This work was supported by the Ministry of Education, Culture, Sports, Science, and Technology of Japan [Grant number 18K07406]. 9 Vol.:(0123456789) Scientific Reports \| (2022) 12:9749 \| https://doi.org/10.1038/s41598-022-13965-w www.nature.com/scientificreports/ Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to K.T. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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PMC6436800	RESEARCH ARTICLE Anthropometry-driven block setting improves starting block performance in sprinters Valentina Cavedon1☯, Marco SandriID1, Mariola Pirlo2, Nicola Petrone3, Carlo Zancanaro1, Chiara MilaneseID1☯* 1 Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy, 2 OMR Automotive, Brescia, Italy, 3 Department of Industrial Engineering, University of Padua, Padua, Italy ☯ These authors contributed equally to this work. * chiara.milanese@univr.it Abstract This study tested the effect of two block setting conditions i.e., the usual block setting [US] and an anthropometry-driven block setting [AS] on the kinematic and kinetic parameters of the sprint start. Furthermore, we verified whether this effect is influenced by the relative lengths of the sprinter’s trunk and lower limbs i.e., the Cormic Index by subdividing sprinters into brachycormic, metricormic and macrocormic groups. Forty-two sprinters performed 6 maximal-effort 10 m sprints using the US and AS conditions. Dynamometric starting blocks measured forces generated by the sprinters. The times at 5 m and 10 m in the sprint trials were measured with photocells. Results showed that the anteroposterior block distances were significantly different between the two conditions (P<0.001). Across the sample, the horizontal block velocity, the rear peak force, the rear force impulse, the total force impulse, the horizontal block power, the ratio of horizontal to resultant impulse in the rear block, the first and second step lengths and the times at 5 m and 10 m improved in AS vs. US (P values from 0.05 to 0.001). Considering the interaction between the block setting condition and the Cormic Index, the rear peak force and the rear force impulse were significantly increased in the metricormic and brachycormic groups (P0.001) and the metricormic group (P<0.001), respectively. Kinetic variables in the rear block and the difference (Delta) in the front block/ starting line distance between US and AS were related with each other (Adjusted R2 values from 0.07 to 0.36). In conclusion, AS was associated with improvement in the kinematic and kinetic parameters of the sprint start performance vs. US; however, AS is apparently best suited for metricormic sprinters. Further work is needed to verify how the sprint start kinetic and kinematic parameters are related to the front block/starting line distance and whether a block setting driven by the sprinter’s Cormic Index is able to improve sprint start performance. Introduction In track sprints, the success of the sprint start performance depends on the ability of the sprinter to generate a large impulse over the shortest time and reach the highest running speed as soon as possible [1–3]. This phase is especially important in the 100 m sprint [4–7]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 1 / 20 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Cavedon V, Sandri M, Pirlo M, Petrone N, Zancanaro C, Milanese C (2019) Anthropometry- driven block setting improves starting block performance in sprinters. PLoS ONE 14(3): e0213979. https://doi.org/10.1371/journal. pone.0213979 Editor: Alena Grabowski, University of Colorado Boulder, UNITED STATES Received: June 25, 2018 Accepted: March 5, 2019 Published: March 27, 2019 Copyright: © 2019 Cavedon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: This work was supported in part by Departmental intramural funds (Joint Projects 2009) to CM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study. The funding organization (OMR Automotive) did not play any role in the study design, data Acceleration at the start of a race is influenced by the way sprinters positions themselves in the blocks at the set command and the mechanics of leaving the blocks at the sound of the gun [8,9]. The kinematic and kinetic patterns of elite athletes during the starting block phase and acceleration phase have received considerable attention and many variables have been studied to account for the effects of starting technique [2,3,5, 9–18]. According to the literature an effective sprint start mainly depends on the start block posi- tioning and the joint angles of the lower limbs in the set position [8,9,11]. Furthermore, the pushing time on the blocks and the forces generated by the front and rear legs in the pushing phase [3,9–18] are also important. Recent studies found that the most predictive factor of sprint start performance was the magnitude of force generated by the rear leg [16,19]. What is more, the average horizontal external power (i.e., the ability to translate the centre of mass in the running direction in a short period of time) was identified as an excellent descriptor of start performance in sprinters [2]. The starting technique is greatly influenced by the setting of the block positions with regards to spacing and obliquities [8,20,21]. Bezodis, Salo, & Trewartha [3] demonstrated that “a single optimal set position” for everybody is not recommended due to varying physical fac- tors, and therefore sprinters generally find their own preferred distance between the blocks according to sensations or outcomes. For example, one of the most popular adjustments fre- quently modified by the sprinters is the anteroposterior inter-block spacing and finding the optimum setting for each athlete may take a long period of training. Furthermore, athletes may not actually be selecting their ideal block setting for best performance when only basing their preference on sensation. The importance of anteroposterior inter-block spacing on the sprint movement during the block start phase has been extensively investigated in several studies [3,6,9,12, 21–24]. The three main types of block spacing investigated in the literature [20] are the bunched start (spacing gen- erally <30 cm), the medium start (30 to 50 cm) and the elongated start (>50 cm). A number of studies found that the velocity of the centre of mass at block clearing is higher when the inter- block spacing increases due to an increase of force impulse [2,11,22]. This is due to an increased duration of force generated against the blocks and a greater contribution of total force impulse from the rear leg [21,25–27]. What is more, an increase in force generation from the rear leg has been associated with higher block clearing velocities in elite sprinters [7,14,18]. However, a recent study [12] demonstrated that in the elongated start, despite a greater velocity of the center of mass at block clearing, the performance at 5 m and 10 m is significantly worse compared to the bunched start. It is known that in the elongated start, the duration of force application is increased during the block phase [9]. Spending longer on the blocks increases the total run time which then conflicts with the objective of a sprint. Instead, at 10 m the best performance results were obtained from the medium start. Further, a number of studies [3,9,12,22–24] suggested that the medium start creates the best balance between total force generated and the increased time of force generation to obtain the best performance in the early acceleration phase. When looking for the best front block/starting line and inter-block distances for an individ- ual athlete, it would seem obvious that the block distances should be relative to the individual’s body dimensions. However, very few studies considered the anteroposterior block distances in relation to the individual body dimensions of the athlete [11,20,22]. A study conducted by Dickinson [20] stated that the distance of the front block from the starting line should depend on the height of the individual, and the distance of the rear block from the starting line on the leg and the thigh lengths, irrespective of the type of the start used (bunched, medium, elon- gated). Henry [22] investigated if the optimum block spacing is related to the individual leg length by analyzing the interaction between four experimental inter-block distances (27.9, 40.6, 53.3 and 66.0 cm) and the individual’s leg length. Contrary to Dickinson, Henry Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 2 / 20 collection and analysis, decision to publish, or preparation of the manuscript. The funder provided support in the form of salaries for an author [PM], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author are articulated in the ‘author contributions’ section. Competing interests: We confirm that the commercial affiliation (OMR Automotive) does not alter our adherence to all PLOS ONE policies on sharing data and materials. concluded that leg length is not important in determining the best block spacing. A later study conducted by Schot & Knutzen [11] went further in associating the athlete’s physical charac- teristics with the anteroposterior block spacing. In this study, the authors stated that a medium start could be achieved according to a calculation based on the individual’s leg length from the greater trochanter to the lateral malleolus (60% of this length was used as the front block/start- ing line distance and 45% for the inter-block spacing). Anthropometry has been shown to play an important role in sports where body proportion- ality differences may affect the biomechanics of movement and the resulting performance (e.g. in running and in gymnastics) [28,29]. However, there is a lack of research addressing anthro- pometric characteristics in the sprint start. Accordingly, research is needed to elucidate any connection between the physical characteristics of the athlete, the block settings and the kine- matic and kinetic parameters during the sprint start. In starting block performance, it is rea- sonable to assume that body proportionality would influence optimal anteroposterior block distances. The starting block performance involves a closed kinematic chain of movements where the body extremities are in a fixed position and the only modifiable parameters are the anteroposterior block distances. Thus, it can be argued that, in addition to body dimensions, the proportion between the leg and trunk length may also affect the optimal anteroposterior block distances. It is well known that individuals present different proportionality characteris- tics between the leg and the trunk lengths and a way to assess such a proportionality is the Cor- mic Index [30]. The Cormic Index expresses sitting height as a proportion of the total height, representing a measure of the relative lengths of the trunk and lower limbs. Individuals are classified as brachycormic, metricormic and macrocormic according to a Cormic Index 51%, 51–53%, or 53%, respectively [31]. Using instrumented starting blocks and high speed video cameras, the first aim of this study was to test the effect of two different block settings in terms of anteroposterior block dis- tances on the kinematic and kinetic parameters of well-trained sprinters. The two setting con- ditions were the usual personal block setting used by the athlete and a block setting based on a proportion of the individual’s leg length [11]. We hypothesized that an anthropometry-driven intervention may improve the sprint start performance outcome. The second aim was to verify whether an effect of the two block settings persists when the Cormic Index of the participants is considered. The body proportionality characteristics of the sprinters may be of relevance in a sprint start, a skill where the entire body is involved in a closed chain of movements. We hypothesized that the Cormic Index of participants could influence the effect of a block setting based on the leg length. It is expected that results of this work would help coaches and athletes to improve sprint start performance using a quick anthropometry-driven procedure. Materials and methods Participants The required sample was estimated “a priori” and calculated using GPower ver.3.1.9.2 [32]. Setting the type I error [SS3] at α = 0.05, the effect size at f = 0.25 and the correlation among repeated measures at 0.6, the minimum sample size required for a within-between interaction in a mixed-design ANOVA for having an 80% power (i.e., β = 0.20) was 36 subjects. In order to comply with a possible ~15% dropout, forty-two participants were initially recruited. Partic- ipants were well-trained skilled sprinters (20 women and 22 men) with a competitive athletic career of at least 2 years in sprint running. Female and male participants’ age, height and body mass (±SD) were 19.70 ± 2.23 and 19.36 ± 2.11 y, 165.4 ± 5.2 and 176.7 ± 5.9 cm, 55.6 ± 6.8 and 67.1 ± 9.8 kg, respectively. All sprinters were involved in regional and national level com- petitions and trained at least 6 times a week for 2/3 hours per day. Their best time over 100 m Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 3 / 20 ranged between 10.45 s and 11.30 s for men and between 11.45 s and 12.68 s for women. According to the Cormic Index, the sprinters were brachycormic (n = 12), metricormic (n = 19) and macrocormic (n = 11). The mean Cormic Index in the three groups was 50.62% ± 1.14, 52.48% ± 0.98 and 53.78% ± 0.51, respectively. All participants gave their written informed consent to participate in the study, and the protocol was performed in accordance with the Declaration of Helsinki. Ethics approval was obtained from the University of Verona Institutional Review Board. Experimental procedure The sprint testing took place on an outdoor track (Olimpic Plast SWD surface, Olimpia Cost- ruzioni, Forlı`, Italy) during the early competition phase of the outdoor season. One operator (VC) attached ten retro-reflective passive flat markers (14 mm diameter) bilaterally over spe- cific anatomical landmarks on the participant’s body (i.e., right [R] and left [L] acromion, R and L femur greater trochanter, R and L femur lateral epicondyle, R and L fibula apex of lateral malleolus, R and L 5th metatarsal). Following a warm-up consisting of jogging, dynamic stretching and sprints of submaximal intensity, all participants performed a total of 6 maximal-effort 10 m sprints using two differ- ent starting conditions: 1) the athlete’s usual personal block setting (US), and 2) an anthro- pometry-driven setting (AS). The order of the two starting conditions was randomized for each athlete. In the AS condition, the start block positions were set according to a calculation based on the individual’s leg length from the greater trochanter to lateral malleolus [11]; 60% of this length was used as the front block/starting line distance and 45% as the inter-block spacing. The anteroposterior block distances in both the US and AS conditions were measured to the nearest 0.1 cm in the outdoor track with a fiberglass tape. The obliquity of the blocks was that usually used by the participants and was the same in both conditions. Participants used their own spiked shoes for sprint running. For all trials, each sprint was initiated by the same experimenter (MP), who provided standard ‘on your marks’ and ‘set’ commands. The experimenter then pressed a custom-designed trigger button to provide the auditory start sig- nal through a sounder device. The rest period between trials was 5–7 minutes. After an adequate rest period, the standing long jump test was used to measure lower extremity strength. Participants stood behind a line marked on the ground with their feet slightly apart. A two-foot take-off and landing was used, with swinging of the arms and bend- ing of the knees to provide forward drive, the subject attempting to jump as far as possible. The horizontal distance between the starting line and the back of the heel closest to the starting line at landing was recorded via tape measure to the nearest 0.1 cm. Three trials were per- formed (with adequate rest between trials) and the maximum distance was recorded. Although the standing long jump performance is usually expressed in absolute terms as the overall dis- tance covered, it has been suggested that the subject’s leg length can play a significant role in the performance [33]. Accordingly, performance in the standing long jump test was expressed relative to the leg length (SLJ-relative). Data collection Anthropometric data. Anthropometric data were taken by one operator (VC) using con- ventional criteria and measuring procedures [34]. Body mass was assessed to the nearest 0.1 kg using a certified electronic scale (Tanita electronic scale BWB-800 MA, Wunder SA.BI. Srl, Milano, Italy). Standing height and sitting height were measured to the nearest 0.1 cm using a Harpenden portable stadiometer (Holtain Ltd., Crymych, Pembs. UK). For the sitting height, the subject was asked to sit on a flat stool of a known height. Measurement was taken with the Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 4 / 20 subject sitting in a standard position. The sitting height was then obtained by subtracting the height of the stool from the reading on the stadiometer. The lower limb length was measured with a Harpenden anthropometer (Holtain Ltd., Crymych, Pembs. UK) as the distance between the greater trochanter and the lateral malleolus (cm). Body circumferences were mea- sured with a fiberglass tape at the mid-thigh and the calf. The Cormic Index was calculated for each participant as sitting height (cm)/standing height (cm)100. The body mass index (BMI) was also computed for each participant as body mass (kg)/standing height (m)2. Kinetic and kinematic data. Each trial was performed using a set of dynamometric start- ing blocks equipped with a set of load cells (CU K5D and CU K1C models, GEFRAN SpA, Bre- scia, Italy) enabling the measurement of the magnitude and direction of forces generated by the sprinter during the starting block phase. The acquisition frequency was 1 kHz and the sensitiv- ity was 0.01 N. These blocks respected all the features of those normally used in the track sprint start, as well the output characteristics of similar blocks used in previous works [13,16]. There were four monoaxial load cells installed on each block, which were used to determine the loads: three cells were used to measure the vertical loads and one to measure the horizontal loads. The force data were resolved into horizontal and vertical components for each foot (Fig 1). The Fig 1. Example of resultant force curves of the front and rear blocks during a sprint start recorded by the instrumented starting blocks. RT, reaction time; RBT, rear block time; FBT, front block time; TBT, total block time; RPF, rear peak force; FPF, front peak force. https://doi.org/10.1371/journal.pone.0213979.g001 Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 5 / 20 blocks were connected to a portable personal computer (SoftPLC, GEFRAN SpA, Brescia, Italy) that stored data, and handled signal processing and parameter calculation. The dynamometric starting blocks were developed at OMR Automotive (Officine Meccaniche Rezzatesi, Brescia, Italy). The reliability and validity tests were performed at the Department of Industrial Engineer- ing, University of Padua, Italy. Both static and dynamic bench tests were carried out on a mechanical bench equipped with two MTS 242 servo hydraulic actuators to calibrate the force transducers. Several tests were performed in the horizontal and vertical directions on each part of the apparatus in order to obtain the single axis calibration constants of the system. Static and dynamic load tests with combined loading were also performed to validate the results of a realistic usage, showing good results with low error: the difference between the maximum force applied on the block and the measured force was lower than 1% [35]. After bench tests, a series of in-vivo sprint starts were recorded during a training session with beginner, intermedi- ate and expert athletes and comparison was made with a commercial force plate (Model 4060– 10, Bertec Columbus, OH, USA) output [35] included in a SMART DX-6000 motion capture system (BTS Bioengineering, Quincy, MA, USA) after mounting the dynamometric blocks over the force platform. The results showed that both vertical and horizontal forces measured by the dynamometric starting blocks and the force plate were very similar to each other: hori- zontal and vertical forces were able to follow the loads also in the region of rapid loading and unloading with good accuracy. Regression coefficients were found to be on average R = 0.9946 for horizontal loads and R = 0.9978 for vertical loads. The dynamometric starting blocks appa- ratus was portable and made resistant to splash water and could therefore be used outdoors. These dynamometric starting blocks can easily be used on the track during training, giving precise quantitative data which would usually only be available in a biomechanics laboratory setting using sophisticated instruments in a controlled environment. Two high-speed video cameras (Casio Exilim ex-zr 1000, Casio Europe Gmbh, Barcelona, Spain) captured the movement of each athlete in two dimensions during the starting block and acceleration phases (first and second stride lengths). One camera (Camera 1) was posi- tioned for the front block side view of the participant and the other camera (Camera 2) for the rear block side view of the participant. According to Bartlett [36], in order to limit the potential technological errors in the filming set-up connected with the 2D videography (e.g. the parallax errors due to visual distortion), the optical axis of each camera was lined directly perpendicular to the sprinter’s movement plane. Furthermore, the optical axis of each camera was centred in correspondence to an imaginary line perpendicular to the ground and passing through the hip joint of the leg facing the camera at the “on your marks” position. Each camera was placed on a tripod at a height of approximately 1 m and located at approximately 5 m from the partici- pant. Each camera’s field of view provided a sagittal view of each sprinter for the first two full strides. The position of the two cameras was standardized for all participants to ensure no environmental changes during field testing (Fig 2). Each video was calibrated with a 50 cm cube positioned to the rear of the blocks and defined by X the horizontal axis and Y the vertical axis. Images of the starting block phase and acceleration phase were collected at a resolution of 1280 x 1024 pixels using a shutter speed of 1/1000 s and a sampling frequency of 200 Hz. Three pairs of photocells (Polifemo Light Radio, Microgate SRL, Bolzano, Italy) based on a radio impulse transmission system and a reflection system were used to measure the times at 5 m and 10 m in the sprint trials. The timing between the dynamometric starting blocks and the photocells system was synchronized using the digital output available from the block control system and connecting it to the available input for timing available in the Microgate unit. Data analysis. The kinematic and kinetic outputs were stored during the trials in a porta- ble personal computer connected to the instrumented blocks and were then exported for Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 6 / 20 further analysis. Raw data were filtered using a low-pass Butterworth filter (fourth order) with a cutoff frequency of 120 Hz and analyzed using a custom program written in Matlab R2008a (MathWorks, Natick, MA, USA). Force signals were resolved into horizontal and vertical components using a coordinate sys- tem affixed to the runway. The x-axis pointed forward along the running surface (horizontal plane), the y-axis pointed vertically upwards. Force data were used to define the force onset threshold (i.e., when the first derivative of the resultant force-time curve was greater than 500 Ns-1) and the force offset thresholds (i.e., when the resultant force was lower than 50 N). The pushing phase was defined between the first instant of block start (i.e., corresponding to the force onset threshold) to block clearance (i.e., corresponding to the force offset threshold on the front block). The pushing phases of the front and the rear blocks were defined as the period between the instant of the block start and the end of the respective sub-phases for each block. The following temporal parameters were extracted for analysis from the instrumented blocks data: the reaction time (RT), defined as the time from the auditory signal to the first force onset threshold; the front block time (FBT), defined as the pushing time on the front block sub-phase; the rear block time (RBT), defined as the pushing time on the rear block sub-phase and the total time block (TBT), defined as the pushing time on the total pushing phase. The following kinetic variables obtained from the instrumented blocks data during the pushing phase were also measured: the front peak force (FPF), defined as the maximum resultant front force value; the rear peak force (RPF), defined as the maximum resultant rear force value; the horizontal and the vertical front peak force (H_FPF and V_FPF); the horizontal and the verti- cal rear peak force (H_RPF and V_RPF); the average total force (ATF). The front force impulse (FFimpulse), the rear force impulse (RFimpulse) and the total force impulse (Total Fimpulse) were computed according to Otsuka and colleagues [15]. All the kinetic variables were normalized to the body mass of the sprinters expressed in kg. In addition, the following variables were computed: the ratio of horizontal to resultant force impulse of both legs (Ratio_front and Ratio_rear) [37]; the horizontal block velocity (H_BV) measured as the sum between the hori- zontal impulse on the front block plus the horizontal impulse on the rear block (both expressed in Ns) divided by the body mass of the sprinter expressed in kg; the normalized average hori- zontal external block power (NAHEP) calculated according to the procedures by Bezodis and colleagues [2]. Fig 2. Experimental set-up. https://doi.org/10.1371/journal.pone.0213979.g002 Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 7 / 20 For each participant the video material of the 12 video clips (6 trials and 2 cameras) was uploaded onto a PC and digitalized at full resolution with a zoom factor of 2.5 using freeware motion-analysis software (Kinovea; version 0.8.15, available for download at: http://www. kinovea.org). One operator (VC) blinded to condition (AS, US) manually digitalized the mark- ers and quantified the joint angles and stride lengths at specific video frames on each video (see details below). The analysis of the video material was carried out in two stages. Firstly, each video clip was rewound frame by frame and frozen at the set position to directly estimate the angles in the sagittal plane with the digital goniometer built into the Kinovea software. The operator visually digitalized the markers through the “cross marker” function and placed the digital goniometers. The videos from Camera 1 (i.e., with the front block side view) were used to estimate the hip, knee and ankle joint angles on the front leg while the videos from Camera 2 (i.e., with the rear block side view) were used to measure the hip, knee and ankle joint angles on the rear leg (Fig 3). The joint angles were measured to the nearest 1 degree. Secondly, in order to measure the length of the first stride, each video clip from Camera 1 was stopped at the instant of the first foot strike. Stride was estimated at the first frame of the video clip where the sprinter’s foot made contact with the track surface. The length of the inferior side of the cube positioned to the rear of the blocks in the frame was used as a reference to calibrate all other line lengths. The “line drawing tool” function was used to assess the horizontal distance between the front block and the toe of the rear foot at the first foot strike (first stride length [SL1]). Thirdly, in order to measure the length of the second stride, each video clip from Cam- era 2 was stopped at the instant of the second foot strike. The “line drawing tool” function was used to assess the horizontal distance to the nearest 0.1 cm between the rear foot at the first toe off and toe of the front foot at the first foot strike (second stride length [SL2]). SL1 and SL2 were normalized to leg length to account for differences among subjects and labelled as NorSL1 and NorSL2, respectively. In order to limit any technical errors involved in 2D videography as much as possible, the procedure adopted to quantify the joint angles and stride lengths was repeated in three sepa- rate sessions, with a minimum interval of 7 days between sessions and the mean value was recorded only when the coefficient of variation was <0.05. Furthermore, the operator was familiar with the use of high-speed video as a tool to quantify joint angles in sprint running and in sprint starts. It has been noted that the markers, despite being properly positioned dur- ing data collection, can move in relation to the skin throughout the range of motion [38]. Accordingly, in line with Bradshaw and colleagues [39], the operator paid close attention dur- ing data analysis to this fact and visually adjusted for skin movement by only using the markers as a guide. Fig 3. Two-dimensional functional representation of the joint angles on the front (Panel A) and rear blocks (Panel B). https://doi.org/10.1371/journal.pone.0213979.g003 Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 8 / 20 In this study, errors (i.e. parallax and lens distortion errors) in the measurement of the first and second stride lengths has to be taken into account. A typical error (%Error) was estimated starting from the calibration cube (50 cm) positioned behind the blocks and then every 50 cm until the end of the field of view. The %Error was calculated according to the following formula: %Error ¼ jE were found for the sitting height and the lower limb length (F = 13.238 and F = 14.495, respec- tively; P<0.001 for both). Post-hoc analysis with Bonferroni’s correction revealed that the bra- chycormic group had significantly lower sitting height and lower limb length values in comparison with both the metricormic (P = 0.002 for both) and the macrocormic (P<0.001 for both) groups (Table 1). The mean values (±SD) of the anteroposterior block distances and the kinematic and kinetic data in the US and AS conditions in the whole sample and in the three Cormic Index groups are reported in Table 2. One-way ANOVA showed that in the US condition, the front block/starting line distance was significantly different within the three groups (F = 9.500, P<0.001), but the inter-block distance was not. Post-hoc analysis with Bonferroni’s correction revealed a significantly lower front block/starting line distance in the brachycormic group vs. the metricormic group (-5.9 cm, P = 0.001) and macrocormic group (-6.1 cm, P = 0.002). One-way ANOVA also indicated that all of the measured sprint start kinematic and kinetic variables were similar in the US con- dition in the three Cormic Index groups with the exclusion of the TBT (F = 3.369, P = 0.045). However, post-hoc analysis with Bonferroni’s correction, revealed no significant group-group difference in the TBT. A mixed-design ANOVA with three groups (brachycormic, metricormic, macrocormic) and two block setting conditions (US and AS) with repeated measures on the second factor showed a significant main effect of condition for both anteroposterior block distances and for several kinematic and kinetic measurements (Tables 2 and 3). The front block/starting line distance was significantly lower (-3.2 cm) in the AS vs. the US whereas the inter-block distance was significantly greater (+9.2 cm). Furthermore, at the set position, the rear hip and the rear knee joint angles were significantly greater in the AS vs. the US (+6 and +5 degrees, respectively) while the opposite was found at the front hip and knee joint angles (-4 and -3 degrees, respectively). The rear ankle joint angle was significantly lower in the AS vs. the US (-2 degrees); no significant difference was found for the front ankle joint angle. Results also showed that, during the pushing phase, several kinematic and kinetic vari- ables were significantly greater in the AS vs. the US namely, the FBT (+0.02 s), the RPF (+1.63 N/kg), the H_RPF (+0.43 N/kg), the RFimpulse (+0.10 Ns/kg), the Total Fimpulse (+0.17 Ns/kg), the Ratio_rear (+0.01), H_BV (+0.14 m/s), and the NAHEP (+0.03). No significant differences were found for the other variables during the pushing phase. In the acceleration phase, the times at 5 m and 10 m were significantly lower in the AS vs. the US (-0.05 and -0.04 s, respectively). Table 1. Characteristics of the participants in the whole sample and the three Cormic Index groups. Data are means ± SD. Whole sample (n = 42) Brachycormic (n = 12) Metricormic (n = 19) Macrocormic (n = 11) Age (y) 19.52 ± 2.14 19.75 ± 2.38 19.63 ± 2.41 19.09 ± 1.38 Sprinting experience (y) 4.76 ± 2.67 5.83 ± 3.04 3.95 ± 2.27 5.00 ± 2.65 Body mass (kg) 61.6 ± 10.2 59.6 ± 9.9 63.7 ± 10.4 60.3 ± 10.4 Height (cm) 171.3 ±7.9 167.6 ± 8.1 172. 4 ± 6.8 173.6 ± 8.8 BMI (kg/m2) 20.89 ± 2.19 21.10 ± 1.98 21.33 ± 2.44 19.89 ± 1.76 Sitting height (cm) 89.6 ± 5 84.9 ± 5 90.4 ± 3 93.33 ± 4.6 Lower limb length (cm) 81.8 ± 5 77.1 ± 5 82.5 ± 2.8 85.8 ± 4.2 Thigh circumference (cm) 49.04 ± 3.27 49.57 ± 3.97 49.32 ± 3.38 47.98 ± 2.09 Calf circumference (cm) 34.72 ± 2.56 34.73 ± 3.08 35.01 ± 2.41 34.22 ± 2.35 SLJ-relative 2.8 ± 0.2 2.7 ± 0.2 2.9 ± 0.3 2.7 ± 0.2 BMI, body mass index; SLJ-relative, performance in the standing long jump test expressed relative to the leg length. , significantly different (P<0.05; Bonferroni’s post-hoc) vs. metricormic and macrocormic. https://doi.org/10.1371/journal.pone.0213979.t001 Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 10 / 20 Table 2. Block distances, and kinematic and kinetic data in the sprint start for the whole sample as well as for the three Cormic Index groups in their usual block setting condition (US) and an anthropometry-driven block setting (AS) condition. Data are means ± SD. Variable Whole sample (n = 42) Brachycormic (n = 12) Metricormic (n = 19) Macrocormic (n = 11) Block distances US AS US AS US AS US AS FB/SL distance (cm) 52.3 ± 4.8 49.1 ± 3.0 48.0 ± 3.6 46.3 ± 3.1 53. 9 ± 4.3 49.5 ± 1.7 54.2 ± 3.9 51.5 ± 2.5 I-B distance (cm)^ 27.6 ± 2.4 36.8 ± 2.3 27.3 ± 2.5 34.7 ± 2.3# 27.2 ± 2.2 37.1 ± 1.3# 28.6 ± 2.5 38.6 ± 1.9# Set position Front hip (˚) 47 ± 6 43 ± 6 44 ± 6 41 ± 7 49 ± 4 43 ± 4 47 ± 9 44 ± 8 Rear hip (˚) 77 ± 8 84 ± 8 75 ± 8 80 ± 10 79 ± 7 85 ± 5 77 ± 11 85 ± 10 Front knee (˚) 92 ± 9 90 ± 8 94 ± 7 93 ± 5 92 ± 9 88 ± 8 92 ± 9 90 ± 9 Rear knee (˚) 112 ± 11 117 ± 11 115 ± 7 119 ±8 111 ± 12 115±11 112 ± 12 118 ± 15 Front ankle (˚) 92 ± 6 93 ± 7 94 ± 4 95 ± 3 92 ± 7 93 ± 7 92 ± 6 92 ± 8 Rear ankle (˚) 87 ± 6 85 ± 7 88 ± 5 86 ± 6 88 ± 6 86 ± 8 85 ± 6 84 ± 7 Pushing phase RT (s) 0.185 ± 0.035 0.189 ± 0.035 0.195 ± 0.026 0.192 ± 0.029 0.184 ± 0.040 0.189 ± 0.032 0.175 ± 0.035 0.186 ± 0.047 FBT (s) 0.402 ± 0.041 0.416 ± 0.064 0.402 ± 0.051 0.402 ± 0.044 0.394 ± 0.036 0.419 ± 0.086 0.419 ± 0.036 0.424 ± 0.027 RBT (s) 0.211 ± 0.041 0.212 ± 0.041 0.220 ± 0.052 0.225 ± 0.059 0.203 ± 0.038 0.210 ± 0.035 0.215 ± 0.031 0.202 ± 0.025 TBT (s) 0.421 ± 0.047 0.427 ± 0.038 0.411 ± 0.035 0.415 ± 0.028 0.409 ± 0.035 0.424 ± 0.036 0.451 ± 0.066 0.448 ± 0.045 FPF (N/kg) 16.50 ± 2.27 16.60 ± 2.09 15.18 ± 2.16 15.31 ± 1.80 17.13 ± 2.20 17.16 ± 2.29 16.85 ± 2.04 17.03 ± 1.44 RPF (N/kg)^ 11.44 ± 2.48 13.07 ± 3.37 10.83 ± 3.10 12.74 ± 3.10# 11.76 ± 2.03 14.08 ± 2.76# 11.55 ± 2.58 11.68 ± 4.24 H_FPF (N/kg) 6.02 ± 0.71 5.91 ± 0.65 5.65 ± 0.74 5.62 ± 0.67 6.25 ± 0.70 6.02 ± 0.71 6.02 ± 0.56 6.06 ± 0.45 V_FPF (N/kg)^ 6.13 ± 0.92 6.12 ± 0.90 6.01 ± 0.98 5.91 ± 1.20 6.25 ± 0.87 5.99 ± 0.74 6.06 ± 0.98 6.58 ± 0.68# H_RPF (N/kg)^ 4.52 ± 1.09 4.95 ± 1.34 4.13 ± 1.17 4.58 ± 1.21# 4.70 ± 0.97 5.41 ± 1.00# 4.63 ± 1.20 4.55 ± 1.78 V_RPF (N/kg)^ 3.78 ± 1.12 3.96 ± 1.20 3.36 ± 1.04 3.55 ± 1.07 3.76 ± 1.08 4.23 ± 1.00# 4.28 ± 1.16 3.94 ± 1.58 ATF (N/kg) 11.37 ± 1.19 11.55 ± 1.12 11.12 ± 1.58 11.22 ± 1.51 11.63 ± 0.91 11.83 ± 0.87 11.21 ± 1.14 11.44 ± 0.96 FFimpulse (Ns/kg) 3.48 ± 0.50 3.56 ± 0.55 3.32 ± 0.61 3.32 ± 0.62 3.52 ± 0.48 3.57 ± 0.58 3.60 ± 0.38 3.79 ± 0.33 RFimpulse (Ns/kg)^ 1.29 ± 0.40 1.39 ± 0.42 1.25 ± 0.47 1.34 ± 0.40 1.25 ± 0.30 1.48 ± 0.34# 1.40 ± 0.47 1.31 ± 0.56 Total Fimpulse (Ns/kg) 4.76 ± 0.55 4.93 ± 0.56 4.56 ± 0.69 4.64 ± 0.65 4.75 ± 0.45 5.01 ± 0.54 5.01 ± 0.51 5.10 ± 0.38 Ratio_front 0.69 ± 0.05 0.69 ± 0.05 0.68 ± 0.05 0.69 ± 0.06 0.70 ± 0.05 0.70 ± 0.05 0.70 ± 0.05 0.67 ± 0.04 Ratio_rear 0.75 ± 0.05 0.76 ± 0.05 0.75 ± 0.06 0.76 ± 0.06 0.76 ± 0.05 0.77 ± 0.04 0.71 ± 0.03 0.73 ± 0.05 Ratio_total 0.71 ± 0.04 0.72 ± 0.04 0.71 ± 0.05 0.71 ± 0.05 0.72 ± 0.05 0.73 ± 0.04 0.70 ± 0.03 0.69 ± 0.04 H_BV (m/s) 3.36 ± 0.35 3.50 ± 0.39 3.18 ± 0.41 3.27 ± 0.36 3.40 ± 0.28 3.62 ± 0.38 3.48 ± 0.35 3.52 ± 0.34 NAHEP 0.47 ± 0.90 0.50 ± 0.10 0.44 ± 0.10 0.46 ± 0.09 0.49 ± 0.08 0.54 ± 0.10 0.46 ± 0.08 0.47 ± 0.08 Acceleration Phase 5 m (s) 1.34 ± 0.10 1.29 ± 0.09 1.36 ± 0.10 1.31 ± 0.12 1.33 ± 0.10 1.28 ± 0.09 1.33 ± 0.09 1.29 ± 0.06 10 m (s) 2.07 ± 0.13 2.03 ± 0.13 2.12 ± 0.14 2.09 ± 0.16 2.05 ± 0.15 2.00 ± 0.12 2.06 ± 0.10 2.01 ± 0.07 NorSL1 1.09 ± 0.11 1.12 ± 0.12 1.06 ± 0.09 1.12 ± 0.12 1.11 ± 0.14 1.14 ± 0.15 1.07 ± 0.09 1.07 ± 0.07 NorSL2 1.15 ± 0.14 1.19 ± 0.12 1.12 ± 0.15 1.16 ± 0.11 1.18 ± 0.15 1.12 ± 0.13 1.14 ± 0.10 1.15 ± 0.10 FB/SL, front block/starting line; I-B, inter-block; RT, reaction time; FBT, front block time; RBT, rear block time; TBT, total block time; FPF, front peak force; RPF, rear peak force; H_FPF, horizontal front peak force; V_FPF, vertical front peak force; H_RPF, horizontal rear peak force; V_RPF, vertical rear peak force; ATF, average total force; FFimpulse, front force impulse; RFimpulse, rear force impulse; Total Fimpulse, total force impulse; Ratio_front, Ratio of horizontal to resultant force impulse of front leg; Ratio_rear, Ratio of horizontal to resultant force impulse of rear leg; NAHEP, normalized average horizontal external power; H_BV, horizontal block velocity; 5 m, time at 5 meters; 10 m, time at 10 meters; NorSL1, first stride length normalized to leg length; NorSL2, second stride length normalized to leg length. , statistically significant (P<0.05) difference between the AS and US conditions ^, statistically significant (P<0.05) difference of the AS vs. the US effect across the three Cormic Index groups #, statistically significant difference between the AS and US conditions (post-hoc analysis with Bonferroni’s correction). https://doi.org/10.1371/journal.pone.0213979.t002 Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 11 / 20 NorSL1, and NorSL2 were significantly larger in the AS vs. the US (+0.03 and +0.04, respectively). The effect size was large (>0.14) for all the significantly different variables except for FBT, NorSL1, and NorSL2 where the effect size was medium (>0.10 effect size <0.12). Table 3. Results of the mixed-design ANOVA to quantify the effect of condition and effect of group by condition interaction on block distances, and kinematic and kinetic data. Condition Group by condition Variable F value P value ηp 2 F value P value ηp 2 Block distances FB/SL distance 29.232 <0.001 0.428 2.308 0.113 0.106 I-B distance 545.112 <0.001 0.933 4.494 0.018 0.187 Set position Front hip 46.494 <0.001 0.544 2.392 0.105 0.109 Rear hip 60.857 <0.001 0.609 1.520 0.231 0.072 Front knee 11.569 0.002 0.229 1.224 0.305 0.059 Rear knee 10.482 0.002 0.212 1.643 0.531 0.032 Front ankle 3.830 0.058 0.089 0.213 0.809 0.011 Rear ankle 7.485 0.009 0.161 0.150 0.861 0.008 Pushing phase RT 0.925 0.342 0.023 0.621 0.543 0.031 FBT 4.261 0.046 0.098 1.201 0.312 0.058 RBT 0.031 0.862 0.001 2.661 0.083 0.120 TBT 1.647 0.207 0.041 1.770 0.184 0.083 FPF 0.356 0.554 0.009 0.066 0.937 0.003 RPF 25.821 <0.001 0.398 5.402 0.008 0.217 H_FPF 1.969 0.168 0.048 2.247 0.119 0.103 V_FPF 0.274 0.603 0.007 4.935 0.012 0.202 H_RPF 10.706 0.002 0.215 4.436 0.018 0.185 V_RPF 1.299 0.261 0.032 6.770 0.003 0.258 ATF 2.237 0.143 0.054 0.165 0.848 0.008 FFimpulse 1.950 0.171 0.048 0.815 0.450 0.040 RFimpulse 6.821 0.013 0.149 10.191 <0.001 0.343 Total Fimpulse 11.838 0.001 0.233 2.255 0.118 0.104 Ratio_front 1.321 0.257 0.033 3.241 0.050 0.143 Ratio_rear 9.244 0.004 0.192 0.829 0.444 0.041 Ratio_total 0.008 0.930 0.000 1.803 0.178 0.085 H_BV 11.946 0.001 0.234 3.107 0.056 0.137 NAHEP 6.815 0.013 0.149 1.446 0.248 0.069 Acceleration phase 5 m 27.802 <0.001 0.416 0.273 0.762 0.014 10 m 19.531 <0.001 0.334 0.219 0.804 0.011 NorSL1 12.041 0.001 0.236 3.002 0.061 0.133 NorSL2 12.732 0.001 0.246 1.241 0.300 0.060 US, usual sprinter’s block setting; AS, anthropometry-driven block setting; ηp 2, partial eta squared; FB/SL, front block/starting line; I-B, inter-block; RT, reaction time; FBT, front block time; RBT, rear block time; TBT, total block time; FPF, front peak force; RPF, rear peak force; H_FPF, horizontal front peak force; V_FPF, vertical front peak force; H_RPF, horizontal rear peak force; V_RPF, vertical rear peak force; ATF, average total force; FFimpulse, front force impulse; RFimpulse, rear force impulse; Total Fimpulse, total force impulse; Ratio_front, Ratio of horizontal to resultant force impulse of front leg; Ratio_rear, Ratio of horizontal to resultant force impulse of rear leg; NAHEP, normalized average horizontal external power; H_BV, horizontal block velocity; 5 m, time at 5 meters; 10 m, time at 10 meters; NorSL1, first stride length normalized to leg length; NorSL2, second stride length normalized to leg length. https://doi.org/10.1371/journal.pone.0213979.t003 Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 12 / 20 As shown in Table 3, the results of the mixed-design ANOVA (group by condition) revealed a significant main effect for condition (US and AS) by group (brachycormic, metri- cormic and macrocormic) interaction for a number of variables. The effect size for all the sig- nificantly different variables was large (>0.14). Post hoc analysis showed significantly greater mean inter-block distance in the AS vs. the US in the brachycormic, metricormic and macro- cormic groups by respectively +7.4 cm, +9.9 cm and +10.0 cm (P<0.001 for all); in the AS con- dition, RPF was significantly greater in the brachycormic and the metricormic group (+1.91 N/kg, P = 0.001 and +2.32 N/kg, P<0.001, respectively), but not in the macrocormic group. H_RPF was significantly greater for brachicormic group (+0.45 N/kg, P = 0.031) and the metricormic group (+0.71 N/kg, P<0.001). In the AS V_RPF was significantly greater in the metricormic group vs. the US (+0.47 N/kg, P = 0.001) and the V_FPF was significantly higher in the macrocormic group (+0.52 N/kg, P = 0.013). RFimpulse was also significantly greater in the metricormic group (+0.23 Ns/kg, P<0.001) in the AS vs. the US. The estimation of a regression model for each performance variable using the difference (Delta) in the anteroposterior block distances between the two conditions (US and AS) as pre- dictors and the Delta in the performance variable as dependent variable, evidenced some sta- tistically significant relationships which were summarized in Table 4. Discussion The first aim of this study was to investigate the effect of two different block setting conditions (US and AS) on kinematic and kinetic performance outcomes during the sprint start in well- trained sprinters focusing on block phases (set position and pushing-phase), and the early acceleration phase (times at 5 m and 10 m, first and second stride lengths). Results showed that an anthropometry-driven block setting condition (AS) based on the sprinter’s leg length [11] is associated with several statistically significant changes in postural parameters at the set position, as well as in kinetic and kinematic variables at the pushing and acceleration phases in comparison with the sprinter’s usual block setting, leading to improved performance. When using the US condition, which was based on the individual sprinter’s preferences, all participants in the sample adopted an inter-block distance (27.6 ± 2.4 cm) classifiable as a bunched start, suggesting that sprinters prefer a low anteroposterior distance between the front and rear foot. This is in agreement with recent studies [15,18] showing that sprinters with different ability levels chose an anteroposterior inter-block distance from 25 cm to 30 cm Table 4. Multiple linear regression model estimated using the difference (Delta) in the anteroposterior block dis- tances between the two conditions (US and AS) as predictors and the delta in the performance variable as the dependent variable. Delta FB/SL distance Delta I-B distance Adj. R2 Delta RPF r = -0.35; P = 0.024 r = -0.26; P = 0.102 0.11 Delta H_RPF r = -0.31; P = 0.047 r = -0.21; P = 0.175 0.07 Delta V_RPF r = -0.37; P = 0.017 r = -0.19; P = 0.246 0.10 Delta RFimpulse r = -0.34; P = 0.027 r = -0.03; P = 0.836 0.08 Delta Total Fimpulse r = -0.50; P < 0.001 r = -0.10; P = 0.545 0.25 Delta Rear hip r = 0.33; P = 0.036 r = 0.62; P < 0.001 0.36 US, usual sprinter’s block setting; AS, anthropometry-driven block setting; FB/SL, front block/starting line; I-B, inter- block; Adj. R2, adjusted coefficient of determination; r, partial correlation coefficient; RPF, rear peak force; H_RPF, horizontal rear peak force; V_RPF, vertical rear peak force; RFimpulse, rear force impulse; Total Fimpulse, total force impulse. https://doi.org/10.1371/journal.pone.0213979.t004 Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 13 / 20 on the basis of their sensations. Nevertheless, the bunched start has been demonstrated to be the least efficient from a biomechanical perspective because less force is exerted on the starting blocks with a reduction in block velocity [12,21,42]. Several studies (2,7,11,14,18,21,22,25–27) highlighted that the increase in inter-block dis- tance is associated with improved performance in several kinetic and kinematic variables linked to the sprint start (i.e., a greater contribution of total force generation and force impulse from the rear leg and higher block clearing velocities). On the other hand, in the AS condition, which was based on the individual sprinter’s leg length, sprinters decreased the front block/ starting line distance (-6.56%) and increased the inter-block distance (+25.02%). Such varia- tions in the anteroposterior block distances in the AS condition lead to a postural adaptation at the set position resulting in a decrease in the front hip, front knee and rear ankle joint angles and an increase in the rear hip and rear knee joint angles (Table 3). Based on the literature [3,4,17,43,44], it is reasonable to assume that these postural changes at the set position in the AS condition compared to the US condition could be associated with an improvement in per- formance in the subsequent pushing phase such as greater RPF, H_RPF, RFI, Ratio_rear, Total Fimpulse and NAHEP. In fact, the resulting joint angles (front/rear) in the AS condition were similar to those reported in sprinters with ability levels ranging from national-level to world- class [3,4,17]. Consistently, scientific data also showed that the front hip and knee as well as the ankle joint angles were found to be smaller in faster than slower sprinters, allowing for the stretch-reflex of the hip extensor and the soleus muscles and the greatest velocity when leaving the blocks [43,44]. A recent study identified an important role of the rear hip joint angle to assist the genera- tion of NAHEP during the block phase [3]. This is supported by results of regression analysis in the current study showing that the difference between the US and AS conditions in the ante- roposterior front/starting line and inter-block distances predicted the difference between the two conditions in the rear hip angle (R2 = 0.39 for both distances). This suggests that the action of the rear hip extensor can be enhanced by adjusting the anteroposterior block distances. This finding is supported by a study by Slawinski and colleagues [12] reporting changes in the rear and front hip angular velocity among three different inter-block distances (bunched, medium and elongated). What is more, in the AS condition, the more flexed front hip, front knee and rear ankle joint angles as well as the shorter front block/starting line distance led sprinters to assume a lower crouched set position, (i.e., the centre of mass is closer to the ground). In the literature it is reported that a crouched set position is able to generate greater H_ BV [7,18]. From a biomechanical point view, the ability to leave the blocks at a high velocity depends on the horizontal force impulse on the blocks during the pushing phase [7]. In accordance with classic mechanical physics, impulse is equal to the product of force and time. Conse- quently, a higher block velocity could either be due to an increase in the net propulsion force generated or to the push duration. In our sample, no significant difference in the duration of the applied force (TBT) was found between the two starting conditions, thus the increase asso- ciated with the AS in H_BV (+4%) was due to an increased force production, not to an increase in the duration of the push against the blocks. The H_BV is a commonly measured variable when evaluating sprint start performance [6,10,11,22]. However, we agree with the statement of Bezodis and colleagues [2] that the NAHEP best describes the sprint start perfor- mance because it summarizes in a single parameter how much a sprinter is able to increase their velocity and the amount of time duration to achieve this. In our study, results showed a greater NAHEP (+6%) in the AS vs. the US indicating that anthropometry-driven anteropos- terior block distances may assist sprinters in translating their centre of mass in the horizontal direction. Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 14 / 20 In line with previous studies underlining the importance of the rear leg [1,3,7,16,18] in the sprint start, another important result in the current study was the significant increase observed in the AS condition for several kinetic parameters in the rear block such as the RPF, RFimpulse and the Ratio_rear by respectively +12.47%, +7.19%, and +1.32%. It is likely that the greater force generated by the rear leg allowed sprinters to achieve significantly greater H_BV. According to Slawinski et al. [7], the ability of faster sprinters to leave the blocks at a higher velocity depends on the rear block total force and the rate of force development. When considering the peak force components on the front and rear blocks, we found sig- nificantly higher horizontal and vertical peak forces at the rear block in the AS vs. the US (+8.68% and +4.54%, respectively. However, the difference in the mean value on peak force between the front and the rear blocks was -3.53 N/kg (-21.26%) and -5.06 N/kg (-30.66%) in the AS and US conditions, respectively. A similar pattern was observed for the Total Fimpulse which was -2.17 Ns/kg (-60.95%) and -2.19 Ns/kg (-62.93%) in the AS and US conditions, respectively. The finding of a smaller force difference between the rear and front legs in the AS condition suggests that sprinters are able to get a more balanced force generation between the rear and front legs. Although results showed that sprinters generated higher FPF than RPF in both conditions, in the AS condition the RPF was greater than in the US condition. Recent studies have reported that the generation of greater forces against the rear block was the stron- gest predictor of sprint start performance, suggesting that forces at the rear block need to be maximised to increase performance [16,19]. In our study, the results of the regression analysis showed that the difference in the front block/starting line distance between the AS and US conditions is able to predict the difference between the two conditions in RPF, the horizontal and vertical components of RPF, as well as RFI (Table 4). These findings suggest that the front block/starting line distance is an important predictor of the ability to generate greater rear block force. On the other hand, results of the present study showed that the difference between the AS and US conditions in the front block/ starting line distance is not able to predict the difference between the two conditions in the Ratio_rear. This suggests that the reduction of the front block/starting line distance does not increase the ability to direct the forces in a more horizontal direction. This is in line with a recent study [16] highlighting that the ratio of horizontal to resultant impulse against the rear block was less important in predicting sprint start performance because of its low correlation with block phase performance (standardized regression coefficient = 0.010). The changes of the lower limb joint angles at the set position showed in the AS condition and subsequent improvement in several kinetic and kinematic parameters measured in the pushing phase, might explain the significant differences between the two conditions in the acceleration phase. In fact, several studies underpinned the importance of the block phase in the subsequent sprinting times at 5 m and 10 m as well as in the first two step lengths [6,7,9,12]. In our sample, the AS condition was associated with a decrease in the times at 5 m and 10 m (-3.59% and -2.32%, respectively) and an increase of the first and second stride lengths (+2.67% and +3,36%, respectively) vs. the US condition. This is in agreement with pre- vious studies showing that the contributions of the hip and ankle joints to force production play an important role in the acceleration phase of the sprint start [3,6,15,45–47]. The findings of our study expand on previous findings by showing that a number of pos- tural, kinematic and kinetic parameters of the sprint start change when setting the starting blocks according to anthropometry causing sprinters to adopt a medium start. In addition, the findings highlight the importance of the front/starting line distance as predictor of certain kinetic and kinematic variables. However, further studies are needed to better understand how the kinetic and kinematic parameters are related to this block distance. Interestingly enough, modifying the anteroposterior block distances, allowed for an immediate improvement of Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 15 / 20 performance in the sprinters with no period of familiarization. However, achieving an optimal automatized movement is a long-term process. Accordingly, further studies should investigate the retention of improvement of performance after at least 24/48 hours. The second aim of the present study was to assess whether an interaction exists between the two block setting conditions (US and AS) and the body proportionality of the sprinters (bra- chycormic, metricormic and macrocormic) affecting the kinematic and kinetic parameters of sprint start. In other words, the present study tested if the kinematic and kinetic parameters of the sprint start were different between the US and AS conditions depending on the body pro- portionality of the sprinters. It is important to underline that the three Cormic Index groups were similar in age, sprint- ing experience, several anthropometric parameters (e.g. BMI, lower limb circumferences), SLJ-relative (Table 1). All of the measured kinematic and kinetic parameters in the US condi- tion were similar as well (Table 2). This suggests that the three groups were largely comparable. In the US condition the three groups adopted a similar inter-block distance whereas the front block/starting line distance was lower in the metricormic and macrocormic groups in compar- ison with the brachycormic group (Table 2). Actually, the front block/starting line distance increased with the mean trunk length (sitting height) of the sprinters (Tables 1 and 2). Taken together, these findings suggest that body proportionality has some effect on the front block/ starting line distance when the sprinter adopts a block setting on the basis of personal sensation. When considering the interaction effect between the three Cormic Index groups over the two block setting conditions, a significant effect was found for the inter-block distance but not for the front block/starting line distance. This may be explained by the following: first, the finding of a significantly lower limb length in the brachycormic vs. metricormic and macro- cormic groups in the presence of similar stature (Table 1); second, the lower limb length was the parameter used to calculate the anteroposterior block distances in the AS condition; third, in the US condition the three Cormic Index groups chose similar inter-block distances, while the front block/starting line distance increased together with the trunk length (Tables 2 and 3). Intriguingly, it was found that sprinters modified the joint angles at the set position in the US and AS conditions irrespective of their body proportionality. In fact, the changes in the front and rear lower limb joint angles were consistent across the brachycormic, metricormic and macrocormic groups. A statistically significant group by condition interaction was also found in a number of per- formance outcomes (Tables 2 and 3). In particular, the metricormic group showed an increase in the AS condition for the RPF, (+16.52%), the H_RPF and the V_RPF (+13.12% and 11.11%, respectively), the RFimpulse, (+15.54%) (all significant at P<0.05); a significant increase was also found in the brachycormic group for the RPF (+14.99%) and the H_RPF (+9.82%); a sig- nificant increase in the V_FPF in the macrocormic group (+7.90%). These findings show that the AS condition is able to drive important improvements in kinetic variables measured in the pushing phase only for the metricormic and in the brachycormic groups. These findings are not attributable to differences in lower limbs muscle mass or strength, because thigh and calf girth as well as performance in the standing long jump expressed relative to the leg length were similar in the three Cormic Index groups (Table 1). Taken together, the above findings suggest that the anthropometry-driven condition used in this study is not best suited for macrocormic sprinters, while the sprinters with an intermediate proportionality between the trunk and the lower limbs (i.e. the metricormic sprinters) are those who are more likely to receive extensive benefits in the kinetics of the rear leg from a block setting based on their lower limb length. A limitation of this study is the relatively small size of the three Cormic Index groups, mak- ing it difficult to generalize our findings to performance outcomes associated with trunk and Starting block performance in sprinters PLOS ONE \| https://doi.org/10.1371/journal.pone.0213979 March 27, 2019 16 / 20 lower limb proportionality. Moreover, 2D kinematic measurement was used, which is well suited to data acquisition in a number of settings [48]. However, accurate analysis of joint angles and the two first stride lengths would have benefit from 3D acquisition [49]. Accord- ingly, the values of the stride lengths and joint angles presented herein should be interpreted with caution as per the presence of the parallax error and the lens distortion error which have occurred in our set-up using a 2D kinematic measurement. Despite these limitations and uti- lizing instrumented starting blocks, the current data provides important biomechanical evi- dence to further understand the influence of the set position on sprint start performance and on the importance of the rear leg action. Taken together, the findings presented in this study show that a block setting position, cal- culated on the basis of a proportion of the leg length allows sprinters to improve performance in both the block phase and early acceleration, thereby confirming the hypothesis that consid- ering the athlete’s body dimensions when calculating block setting is beneficial to the sprint start. In view of these results, future research is needed to adjust the anthropometry-driven condition investigated in this study in relation to the body proportionality of the sprinter to find the optimal anteroposterior block distances. Conclusion In conclusion, the current study confirmed the role played by an anthropometry-driven block setting on the starting block performance and underpins the relevance of body proportionality in calculating personalized anteroposterior block distances. The results obtained in the present study provide new relevant information that may represent the starting point for future studies aimed to develop new guidelines for helping coaches and athletes to identify the ideal personal anteroposterior block distances. From a practical standpoint, the results of this study should encourage coaches to pay more attention to the anthropometric characteristics of their athletes when searching for a more effective block start position. It would seem that a good starting point for further exploration of this field of research would be the inclusion of the Cormic Index in the identification of an individual block settings. Moreover, current findings showed that a key kinetic and kinematic determinant of the rear block performance is the front block/ starting line distance. Therefore, further research is required to elucidate the effect of this dis- tance with kinetic and kinematic parameters to more completely understand its influence on sprint start performance. Supporting information S1 File. Database. (XLSX) Acknowledgments The authors thank all the participants for kind cooperation. This study was supported by a grant from the University of Verona (Joint Projects 2009) to CM. Author Contributions Conceptualization: Valentina Cavedon, Chiara Milanese. Data curation: Valentina Cavedon, Marco Sandri, Mariola Pirlo, Nicola Petrone, Chiara Milanese. Formal analysis: Marco Sandri. 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PMC7827622	International Journal of Environmental Research and Public Health Article Analysis of Trafﬁc Crashes Caused by Motorcyclists Running Red Lights in Guangdong Province of China Guangnan Zhang 1, Ying Tan 2, Qiaoting Zhong 1,* and Ruwei Hu 3 Citation: Zhang, G.; Tan, Y.; Zhong, Q.; Hu, R. Analysis of Trafﬁc Crashes Caused by Motorcyclists Running Red Lights in Guangdong Province of China. Int. J. Environ. Res. Public Health 2021, 18, 553. http://doi.org/10.3390/ ijerph18020553 Received: 1 December 2020 Accepted: 6 January 2021 Published: 11 January 2021 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional clai- ms in published maps and institutio- nal afﬁliations. Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-Sen University, Guangzhou 510275, China; zhgnan@mail.sysu.edu.cn 2 School of Economics and Trade, Guangdong University of Finance, Guangzhou 510521, China; yingtan.cn@hotmail.com 3 School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China; huruwei@mail.sysu.edu.cn * Correspondence: zhongqt6@mail.sysu.edu.cn Abstract: Motorcycles are among the primary means of transport in China, and the phenomenon of motorcyclists running red lights is becoming increasingly prevalent. Based on the trafﬁc crash data for 2006–2010 in Guangdong Province, China, ﬁxed- and random-parameter logit models are used to study the characteristics of motorcyclists, vehicles, roads, and environments involved in red light violations and injury severity resulting from motorcyclists’ running red lights in China. Certain factors that affect the probability of motorcyclists running red lights are identiﬁed. For instance, while the likelihood of violating red light signals during dark conditions is lower than during light conditions for both car drivers and pedestrians, motorcyclists have signiﬁcantly increased probability of a red light violation during dark conditions. For the resulting severe casualties in red-light-running crashes, poor visibility is a common risk factor for motorcyclists and car drivers experiencing severe injury. Regarding the relationship between red light violations and the severity of injuries in crashes caused by motorcyclists running red lights, this study indicated that driving direction and time period have inconsistent effects on the probability of red light violations and the severity of injuries. On the one hand, the likelihood of red light violations when a motorcycle rider is turning left/right is higher than when going straight, but this turning factor has a nonsigniﬁcant impact on the severity of injuries; on the other hand, reversing, making a U-turn and changing lanes have nonsigniﬁcant effects on the probability of motorcyclists’ red light violations in contrast to going straight, but have a very signiﬁcant impact on the severity of injuries. Moreover, the likelihood of red light violations during the early morning is higher than off-peak hours, but this time factor has a negative impact on the severity of injuries. Measures including road safety educational programs for targeted groups and focused enforcement of trafﬁc policy and regulations are suggested to reduce the number of crashes and the severity of injuries resulting from motorcyclists running red lights. Keywords: trafﬁc violation; injury severity; road safety; risk factor; motorcyclist 1. Introduction Running red lights is a major cause of crashes at intersections, posing a higher risk of injury than other kinds of trafﬁc violations [1,2]. The AAA Foundation for Traf- ﬁc Safety has revealed that red-light running (RLR) deaths in the United States hit a 10-year high in 2017 and 28% of crash deaths that occurred at signalized intersections were the result of a driver running through a red light. More than 184 drivers daily were caught failing to stop at red lights in the United Kingdom in 2015 (more details can be found at https://www.thisismoney.co.uk/money/cars/article-3589194/The-roads- drivers-caught-running-red-lights-revealed.html.). Among all police-reported crashes in 2015 in Thailand, 1.96% were caused by drivers violating the red light signal [3]. According to the statistics revealed by the Ministry of Public Security, 4227 severe-injury crashes and 789 fatalities between January and October 2012 in China were attributed to RLR [4]. Int. J. Environ. Res. Public Health 2021, 18, 553. https://doi.org/10.3390/ijerph18020553 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 553 2 of 11 Previous studies have reported various rates of running red lights among road users. For instance, a study by Yan et al. [5] showed that the RLR rate in Changsha, China was 0.14% for motorists, which was far lower than those for motorcyclists (18.64%), bicyclists (18.74%) and pedestrians (18.54%). Kim et al. [6] compared the odds of running red lights between drivers and pedestrians in Hawaii, United States and concluded that drivers tend to commit proportionately more RLR than pedestrians. It is critical to identify the inﬂuencing factors leading to RLR behavior for different road users in different contexts [5]. However, research efforts have focused on car drivers, e-bike riders, cyclists and pedestrians rather than motorcycle riders, who dominate trafﬁc in developing countries such as China. Not only the riders of motorcycles but also the use of motorcycles in China are common, making the problem of motorcyclists running red lights more prominent in China than in other countries. Most of the motorcyclists in China have not undergone formal training on how to ride a motorcycle, and riding without number plates is quite common [7]. In addition, motorcycles in China are mainly used for delivering food and goods, services with a high demand for efﬁciency, resulting in many motorcyclists choosing to run red lights or violating trafﬁc safety laws to complete their tasks on time. By assessing the impact of various risk factors on motorcyclists’ RLR violations and related accident severity in Guangdong Province of China, results arising from this study will shed lights on the development of similar (adjusted) measures to reduce the number of crashes and the severity of injuries resulting from motorcyclists running red lights, and to promote road safety in other regions. Based on previous limited studies, there are few contributing factors affecting motor- cyclists’ RLR violations, such as human characteristics, driving conditions, and driving environment. For human characteristics, both Chen et al. [8] and Jensupakarn and Kanit- pong [9] revealed that male and young motorcycle riders were more likely to commit RLR violations. Motorcyclists whose occupation is a business person/trader and students are more likely to run red lights than other occupations [9]. The approaching speed and the direction of travel of motorcycles, helmet use, the presence of a pillion passenger, and the distance from the subject to the stop line all signiﬁcantly affect RLR violations. Evidence shows that motorcycle riders carrying pillion passengers are less likely to execute RLR violations [8,9], which could be explained by the ease and stability of riding without a passenger, or a sense of responsibility for the passenger’s safety [8]. While the approaching speed had a negative effect on RLR rates in Jensupakarn and Kanitpong’s study [9] on motorcyclists in Thailand, Chen et al. [8] found that motorcyclists travelling at higher speeds were more likely to commit RLR violations in Taiwan. Jensupakarn and Kanitpong’s research [9] also suggested that motorcyclists riding straight through an intersection are less likely to run a red light compared to when they have to make a turn, and motorcycle riders who do not wear a helmet tend to commit RLR violations. Finally, motorcycle riders are less likely to run a red light if they are further from a junction when the light turns red [8]. With regard to the driving environment, evidence shows that the likelihood of mo- torcyclists running a red light is higher at night time [8,9], in periods with lower trafﬁc volume [8] and during off-peak hours [5,8], but is lower on a weekend or a holiday [5]. Despite the increasing prevalence of motorcyclists running red lights in China, the relevant literature is limited. Only a study by Yan et al. [5] examined the relationship between RLR violation rates and the day (weekend or work day) and time period for motorcyclists in Changsha, China. Although such observational investigation is extremely valuable, it is costly to undertake by its very nature, and thus is performed on selected intersections. Even if the estimates obtained are reliable, they can only reﬂect some speciﬁc circumstances at a particular time and place; therefore, sampling errors may arise, and many important variables (such as individual characteristics of motorcycle riders) may be lost. More importantly, the extent to which the results arising from observation surveys are broadly generalizable is unclear [10], leading to a lack of comprehensiveness in the analyses of risk factors in red light violations and the severity of injuries caused by motorcyclists. Int. J. Environ. Res. Public Health 2021, 18, 553 3 of 11 With this in mind, the speciﬁc objectives of this study were to identify the risk factors related to personal characteristics, vehicle characteristics, road conditions and environmental conditions affecting (1) motorcycle riders’ RLR violations and (2) the severity of injuries caused by motorcycle RLR crashes, using data from the road trafﬁc crash database of China’s Public Security Department from 2006 to 2010 in the Guangdong Province of China. 2. Materials and Methods 2.1. Data The data used in this study, obtained from the Guangdong Provincial Security De- partment, were extracted from the Trafﬁc Management Sector-Speciﬁc Incident Case Data Report. The data were recorded and reported by the trafﬁc police who conducted on- scene assessments and provided feedback within 24 h to the headquarters of the Trafﬁc Management Department. The information was recorded according to the Code of Trafﬁc Crash Information issued by the Computer and Information Processing Standardization Commission under the Security Department of the country. Each sample included detailed indexes about the characteristics of drivers/riders, injury severity, vehicle features, road conditions, crash time, as well as environmental conditions, such as the level, form, and cause of the crash [2]. Reports of multi-vehicle motorcycle-related crashes occurring at intersections between 2006 and 2010 were extracted for the current research study (see Figure 1 for data inclusion and exclusion). Among 8054 cases relevant to red light violations of motorcycle riders together with non-trafﬁc violation accidents, 2317 (28.7%) involved no injury (property damage only, PDO), 3968 (49.3%) involved minor injury, 931 (11.6%) resulted in serious injury, and 838 (10.4%) resulted in death. A lower proportion of PDO crashes than that of crashes involved in minor injury in the dataset is inconsistent with the fact that the number of crashes decreases with the increase in injury severity [11,12]. Given the potential under-reporting of PDO crashes [13], PDO crashes were excluded from our analysis. Moreover, cases with an absence of motorcycle rider characteristics (e.g., rider age), cases involving foreign riders and cases occurring on expressways were also removed. Thus, 5304 motorcycle-related trafﬁc crashes were selected in the ﬁnal sample, among which 409 involved red light violations by motorcyclists. The China Road Trafﬁc Accidents Statistics Report showed that the phenomenon of running red lights was very common during the data period. Previous studies have analyzed the red light violations in China that happened in the same period, but with different road users or in different cities (e.g., [2,14,15]). These works provide a comparable foundation for this study to explore the unique factors behind motorcyclists’ RLR behaviours. Therefore, the results arising from the current research on motorcyclists running red lights in Guangdong province of China during 2006 to 2010 have an appropriate level of generality. In addition, Zhang et al. [2] identiﬁed factors inﬂuencing red light violations by car drivers, cyclists, and pedestrians from Guangdong province of China. Their results provide a comparative foundation to study the unique factors for motorcyclists’ RLR behaviors from the same province. Therefore, the same data were also used here to study the factors for the severity of car drivers’ injuries in RLR crashes so that the risk factors of severe injuries between motorcyclists and car drivers in RLR crashes could be distinguished. Int. J. Environ. Res. Public Health 2021, 18, 553 4 of 11 Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 4 of 11 Figure 1. Data flow diagram for analysis of crashes caused by motorcyclists running red lights. 2.2. Risk Factors The risk factors under consideration in the current study were described in a previ- ous study derived from the same database [2]. Four dimensions, namely personal factors, vehicle factors, road factors and environmental factors, were established as follows. Personal factors: Rider gender, age and occupation are considered to be potential risk factors. Rider age is divided into four categories following the WHO’s age classification criteria: ≤24, 25–44, 45–59, and ≥60 years. Occupation and residential registration are used to capture the education level, income and social status of riders. Rider occupation is di- vided into six categories: self-employed, worker, migrant worker, farmer, no occupation and other. Residential registration is divided into rural and urban. Additionally, the im- pact of head injury on the severity of injuries is examined. Vehicle factors: These mainly include whether motorcycles have number plates, whether motorcycles carry passenger(s), vehicle safety conditions and vehicle driving sta- tus, where vehicle driving status is divided into four types: going straight, turning left, turning right, and other. Road factors: These mainly include road types, junction types and whether there are physical barriers on the roads. Roads are considered as two types, i.e., general highways (including the first-class and second-class or below highways) and urban roads (including general urban roads and other urban roads). Junctions are divided into three types, i.e., fork, crossroads and other. Environmental factors: Environmental factors include street-light conditions, weather conditions, visibility, weekends, holidays, time periods and years. Street-light conditions include daylight, nighttime with lighting and nighttime without lighting. While bad weather conditions include cloudy, snowy, rainy, foggy, and very windy, poor visibility is defined as visibility below 50 meters. Following the previous work of red light viola- tions [2,8,9], the time period is divided into early morning hours (midnight to dawn), morning peak hours (7:00–8:59 am), after work peak hours (5:00–7:59 pm) and other time periods. 2.3. Statistical Data Analysis Binary logit models are widely used in the related literature on motorcycle riders’ RLR behavior at signalized intersections (e.g., [8,9]). To facilitate comparison with the literature using the same method, it was appropriate to adopt binary logit models in this study to estimate the effect of different risk factors on the likelihood of the occurrence of motorcyclists running a red light in China. Specifically, multivariate logistic regression was conducted and the adjusted odds ratios (ORs) of significant factors and their 95% confidence intervals (CIs) were computed using Stata 14 (StataCorp, College Station, TX, USA). Data obtained from the Guangdong Provincial Security Department are extracted from the Traffic Management Sector-Specific Incident Case Data Report, covering 21,132 multi- vehicle motorcycle-related crashes occurring at intersections with 25,013 motorcycle riders for the period of 2006–2010. Together with non-traffic violation accidents, 8054 samples relevant to red light violations of motorcycle riders were selected. Of the 8054 samples reported, 3968 (49.3%) involved minor injury, 931 (11.6%) resulted in serious injury, and 838 (10.4%) resulted in death. Data available for analysis in the current research (5304 in total) Red light violations of motorcyclists (n=409) Given the potential under-reporting of no injury crashes, 2317 (28.7%) property damage only crashes were excluded from our samples. EXCLUDED (Total=433) Foreign riders (n=9) Crashes occurred on the expressways (n=311) Absence of motorcycle rider characteristics (n=113) Figure 1. Data ﬂow diagram for analysis of crashes caused by motorcyclists running red lights. 2.2. Risk Factors The risk factors under consideration in the current study were described in a previous study derived from the same database [2]. Four dimensions, namely personal factors, vehicle factors, road factors and environmental factors, were established as follows. Personal factors: Rider gender, age and occupation are considered to be potential risk factors. Rider age is divided into four categories following the WHO’s age classiﬁcation criteria: ≤24, 25–44, 45–59, and ≥60 years. Occupation and residential registration are used to capture the education level, income and social status of riders. Rider occupation is divided into six categories: self-employed, worker, migrant worker, farmer, no occupation and other. Residential registration is divided into rural and urban. Additionally, the impact of head injury on the severity of injuries is examined. Vehicle factors: These mainly include whether motorcycles have number plates, whether motorcycles carry passenger(s), vehicle safety conditions and vehicle driving status, where vehicle driving status is divided into four types: going straight, turning left, turning right, and other. Road factors: These mainly include road types, junction types and whether there are physical barriers on the roads. Roads are considered as two types, i.e., general highways (including the ﬁrst-class and second-class or below highways) and urban roads (including general urban roads and other urban roads). Junctions are divided into three types, i.e., fork, crossroads and other. Environmental factors: Environmental factors include street-light conditions, weather conditions, visibility, weekends, holidays, time periods and years. Street-light conditions in- clude daylight, nighttime with lighting and nighttime without lighting. While bad weather conditions include cloudy, snowy, rainy, foggy, and very windy, poor visibility is deﬁned as visibility below 50 meters. Following the previous work of red light violations [2,8,9], the time period is divided into early morning hours (midnight to dawn), morning peak hours (7:00–8:59 a.m.), after work peak hours (5:00–7:59 p.m.) and other time periods. 2.3. Statistical Data Analysis Binary logit models are widely used in the related literature on motorcycle riders’ RLR behavior at signalized intersections (e.g., [8,9]). To facilitate comparison with the literature using the same method, it was appropriate to adopt binary logit models in this study to estimate the effect of different risk factors on the likelihood of the occurrence of motorcyclists running a red light in China. Speciﬁcally, multivariate logistic regression was conducted and the adjusted odds ratios (ORs) of signiﬁcant factors and their 95% conﬁdence intervals (CIs) were computed using Stata 14 (StataCorp, College Station, TX, USA). Int. J. Environ. Res. Public Health 2021, 18, 553 5 of 11 Although motorcycle riders’ red light violations are treated as a serious problem in terms of related injuries and fatalities in developing countries such as China, limited research has considered the analysis of inﬂuencing factors for the severity of injuries in motorcycle RLR crashes. Previous injury severity studies have reported a signiﬁcant correlation among unobserved effects crossing discrete injury outcome categories (e.g., [16]); to further estimate the effect of different risk factors on the likelihood of the occurrence of severe casualties for motorcyclists in RLR crashes in comparison with car drivers, random- parameter logit models were applied in the current research. Random parameters were assumed to be normally distributed and 200 Halton draws were used in this study, which have been widely used assumptions in previous research (e.g., [13,17]). 3. Results 3.1. Sample Description Among 5304 motorcycle-related trafﬁc crashes, even though the motorcycle-related crashes caused by RLR are less common (approximately 7.7%) when compared to other causes, they are considered a serious problem. In fact, the ratio of severe injuries among all injuries caused by red light violations of motorcyclists is as high as 44.0%, whereas this ratio for other causes is much smaller, i.e., 38.3% for all motorcycle-related trafﬁc crashes (see Table 1). Table 1. Descriptive statistics of variables. Variables Motorcycle-Related Crashes (n = 5304) Crashes Caused by Red Light Violations of Motorcyclists (n = 409) Crashes Caused by Red Light Violations of Car Drivers (n = 435) Frequency Proportion (%) Frequency Proportion (%) Frequency Proportion (%) Signal violation 409 7.7 409 1 435 1 Killed or seriously injured 2031 38.3 180 44.0 126 29.0 (1) Gender Male 4776 90.0 374 91.4 409 94.0 (2) Age ≤24 1133 21.4 81 19.8 47 10.8 25–44 2988 56.3 241 58.9 322 74.0 45–59 982 18.5 76 18.6 65 15.0 ≥60 201 3.8 11 2.7 1 0.2 (3) Residential registration Rural 1818 34.3 129 31.5 60 13.8 (4) Occupation Farmer 1296 24.4 76 18.6 22 5.0 The self-employed 427 8.1 39 9.5 79 18.2 Worker 1053 19.9 85 20.8 74 17.0 Migrant worker 690 13.0 74 18.1 50 11.5 Unemployed 236 4.4 21 5.1 10 2.3 Other occupations 1602 30.2 114 27.9 143 32.9 (5) Whether motorcycles carry a passenger No passenger 3576 67.4 273 66.7 279 64.1 (6) Whether motorcycles have number plates No number plates 1539 29.0 120 29.3 7 1.6 (7) Vehicle safety condition Unﬁt 394 7.4 17 4.2 16 3.7 (8) Vehicle driving status Straight 4524 85.3 326 79.7 319 73.3 Turning left 308 5.8 47 11.5 74 17.0 Turning right 83 1.6 8 2.0 14 3.2 Others 389 7.3 28 6.8 28 6.4 Int. J. Environ. Res. Public Health 2021, 18, 553 6 of 11 Table 1. Cont. Variables Motorcycle-Related Crashes (n = 5304) Crashes Caused by Red Light Violations of Motorcyclists (n = 409) Crashes Caused by Red Light Violations of Car Drivers (n = 435) Frequency Proportion (%) Frequency Proportion (%) Frequency Proportion (%) (9) Type of road First-class highways 828 15.6 53 13.0 45 10.3 Second-class or below highways 2223 41.9 167 40.8 118 27.1 General urban roads 1663 31.4 164 40.1 208 47.8 Other urban roads 590 11.1 25 6.1 64 14.7 (10) Type of junctions Fork 441 8.3 31 7.6 27 6.2 Crossroads 464 8.7 88 21.5 81 18.6 Others 4399 83.0 290 70.9 327 75.2 (11) Whether there are physical barriers in roads No physical barriers 3182 60.0 192 46.9 193 44.4 (12) Visibility Bad visibility 525 9.9 37 9.0 39 9.0 (13) Street-light condition Daylight 2938 55.4 218 53.3 212 48.7 Dark but lighted 1527 28.8 167 40.8 204 46.9 Dark 839 15.8 24 5.9 19 4.4 (14) Weather condition Bad weather condition 1089 20.5 71 17.4 98 22.5 (15) Holiday Holiday 381 7.2 30 7.3 31 7.1 (16) Day of the week Weekends 1401 26.4 109 26.7 135 31.0 (17) Time of day Early morning 804 15.2 64 15.6 98 22.5 Morning peak hours 712 13.4 51 12.5 48 11.0 After work peak hours 911 17.2 63 15.4 58 13.3 Others 2877 54.2 231 56.5 231 53.1 (18) Year 2006 922 17.4 108 26.4 107 24.6 2007 960 18.1 64 15.6 74 17.1 2008 1075 20.3 71 17.4 75 17.2 2009 1127 21.2 85 20.8 78 17.9 2010 1220 23.0 81 19.8 101 23.2 (19) Injured parts Head 1390 26.2 101 24.7 15 3.4 3.2. Risk Factors Affecting Motorcyclists Running Red Lights The ﬁrst model in Table 2 shows the risk factors associated with motorcyclists running red lights. Concerning personal characteristics, the probability of male riders running red lights is 1.48 times greater than that of female riders, which is consistent with conclusions in most of the literature. Compared with motorcyclists over the age of 60, young motorcyclists under the age of 24 (OR = 1.61) shows a signiﬁcant increase in the probability of running red lights. Chen et al. [8] argued that the young-rider effect could be explained by the fact that young riders, in general, tend to demonstrate risk-taking road behaviors. The risk of running red lights among migrant worker motorcyclists is signiﬁcantly higher than that among farmers (OR = 1.23), but the impact of residential registration of rider is insigniﬁcant. Int. J. Environ. Res. Public Health 2021, 18, 553 7 of 11 Among vehicle factors, whether motorcycles carry passengers, whether motorcycles have number plates, vehicle safety conditions and vehicle driving status are signiﬁcant factors for the probability of motorcyclists running red lights. The probability of running a red light by a motorcyclist riding alone is 1.18 times the probability with passengers. Such results with regard to the presence of a pillion passenger are similar to the study conducted by Chen et al. [8] and Jensupakarn and Kanitpong [9]. The probability of a motorcyclist running a red light is higher when riding a motorcycle that does not satisfy safety requirements (OR = 1.25). However, the probability of a motorcyclist running a red light is lower when riding a motorcycle that does have a number plate (OR = 0.88) As reported in Jensupakarn and Kanitpong’s study [9], the driving direction of a motorcycle also affects whether a motorcyclist runs a red light: compared to driving straight ahead, the probability of a motorcyclist running a red light is signiﬁcantly higher when they are turning (left: OR = 1.52, right: OR = 1.71, respectively). Table 2. Factors inﬂuencing motorcyclists running red lights and resulting severe casualties. Factors Red Light Violations for Motorcyclists Severe Casualties for Motorcyclists in Red-Light-Running Crashes Severe Casualties for Car Drivers in Red-Light-Running Crashes ORs (95% CI) ORs (s.d.) (95% CI) ORs (s.d.) (95% CI) n 5304 409 435 (1) Personal Factors Gender of rider (base: female) Male 1.48 * 0.17 [1.20, 1.82] [0.04, 0.70] Age of rider (base: ≥60) ≤24 1.61*** 1.38 (35.53 ) 7.11 * [1.15, 2.26] [0.16, 12.11] [1.53, 33.05] 25–44 2.79 * [0.92, 8.45] Residential registration of rider (base: urban) Rural 3.21 * [0.87, 11.80] Occupation of rider (base: farmer) The self-employed 0.19 * [0.03, 1.36] Migrant worker 1.23 * [1, 1.52] Worker 0.14 [0.02, 0.97] Injured parts (base: others) Head NA 11.80 * [3.15, 44.15] (2) Vehicle factors Carry passenger (base: yes) No passenger 1.18 0.33 * [1.04, 1.34] [0.16, 0.71] Whether motorcycles have number plates (base: yes) No number plates 0.88 * [0.77, 1.01] Vehicle safety condition (base: ﬁt) Unﬁt 1.25 [1.01, 1.56] Vehicle driving status (base: straight) Turning left 1.52 * [1.19, 1.93] Turning right 1.71 [1.09, 2.68] Int. J. Environ. Res. Public Health 2021, 18, 553 8 of 11 Table 2. Cont. Factors Red Light Violations for Motorcyclists Severe Casualties for Motorcyclists in Red-Light-Running Crashes Severe Casualties for Car Drivers in Red-Light-Running Crashes ORs (95% CI) ORs (s.d.) (95% CI) ORs (s.d.) (95% CI) n 5304 409 435 Others 10.24 * [2.33, 44.95] (3) Road factors Type of road (base: ﬁrst-class highways) Second-class or below highways 1.29 *** 1.35 (11.60) 6.06 * [1.07, 1.54] [0.40, 4.55] [1.41, 26.12] General urban roads 1.22 [0.33, 4.59] Other urban roads 1.23 * 1.54 [0.97, 1.56] [0.33, 7.26] Type of junction (base: others) Fork 0.62 * [0.50, 0.78] Crossroads 0.62 * [0.50, 0.78] (4) Environmental factors Visibility (base: others) Poor visibility 3.28 * 3.15 [0.91, 11.87] [1.14, 8.66] Street-light condition (base: dark) Daylight 0.57 * 0.22 * 0.85 (73.51 ) [0.48, 0.69] [0.04, 1.15] [0.09, 8.31] Dark but lighted 0.75 * 2.45 [0.62, 0.91] [0.54, 11.15] Weather condition (base: others) Bad weather condition 0.29 ** [0.09, 0.92] Holiday (base: others) Holiday 0.13 * [0.02, 1.04] Time of day (base: others) Early morning 1.27 *** 0.35 * [1.07, 1.51] [0.11, 1.12] Morning peak hours After work peak hours 0.16 *** [0.04, 0.62] log likelihood −3371.41 −219.13 −214.66 AIC 6814.83 516.27 511.31 For brevity, insigniﬁcant results are omitted, and standard deviations are presented for signiﬁcant random variables only. * p < 0.1, p < 0.05, * p < 0.01. Compared with the probability of motorcyclists running red lights on ﬁrst-class highways, the probabilities of RLR violations by motorcyclists occurring on second-class or below highways, and other urban roads were 1.29 times and 1.23 times higher, respectively. For motorcyclists, the risk of running red lights at forks or crossroads is signiﬁcantly lower than at other types of intersections. This result differs from the ﬁndings of previous studies (e.g., [4]), likely because the surrounding conditions of motorcyclists also inﬂuence their likelihood of running red lights. Street lighting can signiﬁcantly reduce the risk of a motorcyclist running a red light: the risk of running a red light is lower in the daytime (OR = 0.57) and at night with lighting (OR = 0.75) than that at night without lighting. The risk of red light violations by motorcyclists is higher during early morning (OR = 1.27) Int. J. Environ. Res. Public Health 2021, 18, 553 9 of 11 than during off-peak hours. However, the impact of peak hours on motorcyclists’ red light violations is insigniﬁcant, which is inconsistent with the literature [5,8]. Differing from Yan et al. [5], neither weekends nor holidays have signiﬁcant effects on motorcyclists’ red light violations. 3.3. Factors for the Severity of Injuries in Red-Light-Running Crashes Although motorcyclists are less likely to be involved in RLR trafﬁc accidents than car drivers (7.7% vs. 9.6%), crashes involving motorcyclists running a red light have higher odds of resulting in severe casualties than car drivers (44% vs. 29%) in Guangdong, China; therefore, further comparisons between motorcyclists and car drivers were carried out to examine risk factors related to the severity of outcome in different types of vehicle crashes involving a red light violation. Speciﬁcally, random-parameter logit models were conducted for two sub-samples separately, i.e., motorcyclists and car drivers in RLR crashes. In cases of motorcyclists in RLR crashes, as shown in the second model in Table 2, the estimated parameters for young motorcycle riders were insigniﬁcant and random, with a mean of 1.38 and a standard deviation of 35.53. Motorcycle riders registered as residing in rural areas were more likely to get severely injured than those with urban residential registration (OR = 3.21). Furthermore, a motorcyclist’s occupation impacts the level of injury. The probability of serious casualties sustained by the self-employed (OR = 0.19) and workers (OR = 0.14) was signiﬁcantly lower than that of farmers, but there were no signiﬁcant differences between the probabilities of migrant workers, the unemployed and those employed in other professions. When a head injury occurs for the motorcycle rider in a RLR crash, the risk of serious casualties increases (OR = 11.80). With regard to the vehicle driving state, a motorcycle rider was more strongly associated with serious casualties when reversing, making a U-turn or changing lanes rather than going straight (OR = 10.24). The estimated parameters for second-class or below highways were insigniﬁcant and random, with a mean of 1.35 and a standard deviation of 11.60. Poor visibility can signiﬁcantly increase the risk of serious casualties in motorcyclists (OR = 3.28), but daylight (OR = 0.22) and bad weather (OR = 0.29) conditions signiﬁcantly decrease the risk of serious casualties. The risk of serious casualties in motorcyclists is lower during early morning (OR = 0.35) and after work peak hours (OR = 0.16) than during off-peak hours. By comparing empirical results of the injury models for motorcyclists and car drivers in RLR crashes, this study found that the common risk factor for motorcyclists and car drivers experiencing severe injury in RLR crashes is poor visibility. Moreover, riders/drivers’ residential registration and occupation, vehicle driving status, weather condition, time of a day and whether the rider/driver suffers a head injury are signiﬁcantly associated with severe casualties in motorcycle crashes related to RLR but have no signiﬁcant effects on car drivers in RLR crashes. 4. Discussion 4.1. Red Light Violations and Injury Severity for Motorcyclists By comparing the inﬂuential factors that affect the probability of motorcyclists running red lights and the severity of injuries in crashes caused by red light violations, we found that the common risk factor is daylight condition. Rider gender, rider age, passengers, vehicle number plates, vehicle safety conditions, road type and junction type affect the probability of motorcyclists running red lights but do not affect the severity of injuries. Poor visibility does not affect the likelihood of motorcyclists’ RLR violations, but in the event of RLR crashes, this factor often leads to serious casualties. Notably, the following factors have inconsistent effects on the probability of red light violations and the severity of injuries for motorcycle riders: on the one hand, the likelihood of red light violations when a motorcycle rider is turning left/right is higher than when going straight, but this turning factor has a nonsigniﬁcant impact on the severity of injuries; on the other hand, reversing, making a U-turn and changing lanes have nonsigniﬁcant effects on the probability of motorcyclists’ RLR violations in contrast to going straight, but have a very signiﬁcant Int. J. Environ. Res. Public Health 2021, 18, 553 10 of 11 impact on the severity of injuries. The likelihood of red light violations during the early morning is higher than off-peak hours, but this time factor has a negative impact on the severity of injuries. 4.2. Comparison Between Motorcyclists and Other Road Users Previous studies have reported on the factors contributing to trafﬁc signal violations for car drivers, cyclists and pedestrians in Guangdong, China [2], where the effects of gender have been found to be insigniﬁcant for all three groups. In contrast, in this study, male motorcycle riders are conﬁrmed to be more likely to violate trafﬁc signals than female riders. While the likelihood of violating trafﬁc signals during dark conditions is lower than during light conditions for both car drivers and pedestrians, motorcyclists have a signiﬁcantly increased probability of red light violations during dark conditions. Since travelling on second-class or below highways is a factor contributing to red light violations for car drivers, cyclists, pedestrians and motorcyclists, this factor shall not be treated as a unique factor for motorcyclists running red lights. For the resulting severe casualties in RLR crashes, as reported in a previous section, poor visibility is a common risk factor for motorcyclists and car drivers experiencing severe injury in RLR crashes; therefore, we can infer that the visibility factor is not unique to motorcyclists with regard to severe injuries related to red light violations. 4.3. Policy Implications and Further Remarks The empirical evidence presented in this article suggests the need for an increase in inspections and punishment for riding a motorcycle with a poor vehicle safety status. In addition, supervision of red light violations should be strengthened during the early morning hours. The lack of regard for and awareness of trafﬁc laws and safety is a major factor affecting behavior related to running red lights. According to the empirical results in this study, males under the age of 24 are the main target group that should be engaged in trafﬁc safety promotion, campaigns, and educational activities. Due to data availability, the present study analyzed the trafﬁc crash data for 2006– 2010 in Guangdong Province, China, which limits its implications, because road safety trends may be different for other provinces in China and may have changed in the same province over the years. Using data from other provinces and cities in China would be of merit in future research. Moreover, a meta-analysis of old and new data from the same province could help us to understand the signiﬁcant differences in risky behaviors of motorcycle riders in China. Potential factors, such as trafﬁc volume, speed limits, trafﬁc light characteristics and other risky driving behaviors among motorcyclists were not analyzed in this study. It would be worth exploring the effects of these factors on motorcyclists’ RLR violations and injuries in the future. 5. Conclusions Using data collected from Guangdong Province of China, this study offers insights into risk factors related to personal characteristics, vehicle characteristics, road conditions and environmental conditions affecting red light violations and injury severity resulting from motorcyclists’ running red lights. Measures including road safety educational programs for targeted groups and focused enforcement of trafﬁc policy and regulations are suggested to reduce the number of crashes and the severity of injuries resulting from motorcyclists running red lights. Author Contributions: Conceptualization, G.Z. and Y.T.; formal analysis, G.Z., Y.T. and Q.Z.; method- ology, G.Z., Y.T. and Q.Z.; software, Q.Z.; writing—original draft, Y.T. and Q.Z.; writing—review and editing, G.Z., Y.T., Q.Z. and R.H. All authors have read and agreed to the published version of the manuscript. Int. J. Environ. Res. Public Health 2021, 18, 553 11 of 11 Funding: This research was supported in part by the National Natural Science Foundation of China grant 71573286 and Ministry of Education Project for Humanities and Social Sciences Research (16JJDGAT006). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article. Conﬂicts of Interest: The authors declare no conﬂict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. References 1. Retting, R.A.; Williams, A.F.; Preusser, D.F.; Weinstein, H.B. Classifying urban crashes for countermeasure development. Accid. Anal. Prev. 1995, 27, 281–294. [CrossRef] 2. Zhang, G.; Tan, Y.; Jou, R. 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PMC6939913	royalsocietypublishing.org/journal/rspb Research Cite this article: Bohm S, Mersmann F, Santuz A, Arampatzis A. 2019 The force– length–velocity potential of the human soleus muscle is related to the energetic cost of running. Proc. R. Soc. B 286: 20192560. http://dx.doi.org/10.1098/rspb.2019.2560 Received: 5 November 2019 Accepted: 26 November 2019 Subject Category: Morphology and biomechanics Subject Areas: biomechanics, physiology Keywords: biomechanics, muscle-tendon unit, force–length–velocity relationships, gear ratio, running economy Author for correspondence: Sebastian Bohm e-mail: sebastian.bohm@hu-berlin.de Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare. c.4767113. The force–length–velocity potential of the human soleus muscle is related to the energetic cost of running Sebastian Bohm1,2, Falk Mersmann1,2, Alessandro Santuz1,2 and Adamantios Arampatzis1,2 1Department of Training and Movement Sciences, and 2Berlin School of Movement Sciences, Humboldt- Universität zu Berlin, Berlin, Germany SB, 0000-0002-5720-3672; FM, 0000-0001-7180-7109; AS, 0000-0002-6577-5101; AA, 0000-0002-4985-0335 According to the force–length–velocity relationships, the muscle force potential is determined by the operating length and velocity, which affects the energetic cost of contraction. During running, the human soleus muscle produces mechanical work through active shortening and provides the majority of propulsion. The trade-off between work production and alterations of the force–length and force–velocity potentials (i.e. fraction of maximum force according to the force–length–velocity curves) might mediate the energetic cost of running. By mapping the operating length and velocity of the soleus fascicles onto the experimentally assessed force– length and force–velocity curves, we investigated the association between the energetic cost and the force–length–velocity potentials during running. The fascicles operated close to optimal length (0.90 ± 0.10 L0) with moderate velocity (0.118 ± 0.039 Vmax [maximum shortening velocity]) and, thus, with a force–length potential of 0.92 ± 0.07 and a force–velocity potential of 0.63 ± 0.09. The overall force–length–velocity potential was inversely related (r = −0.52, p = 0.02) to the energetic cost, mainly determined by a reduced shortening velocity. Lower shortening velocity was largely explained ( p < 0.001, R2 = 0.928) by greater tendon gearing, shorter Achilles tendon lever arm, greater muscle belly gearing and smaller ankle angle vel- ocity. Here, we provide the first experimental evidence that lower shortening velocities of the soleus muscle improve running economy. 1. Background Humans are capable runners compared with most other mammals and it has been suggested that the endurance performance has been a crucial aspect for human evolution [1]. Running economy is an important physiological factor for endurance performance [2] and is defined as the mass-specific rate of oxygen uptake or metabolic energy consumption at a given speed [3,4]. The main determinant of the metabolic energy consumption during running is the muscular force needed to support and accelerate the body mass [5]. The level of muscle activation necessary to generate the required force is dictated by the force–length and force–velocity potential of the muscle. The force– length and force–velocity potential express the operating length and velocity of the muscle fibres with respect to the force–length [6] and force–velocity relationships [7] (i.e. fraction of maximum force according to the force– length–velocity curves) [8,9]. When fibres operate at lower shortening velocities and close to the optimal length, the required active muscle volume for a given force diminishes, together with the metabolic energy expenditure [4,10]. Besides the operating length and velocity as the main determinants, the history depen- dence of force generation (i.e. increased force after active muscle lengthening © 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. [11] and decreased force after active shortening [12]) may additionally influence the force potential. Thus, it is reason- able to argue that the fibre dynamics of the large lower limb muscles during running are explanatory factors of the energetic cost and thereby endurance performance. During human running, the soleus actively shortens [13] and is the most important muscle for propulsion [14,15]. How- ever, during active shortening, increased length excursion and shortening velocity reduce the force–length–velocity potential of muscle fibres. Due to the steep slope of the hyperbolic force–velocity curve at low to moderate shortening velocities, the force–velocity potential might be particularly sensitive to changes in shortening velocity. Yet the association between energetic cost and operating fibre dynamics as the force– length and force–velocity potential during human running has not been experimentally investigated thus far. From a mechanical point of view, the soleus fibre operating length and velocity are mainly mediated by the decoupling of the fibre length trajectories from those of the muscle–tendon unit (MTU), the Achilles tendon lever arm and the excursions of the ankle joint. The decoupling of the fibre length trajectories from the MTU is a result of tendon compliance and the variable pennation of muscle fibres within the series muscle belly and can be quantified by the so-called MTU gearing (i.e. ratio of MTU and fibre velocity) [16]. Tendons, due to their compliance, take over important portions of the length changes within the MTU, which substantially reduce the length change and vel- ocity of the series muscle belly. The magnitude of the decoupling of the muscle belly from the MTU by the tendon is expressed by the ratio of MTU velocity and belly velocity and has been termed tendon gearing [16]. Furthermore, the rotation of the fibres (i.e. changes of pennation angle) during muscle shortening and concomitant changes in muscle shape decouple the fibre length change from the length change of the muscle belly, further decreasing the fibre shortening length and velocity [17]. The ratio of muscle belly velocity and fibre velocity defines the effect of the fibre rotation mech- anism on the shortening velocity, i.e. belly gearing (or architectural gear ratio) [16,17]. Independent of the gearing within the soleus MTU, muscle force is transmitted through the Achilles tendon lever arm (i.e. the distance between the tendon’s line of action and the centre of rotation of the ankle joint). It has been shown that shorter lever arms of the Achilles tendon are correlated with lower rates of energy consumption during running [18,19]. The lower energy consumption has been attributed to a greater energy storage and return by the Achilles tendon due to the higher muscle force required for a given joint moment at a smaller lever arm [18]. However, the increased energy storage and release from the tendon is associated with a higher muscle force, which in turn increases the metabolic cost, counteracting or even deteriorating the effects of increased energy storage and release [20]. Yet shorter lever arms can reduce the fibre length excursions and fibre shortening velocity of the soleus muscle at a given ankle joint excursion during the stance phase, which can increase the muscle force–length–velocity potential. Therefore, besides the debated benefits in terms of energy storage and release, a reduction of the fibre operating length changes and velocity by a shorter lever arm of the Achilles tendon could be an important mechanism for the improvement in running econ- omy. Furthermore, the ankle joint excursion during the stance phase of running may also influence the operating length and velocity of the soleus fibres [21]. Although gearing within the MTU contributes to the decoupling of fibre length and MTU length trajectories, smaller ankle joint excursions can decrease fibre length changes and velocities. In fact, Cavagna & Kaneko [22] as well as Williams & Cavanagh [23] reported reduced ankle joint excursions in runners with higher running economy than others [22,23]. In the present study, we investigated the operating length and velocity of the soleus muscle fascicles (i.e. bundles of fibres) during running as a function of the experimentally determined force–length and assessed force–velocity relation- ships (i.e. force–length and force–velocity potential) and their association to the energetic cost of running. We further assessed tendon and belly gearing as well as Achilles tendon lever arm and ankle joint excursions during the stance phase of running as mediating factors for the fascicle operating length and velocity. We hypothesized the force–length– velocity potential to be associated with the energetic cost of running, mainly due to the sensitivity of the force–velocity potential to modulations of fascicle velocity. Finally, we expected that gearing, tendon lever arm and joint excursion would explain the majority of the fascicle velocity variability in the soleus muscle during running. 2. Methods (a) Experimental design Nineteen healthy (age: 29 ± 6 years, height: 177 ± 9 cm, mass: 69 ± 9 kg, 7 female), ambitious runners who trained at least three times per week participated in the present study. The ethics com- mittee of the Humboldt-Universität zu Berlin approved the study and the participants gave written informed consent in accordance with the Declaration of Helsinki. After familiarization, the participants ran on a treadmill at 2.5 m s−1 for 4 min. By integrating ultrasonography, electromyogra- phy (EMG) and kinematic data, we measured muscle architectural parameters (fascicle length, pennation angle and thickness) and EMG activity, and assessed the MTU length of the soleus muscle as well as the ankle joint angle of the right leg. Energetic cost of running was determined by expired gas analysis during an additional 10 min running trial at the same speed. In the second part of the experiment, the individual force–fascicle length relation- ship of the soleus was experimentally determined by means of maximal isometric voluntary plantar flexion contractions (MVC) of the right leg at different ankle joint angles on a dynamometer in combination with ultrasound imaging of the soleus muscle fasci- cles. The force applied to the Achilles tendon was calculated from the ankle joint moment and the individual tendon lever arm. The derived optimal fascicle length for force production was further used to determine the force–velocity relationship of the soleus fas- cicles. The order of the two parts of the experiments (running and MVC) was randomized, yet the ultrasound probe and EMG electrodes remained attached between both ultrasound measure- ments. Based on the assessed force–length and force–velocity relationships, it was possible to calculate the force–length and force–velocity potential of the soleus muscle as a function of the fas- cicle operating length and velocity during the stance phase of running. The product of both potentials then gives the overall force–length–velocity potential. (b) Assessment of the soleus force–length and force–velocity relationship The participants were placed in prone position on the bench of an isokinetic dynamometer (Biodex Medical, Syst. 3, Inc., Shirley, NY) with the knee in fixed flexed position (approx. 120°) royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 286: 20192560 2 to restrict the contribution of the bi-articular m. gastrocnemius to the plantar flexion moment [24] (figure 1a). Following a standard- ized warm-up, MVCs were performed with the right leg in eight different joint angles, including a plateau of around 3 s. The angles ranged from 10° plantar flexion to the individual maximum dorsiflexion angle, set in random order in uniformly dis- tributed intervals. The moments at the ankle joint were calculated taking into account the effects of gravitational and passive moments and any misalignment between ankle joint axis and dynamometer axis by means of an established inverse dynamics approach [25] as well as the contribution of the antagonistic muscles by means of electromyography (description in electronic supplementary material; figure 1a). The force applied to the Achilles tendon during the plantar flexion MVCs was calculated as quotient of the joint moment and the individual tendon lever arm (description in electronic supplementary material). Soleus fascicle behaviour during the MVCs was synchronously captured at 30 Hz by B-mode ultrasonography (Aloka Prosound Alpha 7, Hitachi, Tokyo, Japan) with a 6 cm linear array probe (UST- 5713T, 13.3 MHz). The probe was mounted on the shank over the medial aspect of the soleus muscle belly by means of a custom made antiskid neoprene/plastic cast (figure 1a). The fascicle length was post-processed from the ultrasound images (figure 1a) using a self-developed semi-automatic tracking algorithm [26], described in more detail in the electronic supplementary material. Accordingly, an individual force–fascicle length relationship was calculated for each participant based on a second-order poly- nomial fit (figure 1b) and the maximum muscle force applied to the tendon (Fmax) and optimal fascicle length for force generation (L0) was derived, respectively. Furthermore, we assessed the force– velocity relationship of the soleus using the classical Hill equation [7], and the muscle-specific maximum fascicle shortening velocity (Vmax) and constants of arel and brel. Vmax was derived from the study of Luden et al. [27], which showed Vmax values for type 1 fibres of 0.77 L0 s−1 and 2.91 L0 s−1 for type 2 fibres of the human soleus muscle measured in vitro at 15°C [27]. Considering the temp- erature coefficient [28], Vmax can be predicted as 4.4 L0 s−1 for type 1 fibres and 16.8 L0 s−1 for type 2 fibres under physiological temp- erature conditions (37°C). Using an average fibre type distribution (type 1 fibres: 81%, type 2: 19%) of the human soleus muscle reported in literature [27,29–31], Vmax can be calculated as 6.77 L0 s−1. arel was calculated as 0.1 + 0.4FT, where FT is the fast twitch fibre type percentage (see above), which then equals to 0.175 [32,33]. The product of arel and Vmax then gives brel as 1.182 [34]. After rearrangement of the Hill equation and extension to the eccentric component, the operating velocity normalized to Vmax was used to calculate the individual force potential accord- ing to the force–velocity relationship. (c) Assessment of joint kinematics, muscle architecture and electromyographic activity during running During running on a treadmill (h/p cosmos mercury, Isny, Germany, 2.5 m s−1), kinematic data of the right leg were captured on the basis of anatomically-referenced reflective markers (greater trochanter, lateral femoral epicondyle and malleolus, fifth meta- tarsal and calcaneus) by a Vicon motion capture system (250 Hz). A 2 min warm-up and familiarization phase on the tread- mill preceded the captured interval. The touchdown of the foot and toe off were defined by the kinematic data as the first and second peak in knee extension, respectively [35]. Ultrasonic images of the soleus were obtained synchronously with a capture frequency of 146 Hz and soleus fascicle length was measured as mentioned above. At least nine steps (11.1 ± 1.5) were analysed for each participant and averaged [8]. Pennation angle was calcu- lated based on the angle between the deeper aponeurosis and the reference fascicle and thickness as distance between both apo- neuroses. The corresponding length changes of the soleus muscle belly was calculated as the product of fascicle length and the respective cosine of the pennation angle [36]. Note that this gives not the length of the entire soleus muscle belly but the projection of the instant fascicle length to the plane of the MTU, which can be used to calculate the changes of the belly length. The length change of the soleus MTU was calculated as the product of kin- ematic data-based ankle angle changes and the individual Achilles tendon lever arm [37], while the initial soleus MTU length was determined at neutral ankle joint angle based on the regression equation provided by Hawkins & Hull [38]. The vel- ocities of MTU, fascicles and muscle belly were calculated as the first derivative of the MTU, fascicle and belly lengths over the time. From these data we calculated the MTU gearing (VMTU/ VFascicle [16]), tendon gearing (VMTU/VBelly [16]) and belly gearing (VBelly/VFascicle [16,17]), where V is the stance phase-averaged vel- ocity of the soleus MTU, fascicles and muscle belly in absolute (i.e. positive) values. While belly gearing expresses the effects of fasci- cle rotation, tendon gearing expresses the effects of tendon compliance and MTU gearing is an overall expression of the effects of both components on the fascicle velocity [16]. Surface EMG of soleus was measured by means of the wireless EMG system according to the procedure described above (proces- sing description in electronic supplementary material) and normalized to the maximum processed EMG value obtained from all the individual MVCs (EMGmax). All parameters were averaged over the same steps as for the muscle fascicle assessment. (d) Energetic cost of running After detaching the ultrasound probe, the participants continued with a 10 min running trial at the same speed (2.5 m s−1). (a) (b) upper aponeur. deeper aponeur. 1 cm F 10 4500 500 1000 1500 2000 2500 3000 3500 4000 20 30 40 50 60 70 80 fascicle length (mm) force (N) q Figure 1. Experimental set-up for the determination of the soleus force–fas- cicle length relationship. (a) Maximum isometric plantar flexions (MVC) in eight different joint angles were performed on a dynamometer. During the MVCs, the soleus muscle fascicle length (F), pennation angle (Θ) and muscle thickness were measured based on ultrasound images. (b) Exemplary force–fascicle length relationship of the soleus muscle by the MVCs (squares) and the respective second-order polynomial fit (dashed line). royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 286: 20192560 3 A breath-by-breath cardio pulmonary exercise testing system (MetaLyzer 3B – R2, Cortex Biophysik GmbH, Leipzig, Germany) was used to record the percentage of concentration of both oxygen and carbon dioxide expired and rate of oxygen consump- tion ( _VO2) and carbon dioxide production ( _VCO2) was calculated as average of the last 3 min. Running economy was expressed in units of energy by energetic cost ¼ 16:89 _VO2 þ 4:84 _VCO2, ð2:1Þ where the energetic cost is expressed in [W kg−1] and _VO2 and _VCO2 in [ml s−1 kg−1] [3,39]. (e) Statistics Differences between soleus MTU and soleus fascicle length changes (absolute and normalized to L0) and velocities as well as between belly gearing and tendon gearing were tested by means of a paired t-test for dependent samples. The Pearson correlation coefficient was calculated in order to assess the relationship of the energetic cost of running and the force–velocity potential, force–length–velocity potential and fascicle velocity during the stance phase. As normality was not given for the force–length potential, we used the Spearman correlation coefficient to assess its relationship to the energetic cost. A Pearson correlation coeffi- cient was also used to analyse the relationship of EMG activity (mean and maximum) and force–length–velocity potential. We further conducted a multiple regression analysis to assess the mag- nitude of the effect of the four independent variables of stance phase-averaged tendon gearing, belly gearing, angular velocity of the ankle joint as well as Achilles tendon lever arm on the absol- ute soleus fascicle velocity. The statistics were performed using SPSS Statistics (IBM Corp., Version 20.0, Armonk, USA) and the level of significance was set to α = 0.05. All values are reported as means and standard deviations. 3. Results The experimentally assessed L0 was on average 41.3 ± 5.2 mm and corresponding Fmax was 2887.1 ± 724.2 N. The assessed Vmax based on the values of arel = 0.175 and brel = 1.182 s−1 was 279.0 ± 34.9 mm s−1. Achilles tendon lever arm showed an average length of 56.7 ± 7.4 mm. The averaged stance and swing times during running were 304 ± 23 ms and 439 ± 26 ms, respectively. During the stance phase, the ankle joint showed angles between 17.0 ± 3.8° dorsiflexion and 14.5 ± 6.0° plantarflexion (figure 2), and rotated with an average angular velocity of 164 ± 12°/s. The average activation of soleus normalized to EMGmax throughout the stance phase was 0.32 ± 0.19 EMGmax and the maximum activation was 0.52 ± 0.18 EMGmax at 40 ± 6% of the stance phase (figure 2). While the MTU showed a lengthening–shortening behaviour during the stance phase, the muscle fascicles shortened continuously with signifi- cantly less length changes as the MTU ( p < 0.001; figure 2, table 1). The pennation angle increased coincidentally with fascicle shortening while thickness remained almost unchanged (figure 2, table 1). Operating range (i.e. minimum to maximum) of the fascicles throughout the stance phase covered the top of the ascending limb of the force–length curve (0.75 ± 0.09 L0 to 1.01 ± 0.12 L0; figure 3) with a mean fascicle operating length close to the optimal length (i.e. 0.90 ± 0.10 L0). Accordingly, the averaged force–length poten- tial of the soleus fascicles was high; (i.e. 0.92 ± 0.07; figure 3). The soleus fascicles operated between −0.078 ± 0.045 Vmax and 0.322 ± 0.071 Vmax with an average velocity of 0 20 40 60 80 100 –30 –20 –10 0 10 20 30 ankle angle (°) 0 20 40 60 80 100 250 300 350 400 MTU length (mm) 20 40 60 80 100 0 10 20 30 40 50 60 fascicle length (mm) 20 40 60 80 100 0 10 20 30 40 50 pennation angle (°) 20 40 60 80 100 0 5 10 15 20 25 30 thickness (mm) 20 40 60 80 100 stance phase (%) 0 0.2 0.4 0.6 0.8 1.0 EMGNorm Figure 2. Ankle angle, soleus muscle–tendon unit (MTU) length, muscle fascicle length, pennation angle, thickness and electromyographic (EMG) activity (normalized to maximum voluntary isometric contraction) during the stance phase of running (2.5 m s−1). Individual (n = 19) data are shown in thin grey lines and group averages in thick black lines. royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 286: 20192560 4 0.118 ± 0.039 Vmax throughout the stance phase (figure 3). The decoupling of MTU and fascicle length trajectories (figure 2) enabled a significantly lower absolute operating velocity of the fascicles (40.0 ± 8.2 mm s−1) compared with the MTU (166.5 ± 27.7 mm s−1, p < 0.001), resulting in a force–velocity potential of 0.63 ± 0.09 (figure 3). The achieved total force– length–velocity potential of the soleus muscle during the stance phase of running was 0.58 ± 0.10. The calculated velocity gearing ratios were 4.46 ± 0.98 for MTU gearing, 4.03 ± 0.89 for tendon gearing and 1.11 ± 0.07 for belly gearing. The magni- tude of tendon gearing was significantly greater (p < 0.001) than belly gearing (figure 4). The energetic cost of running in the investigated velocity of 2.5 m s−1 was in average 10.69 ± 0.96 W kg−1. An inverse correlation was observed for the energetic cost and the overall force–length–velocity potential of the soleus muscle (r = −0.520, p = 0.022; figure 5). Energetic cost and the force– velocity potential were also inversely correlated (r = −0.565, p = 0.012; figure 5), and energetic cost and shortening velocity were positively correlated (r = 0.561, p = 0.012), indicative for an association of the economy of running and the operating velocity of the soleus fascicles during running. The force– length potential did not show any significant correlation to the energetic cost (rs = −0.076, p = 0.759; figure 5). A significant inverse correlation was also observed for the force–length–vel- ocity potential and the mean (r = −0.504, p = 0.028) and the maximal EMG activation (r = −0.525, p = 0.021). The multiple regression model for the assessment of the fascicle velocity during the stance phase showed a significant explanatory power ( p < 0.001, R2 = 0.928, adjusted R2 = 0.907) and was expressed by the equation: Fasicle velocity¼9:788 (tendon gearing) þ 0:716 (lever arm) 42:097 (belly gearing) þ 0:209 (ankle angle velocity) þ 51:341: The four included independent variables were all signifi- cant predictors ( p < 0.001 for tendon gearing, tendon lever arm and belly gearing and p = 0.002 for ankle angular vel- ocity). Considering the standardized coefficients of −1.006 for tendon gearing, 0.638 for lever arm, −0.367 for belly gear- ing and 0.310 for the ankle angular velocity, the model showed that tendon gearing and Achilles tendon lever arm had the greatest effect on the fascicle velocity. 4. Discussion By mapping the operating length and velocity of the human soleus muscle during running onto the individual force– length and force–velocity curves, we investigated the associ- ation between the energetic cost of locomotion and the soleus fascicle force–length and force–velocity potential. The findings showed that the soleus fascicles operated close to the optimal length and with moderate continuous shortening during the stance phase. The significant inverse relationship between the energetic cost and the force–velocity potential provides first direct experimental evidence that the fascicle shortening Table 1. Average values (dimension) as well as changes (range) of the ankle joint angle (minus indicates dorsiﬂexion), soleus muscle–tendon unit (MTU) length and fascicle length (absolute and normalized to optimal length), pennation angle and muscle thickness during the stance phase of running (n = 19). dimension range ankle angle −6.1 ± 3.6° 31.5 ± 5.2° MTU 321.4 ± 22.3 mm 32.2 ± 8.2 mm (79.5 ± 22.9%L0) fascicles 36.8 ± 4.2 mm 10.6 ± 3.0 mm* (25.9 ± 7.8%L0) pennation angle 24.0 ± 5.1° 8.9 ± 3.1° thickness 15.0 ± 3.3 mm 1.7 ± 1.0 mm Statistically signiﬁcant difference to MTU (p < 0.05). 0.4 0.6 0.8 1.0 1.2 1.4 1.6 LNorm (L/L0) 0 0.2 0.4 0.6 0.8 1.0 1.2 –0.4 –0.2 0 0.2 0.4 0.6 0.8 1.0 1.2 VNorm (V/Vmax) 0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 FNorm (F/Fmax) FNorm (F/Fmax) Figure 3. Operating length and velocity of soleus muscle fascicles during the stance phase of running mapped onto the averaged normalized force–length and force–velocity curve. White circles indicate the average operating length and velocity of the stance phase of each participant and the black circle the respective group average with the standard deviation of all participants (n = 19). The grey shaded areas illustrate the operating range (maximum to minimum) of the operating length and velocity during the stance phase averaged for all participants. Force is normalized to the maximum force during the maximal isometric plantar flexion contractions, fascicle length to the experimentally determined optimal fascicle length and fascicle velocity to the assessed maximum shortening velocity. Dotted lines in the left graph indicate the standard deviation of the individually measured force–length relationships. Note that the data points do not lie on the average curves because the individual force potentials were calculated for each percentage of the stance phase of each step and then averaged step-wise, which makes a difference to the calculation using the overall subject-based average length or velocity due to the non-linearity of the curves. royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 286: 20192560 5 velocity of the soleus muscle is a determining factor for the economy of human running. The triceps surae muscle group contributes substantially to the overall energetic cost of running [20]. The soleus is the largest muscle of the triceps surae [40] and the main muscle to lift and accelerate the centre of mass during locomotion [14,15]. During the stance phase of running, the fascicles of the soleus muscle shorten when activated, contributing to the ankle joint mechanical work/power output [41]. The fascicles operated on the steep-rising part of the hyperbolic-shaped force–velocity curve, in average at 11.8% of Vmax, where already small changes in fascicle shortening velocity cause relevant effects on the muscle force–velocity potential. As dic- tated by the force–velocity relationship [7], an increase in fascicle shortening velocity is accompanied by a decrease in the muscle force potential. The decrease of the muscle force potential requires an upregulation of the muscle activation to maintain the same level of force to support and accelerate the body mass [4,10]. The observed inverse relationship between force–velocity potential and energetic cost confirmed our hypothesis that the soleus fascicle shortening velocity is a key factor for the energetic cost of running. This link may further be supported by the observed inverse correlation of EMG activation and force–length–velocity potential, although it should be considered that active muscle volume cannot be assessed accurately from EMG activity. The fascicles worked in a small range on the upper portion of the ascending limb of the force–length curve with a high force–length potential of 0.92. An operating range on the ascending limb close to L0 (0.75–1.01 L0) was a quite consistent observation in the investi- gated runners, despite notable differences in the optimal fascicle length (L0 ranging from 33 to 51 mm). In our study, we did not find any relationship between force–length poten- tial and energetic cost of running. However, this does not indicate that the force–length potential is not important for running economy, but rather that the consistently observed high force–length potential explained less of the detected varia- bility in the energetic cost. Besides the favourable high force– length potential for economical force production, operating close to optimal length may additionally preserved from rela- tively higher energetic cost that can arise when contracting at shorter length. In vitro evidence showed that although force is reduced at shorter sarcomere length, the ATPase rate seems not to differ from the rate at optimal length, indicating 0 20 40 60 80 100 stance phase (%) –100 –50 0 velocity (mm s–1) belly fascicle 0 20 40 60 80 100 stance phase (%) –400 –200 0 200 velocity (mm s–1) MTU belly . . Figure 4. Operating velocity of the soleus muscle–tendon unit (MTU) and muscle belly (top) as well as muscle belly and fascicles (bottom) over the stance phase, illustrating the effect of tendon gearing and belly gearing, respectively. Grey shadings indicate the standard deviations (n = 19). 7 8 9 10 11 12 13 14 r = –0.56* force–velocity potential energetic cost (W kg–1) energetic cost (W kg–1) energetic cost (W kg–1) 7 8 9 10 11 12 13 14 rs = –0.08 force–length potential 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 7 8 9 10 11 12 13 14 r = –0.52* force–length–velocity potential 0.2 0.4 0.6 0.8 1.0 Figure 5. Association of the force–length–velocity potential, force–velocity potential and force–length potential of the soleus muscle to the energetic cost of running. *Statistically significant correlation ( p < 0.05). royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 286: 20192560 6 comparably higher cost of contraction at shorter length [42,43]. However, this effect seems more pronounced at very short lengths, a portion of the force–length curve that is probably not covered by the soleus during running (operating range 0.75–1.01 L0). Furthermore, we showed that the soleus shor- tened continuously during the stance phase of running, which reflects a condition for force depression. However, since a depression of force was shown to be accompanied by a decrease in the ATPase activity [44], force depression would have little or no effect on the energetic cost itself. During the stance phase, the MTU showed length changes of 80% L0 while the fascicles showed significantly lower length changes (i.e. 26% L0). The regression model provided evidence that the MTU–fascicle decoupling mechanisms of tendon and muscle belly gearing together with the Achilles tendon lever arm and ankle joint angular velocity determine the fascicle vel- ocity. The R2 for the model was 0.928, demonstrating a high goodness-of-fit and a high explanation of variance of the fasci- cle velocity. The overall MTU gearing ratio of the soleus muscle indicated a 4.5 fold reduction of the fascicle operating velocity during the stance phase of running. The tendon gearing ratio of 4.03 was notably greater than the belly gearing ratio of 1.11, resulting in a higher standardized regression coefficient (−1.006 versus −0.367). The observed gearing ratios indicate that the soleus fascicle velocity during the stance phase of run- ning is mainly governed by the compliance of the series elastic element. The high portion of tendon gearing in the soleus muscle is the consequence of greater length changes of the Achilles tendon and aponeurosis in relation to the muscle belly length changes. The soleus produces mechanical work/ energy for the lift and acceleration of the body throughout the entire stance phase. In the first half, where the MTU is elongated, the fascicles actively shorten. This means that a part of the mechanical energy of the human body is transferred to the tendon. Also, in this setting the muscle fascicles produce work under favourable conditions due to the force–length and force–velocity relationships (both potentials in this phase were very high) and save work as strain energy in the tendon. In the second half, the tendon strain energy is returned and at the same time the fascicles produce work by active shortening at a reduced force–velocity potential (fascicle shortening velocity is higher in this phase). The higher shortening velocity is associated with a reduction in the EMG activity and an increase in belly gearing. It has been suggested that increased gearing at fast shortening velocities and lower forces is a mechanism that allows particularly slower type fascicles to be more effective in generating forces [16]. This supports the idea that the observed activation pattern promoted an economical MTU interaction during running. Belly gearing (or the fascicle rotation component) reduced the shorting velocity of the soleus significantly by 11% in average throughout the stance phase (ratio = 1.11). The main contribution of the fascicle rotation component was in the second half of the stance phase. In situ experiments have shown that belly gearing in pennate muscles is variable with higher ratios during low muscle force to amplify belly shortening at lower fascicle shortening velocity and lower ratios during higher levels of muscle force to facilitate muscle force transmission to the tendon in concentric contrac- tions [17]. In accordance, we found an almost constant belly gearing of ≈1 in the soleus muscle during the first half of the stance phase were activation and consequently force was increased. When the soleus activation level decreased and the MTU shortened in the second half, the pennation angle increased and enabled a greater contribution of the fas- cicle rotation component to the reduction of fascicle shortening velocity (maximum belly gearing ratio of 1.18). As proposed by the variable gearing concept, the low fascicle rotation component shown by the soleus muscle during the first half of the stance phase where muscle activation is increased, facilitated the orientation of the line of action of the fascicles to the line of action of the MTU [17]. Our results provide further evidence that the Achilles tendon lever arm and ankle angular excursions during the stance phase were important explanatory factors of the fascicle shortening velocity. The lever arm is an anthropometric charac- teristic and the results showed that shorter lever arms translated into lower fascicle shortening velocities. The associ- ation of the Achilles tendon lever arm and fascicle shortening velocity in the current study provides first direct experimental evidence that shorter lever arms increase the force–velocity potential of the soleus muscle during running. Thus, the reduced fascicle shortening velocity due to a smaller lever arm is—in addition to tendon and belly gearing—a mechanism that improves running economy. Further, the association of the angular velocity of the ankle joint and fascicle shortening vel- ocity during the stance phase shows that greater angular excursions and velocities and in consequence greater length changes of the soleus MTU lead to uneconomical higher fascicle operating velocities. Although the soleus probably contributes to a great portion of the overall energetic cost during running, other limb muscles that were not considered in the present study are involved. However, the main energy source (positive work) is the ankle joint (41%) [41] and the soleus is the greatest muscle among the main plantar flexors with respect to physiological cross-sec- tional area (soleus 63%, gastroc. med. 25%, gastroc. lat. 12%) and volume (53%, 31% and 16% [40]). The key role of soleus is further supported by the modelling study of Hamner and Delp (2013), which showed that the soleus is by far the biggest contributor to the vertical acceleration and fore-aft acceleration of the centre of mass [14]. This function is achieved by active shortening, which reduces the force–velocity potential and consequently requires a greater active muscle volume. In con- trast, the quadriceps muscle group, the main contributor during early stance, decelerating and supporting body mass [14,15], features more economical fascicle dynamics. Recently, we showed that the fascicles of the vastus lateralis muscle as a representative of the quadriceps muscle group operates with a high force–length (i.e. 0.91) and force–velocity potential (i.e. 0.97) during the stance phase of running [8]. Operating at high force potentials minimizes the cost of this muscle, which is energetically expensive due to its long fascicle length (i.e. L0 = 94 mm [8]), by reducing active muscle volume. This may indicate that the mechanical energy by muscular work required for steady state running is generated by muscles that are metabolically less expensive (i.e. due to shorter fascicle length as the soleus muscle), probably to compensate for the reduction of the force–velocity potential. To assessthe force–velocity potential we used a biologically funded value of Vmax, based on in vitro studies on the human soleus, i.e. 6.77 L0 s−1 (279.0 ± 34.9 mm s−1). However, during submaximal running in vivo the lower activation level and selective slow fibre type recruitment may affect the actual force–velocity potential of the soleus muscle. To evaluate the effect of the choice of Vmax on the observed inverse correlation royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 286: 20192560 7 of force–velocity potential and energetic cost, a sensitivity analysis was conducted by decreasing and increasing Vmax in 10% intervals and calculating the correlation coefficients, respectively. The results did not show any substantial effects on the associations between force–velocity potential and ener- getic cost until a value of Vmax of <2.0 L0 s−1 (i.e. Vmax+30%: r = −0.577, Vmax+20%: r = −0.574, Vmax+10%: r = −0.570, Vmax−10%: r = −0.559, Vmax−20%: r = −0.552, Vmax−30%: r = −0.544, Vmax−40%: r = −0.534, Vmax−50%: r = −0.522, Vmax−60%: r = −0.506, Vmax−70%: r = −0.484; p < 0.05), which confirms and strengthens the overserved association. Further- more, we assessed the force–length curve during maximal isometric contractions and used it to calculate the force– length potential of the soleus muscle during running at submaximal activation. There is evidence from in vitro studies that the force–length curve depends on muscle activation [45,46]. However, in a recent in vivo study by Fontana & Herzog [47] on the human vastus lateralis muscle, a rightward shift of optimal length with submaximal activation was not observed when force was normalized to the maximum EMG signal. The authors suggested that the shift in optimal length phenomenon might be related to the in vitro testing set-up (e.g. non-physiological stimulation frequency range or Ca2+ concentrations). The discrepancy of the in vitro and in vivo evi- dence clearly warrants future investigation to elucidate the shifting length phenomenon in the context of in vivo submaxi- mal locomotion. Given the current human in vivo evidence [47], we can argue that mapping the submaximal fascicle operating length onto the force–length curve in the present in vivo study should not affect the findings. In the present study we focused on the understanding of the contribution of the force–length and force–velocity poten- tial to the energetic cost of running and we showed that the force–velocity potential is inversely related to the energetic cost, explaining about one-third of its variance. We argue that an increase of active muscle volume due to the decreased force–velocity potential would increase the energetic cost of running. However, it must be acknowledged that the energetic cost of muscle contraction is complex and multifactorial. Independent of active muscle volume, in higher shortening vel- ocities the rate of cross-bridge cycling is increased and as a consequence so is the consumed energy. In our study, shorten- ing velocities of the soleus muscle were on average 0.118 Vmax throughout the stance phase, a range where the rate of ATP hydrolysis shows a steep increase [48]. Furthermore, in sub- maximal intensity contractions as during our investigated running velocity selective slow fibre type activation might decrease the energetic cost by reducing the contribution of energetically more expensive fast twitch fibres. 5. Conclusion In conclusion, this study provides for the first time experimen- tal evidence that the energetic cost of running is related to the force–length–velocity potential of the soleus muscle with lower shortening velocities of the fascicles as the main influencing factor (i.e. higher force–velocity potential). The main mechan- ism for the underlying reduction of the fascicle shortening velocity during the stance phase was gearing within the MTU, particularly greater tendon gearing, a shorter Achilles tendon lever arm as well as, to a minor extent, a lower ankle angular velocity. Ethics. The ethics committee of the Humboldt-Universität zu Berlin approved the study and the participants gave written informed con- sent in accordance with the Declaration of Helsinki. Data accessibility. The datasets generated and analysed during the cur- rent study are available at https://dx.doi.org/10.6084/m9.figshare. 10119320.v1. Authors’ contributions. S.B., F.M. and A.A. designed research; S.B., F.M. and A.S. performed research; S.B. analysed data; S.B. and A.A. drafted the manuscript and F.M. and A.S. made important intellectual contributions during revision. Competing interests. We declare we have no competing interests. Funding. 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B 286: 20192560 9	The force-length-velocity potential of the human soleus muscle is related to the energetic cost of running.	12-18-2019	Bohm, Sebastian,Mersmann, Falk,Santuz, Alessandro,Arampatzis, Adamantios	eng
PMC7379642	Supplement Table 3. Change in VO2max (L·min-1 and ml·min-1·kg-1) from 1995-1997 to 2016-2017 in the total population and by age-group. L·min-1 ml·min-1·kg-1 L·min-1 ml·min-1·kg-1 L·min-1 ml·min-1·kg-1 Year n Mean (SD) Change Mean (SD) Change n Mean (SD) Change Mean (SD) Change n Mean (SD) Change Mean (SD) Change 95-97 1 354 3.19 (0.17) Ref 43.9 (0.72) Ref 2 195 2.82 (0.16) Ref 38.0 (0.52) Ref 1 025 2.44 (0.14) Ref 32.5 (0.68) Ref 98-99 1 840 3.19 (0.16) -0,1% 43.7 (0.64) -0,5% 2 849 2.80 (0.14) -0,6% 37.5 (0.39) -1,4% 1 854 2.42 (0.12) -0,7% 32.3 (0.51) -0,7% 00-01 3 469 3.16 (0.16) -1,0% 43.0 (0.76) -2,0% 5 248 2.82 (0.14) -0,1% 37.5 (0.70) -1,3% 3 828 2.40 (0.13) -1,7% 31.8 (0.56) -2,1% 02-03 6 563 3.09 (0.17) -3,3% 42.3 (0.68) -3,7% 9 429 2.78 (0.15) -1,4% 36.8 (0.70) -3,1% 6 637 2.35 (0.13) -3,7% 31.2 (0.60) -3,9% 04-05 9 617 3.09 (0.16) -3,0% 42.2 (0.57) -3,9% 16 294 2.78 (0.15) -1,3% 36.7 (0.69) -3,5% 11 509 2.35 (0.13) -3,6% 31.1 (0.50) -4,4% 06-07 9 743 3.08 (0.15) -3,4% 41.7 (0.61) -4,9% 16 867 2.79 (0.15) -0,9% 36.5 (0.69) -3,9% 11 909 2.37 (0.13) -2,9% 31.2 (0.56) -4,1% 08-09 11 268 3.07 (0.15) -3,7% 41.6 (0.70) -5,2% 18 652 2.82 (0.15) -0,1% 36.6 (0.87) -3,6% 13 559 2.39 (0.12) -2,0% 31.2 (0.59) -4,1% 10-11 10 340 3.06 (0.15) -4,0% 41.3 (0.78) -5,8% 17 618 2.83 (0.15) 0,2% 36.5 (0.86) -3,8% 11 219 2.41 (0.13) -1,2% 31.1 (0.58) -4,2% 12-13 15 737 3.04 (0.14) -4,7% 41.0 (0.82) -6,7% 25 651 2.79 (0.14) -1,1% 36.2 (0.94) -4,7% 15 858 2.38 (0.12) -2,3% 30.8 (0.56) -5,2% 14-15 16 428 2.99 (0.14) -6,4% 40.1 (0.67) -8,5% 23 725 2.75 (0.13) -2,5% 35.6 (0.90) -6,4% 15 431 2.37 (0.12) -2,7% 30.5 (0.65) -6,2% 16-17 11 944 2.98 (0.14) -6,5% 39.9 (0.77) -9,2% 14 693 2.73 (0.13) -3,2% 35.3 (0.84) -7,1% 9 924 2.38 (0.13) -2,3% 30.5 (0.71) -6,1% 18-34 years 35-49 years 50-74 years	Decline in cardiorespiratory fitness in the Swedish working force between 1995 and 2017.	11-15-2018	Ekblom-Bak, Elin,Ekblom, Örjan,Andersson, Gunnar,Wallin, Peter,Söderling, Jonas,Hemmingsson, Erik,Ekblom, Björn	eng
PMC5889786	SYSTEMATIC REVIEW Effects of Strength Training on the Physiological Determinants of Middle- and Long-Distance Running Performance: A Systematic Review Richard C. Blagrove1,2 • Glyn Howatson2,3 • Philip R. Hayes2 Published online: 16 December 2017 The Author(s) 2017. This article is an open access publication Abstract Background Middle- and long-distance running perfor- mance is constrained by several important aerobic and anaerobic parameters. The efﬁcacy of strength training (ST) for distance runners has received considerable atten- tion in the literature. However, to date, the results of these studies have not been fully synthesized in a review on the topic. Objectives This systematic review aimed to provide a comprehensive critical commentary on the current litera- ture that has examined the effects of ST modalities on the physiological determinants and performance of middle- and long-distance runners, and offer recommendations for best practice. Methods Electronic databases were searched using a variety of key words relating to ST exercise and distance running. This search was supplemented with citation tracking. To be eligible for inclusion, a study was required to meet the following criteria: participants were middle- or long-distance runners with C 6 months experience, a ST intervention (heavy resistance training, explosive resis- tance training, or plyometric training) lasting C 4 weeks was applied, a running only control group was used, data on one or more physiological variables was reported. Two independent assessors deemed that 24 studies fully met the criteria for inclusion. Methodological rigor was assessed for each study using the PEDro scale. Results PEDro scores revealed internal validity of 4, 5, or 6 for the studies reviewed. Running economy (RE) was measured in 20 of the studies and generally showed improvements (2–8%) compared to a control group, although this was not always the case. Time trial (TT) performance (1.5–10 km) and anaerobic speed qualities also tended to improve following ST. Other parameters [maximal oxygen uptake ( _VO2max), velocity at _VO2max, blood lactate, body composition] were typically unaffected by ST. Conclusion Whilst there was good evidence that ST improves RE, TT, and sprint performance, this was not a consistent ﬁnding across all works that were reviewed. Several important methodological differences and limita- tions are highlighted, which may explain the discrepancies in ﬁndings and should be considered in future investiga- tions in this area. Importantly for the distance runner, measures relating to body composition are not negatively & Richard C. Blagrove richard.blagrove@bcu.ac.uk Glyn Howatson glyn.howatson@nothumbria.ac.uk Philip R. Hayes phil.hayes@northumbria.ac.uk 1 Faculty of Health, Education and Life Sciences, School of Health Sciences, Birmingham City University, City South Campus, Westbourne Road, Edgbaston, Birmingham B15 3TN, UK 2 Division of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Northumberland Building, Newcastle-upon-Tyne NE1 8ST, UK 3 Water Research Group, Northwest University, Potchefstroom, South Africa 123 Sports Med (2018) 48:1117–1149 https://doi.org/10.1007/s40279-017-0835-7 impacted by a ST intervention. The addition of two to three ST sessions per week, which include a variety of ST modalities are likely to provide beneﬁts to the performance of middle- and long-distance runners. Key Points Strength training (ST) appears to provide beneﬁts to running economy, time trial performance and maximal sprint speed in middle- and long-distance runners of all abilities Maximal oxygen uptake, blood lactate parameters, and body composition appear to be unaffected by the addition of ST to a distance runner’s program Adding ST, in the form of heavy resistance training, explosive resistance training, and plyometric training performed, on 2–3 occasions per week is likely to positively affect performance. 1 Introduction Distance running performance is the consequence of a complex interaction of physiological, biomechanical, psy- chological, environmental, and tactical factors. From a physiological perspective, the classic model [1, 2] identi- ﬁes three main parameters that largely inﬂuence perfor- mance: maximal oxygen uptake ( _VO2max), running economy (RE), and fractional utilization (sustainable per- centage of _VO2max). Collectively, these determinants are capable of predicting 16 km performance with more than 95% accuracy in well-trained runners [3]. The velocity associated with _VO2max (v _VO2max) also provides a com- posite measure of _VO2max and RE, and has been used to explain differences in performance amongst trained dis- tance runners [3, 4]. Whilst _VO2max values differ little in homogenous groups of distance runners, RE displays a high degree of interindividual variability [5, 6]. Deﬁned as the oxygen or energy cost of sustaining a given sub-max- imal running velocity, RE is underpinned by a variety of anthropometric, physiological, biomechanical, and neuro- muscular factors [7]. Traditionally, chronic periods of running training have been used to enhance RE [8, 9]; however, novel approaches such as strength training (ST) modalities have also been shown to elicit improvements [10]. For middle-distance (800–3000 m) runners, cardiovas- cular-related parameters associated with aerobic energy production can explain a large proportion of the variance in performance [11–17]. However a large contribution is also derived from anaerobic sources of energy [14, 18]. Anaerobic capabilities can explain differences in physio- logical proﬁles between middle- and longer-distance run- ners [14] and are more sensitive to discriminating performance in groups of elite middle-distance runners than traditional aerobic parameters [19]. Anaerobic capacity and event-speciﬁc muscular power factors, such as v _VO2max and the velocity achieved during a maximal anaerobic running test (vMART) have also been proposed as limiting factors for distance runners [12, 20, 21]. For an 800-m runner in particular, near-maximal velocities of running are reached during the ﬁrst 200 m of the race [22], which necessitate a high capacity of the neuromuscular and anaerobic system. Both RE and anaerobic factors, (i.e., speed, anaerobic capacity and vMART) rely on the generation of rapid force during ground contact when running [23, 24]. Programs of ST provide an overload to the neuromuscular system, which improves motor unit recruitment, ﬁring frequency, musculotendinous stiffness, and intramuscular co-ordina- tion, and therefore potentially provides distance runners with a strategy to enhance their RE and event-speciﬁc muscular power factors [19]. In addition, an improvement in force-generating capacity would theoretically allow athletes to sustain a lower percentage of maximal strength, thereby reducing anaerobic energy contribution [25]. This reduction in relative effort may therefore reduce RE and blood lactate (BL) concentration. As v _VO2maxis a function of RE, _VO2max and anaerobic power factors, it would also be expected to show improvements following an ST intervention. Several recent reviews in this area have pro- vided compelling evidence that a short-term ST interven- tion is likely to enhance RE [10, 26], in the order of * 4% [10]. Whilst these reviews have provided valuable insight into how ST speciﬁcally impacts RE, studies also typically measure other important aerobic and anaerobic determi- nants of distance running performance, which have not previously been fully synthesized in a review. Body com- position also appears to be an important determinant of distance running performance, with low body mass con- ferring an advantage [27, 28]. Resistance training (RT) is generally associated with a hypertrophic response [29]; however, this is known to be attenuated when RT and endurance training are performed concurrently within the same program [30]. Changes in body composition as a consequence of ST in distance runners have yet to be fully addressed in reviews on this topic. 1118 R. C. Blagrove et al. 123 There are also a number of recent publications [31–38] that have not been captured in previous reviews [10, 26] on this topic, which potentially provide valuable additional insight into the area. Previous papers that have reviewed the impact of ST modalities on distance running perfor- mance have done so alongside other endurance sports [23, 39] or are somewhat outdated [40–42]. Furthermore, although improvements in RE would likely confer a beneﬁt to distance running performance, the outcomes from studies that have used time trials have not been compre- hensively reviewed. Performance-related outcome mea- sures provide high levels of external validity compared to physiological parameters, therefore it is likely that a col- lective summary of results would be of considerable interest to coaches and athletes. Consequently the aim of this review was to systemati- cally analyze the evidence surrounding the use of ST on distance running parameters that includes both aerobic and anaerobic qualities, in addition to body composition and performance-related outcomes. This work also provides a forensic, critical evaluation that, unlike previous work, highlights areas that future investigations should address to improve methodological rigor, such as ensuring valid measurement of physiological parameters and maximizing control over potential confounding factors. 2 Methods 2.1 Literature Search Strategy The PRISMA statement [43] was used as a basis for the procedures described herein. Electronic database searches were carried out in Pubmed, SPORTDiscus, and Web of Science using the following search terms and Boolean operators: (‘‘strength training’’ OR ‘‘resistance training’’ OR ‘‘weight training’’ OR ‘‘weight lifting’’ OR ‘‘plyo- metric training’’ OR ‘‘concurrent training’’) AND (‘‘dis- tance running’’ OR ‘‘endurance running’’ OR ‘‘distance runners’’ OR ‘‘endurance runners’’ OR ‘‘middle distance runners’’) AND (‘‘anaerobic’’ OR ‘‘sprint’’ OR ‘‘speed’’ OR ‘‘performance’’ OR ‘‘time’’ OR ‘‘economy’’ OR ‘‘en- ergy cost’’ OR ‘‘lactate’’ OR ‘‘maximal oxygen uptake’’ OR ‘‘ _VO2max’’ OR ‘‘aerobic’’ OR ‘‘time trial’’). Searches were limited to papers published in English and from 1 January 1980 to 6 October 2017. 2.2 Inclusion and Exclusion Criteria For a study to be eligible, each of the following inclusion criteria were met: • Participants were middle- (800–3000 m) or long-dis- tance runners (5000 m–ultra-distance). Studies using triathletes and duathletes were also included because often these participants possess similar physiology to distance runners and complete similar volumes of running training. • A ST intervention was applied. This was deﬁned as heavy (less than 9 repetition maximum (RM) loads and/ or 80% of 1RM) or isometric resistance training (HRT), moderate load (9–15 RM and/or 60–80% 1RM) RT, explosive resistance training (ERT), reactive ST or plyometric training (PT). Sprint training (SpT) could be used in conjunction with one or more of the above ST methods, but not exclusively as the only intervention activity. • The intervention period lasted 4 weeks or longer. This criteria was employed as neuromuscular adaptations have been observed in as little as 4 weeks in non- strength trained individuals [44, 45]. • A running only control group was used that adopted similar running training to the intervention group(s). • Data on one or more of the following physiological parameters was reported: _VO2max, RE, velocity associ- ated with v _VO2max, time trial (TT) performance, time to exhaustion (TTE), BL response, anaerobic capacity, maximal speed, measures of body composition. • Published in full in a peer-reviewed journal. Studies were excluded if any of the following criteria applied: • Participants were non-runners (e.g., students, untrai- ned/less than 6 months running experience). Further restrictions were not placed upon experience/training status. • The running training and/or ST intervention was poorly controlled and/or reported. • The intervention involved only SpT or was embedded as part of running training sessions. • Participants were reported to be in poor health or symptomatic. • Ergogenic aids were used as part of the intervention. Using the mean _VO2max values provided within each study, participants training status was considered as mod- erately-trained (male _VO2max B 55 ml kg-1 min-1), well- trained (male _VO2max 55–65 ml kg-1 min-1), or highly- trained (male _VO2max C 65 ml kg-1 min-1) [10, 46]. For female participants, the _VO2max thresholds were set 10 ml kg-1 min-1 lower [46]). In the absence of _VO2max values, training status was based upon the training or competitive level of the participants: moderately- trained = recreational or local club, well- Effects of Strength Training on Distance Running 1119 123 trained = Collegiate or provincial, highly-trained = na- tional or international. 2.3 Study Selection Figure 1 provides a visual overview of the study selection process. Search results were imported into a published software for systematic reviews [47], which allowed a blind screening process tobe performed bytwoindependent reviewers(RB and PH). Any disagreements were resolved by consensus. The initial search yielded 454 publications. Following the removal of duplicates (n = 190), publications were ﬁltered by reading the title and abstract [inter-rater reliability (IRR): 95.3%, Cohens k = 0.86] leaving 19 review articles or commentaries, and 47 potentially relevant papers, which were given full con- sideration. Five additional records were identiﬁed as being potentially relevant via manual searches of previously published reviews on this topic and the individual study cita- tions. These 52 studies were considered in detail for appropri- ateness, resulting in a further 26 papers [34, 37, 48–71] being excluded (IRR: 94.2%, Cohens k = 0.88) for the following reasons: not published in full in a peer-reviewed journal [50, 52, 60, 61], absence of a running only control group [48, 49, 54, 57, 59, 62–67, 69], participants were non-runners [51, 53, 56, 68], no physiological parameters were measured [55], dissimilar running training was applied between groups [71], the ST interventionwaspoorly controlled[54],andSTdid not involve one of the aforementioned types [34, 37, 58, 70]. 2.4 Analysis of Results The Physiotherapy Evidence Database (PEDro) scale was subsequently used to assess the quality of the remaining 26 records [31–33, 36, 38, 72–92] by the two independent reviewers. Two studies reported their results across two papers [32, 38, 90, 92], therefore both are considered as single studies hereafter, thus a total of 24 studies were analyzed. The PEDro scale is a tool recommended for assessing the quality of evidence when systematically reviewing randomized-controlled trials [93]. Each paper is scrutinized against 11 items relating to the scientiﬁc rigor of the methodology, with items 2–11 being scored 0 or 1. Papers are therefore awarded a rating from 0 to 10 depending upon the number of items which the study methodology satisﬁes (10 = study possesses excellent internal validity, 0 = study has poor internal validity). No studies were not excluded based upon their PEDro scale score and IRR was excellent (93.2%, Cohens k = 0.86). Results are summarized as a percentage change and the p value for variables relating to: strength outcomes, RE, _VO2max, v _VO2max, BL response, time trial, anaerobic per- formance, and body composition. Due to the heterogeneity of outcome measures in the included studies and the limi- tations associated with conditional probability, where pos- sible, an effect size (ES) statistic (Cohens d) is also provided. Effect size values are based upon those reportedin the studies or were calculated using the ratio between the change score (post-intervention value minus pre-intervention value) and a pooled standard deviation at baseline for intervention and control groups. Values are interpreted as trivial\0.2; small 0.2–0.6; moderate 0.6–1.2; and large[1.2. 3 Results 3.1 Participant Characteristics A summary of the participant characteristics for the 24 studies which met the criteria for inclusion in this review is presented in Table 1. A total of 469 participants (male Fig. 1 Search, screening and selection process for suitable studies 1120 R. C. Blagrove et al. 123 Table 1 Participant characteristics and design of each study Study Participant characteristics Study design n (I/C) Sex Age (years) _VO2max (mL kg-1 min-1) Training background (event specialism) Duration (weeks) Randomized? Running controlled? ST added or replace running? PEDro score Albracht & Arampatzis [84] 26 (13/13) M I = 27, C = 25 – Recreational (C 3 runs wk-1, 30–120 km wk-1) 14 No No Added 5 Beattie et al. [33] 20 (11/9) M = 19 F = 1 I = 29.5, C = 27.4 I = 59.6, C = 63.2 Collegiate and national level (1500 m–10 km) 40 No No Added 4 Berryman et al. [80] 28 (HRT n = 12, PT n = 11, C n = 5) M HRT = 31, PT = 29, C = 29 HRT = 57.5, PT = 57.5, C = 55.7 3–7 runs wk-1. Provincial level (5 km–marathon) 8 Yes Yes Added 5 Bertuzzi et al. [85] 22 (RTWBV n = 8, RT n = 8, C n = 6) M RTWBV = 34, RT = 31, C = 33 RTWBV = 56.3, RT = 57.4, C = 56.1 Local 10 km (35–45 min) race competitors 6 Yes No (monitored) Added 6 Bonacci et al. [83] 8 (3/5) M = 5 F = 3 21.6 – Moderately-trained triathletes (34.8 km wk-1) 8 Yes No (monitored) Added 5 Damasceno et al. [89] 18 (9/9) M I = 34.1, C = 32.9 I = 54.3, C = 55.8 Local 10 km (35–45 min) race competitors 8 Yes No (monitored) Added 6 Ferrauti et al. [81] 20 (11/9) M = 14 F = 6 40.0 I = 52.0, C = 51.1 Experienced (8.7 years) recreational (4.6 h wk-1) 8 Yes No (monitored) Added 6 Fletcher et al. [82] 12 (6/6) M I = 22.2, C = 26.3 I = 67.3, C = 67.6 Regional/national/ international level (1500 m– marathon) 8 Yes No Added 6 Giovanelli et al. [36] 25 (13/12) M I = 36.3, C = 40.3 I = 55.2, C = 55.6 Experienced (11.7 years,[60 km wk-1) ultra-distance competitors 12 Yes No (monitored) Added 6 Johnston et al. [72] 12 (6/6) F 30.3 I = 50.5, C = 51.5 [1 year experience, 20–30 miles wk-1, 4–5 days wk-1 10 Yes No (monitored) Added 6 Karsten et al. [31] 16 (8/8) M = 11F = 5 I = 39, C = 30 I = 47.3, C = 47.0 Recreational triathletes ([2 years, 3–5 days wk-1, 180–300 min wk-1) 6 Yes No Added 6 Mikkola et al. [78] 25 (13/12) M = 18 F = 7 I = 17.3, C = 17.3 I = 62.4, C = 61.8 High-school runners ([2 years) 8 No No (monitored) Replace (I: 19%, C: 4%) 4 Millet et al. [74] 15 (7/8) M I = 24.3, C = 21.4 I = 69.7, C = 67.6 Experienced (6.8 years) triathletes (n = 7 national/ international) 14 Yes No (monitored) Added 6 Effects of Strength Training on Distance Running 1121 123 Table 1 continued Study Participant characteristics Study design n (I/C) Sex Age (years) _VO2max (mL kg-1 min-1) Training background (event specialism) Duration (weeks) Randomized? Running controlled? ST added or replace running? PEDro score Paavolainen et al. [73] 18 (10/8) M I = 23, C = 24 I = 63.7, C = 65.1 Experienced (8 years) cross- country runners (545 h year-1) 9 Unclear (matched on _VO2max and 5 km) Yes Replace (I: 32%, C: 3%) 4 Pellegrino et al. [91] 22 (11/11) M = 14 F = 8 I = 34.2, C = 32.5 I = 48.0, C = 47.7 Experienced recreational (local clubs and races) 6 Yes No Added 6 Piacentini et al. [86] 16 (HRT n = 6, RT n = 5, C n = 5) M = 6 F = 4 HRT = 44.2 RT = 44.8 C = 43.2 – Local ([5 years, 4–5 days wk-1) masters runners (10 km – marathon) 6 Yes No Added 4 Ramı´rez- Campillo et al. [87] 32 (17/15) M = 9 F = 13 22.1 – National/international competitive level (1500 m – marathon) 6 Yes No (monitored) Added 6 Saunders et al. [77] 15 (7/8) M I = 23.4, C = 24.9 I = 67.7, C = 70.4 National/international competitive level (3 km) 9 Yes No (monitored) Added (but C matched with stretching/ CS) 6 Schumann et al. [90, 92] 27 (13/14) M 33 – Recreational ([12 months; C 2 runs wk-1) 24 Unclear (matched by performance) Yes Added 5 Skovgaard et al. [88] 21 (12/9) M 31.1 59.4 Experienced (7.5 years) recreational (29.7 km wk-1, 3.3 runs wk-1) 8 Yes Yes (I only) Replace (I: 42%) 6 Spurrs et al. [75] 17 (8/9) M 25 I = 57.6, C = 57.8 Experienced (10 years); 60–80 km wk-1 6 Yes No (monitored) Added 6 Støren et al. [79] 17 (8/9) M = 9 F = 8 I = 28.6, C = 29.7 I = 61.4, C = 56.5 Well-trained (5 km: M = 18.42, F = 19.23) 8 Yes No (monitored) Added 6 Turner et al. [76] 18 (10/8) M = 8 F = 10 I = 31, C = 27 I = 50.4, C = 54.0 Basic training ([6 months; C 3 runs wk-1) 6 Yes No (monitored) Added 6 Vikmoen et al. [32, 38] 19 (11/8) F I = 31.5, C = 34.9 53.3 Well-trained (duathletes) 11 Yes Yes Added 5 C control group, CS core stability, F female, h hours, HRT heavy resistance training, I intervention group, M male, PT plyometric training, RT resistance training, RTWBV resistance training with whole body vibration, _VO2max maximal oxygen uptake, wk week 1122 R. C. Blagrove et al. 123 n = 352, female n = 96) are included, aged between 17.3 and 44.8 years. Maximal oxygen uptake data was reported for all but ﬁve studies [83, 84, 86, 87, 90, 92] and ranged from 47.0 to 70.4 mL kg-1 min-1. Based upon weighted mean values in the studies that reported participant char- acteristics for each group, age (30.2 vs. 29.0 years), body mass (68.1 vs. 70.0 kg), height (1.74 vs. 1.74 m), and _VO2max (57.3 vs. 57.7 mL kg-1 min-1) appeared to differ little at baseline for ST groups and control groups respec- tively. Moderately trained or recreational level runners were used in nine studies [31, 72, 76, 81, 83, 84, 86, 90–92], well-trained participants in ten studies [32, 33, 36, 38, 73, 75, 79, 80, 85, 88, 89], and highly-trained or national/international runners were used in four studies [74, 77, 82, 87]. National caliber junior runners were also used in one investigation [78]. Participants took part or competed in events ranging from the middle-distances to ultra-marathons, and several studies used triathletes [31, 74, 83] or duathletes [32, 38]. 3.2 Study Design and PEDro Scores Table 1 also provides an overview of several important features of study design, including PEDro scale scores. Studies lasted 6–14 weeks with the exception of two investigations, which lasted 24 [90, 92] and 40 weeks [33]. Fourteen studies provided detailed accounts of the running training undertaken by the participants. However, these were usually reported from monitoring records, thus only three studies were deemed to have appropriately controlled for the volume and intensity of running in both groups [32, 38, 73, 80, 90, 92]. Six studies provided little or no detail on the running training that participants performed [31, 33, 82, 84, 86, 91]. Strength training in all but three investigations [73, 78, 88] was supplementary to running training, and one paper provided the control group with alternative activities (stretching and core stability) matched for training time [77]. Studies were all scored a 4, 5, or 6 on the PEDro scale. All investigations had points deducted for items relating to blinding of participants, therapists, and assessors. Differ- ences in the scores awarded were mainly the result of studies not randomly allocating participants to groups and failing to obtain data for more than 85% of participants initially allocated to groups; or this information not being explicitly stated. 3.3 Training Programs Table 2 provides a summary of the training characteristics associated with the ST intervention and running training used concurrently as part of the study period. The ST activities used were RT or HRT [31, 32, 38, 72, 78, 79, 81, 82, 84–86, 89], PT [75, 76, 80, 87, 91], ERT [80], or a combination of these methods [33, 36, 77, 83, 90, 92], which in some cases also included SpT [73, 74, 88]. All studies utilized at least one multi-joint, closed kinetic chain exercise with the exception of two studies that used isometric contractions on the ankle plantarﬂexors [82, 84]. One study employed only resistance machine exercises for lower limb HRT [81], whereas all other studies used free weights, bodyweight resistance or a combination of machines and free weights. Strength training (using lower limb musculature) was scheduled once [33, 80, 81], twice [31–33, 38, 75, 78, 85–87, 89, 90, 92], three times [36, 72, 74–77, 79, 82, 83, 88], or four times [84] per week. One study used 15 sessions over a 6-week period [91] and one study reported 2.7 h of ST activity per week [73]. Heavy RT was typically prescribed in 2–6 sets of 3–10 repetitions per exercise at relatively heavy loads (higher than 70% 1RM or to repetition failure). Plyometric training prescription consisted of 1–6 exercises performed over 1–6 sets of 4–10 repetitions, totaling 30–228 foot contacts per session. Most studies applied the principle of progressive overload and some authors reported periodized models for the intervention period [32, 33, 36, 38, 77, 88, 89]. Studies which included SpT tended to utilize short distances (20–150 m), over 4–12 sets at maximal intensity [73, 74, 88]. Strength training was supervised or part-su- pervised across all studies with the exception of three, one that was unsupervised [76] and two where it was unclear from the report [73, 74]. Running training varied considerably (16–170 km week-1, 3–9 sessions week-1) across the studies, with various levels of detail provided regarding weekly volume and intensity. Importantly, all studies that added ST reported that running training did not differ between groups. 3.4 Strength Outcomes All but two studies [31, 83] measured at least one strength- related parameter (Table 3). Across all studies that used 1RM testing [33, 72, 74, 78, 79, 85, 86, 88–90, 92], the intervention produced a statistically signiﬁcant improve- ment (4–33%, ES: 0.7–2.4). Maximal voluntary contraction (MVC) was also used to assess strength capacity in seven papers, with the majority reporting improved (7–34%, ES: 0.38–1.65) scores following ST [73, 75, 78, 81, 84] but others reporting no difference compared to a control group [81, 82, 90, 92]. Performance on a jump test was shown to improve (3–9%, ES: 0.25–0.65) in some studies [32, 73, 74, 80, 87]; however, other studies showed no Effects of Strength Training on Distance Running 1123 123 Table 2 Intervention and running training variables Study Intervention type Main exercises Frequency Volume per session Intensity ST supervised? Recovery between sessions Running training Albracht & Arampatzis [84] HRT (isometric) Ankle plantarﬂexion (5 dorsiﬂexion, knee extended, 40 hip ﬂexion) 4 per week 4 sets 9 4 reps (3 s loading, 3 s relaxation) 90% MVC (adjusted weekly) Yes – I: 66 km wk-1 C: 62 km wk-1 Beattie et al. [33] HRT/ERT/ PT PT: pogo jumps, depth jumps, CMJ HRT: back squat, RDL, lunge ERT: jump squats Wk 1–20: 2 per week; Wk 21–40: 1 per week 9–12 sets (2–3 sets per exercise); PT: 4–5 reps, HRT: 3–8 reps, ERT: 3 reps Load progressed when competent Yes C48 h between sessions (wk 1–20). Separate session to running Not reported (usual running training) Berryman et al. [80] ERT and PT ERT: concentric squats PT: DJ 1 per week ERT and PT: 3–6 sets 9 8 reps ERT:[95% PPO PT: 20–60 cm so rebound[95% CMJ Yes – 2 9 AIT (1 9 peak speed, 1 9 80% peak speed) 1 9 LSD (30–60 min) Bertuzzi et al. [85] RT and RTWBV Half-squats 2 per week 3–6 sets 9 4–10 reps periodized 70–100% 1RM over 12 wk Yes Different days to runs 57–61 km wk-1 Bonacci et al. [83] PT/ERT PT: CMJ, knee lifts, ankle jumps, bounds, skips, hurdle jumps ERT: Squat jumps, back ext., hamstring curls 3 per week PT: 1–5 sets 9 5–10 reps or 20–30 m RT: 2–5 sets 9 8–15 reps Max height/fast velocity Yes – Same as previous 3 months. I: swim (7.3 km), cycle (137.6 km), run (34.8 km) C: swim (10.1 km), cycle (147.5 km), run (29.0 km) Damasceno et al. [89] HRT Half-squat, leg press, calf raise, knee ext 2 per week 2–3 sets 9 3–10 reps 10RM periodized to 3RM Yes 72 h between HRT sessions. Different days to runs 36–41 km wk-1 @50–70% _VO2max Ferrauti et al. [81] HRT Machines: leg press, knee ext., knee ﬂexion, hip ext., ankle ext.; UB exercises 1 per week LB; 1 per week UB LB: 4 sets 9 3–5 reps 3–5 RM Yes – I: 240 min wk-1, C: 276 min wk-1 Fletcher et al. [82] HRT (isometric) Plantarﬂexions 3 per week 4 sets 9 20 s 80% MVC Yes – 70–170 km wk-1 1124 R. C. Blagrove et al. 123 Table 2 continued Study Intervention type Main exercises Frequency Volume per session Intensity ST supervised? Recovery between sessions Running training Giovanelli et al. [36] CS/RT (4wk) HRT/ERT/ PT (8wk) CS: 6 exercises (e.g., planks) RT/HRT: single leg half-squat, step-up, lunges ERT: CMJ, split squat PT: jump rope, high knees 3 per week 5–8 exercises, 1–3 sets 9 6–15 reps (30 s rest) – Partly (only wk 1 and 2) C48 h between sessions. Not day after races/ AIT I: normal running training C: 70–140 km wk-1, 5–7 sessions wk-1 Johnston et al. [72] HRT Squats, lunge, heel raises (straight- and bent-leg), knee ext./ﬂexion, 8xUB exercises 3 per week 3 sets 9 6 reps squat and lunge; 2 sets 9 20/12 reps bent–/straight–leg heel raise; 3 sets 9 8 reps knee ext./ﬂexion RM each set Yes C48 h between HRT sessions. C 5 h between HRT and running sessions. 4–5 days wk-1, 32–48 km wk-1 Karsten et al. [31] HRT RDL, squat, calf raises, lunges 2 per week 4 sets 9 4 reps 80% 1RM Yes C48 h between HRT sessions. 3–5 sessions/ 180–300 min wk-1 Mikkola et al. [78] HRT Hamstring curl, leg press, seated press, squat, leg ext., heel raise 2 per week 3–5 sets 9 3–5 reps [90% 1RM (reassessed every 3 wk) Yes Separate session to running Total: I = 7 h wk-1, C = 6.6 h wk-1; Running: I = 48 km wk-1, C = 44 km wk-1 Millet et al. [74] SpT/PT/ ERT PT: alternative, calf, squat, hurdle jumps ERT: Squat, calf raise, hurdle jump, leg ext./curl 3 per week (each intervention type once) SpT: 5–10 sets 9 30–150 m PT/ERT: 2–3 sets 9 6–10 reps PT: BW ERT: low load, high velocity Unclear – I: 8.8 h wk-1, C: 8.5 h wk-1 Paavolainen et al. [73] SpT/PT/ ERT PT: alternative, drop and hurdle jumps, CMJ, hops ERT: leg press, knee ext. and ﬂexion Not reported; 2.7 h per week SpT: 5–10 sets 9 20–100 m PT/ERT: 5–20 reps.set-1/ 30–200 reps.session-1 PT: BW or barbell ERT: 0–40% 1RM Unclear – I: 8.4 h wk-1 (9 sessions) C: 9.2 h wk-1 (8 sessions) Pellegrino et al. [91] PT Modiﬁed version of Spurrs et al. (jumps, bounds, hops) 15 sessions total 60–228 foot contacts Progressively increased Yes – I: 34.4–36.2 km wk-1 C: 29.5–31.3 km wk-1 Piacentini et al. [86] HRT and RT Squat, calf press, lunges, eccentric quad, calf raise, leg press ? UB exercises 2 per week HRT: 4 sets 9 3–4 reps RT: 3 sets 9 10 reps HRT: 85–90% 1RM RT: 70% 1RM Yes – 4–5 days wk-1, 50 km wk-1 Effects of Strength Training on Distance Running 1125 123 Table 2 continued Study Intervention type Main exercises Frequency Volume per session Intensity ST supervised? Recovery between sessions Running training Ramı´rez- Campillo et al. [87] PT DJ 2 per week 60 contacts (6 sets 9 10 reps) 20 reps @20 cm, 20 reps @40 cm, 20 reps @60 cm Yes C48 h between PT sessions. Performed before runs. I: 64.7 km.wk-1 C: 70.0 km.wk-1 (AIT preferred) Saunders et al. [77] PT/HRT PT: CMJ, ankle jumps, bounds, skips, hurdle jumps, scissor jumps HRT: back ext., leg press, hamstring curls 3 per week PT: Progress from 1 to 6 sets 9 6–10 reps/10–30 m HRT: 1–5 sets 9 6–10 reps (except back ext.) PT: fast GCT HRT: Leg press 60% 1RM Yes – 107 km.wk-1 (3x AIT, 1 9 LSD 60–150 min, 3 9 LSD 30–60 min, 3–6 9 LSD 20–40 min) Schumann et al. [90, 92] HRT/ERT/ PT HRT: leg press, knee ﬂexion, calf raise ?UB/core exercises ERT: Squat jumps, step-ups PT: Drop jumps, hurdle jumps 2 per week HRT (wk 5–24): 5–12 reps per set HRT (wk 5–24): 60–85% 1RM ERT: 20–30% 1RM Yes Same session as running. [48 h between sessions Weekly: 2x run (35–45 min/ 65–85% HRmax), 2 9 LSD (35–40 min & 70–125 min/ 60–65% HRmax), 1–2 9 AIT and HIIT Skovgaard et al. [88] SpT/HRT HRT: squat, deadlift, leg press SpT 9 2 per week HRT 9 1 per week SpT: 4–12 sets 9 30 s (3 min rest) HRT: 3–4 sets 9 6–8 reps wk 1–4; 4 sets 9 4 reps wk 5–8 SpT: maximal effort HRT: 15RM to 8RM wk 1–4; 4RM wk 5–8 Yes 3–4 d between SpT/HRT sessions. Different days to runs I: AIT (4 9 4 ? 2 min @85% HRmax); 50 min @75–85% HRmax C: 40 km total (4 km AIT) Spurrs et al. [75] PT Jumps, bounds, hops 2–3 per week 60–180 foot contacts Bilateral progressed to unilateral and greater height Yes Separate session to running 60–80 km per week Støren et al. [79] HRT Half-squats 3 per week 4 sets 9 4 reps 4RM Yes – I: 253 min wk-1 (? 119 min other ET) C: 154 min wk-1 (?120 min other ET) Turner et al. [76] PT Vertical jumps and hops (continuous and intermittent), split jumps, uphill jumps 3 per week 40–110 foot contacts (5–30 s per exercise) Bodyweight, short contact time No (logbooks) Performed in running sessions Continued regular running (C 3 runs wk-1, C 10 miles wk-1) 1126 R. C. Blagrove et al. 123 change compared to a control group [33, 76–78, 90–92] and in one study the control group improved to a greater extent than the intervention group [86]. Changes in an ability to produce force rapidly also showed mixed results, with some studies showing improvements in peak power output [80] and rate of force development [78, 79] and others showing no change in these parameters [36, 75, 77]. Similarly, stiffness, when measured directly or indirectly (using reactive strength index) during non-running tasks, has been shown to improve (ES: 0.43–0.90) [75, 84, 86, 87] and remain unchanged [33, 74, 89] following ST. Vertical or leg stiffness during running showed improvements (10%, ES: 0.33) at relatively slow speeds [36] and also at 3 km race pace (ES: 1.2) following ST [74]. 3.5 Running Economy An assessment of RE was included in all but four [31, 85, 87, 90, 92] of the studies in this review (Table 3). Running economy was quantiﬁed as the oxygen cost of running at a given speed in every case, except in three studies where a calculation of energy cost was used [82, 84, 91]. Statistically signiﬁcant improvements (2–8%, ES: 0.14–3.22) in RE were observed for at least one speed in 14 papers. A single measure of RE was reported in four of these papers [31, 79, 80, 88], and a further four studies assessed RE across multiple different speeds and found improvements across all measures taken [72, 74, 75, 84]. Six papers reported a mixture of signiﬁcant and non-sig- niﬁcant results from the intensities they used to evaluate RE [36, 73, 76–78, 86]. Six studies failed to show any signiﬁcant improvements in RE compared to a control group [32, 81–83, 89, 91]. 3.6 Maximal Oxygen Uptake No statistically signiﬁcant changes were reported in _VO2max or _VO2peak for any group in the majority of studies that assessed this parameter [31, 32, 36, 72, 74, 75, 77–80, 85, 88, 89]. Three papers observed improvements for _VO2max in the intervention group, but the change in score did not differ signiﬁcantly from that of the control group [33, 81, 91]. One study detected a signiﬁcant improvement (4.9%) in _VO2max for the control group compared to the intervention group [73]. 3.7 Velocity Associated with _VO2max Nine studies provided data on v _VO2max or a similar metric [31–33, 36, 74, 78, 80, 85, 89]. Just two of these papers reported statistically signiﬁcant improvements (3–4%, ES: 0.42–0.49) in the ST group compared to the control group Table 2 continued Study Intervention type Main exercises Frequency Volume per session Intensity ST supervised? Recovery between sessions Running training Vikmoen et al. [32, 38] HRT Machines: Half- squats, unilateral leg press, cable hip ﬂexion, calf raises 2 per week 3 sets 9 4–10 reps (periodized 3wk cycles) Sets performed to RM failure Partly (1 session per wk 3–11) HRT ﬁrst session or performed on different days 4.3 sessions wk-1; 3.7 h @60–82% HRmax, 1.1 h @83–87% HRmax, 0.8 h @[87% HRmax AIT aerobic interval training, BW body weight, CMJ counter-movement jump, C control group, CS core stability, DJ drop jump, ERT explosive resistance training, ET endurance training (e.g., cycling, swimming, roller skiing), GCT ground contact time, h hours, HIIT high-intensity interval training, HRmax maximum heart rate (predicted from 220-age), HRT heavy resistance training, I intervention group, LB lower body, LSD long slow distance run, MVC maximum voluntary contraction, PPO peak power output, PT plyometric training, RDL Romanian deadlift, RM repetition maximum, RT resistance training, SpT sprint training, ST strength training, UB upper body, RTWBV resistance training with whole body vibration Effects of Strength Training on Distance Running 1127 123 Table 3 Outcomes of the studies. Percentage changes, effect size (ES) and p value only reported for statistically signiﬁcant group results or ES[0.2. All results presented are for the intervention (I) group unless stated (e.g., C = control). Variables measured where no-signiﬁcance (NS) difference for time (pre- vs. post-score) and no group 9 time (G 9 T) interaction was detected, are also listed Study Main strength outcomes Economy _VO2max= _VO2peak v _VO2max Blood lactate Time trial Anaerobic measures Body composition Albracht and Arampatzis [84] Plantarﬂexion MVC (6.7%, ES = 0.56, p = 0.004), max Achilles tendon force (7.0%, ES = 0.55, p\0.01), Tendon stiffness (15.8%, ES = 0.90, p\0.001) _VO2@10.8 km h-1 (5.0%, ES = 0.79) @12.6 km h-1 (3.4%, ES = 0.51) EC@10.8 km h-1 (4.6%, ES = 0.61) @12.6 km h-1 (3.5%, ES = 0.50), all p\0.05 – – BL@10.8 and 12.6 km h-1, NS – – Body mass, NS Beattie et al. [33] 1RM back squat (wk 0–20: 19.3%, ES = 1.2, p = 0.001) DJRSI (wk 0–20: 7.3%, ES = 0.3, NS G 9 T; wk 0–40: 14.6%, ES = 0.5, NS G 9 T) CMJ (wk 0–20: 11.5%, ES = 0.5, NS G 9 T; wk 0–40: 11.5%, ES = 0.6, NS G 9 T) Ave. of 5 speeds Wk 0–20: 5.0%, ES = 1.0, p = 0.01. Wk 0–40: 3.5%, ES = 0.6, NS. Wk 0–20: 0.1%, ES = 0.1, p = 0.013. Wk 0-40, I: 7.4%, ES = 0.5, p = 0.003, C: 2.8%, ES = 0.6, NS Wk 0-20: 3.5%, ES = 0.7, NS. Wk 0-40: 4.0%, ES = 0.9, NS v2 mmol L-1, v4 mmol L-1, NS – – Body mass, fat and lean muscle, NS Berryman et al. [80] PPO (ERT: 15.4%, ES = 0.98, p\0.01; PT: 3.4%, ES = 0.24, p\0.01). CMJ (ERT: 4.5%, ES = 0.25, p\0.01; PT: 6.0%, ES = 0.52, p\0.01) @12 km h-1 ERT: 4%, ES = 0.62, p\0.01. PT: 7%, ES = 1.01, p\0.01 NS ERT: 4.2%, ES = 0.43, p\0.01. PT: 4.2%, ES = 0.49, p\0.01 – 3 km TT ERT: 4.1%, ES = 0.37. PT: 4.8%, ES = 0.46. C: 3.0%, ES = 0.20; all p\0.05, G 9 T NS – Body mass, NS Bertuzzi et al. [85] 1RM half squat (RT: 17%, p B 0.05; RTWBV: 18%, p B 0.05) – NS NS – – – – Bonacci et al. [83] – @12 km h-1 (after 45 min AIT cycle) NS – – – – Body mass, skinfolds, thigh and calf girth, NS 1128 R. C. Blagrove et al. 123 Table 3 continued Study Main strength outcomes Economy _VO2max= _VO2peak v _VO2max Blood lactate Time trial Anaerobic measures Body composition Damasceno et al. [89] 1RM half–squat (23%, ES = 1.41, p\0.05), DJRSI, wingate test NS @12 km h-1 NS NS v _VO2max (2.9%, ES = 0.42, p\0.05) – 10 km TT (2.5%, p = 0.039), increased speed in ﬁnal 7 laps (p\0.05) 30 s Wingate test, NS Body mass and skinfold, NS Ferrauti et al. [81] Leg extension MVC (33.9%, ES = 1.65, p\0.001); leg ﬂexion MVC (9.4%, ES = 0.38, NS) @LT (ES = 0.40, p\0.05, NS G 9 T) @8.6 and 10.1 km h-1, NS FU@10.1 km h-1 (ES = 0.61, p = 0.05 G 9 T) 5.6%, ES = 0.40, NS G 9 T – BL@10.1 km h-1 (I: 13.1%, C: 12.1%, NS G 9 T). v4 mmol L-1 (I: 4.2%, C: 2.6%, NS G 9 T). – – Body mass, NS Fletcher et al. [82] Isometric MVC (I: 21.6%, C: 13.4%), NS G 9 T EC@75,85,95% sLT, NS – – BL@ 75,85,95% sLT, NS. – – – Giovanelli et al. [36] SJ PPO, NS kleg@10 km h-1, (9.5%, ES = 0.33, p = 0.034), @12 km h-1 (10.1%, ES = 0.33, p = 0.038). kvert @8,10,12,14 km h-1, NS @8 km h-1 (6.5%, ES = 0.43, p = 0.005), @10 km h-1 (3.5%, ES = 0.48, p = 0.032), @12 km h-1 (4.0%, ES = 0.34, p = 0.020), @14 km h-1 (3.2%, ES = 0.35, p = 0.022), @RCP NS NS NS – – – Body mass, FFM, fat mass, NS Johnston et al. [72] 1RM squat (40%, p\0.05), knee ﬂexion (27%, p\0.05) @12.8 km h-1 (4.1%, ES = 1.76, p\0.05), @13.8 km h-1 (3.8%, ES = 1.61, p\0.05) NS – – – – Body mass, fat mass, FFM, limb girth, NS Karsten et al. [31] – – NS NS – 5 km TT (3.5%, ES = 1.06, p = 0.002) ARD, NS – Effects of Strength Training on Distance Running 1129 123 Table 3 continued Study Main strength outcomes Economy _VO2max= _VO2peak v _VO2max Blood lactate Time trial Anaerobic measures Body composition Mikkola et al. [78] MVC (8%), 1RM (4%), RFD (31%) on leg press; all p\0.05. CMJ and 5–bounds, NS @14 km h-1 (2.7%, ES = 0.32, p\0.05), @10,12,13 km h-1, NS NS NS BL@12 km h-1 (12%, p\0.05), @14 km h-1 (11%, p\0.05) – vMART (3.0%, p\0.01), v30 m sprint (1.1%, p\0.01) Body mass (2%, ES = 0.32, p\0.01). Thickness of QF (I: 3.9%, ES = 0.35, p\0.01; C: 1.9%, ES = 0.10, p\0.05); fat %, lean mass, NS Millet et al. [74] 1RM half–squat (25%, p\0.01), 1RM heel raise (17%, p\0.01), hop height (3.3%, p\0.05) kleg@3 km pace (ES = 1.2, p\0.05) GCT, hop stiffness, NS @75% v _VO2max (7.4%, ES = 1.14, p\0.05) @ * 92% _VO2max (5.9%, ES = 1.15, p\0.05) NS 2.6%, ES = 0.57, p\0.01, NS G 9 T – – – Body mass, NS Paavolainen et al. [73] MVC knee extension (7.1%, p\0.01), 5BJ (4.6%, p\0.01) @15 km h-1 (8.1%, ES = 3.22, p\0.001) @13.2 km h-1, NS _VO2@LT, NS C: (4.9%, p\0.05) _VO2max demand (3.7%, p\0.05, NS G 9 T) – – 5 km TT (3.1%, p\0.05) v20 m (3.4%, ES = 0.77, p\0.01) vMART (ES = 1.98, p\0.001) Body mass, fat %, calf and thigh girth, NS Pellegrino et al. [91] CMJ (5.2%, p = 0.045, NS G 9 T) @10.6 km h-1 (1.3%, p\0.05 group) NS G 9 T @7.7, 9.2, 12.1, 13.5, 15.0, 16.4 km h-1, NS. 5.2%, ES = 0.49, p = 0.03, NS G 9 T – sLT, NS 3 km TT (2.6%, ES = 0.20, p = 0.04) – – Piacentini et al. [86] 1RM leg press (HRT: 17%, ES = 0.69, p\0.05), CMJ (C: 7%, ES = 0.63, p\0.05), SJ (C: 13%, ES = 0.83, p\0.01), Stiffness (RT: 13%, ES = 0.64, p\0.05) @10.75 km h-1/marathon pace (HRT: 6.2%, p\0.05). @9.75,11.75 km h-1, NS – – – – – Body mass, fat mass, FFM, RMR, NS Ramı´rez- Campillo et al. [87] CMJ (8.9%, ES = 0.51, p\0.01), DJ @20 cm (12.7%, ES = 0.43, p\0.01), DJ @40 cm (16.7%, ES = 0.6, p\0.05) – – – – 2.4 km TT (3.9%, ES = 0.4, p\0.05) 20 m sprint (2.3%, ES = 0.3, p\0.01) Body mass, NS 1130 R. C. Blagrove et al. 123 Table 3 continued Study Main strength outcomes Economy _VO2max= _VO2peak v _VO2max Blood lactate Time trial Anaerobic measures Body composition Saunders et al. [77] SJ RFD and peak force, NS. 5CMJ, NS @18 km h-1 (4.1%, ES = 0.35, p\0.05) @14,16 km h-1, NS NS – BL @14,16,18 km h-1, NS – – Body mass, NS Schumann et al. [90, 92] 1RM leg press (I: NS, C: –4.7%, p = 0.011), MVC leg ﬂexion (– 9.7%, p = 0.031, ES = 0.96, NS G 9 T), MVC leg press NS, MVC knee ext. NS, CMJ NS – – – BL during 6 9 1 km (I: NS, C:, 21%, NS G 9 T) v4 mmol L-1 (I: 6%, C: 8%, NS G 9 T). 1 km TT after 5x 1 km, 60 s rec. (I: 9%, C: 13%, NS G 9 T) – Body mass, NS; CSA vastus lateralis (group diff. I: 7%, C: -6%, NS G 9 T); Total and leg lean mass (I: 2%, NS G 9 T) Skovgaard et al. [88] 1RM squat (wk 4: 3.8%, wk 8: 12%, p\0.001); 1RM leg press (wk 4: 8%, p\0.05; wk 8: 18%, p\0.001), 5RM deadlift (wk 4: 14%, wk8: 22%, p\0.001) @12 km h-1 (wk 8: 3.1%, ES = 1.53, p\0.01) NS – – 10 km TT (wk 4: 3.8%, ES = 1.50, p\0.05) 1500 m TT (wk 8: 5.5%, ES = 0.67, p\0.001) – Body mass, NS Spurrs et al. [75] MTS @75% MVC (left: 14.9%, right: 10.9%, p\0.05), Calf MVC (left: 11.4%, right: 13.6%, p\0.05). RFD NS @12 km h-1 (6.7%, ES = 0.45), 14 km h-1 (6.4%, ES = 0.45), 16 km h-1 (4.1%, ES = 0.30), all p\0.01 NS – – 3 km TT (2.7%, ES = 0.13, p\0.05, NS G 9 T) – Body mass, NS Støren et al. [79] 1RM (33.2%, p\0.01) and RFD (26%, p\0.01) half–squat @70% _VO2max (5%, ES = 1.03, p\0.01) NS – sLT, LT % _VO2max, NS – – Body mass, NS Turner et al. [76] CMJ and SJ, NS Ave. of 3 speeds: M = 9.6, 11.3, 12.9, F = 8.0, 9.6, 11.3 km h-1 (2–3%, p B 0.05) @9.6 km h-1, NS – – – – – – Effects of Strength Training on Distance Running 1131 123 [80, 89]. One study [74] reported a 2.6% improvement (ES: 0.57) and another [33] a 4.0% increase (ES: 0.9) after a 40-week intervention; however, these changes were not signiﬁcantly different to the control group. 3.8 Blood Lactate Parameters Blood lactate value was measured at ﬁxed velocities in six studies [77, 78, 81, 82, 84, 92] and velocity assessed for ﬁxed concentrations of BL (2–4 mmol L-1) or lactate threshold (LT) in six studies [32, 33, 79, 81, 90, 91]. One study using young participants observed signiﬁcantly greater improvements (11–12%) at two speeds compared to the control group [78]. Other studies found no signiﬁcant changes following the intervention [32, 33, 77, 79, 82, 84, 91] or a change which was not superior to the control group [81, 90, 92]. 3.9 Time-Trial Performance To assess the impact of ST directly upon distance running performance, studies utilized time trials over 1000 m (preceded by 5 9 1 km) [90, 92], 1500 m [88], 2.4 km [87], 3 km [75, 80, 91], 5 km [31, 73], 10 km [88, 89], 5 min [32], and 40 min [38]. There were similarities to competitive scenarios in most studies, including perfor- mances taking place under race conditions [31, 75, 87, 90–92], on an outdoor athletics track [31, 87–89], on an indoor athletics track [73, 75, 80, 90–92], and fol- lowing a prolonged (90-min) submaximal run [38]. Per- formance improvements were statistically signiﬁcant compared to a control group for eight of the 12 trials. The exceptions were a 40-min time trial [38], a 1000-m rep- etition [90, 92], and two studies that used a 3 km time trial [75, 80]. Statistically signiﬁcant 3 km improvements were observed for all groups in one case [80]; however, the ES was larger for the two intervention groups (0.37 and 0.46) compared to the control group (0.20). Improvements over middle-distances (1500–3000 m) were generally moderate (3–5%, ES: 0.4–1.0). Moderate to large effects (ES:[1.0) were observed for two studies [31, 88] that evaluated performance over longer distances (5–10 km); however, the relative improvements were quite similar (2–4%) over long distances compared to shorter distances [31, 73, 88, 89]. 3.10 Anaerobic Outcomes Tests relating to anaerobic determinants of distance run- ning performance were used in ﬁve investigations. Sprint speed over 20 m [73, 87] and 30 m [78] showed statis- tically signiﬁcant improvements following ST (1.1–3.4%). Two studies provided evidence for enhancement of Table 3 continued Study Main strength outcomes Economy _VO2max= _VO2peak v _VO2max Blood lactate Time trial Anaerobic measures Body composition Vikmoen et al. [32, 38] 1RM half–squat (45%, ES = 2.4, p\0.01), SJ (8.9%, ES = 0.83, p\0.05), CMJ (5.9%, ES = 0.65, p\0.05) @10 km h-1, NS NS NS v3.5 mmol L-1, NS 5 min TT (4.7%, ES = 0.95, p\0.05). 40 min TT, NS I: Leg mass (3.1%, ES = 1.69, p=p\0.05), body mass, NS C: Leg mass (-2.2%), body mass decrease (-1.2%, p\0.05) ARD anaerobic running distance, BJ broad jump, BL blood lactate, CMJ counter-movement jump, C control group, DJ drop jump, DJRSI drop jump reactive strength index, EC energy cost, EMG electromyography, ERT explosive resistance training, FFM fat-free mass, FU fractional utilization, GCT ground contact time, GRF ground reaction force, HR heart rate, HRT heavy resistance training, I intervention group, kleg leg stiffness, kvert vertical stiffness, (s)LT (speed at) lactate threshold, MAS maximal aerobic speed, MTS musculotendinous stiffness, MVC maximum voluntary contraction, PPO peak power output, PT plyometric training, QF quadriceps femoris, RCP respiratory compensation point (VE/VCO2), RFD rate of force development, RM repetition maximum, RMR resting metabolic rate, RT resistance training, RTWBV resistance training with whole body vibration, SJ squat jump, TT time trial, TTE time to exhaustion, v velocity, vMART velocity during maximal anaerobic running test, _VO2 oxygen uptake, _VO2max= _VO2peak highest oxygen uptake associated with a maximal aerobic exercise test, v _VO2max velocity associated with _VO2max, wk week 1132 R. C. Blagrove et al. 123 vMART [73, 78], and one further study showed no change in anaerobic running distance after 6 weeks of HRT [31]. A 30-s Wingate test was also used in one paper; however, no differences in performance were noted [89]. 3.11 Body Composition Body mass did not change from baseline in 18 of the studies [32, 33, 36, 38, 72–75, 77, 79–81, 83, 84, 86–89]; however, one investigation reported a signiﬁcant increase (2%, ES: 0.32) following ST [78]. This study also docu- mented changes in the thickness of quadriceps femoris muscle in both the intervention (3.9%, ES: 0.35) and control group (1.9%, ES: 0.10) [78]. Similarly, an increase in total lean mass (3%) and leg lean mass (3%) was found following 12 weeks of ST despite little alteration in cross- sectional area of the vastus lateralis and body mass being noted [90, 92]. Another study observed a signiﬁcant decrease (- 1.2%) in body mass in the control group, with no change in the intervention group [32]. A signiﬁcant increase in leg mass (3.1%, ES: 1.69) was also noted in this study [32, 38]. Other indices of body composition that exhibited no signiﬁcant changes were: fat mass [33, 36, 72, 73, 78, 86], fat-free mass [36, 72, 86], lean muscle mass [33, 78], skinfolds [83, 89], and limb girth measurements [72, 73, 83]. 4 Discussion The aim of this systematic review was to identify and evaluate current literature which investigated the effects of ST exercise on the physiological determinants of middle- and long-distance running performance. The addition of new research published in this area, and the application of more liberal criteria provided results for 50% more par- ticipants (n = 469) compared to a recent review on RE [10]. Based upon the data presented herein, it appears that ST activities can positively affect performance directly and provide beneﬁts to several physiological parameters that are important for distance running. However, inconsisten- cies exist within the literature, that can be attributed to differences in methodologies and characteristics of study participants, thus practitioners should be cautious when applying generalized recommendations to their athletes. Despite the moderate PEDro scores (4, 5, or 6), the quality of the works reviewed in this paper are generally consid- ered acceptable when the unavoidable constraints imposed by a training intervention study (related to blinding) are taken into account. 4.1 Running Economy Running economy, deﬁned as the oxygen or energy cost to run at a given sub-maximal velocity, is inﬂuenced by a variety of factors, including force-related and stretch– shortening cycle qualities, which can be improved with ST activities. In general, an ST intervention, lasting 6–20 weeks, added to the training program of a distance runner appears to enhance RE by 2–8%. This ﬁnding is in agreement with previous meta-analytical reviews in this area that show concurrent training has a beneﬁcial effect (* 4%) on RE [10, 26]. In real terms, an improvement in RE of this magnitude should theoretically allow a runner to operate at a lower relative intensity and thus improve training and/or race performance. No studies attempted to demonstrate this link directly, although inferences were made in studies, which noted improvements in RE and performance separately [73, 80, 88]. Other works provide evidence that small alterations in RE (* 1.1%) directly translate to changes (* 0.8%) in sub-maximal [94] and maximal running performance [95]. The typical error of measurement of RE has been reported to be 1–2% [96–99] and the smallest worthwhile change * 2% [94, 98, 100], which is thought to represent a ‘‘real’’ improvement and not simply a change due to variability of the measure. Taken together, it is therefore likely that the improvements seen in RE following a period of concurrent training would represent a meaningful change in performance. Improvements were observed in moderately-trained [72, 76, 84, 86], well-trained [33, 36, 73, 75, 79, 80, 88] and highly-trained participants [74, 77], suggesting runners of any training status can beneﬁt from ST. Different modes of ST were utilized in the studies, with RT or HRT [72, 78, 79, 84, 86], ERT [80], PT [75, 76, 80], and a combination of these activities [33, 36, 77], all augmenting RE to a similar extent. Single-joint isometric RT may also provide a beneﬁt if performed at a high frequency (4 day week-1) [84]. Several studies adopted a periodized approach to the types of ST prioritized during each 3- to 6-week cycle [33, 36, 77, 88], which is likely to provide the best strategy to optimize gains long-term [101]. Six studies [32, 81–83, 89, 91] failed to show any improvement in RE and a further six [36, 73, 76–78, 86] observed both improvements and an absence of change at various velocities. This implies beneﬁts are more likely to occur under speciﬁc conditions relating to the choice of exercises, participant characteristics, and velocity used to measure RE. In most studies that observed a beneﬁt, exercises with free weights were utilized [33, 36, 72, 74, 86, 88]. Multi-joint exercises using free weights are likely to provide a superior neuromuscular stimulus compared to machine-based or single-joint exer- cises as they demand greater levels of co-ordination, multi- Effects of Strength Training on Distance Running 1133 123 planar control, activation of synergistic muscle groups [102, 103] and usually require force to be produced from closed-kinetic chain positions. These types of exercise also have a greater biomechanical similarity to the running action so are therefore likely to provide a greater level of speciﬁcity and hence transfer of training effect [104]. An insufﬁcient overload or a lack of movement pattern speciﬁcity may therefore be the reason for the absence of an effect in studies that used only resistance machines [32, 81] or a single-joint exercise [82]. These studies were also characterized by a lower frequency of sessions com- pared to studies that used similar RT exercises but did observe an improvement in RE [78, 84]. Moderately-trained runners were used in three of the six studies showing an absence of effect [81, 83, 91] and one used triathletes who performed a relatively low volume of running (34.8 km week-1) as part of their training [83]. However, a similar number of studies who used recre- ational athletes did show a positive effect [72, 76, 84, 86], suggesting that training level is unlikely to be the reason for the lack of response in these studies. This is also con- ﬁrmed by recent observations that showed improvement in RE following a period of concurrent training was similar across individuals irrespective of training status and the number of sessions per week ST was performed [10]. The velocity used to assess RE may also explain the discrepancies in results across studies. It has been sug- gested that runners are most economical at the speeds they practice at most [98], and for investigations that utilized PT, stretch–shortening cycle improvements are likely to manifest at high running speeds where elastic mechanisms have greatest contribution [83, 105]. Therefore a velocity- speciﬁc measurement of RE may be the most valid strategy to establish whether an improvement has occurred. For example, Saunders and associates [77] observed an improvement (p = 0.02, ES: 0.35) at 18 km h-1 in elite runners, but an absence of change at slower speeds. Sim- ilarly, Millet and colleagues [74] noted large (ES:[1.1) improvements at speeds faster than 75% v _VO2max (* 15 km h-1) in highly-trained triathletes, and Paavo- lainen et al. [73] detected changes at 15 km h-1 but not slower speeds in well-trained runners. Furthermore, Pia- centini and co-workers [86] found improvement at race- pace in recreational marathon runners but not at a slower and a faster velocity. Improvements observed at faster compared to slower speeds may also reﬂect improvements in motor unit recruitment as a consequence of ST. As running speed increases there is a requirement for greater peak vertical forces due to shorter ground contact times, which elevates metabolic cost [25]. To produce higher forces, yet overcome a reduction in force per motor unit as a consequence of a faster shortening velocity, more motor unit recruitment is required [106]. Thus, an increase in absolute motor unit recruitment following a period of ST would result in a lower relative intensity reducing the necessity to recruit higher threshold motor units during running [25]. Several studies that failed to show any response used a single velocity to assess RE [32, 83, 89], perhaps indicating that the velocity selected was unsuit- able to capture an improvement. Furthermore, only a small number of studies used relative speeds [33, 74, 79, 81, 82], with most choosing to assess participants at the same absolute intensity. A given speed for one runner may rep- resent a high relative intensity, whereas for another runner it may be a relatively low intensity. Therefore selecting the same absolute speed in a group heterogeneous with respect to _VO2max, may not provide a true reﬂection of any changes which take place following an intervention. Moreover, this may also confound any potential improvements observed in fractional utilization of _VO2max. Several common procedural issues exist in the studies reviewed, which may inﬂuence the interpretation of results and therefore conclusions drawn. The majority of studies quantiﬁed RE and _VO2max as a ratio to body mass; how- ever, oxygen uptake does not show a linear relationship with increasing body size [107]. It is also known that the relationship between body size and metabolic response varies across intensities, with a trend for an increasing size exponent as individuals move from low-intensity towards maximal exercise [108, 109]. Moreover, allometric scaling is likely to decrease interindividual variability [110], potentially improving the reliability of observations [99]. Ratio-scaling RE for all velocities to body mass is therefore theoretically and statistically inappropriate [111]. Just two studies [79, 80] used an appropriate allometric scaling exponent (0.75) to account for the non-linearity associated with oxygen uptake response to differences in body mass, both establishing a large ES in their results. The unsuit- ability of ratio-scaling as a normalization technique when processing physiological data is likely to have inﬂuenced the statistical outcomes of some studies and thus inaccurate conclusions may have been generated. Running economy was expressed as oxygen cost in all but three studies [82, 84, 91], which quantiﬁed RE using the energy cost method. As the energy yield from the oxidation of carbohydrates and lipids differs, subtle alter- ations in substrate utilization during exercise can confound measurement of RE when expressed simply as an oxygen uptake value. Energy cost is therefore the more valid [112, 113] and reliable [99] metric for expressing econ- omy, compared to traditional oxygen cost, as metabolic energy expenditure can be calculated using the respiratory exchange ratio, thus accounting for differences in substrate utilization. Despite attempts to control for confounding 1134 R. C. Blagrove et al. 123 variables such as diet and lifestyle in most studies, equiv- alence in inter-trial substrate utilization cannot be guaran- teed, which may have impacted upon the measurement of RE. 4.2 Maximal Oxygen Uptake Maximal oxygen uptake is widely regarded as one of the most important factors in distance running success [114], therefore the objective for any distance runner is to maxi- mize their aerobic power [9]. An individual’s _VO2max is limited by their ability to uptake, transport and utilize oxygen in the mitochondria of working muscles. Endur- ance training involving prolonged continuous bouts of exercise or high intensity interval training induces adap- tations primarily within the cardiovascular and metabolic systems that results in improvements in _VO2max [9, 115]. Conversely, ST is associated with a hypertrophy response that increases body mass and has been reported to decrease capillary density, oxidative enzymes and mitochondrial density [116–118], which would adversely impact aerobic performance. Theoretically there is therefore little basis for ST as a strategy to enhance aerobic power. However it is important to address whether in fact _VO2max is negatively affected when distance running is performed concurrently with ST. Thirteen works in this review found no change in _VO2max following the intervention period, demonstrating that although ST does not appear to positively inﬂuence _VO2max, it also does not hinder aerobic power. Although ST in most studies was supplementary to running training, it appears that the additional physiological stimulus pro- vided by ST was insufﬁcient to elicit changes in cardio- vascular-related parameters [119]. Three studies did observe signiﬁcant increases in aerobic power that did not differ to the change observed in the control group [33, 81, 91], and one further study found an improvement in _VO2max in the control group only [78]. It is perhaps sur- prising that more studies did not ﬁnd an increase in _VO2max (in any group) given that participants continued their nor- mal running training through the study period. Improve- ments in _VO2max of 5–10% have been shown following relatively short periods (\6 weeks) of endurance training [9]; however, the magnitude of changes is dependent upon a variety of factors including the initial ﬁtness level of individuals and the duration and nature of the training programoo [120]. Maximal oxygen uptake is known to have an innate upper limit for each individual, therefore in highly-trained and elite runners, long-term performance improvement is likely to result from enhancement of other physiological determinants, such as RE, fractional utilization and v _VO2max [4, 121, 122]. A number of studies used moderately-trained participants [23, 72, 76, 81, 91], who would be the most likely to show an improvement in _VO2max following a 6- to 14-week period of running, with two investigations demonstrating improvements for both groups [81, 91]. The absence of _VO2max improvement in other papers suggests that the duration of the study and/or the training stimulus, was insufﬁcient to generate an improvement [120]. Indeed, one study of 40 weeks’ dura- tion in Collegiate level runners observed similar improve- ments (ES: 0.5–0.6) in _VO2max in both groups [33], suggesting a longer time period may be required to detect changes in runners with a higher training status. High-in- tensity aerobic training ([80% _VO2max) is a potent stim- ulus for driving changes in _VO2max[123]; however, some studies reported runners predominantly utilized low-inten- sity (\70% _VO2max) continuous running [74, 78, 89], which may also explain the lack of changes observed. 4.3 Velocity Associated with _VO2max An individual’s v _VO2max is inﬂuenced by their _VO2max, RE and anaerobic factors including neuromuscular capacity [4, 124]. The amalgamation of several physiological qualities into this single determinant appears to more accurately differentiate performance, particularly in well-trained run- ners [3, 98, 125, 126], therefore v _VO2max has been labelled as an important endurance-speciﬁc measure of muscular power [127]. Improvements for v _VO2max (3–4%, ES: 0.42–0.49) were found in two investigations [80, 89], with a further two studies observing improvements (2.6–4.0%, ES: 0.57–0.9) that could not be ascribed to the training differences between the groups [33, 74]. A number of studies also found little change in v _VO2max following an intervention [31, 32, 36, 78, 85]. As v _VO2max is the product of the interaction between aerobic and anaerobic variables, a small improvement in one area of physiology may not necessarily result in an increase in v _VO2max. Damasceno et al. [89] found an improvement in v _VO2max (2.9%, p\0.05, ES: 0.42) despite detecting no change in _VO2max, RE or Wingate performance, therefore attributed the change to the large improvements (23%, ES: 1.41) in the force-producing ability they observed in participants. Conversely, Berryman and associates [80] found changes in v _VO2max (4.2%, ES: 0.43–0.49) alongside improvements in RE (4–7%, ES: 1.01), moderate increases in power output, and no change in _VO2max scores. Beattie and co- workers [33] credited the change in v _VO2max they observed (20-weeks: 3.5%, ES: 0.7) to the accumulation of Effects of Strength Training on Distance Running 1135 123 improvements in RE, _VO2max and anaerobic factors; how- ever, these were not sufﬁciently large enough to provide a signiﬁcant group 9 time interaction. Millet and colleagues [74] found notable improvements in RE (7.4%, ES: 1.14); however, changes in RE could not explain the changes observed in v _VO2max (r = - 0.46, p = 0.09). It may also be the case that longer periods of ST are required before an improvement in v _VO2max is detected, as studies showing an improvement (2.6–4.0%, ES: 0.57–0.9) from baseline las- ted 14 weeks or more [33, 74], and studies showing little change tended to be 6–8 weeks in duration [31, 78, 85]. The conﬂicting results could also be explained by the inconsistency in methods used to deﬁne v _VO2max. A number of different protocols and predictive methods have been suggested to assess v _VO2max [4], including determi- nation from the _VO2-velocity relationship [128] and the peak running speed attained during a maximal test using speed increments to achieve exhaustion [21, 127]. All studies that measured v _VO2max in this review did so via an incremental run to exhaustion progressed using velocity. Velocity at _VO2max was taken as the highest speed that could be maintained for a full 60-s stage [78, 80, 85], an average of the ﬁnal 30-s [31, 36], the mean velocity in the ﬁnal 120-s [32], or the minimum velocity that elicited _VO2max [33, 74]. Although a direct approach to the mea- surement of v _VO2max has been recommended [4], due to the velocity increments (0.5–1.0 km h-1) used in these investigations, this may not provide sufﬁcient sensitivity to detect a change following a short- to medium-term inter- vention. Damasceno and associates [89] calculated v _VO2max using a more precise method based upon the fractional time participants reached through the ﬁnal stage of the test multiplied by the increment rate. This perhaps provided a greater level of accuracy which allowed the authors to identify the differences in changes which existed between the groups. Taken together, there is weak evidence that v _VO2max can be improved following an ST interven- tion, despite constituent physiological qualities often exhibiting change. Differences in the protocols used to determine v _VO2max makes comparison problematic; how- ever, a more precise measurement of v _VO2max that accounts for partial completion of a ﬁnal stage is likely to provide the sensitivity to identify subtle changes that may occur. The critical velocity model, which represents exercise tolerance in the severe intensity domain, potentially offers an alternative to measurement of v _VO2max that is currently uninvestigated in runners [35, 129]. Two main parameters can be assessed using the critical velocity model; critical velocity itself, which is deﬁned as the lower boundary of the severe intensity domain which when maintained to exhaustion leads to attainment of _VO2max, and the curva- ture constant of the velocity–time hyperbola above critical velocity, which is represented by the total distance that can be covered prior to exhaustion at a constant velocity [130]. Middle-distance running performance (800 m) is strongly related to critical velocity models (r = 0.83–0.94) in trained runners [131], and may be more important than RE in well-trained runners [35]. Evidence from studies using untrained participants has demonstrated that the total amount of work that can be performed above critical power during high-intensity cycling exercise is improved (35–60%) following 6–8 weeks of RT [132, 133]. Future investigations should therefore address the dearth in liter- ature around how ST might positively inﬂuence parameters related to the critical velocity model [35]. 4.4 Blood Lactate Markers A runner’s velocity at a reference point on the lactate- velocity curve (e.g., LT) or BL for a given running speed are important predictors of distance running performance [134–136]. A runners LT also corresponds to the fractional utilization of _VO2max that can be sustained for a given distance [114], therefore an increase in LT also allows a greater proportion of aerobic capacity to be accessed. In contrast to RE, ST appears to have little impact upon BL markers. This is quite surprising as an improvement in RE should theoretically result in an enhancement in speed for a ﬁxed BL concentration. This suggests that adaptations to RE can occur independently to changes in metabolic markers of performance. An absence of change in BL also implies that ST does not alter anaerobic energy contribu- tion during running, thus assuming aerobic energy cost of running is reduced following ST, it can be inferred that total energy cost (aerobic plus anaerobic energy) is also likely to be reduced. Previous studies have shown as little as 6 weeks of endurance training can improve BL levels or the velocity corresponding to an arbitrary BL value in runners [137–139]. The intensity of training is important to elicit improvement in BL parameters [140], therefore it appears that the running training prescription may have been insufﬁcient to stimulate improvements, or the training status of participants meant a longer period was required to realize a meaningful change. In addition, the inter-session reliability of BL measurement between 2–4 mmol L-1 is * 0.2 mmol L-1 [99], therefore over a short study duration this metric may not provide sufﬁcient sensitivity to detect change. Training at an intensity above the LT is likely to result in a reduction in the rate of BL production (and therefore accumulation), or an improved lactate clearance ability from the blood [9]. Short duration high-intensity bouts of 1136 R. C. Blagrove et al. 123 activity generate high levels of BL so drive metabolic adaptations which can result in an improvement in per- formance [141–143]. Studies that have utilized high-repe- tition, low-load RT in endurance athletes therefore have the potential to produce high BL concentrations so may pro- vide an additional stimulus to improve performance via BL parameters. This theory is supported by works that have demonstrated improvements in BL-related variables in endurance athletes following an intervention that uses a strength-endurance style of conditioning with limited rest between sets [54, 62, 144]. The ST prescription in the studies reviewed was predominantly low-repetition, high- intensity RT or PT, which is unlikely to have provided a metabolic environment sufﬁcient to directly enhance adaptations related to BL markers. 4.5 Time-Trial Performance Physiological parameters such as _VO2max, v _VO2max, RE and LT are clearly important determinants that can be quantiﬁed in a laboratory; however, for a runner, TT per- formance possesses a far higher degree of external validity. Similar improvements in TT performance were observed for middle-distance events (3–5%, ES: 0.4–1.0) and long- distance events up to 10 km (2–4%, ES: 1.06–1.5). In the majority of these studies, time trials took place in a similar environment and under comparable conditions to a race, therefore these ﬁndings have genuine applicability to ‘‘real- life’’ scenarios. These improvements are likely to be a consequence of signiﬁcant enhancements in one or more determinants of performance. Interestingly, Damasceno and co-authors [89] found an improvement in 10 km TT performance due to the attainment of higher speeds in the ﬁnal 3 km, despite observing no change in RE during a separate assessment. This suggests that greater levels of muscular strength may result in lower levels of relative force production per stride, thereby delaying recruitment of higher threshold muscle ﬁbers and thus providing a fatigue resistant effect [145]. This subsequently manifests in a superior performance during the latter stages of long-dis- tance events [89]. Four studies observed no difference in performance change compared to a control group [38, 75, 80, 90, 92]. Vikmoen and colleagues [38] attributed a lack of effect in their 40 min TT to the slow running velocity caused by the 5.3% treadmill inclination used in the test. This was also the only study to use a treadmill set to a pre-determined velocity which participants could control once the test had commenced. The absence of natural self-pacing may therefore have prevented participants achieving their true potential on the test. Spurrs et al. [75] and Berryman et al. [80] both found improvements in 3 km performance compared to a pre-training measure of a comparable magnitude to other studies (2.7–4.8%, ES: 0.13–0.46); however, changes were not signiﬁcantly different to a control group, suggesting ST provided no additional beneﬁt or there was a practice effect associated with the test. It could be possible that enhancement of physiological qualities in some studies could be attributed to RT being positioned immediately after low-intensity, non-depleting running sessions [146]. This arrangement of activities in concurrent training programs has been shown to provide a superior stimulus for endurance adaptation compared to performing separate sessions, and without compromising the signaling response regulating strength gains [147, 148]. This, however, appears not to be the case, as most studies reported ST activities took place on different days to run- ning sessions [85, 88, 89] or were at least performed as separate sessions within the same day [33, 36, 38, 72, 75, 78]. Only three studies performed ST and running imme- diately after one another, with one positioning PT before running [87] and one lacking clarity on sequencing [76]. Schumann and colleagues [90, 92] observed no additional beneﬁt to both strength and endurance outcomes compared to a running only group, when ST was performed imme- diately following an incremental running session (65–85% maximal heart rate), citing residual fatigue which com- promised quality of ST sessions as the reason. 4.6 Anaerobic Running Performance The contribution of anaerobic factors to distance running performance is well established [127, 149]. In particular, anaerobic capacity and neuromuscular capabilities are thought to play a large role in discriminating performance in runners who are closely matched from an aerobic per- spective [124, 150]. An individual’s v _VO2max perhaps provides the most functional representation of neuromus- cular power in distance runners; however, measures of maximal running velocity and anaerobic capacity are also potentially important [127]. Tests for pure maximal sprinting velocity (20–30 m) were used in three studies [73, 78, 87] and showed improvements (1.1–3.4%) following ST in every case. This conﬁrms results from previous studies that have shown sprinting performance can be positively affected by an ST intervention in shorter-distance specialists [151–153]. This ﬁnding has important implications for distance runners, as competitive events often involve mid-race surges and outcomes are frequently determined in sprint-ﬁnishes, particularly at an elite level [154–157]. Middle-distance runners also beneﬁt from an ability to produce fast running speeds at the start of races [158], therefore improving maximum speed allows for a greater ‘‘anaerobic speed Effects of Strength Training on Distance Running 1137 123 reserve’’ [159], resulting in a lower relative work-rate, and thus decreasing anaerobic energy contribution [41]. Inter- estingly, endurance training in cyclists has been shown to improve critical power [160] but reduce work capacity for short duration exercise [161, 162]. It is unknown whether long-term aerobic training has a similar effect on anaerobic running qualities; however, ST offers a strategy to avoid this potential negative consequence. The velocity attained during a maximal anaerobic run- ning test provides an indirect measure of anaerobic and neuromuscular performance, and has a strong relationship (r = 0.85) to v _VO2max [19]. The vMART is particularly relevant to middle-distance runners because it requires athletes to produce fast running speeds under high-levels of fatigue caused by the acidosis and metabolites derived from glycolysis [163]. Both studies that included this test observed signiﬁcant improvements in vMART (1.1–3.4%), which can be attributed to changes observed in neuro- muscular power as a result of the ST intervention [73, 78]. One study showed no alteration in the predicted distance achieved on an anaerobic running test following 6 weeks of HRT; however, the validity and reliability of the test was questioned by the authors [31]. Performance on a 30 s Wingate test was also unchanged following 8 weeks of running training combined with HRT in recreational par- ticipants [89]. This ﬁnding perhaps underlines the impor- tance of selecting tests which are speciﬁc to the training which has been performed in the investigation. 4.7 Strength Outcomes Changes in strength outcomes were evident in most studies despite all but one [78] observing no change in body mass. Since strength changes can be ascribed to both neurological and morphological adaptations [164], it is therefore likely that improvements are primarily underpinned by alterations in intra- and inter-muscular co-ordination. It is also known that initial gains in strength in non-strength trained indi- viduals are the consequence of neural adaptations rather than structural changes [118]. An improvement in force producing capability is perhaps expected in individuals who have little or no strength-training experience [165]; however, concurrent regimens of training have consistently been shown to attenuate strength-related adaptation [30]. The seminal paper published by Hickson et al. [48] was the ﬁrst to identify the potential for endurance exercise to mitigate strength gains, when both training modalities were performed concurrently within the same program. Follow- up investigations have since shown mixed results [166–171], but evidence from this review clearly demon- strates that, for the distance runner at least, strength-related improvements are certainly possible following a concurrent period of training. Nevertheless, the study designs adopted by the works under review did not include a strength-only training group, thus it is not possible to determine whether strength adaptation was in fact negated under a concurrent regimen. One study using well-trained endurance cyclists with no ST experience, observed a blunted strength response in a group who added ST to their endurance training compared to a group who only performed ST [170]. Based upon this ﬁnding and other similar observa- tions [167, 172, 173] it seems likely that although distance runners can signiﬁcantly improve their strength using a concurrent approach to training, strength outcomes are unlikely to be maximized. Moreover, the degree of inter- ference with strength-adaptation also appears to be exac- erbated when volumes of endurance training are increased and the duration of concurrent training programs is longer [30, 146]. 4.8 Body Composition Resistance training performed 2–3 times per week is associated with increases in muscle cross-sectional area as a principal adaptation [174]. Although gains in gross body mass may appear to be an unfavorable outcome for dis- tance runners, the addition of muscle mass to proximal regions of the lower limb (i.e., gluteal muscles) should theoretically provide an advantage, via increases in hip extension forces, minimizing moment of inertia of the swinging limb, and reducing absolute energy usage [25]. It is somewhat surprising that virtually all studies demon- strated an absence of change in body mass, fat-free mass, lean muscle mass, and limb girths. Other than one inves- tigation [33], the duration of the studies that observed no effect on measures of body composition was\14 weeks, suggesting this may not have been sufﬁciently long to demonstrate a clear hypertrophic response. There is also a possibility that small increases in muscle mass within speciﬁc muscle groups (e.g., gluteals) were present, and contributed to the improvements observed in RE, but these may not have been detectable using a gross measure of mass. Evidence for this may have occurred in the Schu- mann et al. study [90, 92], who observed increases in total lean mass (3%) despite noting no signiﬁcant change in body mass or cross-sectional area of the vastus lateralis compared to baseline measures. The interference effect observed during concomitant integration of endurance and ST as part of the same pro- gram may also provide an explanation for the lack of change in measures of mass. Following a bout of exercise, a number of primary and secondary signaling messengers are up regulated for 3–12 h [175], which initiate a series of molecular events that serve to activate or suppress speciﬁc 1138 R. C. Blagrove et al. 123 genes. The signaling messengers which are activated, relate to the speciﬁc stress which is imposed on the physiological systems involved in an exercise bout. Strength training causes mechanical perturbation to the muscle cell, which elicits a multitude of signaling pathways that lead to a hypertrophic response [176]. In particular, the secretion of insulin-like growth factor-1 as a result of intense muscular contraction is likely to cause a cascade of signaling events which increase activity of phosphoinositide-3-dependent kinase (Pl-3 k) and the mammalian target of Rapamycin (mTOR) [177–179]. There is strong evidence that mTOR is responsible for mediating skeletal muscle hypertrophy via activation of ribosome proteins which up regulate protein synthesis [180]. Prolonged exercise bouts, such as those associated with endurance training, activate metabolic signals related to energy depletion, uptake and release of calcium ions from the sarcoplasmic reticulum and oxida- tive stress in cells [181]. Adenosine monophosphate acti- vated kinase (AMPK) is a potent secondary messenger which functions to monitor energy homeostasis [182] and when activated, modulates the release of peroxisome pro- liferator co-activator-1a, which along with calcium- calmodulin-dependent kinases increase mitochondrial function to enhance aerobic function [181, 183, 184]. Crucially though, AMPK also acts to inhibit the Pl-3 k/ mTOR stage of the pathway via activation of the tuberous sclerosis complex thereby suppressing the ST induced up regulation of protein synthesis [185, 186]. This conﬂict arising at a molecular signaling level therefore appears to impair the muscle ﬁber hypertrophy response to ST and attenuate increases in body mass [186]. 4.9 Muscle–Tendon Interaction Mechanisms The potential mechanisms for the positive changes observed in physiological parameters underpinning running performance were directly investigated in three studies [82, 84, 91], and were inferred from gait measures [36, 73–75, 77] and strength outcomes in others. It is well documented that muscle–tendon unit stiffness correlates well with RE [187–189]. Tendons are also highly adaptable to mechan- ical loading and have been shown to increase in stiffness in response to HRT and PT [84, 190, 191]. Despite observing no statistical effect for HRT on RE, Fletcher and col- leagues [82] also found a relationship between the change in RE and the changes observed in Achilles tendon stiff- ness. Despite these associations, it is likely that improve- ments in RE are a consequence of the interaction between adaptations to tendon properties and improvements in motor unit activation which inﬂuence behavior of force– length-velocity properties of muscles [25]. It tends to be assumed that improved tendon stiffness allows the body to store and return elastic energy more effectively, which results in a reduction in muscle energy cost due to a greater contribution from the elastic recoil properties of tendons [192]. Indeed, authors of studies in the present review have argued that the improvements observed in RE following a period of ST are due to an enhanced utilization of elastic energy during running [36, 73–75]. An alternative pro- posal, based upon more recent evidence, suggests the Achilles tendon provides a very small contribution to the total energy cost of running therefore improvements in stiffness provide a negligible reduction in energy cost [193, 194]. Instead, a tendon with an optimal stiffness con- tributes to reducing RE by minimizing the magnitude and velocity of muscle shortening, thus allowing muscle fas- cicles to optimize their length and remain closer to an isometric state [25]. A reduction in the amount and velocity of ﬁber shortening therefore reduces the level of muscle activation required and hence the energy cost of running [193]. The improvements observed in maximal and explosive strength, which can be attributed to increases in motor unit recruitment and ﬁring frequency, enable the lower limb to resist eccentric forces during the early part of ground contact [165] and thus contribute to the attainment of a near isometric state during stance. As the force required to sustain speed during distance running performance is submaximal, the level of motor unit activation needed can be minimized when fascicles contract isometrically [25]. This enables the Achilles tendon in particular to accom- modate a greater proportion of the muscle–tendon unit length change during running thereby reducing metabolic cost [194]. Variables which provide an indirect measure of the neuromuscular systems ability to produce force rapidly and utilize tendon stiffness were found to improve in other studies that showed improvements in running performance and/or key determinants [73, 74, 78–80, 87]. However, some studies found improvements in running-related parameters despite observing no alterations in jump per- formance [33, 76–78, 91], rate of force development [36, 75, 77], or stiffness [33, 74, 89] illustrating that measures were insufﬁciently sensitive to detect change, or a combi- nation of mechanisms is likely to be contributing towards the enhancements observed. Heavy RT causes a shift in muscle ﬁber phenotype, from the less efﬁcient myosin heavy chain (MHC) IIx to more oxidative MHC IIa, [195, 196]. A higher proportion of MHC IIa has been shown to relate to better running economy [91, 197, 198]; however, whether changes to MHC properties as a result of ST contribute to an improvement in RE and performance remains to be deter- mined. One previous study provided evidence that 4 weeks of sprint running (30-s bouts) improve RE and also the percentage of MHC IIx [199]; however, the absence of endurance training may partly explain the shift in Effects of Strength Training on Distance Running 1139 123 phenotype. Over a longer period (6 weeks), Pellegrino and co-workers [91] found no measurable changes in MHC isoforms following a PT intervention despite a signiﬁcant improvement in 3 km TT performance, suggesting that a contribution from this mechanism is unlikely for distance running. It could also be speculated that improvements in RE due to improved strength might have resulted in subtle changes to running kinematics, thus enabling participants to per- form less work for a given submaximal speed [72]. There is currently little direct support for this conjecture; however, previous work has shown that running technique is an important component of RE [200, 201], and improving hip strength can reduce undesirable frontal and transverse plane motion in the lower limb during running [202]. One study in this review did observe a reduction in EMG amplitude in the superﬁcial musculature of the lower limb following ST; however, this wasn’t accompanied by an improvement in RE [83]. This suggests that favorable adaptations in neuromuscular control do not necessarily translate to reducing the metabolic cost of running. Addi- tionally, two studies showed signiﬁcant increases (3.0–4.4%) in ground contact time during submaximal running after an ST intervention [36, 81]; however, only Giovanelli and colleagues [36] found a corresponding improvement in RE. Several papers have demonstrated an inverse relationship between RE and ground contact times [201, 203, 204], since a lower peak vertical force is required to generate the same amount of impulse during longer compared to short ground contacts [25]. Although there is currently minimal evidence to suggest an ST intervention increases ground contact time during sub- maximal running, this mechanism may in part explain the improvements in RE. 4.10 Strength-Training Prescription 4.10.1 Modality and Exercise Selection The works included in this review used a variety of ST modalities; however, the most effective type of training is currently difﬁcult to discern. Adaptations are speciﬁc to the demands placed upon the body, therefore it would be expected that HRT, ERT and PT produce somewhat dif- ferent outcomes [205]. This can be observed in the study by Berryman and co-workers [80], who observed larger improvements in explosive concentric power in a group following an ERT program compared to a group who used PT. The opposite result occurred for the counter-movement jump, which places a greater reliance on a plyometric action; the PT group displayed greater improvements than the ERT group [80]. Heavy RT, which is characterized by slow velocities of movement, is likely to improve agonist muscle activation via enhanced recruitment of the motor neuron pool, whereas ERT, which involves lighter loads being moved rapidly, tends to enhance ﬁring frequency and hence improve rate of force development [164, 165]. Ply- ometric training develops properties related to the stretch– shortening cycle function [206], and uses movements pat- terns which closely mimic the running action (e.g., hopping and skipping). It is therefore likely that although a variety of ST methods are capable of improving physiological parameters relating to distance running performance, the mechanisms underpinning the response may differ. In less strength-trained individuals, such as those used in the studies reviewed, any novel ST stimulus is likely to provide a sufﬁcient overload to the neuromuscular system to induce an adaptation in the short term [207]. This is perhaps why ST is effective even in highly-trained distance runners [74, 77, 87]. Studies that have attempted to com- pare ST techniques in distance runners have generally shown HRT to be superior to ERT or a mixed methods approach at improving aerobic parameters [57, 63] and maximal anaerobic running speed [62]. Plyometric training has also shown superiority to ERT for improvement of RE in moderately trained runners [80]. Other investigations have found no differences in the physiological changes between groups using HRT, ERT or a mixture of modali- ties [62, 65]. A number of studies have also shown HRT and/or ERT to be more beneﬁcial to a muscular endurance style of ST [59, 64, 65, 67, 86]. The addition of whole body vibration to RT also provides no extra beneﬁt [85]. Although ERT and PT may have more appeal compared to HRT due to their higher-level of biomechanical similarity to running, an initial period of HRT is likely to provide an advantage long-term in terms of reducing injury risk [208] and eliciting a more pronounced training effect [209]. Taken together, it seems that long-term, a mixed modality approach to ST is most effective, as this provides the variety and continual overload required to ensure the neuromuscular system is constantly challenged. One study that used a longer intervention period lends support to this notion, as signiﬁcant improvements were observed in strength and physiological measures after 20 and 40 weeks with a periodized methodology that used several types of ST [33]. Further research is required to ascertain the long- term beneﬁts of various ST modalities and the relative merits of different approaches to sequencing and pro- gressing these modalities. As discussed in Sect. 4.1, the exercises selected in an ST program can potentially inﬂuence the magnitude of neu- romuscular adaptation and thus the impact on physiological determinants of performance. Exercises using free weights, which require force to be generated from the leg extensor muscles in a close-kinetic chain position, are the most likely to positively transfer to running performance [210]. 1140 R. C. Blagrove et al. 123 Examples of RT exercises commonly used include: barbell squat, deadlifts, step-ups and lunging movement patterns [31, 33, 36, 72, 79, 85, 88]. Isometric HRT may also have value for the plantarﬂexors [84]. Explosive RT, by its very nature, should avoid a deceleration phase, therefore exer- cises such as squat jumps and Olympic weightlifting derivatives should be utilized [33, 80]. To maximize transfer to distance running performance, particularly at faster speeds, PT exercises should exhibit short ground contact times (\0.2 s) [36, 72], which approximates the contact times observed in competitive middle- [211] and long-distance running [212], and encourages a rapid exci- tation–contraction coupling sequence and improved mus- culotendinous stiffness [36, 73–75]. Exercises which possess a low to moderate eccentric demand such as depth jumps (from a 20–30 cm box), skipping, hopping, speed bounding appear most suitable [33, 73, 75, 77, 80, 83]. 4.10.2 Intra-Session Variables For non-strength trained individuals, exercise prescription and gradual progression is important to avoid injury and overtraining [213]. Most studies initially used 1–2 sets and progressed to 3–6 sets over the course of the intervention period for HRT, ERT and PT, which appears appropriate to circumvent these risks. Several studies utilized a low (3–5) repetition range in every HRT session [31, 79, 81, 86] at loads which approached maximum (C 80% 1RM or repe- tition failure), but did not observe superior beneﬁts com- pared to investigations that prescribed RT at moderate loads (60–80% 1RM) and higher repetition ranges (5–15 repetitions). Sets were performed to RM in a number of studies [32, 38, 72, 79, 81, 88, 89], which was likely employed as a means of standardizing the intensity of each set in the absence of 1RM data for participants. Performing sets which leads to repetition failure induces a high level of metabolic and neuromuscular fatigue, which may delay recovery [214]. Although training to repetition failure may be more important than the load lifted for inducing a hypertrophy response [215], this is both unfavorable and unnecessary to optimize gains in strength compared to a non-repetition failure strategy [216]. Not working to rep- etition failure also appears to become a more important feature of RT as ST status increases [216]. Participants were often instructed to move the weights as rapidly as possible when performing the concentric phase of RT exercises, which increases the likelihood of maximizing neuromuscular adaptations [217]. Plyometric training is characterized by high eccentric forces compared to running and RT, therefore repetitions per set were typically low (4–10 repetitions). Total foot contacts progressed from 30 to 60 repetitions in the ﬁrst week of an intervention up to 110–228 repetitions after 6–9 weeks [73, 75, 76, 91]. Plyometric exercises were all performed without additional external resistance in all but one study [73] and in many cases a short ground contact time [76, 77, 83] and maximal height [80, 83] were cued to amplify the intensity. An inter- set recovery period of 2–3 min was typical for HRT, ERT and PT, which is in line with recommendations for these training techniques [213]. Where SpT was incorporated into ST programs, repetition distances were short (20–150 m) and performed at or close to maximal running speed [73, 74, 88]. 4.10.3 Inter-Session Variables The majority of studies that demonstrated improvements in running physiology scheduled ST 2–3 times per week, which is in line with the guidelines for non-strength trained individuals [213]. One study used just one session per week (ERT or PT) and achieved moderate improvements in strength outcomes and RE after 8 weeks of training [80]. Beattie and associates [33] observed small improvements (ES: 0.3) in RE using a single ST session (mixed activities) each week for 20 weeks; however, the participants had already experienced moderate improvement (ES: 1.0) in this parameter using a twice weekly program in the 20 weeks prior. For well-trained runners who complete 8–13 running sessions per week [73, 77], it would be useful to establish the minimal ST dosage required to elicit a beneﬁcial effect to reduce the risk of overtraining. Equally, for the recreational runner, ST may take up valuable leisure time that could be spent running, therefore identifying the optimal volume and frequency of ST to achieve an improvement in performance would be desirable. A pre- vious meta-analysis indicated that two or three sessions per week provides a large effect on strength, but for the non- strength trained individual, three sessions is superior to two sessions per week [218]. More recently, a weak relation- ship was established between improvement in RE and weekly frequency of ST sessions in 311 endurance runners [10]. This suggests that higher weekly volumes of ST would not necessarily provide greater RE improvements, therefore two sessions per week is likely to be sufﬁcient [10]. Given the volume of endurance training participants were exposed to and the duration of each study, it seems likely that an attenuation of strength-related adaptation would have occurred. To minimize this interference phe- nomenon, it is therefore recommended that a recovery period of[3 h is provided following high-intensity run- ning training before ST takes place [146]. In many studies running training and ST took place on different days [33, 36, 85, 88, 89], and several papers noted a gap of[3 h between running and ST on the same day [32, 38, 72, 78, 79]. This feature of concurrent training prescription Effects of Strength Training on Distance Running 1141 123 therefore appears important in ensuring sufﬁcient strength- adaptations are realized but without compromising running training. Although there is very little evidence that the dosage of ST prescribed impaired any endurance-related adaptations, recent work has highlighted that acute bouts of RT may cause fatigue sufﬁcient to impair subsequent running performance, which long term may result in sub- optimal adaptation [219]. It is therefore recommended that this potential fatigue is accounted for by allowing at least 24 h recovery between an ST session and an intensive running session [33, 85, 88, 89]. The results provide compelling evidence that a rela- tively short period (6 weeks) of ST can enhance physio- logical qualities related to distance running performance. Improvements in RE [57] and 10 km TT performance [88] have also been shown in as little as 4 weeks. A relationship between intervention duration and improvement in RE has previously been reported [10], suggesting that longer periods of ST provide a larger beneﬁt. The same may be true for v _VO2max; however, more research using longer periods of ST is required to establish if this is indeed the case. The beneﬁts to performance also seem to be depen- dent on study duration as most short interventions (6 weeks) tended to produce small TT improvements (2.4–2.7%, ES: 0.13–0.4) [75, 87, 91], whereas longer programs (8–11 weeks) resulted in moderate or large per- formance effects (3.1–5.5%, ES: 0.67–1.50) [32, 73, 88]. It would seem reasonable to assume that highly-trained dis- tance runners would require a higher volume of ST to achieve the same beneﬁt as less experienced runners; however, this does not appear to be the case. Relatively short (6–9 weeks) periods of ST improved RE and TT performance to a similar extent in highly-trained individ- uals [77, 87] and recreational runners [76, 86, 91]. It is therefore recommended that future investigations use periods of 10 weeks or longer to provide further insight into how ST modalities may impact physiological param- eters long-term in different types of distance runner. The time of year or phase of training when the research was conducted was not reported in the majority of studies. Several papers indicated that the intervention formed part of an off-season preparation period [73, 74, 78, 82, 86], but others scheduled the intervention within the competition period [32, 38, 87]. Based upon the literature reviewed, it is currently not possible to provide speciﬁc recommendations for ST in different phases of a runners training macrocycle, as most studies found at least some physiological or per- formance beneﬁts to concurrent training. Importantly though, evidence suggests that choosing to exclude ST following a successful intervention period results in a detraining effect which causes improvements to return to baseline levels within 6 weeks [31]. The 40-week intervention conducted by Beattie and colleagues [33] provides evidence that reducing ST volume from two sessions per week (both with a lower limb HRT emphasis) during the preparatory phase to one weekly session (ERT and PT emphasis) during the in-season racing period is sufﬁcient to at least maintain previous strength and phys- iological gains. This ﬁnding corroborates with a mainte- nance effect observed in cyclists [220, 221] and soccer players [222] showing one ST session per week is sufﬁcient to preserve the strength qualities developed during a pre- ceding phase of training. Therefore, runners can decrease ST volume from 2–3 sessions per week (each with a lower limb focus) in preparatory phases of training to a single session each week during the competitive season without fearing a loss of adaptation as a consequence of the reduction in training density. It is currently uncertain what volume and intensity of running and ST are most likely to avoid the interference effect associated with concurrent training practices. One option to minimize attenuation of strength development is to organize activities into periods that concentrate on developing either strength or endurance adaptation [223]. This polarized approach to planning seems unnecessary and counterintuitive for distance runners who generally possess little ST experience, therefore require a minimal stimulus to create an adaptation. Indeed, studies that replaced running training with ST [73, 78, 88] found no greater beneﬁt than those which included ST in a supple- mentary manner. 4.10.4 Training Supervision In most studies, the ST routine was supervised and tightly monitored; however, similar controls were often absent for the running training participants performed. It seems rea- sonable to assume that any errors in participants training logbooks would be similar across intervention and control groups; however, validity of ﬁndings would be improved if the running component of training had been more tightly deﬁned. Where supervision of the ST exercises was not included [76] or only included for the ﬁrst 2 weeks [36], strength measures did not improve following the inter- vention period. This indicates that a suitably qualiﬁed coach is an important feature of an ST programme for a distance runner who lacks ST experience. 4.11 Limitations In addition to the limitations already highlighted in this review, there are other weaknesses that should be acknowledged. For many of the studies reviewed, calcu- lation of an ES was possible for the variables measured, which provides insight into the meaningfulness and 1142 R. C. Blagrove et al. 123 substantiveness of results. However, despite the qualitative nature of this review, interpretation of ﬁndings was pre- dominantly based upon reported probability values, which can be misleading due to low sample sizes and the heterogeneity in the pool of participants studied. A rela- tively large number of studies have been included in this review; however, several parameters (e.g., v _VO2max and BL) were measured in only a small number of studies, which increases the possibility that false conclusions may be drawn. There was also a lack of detail concerning several important confounding variables in studies, such as the nature of running training prescription and participant’s previous experience in ST. All but seven studies [31, 73, 74, 76, 84, 86, 90, 92] identiﬁed that participants had not been engaged in a program of ST for at least 3 months prior to the study commencing. Although it is perhaps unlikely that participants in these seven studies were strength- trained, this cannot be discounted and may therefore have inﬂuenced ﬁndings in these investigations. 5 Conclusion and Future Research This review is the most comprehensive to date surrounding the potential impact of ST on the physiological determi- nants of distance running. The research reviewed suggests that supplementing the training of a distance runner with ST is likely to provide improvements to RE, TT perfor- mance and anaerobic parameters such as maximal sprint speed. Improvements in RE in the absence of changes in _VO2max, BL and body composition parameters suggests that the underlying mechanisms predominantly relate to alterations in intra-muscular co-ordination and increases in tendon stiffness which contribute to optimizing force– length-velocity properties of muscle. Nevertheless, it is clear that the inclusion of ST does not adversely affect _VO2max or BL markers. The addition of two to three supervised ST sessions per week is likely to provide a sufﬁcient stimulus to augment parameters within a 6- to 14-week period, and beneﬁts are likely to be larger for interventions of a longer duration. A variety of ST modalities can be used to achieve similar outcomes assuming runners are of a non-strength trained status; however, to maximize long-term adaptations, it is sug- gested that a periodized approach is adopted with HRT prioritized initially. Although changes in fat-free mass were not observed in the majority of studies, a targeted RT program, which aims to increase muscle mass speciﬁcally around the proximal region of the lower limb may enhance biomechanical and physiological factors which positively inﬂuence RE. A number of methodological issues are likely to have contributed towards the discrepancies in results and should be acknowledged in future research conducted in this area. In particular, the measurement of RE should be quantiﬁed as energy cost (rather than oxygen cost) and a variety of speeds assessed which are relative to the maximum steady state of each participant. Furthermore, when quantifying RE and _VO2max, differences in body size should be accounted for by using scaling exponents which are appropriate for the cohort under investigation. Although a direct measure of v _VO2max has obvious validity, the dis- crete increments utilized during a maximal test may not provide the sensitivity required to detect changes which exist in this parameter following a relatively short inter- vention. Alternative strategies to quantifying v _VO2max may provide a solution. It is therefore recommended that future studies focus their time and efforts on investigating the effects of ST on physiological variables other than _VO2max and BL responses, such as RE, v _VO2max and parameters associated with the critical power model. The nature of the running training undertaken by participants and strength training history potentially confounds the outcomes of studies in this area, therefore attempts should also be made to control these variables as much as possible. Although the interference phenomenon is likely to have blunted the strength adaptations observed, the extent to which this occurs is currently uncertain due to the absence of a strength-only training group in the studies reviewed. For longer term interventions, where improvements inevi- tably plateau, minimizing attenuation to strength outcomes (and equally augmenting aerobic adaptation) potentially becomes more important. Therefore the organization of ST around running training provides a further avenue for investigation. Similarly, it would be useful for practitioners to understand the optimal sequencing of ST modalities within a long-term program in order to optimize training outcomes and facilitate a peaking response. Finally, very few investigations have examined the effect of ST on speciﬁc populations of runners such as young [78], female [32, 38, 72], and masters’ age [86] competitors, therefore future research should attempt to address this dearth in literature. Compliance with ethical standards Conﬂict of interest Richard Blagrove, Glyn Howatson and Philip Hayes declare that they have no conﬂict of interest. No funding was provided to support the preparation of this manuscript. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a Effects of Strength Training on Distance Running 1143 123 link to the Creative Commons license, and indicate if changes were made. References 1. di Prampero PE, Atchou G, Bruckner JC, Moia C. The ener- getics of endurance running. Euro J Appl Physiol Occ Physiol. 1986;55(3):259–66. 2. Joyner MJ. Modeling: optimal marathon performance on the basis of physiological factors. J Appl Physiol. 1991;70(2):683–7. 3. 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Effects of Strength Training on Distance Running 1149 123	Effects of Strength Training on the Physiological Determinants of Middle- and Long-Distance Running Performance: A Systematic Review.	[]	Blagrove, Richard C,Howatson, Glyn,Hayes, Philip R	eng
PMC10648636	Citation: Martinez-Torremocha, G.; Sanchez-Sanchez, J.; Alonso-Callejo, A.; Martin-Sanchez, M.L.; Serrano, C.; Gallardo, L.; Garcia-Unanue, J.; Felipe, J.L. Physical Demands in the Worst-Case Scenarios of Elite Futsal Referees Using a Local Positioning System. Sensors 2023, 23, 8662. https://doi.org/10.3390/s23218662 Academic Editors: John Komar and Ludovic Seifert Received: 3 October 2023 Revised: 19 October 2023 Accepted: 20 October 2023 Published: 24 October 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sensors Article Physical Demands in the Worst-Case Scenarios of Elite Futsal Referees Using a Local Positioning System Gemma Martinez-Torremocha 1 , Javier Sanchez-Sanchez 2,* , Antonio Alonso-Callejo 1, Maria Luisa Martin-Sanchez 2, Carlos Serrano 2 , Leonor Gallardo 1 , Jorge Garcia-Unanue 1 and Jose Luis Felipe 1 1 IGOID Research Group, Physical Activity and Sport Sciences Department, University of Castilla-La Mancha, 45071 Toledo, Spain; gemma.martinez@uclm.es (G.M.-T.); antonio.alonso@uclm.es (A.A.-C.); leonor.gallardo@uclm.es (L.G.); jorge.garciaunanue@uclm.es (J.G.-U.); joseluis.felipe@uclm.es (J.L.F.) 2 School of Sport Sciences, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain; marialuisa.martindesanpablo@universidadeuropea.es (M.L.M.-S.); carlos.serrano2@universidadeuropea.es (C.S.) * Correspondence: javier.sanchez2@universidadeuropea.es Abstract: The aim of this study is to analyze the worst-case scenarios of professional futsal referees during the ﬁrst and second half of ofﬁcial matches in the Spanish Futsal Cup using a Local Posi- tioning System (LPS) for monitoring their movement patterns. Eight professional futsal referees (40 ± 3.43 years; 1.80 ± 0.03 m; 72.84 ± 4.01 kg) participated in the study. The external load (total distance, high-speed running distance and efforts, sprint distance and efforts, and accelerations and decelerations distances) of the referees was monitored and collected using an LPS. The results revealed signiﬁcant differences in the worst-case scenarios of the futsal referees during the match according to the time window analyzed (p < 0.05). The longest time windows (120 s, 180 s, and 300 s) showed lower relative total distances in the worst-case scenarios (p < 0.05). The high-speed running distances were signiﬁcatively higher in the ﬁrst half for the 120 s (+2.65 m·min−1; ES: 1.25), 180 s (+1.55 m·min−1; ES: 1.28), and 300 s (+0.95 m·min−1; ES: 1.14) time windows (p < 0.05). No differences were found between the ﬁrst and second half for the high-intensity deceleration distance (p > 0.05). These results will serve to prepare the referees in the best conditions for the competition and adapt the training plans to the worst-case scenarios. Keywords: team sport; competition; endurance; game analysis; physical performance 1. Introduction Team sports have a referee who must regulate the rules so that matches can be carried out correctly. Referees have an essential task since they must pay attention and perform precise control over the game’s every single moment [1]. For that reason, the physical demands of referees have been studied for some years thanks to Global Positioning Systems (GPS) which are easy to transport and use [2]. Throughout the years, the physical demands in football referees have been stud- ied [1,3,4]. However, football has different characteristics than futsal [5]. Futsal is a high-intensity, intermittent team sport which is played indoors and involves short actions of high intensity with a short recovery time between efforts such as changes of direction, sprints, accelerations (Acc), and decelerations (Dec) [6]. It is a team sport with two times of 20 min each per game, at a standstill time. So, every time the ball comes out of the ﬁeld, time stops until the game resumes [7]. Moreover, in futsal there are two main referees on the court, who have different functions and must have extraordinary positions on the futsal ﬁeld to observe the possible infractions [5]. Football referees cover between 10 km and 12 km per game and about 15% of the total distance at a high speed (>18 km·h−1) [3,4]. Furthermore, they reach a maximum Sensors 2023, 23, 8662. https://doi.org/10.3390/s23218662 https://www.mdpi.com/journal/sensors Sensors 2023, 23, 8662 2 of 10 speed of 28.76 km·h−1 and cover 212.98 m at sprint speed [1]. Weston et al. [8] showed that the distance covered at a high intensity and the total distance travelled in the ﬁrst 15 min of the ﬁrst half of the match are higher than the distance covered in the ﬁrst 15 min of the second half. This indicates that referees are subjected to high loads, which may cause injuries if they do not train at optimal levels that are equivalent to the physical demands that they have in ofﬁcial matches [9]. Nevertheless, in reference to futsal, Rebelo et al. [5] demonstrated that referees perform intermittent endurance, of moderate–high intensity, during a match, with several periods of running and sprints, while also performing long recovery periods of low intensity. There are several studies conducted on the proﬁles of futsal players [7,10], but there is very little research about futsal referees [5,11,12]. It has been demonstrated that futsal players from the 1st division of the Portuguese, Spanish, and Russian leagues perform high-intensity efforts every 43 s, medium-intensity efforts every 37 s, and low-intensity efforts every 14 s during futsal playoffs’ matches [13]. In addition, a recent study published that futsal players perform around 70 Acc and Dec at a high intensity and, approximately, 170 changes of direction during ofﬁcial matches [14]. Also, players cover 3749 m in a match, of which 134.9 m are carried out at a high speed (>18 km·h−1) [10]. Additionally, Serrano et al. [12] showed that futsal referees cover 5719 m of long distance at slow and moderate speeds, with a reduction in the second half of the games in the sprints’ distance and high-speed running (HSR). Ahmed et al. [11] demonstrated that futsal referees have 150.9 beats per minute (bpm) and cover 161.1 m and 114.4 m at a high intensity in the ﬁrst and second half, respectively. For the analysis of the physical demands of the matches of indoor and outdoor sports, different methods have been used, but GPS is the most common for providing measurements with validity and accuracy [2]. Additionally, the physical demands have been analyzed in different ways, as follows: with video for futsal referees [5,11]; via GPS for ofﬁcial football referees [1,3,4]; and through a Local Positioning System (LPS) with Ultra- Wide Band (UWB) technology for futsal referees [12] and players [10,15,16]. Therefore, new methods have been used to detect worst-case scenarios (WCSs). This is the case for professional football players, as using averages may underestimate peak requirements [17]. The WCS is the most intense time of a game or training [17] (e.g., 30 s). WCSs have recently started to be included in different studies [15,17] to examine the peak physical demands at different playing times during matches [18] in several team sports such as rugby [19], football [17], and futsal [15]. As a result, it would be interesting to use it for referees. However, although there is some information about the physical demands of professional futsal referees [11,12], much more evidence is needed to accurately establish their activity. Furthermore, there is no prior information on WCSs of professional futsal referees collected using tracking technology devices during ofﬁcial matches. Therefore, the aim of this study is to analyze the WCSs of professional futsal referees in the ﬁrst and second half of ofﬁcial matches in the Spanish Futsal Cup using a LPS for monitoring their movement patterns. The results of this study will facilitate the design of training plans according to the physical demands of a match. 2. Materials and Methods 2.1. Experimental Approach to the Problem The data from seven ofﬁcial Spanish Futsal Cup 2020 matches (ﬁrst division teams of the National Spanish Futsal League (LNFS)) were gathered using an UWB technology sys- tem. This allowed for the quantiﬁcation of the absolute and relative external training loads, which were then divided into the ﬁrst and second halves of the games. The competition was organized over 4 days divided into four quarterﬁnals’ games, two semi-ﬁnals’ games, and a ﬁnal game. Sensors 2023, 23, 8662 3 of 10 2.2. Participants Eight professional Spanish futsal referees (age 40 ± 3.43 years; height 1.80 ± 0.03 m; weight 72.84 ± 4.01 kg), with similar characteristics and the same number of training sessions per week, were monitored during this study. They were studied over seven games, which were spread across the quarterﬁnals, semiﬁnals, and championship game over four days. The referees were selected by the National Committee of Referees (CTA) for participating in the Spanish Futsal Cup 2020. All of them had at least 6 years of experience in the ﬁrst division of the National Spanish Futsal League, and they must pass different physical tests each year. Informed written consent was obtained from each participant after being informed of the study’s requirements. The project was approved and followed the guidelines established by the local institution—the Bioethics Committee for Clinical Research of the Virgen de la Salud Hospital in Toledo (Ref.: 2551;17/02/2021). 2.3. Equipment The referees’ movement patterns throughout each game were tracked using WIMU PROTM LPS (RealTrack Systems SL, Almería, Spain) with an UWB technology. The reference technology and the WIMU PROTM inertial device (which the referees transported) are the two components that make up this technology. The WIMU PROTM has shown an accuracy (bias: 0.57–5.85%), test–retest reliability (%TEM: 1.19), and inter-unit reliability (bias: 0.18) in Bastida Castillo et al. [20], as well as a large ICC for the x-coordinate (0.65) and a very large ICC for the y-coordinate (0.88), with a good 2%TEM [21]. For data recording, storage, and uploading, each device has a dedicated internal microprocessor with a fast USB interface [21]. The devices are made up of a variety of sensors, including an UWB chipset with a signal frequency of 18 Hz, a gyroscope, four accelerometers, a magnetometer, and a Global Navigation Satellite System (GNSS) [22]. Six antennas make up the reference system, each of which can transmit and receive radio-frequency signals. The radio-frequency signal works almost exactly like the GPS system, with the antennas (mainly the master antenna) calculating the position of the devices in their range as they receive the calculation [21]. In terms of calculating the distance traveled, speed, mean velocity, ACC, and DEC for intermittent activities, LPS has proven to be accurate and reliable [23,24]. 2.4. Procedures The LPS was installed on the futsal pitch where the games were played, and the individual WIMU PROTM devices (RealTrack Systems SL, Almería, Spain) were used to register the physical parameters’ data of external load. The LPS was activated after the warmup of the referees with an autocalibration of the antennas lasting 5 min [21]. The placement of the six antennas (Figure 1) was set 5 m apart, forming a hexagon, except for those positioned at the ﬁeld’s middle line, which were set 7 m from the perimeter. The hexagonal shape of the antennas improved signal transmission and reception. The antennas were then self-calibrated for ﬁve minutes, while the master antenna synchronized all the antennas to a single clock after they had been installed. They were then switched on, one at a time, with the master antenna being turned on last. Sensors 2023, 23, 8662 4 of 10 Sensors 2023, 23, x FOR PEER REVIEW 4 of 10 Figure 1. Antenna distribution of the Local Positioning System and distance reference from the fut- sal pitch. The arrows indicate the distance from the antennas to the court and the black lines indicate the communication between the antennas [16]. 2.5. Data Processing The physical activity variables were considered in line with previous futsal studies [12,16]. A speciﬁc software (SPROTM v.990) was used to analyze each match’s referees’ performance data. The WCSs were assessed using the WIMU SPROTM software version 990 (RealTrack Systems SL, Almería, Spain), with a rolling average method over each physical variable selected using ﬁve diﬀerent time windows (30, 60, 120, 180, and 300 s). This method had been used in previous futsal investigation [15] and other team sports’ [17,25,26]. The physical variables examined were the following: the total distance covered (TD); the HSR distance (distance covered above 15 km·h−1); the HSR eﬀorts (number of eﬀorts above 15 km·h−1); the sprint distance (distance covered above 18 km·h−1); the sprint eﬀorts (number of eﬀorts above 15 km·h−1); and the number (n) and distance (m) of high- intensity ACC (>3 m·s−2) and DEC (<−3 m·s−2). All the speed variables and Acc and Dec thresholds selected were in line with previous referee futsal research [12]. 2.6. Statistical Analysis The Shapiro–Wilk test was used to test for the normality of each variable and time window, resulting in a non-normal distribution (p < 0.05). The non-parametric Wilcoxon test for paired samples was run for each variable and time window. The same method was used for comparing values between half-times. The conﬁdence level was set to 95%, and the p-values < 0.05 were considered signiﬁcant. The standardized eﬀect size was calculated for each comparison and classiﬁed as negligible (Eﬀect Size (ES) < 0.2), small (ES between 0.2 and 0.6), moderate (ES between 0.6 and 1.2), and large (ES > 1.2). The statistical analysis was carried out and the ﬁgures were created using the RStudio software (R version 4.2.2, RStudio 2022.12.0, © 2009–2022 Posit Software, PBC). 3. Results The results revealed signiﬁcant diﬀerences in the WCS of futsal referees during the match according to the time window analyzed (p < 0.05). The longest time windows (120 s, 180 s, and 300 s) showed lower relative distances in the WCS in comparison to the short- est intervals (Table 1; p < 0.05). According to the diﬀerences between the ﬁrst and the sec- ond half in the WCS (Table 2), the HSR distance was signiﬁcatively higher in the ﬁrst half for the 120 s (+2.65 m·min−1; ES: 0.38), 180 s (+1.55 m·min−1; ES: 0.29), and 300 s (+0.95 m·min−1; ES: 0.27) time windows (p < 0.05). The WCS for the total distance was only Figure 1. Antenna distribution of the Local Positioning System and distance reference from the futsal pitch. The arrows indicate the distance from the antennas to the court and the black lines indicate the communication between the antennas [16]. 2.5. Data Processing The physical activity variables were considered in line with previous futsal stud- ies [12,16]. A speciﬁc software (SPROTM v.990) was used to analyze each match’s referees’ performance data. The WCSs were assessed using the WIMU SPROTM software version 990 (RealTrack Systems SL, Almería, Spain), with a rolling average method over each physical variable selected using ﬁve different time windows (30, 60, 120, 180, and 300 s). This method had been used in previous futsal investigation [15] and other team sports’ [17,25,26]. The physical variables examined were the following: the total distance covered (TD); the HSR distance (distance covered above 15 km·h−1); the HSR efforts (number of efforts above 15 km·h−1); the sprint distance (distance covered above 18 km·h−1); the sprint efforts (number of efforts above 15 km·h−1); and the number (n) and distance (m) of high-intensity ACC (>3 m·s−2) and DEC (<−3 m·s−2). All the speed variables and Acc and Dec thresholds selected were in line with previous referee futsal research [12]. 2.6. Statistical Analysis The Shapiro–Wilk test was used to test for the normality of each variable and time window, resulting in a non-normal distribution (p < 0.05). The non-parametric Wilcoxon test for paired samples was run for each variable and time window. The same method was used for comparing values between half-times. The conﬁdence level was set to 95%, and the p-values < 0.05 were considered signiﬁcant. The standardized effect size was calculated for each comparison and classiﬁed as negligible (Effect Size (ES) < 0.2), small (ES between 0.2 and 0.6), moderate (ES between 0.6 and 1.2), and large (ES > 1.2). The statistical analysis was carried out and the ﬁgures were created using the RStudio software (R version 4.2.2, RStudio 2022.12.0, © 2009–2022 Posit Software, PBC). 3. Results The results revealed signiﬁcant differences in the WCS of futsal referees during the match according to the time window analyzed (p < 0.05). The longest time windows (120 s, 180 s, and 300 s) showed lower relative distances in the WCS in comparison to the shortest intervals (Table 1; p < 0.05). According to the differences between the ﬁrst and the second half in the WCS (Table 2), the HSR distance was signiﬁcatively higher in the ﬁrst half for the 120 s (+2.65 m·min−1; ES: 0.38), 180 s (+1.55 m·min−1; ES: 0.29), and 300 s (+0.95 m·min−1; ES: 0.27) time windows (p < 0.05). The WCS for the total distance was only signiﬁcatively Sensors 2023, 23, 8662 5 of 10 lower in the second half for the 120 s time window (−7.29 m·min−1; ES: 1.44). Finally, the high intensity Acc distance was also higher in the ﬁrst half, but only for the 120 s (+3.71 m·min−1; ES: 0.87) and 180 s (+2.68 m·min−1; ES: 0.72) time windows (p < 0.05; Figure 2). No differences were found between the ﬁrst and second half for the high intensity Dec distance (p > 0.05). Sensors 2023, 23, x FOR PEER REVIEW 5 of 10 signiﬁcatively lower in the second half for the 120 s time window (−7.29 m·min−1; ES: 1.44). Finally, the high intensity Acc distance was also higher in the ﬁrst half, but only for the 120 s (+3.71 m·min−1; ES: 0.87) and 180 s (+2.68 m·min−1; ES: 0.72) time windows (p < 0.05; Figure 2). No diﬀerences were found between the ﬁrst and second half for the high inten- sity Dec distance (p > 0.05). Figure 2. Worst-case scenarios of the elite futsal referees in the diﬀerent time windows in accelera- tions and decelerations. * Signiﬁcant diﬀerences between halves. a,b,c Signiﬁcant diﬀerences between time windows [30 (a), 60 (b), 120 (c), 180 (d), 300 (e)]. Table 1. Worst-case scenarios of the elite futsal referees in the diﬀerent time windows. Time Window (s) 30 (a) 60 (b) 120 (c) 180 (d) 300 (e) n = 28 Mean SD Mean SD Mean SD Mean SD Mean SD Total distance (m·min−1) 143.30 14.61 115.39 a 9.28 94.47 a,b 6.21 87.20 a,b,c 4.76 66.22 a,b,c,d 3.22 HSR Distance (m·min−1) 66.44 17.73 40.23 a 11.33 24.21 a,b 7.06 18.90 a,b,c 5.38 12.89 a,b,c,d 3.40 HSR count (n·min−1) 6.79 2.27 4.00 a 1.12 2.64 a,b 0.61 2.13 a,b,c 0.55 1.48 a,b,c,d 0.27 Sprint Distance (m·min−1) 51.21 15.81 28.38 a 10.90 17.11 a,b 6.67 12.78 a,b,c 4.80 8.39 a,b,c,d 3.06 Sprint count (n·min−1) 5.57 2.13 3.14 a 1.35 1.95 a,b 0.70 1.48 a,b,c 0.57 1.00 a,b,c,d 0.36 HI Acc Distance (m·min−1) 42.46 10.67 26.20 a 7.34 17.21 a,b 4.60 13.63 a,b,c 3.90 9.48 a,b,c,d 3.03 HI Dec Distance (m·min−1) 39.79 10.83 24.99 a 6.30 16.01 a,b 4.20 13.34 a,b,c 3.53 9.31 a,b,c,d 2.56 a,b,c,d Signiﬁcant diﬀerences between time windows [30 (a), 60 (b), 120 (c), 180 (d), 300 (e)]. Figure 2. Worst-case scenarios of the elite futsal referees in the different time windows in accelerations and decelerations. * Signiﬁcant differences between halves. a,b,c Signiﬁcant differences between time windows [30 (a), 60 (b), 120 (c), 180 (d), 300 (e)]. Table 1. Worst-case scenarios of the elite futsal referees in the different time windows. Time Window (s) 30 (a) 60 (b) 120 (c) 180 (d) 300 (e) n = 28 Mean SD Mean SD Mean SD Mean SD Mean SD Total distance (m·min−1) 143.30 14.61 115.39 a 9.28 94.47 a,b 6.21 87.20 a,b,c 4.76 66.22 a,b,c,d 3.22 HSR Distance (m·min−1) 66.44 17.73 40.23 a 11.33 24.21 a,b 7.06 18.90 a,b,c 5.38 12.89 a,b,c,d 3.40 HSR count (n·min−1) 6.79 2.27 4.00 a 1.12 2.64 a,b 0.61 2.13 a,b,c 0.55 1.48 a,b,c,d 0.27 Sprint Distance (m·min−1) 51.21 15.81 28.38 a 10.90 17.11 a,b 6.67 12.78 a,b,c 4.80 8.39 a,b,c,d 3.06 Sprint count (n·min−1) 5.57 2.13 3.14 a 1.35 1.95 a,b 0.70 1.48 a,b,c 0.57 1.00 a,b,c,d 0.36 HI Acc Distance (m·min−1) 42.46 10.67 26.20 a 7.34 17.21 a,b 4.60 13.63 a,b,c 3.90 9.48 a,b,c,d 3.03 HI Dec Distance (m·min−1) 39.79 10.83 24.99 a 6.30 16.01 a,b 4.20 13.34 a,b,c 3.53 9.31 a,b,c,d 2.56 a,b,c,d Signiﬁcant differences between time windows [30 (a), 60 (b), 120 (c), 180 (d), 300 (e)]. Sensors 2023, 23, 8662 6 of 10 Table 2. Worst-case scenarios of the elite futsal referees in the different time windows by half-time. Time Window (s) 30 (a) 60 (b) 120 (c) 180 (d) 300 (e) n = 14 Mean SD Mean SD Mean SD Mean SD Mean SD First Half Total distance (m·min−1) 144.90 12.72 116.42 8.91 98.12 ,a 5.15 88.32 a,b 4.99 66.43 a,b,c 2.76 HSR Distance (m·min−1) 66.15 15.64 41.35 9.72 25.54 ,a 5.87 19.68 a,b 4.92 13.36 ,a,b,c 2.66 Sprint Distance (m·min−1) 51.93 15.01 29.08 10.27 17.59 a 6.10 13.38 a,b 4.45 8.71 a,b,c 2.63 HI Acc Distance (m·min−1) 45.44 11.99 28.24 8.49 19.06 ,a 5.30 14.97 ,a,b 4.59 10.39 a,b,c 3.72 HI Dec Distance (m·min−1) 42.32 11.88 27.43 6.33 17.38 a 4.07 14.03 a,b 3.62 10.06 a,b,c 2.72 Second Half Total distance (m·min−1) 141.71 16.61 114.35 9.85 90.83 a 4.98 86.07 a,b 4.41 66.01 a,b,c 3.72 HSR Distance (m·min−1) 66.73 20.21 39.12 13.03 22.89 a 8.08 18.13 a,b 5.89 12.42 a,b,c 4.07 Sprint Distance (m·min−1) 50.49 17.11 27.68 11.85 16.62 a 7.40 12.18 a,b 5.21 8.07 a,b,c 3.51 HI Acc Distance (m·min−1) 39.48 8.59 24.17 5.57 15.35 a 2.90 12.29 a,b 2.58 8.58 a,b,c 1.88 HI Dec Distance (m·min−1) 37.25 9.40 22.55 5.44 14.62 a 4.00 12.64 a,b 3.43 8.56 a,b,c 2.23 Signiﬁcant differences between halves. a,b,c Signiﬁcant differences between time windows [30 (a), 60 (b), 120 (c), 180 (d), 300 (e)]. 4. Discussion The ﬁndings of this study revealed interesting results which deserve further inves- tigation. The aim of this research was to analyze the WCS in professional futsal referees in the ﬁrst and second half of the match in the Spanish Futsal Cup using an LPS. To our knowledge, this is the ﬁrst study that examined the WCS during ofﬁcial matches in the Spanish Futsal Cup and futsal in general. The main ﬁndings of the study were that there is an extreme inﬂuence on the time window analyzed, which conﬁrmed that the longer the time analyzed, the lower the physical demands’ requirement is during the match. Additionally, the referees covered longer high-intensity distances and more high-intensity Acc in the ﬁrst half of the matches. Previous studies have analyzed the physical demands of futsal referees [5,11,12,27] and futsal players [15,16]. The results of this research determined the physical demands without the time windows, except for the study of Illa et al. [15], which researched the positional differences in the WCS of elite futsal players. Therefore, there are studies on the physical demands of referees with similar results, although they do not analyze the WCS. Our study highlights some interesting results. Curiously, all the variables obtained a decreasing result among the different time windows in the ﬁrst and the second half of the match. These results have important practical applications for training design, as referees might have shown fatigue during the match. Nevertheless, this fatigue may have been due to the contextual variables of the match, which should also be studied in the future to determine whether they really interfere with the results. In comparison to previous research, similarities were found in the total distance, as it decreases with time over the course of the match. This study, in contrast with Illa et al.’s [15], showed that, during the WCS of the match play, the futsal players cover more distance than the futsal referees for each time window (30 s: 37%, 60 s: 34%, 120 s and 180 s: 31%, and 300 s: 43%). An explanation for these differences might be the continuous displacement of the players as they are moving constantly or even the type and categories of the match, although, in this case, it was the same division in the same country. Another possible reason for the differences is substitutions: while a player can rest during the match, the referee cannot; however, they do not start under the same circumstances. On the other hand, we agree with Illa et al. [15] that the WCS decreases as the time window analyzed increases; this may be due to fatigue occurring during the match. Moreover, Ahmed et al. [11], in the comparison of performance between halves of a match, determined a decline in the total distance cover by the Iraq Futsal Premier League referees (3093 m vs. 2850 m), while the ﬁndings in this study reported a similar decrease in the relative total distances covered in both halves in the different time windows. The total distance is also analyzed by Serrano et al. [12] (2888.39 ± 122.55 vs. 2831.51 ± 150.26 m), showing similarities to this study in Sensors 2023, 23, 8662 7 of 10 the decrement of the variable and, also, with Ahmed et al. [11]. The physical demands were monitored using different devices in Ahmed et al. [11], so this could explain the little difference with the total results. Moreover, the competitions were different and, therefore, the contextual demands were also dissimilar. In addition, there are studies that look at the relative distances of elite futsal players in ofﬁcial matches, showing that they have similar values between halves [16] or even experience an increase [10] compared to the results of this research. The contextual variables during the matches might be the reason for these differences, as well as the kind of competition, since the referees must cover the different distances depending on the course of the match. Furthermore, futsal referees cover more distance at sprint speed (>18 km·h−1) during the WCS of the match play (30 s: +8%, +16%; 60 s: +5%, 15%; 120 s: +6%, +12%; 180 s: +6%, +22%; and 300 s: +5%, +17%) than futsal defenders and pivots during the match in all time windows, but cover less distance than wingers (30 s: −5%, 60 s: −13%, 120 s: −17%, 180 s: −17%, and 300 s: −31%) [15]. However, Illa et al. [15] only described the HSR as >18 km·h−1, while in this study it is studied as >15 km·h−1 and the sprint as >18 km·h−1. The differences in the distance covered at a high intensity by the referees and players may be due to the speciﬁc nature of their individual roles during the matches. The referees must always follow the game, as being too far away from fouls may result in incorrect decisions. Even so, rugby union players [19] had lower values in the longest time window than the futsal referees (−6%). The reason for the dissimilarity with rugby union players might be due to the interruptions during the futsal matches (every action that stops the match clock), in which the futsal referees and players continue moving even when the match is detained, for example when there are outsides, corner-kicks, etc. [28]. Nevertheless, Serrano et al. [12] did not study the WCS but showed that referees covered less distance at HSR (>15 km·h−1) in the second half (235.06 ± 67.58 m vs. 207.78 ± 57.86 m, respectively). The same occurred in this study, as the distance travelled at HSR decreased from the ﬁrst to the second half based on the time windows. The speciﬁc situations of futsal matches may slow down the action in the second half, which could have an impact on the referees’ ability to handle these demands. Moreover, the number of actions over 18 km·h−1 during matches is higher in the referees, in the lower windows (30 s, 60 s, and 120 s), than in the players in the different positions; however, in the longer windows (180 s and 300 s), the wingers have a higher number of such actions [15]. Nevertheless, the course of the match and the type of competition of futsal matches might also explain the differences in the results. Another possible explanation for this difference could be that they have a wider range of motion to achieve a greater speed. Additionally, the sprint distance of the present results showed a lower sprint distance (>18 km·h−1) in the second half from every time window, as occurred in Serrano et al.’s study [12], although they did not study the WCS. On the other hand, the Acc and Dec variables were barely discussed in this study. However, these results can be discussed using previous studies. Acc and Dec are an impor- tant part of the physical demands of futsal referees since the number of high-intensities Acc and Dec is very high during the matches [12]. Other studies have analyzed stops, sideways running, and turns during matches [5,11] to report these important actions, as they are one of the causes of sport injuries. While previous studies have examined the results as whole, without taking the WCS into account [5,11,12], the result of the present study shows values of high-intensity Acc and Dec distances of the different time windows. In addition, the high-intensity Acc and Dec values show a decrease in the second half; these results are similar to the results of a previous futsal referees’ study [12]. Therefore, high-intensity Acc and Dec require a high eccentric force which may produce muscular fatigue, so monitoring them could be useful for designing strength training and injury-prevention programs. Player performance was not examined in the current study, but player activity and game development may affect compliance with these requirements because referee activity is inﬂuenced by the match activity [11]. Regardless, based on the data obtained in this study, training programs should be adjusted to the characteristics of the speciﬁc competition to improve referees’ physical performance and prevent injuries. Illa et al. [15] had studied Sensors 2023, 23, 8662 8 of 10 the seasonal trend on all the dependent variables for the different positions, which may be useful for developing training plans to prevent injuries. So, this could be a future, possible focus for research on futsal referees. This research had different limitations, as there was a low number of matches and referees involved. Nevertheless, the study contains all the Spanish Futsal Cup matches. Understanding the physical proﬁle and performance of the referees may be aided by the potential correlation between the physical requirements placed on the players during competitions and the referees’ physical parameters [11,29]. 5. Conclusions Finally, the use of LPS to monitor physical performances provides knowledge of the speciﬁc activity proﬁles of futsal referees. This information could be useful for making more accurate training programs and even for developing new physical tests. With all of this, referees will know their workload requirements for each match during the different seasons and, also, it will serve them to be able to have different references so that they can be in the best physical conditions to face the different matches. One of the most signiﬁcant ﬁndings to emerge from this study is the extreme effect of the time window, which conﬁrmed that the longer the time analyzed, the lower the requirement is during the matches. Therefore, time windows can be used in different ways, depending on their length. Shorter time windows (30 s, 60 s and 120 s) are useful for designing high-intensity tasks in short periods of time, while longer time windows (180 s and 300 s) are effective for tasks that are performed over larger spaces where extensive, high-intensity actions are to be performed. In addition, the present study also identiﬁed that referees have their best results in the ﬁrst half of the matches over the longer time windows. The decline in their physical performance in the second half may be attributed to the referees’ need to keep up with the players’ pace and the situations that arise during the futsal matches. This could lead to a reduction in the intensity of the match in the second half, which would affect the performance of the referees. Finally, it should be considered that it could be helpful to study contextual variables when carrying out future studies. Moreover, these results will serve to prepare referees in the best conditions for the competition and allow to adapt the training plans to critical match scenarios that may be accompanied by relevant decision making. Author Contributions: Conceptualization, G.M.-T. and J.S.-S.; methodology, G.M.-T. and C.S.; soft- ware, M.L.M.-S. and C.S.; validation, G.M.-T., J.S.-S. and J.L.F.; formal analysis, A.A.-C.; investigation, G.M.-T.; resources, J.G.-U.; data curation, A.A.-C.; writing—original draft preparation, G.M.-T.; writing—review and editing, G.M.-T., J.S.-S. and J.L.F.; visualization, G.M.-T.; supervision, L.G. and J.S.-S.; project administration, J.L.F.; funding acquisition, J.G.-U. and L.G. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and approved by the Bioethics Committee for Clinical Research of the Virgen de la Salud Hospital in Toledo (Ref.: 2551;17/02/2021). Informed Consent Statement: Informed consent was obtained from all the subjects involved in the study. Data Availability Statement: Due to the privacy terms, the data are not available. Acknowledgments: The authors would like to thank all the ofﬁcials and The National Commit- tee of Referees from the Spanish Football Federation. G.M.-T. acknowledges the University of Castilla-La Mancha and the “Fondo Social Europeo Plus (FSE+)” for funding the development of her Ph.D. BDNS (identif.): 651201. (2022/9249). A.A.-C. acknowledges the Spanish Ministry of Science, Innovation, and Universities for funding the development of his Ph.D (Grant Number: FPU 21/04332). This research has been developed with the help of Grant EQC2021-006804-P funded Sensors 2023, 23, 8662 9 of 10 by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”, Grant EQC2019-005843-P funded by MCIN/AEI/10.13039/50110001103 and ERDF ‘A way of making Europe’. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. 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Physical Demands in the Worst-Case Scenarios of Elite Futsal Referees Using a Local Positioning System.	10-24-2023	Martinez-Torremocha, Gemma,Sanchez-Sanchez, Javier,Alonso-Callejo, Antonio,Martin-Sanchez, Maria Luisa,Serrano, Carlos,Gallardo, Leonor,Garcia-Unanue, Jorge,Felipe, Jose Luis	eng
PMC4831173	STUDY PROTOCOL Open Access Protocol for evaluating the effects of a therapeutic foot exercise program on injury incidence, foot functionality and biomechanics in long-distance runners: a randomized controlled trial Alessandra B. Matias1, Ulisses T. Taddei1, Marcos Duarte2 and Isabel C. N. Sacco1* Abstract Background: Overall performance, particularly in a very popular sports activity such as running, is typically influenced by the status of the musculoskeletal system and the level of training and conditioning of the biological structures. Any change in the musculoskeletal system’s biomechanics, especially in the feet and ankles, will strongly influence the biomechanics of runners, possibly predisposing them to injuries. A thorough understanding of the effects of a therapeutic approach focused on feet biomechanics, on strength and functionality of lower limb muscles will contribute to the adoption of more effective therapeutic and preventive strategies for runners. Methods/Design: A randomized, prospective controlled and parallel trial with blind assessment is designed to study the effects of a "ground-up" therapeutic approach focused on the foot-ankle complex as it relates to the incidence of running-related injuries in the lower limbs. One hundred and eleven (111) healthy long-distance runners will be randomly assigned to either a control (CG) or intervention (IG) group. IG runners will participate in a therapeutic exercise protocol for the foot-ankle for 8 weeks, with 1 directly supervised session and 3 remotely supervised sessions per week. After the 8-week period, IG runners will keep exercising for the remaining 10 months of the study, supervised only by web-enabled software three times a week. At baseline, 2 months, 4 months and 12 months, all runners will be assessed for running-related injuries (primary outcome), time for the occurrence of the first injury, foot health and functionality, muscle trophism, intrinsic foot muscle strength, dynamic foot arch strain and lower-limb biomechanics during walking and running (secondary outcomes). Discussion: This is the first randomized clinical trial protocol to assess the effect of an exercise protocol that was designed specifically for the foot-and-ankle complex on running-related injuries to the lower limbs of long-distance runners. We intend to show that the proposed protocol is an innovative and effective approach to decreasing the incidence of injuries. We also expect a lengthening in the time of occurrence of the first injury, an improvement in foot function, an increase in foot muscle mass and strength and beneficial biomechanical changes while running and walking after a year of exercising. (Continued on next page) * Correspondence: icnsacco@usp.br 1Department of Physical Therapy, Speech, and Occupational Therapy, School of Medicine, University of São Paulo, Rua Cipotânea, 51 - Cidade Universitária, 05360-160 São Paulo, São Paulo, Brazil Full list of author information is available at the end of the article © 2016 Matias et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Matias et al. BMC Musculoskeletal Disorders (2016) 17:160 DOI 10.1186/s12891-016-1016-9 (Continued from previous page) Trial registration: Clinicaltrials.gov Identifier NCT02306148 (November 28, 2014) under the name “Effects of Foot Strengthening on the Prevalence of Injuries in Long Distance Runners”. Committee of Ethics in Research of the School of Medicine of the University of Sao Paulo (18/03/2015, Protocol # 031/15). Keywords: Running, Sports injuries, Exercise therapy, Foot, Biomechanics Background Human performance, particularly in one of the most popular sports activities such as running, is typically influ- enced by the state of the musculoskeletal system, either by the level of training and conditioning of the biological structures, or by the aging process. Although popular worldwide due to its low cost, versatility, convenience [1], and health benefits to people of all ages [2], running is as- sociated with a high prevalence of lower extremity injuries (between 19.4 and 79.3 %) [3]. The occurrence of injuries limits the intended benefits by inducing changes in prac- tice habits [4] or temporary or even permanent cessation of running. In addition, injuries lead to increased costs due to medical treatment and/or work absence [5]. The understanding of risk factors associated with these injuries, particularly the intrinsic factors, can provide important benefits for runners. Among these intrinsic factors, those that are noteworthy include biomechanical factors and muscle functionality of the lower extremities, particularly the feet. A systematic review by van der Worp et al. [5] included 11 high-quality longitudinal studies and concluded that alterations in the biomechan- ical force distribution patterns, amount of training, his- tory of previous injuries, increased index of the navicular drop, and the misalignment of the ankle, knee, and hip are among the main intrinsic risk factors for running- related injuries. In addition, extrinsic factors such as the training surface and the type of footwear are also relevant risk factors [5]. It is noteworthy that out of these seven di- verse risk factors, two are related to the foot-ankle com- plex, demonstrating the importance of maintaining the health and functionality of its musculoskeletal structures to prevent injuries. It is also believed that any biomechan- ical alteration in the musculoskeletal system, in particular the foot-ankle complex, broadly influences a runner’s functionality, predisposing him/her to a lesser or greater extent to injuries, in addition to the possibility of com- promising his/her quality of life [2, 6]. The foot has a complex structure that can perform a broad variety of functions in different postural and dy- namic tasks [7, 8]. This versatility can only be achieved through its unique arch-shaped architecture and its powerful intrinsic and extrinsic muscular activity, which is responsible for the maintenance and control of foot arches, postural corrections during disturbances, and torque generation during body displacement [9, 10]. Even with this unique and specialized structure, a high prevalence of injuries associated with running practices occurs in this complex. Among the most common hy- potheses used to explain this high prevalence are factors such as the excessive ankle/foot pronation in the stance phase of running [11], the lowering of the medial longi- tudinal arch due to navicular drop [12, 13], the alteration of rigidity of the plantar arches [14], and the increase in impact and acceleration of the tibia during running [15]. Evidence suggests the importance of the intrinsic foot musculature, showing that fatigue can cause a significant increase in pronation, which is evaluated by the navicu- lar drop [12]. In addition, weakness may be a risk factor for falls in the elderly population [16]. Therefore, it is understandable that the specific training of foot [13, 17] and ankle muscles [18–20] is an important tool that im- proves functions and functionalities of the lower extrem- ities, as has been shown in recent studies [13, 19–21]. In one of those studies, the unsupervised practice of a single exercise for the feet (short-foot exercise) four times a week promoted a decrease in the navicular drop, an increase in the medial longitudinal arch index, and an increase in the functionality quality of the intrinsic foot muscles in asymptomatic individuals [13]. These results were maintained 1 month after the training had been completed. Although the results of Mulligan and Cook [13] are promising, they only measured the foot function in static conditions and the unsupervised practice of an isolated exercise for 4 weeks may not have been suffi- cient to cause a transfer of the static gains for a more dynamic task where the foot would be more robustly utilized, according to the star excursion balance test. In contrast, one study compared two groups: one group performed a 4-week period of short-foot exercises, in- cluding 100 repetitions for five seconds each, and the second group performed a 4-week period of towel-curl exercises with the same amount of exercise [20]. This controlled study showed that both groups exhibited de- creased displacement of the centre of pressure during the modified star excursion test. Therefore, a load in- crease in the same exercises used by Mulligan and Cook [13] resulted in positive effects for postural control. The same short-foot exercise was practiced by individ- uals with flat feet in a randomized controlled trial to in- vestigate its effect on the use of foot orthoses [17]. The protocol consisted of three to five sets of exercises with Matias et al. BMC Musculoskeletal Disorders (2016) 17:160 Page 2 of 11 five repetitions each, twice a day, for 8 weeks. In both study groups, the isometric force and the transversal sec- tion area of the abductor hallucis muscle were increased after the interventions, with a significant increase in the group that used orthoses during exercises. These results demonstrated that even in structurally unfavourable conditions, exercise for the foot muscles leads to import- ant strength gains. It is noteworthy that even with a well- planned intervention, the lack of a control group and the evaluation of the muscle strength alone limit the study conclusions. In addition, the study did not take into ac- count the potential clinical and functional changes of the plantar arches, as performed by Goldmann et al. [19]. This group of researchers investigated the effects of the hallucis flexors strengthening in the kinetic and kinematic of foot and ankle during walking, running, and vertical jumping among university athletes. Training of the experimental group consisted of isometric contractions of the hallucis flexors at 90 % of the maximum voluntary contraction using a dynamometer four times a week for 7 weeks. The authors observed a significant increase in the performance of vertical jumping and extensor and flexor momentum of the metatarsal-phalangeal joint and a gain of 60 to 70 % in the strength of the hallucis flexors. This study shows that the flexor muscles of the foot respond in a quick and in- tense manner to training; even for simple training, the strengthening of the muscles in question results in global kinematic and kinetic alterations. It would still be interest- ing to determine how long these gains would last after the completion of the intervention and whether more elabor- ate training, involving more muscles and different pos- tures and loads, would alter the study outcome, especially with regard to foot biomechanics during locomotor tasks. The understanding of the effects of a therapeutic ap- proach focused on the foot biomechanics of walking and running, on the strength and functionality of lower ex- tremity muscles will contribute to the adoption of more effective therapeutic and preventive strategies for runners. However, no evidence exists that supports the efficacy of the therapeutic exercises already used and recommended for the health of the feet [7, 17, 19, 20, 22] with regard to preventing recurrent injuries in long-distance runners. However, one research protocol aims to assess the effects of ankle and hip muscle strengthening and functional bal- ance training on running mechanics, postural control, and injury incidence in novice runners with less than 1 year of running experience but without focusing on the interven- tion of intrinsic and extrinsic muscles of the feet [23]. Therefore, a controlled and randomized clinical trial would determine whether these interventions are effi- cacious by using the incidence of running-related in- juries as the primary outcome and following both intervention and control subjects during a period of time equal to or greater than 1 year (the period during which the incidence and prevalence of these injuries are reported) [4, 16, 24–27]. It is important to highlight that rehabilitation programs rarely include the intrinsic muscles of the feet in their therapeutic protocols. The present proposal uses a new paradigm in which the focus of training and preventive in- terventions in runners is a “ground-up” approach rather than the traditional "top-down" approach, which focuses on the hip strengthening. This new approach, advocated by Baltich et al. [23], will seek to improve the function of the ankle-foot complex, which is directly associated with the absortion and transmission of body forces to the ground and vice-versa during running. Hypotheses Our hypotheses are that the therapeutic exercise proto- col for the foot-ankle as practiced by long-distance rec- reational runners for 1 year will: H 1. Reduce the incidence of running-related injury in the lower limbs, H 2. Lengthening the time for the occurrence of the first running-related injury in the lower limbs, H 3. Increase intrinsic foot muscle strength, H 4. Increase foot muscle cross-sectional area and volume, H 5. Improve foot health and functionality status, H 6. Reduce dynamic strain on the foot’s longitudinal arch during running and walking, and H 7. Produce beneficial biomechanical changes during running that denote an improvement in the mechanical efficiency of absorbing loads and propelling the body while walking and running. Such changes would include an increase in the ankle range of motion in the sagittal plane and increases in 1) ankle extensor moment and power and 2) knee extensor moment and power during the second half of the stance phase. Our aim is therefore to investigate the effects of a "ground-up" therapeutic approach focused on the foot- ankle for 1 year as they relate to 1) the incidence of running-related injuries in the lower limbs of long-distance runners, 2) time of occurrence of the first injury, 3) foot health and functionality, 4) strength of the intrinsic foot muscles; 5) foot muscle trophism, 6) dynamic foot arch strain and 7) lower-limb biomechanics during walking and running. Methods/Design Overview of the research design A randomized, prospective controlled and parallel trial with blind assessment is designed to study the effects of a "ground-up" therapeutic approach focused on the foot- ankle concerning the incidence of running-related injuries Matias et al. BMC Musculoskeletal Disorders (2016) 17:160 Page 3 of 11 to the lower limbs of long-distance runners. This trial has an allocation ratio of 1:1. Its framework is exploratory to gather preliminary information on the intervention of conducting a full scale trial. The trial follows all recom- mendations established by SPIRIT [28]. Long-distance recreational runners are recruited from the vicinity of the city of São Paulo and referred to a physical therapist, who performs the group allocation. The participants are then referred to another physical therapist, who performs the initial blind assessment. All runners allocated to the intervention group (IG) partici- pate in a protocol of therapeutic exercises for the foot- ankle complex for 8 weeks, with one session per week supervised by a physical therapist and three sessions per week remotely supervised by web-enabled software [29]. They receive access to the web software on the first day and use it for 8 weeks. After the 8-week period, the IG runners will continue exercising for 10 more months, supervised only by the web software three times a week. The runners allocated to the control group (CG) do not receive any intervention training, but receive a placebo stretching exercise program. All runners will be assessed at baseline and 2 months (end of intervention). They are then assessed twice more for follow-up purposes, at 4 and 12 months after the baseline. Assessments will concern the incidence of running-related injuries (primary outcome), and all other secondary outcomes. The design and flowchart of the protocol are pre- sented in Fig. 1. The assessments are performed at the Laboratory of Biomechanics of Human Movement and Posture (LaBiMPH) at the Physical Therapy, Speech and Occupational Therapy department of the School of Medicine of the University of São Paulo, São Paulo, Brazil. Participants and recruitment This study is currently recruiting patients (study start date: April 2015) The eligibility criteria for the volunteer runners are: – aged between 18 and 55 years old – at least 1 year of running experience – a weekly training distance greater than 20 km an less than 100 km as their main physical activity – within 2 months prior to baseline assessment, lack of any lower limb musculoskeletal injury or pain that might lead to stopping running practice – no prior experience within the last year of isolated foot and ankle strength training – not receiving any physical therapy intervention – no history of using minimalist shoes for running practice – no prior experience of barefoot running Runners are not selected if they have other neuro- logical or orthopedic impairments (such as congenital foot malformations, stroke, cerebral palsy, poliomyelitis, rheumatoid arthritis, prosthesis or moderate or severe osteoarthritis), major vascular complications (venous or arterial ulcers), diabetes mellitus, sequelae from poorly healed fractures or prior lower-limb surgeries. These runners may use the running technique of fore-, mid- or rear-foot ground contact, which will be classified by the strike index, according to Cavanagh and Lafortune [30]. One hundred and eleven (111) runners will be re- cruited by radio advertisements, print media and run- ning association groups at their site of practice around the city of São Paulo. The potential subjects will be interviewed by telephone and, when selected, assessed in the laboratory to confirm all the eligibility criteria. This first laboratory assessment represents the baseline con- dition (blind assessment). The runners allocated to the IG will be treated during their locally supervised session at the Physical Therapy Department in an ambulatory setting that assists all the physical therapy treatments of the Department, providing a reliable therapeutic environment for the intervention. Randomization, allocation and blinding The randomization schedule was prepared using Clinstat software [31] by an independent researcher (Researcher 1) who was not aware of the numeric code for the CG and IG groups. A numeric block randomization se- quence will be kept in opaque envelopes. After the runners’ agreement to participate and assign- ment in the research, the allocation into the groups will be made by another independent researcher (Researcher 2), who also will be unaware of the codes. Only the physiotherapist (Researcher 3) responsible for the locally supervised training knows who is receiving the interven- tion. Researcher 3 will also be responsible for the remote monitoring of the training by web software [29] and tele- phone. One physiotherapist (Researcher 4), who will also be blind to the treatment allocation, will be responsible for all clinical, functional and biomechanical assess- ments. Both physiotherapists (researchers 3 and 4) will be blind to the block size used in the randomization procedure. To guarantee the blindness of researcher 4, before each evaluation, runners will be instructed not to reveal whether they are in the CG or IG; their questions should be asked only to the physiotherapist in charge of web software [29] and local training (Researcher 3). The trial statistician will also be blind to treatment al- location until the main treatment analysis has been completed. Matias et al. BMC Musculoskeletal Disorders (2016) 17:160 Page 4 of 11 Treatment arms The CG runners will receive a 5-min placebo routine of warm-up and muscle-stretching exercises to be per- formed immediately before every running practice dur- ing their 8-week study (Additional file 1: Table S3). The IG runners will receive a therapeutic foot-ankle exercise protocol for strengthening and improving func- tionality under the supervision of a physiotherapist (Researcher 4) once a week for 8 weeks, and a series of foot-ankle exercises to be performed under remote supervision through web software [29] three times a week for the full 1-year length of the study (1 year). Both locally (Additional file 1: Table S1) and remotely super- vised therapeutic routines (Additional file 1: Table S2) will take from 20 to 30 min. In particular, the remotely supervised practice will be preferentially performed at Fig. 1 Flow chart of the study’s design Matias et al. BMC Musculoskeletal Disorders (2016) 17:160 Page 5 of 11 home; the web software includes written descriptions, photos and videos of each exercise. Each week, IG runners will be requested to evaluate the subjective effort of each exercise’s performance using a score of 0 to 10 either with the web software [29] or to the physiotherapist during locally supervised practice. If the effort score ranges from 0 to 5 and the runner’s per- formance of each exercise is found adequate during the supervised session by the physiotherapist, the exercises will increase in difficulty according to the progression chart in Additional file 1: Table S1 and Table S2. If the effort score ranges from 6 to 7, the exercise will not in- crease in difficulty and no progression would be done on that exercise. Thus, the runner remains in the same ex- ercise progression until he/she scores 0 to 5 in each par- ticular exercise. Finally, if an IG runner reports a score from 8 to 10, the exercise will decrease in difficulty, if possible, until the subject is able to perform it without pain or discomfort. Assessments A physiotherapist (Researcher 3) who is blind to group allocation will perform all assessments. Each assessment will consist of taking a clinical history of personal details, anthropometry, running practice details (years of prac- tice, weekly frequency and volume, usual shoe and train- ing surface, number of races and whether the runner trains with a running coach), previous orthopedic sur- gery, other physical activity practiced regularly (previous to running practice or simultaneously with running) and an injury history concerning the most important risk factors previously published [3, 32, 33]. A foot-health status questionnaire [34] will be used to characterize foot health and functionality. We will use a Brazilian-Portuguese version (FHSQ-BR) translated and validated by Ferreira et al. [35]. This instrument is di- vided into three sections. Section I evaluates foot health in four domains: foot pain, foot function, footwear and general foot health. Section II evaluates general health in four domains: general health, physical activity, social capacity and vigour. Sections I and II are composed of questions with answer options presented in affirmative sentences and corresponding numbers. Section III col- lects general demographic data of the individuals [36]. We will not use the scores from Section III. Each do- main scores from 0 to 100 points, where 100 is the best condition and 0 the worst. We will access variations in foot posture of the run- ners using the Foot Posture Index (FPI) [36]. The FPI is a six component measures that allows multiple segment evaluation of foot posture on a static measurement and requires that subjects stand in their relaxed stance pos- ition looking straight ahead while the assessment is in process. The assessment consists on the (1) palpation of the talar head, (2) observation of supra and infra malleo- lar curvature, (3) observation of the calcaneal frontal plane position, (4) observation of the bulging in the re- gion of the talo-navicular joint, (5) observation of the height and congruence of the medial longitudinal arch and (6) presence of abduction or adduction of the fore- foot. Scores reaching from -12 to +12 and normative values are presented on the literature. Subjects will then be assessed for intrinsic foot muscles strength, lower-limb running kinematics and kinetics, and dynamic foot-arch strain. The feet of 30 % of the partici- pants in each group (41 participants) will be imaged by magnetic resonance imaging (MRI) to assess trophism and strength of the foot intrinsic muscles; this will be scheduled for the same week of each subject’s baseline measurements. After baseline assessment, all subjects will be sched- uled for two follow-ups assessments, one at 8 weeks and the other at 16 weeks. They will maintain contact with the Researcher 3 through the follow-up period by the web software [29], e-mail and telephone. Running-related injuries Running-related injuries will be assessed initially at the baseline and will be assessed continually throughout the study by the web software [29]. The definition of running-related injury was set according to the study of Macera et al. [4]. They stated that any musculoskeletal pain or injury that was caused by running practice and that induces changes in the form, duration intensity or frequency of training for at least 1 week will be consid- ered a running-related injury. Only lower-limb injuries will be accounted during the 12-month period after the baseline assessment; both the incidence and time of oc- currence of the first injury will be analyzed. If any subject presents a new injury during his or her participation in the study, the injury will be accounted for and the intervention or placebo intervention will be discontinued, even though all subjects will still keep be- ing followed for the completion of the study. Isometric intrinsic foot muscles strength Strength of the foot’s intrinsic muscles will be assessed in trials using a pressure platform (EMED: Novel, Germany) on which the subjects will place their domin- ant foot while standing with knees extended. They will push down as hard as possible using only their hallux and toes, particularly the metatarsophalangeal joints and not the hallux interphalangeal joint. A physiotherapist will determine whether the subject lifted the heel, and inspect fluctuations in the line of gravity and trunk pos- ture during each trial. If any changes are observed in the line of gravity or positioning of the heel or trunk, the trial will be excluded. Three trials will be completed on Matias et al. BMC Musculoskeletal Disorders (2016) 17:160 Page 6 of 11 each foot (left and right) according to Mickle et al. (2006) [37]. Maximum force will be normalized by body weight and analyzed for hallux and toes areas separately. Foot muscle trophism and strength One indirect method of measuring foot strength is through MRI, which, combined with other techniques, offers good reliability and a way to follow changes in muscular volume [38]. In addition, MRI can facilitate understanding the etiology of running-related injuries and rehabilitation of the foot-ankle complex [39]. The MRI of the foot will be performed with a 1.5 T sys- tem. Foot images will be acquired by the same technician using a coil of four channels positioned in the magnetic centre. Participants will be placed in supine position with the ankle at 45° of plantar flexion inside the coil. Images will be acquired in the frontal, sagittal and transverse planes to confirm the position of the feet, and the subject will be repositioned if necessary. T1-weighted images of the entire foot length will be acquired perpendicular to the plantar aspect of the foot using a spin-echo sequence (repetition time = 500 ms, echo time = 16 ms, averages = 3, slice thickness = 4 mm, gap between slices = 0 mm, field of view = 120 × 120 mm, flip angle = 90°, matrix = 512 × 512) [39]. The set of images will cover the distance between the most proximal and most distal images in which every in- trinsic foot muscle is visible. To assess changes in the cross-sectional area (CSA) and volume of the intrinsic foot muscles, 30 % of the subjects from each group will have MRI of the foot at three times: baseline, 8 weeks and 16 weeks. The CSA will be measured by ImageJ planimeter soft- ware [40]. Following, Miller et al. [14] for each muscle at each slice and muscle volume will be calculated by multiplying the CSA of all slices for a muscle by their linear distance (4 mm) and adding these volumes. Walking and running biomechanics To ensure maximum reliability, all biomechanical testing sessions will be completed by the same researcher. Gait and running kinematics will be acquired using three-dimensional displacements of passive reflective markers (10 mm in diameter) tracked by nine infrared cameras at 100 Hz (OptiTrack FLEX: V100, Natural Point, Corvallis, OR, USA) [41, 42]. Some 14 markers will be placed on the right subject’s foot according to Leardini’s protocol [43]. Extra markers will be placed at the medial knee joint line, lateral knee joint line and bi- laterally at the iliac spine antero-superior, superior as- pect of the greater trochanter, and sacrum. These markers will be used to determine relative joint centres of rotation for the longitudinal axis of the foot, ankle and knee. The extra markers from the medial aspect of the knee joint line will be removed during the dynamic trial. In addition, three non-collinear reflective markers will be fixed at two technique clusters. One of the clus- ters will be placed in the lateral thigh and the other over the shank. The laboratory coordinate system will be established at one corner of the force plate and all initial calculations will be based on this coordinate system. Each lower-limb segment (shank and thigh), will be modelled based on surface markers as a rigid body with a local coordinate system that coincides with the anatomical axes. Transla- tions and rotations of each segment will be reported relative to the neutral positions defined during the initial static standing trial. All joints will be considered to be spherical (i.e., with three rotational degrees of freedom). The foot will be modeled according to Leardini et al. [43]. That is, the calcaneus, mid-foot and metatarsus are considered rigid bodies and the longitudinal axis of the first, second and fifth metatarsal bones and proximal phalanx of the hallux will be tracked independently. Ground reaction forces will be acquired by a force plate (AMTI OR-6-1000, Watertown, MA, USA) with a sampling frequency of 1 kHz embedded in the centre of the walkway. Force and kinematic data acquisition will be synchronized and sampled by an A/D card (AMTI, DT 3002, 12 bits). The subjects will go through a habituation period before the data acquisition to establish confidence and comfort in the laboratory environment, and to ensure appropriate movement velocity. To assess lower-extremity running mechanics, subjects will perform 10 valid over-ground walking trials and 10 valid over-ground running trials at a constant velocity (9.5 km/h to 10.5 km/s); these will be monitored by two photoelectrical sensors (Speed Test Fit Model, Nova Odessa, Brazil). The automatic digitizing process, 3D reconstruction of the markers’ positions and filtering of kinematic data will be performed using AMASS software (C-motion, Kingston, ON, Canada). Kinematic data will be processed using a zero-lag second-order low-pass filter with cutoff frequen- cies of 6Hz for walking and 12 Hz for running. Ground reaction force data will be processed using a zero-lag low- pass Butterworth fourth-order filter with cutoff frequencies of 50Hz for walking and 200 Hz for running. A bottom-up inverse dynamics method will be used to calculate the net moments in the sagittal and frontal planes of the ankle and knee joints using Visual3D software (C-motion, Kingston, ON, Canada). The human body will be modeled by three linked segments (foot, shank and thigh) and the inertial properties will be based on Dempster’s standard regression equations. The moment of inertia and location of center of mass will be computed assuming the thigh and shank segments as cylinders. Calculation of all variables will be performed using a custom-written MATLAB function (MathWorks, Natick, Matias et al. BMC Musculoskeletal Disorders (2016) 17:160 Page 7 of 11 MA, USA). Data of only one lower limb (randomly chosen) per subject will be analyzed and compared. The following ankle kinematic variables will be analysed: maximum dorsiflexion at foot contact, maximum plantar- flexion, maximum dorsiflexion at the toe-off and dorsiflex- ion range of motion (ROM) in the sagittal plane during the stance phase. The knee kinematic variables are: maximum flexion at foot contact, maximum extension, maximum flexion in the stance phase, ROM on sagittal plane, max- imum abduction and adduction in the stance phase. The foot kinematic variables are: elevation/drop of the longitu- dinal arch angle and of the first, second and fifth metatarsal bones; rearfoot to forefoot rotation; transverse plane angle between first and second metatarsal bones and between second and fifth metatarsal bones; and maximum inversion and eversion of the calcaneus (frontal plane). The ankle and knee kinetic variables to be analysed are net ankle and knee moments normalized by body weight times height and power normalized by body weight in the sagittal plane. The ground reaction force variables will be normalized by body weight and are as followings: first peak force (body weight – BW), second peak (BW), loading rate 80 [N/ms], defined as the force rate between 20 and 80 % of the contact of the foot with the ground during the first peak; loading rate 100 [N/ms], as determined by the force rate between 0 and 100 % of the first peak and push-off rate [N/ms], as defined as the rate of the second peak force, be- tween the minimal values until the second peak. Dynamic longitudinal foot arch strain The dynamic longitudinal foot arch strain will be measured according to Liebermann et al. [44]. The measurement in- volves navicular height (NH), which is the minimum distance from the navicular tuberosity relative to the line formed by the first metatarsal head and the medial process of the calca- neus. These three landmarks form a plane and NH is inde- pendent of rear-foot inversion or eversion. Arch strain can also be quantified by fitting a parabola to markers (with the navicular head as the vertex) and then measuring the average curvature at 100 points evenly spaced along the curve. Outcome measurements The primary outcome measurement will be the inci- dence of running-related injuries in the lower limbs accounted at the end of 12 months of study. The secondary outcomes will be: 1) the time of the occurrence of the first injury along the study period (time to event); 2) foot health and functionality (change from baseline); 3) foot, ankle and knee kinematics, ankle and knee joint moments, and knee and ankle power during walking and running (change from baseline); 4) strength of the intrinsic foot muscles (change from baseline); 5) foot muscle trophism (change from baseline); and 6) dynamic foot arch strain (change from baseline). Interventions Runners allocated to the IG will receive a foot-ankle thera- peutic exercise protocol for strengthening and improving functionality. Part of the exercise protocol (12 exercises) is to be performed once a week under the supervision of a physiotherapist for 8 weeks (Additional file 1: Table S1). And a series of eight foot-ankle exercises is also to be performed three times a week remotely supervised by web software [29] (Additional file 1: Table S2) for the full 1-year completion time of the study. Each session, whether supervised locally or remotely, lasts 20 to 30 min. The therapeutic exercise protocol is described in details in Additional file: 1 Table S1 and S2. Gradual and progressive difficulty will be offered to the runner, respecting any limitation due to pain, fatigue and/or decrease in performance during execution. The runners in the IG will access the web software [29] daily, entering their data regarding performance of the foot exercise training and ranking their level of difficulty in each exercise from 0 to 10. During the locally supervised sessions, the physiotherapist will focus on proper alignment of the foot-ankle segments, especially if the runner has difficulty in maintaining it, in a way that allows no movement compensations. Runners allocated to the CG will receive a 5-min placebo warm-up and muscle stretching exercise routine (Additional file 1: Table S3) that they are to perform for 8 weeks immediately before each running practice. This placebo training can also be assessed and followed through the web software [29]. The stretching exercises are described in detail in Additional file: 1 Table S3. We hypothesized that a warm-up combined with muscle stretching exercises would not have any effect on foot muscular strength and functionality, lower extremity biomechanics or injury prevention. Both groups will access the web software [29] daily, entering their running practice data (daily training duration and volume) and information concerning the occurrence of any injury event. The discontinuation criteria for the exercises during any session includes cramps, moderate to intense pain, fatigue or any other condition that exposes the runner to any discomfort. The discontinuation criteria for the training includes an occurrence of a running-related injury in the lower limbs. If any subject fails to access the web software [29] for three consecutive weeks without explanation, or fails to attend the locally supervised training three consecutive times, that subject will be terminated from the study. To improve adherence, several actions will be per- formed by the researchers in the web software [29]. Data regarding the subjects running practice, such as training volume, time of practice and occurrence of injuries, will Matias et al. BMC Musculoskeletal Disorders (2016) 17:160 Page 8 of 11 be reported to the web software, which will summarize it and make it viewable in the users’ area. In addition, for the duration of the study, runners' responses in the web software concerning their foot-ankle exercise prac- tice and running training will be stored and be accessible to the researchers and subjects at any time. If any sub- ject fails to log in to the web software for more than five consecutive days, an e-mail will be automatically be sent, asking the subject to log in to his or her account and re- port data on the training (or lack of it) for the past week. The physiotherapist responsible for the therapeutic protocol will make phone contact with subjects who fail to attend to any of the weekly locally supervised ses- sions. They will also make phone contact with subjects who do not respond to e-mail reminders from the web software. Subjects will also be contacted by personal phone calls if data they reported on the web software is found to be inconsistent [45]. After the period of intervention and after 12 weeks of follow up all runners will be questioned about their sat- isfaction to the training protocol with one question (Did you enjoy doing the exercises?) with three answer possi- bilities (No; A Little; A lot). To avoid evaluation bias, runners will answer this question secretly through an online-unidentified form sent to their e-mail. Runners will be informed about the anonymity and this form will only be accessed after completion of the study. For the duration of the trial, subjects will be advised not to engage in any new physical activity or preventive train- ing protocols for the foot and ankle. If any subject cannot avoid such behavior, he or she must report this situation during web software [29] access. Concomitant care, such as physical therapy, acupuncture or other conventional medical care, will not be permitted except for runners who are injured during the study. At the end of 12 months, CG participants that are interested will receive access to the software for the foot exercise protocol. Sample size and statistical analysis The sample size calculation was made using an effect size of 0.28 (proportion), considering the categorical primary outcome variable, which is the incidence of running- related lower-limb injuries [33]. A sample size of 101 run- ners is needed to provide 80 % power to detect a moderate effect difference between the highest and lowest group injury incidence medians, assuming an alpha of 0.05 and a χ2 (chi-squared test) statistical design – contingency tables (df = 1) [46]. Assuming a 10 % dropout rate during the study, a sample size of 111 runners is needed. The statistical analysis will be based on intention-to- treat analysis, and mixed general linear models of analysis of variance for repeated measure will be used to detect treatment-time interactions (α = 5 %). The outcome mea- sures will be compared at baseline, 2, 4 and 12 months. Effect sizes (Cohen´s d coefficient) will also be provided between baseline and 2 months and between 2 months and follow-up (4 and 12 months), if the intervention shows any treatment effect. The missing data will be treated by imputation methods depending on the type of the missing data we will face: missing completely at ran- dom, missing at random, or missing not at random [47]. Discussion This clinical trial will provide important data on foot- training effectiveness, its influence on the incidence of injuries and its efficacy on strengthening the muscles of the foot-ankle complex. It will also facilitate the identification of risk factors and biomechanical mechanisms involved in injury processes and prevention. We also intend to contrib- ute new evidence that could be used as a guide for further studies on biomechanical changes in dynamic tasks result- ing from the strengthening of the foot-ankle complex. The few existing clinical trials that have proposed exercise protocols to reduce the incidence of runners’ injuries have not included the incidence of injury as a primary outcome. They also have had short follow-up periods and usually failed to follow the subjects’ adherence to the program and the correctness of exercise performance throughout the study [13, 17, 19, 20]. In contrast, this trial has the incidence of running-related injuries as a primary outcome, will have a long period of follow-up (12 months), proposes an interven- tion training protocol with several exercises that are easy to perform with short durations for each session (20–30 min) and does not require subjects to be continuously supervised by a health professional. In addition, it utilizes open-access web software [29] that will support adherence control. We understand that the number of MRIs that we are performing (on 30 % of the subjects) is limited and might prevent a broad conclusion about changes in intrinsic foot muscle cross-sectional area (CSA) and volume. Running-related injuries in this population cause inter- ruptions and abandonment of physical activity. They also could lead to the development of chronic injury that would prevent the practice of other sports and hence frustrate the individual’s pursuit of a healthy lifestyle. Runners are constantly looking for ways to remain free from injury and the information they receive from coa- ches or media is often conflicting and varied [48]. Our protocol has the potential to change the course of this vicious cycle experienced by long-distance runners. If our hypothesis that such an exercise protocol reduces the incidence of running-related injuries to long-distance runners is confirmed, it could be easily incorporated into their warm-up routine prior to running practice. Ethics approval and consent to participate This trial was approved by the Ethics Committee of the School of Medicine of the University of São Paulo (Protocol Matias et al. BMC Musculoskeletal Disorders (2016) 17:160 Page 9 of 11 number n°031/15). Additionally, this trial is registered in ClinicalTrials.gov (a service of U.S. National Institutes of Health) Identifier NCT02306148 (November 28, 2014) under the name “Effects of Foot Strengthening on the Preva- lence of Injuries in Long Distance Runners”. All runners will be asked for written informed consent according to the standard forms and the researcher 4 will obtain them. Consent to publish Written informed consent for publication of all images was obtained from the models. Availability of data and materials All personal data from potential or enrolled runners will be maintained confidential before, during and after the trial by encoding participant’s name. All data access and storage are in keeping with National Health and Medical Research Council guidelines, as approved. All files will be available from the database published at figshare.com. All important protocol amendments will be reported to investigators, re- view boards and trial registration by the Researcher 3. Additional file Additional file 1: Table S1. Exercises included in the supervised sessions by a physiotherapist. Table S2. Exercises included in the remotely supervised sessions in the web software. Table S3. Warm up and stretching exercises - Control group. (DOCX 2425 kb) Abbreviations CG: Control group; CSA: Cross-sectional area; FHSQ-BR: Foot-health status questionnaire - Brazilian-Portuguese version; FPI: Foot Posture Index; IG: Intervention group; MRI: Magnetic resonance imaging; NH: Navicular height. Competing interests The authors affirm that this study has not received any funding/assistance from a commercial organization which may lead to a conflict of interests. Authors’ contributions All authors have made substantial contributions to all three of sections (1), (2) and (3): (1) The conception and design of the study, or acquisition of data, or analysis and interpretation of data (2) drafting the article or revising it critically for important intellectual content (3) final approval of the version to be submitted. And in the protocol the following roles will be played by the authors: UTT is responsible for the study design, intervention, interpretation of the data, writing the report and submission of the manuscript. ABM is responsible for the study design, data collection, management, analysis, and interpretation, writing the report and submission of the manuscript. ICNS is responsible for the study design, interpretation of the data, writing the report and submission of the manuscript. Acknowledgements The authors are grateful to the State of São Paulo Research Foundation (FAPESP 2014/27311-9; 2015/14810-0), and the Agency Coordination of Improvement of Higher Education Personnel (CAPES) for the funding granted to this study. The funders do not have any role in the study and do not have any authority over any study activity or in the decision to submit the report for publication. The authors acknowledge Oliveira CC, Soares L, Amorim LG and Vilas Boas C for the help with the web-software’s construction. Author details 1Department of Physical Therapy, Speech, and Occupational Therapy, School of Medicine, University of São Paulo, Rua Cipotânea, 51 - Cidade Universitária, 05360-160 São Paulo, São Paulo, Brazil. 2Federal University of ABC, Biomedical Engineering, São Bernardo, São Paulo, Brazil. 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Advanced Statistics: Missing Data in Clinical Research-Part 1: An Introduction and Conceptual Framework. Acad Emerg Med. 2007;14:662–8. 48. Heiderscheit B. Always on the run. J Orthop Sports Phys Ther. 2014;44:724–6. • We accept pre-submission inquiries • Our selector tool helps you to ﬁnd the most relevant journal • We provide round the clock customer support • Convenient online submission • Thorough peer review • Inclusion in PubMed and all major indexing services • Maximum visibility for your research Submit your manuscript at www.biomedcentral.com/submit Submit your next manuscript to BioMed Central and we will help you at every step: Matias et al. BMC Musculoskeletal Disorders (2016) 17:160 Page 11 of 11	Protocol for evaluating the effects of a therapeutic foot exercise program on injury incidence, foot functionality and biomechanics in long-distance runners: a randomized controlled trial.	04-14-2016	Matias, Alessandra B,Taddei, Ulisses T,Duarte, Marcos,Sacco, Isabel C N	eng
PMC9941527	Estimated power output for a distance run and maximal oxygen uptake in young adults Gen-Min Lin1,2, Kun-Zhe Tsai 1,2,3, Xuemei Sui4 and Carl J. Lavie5 1Department of Internal Medicine, Hualien Armed Forces General Hospital, Hualien City, Taiwan, 2Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, 3Department of Stomatology of Periodontology, Mackay Memorial Hospital, Taipei, Taiwan, 4Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States, 5Ochsner Clinical School, John Ochsner Heart and Vascular Institute, The University of Queensland School of Medicine, New Orleans, LA, United States Background: Both cardiopulmonary exercise testing (CPET) and run ﬁeld tests are recommended by the American Heart Association for assessing the maximal oxygen uptake (VO2 max) of youth. Power output was highly correlated with VO2 max in CPET. However, it is unclear regarding the correlations of time and estimated power output (EPO) for a run ﬁeld test with VO2 max obtained from CPET in young adults. Methods: This study included 45 participants, aged 20–40 years, from a sample of 1,120 military personnel who completed a 3,000-m run ﬁeld test in Taiwan in 2020. The participants subsequently received CPET using the Bruce protocol to assess VO2 max in the same year. According to the physics rule, EPO (watts) for the run ﬁeld test was deﬁned as the product of half body mass (kg) and [distance (3000-m)/time (s) for a run ﬁeld test]. Pearson product–moment correlation analyses were performed. Results: The Pearson correlation coefﬁcient (r) of time against EPO for the run ﬁeld test was estimated to be 0.708 (p <0.001). The correlation coefﬁcient between the time for the run ﬁeld test and VO2 max (L/min) in CPET was estimated to be 0.462 (p = 0.001). In contrast, the correlation coefﬁcient between time for the run ﬁeld test and VO2 max scaled to body mass in CPET was estimated to be 0.729 (p <0.001). The correlation coefﬁcient of EPO for the run ﬁeld test against VO2 max in CPET was estimated to be 0.813 (p <0.001). Conclusion: In young adults, although the time for a run ﬁeld test was a reliable estimate of VO2 max scaled to body mass, EPO proportional to the mean square velocity was found as a superior estimate of VO2 max. KEYWORDS cardiopulmonary exercise testing, maximal oxygen uptake, estimated power output, run ﬁeld test, velocity Introduction Cardiorespiratory ﬁtness (CRF) is inversely associated with the risk of metabolic syndrome, diabetes mellitus, cardiovascular diseases (CVD), and mortality in the general population (Carnethon et al., 2009; Mehta et al., 2020; Wang et al., 2021; Lin et al., 2022; Sui et al., 2022). Obtaining greater CRF levels for sedentary individuals has been proposed as one of the major preventive measures to reduce the severity and burden of atherosclerosis in developed countries (Lavie et al., 2019; Sanchis-Gomar et al., 2022). The gold standard for CRF assessment is maximal oxygen uptake (VO2 max) with or without an adjustment for body mass from cardiopulmonary exercise testing (CPET). The main advantages of CPET include a strictly OPEN ACCESS EDITED BY Elisabetta Salvioni, Monzino Cardiology Center (IRCCS), Italy REVIEWED BY Maurizio Bussotti, Scientiﬁc Clinical Institute Maugeri (ICS Maugeri), Italy Yaoshan Dun, Xiangya Hospital, Central South University, China *CORRESPONDENCE Gen-Min Lin, farmer507@yahoo.com.tw Carl J. Lavie, clavie@ochsner.org SPECIALTY SECTION This article was submitted to Exercise Physiology, a section of the journal Frontiers in Physiology RECEIVED 05 December 2022 ACCEPTED 20 January 2023 PUBLISHED 07 February 2023 CITATION Lin G-M, Tsai K-Z, Sui X and Lavie CJ (2023), Estimated power output for a distance run and maximal oxygen uptake in young adults. Front. Physiol. 14:1110802. doi: 10.3389/fphys.2023.1110802 COPYRIGHT © 2023 Lin, Tsai, Sui and Lavie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Frontiers in Physiology frontiersin.org 01 TYPE Original Research PUBLISHED 07 February 2023 DOI 10.3389/fphys.2023.1110802 controlled environment, e.g., a maintained indoor temperature, atmospheric pressure, and calibration gas. However, the limitation to CPET is facility-dependent, and examinees require a tolerance to wear a mask during the graded exercise testing. Therefore, CPET may not be feasible for some speciﬁc populations, such as children and the elderly, and might not be practical at a large population level. Based on the ﬁndings of previous studies, the American Heart Association (AHA) has recommended VO2 max and some alternative measures for the CRF levels of youth (Mayorga-Vega et al., 2015; Raghuveer et al., 2020). For instance, the performance of a ﬁeld-based 20-m shuttle run test or distance run test has a moderate-to-high correlation with VO2 max obtained from CPET, whereas the performance of a ﬁeld-based step test or 6-minute walk test has only a low-to-moderate correlation with VO2 max in children or adolescents (Raghuveer et al., 2020). In addition, previous studies also revealed that the peak power output of the heart or body measured in CPET has a high correlation with VO2 max in patients with recovering heart failure and athletes (Hawley and Noakes, 1992; Jakovljevic et al., 2011). Physiologically, most oxygen uptake is translated to energy output during peak exercise. However, to the best of our knowledge, there have been no studies with regard to the correlation between estimated power output (EPO) for a run ﬁeld test and VO2 max in CPET. The correlation between a run test performance and VO2 max in CPET was not clariﬁed in adults and varied by study (Cooper, 1968; O’Gorman et al., 2000; Casajus and Castagna, 2007). The aim of this study was to investigate the correlations of time and EPO for a distance run ﬁeld test with VO2 max measured from CPET in a group of young military adults. Materials and methods Study population The study included 45 participants, aged 20–40 years, without any medication use, randomly selected from the cardiorespiratory ﬁtness and health in eastern armed forces (CHIEF) study participants (N = 1,120) (Lin et al., 2021; Liu et al., 2021) in 2020, which aimed to carry out a preliminary study on the correlation of time for a 3000-m run ﬁeld test with VO2 max from CPET. All participants received exercise training daily, e.g., a 3,000-m run within 25 min in the morning at the military base for over 6 months. A history of moderate physical activity, such as a limited-time 3,000-m run in the morning per week in the past half year, was obtained from each participant. Each participant underwent the 2020 annual health examinations for physical examinations (Hsu et al., 2022) in the Hualien Armed Forces General Hospital of Taiwan. Each participant also underwent the 2020 annual military exercise test for a 3,000-m run ﬁeld test to assess endurance capacity. Within 2 weeks of the 3,000-run ﬁeld test, the 45 volunteers were scheduled for CPET to objectively assess VO2 max. Anthropometric and blood pressure (BP) measurements Anthropometric parameters, i.e., body height and weight, were measured in a standing position by a medical staff member in the annual military exercise test. Body mass index was deﬁned as the body weight divided by body height squared (kg/m2). Overweight or obesity was deﬁned as a body mass index ≥27.5 kg/2 for Asians, according to the recommendations of the World Health Organization (Jih et al., 2014). The BP and pulse rate of each participant were automatically measured using the same device (FT201, Parama-Tech Co., Ltd., Fukuoka, Japan), which utilized the oscillometric method (Lin et al., 2016; Lin et al., 2020a; Lin et al., 2020b). The BP was measured once over the right arm in a sitting position after resting for longer than 15 min and was recorded by a medical staff member. If the pulse rate was ≥90 beats per minute, systolic BP ≥140 mmHg, or diastolic BP ≥90 mmHg was found, the participant would undergo another two rounds of hemodynamic parameter measurements, which were averaged as the ﬁnal result. The 3000-m run ﬁeld test The 3,000-m run ﬁeld test was performed outdoors on a ﬂat playground of the Military Physical Training and Testing Center in Hualien, Taiwan, at 16:00. Each participant wore sweat suits and did not carry any additional objects. The whole running process of each participant was video recorded and supervised by eight military sports ofﬁcers. The 3,000-m run ﬁeld test was carried out if there was no heavy rain, and the coefﬁcient of the heat stroke risk formula, the product of relative humidity (%) and outdoor temperature (Celsius scale) x 0.1, was less than 40. The time for the 3,000-m run ﬁeld test was utilized to evaluate the endurance capacity of each participant. EPO for the run ﬁeld test was deﬁned as “1/2 x body mass (kg) x square of mean velocity (m/s),” on the basis of the physics rule of the kinetic energy theorem (Serway and Jewett, 2004). The mean velocity was calculated by the formula “distance (3,000- m) divided by time (s) for the run ﬁeld test.” The performance of CPET CPET was performed on a Trackmaster TMX-428 stress treadmill (SCHILLER, Baar, Switzerland) using the standard Bruce protocol. The same supervisor conducted all of the CPETs throughout the study. All participants were asked not to consume caffeine or alcohol for 12 h or longer before the CPET and exercised after a 2-h postprandial period. The room for the CPET used an air conditioning system to maintain a constant temperature of approximately 22 degrees Celsius. Throughout the CPET, electrocardiography and BP were monitored. The rates of oxygen uptake (VO2), production of carbon dioxide (VCO2), tidal volume (Vt), end-tidal partial pressure of carbon dioxide (PETCO2), and respiratory rate were recorded breath by breath using a Cardiovit CS-200 Excellence Ergo-Spiro analytic system (SCHILLER, Baar, Switzerland). VO2 max was deﬁned as the average of VO2 during the last minute of maximal exercise. Statistical analysis Characteristics of overall participants for a 3,000-m run ﬁeld test and those for both a 3,000-m run ﬁeld test and CPET were presented as numbers (%) for categorical variables and mean ± standard deviation for continuous variables, respectively. Continuous variables were compared using analysis of variance if the Kolmogorov–Smirnov test for the normal distribution was met; otherwise, the Wilcoxon signed-rank test was used. Categorical variables were compared using Fisher’s exact test. The Pearson Frontiers in Physiology frontiersin.org 02 Lin et al. 10.3389/fphys.2023.1110802 product–moment correlation coefﬁcient was used to determine the association strength of time and EPO for a 3,000-m run ﬁeld test with VO2 max scaled to body mass or not in CPET. The correlations of time and EPO for a 3,000-m run ﬁeld test were performed for a comparison between selected participants for both a 3,000-m run ﬁeld test and CPET and a sample of age-, sex-, body mass index-, and BP-matched participants from the overall study participants. Scatter plots between time and EPO for the 3,000-m run ﬁeld test and VO2 max scaled for body mass or not in CPET were obtained. Internal validation was performed for those whose body mass index <27.5 kg/m2. A value of p <0.05 was considered signiﬁcant. All analyses were carried out using SPSS version 25.0 for Windows (IBM Corp., Armonk, NY, United States). This study has been approved by the Institutional Review Board of the Clinical Ethics Committee of the Mennonite Christian Hospital (No. 16-05-008), Hualien City, Eastern Taiwan, R.O.C., and written informed consent was obtained from all participants. Results Clinical characteristics of the participants Table 1 shows the clinical characteristics of the participants for a 3,000-m run ﬁeld test (N = 1,120) and those for both a 3,000-m run ﬁeld test and CPET (N = 45). The characteristics of the selected participants for CPET were similar to the original overall sample, except greater age, pulse rate, and BP levels were observed in participants for CPET. The mean age of the participants for CPET was approximately 30 years old, and over 90% of the participants were males. Since only one woman was included for the CPET, the characteristics of men were also compared between the original group (N = 911) and the selected group (N = 44), and the results are provided in Supplementary Table S1, which show consistent ﬁndings. Correlations between time and EPO for a 3000-m run ﬁeld test The correlation coefﬁcient (r) of time against EPO for a 3,000-m run ﬁeld test was estimated to be 0.708 (p <0.001) in participants for both a 3,000-m run ﬁeld test and CPET (Figure 1A), which was close to the correlation coefﬁcient (r = 0.703 and p <0.001) in the age-, sex-, body mass index-, and BP-matched samples of 707 participants for a 3000-m run test (Figure 1B). The characteristics of the variable- matched population are provided in Supplementary Table S2. The correlation coefﬁcients for men only were in line with the main ﬁndings, and the results are provided in Supplementary Figure S1. Correlations of time for a 3000-m run ﬁeld test against VO2 max in CPET The correlation coefﬁcient of time for a 3000-m run ﬁeld test against VO2 max (L/min) in CPET was estimated to be 0.462 (p = 0.001) (Figure 2A). In contrast, the correlation coefﬁcient between time for a 3000-m run ﬁeld test and VO2 max scaled to body mass (kg) in CPET was estimated to be 0.729 (p <0.001) (Figure 2B). The correlation coefﬁcients for men only were in line with the main ﬁndings, and the results are provided in Supplementary Figure S2. Correlations of EPO for a 3000-m run ﬁeld test against VO2 max in CPET The correlation coefﬁcient of EPO for a 3,000-m run ﬁeld test against VO2 max (L/min) in CPET was estimated to be 0.813 (p <0.001) (Figure 3A). However, the correlation coefﬁcient between EPO for a 3,000-m run ﬁeld test and VO2 max scaled to body mass (kg) in CPET was estimated to be only 0.364 (p <0.001) (Figure 3B). The correlation coefﬁcients for men only were in line with the main ﬁndings, and the results are provided in Supplementary Figure S3. Internal validation for non-obese participants The results of interval validation for participants with body mass index <27.5 kg/m2 (N = 35) are revealed in Figure 4. The correlation coefﬁcient of time for a 3,000-m run ﬁeld test against VO2 max (L/min) was 0.453 (p = 0.006) (Figure 4A), and the correlation coefﬁcient of time for a 3,000-m run ﬁeld test with VO2 max scaled to body mass (kg) was 0.485 (p = 0.003) (Figure 4B). The correlation coefﬁcient of EPO for a 3,000-m run ﬁeld test with VO2 max (L/min) was 0.757 (p <0.001) (Figure 4C), and the correlation coefﬁcient of EPO for a 3,000-m run ﬁeld FIGURE 1 (A) Correlation coefﬁcient (r) of time against EPO for a 3,000-m run ﬁeld test, estimated to be 0.708 (p <0.001) in participants for both a 3,000-m run ﬁeld test and CPET. (B) Correlation coefﬁcient estimated to be 0.703 (p <0.001) in the variable-matched participants for a 3,000-m run ﬁeld test. Frontiers in Physiology frontiersin.org 03 Lin et al. 10.3389/fphys.2023.1110802 TABLE 1 Clinical Characteristics of the Overall Participants for a Run Field Test and the Selected Participants for a Cardiopulmonary Exercise Testing. N = 45 N = 1120 p-value Sex, males (%) 44 (97.8) 911 (81.3) 0.07 Age, years 29.93 ± 7.05 27.61 ± 5.87 0.01 Body height, cm 170.74 ± 6.47 170.80 ± 6.67 0.93 Body mass, kg 72.74 ± 11.81 71.93 ± 12.21 0.66 Body mass index, kg/m2 24.95 ± 3.86 24.58 ± 3.54 0.49 Pulse rate, beats per min 77.50 ± 11.18 67.09 ± 10.95 <0.001 Systolic blood pressure, mmHg 127.68 ± 11.83 116.97 ± 12.83 <0.001 Diastolic blood pressure, mmHg 80.32 ± 10.39 69.09 ± 9.88 <0.001 Time for a 3000-m run, secs 876.62 ± 94.34 893.55 ± 106.19 0.29 EPO for a 3000-m run, watts 437.44 ± 105.22 421.12 ± 114.18 0.34 Moderate activity per week 100-150 minutes 8 (17.8) 224 (20.0) 0.92 150-300 minutes 18 (40.0) 423 (37.8) >300 minutes 19 (42.2) 473 (42.2) Abbreviation: EPO, estimated power output. EPO was deﬁned as 1/2 x body mass (kg) x (3000-m/time for a 3000-m run test)2. FIGURE 2 In 45 participants, for both a 3,000-m run ﬁeld test and CPET: (A) correlation coefﬁcient of time for a 3,000-m run ﬁeld test against VO2 max (L/min) was 0.462 (p = 0.001); (B) correlation coefﬁcient of time for a 3,000-m run ﬁeld test with VO2 max scaled to body mass (kg) was 0.729 (p <0.001). FIGURE 3 In 45 participants, for both a 3,000-m run ﬁeld test and CPET: (A) correlation coefﬁcient of EPO for a 3000-m run ﬁeld test against VO2 max (L/min) was 0.813 (p <0.001); (B) correlation coefﬁcient between EPO for a 3,000-m run ﬁeld test and VO2 max scaled to body mass (kg) was estimated to be 0.364 (p <0.001). Frontiers in Physiology frontiersin.org 04 Lin et al. 10.3389/fphys.2023.1110802 test against VO2 max scaled to body mass (kg) was 0.349 (p = 0.04) (Figure 4D). Although the correlation coefﬁcients in the sample for interval validation were all lower than that in the overall sample (N = 45) receiving the CPET, all of the associations of time and EPO for a 3,000-m run with VO2 max in a CPET were signiﬁcant, and the EPO association remained with the greatest strength. Estimation of VO2 max in CPET from time and EPO for a run ﬁeld test Based on the regression line in Figure 2B, formula 1, with regard to time for a run ﬁeld test to estimate VO2 max scaled to body mass, can be derived as follows: Formula 1 Y = −0.0386X + 67.151 X: Time for a 3,000-m ﬁeld run (s) Y: VO2 max scaled to body mass (mL/min/kg) Based on the regression line in Figure 3A, formula 2, with regard to EPO for a run ﬁeld test to estimate VO2 max, can be derived as follows: Formula 2 Y = 3.7943X + 757.6 X: EPO for a 3000-m ﬁeld run (watts) Y: VO2 max (mL/min) Discussion The principal ﬁnding of this study was that in young adults, the time for a run ﬁeld test can be an acceptable estimate of VO2 max scaled to body mass obtained in CPET. In addition, the EPO for a run ﬁeld test, calculated according to the kinetic energy theorem, can be a more precise estimate of VO2 max (L/min) than the time for a run ﬁeld test. Numerous studies have investigated the correlation between distance- or time-based run ﬁeld test performance and VO2 max in CPET in adults (Mayorga-Vega et al., 2016). However, the correlation coefﬁcients were distributed widely, ranging from 0.60 to 0.90, among various run ﬁeld tests (Cooper, 1968). In addition, no consensus has been reached in previous studies to unify the VO2 max unit, with or without an adjustment for body mass, when analyzing the correlation. Furthermore, the correlation coefﬁcients might vary even in the same run ﬁeld test among different studies (Cooper, 1968; O’Gorman et al., 2000; Casajus and Castagna, 2007). For instance, Cooper found a high correlation between running distance and VO2 max scaled to body mass in a 12-min run ﬁeld test in a sample of military males, in whom the correlation coefﬁcient was estimated to be 0.897 (Cooper, 1968). In the O’Gorman et al. (2000) study, there was a moderate correlation in a 12- min run ﬁeld test and in a 3,000-m run ﬁeld test using a sample of physically ﬁt young males, in whom the correlation coefﬁcient was estimated equally to be 0.67 and −0.67. In contrast, the Casajus and Castagna study revealed a relatively lower correlation in a 12-min run ﬁeld test in a sample of elite soccer players, in whom the correlation coefﬁcient was estimated to be only 0.46 (Casajus and Castagna, 2007). In the present study, we demonstrated a moderate correlation of time for a 3,000-m run ﬁeld test with VO2 max scaled to body mass, whereas the results showed a relatively lower correlation with VO2 max, which was not scaled to body mass. These ﬁndings are in line with previous studies and a meta-analysis (Serway and Jewett, 2004) made by Mayorga-Vega et al., which showed the correlation coefﬁcient between time for a 3,000-m run ﬁeld test and VO2 max scaled to a body mass of 12 studies, including 951 young adults, was estimated to be 0.70. We further highlighted the importance of the VO2 max unit for examining the correlation. It is reasonable that the FIGURE 4 In 35 participants with body mass index <27.5 kg/m2, for both a 3000-m run ﬁeld test and CPET: (A) correlation coefﬁcient of time for a 3000-m run ﬁeld test against VO2 max (L/min) was estimated to be 0.453 (p = 0.006); (B) correlation coefﬁcient of time for a 3,000-m run ﬁeld test against VO2 max scaled to body mass (kg) was estimated to be 0.485 (p = 0.003); (C) correlation coefﬁcient of EPO for a 3,000-m run ﬁeld test with VO2 max (L/min) was 0.757 (p <0.001). (D) correlation coefﬁcient between EPO for a 3,000-m run ﬁeld test and VO2 max (L/min) scaled to body mass was 0.349 (p = 0.04). Frontiers in Physiology frontiersin.org 05 Lin et al. 10.3389/fphys.2023.1110802 examinees’ running velocity in run ﬁeld tests was inversely related to their body mass. The correlation between the running velocity in a run ﬁeld test and VO2 max scaled to body mass in CPET would theoretically result in the optimal result, except that the examinees had a similar level of body mass at baseline. Some reports have shown a moderate-to-high correlation between peak cardiac power output (Watts) and VO2 max (L/min) not scaled to body mass in CPET in patients with heart failure, in whom the greatest correlation coefﬁcient was 0.85, observed in those with recovering heart function (Jakovljevic et al., 2011), and a high correlation of peak power output with VO2 max in cycling athletes (correlation coefﬁcient greater than 0.90) (Hawley and Noakes, 1992). The present study is the ﬁrst report using EPO for a run ﬁeld test to estimate VO2 max in CPET in young adults. Oxygen consumption generates energy, heat, and waste. Accordingly, it is apt to use peak exercise power output to assess VO2 max not adjusted for body mass in CPET for adults. In the present study, EPO was calculated according to the kinetic energy theorem and proportional to the square of the mean velocity in a 3,000-m run ﬁeld test. The use of the square of the mean velocity was better than the mean velocity in the run ﬁeld test to correlate with VO2 max in CPET in adults. This ﬁnding is consistent with a previous study on CPET, where peak power output was superior to the cycling speed to estimate VO2 max in athletes (Hawley and Noakes, 1992). The present study, however, has a few limitations. First, the limited number of enrolled subjects is the major limitation to this preliminary study, and further study is required to expand the sample size to obtain greater power. Second, this study included only one woman and lacked multi-ethnic/racial diversity, making generalization of the results difﬁcult. Third, although the annual military exercise test was held with some restrictions of the weather, there could have been a bias for differences in outdoor temperature and humidity, which may affect the running performance and EPO estimation. Fourth, as the study included merely healthy subjects, the results may not be appropriately applied to those with a mismatch between heart and lung functions. External validation should be performed in further study. Finally, since we chose mean running velocity in the kinetic formula, the maximum actual power output during the run test might be underestimated, possibly leading to a bias for the correlation with VO2 max. Conclusion There have been no recommendations from the AHA regarding the role of time and EPO for a run ﬁeld test to evaluate VO2 max in adults, and the VO2 max unit was not emphasized. Our ﬁndings suggest that in young adults, although the time for a distance run ﬁeld test was an acceptable estimate of VO2 max scaled to body mass, EPO proportional to the square of the mean velocity in a run ﬁeld test was found as a superior estimate of VO2 max than the time for a run ﬁeld test in this population. Further studies are needed involving young women. Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Ethics statement The studies involving human participants were reviewed and approved by the Institutional Review Board of the Clinical Ethics Committee of the Mennonite Christian Hospital (No 16-05-008) Hualien City, Eastern Taiwan. The patients/participants provided their written informed consent to participate in this study. Author contributions GL collected and interpreted the data and wrote the manuscript; KT analyzed the data; XS and CL raised critical comments and edited the text; GL was the principal investigator for the administration of the study. Funding The present study was supported by grants from the Hualien Armed Forces General Hospital (HAFGH-D-112004) and the Medical Affairs Bureau, Ministry of National Defense (MND-MAB-D-112182). Conﬂict of interest The authors declare that the research was conducted in the absence of any commercial or ﬁnancial relationships that could be construed as a potential conﬂict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their afﬁliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. 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PMC5133111	nutrients Article Cardiorespiratory Fitness and Peak Torque Differences between Vegetarian and Omnivore Endurance Athletes: A Cross-Sectional Study Heidi M. Lynch , Christopher M. Wharton and Carol S. Johnston Arizona State University, School of Nutrition and Health Promotion, Phoenix, AZ 85004, USA; Christopher.Wharton@asu.edu (C.M.W.); Carol.Johnston@asu.edu (C.S.J.) Correspondence: Hnetland@asu.edu; Tel.: +1-847-828-1332 Received: 1 September 2016; Accepted: 10 November 2016; Published: 15 November 2016 Abstract: In spite of well-documented health beneﬁts of vegetarian diets, less is known regarding the effects of these diets on athletic performance. In this cross-sectional study, we compared elite vegetarian and omnivore adult endurance athletes for maximal oxygen uptake (VO2 max) and strength. Twenty-seven vegetarian (VEG) and 43 omnivore (OMN) athletes were evaluated using VO2 max testing on the treadmill, and strength assessment using a dynamometer to determine peak torque for leg extensions. Dietary data were assessed using detailed seven-day food logs. Although total protein intake was lower among vegetarians in comparison to omnivores, protein intake as a function of body mass did not differ by group (1.2 ± 0.3 and 1.4 ± 0.5 g/kg body mass for VEG and OMN respectively, p = 0.220). VO2 max differed for females by diet group (53.0 ± 6.9 and 47.1 ± 8.6 mL/kg/min for VEG and OMN respectively, p < 0.05) but not for males (62.6 ± 15.4 and 55.7 ± 8.4 mL/kg/min respectively). Peak torque did not differ signiﬁcantly between diet groups. Results from this study indicate that vegetarian endurance athletes’ cardiorespiratory ﬁtness was greater than that for their omnivorous counterparts, but that peak torque did not differ between diet groups. These data suggest that vegetarian diets do not compromise performance outcomes and may facilitate aerobic capacity in athletes. Keywords: vegetarian; endurance; VO2 max; dynamometer; protein; sustainability; torque; body composition; Dual X-ray Absorptiometry (DXA) 1. Introduction Vegetarian diets are increasingly being adopted for a variety of reasons including health, sustainability, and ethics-related concerns. Adherence to a vegetarian diet has been associated with a reduced risk of developing coronary heart disease [1], breast cancer [2], colorectal cancers [3], prostate cancer [4], type 2 diabetes [5], insulin resistance [6], hypertension [7], cataracts [8] and dementia [9]. Vegetarians also typically have a lower body mass index (BMI) [10] and an improved lipid proﬁle [11]. In addition to promoting physical health, reducing or eliminating meat from the diet is environmentally advantageous since producing meat requires more land, water, and energy resources than growing plants for food [12], and producing meat creates more greenhouse gases compared to a plant-based diet [13,14]. In spite of the many health aspects of vegetarian diets some concern has been raised pertaining to the nutrient adequacy of vegetarian diets for supporting athletic performance. Vegetarian diets are typically lower in vitamin B12, protein, creatine, and carnitine [15,16], and iron and zinc from plant sources are less bioavailable than from meat sources [17]. However, vegetarian diets are typically higher in carbohydrate and antioxidants [18,19], which may be advantageous for athletic performance, particularly for endurance activities [20]. Nutrients 2016, 8, 726; doi:10.3390/nu8110726 www.mdpi.com/journal/nutrients Nutrients 2016, 8, 726 2 of 11 Despite these issues, little research directly examining vegetarian diets and athletic performance is available. There have been mixed results regarding hypertrophic potential when comparing vegetarian diets with omnivore diets during resistive exercise training; however, in all cases these differences did not translate to differential strength gains at the completion of the trials [21–24]. Adoption of a lacto-ovo vegetarian (LOV) diet for six weeks did not signiﬁcantly affect endurance performance among a group of trained, male endurance athletes, in spite of a decrease in total testosterone while on the vegetarian diet [25]. There were also no group differences between 20 participants adopting an LOV diet compared to maintaining their usual omnivorous diet in terms of muscle buffering capacity in conjunction with sprint training for ﬁve weeks [26]. These studies provide some insight into the effect of a vegetarian diet on athletic performance. However, a considerable limitation in many of these studies is the inclusion of participants who typically consume meat but subsequently adopt a vegetarian diet only for the duration of the study rather than comparing participants who have adhered to a vegetarian or meat-containing diet long-term. In a 1986 observational trial, Hanne and colleagues compared athletes who had maintained either an LOV or omnivore diet for at least two years and found no group differences for aerobic or anaerobic capacity [27]. However, aerobic capacity was estimated using cycle ergometry and predicted VO2 max, and strength or torque were not measured. Moreover, body adiposity was estimated using skinfold thickness. Given the current interest in vegetarian diets, in terms of both long-term health and environmental beneﬁts, it is important to reafﬁrm, using leading-edge technology, that high-level athletic performance is supported by these diets. The purpose of the present cross-sectional study was to examine body composition and performance measures in vegetarian and omnivore adult endurance athletes who had adhered to their respective diet plans for at least three months. Body composition, including visceral adiposity, was measured using dual-energy X-ray absorptiometry (DXA), leg strength was measured using a dynamometer, and aerobic capacity was determined using the Bruce protocol treadmill test. It was hypothesized that there would be no differences between groups on any parameters. 2. Materials and Methods 2.1. Participant Recruitment Healthy men and women, both vegetarians and omnivores, were recruited through advertisements on Stevebay.org (a popular website for endurance athletes), Facebook, and through word of mouth. Participants were either on a competitive club sports team at a National Collegiate Athletic Association (NCAA) Division 1 university or training for a major endurance race (such as a marathon, triathlon, cycling race, or other ultra-endurance event). An equal number of omnivore and vegetarian athletes were enrolled in the study between the ages of 21–58 years (35 per group); however, answers to diet questions indicated that eight of the vegetarians ate meat on occasion, and these subjects were reclassiﬁed as omnivores. Participants completed a health history questionnaire and were excluded if they had any chronic disease. All participants had the study verbally explained to them and provided their written consent; this study was approved by the Institutional Review Board at Arizona State University, number HS1211008557. Study recruitment and all study measurements took place between August and November 2015. 2.2. Experimental Approach In this cross-sectional investigation participants completed all study measurements in a single visit. Prior to the visit, participants completed a seven-day food log. Fifty-seven out of seventy participants returned completed food logs, all of which were used in dietary analysis using Food Processor SQL Nutrition and Fitness Software by ESHA Research, Inc. (version 10.11.0, Salem, OR, USA). Height and body mass were measured using a SECA directprint 284 digital measuring station when participants were wearing light clothing and no shoes. Participants also completed a full-body Nutrients 2016, 8, 726 3 of 11 DXA scan (Lunar iDXA, General Electric Company, East Cleavland, OH, USA), which was conducted by a certiﬁed radiology technologist. Maximal oxygen uptake was determined by following the Bruce protocol [28] on a Trackmaster TMX425C treadmill using the Parvo Medics TrueOne 2400 (Sandy, UT, USA) metabolic measurement system. Prior to beginning the test, participants were instructed how to report their fatigue level using the Borg rating of perceived exertion (RPE) scale [29]. When asked by a research assistant, they reported their RPE at the end of each minute of the test by pointing to a printed Borg RPE chart being held by a research assistant. Participants were verbally encouraged by the research team to push as long as they could and to try to reach a true maximal effort. Handrail support was not allowed during the test. Maximal respiratory exchange ratio (RER) was recorded to help determine whether subjects had reached a “true” maximal effort during the test. Maximal RER values of ≥1.1 were considered indicative of true maximal oxygen uptake [30,31]. Peak oxygen uptake reported is the highest oxygen uptake measured during the test. Finally, participants completed a series of leg extensions and ﬂexions on the HumacNorm isokinetic dynamometer (Computer Sports Medicine Inc. (CSMi, Stoughton, MA, USA) at 60 degrees per second (d/s), 180 d/s, and 240 d/s. Participants were familiarized with the protocol and conducted one practice repetition at each speed prior to performing three maximal effort repetitions at each speed. All sets, including practice repetitions, were performed on both legs, and self-reported dominant side was recorded. Participants moved from the VO2 max test immediately into the dynamometer testing, and there were 30 s of rest between sets on the dynamometer. 2.3. Statistical Analyses Based on the data of Hanne et al. [27], at 80% power and an alpha level of 5%, 15 participants per group would be needed to detect a 10% difference in strength and 80 participants per group would be needed to detect a 10% change in aerobic capacity between groups. Data were analyzed for normality and log transformed if necessary, and outliers (values > 3 standard deviations (SD) from the mean) were removed prior to data analyses. Data reported are the mean ± SD, and participant characteristics are displayed by gender and diet group. A 2-way analysis of variance (ANOVA) analysis was used to determine differences between diet groups for participant characteristics followed by an independent t-test for post-hoc examination by diet within gender if indicated. Dietary data are reported by group, and a general linear model analysis was used to examine differences between groups controlling for gender. Data were analyzed using the Statistical Package for Social Sciences (SPSS) 23.0 for Mac (SPSS, Inc., Chicago, IL, USA). 3. Results In the vegetarian group, 24 of the 27 participants (89%) had adhered to a vegetarian diet for >2 years. Of the remaining three participants, the diet had been followed for three, six, or eleven months. Fifteen of the vegetarians were vegans (nine men and six women), and twelve were lacto-ovo vegetarians (ﬁve men and seven women). There were no signiﬁcant age or gender differences between groups (Table 1). Signiﬁcant differences were noted between diet groups for body mass and for lean body mass (LBM): female vegetarians tended to have a lower total body mass and LBM compared to the female omnivores (−11% and −7% respectively). Adiposity, however, did not differ between diet groups. Physical activity levels, recorded as kcal·kg−1·week−1, were 20% higher for vegetarians compared to omnivores (p = 0.018) (Table 1). Maximal oxygen uptake (mL/kg/min) differed signiﬁcantly between diet groups, and post-hoc analyses revealed a signiﬁcantly greater aerobic capacity in the female vegetarians in comparison to the female omnivores (+13%, p < 0.05) (Table 1); however, absolute maximal oxygen uptake (L/min) did not differ between diet groups. Peak torque when doing leg extensions was not different between diet groups. The 7-day diet records revealed several differences in nutrient intake between diet groups. Although total energy intakes were similar between the diet groups, Nutrients 2016, 8, 726 4 of 11 the vegetarians consumed more carbohydrate, ﬁber, and iron daily compared to omnivores (Table 2). However, daily intakes for protein, saturated fat, cholesterol, vitamin B12, and selenium were lower among the vegetarians in comparison to the omnivores. Table 1. Participant characteristics by diet group (vegetarian, VEG; omnivorous, OMN) 1. VEG OMN p Measure Male (14) Female (13) Male (26) Female (17) Age, year 36.1 ± 10.2 36.7 ± 7.7 38.0 ± 10.0 37.1 ± 8.7 0.608 Body mass, kg 73.3 ± 14.8 58.3 ± 7.6 78.0 ± 11.0 65.4 ± 11.6 0.043 BMI, kg/m2 24.0 ± 4.4 21.8 ± 2.5 24.8 ± 2.6 23.5 ± 3.8 0.123 Lean mass, kg 56.3 ± 7.4 42.0 ± 4.9 60.2 ± 7.3 45.4 ± 5.1 0.026 Waist, cm 81.6 ± 10.7 69.0 ± 14.8 85.2 ± 7.4 73.8 ± 8.2 0.093 Body fat, % 19.2 ± 6.5 25.5 ± 4.2 19.2 ± 6.4 26.9 ± 8.1 0.659 Visceral fat, cm3 447.4 ± 419.8 110.4 ± 123.0 538.5 ± 404.3 206.4 ± 254.6 0.656 METS, kcal·kg−1·week−1 108.8 ± 32.9 106.1 ± 36.6 ** 91.7 ± 33.2 85.6 ± 20.8 0.018 VO2 max, mL/kg/min 62.6 ± 15.4 53.0 ± 6.9 * 55.7 ± 8.4 47.1 ± 8.6 0.011 VO2 max, L/min 4.44 ± 0.81 3.21 ± 0.67 4.29 ± 0.59 3.03 ± 0.49 0.295 Peak torque, ft-lbs 114.4 ± 26.2 65.5 ± 12.8 124.2 ± 24.5 73.6 ± 18.6 0.104 1 Data are the mean ± SD; n in parentheses; gender distribution did not differ by diet group (p = 0.460; Chi Square analysis). p for 2-way ANOVA analyses by diet (non-normal data transformed prior to analysis (visceral fat)). The single asterisk () indicates signiﬁcant difference within gender by diet group (p < 0.05); the double asterisk (*) indicates a trend for difference within gender by diet group (0.05 < p < 0.10). Table 2. Nutrient differences by diet group (vegetarian, VEG; omnivorous, OMN) 1. VEG (22) OMN (35) p Reference Range 2 Total kilocalories (kcal) 2443 ± 535 2266 ± 612 0.072 - Carbohydrate (CHO) (g) 328 ± 70 248 ± 101 0.001 - CHO (% energy) 53 ± 6 48 ± 7 0.010 45%–65% Fiber (g) 38 ± 13 24 ± 9 <0.001 38/25 g [M/F] Protein (g) 78 ± 19 101 ± 35 0.006 - Protein (% energy) 12 ± 2 17 ± 4 <0.001 10%–35% Protein (g/kg body mass) 1.2 ± 0.3 1.4 ± 0.5 0.220 0.8 g/kg Fat (g) 90 ± 26 83 ± 33 0.901 - Fat (% energy) 32 ± 5 32 ± 6 0.952 20%–35% Saturated fat (g) 22.8 ± 11.2 25.7 ± 10.1 0.207 - Saturated fat (% energy) 8.3 ± 3.1 11.6 ± 6.3 0.002 <10% Cholesterol (mg) 102.8 ± 119.5 301.2 ± 165.6 <0.001 - Vitamin C (mg) 117.0 ± 64.0 83.0 ± 46.5 0.076 90/75 mg [M/F] Vitamin D (IU) 115.4 ± 111.4 129.0 ± 115.5 0.201 600 IU Vitamin B12 (mcg) 3.0 ± 3 4.8 ± 4.6 0.006 2.4 mcg Selenium (mcg) 41.8 ± 36.0 62.6 ± 33.6 0.002 55 mcg Sodium (mg) 2931.2 ± 783.1 2972.8 ± 887.5 0.794 <2300 mg Iron (mg) 19.4 ± 7.8 15.4 ± 5.4 0.017 8/18 mg [M/F] Zinc (mg) 8.5 ± 9.1 8.9 ± 4.9 0.149 11/8 mg [M/F] Calcium (mg) 971.0 ± 401.6 878.1 ± 314.9 0.378 1000 mg Phosphorus (mg) 782.0 ± 378.0 831.2 ± 336.4 0.507 700 mg Omega-3 fatty acid (g) 1.6 ± 2.5 0.9 ± 0.7 0.326 - Omega-3 fatty acid (% energy) 0.004 ± 0.005 0.004 ± 0.003 0.613 0.6%–1.2% Omega-6 fatty acid (g) 7.7 ± 5.4 6.1 ± 4.4 0.145 - Omega-6 fatty acid (% energy) 2.8 ± 1.6 2.4 ± 1.3 0.358 5%–10% 1 Data are the mean ± SD; sample size in parentheses. p for general linear model analyses (non-normal data transformed prior to analysis (all variables except carbohydrate variables and fat percentage) and 2 outliers (VEG group) removed prior to analysis for saturated fat); 2 Reference ranges are the Recommended Dietary Allowance or the Acceptable Macronutrient Distribution Range; note the American College of Sports Medicine recommends that athletes consume 1.2–2.0 g protein/kg body mass. 4. Discussion Results from this study indicate that compared to their omnivore counterparts, vegetarian endurance athletes have comparable strength as indicated by leg extension peak torque, and possibly a greater degree of aerobic capacity, particularly in females, as indicted by a progressive maximal Nutrients 2016, 8, 726 5 of 11 treadmill test to exhaustion. Dietary intake on several key nutrients differed considerably between groups. Some, but not all, results are consistent with previous reports. Our study is signiﬁcant for its increased rigor in measurement assessments compared to previous comparisons of vegetarian and omnivore athletes. We determined maximal oxygen uptake by a graded test to exhaustion on a treadmill instead of predicting VO2 max using a cycle ergometer, as recommended by Shepard and colleagues [32]. Additionally, we measured body composition using a DXA scan, currently regarded as the clinical gold standard for body composition assessment, instead of skinfolds [33]. Finally, we assessed both athletic performance and nutrient intake differences between vegetarians and omnivores, whereas most previously published studies focus exclusively on one of these areas. 4.1. Body Mass and BMI Like other studies of vegetarians in the general population, vegetarian participants in the present study had signiﬁcantly lower body mass compared to omnivores [10,34]. This is in spite of the fact that our study included participants engaged in considerable endurance activities, which could be very different in multiple ways from the general population. One prior study in athletes, conducted by Hanne et al. compared vegetarians and omnivores anthropometrically and found no signiﬁcant differences between groups for weight [27]. It is noteworthy that the athletes in the Hanne et al. study included football, basketball, and water polo players in addition to endurance athletes. 4.2. Lean Body Mass LBM was signiﬁcantly lower for the vegetarian athletes compared to their omnivore counterparts, a difference which was most prominent among the female participants with female vegetarian athletes possessing 7% less LBM as compared to the female omnivore athletes. In spite of this, there were no signiﬁcant differences in body fat percentage or BMI between groups. To our knowledge, this is the ﬁrst study to examine lean body mass differences between vegetarian and omnivore athletes. It is important to note, however, that this difference in lean body mass did not translate into differential peak torque on the leg extension. Although other studies have not assessed lean body mass of vegetarian athletes speciﬁcally, Campbell and colleagues compared resistance-training induced changes in lean body mass and strength between groups assigned to either an omnivorous diet or a lacto-ovo-vegetarian diet for the duration of the study and found that, in spite of differential lean body mass gains, the two groups increased strength similarly [21]. Conversely, a 12-week training study by Haub and colleagues showed no signiﬁcant differences in strength, body composition, or muscle cross-sectional area between groups assigned to either a lacto-ovo-vegetarian or beef-containing diet. 4.3. Body Fat Percent and Visceral Adipose Tissue (VAT) Contrary to the female vegetarian athletes in Hanne’s group, no signiﬁcant differences in body fat percentage were found between vegetarian and omnivore athletes in this study. Additionally, there were no signiﬁcant differences between groups for visceral adipose tissue (VAT). Participants in the present study had VAT values above those reported for similar aged healthy lean sedentary adults (~250 cm3), both omnivores and vegetarians [35,36], but lower than those noted for older adults (1000–1560 cm3) [37]. Although there are no standard reference ranges for VAT, values near 1000 cm3 were associated with BMI values near 25 kg/m2 and values > 300 cm3 have been suggested as predictive of risk for metabolic syndrome in young adults [36,37]. As technology permitting quantiﬁcation of visceral adipose tissue is relatively new for research purposes, this study contributes to the emerging literature by providing VAT values for athletes. VAT and BMI is strongly correlated in this study (p = 0.742), a factor that may be important for estimating VAT inexpensively without a DXA scan. Nutrients 2016, 8, 726 6 of 11 4.4. VO2 Max Unlike athletes in Hanne’s study, vegetarians in the present study had signiﬁcantly higher maximal oxygen uptake than their omnivore counterparts [27]. This difference was most predominant in the female participants with a 13% greater VO2 max score for the female vegetarians as compared to the female omnivores, but this difference was not observed for absolute VO2 max (L/min), which suggests that body weight factored into this difference. This gender difference is intriguing and merits further investigation in future studies. One potential reason that athletes in the present study had higher VO2 max values than those in Hanne’s study may be due to the difference between cycle ergometry and treadmill testing methods. However, it is possible that the athletes in our study simply were more trained and that diet effects on differences in VO2 potential emerge only at higher levels of ﬁtness. Other work that contributes to our understanding of aerobic and anaerobic performance differences by diet include the study of Hietavala et al. that found no signiﬁcant difference in time to exhaustion (albeit a higher oxygen uptake at a given percent of maximal oxygen consumption) between participants following a low-protein vegetarian diet compared to a mixed diet [38]. Subjects in this study adhered to the low protein vegetarian diet (0.80 ± 0.11 g of protein per kilogram of body mass (g/kg) vs. 1.59 ± 0.28 g/kg on their normal diet) for four days before being tested on a cycle ergometer. As this study did not use participants who practiced vegetarianism outside of the study, and the amount of protein that subjects were allowed to consume on the vegetarian diet was restricted, true differences between vegetarians and omnivores may not be evident. Baguet et al. found no differences in repeated sprint ability between participants following a vegetarian or mixed diet for ﬁve weeks; again, these subjects were not following a vegetarian diet long-term [26]. Raben et al. found no differences in maximal oxygen uptake among subjects after adoption of a lacto-ovo vegetarian diet for six weeks [25]. However, the major disadvantage of interpreting results of these studies for vegetarian athletes is that participants in these studies only adhered to a vegetarian diet brieﬂy for the duration of the study. 4.5. Peak Torque Similar to the Hanne et al. study that compared the power output of vegetarian and omnivore athletes [27], we found no signiﬁcant differences by diet in terms of peak torque using leg extensions. Other studies in untrained older men that have examined strength development over time in response to a training program have found mixed results when comparing participants following a vegetarian or mixed diet [21,24]. This is noteworthy, particularly since strength and lean body mass were strongly correlated (r = 0.764) in the present study, as well as the fact that omnivores had signiﬁcantly more lean body mass vs. the vegetarians. A nonsigniﬁcant trend for omnivores to produce higher peak torque is observed, however. It is conceivable that the omnivore diet pattern may be preferred for sports that rely on greater lean mass, and subsequently peak torque. To further investigate this, future work ought to examine if strength can be increased similarly by vegetarian and omnivore athletes engaged in strength training (not just by participants following a vegetarian diet for a few weeks). 4.6. Nutrient Intake Nutrient intake was calculated from food and beverage intakes only and did not include any supplements. There were no signiﬁcant differences in caloric intake or total fat intake between vegetarians and omnivores. However, vegetarians reported signiﬁcantly more dietary carbohydrate (both in terms of absolute intake and as a percent of daily calories), ﬁber, and iron intake. Omnivores consumed more dietary protein (both in terms of absolute intake and as a percent of daily calories), saturated fat, cholesterol, and vitamin B12. However, when expressed relative to body mass, there were no differences in dietary protein intake. Nutrients 2016, 8, 726 7 of 11 That vegetarians and omnivores in the present study did not differ in terms of caloric intake is consistent with ﬁndings by Janelle and Barr from their comparison of 45 vegetarian and omnivore women [16], yet it is in contrast to results from Calkins and colleagues who compared 50 vegetarian, vegan, and omnivores. They found vegetarians consumed about 200 fewer kcal than omnivores [19]. These studies were both in the general population, not speciﬁcally with athletes. Calkins et al. also reported that omnivores consumed more fat than vegetarians, a fact that partially contributed to the higher caloric intake. This too is in contrast to the ﬁndings in the present study which found no signiﬁcant difference either in grams of fat consumed or the percent contribution of fat to the daily calorie intake, even though saturated fat was signiﬁcantly higher in omnivorous diets. Other studies involving the general population have also reported omnivores eating more energy and total fat than vegetarians [10,39–41]. Higher carbohydrate (when expressed either as an absolute amount or as a percent of total daily calories) and ﬁber intake among vegetarians in comparison to omnivores in the present study is consistent with ﬁndings in other studies [10,39,41–44]. As these studies have been conducted in the general population, the present study contributes to the literature by demonstrating that this dietary pattern can be extended to endurance athletes as well. One study by Janelle and Barr stands in contrast to these ﬁndings, as they did not ﬁnd signiﬁcant differences in carbohydrate or ﬁber intake between vegetarian and omnivore women; those participants were not athletes [16]. That vegetarians in the present study consumed more carbohydrates than omnivores is notable since they are all athletes, and the importance of carbohydrates for exercise is well-established [45–47]. Like the present study, other studies have also reported that vegetarians consume less protein (both absolute intake and as a percent of the daily calories) [10,16,39,42] and vitamin B12 [40,48] than omnivores. Our study contributes to the literature since other reports have been in the general population instead of within athletic groups. Of note, though, differences in dietary protein intake are not signiﬁcant when expressed relative to body mass, which is typically the preferred method for recommending protein for athletes [47]. Nonetheless, dietary protein intake was weakly correlated with peak torque (r = 0.359, p = 0.006) in the present study, and dietary protein intake was moderately correlated with lean body mass (r = 0.415, p = 0.001). Expectantly, lean body mass was strongly correlated with peak torque (r = 0.764, p < 0.001). Hence, it is conceivable that protein intake could inﬂuence strength if intakes had been inadequate. In the present evaluation, protein intakes in the vegetarian participants averaged 1.2 g/kg body mass, which falls in the recommended range for athletes [47,49]. There are conﬂicting ﬁndings in the rest of the literature regarding whether omnivores or vegetarians consume more iron. The Wilson et al. study of vegetarian men found that vegetarians consumed more iron [41], but Ball and Bartlett reported no difference in dietary iron intake between female vegetarian and omnivores [50]. Clary et al. compared 1475 vegans, vegetarians, semi-vegetarians, pescetarians, and omnivores and also showed that vegetarians consume more iron than omnivores [39]. Although vegetarians consumed more iron than omnivores in the present study, iron bioavailability was likely reduced as has been shown in other trials [17]. Dietary intakes of zinc did not vary by diet group herein, but generally the literature suggests that vegetarians consume somewhat less dietary zinc than omnivores [16,51–53]. The lower intakes of selenium by vegetarians in comparison to omnivores has also been reported by others and reﬂects the low levels of selenium in plant foods relative to ﬂesh foods [54,55]. 4.7. Limitations In addition to the small sample size, limitations to the study include the variable level of experience of the athletes for their respective sports, and related ﬁtness levels. Although most participants were training for and competing in races such as marathons, Ironman-distance triathlons, and competitive cycling, there were a few participants who were training for shorter distance races. However, this variation makes results more generalizable to athletes of various ﬁtness levels. Nutrients 2016, 8, 726 8 of 11 4.8. Future Directions Future work is needed to compare vegetarian and omnivore endurance athletes’ performance on events more similar to actual sporting events (such as time trials or peak power on a cycle ergometer) and probe differences by type of vegetarian diet (lacto-ovo vegetarian or vegan). Additional work is needed to explore the adequacy of long-term adherence to vegetarian and vegan diets for supporting development of lean body mass. 5. Conclusions Our cross-sectional comparison of vegetarian and omnivore adult endurance athletes shows higher maximal oxygen uptake values among vegetarians and comparable strength, in spite of anthropometric and dietary differences. This study suggests that following a vegetarian diet may adequately support strength and cardiorespiratory ﬁtness development, and may even be advantageous for supporting cardiorespiratory ﬁtness. Certainly many factors affect an athlete’s sports performance, and there is no dietary substitute for quality training. However, our study contributes to the literature about cardiorespiratory and strength comparisons between vegetarian and omnivore endurance athletes, and may provide a rationale about the adequacy of vegetarian diets for sport performance. As this was a small cross-sectional study using endurance athletes, larger intervention trials are necessary to bolster conclusions about adequacy of vegetarian diets to support performance in strength and power-focused sports. Acknowledgments: This study was supported by a grant through the Graduate and Professional Student Association (GPSA) at Arizona State University. Author Contributions: H.M.L. and C.S.J. conceived and designed the experiments; H.M.L. performed the experiments; H.M.L. and C.S.J. analyzed the data; H.M.L., C.S.J., and C.M.W. wrote the paper. Conﬂicts of Interest: The authors declare no conﬂict of interest. The funding sponsor had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. References 1. Fraser, G. A comparison of ﬁrst event coronary heart disease rates in two contrasting California populations. J. Nutr. Health Aging 2004, 9, 53–58. 2. 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Content and bioavailability of trace elements in vegetarian diets. Am. J. Clin. Nutr. 1994, 59, 1223S–1232S. [PubMed] 54. Larsson, C.L.; Johansson, G.K. Dietary intake and nutritional status of young vegans and omnivores in Sweden. Am. J. Clin. Nutr. 2002, 76, 100–106. [PubMed] 55. Letsiou, S.; Nomikos, T.; Panagiotakos, D.; Pergantis, S.A.; Fragopoulou, E.; Antonopoulou, S.; Pitsavos, C.; Stefanadis, C. Dietary habits of Greek adults and serum total selenium concentration: The ATTICA study. Eur. J. Nutr. 2010, 49, 465–472. [CrossRef] [PubMed] © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).	Cardiorespiratory Fitness and Peak Torque Differences between Vegetarian and Omnivore Endurance Athletes: A Cross-Sectional Study.	11-15-2016	Lynch, Heidi M,Wharton, Christopher M,Johnston, Carol S	eng
PMC7365446	See responses in green. Reviewer #1: General Comments: The reviewer would like to commend the authors for undertaking an important and interesting topic. Determining the shoe recommendations for different running levels is an important topic, that can aid clinicians and running coaches in choosing the right foot wear for different runners of different abilities. Overall this is a well written manuscript, with good methodology. There are some specific comments which are written below. >>Thank you for your compliments and suggestions. They have improved our manuscript. Abstract: General comment: For an abstract, the background should be brief. Suggest only have 2 sentence for the background. I do not think you need to describe why a Delphi study is powerful within the abstract. I think the first 3 sentences would suffice, and potentially reduce these three sentences into 2. Within the abstract methods, a little bit more information is needed. For example, how many questions did the study begin with, and how were they whittled down through the three rounds, and how was data tallied. Further, within the results, you describe that there were originally 20 proposed variables. This is an example of something that needs to be in the methods. >>We have updated the abstract as suggested with the following: “Providing runners with footwear that match their functional needs has the potential to improve footwear comfort, enhance running performance, and reduce the risk of overuse injuries. It is currently not known how footwear research experts make decisions about different shoe features and their properties for runners of different levels. We performed a Delphi study in order to understand: 1) definitions of different runner levels, 2) which footwear features are considered important, and 3) how these features should be prescribed for runners of different levels. Experienced academics, journalists, coaches, bloggers and physicians that examine the effects of footwear on running were recruited to participate in three rounds of a Delphi study. Three runner level definitions were refined throughout this study based on expert feedback. Experts were also provided a list of 20 different footwear features. They were asked which features were important and what the properties of those features should be.” (line 26-35) Need key words at the end of the abstract. >>Thank you for the reminder. We have included the following key words: Individualized footwear, running biomechanics, runner abilities, footwear experts, midsole hardness Introduction: Line 54: Delete the parenthetical citation fully written citation, should just be a reference number. >>This citation has been replaced with the appropriate number. Line 67: Same here, please deleted written citation, should just be a reference number. >>This citation has been replaced with the appropriate number. Line 69-70: Reword this to not be a numbered list. Within the intro, it should just be written sentences. >>We have removed the numbers from the sentence and updated the text to the following: “On the other hand, there has been little scientific attention on footwear features such as outsole traction or forefoot flares which could indicate: the prescription of these features to different runner levels is trivial, or that these features are not considered important by footwear professionals, or little is known on how to prescribe these features.” (line 78-81) Line 71: You state, “it is close to impossible for running footwear professionals to provide evidence-based recommendations for footwear properties for runners of different levels.” But then you go on to say you are performing a Delphi to find the best recommendations from the experts. I think this is contradictory. I think you should focus more on how there is not clarity on professional recommendations for footwear for different running skills or groups. >>We have removed the last comment from the introduction to here as we addressed these two comments together. The corresponding statements now read: “In summary, there is a need to better understand how footwear research experts make decisions about different footwear features and their properties” (Lines 81-83) I think the third to last and second to last paragraphs can be amalgamated into one paragraph. Further, the second to last paragraph ends abruptly and a better conclusion is need to set up the purpose paragraph. >>We have combined the two paragraphs and updated the phrasing so that it is more focused on “how there is not clarity in professional recommendations”: “Modern running shoes are complex systems. They incorporate many different features (e.g., crash-pads, heel counters, flares, midsole hardness) and each of these features can be included, excluded, and/or tuned individually to modify the characteristics of the final running shoe system (e.g., cushioning, stability, heel-to-toe transition, energy return). Some of these shoe features have been studied more extensively, such as rearfoot midsole hardness, while others have received little attention, such as upper breathability (12). Nevertheless, a strong research focus on certain footwear features (e.g., midsole hardness) does not necessarily translate into agreement on how modifying these features may affect the running mechanics, performance, injury risk, or footwear comfort in runners of different levels. For example, a recent review found inconclusive evidence regarding the biomechanical effects of different midsole hardness – one of the most studied footwear features (12). On the other hand, there has been little scientific attention on footwear features such as outsole traction or forefoot flares which could indicate: the prescription of these features to different runner levels is trivial, or that these features are not considered important by footwear professionals, or little is known on how to prescribe these features. In summary, there is a need to better understand how footwear research experts make decisions about different footwear features and their properties. A powerful way to examine these decisions is to gather and summarize opinions of experts in the field of running biomechanics and footwear using a Delphi study. The Delphi method has been utilized for gathering and summarizing opinions via survey-based responses of an expert panel in order to obtain consensus on complex topics. For example, this technique has been successfully applied to establish the now frequently reported “Minimalist Index” of running shoes (13). Such an understanding can target future systematic investigations around the presumed optimal property of important footwear features.” (Lines 69-88) Line 73-76: Why are aims here and the purpose in the final introduction paragraph? This is confusing for the reader. Suggest only having the purpose at the last intro paragraph and deleting the aims. >>We have deleted the aims as suggested. Methods: General comments: An overall study design sub section is needed at the beginning of the methods. This should give the 10,000 foot view of the study. >>We have included an overview at the beginning of the Methods section: “Footwear research experts were asked to complete three rounds of a Delphi study, with each successive round building on the results gathered from the previous round. Three runner level definitions were refined throughout the three rounds of the Delphi study through expert feedback. Experts were also provided a list of 20 different footwear features. Through the three rounds of the study, experts provided opinions on which features are important and what the properties should be for the footwear features for the three different running levels.” (lines 96- 101) You need to give inclusion/exclusion criteria for who was considered an expert for this study. >>We have added the following exclusion criteria in the methods: “Participants were excluded if they had under two years of research experience related to running footwear.” (line 113-114) Lines 103-117: I see that 142 experts were contacted. How many responded and were included. A flow chart might help the reader to understand this process. >>We have included a consort diagram (new Figure 1) to show the number of experts in each round. Line 122: Need to cite the running lit used. Further, it is confusing with the parenthetical statement “as detailed below. Suggest deleting this. >>We have deleted the parenthetical statement and replaced it with the citations used in the subsequent paragraph (line 128). Novice versus Recreational runner definition: In the novice group, you state that they run no more than 20km/week, but in the recreational group, they run 10-50 km/week? How do delineated between someone that runs 15-20 km/week? Is this based off of times per week (0-3 v 1-5)? Please clarify. High Caliber runners: I see the same thing here, they run 30km+/week. Please clarify >>Thank you for clarifying this. We have included the following description to clarify the overlapping mileage: “The proposed characteristics provide guidelines for runner classification. As such, there is overlap in the running distance per week between the different running levels in order to accommodate runners that train less and have a better running performance.” (line 128-130) Line 211: Can you explain further why the ‘don’t know’ questions were not included in round 3? >>We have expanded upon our explanation with the following: “These questions were only included in the second-round as we received feedback from the experts that the questionnaire was time consuming which may have increased the drop out rate if these questions were asked in the third-round again.” (line 231-233) Line 218-219: Please add the software program used to calculate these statistics. >>We added the software program that we used to calculate the statistics in the “Analysis and Visualization” section: “All statistical analyses were performed in MATLAB (MathWorks, Natick, MA, USA)”.(line 240) Results: General comment: It is not recommended to use bullet points within the results. Please edit accordingly >>We have eliminated the bullet points and updated the text to the following: “The respondents’ rating of the running level definitions improved as the Delphi study progressed. The median score given to the running level definitions increased each round and the interquartile range decreased as 88% of respondents rated the running level definitions between 7 and 10 in the third-round as opposed to 69% in the first-round (see Fig. 3). The changes to the running level definitions for the second-round were: increased “novice” running experience to one year (from six months) and increased “recreational” running experience to greater than one year (from six months), increased “high caliber” running habits to >4 sessions/week (from >3 sessions/week) and >50 km/week (from >30 km/week), specified the running performance as males between the ages of 18 to 34, replaced “stress management” with enjoyment for running motivation for all levels, re-order the “high caliber” running motivation from 1) Improve general health, 2) Stress management, 3) Competition to: 1) Competition, 2) Improve general heath, and 3) Enjoyment, and re-order the priorities for footwear design for “High caliber” from: 1) Improve performance, 2) Improve comfort, 3) Reduce injury risk, to: 1) Improve performance, 2) Reduce injury risk, 3) Improve comfort. Subsequent changes to the running level definitions were to ensure that the high caliber and recreational runner 5km and 10 km time were indicative of the respective marathon times. These updates resulted in the final updated runner level definitions in Table 3.” (line 251-266) Overall the results are well written. >>Thank you! Discussion: Line 307-10: These are more article strengths and should be moved to the strength and limitations section, not the summary discussion paragraph. >>We have eliminated the sentences in question. Line 315-17: This is a future research, implications, and/or conclusion sentence and should be moved >>I understand that this concluding sentence pertains to future research, in our opinion it is a major point of discussion. We have kept this sentence as it wraps together the discussion summary paragraph. Line 369: Suggest deleting the term ground truth and just state that this should serve as valuable information, etc. >>We have eliminated “ground truth” and updated the sentence to the following: “As such, the findings from this study can serve as a valuable starting point for future systematic biomechanical investigations examining the influence of footwear features on runners with different training/performance levels.” (line 412-414) Line 373-381: suggest that this paragraph be moved before the limitations paragraph. >>We have moved the paragraph before the Limitation paragraph as suggested. (line 379-387) Line 384-386: Delete the first sentence, this can be said in the strengths paragraph but the conclusion paragraph should focus on the findings and future directions. >>We have removed the strengths portion of the sentence and updated it to: “Footwear research experts provided feedback on the effects of different footwear features on running biomechanics across three running levels as well as provided a consensus on the characteristics of runners in these different running levels.” (line 418-427) Reviewer #2: Overall this manuscript fills an obvious void in the literature and aims to assist researchers, clinicians, coaches, and running enthusiasts with shoe prescriptions, while also informing future running shoe research. This work is generally well written and free from fundamental flaws; however, several minor revisions to the proposed article will undoubtedly improve this already great work. >>Thank you for your kind comments. Your suggestions have improved the manuscript. 1. The words "the participants" are over utilized throughout the manuscript. Varied diction will help to maintain reader interest and attention. >>We have updated the manuscript so that there is more varied diction. 2. As this is a study employing Delphi techniques no statistical analyses are necessary and furthermore, no analyses were actually conducted. The "Statistical Analysis" section is therefore unnecessary and the subsequent descriptive statistics can simply be presented in the "Results" as well as Fig 3. >> Together with your suggestion and the Reviewer #1’s comment about what program the statistics were performed in and your #11 comment, we have updated the “Statistical Analysis” section to “Analysis and Visualization” section. 3. A more clear and consistent distinction between footwear properties and features throughout the manuscript would improve readability. >>We have checked the entire manuscript and ensured that “property” and “feature” were used correctly. 4. "Appendix A" utilizes the term "categories" as opposed to "properties" further illustrating the previous point. >>We have updated “categories” to “Property categories” throughout the Appendix and updated the file name of Appendix A to: “S1 Appendix A – Shoe Feature Descriptions and Properties” 5. Additional headings for the "Footwear Properties" in the "Methods" and "Results" sections would assist readers navigating between parts of the manuscript. >>We have added sections titled: “Footwear Feature Properties” in both the Methods and Results sections. 6. The described methods for determining footwear features and feature properties importance is challenging to read at times (particularly lines 169-179; lines 188-192); please try to concisely and succinctly explain these steps. >>We have updated the mentioned sections with the following: “The importance of the footwear features was assessed in the first-round and verified in the second-round. In the first-round, participants were asked if footwear features are important when designing footwear for different running levels. The experts could choose between the following for each footwear feature: (a) is important, (b) is not important or (c) they do not know if it is important. The footwear features were important if over 75% of the first-round participants selected option (a). Prior Delphi studies have defined consensus between 51% (21) and 80% (22) of respondents. The important features were then presented to the second-round participants. The participants were asked if they agreed with each of the features selected as important/non important on a 10-point scale where “1” indicated that the list of important/non important features were “Not at all appropriate” and “10” indicated “Most Appropriate”. The list of important features was verified if over 75% of the second-round participants answered with a seven or higher on the 10 point-scale. The second- and third-rounds of the Delphi study were then limited to the important footwear features. In each round, the experts were asked if other footwear features should be included in the Delphi study. If there were at least five suggestions to add a certain feature, this new footwear feature was added to the subsequent round and the participants were asked if the newly added footwear features were important.” (lines 187-201) and “The experts were asked to recommend footwear feature properties for the different running levels in each round of the study from a multiple-choice selection (see Appendix A for the lists of footwear feature properties). Most footwear feature properties were obtained through literature; however, if there was no related literature (e.g., upper elasticity), properties were provided based on commercially available shoes. In rounds 2 and 3, the results from the previous round were presented to the participants. If at least 51% of the participants agreed on a footwear feature property for a specific running level (e.g., high breathability for novice runners), the participants would be asked if they agreed with the consensus the next round. If at least 51% of the participants verified the consensus, the experts were not asked again to recommend a footwear feature property for that running level (see Fig. 2). In comparison to the consensus for the importance of shoe features (agreement of 75% of respondents), the threshold for consensus was set lower for agreement on footwear feature properties (51%) because of the greater number of available response options.” (lines 204-215) 7. Lines 181-184 seem somewhat redundant. >>We have removed the sentence. 8. The reference to Fig 2 in line 182 seems somewhat premature. Describing the general flow of these methods prior to interpreting Fig 2 made this section easier for this reviewer to understand. >>We have moved the reference to Fig. 2 to near the end of the paragraph after the explanation of the methods. 9. It is not clear how the Likert scale used to rate footwear features (as described in the "Methods" section) is actually used in this study. >>We have clarified the use of the Likert scale by including the following in our methods: “The list of important features was verified if over 75% of the second-round participants answered with a seven or higher on the 10 point-scale.” (line 196-197) 10. Fig 2 is very helpful, but a threshold of >50% is provided when the text describes using a 51% threshold. >>We have updated the Fig. 2 and replaced “>50%” with “≥51%”. 11. While minor, the software used to produce images was not stated. >>We have added the following in methods: “Figures were created in MATLAB and Adobe Illustrator (San Jose, CA, USA).” (line 220-241) 12. Line 162 - Explicitly cite why/where the 20 features considered comes from. >>We have included the following to describe how we came to the 20 footwear features: “. These 20 features were chosen from a list of 31 running shoe footwear features that were identified based on an initial literature review, market analysis, and internal discussion. Two influential studies during this process were reports from (6) and (13). The initial list of 31 was reduced to 23 features by removing or joining related features that were reflected in other features or similar in their function, respectively (e.g., remove midfoot midsole hardness and only retain forefoot and rearfoot midsole hardness). Pilot testing with four footwear science experts (not included in the main study) indicated that 23 features resulted in a questionnaire that would require more than an hour to complete and could potentially lead to a high-drop out rate. Therefore, we limited the number of footwear features to 20, by removing features for which pilot participants indicated low relevance (e.g. upper overlays or varus alignment). In return, the option was added for experts of the main study to suggest footwear features, that should be added to the questionnaire.” (line 169-180) 13. The inclusion of 2 aims and 3 purposes is somewhat confusing. I recommend removing the aims from your "Introduction" as they do not match the "Methods" and "Results" sections as obviously. >>We have removed the two aims from the introduction. 14. Please ensure that permissions for any adapted images (i.e. Figs 1 & 4) are provided as necessary. >>Figures 1 and 4 have been removed as we have replaced the Hoitz article (currently still in review) with another recent running shoe construction review paper (Sun et al., 2020). “Sun, X, Lam, WK, Zhang X., Wang J, & Fu W (2020). Systematic Review of the Role of Footwear Constructions in Running Biomechanics: Implications for Running-Related Injury and Performance. Journal of Sports Science and Medicine,19, 20-37” 15. A limitation that seems somewhat overlooked is that the definitions of runner levels changed throughout iterations. As these definitions changed, so too may have respondents' recommended properties. While the 3 repetitions and consensus measures may help to quell these concerns, it seems important to consider the implications of these interconnected moving targets. >>We have added the following to the limitations as you suggested: “The recommended footwear feature properties may have been influenced by a dynamic definition of the runner levels, which changed slightly throughout the study. These changing definitions, however, seemed to have little effect on expert opinions on the footwear feature properties as the verifying consensus level was generally higher than the original consensus level (Table 4, last vs. second-to-last column).” (line 403-408) 16. If possible, I would like to know more about your "Additional Delphi Questions" results in the discussion. I read some of the statements in your raw data set and found the additional insights very compelling. You do a good job of introducing some of the identified themes in your "Discussion" but I feel that a bit more would elevate the current manuscript. >>We have integrated some expert feedback in the running level definitions discussion paragraph (line 434-451). Please see our response below #20 and #21. 17. Tables 5 and 6 both seem to provide complementary results. Is there a way to combine them or make the more exclusive from one another? >>We have eliminated Table 6 and added a column for “% Participant in agreement with consensus” to Table 5. 18. Consider a CONSORT diagram so readers can better understand the development of the expert panel round by round. >>We have included a consort diagram, as suggested, to show the number of experts in each round as the new Figure 1. 19. Please expand on how your panel may or may not influence your conclusions in the "Discussion" (e.g. Where they all from the US? Do they disproportionately represent companies with financial interests in designing complicated shoes? Etc.). >>We have included the following to expand on our panel in the limitation: “Furthermore, the final recommendation may have been biased as more experts that completed the survey were male (e.g., 22/26 of the final participants). This expert panel was otherwise diverse as nine countries were represented.” (line 401-403) 20. Please discuss how providing the expert panel with definitions in round 1 for running level as opposed to forming definitions built by the panel may have influenced your conclusions. 21. Please expand on the results of your running level definitions in your "Discussion" section. >>We have expanded our running level definitions discussion that includes discussion of #20: “The footwear experts came to a consensus on the running level definitions through slight adjustments to the initial definitions proposed and derived from literature. We opted to provide initial running level definitions to our expert panel rather than letting the panel formulate definitions independently. This latter approach would have required additional Delphi rounds prior to the recommendation of footwear features and their properties. Panel formulated definitions may have resulted in different running level definitions compared to the approach presented here and different running level definitions could have led to altered footwear feature recommendations. However, the experts’ consensus on the running level definitions were in agreement with prior literature. This is exhibited by the novice runner level definition which is similar to a definition created based on subjective running questionnaires (7). The experts did recommend an increased workload for high caliber runners in comparison to literature (7) as participant feedback resulted in the distance per week to be increased from >30 km/week to >50 km/week. These definitions may be viewed more as guidelines as one footwear expert mentioned that “Even elite athletes perform training runs with different intensities, durations, on different surfaces and so on. For each of these runs they might select a different type of footwear.” This comment touches on the competing requirements for running shoes and there may be multiple “correct” shoes for a given running level, especially in the high caliber category.” (lines 362-377) Reviewer #3: General The paper is well written and the study uses appropriate methodology for reaching consensus regarding standards for classifying runners as well as for recommendations for running footwear. >>Thank you for your compliments and suggestions. One major concern that I have is that while the data was collected anonymously, the country and region of the country is provide din the raw data. This information along with the acknowledgment to specific participants, makes it quite easy to identify the responses of many of the participants in the raw data. The country and region data collected in the survey needs to be deleted to de-identify the data and preserve anonymity of the participants responses. >>We have de-identified the raw data by removing the country and region for each participant. Another concern I have is the use of a manuscript in review as a major reference for this study. The Hoitz et al, manuscript that is listed as in review is not available to the reviewers of the current manuscript. As such it is difficult to discern how the current manuscript contributes to the literature. Moreover, depending on when or if the Hoitz, et al manuscript is accepted, it may not be available to the readers of the current manuscript. It would be acceptable to reference a manuscript that has been accepted and is in press. >> We have removed the citation in question (as the mentioned manuscript is still in review) and replaced it with the following: Sun, X, Lam, WK, Zhang X., Wang J, & Fu W (2020). Systematic Review of the Role of Footwear Constructions in Running Biomechanics: Implications for Running-Related Injury and Performance. Journal of Sports Science and Medicine,19, 20-37 Minor Line 111: the phase “reached out to”, is awkward perhaps “contacted” or similar >>We have updated the phrasing as recommended. (line 112) Table 3 or discussion of runner classification. While consensus was reached on runner classification, was consensus reached on how to classify runners who may meet standards across categories (e.g. run at novice speed but with the habit or experience of recreational runners). For example, for a runner to be in a category do they have to meet 4 of the 5 categories or … ? >>While we did not specify how many criteria had to be fulfilled in order to decide the runner’s category at the beginning of the survey, we acknowledge your points and added the following to the limitation section: “A limitation of the consensus process for the running level definitions was that we did not specify to the experts how many of the of the categories a runner must match to be considered a “novice”, “recreational”, or “high caliber” runner. As such, the definitions may lead to minor variations when different footwear experts categorize runners.” (lines 408-411) Table 6. I re-read the methods paragraph describing the manner of reaching consensus multiple times, lines 181-194. I also read the results paragraph regarding shoe properties, lines 283 to 293, multiple times. However, it is not clear to be which specific variables qualified to be presented in table 6. >>We have eliminated Table 6 and added the “% Participants in agreement with consensus” column from Table 6 to Table 5.	Shoe feature recommendations for different running levels: A Delphi study.	07-16-2020	Honert, Eric C,Mohr, Maurice,Lam, Wing-Kai,Nigg, Sandro	eng
PMC8609846	Baygutalp et al. BMC Sports Science, Medicine and Rehabilitation (2021) 13:145 https://doi.org/10.1186/s13102-021-00375-0 RESEARCH Impacts of different intensities of exercise on inflammation and hypoxia markers in low altitude Fatih Baygutalp1, Yusuf Buzdağlı2, Murat Ozan3, Mitat Koz4, Nurcan Kılıç Baygutalp5 and Gökhan Atasever6 This study will be presented as an oral presentation in Eastern Black Sea Rheumatology Days on 18-19 December 2021 in Turkey. Abstract Background: This study aims to determine and compare the effects of exercise modalities with different intensities on the secretion of key inflammation and hypoxia markers in amateur athletes. Methods: Twenty-three athletes with a mean age of 20.1 years, living at low altitude (1850 m) participated in this study. The participants’ maximal oxygen consumption values (VO2 max) were determined with an incremental cycle exercise test as 54.15 ± 6.14 mL kg min−1. Athletes performed four protocols: at rest, 50% VO2 max, 75% VO2 max and 100% VO2 max (until exhaustion) with one-week intervals. 50% VO2 max, 75% VO2 max sessions were performed continuously for 30 min on a bicycle ergometer and 100% VO2 max session was performed by cycling until exhaus- tion. Blood samples were obtained at rest and immediately after each exercise session. Serum tumor necrosis factor alpha (TNF-α), C-reactive protein (CRP), interleukin-10 (IL-10), and hypoxia inducible factor-1 alpha (HIF-1α) levels were measured. Results: There were significant differences in serum TNF-α levels in 75% VO2 max and 100% VO2 max sessions (489.03 ± 368.37 and 472.70 ± 365.21 ng/L, respectively) compared to rest conditions (331.65 ± 293.52 ng/L). Serum CRP levels of 50% VO2 max and 75% VO2 max sessions (1.19 ± 0.50; 1.07 ± 0.52 mg/L) were significantly higher than the rest condition (0.74 ± 0.35 mg/L). There were significant differences in serum IL-10 levels of rest condition and 50% VO2 max; 50% VO2 max, and 100% VO2 max sessions (328.09 ± 128.87; 446.36 ± 142.84; 347.44 ± 135.69; 324.88 ± 168.06 pg/mL). Serum HIF-1α levels were significantly higher in 75% VO2 max session compared to rest (1.26 ± 0.16; 1.08 ± 0.19 ng/mL) (P < 0.05 for all comparisons). Conclusions: Both inflammatory and anti-inflammatory pathway is induced on different exercise intensities. Exer- cise protocols performed until exhaustion may lead to activation of inflammatory pathways and hypoxia-induced damage. Keywords: Anti-inflammatory cytokine, Exercise, Health, Hypoxia, Pro-inflammatory cytokine © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Background The optimal exercise type and intensity should be care- fully determined, particularly in acute exercise protocols. While moderate exercise generally improves immune function, excessive amounts of prolonged, high-intensity Open Access Correspondence: drbaygutalp@gmail.com 1 Department of Physical Medicine and Rehabilitation, Ataturk University Faculty of Medicine, Erzurum, Turkey Full list of author information is available at the end of the article Page 2 of 9 Baygutalp et al. BMC Sports Science, Medicine and Rehabilitation (2021) 13:145 exercise may lead to impairments in immune function [1]. There are promising results from comparison stud- ies demonstrating the equality or superiority of high- intensity intermittent training (HIIT) programs over low-intensity regular exercise programs based on car- diorespiratory and metabolic parameters [2–4]. For this reason, there is a growing interest in the studies with high-intensity intermittent training protocols since this training type is considered helpful for people who cannot exercise regularly, and HIIT protocols are time efficient [5, 6]. Exercise and intense training affect hormonal release, creating adaptive responses that will facilitate the organ- ism to cope with exercise stress [7]. Further, exercise may be considered as a medicine against metabolic syn- drome. The inflammatory response is the body’s reaction and defence against homeostasis disorders, particularly infection and injury [8]. It has long been recognized that exercise is related to anti-inflammatory pathways [9, 10]. However, the pro-inflammatory pathway may be acti- vated, in eccentric exercise protocols [11, 12]. Therefore, optimal exercise protocol should be used for athletes and sedentary people to improve outcomes and prevent mus- culoskeletal damages, cardiovascular, neurological, and endocrinological side effects [13, 14]. The release of pro-inflammatory cytokines and anti- inflammatory cytokines into the circulation in response to exercise varies according to exercise type, duration, and intensity [1, 13]. Tumor necrosis factor alpha (TNF-α) is an essen- tial mediator of the acute inflammatory response [15]. Interleukin-10 (IL-10) is one of the most important anti- inflammatory cytokines [5]. Increased serum TNF- α and C-reactive protein (CRP) levels and decreased IL-10 levels can be regarded as typical signs of a pro-inflamma- tory state [9]. In addition, the IL-10/TNF-α ratio can be used as an indicator of the beneficial effects of exercise [16]. Many studies investigate the inflammatory response in exercise [11, 12] or training sessions [9, 17], and the results are inconsistent. These inconsistencies may arise from various exercise or training protocols (type, dura- tion and intensity), blood sampling timing, lack of con- trol group, small sample sizes, ethnicity and biological variations. CRP is the main acute phase protein in tissue damage and other inflammatory conditions and is a sensitive and objective marker [18]. As a result of a systematic review on CRP, it was found that in trained athletes, when a single exercise protocol was applied, CRP temporarily increased as the acute phase response after exercise. In contrast, those who did higher levels of physical activity in longitudinal studies had lower CRP levels. In this con- text, although physical activity has been found to raise the CRP level acutely, it has been found that chronically physical activity reduces CRP levels [19]. Hypoxia is one of the stress factors that can promote the inflammation process, including pro-inflammatory and anti-inflammatory pathways. Also inflammatory tis- sues usually become hypoxic [20]. Hypoxia or decreased oxygen levels result in many changes at the cellular level, including mitochondrial biogenesis and angio- genesis in rest [21, 22] and exercise conditions [23]. Peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC-1α), hypoxia-inducible fac- tor-1 alpha (HIF-1α) and vascular endotheial growth factor (VEGF) play key roles in these adaptation mecha- nisms. HIF-1α is a hypoxia-induced transcription factor that transcribes more than 100 enzymes and proteins involved in cellular responses caused by hypoxia [24]. There is a relationship between the body’s response to inflammation or exercise stress and its response to hypoxia, and in both cases, the hypoxia-inducible factor- 1a (HIF-1a) signaling pathway can be induced [25]. It is known that both exercise and hypoxia can alter mRNA expression and protein release of pro-and anti-inflam- matory cytokines, activate lymphocytes, alter chemokine receptors, or induce other signaling pathways of the hypoxic inflammatory response [26, 27]. This study was conducted in a low altitude (1850 m) city [28], which is the highest city of Turkey. People living in altitudes higher than sea level have reduced hypoxic ventilatory response, decreased pulmonary hyperten- sion under hypoxia, increased heart rate, and improved peripheral oxygen saturation [29]. The fact that the hypoxia and inflammatory responses were evaluated on different exercise intensities and that the study was con- ducted in a low altitude region is prominent in our study. For these reasons, in this study, the acute effects of exer- cise intensity on hypoxia and inflammatory responses in an amateur athlete group living in a low altitude region were investigated. The combined effects of hypoxia marker and exercise on inflammatory pathways were assessed. Methods Twenty-three amateur male athletes (soccer) living at low altitude (1850 m), training 2 h/day, 5 days/week, were included in this study. Inclusion criteria were; to be an amateur athlete between the ages of 18–22, male gender, living in this location for at least 5 years, vol- unteering to participate in research being healthy and not having a chronic or acute illness. Exclusion criteria were; having any chronic disease, using any medication or stimulants, smoking and alcohol use, having limitation of movement due to injury for any reason. Twenty-three Page 3 of 9 Baygutalp et al. BMC Sports Science, Medicine and Rehabilitation (2021) 13:145 people who met the inclusion criteria were included to the study. Demographical characteristics of athletes are given in Table 1. Athletes were evaluated at rest and at three different exercise intensities: 30 min of exercise on a bicycle ergometer at 50% of the predetermined maxi- mal oxygen consumption capacity (VO2 max) values, 75% of the predetermined VO2 max and exercise at 100% VO2 max-until the individual is exhausted. Venous blood sam- ples were taken at rest state (1st session) and immediately after each exercise sessions (2nd, 3rd and 4th sessions). Ethical ıssues The informed consent form was obtained from all partic- ipants, and they were enlightened with all matters related to the study. The study was approved by the Clinical Research Ethics Committee of Ataturk University Faculty of Medicine (27.05.2021). Study design The study design is summarized in Fig. 1. 2nd session can be defined as mild intensity exercise (50% for 30 min), 3rd session as moderate-intensity exercise (75% for 30 min) and 4th session as high-intensity exercise (100% to exhaustion). Exercise sessions were carried out at one-week inter- vals to prevent physiological adaptation. Data collection Demographical characteristics and height measurement The athletes’ ages in the study were recorded based on their identity information, and the sports ages based on their declarations. The height of the athletes was meas- ured with a mechanical measuring rod (Seca 216, Medis- ave UK Co., UK). Body composition measurement Body composition parameters (weight and body fat per- centage) were measured with the BOD POD body com- position tracking system (Cosmed, USA). Body mass index (BMI) was calculated with the following formula: BMI = weight/height2 (kg/m2). Maximal oxygen consumption capacity (VO2 max) measurement (pre‑test − incremental cycle exercise test) The participants were subjected to a maximal exercise test with an exercise protocol with increasing intensity following the test completion criteria used in the bicycle Table 1 Demographical characteristics of athletes SD: standart deviation, CI: confident interval, BMI: body mass index, BFP: body fat percentage, VO2 max: maximal oxygen consumption Male athletes (n = 23) Mean ± SD 95% CI Age (year) 20.12 ± 0.15 13.51–26.49 BMI (kg/m2) 23.44 ± 1.29 22.44–23.56 BFP 15.19 ± 7.19 11.89–18.11 Sports age (year) 10.21 ± 2.31 9.00–11.00 VO2 max (mL kg min−1) 54.15 ± 6.14 51.34–56.66 Fig. 1 Study design Page 4 of 9 Baygutalp et al. BMC Sports Science, Medicine and Rehabilitation (2021) 13:145 ergometer protocols used for maximal oxygen consump- tion capacity and power measurements. Participants first cycled on the bicycle ergometer for 5 min (50–60 RPM) to warm up. Then, the warming was completed by stretching for 2 min. As soon as the par- ticipant is fully ready, the test is started with the start command and the continuously increasing load test is applied. The participant started the test at 60 revolutions per minute and by pedaling at 150 watts. Then, 30 watts were increased every 2 min, and the trial continued until the test pedal speed fell below 50 revolutions per minute or the subject could not continue anymore. The Rating of Perceived Exertion (RPE) was defined with the Borg scale to determine the VO2 max of the participants [30]. Participant-reported Borg scale scores were used at each load-increasing phase of the exercise test in the first ses- sion they visited the laboratory and immediately after each session. The observation of three of the criteria simultaneously was accepted to indicate that the maximal oxygen use capacity was reached, then the test was terminated. The criteria were; reporting a Borg scale score of 20 by the participant, the oxygen consumption does not increase despite the increase in workload, the ratio of carbon diox- ide production to oxygen consumption RER (respiratory exchange ratio) reaches 1.15 and above, the heart rate is 85% and above the maximum number of heart rates, the increase in the number of heart rates despite the increas- ing workload [31]. In the gas analysis, minute ventila- tion (VE), oxygen volume per minute (VO2), produced carbon dioxide volume (VCO2) per minute were directly measured and recorded. At the same time, the heart rate (HRmax) at which the athletes reached the maximal oxy- gen use and the perceived difficulty values at each step of increase were also recorded. Before the measurement sessions, the Cosmed K5 oxygen analyzer was calibrated with high-grade calibration gases provided by the man- ufacturers. Gas was pumped from the flow meters with a 3-L calibration syringe following the manufacturer’s recommendations and heated for a minimum of 15 min. Mask size was determined individually before the first test, and measurements were taken with the same size in subsequent sessions. Exercise protocols All of the test protocols were carried out in our univer- sity’s Sports performance laboratory and measurement center. Athletes were not allowed to perform vigorous exercise, using drugs, caffeine, alcohol and performance- enhancing ergogenic supplements from 48 h before exercise protocols. Before starting exercise protocols (pre-test), the participants’ maximal oxygen consumption values (VO2 max) were determined using the oxygen analyzer K5 (Cosmed, USA) as a pre-test with the gradu- ally increasing load exercise test on the bicycle ergom- eter. Participants were required to cycle continuously for 30 min on a bicycle ergometer at 50% and 75% of the predetermined maximal oxygen consumption capacity values. Finally the participants were required to cycle at 100% VO2max until exhaustion.The term 100% VO2 max defines the situation when the participant reaches exhaustion during exercise. [32]. The mean time to exhaustion of athletes was 16.35 ± 3.38 min. Venous blood samples were taken immediately after each session. All measurements were taken at the same time of day (morning time). Biochemical analysis 5 mL of venous blood samples were taken from each athlete. After the serum was obtained, the samples were aliquoted and stored at -80 C° until analysis. Serum CRP, TNF-α, interleukin-10 and HIF-1α levels in all samples were analyzed by ELISA method with commercial kits (Bioassay Technology Laboratory-BT Lab, China pro- duced all kits). Samples were collected once and meas- ured duplicated. Sample size calculation The minimum number of patients required for the study was calculated in the G Power sample calculation pro- gram (version 3.1.9.4) at the level of Type I error (α) 0.05) and Type II error (1-β) 0.95, with an effect size (Cohen’s f) of 0.4 (large) for a priori calculation of ANOVA test for 4 repeated groups. Accordingly, the minimum number of samples was determined as 16. We included 23 partici- pants to the study, in order to prevent a limitation caused by small sample size. Statistical analyses Statistical analysis was performed in SPSS 23.0 pack- age program. Kolmogorov–Smirnov test was used to determine the normality of data. Descriptive statistical analysis, repeated measures ANOVA test, and Pearson correlation analysis were performed. Data were pre- sented as mean ± SD (standard deviation). Kolmogo- rov–Smirnov test revealed that data were distributed normally, and repeated measures ANOVA test was used to compare biochemical values of different sessions. Val- ues of P < 0.05 at a 95% confidence interval were consid- ered statistically significant. Eta-squared value (η2) was used to determine effect sizes within the ANOVA calcu- lation. η2 values of 0.01, 0.06, and 0.14 were interpreted as “small”, “medium” and “large” effect sizes, respectively. Page 5 of 9 Baygutalp et al. BMC Sports Science, Medicine and Rehabilitation (2021) 13:145 Results Serum IL-10, TNF-α, CRP and HIF-1α values obtained at rest conditions and different exercise sessions are given in Table 2. Additionally, IL-10/TNF-α ratio was used as a positive predictor of exercise and presented the results in Table 2. Results show that IL-10/TNF-α ratio was decreased in 100% VO2 max session compared to both rest and 50% VO2 sessions (P = 0.008 and P = 0.041, respectively). The pairwise comparisons of biochemical values between rest state and different sessions were per- formed with the repeated measures ANOVA test, and the results are summarized in Table 2. Results showed significant differences in serum TNF-α levels between rest condition and 75% VO2 max; rest and 100% VO2 max session. There were significant differences in serum CRP levels between rest and 50% VO2 max; rest and 75% VO2 max sessions. There were significant dif- ferences in serum IL-10 levels between rest and 50% VO2 max, 50% VO2 max, and 100% VO2 max sessions. There were significant differences in serum HIF-1α lev- els between rest and 75% VO2 max session (P < 0.05 for all comparisons). All other comparisons were not sta- tistically significant (P > 0.05 for all other pairs). The alterations in pro-inflammatory and anti-inflammatory pathways are shown in Fig. 2 with the results of IL-10 and TNF-α. Pearson correlation analyses were performed to evalu- ate the relationships between biochemical parameters in rest conditions and each exercise session. Results showed a high negative correlation between serum HIF-1α and TNF-α levels on 50% VO2 max session (r: − 0.634, P = 0.003). There was a moderate positive correlation between serum HIF-1α and IL-10 levels at 75% VO2 max session (r: 0.593, P = 0.006) (Fig. 3). Correlation analysis showed that serum HIF-1α levels were negatively related to serum TNF-α levels and posi- tively related to serum IL-10 levels. Changes in HIF-1α concentrations during exercise may have negatively affected the pro-inflammatory pathway and positively affected the anti-inflammatory pathway as a protection mechanism. Discussion In this study, the acute effects of different exercise inten- sities on serum IL-10, TNF-α, CRP and HIF-1α levels were reported for the first time. Additionally, the study was conducted in a low altitude (1850 m) city. Exercise practice until exhaustion caused significant pro-inflam- matory effects (demonstrated with TNF-α) and the optimal IL-10 response on 50% VO2 max decreased to nearly baseline level as the exercise intensity reached to 100% VO2 max. Thus, we can suggest that exercise inten- sity should not reach to exhaustion due to there was no improvement in the anti-inflammatory marker IL-10 and there was an increment in the pro-inflammatory marker TNF-α with the potential increase in inflammation. There is a high altitude camping center for athletes in our Table 2 Biochemical values of athletes η2: Eta-squared value a,b,c,d Show repeated measures ANOVA Bonferroni post-hoc test P values a Significant difference (P < 0.05) between rest state and 50% VO2 max session b Significant difference (P < 0.05) between rest state and 75% VO2 max session c Significant difference (P < 0.05) between rest state and 100% VO2 max session d Significant difference (P < 0.05) between 50% VO2 and 100% VO2 max sessions Rest state 50% VO2 max 75% VO2 max 100% VO2 max η2 IL-10 (pg/mL) 328.09 ± 128.87a 446.36 ± 142.84d 347.44 ± 135.69 324.88 ± 168.06 0.546 TNF-α (ng/L) 331.65 ± 293.52b,c 395.59 ± 319.82 472.70 ± 365.21 489.03 ± 368.37 0.309 IL-10/ TNF-α 1.63 ± 1.20 c 1.49 ± 0.93d 1.34 ± 0.97 0.99 ± 0.67 0.566 CRP (mg/L) 0.74 ± 0.35a,b 1.19 ± 0.50 1.07 ± 0.52 0.97 ± 0.55 0.773 HIF-1α (ng/mL) 1.08 ± 0.19b 1.12 ± 0.32 1.26 ± 0.16 1.18 ± 0.21 0.453 Fig. 2 Alterations of cytokine levels in rest state and different seessions Page 6 of 9 Baygutalp et al. BMC Sports Science, Medicine and Rehabilitation (2021) 13:145 city, and athletes from all over the country use this center. For this reason, the study has regional and national added value. Several studies have investigated the responses of pro- inflammatory cytokines, inflammatory cytokines and inflammatory markers to different exercise intensities and modalities, and these studies report distinct results [11, 13, 16, 17, 33, 34]. The conflicting results from pre- vious literature may arise from differences in exercise intensities, exercise modalities, VO2 max capacities, the timing of blood sampling and biological variations. In a study conducted with 20 soccer players with a mean age of 25.75 ± 3.99 years, participants were sub- jected to a single bout high high-intensity interval train- ing and plasma IL-6, IL-1 and TNF- α levels determined before and immediately after training. There was a sig- nificant increase in plasma interleukin-6 levels after exer- cise; however, no significant increase in IL-1 and TNF- α levels showing an anti-inflammatory condition might occur through high-intensity interval training sessions [34]. The training protocol of this study and the exer- cise protocol of the current research is different, and it’s known that metabolic changes may occur differently in training and exercise. However, the type of sports and age of the participants in the two studies are similar. We observed increments in both anti-inflammatory and pro-inflammatory cytokines; anti-inflammatory marker (IL-10) was increased at 50% VO2 session, and pro- inflammatory marker (TNF-α) was increased 75% VO2 and 100% VO2 sessions, and inflammatory (CRP) marker was increased at 50% VO2 and 75% VO2 sessions. We used high-intensity exercises, and researchers have used high-intensity interval training (HIIT). We could not show the anti-inflammatory effects of high intensity exer- cise in our study, although other researchers have shown the anti-inflammatory effects in their study using high- intensity interval training (HIIT). The TNF-α level is decreased by moderate exercise (exercise intensity HRmax 60–70%) [33], and mRNA expression of TNF-α is known to be slightly elevated in skeletal muscle by endurance exercise [35]. In the previ- ous study, a gradient increment was observed in serum TNF-α levels as exercise intensity increases. The high- est TNF-α response to exercise was found at 100% VO2 max session when the athlete presents his maximum endurance. A systematic review including 18 articles investigat- ing the effects of moderate and intense exercise on inflammatory response concluded that intense long exercise protocols might activate pro-inflammatory pathways. Instead of this, moderate or high-intensity intermittent exercise protocols with suitable rest condi- tions may be preferred [1]. We are in line with this con- clusion since we observed an inflammatory profile by determining high TNF- α and CRP levels in 75% VO2 max and 100% VO2 max sessions and optimal IL-10 concentration at 50% VO2 max session. Although there is evidence of minimal pro-inflammatory cytokine response and high anti-inflammatory cytokine release from a study conducted on athletes competing in an ironman triathlon race [36], it should be considered that triathlon race is a type of ultra-endurance exer- cise. We suggest IL-10 levels were not increased as expected at 75% VO2 max and 100% VO2 max exercise Fig. 3 Scatter-dot graphs of Pearson correlation analysis. A Significant negative correlation between HIF-1α and TNF-α at 50% VO2 max session; (r: − 0.634, P = 0.003). B Significant positive correlation between HIF-1α and IL-10 at 75% VO2 max session (r: 0.593, P = 0.006). Page 7 of 9 Baygutalp et al. BMC Sports Science, Medicine and Rehabilitation (2021) 13:145 intensities because of pro-inflammatory effects. CRP level partially supported this suggestion, being signifi- cantly higher in the 75% VO2 max session than the rest state. Among studies investigating the impact of exer- cise on CRP release, most of them reveal increased CRP levels immediately after moderate [37]and intense exercise [38]. Yet, a study reports no effect of exercise modality on acute CRP response [39]. As determined the highest CRP value in 75% VO2 max session and elevated values in 75% VO2 max session compared to rest state in the present study, we can conclude that CRP partly acts together with the pro-inflammatory pathways. However, we could not determine any signifi- cant correlation between CRP and TNF- α. Although we determined optimal IL-10 levels and relatively low TNF- α levels (compared to 75% VO2 max and 100% VO2) at 50% VO2 max session, we can not recommend using this intensity to athletes since this intensity is not related to training for fitness improvements/adap- tations, and as well as for soccer as the participants in the current investigation were indeed soccer players. Further, 50% intensity will not be adequate to stress the body to induce an adaptation. Acute exercise sessions lead to a complex cascade of inflammatory and pro- inflammatory pathways [40–42]. We are in line with this conclusion with altered TNF-α, IL-10, and CRP levels among sessions. Considering the current study results and related stud- ies, we can speculate that moderate intensity exercise with durations longer than 30 min (providing higher endurance than the present study) may be beneficial to prevent/reduce pro-inflammatory response. It is known that disease-induced hypoxia is closely related to the activation of inflammatory pathways, but less information is available about the effects of exercise- induced hypoxia on inflammation. There is a relationship between hypoxia and the release of pro-inflammatory cytokines. Moreover, HIF-1α is important in control- ling excessive inflammation [23]. Also, hypoxia, inflam- mation, and exercise can induce the HIF-1α pathway. It was shown that skeletal muscle HIF-1 protein content increased by 120% with hypoxia, and HIF-1α released in response to hypoxia was triggered by the effect of exer- cise [43]. In the present study, serum HIF-1α levels were significantly increased in 75% VO2 max session com- pared to rest state in a high-lander athlete population liv- ing in this location for at least 5 years. At hypoxia conditions in the exercising person, the inflammatory pathways are regulated differently. The hypoxic and exercise stimuli are stronger in vivo than the hypoxic or inflammatory stimuli isolated in vitro [24]. Of note, when considering HIF-1α results, it should be kept in mind that high interindividual variability may be seen in the expression of HIF and its target genes in response to inflammatory or hypoxic stimuli, and sin- gle nucleotide polymorphisms (SNPs) are thought to be involved in these changes [25]. There is a relation between hypoxia and inflammation. Hypoxia can induce inflammation, and inflamed tissues may become hypoxic [20]. Limited studies investigat- ing the effects of exercise on inflammatory pathways in hypoxic conditions revealed no changes in pro-inflam- matory cytokines, and increases in anti-inflammatory cytokines, indicating the positive effects of exercising in hypoxic conditions. The triple relation of exercise, inflammatory pathways, and oxygen consumption in a low altitude location were investigated in a previous study. HIF-1 α response is maximum on 75% VO2 max session and decreases from this maximum value on exhaustion. This result follows the finding that high-intensity exercise in hypoxia can further induce HIF-1α expression [43]. It is well known that high-lander athletes show better exercise perfor- mance and greater VO2 max capacity than sea-landers since athletes have adapted to hypoxia, and maybe some have a genetic basis, thanks to the effect of altitude [44, 45]. Although there is no agreement to define the term “high-intensity”, it widely refers to exercise intensity higher than 75% VO2 max [23]. A speculative model suggests that HIF-1α and PGC-1α act as mediators in the adaptation of skeletal muscle. The mediators lead to upregulation of mitochondrial biogenesis, angiogenesis via activation of VEGF and a shift in the skeletal mus- cle fibre type. Both high-intensity exercise/training and hypoxia lead to this mechanism to upregulate skeletal muscle adaptation [23]. We observed the optimum HIF-1 α response in a 75% VO2 max session in the present study. HIF-1 α response did not increase when the exercise intensity was reached from 75% VO2 max to 100% VO2 max in the present study. We attribute this because the athletes have developed a physiological adaptation to hypoxia thanks to living at low altitudes. Correlation analyses revealed a high negative correlation between serum HIF-1α and TNF-α levels on 50% VO2 max ses- sion (r: − 0.634, P = 0.003) and a moderate positive cor- relation between serum HIF-1α and IL-10 levels at 75% VO2 max session (r: 0.593, P = 0.006). Results suggest that increased HIF-1α levels reflect the pro-inflammatory condition in 50% VO2 max session and the anti-inflam- matory condition in 75% VO2 max session. We deter- mined maximum TNF-α response and similar IL-10 response compared to baseline in 100% VO2 max ses- sion. We can conclude that the pro-inflammatory effects of hypoxia and anti-inflammatory effects of the exercise Page 8 of 9 Baygutalp et al. BMC Sports Science, Medicine and Rehabilitation (2021) 13:145 was probably due to activating the release of anti-inflam- matory cytokines and downregulating toll-like receptor (TLR) signalling [23]. Studies with different exercise protocols have shown that high-intensity exercise (above 75% of the peak power output) provides similar or even higher benefits than a low-intensity continuous exercise in improving heart health, respiratory health, and metabolic health. Increases in peak power outputs during exercise result in increased metabolic responses, compromising skel- etal muscle integrity, which can cause early onset of fatigue and exhaustion. Therefore, the selection of exer- cise intensity should be made carefully to avoid undesir- able consequences. Taken together, TNF-α, IL-10, CRP, and HIF-1α results, we again suggest that exercise inten- sity should not reach to exhaustion. Despite its original- ity, the current study has a limitation. It would be better ELISA results should be supported with western blotting analysis and mRNA expression levels of proteins. Conclusions There is a tight connection between hypoxia and inflam- mation, and studies investigating the effects of exercise intensity in hypoxic and inflammatory pathways are limited. There is no available study in any athletic popu- lation reporting the acute changes on serum IL-10, TNF- α, CRP and HIF-1α levels induced by different exercise intensities. We noted that both inflammatory and anti- inflammatory pathway is induced on different exercise intensities. As the need for oxygen increases, the inflam- matory pathway (by TNF- α and CRP) is induced, and anti-inflammatory cytokine IL-10 reaches optimal value on exercise intensity of 50% VO2 max. Exercise regimens (not reached to exhaustion) are recommended to prevent inflammation, hypoxia-induced damage, and existing muscle damage progression if any. Further studies on dif- ferent athlete groups should be conducted to determine the optimum exercise intensity and maximum benefit. Abbreviations VO2 max: Maximal oxygen consumption values; TNF-α: Tumor necrosis factor alpha; IL-10: Interleukin-10; CRP: C-reactive protein; HIF-1 α: Hypoxia inducible factor-1 alpha. Acknowledgements The authors would like to thank the athletes that took part in this study. The authors would like to thank to Prof. Mostafa Abdelaty HASSIBELNABY for his scientific contribution to the study. Authors’ contributions FB: concept, design, inspection of all participants, writing article, critical review of the article; YB: performing exercise protocol, writing article, critical review of the article; MO: performing exercise protocol, writing article, critical review of the article; MK: critical review of the article; NKB: biochemical analysis, statistical analysis, critical review of the article; GA: determining exercise protocol, critical review of the article. All authors read and approved the final manuscript. Funding None. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate The informed consent form was obtained from all participants, and they were enlightened with all matters related to the study. The study was approved by the Clinical Research Ethics Committee of Ataturk University Faculty of Medicine (27.05.2021). Consent for publication Not applicable. Competing interests Not applicable. Author details 1 Department of Physical Medicine and Rehabilitation, Ataturk University Faculty of Medicine, Erzurum, Turkey. 2 Department of Physical Education and Sports, Erzurum Technical University Faculty of Sport Sciences, Erzurum, Turkey. 3 Department of Physical Education and Sports, Ataturk University Kazım Karabekir Education Faculty, Erzurum, Turkey. 4 Department of Sports Health Sciences, Ankara University Faculty of Sport Sciences, Ankara, Turkey. 5 Department of Biochemistry, Ataturk University Faculty of Pharmacy, Erzu- rum, Turkey. 6 Department of Recreation, Ataturk University Faculty of Sport Sciences, Erzurum, Turkey. Received: 25 June 2021 Accepted: 15 November 2021 References 1. Cerqueira É, Marinho DA, Neiva HP, Lourenço O. 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Van Thienen R, Masschelein E, D’Hulst G, Thomis M, Hespel P. Twin resem- blance in muscle HIF-1α responses to hypoxia and exercise. Front Physiol. 2016;7:676. 44. Marconi C, Marzorati M, Cerretelli P. Work capacity of permanent residents of high altitude. High Alt Med Biol. 2006;7(2):105–15. 45. Linthorne NP. Improvement in 100-m sprint performance at an altitude of 2250 m. Sports (Basel). 2016;4(2):29. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- lished maps and institutional affiliations.	Impacts of different intensities of exercise on inflammation and hypoxia markers in low altitude.	11-22-2021	Baygutalp, Fatih,Buzdağlı, Yusuf,Ozan, Murat,Koz, Mitat,Kılıç Baygutalp, Nurcan,Atasever, Gökhan	eng
PMC10651037	PONE-D-23-25168 Dose response of running on blood biomarkers of wellness in the generally healthy Reviewer: Dr. Subir Gupta Upon a meticulous review of the article in question, I wish to commend the authors for crafting a piece that not only carries immense scientific weight but is also articulated with great clarity. Such insightful work surely merits publication in your distinguished journal. It's admirable how the authors have navigated through a myriad of physiological and biochemical variables (blood biomarkers) across five distinct participant categories and presented their results with lucidity. The experimental framework is robust, the statistical evaluations are apt, and the narrative progresses seamlessly. The references provided are both relevant and adequate. Nevertheless, I'd like to offer a few observations and suggestions: Original Title: “Dose response of running on blood biomarkers of wellness in the generally healthy.” Proposed Title: “Dose-response relationship between running and blood biomarkers of wellness in generally healthy individuals.” Page 2, Line 8: The mention of “exposure to sunlight” seems somewhat out of context. Could the authors clarify its relevance or indicate if it has been discussed elsewhere in the article? Page 17, Lines 17-18: The text reads: "These observations suggest that elite endurance runners………to their magnesium status." Comments: It would be helpful to clarify whether the professional athletes (PRO) participating in this study are specifically elite endurance runners. Kindly integrate this distinction into the main text if accurate. Page 19, Lines 1-2: The assertion: “Indeed whether exercise………..is inconclusive,” needs to be substantiated with a relevant citation. Table 1: Please include standard deviation (SD) values. I also recommend expressing exercise duration in terms of "h/week" instead of "hr".	Dose response of running on blood biomarkers of wellness in generally healthy individuals.	11-15-2023	Nogal, Bartek,Vinogradova, Svetlana,Jorge, Milena,Torkamani, Ali,Fabian, Paul,Blander, Gil	eng
PMC7435036	Physiological Reports. 2020;8:e14551. \| 1 of 13 https://doi.org/10.14814/phy2.14551 wileyonlinelibrary.com/journal/phy2 DOI: 10.14814/phy2.14551 O R I G I N A L R E S E A R C H Indices of leg resistance artery function are independently related to cycling V̇O2max Jayson R. Gifford1,2 \| Brady E. Hanson1 \| Meagan Proffit1,2 \| Taysom Wallace1 \| Jason Kofoed1 \| Garrett Griffin1 \| Melina Hanson1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society 1Department of Exercise Sciences, Brigham Young University, Provo, UT, USA 2Program of Gerontology, Brigham Young University, Provo, UT, USA Correspondence Jayson R. Gifford, Department of Exercise Sciences, Brigham Young University, Provo, UT 84602, USA. Email: jaysongifford@byu.edu Funding information Bobbit Heart Disease Award, Grant/Award Number: 1 Abstract Purpose: While maximum blood flow influences one's maximum rate of oxygen consumption (V̇O2max), with so many indices of vascular function, it is still unclear if vascular function is related to V̇O2max in healthy, young adults. The purpose of this study was to determine if several common vascular tests of conduit artery and resistance artery function provide similar information about vascular function and the relationship between vascular function and V̇O2max. Methods: Twenty-two healthy adults completed multiple assessments of leg vas- cular function, including flow-mediated dilation (FMD), reactive hyperemia (RH), passive leg movement (PLM), and rapid onset vasodilation (ROV). V̇O2max was assessed with a graded exercise test on a cycle ergometer. Results: Indices associated with resistance artery function (e.g., peak flow during RH, PLM, and ROV) were generally related to each other (r = 0.47–77, p < .05), while indices derived from FMD were unrelated to other tests (p < .05). Absolute V̇O2max (r = 0.57–0.73, p < .05) and mass-specific V̇O2max (r = 0.41–0.46, p < .05) were related to indices of resistance artery function, even when controlling for fac- tors like body mass and sex. FMD was only related to mass-specific V̇O2max after statistically controlling for baseline artery diameter (r = 0.44, p < .05). Conclusion: Indices of leg resistance artery function (e.g., peak flow during RH, PLM, and ROV) relate well to each other and account for ~30% of the variance in V̇O2max not accounted for by other factors, like body mass and sex. Vascular inter- ventions should focus on improving indices of resistance artery function, not conduit artery function, when seeking to improve exercise capacity. K E Y W O R D S flow-mediated dilation, passive leg movement, rapid onset vasodilation, vascular function, V̇O2max 2 of 13 \| GIFFORD et al. 1 \| INTRODUCTION One's maximum rate of oxygen consumption (V̇O2max) strongly influences exercise performance and is also a strong predictor of cardiovascular risk (Poole, Behnke, & Musch, 2020). While many systems may limit V̇O2max (Wagner, 2008), the cardiovascular system often serves as a significant bottleneck, with untrained individuals often ex- hibiting a much lower cardiac output and muscle blood flow than endurance-trained individuals (Gifford et al., 2016; Levine, 2008). Evidence indicates that cardiac output and muscle blood flow during exercise are both strongly in- fluenced by the ability of the peripheral vasculature to di- late (Bada, Svendsen, Secher, Saltin, & Mortensen, 2012; Hanson, Proffit, & Gifford, 2020; Joyner & Casey, 2015). While large conduit arteries, like the brachial or femoral ar- teries, may dilate during exercise (Tremblay & Pyke, 2018), it is the dilation of the network of small resistance arteries, whose total cross-sectional area far exceeds that of the large conduit arteries (Wiedman, 1963), that primarily regulates the increase in blood flow during exercise (Joyner & Casey, 2015; VanTeeffelen & Segal, 2006). With fluctuations in the radius of the arterial circulation having such a profound impact on blood flow and cardiac output, several studies have sought to determine if the ability of the vasculature to dilate, often termed vascular function (Gifford & Richardson, 2017), is related to exercise capacity (Montero, 2015). In such studies vascular function has usually (Montero, 2015) been quantified with a technique that measures the vasodilator ability of a conduit artery (Flow- Mediated Dilation, FMD) in a region not majorly involved in most tests of V̇O2max, the arm. Despite vascular function being measured in a conduit artery that does not perfuse the main exercising muscles, most studies demonstrate a sig- nificant, positive relationship between brachial FMD and V̇O2max during running or cycling exercise (Montero, 2015). However, as noted by Montero (Montero, 2015), the com- parison of conduit artery function of an upper limb to the V̇O2max elicited by a lower-body exercise (e.g., cycling or running) is problematic since upper-limb vascular function is not reflective of lower-limb vascular function (Thijssen, Rowley, et al., 2011), and exercise training is known to elicit local adaptations in conduit artery structure that may mask any adaptation in local vascular function (Green, Spence, Rowley, Thijssen, & Naylor, 2012). Moreover, given the neg- ligible role of conduit arteries in regulating exercise blood flow (Joyner & Casey, 2015), the relevance of conduit artery function to exercise capacity is unclear. Multiple noninvasive assessments intended to interrogate the function of the resistance arteries (Limberg et al., 2020) of the lower limbs have been developed in recent years. Indeed, tests such as reactive hyperemia (RH) in response to cuff occlusion and removal, the hyperemic response to pas- sive leg movement (PLM), and the rapid onset of vasodila- tion and hyperemia in response to a single muscle contraction (ROV) have been shown to be NO dependent (Broxterman et al., 2017; Casey, Walker, Ranadive, Taylor, & Joyner, 2013; Gifford & Richardson, 2017; Limberg et al., 2020) and related to peak blood flow during knee extension exercise (Hanson et al., 2020). Nevertheless, it is not clear if these distinct indi- ces of vascular function actually reflect the same underlying physiology and are related to each other. It is also unclear how relevant these indices of resistance artery function (Limberg et al., 2020) are to exercise capacity. If these indices of resis- tance artery vascular function truly are representative of the function of the resistance arteries, which are largely respon- sible for blood flow control (Joyner & Casey, 2015), one may expect them to relate well to exercise capacity. To date, the relationships between the various tests of vas- cular function in the lower limb and their relevance to exer- cise capacity have not been extensively explored. Therefore, the purpose of this study was twofold. First, we sought to determine how well the various indices of conduit and re- sistance artery function relate to each other. Second, we sought to determine if aerobic exercise capacity, assessed by V̇O2max during cycling exercise, is related to various indices of conduit artery and resistance artery function. 2 \| METHODS 2.1 \| Subjects Twenty-two young, healthy subjects (13 males, 9 females, 18–30 years old) completed the study. All subjects were healthy, nonobese, nonsmokers, free from medications that would affect their hemodynamic responses to exercise (Gifford & Richardson, 2017). Data for females were col- lected within the first 7 days of the menstrual cycle to mini- mize variability attributable to hormonal fluctuations. Prior to starting the study, the Institutional Review Board (IRB) at Brigham Young University (BYU) found the study to be safe, ethical, and in agreement with the main principles out- lined in the Declaration of Helsinki. Prior to participation, all subjects provided informed consent. While the study was per- formed in accordance principles outlined in the Declaration of Helsinki, it was not registered on clinicaltrials.gov before data collection. 2.2 \| Procedures Subjects reported to the laboratory on three occasions hav- ing fasted for 4 hr, rested from exercise and refrained from alcohol or caffeine consumption for ~24 hr (Gifford & \| 3 of 13 GIFFORD et al. Richardson, 2017). Each visit was separated by a minimum of 24 hr. All data collection was completed on the subject's right leg, regardless of leg dominance. On the first visit, body measurements including height (cm), body mass (kg), and body mass index (BMI, kg·m−2). After resting supine for 20 min, vascular function was assessed by the FMD and RH techniques on the superficial femoral artery as described below. Subsequently, the maximum rate of oxygen consumption during cycling (i.e., V̇O2max) was assessed with a graded exercise test (25 watt increments per minute) on a cycle ergometer (Excaliber Sport, Lode, Groningen, Netherlands) with a Parvo metabolic cart (True One, Parvo-Medics Inc., Sandy, Utah, USA) (Gifford et al., 2016)). The greatest power sustained for 1 min during the graded exercise test was identified as Graded Exercise Test Max (GXTmax). Following 30 min of rest from the initial graded exercise test, a constant-load test (100% GXTmax) until exhaustion test was performed to verify the initial V̇O2max results (Poole & Jones, 2017). The verifi- cation V̇O2max for all subjects was within ±5% of the initial V̇O2max, supporting the attainment of V̇O2max. The higher of the two values was recorded as the final V̇O2max. On the second visit, vascular function was assessed first with the passive leg movement (PLM) technique in triplicate. Following a ~ 5-min recovery period, vascular function was then assessed by the rapid onset vasodilation (ROV) tech- nique elicited by single kick knee extension exercise as de- scribed below. 2.3 \| Assessments of vascular function 2.3.1 \| Flow-mediated dilation (FMD) and reactive hyperemia (RH) During the first visit subjects reported to the laboratory to have vascular function assessed via FMD and RH on the superficial femoral artery according to current recommendations (Harris, Nishiyama, Wray, & Richardson, 2010) and as previously described (Hanson et al., 2020). While lying in the supine po- sition, a 9 cm blood pressure cuff (Hokanson Inc., Bellevue, WA, USA) was placed on the thigh proximal to the knee- cap. Following a 20-min acclimation/resting period, baseline measurements (diameter and blood flow) were gathered for 60 s at the superficial femoral artery ~10 cm proximal to the cuff with a GE Logiq E ultrasound (General Electric Medical Systems, Milwaukee, WI, USA) operating with a B-mode frequency of 9 MHz and a Doppler frequency of 5 MHz. The cuff was then inflated for 5 min to 250 mmHg. Blood velocity and diameter data were collected for 2 min immediately after the release of the cuff pressure. Following the study, artery diameter was analyzed frame-by-frame by automated edge detection software (Quipu srl., Pisa, Italy) and averaged into 1-s bins corresponding to 1-s average velocities. A 3-s rolling average was applied to smooth diameter and velocity data. Blood flow (ml·min−1) was calculated using the equation: blood ﬂow=[ (mean blood velocity)×(휋 ×(vessel radius2) ) x 60] , where mean blood velocity is expressed in cm·s−1 and radius is expressed in cm. FMD measurements were expressed as a percent change in diameter and calculated with the equation: Shear rate was calculated with the following equation Shear rate= 8×mean blood velocity Diameter . Subsequently FMD was also normalized for total shear area under the curve (i.e., FMD/ shear) as recommended and described by Harris et al (Harris et al., 2010). Peak flow during RH following the release of the cuff was identified as the greatest 1-s average of flow achieved following cuff release (Harris et al., 2010). 2.3.2 \| Passive leg movement (PLM) The hyperemic response induced by PLM, which is NO- dependent (Broxterman et al., 2017; Mortensen, Askew, Walker, Nyberg, & Hellsten, 2012; Trinity et al., 2012) and strongly related to acetylcholine-induced hyperemia (Mortensen et al., 2012), was utilized to assess thigh vascular function according to recently published guidelines (Gifford & Richardson, 2017). Subjects were seated in an upright posi- tion with knees fully extended (180°) for a 20-min acclimation period before any data were collected. Subsequently, resting blood flow was measured for 60 s at the common femoral artery utilizing a GE Logiq E ultrasound (General Electric Medical Systems, Milwaukee, WI, USA) operating with a B-mode frequency of 9 MHz, a Doppler frequency of 5 MHz, and an insonation angle of 60°. Subsequently, researchers manually moved the subject's leg back and forth from the extended posi- tion of the knee (180°) to the flexed position (90°), at a rate of 60 knee extensions per min, while the subjects stayed re- laxed with no voluntary muscle contraction, while blood flow was measured at the common femoral artery throughout. This procedure was completed three times with a ~15-min period of rest between each trial. Blood flow data were analyzed second- by-second and a 3-s rolling average was applied to smooth the data. The peak blood flow and the area under the curve (PLM Total Flow) were identified for each of the three trials and then averaged together (Gifford & Richardson, 2017). The data pre- sented in this manuscript are the average of the three trials. 2.3.3 \| Rapid onset vasodilation (ROV) The hyperemic response to a single muscle contraction (e.g., one leg extension) has also been shown to be NO dependent FMD (%)= (Peak Diameter−Baseline Diameter) Baseline Diameter ×100. 4 of 13 \| GIFFORD et al. (Casey et al., 2013) and is indicative of the responsiveness of the vasculature to an exercise stimulus (Credeur et al., 2015; Hughes, Ueda, & Casey, 2016). For this study, the hyperemic response to a single knee extension of 60 Nm of work was used to quantify ROV. Subjects were seated in an upright position with legs hanging over the end of a seat with knees in a flexed position (knee at 90° flexion at rest). The right ankle was then connected to the cable of a knee extension machine (a basic pulley system that vertically displaces a se- lected amount of weight – N.K. Products, Lake Elsinore, CA, USA). Subjects then fully extended their leg so that the verti- cal displacement distance associated with a fully extended kick could be measured using a standard tape measure. This displacement distance was subsequently used to calculate the total work performed during the different kicks. Subjects were then familiarized with the kicking motion at various dif- ferent weights. Following 20-min recovery, ROV was assessed in du- plicate in response to a full knee extension totaling 60 Nm of work. Repeated trials were separated by at least 2 min of recovery. As subjects of different leg lengths displaced the weights to different distances, the mass each subject lifted was adjusted for the absolute work kick so that the total work (i.e., Total Work = mass × gravity × displacement distance) was 60 Nm when extending the leg through a full 90° range of motion. For each kick, 1 min of baseline data was collected while the leg was rested in a flexed position. Subsequently, subjects extended the knee to ~180° and then passively allowed the weight to flex the knee back to 90° with no engagement of knee flexor or extensor muscles during the knee flexion phase (e.g., active contraction during knee ex- tension and no contraction during flexion). Femoral blood flow was assessed, as described for the PLM technique, for 1 min of baseline prior to contraction, during the kick and for 1 min following the kick. Data were subsequently analyzed second-by-second and a 3-s rolling average was applied to smooth the data. The peak blood flow (ROV Peak Flow) was subsequently identified as the greatest 1-s average of blood flow, while the total flow response (ROV Total Flow) was identified as the area under the curve for 60 s. As each ex- ercise was performed in duplicate, the data reported in this manuscript are the average of both trials. 2.4 \| Statistical analysis Test–retest reliability of the variables that were performed in repeated measures (PLM-based indices in triplicate and ROV-based indices in duplicate) was assessed with intra- class correlation (ICC) using a two-way mixed model based on absolute agreement. As the average of the multiple meas- urements was used for the analysis in this study, the ICC for the average of the repeated measures, not the ICC for an individual measure, is reported. Criteria for classifying the level of reliability of measurements were based up those set forth by Koo & Li (2016), in which an ICC between 0.50 and 0.75 is evidence of “moderate reliability”, an ICC be- tween 0.75 and 0.90 is evidence of “good reliability”, and an ICC > 0.90 is evidence of “excellent reliability”. Pearson correlation and a linear regression were utilized to determine the relationship between the various assess- ments of vascular function and other variables. Categorical data, like sex, were dummy coded into correlations. Part/ partial correlation was utilized to determine the amount of unique variance shared by two variables when removing that related to a third variable. Principal components analysis was utilized to combine the large amount of information pro- vided by the multiple indices of vascular function into fewer, discrete variables based on the shared variance among the different indices of vascular function. Specifically, major variables derived from the tests of vascular function (FMD % dilation, FMD/shear, RH Peak Flow, RH Total Flow, PLM Peak Flow, PLM Total Flow, ROV Peak Flow, and ROV Total Flow) were entered into a principal components analysis with orthogonal rotation (varimax). Factors with an eigenvalues greater than 0.7 were accepted and only variables with load- ings greater than 0.7 were included in a factor (Field, 2009). An independent sample t-test was conducted to identify sex differences among the indices of vascular function. Alpha was set at p ≤ .05 a priori. All statistical analyses were completed using SPSS ver- sion 26 (SPSS Inc.). Data are expressed as the mean ± SE unless otherwise stated. 3 \| RESULTS 3.1 \| Test–retest reliability of PLM and ROV measurements The repeated measurements of PLM Peak Flow and PLM Total Flow both exhibited “excellent reliability” with ICC equal to 0.91. The repeated measurements of ROV Peak Flow exhibited “excellent reliability” with ICC equal to 0.96. The repeated measurements of ROV Total Flow exhib- ited “moderate reliability” with ICC equal to 0.72. As men- tioned in the methods section, the average of the repeated measurements was utilized for all subsequent analyses in this study. 3.2 \| Relationship between the various assessments of vascular function As illustrated in Figure 1 and further described in Table 1, the relationships between the multiple indices of vascular \| 5 of 13 GIFFORD et al. function were examined with Pearson correlation. In general, indices derived from resistance artery function tests (i.e., RH, PLM, and ROV) were related to each other (p < .05), but not to indices derived from conduit artery function tests (e.g., FMD, p > .05). Principal components analysis of the variables listed in Table 1 was utilized to group indices that share substantial variance to condense the multiple indices of vascular func- tion to fewer factors. In essence, this analysis determines the extent to which the various assessments of vascular function represent similar or distinct factors. The Kaiser- Meyer-Olkin measure (KMO = 0.54) supported the sam- pling adequacy for the factor analysis. Visual analysis of a scree plot indicated a breakpoint at two factors, support- ing the inclusion of two different factors with eigenvalues greater than 0.7. Factor #1 was exclusively comprised of factors related to FMD (FMD % dilation and FMD/shear) with loading factors of 0.72 and 0.89, respectively. Factor #2 was comprised of the following variables with the load- ing factors indicated in parentheses: RH Peak Flow (0.84), RH Total Flow (0.75), PLM Peak Flow (0.83), PLM Total Flow (0.74), and ROV Peak Flow (0.84). ROV total flow was not included in either factor. 3.3 \| Relationship between indices of vascular function and V̇O2max As illustrated in Figure 2 and further described in Table 2, variables associated with FMD were unrelated to mass- specific and absolute V̇O2max (p = .12–0.40). Meanwhile, variables associated with the second factor revealed in fac- tor analysis (e.g., RH Peak Flow, PLM Peak Flow, and ROV Peak Flow) exhibited moderate-to-strong correla- tions with absolute and mass-specific V̇O2max (r = 0.56– 73, p < .05). FIGURE 1 Relationship between Different Indices of Vascular Function. (a) Relationship between the peak flow achieved during passive leg movement (PLM) and the peak flow achieved during the rapid onset vasodilation (ROV) test. (b) Relationship between PLM peak flow and the peak flow observed during a reactive hyperemia (RH) test. (c) Relationship between the peak flow achieved during the ROV and RH tests. (d) Relationship between flow-mediated dilation (FMD) of the superficial femoral artery and the peak flow achieved during an ROV test. (e) Relationship between FMD of the superficial femoral artery and the peak flow achieved during RH. (f) Relationship between FMD of the superficial femoral artery and the peak flow achieved during PLM. A solid trendline indicates a significant relationship between the two variables (p ≤ .05) while a dotted trendline indicates a nonsignificant relationship between the two variables (p > .05). Light gray circles represent data for females and dark gray circles represent data for males 6 of 13 \| GIFFORD et al. TABLE 1 Relationship between various indices of vascular function FMD (% Dilation) FMD (%/ Shear) RH peak flow (ml/min) RH total flow (ml) PLM peak flow (ml/min) PLM total flow (ml) ROV peak flow (ml/min) ROV total flow (ml) Factor 1 FMD (% Dilation) - r = 0.47 p = .04 r = −0.14 p = .55 r = −0.01 p = .99 r = −0.11 p = .64 r = 0.01 p = .96 r = −0.33 p = .15 r = −0.04 p = .85 FMD (%/ Shear) r = 0.47 p = .04 - r = −0.21 p = .37 r = −0.54 p = .01 r = −0.31 p = .18 r = −0.07 p = .78 r = −0.32 p = .17 r = 0.18 p = .45 Factor 2 RH peak flow (ml/min) r = −0.14 p = .55 r = −0.21 p = .37 - r = 0.82 p < .01 r = 0.47 p = .03 r = 0.31 p = .17 r = 0.77 p < .01 r = 0.36 p = .11 RH total flow (ml) r = −0.01 p = .99 r = −0.54 p = .01 r = 0.82 p < .01 - r = 0.40 p = .08 r = 0.23 p = .32 r = 0.57 p = .01 r = 0.09 p = .71 PLM peak flow (ml/min) r = −0.11 p = .64 r = −0.31 p = .18 r = 0.47 p = .03 r = 0.40 p = .08 - r = 0.89 p < .01 r = 0.64 p < .01 r = 0.24 p = .28 PLM total flow (ml) r = 0.01 p = .96 r = −0.07 p = .78 r = 0.31 p = .17 r = 0.23 p = .32 r = 0.89 p = .01 - r = 0.45 p = .04 r = 0.18 p = .41 ROV peak flow (ml/min) r = −0.33 p = .15 r = −0.32 p = .17 r = 0.77 p < .01 r = 0.57 p = .01 r = 0.64 p < .01 r = 0.45 p = .04 - r = 0.63 p < .01 ROV total flow (ml) r = −0.04 p = .85 r = 0.18 p = .45 r = 0.36 p = .11 r = 0.09 p = .71 .01 r = 0.24 p = .28 r = 0.18 p = .41 r = 0.63 p < .01 Note: The terms “Factor 1” and “Factor 2” at the left of the table refer to the variables that were grouped together via principal components analysis. Significant relationships are in bold font Abbreviations: FMD, flow-mediated dilation; PLM: passive leg movement; RH: Reactive Hyperemia; ROV: Rapid onset vasodilation. \| 7 of 13 GIFFORD et al. 3.4 \| Other factors that relate to the indices of vascular function As described in Table 3, factors related to a subject's anat- omy, sex, and body mass were related to the outcomes of the vascular function tests. Notably, FMD exhibited a negative correlation with the artery diameter at baseline (r = −0.64, p = .002), such that individuals with larger arteries tended to exhibit lower FMD (Table 3). Meanwhile, body mass was positively related with RH Peak Flow (r = 0.47, p = .01), PLM Peak Flow (r = 0.62, p < .01), and ROV Peak Flow (r = 0.53, p = .01). 3.4.1 \| Sex differences in indices of vascular function PLM Peak Flow (Female: 1,140 ± 98 ml min−1, Male: 1626 ± 114 ml min−1; p = .006) and PLM Total Flow (Female: 333 ± 57 ml, Male: 566 ± 56 ml; p = .01) were both significantly greater in males than females. ROV peak flow also tended to be greater in males than females (Female: 1762 ± 149 ml min−1, Male: 2,196 ± 186 ml min−1; p = .10), while FMD (% Dilation) tended to be lower in males than fe- males (Female: 6.98 ± 0.78%, Male: 4.73 ± 0.85%; p = .09). The sex difference in PLM Peak Flow and Total Flow disap- peared when controlling for body mass (p = .98), which was significantly different between the females and males in the study (57.00 ± 1.69 kg vs. 82.35 ± 1.69 kg, respectively, p < .01). 3.5 \| Relationship between vascular function and V̇O2max when controlling for other variables Recognizing that several other factors may potentially influ- ence the responses observed in the different vascular func- tion tests (see Table 3), the relationship between V̇O2max and the various indices of vascular function was examined when controlling for potentially confounding variables. When controlling for the variation in FMD accounted for by baseline diameter, FMD was found to be significantly related to the mass-specific V̇O2max (r = 0.44, p = .04; Table 4). Moreover, when simultaneously accounting for the variance related to body mass, sex, and BMI with partial correlation, RH Peak Flow, PLM Peak, and ROV Peak Flow were still significantly related to absolute V̇O2max (r = 0.49–0.59, p < .05) and mass-specific V̇O2max (r = 0.46–0.55, p ≤ .05). Finally, stepwise linear regression was performed to explore the possibility of predicting V̇O2max with vascu- lar function data and other subject characteristics. Of the five variables entered into the regression (body mass, sex, height, BMI, and PLM Peak Flow), only body mass, PLM FIGURE 2 The Relationship between Vascular Function and The Maximum Rate of Oxygen Consumption (V̇O2max) during Cycling. The relationship between absolute V̇O2max and (a) flow-mediated dilation (FMD) of the superficial femoral artery (b) peak flow during Reactive hyperemia (RH), (c) peak flow during passive leg movement (PLM) and (d) peak flow during a rapid onset vasodilation (ROV) test. V̇O2max. A solid trendline indicates a significant relationship between the two variables (p ≤ .05), while a dotted trendline indicates a nonsignificant relationship between the two variables (p > .05). Light gray circles represent data for females and dark gray circles represent data for males 8 of 13 \| GIFFORD et al. Peak Flow, and BMI were retained by the stepwise regres- sion, yielding the following equation (R2 = 0.83, p < .01): Absolute V̇O2max = 970.82 + 55.83 (Body Mass) + 0.68 (PLM Peak Flow) – 121.75 (BMI). 4 \| DISCUSSION The purpose of this study was to determine how well the vari- ous indices of vascular function relate to each other and if aerobic capacity, assessed by V̇O2max, is related to these in- dices of vascular function. The results of this inquiry yielded two major findings. First, in agreement with current thought (Limberg et al., 2020; Thijssen, Black, et al., 2011), the as- sessments of conduit artery function (FMD and its deriva- tives) and resistance artery function (derivatives of RH, PLM, and ROV) appear to reflect two different aspects of vascular function, with the indices derived from the RH, PLM, and ROV being strongly correlated with each other, but not with FMD and its derivatives. The second major finding of this study is that leg vascular function, especially resistance ar- tery function, is strongly related to V̇O2max, accounting for approximately 30% of the variance in V̇O2max not accounted for by known influencers, like body mass, sex, and BMI. 4.1 \| Are the various indices of vascular function interchangable with one another? Multiple methods exist for quantifying a person's vascular function, yet it is unclear if these various methods are related to each other. Therefore, in the current study vascular func- tion was measured in multiple ways (FMD, RH, PLM, and ROV) on a group of young, healthy adults. As illustrated in Figure 1 and further described in Table 1, lower limb vas- cular function assessed by the resistance artery tests RH, PLM, and ROV exhibits strong relationships with each other (r = 0.54–0.83, p < .05), supporting the notion that they reflect some of the same physiological processes (Limberg et al., 2020). This comes in agreement with data from Rossman, Groot, Garten, Witman, & Richardson (2016) and Walker et al. (2016) who observed significant correlations between PLM-induced hyperemia and RH in various popu- lations. However, as was the case for Rossman et al. (2016), vascular function assessed by FMD of the superficial femo- ral artery was not related to the other measurements of vas- cular function (e.g., PLM-induced hyperemia) examined in the current study (Figure 1, Table 1). The lack of relationship between FMD and the other vari- ables should not be interpreted as evidence of superiority or inferiority of one test over another, but as an indication that these validated tests of vascular function capture differ- ent aspects of cardiovascular physiology. Indeed, principal components analysis, which consolidated the various indices of vascular function into two different factors, supports the idea that the results of the various tests capture two general aspects of vascular physiology. As illustrated in Table 1, Factor 1 is comprised exclusively of FMD and factors de- rived from the FMD test, which have been suggested to rep- resent conduit artery function (Thijssen, Black, et al., 2011). Meanwhile, Factor 2 was comprised of the main indices de- rived from RH, PLM, and ROV, all of which have recently been referred to as tests of resistance artery or resistance vessel function (Limberg et al., 2020). Thus, the current data indicate that the tests of resistance artery function used in the current study are relatively interchangeable, but that tests reflecting conduit artery function should not be considered as surrogates for tests of resistance artery function, or vice versa. TABLE 2 Relationship between different indices of vascular function and maximum rate of oxygen consumption (V̇O2max) achieved during cycling Mass-Specific V̇O2max (ml/kg/min) Absolute V̇O2max (ml/min) FMD (% Dilation) r = 0.20 p = .40 r = −0.24 p = .31 FMD (%/shear) r = −0.27 p = .24 r = −0.35 p = .12 RH Peak Flow (ml/min) r = 0.26 p = .25 r = 0.49 p = .02 RH Total Flow (ml) r = 0.41 p = .06 r = 0.44 p = .04 PLM Peak Flow (ml/min) r = 0.48 p = .03 r = 0.72 p < .01 PLM Total Flow (ml) r = 0.42 p = .05 r = 0.65 p < .01 ROV Peak Flow (ml/min) r = 0.36 p = .11 r = 0.58 p < .01 ROV Total Flow (ml) r = 0.10 p = .65 r = 0.03 p = .91 Body Mass (kg) r = 0.16 p = .49 r = 0.84 p < .01 Body Mass Index (kg/m2) r = −0.16 p = .49 r = 0.64 p < .01 Sex (Female = −1, Male = +1) r = 0.01 p = .98 r = 0.79 p < .01 Note: Note that sex has been dummy coded with females being coded as −1 and males being entered as + 1. In this dummy coding scenario, a negative correlation indicates greater values are associated with the female sex, while a positive correlation indicates greater values are associated with the male sex. Significant relationships are in bold font Abbreviations: FMD, flow-mediated dilation; PLM, passive leg movement; RH, reactive hyperemia; ROV, rapid onset vasodilation. \| 9 of 13 GIFFORD et al. 4.2 \| Are conduit and/or resistance artery function related to V̇O2max? The overarching aim of this study was to answer the ques- tion, “Is vascular function related to V̇O2max?” However, the data in Table 1 make it clear that one must clarify which aspect of vascular function is of interest when answering this question, since indices of conduit artery function and resistance artery function are not well cor- related. As illustrated in Figure 2, resistance artery, but not conduit artery, function was strongly related to ab- solute V̇O2max, meaning that an individual with a large hyperemic response to the vascular tests would be likely to achieve a greater maximal rate of oxygen consumption and power output (e.g., GXTmax) during a graded exer- cise test. Meanwhile, mass-specific V̇O2max was only re- lated to resistance artery function assessed by PLM Peak Flow (r = 0.46, p = .03) and RH Total Flow (r = 0.41, p = .05), but not conduit artery function assessed by FMD (r = 0.20, p = .40). The strong relationship between Baseline Artery Diameter (mm) Body Mass (kg) BMI (kg/m2) Sex (Female = −1, Male = +1) FMD (% Dilation) r = −0.64 p < .01 r = −0.47 p = .03 r = −0.48 p = .03 r = −0.38 p = .09 FMD (%/shear) r = −0.49 p = .03 r = −0.27 p = .26 r = −0.33 p = .17 r = −0.15 p = .54 RH Peak Flow (ml/min) r = 0.65 p < .01 r = −0.47 p = .03 r = 0.48 p = .03 r = 0.30 p = .19 RH Total Flow (ml) r = 0.48 p = .03 r = 0.28 p = .22 r = 0.31 p = .17 r = 0.17 p = .46 PLM Peak Flow (ml/min) r = 0.85 p < .01 r = 0.62 p < .01 r = 0.59 p < .01 r = 0.56 p < .01 PLM Total Flow (ml) r = 0.74 p < .01 r = 0.58 p < .01 r = 0.54 p < .01 r = 0.54 p = .01 ROV Peak Flow (ml/min) r = 0.75 p < .01 r = 0.53 p = .01 r = 0.61 p < .01 r = 0.35 p = .11 ROV Total Flow (ml) r = 0.18 p = .43 r = 0.01 p = .99 r = 0.15 p = .50 r = 0.03 p = .98 Note: Note that sex has been dummy coded with females being coded as −1 and males being entered as +1. Significant relationships are in bold font. Abbreviations: BMI, body mass index; FMD, flow-mediated dilation; PLM, passive leg movement; ROV, rapid onset vasodilation. TABLE 3 Relationship between indices of vascular function and other subject characteristics Mass-Specific V̇O2max (ml/kg/min) Absolute V̇O2max (ml/min) FMD (% Dilation) Controlling for baseline diameter r = 0.45 p = .04 r = 0.35 p = .14 Peak Reactive Hyperemia (ml/ min) Controlling for body mass, sex, and BMI r = 0.46 p = .05 r = 0.49 p = .04 PLM Peak Flow (ml/min) Controlling for body mass, sex, and BMI r = 0.53 p = .02 r = 0.58 p = .01 ROV Peak Flow (ml/min) Controlling for body mass, sex, and BMI r = 0.55 p = .01 r = 0.59 p < .01 Note: Note that sex has been dummy coded with females being coded as −1 and males being entered as +1. Significant relationships are in bold font. Abbreviations: BMI, body mass index; FMD, flow-mediated dilation; PLM, passive leg movement; ROV, rapid onset vasodilation. TABLE 4 Partial correlations between indices of vascular function and the maximum rate of oxygen consumption (V̇O2max) during cycling exercise when controlling for potentially confounding variables 10 of 13 \| GIFFORD et al. resistance artery function and V̇O2max in these healthy young adults is consistent with previous studies that have reported relationships between V̇O2max and the hypere- mic responses to RH (Robbins et al., 2011) and ROV (19) in various populations. It makes sense that V̇O2max would be more related to resistance artery function than conduit artery func- tion since V̇O2max is strongly influenced by maximum blood flow (Gifford et al., 2016; Levine, 2008) which is primarily controlled by the dilation and constriction of the myriad of resistance arteries (Dodd & Johnson, 1991; Joyner & Case y, 2015). Along these lines, our group re- cently reported that factors associated with resistance ar- tery function (e.g., PLM Peak Flow and ROV Peak Flow) were very predictive of peak blood flow achieved during knee extension exercise, while FMD was not (Hanson et al., 2020). A large PLM, RH, or ROV response seems to be indicative of a limb with a network of resistance arteries that can accommodate high rates of blood flow, thereby facilitating a greater V̇O2max. Thus, interven- tions targeting resistance artery function may potentially have more impact on exercise tolerance in healthy adults than interventions seeking to improve conduit artery function. Future studies could potentially further exam- ine the relationship between conduit artery function and V̇O2max by measuring conduit artery diameter during a V̇O2max test. Unfortunately, such precise diameter measurements are not currently possible during cycling exercise. Contrary to our findings, previous research (Montero, 2015) has indicated that FMD is typically re- lated to V̇O2max, most commonly the mass-specific V̇O2max. The reason for the disagreement between find- ings may be due to measurement location. In contrast to most previous studies, which measured FMD in the arm, the current study compared vascular function, including FMD, assessed in the lower limb to cardiorespiratory fit- ness assessed during a predominantly lower-limb exercise like cycling or running. Indeed, the aforementioned me- ta-analysis (Montero, 2015) concluded “further studies are needed to elucidate the association of cardiorespi- ratory fitness with lower limb endothelial function.” As mentioned earlier, exercise-induced adaptations to arterial structure and diameter appear to be of a greater magnitude in exercise-trained muscles than in nontrained muscles (Rowley et al., 2012). It is possible that exercise-induced adaptations in the diameter of the superficial femoral ar- tery masked the relationship between FMD in the lower limb and V̇O2max. Thus, further investigation into the relationship between vascular function and V̇O2max, when controlling for potentially confounding variables, is warranted. 4.3 \| What other factors influence the indices of vascular function? It is important to recognize that although these indices of vas- cular function are related to NO bioavailability and endothe- lial function (Casey & Joyner, 2011; Green, 2005; Mortensen et al., 2012), multiple other factors, besides endothelial func- tion, can influence the results of these vascular function tests. As listed in Table 3, the measures of vascular func- tion utilized in the current study are sensitive to several fac- tors that should be considered when interpreting the results of a test. For example, in agreement with previous research (Anderson et al., 1995; Celermajer et al., 1992), FMD was negatively related to baseline artery diameter, such that indi- viduals with a large diameter artery at baseline tend to exhibit a lower FMD. In the initial paper to link brachial artery FMD to coronary endothelial dysfunction (Anderson et al., 1995), the authors indicated that baseline brachial artery diameter was the strongest predictor of a decreased FMD, not the coro- nary endothelial dysfunction for which the paper is famous. With ~41% of the variation in FMD in the current sample being related to baseline diameter (i.e., R2 = 0.41, p < .01), it is possible that the arterial enlargement associated with ha- bitual exercise (Green et al., 2012) may have masked any potential relationship between FMD and V̇O2max in the cur- rent study. As depicted in Table 3 tests of resistance artery func- tion are strongly related with body mass and BMI, such that larger individuals with larger thighs tend to exhibit a greater RH Peak Flow, PLM Peak Flow, and ROV Peak Flow. It is not possible to conclude why this relationship exists from the current data, but it seems likely that larger limbs have a larger vascular network, which can accommo- date greater flows. Whatever the mechanism, the influence of body mass on the measures of resistance artery function is not trivial and should be considered when interpreting these tests, especially when relating vascular function to V̇O2max, which is also strongly influenced by body mass (Proctor & Joyner, 1997). Sex is also related to resistance artery function (Table 3), with males exhibiting a greater peak flow response to PLM. A similar tendency was also observed with ROV Peak Flow (p = .10). However, this sex difference in resistance artery function appears to be driven by differences in body mass between females and males (males were 25.35 ± 2.73 kg heavier than the females in this study, p < .01), since the sex differences in PLM Peak Flow disappeared when statistically removing variance in PLM Peak Flow accounted for by body mass (p = .98). In addition to the factors mentioned above, previous re- search has revealed other factors that must be considered when performing and interpreting tests of vascular function. For \| 11 of 13 GIFFORD et al. example, the placement of the cuff proximal or distal to the site of measurement may impact the results of an FMD test (Doshi et al., 2001), the frequency of movement and the range of mo- tion of PLM (Gifford et al., 2019), and the amount of work performed during ROV (Tschakovsky et al., 2004) have been shown to strongly impact the results. Therefore, these factors should be considered when exploring the relationship between vascular function and other variables, like V̇O2max. 4.4 \| Is vascular function related to V̇O2max when controlling for potentially confounding variables? As described above, several factors, independent of the health of the vascular system, may impact the results of a vascular function test. Thus, it is possible that the underlying influences of variables, like artery diameter and body size, either mask potential relationships between vascular func- tion and V̇O2max or potentially account for them. Partial correlations between the indices of vascular function and V̇O2max were performed to statistically remove variance ac- counted for potentially confounding variables. As described in Table 4, when statistically controlling for the variance in FMD related to baseline artery diameter, superficial femoral artery FMD does exhibit the weak relationship with mass- specific V̇O2max (r = 0.45, p = .05) that has been indicated by studies measuring FMD in the arm (Montero, 2015). No such relationships were observed with absolute V̇O2max (p > .05). Thus, conduit artery function does appear to be weakly related to mass-specific V̇O2max, but the relationship is obscured by variation in artery diameter. While indices of resistance artery function are related to V̇O2max (Table 2), this relationship could potentially be completely dependent upon body mass, sex, and BMI, which are also strongly related to vascular function (Table 3) and V̇O2max (Table 2). Thus, the partial correlation between the indices of resistance artery function and V̇O2max was ex- plored when simultaneously controlling for body mass, sex, and BMI. As described in Table 4, the relationship between resistance artery function and absolute V̇O2max persists, while the relationship between resistance artery function and mass-specific V̇O2max is apparently strengthened when removing any variance in vascular function and V̇O2max related to body mass, sex, and BMI. Similarly, previous re- search indicated that PLM Peak Flow was related to peak exercise blood flow in a mass-independent manner (Hanson et al., 2020). Thus, the relationship between resistance artery function and V̇O2max occurs independently and is not merely a product of sex, mass, or BMI. As described by Wagner (Wagner, 2008), V̇O2max can be simultaneously influenced by the function of many systems, including the lungs, heart, arteries, skeletal muscle mass, and mitochondria. With so many factors influencing V̇O2max in healthy young adults that resistance artery function accounts for ~30% of the variance in V̇O2max not accounted for by body mass, sex, and BMI is quite notable. Factors that were not measured in the current study, like maximal cardiac out- put, mitochondrial density, and muscle oxygen diffusion are likely to account for some of the remaining variance (Gifford et al., 2016; Wagner, 2008). Since V̇O2max is limited by dif- ferent factors in different populations (Gifford et al., 2016; Wagner, 2008), the amount of variance in V̇O2max ac- counted for by resistance artery function likely differ in other populations. 4.5 \| Clinical relevance Since V̇O2max strongly influences exercise performance and is also a strong predictor of cardiovascular risk (Poole et al., 2020), there is great interest in identifying what limits or reduces an individual's V̇O2max (Wagner, 2008) so that appropriate steps may be taken to improve it. With resistance artery function being related to both maximum exercise blood flow (Hanson et al., 2020) and V̇O2max (Table 2), noninvasive assessments, like passive-leg movement (PLM)-induced hyperemia, may conceivably be used to easily determine the likelihood that im- pairments in muscle resistance artery function and leg blood flow impair a person's V̇O2max. Since the PLM technique oc- curs while the subject is in a completely rested state, this could be particularly useful in scenarios in which direct assessment of exercise blood flow may not be possible or practical. Given the strong relationship between resistance artery function and V̇O2max, vascular function data collected at rest could potentially be used to predict V̇O2max. For example, stepwise linear regression revealed that absolute V̇O2max (expressed in ml·min−1) could be predicted (R2 = 0.83, p < .01, n = 22) when considering the peak flow response to PLM (expressed in ml·min−1), body mass (expressed in kg), and BMI (expressed in kg·m−2): Clearly, these data are very preliminary, and a much larger, more heterogeneous sample is needed before a pre- diction equation may be validated and standardized, but the prospect of accurately predicting V̇O2max without breaking a sweat is enticing. 4.6 \| Conclusions This study supports the notion that noninvasive indices of vascular function generally reflect two different aspects of Absolute ̇VO2max =970.82+55.83 (Body Mass) +0.68 (PLM Peak Flow)−121.75 (BMI) . 12 of 13 \| GIFFORD et al. vascular function: conduit artery function (e.g., FMD) and resistance artery function (e.g., RH Peak Flow, PLM Peak Flow, and ROV Peak Flow). Importantly, the results of the tests within each aspect of vascular function (i.e., conduit or resistance artery function) relate well to one another, such that inferences about one test may be made based on the results of another. While only a weak relationship between conduit artery function (e.g. FMD) and V̇O2max is observed when accounting for baseline artery diameter, resistance artery function, assessed by multiple different tests, is con- sistently and independently related to V̇O2max. While FMD has been related to various aspects of cardiovascular health (Broxterman et al., 2019), it is the function of the resist- ance arteries, not the conduit arteries, that is tightly related to exercise capacity and physical function. Thus, vascu- lar interventions, like exercise training (Montero, Walther, Diaz-Cañestro, Pyke, & Padilla, 2015), seeking to improve exercise capacity should target resistance artery function, as represented by factors like peak flow during PLM, RH, or ROV. ACKNOWLEDGMENTS The authors acknowledge and thank the participants for their gracious participation, and the peer reviewers for their ef- forts in refining this manuscript. This study was funded by the Bobbitt Heart Disease Research Award and the BYU Graduate Student Mentorship Award. The authors have no conflicts of interest to report. AUTHOR CONTRIBUTIONS JG: Designed and performed the study, analyzed the data, and wrote the manuscript. BH: Designed and performed the study and wrote the manuscript. MP: Performed the study, analyzed the data, and approved the final manuscript. TW: Performed the study, analyzed the data, and approved the final manuscript. GG: Performed the study, analyzed the data, and approved the final manuscript. JK: Performed the study, analyzed the data, and approved the final manuscript. MH: Performed the study, analyzed the data, and approved the final manuscript. ORCID Jayson R. Gifford https://orcid.org/0000-0002-6034-306X REFERENCES Anderson, T. 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PMC3409846	EDITORIAL Open Access Born to run. Studying the limits of human performance Andrew Murray1* and Ricardo JS Costa2 Abstract It is recognised that regular physical activity and a high level of fitness are powerful predictors of positive health outcomes. There is a long and rich history of significant feats of human endurance with some, for example, the death of the first marathon runner, Pheidippides, associated with negative health outcomes. Early studies on endurance running used X-ray and interview techniques to evaluate competitors and comment on performance. Since then, comparatively few studies have looked at runners competing in distances longer than a marathon. Those that have, tend to show significant musculoskeletal injuries and a remarkable level of adaptation to this endurance load. The TransEurope Footrace Project followed ultra-endurance runners aiming to complete 4,500 Km of running in 64 days across Europe. This pioneering study will assess the impact of extreme endurance on human physiology; analysing musculoskeletal and other tissue/organ injuries, and the body’s potential ability to adapt to extreme physiological stress. The results will be of interest not only to endurance runners, but to anyone interested in the limits of human performance. Please see related article: http://www.biomedcentral.com/1741-7015/10/78 Keywords: Physical inactivity, ultra-marathon, endurance, runners, musculoskeletal, nutrition, hydration, race, Trans- Continental Background Professor Steven Blair describes physical inactivity as “one of the most important public health challenges of the 21st Century” [1]. It is recognized that regular physical activity and a high level of fitness are powerful predictors of posi- tive health outcomes, with Professor Karim Khan, who is a prominent sports and exercise medicine researcher, fram- ing Blair’s data, to show that low fitness may be responsi- ble for a larger attributable fraction of mortality than “Smokadiabesity"- that is smoking, diabetes, and obesity combined [2]. Can there ever be too much of a good thing? Can we ever do too much physical activity? History suggests the human body is perfectly adapted to run long distances. Humans have an unmatched ability in the animal king- dom to run these distances, capabilities that probably emerged around 2 million years ago to assist with persistence hunting - a tactic still used by the San Bush- men of the Kalahari [3]. History celebrates the run in 490 BC from Marathon to Athens by Pheidippides as the inspiration for the modern marathon, whilst remembering that this hero of ancient Greece died following his exertions. The traditional story tells that Pheidippides had, in fact, run from Athens to Sparta, a distance of 240 km in less than 48 hours shortly before. This would be defined as an ultra-marathon, which is considered any distance in excess of the stan- dard marathon distance of 42.195 km. Of interest are the physiological changes that accompany such extreme chal- lenges. In a study published in BMC Medicine, Schutz et al. [4] followed 44 ultra-marathon runners in the TransEurope Footrace 2009, which is a distance of over 4,487 km from South Italy to North Cape. Here, they recorded daily sets of data from magnetic resonance ima- ging, psychometric, body composition and biological measurements with the aim of uncovering new knowl- edge on the physiological and pathological changes that * Correspondence: docandrewmurray@googlemail.com 1SportScotland Institute of Sport, Aithrey Road, Stirling, FK9 5PH, UK Full list of author information is available at the end of the article Murray and Costa BMC Medicine 2012, 10:76 http://www.biomedcentral.com/1741-7015/10/76 © 2012 Murray and Costa; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. accompany cellular and organ systems under extreme strain. Previous studies: What is known about ultra-endurance running? Humans have been racing across continents since the 1928 and 1929 U.S. Trans-Continental Races. In his seminal work “Lore of Running” Tim Noakes notes that 2 separate medical reports were compiled from the 4,960 km, 84 stage 1928 race. J.T. Farrell et al. con- cludes “the immediate effects of long-distance running are inconsequential” from his team’s X-ray studies of the heart, bones and joints; while Gordon and Baker concluded only 40 of the 199 competitors were capable of sustaining this physical workload [5]. Musculoskeletal injuries and financial difficulties were cited as principal reasons that only 55 of the 199 competitors finished [5]. Since these early studies, little research has been con- ducted on extreme endurance runners. Numerous studies have looked at musculoskeletal injuries in marathon run- ners, but few exist of athletes running further than this. Fallon studied musculoskeletal injuries in athletes run- ning 1,005 km from Sydney to Melbourne finding injuries to the knee, and ankle to be most prevalent [6]; while Scheer and Murray amongst others also found lower limb musculoskeletal injuries to be common [7]. Fallon also described an injury fairly specific to ultra-endurance runners, tendinopathy of the ankle dorsiflexors, a condi- tion subsequently called “Ultra-marathoner’s ankle” [6]. Other studies have looked at immune status, nutrition, and hydration in ultra-endurance runners. Perturbed immune function is a common feature after endurance type exercise, with clinical significance associated with increased risk of illness, infection, suppressed tissue repair and wound healing abilities, and exertional heat ill- nesses [8]. Consuming adequate nutrition to meet nutri- ent demands and maintaining euhydration has shown to attenuate some of the immune perturbing effects of extreme endurance running [9,10]; this not only has immune and health consequences, but will affect running performance on consecutive days of competition [11]. A perceived common outcome of running exercise in the heat is dehydration, and thus much focus has pre- viously been on promoting hyperhydration strategies dur- ing ultra-endurance events. Evidence from previous reviews and preliminary data from Coventry University actually suggests that fluid-overconsumption behaviours are a common feature of ultra-endurance running, with large ingestions of plain water and insufficient sodium replacement frequently observed [12]. This type of drink- ing behaviour is associated with the manifestation of hypo- natraemia, which recently has had much interest, with cases of asymptomatic and symptomatic hyponatraemia becoming better recognised during ultra-endurance events [13]. O’Keefe et al. recently found prolonged endurance exercise may cause pathologic remodelling of the heart and be pro-arrythmogenic with atrial fibrillation as much as five times more prevalent in the population studied [14]. Moreover, interesting case studies exist. In his book “Survival of the Fittest” Dr Mike Stroud described the destruction of the human body when studying the effects of a brutal 95 day Antarctic crossing, at times burning >10,000 kcal·day-1 [15]. Unpublished blood values from Andrew Murray’s ultra-endurance run from Scotland to the Sahara desert showed a drop in haemoglobin to 10.8 g·dl-1, and a serum ferritin of 2 ng·ml-1, from pre- viously normal values, despite a dietary iron intake 450% of recommended nutritional intake (RNI), and body weight being maintained. What this study adds: assessing the impact of extreme endurance on human physiology The TransEurope Footrace Project followed 44 ultra- endurance runners aiming to complete 4,500 km of run- ning in 64 days across Europe. Comfortably the largest and most comprehensive of its kind to date, it aimed at demonstrating the feasibility of conducting a longitudinal study over this period collecting a large and wide ranging amount of data which included: 741 magnetic resonance imaging (MRI) examinations, 5,720 urine samples, 244 blood samples, 205 electrocardiogram examinations, 1,018 bioelectrical impedance analysis measurements, 539 anthropological measurements, and 150 psychologi- cal questionnaires. This pioneering study will assess the impact of extreme endurance on human physiology; ana- lysing musculoskeletal and other tissue/organ injuries, and the body’s potential ability to adapt to extreme phy- siological stress. Although running is one of the most popular forms of recreation worldwide, not many will wish to gallop across continents. But ultra-endurance running is increasing dramatically in popularity and this study will be of inter- est to anyone with an interest in the limits of human per- formance, and the ability of man to adapt to seemingly impossible challenges. Comparisons with athletes under- taking feats of endurance including cycling’s Tour de France will be interesting. Conclusions There is a long and rich history of significant feats of human endurance. Remarkably, studies have been con- ducted on trans-continental races since 1928. Similarities between these early studies and the TransEurope Foo- trace Project include the distance covered, and the use of imaging. However, advances in technology has meant Murray and Costa BMC Medicine 2012, 10:76 http://www.biomedcentral.com/1741-7015/10/76 Page 2 of 3 that the TransEurope Footrace Project has been able to acquire longitudinal data from a relatively large volunteer cohort of ultra-marathon runners including data on mus- culoskeletal, cardiac, and brain MRI, along with a raft of other data on immune function, hydration and nutrition. Data is likely to show that competing in such an event can lead to significant musculoskeletal and other inju- ries, but also that the human body is capable of adapting to incredible endurance loads, and can run well in excess of a marathon per day despite seemingly signifi- cant medical issues. Like the runners, research in this field will continue to move forward. Author details 1SportScotland Institute of Sport, Aithrey Road, Stirling, FK9 5PH, UK. 2Department of Health Professions, Coventry University, Priory Road, Coventry, CV1 5FB, UK. Authors’ information AM and RC have completed over 100 ultra-marathons between them. AM completed a run across Europe in 2011 and is a Sports and Exercise Medicine doctor. He has worked at numerous ultra-marathon events with Marathon Medical Services. RC is a former professional triathlete and is currently a Senior Lecturer and Researcher Fellow in Dietetics and Human Nutrition at Coventry University. AM and RC have both produced original research from ultra-marathon competition. Received: 13 July 2012 Accepted: 19 July 2012 Published: 19 July 2012 References 1. Blair SN: Physical inactivity: The biggest public health problem of the 21st Century. Br J Sports Med 2009, 43:1-2. 2. Khan KM, Tunaiji HA: As different as Venus from Mars: time to distinguish efficacy (can it work?) from effectiveness (does it work?). Br J Sports Med 2011, 45:759-760. 3. Lieberman DE, Bramble DM: The evolution of marathon running: capabilities in humans. Sports Med 2007, 37:288-90. 4. Schulz , et al:, (to be added when published). 5. Noakes T: From Learning from the experts in Lore of Running.Edited by: Noakes T. Oxford: Oxford University Press; 2001:361-483. 6. Fallon KE: Musculoskeletal injuries in the ultra-marathon: the 1990 Westfield Sydney to Melbourne run. Br J Sports Med 1996, 30:319-323. 7. Scheer BV, Murray AD: Al Andalus Ultra Trail: An Observation of Medical Interventions During a 219-km, 5-Day Ultramarathon stage race. Clin J Sports Med 2011, 21:444-446. 8. Walsh NP, Gleeson M, Shephard RJ, Gleeson M, Woods JA, Bishop NC, Fleshner M, Green C, Pedersen BK, Hoffman-Goetz L, Rogers CJ, Northoff H, Abbasi A, Simon P: Position statement. Part one: Immune function and exercise. Exerc Immunol Rev 2011, 17:6-63. 9. Walsh NP, Gleeson M, Pyne DB, Nieman DC, Dhabhar FS, Shephard RJ, Oliver SJ, Bermon S, Kajeniene A: Position statement. Part two: Maintaining immune health. Exerc Immunol Rev 2011, 17:64-103. 10. Costa RJS, Walters R, Bilzon JLJ, Walsh NP: Effects of immediate postexercise carbohydrate ingestion with and without protein on neutrophil degranulation. Int J Sport Nutr Exerc Metab 2011, 21(3):205-213. 11. American College of Sports Medicine, American Dietetic Association, Dietitians of Canada: American College of Sports Medicine position stand. Nutrition and athletic performance. Med Sci Sports Exerc 2009, 41(3):709-731. 12. Hew-Butler T, Ayus JC, Kipps C, Maughan RJ, Mettler S, Meeuwisse WH, Page AJ, Reid SA, Rehrer NJ, Roberts WO, Rogers IR, Rosner MH, Siegel AJ, Speedy DB, Stuempfle KJ, Verbalis JG, Weschler LB, Wharam P: Statement of the Second International Exercise-Associated Hyponatremia Consensus Development Conference. Clin J Sport Med 2007, 18(2):111-121. 13. 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Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color ﬁgure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit Murray and Costa BMC Medicine 2012, 10:76 http://www.biomedcentral.com/1741-7015/10/76 Page 3 of 3	Born to run. Studying the limits of human performance.	07-19-2012	Murray, Andrew,Costa, Ricardo J S	eng
PMC6843975	International Journal of Environmental Research and Public Health Article Analysis of the Association between Running Performance and Game Performance Indicators in Professional Soccer Players Toni Modric 1,2, Sime Versic 1,2, Damir Sekulic 1,* and Silvester Liposek 3,* 1 Faculty of Kinesiology, University of Split, 21000 Split, Croatia; toni.modric@yahoo.com (T.M.); simeversic@gmail.com (S.V.) 2 HNK Hajduk Split, 21000 Split, Croatia 3 University of Maribor, 2000 Maribor, Slovenia * Correspondence: dado@kifst.hr (D.S.); silvester.liposek@um.si (S.L.); Tel.: +385-21-302-440 (D.S.) Received: 2 October 2019; Accepted: 18 October 2019; Published: 21 October 2019 Abstract: Running performance (RP) and game performance indicators (GPI) are important determinants of success in soccer (football), but there is an evident lack of knowledge about the possible associations between RP and GPI. This study aimed to identify associations between RP and GPI in professional soccer players and to compare RP and GPI among soccer playing positions. One hundred one match performances were observed over the course of half of a season at the highest level of national competition in Croatia. Players (mean ± SD, age: 23.85 ± 2.88 years; body height: 183.05 ± 8.88 cm; body mass: 78.69 ± 7.17 kg) were classiﬁed into ﬁve playing positions (central defenders (n = 26), full-backs (n = 24), central midﬁelders (n = 33), wide midﬁelders (n = 10), and forwards (n = 8). RP, as measured by global positioning system, included the total distance covered, distance covered in ﬁve speed categories (walking, jogging, running, high-speed running, and maximal sprinting), total number of accelerations, number of high-intensity accelerations, total number of decelerations, and number of high-intensity decelerations. The GPI were collected by the position-speciﬁc performance statistics index (InStat index). The average total distance was 10,298.4 ± 928.7 m, with central defenders having the shortest and central midﬁelders having the greatest covered distances. The running (r = 0.419, p = 0.03) and high-intensity accelerations (r = 0.493, p = 0.01) were correlated with the InStat index for central defenders. The number of decelerations of full-backs (r = −0.43, p = 0.04) and the distance covered during sprinting of forwards (r = 0.80, p = 0.02) were associated with their GPI obtained by InStat index. The speciﬁc correlations between RP and GPI should be considered during the conditioning process in soccer. The soccer training should follow the speciﬁc requirements of the playing positions established herein, which will allow players to meet the game demands and to perform successfully. Keywords: GPS; football; accelerations; decelerations; eﬃcacy 1. Introduction Soccer is a highly complex team sport with changing dynamics and multistructural movements played by two teams. Each team consists of 10 outﬁeld players and a goalkeeper and the ﬁnal game achievement depends directly on the performance of all 11 players [1,2]. Therefore, performance analysis is crucial in the evaluation of players’ achievement [3]. The global popularity of soccer has led to the implementation of scientiﬁc and technological knowledge in everyday use, and this is particularly evident within the ﬁeld of performance analysis. One of the important aspects of performance analysis is termed “running performance”, which is nowadays mostly evidenced by global positioning software systems (GPS) [4]. Int. J. Environ. Res. Public Health 2019, 16, 4032; doi:10.3390/ijerph16204032 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2019, 16, 4032 2 of 13 GPS technology is known to be highly applicable in evaluation of mobility and physical activity patterns within the ﬁeld of public health [5–7]. With the improvement of their accuracy/precision, design, usability and safeness (Figure 1), the GPS-based devices are becoming prevalent even in competitive sports, including soccer [8,9]. Figure 1. Global positioning device (GPS) used for the measurement of running performances in soccer. Speciﬁcally, GPS allows collecting data about players’ running performance, such as the total distance covered, the distance covered at diﬀerent intensities (i.e., speeds), and the number of accelerations and decelerations. Studies conducted so far have provided playing-position-speciﬁc evidence with regard to running at diﬀerent intensities, with midﬁelders covering the largest total distance and wingers performing the most high-intensive sprints [10]. Furthermore, match running performance in Brazilian professional soccer players indicated that winning teams, home playing teams and teams that play against “weaker” opponents had the greatest total distance covered [11]. A study performed with <21- and <18-year old soccer players found that a 3–5–2 formation elicited the highest total distance, with a 4–2–3–1 formation eliciting the highest number of accelerations and decelerations [12]. The results of the previously cited studies that used GPS technology as a measurement tool were generally consistent with those from investigations where authors used diﬀerent video-based computerized match analysis systems in the evaluation of players’ running performances [13–17]. Game performance indicators are another set of variables that are used in performance analysis in soccer. Basically, game performance indicators are deﬁned as a “selection and combination of variables that deﬁne some aspect of performance and that help achieve athletic success” [18]. The most frequently used game performance indicators are passes, shots, crosses, dribbles, challenges etc. [19]. Currently, numerous video-based platforms that track performance indicators of soccer players are available (InStat, Optasport, Wyscout). Such platforms quickly and accurately provide a large range of data about game performance indicators, allowing the simultaneous analysis of the physical eﬀorts, movement patterns, and technical actions of players, both with and without the ball [20–22]. Previous studies conducted in the ﬁeld of performance analysis in soccer found that both the physical (i.e., total distance covered, high-intensity running, accelerations and decelerations) and technical–tactical performances (i.e., shots, crosses, challenges, and dribbles) of players were correlated with speciﬁc conditions such as match outcome (win/draw/loss), match location (home/away), type of match (league/cup/friendly), and strength of the opponent team [19,23–28]. Situational variables, such as ball possession, total shots, shots on target, crosses, dribbles, clearances, challenges, and interceptions, and their inﬂuence on technical–tactical parameters were mostly evaluated by the variation of counts of technical match actions, which include shots, passing, tackles, aerial duels, and dribbles [18,27,29,30]. Brieﬂy, situational variables that discriminate among winning, drawing and losing were mostly those related to ball possession and oﬀensive actions (e.g., total shots, shots on goal, and crosses) [29,30], Int. J. Environ. Res. Public Health 2019, 16, 4032 3 of 13 while some studies found that indicators of defensive eﬃcacy (e.g., interceptions, clearance, and aerial challenges) were the variables most related to the match outcome [27]. Although game performance indicators and running performance are often investigated separately, to the best of our knowledge, there is no study that simultaneously observed both groups of performance variables during oﬃcial soccer matches. Additionally, there is no information about the relationship that may exist between these two groups of variables. Therefore, the aim of this study was to identify possible associations that may exist between running performance and game performance in professional soccer players. Additionally, running performance and standard soccer performance variables were compared among playing positions. Authors were of the opinion that a study of this type would allow a better understanding of the relationships that exist between running performance and game performance indicators and that such understanding would therefore improve the applicability of both sets of variables in soccer training and competition. 2. Materials and Methods 2.1. Participants and Design The participants in this study were professional soccer players from Croatia (mean ± SD, age: 23.85 ± 2.88 years; body height: 183.05 ± 8.88 cm; body mass: 78.69 ± 7.17 kg), and all were members of one team competing at the highest national. Players were observed over one competitive half season, resulting in 101 match performances which were used as cases for this study. All data were collected during 14 matches of the Croatian Soccer League 2018/2019 season, and for the purpose of this study only the results of those players who participated in the whole game were analyzed. Players were classiﬁed in ﬁve groups based on playing positions: central defenders (CD; n = 26), full-backs (FB; n = 24), central midﬁelders (CM; n = 33), wide midﬁelders (WM; n = 10), and forwards (FW; n = 8), as suggested previously [10]. Sociodemographic and anthropometric data of observed players are presented in Table 1. In the observed half-season, the team played seven home and seven guest matches, with three wins, eight draws and three losses. At the end of the observed half-season, the team ranked 6th of 10 teams which competed in Croatian Soccer League. The investigation was approved by Ethical Board of the University of Split, Faculty of Kinesiology, Split, Croatia (approval number: 2181-205-02-05-19-0020). Table 1. Sociodemographic and anthropometric characteristics of the studied players with diﬀerences among playing positions (F-test). Age (years) Body height (cm) Body mass (kg) Mean ± SD Mean ± SD Mean ± SD Total sample (n = 101) 23.85 ± 2.88 183.05 ± 6.88 78.69 ± 7.17 Central Defenders (n = 26) 23.25 ± 2.21 192.25 ± 5.61 87.27 ± 7.38 Full-Backs (n = 24) 23.2 ± 3.56 176.6 ± 3.36 73.4 ± 4.34 Central Midﬁelders (n = 33) 22.66 ± 2.73 175 ± 6.08 76.51 ± 5.02 Wide Midﬁelders (n = 10) 26.0 ± 1.0 183 ± 3.46 76.2 ± 4.17 Forwards (n = 8) 27.0 ± 2.82 181.5 ± 0.7 85 ± 4.1 F-test (p) 1.51 (0.24) 5.92 (0.01) 5.04 (0.01) 2.2. Procedures The variables in this study were two sets of soccer performance variables (running performance and game performance indicators) and the ﬁnal game outcome (observed as loss, draw, win). Data on the running performance of the players were collected by GPS technology (Catapult S5 and X4 devices, Melbourne, Australia) with a sampling frequency of 10 Hz. Such device was already investigated for metrics, and was found to be appropriately reliable and valid in sport settings (i.e., less than 1% measurement error, and 80% of common variance with running speed measured by timing gates) [31,32]. Int. J. Environ. Res. Public Health 2019, 16, 4032 4 of 13 The variables included the following: total distance covered (m); distance in ﬁve speed categories (walking (<7.1 km/h), jogging (7.2–14.3 km/h), running (14.4–19.7 km/h), high-speed running (19.8–25.1 km/h), and maximal sprinting (>25.2 km/h)); number (frequency) of total accelerations (>0.5 m/s2); number of high-intensity accelerations (>3 m/s2); number of total decelerations (less than –0.5 m/s2) and number of high-intensity decelerations (less than –3 m/s2). The game performance indicators for each player were determined by the position-speciﬁc InStat index (InStat, Moscow, Russia). The InStat index is calculated on the basis of a unique set of key parameters for each playing position (12–14 performance parameters, depending on the position during the game), with a higher numerical value indicating better performance. The exact calculations are trademarked and known only to the manufacturer of the platform. In most general terms, an automatic algorithm considers the player’s contribution to the team’s success, the signiﬁcance of their actions, opponent’s level and the level of the competition they play in (i.e., the same performance done in European Champions League and some national-level ﬁrst division will not be rated with same values). The rating is created automatically, and each parameter has a factor which changes depending on the number of actions and events in the match. The weight of the action factors diﬀers depending on the player’s position. For example, grave mistakes done by CD and their frequency aﬀect InStat index to a greater extent than those done by FWD. The key factors included in the calculation of the InStat index are position speciﬁc and include tackling, aerial duels, set pieces in defense, interceptions (for CD); number of crosses, number of passes to the penalty area, pressing (for FB); playmaking, number of key passes, ﬁnishing (for CM); pressing, dribbling, ﬁnishing, counterattacking (for WM); shooting, ﬁnishing, pressing, dribbling (for FWD). In order to calculate the InStat Index, the player has to spend a certain amount of time on the ﬁeld and perform a minimum number of actions, but in this study this issue was solved simply by including only those players who played the whole game (as explained in Section 2.1). 2.3. Statistics The normality of the distributions was checked by the Kolmogorov–Smirnov test, and the data are presented as the means ± standard deviations. The homoscedasticity of all variables was conﬁrmed by Levene’s test. The statistical analyses were performed throughout several phases. In the ﬁrst phase the data obtained by InStat index were associated with ﬁnal game outcome by one-way analysis of variance (ANOVA). For this procedure the game outcomes (loss, draw, win) were considered as the grouping (independent) variable, and diﬀerences were established for total sample of players, and separately for each playing position. This allowed identiﬁcation of the validity of the InStat index as an indicator of the ﬁnal game achievement for the total sample, and for the ﬁve observed playing positions. The second phase of data analysis comprised calculation of diﬀerences among playing positions in running performance and InStat index. This was done by ANOVA with a consecutive Scheﬀe post hoc test. Throughout these analyses the information of running performance speciﬁcs for each playing position were obtained. Also, the analysis of diﬀerences in InStat allowed identiﬁcation of the applicability of the InStat index for the analysis of game achievement for each playing position. To evaluate the eﬀect sizes (ES), partial eta-squared values (η2) were presented (small ES: >0.02; medium ES: >0.13; large ES: >0.26) [33]. In the third phase, the associations between running performance (obtained by GPS) and game performance indicators (evaluated by InStat) were identiﬁed by calculating Pearson’s product moment correlation coeﬃcients. For all analyses, Statistica 13.0 (TIBCO Software Inc., Greenwood Village, CO, USA) was used, and a p < 0.05 was applied. Int. J. Environ. Res. Public Health 2019, 16, 4032 5 of 13 3. Results The ANOVA indicated signiﬁcant (p < 0.05) association between the InStat index and match outcome for the total sample (n = 101, F-test: 23.69, η2 = 0.30 (large E)), CD (n = 26, F-test: 3.89, η2 = 0.24 (medium ES)), FB (n = 24, F-test: 4.98, η2 = 0.31 (large ES)) and CM (n = 33, F-test: 15.71, η2 = 0.50 (large ES)). The InStat index was not signiﬁcantly associated with game outcomes for WM (n = 10, F-test: 0.98, η2 = 0.21 (medium ES)), and FW (n = 8, F-test: 2.61, η2 = 0.52 (large ES)) (Figure 2). Figure 2. InStat index in relation to the outcome of the match for total sample (Total) and diﬀerent playing positions (CD, central defenders; FB, full-backs; CM, central midﬁelders; WM, wide midﬁelders; FW, forwards); * indicates statistically signiﬁcant diﬀerences at p < 0.05 derived by analysis of variance The descriptive parameters for running performances and InStat index in total sample, and for each playing positions are presented in Table 2. Signiﬁcant ANOVA diﬀerences were found among playing positions (p < 0.05) in all running performances, with large ES for diﬀerences in: (i) total distance covered (η2 = 0.59); (ii) distance covered while jogging (η2 = 0.41); (iii) running (η2 = 0.62); (iv) high-speed running (η2 = 0.53); (v) sprinting (η2 = 0.39); (vi) number of performed accelerations (η2 = 0.27); (vii) number of decelerations (η2 = 0.45); (viii) number of high-intensity accelerations (η2 > 0.30); and (ix) number of high-intensity decelerations (η2 = 0.41). Small ES was found for diﬀerences in distance covered while walking (η2 = 0.11) (Table 3). Speciﬁcally, CM covered the longest total distance (signiﬁcant post-hoc diﬀerences when compared to all other playing positions), the longest distance in jogging (signiﬁcant post-hoc diﬀerences when compared to all other playing positions), and the longest distance while running (signiﬁcantly diﬀerent from CD and FB). WM covered the longest distance in high-speed running, and in sprinting (signiﬁcant post-hoc diﬀerences to CD, CM, and FW). CD carried out the highest number of accelerations and highest number of decelerations (signiﬁcantly diﬀerent from FW). Finally, FW carried out the highest number of high-intensity accelerations (signiﬁcant post-hoc diﬀerences when compared to CD, FB, and WM) and high-intensity decelerations (signiﬁcantly diﬀerent to WM) (Table 3). The total running distance and high-intensity accelerations were correlated with the InStat index for CD (r = 0.42 and r = 0.49, respectively). Furthermore, the number of decelerations was signiﬁcantly correlated with the InStat in FB (r = –0.43), while distance covered during sprinting was correlated with InStat index in FW (r = 0.80). In general, the running performances of players in central and wide midﬁeld positions were not signiﬁcantly associated with the InStat index (Table 4). Int. J. Environ. Res. Public Health 2019, 16, 4032 6 of 13 Table 2. Descriptive statistics for running performances and game performance indicator (InStat). Variables Total Central Defenders Full-Backs Central Midﬁelders Wide Midﬁelders Forwards Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD Total distance (m) 10,298.4 ± 928.68 9313.5 ± 599.4 10,368 ± 612 11,155.1 ± 635.3 10,264.8 ± 275.2 9796.7 ± 703.7 Walking (m) 4220.57 ± 362.33 4076.6 ± 378.3 4297.9 ± 338.5 4258.5 ± 340.7 4074.8 ± 194.3 4482.1 ± 442.2 Jogging (m) 4092.94 ± 569.73 3859 ± 380.2 3975.4 ± 372.8 4599.7 ± 471.4 3761.2 ± 324.1 3530 ± 729.9 Running (m) 1363.27 ± 339.68 999.2 ± 197.7 1320.7 ± 236.1 1674.9 ± 226.1 1526.5 ± 117.4 1184.4 ± 207.9 High-speed running (m) 461.83 ± 160.15 288.2 ± 63.8 533.9 ± 134.1 492.7 ± 139.9 640.7 ± 105.4 458.7 ± 94.7 Sprinting (m) 155.89 ± 97.13 87.7 ± 59.9 236.6 ± 97.2 123.7 ± 69.5 260.6 ± 68.8 137.1 ± 46.9 Accelerations (count) 716.19 ± 73.15 743.5 ± 56.2 710 ± 66.2 733.4 ± 72.4 688 ± 34.2 610.1 ± 83.7 Decelerations (count) 674.44 ± 69.29 714.1 ± 51.5 672.4 ± 56 681.9 ± 55.8 661.8 ± 36.7 536.6 ± 69 High-intensity accelerations (count) 3.16 ± 2.67 2.5 ± 1.8 3.1 ± 1.7 1.9 ± 2.2 7 ± 2.6 6 ± 2.9 High-intensity decelerations (count) 11.39 ± 6.27 6.1 ± 2.8 13.1 ± 4.9 11.5 ± 5.9 20.8 ± 5.5 11 ± 3.1 InStat (index) 284.5 ± 31.04 247.4 ± 29.2 243 ± 28.7 254.1 ± 29.3 251.1 ± 32.1 242 ± 49.5 Table 3. Diﬀerences among playing positions for running performances and game performance indicator (InStat) determined by analysis of variance (ANOVA), with Scheﬀe post-hoc test diﬀerences. Variables ANOVA Eﬀect Size Post hoc F (p) η2 Central Defenders Full-Backs Central Midﬁelders Wide Midﬁelders Forwards Total distance (m) 35.02 (0.01) 0.59 FB, CM, WM CD, CM CD, FB, WM, FW CD, CM CM Walking (m) 3.18 (0.02) 0.11 - - - - - Jogging (m) 16.71 (0.01) 0.41 CM CM CD, FB, WM, FW CM CM Running (m) 39.30 (0.01) 0.62 FB, CM, WM CD, CM CD, FB CD, FW CM High-speed running (m) 29.30 (0.01) 0.53 FB, CM, WM, FW CD CD, WM CD, CM, FW CD, WM Sprinting (m) 15.72 (0.01) 0.39 FB, WM CD, CM, FW FB, WM CD, CM, FW FB, WM Accelerations (count) 9.06 (0.01) 0.27 FW FW FW CD, CM, FW Decelerations (count) 20.11 (0.01) 0.45 FW FW FW FW CD, FB, CM, WM High-intensity accelerations (count) 8.53 (0.01) 0.30 WM, FW WM, FW WM, FW CD, FB, CM CD, FB, WM High-intensity decelerations (count) 16.70 (0.01) 0.41 FB, CM, WM CD, WM CD, WM CD, FB, CM, FW WM InStat (index) 0.64 (0.62) 0.03 - - - - - Superscripted letters indicate signiﬁcant post-hoc diﬀerences when compared to speciﬁc playing position (CD, central defenders; FB, full-backs; CM, central midﬁelders; WM – wide midﬁelders; FW, forwards). Int. J. Environ. Res. Public Health 2019, 16, 4032 7 of 13 Table 4. Pearson’s product moment correlations between running performances and game performance indicator (InStat) for diﬀerent playing positions. Variables Total (n = 101) Central Defenders (n = 26) Full-Backs (n = 24) Central Midﬁelders (n = 33) Wide Midﬁelders (n = 10) Forwards (n = 8) Total distance 0.08 0.18 –0.04 –0.02 –0.17 0.01 Walking −0.02 0.09 0.01 –0.12 0.07 0.04 Jogging 0.05 0.02 –0.10 0.05 –0.41 –0.05 Running 0.16 0.42 * 0.02 0.12 0.01 –0.13 High-speed running 0.02 –0.04 –0.06 –0.10 0.54 0.17 Sprinting 0.01 –0.24 0.17 –0.04 0.22 0.80 * Accelerations –0.01 0.12 –0.39 0.07 –0.24 –0.02 Decelerations –0.09 0.07 –0.43 * –0.05 –0.26 –0.33 High-intensity accelerations 0.18 0.49 * 0.20 0.29 –0.08 0.26 High-intensity decelerations 0.05 0.29 –0.04 –0.01 0.44 –0.18 * denotes statistical signiﬁcance of p < 0.05. 4. Discussion With regard to study aims there are two most important ﬁndings. First, the total distance covered and the intensity of running varied according to the diﬀerent playing positions. Second, running performance parameters (e.g., the number of accelerations or decelerations and the distance covered in diﬀerent speed zones) aﬀect successful performance in soccer for some playing positions. Prior to discussion of these ﬁndings, an overview of the analyses done in order to evaluate the applicability and validity of InStat index as a measure of ﬁnal match outcome will be provided. Studies have already investigated the association between diﬀerent variables explaining situational eﬃcacy (i.e., game performance indicators) and match outcomes. For example, when losing the game, teams had more ball possession [30,34,35] and performed more crosses and dribbles [27]. Additionally, when winning, the teams performed more interceptions, clearances and aerial challenges, fewer passes and dribbles [27], and less high-intensity activities [18,34]. However, previous studies regularly investigated the performance indicators of the whole team, while there has been limited research investigating the position-speciﬁc performances in relation to game outcome, even though technical indicators have been considered good predictors of soccer match success [36]. Also, the quality of technical skills in real-game performance, which is actually obtained throughout the InStat index and other similar platforms, has been included as a main component in soccer talent identiﬁcation and development systems [37,38]. InStat index in soccer is based on wide range of team- and individual-statistics, which are linked to the supporting video episodes. At the ﬁnal stage, the calculated index should be related to ﬁnal game outcome, and consequently should be a valid measure of ﬁnal team achievement (i.e., game outcome). Results of this study indicated signiﬁcant diﬀerences among game outcomes (loss, draw, or win) in InStat index for the total sample and speciﬁcally for CD, FB and CM. Although the statistical signiﬁcance of the F-test did not reach statistical signiﬁcance for WM and FW, this may be attributed to small number of players in these groups (WM: 10 players, FW: 8 players) and consequent small number of degrees of freedom [39]. Therefore, it might be said that the results presented here conﬁrmed the validity of InStat in evaluation of ﬁnal game achievement in Croatian professional soccer. It is also important to note that InStat index is speciﬁcally calculated for diﬀerent positions on the basis of position-speciﬁc parameters (please see Section 2 for more details). Therefore, the lack of diﬀerences among playing positions in InStat (please see Tables 2 and 3 for more details) indicates that this index might be observed as an applicable measure of position-speciﬁc game performance in soccer. Int. J. Environ. Res. Public Health 2019, 16, 4032 8 of 13 4.1. Running Performances and Diﬀerences Among Playing Positions Considering the diﬀerent tactical roles of diﬀerent playing positions in soccer games, recent studies conﬁrm that the distance covered during the match appears to be related to playing position [11,14,16,20]. Results of this study evidenced signiﬁcant diﬀerences in running performance among playing positions, and such results are generally in agreement with previous studies that investigated these issues in the English Premier League, the Spanish ﬁrst division, the Italian Serie A, the French League 1, and the Brazilian ﬁrst division [13,14,16,20,40]. Speciﬁcally, analysis of the Brazilian ﬁrst division evidenced that the total distance covered by FB, CM and WM was greater than that covered by CD and FW [13]. Supporting this, the lowest total distance was found for CD (9313 m on average). At the same time, CM covered signiﬁcantly more distance than players in all other positions (11,155 m, on average), which is known to be related to speciﬁc playing duties (i.e., CM are responsible for the connection between defense and attack, and such tactical roles require them to achieve greater distances) [14,16]. Previous studies performed indicated 10.7 km as the average total distance covered in Spanish and English top divisions [10,40,41]. Meanwhile players observed herein covered total distance of 10.3 km in average. Therefore, it seems that the total distance covered is not the factor that distinguishes Croatian players from those playing in elite European divisions. On the other hand, there is an evident diﬀerence in the intensity of running. More precisely, top-level European soccer players cover 10% of the total distance at a high intensity, which includes high-speed running and maximal sprinting [17,42]. Meanwhile, here presented results indicated that Croatian players perform 6.4% of the total distance covered at a high-intensity running pace. It is generally accepted that low-intensity activities, such as walking and jogging, are not crucial in elite soccer performance [43]. However, knowledge of these indicators is important to properly understand the position-speciﬁc demands. Thus, considering the percentage of the total distance, the most time spent walking and jogging is observed in CD (an average of 85.2% (7935 m) of their total distance covered (9313 m)). On the other hand, the least time spent walking and jogging is observed in WF (76.3%), followed by CM (79.4%), FB (79.8%) and FW (81.8%). Collectively, these ﬁndings support previous considerations that Croatian ﬁrst division players generally play at a lower game pace when compared to elite European national division players, who spend a much lower percentage of time in low-intensity activities (from 74.9% to 79.6% of total distance) [10]. The distance covered while jogging among CM is signiﬁcantly higher than for any other position. As mentioned before, CM had the greatest total distance, which is directly inﬂuenced by the distance covered while jogging. Furthermore, CM have the greatest distances in the “running zones” (e.g., 14.4–19.7 km/h). Therefore, results support the ﬁndings from previous studies in which authors reported similar ﬁgures and concluded that the physical performance of CM is characterized by covering a high overall distance, especially at moderate to high speeds such as jogging and running [10]. High-intensity activities are usually deﬁned as all activities with running speeds of 19.8 km/h and above, and the distance covered at high intensities has been traditionally identiﬁed as a key performance indicator of physical match performance [44] and one of the crucial elements of success in soccer [43]. The results showed that the greatest amount of high-intensity running (high-speed running + sprint running) is covered by WM, while the CD have the lowest values for these indicators. This is consistent with previous investigations in which authors reported similar results for the English Premier League and the Spanish ﬁrst division [10,14,20,40]. It is known that outside players (e.g., WM and FB) perform signiﬁcantly more sprints than players in central playing positions [14]. Supporting this, our results showed that the greatest sprint distance was covered by WM and FB. However, despite similar diﬀerences among playing positions between our study and previous studies, values of high-intensity running in Croatian players were evidently lower than those from the best European national competitions [10,40]. More speciﬁcally, the mean high-intensity distance covered among all playing positions in the English Premier League was 936 m, in the Spanish ﬁrst division an average of 821 m was reported, while the average value for high-intensity running in Croatian players was 652 m. Int. J. Environ. Res. Public Health 2019, 16, 4032 9 of 13 The highest number of accelerations and decelerations was found for CD and the lowest for FW, which is consistent with some similar studies on friendly matches in the Spanish ﬁrst division [10]. Speciﬁcally, one of the most important tactical roles of FW is to keep the ball in possession in the central position, so it is expected that FWs do not cover a large distance. On the other hand, CD must be constantly prepared for defensive reactions. While trying to ﬁnd appropriate positioning, they frequently change running directions, but also the type of running (i.e., frontal running to make a defensive line to catch opposing players in oﬀsides and lateral shuﬄes to obtain better positions versus FW). This certainly results in a high number of accelerations and decelerations for CD. However, the kind of accelerometer unit and the way that the data are mathematically treated could have a signiﬁcant eﬀect on the calculation of accelerations and decelerations, which actually limits the comparability between diﬀerent studies [10]. Speciﬁcally, while the capacity to accelerate and decelerate plays a critical role in elite soccer, as it represents high energy demanding activities, the determination of accelerations might still have unresolved methodological issues [10]. 4.2. Associations between Game Performance Indicators and Running Performances— Playing Position Approach The results suggest that CD covered the shortest distance while running out of all playing positions, and this is in agreement with previous reports where authors found that CD exert the fewest high-intensity eﬀorts compared to all other playing positions [10,14,20,40]. This is understandable knowing that their technical roles (i.e., aerial duels, tackles, positioning, and interceptions of the balls passed to the attackers) are generally more focused on the reactions or accelerations and then on high-speed running. As a result, most of their high-intensity eﬀorts are performed in the zone of running (14.4–19.7 km/h) simply because they do not have many opportunities to develop running speeds above the high-intensity zone threshold (>19.8 km/h). However, because of the positive correlation between the InStat index and the distance covered while running (14.4–19.7 km/h) for CD, running should be considered an important determinant that aﬀects success for this position. Furthermore, a positive correlation between the numbers of high-intensity accelerations and the InStat index among CD shows that a greater number of high-intensity accelerations directly aﬀects real game performance for this playing position. Speciﬁcally, stepping out to the duels and putting pressure on opponent players are two of the most important tactical roles of CD. If performed rapidly and aggressively (in other words, with a high acceleration), the chances of winning a duel increase, which consequently has positive repercussions on ﬁnal match achievement as well. The total number of decelerations was inversely associated with the InStat index among FB, meaning that a higher number of decelerations negatively aﬀected real game eﬃcacy for that playing position. Although FB are basically defensive players and their starting tactical line-up is in the ﬁrst third of the pitch, the main technical requirements for FB are the number of entries to the third part of the pitch (i.e., pressing) and the number of crosses [38,45]. These duties are actually performed on the opponent’s half. Therefore, some of the most important tactical roles of the FB are actually in attacking. To create more of these activities, FB frequently have to move away from the starting tactical line-up, which actually enables them to make crosses and press. Consequently, if FB have a higher number of stoppings (i.e., decelerations), it probably negatively aﬀects their ability to participate in attacking actions and to perform crosses and entries to the third part of the pitch. Collectively, it seems that soccer success of FB is more aﬀected by their attacking activities, regardless of the fact that they are defensive players. Previously, it was highlighted that FW had evidently shorter sprinting distances than players in the same position during games from other European competitions (please see previous discussion for details). However, results indicated a strong correlation between the InStat index and the sprint distance for this playing position, which led to conclusion that the sprint distance covered during the game was a highly important determinant of overall game performance for FW. Indeed, FW are positioned close to the opponent’s goal, and almost every sprint presents the opportunity to perform Int. J. Environ. Res. Public Health 2019, 16, 4032 10 of 13 attacking actions. In addition, FW have the lowest number of tackles, interceptions and clearances compared with other playing positions [38], which suggests that most of their activities are focused on attacking. With the higher number of attacking actions, there is a growing chance to enter the penalty area, shoot, and score. As a result, the number of attacking situations increases the likelihood for positive game outcomes [28–30]. The main role of CM is to organize the oﬀense by proper ball control and passes, rather than by invasion into the opponent’s area [38]. Considering the lack of signiﬁcant association between running parameter and the InStat index for this playing position, it seems that CM soccer success is more inﬂuenced by some variables, other than those obtained by GPS, such as ball possession, number of key passes, dribbles, and shots. Also, the running indicators obtained by GPS measurements were not correlated to InStat variables in WM, which may be observed as surprising since WM experience the greatest physical requirements during the game, both in terms of total distance covered and high-intensity running [10]. The possible explanation may be the previously discussed ﬁnding of the small amount of overall distance covered in the studied Croatian players, which actually resulted in truncated variance and consequently statistically/mathematically decreased the possibility of achieving signiﬁcant correlations. 4.3. Limitations and Strengths The main limitation comes from the fact that this study observed only one team which was observed during one half season. Therefore, some speciﬁc covariates (limited number of observed players, strength of the opponent, speciﬁc tactical requirements) may inﬂuence reported results. Next, in this study no data were collected about psycho-physiological responses of the players (e.g., heart rate and RPE), which are known to be important determinants of overall performance. Further, this study actually studied relatively simple “game-related outcomes” (i.e., running performances obtained by GPS and game performance indicators obtained by InStat index), while sport-performances, especially those in team sports, are far more complex (i.e., include interaction, cooperation, and opposition) [46]. Also, in this study relatively simple methodology was applied, while complex systems like sport games may ask for more detailed experimental approaches and the use of mixed methods as an observational methodology [47]. On the other hand, this study has several strengths. First, this is one of the ﬁrst studies which simultaneously evaluated two sets of performance variables (i.e., running performances and game performance indicators) and probably the ﬁrst one where associations between these two groups of performances were analyzed. Also, the data were collected during oﬃcial games, among professional players, and at the highest national competitive level. Therefore, results are generalizable to similar samples of participants and levels of competition. Furthermore, the position-speciﬁc approach in identiﬁcation of the relationships between running performances and game performance indicators is important strength of the investigation. Therefore, despite the evident limitations, the authors believe that this study may contribute to the knowledge on this ﬁeld and initiate further research. 5. Conclusions The total distance covered during the match did not distinguish Croatian ﬁrst division players from players who compete in elite European divisions (i.e., Spain and Germany). However, the players studied here achieved total distances at lower running speeds than their peers involved in top-level European competitions, which clearly indicates the lower game pace in Croatian soccer competition. These ﬁndings can be useful for determining the physical requirements and proﬁles of the players in the Croatian ﬁrst division, especially with regard to international competitions (i.e., the European League, Champions League). This study conﬁrmed the association between the running performance of players involved in certain playing positions and overall game performance. Speciﬁcally, it seems that CD distance in Int. J. Environ. Res. Public Health 2019, 16, 4032 11 of 13 the running zone and number of high-intensity accelerations, FB number of decelerations, and FW sprinting distance are crucial physical requirements of team success. Training prescriptions in soccer should be based on established requirements speciﬁc to the playing positions, thereby ensuring that players are more able to fulﬁll their game duties and tactical responsibilities over the soccer match. In further studies it would be important to identify possible associations that might exist between diﬀerent parameters of players’ conditioning status and indicators of real game performance. Author Contributions: Conceptualization: T.M.; Data curation: T.M. and S.V.; Formal analysis: T.M.; Funding acquisition: T.M.; Investigation: T.M., S.V. and S.L.; Methodology: D.S.; Project administration: D.S.; Supervision: D.S.; Validation: S.V. Funding: This research received no external funding. 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PMC5803184	ORIGINAL RESEARCH Effect of speed endurance training and reduced training volume on running economy and single muscle ﬁber adaptations in trained runners Casper Skovgaard1,2, Danny Christiansen3, Peter M. Christensen1,2, Nicki W. Almquist1, Martin Thomassen1 & Jens Bangsbo1 1 Department of Nutrition, Exercise and Sports, Section of Integrative Physiology, University of Copenhagen, Copenhagen, Denmark 2 Team Danmark (Danish Elite Sports Organization), Copenhagen, Denmark 3 Institute of Sport, Exercise and Active Living (ISEAL), Victoria University, Melbourne, Australia Keywords Intense training, muscle ﬁber type-speciﬁc adaptations, muscular adaptations, sprint interval training. Correspondence Jens Bangsbo, University of Copenhagen, Department of Nutrition, Exercise and Sports, Section of Integrative Physiology, August Krogh Building, Universitetsparken 13, 2100 Copenhagen O, Denmark. Tel: +45 35 32 16 23 Fax: +45 35 32 16 00 E-mail: jbangsbo@nexs.ku.dk Funding Information The study was supported by a grant from Team Danmark (Danish elite sports organization), Copenhagen, Denmark. Received: 26 October 2017; Revised: 7 January 2018; Accepted: 9 January 2018 doi: 10.14814/phy2.13601 Physiol Rep, 6 (3), 2018, e13601, https://doi.org/10.14814/phy2.13601 Abstract The aim of the present study was to examine whether improved running economy with a period of speed endurance training and reduced training vol- ume could be related to adaptations in speciﬁc muscle ﬁbers. Twenty trained male (n = 14) and female (n = 6) runners (maximum oxygen consumption (VO2-max): 56.4 4.6 mL/min/kg) completed a 40-day intervention with 10 sessions of speed endurance training (5–10 9 30-sec maximal running) and a reduced (36%) volume of training. Before and after the intervention, a muscle biopsy was obtained at rest, and an incremental running test to exhaustion was performed. In addition, running at 60% vVO2-max, and a 10-km run was performed in a normal and a muscle slow twitch (ST) glycogen-depleted con- dition. After compared to before the intervention, expression of mitochondrial uncoupling protein 3 (UCP3) was lower (P < 0.05) and dystrophin was higher (P < 0.05) in ST muscle ﬁbers, and sarcoplasmic reticulum calcium ATPase 1 (SERCA1) was lower (P < 0.05) in fast twitch muscle ﬁbers. Running econ- omy at 60% vVO2-max (11.6 0.2 km/h) and at v10-km (13.7 0.3 km/h) was ~2% better (P < 0.05) after the intervention in the normal condition, but unchanged in the ST glycogen-depleted condition. Ten kilometer performance was improved (P < 0.01) by 3.2% (43.7 1.0 vs. 45.2 1.2 min) and 3.9% (45.8 1.2 vs. 47.7 1.3 min) in the normal and the ST glycogen-depleted condition, respectively. VO2-max was the same, but vVO2-max was 2.0% higher (P < 0.05; 19.3 0.3 vs. 18.9 0.3 km/h) after than before the inter- vention. Thus, improved running economy with intense training may be related to changes in expression of proteins linked to energy consuming pro- cesses in primarily ST muscle ﬁbers. Introduction Speed endurance training (SET; 10–40 sec repeated “all- out” efforts with rest periods lasting >5 times the exercise bouts) with a concomitant reduced training volume has been found to improve endurance performance in associ- ation with better running economy at submaximal speeds in trained runners (Bangsbo et al. 2009; Bangsbo 2015). However, the mechanisms causing the improved running economy are not clearly identiﬁed, but may be related to metabolic changes in the trained muscles (Saunders et al. 2004). Training-induced improvement in running economy may be due to higher mitochondrial efﬁciency, that is, higher ATP/O2, which could be due to reduced uncou- pled respiration. The mitochondrial uncoupling protein 3 ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 2018 \| Vol. 6 \| Iss. 3 \| e13601 Page 1 Physiological Reports ISSN 2051-817X (UCP3) is suggested to be involved in thermogenesis by dispersing energy as heat instead of converting it to ATP (Gong et al. 1997; Boss et al. 2000) and improved run- ning economy may therefore be related to reduced levels of muscle UCP3. In agreement, cross-sectional studies have shown that endurance-trained subjects have lower muscle UCP3 expression and better running economy than untrained subjects (Russell et al. 2003a,b; Mogensen et al. 2006). However, Iaia et al. (2009) found no change in whole muscle UCP3 level although running economy improved after 4 weeks of SET and a 65% reduced train- ing volume. Thus, studies should investigate whether changes in the single muscle ﬁber expression of UCP3 could be related to changes in running economy. The transfer of muscle force produced by the acto- myosins involves a secondary matrix of proteins that trans- mit the muscle force along and between muscle ﬁbers and out to the extracellular matrix. Cytoskeleton proteins, such as dystrophin, have been identiﬁed as playing a role in this muscle force transmission (Rybakova et al. 2000; Prins et al. 2009) and changes in the expression of these proteins could inﬂuence the integrity and the strength of the muscle (Hughes et al. 2015). Hence, increased expression of mus- cle dystrophin may result in increased rate of force devel- opment, increased muscular power output and greater storage and return of elastic energy thereby lowering the cost of running (i.e., improve running economy). Another potential cause of training-induced improve- ments in running economy is lowered muscle expression of the sarcoplasmic reticulum (SR) Ca2+-ATPase (SERCA) pumps, as they are suggested to be responsible for up to 50% of the ATP used during muscle activity (Clausen et al. 1991; Walsh et al. 2006; Smith et al. 2013). Studies have shown that speed endurance training modulates skeletal muscle ﬁber type distribution in soccer players (Gunnarsson et al. 2012) and runners (Skovgaard et al. 2014), which has been found together with lowered SERCA1 expression (Skovgaard et al. 2014) and improved running economy. Muscle ﬁbers with high SERCA1 expression have a faster release and uptake of Ca2+ (Del- bono and Meissner 1996; Froemming et al. 2000) and lowered expression of SERCA1 may therefore reduce the energy turnover during exercise. An increase in the respiratory capacity of skeletal mus- cle permits the use of less oxygen per mitochondrial res- piratory chain for a given submaximal running speed (Saunders et al. 2004). Slow twitch (ST) muscle ﬁbers have higher mitochondrial content and are more depen- dent on oxidative metabolism than fast twitch (FT) mus- cle ﬁbers (Berchtold et al. 2000; Schiafﬁno and Reggiani 2011). However, Jansson and Kaijser (1977) reported that, unlike a control group of varying physical ﬁtness, there was no difference in succinate dehydrogenase muscle activity between ST and FT ﬁbers in gastrocnemius mus- cle of elite orienteers, suggesting that FT ﬁbers have the ability to metabolically adapt to high oxidative demands (Jansson and Kaijser 1977). Metabolic adaptations in FT ﬁbers may therefore contribute to improving running economy after intense training, such as SET, targeting both ST and FT ﬁbers (Egan and Zierath 2013). In sup- port, augmented mRNA response related to mitochon- drial biogenesis (peroxisome proliferator-activated receptor-c coactivator-1, PGC-1a) and metabolism (hex- okinase II and pyruvate dehydrogenase kinase-4, PDK4) in trained subjects was observed following a SET session (Skovgaard et al. 2016). Furthermore, PGC-1a mRNA has been shown to increase in an exercise intensity-dependent manner (Egan et al. 2010; Nordsborg et al. 2010). Regular intense training may therefore lead to higher oxidative capacity, possibly due to oxidative adaptations in FT ﬁbers, which in turn could contribute to the improved running economy as a result of the intense training (Iaia et al. 2008; Bangsbo et al. 2009; Iaia and Bangsbo 2010; Skovgaard et al. 2014). In vitro studies have shown that the energy cost of con- traction is higher in FT than ST ﬁbers (Crow and Kushmer- ick 1982; Barclay et al. 1993; He et al. 2000). This was conﬁrmed in vivo by Krustrup et al. (2008) who observed that the oxygen uptake for at given exercise intensity was higher when ST ﬁbers were blocked by a neuromuscular blocking agent. And reports by Krustrup et al. (2004), who depleted the ST ﬁbers the day before submaximal exercise, that the glycogen depletion of ST ﬁbers enhanced the recruitment of FT ﬁbers and elevated the energy require- ment by 7% (Krustrup et al. 2004). By using the approach, of depleting ST ﬁbers the day before exercise (Krustrup et al. 2004), before and after a SET period, it may be possi- ble to study whether a change in running economy is caused by speciﬁc adaptations in FT ﬁbers. Thus, the aims of the present study were in trained run- ners to investigate the effect of intensiﬁed training, in the form of speed endurance training and a reduced volume of aerobic training, on running economy and adaptation of single muscle ﬁbers. We hypothesized that FT muscle ﬁbers would adapt to the training by lowered expression of UCP3 and SERCA1, and increased expression of dys- trophin and CS, which would be associated with improved running economy and 10-km running performance. Methods Subjects Twenty-six trained runners commenced the study. Six sub- jects did not complete the intervention period due to per- sonal circumstances (n = 4) or low adherence to the 2018 \| Vol. 6 \| Iss. 3 \| e13601 Page 2 ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society Muscle Fiber Type Adaptations to Speed Endurance Training in Runners C. Skovgaard et al. training program (n = 2). Thus, a total of twenty trained male (n = 14) and female (n = 6) runners with an average age, height, body mass, and maximum oxygen consump- tion (VO2-max) of 28.1 4.5 years, 177.5 9.9 cm, 72.5 10.6 kg, and 56.4 4.6 mL/min/kg, respectively, (males: 28.8 4.8 years, 181.8 7.9 cm, 77.8 6.6 kg, 58.1 3.4 mL/min/kg; females: 27.4 3.7 years, 169.0 5.6 cm, 59.9 6.9 kg, 52.5 4.9 mL/min/kg; means SD), completed the study. After receiving written and oral information about the study and the possible risks and discomforts associated with the experimental proce- dures, all subjects gave their written informed consent to participate. The study conformed to the Code of Ethics of the World Medical Association (Declaration of Helsinki) and was approved by the Ethics Committee of the capital region of Copenhagen (Region Hovedstaden). Design The study lasted 40 days and consisted of 10 sessions of supervised speed endurance training (SET) and 10 ses- sions of aerobic moderate-intensity (AM) training (Fig. 1). Total running distance during the intervention period was reduced (P < 0.05) by 36% compared to before the intervention (mean SE, 16 1 vs. 25 2 km/week). Screening and familiarization Before being included in the study, subjects performed a 10-km running test and an incremental treadmill test to exhaustion with pulmonary VO2 measured by a breath- by-breath gas analyzing system (Oxycon Pro; Viasys Healthcare, Hoechberg, Germany), and heart rate (Polar Team2 transmitter; Polar Electro Oy, Kempele, Finland) collected throughout the test. Training SET was performed on day two and six of an 8-day cycle at Østerbro Stadium, Copenhagen, on an outdoor 400-m running track. In ﬁrst and ﬁnal SET session, subjects completed six bouts of 30-sec running. The ﬁrst bout was performed with near-maximal intensity, whereas the remaining ﬁve bouts were performed with maximal inten- sity and distance covered was measured. For the remain- ing eight SET sessions, subjects completed ten bouts of 30-sec “all-out” running. In all sessions, running bouts were separated by 3.5 min of recovery (walking ~200 m to the start-line). SET sessions were supervised, but the subjects performed the SET sessions on their own, if they were unable to participate in the supervised training (85 4% adherence to the supervised SET). AM training was performed on the ﬁrst and ﬁfth day during the 8-day cycle. These sessions were not super- vised, but subjects kept a training log to record exercise distance, time and intensity. A Polar FT7 (Polar Electro Oy, Kempele, Finland) or personal watch with HR moni- tor was used to record exercise intensity and training logs was continuously analyzed. The adherence to the AM training sessions was 93 3% with a weekly duration of 68 5 min and with an average heart rate of 83 1% of HRmax. Testing Tests were performed on separate days interspersed by at least 48 hours, on the same treadmill in the Exercise Physiology laboratory at August Krogh Institute, Depart- ment of Nutrition, Exercise and Sports, University of Copenhagen, before and after the intervention. Tests included: (1) an incremental running test to exhaustion (INC); (2) repeated bouts of 6-min submaximal running followed by a 10-km running test on a running track in a normal condition; (3) repeated bouts of 6-min submaxi- mal running followed by a 10-km running test on a run- ning track in a ST glycogen-depleted condition; (4) a muscle biopsy and a blood sample collected at rest after an overnight fast (Fig. 1). All tests were carried out at the same time of day. Sub- jects refrained from strenuous physical activity, alcohol and caffeine 24 h before testing. Subjects were instructed to keep a diary journal 2 days before and during the ﬁrst series of tests, and to replicate this diet when tested again. 0 8 16 24 32 40 days Pre testing: • INC • 10-km normal • • 10-km depleted muscle and blood sampling • muscle and blood sampling Post testing: • INC • 10-km normal • 10-km depleted Figure 1. Testing before (Pre) and after (Post) 5 blocks/40 days of speed endurance training and reduced training volume in trained runners. Small grey, black and white boxes on the timeline are days with aerobic moderate-intensity training, speed endurance training and rest days, respectively. INC: incremental test to exhaustion. ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society 2018 \| Vol. 6 \| Iss. 3 \| e13601 Page 3 C. Skovgaard et al. Muscle Fiber Type Adaptations to Speed Endurance Training in Runners The incremental running test to exhaustion INC consisted of 2 min of walking at 5 km/h, 6 min at the subject’s individual average 10-km running pace determined at the 10-km screening test before the inter- vention (v10 km; 13.7 0.3 km/h), and 2 min at 14 or 15 km/h (dependent on v10 km), after which the speed increased by 1 km/h every minute until exhaustion. Dur- ing INC, VO2-max, deﬁned as the highest average value achieved over a 30-sec period (Howley et al. 1995), and maximal incremental speed (vVO2-max) {[vVO2- max = Vf + (Ti/60)], where Vf is the ﬁnal velocity obtained and Ti is the time spent at the ﬁnal speed level} were determined. Attaining of maximal heart rate (HR) (judged against the screening test) and an RER value of >1.15 were used as criterions. During the last part of the test, the subjects were verbally encouraged to continue their effort until voluntary termination of the test. Before the test, body mass was measured and subjects wore a Polar Team2 HR monitor around their chest for continu- ous HR recordings. Pulmonary VO2 was measured by use of Oxycon Pro, which was calibrated prior to each test. Muscle and blood sampling Sampling of muscle and blood was performed between 7 and 11 AM after an overnight fast. Using the Bergstr€om procedure (Bergstrom 1962), a muscle biopsy was col- lected with a 5-mm needle from a standardized depth of 5 cm in the middle of m. vastus lateralis of the right leg at rest using local anesthesia (1 mL; 20 mg/L lidocaine without adrenaline). The muscle sample (~100 mg wet weight) was immediately frozen in liquid N2 and stored at 80°C until further analysis. Next, a catheter was inserted in the antecubital vein, and a ~7-mL blood sam- ple was collected and stored on ice until being analyzed. 10-km running tests Both before and after the intervention, two 10-km run- ning tests were performed on a 400-m outdoor running track (Østerbro Stadium, Copenhagen) under similar weather conditions (~20°C, partly cloudy, light winds) between the beginning of July and end of August. The 10-km tests were conducted in a randomized order either without (normal) or after a muscle ST glycogen depletion protocol that was performed the day before the test (see later). Each 10-km running test was preceded by two bouts of 6 min of running, separated by 20 min of rest, on a treadmill at the subject’s individual 60% vVO2-max (11.6 0.2 km/h) with respiratory and HR measure- ments. After these bouts, subjects biked to Østerbro Sta- dium (1-km, slow pace) for the 10-km test. Muscle slow-twitch glycogen depletion protocol The protocol was based on the ﬁndings from the study by Krustrup et al. (2004) who used a 3-h cycling protocol (~50% VO2-max) to deplete ST ﬁbers the day before 20- min of submaximal cycling. The authors reported that the glycogen depletion of ST ﬁbers (51 and 44% of the ST ﬁbers were empty and almost empty of glycogen, respec- tively, and less than 2% of the FT ﬁbers were empty of glycogen) enhanced the recruitment of FT ﬁbers (Krus- trup et al. 2004). The protocol is veriﬁed by previous ﬁndings that ST ﬁbers are exclusively active at 50% VO2- max when subjects have normal muscle glycogen levels (Gollnick et al. 1974; Vøllestad and Blom 1985). The subjects completed a 3-h exercise protocol consist- ing of 60 min of cross-training, 30 min of cycling, 30 min of running, and 60 min of striding at a low speed to deplete glycogen in ST muscle ﬁbers of the calves and thigh muscles. The protocol was chosen to minimize muscle soreness from eccentric contractions while mim- icking the movement pattern of running. During the pro- tocol, subjects’ HR was monitored to ensure they exercised at 60–65% of HRmax (~50% VO2-max). Average HR during the 3-h depletion protocol was the same before and after the intervention (120 1 vs. 120 1 bpm; 63 0 vs. 63 0% HRmax). The protocol started at 6:30 PM and ﬁnished around 10:00 PM and sub- jects were allowed water ad libitum. After termination of the protocol, subjects were given a diet consisting of 5E% carbohydrate, 35E% protein, and 60E% fat with a total energy intake of 30 kJ/kg body mass at dinner and 10 kJ/ kg at breakfast. Breakfast was consumed 2 h before the 10-km running test, which started at 8:00 AM. Whole muscle protein expression Western blotting was performed to determine protein expression as described previously (Skovgaard et al. 2014). In short, ~2.5 mg dry weight (dw; freeze-dried for a minimum of 24 h) of each muscle sample was dissected free from blood, fat, and connective tissue. Samples were homogenized for 1 min at 28.5 Hz (Qiagen Tissuelyser II; Retsch) in a fresh batch of ice-cold buffer containing (in mM) 10% glycerol, 20 Na-pyrophosphate, 150 NaCl, 50 HEPES (pH 7.5), 1% NP-40, 20 b-glycerophosphate, 2 Na3VO4, 10 NaF, 2 PMSF, 1 EDTA (pH 8), 1 EGTA (pH 8), 10 lg/mL aprotinin, 10 lg/mL leupeptin, and 3 ben- zamidine, after which they rotated for 1 h at 4°C, and centrifuged at 18,320g for 20 min at 4°C to exclude nondissolved structures. The supernatant (lysate) was col- lected and used for further analysis. Total protein concen- tration in each sample was determined by a BSA standard kit (Thermo Scientiﬁc), and samples were mixed with 69 2018 \| Vol. 6 \| Iss. 3 \| e13601 Page 4 ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society Muscle Fiber Type Adaptations to Speed Endurance Training in Runners C. Skovgaard et al. Laemmli buffer (7 mL 0.5 mol/L Tris-base, 3 mL glycerol, 0.93 g DTT, 1 g SDS, and 1.2 mg bromophenol blue) and ddH2O to reach equal protein concentration before protein expression was determined by western blotting. Equal amounts of total protein (6–12 lg depending on the protein of interest) were loaded in each well of precast gels (Millipore). All samples from each subject were loaded on the same gel. Proteins were separated according to their molecular weight by SDS-PAGE and semi-dry transferred to a 0.45 lm PVDF membrane (Bio-Rad). The membranes were blocked in either 2% skimmed milk or 3% BSA in TBST, including 0.1% Tween-20 before an overnight incu- bation with rocking in primary antibody at 4°C. The pri- mary antibodies used were: (ab. cat number and company, respectively): sarcoplasmic reticulum Ca2+-ATPase 1 (SERCA1; MA3-912; Thermo Scientiﬁc), sarcoplasmic reticulum Ca2+-ATPase 2 (SERCA2; N-19 Sc-8095; Santa Cruz Technology), actin (A2066; Sigma Aldrich), mito- chondrial uncoupling protein 3 (UCP3; AB3046; Milli- pore). The membranes were then incubated for 1 h at room temperature in horseradish peroxidase conjugated secondary antibody (rabbit anti-sheep (P-0163, DAKO), rabbit anti-goat (P-0449, DAKO), goat anti-mouse (P- 0447, DAKO) or goat anti-rabbit IgM/IgG (4010-05; South- ern Biotech), depending on the primary antibody source). The protein bands were visualized with ECL (Millipore) and recorded with a digital camera (ChemiDoc MP Imag- ing System, Bio-Rad Laboratories). For each muscle sam- ple, protein expression was determined in duplicate on individual gels. Quantiﬁcation of the band intensity was performed using Image Lab version 4.0 (Bio-Rad Labora- tories). Each band was normalized to two control samples of human, whole-muscle homogenate that were loaded onto every gel. Single muscle ﬁber protein expression To determine the protein expression of citrate synthase (CS), UCP3 as well as SERCA- and myosin heavy chain (MHC) isoforms in different muscle ﬁber types, 88 5 single-ﬁber segments were collected from each freeze-dried muscle biopsy. Individual segments were isolated under a microscope at room temperature using ﬁne jeweler’s for- ceps, and were individually incubated for 1 h at room tem- perature in microfuge tubes containing 10 lL of denaturing buffer (0.125 mol/L Tris-HCl, 10% glycerol, 4% SDS, 4 mol/L urea, 10% mercaptoethanol, and 0.001% bromophenol blue, pH 6.8) (Murphy, 2011). The dena- tured segments were stored at 80°C until being analyzed for ﬁber type and grouped accordingly as described below. The ﬁber type of ﬁber segments was determined using dot blotting. 1.5 lL of each denatured sample was spotted onto two PVDF membranes, which were pre-activated in 95% ethanol and pre-equilibrated in transfer buffer (25 mmol/L Tris, 192 mmol/L glycine, pH 8.3, 20% methanol). After drying completely at room temperature, the membranes containing samples were reactivated in ethanol and re-equilibrated in transfer buffer, before being blocked in 5% skim milk in TBST for 5–30 min. One membrane was then incubated by gentle rocking with MHCI antibody (1:200 in 1% BSA with PBST; mouse monoclonal IgM, clone A4.840, Developmental Studies Hybridoma Bank (DSHB)), and the other with MHCIIa antibody (mouse monoclonal IgG, clone A4.74, DSHB) for 2 h at room temperature. After a quick wash in TBST, secondary antibody was applied (1:10,000), and protein signals quantiﬁed as described under Whole mus- cle protein expression (section above). The remaining part of each denatured ﬁber segment (7 lL) was pooled into groups of ST or FTa ﬁbers depend- ing on MHC expression. The number of segments entailed in each pool of ﬁbers per biopsy was 15 2 (range: 8–42) for ST and 18 2 (range 8–39) for FTa ﬁbers before the intervention, and 19 3 (range: 7–55) and 18 2 (range 7–41), respectively, after the intervention. Hybrid ﬁbers (expressing multiple MHC isoforms) were excluded from analysis. Protein expression was determined in pools of ST and FTa ﬁbers using western blotting as detailed in the sec- tion above. The primary antibodies used were: (ab. cat number and company, respectively): CS (ab96600, Abcam), UCP3 (AB3046; Millipore) SERCA1 (MA3-912; Thermo Scientiﬁc), SERCA2 (N-19 Sc-8095; Santa Cruz Technol- ogy). Pools of ﬁbers from biopsies obtained before and after the intervention was loaded on the same gel (stain- free, 4–15%, precast), along with either a calibration curve or two loading controls of whole-muscle homogenate. Pro- tein bands were quantiﬁed by normalizing each band to the total protein content in each lane on the stain-free gel. Muscle enzyme activity Muscle enzyme activity was determined by use of ~2.5 mg dw muscle tissue dissected free from blood, fat, and connective tissue, which was homogenized (1:400) in a 0.3 mol/L phosphate buffer (pH 7.7) by 2 rounds of 30-sec using a TissueLyser II (Retch, Germany). Maximal activity of CS, b-hydroxyacyl-CoA-dehydrogenase (HAD) and phosphofructokinase (PFK) was determined ﬂuoro- metrically with NAD-NADH coupled reactions (Lowry and Passonneau 1972) on a Fluoroskan Ascent apparatus (Thermo Scientiﬁc) using Ascent Software version 2.6. Blood analysis A total of ~7 mL blood was drawn in a heparinized 2-mL syringe and a 5-mL syringe at rest. A part of the 2-mL blood ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society 2018 \| Vol. 6 \| Iss. 3 \| e13601 Page 5 C. Skovgaard et al. Muscle Fiber Type Adaptations to Speed Endurance Training in Runners sample (~1.5 mL) and the 5-mL sample (split into 2 9 2 mL tubes containing 30 lL EDTA) were centrifuged at 20,000 g for ~2 min and the remaining whole blood from the 2-mL sample (~0.5 mL) was stored on ice for further analyses. After centrifugation, the plasma was transferred into tubes that were placed in ice-cold water until they were stored at 20°C. Plasma samples were subsequently ana- lyzed for testosterone and cortisol, creatine kinase (CK) and immunoglobulin A (IgA). CK activity was analyzed by enzy- matic kinetic assay methods (Roche Diagnostic, Mannheim, Germany) using a Hitachi 912 (Roche Diagnostic, Indi- anapolis). IgA was determined using an immunoturbidi- metric assay method (Horiba, Montpellier, France) on an automatic analyzer (Pentra C400, Horiba, Montpellier, France). Testosterone and cortisol was determined using ELISA kits (R&D Systems, Inc. Minneapolis). Whole blood was analyzed for hemoglobin, hematocrit and HCO3 at rest (ABL800 Flex; Radiometer Medical, Copenhagen, Den- mark). Running economy Running Economy (RE) was calculated using the follow- ing formula: REðmLO2=kg=kmÞ ¼ VO2ðmL/minÞ 60 min/h=BM (kg) running speedðkm/hÞ where VO2 is the average value during the last 2 min of running for the two intervals at 60% vVO2-max and v10- km, and BM is body mass. Statistics Paired t tests were used to evaluate the effect of the inter- vention (Pre vs. Post) with two-way ANOVA repeated mea- sures being used to evaluate the effect of glycogen condition (normal vs. ST glycogen-depleted) on 10-km running per- formance and running economy (at 60% vVO2-max). Level of signiﬁcance was set at P < 0.05. A Student-Newman Keuls post-hoc test was applied in case signiﬁcance was reached in the ANOVA. Absolute data values was used and presented as means SE unless otherwise stated. Results Pulmonary oxygen uptake and heart rate during submaximal exercise Pulmonary VO2 during running at v10-km was 1.9% lower (P < 0.05) after compared to before the interven- tion (3.46 0.14 vs. 3.53 0.14 L/min), and running economy was improved by 2.1% (P < 0.05; 207.6 2.6 vs. 212.1 2.8 mL/kg/km) (Fig. 2). Mean HR at v10-km was 1.7% lower (P < 0.05) after than before the interven- tion (162 2 vs. 165 2 bpm). In the normal condition, pulmonary VO2 at 60% vVO2-max was the same before and after the intervention (3.01 0.13 vs. 2.99 0.13 L/min), whereas running economy was 1.7% better (P < 0.05) after compared to before the intervention (210.4 2.9 vs. 214.1 3.2 mL/ kg/km) (Fig. 3). In the ST glycogen-depleted condition, pulmonary VO2 at 60% vVO2-max (3.05 0.15 (Post) vs. 3.04 0.13 (Pre) L/min) and running economy (216.5 2.9 (Post) vs. 217.4 2.9 (Pre) mL/kg/km) did not change with the intervention (Fig. 3). Before the intervention, pulmonary VO2 at 60% vVO2- max was the same in normal and ST glycogen-depleted condition, whereas after the intervention, pulmonary VO2 was 2.0% lower (P < 0.01) in normal than ST glycogen- depleted condition. Before and after the intervention, running economy was 1.6% and 2.9% better (P < 0.05), respectively, in the normal compared to the ST glycogen- depleted condition (Fig. 3). HR during running at 60% vVO2-max in normal and ST glycogen-depleted condition did not change with the intervention, and there were no differences between conditions. Expression of proteins in muscle homogenate Expression of SERCA2 in muscle homogenate was 20% higher (P < 0.05) after compared to before the interven- tion, whereas expression of muscle SERCA1 was 22% lower (P < 0.05). Expression of muscle actin and UCP3 did not change with the intervention (Fig. 4). Expression of proteins in single muscle ﬁbers After compared to before the intervention, expression of muscle CS and UCP3 in ST ﬁbers was 22% and 25%, respectively, lower (P < 0.05), and expression of muscle dystrophin in ST ﬁbers was 41% higher (P < 0.05) (Fig. 5). Expression of muscle SERCA1 was 19% lower (P < 0.05) in FTa ﬁbers, and expression of MHCIIa was 19% higher (P < 0.05) in FTa ﬁbers after than before the intervention. Expression of SERCA2 and MHCI in the single ﬁber pools was unchanged with the intervention (Fig. 5). Muscle enzymatic activity Maximal activity of CS, HAD, and PFK was 10.7%, 9.1%, and 23.4%, respectively, higher (P < 0.05) after than before the intervention (Table 1). 2018 \| Vol. 6 \| Iss. 3 \| e13601 Page 6 ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society Muscle Fiber Type Adaptations to Speed Endurance Training in Runners C. Skovgaard et al. 10-km run Compared to before, 10-km performance in the normal condition improved (P < 0.01) by 3.2% (43.7 1.0 vs. 45.2 1.2 min) after the intervention (Fig. 6). In the ST glycogen-depleted condition, 10-km performance was 3.9% better (P < 0.001) after compared to before the intervention (45.8 1.2 vs. 47.7 1.3 min; Fig. 6). Ten kilometer performance was reduced (P < 0.001) to the same degree in the ST glycogen-depleted compared to the normal condition before (5.3%) and after (4.7%) the intervention (Fig. 6). Maximum oxygen uptake, body mass, and heart rate VO2-max was the same before and after the intervention (4.06 0.16 vs. 4.13 0.18 L/min; 56.4 1.0 vs. 56.3 1.2 mL/min/kg), but vVO2-max was 2.0% higher (P < 0.05) after compared to before (19.3 0.3 vs. 18.9 0.3 km/h). Peak heart rate during INC was the same before and after the intervention (187 2 vs. 188 2 bpm) as well as body mass (72.5 2.4 vs. 72.9 2.3). Blood variables Blood hematocrit and concentration of hemoglobin as well as plasma concentrations of testosterone, cortisol, CK and HCO3 were the same before and after the interven- tion. Compared to before the intervention, testosterone to cortisol ratio was 31.3% higher (P < 0.05) and plasma IgA level was 4.0% higher (P < 0.05) after (Table 2). Discussion The main ﬁndings of the present study were that a period of intense and reduced volume of training in trained 202 204 206 208 210 212 214 216 Pre Post Running economy (ml/kg/km) at v10-km B * 3350 3400 3450 3500 3550 3600 3650 3700 Pre Post Oxygen uptake (L/min) at v10-km A * Figure 2. Oxygen uptake (A) and running economy (B) at v10-km before (Pre) and after (Post) 5 blocks/40 days of speed endurance training and reduced training volume in trained runners. Values are means SE. **Post different (P < 0.001) to Pre. 204 206 208 210 212 214 216 218 220 222 Pre Post Running Economy (ml/kg/km) at 60% VO2-max Depleted Normal B # # 2850 2900 2950 3000 3050 3100 3150 3200 3250 Pre Post Oxygen uptake (L/min) at 60% VO2-max Depleted Normal A # Figure 3. Oxygen uptake (A) and running economy (B) at 60% VO2-max in depleted and normal conditions before (Pre) and after (Post) 5 blocks/40 days of speed endurance training and reduced training volume in trained runners. Values are means SE. Post different (P < 0.05) to Pre; #difference (P < 0.05) within time-point. ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society 2018 \| Vol. 6 \| Iss. 3 \| e13601 Page 7 C. Skovgaard et al. Muscle Fiber Type Adaptations to Speed Endurance Training in Runners runners improved running economy together with higher expression of dystrophin and lowered expression of UCP3 in ST muscle ﬁbers as well as lower expression of SERCA1 in FTa muscle ﬁbers. In addition, compared to the normal condition, 10-km running performance and running economy was equally reduced after the ST mus- cle glycogen-depletion protocol before and after the inter- vention period. The better running economy at 60% vVO2-max and v10-km after the intervention period is in accordance with ﬁndings in other studies of intense training and low- ered training volume in trained runners (Bangsbo et al. 2009; Iaia and Bangsbo 2010; Skovgaard et al. 2014). In the ST glycogen-depleted condition, where a higher recruitment of FT ﬁbers would be expected, the running economy remained unchanged with training, suggesting that it was mainly changes in ST ﬁbers that caused the improvement in running economy in the normal condi- tion. In accordance, the expression of UCP3 in ST ﬁbers was lowered by training in the present study. As mechani- cal energy efﬁciency is negatively related to UCP3 expres- sion (Russell et al. 2003a,b; Mogensen et al. 2006), this suggests that the reduced UCP3 expression in ST ﬁbers may have improved the mitochondrial efﬁciency, and thereby running economy. On the other hand, reduced energy expenditure during submaximal exercise was –50% –40% –30% –20% –10% 0% 10% 20% 30% 40% 50% 60% Actin UCP3 SERCA1 SERCA2 Muscle expression (Post relative to Pre) * Figure 4. Protein expression of actin; UCP3, mitochondrial uncoupling protein 3; SERCA1 and 2, sarcoplasmic reticulum calcium ATPase, before (Pre) and after (Post) 5 blocks/40 days of speed endurance training and reduced training volume in trained runners. Values are geometric means 95% conﬁdence interval (CI) (Post relative to Pre). Post different (P < 0.05) from Pre. 40% 60% 80% 100% 120% 140% 160% 180% MHCIIa SERCA1 CS UCP3 Dystrophin FTa single muscle fiber expression (relative to Pre) Post FTa * 40% 60% 80% 100% 120% 140% 160% 180% MHCI SERCA2 CS UCP3 Dystrophin ST single muscle fiber expression (relative to Pre) Post ST * * * A B Figure 5. ST (A) and FTa (B) single muscle ﬁber expression of MHCI and II, myosin heavy chain; SERCA1 and 2, sarcoplasmic reticulum calcium ATPase; CS, citrate synthase; UCP3, mitochondrial uncoupling protein 3; and dystrophin, before (Pre) and after (Post) 5 blocks/40 days of speed endurance training and reduced training volume in trained runners. Values are means SE (relative to Pre). Post different (P < 0.05) from Pre. Table 1. Maximal activity of muscle citrate synthase (CS), b-hydroxyacyl-CoA-dehydrogenase (HAD); phosphofructokinase (PFK) at rest before (Pre) and after (Post) 5 blocks/40 days of speed endurance training and reduced training volume in trained runners. Pre Post CS (lmolg/dw/min) 17.7 2.9 19.6 2.9 HAD (lmolg/dw/min) 15.6 0.9 17.0 0.7* PFK (lmolg/dw/min) 72.1 15.3 88.9 13.7* Data are presented as means SE. Post different (P < 0.05) to Pre. 2018 \| Vol. 6 \| Iss. 3 \| e13601 Page 8 ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society Muscle Fiber Type Adaptations to Speed Endurance Training in Runners C. Skovgaard et al. reported in a study where trained subjects (VO2-max: 56 1 mL/min/kg) performed 4 weeks of speed endur- ance training (8–12 9 30-sec at maximum speed; 3 times/week) with a 65% reduced training volume without change in the expression of whole muscle UCP3 (Iaia et al. 2009). It could be speculated that the reduction in training volume was too large to elicit changes in UCP3 expression or that a potential reduced expression of UCP3 in ST ﬁbers was undetected by the analysis of whole muscle tissue (Iaia et al. 2009). In addition, the expression of dystrophin, a protein that connects the sarcomere and the extracellular matrix (Hughes et al. 2015), increased in ST muscle ﬁbers with the training intervention. An important function of dys- trophin is to transmit forces generated by the actin-myo- sin cross-bridge (Chopard et al. 2005), and the higher expression of dystrophin in ST ﬁbers may have enhanced the structural integrity of the ST muscle ﬁbers and thereby inﬂuenced running economy. The intervention period also led to lowered expression of SERCA1 in FT muscle ﬁbers, which has also been found in studies of endurance training (Majerczak et al. 2008, 2012; Green et al. 2011). The lower expression of muscle SERCA1 may have reduced the energy turnover during exercise, since calcium handling by the ATP dependent SERCA pumps is reported to be responsible for up to 50% of total energy usage (Clausen et al. 1991; Walsh et al. 2006; Smith et al. 2013), and, thus, may have contributed to the better running economy after the intervention period. The ﬁnding of improved 10-km running performance after the intervention period is in agreement with other studies investigating the effect of intense training and low- ered training volume in trained runners (Bangsbo et al. 2009; Iaia and Bangsbo 2010; Skovgaard et al. 2014). The novel ﬁnding in the present study was that the magnitude of the difference between 10-km running in normal versus ST ﬁber glycogen-depleted condition was the same before and after the training period. This observation suggests that any effect of the intervention on the oxidative capacity of the FT ﬁbers was small, which is supported by the ﬁnding that the expression of CS in the FT ﬁbers did not change with the intervention. In agreement, a 7-week intense training period (12 9 30-sec maximal sprints 2.5 times/week and 5 9 4- min intervals (at a heart rate (HR) of 89% HRmax) 1.5 times/week) with a 50% reduction in training volume, did not change expression of muscle CS and COX-4 in segments of FT ﬁbers in well trained cyclists (VO2-max: 59 4 mL/ min/kg) (Christensen et al. 2015). Collectively, these ﬁnd- ings suggest that intense training with a decrease (36–50%) in training volume does not affect oxidative proteins in FT muscle ﬁbers in trained subjects. Nevertheless, the mixed muscle CS activity was elevated with the intervention and may have contributed to the better 10-km performance. In agreement with other studies on the effect of speed endurance training and reduced training volume in run- ners (Bickham et al. 2006; Iaia et al. 2008; Bangsbo et al. 2009; Iaia and Bangsbo 2010; Skovgaard et al. 2014), VO2-max did not change with the intervention and can- not explain the improved 10-km performance. Based on the performance during the 10-km run, VO2-max and running economy, the fraction of FVO2-max [FVO2- max = 10-km velocity (km/hr)running economy at v10- km (mL/kg/km)/VO2-max (mL/min/kg)100] during the 10-km run was calculated. It showed that FVO2-max did not change with the intervention period (Pre: 84.1 1.3% vs. Post: 85.1 1.2%). In agreement, Iaia et al. (2009) observed a FVO2-max of 84.8% and 81.6% at v10-km (14.5 km/h) before and after, respectively, a 4- wk intervention period with speed endurance training. 41 42 43 44 45 46 47 48 49 50 Pre Post 10-km time (min) Depleted Normal ### ### Figure 6. Time to complete a 10-km run in depleted and normal conditions before (Pre) and after (Post) 5 blocks/40 days of speed endurance training and reduced training volume in trained runners. Values are means SE. *Post different (P < 0.01) to Pre; ###difference (P < 0.001) within time-point. Table 2. Plasma testosterone (T), cortisol (C) and T:C ratio, hemo- globin and hematocrit, creatine kinase, (CK), and Immunoglobulin A, (IgA) and HCO3 before (Pre) and after (Post) 5 blocks/40 days of speed endurance training and reduced training volume in trained runners. Pre Post Testosterone (nmol/L) 24.2 2.9 25.4 3.5 Cortisol (nmol/L) 195.8 21.2 168.3 25.2 t:c ratio 0.16 0.03 0.21 0.03 Hemoglobin (mmol/L) 8.9 0.2 8.5 0.2 Hematocrit (%) 44.2 1.1 41.8 0.9 CK (U/L) 208 41 155 17 IgA (g/L) 1.98 0.19 2.06 0.19* HCO3 (mmol/L) 24.3 0.4 25.0 0.7 Data are presented as means SE. *Post different (P < 0.05) to Pre. ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society 2018 \| Vol. 6 \| Iss. 3 \| e13601 Page 9 C. Skovgaard et al. Muscle Fiber Type Adaptations to Speed Endurance Training in Runners And in the study by Bangsbo et al. (2009), FVO2-max was 85.7% and 83.6% at v10-km (16.0 km/h) before and after, respectively, a 6–9-week period with speed endur- ance training and a basic volume of aerobic training in trained runners. These observations suggest that changes in FVO2-max do not explain the improved 10-km perfor- mance with speed endurance training and reduced train- ing volume. Thus, the improved performance of the 10- km run appears mainly to be caused by the better run- ning economy. It should be noted, however, that the anaerobic energy production during the 10-km run, which is suggested to amount up to 20% of the energy provided during a 10-km run (Joyner and Coyle 2008), is not taken into account in the calculation. In the present study, anaerobic energy production may have been higher after the speed endurance training period due to a possi- ble higher anaerobic capacity reﬂected by the ﬁnding of unchanged VO2-max and higher maximal speed during the incremental test. In support, maximal activity of PFK was higher after the intervention period, which theoreti- cally may have promoted a higher energy production from glycolysis during the 10-km run. In summary, running economy was improved after 40 days of intense and reduced volume of training, which may have been related to a reduced expression of UCP3 and higher expression of dystrophin in ST muscle ﬁbers and lower expression of SERCA1 in FT muscle ﬁbers. The ﬁnding that running economy at 60% VO2-max in a ST muscle ﬁber glycogen-depleted condition was unchanged, suggests that the change in running economy was due to adaptation in ST muscle ﬁbers. The better running econ- omy may explain the improved 10-km running perfor- mance together with a possibly higher anaerobic capacity. Acknowledgments We thank all the participants for their valuable time and extraordinary effort during training and testing. 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Effect of speed endurance training and reduced training volume on running economy and single muscle fiber adaptations in trained runners.	[]	Skovgaard, Casper,Christiansen, Danny,Christensen, Peter M,Almquist, Nicki W,Thomassen, Martin,Bangsbo, Jens	eng
PMC10453861	Citation: Poole, G.; Harris, C.; Greenough, A. Exercise Capacity in Very Low Birth Weight Adults: A Systematic Review and Meta-Analysis. Children 2023, 10, 1427. https://doi.org/10.3390/ children10081427 Academic Editor: Srinivas Bolisetty Received: 28 June 2023 Revised: 31 July 2023 Accepted: 5 August 2023 Published: 21 August 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). children Systematic Review Exercise Capacity in Very Low Birth Weight Adults: A Systematic Review and Meta-Analysis Grace Poole 1, Christopher Harris 1 and Anne Greenough 2,* 1 Neonatal Intensive Care Centre, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK; grace.poole5@nhs.net (G.P.); christopher.harris@kcl.ac.uk (C.H.) 2 Department of Women and Children’s Health, Faculty of Life Sciences and Medicine, King’s College London, London SE5 9RS, UK * Correspondence: anne.greenough@kcl.ac.uk Abstract: There is an association between very low birth weight (VLBW) and cardiovascular mor- bidity and mortality in adulthood. Aerobic ﬁtness, measured as the maximal oxygen consumption (VO2 max), is a good indicator of cardiopulmonary health and predictor of cardiovascular mortal- ity. Our aim was to determine the effect of birth weight on aerobic exercise capacity and physical activity. We systematically identiﬁed studies reporting exercise capacity (VO2 max and VO2 peak) and physical activity levels in participants born at VLBW aged eighteen years or older compared to term-born controls from six databases (MEDLINE, OVID, EMBASE, CI NAHL, CENTRAL, and Google Scholar). Meta-analysis of eligible studies was conducted using a random effect model. We screened 6202 articles and identiﬁed 15 relevant studies, 10 of which were eligible for meta- analysis. VLBW participants had a lower VO2 max compared to their term counterparts (−3.35, 95% CI: −5.23 to −1.47, p = 0.0005), as did VLBW adults who had developed bronchopulmonary dysplasia (−6.08, 95% CI −11.26 to −0.90, p = 0.02). Five of nine studies reported signiﬁcantly re- duced self-reported physical activity levels. Our systematic review and meta-analysis demonstrated reduced maximal aerobic exercise capacity in adults born at VLBW compared to term-born controls. Keywords: very low birth weight; VLBW; neonatal intensive care; VO2 max; exercise capacity; exercise tolerance; physical activity 1. Introduction According to the World Health Organization (WHO), 15–20% infants worldwide are born at a low birth weight (LBW, <2500 g) [1]. Since the introduction of neonatal intensive care units, there has been a dramatic improvement in survival rates of very low birth weight infants (VLBW, <1500 g) [2,3]. The increased survival has resulted in a focus on morbidity and mortality of these cohorts in later life. The impact of preterm birth on multi-organ development can have deleterious effects on the cardiopulmonary system [4–7]. Cardiac magnetic resonance imaging (CMR) has demonstrated structural myocardial changes in adolescents and adults born prematurely which may be associated with reduced functional reserve [4,8]. A study of 102 adults born prematurely demonstrated they had greater left ventricular (LV) mass, smaller in- ternal diameters, and poorer LV strain compared to term-born controls [4]. Mohamed et al. supported those ﬁndings reporting smaller LV volumes and reduced LV function in 200 preterm adults [9]. On stress echocardiography, a lower ejection fraction (EF) propor- tional to exercise intensity was demonstrated [8]. Given the increased prevalence of cardiovascular risk factors described in prematurely born individuals, it is not surprising that birthweight is inversely proportional to adult morbidity and mortality from cardiovascular disease [6,10,11]. In 1991, Barker et al. re- ported diminished airway function in adults born of reduced birthweight and speculated Children 2023, 10, 1427. https://doi.org/10.3390/children10081427 https://www.mdpi.com/journal/children Children 2023, 10, 1427 2 of 15 that this may be secondary to poor prenatal nutrition [12]. Subsequent evidence has shown that LBW is associated with excess respiratory morbidity, independent of or secondary to premature birth or in utero growth retardation (small for gestational age—SGA) [7]. Indeed, prematurity and being born SGA are associated with different risk factors, but many prematurely born infants are SGA. Individuals who developed bronchopulmonary dysplasia (BPD) are particularly at increased risk of chronic respiratory morbidity includ- ing an increased requirement for supplementary oxygen following neonatal discharge, more hospital readmissions particularly for respiratory viral infections, and lung function abnormalities persisting even into adulthood. Sadly, such infants may also suffer hear- ing and visual impairment, feeding difﬁculties, growth restriction, and chronic kidney disease [13,14]. The impact of preterm birth on multi-organ development can have deleterious effects on the cardiopulmonary system [4–7]. Furthermore, birthweight has been shown to be inversely proportional to adult morbidity and mortality from cardiovascular disease [8,9]. Measurement of the maximal oxygen consumption (VO2 max) is considered the gold standard assessment of cardiorespiratory ﬁtness [15]. It has been shown to be a strong predictor of cardiovascular health, morbidity, and mortality [16,17]. A systematic review of studies of maximal aerobic exercise capacity found a 13% reduction in VO2 max in children and adults born prematurely, compared to their term-born counterparts. While the pooled results were signiﬁcantly different, it was highlighted that the majority of included observational studies showed no signiﬁcant difference in VO2 max between the two groups [18–20]. To our knowledge, no systematic review has exclusively focused on determining whether there was an association between VLBW and maximal aerobic exercise capacity in adults. As a strong predictor of cardiovascular health, an improvement in VO2 max may re- duce the risk of cardiovascular disease and associated mortality [21]. It is well-established that regular exercise is an effective means of increasing VO2 max [22]. In addition to its important association with VO2 max, physical activity levels are an important independent protective factor for cardiovascular health [23]. Several studies have suggested that prema- turely born individuals are less physically active than their term-born peers, independent of whether they had developed bronchopulmonary dysplasia (BPD) and socio-economic confounders during childhood [24–26]. However, the evidence is conﬂicting [27]; the Epicure study did not reveal any signiﬁcant differences in physical activity levels between school-age children born prematurely and term-born controls when measured using ac- celerometers. Therefore, the relationship between birth weight and physical activity levels in adulthood would beneﬁt from further clariﬁcation. The evidence, however, is conﬂicting [19]. A study of 61 children found no signiﬁcant differences in physical activity levels assessed using an accelerometer [19]. In addition to its important association with VO2 max, physical activity levels are also an independent risk factor that may contribute to cardiovascular morbidity and mortality. Our primary aim was to undertake a systematic review to evaluate the impact of VLBW on exercise capacity in adults as assessed by VO2 max. Our secondary outcome was to compare self-reported physical activity levels between VLBW and term-born adults and assess whether this impacted on exercise capacity. 2. Materials and Methods 2.1. Methods This systematic review and meta-analysis was prospectively registered on PROSPERO at https://www.crd.york.ac.uk/prospero/ (accessed on 25 May 2023) as CRD42023429309 [28]. The literature search was conducted according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines [29]. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRIMSA) guidelines were used to prepare the manuscript [30]. Children 2023, 10, 1427 3 of 15 2.2. Search Strategy Relevant studies were identiﬁed through searching six electronic databases (EMBASE, OVID MEDLINE(R.), Scopus, CENTRAL, CINAHL, and Google Scholar) between the 1 March 2023 and 15 March 2023. A repeat search was conducted on the 1 June 2023 to identify any further articles that met inclusion criteria. We also hand-searched references from included articles. Search strategies were based on the Cochrane library Neonatal Search Terms [31]. 2.3. Eligibility Criteria Studies on exercise capacity in adults (deﬁned as greater than 18 years old) born at VLBW compared to term controls with results of VO2 max (mL/kg/min) or VO2 Peak (mL/kg/min) using a treadmill or cycle ergometer were eligible. VO2 max refers to the maximum rate of oxygen consumption attainable during physical exertion. VO2 peak, directly reﬂective of VO2 max, is the highest value VO2 attained upon incremental or other high-intensity exercise testing [32]. The recruitment of subjects in some papers was based on gestational age, but only those which reported birthweight were included in the review. BPD was deﬁned as either dependence on supplementary oxygen at 28 days of life or dependence on supplementary oxygen at 36 weeks postmenstrual age (PMA). Other measures of cardiorespiratory exercise capacity were reviewed (such as anaerobic threshold and minute ventilation), but there were insufﬁcient data for a meta-analysis. Given the authors’ capabilities, studies were restricted to those reported in the English language. Using pre-agreed inclusion criteria, two independent authors (GP and CH) removed duplicates, screened titles and abstracts of retrieved articles, and obtained full-text articles. Any disagreements were resolved through discussion between the two reviewers until a consensus was achieved. 2.4. Data Analysis Data extraction was performed by a single reviewer (GP) using a pre-speciﬁed data extraction form. A second reviewer (CH) independently checked the accuracy of the ﬁrst extraction. Study characteristics, sample size, the method of assessing exercise capacity, and reference values were summarised for each study. For each study, VO2 max, VO2 peak, and activity levels were extracted for adults born at VLBW and term-born controls. To be eligible for meta-analysis, a study had to fulﬁl the following criteria, deﬁned a priori: an original report on the relation between exercise capacity in adults that were born at VLBW, odds ratios (OR), and 95% conﬁdence intervals (95% CI) for exercise tolerance in at least two strata of birth weight. To assess exercise capacity, we analysed results for VO2 max and VO2 peak. Meta-analyses were conducted using Review Manager (RevMan) version 5.4 [33]. 2.5. Quality Assessment The risk of bias for each study was assessed by two independent reviewers (GP and CH) using the Newcastle–Ottawa Scale for cohort and cross-sectional studies [34,35]. Studies were scored across three domains: case selection, comparability, and outcome. Scores across three domains were tabulated to give an overall rating of good, fair, or poor quality. The data extraction for quality was performed by a single reviewer (GP) and three randomly chosen papers were checked for consistency by a second reviewer (CH), with no discrepancies being identiﬁed. For cohort studies, we considered that participants lost to follow-up were unlikely to introduce bias if follow-up rates were greater than 80% or between 70% and 80% with an accompanying statement describing those lost to follow up. 3. Results 3.1. Identiﬁed Studies and Characteristics The course of the systematic review is outlined in a PRIMSA 2020 ﬂow diagram (Figure 1). Seven thousand, eight hundred and seven studies were identiﬁed through Children 2023, 10, 1427 4 of 15 database searching. A total of 1605 duplicates were removed, and 6202 abstracts were screened. Eighty-one full-text articles were screened for eligibility, and the quality of ﬁfteen studies was evaluated [8,24,36–47]. The characteristics of the studies included are summarised in Table 1. From all included studies, there were 1132 VLBW participants, 914 controls, and 75 VLBW who had had BPD. Individuals were born between 1984 and 1998. Participants were assessed at ages 18 to 30 years old [8,24,36–47]. Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRIMSA) ﬂow diagram outlining the course of our systematic literature search for articles evaluating maximal aerobic exercise capacity in adults born at very low birth weight. Six databases (SCOPUS, EMBASE, MEDLINE, CINAHL, CENTRAL, and Google Scholar) were searched. A total of 6202 articles were screened and 81 articles assessed for eligibility. Fifteen studies were included in the review and ten in the meta-analysis. Children 2023, 10, 1427 5 of 15 Table 1. Study characteristics of included studies. Author Country Study Design Number of VLBW Infants Number of Control Subjects Age at Follow Up (Years) Outcome Measures Vrijlandt et al., 2006 [36] Netherlands Prospective cohort study 42 48 18–22 • VO2 max • Physical activity level Evensen et al., 2009 [37] Norway Prospective cohort study 32 51 18 • VO2 max Narang et al., 2009 [38] UK Prospective cohort study 57 50 20–25 • Physical activity level Sipola- Leppanen et al., 2011 [39] Finland Prospective cohort study 116 118 20–28 • Physical activity level Lovering et al., 2013 [40] USA Prospective cohort study 12 12 18–27 • VO2 peak Clemm et al., 2014 [24] Norway Prospective cohort study 34 33 24–25 • VO2 peak • VO2 peak at anaerobic threshold • Physical activity level Duke et al., 2014 [48] USA Prospective cohort study 13 14 20–25 • VO2 peak Saarenpaa et al., 2015 [42] Finland Prospective cohort study 160 162 20–25 • Physical activity level Farrell et al., 2015 [43] USA Prospective cohort study 14 16 20–23 • VO2 max Caskey et al., 2016 [44] UK Prospective cohort study 20 24 23–30 • VO2 peak Kasaeva et al., 2012 [45] Finland Prospective cohort study 94 101 21–27 • Physical activity level Haraldsdottir et al., 2020 [46] USA Prospective cohort study 12 12 24–28 • VO2 max at normoxia and hypoxia Huckstep et al., 2018 [8] UK Prospective cohort study 47 54 20–26 • VO2 max • Physical activity level Yang et al., 2022 [47] New Zealand Prospective cohort study 202 93 26–30 • VO2 peak • Physical activity level Cheong et al., 2023 [49] Australia Prospective cohort study 128 126 25 • Six-minute walk test • Maximum beep test level 3.2. VO2 Max in VLBW Infants Ten studies assessed VO2 max in VLBW adults compared to term-born controls [24,36,37,40,41,43,44]. Three studies undertook a subgroup analysis for VLBW individuals who had BPD, including 75 participants. Table 2 summarises the characteristics and results of the studies included in the analysis. The average weight of VLBW adults at follow-up was 68.85 kg, compared to 73.75 kg in the control group (Student’s t test, p = 0.034). Four studies assessed VO2 max, three of which used a cycle ergometer [8,36,37,46]. Six studies assessed VO2 peak using a combination of cycle ergometry and treadmill exercise protocols [24,40–43,47]. Children 2023, 10, 1427 6 of 15 Table 2. Summary of physical maximum aerobic exercise capacity in adults born at very low birth weight. Study Birth Weight VLBW Infants (g) Birth Weight Control Subjects (g) Age at Follow Up (years) Weight VLBW Adults (kg) Weight Control Group (kg) VO2 Max/Peak Measurement VO2 Mea- surement in VLBW Group (mL/kg/min) VO2 Mea- surement in Control Group (mL/kg/min) VO2 Mea- surement in BPD Group (mL/kg/min) Vrijlandt et al., 2006 [36] 1246 ± 232 - 18–22 65 ± 10 72 ± 10 • VO2 Max • Cycle Ergometer 35.3 ± 6.9 20.8 ± 1.2 - Evensen et al., 2009 [37] 1245 (800–1500) 3700 (2670– 5140) 18 64.2 ± 1.7 69.8 ± 1.3 • VO2 Max • Treadmill 48.8 ± 1.4 48.5 ± 1.1 - Lovering et al., 2013 [40] 1160 ± 450 - 21–24 64.7 ± 9.3 75.7 ± 10.4 • VO2 Peak • Cycle Ergometer 40.6 ± 9.4 48.8 ± 7.6 40.7 ± 14.3 Clemm et al., 2014 [24] 1173 ± 163 - 24–25 71.5 ± 4.3 72.3 ± 5.9 • VO2 Peak • Treadmill 40.7 ± 2.8 44.2 ± 3.2 - Duke et al., 2014 [48] 1080 ± 430 - 20–25 65 ± 10 72 ± 12 • VO2 Peak • Cycle Ergometer 35.0 ± 9.0 48.0 ± 9.0 - Farrell et al., 2015 [43] 1027 ± 296 >1500 20–23 76.3 ± 5.0 71.8 ± 5.4 • VO2 Peak • Cycle Ergometer 39.5 ± 1.7 38.9 ± 1.6 - Caskey et al., 2016 [44] 1234 ± 205 3569 ± 297 21–30 - - • VO2 Peak • Cycle Ergometer 45.2 ±11.3 39.3 ± 8.8 35.6 ± 7.5 Haraldsdottir et al., 2020 [46] <1500 g - 24–28 70.1 ± 13.3 75.6 ± 0.7 • VO2 Max • Cycle Ergometer 34.88 ± 9.26 45.79 + 8.71 - Huckstep et al., 2021 [50] 1916 ± 806 3390 ± 424 22–27 - - • VO2 Max • Cycle Ergometer 33.6 ± 8.6 40.1 ± 9.0 - Yang et al., 2022 [47] 1131 ± 233 3362 ± 529 28–29 74.1 ± 18.8 80.8 ± 16.3 • VO2 Peak • Cycle Ergometer 30.46 ± 8.06 31.45 ± 7.86 28.3 ± 1.1 Meta-analysis indicated that adults born at VLBW had a signiﬁcantly lower VO2 max/VO2 peak compared to controls (mean difference: −3.35 [95% CI −5.23 to −1.47] mL/kg/min, p = 0.0005) (Figure 2). However, there was high heterogeneity between studies included in the analysis (I2 = 87%). Meta-analysis of studies solely reporting on those who had BPD found they were more likely to have a lower VO2 max (mean difference: −6.08 [95% CI −11.26 to −0.90] mL/kg/min, p = 0.02,) than term-born controls. There were no signiﬁcant differences in VO2 max between VLBW participants who had BPD and those that did not (p = 0.33). Children 2023, 10, 1427 7 of 15 Children 2023, 10, x. https://doi.org/10.3390/xxxxx www.mdpi.com/journal/children Figure 2. Forest plot of aerobic exercise capacity (VO2 max/VO2 peak) in adults born at very low birth weight compared to their term-born counterparts [24,36,37,40,43,44,46–48,50]. 3.3. Levels of Physical Activity in VLBW Infants Nine studies from ﬁve different countries reported on physical activity levels in VLBW adults compared to controls (Table 3) [8,26,37–39,42,44,47]. Three studies followed up individuals from the Helsinki Study of VLBW Adults [39,41,44]. Three studies used the European Community Respiratory Health Survey II to identify adult’s physical activity levels [36,44,47,51]. Five studies did not report on questionnaires or tools utilised to assess physical activity levels [8,24,37,39,42]. Five studies found signiﬁcant differences in self-reported activity levels in VLBW adults compared to controls [36,39,44,45,47]. In four studies, VLBW adults were less likely to engage in weekly vigorous physical activity [36,44,45,47]. In one study, despite no signiﬁcant difference in the frequency of exercise, VLBW adults were more likely to engage in less intense physical activity for a shorter duration of time [39]. Four studies found no signiﬁcant difference in the frequency of physical activity between VLBW and control groups [8,24,37,42]. Due to a difference in measurable outcomes and assessment tools, the results were unsuitable for meta-analysis. Children 2023, 10, 1427 8 of 15 Table 3. Summary of physical activity levels in adults born at very low birth weight. Study Weight VLBW Infants (g) Weight Control Subjects (g) Age at Follow Up VLBW Infants (Years) Age at Follow Up Control Subjects (Years) PA Assessment Measure Results Summary of Impact Vrijlandt et al., 2006 [36] 1246 ± 232 - 19 ± 0.3 20.8 ± 1.2 • European Community Respiratory Health Survey II • Mean hours of vigorous exercise per week • Preterm born infants undertake signiﬁcantly less vigorous exercise per week (1.9 h ± 2) compared to term born controls (2.9 h ± 2) Narang et al., 2009 [38] 1440 ± 550 3410 ± 2390 21.7 ± 1.2 23.1 ± 2.0 • No formal questionnaire reported • Mean days engaged with physical activity per week • There was no statistical difference in time spent being active per week between VLBW infants (3.0 ± 2.42) and the control group (3.0 ± 1.79) Sippola-Leppanen et al., 2011 [39] 1125 ± 223 3606 ± 469 22.3 ± 2.2 22.6 ± 2.2 • No formal questionnaire reported • Frequency, duration, and intensity of exercise • There was signiﬁcant difference between VLBW and control subjects in the intensity and duration of physical activity during a typical week • There was no difference in the frequency of activity between groups Kaseva et al., 2012 [45] 1157 ± 208.7 3608 ± 492 24.9 ± 2.1 25.1 ± 2.2 • Modiﬁed Kuopio Ischaemic Heart Disease Risk Factor Study • Frequency, time, and intensity of conditioning exercise • Frequency, time, and intensity of leisure-time physical activity • No signiﬁcant difference in commuting, leisure-time, or conditioning physical activity between groups • VLBW infants were more likely to do less vigorous activity compared to their counterparts Clemm et al., 2014 [24] 1173 ± 163 - 24.7 ± 1.2 25.1 ± 1.2 • No formal questionnaire reported • Categorical hours spent exercising per week • No statistically signiﬁcant difference in leisure time spent doing physical activity between EP and term-born individuals. Caskey et al., 2016 [44] 1234 ± 205 3569 ± 297 26.4 ± 3.7 28.3 ± 3.3 • European Community Respiratory Health Survey II • Frequency exercised 2–3 h per week • Statistically signiﬁcant difference in the frequency individuals exercised 2–3 h per week between BPD, non-BPD adults, and term-born controls Saarenpaa et al., 2015 [42] 1126 ± 218 3599 ± 466 22.4 ± 2.1 22.5 ± 2.5 • No formal questionnaire reported • Frequency of exercise per week • No statistically signiﬁcant difference in the frequency of exercise per week between VLBW and non VLBW individuals Children 2023, 10, 1427 9 of 15 Table 3. Cont. Study Weight VLBW Infants (g) Weight Control Subjects (g) Age at Follow Up VLBW Infants (Years) Age at Follow Up Control Subjects (Years) PA Assessment Measure Results Summary of Impact Huckstep et al., 2018 [8] 1916 ±806 3390 ± 424 22.7 ± 3.04 23.6 ± 3.8 • No formal questionnaire reported • Hours spent doing moderate and vigorous physical activity per week • No statistical difference in hours spent doing moderate or vigorous activity between groups Yang et al., 2022 [47] 1131 ±233 3362 ± 529 28.3 ± 1.1 28.2 ± 0.9 • European Community Respiratory Health Survey II • Mean days engaged with physical activity per week • VLBW exercised signiﬁcantly less (2.9 h ± 2.6) per week compared to term-born controls (37 ± 2.4). Children 2023, 10, 1427 10 of 15 4. Discussion We have demonstrated that exercise capacity is signiﬁcantly reduced in adults born at VLBW, independent of whether they had BPD, compared to term born controls. It is important to consider the origin of differences in VO2 max between VLBW adults and TB term-born controls. Maximal aerobic exercise capacity is impacted by age, sex, weight, size, body composition, and physical activity levels. Due to a lack of data reported in individual papers, we were unable to analyse results to determine if there were differences related to sex. A follow-up study of 150 adults born prematurely recruited into the United Kingdom Oscillation Study (UKOS) found males compared to females completed signiﬁ- cantly greater distances during shuttle sprint testing and reported exercising more each week [52]. While VO2 max was not assessed in that study, sex differences in the amount of exercise undertaken could potentially impact on VO2 max. Furthermore, in a study of elite endurance athletes, women were found to have a VO2 max 10% lower than their male counterparts [53]. Given the absolute value is highly impacted by body weight, VO2 max and VO2 peak are typically expressed as milliliter/kg/minute. While this enables results to be adjusted for body weight, body composition remains a likely confounder. Eight out of ten included studies reported the participants’ weight [8,24,36,37,40,41,43,46,47]. On pooled analysis, there was a signiﬁcant difference in the mean weight of adults born at VLBW compared to term-born controls. While impossible to predict based exclusively on weight, BMI is generally well-correlated to percentage body fat [54–56]. One study of 25 female athletes aged between 17 and 22 years found a non-signiﬁcant negative correlation between percentage body fat and VO2 max [57]. Goren et al. demonstrated a strong correlation between fat-free mass (FFM) and VO2 max [58]. This may explain the greater effect size observed in our meta-analysis which focused exclusively on adults compared to the results of Edwards et al. which also included children [18]. Dual energy X-ray absorptiometry (DXA) of 433 healthy subjects demonstrated a decline in FFM with age [59]. This emphasises the importance of including anthropometric measurements in studies investigating maximal aerobic exercise capacity in future. There is a well-established association between physical activity levels and maximal aerobic exercise capacity. Meta-regression and analysis of 28 articles highlighted an increase in VO2 max with physical activity training, independent of the volume and intensity of exercise sessions [60]. Crowley et al. reported in their systematic review that both high and low intensity training when undertaken frequently increased VO2 max [61]. Interestingly, despite adjusting for physical activity levels, Gostelow and Stohr found a signiﬁcantly lower VO2 max in individuals born at VLBW [19]. In our review, ﬁve studies reported that adults born at VLBW exercised less than their term-born counterparts [36,39,44,45,47], whereas four studies found no such association [4,8,24,38]. This highlights the need for further research assessing the relationship of physical activity levels and VO2 max in relation to birth weight. If frequent exercise improves maximal aerobic exercise capacity, an important predictor of cardiovascular morbidity and mortality, a pertinent public health strategy would be to target educational interventions and physical activity programs at VLBW adults. Interestingly, despite adjusting for PA levels, Gostelow and Stohr found a signiﬁcantly lower VO2 max in individuals born at VLBW [52,53]. There is improvement in maximal aerobic exercise capacity with regular exercise, that PA may be used as a potential interven- tion [52,53]. This raises a question as to whether VLBW infants should be recommended targeted exercise regimens as a preventative cardiovascular strategy. The physiological mechanisms resulting in reduced maximal aerobic exercise capacity in adults born at VLBW remain poorly understood. Aerobic exercise capacity is determined by the integrative responses of the cardiovascular and respiratory systems, in addition to oxygen uptake by skeletal muscles [62]. Several studies have demonstrated that [54]. Our ﬁndings support previous research demonstrating a reduced maximal aerobic exercise capacity, independent of prematurity-related perinatal factors such as BPD [40]. Pulmonary gas exchange during Children 2023, 10, 1427 11 of 15 exercise, assessed by the alveolar-to-arterial oxygen difference (A-aDO2), is comparable between prematurely born and term-born TB adults during exercise [40,43,63]. It has however been hypothesised that adults born at VLBW may have higher airway resistance and smaller peripheral airways, requiring a greater concentration of oxygen to maintain ventilation respiration during exercise [64]. Follow-up of the UKOS cohort found males born prematurely were more likely to have poorer smaller airway function, however, they performed better on exercise testing compared to their female counterparts, possibly in- dicating that other factors’ physiological mechanisms may have a greater inﬂuence [52]. Adults born prematurely have been shown to have a signiﬁcantly increased pulmonary ar- terial pressure during exercise, which may reduce pulmonary blood ﬂow and subsequently VO2 max [64,65]. Physiological mechanisms contributing to a reduced maximal aerobic exercise capacity are multi-factorial and complex, where further research is required to fully understand the impact of birth weight and prematurity. In addition to a reduced VO2 max, a predictor of increased cardiopulmonary mortality, adults born at VLBW are also at higher risk due to the increased prevalence of hyperten- sion [66], heart failure [67], diabetes [68], and cardiometabolic syndromes [66]. Given this, a more detailed cardiovascular risk assessment in adults known to be born at VLBW may be of beneﬁt. One suggestion is to screen adults born at VLBW in general practice using a risk scoring system, such as the widely utilised QRISK2, to predict individuals 10-year risk of cardiovascular disease [69]. It would be important however to evaluate the ﬁnancial cost, resource implications, and the most appropriate and effective age-range to target such an intervention. Furthermore, adults born preterm have been shown to have a signiﬁcant increase in pulmonary arterial pressure during exercise, which may reduce pulmonary blood ﬂow and subsequently VO2 max [59,60]. Physiological mechanisms contributing to the observed reduction in maximal aerobic exercise capacity are multi-factorial and it is clear further research is required to fully understand the impact of birth weight and prematurity. Adults born at VLBW are at a higher risk of hypertension [61], heart failure [62], dia- betes [63], and cardiometabolic syndromes [61]. Interestingly, despite a greater prevalence of cardiovascular risk factors, an association with ischemic heart disease remains inconclu- sive [64,65]. An increased relative-risk of all-cause mortality in adults born prematurely, however, is well-established [66]. A more detailed cardiovascular health assessment in adults known to be born at VLBW may be of beneﬁt, but evaluation of the efﬁcacy and resource implications of adult-targeted interventions would be important. Studies such as the trial of exercise to prevent hypertension in young adults are therefore very welcome, even though only 38.7% of those born prematurely were born at less than 32 weeks of gestation [70]. Despite our efforts to generate a precise effect of being born at VLBW on maximal aerobic exercise capacity, our review has some limitations. On analysis, there was a high proportion of heterogeneity between studies reporting maximal aerobic exercise capacity. In part, this may be secondary to our decision to include studies reporting VO2 peak and VO2 max, however, prior studies have shown that VO2 peak is reﬂective of VO2 max [32]. The heterogeneity is possibly attributable to different methodologies and protocols used between studies to estimate maximal aerobic exercise capacity. Eight studies utilised cycle ergometry [8,36,37,39,41,44,46,47] whereas two studies utilised a treadmill [24,27]. While studies utilising a treadmill demonstrated comparable results to those using a cycle ergometer in this review, prior studies have commented on lower values of VO2 max using cycle ergometry when intra-subject comparisons of both methods were utilised in the same study [71,72]. While challenging, this perhaps highlights a need to standardise methodology and protocols utilised to measure maximal aerobic exercise capacity. All participants included in the meta-analysis were between their second and third decade of life, a period well-established to correlate to peak maximal aerobic exercise capacity. Generally, it is estimated that VO2 max declines 10% per decade after the age of 25 years and 15% between the ages of 50 and 75 [73–75]. Most studies included in our Children 2023, 10, 1427 12 of 15 meta-analysis followed up participants in their third decade of life, with latter follow-up. It will be interesting to observe the impact of age on differences in VO2 max between adults born at VLBW and at term. Due to differences in outcome measures between studies, a meta-analysis could not be performed to assess self-reported physical activity levels in adults born at VLBW. Given the correlation between physical activity levels and cardiovascular morbidity and mortality, in addition to the possibility of reduced activity levels in adults born at VLBW, standardisation of outcome measures between studies is of vital importance. In studies evaluating maximal aerobic exercise capacity and physical activity levels, it is important to critically evaluate participant recruitment given the high risk of recruitment bias associated with exercise- based studies [76]. 5. Conclusions In conclusion, maximal aerobic exercise capacity was signiﬁcantly reduced in adults born at VLBW compared to term-born controls. Given the relationship between exercise capacity and cardiovascular morbidity and mortality, this could have signiﬁcant impli- cations for individuals’ long-term health. The variability in outcome measures assessing physical activity meant it was difﬁcult to accurately assess the association with birthweight. We recommend a standardised approach of assessing physical activity levels for future studies, such as the European Respiratory Health Community Questionnaire II or Metabolic Equivalent of a Task levels. Author Contributions: Conceptualization, G.P. and A.G.; methodology, G.P.; validation, C.H.; formal analysis, G.P.; writing—original draft preparation, G.P.; writing—review and editing, A.G.; visual- ization, G.P.; supervision, A.G. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Acknowledgments: The authors would like to acknowledge the work of Deidre Gibbons in the preparation of this manuscript. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. World Health Organization. Global Nutrition Targets 2025. Low Birth Weight Policy Brief. Available online: https://www.who. int/publications/i/item/WHO-NMH-NHD-14.5 (accessed on 17 October 2014). 2. Jeschke, E.; Biermann, A.; Günster, C.; Böhler, T.; Heller, G.; Hummler, H.D.; Bührer, C. 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BMJ Open 2018, 8, e019546. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.	Exercise Capacity in Very Low Birth Weight Adults: A Systematic Review and Meta-Analysis.	08-21-2023	Poole, Grace,Harris, Christopher,Greenough, Anne	eng
PMC9206639	1 Vol.:(0123456789) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports Health status of recreational runners over 10‑km up to ultra‑marathon distance based on data of the NURMI Study Step 2 Katharina Wirnitzer1,2,3, Patrick Boldt4, Gerold Wirnitzer5, Claus Leitzmann6, Derrick Tanous1,2, Mohamad Motevalli1,2, Thomas Rosemann7 & Beat Knechtle7,8* Endurance running is well‑documented to affect health beneficially. However, data are still conflicting in terms of which race distance is associated with the maximum health effects to be obtained. Therefore, the aim of this study was to compare the health status of endurance runners over different race distances. A total of 245 recreational runners (141 females, 104 males) completed an online survey. Health status was assessed by measuring eight dimensions in two clusters of health‑related indicators (e.g., body weight, mental health, chronic diseases and hypersensitivity reactions, medication intake) and health‑related behaviors (e.g., smoking habits, supplement intake, food choice, healthcare utilization). Each dimension consisted of analytical parameters derived to a general domain score between 0 and 1. Data analysis was performed by using non‑parametric ANOVA and MANOVA. There were 89 half‑marathon (HM), 65 marathon/ultra‑marathon (M/UM), and 91 10‑km runners. 10‑km runners were leaner than both the HM and M/UM runners (p ≤ 0.05). HM runners had higher health scores for six dimensions (body weight, mental health, chronic diseases and hypersensitivity reactions, medication intake, smoking habits, and health care utilization), which contributed to an average score of 77.1% (score range 62–88%) for their overall state of health. Whereas 10‑km and M/UM runners had lesser but similar average scores in the overall state of health (71.7% and 72%, respectively). Race distance had a significant association with the dimension “chronic diseases and hypersensitivity reactions” (p ≤ 0.05). Despite the null significant associations between race distance and seven (out of eight) multi‑item health dimensions, a tendency towards better health status (assessed by domain scores of health) among HM runners was found compared to other distance runners. However, the optimal state of health across all race distances supported the notion that endurance running contributed to overall health and well‑being. Trial registration number: ISRCTN73074080. Retrospectively registered 12th June 2015. As the basic form of human movement, running is the most popular leisure-time physical activity1. This low-cost and convenient activity can be practiced at any age with little effort and a lower level of expertise and mastery2. Over the past decades, the number of recreational and professional runners has increased across various dis- tances, marathons in particular3, and various reasons for actively following a running routine have been reported by runners. While health-oriented purposes have been shown to be the most significant motive for running1,4, literature indicates that several motives including but not limited to leisure, hobby, weight control, winning, OPEN 1Department of Research and Development in Teacher Education, University College of Teacher Education Tyrol, Innsbruck, Austria. 2Department of Sport Science, University of Innsbruck, Innsbruck, Austria. 3Research Center Medical Humanities, Leopold-Franzens University of Innsbruck, Innsbruck, Austria. 4Department of Child and Adolescent Psychiatry and Psychotherapy, LVR-Klinik Viersen, Viersen, Germany. 5adventureV & change2V, Stans, Austria. 6Institute of Nutrition, University of Gießen, Gießen, Germany. 7Institute of Primary Care, University of Zurich, Zurich, Switzerland. 8Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland. *email: beat.knechtle@ hispeed.ch 2 Vol:.(1234567890) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ and social reasons encourage runners to engage in running activities/events3,5. Motivations for running could potentially influence the intensity, duration, and frequency of training routines as well as lifestyle behaviors in endurance runners, which together might affect short- and long-term health status4,5. Despite the fact that distance runners are depicted as the healthiest fraction of the general population, it has been reported that a “faster and further” dosage fails6. Research indicated that among 26 different kinds of sport, endurance running provides the most favorable health implications9. Regular participation in recreational running was found to positively affect body weight (BW), body fat, blood pressure, blood glucose levels, insulin sensitivity, blood-lipid profile, and musculoskeletal health10–12. Additionally, running could favorably influence mood, well-being, and mental status13,14. Other mental feelings, including fear, depression, worries, anxiety, and anger within the context of an adjustment disorder, might be positively affected following regular endurance running14. Distance running contributes to the prevention of chronic diseases by lowering the risks, such as cardiovascular disease (e.g., coronary artery disease, stroke)15,16 and different types of cancer17,18. As a potential link between running and overall mortality, cardiorespiratory fitness is a strong predictor for morbidity and mortality, and further reduces total mortality from cardiovascular disease, cancer, infections, and other causes15,19. Substantial health-related advantages fol- lowing endurance running are correlated with running exposure in a dose–response association, as the larger effects on health are achieved with increased loads of running8. Moreover, evidence supports more beneficial health effects of regular endurance running on cardiovascular risk factors, particularly artery carotid diameter thickness20 and low-grade inflammation21 compared to irregular endurance running. In addition to the well- established fact that endurance running is an effective tool to improve individual health7, regular and long-term involvement in running activities could be a powerful tool to affect public health positively and thus tackle global health problems8,9. It has been shown that marathon runners benefit from a greater metabolic fitness (e.g., insulin response, fast- ing lipids, fasting insulin), aerobic performance (e.g., velocity at VO2max, running economy), exercise metabo- lism (e.g., lactate threshold), and skeletal muscle levels of mitochondrial proteins compared to sedentary subjects with matched cardiovascular fitness, age, gender, and body mass index (BMI)22. Marathon running was found to significantly diminish the risk of coronary plaque prevalence as a result of reducing the relevant risk factors (e.g., hypertension and hyperlipidemia)23. In addition to a low incidence of cardiovascular disease, marathoners are shown to have an extended longevity compared to the general population24. The favorable health consequences of distance running are not limited to marathoners, as distance runners in lower and higher mileages were also shown to have comparable outcomes. Evidence indicates that ultra-marathoners were healthier and less often sick compared to the general population25. Half marathon running was found to positively affect immune cell proportions, pro-inflammatory cytokine levels, and recovery behavior on a short-term basis as a midterm anti- inflammatory effect26. Research on 10-km running demonstrates a positive relationship between running and cardio-metabolic health, independent of exercise volume and cardiorespiratory fitness27. Furthermore, compared to longer-distance runners, 10-km runners also appear to be at a lower risk of injuries; however, weekly mileage and race distance were identified as risk factors for injuries in endurance runners7,28. Despite the aforementioned advantageous influences, there have been reports of some adverse effects of distance running on health status (e.g., musculoskeletal injuries, unintended weight reduction, cardiovascular abnormalities) that potentially increase with age28–30. The increased exercise-induced stress during an ultra- marathon run leads to several pathophysiological changes, such as an increase in acute phase proteins, a decrease in testosterone, an increase in liver values, hemolysis, skeletal muscle cell damage, micro-hematuria, and a loss of bone mass31. Ultra-marathoners also tend to suffer from more knee pain, stress fractures, allergies, and asthma than the general population25. In addition, intensive and long-lasting endurance running was found to lead arterial changes toward constricting the coronary, cerebral, and peripheral arteries1,32,33, which not only affects performance but could also be associated with an increased risk for acute cardiac disorders (e.g., cardiac death, clinical arrhythmias, angina, myocardial infarcts)34,35. While older individuals are at a higher risk36, the incidence of race-related cardiac arrest was found to be significantly higher in males than female marathoners—although the overall risk is low34. It was found that a half marathon running could also significantly increase post-exercise levels of biomarkers related to cardiovascular damage and dysfunction37, which is associated with an increased risk for race-related cardiac arrest34. Moreover, activation of the inflammatory response and the detoxification process was shown by proteomic profile changes after a half marathon race, and additional pathways associated with immune response, lipid transport, and coagulation were involved38. Distance running is also associated with a high risk of running-induced injuries, as approximately half of the active runners reported having more than one injury per year, with excess BW, the weekly mileage, and the race distance recognized as relevant risk factors7,28. Furthermore, gastrointestinal complaints (due to decreased exercise-induced mesenteric blood flow)39, symptomatic hyponatremia35, and exercise-induced asthma, as well as hay fever, are reported in distance runners39,40. In spite of the well-recognized effects of endurance running on different health parameters, there is a paucity of research comparing the health status among different groups of endurance runners. The available health- associated reports did not distinguish different race distances and instead have focused on 10-km runners41, half marathoners26,37,38, marathoners12,22,24, or ultra-marathoners31,32. Therefore, the aim of the present study was to investigate the health-related indicators and behaviors of recreational endurance runners and compare their health status across different race distances. It was hypothesized that the health status differs between endurance runners over 10-km, half-marathon, and marathon/ultra-marathon race distances. 3 Vol.:(0123456789) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ Methods Study design and ethical approval. The present study is a part of the NURMI (Nutrition and Run- ning High Mileage) Study and has been conducted following a cross-sectional design42. The NURMI study was designed by an interdisciplinary team of scientists and aims to assess and compare recreational endurance run- ners by sex, race distance, diet type, etc. Data collection was conducted via a series of self-reported online sur- veys in three separate but subsequent steps. The NURMI Study Step 1 will therefore examine epidemiological aspects (e.g., age, sex, and prevalence of diet type at running events), Step 2 focuses on behaviors considering running training and racing, nutrition, health, etc., and Step 3 investigates running performance linked to diet and sports-psychological parameters. The subsequent method was introduced in detail elsewhere10,42,43, to which the interested readers are kindly referred. The study protocol was approved by the ethics board of St. Gallen, Switzerland, on May 6, 2015 (EKSG 14/145). The trial registration number is ISRCTN73074080. Experimental approach and inclusion criteria. Endurance runners in the NURMI study were mostly engaged from German-speaking countries, including Germany, Austria, and Switzerland. Runners were con- tacted and recruited mainly via social media, websites of the organizers of marathon events, online running communities, email-lists and runners’ magazines, as well as via magazines for health, nutrition and lifestyle, trade fairs on sports, plant-based nutrition and lifestyle, as well as through personal contacts. Participants completed an online survey within the NURMI Study Step 2, which was available in German and English at www. nurmi- study. com. Prior to completion of the questionnaire, participants were provided a writ- ten description of the procedures and gave their informed consent to take part in the study. In parallel, physical and psychological information—including the assignment to one of three basic areas of sports (as participants are mainly active in running due to either health, leisure, or performance foci)—motivation and aim of run- ning activities, and details regarding other sports activities to balance for running were obtained to differentiate between a health, leisure, or predominantly performance-orientated approach. For successful participation in the study, the following inclusion criteria were determined initially: (1) written informed consent; (2) at least 18 years of age; (3) questionnaire Step 2 completed; (4) having a BMI < 30 kg/m2; and (5) successful participation in a running event of at least a half-marathon distance in the past two years. However, to avoid an irreversible loss of valuable data sets, those who met the inclusion criteria 1–4 but stated being 10-km runners were included as additional participants and were assigned to a further race distance group. To control for a minimal status of health linked to a minimum level of fitness and to further enhance the reliability of data sets, BMI-associated criteria were implemented in the present study. With a BMI ≥ 30 kg/m2, however, other health-protective and/or weight loss strategies other than running are necessary to reduce body weight safely, and could thus potentially affect health-related data. Therefore, participants with a BMI ≥ 30 kg/ m2 (n = 3) were excluded from data analysis. Data clearance and classification of participants. Control questions were included throughout differ- ent sections of the survey to control for self-reported information of running-related variables (history, training, racing, etc.), and consequently, to identify inconsistent or conflicting data. In general, from the initial number of 317 endurance runners, 72 participants who did not meet the inclusion criteria or did not provide consistent or complete answers to essential questions (e.g., sex, age, race distance, health-related questions) were excluded from the study. As a result, a total of 245 runners with complete data sets were included for descriptive statistical analysis after data clearance (Fig. 1). Participants were initially categorized according to race distance: half-marathon and marathon/ultra-mara- thon (data were pooled since the marathon distance is included in an ultra-marathon). The shortest distance for ultra-marathon was 50 km, and the longest distance was 160 km in the present study. In addition, a total of 91 highly-motivated 10-km runners provided accurate and complete answers; however, they had not successfully participated in either a half-marathon or marathon. In general, the most frequently stated race distance was considered the main criterion to assign runners to the respective study groups. It is well-established that the BMI of active runners is lower than the general population44, and people with a higher BMI might have a different health status, as their main goal to engage in running activities is to achieve and maintain a healthy BW. The World Health Organization45,46 recommends maintaining a BMI in the range of 18.5–24.9 kg/m2 (BMINORM) for individuals, while at the same time pointing to an increased risk of co-morbidities for a BMI 25.0–29.9 kg/m2 and moderate to severe risk of co-morbidities for a BMI > 30 kg/m2. Therefore, calcu- lated BMI was classified into three categories, under 18.49, BMINORM, and over 25, to differentiate health-related findings based on BMI subgroups. In addition, given the importance of diet types in endurance runners’ health status10,20, participants were assigned into three dietary subgroups of omnivores, vegetarians, and vegans47. Health‑related dimensions. As a latent variable, health status was derived by using both the two clusters of health-related indicators and health-related behaviors10,48. Each cluster pooled four dimensions defined by specific items based on manifest measures. The following dimensions described health-related indicators: (1) BW and BMI; (2) mental health (stress perception); (3) chronic diseases and hypersensitivity reactions: preva- lence of chronic diseases (incl. heart disease, state after heart attack, cancer), prevalence of metabolic diseases (incl. diabetes mellitus 1, diabetes mellitus 2, hyperthyroidism, hypothyroidism), prevalence of hypersensitiv- ity reactions (incl. allergies, intolerances); and (4) medication intake (for thyroid disease, for hypertension, for cholesterol level, for contraception). The following dimensions described health-related behaviors: (1) smoking habits (current and history of smoking); (2) supplement intake (supplements prescribed by a doctor, supple- ments for performance enhancement, supplements to cope with stress); (3) food choice (motivation, desired 4 Vol:.(1234567890) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ ingredients, avoided ingredients); and (4) healthcare utilization and regular check-ups. Together, these eight dimensions described health outcomes. Resulting from this, eight domain scores were derived, which generated scores between 0 and 1. Low scores indicate detrimental health associations, while higher scores indicate benefi- cial health associations [given as mean scores plus standard deviation and percentage (%)]. Statistical analysis. The statistical software R version 3.5.0 Core Team 2018 (R Foundation for Statistical Computing, Vienna, Austria) was used to perform all statistical analyses. Exploratory analysis was performed by descriptive statistics (median and interquartile range (IQR)). Significant differences between race distance subgroups and domain scores to describe health status were calculated by using a non-parametric ANOVA. Chi-square test and Kruskal–Wallis test were used to examine the association between race distance subgroups and domain scores with nominal scale variables, and Wilcoxon test and Kruskal–Wallis test (ordinal and metric scale) approximated by using the F distributions. State of health was statistically modeled as a latent variable and was derived by manifest variables (e.g., BW, cancer, smoking). In order to scale the state of health described by the respective dimensions of health, a heuristic index between 0 and 1 was defined (equivalence in all items). In order to test the statistical hypothesis considering significant differences between subgroups of race distance, sex, age, academic qualification, and weekly mileage of running for each dimension, a MANOVA was performed Figure 1. Enrollment and categorization of participants. 5 Vol.:(0123456789) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ to define health status. The assumptions of the ANOVA were verified by residual analysis. The level of statistical significance was set at p < 0.05 (statistical trend: 0.05 ≥ p < 0.10). Ethics approval. The study protocol was approved by the ethics board of St. Gallen, Switzerland on May 6, 2015 (EKSG 14/145). The study was conducted in accordance with the ethical standards of the institutional review board, medical professional codex and the with the 1964 Helsinki declaration and its later amendments as of 1996 as well as Data Security Laws and good clinical practice guidelines. Consent to participate. All participants gave written informed consent prior to the testing procedure. Results Sociodemographic data. A total of 245 endurance runners (141 women and 104 men) with a mean age of 39 (IQR 17) years and a BMI of 21.72 (IQR 3.50) kg/m2 were included for final data analysis. Germany (n = 177), Austria (n = 44), and Switzerland (n = 13) had the majority of endurance runners, but 4.5% of participants (n = 11) were from other countries, including Belgium, Brazil, Canada, Italy, Luxemburg, Netherlands, Poland, Spain, and the UK. There were 154 NURMI-Runners (89 half-marathoners, 65 marathoners/ultra-marathoners) and 91 runners over the 10-km distance. The participants reported following an omnivorous diet (44%), vegetar- ian diet (18%), or vegan diet (37%). Moreover, with regard to the level of academic qualification, 34% of endur- ance runners (n = 83) had upper secondary/technical education or a university (or higher) degree. In addition, 67% of endurance runners were married or living with partner (Table 1). The characteristics of the subjects are presented in Tables 1 and 2. The basic assignment of endurance runners to sports areas was 54% for leisure activity, 36% for sports achievement, and 10% for health concerns. The main motivation of endurance runners to start running was for hobby (35%), health (19%), or BW loss (18%). The major goal for participation in running events reported was to achieve a specific runtime (51%) followed by the pleasure of running (39%). As a supplementary physical activity, summer sports (53% cycling, 31% respectively swimming, hiking/rambling and trail/uphill running) were reported to be more prevalent than winter sports. Table 1. Anthropometric and sociodemographic characteristics of the endurance runners. Data are presented as “percentage of prevalence (n)” or “median (IQR)”. BMI body mass index, BW body weight, HM half- marathon, IQR interquartile range, km kilometers, M/UM marathon/ultra-marathon. Total HM M/UM 10 km Number of Subjects 100% (245) 36% (89) 27% (65) 37% (91) Sex Female 58% (141) 55% (49) 38% (25) 74% (67) Male 42% (104) 45% (40) 62% (40) 26% (24) Age (years) (median) 39 (IQR 17) 37 (IQR 18) 44 (IQR 17) 37 (IQR 18) BW (kg) (median) 65.0 (IQR 14.2) 65.0 (IQR 13.0) 67.5 (IQR 17.5) 62 (IQR 11.0) BMI (kg/m2) (median) 21.72 (IQR 3.50) 21.97 (IQR 3.28) 22.15 (IQR 3.25) 21.30 (IQR 3.94) Diet Omnivorous 44% (109) 44% (39) 51% (33) 41% (37) Vegetarian 18% (45) 22% (20) 15% (10) 16% (15) Vegan 37% (91) 34% (30) 34% (22) 43% (39) Academic qualification No Qualification < 1% (1) 1% (1) – – Upper Secondary Education/Technical Qualification/GCSE or Equivalent 34% (83) 37% (33) 40% (26) 26% (24) A Levels or Equivalent 22% (53) 17% (15) 23% (15) 25% (23) University Degree/Higher Degree (i.e., doctorate) 34% (83) 30% (27) 28% (18) 42% (38) No Answer 10% (25) 15% (13) 9% (6) 7% (6) Marital status Divorced/Separated 6% (15) 6% (5) 6% (4) 7% (6) Married/Living with Partner 67% (164) 63% (56) 72% (47) 67% (61) Single 27% (66) 31% (28) 22% (14) 26% (24) Country of residence Austria 18% (44) 17% (15) 20% (13) 18% (16) Germany 72% (177) 73% (65) 69% (45) 74% (67) Switzerland 5% (13) 7% (6) 8% (5) 2% (2) Other 4% (11) 3% (3) 3% (2) 7% (6) 6 Vol:.(1234567890) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ The median number of events completed in our sample was eight races, and the marathoners/ultra-maratho- ners finished the highest number of races. Depending on the stage of preparation for the main event and/or sea- son within the course of the year, 70% of runners reported their weekly mileage at a medium volume (19–36 km), while 17% and 13% of runners reported low (< 19 km) and high (> 36 km) volumes, respectively (Table 2). Health‑related indicators. Dimension of BW and BMI. There was a significant difference in BW between race distance subgroups (F(2, 242) = 5.05, p = 0.007), with 10-km runners weighing less than half-marathoners and Table 2. Characteristics of running activity of the subjects. Data are presented as “percentage of prevalence (n)” or “median (IQR)”. HM half-marathon, IQR interquartile range, km kilometers, M/UM marathon/ultra- marathon. a Sport for health: Those who take part in sports for health reasons and train 2–3 times a week for 30–60 min at a low to moderate intensity with the aim of maintaining or improving their health. b Sport for leisure: Those who take part for leisure reasons and train 2–5 times a week for 60–90 min at a moderate intensity with the aim of enjoying their free time actively. c Sport for performance: Performance athletes train 3–6 times a week, at moderate to high intensities and assiduously follow a long-term training plan, including assessing their performance, with the aim of ascertaining and improving it and measuring it against that of other athletes in competitions. Total HM M/UM 10 km Number of subjects 100% (245) 36% (89) 27% (65) 37% (91) Basic assignment to areas of sport Sport for Healtha 10% (23) 8% (7) 5% (3) 14% (13) Sport for Leisureb 54% (133) 64% (57) 37% (24) 57% (52) Sport for Performancec 36% (89) 28% (25) 58% (38) 29% (26) Motive for running Initial Motivation for Running Counteraction to Job 9% (22) 10% (9) 11% (7) 7% (6) Leisure Activity 4% (11) 7% (6) 5% (3) 2% (2) Hobby 35% (85) 33% (29) 38% (25) 34% (31) Weight Maintenance 7% (17) 9% (8) 6% (4) 5% (5) Weight Loss 18% (45) 17% (15) 15% (10) 22% (20) Health 19% (46) 19% (17) 18% (12) 19% (17) Other 8% (19) 6% (5) 6% (4) 11% (10) Aim for running events For the Pleasure of Running 39% (90) 40% (35) 47% (27) 32% (28) Specific Placing 3% (8) 2% (2) 5% (3) 3% (3) Specific Time 51% (117) 51% (44) 44% (25) 55% (48) Taking Part is All that Matters 7% (16) 7% (6) 4% (2) 9% (8) Completion of running events Total Races Completed Before the NURMI Study (median) 8 (IQR 11) 7 (IQR 11) 10 (IQR 10) 7 (IQR 12) Races Completed in the Past 2 Years Over Distances (median) 8 (IQR 11) 6 (IQR 11) 10 (IQR 11) 7 (IQR 11) Half-Marathon 2 (IQR 3) 3 (IQR 4) 2 (IQR 3) 1 (IQR 2) Marathon/Ultra-Marathon 1 (IQR 2) 0 (IQR 1) 2 (IQR 3) 0 (IQR 1) Running Training per week (Mean mileage, km) Low Mileage (≤ 1 km) 17% (41) 26% (23) 5% (3) 16% (5) Medium Mileage (> 19–36 km) 70% (172) 65% (58) 63% (41) 80% (73) High Mileage (> 36–100 km) 13% (32) 9% (8) 32% (21) 3% (3) Other sports to balance for running Summer Sports Cycling 53% (130) 55% (49) 57% (36) 49% (45) Swimming 31% (75) 35% (31) 22% (14) 33% (30) Hiking/Rambling 31% (75) 33% (29) 32% (20) 29% (26) Trail/Uphill Running 31% (75) 33% (29) 46% (29) 19% (17) Triathlon 19% (46) 21% (19) 17% (11) 18% (16) Winter Sports Skiing (alpine) 14% (34) 15% (13) 16% (10) 12% (11) Cross Country Skiing 11% (26) 12% (11) 13% (8) 8% (7) Snowboarding 7% (16) 9% (8) 5% (3) 5% (5) Ski Touring 4% (9) 7% (6) 5% (3) – 7 Vol.:(0123456789) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ marathoners/ultra-marathoners. However, there was no difference in the health-related item BMI between the subgroups (χ2 (4) = 1.35, p = 0.854) (Table 3). In addition, 10-km runners showed the lowest calculated BMI, while half-marathoners contributed the largest fraction of BMINORM (85%). Although no significant between-group difference was observed in the dimension of “BW and BMI” (F(2, 242) = 0.84, p = 0.433), comparative data showed that half-marathoners had the highest score for the health-related indicator “BW and BMI” (0.69 ± 0.39), and were followed closely by marathon/ultra-marathon runners (0.67 ± 0.39) (Table 4). Dimension of mental health. There was no significant association between race distance and mental health (χ2 (2) = 5.83, p = 0.054) (Table 3). However, half-marathoners reported least often to suffer from perceived stress (27%, n = 23). Although no significant between-group difference was observed in the dimension of “mental health” (F(2, 219) = 2.95, p = 0.054), comparative data showed that half-marathoners had the highest score with regard to mental health (0.73 ± 0.45) (Table 4). Dimension of chronic diseases and hypersensitivity reactions. There was no significant association between race distance and the prevalence of (1) cardiovascular diseases and cancer (χ2 (4) = 4.76, p = 0.313), (2) metabolic diseases (χ2 (10) = 13.25, p = 0.210), and (3) hypersensitivity reactions (χ2 (4) = 8.90, p = 0.064). However, none of the half-marathoners reported having chronic diseases, and half-marathoners most often reported having no metabolic diseases (92%, n = 78) and no hypersensitivity reactions (73%, n = 62) while having allergies the least often (22%, n = 19), (Table 3). Overall, half-marathoners scored highest significantly with regard to the health- related indicator chronic diseases and hypersensitivity reactions, and it was the only dimension with significant between-group differences (0.88 ± 0.18, F(2, 219) = 3.31, p = 0.038) (Table 4). Dimension of medication intake. There was no significant association between medication intake and race dis- tance (χ2 (6) = 2.64, p = 0.852). Furthermore, there was no significant association between race distance and the intake of contraceptives (χ2 (2) = 5.93, p = 0.051) (Table 3). However, half-marathoners most often reported hav- ing no regular medication intake (87%, n = 74). Although no significant between-group difference was observed in the dimension of “medication intake” (F(2, 219) = 0.20, p = 0.817), comparative data showed that half-maratho- ners had the highest score with regard to medication intake (0.87 ± 0.34) but were closely followed by two other groups (Table 4). Health‑related behaviors. Dimension of smoking habits. Race distance and current or former smoking were not significantly associated (χ2 (4) = 4.00, p = 0.406) (Table 3). In addition, half-marathoners showed the highest fraction of non-smokers (67%, n = 57). Although no significant between-group difference was observed in the dimension of “smoking habits” (F(2, 219) = 2.00, p = 0.138), comparative data showed that half-marathoners showed the best health-related behaviors with regard to smoking habits (0.83 ± 0.25) (Table 4). Dimension of supplement intake. There was no significant association between race distance and (1) supple- ment intake prescribed by a doctor (χ2 (2) = 0.07, p = 0.968), (2) the consumption of performance-enhancing substances (χ2 (4) = 3.52, p = 0.476), or (3) the intake of substances to cope with stress (χ2 (4) = 6.66, p = 0.155) (Table 3). Although no significant between-group difference was observed in the dimension of “supplement intake” (F(2, 219) = 0.92, p = 0.400), comparative data showed that 10-km runners had the highest health scores with regard to supplement intake (0.92 ± 0.17) but were closely followed by two other groups (Table 4). Dimension of food choice. There was no significant association between race distance and motives for food choice (1) because it is healthy (χ2 (2) = 0.74, p = 0.690), health-promoting (χ2 (2) = 1.00, p = 0.607), and good for maintaining health (χ2 (2) = 2.15, p = 0.341); (2) in order to obtain vitamins (χ2 (2) = 0.15, p = 0.928), minerals/trace elements (χ2 (2) = 0.10, p = 0.953), antioxidants (χ2 (2) = 1.06, p = 0.587), phytochemicals (χ2 (2) = 2.92, p = 0.232), and fiber (χ2 (2) = 2.58, p = 0.276); or (3) with regard to the avoidance of the following ingredients (Table 3): refined sugar (χ2 (2) = 1.89, p = 0.390), sweeteners (χ2 (2) = 5.63, p = 0.060), fat in general (χ2 (2) = 3.13, p = 0.210), saturated fats (χ2 (2) = 0.21, p = 0.899), cholesterol (χ2 (2) = 0.46, p = 0.794), alcohol (χ2 (2) = 1.22, p = 0.542), and caf- feine (χ2 (2) = 3.04, p = 0.219). However, there was a significant association between race distance and food choice with regard to the avoidance of the following ingredients (Table 3): white flour (χ2 (2) = 8.70, p = 0.013), sweets (χ2 (2) = 6.29, p = 0.043), and nibbles (χ2 (2) = 6.11, p = 0.047), with 10-km runners reporting doing so more often (all three food items) than the other distance runners. Although no significant between-group difference was observed in the dimension of “food choice” (F(2, 219) = 1.32, p = 0.270), comparative data showed that 10-km run- ners had the best health-related behaviors with regard to food choice (0.72 ± 0.20) (Table 4). Dimension of healthcare utilization. There was no significant association between the use of regular health check-ups and race distance (χ2 (2) = 2.64, p = 0.268) (Table 3). Although no significant between-group difference was observed in the dimension of “healthcare utilization” (F(2, 219) = 1.32, p = 0.270), comparative data showed that half-marathoners had the highest scores with regard to healthcare utilization (0.62 ± 0.49) while maratho- ners/ultra-marathoners scored lowest (0.49 ± 0.50) (Table 4). Results of the MANOVA. The findings of the MANOVA considering the health status of endurance run- ners are presented in Table 5, indicating significant differences for the following results: (1) education (academic qualification) had an association with BW and BMI (p = 0.004), smoking habits (p = 0.005), and supplement intake (p = 0.022); (2) race distance had a significant association with the dimension “chronic diseases and hyper- 8 Vol:.(1234567890) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ Cluster and respective Dimensions HM M/UM 10 km Statistics ‘Health-Related Indicators’ BW and BMI BW (kg) (median) 65.0 (IQR 13.0) 67.5 (IQR 17.5) 62 (IQR 11.0) F(2, 242) = 5.05, p = 0.007 BMI (median) 21.97 (IQR 3.28) 22.15 (IQR 3.25) 21.30 (IQR 3.94) χ2 (4) = 1.35, p = 0.854 ≤ 18.49 4% (4) 6% (4) 8% (7) 18.50–24.99 85% (76) 82% (53) 79% (72) ≥ 25–29.99 10% (9) 12% (8) 13% (12) Mental health χ2 (2) = 5.83, p = 0.054 Stress Perception Yes 27% (23) 42% (23) 44% (36) No 73% (62) 58% (32) 56% (46) Chronic diseases/hypersensitivity reactions Prevalence of Chronic Diseases χ2 (4) = 4.76, p = 0.313 Heart Disease – 2% (1) – Heart Attack – – – Cancer – – 1% (1) No Diseases 100% (85) 98% (54) 99% (81) Prevalence of Metabolic Diseases χ2 (10) = 13.25, p = 0.210 Diabetes Mellitus 1 – 4% (2) – Diabetes Mellitus 2 1% (1) – 1% (1) Hyperthyroidism – 2% (1) 2% (2) Hypothyroidism 7% (6) 7% (4) 4% (3) Other Diseases – – 2% (2) No Diseases 92% (78) 87% (48) 90% (74) Prevalence of Hypersensitivity Reactions χ2 (4) = 8.90, p = 0.064 Allergies 22% (19) 25% (14) 35% (29) Intolerances 5% (4) 4% (2) 11% (9) No Reactions 73% (62) 71% (39) 54% (44) Medication intake (regularly) χ2 (6) = 2.64, p = 0.852 Thyroid Disease 7% (6) 11% (6) 7% (6) Hypertension 4% (3) 2% (1) 2% (2) Cholesterol Level – – – Other Medication 2% (2) 4% (2) 6% (5) No Medication 87% (74) 84% (46) 84% (69) Contraceptives (females only) 12% (10) 5% (3) 20% (16) χ2 (2) = 5.93, p = 0.051 ‘Health-Related Behaviors’ Smoking habits χ2 (4) = 4.00, p = 0.406 Non-Smoker 67% (57) 56% (31) 52% (43) Ex-Smoker 32% (27) 42% (23) 45% (37) Smoker 1% (1) 2% (1) 2% (2) Supplement intake Prescribed by doctor 8% (7) 7% (4) 7% (6) χ2 (2) = 0.07, p = 0.968 To boost your performance χ2 (4) = 3.52, p = 0.476 Occasionally 16% (14) 11% (6) 9% (7) Regularly/every day 2% (2) 4% (2) 1% (1) To cope wit stress χ2 (4) = 6.66, p = 0.155 Occasionally 6% (5) 7% (4) 6% (5) Regularly/every day 5% (4) – – Food Choice Motivation Because it is healthy 74% (63) 73% (40) 68% (56) χ2 (2) = 0.74, p = 0.690 Because it is health-promoting 81% (69) 82% (45) 87% (71) χ2(2) = 1.00, p = 0.607 Because it is good for maintaining health 88% (75) 87% (48) 94% (77) χ2(2) = 2.15, p = 0.341 Avoided ingredients Refined Sugar 66% (56) 58% (32) 70% (57) χ2(2) = 1.89, p = 0.390 Sweetener 82% (73) 64% (35) 82% (67) χ2(2) = 5.63, p = 0.060 Continued 9 Vol.:(0123456789) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ sensitivity reactions” (p = 0.038); (3) there was an association between sex and smoking habits (p = 0.048); (4) training (weekly mileage) had an association with food choice (p = 0.003); and (5) there was an association between age and healthcare utilization (p = 0.002). However, no significant associations were found considering the dimensions of mental health and medication intake. Discussion This study aimed to investigate the potential differences in the health status of recreational half-marathoners, marathoners/ultra-marathoners, and 10-km runners. Mental health, BW and BMI, the prevalence of chronic diseases and hypersensitivity reactions, medication and supplement intake, smoking habits, food choice from ingredients to be avoided or desired, and regular or routine health checkups were measured and compared between the study groups. The main findings were (1) that while no association between race distance and seven health dimensions were found, “chronic diseases and hypersensitivity reactions” had a significant association with race distance, and (2) compared to 10-km and marathon/ultra-marathon runners, half-marathoners showed a tendency towards better scores in six out of eight dimensions of health (BW/BMI, mental health, chronic diseases and hypersensitivity reactions, medication intake, smoking habits, and health care utilization) with an average score of 77.1%; the half-marathon distance was found to contribute best to the overall health status among endurance runners. Interestingly, only 8% of half-marathon runners and 10% of the overall sample reported “sport for health” as the basic assignment to a sports area, while “sport for leisure” (54% of total participants, 64% of half-marathoners) and “sport for performance” (36% of total participants, 28% of half-marathon runners) were ranked higher. Cluster and respective Dimensions HM M/UM 10 km Statistics Fat in General 38% (32) 44% (24) 51% (42) χ2(2) = 3.13, p = 0.210 Saturated Fats 58% (49) 58% (32) 61% (50) χ2(2) = 0.21, p = 0.899 Cholesterol 42% (36) 45% (25) 48% (39) χ2(2) = 0.46, p = 0.794 White Flour 60% (51) 60% (33) 79% (65) χ2 (2) = 8.70, p = 0.013 Sweets 62% (53) 51% (28) 72% (59) χ2 (2) = 6.29, p = 0.043 Nibbles 58% (59) 53% (29) 72% (59) χ2 (2) = 6.11, p = 0.047 Alcohol 52% (44) 53% (29) 60% (49) χ2 (2) = 1.22, p = 0.542 Caffeine 38% (32) 25% (14) 39% (32) χ2 (2) = 3.04, p = 0.219 Desired ingredients Vitamins 82% (70) 80% (44) 80% (66) χ2 (2) = 0.15, p = 0.928 Minerals/trace elements 73% (62) 71% (39) 73% (60) χ2 (2) = 0.10, p = 0.953 Antioxidants 54% (46) 45% (25) 52% (43) χ2 (2) = 1.06, p = 0.587 Phytochemicals 44% (37) 40% (22) 54% (44) χ2 (2) = 2.92, p = 0.232 Fiber 71% (60) 69% (38) 70% (57) χ2 (2) = 0.04, p = 0.980 Health care utilization Regular check-ups or routine health checks 62% (53) 49% (27) 54% (44) χ2 (2) = 2.64, p = 0.268 Table 3. Descriptive and ANOVA results for the eight dimensions of health status displayed by race distance. Data are presented as “percentage of prevalence (n)” or “median (IQR)”. BMI body mass index, BW body weight, HM half-marathon, IQR interquartile range, km kilometers, M/UM marathon/ultra-marathon. Table 4. Domain scores of ‘health-related indicators’ and ‘health-related behaviors’ of endurance runners, displayed by race distance groups. Data are presented as Domain Scores and (SD): Low scores indicate detrimental health effects; high scores indicate beneficial health effects (scales: 0–1). BMI body mass index, BW body weight, HM half-marathon, km kilometers, M/UM marathon/ultra-marathon. Total HM M/UM 10 km Statistics Health-Related Indicators BW and BMI 0.65 (0.40) 0.69 (0.39) 0.67 (0.41) 0.60 (0.42) F(2, 242) = 0.84, p = 0.433 Mental Health 0.63 (0.48) 0.73 (0.45) 0.58 (0.50) 0.56 (0.50) F(2, 219) = 2.95, p = 0.054 Chronic Diseases/Hypersensitivity Reactions 0.85 (0.19) 0.88 (0.18) 0.85 (0.19) 0.81 (0.20) F(2, 219) = 3.31, p = 0.038 Medication Intake 0.85 (0.36) 0.87 (0.34) 0.84 (0.37) 0.84 (0.37) F(2, 219) = 0.20, p = 0.817 Health-Related Behaviors Smoking 0.79 (0.27) 0.83 (0.25) 0.77 (0.27) 0.75 (0.27) F(2, 219) = 2.00, p = 0.138 Supplement Intake 0.90 (0.20) 0.88 (0.23) 0.91 (0.21) 0.92 (0.17) F(2, 219) = 0.92, p = 0.400 Food Choice 0.68 (0.22) 0.67 (0.21) 0.65 (0.26) 0.72 (0.20) F(2, 219) = 1.32, p = 0.270 Healthcare Utilization 0.56 (0.50) 0.62 (0.49) 0.49 (0.50) 0.54 (0.50) F(2, 219) = 1.32, p = 0.270 10 Vol:.(1234567890) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ “Hobby” and “health” with 34% and 19% of total participants, respectively, were ranked highest among other initial motives for running, with no considerable difference between the study groups. The number of completed races shows that endurance athletes in the present study are not novices but rather active in recreational (not professional) running. It has been shown that recreational participation in running activities could affect some health-related findings49, which could be linked to the participants’ slight emphasis on specific personal achieve- ments versus the joy of running (53% vs. 47%) as the main goal to participate in running events. Consistent with the present findings, it has been reported that “the joy of running races” was a top reason, and “winning” was identified as an unimportant reason to participate in running events4. Although “health” was the second-highest ranked reason among the seven motivations for running, it could be considered as the 1st rank (by 44%) when pooled with two other health-related motivations (BW loss and maintenance). This finding is consistent with the literature available, with the main underlying intention probably being to achieve the advantageous effects and pronounced benefits associated with health1,4, especially for long-term adherence to running activity4,50. Run- ning is expected to be a powerful strategy in the prevention of diseases, promotion of health, and maintenance of a good state of health, especially in elderly populations with an age of ≥ 50 years50. Table 5. MANOVA results for the eight dimensions of health status. BMI body mass index, BW body weight, df degrees of freedom. F F-value, η2 partial effect (small: 0.01; medium: 0.059; large: 0.138), p p value for between-group differences. Cluster Dimension Subgroup F df η2 p Health-Related Indicators BW and BMI Race Distance 0.39 2 0.00 0.677 Sex 1.17 1 0.01 0.281 Age 0.00 1 0.00 0.999 Education (academic qualification) 5.66 2 0.05 0.004 Training (weekly mileage) 0.23 2 0.00 0.797 Mental health Race Distance 2.97 2 0.03 0.053 Sex 3.43 1 0.02 0.065 Age 1.04 1 0.00 0.310 Education (academic qualification) 0.48 2 0.00 0.619 Training (weekly mileage) 0.95 2 0.01 0.390 Chronic diseases/hypersensi- tivity reactions Race Distance 3.04 2 0.03 0.050 Sex 0.61 1 0.00 0.435 Age 0.24 1 0.00 0.623 Education (academic qualification) 0.65 2 0.01 0.525 Training (weekly mileage) 0.71 2 0.01 0.492 Medication Intake Race Distance 0.20 2 0.00 0.815 Sex 0.92 1 0.00 0.340 Age 3.05 1 0.01 0.082 Education (academic qualification) 1.43 2 0.01 0.241 Training (weekly mileage) 0.56 2 0.01 0.573 Health-Related Behaviors Smoking habits Race Distance 2.08 2 0.02 0.128 Sex 3.96 1 0.02 0.048 Age 1.97 1 0.01 0.161 Education (academic qualification) 5.35 2 0.05 0.005 Training (weekly mileage) 0.25 2 0.00 0.776 Supplement intake Race Distance 1.04 2 0.01 0.356 Sex 1.74 1 0.01 0.189 Age 3.05 1 0.01 0.082 Education (academic qualification) 3.88 2 0.04 0.022 Training (weekly mileage) 0.37 2 0.00 0.686 Food choice Race Distance 1.62 2 0.02 0.200 Sex 0.20 1 0.00 0.655 Age 0.55 1 0.00 0.459 Education (academic qualification) 0.29 2 0.00 0.749 Training (weekly mileage) 6.06 2 0.06 0.003 Healthcare utilization Race Distance 1.37 2 0.01 0.256 Sex 2.86 1 0.01 0.092 Age 9.62 1 0.05 0.002 Education (academic qualification) 1.40 2 0.01 0.249 Training (weekly mileage) 0.11 2 0.00 0.899 11 Vol.:(0123456789) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ BW and BMI. Four out of five endurance runners in this study were found to have a BW that corresponds to a healthy BMINORM. Half-marathoners most often matched the BMINORM and consequently had higher health scores compared to marathoners/ultra-marathoners and 10-km runners. However, 10-km runners were found to have lower BW than half- to ultra-marathoners, nicely matching their reports where BW loss was ranked 2nd highest motivation to start running. In addition, the higher score of 10-km runners in food choices compared to runners over longer distances could be partially associated with the existing findings regarding their trend toward having a lower BW. Another justification could be the higher number of vegan runners in 10-km com- pared to half-marathon and marathon/ultra-marathon groups in the present study. About 25% of runners in the present study stated BW management (loss: 18%, and maintenance: 7%) as the reason to start running. However, the half-marathoners seem to established a good balance between running- induced energy required and dietary intake, as they reported least often a decrease in BW due to a change in their diet. These findings emphasize the significance of BW control strategies for endurance runners as dietary changes potentially cause unintended BW loss29,51, and adherence to appropriate nutrition strategies for sustainable BW management is highly advised to endurance runners29. Although the lower BMI and being leaner were found to be associated with increased endurance running performance52, and training/competing in longer race dis- tances correlates with a decrease in BW and body fat53, evidence excludes marathon runners or ultra-endurance athletes from this fact54,55. This is consistent with the present findings where marathon/ultra-marathon runners had a slight but non-significant higher BMI. The higher BMI of ultra-marathon runners compared to shorter distance endurance runners might be due to the lower importance of running speed in long-distance compared to shorter distance runs. In general, however, reports from the successful runners over 10-km and marathon distance indicate that an optimal BMI for health and performance was found to be between 19 and 20 kg/m256. The vegan diet was shown to effectively reduce BW and particularly body fat57,58, with favorable effects on run- ning performance, if planed appropriately59. Consistently, previous data from our laboratory show that vegan endurance runners are significantly leaner than omnivores (64 kg vs. 68 kg), contributing to their overall state of health with the highest health score (69%)10. Mental health. While most participants were not suffering from mental stress, half-marathoners reported lower perception of pressure and stress compared to 10-km runners and marathoners/ultra-marathoners. In line with the present findings, it has been found that endurance running leads to stress reduction, a better mood, and higher resilience to psychological pressure and anxiety43,60. However, data in terms of the appropriate amount of physical activity in order to maximize these positive effects while avoiding negative effects is sparse. Too little exercise does not evoke beneficial effects, but too much exercise (defined as overtraining) can cause the percep- tion of stress60. Half-marathon allows performance to increase within a short period of time, which provides the feeling of success38. These characteristics are supposed to lead to a certain degree of life satisfaction and thus a resilience to stress and pressure perception43. Chronic disease and hypersensitivity reactions. The present study revealed a significant difference between the race distance groups and the dimension, “chronic diseases and hypersensitive reactions”, most ben- eficially contributing to the half-marathoners’ state of health. Recreational endurance running is well accepted, having various health effects with robust evidence for regular running to add benefits in aerobic, metabolic, and cardiovascular function at rest. Consistent with the study findings, running has beneficial influences on the prevention of chronic and cardio-metabolic diseases, including but not limited to coronary heart disease, stroke, hypertension, diabetes mellitus type 2, and hypercholesterolemia, mainly via increasing cardiorespiratory fitness as a strong predictor for morbidity and mortality8,9,12,15. This is in line with another finding from the present study, where race distance was found to have a significant association with chronic diseases and hypersensitivity reactions. These exercise-induced advantageous effects are based on various mechanisms, such as adaptations to the cardiorespiratory and cardio-metabolic system (e.g., changes in the musculoskeletal system and heart muscle cells, increased maximal oxygen uptake), modifications in hormonal response and enzymatic activity, the acti- vation of both inflammatory response and detoxification processes, the involvement of pathways associated to immune response, lipid transport and coagulation, and further genetic adaptions38,61. The present findings could be influenced by the distribution of diet types, particularly vegetarians and vegans, among the endurance runners. It has been reported that appropriately planned vegetarian and vegan diets are healthful and nutritionally adequate even for athletes and provide health benefits for the prevention and treat- ment of cardio-metabolic disorders and certain diseases such as ischemic heart disease, type 2 diabetes, hyper- tension, inflammatory problems, and some types of cancer47,62. More specifically, the higher prevalence of plant diets together with the null association between race distance and the incidence of allergies in the present study is in line with the available data on the protective effects of fruits and vegetables on the incidence of food aller- gies, including allergic asthma18 as well as the lower prevalence of allergies in vegan endurance runners (20%) compared to omnivores (32%) and vegetarians (36%)10. Despite the null association between the occurrence of food intolerances and race distance in the present study, gastrointestinal complaints due to food intolerances are common among endurance runners63, probably caused by subclinical food sensitivities that occur during vigorous exercise64. Medication intake. Medication intake in the form of contraceptives was lower with a statistical trend (p = 0.051) in marathoners/ultra-marathoners compared to half-marathoners and 10-km runners. This finding, however, could be explained by a sex-based bias as there were fewer females (38%) among marathoners/ultra- marathoners than in half-marathoners (55%) and 10-km runners (74%). Indeed, 85% of those who reported an intake of thyroid hormones were women, and 100% of those who reported an intake of other hormones than 12 Vol:.(1234567890) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ thyroid medication were women who reported the intake of contraceptives. However, there was no associa- tion between sex and the dimension “medication intake” when runners were pooled for the MANOVA. As a well-established fact associated with the present findings, women suffer more often from hypothyroidism than men65, and importantly, more than 100 million women worldwide use contraceptive pills to avoid undesired pregnancies66. Although there were no associations between race distance and the intake of any medication, race distance had a considerable association (score range 0.82–0.86) with medication intake. However, as the major- ity of distance runners (84–87%) reported no medication intake, caution must be considered when interpreting the present limited data concerning the intake of non-contraceptives medications across different subgroups of distance runners. Smoking habits. A low rate of smoking (< 2%) was found in endurance runners across all race distances. Consistently, data indicate that smoking prevalence is usually quite low among endurance runners67. This can be justified by undesired performance limitations due to smoking68 and the health-consciousness of athletes in general69. On the other hand, adhering to regular physical exercise, particularly endurance running, can be an effective way to prevent people from smoking or even help in smoking cessation by reducing cessation-related mood symptoms, cigarette cravings, and withdrawal symptoms among temporarily abstinent smokers68. In the present study, there was no association between smoking habits and race distance, but half-marathoners showed a better score in this dimension. While no comparable data are available in the literature, evidence has found a positive association between smoking quitters and running activity in terms of weekly training mileage67. Supplement intake and performance‑enhancing substances. The most commonly reported sup- plement by the runners was vitamin D. Several studies have detected a huge difference between required and real vitamin D intake in athletes worldwide70,71. In addition to dietary intake, athletes’ vitamin D level depends on skin color, training day-time, indoor/outdoor training, and geographic location71. Although supplement intake was not associated with race distance, it was found to have high scores (score range 0.88–0.92) among race dis- tance groups, with a slight predominance in 10-km runners. However, the prevalence of intake was generally low, reflected by high health scores across all race distance subgroups. Compared with the highest rate of supple- ment intake reported by half-marathoners (16%), a recent study reported that 30% of female and 40.2% of male endurance runners consume supplements in order to enhance performance72. Although few studies have yet compared different groups of endurance runners regarding the patterns of supplement intake73, it has been well- documented that endurance athletes use supplements to a greater extent than non-endurance athletes74, proba- bly due to the higher exercise-induced nutritional requirements associated with long-time training, competition, and recovery75. Reports from a recent study on elite track and field athletes indicated that distance runners have a significantly higher prevalence in supplemental micronutrient but not macronutrient intake when compared to runners in other track and field disciplines76. Moreover, there is some evidence for an increasing problem of doping among elite endurance runners77. However, as the participants in the present study were mostly recrea- tional runners, they may have different choices of dietary supplements, which could be associated with their different goals for engaging in training and competition compared to elite athletes49. In addition, findings from the present study regarding the participants’ attitudes towards food choices characterize them as being health- conscious, so they might have been aware of potential detrimental effects of risky performance-enhancing sub- stances. In general, despite the fact that the beneficial effects of many supplements on the promotion of health, prevention of chronic disease, and enhancement of athletic performance remain unclear78, it is well-established that these products significantly contribute to the nutrient requirements of athletes78–80. Food choice. The present study showed that food choice was not associated with race distance, but the run- ners over the 10-km distance reported choosing food in order to avoid white flour, sweets, and nibbles more often than half to ultra-marathoners. This is even reflected by their higher score for food choice (72% vs. 67% and 65%) along with their motivation for choosing food based on health-promoting and health-maintaining reasons. However, caution must be warranted while interpreting the findings, as the higher score of 10-km run- ners in food choice could be potentially associated with their lower BMI among the study groups. Although the majority of the runners in this study reported following a mixed diet, 59% of 10-km and 56% of half-marathon runners reported following vegetarian/vegan diets, which were recently found to add most advantageous ben- efits to the runners’ state of health mainly due to maximizing favorable food choice behaviors in endurance runners10. The imbalanced distribution of vegans in the 10-km group (compared to the overall groups) might explain, in part, the highest scores for both supplement intake and food choice, as vegans are known to be more health-conscious and thus take special care and compensate for potential deficiencies considering critical nutri- ents such as vitamin B12 10,59,81. Considering a health-related food choice to get desired ingredients by a specific choice of healthy and health-maintaining items, most participants reported health-conscious behavior across all race distance subgroups. This finding was in line with available literature2,69, where athletes were characterized as being health-conscious, particularly with regard to food choice10. Healthcare utilization. Overall, most athletes reported seeing a doctor at least once a year and making use of regular health checkups. These findings were consistent with the previous literature82 and emphasize the fact that regular and sustainable physical activity can diminish morbidity rates and thus the necessity for doctor consultations83. The endurance runners of the present study were found to have a good balance between healthy physical activity and vigorous exercise, which could be advantageous for gaining the desired health effects2, and importantly for the avoidance of the detrimental consequences of overtraining following excessive running or training activities. In the present study, there was a statistically significant association between race distance and 13 Vol.:(0123456789) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ age. Interestingly, and although being older than runners over other distances, marathoners/ultra-marathoners had a low score for regular and routine health checkups, indicating disadvantageous contribution to overall health from weak healthcare utilization. Limitations, strengths, and future perspectives. There are limitations worth mentioning. The pre- sent study shares with others the limitations of the cross-sectional design. The fact that the findings relied on self-reported records should be considered as the primary limitation since under- and over-reporting are poten- tially prevalent in self-reported data. However, this effect was compensated by using control questions. Also, the high intrinsic motivation of the participants could be consequential to increase the accuracy of their answers to provide a high quality of the data set. The operationalization of state of health as a latent variable (domain scores) should also be considered as a statistical limitation. Nonetheless, the health score was identified as a meaningful tool to assess the health status. In this regard, however, retrospective rating of the cross-sectional design might raise misunderstandings about the associations between health-related variables and race distance, and thus, caution must be warranted in the representativeness of the present findings. Moreover, the sex-based imbalance in the study groups (particularly the higher number of males in the marathon/ultra-marathon group and females in the 10-km group) could be influential on the health-related findings, as females are well-known to be more health-conscious than males considering favorable habits and healthy lifestyles (e.g., physical activity, alcohol/ nicotine, plant-based diets). Nevertheless, the data contribute to the growing scientific interest and knowledge in health-related consequences of endurance exercise for distance running in particular, and can be taken as a step towards broadening the body of evidence in the field. Although it is well-established that endurance running offers various health benefits, the body of science is still contradictory considering both quantity and quality of running activity that enables obtaining the maximum beneficial health effects and preventing the minimum undesired or adverse effects. Therefore, specific knowledge about the interconnectedness of running distance (in training and racing) and health can provide a better basis for athletes, coaches, physicians, and specialists to optimize health-related training and racing strategies. Thus, the results might be useful for different populations by providing such knowledge to aid the decision of an active and healthy lifestyle, with regular involvement in running training, and also to advise individuals to run for sustain- able health outcomes. Even at community and public health levels, health authorities can use this information to support policies towards investing in running programs that promote sustainable running training strategies. Conclusions Regardless of the race distance, endurance runners in the present study showed an optimal state of health. This finding supports the notion that endurance running contributes beneficially to an increased level of health. Half- marathon running was found to contribute to 62–88% of their overall state of health; in addition, the higher score of half-marathon runners in overall state of health (77.1% vs. 72.0% in marathon/ultra-marathon runners and 71.7% in 10-km runners), along with the predominance of half-marathoners in six out of eight dimensions, might suggest that recreational runners over the half-marathon distance have a tendency toward a better health status compared to runners over shorter and longer distances. However, among eight health-related dimensions investigated in the present study, only the “chronic diseases and hypersensitivity reactions” dimension was found to have a significant association with race distance, with a significantly better status for half-marathon runners compared to marathoners/ultra-marathoners and 10-km runners. 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P.B. and K.W. drafted the manuscript, M.M., D.T. and T.R. helped in drafting 16 Vol:.(1234567890) Scientific Reports \| (2022) 12:10295 \| https://doi.org/10.1038/s41598-022-13844-4 www.nature.com/scientificreports/ the manuscript, and B.K. and K.W. critically reviewed it. Technical support was provided by G.W. All authors read and approved the final manuscript. Funding This research did not receive any specific grant or funding from funding agencies in the public, commercial, or not-for-profit sectors. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to B.K. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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PMC9956911	Citation: Manresa-Rocamora, A.; Fuertes-Kenneally, L.; Blasco-Peris, C.; Sempere-Ruiz, N.; Sarabia, J.M.; Climent-Paya, V. Is the Veriﬁcation Phase a Suitable Criterion for the Determination of Maximum Oxygen Uptake in Patients with Heart Failure and Reduced Ejection Fraction? A Validation Study. Int. J. Environ. Res. Public Health 2023, 20, 2764. https:// doi.org/10.3390/ijerph20042764 Academic Editor: Cristian Álvarez Received: 12 January 2023 Revised: 31 January 2023 Accepted: 2 February 2023 Published: 4 February 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article Is the Veriﬁcation Phase a Suitable Criterion for the Determination of Maximum Oxygen Uptake in Patients with Heart Failure and Reduced Ejection Fraction? A Validation Study Agustín Manresa-Rocamora 1,2 , Laura Fuertes-Kenneally 1,3 , Carles Blasco-Peris 1,4 , Noemí Sempere-Ruiz 1,2 , José Manuel Sarabia 1,2,* and Vicente Climent-Paya 1,3 1 Institute for Health and Biomedical Research of Alicante (ISABIAL), 03010 Alicante, Spain 2 Department of Sport Sciences, Sports Research Centre, Miguel Hernández University of Elche, 03202 Elche, Spain 3 Cardiology Department, Dr. Balmis General University Hospital, 03010 Alicante, Spain 4 Department of Physical Education and Sport, University of Valencia, 46010 Valencia, Spain * Correspondence: jsarabia@umh.es; Tel.: +34-96-522-25-68 Abstract: The veriﬁcation phase (VP) has been proposed as an alternative to the traditional criteria used for the determination of the maximum oxygen uptake (VO2 max) in several populations. Nonetheless, its validity in patients with heart failure with reduced ejection fraction (HFrEF) remains unclear. Therefore, the aim of this study was to analyse whether the VP is a safe and suitable method to determine the VO2 max in patients with HFrEF. Adult male and female patients with HFrEF performed a ramp-incremental phase (IP), followed by a submaximal constant VP (i.e., 95% of the maximal workload during the IP) on a cycle ergometer. A 5-min active recovery period (i.e., 10 W) was performed between the two exercise phases. Group (i.e., median values) and individual comparisons were performed. VO2 max was conﬁrmed when there was a difference of ≤ 3% in peak oxygen uptake (VO2 peak) values between the two exercise phases. Twenty-one patients (13 males) were ﬁnally included. There were no adverse events during the VP. Group comparisons showed no differences in the absolute and relative VO2 peak values between both exercise phases (p = 0.557 and p = 0.400, respectively). The results did not change when only male or female patients were included. In contrast, individual comparisons showed that the VO2 max was conﬁrmed in 11 patients (52.4%) and not conﬁrmed in 10 (47.6%). The submaximal VP is a safe and suitable method for the determination of the VO2 max in patients with HFrEF. In addition, an individual approach should be used because group comparisons could mask individual differences. Keywords: cardiorespiratory ﬁtness; VO2 max; HFrEF; exercise testing; respiratory exchange ratio; gradual exercise test; VO2 peak 1. Introduction Heart failure with reduced ejection fraction (HFrEF) is a cardiovascular disorder characterised by symptoms of breathlessness, ﬂuid retention, and exercise intolerance [1–3]. The maximal ramp or step incremental exercise test, coupled with breath-by-breath and gas exchange measurements, is widely used in patients with HFrEF to measure maximum oxygen uptake (VO2 max) and for risk stratiﬁcation [4–6]. VO2 max is deﬁned as the physiological limit of oxygen utilisation [7] and is considered a strong predictor of mortality in patients with HFrEF [8,9]. In fact, VO2 max is considered a prognostic factor in advanced heart failure and is currently used as a key criterion for the selection of candidates for heart transplantation (i.e., ≤14 mL·kg−1·min−1) [4]. The measurement of VO2 max requires the patient to perform a maximal exercise effort (i.e., volitional exhaustion) and thus might be substantially underestimated due to muscle Int. J. Environ. Res. Public Health 2023, 20, 2764. https://doi.org/10.3390/ijerph20042764 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2023, 20, 2764 2 of 10 fatigue, breathlessness, and reduced motivation (i.e., submaximal exercise test). In these circumstances, the peak oxygen uptake (VO2 peak) instead of the VO2 max is obtained. Consequently, it is important to determine the criteria to accurately categorise an effort as maximal. The primary criterion used to verify maximal exercise relies on the presence of the oxygen uptake (VO2) plateau, which is deﬁned as no increase in VO2 despite an increase in workload rate [10,11]. Nonetheless, data indicate that only a small percentage of VO2 assessments actually exhibit a VO2 plateau [12–15], supporting the argument that this physiological phenomenon is not necessary to acutely determine the VO2 max. Thus, secondary criteria such as the value of the respiratory exchange ratio (RER)—which is the most frequently used variable in cardiac patients—age-predicted maximal heart rate (HR), or blood lactate concentrations are commonly used to verify that a maximal exercise effort has been achieved [13,16,17]. However, evidence suggests that these criteria lack validity since they can be met with either a maximal or submaximal exercise effort or even, not reached at all despite a maximal effort [11,18,19]. In summary, traditional criteria (i.e., including both primary and secondary) are not reliable methods to ensure that the VO2 max is reached at the end of an incremental exercise test [20]. In an attempt to overcome the shortcomings of traditional criteria, a new criterion for the determination of the VO2 max has emerged, known as the veriﬁcation phase (VP) [13]. The VP is a constant-load phase performed following the incremental phase (IP) and a short recovery period (e.g., 3–5 min). Other protocols have also been used previously [20]. Regarding its intensity, it can be performed either above (i.e., supramaximal veriﬁcation phase) or below (i.e., submaximal veriﬁcation phase) the peak work rate attained in the previous IP [10,15]. There is evidence demonstrating that the VP is an adequate standard for validating the VO2 max in healthy individuals [21] and patients with a wide range of pathologies [22]. In this regard, Bowen et al. [23] investigated whether the submaximal VP was a valid method to determine the VO2 max in patients with HFrEF. According to the authors, the VP was well tolerated by patients with HFrEF, and its precision was greater than that of secondary criteria (i.e., RER). Nonetheless, only male patients were included, and further research is needed to determine whether VP is suitable and well tolerated by female patients with HFrEF. Furthermore, although both group and individual comparisons can be used to validate the VO2 max, group comparisons could mask individual differences between the VO2 peak values attained in each exercise phase [19,20]. Also, the clinical utility of the exercise test is its application to the individual rather than the group. For these reasons, individual comparisons are more useful than group comparisons [24]. In order to perform these individual comparisons and assess whether or not the VO2 max was reached, a standard cut-off point should be established, preferentially using relative differences (e.g., ≤3%) [20]. In contrast, Bowen et al. [23], who included group and individual comparisons, carried out statistical comparisons. In this study, the VO2 max was conﬁrmed when statistical signiﬁcance was not reached (p > 0.050). Nonetheless, the use of statistical comparisons is a ﬂawed approach because it is designed to detect differences and depends on the sample size [25]. Thus, the use of different approaches to conduct individual comparisons and conﬁrm the VO2 max warrants future studies in patients with HFrEF. Therefore, the main purpose of the current study was to investigate the utility of the submaximal VP for validating the VO2 max in male and female patients with HFrEF. In addition, we compared the level of agreement between the VP and traditional (i.e., RER) criteria for the determination of the VO2 max. Based on previous evidence, we hypothesised that the submaximal VP would be an adequate criterion to verify the VO2 max in male and female patients with HFrEF when an individual approach is used, and no agreement would be found between both criteria. Int. J. Environ. Res. Public Health 2023, 20, 2764 3 of 10 2. Materials and Methods 2.1. Patients Participants needed to fulﬁl the following inclusion criteria to be eligible: (a) male or female aged between 50 and 70 years old; (b) diagnosed with HFrEF (left ventricular ejection fraction < 50%); (c) stable phase of the disease with no recent hospitalisation or visit to the emergency department due to heart failure (within the last six months before the beginning of the study); (d) New York Heart Association (NYHA) functional class I, II, or III; (e) under treatment with B-blockers; and (f) sedentary (i.e., not involved in exercise training for six months). The exclusion criteria were: (a) use of intravenous diuretics in the last six months; (b) unstable angina or evidence of severe ventricular arrhythmia; (c) atrial ﬁbrillation; (d) supraventricular arrhythmias; (e) chronic obstructive pulmonary disease; (f) recent of haemoglobin concentrations outside optimal parameters (13–16.5 g·dL−1); (g) physical limitations that impeded the completion of the ergometry; and (h) the presence of ischaemia, arrhythmias, or high frequency of ectopic heartbeats. All patients were fully informed and signed the informed consent before any procedure related to the study was performed. The protocol of this study was approved by the competent ethics committee of the host institution (PI2021-177). 2.2. Measurements Participants performed a symptom-limited exercise test which comprised two phases; (a) the ramp-incremental exercise phase (i.e., IP); and (b) the steady-state exercise phase (i.e., VP). The test was carried out on an electromagnetically braked cycle ergometer (SanaBike 500 easy, Truchtelﬁnger, Germany). Before the start of the IP, a 3 min warm-up at 10 W and a cadence of 50 revolutions per minute (rpm) was performed. The IP ended when the patient reached volitional exhaustion or was unable to maintain a cadence of at least 45 rpm. The exercise test was terminated, and the VP was not carried out in the presence of symptoms of ischaemia or multifocal ectopic heartbeats (symptom-limited). Otherwise, a free-cadence recovery period of 5 min at 10 W was performed after ﬁnishing the IP. Subsequently, the VP was carried out at 95% of the maximum power reached during the IP. Throughout the protocol, gas exchange was recorded with the Metalyzer 3B gas analyser (CORTEX Biophysik, Leipzig, Germany), and HR was monitored with a 12-lead electrocardiograph. Patients were asked to fast (at least three hours prior to the test), as well as to refrain from strenuous physical activity (24 h), alcohol, and smoking (three hours prior). 2.3. Data and Statistical Analyses Gas exchange and ventilatory variables were analysed to remove atypical breaths (four standard deviations from the local mean) due to swallows, coughs, and so on [26]. VO2 peak was deﬁned as the highest VO2 occurring during each exercise phase (i.e., IP and VP). VO2 peak, as well as the remaining ventilatory variables obtained at exercise peak (i.e., VCO2, oxygen pulse, RER, VE, VE/VCO2, and VE/VO2), were identiﬁed using a 12-breath rolling average [23]. Breathing frequency and HR were averaged over 10 s. Data are displayed as median (25th and 75th percentiles) and frequency (percentage) for continuous and categorical variables, respectively, unless stated otherwise. Overall, the Fisher-Pitman permutation test [27] and the non-parametric 95% conﬁdence interval (CI) of the difference [28] were used to conduct between-phase comparisons (i.e., VP vs. IP). The Bland-Altman plot was used to test the agreement between VO2 peak values measured during the IP and VP. Individual comparisons between VO2 peak values reached during the two exercise phases were also conducted. In this regard, the IP-derived VO2 peak was conﬁrmed (i.e., VO2 max) if the difference with the VP-derived VO2 peak value was ≤ 3%. After- wards, patients were classiﬁed into two groups, depending on whether IP-derived VO2 max values were conﬁrmed or not. Fisher’s exact test, Mann-Whitney test, and Bonett- Price 95% CI were used to conduct between-group comparisons (i.e., conﬁrmed vs. not conﬁrmed groups). Int. J. Environ. Res. Public Health 2023, 20, 2764 4 of 10 The traditional criterion (i.e., RER peak ≥ 1.10) was also used to verify VO2 max [17]. Compared to the VP, the results were classiﬁed as follows: (a) agreement, RER ≥ 1.10 and VO2 max conﬁrmed by VP or RER < 1.10 and VO2 max not conﬁrmed by VP; (b) false positive, RER ≥ 1.10 and VO2 max not conﬁrmed by VP; and (c) false negative, RER < 1.10 and VO2 max conﬁrmed by VP. The Kappa index was used to analyse the degree of agreement between the two criteria (i.e., RER vs. VP). All tests were two-sided, and p values ≤ 0.050 were considered signiﬁcant. All analyses were performed using STATA software (version 16.0; Stata Corp LLC, College Station, TX, USA). 3. Results 3.1. Patients Thirty patients with HFrEF (22 males; 73.3%) fulﬁlled the inclusion criteria to be eligible to participate in the current study. Nonetheless, we excluded a total of eight patients (26.6%) because the VP was considered contraindicated (i.e., symptoms of ischaemia, arrhythmias, or high frequency of ectopic heartbeats during the IP). Moreover, one patient (3.3%) did not complete the VP due to knee pain and was also excluded from the analysis. All excluded patients were male. Therefore, 21 patients (13 males; 61.9%) were ﬁnally included. The characteristics of these patients are shown in Table 1. No adverse events occurred during the exercise tests. The median age was 64.0 years (57.5; 68.5), and the median left ventricular ejection fraction was 39.1% (33.0; 43.1). Ischemic etiology was the cause of HFrEF in almost half of the patients. Most of the included patients were smokers (81%). Table 1. Baseline participant characteristics. Variable n = 21 Conﬁrmed Group (n = 11) Not Conﬁrmed Group (n = 10) p Age, years 64.0 (57.5; 68.5) 64.0 (56.0; 66.0) 65.0 (58.0; 69.3) 0.717 Height, cm 164 (158; 171) 164 (157; 168) 164 (159; 174) 0.617 Weight, kg 70.0 (66.5; 87.3) 72.0 (64.0; 88.5) 69.6 (67.8; 80.6) 0.850 Body mass index, kg/m2 27.8 (24.8; 31.6) 27.8 (25.1; 32.0) 27.7 (24.1; 31.4) 0.557 LVEF, % 39.1 (33.0; 43.1) 39.0 (33.0; 45.0) 37.5 (32.3; 43.0) 0.414 Male (%) 13 (61.9) 7 (63.6) 6 (60.0) 0.999 Ischemic etiology (%) 10 (47.7) 6 (54.6) 4 (40.0) 0.670 Diabetes mellitus (%) 9 (42.9) 4 (36.4) 5 (50.0) 0.670 Hypertension (%) 9 (42.9) 4 (36.4) 5 (50.0) 0.670 Dyslipidaemia (%) 11 (52.4) 6 (54.6) 5 (50.0) 0.999 Smokers (%) 17 (81.0) 11 (100) 6 (60.0) 0.035 ICD (%) 9 (42.9) 5 (45.5) 4 (40.0) 0.999 Drug therapy: ACEI/ARBs (%) 8 (38.1) 4 (36.4) 4 (40.0) 0.999 ARNI (Sac/Val) (%) 13 (61.9) 7 (63.4) 6 (60.0) 0.999 MRA (%) 17 (81.0) 9 (81.2) 8 (80.0) 0.999 Antiplatelet (%) 8 (38.1) 4 (36.4) 4 (40.0) 0.999 Anticoagulants (%) 2 (9.5) 2 (18.2) 0 (0) 0.476 Diuretics (%) 3 (14.3) 1 (9.1) 2 (20.0) 0.586 ACEI, Angiotensin-converting enzyme inhibitors; ARNI (Sac/Val), angiotensin receptor-neprilysin inhibitor (sacubitril/valsartan); ICD, implantable cardioverter deﬁbrillator; LVEF, left ventricular ejection fraction; MRA, mineralocorticoid receptor antagonist. Data are presented as median (25th and 75th percentiles) or frequency (percentage); p values refer to between-group differences; bold values refer to statistical signiﬁcance (p ≤ 0.050). 3.2. Group Comparisons Descriptive group data from the IP and VP, as well as between-phase comparisons, are presented in Table 2. The median peak work rate during the IP was 55.0 W (46.5; 92.5). The absolute and relative VO2 peak values did not differ between exercise phases (p = 0.557 and p = 0.400, respectively). RER and VE/VCO2 peak values were lower and higher, respectively, in the VP than in the IP (p = 0.004 and p = 0.003). The results did not Int. J. Environ. Res. Public Health 2023, 20, 2764 5 of 10 change when exclusively male or female patients were included in the analyses. Figure 1 shows the Bland-Altman plot for the relative VO2 peak. The mean difference between both exercise phases was −0.07 mL·kg−1·min−1, while the lower and upper limits of agreement were −1.59 mL·kg−1·min−1 and 1.45 mL·kg−1·min−1, respectively. Table 2. Cardiopulmonary responses to the two exercise phases and between-phase comparisons (n = 21). Variable IP VP Difference (95% CI) p Duration, min 8.5 (7.3; 12.5) 2.8 (2.1; 3.5) −7.25 (−9.30 to −5.20) <0.001 HR peak, beats·min−1 112.0 (108.0; 127.0) 109.0 (105.5; 128.0) −1.00 (−5.92 to 4.92) 0.720 RER peak 1.10 (1.04; 1.12) 1.00 (0.95; 1.08) −0.08 (−0.13 to −0.02) 0.004 VO2 peak, ml·min−1 1.02 (0.89; 1.59) 1.01 (0.89; 1.58) −0.007 (−0.026 to 0.012) 0.557 VO2 peak, ml·kg−1·min−1 14.7 (13.1; 17.7) 15.3 (12.7; 17.3) −0.09 (−0.35 to 0.18) 0.400 O2 pulse, ml·beat−1 10.0 (8.0; 13.0) 10.0 (8.0; 13.0) 0.00 (−0.002 to 0.002) 0.999 VE peak, l·min−1 44.9 (36.4; 64.0) 43.6 (35.8; 62.4) 0.40 (−3.64 to 4.44) 0.550 VE/VO2 peak 36.5 (32.9; 40.9) 35.6 (32.5; 39.1) 0.90 (−2.73 to 4.53) 0.338 VE/VCO2 peak 35.5 (32.4; 36.7) 36.4 (33.4; 39.9) 2.90 (1.33 to 4.47) 0.003 BF peak, breaths·min−1 36.0 (29.5; 41.0) 37.0 (30.5; 41.5) 0.50 (−2.51 to 3.51) 0.746 BF peak, peak breath frequency; CI, conﬁdence interval; HR peak, peak heart rate; IP, incremental phase; O2 pulse, Oxygen pulse; RER peak, peak respiratory exchange ratio, VE peak, peak ventilation; VE/VCO2 peak, peak ventilatory equivalent for carbon dioxide; VE/VO2 peak, peak ventilatory equivalent for oxygen; VO2 peak, peak oxygen uptake; VP, veriﬁcation phase. Exercise phase data are presented as median (25th and 75th percentiles); p values refer to within-subject comparisons (VP vs. IP); bold values refer to statistical signiﬁcance (p ≤ 0.050). Descriptive group data from the IP and VP, as well as between-phase comparisons, are presented in Table 2. The median peak work rate during the IP was 55.0 W (46.5; 92.5). The absolute and relative VO2 peak values did not differ between exercise phases (p = 0.557 and p = 0.400, respectively). RER and VE/VCO2 peak values were lower and higher, respectively, in the VP than in the IP (p = 0.004 and p = 0.003). The results did not change when exclusively male or female patients were included in the analyses. Figure 1 shows the Bland-Altman plot for the relative VO2 peak. The mean difference between both exercise phases was −0.07 mL·kg−1·min−1, while the lower and upper limits of agreement were −1.59 mL·kg−1·min−1 and 1.45 mL·kg−1·min−1, respectively. Table 2. Cardiopulmonary responses to the two exercise phases and between-phase comparisons (n = 21). Variable IP VP Difference (95% CI) p Duration, min 8.5 (7.3; 12.5) 2.8 (2.1; 3.5) −7.25 (−9.30 to −5.20) <0.001 HR peak, beats·min−1 112.0 (108.0; 127.0) 109.0 (105.5; 128.0) −1.00 (−5.92 to 4.92) 0.720 RER peak 1.10 (1.04; 1.12) 1.00 (0.95; 1.08) −0.08 (−0.13 to −0.02) 0.004 VO2 peak, ml·min−1 1.02 (0.89; 1.59) 1.01 (0.89; 1.58) −0.007 (−0.026 to 0.012) 0.557 VO2 peak, ml·kg−1·min−1 14.7 (13.1; 17.7) 15.3 (12.7; 17.3) −0.09 (−0.35 to 0.18) 0.400 O2 pulse, ml·beat−1 10.0 (8.0; 13.0) 10.0 (8.0; 13.0) 0.00 (−0.002 to 0.002) 0.999 VE peak, l·min−1 44.9 (36.4; 64.0) 43.6 (35.8; 62.4) 0.40 (−3.64 to 4.44) 0.550 VE/VO2 peak 36.5 (32.9; 40.9) 35.6 (32.5; 39.1) 0.90 (−2.73 to 4.53) 0.338 VE/VCO2 peak 35.5 (32.4; 36.7) 36.4 (33.4; 39.9) 2.90 (1.33 to 4.47) 0.003 BF peak, breaths·min−1 36.0 (29.5; 41.0) 37.0 (30.5; 41.5) 0.50 (−2.51 to 3.51) 0.746 BF peak, peak breath frequency; CI, confidence interval; HR peak, peak heart rate; IP, incremental phase; O2 pulse, Oxygen pulse; RER peak, peak respiratory exchange ratio, VE peak, peak ventilation; VE/VCO2 peak, peak ventilatory equivalent for carbon dioxide; VE/VO2 peak, peak ventilatory equivalent for oxygen; VO2 peak, peak oxygen uptake; VP, verification phase. Exercise phase data are presented as median (25th and 75th percentiles); p values refer to within-subject comparisons (VP vs. IP); bold values refer to statistical significance (p ≤ 0.050). Figure 1. Bland-Altman plot for relative peak oxygen uptake response between the two exercise phases. Dashed line represents the mean bias, and highlighted zone indices are the limits of agreement (mean ± 1.96 standard deviation). 3.3. Individual Comparisons An IP-derived VO2 peak was conﬁrmed (i.e., VO2 max) in 11 (52.4%) patients and not conﬁrmed (i.e., VO2 peak) in 10 (47.6%). Regarding the patients in whom the VO2 peak was attained, ﬁve showed higher IP-derived VO2 peak values and ﬁve showed lower IP- derived VO2 peak values, compared with the VP-derived VO2 peak value. As to the patient characteristics, comparisons showed that the proportion of smokers was higher (p = 0.035) in the conﬁrmed group (100%) than in the not conﬁrmed group (60%). Interestingly, the percentage of female participants did not differ between groups (p = 0.999). Moreover, there were no between-group differences in any of the remaining analysed variables (p > 0.050) (see Table 1). Int. J. Environ. Res. Public Health 2023, 20, 2764 6 of 10 3.4. Conﬁrmed and Not Conﬁrmed Groups The patients’ responses to both exercise phases in the conﬁrmed and not conﬁrmed groups can be found in Table S1. Between-phase comparisons showed the same results as those found when all patients had been included (see Table 2). On the other hand, between-group comparisons during each exercise phase are shown in Table S2. Although no statistically signiﬁcant differences were found (p > 0.050), the relative VO2 peak value was higher in the conﬁrmed group compared to the not conﬁrmed group both in the IP (2.62 mL·kg−1·min−1 [95%CI = −2.38 to 7.62]; p = 0.305) and the VP (2.35 mL·kg−1·min−1 [95%CI = −2.90 to 7.60]; p = 0.380). 3.5. Agreement between the Traditional and Veriﬁcation Phase Criteria When the traditional criterion (i.e., RER peak) was used, VO2 max was conﬁrmed in 10 patients and VO2 peak was attained in 11 patients. The median VO2 peak values in the conﬁrmed and not-conﬁrmed groups during the IP were 16.2 mL·kg−1·min−1 (13.0; 19.9) and 14.1 mL·kg−1·min−1 (13.0; 17.6), while the median values during the VP were 15.7 mL·kg−1·min−1 (13.6; 21.6) and 13.7 mL·kg−1·min−1 (11.5; 16.8), respectively. Regard- ing the agreement between the two criteria for the determination of VO2 max, there were 10 agreements (47.6%), six false negative cases (28.6%), and ﬁve false positive cases (23.8%). Moreover, the Kappa index showed that there was no signiﬁcant agreement between both criteria (Kappa = −0.045; p = 0.583). 4. Discussion The main objective of this study was to investigate whether the submaximal VP is a safe and reliable method to validate VO2 max in male and female patients with HFrEF. To accomplish this, we used both group and individual approaches. Additionally, we investigated the agreement between the RER and VP criteria to determine VO2 max. Regarding our results, no adverse events were observed during the exercise tests, suggesting that VP is a safe method for determining VO2 max in patients with HFrEF. In agreement with our ﬁndings, Bowen et al. [23] also reported no adverse events in patients with HFrEF. There is also previous evidence showing that the use of the VP was well- tolerated in patients with other diseases, such as cancer [29], prehypertension [30], and metabolic syndrome [31], who are normally sedentary and not familiarised with high- intensity exercise. Nonetheless, it should be noted that, in the current study, patients who had a high risk of adverse events (those who presented symptoms of ischaemia or ectopic heartbeats during the IP) were exempt from performing the VP. Moreover, several patients had difﬁculty cycling since they were unfamiliar with the cycle ergometer. In this regard, Manresa-Rocamora et al. [32] reported a greater improvement in the VO2 max after an exercise-based cardiac rehabilitation programme in studies that conducted the incremental exercise test on a cycle ergometer compared to studies that used a treadmill in patients with coronary artery disease. The lack of habituation to the cycle ergometer could explain, in part, the higher training-induced effect found in these studies, seeing as their baseline VO2 max results were worse than those who used a treadmill. Therefore, a familiarisation period should be performed before conducting the incremental exercise test to avoid terminating the test due to peripheral fatigue. As for the use of individual versus group comparisons for the analysis of VP, con- tradictory ﬁndings were obtained based on the type of approach used to conduct the analyses. Group comparisons showed that both exercise phases (i.e., IP and VP) led to similar median VO2 peak values. These results did not change when only male or female patients were included in the analysis. Therefore, based on this approach, the VO2 peak values reached during the IP can be considered as maximal (i.e., VO2 max) in all patients. This ﬁnding is in line with those of Murias et al. [33] and Bowen et al. [23] in healthy males and patients with HFrEF, respectively. In this regard, Murias et al. [33], who did not conduct individual comparisons, concluded that both the submaximal VP (i.e., 85% of peak power output) and supramaximal VP (i.e., 105% of peak power output) were not Int. J. Environ. Res. Public Health 2023, 20, 2764 7 of 10 necessary to conﬁrm the VO2 max values reached during the preceding IP. In the same line, Astorino and Emma [22] and Costa et al. [34], who respectively conducted a review and a meta-analysis (54 studies), reported no differences in mean VO2 peak values between the two exercise phases in a sizable number of studies conducted with healthy adults and individuals with pathology. Previous studies also failed to ﬁnd differences between both exercise phases in endurance-trained athletes [35,36]. Interestingly, in line with our results, Costa et al. [34] found that the sex of the participants did not inﬂuence their results and reported no differences in the aggregate VO2 peak values in male and female participants. In contrast to these ﬁndings, Moreno-Cabañas et al. [31] and Schaun et al. [37] found higher mean VO2 peaks during the VP than during the IP in male and female older adults with obesity and hypertension, respectively. It should be noted that a supramaximal VP (i.e., constant load and multistage) preceded by a passive recovery period (i.e., 10–15 min) in the seated position was conducted, which could explain in part these controversial ﬁndings. In contrast, Costa et al. [34] reported in their meta-analysis no differences in mean VO2 peaks regardless of the VP intensity (i.e., submaximal vs. supramaximal), type of recovery (i.e., active vs. passive), veriﬁcation timing (i.e., same day vs. different day), and veriﬁcation phase duration (e.g., less than 80 s) in apparently healthy adults. Bhammar and Chien [30], who conducted a supramaximal VP, also found no differences in VO2 peak values in adults with prehypertension. Therefore, our ﬁndings and previous evidence support that the submaximal VP is not necessary to conﬁrm VO2 max when group comparisons are used, while the utility of the supramaximal VP, which could lead to controversial ﬁndings, in patients with HFrEF requires future study. Nonetheless, the achievement of a VO2 peak is an individual phenomenon and group comparisons may cloud individual differences. In relation to individual comparisons, our results showed that VO2 max was conﬁrmed by the VP in 52% of the patients with HFrEF, while a VO2 peak was attained (i.e., the individual between-phase difference in VO2 peak values higher than 3%) in the remaining patients (48%). Moreover, the percentage of female patients was the same in the conﬁrmed and not conﬁrmed groups, suggesting that individual comparisons could be used in both male and female patients. Nonetheless, the low number of female patients included warrants future studies to conﬁrm our results. Similarly, Bowen et al. [23], who only recruited male patients, found that the VO2 max was conﬁrmed in 58% of the patients with HFrEF included in their study. It should be highlighted that, in contrast to our study, statistical comparisons between both exercise phases were performed to conduct individual comparisons and validate the VO2 max. In conclusion, regardless of the criteria used to carry out individual comparisons, an individual approach should be prioritised to determine the VO2 max in patients with HFrEF, in accordance with previous reports in the literature [24,37]. On the other hand, we found no difference in median VO2 peak values between the two exercise phases in the conﬁrmed and not conﬁrmed groups, which also agrees with the results of Bowen et al. [23]. In the same line, the current and the former study showed no difference between the two groups in aggregate VO2 peak values reached during the IP. However, although statistical signiﬁcance was not reached, both studies showed that the group VO2 peak values achieved during the IP were higher in the conﬁrmed group (15.9 and 15.1 mL·kg−1·min−1) than in the not conﬁrmed group (13.3 and 13.7 mL·kg−1·min−1). These ﬁndings seem to support a greater difference in VO2 peak values between both exercise phases (i.e., VO2 peak attained) in patients with lower cardiorespiratory ﬁtness. Furthermore, Moreno-Cabañas et al. [31], who included older and less physically ﬁt par- ticipants with obesity, observed higher VP-derived VO2 peak in 40% of the participants, while Wood et al. [38], who recruited younger and ﬁtter patients with obesity, only found a difference in VO2 peak values between the two exercise phases in 15% of the participants. Therefore, our ﬁndings and previous evidence seem to support that patients with lower cardiorespiratory ﬁtness may show a greater difference in VO2 peaks between the two exercise phases, with the use of the VP being even more important for the validation of the VO2 max in this group of patients. Int. J. Environ. Res. Public Health 2023, 20, 2764 8 of 10 Finally, regarding the comparison between traditional criteria (i.e., RER) and the VP for VO2 max determination, we found no agreement between both methods, which is similar to previous evidence [23]. Interestingly, based on the RER criterion, the VO2 max was conﬁrmed in ﬁve patients who showed higher VO2 peaks during the VP compared with the IP (i.e., false positive). There is evidence showing that the RER criterion can be reached at submaximal intensities (e.g., 80% VO2 max) [11,18], which concurs with our ﬁndings. Moreover, Bowen et al. [23] showed a direct relationship between RER and workload increase in patients with HFrEF. In the same line, Moreno-Cabañas et al. [31] observed that the VO2 plateau was not reliable for determining the VO2 max in participants with obesity. Therefore, the results of the current and previous studies conﬁrm that traditional criteria (e.g., RER and VO2 plateau) should not be due to their lack of validity to verify the VO2 max. 5. Limitations Some limitations should be mentioned. First, we did not perform a familiarisation period with the equipment (e.g., cycle ergometer) and, consequently, some patients showed difﬁculty cycling. Future studies conducted with patients who are sedentary should include a familiarisation phase before starting the study protocol. Second, there was an uneven sex distribution among the participants (i.e., 13 males and 8 females). Therefore, to support our ﬁndings, additional research including more female patients with HFrEF should be conducted. Third, no prior power analysis was conducted to estimate the optimum number of patients the study should include. 6. Conclusions The submaximal VP is a safe and suitable method to determine the VO2 max in patients with HFrEF. When comparing both exercise phases, an individual approach is preferable, seeing as aggregate comparisons could mask patients who showed differences in VO2 peaks between both exercise phases. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/ijerph20042764/s1, Table S1: Cardiopulmonary responses to the two exercise phases in the conﬁrmed and not conﬁrmed groups, and between-phase comparisons; Table S2: Between-group comparisons during the two exercise phases. Author Contributions: Conceptualization, A.M.-R., N.S.-R. and J.M.S.; Methodology, A.M.-R., L.F.-K., C.B.-P., N.S.-R., J.M.S. and V.C.-P.; Formal analysis, A.M.-R., L.F.-K. and C.B.-P.; Data curation, A.M.-R., L.F.-K. and C.B.-P.; Writing—original draft, A.M.-R., C.B.-P. and J.M.S.; Writing—review & editing, L.F.-K. and V.C.-P. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Institute of Health Carlos III (ISCIII, grant number DTS21/00171, European Commission, FEDER funds) and by the Institute for Health and Biomedical Research of Alicante (ISABIAL, grant number A2022-0018). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The datasets generated from the current study are available from the corresponding author upon reasonable request. Conﬂicts of Interest: The authors declare no conﬂict of interest. Int. J. Environ. Res. 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Costa, V.A.B.; Midgley, A.W.; Carroll, S.; Astorino, T.A.; de Paula, T.; Farinatti, P.; Cunha, F.A. Is a veriﬁcation phase useful for conﬁrming maximal oxygen uptake in apparently healthy adults? A systematic review and meta-analysis. PLoS ONE 2021, 16, e0247057. [CrossRef] [PubMed] 35. Midgley, A.W.; Carroll, S.; Marchant, D.; McNaughton, L.R.; Siegler, J. Evaluation of true maximal oxygen uptake based on a novel set of standardized criteria. Appl. Physiol. Nutr. Metab. 2009, 34, 115–123. [CrossRef] 36. Foster, C.; Kuffel, E.; Bradley, N.; Battista, R.A.; Wright, G.; Porcari, J.P.; Lucia, A.; Dekoning, J.J. VO2max during successive maximal efforts. Eur. J. Appl. Physiol. 2007, 102, 67–72. [CrossRef] 37. Schaun, G.Z.; Alberton, C.L.; Gomes, M.L.B.; Santos, L.P.; Bamman, M.M.; Mendes, G.F.; Häfele, M.S.; Andrade, L.S.; Alves, L.; DE Ataides, V.A.; et al. Maximal Oxygen Uptake Is Underestimated during Incremental Testing in Hypertensive Older Adults: Findings from the HAEL Study. Med. Sci. Sports Exerc. 2021, 53, 1452–1459. [CrossRef] 38. Wood, R.E.; Hills, A.P.; Hunter, G.R.; King, N.A.; Byrne, N.M. Vo2max in overweight and obese adults: Do they meet the threshold criteria? Med. Sci. Sports Exerc. 2010, 42, 470–477. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.	Is the Verification Phase a Suitable Criterion for the Determination of Maximum Oxygen Uptake in Patients with Heart Failure and Reduced Ejection Fraction? A Validation Study.	02-04-2023	Manresa-Rocamora, Agustín,Fuertes-Kenneally, Laura,Blasco-Peris, Carles,Sempere-Ruiz, Noemí,Sarabia, José Manuel,Climent-Paya, Vicente	eng
PMC6239296	S2 Appendix. Comparison to oxygen uptake measurement While it is assuring to see below that our model can explain and predict record and individual racing times, a more direct comparison to power output during running is desirable to probe the logarithmic decline of the maximal power output with exercise duration, as predicted by Eq. (6). This is of particular importance in the anaerobic range where different functional forms, e.g., exponential decays, have been proposed [6]. However, running power, as measured by oxygen utilization, can be directly determined only in the aerobic regime. For (supra-maximal) exercise with substantial contributions from anaerobic systems where power output exceeds maximal oxygen uptake, Medbo et al. showed that the oxygen demand can be estimated by extrapolating each runner’s individual nominal linear relationship between running speed and submaximal oxygen uptake [1]. The difference between the extrapolated oxygen utilization and the measured oxygen uptake is the accumulated oxygen deficit. Using this method, Medbo et al. determined from treadmill exercise at speeds that caused exhaustion within different predetermined durations the oxygen demand relative to the maximal uptake. Translated to percent of maximal aerobic power output, this oxygen demand is given by 100 × Pmax(T)/Pm in our model, with Pmax(T) given in Eq. (6). While a logarithmic dependence for Pmax(T) has been deduced from purely empirical data analyses for world records for times above tc before [6], to our knowledge a logarithmic scaling has not been proposed for shorter exercise with large anaerobic involvement. Hence, it is interesting that there exists experimental estimates of the maximal oxygen utilization that can be maintained for a given duration. As explained above, Medbo et al. [1] obtained for 11 runners data that correspond to 100 × Pmax(T)/Pm which is shown as function of T < 5min ∼ tc in Fig. A. We have fitted the prediction of our model to the data, and the results for the runner with smallest and largest oxygen demand are shown in the same figure. The agreement between the data and our model prediction appears to be rather convincing. This suggests that there exists indeed a logarithmic relation between maximally sustainable power and duration in the range of supra-maximal intensities, resembling observation that were made before in the sub-maximal zone. PLOS 1/2 Fig A. Relative nominal oxygen demand as function of the maximum duration over which it can be sustained. Original plot and data for 11 runners from Ref. [1]. The two curves are fits of Eq. (6) to the data for the runners with smallest and largest relative oxygen demand. References 1. Medbo JI, Mohn AC, Tabata I, Bahr R, Vaage O, Sejersted OM. Anaerobic capacity determined by maximal accumulated O2 deficit. J Appl Physiol. 1988;64(1):50–60. PLOS 2/2	A minimal power model for human running performance.	11-16-2018	Mulligan, Matthew,Adam, Guillaume,Emig, Thorsten	eng
PMC10739691	Supplementary Online Material Anaerobic Threshold Using Sweat Lactate Sensor under Hypoxia Hiroki Okawara, Yuji Iwasawa, Tomonori Sawada, Kazuhisa Sugai, Kyohei Daigo, Yuta Seki, Genki Ichihara, Daisuke Nakashima, Motoaki Sano, Masaya Nakamura, Kazuki Sato, Keiichi Fukuda, Yoshinori Katsumata This supplementary material has been provided by the authors to give readers additional information about their work. APPENDIX Supplementary Figure 1. Imaging of sweat lactate levels, local sweat rate, and blood lactate values during incremental exercise under normoxia Representative graphs of sweat lactate levels (orange), local sweat rate (blue), and blood lactate values (red) during hypoxic exercise with a stepwise incremental protocol (25 W/min) ergometer are shown. Abbreviations: VT=ventilatory threshold; sLT=sweat lactate threshold. Supplementary Figure 2. Measured parameters in normoxia. The graph shows the measured parameters (a; VO2/Body weight, b; Heart rate, c; Sweat lactate, d; sweat rate) at rest, warm up, VT, and peak in hypoxia. Data are shown as mean (±standard deviation). Abbreviations: VO2=oxygen uptake: VT=ventilatory threshold: HR=heart rate: sLA=sweat lactate: SR=sweat rate. a) VO2 c) sLA d) SR b) HR Supplementary Figure 2 Supplementary Figure 3. Reliability testing of the time at sLT determined by the same evaluator in normoxia. (a) The graph shows the relationship between the repeatedly determined sweat lactate threshold (sLT) by the same evaluator (b) The graph shows the Bland–Altman plots, which indicate the respective differences between the repeatedly determined sLT by the same evaluator (y-axis) for each individual against the mean of the time at the repeatedly determined sLT (x-axis) in normoxia. R, correlation coefficient; p, p-value; ventilatory threshold; sLT, sweat lactate threshold. Supplementary Figure 3 a) b) R = 0.70 p < 0.01 Supplementary Figure 4. Validity testing of the time at VT and sLT in normoxia (a) The graph shows the relationship between the time from the start of the measurement (seconds) at VT and sLT. (b) The graph shows the Bland–Altman plots, which indicate the respective differences between the time from the start of measurement (s) at the VT and sLT (y-axis) for each individual against the mean of the time at the VT and sLT (x-axis) in hypoxia. R, correlation coefficient; VT, ventilatory threshold; sLT, sweat lactate threshold. pp y g a) b) R = 0.69 p < 0.01 Supplementary Table 1. Intra-evaluator reliability of sweat lactate threshold determination in normoxia Hypoxia N Evaluator 1 Evaluator 2 Evaluator 3 ICC (95%CI) sLT [sec] Mean 20 553.3 486.3 533.6 0.782 (0.607 - 0.898) SD 84.4 89.8 80.8 bLT [sec] Mean 20 643.9 605.6 611.3 0.621 (0.363 - 0.813) SD 77.1 67.4 81.9 VT [sec] Mean 20 563.1 552.2 552.6 0.711 (0.500 - 0.861) SD 56.5 45.3 60.0 ICC, intraclass correlation; sLT, sweat lactate threshold; bLT, blood lactate threshold; VT, ventilatory threshold; SD, standard deviation.	Anaerobic threshold using sweat lactate sensor under hypoxia.	12-21-2023	Okawara, Hiroki,Iwasawa, Yuji,Sawada, Tomonori,Sugai, Kazuhisa,Daigo, Kyohei,Seki, Yuta,Ichihara, Genki,Nakashima, Daisuke,Sano, Motoaki,Nakamura, Masaya,Sato, Kazuki,Fukuda, Keiichi,Katsumata, Yoshinori	eng
PMC8059023	Identification of heart rate dynamics during treadmill exercise: comparison of first‑ and second‑order models Hanjie Wang* and Kenneth J. Hunt Background Characterisation of heart rate (HR) dynamics with respect to changes in exer- cise intensity provides models that can be used to synthesise control algorithms to maintain target HR levels [1]. The control of HR is important in the design of train- ing protocols that aim both to maintain and to improve cardiorespiratory fitness; this applies to healthy individuals [2] and also in different patient populations [3, Abstract Background: Characterisation of heart rate (HR) dynamics and their dependence on exercise intensity provides a basis for feedback design of automatic HR control systems. This work aimed to investigate whether the second-order models with separate Phase I and Phase II components of HR response can achieve better fitting performance com- pared to the first-order models that do not delineate the two phases. Methods: Eleven participants each performed two open-loop identification tests while running at moderate-to-vigorous intensity on a treadmill. Treadmill speed was changed as a pseudo-random binary sequence (PRBS) to excite both the Phase I and Phase II components. A counterbalanced cross-validation approach was implemented for model parameter estimation and validation. Results: Comparison of validation outcomes for 22 pairs of first- and second-order models showed that root-mean-square error (RMSE) was significantly lower and fit (normalised RMSE) significantly higher for the second-order models: RMSE was 2.07 bpm ± 0.36 bpm vs. 2.27 bpm ± 0.36 bpm (bpm = beats per min), second order vs. first order, with p = 2.8 × 10−10 ; fit was 54.5% ± 5.2 % vs. 50.2% ± 4.8 %, p = 6.8 × 10−10. Conclusion: Second-order models give significantly better goodness-of-fit than first- order models, likely due to the inclusion of both Phase I and Phase II components of heart rate response. Future work should investigate alternative parameterisations of the PRBS excitation, and whether feedback controllers calculated using second-order models give better performance than those based on first-order models. Keywords: Heart rate dynamics, System identification, Treadmills Open Access © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. RESEARCH Wang and Hunt BioMed Eng OnLine (2021) 20:37 https://doi.org/10.1186/s12938‑021‑00875‑7 BioMedical Engineering OnLine Correspondence: hanjie.wang@bfh.ch Department of Engineering and Information Technology, Division of Mechanical Engineering, Institute for Rehabilitation and Performance Technology, Bern University of Applied Sciences, 3400 Burgdorf, Switzerland Page 2 of 10 Wang and Hunt BioMed Eng OnLine (2021) 20:37 4]. Target heart rate profiles come in various forms such as high-intensity inter- val training (HIIT) that repeats high-intensity exercise connected by low-intensity recovery intervals; HIIT has potential to enhance cardiovascular health and fitness when compared to training at constant work rates (systematic reviews: [5, 6]). Recent work investigated the effect of exercise intensity and time on HR dynamics using first-order models [7], but it may be beneficial to include higher order effects: based on physiological study, the dynamics of both oxygen uptake and HR responses to changes in exercise intensity are known to have three distinct phases [8]. These are: (i) a Phase I component lasting ∼ 15 s with a relatively small-magnitude venti- latory response, but where HR can increase by about 50% of its total response [9]; (ii) a Phase II component between around 15 s and 3 min contributing the further increase of cardiopulmonary response; and then, (iii) if the applied exercise inten- sity exceeds the anaerobic threshold, a Phase III component is prolonged and rises slowly. The three components can each be modelled as single exponentials (first- order systems) each with their own time delay, gain, and time constant [10]. In addi- tion to these primary dynamic responses, the phenomenon of heart rate variability (HRV) can be added to the model to represent the regulatory activities of the auto- nomic nervous system; in the context of feedback control of HR, HRV represents a broad-spectrum disturbance term [1]. Because it can be challenging to estimate the separate Phase I and II components using data which is noisy, those two phases have often been identified as a com- bined single exponential model with a time constant termed the mean response time (MRT, [8]), which is effectively the first-order approach taken in the previous studies that focused on system identification [7] and feedback control [1]. In feedback con- trol, the slow Phase III component can readily be neglected as it is compensated by inclusion of an integrator in the controller. The focus of the present work is there- fore the investigation of whether the separate identification of Phase I and II compo- nents, i.e., the employment of a second-order model, can give better model fidelity. Other recent approaches to HR dynamics identification focused mainly on the modelling of the Phase II and III components of the HR response. Several studies employed a non-linear state-space model structure comprising two different states ( x1 and x2 ) to separately describe the Phase II and Phase III dynamics [11–15]. Other work used linear time-varying systems to model the slow Phase III dynamic [16, 17]. While inclusion of Phase III may improve overall model fidelity, it will, as noted above, have negligible impact on feedback-control performance as it will be elimi- nated by the integral action. In contrast, it can be anticipated that separate mod- elling of the Phase I and II components might lead to better control performance when the model is used as the basis of an analytical, model-based feedback design. To this end, this work aimed to investigate whether second-order models with sep- arate Phase I and Phase II components of HR response can achieve better fitting performance compared to first-order models that do not delineate the two phases. Here, an input signal of PRBS (pseudo-random binary sequence) form was designed to excite both the Phase I and Phase II components. Page 3 of 10 Wang and Hunt BioMed Eng OnLine (2021) 20:37 Results To illustrate the procedures of data preprocessing and model validation, an exemplary result from participant P04 is shown (Fig. 1); the raw data for the same participant are shown above below in the section ‘Method’. For this example, the second-order model P2 gave better performance than the first-order model P1 : fit was 51.9% vs. 50.9% ( P2 vs. P1 ) and RMSE was 2.01 bpm vs. 2.05 bpm. The overall statistical comparison of validation outcomes for the 22 pairs of first- and second-order models showed that RMSE was significantly lower and fit significantly higher for the second-order models: RMSE was 2.07 bpm ± 0.36 bpm vs. 2.27 bpm ± 0.36 bpm, P2 vs. P1 , with p = 2.8 × 10−10 (Table 1; Fig. 2a); fit was 54.5% ± 5.2 % vs. 50.2% ± 4.8 %, p = 6.8 × 10−10 (Table 1; Fig. 2b). The graphical illustration of overall outcomes (Fig. 2) shows how widely individual samples and their differences are dis- persed, together with means and their 95% confidence intervals (CIs). These plots allow visual determination of significant differences, if they exist: whenever there is a signifi- cant difference, the value 0 will not be contained within the corresponding CI. The sample size was estimated a priori by a statistical power calculation that used esti- mates of expected effect sizes and sample standard deviations, with significance level set to 5% ( α = 0.05) and with a statistical power of 80% ( 1 − β = 0.8). The observed outcomes show large effect sizes (approximately 9% for both outcomes) and extremely low p values (on the order of 10−10 ), thus pointing to a well-powered sta- tistical analysis. In fact, post hoc statistical power analysis based on observed effect sizes and sample dispersions gives an observed power of 100% for both outcomes. A graphical illustration of the dispersion of estimated model parameters for the 22 first- and 22 second-order models is provided (Fig. 3). The overall first- and second- order models were obtained by averaging the individual gains and time constants. For the first-order models, the overall gain was k1 = 28.57 bpm/(m/s) ± 5.27 bpm/(m/s) time/s 300 600 900 1200 1500 1800 -20 -10 0 10 20 heart rate/bpm 300 600 900 1200 1500 1800 -0.5 -0.25 0 0.25 0.5 speed/(m/s) Fig. 1 Data preprocessing and model validation: exemplary data for participant P04 (the raw data for this test are shown in section ‘Method’). Upper plot: HR measurement from validation data set after detrending (solid black line), simulated HR response of first-order model ( P1sim , blue dashed line), and simulated HR response of second-order model ( P2sim , green dashed line). Lower plot: treadmill speed from validation data set after mean removal Page 4 of 10 Wang and Hunt BioMed Eng OnLine (2021) 20:37 (mean ± standard deviation) while the time constant was τ1 = 70.56 s ± 16.84 s. For the second-order models, the overall gain was k2 = 24.70 bpm/(m/s) ± 5.07 bpm/(m/s) and the overall time constants were τ21 = 18.60 s ± 7.88 s and τ22 = 37.95 s ± 16.01 s. This gives the average transfer functions for first- and second-order models as follows: Discussion This study aimed to investigate whether second-order models with separate Phase I and Phase II components of heart rate response can achieve better fitting perfor- mance compared to first-order models that do not delineate the two components. (1) u → y: P1(s) = 28.57 70.56s + 1, (2) u → y: P2(s) = 24.70 (18.60s + 1)(37.95s + 1). Table 1 Overall outcomes for first- and second-order models and comparison of outcome differences (see also Fig. 2) n = 22 P1 first‑order models, P2 second‑order models, SD standard deviation, MD mean difference, 95% CI confidence interval for the mean difference , p-value paired one‑sided t tests, RMSE root‑mean‑square error, fit normalised root‑mean‑square error, bpm beats per min Mean ± SD MD (95% CI) p-value P1 P2 P2 − P1 RMSE/bpm 2.27 ± 0.36 2.07 ± 0.36 −0.19 ( −∞ , −0.16) 2.8 × 10−10 fit/% 50.2 ± 4.8 54.5 ± 5.2 4.3 (3.6, +∞) 6.8 × 10−10 * a Root-mean-square error, RMSE. b Normalised root-mean-square error, ﬁt. Fig. 2 Primary outcomes: data samples and differences for RMSE and fit between 22 first-order models, P1 , and 22 second-order models, P2 (see also Table 1). Sample pairs for each participant are connected by green lines; mean values are shown as red horizontal bars (with numerical values given in Table 1). Sample-pair differences are shown as D ( P2 − P1 ). The mean difference (MD) is depicted as a red bar and the blue arrow is the corresponding 95% confidence interval (CI). For both RMSE and fit, the 95% CI does not contain the value 0, thus showing a significant improvement for P2 vs. P1 ( p < 0.05 , Table 1; the notation ** denotes p < 0.0001) Page 5 of 10 Wang and Hunt BioMed Eng OnLine (2021) 20:37 The results clearly demonstrate that second-order models give significantly better goodness-of-fit, in terms of both RMSE and fit (NRMSE): RMSE was on average 10 15 20 25 30 35 40 45 50 0 20 40 60 80 100 120 first-order models average model a First-order models. 10 15 20 25 30 35 40 45 50 0 20 40 60 80 second-order models average model 10 15 20 25 30 35 40 45 50 0 20 40 60 80 second-order models average model b Second-order models. Fig. 3 Dispersion of estimated model parameters for 22 first- and 22 second-order models. The stars depict the average models. The 95% confidence intervals for the mean gains and time constants are shown as rectangular boxes -25 -10 speed/(m/s) evaluation period (290 s to 2085 s ) 0.5 m/s time/min PRBS (schematic) warm up rest formal measurement phase cool down 0 10 20 30 40 50 - 5 2.0 + 5 2.0 a 0 300 600 900 1200 1500 1800 2100 100 150 200 heart rate/bpm 0 300 600 900 1200 1500 1800 2100 time/s 1 1.5 2 2.5 3 speed/(m/s) b Fig. 4 Identification test protocol. a Test phases and treadmill speed. b Original data record from one participant (P04; upper plot—HR measurement; lower plot—speed of the treadmill); the evaluation period is depicted by the red horizontal bar Page 6 of 10 Wang and Hunt BioMed Eng OnLine (2021) 20:37 0.19 bpm lower and fit 4.3% higher for the second-order model structure (p values were on the order of 10−10 in both cases); that these significance levels were achieved with a small sample size of only 11 participants underline the difference. The approach taken here focused on control-orientated model structures, in the sense that the estimated models would be intended to be used for analytical (model- based) design of heart rate control systems. For this reason, slow Phase III com- ponents in the data were eliminated by detrending prior to parameter estimation. This is consistent with feedback-control scenarios where slowly drifting Phase III variations in heart rate are automatically compensated using integral action in the controller. A further difference between the methodology employed here and heart rate mod- elling approaches taken in the physiological literature, [8], is that a nominal operat- ing point was assumed, and small deviations around this point were considered (in this case, the operating point was set at the transition between exercise levels con- sidered to be moderate and vigorous). This is consistent with linear feedback design approaches, which are implicitly based on models that are small-signal linearisations around an operating point; the purpose of feedback control is indeed to maintain the controlled variable, viz., heart rate, close to a target level. For these reasons, it is not possible to compare the overall estimated model param- eters (gains and time constants, Eqs. (1) and (2) with values given in the physiologi- cal literature (e.g., [9, 10]), because, there, responses are usually recorded using large steps from a resting or low-intensity baseline. A consequence of the control-orientated methodology followed here is that the design of the PRBS input signal becomes important. For non-linear systems, it is known that the parameters of linear approximations are input dependent [18], which motivates further work to explore the effect of PRBS amplitude and frequency con- tent on model fidelity; in particular, it is important to focus the information content on frequencies around the intended crossover band of the closed-loop system [19]. Future work should investigate whether the observed improvement in model fidel- ity translates into better feedback-control performance, i.e., whether controllers designed on the basis of second-order models perform better, in some sense, than those designed using first-order models. Because of the fundamental property of feedback that plant uncertainty (including modelling error) is reduced, the answer to this question will likely not be as clear cut as in the open-loop identification case. Conclusions Second-order models give significantly better goodness-of-fit than first-order mod- els, likely due to the inclusion of both Phase I and Phase II components of heart rate response. Future work should investigate alternative parameterisations of the PRBS excitation, and whether feedback controllers calculated using second-order models give better performance than those based on first-order models. Page 7 of 10 Wang and Hunt BioMed Eng OnLine (2021) 20:37 Methods Participants Eleven healthy participants were recruited (8 males, 3 females) with age 32.5 years ± 12.3 years (mean ± standard deviation), body mass 75.5 kg ± 14.4 kg, and height 179 cm ± 12 cm. For inclusion, each participant was required to be a regular exerciser (30-min bouts, 3 times per week) and non-smoker, and to be free of injury and illness. Test protocols To generate separate estimation and validation data sets, each participant took part in two identification tests; there was an interval of at least 48 h between the two tests. Before each test, participants were asked to meet the following requirements: refrain from strenuous activity for 24 h, caffeine for 12 h, avoid large meals for 3 h. Each test session had four phases: a 15 min warm up, a 10 min rest, a 36 min formal measure- ment, and a 10 min cool down (Fig. 4a). In the warm up, a feedback-control system was employed to automatically regulate the speed of the treadmill to maintain a constant target HR. The target HR, denoted HRref , was computed individually for each participant and corresponded to the HR at the transition between intensity levels considered to be moderate or vigorous [3], as follows: HRref = 0.765 × (220 − age) [beats/min, bpm] (except for participant P03, for whom the factor 0.7 was used, because 0.765 led to HR remaining in the vigor- ous-intensity regime). The mean speed of the treadmill during the final 2 min of the warm up phase was subsequently used as the mid-level speed, denoted vm , for the next phase. In the formal measurement phase, the speed of the treadmill, denoted v, was designed as a fifth-order PRBS with mean speed vm and amplitude 0.25 m/s, i.e., v = vm ± 0.25 m/s (to illustrate, a single original data record is provided; Fig. 4b). Model parameter estimation and validation was performed over a full cycle of the PRBS using an evaluation period from 290 s to 2085 s (Fig. 4); the first 5 min were excluded to eliminate the initial transient. During the cool down phase, the speed of the treadmill was kept constant at v = vm − 0.5 m/s. Equipment All tests were carried out using a treadmill (model Venus, h/p/cosmos Sports & Med- ical GmbH, Germany) controlled by a PC running real-time Matlab/Simulink (The MathWorks, Inc., USA). HR recording was performed with a chest strap (H10, Polar Electro Oy, Finland) and a wireless receiver (Heart rate Monitor Interface, Spark- fun Electronics, USA) connected to the Simulink model via a serial port. HR meas- urements were received at a rate of 1 Hz and then downsampled to a sample rate of 0.2 Hz (sample period 5 s) by averaging consecutive batches of five individual samples. Data preprocessing, model identification, and outcome measures As noted above, each participant completed two identification tests, thus gener- ating individual data sets (I and II) for model parameter estimation and validation. To prevent over-fitting and to eliminate potential order-of-presentation effects, a Page 8 of 10 Wang and Hunt BioMed Eng OnLine (2021) 20:37 counterbalanced cross-validation approach was implemented: for each participant, data set I was used to estimate model parameters and data set II was used as valida- tion data for the estimated models; then, for the same participant, data set II was used for model estimation and data set I for validation. Thus, for the 11 participants, a total of 22 estimation data sets and 22 validation data sets were obtained. According to the test protocol (Sect. 5.2, Fig. 4a), an evaluation interval from 290 s to 2085 s was used to estimate and validate model parameters. This interval, within one single PRBS period, was selected, such that the number of samples where the input was high ( v = vm + 0.25m/s) equalled the number of samples where the input was low ( v = vm − 0.25m/s). Here, on the evaluation period from 290 s to 2085 s and with a sam- ple period of 5 s, the total number of samples was N = 360, thus giving 180 low samples and 180 high samples. To remove any potential drifting Phase III dynamic of the HR response, the mean value and any trend were removed (Matlab “detrend” function) prior to estimation and validation; the mean value of the input signal was also removed. An exemplary data set following this preprocessing procedure is provided (Fig. 1), with raw data are shown above (Fig. 4b). For each estimation data set, two linear time-invariant transfer functions were employed to model the dynamic response from treadmill speed to HR: a first-order transfer function (Eq. 3) which combined Phases I and II into a single time constant, and a second-order transfer function (Eq. 4) with separate time constants for Phases I and II. Hence, for the 11 participants, a total of 22 first-order models and 22 second-order models were estimated: Here, k1 and k2 are steady-state gains, and τ1 , τ21 , and τ22 are time constants. Model parameters were calculated from the estimation data sets using a least-squares optimisa- tion procedure (“procest” function from the Matlab System Identification Toolbox; The Mathworks, Inc., USA). After model estimation, the corresponding validation data sets were used to compute goodness-of-fit measures for the resulting first- and second-order models. Two outcome measures were used: the normalised root-mean-square error [denoted fit, Eq. (5)], and the root-mean-square error [denoted RMSE, Eq. (6)], as follows: (3) u → y: P1(s) = k1 τ1s + 1, (4) u → y: P2(s) = k2 (τ21s + 1)(τ22s + 1). (5) ﬁt (NRMSE) [%] =  1 − N i=1(HR(i) − HRsim(i))2 N i=1(HR(i) − HR)2   × 100 %, (6) RMSE [bpm] = 1 N N i=1 (HRsim(i) − HR(i))2. Page 9 of 10 Wang and Hunt BioMed Eng OnLine (2021) 20:37 Here, HRsim is the simulated HR response obtained using the estimated models and the input signal, and HR is the measured HR from the validation data. ¯ HR is the mean value of HR . i is the discrete time index and N is the number of discrete samples considered (as described above, N = 360 ). Both of the above outcomes were calculated using the “compare” function from the Matlab System Identification Toolbox. Statistics Statistical analysis was performed to test the hypothesis that the goodness-of-fit out- comes of second-order models are better (higher fit and lower RMSE) compared to first- order models. Prior to analysis, normality of differences between the goodness-of-fit outcomes was formally assessed using the Matlab “lilliefors” function (this implements a Kolmogorov–Smirnov test with correction according to the Lillifors method). As it transpired that all differences were not significantly different from a normal distribution, paired one-sided t tests were employed for hypothesis testing. Hypothesis testing used a significance threshold of 5% ( α = 0.05 ). The Matlab Statistics and Machine Learning Toolbox (The Mathworks, Inc., USA) was employed. Abbreviations bpm: Beats/min; CI: Confidence interval; fit/NRMSE: Normalised root-mean-square error; HR: Heart rate; HRV: Heart rate variability; MD: Mean difference; PRBS: Pseudo-random binary sequence; RMSE: Root-mean-square error; SD: Standard deviation; k: Steady-state gain; τ: Time constant. Acknowledgements Lars Brockmann (Institute for Rehabilitation and Performance Technology, Bern University of Applied Sciences) critically reviewed the manuscript for important intellectual content. Authors’ contributions KH and HW designed the study. HW did the data acquisition. HW and KH contributed to the analysis and interpretation of the data. HW wrote the manuscript; KH revised it critically for important intellectual content. Both authors read and approved the final manuscript. Funding This study was funded by the Swiss National Science Foundation as part of the project “Heart Rate Variability, Dynamics and Control During Exercise” (Ref. 320030-185351). Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate This research was performed in accordance with the Declaration of Helsinki. The study was reviewed and approved by the Ethics Committee of the Swiss Canton of Bern (Ref. 2019-02184). All participants provided written, informed consent. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Received: 13 January 2021 Accepted: 28 March 2021 References 1. Hunt KJ, Fankhauser SE. Heart rate control during treadmill exercise using input-sensitivity shaping for disturbance rejection of very-low-frequency heart rate variability. Biomed Signal Process Control. 2016;30:31–42. 2. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee I-M, Nieman DC, Swain DP. American College of Sports Medicine Position Stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, Page 10 of 10 Wang and Hunt BioMed Eng OnLine (2021) 20:37 • fast, convenient online submission • thorough peer review by experienced researchers in your ﬁeld • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year • At BMC, research is always in progress. Learn more biomedcentral.com/submissions Ready to submit your research Ready to submit your research ? 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PMC7037403	International Journal of Environmental Research and Public Health Article Seven Weeks of Jump Training with Superimposed Whole-Body Electromyostimulation Does Not Aﬀect the Physiological and Cellular Parameters of Endurance Performance in Amateur Soccer Players Nicolas Wirtz 1,* , André Filipovic 2, Sebastian Gehlert 2,3 , Markus de Marées 4, Thorsten Schiﬀer 5, Wilhelm Bloch 2 and Lars Donath 1 1 Institute of Training Science and Sport Informatics, Department of Intervention Research in Exercise Training, German Sport University Cologne, 50933 Cologne, Germany; l.donath@dshs-koeln.de 2 Institute of Cardiology and Sports Medicine, Department of Molecular and Cellular Sports Medicine, German Sport University Cologne, 50933 Cologne, Germany; andre.ﬁlipovic@gmx.net (A.F.); gehlert@dshs-koeln.de (S.G.); w.bloch@dshs-koeln.de (W.B.) 3 Institute of Sport Science, University of Hildesheim, 50933 Hildesheim, Germany 4 Section of Sports Medicine and Sports Nutrition, Faculty of Sports Science, Ruhr University of Bochum, 44801 Bochum, Germany; Markus.deMarees@ruhr-uni-bochum.de 5 Outpatient Clinic for Sports Traumatology and Public Health Consultation, German Sport University Cologne, 50933 Cologne, Germany; t.schiﬀer@dshs-koeln.de * Correspondence: n.wirtz@dshs-koeln.de; Tel.:+49-221-4982-6044 Received: 6 January 2020; Accepted: 1 February 2020; Published: 10 February 2020 Abstract: Intramuscular density of monocarboxylate-transporter (MCT) could aﬀect the ability to perform high amounts of fast and explosive actions during a soccer game. MCTs have been proven to be essential for lactate shuttling and pH regulation during exercise and can undergo notable adaptational changes depending on training. The aim of this study was to evaluate the occurrence and direction of potential eﬀects of a 7-weeks training period of jumps with superimposed whole-body electromyostimulation on soccer relevant performance surrogates and MCT density in soccer players. For this purpose, 30 amateur soccer players were randomly assigned to three groups. One group performed dynamic whole-body strength training including 3 x 10 squat jumps with WB-EMS (EG, n = 10) twice a week in addition to their daily soccer training routine. A jump training group (TG, n = 10) performed the same training routine without EMS, whereas a control group (CG, n = 8) merely performed their daily soccer routine. 2 (Time: pre vs. post) x 3 (group: EG, TG, CG) repeated measures analyses of variance (rANOVA) revealed neither a signiﬁcant time, group nor interaction eﬀect for VO2peak, Total Time to Exhaustion and Lamax as well as MCT-1 density. Due to a lack of task-speciﬁcity of the underlying training stimuli, we conclude that seven weeks of WB-EMS superimposed to jump exercise twice a week does not relevantly inﬂuence aerobic performance or MCT density. Keywords: electrostimulation; soccer; lactate; VO2peak; monocarboxylate transporter 1. Introduction The physical demands of soccer players have increased notably within the last 10 to 20 years due to modern game tactics and their variability. For example, the ability of a team to successfully play high pressing mainly depends on the physical characteristics of the players. The distances covered in the higher intensities and the number of quick and explosive actions such as accelerations, turns, and jumps have increased within the recent years [1–3]. A player’s capacity to perform numerous of Int. J. Environ. Res. Public Health 2020, 17, 1123; doi:10.3390/ijerph17031123 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 1123 2 of 13 those actions with a highly intense load is considered crucial in modern soccer. This ability relies on (1) adequate intra- and intermuscular coordination of soccer-speciﬁc movements and (2) metabolism that ensures proper energy delivery [4]. Both has been shown to be aﬀected by electromyostimulation (EMS) training [5,6]. Jumps with superimposed Whole-Body EMS (WB-EMS) in addition to soccer training sessions can be eﬀective for improving accelerations, turns, jumps, and kicking velocity [5]. WB-EMS potentially supports the athlete achieving higher power outputs and faster sport-speciﬁc movement velocities using resistance training [7] by increased ﬁring rates and synchronization of motor units, resulting in a more pronounced activation of fast-twitch ﬁbers at relatively low force levels [8]. Previous studies showed that local EMS is beneﬁcially aﬀecting muscle metabolism and can elevate energy expenditure and carbohydrate oxidation to a higher degree than voluntary contraction only [9–11]. Moreover, WB-EMS seem to stimulate anaerobic glycolysis for energy production with higher lactate accumulation [12,13]. The beneﬁcial eﬀects of EMS on transportation of lactate have to be taken into account as lactate shuttling via monocarboxylate transporters (MCTs) has been shown to improve high-intensity intermittent exercise performance [14,15]. MCTs are considered essential for lactate shuttling and pH regulation during exercise and can undergo notable adaptational changes depending on physical activity levels [16,17]. Due to a 1:1 ratio of lactate and H+ being transported by MCTs, an increase in the two isoforms MCT-1 and MCT-4 in skeletal muscle reduce the intracellular pH perturbations [18]. In line with this, studies revealed that the density of MCT-1 and MCT-4 proteins in muscle is elevated after a macrocycle of endurance training [19–21]. However, some training studies did not ﬁnd relevant increases in MCT-4 density [22–24]. It has been assumed that MCT-1 production is more sensitive to physical stress than MCT-4. Since the biochemical characteristics of MCT-1 favors lactate uptake, it has been suggested that erythrocytes provide a lactate storage compartment in situations of physical exercise, thereby reducing the exercise-induced increase in plasma lactate concentration [25]. Interestingly, Fransson et al. [26] showed remarkable changes in MCT-4 protein expression after 4 weeks of soccer speciﬁc training regimes like speed endurance (+30%) and small sided games (+61%) in well-trained soccer players. An increase in MCT-1 and MCT-4 density in skeletal muscle after 6 weeks of strength training was however merely reported by Juel et al. [27]. No available study investigated the eﬀects of WB-EMS on relevant endurance capacities like VO2max and MCT-1 and MCT-4 in soccer players. Against this background, the aim of our 3-armed randomized controlled trial was to elucidate whether WB-EBS supplemented to a traditional soccer training routine can improve endurance capacities indices and MCT density of soccer players. Our primary hypothesis was that a training program of jumps with superimposed EMS may pronouncedly stimulate MCT-1 and MCT-4 density. Our secondary hypothesis was that endurance performance surrogates will not be aﬀected by the training program, because of the relatively low additional training volume and the subject´s high overall training status. 2. Material and Methods 2.1. Participants Only healthy ﬁeld soccer players were included which means no cardiovascular or metabolic diseases and no preinjury in the tested muscle groups. Participants needed to compete on a regional level for the last 3 years and train 2–4 session per week with strength and conditioning training contents and play one soccer match per week. In a randomized control trial twenty-eight soccer players from 10 diﬀerent teams were assigned to three diﬀerent groups. Control group was assigned based on preferences and availability, whereas both intervention arms have been assigned based on coin toss. The EMS group (EG, n = 10) performed jumps with superimposed WB-EMS twice a week accompanied by 3 × 10 squat jumps in addition to the daily soccer routine over a period of 7 weeks that is a suﬃcient intervention period with WB-EMS to improve strength abilities [5,28,29]. To diﬀerentiate between the Int. J. Environ. Res. Public Health 2020, 17, 1123 3 of 13 eﬀects caused by EMS and by the squat jumps and soccer training respectively, two control groups were included. A jump training group (TG, n = 10) performed the same number of squat jumps without EMS on the same days as the EG and a control group (CG, n = 8) that only performed the daily soccer routine. All subjects were non-smokers. Basal anthropometric parameters of the subjects were presented in Table 1. This study was carried out in accordance with the recommendations of the “Ethics Committee of the German Sports University Cologne”. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the “Ethics Committee of the German Sports University Cologne” (06–02–2014). Table 1. Anthropometric data (mean ± SD) and Total Training Load (arbitrary units) during the 7-weeks intervention period calculated by Polar Team-2 Software according to training time spent in deﬁned heart rates zones. Group Age [Year] Height [m] Weight [kg] Bodyfat [%] relVO2peak [ml/kgmin-1] Sessions/ Week Total Training Load [a.u.] EG (n = 10) 24.4 ± 4.2 1.82 ± 0.03 81.4 ± 5.3 12.9 ± 2.1 52.1 ± 3.4 3.4 ± 1.2 3431 ± 911 TG (n = 10) 21.1 ± 1.9 1.83 ± 0.06 79.7 ± 5.5 10.8 ± 2.8 56.3 ± 5.7 3.4 ± 1.3 3479 ± 1723 CG (n = 8) 23.6 ± 3.9 1.82 ± 0.05 79.7 ± 7.5 14.1 ± 3.6 54.3 ± 7.2 2.6 ± 0.7 2644 ± 1437 2.2. Daily Soccer Routine The participants performed 3.2 ± 1.0 soccer training sessions per week and competed once a week in the championships. The standard training sessions lasted approximately 90 min including technical skill activities, oﬀensive and defensive tactics, athletic components with various intensities, small-sided game plays and continuous play. In a normal training week during season with a match on Sunday training was scheduled on Tuesdays, Wednesdays (optional), Thursdays and Fridays. Number of training sessions and the training days varied according to the game schedule playing Sunday-Sunday or Sunday-Saturday. The number of training sessions and the total training minutes were documented. The training load was measured according to the training time spent in deﬁned heart rate zones during soccer training or match via Polar Team-2 Software (Polar Electro, Büttelborn, Germany) (see Table 1). The training load [arbitrary units] provided by the Polar-Software aims to determine internal training load based on background variables (sex, training history, metabolic thresholds, and maximal oxygen consumption [VO2max]) and parameters measured during training sessions (exercise mode, and energy expenditure) (c.f. [30]). The heart rate zones (100–90%, 89–80%, 79–70%, 69–60%, 59–50%) were deﬁned according to the individual maximum heart rate measured in the maximal ramp test (see endurance test). The players were asked to maintain their usual food intake und hydration according to the recommendations for soccer players [31] and no nutrition supplementation was used. Additional strength training was not allowed during the study. All players had a constant training volume during the ﬁrst half of the season (July till December) and were in a well-trained condition with a relative VO2peak of 54.2 ± 5.9 mL/kg·min−1. All players regularly conducted strength training during ﬁrst half of the season and had overall experience in strength training of 5.4 ± 3.9 years. The intervention period started after the three weeks mid-season break from end of December till mid of January. During these three weeks the training load was relatively low (moderate endurance training twice per week) in order to maintain ﬁtness level and not negatively aﬀect Baseline testing. 2.3. WB-EMS Application and Protocol In order to obtain a rest interval of 48 h between the two sessions and the championship game on Sunday WB-EMS training was conducted on Tuesdays and Friday. All subjects abstained from alcohol consumption for 24 h prior to and during the training intervention. The EMS Training was conducted with a WB-EMS-system by Miha Bodytec (Augsburg, Germany). WB-EMS was applied Int. J. Environ. Res. Public Health 2020, 17, 1123 4 of 13 with an electrode vest to the upper body with integrated bilaterally two paired surface electrodes for the chest (15 × 5 cm), upper and lower back (14 × 11 cm), latissimus (14 × 9 cm), and the abdominals (23 × 10 cm) and with a belt system to the lower body including the muscles of the glutes (13 × 10 cm), thighs (44 × 4 cm) and calves (27 × 4 cm). Biphasic rectangular wave pulsed currents (80 Hz) were used with an impulse width of 350 µs [5]. The stimulation intensity (mA) was determined and set separately for each muscle group (0–120 mA) by using a Borg Rating of Perceived Exertion [32]. The training intensity was deﬁned for each player in a familiarization session two weeks before and set at a sub-maximal level that still assures a clean dynamic jump movement (RPE 16–19 “hard to very hard”) and was saved on a personalized chip card. The EG performed 3 × 10 maximal squat jumps with a set pause of 60 s (no currents) per session. Every impulse for a single jump lasted for 4 s (range of motion: 2 s eccentric from standing position to an knee angle of 90◦–1 s isometric–0.1 s explosive concentric–1 s landing and stabilisation) followed by a rest period of 10 s (duty cycle approx. 28%). This results in an overall time of 8.5 min per session an eﬀective stimulation time of 2 min per session. The players started with a 2–3 min standardized warm-up with movement preparations including squats, skipping and jumps in diﬀerent variations (squat jumps, jumps out of skipping or double jumps) at a light to moderate stimulation intensity. The players were told to slowly increase the intensity every few impulses. The training started when the players reached the deﬁned training intensity that was saved on the chip card from the last session according to the RPE 16–19 (“hard to very hard”). The stimulation intensity was constantly increased individually every week (Tuesdays) controlled by the coaches in order to maintain a high stimulation intensity. The intensity was increased after the warm-up during the ﬁrst and the second set of 10 squat jumps starting from calves up to the chest electrodes. The TG conducted the same standardized warm-up and performed the same amount of jumps with identical interval and conduction twice per week without EMS. The CG only performed the 2–4 soccer training session plus one match per week. 2.4. Experimental Protocol 2.4.1. Endurance Test and Assessment of Anthropometrics For determination of the endurance parameters spirometry was performed on a WOODWAY treadmill (Woodway GmbH, Weil am Rhein, Germany) one week before (Baseline) and after the 7-weeks intervention period (Post-test) (Figure 1). Furthermore, bodymass and body composition were determined via bioelectrical impendence analysis (TANITA corp., Tokyo, Japan). Endurance tests were conducted three days after the soccer match to assure adequate recovery and not negatively inﬂuence performance. Respiratory gases were analyzed via the ZAN600-System and ZAN-Software GPI 3.xx (ZAN Austria e.U., Steyr-Dietach, Austria), using standard algorithms with dynamic account for the time delay between the gas consumption and volume signal. To calibrate the device according to the manufacturer´s guidelines, a gas mixture consisting of 5% CO2, 16% O2, and rest nitrogen was used (Praxair Deutschland GmbH, Düsseldorf, Germany). To measure the maximum oxygen uptake (VO2peak), the subjects performed an incremental ramp test [33]. Thereby, the players performed a warmup at moderate speed (3 m·s−1) with 1% incline for 3 min. In the last 30 s the incline was increased to 2.5%. Subsequently, running speed was then increased every 30 sec by 0.3 m/s until subjective exhaustion was reported. Heart rate was documented in the last 10s of a ramp stage. The VO2peak was determined as average maximum oxygen uptake of the ﬁrst 20 s after ending the test. Additionally, maximum heart rate, time to exertion (TTE) and maximum lactate concentration (Lamax) was recorded. 27 players completed the two endurance diagnostics. One player of the TG was removed from the study due to an ankle joint injury prior to post testing. Int. J. Environ. Res. Public Health 2020, 17, 1123 5 of 13 Figure 1. Timeline of endurance testing and muscle biopsy withdrawal during the study in the 2nd half of the season. 2.4.2. Muscle Biopsies and Tissue Treatment. Muscle biopsies via Bergström method [34] were taken from each player two weeks before (Baseline) and in the week after the last training intervention (Post-test). All biopsies were obtained under local anaesthetic from the middle portion of the vastus lateralis between the lateral part of the patella and spina iliaca anterior superior 2.5 cm below the fascia. The muscle samples were freed from blood and non-muscular material and embedded in tissue freezing medium (TISSUE TEK, Sakura, Zoeterwoude, The Netherlands). Samples were frozen in liquid nitrogen-cooled isopentane and stored at −80 ◦C for further analysis. The distance between the Baseline and Post-test incision was approx. 2.5 cm. 2.5. Immunohistochemistry Muscle samples from 26 subjects were used for histology. 7 µm cross-sectional slices were obtained from the frozen muscle tissue using a cryo-microtome Leica CM 3050 C (Leica Microsystems, Nußbach, Germany) and placed on Polysine™ microscope slides (VWR International, Leuven, Belgium) [35]. Sections were ﬁxed for 5 min in −20 ◦C pre-cooled acetone and air dried for 10 min at room temperature (RT), before blocking for one hour at RT with TBS (tris buﬀered saline, 150 mM NaCl, 10 mM Tris-HCl, pH 7.6) containing 5% BSA (bovine serum albumin). After blocking, sections were incubated overnight (4 ◦C) with primary antibody for MCT-1 (ab3538P; 1:500; Merck Millipore, Burlington, MA, USA) and MCT-4 (sc-376140; 1:400; Santa Cruz Biotechnology, Dallas, TX, USA), diluted in 0.8% BSA. To conﬁrm antibody speciﬁcity, control sections were incubated in TBS containing 0.8% BSA but without primary antibody. After incubation, sections were washed 5 times short and twice for 10 min with TBS and incubated for one hour with biotinylated goat anti-rabbit secondary antibody for MCT-1 (VECTOR Laboratories, Burlingame, CA, USA), diluted 1:500 in TBS and goat anti-mouse for MCT-4 (VECTOR Laboratories), diluted 1:400 in TBS, at RT. After that, sections were washed again 5 times for 30 s and twice for 10 min before incubation with ﬂuorescent Alexa 488 secondary antibodies (Life Technologies, Carlsbad, CA, USA); diluted 1:500 in TBS for an hour. Afterwards sections were blocked with 5% BSA (TBS-Tween) for 30 min. Slides were then incubated overnight at 4 ◦C with A4.951primary antibodies (A4951; type-I myosin heavy chain; Developmental Studies Hybridoma Bank, Iowa City, IA, USA) diluted 1:200 in 0.8% BSA. On the third day, sections were washed 5 times short and twice for 10 min with TBS before incubated again with secondary antibodies and ﬂuorescent Alexa 555 (red) diluted 1:500 in TBS for an hour at RT. After washing again the samples, ﬁxed on microscope glass slides, were embedded with aqualpolymount and stored at RT. Int. J. Environ. Res. Public Health 2020, 17, 1123 6 of 13 2.6. Data Analysis The analysis of immunoﬂuorescence stained myoﬁbers were conducted with a confocal laser scanning microscope (LSM 510, Zeiss, Jena, Germany) at 63X fold magniﬁcation. For the analysis of MCT density in sarcoplasm and myoﬁber membranes, two laser channels 543 nm (for Alexa 555) and 488 nm (for Alexa 488) were used. Two separate line-scans were conducted per measurement of membrane and sarcoplasmic areas of single myoﬁbers to determine the staining intensity for MCT 1 and MCT 4 and the means was used for analysis (Figure 2). 1000 pixels were standardized analyzed per line scan along the membrane and the sarcoplasm. MCT density was then calculated as the mean staining intensity of all pixels along each line scan. For the analysis of type I ﬁbers, only the green channel (Alexa 488) was used for analysis and the red channel (Alex 555) was used for ﬁber type determination. Laser intensity was standardized for each subject without changing throughout the analysis. Figure 2. Representative pictures of immunoﬂuorescence stained myoﬁber cross-sections showing speciﬁc MCT-1 and MCT-4 staining (green) and type 1 myoﬁber staining (red) within membrane and sarcoplasmic areas of myoﬁbers (10× fold magniﬁcation). (A) MCT-4 Posttest, (B) MCT-1 Posttest. 2.7. Statistical Analysis To determine the eﬀect of the training interventions on endurance parameters, MCT-1 and MCT-4, separate 2 (time: pre vs. post) × 3 (group: EG, TG, CG) mixed ANOVA with repeated measures were conducted. ANOVA assumption of homogenous variances was tested using Maulchy-test of Sphericity. A Greenhouse-Geisser correction was used when a violation of Mauchly´s test was observed. To estimate overall time and interaction eﬀect sizes, partial eta squared (η2p) was computed with η2p ≥ 0.01 indicating small, ≥0.059 medium and ≥0.138 large eﬀects [36]. If 2 × 3 mixed ANOVA revealed a timegroup interaction eﬀect on any variable, this eﬀect was further investigated using Bonferroni post hoc tests for pairwise comparison. For all inferential statistical analyses, signiﬁcance was deﬁned as a p-value less than 0.05. All descriptive and inferential statistical analyses were conducted using SPSS 25® (IBM®, Armonk, NY, USA). Results were presented as means and standard deviations (SDs). Figures were created with Prism 6 (GraphPad Software Inc., La Jolla, CA, USA). Int. J. Environ. Res. Public Health 2020, 17, 1123 7 of 13 3. Results 3.1. Training Load No signiﬁcant diﬀerences were observed between the groups in the total number of training sessions (EG 23.9 ± 7.8; TG 25.9 ± 6.6; CG 18.1 ± 5.6 sessions), training minutes (EG 2103 ± 630; TG 1812 ± 919; CG 1437 ± 381Min), and the total recorded training load via Polar Team-2 software (Table 1). All subjects of TG and EG had a compliance of 100% (14 training sessions) for jump training and WB-EMS sessions, respectively. 3.2. Endurance Parameters 2 × 3 (time × group) ANOVA of repeated measures revealed no signiﬁcant time, group or interaction eﬀect for relative VO2peak, TTE and Lamax. No group diﬀerences were observed at Baseline or Posttest in none of the analyzed parameters (Figure 3). Figure 3. (A) Relative maximum oxygen uptake (relVO2peak), (B) maximal lactate concentration, (C) maximal running time till exertion (TTE), and (D) maximal heart rate) determined at the endurance ramp-test on the treadmill in EMS-Group (EG), Training-Group (TG) and Control-Group (CG) measured before (Baseline) and after the 7 weeks intervention period (Posttest). Values are presented in means ± SD. 3.3. MCT-4 3.3.1. Type-I Fibers The 2 × 3 (time × group) repeated measures ANOVA revealed no signiﬁcant time (p = 0.119, η2p = 0.102), group or intervention eﬀect (p = 0.165, η2p = 0.145) for the MCT-4 density in the membrane of type-I muscle ﬁbers. Regarding cytoplasm density of the MCT-4, a large signiﬁcant eﬀect over time (p = 0.009, η2p = 0.26) was shown. No grouptime eﬀect (p = 0.318, η2p = 0.095) was however observed. Subsequent post-hoc analysis showed a signiﬁcant decrease in MCT-4 density after 7 weeks for TG only (p = 0.005). No group diﬀerences were detected at Baseline and Posttest for MCT-4 density in membrane and cytoplasm in type-I ﬁbers (Figure 4). Int. J. Environ. Res. Public Health 2020, 17, 1123 8 of 13 3.3.2. Type-II Fibers With respect to the membrane density of the MCT-4, no time eﬀect (p = 0.172, η2p = 0.079) or grouptime interaction (p = 0.315, η2p = 0.096) of type-II ﬁbers was shown. For the cytoplasm density of the MCT-4 a large signiﬁcant main eﬀect for the factor time (p = 0.001, η2p = 0.382) was observed in type-II ﬁbers. No grouptime interaction eﬀect (p = 0.333, η2p = 0.091) was observed. Subsequent post-hoc analysis showed a signiﬁcant decrease in MCT-4 distribution only for TG (p = 0.004). No diﬀerences were shown between the groups at Baseline or Posttest for MCT-4 density in the membrane and cytoplasm of the type-II ﬁbers (Figure 4). Figure 4. MCT-4 density in type-I ﬁber (A) membrane and (B) cytoplasm, and in type-II ﬁber (C) membrane and (D) cytoplasm for EMS-Group (EG), Training-Group (TG) and Control-Group (CG) measured before (Baseline) and after the 7 weeks intervention period (Posttest). Values are presented in means ± SD. 3.4. MCT-1 3.4.1. Type-I Fibers The 2 × 3 (time × group) repeated measure ANOVA showed no signiﬁcant eﬀect over time (p = 0.230, η2p = 0.065) as well as no signiﬁcant grouptime interaction eﬀect (p = 0.045, η2p = 0.246) for MCT-1 density in the membrane. Post-hoc analysis showed a signiﬁcant decrease in density for the TG (p = 0.032). For the cytoplasm density of the MCT-1 no main eﬀects over time (p = 0.114, η2p = 0.110) or group*time (p = 0.416, η2p = 0.077) were found. No group diﬀerences were detected at Baseline and Posttest for MCT-1 density in the membrane and cytoplasm of the type-I ﬁbers (Figure 5). 3.4.2. Type-II Fibers The 2 × 3 ANOVA revealed a large signiﬁcant time eﬀect for cytoplasm MCT-1 (p = 0.009, η2p = 0.269) but no group × time interaction eﬀect (p = 0.933, η2p = 0.006). However, no signiﬁcant alternations in the cytoplasm were found in the three diﬀerent groups over time. For membrane density of the MCT-1 neither a time (p = 0.104, η2p = 0.115) nor an interaction eﬀect (p = 0.480, η2p = 0.065) was observed in type-II ﬁbers. Group comparison revealed no diﬀerences between the three groups at baseline and post-testing for MCT-1 density in the membrane and cytoplasm of the type-II ﬁbers (Figure 5). Int. J. Environ. Res. Public Health 2020, 17, 1123 9 of 13 Figure 5. MCT-1 density in type-I ﬁber (A) membrane and (B) cytoplasm, and in type-II ﬁber (C) membrane and (D) cytoplasm for EMS-Group (EG), Training-Group (TG) and Control-Group (CG) measured before (Baseline) and after the 7 weeks intervention period (Posttest). Values are presented in means ± SD. 4. Discussion The main ﬁnding of this intervention is that 7 weeks of a dynamic WB-EMS program (2 sessions per week) in addition to the regular soccer training does not relevantly inﬂuence endurance performance indices as well as MCT-1 or MCT-4 density in the muscle. We surprisingly observed that MCT-4 density in the cytoplasm and MCT-1 density in the membrane of type I muscle ﬁbers notably decreased in TG, the group that completed jumps without EMS. Participants of all three groups (EG, TG, CG) did their weekly soccer sessions since years and training volume and training intensity was not changed during the intervention period. Thus, the results could be explained by the high overall training status of the subjects. Due to the documented eﬀects of strength training on runner´s performance [37] and eﬀects of EMS application on runner´s VO2max [6], we analysed eﬀects on some endurance parameter for EG. Indeed, WB-EMS intervention with dynamic exercises and training status of the subjects (VO2max: 53 mL/min/kg) were similar to the study of Amaro-Gahete et al. [6]. However, the diﬀerential results might be attributed to diﬀerences in current frequency (12–90 vs. 85 Hz), higher time under tension (6 vs. 2 min) and higher intensities of exercises with superimposed EMS (strength and interval exercise vs. jumps) in the cited study. With regard to the training status of the subjects and the general high metabolic demand in soccer games and -training, the EMS stimulus could have been too low for further adaptations in endurance capacities. Consequently, no changes were observed in any parameter obtained during incremental treadmill running test. With respect to the results of Amaro-Gahete and coworkers and in order to improve endurance parameter, it might be promising to adjust exercise to higher training intensities, e.g., by shortening rest intervals of jumps or include other exercises within high intensity intervals. The authors provided recommendations for an undulating modulation of current adjustments [38], but without physiological explanation or reasoning. There are no studies available that support these results and we found only one study that applied EMS during endurance training. In this regard, Mathes and coworkers showed that, although metabolic stimuli and markers of muscle damage were higher in cycling with superimposed EMS compared to cycling without EMS, improvements of endurance performance and capacity were not signiﬁcantly diﬀerent between both training methods [39]. Int. J. Environ. Res. Public Health 2020, 17, 1123 10 of 13 The disposed EMS-protocol concurrently to soccer training enhanced strength and myoﬁber adaptations [40]. Furthermore it revealed to be eﬀective for accelerations, direction changes, vertical jumping ability and kicking velocity in elite soccer players [5]. Training design was identical within the present study. Improving such surrogate parameters of aerobic or anaerobic endurance capacities seems also promising to improve indices of soccer performance. The ability to perform sprints with high intensity bouts is inﬂuenced by anaerobic capacity. The ability to do that repeatedly critically depends on the aerobic metabolism. Both metabolic pathways are inter-linked with each other. However, for the recommendation of WB-EMS, it would be also important that no degradation occurs since soccer players need concurrent abilities of strength, speed, and endurance in the sense of repeated high-intensity actions. MCT-1 and MCT-4 content in the muscle was not inﬂuenced by the intervention of EG. Interventions that showed increases of MCT-1 and MCT-4 conducted higher intensities and metabolic demanding exercises. It is known that high-intensity endurance training increases MCT-1 in trained subjects [41,42] and strength training increases MCT-1 and MCT-4 in untrained subjects [27]. The training program of 3 × 10 maximum jumps and 10 s between each jump was seemingly less intense. Indeed, jumps are metabolically demanding, but 10 s of rest enable adequate delivery of oxygen. Unfortunately, lactate accumulation was not measured during training in the present study. However, hundreds of repeated jumps with 8 s rest between the jumps can result in moderate steady state lactate concentrations of 3–4 mmol·L−1 [43]. It might be a question of the stimulus´ intensity or accumulation that need to be analyzed in exercise constellations that increase metabolic stimulus like high intensity interval training. The superimposed WB-EMS on/oﬀ-time ratio should be increased accordingly. Our results show a signiﬁcant decrease in MCT-4 and MCT-1 content after jump training without EMS (TG), which can be hardly explained, as EG and CG did not show signiﬁcant diﬀerences in MCT-4 and MCT-1 content. Although the EG and TG showed equal training load (see Table 1) generally, high-intensity anaerobic eﬀort in soccer greatly varies according to the playtime and diﬀerent playing position requirements within a squad [44,45]. Furthermore, there can be diﬀerences of intensity in daily soccer training routine that could lead to ﬂuctuations of MCT´s. This speculation is indicated by large standard deviations in total training load of TG (Table 1). Although subjects were assigned to play and train as usual, it was not possible to adjust for this inﬂuence in the study. Replication studies with accelerometer-based monitoring of the total loads are required to verify this issue. Additionally, ﬁndings warrant further studies about strength training eﬀects to MCT. In this regard, authors have demonstrated the importance of detailed characterization of the training stimulus and the subjects [46,47]. A speciﬁcation of muscular time under tension and movement dynamics like reactivity are missing in recent studies that dealt with EMS or MCT´s. The reduction eﬀect of MCT could also be attributed to a shift of MCTs to the membrane. A previous study has shown that MCT-1 localisation after training in diabetic patients increased in the sarcolemma of muscle ﬁbers while the sarcoplasmic content was reduced [48]. Some more limitations of the present study have to be mentioned for further research on the eﬀects of WB-EMS to endurance capacities. Due to the small sample size the study has pilot character. Since we did not measure lactate production during training, we are not able to characterize the metabolic stimulus of the training. Moreover, the eﬀects of running performance mainly include improved running economy, time trial performance and sprint performance all of which were not tested in the current work. A more sports-speciﬁc testing set such as the Yo-Yo Intermittent Recovery test in combination to sprint tests would have been useful in terms of ecological validity. A Further aspect of limitation is that including players from diﬀerent teams can result in diﬀerences in training sessions for the CG (Table 1). A more detailed documentation of players match and training loads would be helpful to avoid bias. Future research may consider changing study design to evoke higher metabolic stimuli by increasing time under tension, reducing rest intervals or increasing intervention duration. However, the present training stimulus was designed to improve soccer speciﬁc high-intensity actions and could be integrated into daily training on a professional level [5]. Int. J. Environ. Res. Public Health 2020, 17, 1123 11 of 13 5. Conclusions We conclude despite ﬁndings that the disposed WB-EMS protocol can enhance strength [5] and myoﬁber adaptations [40] it is not a potent stimulation to improve VO2max and lactate transport proteins. Author Contributions: Conceptualization: A.F. and N.W.; Methodology: A.F., T.S. and M.d.M.; Software: A.F.; Validation: S.G., N.W. and A.F.; Formal Analysis: A.F.; Investigation: A.F., T.S. and M.d.M.; Resources: W.B.; Data Curation: A.F.; Writing—Original Draft Preparation: N.W.; Writing—Review and Editing: L.D., S.G. and N.W.; Visualization: A.F.; Supervision: L.D.; Project Administration: A.F.; Funding Acquisition: A.F. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the German Federal Institute of Sport Science, grant number AZ 070101/16-17. 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Physiol. Pharm. 2014, 92, 259–262. [CrossRef] [PubMed] © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).	Seven Weeks of Jump Training with Superimposed Whole-Body Electromyostimulation Does Not Affect the Physiological and Cellular Parameters of Endurance Performance in Amateur Soccer Players.	02-10-2020	Wirtz, Nicolas,Filipovic, André,Gehlert, Sebastian,Marées, Markus de,Schiffer, Thorsten,Bloch, Wilhelm,Donath, Lars	eng
PMC4473093	Electronic Supplementary Material Appendix S1 Specification of the search strategy used in the Pubmed database: (488 HITS, 23th of june, 2014) (((("Running"[Mesh]) AND (((("Athletic Injuries"[Mesh]) OR running injur) OR running-related injur) OR "1000 hours")) NOT ((((((((((((((((((((((((("Addresses"[Publication Type]) OR "Bibliography"[Publication Type]) OR "Biography"[Publication Type]) OR "Case Reports"[Publication Type]) OR "Clinical Conference"[Publication Type]) OR "Comment"[Publication Type]) OR "Congresses"[Publication Type]) OR "Dictionary"[Publication Type]) OR "Directory"[Publication Type]) OR "Editorial"[Publication Type]) OR "Festschrift"[Publication Type]) OR "Government Publications"[Publication Type]) OR "Interview"[Publication Type]) OR "Lectures"[Publication Type]) OR "Legal Cases"[Publication Type]) OR "Legislation"[Publication Type]) OR "Letter"[Publication Type]) OR "News"[Publication Type]) OR "Newspaper Article"[Publication Type]) OR "Retracted Publication"[Publication Type]) OR "Retraction of Publication"[Publication Type]) OR "Review"[Publication Type]) OR "Scientific Integrity Review"[Publication Type]) OR "Technical Report"[Publication Type]) OR "Validation Studies"[Publication Type])) NOT "Soccer"[Mesh]) NOT "Football"[Mesh] Filters: Danish; English	Incidence of Running-Related Injuries Per 1000 h of running in Different Types of Runners: A Systematic Review and Meta-Analysis.	[]	Videbæk, Solvej,Bueno, Andreas Moeballe,Nielsen, Rasmus Oestergaard,Rasmussen, Sten	eng
PMC8838374	Citation: Martínez-Rodríguez, A.; Miralles-Amorós, L.; Vicente-Martínez, M.; Asencio-Mas, N.; Yáñez-Sepúlveda, R.; Martínez-Olcina, M. Ramadan Nutritional Strategy: Professional Soccer Player Case Study. Nutrients 2022, 14, 465. https://doi.org/ 10.3390/nu14030465 Academic Editor: Lauri Byerley Received: 17 December 2021 Accepted: 18 January 2022 Published: 21 January 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). nutrients Article Ramadan Nutritional Strategy: Professional Soccer Player Case Study Alejandro Martínez-Rodríguez 1,2,* , Laura Miralles-Amorós 1 , Manuel Vicente-Martínez 3, Nuria Asencio-Mas 1, Rodrigo Yáñez-Sepúlveda 4 and María Martínez-Olcina 1 1 Department of Analytical Chemistry, Nutrition and Food Science, Faculty of Sciences, University of Alicante, 03690 Alicante, Spain; laura.miralles@ua.es (L.M.-A.); niam1@gcloud.ua.es (N.A.-M.); maria.martinezolcina@ua.es (M.M.-O.) 2 Alicante Institute for Health and Biomedical Research (ISABIAL Foundation), 03010 Alicante, Spain 3 Faculty of Health Science, Miguel de Cervantes European University, 47012 Valladolid, Spain; mvmartinez11006@alumnos.uemc.es 4 Escuela de Educación, Pedagogía en Educación Física, Universidad Viña del Mar, Viña del Mar 7055, Chile; rodrigo.yanez@uvm.cl * Correspondence: amartinezrodriguez@ua.es Abstract: The period of Ramadan induces changes in the usual eating patterns of individuals. During this period, Muslims must abstain from drinking and eating from dawn to dusk. Therefore, some research conducted on professional soccer players has observed that during and/or after Ramadan, performance, running speed, agility, dribbling speed, and endurance and/or skill performance in athletic events may be negatively affected by Ramadan intermittent fasting (RIF). The objective of this study was to analyze the inﬂuence of a dietary plan during RIF on performance and body composition in a professional soccer player. A 20-year-old elite player (86.0 kg, 188.5 cm) followed a dietary- nutritional plan with an isocaloric diet and was supplemented with glycerol. The athlete’s strength and power in the lower limbs was assessed by performing a countermovement jump (CMJ) and Abalakov vertical jump (ABK) before and after Ramadan. After nutritional planning, the patient’s body composition improved in terms of fat loss (6.61 to 5.70%) and muscle mass gain (50.26 to 51.50%). In addition, this translated into improvements in performance tests, both in the CMJ (36.72 to 40.00 cm) and ABK (39.16 to 49.34 cm). In conclusion, during a period of fasting, personalised nutritional planning and an appropriate supplementation and rest protocol can improve the body composition and performance of soccer players. Keywords: nutrition; sport; athletic performance; sports supplements; body composition; dietary tools 1. Introduction The period of Ramadan induces changes in the usual dietary patterns of individu- als; during this period, Muslins must abstain from drinking and eating from sunrise to sunset [1]. According to the Holy Quran, it takes place in the ninth month of the Islamic calendar, and, as a lunar month, it lasts between 29 and 30 days [2]. According to the Federation International Football Association (FIFA), soccer has pro- moted fair play, heterogeneity and fairness and is an internationally recognized sport [1]. The month of Ramadan often coincides with the calendars of many soccer leagues [1]. Researchers have proceeded to conduct studies on adult and youth Muslim soccer players because of their commitment to their faith and the game. It has been shown that, dur- ing and/or after Ramadan, performance, running speed, agility, dribbling speed, and endurance and/or skill performance in athletic tests can be negatively affected by Ramadan intermittent fasting (RIF) [3,4]. In the same way, sleep duration is compromised, as dinners and breakfasts are usually early during Ramadan [5,6]. Indeed, by altering the sleep-wake cycle of circadian rhythms, Nutrients 2022, 14, 465. https://doi.org/10.3390/nu14030465 https://www.mdpi.com/journal/nutrients Nutrients 2022, 14, 465 2 of 8 a modiﬁcation in anticipation and adaptation to environmental changes occurring during the day may occur in the metabolism and cardiovascular system [2]. Therefore, biological rhythms and how they affect human performance must be taken into account, as some factors, such as hepatic and muscular glucogen stores, ﬂuid stores, decreased blood glucose levels, increased uric acid and the risk of dehydration during prolonged physical activity, could negatively impair athletic performance [7]. Currently, there are no studies evaluating the effects of Ramadan that intervene at the dietary-nutritional level, so there are still no adapted plans for practitioners practicing RIF [1,8]. However, the variation in total energy intake before and after Ramadan has been evaluated [6,9,10]. In these investigations, it has been observed that the intake was insufﬁcient to meet the needs of the athletes and, in addition, the estimated total daily energy intake and the relative proportions of carbohydrate, fat and protein in the diet did not change during Ramadan in the participants [6,9,10]. Physical performance tests such as counter movement jump (CMJ) and Abalakov jump are often used in these investigations to assess recovery from baseline levels and serve to measure power and fatigue [11,12]. Results for CMJ have been inconclusive in the literature [8]. Rebaï et al. [13] observed signiﬁcant increases in CMJ during Ramadan; the increases were signiﬁcant during versus before Ramadan for the group that reduced their training load. However, some of the studies found do not evaluate the effect of RIF on Abalakov’s jumping tests, with free arm jumps being predominant in this sport [9,10,13]. Another aspect that inﬂuences sports performance and can be affected by RIF is body composition. In soccer, it is important to control body fat, as optimal fat levels allow players to move more efﬁciently during training and matches [11,14]. Further relevant is the lean mass compartment, speciﬁcally muscle mass, because insufﬁcient or excessive training loads can impact the physique, affecting performance, as well as speed, strength, power and injury risk [15]. Although inconsistent ﬁndings on body composition for RIF have been observed in the scientiﬁc literature [1,8], some studies use data obtained from bioimpedance or the summation of 4 folds [6,9]. In addition, other research proposes, but does not carry out, the complete proﬁle of 5 components: skin, body fat, muscle mass, bone tissue and residual mass. This model allows to get a photographic shape of the athlete’s proﬁle [16]. Another aspect that lacks research so far is the medio somatotype and the Heath and Carter anthropometric method classiﬁcation [17] for Muslim RIF performers. In order to respond to all these gaps in the scientiﬁc literature, the main objective of this research was to describe the effects of RIF with personalized dietary-nutritional planning on the athletic performance and body composition of an elite soccer player. The initial hypothesis was that performance would be negatively affected by the month of Ramadan. 2. Materials and Methods 2.1. Study Design A case report was used to analyse the inﬂuence of RIF on performance and body composition in a professional soccer player. In this design, an initial measurement prior to the intervention (start) and a measurement after the intervention (ﬁnal) was conducted. The participant was informed about the possible risks and discomforts that could arise and was asked to complete a health history questionnaire and sign a consent form. The research strictly followed the Declaration of Helsinki (Edinburgh review in 2000), and the procedures were also in accordance with the recommendations of the EEC Good Clinical Practice (document 111/3976/88 of July 1990). The subject signed an informed consent to participate in the study after receiving all the information. All procedures were previously approved by the Ethics Committee of the University of Alicante (UA-2021-03-11). 2.2. Participant The participant was a 20-year old African soccer player since he was 5 years old, having now 15 years of experience. He has been playing soccer at a professional level, for Nutrients 2022, 14, 465 3 of 8 the team in La Liga for the last two seasons. He usually trains 12 h per week and his only dedication is soccer. He is healthy, does not take any medication, has never undergone surgery and has an optimal blood biochemistry. 2.3. Data Collection 2.3.1. Body Composition Body weight was measured early in the morning, fasting and with minimal clothing, using a Tanita BC-545n, (Tanita Corporation, Arlington Heights, IL, USA) to the near- est 0.1 kg. Standing height without shoes was measured using a Seca 213 stadiometer (Seca, Hamburg, Germany) to the nearest 0.1 cm. To minimize the potential source of Bioimpedance systems (BIA) related to total body weight and height, body composition assessment was performed by a level three anthropometrist following the International Society for the Advancement of Kinanthropometry (ISAK) recommendations [18]. All mea- surements were performed at the same location, at room temperature and under baseline conditions. The technical error of measurement for perimeters, circumferences, lengths and heights was less than 1% and less than 5% for skinfolds. Anthropometric measurements were performed following the complete proﬁle of ISAK II methodology [18]. Skinfolds, girths, lengths and breadths were measured with a caliper, ﬂexible metallic tape, segmometer and pachymeter, respectively (Holtain, Crymych, UK). Bone and muscle mass were obtained through Rocha’s equation [19] and Lee’s for- mula [20], respectively. Fat mass was estimated using Carter’s formula [21], Faulkner formula [22] and Withers’ equation [23]. Residual mass was calculated from the difference between the total body weight minus the sum of the bone, muscle and fat masses. Accord- ing to the Spanish Committee of Kinanthropometry, these methods are the most suitable for high performance players [24]. Somatotype components were calculated from the assessment of different body com- partments, including fat mass for endomorphy, muscle mass for mesomorphy, and leanness and relative bone linearity for ectomorphy. The differences between each individual so- matopoint with respect to the mean value was calculated using the somatotype attitudinal mean (SAM) [25,26]. Proportionality analyses were performed using the Phantom stratum [19]. Each variable was ﬁtted to the Phantom size using the z-score. The z-values have a mean of 0, so a z-value of 0.0 indicates that the given variable is proportionally equal to the Phantom; a z- value greater than 0.0 means that the subject is proportionally greater than the Phantom for that variable; and, conversely, a z-value less than 0.0 shows that the subject is proportionally less than the Phantom for the variable [19]. 2.3.2. Performance Measures As indicators of global strength, the countermarch jump (CMJ) and the Abalakov jump test [27] were performed. In the CMJ test, the participant performed a maximal vertical jump starting from a standing position, without allowing the arms to swing, and bending the knees 90◦. The participant performed several familiarisation trials prior to the test [28]. A contact platform (Optojump Next Microgate, Bolzano, Italy) was used to measure the CMJ. Three measurements were performed with 30 s recovery in between. The ﬂight time was used to calculate the jump height. The best jump was used for subsequent analysis [29,30]. The Abalakov jump test followed the same protocol. The participant also performed 3 countermovement jumps with 30 s rest between jumps, on a platform with an optical (infrared) data collection system (Optojump Next Microgate, Bolzano, Italy) to calculate the Abalakov jump height [31]. The player had to stand up and perform a 90◦ knee ﬂexion followed by the fastest extension to reach the highest possible jump height. Of the three results, the best one was used for statistical analysis. Nutrients 2022, 14, 465 4 of 8 2.4. Nutritional Intervention Protocol 2.4.1. Nutrient Intake For the dietary-nutritional intervention, quantitative estimates were made of total energy expenditure based on basal metabolism, using the Harris–Benedict formula [32] and corrected body weight. Physical activity expenditure was estimated from standardized factors [33]. The proposed diet was based on an isocaloric intake following the recommen- dations for elite soccer players [34]. Table 1 shows the values of macronutrients and vitamin B12 provided by the dietary- nutritional guideline established according to the recommendations for elite soccer players. Fiber intake was around 25 g per day and total cholesterol was less than 200 mg per day. The software used in the elaboration of the diet was Dietopro (Dietopro, Valencia, Spain) [35]. Table 1. Dietary nutrient intake. Calories (Kcal) 3432.1 Carbohydrates (g) 398.1 Proteins (g) 219.4 Fat (g) 112.7 Vitamin B12 (mg) 17.3 Carbohydrates (g/kg/day) 4.6 Protein (g/kg/day) 2.5 PFA/MFA 1.2 Kcal = kilocalories; g = grams; kg = kilograms; PFA = polyunsaturated fatty acids; MFA = monounsaturated fatty acids. Because the time of ingestion is different than usual during the RIF, a schedule was established with SK for each of the different meals (Table 2). Table 2. Meal times. Intake Time of Day Breakfast 5:30–6:00 Snack 20:00 Dinner 22:30 Snack 0:30 2.4.2. Supplementation Glycerol (1,2,3-propanetriol) is produced from glucose, protein, pyruvate, triacylglyc- erols and other glycerolipid metabolic pathways and is a binding metabolite in numerous pathways [27]. During RIF, glycerol is the only source of gluconeogenesis, as glycogen stores are depleted within two days of fasting [28]. Considering its role as an energy substrate, glycerol could effectively improve sports performance [28]. The combined ingestion of glycerol and ﬂuid has been used to increase body water volume, thus maintaining hydration by reducing the rate of water elimination from the kidneys [27]. The intake protocol is shown in Table 3. Table 3. Glycerol intake protocol. Time Glycerol (g) Liquid (L) 1–3 days 25 0.5 4–6 days 50 1 7–10 days 75 1.5 11–ﬁnal 100 2 g = grams; L = liters. Nutrients 2022, 14, 465 5 of 8 3. Results 3.1. Body Composition The soccer player remained weight-stable throughout the study period (the initial weight was 86.1 kg, while at the end of the intervention the weight reached 85.9 kg). The athlete claimed to have followed the entire nutritional plan, based on 3400 kcal, 4.6 g/kg/day of carbohydrates and 2.5 g/kg/day of protein. Table 4 shows the results of the body composition assessment between the beginning and the end of the RIF. It can be observed that both weight and fat mass percentage decreased after the intervention. However, muscle mass increased (from 50.26 to 51.50%). The somatotype presented is ecto-mesomorphic, where mesomorphy predominates and ectomorphy is higher than endomorphy. Table 4. Body composition results. Body Composition Start Final Weight (kg) 86.1 85.9 Height (cm) 188.5 188.5 BFM Carter (%) 3.80 3.30 BFM Faulkner (%) 9.30 8.90 BFM Withers + Siri (%) 6.61 5.70 Muscle mass (kg) Lee 2000 43.27 44.20 Muscle mass (%) Lee 2000 50.26 51.50 Bone mass (kg) Rocha 13.96 14.01 Bone mass (%) Rocha 16.21 16.31 Residual mass (kg) 23.18 22.81 Residual mass (%) 26.92 26.55 Endomorphy 1.38 1.16 Mesomorphy 5.46 5.98 Ectomorphy 2.67 2.69 BFM: Body Fat Mass; kg = kilograms; cm = centimeters; % = percentage. With regard to skinfolds, the triceps (4.5 to 3.5 mm), subscapular (8.2 to 7.6 mm), biceps (3.4 to 2.5 mm), supraspinal (4.5 to 4.2 mm), abdominal (5.8 to 4.8 mm), thigh (6.3 to 6.1 mm) and leg (4.3 to 2.8 mm) skinfolds decreased, while the triceps (4.3 to 2.8 mm) skinfold decreased, (5 to 4.2 mm), abdominal (5.8 to 4.8 mm), thigh (6.3 to 6.1 mm) and leg (4.3 to 2.8 mm) decreased, while the iliac crest crease increased (6.1 to 6.4 mm). Differences were observed in the different variables for anthropometric dimensions and proportionality proﬁle. The results for Z-perimeter calf (0.06 to 0.20), Z-perimeter thigh (−0.08 to −0.25), Z-perimeter hip (−0.95 to −1.72), Z-perimeter waist (−1.17 to −1.20), Z-perimeter forearm (1.96 to 2.28), Z-perimeter ﬂexed arm (2.49 to 3.15) and Z-perimeter relaxed arm (1.98 to 2.06) were higher after the intervention. However, the Z-perimeter mesosternale decreased from −0.28 to −0.20. 3.2. Sport Performance Regarding sport performance results, lower limb power measured with the CMJ and Abalakov jumps tests shows a considerable improvement in performance after the RIF and the dietary-nutritional intervention performed. The variation of the Abalakov jump was from 39.16 to 49.34 cm, while the countermovement jump improved from 36.72 to 40 cm. 4. Discussion The main objective of this research was to evaluate body composition, physical con- dition (through different physical tests) and a personalized dietary-nutritional plan in a professional soccer player at the beginning and at the end of the Ramadan period. Differences in daily energy intake among different studies could be due to dietary habits, cultural customs, biopsychosocial environment, and geographical differences of the study participants. The present ﬁndings demonstrate that the estimated total daily energy Nutrients 2022, 14, 465 6 of 8 intake was not affected by Ramadan fasting, since the player followed the dietary pattern, where the nutritional plan was adapted to the fasting schedule [29–31]. In terms of body composition, the player presented lower initial characteristics of muscle mass and higher fat mass compared to the end of the study. This can also be seen in the measurement of skinfolds, where all decreased, except for the iliac crest. So far, no study has analyzed these parameters in such depth. Only one study by Durnin and Womersley et al. [36] analyzed the sum of 4 skinfolds (triceps, biceps, subscapular and suprailiac); it was observed that there was no signiﬁcant difference after the RIF period [37]. Another investigation by Meckel et al. [9] found a slight increase in skinfolds. These studies did not include the implementation of a personalized dietary plan, so the results may be due to an inadequate diet. It should be noted that this is the ﬁrst study to investigate the effect of a dietary- nutritional planning adapted to Ramadan on lower limb physical performance variables and 5-component body composition. This study consists of the CMJ and Abalakov jump tests, being the ﬁrst study to evaluate Abalakov in a Muslim professional athlete. In reference to the scores, all were higher after the intervention. Therefore, the player had better lower body power at the end of the study than at the beginning. These results at ﬁrst indicate an increase in physical performance tests, however, they are different from those obtained in previous studies, as some studies showed that Ramadan fasting decreases physical performance [9,37], while others revealed no effect [3,4,6]. Furthermore, since Abalakov tests can be used as a performance indicator and as part of the selection process of youth soccer players for national teams [38], future research should include this test in its variables. On the other hand, some researchers [27] underline the importance of the qualities of glycerol for success in aerobic and anaerobic activities, emphasizing the effect on perfor- mance, since it can be used as an energy substrate. The ﬁndings of such supplementation evidence its relevance and usefulness in the athlete population, since it has been observed that hypohydration decreases maximal aerobic power [27]. Therefore, the effects of such supplementation could have had a positive impact on the performance tests of the subject included in the present study, following the same line as other investigations [39]. Existing scientiﬁc evidence shows that adequate hydration and nutrition are essential to improve performance in soccer during Ramadan periods [1,40]. It should be borne in mind that soccer is a high-intensity intermittent sport played in hot environments, so there is an increased risk of dehydration, with sweating rates in elite players of 1–2.5 L/h [39]. Therefore, both physiological function and cognitive and athletic performance are com- promised. To maximise the effects of training during Ramadan and better adapt to the conditions presented, it is essential to make appropriate dietary choices, ensuring optimal energy intake during periods of high-intensity, long-duration training [1]. The results of this case study should be interpreted with caution, as they are based on the performance of a single athlete using an experimental protocol (n = 1) in which neither the participant nor the researchers were unaware of the project during the intervention phase. In fact, only one set of performance tests was conducted at the beginning and end of the intervention. Future research will consider the previously mentioned limitation. It is recommended that researchers in the ﬁeld provide more detailed information on ﬁtness assessment, body composition and dietary habits in future studies on professional soccer players and the RIF, as research is still scarce. 5. Conclusions During a fasting period such as Ramadan, an optimal and personalized nutritional planning according to the demands and needs of each athlete, accompanied by a supple- mentation of glycerol and rest protocol, allows for improvements on body composition and lower limb performance indicators in soccer players. Author Contributions: Conceptualization, A.M.-R. and M.M.-O.; methodology, A.M.-R. and R.Y.-S.; software, M.V.-M.; validation, R.Y.-S. and A.M.-R.; formal analysis, M.M.-O.; investigation, M.M.-O., Nutrients 2022, 14, 465 7 of 8 L.M.-A., N.A.-M. and M.V.-M.; resources, M.V.-M.; data curation, L.M.-A. and N.A.-M.; writing— original draft preparation, M.M.-O. and L.M.-A.; writing—review and editing, A.M.-R. and R.Y.-S.; visualization, M.V.-M.; supervision, A.M.-R.; project administration, A.M.-R. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and also approved by the Ethics Committee of the University of Alicante (UA-2021-03-11, 26 June 2021). Informed Consent Statement: Written informed consent has been obtained from the patient(s) to publish this paper. 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PMC4687124	RESEARCH ARTICLE The Dynamics of Speed Selection and Psycho-Physiological Load during a Mountain Ultramarathon Hugo A. Kerhervé1,2, Guillaume Y. Millet3,4, Colin Solomon1 1 School of Health and Sport Sciences, University of the Sunshine Coast, Sippy Downs, Australia, 2 Laboratoire de Physiologie de l’Exercice, EA-4338, Université Savoie Mont Blanc, Le Bourget-du-Lac, France, 3 Human Performance Laboratory, University of Calgary, Calgary, Canada, 4 Laboratoire de Physiologie de l’Exercice, Université de Lyon, F–42023, Saint–Etienne, France hugo-alain.kerherve@univ-smb.fr Abstract Background Exercise intensity during ultramarathons (UM) is expected to be regulated as a result of the development of psycho-physiological strain and in anticipation of perceived difficulties (duration, topography). The aim of this study was to investigate the dynamics of speed, heart rate and perceived exertion during a long trail UM in a mountainous setting. Methods Fifteen participants were recruited from competitors in a 106 km trail mountain UM with a total elevation gain and loss of 5870 m. Speed and gradient, heart rate (HR) and ratings of perceived exertion (dissociated between the general [RPEGEN] and knee extensor fatigue [RPEKE] and collected using a voice recorder) were measured during the UM. Self-selected speed at three gradients (level, negative, positive), HR, RPEGEN and RPEKE were deter- mined for each 10% section of total event duration (TED). Results The participants completed the event in 18.3 ± 3.0 h, for a total calculated distance of 105.6 ± 1.8 km. Speed at all gradients decreased, and HR at all gradients significantly decreased from 10% to 70%, 80% and 90%, but not 100% of TED. RPEGEN and RPEKE increased throughout the event. Speed increased from 90% to 100% of TED at all gradients. Average speed was significantly correlated with total time stopped (r = -.772; p = .001; 95% confidence interval [CI] = -1.15, -0.39) and the magnitude of speed loss (r = .540; p = .038; 95% CI = -1.04, -0.03), but not with the variability of speed (r = -.475; p = .073; 95% CI = -1.00, 0.05). Conclusions Participants in a mountain UM event combined positive pacing strategies (speed decreased until 70–90% of TED), an increased speed in the last 10% of the event, a decrease in HR at PLOS ONE \| DOI:10.1371/journal.pone.0145482 December 21, 2015 1 / 13 OPEN ACCESS Citation: Kerhervé HA, Millet GY, Solomon C (2015) The Dynamics of Speed Selection and Psycho- Physiological Load during a Mountain Ultramarathon. PLoS ONE 10(12): e0145482. doi:10.1371/journal. pone.0145482 Editor: Pedro Tauler, University of the Balearic Islands, SPAIN Received: October 12, 2015 Accepted: December 6, 2015 Published: December 21, 2015 Copyright: © 2015 Kerhervé et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper. Funding: No specific funding was used for this study. The first author was supported by university research funds and scholarship (USC and Queensland Education and Training). Competing Interests: The authors have declared that no competing interests exist. 70–90% of TED, and an increase in RPEGEN and RPEKE in the last 30% of the event. A greater speed loss and less total time stopped were the factors associated with increased total performance. These results could be explained by theoretical perspectives of a com- plex regulatory system modulating motor drive in anticipation of perceived difficulties such as elevation changes. Introduction Exercise intensity during self-paced events is regulated by a complex protective system inte- grating instantaneous somatosensory feed-back and anticipatory mechanisms in order to maintain homeostasis and prevent from catastrophic failure [1–3]. As in shorter duration, self- paced exercises [2, 4–8], the dynamics of exercise intensity during ultramarathons (UM) are expected to be regulated as a result of the development of psycho-physiological strain and in anticipation of perceived difficulties (duration, topography) [1]. Despite being sensitive to environmental factors such as gradient and wind [9], speed is commonly used as an indicator of exercise intensity in the study of athletic performance. Speed is predicted to decrease throughout UM events [10], and systematic descriptions of pacing during field UM events have been used to indicate that speed decreases overall and becomes more variable as a func- tion of increased finishing times [11–14]. UM events performed on trails and in mountainous settings include changes in elevation, surface, obstacles, altitude, remoteness, and adverse atmospheric conditions, which could alter the dynamics of pacing, compared to flat events. For instance, significant peripheral fatigue (low-frequency fatigue, indicative of the failure of excitation-contraction coupling typical of exercises involving intense eccentric and stretch-shortening contractions [15, 16]) was observed following a ~37 h mountain UM event [17], but not following a 24 h level, treadmill run [14]. Therefore, it is possible that the dynamics of speed during a mountain UM would dif- fer compared to a level UM. Some measures of psycho-physiological load, such as the ratings of perceived exertion (RPE) and heart rate (HR), are strong predictors of pacing during self-paced exercise [1, 18], and could assist in characterising the psycho-physiological load pertaining to a runner at vari- ous stages of an UM. HR is an indicator of the cumulative systemic physiological response to variations in physiological and psychological load, and is routinely used to monitor exercise intensity [19]. The RPE are scores of specifically-developed subjective scales [20] widely used as indicators of psycho-physiological strain [2], and have been proposed to be a primary vari- able involved in the selection of work rate [1, 2]. During a 68km UM lasting 9.8 ± 0.4 hr, HR decreased in the second half, and RPE increased throughout the event without reaching maxi- mal values (15.4 ± 0.4) [21]. RPE was also found to increase and to have slightly higher values (14.1 ± 2.0, with a maximum under ~18) throughout a 73 km mountain UM lasting 11.5 ± 0.5 hr [22]. More recently, near-maximal RPE values (19.5 ± 1.5) were reported at the end of a 54 km mountain UM despite a moderate exercise intensity as evidenced by the relatively low aver- age speed (3.83 km h−1), and HR (111.7 ± 5.9 bpm) [23]. However, no description of psycho- physiological load and pacing has been performed in long, mountain UM events. Therefore, the aim of this study was to investigate the dynamics of speed, HR and perceived exertion during a long trail UM in a mountainous setting. We hypothesised that a progressive decrease in speed (positive pacing) and increased variability would be observed in participants. Based on shorter duration events [11, 24], we also hypothesised that changes in speed would be Psycho-Physiological Load during a Mountain Ultramarathon PLOS ONE \| DOI:10.1371/journal.pone.0145482 December 21, 2015 2 / 13 correlated to changes in gradient, and would exhibit different dynamics at level gradients com- pared to positive and negative gradients. In association with these changes, we hypothesised that HR would decrease (reverse HR drift), and that RPE would increase throughout the event. Together, these findings would indicate that pacing is regulated not only as a consequence of the development of fatigue (simultaneous decrease in speed and HR, [25]), but also in an antic- ipatory manner to prevent from reaching high levels of exertion prematurely. Methods Ethics statement and participants Ethical approval for the study was granted by the research ethics committee of the University of the Sunshine Coast (project code S/12/432). The participants were recruited from experi- enced runners registered to compete in a ~167 km UM running event with ~10,000 m of eleva- tion gain and loss, and held in Chamonix, France (The North Face1 Ultra-Trail du Mont- Blanc, UTMB). Nineteen male participants provided written informed consent and were ini- tially included in the study, fifteen of which completed the entire event and constitute the study group (age: 43 ± 10 yr, height: 1.78 ± 0.60 m, weight: 74 ± 8 kg). Study procedures The participants were individually familiarised with the following study procedures in the 3 days preceding the event. Due to adverse meteorological conditions, the course was shortened to ~106 km and the elevation gain and loss reduced to ~6,000 m on race day. Measures of distance, speed and gradient. The participants were equipped with a Non- differential Global Positioning System (GPSND) device (BT-Q1000, Qstarz International, Tai- wan) secured on top of their clothing or gear to record the distance, speed and elevation for the entire event. The GPSND data were retrieved using the proprietary software, and exported in columnar format for data analysis. The variables contained in the files were date and time (uni- versal time constant, UTC), position (latitude, longitude, elevation) and speed (via Doppler shift). From the successive geographical coordinates recorded, we calculated the distance between each data point using the Vincenty great-circle formulae [26], which are spherical trigonome- try functions calculating the shortest distance between spatial coordinates at the surface of an ellipsoid (earth dimensions used were the WGS-84 GPS model of reference with equatorial radius 6,378.137 km, polar radius 6,356.752 314 245 km and flattening f 1=298:257223563). An automated calculation of the Vincenty formulae can be obtained from an internet-based utility (GPS Visualizer; www.gpsvisualizer.com), which we compared to our preliminary mea- sures for 10 data sets and found to be in exact agreement (r = 1.00, p < .001). Therefore, we used the automated formulae as a simple and generalisable procedure to obtain point-to-point distances. Point-to-point speed was subsequently calculated using the ratio of the point-to- point distances and of the GPS epoch time (one data point every 5 s in the current study). To reduce the effect of signal errors in the analysis, a two-step treatment procedure was then applied to the data. Preliminary calculations revealed that GPSND devices did not discrim- inate speeds slower than 1 km h−1 (0.28 m s−1 or 1.39 m in 5 s) based on the typical error in speed measured in a static position (drift, when a device will record speed values due to the non geo-synchronous nature of the constellation of satellites). For the high end of the speed range, it was considered that speeds higher than 20 km h−1 (5.56 m s−1 or 27.8 m in 5 s) were not expected during a long UM and were likely due to signal jamming (which occurs mainly when the signal from a satellite becomes too weak and forces the ground based receiver to pair to another satellite). These erroneous distance and speed data were first assigned a value of Psycho-Physiological Load during a Mountain Ultramarathon PLOS ONE \| DOI:10.1371/journal.pone.0145482 December 21, 2015 3 / 13 zero, and all speed values were then smoothed in order to further increase the signal-to-noise ratio. For smoothing, a 3, 9, and 15-pt weighted averages were graphically compared. The 9-pt weighted average was considered satisfactory as it provided a balanced sensitivity to individual observations of slow and high speeds. This two-step procedure facilitated the reduction of the effect of signal drift and jamming, which both artificially increase the distance and speed mea- sured using GPS devices, while remaining sensitive to periods of null speed values (Fig 1). GPS-based elevation is considered to be inaccurate [27] due to differences between the model of reference of the earth used for calculations and the actual shape of the earth, and therefore an independent source was sought in order to increase the quality of the elevation data. Due to the size of a typical file containing UM data at the relatively high recording rates of GPS devices (12 h of data recording at 5 s equals 8640 observations), a digital elevation model (DEM) was used in order to automate the treatment procedure. Elevation values were reconstructed from the geographical positions using a DEM (in this study, the NASA SRTM3) available from the same online utility (GPS Visualizer; gpsvisualizer.com). Data was smoothed using a 9-pt weighted average. The gradient between two consecutive data points was then Fig 1. Total time stopped. Total time stopped for each participant, including the relative position of official event checkpoints. “CP” are official event checkpoints. CP 1 and 6: distance ~7 km (outbound) and ~69 km (inbound), altitude ~1015 m. CP 2: distance ~ 19 km, altitude ~815 m. CP 3 and 5: distance ~29 km (outbound) and ~52 km (inbound), elevation ~1160 m. CP 4: distance ~36.5 km, elevation ~1699 m. CP 7: distance ~93 km, elevation ~1263 m. doi:10.1371/journal.pone.0145482.g001 Psycho-Physiological Load during a Mountain Ultramarathon PLOS ONE \| DOI:10.1371/journal.pone.0145482 December 21, 2015 4 / 13 calculated as the change in elevation divided by the horizontal distance between two points (the amount of vertical gain as a function of horizontal distance). Measures of psycho-physiological load. HR was measured continuously using a chest strap and watch (RS800, RS800cx, RS400, or S810, Polar Electro, Kempele, Finland). Each watch was set to record one data point every 15 s in order to optimise battery life and memory. RPE scores were recorded using a portable voice recorder (ICD PX312, Sony, Tokyo, Japan). We instructed the participants to record the time of observation, a general (RPEGEN) and a local (muscular) RPE focused on the sensation of fatigue or pain of the knee extensor muscles and excluding any psychological/psychic contribution to exertion (RPEKE) using Borg’s 10 point category-ratio scale (CR-10) that the subjects carried over the entire race. Variables and statistical analyses We reported all variables as a function of total event distance (Figs 1 and 2) or duration (Figs 3 and 4) in order to represent all participants on a comparable scale (where 100% represents the distance or duration at event completion for every participant). In order to ensure sufficient data was used at each stage, data for each dependent variable was computed for every 10% Fig 2. UTMB course outline and elevation profile. (A) Mean (±SD) group elevation data from the Digital Elevation Model values associated with measures of geographical positions, and (B) group speed data associated with measures of geographical positions (CP, refer to legend of Fig 1 for description). doi:10.1371/journal.pone.0145482.g002 Psycho-Physiological Load during a Mountain Ultramarathon PLOS ONE \| DOI:10.1371/journal.pone.0145482 December 21, 2015 5 / 13 Fig 3. Dynamics of speed. Mean (±SD) group speed as a function of event duration in (A) level, (B) negative (C) and positive gradients, respectively. Symbols denote significant differences to () 10%, ($) 20%, (#) 30%, (θ) 40%, (&) 50%, (€) 60%, (ϕ) 70%, (Ω) 90% and (£) 100% of total event duration, at p < .05. doi:10.1371/journal.pone.0145482.g003 Psycho-Physiological Load during a Mountain Ultramarathon PLOS ONE \| DOI:10.1371/journal.pone.0145482 December 21, 2015 6 / 13 section of the total duration of the event. All statistical analyses were performed using SPSS (version 21, IBM Corporation, Armonk NY, USA). Data are reported as mean ± SD, and the level of significance was set at p < .05. Dynamics of exercise intensity. We determined the relationship between the variations of speed and changes in elevation for the entire event using a quadratic regression of individual speed and gradient. After confirming the assumption of the equality of variances were met, the effect of exercise duration on speed at each gradient (level, negative and positive inclines) HR, RPEGEN and RPEKE were determined using a multivariate ANOVA (MANOVA). Post-hoc one-way, repeated measures ANOVAs with a Fisher’s LSD post-hoc test were used in order to locate the differences in means for speed at all gradients. Due to incomplete data sets, the dynamics of HR, RPEGEN and RPEKE were assessed using a one-way ANOVA on ranks (Krus- kal-Wallis test) with a Student-Newman-Keuls post-hoc test. As HR does not adjust to exercise intensity instantaneously, it is not possible to treat HR data in the same way as speed. Instead, we investigated the dynamics of exercise intensity using Fig 4. Dynamics of psycho-physiological load. (A) Mean (±SD) group heart rate (HR) as a function of total event duration, and (B) general and muscular (knee extensors) ratings of perceived exertion (RPE) as a function of total event duration. Symbols denote significant differences to () 10% and ($) 20%, at p < .05. Bpm: beats per minute. CR-10: 10-point category-ratio Borg scale. doi:10.1371/journal.pone.0145482.g004 Psycho-Physiological Load during a Mountain Ultramarathon PLOS ONE \| DOI:10.1371/journal.pone.0145482 December 21, 2015 7 / 13 sections of sustained uphill running, in order to maximise the contribution of metabolic work compared to passive energy recovery (since the ability to perform eccentric contractions decreases with the development of peripheral fatigue, refer to [15, 16]) in total work rate [28]. We identified 6 sections of sustained uphill combining at least 300 m of vertical gain at a 10% average gradient (refer to Fig 2; the section from CP2 to CP3 was only 4.9% gradient and was not included in the analysis). The effect of hill order (1 to 6) on HR was assessed using a repeated-measures, one-way ANOVA and a Bonferroni post-hoc test. However, during uphill running, overground speed is a less relevant metrics than the amount of vertical gain (in m h−1) to characterise exercise intensity, which is also dependent on the gradient of the slope [28]. Therefore, to determine whether any drift in HR existed inde- pendent of exercise intensity, we used a 1-factor principal component analysis to reduce the dimension of these three variables (termed SVG for speed, vertical gain, gradient). After con- firming the assumptions of normality using a Shapiro-Wilk test, the relationship between aver- age HR and SVG in the 6 main ascents was assessed using Pearson’s product-moment correlation. We further tested the effect of hill order (1–6) on HR using a repeated-measures one-way ANCOVA, and a Bonferroni post-hoc test, using SVG as a covariate of HR. Factors of performance. The relationship between final performance (using the individual average speed, as it allows comparison of various UM distances) and 1) the variability of speed (using the coefficient of variation of point-to-point speed values), 2) the magnitude of speed loss (using the slope of the linear regression of speed over the entire event) and 3) the total time stopped (assumed to correspond to resting, eating, clothing and gear change, toilet, other) were tested using Pearson’s product-moment correlation after confirming assumptions of normality were met (Shapiro-Wilk test). The 95% confidence intervals (CI) of the correlations were calcu- lated using the unstandardised beta-weights of the linear regression of the Z-scores of each variable. Results The following data sets were retrieved: 15 complete GPS traces, 9 HR data sets with at least 80% of event data (due to equipment issues and loss of signal), and 6 RPE data sets with at least 80% of data. The average distance for the event, calculated using filtered point-to-point ortho- drome, was 105.6 ± 1.8 km (range: 103.0–107.5 km), and the total elevation gain and loss was 5871 ± 239 m. The 15 participants completed the event in 18.3 ± 3.0 h (range: 13.8–23.9 h) at an average speed of 5.88 ± 0.9 km h−1 (range: 4.58–7.58 km h−1) and an average HR of 132 ± 10 bpm (range: 112–146 bpm). The mean group elevation and speed profiles are repre- sented in Fig 2. There was a significant quadratic correlation between point-to-point speed and elevation changes (linear factors model: r = .49, R2 = .24, F-linear = 316.76, p < .001; quadratic factors model: r = .52, R2 = .27, F-change = 40.99, p < .001; Total factors: F-total = 185.22, p < .001). Dynamics of exercise intensity The changes in speed as a function of total event duration are presented in Fig 3 (panels A, B and C for level, negative and positive gradients, respectively). Positive pacing was observed on level (speed loss: -2.91 ± 2.15%), negative (-2.61 ± 0.92%) and positive gradients (-1.31 ± 0.84%). The MANOVA indicated a difference in speed between the speed at level gradient, and at the negative and positive gradients (p < .001). Speed was not significantly different between the negative and positive gradients (p = .10). Post-hoc ANOVAs indicated that speed decreased from 10% to all sections up to 70% of total duration at level inclines (except 60%, where it sig- nificantly increased compared to 40% and 50%), and that speed increased at 90% compared to Psycho-Physiological Load during a Mountain Ultramarathon PLOS ONE \| DOI:10.1371/journal.pone.0145482 December 21, 2015 8 / 13 40%, 50%, 70%, 80%, and increased at 100% compared to all sections between 30% and 90% (Fig 3A). For negative gradients, speed decreased from 10% to all other sections, and from 20% and 30% to 70% and 90%. Speed then increased between 90% and 100% (Fig 3B). For positive gradients, speed decreased at all sections until 80% of event duration except 60%. Speed increased at 100% compared to all observations between 40% and 90% (Fig 3C). Mean group HR averaged 132.6 ± 13.6 bpm, and decreased -34.2 ± 17.2 bpm over the UM. HR decreased from 10% to 70%, 80% and 90%, but not 100% (Fig 4A). Mean group RPEGEN and RPEKE averaged 4.3 ± 1.1 and 3.8 ± 1.3, respectively, and increased 6.9 ± 1.4 and 6.9 ± 2.2, respectively during the event (Fig 4B). Mean group RPEGEN increased significantly from 10% to 70%, 80%, 90% and 100%, and from 20% to 70%, 80% and 90% (Fig 4B). Mean group RPEKE increased significantly from 10% to 70%, 80%, 90% and 100% (Fig 4B). Mean group RPEGEN and RPEKE were significantly positively correlated (r = .980, p < .001). There was no significant change in HR as a function of time in the 6 main climbs, as evi- denced in the ANOVA (using HR alone) as well as in the ANCOVA (using HR and SVG as covariates). HR was significantly positively correlated with SVG on all uphill sections (r = .663, p < .001). Factors of performance Performance (average speed) was negatively correlated with total time stopped (r = -.772, p = .001; 95% CI = -1.15, -0.39) and positively correlated with the magnitude of speed loss (r = .540, p = .038; 95% CI = -1.04, -0.03) but not with the variability of speed (r = -.475, p = .073; 95% CI = -1.00, 0.05). Discussion There were three main findings in this study: (i) speed decreased overall at all inclines during the event (positive pacing), but increased significantly in the last section at all inclines; (ii) faster participants stopped less and decreased their speed more than slower participants throughout the event; and (iii) the measures of psycho-physiological load indicated that despite evidence of a reverse HR drift and increased RPE throughout the event, HR in sustained climbs did not change, and maximal RPE values were relatively low, suggesting that participants actively regulated (paced) their physiological and psychological load to complete the event and avoid premature exhaustion. During self-paced running exercise, the optimal locomotor speed is adjusted as a function of environmental factors [10]. One of the main factors influencing speed selection is gradient, where additional energy is required to run at high negative (to generate braking forces limiting downward acceleration) and positive (to elevate a runner’s mass against gravity) gradients compared to level or slightly negative gradients [28, 29]. The curvilinear relationship between locomotor speed and gradient measured in this study (indicating that speed varies directly as a function of gradient; Fig 2) had so far been assumed to exist [11] but not measured in UM events due to limitations in the ability to measure speed and gradient of individual participants. This relationship was measured despite the overall low speeds (especially at positive gradients) and positive pacing strategies characteristic of mountain UM events, which could both have affected the relationship. The overall decrease in speed during the event indicates that the study participants used positive pacing strategies (progressively slowing down), in agreement with previous research findings specific to UM running [10–13, 25, 30]. Speed losses on level gradients were more pro- nounced (the slope of the linear regression was greater, and the MANOVA indicated that sig- nificant differences existed with both negative and positive gradients) and occurred at an Psycho-Physiological Load during a Mountain Ultramarathon PLOS ONE \| DOI:10.1371/journal.pone.0145482 December 21, 2015 9 / 13 earlier point in the event (reaching a minimum at 70% of event duration) compared to both negative and positive gradients (minimum at 90% of event duration). The increase in speed at 60% of total event duration on level gradients could not be explained using the data we col- lected. This section corresponded to a section of the course in the main valley of Chamonix, and therefore we hypothesise that the terrain and surface were conducive for running (in con- trast to walking), and that the course was accessible by spectators and crew members (which could have provided support and motivation). The existence of an increase in speed at the end of the event at all inclines in the current study is unique compared to other studies of long UM on level ground [13]. Two combined factors could, in part, explain the presence of an increase in speed in the last section: first, the mainly descending profile at the end of the event (after hill 6, refer to Fig 2), and second, a phenomenon termed cardio-pulmonary [7] or speed reserve [1] predicting the increase of exercise intensity at the end of self-paced exercises. In this mountain UM event, the marked elevation gain and loss may have favoured the use of conservative pac- ing strategies decreasing the risk of premature exhaustion in anticipation of difficulties, when compared to level or hilly UM events [1]. Together, these findings are indicative of mixed pac- ing strategies, which is a subset of the three main types of pacing associating positive pacing for the main part of the event and an increase in speed for the final section of the event. The significant relationship between performance and speed loss in this project contrasts with findings in other UM studies [11, 13], where participants with a higher performance level had greater speed losses. Future studies are required to further investigate this unexpected result, which could potentially originate from faster participants pacing their race less conser- vatively from the start aiming to decrease overall time, compared to slower participants, for whom finishing the event could have been the main goal. Future studies should investigate a priori pacing strategies, and performance goals and attitudes toward risk taking as a function of performance level. The inverse correlation between performance and time stopped was novel in UM running, and extends findings in an ultra-endurance cycling event [31] where faster athletes spent less time napping. While this result is expected in shorter duration events, it is commonly believed among participants of ultra-long duration events that a bout of passive rest can be beneficial to final performance. This finding indicates the marked differences in the physiological demands of an UM event as a function of performance level due to the differ- ences in time spent on course, where passive rest may be a relatively important feature of pac- ing strategies for slower participants. Future studies are also required to determine whether a threshold exists as a function of performance level in longer (> 300 km) UM events. We reported that HR decreased from 10% to 70%, 80% and 90%, but not 100% of total event duration. Although we could not determine the relationship between the dynamics of speed and HR at each gradient over the entire event (due to the relatively low accuracy of GPSND devices), it is likely that the variations of exercise intensity (speed) explained most of the observed changes in HR (reverse HR drift), as this well-described physiological response is typical of ultra-long duration exercise [1, 25]. Still, we reported that HR in sustained uphill sec- tions distributed throughout the event (hills 1–6) did not change, including with the use of HR scaled for exercise intensity (using the factorial component SVG), which indicates that bouts of sustained uphill running may be regulated differentially given the high risk of exhaustion they present. The dissociated RPE (RPEGEN and RPEKE) had similar dynamics (the two measures of RPE increased from 10% to all sections between 70% and 100% of event duration), and were highly correlated, suggesting either (i) that the dynamics and magnitude of change of RPEGEN and RPEKE are similar throughout the course of an UM (which would indicate that the measure of one variable is sufficient), or (ii) that the two scales measured the same underlying construct (and therefore, that RPE may be a general, but not location-specific indicator of psycho-physiological Psycho-Physiological Load during a Mountain Ultramarathon PLOS ONE \| DOI:10.1371/journal.pone.0145482 December 21, 2015 10 / 13 load). Future research is required to investigate the relative contribution of the perceived exertion specific to a muscle group (knee extensors, plantar flexors) or physiological system (respiratory, gastric) to the general RPE. The role of pain will also need to be investigated as it may alter the perception of exertion and fatigue [1], and will be heightened following sustained downhill loco- motion [32]. Further, although the highest values in RPE (both RPEGEN and RPEKE) were recorded in the last section of the event, the maximal values were relatively low (6.3 and 6.7 group mean for RPEGEN and RPEKE, respectively). The relatively low maximum values distin- guishes it from other studies in shorter UM [23], and could contribute to identify the protective nature of fatigue in preventing the participants from attaining maximal values at the end of the event [1, 3]. As such, the combined results of the dynamics of pacing and psycho-physiological load may indicate that participants relied on relatively conservative pacing strategies and used a functional reserve [1, 7] permitting an increase in speed observed in the last section of the event. These findings are consistent with theoretical perspectives of a complex protective system regu- lating work rate based on the interaction of the instantaneous and anticipated psycho-physiologi- cal state of a participant, and of the environmental conditions in which the exercise is performed [1–3]. Still, some questions remain regarding the regulation of speed at different gradients, since some of our results (the pacing on level gradients differed to both downhill and uphill gradients) were not expected. Previous research hypothesised that the changes in stride patterns were altered differentially following a level [33] and a mountain UM [32] due to the increased reliance on eccentric contractions typical of downhill running. Recently, Vernillo and colleagues [34] measured an increase in the energy cost of running specifically at mild downhill (-5%) gradients but not on level or uphill (5%) gradients as a function of the development of fatigue in an UM event. Therefore, further studies are required to investigate the simultaneous variations of pacing and psycho-physiological load as a function of gradient during an UM event with a greater resolution. Limitations In this study, the main limitation was related to the resolution of measurements made using non-differential GPS devices. We optimised the calculations to be able to define broad catego- ries of total distance and duration (10% sections) and gradients (negative [-100 to -2.5%], level [-2.5 to 2.5%], positive [2.5 to 100%]). Still, the temporal resolution of observations (10% of event duration) was comparable to other UM studies [13], and was selected as a robust approach ensuring a sufficient number of observations was available for each variable. Unfore- seen issues with recording equipment reduced the numbers of data sets in HR, and therefore limited the findings as we were unable to establish the relationship between HR and pacing at each stage. In future studies, the use of GPS devices with higher temporal and spatial resolution could also lead to the development of various indices of running performances in conditions of trail running, such as the rate of ascent as a function of gradient which would be useful for ath- letes and coaches, and in scientific research for the analysis of performance. Conclusion During a mountain UM, speed decreased over the first 90% of the event and at all gradients, and speed increased in the last 10% section of the event. A greater speed loss and less total time stopped were the factors associated with increased total performance. HR decreased overall, but remained constant in the main ascents of the race, indicating the potential effect of conser- vative pacing strategies to avoid premature exertion. Perceived exertion increased throughout the event, but without reaching maximal values. These observations are supported by Psycho-Physiological Load during a Mountain Ultramarathon PLOS ONE \| DOI:10.1371/journal.pone.0145482 December 21, 2015 11 / 13 theoretical perspectives of a complex protective system regulating motor drive in anticipation of remaining exercise duration and changes in elevation. Acknowledgments The authors would like to thank all the participants for their effort and valuable time. We would also like to thank the organisers of the UTMB and other researchers who have permitted this research effort. In particular, the authors would like to thank Dr Roger Ouillon and Dr Pascal Edouard for conducting medical screenings, Mr Sylvain Battault for providing the voice recording equipment, and Dr Léonard Féasson for organising and managing the testing facility. We also thank Ms Benjie Bartos and Mr Dylan Astley for proof-reading and editing the manuscript. Author Contributions Conceived and designed the experiments: HK CS. Performed the experiments: HK GM. 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PMC4237511	Speed Trends in Male Distance Running Timothy N. Kruse1, Rickey E. Carter2, Jordan K. Rosedahl2, Michael J. Joyner3* 1 The University of Washington School of Medicine, 1959 N. E. Pacific Street, Seattle, WA, 98195, United States of America, 2 Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, United States of America, 3 Department of Anesthesiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, United States of America Abstract The major cycling ‘‘Grand Tours’’ have shown an attenuation of performance over the last decade. This has been interpreted as circumstantial evidence that newer anti-doping strategies have reduced the use of performance-enhancing drugs. To examine this idea under more controlled conditions, speed trends for world class 5000 m, 10000 m, and marathon performances by men from 1980 to 2013 were analyzed. We obtained comprehensive records from the International Association of Athletics Federations, Association of Road Racing Statisticians, and the Track and Field All-time Performances database webpages. The top 40 performances for each event and year were selected for regression analysis. For the three distances, we noted cumulative performance improvements in the 1990s thru the mid-2000s. After the peak speed years of the mid 2000 s, there has been limited improvement in the 5000 m and 10,000 m and world records set during that time remain in place today, marking the longest period of time between new records since the early 1940s. By contrast marathon speed continues to increase and the world record has been lowered four times since 2007, including in 2013. While the speed trends for 5000 m and 10000 m track results parallel those seen in elite cycling, the marathon trends do not. We discuss a number of explanations other than improved anti-doping strategies that might account for these divergent findings. Citation: Kruse TN, Carter RE, Rosedahl JK, Joyner MJ (2014) Speed Trends in Male Distance Running. PLoS ONE 9(11): e112978. doi:10.1371/journal.pone.0112978 Editor: Maria F. Piacentini, University of Rome, Italy Received June 18, 2014; Accepted October 17, 2014; Published November 19, 2014 Copyright: 2014 Kruse et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. Ethical restrictions prevent public deposition of data. Requests for data may be sent to Michael Joyner, joyner.michael@mayo.edu. Funding: This work was supported by the National Institutes of Health grants R25 GM075148 and UL1 TR000135. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * Email: Joyner.Michael@Mayo.edu Introduction The use of performance-enhancing drugs (doping) can be dated back to the ancient Olympics [1], [2]. Since then athletes have used a wide range of substances including red wine, caffeine, nitroglycerin, cocaine, opiates, amphetamines, growth hormone, blood transfusions, anabolic steroids, and erythropoietin (EPO) in an effort to gain a physiological advantage [3]. Because performance-enhancing drugs compromises the idealized princi- ples of pure competition, the World Anti-Doping Agency (WADA) was created in 1999 [4]. Since the formation of WADA, it has developed widely applied policies and drug testing protocols (including regular out of competition testing) in an attempt to stop the apparently wide spread doping in elite sports competition [5]. If recent improvements in athletic performance have been driven by doping, then improved doping control might be reflected by a leveling off or declining performances in sports where doping is thought to be ubiquitous. In recent analyses of major cycling races including the Tour de France, Giro d’Italia, Vuelta A Espan˜a, the average speed has been leveling off or declining [6], [7] since the introduction of improved techniques to detect use of exogenous EPO in 2005 [4]. However, the analysis of cycling is confounded by varying race distances, yearly changes in course, and weather. Endurance running eliminates many of these confounding factors. The tracks and courses are identical from year to year. For the two shorter distances, there are numerous competitive opportunities per year and at least some would likely have nearly ideal environmental conditions. With this information as a background, we tested the hypothesis that elite distance running times would show a pattern of leveling in the middle 2000 s similar to that seen in cycling. Such a finding could be explained by improved doping control. We also discuss alternate explanations including that humans are reaching the biological limits of performance and the potential role technical innovations in training and equipment [8], [9]. Finally, any potential explanation might also be confounded by changes in the economic landscape associated with world class distance running. Materials and Methods We obtained the top male performances from 1980–2013, by year, in major endurance running races (5000 m, 10000 m on the track; marathon on road courses) from the International Associ- ation of Athletics Federations (IAAF: http://www.iaaf.org/home), Association of Road Racing Statisticians (ARRS: http://www. arrs.net), and the Track and Field all-time Performances data- base websites (http://www.alltime-athletics.com/index.html) [10], [11], [12]. The 1980–2013 epoch was selected because besides new performance-enhancing drugs (PEDs), a case can be made that potentially transformative changes in training or equipment has not occurred. For example, by 1980 high volume and high intensity training had been widely adopted by top competitors for PLOS ONE \| www.plosone.org 1 November 2014 \| Volume 9 \| Issue 11 \| e112978 several decades and athletes from East Africa had been participating at the international level since the early 1960s. High quality synthetic tracks were also widely available by 1980 and carbohydrate loading was widely practiced in the marathon and it is unclear if technical changes in shoes have had a measureable impact on performance. While ideas about training have been refined the extent to which these have been uniformly adopted by elite athletes, especially the East Africans, is not known [13], [14], [15]. Beyond these training and globalization related factors, professionalism also emerged during the 1980s. We also restricted our analysis to men because women were not routinely permitted to participate in long distance racing until the 1970s and performances dropped dramatically in the early years of widespread competition by women. While the gap in world records has been steady since that time women still lag in competitive depth in many events [16], [17]. Finally, the first synthetic EPO (Epogen) was approved by the FDA in 1989 and within a few years it was clear that EPO can have profound effects on maximal oxygen transport (VO2 max) [18] in humans and was being used to enhance athletic performance by the early 1990s. Additionally, because EPO and related analogues are injectable the logistical challenges of traditional blood doping (autologous red cell transfusions) are eliminated [19]. The abstracted data consisted of the total number of perfor- mances below 2012’s Olympic A standard: 5000 m-13:20, 10,000 m-27:45, and also performances under 2:10:00 for the marathon (the Olympic A standard was 2:15 in 2012, a time that equals an estimated 29.22 10,000 m [20]). Performances below these standards were considered ‘elite’. To study the speed tends more formally, the 40 fastest athlete performances (the fastest performance of the 40 fastest athletes) were recorded per year and event for regression modeling (described below). Age and country of residence at time of race were also abstracted. Initial data summaries included the frequency tabulation elite performances by event and distance. To analyze the changes in speed trends over the study period, we used regression techniques consisting of quadratic splines (cubic B-splines with 3 equally spaced interior knots) against our years of interest using the top 40 athlete performances per year. These generalized regression models allowed for flexibility of estimating the change in performance over time while providing traditional measures of model fit (e.g., R-square value). It was hypothesized that different regression profiles would be observed between the top 40 finishing times and the fastest yearly performances, so the percentage difference in speed of the fastest performance relative to the 40th fastest time per year was also modeled using regression splines and locally weighted smoothers (LOESS). When reporting measures of model fit for the cubic B-splines regression models (i.e., change in speed as a function of year), the omnibus F test for the regression model and R-square were reported. The LOESS curves, which were used for illustrative purposes of changes in pacing based on the relative placing, were not summarized using traditional regression summaries such as R-square on account of their intended visual utilization. Cubic B-spli ne regression analyses were conducted using The SAS System (v9.3, Cary, NC) using PROC ORTHOREG. LOESS smoothing was conducted using PROC SGPLOT using default parameters. Results The number of performances below the 2012 Olympic A qualifying standard plus sub 2:10:00 for each distance increased over the 1980–2013 (Figure 1a–c). The world record for the 5000 m was set in 2004 while the 10000 m world record was set in 2005; these records stand today, which is the longest gap between world records since the 1940s. The number of performances below the 2012 Olympic A qualifying standard for the 5000 m and 10,000 m also appears to have leveled off since the middle 2000 s. Similarly the number of athletes breaking 2:10:00 for the marathon has also leveled off. 2:10:00 was chosen as a comparable standard for the marathon because this time is considered generally similar to or slightly slower than the A standards for shorter distances based on various empirical point tables, scoring systems and time conversion programs [20]. All three regression splines presented in Figures 2a–c were statistically significant (p,0.0001 for each). Furthermore, year alone explained a large percentage of the variation in the speed trends (R-square: 53%, 37% and 69% for 5000 m, 10000 m and marathon, respectively). Consistent with these overall model estimates, the 5000 m and 10000 m had significant increases in speed during the 1990s whereas the marathon showed an increase over the entire three plus decades (Figures 2a–c). In particular, the 5000 m the speed trend levels off starting around 2000. The marathon and 10000 m times do not show this as a pronounced tendency. Figures 3a–c explores the speed trends using an alternative classification approach to provide additional insights into the temporal effects. The fastest performance of the year is plotted alone and summarized using a LOESS smoother. Speed trends of the mean top 10, mean top 20 and mean top 40 athlete performances are superimposed in these same figures. With the 5000 m and 10000 m, there is a pronounced ‘outlying’ effect of the top performance from 1995 to late 2010. The marathon, however, displays no attenuation of the increased speed over the epoch sampled and the relative speed of the fastest annual time does not appear to be an outlier (i.e., the figure lines appear as roughly parallel). To better quantify the observations made from Figure 3, the percentage changes in speeds over time were examined and found to be consistent with the differential findings of the top performance vs. the 40th fastest athlete performance. The degree to which the fastest times were relatively fast (compared to other years) was observed during the 2000s in the 5000 m and 10000 m distances. The regression spline analyses supported these findings that the fastest relative times for the 5000 m and 10000 m varied over the epoch (5000 m: p = 0.048, R-square = 32%; 10000 m: p = 0.0007, R-square = 51%). As illu strated in Figure 3c, trends for the marathon distance were not clearly identified in the data (p = 0.29, R-square = 19%). Discussion The speed and performance trends for top 5000 m and 10000 m distance running performers on the track show a period of increased speed among the fastest runners to the mid-2000 s with an attenuation of speed in either all (5000 m) or the fastest performances (10000 m) after this period of time. For the marathon, all indices of speed show a nearly linear increase in speed with an increased number of elite performances over the three plus decades we sampled. We believe there are a number of possible explanations for these findings. First, the findings for the 5000 m can be interpreted as consistent with the hypothesis that improved drug testing has Doping and Running Times PLOS ONE \| www.plosone.org 2 November 2014 \| Volume 9 \| Issue 11 \| e112978 limited the ability of elite athletes to manipulate their oxygen transport systems with EPO (or other techniques to improve oxygen transport during exercise) since the middle 2000 s. These observations are also broadly consistent with recent speed trends in elite cycling races [6], [7]. This interpretation can also be applied to the 10000 m results, but only when considering the fastest times. By contrast, the data for the marathon shows continued improvements in running speed during the same time period along with more total elite performances and world records. These observations challenge the idea that the speed leveling seen in the 5000 m on the track and in the so-called ‘‘Grand Tours’’ of cycling is due primarily to better drug testing and the reduced use of performance enhancing drugs. A second possible interpretation is that world class performanc- es are leveling off and reaching a physiological upper limit as has been postulated for equine and canine athletes [8], [9,], [21]. In the case of the marathon a number of empirical estimates and physiological modeling suggest the record is relatively slow in comparison to the 5,000 m and 10,000 m times and is merely catching up by comparison [20], [22], [23]. In this context, it is Figure 1. Total number of elite performances by year. Times under 13:20 for the 5000 m, 27:45 for the 10000 m, and 2:10 for the marathon. doi:10.1371/journal.pone.0112978.g001 Doping and Running Times PLOS ONE \| www.plosone.org 3 November 2014 \| Volume 9 \| Issue 11 \| e112978 interesting to note that top speeds have not fallen for the shorter races but only leveled off. The third element of any interpretation focuses on the changing financial incentives in professional distance running. Prize money for top marathon performances has increased. Specifically, in 1980 the highest total payout for any marathon was $50,000; just over two decades later the first million dollar race was run [24]. These incentives could be attracting a stronger pool of competitors to ‘‘move up’’ and focus on the marathon and forgo record setting attempts at shorter distances. This could lead to more competitive races among top runners at the major marathons. Second the Figure 2. Top 40 athlete performances of the 5000 m, 10000 m, and marathon (circles). The solid line is a quadratic spline fit with three equally spaced knots (points of inflection). doi:10.1371/journal.pone.0112978.g002 Figure 3. LOESS smoothers fit through the fastest yearly performance by year for the 5000 m (A), 10000 m (B) and marathon (C). In addition, the mean speed for the top 10, top 20 and top 40 athlete performances are also plotted for reference. doi:10.1371/journal.pone.0112978.g003 Doping and Running Times PLOS ONE \| www.plosone.org 4 November 2014 \| Volume 9 \| Issue 11 \| e112978 highest profile marathon races are now being staged in a way designed for world record attempts that include the use of pacers. Along these lines, the use of pacers has been wide spread for races on the track for many years, and many top athletes have bonus plans and other financial incentives from sponsors that reward fast times at the shorter distances. There are a number strengths and limitations to this study. A major strength of our data set and analysis is that it includes standardized distances and courses with numerous competitive opportunities at the shorter two distances when environmental conditions are likely to be optimal. By contrast, a limitation to our analysis is that we have no idea if improved approaches to training or equipment (shoes and tracks) might have contributed to the trends we report. However, we favor the interpretation that the entire epoch we have analyzed has been relatively stable from a technical perspective. This includes widespread use of high volume and high intensity training, widespread availability of synthetic tracks, and adequate footwear. Additionally, while ideas about training have been refined it is not known if how uniformly these have been adopted by elite athletes, especially the East Africans [13], [14], [15]. This perspective contrasts to the major improvements in equipment for cycling that includes use of advanced materials and improved aerodynamic designs to construct faster bikes. A final concern whenever the topic of doping is raised is discussed relates to what might be called the continuous ‘‘cat and mouse’’ game between those trying to enforce the rules with improved testing and those trying to circumvent them. This has engendered speculation that micro-doses of EPO can be titrated by athletes in a way to achieve high levels of performance and yet avoid a positive drug test [19], [25], [26]. There is also widespread speculation about the use less or undetectable compounds and so- called designer performance enhancing drugs. Advocates of these points of view have argued that while doping is considered widespread the number of positive tests in major competitions is quite low [5]. The counter argument is that the low number of positive results demonstrates that the testing is working and deterring doping. The lack of hard data on the true incidence of doping and how it has or has not been influenced by improved testing is unknown and a major limitation to any discussion on this topic. However, it is clear that anonymous questionnaire based surveys suggest the true incidence of doping it is much higher (14– 39%) than ,2% rate of positive tests suggests [27]. This is clearly an area of sports sociology that requires increased attention. It should also be noted that the sociology surrounding the doping phenomenon along with the ongoing incentives to dope are complex. In this context, strategies beyond testing alone will be required to improve the efficacy of doping control. A compre- hensive discussion of this complex topic is beyond the scope of our analysis, but there has been much thoughtful discussion of related topics [28], [29], [30], [31], [32], [33], [34]. Conclusion In summary, our analysis demonstrates that speed trends for elite distance running are divergent depending on distance and have event specific patterns. Thus, any generalizations about performances in world class competition providing evidence that drug testing is or is or is not ‘‘working’’ need to be viewed with caution. Further caution is required given the many caveats and potential factors that could explain our findings. Acknowledgments We would like to thank IAAF, Peter Larsson, and Ken Young for compiling the data. This work was supported by the National Institutes of Health grants R25 GM075148 and UL1 TR000135. Author Contributions Conceived and designed the experiments: TNK MJJ REC. Performed the experiments: TNK MJJ. Analyzed the data: TNK MJJ REC JKR. Contributed reagents/materials/analysis tools: TNK MJJ REC JKR. Contributed to the writing of the manuscript: TNK MJJ REC. References 1. Conti AA (2010) Doping in sports in ancient and recent times. Med Secoli 22: 181–190. 2. Papagelopoulos PJ, Mavrogenis AF, Soucacos P (2004) Doping in ancient and modern Olympic Games. Orthopedics 27: 1226–1231. 3. Gaudard A, Varlet-Marie E, Bressolle F, Audran M (2003) Drugs for increasing oxygen transport and their potential use in doping - A review. Sports Med 33: 187–212, doi:10.2165/00007256-200333030-00003. 4. World Anti-Doping Agency (2010) A Brief History of Anti-Doping. 5. International Olympic Committee (2014) The Fight Against Doping and Promotion of Athletes’ Health. 6. El Helou N, Berthelot G, Thibault V, Tafflet M, Nassif H, et al. (2010) Tour de France, Giro, Vuelta, and classic European races show a unique progression of road cycling speed in the last 20 years. J Sports Sci 28: 789–796, doi:10.1080/ 02640411003739654. 7. Perneger TV (2010) Speed trends of major cycling races: does slower mean cleaner? Int J Sports Med 31, 261–264, doi:10.1055/s-0030-1247593. 8. Berthelot G, Tafflet M, El Helou N, Len S, Escolano S, et al. (2010) Athlete atypicity on the edge of human achievement: performances stagnate after the last peak, in 198. PLoS One 5: e8800, doi:10.137/journal.pone.0008800. 9. Berthelot G, Thibault V, Tafflet M, Escolano S, El Helou N, et al. (2008) The citius end: world records progression announces the completion of a brief ultra- physiological quest. PLoS One 3: e1552, doi:10.1371/journal.pone.0001552. 10. Home of World Athletics (2014) International Association of Athletics Federation. Available: http://www.iaaf.org/records/toplists. Accessed 15 May 2014. 11. Young K (13 June 2014) Yearly Rankings- Marathon. Association of Road Racing Statisticians. Available: http://www.arrs.net. Accessed 15 May 2014. 12. Larsson P (16 Jun 2014) Men’s standard events. 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Med Sci Sports Exerc 43: 656–664, doi:10.1249/MSS.0b013e3181fb4e00. 18. Joyner MJ (2003) VO2MAX, blood doping, and erythropoietin. Br J Sports Med 37: 190–191. 19. Lundby C, Achman-Andersen NJ, Thomsen JJ, Norgaard AM, Robach P (1985) Testing for recombinant human erythropoietin in urine: problems associated with current anti-doping testing. J Appl Physiol 105: 417–419, doi:10.1152/ japplphysiol.90529.2008. 20. Hill R (1 Jun 2014) Race Conversion. Hill Runner. Available: http://www. hillrunner.com/calculators/raceconversion.php. Accessed 17 May 2014. 21. Denny MW (2008) Limits to running speed in dogs, horses and humans. J Exp Biol 211: 3836–3849, doi:10.1242/jeb.024968. 22. Joyner MJ, Ruiz JR, Lucia A (2011) The two-hour marathon: who and when? J Appl Physiol (1985) 110: 275–277. doi:10.1152/japplphysiol. 00563.2010. 23. Joyner MJ (1991) Modeling: optimal marathon performance on the basis of physiological factors. J Appl Physiol (1985) 70: 683–687. 24. Miller T, Lawrence G, McKay J, Rowe D (2001) Globalization and Sports. (SAGE). 25. Jelkmann W, Lundby C (2011) Blood doping and its detection. Blood 118: 2395–2404, doi:10.1182/blood-2011-02-303271. 26. Lonnberg M, Lundby C (2013) Detection of EPO injections using a rapid lateral flow isoform test. Anal Bioanal Chem 405: 9685–9691, doi:10.1007/s00216- 013-6997-8. Doping and Running Times PLOS ONE \| www.plosone.org 5 November 2014 \| Volume 9 \| Issue 11 \| e112978 27. de Hon O, Kuipers H, van Bottenburg M (2014) Prevalence of doping use in elite sports: a review of numbers and methods. Sports Med Aug 29 [Epub ahead of print]. 28. Delanghe JR, Joyner MJ (2008) Testing for recombinant human erythropoietin. J Appl Physiol (1985) 105: 395–396, doi:10.1152/japplphysiol.90746.2008. 29. D’Angelo C, Tamburrini C (2010) Addict to win? A different approach to doping. J Med Ethics 36: 700–707. 30. 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PMC7663387	International Journal of Environmental Research and Public Health Review A Scoping Review of the Relationship between Running and Mental Health Freya Oswald 1,* , Jennifer Campbell 1 , Chloë Williamson 2, Justin Richards 3 and Paul Kelly 2 1 Edinburgh Medical School, The University of Edinburgh, Edinburgh EH16 4TJ, UK; s1604637@sms.ed.ac.uk 2 Physical Activity for Health Research Centre (PAHRC), University of Edinburgh, Edinburgh EH8 8AQ, UK; chloe.williamson@ed.ac.uk (C.W.); p.kelly@ed.ac.uk (P.K.) 3 Faculty of Health, Victoria University Wellington, Wellington 6140, New Zealand; justin.richards@vuw.ac.nz * Correspondence: s1504283@sms.ed.ac.uk Received: 10 October 2020; Accepted: 29 October 2020; Published: 1 November 2020 Abstract: Poor mental health contributes signiﬁcantly to global morbidity. The evidence regarding physical beneﬁts of running are well-established. However, the mental health impacts of running remain unclear. An overview of the relationship between running and mental health has not been published in the last 30 years. The purpose of this study was to review the literature on the relationship between running and mental health. Our scoping review used combinations of running terms (e.g., Run* and Jog) and mental health terms (general and condition speciﬁc). Databases used were Ovid(Medline), Ovid(Embase), ProQuest and SportDiscus. Quantitative study types reporting on the relationships between running and mental health were included. Database searches identiﬁed 16,401 studies; 273 full-texts were analysed with 116 studies included. Overall, studies suggest that running bouts of variable lengths and intensities, and running interventions can improve mood and mental health and that the type of running can lead to diﬀerential eﬀects. However, lack of controls and diversity in participant demographics are limitations that need to be addressed. Cross-sectional evidence shows not only a range of associations with mental health but also some associations with adverse mental health (such as exercise addiction). This review identiﬁed extensive literature on the relationship between running and mental health. Keywords: exercise; mental health; psychology; physical activity; running; jogging 1. Introduction Poor mental health contributes signiﬁcantly to the global health burden [1]. The strain of mental health and behavioural disorders is estimated to account for more years of lived disability than any other chronic health ailment [1,2]. The global proportion of disability-adjusted life years caused by mental ill-health has increased from 12.7% to 14% (males) and 13.6% to 14.4% (females) from 2007 to 2017 [3]. Due to the burden and increasing prevalence of mental ill-health, eﬀective management of mental health disorders is vital [4]. There is substantial evidence to support the relationship between physical activity (PA) and various mental health outcomes across the lifespan [5–7]. There has been investigation of low-intensity PA on mental health; for example, Kelly et al. (2018) reported the positive relationships between walking and mental health in an earlier scoping review [8]. However, a similar synthesis for higher-intensity PA such as running has not been reported. While the evidence base for the beneﬁts of running on physical health is well-established, the mental health changes from running remain unclear. Addressing the gap within this knowledge is valuable as running is a form of PA popular among many population groups [9]. Inclusive organisations such as “Couch to 5k” [10], “Girls on the run” [11] and “Parkrun” can support running while promoting Int. J. Environ. Res. Public Health 2020, 17, 8059; doi:10.3390/ijerph17218059 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 8059 2 of 39 well-being and satisfaction with physical health, facilitating socialisation and community connectedness, and reducing loneliness [12–14]. In primary care settings, national initiatives such as “Parkrun-Practice” promote well-being through running [15]. In recent years, there has been a transition within healthcare to focus on disease morbidity rather than disease mortality, in particular with a drive to improve global mental health [16]. There is increasing prevalence of mental ill-health; therefore, eﬀective management of mental health disorders is vital [4]. In order to investigate any diﬀerences in mental health eﬀects between high and low intensities of running, all genres of running must be considered including jogging, sprinting, marathon running and orienteering. To the best of the authors’ knowledge, no recent reviews of the relationship between running and mental health are available. The synthesis provided by this review will enable healthcare practitioners, psychologists and policy makers to better advise on running for mental health. It will also identify key gaps in the literature for future research. The aims of this scoping review are the following: (1) to provide an overview of what is known regarding the relationship between running and mental health outcomes in all age groups and populations (2) to highlight current knowledge gaps and research priorities 2. Materials and Methods A scoping review was concluded to be the most appropriate to address the research aims as it provides an overview of the volume and distribution of the evidence base as well as highlights where more research is warranted. The review followed the ﬁve-stage scoping review framework proposed by Arksey and O’Malley and was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scoping review extension checklist (Appendix A) [17,18]. 2.1. Identify Research Question Research questions were developed to address the research aims: “What is known about the eﬀects of running on mental health outcomes?” and “What are the current knowledge gaps?”. Research question formulation was guided by item 4 in the PRISMA scoping review extension checklist (Appendix A). The deﬁnition of running included jogging, sprinting, marathon running, orienteering and treadmill running. A wide range of intensities were included as the aim of the scoping review was to provide an overall picture of the relationship between running (of various intensities) and mental health. 2.2. Identify Relevant Outcomes Mental health outcomes were informed by Kelly et al. (2018) [8], who reviewed the relationships between walking and mental health (Table 1). Measures or disorders of cognitive dysfunction were considered neurological and thus outside the scope of this review. Eating disorders were included as they signiﬁcantly impair physical health or psychosocial functioning. Health-related quality-of-life was excluded as it was considered to incorporate physical, social, emotional and mental factors. Int. J. Environ. Res. Public Health 2020, 17, 8059 3 of 39 Table 1. Deﬁnitions of the mental health outcomes included within the review: the outcomes were informed by Kelly et al. (2018) [8]. Outcome Description Depression Depression is a mood disorder with prolonged periods of low mood and a lack of interest and/or pleasure in normal activities most of the time. This includes major depressive disorder [19]. Anxiety Anxiety is characterised by uncomfortable or upsetting thoughts and is usually accompanied by agitation, feelings of tension and activation of the autonomic nervous system. It is important to note the distinction between transient anxiety symptoms (state anxiety), persistent symptoms (trait anxiety) and anxiety disorders: a collection of disabling conditions characterised by excessive, chronic anxiety. Examples of anxiety disorders are speciﬁc phobias, social phobia, generalised anxiety disorder, panic disorder, obsessive–compulsive disorder and post-traumatic stress disorder [20]. Self-eﬃcacy Self-eﬃcacy is a situation-speciﬁc form of self-conﬁdence. Self-eﬃcacy beliefs inﬂuence how people think, feel, motivate themselves and act [21]. Psychological stress Psychological stress or distress can be deﬁned as the unique discomforting, emotional state experienced by an individual in response to a speciﬁc stressor or demand that results in harm, either temporary or permanent, to that person [22]. Eating pathology Eating pathology or disorder can be described as persistent disturbance of eating behaviours or behaviours intended to control weight, which signiﬁcantly impairs physical health or psychosocial functioning. This disturbance should not be secondary to any recognised general medical disorder, e.g., hypothalamic tumour. This deﬁnition includes anorexia nervosa and bulimia nervosa [23]. Self-esteem Self-esteem is the feelings of value and worth that a person has for oneself. It contributes to overall self-concept as a construct of mental health [24]. Addiction Addiction designates a process whereby a behaviour that can function both to produce pleasure and to provide escape from internal discomfort is employed in a pattern characterized by (1) recurrent failure to control the behaviour (powerlessness) and (2) continuation of the behaviour despite signiﬁcant negative consequences (unmanageability) [25]. Psychological well-being Psychological well-being links with autonomy, environmental mastery, personal growth, positive relations with others, purpose in life and self-acceptance. This is often referred to as eudemonic well-being [26]. Self-concept Self-concept is the organisation of qualities that the individual attributes to themself, which in turn guides or inﬂuences the behaviour of that individual [27]. Mood Mood is a transient state of a set of feelings, usually involving more than one emotion. Seen as a conscious summative recognition of feelings that can vary in intensity and duration [28]. 2.3. Identify Relevant Studies Studies were included based on the following criteria: • Any geographical location • All years between 1970 and 2019 • Quantitative eﬀects of running on predetermined mental health outcomes # Preventive eﬀects (negative) # Health promotion eﬀects (positive) # Intervention eﬀects • Any age group or sex • Human studies • Designs including primary research (cross-sectional, longitudinal, interventions and natural experiments with pre-post measures with or without non-running comparisons) • Studies that mentioned walking as well as running were included because it is not possible to diﬀerentiate walkers from runners in events such as Parkrun. Studies were excluded based on the following criteria: • Specialist groups including elite, professional or competitive athletes. • General physical or aerobic activity, rather than exclusively running • Qualitative and ethnographic designs • Systematic and scoping reviews (individual studies from identiﬁed reviews were included if relevant) Int. J. Environ. Res. Public Health 2020, 17, 8059 4 of 39 • Editorials, opinion pieces, magazine/newspaper articles, case reports and papers without primary data • Focus on secondary mental health within clinical groups with speciﬁc physical or mental conditions that is not the condition being treated with running (e.g., eﬀects on depression in patients with cancer) • Evidence types including guidelines, unpublished and ongoing trials, annual reports, dissertations and conference proceedings • Animal studies • Unavailable in English • Running intervention was part of a wider study where diﬀerentiating the individual eﬀect of running was not possible (e.g., combined with weight management). • Conference abstracts that were not published as full articles Search Strategy and Databases Databases searched were Ovid (Medline), Ovid (Embase), ProQuest and SportDiscus. Databases were searched for titles and abstracts that included at least one running term with one mental health outcome term. Appropriate truncation symbols were used to account for search term variations. Common running terms were combined. Search terms and the full search syntax can be found in Appendix B. Searches were conducted for papers published up to August 2019. 2.4. Study Selection All identiﬁed records were uploaded to Covidence (https://www.covidence.org), and duplicates were automatically removed. Titles and abstracts were screened, with 20% cross-checked early in the process to assess agreement between authors. Full texts were reviewed by 2 authors. 2.5. Charting the Data Data extraction was completed by the lead author (F.O.) with 5% double screened by a second author (J.R.). The data extraction form was pilot tested with the ﬁrst 20 studies and informed the following standardised extraction agreed upon by all authors: (1) Author(s), year of publication and geographical location of study (2) Mental health conditions examined (3) Sample size and population details (4) Study design (5) Measures used to quantify any change in mental health outcome(s) (6) Running dose (if applicable) and compliance (if applicable) (7) Whether running was beneﬁcial and the main ﬁndings In studies that used “Proﬁle of Mood States” (POMS) as a measurement of mood state, total mood disturbance was used in this review if reported by the authors. If the authors only reported one/some of the POMS subdimensions, these data were extracted instead. 2.6. Collating, Summarising and Reporting Results Included studies were organized into 3 categories: cross-sectional studies, acute (single, double or triple) bouts of running, and long-term running interventions. For each of these 3 categories, the results were presented in two ways: (a) a descriptive numerical analysis to highlight the prevailing domains of research regarding geographical location, mental health outcomes and research methods and (b) a narrative summary of the key ﬁndings. Int. J. Environ. Res. Public Health 2020, 17, 8059 5 of 39 3. Results 3.1. Included Studies From initial searches, 29,851 papers were identiﬁed. Following removal of duplicates, 16,401 were screened at the title and abstract levels and 273 papers were retained for full-text assessments. Ultimately, 116 papers met the inclusion criteria for this review. Figure 1 displays the PRISMA study ﬂowchart. The results are presented in the following 3 categories: cross-sectional studies, acute bouts of running and longer-term interventions. Int. J. Environ. Res. Public Health 2020, 17, x 5 of 66 From initial searches, 29,851 papers were identified. Following removal of duplicates, 16,401 were screened at the title and abstract levels and 273 papers were retained for full-text assessments. Ultimately, 116 papers met the inclusion criteria for this review. Figure 1 displays the PRISMA study flowchart. The results are presented in the following 3 categories: cross-sectional studies, acute bouts of running and longer-term interventions. Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) depicting the identification, screening, eligibility and inclusions of texts within the scoping review. 3.2. Category 1: Cross-Sectional Studies Forty-seven studies utilised cross-sectional designs (with and without non-running comparison groups) (Table 2) [29–75]. These studies assessed exposure to regular running by questionnaire. Narrative description of findings of the 47 cross-sectional studies are included within Table S1 within the supplementary material. Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) depicting the identiﬁcation, screening, eligibility and inclusions of texts within the scoping review. 3.2. Category 1: Cross-Sectional Studies Forty-seven studies utilised cross-sectional designs (with and without non-running comparison groups) (Table 2) [29–75]. These studies assessed exposure to regular running by questionnaire. Narrative description of ﬁndings of the 47 cross-sectional studies are included within Table S1 within the supplementary material. Int. J. Environ. Res. Public Health 2020, 17, 8059 6 of 39 Table 2. Summary of data extraction from the 47 cross-sectional studies. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Wilson et al. [29] (1980) Canada Cross-sectional n = 30, all male; age range 20–45; 10 marathoners, 10 regular joggers and 10 non-exercisers Mood (Proﬁle of Mood States) Comparing mood states of marathon runners, regular joggers and non-exercisers Marathoners and joggers reported less depression (F(2,28) = 7.51, p < 0.003), anger (F = 10.11, p < 0.001) and confusion (F = 12.41, p < 0.001) and more vigour (F = 103.21, p < 0.001) than non-exercisers. Marathoners reported less fatigue (F = 10.26, p < 0.001) and tension (F = 7.51, p < 0.003) than non-exercisers. Marathoners and joggers did not signiﬁcantly diﬀer on reported fatigue and tension; however, marathoners had signiﬁcantly less depression, anger and confusion but more vigour than joggers. Joesting [30] (1981) USA Controlled cross-sectional n = 100 runners; 79 males, mean age 18.36; 21 females, mean age 16.53 Depression (Depression Adjective Checklist) Investigating the relationship between running and depression Signiﬁcantly (p < 0.01) decreased depression in males and female runners compared to Lubin’s data for nonpsychiatric patients: male and female runners mean depression scores were 4.59 and 4.33, respectively, while the normative nonpsychiatric sample means were 8.02 and 7.32, respectively. Jorgenson et al. [31] (1981) USA Cross-sectional n = 454 regular runners; 390 males and 64 females; majority aged 30–39 Emotional well-being (structured questionnaire consisting of 55 items designed by the author) Investigating the relationship between emotional well-being and running Of the runners, 92.3% (n = 419) indicated an increase in emotional well-being (p < 0.01) but no report on the scale of improvement. Age and emotional well-being were signiﬁcantly correlated (gamma value = 0.42, p < 0.001), with the older runner having the greater perception of emotional well-being resulting from running. There was a signiﬁcant inverse relationship between average hours per week running and emotional well-being (gamma value = −0.43, p < 0.001). Valliant et al. [32] (1981) Canada Cross-sectional n = 68 male runners; 30 marathon runners, mean age 34.4; 38 recreational runners, mean age 20.6 Self-suﬃciency and personality proﬁles (a 1-h “Sixteen Personality Factor Questionnaire”) Comparing self-suﬃciency and personality proﬁles in marathon runners vs. recreational joggers Marathon runners had a more self-suﬃcient personality compared to joggers who were less assertive and more conscientious and had controlled personality types: On average, marathoners more reserved (F = 17.07, df = 1,66, p < 0.001), intelligent (F = 12.69, df = 1,66, p < 0.001), tender-minded (F = 11.79, df = 1,66, p < 0.001), imaginative (F = 11.09, df = 1,66, p < 0.005) and self-suﬃcient (F = 19.84, df = 1,66, p < 0.001) than joggers. Conversely, joggers were more happy-go-lucky (F = 10.05, df = 1,66, p < 0.005), apprehensive (F = 10.51, df = 1,66, p < 0.005) and controlled (F = 7.09, df = 1,66, p < 0.01). Francis et al. [33] (1982) USA Cross-sectional n = 44 male participants; mean age 32; non-running controls who ran 0 miles weekly (n = 16), 20 miles (n = 10), 30–40 miles (n = 8) and 50–60 miles (n = 10) Anxiety, depression and hostility (State-Trait Anxiety Inventory and the Multiple Aﬀect Adjective Check List) Comparing anxiety, depression and hostility in various groups of runners vs. sedentary controls Compared to sedentary controls, runners had lower anxiety (4.2 vs. 7.2, p < 0.01), depression (8.6 vs. 12.3, p < 0.01) and hostility (4.8 vs. 6.8, p < 0.01). Hailey et al. [34] (1982) USA Cross-sectional n = 60 male runners; aged 13–60; Those who ran for less than 1 year (n = 12), those who ran for 1–4 years (n = 32) and those who ran for over 4 years (n = 16) Negative addiction (Negative addiction scale) Investigating the relationship between running and negative addiction The more years that a male had been running, the greater the risk of developing negative addiction (F(2,58) = 3.48, p < 0.05). Runners with a running history of <1 year scored a mean of 3.84 (scale of 1–14), those running for 1–4 years scored 5.63 and those running for 4+ years scored 6.38. Addiction scores for runners of 4+ years was greater than the addiction score for runners of <1 year (t(59) = 2.72, p < 0.005). Likewise, the addiction score for runners of between 1–4 years was greater than the score for runners <1 year (t(59) = 2.52, p < 0.01). No statistically signiﬁcant diﬀerence in addiction scores were found between the 1–4-year group and the 4+ year group. Callen [35] (1983) USA Cross-sectional n = 424 non-professional runners who ran on average more than 28.8 miles per week; 303 males and 121 females; mean age 34 Mental and emotional aspects (a questionnaire designed by the author) Investigating mental and emotional aspects associated with long-distance running in non-professional runners, including depression, tension, mood, happiness, self-conﬁdence and self-image Ninety-six percent of subjects noticed mental/emotional beneﬁts from running, but none reported the size of beneﬁts. Beneﬁts included relief of tension (86% of all respondents, n.s.), improved self-image (77%, n.s.), better mood (66%, p < 0.05), improved self-conﬁdence (64%, n.s.), relieved depression (56%, p < 0.05) and improved happiness (58%, n.s.). However, 25% stated they had experienced emotional problems associated with running, with almost every instance being a problem of depression, anger or frustration associated with not being able to run due to injury, but no details of size or signiﬁcance were reported. Sixty-nine percent of runners experienced an emotional “high” while running. Galle et al. [36] (1983) USA Controlled cross-sectional n = 391 female subjects; aged 15 to 50; runners (n = 102), infertility patients (n = 103), fertile subjects (n = 139) and Clomid study patients whose only infertility abnormality was ovulation dysfunction (n = 47) Anxiety and depression (Hopkins Symptom Checklist-90) Comparing psychologic proﬁles including anxiety and depression in runners, infertility patients, fertile subjects and Clomid study patients whose only infertility abnormality was ovulation dysfunction Emotional distress scores of runners were not signiﬁcantly diﬀerent from the fertile control subjects (F = 1.19, ns), but both groups of infertility patients showed greater distress on items in the depression subscale than the runners and fertile control subjects (F = 3.42, p < 0.025). The only signiﬁcant diﬀerence between runners and fertile control subjects was that control subjects had higher hostility (p < 0.05). Regarding just runners, there was signiﬁcant diﬀerences in depression between amenorrhoeic (n = 15) and regular cycling runners (n = 87), with amenorrhoeic runners scoring higher in the depression factor than regular ovulation cycle runners (F = 3.0, p < 0.10). Int. J. Environ. Res. Public Health 2020, 17, 8059 7 of 39 Table 2. Cont. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Lobstein et al. [37] (1983) USA Pre-post controlled between subject design n = 22 medically healthy men; 11 physically active men and 11 sedentary men; aged 40–60 Depression (Minnesota Multiphasic Personality Inventory) Assessing the impact of a treadmill run with increasing gradient on depression Sedentary men were signiﬁcantly more depressed than men who ran (mean = 61.36 vs. 50.73, respectively, p < 0.01), but both groups were within clinical limits for normal, mentally healthy, middle aged men. Rudy et al. [38] (1983) USA Cross-sectional n = 319 female regular runners; aged between 16 and 60 Anxiety and self-esteem (Rosenberg Self-esteem Scale and Zuckerman’s Anxiety Adjective Checklist) Investigating how levels of anxiety and self-esteem related to intensity of jogging Female runners jogging with great intensity had signiﬁcantly less anxiety than lower intensities (x2 = 22.83; p < 0.001). Results indicate that intensity of jogging inﬂuences self-esteem but was not signiﬁcant: 89% of women scored in the range of high self-esteem, and in the open-ended answers, 29% of responses stated that they feel better about themselves, 12% had increased self-conﬁdence and 6% stated a sense of accomplishment. Goldfarb et al. [39] (1984) USA Cross-sectional n = 200 distance runners; 136 males and 64 females Anorexia nervosa traits (Goldfarb Fear of Fat scale and Activity Vector Analysis) Investigating anorexia nervosa traits within distance runners Runners had a mean score of 2.91 (on a 10-point scale), indicating a low–normal fear of fat, and only 29 (14.5%) participants reported a high fear-of-fat score (score between 6 and 10). Fear-of-fat scores did not correlate signiﬁcantly with measures of running zealousness: miles run per week (r = −0.04), number of workouts per week (r = 0.09), number of road races (r = 0.05), marathons completed (r = −0.05) or degree of importance placed on running (r = −0.03). Runners who demonstrated the greatest zealousness demonstrated Activity Vector Analysis proﬁles that clustered around one particular proﬁle (r = 0.64, p < 0.05) indicating assertive, obsessive, perfectionistic and anxious individuals. Results do not support a correlation between running and fear of fat; however, runners most closely resembling “obligatory runners” exhibited traits characteristic of anorexia nervosa patients. -Guyot et al. [40] (1984) USA Controlled cross-sectional n = 126 participants; 64 runners (44 males and 20 females) vs. 62 non-runners (37 males and 25 females) Death anxiety (Death Concern Scale) Comparing death anxiety in runners vs. non-runners Runners experienced more death thoughts (F(1,122) = 4.49, p < 0.05) but less death anxiety (F(1,122) = 6.35, p < 0.05) than non-runners. Rape [41] (1987) USA Controlled cross-sectional n = 42 male participants; aged 18–25; 21 runners vs. 21 non-exercisers Depression (Beck Depression Inventory) Comparing depression scores in runners vs. non-exercisers Runners were signiﬁcantly less depressed (M = 4.38, SD = 3.88) than the non-exercisers (M = 9.55, SD = 5.40) (t40 = 3.55, p < 0.001). Overall results suggest that running reduces depression. Weight et al. [42] (1987) South Africa Controlled cross-sectional n = 135 female participants consisting of marathon runners (n = 85) vs. cross country runners (n = 25) vs. non-running controls (n = 25); aged 18–56 Eating attitudes and disorders (Eating Attitudes Test and the Eating Disorder Inventory) Comparing eating attitudes and disorders in marathon runners vs. cross country runners vs. non-running controls. No signiﬁcant diﬀerences were found between groups on any of the Eating Attitudes Test scores (mean scores = 8.4, 14.3 and 11.8). Eating Disorder Inventory scores also did not follow a deﬁnite pattern (mean scores for marathoners, cross country runners and non-running controls were 24.8, 27.1 and 32.0, respectively), indicating that abnormal eating attitudes and the incidence of anorexia was no more common among competitive female runners than among the general population, with a low incidence of anorexia in the total group (2 out of 135 participants). Chan et al. [43] (1988) USA Cross-sectional n = 60 runners who ran at least 3× per week for a minimum of a year; 28 males and 32 females; prevented runners n = 30 vs. continuing runners n = 30; aged 15–50. Depression, self-esteem and mood (Zung depression Scale, Rosenberg Self-esteem Scale and Proﬁle of Mood States) Comparing depression, self-esteem and mood in prevented runners vs. continuing runners Prevented runners reported signiﬁcantly greater overall psychological distress (Wilks’s = 0.63, p < 0.01: X92 = 24.38, p < 0.01), depression (F(1,58) = 11.57, p < 0.01) and overall mood disturbance (F(1,58) = 11.03, p < 0.01) than continuing runners. Prevented runners reported signiﬁcantly lower self-esteem (F(1,58) = 3.17, p < 0.05), less satisfaction with the way their bodies’ present looks (F(1,58) = 4.17, p < 0.05) and had greater desire to change something about the way their bodies look (F(1,58) = 4.54, p < 0.05) compared to continuing runners. Frazier [44] (1988) USA Post only, nonrandomised long-term observational study n = 86 regular, distance runners who had all completed a marathon; 68 males with mean age of 33.7 and 18 females with a mean age of 32.2 Mood (Proﬁle of Mood States) Investigating the relationship between running and mood in regular distance runners Results suggest that regular, distance running improves mood in both males and females. Running subjects had lower mean scores on tension, anger, depression, fatigue and confusion and a higher mean of vigour compared to scores for test norms; however, statistical signiﬁcance was not reported here. The only signiﬁcant diﬀerence between males and females was on the confusion subscale: female mean = 7.8 vs. male = 5.5 (F(1,84) = 5.33, p < 0.05). Lobstein, Ismail et al. [45] (1989) USA Controlled cross-sectional n = 36 male participants; aged 40–60; runners (n = 21) vs. sedentary controls (n = 15) Anxiety and depression (Minnesota Multiphasic Personality Inventory and Eysenck Personality Inventory) Comparing anxiety and depression levels in runners vs. sedentary controls Overall, running reduced anxiety (mean = 48.95 vs. 61.48 respectively, p < 0.05, standardised canonical coeﬃcient = −1.07) and depression (mean = 50.76 vs. 57.93, respectively, p < 0.05, standardised canonical coeﬃcient = 0.00) compared to being sedentary. Lobstein, Rasmussen et al. [46] (1989) USA Controlled cross-sectional n = 20 psychologically normal, medically healthy men; aged 40–60; physically active joggers (n = 10) vs. sedentary (n = 10) Depression and stress (Eysenck Personality Inventory and Minnesota Multiphasic Personality Inventory) Comparing depression and stress in sedentary men to physically active joggers The ﬁndings suggest that regular jogging increases emotional stability (t = −2.84, p < 0.01) and decreases subjective depression with MMPI subscales of depression and Wiggins depression both being signiﬁcantly lower in the joggers (t = 3.70, p < 0.01; t = 2.40, p < 0.05; respectively). Int. J. Environ. Res. Public Health 2020, 17, 8059 8 of 39 Table 2. Cont. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Nouri et al. [47] (1989) USA Cross-sectional n = 100 male participants; aged 18–62; non-exercisers (n = 28), drop-out joggers (n = 21), beginning joggers (n = 15), intermediate joggers (n = 16) and 20 advanced joggers (n = 20) Anxiety and addiction/commitment (State-Trait Anxiety Inventory, Commitment to Running Scale, and The Buss–Dutkee Inventory measuring hostility and aggression) Investigating the relationship between various levels of jogging vs. non-exercising on anxiety and addiction/commitment Running reduced anxiety levels compared to physical inactivity (F(4,89) = 4.43, p < 0.01), with advanced joggers scoring signiﬁcantly lower on trait anxiety than beginner and intermediate joggers (1.42 vs. 1.77 vs. 1.69, respectively, p < 0.01) and commitment to running signiﬁcantly higher for the joggers than the non-exercisers (F(4,89) = 14.30, p < 0.01). Chan et al. [48] (1990) Hong Kong Cross-sectional n = 44 male runners of track clubs who ran a mean of 57.2 km per week; mean age 27.8 Depression, stress, tension and personality proﬁles (Chinese version of the Personality Research Form) Investigating the relationship between running and depression, stress, tension and personality proﬁles Running increased mood, happiness and outlook, while relieving anger, depression and aggression, but none reported the size of changes or signiﬁcance; 36.4% of participants reported “improving mental health” as a reason to start running. Emotional beneﬁts from running reported were more self-conﬁdent (59.1% of respondents), happier (56.8%), better mood (50.0%), relieved tension (45.5%), better self-image (36.4%), relieved depression (36.4%), more aggression (36.4%), improved outlook (34.1%), more content (31.8%) and better family relationship (15.9%). However, when participants stopped running, 38.6% experienced low mood and 25.0% experienced anxiousness. More experienced runners, compared to less experienced runners, were less aggressive or easily angered (t = 2.92, df = 42, p < 0.01), less guarded or defensive (t = 2.13, df = 42, p < 0.005) and more likely to present themselves favourably (t = 2.68, df = 35, p < 0.05). Chapman et al. [49] (1990) USA Cross-sectional n = 47 runners; 32 males aged 34–57 and 15 females aged 35 to 59 Running addiction, psychological characteristics and running (Running Addiction Scale, Commitment to Running Scale, Symptom Checklist (SCL-90-R) and Levenson’s Locus of Control Scale) Investigating the relationship between running addiction, psychological characteristics and running Results suggest a sex diﬀerence in the relationship between addiction and commitment, in that commitment to running can occur without addiction in females. Running Addiction Scale (RAS) scores correlated strongly for both sexes with self-rated addiction (p < 0.05) and moderately with discomfort (p < 0.05). However, the Commitment to Running Scale (CR) did not signiﬁcantly correlate with self-rated addiction in females (0.246, ns) while RAS did (0.753, p < 0.05) (z = 2.00, p < 0.05). Running addiction was associated with a high frequency of running (p < 0.05) and longer duration of running (males = p < 0.05; females = ns). The CR score correlated signiﬁcantly with run frequency for male (0.59, p < 0.05) but not female runners (0.14, ns), while CR and run duration did not correlate signiﬁcantly for either sex (males = 0.16, females = 0.28, n.s.). Duration of running was associated with mood enhancement, implying that the beneﬁts of running to mood may be obtained without addiction. Males were above the norm for obsessive–compulsive tendencies (SCL-90 score) and signiﬁcantly higher than for females (p < 0.05), with running addiction associated with male-positive personality characteristics (p < 0.05) but not with mood enhancement. There were no signiﬁcant correlations with personality traits for females. Guyot [50] (1991) USA Cross-sectional n = 370 regular long-distance runners; 289 males, mean age 38; 81 females, mean age 35 Addiction and death anxiety (Dickstein Death Concern Scale and author-created questionnaires for pain running, running motives, risk taking and medical symptoms) Investigating the relationship between addiction and death anxiety between pain runners and non-pain runners Of the 370 runners, 56% pushed themselves during running until they felt pain. Compared to non-pain runners, pain runners were more likely to be male, taller (F(1,366) = 11.45, p < 0.05), heavier (F(1,366) = 9.19, p < 0.05) and younger (F(1,366) = 5.75, p < 0.05). Overall, results suggest that runners classiﬁed as pain runners experienced signiﬁcantly more death thoughts (F(1,364) = 5.04, p < 0.05) and death anxiety (F(1,364) = 8.86, p < 0.05) than non-pain runners. Maresh et al. [51] (1991) USA Cross-sectional n = 29 male distance runners; mean age 40.1 Psychological characteristics including anxiety, depression and stress (Myers–Briggs Type Indicator Form and Multidimensional Anger-Inventory) Investigating psychological characteristics including anxiety, depression and stress in distance runners Results suggest that long-term involvement in running is associated with low levels of self-reported anxiety (m = 2.5 on a 6-point scale), depression (M = 1.8) and stress (m = 2.5). Runners’ personality proﬁles diﬀered from the normative sample, suggesting that running is associated with more introverted personalities compared to men in the general population. Compared to a normative sample of male control students, runners were less angry overall, less frequently angry, and angry across fewer situations. However, 82% of runners reported withdrawal symptoms when forced to be inactive, with a self-reported addiction average of 4.4 (“moderately” to “very”) on a 6-point scale. Gleaves et al. [52] (1992) USA Controlled cross-sectional n = 60 female participants; runners (n = 20), bulimia patients (n = 20) and a non-exercising, non-dieting control group (n = 20) Depression, body image and bulimia nervosa symptomology (Beck’s Depression Inventory, Body Image Assessment Procedure, subscales from the Eating Disorder Inventory, Automatic thoughts Questionnaire and dieting/weight loss questionnaire) Comparing depression, body image disturbance and bulimia nervosa symptomology in runners, bulimia patients and a non-exercising, non-dieting control group No diﬀerences were found between runners and controls throughout the study. Bulimics had signiﬁcantly higher depression scores than runners and controls (20.65, 3.30 and 4.80 respectively, F = 56.95, p < 0.0001), but runners and controls did not diﬀer from each other. The same pattern of results was found for the Autonomic Thoughts Questionnaire (F = 45.87, p < 0.0001) and Eating Disorder Inventory (F = 34.95, p < 0.0001), with bulimics scoring higher, but no signiﬁcant diﬀerence was found between runners and controls: ATQ means = 85.40, 41.10 and 41.50, respectively, and EDI means = 12.80, 0.80 and 1.60, respectively. There were signiﬁcant group eﬀects for all three variables of body image (p < 0.01); again, bulimics diﬀered from runners and controls. Int. J. Environ. Res. Public Health 2020, 17, 8059 9 of 39 Table 2. Cont. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Coen et al. [53] (1993) USA Cross-sectional n = 142 male marathon runners; mean age 44.07; obligatory runners (n = 65) vs. non-obligatory runners (n = 77) Anxiety, anorexia and self-identity (Obligatory Exercise Questionnaire, State-Trait Personality Inventory and The Ego Identity Scale) Investigating the relationship between obligatory running vs. non-obligatory running on anxiety, anorexia and self-identity Compared to the non-obligatory runners, the obligatory group ran signiﬁcantly more miles per week,0 spent more time running each week (t(140) = 13.19, p < 0.001) and had signiﬁcantly higher levels of anxiety (18.85 vs. 6.45, respectively, (p < 0.01), suggesting that running represents a successful coping mechanism to reduce anxiety. There was no statistically signiﬁcant diﬀerence in Ego Identity Scale score (p > 0.05), indicating that neither group showed a higher developed sense of identity. Furst et al. [54] (1993) USA Controlled cross-sectional n = 188 participants, with n = 98 runners: 72 males and 26 females vs. n = 90 gym exercisers: 60 males and 30 females; majority aged 20–29 Negative addiction (Negative Addiction Scale) Comparing negative addiction in runners vs. gym exercisers A signiﬁcant association was found between years of participation in physical activity and addiction scores (F(5,182) = 6.39, p < 0.01) regardless of the type of activity, with no signiﬁcant diﬀerences in addiction scores between runners and gym exercisers. Masters et al. [55] (1993) USA Cross-sectional n = 712 participants in a marathon; 601 males and 111 females; aged 16–79 Self-esteem and psychological coping of runners (Motivation of Marathoners Scales, Sport Orientation Questionnaire, Marlowe–Crowne Social Desirability Scale, Attentional Focusing Questionnaire, and 3 body satisfaction and composition questions) Investigating self-esteem and psychological coping of marathon runners Participation in marathon running and training was used as a way to problem solve with self-distraction for psychological coping (r(66) = 0.54, p < 0.001), improving self-esteem (r(66) = 0.31, p < 0.01) and life meaning (r(66) = 0.36, p < 0.01). Marathon runners reporting higher anxiety levels were more likely to endorse psychological motives for marathon running, indicating that their running helps them avoid or dampen negative emotional experiences: psychological coping (r(62) = 0.38, p < 0.01) and self-esteem (r(62) = 0.36, p < 0.01). Women more strongly endorsed weight concern as a reason for involvement in marathons (t(588) = −3.52, p < 0.001). Personal goal achievement and competition were both positively related to training miles per week (r(575) = 0.22, p < 0.001 and r(576) = 0.30, p < 0.001, respectively). Pierce et al. [56] (1993) USA Cross-sectional n = 89 male runners; n = 33 non-competitive runners vs. n = 24 5-km runners vs. n = 32 marathoner runners Exercise dependence (negative addiction scale) Comparing exercise dependence in recreational (non-competitive) runners vs. 5-km runners vs. marathoner runners Training mileage was signiﬁcantly correlated with exercise dependence, with marathoners showing signiﬁcantly higher (p < 0.05) mean exercise dependence scores (3.78) compared to 5K (2.9) and recreational runners (2.16). There was no signiﬁcant diﬀerence in exercise dependence scores found between recreational and 5K runners. Klock et al. [57] (1995) USA Controlled cross-sectional n = 22 females who were not currently pregnant or taking oral contraceptives; amenorrhoeic runners (n = 7, mean age 28.0), eumenorrheic runners (n = 9, mean age 32.1) and eumenorrheic sedentary women as controls (n = 6, mean age = 27.5) Depression, anorexia nervosa, excessive exercise and eating disorder (The modiﬁed Body Image Questionnaire, the Beck Depression Inventory, the Symptom Checklist-90 and the Eating Disorders Inventory) Comparing depression, anorexia nervosa, excessive exercise and eating disorders in amenorrhoeic runners, eumenorrheic runners and eumenorrheic sedentary women as controls No signiﬁcant diﬀerences were found between amenorrhoeic runners, eumenorrheic runners and eumenorrheic sedentary controls on any of the psychological measures; hence, these results do not suggest that there are psychological similarities between obligatory runners and anorexics. However, there was a subgroup of amenorrhoeic runners (3 out of 9) who scored in the clinically depressed range on the BDI, indicating that they were mild to moderately depressed, and who also had the highest scores in their group on the Eating Disorder Inventory measures. Thornton et al. [58] (1995) UK Cross-sectional n = 40 long-standing, habitual male runners who ran on average 4 times per week with a weekly mileage of 42.5 miles; mean age 38 Addiction (Rudy and Estok Running Addiction Scale, the Hailey and Bailey Running Addiction Scale, and the Personal Incentives for Exercise questionnaire) Investigating a relationship between habitual running and addiction A high level of commitment in runners was found, with 55% classiﬁed as moderately committed (scores 13–20) and 22% classiﬁed as highly “addicted” (scores +20), but no relationship between years of running and addiction scales was found. This contrasts the signiﬁcant correlations between both the Estok RAS and frequency of running (rs = 0.38; p < 0.05) and the Bailey RAS and number of runs per week (rs = 0.55; p < 0.01). The correlation between the two addiction scales revealed a strong positive relationship (rs = 0.81; p < 0.001). The primary motivation for running was mastery (mean Personal Incentives for Exercise score of 4.2), followed by competition (3.93), weight regulation (3.9), health beneﬁts (3.89), ﬁtness (3.87) and social recognition (3.01). Powers et al. [59] (1998) USA Controlled cross-sectional n = 57; habitual male runners (n = 20), habitual female runners (n = 20) and female anorexia nervosa patients (n = 17) Psychological proﬁles (Minnesota Multiphasic Personality Inventory, Leyton Obsessional Inventory, Obligate Running Questionnaire, Becks Depression Inventory and three body image tests) Comparing psychological proﬁles of habitual male runners, habitual female runners and female anorexia nervosa patients Signiﬁcant diﬀerences in body image between groups (F = 7.969, p < 0.001) were found, but no signiﬁcant diﬀerences between female groups were found. Anorexics scored higher than either group of runners (p < 0.001) for MMPI subscales of depression, hysteria and psychopathic deviate, while none of the mean scores for either set of runners were considered clinically signiﬁcant. Anorexics scored higher on the Becks Depression Inventory than both male and female runners (F = 68,645, p < 0.0001, mean scores = 23, 2.4 and 3.45, respectively), but again, there was no signiﬁcant diﬀerences between the runners. While there were suggestive similarities between female runners and anorexics on body image, the overall results found few psychological similarities between anorexia patients and habitual runners, with evidence of signiﬁcant psychopathology on all psychological measures in the anorexia group, while both groups of runners were consistently in the normal range. Int. J. Environ. Res. Public Health 2020, 17, 8059 10 of 39 Table 2. Cont. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Slay et al. [60] (1998) USA Cross-sectional n = 324 regular runners; 240 males and 84 females; 84 classiﬁed as obligatory runners: 63 males and 21 females; ages 15–71 Eating pathology traits (Eat Attitudes Test, and Obligatory Running and Motivations for Running Questionnaire) Comparing eating pathology traits between obligatory and non-obligatory runners Obligatory runners, particularly females, are most at risk of eating pathophysiology, as obligatory runners scored signiﬁcantly higher on the EAT test, with female obligatory runners having the highest mean EAT score (r = 0.40, p < 0.0002). At low levels of obligatory running, women and men scored similarly on the EAT test (F(1,164) = 2.78, p > 0.05); however, at higher levels of obligatory running, women demonstrated signiﬁcantly higher EAT scores than men (F(1,164) = 29.50, p < 0.001). Ryujin et al. [61] (1999) USA Controlled cross-sectional n = 55 female participants; collegiate distance runners (n = 20) vs. non-running undergraduate student controls (n = 35) Eating disorder symptomology (Eating Disorders Inventory 2) Comparing eating disorder symptomology in collegiate distance runners to non-running undergraduate student controls Distance runners showed no enhanced symptomatology of eating disorders; instead, female distance runners exhibited fewer symptoms of eating disorders on all subscales of the Eating Disorder Inventory-2 except Perfectionism: drive for thinness (t(107) = 3.34, p < 0.005), bulimia (t(107) = 2.48, p < 0.05) and body dissatisfaction (t(107) = 4.23, p < 0.001). Leedy [62] (2000) USA Controlled cross-sectional n = 276 runners with an average of 11.5 years of running experience; 239 men, mean age 37.9; 37 women, mean age 40.5 Depression and anxiety (Diagnostic and Statistical Manual-IV, and an author-adapted scale based on the Running Addiction Scale) Comparing depression and anxiety in runners to non-runners Of the non-runners and runners, 16.2% and 4.6%, respectively, had been diagnosed with an anxiety disorder or prescribed an anxiolytic medication. These participants had signiﬁcantly higher anxiety trait scores than those without a diagnosis: F(1,274) = 18.87, p < 0.0001; 27% of non-runners and 11.8% of runners reported a diagnosis of depression or were prescribed an antidepressant. These participants had signiﬁcantly higher measures of depression traits: F(1,274) = 22.46, p < 0.0001. Women’s Stress Relief scores were signiﬁcantly higher than men’s (F(1,229) = 20.51, p < 0.001). Stress relief scores also varied across race length (F(2,229) = 6.47, p < 0.005), indicating that runners entered in the 5–10K runs had lower scores than those running the half/full marathon. Results indicate that highly committed runners (n = 31) had signiﬁcantly lower anxiety (F(2,113) = 5.73, p < 0.01) and depression scores (F(2,113) = 8.00, p < 0.001) than recreational runners (n = 46) and non-runners (n = 39). Edwards et al. [63] (2005) South Africa Cross-sectional n = 277 participants; 94 males and 183 females; mean age 25.2; hockey players (n = 60), runners (n = 40) and health club gym members (n = 69) vs. a control group of non-exercisers (n = 108) Psychological well-being and physical self-perception (Ryﬀ’s Short Standardized 18-item scale of Objective Psychological Well-being, Fox’s Physical Self-Perception Proﬁle (PSPP) and the Physical Self-Perception Proﬁle) Comparing psychological well-being and physical self-perception in hockey players, runners and health club gym members vs. a control group of non-exercisers. All three forms of physical activity were associated with signiﬁcantly higher (p < 0.01) scores on 11 out of the 15 dimensions of psychological well-being and physical self-perception scales compared to the control group: autonomy (F = 11.3), personal growth (F = 35.4), environmental mastery (F = 9.6), purpose in life (F = 149.2), positive relations with others (F = 81.6), self-acceptance (F = 50.4), sport competence (F = 41.3), conditioning (F = 28.1), sport importance (F = 11.7), conditioning importance (F = 28.1) and body importance (F = 31.0). Schnohr et al. [64] (2005) Denmark Observational cohort study n = 12,028 participants; 5479 males and 6549 females; aged 20–79 Stress (An author-created questionnaire) Comparing stress levels between jogging and various levels of physical (in)activity in leisure time Those who were vigorously physically active (joggers) had the lowest level of stress compared to those with low activity levels (males, 3.1% vs. 12.8%, respectively; female, 3.3% vs. 19.3%, respectively). With increasing physical activity in leisure time, there was a decrease in level of stress between sedentary persons and joggers (Odds Ratio (OR) = 0.30) and a decrease in life dissatisfaction between sedentary persons and joggers (OR = 0.30). The highest levels of stress and dissatisfaction was seen in sedentary persons who remained inactive at follow-up, while the group that changed from sedentary to active had an adjusted OR < 0.50. Strachan et al. [65] (2005) Canada Prospective longitudinal study n = 67 regular runners; 32 were male and 35 were female; mean age of 40.6 Self-eﬃcacy and self-identity (author-created measures of task self-eﬃcacy and self-regulatory eﬃcacy, and a 10-item, validated athletic identity measurement scale) Investigating the relationship between running and self-eﬃcacy and self-identity Signiﬁcant comparisons were made between extreme self-identity groups on social cognitive and behavioural variables (F(5,37) = 4.72, p < 0.002), with those higher in self-identity scoring higher on task self-eﬃcacy (p < 0.001), scheduling self-eﬃcacy (p < 0.03), running more frequently (p < 0.001) and running for longer durations (p < 0.005) than those who scored lowest on self-identity. Both scheduling self-eﬃcacy (R2 change = 0.16, p < 0.001) and barriers to self-eﬃcacy (R2 change = 0.22, p < 0.001) were correlated with self-identity to prospectively predict running frequency (F(2,64) = 9.98, p < 0.001; F(2,63) = 12.89, p < 0.001, respectively). Both task self-eﬃcacy (R2 change = 0.06, p < 0.05) and self-identity (R2 change = 0.06, p < 0.04) were signiﬁcant predictors of maintenance duration. Int. J. Environ. Res. Public Health 2020, 17, 8059 11 of 39 Table 2. Cont. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Galper et al. [66] (2006) USA Retrospective cross-sectional n = 6728 participants; 5451 males with a mean age of 49.5 and 1277 females with a mean age of 48.1; inactive (n = 1454 men and n = 422 women), insuﬃciently active (n = 1892 men and n = 443 women), suﬃciently active (n = 1396 men and n = 283 women) and highly active (n = 709 men and n = 129 women) Depression and emotional well-being (Center for Epidemiological Studies Scale for Depression and the General Well-Being Schedule) Assessing retrospectively if the level of walking/running impacted depression and emotional well-being Signiﬁcant inverse association between increased physical activity and reduced depression scores for both men (F(6, 5306) = 20.93, p < 0.0001) and women (F(6, 1247) = 11.80, p < 0.0001) and a positive association between increased physical activity and increased well-being scores in men (F(6, 5306) = 78.65, p < 0.0001) and women (F(6, 1247) = 24.82, p < 0.0001) were found. These eﬀects peaked at 11–19 miles per week (the suﬃciently active category). Luszcynska et al. [67] (2007) UK Longitudinal prospective cohort study n = 139 runners; 111 males and 29 females; mean age of 29.5; strong (n = 72) and weak (n = 66) maintenance self-eﬃcacy, strong (n = 72) and weak (n = 61) recovery self-eﬃcacy, and strong (n = 87) and weak (n = 45) intentions Self-eﬃcacy and running behaviour (an author-created questionnaire) Investigating the relationship between self-eﬃcacy and running behaviour with data collected twice with a time gap of 2 years Participants decreased the frequency of running sessions after 2 years, regardless of baseline intensions or self-eﬃcacy; however, those with stronger recovery in self-eﬃcacy jogged more than those with weaker recovery in self eﬃcacy 2 years later. All participants reduced the number of jogging or running sessions over 2 years (F(1,131) = 43.43, p < 0.001); however, those with strong baseline recovery self-eﬃcacy ran/jogged more often at 2 years than those who had weak recovery self-eﬃcacy at baseline (F(1,131) = 6.12, p < 0.05). Participants reduced the number of running or jogging sessions over the 2 years, regardless of strong or weak intentions at baseline (F(1,130) = 34.55, p < 0.001) or of strong or weak baseline maintenance of self-eﬃcacy (F(1,130) = 42.12, p < 0.001). No eﬀects of maintenance self-eﬃcacy were found. Recovery self-eﬃcacy at T1 predicted recovery self-eﬃcacy (p < 0.05), maintenance self-eﬃcacy (p < 0.05), and jogging or running behaviour (p < 0.05) assessed 2 years later. Overall, social-cognitive variables predicted behaviour, whereas behaviour did not predict social-cognitive variables. Smith et al. [68]. (2010) UK Cross-sectional n = 93 non-competitive, regular runners; 47 males and 46 females Exercise dependence, running addiction and social physique anxiety (Exercise Dependence Scale, Running Addiction Scale and Social Physique Anxiety Scale) Comparing exercise dependence, running addiction and social physique anxiety in male vs. female runners While a signiﬁcant proportion of runners displayed symptoms of exercise dependence, results did not ﬁnd that exercise dependence was linked to social physique anxiety (F(3.179) = 1.21, p > 0.05) or that there was a diﬀerence between men and women (p > 0.05 in all cases). There was no signiﬁcant diﬀerence between males and females for running addiction scale (22.64 and 20.91, respectively), social physique anxiety scale (22.30 and 22.61, respectively) or total exercise dependence scale scores (72.56 and 66.86, respectively). Gapin et al. [69] (2011) USA Cross-sectional n = 179 regular runners; 88 males and 91 females; 91 obligatory and 88 non-obligatory runners Disordered eating (Eating Disorder Inventory, Athletic Identity Measurement Scale and Obligatory Exercise Questionnaire) Comparing disordered eating in obligatory and non-obligatory runners. Obligated running (exercising to maintain identiﬁcation with the running role) may be associated with pathological eating and training practices, with obligatory runners scoring signiﬁcantly higher on all of the Eating Disorder Inventory measures (F(1,166) = 9.75, p < 0.002),and the Athletic Identity Measure Scale (F(8,161) = 8.85, p < 0.001). Results also indicated that runners in the obligatory group demonstrated greater concern with dieting, preoccupation with weight and pursuit of thinness. Wadas [70] (2014) USA Cross-sectional n = 68 male, high school cross country runners; mean age 15.9 Disordered eating behaviours (questionnaire consisting of The Exercise Motivation Inventory 2, the Eating Attitudes Test 26 and the ATHLETE questionnaire) Investigating any relationship between male runners with disordered eating behaviours and eating attitudes Risk factors associated with eating disorders within high school male cross-country runners were found. Factors that had a signiﬁcant relationship with disordered eating were weight management (r = 0.31, p = 0.011), drive for thinness and performance (r = 0.36, p < 0.05), and feelings about performance/performance perfectionism (r = 0.26, p < 0.05). No signiﬁcant relationships were found between disordered eating behaviours and personal body feelings (r = 0.19, p = 0.109), feelings about eating (r = 0.18, p = 0.137), and feelings about being an athlete (r = 0.12, p = 0.345); 4.41% (n = 3) of participants scored 20 or higher on the EAT-26, indicating being at risk for disordered eating and displaying symptoms. An additional 13.2% (n = 9) met the cutoﬀ score of 14 for disordered eating behaviours. Samson et al. [71] (2015) USA Cross-sectional n = 308 marathon runners; 177 males and 191 females; mean age 41 Self-esteem and psychological coping (Motivation for Marathons Scale, The Perceived control questionnaire and Sport Mental Toughness Questionnaire) Investigating the relationship between self-esteem and psychological coping with marathon running Self-esteem was positively associated with perceived control (r = 0.40) (x27 = 47.08, p < 0.001, CFI = 0.85 and RMSEA = 0.14) but negatively associated with mental toughness. There was also a positive relationship between perceived control and psychological coping (r = 0.42) (x28 = 45.65, p < 0.001, CFI = 0.85 and RMSEA = 0.12). Results of the Motivations of Marathoners Scales suggested than females were more likely to run to improve psychological coping (4.8 and 4.42, respectively) and self-esteem (5.22 and 4.62, respectively) than men. Int. J. Environ. Res. Public Health 2020, 17, 8059 12 of 39 Table 2. Cont. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Lucidi et al. [72] (2016) Italy Cross-sectional prospective ﬁeld study n = 669 runners training for a marathon; 569 males and 100 females; mean age 42.07 Stress (Perceived Stress Scale, the Passion Scale and The Italian version of the Locomotion and Assessment Scales) Investigating the relationship between running and stress in runners training for a marathon Results suggest that running does not directly impact stress (β = −0.01; p = 0.75); however, running increases harmonious passion (β = 0.37; p < 0.001), which in turn reduced athletes’ experience of stress. The indirect eﬀect of running on anticipatory stress perception through harmonious passion was statistically signiﬁcant (αβ = −0.10; 95% conﬁdence interval: from −0.15 to −0.05). Similarly, the indirect eﬀect of assessment on stress through obsessive passion was statistically signiﬁcant (αβ = 0.12; 95% conﬁdence interval: from 0.07 to 0.17). Results also indicated a signiﬁcant direct eﬀect of assessment on the athletes’ experience of stress (β = 0.22; p < 0.001). Batmyagmar et al. [73] (2019) Austria Prospective longitudinal study n = 99 participants; n = 50 elderly marathon runners, mean age of 66, with 46 men and 4 women vs. n = 49 non-exercising controls, mean age of 66, with 44 men and 5 women Self-reported health and well-being and quality of life (Short Form Health Survey-36) Comparing self-reported health and well-being, and quality of life over 4 years in elderly marathon runners to non-exercising controls Findings suggested that extensive high-intensity endurance exercise is linked with improved subjective health and well-being in elderly persons, with athletes evaluating their health as better than non-athletes in the following categories: general health perception (F = 14.21, p < 0.001), vitality (F = 13.37, p < 0.001), social functioning (F = 11.30, p < 0.001), emotional role functioning (F = 1.42, p < 0.002) and mental health (F = 6.07, p < 0.0016). Cleland et al. [74] (2019) Australia Cross-sectional 372 participants of “Parkrun” events; mean age 43.8 Enjoyment, self-eﬃcacy and factors of participation in Parkrun event (author-created questionnaires). Investigating enjoyment, self-eﬃcacy and factors of participation in Parkrun event participants Overall results suggested that perceived social beneﬁts (B coeﬃcient = 0.43) and self-eﬃcacy for Parkrun (B coeﬃcient = 0.18) were positively associated with Parkrun participation. Perceived beneﬁts of Parkrun including enjoyment and social factors (B = 0.70) were positively associated with participation, as was overall enjoyment (B = 0.30), self-eﬃcacy for Parkrun (B = 0.46), social support for Parkrun from family (absolute: B = 0.05) and social support from friends (B = 0.04) related to Parkrun. Lukacs et al. [75] (2019) Hungary Cross-sectional n = 257 amateur runners with at least 2 years of running experience; 131 males and 126 females; mean age 40.49 Exercise addiction and psychological features (Exercise Dependence Scale; a Cantril ladder for Overall life satisfaction; SCOFF eating disorder questionnaire; the UCLA 3-item Loneliness Scale; Body Image Subscale from Body Investment scale; and the Depression, Anxiety and Stress Scale-21). Investigating the prevalence of exercise addiction and psychological features in amateur runners, including perceived health, life satisfaction, loneliness, stress, anxiety, depression, body shape and eating disorders Respondents (137) were characterized as nondependent symptomatic, 97 were nondependent asymptomatic and 23 were at risk of exercise addiction. Results found that ﬁve variables signiﬁcantly predicted the risk of exercise addiction in runners: weekly time spent running, childhood physical activity, lower educational attainment, anxiety and loneliness (ranges of B = 0.47 to 2.06, 95% CI for odds ratio = 1.61 to 7.86, p < 0.001 to p = 0.023). Int. J. Environ. Res. Public Health 2020, 17, 8059 13 of 39 3.2.1. Runners Versus Non-Running Comparisons Sixteen of the 47 studies directly compared measures of mental health in runners and non-running comparisons [29,33,36,37,40–42,45–47,57,61–64,73]. They found that runners had lower depression and anxiety [33,36,37,40,41,45–47,62], lower stress [64], higher psychological well-being [63,73], and better mood [29] compared to sedentary controls. In these studies, there was no evidence of increased prevalence of eating psychopathology in non-elite runners [42,57,61]. 3.2.2. Runners Only Nineteen studies only included runners [30,31,34,35,39,44,48,49,51,55,58,65–67,70,74–76] and compared diﬀerent levels and types of running. Some studies found a positive association with higher self-identity runners and low levels of depression and high self-eﬃcacy [30,65–67,74]. Studies investigating marathon training found a positive relationship of marathon training with self-esteem and psychological coping [55,71]. Two questionnaires of long-distance runners found a correlation between long-distance running and disordered eating behaviours, with obligatory runners (obsessive runners who sacriﬁced commitments and relationships for running and suﬀered withdrawal symptoms if they missed a run) exhibiting traits characteristic of anorexia nervosa patients [39] and risk factors for eating disorders identiﬁed within male high school cross-country runners [70]. One study of runners training for a marathon suggested that running did not directly impact stress [72]. There were conﬂicting results from papers investigating negative addiction; one indicated that with more years spent running came a greater risk of negative addiction [34], while another found no relationship between years of running and addiction [58] and another found a sex diﬀerence in that commitment to running can occur without addiction in female runners but not in males [49]. Another paper found that ﬁve variables signiﬁcantly predicted risk of exercise addiction in runners: weekly time spent running, childhood PA, lower educational attainment, anxiety and loneliness [75]. The remaining four cross-sectional studies of runners only found that, since participating in running, they had better emotional well-being, relief of tension, self-image and self-conﬁdence, mood, depression, aggression and anger, anxiety and happiness, but not all reported signiﬁcance or eﬀect size [31,35,44,48,51]. A further eight studies compared groups of runners [32,38,50,53,56,60,68,69]. One paper found that females jogging with greater intensity had signiﬁcantly less anxiety than those jogging at lower intensities [38]. The results from these studies showed that obligatory runners had signiﬁcantly higher anxiety [53] and eating disorder measures [60,69] than non-obligatory runners and that female obligatory runners are most at risk of eating pathophysiology [60]. Non-elite marathoners showed signiﬁcantly higher exercise dependence scores [56] but had more self-suﬃcient personalities compared to recreational runners who did not run marathons [32]. One paper did not ﬁnd that exercise dependence was linked to social physique anxiety [68], while another found that runners classiﬁed as pain runners (pushed themselves until they felt pain) experienced signiﬁcantly more death thoughts and death anxiety than non-pain runners [50]. 3.2.3. Runners Compared to Individuals with Eating Disorders Two studies compared runners to individuals with diagnosed eating disorders but neither indicated that habitual running led to development of disordered eating or body-image problems [52,59]. 3.2.4. Prevented Runners One study found that habitual runners prevented from running by illness or injury had signiﬁcantly greater overall psychological distress, depression and mood disturbance than continuing runners as well as signiﬁcantly lower self-esteem and body-image [43]. Int. J. Environ. Res. Public Health 2020, 17, 8059 14 of 39 3.2.5. Runners Compared to Gym Exercisers A study comparing negative addiction in runners versus gym exercisers found signiﬁcant association between years of participation in running and gym exercise with negative addiction, regardless of activity type [54]. 3.2.6. Summary of Cross-Sectional Evidence Consistent evidence was found for a positive association between positive mental health outcomes and habitual or long-term recreational running compared to non-runners. In contrast, there was evidence that high or extreme levels of running (high frequency and long distance including marathon running) were associated with markers of running ill-health compared to levels of moderate running. 3.3. Category 2: Acute Bouts of Running Narrative description of ﬁndings of the 35 studies with an acute bout of running are included within Tables S2–S4 within the supplementary material. 3.3.1. Single Bouts Twenty-three studies incorporated a design using a single bout of running to compare pre-post measurements of mood and short-term measures of mental health (Table 3) [77–99]. Twenty-two of these found positive improvement in measures of mental health (including anxiety, depression and mood); however, one found a decrease in self-eﬃcacy of children following participation in gymnasium PACER (progressive aerobic cardiovascular endurance run) running challenge [95]. Eleven studies used a single bout of treadmill running, and all found positive pre-post diﬀerences in mental health outcomes [84–86,88–93,97,99]. Results found signiﬁcant reductions in state-trait anxiety; total mood disturbance; and POMS subscales of anxiety, depression and confusion. A single bout of treadmill running also signiﬁcantly improved self-esteem; psychological well-being; children and adolescent self-eﬃcacy; state anxiety, depression and totally mood disturbance; adult self-eﬃcacy; and general aﬀective response. One study found that mood improvements were not evident until 40 min of running [88], while another found that depressed individuals participating in a treadmill run with increasing gradient improved depressed mood immediately post-run but that depressed mood increased at 30-min postexercise [93]. Three studies used a single bout of track running and found signiﬁcant decreases in anxiety [78,87] and total mood disturbance [81]. Two studies found that a single outdoor run signiﬁcantly improved depression scores and that even a 10-min jog caused signiﬁcant mood enhancement [80,94]. Two studies found that a single bout of self-paced running signiﬁcantly reduced all but one of the POMS subscales and had signiﬁcant positive changes in all measures of states of aﬀect [82,96]. There were signiﬁcant improvements for self-esteem, stress and total mood disturbance following a 5-km Parkrun [98], while a 3-mile “fun-run” increased positive mood and decreased negative mood [83]. Two studies used longer runs as exposures: one found that a 1-h run signiﬁcantly reduced anxiety and nonsigniﬁcantly reduced depression [79], while the other found that a 12.5-mile jog signiﬁcantly improved pleasantness; decreased trait anxiety; nonsigniﬁcantly increased activation; and reduced state-anxiety, sadness, anxiety, depression and relaxation subscales [77]. Int. J. Environ. Res. Public Health 2020, 17, 8059 15 of 39 Table 3. Summary of data extraction from the 23 single-bout studies. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Nowlis et al. [77] (1979) Canada Pre-post non-controlled study n = 18, experienced joggers; 5 females and 13 males; age range 17 to 55 Mood and anxiety (Mood Adjective Checklist and State Trait Anxiety Inventory) Impact of a 12.5-mile jog on mood and anxiety Signiﬁcant improvement in measures of pleasantness (2.00 to 2.67, p < 0.01); a signiﬁcant decrease in trait anxiety (34.81 to 33.31, p < 0.10); a nonsigniﬁcant increase in activation; a reduction in state-anxiety; and a reduction of sadness, anxiety, depression and relaxation subscales Wilson et al. [78] (1981) Canada Pre-post controlled study n = 42; 20 runners, 12 aerobics class exercisers vs. 10 people having lunch; 23 females and 19 males; age range 21 to 28 Anxiety (State-Trait Anxiety Inventory) Impact of a solo indoor track run on anxiety Signiﬁcant decrease in anxiety post-activity (F(1,39) = 15.63, p < 0.003) Markoﬀ et al. [79] (1982) Hawaii Pre-post non-controlled n = 15, all had run at least 1 marathon; 11 males and 4 females; aged 23–45 Mood (Proﬁle of Mood States) Impact of a 1-h run on mood Signiﬁcant reduction in anxiety (t = 2.72, p < 0.01) and a nonsigniﬁcant reduction in depression (t = 1.80, n.s.) Thaxton et al. [80] (1982) USA Non-randomised controlled trial n = 33, regular runners; 24 males and 9 females; mean age 36; 4 groups: outdoor running test (n = 6), pre-test but no running test (n = 9), no pre-test but running test (n = 11), and no pre-test and no running test (n = 7). Mood (Proﬁle of Mood States) Impact of 30 min outdoor running on mood Signiﬁcant diﬀerences in the depression scores between the 30 min outdoor running group and abstaining groups (F(1,29) = 4.8, p < 0.05) but no signiﬁcant diﬀerences between anxiety, vigour and fatigue scores McGowan et al. [81] (1991) USA Non-randomised controlled trial n = 72, college students; 25 joggers vs. 11 karate vs. 26 weight lifters vs. 10 science lecture class members Mood (Proﬁle of Mood States) Impact of 75 min of jogging on an outdoor track on mood Signiﬁcant decrease in total mood disturbance from pre- (35.68) to post- (24.16) test (t24 = 2.84, p < 0.009) following 75 min of jogging on a track Goode et al. [82] (1993) USA Pre-post non-controlled n = 150, regular runners; 104 males, 36 females; mean age 31.7 Mood (Proﬁle of Mood States) Impact of own training for running on mood Signiﬁcant alterations in all but one (vigor) of the POMS scales, with a signiﬁcant reduction post-run in tension/anxiety (mean change of −3.1, p < 0.1), depression (mean change of −1.5, p < 0.1), confusion (mean change of −1.1, p < 0.1) and anger (mean change of −1.8, p < 0.1) and a signiﬁcant increase in fatigue post-run (mean change of +1.8, p < 0.1). Morris et al. [83] (1994) UK Pre-post non-controlled n = 165, members of a road runners club; 98 males and 67 females; mean age 34 Mood (author-devised adjective checklist based on POMS) Impact of a 3 mile “fun-run” on mood Increase in positive mood (F(1,163) = 68.18, p < 0.001), decrease in negative mood (F(1.163) = 47.62, p < 0.001) and greater improvements in mood in women than in men that was not signiﬁcant (p > 0.1). Rudolph et al. [84] (1996) USA Randomised non-controlled trial n = 36, moderately active female university students; n = 12 for 10-, 15- and 20-min interventions; mean age 20.6 Self-eﬃcacy (Exercise-Eﬃcacy Scale) Impact of various timings of treadmill running on self-eﬃcacy (10, 15 and 20 min) Signiﬁcant increase in mean scores of self-eﬃcacy in all 3 groups, from pre to postexercise (F(1, 33) = 74.57, p < 0.001), and moderate eﬀect sizes in the 15- (ES = 0.36) and 10- (ES = 0.49) minute conditions although the largest eﬀect size (ES) occurred in the 20-min condition (ES = 0.68) Cox et al. [85] (2001) USA Randomised controlled trial n = 24, physically active male university students; mean age of 28.3 Psychological aﬀect and well-being (Subjective Exercise Experiences Scale) Impact of 30 min of treadmill jogging at either 50% or 75% predicted VO2 max on psychological aﬀect and well-being Signiﬁcant improvement in psychological well-being following an acute bout of aerobic exercise (p = 0.037, η2p = 0.07) O’Halloran et al. [86] (2002) Australia Pre-post non-controlled n = 50, regular runners; 25 males and 25 females; mean age 26.6 Mood (Proﬁle of Mood States and Beliefs Concerning Mood Improvements Associated with Running Scale) Impact of a 60-min treadmill run on mood Signiﬁcant reductions in anxiety (composed-anxious POMS scale = 25.6 to 29.12, p < 0.05, i.e., more composed-less anxious) and depression (elated-depressed POMS scale = 24.56 to 27.10, p < 0.01, i.e., more elated-less depressed) Szabo et al. [87] (2003) UK Pre-post non-controlled time series quasi-experimental n = 39, sports science university students; 22 males and 17 females; aged 20–23 Anxiety, positive well-being and psychological distress (Spielberger State Anxiety Inventory and Exercise induced Feeling Inventory) Impact of 20 min of track running on anxiety and feelings Signiﬁcant reduction in state anxiety (F(1.5, 58.3) = 5.32, p < 0.01) and a positive eﬀect on psychological distress and positive well-being O’Halloran et al. [88] (2004) Australia Randomised controlled trial n = 160 regular runners; 80 did run vs. 80 no running; 80 males and 80 females; aged 18–40 Mood (Proﬁle of Mood States and Beliefs Concerning Mood Improvements Associated with Running Scale). Impact of a 60-min treadmill run on mood Improvements in composure, energy, elation and mental clarity during the run; in the energetic-tired subscale, evident improvements at 25 min (F(1,156) = 10.09, p = 0.002); in the rest, non-evident mood improvements until 40 min of running; and more composure (less anxious) (F(1,156) = 9.47, p = 0.002) and more clear headedness (less confused) (F(1,156) = 5.57, p = 0.02) in runners Robbins et al. [89] (2004) USA Pre-post non-controlled n = 168, inactive children and adolescents; 86 males and 82 females; mean age 12.6 Self-esteem using the Walking Eﬃcacy Scale Impact of a 20-min treadmill run on self-eﬃcacy in children and adolescents Signiﬁcant increase in children and adolescents’ self-eﬃcacy postexercise (F(1, 158) = 84.31, p < 0.001) but signiﬁcantly lower pre-activity self-eﬃcacy in African American girls reported than the other three race-gender groups (F(3,164) = 5.55, p < 0.01) Int. J. Environ. Res. Public Health 2020, 17, 8059 16 of 39 Table 3. Cont. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Pretty et al. [90] (2005) UK Randomised controlled trial n = 100; 45 males and 55 females; mean age 24.6 Mood and self-esteem (Proﬁle of Mood States and Rosenberg Self-Esteem Questionnaire) Impact of a 20-min treadmill run with rural vs. urban stimuli on mood and self-esteem Signiﬁcant increase in self-esteem (from 19.4 to 18.1 on the Rosenberg Self-Esteem Questionnaire, p < 0.001), with rural and urban pleasant stimuli producing a signiﬁcantly greater positive eﬀect on self-esteem than exercise alone, while both rural and urban unpleasant scenes reduced the positive eﬀects of exercise on self-esteem Hoﬀman et al. [91] (2008) USA Pre-post pre-experimental study n = 32; 16 regular exercisers and 16 non-exercisers; 8 males and 8 females in each group Mood (Proﬁle of Mood States) Impact of a 30-min treadmill run on mood Decreased total mood disturbance in a 30-min treadmill run in both regular exercisers (−16 points, 95% CI = 7–24) and non-exercisers (−9 points, 95% CI = 1–18) but almost double the eﬀect in exercisers. Kwan et al. [92] (2010) USA Pre-post non-controlled n = 129; 62 males and 67 females; mean age 22 General aﬀective response (Physical Activity Aﬀect scale) Impact of a 30-min treadmill run on general aﬀective response Positive eﬀect of the run on general aﬀective response during exercise (b = 0.52, SE = 0.09, p < 0.0001) and 15 min postexercise (b = 0.73, CI.95 = 0.56, 0.89, t(126) = 8.63, p < 0.0001). Weinstein et al. [93] (2010) USA Pre-post controlled study n = 30; 15 males and 15 females; 2 with minor depressive disorder, 12 with major depressive disorder and 16 as controls; mean age 39.8 Mood and depression (Becks depression Inventory scale and Proﬁle of Mood States) Impact of 25 min of increasing graded treadmill running on mood and depression Not only improvements in depressed mood immediately following exercise (p = 0.02) of 25 min of increasing graded treadmill running but also increased depressed mood at 30 min postexercise (F(1,27) = 3.98; p = 0.05; ηp2 = 0.13) and signiﬁcant relation between the severity of depression and increases in depressed mood (r = 0.60, p = 0.001) at 30 min postexercise Anderson et al. [94] (2011) UK Randomised controlled trial 2 × 2 mixed design n = 40, from various sports clubs; aged 18–25 Mood (“Incredibly Short Proﬁle of Mood States”) Impact of a light 10-min outdoor jog on mood Signiﬁcant mood enhancement even with a light 10=min jog on a grass playing ﬁeld (F(1,38) = 24.18, p < 0.001, n2p = 0.39) compared with a 10-min cognitive task Kane et al. [95] (2013) USA Pre-post non-controlled n = 34 school children; 16 males and 18 females; aged 11–14 Self-eﬃcacy (Self-eﬃcacy questionnaire adapted for children) Impact of the running PACER challenge (20 m sprints with increasing pace inside a gymnasium) on self-eﬃcacy in children Decrease in self-eﬃcacy following participation in the run (from 2.7 to 2.3 following exercise, t = 4.6, p < 0.001, large eﬀect size of d = 0.79) but positive correlation between PACER laps and pre- and post-measures of exercise self-eﬃcacy Szabo et al. [96] (2013) Hungary Pre-post non-controlled n = 50 recreational runners; 37 males and 13 females mean age 29.02 States of aﬀect using the Exercise Induced Feeling Inventory Impact of a 5 km self-paced run along a public running path on states of aﬀect Signiﬁcant positive changes in all 4 measures of states of aﬀect following a 5-km self-paced run: revitalisation (F(1,48) = 145.93, p < 0.001, partial n2 = 0.75, ES = 2.0), positive engagement (F(1,48) = 97.11, p < 0.001, partial n2 = 0.67, ES = 1.6), tranquillity (F(1,48) = 85.02, p < 0.001, partial n2 = 0.64, ES = 1.5) and exhaustion (F(1,48) = 32.25, p < 0.001, partial n2 = 0.40, ES = 1.0) McDowell et al. [97] (2016) Ireland Randomised controlled trial n = 53; 27 males and 26 females; mean age of 21.2 Mood and anxiety (Proﬁle and Mood States and State-Trait Anxiety Inventory) Impact of a 30-min treadmill run on mood and anxiety Signiﬁcantly improved state anxiety (F1,92 = 12.52, p < 0.001), feelings of depression (F1,86 = 5.05, p < 0.027) and total mood disturbance (F = 36.91, p < 0.001) compared to 30 min of seated quiet rest Rogerson et al. [98] (2016) UK Pre-post non-controlled mixed between-within n = 331 Parkrun attendees; 180 males and 151 females; mean age 40.8 Psychological well-being (Questionnaire containing parts of the Proﬁle of Mood States, Rosenberg Self-esteem scale and Perceived Stress Scale) Impact of a 5-km park run on psychological well-being Signiﬁcant (p < 0.001) improvements post-run for self-esteem (7.7% improvement; F(1, 324) = 100.58, η2 = 0.24), stress (18.4% improvement; F(1, 315) = 50.78, η2 p = 0.139) and total mood disturbance (14.2% improvement; F(1, 278) = 22.15, η2p = 0.07) Edwards et al. [99] (2017) USA Randomised controlled trial n = 27; 8 joggers vs. 9 walkers vs. 10 stretchers; aged 18–35 Stress and anxiety (Exercise-Induced Feeling Inventory and Aﬀective Circumplex Scale, and the Strait-Trait Anxiety Inventory) Impact of a 15-min treadmill jog on stress and anxiety Emotionally protective eﬀect from a 15-min bout of treadmill jogging (n = 8) compared to an equivalent amount of time walking (n = 9) or stretching (n = 10) after exposure to a ﬁlm clip intended to elicit a negative emotional response, with reduced anxiousness from baseline to post-jog (28.8 to 13.1, p = 0.06) on the State-Trait Anxiety Inventory, and increased anger score from baseline to post-ﬁlm clip in the stretching group (1.2 to 26.0, p = 0.048) unlike the walking (p = 0.11) and jogging (11.3 to 9.4, p = 0.19) groups Int. J. Environ. Res. Public Health 2020, 17, 8059 17 of 39 3.3.2. Double Bouts There were nine studies that had a double-bout design [100–108] (Table 4). Eight of the nine studies were primarily designed to compare conditions rather than to compare the impact of running on mental health, including green/park versus urban, solo versus group, diﬀerent pacing and diﬀerent durations of running [101–108]. Seven of the eight studies found that markers of mental health improved signiﬁcantly after running [101–107]. Only one study was designed to primarily assess the impact of running on mental health, and although there was no control, they found higher mood and feelings of pleasantness post-run but these “did not reach signiﬁcance” [100]. Four studies compared park/rural versus urban running, and all found measures of mental health including anxiety, depression, mood and self-esteem improved post-run [103–105,107]. No paper reported a statistically signiﬁcant diﬀerence in emotional beneﬁt between park and urban conditions. Two studies compared solo versus group running: one found that anxiety reduced following both group and solo running [101], while the other found that children’s anxiety levels increased nonsigniﬁcantly following individual and group running [108]. One study compared 10- and 15-min runs and found that they produced similar psychological beneﬁts to mood [102]. Another compared a self-paced versus prescribed-paced run and found higher self-eﬃcacy before the prescribed-paced run compared to the self-paced run [106]. 3.3.3. Triple Bouts Three studies used three bouts of running (Table 5) [109–111]. One study found that, while two indoor runs had a positive eﬀect on mood, the outdoor run had an even greater beneﬁt to mood with subjects feeling less anxious, depressed, hostile and fatigued and feeling more invigorated [109]. Another study also used 3 runs of varying intensities and found signiﬁcant overall mood beneﬁts postexercise but no signiﬁcant diﬀerences between intensities [110]. One study compared 3 intensities of treadmill exercise to a sedentary control condition and found that state anxiety improved following running at 5% below and at the lactate threshold but that anxiety increased after running at 5% above the lactate threshold [111]. Overall, these studies suggest that running improves mood, that outdoor running has a greater beneﬁt to mood and that most intensities of running improve mood, with the exception of an intensity markedly above the lactate threshold. However, only one study included a control condition [111]. 3.3.4. Summary of Acute Bouts Overall, these studies suggest that acute bouts of running can improve mental health and that the type of running can lead to diﬀerential eﬀects. The evidence suggests that acute bouts of treadmill, track, outdoor and social running (2.5–20 km and 10–60 min) all result in improved mental health outcomes. There were few diﬀerences between high and low intensities. Studies consistently show that any running improves acute/short-term mood markers, but the lack of inactive comparison conditions is a limitation to the strength of the evidence. Little variation in the demographics of participants and small sample sizes limit generalizability and precision of ﬁndings. Int. J. Environ. Res. Public Health 2020, 17, 8059 18 of 39 Table 4. Summary of data extraction from the 9 double-bout studies. Author Year Country Design Population Mental health Outcome (Measurement) Study Aim Main Findings Krotee [108] (1980) USA Pre-post pre-experimental non-controlled n = 78, children aged 7–12. Anxiety (State-Trait Inventory for Children) Impact of 50-metre group vs. solo run on anxiety Children’s anxiety levels increased nonsigniﬁcantly following a run in either an individual (30.54 to 32.72, n.s.) or a group setting (30.67 to 31.83, n.s.). Wildmann et al. [100] (1986) Germany Pre-post non-controlled n = 21, male long-distance runners; mean age of 29.8 Feelings of pleasantness and changes of mood (Eigenschaftsworterliste scale adjective checklist) Impact of 2 identical 10-km runs (1 week apart) on feelings of pleasantness and change of mood Higher scores of good mood and feelings of pleasantness were found following the runs (mean increase of the two runs for all subjects was 2.79 from a total of 19 items, but the increase did not reach signiﬁcance). O’Connor et al. [101] (1991) USA Pre-post non-controlled n = 17, members of local running clubs; 10 males and 7 females; mean age 25 Anxiety and body awareness (State-Trait Anxiety Inventory and Body Awareness Scale) Impact of a 5-mile outdoor group vs. solo run on anxiety Anxiety levels were reduced following both a group (mean baseline = 34.0 vs. pre-exercise = 42.5 vs. postexercise = 27.5, p < 0.05) and solo run (mean baseline = 34.0 vs. pre-exercise = 40.0 vs. postexercise = 30.0, p < 0.05). Nabetani et al. [102] (2001) Japan Pre-post non-controlled n = 15, healthy, moderately active male graduate students Mood (Mood Checklist Short-form 1) Impact of a 10-min vs. a 15-min treadmill run on mood Following the 10-min trial, anxiety signiﬁcantly decreased (ES = 0.61, p < 0.01), whilst there was no signiﬁcant diﬀerence in pleasantness (ES = 0.86) and relaxation (ES = 0.33). Following the 15-min trial, anxiety (ES = 0.51) and pleasantness (ES = 0.62) signiﬁcantly decreased (p < 0.01), but relaxation (ES = 0.07) had no signiﬁcant pre-post diﬀerence. Bodin et al. [103] (2003) Sweden Pre-post non-controlled within-subjects n = 12, regular runners. 6 male and 6 female. Mean age of 39.7. Emotional restoration ie. depression/anxiety (Exercise-Induced Feeling Inventory and the Negative Mood Scale). Impact of 1 h park vs. urban run on depression and anxiety. Runners preferred the park to the urban environment and perceived it as more psychologically restorative; there was no statistical diﬀerence in results for park vs. urban settings, with running in both settings causing a signiﬁcant decline in anxiety/depression (F(1,10) = 16.2, p < 0.002, r = 0.78, eﬀect size = 0.30). Butryn et al. [104] (2003) USA Pre-post non-controlled within-subjects n = 30, non-elite female distance runners; mean age 31 Mood, feeling states and cognition states (Proﬁle of Mood States, Exercise-Induced Feeling Inventory and Thoughts During Running Scale) Impact of a 4-mile park vs. urban run on mood Total mood disturbance scores decreased by 8.97 (p < 0.001), with a similar eﬀect following the urban run: total mood disturbance scores decreased by 9.13 (p < 0.001). Kerr et al. [105] (2006) Japan Pre-post non-controlled n = 22, recreational runners; mean age 22.7 Stress and emotions (Tension and Eﬀort Stress Inventory) Impact of indoor vs. outdoor 5-km run on stress and emotions Signiﬁcant pre-post eﬀects irrespective of running condition were found, with an increase in relaxation (F(1, 21) = 5.60, p < 0.05) and excitement (F(1, 21) = 24.65, p < 0.001) and a decrease in anxiety (F(1, 21) = 9.90, p < 0.01). Rose et al. [106] (2012) New Zealand Pre-post controlled n = 32, all females; 17 sedentary and 15 active; mean age 45 Self-eﬃcacy (Self-Eﬃcacy for Exercise Scale) Impact of self-paced vs. prescribed pace 30-min treadmill run on self-eﬃcacy Higher self-eﬃcacy was observed before the prescribed paced run compared to the self-paced run (F1,28 = 5.81; p < 0.023; n2 = 0.17). Reed et al. [107] (2013) UK Pre-post non-controlled n = 75, children aged 11 and 12 Self-esteem (Rosenberg Self Esteem Scale) Impact of rural vs. urban 1.5-mile run on self-esteem Signiﬁcant increase in self-esteem (F(1,74) = 12.2, p < 0.001) was found, but no signiﬁcant diﬀerence between the urban or green exercise condition (F(1,74) = 0.13, p = 0.72) or any signiﬁcant diﬀerence between boys and girls were found. Int. J. Environ. Res. Public Health 2020, 17, 8059 19 of 39 Table 5. Summary of data extraction from the 3 triple-bout studies. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Harte et al. [109] (1995) Australia Pre-post non-randomised controlled-repeated measure design n = 10, male amateur triathletes or marathon runners with a mean age of 27.1 Mood (Proﬁle of Mood States) Impact of a 12-km outdoor run vs. indoor treadmill run with external stimuli vs. an indoor treadmill run with internal stimuli on mood While the two indoor runs had a positive eﬀect on mood, outdoor running had an even greater beneﬁt to mood with subjects less anxious (F(3,35) = 14.12, p < 0.005), less depressed (F(3,35) = 4.16, p < 0.01), less hostile (F(3,35) = 13.13, p < 0.005), less fatigued (F(3,35) = 15.09, p < 0.005) and more invigorated F(3,35) = 13.01, p < 0.005). Berger, Owen + Motl [110] (1998) USA Pre-post non- controlled study Study 1: n = 71 college students (32 males and 39 females) with a mean age of 21.39; study 2: n = 68 college students (28 males and 40 females) with a mean age of 22.22 Mood (Proﬁle of Mood States) Impact of three 15-min runs of varying intensities (50, 65 or 80% age-adjusted HR max) on mood Signiﬁcant overall mood beneﬁts postexercise (F(6.57) = 6.43, p < 0.0001) for all intensities but no signiﬁcant diﬀerences between intensities were found. Markowitz et al. [111] (2010) USA Pre-post controlled trial n = 28, college-aged students; 14 active vs. 14 sedentary controls; mean age 21 Anxiety (State-Trait Anxiety Inventory) Impact of three 20-min treadmill runs of varying intensities (5% below, 5% above and directly at lactate threshold) on anxiety This was the only triple-bout study with a sedentary control condition. State anxiety improved postexercise at 5% below (eﬀect size = −0.38, p < 0.001) and after exercise at the lactate threshold (eﬀect size = −0.20, p < 0.001), but anxiety increased at 5% above the lactate threshold (eﬀect size = +0.13, p = 0.0030). Int. J. Environ. Res. Public Health 2020, 17, 8059 20 of 39 3.4. Category 3: Longer-Term Interventions Thirty-four studies investigated the eﬀects of more than three bouts of running on measures of mental health ranging from 2-week interventions to 1-year marathon training programmes (Table 6) [112–144]. Narrative description of 34 studies are available in Table S5 within the supplementary material. Eight studies used 2–8 week running interventions [121,122,125,127,128,132,137,139]. Male regular runners deprived of running for 2 weeks had increased anxiety and depression symptoms compared to continuing runners [125]. Two 3-week interventions both found that mood improved while amateur runners had lesser anxiety on running days compared to non-running days; perceived stress in adolescents did not signiﬁcantly change [132,137]. A 4-week intervention of regular treadmill running at set paces in moderately trained male runners found that an increase in intensity of runs was associated with signiﬁcant increase in total mood disturbance while running at a pace with more economical values was associated with more positive mental health proﬁles [127]. A 7-week non-controlled intervention of weekly 40-min ﬁxed distance outdoor rural runs increased mood in both male and female regular exercising university students, with faster runners scoring higher than slower runners [128]. An 8-week intervention of a combination of weekly group and solo jogging in middle-aged chronically stressed, sedentary women found lower anxiety and greater self-eﬃcacy than baseline and compared to relaxation group controls [121]. Two studies used a 8-week intervention of walking/running with non-treatment controls and found signiﬁcant improvements in mood and decrease of depression, including in outpatients diagnosed with mild to severe depression [122,139]. Eleven studies used 10–20 week running interventions [114–116,119,123,126,129–131,140,143]. Three 10-week walking/jogging interventions found reductions in anxiety measures, improvement of well-being and conflicting results for changes in depression measures compared to controls [115,119,129]. Another 10-week running intervention found that depression, trait anxiety and state anxiety all decreased significantly while mood improved significantly [114]. A further 10-week running intervention found that, although the exercise group was more likely to use exercise to cope with stress, there were no significant differences in stress or coping measurements between the running and comparison group [123]. Three 12-week interventions found significantly reduced stress and improvements in mood in college students compared to controls, with more mood improvement in males and in females with higher masculinity [126,130,143]. One 12-week intervention of self-directed running in recreational runners found that well-being was significantly higher during weeks when individuals ran further and ran more often while self-efficacy was related to distance ran but not to frequency of running [143]. Running interventions of 14–20 weeks improved mood and self-esteem and lowered emotional stress reactivity in college/university students compared with controls [116,131,140]. Int. J. Environ. Res. Public Health 2020, 17, 8059 21 of 39 Table 6. Summary of data extraction from the 34 longer-term intervention studies. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Lion [112] (1978) USA Randomised controlled trial n = 6, chronic psychiatric patients; 2 males and 4 females; 3 had the running intervention and 3 were controls; middle aged Anxiety and body image (State-Trait Anxiety Inventory and Rorschach Inkblot Test for body-boundary image) Impact of running a mile 3 times per week for 2 months on anxiety and body image in chronic psychiatric patients Post-test anxiety was signiﬁcantly reduced in the jogging group vs. control group (t = 3.2, df = 4, p < 0.05). Joggers showed an average drop of 9 points on the STAI (39.3 to 30.3) from pre- to post-test, while controls showed an average rise of 4 points (32.6 to 36.6). No statistical diﬀerence was found between groups for post-test body image scores. Blue [113] (1979) USA Pre-post non-controlled n = 2 former inpatients of a psychiatric hospital; 1 male aged 37 and 1 female aged 32 Depression (Zung depression scale) Impact of 3 runs per week for 9 weeks on depression Following running intervention, both patients’ depression scores reduced from “moderately depressed” to “mildly depressed” (decrease of 18 and 15 points on the Zung Depression Scale). Young [114] (1979) USA Pre-post non-controlled n = 32 adults; 4 groups: young males (n = 8, mean age 30.13), middle-aged males (n = 8, mean age 53.0), young females (n = 8, mean age 28.25) and middle aged females (n = 8, mean age 50.25) Anxiety and depression using the Multiple Aﬀect Adjective Checklist Impact of a 10-week walking/jogging programme consisting of 1 h 3× per week on anxiety and depression Signiﬁcant reductions in pre- to post-test anxiety (F(1,28) = 6.01, p < 0.05) were found. Results for anxiety and depression both showed signiﬁcant age diﬀerences in favour of older subjects ((F(1,28) = 5.37, p < 0.05) and (F(1,28) = 5.21, p < 0.05), respectively). However, there was no signiﬁcant improvement with subject depression scores. Blumenthal et al. [115] (1982) USA Non-randomised controlled cohort n = 16 healthy adults; 5 males and 11 females; mean age 45.1 Anxiety and mood (Proﬁle of Mood States and the State-Trait Anxiety Inventory) Impact of 3 times weekly walking-jogging programme for 10 weeks vs. 10 weeks of sedentary controls on anxiety and mood The exercise group exhibited less tension (F(1,30) = 4.49, p < 0.04), depression (F(1,15) = 4.82, p < 0.04), fatigue (F(1,30) = 3.88, p < 0.05) and confusion (F(1,15) = 4.40, p < 0.05) but more vigour (F(1,15) = 3.28, p < 0.09) than sedentary controls. No change was observed for either group on the POMS anger subscale. After the 10-week programme, exercisers also exhibited less state anxiety (F(1,26) = 4.15, p < 0.05) and less trait anxiety (F(1,26) = 6.05, p < 0.02). Trujillo [116] (1983) USA Randomised controlled trial n = 35 female college students; 13 weight trainers, 12 runners and 10 controls Self-esteem (Tennessee Self-concept Scale and the Bem Sex Role Inventory) Impact of a 16-week running programme vs. weight training vs. a control on self-esteem Both the running and weight training groups showed a signiﬁcant increase in self-esteem from pre- to post-programme (t(11) = 2.11, p < 0.05), while the control group showed a nonsigniﬁcant loss in self-esteem (t(9) = 0.55, p > 0.05). Tuckman et al. [117] (1986) USA Randomised non-controlled trial n = 154 children; aged 9–11 Psychological aﬀects such as creativity, perceptual function, behaviour and self-concept (Alternate Uses Test, Bender–Gestalt Test, Devereaux Elementary School Behaviour Rating Scale and Piers–Harris Children’s Self-Concept Scale) Impact of three 30-min runs per week on an outdoor running track for 12 weeks on psychological aﬀects in children (creativity, perceptual function, behaviour and self-concept, compared to 12 weeks of the school’s regular physical education Running signiﬁcantly improved creativity of school children compared to regular physical education participants (F ratio = 17.00, p < 0.001) but had no signiﬁcant eﬀect on classroom behaviour, perceptual functioning or self-concept. Doyne et al. [118] (1987) USA Randomised controlled trial n = 40 women; all with a diagnosis of minor or major depression; mean age of 28.52 Depression (Beck’s Depression Inventory, Hamilton Rating Scale for Depression and Depression Adjective Checklists) Impact of 3 runs per week on an indoor track for 8 weeks on depression in women diagnosed with depression, compared to 8 weeks of weight lifting vs. control Running statistically and clinically signiﬁcantly decreased depression scores (F(4,138) = 14.98, p < 0.01) relative to the wait-list control group, with improvements reasonably well maintained at 1 year follow-up. Fremont et al. [119] (1987) USA Randomised non-controlled trial n = 49; 13 males and 36 females; aged 19–62 Depression, anxiety and mood state (Beck’s Depression inventory, State-Trait Anxiety Inventory and The Proﬁle of Mood States) Impact of 3 runs per week for 10 weeks on depression, anxiety and mood vs. 10 weeks of counselling vs. 10 weeks of a combination of running and counselling Depression, trait anxiety and state anxiety all decreased signiﬁcantly ((F(4,184) = 50.3, p < 0.0001), (F(1, 46) = 27.1, p < 0.0001), (F(1,46) = 21.9, p < 0.0001), respectively), while mood improved signiﬁcantly over the 10 weeks (F(18,378) = 4.5, p < 0.001). Hannaford et al. [120] (1988) USA Randomised controlled trial n = 27 male psychiatric patients with major psychiatric disorders; age range 25–60; 9 runners, 9 in corrective therapy for 8 weeks and 9 waiting list controls Depression and anxiety (Zung Self Rating Depression Scale and State Trait Anxiety Index) Impact of three 30-min runs per week for 8 weeks on depression and anxiety in psychiatric patients with major psychiatric disorders Signiﬁcant reductions were observed in depression scores (F(2,23) = 3.61, p = 0.043) compared to the waiting list controls, and nonsigniﬁcant reductions were observed in anxiety scores (F(2,23) = 1.085, p = 0.354) compared to the waiting list control group. Int. J. Environ. Res. Public Health 2020, 17, 8059 22 of 39 Table 6. Cont. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Long et al. [121] (1988) Canada Randomised non-controlled trial n = 39 chronically stressed, sedentary working women; mean age 40; 18 joggers vs. 21 relaxation intervention Stress, anxiety and self-eﬃcacy (Trait Anxiety Inventory, Sherer et al.’s Inventory for Self-Eﬃcacy and a modiﬁed version of the Ways of Coping Checklist) Impact of an 8-week running programme consisting of a weekly group session plus twice weekly solo jogs on stress, anxiety and self-eﬃcacy Runners had signiﬁcantly less anxiety and greater self-eﬃcacy than baseline; 24% of subjects reached clinically signiﬁcant improvements at the end of treatment, and 36% reached clinically signiﬁcant improvements at 14-month follow-up. The jogging group exhibited higher self-eﬃcacy, and the time eﬀect for the pre to the post/follow-up average was signiﬁcant for both self-eﬃcacy and trait anxiety (F(2, 36) = 15.38, p < 0.001), while total coping scores did not change (F(2, 35) = 2.88, p < 0.07) from pre to post/follow-up. Simons et al. [122] (1988) USA Non-randomised controlled trial n = 128; 53 experimental subjects (24 male, 30 female, mean age 44.9); 75 control subjects (28 male, 47 female, mean age of 42.0) Mood (Proﬁle of Mood States, Nowicki–Strickland Internal–External Control Scale for Adults and Marlowe–Crowne Social Desirability Scale) Impact of two 30 min walk/runs per week for 8 weeks on mood, compared to a weekly 30-min ﬁtness lecture for 8 weeks Signiﬁcant improvement in mood pre- to post-test intervention compared to non-treatment controls (F(1,126) = 4.46, p < 0.05) as well as signiﬁcant improvement in pre- to follow-up mood change scores (F(1,98) = 7.63, p < 0.01) were observed. Moses et al. [123] (1989) UK Randomised controlled trial n = 75 sedentary adult volunteers; mean age 38.8; four 10-week conditions: high-intensity aerobic walk-jog programme (n = 18); moderate intensity walk-jog programme (n = 19); attention-placebo including strength, mobility and ﬂexibility exercises (n = 18); or waiting list control (n = 20). Mood and mental well-being (Proﬁle of Mood States and the Hospital Anxiety and Depression Scale) Impact of varying intensity 10 week walk-jog programmes on mood and mental well-being Signiﬁcant reductions in tension/anxiety (F(3,71) = 2.94, p < 0.05) were reported only by subjects in the moderate exercise condition. Signiﬁcant diﬀerences in the confusion subscale were found over time (F(1,71) = 3.70, p < 0.06), with greater decreases in the moderate exercise group than in the high exercise, attention-placebo or waiting list conditions. No signiﬁcant eﬀects were found on the perceived coping scales, but there was signiﬁcant improvement on the physical well-being scale in the exercisers (F(3,71) = 3.82, p < 0.01) after 10 weeks, while the waiting list group ratings decreased. At follow-up, only subjects in the moderate exercise condition reported decreased ratings of depression/dejection (F(2,55) = 3.00, p < 0.06) and positive changes that approached signiﬁcance for the perceived coping assets scale (F(2,55) = 2.56, p < 0.08), but this was not the case for the high exercise or attention-placebo conditions. Ossip-Klein et al. [124] (1989) USA Randomised controlled trial n = 32 clinically depressed women; mean age 28.52 Self-concept (Beck Self-Concept Test) Impact of running on an indoor track 4 times per week for 8 weeks on self-concept in clinically depressed women compared to weight lifting 4 times weekly vs. a delayed treatment (assessment only) control Self-concept signiﬁcantly improved in the clinically depressed women compared to controls (F(3,99) = 7.62, p < 0.0001). Self-concept scores were also signiﬁcantly higher in those in the running condition compared to the wait-list condition at post-treatment (F(2, 33) = 4.69, p < 0.05), with improvements also reasonably well-maintained over time. Morris et al. [125] (1990) UK Pre-post study with randomised comparison n = 30 male regular runners; mean age 37; 20 participants stopped running for 2 weeks vs. 20 continued running as normal Anxiety and depression (General Health Questionnaire and short forms of the Zung Anxiety and Zung Depression scales) Impact of stopping running for 2 weeks on anxiety and depression Somatic symptoms, anxiety/insomnia and social dysfunction, symptoms of depression (p < 0.05), were all signiﬁcantly greater in deprived than in continuing runners, and Zung depression (F(1,37) = 22.64, p < 0.001) and anxiety (F(1,37) = 11.51, p < 0.01) scores were signiﬁcantly higher after the two weeks. Signiﬁcantly more deprived than non-deprived subjects exceeded the suggested cutoﬀ score for a psychiatric case after both weeks of deprivation (x2 = 5.38 and 4.51, respectively, df = 1, p < 0.05), but there was no statistical diﬀerence between groups once the deprived group resumed running. Int. J. Environ. Res. Public Health 2020, 17, 8059 23 of 39 Table 6. Cont. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Friedman et al. [126] (1991) USA Randomised controlled trial n = 387 students; 177 males and 188 females; mean age 20; 84 joggers, 96 relaxation, 100 group interaction and 107 lecture-control Stress and mood (Proﬁle of Mood States and Bem Sex Role Inventory) Impact of 12 weeks of jogging on stress and mood High masculinity male and female joggers reported signiﬁcantly more mood improvement than those with low masculinity (p < 0.004). All women joggers reported signiﬁcant reductions in depression after jogging, but those with high psychological masculinity experienced signiﬁcantly greater reductions than low masculinity joggers (p < 0.04). Femininity had a signiﬁcant eﬀect on combined POMS scores (F(6,297) = 2.79, p < 0.02), with higher psychological femininity associated with higher tension, depression and fatigue and with lower vigour and confusion scores compared to those low in femininity. There were signiﬁcant pre-post session × technique interactions for high and low masculinity women (F(18,843.36) = 2.47, p < 0.0007; F(18,843.36) = 2.49, p < 0.0006, respectively). Short-term improvements in POMS scores depended upon masculinity for women joggers and participants in group interaction. Williams et al. [127] (1991) USA Pre-post non-controlled within subject design n = 10 moderately trained male runners; mean age 25.6 Mood (Proﬁle of Mood States) Impact of 4 weeks of treadmill running 5 times per week at set paces reﬂecting 50, 60 and 70% VO2 max on mood Regarding within-subject data, an increase in mean VO2 was associated with a signiﬁcant increase in total mood disturbance (r = 0.88, p < 0.01), while running at a pace with more economical values was associated with more positive mental health proﬁles. However, when considered as a group, there was no relationship between running eﬃciency in moderately trained male runners and total mood disturbance. Kerr et al. [128] (1993) Holland Pre-post non-controlled n = 32 regular exercising university students (18 males and 14 females) aged 18–22 Mood (Stress-Arousal Checklist and Telic State Measure) Impact of a weekly 40-min ﬁxed distance run (5 km for females, 6.6 km for males) through a wooded area for 7 weeks on mood In both males and females, there were signiﬁcant increases from pre- to post-running intervention in telic state measure felt arousal scores (F(1,16) = 52.37, p = 0.0001 and F(1,12) = 16.16, p = 0.002, respectively), stress-arousal checklist arousal scores (F(1,16) = 15.34, p = 0.001 and F(1,12) = 25.19, p = 0.0001, respectively) and telic state measure preferred arousal scores (F(1,16) = 4.49, p = 0.05 and F(1,12) = 11.82, p = 0.005, respectively). In contrast, telic state measure arousal discrepancy scores decreased signiﬁcantly for males (F(1,16) = 6.74, p = 0.02) and females (F(1,12) = 11.86, p = 0.005) pre- to post-running. Long [129] (1993) Canada Randomised controlled trial n = 35; 14 males and 21 females; mean age 35.6; 12 runners, 9 stress inoculation and 14 wait-list control Anxiety and stress (Cornell Medical Symptom Checklist) Impact of 3 runs per week for 10 weeks on anxiety and stress Although the exercise group was more likely to report using exercise to cope with stress, there was no signiﬁcant diﬀerences found between groups on stress or coping classiﬁcations. There was also no signiﬁcant diﬀerence in scores of the Cornell Medical Symptom Checklist between the running group and the stress inoculation treatment groups (F < 1; M = 87.4 vs. M = 86.2, respectively). Berger and Friedman [130] (1998) USA Randomised controlled trial n = 387 undergraduate college students; 117 males and 188 females; mean age 20.0; 84 joggers vs. 96 relaxation response vs. 100 in discussion groups vs. 107 in the control group Stress and mood (Proﬁle of Mood States) Impact of three jogs per week for a minimum of 20 min over 12 weeks on stress and mood Jogging was signiﬁcantly more eﬀective in reducing stress than the control activity (F(18,280) = 1.79 to 1.85, p < 0.03), and joggers reported larger and more numerous reductions in tension, depression and anger than the control group; however, changes in vigour, fatigue and confusion were sporadic. There were no long-term beneﬁts observed. Berger and Owen [131] (1998) USA Pre-post with comparison n = 91 college students; n = 67 in weekly walking/jogging (32 males and 35 females) vs. n = 24 in a weekly health science class (9 males and 15 females) Mood and anxiety (Proﬁle of Mood States and State-Trait Anxiety Inventory) Impact of twice weekly walking/jogging for 14 weeks on mood and anxiety No signiﬁcant interaction between exercise intensity and pre-post mood beneﬁts was observed. Joggers reported signiﬁcant short-term mood beneﬁts following running regardless of exercise intensities (F(6,56) = 4.87, p < 0.0005). Joggers reported signiﬁcant pre-post exercise changes on all POMS subscales: tension (F = 15.67, p < 0.0002), depression (F = 15.64, p < 0.0002), anger (F = 12.77, p < 0.0007), vigour (F = 22.29, p < 0.00005), fatigue (F = 20.14, p < 0.00005) and confusion (F = 26.34, p < 0.00005). Int. J. Environ. Res. Public Health 2020, 17, 8059 24 of 39 Table 6. Cont. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Szabo et al. [132] (1998) UK Pre-post non-controlled observational cohort study n = 40 members of an amateur running club; 30 males, mean age 40.5, and 10 females, mean age 37 Anxiety and mood, i.e., exhaustion, tranquillity, positive engagement and revitalization (Commitment to running scale, Exercise-induced Feeling Inventory and Spielberger State Anxiety Inventory) Impact of running vs. non-running days on anxiety and mood over 21 consecutive days Reported diﬀerences (eﬀect sizes ranging from 0.07 to 0.56, all p < 0.05) all favour running days over non-running days, concluding that, on running days, runners experienced less anxiety (F(1,38) = 5.22, p < 0.03) and better subscales of mood: exhaustion (F(1,38) = 4.34, p < 0.04), tranquillity (F(1,38) = 5.56, p < 0.02), revitalisation (F(1,38) = 18.32, p < 0.001) and positive engagement (F(1,38) = 11.79, p < 0.001). Broman-Fulks et al. [133] (2004) USA Randomised non-controlled trial n = 54 participants with elevated anxiety sensitivity scores; 13 males and 41 females; mean age 21.17; 29 high-intensity aerobic exercisers vs. 25 low-intensity aerobic exercisers Anxiety sensitivity (Anxiety Sensitivity Index, State-trait Anxiety Inventory and Body Sensations Questionnaire) Impact of six 20-min treadmill sessions of either high/low-intensity aerobic exercise across 2 weeks on anxiety sensitivity in participants with elevated anxiety sensitivity scores Six 20-min treadmill sessions of both high-intensity and low-intensity running across 2 weeks reduced anxiety sensitivity (F(2,56) = 42.50, p < 0.001, n2 = 0.60; F(2, 48) = 13.72, p < 0.001, n2 = 0.36; respectively). State anxiety also decreased from pre-post high-intensity running (35.10 to 32.03); however, it increased following low-intensity running (42.72 to 42.32), but neither of these eﬀects were signiﬁcant. Haﬀmans et al. [134] (2006) Holland Randomised controlled trial n = 60 psychiatric patients all suﬀering from a depressive disorder; 19 males and 41 females; mean age 39; 20 runners vs. 21 in physiotherapy training vs. 19 controls Depression and self-eﬃcacy (Hamilton Rating Scale for Depression, Becks Depression Inventory, Self-Eﬃcacy Scale and Physical Self-Eﬃcacy Scale) Impact of running therapy for 3 days per week for 12 weeks on depression and self-eﬃcacy in psychiatric patients all suﬀering from depression While after 6 weeks of running, self-eﬃcacy was signiﬁcantly higher (p = 0.03), after the full 12 weeks of running, there was no signiﬁcant diﬀerence in depression (26.7 to 25.5, n.s.) or self-eﬃcacy (46.6 to 49.1, n.s.) scores from baseline. Thornton et al. [135] (2008) USA Repeated measures design n = 50 runners over age 18 Anxiety (Beck Anxiety Inventory) The relationship between anxiety and marathon Marathon training decreased Beck Anxiety Inventory scores (0.9) initially from baseline pre-training levels compared to 2 months prior to marathon day (0.7; 72% had no change from baseline, 22% were less anxious and 6% were more anxious). However, anxiety scores increased as race day approached: at 1 month prior to race day (1.4; 46% had no change from baseline, 19% were less anxious and 35% were more anxious than baseline) and 1 week prior to race (2.6; 22% had no change from baseline, 14% were less anxious and 64% were more anxious than baseline, respectively). Scholz et al. [136] (2008) Switzerland Pre-post non-controlled non-experimental longitudinal study n = 30 untrained participants; 4 males and 26 females; mean age 41.2 Self-eﬃcacy (4-part author-created measurement) Impact of a 1-year marathon training programme on self-eﬃcacy The trend between running and self-eﬃcacy had substantial correlation but was not signiﬁcant. No statically signiﬁcant diﬀerences was observed in the baseline level, trend or ﬂuctuation of self-eﬃcacy between the participants who successfully completed the marathon and those who did not, but the baseline level of self-eﬃcacy was positively associated with the baseline level in running (correlation analyses = 0.27; p < 0.05; 95% CI = 0.00; 0.53) and ﬂuctuation in self-eﬃcacy correlated positively with ﬂuctuation in running (0.39; p < 0.05; 95% CI = 0.03; 0.74). As this was a non-experimental longitudinal study, no causal statements can be drawn. Kalak et al. [137] (2012) Switzerland Randomised controlled trial n = 51 adolescents; 24 males and 27 females; mean age 18.3; 27 runners vs. 24 controls Mood and stress (A-a daily mood log, a questionnaire assessing positive and negative comping strategies, and Perceived Stress Scale) Impact of daily 30-min morning runs on weekdays for 3 weeks (i.e., 3 × 5 runs) on stress and mood Perceived stress did not diﬀer signiﬁcantly between running and control groups over time (F(1,49) = 1.71, n2 = 0.034, n.s.), while mood in the morning increased signiﬁcantly over time in the running group compared with controls (F(5,245) = 16.08, n2 = 0.247, p < 0.05). However, irrespective of group, mood in the evening improved, and there was no signiﬁcant diﬀerence of mood in the evening between groups. Inoue et al. [138] (2013) USA Pre-post non-controlled n = 148 homeless people; 134 males and 14 females; mean age 29.9 Self-suﬃciency (author-created scale) Impact of 10 organised runs on self-suﬃciency in homeless people Running involvement had a signiﬁcant positive correlation with perceived self-suﬃciency (r = 0.30, p < 0.01). Results suggested that participants gained higher levels of perceived self-suﬃciency as they became more involved with running during the program (F = 3.39, p < 0.01, Adjusted R2 = 0.08), and increases in running involvement were the sole signiﬁcant predictor of the outcome (β = 0.29, t = 3.57, p < 0.01). Samson et al. [76] (2013) USA Pre-post non-controlled n = 39 university students who all had running experience; 11 males and 28 females; mean age 20.5 General aﬀect and self-eﬃcacy (Positive and Negative Aﬀect Scale and author-created measurements for self-eﬃcacy) Impact of a 15-week marathon training program of 3 group training days per week and one run of 8–20 miles on the weekend on general aﬀect and self-eﬃcacy Self-eﬃcacy signiﬁcantly increased over the training programme (F(12,444) = 5.81, p < 0.01), but there was a signiﬁcant decrease of positive aﬀect over time (F(12,444) = 8.35, p < 0.01) and no signiﬁcant change was found for negative aﬀect over the programme. Int. J. Environ. Res. Public Health 2020, 17, 8059 25 of 39 Table 6. Cont. Author Year Country Design Population Mental Health Outcome (Measurement) Study Aim Main Findings Doose et al. [139] (2015) Germany Randomised controlled trial n = 46 outpatients diagnosed with mild to severe depression; aged 18–65; 30 walker/runner vs. 16 controls Depression (Hamilton Rating Scale and Beck Depression Inventory) Impact of group walking/running 3 times per week for 8 weeks on depression Depression clinically signiﬁcantly decreased on the Hamilton Rating Scale (Cohen’s d = 1.8; mean change = 8.24; p = <0.0001), and while there were reductions, they were without clinical signiﬁcance (Cohen’s d = 0.50; mean change = 4.66; p = 0.09) in the Becks Depression Inventory scores. Von Haaren et al. [140] (2015) Germany Randomised controlled trial, within subject design n = 61 inactive male university students; mean age 21.4 Stress and mood (a shorten mood scale based on the Multidimensional Mood Questionnaire and a one-item test for perceived control and stress) Impact of a 20-week running training course on stress and mood during academic examinations, compared to waiting list controls Signiﬁcant emotional stress reactivity was observed in both groups during academic assessment episodes; participants in aerobic training showed lower emotional stress reactivity compared with the control participants after the 20-week training programme, with perceived stress of the aerobic group remaining similar during both exam periods (2.27 to 2.24), while it increased further in the control group (2.43 to 2.51). Kahan et al. [141] (2018) USA Pre-post with comparison n = 11 children; 9 males and 2 females; aged 9 and 10 Self-esteem and self-eﬃcacy (50-item, author-created questionnaire) Impact of 20 running sessions alternating between game vs. lap running on self-esteem and self-eﬃcacy in children Means for self-esteem and task-eﬃcacy were 3.63 and 4.16, respectively, on a 5-point scale, while the mean for task-eﬃcacy was 4.16 on a 5-point scale, and high inherent-interest participants (i.e., higher moderate–vigorous physical activity in the running laps condition) had statistically signiﬁcant higher scores than low inherent-interest participants on recognition (p = 0.01), ego orientation (p = 0.03) and expectancy beliefs (p = 0.03) subscales. There were no direct comparisons of self-esteem and self-eﬃcacy in game vs. lap running. Keating et al. [142] (2018) Canada Pre-post non-controlled n = 46 participants with complex mood disorders; 11 males and 35 females; 29 youths (mean age 22.1) and 17 adults (mean age 45.2) Stress, anxiety and depression (Cohen’s Perceived Stress Scale, Becks Depression Inventory, Becks Anxiety Inventory and Short Form Survey) Impact of 12 weeks of twice weekly running in a group setting that oﬀers social support supervised by clinical professionals on stress, anxiety and depression Signiﬁcant decreases in depression (F(11,201) = 4.5, p < 0.0001), anxiety (F(11,186) = 4.8, p < 0.0001) and stress (F(11,186) = 2.3, p = 0.01) from baseline was observed. Following intervention, mean depression scores decreased by 39% in adults from high to low levels and by 27% in youths from moderate to reduced moderate levels. Younger participant age, younger age at onset of illness and higher perceived levels of friendship with other running group members (ps ≤ 0.04) were associated with lower depression, anxiety and stress scores. Higher attendance was linked with decreasing depression and anxiety (ps ≤ 0.01) scores over time. Nezlek et al. [143] (2018) Poland Pre-post observational cohort study over 3 months with no control n = 244 recreational runners; 127 males and 117 females; mean age 32.5 Psychological well-being, self-esteem, self-eﬃcacy and aﬀect (Rosenberg Self-esteem Scale, Satisfaction with Life Scale, and a circumplex model that distinguishes the valence and arousal of aﬀect) Impact of 3 months of self-prescribed running on psychological well-being, self-esteem, self-eﬃcacy and aﬀect Positive within-person relationships between how much people ran each week and self-reports of well-being were observed, with well-being signiﬁcantly higher during weeks when individuals ran more often and further. Self-eﬃcacy was related to distance run but not to frequency. For the km that people ran each week, signiﬁcant moderation was found for weekly Satisfaction with Life Scale (γ11 = −0.0002, p = 0.013), self-esteem (γ11 = −0.0002, p = 0.015), positive activated aﬀect (γ11 = −0.0003, p < 0.001), positive deactivated aﬀect (γ11 = −0.0008, p < 0.01), negative activated aﬀect (γ11 = 0.0002, p = 0.046) and negative deactivated aﬀect (γ11 = 0.0003, p = 0.01). Kruisdijk et al. [144] (2019) Holland Randomised controlled trial n = 48 participants with major depressive disorder; mean age 42.6; 25 runner-walkers vs. 23 controls Depression (Hamilton Depression Scale) Impact of 6 months of running-walking for one hour twice a week on depression in subjects with major depressive disorder No signiﬁcant diﬀerence or eﬀect on depression in favour of the intervention group (Cohen’s d < 0.2, F = 0.13, p = 0.73) with only 9 participants (19%) completing the study was found, with low statistical power and lack of follow-up at six and 12 months. Int. J. Environ. Res. Public Health 2020, 17, 8059 26 of 39 A number of studies looked at speciﬁc populations. One investigated the impact of 10 organised runs on homeless people and found signiﬁcant positive correlation with perceived self-suﬃciency [138]. Two investigated the eﬀects in children and found that running signiﬁcantly improved creativity and higher self-esteem subscales [117,141]. Three looked at marathon training programmes: one found a positive correlation between the trend in running and self-eﬃcacy but was not signiﬁcant [136], while another found a signiﬁcant increase in self-eﬃcacy over the programme [76]. The remaining study used participants who were already self-enrolled in a marathon, and researchers found that, while anxiety decreased initially during training, anxiety increased as marathon day approached [135]. Nine studies used subjects with known psychiatric disorders and found that longer-term interventions generally improved markers of mental health in psychiatric populations, particularly markers of depression [112,113,118,120,124,133,134,142,144]. Running interventions from 2 to 12 weeks all resulted in signiﬁcant positive eﬀects on mental health [112,118,120,124,133,142,144]. While an anti-depressive eﬀect of exercise was apparent in patients with minor to moderate psychiatric problems, one study found that this was not reﬂected in patients with major depressive disorder due to issues with compliance and motivation towards the intervention [144]. Summary of Longer-Term Interventions Overall, running interventions of 2–20 weeks generally show improved markers of a range of mental health outcomes compared to non-running controls, including mental health outcomes in psychiatric and homeless populations. The risk of longer-term running interventions on adverse mental health outcomes remains unclear. 3.5. Summary of Key Findings The key ﬁndings of the each of the three categories of studies are summarised in Table 7. Table 7. Summary of key ﬁndings within each of the three categories. Study Type Number of Studies Summary of Evidence Cross-sectional 47 studies Consistent evidence was found for a positive association between mental health and habitual or long-term recreational running compared to non-runners. In contrast, there was evidence that high or extreme levels of running were associated with markers of running ill-health compared to levels of moderate running. Acute: single/double/triple bout 35 studies Overall, these studies suggest that acute bouts of running can improve mental health and that the type of running can lead to diﬀerential eﬀects. Evidence suggests that acute bouts of treadmill, track, outdoor and social running (2.5–20 km and 10–60 min) all result in improved mental health outcomes. There were few diﬀerences between high and low intensities. Studies consistently show that any running improves acute/short-term mood markers but that lack of inactive comparisons limits the strength of evidence. Little variation in the demographics of participants and small sample sizes limit generalizability and precision of ﬁndings. Interventions (2 weeks or more) 34 studies Overall, running interventions of 2–20 weeks generally show improved markers of a range of mental health outcomes compared to non-running controls, including mental health outcomes in psychiatric and homeless populations. The risk of longer-term running interventions on adverse mental health outcomes remains unclear. 3.6. Evidence Gaps As well as reporting the available evidence, this review also aimed to identify key gaps in the evidence base for running and mental health. Consideration of sample demographics in the n = 116 included studies resulted in the following gaps being identiﬁed: • lack of studies in those aged under 18 (Only four acute bout studies [89,95,107,108] and two longer term interventions [117,141] looked directly at children under age 15, while a further 2 studies looked speciﬁcally at adolescents [70,137]); • lack of studies in those aged over 45; • lack of gender-speciﬁc approaches; • few studies investigating clinical populations; and • limited diversity in patient demographics. Int. J. Environ. Res. Public Health 2020, 17, 8059 27 of 39 4. Discussion 4.1. Principal Findings There is a growing body of literature exploring the relationships of running on certain mental health outcomes. There were variations in methods and outcomes studied, but there were similar overall beneﬁcial trends. Generally, evidence supported positive eﬀects of a range of lengths and intensities of running on mental health. However, there was limited diversity in participant demographics. Attribution was also compromised by the limited number of studies with comparisons/control groups. Synthesis of quantiﬁed eﬀects is made challenging by large variations in reporting methods. Consistency and appropriateness of mental health measures was also varied throughout the literature. The review identiﬁed a smaller evidence-base focused on clinical populations. Behaviour change and compliance can be challenging in populations with clinical depressive disorders [145], and there is limited evidence regarding the long-term impact of PA in the treatment of depression [7,146,147]. Further investigations of the eﬀects of running in populations with prior diagnoses of mental health disorders may help to address the global burden of mental illness. 4.2. Plausible Explanations for Findings Our ﬁndings suggest that, throughout cross-sectional evidence, acute bouts of running and longer-term running interventions are associated with improvements in a range of mental health outcomes. This is likely explained by running supplying a suﬃcient dose of moderate to vigorous PA to stimulate the known mental health beneﬁts associated with PA. These beneﬁts are thought to be mediated by neurobiological, psychosocial and behavioural mechanisms, all of which an eﬀective running intervention of any genre has the potential to inﬂuence [148]. The diﬀerential eﬀects of these mechanisms remain unclear and may explain the variation in ﬁndings by running duration, intensity, setting, and social or individual participation. 4.3. Comparison to Literature This review does not present running and mental health as a novel idea. As early as 1979, scholars discussed the relationship between psychotherapy and running [149]. An early review by Vezina et al. (1980) reported that regular running causes positive mood changes, increases self-esteem and decreases anxiety [150]. Another review by Hinkle (1992) found positive psychological eﬀects in both adults and children including reductions in depressive mood and anxiety, and enhanced self-esteem [151]. However, a review by Weinstein et al. (1983) found that the volume of literature examining running and depression was scarce, and while running appeared to improve a sense of well-being, there was minimal evidence to strongly support reductions in depression and anxiety [152]. Studies from 1986 [153] and 1991 [154] warned that long-distance running had the potential to trigger development of eating disorders in people who were psychologically or biologically at risk. Early research also highlighted that runners should be aware of the possibility of addiction [155] and that women may be linked more strongly to negative addiction than men [156]. This review agrees with these earlier ﬁndings but is the ﬁrst to use systematic scoping review methods. This means that it presents a transparent search and inclusion strategy and is less prone to bias in terms of included studies and resulting ﬁndings. As such, this review has contributed to the evidence base by demonstrating that the weight of evidence up to 2019 favours positive mental health relationships with running. 4.4. Strengths and Limitations The authors acknowledge the limitation that this review was designed to assess the behaviour of running but that there are ﬁelds of studies including treadmill-based exercise which our review may not have picked up. However, the strength of this review is that the review does not focus on laboratory-based exercise but instead on what a healthcare professional may recommend to a free-living Int. J. Environ. Res. Public Health 2020, 17, 8059 28 of 39 patient or the general public for mental health beneﬁts. However, subjective measures of running intensity were not considered in detail, which may impact the conclusions of the review. The authors acknowledge that the results were not separated by means of running type due to the method of prioritization used to report the results, and thus, this remains a research gap. As with any scoping review, it is possible that the search and inclusion strategy led to omission of some key research. Synthesis of quantiﬁed eﬀects was also made challenging by the large range of reporting methods used within the studies. This scoping review did not attempt to undertake quality appraisal of the included studies. The wide range of study designs and methods included within the review does not allow a statistical synthesis of the eﬀectiveness of the studies. 4.5. Implications Pharmacological management is often used as a ﬁrst-line of defence for mental health disorders [157]; however, it is not always eﬀective due to poor adherence and relapse [158]. Ineﬀective management adds to the global burden of poor mental health [159], With increasing pressures on healthcare budgets, PA oﬀers an augmentative therapeutic option for mental health management [160]. It is likely that using a cost-eﬀective therapy such as running to improve mental health would prove economical as well. An integrated lifestyle intervention (i.e., iterative process) may be more feasible than a single add-on exercise intervention (i.e., addition of an individual behaviour) for patients with major depressive disorder who are deemed suitable for running therapy by clinicians. This review presents the eﬀects of running on mental health and can inform healthcare professionals and psychologists who advise on management of mental health conditions. The authors’ interpretation of the evidence base is that, with appropriate clinical judgement, practitioners may identify patients with an interest in running or previous history of running as an ideal candidate for running as a form of psychotherapy. Findings from this review indicate that characteristics of running to be recommended may include self-pacing, distance and time feasibility to the individual, and being within the lactate threshold. There were consistent trends within ﬁndings despite a variety of running interventions, which suggest that it would be appropriate to recommend track running, outdoor urban and rural running, and treadmill running to improve mental health. However, a large number of studies used healthy, active college-aged participants, which may limit the relevance of these recommendations to other population groups. It is acknowledged that running will not be a suitable recommendation for everyone and that prescription of running is not as simple as just instructing people to run; it will require clinical expertise with regard to mental health in the way it is prescribed [161]. 4.6. Future Research This review identiﬁes research gaps regarding patient demographics, but we have further recommendations about increasing sample sizes, quantitative study design and more coherent mental health outcomes. There was great variability in mental health outcome measures, particularly within the acute bout studies, where short-term measures of mental health could have equally been deﬁned as mood and aﬀect. We recommend that future research seeks more clarity on appropriate outcome measures. A comparison of types, settings and intensities of running is needed to better inform running and mental health recommendations. Recommendations for future research include addressing the eﬀect of running on mental health of those under 18, those over 50s and clinical populations. A meta-analysis of the subset of study types such as interventions should be carried out. While the appropriateness of running interventions in those over 50 may be questioned, there is evidence that older adults do also beneﬁt from the anti-depressive eﬀect of exercise [162]. We know that children running can be used as a population intervention, for example, in “The Daily Mile” [163], which signiﬁes the importance of addressing this gap around the mental health impact of running in those under 18. Future systematic reviews and meta-analyses are needed to quantify the beneﬁts of running on speciﬁc outcomes. Int. J. Environ. Res. Public Health 2020, 17, 8059 29 of 39 5. Conclusions This review is the most recent to comprehensively report the breadth of literature on the relationship between running and mental health. We conclude that running has important positive implications for mental health, particularly depression and anxiety disorders, but synthesis of quantiﬁed eﬀects is made challenging by variation in reporting methods and remains a gap. This scoping review may have consequences for researchers, practitioners and relevant organisations and may inform the practice of healthcare professionals. Knowledge gaps concerning running on the mental health of children, older adults and clinical populations provide guidance for future research Supplementary Materials: The following are available online at http://www.mdpi.com/1660-4601/17/21/8059/s1, Table S1: Narrative description of ﬁndings of the 47 cross-sectional studies. Table S2–S4: Narrative description of ﬁndings of the 35 studies with an acute bout of running. Table S5: Narrative description of ﬁndings of the 34 studies with a longer-term intervention of running. Author Contributions: P.K., J.R. and F.O. conceived the study. P.K., C.W. and F.O. designed the search strategy. F.O. conducted searching of databases. J.C., P.K., F.O. and C.W. screened the records. F.O. and P.K. screened the full texts. F.O. completed all data extraction, and J.R. conducted quality checks. F.O. drafted the full manuscript, and all authors reviewed and approved ﬁnal submission. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: The authors would like to thank a number of people for their assistance during the scoping review: Thelma Dugmore for her support and administration help within the Edinburgh University Physical Activity for Health Research Centre (PAHRC), Marshall Dozier for her assistance setting up a Covidence account for the project to run through, all the staﬀ at PAHRC for being so welcoming and interested in the project and, ﬁnally, Colin Oswald who was a source of great encouragement and support throughout the project. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A Table A1. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist [18]. Section Item PRISMA-ScR CHECKLIST ITEM Reported on Page Number Title Title 1 Identify the report as a scoping review. 1 Abstract Structured summary 2 Provide a structured summary that includes (as applicable) background, objectives, eligibility criteria, sources of evidence, charting methods, results and conclusions that relate to the review questions and objectives. 1 Introduction Rationale 3 Describe the rationale for the review in the context of what is already known. Explain why the review questions/objectives lend themselves to a scoping review approach. 1–2 Objectives 4 Provide an explicit statement of the questions and objectives being addressed with reference to their key elements (e.g., population or participants, concepts and context) or other relevant key elements used to conceptualize the review questions and/or objectives. 1–2 Methods Protocol and registration 5 Indicate whether a review protocol exists; state if and where it can be accessed (e.g., a Web address), and if available, provide registration information, including the registration number. 2 Eligibility criteria 6 Specify characteristics of the sources of evidence used as eligibility criteria (e.g., years considered, language and publication status), and provide a rationale. 3 Information sources 7 Describe all information sources in the search (e.g., databases with dates of coverage and contact with authors to identify additional sources) as well as the date that the most recent search was executed. 4 Search 8 Present the full electronic search strategy for at least 1 database, including any limits used, such that it could be repeated. Appendix B Selection of sources of evidence † 9 State the process for selecting sources of evidence (i.e., screening and eligibility) included in the scoping review. 4 Int. J. Environ. Res. Public Health 2020, 17, 8059 30 of 39 Table A1. Cont. Section Item PRISMA-ScR CHECKLIST ITEM Reported on Page Number Data charting process ‡ 10 Describe the methods of charting data from the included sources of evidence (e.g., calibrated forms or forms that have been tested by the team before their use and whether data charting was done independently or in duplicate) and any processes for obtaining and conﬁrming data from investigators. 4 Data items 11 List and deﬁne all variables for which data were sought and any assumptions and simpliﬁcations made. 4 Critical appraisal of individual sources of evidence § 12 If done, provide a rationale for conducting a critical appraisal of included sources of evidence; describe the methods used and how this information was used in any data synthesis (if appropriate). N/A Synthesis of results 13 Describe the methods of handling and summarizing the data that were charted. 4 Results Selection of sources of evidence 14 Give numbers of sources of evidence screened, assessed for eligibility and included in the review, with reasons for exclusions at each stage, ideally using a ﬂow diagram. 4 Characteristics of sources of evidence 15 For each source of evidence, present characteristics for which data were charted and provide the citations. Tables 2–6 Critical appraisal within sources of evidence 16 If done, present data on critical appraisal of included sources of evidence (see item 12). N/A Results of individual sources of evidence 17 For each included source of evidence, present the relevant data that were charted that relate to the review questions and objectives. Tables 2–6 Synthesis of results 18 Summarize and/or present the charting results as they relate to the review questions and objectives. 5–40 Discussion Summary of evidence 19 Summarize the main results (including an overview of concepts, themes and types of evidence available), link to the review questions and objectives and consider the relevance to key groups. 40–41 Limitations 20 Discuss the limitations of the scoping review process. 41 Conclusions 21 Provide a general interpretation of the results with respect to the review questions and objectives as well as potential implications and/or next steps. 42 Funding Funding 22 Describe sources of funding for the included sources of evidence as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review. 43 JBI = Joanna Briggs Institute; PRISMA-ScR = Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. * From where sources of evidence (see second footnote) are compiled, such as bibliographic databases, social media platforms and websites. † A more inclusive/heterogeneous term used to account for the diﬀerent types of evidence or data sources (e.g., quantitative and/or qualitative research, expert opinion and policy documents) that may be eligible in a scoping review as opposed to only studies. This is not to be confused with information sources (see ﬁrst footnote). ‡ The frameworks by Arksey and O’Malley (1) and Levac and colleagues (2) and the JBI guidance (3,4) refer to the process of data extraction in a scoping review as data charting. § The process of systematically examining research evidence to assess its validity, results and relevance before using it to inform a decision. This term is used for items 12 and 19 instead of “risk of bias” (which is more applicable to systematic reviews of interventions) to include and acknowledge the various sources of evidence that may be used in a scoping review (e.g., quantitative and/or qualitative research, expert opinion and policy document). Appendix References (1) Arksey, H.; O’Malley, L. Scoping studies: Towards a methodological framework. Int. J. Soc. Res. Methodol. 2005, 8, 19–32. (2) Levac, D.; Colquhoun, H; O’Brien, K.K. Scoping studies: Advancing the methodology. Implement Sci. 2010, 5, 69, doi:10.1186/1748-5908-5-69. (3) Peters, M.D.; Godfrey, C.M.; Khalil, H.; McInerney, P.; Parker, D.; Soares, C.B. Guidance for conducting systematic scoping reviews. Int. J. Evid. Based Healthc. 2015, 13, 141–146, doi:10.1097/XEB.0000000000000050. (4) Peters, M.D.J.; Godfrey, C.; McInerney, P.; Baldini Soares, C.; Khalil, H.; Parker, D. Scoping reviews. In Joanna Briggs Institute Reviewer’s Manual; Aromataris, E., Munn, Z., Eds.; Joanna Briggs Inst: Adelaide, Australia, 2017. Int. J. Environ. Res. Public Health 2020, 17, 8059 31 of 39 Appendix B Table A2. Details of the search strategy used in all 4 databases. Notes for Database Ovid (Embase) Ovid (Medline) Sport DISCUS (Ebscohost) ProQuest Social Science Journals Ti,ab Searches Title & Abstract Ti,ab Searches Title & Abstract AB Searches Abstract Ab Searches Abstract Running Search Terms Run, Jog, Sprint, Park-run, Orienteer, Orienteering, Marathon, Marathon-running, Treadmill Mental Health Search Terms Mental Health, Mental illness, Mental state, Emotions, Emotional, Depression, Depressive disorder, depressive therapy, Postnatal depression, Postpartum depression, Seasonal aﬀective disorder, Situational depression, Atypical depression, Persistent depressive disorder, anxiety, loneliness, stress, mood, self-eﬃcacy, sleep, psychological, psychological characteristics, psychology, eating disorder, disordered eating, anorexia, bulimia, exercise, health status disparities, quality of life, motivation, adjustment disorder, sick role. relaxation, lifestyle, exercise therapy, social support NOT search Terms Rodent, mouse, rat, bovine, pig, animal, horses, mice, ecology, dermatology, epigenetics, gene, molecule, cell, phenotype, drug, hormone, food, nutrient, glucose, imaging, football, tennis, swimming, heart, troponin, cardiology, lung, respiratory, bone, cesarean, newborn, breast-feeding, HIV, cough, rectal, protocol, procedure, surgery, operation, stroke, sacroiliitis, COPD, asthma, Apnoea, angina, allergy, railway, falling Search Syntax (remember the ‘adj’ function) (remember the ‘adj’ function) (remember the ‘adj’ function) (remember the ‘adj’ function) (((“mental-health” or “mental-illness” or “mental-state” or emotions or emotional or depression or “depressive-disorder” or “depressive-therapy” or “postpartum-depression” or “seasonal-aﬀective-disorder” or “situational-depression” or “atypical-depression” or “persistent-depressive-disorder” or anxiety or loneliness or stress or mood or “self-eﬃcacy” or sleep or psychological or “psychological-characteristics” or psychology or “eating-disorder” or “disordered-eating” or anorexia or Bulimia or exercise or “health-status-disparities” or “quality-of-life” or motivation or “adjustment-disorder” or “sick-role” or relaxation or lifestyle or “exercise-therapy” or “social-support”)) AND (run* or Jog* or sprint* or “park-run” or orienteer or orienteering or marathon or “Marathon-running” or treadmill) NOT (dermatology OR epigenetics OR gene* OR drug* OR surgery OR hormone* OR food OR imaging OR animal* OR football OR tennis OR swimming OR rodent OR mouse OR rat OR pig OR bovine OR phenotype or Heart or cardiology OR lung or bone OR caesarean OR HIV OR troponin OR cough OR protocol OR breast-feeding OR cell* OR sacroiliitis OR rectal or procedure OR COPD or respiratory OR nutrient* OR glucose or newborn OR stroke OR asthma OR operation OR horses OR falling OR railway OR molecule* OR apn?ea OR angina OR allergy OR mice OR ecology)).ab,ti. (((“mental-health” or “mental-illness” or “mental-state” or emotions or emotional or depression or “depressive-disorder” or “depressive-therapy” or “postpartum-depression” or “seasonal-aﬀective-disorder” or “situational-depression” or “atypical-depression” or “persistent-depressive-disorder” or anxiety or loneliness or stress or mood or “self-eﬃcacy” or sleep or psychological or “psychological-characteristics” or psychology or “eating-disorder” or “disordered-eating” or anorexia or Bulimia or exercise or “health-status-disparities” or “quality-of-life” or motivation or “adjustment-disorder” or “sick-role” or relaxation or lifestyle or “exercise-therapy” or “social-support”) and (run* or Jog* or sprint* or “park-run” or orienteer or orienteering or marathon or “Marathon-running” or treadmill)) not (dermatology or epigenetics or gene* or drug* or surgery or hormone* or food or imaging or animal* or football or tennis or swimming or rodent or mouse or rat or pig or bovine or phenotype or Heart or cardiology or lung or bone or caesarean or HIV or troponin or cough or protocol or breast-feeding or cell* or sacroiliitis or rectal or procedure or COPD or respiratory or nutrient* or glucose or newborn or stroke or asthma or operation or horses or falling or railway or molecule* or apn?ea or angina or allergy or mice or ecology)).ab,ti. (AB(run* OR jog* OR sprint OR “park run” OR orienteer OR orienteering OR marathon OR “marathon-running” OR treadmill) AND AB(“mental health” OR “mental illness” OR “mental state” OR emotions OR emotional OR depression OR “depressive disorder” OR “depressive therapy” OR “postpartum depression” OR “seasonal aﬀective disorder” OR “situational depression” OR “atypical depression” OR “persistent depressive disorder” OR anxiety OR loneliness OR stress OR mood OR “self-eﬃcacy” OR sleep OR psychological OR “psychological characteristics” OR psychology OR “eating disorder” OR “disordered eating” OR anorexia OR bulimia OR exercise OR “health status disparities” OR “quality-of-life” OR motivation OR “adjustment disorder” OR “sick role” OR relaxation OR lifestyle OR “exercise therapy” OR “social-support”)) NOT (dermatology OR epigenetics OR gene* OR drug* OR surgery OR hormone* OR food OR imaging OR animal* OR football OR tennis OR swimming OR rodent OR mouse OR rat OR pig OR bovine OR phenotype or Heart or cardiology OR lung or bone OR caesarean OR HIV OR troponin OR cough OR protocol OR breast-feeding OR cell* OR sacroiliitis OR rectal or procedure OR COPD or respiratory OR nutrient* OR glucose or newborn OR stroke OR asthma OR operation OR horses OR falling OR railway OR molecule* OR apnoea OR angina OR allergy OR mice OR ecology) (ab((run* OR jog* OR sprint OR “park run” OR orienteer OR orienteering OR marathon OR “marathon-running” OR treadmill)) AND ab((“mental health” OR “mental illness” OR “mental state” OR emotions OR emotional OR depression OR “depressive disorder” OR “depressive therapy” OR “postpartum depression” OR “seasonal aﬀective disorder” OR “situational depression” OR “atypical depression” OR “persistent depressive disorder” OR anxiety OR loneliness OR stress OR mood OR “self-eﬃcacy” OR sleep OR psychological OR “psychological characteristics” OR psychology OR “eating disorder” OR “disordered eating” OR anorexia OR bulimia OR exercise OR “health status disparities” OR “quality-of-life” OR motivation OR “adjustment disorder” OR “sick role” OR relaxation OR lifestyle OR “exercise therapy” OR “social-support”)) NOT ab((dermatology OR epigenetics OR gene* OR drug* OR surgery OR hormone* OR food OR imaging OR animal* OR football OR tennis OR swimming OR rodent OR mouse OR rat OR pig OR bovine OR phenotype OR Heart OR cardiology OR lung OR bone OR caesarean OR HIV OR troponin OR cough OR protocol OR breast-feeding OR cell* OR sacroiliitis OR rectal OR procedure OR COPD OR respiratory OR nutrient* OR glucose OR newborn OR stroke OR asthma OR operation OR horses OR falling OR railway OR molecule* OR apnoea OR angina OR allergy OR mice OR ecology))) AND (stype.exact(“Scholarly Journals”) AND la.exact(“ENG”)) Search complete? Yes Yes Yes Yes Search saved? Yes Yes Yes Yes Saved under: Embase RunningMH Medline RunningMH Sport Discus Running MH ProQuest RunningMH Number of hits: 10,131 Text results (this had a limit of only human studies, as well as a limit for articles and articles in press applied to the search) 10,154 text results (this had a limit of human studies applied to the search) 3461 (this had a limit of English studies only, and academic journal only applied to the search) 5933 (this search was carried out within the sports medicine and education index database and in the social sciences database) Uploaded to Covidence? Yes Yes Yes Yes Int. J. Environ. Res. Public Health 2020, 17, 8059 32 of 39 References 1. Whiteford, H.A.; Ferrari, A.J.; Degenhardt, L.; Feigin, V.; Vos, T. The global burden of mental, neurological and substance use disorders: An analysis from the Global Burden of Disease Study 2010. PLoS ONE 2015, 10, e0116820. [CrossRef] [PubMed] 2. Lopez, A.D.; Murray, C.C.J.L. 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A Scoping Review of the Relationship between Running and Mental Health.	11-01-2020	Oswald, Freya,Campbell, Jennifer,Williamson, Chloë,Richards, Justin,Kelly, Paul	eng
PMC7739736	BRIEF RESEARCH REPORT published: 04 September 2019 doi: 10.3389/fspor.2019.00017 Frontiers in Sports and Active Living \| www.frontiersin.org 1 September 2019 \| Volume 1 \| Article 17 Edited by: Giuseppe D’Antona, University of Pavia, Italy Reviewed by: Lars R. McNaughton, Edge Hill University, United Kingdom Jeremy Coquart, Université de Rouen, France Correspondence: Chen Fleischmann chen.ﬂeischmann@sheba.health.gov.il Specialty section: This article was submitted to Exercise Physiology, a section of the journal Frontiers in Sports and Active Living Received: 13 June 2019 Accepted: 16 August 2019 Published: 04 September 2019 Citation: Fleischmann C, Horowitz M, Yanovich R, Raz H and Heled Y (2019) Asthaxanthin Improves Aerobic Exercise Recovery Without Affecting Heat Tolerance in Humans. Front. Sports Act. Living 1:17. doi: 10.3389/fspor.2019.00017 Asthaxanthin Improves Aerobic Exercise Recovery Without Affecting Heat Tolerance in Humans Chen Fleischmann 1,2,3, Michal Horowitz 2, Ran Yanovich 1,2,4, Hany Raz 5 and Yuval Heled 2 1 Institute of Military Physiology, IDF Medical Corps, Tel-Hashomer, Israel, 2 Heller Institute of Medical Research, Sheba Medical Center, Ramat Gan, Israel, 3 Laboratory of Environmental Physiology, Dentistry Faculty, Hebrew University of Jerusalem, Jerusalem, Israel, 4 The Academic College at Wingate, Wingate Institute, Netanya, Israel, 5 The Faculty of Agriculture, Food and Environment, Hebrew University, Rechovot, Israel Objectives: To examine the supplementation effects of the xanthophyll carotenoid Astaxanthin on physical performance and exertional heat strain in humans. Design: A randomized double blind placebo controlled trial. Methods: Twenty two male participants (Age: 23.14 ± 3.5 y, height: 175 ± 6 cm, body mass: 69.6 ± 8.7 kg, % body fat: 16.8 ± 3.8) received placebo (PLA, n = 10) or Astaxanthin (ATX, n = 12) 12 mg/day Per os (P.O), for 30 days, and were tested pre and post-supplementation with a maximal oxygen uptake (VO2 Max) test and the heat tolerance test (HTT) (2 h walk at 40◦C, 40% relative humidity (RH), 5 kph, 2% incline). NIH database registration no. NCT02088242. Gas exchange, Heart rate (HR), Relative perceived exertion (RPE), and blood lactate were measured during the VO2 Max test. Heart rate (HR), rectal (Trec), and skin (Tskin) temperatures, RPE, and sweat rate (SR) were monitored in the HTT. Serum heat shock protein 72 (HSP72), Creatine phospho- kinase (CPK), C-reactive protein (CRP), and lipid proﬁle were measured before and after the test. Results: The rise in blood lactate caused by the VO2 Max test was signiﬁcantly diminished in the ATX group (9.4 ± 3.1 and 13.0 ± 3.1 mmole∗l−1 in the ATX and PLA groups, respectively P < 0.02), as was the change in oxygen uptake during recovery (−2.02 ± 0.64 and 0.83 ± 0.79% of VO2 Max in the ATX and PLA group, respectively, p = 0.001). No signiﬁcant differences were observed in the anaerobic threshold or VO2 Max. In the HTT, no signiﬁcant physiological or biochemical differences were observed (HR <120 bpm, Trec rose by ∼1◦C to <38◦C, no difference in SR). Conclusions: Astaxanthin supplementation improved exercise recovery. No beneﬁt was observed for ATX over PLA in response to heat stress. Further examination of Astaxanthin in higher exertional heat strain is required. Keywords: astaxanthin, supplementation, exercise nutritional physiology, aerobic exercise, exercise-recovery, heat tolerance Fleischmann et al. Astaxanthin Exercise Recovery and Heat Tolerance INTRODUCTION Astaxanthin is a xanthophyll carotenoid food supplement prevalent in marine organisms (Kidd, 2011). This potent antioxidant (Kidd, 2011) aﬀects the Insulin\Insulin growth factor I (IGF1) and the nuclear kinase mitogen and stress- activated protein kinase-1 (MSK1) signaling pathways, which were found to be implicated in preconditioning, survival and longevity in vitro in human keratinocytes (Terazawa et al., 2012) and in vivo in Caenorhabditis elegans (Yazaki et al., 2011) Other in vivo experiments in animals have shown Astaxanthin is associated with reductions in C reactive protein and DNA damage and improvement of the cell-mediated and humoral immune responses (Park et al., 2011) and with improvement in cardiovascular parameters (Fassett and Coombes, 2012). In exercising mice Astaxanthin induced diminished fatigue, and reductions in blood lactate, oxidative damage to lipids and DNA, and muscle injury (Aoi et al., 2003; Ikeuchi et al., 2006). However, exercise experiments in humans were equivocal, showing improved endurance as time trial performance in competitive cyclists (Earnest et al., 2011), vs. no signiﬁcant improvement in well-trained cyclists (Res et al., 2013) and soccer players (Djordjevic et al., 2012). During exercise, some evidence from animal experiments supports enhanced fat utilization over carbohydrates (Ikeuchi et al., 2006; Aoi et al., 2008), yet no supplementation eﬀect in endurance exercise and recovery was established (Brown et al., 2018). Exertional heat injury is a life threatening condition inﬂicting many young, healthy individuals, commonly aﬀecting highly motivated, physically active populations such as military personnel and athletes (Carter et al., 2005; Casa et al., 2005). Risk reduction of heat injury includes avoidance of strenuous physical activity in severe heat load conditions, application of heat acclimation protocols, and the use of external cooling methods (Epstein et al., 2000). Preparation for planned military and athletic activities could potentially enhance resilience to extreme physical and environmental conditions, reduce the chance of heat injury and improve injury response and recovery. Several exogenous agents have been studied either as prophylactic to heat stress exposure or as post injury treatment yet none were eﬀective (Moran et al., 1999; Kuennen et al., 2011). Heat load is a signiﬁcant stressor during exercise. Astaxanthin’s activity against stressor induced generation of reactive oxygen and nitrogen species (RONS) and inﬂammatory cytokines (Brown et al., 2018) may thus be beneﬁcial in heat stress conditions. Accordingly, Do et al. demonstrated that Abbreviations: AT, Anaerobic threshold; ATX, Astaxanthin; BMI, Body mass index; BSA, Body surface area; CPET, Cardio pulmonary exercise test; CPK, Creatine phospho kinase; CRP, C- reactive protein; ELISA, Enzyme-Linked Immunosorbent Assay; FDA, U.S. Food and Drug Administration; GI, Gastro intestinal; HR, Heart rate; HSP72, Heat shock protein 72; HSR, Heat shock response; HTT, Heat tolerance test; IGF-1, Insulin-like growth factor 1; MAP, Mean arterial pressure; MPO, Myeloperoxidase; MSK-1, Mitogen- and stress- activated protein kinase-1; OBLA, Onset of blood lactate accumulation; OD, Optical density; PLA, Placebo; PP, Pulse pressure; RER, Respiratory exchange ratio; RH, relative humidity; RONS, Reactive oxygen and nitrogen species; ROS, Reactive oxygen species; RPE, Relative perceived exertion; SR, Sweat rate; TCR, Thermal comfort rate; VO2, Oxygen uptake; VO2 Max, Maximal oxygen uptake. during development, treatment with Astaxanthin increased protection of porcine oocytes against heat shock, along with increased resilience to oxidative stress (Do et al., 2015). In rodents, Astaxanthin enhanced protection against heat related damages and oxidative stress (Preuss et al., 2009) and resilience against heat stress combined with gravitational unloading (Yoshihara et al., 2018). In a preliminary experiment our group demonstrated improved heat tolerance with elevated cardiac tissue concentration of HSP72 protein and HSP70 mRNA in rats (Horowitz M. & Abbas A., unpublished data). In yellow catﬁsh, Astaxanthin pretreatment improved overall stress resistance, while elevating hepatic heat shock protein 70 (HSP70) mRNA levels, with increased antioxidant capacity, and decreased expression of the stress related hormone cortisol and glucose levels (Liu et al., 2016). Puﬀerﬁsh fed with a diet containing Astaxanthin produced less reactive oxygen species (ROS) when exposed to heat stress, and increased production of superoxide dismutase (SOD), catalase (CAT), and HSP70 mRNA under high temperature stress, in comparison with the control (Cheng et al., 2018). Elevation of HSP70 mRNA and HSP72 protein is an important part of the heat shock response (HSR), representing innate cellular defense mechanisms against heat related damage (Horowitz, 1998). Overall, across several animal models, Astaxanthin treatment enhances cellular protection against heat with corresponding increased levels of HSP70, possibly by priming key components of the HSR for activation, and by acting as a potent antioxidant via protection against the heat stress induced generation of RONS. Based on the aforementioned knowledge, and in the absence of known safe substances applicable as preemptive measures for anticipated heat stress exposure, Astaxanthin emerges as a potential candidate for enhancing heat resilience through increased cellular protection, pertinent to both heat and exercise exposure, as it is a safe food supplement, which may potentially be consumed chronically without adversely aﬀecting active populations. Therefore, we set out to determine whether Astaxanthin supplementation, as a preemptive strategy, could have an inﬂuence on performance in heat stress combined with exercise scenarios in humans, and potentially serve as an added line of defense against heat related injury for individuals anticipating exposure to heat and exercise. We also chose to separately evaluate the inﬂuence of Astaxanthin supplementation on aerobic ﬁtness, since it is a key contributing component to endurance in the heat (Mclellan et al., 2012) and to determine whether the potential added cellular protection might inﬂuence aerobic performance, independently of heat exposure. The study goals were to determine whether Astaxanthin pre- supplementation could inﬂuence performance in exercise alone or in combination with heat stress. METHODS In order to evaluate the inﬂuence of Astaxanthin supplementation on heat tolerance and on aerobic capacity, we employed a double blind placebo controlled randomized trial. The heat tolerance test (HTT), which involves exposure Frontiers in Sports and Active Living \| www.frontiersin.org 2 September 2019 \| Volume 1 \| Article 17 Fleischmann et al. Astaxanthin Exercise Recovery and Heat Tolerance to mild physical activity in controlled heat load conditions and the maximal oxygen uptake (VO2 Max) test were used before supplementation and repeated after 1 month of daily supplementation. The study was approved by the ethical review boards of the Sheba medical center (reg. no. 1295-13) and of the IDF Medical Corps (reg. no. 0471-13) and was registered in the NIH database (reg. no. NCT02088242). Data collection took place between March of 2015 and March of 2016 at the Heller institute of medical research located in the Sheba medical center, Tel-Hashomer, Israel. Participants Twenty two young healthy male volunteers, free from illness and not consuming medications or dietary supplements, completed their participation in the study after giving their informed consent and being examined by the study physician. Participants were interviewed by a nutritionist to ensure an Astaxanthin free diet and instructed to avoid changing their exercise routine for the duration of the study, and refrain from consuming Astaxanthin containing foods, as well as any dietary supplements for 2 weeks prior to participating in the physical tests and throughout the duration of the study. They were randomly assigned to either the supplementation group, who received 12 mg of Astaxanthin P.O daily as 3 soft gel capsules of Astapure R⃝ (10% Oleoresin) 4 mg or a placebo identical in appearance and taste, which contained no Astaxanthin (Algatech, Ktora, Israel). The Supplement and placebo capsules were purchased directly from the manufacturer, to guarantee production of a placebo identical in every way to the supplement, apart from the presence of the active ingredient. Certiﬁcates of analysis were issued for each purchased batch, ensuring a 95% purity at least of the active ingredient (Astaxanthin) in the oleoresin contained in the soft gel capsules. Treatment The dose (12 mg) was chosen in accordance with the highest daily dose approved for human consumption by the U.S. Food and Drug Administration (FDA) at the time of study approval and with literature evidence from human experimentation, demonstrating safety and eﬃcacy at this and higher doses (Kupcinskas et al., 2008; Yoshida et al., 2010; Choi et al., 2011; Nakagawa et al., 2011). Supplementation duration (over 30 days) was chosen in order to ensure adequate time for achieving a supplemented state and initiating the necessary long term eﬀects, based on other human experiments involving exercise related aspects, without any known threat to the subjects’ health and well-being (Spiller and Dewell, 2003; Bloomer et al., 2005; Earnest et al., 2011; Miyazawa et al., 2011). Randomization and assignment to the Astaxanthin or placebo group was performed by an independent party (the clinical research division of the Sheba medical center pharmaceutical services), which also individually dispensed the study product to the participants. Treatment allocation was disclosed to the researchers only after study completion. In order to ensure maximal gastro-intestinal (GI) absorption, participants were instructed to ingest the supplement or placebo with a meal containing 15 grams of fat. Supplementation lasted for 30 days, immediately followed by an additional supplementation period of 5–10 days, during which the physical tests (HTT and VO2 Max) were repeated, on separate days, in-order to maintain an eﬀective concentration of the supplement and ensure the tests were performed under a supplemented state. Treatment adherence by participants was monitored by keeping a supplementation log and sending a daily text message after supplement consumption. A dietary log was also kept for 3 days before each physical examination day. Experimental Design and Procedures Twenty two participants completed the study, after being randomly assigned to either the Astaxanthin (ATX, n = 12, age: 22.3 ± 4.0 years) or placebo (PLA, n = 10, age: 24.1 ± 2.60) groups in a double blind manner. Participants in both groups were of average anthropometrics (Height=173.95 ± 4.0 cm, and 1.75 ± 7.6 cm; Body mass = 68.46 ± 8.0, and 70.96 ± 9.8; BMI = 22.6 ± 2.33, and 23.02 ± 2.40; %body fat = 13.32 ± 4.15% vs. 17.33 ± 3.41%, in the ATX and PLA groups, respectively, no signiﬁcant diﬀerence between treatment groups). Supplementation began after completion of the initial HTT and VO2 Max tests, and lasted a total of 35–40 days. The HTT and VO2 Max tests where repeated after 30 days under ongoing supplementation. Figure 1 is a ﬂow diagram of the study, detailing the process of participant recruitment, assignment and testing. Anthropometric measurement (height, body mass, body fat from a four points skinfold measurement) was followed by evaluation of aerobic capacity and heat tolerance which were conducted on separate days, at least 48 h apart, and followed by commencement of daily supplementation. Aerobic capacity and heat tolerance assessment were repeated during the 31–40 day period of supplementation, while still consuming the supplement or placebo. Anthropometry included height (roll-up stadiometer, model 206, Seca medical measuring systems and scales, Germany), body mass (electronic scales), and determination of body composition by the four sites (biceps brachii, triceps brachii, suprailiac, subscapular) skinfold measurement (Lange skinfold caliper, Beta technology, Santa Cruz, CA) and calculation of fat content and lean body mass, based on an equation suited to the participant’s age (Durnin and Womersley, 1974). The heat tolerance test (HTT) was described by Moran et al. (2007). Participants were dressed in shorts and tennis shoes and exposed to 2 h of extreme heat stress (40◦C, 40% RH) in a climatic chamber, while walking on a motor-driven treadmill (5 kph, 2% incline). Rectal temp. (Trec), skin temp. (Tsk), and heart rate (HR) were continuously monitored. Fluid consumption (cold water) was provided ad-libitum from pre-weighed drinking cans. Trec was measured with a rectal thermistor (YSI-401, Yellow Springs Incorporated, USA) inserted 10 cm beyond the anal sphincter. Skin temp. (Tsk) at the chest, upper arm and calf, was measured using a skin thermistor (YSI-409B, Yellow Springs Incorporated, USA). Mean Tsk was calculated by Burton’s equation (Burton, 1935). All temperatures were continuously recorded (MP150 and Acqknowledge software, version 3.9, Biopac systems, USA). Heart rate (HR) was continuously Frontiers in Sports and Active Living \| www.frontiersin.org 3 September 2019 \| Volume 1 \| Article 17 Fleischmann et al. Astaxanthin Exercise Recovery and Heat Tolerance FIGURE 1 \| Study ﬂow diagram. monitored by a heart rate monitor (model: RS800CX, POLAR, Finland). Blood pressure (BP) was monitored at pre-set time points (before the test, after 1 h of walking, at the end of the test, and every 15 min during recovery, for 1 h after the test) using an automated blood pressure monitor (Omron m6 comfort, Omron healthcare, Japan). Fluid balance was determined from nude body mass, measured before and after each trial, adjusted for ﬂuid intake and urine volume, and used to calculate sweat loss, which was then normalized to body surface area and presented as the hourly sweat rate (SR). Relative perceived exertion (RPE) was assessed every 15 min during the HTT using the Borg scale (Borg, 1998), and a scale from 1 to 13 (unbearably cold to unbearably hot sensation, respectively), was used to rate the subjective sensation of thermal comfort (Thermal comfort rate, TCR). Safety thresholds for test cessation were set at Trec = 39◦C or Frontiers in Sports and Active Living \| www.frontiersin.org 4 September 2019 \| Volume 1 \| Article 17 Fleischmann et al. Astaxanthin Exercise Recovery and Heat Tolerance HR = 180 bpm, at the study physician’s discretion or at the participant’s request. Maximal oxygen uptake (VO2 Max) was determined by cardio pulmonary exercise testing (CPET), using a modiﬁed Bruce protocol composed of 5 min seated rest, followed by 5 min of walking on a treadmill at 5 kph, and 2% incline, followed by running at 9 kph, with an incrementally increasing incline (2% every 2 min), until reaching VO2 Max, which was determined by 3 of 4 criteria during the test: (1) leveling oﬀ of the VO2 curve to a plateau, (2) reaching >90% of the participant’s predicted maximal heart rate (210–0.65 × Age), (3) reaching a respiratory exchange ratio (RER) ≥1.1 or 4) at the participant’s request, after reaching a subjective state of extreme physical tiredness. Additional supportive indications after test completion were reaching >8 mmol/l of blood lactate, or RPE > 17 (Edvardsen et al., 2014; Debeaumont et al., 2016). The ventilatory anaerobic threshold (AT) was determined visually by two trained examiners according to the American heart association guidelines (Balady et al., 2010). Continuous monitoring lasted throughout recovery, which consisted of 3 min at 5 kph and 2% incline, followed by 3 kph at 0% incline, and ﬁnally, 1 min, seated. The test was performed on a CPET machine (ZAN 600, Nspire Health, USA) connected to a treadmill ergometer (Model 770 S, RAM medical and industrial instruments, Germany). Reaching onset of blood lactate accumulation (OBLA) was conﬁrmed by examining blood lactate level before and after the test (lactate scout+ analyzer, Sports Resource Group Inc., USA). Heart rate was continuously monitored by a heart rate monitor (model: RS800CX, POLAR, Finland). Assessment of RPE took place before and after the VO2 Max test. Blood was drawn on physical testing days before the VO2 Max test and on HTT days before, immediately after and at 60 min after the end of the HTT. Blood was collected in yellow gel chemistry collection tubes (Becton, Dickinson and Co., NJ, USA), allowed to clot for 30 min and centrifuged. Serum was separated immediately and stored at −80◦C pending analysis. Serum lipid and triglyceride (TG) proﬁle, CRP, and CPK were analyzed by the central laboratories at the Sheba medical center. A commercially available ELISA kit for High-Sensitivity HSP72 detection was used to measure serum HSP72 levels in optical density (OD), which was used to calculate the HSP72 concentration in ng/ml, according to the manufacturer’s instructions (AMP’D R⃝ HSP70 high sensitivity ELISA kit, ENZ-KIT-101, Enzo life sciences, NY, USA). STATISTICS Anthropometric, physiological and biochemical parameters were statistically analyzed using the SPSS software (version 23, IBM, USA). Treatments and time point were taken as the independent variable and participants were considered a random sample of the general population. Normality of distribution was assessed by the Kolomogorov-Smirnov test and comparison between treatment groups and between pre- and post-supplementation time points was made with 1-way ANOVA, with Tukey post hoc analysis for normally distributing variables, or Mann-Whitney U-test for non-normally distributing variables. Analysis of the diﬀerence in the change in parameters due to supplementation between treatment groups was conducted by calculating the delta between the pre- and post-supplementation states (pre-supplemented state subtracted from the post-supplemented state). Normality of distribution was assessed by the Kolomogorov-Smirnov test and comparison between treatment groups was made by T-test for normally distributing variables and Mann-Whitney U-test for non-normally distributing variables. Leven’s test was used to evaluate the equality of variance between treatment groups, followed by the appropriate Student’s t-test (2-tailed) to assess signiﬁcance. In order to assess the signiﬁcance of diﬀerence between repetitive blood tests, ANOVA for repeated measures followed by Bonferroni post-hoc analysis or Friedman’s omnibus test followed by Wilcoxon’s signed-rank test with Bonferroni adjustment were used, for normally or non-normally distributing variables, respectively. A signiﬁcant p-value was set at 0.05. RESULTS Table 1 lists key parameters of aerobic capacity, as recorded by the VO2 Max test. In both groups, anaerobic threshold (AT) was achieved at approximately 72% of the VO2 Max value, VO2 Max was similar and did not improve post-supplementation. Aerobic characteristics did not diﬀer between the ATX and PLA groups both before and after supplementation, as seen in the unchanged AT, maximal oxygen uptake, reduction in heart rate during recovery, and in substrate utilization demonstrated by the scatter plot of respiratory exchange ratio (RER) vs. oxygen uptake (VO2) (Supplemental Figure 1). However, a signiﬁcant diﬀerence was observed between the two groups post supplementation in the blood lactate concentration measured after the VO2 Max test. Additionally, a signiﬁcant reduction was observed in oxygen uptake at the end of recovery between the pre-supplementation and post- supplementation time points in the ATX group compared to the PLA group (Table 1). Supplemental Figure 2 depicts the VO2 values during the test by group, before and after supplementation. Table 2 lists the results from the HTT. The physiological parameters monitored continuously during the test, including HR, Trec, and Tsk displayed no signiﬁcant diﬀerence between the ATX and PLA groups. During the ﬁrst, un-supplemented HTT, and the second, supplemented HTT, Basal Trec in both groups was below 37◦C, and increased by about 1◦C. Heart rate began at nearly 80 bpm and increased to just under 120 bpm in both groups. Pre-supplementation sweat rate in the PLA group was signiﬁcantly higher than the ATX group (which disappeared post-supplementation), and post-supplementation in the PLA group (p < 0.001). The subjective scales representing sensations of relative perceived exertion (RPE) and thermal comfort (TCR), which were monitored every 15 min during the test and for 1 h after its completion, also displayed no diﬀerence between groups or exposures. Participants perceived a mild to moderate eﬀort in reporting their subjective sensations in the Borg scale (RPE) and moderate heat in the TCR scale. Frontiers in Sports and Active Living \| www.frontiersin.org 5 September 2019 \| Volume 1 \| Article 17 Fleischmann et al. Astaxanthin Exercise Recovery and Heat Tolerance TABLE 1 \| Main VO2 Max ﬁndings: This table lists the main ﬁndings from the maximal oxygen uptake tests performed before (pre) and after (post) supplementation in the two study groups. Test parameter ATX pre ATX post PLA pre PLA post ANOVA/Mann-Whitney U Delta ATX Delta PLA T-test/ Mann-Whitney U Mean ± St. Error Mean ± St. Error Mean ± St. Error Mean ± St. Error p-value Post hoc Tukey p-value Mean ± St. Error Mean ± St. Error p-value RPE Before 6 ± 0 7 ± 0 7 ± 1 7 ± 0 N.S 0 ± 1 −2 ± 1 N.S RPE After 18 ± 1 17 ± 0 16 ± 1 17 ± 0 N.S −2 ± 2 −3 ± 3 N.S 1 RPE 12 ± 1 11 ± 0 9 ± 1 11 ± 1 N.S −2 ± 1 −1 ± 2 N.S BLA before (mmole.l−1) 2.3 ± 0.31 2.57 ± 0.21 2.04 ± 0.24 2.08 ± 0.2 N.S 0.27 ± 0.37 −0.17 ± 0.38 N.S BLA after (mmole.l−1) 13.46 ± 0.94 11.92 ± 0.85 12.65 ± 0.68 15.09 ± 1.02 N.S −1.54 ± 0.85 0.93 ± 1.84 N.S 1 BLA (mmole.l−1) 11.16 ± 0.78 9.35 ± 0.89 10.61 ± 0.73 13.01 ± 1.05 0.044 0.027* −1.81 ± 0.89 1.1 ± 1.67 N.S Sys. BP before (mmHg) 119 ± 2.77 120.64 ± 3.96 118.5 ± 2.87 121.29 ± 2.9 N.S −8.42 ± 10.34 −33.6 ± 19.81 N.S Dias. BP before (mmHg) 78.58 ± 3.14 77.64 ± 3.65 72.7 ± 3.09 79.57 ± 5.07 N.S −7.42 ± 8.94 −17 ± 14.49 N.S Sys. BP after (mmHg) 118.9 ± 4.42 127.2 ± 3.84 122.9 ± 5.34 117.71 ± 5.51 N.S 6.92 ± 21.73 −40.5 ± 17.89 N.S Dias. BP after (mmHg) 75.1 ± 2.93 79.4 ± 3.11 76 ± 3.24 73.29 ± 4.36 N.S 3.58 ± 14.96 −24.7 ± 11.12 N.S AT VO2 (ml.kg−1.min−1) 37.51 ± 3.35 40.1 ± 1.97 37.27 ± 3.51 38.53 ± 3.98 N.S 2.59 ± 3.68 1.26 ± 2.09 N.S AT (% of VO2 Max) 72.32 ± 6.08 78.27 ± 3.17 71.3 ± 3.77 73.23 ± 4.51 N.S 5.95 ± 6.86 1.94 ± 3.79 N.S Max load (Watt) 284.67 ± 11.46 288.5 ± 12.66 276.7 ± 19.6 281.1 ± 19.06 N.S 3.83 ± 5.92 4.4 ± 2.61 N.S VO2 Max (ml.kg−1.min−1) 52.55 ± 1.83 51.24 ± 1.6 51.49 ± 2.32 51.54 ± 2.66 N.S −1.31 ± 0.7 0.05 ± 0.71 N.S HR Max (bpm) 190.67 ± 2.85 191.17 ± 2.33 191.2 ± 2.62 190.1 ± 2.85 N.S 0.5 ± 1.58 −1.1 ± 1.42 N.S RER Max 1.14 ± 0.02 1.13 ± 0.02 1.15 ± 0.02 1.15 ± 0.03 N.S 0 ± 0.02 0 ± 0.02 N.S #End recov. VO2 (mlkg−1min−1) 12.08 ± 0.32 10.76 ± 0.45 11.43 ± 0.43 11.85 ± 0.51 N.S −2.22 ± 0.99 0.42 ± 0.48 ##0.006 #End recov. VO2 (% of VO2 Max) 23.52 ± 0.64 21.31 ± 0.68 22.44 ± 0.98 23.27 ± 1.04 N.S −2.02 ± 0.64 0.83 ± 0.79 0.01 1#end recov. HR (bpm) 47.25 ± 2 43.08 ± 2.24 40.3 ± 4 40.3 ± 4.7 N.S −4.17 ± 2.15 0 ± 5.19 N.S RPE, relative perceived exertion; BLA, blood lactate; Sys., systolic; Dias., diastolic; BP, blood pressure; AT, Anaerobic threshold; Max, maximal; HR, heart rate; bpm, beats per minute; recov., recovery. #End recovery is the average value of the last 30 s recorded while seated at the end of the test. Between ATX post and PLA post. ##Value calculated with Mann-Whitney U-test, for non-normally distributing variables. Frontiers in Sports and Active Living \| www.frontiersin.org 6 September 2019 \| Volume 1 \| Article 17 Fleischmann et al. Astaxanthin Exercise Recovery and Heat Tolerance TABLE 2 \| Main HTT ﬁndings: This table lists the main ﬁndings from the HTT performed before (pre) and after (post) supplementation in both treatment groups. Test parameter ATX pre ATX post PLA pre PLA post ANOVA/Mann-Whitney U Delta ATX Delta PLA T-test/ Mann-Whitney U Mean ± St. Error Mean ± St. Error Mean ± St. Error Mean ± St. Error p-value post hoc Tukey p-value Mean ± St. Error Mean ± St. Error p-value Basal Trec (◦C) 36.93 ± 0.08 36.87 ± 0.08 36.95 ± 0.07 36.82 ± 0.12 N.S −3.13 ± 3.04 −0.12 ± 0.12 N.S Max Trec (◦C) 37.79 ± 0.1 37.9 ± 0.1 37.79 ± 0.1 37.91 ± 0.17 N.S −3.05 ± 3.12 0.12 ± 0.16 N.S 1 Trec (◦C) 0.83 ± 0.09 0.93 ± 0.12 0.9 ± 0.1 1.1 ± 0.18 N.S 0.1 ± 0.09 0.2 ± 0.2 N.S End + 1 h Trec (◦C) 37.29 ± 0.06 37.26 ± 0.13 37.26 ± 0.07 37.19 ± 0.14 N.S 3.07 ± 3.13 7.38 ± 4.97 N.S Basal Tskin (◦C) 35.41 ± 0.21 35.12 ± 0.24 35.68 ± 0.24 35.49 ± 0.24 N.S −0.26 ± 0.2 −7.29 ± 4.76 N.S Max Tskin (◦C) 36.4 ± 0.14 36.43 ± 0.23 36.12 ± 0.17 36.28 ± 0.29 N.S −3 ± 3.01 −7.1 ± 4.83 N.S 1 Tskin (◦C) 0.99 ± 0.22 −2 ± 3.41 0.4 ± 0.27 0.67 ± 0.37 N.S −2.74 ± 3.07 0.2 ± 0.36 N.S Sweat rateBSA−1 −342.81 ± 23.25 −334.17 ± 22.53 −472.89 ± 29.72 −333 ± 19.71 <0.001 0.002* 8.64 ± 36.11 139.89 ± 39.16 0.023 HR Start (bpm) 79.17 ± 3.29 79.67 ± 3.17 80.4 ± 4.37 79.1 ± 3.84 N.S 0.5 ± 2.91 −1.3 ± 2.78 N.S HR end (bpm) 118 ± 3.84 116.08 ± 4.71 117.7 ± 6.38 118.9 ± 5.47 N.S −1.92 ± 4.15 1.2 ± 3.57 N.S 1 HR (bpm) 38.83 ± 2.29 36.42 ± 5.09 37.3 ± 5.11 39.8 ± 3.68 N.S −2.42 ± 5.32 2.5 ± 3.42 N.S RPE start 8 ± 1 7 ± 0 7 ± 0 7 ± 0 N.S 0 ± 1 0 ± 1 N.S RPE end 11 ± 1 8 ± 0 9 ± 1 10 ± 2 N.S −1 ± 0 ± 2 N.S TCR start 9 ± 1 7 ± 1 8 ± 0 8 ± 1 N.S −1 ± 1 1 ± 1 N.S TCR end 11 ± 1 11 ± 1 9 ± 1 9 ± 0 N.S 0 ± 0 ± 1 N.S MAP Start (mmHg) 83.78 ± 2.72 89.39 ± 5.03 80.57 ± 9.23 83.73 ± 2.36 N.S 5.61 ± 5.19 3.17 ± 8.96 N.S MAP end (mmHg) 81.33 ± 2.36 80.36 ± 2.96 83.8 ± 3.69 80.97 ± 2.01 N.S 1.27 ± 2.92 −2.83 ± 4.7 N.S 1 MAP (mmHg) −9.22 ± 6.94 −9.03 ± 5.13 3.23 ± 11.59 −2.77 ± 2.4 N.S 0.19 ± 7.92 −6 ± 10.89 N.S MAP HTT + 1 h (mmHg) 83.48 ± 2.47 90.42 ± 1.23 87.6 ± 4.01 85.17 ± 2.24 N.S 6.67 ± 2.41 −2.43 ± 4.46 N.S Max, maximal; Trec, rectal temp.; Tskin, skin temp.; BSA, body surface area; HR, heart rate; bpm, beats per minute; RPE, relative perceived exertion; TCR, thermal comfort rate; MAP, Mean arterial pressure. Between PLA pre and ATX pre and between PLA pre and PLA post. Frontiers in Sports and Active Living \| www.frontiersin.org 7 September 2019 \| Volume 1 \| Article 17 Fleischmann et al. Astaxanthin Exercise Recovery and Heat Tolerance Biochemical analyses: Table 3 depicts measured serum concentrations of CRP, CPK, HSP72, and the lipid proﬁle, including, high density lipoproteins (HDL), low density lipoproteins LDL total cholesterol and Triglycerides. No signiﬁcant diﬀerences were observed between the ATX and PLA groups in the serum levels of HSP72 protein, in the lipid and triglyceride proﬁle, in CRP or in CPK concentrations, both before and after the eﬀort. However, during all HTT testing days, CPK levels obtained before the test were signiﬁcantly lower than those obtained immediately after the test, in both groups, both before and after supplementation. DISCUSSION We examined the inﬂuence of 1 month of 12 mg daily Astaxanthin supplementation on heat tolerance and aerobic capacity. Astaxanthin improved exercise recovery but had no inﬂuence on performance in the heat. Human exercise models, in contrast to animal studies have shown conﬂicting results regarding the eﬀects of Astaxanthin on performance. For example: the beneﬁcial eﬀects of Astaxanthin in competitive cyclists shown while consuming 4 mg/day (Earnest et al., 2011), vs. no signiﬁcant diﬀerence in performance of well-trained cyclists while consuming 20 mg/day (Res et al., 2013). Neither metabolic markers nor blood biochemistry of human cohorts revealed dose or time dependent metabolic changes attributable to Astaxanthin supplementation (Karppi et al., 2007; Earnest et al., 2011; Res et al., 2013). The variance of substrate oxidation proﬁles during exercise existing in the general population and the steady state nature of the measurement may have masked a metabolic supplementation eﬀect. The graded VO2 Max test used in our study, designed to answer the questions raised regarding the inﬂuence of Astaxanthin on substrate utilization in exercising humans over a range of exercise intensities (Brown et al., 2018), showed no eﬀect on aerobic capacity or its components: energy substrate use during the VO2 Max test displayed no supplementation eﬀect to inﬂuence fat utilization over carbohydrates in either group, as demonstrated in Supplemental Figure 1. However, the change in blood lactate concentration after the VO2 Max test (Table 1), along with the signiﬁcant reduction in oxygen uptake at the end of recovery in the ATX group compared to the PLA group, may suggest less oxidative stress and faster recovery in comparison with the control, which is a possible advantage for Astaxanthin supplementation. In Supplemental Figure 2, a more rapid return to lower VO2 values during recovery is seen in ATX after supplementation compared to before supplementation. Though evidence from animal models suggests that post exercise recovery may improve with Astaxanthin administration, particularly, by diminishing exercise induced tissue damage markers such as creatine kinase (CK) and myeloperoxidase (MPO), through anti-oxidative and anti-inﬂammatory pathways (Aoi et al., 2003; Guo et al., 2018), human studies are ambiguous: muscle soreness, exercise force production and plasma CK displayed no signiﬁcant diﬀerence in highly trained individuals who received 3 weeks of 4 mg/day Astaxanthin (Bloomer et al., 2005). However, longer supplementation (90 days) in young soccer players was associated with improved indirect damage markers like reduced lactate dehydrogenase (LDH), and non-signiﬁcant improvements in CK and inﬂammatory markers including CRP and leukocyte and neutrophil counts (Djordjevic et al., 2012). Validated information on the eﬀects of Astaxanthin supplementation on exercise performance and recovery, particularly in diverse populations, is lacking. In the present experiment, though exercise recovery of oxygen uptake was improved in the Astaxanthin group post- supplementation, contrastingly, serum inﬂammation (CRP), muscle damage (CPK) and lipid proﬁle remained unaﬀected by supplementation in both groups. The physiological strain induced by the HTT, was mild for both groups (Trec < 38◦C and HR < 120 bpm), as supported by the lack of change in HSP72 post-exercise, pointing to an insuﬃcient perturbation of the thermoregulatory system and the absence of an HSR. Notably, experimental conditions, particularly the physiological safety thresholds, were limited by ethical constraints, and could not induce a higher thermal threshold. Under the experimental conditions employed in this study, no participant reached the safety threshold during heat exposure. An Additional component contributing to the observed physiological response may have been the ﬁtness level of participants and the relatively mild eﬀort undertaken by them during the HTT. An average VO2 Max of 51–52 ml × kg−1 × min−1 was typical of the study participants. The average HR elevation during the HTT was ∼40 bpm, reﬂecting a 21% change relative to the measured maximal HR in the VO2 Max test (Table 1), indicating a state of mild stress experienced during the HTT across treatment groups and exposures. Nevertheless, the signiﬁcant diﬀerence in CPK levels from the beginning to the end of the HTT, in both groups indicates some muscle damage resulting from the HTT, which was unaﬀected by supplementation (Table 3). The signiﬁcantly higher sweat rate in the pre-supplemented PLA group compared to PLA post-supplementation and to ATX pre- and post-supplementation cannot be explained by an eﬀect of supplementation, and can only be attributed to a diﬀerence between participant groups. This was, however, insigniﬁcant when the change in sweat rate from pre- to post-supplementation was compared between treatment groups (Table 2). The daily dose of Astaxanthin used in this work (12 mg) was reﬂective of the highest recommended dose for humans at the time, which has been substantially increased since then to 24 mg daily (Visioli and Artaria, 2017). Consumption of a larger dose may have evoked greater eﬀects in aerobic function and cellular protective aspects important to coping with the damages of heat stress exposure. CONCLUSION Preemptive nutritional supplementation is a promising avenue for exercise science research as a way of improving physiological Frontiers in Sports and Active Living \| www.frontiersin.org 8 September 2019 \| Volume 1 \| Article 17 Fleischmann et al. Astaxanthin Exercise Recovery and Heat Tolerance TABLE 3 \| Serum levels of CPK (mg/Liter), CRP (mg/Liter), HSP72 (ng/mL), and lipid proﬁle: HDl, LDL, total cholesterol and triglycerides (mg/dL). Supp. Time point ATX PLA T-test\Mann-Whitney U Mean ± St. Error Mean ± St. Error CRP pre-supp. Before HTT 1.09 ± 0.32 0.6 ± 0.14 N.S After HTT 1.08 ± 0.33 0.63 ± 0.14 After HTT + 1 h 1.06 ± 0.3 0.62 ± 0.13 #1 HTT −0.01 ± 0.03 0.03 ± 0.01 ##1 HTT + 1 h −0.03 ± 0.03 0.02 ± 0.02 CRP post-supp. Before HTT 1.54 ± 0.46 1.31 ± 0.79 After HTT 1.53 ± 0.45 1.22 ± 0.72 After HTT + 1 h 1.55 ± 0.46 1.25 ± 0.76 #1 HTT −0.01 ± 0.04 −0.08 ± 0.07 ##1 HTT + 1 h 0.01 ± 0.07 −0.05 ± 0.03 CPK pre-supp. Before HTT 221.42 ± 61.68 188.7 ± 36.69 After HTT 260.09 ± 69.54 208 ± 39.68 After HTT + 1 h 234.42 ± 57.17 198.8 ± 33.96 #1 test 27.64 ± 9.4 19.3 ± 5.06 ##1 recovery −15 ± 8.21 −9.2 ± 4.64 CPK post-supp. Before HTT 191.5 ± 42.54 134.5 ± 25.83 After HTT 207.42 ± 43.94 151.3 ± 26.3 After HTT + 1 h 204.75 ± 43.39 146.1 ± 25.9 #1 test 15.08 ± 4.7 16.8 ± 3.11 ##1 recovery −2.17 ± 3.35 −5.2 ± 2 HSP72 pre-supp. Before HTT 1.9 ± 0.75 4.06 ± 0.62 0.021 After HTT 2.68 ± 0.88 3.77 ± 0.67 N.S 1 HTT 0.77 ± 0.54 −0.3 ± 0.53 HSP72 post-supp. Before HTT 2.25 ± 0.79 3.75 ± 0.77 After HTT 2.31 ± 0.76 3.77 ± 0.77 1 HTT 0.06 ± 0.09 0.03 ± 0.09 HSP72 1Supp. 1 before HTT 0.35 ± 0.12 −0.32 ± 0.34 1 after HTT −0.37 ± 0.59 0.01 ± 0.78 Total cholesterol Pre-supp. 139 ± 9.82 156.6 ± 5.98 Post-supp. 144.5 ± 7.12 160.5 ± 7.12 1 5.5 ± 8.14 3.9 ± 5.12 Triglycerides Pre-supp. 88.25 ± 16.18 98 ± 11.62 Post-supp. 94.33 ± 15.78 95.2 ± 11.62 1 6.08 ± 8.25 −2.8 ± 9.88 HDL Pre-supp. 46 ± 2.7 47.2 ± 2.92 Post-supp. 46.42 ± 2.04 47.8 ± 2.12 1 0.42 ± 1.66 0.6 ± 1.42 LDL Pre-supp. 96.17 ± 5.29 105.7 ± 6.29 Post-supp. 94.42 ± 5.68 108.8 ± 6.69 1 −1.75 ± 3.85 3.1 ± 3.74 n = 12 in the ATX group, and 10 in the PLA group. HSP72 protein levels were measured in eight participants of each group using ELISA before the HTT and 1 h after completion of the test. Analysis was made in triplicate and averaged for each measurement. #Calculated by subtracting before HTT from after HTT values. ##Calculated by subtracting before HTT from after HTT + 1 h values. Supplementation delta was calculated by subtracting pre supplementation values from the post supplementation values for the appropriate time point. *Delta (1) at each HTT was calculated by subtracting Before HTT results from the After HTT results. Supp., supplementation; HTT, heat tolerance test; 1, delta, HDL, high density lipoproteins; LDL, low density lipoproteins. For CRP, Friedman’s omnibus test between time points within treatments revealed no statistically signiﬁcant difference. For CPK, 1-way ANOVA revealed no signiﬁcant difference for any of the time points between the ATX and PLA groups. Friedman’s omnibus test, followed by Wilcoxon signed-ranks test revealed signiﬁcant differences between CPK values before and after the HTT (p = 0.005 and p < 0.01 in the ATX and PLA groups, respectively) and in the ATX group, between after the HTT and 1 h after its completion (p = 0.006). Analysis of HSP72 with Wilcoxon signed ranks test revealed no signiﬁcant difference between measurements within treatment groups. Analysis of TG’s by Wilcoxon signed-ranks test revealed no signiﬁcant difference between time points within treatment groups. Analysis of Cholesterol and lipids by paired samples t-test revealed no signiﬁcant difference between measurements within treatment groups. Frontiers in Sports and Active Living \| www.frontiersin.org 9 September 2019 \| Volume 1 \| Article 17 Fleischmann et al. Astaxanthin Exercise Recovery and Heat Tolerance resilience in preparation for an anticipated exposure to adverse conditions and to strenuous eﬀorts. Long term supplementation of 12 mg\daily Astaxanthin contributed to improved aerobic recovery, but was not beneﬁcially manifested under the examined heat load conditions. It remains to be seen if administration of larger doses of Astaxanthin or exposure to greater environmental and physiological stress that elicit a heat shock response might bring additional protective mechanisms of Astaxanthin supplementation into light. DATA AVAILABILITY The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualiﬁed researcher. AUTHOR CONTRIBUTIONS CF contributed to the conception and design of the study, conducted the experiments, analyzed the data, and wrote the manuscript. MH contributed to data analysis and to manuscript design and reviewed the manuscript. RY contributed to conducting the experiments, to data analysis, and reviewed the manuscript. HR participated as the study nutritionist and contributed to conducting the experiments, to data analysis, and reviewed the manuscript. YH contributed to the conception and design of the study, to conducting the experiments, and reviewed the manuscript. FUNDING This work was funded by the IDF medical corps research fund, grant number: 44405899192. ACKNOWLEDGMENTS The authors would like to acknowledge the participants who took part in this study. SUPPLEMENTARY MATERIAL The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fspor. 2019.00017/full#supplementary-material Supplemental Figure S1 \| Scatter plot of RER vs. VO2 during the VO2 Max tests, for the ATX and PLA supplementation groups. The ATX group before and after supplementation is represented by the blue and gray dots, respectively. The PLA group before and after supplementation is represented by the orange and yellow dots, respectively. Supplemental Figure S2 \| VO2 Max test. Depicts the VO2 Max test graphs by group and stage: (A) upper left, green lines: ATX group, before supplementation; (B) lower left, brown lines: ATX group, after supplementation; (C) upper right, blue lines: PLA group, before supplementation; (D) lower right, black lines: PLA group, after supplementation. REFERENCES Aoi, W., Naito, Y., Sakuma, K., Kuchide, M., Tokuda, H., Maoka, T., et al. (2003). Astaxanthin limits exercise-induced skeletal and cardiac muscle damage in mice. Antioxid. Redox Signal. 5, 139–144. doi: 10.1089/152308603321223630 Aoi, W., Naito, Y., Takanami, Y., Ishii, T., Kawai, Y., Akagiri, S., et al. (2008). Astaxanthin improves muscle lipid metabolism in exercise via inhibitory eﬀect of oxidative CPT I modiﬁcation. Biochem. Biophys. Res. Commun. 366, 892–897. doi: 10.1016/j.bbrc.2007.12.019 Balady, G. J., Arena, R., Sietsema, K., Myers, J., Coke, L., Fletcher, G. F., et al. (2010). 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The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Frontiers in Sports and Active Living \| www.frontiersin.org 11 September 2019 \| Volume 1 \| Article 17	Asthaxanthin Improves Aerobic Exercise Recovery Without Affecting Heat Tolerance in Humans.	09-04-2019	Fleischmann, Chen,Horowitz, Michal,Yanovich, Ran,Raz, Hany,Heled, Yuval	eng
PMC7181553	Vol.:(0123456789) 1 3 European Journal of Applied Physiology (2020) 120:961–968 https://doi.org/10.1007/s00421-020-04312-w ORIGINAL ARTICLE Gross and delta efficiencies during uphill running and cycling among elite triathletes Magnus Carlsson1,2 · Viktor Wahrenberg1 · Marie S. Carlsson1 · Rasmus Andersson1 · Tomas Carlsson1,2 Received: 18 October 2019 / Accepted: 31 January 2020 / Published online: 1 April 2020 © The Author(s) 2020 Abstract Purpose To investigate the gross efficiency (GE) and delta efficiency (DE) during cycling and running in elite triathletes. Methods Five male and five female elite triathletes completed two incremental treadmill tests with an inclination of 2.5° to determine their GE and DE during cycling and running. The speed increments between the 5-min stages were 2.4 and 0.6 km h−1 during the cycling and running tests, respectively. For each test, GE was calculated as the ratio between the mechanical work rate (MWR) and the metabolic rate (MR) at an intensity corresponding to a net increase in blood-lactate concentration of 1 mmol l−1. DE was calculated by dividing the delta increase in MWR by the delta increase in MR for each test. Pearson correlations and paired-sample t tests were used to investigate the relationships and differences, respectively. Results There was a correlation between GEcycle and GErun (r = 0.66; P = 0.038; R2 = 0.44), but the correlation between DEcycle and DErun was not statistically significant (r = − 0.045; P = 0.90; R2 = 0.0020). There were differences between GEcycle and GErun (t = 80.8; P < 0.001) as well as between DEcycle and DErun (t = 27.8; P < 0.001). Conclusions Elite triathletes with high GE during running also have high GE during cycling, when exercising at a treadmill inclination of 2.5°. For a moderate uphill incline, elite triathletes are more energy efficient during cycling than during run- ning, independent of work rate. Keywords Triathlon · Cycling economy · Running economy · Incline · Metabolic rate · Mechanical work rate Abbreviations α Treadmill inclination DE Delta efficiency DEcycle Delta efficiency during uphill cycling DErun Delta efficiency during uphill running ΔMR Change in metabolic rate ΔMWR Change in mechanical work rate g Gravitational acceleration GE Gross efficiency GEcycle Gross efficiency during uphill cycling at the lactate threshold GErun Gross efficiency during uphill running at the lactate threshold LT Lactate threshold, i.e. the mechanical work rate at which the blood-lactate concentration increased 1 mmol l−1 above the lowest meas- ured value mtot Total mass of participant and equipment MR Metabolic rate MWR Mechanical work rate RERmean Mean respiratory exchange ratio μ Rolling-resistance coefficient of the bicycle ̇VO2max Maximal oxygen uptake ̇VO2mean Mean oxygen uptake Introduction Triathlon comprises stages of swimming, cycling and run- ning in a sequential order. In a World Cup Olympic-distance competition (i.e. 1.5 km swimming, 40 km cycling and 10 km running), all three stages are important for overall race performance (Landers et al. 2000; Ofoghi et al. 2016). An analysis of the International Triathlon Union’s champi- onship results from 2008 to 2012 revealed that the winners’ Communicated by Jean-René Lacour. * Tomas Carlsson tca@du.se 1 School of Education, Health and Social Studies, Dalarna University, Högskolegatan 2, 791 88 Falun, Sweden 2 Swedish Unit for Metrology in Sports, Dalarna University, Högskolegatan 2, 791 88 Falun, Sweden 962 European Journal of Applied Physiology (2020) 120:961–968 1 3 mean race times were 1 h 46 min (men) and 1 h 58 min (women) (Ofoghi et al. 2016). Moreover, it was found that elite triathletes’ mean heart rate during an Olympic-distance competition was 92% of their maximal heart rate, which indicates the competition’s high-intensity character (Le Meur et al. 2009). From a physiological perspective, endurance perfor- mance is determined by the sum of the aerobic and anaero- bic energy contribution multiplied by gross efficiency (GE) (Joyner and Coyle 2008). In triathlon, performance is mainly determined by maximal oxygen uptake ( ̇VO2max ), lactate/ ventilatory threshold and oxygen uptake kinetics, which together reflect the aerobic energy contribution, and exer- cise economy (i.e. GE during the specific exercise mode) (Jones and Carter 2000). In line with these findings, lactate- threshold variables and peak oxygen uptake in cycling and running were found to be predictors of Olympic-distance triathlon performance (Miura et al. 1997; Schabort et al. 2000). Hence, the ability to exercise at a lower percentage of ̇VO2max for a given submaximal workload (i.e. better econ- omy) has been suggested to be of great importance for suc- cess in triathlon (Dengel et al. 1989; Sleivert and Rowlands 1996). Accordingly, cycling economy and running economy have been reported to be correlated with performance in triathlon (Miura et al. 1997), and economy of movement has been suggested to be an important determinant of triathlon performance (Dengel et al. 1989; Tucker and Tucker 2013). The economy of movement is reflected by the functioning of the cardiorespiratory, metabolic, neuromuscular and bio- mechanical systems (Barnes and Kilding 2015; Ettema and Lorås 2009). In line with this concept, it has been suggested that running economy is related to factors such as muscle morphology, elastic elements and joint mechanics (Barnes and Kilding 2015; Joyner and Coyle 2008; Lacour and Bour- din 2015). In cycling, mechanisms such as muscle-fibre-type transformation, changes in muscle-fibre-shortening veloci- ties, changes within the mitochondria and biomechanical factors have been proposed to be related to improved cycling economy (Coyle et al. 1991; Hopker et al. 2009). There are several ways to express cycling efficiency. Two of these measures of efficiency are based on the relationship between the work performed and the energy expenditure; GE is the ratio between the mechanical work rate (MWR) and the metabolic rate (MR) (i.e. GE = MWR/MR), whereas delta efficiency (DE) is the ratio between the delta increase in MWR and the delta increase in MR (i.e. DE = ΔMWR/ ΔMR). In cycling, GE varies between approximately 18 and 23% in different individuals (Coyle et al. 1992), and the corresponding range in DE is approximately 18–27% (Coyle et al. 1992; Ettema and Lorås 2009). The DE is usu- ally somewhat higher than GE because the basal metabolic rate and metabolic cost of zero-load exercise are excluded from DE calculations. Running economy is often measured as oxygen uptake at a given submaximal running speed (e.g. 16 km h−1) while running on a level treadmill, where a better running econ- omy is indicated by a lower oxygen consumption (Barnes and Kilding 2015). During level treadmill running, zero external work is performed against gravity, frictional forces or air resistance; hence, it is not appropriate to express run- ning economy as GE or DE using a treadmill inclination of 0°. Previously, it has been found that GE during running increases with steeper inclines (Minetti et al. 2002), which emphasize the importance of taking the incline into account when running efficiency is evaluated. A recent study investigated the relationship between triathletes’ energy expenditure during level running at 12 km h−1 and during ergometer cycling at a power output of 200 W, and no significant correlation was found between the gross metabolic rates (Swinnen et al. 2018). Other studies have compared DE during running and cycling using differ- ent methods to apply external loads (e.g. running up different inclines, applying impeding horizontal forces during level treadmill running and treadmill cycling on a tricycle), but the relationship between running and cycling DE was not investigated in either study (Bijker et al. 2001, 2002). To the best of our knowledge, no previous study has used a fixed treadmill inclination to investigate elite triathletes’ running and cycling efficiencies. The purpose of this study was to investigate gross efficiency and delta efficiency dur- ing cycling and running in elite triathletes. Methods Participants Five male (age: 24 ± 5 years, stature: 181 ± 4 cm, and body mass: 73 ± 4 kg) and five female (age: 22 ± 6 years, stature: 169 ± 8 cm, and body mass: 64 ± 9 kg) elite triathletes volun- teered to participate in the study and completed the GE and DE tests. During a 5-year period, all ten triathletes had been in the top 8 in the Swedish championships; seven of the par- ticipants had at least one podium finish, and two participants had previously won the Swedish championships in triathlon. Testing procedures The participants were instructed to only perform light train- ing on the 2 days preceding their scheduled test days, to be well hydrated, to refrain from alcohol (24 h) and caffeine (12 h) and to avoid eating within 2 h prior to testing. On the day of the tests, the participants completed a health- status questionnaire, and thereafter, the participant’s stat- ure (Harpenden Stadiometer, Holtain Limited, Crymych, Great Britain) and body mass (Midrics 2, Sartorius AG, 963 European Journal of Applied Physiology (2020) 120:961–968 1 3 Goettingen, Germany) were measured. Additionally, the mass of the equipment the participant used in the cycling test (i.e. bicycle, cycling shoes, helmet and harness) and running test (i.e. running shoes and harness) were weighed. The cycling and running tests were performed on a motor- driven treadmill (Saturn 450/300rs, h/p/cosmos sports & medical GmbH, Nussdorf-Traunstein, Germany). Through- out the tests, expired air was continuously analysed using a metabolic cart in mixing-chamber mode (Jaeger Oxycon Pro, Erich Jaeger Gmbh, Hoechberg, Germany). The meta- bolic cart was calibrated according to the specifications of the manufacturer before each test, and at the start of each new 5-min stage, a ‘zeroing’ of the O2 and CO2 sensors was performed. After the warm-ups and after each stage was completed, capillary-blood samples were collected from a fingertip and thereafter analysed to determine blood-lactate concentrations (Biosen 5140, EKF-diagnostic GmbH, Bar- leben, Germany). Cycling test Prior to the cycling test, the participants performed a standardized warm-up. The 7.5-min warm-up started with 5 min at a treadmill inclination of 1° and treadmill speed of 5.56 m s−1 (20 km h−1) for the men and 5.00 m s−1 (18 km h−1) for the women, which was followed by 2.5 min at the initial work intensity of the cycling test (i.e. incli- nation, 2.5°; speed, 4.56 m s−1 (16.4 km h−1) (men) or 3.22 m s−1 (11.6 km h−1) (women)). After the warm-up was completed, a capillary-blood sample was collected, and thereafter the rolling-resistance coefficient of the partici- pant’s bicycle was determined using a previously described method (Carlsson et al. 2016). In brief, the treadmill speed was set at 5.56 m s−1 (20 km h−1), with the rider facing downhill, and the treadmill’s negative inclination was then adjusted until the participant sitting on the bicycle (without pedalling) did not move in either the backward or forward direction on the treadmill. Based on the equilibrium incli- nation, the bicycle’s rolling-resistance coefficient (μ) was calculated from the formula μ = mtot · g · sin α/mtot · g · cos α, where mtot is the mass of the participant, including the mass of the equipment (kg), g is the acceleration due to gravity (9.82 m s−2 at the location of the sport-science laboratory) and α is the treadmill inclination (°). Throughout the cycling test, the treadmill inclination was 2.5°, and the participants were permitted to use a self-chosen cadence. For each of the subsequent stages, the speed was increased by 0.67 m s−1 (2.4 km h−1). Each stage lasted for 5 min, and the mean oxygen uptake ( ̇VO2mean ) and mean respiratory exchange ratio (RERmean) during the last 2 min of the stages were used for calculation of gross efficiency (GEcycle) and delta efficiency (DEcycle) during cycling. The stages were separated by a 1-min pause to collect a capillary-blood sample, and the participants rated their per- ceived exertion (RPE) on a scale of 6–20 (Borg 1970). The pre-determined criteria for permitting the participants to commence another stage were as follows: the participant’s RPE had to be lower than 17 (“Very hard”), and the previous stage’s RER had to be lower than 1.0. Running test To minimize the influence of the cycling test on the sub- sequent running test, the running test was initiated 60 min after the completion of the cycling test. It has previously been reported that approximately 30 min of passive recovery is sufficient to reduce the blood-lactate concentration from 3.9 ± 0.3 mmol l−1 to baseline values (1.0 ± 0.1 mmol l−1) in moderately trained adults (Menzies et al. 2010); hence, the time for the elite triathletes to recover after the submaximal cycling test was considered to be sufficient. The partici- pants were permitted to use a self-chosen stride frequency throughout the test. Prior to the start of the running test, the participants performed a 5-min warm-up at a treadmill incli- nation of 2.5°, and the treadmill speeds were 10.0 km h−1 and 8.2 km h−1 for the men and women, respectively. After the warm-up was completed, a capillary-blood sample was collected. Thereafter, the running test was initiated with the same intensities as in the warm-up; it consisted of 5-min stages with a 1-min pause between stages to collect a cap- illary-blood sample, and to have the participants rate their perceived exertion. Throughout the test, the treadmill incli- nation was fixed at 2.5°, and the treadmill speed increment was 0.6 km h−1 between stages. The criteria for permitting the participants to commence another stage were the same as those for the cycling test. The ̇VO2mean and RERmean during the last 2 min of the stages were used for calculation of the gross efficiency (GErun) and delta efficiency (DErun) during running. Calculation of delta efficiency For each completed stage in both tests, i.e. when RER was < 1.0 and blood-lactate concentration was < 4.0 mmol l−1, the mechanical work rate (MWR) and metabolic rate (MR) were calculated. The MWR (W) dur- ing the cycling test was the sum of the work against gravity and the work related to overcoming the rolling resistance of the bicycle: MWR = (mtot · g · sin α · v + mtot · g · cos α · μ · v), where v is the treadmill speed (m s−1). In the running test, only the component related to work performed against gravity was included in the MWR calculation. The MR (W) was based on the participant’s ̇VO2mean (l s−1) and RERmean: MR = k1 · ̇VO2mean · k2, where k1 is 3.815 + 1.232 · RERmean (Lusk 1928) and k2 is 4186 and converts kcal to J. Linear regression was used to determine the relationship between 964 European Journal of Applied Physiology (2020) 120:961–968 1 3 MWR and MR for each participant. Based on the relation- ship, DEcycle and DErun were calculated by dividing the delta increase in MWR by the delta increase in MR for each test. Calculation of gross efficiency For both tests, the GE was calculated as the ratio between the MWR and MR during the last 2 min of each stage. To make an adequate comparison between GEcycle and GErun, the MWR when the blood-lactate concentration had increased 1 mmol l−1 above the lowest measured value (LT) was used. To establish the work rate at the LT, a third-order polyno- mial equation was fitted to each of the participant’s obtained MWR/blood-lactate concentration combinations, i.e. it was calculated even for those stages that resulted in lactate val- ues exceeding 4 mmol l−1. The polynomial equation was then used to calculate the MWR at the LT. Thereafter, linear regression was used to determine the relationship between MWR and GE for each participant. Based on the linear equa- tion and the participant’s MWR at the LT, the GE at the LT was calculated. Statistical analyses The test results are presented as the mean and standard deviation (SD). The agreement of test variables with a nor- mal distribution was assessed with the Shapiro–Wilk test. Pearson’s product–moment correlation coefficient (r) test was used to investigate the relationship between GEcycle and GErun as well as between DEcycle and DErun. The guide- lines for the interpretation of the strength of the correlation are as follows: small correlation for 0.1 ≤\|r\|< 0.3, moder- ate correlation for 0.3 ≤\|r\|< 0.5, and large correlation for \|r\|≥ 0.5 (Cohen 1988). Paired-samples t tests were used to investigate differences between GEcycle and GErun as well as between DEcycle and DErun. The Cohen’s effect-size cri- teria were used to interpret the magnitude of the effect size (η2) and to enable making more informative inferences from the results. The substantial effects were divided into more fine-graded magnitudes as follows: small effect for 0.01 ≤ η2 < 0.06, moderate effect for 0.06 ≤ η2 < 0.14, and large effect for η2 ≥ 0.14 (Cohen 1988). All statistical analy- ses were assumed to be significant at an alpha level of 0.05. The statistical analyses were conducted using the IBM SPSS Statistics software, Version 25 (IBM Corporation, Armonk, NY, USA). Results The test results of the cycling and running test are pre- sented in Tables 1 and 2, respectively. The mass of the equipment was 9.6 ± 0.5 kg during the cycling test and 0.9 ± 0.2 kg during the running test. The bicycles’ μ was determined to 0.0042 ± 0.0006 N N−1. The intercepts for the relationship between MWR and oxygen uptake were 0.44 ± 0.12 l min−1 and 0.28 ± 0.20 l min−1 for the cycling and running test, respectively. The blood-lactate concentra- tions at LT were 1.9 ± 0.2 mmol l−1 for the cycling test and 2.2 ± 0.3 mmol l−1 for the running test. The maximum blood- lactate concentration after each test was 4.3 ± 1.1 mmol l−1 and 4.0 ± 1.8 mmol l−1 for the cycling and running test, respectively. The test results in the DE tests were DEcycle = 23.5 ± 1.6% and DErun = 8.3 ± 0.5%. All test variables were normally distributed (all P > 0.05). There was a correlation between GEcycle and GErun (r = 0.66; P = 0.038; R2 = 0.44) (Fig. 1), and the participants’ sex was not a contributing factor Table 1 Test results from the cycling test All values are presented as mean ± standard deviation ̇VO2mean mean oxygen uptake (l min−1), RER respiratory exchange ratio (l l−1), MR metabolic rate (W), MWR mechanical work rate (W), GE gross efficiency (%), LT the MWR at which the blood-lactate con- centration increased 1 mmol l−1 above the lowest measured value. All ten participants completed stage 1–5. Stages 6 and 7 were completed by eight and zero participants, respectively, N/A not applicable Stage ̇VO2mean RER MR MWR GE 1 2.16 ± 0.50 0.80 ± 0.03 726 ± 170 144 ± 37 19.7 ± 0.8 2 2.42 ± 0.51 0.84 ± 0.03 820 ± 175 168 ± 39 20.4 ± 0.7 3 2.72 ± 0.55 0.85 ± 0.03 921 ± 187 193 ± 41 20.9 ± 0.7 4 3.03 ± 0.60 0.86 ± 0.03 1032 ± 206 217 ± 44 21.0 ± 0.6 5 3.32 ± 0.63 0.90 ± 0.02 1140 ± 216 242 ± 46 21.2 ± 0.7 6 3.61 ± 0.67 0.93 ± 0.02 1250 ± 232 269 ± 47 21.5 ± 0.7 7 N/A N/A N/A N/A N/A LT 3.14 ± 0.62 0.87 ± 0.04 1069 ± 209 226 ± 45 21.1 ± 0.7 Table 2 Test results from the running test All values are presented as mean ± standard deviation ̇VO2mean mean oxygen uptake (l min−1), RER respiratory exchange ratio (l l−1), MR metabolic rate (W), MWR mechanical work rate (W), GE gross efficiency (%), LT the MWR at which the blood-lactate con- centration increased 1 mmol l−1 above the lowest measured value. All ten participants completed stage 1–5. Stage 6 and 7 was completed by 9 and 5 participants, respectively. Stage ̇VO2mean RER MR MWR GE 1 2.73 ± 0.47 0.83 ± 0.03 923 ± 157 76 ± 15 8.2 ± 0.5 2 2.89 ± 0.49 0.84 ± 0.03 977 ± 164 81 ± 15 8.2 ± 0.6 3 3.03 ± 0.51 0.85 ± 0.03 1027 ± 172 86 ± 16 8.3 ± 0.6 4 3.23 ± 0.54 0.86 ± 0.04 1097 ± 180 90 ± 17 8.2 ± 0.6 5 3.38 ± 0.53 0.87 ± 0.04 1152 ± 177 95 ± 17 8.3 ± 0.5 6 3.57 ± 0.63 0.89 ± 0.05 1222 ± 210 101 ± 19 8.2 ± 0.5 7 3.79 ± 0.52 0.89 ± 0.05 1298 ± 171 112 ± 15 8.6 ± 0.2 LT 3.41 ± 0.69 0.88 ± 0.04 1165 ± 227 96 ± 22 8.2 ± 0.5 965 European Journal of Applied Physiology (2020) 120:961–968 1 3 (P = 0.79). There was a significant relationship between oxygen uptake values during cycling and running at LT (r = 0.95; P < 0.001; R2 = 0.90). No significant relationship was found between DEcycle and DErun (r = − 0.045; P = 0.90; R2 = 0.0020) (Fig. 2), and the participants’ sex was not a contributing factor (P = 0.38). There were differences between GEcycle and GErun (t = 80.8; P < 0.001; η2 = 0.99) as well as between DEcycle and DErun (t = 27.8; P < 0.001; η2 = 0.98) (Fig. 3). Moreo- ver, there was a difference between GEcycle and DEcycle (t = − 5.85; P < 0.001; η2 = 0.79); however, no difference was found between GErun and DErun (t = − 0.40; P = 0.70; η2 = 0.018). Discussion The results of this study demonstrate that there is a large correlation between elite triathletes’ GE during running and cycling on a moderate uphill incline. However, no corre- lation was found between DE during running and cycling. The results reveal that GE and DE differ between cycling and running, with large effect sizes, where cycling is more energy efficient than running on a moderate uphill incline. The finding that GErun and GEcycle are strongly correlated (Fig. 1) is consistent with results of previous studies that reported a significant positive correlation between cyclists’ running economy and cycling economy when the economy of movement was measured during level running and ergom- eter cycling, respectively (Lundby et al. 2017; Swinnen et al. 2018). Moreover, in the current study, there was a signifi- cant correlation between gross metabolic rates (i.e. oxygen uptake) during cycling and running. This result contradicts the result from recent study that found no significant rela- tionship between the gross metabolic rates during running and cycling for nine sub-elite triathletes (Swinnen et al. 10 9 8 7 26 25 24 23 22 21 20 19 GEcycle (%) GErun (%) Fig. 1 Significant relationship between gross efficiency during run- ning (GErun) and gross efficiency during cycling (GEcycle) (P < 0.05) 10 9 8 7 26 25 24 23 22 21 20 19 DEcycle (%) DErun (%) Fig. 2 No significant relationship between delta efficiency dur- ing running (DErun) and delta efficiency during cycling (DEcycle) (P > 0.05) Fig. 3 Significant differences between gross efficiency during run- ning (GErun) and cycling (GEcycle) is reported as †P < 0.001, and between delta efficiency during running (DErun) and cycling (DEcycle) is reported as ‡P < 0.001. Squares and circles represent mean values, and error bars represent ± 1 standard deviation 966 European Journal of Applied Physiology (2020) 120:961–968 1 3 2018); however, the relationship was close to significance (P = 0.053). The large correlation between exercise-mode efficiencies found in the current study indicates that an elite triathlete with a high GErun also has a high GEcycle. This result con- tradicts previous findings that the efficiency in one exercise mode does not predict the efficiency in other exercise modes (Daniels et al. 1984). However, it should be noted that in the study by Daniels et al. (1984), trained runners were tested in exercise modes outside their specific sport (i.e. bench step- ping, arm cranking, graded walking and cycling) in addi- tion to running. In the case of the participants in the current study, one can assume that to become a national level elite triathlete it is important to be efficient at all three disciplines in triathlon, which partly could explain the interrelationship of GErun and GEcycle. The exercise efficiency is determined by the cardiores- piratory, metabolic, neuromuscular and biomechanical effi- ciencies (Barnes and Kilding 2015). The cardiorespiratory and metabolic efficiencies reflect the delivery of oxygen to the force-producing muscles and the adenosine triphos- phate re-synthesis therein (Barnes and Kilding 2015; Saun- ders et al. 2004). The neuromuscular and biomechanical efficiencies reflect the interactions between the neural and musculoskeletal systems as well as the efficiency of convert- ing produced power to forward propulsion (Anderson 1996; Barnes and Kilding 2015). The energy expenditure during cycling and running is related to the increase in potential energy during the pedal cycle/stride cycle (i.e. the raising of centre of mass vertically during the pedal cycle/stride cycle), the translational kinetic energy (i.e. the braking and propelling of the body mass in the forward direction paral- lel to the surface) and the rotational kinetic energy (i.e. the swinging of the legs and arms) as well as the maintenance of balance and energy cost of supporting body weight (Bergh 1987; Hoogkamer et al. 2014). Hence, a triathlete’s GE is determined by these four underlying efficiencies (i.e. cardi- orespiratory, metabolic, neuromuscular and biomechanical efficiency) and at least one of these underlying efficiencies is significantly higher for a ‘more efficient’ triathlete compared to their ‘less efficient’ counterpart. In the current study, it was found that GEcycle was sig- nificantly lower than DEcycle (Fig. 3), and the difference was associated with a large effect size. This difference is to a large extent explained by the influence of baseline energy expenditure in the GE calculations, which previously has suggested being an artefact (Gaesser and Brooks 1975). The relative contribution of the baseline energy expendi- ture in the GE calculations decreases gradually with increasing work intensity; hence, it should be expected that the GE during cycling is related to work intensity. This is in line with a previous study that reported a positive relationship between cyclists’ GE and crank inertial load (Bertucci et al. 2012). In the running test, there was no dif- ference between GErun and DErun, which means that triath- letes’ GE during moderate uphill running does not change significantly with increasing work rate. Calculations of GE during uphill running, based on reported values for run- ning speed and treadmill inclination as well as the mean values of body mass and oxygen uptake (Hoogkamer et al. 2014), showed that GE was independent of running speed (2–3 m s−1); GE was calculated to be approximately 7% at a 2° incline and 10% at a 3° incline, which are in good agreement with the results in the current study. When comparing GErun with GEcycle at the same exter- nal work rate and treadmill incline, cycling was shown to be more energy efficient than running (21.1% versus 8.2%, respectively) (Fig. 3), despite the limitation that the equation for calculating MWR during cycling does not account for the work done to overcome the friction of the drivetrain of the bicycle. Hence, the GEcycle is therefore somewhat underestimated. At an equivalent metabolic energy expenditure rate, the mechanical power output during cycling was approximately 2.5 times higher than during running. Hence, based on the previously presented equation that endurance performance is equal to the sum of aerobic and anaerobic energy contributions multiplied by GE (Joyner and Coyle 2008), it can be concluded that the cycling speed is much higher than the running speed for a moderate uphill incline at the same work intensity as a consequence of the higher GE during cycling. The GE difference between cycling and running is to a large extent explained by differences in the factors related to force gen- eration to support body weight, an increase in potential energy during the pedal cycle/stride cycle and to trans- lational kinetic energy. Running entails a considerably higher vertical raising of the centre of mass (~ 8–10 cm) (Cavagna et al. 2005; Tartaruga et al. 2012) compared to cycling, where the raising of the centre of mass is mini- mal during pedalling (Connolly 2016). The importance of having a relatively low vertical centre-of-mass displace- ment during running is indicated by a lower energy cost and thus better running economy (Folland et al. 2017). Moreover, the stride cycle during running implies a decel- eration at the foot plant, followed by an acceleration of body mass at push-off (Hamner et al. 2010). Based on fun- damental physics, these deceleration/acceleration phases are associated with a significant energy cost; however, the relative energy-cost contribution related to translational kinetic energy decreases during uphill running, because on inclines the braking forces at the foot plant decrease (Gottschall and Kram 2005). In cycling, the fluctuation in speed during the pedal cycle is lower than in running due to the continuous supply of power (Fintelman et al. 2016; van Ingen Schenau et al. 1990). The described energy- expenditure differences between the exercise modes result 967 European Journal of Applied Physiology (2020) 120:961–968 1 3 in a reduced biomechanical efficiency during running com- pared to cycling, which is the major factor explaining the lower GE during running. The corresponding reasoning could be applied to under- stand the difference, and large effect size, between DErun and DEcycle (Fig. 3), because DE reflects how much a tri- athlete needs to increase his/her MR for an increment in MWR. Hence, the enhanced biomechanical efficiency dur- ing cycling compared to running is reflected in a higher DEcycle than DErun. This finding contradicts results from previous studies that reported higher DE during running (~ 44%) compared to cycling (~ 25%), where DE was derived from tests using a constant running speed with an incremental increase in treadmill inclination and station- ary ergometer cycling (Bijker et al. 2001, 2002). Previ- ously it was reported that the metabolic cost of running parallel to the running surface decreases with incline, whereas the efficiency of producing mechanical power to lift the centre of mass vertically is constant and inde- pendent of incline and running speed (Hoogkamer et al. 2014). Hence, the methodological differences (i.e. constant speed and incremental increase in incline vs. incremental increase in speed and constant incline) could to a large extent explain the contradicting DE values during running. The correlation analysis showed that participants’ sex was not a contributing factor to the relationship between GEcycle and GErun. This result is in line with previously reported results where GE during cycling did not differ between male and female competitive cyclists when the sexes were com- pared at the same relative intensity (Hopker et al. 2010). Moreover, in a recently published review investigating fac- tors affecting the energy cost of running, it was concluded that men and women with the same body mass have similar running economies (Lacour and Bourdin 2015). However, because of the low number of participants in the current study further research is warranted to investigate potential sex dif- ferences in GE for elite triathletes during cycling and running. Conclusions The results show that elite triathletes with high GE dur- ing running also have a high GE during cycling, when exercising at a treadmill inclination of 2.5°. However, a triathlete’s relative efficiency in attaining an increased power output in terms of DE is not transferable between the two exercise modes. In a moderate uphill incline, elite triathletes are more energy efficient during cycling than running, independent of work rate. Acknowledgements Open access funding provided by Dalarna University. Author contributions All authors contributed to the study concep- tion and design, data collection, and analysis. The first draft of the manuscript was written by MC and TC and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Compliance with ethical standards Conflict of interest The authors declare that they have no conflicts of interest. Ethical approval The study was approved by the Research Ethics Com- mittee at Dalarna University, Falun, Sweden, and the test procedures were performed in accordance with the World Medical Association’s Declaration of Helsinki—Ethical Principles for Medical Research Involving Human Subjects 2008. Informed consent Written informed consent was obtained from all individual participants included in the study. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. References Anderson T (1996) Biomechanics and running economy. 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PMC10358465	Assessing the agreement between a global navigation satellite system and an optical- tracking system for measuring total, high-speed running, and sprint distances in ofﬁcial soccer matches Piotr Makar1, Ana Filipa Silva2,3,4, Rafael Oliveira4,5,6 , Marcin Janusiak7, Przemysław Parus8, Małgorzata Smoter9 and Filipe Manuel Clemente2,10 1Gdańsk University of Physical Education and Sport, Gdańsk, Poland 2Escola Superior Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal 3Research Center in Sports Performance, Recreation, Innovation and Technology (SPRINT), Melgaço, Portugal 4The Research Centre in Sports Sciences, Health Sciences and Human Development (CIDESD), Vila Real, Portugal 5Sports Science School of Rio Maior–Polytechnic Institute of Santarém, Rio Maior, Portugal 6Life Quality Research Centre, Rio Maior, Portugal 7Śląsk Wrocław Basketball, Physiology Department, Wrocław, Poland 8FC WKS Śląsk Wrocław, Physical Performance Department, Wrocław, Poland 9Department of Basics of Physiotherapy, Gdansk University of Physical Education and Sport, Gdańsk, Poland 10Instituto de Telecomunicações, Delegação da Covilhã, Lisboa, Portugal Abstract This study aimed to compare the agreement of total distance (TD), high-speed running (HSR) dis- tance, and sprint distance during 16 ofﬁcial soccer matches between a global navigation satellite Corresponding author: Filipe Manuel Clemente, Escola Superior Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Álvares, 4900-347 Viana do Castelo, Portugal. Email: ﬁlipe.clemente5@gmail.com Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as speciﬁed on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). Sports Sciences, Exercise, and Health – Original Manuscript SCIENCE PROGRESS Science Progress 2023, Vol. 106(3) 1–14 © The Author(s) 2023 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/00368504231187501 journals.sagepub.com/home/sci system (GNSS) and an optical-tracking system. A total of 24 male soccer players, who are actively participating in the Polish Ekstraklasa professional league, were included in the analysis conducted during ofﬁcial competitions. The players were systematically monitored using Catapult GNSS (10- Hz, S7) and Tracab optical-tracking system (25-Hz, ChyronHego). TD, HSR distance, sprint dis- tance, HSR count (HSRC), and sprint count (SC) were collected. The data were extracted in 5- min epochs. A statistical approach was employed to visually examine the relationship between the systems based on the same measure. Additionally, R2 was utilized as a metric to quantify the proportion of variance accounted for by a variable. To assess agreement, Bland–Altman plots were visually inspected. The data from both systems were compared using the estimates derived from the intraclass correlation (ICC) test and Pearson product–moment correlation. Finally, a paired t-test was employed to compare the measurements obtained from both systems. The interaction between Catapult and Tracab systems revealed an R2 of 0.717 for TD, 0.512 for HSR distance, 0.647 for sprint distance, 0.349 for HSRC, and 0.261 for SC. The ICC values for absolute agreement between the systems were excellent for TD (ICC = 0.974) and good for HSR distance (ICC = 0.766), sprint distance (ICC = 0.822). The ICC values were not good for HSRCs (ICC = 0.659) and SCs (ICC = 0.640). t-test revealed signiﬁcant differences between Catapult and Tracab for TD (p < 0.001; d = −0.084), HSR distance (p < 0.001; d = −0.481), sprint distance (p < 0.001; d = −0.513), HSRC (p < 0.001; d = −0.558), and SC (p < 0.001; d = −0.334). Although both systems present acceptable agreement in TD, they may not be perfectly inter- changeable, which sports scientists and coaches must consider when using them. Keywords : Football, athletic performance, player tracking systems, training load monitoring, locomotor demands Introduction Monitoring locomotor demands through technological devices has become a widespread and recurring practice in soccer training. A survey conducted among 94 coaches and 88 practitioners belonging to elite English soccer found that tracking systems (e.g., global navigation satellite system [GNSS]) were used more than other different training load monitoring methods (e.g., blood lactates, ratings of perceived exertion, heart rates).1 In another survey conducted among 82 high-level soccer teams competing in the top leagues of countries such as the United Kingdom, Spain, France, Germany, and Italy, the ﬁndings revealed that approximately 40% of the teams utilized time motion analysis and accelerometers as the primary tools for quantifying training load.2 It is noteworthy to acknowledge that the term “training load” has been a topic of discussion regarding its accuracy. This is primarily due to the conventional association of the term “load” with a mechanical variable measured in newtons within the International System of Units.3 However, it is crucial to emphasize that in the speciﬁc context of training load, it serves as a scientiﬁc construct rather than a direct “measurement” per se.4 Therefore, its usage does not contravene any scientiﬁc principles.4 To enhance the efﬁcacy of collecting sports science training data, it is crucial to acknowledge the inherent value of coaches and performance staff. This recognition plays a pivotal role in facilitating the widespread adoption and seamless integration of meticulously engineered tracking systems that are purposefully designed to augment 2 Science Progress 106(3) training load practices. Supporting this notion, a comprehensive survey was conducted among 176 soccer coaches and performance coaches, revealing that sport science data holds substantial importance in guiding their practice, being perceived as both somewhat important and very important.5 In light of the proliferation of tracking systems, it is evident that a multitude of options are now available on the market. However, it becomes increasingly challenging to conduct fair comparisons and establish the interchangeability of data among these diverse alternatives.6 Among the various technological options, GNSS, ultra-wideband technology, and optical video tracking systems are prominent examples. The usability of these options is contingent upon speciﬁc contexts and objectives. GNSS, while cost- effective, is primarily applicable in outdoor facilities. Conversely, ultra-wideband tech- nology, despite its higher cost, offers versatility by functioning effectively in both outdoor and indoor settings.7 In the context of outdoor facilities, GNSS remains widely utilized, potentially owing to its user-friendly nature compared to installation-dependent optical video tracking systems, as well as its relatively lower cost in comparison to ultra-wideband technology. It is important to note, however, that optical video tracking systems can present an alter- native and intriguing solution for sports scientists and players in outdoor facilities. These systems ensure high-quality data collection and provide the opportunity to combine time- motion analysis with notational analysis, all without necessitating any devices to be placed on the players. Having in mind the multiplicity of the options and technical aspects of the devices, it is particularly essential to focus on the agreement between such systems,7 considering that data collected can vary from system to system with a remarkable impact on data interpretation. Taking that into consideration, different studies have focused on testing absolute agree- ment between different tracking systems.6,8,9 For example, a 10-Hz multi-GNSS GNSS device (vector, Catapult) and two optical tracking systems (25-Hz Tracab and Second Spectrum) were compared. The results indicated that in comparison to GNSS, Tracab revealed signiﬁcantly higher values for most locomotor measures followed by the other optical system (Second Spectrum).6 Another study comparing two 10-Hz GNSS systems (Viper, StatSports; and Apex, StatSports) with Tracab optical tracking system demonstrated signiﬁcant differences between GNSS and the optical tracking systems for locomotor mea- sures such as total distance (TD), high-speed running (HSR) distance, and sprint distance.8 However, the different systems presented very large correlations.8 Another comparison of Tracab and a 10-Hz GNSS (Wimu) revealed that the optical tracking system slightly overes- timated most locomotor measures compared to GNSS.9 These aforementioned studies6,8,9 suggest notable differences in locomotor measures when comparing different technologies, speciﬁcally GNSS and optical video tracking systems. However, they also demonstrate a strong correlation between the two, indicating the potential for interchangeability. Despite these ﬁndings, the current research has not focused on analyzing peak demands within 5-min epoch periods. Peak demands, or worst-case scenarios, have recently garnered attention,10 raising concerns about the accuracy, precision, and interchangeability of different systems. Although Ellens’ study6 investigated the interchangeability between a 10-Hz GNSS Vector and Tracab (an optical tracking system with 25-Hz), the analysis did not Makar et al. 3 speciﬁcally examine the interchangeability of epoch periods. Therefore, further research is needed to determine whether interchangeability can also be achieved within 5-min epochs. Such research would provide another independent assessment of absolute agree- ment between different systems and models, particularly comparing new GNSS systems like the Catapult S7 with Tracab (an optical tracking system with 25-Hz). Testing for interchangeability holds signiﬁcant importance for several reasons. Firstly, it allows for better control over the comparisons made between scientiﬁc articles and bench- marks conducted on players. By establishing the interchangeability of devices and technolo- gies, it becomes possible to ensure the validity and reliability of such comparisons. Secondly, considering that clubs often change their devices and technologies, having access to interchangeability values becomes crucial. It provides clubs with the necessary information to determine whether fair and accurate comparisons can be made or if caution should be exercised due to potential discrepancies between different measurement systems. This knowledge empowers clubs to make informed decisions regarding the compatibility and comparability of data collected from different sources. Therefore, the objective of this study is to assess the absolute agreement between the 10-Hz Catapult S7 GNSS and the 25-Hz Tracab optical video tracking system in terms of TD, HSR distance, sprint distance, as well as the number of HSR and sprints. These measures were spe- ciﬁcally chosen due to their signiﬁcance in quantifying training load within the given context. TD serves as a comprehensive measure of locomotor demand, which is closely asso- ciated with internal load responses.11 It provides valuable insights into the magnitude of demands imposed on players. HSR and sprint distances were selected as they represent the most demanding locomotor demands observed during matches. Moreover, these mea- sures are known to have traditionally lower values of precision,12 necessitating further examination to ensure optimal accuracy and precision of the collected data. Methods Study design This study employed a longitudinal design, focusing on a group of twenty-one soccer players from a single professional team. Over a period of 16 ofﬁcial soccer matches, which took place outdoors in stadium facilities, the players were consecutively observed. The observation period spanned from July 15, 2022, to November 13, 2022, correspond- ing to the competitive phase of the season. For the analysis, only data from ofﬁcial matches in the domestic competition, including league matches and cup matches, were considered. The players were monitored using two tracking systems: (a) a GNSS and (b) an optical-tracking system. The study aimed to test the agreement between both systems for monitoring locomotor demands of the players during the match. Participants We used nonprobabilistic convenience sampling. A group of 21 male professional foot- ball players (231 observations) from the ﬁrst team of one of the Polish Ekstraklasa clubs (age: 25 ± 3 years, body height: 179.6 ± 5.5 cm, body mass: 76.1 ± 5.0 kg) participated in 4 Science Progress 106(3) the research. The data were collected over 16 matches played in the autumn round of the 2022/2023 season and were recorded simultaneously during each of the observed games. Goalkeepers were not included in this study due to the unique nature of their position. Considering the speciﬁc movements and actions performed by goalkeepers during matches, the use of GNSS instruments may potentially cause damage or interfere with their typical movements. Hence, to ensure the integrity of the study and avoid any poten- tial harm to the goalkeepers, their data was not collected or analyzed as part of this research. All data were created as a condition of employment where players were rou- tinely monitored throughout league play. In order to uphold ethical standards, all players involved in the study were provided with detailed information about the study design, the associated risks, and the potential beneﬁts of participation. Only after obtaining their informed consent was the study con- ducted. The informed consent process ensured that the players were fully aware of the study’s objectives, procedures, and potential implications before agreeing to participate. This study adhered to the ethical guidelines outlined in the Declaration of Helsinki for research involving human participants. Conﬁdentiality was strictly maintained through- out the study by anonymizing all data prior to analysis. Methodological procedures Two tracking systems were used simultaneously: (a) a GNSS unit (Vector S7, Catapult Innovations, Melbourne, Australia; 81mm×43mm×16mm), operating at a frequency of 10-Hz and (b) an optical tracking system (TRACAB, ChyronHego, New York, USA) using two multicamera units (each containing three HD-SDI cameras with a resolution of 1920 × 1080 pixels) with a sampling frequency of 25-Hz. On average, the number of satellites connected during data collection was 15, and the average horizontal dilution of precision (HDOP) was 0.7. Vector S7 was preliminarily tested for its ability to assess a force-velocity proﬁle.13 Furthermore, the Tracab system underwent a validation process to assess its accuracy and precision in measuring locomotor demands across various running speed thresholds.14 The players always used the same GNSS unit to reduce inter-unit variability.15 The GNSS units were placed between the players’ shoulder blades and were activated accord- ing to a manufacturer’s guidelines before kickoff. To avoid potential unit differences, the players wore the same GNSS unit for each match.8 The data recorded by the units were downloaded after each match for further analysis using Catapult OpenField Cloud Analytics (OpenField 3.9.0 Catapult Sports, Melbourne, Australia). The following vari- ables were selected for analysis during this study: ﬁeld time, deﬁned as the time spent on the ﬁeld (FT; min), TD (m), distance in HSR, deﬁned as a running speed between 19.81 and 25.2 km/h (HSR; m), sprint, deﬁned as velocity greater than 25.2 km/h (SPR; m), High-speed running count (HSRC) and sprint count (SC). The HSR speed threshold of 19.81 km/h was determined based on the research conducted by Abt and Lovell,16 who identiﬁed this value as the reference for the second ventilatory threshold. This spe- ciﬁc threshold has gained broad acceptance and is widely used as a prevalent measure to deﬁne arbitrary speed thresholds in soccer players. The selection of the 25.2 km/h speed threshold for sprints aligns with established conventions based on previous research Makar et al. 5 conducted on sprinting in soccer players.17 The velocity thresholds chosen are those deﬁned by both tracking system providers. All data from the Tracab system were pro- vided by ChyronHego as a match report. Data from both the Catapult and Tracab systems were extracted in 5-min epochs, which involved dividing the ofﬁcial match time into consecutive 5-min time periods. This approach ensured that all 5-min epochs within the match time were considered and included in the analysis. Statistical procedures Descriptive statistics are presented in the form of mean and standard deviation. Plotting data was performed to visually inspect the relationship between the systems for the same measure. At the same time, R2 was used as a measure to represent the proportion of the variance for a variable. Measuring agreement was visually inspected by Bland–Altman plots with a 95% conﬁdence interval using the mean difference between measures. The estimates of the intraclass correlation (ICC) test and their 95% conﬁdence intervals were calculated by means of SPSS statistical package (28.0.0.0, IBM, Chicago, IL) based on a mean-rating (k = 2), absolute-agreement, a two-way mixed effects model. The clas- siﬁcation of the agreement18 was considered good between ICC = 0.75 and ICC = 0.90, while the values above ICC = 0.90 were considered excellent. The Pearson–product cor- relation test was executed on SPSS (version 28.0.0., IBM, Chicago, USA) for a p-value less than 0.05 to analyze the strength of the relationship between the systems. The cor- relation coefﬁcients19 between r = 0.50 and r = 0.7 were considered large, between r = 0.7 and r = 0.9 very large, and above r = 0.9 nearly perfect. The paired t-test was used to compare the measures obtained for both systems, followed by the standardized effect size (Cohen’s d) that was interpreted as20: 0.0–0.2, trivial; 0.2–0.5, medium; 0.5–0.8, large; and >0.8, very large. The statistical procedures were executed in SPSS (version 28.0.0., IBM, Chicago, USA) for a p < 0.05. Results Figure 1 displays a scatter plot comparing the Catapult and Tracab systems for the various running-based measures extracted during the matches. It is important to note that all data presented in the results correspond to the values obtained for the 5-min epochs. The interaction between Catapult and Tracab systems revealed an R2 of 0.717 for TD, 0.512 for HSR distance, 0.647 for sprint distance, 0.349 for HSRC, and 0.261 for SC. Table 1 presents the ICC and the Pearson–product correlation tests comparing both systems for the different running-based measures. The ICC values for absolute agreement between the systems were excellent for TD (ICC = 0.974) and good for HSR distance (ICC = 0.766), and sprint distance (ICC = 0.822). The ICC values were not good for HSRCs (ICC = 0.659) and SCs (ICC = 0.640). The measurement agreement was performed by visual inspection of the data. Figure 2 presents the Bland–Altman plots for the different running-based measurements. The mean difference for TD (for 5-min epochs) was −11.5 [95% CI: −165; 142], while for HSR distance was −11 [95% CI: −44; 22] and for sprinting −8.0 [95% CI: −26; 11]. 6 Science Progress 106(3) Figure 1. Scatter plot between Catapult and Tracab for TD, HSR distance, sprint distance, HSRC, and SC. The data displayed in the ﬁgure represents the aggregation of values over 5-min epochs. Makar et al. 7 Regarding HSR and SCs, the mean difference was −1 [95% CI: −3; 2] and 0 [95% CI: −1; 1]. Table 2 presents descriptive statistics of the running-based measures collected in both the Catapult and Tracab systems. t-test revealed signiﬁcant differences between Catapult and Tracab for TD (p < 0.001; d = −0.084), HSR distance (p < 0.001; d = −0.481), sprint distance (p < 0.001; d = −0.513), HSRC (p < 0.001; d = −0.558), and SC (p < 0.001; d = −0.334). Discussion The objective of this study was to evaluate the absolute agreement of TD, HSR distance, and sprint distance between the 10-Hz Catapult S7 GNSS and the 25-Hz Tracab optical video tracking system. This evaluation was conducted using data collected in 5-min epochs. The main ﬁndings showed excellent absolute agreement between the systems for TD and good absolute agreement for HSR distance, sprint distance, HSRC, and SCs. Nonetheless, it was noted that Catapult underestimated the values in comparison to Tracab, particularly for HSR and sprint distances/counts. This corresponds with the ﬁndings of previous studies that revealed higher values for tracking systems when com- pared with GNSS6 Regarding TD, the present study showed higher values for Tracab in comparison to Vector S7, which was also previously conﬁrmed.6 Moreover, the ﬁndings concerning speed values, higher for the tracking system when compared to GNSS, are similar to the ﬁndings of previous research.7 The observed discrepancies may arise from disparities in data ﬁltering methodologies employed by device software. Notably, the implementa- tion of ﬁlters such as moving averages has been shown to yield more reﬁned speed data. However, it is crucial to elucidate that these ﬁltering techniques do not affect the funda- mental principle of peak velocity. Peak velocity signiﬁes the utmost speed attained within a speciﬁed timeframe, irrespective of any averaged HSR or sprinting encompassed during the said interval. It is noteworthy that the present study adopted 5-min epochs as the tem- poral units for data analysis. This study also found that the above differences tend to increase signiﬁcantly with higher speed distances and counts, which was found in the previous study that analyzed Tracab and GNSS (GPEXE®, Exelio, Udine, Italia) with different epochs (15, 30, and 45 min).21 Hence, it can be inferred that as the velocity achieved during high-speed distances Table 1. ICC and Pearson-product correlation tests comparing both systems for the different running-based measures. Intraclass correlation r Pearson Total distance (m) 0.906 [95% CI: 0.897; 0.914] 0.851 [0.841; 0.860] \| p < 0.001 High-speed running (m) 0.766 [95% CI: 0.522; 0.864] 0.716 [0.697; 0.734] \| p < 0.001 Sprint (m) 0.822 [95% CI: 0.445; 0.917] 0.804 [0.780; 0.826] \| p < 0.001 High-speed running (n) 0.659 [95% CI: 0.399; 0.784] 0.590 [0.564; 0.615] \| p < 0.001 Sprint (n) 0.640 [95% CI: 0.542; 0.712] 0.523 [0.468; 0.574] \| p < 0.001 8 Science Progress 106(3) Table 2. Descriptive statistics (mean ± standard deviation) for the different running-based measures collected by 5-min epoch and inferential comparisons. Catapult Tracab Mean difference (Catapult − Tracab) p-value d Total distance (m) for a 5-min epoch 491.0 ± 116.6 502.7 ± 144.8 −11.8 [95% CI: −14.4; −9.2] <0.001 −0.084 [95% CI: −0.103; −0.065] High-speed running (m) for a 5-min epoch 27.5 ± 19.1 38.2 ± 24.0 −10.8 [95% CI: −11.4; −10.1] <0.001 −0.481 [95% CI: −0.512; −0.450] Sprint (m) for a 5-min epoch 18.0 ± 13.5 25.7 ± 15.9 −7.7 [95% CI: −8.4; −7.1] <0.001 −0.513 [95% CI: −0.560; −0.465] High-speed running (n) for a 5-min epoch 2.1 ± 1.2 2.9 ± 1.5 −0.8 [95% CI: −0.8; −0.7] <0.001 −0.558 [95% CI: −0.597; −0.519] Sprint (n) for a 5-min epoch 1.2 ± 0.4 1.4 ± 0.6 −0.2 [95% CI: −0.2; −0.1] <0.001 −0.334 [95% CI: −0.408; −0.261] Makar et al. 9 Figure 2. Difference against mean for running-based measures. 10 Science Progress 106(3) increases, the likelihood of encountering greater differences between the systems also ampliﬁes. In addition, the number of HSR and sprint efforts detected was the greatest for TRACAB, which is also in line with a previous study.6 In this regard, it is important to acknowledge that the count detection of speed distances requires a minimum duration above a ﬁxed velocity. For instance, Varley et al.22 showed moderate to large differences when different minimum effort durations were applied in the number of HSR detected with 10-Hz GNSS during a soccer match (∼150 efforts detected for 0.1 s duration com- pared to ∼90 efforts detected for 1 s duration). Thus, when comparing different systems, it is relevant to consider ﬁltering technology differences to understand the pros and cons of each system23 as well as other factors, such as the sampling rate, the number of satel- lites, HDOP, and the software analysis of different systems.7 A limitation of the study is a small sample size of participants and, consequently, speculation that a larger sample size could potentially provide a different result when comparing both systems. However, in the context of professional soccer matches, collect- ing data by means of two different systems is complex and limits the opportunity to gather larger sample sizes. Nonetheless, this had also been pointed as a limitation due to non-ecological environments by the simulation of circuits or matches.24 Besides, such devices are expensive and not all teams have access to them. The lack of analysis of accelerometer-based variables is another limitation that may provide further state-of-the-art knowledge, considering that accelerating or decelerating with or without changing direction has been reported as very important in soccer.25 Therefore, the investigation with a similar design but with a larger sample size and analysis of accelerometer-based variables is recommended for future studies. Conclusions Although both systems present a strong relationship and acceptable agreement for TD, the interchangeability should be considered cautiously, mainly regarding signiﬁcant dif- ferences in the collected measures. Coaches and sports scientists should be mindful of the differences between both systems when comparing data and avoid using them interchangeably. Acknowledgments The authors appreciate the study participants. All authors have read and agreed to the published version of the manuscript. Author contributions Conceptualization by P.M. and F.M.C.; methodology by P.M. and F.M.C.; formal analysis by F.M.C.; investigation by P.M., M.J., P.P., F.M.C.; data curation by F.M.C.; writing—original draft preparation by P.M., A.F.S., R.O., M.J., P.P., M.S., F.M.C.; writing—review and editing by P.M., A.F.S., R.O., M.J., P.P., M.S., F.M.C.; project administration by F.M.C. Makar et al. 11 Availability of data and materials All data generated or analyzed during this study are available at the request of the corresponding author. Consent for publication Not applicable. Declaration of conﬂicting interests The author(s) declared no potential conﬂicts of interest with respect to the research, authorship, and/ or publication of this article. Ethics approval Medical Ethic Committee in University of Gdańsk (Decision number 62/2022). Funding The author(s) disclosed receipt of the following ﬁnancial support for the research, authorship, and/ or publication of this article: Filipe Manuel Clemente and this work are funded by the Fundação para a Ciência e Tecnologia/Ministério da Ciência, Tecnologia e Ensino Superior through national funds, and when applicable, co-funded by EU funds under the project UIDB/50008/2020. Informed consent All participants were informed about the study and signed free consent. ORCID iDs Rafael Oliveira https://orcid.org/0000-0001-6671-6229 Filipe Manuel Clemente https://orcid.org/0000-0001-9813-2842 References 1. Weston M. Training load monitoring in elite English soccer: a comparison of practices and per- ceptions between coaches and practitioners. Sci Med Footb 2018; 2: 216–224. 2. Akenhead R and Nassis GP. Training load and player monitoring in high-level football: current practice and perceptions. Int J Sports Physiol Perform 2016; 11: 587–593. 3. Staunton CA, Abt G, Weaving D, et al. 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Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009; 41: 3–13. 20. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Vol 2. Hillsdale, NJ, USA: Lawrence Erlbaum Associates, 1988. 21. Castellano J, Casamichana D, Campos-Vázquez MA, et al. Interchangeability of two tracking systems to register physical demands in football: multiple camera video versus GPS technol- ogy. Arch de Med del Deporte 2019; 36: 268–269. 22. Varley MC, Jaspers A, Helsen WF, et al. Methodological considerations when quantifying high-intensity efforts in team sport using global positioning system technology. Int J Sports Physiol Perform 2017; 12: 1059–1068. 23. Delves RIM, Aughey RJ, Ball K, et al. The quantiﬁcation of acceleration events in elite team sport: a systematic review. Sports Med Open 2021; 7: 45. 24. Cust EE, Sweeting AJ, Ball K, et al. Machine and deep learning for sport-speciﬁc movement recognition: a systematic review of model development and performance. J Sports Sci 2019; 37: 568–600. 25. Delaney JA, Cummins CJ, Thornton HR, et al. Importance, reliability, and usefulness of accel- eration measures in team sports. J Strength Cond Res 2018; 32: 3485–3493. Author biographies Piotr Makar is Head of Department at Gdansk University of Physical Education and Sport (Poland). Ana Filipa Silva is an assistant professor at Instituto Politécnico de Viana do Castelo (Portugal). Rafael Oliveira is an assistant professor at Instituto Politénico de Santarém. Makar et al. 13 Marcin Janusiak is professor at Ś ląsk Wrocław Basketball, Physiology Department, Wrocław, Poland. Przemysław Parus is sport scientist at FC WKS Ś ląsk Wrocław, Physical Performance Department, Wrocław, Poland. Małgorzata Smoter is professor at Department of Basics of Physiotherapy, Gdansk University of Physical Education and Sport, Gdańsk, Poland. Filipe Manuel Clemente is an assistant professor at Instituto Politécnico de Viana do Castelo, Portugal. 14 Science Progress 106(3)	Assessing the agreement between a global navigation satellite system and an optical-tracking system for measuring total, high-speed running, and sprint distances in official soccer matches.	[]	Makar, Piotr,Silva, Ana Filipa,Oliveira, Rafael,Janusiak, Marcin,Parus, Przemysław,Smoter, Małgorzata,Clemente, Filipe Manuel	eng
PMC10708873	Citation: Bascuas, P.J.; Gutiérrez, H.; Piedraﬁta, E.; Rabal-Pelay, J.; Berzosa, C.; Bataller-Cervero, A.V. Running Economy in the Vertical Kilometer. Sensors 2023, 23, 9349. https:// doi.org/10.3390/s23239349 Academic Editor: Nicola Francesco Lopomo Received: 16 October 2023 Revised: 16 November 2023 Accepted: 17 November 2023 Published: 23 November 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sensors Article Running Economy in the Vertical Kilometer Pablo Jesus Bascuas , Héctor Gutiérrez , Eduardo Piedraﬁta , Juan Rabal-Pelay , César Berzosa * and Ana Vanessa Bataller-Cervero Facultad de Ciencias de la Salud, Universidad San Jorge, Autov. A-23 Zaragoza-Huesca, 50830 Villanueva de Gallego, Spain; pbascuas@usj.es (P.J.B.); hgutierrez@usj.es (H.G.); epiedraﬁta@usj.es (E.P.); jrabal@usj.es (J.R.-P.); avbataller@usj.es (A.V.B.-C.) * Correspondence: cberzosa@usj.es Abstract: New and promising variables are being developed to analyze performance and fatigue in trail running, such as mechanical power, metabolic power, metabolic cost of transport and mechanical efﬁciency. The aim of this study was to analyze the behavior of these variables during a real vertical kilometer ﬁeld test. Fifteen trained trail runners, eleven men (from 22 to 38 years old) and four women (from 19 to 35 years old) performed a vertical kilometer with a length of 4.64 km and 835 m positive slope. During the entire race, the runners were equipped with portable gas analyzers (Cosmed K5) to assess their cardiorespiratory and metabolic responses breath by breath. Signiﬁcant differences were found between top-level runners versus low-level runners in the mean values of the variables of mechanical power, metabolic power and velocity. A repeated-measures ANOVA showed signiﬁcant differences between the sections, the incline and the interactions between all the analyzed variables, in addition to differences depending on the level of the runner. The variable of mechanical power can be statistically signiﬁcantly predicted from metabolic power and vertical net metabolic COT. An algebraic expression was obtained to calculate the value of metabolic power. Integrating the variables of mechanical power, vertical velocity and metabolic power into phone apps and smartwatches is a new opportunity to improve performance monitoring in trail running. Keywords: performance monitoring; energy expenditure; human movement; trail running 1. Introduction Over the past decade, there has been a signiﬁcant increase in interest in sport ﬁeld applications, driven by both users and technological companies. This interest has been propelled by advancements in the development of wearable sensors based on micro- electromechanical systems (MEMSs) [1]. These sensors ﬁnd application during training sessions and sports competitions, serving the purpose of monitoring the internal training load [2], scheduling workouts and tracking the athlete’s ﬁtness level progression. To achieve this objective, it is essential to develop automated assessment methods that analyze highly accurate variables capable of reﬂecting the physiological, metabolic, biomechanical and neuromuscular state of the athlete. Additionally, these methods should be easily implemented in low-cost sensors, such as inertial measurement units, linear transducers, potentiometers and global navigation satellite systems, among others [3]. Trail running races have increasingly gained the interest of amateur and professional runners around the world due to their great accessibility and low economic cost. Speciﬁcally, the vertical kilometer is a trend in trail running. In this modality, the athletes must complete a course of an approximately 1000 m vertical climb in a maximum of 5000 m total race length, although these parameters could change between different races, according to the rules of the International Skyrunning Federation [4]. Research on key performance parameters, both in road and trail running, has been a growing target of analysis by numerous health and sport science researchers. The aim of these studies is to understand in more depth those factors correlated with running Sensors 2023, 23, 9349. https://doi.org/10.3390/s23239349 https://www.mdpi.com/journal/sensors Sensors 2023, 23, 9349 2 of 19 performance to later be able to apply this knowledge in the creation of personalized trackers that can be implemented in phone apps and smartwatches. With technological advances, many scientists have developed new promising concepts whose assessment seems to be sensitive to physiological and biomechanical modiﬁcations during running and which may be suitable real feedback measures of performance and training monitoring in trail running and vertical kilometers. These concepts are the running economy, the net metabolic power, the mechanical vertical center of mass power, the net mechanical efﬁciency, the net metabolic cost of transport and the vertical net metabolic cost of transport. Running economy is deﬁned as the oxygen uptake (VO2) required to run a given distance or run at a given submaximal velocity [5]. This parameter can also be deﬁned and calculated in energy terms as the amount of energy liberated per liter of oxygen, denomi- nated in this case as net metabolic rate or power (Cmetab) (kcal·min−1·kg−1·or W·kg−1). It is calculated by measuring the steady-state consumption of oxygen (VO2) and the respira- tory exchange ratio [6] and is considered a physiological determinant of endurance running. This variable is multifactorial, depending on metabolic, cardiorespiratory, biomechanical and neuromuscular factors [7], such as heart rate, minute ventilation, substrate utilization, muscle ﬁber type and core temperature, among many other variables, and is a new concept that reﬂects the physiological and neuromuscular state of the athlete [8]. It is currently considered more sensitive than VO2 itself when used to observe performance differences between runners [7,9]. The mechanical vertical power of the center of mass (Cmec) is deﬁned as the external mechanical work performed to lift the body mass at each running stride, calculated by multiplying the vertical running velocity by the weight of the subject. Recent studies related to running power have found a linear relationship between running power and aerobic power (VO2 consumption) [10,11]. In addition, lower limb power is related to running spatiotemporal improvements (increased contact time), reduction in the energy cost of running [12] and reduction in the increase in energy cost of running due to fatigue in trail running [13]. Speciﬁcally, in vertical kilometers, runners must overcome extreme uphill running slopes, lifting the center of body mass in each step more than in level running by increasing the net mechanical work. This mechanism entails an increase in energy expenditure and a poorer mechanical advantage for producing force against the ground by the hip extensors [14]. Finally, from the previous concepts, the parameters of net mechanical efﬁciency, net metabolic cost of transport and vertical net metabolic cost of transport have emerged. The ﬁrst authors to evaluate these parameters were Margaria et al., (1963) [15] and Minetti et al., (2002) [16]. They calculated the net metabolic cost of transport (both walking and running) (cost of walking (Cw) and cost of running (Cr)) by dividing the metabolic power or rate by running or walking velocity (vertical velocity for the vertical net metabolic cost of transport (VCw and VCr)). This parameter is a key factor in road running [4] and describes the amount of energy needed to transport a kilogram of body mass per unit of distance covered (kcal·kg−1·km−1 or J·kg−1·m−1). In their studies, Margaria et al., (1963) [15] and Minetti et al., (2002) [16] observed that the metabolic cost of running (Cr) was dependent on gradient and independent of speed, except for the steepest positive slopes (above 15% or 8.5◦). Based on these data, subsequent studies have found a great increase in Cr between slopes among runners, whose cause is still unknown, since uphill Cr correlates with neither level Cr nor with biomechanical parameters, such as stride frequency, stride length and body mass index [17]. Likewise, there is no correlation between either the initial Cr values or the changes in Cr values before and after the trail running race with performance time, in contrast to the observed correlation in road running [18]. The increase in Cr with a positive incline is due to an increase in power output and greater muscular activity at all joints, especially in the hip [19]. Unlike level running, where the center of mass behavior oscillates cyclically and both potential and kinetic energy ﬂuctuation are in-phase during the stride [20], in uphill running above 15% (8.5◦), positive work predominately Sensors 2023, 23, 9349 3 of 19 lifts the center of mass and decreases the use of elastic energy (the stretch–shortening cycle mechanism disappears) and bouncing mechanisms [21,22]. Consequently, the metabolic demand increases, coinciding with an increase in blood lactate values and cardiorespiratory values [17,19,23]. In connection with the concepts of mechanical and metabolic power, Margaria et al., (1963) [15] and Minetti et al., (2002) [16] also introduced the concept of net mechanical efﬁciency (Eff) by explaining the ratio of these two variables. In their analysis, they observed that trained athletes were only 5–7% more efﬁcient than non-athletes [15]. They predicted that mechanical efﬁciency was approximately 22–24% with positive slopes above 15% (8.5◦) and 25% above 20% (11.3◦), corresponding to concentric muscle contraction [15,16]. Peyré-Tartaruga et al., (2018) [24] proposed that overall efﬁciency in locomotion (walking and running) is determined by muscular efﬁciency, deﬁned as the fraction of metabolic energy transformed into muscular mechanical work, and transmission efﬁciency, deﬁned as the fraction of muscular mechanical work utilized as total work. However, for practical purposes, the concept net mechanical efﬁciency (Eff) is considered the fraction of metabolic power transformed into mechanical power or total work. These authors also contended that if the efﬁciency value was close to 25% (indicating pure concentric muscle efﬁciency), it would suggest good efﬁciency transmission. If the value exceeded 25%, it would indicate that passive elastic elements in series within muscles (fascial tissues) and tendons provided either the same or signiﬁcant negative work. Based on the studies analyzed to date, most research has been conducted on a treadmill in trail running, and any study of the vertical kilometer was executed through a ﬁeld test. For these reasons, the present study aims to determine the correlation with performance in the previously mentioned concepts (Cmec, Cmetab, Cw, Cr, Vcw, VCr and Eff), as well as to observe the effect of fatigue on these concepts during the progress of a vertical kilometer ﬁeld test. 2. Materials and Methods 2.1. Participants Fifteen trained trail runners participated in the study (eleven males, four females). Demographic, anthropometric and training level data are presented in Table 1. All runners had been training regularly for more than 3 years, and none of them had a history of musculoskeletal injuries in the last year. Before the experiment, all subjects were informed about the objectives, beneﬁts and risks of the investigation, and they signed an informed consent form. The experimental protocol received approval from the University Ethics Committee (Ref 005-19/20), and all procedures adhered to the principle of the Declaration of Helsinki. Table 1. Demographic, anthropometric and training level data. Men Women Age (years) 22–38 * 19–35 * 28.4 ± 5.11 27.7 ± 6.70 Height (cm) 174 ± 4.54 163 ± 2.36 Body mass (kg) 69.8 ± 5.56 54 ± 4.08 BMI (kg/m2) 22.8 ± 1.63 20.2 ± 1.01 Running training duration per session (min) 52 ± 7.58 60 ± 21.6 Running training frequency per week (days/week) 4.40 ± 1.14 4.75 ± 1.26 Pre-test heart rate (bpm) 73.8 ± 10.7 79.5 ± 3.31 HR change (%) 16.1 ± 4.99 61.2 ± 56.6 VO2 peak (mL/kg/min) 65.8 ± 7.00 57.9 ± 6.61 Values: Mean ± SD. BMI: body mass index. HR change: percentage change in heart rate during the vertical kilometer test. VO2 peak achieved in the vertical kilometer test. : age range of participants. Sensors 2023, 23, 9349 4 of 19 2.2. Procedure Each participant completed a vertical kilometer (VK) route spanning 4.64 km with a positive slope of 835 m. The vertical kilometer entails a continuous uphill test, comprising natural segments with varying positive inclinations ranging from 0◦ to 20◦ on this speciﬁc route. To facilitate analysis, the route was divided into three equal parts, each measuring 1.58 km, as illustrated in Figure 1. Within each of these segments, ﬁve sections with a constant slope were chosen (0◦, 5◦, 10◦, 15◦, and 20◦ positive slope). Each section had to last a minimum of 30 s to extract stable physiological data. Furthermore, to ensure data stability, only the central 20 s of each section were analyzed, excluding the initial and ﬁnal portions of the positive slope. Values: Mean ± SD. BMI: body mass index. HR change: percentage change in heart r vertical kilometer test. VO2 peak achieved in the vertical kilometer test. : age range o 2.2. Procedure Each participant completed a vertical kilometer (VK) route spanning 4.6 positive slope of 835 m. The vertical kilometer entails a continuous uphill test natural segments with varying positive inclinations ranging from 0° to 20° on route. To facilitate analysis, the route was divided into three equal parts, eac 1.58 km, as illustrated in Figure 1. Within each of these segments, ﬁve section stant slope were chosen (0°, 5°, 10°, 15°, and 20° positive slope). Each section minimum of 30 s to extract stable physiological data. Furthermore, to ensure d only the central 20 s of each section were analyzed, excluding the initial and ﬁ of the positive slope. Figure 1. Vertical kilometer track. Race course divided into 3 sections of 1.58 km. 2.3. Measurements 2.3.1. Metabolic Data Throughout the entire course, the runners were equipped with a porta lyzer (Cosmed K5 (Rome, Italy)) to assess cardiorespiratory and metabolic re breath-by-breath basis. This measurement was facilitated by a turbine ﬂowm to a properly ﬁtted face mask. The gas analyzer was secured to the runner’s harness, and the entire system weighted 900 g. To ensure time alignment, t parameters from the gas analyzer (including GPS data) were synchronized a the data logger. Calibration of the Cosmed system was performed before ea ment, using a calibration syringe (3L) for the turbine. The oxygen (O2) and ca (CO2) sensors of the gas analyzer were also calibrated to ambient air condit O2 and 0.03% CO2), along with delay calibration. Each experimental day comm determining the metabolic rate during a 10-min standing trial. Subsequently ygen consumption (VO2) and carbon dioxide production (VCO2) were measur Cosmed K5 analyzer. For statistical analysis, the data for each slope and sect d th l t d 20 i t l 1600 m 1400 m 1200 m 1000 m 800 m Figure 1. Vertical kilometer track. Race course divided into 3 sections of 1.58 km. 2.3. Measurements 2.3.1. Metabolic Data Throughout the entire course, the runners were equipped with a portable gas analyzer (Cosmed K5 (Rome, Italy)) to assess cardiorespiratory and metabolic responses on a breath- by-breath basis. This measurement was facilitated by a turbine ﬂowmeter attached to a properly ﬁtted face mask. The gas analyzer was secured to the runner’s back using a harness, and the entire system weighted 900 g. To ensure time alignment, the analyzed parameters from the gas analyzer (including GPS data) were synchronized and stored in the data logger. Calibration of the Cosmed system was performed before each measurement, using a calibration syringe (3L) for the turbine. The oxygen (O2) and carbon dioxide (CO2) sensors of the gas analyzer were also calibrated to ambient air conditions (20.93% O2 and 0.03% CO2), along with delay calibration. Each experimental day commenced with determining the metabolic rate during a 10-min standing trial. Subsequently, rates of oxygen consumption (VO2) and carbon dioxide production (VCO2) were measured using the Cosmed K5 analyzer. For statistical analysis, the data for each slope and section were averaged over the selected 20-s intervals. Sensors 2023, 23, 9349 5 of 19 2.3.2. Calculations The calculation of mechanical vertical center of mass (COM) power (Watts/kg) utilized GPS velocity and incline, as expressed in (Equation (1)): Mechanical vertical COM power = g × v × sin (θ) (1) where θ represents the incline in degrees, and v is the instantaneous velocity in m/s. Net metabolic power (Watts/kg) was calculated from running respiratory measure- ments using the Peronnet and Massicot equation [6], adjusted by subtracting the standing metabolic rate measured 10 min before the test. The calculation is outlined in (Equation (2)): Net Metabolic power = ((16.89 × VO2 + 4.84 × VCO2)/kg) − standing metabolic rate (2) The net mechanical efﬁciency was derived by dividing the mechanical vertical COM power by the net metabolic power, as illustrated in (Equation (3)) [25]: Net mechanical efﬁciency = Mechanical vertical COM power/Net metabolic power (3) The net metabolic cost of transport (J/kg/m) was computed by dividing the net metabolic power by the running velocity, representing the mean net metabolic cost per unit distance traveled parallel to the running surface. (Equation (4)) summarizes this calculation: Net Metabolic COT = Net metabolic power/v (4) The vertical net metabolic cost of transport (J/kg/m) was determined by dividing the net metabolic power by vertical velocity, factored by the mean net metabolic cost to ascend a vertical meter. (Equation (5)) outlines this computation: Vertical Net Metabolic COT = Net metabolic power/v × sin (θ) (5) 2.4. Statistical Analysis The following statistical analysis of the data was conducted: • Normality testing: the Shapiro–Wilk test was used to assess the normality of the variables. • Gender and performance level comparison: A T-student parametric test was employed to compare gender and performance level differences. The sample was divided into quartiles based on the ﬁnal test time, and values from the ﬁrst quartile were compared to the remaining quartiles. • Comparison of assessed variables: A two-factor repeated-measures ANOVA was utilized to compare means across multiple analyzed variables. The analysis compared three sections and ﬁve positive slopes in each section. Before applying ANOVA, the Mauchly’s sphericity test was performed. If sphericity was rejected, the univariated F-statistic was used, adjusted with the Greenhouse–Geisser correction index. Bonfer- roni’s post hoc analysis was performed when signiﬁcant differences were found for pairwise comparison. • Statistical power and effect size determination: The statistical power (SP) and effect size (partial eta squared, ηp2) were determined. The effect size was categorized as trivial (ηp2 ≤ 0.01), small (0.01 ≤ ηp2 < 0.06), moderate (0.06 ≤ ηp2 < 0.14) or large (ηp2 ≥ 0.14) [26]. • Relationship analysis with ﬁnal uphill time: Multiple regression and correlation models were calculated using an “intro” method. Mechanical vertical COM power was considered the dependent variable, and net metabolic power and vertical net metabolic cost of transport were the independent variables in the three VK sections. The entry and exit criteria were set at F probabilities greater than 0.05 and 0.10, re- spectively. The residual linearity and independence assumptions were checked with the Durbin–Watson test. The homoscedasticity was studied in a partial standardized Sensors 2023, 23, 9349 6 of 19 residual-standardized prediction plot. The method of Bland and Altman was used to determine systematic bias and random error in the prediction model, as well as the lower and upper limits of agreement (1.96 × SD). The multicollinearity was estimated using a variance inﬂation factor (VIF), with values greater than 10 considered exces- sive. Inﬂuential cases (Cook’s distance > 1) and atypical cases (residual > 3 standard deviations) were removed from the analysis. • A signiﬁcance level of p < 0.05 was established. All statistical tests were conducted using the statistical package SPSS version 25.0 (SPSS, Chicago, IL, USA). 3. Results Mean values for the three sections and ﬁve slope conditions are presented in Table 2. Regarding gender, no statistical differences were observed. Furthermore, when analyzing the aforementioned variables based on runner performance level (vertical kilometer ﬁnal time) (Table 3), signiﬁcant differences emerged between the ﬁrst quartile and the remaining quartiles in the variables mechanical vertical COM power, net metabolic power, velocity and vertical velocity. On the other hand, no signiﬁcant differences were identiﬁed in the variables net mechanical efﬁciency, net metabolic cost of transport and vertical net metabolic cost of transport. A repeated-measures ANOVA revealed signiﬁcant differences between sections, in- cline and the interaction of section x incline in all the variables presented in Table 4. These ﬁndings indicate a “Large” effect size of fatigue on all variables as the VK progresses. Additional distinctions are detailed in Table 5 through percentages. Conducting a two-way ANOVA with performance level as a factor (ﬁrst quartile versus remaining quartiles), signiﬁcant differences were only identiﬁed in mechanical vertical COM power (incline p < 0.001, SP = 0.967, ηp2 = 0.320) and vertical velocity (incline p < 0.001, SP = 0.972, ηp2 = 0.307). No signiﬁcant differences were observed in net metabolic power, net mechanical efﬁciency, net metabolic COT, vertical net metabolic COT, and velocity. The percentage of change with corresponding p-values is depicted in Figures 2 and 3. Sensors 2023, 23, 9349 7 of 19 Table 2. Descriptive data of values in the three sections and ﬁve slope conditions. Section 1 Section 2 Section 3 0◦ 5◦ 10◦ 15◦ 20◦ 0◦ 5◦ 10◦ 15◦ 20◦ 0◦ 5◦ 10◦ 15◦ 20◦ Velocity (m/s) 3.42 ± 0.39 1.98 ± 0.34 1.52 ± 0.19 1.35 ± 0.21 1.00 ± 0.13 2.26 ± 0.38 1.95 ± 0.30 1.40 ± 0.24 1.03 ± 0.14 0.88 ± 0.14 2.39 ± 0.60 1.67 ± 0.27 1.33 ± 0.19 1.00 ± 0.22 0.73 ± 0.16 Vertical velocity (m/s) 0 ± 0 0.17 ± 0.03 0.26 ± 0.03 0.35 ± 0.05 0.34 ± 0.05 0 ± 0 0.17 ± 0.03 0.24 ± 0.04 0.27 ± 0.04 0.30 ± 0.05 0 ± 0 0.14 ± 0.02 0.23 ± 0.03 0.26 ± 0.06 0.25 ± 0.06 RER 0.94 ± 0.08 0.82 ± 0.08 0.88 ± 0.10 0.90 ± 0.10 0.81 ± 0.08 0.82 ± 0.08 0.81 ± 0.09 0.81 ± 0.08 0.80 ± 0.08 0.80 ± 0.08 0.79 ± 0.08 0.80 ± 0.08 0.78 ± 0.08 0.79 ± 0.07 0.80 ± 0.08 Mechanical vertical COM power (W/kg) 0 ± 0 1.69 ± 0.29 2.58 ± 0.32 3.42 ± 0.54 3.38 ± 0.47 0 ± 0 1.67 ± 0.26 2.37 ± 0.40 2.62 ± 0.36 2.94 ± 0.48 0 ± 0 1.42 ± 0.23 2.25 ± 0.33 2.54 ± 0.55 2.46 ± 0.55 Net metabolic power (W/kg) 17 ± 2.41 17.4 ± 3.10 18.8 ± 2.97 18.7 ± 2.80 17.5 ± 2.69 16.3 ± 2.89 16.4 ± 2.84 16.3 ± 2.93 16.5 ± 2.82 16.3 ± 2.97 15.1 ± 3.33 16 ± 2.92 16.3 ± 2.65 16.7 ± 2.77 16.8 ± 2.91 Net mechanical efﬁciency 0 ± 0 9.88 ± 1.54 13.9 ± 2.15 18.6 ± 3.10 19.5 ± 2.83 0 ± 0 10.3 ± 1.66 14.8 ± 2.91 16 ± 1.92 18.1 ± 2.05 0 ± 0 9.03 ± 1.37 14.0 ± 1.75 15.6 ± 4.48 14.7 ± 2.23 Net metabolic cost of transport (J/kg/m) 5.01 ± 0.72 8.84 ± 1.37 12.4 ± 1.81 14.0 ± 2.36 17.4 ± 2.16 7.26 ± 0.97 8.47 ± 1.19 11.8 ± 2.15 16.0 ± 1.96 18.7 ± 2.03 6.77 ± 2.47 9.66 ± 1.39 12.3 ± 1.54 17.2 ± 4.19 23.4 ± 3.72 Vertical net metabolic cost of transport (J/kg/m) 0 ± 0 101.6 ± 15.7 71.9 ± 10.5 54.2 ± 9.15 51.0 ± 6.32 0 ± 0 97.3 ± 13.6 68.5 ± 12.4 62.1 ± 7.59 54.8 ± 5.93 0 ± 0 111.1 ± 15.9 71.3 ± 8.92 66.8 ± 16.2 68.5 ± 10.9 Values: mean ± standard deviation. RER: respiratory exchange rate; COM: center of mass. Table 3. Differences in values in the three sections and ﬁve slope conditions between ﬁrst quartile and remaining quartiles. Section 1 Section 2 Section 3 0◦ 5◦ 10◦ 15◦ 20◦ 0◦ 5◦ 10◦ 15◦ 20◦ 0◦ 5◦ 10◦ 15◦ 20◦ Vertical velocity (m/s) 1st quartile (n = 5) 0 ± 0 0.20 ± 0.02 * 0.29 ± 0.02 * 0.39 ± 0.06 * 0.37 ± 0.02 * 0 ± 0 0.19 ± 0.01 * 0.26 ± 0.04 0.30 ± 0.02 * 0.35 ± 0.03 ** 0 ± 0 0.16 ± 0.02 * 0.26 ± 0.03 * 0.28 ± 0.02 * 0.30 ± 0.01 ** Remaining quartiles (n = 9) 0 ± 0 0.16 ± 0.02 0.26 ± 0.03 0.33 ± 0.04 3.33 ± 0.05 0 ± 0 0.16 ± 0.02 0.23 ± 0.04 0.25 ± 0.03 0.28 ± 0.03 0 ± 0 0.14 ± 0.02 0.21 ± 0.02 0.24 ± 0.06 0.22 ± 0.04 Velocity (m/s) 1st quartile 3.74 ± 0.23 * 2.26 ± 0.27 * 1.67 ± 0.15 * 1.53 ± 0.22 * 1.10 ± 0.07 * 2.64 ± 0.14 * 2.25 ± 0.15 * 1.52 ± 0.24 1.17 ± 0.09 ** 1.02 ± 0.09 * 2.79 ± 0.24 * 1.90 ± 0.24 * 1.50 ± 0.20 * 1.10 ± 0.68 * 0.89 ± 0.03 ** Remaining quartiles 3.33 ± 0.41 1.86 ± 0.28 1.49 ± 0.22 1.27 ± 0.15 0.96 ± 0.14 2.10 ± 0.32 1.83 ± 0.27 1.35 ± 0.22 0.98 ± 0.12 0.81 ± 0.09 2.22 ± 0.61 1.58 ± 0.23 1.23 ± 1.10 0.94 ± 0.25 0.65 ± 0.13 Mechanical vertical COM power (W/kg) 1st quartile 0 ± 0 1.92 ± 0.23 * 2.82 ± 0.25 * 3.87 ± 0.56 * 3.62 ± 0.23 * 0 ± 0 1.92 ± 0.13 * 2.58 ± 0.41 * 2.97 ± 0.24 * 3.43 ± 0.32 ** 0 ± 0 1.62 ± 0.20 * 2.54 ± 0.35 * 2.77 ± 0.17 2.98 ± 0.12 ** Remaining quartiles 0 ± 0 1.56 ± 0.24 2.44 ± 0.28 3.19 ± 0.37 3.25 ± 0.53 0 ± 0 1.53 ± 0.21 2.25 ± 0.37 2.43 ± 0.26 2.66 ± 0.30 0 ± 0 1.32 ± 0.18 2.09 ± 0.19 2.42 ± 0.65 2.17 ± 0.48 Net metabolic power (W/kg) 1st quartile 19 ± 0.64 # 20.1 ± 2.62 # 22 ± 1.10 # 21.6 ± 1.66 # 20.1 ± 1.32 # 19.5 ± 0.93 # 19.6 ± 1.61 # 19.7 ± 1.35 # 19.7 ± 1.24 # 19.8 ± 1.46 # 18.6 ± 2.16 # 19.4 ± 1.42 # 19.3 ± 1.24 # 19.9 ± 1.11 # 20.2 ± 1.46 # Remaining quartiles 15.9 ± 2.31 15.8 ± 2.21 17.1 ± 2.06 17.1 ± 1.81 16 ± 2.08 14.5 ± 1.75 14.6 ± 1.31 14.4 ± 1.28 14.8 ± 1.52 14.4 ± 1.19 13.1 ± 1.88 14.1 ± 1.13 14.6 ± 1.25 14.9 ± 1.42 14.9 ± 1.25 Values: mean ± standard deviation. COM: center of mass. p-value < 0.05. ** p-value < 0.001. #: strong effect size (g Hedges > 0.8). Sensors 2023, 23, 9349 8 of 19 Table 4. Repeated-measures ANOVA results. p-Value Power (SP) Effect Size (ηp2) Vertical velocity (m/s) Section <0.001 1 0.779 Large Slope <0.001 1 0.973 Large Interaction <0.001 1 0.463 Large Velocity (m/s) Section <0.001 1 0.872 Large Slope <0.001 1 0.949 Large Interaction <0.001 1 0.654 Large Mechanical vertical COM power (W/kg) Section <0.001 1 0.776 Large Slope <0.001 1 0.972 Large Interaction <0.001 1 0.452 Large Net metabolic power (W/kg) Section <0.001 0.993 0.600 Large Slope <0.001 1 0.489 Large Interaction <0.001 0.991 0.243 Large Net mechanical efﬁciency Section <0.001 1 0.626 Large Slope <0.001 1 0.969 Large Interaction <0.001 0.994 0.379 Large Net metabolic cost of transport (J/kg/m) Section <0.001 1 0.706 Large Slope <0.001 1 0.952 Large Interaction <0.001 0.997 0.406 Large Vertical net metabolic cost of transport (J/kg/m) Section <0.001 1 0.648 Large Slope <0.001 1 0.972 Large Interaction <0.001 0.964 0.304 Large Table 5. Differences in values between sections and inclines. Sections 1 vs. 2 Sections 1 vs. 3 Sections 2 vs. 3 Vertical velocity (m/s) 5◦ =0% ↓21.4% * ↓21.4% * 10◦ ↓8.33% ↓13% * ↓4.35% 15◦ ↓29.6% ↓35.6% ↓3.85% 20◦ ↓13.3% * ↓36% ** ↓20% * Velocity (m/s) 0◦ ↓51.3% ↓43.1% ↑5.75% 5◦ ↓1.53% ↓18.6% * ↓16.8% * 10◦ ↓8.6% ↓14.3% * ↓5.3% 15◦ ↓31% ↓35% ↓3% 20◦ ↓13.6% * ↓37% ** ↓20.5% * Mechanical vertical COM power (W/kg) 5◦ ↓1.19% ↓19% * ↓17% * 10◦ ↓8.86% ↓14.7% * ↓5.33% 15◦ ↓30.5% ↓34.6% ↓3.15% 20◦ ↓15% * ↓37.4% ** ↓19.5% * Net metabolic power (W/kg) 0◦ ↓4.3% ↓12.6% ↓7.95% 5◦ ↓6.10% ↓8.75% ↓2.50% 10◦ ↓15.3% ↓15.3% =0% 15◦ ↓13.3% ↓12% ↑1.21% 20◦ ↓7.36% ↓4.17% ↑3.07% Net mechanical efﬁciency 5◦ ↑4.25% ↓9.41% * ↓14.1% * 10◦ ↑6.47% ↓0.72% ↓5.71% 15◦ ↓16.2% * ↓19.2% * ↓2.56% 20◦ ↓7.73% ↓32.6% ** ↓23.1% * Sensors 2023, 23, 9349 9 of 19 Table 5. Cont. Sections 1 vs. 2 Sections 1 vs. 3 Sections 2 vs. 3 Net metabolic cost of transport (J/kg/m) 0◦ ↑44.9% ** ↑35.1% * ↓7.24% 5◦ ↓4.37% ↑9.28% * ↑14% * 10◦ ↓5.08% ↓0.81% ↑4.24% 15◦ ↑14.3% * ↑22.8% * ↑7.5% 20◦ ↑7.47% ↑34.5% ** ↑25.1% * Vertical net metabolic cost of transport (J/kg/m) 5◦ ↓4.42% ↑9.35% * ↑14.2% * 10◦ ↓4.96% ↓0.84% ↑4.10% 15◦ ↑14.6% * ↑23.2% * ↑7.57% 20◦ ↑7.45% ↑34.3% ** ↑25% * % of change in mean values with p-values of Bonferroni post hoc. Up and down arrows correspond to increases and decreases respectively; equal symbols indicate no change. * p-value < 0.05. ** p-value < 0.001. Sensors 2023, 23, x FOR PEER REVIEW 9 of 19 velocity. The percentage of change with corresponding p-values is depicted in Figures 2 and 3. (a) (b) Figure 2. Percentage of change in mechanical vertical center of mass power between slopes. * p-value < 0.05. p-value < 0.001. (a) Percentage of change in the runners of the ﬁrst quartile. The ﬁgures are arranged according to VK section (a1: ﬁrst section; a2: second section; a3: third section). (b) Percent- age of change in the runners of the remaining quartiles (b1: First section; b2: second section; b3: third section). 1.62 2.54 2.77 2.98 0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50 5º 10º 15º 20º Section 3 1st Quartile 3 1.93 2.83 3.87 3.63 0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50 4,00 4,50 5º 10º 15º 20º Section 1 1st Quartile 1 88% 28%* 1.92 2.58 2.97 3.43 0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50 4,00 5º 10º 15º 20º Section 2 1st Quartile 2 46% 100% 37%* 34%* 55% 79% 33%* 57% 71% 84% 9% 17% 7% − 7% 15% 15% 1.65 2.45 3.19 3.25 0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50 5º 10º 15º 20º Section 1 Remaining Quartiles 1 1.53 2.25 2.43 2.67 0,00 0,50 1,00 1,50 2,00 2,50 3,00 5º 10º 15º 20º Section 2 Remaining Quartiles 2 1.32 2.09 2.42 2.18 0,00 0,50 1,00 1,50 2,00 2,50 3,00 5º 10º 15º 20º Section 3 Remaining Quartiles 3 48% 93% 97% 30%* 33%* 8% 47% 59% 74% 2% 19% 10% − 11% 4% 16% 65% 83% 58% 5° 10° 15° 20° 5° 10° 15° 20° 5° 10° 15° 20° 5° 10° 15° 20° 5° 10° 15° 20° 5° 10° 15° 20° 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 3.50 0.00 3.00 2.50 0.50 2.00 1.50 1.00 3.50 4.00 3.00 2.50 2.00 1.50 1.00 0.50 0.00 3.00 2.50 2.00 1.50 1.00 0.50 0.00 3.00 3.50 2.50 2.00 1.50 1.00 0.50 0.00 3.00 2.50 2.00 1.50 1.00 0.50 0.00 Figure 2. Percentage of change in mechanical vertical center of mass power between slopes. * p-value < 0.05. ** p-value < 0.001. (a) Percentage of change in the runners of the ﬁrst quartile. The ﬁgures are arranged according to VK section (a1: ﬁrst section; a2: second section; a3: third sec- tion). (b) Percentage of change in the runners of the remaining quartiles (b1: First section; b2: second section; b3: third section). Sensors 2023, 23, 9349 10 of 19 Sensors 2023, 23, x FOR PEER REVIEW 10 of 19 (a) (b) Figure 3. Percentage of change in vertical velocity between slopes. * p-value < 0.05. p-value < 0.001. (a) Percentage of change in the runners of the ﬁrst quartile. The ﬁgures are arranged according to VK section (a1: ﬁrst section; a2: second section; a3: third section). (b) Percentage of change in the runners of the remaining quartiles (b1: ﬁrst section; b2: second section; b3: third section). A multiple regression analysis was conducted to predict mechanical vertical COM power from the remaining variables. The analysis revealed that the variable mechanical vertical COM power can be statistically signiﬁcantly predicted using net metabolic power and vertical net metabolic cost of transport. This relationship held true across all sections and slopes. The resulting model can be expressed algebraically as follows (Equation (6)): Mechanical Vertical COM power = 𝛼𝐶𝑚𝑒𝑡𝑎𝑏 + 𝛽𝑉𝐶𝑟 + 𝛾 (6) where Cmetab represents net metabolic power (Equation (2)) and VCr stands for vertical net metabolic cost of transport (Equation (5)). 0.16 0.26 0.28 0.30 0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 5º 10º 15º 20º Section 3 1st Quartile 3 0.20 0.29 0.39 0.37 0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 0,40 0,45 5º 10º 15º 20º Section 1 1st Quartile 1 0.20 0.26 0.30 0.35 0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 0,40 5º 10º 15º 20º Section 2 1st Quartile 2 45% − 5% 95% 85% 30%* 34%* 27%* 50% 75% 15% 35%* 17% 62% 75% 7% 87% 8% 15% 0.16 0.26 0.33 0.33 0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 5º 10º 15º 20º Section 1 Remaining Quartiles 1 0.16 0.23 0.25 0.28 0,00 0,05 0,10 0,15 0,20 0,25 0,30 5º 10º 15º 20º Section 2 Remaining Quartiles 2 0.14 0.21 0.24 0.22 0,00 0,05 0,10 0,15 0,20 0,25 0,30 5º 10º 15º 20º Section 3 Remaining Quartiles 3 106% 106% 62% 27%* 27% 0% 44% 56% 75% 9% 22%* 12% 50% 71% 57%** 14% 5% − 9% 5° 10° 15° 20° 5° 10° 15° 20° 5° 10° 15° 20° 5° 10° 15° 20° 5° 10° 15° 20° 5° 10° 15° 20° 0.00 0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 0.00 0.00 0.00 0.00 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.30 0.25 0.20 0.15 0.10 0.05 0.35 0.30 0.25 0.20 0.10 0.15 0.05 0.30 0.20 0.25 0.15 0.10 0.05 Figure 3. Percentage of change in vertical velocity between slopes. * p-value < 0.05. ** p-value < 0.001. (a) Percentage of change in the runners of the ﬁrst quartile. The ﬁgures are arranged according to VK section (a1: ﬁrst section; a2: second section; a3: third section). (b) Percentage of change in the runners of the remaining quartiles (b1: ﬁrst section; b2: second section; b3: third section). A multiple regression analysis was conducted to predict mechanical vertical COM power from the remaining variables. The analysis revealed that the variable mechanical vertical COM power can be statistically signiﬁcantly predicted using net metabolic power and vertical net metabolic cost of transport. This relationship held true across all sections and slopes. The resulting model can be expressed algebraically as follows (Equation (6)): Mechanical Vertical COM power = αCmetab + βVCr + γ (6) Sensors 2023, 23, 9349 11 of 19 where Cmetab represents net metabolic power (Equation (2)) and VCr stands for vertical net metabolic cost of transport (Equation (5)). The adjusted R2 of the multiple linear regression indicates that 94% of the variation in mechanical vertical COM power is explained by the proposed model (R2adjusted = 0.942). The scatter plot for this model is illustrated in Figure 4. The model reached a signiﬁcance level of p < 0.001. All variables included in the model exhibited a signiﬁcance level below 0.001, suggesting their retention in the considered model. The Durbin–Watson test fell within the critical interval (1 < D–W < 3), allowing the acceptance of residual linearity and independence assumptions. However, Bland and Altman plots (Figure 5) revealed randomly distributed residuals concerning the average net mechanical vertical COM power predicted values. Only one value outside ±1.96 × SD was observed, and the residuals exhibited normal distribution based on the Shapiro–Wilk test (SW = 0.941; p = 0.434). All values presented a variance inﬂation factor (VIF) lower than 10 units. Therefore, the multicollinearity assumption is satisﬁed. Sensors 2023, 23, x FOR PEER REVIEW 11 of 19 The adjusted R2 of the multiple linear regression indicates that 94% of the variation in mechanical vertical COM power is explained by the proposed model (R2adjusted = 0.942). The scatter plot for this model is illustrated in Figure 4. The model reached a signiﬁcance level of p < 0.001. All variables included in the model exhibited a signiﬁcance level below 0.001, suggesting their retention in the considered model. The Durbin–Watson test fell within the critical interval (1 < D–W < 3), allowing the acceptance of residual linearity and independence assumptions. However, Bland and Altman plots (Figure 5) revealed ran- domly distributed residuals concerning the average net mechanical vertical COM power predicted values. Only one value outside ±1.96 × SD was observed, and the residuals ex- hibited normal distribution based on the Shapiro–Wilk test (SW = 0.941; p = 0.434). All values presented a variance inﬂation factor (VIF) lower than 10 units. Therefore, the mul- ticollinearity assumption is satisﬁed. Figure 4. Scatter plot of the multiple linear regression model. Each data point represents the value of a subject in the study. Figure 4. Scatter plot of the multiple linear regression model. Each data point represents the value of a subject in the study. Sensors 2023, 23, x FOR PEER REVIEW 12 of 19 Figure 5. Bland–Altman plot of the multiple linear regression model. Each data point represents the value of a subject in the study. The results of the Bland–Altman analysis indicate the absence of systematic biases and random errors in our regression model, attributed to the randomness of the scatter- plot dispersion and the absence of outliers. Based on these results the following prediction equations are derived (Equations (7) -0,15 -0,1 -0,05 0 0,05 0,1 0,15 U nstandarized residuals A verage net m echanical vertical C O M pow erpredicted values 1.96×SD=0.12 2 2.1 2.2 2.3 2.4 2.6 2.5 2.7 2.8 2.9 3 0.1 0.05 0 −0.05 −0.15 −0.1 0.15 Figure 5. Bland–Altman plot of the multiple linear regression model. Each data point represents the value of a subject in the study. Sensors 2023, 23, 9349 12 of 19 The results of the Bland–Altman analysis indicate the absence of systematic biases and random errors in our regression model, attributed to the randomness of the scatterplot dispersion and the absence of outliers. Based on these results, the following prediction equations are derived (Equations (7) and (8)): Mechanical Vertical COM power = 0.133 × Cmetab − 0.030 × VCr + 2.376 (7) Net metabolic power = V × g × sinθ − 2.376 0.133 − 0.030 × (V × g × sinθ)−1 (8) Through mathematical calculation, the obtained algebraic expression allows us to calculate the value of net metabolic power solely from the subject’s vertical velocity (parallel velocity × sin θ (positive slope)). 4. Discussion Metabolic efforts in trail running have recently become a signiﬁcant focus of research, with studies conducted in both ultra-distance events and short trail running. In the ma- jority of these studies, simulations of race slopes have been conducted using treadmill tests [27–29]. However, the metabolic demand appears to differ when the test is conducted outdoors, potentially making it a more suitable method [30]. To our knowledge, there are no metabolic studies during a vertical kilometer ﬁeld test simulating a real race. For these reasons, the primary objective of this study was to evaluate new concepts such as mechanical vertical COM power, net metabolic power, net metabolic cost of trans- port, vertical net metabolic cost of transport, and net mechanical efﬁciency during a real outdoor vertical kilometer ﬁeld test, examining their changes with fatigue and the perfor- mance level of the athletes. The secondary goal was to analyze their relationships with the ﬁnal time of the test. 4.1. Vertical Kilometer Performance Analysis The T-test results showed no signiﬁcant differences between genders, while revealing distinctions based on the subjects’ performance level, as despicted in Table 3. Concerning the mean value differences between the ﬁrst quartile and the remaining quartiles, better mean values were observed in top-level runners across all sections and inclines, achieving higher values in mechanical vertical COM power, net metabolic power, velocity and vertical velocity. The results suggest that better runners can apply more force and achieve greater vertical velocities as the slope increases. These disparities in power and vertical velocity persist throughout the entire duration of the VK. These outcomes align with expectations, as several researchers have observed that uphill running requires an increase in net mechanical work to increase the potential energy of the body, with concurrent increases in parallel propulsive force peaks and impulses with positive grades [31], since the bouncing mechanism gradually disappears as speed and slope increase [22]. The hip and knee joints are identiﬁed as the primary contributors to the augmented mechanical power [14,32]. Additionally, in short trail running, it has been observed that local endurance of knee extensors, assessed through repeated maximal concentric contractions, is a key performance factor in uphill running sections [33]. Net metabolic power reﬂects the instantaneous energy requirement for running, and it has been observed to increase linearly with speed in VK runners [34], attributable to the rise in O2 consumption and CO2 production. The higher metabolic power values among ﬁrst quartile athletes are primarily explained by their greater velocity, stemming from either enhanced cardiorespiratory development or greater strength and power values. Moreover, the same study suggest that running is more efﬁcient than walking above 0.8 m/s [34]. This reference value is crucial, as ﬁrst-quartile runners could maintain speeds greater than 0.8 m/s with 20◦ positive grade in the section 3 of our VK test, while the remaining Sensors 2023, 23, 9349 13 of 19 quartiles’ runners could not. This decision to walk instead of running may partly account for the observed difference in test performance. Regarding the remaining variables, no signiﬁcant differences were found based on performance level. Our results align with other studies where no differences were identiﬁed in the cost of running [35], and only a 5–7% difference in efﬁciency values [15] was observed among trail runners of different levels. The minimal variation in the net metabolic cost of transport in a real VK race could indicate that, despite ﬁrst-quartile runners exhibiting higher metabolic power, their ability to attain higher speeds resulted in comparable cost of transport. This observation implies that net mechanical vertical COM power and net metabolic power may serve as more informative indicators of trail running performance compared to net metabolic cost of transport, as suggested by the existing literature [35]. These variables could prove more suitable for real-time tracking outdoors, utilizing po- tentiometers [36] or mobile applications, or for analyzing average values in both men and women to observe changes with training. 4.2. The Impact of Fatigue on the Vertical Kilometer Analyzing the impact of fatigue throughout the progression of the VK (Table 4), we observed a deterioration in mean values across all monitored variables, occurring with all slopes, particularly notable between the ﬁrst section and subsequent sections, and to a lesser extent between the second and third sections. The changes were more pronounced with steeper inclines (20◦). There was a reduction in velocity and vertical velocity, possibly associated with the diminished ability to apply force (indicated by lower mechanical vertical COM power values). This reduction was more signiﬁcant between the ﬁrst and third sections, especially with 15◦ and 20◦ inclines, which are the most demanding due to lower use of elastic energy [21,37] and biomechanical changes during the transition from running to walking [38]. This power loss could stem from central fatigue (decreased amplitude and frequency of motor unit recruitment) or peripheral fatigue (alterations in potential transmission along the sarcolemma, excitation–contraction coupling and actin–myosin myoﬁlament interaction) [39]. Both types of fatigue might be implicated based on previous ﬁndings in ultra-trail running [39–43]. Decreases in mechanical vertical COM power values could be attributed to fatigue in both plantar ﬂexors and knee extensor muscles. Recent studies suggest that central fatigue tends to affect knee extensors more, while peripheral fatigue affects the plantar ﬂexors [39,41,44]. However, caution is warranted in applying these conclusions to the VK, as these data were observed after an ultra-marathon. A potential factor contributing to the onset of fatigue, particularly of central origin as posited by the central command theory [45], is muscle damage and inﬂammation. However, Pokora et al., (2014) [46] did not observe changes in creatine kinase (a marker of muscle damage) after 1 h of uphill running (10◦) at 60%VO2max. Therefore, investigating muscle damage as a cause of fatigue in uphill running requires further exploration [46]. Decreases in metabolic power values were also observed, possibly caused by impair- ments in running biomechanics (such as increased step frequency, ankle joint changes and duty-free alterations) [39], arising from neuromuscular fatigue and behavioral changes in runners, especially with 15◦ and 20◦inclines, choosing gaits that minimize metabolic cost [47]. Concerning net metabolic COT and vertical net metabolic COT, both continuously increased across all sections with steeper uphill inclines due to greater loss of velocity than metabolic power values as the test progressed. This suggests that neuromuscular, rather than cardiorespiratory factors, may be the primary contributors to the decline in performance in the VK. These increases align with observations in the literature after short- distance running races [48,49], 1 h of treadmill running [50] and the vertical kilometer [34]. Multiple reasons have been proposed for this increase in COT. Firstly, the steep inclines of the VK, coupled with a decrease in velocity, induce changes in running biomechanics, Sensors 2023, 23, 9349 14 of 19 such as decreased step length, increased non-optimal step frequency, mid- to fore-foot strike patterns, and decreased leg stiffness, all associated with increased COT [9,51–53]. Prolonged running step contact times (“Groucho running” pattern concept) [54] could impair spring-like bouncing and elevate the COT due to changes in potential-kinetic energy savings [34,55]. These biomechanical changes may be induced by neuromuscular fatigue (reﬂected in decreased mechanical power) [39,56] or serve as a protective mechanism to reduce running impacts [57]. Regarding net mechanical efﬁciency changes, this variable decreased due to greater losses in mechanical power than metabolic power. The substantial and continuous losses in mechanical power could signify a decrease in workload due the loss of velocity, providing a signiﬁcant limitation to performance due to the inability to utilize maximum metabolic potential. This theory is supported by data from Ettema et al., (2009) [58], who stated that power output is the main determinant of efﬁciency (more power leads to more efﬁciency and vice versa), owing to a greater utilization of metabolic power in running. The imbalance between mechanical power and metabolic power, resulting in a decrease in net mechanical efﬁciency, could be attributed to decreased energy transduction (due to decreased speed and stretch-shortening cycle) coupled with an increase in respiratory cost [24]. 4.3. Examining Fatigue Effects Based on Runners’ Performance Levels When examining the impact of fatigue based on the runners’ performance levels, we observed differential changes in only two variables, namely mechanical vertical COM power (Figure 2) and vertical velocity (Figure 3). Notably, elite runners demonstrated a better ability to sustain power values across all slopes, particularly evident with 10◦ and 20◦ inclines, resulting in more pronounced differences in power values between slopes. This phenomenon suggests their enhanced capability to exert force consistently across all slopes throughout the entire race. Similarly, top-level runners exhibited a superior ability to maintain vertical velocity values across all inclines, likely attributable to their heightened application of force throughout the entire VK. These ﬁndings align with prior research indicating a signiﬁcant correlation between performance in short trail running races and neuromuscular capacity, as assessed by isometric knee extensor muscle torque, maximal theoretical force and maximal power from the force–velocity curve [59]. This underscores the importance of incorporating resistance training [60], uphill interval run- ning training [61] and pulled running training [62] to enhance power and neuromuscular function [39,62] in runners. Additionally, it emphasizes the signiﬁcance of monitoring these two variables using apps that measure speed and incline or smartwatches, which are increasingly employed in outdoor races and training sessions. 4.4. Metabolic Power Calculation The outcomes of the multiple regression analysis revealed that 94% of the variance in mechanical vertical COM power during the VK test could be accounted for by net metabolic power and the vertical metabolic COT. This substantial explanation is primarily attributed to the fact that these two variables elucidate the vertical velocity, a key component of mechanical vertical COM power. From the derived equation (Equation (6)), three coefﬁcients sensitive to the progression of the test and inclination were obtained (Table 6). These coefﬁcients are likely subject to variations depending on the characteristics of the uphill test, such as slope, section lengths and their interaction. This observation is consistent with our study’s results, where net metabolic power levels exhibited changes due to slope and fatigue. The ﬁndings of this regression analysis suggest that, once an ascent has been characterized, net metabolic power can be estimated based on the runner’s vertical velocity. Consequently, a reliable equation (Equation (8)) was established from the multiple regression to calculate the runner’s metabolic power during a VK ﬁeld test. This equation utilizes only the vertical velocity and the coefﬁcients found in the model, eliminating the need for expensive portable gas analyzers. The ease of analysis with common devices like phones, smartwatches and GPS is a notable advantage [63]. These results align with Sensors 2023, 23, 9349 15 of 19 the increasing interest among researchers to determine metabolic power during actual competitions in various sports. This pursuit aims to enhance the understanding of the real workload for athletes, thereby improving training methods and periodization [64–67]. Table 6. Multiple linear regression model for mechanical vertical COM power. R R2 adR2 SEE p Durbin– Watson B SE Beta p B VIF LL95% UL95% 0.975 0.951 0.942 0.07 <0.001 1.911 α 0.133 0.009 1.243 <0.001 0.113 0.152 1.626 β −0.030 0.003 −0.797 <0.001 −0.037 −0.023 1.626 γ 2.376 0.183 <0.001 1.973 2.779 R: correlation coefﬁcient, R2: determination coefﬁcient; adR2: adjusted determination coefﬁcient; SEE: standard error of the estimation; p: signiﬁcance level; LL95%: lower limit for 95% conﬁdence interval; UL95%: upper limit for 95% conﬁdence interval, B: multiple linear regression coefﬁcients of each variable; SE: B-standard error; Beta: standardized coefﬁcients; VIF: variance inﬂation factor; α: net metabolic power coefﬁcient; β: vertical net metabolic COT coefﬁcient; γ: independent coefﬁcient of the multiple regression. Our formula, combined with the VO2 submax at 30◦ formula developed by Giovanelli et al. [38], can serve as a valuable tool for characterizing VK runners based on easily measured variables in a real ﬁeld test. The study results offer novel insights into the signiﬁcance of utilizing mechanical power, metabolic power and vertical velocity variables for performance analysis in vertical kilometer runners, regardless of gender. Furthermore, it underscores their susceptibility to impairment due to the inﬂuence of fatigue. These ﬁndings align with the increasing interest in acquiring high-quality information on athletes’ internal load through the progressive improvement of technology and data analysis methods [3]. Moreover, it opens up the possibility of conducting further research to deeper analyze these variables across various running modalities and both cyclic and acyclic sports. A major strength of the present study lies in the simplicity with which these variables can be implemented in any existing wearable sensor on the market that utilizes IMUs and GNSS to calculate real-time velocity, accelerations, anthropometric data and terrain characteristics. These data facilitate the calculation of key parameters, eliminating the need for athletes and coaches to undergo time-consuming and fatiguing tests and allowing data collection during training and competition [2]. Finally, possessing a comprehensive understanding of key variables within each sporting context is crucial. This clarity is essential for precise data collection, enabling researchers and companies to save a signiﬁcant amount of time developing software and sensors [2]. As future lines of research, it would be interesting to validate the metabolic power formula and continue studying runners through real ﬁeld tests. 5. Limitations The main limitation of the study is the small sample size, with only 11 male and 4 female participants. This limitation arose from the technical complexity and time cost associated with conducting the analyses in a true vertical kilometer ﬁeld test. Future extensive analyses with a larger and more diverse sample should be conducted, particularly for the reliability and validity assessment of the metabolic power formula identiﬁed in this study. Additionally, the absence of anthropometric analysis to determine the body fat per- centage and the level of lower limb muscle mass among the runners represents another limitation. This lack of information prevents readers from gaining insights into the sub- jects’ ﬁtness levels, which would provide better context for the study’s ﬁndings. Notably, individuals with lower body fat percentages and higher levels of leg muscle mass are often observed to perform better in trail running tests. Consequently, future studies should Sensors 2023, 23, 9349 16 of 19 incorporate analyses of these parameters. Lastly, another limitation is the absence of a pre-vertical kilometer maximum treadmill test to assess the physiological condition of the runners, as well as a strength test to gauge their neuromuscular level. These factors are also crucial for race performance and should undergo thorough examination in future studies. 6. Conclusions The study results revealed signiﬁcant differences in the mean values of variables such as velocity, vertical velocity, mechanical vertical COM power and net metabolic power when comparing top-level runners to low-level runners during a vertical kilometer ﬁeld test. Additionally, all analyzed variables were affected by fatigue as the test progressed, showing signiﬁcant differences in how fatigue altered mechanical vertical COM power and vertical velocity when comparing top-level to low-level runners. A multiple regression analysis demonstrated that 94% of the mechanical vertical COM power during the vertical kilometer test could be explained by net metabolic power and vertical net metabolic cost of transport. Subsequently, a reliable equation was derived from the multiple regression to calculate each runner’s metabolic power during a vertical kilometer ﬁeld test, utilizing only the vertical velocity and the coefﬁcients identiﬁed in the model. These ﬁndings present an opportunity to explore new variables correlated with performance in short trail running, particularly in vertical kilometer races. These new variables are sensitive to performance disparities, exhibit changes with fatigue and are applicable to both male and female athletes. Importantly, they can be easily measured through apps, smartwatches, foot-pod potentiometers and GPS. Author Contributions: Conceptualization, P.J.B., A.V.B.-C. and C.B.; methodology, H.G., J.R.-P. and C.B.; formal analysis, P.J.B. and H.G.; investigation, A.V.B.-C., E.P. and J.R.-P.; data curation, P.J.B. and H.G.; writing—original draft preparation, P.J.B. and H.G.; writing—review and editing, A.V.B.-C., C.B., E.P. and J.R.-P.; funding acquisition, C.B. All authors have read and agreed to the published version of the manuscript. Funding: This work was partially funded by Departamento de Ciencia, Universidad y Sociedad del Conocimiento, from the Gobierno de Aragón (Spain) (Research Group ValorA, under grant S08_23R. In addition, this research was partially supported by the Spanish Ministry of Universities (FPU grant FPU19/00967). Institutional Review Board Statement: This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of the Universidad San Jorge, protocol code Ref 005-19/20. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent was obtained from the subjects to publish this paper. Data Availability Statement: The raw data belong to the Universidad San Jorge and can be requested from the corresponding author with the permission of Universidad San Jorge. Conﬂicts of Interest: The authors declare no conﬂict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. References 1. Bunn, J.A.; Navalta, J.W.; Fountaine, C.J.; Reece, J.D. Current State of Commercial Wearable Technology in Physical Activity Monitoring 2015–2017. Int. J. Exerc. Sci. 2018, 11, 503–515. [PubMed] 2. Helwig, J.; Diels, J.; Röll, M.; Mahler, H.; Gollhofer, A.; Roecker, K.; Willwacher, S. 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PMC10335662	RESEARCH ARTICLE The relationship between ambient temperature and match running performance of elite soccer players Ryland MorgansID1,2, Eduard Bezuglov2, Dave RhodesID1, Jose TeixeiraID3,4,5, Toni Modric6, Sime Versic6, Rocco Di Michele7, Rafael OliveiraID3,8,9 1 Football Performance Hub, University of Central Lancashire, Preston, United Kingdom, 2 Department of Sports Medicine and Medical Rehabilitation, Sechenov State Medical University, Moscow, Russia, 3 Research Centre in Sports Sciences, Health and Human Development, Vila Real, Portugal, 4 Departamento de Desporto e Educac¸ão Fı´sica, Instituto Polite´cnico de Braganc¸a, Braganc¸a, Portugal, 5 Instituto Polite´cnico da Guarda, Guarda, Portugal, 6 Faculty of Kinesiology, University of Split, Split, Croatia, 7 Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy, 8 Sports Science School of Rio Maior–Polytechnic Institute of Santare´m, Rio Maior, Portugal, 9 Life Quality Research Centre, Rio Maior, Portugal rylandmorgans@me.com Abstract The influence of environmental factors on key physical parameters of soccer players during competitive match-play have been widely investigated in the literature, although little is known on the effects of sub-zero ambient temperatures on the performance of adult elite soccer players during competitive matches. The aim of this study was to assess how the teams’ match running performance indicators are related to low ambient temperature during competitive matches in the Russian Premier League. A total of 1142 matches played during the 2016/2017 to 2020/2021 seasons were examined. Linear mixed models were used to assess the relationships between changes in ambient temperature at the start of the match and changes in selected team physical performance variables, including total, running (4.0 to 5.5 m/s), high-speed running (5.5 to 7.0 m/s) and sprint (> 7.0 m/s) distances covered. The total, running and high-speed running distances showed no significant differences across temperatures up to 10˚C, while these showed small to large decreases at 11 to 20˚C and especially in the >20˚C ranges. On the contrary, sprint distance was significantly lower at temperature of -5˚C or less compared to higher temperature ranges. At sub-zero temper- atures, every 1˚C lower reduced team sprint distance by 19.2 m (about 1.6%). The present findings show that a low ambient temperature is negatively related to physical match perfor- mance behavior of elite soccer players, notably associated with a reduced total sprint distance. Introduction Soccer is a worldwide sport that is played in differing environmental conditions varying from extreme heat to severely cold temperatures. The physical demands that are required on players PLOS ONE PLOS ONE \| https://doi.org/10.1371/journal.pone.0288494 July 11, 2023 1 / 9 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Morgans R, Bezuglov E, Rhodes D, Teixeira J, Modric T, Versic S, et al. (2023) The relationship between ambient temperature and match running performance of elite soccer players. PLoS ONE 18(7): e0288494. https://doi.org/ 10.1371/journal.pone.0288494 Editor: Emiliano Cè, Università degli Studi di Milano: Universita degli Studi di Milano, ITALY Received: February 6, 2023 Accepted: June 28, 2023 Published: July 11, 2023 Copyright: © 2023 Morgans et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper. Funding: This research was funded by the Portuguese Foundation for Science and Technology, I.P., Grant/Award Number UIDP/ 04748/2020. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. can vary significantly as a result of the environmental conditions in which soccer matches are played [1–3]. Over the course of a soccer season, environment conditions can change signifi- cantly from warm summer months to cold winter months within most European soccer lea- gues. These changes may dictate important considerations for the physical preparation of players over the course of a soccer season in relation to the specific environmental conditions experienced locally. Much of the previous research that has been undertaken into the effects of environmental conditions on soccer performance has focused on the impact of hot environments and expo- sure to altitude [4]. While this may be of significant importance in certain areas of the world, it would appear that it is also important to investigate the impact of extremely cold temperatures which are more commonly seen during the winter months of many European leagues. There is scant previous research in this area, however, some studies have assessed the effects of cold environments on physical performance during soccer match-play [5]. For instance, this study showed greater distances covered by midfielders at high-intensity running (>19.8 km/h), between the 30-45-minute period, in temperatures 5˚C versus 21˚C. However, total dis- tance covered was unaffected in colder conditions, although this finding could not be con- firmed across all positions as defenders and strikers were not assessed [5]. Assessment of the effects of different temperatures on the physiological response to sub- maximal exercise in soccer players shows that exercise capacity is reduced in cool environ- ments when compared with moderate conditions [6]. This was evident by changes in players’ heart rate responses and ventilation rates in cool environments when cycling for 20 minutes at 60% maximum oxygen uptake and until reaching exhaustion in three different environmental conditions (10, 22, 35˚C) [6]. Furthermore, Armstrong [7] stated that exercising in cold envi- ronments has various effects on physiological processes such as an increase in muscle glycogen utilization, a reduction in aerobic capacity, and a reduction in muscular strength and power. More specifically, Carling et al. [5] investigated the effect of cold temperature on physical activity profiles in matches over four seasons in the French Ligue 1. The authors reported that generally physical outputs were unaffected by changes in environmental temperatures in the matches analyzed. However, the lowest match temperature involved in this study were 5˚C. This temperature accounts for a large range of temperatures which may significantly affect physical match performance. Although, further research may be required to assess the differ- ences in the effect on physical match performance of very cold and freezing temperatures (<5˚C). Therefore, the aim of this study was to investigate the relationships between ambient tem- peratures and physical match activity of elite soccer players during official competitive matches in the Russian Premier League, where matches are often played in a cold environment, espe- cially during autumn and winter months. Given the different findings of the previous studies about the lower capacity of exercising in a cold environment on physical performance [6, 7], and at the same time that midfielders run higher distances at a speed > 19.8 km/h [5], it was hypothesized that playing at very low temperatures would impact physical match behavior when compared with warmer environmental conditions, specially at speeds < 19.8 km/h. Materials and methods Experimental design An observational design to assess the impact of ambient temperature on soccer match physical performance in official matches played in the Russian Premier League over a period of five competitive seasons was employed. PLOS ONE Cold exposure and match running performance PLOS ONE \| https://doi.org/10.1371/journal.pone.0288494 July 11, 2023 2 / 9 Match sample The ambient temperature at the start of the match, and a selection of the team’s physical match performance variables, were assessed for all official matches played in the Russian Premier League during five consecutive seasons, 2016/2017 to 2020/2021. Due to some missing temper- ature and physical performance data, and the poor quality of some match data due to environ- mental or other reasons, a total of 1146 out of 1200 matches were examined. For every match, data for the home and away team were examined, resulting in a total of 2292 team perfor- mance data points, from a total of 24 different teams. Ethical approval for the study was granted by the Ethics Committee of Sechenov University (N06-21 dated 04/07/2021). The study was performed in accordance with the Helsinki Declaration principles. All professional players and the Russian Premier League agreed to the collection of match performance data and for research purposes. Data collection League match data across each season was recorded and analyzed via a two-camera Optical Tracking System (InStat, Moscow, Russia) to report physical performance data. The matches were filmed using two full HD, static cameras positioned on the centre line of the field, not less than 3-metres from the field and 7-metres in height. A consistent 25Hz format was provided. Data were linearly interpolated to 50Hz, smoothed using a 5-point moving average and then down-sampled to 10Hz. The reliability and validity of InStat have been demonstrated by assessing velocity and position data collected during soccer-specific exercises in comparison with a reference stereophotogrammetric system (Vicon Motion Systems Ltd., Oxford, UK). These assessments are included in the official FIFA test protocol for Electronic and Perfor- mance Tracking Systems document, stating that the system has passed [8]. The InStat Analysis Software System was used to measure and analyze physical performance. InStat provided writ- ten permission to allow all match data to be used for research purposes. The following distances, describing the whole team physical match performance, were assessed: total distance covered by all team players (m); running distance covered by all team players (m; total distance covered 4.0 to 5.5 m/s); high-speed running (HSR) distance covered by all team players (m; total distance covered 5.5 to 7.0 m/s); and sprint distance covered by all team players (m; total distance covered >7.0 m/s). These variables were selected based on pre- vious studies [9, 10] to facilitate comparisons. The League Development Department gathered temperature data on all official matches during the 2015 to 2021 period from the official Hydrometeorological Center. Meteorological data were retrieved from the meteorological stations located nearest the stadiums, available from the Russian Weather Service (http://aisori-m.meteo.ru/waisori/) and Ogimet (https:// www.ogimet.com/synops.phtml.en) databases. As far as data from the stadiums were con- cerned, meteorological data were thus taken per hour during the match-day and one hour prior to kick-off, measured from the centre of the field, as stated by League requirements. Data on air temperature during matches were retrieved from the stadiums’ meteorological stations and reported by the official match delegate. Two experts retrospectively examined and verified the official Hydrometeorological Center data by comparisons with the League delegate reports. All matches were classified into six groups depending on the ambient temperature at the start of the match: <-5˚C; -4 to 0˚C; 1 to 5˚C; 6 to 10˚C; 11 to 20˚C; >20˚C. Ranges for tempera- tures higher than 5˚C were set according to Carling et al. [5], while two additional ranges (-4 to 0˚C and 1 to 5˚C) were added for temperatures up to 5˚C. The lowest temperature threshold (<-5˚C) was set according to Link and Weber [11]. All physical match performance and PLOS ONE Cold exposure and match running performance PLOS ONE \| https://doi.org/10.1371/journal.pone.0288494 July 11, 2023 3 / 9 match temperature data were analysed by the same experienced investigator to ensure inter- observer reliability was not a limitation of the study. Statistical analysis The statistical analysis was conducted using the software R, version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). Data are presented as mean ± standard deviation. Lin- ear mixed models with random intercept on team IDs were used to compare the examined physical performance variables across temperature ranges. When there was a significant (p<0.05) difference between temperature ranges, Tukey’s tests were used to determine which ranges differed. The estimated differences were standardized by dividing them by the esti- mated between-team standard deviation to determine the effect size (ES). Absolute ES values were evaluated as <0.2, trivial; 0.2–0.6, small; 0.6–1.2, moderate; 1.2–2.0, large; 2.0–4.0, very large; >4.0 extremely large [12]. The effect of low temperature on physical match performance was further explored by tak- ing temperature as a continuous variable. Linear mixed models with random intercept on team IDs were used to examine the effect of 1˚C ambient temperature decrease on physical performance variables. For this analysis, only matches started at ambient temperature < 0˚C were included. Effect sizes were calculated as Cohen’s d from the coefficients of linear mixed models coefficient with the lme.dscore function from package EMAtools [13], and interpreted as very small (<0.2), small (0.2–0.5), medium (0.5–0.8), large (>0.8) [14]. For all models, it was ensured that the assumptions of linearity, homoscedasticity and nor- mality of residuals were met by visually inspecting histograms of residuals and plots of residu- als vs. fitted values. For all analyses, statistical significance was set at p<0.05. Results In the examined matches, the mean ambient temperature at the start of the match was 11.7 ± 10.2˚C. Table 1 presents the mean values for the examined physical performance variables in each temperature range, while Table 2 displays the standardized differences (ES). The total distance was similar from the <-5˚C to the 6 to 10˚C ranges, while it decreased in the 11 to 20˚C range Table 1. Mean ± SD values, with 95% Confidence Intervals (CI), for physical match performance variables across temperature ranges. <-5˚C -4 to 0˚C 1 to 5˚C 6 to 10˚C 11 to 20˚C >20˚C (n = 134) (n = 222) (n = 322) (n = 372) (n = 726) (n = 516) Total distance (km) Mean ±SD 114.9de ± 4.8 114.3de ± 5.1 114.5de ± 4.7 114.6de ± 4.6 113.7e ± 4.8 112.3 ± 5.5 95% CI 114.1–115.8 113.6–114.9 114.0–115.0 114.1–115.1 113.4–114.0 112.8–111.9 Running distance (km) Mean ±SD 20.0de ± 1.8 20.0de ± 2.0 20.1de ± 1.8 20.0de ± 2.0 19.3e ± 1.8 18.3 ± 1.8 95% CI 19.7–20.3 19.7–20.3 19.9–20.3 19.8–20.2 19.2–19.5 18.2–18.5 High-speed distance (km) Mean ±SD 7.7e ± 0.9 7.9de ± 0.9 7.9de ± 0.9 7.9de ± 1.0 7.7e ± 0.9 7.2 ± 0.9 95% CI 7.5–7.8 7.8–8.0 7.8–8.0 7.8–8.0 7.6–7.7 7.1–7.3 Sprint distance (km) Mean ±SD 1.1abcd ± 0.4 1.2e ± 0.4 1.2e ± 0.3 1.3e ± 0.4 1.3e ± 0.4 1.1 ± 0.4 95% CI 1.0–1.1 1.2–1.3 1.2–1.3 1.2–1.3 1.2–1.3 1.1–1.2 a denotes a significant difference vs. -4 to 0˚C b denotes a significant difference vs. 1 to 5˚C c denotes a significant difference vs. 6 to 10˚C d denotes a significant difference vs. 11 to 20˚C e denotes a significant difference vs. >20˚C. Number of data is referred to team performance data points https://doi.org/10.1371/journal.pone.0288494.t001 PLOS ONE Cold exposure and match running performance PLOS ONE \| https://doi.org/10.1371/journal.pone.0288494 July 11, 2023 4 / 9 with significant small differences vs. all the lower temperature ranges. In the >20˚C range, the total distance shows a further increase, with significant moderate differences vs. all the lower temperature ranges. A similar trend was observed for running distance, with no significant dif- ferences between ranges from <-5˚C to 6 to 10˚C, a decrease in the 11 to 20˚C range, with sig- nificant moderate differences vs. all the lower temperature ranges and a marked decrease in the >20˚C range, with significant moderate to large differences vs. all lower temperature ranges (Tables 1 and 2). The HSR distance was slightly though not significantly lower in the <-5˚C range when compared to temperature ranges from -4 to 0˚C to 6 to 10˚C. In the 11 to 20˚C range, HSR was significantly lower than in the -4 to 0˚C, 1 to 5˚C, and 6 to 10˚C ranges, with small differences. In the >20˚C range, HSR distance was significantly lower than all lower temperature ranges, with moderate to large differences. Sprint distance was similar across ranges from -4 to 0˚C to 11 to 20˚C, while it was significantly lower in the <-5˚C range when compared to all higher temperature ranges except >20˚C with moderate differences, and in the >20˚C range when compared to all lower temperature ranges except <-5˚C, with differences ranging from small to moderate (Tables 1 and 2). The linear mixed model analysis performed using ambient temperature as a quantitative independent variable including matches when temperature was equal to zero or lower, when examining the estimated fixed effect coefficients, revealed small (d = 0.21–0.23) though non- significant (p >0.05) increases of total distance and running distance for a 1˚C decrease of ambient temperature. High-speed running distance showed a very small (d = 0.07) yet non- significant decrease with decreasing temperature. Conversely, there was a significant (p<0.01) decrease of 19.2 m (approximately 1.6%) for every 1˚C decrease of ambient temperature (d = 0.48, small). Table 2. Standardized differences (ES), with 95% Confidence Intervals (CI), between temperature ranges (values of temperature categories in columns minus values of temperature categories in rows). <-5˚C -4 to 0˚C 1 to 5˚C 6 to 10˚C 11 to 20˚C Total distance -4 to 0˚C 0.16 (-0.27 to 0.59) 1 to 5˚C 0.12 (-0.29 to 0.59) -0.04 (-0.38 to 0.30) 6 to 10˚C 0.08 (-0.31 to 0.48) -0.07 (-0.41 to 0.27) -0.03 (-0.33 to 0.27) 11 to 20˚C 0.55* (0.18 to 0.92) 0.39* (0.09 to 0.70) 0.43* (0.17 to 0.70) 0.47* (0.21 to 0.72) >20˚C 1.18* (0.80 to 1.57) 1.02* (0.70 to 1.35) 1.06* (0.78 to 1.35) 1.10* (0.83 to 1.37) 0.63* (0.40 to 0.86) Running distance -4 to 0˚C 0.11 (-0.32 to 0.53) 1 to 5˚C 0.01 (-0.39 to 0.41) -0.10 (-0.43 to 0.24) 6 to 10˚C 0.10 (-0.29 to 0.49) -0.01 (-0.34 to 0.32) 0.09 (-0.21 to 0.38) 11 to 20˚C 0.72* (0.35 to 1.08) 0.61* (0.32 to 0.91) 0.71* (0.45 to 0.97) 0.62* (0.37 to 0.87) >20˚C 1.73* (1.36 to 2.11) 1.63* (1.31 to 1.94) 1.72* (1.45 to 2.00) 1.64* (1.37 to 1.90) 1.01* (0.79 to 1.24) High-speed distance -4 to 0˚C -0.28 (-0.90 to 0.34) 1 to 5˚C -0.33 (-0.91 to 0.25) -0.05 (-0.55 to 0.44) 6 to 10˚C -0.34 (-0.91 to 0.23) -0.06 (-0.55 to 0.42) -0.01 (-0.44 to 0.42) 11 to 20˚C 0.21 (-0.33 to 0.74) 0.48* (0.05 to 0.92) 0.54* (0.16 to 0.92) 0.55* (0.18 to 0.91) >20˚C 1.31* (0.76 to 1.86) 1.59* (1.13 to 2.05) 1.64* (1.24 to 2.05) 1.65* (1.27 to 2.04) 1.11* (0.78 to 1.43) Sprint distance -4 to 0˚C -0.70* (-1.23 to -0.18) 1 to 5˚C -0.85* (-1.34 to -0.35) -0.15 (-0.57 to 0.27) 6 to 10˚C -0.93* (-1.42 to -0.45) 0.23 (-0.63 to 0.18) -0.08 (-0.45 to 0.29) 11 to 20˚C -0.84* (-1.29 to -0.39) -0.13 (-0.50 to 0.24) 0.02 (-0.31 to 0.33) 0.10 (-0.21 to 0.40) >20˚C 0.18 (-0.29 to 0.65) 0.52* (0.13 to 0.92) 0.67* (0.32 to 1.06) 0.75* (0.42 to 1.08) 0.66* (0.38 to 0.94) * denotes a significant difference (p<0.05). https://doi.org/10.1371/journal.pone.0288494.t002 PLOS ONE Cold exposure and match running performance PLOS ONE \| https://doi.org/10.1371/journal.pone.0288494 July 11, 2023 5 / 9 Discussion The present study assessed the relationships between low ambient temperature and physical match behavior in elite soccer players. To this aim, competitive matches played across five con- secutive seasons in the Russian Premier League were examined. Although there is a mid-sea- son winter break (mid-December to end-February) during the extremely cold months of the year in this League, there is still a significant number of matches played under cold or very cold conditions, mainly in the weeks just before or after the winter break. Thus, an evaluation into how low temperatures may impact the physical match output of soccer players in a real- world setting is of significant interest to the support staff tasked with maintaining the well- being of players. Together, our results show that a very cold (<-5˚C) ambient temperature at the start of the match has no impact on total distance covered or distance when running up to 5.5 m/s, while there is a trend suggesting a slight reduction of HSR distance (5.5 to 7.0 m/s), and an evident decrease in sprint distance (>7.0 m/s). On average, in matches played at a temperature of -5˚C or less, team sprint distance was approximately 10–15% lower than in matches played at less cold or warmer temperatures (Table 1). Moreover, with a mixed-effects regression analysis, a moderate but relevant decrease of approximately 19.2 m (1.6%) of sprint distance covered by the team for every 1˚C decrease of ambient temperature when the temperature was 0˚C or less was reported. Thus, players performed less sprinting at low temperatures. Overall, the results support the study hypothesis that physical match performance is negatively affected by playing at low temperatures, though the impact of cold environment was evident, among the examined variables, distances covered at high- to maximal intensity were notable. To date, a number of previous studies have investigated how environmental conditions, including ambient temperature, affects physical performance in soccer players [1, 3, 5, 11, 15– 18]. There is some evidence that total distance covered during a soccer match, as well as HSR distance, are reduced in hot environments compared to neutral temperature conditions [1, 11, 16]. Though the present study mainly focused on the impact of low temperatures, the sample included all available matches from the examined Russian Premier League seasons, thus allow- ing a comparison between physical match performance across all temperature conditions. Consistently with previous studies [1, 11, 17], it was observed that total distance covered, as well as running and HSR distances, were reduced in the 11 to 20˚C and more evidently in the >20˚C condition than in conditions where the temperature was between -4 and 20˚C. Sprint distance also showed a decrease when the temperature at the start of the match was 20˚C or higher when compared to lower temperature ranges (Table 1). Currently, scant literature is available examining the impact of low temperatures on physi- cal match performance in elite soccer. Carling et al. [5], analyzed physical match performance in players from a French Ligue 1 team, and showed no detrimental effect of low temperature on distances covered in the 0.0–4.0, 4.0–5.5, and >5.5 m/s speed ranges. However, the authors employed <5˚C as the lower temperature condition. Our findings showed no substantial effect of temperature in the 1 to 5˚C range, on physical match outcomes, while the effects of low tem- perature became more evident at -5˚C or less. Therefore, the present results are consistent with those of Carling and colleagues [5]. Link and Weber [11] examined the effects of ambient temperature on total distance covered by players from 38 teams from the top two German lea- gues, collected across 1211 league matches. Those authors used a lower temperature range of <-5˚C, as in the present study, and also reported no substantial differences in the total distance covered between matches starting at a temperature of -5˚C or less and matches played in the -4˚ to 13˚C and 14 to 27˚C temperature ranges. This observation is also consistent with our finding that total distance covered is unaffected by low temperatures (Tables 1 and 2). PLOS ONE Cold exposure and match running performance PLOS ONE \| https://doi.org/10.1371/journal.pone.0288494 July 11, 2023 6 / 9 A hypothesis that may partly explain why sprint distance may decrease during soccer matches played in very cold (<-5˚C) temperatures is that of a reduced sprinting capacity, that may occur due to the negative effects of low temperature on muscle function and power pro- duction. Indeed, it has been shown that speed, as well as agility and lower-limb power, is impaired immediately after the application of different cryotherapy modalities [18–20]. To our knowledge, only the study of Carlson et al. [21] investigated the effects of whole-body exposure to low temperature on the outcomes of lower-limb power, agility and sprint tests. Reduced ver- tical jump and agility performance was observed, although unaffected sprinting performance, in recreational athletes, after a 15-minute cool (6.1˚C) exposure vs. a thermoneutral (17.2˚C) environment was reported [21]. However, the temperature administered in this study for the cool environment is higher than the lower ranges of ambient temperature in the present sam- ple of soccer matches (<-5˚C). A colder temperature (-14˚C) was utilized by Wiggen and col- leagues [22] to examine the effect of cold exposure on double poles sprint performance in cross-country skiers. The authors reported lower performance in terms of power output at 14˚C vs. 6˚C temperatures. Such evidence supports the assumption that very cold ambient temperature can impair sprinting performance in soccer players, however future studies are warranted to further explore the effect of low ambient temperature on sprinting performance in training and competitive match-play in players from different leagues. A further factor that may potentially have an impact on reduced sprint distance at low tem- peratures is possibly related to playing surface conditions. At sub-zero temperatures, the play- ing turf may be frozen or partly frozen and slippery, decreasing stability and traction, increasing the player ground contact and surface interaction, and therefore making it more difficult for players to execute maximal or near-maximal actions, including sprints, than in normal conditions. Additionally, the team playing strategy may be altered due to elements such as unpredictable ball roll, bounce and ball-speed, leading players to execute more shorter passes and thus reducing the number of longer, forward passes and subsequent physical actions involving long (>30 m) sprinting. In this respect, in future investigations it would be practically interesting to assess how the technical and tactical performance is affected when matches are performed in cold or very cold conditions. Limitations Despite the findings, several limitations of this study were identified. Due to data availability, only ambient temperature at the start of the match was examined as an indicator of environmental conditions, while other atmospheric conditions such as atmospheric pressure, wind, wind child, humidity, heat index, wet-bulb globe temperature, precipitation and cloudiness that may also influence physical performance were not considered. However, depending on other factors such as kick-off time, ambient temperature could to some extent increase or decrease throughout the duration of approximately 2-hours of a soccer match, especially if the kick-off time is in the even- ing hours. A perspective for future studies is therefore to consider the average ambient tempera- ture during the match. Furthermore, our study lacked data related to other situational variables such as match location, that may also potentially modulate the impact of low temperatures on physical match performance, or others such as match result and quality of the teams that could also cause different running-based results considering the different scenario of the analyzed team. For such reasons, it is recommended to consider those variables in future studies. Conclusion The present study has shown that sub-zero ambient temperatures, especially when equal to or lower than -5˚C, are related to changes in the match running performance behavior in elite PLOS ONE Cold exposure and match running performance PLOS ONE \| https://doi.org/10.1371/journal.pone.0288494 July 11, 2023 7 / 9 soccer players. A novel finding of our study is that low temperatures are associated with reduced sprint performance. The present results may be of real practical interest to coaching staff who are responsible for improving and maintaining the health and well-being of soccer players that regularly play in cold or very cold conditions. Author Contributions Conceptualization: Ryland Morgans, Eduard Bezuglov, Dave Rhodes, Jose Teixeira, Toni Modric, Sime Versic, Rafael Oliveira. Formal analysis: Rocco Di Michele. Investigation: Ryland Morgans, Dave Rhodes, Jose Teixeira, Toni Modric, Sime Versic, Rafael Oliveira. Methodology: Ryland Morgans. Project administration: Ryland Morgans. Resources: Eduard Bezuglov. Software: Rocco Di Michele. Supervision: Ryland Morgans. Validation: Ryland Morgans, Eduard Bezuglov, Rafael Oliveira. Visualization: Ryland Morgans, Rocco Di Michele. Writing – original draft: Ryland Morgans, Rocco Di Michele, Rafael Oliveira. Writing – review & editing: Ryland Morgans, Eduard Bezuglov, Dave Rhodes, Jose Teixeira, Toni Modric, Sime Versic, Rafael Oliveira. References 1. Ozgunen KT, Kurdak SS, Maughan RJ, et al. Effect of hot environmental conditions on physical activity patterns and temperature response of football players. Scand J Med Sci Sports 2010; 20: 140–147. https://doi.org/10.1111/j.1600-0838.2010.01219.x PMID: 21029201 2. Nassis GP, Brito J, Dvorak J. et al. The association of environmental heat stress with performance: analysis of the 2014 FIFA World Cup Brazil. Br J Sports Med 2015; 49: 609–613. https://doi.org/10. 1136/bjsports-2014-094449 PMID: 25690408 3. Chmura P, Konefal M, Andrzejewski M. et al. Physical activity profile of 2014 FIFA World Cup players, with regard to different ranges of air temperature and relative humidity. Int J Biometerol 2017; 61: 677– 684. https://doi.org/10.1007/s00484-016-1245-5 PMID: 27618828 4. Draper G, Wright MD, Ishida A. et al. Do environmental temperatures and altitudes affect physical out- puts of elite football athletes in match conditions? A systematic review of the ‘real world’ studies. Sci Med Football 2022; In press. https://doi.org/10.1080/24733938.2022.2033823 PMID: 35068376 5. Carling C, Dupont G, Le Gall F. et al. The effect of a cold environment on physical activity profiles in elite soccer match-play. Int J Sport Med 2011; 32: 542–545. https://doi.org/10.1055/s-0031-1273711 PMID: 21563033 6. No M and Kwak HM. Effects of environmental temperature on physiological responses during submaxi- mal and maximal exercises in soccer players. Integr Med Res 2016; 5: 216–222. https://doi.org/10. 1016/j.imr.2016.06.002 PMID: 28462121 7. Armstrong LE. Nutritional strategies for football: counteracting heat, cold, high altitude, and jet lag. J Sports Sci 2006; 24: 723–740. https://doi.org/10.1080/02640410500482891 PMID: 16766501 8. FIFA. R&D test report. Electronic Performance & Tracking Systems. https://www.fifa.com/technical/ football-technology/resource-hub?id=aca57303eb0449f2835ac891b1beeb24 (2022, accessed 16 May 2023). 9. Morgans R, Bezuglov E, Orme P. et al. Technical and Physical Performance Across Five Consecutive Seasons in Elite European Soccer. Int J Sports Sci Coach 2022; 0: 1–9. PLOS ONE Cold exposure and match running performance PLOS ONE \| https://doi.org/10.1371/journal.pone.0288494 July 11, 2023 8 / 9 10. Morgans R, Orme P, Di Michele R. Impact of technical and physical performance on match outcome over five elite European soccer seasons. Int J Sports Med Phys Fitness 2022; In press. https://doi.org/ 10.23736/S0022-4707.22.14018-1 PMID: 35816143 11. Link D and Weber H. Effect of ambient temperature on pacing in soccer depends on skill level. J Strength Cond Res 2017; 31: 1766–1770. https://doi.org/10.1519/JSC.0000000000001013 PMID: 25992664 12. Hopkins W, Marshall S, Batterham A, et al. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exercise 2009; 41: 3–13. https://doi.org/10.1249/MSS. 0b013e31818cb278 PMID: 19092709 13. Kleiman E. Data Management Tools for Real-Time Monitoring/Ecological Momentary Assessment Data [R Package EMAtools version 0.1.3], 2017; Available online: https://cran.r-project.org/web/packages/ EMAtools/index.html (Accessed 25 Jul 2022). 14. Cohen J. Statistical power analysis for the behavioral sciences (2nd Edition). 2013; Academic Press. https://doi.org/10.4324/9780203771587. 15. Chmura P, Liu H, Andrzejewski M. et al. Is there meaningful influence form situational and environmen- tal factors on the physical and technical activity of elite football players? Evidence from the data of 5 consecutive seasons of the German Bundesliga. PLoS One 2021; 16: 1–16. 16. Zhou C, Hopkins WG, Mao W. et al. Match performance of soccer teams in the Chinese super league– effects of situational and environmental factors. Int J Environ Res Public Health 2019; 16: 1–13. https:// doi.org/10.3390/ijerph16214238 PMID: 31683754 17. Mohr M, Nybo L, Grantham J. et al. Physiological responses and physical performance during football in the heat. PLoS One 2012; 7: e39202. https://doi.org/10.1371/journal.pone.0039202 PMID: 22723963 18. Cross KM, Wilson RW, Perrin DH. et al. Functional performance following an ice immersion to the lower extremity. J Athl Train 1996; 31: 131–136. 19. Richendollar ML, Darby LA, Brown TM. et al. Ice bag application, active warm-up, and 3 measures of maximal functional performance. J Athl Train 2006; 41: 364–370. PMID: 17273459 20. Patterson SM; Udermann BE; Doberstein ST. et al. The effect of cold whirlpool on power, speed, agility, and range of motion. J Sports Sci Med 2008; 7: 387–394. 21. Carlson LA, Fowler C, Lawrence MA. et al. Agility and vertical jump performances are impacted by acute cold exposure. J Strength Cond Res 2019; 33: 1648–1652. 22. Wiggen ON, Waagaard SH, Heidelberg CT. et al. Effect of cold conditions on double poling sprint per- formance of well-trained male cross-country skiers. J Strength Cond Res 2013; 27: 3377–3383. https:// doi.org/10.1519/JSC.0b013e3182915e7d PMID: 23539076 PLOS ONE Cold exposure and match running performance PLOS ONE \| https://doi.org/10.1371/journal.pone.0288494 July 11, 2023 9 / 9	The relationship between ambient temperature and match running performance of elite soccer players.	07-11-2023	Morgans, Ryland,Bezuglov, Eduard,Rhodes, Dave,Teixeira, Jose,Modric, Toni,Versic, Sime,Di Michele, Rocco,Oliveira, Rafael	eng
PMC3518245	RESEARCH ARTICLE Open Access Comparison of vertical ground reaction forces during overground and treadmill running. A validation study Bas Kluitenberg1, Steef W Bredeweg1, Sjouke Zijlstra1, Wiebren Zijlstra2,3 and Ida Buist1 Abstract Background: One major drawback in measuring ground-reaction forces during running is that it is time consuming to get representative ground-reaction force (GRF) values with a traditional force platform. An instrumented force measuring treadmill can overcome the shortcomings inherent to overground testing. The purpose of the current study was to determine the validity of an instrumented force measuring treadmill for measuring vertical ground-reaction force parameters during running. Methods: Vertical ground-reaction forces of experienced runners (12 male, 12 female) were obtained during overground and treadmill running at slow, preferred and fast self-selected running speeds. For each runner, 7 mean vertical ground-reaction force parameters of the right leg were calculated based on five successful overground steps and 30 seconds of treadmill running data. Intraclass correlations (ICC(3,1)) and ratio limits of agreement (RLOA) were used for further analysis. Results: Qualitatively, the overground and treadmill ground-reaction force curves for heelstrike runners and non-heelstrike runners were very similar. Quantitatively, the time-related parameters and active peak showed excellent agreement (ICCs between 0.76 and 0.95, RLOA between 5.7% and 15.5%). Impact peak showed modest agreement (ICCs between 0.71 and 0.76, RLOA between 19.9% and 28.8%). The maximal and average loading-rate showed modest to excellent ICCs (between 0.70 and 0.89), but RLOA were higher (between 34.3% and 45.4%). Conclusions: The results of this study demonstrated that the treadmill is a moderate to highly valid tool for the assessment of vertical ground-reaction forces during running for runners who showed a consistent landing strategy during overground and treadmill running. The high stride-to-stride variance during both overground and treadmill running demonstrates the importance of measuring sufficient steps for representative ground-reaction force values. Therefore, an instrumented treadmill seems to be suitable for measuring representative vertical ground-reaction forces during running. Keywords: Running, Kinetics, Biomechanics, Validity, Overuse injuries Background One major drawback in measuring ground-reaction forces during running is that it is time consuming to get representative ground-reaction force (GRF) values with a traditional force platform. A single force platform is only capable of measuring GRFs of one single stance phase per trial [1,2]. Therefore, multiple force platforms are necessary for measuring consecutive steps which is space consuming and expensive. The limited length of a run- way, also makes it difficult to simulate natural running at a constant speed in a laboratory situation [3]. For de- tection of small differences in GRFs during running, however, it is important to record sufficient steps during a stable running pattern [4]. An instrumented treadmill capable of measuring GRFs can overcome the limitations inherent to overground GRF testing during running at a short runway. With an instrumented treadmill it is possible to measure GRFs of multiple steps during Correspondence: b.kluitenberg@umcg.nl 1Center for Sports Medicine, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, The Netherlands Full list of author information is available at the end of the article © 2012 Kluitenberg et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Kluitenberg et al. BMC Musculoskeletal Disorders 2012, : http://www.biomedcentral.com/1471-2474// one trial without interruptions in running speed, result- ing in a more stable running pattern during the measurements [3]. In running, most runners make first ground contact with the posterior part of the foot, this is called heel- strike running. This running style results in a typical ver- tical GRF force-time curve that is characterized by two peaks, the impact peak and the active peak, as depicted in Figure 1. Magnitude of the impact peak is speed depen- dent and occurs during the first 10% of stance (10-30ms) [5]. The active peak is reached approximately during mid- stance and can last up to 200ms. The absence of a separ- ate impact peak in the force-time curve is typical for a non-heelstrike runner, as depicted in Figure 1 [6]. Besides a vertical component, GRFs also have an anterior- posterior and medio-lateral component. During running, the anterior-posterior force component shows a typical braking and propulsive phase while the medio-lateral force component is characterized by more variability [7]. Compared to the vertical GRF component, anterior- posterior and medio-lateral forces are small [7]. An underlying assumption when using a treadmill for running analysis is that running on a treadmill is similar to overground running. A comparison of spatio- temporal variables during overground and treadmill running was made in several studies. During treadmill running, runners tend to run with a shortened stride length and an increased stride rate [3,8,9]. Despite of these spatio-temporal differences, only small differences in knee flexion and a more flattened landing style during treadmill running were observed [3,10]. An overground- treadmill comparison with respect to GRFs was made in only two studies [1,3]. No systematic errors or extraor- dinary differences in vertical GRFs were found. Impact peaks and loading rates, however, have not been studied in these previous studies. The purpose of this study was to determine the valid- ity of a custom made instrumented force measuring treadmill to measure vertical GRF parameters during running. Validation of the treadmill was performed by comparing overground and treadmill measured vertical GRF parameters during running. Methods Participants Twenty-four experienced runners (12 male, 12 female) between 18 and 35 years old participated in this study. The runners were voluntarily recruited by contacting two local track and field clubs. The criteria for inclusion in this study included a minimal training frequency of two times a week for at least a period of one year. Run- ners who reported an injury at time of measurement were excluded. Both heelstrike and non-heelstrike run- ners were allowed to participate in this study. All partici- pants signed an informed consent before measurements started. The study was approved by the Medical Ethical Committee at the University Medical Center Groningen, The Netherlands; M12.112668. Overground measurements During the overground measurements, GRFs were mea- sured at three different individual speed conditions. Par- ticipants were instructed to run at their preferred speed (running speed for a normal endurance run), slower speed (running speed during a warming-up), and a faster speed (10km race speed) respectively. GRFs were col- lected with a force platform (0.60m x 0.40m) which was mounted in the middle of a 17.5m long runway. The sample frequency of the force platform was set at 1000Hz. Running speed was monitored with two pairs of photocells placed 2.5m before and after the force platform. 0 20 40 60 80 100 0 0.5 1 1.5 2 2.5 3 % Of Stance Vertical-GRF (BW) Fz2 Fz1 LR tFz1 tFz2 Contact Time (CT) 0 20 40 60 80 100 0 0.5 1 1.5 2 2.5 3 % Of Stance Vertical-GRF (BW) Fz2 tFz2 LR Contact Time (CT) Heelstrike landing Non-Heelstrike landing Figure 1 Outcome measures in a typical vertical ground- reaction force (GRF) curve for a heelstrike runner and a non- heelstrike runner. Figure is created from personal data. Kluitenberg et al. BMC Musculoskeletal Disorders 2012, : Page 2 of 8 http://www.biomedcentral.com/1471-2474// Before the actual overground measurements started, the participants performed several accommodation runs. During these accommodation runs, the exact start pos- ition for the measurements was determined. The start position was based on the position of foot placement at the force platform. Foot strike of the right foot should be completely at the force platform without an alteration in running pattern. An alteration can indicate aiming for the force platform, which modifies the GRF pattern [11]. Position of foot placement and running pattern were evaluated on sight. When participants were able to run several trials at the same speed, while landing with the right foot completely placed at the force platform, with- out visible alterations in running pattern, the actual measurements started. Since the participants were tested at three different speed conditions, accommodation runs were performed for each speed condition (preferred, slow and fast). The accommodation runs for the pre- ferred speed were combined with a short warming-up and took longer (approximately 10 min), where the subsequent accommodation runs took approximately 5 minutes. During the actual measurements, GRF data were cap- tured until five clean strikes of the right leg within a 5% speed range were recorded for all speed conditions. Trials with visible alterations in running pattern were not included in these five clean strikes. Afterwards, the mean running speed of the five steps was calculated for each speed condition. Treadmill measurements In this study, an instrumented treadmill (Entred, Force- link, Culemborg, The Netherlands) with a running sur- face of 1.60m by 0.60m that was driven by a 1.8 kW motor was used to measure vertical GRFs during running. The treadmill was equipped with three strain gage force transducers (ACB-500kg, Vishay Revere Transducers, Breda, The Netherlands) which were con- nected to bridge amplifiers. The force transducers were mounted on a stiff plate which was enforced with a non-deformable frame and were positioned as shown in Figure 2. The signals from the amplifiers were sampled at 1000 Hz, digitized into a 16-bit signal by an AD con- verter (PCI-6220, National Instruments, Austin, TX, USA) and were connected to a computer. Before the treadmill measurements started, partici- pants started with an accommodation run of 10 minutes at 10 km·h-1. After this accommodation period, partici- pants were tested at three different individual speed con- ditions (slow, preferred and fast). Treadmill speed was matched to the average overground running speed for each speed condition because GRF parameters are speed dependent [7]. The three speed conditions lasted three minutes and were offered in random order. GRFs were recorded during the last 30 seconds of each speed condi- tion. When treadmill measurements were finished, parti- cipants were given the opportunity for cooling-down at the treadmill. All measurements were conducted while participants were running in their personal running shoes. Data analysis Vertical force data from both the force platform and the treadmill were processed using custom programs written in MATLAB R2010a (The MathWorks, Inc, Natick, MA). All steps which were recorded during the treadmill measurement were entered into the analysis. A 13-point moving average low-pass filter with a cut-off frequency of 33.3Hz was used to filter the GRF data that was recorded during the overground and treadmill measure- ments. Foot strikes in the overground and treadmill data were detected with a threshold of 30 Newton for impact and toe-off phase. Outcome measures for all right foot steps were identified, as described in Table 1. For each speed condition outcome measures of each participant were averaged. A distinction between heelstrike and S1 S2 S3 Front 0.60m 1.60m Figure 2 Positioning of the three strain gage force transducers S1, S2 and S3. Kluitenberg et al. BMC Musculoskeletal Disorders 2012, : Page 3 of 8 http://www.biomedcentral.com/1471-2474// non-heelstrike landing patterns was made based on the existence of an impact peak Fz1. Peak values Fz1 and Fz2 and the loading-rate were normalized to bodyweight. Statistical analysis A within-subject repeated measures design was used to determine the validity of the instrumented treadmill for measuring vertical GRF parameters during running. Therefore, a two-way mixed-effects, consistency, single measure intraclass correlation coefficient (ICC(3,1)) model was used to examine the agreement between overground and treadmill measured GRF-parameters. Interpretation of the intraclass coefficients were as fol- lows: poor (0 – 0.39), modest (0.4 – 0.74), or excellent (0.75 – 1) [12]. ICCs were calculated by using SPSS (SPSS inc. Version 18.0, Chicago, IL, U.S.A.). Besides the intraclass correlations, Bland-Altman plots were used to examine the agreement between overground and tread- mill measurements [13]. These plots were made for each outcome measure and each speed condition. The limits of agreement (LOA) were calculated (mean difference +/− 1.96 times the standard deviation of the difference). Also ratio limits of agreement (RLOA) were calculated to express the LOA as percentage of the mean overground-treadmill value. The upper and lower LOA and the RLOA provide insight into how much random variation may be influencing the measurements. Results Ground-reaction force (GRF) parameters of a different landing strategy cannot be compared, therefore only GRF parameters of participants who showed a consistent landing strategy during overground and treadmill run- ning within a speed condition were examined. During overground running at preferred speed, 19 participants showed a heelstrike (HS) landing, while 16 of these run- ners showed a HS landing during treadmill running. This shows that 82.4% of the runners used a similar landing strategy during treadmill running at preferred speed. Results for the two other speeds can be found in Table 2. Qualitatively, the overground and treadmill GRF curves for both HS and NHS running at slow, preferred and fast running speeds, were very similar, as can be seen in Figure 3. In Table 3 a quantitative evaluation of the vertical GRF-parameters of both HS and NHS run- ners can be found. The levels of agreement between overground and treadmill running for the time related variables (tFz1, tFz2 and CT) were excellent (ICCs be- tween 0.76 and 0.95 and RLOAs between 5.7% and 15.5%). Also the active peak (Fz2) measured with both devices showed excellent agreement (ICCs between 0.77 and 0.89, RLOAs between 7.8% and 9.9%). Modest agreement was found for the impact peak, Fz1 (ICCs be- tween 0.71 and 0.76, RLOAs between 19.9% and 28.8%). Maximal loading rate (LR) and average loading rate (ALR) also showed modest to excellent intraclass corre- lations (ICCs between 0.70 and 0.89), however the ratio limits of agreement were higher (RLOA values between 34.3% and 45.4%). Discussion The instrumented treadmill is capable of measuring ver- tical ground-reaction forces (GRFs) during running and seems to be a usable tool for simulating overground run- ning kinetics. The results of this study demonstrated that the instrumented treadmill is a highly valid tool for the assessment of the vertical GRF parameters: tFz1, tFz2, CT and Fz2 and moderately valid for the assess- ment of Fz1, LR and ALR for runners who showed a consistent landing strategy during overground and tread- mill running. A qualitative evaluation of the overground and treadmill vertical GRF curves as shown in Figure 3, Table 1 Definition of outcome measures, as displayed in Figure 1 Outcome measure Description Fz1 Local maximum in the vertical GRF data, normalized to body weight (BW). Fz2 Maximum value in the vertical GRF data, normalized to BW. LR The steepest part of the vertical GRF curve, from stance to impact peak. Expressed in BW/s. ALR Average loading rate, the slope of the line from 20% to 80% of Fz1. Expressed in BW/s. tFz1 Time from heelstrike to Fz1 in ms. tFz2 Time from heelstrike to Fz2 in ms. CT Contact time, from heelstrike to toe-off in ms. Outcome measures for the overground and treadmill data were identified with the same routine. Foot strikes were detected with a threshold of 30 Newton for both heelstrike and toe-off. Table 2 Overground landing strategy compared to treadmill landing strategy, displayed as number of persons and corresponding percentages of runners who showed a consistent landing strategy Heelstrike landing Non-heelstrike landing Overground Treadmill Consistency Overground Treadmill Consistency Slow 17 14 82.4% 7 5 71.4% Preferred 19 16 84.2% 5 5 100.0% Fast 12 12 100.0% 12 6 50.0% Kluitenberg et al. BMC Musculoskeletal Disorders 2012, : Page 4 of 8 http://www.biomedcentral.com/1471-2474// demonstrated that the vertical GRFs for both the heel- strike (HS) runners and the non-heelstrike (NHS) run- ners were similar during overground and treadmill running. The excellent intraclass correlations and low limits of agreement for contact time (CT), time to im- pact peak force (tFz1) and time to the active peak (tFz2) reflect this qualitative similarity. After all, these para- meters show that the timing of peak values in the verti- cal GRF curve is not different for overground and treadmill running. The qualitative similarity of these GRF curves was also observed in other studies [1,3]. In the current study, the overground and treadmill mea- sured active peak (Fz2) showed no noteworthy differ- ences. This is in accordance with the results of Riley et al., who also compared overground and treadmill run- ning kinetics in a group of 20 runners [3]. Overground and treadmill measured impact peaks (Fz1), maximal loading rates (LR) and average loading rates (ALR), showed less consistent results with modest to excellent intraclass correlations and wider limits of agreement. To our knowledge this study is the first to compare overground and treadmill measured impact peaks and loading rates during running, therefore it is not possible to evaluate these results with previous studies. For an overground-treadmill comparison with respect to vertical GRF parameters, a consistent landing strategy during both running conditions (overground and tread- mill) is required. While most runners showed a consist- ent landing strategy during overground and treadmill running, some runners switched to another landing strategy. During slow and preferred running speed, these inconsistent runners mostly switched from an over- ground HS landing to a NHS landing during treadmill running. Considering that this behavior is in line with the more flattened landing style as observed in a previ- ous study [14], it is likely that these inconsistencies in landing strategy are the result of accommodation to treadmill running. At fast self selected speed, however, the inconsistent runners switched from a NHS to a HS landing during treadmill running. These differences in landing strategy may indicate overground and treadmill differences in anterior-posterior GRFs which were not 0 20 40 60 80 100 0 0.5 1 1.5 2 2.5 3 % of stance Vertical-GRF (BW) Slow running speed (N=14) 0 20 40 60 80 100 0 0.5 1 1.5 2 2.5 3 % of stance Vertical-GRF (BW) Preferred running speed (N=16) 0 20 40 60 80 100 0 0.5 1 1.5 2 2.5 3 % of stance Vertical-GRF (BW) Fast running speed (N=12) 0 20 40 60 80 100 0 0.5 1 1.5 2 2.5 3 % of stance Vertical-GRF (BW) Slow running speed (N=5) 0 20 40 60 80 100 0 0.5 1 1.5 2 2.5 3 % of stance Vertical-GRF (BW) Preferred running speed (N=5) 0 20 40 60 80 100 0 0.5 1 1.5 2 2.5 3 % of stance Vertical-GRF (BW) Fast running speed (N=6) Overground Treadmill Overground Treadmill Non-heelstrike running Heelstrike running Figure 3 Average GRF plots from all runners for overground (mean, solid black line; ± SD, dotted black lines) and treadmill running (mean, solid grey line) at slow, preferred and fast running speed for heelstrike and non-heelstrike runners. Forces are in body weight (BW). Kluitenberg et al. BMC Musculoskeletal Disorders 2012, : Page 5 of 8 http://www.biomedcentral.com/1471-2474// Table 3 Outcome measures for overground and treadmill running OG mean ± SD TM mean ± SD ICC(3,1) (95%CI) Mean diff (LOA) diff (lowLim, upLim) RLOA (%) Fz1 (BW) HS Slow 1.67 ± 0.26 1.70 ± 0.23 0.74 (0.37, 0.91) 0.03 (−0.32, 0.38) 20.8 Preferred 1.94 ± 0.45 1.93 ± 0.30 0.71 (0.35, 0.89) −0.01 (−0.57, 0.55) 28.8 Fast 1.94 ± 0.25 2.06 ± 0.32 0.76 (0.35, 0.92) 0.12 ( −0.28, 0.52) 19.9 Fz2 (BW) HS Slow 2.54 ± 0.20 2.53 ± 0.18 0.77 (0.49, 0.91) −0.02 (−0.27, 0.23) 9.9 Preferred 2.70 ± 0.26 2.65 ± 0.25 0.89 (0.76, 0.96) −0.03 (−0.25, 0.17) 7.9 Fast 2.77 ± 0.24 2.70 ± 0.22 0.86 (0.67, 0.95) −0.06 (−0.27, 0.15) 7.8 NHS Slow 2.56 ± 0.17 2.55 ± 0.20 Preferred 2.61 ± 0.15 2.58 ± 0.13 Fast 2.79 ± 0.15 2.78 ± 0.20 LR (BW/s) HS Slow 81.11 ± 25.62 87.28 ± 23.39 0.76 (0.47, 0.90) 3.25 (−28.62, 35.12) 39.9 Preferred 95.34 ± 26.67 105. 33 ± 25.08 0.80 (0.57, 0.91) 6.11 (−26.21, 38.42) 34.3 Fast 104.40 ± 29.29 118.08 ± 33.73 0.70 (0.36, 0.88) 7.17 (−37.69, 52.02) 42.7 NHS Slow 70.03 ± 14.68 65.09 ± 13.74 Preferred 77.00 ± 22.35 74.25 ± 16.47 Fast 95.81 ± 26.02 87.41 ± 18.74 ALR (BW/s) HS Slow 68.89 ± 20.26 73.92 ± 20.22 0.84 (0.63, 0.93) 2.98 (−24.74, 30.70) 45.3 Preferred 82.14 ± 21.38 88.70 ± 20.75 0.89 (0.74, 0.95) 3.60 (−23.01, 30.21) 36.4 Fast 90.70 ± 23.66 100.77 ± 29.10 0.86 (0.67, 0.95) 4.08 (−31.26, 39.42) 45.4 NHS Slow 33.96 ± 6.07 31.21 ± 5.01 Preferred 47.09 ± 22.92 33.78 ± 4.20 Fast 43.63 ± 13.89 36.00 ± 4.20 tFz1 (ms) HS Slow 35 ± 4.08 35 ± 4.86 0.76 (0.40, 0.92) 0.0 ( −5.4, 5.4) 15.5 Preferred 34 ± 4.42 34 ± 3.35 0.82 (0.56, 0.93) 0.3 ( −4.4, 5.0) 13.8 Fast 32 ± 5.00 33 ± 4.88 0.87 (0.61, 0.96) 0.6 ( −3.7, 4.8) 13.0 tFz2 (ms) HS Slow 112 ± 13.55 109 ± 10.22 0.84 (0.63, 0.94) −1.8 (−15.4, 11.8) 12.6 Preferred 102 ± 13.28 100 ± 12.19 0.94 (0.85, 0.97) −1.6 (−10.1, 6.8) 8.5 Fast 99 ± 10.00 96 ± 11.47 0.87 (0.68, 0.95) −3.0 (−13.5, 7.5) 11.0 NHS Slow 102 ± 13.00 103 ± 15.00 Preferred 99 ± 12.00 98 ± 11.00 Fast 92 ± 80 0 91 ± 10.00 CT (ms) HS Slow 258 ± 22.00 254 ± 21.13 0.92 (0.80, 0.97) −4.0 (−21.4, 13.4) 6.9 Preferred 232 ± 23.34 232 ± 20.49 0.92 (0.82, 0.97) −2.0 (−17.2, 13.2) 6.6 Fast 223 ± 21.00 220 ± 21.14 0.95 (0.87, 0.98) −3.3 (−15.6, 9.1) 5.7 NHS Slow 240 ± 17.00 237 ± 19.00 Preferred 229 ± 12.00 222 ± 12.00 Fast 213 ± 12.00 206 ± 12.00 Intraclass correlations, mean-differences with limits of agreement (LOA), and ratio limits of agreement (RLOA) were reported. Both HS and NHS runners were taken into account in the statistical analysis. Number of participants: HS (slow: N=14, preferred: N=16, fast: N=12), NHS (slow: N=5, preferred: N=5, fast: N=6). HS: Heelstrike-runner, NHS: Non-Heelstrike-runner, CI: Confidence Interval, LOA: Limit of Agreement, RLOA: Ratio Limit of Agreement, OG: Overground, TM: Treadmill, BW: Body Weight. Slow running speed: HS runners: 11.0 ± 1.3 km·h-1, NHS runners: 10.9 ± 1.5 km·h-1. Preferred running speed: HS runners: 12.7 ± 1.6 km·h-1, NHS runners: 11.8 ± 1.5 km·h-1. Fast running speed: HS runners: 14.1 ± 2.0 km·h-1, NHS runners: 13.9 ± 1.9 km·h-1. Kluitenberg et al. BMC Musculoskeletal Disorders 2012, : Page 6 of 8 http://www.biomedcentral.com/1471-2474// compared in the current study. The results of this study demonstrated that the inconsistencies in landing strategy are smallest during running at preferred speed. There- fore, to maximize certainty, it can be recommended to determine landing strategy with a treadmill measure- ment at preferred running speed. The use of a treadmill in a research setting has been subject of much debate. Several factors are mentioned which may cause biomechanical differences between overground and treadmill running [9]. First, non- mechanical factors as accommodation to the changed visual and auditory surroundings or fear during treadmill running may result in differences between overground and treadmill running biomechanics [15]. Second, differ- ences in air resistance may have an effect on treadmill running form [16]. The effects of air resistance on run- ning kinematics, however, will only be visible during running at high speeds [17]. Third, intra-stride belt speed variations, due to an energy exchange between the treadmill and the runner, can cause differences in run- ning kinematics compared to overground running. In particular low powered treadmills are more sensitive for opposite forces acting on the belt during running, result- ing in larger belt speed variations. These variations in belt speed may lead to biomechanical differences during treadmill running when compared to overground run- ning [15]. Fourth, during running, leg stiffness is adjusted to the stiffness of the running surface [18]. Adjusting leg stiffness results in subtle changes in the kinematics of the lower extremity [19]. Therefore, differ- ences in running surface may lead to biomechanical dif- ferences when comparing overground and treadmill running. Several studies compared overground and treadmill running biomechanics [3,8,14]. Even though runners tend to run with a shortened stride length and an increased stride rate during treadmill running [3,8,9], overground and treadmill running kinematics are re- markably similar [3,9,14]. Only small differences in knee and ankle joint kinematics were reported. Nigg et al. observed a more flattened landing style during treadmill running [14]. Riley et al. did not find differences in ankle joint kinematics, but did find differences in minimal and maximal knee flexion [3]. Maximal knee flexion was lower and minimal flexion was higher during treadmill running, which could be a result of the observed de- crease in flight phase and higher stride rate [3]. Thus, despite the theoretical factors which may influence treadmill running biomechanics, only small differences in overground and treadmill kinematics were observed. In the current study, also no significant differences in GRF parameters between overground and treadmill run- ning were found. These findings are in line with previ- ous studies where overground and treadmill running kinetics were compared [1,3]. The between person vari- ance in Fz1, LR and ALR during both overground and treadmill running was high, as indicated by the high standard deviations for these parameters. Stride-to-stride variance for these parameters was also high, which demonstrates the importance of measuring sufficient steps for representative GRF values. This is especially important for detecting small differences between differ- ent conditions or persons [20]. Because a treadmill makes it possible to measure multiple steps during one test trial, it can be argued that a treadmill measurement is more suitable for detecting small differences in verti- cal GRFs during running. However, this assumption was not assessed in the current study. Since the treadmill used in the current study only is capable of measuring vertical GRFs it cannot be used to assess joint kinetics using the standard inverse dynamics methodology, because anterior-posterior and medio- lateral GRFs are also needed for these calculations. It should also be noted that the inconsistencies in landing strategy may indicate differences in anterior-posterior GRFs between overground and treadmill running. Fur- thermore, this instrumented treadmill would have lim- ited usefulness for walking studies, because the double support phase in walking cannot be measured directly. For measuring GRFs during walking, an instrumented split-belt treadmill may be more convenient. A limitation of this study was that participants first performed the overground measurements after which the treadmill measurements started. Due to this fixed order of the measurements, fatigue may have influenced the later treadmill measurements [21]. Nevertheless, this influence is expected to be low, since all participants were experienced runners who did not have to deliver a maximal performance and participants did not show signs of exaggerated fatigue during the measurements. Conclusions The results of this study demonstrated the treadmill is a moderate to highly valid tool for the assessment of verti- cal GRFs during running for runners who showed a con- sistent landing strategy during overground and treadmill running. Therefore, an instrumented treadmill can be used to measure vertical GRF parameters which corres- pond to normal overground values during running. In a future study, the treadmill can be used to measure vertical GRF parameters in a large group of runners, for instance to identify possible kinetic risk-factors for run- ning related injuries prospectively. Abbreviations GRF: Ground-reaction force; HS: Heelstrike; NHS: Non-heelstrike; Fz1: Impact peak; Fz2: Active peak; LR: Loading Rate; ALR: Average Loading Rate; CT: Contact Time; tFz1: Time to impact peak; tFz2: Time to active peak; Kluitenberg et al. BMC Musculoskeletal Disorders 2012, : Page 7 of 8 http://www.biomedcentral.com/1471-2474// BW: Body Weight; ICC: Intraclass correlation; LOA: Limit of Agreement; RLOA: Ratio Limit of Agreement. Competing interests In this study, an instrumented treadmill was used. The research group had no financial or other interest in the treadmill product or distributor of the treadmill. The project was not dependent on external financial assistance and the authors declare that they have no competing interests. Authors’ contributions SB, SZ, WZ and IB provided advice on the study design. BK recruited the participants, was responsible for the data acquisition/analysis and wrote the article. WZ provided advice on the data analysis. SB, SZ, WZ and IB contributed to the content of the article. All authors read and approved the final manuscript. Author details 1Center for Sports Medicine, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, The Netherlands. 2Center for Human Movement Sciences, University Medical Center Groningen, P.O. Box 196, Groningen, AD 9700, The Netherlands. 3Institute of Movement and Sport Gerontology, German Sport University Cologne, Cologne, Germany. Received: 22 May 2012 Accepted: 1 November 2012 Published: 27 November 2012 References 1. Kram R, Powell AJ: A treadmill-mounted force platform. J Appl Physiol 1989, 67(4):1692–1698. 2. Kram R, Griffin TM, Donelan JM, Chang YH: Force treadmill for measuring vertical and horizontal ground reaction forces. J Appl Physiol 1998, 85(2):764–769. 3. Riley PO, Dicharry J, Franz J, Della Croce U, Wilder RP, Kerrigan DC: A kinematics and kinetic comparison of overground and treadmill running. Med Sci Sports Exerc 2008, 40(6):1093–1100. 4. 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Challis J: The variability in running gait caused by force plate targeting. J Appl Biomech 2001, 17(1):77–83. 12. Fleiss J: The design and analysis of clinical experiments. New York: John Wiley; 1986. 13. Bland J, Altman D: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986, 327(8476):307–310. 14. Nigg BM, De Boer RW, Fisher V: A kinematic comparison of overground and treadmill running. Med Sci Sports Exerc 1995, 27(1):98–105. 15. Savelberg H, Vorstenbosch M, Kamman E, Weijer J, Schambardt H: Intra-stride belt-speed variation affects treadmill locomotion. Gait Posture 1998, 7(1):26–34. 16. Pugh L: Oxygen intake in track and treadmill running with observations on the effect of air resistance. J Physiol (Lond) 1970, 207(3):823–835. 17. van Ingen Schenau GJ: Some fundamental aspects of the biomechanics of overground versus treadmill locomotion. Med Sci Sports Exerc 1980, 12(4):257–261. 18. Ferris D, Liang K, Farley C: Runners adjust leg stiffness for their first step on a new running surface. J Biomech 1999, 8:787–794. 19. Dixon S, Collop A, Batt M: Surface effects on ground reaction forces and lower extremity kinematics in running. Med Sci Sports Exerc 2000, 32(11):1919–1926. 20. Bates BT, Osternig LR, Sawhill JA, James SL: An assessment of subject variability, subject-shoe interaction, and the evaluation of running shoes using ground reaction force data. J Biomech 1983, 16(3):181–191. 21. Morin J, Samozino P, Millet G: Changes in running kinematics, kinetics, and spring-mass behavior over a 24-h run. Med Sci Sports Exerc 2011, 43(5):829–836. doi:10.1186/1471-2474-13-235 Cite this article as: Kluitenberg et al.: Comparison of vertical ground reaction forces during overground and treadmill running. A validation study. BMC Musculoskeletal Disorders 2012 :. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color ﬁgure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit Kluitenberg et al. BMC Musculoskeletal Disorders 2012, : Page 8 of 8 http://www.biomedcentral.com/1471-2474//	Comparison of vertical ground reaction forces during overground and treadmill running. A validation study.	11-27-2012	Kluitenberg, Bas,Bredeweg, Steef W,Zijlstra, Sjouke,Zijlstra, Wiebren,Buist, Ida	eng
PMC9718750	1 Vol.:(0123456789) Scientific Reports \| (2022) 12:20843 \| https://doi.org/10.1038/s41598-022-25253-8 www.nature.com/scientificreports Premorbid beta blockade in sepsis is associated with a lower risk of a lactate concentration above the lactate threshold, a retrospective cohort study Liam Schneider 1, Debra Chalmers 1, Sean O’Beirn 1, Miles Greenberg 2 & Grant Cave 1* Sepsis and septic shock represent a significant worldwide mortality burden. A lactate greater than 4 mmol/L is associated with increased mortality in septic patients. This is the concentration at the “lactate threshold” where serum lactate concentrations rise markedly with increased workload in exercise. Hyperlactatemia in both sepsis and exercise is contributed to by adrenergic agonism which stimulates aerobic glycolysis, increasing lactate production and decreasing lactate clearance. Our hypothesis is that in patients with sepsis, treatment with beta blockers in the community will be associated with a lower probability of initial lactate ≥ 4 mmol/L. This was single centre retrospective cohort study. We used an in-house SQL Database for all admissions to ICU/HDU for the 2017–2020 calendar years. The dataset was filtered for an APACHE III Diagnosis of sepsis. T-tests were used for continuous data, Chi squared and Fisher’s exact test were used as appropriate to compare proportions. Logistic regression was used to investigate covariate effects. Of the 160 patient records analysed, 49 were prescribed beta blockers. A greater proportion of patients not prescribed beta blockers in the community had a first lactate ≥ 4 mmol/L (p = 0.049). This was robust to regression analysis. There was no difference in the proportion of patients with lactate ≥ 2 mmol/L (p = 0.52). In our cohort patients previously prescribed beta blockers were less likely to have a lactate of ≥ 4 mmol/mL. This supports the proposed mechanism that treatment with beta blockers increases the lactate threshold in sepsis. Further study is warranted. Sepsis is a major global health issue accounting for 20% of global mortality and 11 million deaths in 20171. Sepsis represents a significant proportion of intensive care unit (ICU) caseload2. Hyperlactatemia in sepsis and septic shock correlates with the severity of sepsis and associated mortality3. Reduction in serial lactate concentrations is associated with improved outcomes4,5, while goal directed therapy targeting lactate clearance is part of the 2021 surviving sepsis guidelines6. Given the position serum lactate holds in the assessment and management of sepsis, any premorbid factors which impact on lactate concentrations are of potential interest to the treating clinician. Lactate kinetics in health and the lactate threshold. Lactate is produced from pyruvate during gly- colysis. This reaction regenerates NAD+ for the production of adenosine triphosphate (ATP)7. Lactate is metab- olised via gluconeogenesis in liver and kidney and oxidation in skeletal muscles8–10. At rest, lactate clearance is evenly distributed between these two mechanisms; during moderate to high exercise 60–80% of lactate clearance occurs in skeletal muscle11. The lactate threshold refers to the level of exercise intensity at which serum lactate accumulation rapidly increases12. This has been attributed to anaerobic metabolism and/or to an imbalance between lactate production and lactate clearance in the absence of tissue hypoxia13,14. The lactate threshold can be taken as 4 mmol/L with reasonable accuracy15. This threshold in health has a correlate in critical illness, with a marked increased mortality in sepsis when initial lactate is greater than 4 mmol/L16. In healthy subjects exercis- ing at the lactate threshold, the amount of lactate metabolised is reduced compared to moderate exercise17. This suggests that part of the marked increase in lactate with increased exercise intensity above the lactate threshold OPEN 1Hawkes Bay Hospital Intensive Care Unit, Hastings, New Zealand. 2University of Notre Dame, Freemantle, Australia. *email: grantcave@gmail.com 2 Vol:.(1234567890) Scientific Reports \| (2022) 12:20843 \| https://doi.org/10.1038/s41598-022-25253-8 www.nature.com/scientificreports/ is due to decreased metabolic clearance of lactate, an effect which may be mediated by beta agonism. Adrenergic stimulation of phosphofructokinase and Na/K ATPase result in increased conversion of glucose to pyruvate and cytosolic ATP to ADP, respectively. This increases production of lactate from pyruvate and ADP via lactate dehydrogenase in the cellular cytosol, resulting in decreased uptake of lactate by non-exercising tissues under adrenergic stimulation. This proposed mechanism is illustrated below in Fig. 1. This physiology in health could also occur in sepsis—an adrenergically mediated decrease in cellular metabo- lism of lactate could hypothetically account for part of the hyperlactataemia in sepsis. Any such effect (and the effect of its blockade) may be more marked near the lactate threshold of 4 mmol/L. Hyperlactatemia in sepsis. Hypoperfusion and the resultant tissue hypoxia is traditionally held to explain the hyperlactatemia seen in sepsis7,18,19. Studies measuring partial pressures of oxygen in septic patients have however not demonstrated tissue hypoxia20–24, which has led to the consideration of other mechanisms7. Activation of beta-2 adrenergic receptors, stimulated as part of a stress response is one such explanation7,25. This pathway has been experimentally blocked at various points, with a resultant reduction in lactate22,26,27. Esmo- lol infusion in septic patients was found to reduce lactate, and in beta blocker overdose a lower than expected lactate concentration is seen for the degree of haemodynamic compromise28,29. Other mechanisms have been proposed. Impaired oxygen utilisation rather than inadequate oxygen delivery (DO2–VO2 mismatch) has been suggested, though there is little research correlating DO2–VO2 mismatch to lac- tate levels30–33. Gattinoni et al. hypothesised a combination of tissue hypoxia and inadequate oxygen utilisation as an explanation, finding that high lactate correlated in septic patients with either the highest or lowest central venous oxygen saturation (ScVO2)34. Other proposed mechanisms are the Warburg effect in immune activation and microcirculatory dysfunction35–37. It has been suggested that many or all of these suggested mechanisms play a role in hyperlactatemia in sepsis38. Premorbid beta blockade and lactate levels in sepsis. Five observational studies have assessed the effect of pre-morbid beta-blockade on lactate levels in patients presenting with sepsis39–43. Three trials found a significant reduction in lactate levels with beta-blockade39,41,43, while two showed no difference40,42. A recent meta-analysis of these studeis found lactate levels to be lower in patients on beta-blockers44. If the effect of beta blockade is mediated by an alteration of the lactate threshold, the effects on serum lactate would be expected to be greater in patient cohorts with higher lactates. Figure 2 represents data from these five studies. Figure 1. The proposed effect of beta adrenergic stimulation on lactate metabolism. 3 Vol.:(0123456789) Scientific Reports \| (2022) 12:20843 \| https://doi.org/10.1038/s41598-022-25253-8 www.nature.com/scientificreports/ All these trials have limitations—the majority of studies are retrospective observational in design; only one study was multicenter; two trials looked at lactate concentrations as a secondary outcome; inclusion criteria and sepsis definitions were variable between studies as was the timing of lactate measurement39–43. However, the pat- tern of data from the studies supports the theory that the effect of beta blockers on lactate is more pronounced in populations with higher lactate levels. Effect of premorbid beta-blockers on mortality. Pre morbid beta blockers may confer a mortality benefit in patients presenting with sepsis40,43–47, however the evidence is not homogenous48,49. The use of ultra- short acting beta blockers infusions in patient admitted with sepsis has shown a mortality benefit28,50–56. A meta- analysis including ten studies found premorbid beta blockade was associated with lower short term mortality in patients admitted with sepsis44. The proposed mechanisms of a mortality benefit include direct and indirect cardio protective effects; enhanced microvascular circulation due to reduction in coagulopathy and indirect immune modulatory effects42,43,47,48. In this regard evidence of a potentially beneficial effect on lactate metabo- lism of beta blockade would be of interest. Aim. To assess whether previous beta blocker prescription affected the probability that the first lactate in patients admitted from the Emergency Department to our intensive care unit with sepsis was above the lactate threshold. Hypothesis. The hypothesis was that in patients admitted with sepsis, treatment with beta blockers in the community will be associated with a lower probability of a lactate ≥ 4 mmol/L. Methods Setting. This was a retrospective cohort study conducted in the intensive care unit (ICU) of the Hawkes Bay Fallen Soldiers Memorial Hospital in Hastings, New Zealand. The hospital has 364 beds and approximately 1000 ICU and High Dependency Unit (HDU) admissions a year. The unit can provide mechanical ventilation and continuous renal replacement therapy, and cares for both adult and paediatric patients with medical and surgical conditions. Approval for the audit was granted by the Hawkes Bay DHB audit registration committee and the Northern B Health and Disability Ethics Committee of New Zealand. As only de-identified date was used a consent waiver was given by both committees. All research was performed in accordance with relevant guidelines/regulations. We used an In-house SQL Database that tracked all admissions to ICU/HDU for the 2017–2020 calendar years, which also allows collection of the ANZICS CORE Dataset. This was then filtered by a diagnosis of sepsis. A keyword search of free-text fields that supplemented the APACHE III Diagnosis that contained terms such as “Sepsis”, or “Septic Shock”, was carried out to identify those patients admitted with a co-diagnosis of sepsis. The data was then reviewed, and all duplicate or non-sepsis admissions were removed. Initial serum lactate level at our centre was measured on an ABL 800 Flex blood gas analyser (Radiometer Medical ApS, Bronshoj, Denmark). Serum lactate was defined as the first lactate measured during an Emergency Department (ED) presentation. Only patients admitted directly from the ED were included. Serum lactate, current medications, presenting vital signs, illness severity scores, laboratory data and mortality outcome were extracted from patients’ electronic medical record and the unit’s clinical database. Inclusion. A single investigator (GC) blinded to beta blocker treatment status evaluated the electronic medi- cal record to assess whether the clinical or microbiologic picture was consistent with infection. Exclusion. A single investigator (LS) reviewed the Emergency Department electronic medical record was reviewed and a qSOFA score calculated for each patient prior to evaluation of beta blocker status. Patients with 0 0.5 1 1.5 2 2.5 3 0 1 2 3 4 5 6 Difference in latate between BB and non BB Average lactate in trial Average lactate in trial vs Mean difference BB/non BB (95% CI) Figure 2. Average lactate in individual trials vs. difference in mean lactate for those prescribed and not previously prescribed beta blockers. 4 Vol:.(1234567890) Scientific Reports \| (2022) 12:20843 \| https://doi.org/10.1038/s41598-022-25253-8 www.nature.com/scientificreports/ qSOFA < 2 were excluded from analysis as patients with a qSOFA score < 2 are identified as low risk of sepsis57. Patients who did not have lactate measured were also excluded from analysis. Calculation of sample size. We used unpublished data from previous work where 25% fewer patients who were prescribed beta blockers in the community had an initial lactate > 4 mmol/L when compared with those not prescribed beta blockers (20% vs 45%). The study was powered under the assumptions that there would be the same proportion of beta blocked patients in the population and the proportions of patients with lactate > 4 mmol/L would be the same as in our previous work41. Under these assumptions, our study was > 80% powered at and alpha of 0.05 with 180 patient records included in the analysis. We anticipated that abstraction of four calendar years of data would provide these patient numbers. Statistical analysis. Our data were analysed using the Graphpad Prism version 9. Continuous data are presented as means with 95% confidence intervals. Proportions are presented as percentages with 95% confi- dence intervals. Students T-test was used for continuous data, Chi squared and Fisher’s exact test as appropriate to compare proportions. Logistic regression was used to investigate for significance and magnitude of covariate effects. Criterion for covariate entry into multiple regression modelling were a statistically significant difference in distribution of the variable between groups, an effect demonstrated in previously published work or a plausi- ble covariate effect and a p value < 0.1 on univariate regression. Ethics approval and consent to participate. Approval for the use of de-identified data from this study was given by the Hawkes Bay hospital clinical audit committee and the Northern B Health and Disability Ethics Committee of New Zealand. Results 293 patient records were identified for audit. Of these, 129 were excluded for a qSOFA < 2 and a further 3 were excluded as the clinical situation and/or microbiology did not fit with the diagnosis of sepsis. One patient record of the remaining 161 did not have their lactate measured and was excluded from analysis. Baseline characteristics. The baseline characteristics for the 160 patients included in the analysis are pre- sented in Table 1. At baseline the beta blocker group was older and had lower lactate than those not exposed to beta blockers. The site of sepsis by group is shown in Table 2. Fewer beta blocker patients had a respiratory source of sepsis. There was no statistically significant difference in mean lactate between those with a respiratory source of sepsis and those with other sites (mean difference 0.48, 95% CI − 1.04 to 2.0 mmol/L) nor between those with APACHE classified chronic cardiovascular disease (mean difference 0.08, 95% CI − 1.75 to 1.91 mmol/L). Primary endpoint. A greater proportion of patients not prescribed beta blockers in the community had a first lactate ≥ 4 mmol/L (48% of patients no beta blockers vs 29% prescribed beta blockers, p = 0.049). There was no significant difference in the proportion of patients with lactate ≥ 2 mmol/L (79% of non-beta blocker patients versus 83% of beta blocker patients, p = 0.52). These results are displayed graphically in Fig. 3. Covariates and regression analysis. Of the covariates in table 3 only APACHE 3 score was significantly correlated with lactate. Logistic regression was undertaken to evaluate the effect of covariates as per the analysis Table 1. Baseline characteristics. Beta blocker Non-beta blocker p value for difference Number (n) 49 111 Male (%) 32 (65) 64 (58) 0.36 Age (years) 71 63 < 0.01 First lactate (mmol/L) 3.54 4.46 0.04 Lowest systolic blood pressure in ED (mmHg) 90 91 0.64 Lowest HR first 24 h in ICU 75.6 78 0.42 SaO2 (%) 92 90 0.74 Highest Creatinine first 24 h (mmol/L) 173 187 0.55 Lowest Haematocrit first 24 h 0.32 0.33 0.34 APACHE III score 75 70 0.31 Number qSOFA score 3 n(%) 7 (14) 16 (14) 0.98 Vasopressors used in ED, n (%) 24 (49) 63 (57) 0.36 Prescribed metformin, n (%) 14 (29) 22 (19) 0.22 Chronic cardiovascular disease (APACHE) n (%) 6 (12) 5 (5) 0.07 Chronic respiratory disease (APACHE) n (%) 1(2) 7(6) 0.67 Mortality, n (%) 8 (16) 17 (15) 0.87 5 Vol.:(0123456789) Scientific Reports \| (2022) 12:20843 \| https://doi.org/10.1038/s41598-022-25253-8 www.nature.com/scientificreports/ plan in the methods. The odds ratio for beta blocker prescription for first lactate being ≥ 4 mmol/L regressed for the covariates age, APACHE 3, metformin prescription, site of sepsis being respiratory and lowest haemotocrit (Hct Lo) in the first 24 h of ICU admission was 0.31 (0.13–0.71). Of note the upper bound of the 95% CI for the odds ratio was < 1. This finding was robust to regression with covariates individually, the removal from the model of age (which exhibited a linear correlation with Apache 3), and addition of lowest systolic blood pressure in the Emergency Department to the model. Table 2. Site of sepsis by group. Site of sepsis Beta blocker, n (%) Non-beta blocker, n (%) p value Respiratory 7(14) 32 (29) 0.05 Skin/soft tissue/joint 16 (33) 26 (23) 0.22 Genitourinary 12 (25) 18 (16) 0.22 Unknown 5 (10) 24 (22) 0.08 Biliary 5 (10) 3 (3) n/s Intra-abdominal 3 (6) 3 (3) n/s Cardiac 0 (0) 2 (2) n/s Vascular catheter 1 (2) 0 (0) n/s CNS 0 (0) 1 (1) n/s Figure 3. Percentages of those prescribed and not prescribed beta blockers with lactate greater than 2 and 4 mmol/L at presentation. Table 3. Covariate analysis. Covariate 95% CI odds ratio, p value Male sex 0.8–1.75 to 0.21, p 0.82 Age (years) 0.98–1.01, p 0.99 Lowest recorded SpO2 in ED (%) 0.96–1.07, p 0.36 Lowest haematocrit first 24 h 0.99 to 1.1, p 0.053 APACHE III score 1.013 to1.04, p < 0.001 Vasopressors used in ED 0.39 to 1.4, p 0.44 SBP in ED 0.96–1, p 0.11 Prescribed metformin 0.8–3.8, p 0.31 Site of sepsis respiratory 0.31–1.4, p = 0.6 Cardiovascular disease (APACHE) 0.35–4.4, p = 0.89 6 Vol:.(1234567890) Scientific Reports \| (2022) 12:20843 \| https://doi.org/10.1038/s41598-022-25253-8 www.nature.com/scientificreports/ Discussion In this patient cohort pre-morbid beta blocker treatment was associated with a lower initial lactate, driven by a reduction in the proportion of patients with a lactate of ≥ 4 mmol/L. This effect was robust to regression analysis. There was no significant difference in the proportion of beta blocker/non beta blocker exposed patients with lactate ≥ 2 mmol/L. Extension from our findings holds obvious caveats in that our methodology permits identi- fication of association only, and lactate threshold is a concept from exercise physiology that has not been proven to have an effect in the clinical context. Nonetheless, these findings provide inferential support for premorbid beta blockade reducing serum lactate in sepsis by increasing the proportion of patients below the concentration where lactate production and metabolism uncouple in response to metabolic stress. Restated, our findings offer support for the view that beta blockers increase the lactate threshold in sepsis. This data fits with the pattern observed the previous five studies as demonstrated in Fig. 2, that the effect of premorbid beta-blockers on initial lactate was most significant in patient populations with higher mean lactate concentrations. The papers which found no effect analysed cohorts with an average initial lactate of ≤ 2 mmol/L40,42; we did not identify any association for premorbid beta blockade with the probability of lac- tate being ≥ 2 mmol/L. The three other studies which demonstrated an effect of beta blockade when looking at cohorts with higher average lactates39,41,43. Our proposed mechanism to explain the findings seen in our study, while hypothetical, offers a unifying explanation for the current heterogeneity of evidence in this area. Our study has several limitations. The most significant is that seeking an association based on a hypothesized mechanism can establish the association while the mechanism remains hypothetical. While there is a mark- edly increased mortality with lactate ≥ 4 mmol/L in sepsis the lactate threshold is a concept proven in exercise physiology rather than established in clinical medicine. Our study design is a retrospective observational and as such can only demonstrate an association rather than prove causation for the effects of premorbid beta blockade on serum lactate concentrations. Additionally, our study is single centre creating limits on external validity. The mean initial lactate was 3.54 mmol/L for the beta blocked patients compared to 4.46 mmol/L in those not premorbidly prescribed beta blockade. Both concentrations are above a threshold which would trigger clini- cal action—viewed from this perspective the clinical significance of the concentration difference is uncertain. This study is not powered to assess whether statistically significant difference in lactates concentration affected clinical outcomes. A qSOFA score of ≥ 2 was used as part of the inclusion criteria. The qSOFA score has been found to have a decreased predictive value in ICU compared to SOFA score but a better predictive value outside ICU57. A flaw with the qSOFA is the potential for inter-user variability in the recording of scores particularly in the altered mental status variable. In addition, some of the cohort was excluded due to incomplete data from the Emergency Department admission. As with other studies, it is only possible to ascertain whether patients were prescribed beta blockers at the time of to their admission, the actual compliance in the cohort is unknown. The reason for beta blocker prescription is not available for this patient cohort and as such the role of any underlying cardiac dysfunction is difficult to quantify. There may also be an effect of other unmeasured variables such as amount of fluid resuscitation prior to initial lactate measurement. We abstracted patient data in blocks of calendar years and anticipated 4 years would provide 180 records for study. The 5% reduction in power from analysis of 160 subjects from this period did not result in a type I error—our results were positive. Underpowered studies which return positive results additionally tend to overestimate the magnitude of effect—a lower proportion of underpowered studies are expected to be positive with the tendency to exhibit more extreme results. This was again not the case in our work as the overall difference in proportion of patients with lactate ≥ 4 in was lower than that powered for (19% vs 25%). Further research is recommended into the effect of beta blockade on the lactate threshold and its significance. Our group has commenced bench top mechanistic work in a prospective study of the effect esmolol infusions on lactate in animal models of sepsis aiming to further examine the proposed mechanism of the effect of beta blockade on lactate. Conclusion In our cohort patients previously prescribed beta blockers presenting with sepsis were less likely to have a lactate of ≥ 4 mmol/ml. This is in keeping with the pattern of results seen in the current literature and supports the pro- posed mechanism that treatment with beta blockers increases the lactate threshold. Further study is warranted as such a mechanism could have clinical significance. Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Received: 13 June 2022; Accepted: 28 November 2022 References 1. World Health Organization. Global report on the epidemiology and burden of sepsis: Current evidence, identifying gaps and future directions. World Health Organization. (2020) [cited 2022 Jun 5]. https:// apps. who. int/ iris/ handle/ 10665/ 334216. 2. Fleischmann-Struzek, C. et al. Incidence and mortality of hospital- and ICU-treated sepsis: Results from an updated and expanded systematic review and meta-analysis. Intensive Care Med. 46(8), 1552–1562 (2020). 3. Shapiro, N. I. et al. 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Efficacy and safety of landiolol, an ultra-short-acting β1-selective antagonist, for treatment of sepsis-related tachyarrhythmia (J-Land 3S): A multicentre, open-label, randomised controlled trial. Lancet Respir. Med. 8(9), 863–872 (2020). 57. Seymour, C. W. et al. Assessment of clinical criteria for sepsis: For the third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315(8), 762–774 (2016). Acknowledgements The authors would like to acknowledge Penny Park, ICU research nurse at the Hawkes Bay hospital for her assistance with data extraction. Author contributions G.C. and M.G. were responsible for study conception. G.C. was responsible for analysis. L.S. and S.O. were responsible for data extraction. L.S. authored the first draft of the manuscript. All authors subsequently con- tributed to revisions. No individual identifying patient data is included in this manuscript. The requirement for informed consent was waived by the Hawkes Bay hospital clinical audit committee and the Northen B Health and Disability Ethics Committee of New Zealand. Funding No funding was received to undertake this study. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to G.C. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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PMC6747064	International Journal of Environmental Research and Public Health Article Acute Eﬀects of a Speed Training Program on Sprinting Step Kinematics and Performance Krzysztof Mackala 1,, Marek Fostiak 2, Brian Schweyen 3, Tadeusz Osik 4 and Milan Coch 5 1 Department of Track and Field University School of Physical Education in Wroclaw, Poland, Ul. Paderewskiego 35, 51-612 Wrocław, Poland 2 Department of Track and Field, Gdansk University of Physical Education and Sport, ul. Kazimierza Górskiego 1, 80-336 Gdansk, Poland 3 PZLA (Polish Track and Field Association), Mysłowicka 4, 01-612 Warszawa, Poland 4 Athletics Department, University of Montana, Adams Center 32 Campus Drive, Missoula, MT 59812, USA 5 Faculty of Sport, University of Ljubljana, Gortanova ulica 22, 1000 Ljubljana, Slovenia Correspondence: krzysztof.mackala@awf.wroc.pl; Tel.: +48-605-272-433 Received: 4 July 2019; Accepted: 24 August 2019; Published: 28 August 2019 Abstract: The purpose of this study was to examine the eﬀects of speed training on sprint step kinematics and performance in male sprinters. Two groups of seven elite (best 100-m time: 10.37 ± 0.04 s) and seven sub-elite (best 100-m time: 10.71 ± 0.15 s) sprinters were recruited. Sprint performance was assessed in the 20 m (ﬂying start), 40 m (standing start), and 60 m (starting block start). Step kinematics were extracted from the ﬁrst nine running steps of the 20-m sprint using the Opto-Jump–Microgate system. Explosive power was quantiﬁed by performing the CMJ, standing long jump, standing triple jump, and standing ﬁve jumps. Signiﬁcant post-test improvements (p < 0.05) were observed in both groups of sprinters. Performance improved by 0.11 s (elite) and 0.06 s (sub-elite) in the 20-m ﬂying start and by 0.06 s (elite) and 0.08 s (sub-elite) in the 60-m start block start. Strong post-test correlations were observed between 60-m block start performance and standing ﬁve jumps (SFJ) in the elite group and between 20-m ﬂying start and 40-m standing start performance and standing long jump (SLJ) and standing triple jump (STJ) in the sub-elite group. Speed training (ST) shows potential in the reduction of step variability and as an eﬀective short-term intervention program in the improvement of sprint performance. Keywords: sprinting; speed; sprint exercises; step variability; kinematics 1. Introduction Past investigations have indicated a signiﬁcant commonality between sprinting performance (across distances from 20 to 100 m) and explosive power [1–5]. As the largest inhibitor in the sprinting movement is gravity, sprinters must produce large vertical ground reaction force during step take-oﬀ to achieve maximal velocity [6]. This is achieved via application of plyometric training as it uses jump-based movements to train and improve explosive power. However, this training modality does not involve a running component. A mixture of plyometric characters exercises (skips and bounds) and some speed running drills created an alternative sprint-speciﬁc running methodology, which represents a high-intensity speed training (ST). ST is treated as a low-volume training strategy and credited with producing signiﬁcant gains in maximal running velocity [7]. This form of training is based on short bouts of all-out sprints separated by rest periods from 90 s to 5–6 min to enhance post-exercise recovery and avoid fatigue, including central nervous system fatigue [8–10]. Similar in one regard to plyometrics, sprint-speciﬁc running also involves a rapid eccentric movement followed by a short amortization phase that is Int. J. Environ. Res. Public Health 2019, 16, 3138; doi:10.3390/ijerph16173138 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2019, 16, 3138 2 of 13 then followed by an explosive concentric movement [6]. This enables the synergistic muscles to engage the myotatic-stretch reﬂex during the stretch-shortening cycle [11]. Such a training stimulus activates the elastic properties of the muscle ﬁbers and connective tissue, allowing the muscles to store more elastic energy [11–13]. This then enhances the release of the accumulated mechanical energy during step take-oﬀ, a process which is easily visible during skipping and speed bounding. Hence, improvements in elastic energy storage via ST could be interrelated with improvements in single sprint step performance and, therefore, maximal velocity. Running velocity is a product of step frequency (SF) and step length (SL) [3,14,15] in which a step deﬁnes half a running cycle or the interval between ground contact of one foot to the moment when contact is made by the opposing foot [16]. SF and SL are mutually interdependent in which an increase in SL results in a decrease in SF and vice versa [17–21]. Both variables are determined by anthropometric characteristics, movement regulation processes, motor abilities, and energetic processes [16,22]. Hence, the relationship between SF and SL is unique for each sprinter [19,23]. Previous research investigating the interaction between SL and SF in running has generally been inconclusive. There is much debate as to whether sprinters achieve greater beneﬁts from increased SL or SF. Nonetheless, the strong interdependency between SL and SF and performance at sub-maximal and maximal velocities posits that these are important outcome measures when assessing variability in sprinting technique [3,24]. In addition, little is known on how other kinematic variables such as ﬂight phase time, ground contact time, and step velocity interact and inﬂuence maximal running velocity. The fundamental goal of this study was to investigate the potential eﬀects of acute ST on sprint steps kinematics—how these spatial-temporal characteristics can inﬂuence sprint running. A secondary goal was to examine performance including maximal running velocity and power of lower extremity. Sprinters of diﬀerent competitive levels were recruited to better generalize the results of the study. To mitigate the eﬀects of overtraining, only eight sessions were administered in two 10- and 12-day modules separated with 10 days of recovery. Furthermore, the intervention was administered during the pre-season period to avoid additional confounding variables. A pre-test/post-test design was adopted and included a wide battery of tests and exercises commonly applied in sprint training. Based on this consideration, it was hypothesized that the information of selecting the relevant biomechanical parameters obtained after high-speed training, may indicate the elite of the sprinters (seniors and 100 m < 10.50 s) will show less variability of the sprinting step in the 20-m sprint from a ﬂying start than the sub-elite sprinters (juniors and 100 m > 10.50 s). 2. Materials and Methods 2.1. Participants Seven elite and seven sub-elite healthy male sprinters with a minimum of four to ﬁve years of regular sprint training were recruited. Sprinters participating in the experiment were members of the national team. For the purposes of the experiment, they were divided into two groups: Elite and sub-elite. The main division criterion was age (juniors up to 19 years, seniors above) and the personal best times in the 100 m sprint (below 10.50 s—elite). Each participant was medically cleared to participate in sprint training and presented no orthopedic or physiological limitation or injury that could aﬀect sprint performance. All participants had previous experience (minimum six months) in sprint-related strength, speed, and plyometrics training before enrollment. Written consent was obtained after the protocol and procedures were explained in full. Parental consent was also obtained from those individuals under 18 years of age. Sprinters were instructed to maintain their normal intake of food and ﬂuids during the study period. Additionally, they were instructed to avoid any strenuous physical activity 24 h prior to testing as well as refrain from eating 3 h before test commencement. The study design was approved by the Institutional Ethics Committee. Int. J. Environ. Res. Public Health 2019, 16, 3138 3 of 13 2.2. Design The study was performed over a four-week period during the pre-season (April–May) just prior to the outdoor racing season. All participants had been engaged in a standard sprint training regimen (ﬁve to seven days of a weekly microcycle) from October to November until inclusion in the study. This training regimen involved general and specialized ﬁtness exercises designed to enhance sprinting technique, increase maximal velocity, and develop strength, power, and endurance. Additionally, many of the participants competed during the indoor competition season (January–February). Eight ST sessions were executed in a 10-day and then 12-day module (Table 1). The modules were separated by 10 days of recovery training with minimal load to promote central nervous system regeneration. ST in the ﬁrst 10-day module was executed every other day and in the 12-day module every third day. The days with training involved the main SST workout and one short supplementary workout (general ﬁtness) with the order reversed in each subsequent training session. Post-workout recovery was also provided and predominately involved massage treatment and low-intensity swimming. Table 1. Training characteristics of the training modules. Type of Exercise Modality Training Module 10-day Module 12-day Module Number of Workouts (n) Strength (combined with a short plyometrics session) 3 2 Plyometrics − 1 Speed 4 4 Speed−endurance 1 2 Tempo 2 1 General ﬁtness (supplementary session) 4 4 Recovery (swimming, massage, cryotherapy) 5 5 Day oﬀ (rest) − 1 Testing − 1 Total training workouts per module 36 ST focused primarily on developing sprint technique and maximal velocity by various exercises that consisted of various skips, bounds, accelerations, starts, and maximal intensity sprints (Table 2). Training duration was approximately 90 min. This included a 20–30 min warm-up of jogging, stretching, light jumping, skipping, and submaximal accelerations and a 10–15 min cooldown. Training load was progressively ampliﬁed by varying task complexity (running distance or number of foot contacts during skip and bound exercises) and by increasing running velocity (from 85–90% to 100%). Int. J. Environ. Res. Public Health 2019, 16, 3138 4 of 13 Table 2. Speed training (ST) characteristics. Exercises Module 1 Module 2 1st session 2nd session 3rd session 4th session 5th session 6th session 7th session 8th session Sub−maximal speed (85–90%) 10 m skip A + 20 m acceleration 2 rep. 3 rep. 2 rep. 2 rep. 10 m skip C + 20 m acceleration 2 rep. 3 rep. 2 rep. 2 rep. 20 m sprint bounding + 20 m acceleration 3 rep. 3 rep. 3 rep. 4 rep. Falling start + 20 m build-up 3 rep. 2 rep. 3 rep. 2 rep. Block starts + 20 m build-up 4 rep. 4 rep. 4 rep. 4 rep. 40 m acceleration 3 rep. 4 rep. 3 rep. 6 rep. Total distance [m] 540 600 540 640 Maximal speed (ca. 100%) Falling start + 20 m build up 2 rep. 2 rep. 2 rep. 2 rep. Block starts + 20 build up 3 rep. 4 rep. 3 rep. 4 rep. 30 m sprint 3 rep. 4 rep. 3 rep. 4 rep. 40 m sprint 3 rep. 4 rep. 3 rep. 4 rep. 50 m sprint 1 rep. 0 rep. 1 rep. 1 rep Total distance [m] 360 400 360 450 2.3. Testing Protocol Maximal running velocity and lower extremity explosive power were measured one day before the intervention and 48 h after the last training session was completed to ascertain acute training eﬀects. Pre- and post-testing was executed at the same time of the day and under identical conditions. No familiarization was provided as all of the testing protocols were well-known to the participants. A warm-up similar to the one used during the training intervention was administered prior to testing (light jogging, stretching, skipping drills, light jumps and bounds, and 30- to 50-m accelerations). The test battery included the countermovement jump (CMJ), standing long jump (SLJ), standing triple jump (STJ), and standing ﬁve jump (SFJ) to assess explosive power. After 60 min of rest, the participants performed the 20-m sprint (from the ﬂying start) to evaluate sprint step kinematics and then 40 m (from a standing start) and 60 m (using starting blocks) to determine maximal running velocity. 2.4. Assessment of Lower Extremity Explosive Power The CMJ was used to determine vertical jumping performance. The OptoJump–Microgate optical measurement system (Optojump, Bolzano, Italy) determined contact and ﬂight times with an accuracy of 0.001 s. No restrictions were placed on knee angle during the eccentric phase and the participants were instructed to perform a dynamic double arm swing to attain maximal height. Three trials were separated by 1 min of rest with the highest jump selected for analysis. Horizontal jump performance was measured in the following order: SLJ, STJ, and SFJ. From an erect position with parallel feet placement, the participant executed the SLJ and was required to land on both feet in the long jump pit without falling backwards. Jumping distance was measured to the nearest 1.0 cm. The starting position for the STJ and SFJ was similar. In these jumps, after one or more arm and leg swings, the participant performed the required number of forward jumps with each step on an alternating leg and was required to land on both feet in the half-squat position on a special jumping mattress. Three trials were executed for each jump and the longest distance to the nearest 1.0 cm was recorded. A 2 min rest was provided between each trial and 5–6 min between each jump modality. The reliability of the vertical and horizontal jumping tests was measured using intraclass correlation coeﬃcients (ICC). A posteriori analysis obtained correlations of 0.92 for CMJ jump height and 0.93 for Int. J. Environ. Res. Public Health 2019, 16, 3138 5 of 13 SLJ, 0.93 for STJ, and 0.90 for SFJ jump distances. The large coeﬃcients indicate satisfactory test–retest reliability and may be explained by the extensive familiarization of all participants with executing these jumps. 2.5. Assessment of Sprint Performance The 20-m ﬂying start, 40-m standing start, and 60-m block start were performed on an indoor track integrated with the Brower Timing TC-System (Draper, Utah, USA). The photocells were positioned on the track at the start and ﬁnish according to the sprint distance [3]. In the 20-m ﬂying start, the sprinter began from a standing start and accelerated as quickly as possible to attain maximal running velocity within a 20 m run-up. Upon reaching the 20 m mark, the sprinter continued to sprint for exactly 20 m at their maximal velocity. This sprint modality had been previously applied in research and is considered suﬃcient to achieve maximal velocity [3,6,16,25,26]. Two trials were executed and separated by 2 min of rest. The same OptoJump measuring system was used to measure the spatial-temporal characteristics of the ﬁrst nine running steps at maximal velocity including step length, step frequency, ground contact time, ﬂight time, and step velocity. In a track conﬁguration, the measurement system uses a series of interconnected rods (100 cm x 4 cm x 3 cm) ﬁtted with optical sensors. Each rod (RX bars and TX bars) is ﬁtted with 32 photocells, arranged 4 cm one from another and 0.2 cm above the ground. The rods were distributed along the length and width of the track (20 m x 1.22 m). The device was integrated with a computer for data storage and processing. After completing the 20-m ﬂying start sprint, the participants performed two trials of the 40 m from a standing start and 60 m from a block start. Rest intervals of 4 and 6 min were provided between trials, respectively. The fastest time in each distance was selected for analysis. 2.6. Statistical Analysis Means (x) and standard deviations (SD) were calculated for all dependent variables. Student’s t-test was used to examine pre- and post-test diﬀerences in running velocity and jumping performance. Fisher’s least signiﬁcant diﬀerence (LSD) tests were performed post hoc to determine pairwise diﬀerences when signiﬁcant F ratios were obtained. Variability in the nine steps was quantiﬁed by calculating the SD and conﬁdence intervals (95%CI). The associations between the performance variables (sprint times and jump distance/heights) were determined by Pearson product–moment correlations. Additionally, hierarchical cluster analysis using Ward’s method was used to determine the linkage distances among the kinematic characteristics grouped as elementary determinants of sprint velocity. A statistical power of 0.90 was determined satisfactory and an alpha level of 0.05 was accepted as statistically signiﬁcant (denoted in bold font). 3. Results Table 3 provides the anthropometric and personal bests in the 60 m and 100 m of the elite and sub-elite sprinters. Table 3. Descriptive statistics and Student’s t-test results of group age, anthropometric characteristics, and personal best (PB) times. Variables Sub-Elite Elite t p x SD x SD Age (years) 18.71 0.75 24.71 2.43 −6.24 0.000043 Height (cm) 182.00 5.35 179.42 3.91 0.78 0.449165 Body mass (kg) 73.28 4.49 74.43 8.24 −0.32 0.753007 BMI (kg/m2) 22.17 1.10 22.79 0.74 −1.22 0.244110 60 m PB 6.97 0.08 6.69 0.79 6.52 0.000028 100 m PB 10.71 0.15 10.37 0.04 5.71 0.000097 Bold format: signiﬁcant diﬀerences. Int. J. Environ. Res. Public Health 2019, 16, 3138 6 of 13 Height, body mass, and BMI were similar between the groups. The diﬀerences between the elite and sub-elite group for age and personal bests were signiﬁcant (p < 0.05). Table 4 presents the pre- and post-test results in sprint and jump performance. Signiﬁcant diﬀerences were observed in all variables in which jumping distances increased and running times decreased in both the elite and sub-elite sprinters (p < 0.05). Sprint step characteristics are presented in Figure 1. In the elite sprinters, contact time (CT) decreased post-test in steps four to seven whereas the lowest value was attained in the ninth step. CT showed a decreasing trend from the ﬁrst to ninth step. Among the sub-elite sprinters at pre-test, a trend towards increased CT between the ﬁrst and ninth steps was observed. At post-test, increased CT was observed in steps four to eight. The ﬂight time (FT) in both groups increased post-test from the ﬁrst to the eighth step. Greater FT was achieved by the sub-elite sprinters at post-test. Step length (SL) in both the elite and sub-elite sprinters showed an upward trend at pre-test, with increased SL from the ﬁrst to the eighth step, only to become more linear at post-test. Additionally, post-test SL magnitudes increased in both groups. Step frequency (SF) showed an irregular pre-test trend in both the elite and sub-elite sprinters. Following the intervention, SF increased in the elite sprinters, particularly in the last three steps. Changes in step velocity (SV) were more pronounced compared with the other variables in both groups particularly at pre-test (an increase in two consecutive steps followed by a decrease in the next two steps). Table 4. Student’s t test results for the dependent variables. Variable x SD x SD ∆x ∆xSD t p Conﬁdence −95.00% Conﬁdence +95.0% Sub-elite Pre−test Post−test 60m–60m_t2 (s) 7.10 0.09 7.02 0.05 0.08 0.04 4.77 0.0030 0.038 0.121 20m ﬂying–20m ﬂying_2t (s) 2.21 0.08 2.13 0.05 0.07 0.05 3.57 0.0116 0.024 0.129 40m –40m_2t (s) 4.37 0.04 4.32 0.03 0.06 0.01 7.94 0.0000 0.040 0.076 SLJ–SLJ_2t (cm) 2.91 0.06 2.99 0.07 −0.08 0.04 −4.81 0.0029 −0.120 −0.039 STJ–STJ_2t (m) 8.56 0.16 8.80 0.18 −0.24 0.07 −8.67 0.0001 −.0313 −0.175 SFJ–SFJ_2t (m) 14.90 0.62 15.56 0.53 −0.66 0.13 −13.66 0.0000 −0.775 −0.539 CMJ –CMJ_2t (cm) 76.43 4.89 82.71 5.34 −6.29 1.70 −9.76 0.0000 −7.862 −4.709 Elite Pre−test Post−test 60m–60m_t2 (s) 6.79 0.08 6.72 0.08 0.06 0.02 7.17 0.0003 0.040 0.082 20m ﬂying–20m ﬂying_2t (s) 2.07 0.04 1.97 0.07 0.11 0.07 4.17 0.0058 0.047 0.179 40m –40m_2t (s) 4.12 0.02 4.08 0.01 0.04 0.02 4.58 0.0037 0.018 0.061 SLJ –SLJ_2t (m) 3.15 0.10 3.23 0.11 −0.07 0.05 −3.92 0.0078 −0.118 −0.027 STJ–STJ_2t (m) 9.39 0.52 9.89 0.48 −0.49 0.21 −6.28 0.0007 −0.693 −0.304 SFJ–SFJ_2t (m) 15.81 0.44 16.59 0.57 −0.78 0.50 −4.11 0.0062 −1.244 −0.316 CMJ –CMJ_2t (cm) 81.57 2.57 87.86 1.07 −6.28 2.06 −8.08 0.0001 −8.189 −4.382 _2t—post−test results, bold format—signiﬁcant diﬀerences. Int. J. Environ. Res. Public Health 2019, 16, 3138 7 of 13 Figure 1. Sprint step kinematics characterizing the ﬁrst nine steps (S) in the 20-m ﬂying start sprint. Post-test variability in the sprint step characteristics was signiﬁcant for group and step (p < 0.05) (Table 5). The interactions ST × group, SL × group, ST × SL, and ST × SL × group did not show variability except SV and CT (p < 0.05). Consequently, the consistent generation of high horizontal velocity in the run-up resulted in greater running velocity with stable SV when sprinting the 20 m distance. Table 5. ANOVA results of sprint step kinematics. Feature Main Eﬀect Group ST PT × Group Step Step × Group ST × step ST × step × group F p F p F p F p F p F p F p CT 0.27 0.6129 4.09 0.0660 2.79 0.1206 2.14 0.0388 0.74 0.6540 0.19 0.9918 2.34 0.0240 FT 9.05 0.0109 0.53 0.4789 0.85 0.3756 10.58 0.0000 0.24 0.9812 0.54 0.8220 0.57 0.8016 SF 4.91 0.0468 0.33 0.5763 0.73 0.4100 0.90 0.5167 1.68 0.1121 0.89 0.5247 1.13 0.3484 SL 0.61 0.4489 0.32 0.5841 0.08 0.7828 18.23 0.0000 0.25 0.9798 0.50 0.8503 0.50 0.8519 SV 56.64 0.0000 2.72 0.1249 0.74 0.4080 8.22 0.0000 2.06 0.0475 0.48 0.8703 0.80 0.6012 SST—sprint-speed training; bold format—signiﬁcant diﬀerences. Table 6 presents the pre- and post-test correlation coeﬃcients for the performance measures in the 20-m ﬂying start, 40-m standing start, and 60-m block start sprints and vertical and horizontal jumping tests. At pre-test, the only signiﬁcant correlation in the sub-elite group was between the 60-m block start and SLJ (r = −0.76) and SFJ (r = −0.77). In the elite group, a signiﬁcant relationship was found between the 60-m block start and the 20-m ﬂying start (r = 0.81) and 40 m standing start (r = −0.76). No other signiﬁcant pre-test correlations were found. Post-test analysis revealed correlations between the 20-m ﬂying start and 40-m standing start and SLJ and STJ performance in the sub-elite group. In the elite sprinters, only the correlation between 60-m block start and SFJ performance was signiﬁcant (r = −0.87). Many of the horizontal jump tests (SLJ, STJ, SFJ) were strongly associated with each other, however, no signiﬁcant pre- and post-test correlations were observed between CMJ with any of the variables in either group. Int. J. Environ. Res. Public Health 2019, 16, 3138 8 of 13 Table 6. Spearman rank correlation coeﬃcients between sprint performance and lower extremity explosive power variables. Sub-Elite Variable Elite [7] [6] [5] [4] [3] [2] [1] [1] [2] [3] [4] [5] [6] [7] - −0.77 −0.76 0.85 - - 60 m [1] - 0.81 −0.76 - −0.87 * - - - - −0.76 * 0.90 * - - - - - - - - - - - −0.85 * - - - - 20 m ﬂying start [2] - - - - - - - - - −0.76 * - - - - 40 m [3] - - - - - - - −0.79 0.82 * 0.79 * - - - - SLJ [4] - - - - - 0.85 * - - −0.90 - - - - - STJ [5] - - - - - 0.79 - - 0.79 * - - - - - - - - - - - - - - - - - - - SFJ [6] - - - - - - - CMJ [7] Italic means pre-test result, bold with * means post-test result. Hierarchical cluster analysis of the grouped variables is illustrated in Figure 2 (pre-test) and Figure 3 (post-test). Comparison of the two dendrograms did not reveal any diﬀerences between the emerging clusters. At both time points, the individual clusters were grouped similarly to form two large characteristic aggregations. This suggests a congruency of the kinematic variables and a relatively loose relationship with no signiﬁcant eﬀect of one cluster on the other. Additionally, the Euclidean distances of the clusters in the pre-test and post-test clusters were similar. Figure 2. Dendrogram clustering of pre-test sprint step kinematics. Int. J. Environ. Res. Public Health 2019, 16, 3138 9 of 13 Figure 3. Dendrogram clustering of post-test sprint step kinematics. 4. Discussion Signiﬁcant post-test improvements in 60-m block start times were observed in both the elite and sub-elite sprinters by 1.04% and 1.23%, respectively. Improvements were also noted in 20-m ﬂying start performance by 4.84% and 3.62% in the elite and sub-elite sprinters, respectively. Only marginal improvements (not statistically signiﬁcant) were found for 40-m standing start performance, in which sprinting time improved by 0.98% in the elite group (Table 5). While indicative that ST has a positive eﬀect on sprint performance, the ﬁndings are diﬃcult to interpret due to the lack of similar data in the literature. However, comparisons can be made with studies that examined the eﬀects of plyometrics training on maximal running velocity. In this context, the results of the present study are comparable with those reported by Kreamer et al. [27], Hennessy and Kilty [12], and Mackala and Fostiak [3]. There is strong evidence that plyometrics training enhances the stretch-shortening cycle of muscle to improve elastic energy storage [28] and generate faster and more powerful movements [29]. ST is similar in this regard in that it can likewise activate the elastic properties of muscle ﬁbers and connective tissue to also allow greater elastic energy storage that, after its release, can provide additional impetus during running [6]. An important question in this regard is whether ST is more eﬀective than plyometrics training in enhancing explosive power and maximal running velocity. The data from this study can be compared with previous research that used an identical testing protocol to assess the eﬀects of six sessions of plyometrics training [3]. When compared with this study, ST shows greater improvements in 20- and 60-m ﬂying start sprint times (by 0.5% to 2%, respectively) and horizontal jump performance (STL and STJ). In turn, the plyometrics intervention showed greater increases in standing jump (SJ) and CMJ performance [3]. Some studies reported strong correlations (r) from 0.65 to 0.90 between sprinting Int. J. Environ. Res. Public Health 2019, 16, 3138 10 of 13 and drop jump (DJ), SJ, CMJ performance, depending on the sprint distance (20–100 m) and type of jump [12,30,31]. These ﬁndings are comparable with the present investigation, in which signiﬁcant post-test correlations were observed between 60-m block start times and SLJ in the sub-elite (r = −0.76) and STJ in the elite (r = −0.85) group. Strong correlations were also observed between 20-m ﬂying start times and STJ performance (r = −0.85) in the sub-elite group but not in the elite group. The secondary purpose of the study was to examine the eﬀects of ST on sprint step kinematics (SL, SF, FT, CT, and SV). Analysis of SL in both groups of sprinters was compared with other research where high-performance sprinters were investigated [16,22,32–34]. These studies reported that SL increased with running velocity and with sprint distance. In our study, linear increases in SL (between steps two and eight in the elite and steps two and six and also step eight in the sub-elite sprinters) were observed in both groups at both time points. SL increased only in the sub-elite group by approximately 4 cm at post-test with no changes observed in the elite group. SL was maintained at 228 and 230 cm (sub-elite and elite, respectively) in the last three steps (Figure 1). This may be explained by the fact that the ﬂying start involved a 20-m run-up and, therefore, a combined distance of 40 m. Therefore, it is possible that SL was still increasing with each step (build-up phase) that only plateaued at the end of the 20 m sprint distance when maximal velocity was attained. In turn, the changes in SF were less pronounced and remained relatively similar in the sub-elite group but slightly decreased from 4.38 to 4.33 Hz in the elite group. Post-test SF in the elite group was similar between the second and sixth step (diﬀerence of only 0.03 Hz), whereas greater variability was observed between the second and sixth step in the sub-elite group at pre- and post-test. Considering both SF and SL, the elite sprinters presented greater SF and slightly longer SL than the sub-elite sprinters (Figure 1). This result suggests that the improvement in sprint performance via increased running velocity does not demonstrate the classic dependency between SL and SF. This contradicts the study of Bezodis et al. [19], who reported a weak correlation (r = –0.192) between SL and running velocity but a strong correlation between SF and running velocity (r = 0.886). In turn, Hunter et al. (2004) found a strong correlation between sprint velocity and SL (r = 0.73) and only a weak correlation between sprint velocity and SF (r = –0.14). While this contradicts the previous ﬁnding, it does conﬁrm Delecluse et al. [35] who used regression analysis to ﬁnd that ca. 85% of variance in running velocity can be explained by variance in SL. Similarly, Mackala [34] examined whether an increase in SF or SL would increase running velocity to ﬁnd that SL was more strongly associated with running velocity than SF. Post-test SV increased from 9.10 m/s to 9.26 m/s in the sub-elite and from 9.93 m/s to 9.98 m/s in the elite group. Single sprint step execution in the elite sprinters showed a linear increase across steps one to nine at both pre- and post-test with no changes in SL in step four and seven when compared with earlier steps three and six. In turn, the sub-elite sprinters showed more pronounced variation in SV and SL, which, in turn, may have perturbed the sprinting movement and thus explain the slower running velocity (Figure 1). As SV is a product of reduced CT during the support phase, this may explain the increased running velocity in the elite sprinters. According to Coh et al. [17], and Alcaraz et al. [36] the most important factor in sprint step eﬃciency is the support phase, especially the ratio between the braking and propulsion phases. Therefore, maximal running velocity can be achieved only if the force impulse is as small as possible during the braking phase and may be possible by positioning the foot of the push-oﬀ leg as close as possible to the vertical projection of the body’s center of gravity on the surface. Although this was not measured in the present study, this is the most rational explanation for the increases in running velocity in the 20-m ﬂying start. Hence, the various sprint distances involved in the SST intervention may have increased execution economy during the support phase by positioning the center of gravity closer to the fulcrum upon landing, thereby increasing the velocity of each step. No signiﬁcant changes were also observed in CT and FT in either group of sprinters (Figure 1). The diﬀerence between CT and FT was ca. 0.04 s and was relatively linear from step two to step eight at both pre- and post-test. More variability was observed in post-test FT in the elite sprinters Int. J. Environ. Res. Public Health 2019, 16, 3138 11 of 13 although the diﬀerence between the minimum and maximum values is 0.03 s. CT was reduced in the elite sprinters and was comparable with values reported in other studies during maximal sprinting (90–120 ms) [15,37,38] noted a decreasing trend in CT in the ﬁrst 10 sprint steps after which CT stabilizes. To better understand the eﬀects of ST on the spatial and temporal variability of sprinting, hierarchical cluster analysis was applied. Post-test analysis revealed that the variables in the ﬁrst cluster show greater Euclidean distances between CT and FT (6.5 at pre-test and 7.5 at post-test). Changes were also observed in the grouping order regarding the CT, which are arranged in the order of the executed steps. Post-test changes in the second cluster (SF and SV) revealed closer sub-cluster linkages (2.5 units at pre-test and 2 at post-test). These results suggest that SF and SV show considerable dependency and may be associated with improvements in running velocity. Similar conclusions can be assumed for CT and FT based on the Euclidean distances. 5. Conclusions In summary, this study has shown that the application of eight SST sessions are eﬀective in signiﬁcantly increasing 60-m block start and 20-m ﬂying start sprint performance. Signiﬁcant improvements were also observed in lower extremity explosive power as ascertained by vertical and horizontal jump testing. Greater increases were observed in the CMJ (mean 7.95% increase) than in the horizontal jumps (mean 2.5–5.3% increase). Increased 20-m running velocity was associated with increases SV, as CT and FT did not change after SST and only relatively linear increases were observed in SL and SF from step two to step eight. Additionally, sprint-speed training can be recommended as an eﬀective short-term intervention to improve sprint performance and lower extremity explosive power, particularly when considering the required training volume of eight sessions (across 22 days). Sprint performance gains can also be optimized by decreasing variability in sprint step kinematics during maximal velocity running in both lower and higher performing sprinters. Author Contributions: Conceptualization, M.K., F.M., O.T.; methodology, M.K., F.M., C.M., software, M.K., F.M.; validation, C.M., O.T.; formal analysis, M.K., F.M., C.M., O.T., S.B.; investigation, M.K., F.M., O.T.; a resources, F.M.; data curation, M.K., C.M., S.B.; writing—original draft preparation, MK., F.M.; writing—review and editing, M.K., SB.; visualization, M.K., F.M., C.M.; supervision, M.K., C.M. Funding: This research received no external funding. Conﬂicts of Interest: The authors have no conﬂict of interest to declare. 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Kinematic parameters of the running stride in 1- to 18-yeard-old youth. Kinesiol. Slov. 2011, 17, 63–75. 26. Ozsu, I. Biomechanical structure of sprint start and eﬀect of biological feedback methods on sprint start performance. Turk. J. Sport Exerc. 2014, 16, 72–79. [CrossRef] 27. Kraemer, J.W.; Ratamess, A.N.; Volek, S.J.; Mazzetti, A.S.; Gomez, I.A. The eﬀect of the Meridian Shoe on vertical jump and sprint performances following short-term combined plyometric/sprint and resistance training. J. Strength Cond. Res. 2000, 14, 228–238. 28. Cormie, P.; McGuigan, M.R.; Newton, R.U. Developing maximal neuromuscular power: Part 1-biological basis of maximal power production. Sports Med. 2011, 41, 17–38. [CrossRef] [PubMed] 29. Fatahi, A.; Sadeghi, H. Resistance, plyometrics and combined training in children and adolescents’ volleyball players: A review Study. J. Sci. Res. Rep. 2014, 20, 2584–2610. [CrossRef] [PubMed] 30. Mehmet, K.; Alper, A.; Coskun, B.; Caner, A. Relationship among jumping performance and sprint parameters during maximum speed phase in sprinters. J. Strength Cond. Res. 2009, 23, 2272–2279. 31. Wild, J.; Bezodis, N.; Blagrove, R.; Bezodis, I. Biomechanical comparison of accelerative and maximum velocity sprinting: Speciﬁc strength training consideration. U. K. Strength Cond. Assoc. 2011, 21, 23–36. 32. Bruggemann, G.P.; Koszewski, D.; Muller, D. Biomechanical Research Project: Athens 1997; Final Report; Meyer & Meyer Sport: Oxford, UK, 1999; pp. 12–41. Int. J. Environ. Res. Public Health 2019, 16, 3138 13 of 13 33. Shen, W. The eﬀects of stride length and frequency on the speeds of elite sprinters in 100 meter dash. In Proceedings of the XVIII International Symposium of Biomechanics in Sports (ISBS), Hong-Kong, China, 25–30 June 2000; pp. 333–336. 34. Mackala, K. Optimization of performance through kinematic analysis of the diﬀerent phases of the 100 meters. New Stud. Athlet. 2007, 22, 7–16. 35. Delecluse, C.H.; van Coppenolle, H.; Willems, R.; Diels, M.; Goris, M.; van Leempurte, M.; Vuylsteke, M. Analysis of 100 meter sprint performance as a multi-dimensional skill. J. Hum. Mov. Stud. 1995, 28, 87–101. 36. Alcaraz, P.E.; Palao, J.M.; Elvira, J.L.L.; Linthorne, N.P. Eﬀects of three types of resisted sprint training devices on the kinematics of sprinting at maximum velocity. J. Strength Cond. Res. 2008, 22, 890–897. [CrossRef] 37. Hunter, J.P.; Marshall, R.N.; McNair, P.J. Relationships between ground reaction force impulse and kinematics of sprint-running acceleration. J. Appl. Biomech. 2001, 21, 31–43. [CrossRef] 38. Coh, M.; Tomazin, K.; Stuhec, S. The biomechanical model of the sprint start and block acceleration. Facta Univ. Ser. Phys. Educ. Sport 2006, 4, 103–114. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).	Acute Effects of a Speed Training Program on Sprinting Step Kinematics and Performance.	08-28-2019	Mackala, Krzysztof,Fostiak, Marek,Schweyen, Brian,Osik, Tadeusz,Coch, Milan	eng
PMC3966785	Figure S1. Gaze in two representative trials. Distance between the ball image and the point of gaze as a function of time. A) Gaze for a participant who successfully caught the projected 9ly ball (after 2.64 s); B) Gaze for a participant who indicated that the projected 9ly ball was uncatchable for her (after 1.37 s). See also Movies S1 and S2, which show scene camera recordings of these trials. !! 0 75 150 225 0 0.5 1 1.5 2 2.5 3 Caught Tracking Other Distance (pixels) Time to catch (s) 0 0.5 1 1.5 2 2.5 3 Judged to be uncatchable Time to 'no' (s) A B	Keeping your eyes continuously on the ball while running for catchable and uncatchable fly balls.	03-26-2014	Postma, Dees B W,den Otter, A Rob,Zaal, Frank T J M	eng
PMC10739691	1 Vol.:(0123456789) Scientific Reports \| (2023) 13:22865 \| https://doi.org/10.1038/s41598-023-49369-7 www.nature.com/scientificreports Anaerobic threshold using sweat lactate sensor under hypoxia Hiroki Okawara 1,4, Yuji Iwasawa 2,4, Tomonori Sawada 1, Kazuhisa Sugai 3, Kyohei Daigo 2, Yuta Seki 2, Genki Ichihara 2, Daisuke Nakashima 1, Motoaki Sano 2, Masaya Nakamura 1, Kazuki Sato 3, Keiichi Fukuda 2 & Yoshinori Katsumata 2,3* We aimed to investigate the reliability and validity of sweat lactate threshold (sLT) measurement based on the real-time monitoring of the transition in sweat lactate levels (sLA) under hypoxic exercise. In this cross-sectional study, 20 healthy participants who underwent exercise tests using respiratory gas analysis under hypoxia (fraction of inspired oxygen [FiO2], 15.4 ± 0.8%) in addition to normoxia (FiO2, 20.9%) were included; we simultaneously monitored sLA transition using a wearable lactate sensor. The initial significant elevation in sLA over the baseline was defined as sLT. Under hypoxia, real-time dynamic changes in sLA were successfully visualized, including a rapid, continual rise until volitionary exhaustion and a progressive reduction in the recovery phase. High intra- and inter-evaluator reliability was demonstrated for sLT’s repeat determinations (0.782 [0.607–0.898] and 0.933 [0.841–0.973]) as intraclass correlation coefficients [95% confidence interval]. sLT correlated with ventilatory threshold (VT) (r = 0.70, p < 0.01). A strong agreement was found in the Bland–Altman plot (mean difference/mean average time: − 15.5/550.8 s) under hypoxia. Our wearable device enabled continuous and real-time lactate assessment in sweat under hypoxic conditions in healthy participants with high reliability and validity, providing additional information to detect anaerobic thresholds in hypoxic conditions. It is presumed that hypoxic training helps improve endurance performance in athletes1. Traditional high-altitude training refers to a state where atmospheric and oxygen pressure decrease and athletes are exposed to chronic hypobaric hypoxia for many weeks2. Recent studies on normobaric hypoxic exercise have investigated the impact of the recently popular live low-train high-altitude interventions on athletes’ lifestyles3,4, as prolonged exposure to low-pressure conditions is not always feasible (travel time, engagement, and expenses) and can lead to health problems5,6. Anaerobic threshold (AT) and peak oxygen uptake (peak VO2) should be routinely assessed in hypoxic conditions to practice efficient fitness training during hypoxia7. To date, the ventilatory threshold (VT), calculated as a noninvasive index of metabolic response to incremental exercise, has been used to determine AT8,9. The VT assessment method is beneficial; however, VT assessment requires an expensive analyzer and expertise due to the difficulty in confirming VT based on the oscillations in minute ventilation and inconsistencies among several factors10. The difference in expertise is reported to worsen the VT determination agreement11. This is because the respiratory gas analyzer is not readily available in sports settings. Therefore, there is an urgent need to apply an innovative and simple system to determine AT with high reliability for fitness training under hypoxia. Flexible, wearable sensing devices can yield vital information about the underlying physiology of a human participant in a continuous, real-time, and noninvasive manner12,13. Sampling human sweat, rich in physiologi- cal information, can enable noninvasive monitoring14. We developed a sweat sensor to monitor sweat lactate levels (sLA) in real-time during progressive exercise in the clinical setting, investigating its use in detecting AT in healthy individuals and patients with cardiovascular diseases15. sLA has been reported to not reflect blood lactate during exercise16,17; however, our research group has examined sLA transitions during incremental load exercise and reported that the sweat lactate threshold was strongly approximated to AT by focusing on the inflec- tion point where the value increases rapidly during incremental exercise, not the absolute value15,18. Our sLA sensor is portable and easy to carry, enabling convenient measurements in various environments, and the continuous collection of only 1 Hz sLA values promises a simpler determination of the inflection point. Moreover, the need for invasive collection methods, including blood collection, is undesirable considering human OPEN 1Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan. 2Department of Cardiology, Keio University School of Medicine, Tokyo, Japan. 3Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan. 4These authors contributed equally: Hiroki Okawara and Yuji Iwasawa. *email: goodcentury21@keio.jp 2 Vol:.(1234567890) Scientific Reports \| (2023) 13:22865 \| https://doi.org/10.1038/s41598-023-49369-7 www.nature.com/scientificreports/ resources for multi-measurements, the possibility of any person to evaluate, and acceptance of the evaluation target. Under hypoxia, some researchers previously reported AT evaluation results7. However, similar to normoxia, it is problematic that the VT evaluation method was applied to broadly cover the sports setting. Therefore, we aimed to investigate the validity of AT estimation and reliability of the sLA continuously obtained using our sLA sensor during exercise under hypoxia in healthy participants. Results The baseline characteristics of the healthy participants are summarized in Table 1. The participants were males (100%) with a median (IQR) age of 21 (20–21) years. The temperature and humidity were 28 ± 1 °C and 62 ± 6% under hypoxia, respectively. Figures 1 and 2 show the sLA during exercise in hypoxia. During the exercise tests, dynamic changes in the sLA were continuously measured and projected onto the wearable device without delay, even under hypoxia. Because of the lack of sweat, the lactate biosensor measured a negligible current response at the commence- ment of cycling activity. During exercise, sLA increased drastically, and the sweat rate continuously increased as cycling continued until volitional exhaustion. This drastic sLA increase was not associated with the onset of sweating (Fig. 1). Contrary to sLA, the heart rate and VO2 gradually increased from incremental-load exercise initiation to its end (Fig. 2). At the end of the exercise period, the sLA continued to decrease relatively slowly, mirroring the decrease in heart rate. The results under normoxia are shown in Supplementary Figs. 1 and 2. We easily identified the conversion from steady low lactate values to a continuous increase under hypoxia. Repeated sLT and VT determinations by the same evaluator demonstrated high intra-evaluator reliability Table 1. Baseline characteristics of participants. Data are presented as median (IQR). BMI body mass index. Demographic and anthropometric data Healthy male participants (n = 20) Age (years) 21.0 (20.0–21.0) Height (cm) 174.0 (171.0–176.4) Body weight (kg) 69.0 (62.5–71.1) BMI (kg/m2) 22.2 (20.5–23.4) Body fat/body weight (%) 14.2 (12.3–16.7) Body muscle/body weight (%) 81.3 (78.6–83.1) Body water/body weight (%) 60.2 (57.5–62.8) Figure 1. Imaging of sweat lactate levels, local sweat rate, and blood lactate values during incremental exercise under hypoxia. Representative graphs of sweat lactate levels (orange), local sweat rate (blue), and blood lactate values (red) during hypoxic exercise with a stepwise incremental protocol (25 W/min) ergometer are shown. VT ventilatory threshold, sLT sweat lactate threshold. 3 Vol.:(0123456789) Scientific Reports \| (2023) 13:22865 \| https://doi.org/10.1038/s41598-023-49369-7 www.nature.com/scientificreports/ (intraclass correlation [ICC] [2, 1] measured value [95% confidence interval] normo, 0.893 [0.794–0.952]; hypo, 0.782 [0.607–0.898]; and normo, 0.711 [0.500–0.861]; hypo, 0.919 [0.841–0.964]), respectively (Fig. 3 and Sup- plementary Fig. 3). Moreover, these were reproducible between both blinded reviewers (ICC [1, 1] measured value [95% confidence interval]; sLT; normo, 0.898 [0.765–0.958], hypo, 0.933 [0.841–0.973], and VT, normo, 0.933 [0.841–0.973], hypo, 0.836 [0.638–0.931]) as shown in Table 2 and Supplementary Table 1. However, the intra- and inter-evaluation reliability for bLT was low (ICC [1, 1] measured value [95% confidence interval]; normo, 0.529 [0.134–0.781], hypo, 0.652 [0.314–0.845], ICC [2, 1] measured value [95% confidence interval]; normo, 0.621 [0.363–0.813], hypo, 0.586 [0.331–0.790]) as shown in Fig. 3, Table 2, Supplementary Fig. 3, and Supplementary Table 1. The relationships between sLT and VT are shown in Fig. 4A and Supplementary Fig. 4A, describing the strong relationships between each threshold (normo, r = 0.69; hypo, r = 0.70). The Bland–Altman plot revealed that the mean difference between each threshold was 4.9 s under normoxia and − 15.5 s under hypoxia, and there was no bias between the mean values, displaying strong agreements between sLT and VT (Fig. 4B and Supplementary Fig. 4B). Discussion The noninvasive sLA sensor enabled continuous and real-time measurement of sLA during an exercise test under hypoxia. Furthermore, sLT determination had high intra- and inter-evaluator reliability, and sLT was strongly correlated with VT. Real-time sweat lactate monitoring could be applied to detect aerobic threshold, even under hypoxia (Fig. 5). Lactate levels are measured to track an individual’s performance and exertion level19,20. Blood lactate levels are measured by athletes or their supporters21,22, but these are not continuous, real-time measurements, limiting their utility to applications where stationary, infrequent tests are sufficient. In particular, applying bLT relies on Figure 2. Measured parameters in hypoxia. The graph shows the measured parameters [(a) VO2/body weight, (b) Heart rate, (c) Sweat lactate, (d) sweat rate] at rest, warm up, VT, and peak in hypoxia. Data are shown as mean (± standard deviation). VO2 oxygen uptake, VT ventilatory threshold, HR heart rate, sLA sweat lactate, SR sweat rate. 4 Vol:.(1234567890) Scientific Reports \| (2023) 13:22865 \| https://doi.org/10.1038/s41598-023-49369-7 www.nature.com/scientificreports/ the measurement’s reliability; in this study, the intra- and inter-evaluation reliability for bLT was low. Conversely, even under hypoxia, our devices captured the sLA during fitness in a real-time, noninvasive, and continuous manner at 1 Hz instead of cumulative values as in the conventional method, which detects the “timing of change” in a real-time and sensitive manner. Therefore, it is easy to identify the inflection point (sLT) from the plots of the sLA values. Using sLT demonstrated lower intra- and inter-observer bias and superior determination accuracy. Figure 3. Reliability testing of the time at sLT determined by the same evaluator in hypoxia. (a) The graph shows the relationship between the repeatedly determined sweat lactate threshold (sLT) by the same evaluator. (b) The graph shows the Bland–Altman plots, which indicate the respective differences between the repeatedly determined sLT by the same evaluator (y-axis) for each individual against the mean of the time at the repeatedly determined sLT (x-axis) in hypoxia. R correlation coefficient, p p-value, VT ventilatory threshold, sLT sweat lactate threshold. Table 2. Intra-evaluator reliability of sweat lactate threshold determination in hypoxia. ICC intraclass correlation, sLT sweat lactate threshold, bLT blood lactate threshold, VT ventilatory threshold, SD standard deviation. Hypoxia N Evaluator 1 Evaluator 2 Evaluator 3 ICC (95% CI) sLT [s] Mean 20 553.3 486.3 533.6 0.782 (0.607–0.898) SD 84.4 89.8 80.8 bLT [s] Mean 20 581.8 558.7 597.6 0.586 (0.331–0.790) SD 69.7 58.4 73.7 VT [s] Mean 20 546.8 547.2 540.9 0.919 (0.841–0.964) SD 66.2 49.8 59.7 Figure 4. Validity testing of the time at VT and sLT in hypoxia. (a) The graph shows the relationship between the time from the start of the measurement (seconds) at VT and sLT. (b) The graph shows the Bland–Altman plots, which indicate the respective differences between the time from the start of measurement (s) at the VT and sLT (y-axis) for each individual against the mean of the time at the VT and sLT (x-axis) in hypoxia. R correlation coefficient, VT ventilatory threshold, sLT sweat lactate threshold. 5 Vol.:(0123456789) Scientific Reports \| (2023) 13:22865 \| https://doi.org/10.1038/s41598-023-49369-7 www.nature.com/scientificreports/ Another possible explanation to support this positive result is that several operations, including the exchange of the sensor chip, cleaning the upper arm which the sensor fixed, and flushing out any residual sweat from the duct in the perspiration meter, certainly could eliminate the bias due to contaminations from previous experi- ments or original sweating. sLA has been reported to not reflect blood lactate during exercise16,17; however, our data showed that the AT point coincided with that in the sLA level during progressive exercise, consistent with the finding of the previous report15,18. This could be because an increase in lactate production from muscle cells, reflecting LT, may induce a simultaneous rise in sLA levels through changes in autonomic nervous balance, hor- mones, acid–base equilibrium, and metabolic dynamics23,24 similar to VT25. A previous study has demonstrated a rapid increase in blood catecholamine concentrations during incremental exercise loads26. Furthermore, it has also been indicated that sweat gland metabolism is activated by catecholamines27. Therefore, we are evaluating the timing of physiological responses to increasing exercise loads using completely different analytes and not estimating the bLA levels by observing the sweat lactate levels. Measuring VT and peak VO2 with respiratory gas analysis helps in efficient training under hypoxia. However, it is often difficult to determine VT because of inconsistencies among the several factors required for detecting VT, such as the ventilation (VE)/oxygen uptake (VO2) or carbon dioxide production (VCO2)/VO2 slope and oscil- lations in minute ventilation10. Further, a respiratory gas analyzer is unavailable in a small hypoxic booth because of its size. Moreover, using a facemask, respiratory gas cannot be collected under hypoxic exercise. In addition, in a respiratory infection epidemic such as COVID-19, using respiratory gas analyzers has become difficult due to the possibility of cross-infection. Determining sLT using only sweat-based monitoring could overcome these problems, and the newly developed device enables AT measurements in various hypoxic environments (a small private booth and facemask). It has been reported that sweat rate decreases in hypoxia28. As our sensor showed non-response in the absence of sweating, evaluating sweat rate is paramount to successfully determining sLT in hypoxia. This study quantified the amount of sweating per unit area near the sensor; the results showed no difference in the local sweat rate during exercise under hypoxia compared with that under normoxia. The relationship between the local sweat rate/response in the sLA sensor, humidity, and temperature during exercise warrants further investigation. Figure 5. Schematic of the lactate-sensing device under hypoxia. This figure is licensed by © Medical FIG. ICC interclass correlation coefficients, bLT blood lactate threshold, VCO2 carbon dioxide output, VO2 oxygen uptake, VT ventilatory threshold, sLT sweat lactate threshold, FiO2 fraction of inspiratory oxygen. 6 Vol:.(1234567890) Scientific Reports \| (2023) 13:22865 \| https://doi.org/10.1038/s41598-023-49369-7 www.nature.com/scientificreports/ The device used in our study is suitable for use in remote monitoring or remote training settings during isolation measures, such as those taken during a respiratory infection epidemic. Furthermore, real-time assess- ments of sLA through a wireless data transfer system can offer a rigorous training menu under hypoxia based on the day-to-day physical conditions of trainees. In addition, exercise under hypoxia has been recognized as a new therapeutic modality for health promotion and disease prevention or treatment, such as for diabetes29, cardiovascular diseases30, hypertension31, obesity32, and age-related diseases33. Disease prevention and treatment can be more efficiently and safely provided by combining sLA sensors with exercise under hypoxia. The study has some limitations. First, due to the observational study design, we could not exclude the influ- ence of a selection bias. Second, our study had a relatively small number of cases. Third, the current study included healthy college-aged male individuals. Recent findings could be applied to various age groups and genders; however, further research, including females and young athletes, is required considering a sweat func- tional difference between sexes. Fourth, the sLA sensor used in this study exhibited the current value, not the sLA concentration. Conversion to concentration from the current value is possible; however, it is sufficient to display the current values to determine the inflection point based on the constant value of sLA during exercise. The effect of sLA dilution by high sweat rate on sLT determination is minimal due to the low sweat rate at AT and, therefore, does not negate our study’s result. Finally, exercise training has been performed under various hypoxic conditions; however, only a hypoxia of 15.5% was verified. Further verification is required to overcome these limitations. In conclusion, the noninvasive sweat lactate sensor enabled continuous and real-time measurement of sweat lactate during exercise under hypoxia. The sweat lactate threshold can also be reliably determined by non- experts, even under hypoxia. Real-time sweat lactate monitoring could be used to detect aerobic threshold in a noninvasive and feasible manner under hypoxia and normoxia. It is expected that these findings enhance the effectiveness of exercise under hypoxia. This was the first study to show real-time monitoring of sLA during progressive exercise under hypoxia. Given the difficulty in deciding VT, such as in hypoxia, sLA monitoring could be beneficial in improving VT detection with high reliability. Methods Experimental approach to the problem We conducted a cross-sectional study with 20 healthy participants who underwent exercise tests with respiratory gas analysis under hypoxia or normoxia and simultaneously monitored changes in sLA using a wearable lactate sensor to investigate the capability of sweat lactate sensor to monitor sLA under hypoxia and the relationship between sLT and VT. In addition, Intraclass correlation was determined for the intra- and inter-evaluator reli- ability of each threshold in this study. Subjects Participants aged 20–80 years were recruited through a web system in June 2021. The exclusion criteria were patients receiving medication, having comorbidities like hypertension, diabetes, and active lung diseases, and having low local sweat rates of < 0.4 mg/cm2/min at the upper arm during maximal exercise. This sweat rate threshold was defined based on previous reports15 and preliminary studies. Twenty healthy participants were enrolled, including athletes and those with a broad spectrum of aerobic capacities and fitness levels. Notably, all participants exercised regularly for more than twice weekly. The study protocol was approved by the Institutional Review Board (IRB) of Keio University School of Medi- cine (approval number 20190229), and the study was conducted following the principles of the Declaration of Helsinki. Verbal informed consent was obtained from all participants because the IRB approved using verbal con- sent following the Japanese guidelines for clinical research. Verbal consent was recorded as an experimental note. Procedures The twice exercise tests with a minimum of 2 days intervals were performed using an electromagnetically braked ergometer (POWER MAX V3 Pro, Konami Sports Co., Ltd., Tokyo, Japan) with respiratory gas analysis under hypoxia (hypo; a fraction of inspired oxygen [FiO2], 15.4 ± 0.8% equivalent to a simulated altitude of 2500 m) or normoxia (normo; FiO2, 20.9%). Hypoxic conditions were created in an exercise booth with an oxygen filtration hypoxic generator (Hypoxico Everest Summit II; WILL Co., Tokyo, Japan) by insufflating nitrogen as a target of FiO2 15.5%34. During exercise, the sLA was monitored using an sLA sensor (Grace Imaging Inc., Tokyo, Japan) attached to the upper arm, and the local sweat rate was measured at a sampling rate of 1 Hz in the same area as the sLA sensor using a perspiration meter (SKN-2000M; SKINOS Co., Ltd., Nagano, Japan). A perspiration meter ensured the value returned to zero before the new experiment by flushing out any residual sweat from the duct. Heart rate was monitored using Duranta (Zaiken, Tokyo, Japan), and blood lactate levels were measured using a standard enzymatic method on a lactate analyzer (Lactate Pro2®, ARKRAY, Kyoto, Japan). On the day of the exercise test, the participants avoided any prior heavy physical activity. The participants performed the test upright on an electronically braked ergometer. Following a 2-min rest to stabilize the heart rate and respiratory condition, the participants performed a 4-min warm-up pedaling at 20 W. Then, they exercised at increasing intensity until they could no longer maintain the pedaling rate (volitional exhaustion). The resistance was increased in 25-W increments from 50-W at 1-min intervals. Once the exercise tests were terminated, the participants were instructed to stop pedaling and remain on the ergometer for 3 min. The expired gas flow collected through the mask was measured using a breath-by-breath automated system (Aeromonitor®, Minato Medical Science Co., Ltd., Osaka, Japan). This system was subjected to a three-way cali- bration process involving a flow volume sensor, gas analyzer, and delay time calibration. The gas analyzer was calibrated under hypoxia using 8% O2, assuming a minimum oxygen concentration of 8% in exhaled air during 7 Vol.:(0123456789) Scientific Reports \| (2023) 13:22865 \| https://doi.org/10.1038/s41598-023-49369-7 www.nature.com/scientificreports/ hypoxic exercise. Respiratory gas exchange, including VE, VO2, and VCO2, was continuously monitored and measured using a 10-s average. VT was determined using the ventilatory equivalent, excess carbon dioxide, and modified V-slope methods10 through manual operating software. First, two of the three experienced researchers independently and randomly evaluated each participant’s VT using the three methods. The researchers used all three methods to assess concurrent breakpoints and eliminate false breakpoints. Second, if the VO2 values determined by the independent researchers were within 3%, then the VO2 values from the two investigators were averaged. Third, if the VO2 values determined by the independent evaluators were not within 3% of one another, a third researcher independently determined VO2. The third VO2 value was then compared with that obtained by the initial investigators. If the adjudicated VO2 value was within 3% of either of the initial investigators, the two VO2 values were averaged. Blood lactate values were obtained by auricular pricking and gentle squeezing of the ear lobe to obtain a capillary blood sample at rest, warm-up, and every minute after the start of progressive intensity. The samples were immediately analyzed for whole-blood lactate concentrations (mmol/L). bLT was determined through graphical plots of the bLA value vs. time8. Visual interpretation was indepen- dently made for each participant by two experienced researchers to locate the first rise from baseline. If the independent determinations of the stage at LT differed between the two researchers, a third researcher adjudi- cated the difference by independently determining LT. The three researchers then jointly agreed on the LT point. The sLA was measured using a sLA sensor, which quantifies lactate concentration as a current value because it reacts with sLA and generates an electric current15. The sLA sensing system comprises a disposable sensor chip and a sensor. The sensor chip generates the current value proportional to the lactate concentration by catalyzing the enzymatic immobilization on its surface to oxidize lactate, which reduces hydrogen peroxide. In addition, a protective film formed by exposure using a UV lamp allows the achievement of immediate responsiveness (response delay < 1 s) and sustainability without the enzyme reacting all at once15. The current value can be obtained as continuous data within 0.1–80 μA in 0.1-μA increments. The sLA sensor responded linearly to the lactate concentrations, especially in the 0–5 mmol/L range, which were most significant in determining the LT because the LT had normal lactate values from 2 to 4 mmol/L15. Moreover, it is also validated that the sLA values obtained from this sensor can show a significant enough difference to determine the inflection point under various sweat environments35. After calibration using saline for 2 or 3 min, the sensor chip connected to the sensor device was attached to the superior right upper limb of the participants and cleaned with an alcohol-free cloth to eliminate the influence of original sweat. In addition, the data were recorded at a sampling frequency of 1 Hz for mobile applications with Bluetooth connection. The recorded data were converted to moving average values over 13-s intervals and underwent zero correction using the baseline value. sLT was defined as the first significant increase in the sLA above baseline based on graphical plots15. Three researchers, independent of those who analyzed respiratory gas exchange, agreed on the point of sLT. Statistical analyses The results are represented as mean ± standard deviation for continuous variables and percentages for categorical variables, as appropriate. ICC was determined for intra- and inter-evaluator reliability of each threshold36. The intra-evaluator reliability was tested by one of the blinded reviewers. The inter-observer reliability was tested by estimating each threshold using three blinded reviewers. The relationship between exercise time at sLT and VT was investigated using Pearson’s correlation coefficient test. In addition, the Bland–Altman technique was applied to verify the similarities among the different methods37. The graphical representation of the difference between the methods and the average WAS compared. Statistical significance was set at two-tailed p-values < 0.05. All statistical analyses were performed using IBM SPSS Statistics for Windows, version 27.0 (IBM Corporation, Armonk, NY, USA). Data availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Received: 5 July 2023; Accepted: 7 December 2023 References 1. Lundby, C., Millet, G. P., Calbet, J. A., Bartsch, P. & Subudhi, A. W. Does “altitude training” increase exercise performance in elite athletes?. Br. J. Sports Med. 46, 792–795 (2012). 2. Owen, J. R. 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Estimation of maximal lactate steady state using the sweat lactate sensor. Sci. Rep. 13, 10366 (2023). 36. Rousson, V., Gasser, T. & Seifert, B. Assessing intrarater, interrater and test-retest reliability of continuous measurements. Stat. Med. 21, 3431–3446 (2002). 37. Bland, J. M. & Altman, D. G. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1, 307–310 (1986). Acknowledgements We are grateful to Editage for editing this manuscript. This study was funded by the Grant-in-Aid from Scien- tific Research from the Japan Agency for Medical Research and Development (ID. 21ek0210130h0003) and by a grant from the Kimura Memorial Heart Foundation Research Grant for 2019, Suzuken Memorial Foundation, Foundation for Total Health Promotion, and Research Grant for Public Health Science. The funders had no role in study design, data collection and analysis, and the decision to publish or prepare the manuscript. Author contributions The author contributions are stated as follows; the manuscript was drawn by Y.I., H.O., and T.S. The images were prepared by Y.I., H.O., T.S., and Y.K. The patient information was collected by H.O., T.D., K.S., K.D., Y.S., G.I., D.N., and Y.K. A critical revision of the manuscript for key intellectual content and supervision was provided by M.S., K.S., K.F., and Y.K. All authors have approved all aspects of our work, read, and approved the manuscript. Competing interests D.N. is a founder and shareholder of Grace Imaging Inc. All other authors declare no competing interests. Additional information Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1038/ s41598- 023- 49369-7. Correspondence and requests for materials should be addressed to Y.K. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 9 Vol.:(0123456789) Scientific Reports \| (2023) 13:22865 \| https://doi.org/10.1038/s41598-023-49369-7 www.nature.com/scientificreports/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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PMC5266769	ORIGINAL RESEARCH ARTICLE Health and Economic Burden of Running-Related Injuries in Dutch Trailrunners: A Prospective Cohort Study Luiz Carlos Hespanhol Junior1 • Willem van Mechelen1,2,3,4 • Evert Verhagen1,3,5 Published online: 25 May 2016 The Author(s) 2016. This article is published with open access at Springerlink.com Abstract Background Trailrunning is becoming very popular. However, the risk and burden of running-related injuries (RRI) in trailrunning is not well established. Objective To investigate the prevalence, injury rate, severity, nature, and economic burden of RRIs in Dutch trailrunners. Methods This prospective cohort study included 228 trailrunners aged 18 years or over (range 23–67), and was conducted between October 2013 and December 2014. After completing the baseline questionnaire, the Oslo Sports Trauma Research Center Questionnaire on Health Problems was administered every 2 weeks to collect data on RRIs. Participants who reported RRIs were asked about healthcare utilization (direct costs) and absenteeism from paid work (indirect costs). RRI was deﬁned as disorders of the musculoskeletal system or concussions experienced or sustained during participation in running. Results The mean prevalence of RRIs measured over time was 22.4 % [95 % conﬁdence interval (CI) 20.9–24.0], and the injury rate was 10.7 RRIs per 1000 h of running (95 % CI 9.4–12.1). The prevalence was higher for overuse (17.7 %; 95 % CI 15.9–19.5) than for acute (4.1 %; 95 % CI 3.3–5.0) RRIs. Also, the injury rate was higher for overuse (8.1; 95 % CI 6.9–9.3) than for acute (2.7; 95 % CI 2.0–3.4) RRIs. The median of the severity score was 35.0 [25–75 %, interquartile range (IQR) 22.0–55.7], and the median of the duration of RRIs was 2.0 weeks (IQR 2.0–6.0) during the study. The total economic burden of RRIs was estimated at €172.22 (95 % CI 117.10–271.74) per RRI, and €1849.49 (95 % CI 1180.62–3058.91) per 1000 h of running. An RRI was estimated to have a direct cost of €60.92 (95 % CI 45.11–94.90) and an indirect cost of €111.30 (95 % CI 61.02–192.75). Conclusions The health and economic burden of RRIs presented in this study are signiﬁcant for trailrunners and for society. Therefore, efforts should be made in order to prevent RRIs in trailrunners. Electronic supplementary material The online version of this article (doi:10.1007/s40279-016-0551-8) contains supplementary material, which is available to authorized users. & Luiz Carlos Hespanhol Junior l.hespanhol@outlook.com 1 Amsterdam Collaboration on Health and Safety in Sports, Department of Public and Occupational Health and the EMGO? Institute for Health and Care Research, VU University Medical Center Amsterdam, Van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands 2 School of Human Movement and Nutrition Sciences, Faculty of Health and Behavioural Sciences, University of Queensland, Brisbane, QLD, Australia 3 UCT/MRC Research Unit for Exercise Science and Sports Medicine (ESSM), Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa 4 School of Public Health, Physiotherapy and Population Sciences, University College Dublin, Dublin, Ireland 5 Australian Centre for Research into Injury in Sport and its Prevention, Federation University Australia, Ballarat, VIC, Australia 123 Sports Med (2017) 47:367–377 DOI 10.1007/s40279-016-0551-8 Key Messages At any given time, one in ﬁve trailrunners report having a running-related injury (RRI). Of the RRIs in trailrunners, 75.2 % were overuse injuries, and the prevalence of overuse RRIs was fourfold higher than acute RRIs. The indirect cost of RRIs (related to absenteeism from paid work) was twofold higher than the direct cost (related to healthcare utilization). 1 Introduction Physical activity is a cost-effective and cost-saving inter- vention to improve overall health and gain healthy life- years [1–4]. There is evidence claiming that physical activity participation in outdoor environments has a larger beneﬁcial effect on physical and mental wellbeing than participation in indoor physical activities [5]. Coinciden- tally, trailrunning, a mode of running consisting of running in the outdoors on unpaved and hilly/mountain terrains, is quickly gaining in popularity worldwide. The trailrunning community is composed of well trained trailrunners who participate in ultra-marathon events ([42.2 km), but also increasingly by trailrunning enthusiasts who partake in trailrunning events with shorter distances. Running is a very popular mode of exercise among people seeking an active lifestyle [6, 7]. Next to being beneﬁcial for health [8–10], running also carries a risk of running-related injuries (RRI) with incidence rates ranging from 7.7 [95 % conﬁdence interval (CI) 6.9–8.7] to 17.8 (95 % CI 16.7–19.1) RRIs per 1000 h of running in recreational and novice runners, respectively [11]. How- ever, prospective data on the risk and burden (including costs) of RRIs in trailrunning are sparse, especially in cohorts including trailrunning enthusiasts that compose the general trailrunning population. Most RRIs have an overuse nature [12] of which the symptoms can last for several weeks [13]. Also, these injuries can negatively inﬂuence physical activity partici- pation [14, 15]. Consequently, measuring overuse injuries next to acute injuries is important to understand the overall burden of RRIs [15]. However, measuring overuse injuries is challenging, because of their non-identiﬁable and gradual onset, and also due to ﬂuctuation of symptoms over time [16]. Most studies about running have measured RRIs leading to consequences, such as time loss (i.e. running sessions not fully accomplished or completely missed due to RRIs) and/or medical attention [17]. Deﬁning RRI based only on these consequences could underestimate the overall burden of RRIs, since minor injuries not resulting in such consequences would be neglected [18, 19]. Also, to register overuse injuries accurately, one needs a long follow-up time including regular measurement intervals in order to chart the gradual onset and ﬂuctuations of symptoms related to overuse RRIs [16, 18]. Such data are sparse in the RRI literature, and completely missing in trailrunning. The purpose of this study was therefore to prospectively investigate the prevalence, injury rate, severity, nature, and economic burden of acute and overuse RRIs in Dutch trailrunners. Such data may assist in the development of RRI prevention programs in this mode of running, and also may assist in decisions related to allocation of public health ﬁnancial resources. 2 Methods 2.1 Participants This study was composed of a convenience sample of the general Dutch trailrunning population. Individuals engaged in trailrunning were invited to partake in the study via ﬂyer cards distributed during trailrunning events in The Netherlands, and also by social media channels, newslet- ters, and the MudSweatTrails (MST) website [20]. The ﬂyer cards and additional recruitment sources guided the individuals to the project’s website containing further information and the option to enroll in the study. Individ- uals who agreed to participate through online informed consent, aged 18 years or over, reported running on unpaved surfaces on a regular basis, and who completed the baseline questionnaire were included in the study. A sample size calculation a priori was not possible because of a lack of information on the prevalence of RRIs repeatedly measured over time at the commencement of this study. The study was approved by the medical ethics committee of the VU University Medical Center Amsterdam, The Netherlands. 2.2 Study Design This was a prospective open cohort study conducted between October 2013 and December 2014. This cohort was composed of a dynamic sample, i.e., the participants entered into the study at different time-points and, there- fore, they had different follow-up periods. However, all participants were followed for at least 6 months. After giving informed consent, a link to a secure online baseline questionnaire was sent by e-mail to the participants. This 368 L. C. H. Junior et al. 123 questionnaire asked about demographics, running experi- ence, participation in other sports, current medical condi- tions, previous (last 12 months) RRIs, and current RRIs. Online follow-up questionnaires were completed every 2 weeks via a secure link sent by email. The aim of these follow-up questionnaires was to collect data about the participants’ running exposure (overall exposure and on unpaved surfaces speciﬁcally) and to record any health problems experienced in the preceding 2 weeks. In case of a sustained RRI, information about healthcare utilization and absenteeism from paid work related to the RRI were also registered through the same follow-up questionnaires (conditional branching questions). If no response was received within 1 week, a reminder was sent by e-mail encouraging the participant to complete the follow-up questionnaire. 2.3 Health Problems Registration In order to prospectively register health problems during the follow-up, the translated and adapted Dutch version of the Oslo Sports Trauma Research Center (OSTRC) Ques- tionnaire on Health Problems was included in the follow- up questionnaires [21, 22]. The OSTRC questionnaire was proposed and validated to register and monitor sports-re- lated health problems over time, i.e., acute injuries, overuse injuries, and illnesses [23]. The internal consistency (Cronbach’s a) of the OSTRC questionnaire was estimated at 0.96 and 0.91 for overall problems (including illnesses) and overuse injuries, respectively [21, 23]. The OSTRC questionnaire consisted of four key ques- tions on: (1) the extent to which injury, illness, or other health problems have affected running participation; (2) running volume; (3) running performance; and (4) the extent to which the individual has experienced symptoms during the previous 2 weeks. If no problems were reported on these four key questions, the questionnaire was ﬁnished. If a problem was reported on any of the four key questions, the participant was asked to specify whether the problem was an illness or an injury. In the case of an illness, the questionnaire was ﬁnished. In case of an injury, partici- pants were asked to report the anatomical location (one possible answer per RRI), injury type (one possible answer per RRI), a description of the symptoms (open question), injury onset, the number of days of time loss (deﬁned as the number of training sessions not fully accomplished or completely missed due to injury), and whether the injury was related to running. In the case of multiple injuries within the fortnight, the participants were asked to register the injury that caused most complaints. Other injuries could be reported in an open question. Participants were instructed to report all problems, regardless of whether or not they had already reported the same problem in previous follow-up questionnaires. 2.4 Classiﬁcation of Health Problems Health problems were classiﬁed as injuries if they were ‘‘disorders of the musculoskeletal system or concussions,’’ and were classiﬁed as illnesses if they ‘‘involved other body systems’’ [21]. One investigator who is also a phys- iotherapist (LCHJ) evaluated each reported injury case by case. Injuries were classiﬁed as RRI when they were reported as such by the participants, and when the phys- iotherapist conﬁrmed that they were experienced or sus- tained during participation in running. Subsequently, RRIs were subcategorized into acute (the onset could be linked to a speciﬁc injury event) or overuse injuries (could not be linked to a clearly identiﬁable event) [21]. The Orchard Sports Injury Classiﬁcation System version 10 (OSICS-10) [24] was used to provide a diagnostic classiﬁcation for each RRI. Substantial health problems were deﬁned as those leading to moderate or major reductions in training vol- ume, moderate or major reductions in running perfor- mance, or complete inability to run, as identiﬁed in the response options of the key questions 2 or 3 of the OSTRC questionnaire [21]. A recurrent RRI was deﬁned as an RRI at the same location and of the same type of the index RRI, even if it concerned re-injuries (after full recovery) or exacerbations (not full recovery) [25]. 2.5 Economic Consequences of Running-Related Injuries (RRIs) Participants who had reported an RRI were asked about their healthcare utilization (direct costs) and days of pro- ductivity loss related to paid work (indirect costs) due to RRIs for the duration of their reporting of symptoms. This information was collected through conditional branching questions in the follow-up questionnaires. The cost evalu- ation was performed from a societal perspective, consid- ering all RRI-related costs regardless of who pays or beneﬁts [26]. Table 1 provides the cost categories that were registered and related monetary costs used in this evalua- tion. All prices were standardized to the year 2009 according to the Dutch Health Insurance Board [27] and corrected for inﬂation until the year 2014 [28]. Costs of absenteeism from paid work were estimated based on the mean income [27] and working hours of the Dutch popu- lation according to age and gender [29]. Health and Economic Burden of Running Injuries in Trailrunners 369 123 2.6 Data Analysis Microsoft Excel 2011 version 14.5.8 (Microsoft Cor- poration, Redmond, WA, USA) and R version 3.2.3 (R Foundation for Statistical Computing, Vienna, Austria) were used to analyze the data. Descriptive analysis was performed to present baseline and follow-up data. Per- centages were calculated for categorical variables. The mean and its 95 % CI were calculated for continuous data with Gaussian distribution, otherwise the median and the 25–75 % interquartile range (IQR) were calculated. 2.6.1 Prevalence and Injury Rate Calculations Prevalence repeatedly measured over time is considered the preferable measure to describe the overall burden of injuries in sports involving overuse injuries [16]. The mean prevalence of RRIs repeatedly measured over time was calculated according to previous recommendations [16, 21, 23]. For each 2-week period, the prevalence was calculated by dividing the number of participants report- ing RRIs during that period by the number of total questionnaire respondents during the same period. Thereafter, the mean prevalence and its 95 % CI were calculated by summing all prevalences measured every 2 weeks, divided by the number of 2-week time-periods. The injury rate was calculated by dividing the number of RRIs by the sum of total running exposure in hours [18, 30]. The number of RRIs was calculated based on the number of unique RRIs identiﬁed during the follow-up. Results were expressed as the number of RRIs per 1000 h of running and its 95 % CI. 2.6.2 Severity In order to monitor the progress of the RRIs over time, a severity score ranging from 0 to 100 was calculated for each RRI based on the response options of the four key questions of the OSTRC questionnaire [21]. Average severity scores were calculated by taking the mean of the severity scores measured every 2 weeks for each RRI. The cumulative severity score (sum of the severity scores measured every 2 weeks) was calculated as an estimation of the total impact that each RRI had had over the course of the study. The average and cumulative time loss were also calculated for each RRI as the same manner as the severity score. 2.6.3 Costs Mean direct, indirect, and total costs were estimated per RRI, per 1000 h of running, and per most commonly reported RRIs. The participants could present more than one RRI during the study, resulting in dependent obser- vations. Therefore, the difference in costs between overuse and acute RRIs were estimated using linear mixed models with random intercept at the participant level, adjusted for the following possible confounders measured at baseline: age, gender, body mass index (BMI), running experience, practice of other sports, chronic condition, medication use, current RRIs, and previous RRIs. As the cost per 1000 h of running is a rate between cumulative measures at the population level (i.e., sum of costs divided by the sum of total running exposure in hours multiplied by 1000), adjustment for possible confounders was not possible. Cost data are nonparametric, therefore, 95 % CIs were obtained by bootstrapping the data with 2000 replications [31–33], as recommended for economic evaluations [26]. 3 Results 3.1 Participants, Response Rate, and Running Exposure A total of 228 trailrunners, 171 males (75.0 %) and 57 females (25.0 %), were included in the study. The baseline results are summarized in Table 2. Five male participants entered no data in the follow-up questionnaires, corre- sponding to an attrition rate of 2.2 %. As the participants entered in the study in different time-points, they had dif- ferent follow-up periods. However, all participants were followed for at least 6 months. The median of the follow- up period was 34.0 weeks (IQR 28.0–36.0), and the response rate measured every 2 weeks was 77.3 % (IQR 57.6–88.1). The median and IQR for the weekly running exposure can be found in Table 3. On average, 22.8 % Table 1 Monetary costs applied in the cost analysis Description Cost, € Healthcare costs (direct costs) General practitioner (per visit, 10 min) 30.79 General practitioner (per telephone consultation) 15.40 Medical specialist (per visit) 79.17 Physiotherapist (per visit) 39.59 Costs of productivity loss (indirect costs) Absenteeism from paid work (per hour)* 31.22 (9.78–43.95) Prices standardized to the year 2009 according to the Dutch Health Insurance Board [27] and adjusted for inﬂation until the year 2014 [28] * Indirect costs for paid work were estimated based on the mean income [27] and working hours [29] of the Dutch population according to age and gender. The value for paid work is the mean price followed by the minimum and maximal values according to standardized prices by age and gender, adjusted for inﬂation [28] 370 L. C. H. Junior et al. 123 (95 % CI 20.1–25.6) of the trailrunners participated in trailrunning events every 2 weeks. The median of the distance of the trailrunning events was 28.0 km (IQR 17.5–39.1), ranging from 3 (minimum) to 230 km (maximum). 3.2 Prevalence, Injury Rate, Severity, and Nature of RRIs The absolute number, prevalence, injury rate, and severity measures of RRIs can be found in Table 4. A total of 148 participants (66.4 %) reported 242 RRIs during the follow- up. Of the injured participants, 68 (45.9 %) reported mul- tiple RRIs (i.e., different OSICS-10 diagnostic classiﬁca- tions). The percentage of injured participants who reported other RRIs within the 2-week time-period was 4.7 % (IQR 4.0–7.2). The mean prevalence of RRIs measured every 2 weeks was 22.4 % (95 % CI 20.9–24.0). For males, the mean prevalence of RRIs was 23.0 % (95 % CI 21.3–24.7), and for females this was 20.7 % (95 % CI 18.2–23.2), with a mean difference of 2.3 percentage points (95 % CI -1.0 to 5.6). The mean prevalence of RRIs was higher for overuse than for acute RRIs, with a mean difference of 13.6 per- centage points (95 % CI 10.3 to 16.9). The injury rate was 10.7 RRIs per 1000 h of running (95 % CI 9.4–12.1). For males, the injury rate was 11.3 (95 % CI 9.7–12.9), and for females this was 9.1 (95 % CI 6.6–11.6), with an injury rate difference of 2.2 RRIs per 1000 h of running (95 % CI -0.7 to 5.1). The injury rate Table 2 Baseline data of the participants All participants n = 228 Male n = 171 Female n = 57 Age, years 43.4 (42.2–44.6) 43.8 (42.4–45.2) 42.4 (39.9–44.8) Height, cm 178.9 (177.8–180.1) 182.4 (181.4–183.4) 168.4 (166.8–170.0) Weight, kg 72.5 (71.1–74.0) 76.5 (75.2–77.9) 60.6 (58.9–62.2) BMI, kg/m2 22.6 (22.3–22.8) 23.0 (22.7–23.3) 21.3 (20.9–21.8) Total running experience, n (%) Up to 1 year 7 (3.1 %) 7 (4.1 %) – 1–2 years 18 (7.9 %) 13 (7.6 %) 5 (8.8 %) 2–5 years 43 (18.9 %) 35 (20.5 %) 8 (14.0 %) More than 5 years 160 (70.2 %) 116 (67.8 %) 44 (77.2 %) Trailrunning experience, n (%) Up to 6 months 22 (9.6 %) 16 (9.4 %) 6 (10.5 %) 6–12 months 38 (16.7 %) 31 (18.1 %) 7 (12.3 %) 1–2 years 59 (25.9 %) 38 (22.2 %) 21 (36.8 %) 2–5 years 71 (31.1 %) 56 (32.7 %) 15 (26.3 %) More than 5 years 38 (16.7 %) 30 (17.5 %) 8 (14.0 %) Practice of other sports, n (%) Yes 152 (66.7 %) 111 (64.9 %) 41 (71.9 %) No 76 (33.3 %) 60 (35.1 %) 16 (28.1 %) Chronic condition, n (%) Yes 40 (17.5 %) 27 (15.8 %) 13 (22.8 %) No 188 (82.5 %) 144 (84.2 %) 44 (77.2 %) Current medication use, n (%) Yes 26 (11.4 %) 16 (9.4 %) 10 (17.5 %) No 202 (88.6 %) 155 (90.6 %) 47 (82.5 %) Current RRI, n (%) Yes 41 (18.0 %) 33 (19.3 %) 8 (14.0 %) No 187 (82.0 %) 138 (80.7 %) 49 (86.0 %) Previous RRI (last 12 months), n (%) Yes 96 (42.1 %) 71 (41.5 %) 25 (43.9 %) No 132 (57.9 %) 100 (58.5 %) 32 (56.1 %) Continuous data are given as mean and 95 % conﬁdence interval BMI body mass index, RRI running-related injury Health and Economic Burden of Running Injuries in Trailrunners 371 123 was higher for overuse than for acute RRIs, with an injury rate difference of 5.4 RRIs per 1000 h of running (95 % CI 4.1 to 6.8). A total of 54.1 % (n = 131) of the RRIs were classiﬁed as substantial (i.e., leading to moderate or major reductions in training volume, moderate or major reductions in run- ning performance, or complete inability to run). Fifty-nine RRIs (24.4 %) neither resulted in time loss nor in medical attention. Overuse RRIs lasted longer and presented a higher cumulative severity score than acute RRIs (Table 4). The most commonly reported RRIs were Achilles tendon injury (12.8 %, n = 31), calf muscle injury (10.7 %, n = 26), knee pain undiagnosed (8.7 %, n = 21), and ankle sprain (7.0 %, n = 17). A breakdown list with all RRIs reported during this study can be found in the Electronic Supplementary Material. Table 3 Running exposure during the follow-up All participants n = 223 Male n = 166 Female n = 57 Total running exposure Duration (h/week) 3.5 (2.0–5.0) 3.5 (2.0–5.0) 3.5 (2.0–5.3) Frequency (times/week) 2.5 (1.5–3.5) 2.5 (1.5–3.5) 2.5 (2.0–3.5) Distance (km/week) 33.6 (19.5–50.0) 35.0 (20.0–50.0) 32.5 (17.5–50.0) Running exposure on unpaved surfaces Duration (h/week) 1.5 (0.5–3.0) 1.5 (0.5–2.8) 1.8 (0.8–3.0) Frequency (times/week) 1.0 (0.5–2.0) 1.0 (0.5–2.0) 1.5 (0.5–2.0) Distance (km/week) 15.0 (6.0–28.0) 15.0 (6.0–27.5) 16.0 (7.5–30.0) Results are given as median and 25–75 % interquartile range (IQR) Table 4 Absolute number, mean prevalence measured over time (every 2 weeks), injury rate, and severity measures of running-related injuries (RRIs) RRIs Total Overuse Acute Time loss Medical attention Overall Number of RRIs registered n = 242 n = 182 n = 60 n = 174 n = 72 Prevalence, mean (95 % CI) 22.4 % (20.9–24.0) 17.7 % (15.9–19.5) 4.1 % (3.3–5.0) 15.1 % (14.0–16.2) 5.9 % (5.1–6.7) Injury rate, number of RRIs per 1000 h of running (95 % CI) 10.7 (9.4–12.1) 8.1 (6.9–9.3) 2.7 (2.0–3.4) 7.7 (6.6–8.9) 3.2 (2.4–3.9) Severity measures, median (IQR) Average severity score 35.0 (22.0–55.7) 31.1 (20.0–55.0) 37.0 (28.0–57.2) 43.0 (28.6–63.0) 55.0 (34.5–70.2) Cumulative severity score 55.5 (28.0–122.0) 63.0 (25.2–122.0) 50.0 (33.8–116.0) 78.0 (37.0–165.0) 132.0 (66.0–278.0) Average time loss, days 2.0 (0.0–4.7) 2.0 (0.0–4.5) 2.8 (1.0–5.1) 3.3 (1.8–6.0) 4.0 (1.5–7.3) Cumulative time loss, days 3.0 (0.0–10.0) 3.0 (0.0–10.0) 3.5 (1.0–8.0) 5.0 (3.0–15.5) 12.0 (3.0–28.2) Duration, weeks 2.0 (2.0–6.0) 4.0 (2.0–6.0) 2.0 (2.0–4.0) 4.0 (2.0–6.0) 6.0 (3.5–10.0) Substantial Number of RRIs registered n = 131 n = 94 n = 37 n = 120 n = 58 Prevalence, mean (95 % CI) 9.9 % (9.1–10.8) 7.3 % (6.5–8.0) 2.3 % (1.4–3.1) 9.4 % (8.6–10.2) 3.7 % (3.1–4.3) Injury rate, number of RRIs per 1000 h of running (95 % CI) 5.8 (4.8–6.8) 4.2 (3.3–5.0) 1.6 (1.1–2.2) 5.3 (4.4–6.3) 2.6 (1.9–3.3) Severity measures, median (IQR) Average severity score 54.5 (39.9–68.3) 54.5 (39.7–68.8) 51.0 (41.2–67.3) 54.8 (41.1–69.2) 59.6 (44.1–76.6) Cumulative severity score 109.0 (66.0–198) 113.0 (71.2–230.5) 80.0 (50.0–159.0) 117.5 (66.0–226.0) 168.0 (80.0–287.2) Average time loss, days 4.0 (2.0–6.8) 4.0 (2.0–6.9) 4.0 (2.0–6.0) 4.2 (2.8–7.0) 5.0 (3.0–8.3) Cumulative time loss, days 7.0 (4.0–20.0) 8.5 (4.0–23.8) 5.0 (3.0–16.0) 9.5 (4.0–21.5) 14.0 (4.0–31.5) Duration, weeks 4.0 (2.0–8.0) 5.0 (2.0–9.5) 4.0 (2.0–6.0) 4.0 (2.0–8.0) 6.0 (4.0–10.0) Substantial RRIs were deﬁned as those leading to moderate or major reductions in training volume, moderate or major reductions in running performance, or complete inability to run 95 % CI 95 % conﬁdence interval, IQR 25–75 % interquartile range 372 L. C. H. Junior et al. 123 3.3 Economic Burden of RRIs In total, 332 healthcare consultations (21 general practi- tioner, 47 medical specialist, and 264 physiotherapy con- sultations) and 102 days of productivity loss related to paid work were registered. A total (direct plus indirect) cost of €41,677.13 was calculated for the 242 RRIs. The direct cost was €14,742.39 (€569.64 related to general practi- tioner, €3720.99 related to medical specialist and €10,451.76 related to physiotherapy consultations) and the indirect cost was €26,934.74 (related to absenteeism from paid work). The costs per RRI, per 1000 h of running and per most commonly reported RRIs can be found in Table 5. Overuse RRIs presented higher physiotherapy costs than acute RRIs, and acute RRIs presented higher costs related to general practitioner than overuse RRIs. There were no statistically signiﬁcant differences in costs per 1000 h of running between males and females. Of the four most commonly reported RRIs, calf muscle injuries presented the highest direct and indirect costs. 4 Discussion 4.1 Trailrunners and Running Exposure The sample of the current study was composed by Dutch trailrunners who were recruited during trailrunning events, or through trailrunning channels, like the MST website [20], regardless of age, gender, running experience, com- petition level, or training exposure (e.g., volume and intensity). As presented in Table 3, Dutch trailrunners usually train on paved and unpaved tracks. This could be explained by the fact that most Dutch trailrunners live in city areas, and, therefore, they do not have easy and fast access to trail tracks that usually are composed by rugged, muddy, and/or mountain terrains. However, trailrunners need to train on a regular basis to be prepared for the trailrunning events that usually have longer distances (median of 28 km in the current study). Therefore, the sample of trailrunners in the current study can be consid- ered representative of the general Dutch trailrunning pop- ulation. Furthermore, the characteristics of the Dutch trailrunners who participated in this study may also be similar to recreational trailrunners in other countries. 4.2 Prevalence and Injury Rate of RRIs The results of this study have shown that the mean prevalence of RRIs measured every 2 weeks is between 20.9 and 24.0 % (95 % CI) in trailrunners. In other words, one out of ﬁve trailrunners may be expected to sustain RRIs during a 2-week time-period. The prevalence esti- mates of this study are not comparable with other studies in the literature, since this is the ﬁrst study to report the prevalence of RRIs repeatedly measured over time in trailrunners. In addition, previous studies on trailrunning have used different methods and RRI deﬁnitions [34, 35]. This hampers comparisons. For example, the incidence proportion of lower limb musculoskeletal injuries (22.2 %) found during the Al Andalus Ultimate Trail 2010 held in southern Spain [35] was similar to the prevalence repeat- edly measured over time reported in the current study, although these are two different measures. Hespanhol Junior et al. [15] have used similar methods as the one used in the current study to investigate RRIs in inexperienced runners training for an event. The study design, surveillance system, RRI deﬁnition and RRI clas- siﬁcations were the same in both studies, although the population and the follow-up period were different. The mean prevalence of all RRIs and the mean prevalence of overuse RRIs found in the current study were lower than the mean prevalences reported by Hespanhol Junior et al. [15]. This may be explained by differences in running experience [11] and training volume [36] between these two populations. As explained in the methods, a priori sample size cal- culation was not possible because of missing information on the prevalence of RRIs repeatedly measured over time in a general trailrunning population at the commencement of this study. However, the study of Hespanhol Junior et al. [15] was recently available. Therefore, a post hoc sample size calculation based on the results reported in Hespanhol Junior et al. [15] and the results of the current study was possible. The sample size was estimated based on calcu- lations for longitudinal studies with repeated measurements [37]. The prevalence of RRIs repeatedly measured over time in the study of Hespanhol Junior et al. [15] was 30.8 % (95 % CI 25.6–36.0), and in the current study was 22.4 % (95 % CI 20.9–24.0). Considering a = 0.05, b = 0.8, 17 repeated measurements (i.e., median of 34 weeks of follow-up with repeated measurements every 2 weeks), a correlation coefﬁcient of the repeated mea- surements of 0.24 (calculated in the current study for the purpose of this sample size calculation), and a response rate of 77.3 % (reported in the current study), the sample size calculation suggested a cohort of 152 participants. Based on this calculation, the sample size of the current study was appropriate. This calculation may be useful as a reference for sample size calculations for future longitu- dinal studies with repeated measurements on RRIs. Comparisons of injury rates of RRIs across studies are difﬁcult because of differences in RRI deﬁnitions [11, 17, 19]. However, the time loss injury rate in trailrunners found in the current study [7.7 RRIs per 1000 h (95 % CI Health and Economic Burden of Running Injuries in Trailrunners 373 123 Table 5 Economic burden of running-related injuries (RRIs) in trailrunners Overall Direct cost Indirect cost Total General practitioner Medical specialist Physiotherapy Absenteeism from paid work Cost per RRI, € All injuries, n = 242 172.22 (117.10 to 271.74) 60.92 (45.11 to 94.90) 2.35 (1.08 to 4.14) 15.38 (8.51 to 27.41) 43.19 (30.96 to 60.20) 111.30 (61.02 to 192.75) Overuse, n = 182 174.40 (108.52 to 302.65) 69.96 (48.18 to 102.90) 1.44 (0.51 to 3.54) 17.84 (9.57 to 31.88) 50.68 (35.02 to 72.00) 104.44 (50.88 to 205.16) Acute, n = 60 165.61 (78.19 to 363.54) 33.50 (18.55 to 55.33) 5.13 (2.05 to 10.52) 7.92 (1.32 to 22.43) 20.45 (9.90 to 41.57) 132.11 (44.36 to 301.15) Difference (overuse minus acute) 16.60 (-161.98 to 179.61) 31.05 (16.31 to 76.70)* -3.85 (-10.42 to -3.66)* 3.79 (-14.19 to 23.45) 27.43 (23.68 to 50.98)* -14.88 (-210.93 to 99.40) Cost per 1000 h of running, € All participants, n = 223 1849.49 (1180.62 to 3058.91) 654.22 (465.82 to 942.68) 25.28 (11.61 to 44.41) 165.13 (91.35 to 291.60) 463.81 (323.26 to 686.48) 1195.27 (635.76 to 2346.03) Male, n = 166 1783.44 (1013.66 to 3321.43) 548.35 (353.32 to 966.17) 19.46 (7.41 to 37.99) 142.93 (57.17 to 290.63) 385.96 (238.25 to 631.36) 1235.09 (582.51 to 2622.02) Female, n = 57 2034.97 (1032.15 to 4630.62) 951.52 (621.73 to 1630.91) 41.63 (10.41 to 104.08) 227.45 (80.28 to 454.90) 682.44 (398.56 to 1137.39) 1083.45 (279.82 to 3134.15) Difference (males minus females) -251.52 (-2671.36 to 1380.54) -403.17 (-1107.63 to 118.15) -22.17 (-92.86 to 10.32) -84.52 (-339.59 to 109.56) -296.47 (-801.50 to 50.16) 151.64 (-1751.25 to 1485.12) Cost per most commonly reported RRIs, € Achilles tendon injury, n = 31 67.60 (30.08 to 148.10) 46.68 (19.16 to 111.10) 1.99 (0.00 to 12.32) 10.22 (0.00 to 53.23) 34.48 (13.68 to 69.77) 20.91 (0.00 to 128.43) Calf muscle injury, n = 26 135.85 (49.22 to 391.91) 56.00 (23.19 to 123.85) 1.18 (0.00 to 6.58) 21.32 (5.66 to 63.18) 33.50 (11.50 to 80.89) 79.86 (0.00 to 384.14) Knee pain undiagnosed, n = 21 13.20 (2.83 to 33.33) 13.20 (3.05 to 31.67) – – 13.20 (2.92 to 33.73) – Ankle sprain, n = 17 82.20 (8.48 to 346.78) 20.96 (5.16 to 50.47) – – 20.96 (5.28 to 53.78) 61.24 (0.00 to 319.48) All costs are presented in euros (€). Mean values are followed by the bias-corrected and accelerated 95 % conﬁdence interval estimated by bootstrapping (2000 replications) * Signiﬁcant difference between overuse and acute RRIs The difference in costs between overuse and acute RRIs were estimated using linear mixed models with random intercept at the participant level, adjusted for the following possible confounders measured at baseline: age, gender, body mass index (BMI), running experience, practice of other sports, chronic condition, medication use, current RRIs, and previous RRIs 374 L. C. H. Junior et al. 123 6.6–8.9)] was similar to the injury rate in recreational runners reported by Videbaek et al. [7.7 RRIs per 1000 h (95 % CI 6.9–8.7)] [11], that was summarized based on studies with time loss RRI deﬁnitions. According to the literature, overuse RRIs occur more frequently than acute RRIs [12, 15]. The results of the current study support this observation for trailrunning, since the prevalence of overuse RRIs was fourfold higher than acute RRIs, and the injury rate of overuse RRIs was threefold higher than acute RRIs. Most of the time, running can be described as an aerobic physical activity that requires long duration exertion with few changes in movement patterns. Therefore, overuse injuries with a gradual onset mechanism resulting from repetitive micro- trauma would be more expected in trailrunning than inju- ries with a sudden onset. 4.3 Severity of RRIs Severity measures are important to understand the extent to which sports injuries affect health [38]. A strength of this study was the continuous and valid method used to monitor the severity of sports injuries, irrespective of time loss or medical attention [21]. In fact, 24.4 % of the RRIs reported in this study neither resulted in time loss nor medical attention. Therefore, the results of this study support the hypothesis that measuring RRIs based only on time loss or medical attention deﬁnitions will lead to an underestima- tion of the burden of RRIs. The longer duration of overuse RRIs can explain why the cumulative severity score was higher for overuse than for acute RRIs. More than half of the RRIs were classiﬁed as substantial, meaning that they caused a moderate or major reduction in running volume or running perfor- mance, or had caused a complete inability to participate in running. This result supports the hypothesis that RRIs may reach such severity levels that they can lead to dropping out of running participation [14, 15]. The implication is that RRIs may lower the motivation to participate in running, a great ally against the burden of physical inactivity, which is a leading risk factor for the global disease burden [39] and mortality [40]. In fact, running is effective in reducing mortality and disability [8, 9]; however, the adherence to running participation is essential to reach such health beneﬁts [9, 10]. 4.4 Economic Burden of RRIs To the best of our knowledge, this is the ﬁrst study reporting the total, direct, and indirect costs of RRIs in trailrunners. The cost per RRI in trailrunners found in the current study was €172.22 (95 % CI 117.10–271.74), which was comparable to the cost per RRI found in runners training for an event (€173.72; 95 % CI 57.17–318.76) [15] and higher than the costs per RRI found in novice runners (€83.22; 95 % CI 50.42–116.02) [41]. These cost estimates are lower than the economic burden generally reported for sports injuries in other athletic populations [42, 43]. However, comparisons with other sports and populations should be made with caution where the study methods and follow-up periods were different. Healthcare consultations related to RRIs were threefold higher than the number of days of productivity loss related to paid work. However, the indirect cost of RRIs was twofold higher than the direct cost. Interestingly, the indirect-direct cost ratio was higher for acute RRIs (indi- rect cost fourfold higher than direct cost) than for overuse RRIs (indirect cost 1.5-fold higher than direct cost), indi- cating that the productivity loss impact may be higher for acute RRIs. Other studies have also shown higher indirect than direct costs related to sports injuries [43–47]. These results indicate that productivity loss is the main contrib- utor to the economic burden of sports injuries, with a sig- niﬁcant impact on societal ﬁnancial resources. As such, policymakers should always take into account the direct and especially the indirect costs of sports injuries to drive their policies. To put our results into perspective: according to MST, 7500 people participate in trailrunning events organized by them each year. Based on the results of the current study, one trailrunner runs approximately 3.5 h per week (i.e., 182 h per year). Therefore, one could expect to have a total cost related to RRIs of more than €2.5 million yearly, only accounting for trailrunners participating in the MST events. This ﬁgure represents around 0.4 % of all annual sports injury costs in The Netherlands [47]. Although not a large proportion, if RRIs in trailrunning are prevented, maybe hundreds of thousands of euros could be saved and redi- rected to other public health areas. This assumption shows the ﬁnancial impact that RRIs in trailrunning could have for society. There is sound evidence showing that physical activity is a cost-effective method to improve overall health, and gain healthy life-years [1–4]. Evidence also suggests that the health beneﬁts of running outweigh the related risks and costs [4, 8–10]. Therefore, running may be advised for people who seek to improve their health by means of engaging in strenuous physical activity. Nonetheless, RRIs are a preventable side effect of such active engagement and prevention is warranted. Effective prevention of injuries will not only reduce the individual burden in terms of injury and costs, but will also improve joyful and contin- uing participation in running. Health and Economic Burden of Running Injuries in Trailrunners 375 123 4.5 Limitations This study was composed of a convenience sample. As presented in Table 4, most RRIs reported in the current study were overuse injuries, i.e., those that have a non- identiﬁable and gradual onset, and also present ﬂuctuation of symptoms over time. Consequently, the RRIs reported in the current study represent all RRIs that could be a result of running exposure on paved, unpaved, or both surfaces (the most likely assumption). The RRIs were self-reported and then classiﬁed by a healthcare professional (LCHJ) based on the RRI description given by the participants. A con- ﬁrmation of the RRI diagnoses during face-to-face con- sultations was not possible due to logistic reasons. Data about medicines taken and diagnostic tests due to RRIs were not collected. This could have lead to an underesti- mation of the direct costs of RRIs. The cost analysis was an estimation based on Dutch standardized prices for health- care utilization [27], and the mean income [27] and working hours of the Dutch population for absenteeism from paid work [29], all adjusted for inﬂation [28]. Despite the fact that this methodology has been accepted and rec- ommended [26], it is important to realize that the cost results were estimated and do not represent actual costs. 5 Conclusions On average, one out of ﬁve trailrunners reported RRIs every 2 weeks. Overuse RRIs represented 75.2 % of all RRIs registered during the follow-up. A total of 54.1 % of all RRIs were classiﬁed as substantial. The economic burden (direct plus indirect costs) of RRIs was estimated at €172.22 (95 % CI 117.10–271.74) per RRI, and €1849.49 (95 % CI 1180.62–3058.91) per 1000 h of running. Healthcare uti- lization (direct costs) contributed to 35.4 % of these costs and absenteeism from paid work (indirect costs) to 64.6 %. Acknowledgments Luiz Carlos Hespanhol Junior is a PhD candidate supported by CAPES (Coordenac¸a˜o de Aperfeic¸oamento de Pessoal de Nı´vel Superior), process number 0763/12-8, Ministry of Education of Brazil. The authors wish to thank MudSweatTrails and Marc Weening for their assistance during the recruitment, and all trail- runners who participated in this study. Compliance with Ethical Standards Funding This study had no funding sources. Conﬂict of interest Luiz Carlos Hespanhol Junior and Evert Ver- hagen declare that they have no conﬂicts of interest. Willem van Mechelen declares that he is director-shareholder of VU University Medical Center spin-off company Evalua Nederland B.V. (http:// www.evalua.nl), and non-executive board member of Arbo Unie B.V. (http://www.arbounie.nl). Both companies operate on the Dutch Occupational Health Care market. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. 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Global health risks: mortality and burden of disease attributable to selected major risks. Geneva: World Health Organization; 2009. 41. Hespanhol Junior LC, Huisstede BM, Smits DW, et al. The NLstart2run study: economic burden of running-related injuries in novice runners participating in a novice running program. J Sci Med Sport. 2015;. doi:10.1016/j.jsams.2015.12.004. 42. King DA, Hume PA, Milburn P, et al. Rugby league injuries in New Zealand: a review of 8 years of accident compensation corporation injury entitlement claims and costs. Br J Sports Med. 2009;43(8):595–602. doi:10.1136/bjsm.2009.061481. 43. Verhagen EA, van Tulder M, van der Beek AJ, et al. An eco- nomic evaluation of a proprioceptive balance board training programme for the prevention of ankle sprains in volleyball. Br J Sports Med. 2005;39(2):111–5. doi:10.1136/bjsm.2003.011031. 44. Cumps E, Verhagen E, Annemans L, et al. 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Health and Economic Burden of Running Injuries in Trailrunners 377 123	Health and Economic Burden of Running-Related Injuries in Dutch Trailrunners: A Prospective Cohort Study.	[]	Hespanhol Junior, Luiz Carlos,van Mechelen, Willem,Verhagen, Evert	eng
PMC9250525	1 Vol.:(0123456789) Scientific Reports \| (2022) 12:11223 \| https://doi.org/10.1038/s41598-022-14616-w www.nature.com/scientificreports Effects of different inspiratory muscle warm‑up loads on mechanical, physiological and muscle oxygenation responses during high‑intensity running and recovery Anita B. Marostegan1, Claudio A. Gobatto1, Felipe M. Rasteiro1, Charlini S. Hartz2, Marlene A. Moreno2 & Fúlvia B. Manchado‑Gobatto1 Inspiratory muscle warm‑up (IMW) has been used as a resource to enhance exercises and sports performance. However, there is a lack of studies in the literature addressing the effects of different IMW loads (especially in combination with a shorter and applicable protocol) on high‑intensity running and recovery phase. Thus, this study aimed to investigate the effects of three different IMW loads using a shorter protocol on mechanical, physiological and muscle oxygenation responses during and after high‑intensity running exercise. Sixteen physically active men, randomly performed four trials 30 s all‑out run, preceded by the shorter IMW protocol (2 × 15 breaths with a 1‑min rest interval between sets, accomplished 2 min before the 30 s all‑out run). Here, three IMW load conditions were used: 15%, 40%, and 60% of maximal inspiratory pressure (MIP), plus a control session (CON) without the IMW. The force, velocity and running power were measured (1000 Hz). Two near‑infrared spectroscopy (NIRS) devices measured (10 Hz) the muscle’s oxygenation responses in biceps brachii (BB) and vastus lateralis (VL). Additionally, heart rate (HR) and blood lactate ([Lac]) were also monitored. IMW loads applied with a shorter protocol promoted a significant increase in mean and minimum running power as well as in peak and minimum force compared to CON. In addition, specific IMW loads led to higher values of peak power, mean velocity (60% of MIP) and mean force (40 and 60% of MIP) in relation to CON. Physiological responses (HR and muscles oxygenation) were not modified by any IMW during exercise, as well as HR and [Lac] in the recovery phase. On the other hand, 40% of MIP presented a higher tissue saturation index (TSI) for BB during recovery phase. In conclusion, the use of different loads of IMW may improve the performance of a physically active individual in a 30 s all‑out run, as verified by the increased peak, mean and minimum mechanical values, but not in performance assessed second by second. In addition, 40% of the MIP improves TSI of the BB during the recovery phase, which can indicate greater availability of O2 for lactate clearance. High-intensity exercise such as tethered sprints is often applied in training programs1–3. Considering the sig- nificance of the running power in sports and training programs, investigations have been conducted to improve the consistency of this parameter by making it measurable using a tethered system capable of monitoring the force, the velocity, and consequently the running power2,4–6. Furthermore, by applying a high-intensity exer- cise test, especially with duration around 30 s (i.e., 30 s all-out run), it is possible to assess the peak, mean and minimum power, as well as the fatigue index7–9, which contribute to training programs applied to athletes and active participants. The 30 s all-out run is characterized by a predominantly anaerobic stimulus that increases the physiological responses, such as heart rate and blood lactate production7,8,10, by locomotor muscles (more OPEN 1Laboratory of Applied Sport Physiology, School of Applied Sciences, University of Campinas, Limeira, São Paulo, Brazil. 2Postgraduate Program in Human Movement Sciences, Methodist University of Piracicaba, Piracicaba, Sao Paulo, Brazil. email: 2 Vol:.(1234567890) Scientific Reports \| (2022) 12:11223 \| https://doi.org/10.1038/s41598-022-14616-w www.nature.com/scientificreports/ active muscles). Despite the significance of the anaerobic metabolism, the aerobic metabolism also plays an important role during high-intensity exercise, as observed in the Wingate test11–13 and the 30 s all-out tethered run7. In addition, the recovery process is essential to reestablish the energy stores, with significant participation of the aerobic metabolism during and after high-intensity exercise 10,14, acting as an ally in the performance of subsequent intense efforts15. Recently, our group observed that during an all-out 30 s running test the biceps brachii (less active muscle) showed increased deoxygenation and a quick adjustment of post-effort oxygen levels compared to the vastus lateralis10. These findings reinforce the tissue-dependent response, evidencing that the organism adapts to a stressful stimulus in an integrative way. Considering the integrative biology during physical effort16 in high-intensity exercise, it is known that the oxygenation is directed to areas with higher demand, promoting vasoconstriction in less active muscles and vaso- dilation in more active muscles. In this sense, the accumulation of metabolites (e.g., blood lactate and H + ions) in the fatigued inspiratory muscles (IM) can initiate the inspiratory muscle metaboreflex, which triggers sympathetic nerve activity that promotes adrenergic vasoconstriction, provoking competition with locomotor muscles for oxygenation17–20, and consequently impairing the exercise performance. In line with this, it is reported that IM plays a significant role during exercise and recovery19–22. Therefore, some studies have focused on IM training, aiming at reducing the IM fatigue in order to maximize performance and oxygenation redistributions23–25. Moreo- ver, the warm-up or pre-activation of inspiratory muscles (IMW) using a portable device has been suggested to prepare this specific musculature before exercise, and consequently improve the physiological responses during effort26–28, as well as sports performance29–32. Positive effects of IMW were observed during high-intensity, short- duration exercises, with increased power in the Wingate test33,34. Furthermore, better oxygenation distributions in more active muscles35 and decreased lactate accumulation in athletes after intermittent running36 were also observed. However, the available literature lacks studies on the effects of IMW on more and less active muscles, which are essential for oxygen uptake and metabolite removal, respectively10,37. Different IMW protocols (i.e., number of sets, repetitions and time between sets) and loads (characterized by airflow restriction in the inspiration phase) ranging from 5 to 80% of maximal inspiratory pressure (MIP) have been proposed29,31,32,38–41. Nonetheless, most studies that applied this strategy used efforts based on indi- vidual MIP, generally composed of 2 sets of 30 repetitions at 40% of MIP. This protocol has been suggested to improve exercise performance through the preparation of the inspiratory muscles, to minimize the effects of inspiratory muscle metaboreflex, reducing fatigue and improving oxygen delivery between the locomotor and respiratory muscles29,31,34,40. The application of higher inspiratory loads such as 60–80% of MIP38,39 to improve subsequent results is still controversial and lower loads such as 5–15% of MIP are commonly applied as a placebo condition27,31,35,36,39. Concerning the number of repetitions, recent investigations have suggested a shorter IMW protocol consisting of 2 sets of 15 inspiratory efforts in order to minimize the time spent during warm-up and improve its application in a sports/physical training environment32,38,39. However, there is a lack of knowledge about the effects of different inspiratory loads of IMW on the mechanical and physiological performance in high-intensity and short-duration running, especially when performing such a short inspiratory protocol. Therefore, this study aims to investigate the effects of different IMW loads (15%, 40% and 60% of MIP) using a shorter protocol (2 sets of 15 repetitions with a 1-min rest interval between them) on the mechanical (i.e., power, force and velocity), physiological (i.e., heart rate and blood lactate) and muscle oxygenation responses (in vastus lateralis and biceps brachii, considered the more and less active muscles, respectively) during high- intensity running and passive recovery. Our hypothesis is that IMW at 40% of MIP will minimize IM fatigue, allowing redirect the oxygenation to more active muscles with higher percentages of tissue saturation index (TSI), consequently promoting a positive impact on the mechanical responses during the 30 s all-out run (especially on mean running power and fatigue index), as well as contributing to a better post-effort blood lactate removal in relation to other loads. In addition, as studies are controversial and there are gaps about the IMW effects on high-intensity running, compared with the responses already observed in the literature in different populations and types of exercise it is estimated that the load with 15% of MIP has a placebo effect and is equal to the control session, while the 60% load does not improve performance, as it is considered a heavy load that will fatigue the respiratory muscles. Materials and methods Ethics approval. All procedures were approved by the local Research Ethics Committee of the School of Medical Sciences of the State University of Campinas (protocol number: 99783318.4.0000.5404) and were in accordance with the ethical recommendations in the Declaration of Helsinki. Participants were only evaluated after having received information about the experimental procedures and risks and signing an informed consent form. Participants. Sixteen physically active young men (local sports team players and street running partici- pants) were evaluated (23 ± 1 years old, 73.2 ± 2.0 kg; 177.4 ± 1.9 cm; 9.0 ± 0.5% body fat, IPAQ at 4539 ± 942 metabolic equivalent-min/week; MIP at 145.6 ± 9.9 cmH2O, peak of global strength index (S-Index) at 139 ± 3 and mean S-Index at 123 ± 4, cmH2O). The analysis with GPower software showed that a sample size of at least 12 individuals would be necessary to obtain a power of 80% with a significance level α = 5%, based on previously published data10. The participants were invited to answer questionnaires about their levels of physical activity (International Physical Activity Questionnaire—IPAQ), sports practice and health history. Only participants that presented a minimum score to classify them as ‘physically active’ were included in the study42. Individuals that reported metabolic, cardiovascular, respiratory or orthopedic disease were excluded from this research. 3 Vol.:(0123456789) Scientific Reports \| (2022) 12:11223 \| https://doi.org/10.1038/s41598-022-14616-w www.nature.com/scientificreports/ Experimental approach to the problem. The experimental design involved six visits to the laboratory under similar conditions and at identical daytime (± 60 min), separated by 48–72 h (Fig. 1). On the first day, after signing the informed consent form and completing the questionnaires, anthropometric and body composi- tion measurements were performed. In the second visit, MIP and S-Index were determined 1 h apart to prevent inspiratory fatigue. On the same day, the participants were familiarized with the inspiratory muscle warm-up protocol and the non-motorized treadmill (NMT), when they were asked to perform five sprints of 10 s. In the next four visits to the laboratory, all participants were submitted to high-intensity tethered exercise (30 s all-out run), preceded by different IMW loads protocol (15%, 40% and 60% of MIP plus a control (CON) session with- out the IMW protocol). These sessions were randomly performed by the individuals under the IMW protocols used. Upon arrival at the laboratory, the participants were equipped with near-infrared spectroscopy (NIRS) devices attached to the biceps brachii (BB) and vastus lateralis (VL) muscles, and a heart rate monitor (HR) for data acquisition throughout the session. The participants remained at rest for 3 min for the determination of baseline values (BL), including blood lactate concentration ([Lac]) at rest. Then, they were asked to warm-up on a motorized treadmill for 5 min (7 km/h and 1% inclination) and rest for another 5 min. Subsequently, the IMW protocol was performed. Two minutes after, the individuals were submitted to 30 s high-intensity tethered run- ning for data acquisition (i.e., force, velocity and running power). Immediately after the test (T0), blood samples were collected every 2 min up to 18 min of passive recovery (T2‒T18). In all sessions, the participants were instructed to have a light meal, not to consume alcohol/caffeine and not to practice moderate-intense exercise 24 h before the tests. The procedures were performed in a controlled and isolated laboratory environment, and the participants did not receive any information about each intervention. Figure 1. (A) Experimental protocol illustrating the procedures performed on each day during the whole study period. (B) Timeline of the protocol sessions. Physiological responses were monitored throughout the exercise and the 18 min of passive recovery. Near-infrared spectroscopy devices (NIRS, represented by the red rectangles) provided the biceps brachii and vastus lateralis oxygenation data. S-Index—global strength index; CON—Control session; WU15, WU40 and WU60—Warm-up with 15%, 40% and 60% of maximal inspiratory pressure (MIP), respectively. 4 Vol:.(1234567890) Scientific Reports \| (2022) 12:11223 \| https://doi.org/10.1038/s41598-022-14616-w www.nature.com/scientificreports/ Inspiratory measurements and inspiratory muscle warm‑up. The analysis was conducted by a trained researcher who demonstrated the correct performance of the respiratory maneuver. The participants remained seated on a chair, wearing a nose clip and a plastic mouthpiece connected to an analogical manovacu- ometer (± 300 cmH2O; GER-AR, São Paulo, SP, Brazil) used to measure maximal pressures. A small hole (2 mm) was introduced in the rigid mouthpiece in order to prevent glottic closure. The participants were instructed to complete three to five acceptable and reproducible maximum maneuvers (i.e., differences of 10% or less between values), with 1 min interval between maneuvers43,44. Each inspiratory effort was sustained for at least 1 s, and the MIP was considered the highest value between these attempts45. After 1 h, a dynamic proposal to characterize the strength of the participants’ IM, the global strength index (S-Index), was assessed by an inspiratory threshold (POWERbreathe K5, IMT Technologies Ltd., Birmingham, UK), with the participants in standing position and using a nose clip. Thirty dynamic inspirations resistance- free were performed slowly, with verbal encouragement to inhale a greater air capacity32. During the protocol, breathing pattern curves were monitored by graphic records provided by Breathe-link® software. At the end of the test, the algorithm provided the mean and peak values of the S-Index (in units of cmH2O). The inspiratory muscle warm-up (IMW) protocol loads were also applied using the inspiratory device POW- ERbreathe K5. The participants initiated every breath from residual volume and were encouraged to continue the respiratory effort until further excursion of the thorax was not possible, with a diaphragmatic breathing pat- tern. Subsequently, they were instructed to keep the same inspiratory pressure and the breathing pattern curves were also monitored by Breathe-link® software. The total protocol was comprised of two sets of 15 inspirations with a 1-min rest interval between them. The loads were equivalent to 15% (WU15), considered placebo by the literature32,35,39, 40% (WU40) and 60% (WU60) of MIP. All experimental trials were randomly distributed. The 30 s all‑out run test and mechanical measurements. The 30 s all-out run was performed on a non-motorized treadmill (NMT) (Inbramed Super ATL, Inbrasport, Porto Alegre, Brazil), as detailed by Man- chado-Gobatto et al.10. Two minutes after the IMW or CON protocols, the participants were asked to run at maximum intensity for 30 s, tethered by their waist to an inextensible steel cable attached to a load cell (CSL/ ZL-250, MK Controle e Instrumentação Ltda, Brazil) for horizontal force measurement. Other four load cells (CSAL/ZL-500, MK Controle e Instrumentação Ltda, Brazil) were positioned under the NMT platform to meas- ure the vertical force (signal frequency at 1000 Hz). A hall-effect sensor in the frontal axis of the NMT provided pulses for velocity acquisition. Therefore, both vertical and horizontal force components were measured during the running exercise along with velocity to calculate the power running. The signals were synchronized and the product between force and velocity resulted in the running power, with the peak, mean and minimum values relativized to body mass. Fatigue index (FI) was also calculated by the following equation: FI = (peak power— minimum power)/peak power 100). The system was calibrated on the test days. Physiological responses. Blood lactate concentration and heart rate. For lactate concentrations ([Lac]) at rest, post-effort and every 2 min up to 18 min of passive recovery, blood samples (25 µl) were collected from the participants’ earlobe with heparinized capillaries, deposited in microtubes (Eppendorf, 1.5 ml containing 50 µl of 1% sodium fluoride—NaF) and frozen at − 20 °C. The [Lac] were determined by a lactate analyzer (YSI- 2300-STAT-Plus™, Yellow Springs, USA). Throughout the protocols, the heart rate (HR) was constantly recorded (at 1 Hz) (Polar V800, Finland). For all variables, the peak, mean and minimum values were calculated (during the test we used the 30 s responses, while during passive recovery we considered the mean of 18 min). Muscle oxygenation by NIRS measurements. Total hemoglobin ([tHb] = oxyhemoglobin ([O2Hb]) + deoxyhemoglobin [HHb]) and the equilibrium between oxygen supply and consumption were calcu- lated using the tissue saturation index (TSI = [O2Hb]/([O2Hb] + [HHb]) × 100%)46 throughout the experimental protocol by two PortaMon devices (Artinis, Medical Systems BV, Zetten, Netherlands) working on the modified Beer–Lambert law. Each device has three light source transmitters (with two wavelengths of 760 and 850 nm), positioned at 30, 35 and 40 mm from the receiver. The devices were safely fixed and covered to eliminate back- ground light after shaving and cleaning the skin surface. While one was positioned in the medial part (belly) of the BB10,37,47 of the right arm, considered less active during running, the other was allocated in the VL of the right leg (considered more active), 15 cm above the proximal edge of the patella and 5 cm to the external side10,48–50. Skinfolds for BB (3.3 ± 0.2 mm) and VL (11.2 ± 1.2 mm) were less than half the distance between the source and the deepest detector (i.e., 20 mm). Different path lengths (DPF) were used for BB (3.78) and VL (3.83)10. The signals were smoothed using a 10th order low-pass zero-phase Butterworth filter (cutoff frequency of 0.1 Hz)50, and recorded and analyzed on Oxysoft® software (Artinis Medical System, Netherlands). The (Δ)[tHb] in micro- molar units (μM) was obtained by subtracting these values from the final 30 s of the baseline period of 3 min. Examples of NIRS signals in BB and VL muscles of one participant during the four sessions (CON, WU15, WU40 and WU60) are displayed in Supplementary file 1, whereas the descriptive graphics of [O2Hb] and [HHb] during and after the 30 s all-out run are in Supplementary file 2. Statistical analysis. All analyses were performed using Statistica software (version 7.0), and the results are expressed as mean ± standard error of the mean (SEM). Data normality and homogeneity distribution were tested by Shapiro–Wilk and Levene’s test, respectively. Two-way repeated measures analysis of variance (ANOVA) was applied to study the effect of IMW (CON x WU15 x WU40 x WU60) and time during the 30 s of exercise (on mechanical and physiological responses) as well as the 18 min (every 2 min) of passive recovery (on physiological responses) compared to the baseline condition (BL). Three-way repeated measures ANOVA investigated the effect of IMW, time, and limb muscles (BB vs VL) on muscle oxygenation during the 30 s all-out 5 Vol.:(0123456789) Scientific Reports \| (2022) 12:11223 \| https://doi.org/10.1038/s41598-022-14616-w www.nature.com/scientificreports/ run and recovery phase. One-way repeated measures ANOVA was applied to investigate the effects of IMW pro- tocols on peak, mean, minimum values for force (N/kg), velocity (m/s), running power (W/kg), FI, as well HR and [Lac] values. Two-way repeated measures ANOVA (effects of IMW and limb muscles) analyzed the peak, mean and minimum values of muscle oxygenation responses. Lastly, post hoc Newman−Keuls test was used to detect differences (P ≤ 0.05). The partial eta-squared (ηp 2: 0.01 = small, 0.06 = moderate, 0.14 = large) statistics provided estimates of the effect sizes. Results Analyses during the 30 s all‑out run. The mechanical and heart rate results during the 30 s all-out run are shown in Fig. 2. Panel A shows similar running power responses between interventions during the 30 s all-out run test. The two-way repeated measures ANOVA demonstrated a significant effect of time on this parameter (F(29,1740) = 255.50, P ˂ 0.001, ηp 2 = 0.790). There was an increase in running power in the first second until a peak power was reached at approximately 6 s and a consequent decrease after this time for all interven- tions, without any effects of IMW on the studied parameter. Panels B, C, D and E display the peak, mean and minimum values for power, force, velocity and FI, respectively. For these measurements, the one-way repeated measures ANOVA revealed an effect of IMW (F(3,45) = 2.98, P = 0.041, ηp 2 = 0.166) on peak power (Panel B), with higher values in WU60 (34.1 ± 1.0 W/kg, P = 0.029) compared to CON (31.2 ± 1.4 W/kg). For mean power (Panel B; F(3,45) = 4.5, P = 0.007, ηp 2 = 0.232), minimum power (Panel B; F(3,45) = 3.3, P = 0.028, ηp 2 = 0.181), peak force (Panel C; F(3,45) = 3.9, P = 0.013, ηp 2 = 0.208), minimum force (Panel C; F(3,45) = 5.5, P = 0.002, ηp 2 = 0.269), all IMW loads led to higher values in comparison with CON. Additionally, regarding specific IMW effects on mean force (Panel C; F(3,45) = 3.2, P = 0.028, ηp 2 = 0.180), higher values were observed in WU40 (5,6 ± 0.1 N/kg, P = 0.035) and WU60 (5.6 ± 0.1 N/kg, P = 0.020) than in CON (5.2 ± 0.1 N/kg), whereas a higher mean velocity value (Panel D; F(3,45) = 3.5, P = 0.022, ηp 2 = 0.190) was obtained for WU60 (4.5 ± 0.1 m/s) than for CON (4.3 ± 0.1 m/s). Panel Figure 2. Mechanical and physiological results observed during the 30 s all-out run under control conditions (CON, black color) and after IMW loads at 15% (WU15, blue color), 40% (WU40, green color) and 60% of MIP (WU60, red color). ✝ Indicates difference in relation to the control session. (P < 0.05). 6 Vol:.(1234567890) Scientific Reports \| (2022) 12:11223 \| https://doi.org/10.1038/s41598-022-14616-w www.nature.com/scientificreports/ F represents the HR responses second by second during the 30 s all-out run. The two-way repeated measures ANOVA pointed to a significant effect of time (F(29,1740) = 350.76, P ˂ 0.001, ηp 2 = 0.854), yet no effects among interventions were detected. The HR constantly increased, reaching the highest values in the last second of the test. Furthermore, the peak, mean and minimum HR values were not influenced by IMW (Panel G). The changes in muscle oxygenation are shown in Fig. 3. Panels A and B illustrate the TSI responses in BB and VL, respectively. The three-way repeated measures ANOVA revealed effects of limb muscle (F(1,120) = 85.83, P ˂ 0.001, ηp 2 = 0.419), time (F(29,3480) = 928.57, P ˂ 0.001, ηp 2 = 0.886) and significant interaction between time x limb muscle (F(29,3480) = 36.39, P ˂ 0.001, ηp 2 = 0.233), but no IMW effects. The peak, mean and minimum TSI data (Panel C) were analyzed by two-way ANOVA, which indicated the effect of limb muscle (TSI; peak: F(1,30) = 20.67, P ˂ 0.001, ηp 2 = 0.408; mean: F(1,30) = 33.49, P ˂ 0.001, ηp 2 = 0.527 and minimum: F(1,30) = 71.53, P ˂ 0.001, ηp 2 = 0.705) and IMW only for TSI mean values (F(1,90) = 3.72, P = 0.014, ηp 2 = 0.110). Despite these findings, no interaction between IMW and limb muscles was observed. The Δ [tHb] for BB and VL are presented in Panels D and E. The three-way repeated measures ANOVA showed the effects of limb muscle (F(1,119) = 15.03, P ˂ 0.001, ηp 2 = 0.112), time (F(29,3451) = 82.66, P ˂ 0.001, ηp 2 = 0.410), and an interaction between time x limb muscle (F(29,3451) = 16.86, P ˂ 0.001, ηp 2 = 0.124). However, no difference was observed through post-hoc analyses (IMW x time x muscle limb). Whereas for peak values no differences were observed, for mean (F(1,30) = 10.00, P = 0.003, ηp 2 = 0.250) and minimum values (Panel F; F(1,30) = 23.73, P ˂ 0.001, ηp 2 = 0.442) a limb effect was detected without interaction with IMW. Analyses in passive recovery. Physiological responses obtained at baseline (BL) immediately after the 30 s all-out run (T0) and during passive recovery (every 2 min: T2–T18) are displayed in Fig. 4. Panel A shows that the curves of blood lactate were similar in all interventions, with a clear effect of time (F(10,600) = 1053.76, P ˂ 0.001, ηp 2 = 0.946). A [Lac] peak was observed at 8 min for all participants ([Lac] peak in CON: 17.14 ± 0.62; WU15: 16.00 ± 0.51; WU40: 16.11 ± 0.56 and WU60: 16.60 ± 0.65, mM). No effects of IMW were observed. [Lac] values did not return to their initial values (BL) after 18 min of recovery, independently of the IMW load applied. A similar HR behavior was observed for all conditions (Panel C), with a significant effect of time (F(10,600) = 1646.51, P ˂ 0.001, ηp 2 = 0.965). The HR peak was obtained immediately after the 30 s all-out run (post- effort, T0), reaching higher values than T2–T18 and decreasing over time. After 18 min, the HR values did not Figure 3. Results of tissue saturation indexes (TSI, Panels A–C) and total hemoglobin ([tHb], Panels D–F) in biceps brachii (BB) and vastus lateralis (VL) at each second during the 30 s all-out run on a non-motorized treadmill under control conditions (CON, black color) and after the IMW loads with 15% (WU15, blue color), 40% (WU40, green color) and 60% of MIP (WU60, red color), in line graphs. In bar graphs, the dark colors represent the BB, while the light colors correspond to the VL. MIP = maximum inspiratory pressure. 7 Vol.:(0123456789) Scientific Reports \| (2022) 12:11223 \| https://doi.org/10.1038/s41598-022-14616-w www.nature.com/scientificreports/ reach the initial values under any condition. Peak, mean and minimum values for [Lac] and HR are shown in panels B and D, respectively. IMW was not able to influence these results. Changes in muscle oxygenation during passive recovery are shown in Fig. 5. Panels A and B display the TSI responses in BB and VL, respectively. The three-way repeated measures ANOVA revealed effects of limb muscle (F(1,120) = 93.23, P ˂ 0.001, ηp 2 = 0.437), time (F(10,1200) = 643.46, P ˂ 0.001, ηp 2 = 0.843) and a significant interaction between time x limb muscle (F(10,1200) = 32.43, P ˂ 0.001, ηp 2 = 0.213), but no interaction with IMW. In all interventions, BB presented lower saturation in relation to BL at T0 and T2, and after 4 min of recovery (T4) the saturation returned to BL values. An interesting finding was observed in WU40, which reached higher oxygenation values from T4 to T10 compared to BL. In VL, only WU60 at T2 presented different values from BL. Moreover, higher saturation was detected in VL than in BB at T0. The two-way repeated measures ANOVA revealed an effect of limb muscle on BL (F(1,30) = 61.16, P ˂ 0.001, ηp 2 = 0.671), peak (F(1,30) = 17.87, P ˂ 0.001, ηp 2 = 0.373), mean (F(1,30) = 30.06, P ˂ 0.001, ηp 2 = 0.501) and minimum (F(1,30) = 55.17, P ˂ 0.001, ηp 2 = 0.648) values (Panel C) without IWM interaction. Regarding [tHb] (Panel D‒E), there were a significant effect of time (F(10,1200) = 44.73, P ˂ 0.001, ηp 2 = 0.272) and interaction between time x limb muscle (F(10,1200) = 2.95, P ˂ 0.001, ηp 2 = 0.024). Compared to BL values, the passive recovery time showed lower values for BB at T0 only in WU40, while higher values were noted at T2–T12 in WU60, subsequently returning to BL values. In VL, differences from BL values were observed at specific moments in CON (T2‒T8), WU40 (T18) and WU60 (T4‒T18). IMW strategies did not affect peak, mean and minimum [tHb] values in passive recovery (Panel F). Discussion To the best of our knowledge, this is the first study dedicated to investigating the effects of different IMW loads (15, 40 and 60% of MIP) on mechanical and physiological responses, including oxygenation in more and less active muscles, during and after high-intensity, short-duration running exercise. Additionally, we studied these acute inspiratory strategies using a shorter protocol (i.e., lower number of exercise repetitions). Our main findings revealed some effects of IMW, performed with 2 sets of 15 repetitions with a 1-min rest interval between the sets, on the high-intensity running effort and recovery, independently of the load applied. Regarding the mechanical parameters, all IMW promoted a significant increase in mean and minimum running power, as well as in peak and minimum running force compared to CON. Additionally, when applying specific IMW loads higher values were observed for peak power, mean velocity (WU60) and mean force (WU40 and WU60) in relation to CON. The physiological responses, including HR and oxygenation in more and less active muscles during the running exercise, were not modified by IMW, at least not during the 30 s high-intensity running nor for HR and [Lac] in the post-effort phase. By comparing the responses in BB and VL, no differences were observed in mechanical and muscle oxygenation during the 30 s all-out test. In passive recovery, higher TSI values for VL were detected in the post-effort phase (T0) for all protocols. An interesting finding was observed in WU40, which reached Figure 4. Physiological results of passive recovery immediately after the 30 s all-out run and every 2 min up to 18 min (mean ± EPM). Results under control conditions (CON, black color) and after IMW protocols with loads at 15% (WU15, blue color), 40% (WU40, green color) and 60% of MIP (WU60, red color). # corresponds to difference in relation to baseline (BL) for each protocol. Dashed line means no differences among protocols. 8 Vol:.(1234567890) Scientific Reports \| (2022) 12:11223 \| https://doi.org/10.1038/s41598-022-14616-w www.nature.com/scientificreports/ higher oxygenation values from T4 to T10 compared to BL. It can be then suggested that the use of different loads of IMW promotes an improvement in performance corroborated by the increased peak, mean and minimum mechanical values, but not in the performance assessed second by second. Also, WU40 may improve recovery phase with higher oxygenation in BB. Inspiratory muscle warm‑up and performance. Our choice to investigate the effect of different IMW loads on the performance of high-intensity, short-duration running exercise was based on previous studies that indicate the positive effect of IMW, but used different IWM protocols in intermittent running27, in Wingate tests33,34, in 100 m freestyle swimming29, in specific hockey drills31 and in a simulate judo match32. Additionally, considering the large use of tethered efforts in physical and sports programs together with the significance of high-intensity exercise in this context, we focused on the evaluation of the IMW impact on the force, velocity and running power of 30 s all-out run sessions using different inspiratory loads. As shown in Fig. 2, the same characteristics were observed for the curve of running power throughout the tests, with no differences among the IMW load interventions during the 30 s all-out run sessions. As previously mentioned, running power, force and velocity were improved by the IMW loads, more specifically the WU40 and WU60, which significantly influenced the mechanical variables compared to CON, suggesting an improvement in running performance for active participants. Regarding the exercise performance of athletes, the IMW combined with specific warm-up was capable of reducing the time in 100 m freestyle swimming29, treadmill sprint performance51 and interactions among the technical-tactical, physical, physiological, and psychophysiological parameters in a simulated judo match32. Studies that used IMW as the only means of prior effort to main motor task also observed a reduction in the sensation of dyspnea and an improvement in the distance walked in one badminton-footwork test36, as well as in one shuttle run test52. Similarly, Özdal and colleagues34 observed an increase in peak and relative power in Figure 5. Results of tissue saturation indexes (TSI, Panels A–C) and total hemoglobin ([tHb], Panels D–F) in passive recovery immediately after the 30 s all-out run (T0) and every 2 min up to 18 min (T2–T18) compared to BL values. Timeline panels show the biceps brachii (BB) and vastus lateralis (VL) under control conditions (CON, black color) and after the IMW loads with 15% (WU15, blue color), 40% (WU40, green color) and 60% of MIP (WU60, red color). Bar graphs present the peak, mean and minimum values (mean ± SEM), with dark colors for BB and light colors for VL. MIP = maximum inspiratory pressure. # corresponds to difference compared to baseline (BL); * means significantly different from BB. Dashed line means no differences among protocols. Colored line means differences in a specific protocol. 9 Vol.:(0123456789) Scientific Reports \| (2022) 12:11223 \| https://doi.org/10.1038/s41598-022-14616-w www.nature.com/scientificreports/ anaerobic Wingate test performed by hockey athletes after IMW (2 × 30 at 40% of MIP with a 2-min rest interval between the sets). Some additional positive effects of IMW on respiratory response and performance in a shuttle run test27, on respiratory muscle strength28,53, on performance in a knee flexion–extension protocol accomplished in an isokinetic test by healthy sedentary participants41 and on long-distance test30 were previously observed. On the other hand, other studies did not find significant effects of IMW for both active individuals and athletes perform- ing different types of exercise and tests39,43,46,53–56. It is important to consider the diversity of the IMW protocols when applied to different populations, sports modalities and exercise tests, which makes it difficult to compare the results obtained with other findings. Moreover, most investigations do not describe the time interval between the IMW sets and the time between the IMW application and the test or main exercise. Knowing that the effects of warm-up can be affected by several factors, such as protocol, load, performance level, type of exercise, time interval between the conditioning stimulus and the performance testing, etc.57,58, more attention could be paid to these aspects. In this sense, we focused on a shorter IMW protocol (2 sets of 15 repetitions with a 1-min rest interval between them, concluded 2 min before the running test), using different inspiratory loads in each session (without and with 15, 40 and 60% of MIP) applied to active participants. Regarding the respiratory parameters, even though our participants did not have any experience with respiratory training or inspiratory warm-up, they reached good MIP values (145.6 ± 9.9 cmH2O), similar to Japanese athletes in triathlon and wrestling (light category) modalities (145.8 and 147.3 cmH2O, respectively)59. Inspiratory muscle warm‑up and physiological responses. The ventilatory responses in high-inten- sity exercises can affect the perfusion dynamics of the locomotor muscles and tissue saturation indices, rep- resenting a limitation of exercise performance19,20,60. According to the literature, inspiratory muscle warm-up can be a strategy to potentialize oxygenation redistribution to more active muscles during physical exercise35. However, improvements in post-effort recovery process remain unexplored. Thus, we measured for the first time the physiological responses in exercise and recovery, including oxygenation analysis in more or less active muscles (which are relevant to providing oxygen and removing metabolites, respectively). Few scientific inves- tigations, especially in sports, have been conducted to study the IMW potential to minimize respiratory fatigue and improve the oxygenation redistribution in high-intensity exercise35,40,46,51,61. When studying the oxygena- tion in the gastrocnemius muscle of female soccer players by submitting them to submaximal cycling test and intermittent cycling test, Cheng et al.35 demonstrated that the IMW protocol can enhance oxygen saturation in this tissue. However, the authors did not observe changes in performance, possibly due to the lack of specificity in the test for these athletes. In our study, second-by-second NIRS analyses did not reveal the effects of IMW on muscle responses, regard- less of the load (Fig. 3, Panels A‒B). Despite the evidence of limb muscle effect on TSI peak, mean and mini- mum values (Fig. 3, Panel C) and [tHb] mean and minimum values (Fig. 3, Panel F), no interaction with IMW protocols was observed, indicating a similar behavior in BB and VL oxygenation. Whether the IMW promotes a positive effect on the oxygenation redistribution35, this was not observed in more and less active muscles. Con- sidering the increased respiratory muscle work and the competition with locomotor muscles for O2 supply17–20, the analysis of inspiratory muscle oxygenation could provide some insight into the oxygenation between these muscles. However, the analysis of oxygenation occurs only in accessory and secondary inspiratory muscles62, and it does not directly reflect the oxygenation of the muscle with the greatest potential for oxygen uptake and the most affected one by IMW, the diaphragm. Recently, studies addressing the IMW applied to speed skaters on ice time trial also did not report any improvement in muscle oxygenation variables in the right VL, with some limitations pointed out by the authors, such as leg compression garments and small sample size40,46. On the other hand, in high-intensity sprint10 and high-intensity cycling37 a difference in more and less active muscles was observed, suggesting adjustments in oxygenation during effort in a tissue-dependent manner. In order to support the high demand of the respiratory muscles during exercise, the VO2 and oxygenation in this region are increased, and may compromise cardiac output by 14–16% in well-trained individuals63, thus affecting oxygenation distribution to locomotor muscles64. In high-intensity effort, these locomotor muscles also use predominantly anaerobic pathways, resulting in lactate production. According to previous studies, inspiratory muscles may play an important role as lactate consumers21,22. In this context, Lin et al.36 indicated a reduction in [Lac] in badminton players after IMW. Regarding [Lac], the peak values observed here (~ 16 mM) confirmed their significant anaerobic contribution in the 30 s all-out run. These findings corroborate previous studies on exercises characterized by anaerobic contribution9,10,65,66. In our study, both HR and [Lac] were not affected by IMW interventions, and 18 min post-effort in passive recovery was not sufficient to make these physiological responses return to baseline values (Fig. 4, Panels A–D). With respect to passive recovery, a higher decrement in oxygenation was observed immediately after the exercise, the so-called post-effort phase (T0), with quick adjustments after T2 for both muscles (Fig. 5). Osawa et al.37 reported that tissue oxygenation did not begin immediately after high-intensity cycling effort and that deoxygenation occurred for a few seconds. In the present study, TSI percentages started to rise immediately after the exercise, and after 4 min (T4) they returned to baseline values (Fig. 5, Panels A‒B). Interestingly, only WU40 presented higher TSI values in BB from T4 to T10 in relation to BL. We did not perform correlation analyses, however, Manchado-Gobatto et al.10 observed a significant correlation between the [Lac] peak and TSI and [tHb] values in BB. In this regard, higher TSI in BB suggests an important role of this parameter during recovery since O2 is considered to be essential in oxidative pathways for lactate clearance67. In VL, only WU60 at T2 reached lower values than BL. Despite our initial hypothesis, which proposed that 40% of MIP would redirect oxygena- tion to VL with a consequent positive impact on mechanical responses during a tethered 30 s all-out run, in 10 Vol:.(1234567890) Scientific Reports \| (2022) 12:11223 \| https://doi.org/10.1038/s41598-022-14616-w www.nature.com/scientificreports/ addition to a better blood lactate removal during recovery in relation to other loads, we observed that different inspiratory loads can improve mechanical parameters and recovery oxygenation. Recent studies investigated the effects of IMW as a warm-up strategy combined with core warm-ups on recov- ery period between intermittent exercise and repeated sprints on NMT51 and on recovery periods of sprints on a cycle ergometer68. Although the authors evaluated recovery, muscle oxygenation was only observed during exer- cise. In our study, the comparison between BB and VL revealed that only immediately after the 30 s all-out run the VL presented higher values for all interventions (Fig. 5, Panels A–B). These results corroborate those reported by Manchado-Gobatto et al.10, who did not use inspiratory strategy to improve the running performance. Finally, the responses during exercise and recovery are a complex process. Thus, to improve the interpretation of IMW on running and recovery, integrative analyses could reveal responses beyond conventional statistics. For example, our group recently observed the improvement in the technical and tactical parameters in a judo simulated fight using the same shorter IMW protocol used herein, based on a complex network analysis32. In such study, the centrality metrics revealed that the IWM at 15% of MIP favored the interactions among the psychophysiological, physical and physiological parameters, while the IWM at 40% of MIP was able to improve performance in the judo match. Therefore, our next investigations will be considering these findings. Furthermore, a recent study indicated NIRS measurements as a future physiological marker, showing no significant differences regarding the respiratory compensation point69. These findings highlight the relationship between systemic (i.e., ventilatory) and peripheral (i.e., oxygenation of locomotor and non-locomotor muscles) physiological breakpoints. In this sense, future studies should consider respiratory strategies associated with the NIRS technique to improve knowledge about the intensity of the training zone. Moreover, the IMW can attenuate muscle deoxygenation during exercise35 and the NIRS technique can contribute to the monitoring of oxygenation in clinical practice70, especially in patients with exercise-induced ischemic pain caused by reduced blood flow to the lower extremities71. Limitations and strengths Despite the use of technologies with high-frequency signal acquisition, some limitations regarding our results must be addressed. First, the all-out run performed on a NMT has shown reliable results in the scientific literature5,7–10,66,72. However, we did not test the reproducibility of the four IMW interventions – although it is safe to say that they exhibited similar results in the 30 s all-out run tests (Fig. 2) with no differences in power running among the IMW loads second by second. We chose to investigate a classic anaerobic test (30 s all-out run), considering the aerobic component around ~ 18–20% in these efforts7,13. We observed an effect of IMW on the mechanical parameters, which did not result in greater muscle oxygenation differentiation. It is possible that by applying another slightly longer exercise protocol or repeated sprints the impact of IMW can be observed on both mechanical parameters (second by second) and physiological responses. Additionally, we did not use the gas analyzer to investigate the oxygen uptake due to our experimental design, nor investigated oxygenation of the inspiratory muscles. Still, we are aware that the association of NIRS measurements and VO2 exchange would improve our data interpretation, but we have not tested whether this equipment can interfere with breathing pattern or breath frequency altering the isolated effects of IMW. Another limitation refers to our participants’ characteristics, as only healthy active males, non-athletes performed the test. In future studies, we suggest the inclusion of female participants, the comparison of IMW with a shorter protocol and the analysis of effects in both high-performance athletes and non-athletes in different types of exercises, such as repeated-sprint effort. The strengths of this study include: (i) the investigation of the inspiratory muscle strategy through a shorter protocol applied with methodological rigor, performed with practical and high-quality inspiratory devices, (ii) the running effort performed on a non-motorized treadmill able to identify, with high signal capture, minimal changes in mechanical variables during high-intensity exercise; (iii) monitoring oxygenation responses in dif- ferent muscle groups, associated or not with respiratory strategies, allows a more integrative interpretation of this variable during effort and also during recovery. Conclusion In summary, different IMW loads with a shorter protocol (2 sets of 15 repetitions with a 1-min rest interval between sets and 2 min before exercise) applied on high-intensity running exercise suggested an improvement in performance corroborated by increased peak, mean and minimum mechanical values, but not in power and oxygenation assessed second by second. With respect to muscle oxygenation, these measurements demonstrated that the mechanisms by which IMW could possibly exert an effect on performance were not affected by these protocols, as all interventions showed similar and rapid adjustments of oxygenation responses during exercise demands. Interestingly, during passive recovery WU40 presented a pronounced TSI value for BB, indicating a greater availability of O2 for lactate clearance in a tissue-dependent manner. Data availability The data that support the findings of this study are available from the corresponding author on reasonable request. 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Physiol. 9, 843. https:// doi. org/ 10. 3389/ fphys. 2018. 00843 (2018). Author contributions F.B.M.G., C.A.G and M.A.M. conceived the main conceptual ideas, designed the project, performed data analysis and interpretation, and were responsible for funding acquisition. A.B.M. and F.M.R. conducted data collection, analysis and interpretation, and designed figures and table. A.B.M., C.A.G and F.B.M.G. wrote the main text of the manuscript. M.A.M. and C.S.H. provided support on inspiratory muscle protocols and performed data analysis. All authors reviewed the manuscript and have approved the submitted version. Funding São Paulo Research Foundation—FAPESP (2009/08535-5, 2012/06355-2, 2016/50250-1, 2018/05821-6, 2019/10666-2 and 2019/20894-2), the National Council for Scientific and Technological Development—CNPq 13 Vol.:(0123456789) Scientific Reports \| (2022) 12:11223 \| https://doi.org/10.1038/s41598-022-14616-w www.nature.com/scientificreports/ (307718/2018-2, 308117/2018-2) and the Coordination for the Improvement of Higher Education Personnel— CAPES (Finance Code 001). We also thank all the participants for the voluntary participation. Competing interests The authors declare no competing interests. Additional information Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1038/ s41598- 022- 14616-w. Correspondence and requests for materials should be addressed to A.B.M. Reprints and permissions information is available at www.nature.com/reprints. 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PMC7893283	royalsocietypublishing.org/journal/rspb Research Cite this article: Bohm S, Mersmann F, Santuz A, Arampatzis A. 2021 Enthalpy efficiency of the soleus muscle contributes to improvements in running economy. Proc. R. Soc. B 288: 20202784. https://doi.org/10.1098/rspb.2020.2784 Received: 6 November 2020 Accepted: 5 January 2021 Subject Category: Morphology and biomechanics Subject Areas: biomechanics, physiology Keywords: force–length and force–velocity relationship, enthalpy–velocity relationship, triceps surae, endurance running, strength training, tendon stiffness Author for correspondence: Sebastian Bohm e-mail: sebastian.bohm@hu-berlin.de Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare. c.5271347. Enthalpy efficiency of the soleus muscle contributes to improvements in running economy Sebastian Bohm1,2, Falk Mersmann1,2, Alessandro Santuz1,2 and Adamantios Arampatzis1,2 1Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, Philippstr. 13, 10115 Berlin, Germany 2Berlin School of Movement Science, Humboldt-Universität zu Berlin, Berlin, Germany SB, 0000-0002-5720-3672; FM, 0000-0001-7180-7109; AS, 0000-0002-6577-5101; AA, 0000-0002-4985-0335 During human running, the soleus, as the main plantar flexor muscle, gen- erates the majority of the mechanical work through active shortening. The fraction of chemical energy that is converted into muscular work (enthalpy efficiency) depends on the muscle shortening velocity. Here, we investigated the soleus muscle fascicle behaviour during running with respect to the enthalpy efficiency as a mechanism that could contribute to improvements in running economy after exercise-induced increases of plantar flexor strength and Achilles tendon (AT) stiffness. Using a controlled longitudinal study design (n = 23) featuring a specific 14-week muscle–tendon training, increases in muscle strength (10%) and tendon stiffness (31%) and reduced metabolic cost of running (4%) were found only in the intervention group (n = 13, p < 0.05). Following training, the soleus fascicles operated at higher enthalpy efficiency during the phase of muscle–tendon unit (MTU) lengthening (15%) and in average over stance (7%, p < 0.05). Thus, improve- ments in energetic cost following increases in plantar flexor strength and AT stiffness seem attributed to increased enthalpy efficiency of the operating soleus muscle. The results further imply that the soleus energy production in the first part of stance, when the MTU is lengthening, may be crucial for the overall metabolic energy cost of running. 1. Introduction Habitual bipedalism has been recognized as a defining feature of humans [1], and an exceptional endurance running ability has been linked to the evolution of the human lineage [2]. Economy, which is the mass-specific rate of oxygen uptake or metabolic energy consumption at a given speed [3,4], plays a crucial role in endurance running performance [5]. The cost of generating force and work through muscles to support and accelerate the body mass is the main source of metabolic energy expenditure during locomotion [6]. The force– length–velocity potential of muscles (defined as the fraction of maximum force according to the force–length [7] and force–velocity relationships [8]) at which muscles operate during running [9,10] largely dictates the required active muscle volume and consequently the energetic cost of contraction [3,9,11]. In human running, the triceps surae is the major contributor to propulsion and the main plantar flexor muscle group that transmits force through the Achilles tendon (AT) [12], consuming a significant amount of metabolic energy [13]. In earlier studies, we provided evidence that both the contractile capacities of the triceps surae and the mechanical properties of the AT (i.e. its stiffness) influence running economy [14,15]. We found that the most © 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. economical runners feature a combination of higher plantar flexor muscle strength and AT stiffness [14], and that a specific training of muscle strength and AT stiffness can, in fact, improve running economy [15]. Although the associ- ation of AT stiffness and energetic cost of running has been confirmed by other research groups [16,17], the underlying physiological mechanisms remain unclear. The soleus is the greatest muscle of the triceps surae [18] and generates the majority of work/energy to lift and accel- erate the body [12] by actively shortening throughout the entire stance phase of running [9,19]. In the first part of the stance phase, the fascicle shortening is paralleled by a lengthening of the muscle–tendon unit (MTU) [9], indicating that a part of the body’s mechanical energy is stored as strain energy in the AT, but also that the fascicles generate work and save this work as strain energy in the AT. In the second part of the stance phase, where the MTU shortens (propulsion phase), the tendon strain energy is returned to the body and contributes to the ongoing work generation [9]. The metabolic cost of generating work by active shortening of muscles depends on the velocity of the shortening [20]. The enthalpy efficiency (or mechanical efficiency) quantifies the fraction of chemical energy from ATP hydrolysis that is con- verted into mechanical muscular work [21]. The relation of enthalpy efficiency and shortening velocity shows a steep increase at low velocities with the peak at around 20% of the maximum shortening velocity [21,22]. During submaxi- mal running, the soleus operates below the optimal velocity for maximal efficiency [9], suggesting that small changes in the shortening velocity may substantially influence the enthalpy efficiency of the soleus muscular work production. The mechanical interaction of the soleus muscle with the series AT regulates the fascicle shortening dynamics. The AT takes over a great part of the length changes of the entire soleus MTU, thereby decoupling the muscle fascicle and MTU behaviour and, beside the storage and release of strain energy, allowing the fascicles to operate at velocities favourable for economical force generation [9,19]. The mech- anical properties of the tendon in combination with the strength capacity of the muscle may determine the amount of fascicle decoupling during the stance phase of running. However, similar to an increase in muscle strength [23], ten- dons can adapt to periods of higher mechanical loading by increasing stiffness [24]. Our earlier findings of improved ener- getic cost after an exercise-induced increase in AT stiffness and plantar flexor muscle strength evidenced a direct association between a balanced adaptation of tendon and muscle and improvements in running economy [15]. Considering a given work produced by the soleus muscle during the stance phase, the energetic cost depends on the enthalpy efficiency under which this muscular work is generated. Assuming that a combination of increased plantar flexor strength and AT stiffness may influence the soleus fascicle shortening pat- tern, the overall enthalpy efficiency might improve. This would provide an explaining mechanism to the previously reported improvements in running economy following effec- tive muscle–tendon training [15]. To the best of our knowledge, no study has experimentally examined the operat- ing soleus muscle fascicles with respect to the enthalpy efficiency and its association to the energetic cost of running. Here, we investigated the effect of a specific muscle- tendon training, which has been shown to increase plantar flexor strength and AT stiffness [15], on the enthalpy efficiency of the operating soleus fascicles during running. Based on our earlier study [15], we expected an improvement in running economy after 14 weeks of training. We hypoth- esized that the training-induced increase in plantar flexor muscle strength and AT stiffness modulates the soleus fascicle velocity pattern throughout the stance phase towards vel- ocities associated with a higher enthalpy efficiency, thereby reducing the energetic cost of running. 2. Methods (a) Participants and experimental design A statistical power analysis was performed a priori and revealed a required sample size of n = 12 for the intervention group (see electronic supplementary material for details). Considering potential dropouts, we recruited 36 participants and randomly assigned them to an intervention (n = 19) or control group (n = 17). Inclusion criteria were an age of 20–40 years, at least two running sessions weekly on a recreational basis and no mus- cular–tendinous injuries in the previous year. Only habitual rearfoot-striking runners were considered because it is the most common foot strike pattern [25] and also to avoid potential con- founding effects of the strike pattern on our outcome measures. To quantify the foot strike pattern, we assessed the strike index [26] (i.e. centre of pressure position with respect to the heel relative to foot length at touchdown) during a pre-test session (0 equals rearfoot-striking, <0.3 inclusion threshold). Twenty- three participants completed the study, of which 13 were the intervention group (age 29 ± 5 years, height 178 ± 8 cm, body mass 73 ± 8 kg, four females) and 10 the control group (31 ± 3 years, 175 ± 10 cm, 70 ± 11 kg, seven females). For the interven- tion group, the same 14-week muscle–tendon training was added to the regular ongoing training habits as in our earlier study [15]. Before and after the intervention period, the maximal plantar flexion moment and AT stiffness as well as energetic cost of run- ning at 2.5 m s−1 were assessed in both groups. To explain the expected improvements in energetic cost following the training, we experimentally determined (i) the foot strike pattern, joint kinematics and temporal gait parameters as well as (ii) the soleus MTU and fascicle behaviour in addition to the electromyo- graphic (EMG) activity during running. We further determined (iii) the soleus force–fascicle length relationship and force–fasci- cle velocity relationship in order to calculate the force–length and force–velocity potential of the fascicles during running (i.e. fraction of maximum force according to the force–length and force–velocity curve [9,10,27]) and assessed (iv) the enthalpy effi- ciency–fascicle velocity relationship to calculate the efficiency of the soleus muscle during running. Because changes in running economy were not expected without any intervention [15], the assessment of the fascicle behaviour was not conducted in the controls. The university ethics committee approved the study, and participants gave written informed consent in accordance with the Declaration of Helsinki. (b) Exercise protocol The supervised and biofeedback-based resistance training was performed for 14 weeks and was characterized by five sets of four repetitive isometric ankle plantar flexion contractions (3 s loading and 3 s relaxation) at 90% of the maximum voluntary contraction (MVC) strength (adjusted every two weeks), three to four times a week (see electronic supplementary material for illustration). This loading regimen has been shown to pro- vide a sufficient magnitude and duration of tendon strain to promote AT adaptation in addition to increases in plantar flexor muscle strength [15,24,28]. royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20202784 2 (c) Strength of the plantar flexors and Achilles tendon stiffness The plantar flexor strength of the right leg was measured using an inverse dynamics approach. For the determination of AT stiff- ness, ramp-MVCs were conducted and the force applied to the AT was calculated as quotient of joint moment and individual tendon lever arm, which was determined using the tendon- excursion method. The corresponding AT elongation was ana- lysed based on the displacement of the gastrocnemius medialis-myotendinous junction visualized by ultrasonography. Stiffness was calculated between 50 and 100% of the maximum tendon force and strain by dividing elongation by resting length (see electronic supplementary material for details). (d) Energetic cost of running During an 8 min running trial on a treadmill at 2.5 m s−1, expired gas analysis was conducted and the rate of oxygen consumption ( _VO2) and carbon dioxide production ( _VCO2) was calculated as the average of the last 3 min [15]. Running economy was then expressed in units of energy as Energetic cost ¼ 16:89 _VO2 þ 4:84 _VCO2, where energetic cost is presented in (W kg−1) and _VO2 and _VCO2 in (ml s−1 kg−1) [4,29]. The steady state was visually confirmed by the rate of _VO2 during each trial, and a respiratory exchange ratio (RER) of <1.0 was controlled for during the post analysis (see electronic supplementary material for details). (e) Joint kinematics and foot strike pattern Kinematics of the right leg were captured (250 Hz) by a Vicon motion capture system (Nexus 1.8, Vicon, Oxford, UK) using ana- tomical-referenced markers [9]. The touchdown and the toe-off were determined from the kinematic data as consecutive minimum in knee joint angle over time [30]. The foot strike pattern was ana- lysed by means of the strike index [26]. A self-developed algorithm [25] was used to calculate the strike index from the plantar pressure distribution (120 Hz) captured by the integrated pressure plate (FDM-THM-S, Zebris Medical GmbH, Isny, Germany). (f) Soleus muscle-tendon unit length changes, fascicle behaviour and electromyographic activity during running During an additional 3 min running trial at the same speed, kin- ematics of the ankle joint served to calculate the length change of the soleus MTU as the product of ankle angle changes and the previously assessed individual AT lever arm [31]. The initial soleus MTU length was determined at a neutral joint angle using the previously reported regression equation by Hawkins & Hull [32]. Ultrasonic images of the soleus muscle fascicles were obtained synchronously at 146 Hz (Aloka Alpha7, Tokyo, Japan). The probe (6 cm linear array, 13.3 MHz) was mounted over the medial soleus muscle belly. The fascicle length was post-processed from the images using a semi-automatic tracking algorithm [33] (figure 1), and corrections were made if necessary. At least nine steps were averaged [10]. The velocities of MTU and fascicles were calculated as the first derivative of the lengths over the time. Synchronized surface EMG of soleus was measured (1000 Hz) by means of a wireless EMG system (Myon m320RX, Baar, Switzerland) and is presented as normalized to the maximum EMG value observed from the individual MVCs [9]. (g) Soleus force–length, force–velocity and efficiency– velocity relationship To determine the soleus force–fascicle length relationship (for details [9]), the participants were placed in the prone position on the bench of the dynamometer (Biodex Medical, Shirley, NY) with the knee fixed in a flexed position (figure 1) to restrict the contri- bution of the bi-articular gastrocnemius muscle to the plantar flexion moment (approx. 120°) [34]. MVCs were performed with the right leg in eight different joint angles, and the joint moments and force acting on the AT were calculated as described in section 2c above. The corresponding soleus fascicle behaviour was cap- tured synchronously at 30 Hz by ultrasonography, and fascicle length was measured accordingly (figure 1). The probe remained attached between the running trial and MVCs. An individual force–fascicle length relationship was calculated by means of a second-order polynomial fit (figure 1), giving the maximum force (Fmax) and optimal fascicle length for force generation (L0). The force–velocity relationship of the soleus was assessed using the classical Hill equation [8] and the maximum fascicle shortening velocity (Vmax) and constants of arel and brel. For Vmax, we took reported values of human soleus type 1 and 2 fibres [35], adjusted those for physiological temperature [36] and applied an average fibre-type distribution (81% type 1 fibres and 19% type 2 [9]), giving Vmax as 6.77 L0 s−1 [9]. arel was calculated as 0.1 + 0.4 × type 2 fibre percentage [37], which equals to 0.175. The product of arel and Vmax gives brel as 1.182 [37]. Based on the assessed force–length and force–velocity relationships, it was possible to calculate the individual force–length and force– velocity potential of soleus as a function of the fascicle length (figure 1) and velocity during running [9,10,27]. Furthermore, we determined the enthalpy efficiency– shortening velocity relationship for the soleus fascicles to calculate the enthalpy efficiency of the soleus as a function of the fascicle velocity during running. We referred to the fascicle length (mm) 5000 4000 3000 2000 1000 0 force (N) 20 40 60 80 (a) (b) Figure 1. (a) Experimental set-up for the determination of the soleus force– fascicle length relationship. MVCs at eight different joint angles were per- formed on a dynamometer. During the MVCs, the soleus muscle fascicle length was measured by ultrasonography as an average (F) of multiple fascicle portions (short-dashed white lines) identified from the images. (b) Exemplary force–length relationship of the soleus fascicles obtained from the MVCs (squares) and the respective second-order polynomial fit (dashed line). royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20202784 3 experimental efficiency values provided by the paper of Hill [20], where the values are presented as a function of relative load which we then transposed to the shortening velocity (normalized to Vmax) using the classical Hill equation [8]. The corresponding values of enthalpy efficiency and shortening velocity were fitted using a cubic spline, giving the right-skewed parabolic-shaped curve with a peak efficiency of 0.45 at a velocity of 0.18 Vmax. The resulting function was then used to calculate the soleus efficiency during running. (h) Statistics An analysis of variance for repeated measures including post hoc analysis (adjusted p-values reported) was performed for the group comparison. Anthropometric group differences as well as baseline differences of the plantar flexion moment, AT stiffness and energetic cost were tested using a t-test for independent samples. A paired t-test was used to analyse the training effects on the assessed gait characteristics, kinematics and MTU and fas- cicle parameters. The level of significance was set to α = 0.05. Effect sizes (Hedges’s g) assess the strength of the intervention effects (see electronic supplementary material for details). 3. Results There were no significant differences in age (p = 0.421), height ( p = 0.361) and body mass ( p = 0.382) between the interven- tion and control groups. No baseline differences between groups were observed for the maximum plantar flexion moment ( p = 0.894), AT stiffness ( p = 0.421) and energetic cost ( p = 0.143; table 1). Both the plantar flexion moment and AT stiffness increased significantly in the intervention group ( p = 0.024, p = 0.048) without significant changes in the controls ( p = 0.296, p = 0.745; table 1). Furthermore, we found a significant decrease in the energetic cost of running following the 14 weeks of training in the intervention group ( p = 0.028) and no significant changes in the control group ( p = 0.688; table 1). Neither group showed any significant changes in the strike index (intervention p = 0.868, control p = 0.868), stance time ( p = 0.283, p = 0.283), flight time ( p = 0.981, p = 0.252) and cadence ( p = 0.310, p = 0.384; table 1) after training, indicating that the training intervention did not influence the foot strike pattern. Following the intervention, ankle and knee joint kin- ematics did not significantly change during the stance phase, i.e. joint angles at touchdown (ankle p = 0.108, knee p = 0.064), toe-off ( p = 0.161, p = 0.844), maximal ankle dorsi- flexion (p = 0.576) and maximal knee flexion (p = 0.138; table 2 and figure 2). The soleus MTU showed a lengthening– shortening behaviour during the stance phase, with shortening starting at 59 ± 2% of the stance phase similarly pre- and post- intervention (p = 0.266, g = 0.30; see the Statistics section; figure 3). The training had no effect on the MTU length, length changes and velocity, neither when averaged over the entire stance phase (p = 0.943, p = 0.273, p = 0.274) nor over the subphase of MTU lengthening (p = 0.931, p = 0.893, p = 0.788) or MTU shortening (p = 0.946, p = 0.470, p = 0.189; table 3 and figure 3). Despite the MTU lengthening, the soleus muscle fascicles shortened continuously throughout the entire stance phase (figure 3). Following the intervention, the fascicle shortening was not significantly different over the entire stance phase (p = 0.662) and the phase of MTU Table 1. Maximal plantar ﬂexion moment and Achilles tendon stiffness as well as energetic cost, foot strike index and temporal step characteristics during running before and after the training period for the intervention and control group (mean ± s.d., effect size g). intervention (n = 13) control (n = 10) pre post g pre post g moment (Nm kg−1)a 3.12 ± 0.48 3.44 ± 0.37c 0.77 3.10 ± 0.46 2.99 ± 0.32 0.32 stiffness (kN strain−1)a 85 ± 36 111 ± 59c 0.67 73 ± 29 71 ± 28 0.10 energy cost (W kg−1)b 10.6 ± 0.6 10.2 ± 0.7c 0.74 11.2 ± 1.0 11.1 ± 1.0 0.12 strike index 0.08 ± 0.12 0.10 ± 0.16 0.09 0.06 ± 0.03 0.06 ± 0.03 0.05 stance time (ms) 310 ± 23 316 ± 23 0.29 327 ± 17 324 ± 23 0.34 ﬂight time (ms) 53 ± 31 53 ± 24 0.01 50 ± 31 54 ± 31 0.48 cadence (steps min−1) 160 ± 11 159 ± 9 0.39 162 ± 9 161 ± 9 0.26 aSigniﬁcant time by group interaction effect (p < 0.05). bSigniﬁcant main effect of time (p < 0.05). cSigniﬁcant difference (post hoc analysis) to pre (p < 0.05). Table 2. Ankle and knee joint angles at touchdown, toe-off and at the maximal ankle dorsiﬂexion and knee ﬂexion angle, respectively, during running before and after the training intervention (mean ± s.d., effect size g, n = 13). touchdown toe-off maximum dorsiﬂexion/knee ﬂexion pre post g pre post g pre post g ankle joint (°) −1.3 ± 5.1 −0.0 ± 6.2 0.45 13.7 ± 7.8 15.1 ± 6.0 0.39 −18.0 ± 3.7 −18.4 ± 4.4 0.15 knee joint (°) −3.7 ± 3.9 −6.5 ± 6.0 0.53 −11.6 ± 4.5 −11.3 ± 4.6 0.05 −32.8 ± 5.8 −34.6 ± 5.8 0.41 royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20202784 4 lengthening (p = 0.106) but in the phase of MTU shortening (p = 0.016; table 3). L0 (pre 43.1 ± 5.7 mm, post 44.1 ± 8.9 mm, p = 0.767, g = 0.08) and thus Vmax (pre 291 ± 38 mm s−1, post 298 ± 17 mm s−1, p = 0.767, g = 0.08) were not significantly altered due to training. The operating fascicle length averaged over the stance phase (pre 0.87 ± 0.11 L0, post 0.85 ± 0.13 L0, p = 0.360, g = 0.16), but also during MTU lengthening (pre 0.92 ± 0.12 L0, post 0.91 ± 0.15 L0, p = 0.772, g = 0.07) and short- ening (pre 0.81 ± 0.10 L0, post 0.76 ± 0.11 L0, p = 0.226, g = 0.32), was not significantly changed following training. Conse- quently, the force–length potential was not significantly different between pre- and post-training in the different phases (stance p = 0.172, g = 0.14, MTU lengthening p = 0.713, g = 0.10, MTU shortening p = 0.640, g = 0.12; figure 4). After training, the soleus force–velocity potential was sig- nificantly lower in the phase of MTU lengthening ( p = 0.030, g = 0.64) and significantly higher when the MTU shortened ( p = 0.045, g = 0.58) with no significant difference over the entire stance ( p = 0.249, g = 0.31; figure 4). This was the conse- quence of a tendency towards higher fascicle shortening velocity during MTU lengthening (pre −0.088 ± 0.054 Vmax, post −0.129 ± 0.061 Vmax, p = 0.073, g = 0.51) and a signifi- cantly lower velocity during MTU shortening after training (pre −0.174 ± 0.057 Vmax, post −0.127 ± 0.008 Vmax, p = 0.007, g = 0.83). Furthermore, the averaged EMG activation over the phase of MTU shortening ( p = 0.028, g = 0.67) and the entire stance phase was significantly reduced following the intervention ( p = 0.017, g = 0.60; figures 3 and 4). Com- pared with pre-intervention running, the fascicle velocity in the phase of MTU lengthening was closer to the velocity for optimal enthalpy efficiency after the training (figure 5). Consequently, the fascicles operated at a significantly higher enthalpy efficiency in the phase of MTU lengthening after the training ( p = 0.006, g = 0.85; figures 5 and 6), while there was no significant pre–post difference in the phase of MTU shortening ( p = 0.640, g = 0.12; figure 6). Over the entire stance phase of running, the efficiency of the fascicle 0 20 40 60 80 100 stance phase (%) –30 –20 –10 0 10 20 30 ankle angle (°) pre post 0 20 40 60 80 100 stance phase (%) –50 –40 –30 –20 –10 0 10 knee angle (°) pre post dorsiﬂexion plantar flexion flexion extension (a) (b) Figure 2. (a) Ankle joint angle and (b) knee joint angle during the stance phase of running before and after the training intervention (mean ± s.e.m., n = 13). 20 40 60 80 100 stance phase (%) 0 0.2 0.4 0.6 0.8 1.0 EMGnorm pre post 0 20 40 60 80 100 stance phase (%) 20 30 40 50 60 fascicle length (mm) pre post 0 20 40 60 80 100 stance phase (%) 280 300 320 340 360 MTU length (mm) pre post * (a) (b) (c) Figure 3. (a) Soleus MTU length, (b) muscle fascicle length and (c) EMG activity (normalized to a maximum voluntary isometric contraction, during the stance phase of running before and after the training intervention (mean ± s.e.m., n = 13). Significant difference of the stance phase-averaged EMG activation between pre and post (p < 0.05). royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20202784 5 shortening was also significantly increased following the training ( p = 0.025, g = 0.66; figure 6). 4. Discussion Our current study showed for the first time that specific muscle–tendon training that increases plantar flexor muscle strength and AT stiffness facilitates the enthalpy efficiency of the soleus muscle during the stance phase of running. The increased enthalpy efficiency was found in the first part of the stance phase where the soleus muscle produces work by active shortening and transfers muscular work to the tendon as strain energy. Furthermore, the results provide additional evidence that a combination of greater plantar flexor muscle strength and AT stiffness decreases the energy cost of running [14,15] and indicates that the soleus enthalpy efficiency is a contributive determinant. Following the intervention, the energetic cost of running was significantly reduced by about 4%, a quantity reported to be above test–retest typical errors [38] and to substantially enhance endurance running performance [39]. At the same time, the soleus, which is the main muscle for work/energy production during running [12], operated at a significantly increased (7%) enthalpy efficiency throughout the stance phase. The enthalpy efficiency quantifies the portion of energy from ATP hydrolysis used by a muscle that is con- verted into mechanical muscular work [21]. Enthalpy efficiency depends on the velocity of muscle shortening with a steep increase at low velocities until the peak at around 0.18 Vmax and again decreasing at higher shortening velocities [20,21]. For the whole stance phase, fascicle short- ening, the force–length potential and the force–velocity potential of the soleus muscle were not significantly different before and after the intervention, indicating a similar energy production through muscular work of the soleus muscle. During the propulsion phase of running (i.e. MTU shorten- ing), where both tendon and muscle transfer energy/work to the skeleton [19,40], the enthalpy efficiency of the operat- ing soleus muscle was high pre- and post-intervention (94% and 93% of the maximum efficiency). By contrast, during the first part of the stance phase (i.e. MTU lengthening), where energy is transferred from the contractile element to the tendon, the efficiency was lower during pre-intervention running (77% of the maximum efficiency). The relevant part of the soleus fascicle shortening occurred during this first part of stance (59% of the entire shortening range). In combi- nation with the high muscle activation (higher during MTU lengthening than during MTU shortening), this indicates an important energy production through muscular work during the phase of MTU lengthening. The exercise-induced increase in plantar flexor muscle strength and AT stiffness was associated with an alteration of the operating fascicle velocity profile and a significant increase of the enthalpy efficiency of the soleus in the phase of MTU lengthening (88% of maximum), potentially improv- ing the enthalpy efficiency of muscular work production. The significant increase of the enthalpy efficiency following train- ing in the phase of MTU lengthening demonstrates that a substantial part of the entire muscular work was generated more economically. In the second part of the stance phase, where the MTU shortened, the high efficiency was main- tained after the intervention and, further, the fascicles operated at a significantly higher force–velocity potential. This was possible due to a shift of the shortening velocity around the plateau of the efficiency–velocity curve, from the descending part before the training to the ascending part after training (figure 5), without a significant decline in the efficiency. Consequently, the overall enthalpy efficiency throughout the stance phase of each step was increased. The phase of MTU shortening was accompanied by a reduced soleus EMG activation after the intervention, and the overall EMG activity during the stance phase was signifi- cantly lower as well (12%). However, the higher maximum plantar flexion moment along with no significant changes in EMGmax during the MVCs (pre 0.409 ± 0.114 mV, post 0.410 ± 0.092 mV, p = 0.300) and antagonistic co-activation (tibialis anterior EMG pre 0.034 ± 0.016 mV, post 0.034 ± 0.013 mV, p = 0.923) as measures for neural adaption after training strongly indicate muscle hypertrophy, resulting in a 13% increase of Fmax. Therefore, the reductions in EMG acti- vation may not correspond to a reduced active muscle volume. To examine this possibility, we calculated the aver- age force of the soleus muscle (Fs) during the stance phase, adopting a ‘Hill-type muscle model’ as a function of the aver- age force–length potential (λl), force–velocity potential (λv), Table 3. Soleus MTU length, length changes and velocity as well as muscle fascicle length, fascicle shortening distance and fascicle velocity averaged over the phase of MTU lengthening, MTU shortening and over the entire stance phase during running before and after the training intervention (mean ± s.d., effect size g, n = 13). MTU lengthening MTU shortening stance phase pre post g pre post g pre post g MTU length (mm) 325 ± 20 325 ± 21 0.02 323 ± 20 323 ± 21 0.02 324 ± 20 324 ± 21 0.02 MTU length changes (mm) 18.4 ± 2.0 18.2 ± 3.2 0.04 −33.9 ± 9.3 −32.5 ± 5.6 0.19 −16.4 ± 9.0 −14.8 ± 5.6 0.30 MTU velocity (mm s−1) 97 ± 15 98 ± 22 0.07 −259 ± 52 −244 ± 33 0.36 −173 ± 29 −164 ± 21 0.30 fascicle length (mm) 39.2 ± 4.4 39.0 ± 5.1 0.03 34.5 ± 4.3 33.1 ± 4.5 0.23 37.2 ± 4.3 36.5 ± 4.8 0.12 fascicle shortening (mm) −5.21 ± 2.68 −6.75 ± 3.08 0.45 −6.49 ± 2.02 −4.98 ± 1.23a 0.72 −11.05 ± 3.32 −11.53 ± 3.47 0.12 fascicle velocity (mm s−1) −21.2 ± 16.7 −33.4 ± 17.5 0.52 −49.1 ± 16.7 −35.8 ± 10.1a 0.71 −33.0 ± 10.8 −34.6 ± 11.0 0.10 aSigniﬁcant difference to pre (p < 0.05). royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20202784 6 EMG activity (α) and Fmax (Fs ¼ lllva FmaxÞ. The average force of the soleus muscle after the intervention (Fs = 353 ± 122 N) did not show significant differences compared with the pre-values (Fs = 372 ± 112 N, p = 0.660), indicating a simi- lar active muscle volume. Similarly, the rate of muscle force generation during the stance phase (_Fs ¼ Fs=tstance) did not differ before (_Fs = 1215 ± 413 N s−1) and after the intervention (_Fs = 1126 ± 400 N s−1, p = 0.498). The above assessments suggest that the active muscle volume and the rate of muscle force generation were not the reason for the improved running economy, but rather the increase in soleus muscle operating enthalpy efficiency. Previous studies provided evidence that the cost of force to support the body mass and the time course of force appli- cation to the ground are the major determinants of the energetic cost of running [6,41]. According to the ‘cost of generating force hypothesis’ [6], the rate of metabolic energy consumption is directly related to the body mass and the time available to generate force, which results in a constant cost coefficient (i.e. energy required per unit force). However, modifications in the muscle effective mechanical advantage (i.e. ratio of the muscle moment arm to the moment arm of the ground reaction force [42]) within the lower extremities can influence the cost coefficient of loco- motion [43,44]. In our study, the metabolic energy cost of running was reduced after the training without any changes in the contact time and body mass, indicating a decrease of the cost coefficient. The similar strike index and lower leg kin- ematics before and after the intervention suggest unchanged effective mechanical advantages within the lower extremities; therefore, this would not be the reason for the reduced cost coefficient. Instead, our findings show that an adjusted time course of the soleus shortening velocity during the stance phase following the training can influence the cost coefficient as a result of increased enthalpy efficiency of the soleus and, thus, complement the earlier studies on the mechanical advantage and cost coefficient interaction [41,42]. The observed continuous soleus fascicle shortening during the stance phase is in agreement with other experiments using the ultrasound methodology and comparable running speeds [19,45]. The importance of the energy production by the plantar flexor muscles for the propulsion phase (i.e. short- ening of the MTU) during running is well accepted [19,46], because the mechanical power produced at the ankle joint in this phase is highest and determines running performance [47]. Our current results regarding the enthalpy efficiency of muscular energy generation and running economy show for the first time that also the phase of the MTU lengthening is crucial for the overall metabolic energy consumption during running. Recently, findings of our group [9] but also others [48,49] provided evidence that soleus muscle dynamics may improve the economy of locomotion by a modulation of the force–length–velocity potential, thus decreasing the active muscle volume. In the present study, the soleus force– length–velocity potential throughout stance was not signifi- cantly changed following the intervention, while in the same time the adjusted time course of the shortening velocity increased the efficiency of muscle work production. Thus, the present study expands the importance of the soleus fas- cicle dynamics towards the efficiency–velocity dependency as a further factor for improvements of locomotor economy. The findings of the current study provide further evi- dence [15,16] that strength training of the plantar flexors has the potential to enhance running economy. We used a specific high-intensity muscle–tendon training programme [24,28], targeting an adaptation of both AT stiffness and plan- tar flexor muscle strength [14,15], to maintain the functional integrity of the contractile and series elastic element. Strength increases without concomitant stiffening of the AT after a period of training can increase levels of operating and maxi- mum strain [24], which have been associated with pathologies [50], and also possible functional decline [51]. On the other hand, increased stiffness without higher muscle strength may also limit function by reducing relevant operating tendon strains [51]. In our study, the maximum AT strain during the MVCs was not affected by the training (pre 6.2 ± 1.6%, post 6.0 ± 1.2%, p = 0.501) despite an increase in the plantar flexor muscle strength, indicating a balanced adaptation of muscle and tendon. Therefore, a specific 1.0 0.8 0.6 0.4 0.2 force–length potential MTU lengthening MTU shortening stance phase MTU lengthening MTU shortening stance phase MTU lengthening MTU shortening stance phase 1.0 0.8 0.6 0.4 0.2 EMGnorm 1.0 0.8 0.6 0.4 0.2 force–velocity potential pre post * * * (a) (b) (c) Figure 4. (a) Soleus fascicle force–length potential, (b) force–velocity poten- tial and (c) EMG activity (normalized to a maximum voluntary isometric contraction, averaged over the phase of MTU lengthening, MTU shortening and the entire stance phase of running before and after the training inter- vention (n = 13). Significant difference between pre and post ( p < 0.05). royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20202784 7 muscle-tendon training [24,28] can be recommended to improve running economy. To assess the enthalpy efficiency–shortening velocity relationship, we used a biologically founded value of Vmax (i.e. 6.77 L0 s−1). However, during submaximal running, the lower activation level and selective slow fibre-type recruit- ment may affect the actual relationship. Furthermore, differences in fibre-type distribution may also affect the shape of the enthalpy efficiency–shortening velocity curve [22]. We evaluated the effect of (i) decreasing Vmax by 10% intervals and (ii) replacing the underlying efficiency values measured at the frog sartorius at 0°C from Hill [20] by the data presented by Barclay et al. [22] for the predominantly slow fibre-type soleus mouse muscle at 21°C, comparable with the human soleus muscle. The significant pre- to post- enthalpy efficiency increase for the MTU lengthening phase and the entire stance phase persisted for values till Vmax−30% both using the data of Hill or Barclay et al. ( p < 0.05), which confirms and strengthens the observed intervention effect (for descriptive values and p-values see electronic supplemen- tary material, S2). Furthermore, since we calculated the efficiency as a function of the soleus muscle shortening vel- ocity (adjusted for physiological temperature) and only discussed our findings in terms of percentage change, any uncertainties about the magnitude of the enthalpy efficiency would not affect our results. The soleus fascicle dynamics were not assessed in the control group because alterations were not expected with continued training habits as pre- viously evidenced [45]. Furthermore, the controls did not show alterations in any of the assessed parameters, giving strong support for an unchanged fascicle behaviour after the intervention period. 5. Conclusion In conclusion, the current study gives new insights into the soleus muscle mechanics and metabolic energetics during human running. In support of our earlier study, an exercise- induced increase of plantar flexor muscle strength and AT stiffness reduced the metabolic energy cost of running. The proposed reason for this improvement is an alteration in the soleus fascicle velocity profile throughout the stance phase, which led to a significantly higher enthalpy efficiency of the operating soleus muscle. The enthalpy efficiency was particu- larly increased in the phase of MTU lengthening, where the activation is high and the soleus generates an important part of the mechanical energy required for running. Ethics. The ethics committee of the Humboldt-Universität zu Berlin approved the study and the participants gave written informed consent in accordance with the Declaration of Helsinki. Data accessibility. The processed datasets generated and analysed during the current study are available as part of the electronic supplementary material.. Authors’ contributions. S.B., F.M., A.S. and A.A. designed research. S.B., F.M. and A.S. performed research. S.B. analysed data. S.B. and A.A. drafted the manuscript. F.M. and A.S. made important intellectual contributions during revision. Competing interests. We declare we have no competing interests. 0.2 0.4 0.6 0.8 1.0 Vnorm (V/Vmax) Vnorm (V/Vmax) 0 0.1 0.2 0.3 0.4 enthalpy efficiency pre post 0 20 40 60 80 100 stance phase (%) –0.2 –0.1 0 0.1 0.2 0.3 0.4 pre post } MTU lengthening MTU shortening (a) (b) Figure 5. (a) Soleus muscle fascicle operating velocity over the stance phase of running before and after the intervention (mean ± s.e.m.) and velocity of maximum enthalpy efficiency (i.e. 0.18 Vmax, horizontal dashed line). Following the intervention, the fascicle shortening velocity was closer to the velocity optimal for maxi- mum enthalpy efficiency during most of the MTU lengthening phase. (b) Enthalpy efficiency–fascicle velocity relationship with average values of the phase of MTU lengthening, showing that the fascicles operated at a significantly higher enthalpy efficiency following the intervention (p < 0.05). Circles indicate that the single participant values before (white) and after (black) the intervention and squares show the respective mean with standard error bars (n = 13). The vertical dotted line shows the velocity of maximum efficiency. enthalpy efficiency 0.50 0.45 0.40 0.35 0.30 0.25 pre post MTU lengthening MTU shortening stance phase 0 * Figure 6. Soleus muscle fascicle enthalpy efficiency averaged over the phase of MTU lengthening, MTU shortening and the entire stance phase of running before and after the training intervention (n = 13). *Significant difference between pre and post ( p < 0.05). royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20202784 8 Funding. Funding for this research was supplied by the German Federal Institute of Sport Science (grant no. ZMVI14-070604/ 17-18). Acknowledgements. We acknowledge the support of Antonis Ekizos, Arno Schroll, Leon Brüll and Victor Munoz-Martel for data recording and analysis. References 1. Pontzer H. 2017 Economy and endurance in human evolution. Curr. Biol. 27, R613–R621. (doi:10.1016/ j.cub.2017.05.031) 2. Bramble DM, Lieberman DE. 2004 Endurance running and the evolution of Homo. Nature 432, 345–352. (doi:10.1038/nature03052) 3. Fletcher JR, MacIntosh BR. 2017 Running economy from a muscle energetics perspective. Front. Physiol. 8, 433. 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PMC6021049	RESEARCH ARTICLE On the apparent decrease in Olympic sprinter reaction times Payam Mirshams Shahshahani1, David B. Lipps2, Andrzej T. Galecki3,4, James A. Ashton-Miller1,2,3 1 Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America, 2 School of Kinesiology, University of Michigan, Ann Arbor, Michigan, United States of America, 3 Institute of Gerontology, University of Michigan, Ann Arbor, Michigan, United States of America, 4 Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America mirshams@umich.edu Abstract Reaction times of Olympic sprinters provide insights into the most rapid of human response times. To determine whether minimum reaction times have changed as athlete training has become ever more specialized, we analyzed the results from the Olympic Games between 2004 and 2016. The results for the 100 m and 110 m hurdle events show that minimum reac- tion times have systematically decreased between 2004 and 2016 for both sexes, with women showing a marked decrease since 2008 that eliminated the sex difference in 2012. Because overall race times have not systematically decreased between 2004 and 2016, the most likely explanation for the apparent decrease in reaction times is a reduction in the pro- prietary force thresholds used to calculate the reaction times based on force sensors in starting blocks—and not the result of more specialized or effective training. Introduction The ability to rapidly respond to an external auditory stimulus is important when encounter- ing emergent situations in daily activities such as driving or when operating machinery. Reac- tion times of sprinters at the Olympic Games offer insights into the fastest human reaction times because there is little question as to their states of arousal, motivation, learning or train- ing [1]. (To avoid confusion, since this paper employs International Association of Athletics Federation (IAAF) data on ‘reaction time’, that term is used throughout this paper rather than the more usual scientific term ‘response time’.) It is unknown if the reaction times of sprinters at the Olympic Games remain stable from year to year, or whether changes to the focused training that athletes undergo in preparation for each Olympics can improve their reaction times. Understanding the effect of this focused training on the fastest human reaction times could provide insights into the trainability of athletes and other individuals for time-critical situations. Investigating potential sex differences in the fastest human reaction times in elite Olympic sprinters has important implications on the design of human-machine interfaces for handling time-critical behaviors. Such interfaces, as in the braking system of an automobile, often do PLOS ONE \| https://doi.org/10.1371/journal.pone.0198633 June 27, 2018 1 / 7 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Mirshams Shahshahani P, Lipps DB, Galecki AT, Ashton-Miller JA (2018) On the apparent decrease in Olympic sprinter reaction times. PLoS ONE 13(6): e0198633. https://doi.org/ 10.1371/journal.pone.0198633 Editor: Maria Francesca Piacentini, University of Rome, ITALY Received: February 13, 2018 Accepted: May 22, 2018 Published: June 27, 2018 Copyright: © 2018 Mirshams Shahshahani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All the raw data are already available at the International Association of Athletics Federations webpage at https://www.iaaf. org/results. We also uploaded the dataset that we used for this manuscript on the Deep Blue webpage at https://deepblue.lib.umich.edu/data/ concern/generic_works/cr56n184r. Funding: This work was supported by the Claude Pepper Center Grant AG024824 from the National Institute of Aging, (JAAM and ATG), https://www. nia.nih.gov/. The funders had no role in study not consider potential sex differences in reaction times despite women having faster auditory latencies [2,3], shorter neural pathways [4] but less muscle strength [5]. A significant sex differ- ence was found in the reported reaction times of sprinters at the 2008 Beijing Olympics Games, but this may have been an artifact of the algorithm used to calculate the reaction times [6]. One goal of this paper was to determine whether sex differences in reported reaction times have occurred at other Olympic Games. A second goal was to determine whether reaction times have decreased over Olympic years. To measure reaction times on the Olympic Track Swiss Timing, a subsidiary of Omega SA, uses their ASC3 (Automatic Start Control) false start detection system which includes instru- mented starting blocks to measure the time course of the force applied by the sprinter to the blocks with a precision of 1 ms following the starting gun. If this force, which their datasheet shows can reach ~2 kN, exceeds a given (unpublished) force threshold before 100 ms has elapsed from the onset of the start gun signal, a false start is registered (Rule 162, IAAF Com- petition Rules). A reaction time is reported in the published Olympic results if the force crosses the designated threshold after 100 ms, but no reaction time is reported in the event of a false start (< 100 ms) or other reasons for disqualification. Any longitudinal comparisons of reaction time of sprinters competing at the Olympic Games between 2004 and 2016 is confounded by an IAAF rule change in 2010 (Rule 162.7, IAAF Competition Rules) that disqualified any athlete who false started, rather than permit- ting a second chance as the prior rule allowed. This change apparently led runners to adopt slightly more conservative strategies in their reaction times in order to avoid disqualification [7]. However, since there was no rule change between 2004 and 2008 or between 2012 and 2016, the results from those years should permit a direct comparison of athlete reaction times independent of the rule change. Methods Official reaction times from every heat in the 100, 110, 200, 400 and 440 m track events in 2004, 2008, 2012 and 2016 were downloaded from the official IAAF web site. All names were stripped from the record to blind the analyses and University of Michigan Institutional Review Board Approval was received (Exempt—Not Regulated Research, HUM00135664, dated 9/8/ 2017). Runners who were disqualified in a heat were excluded, as were those who did not start. The reaction time (RT) data were positively skewed, so a power transformation (RT-1.5) was used to obtain a normal distribution [6]. Since we are interested in the minimum human auditory reaction times, we focused the analysis on the races with the shortest reaction times: in an initial analysis these proved to be the 100 m and 100 and 110 m hurdles races (S2 Fig and S1 Table). Since one reaction time was an outlier, exceeding 300 ms, we excluded it for being non-competitive; whether or not it was included in the analyses would prove not to affect the results. All data analyses were performed in R (a language and environment for statistical comput- ing) version 3.4.2. Linear mixed-effect models (LMM) with a random intercept for each athlete were fit to the data using the ‘nlme’ version 3.1–132 package. We allowed for different residual variations for each Olympic year and sex. Likelihood Ratio and t-tests were used to examine the roles of sex and year on an individual’s reaction time for the years 2004–2016. We only considered the minimum reaction time for each athlete for a given Olympic year; Those results were then back transformed to find the mean value as well as the -3SD minimum reaction time values (S1 Text) by sex and year [6]. Official reaction time results for the athletes who competed in the 2004, 2008, 2012 and 2016 Olympic sprinting competitions were included in our analyses (Fig 1A). To account for On the apparent decrease in Olympic sprinter reaction times PLOS ONE \| https://doi.org/10.1371/journal.pone.0198633 June 27, 2018 2 / 7 design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Fig 1. Sprinter minimum reaction times by sex and year in the 2004–2016 Olympics. (A) Scatter plot of reaction times by sex and year. The solid black circles and bars represent the mean and ±3SD reaction times after back- transformation. The hatched area designates reaction times deemed by IAAF rule to be a false start. The number of On the apparent decrease in Olympic sprinter reaction times PLOS ONE \| https://doi.org/10.1371/journal.pone.0198633 June 27, 2018 3 / 7 repeated measures a mixed-effect model with Olympic year and sex (4- and 2-level factors, respectively) was fit to the set of transformed minimum reaction times. A Likelihood Ratio (LR) test revealed a significant interaction between Olympic year and sex [LR = 22.82, p < 0.0001]. After considering the plots of the transformed minimum reaction times (Fig 1B), the mixed effect model was simplified based on the results of likelihood ratio tests for nested models. In particular the model was simplified by combining the 2004 and 2008 years into one category both in main effect of year as well as in year by sex interaction terms [LR = 1.20, p = 0.54]. Results The coefficients for the resulting fixed effects can be seen in Table 1 and, when considered with the results in Fig 1B, they suggest that reaction times decreased significantly in the more recent Olympics. When only the 2012 and 2016 data were considered as the fixed effects in the model, there remained significant year and sex differences, but the interaction was no longer significant [LR = 2.54, p = 0.111]. This suggests that the large sex differences in 2004 and 2008, which disappeared in 2012, drove the significant interaction term. Discussion Our starting point for this study was the finding by Lipps et al. [6] of a significant sex differ- ence in the reaction times of sprinters at the 2008 Beijing Olympics. Natural questions were whether this difference would persist at the 2012 London Olympics and whether ever more focused training leads to slight decreases in reaction times for both men and women. The pres- ent results showing the abrupt nature of the decrease for only women at the London Olympics suggests something other than training may have been responsible. One explanation for the apparent decrease in minimum reaction times from 2012 to 2016 is that both sexes became more comfortable competing under the threat of disqualification imposed by the 2010 IAAF rule change, and thereby became less conservative. If so, the results do not support this false starts reported for the 100 m sprints, 100 m hurdles, and 110 m hurdles were: 1 false start in 2004 and 2008 each, 3 in 2012, and 2 in 2016. (B) Mean (±2SE) transformed minimum reaction times (s-1.5) by sex and year. The 100 ms false start threshold when transformed becomes 31.6 s-1.5. The most parsimonious Linear Mixed-effect Model found to fit the data used a random intercept for each athlete. The fixed effects consisted of Olympic year as a factor with 3 levels (2004 and 2008 together, 2012, and 2016), Sex as a factor with 2 levels, with the interaction of Olympic year by sex. As a check, the predicted mean values from the LMM were found (the black dots and dashed lines) and agreed well with the original data (red and blue lines). Minimum reaction times decreased significantly by year (See Results section). Note that the 2010 IAAF rule change decreed that any runner who false started would be disqualified from the race, and that applied to the 2012 and 2016 Olympics. https://doi.org/10.1371/journal.pone.0198633.g001 Table 1. Linear mixed-effect model for transformed minimum reaction times (s-1.5). Fixed effect Parameter estimate (s-1.5) SE p Olympic year (2004, 2008) 13.28 0.22 <0.001 Olympic year (2012) 15.37 0.28 <0.001 Olympic year (2016) 17.68 0.3 <0.001 Olympic year (2004, 2008):Men 3.05 0.31 <0.001 Olympic year (2012):Men 0.51 0.35 0.16 Olympic year (2016):Men 1.3 0.39 0.001 The analysis shows that women’s reaction times decreased significantly each year from 2008 to 2012, and 2012 to 2016. However, men’s reaction times only decreased significantly from 2012 to 2016 (Fig 1B). https://doi.org/10.1371/journal.pone.0198633.t001 On the apparent decrease in Olympic sprinter reaction times PLOS ONE \| https://doi.org/10.1371/journal.pone.0198633 June 27, 2018 4 / 7 explanation because there is no systematic decrease in the race finish times between 2012 and 2016 (S1 Fig). A simpler explanation is that the decreasing reaction times between 2012 and 2016 might be due to a decrease in the force thresholds employed by Swiss Timing to calculate the reaction times of the men and women [8]. It was suggested by Lipps et al. that a 22% decrease in the force threshold could have eliminated the sex difference in the reported 2008 reaction time data [6]. The present data for the 2012 Olympics suggest that the force threshold was indeed reduced for the women in 2012, but not the men; there was no significant sex dif- ference in their reported reaction times, and the reaction times of the men did not change between 2008 and 2012 (Table 1, Fig 1B). There is no physiologic reason why women should be slower than men in their central auditory processing time [2]; put simply, their shorter limbs mean the signal reaches the leg muscles more quickly than in men, but their smaller leg muscles mean than it takes longer to develop a significant plantarflexion force against the start- ing block [6]. This means that it may be fruitful for companies to re-examine how they detect a false start with an automatic starting system. Our results suggest that the back-transformed Mean - 3SD value should be set to 100 ms (S1 Text, Fig 1A). The method for calculating reaction time can vary with the company awarded the timing contract [9], so such a reduction is within the purview of Swiss Timing, who regard the force threshold used as proprietary information [6]. The choice of this force threshold is an impor- tant compromise for the quality of the athletic competitions. If the force threshold is set too low, the slightest twitch could result in a false start being recorded by the IAAF-certified Start Information System (SIS). This would not be practical because too many sprinters would be disqualified and that would spoil the competition. If the force threshold is set too high, sprint- ers would exhibit unreasonably long reaction times. Of course, in the most recent IAAF rules, it is the starter that makes the final disqualification decision based on data from the SIS system as well as whether the athlete initiated his/her starting action before the starter pulled the trig- ger. Any motion that is not part of the continuous starting movement would simply result in a caution the first time (Rule 162.7-Note (i), IAAF Competition Rules). The IAAF has been examining SIS methods for detecting false starts. (see for example [10]) A limitation of this study is the absence of data for reaction times less than 100 ms because the reaction times are not reported for false-starts. This, and not having access to the starting block force-time curves, prevents an accurate calculation of the minimum human reaction time for lower force thresholds than were used; in those cases reaction times could become less than 100 ms. We conclude that the apparent decrease in sprinter reaction times between 2004 and 2016 is caused by decreases in the force thresholds used to calculate the reaction time. The decrease in both men’s and women’s reaction times after 2012 appears to reflect fine tuning of those force thresholds by Swiss Timing rather than a decrease in the acoustic neuromuscular reac- tion time of the athletes due to specialized training. In terms of applicability of the results to other situations, a rapid acoustic reaction time can be important, for example, when a driver needs to brake an automobile in an emergency after hearing a warning horn. Our results suggest that the designer of mechanical or electronic equipment to which humans are to be mechanically coupled should employ the lowest practi- cal force threshold that does not disadvantage women. Conclusions We conclude that the apparent decrease in reaction times is due to a reduction in the proprie- tary force thresholds used to calculate the reaction times, based on measurements from force sensors in the starting blocks, not the result of more specialized or effective training. On the apparent decrease in Olympic sprinter reaction times PLOS ONE \| https://doi.org/10.1371/journal.pone.0198633 June 27, 2018 5 / 7 Supporting information S1 Text. Why the Mean– 3SD value is a good estimate of minimum auditory reaction time for Olympic false start detection in sprinting. (DOCX) S1 Fig. Distribution of overall ‘mark’ times by sex, year, and race type. IAAF terminology designates the overall race time as the ‘mark’ time. The boxplot lines represent the median, and the first and third quartiles. The vertical lines extend up to 1.5 times the interquartile dis- tance from the top and bottom boxplot lines. The graph shows no systematic change in overall race times with Olympic year for men or women. (TIF) S2 Fig. Transformed minimum reaction times for all the sprints in 2016. Distribution of transformed minimum (min) reaction time in 2016 for 196, 46, 63, 148, 138, and 94 athletes who competed in 100 m, 100 m hurdles, 110 m hurdles, 200 m, 400 m, and 400 m hurdles respectively. The error bars show ±2SE. The shorter sprints (100 m, 100 m hurdles, and 110 m hurdles) have significantly faster reaction times than the longer sprints (p = 0.0001). To view the linear mixed effect model for this analysis, please refer to S1 Table. (TIF) S1 Table. Linear mixed-effect model results for transformed minimum reaction times for all sprints in 2016. Transformed minimum reaction time results for the 100 m sprints was chosen as the reference group. We can see that 100 m hurdles, and 110 m hurdles were not sig- nificantly different than the reference. However, 200 m, 400 m, and 400 m hurdles were signif- icantly different than the 100 m sprints. (PDF) Acknowledgments All Olympic Game reaction times are publicly available and downloaded from the official International Association of Athletic Federation webpage. We are grateful for the developers of the R Core Team, and ‘nlme’, ‘ggplot2’ and ‘stargazer’ packages who have made their prod- uct publicly available for free. Author Contributions Conceptualization: Payam Mirshams Shahshahani, David B. Lipps, Andrzej T. Galecki, James A. Ashton-Miller. Data curation: Payam Mirshams Shahshahani. Formal analysis: Payam Mirshams Shahshahani, Andrzej T. Galecki. Funding acquisition: Andrzej T. Galecki, James A. Ashton-Miller. Investigation: Payam Mirshams Shahshahani. Methodology: Payam Mirshams Shahshahani, Andrzej T. Galecki. Project administration: Payam Mirshams Shahshahani. Resources: James A. Ashton-Miller. Software: Payam Mirshams Shahshahani. Supervision: Andrzej T. Galecki, James A. Ashton-Miller. On the apparent decrease in Olympic sprinter reaction times PLOS ONE \| https://doi.org/10.1371/journal.pone.0198633 June 27, 2018 6 / 7 Validation: Payam Mirshams Shahshahani, Andrzej T. Galecki. Visualization: Payam Mirshams Shahshahani. Writing – original draft: Payam Mirshams Shahshahani, James A. Ashton-Miller. Writing – review & editing: Payam Mirshams Shahshahani, David B. Lipps, Andrzej T. Galecki, James A. Ashton-Miller. References 1. Bell DG, Jacobs I. Electro-mechanical response times and rate of force development in males and females. Med Sci Sports Exerc. 1986 Feb; 18(1):31–36. PMID: 3959861 2. Don M, Ponton CW, Eggermont JJ, Masuda A. Gender differences in cochlear response time: An expla- nation for gender amplitude differences in the unmasked auditory brain-stem response. J Acoust Soc Am. 1993; 94(4):2135–48. PMID: 8227753 3. Trune DR, Mitchell C, Phillips DS. The relative importance of head size, gender and age on the auditory brainstem response. Hear Res. 1988; 32(2–3):165–74. PMID: 3360676 4. Uth N. Anthropometric comparison of world-class sprinters and normal populations. J Sport Sci Med. 2005; 4(4):608–16. 5. Thelen DG, Schultz AB, Alexander NB, Ashton-Miller JA. Effects of age on rapid ankle torque develop- ment. J Gerontol. 1996; 51(5):M226–32. 6. Lipps DB, Galecki AT, Ashton-Miller JA. On the implications of a sex difference in the reaction times of sprinters at the Beijing Olympics. PLoS One. 2011; 6(10):4–8. 7. Brosnan KC, Hayes K, Harrison AJ. Effects of false-start disqualification rules on response-times of elite-standard sprinters. J Sports Sci. 2017; 35(10):929–35. https://doi.org/10.1080/02640414.2016. 1201213 PMID: 27351870 8. ASC3 -FALSE START DETECTION SYSTEM [Internet]. [cited 2017 Nov 18]. Available from: https:// www.swisstiming.com/fileadmin/Resources/Data/Datasheets/DOCM_AT_ASC3_ FalseStartDetectionSystem_0715_EN.pdf 9. Pain MTG, Hibbs A. Sprint starts and the minimum auditory reaction time. J Sports Sci. 2007; 25(1):79– 86. https://doi.org/10.1080/02640410600718004 PMID: 17127583 10. Willwacher S, Feldker MK, Zohren S, Herrmann V, Bru¨ggemann GP. A novel method for the evaluation and certification of false start apparatus in sprint running. Procedia Eng [Internet]. 2013; 60:124–9. Available from: http://dx.doi.org/10.1016/j.proeng.2013.07.073 On the apparent decrease in Olympic sprinter reaction times PLOS ONE \| https://doi.org/10.1371/journal.pone.0198633 June 27, 2018 7 / 7	On the apparent decrease in Olympic sprinter reaction times.	06-27-2018	Mirshams Shahshahani, Payam,Lipps, David B,Galecki, Andrzej T,Ashton-Miller, James A	eng
PMC10664904	RESEARCH ARTICLE Partial-body cryostimulation procured performance and perceptual improvements in amateur middle-distance runners Massimo De Nardi1,2☯, Luca FilipasID3,4☯, Carlo Facheris1,3, Stefano RighettiID5,6, Marco Tengattini5, Emanuela Faelli2,7, Ambra BisioID2,7, Gabriele Gallo2,7, Antonio La Torre3,5,8, Piero Ruggeri2,7‡, Roberto CodellaID3,4‡* 1 Krioplanet Ltd, Treviglio, Bergamo, Italy, 2 Department of Experimental Medicine, Università degli Studi di Genova, Genoa, Italy, 3 Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy, 4 Department of Endocrinology, Nutrition and Metabolic Diseases, IRCCS MultiMedica, Milano, Italy, 5 Italian Athletics Federation, Rome, Italy, 6 Interventional Cardiology Department, San Gerardo Hospital, Monza, Italy, 7 Centro Polifunzionale di Scienze Motorie, Università degli Studi di Genova, Genoa, Italy, 8 IRCCS Istituto Ortopedico Galeazzi, Milan, Italy ☯ These authors contributed equally to this work. ‡ PR and RC also contributed equally to this work. * roberto.codella@unimi.it Abstract The purpose of this study was to investigate the effects of partial-body cryostimulation on middle-distance runners before two 3000-m tests at the speed of the first and second venti- latory threshold, and before a time to exhaustion test at 110% of the maximal aerobic speed. Twelve amateur runners (age: 46 ± 9 years; VO2max: 51.7 ± 4.9 mlkg-1min-1) completed six running testing sessions in a randomized counterbalanced cross-over fashion: three of them were preceded by a partial-body cryostimulation and the other three by a control condi- tion. The testing sessions consisted of: 1) a 3000-m continuous running test at the speed of the first ventilatory threshold; 2) a 3000-m continuous running test at the speed of the sec- ond ventilatory threshold; 3) a time to exhaustion test at 110% of the maximal aerobic speed. Heart rate, ratings of perceived exertion and visual analogue scale relative to muscle pain were recorded throughout the tests. Total quality recovery was evaluated 24–48 h after the end of each test. Distance to exhaustion was higher after partial-body cryostimulation than control condition (p = 0.018; partial-body cryostimulation: 988 ± 332 m, control: 893 ± 311 m). There were differences in the ratings of perceived exertion during each split of the 3000-m continuous running test at the speed of the second ventilatory threshold (p = 0.001). Partial-body cryostimulation can be positively considered to enhance middle-distance run- ning performance and reduce perception of effort in amateur runners. Introduction In high-performance sport, coaches and sports scientists strive to identify novel interventions to enhance performance maximizing the physical, psychological and behavioral state before a PLOS ONE PLOS ONE \| https://doi.org/10.1371/journal.pone.0288700 November 22, 2023 1 / 12 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: De Nardi M, Filipas L, Facheris C, Righetti S, Tengattini M, Faelli E, et al. (2023) Partial-body cryostimulation procured performance and perceptual improvements in amateur middle- distance runners. PLoS ONE 18(11): e0288700. https://doi.org/10.1371/journal.pone.0288700 Editor: Nejka Potocnik, University of Ljubljana, Medical faculty, SLOVENIA Received: December 10, 2021 Accepted: July 3, 2023 Published: November 22, 2023 Copyright: © 2023 De Nardi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. competition [1, 2]. Prior to performance, several routines have been pursued with the aim of enhancing oxygen uptake, cardiac output, blood flow to skeletal muscle, neuromuscular activa- tion and mental readiness [3]. Running economy is certainly another typical target. Pre-training typically involves a combination of passive and active elements which can enhance exercise performance [4]. Active warm-ups are used to increase body temperature leading to a higher muscle metabolism, enhanced oxygen uptake and subsequent cardiac out- put [5], while passive warm up has the same goal but is mainly focused on various psychologi- cal techniques or cooling/heating interventions [6]. Preparation through passive modalities is becoming increasingly common for athlete management and performance enhancement [7]. Although every team, staff, strength and conditioning coach, have individualized methods and preferences for their athletes/players, cutting-edge methodologies and techniques are evolving and being available at large scale [8]. Cryotherapy is a relatively new clinical intervention used in different medical treatments to alleviate pain derived from inflammatory conditions [9]. Given the extremely cold air temper- atures of –110˚C or below, reached in short (1–4 min) exposures, cryostimulation can elicit strong physiological responses in users [10], ranging from muscular soreness relief up to a modest immunomodulatory effect [11]. The utilization of cryostimulation techniques before physical efforts is also known as pre- cooling [12]. Usually, precooling aims to a rapid removal of heat of the body prior to exercising in warm environments and to prevent the side effects of heat-stress-induced fatigue [13]. Reducing body temperature before commencing an effort has been found to be successful in improving endurance performance both in hot and thermoneutral environments [14, 15]. Higher precooling effects were also observed in prolonged efforts [16], ambient temperature and for aerobic capacity [17]. Improvements in endurance performance following precooling interventions were achieved both with external (cold air exposure, water immersion, exposure to ice, iced garments, iced towels, etc.), internal modalities (beverage ingestion, ice ingestion) or combining two or more practical precooling methods [12, 17]. Cooling strategies are logisti- cally challenging, therefore it is important to analyze the effectiveness of practical precooling in competition or field setting [12]. In particular, the new transportable models of cryo-saunas have increased the diffusion of partial-body cryostimulation (PBC) treatments before races and training [18], which have been demonstrated to lower core and skin temperature for several hours [19]. Exposures to very-low temperatures (approximately at -120˚C) could have positive impacts on core body temperature, cardiovascular and autonomic functions [20], and perception of effort [21]. Nev- ertheless, the disparities in the literature among various precooling methods, exercise efforts and thermometry protocols remain substantial. This breath of knowledge could be scoped by athletes, coaches and their extended entourage to tailor the maximizing-performance routine. In this study, the effects of PBC on running performance were evaluated with two 3000-m tests at the speed of the first and second ventilatory threshold, and before a time to exhaustion test at 110% of the maximal aerobic speed (MAS). We aimed to test if PBC could be applied as a prior to competition performance enhancement methodology in middle-distance events. Our hypothesis was that running performance would have been improved by cryo-exposures, along with lower value of perceptual fatigue. Materials & methods Experimental approach to the problem A randomized counterbalanced cross-over design was used for the experimental component of the present study. The order of the experimental treatments (PBC; control) and testing PLOS ONE Precooling in endurance runners PLOS ONE \| https://doi.org/10.1371/journal.pone.0288700 November 22, 2023 2 / 12 sessions were randomly allocated based on balanced permutations generated by a computer program (www.randomization.com). A flow-chart of the protocol is offered in Fig 1. Subjects To determine an a priori sample-size (software package, G * Power 3.1.9.2), the following input parameters were selected as per an F test for ANOVA-repeated measures-within factors analysis: a statistical power (1-β) of 0.8, a significance α level of 0.05, an effect size f of 0.38 (which corresponds to a η2 p = 0.13), 1 group, 6 measurements, 0.5 as correlation among repeated measurements. As output parameters, an actual power of 0.83, a critical F of 2.45 Fig 1. Flow-chart of the study. https://doi.org/10.1371/journal.pone.0288700.g001 PLOS ONE Precooling in endurance runners PLOS ONE \| https://doi.org/10.1371/journal.pone.0288700 November 22, 2023 3 / 12 were obtained. Therefore, eight subjects would have been sufficient to assess the sought effects. However, to face a possible drop-out of one third of the subjects, thirteen male amateur run- ners were recruited for this study. Participants were evaluated by a medical doctor who ascer- tained their competitive suitability and excluded any contraindication to systemic cryostimulation. Eligibility criteria were as follows: being free from any known medical dis- eases, medication, injuries, color vision deficiencies. The study design and procedures were approved by the local Ethics Committee and followed the ethical principles for medical research involving human subjects set by the World Medical Association Declaration of Hel- sinki. After ethical approval, written informed consent and medical declaration were obtained from the participants in line with the procedures set by the local Institution’s Research Ethics Committee. Subjects were informed of the procedures and potential risks involved, although these were minimal as already described [22]. Participants were also informed that they could withdraw from the study at any time, for any reason. Procedures Participants performed eight testing sessions on eight different occasions, in a period no lon- ger than three weeks between the first and last visit (with at least 48 h between two visits). Vis- its were carried in a private laboratory. All the experimental procedures were performed in an isolated and air-conditioned room, at the constant temperature of 21 ± 1˚C and at a relative humidity of 40–50%. Before each visit, participants were instructed to sleep for at least 8 h, refrain from the consumption of alcohol and caffeine, and avoid any vigorous exercise for the 24-h preceding the testing sessions. Each participant carried out the visits individually and at the same time of day (within 1 h period, between 7:30 and 10:30). During visit 1, participants’ body weight and height were measured. Afterwards, they familiar- ized with the procedures employed for the testing sessions, i.e. running on the treadmill used for the study (Excite Treadmill, Technogym, Cesena, Italy) for at least 20 min wearing a portable gas exchange system in breath-by-breath mode (Cosmed K5, Cosmed, Rome, Italy). Treadmill speed was validated for each stage using an odometer (Stanley, Milano, Italy) to confirm that it was the one declared by the manufacturer. During visit 2, participants completed an incremental exercise test to determine their maximal oxygen uptake (VO2max). The test started with a standardized warm-up consisting in 10 min of running at a constant speed of 10 kmh-1, followed by 5 min of mobility drills. At the end of the mobility exercises, participants completed 10 min of passive recovery and, after wearing the mask, 3 min in a standing position on the treadmill for the acquisi- tion of basal values. After the warm-up, the incremental test started at a speed equal to 10 kmh-1, with an increase of 0.1 kmh-1 every 12 s until exhaustion. Alveolar gas exchanges were measured breath by breath in the mouth, eliminating any values outside the pre-established range, using a specific software (OMNIA, Cosmed, Rome, Italy). Maximum oxygen uptake was calculated as the 30 s mean oxygen uptake once the plateau was reached. Runner’s VO2max was reached when at least three of the following criteria were fulfilled: i) a steady state of VO2 (change in VO2 at VO2max 150 mLmin-1), ii) final respiratory-exchange ratio (RER) exceeded 1.1, iii) visible exhaustion, iv) a HR at the end of exercise within the 10 bpm of the predicted maximum, and v) a lactate concentration at the end of exercise higher than 8 mmolL-1 [23]. Blood samples were obtained immediately after exhaustion, in a single measurement, through the ear lobe, and were analyzed for whole blood lactate using a portable lactate analyzer (Lactate Pro, Arcray Inc, Kyoto, Japan), reported to have good reliability and accuracy [24]. First and second ventilatory thresholds were detected through the ventilatory equivalents method [25] and the speeds associated were cal- culated. In addition, researchers calculated the maximal aerobic speed (MAS) as the lowest run- ning speed at which VO2max occurred. PLOS ONE Precooling in endurance runners PLOS ONE \| https://doi.org/10.1371/journal.pone.0288700 November 22, 2023 4 / 12 After the first two preliminary visits, participants carried out randomly the three testing ses- sions, preceded either by a PBC or a control condition (six sessions in total, all randomized, Fig 1). The testing sessions consisted of a 3000-m continuous running test at the speed of the first ventilatory threshold, a 3000-m continuous running test at the speed of the second venti- latory threshold and a time to exhaustion test at 110% of the MAS. The order of the tests and the interventions were randomly counterbalanced. The tests were preceded by the same warm-up routine described for the incremental ramp test. The warm-up started within two minutes by the end of the PBC or control condition. The tests at the first and second ventila- tory thresholds were performed over a distance of 3000 m to give enough time to the physio- logical parameters to reach a steady-state. Experimental treatments Before each testing session, participants underwent either a PBC session or a control session. During the PBC duty, participants completed the cryo-session (150 s) in a cryo-cabin (Space Cabin, Criomed Ltd, Kherson, Ukraine). Temperature was set between -130 and -170˚C as recommended [26]. Participants were instructed to turn around continuously (standing rota- tions) in the cabin for the 150-second session. The control condition required to perform simi- lar movement (standing rotations) for the same duration in a thermo neutral environment (21 ± 1˚C). Immediately after the cryo-exposure or control task, the running tests were per- formed. Due to the nature of the cryostimulation, participants were not blinded to their treat- ments. However, the research team was blinded to the treatment because specific personnel oversaw the treatments in a separated room. Physiological and psychological measures Heart rate (HR) was recorded, and averaged for the duration of the tests, using a HR monitor fitted with a chest strap. VO2 was recorded using portable gas exchange system in breath-by- breath mode (Cosmed K5, Cosmed, Rome, Italy) and data were averaged using the specific software (OMNIA, Cosmed, Rome, Italy). During the 3000-m continuous running tests and the 110% of the MAS test, VO2 were reported as the average of the last 30 s of the tests. Rating of perceived exertion (RPE) was registered during the final 10 s of 1000- and 2000-m splits, at the end of the 3000-m continuous running tests, and at the end of the time to exhaustion test. RPE was measured with the 11-point CR10 scale developed by Borg [27]. Participants were familiar with the scale as it had been employed during their daily training sessions for at least six months ahead the tests. Participants recorded their subjective sensation of muscle pain by using a visual analogue scale (VAS), before and after each test. Participants marked their response on a 100-mm line anchored by 0 (no muscle pain at all) and 100 (maximum muscle pain) [28]. The total quality recovery (TQR) scale [29] was used to monitor recovery. After 24 and 48 hours from the end of each test, participants answered the question “How do you feel about your recovery?” using the TQR scale, in which answers are rated from 6 to 20. Statistical analyses All data are presented as mean ± standard deviation. Assumptions of statistical tests such as normal distribution (Shapiro-Wilk test with visual inspection) and sphericity of data (Mauchly’s test) were checked as appropriate. Greenhouse-Geisser correction to the degrees of freedom was applied when violation to sphericity was present. Two-way repeated measured ANOVA was used to determine the treatment factor (2 levels, PBC and Control), time factor (3 levels, at 1000 m, 2000 m, 3000 m splits), and interaction on RPE during the 3000-m tests. Significant main effects and interactions were interpreted through pairwise comparisons with PLOS ONE Precooling in endurance runners PLOS ONE \| https://doi.org/10.1371/journal.pone.0288700 November 22, 2023 5 / 12 Bonferroni correction. Paired sample t tests were used to compare the other variables between the PBC and control conditions. Significance was set at 0.05 (two-tailed) for all analyses. Effect sizes for repeated measure ANOVA are reported as partial eta squared (η2 p), using the small (< 0.13), medium (0.13–0.25) and large (> 0.25) interpretation for effect size [30], while effect sizes for pairwise comparison were calculated using Cohen’s d and considered to be either triv- ial (effect size: < 0.20), small (0.21–0.60), moderate (0.61–1.20), large (1.21–2.00), or very large (> 2.00) [31]. Data analysis was conducted using the Statistical Package for the Social Sciences, version 25 (SPSS Inc., Chicago, IL, USA). Results Participants’ baseline characteristics and data derived from the incremental ramp test are reported in Table 1. One participant was classified as outlier based on his maximum oxygen uptake (VO2max) (more than two standard deviation from the mean of the sample) and excluded from the analysis. Therefore, twelve subjects were included in the study procedures (age: 46 ± 9 years; height: 1.75 ± 0.05 m; mass: 72 ± 8 kg). Performance outcomes Table 2 shows the performance outcomes derived from the 3000-m continuous running test at the speed of the first and second ventilatory threshold and the time to exhaustion test at 110% of the MAS. Running distance (p = 0.018, d = 0.30) and time (p = 0.020, d = 0.31) during the time to exhaustion test were higher after PBC than control condition. No differences were found among the other parameters. Psychological outcomes Table 3 shows the pairwise comparison of the psychological outcomes derived during the 3000-m continuous running test at the speed of the first and second ventilatory threshold and the time to exhaustion test at 110% of the MAS. There was a significant condition x time inter- action in the RPE during the 3000-m continuous running test at the speed of the second venti- latory threshold (F (2,18) = 15.716, p = 0.001, η2 p = 0.43). Pairwise comparison revealed significant differences for RPE in 1000-m (p = 0.010, d = 0.54), 2000-m (p = 0.030, d = 0.65) Table 1. Participants’ characteristics at baseline (mean ± SD). VO2max (mLkg-1min-1) 51.7 ± 4.9 HRmax, incremental test (bpm) 181 ± 16 vVT1 (kmh-1) 11.8 ± 0.9 vVT2 (kmh-1) 13.4 ± 0.9 MAS (kmh-1) 14.7 ± 1.1 VO2VT1 (mLkg-1min-1) 45.2 ± 5.3 VO2VT2 (mLkg-1min-1) 49.8 ± 5.3 HRVT1 (bpm) 156 ± 18 HRVT2 (bpm) 167 ± 17 VO2max: maximum oxygen uptake; HRmax: maximum heart rate; vVT1: velocity at first ventilatory threshold; vVT2: velocity at second ventilatory threshold; MAS: maximum aerobic speed; VO2VT1: oxygen uptake at first ventilatory threshold; VO2VT2: oxygen uptake at second ventilatory threshold; HRVT1: heart rate at first ventilatory threshold; HRVT2: heart rate at second ventilatory threshold. https://doi.org/10.1371/journal.pone.0288700.t001 PLOS ONE Precooling in endurance runners PLOS ONE \| https://doi.org/10.1371/journal.pone.0288700 November 22, 2023 6 / 12 Table 2. Performance outcomes from the three running tests in partial-body cryotherapy (PBC) and control conditions (mean ± SD). 3000-m test at VT1 PBC Control p Cohen’s d Time (s) 910 ± 77 919 ± 70 0.237 0.13 VO2 (mLkg-1min-1) 46.4 ± 5.5 47.1 ± 6.0 0.731 0.12 HR (bpm) 159 ± 17 159 ± 18 0.478 0.04 RER (VCO2VO2 -1) 0.84 ± 0.06 0.85 ± 0.05 0.750 0.13 3000-m test at VT2 Time (s) 693 ± 217 711 ± 152 0.491 0.09 VO2 (mLkg-1min-1) 48.8 ± 5.8 48.1 ± 5.2 0.502 0.13 HR (bpm) 168 ± 15 167 ± 17 0.625 0.05 RER (VCO2VO2 -1) 0.92 ± 0.08 0.92 ± 0.09 0.817 0.06 TTE at 110% of MAS Distance (m) 988 ± 332 893 ± 311 0.018 * 0.30 Time (s) 222 ± 73 201 ± 67 0.020 * 0.31 VO2 (mLkg-1min-1) 49.8 ± 6.1 46.7 ± 6.2 0.233 0.49 HR (bpm) 170 ± 13 167 ± 13 0.227 0.21 RER (VCO2VO2 -1) 1.13 ± 0.11 1.13 ± 0.12 0.781 0.07 VT1: first ventilatory threshold; VT2: second ventilatory threshold; TTE: time to exhaustion; MAS: maximum aerobic speed; VO2: oxygen uptake; HR: heart rate; RER: respiratory exchange ratio. * Significant difference between the conditions (p < 0.05). https://doi.org/10.1371/journal.pone.0288700.t002 Table 3. Psychological outcomes from the three running tests in partial-body cryotherapy (PBC) and control conditions (mean ± SD). 3000-m test at VT1 PBC Control p Cohen’s d VAS pre (mm) 11 ± 14 18 ± 16 0.151 0.44 VAS post (mm) 15 ± 18 17 ± 17 0.638 0.10 1000-m RPE 2.6 ± 1.2 2.3 ± 0.9 0.410 0.24 2000-m RPE 3.2 ± 1.5 3.0 ± 1.2 0.305 0.13 3000-m RPE 3.3 ± 1.9 3.3 ± 1.9 1.000 0.00 24-h TQR 17.5 ± 2.3 17.5 ± 2.6 0.915 0.02 48-h TQR 18.6 ± 2.1 18.1 ± 2.3 0.053 0.35 3000-m test at VT2 VAS pre (mm) 13 ± 11 11 ± 12 0.504 0.15 VAS post (mm) 13 ± 11 18 ± 17 0.141 0.41 1000-m RPE 3.2 ± 1.3 3.9 ± 1.5 0.010 * 0.54 2000-m RPE 4.3 ± 1.7 5.5 ± 2.1 0.030 * 0.65 3000-m RPE 5.3 ± 1.8 6.6 ± 2.7 0.008 * 0.56 24-h TQR 17.9 ± 2.1 16.3 ± 3.3 0.091 0.57 48-h TQR 18.2 ± 2.4 17.2 ± 2.4 0.090 0.44 TTE at 110% of MAS VAS pre (mm) 8 ± 11 10 ± 14 0.748 0.14 VAS post (mm) 15 ± 14 23 ± 28 0.279 0.36 RPE at exhaustion 8.2 ± 2.0 8.3 ± 2.0 0.928 0.02 24-h TQR 17.2 ± 2.0 17.1 ± 2.1 0.903 0.04 48-h TQR 17.8 ± 1.8 17.3 ± 2.7 0.324 0.22 VT1: first ventilatory threshold; VT2: second ventilatory threshold; TTE: time to exhaustion; MAS: maximum aerobic speed; VAS: visual analogue scale; RPE: rating of perceived exertion; TQR: total quality recovery. * Significant difference between the conditions (p < 0.05). https://doi.org/10.1371/journal.pone.0288700.t003 PLOS ONE Precooling in endurance runners PLOS ONE \| https://doi.org/10.1371/journal.pone.0288700 November 22, 2023 7 / 12 and 3000-m (p = 0.008, d = 0.56) splits. No differences were found among the other parameters. Discussion The main finding of this study was that a PBC session increased running time to exhaustion, showing an improved performance at 110% of MAS after a single PBC session compared to a control condition. Further, RPE was significantly reduced after the PBC in each split of the 3000-m continuous running test at the second ventilatory threshold, suggesting that both cen- tral and peripheral mechanisms could be affected by PBC. Partially unexpected, no other dif- ferences were found in the two different conditions for other performance or psychological measures. This study showed a potential implication in adopting this relatively new technique before running performance in a moderate-temperature condition. To date, an abundance of evi- dence demonstrated the favorable use of PBC as a recovery modality after high intensity exer- cise for athletes. Researchers have identified how PBC can improve recovery post-exercise by maximizing anti-inflammatory and decreasing pro-inflammatory actions [9]. Conversely, to the best of our knowledge, the effects of PBC before an exercise are mostly uncertain. Prior investigations have documented the effectiveness of precooling using PBC in ameliorating flexibility without losing the trunk position sense proprioception [32]. In addition, numerous studies showed the positive influence of several precooling strategies in hot environments on endurance performance, while no studies have evaluated the effects of PBC in thermoneutral conditions [14]. Our results indicated PBC as a method capable to induce improvements in middle-distance running performance, probably mediated by a lower RPE for the same exter- nal workload. This is only a hypothesis as in the present study the improvement in perfor- mance and the reduction of RPE occurred not in combination but during different tests. This result is possibly due to the timing of the RPE evaluation, i.e. at the end of the time to exhaus- tion test, and at each 1000-m split in the 3000-m time trials. Therefore, the ceiling effect of the RPE in an exercise-to-exhaustion probably determined the lack of significance in the TTE at 110% of the MAS [27]. This result is not an unicum in the body of literature as a similar one was found in a submaximal exercise in elite synchronized swimmers [33]. Klimek and col- leagues have also shown an improvement in anaerobic capacity after 10 whole-body cryosti- mulation sessions, principally explained by metabolic changes (i.e. increased activity of anaerobic glycolytic enzymes) and a greater tolerance to pain, highlighted by an increase in blood lactate concentration [34]. Reduction in pain could be easily linked to a decrease in perception of effort, a master regu- lator of performance in endurance exercises [35]. Of note, perception of effort is determinant in a TTE test, as it has been shown that maximum values of perception of effort lead to an early stop to exercise, despite athletes could continue both muscularly and metabolically [36]. Physiologically, the reduction of perception of effort could be explained by the modification of peripheral afferent signals generated by PBC, that could play a crucial role in changing the perception of effort during exercise at low- and high-intensity [37]. In fact, together with neu- ral drive from the motor cortex area, previous studies showed that RPE also is influenced by afferent feedback from the periphery for the prediction of the endpoint of the exercise bout [37, 38]. No other changes were observed in the other physiological and psychological parameters. Our results are in line with previous studies that have shown no change in performance, recov- ery, soreness perception parameters [39, 40], and with strategies that altered the perception of effort [41, 42]. Moreover, the reduction of RPE at the same timepoints in the 3000-m tests PLOS ONE Precooling in endurance runners PLOS ONE \| https://doi.org/10.1371/journal.pone.0288700 November 22, 2023 8 / 12 implies that the RPE:VO2 and RPE:HR ratios are reduced in both tests in the PBC condition compared to the control one. This reduction denotes an indirect change in the VO2 and HR parameters, that would have been higher for the same RPE. This influence of PBC on RPE indicates a strong impact of cryostimulation on fatigue, also shown by the lack of significance shown by the VAS relative to muscle pain. We must certainly highlight how the great variability of the measures is a factor to consider in the interpretation of the results. It is known that the coefficient of variation of the TTE is large. Despite constant-work test has a much lower coefficient of variation, with amateur ath- letes we must also consider the variability generated by pacing behavior. Hinckson and Hop- kins [43], using the relationship between exercise duration and power output showed that the reliability of the equivalent mean power calculated from the constant-power test (0.6%) is even better than from the constant-work test (1.0%). In addition, Laursen and colleagues [44] reported that a small random variation in performance results in large variation in time to exhaustion, which causes large value of coefficient of variation. On the other hand, the small change after an intervention also leads to a large change in the time to exhaustion, therefore the signal-to-noise ratio of the constant-power test should be comparable to the constant-work test. In addition, participants were familiar with the experimental procedures, but especially in the TTE, the nature of the performance was far from the performance habits of the participants. An important limitation of the present study is the lack of cardiovascular and physiological measures that compared the effects of the exposure to the PBC itself. Defining cardiovascular changes will be crucial for the assessment of the efficiency of PBC in sport. Since the implementation of cryostimulation prior to competition is relatively recent [45], there is a lack of comparability among methodologies, in terms of cryostimulation modalities, exposure parameters, and types of exercise. Besides, future investigations should evaluate whether the exercise-induced inflammatory response is preserved in warmer conditions, and whether precooling effects are likewise effective with higher temperatures. Moreover, since we included amateur master athletes, future studies should aim to verify if the results of the pres- ent study could be extended to other population of athletes (e.g. young, elite, etc.). In addition, the 48-h TQR values could indicate an inadequate recovery for participants who underwent the subsequent visit after 48 h. However, most of them completed each visit with at least 72 h of recovery in between. Practical applications This study results advocate for the use of cryostimulation before anerobic activities. Beyond the recovery benefits reported in the literature, cryostimulation may boost performance in the pre-execution phase. However, it is essential to evaluate the best timeframe to maximize the potential enhancing-performance effects. Cryostimulation may be also reducing the percep- tion of effort and this effect could be crucial for coaches to modulate the training prescription of their athletes. Considering this result, athletes could potentially increase their external train- ing load given the lower internal/perceptual load for the same external load. In fact, reducing RPE for the same external load could lead to a higher number of repetitions during an interval training session or a higher external intensity for each repetition. Conclusions A single PBC session may represent a favorable set-up routine before running, improving mid- dle-distance running performance and reducing RPE for the same external effort also in mod- erate-temperature conditions. Future studies should investigate the optimal integration of PLOS ONE Precooling in endurance runners PLOS ONE \| https://doi.org/10.1371/journal.pone.0288700 November 22, 2023 9 / 12 PBC with the traditional elements of active of pre-exercise routines, so to maximize middle- distance performance. Supporting information S1 Data. (XLSX) Acknowledgments The authors acknowledge support from the University of Milan through the APC initiative. Author Contributions Conceptualization: Massimo De Nardi, Luca Filipas, Carlo Facheris, Stefano Righetti, Marco Tengattini, Emanuela Faelli, Ambra Bisio, Antonio La Torre, Piero Ruggeri, Roberto Codella. Data curation: Massimo De Nardi, Carlo Facheris, Stefano Righetti, Marco Tengattini. Formal analysis: Massimo De Nardi, Carlo Facheris, Marco Tengattini. Investigation: Massimo De Nardi, Carlo Facheris, Stefano Righetti, Marco Tengattini. Methodology: Massimo De Nardi, Luca Filipas, Ambra Bisio, Roberto Codella. Project administration: Massimo De Nardi, Luca Filipas, Emanuela Faelli, Gabriele Gallo, Piero Ruggeri, Roberto Codella. Supervision: Luca Filipas, Emanuela Faelli, Ambra Bisio, Antonio La Torre, Piero Ruggeri, Roberto Codella. Validation: Massimo De Nardi, Luca Filipas, Stefano Righetti, Gabriele Gallo, Antonio La Torre, Piero Ruggeri, Roberto Codella. 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Med Sci Sports Exerc. 2005; 37(4):696–701. https://doi.org/10.1249/01.mss.0000159023. 06934.53 PMID: 15809572 44. Laursen PB, Francis GT, Abbiss CR, Newton MJ, Nosaka K. Reliability of time-to-exhaustion versus time-trial running tests in runners. Med Sci Sports Exerc. 2007; 39(8):1374–9. https://doi.org/10.1249/ mss.0b013e31806010f5 PMID: 17762371 45. Partridge EM, Cooke J, McKune A, Pyne DB. Whole-body cryotherapy: Potential to enhance athlete preparation for competition? Front Physiol. 2019. https://doi.org/10.3389/fphys.2019.01007 PMID: 31447697 PLOS ONE Precooling in endurance runners PLOS ONE \| https://doi.org/10.1371/journal.pone.0288700 November 22, 2023 12 / 12	Partial-body cryostimulation procured performance and perceptual improvements in amateur middle-distance runners.	11-22-2023	De Nardi, Massimo,Filipas, Luca,Facheris, Carlo,Righetti, Stefano,Tengattini, Marco,Faelli, Emanuela,Bisio, Ambra,Gallo, Gabriele,La Torre, Antonio,Ruggeri, Piero,Codella, Roberto	eng
PMC8914642	Citation: Chahal, A.K.; Lim, J.Z.; Pan, J.-W.; Kong, P.W. Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running. Sensors 2022, 22, 1888. https://doi.org/10.3390/s22051888 Academic Editor: Mario Munoz-Organero Received: 19 January 2022 Accepted: 25 February 2022 Published: 28 February 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sensors Article Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running Amandeep Kaur Chahal, Jolene Ziyuan Lim, Jing-Wen Pan and Pui Wah Kong * Physical Education and Sports Science Academic Group, National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore; amandeep001@e.ntu.edu.sg (A.K.C.); nie20.lzj@e.ntu.edu.sg (J.Z.L.); nie173748@e.ntu.edu.sg (J.-W.P.) * Correspondence: puiwah.kong@nie.edu.sg Abstract: The present study aimed to investigate the inter-unit consistency and validity of multiple 10-Hz Catapult Global Navigation Satellite System (GNSS) units in measuring straight-line sprint distances and speeds. A total of 13 participants performed one 45.72-m linear sprint at maximum effort while wearing all eight GNSS units at once. Total run distance and peak speed recorded using GNSS units during the sprint duration were extracted for analysis. Sprint time and peak speed were also obtained from video recordings as reference values. Inter-unit consistency was assessed using intraclass correlation coefﬁcients (ICC) and standard errors of measurements (SEM). For a validity test, one-sample t-tests were performed to compare each GNSS unit’s distance with the known distance. Additionally, Wilcoxon signed-rank tests were performed to compare each unit’s peak speed with the reference peak speed measured using video analysis. Results showed poor inter-unit consistency for both distance (ICC = 0.131; SEM = 8.8 m) and speed (ICC = 0.323; SEM 1.3 m/s) measurements. For validity, most units recorded a total distance (44.50 m to 52.69 m) greater than the known distance of 45.72 m and a lower peak speed (7.25 (0.51) m/s) than the video-based reference values (7.78 (0.90) m/s). The present ﬁndings demonstrate that there exist variations in distance and speed measurements among different units of the same GNSS system during straight-line sprint running. Practitioners should be aware of the window of errors associated with GNSS measurements and interpret the results with caution. When making comparisons over a season, players should wear the same unit every time if logistically possible. Keywords: Global Navigation Satellite System; reliability; distance; speed; video; movement analysis 1. Introduction In sports, the movement characteristics of players during competitions and training are of interest for in-game analyses. Traditionally, player activity data were manually collected on pen and paper, which was extremely labor-intensive and time-consuming [1,2]. With technological advancements in time-motion analysis, more convenient methods, such as video analysis, have been regularly used to track player movements during competitions and training. However, video analysis can be troublesome to set up and requires extensive manual analysis after data collection. The development of Global Navigation Satellite System (GNSS) and Global Positioning System (GPS) units, which are light, small, and portable, allows for simultaneous movement patterns analyses of multiple players [3,4]. Since then, the use of GNSS/GPS for athlete tracking has become widespread in various sports, such as soccer, rugby, and ﬁeld hockey [3,5–7] due to the ease of data collection and quality of analysis provided by these systems [8–11]. Additionally, GNSS/GPS units are the conventional technology used for the assessment of external training load variables in team sports due to their ability to give real-time feedback. This is essential, given the limited amount of time to process data and carry out post-session analysis [3,12,13]. GPS is a navigation system based on connections to satellites that allows locations of users to be triangulated through signals sent out by the satellites and received by the Sensors 2022, 22, 1888. https://doi.org/10.3390/s22051888 https://www.mdpi.com/journal/sensors Sensors 2022, 22, 1888 2 of 10 units [14]. The accuracy of data is dependent on the conﬁguration of the satellites in relation to the receiver and how evenly spaced they are, known as the dilution of precision (DOP). Triangulation of position is the most accurate when one satellite is directly overhead the receiver while the rest of the satellites are evenly spaced around the horizon (DOP = 1). It has generally been suggested that GPS units require at least 4 satellites for data to be considered accurate. In addition, satellites that are more evenly spaced are considered stronger than when satellites are close together [14]. GPS units are usually combined with microsensors such as accelerometers that are capable of recording movements in three planes, allowing the intensity of body load (also known as player load in some systems) to be measured. In addition, the inclusion of gyroscopes and magnetometers in these units allows for directional orientation and rotational velocity to also be measured [15]. Sampling rates of GPS units may range from 1 to 15 Hz, indicating the multiple speeds at which the GPS units collect data. Existing studies have shown that higher sampling rates increase the accuracy of performance indicators [16–18] recorded by the GPS units. For instance, 10-Hz GPS units are more accurate than those of lower sampling rates in measuring total distance covered during both linear activities and sport-speciﬁc circuits and measuring peak speed [19]. No additional accuracy has been found between 10-Hz and 15-Hz GPS units [19], indicating that a sampling rate of 10 Hz could be sufﬁcient. GNSS/GPS units provide a multitude of movement variables including distance, speed, acceleration/deceleration, and metabolic power [3,20]. These movements can be purposefully analyzed (external training load) to comprehend the positional demands in sports, allowing practitioners to design programs that accurately emulate and equip athletes for their speciﬁc sport [3]. Furthermore, the GNSS/GPS data have also been proven useful in aiding practitioners to understand physiological and technical demands of their players through the extraction of various external training load measures such as volume, intensity, and frequency [21]. Such information can inform and guide coaches and sport scientists to develop appropriate conditioning and recovery plans [22,23]. While GNSS/GPS units provide practical and useful feedback, environmental objects such as surrounding tall buildings [24], atmospheric pressure [25], as well as the level of satellite giving out the signals (with signals from lower satellites having to go through more atmosphere) can result in obstruction of signals, leading to lower signal-to-noise ratio and lower accuracy in measurements. Hence, it is important to establish the validity and reliability of these units before applying them in sports [4,16,26]. Testing validity provides an understanding of the differences between the measures recorded by the units and standard measures. Reliability testing, on the other hand, tests reproducibility of values when the same test is repeated by another unit. While studies have generally agreed that GNSS/GPS devices can be reliable in straight-line running, there is a sizable inconsistency in accuracy among the models of GNSS/GPS manufacturers [12,18,27]. Imparting the validation of one system to another can be imprecise even if it is introduced by the same manufacturer [28]. While Johnston and co-workers [29] used a different software to collect and analyze GPS data collected by other brands of GPS units, the authors cautioned that the mismatching in GPS models may have inﬂuenced the movement demand data. Hence, it is vital to carry out an independent and thorough trial for each new GNSS/GPS device (hardware) and its analysis tool (software). Within the same system, high consistency between different GNSS/GPS units is critical especially in team sports whereby each player wears an independent unit. Previous studies examining the inter-unit reliability of GPS units have placed multiple units on solid objects such as a golf cart and motorcycle [26], plastic sled [30], and a trundle wheel [31]. It should be noted that the movement trajectories of solid objects may differ from those of the human players who can freely move individual body segments in different directions at various magnitudes. There are very few studies placing GPS units on human participants and these studies typically compared among only two to four units each time [27,32]. To the best of the authors’ knowledge, only one study has tested the inter-unit reliability of eight GPS units on an individual [33]. Although their study found inter- and intra-receiver reliability Sensors 2022, 22, 1888 3 of 10 to be acceptable, the GPS units were sampled at 1 Hz which is far below the recommended frequency of 10 Hz for accurate measurements [19]. Thus, there is a need to examine the inter-unit consistency and validity of multiple GNSS/GPS units sampled at sufﬁciently high frequency (i.e., at least 10 Hz) with the units placed on human participants and not solid objects. This study, therefore, aimed to investigate the inter-unit consistency and validity of 10-Hz Catapult GNSS (S5 OptimEye, Catapult Innovations, Melbourne, Australia) units during straight-line sprint running. Eight GNSS units were analyzed using the Sprint software developed by Catapult. It was hypothesized that all GNSS units, when placed on human participants, would be consistent and accurate in measuring distances and peak speeds during sprint running [18]. 2. Materials and Methods 2.1. Participants This study was approved by the Nanyang Technological University Institutional Review Board (IRB-2020-09-033). Thirteen active participants (4 males, 9 females) were recruited via convenient sampling [age 21.6 (1.6) years, height 170.6 (7.7) cm, body mass 63.1 (10.1) kg]. To be eligible for this study, participants must have been training with a sports team at least twice a week and had minimally a year of experience in the speciﬁed sport. Additionally, they were required to be injury-free and pain-free at the time of the study. 2.2. Equipment The Catapult S5 OptimEye GNSS system was used in the present study. Accessing GPS and Global Navigation Satellite System (GLONASS) satellite constellations, this GNSS system ensures high-quality data even in challenging performance environments (https://www.catapultsports.com (accessed on 1 October 2020)). Eight 10-Hz GNSS units were worn at once on each participant during the test (Figure 1). The eight GNSS units were placed near the mid-back area using a custom-made strap, with slightly different positions. While the tightness of strap was adjusted to fit individual body sizes, the relative positions of the GNSS units on the strap remained consistent across all participants. p y y y g different directions at various magnitudes. There are very few studies placing GPS units on human participants and these studies typically compared among only two to four units each time [27,32]. To the best of the authors’ knowledge, only one study has tested the inter-unit reliability of eight GPS units on an individual [33]. Although their study found inter- and intra-receiver reliability to be acceptable, the GPS units were sampled at 1 Hz which is far below the recommended frequency of 10 Hz for accurate measurements [19]. Thus, there is a need to examine the inter-unit consistency and validity of multiple GNSS/GPS units sampled at sufficiently high frequency (i.e., at least 10 Hz) with the units placed on human participants and not solid objects. This study, therefore, aimed to investigate the inter-unit consistency and validity of 10-Hz Catapult GNSS (S5 OptimEye, Catapult Innovations, Melbourne, Australia) units during straight-line sprint running. Eight GNSS units were analyzed using the Sprint software developed by Catapult. It was hypothesized that all GNSS units, when placed on human participants, would be consistent and accurate in measuring distances and peak speeds during sprint running [18]. 2. Materials and Methods 2.1. Participants This study was approved by the Nanyang Technological University Institutional Review Board (IRB-2020-09-033). Thirteen active participants (4 males, 9 females) were recruited via convenient sampling [age 21.6 (1.6) years, height 170.6 (7.7) cm, body mass 63.1 (10.1) kg]. To be eligible for this study, participants must have been training with a sports team at least twice a week and had minimally a year of experience in the specified sport. Additionally, they were required to be injury-free and pain-free at the time of the study. 2.2. Equipment The Catapult S5 OptimEye GNSS system was used in the present study. Accessing GPS and Global Navigation Satellite System (GLONASS) satellite constellations, this GNSS system ensures high-quality data even in challenging performance environments (https://www.catapultsports.com (accessed on 1 October 2020)). Eight 10-Hz GNSS units were worn at once on each participant during the test (Figure 1). The eight GNSS units were placed near the mid-back area using a custom-made strap, with slightly different positions. While the tightness of strap was adjusted to fit individual body sizes, the relative positions of the GNSS units on the strap remained consistent across all participants. Figure 1. Each participant wore eight GNSS units at once in the maximal sprint test. Figure 1. Each participant wore eight GNSS units at once in the maximal sprint test. 2.3. Experimental Protocol This experiment involved one single visit to the ﬁeld hockey pitch at the National Institute of Education, Nanyang Technological University, Singapore. GNSS data were collected outdoors without high surrounding buildings to enhance satellite reception [34]. After sufﬁcient warm-up and putting on the strap with 8 GNSS units, participants were asked to perform one straight-line sprint across the hockey pitch with maximum effort (Figure 2). A sprint distance of 45.72 m was chosen as it is exactly half of the hockey pitch Sensors 2022, 22, 1888 4 of 10 with a clear marked line. This distance also allowed sufﬁcient time for participants to reach their peak speed. To help participants discern the start and end points, the sprint area was marked out using colored cones. Participants were asked to run pass the 45.72 m end-line before they could slow down. The sprint test was recorded using two video cameras at 60 Hz for subsequent determination of sprint times and peak speeds. To minimize the effect of camera lens distortion, we used two smartphone cameras to cover the entire 45.72 m range (Figure 2). The midline distance of 22.86 m was used to calibrate each camera (0 to 22.86 m, 22.86 to 45.72 m) as the midline can be clearly seen from both camera views. This experiment involved one single visit to the field hockey pitch at the National Institute of Education, Nanyang Technological University, Singapore. GNSS data were collected outdoors without high surrounding buildings to enhance satellite reception [34]. After sufficient warm-up and putting on the strap with 8 GNSS units, participants were asked to perform one straight-line sprint across the hockey pitch with maximum effort (Figure 2). A sprint distance of 45.72 m was chosen as it is exactly half of the hockey pitch with a clear marked line. This distance also allowed sufficient time for participants to reach their peak speed. To help participants discern the start and end points, the sprint area was marked out using colored cones. Participants were asked to run pass the 45.72 m end-line before they could slow down. The sprint test was recorded using two video cameras at 60 Hz for subsequent determination of sprint times and peak speeds. To minimize the effect of camera lens distortion, we used two smartphone cameras to cover the entire 45.72 m range (Figure 2). The midline distance of 22.86 m was used to calibrate each camera (0 to 22.86 m, 22.86 to 45.72 m) as the midline can be clearly seen from both camera views. Figure 2. Experimental set-up of the sprint test over half a field hockey pitch (45.72 m) with two smartphone cameras recording the performances (Camera 1: 0 to 22.86 m, Camera 2: 22.86 m to 45.72 m). GNSS units were switched on at least 5 min before the units were strapped on the participants. After strapping on all units, participants were verbally briefed and then asked to familiarize themselves with the task. The GNSS units were switched on for more than 15 min to receive the complete almanac before the commencement of the test. Participants were also instructed to stay still for 30 s, before the start of the sprint. This was to enable subsequent determination of the start time for each trial when the speed increased sharply from zero. 2.4. Data Processing The GNSS movement data were downloaded using the manufacturer’s software (Catapult Sprint Version 5.1.7, Melbourne, Australia) at the default ‘GPS rate’ of 10 Hz. Customized MATLAB codes were written to extract the relevant distance and speed time- series data using MATLAB (R2021a, MathWorks, Natick, MA, USA). The start of the sprint was identified from a sharp and continuous increase in speed above a threshold of 0.5 m/s. The duration each participant took to complete the 45.72 m distance was obtained based on the video recordings of the sprint. This sprint duration was then used to determine the end time of the sprint in the GNSS data. From the start to the end of the sprint, total distance traveled, and peak speeds were obtained from each of the 8 GNSS Figure 2. Experimental set-up of the sprint test over half a field hockey pitch (45.72 m) with two smartphone cameras recording the performances (Camera 1: 0 to 22.86 m, Camera 2: 22.86 m to 45.72 m). GNSS units were switched on at least 5 min before the units were strapped on the participants. After strapping on all units, participants were verbally briefed and then asked to familiarize themselves with the task. The GNSS units were switched on for more than 15 min to receive the complete almanac before the commencement of the test. Participants were also instructed to stay still for 30 s, before the start of the sprint. This was to enable subsequent determination of the start time for each trial when the speed increased sharply from zero. 2.4. Data Processing The GNSS movement data were downloaded using the manufacturer’s software (Catapult Sprint Version 5.1.7, Melbourne, Australia) at the default ‘GPS rate’ of 10 Hz. Customized MATLAB codes were written to extract the relevant distance and speed time- series data using MATLAB (R2021a, MathWorks, Natick, MA, USA). The start of the sprint was identiﬁed from a sharp and continuous increase in speed above a threshold of 0.5 m/s. The duration each participant took to complete the 45.72 m distance was obtained based on the video recordings of the sprint. This sprint duration was then used to determine the end time of the sprint in the GNSS data. From the start to the end of the sprint, total distance traveled, and peak speeds were obtained from each of the 8 GNSS units. Raw GNSS data were used without further down sampling, ﬁltering, or smoothing procedures. Due to transmission and technical errors, it was not possible to obtain complete data sets from all 8 GNSS units throughout all trials. Among the 13 participants, 7 had complete data set and 6 had missing data from either 1 or 2 GNSS units. For validity analysis, a reference value of the gold standard was needed. In the present study, the total distance was 45.72 m, which was the known size of half of a standard ﬁeld hockey pitch. This distance was also conﬁrmed by experimental measurement using a trundle wheel. To calculate the speed from position data, manual digitization of the player’s center of the head was performed through the sprint duration using the software Kinovea (version 0.9.3, Kinovea, Bordeaux, France, available for download at: http:// Sensors 2022, 22, 1888 5 of 10 www.kinovea.org (accessed on 15 April 2021)). The present study used video analysis as the gold standard for kinematics, which is aligned with previous work evaluating the accuracy of 10 Hz GPS system [12]. Kinovea has been demonstrated as a reliable and accurate tool for video-based angular and linear measurements via digitization of x- and y-axis coordinates [35]. While an optimal angle of 90◦ was recommended, an accepted level accuracy was also established when the camera was placed within an angle range of 45◦ to 90◦ [35]. Figure 3 illustrates examples of the speed-time data measured using one GNSS unit and video analysis. The raw speed data from videos were low-passed filter at 10 Hz to remove the noise associated with manual digitization. The peak value of the filtered speed data during the entire sprint duration was then identified. This video-based peak speed was used as a reference value in the subsequent validity analysis of GNSS units. The mean (SD) of the raw and filtered peak speeds were 7.82 (0.81) m/s and 7.78 (0.90) m/s, respectively. present study, the total distance was 45.72 m, which was the known size of half of a standard field hockey pitch. This distance was also confirmed by experimental measurement using a trundle wheel. To calculate the speed from position data, manual digitization of the player’s center of the head was performed through the sprint duration using the software Kinovea (version 0.9.3, Kinovea, Bordeaux, France, available for download at: http://www.kinovea.org (accessed on 15 April 2021)). The present study used video analysis as the gold standard for kinematics, which is aligned with previous work evaluating the accuracy of 10 Hz GPS system [12]. Kinovea has been demonstrated as a reliable and accurate tool for video-based angular and linear measurements via digitization of x- and y-axis coordinates [35]. While an optimal angle of 90° was recommended, an accepted level accuracy was also established when the camera was placed within an angle range of 45° to 90° [35]. Figure 3 illustrates examples of the speed-time data measured using one GNSS unit and video analysis. The raw speed data from videos were low-passed filter at 10 Hz to remove the noise associated with manual digitization. The peak value of the filtered speed data during the entire sprint duration was then identified. This video-based peak speed was used as a reference value in the subsequent validity analysis of GNSS units. The mean (SD) of the raw and filtered peak speeds were 7.82 (0.81) m/s and 7.78 (0.90) m/s, respectively. Figure 3. Representative raw speed-time from one participant measured using Kinovea video analysis and one GNSS unit. 2.5. Statistical Analyses Statistical analyses were carried out on JASP (version 0.14.1, JASP Team 2020) and SPSS (version 26.0, IBM Corp., Armonk, NY, USA). Data are expressed as mean (standard deviation). An alpha level of p < 0.05 was set as the level of significance. Inter-unit consistency was assessed using intraclass correlation coefficients (ICC). ICC was interpreted as slight (<0.20), fair (0.21–0.40), moderate (0.41–60), substantial (0.61–0.80), or Figure 3. Representative raw speed-time from one participant measured using Kinovea video analysis and one GNSS unit. 2.5. Statistical Analyses Statistical analyses were carried out on JASP (version 0.14.1, JASP Team 2020) and SPSS (version 26.0, IBM Corp., Armonk, NY, USA). Data are expressed as mean (standard deviation). An alpha level of p < 0.05 was set as the level of signiﬁcance. Inter-unit consistency was assessed using intraclass correlation coefﬁcients (ICC). ICC was interpreted as slight (<0.20), fair (0.21–0.40), moderate (0.41–60), substantial (0.61–0.80), or almost perfect reliability (>0.80) [36,37]. Standard error of measurement (SEM) was calculated from the ICC results using the formula: SEM = SD × √(1 − ICC). For the validity assessment, one-sample t-tests were performed to compare the distance measured using each GNSS unit with the known distance of 45.72 m. Effect sizes were indicated by Cohen’s d and interpreted as small (0.2 ≤ d < 0.5), medium (0.5 ≤ d < 0.8), or large (d ≥ 0.8). Since the speed data were not normally distributed, non-parametric statistical tests were employed. Specially, Wilcoxon signed-rank tests were used to compare each GNSS unit’s peak speed with the reference speed measured using video analysis. Effect size (r) for the Wilcoxon signed-rank tests was calculated from the Z-value and interpreted as small (0.1 ≤ \|r\| < 0.3), medium (0.3 ≤ \|r\| < 0.5), or large (\|r\| ≥ 0.5). Sensors 2022, 22, 1888 6 of 10 3. Results 3.1. Inter-Unit Consistency The results of ICC analysis showed slight reliability for the total sprint distance and fair reliability for peak speed (Table 1). These results indicate that the 8 tested GNSS units are not sufﬁciently consistent among themselves. Table 1. Reliability statistical outputs to assess inter-unit consistency. GNSS Variables ICC 95% Conﬁdence Intervals SEM Total distance 0.131 [−0.024, 0.556] 8.8 m Peak speed 0.323 [0.101, 0.736] 1.3 m/s Note. ICC denotes intraclass correlation coefﬁcients; SEM denotes standard error of measurement. 3.2. Validity Most GNSS units recorded a total distance greater than the known distance of 45.72 m (Table 2). While statistical signiﬁcance was only found in two units, the effect sizes of the differences were large across all units. These results indicate that GNSS units, although belonging to the same system, do not always measure distance with the same degree of accuracy. Table 2. Validity of GNSS distance measurements against known distance of 45.72 m. GNSS Units Mean (SD) p-Value Effect Size (d) Unit 1 (n = 13) 49.77 (5.92) 0.030 * 8.41 Large Unit 2 (n = 13) 46.69 (10.62) 0.747 4.49 Large Unit 3 (n = 10) 44.50 (8.55) 0.663 5.29 Large Unit 4 (n = 13) 52.23 (10.11) 0.039 * 5.17 Large Unit 5 (n = 11) 52.00 (10.13) 0.067 5.13 Large Unit 6 (n = 12) 50.83 (8.57) 0.063 5.93 Large Unit 7 (n = 12) 47.50 (8.06) 0.460 5.89 Large Unit 8 (n = 13) 52.69 (12.18) 0.061 4.33 Large Note. * Signiﬁcant differences detected using one-sample t-tests (p < 0.05). Compared with the reference speed data obtained from video analysis, Unit 4 mea- sured signiﬁcantly higher peak speed (p = 0.010, large effect size, Table 3). No signiﬁcant differences were identiﬁed between other GNSS units and video analysis, with data of 4 units approaching statistical signiﬁcance (Units 1, 3, 7, 8). In general, most GNSS units measured a lower peak speed (7.25 (0.51) m/s) than the video-based value (7.78 (0.90) m/s) and the effect sizes of the differences were medium to large. Table 3. Validity of GNSS peak speed measurements against video analysis. GNSS Units Mean (SD) p-Value Effect Size (r) Unit 1 (n = 13) 7.04 (1.15) 0.057 0.604 Large Unit 2 (n = 13) 6.89 (1.98) 0.127 0.495 Medium Unit 3 (n = 10) 6.92 (1.03) 0.064 0.673 Large Unit 4 (n = 13) 7.11 (0.89) 0.010 * 0.780 Large Unit 5 (n = 11) 7.37 (1.55) 0.416 0.303 Medium Unit 6 (n = 12) 8.40 (2.53) 0.970 0.026 Negligible Unit 7 (n = 12) 6.86 (1.34) 0.064 0.615 Large Unit 8 (n = 13) 7.37 (1.05) 0.057 0.604 Large Note. * Signiﬁcant differences detected using Wilcoxon signed-rank tests (p < 0.05). Group mean (SD) of video- based peak speed was 7.78 (0.90) m/s. 4. Discussion The aim of the study was to investigate the inter-unit reliability and validity of multiple 10-Hz Catapult GNSS units during straight-line sprint running. Inter-unit consistency was Sensors 2022, 22, 1888 7 of 10 assessed among eight GNSS units worn on each participant, and validity was tested by comparing total distance and peak speed against criterion-referenced values. The most prevailing outcomes were that despite all GNSS units belonging to the same system, low inter-unit reliability and varied accuracies in distance and speed measurements were found during fast speed running. 4.1. Distance We originally expect that all GNSS units, when placed on the participant, would be consistent and accurate in measuring total distance traveled during 45.72-m sprint. However, there was only slight reliability for inter-unit consistency among the eight GNSS units and two out of eight units (Units 1 and 4, Table 2) had signiﬁcantly different values from the criterion distance. In addition, seven out of the eight GNSS units overestimated the values during the straight-line sprint. These results in the present study are somewhat in congruence with previous research which reported moderate errors when measuring total distance over very high-speed running (>5.56 m/s) [17]. Additionally, overestimation of the total distance measured using GNSS units has also been found when the sprinting distances were set as 15 m and 30 m [34,35]. The reliability and accuracy may also be affected by rapid changes in speed during the acceleration phase of the sprint. A previous study revealed that distance measures over the post-acceleration phase of 20–40 m were more accurate than the acceleration phase of 0–20 m in a 40-m linear acceleration run [16], suggesting that smaller variations in speed may facilitate more accurate measures in distance. In the present study, participants started from a stationary position and were asked to sprint as fast as they could using maximal effort. Hence, phases with great variations in speed could have resulted in inconsistent and less accurate total distances measurement across different units. It is also possible that some participants did not sprint in a perfectly straight line hence covering a longer distance than the reference value of 45.72 m. Although the deviation from a straight line can be expected to be quite small, this could partly explain why seven out of eight GNSS units recorded a longer total distance than the reference value based on the distance between standard marked lines on the ﬁeld. Finally, the GNSS units could miss data owing to the poor satellite connection [19]. This may have caused measurement errors in certain GNSS units, leading to inconsistency among the different units. 4.2. Peak Speed This study hypothesized that multiple GNSS units of the same system would be consistent and accurate in measuring peak speed during a maximal effort sprint. The results demonstrated fair reliability among the eight GNSS units and that seven out of the eight units generally measured lower peak speeds than that video-based reference values (Table 3). The results are not in line with previous ﬁndings which suggested conﬁdence in 10-Hz GNSS units being able to accurately measure consistent speeds and velocities [18]. The discrepancy in the peak speeds measured can be attributed to the compromises when measuring instantaneous velocities during great decelerations [38] and accelerations [28]. Hence, rapid changes in speed during the acceleration phase of the 45.72-m sprint in the present study could affect the accuracy of the GNSS units when measuring peak speeds. Higher accuracy and inter-unit reliability may be expected if GNSS units are applied to measure speed during a stable phase with small decelerations or accelerations. Compared with the video analysis which was used as the golden standard for the speed measurement, only one GNSS unit displayed statistically signiﬁcant result (Unit 4, Table 3). It is worth noting that the effect sizes of the differences were medium to large across all units regardless of statistical signiﬁcance. As the GNSS units tend to register lower peak speeds (7.25 (0.51) m/s) than video-based reference values (7.78 (0.90) m/s), such differences cannot be disregarded. Sport practitioners should keep in mind that GNSS readings may slightly underestimate peak speeds during high-speed running and interpret the results with consideration of the error window (SEM = 1.3 m/s). Sensors 2022, 22, 1888 8 of 10 4.3. Limitations There were a few limitations to the current study. Firstly, six participants had miss- ing data due to either faulty units or the poor connection to the satellites. The current sample size of 13 participants was smaller than expected since the experiment was halted prematurely due to the COVID-19 pandemic. A larger sample size may have brought about more reliable results, which was unfortunately not possible due to time constraints. Secondly, environmental factors (e.g., presence of clouds) during the experiment may have occurred and affected the results. Thirdly, we acknowledge that the use of smartphone cameras can reduce the accuracy of data collected due to optical effects, such as lens dis- tortion and parallax error. For fast sprint movements, the relatively low frame rate of 60 Hz could have also compromised accuracy of speed and time data collected. Lastly, the current study investigated only two variables of linear sprints (total distance and peak speed). In the future, researchers should expand to other variables and movement types concerning the utilizations of GNSS units in sports such as change in direction, acceleration, and deceleration. 5. Conclusions In team sports, high consistency between different GNSS units is critical as coaches compare the movement characteristics across players in a game or training. This study revealed that there exist variations in distance and speed measurements among eight GNSS units worn by participants at the same time. In general, GNSS units may lead to an overestimation of total distance and underestimation of peak speed during high-speed sprint running. Practitioners should be aware of the window of errors associated with GNSS measurements and interpret the results with caution. This is especially important for data collected during sport competitions or training which involve movement demands at high speeds. When making comparisons over a season, players should wear the same GNSS unit every time if logistically possible. Despite some limitations, the use of GNSS/GPS technology is still widespread, and it offers practical insights to players’ movements characteristics and playing demands. In view of the rapid advancement in technology, it may be possible to improve current GNSS/GPS systems so as to enhance their inter-unit consistency and measurement accura- cies across different movement types including high-speed sprinting. Author Contributions: Conceptualization, J.Z.L. and P.W.K.; methodology, J.Z.L., A.K.C. and P.W.K.; validation, A.K.C.; formal analysis, A.K.C., J.-W.P. and P.W.K.; writing—original draft preparation, A.K.C., J.Z.L. and J.-W.P.; writing—review and editing, A.K.C., J.Z.L., J.-W.P. and P.W.K.; supervision, P.W.K. All authors have read and agreed to the published version of the manuscript. Funding: We wish to acknowledge the funding support for this project from Nanyang Technological University under the URECA Undergraduate Research Programme. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Nanyang Technological University Institutional Review Board (IRB-2020-09-033). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Acknowledgments: The authors would like to acknowledge all participants for taking their time to participate in this study. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Roberts, S.; Trewartha, G.; Stokes, K. A Comparison of Time–Motion Analysis Methods for Field-Based Sports. Int. J. Sports Physiol. Perform. 2006, 1, 388–399. [CrossRef] 2. Carling, C.; Bloomﬁeld, J.; Nelsen, L.; Reilly, T. The Role of Motion Analysis in Elite Soccer. Sports Med. 2008, 38, 839–862. 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PMC7557486	Vol.:(0123456789) 1 3 European Journal of Applied Physiology (2020) 120:2507–2515 https://doi.org/10.1007/s00421-020-04474-7 ORIGINAL ARTICLE Biomechanical and metabolic aspects of backward (and forward) running on uphill gradients: another clue towards an almost inelastic rebound L. Rasica1 · S. Porcelli1,2 · A. E. Minetti3 · G. Pavei3 Received: 23 June 2020 / Accepted: 10 August 2020 / Published online: 25 August 2020 © The Author(s) 2020 Abstract Purpose On level, the metabolic cost (C) of backward running is higher than forward running probably due to a lower elastic energy recoil. On positive gradient, the ability to store and release elastic energy is impaired in forward running. We studied running on level and on gradient to test the hypothesis that the higher metabolic cost and lower efficiency in backward than forward running was due to the impairment in the elastic energy utilisation. Methods Eight subjects ran forward and backward on a treadmill on level and on gradient (from 0 to + 25%, with 5% step). The mechanical work, computed from kinematic data, C and efficiency (the ratio between total mechanical work and C) were calculated in each condition. Results Backward running C was higher than forward running at each condition (on average + 35%) and increased linearly with gradient. Total mechanical work was higher in forward running only at the steepest gradients, thus efficiency was lower in backward running at each gradient. Conclusion Efficiency decreased by increasing gradient in both running modalities highlighting the impairment in the elastic contribution on positive gradient. The lower efficiency values calculated in backward running in all conditions pointed out that backward running was performed with an almost inelastic rebound; thus, muscles performed most of the mechanical work with a high metabolic cost. These new backward running C data permit, by applying the recently introduced ‘equiva- lent slope’ concept for running acceleration, to obtain the predictive equation of metabolic power during level backward running acceleration. Keywords Backward acceleration · Efficiency · Mechanical work · Metabolic cost · Metabolic power Abbreviations C Metabolic cost CBA Metabolic cost of backward running acceleration BCoM Body centre of mass PE Potential energy of BCoM KE Kinetic energy of BCoM TE Total energy of BCoM WEXT Positive external work WINT Positive internal work WTOT Total work WEXT − Negative external work Introduction Backward running is commonly used in rehabilitation and as an injury prevention strategy (e.g. Soligard et al. 2008; Gilchrist et al. 2008; Heiderscheit et al. 2010; Rössler et al. 2016), thanks to the reduced knee joint forces and lower vertical peak of the ground reaction force compared with forward running (Flynn and Soutas-Little 1995; Sussman et al. 2000; Roos et al. 2012). Moreover, the reverse direc- tion of the movement gives the possibility to involve and Communicated by Jean-René Lacour. * G. Pavei gaspare.pavei@unimi.it 1 Institute of Biomedical Technologies, National Research Council, Segrate, Italy 2 Department of Molecular Medicine, University of Pavia, Pavia, Italy 3 Laboratory of Physiomechanics of Locomotion, Department of Pathophysiology and Transplantation, Physiology Division, University of Milan, Via Mangiagalli 32, 20133 Milan, Italy 2508 European Journal of Applied Physiology (2020) 120:2507–2515 1 3 train different muscles groups (DeVita and Stribling 1991; Flynn and Soutas-Little 1995; Sterzing et al. 2016); for a comprehensive review on backward running see Uthoff et al. (2018). An increasing number of backward running competi- tions have also been organised all over the world (also the RetroRunning world championship), with athletes training specifically backward for improving their performance. On level, the metabolic demand of backward running is higher than forward running (Reilly and Bowen 1984; Flynn et al. 1994; Wright and Weyand 2001) probably due to a higher muscle activation (Flynn and Soutas-Little 1993, 1995; Wright and Weyand 2001; Sterzing et al. 2016) and/ or a reduced elastic energy utilisation (Cavagna et al. 2011, 2012). This lower elastic contribution could be caused by the inverse approach of the foot on the ground that does not allow to store and recoil the energy from Achilles tendon or foot arch. Up to now, on level, no studies have analysed the mechanical work and metabolic cost of backward running concurrently so that conclusions about efficiency and elastic energy were inferred only indirectly. When moving on positive gradient, the energy saving mechanism of forward running is impaired (Minetti et al. 1994). When running uphill the downward trajectory of the body centre of mass is reduced and less energy can be stored in the elastic elements of the lower limbs, which decreases the overall running efficiency (Minetti et al. 1994). There are no studies on the metabolic aspects (or efficiency) of backward running on gradient yet. However, it has been shown that the difference in metabolic cost between forward and backward walking was 100% on level, and decreased to 5–8% at gradients steeper than + 15% (Minetti and Ardigò 2001) and this decrement was addressed to the impairment in the pendulum like motion while walking uphill. Based on this general knowledge, the analysis of mechan- ical and metabolic aspects of backward running on gradi- ent would test the hypothesis of the higher metabolic cost and the possible decreased efficiency in backward than for- ward running due to the impairment in the elastic energy utilisation. Materials and methods Subjects Eight male endurance runners (age: 25.6 ± 3.2 year, height: 1.76 ± 0.07 m, mass: 68.4 ± 6.6 kg, ̇V O2max: 65.7 ± 6.2 mlO2 kg−1 min−1; mean ± SD) took part in the study. Each sub- ject was fully informed about the aims, methods, and risks associated with participation and gave his written informed consent before the start of the study. All procedures were in accordance with the Declaration of Helsinki and the study was approved by the local ethics committee. Subjects undertook three familiarisation sessions with backward run- ning at all speeds and gradients to get used with balance and proprioception while moving backward. After familiarisa- tion, subjects came to the laboratory six times to complete the entire protocol. Experimental protocol Subjects visited the laboratory on six different not-con- secutive days. This protocol was designed to avoid any fatigue effect due to the high metabolic and neuromuscu- lar demand of each acquisition; the comparison between forward and backward running on the same subject was performed to avoid any mechanical or metabolic con- founding factors; a number of speeds were tested to check the metabolic cost behaviour. On day 1, subjects ran for- ward on level at 2.78 m s−1, on gradient + 5% at 2.5 m s−1 and + 10% at 2.22 m s−1, with 15 min of recovery among trials. On day 2, subjects ran forward on gradient + 15% at 1.94 m s−1 and + 20% at 1.67 m s−1, with 15 min of recovery between trials. On day 3, subjects ran backward on level at 1.67 m s−1, on gradient + 5% at 1.53 m s−1 and + 20% at 1.11 m s−1, with 15 min of recovery among trials. On day 4, subjects ran backward on gradient + 10% at 1.11 m s−1, 1.39 m s−1 and 1.67 m s−1, with 15 min of recovery among trials. On day 5, subjects ran backward on gradient + 15% at 1.11 m s−1, 1.25 m s−1, 1.39 m s−1 and 1.67 m s−1, with 15 min of recovery among trials. All acquisitions lasted 5 min. On day 6, kinematics data for all conditions were recorded (see below). The mechanical parameters (and efficiency) were compared between backward and forward running at each slope pairwise at these speeds: 1.67, 1.53, 1.39, 1.25, 1.11, 0.97 m s−1 for backward running and 2.78, 2.50, 2.22, 1.94, 1.67, 1.39 m s−1 for forward running at 0, + 5, + 10, + 15, + 20 and + 25% gradient, respectively. Metabolic measurements Each experimental session was preceded by an 8-min stand resting oxygen consumption ( ̇VO2, mlO2 kg−1 min−1) assessment after which subjects started running on the treadmill. Data acquisition lasted 5 min in order to reach a steady state ̇VO2. Pulmonary ventilation, oxygen con- sumption and carbon dioxide production were analysed breath by breath by a metabolic cart (Vmax229, Sensor- Medics, The Netherlands). The metabolic cost of running (C, J kg−1 m−1, Margaria et al. 1963) was calculated from the data collected during the last minute of exercise by dividing the measured net ̇VO2 (total – resting ̇VO2) by the running speed. The unit conversion from mlO2 to meta- bolic J was achieved by considering the mean respiratory exchange ratio ( ̇VCO2 ̇VO2 −1) for each acquisition. At rest and during recovery (3rd and 5th minute) 20 μL of 2509 European Journal of Applied Physiology (2020) 120:2507–2515 1 3 capillary blood was obtained from a preheated earlobe for the determination of blood lactate concentration ([La−]b) by an enzymatic method (Biosen 5030, EKF, Germany). Kinematics Three-dimensional (3D) body motion was collected by an 8-camera system (6 Vicon MX 1.3, 2 T20-S, Oxford Metrics, UK), by sampling at 100 Hz the spatial coordi- nates of 18 reflective markers located on the main joint centres (Minetti et al. 1993; Pavei et al. 2017), while the subject was running on a treadmill (Ergo LG Woodway, Germany). Marker positions were filtered through a ‘zero- lag’ second-order Butterworth low pass filter with a cutoff frequency detected by a residual analysis on each marker coordinate (Winter 1979). Each acquisition lasted 1 min and the time course of the 3D body centre of mass (BCoM) position was computed from an 11-segment model (Minetti et al. 1993; Pavei et al. 2017) based on Dempster inertial parameters of body segments (Winter 1979). From the BCoM 3D trajectory, the time course of potential (PE) and kinetic (KE) energies was computed to obtain the total mechanical energy (TE = PE + KE). The summation of all increases in TE time course constitutes the positive exter- nal work (WEXT, J kg−1 m−1), the work done to accelerate and lift the BCoM (Cavagna et al. 1963; Cavagna et al. 1976). The work necessary to rotate and accelerate limbs with respect to BCoM (WINT, J kg−1 m−1) (Cavagna and Kaneko 1977; Willems et al. 1995) was also calculated (according to Minetti et al. 1993) and summed to WEXT to obtain the total mechanical work (WTOT, J kg−1 m−1). The frictional component of WINT (Minetti et al 2020) was not included in the present calculation. The negative exter- nal work (WEXT −, J kg−1 m−1), the decreases in TE time course, was analysed as percentage of ‘comprehensive’ external mechanical work (= (WEXT) + (WEXT −)) in gradi- ent locomotion, as suggested by Minetti et al. (1994). The ratio between WTOT and C was used to estimate locomotion efficiency. Elastic energy contribution was estimated at each step as the difference between the mechanical equiva- lent of C and WTOT. C was converted into WTOT by multi- plying by an efficiency value of the positive work of 0.28 (Woledge et al. (1985) reported a range of 0.25–0.30 for positive work muscle efficiency), then the measured WTOT was subtracted from it. The result, multiplied by the pro- gression speed and divided by step frequency, provides an estimate of the elastic energy stored in a step. The elastic energy value of forward running on level was set to 1, and all the other conditions are reported as (sub)multiples. All data were analysed with custom-written Labview programs (release 10, National Instruments, USA). Statistics Data were presented as mean ± SD and compared between running conditions using paired t test; difference among speeds were compared using one-way ANOVA for repeated measures and Bonferroni post hoc test; significance level was set at p < 0.05. Statistical analyses were performed with SPSS version 20 (IBM). Results Metabolic cost Forward running C increased with slope and present data are comparable with Minetti et al. (2002) values (Fig. 1). Back- ward running C was significantly higher than forward run- ning at each slope (P < 0.01, Fig. 1) and speed independent at the analysed gradients. Backward running C (J kg−1 m−1) can be computed as a function of gradient (with same units as in Fig. 1) with the equation: C = 0.31gradient + 4.9 (R2 = 0.99). The difference between forward and backward running was almost constant among gradients 35 ± 7%. Biomechanical parameters The mechanical WEXT, WINT, and WTOT of backward run- ning in all gradient conditions are plotted as a function of speed in Fig. 2. WEXT was the major determinant of WTOT and decreased with speed, but increased with gradient. WINT was almost gradient independent due to the decrease Fig. 1 Metabolic cost (J kg−1 m−1) as a function of gradient (%). Black circles represent backward running, and white circles repre- sent forward running. The superimposed dotted line represents the Minetti et al. 2002 equation of metabolic cost on gradient and well fit the experimental data. Backward running cost is always higher than forward running (p < 0.01) on average of 35%. Data are mean ± SD 2510 European Journal of Applied Physiology (2020) 120:2507–2515 1 3 of speed. In Fig. 3, the mechanical parameters of back- ward and forward running are shown at each slope. Data were collected and presented at these identical gradients (0, + 5, + 10, + 15, + 20 and + 25%), however, at different speeds: 1.67, 1.53, 1.39, 1.25, 1.11, 0.97 m s−1 for back- ward running and 2.78, 2.50, 2.22, 1.94, 1.67, 1.39 m s−1 for forward running. WEXT was greater in backward run- ning from 0 to 10%, whereas WINT was significantly lower in backward running at all gradients (p < 0.01) and WTOT turned to be greater in forward running only at maximal gradients (20–25%, p < 0.05) (Fig. 3). Stride frequency (SF, Hz, Fig. 4) was statistically higher in backward than forward running at all slopes (p < 0.01). Locomotion efficiency (Fig. 5) was greater in forward than backward running (p < 0.001) and decreased with gradi- ent. Backward running reached values close to the muscular efficiency (0.25–0.30) at the steepest gradient where both metabolic and mechanical variable were measured. Estimated elastic energy contribution (Fig. 6) was higher in forward than backward running in all gradient conditions (p < 0.001) and decreased with gradient. Backward running approached no elastic energy contribution at the steepest gradient. Discussion The metabolic cost of backward running was higher than forward running in all the investigated gradients, whereas the total mechanical work was similar in the two gaits at all gradients. Thus, the lower locomotion efficiency of back- ward than forward running (also on gradient) seems to be explained by the lower elastic energy contribution that does Fig. 2 The mechanical external (WEXT), internal (WINT) and total (WTOT) work (J kg−1 m−1) as a function of speed (m s−1) in backward running is represented at the different investigated gradients. Data are mean ± SD Fig. 3 The mechanical external (WEXT), internal (WINT) and total (WTOT) work (J kg−1 m−1) as a function of gradient (%) is represented in backward (black circles) and forward (white circles) running. Sta- tistical difference between backward and forward running: #p < 0.05; p < 0.01. Data are mean ± SD 2511 European Journal of Applied Physiology (2020) 120:2507–2515 1 3 not assist muscles in performing mechanical work, which is carried out with a higher metabolic cost. The metabolic cost of backward running was already shown to be higher than forward running on level over a range of speeds (Flynn et al. 1994; Wright and Weyand 2001) and the percentage difference is close to that reported in the present study. The novelty of this work consists in extending the previous knowledge also to gradients, where we found that the difference in metabolic cost was almost constant between the two running modalities at the different slopes, with a similar increase among gradients (Fig. 1). This behaviour differs from walking, since Minetti and Ardigò (2001) reported a decrease in delta cost between forward and backward walking on gradient, down to a + 5–8% dif- ference at gradients steeper than 15%. They ascribed this decrease in delta cost to the impairment of the pendulum- like energy-saving mechanism of forward walking (energy recovery decreased in parallel with the metabolic cost) on gradient. Running does not rely on this mechanism, there- fore a direct comparison cannot be performed; we will discuss later the energy-saving mechanism of running and its implication on the metabolic cost. The high metabolic power required for running backward forced us to test dif- ferent speeds in the two running modalities, and to decrease speed (in both modalities) by increasing the gradient. The metabolic cost of forward running is speed independent on level and on gradient (Margaria 1938; Margaria et al. 1963; Minetti et al. 2002). Backward running C showed the speed independency on level (Wright and Weyand 2001), and here we extended this speed independency also on gradient [in Fig. 4 Stride frequency (Hz) as a function of gradient (%). Black cir- cles represent backward running, and white circles represent forward running. Statistical difference between backward and forward run- ning: #p < 0.05; p < 0.01; §p < 0.001. Data are mean ± SD Fig. 5 Running efficiency, calculated as the ratio between total mechanical work (WTOT, J kg−1 m−1) and metabolic cost (C, J kg−1 m−1), as a function of gradient (%) is represented in backward (black circles) and forward (white circles) running. Statistical differ- ence between backward and forward running: *p < 0.01; §p < 0.001. Data are mean ± SD Fig. 6 Estimated elastic energy contribution is represented as a func- tion of gradient (%) in backward (black circles) and forward (white circles) running. The mean elastic energy of forward running on level is considered as 1 (see Material and methods for details), and all the other conditions are represented as submultiple. Statistical differ- ence between backward and forward running: §p < 0.001. Data are mean ± SD 2512 European Journal of Applied Physiology (2020) 120:2507–2515 1 3 the tested range of speeds (1.11–1.67 m s−1) and gradients (+ 10%, + 15%)]; thus, this speed difference between run- ning modalities should not affect our metabolic conclusion. The mechanical work values of backward running on level of present investigation showed the same pattern as in Cavagna et al. (2011) values (Fig. 2), whereas no data have been previously reported for backward running on gradi- ent. WEXT decreased with running speed, WINT increased by increasing speed, but its contribution was small, and then WTOT decreased in the investigated range of speeds at all gradients. The mechanical work data for forward running (Fig. 3) revealed similar trend compared with Minetti et al. (1994) values up to + 15%, which was the steepest gradient analysed in that study, whereas data on steeper slopes are not reported in the literature. At the two steepest gradients (+ 20 and + 25%), forward running WEXT increased with the same trend as the previous gradients (Fig. 3). However, WINT that was gradient independent until + 15% (present data and Minetti et al. 1994) showed a tendency to increase probably due to an increased duty factor and more extended limbs that increased the inertia during the swing (thus the compound factor q of the predictive equation for WINT (Minetti 1998) is increased). This WINT tendency to increase at the steep- est gradients is similar to data reported by Nardello et al. (2011). A similar behaviour in the increase of WINT and q factor has been reported at the beginning of the acceleration phase in sprint running (Pavei et al. 2019) and reinforces the idea that the mechanics of constant speed uphill running can be assimilated to running acceleration (di Prampero et al. 2005; Minetti and Pavei 2018). When comparing forward and backward running, albeit not at the same speed, on the different slopes the same trend in WEXT and WTOT was found, with WEXT that increased linearly with gradient (on level WEXT is higher in backward running, as reported by Cavagna et al. (2011)) and was the main determinant of WTOT. WINT was slope independent in backward running, but showed a tendency to increase in forward running, which caused a higher WTOT in forward than backward running at the steep- est gradients. Stride frequency was higher in backward than forward running at all slopes (Fig. 4). On level, a higher stride frequency in backward compared with forward run- ning at paired speed was already reported (Threlkeld et al. 1989; Flynn et al. 1994; Wright and Weyand 2001; Cav- agna et al. 2011, 2012). Our results on level showed that speed (1.39–2.22 m s−1 range) was increased with a constant stride frequency and an increased stride length, similar to the results of Cavagna et al. (2012). The higher stride frequency would increase WINT, but we found higher values in forward than backward running. Other kinematics parameters concur in the computation of WINT: duty factor, defined as the frac- tion of foot contact within the stride duration, mean velocity and a compound q factor that accounts for the limb mass and spatial configuration during the stride (Minetti 1998). When analysing the differences of each WINT component between backward and forward running on gradients, we found the already mentioned increase in stride frequency (+ 9%), an increase in duty factor (+ 27%), together with a decrease in velocity (− 36%) and q (− 32%), which led to a decreased WINT (− 35%) in backward running. Running has been classically represented as a bouncing ball (Cavagna et al. 1964) or a spring mass model (Blick- han 1989), where the lowering trajectory of the BCoM dur- ing the first half of the contact time compresses the spring (or deforms the ball) that can store elastic energy, which is then released to assist muscles while lifting and accelerat- ing BCoM for the next step. Thanks to this elastic recoil of the muscle–tendon structures, running efficiency values are higher than the muscle efficiency (25–30%) and it is also termed ‘apparent efficiency’. In the present study, forward running apparent efficiency on level was ~ 60%, in line with the literature (Cavagna and Kaneko 1977), and decreased with increasing gradient, ~ 40% at + 20%, losing most of the ‘apparent’ part (Fig. 5). This is in accordance with and expand the results of Minetti et al. (1994). Apparent effi- ciency of backward running decreased similarly to forward running, but with about − 20% value in the slope range from level to + 20% (Fig. 5). These results showed that the energy- saving mechanism of running (the storage and release of elastic energy) is impaired on gradient. One explanation can be found by looking at the trajectory of the BCoM and the fraction of positive (WEXT) and negative external work (WEXT −) (Fig. 7). On level, positive (WEXT) and negative (WEXT −) external work equally contributes to the ‘compre- hensive’ external mechanical work (= (WEXT) + (WEXT −)). By moving uphill, WEXT − reduced its contribution as the BCoM trajectory became more ascending (as an effect of the slope) than descending (Minetti et al. 1994). Since the spring is compressed, and elastic energy is stored, during the lower- ing part of the trajectory, and this part is smaller by increas- ing gradient, less elastic energy can be stored. The muscles had then to perform the positive work to lift the BCoM, which increases with slopes, with less assistance from ten- dons; this required more metabolic energy that increased C (which is the denominator of the efficiency equation) and the efficiency decreased (Fig. 5). Since muscles are required to perform more work, a higher sEMG activity can be expected in backward than forward running; we did not assess sEMG, but higher activity was found when running backward on level (Flynn and Soutas-Little 1993, 1995; Sterzing et al. 2016). The partitioning between positive and negative exter- nal work was similar between the two running modalities (Fig. 7), highlighting the same behaviour of the BCoM tra- jectory on gradient. The estimated elastic energy contribu- tion showed the same decreasing tendency with gradient of efficiency (Fig. 6), reinforcing the aforementioned idea that the energy-saving mechanism is impaired. Backward 2513 European Journal of Applied Physiology (2020) 120:2507–2515 1 3 running values were always lower than forward running, and while at the steepest gradient forward running main- tained some kind of elastic contributions, backward running relied only on muscle capability to perform work and power (Fig. 7). The mechanical inefficacy of backward running was already described by Cavagna et al. (2011, 2012) with the reversed landing take-off asymmetry, which resulted in a greater muscle activation during positive work and a lower ability to store and release elastic energy. These mechanical premises for inefficiency were tested here (since Cavagna et al. did not measure metabolic cost), confirmed in their original theory (elastic energy) and extended to the gradient, where we already knew that forward running energy saving was impaired (Minetti et al. 1994). Backward running with a reversed use of the lever system of the limbs that already impaired the efficiency on level showed the same impair- ment of forward running on gradient. However, starting from a lower level of ‘apparent efficiency’, at the steepest gradi- ent backward running reached values of the ‘pure’ muscular efficiency, very likely with no elastic component. Backward running is performed also in various sport activities, e.g. in soccer it has been reported to be as frequent as high-speed running (Mohr et al. 2003). However, up to now, backward running bouts are only counted (frequency of occurrence) and/or considered for their duration. The ‘Equivalent Slope’ concept has been an ingenious idea to infer the metabolic cost of running acceleration (di Pramp- ero et al. 2005) from the metabolic cost of the steady-state uphill running (Minetti et al. 2002). With the present meta- bolic cost data of backward running on gradient (Fig. 1), we can calculate the metabolic cost of backward running over a range of 0–2 m s−2 acceleration (CBA, J kg−1 m−1). However, since the metabolic cost increased linearly with gradient in backward running (as occurred in forward running), we can expect that the proposed equation can be used over a wider range of accelerations. Rearranging the Minetti and Pavei (2018) equation for the metabolic cost in forward running acceleration with present data of backward running C on gradient, the cost of backward running acceleration can be computed as: where ab is the absolute backward acceleration (a positive value, e.g. + 1.5 m s−2, even if it is performed backward, because the negative value is usually given to deceleration). With this new equation, the metabolic power (= instanta- neous CBA × instantaneous speed) of backward acceleration can be computed, with the acceleration and speed values obtained from any GPS system, and added to the metabolic power for forward running acceleration and deceleration (Minetti and Pavei 2018) to obtain a more precise estimate of the metabolic power during different types of sports and activities. Conclusions The metabolic cost of backward running on level and uphill gradient is higher than for forward running, with a similar difference between the two running modalities. This higher cost was not determined by an increased mechanical work; thus, the locomotion efficiency was lower in backward than forward running. When analysing the trajectory of the body centre of mass, the two running modalities showed a similar impairment in the spring mass model behaviour; however, backward running relied less on the elastic energy. With less elastic contribution, the muscles have to perform ‘alone’ the work to lift and accelerate BCoM with a higher meta- bolic demand. With the metabolic cost of backward running on gradient, and the concept of equivalent slope, the new equation for the metabolic cost of backward running accel- eration was computed. The metabolic power of backward acceleration can be now calculated and integrated with the well-known equations for forward running acceleration and deceleration to obtain a more precise estimate of the meta- bolic demand of the sport activities. CBA = (a2 b + 96.2)0.5 × (3.14ab + 4.9) , Fig. 7 Negative external work (WEXT −) as a percentage of ‘compre- hensive’ external mechanical work (= (WEXT) + (WEXT −)) is repre- sented as a function of gradient (%). Black circles represent back- ward running, White circles represent forward running. Data are mean ± SD 2514 European Journal of Applied Physiology (2020) 120:2507–2515 1 3 Author contributions GP, SP, and AEM conceived and designed the study. GP and LR conducted the experiments. GP and LR analysed the data. GP and AEM interpreted the results of the experiments. GP wrote the manuscript. All authors read and approved the manuscript. Funding Open access funding provided by Universitá degli Studi di Milano within the CRUI-CARE Agreement. Compliance with ethical standards Conflict of interest The authors report no conflict of interest. Ethical approval All procedures were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. References Blickhan R (1989) The spring-mass model for running and hopping. 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PMC6466240	International Journal of Environmental Research and Public Health Article Celebrating 40 Years of Ironman: How the Champions Perform Lucas Pinheiro Barbosa 1,†, Caio Victor Sousa 1,2,† , Marcelo Magalhães Sales 3 , Rafael dos Reis Olher 1, Samuel Silva Aguiar 1 , Patrick Anderson Santos 1, Eduard Tiozzo 2, Herbert Gustavo Simões 1, Pantelis Theodoros Nikolaidis 4 and Beat Knechtle 5,6,* 1 Graduate Program in Physical Education, Catholic University of Brasília, 71966-700 Brasília, Brazil; lduarte.barbosa@gmail.com (L.P.B.); cvsousa89@gmail.com (C.V.S.); rﬂolher@gmail.com (R.d.R.O.); ssaguiar0@gmail.com (S.S.A.); patricksantospas@gmail.com (P.A.S.); hgsimoes@gmail.com (H.G.S.) 2 Miller School of Medicine, University of Miami, Coral Gables, FL 33124, USA; etiozzo@med.miami.edu 3 Physical Education Department, Goias State University, Quirinópolis, 75860-000 GO, Brazil; marcelomagalhaessales@gmail.com 4 Exercise Physiology Laboratory, 18450 Nikaia, Greece; pademil@hotmail.com 5 Medbase St. Gallen Am Vadianplatz, 9001 St. Gallen, Switzerland 6 Institute of Primary Care, University of Zurich, 8006 Zurich, Switzerland * Correspondence: beat.knechtle@hispeed.ch; Tel.: +41-(0)71-226-93-00 † These authors contributed equally to this work. Received: 7 February 2019; Accepted: 16 March 2019; Published: 20 March 2019 Abstract: We aimed to determine which discipline had the greater performance improvements in the history of Ironman triathlon in Hawaii and also which discipline had the greater inﬂuence in overall race time. Data from 1983 to 2018 of the top three women and men of each year who competed in the Ironman World Championship were included. In addition to exploratory data analyses, linear regressions between split times and years of achievement were performed. Further, a stepwise multiple linear regression was applied using total race time as the dependent variable and split times as the independent variables. Both women and men signiﬁcantly improved their performances from 1983 to 2018 in the Ironman World Championship. Swimming had the largest difference in improvements between men and women (3.0% versus 12.1%, respectively). A negative and signiﬁcant decrease in each discipline was identiﬁed for both women and men, with cycling being the discipline with the greatest reduction. The results from the stepwise multiple regression indicated that cycling was the discipline with the highest inﬂuence on overall race time for both sexes. Based on the ﬁndings of this study, cycling seems to be the Ironman triathlon discipline that most improved overall race times and is also the discipline with the greatest inﬂuence on the overall race time of elite men and women in the Ironman World Championship. Keywords: triathlon; cycling; running; swimming; endurance 1. Introduction The Ironman triathlon consists of swimming 2.4 miles (3.8 km), cycling 112 miles (180 km), and running 26.2 miles (42.2 km) and is considered as one of the most challenging ultra-endurance events worldwide [1,2]. Although triathlon started in San Diego, California, the history of Ironman triathlon started in 1978 in Hawaii, with the ﬁrst Ironman World Championship being held in Kailua-Kona (Big Island) three years later, also in Hawaii [1–3]. Int. J. Environ. Res. Public Health 2019, 16, 1019; doi:10.3390/ijerph16061019 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2019, 16, 1019 2 of 9 Nowadays, the Ironman events take place all over the world, with amateur and professional athletes competing in these events to qualify for the World Championship in Kailua-Kona. Ironman Hawaii in considered as the toughest Ironman race in the world due to the course, the environmental conditions, and the competitiveness of the event [2,4]. The race itself is one of the most popular triathlon events in the world, with a growing competitiveness and performance improvement in non-elite triathletes [1,5,6]. In addition, it should be highlighted that the best professional triathletes in the world often achieve new records in Kailua-Kona [7]. In order to help coaches and athletes with both training plans and race strategy, performance trends have been analyzed in the past few years in many endurance sports [8–11]. Speciﬁcally in triathlon, relevant studies have been conducted for Olympic distance (1.5 km swim/40 km cycle/10 km run) [12,13], half-distance (half-Ironman: 1.9 km swim/90 km cycle/21 km run) [13,14], full-distance (3.8 km swim/180 km cycle/42.195 km run) [14,15], and ultra-triathlons (distance > Ironman) [16,17]. To date, two studies investigated the performance of amateur triathletes [2,5], but none of them included only elite women and men. Ofoghi et al. [18] investigated which discipline would have the greater inﬂuence on overall performance in an Olympic triathlon and concluded that running was the most decisive, followed by swimming and cycling. On the other hand, Sousa et al. [19] analyzed all sub-8-h performances in full-distance triathlon (i.e., Ironman) and reported that cycling was the discipline with the greatest inﬂuence on the overall result, followed by running and swimming. Additionally, it is noteworthy that in 2018 the female and male winners of the 2018 World Championship improved the course records, showing that the fastest Ironman triathletes worldwide can further improve their performances. However, to the best of our knowledge, the only two studies analyzing the Ironman World Championship results concerned amateur athletes in the analysis, with one of the studies analyzing races up to 2007 [2] and the other analyzing races from 2002 to 2015 [5]. Therefore, we aimed to analyze only elite men and women competing in the Ironman World Championship from 1983 to 2018 in order to determine (i) which discipline had the greatest performance improvement in the last 35 years; (ii) which discipline had the greatest inﬂuence on overall result; and (iii) whether women were really closing the gap to men. 2. Methods 2.1. Ethical Approval All procedures used in the study were approved by the Institutional Review Board of Kanton St. Gallen, Switzerland, with a waiver of the requirement for informed consent of the participants given the fact that the study involved the analysis of publicly available data (1 June 2010). 2.2. Data All data were obtained from a publicly available database (www.ironman.com). All ofﬁcial overall race and split times from the top three women’s and men’s ﬁnishers of the Ironman World Championship from 1983 to 2018 were included in the analysis. Table 1 presents the descriptive distribution of women’s races including the Ironman World Championship Race/Split Record (among top three ﬁnishers), whereas Table 2 presents men’s data. Table 1. Women’s total and split race times in the Ironman World Championship from 1983 to 2018. Race Time Median (25–75 Percentile) Mean (±SD) Ironman World Championship Race/Split Record * Overall 09:16:48 (09:03:51–09:26:18) 09:19:06 (00:26:14) 08:26:18 Swimming 00:57:00 (00:55:26–01:00:09) 00:57:34 (00:03:24) 00:48:14 Int. J. Environ. Res. Public Health 2019, 16, 1019 3 of 9 Table 1. Cont. Race Time Median (25–75 Percentile) Mean (±SD) Ironman World Championship Race/Split Record * Cycling 05:08:39 (05:00:14–05:17:50) 05:11:22 (00:18:06) 04:26:07 Running 03:08:10 (03:04:09–03:16:31) 03:10:10 (00:10:37) 02:50:26 * Within top three ﬁnishers from 1983 to 2018. Table 2. Men’s total and split race times in the Ironman World Championship from 1983 to 2018. Race Time Median (25–75 Percentile) Mean (±SD) Ironman World Championship Race/Split Record * Overall 08:22:02 (08:14:37–08:33:02) 08:26:28 (00:18:26) 07:52:39 Swimming 00:51:43 (00:51:00–00:53:02) 00:53:18 (00:06:07) 00:48:02 Cycling 04:37:47 (04:30:16–04:46:15) 04:42:15 (00:21:01) 04:12:25 Running 03:08:10 (02:46:42–02:57:00) 02:55:21 (± 00:18:57) 02:39:59 * Within top three ﬁnishers from 1983 to 2018. 2.3. Statistical Analysis Initially, an exploratory analysis of the data was carried out, in which central tendency (median and mean), dispersion (interquartile ranges (25 and 75 percentiles and standard deviation), and extreme (lowest value) measures were calculated (Tables 1 and 2). Furthermore, all data were transformed in seconds and non-linear regressions (second order) were performed between each split time and year of achievement. Linear regressions were used for splits because the non-linear ﬁtting line was the same as the linear. The relative difference (percentage) between the ﬁrst (1983) and last (2018) World Championship’s top three performances was calculated for both women and men. Regarding overall race time, non-linear regression analyses were performed since the trend line had a better ﬁt than linear regression. A comparison of average race times between the top three athletes and the chasing group (4th to 10th place ﬁnishers) was performed. Finally, a stepwise multiple linear regression was performed using overall race time as the dependent variable and split times as independent variables. The signiﬁcance level was set as 5% (p < 0.05), and all procedures were performed using SPSS v21.0 (IBM SPSS Statistics for Windows. Armonk, NY: IBM Corp). 3. Results Men improved in overall race time by 13.3% from 1983 to 2018, whereas women improved by 20.8% (Table 3). Swimming showed the largest difference in improvements between men and women (3.0% versus 12.1%, respectively), and running showed the smallest difference (12.5% versus 15.5%, respectively) for the three split disciplines. Table 3. Women’s and men’s percentage performance improvements in the Ironman World Championship from 1983 to 2018. Total Total Difference Decade Average Decade Average Difference Overall Women 20.8% 7.5% 5.20% 1.87% Men 13.3% 3.33% Swimming Women 12.1% 9.1% 3.25% 2.50% Men 3% 0.75% Int. J. Environ. Res. Public Health 2019, 16, 1019 4 of 9 Table 3. Cont. Total Total Difference Decade Average Decade Average Difference Cycling Women 26.4% 9.5% 6.60% 2.37% Men 16.9% 4.23% Running Women 15.5% 3% 3.88% 0.75% Men 12.5% 3.13% Both women and men signiﬁcantly improved their performances from 1983 to 2018 in the Ironman World Championship in Kona, Hawaii (Figures 1 and 2). The world record was improved almost every three years (see Supplementary Table S1 for accurate race time values from each year’s champions). Int. J. Environ. Res. Public Health 2018, 15, x FOR PEER REVIEW 4 of 9 almost every three years (see Supplementary Table S1 for accurate race time values from each year’s champions). Figure 1. Dispersion and non-linear regression of overall race time performances in the Ironman World Championship from 1983 to 2018 of women and men. Gold trophies represent the champion in each year. Figure 1. Dispersion and non-linear regression of overall race time performances in the Ironman World Championship from 1983 to 2018 of women and men. Gold trophies represent the champion in each year. Int. J. Environ. Res. Public Health 2019, 16, 1019 5 of 9 Figure 1. Dispersion and non-linear regression of overall race time performances in the Ironman World Championship from 1983 to 2018 of women and men. Gold trophies represent the champion in each year. Figure 2. Dispersion and non-linear regression overall race time performances between the top three finishers and the chasing group (4th to 10th place finishers) from women and men in the Ironman World Championship from 1983 to 2018. Figure 2. Dispersion and non-linear regression overall race time performances between the top three ﬁnishers and the chasing group (4th to 10th place ﬁnishers) from women and men in the Ironman World Championship from 1983 to 2018. The linear regression of split disciplines shows a negative and signiﬁcant slope for all disciplines for both women (swimming: −6.94 to 0.47; cycling: −71.06 to −36.98 ; running: −45.79 to −26.86 ; Figure 3) and men (swimming: −19.37 to −6.77 ; cycling: −96.01 to −60.52 ; running: −65.06 to −30.58 ; Figure 4) ( indicates p < 0.001). The greatest slope in both sexes was for cycling. Int. J. Environ. Res. Public Health 2018, 15, x FOR PEER REVIEW 5 of 9 The linear regression of split disciplines shows a negative and significant slope for all disciplines for both women (swimming: −6.94 to 0.47; cycling: −71.06 to −36.98; running: −45.79 to −26.86; Figure 3) and men (swimming: −19.37 to −6.77; cycling: −96.01 to −60.52; running: −65.06 to −30.58; Figure 4) ( indicates p < 0.001). The greatest slope in both sexes was for cycling. Figure 3. Dispersion and linear regression of split-times performances in the Ironman World Championship from 1983 to 2018 of women. Figure 3. Dispersion and linear regression of split-times performances in the Ironman World Championship from 1983 to 2018 of women. Int. J. Environ. Res. Public Health 2019, 16, 1019 6 of 9 Figure 3. Dispersion and linear regression of split-times performances in the Ironman World Championship from 1983 to 2018 of women. Figure 4. Dispersion and linear regression of split-times performances in the Ironman World Championship from 1983 to 2018 of men. Figure 4. Dispersion and linear regression of split-times performances in the Ironman World Championship from 1983 to 2018 of men. The best-ﬁtting model from the stepwise multiple regression included swimming, cycling, and running split times for both women and men (Table 4). Cycling was the discipline with the greatest standardized beta for both sexes. The swimming discipline resulted in a negative standardized coefﬁcient for the men. Table 4. Standardized coefﬁcient from stepwise multiple regression using total race time as the dependent variable of Ironman World Championship from 1983 to 2018. Standardized β Coefﬁcient R2 R2aj Swimming Cycling Running Women 0.129 0.690 0.405 0.857 0.856 Men −0.290 0.895 0.250 0.781 0.775 4. Discussion The main ﬁnding of this manuscript was that cycling has been the Ironman triathlon discipline with the greatest improvement rate throughout the years and also has had the greatest inﬂuence on overall race time for both women and men. However, apparently both women and men have improved their performances over the years in all triathlon disciplines. It is worth mentioning that women had a greater improvement than men in all triathlon disciplines and consequently in total race times. Jeukendrup and Martin [20] had previously reported that cycling in aero position and the use of lighter wheels (i.e., elbows on handlebars and carbon wheels, respectively, which had developed for use in time-trial and triathlon bicycles) makes an athlete signiﬁcantly faster. Thus, cycling performance also had new technologies that could inﬂuence the performance increase, from the outﬁt to the bicycle itself, all of which contributed to make the athlete more comfortable, aerodynamic, and consequently faster. Although cycling is the discipline that encompasses more time in comparison to swimming and running in Olympic distance and short distances, it does not have an inﬂuence in overall race time, being the least important of the three disciplines [21]. In Olympic distance and short distances, athletes normally swim really fast to be able to leave transition one with the ﬁrst pack of cyclists and stay within the leading and chasing peloton, thus saving the energy for the running [18]. However, Int. J. Environ. Res. Public Health 2019, 16, 1019 7 of 9 in Ironman races drafting during cycling is not allowed, making cycling a more competitive discipline, which means that athletes have to apply some strategy in order to cycle fast enough to remain in a competitive position but still save energy for the running leg. Similarly, in an analysis only including top full-distance triathlon performances, the authors reported that cycling was the discipline that most inﬂuenced overall performance in elite men racing below 8 h of overall race time, followed by running and swimming [19]. A performance analysis on Ironman races investigated more than 340,000 triathletes racing in 253 different race locations and concluded that the race tactics in an Ironman triathlon should focus on saving energy during the ﬁrst two disciplines for the running split [22]. This conclusion is different from the ﬁndings of the present study, which suggest that athletes seem to apply greater effort in cycling than during running. It is worth mentioning that this analysis was carried out with a majority of age groupers (non-elite), whereas the present study only considered the top three elite professionals from each year. It is noteworthy that 4th to 10th place ﬁnishers in the Ironman World Championship seemed to have a substantial performance improvement in the last decade of the event, with consistently much closer groupings in the top ten athletes for both men and women. With regard to performance throughout the race, the performance analysis of the Ironman World Championship with amateurs reported a performance increase in all disciplines for men and women [23]. However, the authors suggest that this improvement in performance may be due to an increased number of athletes and morphological changes [23]. The overall performance increase throughout the years can be mostly attributed to the development of new nutrition and training strategies [24–27]. A controversial result was the negative coefﬁcient for the swim split in men, which would mean that a slower swim could lead to a better overall race time. We believe that this statistical outcome is due to the speciﬁcity of the sample, as only the top three athletes in the overall race were considered, and these athletes are not always the best swimmers. For example, in the 2018 World Championship, none of the top three overall athletes were among the top 10 swimmers. Concerning the performance gap between men and women, it has markedly reduced in the last decades. At the 2018 Ironman World Championship, women improved by 21% while men improved by 13%; the absolute gap between them reduced from 1 h and 38 min to 33 min. Indeed, in the 2018 Championship, the female champion Daniela Ryf crossed the ﬁnish line ahead of 20 elite professional men who ﬁnished the race. Some previous studies have concluded that women have been closing the gap in swimming [28,29], in running [30,31], and even in triathlon [2]. We believe that women may still close the gap in an Ironman someday despite the body composition and physiological differences that exist between men and women. One of the possible explanations for this can be attributed to cultural changes that have favored a greater participation of women in all sports, including triathlon, thus increasing competitiveness and therefore performance [7,9,10,32]. When comparing performance with other endurance modalities such as ultra-triathlon and marathon running, the performance gaps between women and men are getting smaller each year. Knechtle et al. [33] investigated the performance trends of Double Iron ultra-triathlon (2I; 2x Ironman distance), Triple Iron ultra-triathlon (3I; 3x Ironman distance), and Deca Iron ultra-triathlon (10I; 10x Ironman distance) from 1985 to 2009 and reported a smaller sex difference in 2I and 3I. Conversely, Nikolaidis et al. [31] investigated the performance of male and female athletes running the marathon and concluded that men are still faster than women, but the performance gap remained unchanged for the past few years. Regarding the speciﬁc Ironman World Championship, Kailua-Kona is one of the toughest races within the entire Ironman circuit, which typically requires athletes to swim in choppy waters, cycle with a lot of wind, and run in hot and sunny weather [34,35]. The race course has not always been the same in Ironman Hawaii, with small changes every two or three years in order to accommodate safety precautions and/or local transit logistics. Although this may affect the overall race time, the distances remained standard and we believe that any small course changes affecting a speciﬁc split race time are diluted within the sample and do not represent a great confounder to the general results of this study. Int. J. Environ. Res. Public Health 2019, 16, 1019 8 of 9 5. Conclusions In conclusion, cycling seems to be the triathlon discipline that most improved overall race times and is also the discipline that had the greatest inﬂuence on the overall race time in elite men and women in the Ironman World Championship. Furthermore, within the last 40 years of Ironman Hawaii, both men and women improved overall time performance, but women improved more, thereby closing the gap to men. Supplementary Materials: The following are available online at http://www.mdpi.com/1660-4601/16/6/1019/ s1, Table S1: Overall times in the Ironman World Championship from 1983 to 2018 for women and men. Author Contributions: Conceptualization: C.V.S., L.P.B., P.T.N., and B.K.; methodology: C.V.S., L.P.B., M.M.S., R.d.R.O., S.S.A., P.A.S., P.T.N., and B.K.; formal analysis: C.V.S., L.P.B., and M.M.S.; writing—original draft preparation: C.V.S. and L.P.B.; writing—review and editing: C.V.S., L.P.B., M.M.S., P.A.S., E.T., H.G.S., P.T.N., and B.K.; visualization: C.V.S., L.P.B., M.M.S., P.A.S., E.T., H.G.S., P.T.N., and B.K.; supervision: P.T.N. and B.K.; project administration: P.T.N., and B.K. Funding: This research received no external funding. Conﬂicts of Interest: The authors report no conﬂicts of interest in this work. References 1. Stiefel, M.; Rüst, C.A.; Rosemann, T.; Knechtle, B. A comparison of participation and performance in age-group ﬁnishers competing in and qualifying for Ironman Hawaii. Int. J. Gen. Med. 2013, 6, 67. 2. Lepers, R. Analysis of Hawaii ironman performances in elite triathletes from 1981 to 2007. Med. Sci. Sports Exerc. 2008, 40, 1828–1834. 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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).	Celebrating 40 Years of Ironman: How the Champions Perform.	03-20-2019	Barbosa, Lucas Pinheiro,Sousa, Caio Victor,Sales, Marcelo Magalhães,Olher, Rafael Dos Reis,Aguiar, Samuel Silva,Santos, Patrick Anderson,Tiozzo, Eduard,Simões, Herbert Gustavo,Nikolaidis, Pantelis Theodoros,Knechtle, Beat	eng
PMC6239296	S1 Appendix. Solution of the integral equation for Pmax(T). The maximal power Pmax(T) is determined by the integral equation Pmax(T) + Psup(T) = 1 T Z T 0 Pmax(T − t)dt = 1 T Z T 0 Pmax(t)dt (1) with Psup(T) given by Eq. (3). This equation can be easily transformed into a differential equation by defining the indefinite integral E(T) of Pmax(T) so that the derivative E′(T) = Pmax(T). Without loss of generality, we can chose the initial condition E(0) = 0. The differential equation for E(T) is then E′(T) + Psup(T) = E(T) T (2) which has the general solution E(T) = TPm + TPsup(tc) − T Z T tc Psup(t) t dt (3) where we imposed the initial condition E′(T = tc) = Pm so that Pmax(T = tc) = Pm as required by definition of Pm. Performing the integral with the constant function Psup(t) = Ps for T ≤ tc yields E(T) = T [Pm + Ps − Ps log(T/tc)] (4) and using Psup(t) = Pl(t − tc)/t + Pstc/t for T ≥ tc yields E(T) = T Pm + Ps + (Pl − Ps)T − tc T − Pl log(T/tc) . (5) Taking the derivative of this solution, we finally obtain the solution Pmax(T) = Pm − Ps log(T/tc) (6) for T ≤ tc and Pmax(T) = Pm − Pl log(T/tc) (7) for T ≥ tc. This is the result given in Eq. (6). PLOS 1/??	A minimal power model for human running performance.	11-16-2018	Mulligan, Matthew,Adam, Guillaume,Emig, Thorsten	eng
PMC4919094	RESEARCH ARTICLE Prediction and Quantification of Individual Athletic Performance of Runners Duncan A. J. Blythe1,2☯¤a, Franz J. Király3☯¤b 1 African Institute for Mathematical Sciences, Bagamoyo, Tanzania, 2 Bernstein Centre for Computational Neuroscience, Berlin, Germany, 3 Department of Statistical Science, University College London, London, United Kingdom ☯ These authors contributed equally to this work. ¤a Current address: African Institute of Mathematical Sciences, Tanzania, P.O. Box 176, Alpha Zulu, Chunguuni Street (off Indian Street), Mwambao—Bagamoyo, Pwani—Tanzania ¤b Current address: Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, United Kingdom * duncan@aims.ac.tz (DAJB); f.kiraly@ucl.ac.uk (FJK) Abstract We present a novel, quantitative view on the human athletic performance of individual run- ners. We obtain a predictor for running performance, a parsimonious model and a training state summary consisting of three numbers by application of modern validation techniques and recent advances in machine learning to the thepowerof10 database of British runners’ performances (164,746 individuals, 1,417,432 performances). Our predictor achieves an average prediction error (out-of-sample) of e.g. 3.6 min on elite Marathon performances and 0.3 seconds on 100 metres performances, and a lower error than the state-of-the-art in per- formance prediction (30% improvement, RMSE) over a range of distances. We are also the first to report on a systematic comparison of predictors for running performance. Our model has three parameters per runner, and three components which are the same for all runners. The first component of the model corresponds to a power law with exponent dependent on the runner which achieves a better goodness-of-fit than known power laws in the study of running. Many documented phenomena in quantitative sports science, such as the form of scoring tables, the success of existing prediction methods including Riegel’s formula, the Purdy points scheme, the power law for world records performances and the broken power law for world record speeds may be explained on the basis of our findings in a unified way. We provide strong evidence that the three parameters per runner are related to physiologi- cal and behavioural parameters, such as training state, event specialization and age, which allows us to derive novel physiological hypotheses relating to athletic performance. We con- jecture on this basis that our findings will be vital in exercise physiology, race planning, the study of aging and training regime design. PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 1 / 16 a11111 OPEN ACCESS Citation: Blythe DAJ, Király FJ (2016) Prediction and Quantification of Individual Athletic Performance of Runners. PLoS ONE 11(6): e0157257. doi:10.1371/ journal.pone.0157257 Editor: Nir Eynon, Victoria University, AUSTRALIA Received: February 26, 2016 Accepted: May 26, 2016 Published: June 23, 2016 Copyright: © 2016 Blythe, Király. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data are available from https://figshare.com/articles/thepowerof10/3408202 and https://figshare.com/articles/Ful_code_to_ Prediction_and_Quantification_of_Individual_ Athletic_Performance_of_Runners_/3408250. Funding: DAJB was supported by a grant from the German Research Foundation, research training group GRK 1589/1 “Sensory Computation in Neural Systems.” FJK was partially supported by Mathematisches Forschungsinstitut Oberwolfach (MFO). This research was partially carried out at MFO with the support of FJK’s Oberwolfach Leibniz Fellowship. Introduction Performance prediction and modeling are cornerstones of sports medicine, essential in training and assessment of athletes with implications beyond sport, for example in the understanding of aging, muscle physiology, and the study of the cardiovascular system. Existing research on running performance focuses either on (A) explaining world records [1–6], (B) equivalent scoring [7, 8], or (C) modelling of individual physiology [9–16]. Currently, however, there is no parsimonious model which simultaneously explains individual physiology (C) and collec- tive performance (A,B) of runners. We present such a model, a non-linear low-rank model derived from a database of UK run- ners. It levers an individual power law which explains the power laws known to apply to world records, and which allows us to derive runner-individual training parameters from prior per- formance data. Performance predictions obtained using our approach are the most accurate to date, with an average prediction error of under 4 minutes (2% rel.MAE and 3% rel.RMSE out- of-sample) for elite performances. We anticipate that our framework will allow researchers to leverage existing insights in the study of world record performances and sports medicine for an improved understanding of human physiology. Our work builds on the three major research strands in prediction and modeling of running performance, which we briefly summarize: (A) Power law models of performance posit a power law dependence t = c sα between the time elapsed running t and the distance s, for constants c and α. This is equivalent to assuming a linear dependence log t = α log s + log c of log-time on log-distance. Power law models have been known to describe world record performances across sports for over a century [17], and have been applied extensively to running performance [1–6]. These power laws have been applied to prediction by practitioners: the Riegel formula [18] predicts performance by fitting c to each runner and fixing α = 1.06 (derived from world-record performances). The power law approach has the benefit of modelling performances in a scientifically parsimonious way. (B) Scoring tables, such as those of the international association of athletics federations (IAAF), render performances over disparate distances comparable by presenting them on a single scale. These tables have been published by sports associations for almost a century [19] and catalogue, rather than model, performances of equivalent standard. Performance predic- tions may be obtained from scoring tables by forecasting a time with the same score as an exist- ing attempt, as implemented in the Purdy Points scheme [7, 8]. The scoring table approach has the benefit of describing performances in an empirically accurate way. (C) Explicit modeling of performance related physiology is an active subfield of sports sci- ence. Several physiological parameters are known to be related to athletic performance; these include maximal oxygen uptake ( _VO2-max) and critical speed (speed at _VO2-max) [9, 10], blood lactate concentration, and the anaerobic threshold [11, 20]. Physiological parameters may be used (C.i) to make direct predictions when clinical measurements are available [12, 13, 21], or (C.ii) to obtain theoretical models describing physiological processes [14–16, 22]. These approaches have the beneﬁt of explaining performances physiologically. All three approaches (A), (B), (C) have appealing properties, as explained above, but none provides a complete treatment of running performance prediction: (A) individual perfor- mances do not follow the parsimonious power law perfectly; (B) the empirically accurate scor- ing tables do not provide a simple interpretable relationship. Neither (A) nor (B) can deal with the fact that runners may differ from one another in multiple ways. The clinical measurements in (C.i) are informative but usually available only for a few select runners, typically at most a few dozen (as opposed to the 164,746 considered in our study). The interpretable models in (C. ii) are usually designed not with the aim of predicting performance but to explain physiology Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 2 / 16 Competing Interests: The authors have declared that no competing interests exist. or to estimate physiological parameters from performances; thus these methods are not directly applicable to running performance prediction without additional work. The approach we present unifies the desirable properties of (A), (B) and (C), while avoiding the aforementioned shortcomings. We obtain (A) a parsimonious model for individual athletic performance that is (B) empirically derived from a large database of UK runners. It yields the best performance predictions to date (2% average error for elite runners on all events, average error 3.6 min for Marathon 0.3 seconds for 100 metres) and (C) unveils hidden descriptors for individuals which we find to be related to physiological and behavioural characteristics. Our approach bases predictions on Local Matrix Completion (LMC), a machine learning technique which posits the existence of a small number of explanatory variables which describe the performance of individual runners. Application of LMC to a database of runners allows us, in a second step, to derive a parsimonious physiological model describing the running perfor- mance of individual runners. We discover that a three number-summary for each individual explains performance over the full range of distances from 100m to the Marathon. The three- number-summary relates to: (1) the endurance of a runner, (2) the relative balance between speed and endurance, and (3) specialization over middle distances. The first number explains most of the individual differences over distances greater than 800m, and may be interpreted as the exponent of an individual power law for each runner, which holds with remarkably high precision, on average. The other two numbers describe individual, non-linear corrections to this individual power law. Vitally, we show that the individual power law with its non-linear corrections reflects the data more accurately than the power law for world records. We antici- pate that the individual power law and three-number summary will allow for exact quantitative assessment in the science of running and related sports. Materials and Methods Local Matrix Completion and the Low-Rank Model It is well known that world records over distinct distances are held by distinct runners—no one single runner holds all running world records. Since world record data obey an approximate power law (see above), this implies that the individual performance of each runner deviates from this power law. The left top panel of Fig 1 displays world records and the corresponding individual performances of world record holders in logarithmic coordinates—an exact power law would follow a straight line. The world records align closely to a straight line, while individ- uals deviate non-linearly. Notable is also the kink in the world records which causes them to deviate from an exact straight line, yielding a “broken power law” for world records [5]. Any model for individual performances must model this individual, non-linear variation, and will, optimally, explain the broken power law observed for world records as an epiphenom- enon of such variation over individuals. In the following paragraphs we explain how the LMC scheme captures individual variation in a typical scenario. Consider three runners (taken from the database) as shown in the top-right panel of Fig 1. The 1500m performance of the green runner is not known and is to be predicted. All three run- ners, green, blue and red, achieve similar performance over 800m. Any classical method for performance prediction which only takes this information into account will predict that green performs similarly over 1500m to blue and red. However, this is unrealistic, since it does not take into account event specialization: looking at the 400m performance, we see that red is slowest over short distances, followed by blue and then by green. Thus red is more of an endur- ance type runner than blue, and blue is more of a speed type runner (sprinter) than red; green specializes to a greater extent in speed than both red and blue. Using this additional informa- tion leads to the more realistic prediction that green will be out-performed by red and blue Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 3 / 16 over 1500m. Supplementary analysis (IV) in S1 Supplement validates that this phenomenon illustrated in the example is prevalent throughout the data set. LMC is a quantitative method for taking into account this event specialization. A schematic overview of the simplest variant is displayed in the bottom panel of Fig 1: to predict an event for a runner (figure: 1500m for green) we find a 3-by-3-pattern of performances, denoted by A, with exactly one missing entry—this means the two other runners (figure: red and blue) have attempted similar events and their data are available. Explanation of the green runner’s curve by the red and the blue is mathematically modelled by demanding that the data of the green runner is given as a weighted sum of the data of the red and the blue; i.e., more mathematically, the green row is a linear combination of the blue and the red row. A classical result in matrix algebra implies that the green row is a linear combination of red and blue whenever the deter- minant of A, a polynomial function in the entries of A, vanishes; i.e., det(A) = 0. A prediction is made by solving the Eq det(A) = 0 for “?”. To increase accuracy, candidate solutions from multiple 3-by-3-patterns (obtained from many triples of runners) are averaged in a way that minimizes the expected error in approximation. We shall consider variants of the algorithm which use n-by-n-patterns, n corresponding to the complexity of the model (we later show n = 4 to be optimal). See the methods appendix for an exact description of the algorithm used. Fig 1. Central phenomenon: non-linear deviation from the power law in individuals. Top left: performances of world record holders and a selection of random runners. Curves labelled by runners are their known best performances (y-axis) at that event (x-axis). Black crosses are world record performances. Individual performances deviate non-linearly from the world record power law. Top right: a good model should take into account specialization, illustration by example. Hypothetical performance curves of three runners, green, red and blue are shown, the task is to predict green on 1500m from all other performances. Dotted green lines are predictions. State-of-art methods such as Riegel or Purdy predict green performance on 1500m close to blue and red; a realistic predictor for 1500m performance of green—such as LMC—will predict that green is outperformed by red and blue on 1500m; since blue and red being worse on 400m indicates that out of the three runners, green specializes most on shorter distances. Bottom: using local matrix completion as a mathematical prediction principle by filling in an entry in a (3 × 3) sub-pattern. Schematic illustration of the algorithm. doi:10.1371/journal.pone.0157257.g001 Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 4 / 16 The LMC prediction scheme is an instance of the more general local low-rank matrix com- pletion framework introduced in [23], here applied to performances in the form of a numerical table (or matrix) with columns corresponding to events and rows to runners. The cited frame- work is the first matrix completion algorithm which allows prediction of single missing entries as opposed to all entries. While matrix completion has proved vital in predicting consumer behaviour and recommender systems, the results in the present study show that existing approaches which predict all entries at once cannot cope with the non-uniform missingness and the noise associated with performance prediction in the same way as LMC can (see find- ings and supplemental analysis II.a in S1 Supplement). See the methods appendix for more details of the method and an exact description. In a second step, we use the LMC scheme to fill in all missing performances (over all events considered—100m, 200m etc.) and obtain a parsimonious low-rank model—we remark that first filling in the entries with LMC and only then fitting the model is crucial due to the fact that data are non-uniformly missing (see supplemental analysis II.a in S1 Supplement). The low-rank model explains individual running times t in terms of distance s and has the form: log t ¼ l1f1ðsÞ þ l2f2ðsÞ þ þ lrfrðsÞ; ð1Þ with components f1, f2, . . ., fr that are the same for every runner, and coefﬁcients λ1, λ2, . . ., λr which summarize the runner under consideration. The number of components and coefﬁcients r is known as the rank of the model and measures its complexity. The Riegel power law is a very special case, demanding that log t = 1.06log s + c; that is, a rank 2 model with λ1 = 1.06 for every runner, f1(s) = log s, and a runner-speciﬁc constant λ2 f2(s) = c. Our analyses will show that the best model has rank r = 3 (meaning above we consider patterns or matrices of size n × n = 4 since above n = r + 1). This means that the model has r = three universal components f1(s), f2(s), f3(s), and every runner is described by their individual three-coefﬁcient-summary λ1, λ2, λ3. Remarkably, we ﬁnd that f1(s) = log s (for a suitable unit of distance/time, see supple- mental analysis II.b in S1 Supplement), yielding an individual power law; the corresponding coefﬁcient λ1 thus has the natural interpretation as an individual power law exponent. Table 1 contains the exact form for the components f1, f2, f3 in our model; they are also dis- played in Fig 2 top left. More details on how to obtain components and coefficients can be found in the methods section, “obtaining the low-rank components and coefficients”, and in supplementary analysis II.b in S1 Supplement. Data Set, Analyses and Model Validation The basis for our analyses is the online database www.thepowerof10.info, which catalogues British individuals’ performances achieved in officially ratified athletics competitions since 1954. The excerpt we consider contains performances between 1954 and August 3, 2013. Our study does not use performances prior to 1954 since the database does not contain perfor- mances dating prior to 1954. It contains (after error removal) records of 164,746 individuals of both genders, ranging from the amateur to the elite, young to old, comprising a total of 1,417,432 individual performances over 10 different distances: 100m, 200m, 400m, 800m, 1500m, the Mile, 5km, 10km, Half-Marathon, Marathon. All British records over the distances considered are contained in the dataset; the 95th percentile for the 100m, 1500m and Marathon are 15.9, 6:06.5 and 6:15:34, respectively. As performances for the two genders distribute differ- ently, we present only results on the subset of 101,775 male runners in the main corpus of the manuscript; female runners and further subgroup analyses are considered in the supplemen- tary results. The data set is available upon request, subject to approval by British Athletics. Full code and data for our analyses can be obtained from [24, 25]. Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 5 / 16 Adhering to state-of-the-art statistical practice (see [26–29]), all prediction methods are val- idated out-of-sample, i.e., by using only a subset of the data for estimation of parameters (train- ing set) and computing the error on predictions made for a distinct subset (validation or test set). As error measures, we use the root mean squared error (RMSE) and the mean absolute error (MAE), estimated by leave-one-out validation for 1000 single performances omitted at random. We would like to stress that out-of-sample prediction error is the correct way to evaluate the quality of prediction, as opposed to merely reporting goodness-of-fit in-sample, since out- putting an estimate for an instance that the method has already seen does not qualify as prediction. More details on the data set and our validation setup can be found in the supplementary material. Table 1. The three components of the low-rank model of Eq (1). s 100m 200m 400m 800m 1500m Mile 5km 10km HM Mar f1 2.254 2.875 3.574 4.305 4.964 5.049 6.179 6.844 7.555 8.243 f2 0.4473 0.4721 0.5265 0.3045 0.0798 0.0806 -0.1597 -0.1983 -0.2279 -0.2785 f3 -0.1750 -0.2004 -0.1145 0.2224 0.3263 0.3092 0.3157 0.2717 -0.1153 -0.6912 v 0.1291 0.1647 0.2047 0.2466 0.2843 0.2892 0.3539 0.3920 0.4327 0.4721 An entry in the rows i = 1,2,3 is fi(s), where s is the column header; HM is the half-Marathon, Mar is the Marathon. The components are obtained as described in methods, “obtaining the low-rank components and coefﬁcients”. v is the raw singular vector described there from which f1 is obtained by rescaling. v, f2, f3 are displayed in Fig 2 top left with standard error tubes per entry. The entries for v have, on average, an estimated standard error of 0.005, the entries for f2 have, on average, an estimated standard error of 0.02, and the entries for f3 have, on average, an estimated standard error of 0.04. doi:10.1371/journal.pone.0157257.t001 Fig 2. The three components of the low-rank model, and explanation of the world record data. Left: the components displayed (unit norm, log-time vs log-distance). Tubes around the components are one standard deviation, estimated by the bootstrap. The first component is an exact power law (straight line in log-log coordinates); the last two components are non-linear, describing transitions at around 800m and 10km. Middle: Comparison of first component and world record to the exact power law (log-speed vs log-distance). Right: Least-squares fit of rank 1-3 models to the world record data (log-speed vs log- distance). doi:10.1371/journal.pone.0157257.g002 Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 6 / 16 Results (I) Prediction accuracy. We evaluate prediction accuracy of ten methods, including our pro- posed method, LMC. We include, as naive baselines: (1.a) imputing the event mean, (1.b) imputing the average of the k-nearest neighbours; as representative of the state-of-the-art in quantitative sports science: (2.a) the Riegel formula, (2.b) a power law predictor with exponent estimated from the data, which is the same for all runners, (2.c) a power law predictor with exponent estimated from the data, with one exponent per runner, (2.d) the Purdy points scheme [7]; as representatives for the state-of-the-art in matrix completion: (3.a) imputation by expectation maximization on a multivariate Gaussian [30] (3.b) nuclear norm minimization [31, 32]. We instantiate our low-rank local matrix completion (LMC) in two variants: (4.a) rank 1, and (4.b) rank 2. Methods (1.a), (1.b), (2.a), (2.b), (2.d), (4.a) require at least one observed performance per runner, methods (2.c), (4.b) require at least two observed performances in distinct events. Meth- ods (3.a), (3.b) will return a result for any number of observed performances (including zero). Prediction accuracy is therefore measured by evaluating the RMSE and MAE out-of-sample on the runners who have attempted at least three distances, so that the two necessary performances remain to calculate the prediction when one is removed for leave-one-out validation. Prediction is further restricted to the best 95-percentile of runners (measured by performance in the best event) to reduce the effect of outliers. Whenever the method demands that the predicting events need to be specified, the events which are closest in log-distance to the event to be predicted are taken. The accuracy of predicting time (normalized w.r.t. the event mean), log-time, and speed are measured. We repeat this validation setup for the year of best performance and a random cal- endar year. Moreover, for completeness and comparison we treat 2 additional cases: the top 25% of runners and runners who have attempted at least 4 events, each in log time. More details on methods and validation are presented in the methods appendix. The results are displayed in Table 2 (RMSE of log-time prediction) and supplementary Table B in S1 Supplement (MAE of log-time prediction), S4 (rel.RMSE of time prediction) and S5 (rel. MAE of time prediction). Of all benchmarks, k-nearest neighbours (1.b), Purdy points (2.d) and Expectation Maximization (3.a) perform best. LMC rank 2 substantially outperforms k-nearest neighbours, Purdy points and Expectation Maximization (two-sided Wilcoxon signed-rank test significant on the validation samples of absolute prediction errors; p2.0e-8 on top 95% in log-time and p1.4e-11 for top 25% in log-time); rank 1 outperforms Purdy points on the year of best performance data (p3.0e-3) for the best runners, and is on a par on runners up to the 95th percentile. Both rank 1 and 2 outperform the power law models (p1.1e-42), the improvement in RMSE of LMC rank 2 over the power law models reaches over 50% for data from the fastest 25% of runners. (II) The rank (number of components) of the model. Paragraph (I) establishes that LMC is the best method for prediction. LMC assumes a fixed number of prototypical runners, viz. the rank r, which is the complexity parameter of the model. We establish the optimal rank by comparing prediction accuracy of LMC with various ranks. The rank r algorithm requires r attempted events for prediction, thus r + 1 observed events are needed for validation. Table F in S1 Supplement displays prediction accuracies for LMC ranks r = 1 to r = 4, on the runners who have attempted k > r events, for all k 5. The data is restricted to the top 25% in the year of best performance in order to obtain a high signal to noise ratio. We observe that rank 3 out- performs all other ranks, when applicable; rank 2 aways outperforms rank 1 (both p1e-4). We also find that the improvement of rank 2 over rank 1 depends on the event predicted: improvement is 26.3% for short distances (100m, 200m), 29.3% for middle distances (400m, Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 7 / 16 Table 2. Out-of-sample RMSE for prediction methods on different data setups. Generic Baselines State of art Performance Predictors State of art Matrix Completion Proposed Method: LMC evaluation percentiles no.events data type r.mean k-NN individual power law riegel power law purdy nuclear norm EM LMC rank 1 LMC rank 2 log time 0-95 3 best 0.1308 ± 0.0032 0.0618 ± 0.0027 0.1033 ± 0.0042 0.0982 ± 0.0046 0.0973 ± 0.0046 0.0610 ± 0.0031 0.3909 ± 0.0457 0.0566 ± 0.0028 0.0586 ± 0.0026 0.0515 ± 0.0027 normalized 0-95 3 best 0.1364 ± 0.0044 0.0716 ± 0.0046 0.1067 ± 0.0048 0.1059 ± 0.0066 0.1050 ± 0.0065 0.0684 ± 0.0043 0.1900 ± 0.0120 0.0634 ± 0.0045 0.0643 ± 0.0038 0.0576 ± 0.0039 speed 0-95 3 best 0.6655 ± 0.0147 0.3057 ± 0.0146 0.6096 ± 0.0245 0.5467 ± 0.0251 0.5415 ± 0.0243 0.3077 ± 0.0176 26.6210 ± 11.4828 0.2922 ± 0.0165 0.3123 ± 0.0149 0.2530 ± 0.0129 log time 0-95 3 random 0.1380 ± 0.0032 0.0544 ± 0.0025 0.0931 ± 0.0035 0.0931 ± 0.0038 0.0919 ± 0.0038 0.0591 ± 0.0028 0.4416 ± 0.0428 0.0561 ± 0.0031 0.0567 ± 0.0027 0.0471 ± 0.0024 normalized 0-95 3 random 0.1450 ± 0.0043 0.0623 ± 0.0037 0.0951 ± 0.0039 0.1011 ± 0.0048 0.0998 ± 0.0046 0.0682 ± 0.0038 0.2046 ± 0.0117 0.0634 ± 0.0039 0.0640 ± 0.0037 0.0538 ± 0.0035 speed 0-95 3 random 0.6935 ± 0.0143 0.2585 ± 0.0117 0.5917 ± 0.0312 0.5052 ± 0.0176 0.4979 ± 0.0167 0.2835 ± 0.0137 24.7206 ± 10.9157 0.2801 ± 0.0196 0.2863 ± 0.0120 0.2261 ± 0.0105 log time 0-95 4 best 0.1268 ± 0.0032 0.0735 ± 0.0030 0.0777 ± 0.0024 0.0819 ± 0.0032 0.0822 ± 0.0032 0.0581 ± 0.0023 0.1779 ± 0.0199 0.0529 ± 0.0024 0.0536 ± 0.0021 0.0467 ± 0.0022 log time 0-25 3 best 0.0557 ± 0.0015 0.0416 ± 0.0014 0.0806 ± 0.0031 0.0683 ± 0.0026 0.0720 ± 0.0026 0.0411 ± 0.0012 0.3008 ± 0.0275 0.0383 ± 0.0013 0.0411 ± 0.0014 0.0306 ± 0.0011 Predicted performance is of the 95 and 25 top percentiles of male runners who attempted 3 or 4 events resp., in their best year and a random calendar year. Standard errors are bootstrap estimates over 1000 repetitions. Compared method classes are (1) generic baselines, (2) state-of-the-art in performance prediction, (3) state-of-the-art in matrix completion, (4) local matrix completion (columns). Methods are (1.a) r.mean: predicting the mean of all runners (1.b) k-NN: predicting the nearest neighbor. (2.a) riegel: Riegel’s formula (2.b) power law: power law with free exponent and coefﬁcient. Exponent is the same for all runners. (2.c) ind.power law: power law with free exponent and coefﬁcient. (2.d) purdy: Purdy points scheme (3.a) EM: expectation maximization (3.b) nuclear norm: nuclear norm minimization (4.a) LMC with rank 1 (4.b) LMC with rank 2. Data setup is speciﬁed by (i) evaluation: what is predicted. log-time = natural logarithm of time in seconds, normalized = time relative to mean performance, speed = average speed in meters per seconds, (ii) percentiles: selected percentile range of runners, (iii) no.events tried = sub-set of runners who have attempted at least that number of different events, (iv) data type: collation mode of performance matrix; best = 1 year around best performance, random = random 2 year period. LMC rank 2 signiﬁcantly outperforms all competitors in either setting. doi:10.1371/journal.pone.0157257.t002 Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 8 / 16 800m, 1500m), 12.8% for the mile to half-marathon, and 3.1% for the Marathon (all significant at p = 1e-3 level) (see Fig A in S1 Supplement). These results indicate that inter-runner variabil- ity is greater for short and middle distances than for Marathon. (III) The three components of the model. The findings in (II) imply that the best low-rank model assumes 3 components. To estimate the components (fi in Eq (1)) we impute all missing entries in the data matrix of the top 25% runners who have attempted 4 events and compute its singular value decomposition (SVD) [33]. From the SVD, the exact form of components may be directly obtained as the right singular vectors (in a least-squares sense, and up to scaling, see supplemental analysis II.b in S1 Supplement). We obtain three components in log-time coordi- nates, which are displayed in the left hand panel of Fig 2. The first component for log-time pre- diction is linear (i.e., f1(s) / log s in Eq (1)) to a high degree of precision (R2 = 0.9997) and corresponds to an individual power law, applying distinctly to each runner. The second and third components are non-linear; the second component decreases over short sprints and increases over the remainder, and the third component resembles a parabola with extremum positioned around the middle distances. In speed coordinates, the first, individual power law component does not display the “bro- ken power law” behaviour of the world records (rank 1 component: goodness-of-fit for linear model R2 = 0.99; world-record data: R2 = 0.93). Deviations from an exact line can be explained by the second and third component (Fig 2 middle). The three components together explain the world record data and its “broken power law” far more accurately than a simple linear power law trend—with the rank 3 model fitting the world records almost exactly (Fig 2 right). (IV) The three runner-specific coefficients. The three summary coefficients for each run- ner (λ1, λ2, λ3 in Eq (1)) are obtained from the entries of the left singular vectors (see methods appendix). Since all three coefficients summarize the runner, we refer to them collectively as the three-number-summary. (IV.i) Fig 3 displays scatter plots and Spearman correlations between the coefficients and performance over the full range of distances. The individual exponent correlates with perfor- mance over distances greater than 800m. The second coefficient correlates positively with per- formance over short distances and displays a non-linear association with performance over Fig 3. Matrix scatter plot of the three-number-summary vs performance. For each of the scores in the three-number-summary (rows) and each event distance (columns), the plot matrix shows: a scatter plot of performances (time) vs the coefficient score of the top 25% (on the best event) runners who have attempted at least 4 events. Each scatter plot in the matrix is colored on a continuous color scale according to the absolute value of the scatter sample’s Spearman rank correlation (red = 0, green = 1). doi:10.1371/journal.pone.0157257.g003 Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 9 / 16 middle distances. The third coefficient correlates with performance over middle distances. (All correlations are significant at p1.0e-4; t-distribution approximation to the distribution of Spearman’s correlation coefficient.) The associations for all three coefficients are non-linear, with the notable exception of the individual exponent on distances exceeding 800m. (IV.ii) Fig 4 top displays the three-number-summary for the top 95% runners in the data- base. The runners separate into (at least) four classes, which are associated with the runner’s preferred distance. A qualitative transition can be observed over middle distances. Three-num- ber-summaries of world class runners (not all in the UK runners database), computed from their personal bests, are listed in Table 3; they and also shown as highlighted points in Fig 4 top right. The elite runners trace a frontier around the population: all elite runners are subject to a low individual exponent. A hypothetical runner holding all the world records is also shown in Fig 4 top right, obtaining an individual exponent which comes close to the world record expo- nent estimated by Riegel [3] (1.08 for elite runners, 1.06 for senior runners). (IV.iii) Fig 4 bottom left shows that a low individual exponent correlates positively with per- formance in a runner’s preferred event. The individual exponents are higher on average (median = 1.12; 5th, 95th percentiles = 1.10, 1.15) than the world record exponents estimated by Riegel. Fig 4. Scatter plots exploring the three number summary. Top left and right: 3D scatter plot of three- number-summaries of runners in the data set, colored by preferred distance and shown from two angles. A negative value for the second score is a indicates that the runner is a sprinter, a positive value an endurance runner. In the top right panel, the summaries of the elite runners Usain Bolt (world record holder, 100m, 200m), Mo Farah (world beater over distances between 1500m and 10km), Haile Gabrselassie (former world record holder from 5km to Marathon) and Takahiro Sunada (100km world record holder) are shown; summaries are estimated from their personal bests. For comparison we also display the hypothetical data of a runner who holds all world records. Bottom left: preferred distance vs individual exponents, color is percentile on preferred distance. Bottom right: age vs. exponent, colored by preferred distance. doi:10.1371/journal.pone.0157257.g004 Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 10 / 16 (IV.iv) Fig 4 bottom right shows that in cross-section, the individual exponent decreases with age until 20 years, and subsequently increases. (All correlations significant at p1.0e-4; t- distribution approximation to the distribution of Spearman’s correlation coefficient.) (V) Phase transitions. We observe two transitions in behaviour between short and long dis- tances. The data exhibit a phase transition around 800m: the second component exhibits a kink and the third component makes a zero transition (Fig 2); the association of the first two scores with performance shifts from the second to the first score (Fig 3). The data also exhibits a transition around 5000m. We find that for distances shorter than 5000m, holding the event performance constant and increasing the standard of shorter events leads to a decrease in the predicted standard of longer events and vice versa. On the other hand for distances greater than 5000m this behaviour reverses; holding the event performance constant, and increasing the standard of shorter events leads to an increase in the predicted standard of longer events. See supplementary analysis IV in S1 Supplement for details. (VI) Universality over subgroups. Qualitatively and quantitatively similar results to the above can be deduced for female runners, and subgroups stratified by age or training standard; LMC remains an accurate predictor, and the low-rank model has similar form. See supplemen- tal analysis II.c in S1 Supplement. Discussion We have presented the most accurate existing predictor for running performance to date— local low-rank matrix completion (finding I); its predictive power confirms the validity of a three-component model (finding II) that offers a parsimonious explanation for many known phenomena in the quantitative science of running, including answers to some of the major open questions of the field. More precisely, we establish: The individual power law. In log-time coordinates, the first component of our physiologi- cal model is linear with high accuracy, yielding an individual power law (finding III). This is a novel and rather surprising finding, since, although world-record performances are known to obey a power law [1–6], there is no reason to suppose a-priori that the performance of individ- uals is governed by a power law. Striking is that the power-law derived is considerably more accurate when considered in log-distance—log-speed coordinates than the power-law which applies to world-record data. This parsimony a-posteriori unifies (A) the parsimony of the Table 3. Estimated three-number-summary (λi) for a selection of elite runners. runner Specialization Individual Exponent (λ1) Score 2 (λ2) Score 3 (λ3) Usain Bolt Sprints 1.11 -0.367 0.081 Mo Farah Middle-Long 1.08 0.033 -0.076 Haile Gabrselassie Long 1.08 0.114 -0.056 Galen Rupp Long 1.08 0.104 -0.040 Seb Coe Middle 1.09 -0.085 -0.036 Takahiro Sunada Ultra-Long 1.09 0.138 -0.010 Paula Radcliffe Long (Female) 1.10 0.189 0.025 The scores λ1, λ2, λ3 are as in Eq (1). Since component 1 is a power law (see the top-left of Fig 2), λ1 may be interpreted as an individual exponent. See the bottom right panel of Fig 4 for a scatter plot of the runners in the database. doi:10.1371/journal.pone.0157257.t003 Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 11 / 16 power law with the (B) empirical correctness of scoring tables. To what extent this individual power law is exact is to be determined in future studies. An explanation of the world record data. The broken power law [5] of world record data can be seen as a consequence of the individual power law and the non-linearity in the second and third component (finding III) of our low-rank model. The breakage point in the world rec- ords can be explained by the differing contributions in the non-linear components of the dis- tinct individuals holding the world records. Savaglio and Carbone [5] hypothesize that the breakpoint in the log-speed—log-distance slope of world-record data, which occurs between short and long distance events, is due to a transition in the physiology required between short and long-distance events. Our analyses indeed show that their exist breakpoints, manifested in the second and third components of the low-rank model. However our findings show that the claim that there is a universal physiological transition manifesting itself in the differing slopes of short and long-distance world-record data is unwarranted. Runners who exhibit small values for the 2nd and 3rd numbers in their three number summaries will exhibit performances close to log-log with little or no transition; this is because the first component of the model is much closer to scale-free (log-log straight line) than world-record data. Some runners will indeed dis- play an upward kink in their average speed as is the case with world-record data. Other runners will exhibit transitions corresponding to a quicker fall off in average speed rather than faster, i.e. a downwards kink. Thus the validity of the three component model points to a far more complex description and diversity of average speed than world record data suggest. Crucially both the power law and the broken power law on world record data can be under- stood as epiphenomena of the individual power law and its non-linear corrections. Universality of our model. The low-rank model remains unchanged when considering dif- ferent subgroups of runners, stratified by gender, age, or calendar year; only the individual three-number-summaries change (finding VI). This shows the low-rank model to be universal for running. The three-number-summary reflects a runner’s training state. Our predictive validation implies that the number of components of our model is three (finding II), which yields three numbers describing the training state of a given runner (finding IV). The most important sum- mary is the individual exponent for the individual power law which describes overall perfor- mances for distances longer than 400m (IV.iii). The second coefficient describes whether the runner has greater endurance (positive) or speed (negative) and predicts performances over the sprint distances, the third describes specialization over middle distances (negative) vs. short and long distances (positive). All three numbers together clearly separate the runners into four clusters, which fall into two clusters of short-distance runners and one cluster of middle-and long-distance runners respectively (IV.i). Our analysis provides strong evidence that the three- number-summary captures physiological and/or social/behavioural characteristics of the run- ners, e.g., training state, specialization, and which distance a runner chooses to attempt. While the data set does not allow us to separate these potential influences or to make statements about cause and effect, we conjecture that combining the three-number-summary with specific experimental paradigms will lead to a clarification; further, we conjecture that a combination of the three-number-summary with additional data, e.g. training logs, high-frequency training measurements or clinical parameters, will lead to a better understanding of (C) existing physio- logical models. Some novel physiological insights can be deduced from leveraging our model on the UK runners database: • We find that the individual exponent correlates with performances over distances greater than 400m and especially long distances above 5km (finding III). We also find that LMC is Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 12 / 16 most effective for the longer-sprints and middle distances; the improvement of the higher rank over the rank 1 version is lowest over the marathon distance (supplemental analysis I.c in S1 Supplement). This indicates that the variability in performances on long distances may to a large extent be explained by a single factor, which may imply that there is only one way to be a fast marathoner. On the other hand since we find that the rank-2 and 3 versions far outperform the rank-1 version over middle distances, this may be interpreted in terms of some runners using a high maximum velocity to coast whereas other runners using greater endurance to run closer to their maximum speed for the duration of the race; if the type of running (coasting vs. endurance) is a physiological correlate to the specialization summary (as hypothesized above), it could imply that the “one way” corresponds to possessing a high level of endurance—as opposed to being able to coast relative to a high maximum speed. In any case, the low-rank model predicts that a marathoner who is not close to world class over 10km is unlikely to be a world class marathoner. • The phase transitions which we observe (finding V) provide additional observational evi- dence for a transition in the complexity of the physiology underlying performance between long and short distances. This finding is bolstered by the difference we observe between the increase in performance of the rank 2 predictor over the rank 1 predictor for short/middle distances over long distances. Notice, however, that this is quite different evidence than the kink in the power-law of world-record speeds [5], which we argued above does not necessar- ily imply the presence of transitions at the level of the individual runner. Our results may have implications for existing hypotheses and findings in sports science on the differences in physiological determinants of long and short distance running respectively. These include differences in the muscle fibre types contributing to performance (type I vs. type II) [34, 35], whether the race length demands energy primarily from aerobic or anaerobic metabolism [20, 36], which energy systems are mobilized (glycolysis vs. lipolysis) [37, 38] and whether the race terminates before the onset of a _VO2 slow component [39, 40]. We conjecture that the combination of our methodology with experiments will shed further light on these differences. • An open question in the physiology of aging is whether sprinting power or endurance capa- bilities diminish faster with age. Our analysis provides cross-sectional evidence that training standard decreases with age, and specialization shifts away from endurance: a larger expo- nent is correlated with worse performance on endurance type events (finding IV.i), and exponents increase, in cross-section, with age (finding IV.iv). This confirms observations of Rittweger et al. [41] on masters world-record data. There are multiple possible explanations for this, for example longitudinal changes in specialization, or selection bias due to the dis- tances older runners prefer; our model renders these hypotheses amenable to quantitative validation. • We find that there are a number of high-standard runners who attempt distances different from their inferred best distance; most notably a cluster of young runners (<25 yrs.) who run short distances (mostly in accordance with legal limitations of participation), and a clus- ter of older runners (>40 yrs.) who run long distances, but who we predict would perform better on longer resp. shorter distances. Moreover, the third component of our model implies the existence of runners with very strong specialization in their best event; there are indeed high profile examples of such runners, such as Zersenay Tadese, who holds the half-mara- thon world best performance (58:23) but has as yet to produce a marathon performance even close to this in quality (best performance, 2:10:41). Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 13 / 16 We also anticipate that our framework will prove fruitful in equipping the practioner with new methods for prediction and quantification: • Individual predictions are crucial in race planning, especially for predicting a target perfor- mance for events such as the Marathon for which months of preparation are needed; the abil- ity to accurately select a realistic target speed could potentially make the difference between a runner achieving a personal best performance and “hitting the wall” or at worst dropping out of the race from exhaustion. N.B.: We would like to stress that using a prediction as part of marathon preparation without professional support may lead to injury and is entirely at the risk of the user. • Predictions and the three-number-summary yield a concise description of the runner’s spe- cialization and training state and are thus of immediate use in training assessment and plan- ning, for example in determining the potential effect of a training scheme or finding the optimal event(s) for which to train. N.B.: We would like to stress that our study is not able to assign a conclusive meaning to the three-number summary, due to the limitations of the data set; therefore decisions should not be based on a hypothesized interpretation without consideration. • The presented framework allows, in principle, for the derivation of novel and more accurate scoring schemes, including scoring tables for any type of population. N.B.: We would like to stress that the form of the derived scoring tables may depend on the selection of the data from which they are derived. • Predictions for elite runners allow for a more precise estimation of quotas and betting risk. For example, we predict that a fair race between Mo Farah and Usain Bolt is over 492m (374- 594m with 95% confidence), Chris Lemaitre and Adam Gemili have the calibre to run 43.5 (±1.3) and 43.2 (±1.3) resp. seconds over 400m. Kenenisa Bekele is capable, in a training state where he can achieve his personal bests over 5km, 10km and the half-marathon, of a 2:00:36 marathon (±3.6 mins). N.B.: We would like to stress that such predictions need to be taken with much caution, as they are only correct insofar as our model extends, from the top 25% of UK runners (who success- fully participated in official events), to the very extremes of human performance. We further conjecture that the physiological laws we have validated for running will be immediately transferable to any sport where a power law has been observed on the collective level, such as swimming, cycling, and horse racing. Supporting Information S1 Supplement. Additional analyses and method details with corresponding figures and tables. (PDF) Acknowledgments DAJB was supported by a grant from the German Research Foundation, research training group GRK 1589/1 “Sensory Computation in Neural Systems”. FJK was partially supported by Mathematisches Forschungsinstitut Oberwolfach (MFO). This research was partially carried out at MFO with the support of FJK’s Oberwolfach Leibniz Fellowship. We thank thepo- werof10.info for permission to use their database for this paper, Ryota Tomioka for providing us with his code for matrix completion via nuclear norm minimization, and for advice on its Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 14 / 16 use, Louis Theran for advice regarding the implementation of local matrix completion in higher ranks. We thank Denis Bafounta, Renato Canova, Tim Grose, Florian Lorenz, Klaus- Robert Müller and Franz Wölfle for remarks, and discussion of the concepts and results pre- sented in the manuscript. Author Contributions Conceived and designed the experiments: DAJB FJK. Performed the experiments: DAJB. Ana- lyzed the data: DAJB FJK. Contributed reagents/materials/analysis tools: DAJB FJK. Wrote the paper: DAJB FJK. Conceived the LMC algorithm in higher ranks: FJK. 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Proceedings of the Royal Society B: Biological Sciences. 2009; 276(1657):683–689. doi: 10.1098/rspb.2008.1319 PMID: 18957366 Prediction and Quantification of Individual Athletic Performance of Runners PLOS ONE \| DOI:10.1371/journal.pone.0157257 June 23, 2016 16 / 16	Prediction and Quantification of Individual Athletic Performance of Runners.	06-23-2016	Blythe, Duncan A J,Király, Franz J	eng
PMC7573630	Supplementary Information Efﬁcient trajectory optimization for curved running using a 3D musculoskeletal model with implicit dynamics Marlies Nitschke1,, Eva Dorschky1, Dieter Heinrich2, Heiko Schlarb3, Bjoern M. Eskoﬁer1, Anne D. Koelewijn1,4, and Antonie J. van den Bogert4 1Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-Universit¨at Erlangen-N¨urnberg (FAU), Erlangen, Germany 2Department of Sport Science, University of Innsbruck, Innsbruck, Austria 3adidas AG, Herzogenaurach, Germany 4Department of Mechanical Engineering, Cleveland State University, Cleveland, USA marlies.nitschke@fau.de S1 Model Adaptations The proposed “running model for motions in all directions”, short “runMaD”, was adapted from a model proposed by Hamner et al.1 in order to simulate running with directional changes. We changed the sequence of pelvis rotations from tilt (z), list (x), rotation (y) to rotation (y), obliquity (x), tilt (z) (see Fig. 1 for the global coordinate system). The updated rotation sequence is in agreement with clinical understanding2. Hence, results are interpretable in clinical analysis independently of the movement direction. Furthermore, this sequence simpliﬁes the speciﬁcation of directional tasks in simulations, e.g. in the presented curved running simulation. Pronation and supination of the foot was enabled by unlocking the subtalar joint. The metatarsophalangeal (mtp) joint was also unlocked for roll over of the foot. For both joints, a range of motion from −90◦ to 90◦ was allowed. In order to ﬁt the recorded data of fast running, the upper limit of knee ﬂexion was increased from 120◦ to 160◦. Additionally, the range of motion of the pronation/supination angle at the elbow was enlarged from [0◦,90◦] to [0◦,150◦] and the default pronation/supination angle was set to 90◦ such that the palms were pointing towards the body for zero torque. The default elbow ﬂexion was set to 5◦ to be within the range of motion. S2 System Dynamics The system dynamics were described implicitly as a function f() of the states x, the state derivatives ˙x, and the controls u (Eq. 1). The function f() contained • identities ˙q− dq dt = 0 for each degree of freedom (DOF), • multibody dynamics for each DOF (Eq. S1), • activation dynamics for each muscle tendon unit (MTU) (Eq. S2), and • contraction dynamics for each MTU (Eq. S3). S2.1 Multibody Dynamics The multibody dynamics were deﬁned as follows: M(q) ¨q + C(q, ˙q) ˙q + G(q) − JcT Fc − τ = 0, (S1) where q contained the global position, the global orientation, and the joint angles, ˙q contained the global velocities and joint angular velocities, M(q) was the mass matrix, C(q, ˙q) contained the Coriolis forces, G(q) contained the gravity forces, and Jc was the Jacobian of the contact forces Fc. τ was the sum of active joint torques generated by the muscles τmus (Eq. S12), passive joint torques τpas (Eq. S14), and joint torques due to external actuation torques τext (Eq. S15). The talus was assumed to be weightless to save computation within the multibody dynamics. This assumption can be made since the talus is a small body which cannot move independently from the other segments since no MTU is connected to it. S2.2 Muscle Dynamics The model was operated using 92 MTUs (see Table S1). MTUs were modeled as three element Hill-type muscles with a contractile element (CE), a parallel elastic element (PEE), and a series elastic element (SEE). The muscle dynamics were described as a function of the activation state a and the CE length state s: ˙a − r(ne) (ne − a) = 0, (S2) FSEE(lMTU(q),s) − (FCE(a,s, ˙s) + FPEE(s)) cos(φ(s)) = 0, (S3) where the state variable s = lCE cos(φ) denoted the projection of CE length on the muscle line of action for a speciﬁc pennation angle φ 3, ˙a denoted the time derivative of the activation a, ne denoted the neural excitation, and r(ne) denoted the activation dynamics, which were determined as follows: r(ne) = ne Tact + 1 − ne Tdeact . (S4) The activation time constant Tact = 10ms and the deactivation time constantand Tdeact = 40ms were identically to Hamner’s model1. In Eq. S3, FSEE denoted the force in the SEE, FCE denoted the force in the CE, and FPEE was the force in the PEE. The force in the CE was determined by FCE = a fFL(lCE) fFV(vCE) FISO , (S5) where FISO was the maximum isometric force, fFL(lCE) denoted the force-length relationship, and fFV(vCE) the force-velocity relationship. The force-length relationship was described as fFL(lCE) = exp − lCE − 1 w 2! , (S6) where lCE was the length of the CE normalized to the optimal ﬁbre length lCE,opt, w a width parameter of the muscle, equal to the square root of the shape factor used by Thelen4. The force-velocity relationship was described as fFV(vCE) = ( λ vCE,max + vCE λ vCE,max − vCE / A if vCE < 0 gmax vCE + λ cFV vCE + λ cFV if vCE ≥ 0, (S7) where vCE was the normalized CE velocity, λ = 0.5025 + 0.5341a5 modeled the activation dependence of the normalized maximum shortening velocity vCE,max = 10lCE,opt/s4. gmax = 1.8 was the maximum force ampliﬁcation during lengthening4, A = 0.25 was the Hill curve parameter6, and cFV was determined as follows7: cFV = vCE,max A (gmax − 1) A + 1 . (S8) The force in the SEE and PEE were modeled as non-linear springs7: F(l) = ( FISO k1 (l − lslack) if l ≤ lslack FISO k1 (l − lslack) + FISO k2 (l − lslack)2 if l > lslack , (S9) where l denoted the length of the element normalized to optimal ﬁber length lCE,opt. It was equal to lMTU(q) − lCE for the SEE, and equal to s for the PEE. lslack was the slack length normalized to optimal ﬁber length lCE,opt, k1 = 1 was a small linear stiffness, which was added to aid the optimization such that the derivative with respect to the model states was never zero, and k2 was the non-linear stiffness, which was equal to the following for the PEE and SEE, respectively: k2,PEE = 1 w2 , (S10) k2,SEE = 1 (h lslack,SEE)2 , (S11) where h = 0.04 was the strain of the muscle at isometric force8,9. 2/8 Table S1. Abbreviations of muscles used in the model “runMaD”. Abbreviation Muscle Name glut_min/med/max gluteus minimus/medius/maximus semimem semimembranosus semiten semitendinosus bifemsh/bifemlh biceps femoris (short/long head) sar sartorius add_long/brev/mag adductor longus/brevis/magnus tﬂ tensor fascia latae pect pectineus grac gracilis iliacus iliacus psoas psoas quad_fem quadratus femoris gem gemellus peri piriformis rect_fem rectus femoris vas_med/int/lat vastus medialis/intermedius/lateralis med/lat_gas gastrocnemius (medial/lateral head) soleus soleus tip_post/ant tibialis posterior/anterior ﬂex_dig/hal ﬂexor digitorum/hallucis per_long/brev/tert peroneus longus/brevis/tertius ext_dig/hal extensor digitorum/hallucis ercspn erector spinae intobl/extobl internal/external abdominal oblique S2.3 Muscle-Joint Coupling The torque in DOF j generated by muscle i was determined using the following equation: τmus,i,j = − ∂ lMTU,i(q) ∂ qj FSEE,i (lMTU,i(q),lCE,i), (S12) where lMTU,i(q) denoted the normalized muscle-tendon length depending on the current pose deﬁned by the generalized coordinates q, and ∂ lMTU,i(q) ∂ qj denoted the muscle moment arm. A constant muscle moment arm would not have been accurate enough in the 3D model3. Hence, a polynomial function was ﬁtted to describe the muscle-tendon length depending on the joint angles to match the moment arms available in OpenSim, since polynomials have well deﬁned derivatives. The order of the polynomial was chosen such that the root mean square error in the moment arms was less than 5% of the maximum moment arm for each muscle. The maximum possible order was set to four. Additionally, the range of motion used to determine the polynomial was reduced if the muscles wrapped around the bones incorrectly for large joint angles. Linear interpolation was used outside of this range of motion. The following ranges were used: hip ﬂexion [−28◦,78◦], hip adduction [−13◦,13◦], hip rotation [−3◦,18◦], knee angle [−118◦,8◦], ankle angle [−38◦,38◦], subtalar angle [−13◦,13◦], mtp angle [−8◦,48◦], lumbar extension [−38◦,3◦], lumbar bending [−8◦,8◦], and lumbar rotation [−18◦,18◦]. S2.4 Passive Joint Torques Passive torques were added to the joint torques when the joint angle was outside of the normal range of motion. These torques were determined as follows: τpas,out, j = ( K2 (qj −qj,min)2 if q j < q j,min −K2 (qj −qj,max)2 if q j > q j,max , (S13) where K2 = 5000Nmrad−2. The range of motions, deﬁned by qj,min and qj,max for the trunk and legs are the same as those used for the muscle moment arms. For the arms, the following ranges were used: arm ﬂexion [−88◦,88◦], arm adduction [−118◦,88◦], arm rotation [−88◦,88◦], elbow ﬂexion [2◦,148◦], and pronation/supination [2◦,148◦]. For numerical reasons, a small stiffness was used for the full range of motion, such that the derivative of the joint moment with respect to the joint angle was never zero: τpas, j = τpas,out, j −K1 (qj − q j,neutral) − B ˙qj , (S14) 3/8 where the stiffness was K1 = 1Nmrad−1 and the damping was equal to B = 1Nmsrad−1. qj,neutral was the neutral position of DOF j deﬁned as default values in the OpenSim model ﬁle runMaD.osim (see supplementary material at www.simtk.org). S2.5 Arm Torques The DOF j of a arm was directly actuated by the torque τext,j = m j 10Nm, (S15) with torque control m j. For numerical reasons, a scaling was performed to obtain states and controls of same magnitude. S2.6 Penetration-Based Ground Contact Model Eight contact points were used at each foot to describe the contact with the ground. Four contact points were located at the toe segment and four at the calcaneus segment. In the OpenSim model, their location relative to the respective segment origin was deﬁned using marker objects (see runMaD.osim). The vertical ground reaction force (GRF) in each contact point c was determined based on the ground penetration d: Fc,y(d) = k d (1 − b ˙pc,y), (S16) where k = 100BWm−1 was the stiffness of the ground normalized to body weight (BW) per meter. The damping constant b = 0.75sm−110 was multiplied with the vertical velocity of the contact point ˙pc,y. The ground penetration d was determined from the vertical position of the contact point pc,y and the size of the transition region pc,y,0 = 10−3 m between contact and no contact: d = 1 2 q p2c,y + p2 c,y,0 − pc,y . (S17) The horizontal GRFs in x- and z-directions were determined using a continuous approximation of the Coulomb friction: Fc,x(Fc,y, ˙pc,x) = − µk Fc,y ˙pc,x q ˙p2c,x + ˙p2 c,x,0 , (S18) Fc,z(Fc,y, ˙pc,z) = − µk Fc,y ˙pc,z q ˙p2c,z + ˙p2 c,z,0 , (S19) where µk = 1 was the kinetic friction coefﬁcient, ˙pc,x and ˙pc,z were the sliding velocities of the contact point, and ˙pc,x,0 = ˙pc,z,0 = 10−2 ms−1 was a small velocity parameter that ensured that the force was differentiable around zero velocity. S3 Simulations The bounds of states x and controls u used in the three optimization examples are summarized in Table S2. For standing, smaller ranges were chosen compared to running to avoid the simulation terminating in local optima. S4 Results Pelvis translation and joint moments of straight and curved running are shown in Figs. S1 and S2. 4/8 Table S2. Bounds used to simulate standing, straight running, and curved running. For straight running, the pelvis position at the ﬁrst node was ﬁxed to qpel_tx[0] = 0 and qpel_tz[0] = 0. For curved running, the pelvis position at the ﬁrst node was ﬁxed to qpel_tx[0] = −r and qpel_tz[0] = 0. ∆t denotes the duration between two collocation nodes. Parameter Unit Standing Running Lower Upper Lower Upper qpelvis_rotation degree 0 0 -90 90 qpelvis_obliquity degree -5 5 -90 90 qpelvis_tilt degree -30 30 -90 90 qpel_tx m 0 0 -5 7 qpel_ty m 0.5 1.5 -1 2 qpel_tz m 0 0 -3 3 qhip_flexion degree -30 30 -120 120 qhip_adduction degree -10 10 -120 120 qhip_rotation degree -30 30 -120 120 qknee_angle degree -30 10 -160 10 qankle_angle degree -30 30 -90 90 qsubtalar_angle degree -30 30 -90 90 qmtp_angle degree -30 30 -90 90 qlumbar_extension degree -10 10 -90 90 qlumbar_bending degree -10 10 -90 90 qlumbar_rotation degree -10 10 -90 90 qarm_flex degree -40 40 -40 40 qarm_add degree -40 40 -40 40 qarm_rot degree -40 40 -40 40 qelbow_flex degree 0 150 0 150 qpro_sup degree 0 150 0 150 ˙q rads−1 or ms−1 -30 30 -30 30 s - 0 5 0 5 a - 0 5 0 5 ne - 0 5 0 5 m - -5 5 -5 5 5/8 0 100 0 3.5 Translation in m pelvis_tx 0 100 0.95 1.15 pelvis_ty 0 100 −0.1 0.1 pelvis_tz 0 100 −250 150 Moment in Nm hip_ﬂexion 0 100 −250 100 hip_adduction 0 100 −70 70 hip_rotation 0 100 −150 400 knee_angle 0 100 −300 100 Moment in Nm ankle_angle 0 100 −40 120 subtalar_angle 0 100 −30 30 mtp_angle 0 100 −100 300 lumbar_extension 0 100 −250 150 Moment in Nm lumbar_bending 0 100 −100 100 lumbar_rotation 0 100 −30 30 arm_ﬂex 0 100 −30 30 Gait Cycle in % arm_add 0 100 −30 30 Gait Cycle in % Moment in Nm arm_rot 0 100 −30 30 Gait Cycle in % elbow_ﬂex 0 100 −30 30 Gait Cycle in % pro_sup Simulated Measured: Mean ± SD Torso Right side Left side Figure S1. Pelvis translation and joint moments of the straight running simulation. The degrees of freedom (DOFs) are named according to their deﬁnition in the model ﬁle runMaD.osim. Black, red, and blue solid lines indicate the simulated variables of the torso, the right side, and left side, respectively. Shaded areas show mean ± standard deviation (SD) of inverse dynamics (ID) of the measured gait cycles of straight running. 6/8 0 100 −4 3 Translation in m pelvis_tx 0 100 0.9 1.1 pelvis_ty 0 100 0 2 pelvis_tz 0 100 −250 150 Moment in Nm hip_ﬂexion 0 100 −250 100 hip_adduction 0 100 −70 70 hip_rotation 0 100 −150 400 knee_angle 0 100 −300 100 Moment in Nm ankle_angle 0 100 −40 120 subtalar_angle 0 100 −30 30 mtp_angle 0 100 −100 300 lumbar_extension 0 100 −250 150 Moment in Nm lumbar_bending 0 100 −100 100 lumbar_rotation 0 100 −30 30 arm_ﬂex 0 100 −30 30 Gait Cycle in % arm_add 0 100 −30 30 Gait Cycle in % Moment in Nm arm_rot 0 100 −30 30 Gait Cycle in % elbow_ﬂex 0 100 −30 30 Gait Cycle in % pro_sup Simulated Measured: Mean ± SD Torso Right side Left side Figure S2. Pelvis translation and joint moments of the curved running simulation. The degrees of freedom (DOFs) are named according to their deﬁnition in the model ﬁle runMaD.osim. Black, red, and blue solid lines indicate the simulated variables of the torso, the right side, and left side, respectively. Shaded areas show mean ± standard deviation (SD) of inverse dynamics (ID) of the measured gait cycles of curved running. The horizontal pelvis translation cannot be directly compared to the measured data since the global frames were not aligned but rotated around the vertical axis. 7/8 References 1. Hamner, S., Seth, A. & Delp, S. L. Muscle contributions to propulsion and support during running. J. Biomech. 43, 2709–2716 (2010). 2. Baker, R. Pelvic angles: a mathematically rigorous deﬁnition which is consistent with a conventional clinical understanding of the terms. Gait & Posture 13, 1–6 (2001). 3. Van den Bogert, A. J., Blana, D. & Heinrich, D. Implicit methods for efﬁcient musculoskeletal simulation and optimal control. Procedia IUTAM 2, 297–316 (2011). 4. Thelen, D. G. Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults. J. Biomech. Eng. 125, 70 (2003). 5. Chow, J. W. & Darling, W. G. The maximum shortening velocity of muscle should be scaled with activation. J. Appl. Physiol. 86, 1025–1031 (1999). 6. Winters, J. M. An improved muscle-reﬂex actuator for use in large-scale neuromusculoskeletal models. Annals Biomed. Eng. 23, 359–374 (1995). 7. McLean, S. G., Su, A. & Van den Bogert, A. J. Development and validation of a 3-D model to predict knee joint loading during dynamic movement. J. biomechanical engineering 125, 864–874 (2003). 8. Van Soest, A. J. & Bobbert, M. F. The contribution of muscle properties in the control of explosive movements. Biol. cybernetics 69, 195–204 (1993). 9. Miller, R. H. Hill-based muscle modeling. In Handbook of Human Motion, 373–394 (Springer, 2018). 10. Gerritsen, K. G., van den Bogert, A. J. & Nigg, B. M. Direct dynamics simulation of the impact phase in heel-toe running. J. Biomech. 28, 661–668 (1995). 8/8	Efficient trajectory optimization for curved running using a 3D musculoskeletal model with implicit dynamics.	10-19-2020	Nitschke, Marlies,Dorschky, Eva,Heinrich, Dieter,Schlarb, Heiko,Eskofier, Bjoern M,Koelewijn, Anne D,van den Bogert, Antonie J	eng
PMC8307654	International Journal of Environmental Research and Public Health Article Motion Analysis of Match Play in U14 Male Soccer Players and the Inﬂuence of Position, Competitive Level and Contextual Variables Erling Algroy 1,, Halvard Grendstad 2, Amund Riiser 3, Tone Nybakken 2, Atle Hole Saeterbakken 3 , Vidar Andersen 3 and Hilde Stokvold Gundersen 2 Citation: Algroy, E.; Grendstad, H.; Riiser, A.; Nybakken, T.; Saeterbakken, A.H.; Andersen, V.; Gundersen, H.S. Motion Analysis of Match Play in U14 Male Soccer Players and the Inﬂuence of Position, Competitive Level and Contextual Variables. Int. J. Environ. Res. Public Health 2021, 18, 7287. https:// doi.org/10.3390/ijerph18147287 Academic Editors: Filipe Manuel Clemente, Ana Filipa Silva and Daniele Conte Received: 4 June 2021 Accepted: 5 July 2021 Published: 7 July 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Campus Bergen, NLA University College, 5812 Bergen, Norway 2 Department of Sport, Food and Natural Sciences, Campus Bergen, Western Norway University of Applied Sciences, 5020 Bergen, Norway; halvardg@nih.no (H.G.); tone.nybakken@hvl.no (T.N.); hilde.stokvold.gundersen@hvl.no (H.S.G.) 3 Department of Sport, Food and Natural Sciences, Campus Sogndal, Western Norway University of Applied Sciences, 6851 Sogndal, Norway; amund.riiser@hvl.no (A.R.); atle.saeterbakken@hvl.no (A.H.S.); vidar.andersen@hvl.no (V.A.) Correspondence: eaa@nla.no Abstract: This study aimed to investigate match running performance in U14 male soccer players in Norway, and the inﬂuence of position, competitive level and contextual factors on running performance. Locomotion was monitored in 64 different U14 players during 23 ofﬁcial matches. Matches were played at two different competitive levels: U14 elite level (n = 7) and U14 sub-elite level (n = 16). The inclusion criterion was completed match halves played in the same playing position. The variables’ inﬂuence on match running performance was assessed using mixed-effect models, pairwise comparisons with Bonferroni correction, and effect size. The results showed that the U14 players, on average, moved 7645 ± 840 m during a match, of which 1730 ± 681 m (22.6%) included high-intensity running (HIR, 13.5–18.5 km·h−1) and sprinting (>18.5 km·h−1). Wide midﬁelders (WM) and fullbacks (FB) covered the greatest sprint distance (569 ± 40 m) and, in addition to the centre midﬁeld position (CM), also covered the greatest total distance (TD) (8014 ± 140 m) and HIR distance (1446 ± 64 m). Centre forwards (CF) performed signiﬁcantly more accelerations (49.5 ± 3.8) compared other positions. TD (7952 ± 120 m vs. 7590 ± 94 m) and HIR (1432 ± 57 m vs. 1236 ± 43 m) were greater in U14 elite-level matches compared with sub-elite matches. Greater TD and sprint distances were performed in home matches, but, on the other hand, more accelerations and decelerations were performed in matches played away or in neutral locations. Signiﬁcantly higher TD, HIR and sprinting distances were also found in lost or drawn matches. In conclusion, physical performance during matches is highly related to playing position, and wide positions seem to be the most physically demanding. Further, competitive level and contextual match variables are associated with players’ running performance. Keywords: match running performance; youth soccer; positional differences; competitive levels; contextual variables 1. Introduction In the last decade, several studies have described the physical demands of youth soccer [1]. Previous reports have shown that U14 players cover a total distance between 105–115 m·min−1 during a match and 11.5–14.5 m·min−1 at higher speed (13.1–19 km·h−1) [2,3] Additionally, elite U14 players perform about 0.4 ± 0.2 sprints (speed above 25.2 km·h−1) [2] and 1.82 ± 0.33 accelerations per min during a match [2,4]. Although there is a growing body of evidence related to the physical match characteristics of youth soccer players, very few studies have examined running performance at the U14 level speciﬁcally in relation to high-explosive actions like sprints, accelerations and decelerations [1,5]. Due to the Int. J. Environ. Res. Public Health 2021, 18, 7287. https://doi.org/10.3390/ijerph18147287 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 7287 2 of 9 increasing physical match demands with age, more knowledge about performance at speciﬁc age levels is necessary to gain insights into the prerequisites of competing at U14 level. Additionally, the inﬂuence of competing standard and positional roles on players’ running performance seems to be an important factor in youth soccer [1] and must be accounted for. Although the scientiﬁc evidence is scarce, some studies have examined match running characteristics among youth players. These have reported that U13–U18 players’ physical match characteristics are affected by positional demands, as strikers and wide midﬁelders demonstrated the highest peak game speeds and frequency of high-intensity actions [5,6]. Additionally, centre midﬁelders have been reported to cover the highest total distance [5,6], whereas centre backs covered the lowest total distance [7] and the lowest amount of high-intensity actions [6]. A current limitation in research on youth soccer is the lack of information regarding the inﬂuence of contextual variables like match results, location and match status [1]. One previous study has shown that U14 elite level players outperform non-elite players regarding match running performance [2], suggesting a higher external demand during elite matches [7,8]. Studies from senior soccer have also reported greater match running performance in games lost compared with games won [9]. To date, no study has examined the match performance characteristics of U14 soccer players from Norway or other Scandinavian countries. Comparing data from different countries and regions of the world would be important to enhance our understanding of various approaches to and philosophies of talent development. Especially, more research regarding accelerations and decelerations is necessary in youth soccer. These high-intensity actions have been found to be crucial determinants for successful performance and to discriminate high- and low-level adult players [10]. Accelerations and decelerations also require high rates of force development and are therefore related to the total match load [11]. Additionally, studies on U14 players have, to the best of our knowledge, only been con- ducted in the United Kingdom, Qatar and New Zealand [1]. Hence, the ﬁrst aim of this study was therefore to analyse match running performance in U14 male soccer players in Norway. A second aim was to identify how playing position, competitive level and contextual factors inﬂuenced running performance. We hypothesise that players’ playing positions and competitive level inﬂuenced the amount of high-intensity actions in U14 soccer matches. 2. Materials and Methods The current study is part of a longitudinal research project, examining factors related to talent development in youth soccer [12]. The study design in the present study is descriptive, and match running performance data were collected during the 2018 season, from April to October. Collection of anthropometric data was performed during 2 different days for each player in the middle of the 2018 season. Contextual factors investigated in the present study were match results, match location and match halves. 2.1. Participants Sixty-four U14 male outﬁeld players (age: 14.0 ± 0.3 years, height: 166.3 ± 8.8 cm, weight: 51.9 ± 9.7 kg, body fat: 8.7 ± 3.3%) from 4 clubs with youth soccer academies were included. Three of the players were U13 players but were included as they played in the U14 teams investigated. The Regional Committee for Medical and Health Research Ethics approved the study (2017/1731), which was conducted in accordance with the Helsinki Declaration. Since players were under the legal age of consent, both the players and their parents gave written informed consent to participate. All results were treated anonymously. 2.2. Matches Match running performance was obtained from 23 matches, 15 at the team’s home location and 5 away and 3 on a neutral ﬁeld. In 17 of the matches, the teams won; 1 match Int. J. Environ. Res. Public Health 2021, 18, 7287 3 of 9 ended with a draw and 5 with a loss. All matches were ofﬁcial league or cup matches, played outdoor on regular-sized synthetic grass soccer ﬁelds with 11 players per side. Matches were played at 2 different competitive levels: U14 elite, national level (7 matches, 93 match halves), U14 sub-elite, the highest regional level (16 matches, 249 match halves). Players playing in elite teams are recruited into playing at professional youth academies and matches are played in the U14 national level league, in which youth academies from professional Norwegian clubs participate. Some players played matches at more than 1 level. Match playing time was 2 × 35 min in the U14 local series and 2 × 40 min for the U14 national series. All match performance outcomes were normalised to 35 min of playing time before the analyses to allow direct comparison between matches at different competing levels and to be comparable with other studies examining U14 players. Because of the “rolling substitution” policy resulted in a large variation in playing time between players, all match halves completed by 1 player in the same playing position were included in the dataset. The dataset comprised 342 match halves, including 187 ﬁrst halves and 155 second halves; 278 of the included halves were from complete matches played by 1 player. 2.3. GPS Tracking During the matches, the players wore a portable and previously validated [13] GPS device (Apex, STATSsport, Newry, UK) monitoring their motions and position with a sampling frequency of 18 Hz. The players wore, in most cases, the same unit in every game to limit inter-unit reliability issues, although the present GPS system has been shown to have excellent inter-unit reliability [14]. The units were placed in vests located between the players’ scapulae. The raw GPS data were synchronised to the start and end of each half match period and exported for further analysis. 2.4. Match Running Categories Match activities were divided into different speed categories: walking (0.1–4.5 km·h−1), low-intensity running (LIR, 4.5–8.5 km·h−1), medium-intensity running (MIR, 8.6–13.5 km·h−1), high-intensity running (HIR, 13.6–18.5 km·h−1), sprinting (>18.5 km·h−1) and maximal speed. The speed thresholds were age-speciﬁc [3] and adapted from previous studies on U14 soccer players [3,15] to compare running performance with previously reports on U14 soccer players. In addition, sprint distance with a threshold adapted from studies on senior soccer players (25.2 km·h−1 with a duration of at least 1 s) was included to compare sprint distance between U14 players and senior elite players [16]. Total distance included all locomotion during the match. Data are presented both in absolute (m) and relative (m·min−1) distances. The number of accelerations and decelerations was also examined, and were deﬁned as actions when speed was increased or decreased by more than 3 m·s−2 and lasted more than 0.5 s. Only accelerations and decelerations at speeds above 13.5 km·h−1 were included. 2.5. Playing Position Players were categorised as centre backs (CB, n = 70 match halves), fullbacks (FB, n = 68 match halves), centre midﬁelders (CM, n = 100 match halves), wide midﬁelders (WM, n = 65 match halves) or centre forwards (CF, n = 39 match halves). Players swapping positions were excluded from the analysis. Goalkeepers were excluded from the study due to their low running demands [16,17]. 2.6. Statistical Analyses Data are presented as means with the standard deviation (SD) or 95% conﬁdence inter- val (CI). Visual inspection conﬁrmed that all data were normally distributed. Differences in match running performance between halves were assessed by one-way ANOVA analysis. Int. J. Environ. Res. Public Health 2021, 18, 7287 4 of 9 The ﬁxed effects (E) of the independent variables on match running performance were assessed using mixed-effect models with the player as a random effect to adjust for multiple match observations by the same player. All independent variables were included in the models, as they may inﬂuence match running performance in theory. The variables analysed were playing position (5 positions), competitive level (elite vs. sub-elite), match location (home vs. neutral/away), match results (win vs. draw/loss) and match half (ﬁrst vs. second half). The collinearity between the continuous independent variables was inspected by Pearson’s product moment correlations coefﬁcients and were included if r < 0.6. Pairwise comparisons for competition level and playing position were performed with Bonferroni correction. IBM SPSS Statistics (version 26, IBM, Armonk, NY, USA) was used for all statistical analyses. Signiﬁcance for all analyses was accepted at p ≤ 0.05. When signiﬁcant differences were detected, Cohen’s d effect size (ES) was calculated. An ES of 0–0.2 was considered trivial, >0.2 as small, >0.5 as medium and >0.8 as large [18]. 3. Results 3.1. Match Running Performance During a match, players covered, on average, 22% of the total distance by high- intensity running (13.6–18.5 km·h−1) and sprinting (>18.5 km·h−1), 32% by medium- intensity running (8.6–13.5 km·h−1), 32% by low-intensity running (4.6–8.5 km·h−1) and 16% by walking (0.1–4.5 km·h−1). More distance was covered in the ﬁrst compared with the second half, both in metres (ES = 0.71) and metres pr minute (ES = 0.29). Total distance and high-intensity activities for each half are presented in Table 1. Table 1. An overview of total match distance covered and high-intensity actions in Norwegian U14 players. Data are presented as means ± SD. Match playing time was 2 × 35 min. One-way ANOVA was used when comparing match halves independently of whether the same players played one or both halves. First Half (n = 187) Second Half (n = 155) Full Match p-Values ES Values Total distance m 3883 ± 405 3762 ± 435 7645 ± 840 0.008 0.71 m·min−1 111.0 ± 11.6 107.5 ± 12.4 109.3 ± 12.0 0.008 0.29 HIR (13.6–18.5 km·h−1) m 618 ± 185 600 ± 199 1218 ± 398 0.365 0.09 m·min−1 17.7 ± 5.3 17.1 ± 5.7 17.4 ± 5.5 0.365 0.11 Sprint distance >18.5 km·h−1 (m) a 242 ± 105 229 ± 116 471 ± 221 0.260 0.12 >25.2 km·h−1 (m) b 19.8 ± 31.2 20.8 ± 31.1 40.6 ± 62.3 0.767 0.03 Maximal speed (km·h−1) 27.0 ± 2.2 26.6 ± 2.2 27.0 ± 2.2 0.061 0.18 Accelerations (n) 18.9 ± 9.4 19.7 ± 8.9 38.6 ± 18.3 0.419 0.09 Declarations (n) 23.1± 11.3 24.7 ± 10.9 47.8 ± 22.2 0.194 0.14 HIR: high-speed running. a threshold adapted from previous studies on U14 soccer players, b above 25.2 km·h−1 for more than 1 s (threshold adapted from studies on senior soccer players). Accelerations/decelerations: actions (>13.6 km·h−1) when speed was increased or decreased by more than 3 m·s−2 and lasted more than 0.5 s. Data were not corrected for multiple match observations by the same player. p ≤ 0.05 is statistically signiﬁcant. ES: 0–0.2 = trivial; >0.2 = small; >0.5 = medium; >0.8 = large. 3.2. Playing Position Playing positions were associated with TD and all high-intensity parameters inves- tigated (HIR, sprints, accelerations and decelerations). FB, WM and CM showed signiﬁ- cantly greater total distance and HIR distance compared with CB (ESTD = 0.62–1.05 and ESHIR = 0.91–3.06) and CF (ESTD = 0.62–1.05 and ESHIR = 0.57–2.61). Wide playing posi- tions (WM, FB) covered the most sprint distance (ES = 0.64–1.08), and CF also covered greater sprint distance compared with CB positions (ES = 0.90). CF performed signiﬁcantly more accelerations compared with all other playing positions (ES = 0.48–0.96). WM and CF had the highest number of decelerations (ES = 0.38–1.04), and CB performed signiﬁ- Int. J. Environ. Res. Public Health 2021, 18, 7287 5 of 9 cantly fewer decelerations compared with all other positions (ES = 0.53–1.18). Positional differences according to running performance are described in Table 2. Table 2. Positional differences (mean ± 95% CI) in match running performance during one match half (35 min). Total Distance (m) HIR (m) (13.6–18.5 km·h−1) Sprints (m) (>18.5 km·h−1) Accelerations (n) Decelerations (n) FB ‡ 3987 [3838, 4136] (‡ > §, ∞) * 722 [653, 790] (‡ > §, ∞) * 281 [240, 322] (‡ > §, ¶) * 19.3 [16.3, 22.3] 25.2 [21.7, 28.6] (‡ > §) * WM † 4015 [3883, 4148] († > §, ∞) * 712 [651, 774] († > §, ∞) * 288 [251, 326] († > §, ¶) * 20.0 [17.1, 22.9] 27.9 [24.5, 31.3] († > §, ¶) * CB § 3673 [3506, 3839] 552 [475, 629] 187 [141, 233] 16.2 [13.1, 19.3] 18.0 [14.5, 21.5] CM ¶ 4020 [3887, 4153] (¶ > §, ∞) * 736 [674, 796] (¶ > §, ∞) * 217 [180, 254] 17.5 [14.9, 20.1] 23.6 [20.5, 26.6] (¶ > §) * CF ∞ 3734 [3551, 3917] 614 [529, 699] 257 [205, 309] (∞ > §) * 24.8 [20.1, 28.6] (∞ > ‡, †, §, ¶) * 27.6 [23.2, 32.0] (∞ > §) * HIR: high-intensity running; FB: fullback; WM: wide midﬁelder; CB: centre back; CM: centre midﬁelder; CF: centre forward. Accelera- tions/decelerations: actions (>13.6 km·h−1) when speed was increased or decreased by more than 3 m·s−2 and lasted more than 0.5 s. Data were corrected for multiple match observations by the same player and all independent variables. Pairwise comparisons for playing position were performed with Bonferroni correction. * p ≤ 0.05. 3.3. Competitive Level Players covered signiﬁcantly greater total distance (ES = 0.35) and HIR distance (ES = 0.43) (13.6–18.5 km·h−1) in matches played at U14 elite level compared with the local U14 sub-elite level (Table 3). No differences between groups were found regarding sprints, accelerations and decelerations. Table 3. Match running performance (mean ± 95% CI) in one match half (35 min) in U14 players according to competitive level. Total Distance (m) HIR (m) (13.6–18.5 km·h−1) Sprints (m) (>18.5 km·h−1) Accelerations (n) Decelerations (n) U14 elite matches 3898 [3779, 4016] * 671 [616, 727] * 247 [216, 279] 17.9 [15.1, 20.6] 22.9 [19.6, 26.2] U14 sub-elite matches 3752 [3659, 3845] 596 [554, 639] 245 [222, 267] 20.4 [18.7, 22.2] 24.4 [22.2, 26.5] U14 elite: national U14 elite level; U14 sub-elite: highest regional U14 level. HIR: high-intensity running. Accelerations/decelerations: actions (>13.6 km·h−1) when speed was increased or decreased by more than 3 m·s−2 and lasted more than 0.5 s. Data were corrected for multiple match observations by the same player. Pairwise comparisons for playing position were performed with Bonferroni correction. * p ≤ 0.05. 3.4. Contextual Factors Signiﬁcant greater total distances (p < 0.001, ES = 0.36), HIR distances (p < 0.001, ES = 0.32) and sprint distances (p = 0.016, ES = 0.11) were observed in matches lost or drawn compared with matches won. Match location was also shown to have an impact on most running variables investigated. Players performed greater TD (p = 0.043, ES = 0.13) and sprint distance (p = 0.038, ES = 0.20) when playing at home compared with playing away or at a neutral match location. On the other hand, the number of accelerations (p < 0.001, ES = 0.56) and decelerations (p < 0.001, ES = 0.46) was higher when playing away or at a neutral location compared with playing at home. Greater total distances (3883 ± 405 vs. 3762 ± 435, ES = 0.71) covered in one half were observed (p = 0.008), but we found no other differences between match halves. 4. Discussion This study aimed to analyse match running performance in U14 male soccer players in Norway, and to identify how playing position, competitive level and contextual match variables inﬂuenced running performance. Overall, 22% of the locomotion during matches constituted high-speed running and sprinting, with the rest being performed at lower running intensities. There were signiﬁcant differences in physical performance between positions, in addition to greater running demands when players competed at the highest competitive standard. Furthermore, contextual factors were associated with most running variables investigated. The ﬁndings of the present study showed that Norwegian male U14 soccer players run similar distances or more during a match compared with previous reports on U14 Int. J. Environ. Res. Public Health 2021, 18, 7287 6 of 9 players [1]. The group in our study covered a total distance of 109.2 m·min−1, which is higher than that of 95.7 m·min−1 reported in New Zealand U14 male soccer players [3] and 106.5 m·min−1 in English U14 soccer players [19]. Similarly, in our study, 22% of the total match time was spent as high-intensity running actions (>13.6 km/h−1), whereas reports from youth soccer in New Zealand [3], Italy [20] and England [19] have shown total high-intensity running actions to constitute 7%, 16% and 17%, respectively, with a similar speed threshold applied as in the present study. These results could be explained by how the different countries approach and emphasise the game, as studies from senior soccer highlighted that soccer philosophy, technical performance and physical performance varied between different countries and leagues [21–23]. Across playing positions we found that CF had a greater number of accelerations compared with all other playing positions. In contrast, a previous study on youth soccer showed that attackers perform a high amount of accelerations, but that players in wide positions (WM and FB) in general perform more accelerations than central players (CB, CM and CF) [24]. However, Vigh-Larsen and colleagues [24] only examined 14 outﬁeld players from one single team, and their ﬁndings could reﬂect the team’s formation and tactical dispositions, or the physical capacity of the individual players [25]. Indeed, discrepancies have been found for high-intensity physical performance between different playing forma- tions in elite senior soccer [26]. Regardless, our ﬁnding that CB perform fewer accelerations than other positions is in line with previous ﬁndings [24,27], and also that CB positions in general have a lower running performance compared with other playing positions [1]. Our results showed that wide playing positions performed the highest amount of high-intensity work and, in general, were the most physically demanding playing positions. This was also supported by Pettersen and colleagues (2019), who investigated a group of Norwegian U17 soccer players [27]. In senior soccer, studies have demonstrated that evolving tactics over the last decade have especially impacted the physical demands of wide players, as this position has shown the greatest increase in high-intensity running [17,28]. Our ﬁndings suggest that the high running demands of wide positions found at higher age groups and in senior soccer also seems to be evident at U14 level. Total distance and high-intensity running were signiﬁcantly higher in matches at U14 elite level compared with sub-elite level, which is in line with ﬁndings from a previous study [2]. However, the observed difference was small regarding effect size, and we found no differences regarding sprint distance, or the number of accelerations and decelerations. A possible explanation is that the sub-elite teams represented in our study were the highest ranked sub-elite teams and included highly talented players also recruited in the U14 national and regional teams. The observed difference in our study regarding running performance and competitive level might been greater with a wider range of teams included from the sub-elite group. Our ﬁnding is in contrast to what has previously been reported in top-ranked U17 players, where the best teams outperformed players in lower-ranked teams [29]. Contrarily, studies on senior players from teams that ﬁnished in the top ﬁve at the end of the English Premier League season performed less sprinting than those that ﬁnished outside of the top ﬁve [30]. Match results were associated with running performance. The players covered a greater total distance, HIR distance and sprint distance in matches they lost or drew com- pared with the matches they won. Previous studies in elite senior soccer have shown conﬂicting results regarding the inﬂuence of match results, where Castellano et al. [9] found no relationship with match results, whereas Lago et al. [31] found that players ran less at higher intensities when winning. Playing against high-quality opposition has been found to be associated with lower ball possession [32], and a possible explanation could be that the lower-quality team has to cover a greater distance to regain possession and defend important spaces. Our results also showed that match location inﬂuenced match running performance, as greater TD and sprinting distance (>18.5 km·h−1) were performed when playing at home, even though fewer accelerations and decelerations were performed. Different tactical approaches to home matches versus away matches may be an Int. J. Environ. Res. Public Health 2021, 18, 7287 7 of 9 explanation, but more research is necessary to better understand this. A limitation in the present study according to contextual factors is the preponderance of matches ending with a win compared with matches ending with a draw/loss. Our results showed a signiﬁcant association between contextual factors and several running performance variables; how- ever, these ﬁndings had, in general, a small/medium effect size. More research is necessary in order to better understand the link between match running performance and contextual factors in youth soccer. The inﬂuence of these multivariate factors should not be considered in isolation. A challenge when comparing time motion data from different studies from youth soccer is the different methodological approach to the rolling substitution policy, resulting in a large variety in playing time between players. In the present study, all completed match halves were included to increase the sample size and statistical power, in contrast to most previous studies that only included completed matches. Excluding players who did not play full matches may bias analyses by reducing the sample size and removing variations in running performance. This is important to take into account when interpreting positional differences, as offensive positions are commonly substituted [33]. We observed a signiﬁcantly lower relative total distance covered in the second half (3.9 m·min−1), but the small difference observed between halves was argued to be of less practical meaning. Overall, our results show that physical performance during matches is affected by several factors and differs between leagues and countries. Running demands in soccer and especially different positional running demands are evolving [28], and highlight the need for updated research. This is the ﬁrst study to assess match-related physical performance and positional differences in Scandinavian U14 youth soccer. Our results suggest that physical performance during matches is similar or higher than in reports from other parts of the world and are highly related to playing position. Wide playing positions seem to be the most physically demanding positions. In addition, centre forwards perform more accelerations than all other positions. Given the high physical demands of high- intensity work and the impact of these actions on post-match muscle damage [34], coaches must consider different positional match loads, and individualise training according to positional demands. Further, our data indicate that playing at the highest U14 level was more physically demanding, suggesting a higher external demand during elite matches compared with sub-elite matches. This reveals important information for practitioners, as the physical training could be tailored to the game demands, and/or players could be moved between levels to assist in talent development. Future research should seek to improve our understanding of positional demands related to different tactics and team formations, and to investigate training load according to positional demands. More research is also necessary to better understand the inﬂuence of different contextual factors in youth soccer. 5. Conclusions Physical performance during matches in this group of Norwegian U14 male soccer players was similar or higher than reports from other parts of the world and are highly related to playing position. Total distance and high-intensity running were greater in elite matches compared with sub-elite matches, but no differences were found regarding sprints, accelerations and decelerations. Match result and match arena also inﬂuenced running performance. Author Contributions: Conceptualisation, E.A., H.G. and H.S.G.; formal analysis, E.A., A.R., V.A. and H.S.G.; investigation, E.A, T.N. and H.S.G.; methodology, E.A., A.R. and H.S.G.; project adminis- tration, H.S.G.; supervision, H.S.G.; visualisation, E.A.; writing—original draft, E.A., H.G. and H.S.G.; writing—review and editing, E.A., H.G., A.R., T.N., A.H.S., V.A. and H.S.G. All authors have read and agreed to the published version of the manuscript. Funding: This research was given ﬁnancial support by the University and College Network for Western Norway. Int. J. Environ. Res. Public Health 2021, 18, 7287 8 of 9 Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by The Regional Committee for Medical and Health Research Ethics, Grant number (720025). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical restrictions. Acknowledgments: We would speciﬁcally like to thank all the participants, Knut Kvammen and Håvard Wiersen for contributing to the data collection, and the University and College Network for Western Norway for ﬁnancial support. 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PMC8296310	International Journal of Environmental Research and Public Health Review Periodization and Programming for Individual 400 m Medley Swimmers Francisco Hermosilla 1,2 , José M. González-Rave 1,* , José Antonio Del Castillo 3 and David B. Pyne 4 Citation: Hermosilla, F.; González- Rave, J.M.; Del Castillo, J.A.; Pyne, D.B. Periodization and Programming for Individual 400 m Medley Swimmers. Int. J. Environ. Res. Public Health 2021, 18, 6474. https:// doi.org/10.3390/ijerph18126474 Academic Editors: Matteo Cortesi, Sandro Bartolomei, Giorgio Gatta and Tomohiro Gonjo Received: 12 May 2021 Accepted: 12 June 2021 Published: 15 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Sport Training Lab, University of Castilla-La Mancha, 45008 Toledo, Spain; fhermosilla@nebrija.es 2 Facultad de Ciencias de la Vida y la Naturaleza, Universidad Nebrija, 28248 Madrid, Spain 3 Catalonian Swimming Federation and High Performance Center, Alcalde Barnils, Av. 3-5, Sant Cugat del Vallès, 08174 Barcelona, Spain; kasti71@gmail.com 4 Research Institute for Sport and Exercise, Faculty of Health, University of Canberra, Bruce, ACT 261, Australia; David.Pyne@canberra.edu.au * Correspondence: josemaria.gonzalez@uclm.es; Tel.: +34-666160346 Abstract: Knowledge in the scientiﬁc domain of individual medley (IM) swimming training over a competitive season is limited. The purpose of this study was to propose a detailed coaching framework incorporating the key elements of a periodized training regimen for a 400 m IM swimmer. This framework was based on the available coaching and scientiﬁc literature and the practical experience and expertise of the collaborating authors. The season has been divided in two or three macrocycles, further divided in three mesocycles each (six or nine mesocycles in total), in alignment with the two or three main competitions in each macrocycle. The principal training contents to develop during the season expressed in blood lactate zones are: aerobic training (~2 mmol·L−1), lactate threshold pace (~4 mmol·L−1) and VO2max (maximum oxygen uptake) (~6 mmol·L−1). Strength training should focus on maximum strength, power and speed endurance during the season. Altitude training camps can be placed strategically within the training season to promote physiological adaptation and improvements in performance. A well-constructed technical framework will permit development of training strategies for the 400 m IM swimmer to improve both training and competitive performance. Keywords: swimming; individual medley; training; season 1. Introduction Swimming competitions are performed in four major strokes (front crawl, backstroke, breaststroke and butterﬂy). The individual medley events (IM) comprise all four swimming strokes in the following order: butterﬂy, backstroke, breaststroke and freestyle. The two variants of the IM are the 200 m IM (50 m of each stroke) and the 400 m IM (100 m of each stroke). It is necessary to train all four strokes underpinning the widespread assertion in the high performance swimming community that the IM events are the most complex and challenging on swimming program [1]. Gonjo and Olstad [2] highlight the lack of knowledge on comprehensive guidelines for the preparation of high-level 400 m IM swimmers. In the IM swimming, the energetic and biomechanics differences between the four strokes yield a variable relative contribution of each stroke to the ﬁnal performance. How- ever, it is not clear which stroke(s) are more important in the ﬁnal performance in IM events. In the 400 m IM, breaststroke and freestyle seems to be the most relevant stroke in female swimmers [3,4]. In contrast, for male swimmers, the backstroke and breaststroke appear more important [4,5]. When planning training, coaches need to determine then prescribe the relative proportion of training of each stroke for the 400 m IM event throughout the season. To achieve the best performance in 400 m IM, coaches must ensure that middle- Int. J. Environ. Res. Public Health 2021, 18, 6474. https://doi.org/10.3390/ijerph18126474 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 6474 2 of 14 distance front crawl training is a priority given a positive association between that freestyle and IM swimming [6]. To maximize the IM swimmers’ performance, it is important to establish a detailed understanding of the training characteristics of both the 200 m and 400 m IM events for effective planning and monitoring. A coach needs to consider the training volume, frequency and intensity distribution for maximizing physical capacity and performance. Periodization can be deﬁned as the macromanagement of the delineated stages of training process with respect to the time allocated toward various elements [7]. The key aspects that underpin periodization are: (i) determining relevant dates (e.g., main and minor competi- tions), (ii) determining the sequence of phases for each training cycle and (iii) managing load dynamics with the intent of achieving peak or optimal performance at the critical competitions [8]. There are few studies which have examined the key aspects related to the best performance in 400 m IM events, including the training organization and periodization. Therefore, the aim of this narrative review is to examine the evidence on periodization related to the 400 m IM and identify key elements and best practices for this event. We ad- dress key aspects, such as bioenergetics necessary to plan the periodization for 400 m IM and examine traditional periodization following two or three peaks performance, training methods and ﬁtness phases for each training period in accordance with other narrative reviews in individual sports [9]. A narrative review provides a historical account of the development of theory and research on a topic (although the contribution to knowledge will be relatively minor [10]. Here we address theoretical conceptualizations, training constructs and relevant scientiﬁc literature, to propose a practical framework for preparing 400 m IM swimmers. 2. Literature Search Methodology Electronic searches of PubMed/MEDLINE, SPORTDiscus, Scopus and Web of Science were conducted. The search terms used were “individual medley swimming”, “middle distance swimming training”, “swimming training periodization” and “swimming peri- odization”. Relevant review articles were also examined to uncover studies which might have been missed in the primary search. The reference list of selected manuscripts was also examined for other potentially eligible manuscripts. No limits regarding the year of publication were employed. Studies were included when (a) they were published in English language (b); provided training zones, volumes and/or periodization details about middle distance or IM events and (c) focused on swimming performance in IM events. Exclusion criteria were: (a) swimmers with a current injury or disability and other aquatic participants (e.g., water polo, diving, triathletes) and (b) studies focusing on pacing or performance trends. The initial database search identiﬁed 714 records that were relevant to the search keywords. After removal of duplicates and elimination of papers based on title and abstract screening, 15 manuscripts remained. Finally, four articles were included in this review [11–14]. The 11 studies that did not match the eligibility criteria based on full-text screening were discarded for one or more of the following reasons: not detailing training intensity distributions (n = 6), conducted with master swimmers (n = 2), performance trends in IM events across the years (n = 1) and pacing in 200 and 400 m IM (n = 1) (Figure 1). Int. J. Environ. Res. Public Health 2021, 18, 6474 3 of 14 Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 3 of 14 Figure 1. Flow chart summary of the study selection process. 3. Bioenergetics of Individual Medley Events The 400 IM has a duration ranging from 4 to 4.30 min and is considered as a middle distance swimming event [1]. Middle distance events are supported energetically by a combination of phosphate energy; anaerobic glycolysis and aerobic combustion of carbo- hydrate, fat and protein [15]. Competitive swimmers spend most of their training time improving aerobic endurance, defined as the ability to sustain a high percentage of VO2max for a long period, through careful and repeated interval-based training. This type of training is important for performance in events around 4 min such as the 400 m IM [16]. The physiological preparation for a 400 m IM should address the key physiological factors of the maximal aerobic power (rate of adenosine triphosphate resynthesis), capacity (total amount of adenosine triphosphate resynthesis from available fuels) and VO2max (maxi- mum oxygen uptake) [1]. The velocity associated with VO2max (vVO2max) is the single best predictor of middle-distance swimming performance especially in 400 m events [17,18]. From the data provided by 400 m front crawl swimmers [19,20], the estimated velocity achieved during 400 m IM is ~100% of vVO2max. At these intensities, the attain- ment of a VO2 steady state is delayed due to the emergence of a supplementary slowly developing component of the VO2 response [21]. The VO2 fast component is stable at in- tensities between 95, 100 and 105%; however, the kinetics of the VO2 slow component and the corresponding metabolic profiles showed variations between this intensities [19]. Other important physiological factors include the lactate threshold (LT), the ability to sustain a high percentage of VO2max during the competition and the energy cost of locomotion [15,18,19,22]. The physiological adaptations should align with the periodiza- tion of each swimmers’ training and competition calendar. These physiological adapta- tions are usually prescribed with specific training sets and sessions in the pool and dry- land training. One common approach in elite-level swimming to enhancing physiological and performance adaptations is incorporation of altitude training (either real or simulated to induce hypoxia). 4. Training Monitoring External load monitoring is usually assessed by quantifying the weekly training vol- ume [23]. The training volumes are usually classified into three or five intensities zones [14]. The three training zone model is typically established using swimming velocity and blood lactate concentrations as follows: z1 ≤ 2 mmol·L–1; z2 2–4 mmol·L–1, and z3 ≥ 4 mmol·L–1 [13]. However, [24,25] proposed a modification with z1 ≤ 3 mmol·L–1 and z2 be- tween 3–4 mmol·L–1. In swimming, the most common model adopted in the sports science literature comprises five zones: z1 ≤ 2 mmol·L–1, z2 2–4 mmol·L–1, z3 4–6 mmol·L–1, z4 6– 10 mmol·L–1 and z5 < 10 mmol·L–1 [12,13,26]. Training zones can be categorized according Figure 1. Flow chart summary of the study selection process. 3. Bioenergetics of Individual Medley Events The 400 IM has a duration ranging from 4 to 4.30 min and is considered as a mid- dle distance swimming event [1]. Middle distance events are supported energetically by a combination of phosphate energy; anaerobic glycolysis and aerobic combustion of carbohydrate, fat and protein [15]. Competitive swimmers spend most of their training time improving aerobic endurance, deﬁned as the ability to sustain a high percentage of VO2max for a long period, through careful and repeated interval-based training. This type of training is important for performance in events around 4 min such as the 400 m IM [16]. The physiological preparation for a 400 m IM should address the key physio- logical factors of the maximal aerobic power (rate of adenosine triphosphate resynthesis), capacity (total amount of adenosine triphosphate resynthesis from available fuels) and VO2max (maximum oxygen uptake) [1]. The velocity associated with VO2max (vVO2max) is the single best predictor of middle-distance swimming performance especially in 400 m events [17,18]. From the data provided by 400 m front crawl swimmers [19,20], the es- timated velocity achieved during 400 m IM is ~100% of vVO2max. At these intensities, the attainment of a VO2 steady state is delayed due to the emergence of a supplementary slowly developing component of the VO2 response [21]. The VO2 fast component is stable at intensities between 95, 100 and 105%; however, the kinetics of the VO2 slow component and the corresponding metabolic proﬁles showed variations between this intensities [19]. Other important physiological factors include the lactate threshold (LT), the ability to sustain a high percentage of VO2max during the competition and the energy cost of locomotion [15,18,19,22]. The physiological adaptations should align with the periodization of each swimmers’ training and competition calendar. These physiological adaptations are usually prescribed with speciﬁc training sets and sessions in the pool and dryland training. One common approach in elite-level swimming to enhancing physiological and performance adaptations is incorporation of altitude training (either real or simulated to induce hypoxia). 4. Training Monitoring External load monitoring is usually assessed by quantifying the weekly training volume [23]. The training volumes are usually classiﬁed into three or ﬁve intensities zones [14]. The three training zone model is typically established using swimming velocity and blood lactate concentrations as follows: z1 ≤ 2 mmol·L−1; z2 2–4 mmol·L−1, and z3 ≥ 4 mmol·L−1 [13]. However, [24,25] proposed a modiﬁcation with z1 ≤ 3 mmol·L−1 and z2 between 3–4 mmol·L−1. In swimming, the most common model adopted in the sports science literature comprises ﬁve zones: z1 ≤ 2 mmol·L−1, z2 2–4 mmol·L−1, z3 4–6 mmol·L−1, z4 6–10 mmol·L−1 and z5 < 10 mmol·L−1 [12,13,26]. Training zones can be categorized according to the response in blood lactate concentration: Z1; Aerobic low intensity (A1), z2; Aerobic maintenance (A2), z3; lactate threshold (LT), z4; VO2max and Int. J. Environ. Res. Public Health 2021, 18, 6474 4 of 14 intensity above VO2max as 200 m race pace and z5 maximal swimming speed [26]. These training zones need to be established and then checked periodically during a training season Monitoring of heart rate also has been used during training sessions to indicate training intensities [27] but is subject to substantial biological and measurement error. Nevertheless, blood lactate measurements are considered more useful in determining the training intensity because they facilitate better monitoring of the effect of training workloads on the muscle [23]. Thus, blood lactate is a good indicator of the muscles’ capacity for an athletic performance which allows coaches to identify the type and extent of physiological disturbance and the degree of adaptation that has taken place over time [23]. An increase in blood lactate for the same training stimulus may, for example, point to in- creased anaerobic metabolism, and therefore, higher levels of lactate at slower speeds may be indicative of impending overtraining [28]. Nevertheless, the values of blood lactate are associated with a high between-swimmer variability in swimming techniques, with a range from <2 to >5 mmol·L−1 at lactate threshold intensity. Front crawl (3.3 mmol·L−1) and breaststroke (2.9 mmol·L−1) present lower levels of blood lactate at the lactate threshold intensity than butterﬂy (4.9 mmol·L−1) and backstroke (3.9 mmol·L−1) [29]. It is recom- mended to schedule a blood lactate assessment test using a prescribed testing protocol every few weeks [23]. The Rating of Perceived Exertion (RPE) is another commonly used method for assess- ing the internal training load [30–33]. Studies have showed moderate to large correlations between the heart rate and blood lactate concentrations [34,35]. Although, RPE is a valid method for assessing the training stress in high-intensity exercises [36,37], it is important to acknowledge that personal perceptions of physical efforts is a very complex interaction of many factors [38]. Therefore, some investigators recommend to complement the RPE with an objective assessment of internal training load such as blood lactate and/or heart rate monitoring [39,40]. 5. Training Periodization Periodization is a process that serves as the macromanagement of the training program in the context of the annual plan [7,41]. Various periodized models such as the reverse linear [24,25,42], or block periodization [43], have been established, but the most common periodized model in swimming is the so-called traditional periodization [14]. Over the recent decades, many periodization approaches have evolved including traditional, blocks, and reverse linear periodization, each offering a differing rationale and template for sub-division of the program into sequential elements [14]. Some authors afﬁrm that the traditional model of periodization can take different forms (i.e., reverse) [44]. Reverse linear periodization has been used in combination with a polarized intensity distribution for improving sprint events. However, the small number of relevant studies did not report any differences with the traditional model in 50 m performance, or a modest improvement of 1% in 100 m performance [24,25]. Polarized training is not recommended for middle distance swimmers; 400 m IM swimmers should beneﬁt from speciﬁc periods of training that employ a threshold-oriented training intensity distribution [13]. Training periodization involves the coordination of physical training, psychological capacities training and skill acquisition, providing a comprehensive framework for optimal preparation [45]. Periodization of training leads to a progressive enhancement in the critical physiological and biomechanical factors required for swimming competitions [46]. On this basis, a well-planned and effective periodized approach to training should be established, monitored and reﬁned for swimmers to achieve ﬁtness and peak performance at the major competition for the season [47,48]. In the same way, detailed monitoring of performance and training during the season should be a fundamental aspect to maximize training effectiveness and avoid excessive volume, intensity and/or training load which can cause physiological disturbances (e.g., glycogen depletion, neuromuscular fatigue, decrements in red cell volume and hemoglobin), injuries or illness [49]. Int. J. Environ. Res. Public Health 2021, 18, 6474 5 of 14 The traditional pyramidal model is most commonly used for swimmers characterized by a sequential reduction in training volume moving from zone 1, to zones 2 and 3, respectively. The majority (80%) of the volume is conducted in z1 and the remaining 20% in z2 and z3 [50]. Issurin [51] conceptualized macrocycles as a training period which involved a preparatory and competitive period, usually taking several months. A mesocycle is a medium size training cycle consisting of a number of microcycles which usually involved several weeks, while a microcycle is a small size training cycle consisting of a number of days, frequently one week. The season can been divided into three macrocycles [52], although the majority of studies reported one or two macrocycles including a comprehensive evaluation conducted on 127 elite swimmers and 20 competitive seasons [12]. This option seems to be the most common used by coaches [53]. Our recommendation is to employ two or three macrocycles divided into three mesocycles each (six or nine mesocycles in total), aligning with three main competitions in each macrocycle to establish an annual plan. In many countries, these competitions comprise in order: short course international championships, national championships and main international competition for the calendar. The emergence of the International Swimming League (ISL) in recent years may require more ﬂexible planning and periodization. 5.1. Altitude Training Altitude training camps during a season can be useful in developing aerobic en- durance in world-class endurance athletes [54]. Altitude training elicits an increases in erythropoietic response [55] and hemoglobin mass [56,57]. However, there are also hypoxia- induced non-hematological changes, such as mitochondrial gene expression and enhanced muscle buffering capacity [58,59]. Altitude training can account for ~18–25% of annual training volume in some world-class athletes [60] and is typically performed at altitudes of ~1800–2300 m above sea level or higher [54,60,61]. The duration of an altitude training camp depends on many factors; however, between three to four weeks is suggested for mid- dle distance swimmers [59]. Endurance athletes can undertake altitude training to promote speciﬁc training goals of the macrocycle [62]. For example, the early-season training camp when training intensity is typically lower can focus on higher training volumes at low-to- moderate intensities and capitalize on the hematological effects of the hypoxic stimulus [62]. Subsequent training camps should progress to a focus on lactate threshold, aerobic power and VO2max training later in the macrocycle. However, a period of low-intensity training during the ﬁrst few days of altitude acclimatization is recommended [63]. Altitude training camps are generally placed before the main competitions. Six to nine weeks prior to the main competition is typical time to undertake altitude training [59]. Nevertheless, other training camps could be carry out in the middle of the macrocycles to emphasize aerobic training contents [59]. One key aspect that coaches and swimmers must consider is the timing of return from altitude prior to competition. The timing of a peak performance following altitude training is likely to be inﬂuenced by a combination of altitude acclimatization and de-acclimatization responses, but more importantly are the periodization of and responses to training and tapering conducted at and after alti- tude [59]. Previous studies reported periods of between three and ﬁve weeks (usually three weeks) [64,65] between altitude training camps and the main competition. This period seems to be optimal timing of post-altitude performance peaking, but individualizing training will be important for optimizing the time to compete after an altitude camp [65]. 5.2. Preparatory and Main Competitions through the Season In most individual sports, competitive athletes plan to optimize their performance at the main competition no more than two or three times per year [66]. A retrospective study divided the training season in two macrocycles, the ﬁrst leading to the national selection trials and the second macrocycle leading to the major international competition) [12]. Each macrocycle should include at least two preparatory (minor) competitions before a major Int. J. Environ. Res. Public Health 2021, 18, 6474 6 of 14 competition. Swimmers performance in the main competitions should faster given the effects of the tapering phase [67] and extra motivation at the main competitions [66]. After a 2-week taper period, swimmers can show an improvement of ~3% in the main competition in comparison with the preparatory competitions carried out three to six weeks before [68]. A common strategy adopted by IM coaches in minor competitions is to have swimmers compete in one or more form stroke events (e.g., butterﬂy, backstroke or breaststroke) to complement the speciﬁc IM events. At the major competitions, IM swimmers typically concentrate on their main event (200 m or 400 m IM), but selection of events will depend on qualiﬁcations, team selections and coach/swimmer preferences. 5.3. Training Intensity Distribution Training load variables such as volume, frequency and intensity distribution play an important role in maximizing physical capacity and performance [69]. Annual vol- ume of kilometers for a middle-distance swimmer (400 m freestyle) ranged from 2055 to 2600 km [58]. Increasing the training volume is not be the only way of enhancing perfor- mance and more objective and speciﬁc training sets are required to improve the quality of the swimming training process [18]. Weekly volume and training intensity distribution are used as a reference for determining a swimmers’ training load and prescribing training sets and sessions. Middle distance swimmers show ranges of training volumes between 39,000 and 42,000 m depending on the type of macrocycles used [12]. However, some training plans showed mean training volumes as high as 58,000 m [11]—peak volumes as high as 70,000–80,000 m—have been reported anecdotally for some international IM and dis- tance swimmers. On this basis, both training volume and intensity distribution should be evaluated together for IM swimmers. A retrospective study showed that middle distance swimmers follow a threshold model in which ~40–44% of the training was performed at an intensity of < 2 mmol·L−1 (z1), and 44–46% at 2 to ≤ 4 mmol·L−1 (z2) and 9–14% at >4 mmol·L−1 (z3) [12]. Threshold-oriented intensity distribution (z1 66%, z2 25%, z3 9%) can improve crucial training contents for 400 m IM swimmers at the velocity at 4 mmol·L−1 and VO2max [13]. In summary, 400 m IM swimmers should beneﬁt from speciﬁc periods of training that employ a threshold-oriented intensity distribution. Coaches should also consider that swimmers who train with a threshold intensity distribution might experience additional fatigue induced by the cumulative impacts of threshold and high-intensity training [13] 5.4. Macrocycle Distribution Training cycles should be prescribed according to the principles of individualization and progression [12]. Two or three distinctive peaks of high total load are suggested in the overall training programs of elite swimmers across the year depending on the number of major competitions scheduled for a particular season. Application of suitable wave-like cycles in units such as a two or three week mesocycle is used to promote physiological adaptations and skill acquisition. Swimmers can engage in mono-, bi- or tri-cycled periodized programs depending on the sequencing of important competitions within that year. An annual periodization composed of two to three macrocycles would be appropriate for Olympic and World Championship seasons [14]. For example, three waves of macrocycles could be planned as follows: the ﬁrst cycle is conducted from September to December, second from December to April and third from April to August. A two waves macrocycle timeline could be planned as follows: the ﬁrst cycle from September to April and the second from April to August. The main aim of the ﬁrst macrocycle is to develop the general physical ﬁtness and foundation work for speciﬁc qualities oriented to the event. The goal of second and third macrocycles is to develop the speciﬁc and competitive physiological qualities (VO2max, race pace) required for the event, building from general to sport-speciﬁc qualities required culminating in the taper at the end of the season. In the two-wave macrocycle, the objectives and training contents of the ﬁrst and second macrocycles are included in a single macrocycle Int. J. Environ. Res. Public Health 2021, 18, 6474 7 of 14 with the same duration as the ﬁrst and second cycles in the three wave cycle. Moreover, the second macrocycle of the two-wave cycle has the same duration, training contents and objectives that the third cycle in the three-wave cycle. 5.4.1. First Macrocycle The beginning of a season in this macrocycle requires development of aerobic en- durance up to the lactate threshold, given it is a priority objective on the endurance training for 400 m IM swimmers [53]. The sessions could be performed in short course and long course training depending on the characteristics of the swimmers and the sets planned by coaches. Strength and conditioning (dry-land) training should focus on strength- hypertrophy, maximal strength and strength-metabolic conditioning (e.g., circuit training) with a duration ranging from 50–80 min [70]. Circuit training includes a cardiovascular element in combination with dry-land resistance training using light loads (40–60% one repetition maximum (1 RM)) and brief rest intervals with circuits performed a number of times per session. This work yields metabolic adaptations including an athlete’s buffering capacity [70,71]. Core training sessions can be used to develop stability and postural control of the body position while swimming. Stabilizing muscles can form the basis for generating more strength through the limbs [72]. The competition in this cycle is scheduled in December such as an international short course (for three peaks of performance), but in two peaks of performance, the ﬁrst cycle is scheduled in April (national selection trial (Figure 2)). The main aim of the first macrocycle is to develop the general physical fitness and foundation work for specific qualities oriented to the event. The goal of second and third macrocycles is to develop the specific and competitive physiological qualities (VO2max, race pace) required for the event, building from general to sport-specific qualities required culminating in the taper at the end of the season. In the two-wave macrocycle, the objec- tives and training contents of the first and second macrocycles are included in a single macrocycle with the same duration as the first and second cycles in the three wave cycle. Moreover, the second macrocycle of the two-wave cycle has the same duration, training contents and objectives that the third cycle in the three-wave cycle. 5.4.1. First Macrocycle The beginning of a season in this macrocycle requires development of aerobic endur- ance up to the lactate threshold, given it is a priority objective on the endurance training for 400 m IM swimmers [53]. The sessions could be performed in short course and long course training depending on the characteristics of the swimmers and the sets planned by coaches. Strength and conditioning (dry-land) training should focus on strength-hyper- trophy, maximal strength and strength-metabolic conditioning (e.g., circuit training) with a duration ranging from 50–80 min [70]. Circuit training includes a cardiovascular element in combination with dry-land resistance training using light loads (40–60% one repetition maximum (1 RM)) and brief rest intervals with circuits performed a number of times per session. This work yields metabolic adaptations including an athlete’s buffering capacity [70,71]. Core training sessions can be used to develop stability and postural control of the body position while swimming. Stabilizing muscles can form the basis for generating more strength through the limbs [72]. The competition in this cycle is scheduled in De- cember such as an international short course (for three peaks of performance), but in two peaks of performance, the first cycle is scheduled in April (national selection trial (Figure 2)). Figure 2. Example of macrocycle and mesocycle distribution. Note: %TTL: Total training load per- centage; GP: General Phase; SP: Specific Phase; CP: Competitive Phase. Figure 2. Example of macrocycle and mesocycle distribution. Note: %TTL: Total training load percentage; GP: General Phase; SP: Speciﬁc Phase; CP: Competitive Phase. 5.4.2. Second Macrocycle As a general rule, national championships doubling as selection trials are the main competition in this cycle. In the majority of countries, this competition is the qualifying event for the next international competition. The second macrocycle should be character- ized by high training volume and high amount of training at z2 (2–4 mmol·L−1) and z4 (6–10 mmol·L−1) [12]. The objective of these sessions is to improve the aerobic endurance, up to the level of the lactate threshold pace and VO2max. In addition, sessions aimed at lactate tolerance and speed could be prescribed as a continuation of the workloads performed in the ﬁrst macrocycle. The strength and conditioning training should focus on Int. J. Environ. Res. Public Health 2021, 18, 6474 8 of 14 maximal strength, power and speed endurance with resistance exercise. Circuit training is recommended performed in sessions ranging from 30–90 min. During this cycle, the swimmers must continue with the core training sessions for improving their stability. In ad- dition, progressively, the strength training could be transformed from strength-metabolic conditioning to muscle endurance with exercises that simulate the time frame of the event, using light and moderate weights in every exercise (30–50% 1 RM). 5.4.3. Third Macrocycle The third macrocycle would be the last cycle in the season, featuring the main com- petition of this cycle which is also the main competition of the season for international swimmers. Clearly, the Olympic Games or the World Championship is the main major competition for achieving the peak performance. The previous competitions should focus on increasing the technical and physical exigency leading to peak performance at the end of the cycle. It is crucial in this cycle to emphasize the technical aspects, especially during the early stages of this cycle, including basic training contents (as aerobic endurance), progressing to speciﬁc contents later (as VO2max) and, ﬁnally, the competitive contents (as race pace). Thus, training in the third macrocycle should be characterized by high amount of training at z2 and z4. Strength and conditioning training is similar to the second macrocycle with a focus on maximal strength, power and speed endurance with resistance exercise. These attributes can be maintained by circuit training to improve aerobic ﬁtness; however, this type of training should be reduced when the main competition approaches. 5.5. Mesocycle Distribution Each cycle is divided in preparatory, competitive and transition following the Matveyev’s proposal [73]. Bompa and Haff [74] reported the preparatory phase has 2 subphases: gen- eral phase (GP) and speciﬁc phase (SP). The competitive phase (CP) is when the athletes need to peak for a competition. For example, the mesocycle distribution in each macrocycle could keep the following distribution. Three waves macrocycles timeline could be planned as follows, the ﬁrst cycle: GP: 6 weeks, SP: 10 weeks and CP: 2 weeks; second: GP: 4 weeks, SP: 7 weeks and CP: 3 weeks; and ﬁnally the third cycle: GP: 3 weeks, SP: 10 weeks and CP: 3 weeks. Moreover, a two waves macrocycle timeline could be planned as follows, ﬁrst cycle: GP: 12 weeks, SP: 17 weeks and CP: 3 weeks; second: GP: 3 weeks, SP: 10 weeks and CP: 3 weeks. 5.5.1. General Phase The main objective of the general phase is to induce physiological, psychological, and technical adaptations that serve as the foundation for competitive performances [75]. The development of aerobic or oxidative endurance should be the main objective in this phase (see Table 1). Lactate threshold and VO2max sets could be included in the last few weeks of these mesocycle. Front crawl would be the recommended stroke to perform this type of session because of the large volume required in aerobic sets. However, coaches should consider conducting mixed sets with other strokes. To improve the aerobic en- durance, swimmers should train at hart rate 40 to 30 beats below maximum. The suggested pace is half of personal best 200 m time plus 10 to 15 sec. The repeat distances to use when training in this category are 200 to 1500 m [76]. Moreover, lactate threshold and VO2max training could be placed in the ﬁnal weeks of the general mesocycle as introductory sets for the subsequent mesocycle. It would be recommended that a training volume between 55–65 km per week during this phase is appropriate for most 400 m IM swimmers. Dry- land training during the general mesocycle is focused on the strength and hypertrophy development. During the ﬁrst cycle, strength and hypertrophy development are the main objectives, whereas in the second and third cycles coaches should ensure an appropriated maximal strength development. Int. J. Environ. Res. Public Health 2021, 18, 6474 9 of 14 Table 1. Aerobic development and mixed-endurance training sessions and hypertrophy-maximum strength development training set in the general mesocycle (ﬁnal part) of the ﬁrst cycle. Swimming Training Objective Set Volume (m) Training Intensity Training Zone Stroke Notes Aerobic development and mixed- endurance training 1 2400 3 × (8 × 100)/ 1:20–1:30–1:40 min A2, LT, VO2max Z2, Z3, Z4 FC Performed as training testing, speed control, HR, stroke, frequency and [La-] 2 3600 36 × 100/ 1:20–1:30–1:40 min Sequence: 3 × 100 A2 < 2 × 100 LT < 1 × 100 VO2max Z2, Z3, Z4 FC 3 intensities simultaneously with the [La-] of the previous intensity in the next one Strength Training Objective Cycle Sets Exercise Repetitions Intensity Hypertrophy and maximal strength development 2 4 Short pull Hammer 8 65 < 75%RM Row Hammer 6 80 < 85%RM Pull over 8 70%RM 3 Pulls-Up (Eccentric) 3 Body weight Pulls-UP (Supine grip) 3 6 Squat 8 85%RM Hamstrings 8 75%RM Note: Heart rate (HR); Blood lactate ([La-]); Front Crawl (FC); Aerobic maintenance (A2); LT: lactate threshold (LT); Maximum oxygen uptake (VO2max); Repetition maximum (RM); Zone 1 (Z2); Zone 2 (Z2); Zone 3 (Z3); Zone 4 (Z4); Zone 5 (Z5). The recommendations made in this table are based on both scientiﬁc and empirical data. 5.5.2. Speciﬁc Phase As the swimmers progresses to the speciﬁc phase of training, it is important maintain the level of physical development established during the preparatory phase. The aerobic endurance training needs to be maintained through this phase. However, the training should be focus on the lactate threshold and VO2max development (see Table 2). In the case of the lactate threshold development, swimmers should train at heart rate from 30 to 20 beats below the maximum in repetitions of 50 to 400 m at seven to ten second plus the half of personal best time in 200 m. For 400 m IM swimmers, it is recommended 3000 to 4500 m sets [53]. Finally, VO2max sets should be performed in sets of 300 to 500 m with 50 to 150 m repetitions. The suggested pace for VO2max training is half of personal best 200 m time plus four to seven seconds [76]. Moreover, during the middle and the last part of this period, the race pace training should be included. Race pace training can be carry out as broken swims (a training repeat with more than one break) and splits (a training repeat with one break) [53]. The speciﬁc sets should be performed in a mix of strokes, not only in one stroke, and depending on the week, the training volume should oscillate between 65–90 km per week. During the training ﬁrst cycle, the dryland training should be focus on maximal strength development; nevertheless, during the second and third training cycles, power and speed endurance development are the main objectives of the dryland training. Maximum strength training could involve exercises as Hamstrings (training machine), Leg press, Dumbbell Row, Chin ups, Lunges Back, Squat, Row Hammer, Bench Press and Triceps overhead (pulley) with one or two sets with an intensity between 70–90% 1 RM and four-six repetitions. Int. J. Environ. Res. Public Health 2021, 18, 6474 10 of 14 Table 2. Aerobic and lactate threshold set in the speciﬁc mesocycle of the second cycle of the season. Objective Cycle Set Total Volume (m) Training Intensity Training Zone Stroke Notes Aerobic development and high intensity (lactate threshold) training along with aerobic endurance training 1 1 3600 1 × (300 m/3:45 min) A1 Z1 FC Intensity progression in each distance 1 × (400 m/5 min) + 1 × (500 m/6:30 min) A2, LT Z2-Z3 2 4000 1 × (400 m/5:15 min) + 8 × 50/50 s A2, LT Z2-Z3 BT 2 × (200 m) + 4 × 100/1:30 min) A2, LT Z2-Z3 BS 1 × (400 m/5 min) + 4 × (100 m/1:10 min) A2, LT Z2-Z3 FC 3 1000 4 × (25 m/1:30 min) Max Z5 UUS 4 × (50 m/2 min) + 1 × (100/2 min) 400 Race pace Z4 FC 4 × (150 m/2 min) A2 (last at LT) Z2, Z3 FC Note: Underwater undulatory swimming (UUS); Breaststroke (BK); Front crawl (FC); Butterﬂy (BT); Backstroke (BS); Aerobic low intensity (A1); Aerobic maintenance (A2); LT: lactate threshold (LT); Zone 1 (Z2); Zone 2 (Z2); Zone 3 (Z3); Zone 4 (Z4); Zone 5 (Z5). The recommendations made in this table are based on both scientiﬁc and empirical data. 5.5.3. Competitive Phase Among the main tasks of the competitive phase is perfection of all training factors, which enables the athlete to compete successfully in the main competition or champi- onships targeted by the annual training plan [75]. A primary goal of the competitive and tapering mesocycles is to remove fatigue to stimulate a supercompensation of performance. During the competitive mesocycle, the most important training content is race pace training (see Table 3) and dryland training where the focus on is power (explosiveness) development (see Table 4). The tapering phase prior to international championships can include minor or major competitions, moderate volume including more low intensity training [49,77]. The training volume is progressively decreased for performing a progressive sloped taper phase, with the aim of achieving the best performance in the main competition. An optimal taper duration appears to be between 8–21 days involving a decrease in training load of ~40–60% [75]. Mujika and Padilla [49] recommended a training load reduction between 60–90% and maintaining the training intensity to avoid detraining, provided reductions in the other training variables allow (i.e., fewer training sessions per week or volume) for sufﬁ- cient recovery to optimize performance. The progressive reduction on the training volume begins around 60 km per week and ﬁnishes around 20–30 km per week. The remarkable decrease in volume took place in the second and third macrocycles of the season [77]. Int. J. Environ. Res. Public Health 2021, 18, 6474 11 of 14 Table 3. Race pace through mixed-endurance training set in the beginning of the competitive mesocycle of the second cycle. Objective Cycle Set Volume (m) Training Intensity Training Zones Stroke Notes Race pace through mixed- endurance sessions 3 1 2200 2 × (8 × 50 m/1 min) + 1 × 400 m/5:30 min 400 m race pace + A2 Z4-Z2 FC Combination 400 m speed, focusing on frequency/speed competitive and A2 1000 4 × (1 × 50 m/1:15 min) + 1 × 200/3 min 200 m race pace + A2/LT Z4-Z2- Z3 FC Intensity increase looking for a [La-] accumulation 800 4 × (150 m/2:15 min + 50 m/1 min) LT + Max speed Z3-Z5 FC The aim is to increase the intensity, focusing of AT and 50 m maximal speed 2 800 8 × (50 m/50 s) + 2 × (200/2:30 min) 200 m race pace + A2 Z4-Z2 FC The aim is to focus on frequency/ competitive 400 m speed 1000 10 × (100 m/ 2–2:15 min) 400 m race pace Z4 1 × BT, 2 × BS, 3 × BK, 4 × FC The aim is to simulate the competition as much as possible 400 8 × (50 m/1 min) Max speed Z5 FC The aim is to simulate the last part of the competition 800 4 × (200 m/2:30 min) A2 Z2 FC The aim is to remove the lactate at AT intensity Note: Blood lactate ([La-]); Breaststroke (BK); Front crawl (FC); Butterﬂy (BT); Backstroke (BS); Aerobic low intensity (A1); Aerobic maintenance (A2); LT: lactate threshold (LT); Maximum oxygen uptake (VO2 max); Zone 1 (Z2); Zone 2 (Z2); Zone 3 (Z3); Zone 4 (Z4); Zone 5 (Z5). The recommendations made in this table are based on both scientiﬁc and empirical data. Table 4. Power training set in the beginning of the competitive mesocycle of the second cycle. Objective Cycle Sets Exercise Repetitions Intensity Power development 2 3× Push-ups (additional weight) 4 105–110% BW Bench Press Max 60% RM-0.9 m/s 5× Chin-ups (Eccentric) 4 Body weight Row Hammer Max 60% RM-0.9 m/s 3× Isometric Squat 20” Body weight Squat Max 60% RM-0.9 m/s Note: Body weight (BW); Repetition maximum (RM); Meters per second (m/s). The recommendations made in this table are based on both scientiﬁc and empirical data. 6. Conclusions Knowledge of the preparation and periodization of IM training over a season is limited in both the coaching and sports science literature. Progressive development of the critical energetic and biomechanics variables involves the design, implementation and evaluation of an effective IM training plan. The training program we detailed here was organized with a traditional periodization paradigm using two or three macrocycles for the season (with two to three main competitions) incorporating a series of altitude camps. It is incumbent upon the coach to adjust the programming based on individual responses and swimmers’ characteristics to optimize the training process for each swimmer. Future investigations into IM training should determine the long-term effects of individual elite swimmers. It would also be informative to investigate the effects of different periodization models and training loads distribution on the performance in 400 IM using observation analyses of elite swimmers and controlled studies with high-level emerging swimmers. Int. J. Environ. Res. 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PMC10477198	Physiological Reports. 2023;11:e15801. \| 1 of 12 https://doi.org/10.14814/phy2.15801 wileyonlinelibrary.com/journal/phy2 1 \| INTRODUCTION An important practical challenge in modern sports is to obtain objective information on an athlete's physical per- formance and fitness during the training process. One of the most informative and popular methods for assessing the functional status of athletes is to study the anaerobic threshold (AT) (Ghosh, 2004; Solli et al., 2017). The AT represents the transition to an anaerobic mechanism of energy exchange when performing physical activity at submaximal and maximum power. The production and excretion of blood lactate are in equilibrium at the AT (MLSS, maximal lactate steady state), and a significant increase in the lactate level is observed (i.e., the lactate threshold) then the athlete transitions through the AT zone (Abreu et al., 2016; Gobatto et al., 2001). Despite numerous studies, the metabolic basis of AT has not been fully established. Studies of AT in humans are often Received: 20 March 2023 \| Revised: 10 August 2023 \| Accepted: 14 August 2023 DOI: 10.14814/phy2.15801 O R I G I N A L A R T I C L E Lactate thresholds and role of nitric oxide in male rats performing a test with forced swimming to exhaustion Natalya Potolitsyna \| Olga Parshukova \| Nadezhda Vakhnina \| Nadezhda Alisultanova \| Lubov Kalikova \| Anastasia Tretyakova \| Alexey Chernykh \| Vera Shadrina \| Arina Duryagina \| Evgeny Bojko Institute of Physiology of Kоmi Science Centre of the Ural Branch of the Russian Academy of Sciences, FRC Komi SC UB RAS, Syktyvkar, Russia Correspondence Evgeny Bojko, Institute of Physiology of Kоmi Science Centre of the Ural Branch of the Russian Academy of Sciences, FRC Komi SC UB RAS, Syktyvkar, Russia. Email: boiko60@inbox.ru Funding information Institute of Physiology of Kоmi Science Centre of the Ural Branch of the Russian Academy of Sciences, FRC Komi SC UB RAS, FUUU- 2022- 0063, Grant/Award Number: 1021051201877- 3 Abstract The present study assessed a complex of biochemical parameters at the anaerobic threshold (AT) in untrained male Wistar rats with different times to exhaustion (Tex) from swimming. The first group of rats was randomly divided into six sub- groups and subjected to a swimming test to exhaustion without a load or with a load of 2%– 10% of body weight (BW). In the first group, we established that for untrained rats, the load of 4% BW in the swimming to exhaustion test was optimal for endurance assessment in comparison with other loads. The second group of rats went through a preliminary test with swimming to exhaustion at 4% BW and was then divided into two subgroups: long swimming time (LST, Tex > 240 min) and short swimming time (SST, Tex < 90 min). All rats of the second group per- formed, for 6 days, an experimental training protocol: swimming for 20 min each day with weight increasing each day. We established that the AT was 3% BW in SST rats and 5% BW in LST rats. The AT shifted to the right on the lactate curve in LST rats. Also, at the AT in the LST rats, we found significantly lower levels of blood lactate, cortisol, and NO. K E Y W O R D S anaerobic threshold, exhaustion, lactate biochemical indices, rats, swimming test This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2023 Institute of Physiology of Komi Science Center of the UB of the RAS. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society. 2 of 12 \| POTOLITSYNA et al. complicated by the infeasibility of complete control over the experiments, and in- depth deep, long- term and high- intensity studies of the AT phenomenon are not always possible in humans (Cholewa et al., 2014). Therefore, to understand many fundamental aspects of exercise physiology, research using an adequate animal model is needed. Rodents are among the most popular and eas- ily available laboratory animals for use in such models. Compared to human studies, working with animals al- lows for greater control and regulation of environmental conditions and food intake. These studies make it possible to collect different types of tissues and perform a number of experimental manipulations that cannot be performed in humans (Cholewa et al., 2014). The results of studies on rats are traditionally projected onto humans (Voltarelli et al., 2002). There is evidence that rats adequately reflect the human response to physical activity based on the main biochemical parameters of blood (Goutianos et al., 2015). However, rodents and humans may not have similar reac- tions to physical exercise (Greek et al., 2012; Rice, 2012). The AT in animals is determined by various changes in blood biochemistry (Faude et al., 2009): 1. when animals achieve the reference lactate level in the blood (4 mmol/L) during exercise (Heck et al., 1985); 2. when the first increase in blood lactate levels above the baseline level is detected (onset of blood lactate accu- mulation) (Farrell et al., 2018; Faude et al., 2009); 3. when a notable bend (sharp change in curvature) in the lactate curve caused by massive lactate accumu- lation during physical load is observed (Contarteze et al., 2008; Gobatto et al., 2001); 4. when undergoing a short period of submaximal load to induce hyperlactemia before starting the test with an increasing load (lactate minimum test, LMT) (Voltarelli et al., 2002). In our opinion, fixed or precalculated lactate levels might not take into account considerable interindividual differences and differences between various lactate ana- lyzers (Faude et al., 2009). Therefore, the most objective AT assessment method is by observing a sharp change (bend) in the lactate curve during exercise with an increas- ing load. Then, the AT is determined as the physical load equivalent to MLSS; in other words, the highest exercise load at which the lactate levels in blood do not change sig- nificantly (Contarteze et al., 2008; Faude et al., 2009; Heck et al., 1985). In the case of individual lactate curves, visual curve assessment is used. For groups, we believe a statisti- cally significant increase in blood lactate levels in compar- ison with the previous physical load is more appropriate. It is also important to monitor other biochemical indicators that characterize the level of physiological adaptations of the body during exercise. Indicators in sports physiology, such as cortisol, catecholamines, glu- cose, urea, and other metabolites, are most often used (De Araujo et al., 2016; Halson & Jeukendrup, 2004). Various studies have also proposed other markers of AT, includ- ing blood catecholamines (Davies et al., 1974) and saliva amylase (Chicharro et al., 1999). We showed that elite ath- letes (cross- country skiers) had a nitric oxide- dependent (NO- dependent) mechanism for regulating lactate levels during aerobic exercise, especially when working at the AT. In our previous work, we revealed a positive relation- ship between NO metabolites and blood lactate at the AT, which was reversed at maximum load. This observation suggests the existence of an adaptive mechanism for reg- ulating the level of lactate on the AT in highly qualified cross- country skiers (Parshukova et al., 2022). The time of onset of the anaerobic (lactate) thresh- old largely depends on the duration of the load (Pa- padopoulos et al., 2006; Roecker et al., 1998; Weyand et al., 1994), physical fitness level (Støren et al., 2014; Tanji & Nabekura, 2019), intensity of movement (Wakayoshi et al., 1993), testing methods (Contarteze et al., 2008; De Araujo et al., 2016), and swimming patterns in swimmers (Dos Reis et al., 2018). Perhaps due to the influence of a large number of factors, research results are often contra- dictory. Therefore, doubts are expressed that the AT (as an indirect method for determining aerobic endurance) re- flects the optimal intensity of training, especially for elite athletes (Bosquet et al., 2002). Determination of the du- ration of exercise to exhaustion is a direct method for de- termining aerobic endurance and provides more complete and reliable information (Beck et al., 2014). There are few studies of the AT with measurements of the maximum du- ration of physical activity to exhaustion in untrained rats. Therefore, the hypothesis of the current study was that the characteristics of the AT will be different in rats dis- playing different times to exhaustion (Tex) while perform- ing the same swimming tests in the same conditions. The purpose of our study was to assess the biochemical param- eters at the AT in untrained rats with different endurance levels performing a swimming test to exhaustion. 2 \| MATERIALS AND METHODS 2.1 \| Experimental animals Our study used male Wistar rats (n = 60), aged 8 weeks at the beginning of the experiment, weighing 250– 300 g. Rats were housed in a room with a temperature of 21 ± 1°C and a controlled photoperiod (12 h of light/12 h of dark- ness) on a standard vivarium diet, with access to water ad libitum. The protocol of the study was reviewed for \| 3 of 12 POTOLITSYNA et al. compliance with the “Rules of the European Convention for the Protection of Vertebrates Used for Experimental and Other Scientific Purposes” and approved by the local Ethics Committee of the Institute of Physiology of the Komi Research Center of the Ural Branch of the Russian Academy of Sciences. 2.2 \| Adaptation to water Prior to experiments, all animals were adapted to water, with a subsequent recovery for 14 days (Brito et al., 2015). Adaptation consisted of keeping animals in shallow water at a temperature of 31 ± 1°C for 30 min for 14 days. The purpose of the adaptation period was to familiarize the animals with testing conditions while avoiding physical training adaptations. 2.3 \| Experimental procedures The swimming sessions were performed in cylindrical water tanks (height 60 cm × diameter 45 cm) with a water temperature of 30 ± 1°C and indoor air temperature of 22 ± 1°C. The rats swam in individual tanks with desatu- rated water. After weighing the animals, a metal weight of the necessary mass was affixed to the base of the tail using elastic nontraumatic tape. A stopwatch was started when the animals were placed in the water. Exhaustion was defined as the animals being incapable of staying on the water surface, the loss of symmetrical movements dur- ing swimming, or the animals remaining underwater for more than 10 s (Chimin et al., 2013). No deaths occurred during or after exercise in any of the animal subgroups. 2.4 \| Method for assessing physical activity to exhaustion Rats (n = 50) were randomly divided into six groups. Each group performed a swimming test to exhaustion with one of the following swim loads (SL): without load (SL0, n = 9) or with a load of 2% (SL2, n = 8), 4% (SL4, n = 8), 6% (SL6, n = 10), 8% (SL8, n = 8), and 10% (SL10, n = 7) of body weight (BW). After the animals achieved exhaustion, they were immediately removed from the water tank, dried, anesthetized, and sacrificed via decapitation. 2.5 \| Method of measuring the AT To determine the AT, we used the method of Gobatto et al. (2001) with modifications (Figure 1). A total of 10 rats were used in the study. All rats had previously gone through the test to exhaustion with a load of 4% BW. Based on this test, all rats were divided into two groups: rats with a long swimming time (time to exhaustion, Tex > 240 min, LST, n = 5) and rats with a short swimming time (Tex < 90 min, SST, n = 5). After this test, all rats were allowed to recover for 1 week. One rat from each group was decapitated to assess the recovery of the blood bio- chemistry parameters after exertion to exhaustion (recov- ery control after swimming to exhaustion). The remaining rats were subjected to physical activity to assess the AT. FIGURE 1 The protocol for anaerobic threshold evaluation in rats. 1st day 0% BM swim 20 min 2nd day 2% BM swim 20 min 3rd day 3% BM swim 20 min 4th day 4% BM swim 20 min 5th day 5% BM swim 20 min 6th day 6% BM swim 20 min Swimming test to exhaustion with 4% of body weight Recovery control after swimming to exhaustion n=2 LST Swimming, long time Tex>240 min, n=4 Separation into groups by duration of the swimming SST Swimming, short time Tex<90 min, n=4 Anaerobic threshold test One week recovery Sacrifce, blood sampling 4 of 12 \| POTOLITSYNA et al. Each animal participated in six experimental tests over 6 days with a 24- h interval between tests. Each test con- sisted of continuous swimming for 20 min with a load of 0%, 2%, 3%, 4%, 5%, or 6% BW in a tank filled with de- saturated water at a temperature of 31 ± 1°C. Immediately after performing the exercise, the rats were removed from the water, and their tails were heated with warm water and dried with a towel. Blood samples were taken from the tail vein using a syringe and placed in heparinized Ep- pendorf tubes (1.5 mL capacity). After performing the test with last load (6% BW), the rats were sacrificed. We determined the AT by observing a sharp increase in blood lactate using the lactate curve obtained by per- forming physical load tests (Faria et al., 2021; Faude et al., 2009). The increase between the two consecutive loads was required to be statistically significant. The AT in this case was the lower of the two consecutive physi- cal load intensities, the one after which the increase was observed. 2.6 \| Blood samples and analyses The lactate levels in the blood samples collected from the caudal veins were determined using a lactate ana- lyzer (Accutrendplus, Roche Diagnostics GmbH). Blood (mixed, arteriovenous) after decapitation was collected in tubes containing heparin and centrifuged at 2400 rpm for 10 min at 4°C. The samples were frozen and stored at −40°C. Plasma levels of lactate (Sentinel Diagnostics), urea, glucose, and cortisol (all from Human GmbH) were measured using immunoenzyme assays (ChemWell 2900 biochemical analyzer). Levels of NO in the plasma were measured using the Griess reaction by evaluating stable metabolites of NO, including nitrites (NO2) and nitrates (NO3), which were merged together as an index (NOx). These methods were previously described (Parshukova et al., 2020, 2022). 2.7 \| Statistical analysis All values are expressed as the means ± SD. Statistical analyses were performed using Statistica 8.0 (Statsoft). The statistical significance of differences between the SST and LST groups was estimated using the Mann– Whitney (U) test. For comparisons of multiple independent groups, we used the Kruskal– Wallis test. For comparisons be- tween workload groups within a corresponding group, we used Friedman ANOVA and Kendall's coefficient of con- cordance. When necessary, the Newman– Keuls post hoc comparison test was used. The statistical significance level was set at p < 0.05. 3 \| RESULTS The times of swimming to exhaustion and the parameters of blood biochemistry in rats performing the Tex test with various loads are presented in Table 1. The total swimming time of rats performing the test to ex- haustion expectedly decreased with increasing tail weights and showed significant variation between individual rats. The greatest variation between minimal and maximal times of swimming was observed in the group swimming with weights of 0%– 6% BW. All rats within each group were clearly divided by the duration of swimming into LST and SST groups (Table 2). The duration of swimming did not ex- ceed 4 min in the SL8 and SL10 groups, and these values did not differ significantly between the SL8 and SL10 groups. The concentrations of glucose and lactate in the blood of rats after exercise to exhaustion generally tended to be higher as the weight of the attached load increased. However, the picture became clearer when the rats were separated within each group according to the duration of swimming. Lactate and glucose levels in LST rats were sig- nificantly lower than those in SST rats. The average values of the other parameters did not show regularities or trends that corresponded with differences in load weights or the duration of swimming in groups with- out separation. However, the differences became apparent when the groups were divided into SST and LST groups. The concentrations of cortisol were higher in SST rats than in LST rats, and the concentrations of urea were lower. Levels of nitric oxide metabolites were also notable. The NOx index was significantly higher in LST rats than in SST rats. The significant differences in NOx values primarily de- pended on the levels of NO3, and the levels of NO2 showed no significant differences between groups. 3.1 \| Anaerobic threshold Based on the time of swimming to exhaustion, all rats were divided into rats that swam for more than 240 min (LST, n = 4) and rats that swam for less than 90 min (SST, n = 4). The dynamics of lactate and the level of the AT in these groups are shown in Figure 2. The dynamics of lactate in venous blood differed in these two groups of rats. In the SST group, we observed a sharper increase in this indicator, and starting at the load of 3% BW, we registered a statistically significant differ- ence from the first data point. However, the most signifi- cant increase in lactate levels was detected at a load of 4% BW. Further testing of rats at loads of 5%– 6% BW did not show significant changes in lactate levels compared with a load of 4% BW. Therefore, the AT in SST rats was assessed at the level of 3% BW. The lactate curve in LST rats had a \| 5 of 12 POTOLITSYNA et al. TABLE 1 Swimming time and biochemical parameters of blood in untrained male rats (all group) after performing swimming tests to exhaustion with different loads. Work load groups Swimming time, min Glucose, mmol/L Lactate, mmol/L Cortisol, ng/ mL Urea, mmol/L NOх, μmol/L NO2, μmol/L NO3, μmol/L SL0 419.7 ± 229.1 4.9 ± 1.5 4.1 ± 2.5 21.0 ± 3.3 8.0 ± 3.5 37.0 ± 21.8 5.2 ± 2.3 31.8 ± 21.7 SL6, SL8, SL10 SL10 SL8 SL10 SL2 148.6 ± 130.2 5.2 ± 2.3 6.8 ± 4.4 19.8 ± 8.3 5.3 ± 1.8 27.7 ± 13.5 9.1 ± 2.1 18.5 ± 13.9 SL4 SL4 SL4 SL4 SL4 SL4 89.1 ± 86.9 4.7 ± 2.4 5.7 ± 4.8 23.7 ± 5.5 5.1 ± 2.0 40.9 ± 8.2 3.9 ± 0.8 37.0 ± 7.8 SL2, SL10 SL2, SL8 SL10 SL2, SL8 SL2 SL2, SL6, SL8, SL10 SL6 59.9 ± 83.1 6.0 ± 2.8 11.0 ± 6.7 23.0 ± 5.0 6.3 ± 1.4 22.1 ± 13.0 6.4 ± 3.0 15.7 ± 15.1 SL0 SL4 SL8 3.0 ± 1.1 8.6 ± 2.9 14.2 ± 1.4 16.1 ± 2.4 5.0 ± 2.0 12.3 ± 2.5 5.6 ± 1.4 6.8 ± 2.7 SL0 SL0, SL4 SL4 SL4 SL10 2.2 ± 0.4 9.8 ± 1.8 13.3 ± 1.3 16.5 ± 3.8 2.8 ± 1.1 14.1 ± 2.2 6.6 ± 1.9 7.5 ± 1.7 SL0 SL0, SL4 SL0, SL4 SL4 Note: The values were expressed as means ± SD. The differences between workload groups are significant at <0.05 for the Kruskal– Wallis ANOVA test. TABLE 2 Swimming time and biochemical parameters of blood in short swimming time (SST) and long swimming time (LST) male rats after performing swimming tests to exhaustion with different loads. Work load groups Swimming time, min Glucose, mmol/L Lactate, mmol/L Cortisol, ng/mL Urea, mmol/L NOх, μmol/L NO2, μmol/L NO3, μmol/L SST LST SST LST SST LST SST LST SST LST SST LST SST LST SST LST SL0 168.0 ± 24.0 545.5 ± 163.5 4.9 ± 2.3 5.0 ± 1.3 7.2 ± 1.6 2.5 ± 0.7 24.5 ± 2.2 19.2 ± 2.0 7.2 ± 5.5 8.5 ± 2.6 23.0 ± 18.7 42.0 ± 8.2 5.8 ± 2.1 5.2 ± 2.4 17.3 ± 19.9 36.8 ± 19.5 SL6 # SL6 SL6# # SL2 48.0 ± 14.2 282.7 ± 58.0 6.4 ± 2.4 3.5 ± 0.2 10.3 ± 0.5 2.1 ± 0.6 25.6 ± 5.5 15.4 ± 3.7 4.3 ± 0.7 6.6 ± 2.1 20.0 ± 3.5 37.9 ± 11.1 10.0 ± 1.1 8.0 ± 2.9 10.1 ± 2.4 29.9 ± 15.4 SL6 # # # # # SL4 29.2 ± 7.4 189.0 ± 48.8 5.8 ± 2.5 2.9 ± 0.5 8.3 ± 4.1 1.3 ± 0.2 26.0 ± 5.9 19.9 ± 1.0 4.7 ± 2.1 5.7 ± 2.0 37.0 ± 8.1 47.3 ± 2.2 3.6 ± 0.7 4.2 ± 1.0 33.4 ± 7.6 43.0 ± 3.1 SL6# SL6 SL6 SL6 # SL6 SL6 SL6 8.4 ± 6.8 180.0 ± 2.0 7.0 ± 7.4 3.0 ± 2.8 15.0 ± 1.7 1.5 ± 0.3 26.2 ± 2.9 19.1 ± 3.7 5.7 ± 1.3 7.7 ± 0.5 15.4 ± 8.7 37.8 ± 1.6 6.8 ± 3.6 5.5 ± 0.4 8.6 ± 12.0 32.3 ± 1.8 SL0, SL2 SL4# # SL0, SL4 SL4 # # SL4 # SL4 # Note: The values were expressed as means ± SD. The differences between workload groups (SST, LST accordingly) are significant at <0.05 for the Kruskal– Wallis ANOVA test. #The differences between SST and LST groups are significant at <0.05 for the Mann– Whitney (U) test. 6 of 12 \| POTOLITSYNA et al. flatter slope than the SST curve and was characterized by a more gradual increase in the concentration of lactate in the blood with the increasing weight of the attached loads. The most significant increase in this indicator occurred when the rats performed the test at a load of 6% BW. There- fore, the AT for the LST group was set at a load of 5% BW. The concentrations of lactate, glucose, and cortisol in arteriovenous blood collected after decapitation of rats were higher in SST rats than in LST rats (Table 3). Urea levels in the SST and LST groups showed no signif- icant differences. The NOx levels were significantly higher in SST rats than in LST rats. The levels of NO2 and NO3 did not reveal significant differences in the arteriovenous blood of either group of rats. However, we observed higher values of both metabolites in SST rats than in LST rats. 4 \| DISCUSSION Only a few studies have described swimming Tex in rats (Beck & Gobatto, 2013; Travassos et al., 2018; Venditti & Di Meo, 1996). Some groups used arbitrary loads without describing the reasoning for weight choice (Travassos et al., 2018; Venditti & Di Meo, 1996). Other research- ers used a method in which the load to exhaustion was based on a preliminary calculation of AT. For example, Beck et al. (2014) used the minimum lactate level on the “U- shaped” lactate curve obtained after the blood LMT as the AT. The loads that the rats were subjected to in this test were then used as benchmarks for the swimming to exhaustion experiment. However, the method of calculat- ing AT significantly affects the interpretation of the re- sults and makes it impossible to compare the results with those of other studies. Therefore, we evaluated Tex in rats swimming to exhaustion using various loads in the first stage of our study (Table 1). Predictably, the increase in the load weight affected the duration of the swim and re- duced it from several hours to several minutes. However, the swimming time also strongly depended on the endur- ance of the rats. For most load weights, Tex was divisible into two groups (Table 2): SST and LST. Loads heavier than 8% BW were too heavy for untrained rats (Gobatto FIGURE 2 Blood lactate levels in tail vein blood of rats from SST and LST groups. SST— swimming for short time, LST— swimming for long time. The values are expressed as means ± SD. The statistical significance of differences between SST and LST groups was estimated using Mann– Whitney (U) test; p- values were considered significant at p < 0.05. The statistical significance of differences between workload groups within corresponding group (SST or LST accordingly) was estimated using the Friedman ANOVA and Kendall coefficient of concordance, and is shown in italics. When necessary, the Newman– Keuls post hoc comparison test was used. Statistical significance is indicated in comparison with the specified load weight at #p < 0.05. p=0.0021 p=0.0019 #0 #0,2,3 #0,2,3 #0,2,3 * * * * #0,2 #0,2,3,4 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 Lactate, mmol/l Load weight (% of body mass) SST LST TABLE 3 Biochemical parameters of arteriovenous blood of sacrificed rats after performing the last load (6% BW). Groups Lactate, mmol/L Glucose, mmol/L Cortisol, ng/ mL Urea, mmol/L NOx, μmol/L NO2, μmol/L NO3, μmol/L SST 10.3 ± 4.4 9.5 ± 0.8 34.2 ± 9.3 4.0 ± 1.0 28.1 ± 1.8 9.5 ± 1.6 18.6 ± 3.1 LST 6.3 ± 0.7 8.5 ± 0.7 19.1 ± 3.2 4.5 ± 1.1 24.3 ± 1.8 8.0 ± 1.1 16.3 ± 2.9 p- valueSST- LST 0.049 0.126 0.049 0.512 0.049 0.126 0.512 Note: Data are presented as the means ± SD. p- valueSST- LST— The statistical significance of differences between short swimming time (SST) and long swimming time (LST) groups was estimated using the Mann– Whitney (U) test. p < 0.05 are shown in bold. \| 7 of 12 POTOLITSYNA et al. et al., 2001), and no differences were observed in this case. Despite the lack of similar studies in the literature, there are fragmentary results on rats swimming to exhaustion. For example, Venditti and Di Meo (1996) showed a Tex close to our results (294 ± 32 min) in untrained rats (swim- ming to exhaustion) with a load of 2% of BW. Beck and Gobatto (2013) reported that rats swam 108 ± 46 min with a load weight of 5% BW, which is less than the load of 4%– 6% BW in our study. A greater variation in Tex was described in Travassos et al. (2018). Rats in the Travassos et al. (2018) study swam at a load of 6% BW from 3 to 22 min and were divided into low per- formance (Tex = 3– 12 min) and high performance (Tex = 12– 22 min) groups depending on the time of exhaustion. Notably, the authors excluded all rats that swam longer than 22 min. The variability in the duration of swimming with the load of 6% BW was larger in our study, and therefore, the division into endurance groups was different. All rats that swam less than 21 min with a load of 6% BW were included in the SST group, and rats that swam approximately 180 min were included in the LST group. Therefore, the study of un- trained rats using various load weight protocols allows com- parison of metabolic changes in LST and SST animals and identifies the best load weight for the test. We showed that the blood biochemistry changes in dif- ferent load weights and the time of swimming to exhaus- tion also significantly differed between the LST and SST rats. The SST rats had higher levels of lactate, glucose and cortisol, and the LST rats had higher levels of urea and nitric oxide. Notably, these differences became more ob- vious with increases in the load weight while swimming. Despite all of the rats being of the same age, being housed in the same conditions, being fed the same diet, and having no previous training, some rats showed higher inborn physical endurance. These results are fully con- sistent with the hypothesis that genetics is an important determinant of the response to physical activity (Koch et al., 2005) and may affect the features of anatomy (Britton & Koch, 2001), pulmonary function (Kirkton et al., 2009), insulin response (Schwarzer et al., 2021), and the predominant type of skeletal muscle fibers (Abernethy et al., 1990). It was expected that the metabolism in rats with different physical endurance would be different. A significant increase in blood lactate at the lower loads in SST rats reflects lower aerobic capacities, and hypoxia oc- curs faster in these rats under high- intensity physical exer- cise. Howlett et al. (2009) showed that SST rats had VO2max and oxygen transfer in skeletal muscles that was 50% lower than those in higher endurance rats, despite having higher absolute muscle mass. The maintenance of glucose levels in hypoxia is provided primarily by glycolysis and glycog- enolysis (Brooks & Mercier, 1994; Emhoff et al., 2013). With sufficient oxygen supply during prolonged physical exercise, there is a higher fat utilization. The increased contribution of lipids to energy metabolism makes it pos- sible to significantly increase endurance during physical exercise (Brooks & Mercier, 1994; Nosaka et al., 2009). There are more data on a more complex system of regu- lation of lipid metabolism depending on the intensity of exercise (Lyudinina et al., 2018; Romijn et al., 1993). Physical exercises also stimulate increases in cortisol levels. This hormone plays a significant role in acceler- ating lipolysis, ketogenesis, and proteolysis (Del Corral et al., 1998). The level of cortisol increases in proportion to the intensity of exercise, but the final level depends on the total duration of exercise. Moderate- and high- intensity exercises increase the levels of circulating cortisol. In con- trast, low- intensity exercise does not lead to an increase in cortisol levels (Del Corral et al., 1998; Hill et al., 2008). The levels of cortisol did not show significant differ- ences with respect to Tex in our study. However, cortisol levels were higher in SST rats than in LST rats, especially at loads of 2%– 4% BW. Perhaps, this result occurred be- cause of the different behaviors of rats when performing the test and the levels of individual stress. Glucocorticoids in rodents are often used as biomarkers of stress, with cortisol reacting faster during severe acute stress, unlike corticosterone, which is associated more with adaptation during chronic stress (Gong et al., 2015). NO is another metabolite that allows adaptation to significant physical exercise (Oral, 2021). It is a signaling molecule with a wide variety of effects in mammals, the most well- known of which is the regulation of local vaso- motor tone and resistance to microvascular flow (Baskurt et al., 2011). Skeletal muscles of rodents contain unusu- ally high concentrations of nitrates compared to blood and other tissues, which indicates the high importance of ni- tric oxide for their body (Piknova et al., 2015). Nitric oxide has an extremely short half- life of only a few milliseconds in biological tissues, and it is important that it is con- stantly produced at its sites of effect (Jones et al., 2021). Experimental data indicate that physical exercises lead to an increase in the enzymatic synthesis of nitric oxide and activation of the associated vascular control mechanisms (Baskurt et al., 2011). We previously found a positive cor- relation between nitrogen oxide and lactate at the AT and a negative correlation at maximum load in elite cross- country skiers possessing high endurance (Parshukova et al., 2020). The higher level of NOx we obtained in LST rats, but not SST rats, is consistent with these findings. It characterizes a more adequate response of the vascular bed in response to physical exercise and allows better con- trol of vascular tone for a longer time. The increase in NOx levels in LST rats was observed primarily due to the NO3 fraction. Under conditions of normal and increased oxy- gen consumption by tissues, NO is formed enzymatically 8 of 12 \| POTOLITSYNA et al. via the oxidation of L- arginine, and the final metabolite of this process is primarily NO3 (Cubrilo et al., 2011). Our study showed that the use of a load weight of 4% BW was the most informative for studying the level of physical endurance in untrained rats. At this load weight and a background of a wide Tex range, the rats showed significant changes in most of the biochemical indices as- sessed in our study, which included the most informative indices in relation to the problem under discussion. 4.1 \| Anaerobic threshold There are a large number of methods for studying the lactate threshold in rats (Faude et al., 2009). The choice of method depends on the research goals. However, it is also important to consider the natural abilities of rats and our capacity to project the results on humans in the future. The optimal method for determining the AT in rats is swimming with increasing load. Several studies of the lactate threshold in swimming rats found that loads of 4%– 6% BW were more often used (Contarteze et al., 2008; Gobatto et al., 2001; Voltarelli et al., 2002). For example, Gobatto et al. (2001) showed that the AT corresponded to 6% BW at a blood lactate concentra- tion of 5.5 mmol/L. Another study established the AT at a weight of 4.0% BW and a lactate level of 5.2 mmol/L (Abreu et al., 2016). Similar lactate values were shown at an AT with a load of 4.5% BW (Zhouab et al., 2018). However, these studies do not mention individual en- durance variation in rats. The results of our study showed that this characteristic of laboratory animals may significantly shift the AT to the left or the right on the lactate curve. The ATs in SST and LST rats were 3% BW and 5% BW, respectively. The lactate curve of SST was less flat than that of LST. For increasing endur- ance, it is generally recognized that a shift of the lactate curve to the right is interpreted as an increase in physi- cal performance, and a shift to the left is considered a deterioration in endurance (Abreu et al., 2016; Faude et al., 2009). The lactate concentration at the AT was also different and higher in SST rats than in LST rats (5.8 mmol/L vs. 5.2 mmol/L). A lower lactate level at the end of physical exercise in LST rats may be associated with a lower rate of lactate accumulation and/or a lower metabolic clearance of lactate (Donovan & Brooks, 1983; Yang et al., 2020). Higher endurance augments capaci- ties for lactate production, disposal, and clearance (Mes- sonnier et al., 2013). Our data are generally consistent with the results of other studies, although no data on the AT when swimming in rats with a load below 4% were found. However, rats with higher endurance were likely included for various reasons in the described studies (Abreu et al., 2016; Contarteze et al., 2008; Gobatto et al., 2001; Voltarelli et al., 2002; Zhouab et al., 2018). Biochemical data from the arteriovenous rat blood as- says (Table 3) also demonstrated significant differences between the SST and LST rats. Because the collection of arteriovenous blood occurred within 3– 5 min after the last collection of blood from the caudal vein, the data on lactate from arteriovenous blood showed higher values relative to lactate from the caudal vein. The most significant increase was observed in SST rats, which reflected their lower re- covery abilities compared to LST animals. Glucose, corti- sol, and NOx levels were also significantly higher in the SST rats. This pattern of blood biochemistry generally characterizes more significant rearrangements and higher stress levels in SST rats than in LST rats at a similar level of physical exercise. Notably, the increase in NOx levels in this test occurred due to an increase in nitrites (NO2), unlike in swimming to exhaustion. Under hypoxic con- ditions, NO2 is an alternative source of nitric oxide syn- thesis (Gladwin et al., 2000; Schulman & Hare, 2012) and participates in adaptation to hypoxia caused, for example, by physical exertion (Gladwin et al., 2000). The current understanding of nitrite- dependent mechanisms of adap- tation to hypoxia is based on data on the reduction of NO2 by oxygen- dependent and hypoxic nitrite reductase (Glad- win & Kim- Shapiro, 2008). NO is a mediator of skeletal muscle function and af- fects cellular respiration and contractility. In working skel- etal muscle, inhibition of NOS improves the economy of muscle contraction, decreases the outflow of lactate from the muscles, and reduces the oxygen cost (Krause & Van Etten, 2005). Thiol groups, reactive metal ions in the pro- teins' active centers, can interact with NO, which leads to various responses and further biological events in skeletal muscles. NO- mediated reactions inhibit heme- containing proteins, such as cytochrome C oxidase, thus interfering with the function of cytochrome C oxidase in cell respi- ration (Borutaite & Brown, 1996). Inhibition of this en- zyme and of the sarcoplasmic reticulum Ca2+- ATPase in fast- twitch and slow- twitch skeletal muscle fibers by NOS- generated NO may also lead to inhibition of mitochondrial respiration in skeletal muscle (Klebl et al., 1998). More- over, aconitase and respiratory chain complex I can also be targeted by NO (Clementi et al., 1998). NO is crucial for the activation and inhibition of ryanodine receptors (RyRs) (Stamler & Meissner, 2001), which play a decisive role in the release of Ca2+ into the cytosol and therefore in muscle excitation and contraction (Mazzone & Carme- liet, 2008). In our experimental work, we have shown that elite athletes (cross- country skiers) have an NO- dependent mechanism for regulating lactate levels during aerobic ex- ercise, especially when working at the AT. In particular, at the AT, we have revealed a positive relationship between \| 9 of 12 POTOLITSYNA et al. NOx (nitric oxide metabolites) and blood lactate, with that relationship being reversed at maximum load. This obser- vation suggests the existence of an adaptive mechanism for regulating lactate levels at the AT in highly qualified cross- country skiers (Parshukova et al., 2022). Therefore, our data provide a new understanding of the role of NO- dependent mechanisms in the phenome- non of AT. 5 \| CONCLUSION We found that the level of individual endurance signifi- cantly affected the AT in untrained rats. The AT in SST rats and 5% BW in LST rats. These groups also had dif- ferent blood biochemistry profiles at the AT and after swimming to exhaustion. There was a shift in the AT to the right side on the lactate curve in the zone of the AT in LST rats compared to SST rats, and the levels of lactate, glucose, cortisol, and NOx were lower. At the end of the exercise to exhaustion, SST rats had higher blood levels of lactate, glucose, and cortisol, and LST rats had higher levels of urea and NOx. AUTHOR CONTRIBUTIONS Evgeny Bojko, Nadezhda Vakhnina, Natalya Potolit- syna conceives and designed the experiments; Natalya Potolitsyna, Nadezhda Vakhnina, Nadezhda Alisyl- tanova, Lubov Kalikova, Anastasia Tretyakova, Alexey Chernykh, Vera Shadrina, Arina Duryagina performed experiments; Natalya Potolitsyna, Olga Parshukova, Lubov Kalikova analysed data; Natalya Potolitsyna, Olga Parshukova, Lubov Kalikova, Nadezhda Vakhnina interpreted results of experiments; Natalya Potolitsyna prepared figures; Natalya Potolitsyna, Alexey Chernykh drafted manuscript; Natalya Potolitsyba, Evgeny Bojko, Olga Parshukova, Nadezhda Vakhnina, Alexey Chernykh edited and revised manuscript; Natalya Po- tolitsyna, Evgeny Bojko, Olga Parshukova, Nadezhda Vakhnina, Alexey Chernykh approved final version of manuscript. ACKNOWLEDGMENTS The study was conducted within the framework of the research work of the Institute of Physiology of Kоmi Sci- ence Centre of the Ural Branch of the Russian Academy of Sciences, FRC Komi SC UB RAS, FUUU- 2022- 0063 (No. 1021051201877- 3). FUNDING INFORMATION This research did not receive any specific grant from fund- ing agencies in the public, commercial, or not- for- profit sectors. CONFLICT OF INTEREST STATEMENT No conflicts of interest, financial or otherwise, are de- clared by the authors. ETHICS STATEMENT The Ethics Committee of the Institute of Physiology of the Russian Academy of Sciences, Syktyvkar approved the experimental design and protocol of the study. The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. ORCID Natalya Potolitsyna https://orcid. org/0000-0003-4804-6908 Evgeny Bojko https://orcid.org/0000-0002-8027-898X REFERENCES Abernethy, P., Thayer, R., & Taylor, A. W. 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Effects of nicotinamide N- methyltransferase (NNMT) inhibition on the aerobicand the anaerobic endurance exercise capacity. Science & Sports, 33(4), e159– e165. https://doi.org/10.1016/j.scispo.2018.02.006 How to cite this article: Potolitsyna, N., Parshukova, O., Vakhnina, N., Alisultanova, N., Kalikova, L., Tretyakova, A., Chernykh, A., Shadrina, V., Duryagina, A., & Bojko, E. (2023). Lactate thresholds and role of nitric oxide in male rats performing a test with forced swimming to exhaustion. Physiological Reports, 11, e15801. https://doi.org/10.14814/phy2.15801	Lactate thresholds and role of nitric oxide in male rats performing a test with forced swimming to exhaustion.	[]	Potolitsyna, Natalya,Parshukova, Olga,Vakhnina, Nadezhda,Alisultanova, Nadezhda,Kalikova, Lubov,Tretyakova, Anastasia,Chernykh, Alexey,Shadrina, Vera,Duryagina, Arina,Bojko, Evgeny	eng
PMC7036986	International Journal of Environmental Research and Public Health Article Alterations in Running Biomechanics after 12 Week Gait Retraining with Minimalist Shoes Yang Yang 1, Xini Zhang 1, Zhen Luo 1, Xi Wang 1, Dongqiang Ye 1 and Weijie Fu 1,2,* 1 School of Kinesiology, Shanghai University of Sport, Shanghai 200438, China; yangyang.sus@gmail.com (Y.Y.); xinizhang.sus@gmail.com (X.Z.); luozhen716@gmail.com (Z.L.); zwx252@163.com (X.W.); yedongqiang09@gmail.com (D.Y.) 2 Key Laboratory of Exercise and Health Sciences of Ministry of Education, Shanghai University of Sport, Shanghai 200438, China * Correspondence: fuweijie@sus.edu.cn or fuweijie315@163.com; Tel.: +86-21-65507368; Fax: +86-21-51253242 Received: 20 December 2019; Accepted: 25 January 2020; Published: 28 January 2020 Abstract: Purpose: The intervention of 12 week gait retraining with minimalist shoes was established to examine its eﬀect on impact forces, joint mechanics, and vertical stiﬀness during running. Methods: Thirty male recreational runners were randomly assigned to the gait retraining + minimalist shoe (n = 15, GR) and minimalist shoe (n = 15, MIN) groups. The ground reaction force and marker trajectories were collected before and after intervention at a speed of 3.33 ± 5% m/s. Results: A total of 17 participants (9 in the GR group and 8 in the MIN group) completed the training. After training, (1) the loading rate of both groups decreased signiﬁcantly, and the loading rate of the GR group was lower than that of the MIN group. (2) The foot strike angle of the GR group decreased signiﬁcantly after training, and the plantarﬂexion angle and hip joint angular extension velocity increased in both groups. (3) The moment of ankle joint increased in the GR group, and the stiﬀness of lower limbs was signiﬁcantly improved in both groups. Conclusion: The 12 week gait retraining with minimalist shoes converted rearfoot strikers into forefoot strikers with a rate of 78% (7/9). More importantly, such a combined program, compared to the training with only minimalist shoes, can avoid the peak impact force and decrease the loading rate more eﬀectively, thus providing a potential means of reducing risk of running injury caused by impact forces. Moreover, the increased vertical stiﬀness of lower extremity after gait retraining may improve running economy and corresponding energy utilization. However, these observations also suggest that the sole use of minimalist footwear may have limited eﬀects on reducing running-related impacts. Keywords: gait retraining; running biomechanics; strike pattern; minimalist shoe 1. Introduction As one of the most popular sports in the world, running is attracting increasing attention nowadays [1]. However, a high injury rate (19–79%) in running has been reported [2]. The impact load is two to three times of the body weight at touchdown, which is considered to be the main risk factor for causing damage such as stress fracture/fracture, patellofemoral joint pain syndrome, and plantar fasciitis [2–5]. Thus, how to reduce the impact and risk of running injury has always been a hot issue in the biomechanics, sports medicine, rehabilitation, and related industries [6,7]. In the past 50 years, the injury rate of running has not changed much despite the development of running shoes [8]. Studies show that the cushioning function of running shoes cannot be utilized in actively landing [6,9,10]. Hence, researchers have considered diﬀerent shoe designs and the postural control in lower limbs whilst running. As a result, gait retraining and minimalist shoe training derived Int. J. Environ. Res. Public Health 2020, 17, 818; doi:10.3390/ijerph17030818 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 818 2 of 13 from barefoot running theory have been applied to rehabilitation, medical treatment, and sports ﬁelds [11–14]. Minimalist footwear are shoes with a lighter mass, greater ﬂexibility, and lower heel-to-toe drop than conventional running shoes [15]. Runners who use this type of footwear likely adopt a non-rearfoot strike pattern [16,17], which can reduce impact forces [11,16]. McCarthy et al. [13] showed that after a 12 week simulated barefoot training, in which the participants were free to adopt their own running pattern, 100% used non-rearfoot strike patterns. Latorre-Roman et al. [18] found that a 12 week barefoot training program causes signiﬁcant changes in the foot strike pattern, with a tendency towards midfoot or forefoot strikes. However, not all runners who are used to wearing conventional shoes can switch to non-rearfoot strike when wearing minimalist shoes [18]. Without the cushioning of conventional shoes, the risk of high impact-related injuries likely increases [19,20]. Therefore, combining gait retraining and minimalist shoes may be more eﬀective and secure than adopting the two separately. Gait retraining, an active training program with instruction or feedback, diﬀers from the minimalist shoe training, which is a passive adaptive process of special shoe conditions (e.g., minimalist shoes, barefoot shoes). Promoting a forefoot strike pattern, which is similar to the barefoot movement in the literature, is considered as a possible way forward [17,21]. In addition to promoting non-rearfoot strike patterns, gait retraining encourages forefoot/midfoot strike for a high frequency, light stride and an upright posture [21,22]. Gait retraining reduces loading rate and impact peak by increasing the stride frequency and adopting a non-rearfoot strike pattern [16,21,23]. Warne et al. [12] showed that a 6 week combination program of gait retraining with minimalist shoes causes more signiﬁcant changes than that of gait retraining with conventional shoes. In light of the above information, the combination of gait retraining and minimalist shoes can reduce the loading rate and peak impact force by using a non-rearfoot strike pattern [13,16,23]. The implication is that the shoe condition should match the running posture. However, the long-term impact on the running posture of such a combination program remains unclear. The combined training program with a long incremental load may be eﬀective and safe. The purpose of this study was to establish a combined intervention mode of 12 week gait retraining with minimalist shoes and examine its eﬀect on factors related to the risks of running injury and performance, i.e., impact forces, joint mechanics, and vertical stiﬀness. The hypothesis was that the participants received 12 week gait retraining with minimalist shoes would have a lower loading rate and a decreased foot-strike angle compared to that of those who only used minimalist shoes. 2. Methods 2.1. Participants Thirty recreational male runners (age: 30.0 ± 6.4 years; height: 175.0 ± 5.2 cm; body mass: 71.9 ± 9.4 kg; weekly running volume: 27.4 ± 8.7 km) were recruited. Inclusion criteria are as follows: (1) they ran at least 3 days per week with a minimum of 20 km/week for at least 3 months prior to the study and (2) they were used to running with rearfoot strike in cushioned shoes and had no experience of barefoot running or special sneakers (e.g., ﬁve-ﬁnger shoes, minimalist shoes, and racing spikes). Prior to this experiment, participants completed a basic information questionnaire and signed an informed consent form to ensure that they had no musculoskeletal injuries for the past 6 months. This study was approved by the Institutional Review Board of the Shanghai University of Sport (no. 2017007). 2.2. Experimental Design A parallel randomized control design was used in this study. Thirty participants were randomly (random number sort) divided into gait retraining + minimalist shoe (GR) and minimalist shoe (MIN) groups. The two groups underwent the same testing process but with diﬀerent interventions (Figure 1). Int. J. Environ. Res. Public Health 2020, 17, 818 3 of 13 Foot size was measured, and participants in both groups were provided with a pair of minimalist footwear (type INOV-8 Bare-XF 210 V2: 3 mm outsole, no midsole, 0 mm heel-toe drop, 227 g weight). Figure 1. Flow diagram of this study. 2.3. Testing Procedure A 10-camera motion capture system (100 Hz, T40, Vicon Motion Inc., Oxford, United Kingdom) was used to collect kinematic data including hip, knee, and ankle joints (Figure 2). Two 90 × 60 × 10 cm Kistler 3D force platforms (9287B, Kistler Corporation, Winterthur, Switzerland) were used to collect ground reaction force (GRF) data at a sampling rate of 1000 Hz. Before the over-ground test, the participants performed a 5 min warm-up on a treadmill at optional running speed with the minimalist shoes, followed by 1 min 3.33 m/s experimental speed adaptation. During the over-ground test, three successful right foot contacts on the force plate were required. Its presence was not mentioned to avoid targeting problems. The speed during the over ground test was monitored to ensure the participants ran at 3.33 m/s using a Witty-Manual grating timing system (Witty wireless training timer, Microgate Corp., Bolzano, Italy) with a 5% acceptable variance. Both the GR and the MIN groups were tested before and after the intervention. Int. J. Environ. Res. Public Health 2020, 17, 818 4 of 13 Figure 2. The marker-set and the experimental setup. 2.4. Intervention GR group: The participants were required to run at a medium-intensity self-selected speed with minimalist shoes and strike with forefoot. A pressure sensitive insole (Podoon) was applied to GR runners to distinguish foot strike patterns. The sensors were located at the metatarsophalangeal joint and heel. Sound feedback could be obtained from a mobile application if participants struck with the heel. The gait retraining program lasted 12 weeks and was three times a week. The duration of the training gradually increased from 5 min in the 1st week to 48 min in the 12th week (Table 1) [12,13]. Weekly group training was also provided to ensure the quality of retraining and to minimize the dropout rates. After each training, the experimenter will remind the participants who do not meet the requirements or mismatch with the data in the cloud. Table 1. The 12 week gait retraining intervention. Week 1 2 3 4 5 6 7 8 9 10 11 12 Duration (min) 5 10 15 20 25 30 35 40 42 44 46 48 Times per week 3 3 3 3 3 3 3 3 3 3 3 3 MIN group: During the running training, the participants were required to run at a medium- intensity self-selected speed wearing minimalist shoes without any instructions for the strike pattern. A pressure-sensitive insole (Podoon) was also applied to MIN runners for matching the same insole condition of the GR group, but they did not receive the mobile application that provided sound feedback. The schedule was the same as that of the GR group. The intervention training was only an alternative part of the training [12,13]. The total running distance per week was unchanged. Participants kept record training logs, including the time training start/stop, location, and distance. During training, they were told that any discomfort or injury needed to be reported to the experimenter. The researchers checked the training logs stored in the cloud. Int. J. Environ. Res. Public Health 2020, 17, 818 5 of 13 The participants in both groups were allowed to wear habitual running shoes when out of training. During training sessions, the two groups were prevented from interacting with one another. Inclusion criteria: (1) completed all tests, (2) no more than three absences, and (3) completed the last 3 weeks’ training with no more than six absences. Those who satisﬁed any condition were included. During training, participants were allowed to delay or withdraw due to injury or personal reasons. 2.5. Data Processing Kinematic data and GRF were analyzed via the gait analysis software Visual 3D (v5, C-Motion, Inc., Germantown, MD, USA) using inverse dynamics. The GRF was ﬁltered with a cut-oﬀ frequency of 100 Hz. Marker trajectories were ﬁltered with a cut-oﬀ frequency of 7 Hz [10] via a fourth-order Butterworth low-pass ﬁlter. The hip, knee, and ankle angles of the lower limb were deﬁned on the basis of our previous model [24], and the kinematic features of each joint were calculated. Impact variables included peak impact forces and maximum loading rates. The maximum loading rate (LR) is equivalent to a slope of 20–80% of ﬁrst peak (FP). If FP is non-existent, then LR is calculated by using 13% of the gait cycle as a representative value [25,26]. Kinematic variables included (1) ground contact time (CT), which represents the duration between touchdown to oﬀ-ground; (2) strike angle (θf) which refers to the angle between the foot and ground at initial contact; (3) angles of the hip, knee, and ankle joints when contacting the ground (θ0) and the maximum joint angle (θmax); and (4) joint angular velocities including the angular velocity at initial contact (ω0) and the maximum angular velocity of hip, knee, and ankle joints (ωp). The angle of ankle joint was 0◦ during standing, negative for extension/plantarﬂexion, and positive for ﬂexion/dorsiﬂexion (Figure 3). Kinetic variables included (1) joint moment determined by the net moment generated by the muscles of the hips, knees, and ankles of the lower limbs using the inverse dynamics in Visual 3D biomechanical analysis software; (2) peak extension joint power (p), which is the product of the net moment (M) and joint angular velocities (ω), and (3) vertical stiﬀness (k = GRFi/∆y) [27]. For the joint moment, the maximum extension moment (Mmax) of each joint was selected. GRFi represents the vertical GRF when the center of gravity (CoG) was lowest, and ∆y represents the vertical displacement of CoG during centrifugation. Figure 3. Angles of lower extremity joints. Int. J. Environ. Res. Public Health 2020, 17, 818 6 of 13 2.6. Statistics The mean and standard deviation for each variable were calculated. The results of each group of pre/post were tested for normality. The original value was used in all tables and ﬁgures for comparison. A two-way repeated measure ANOVA was used to examine the eﬀects of retraining (pre- and post-training) and groups (GR and MIN) on each variable (Version 22.0; SPSS, Inc., Chicago, IL, USA). Independent t-tests and paired t-tests were used as post-hoc tests when a signiﬁcant interaction was detected. The signiﬁcance level was set as α = 0.05. 3. Results 3.1. Dropout Rate Seventeen participants completed intervention and met the inclusion criteria (nine in the GR group, eight in the MIN group) (Table 2). Speciﬁcally, an FFS runner in the GR group was excluded after pre-test. During intervention, two participants (one in GR, one in MIN) were excluded due to injuries caused by non-training related events, i.e., walked downstairs carelessly. Two participants (one in GR, one in MIN) were excluded due to mismatch of the cloud data, and they could not provide reliable evidence, such as app or smart watch data. Three participants (one in GR, two in MIN) who lost contact during the training were excluded. Five participants (two in GR, three in MIN) who quit or missed too much training were also excluded. No signiﬁcant diﬀerence was observed in the average running volumes between the GR and MIN groups (GR: 28.3 ± 11.2 km/week, MIN: 26.9 ± 10.7 km/week). Table 2. Information of the participants who completed training. GR: gait retraining + minimalist shoe, MIN: minimalist shoe. Age (years) Height (cm) Body Mass (kg) km per Week (km) GR (n = 9) 32.4 ± 6.1 174.8 ± 5.3 70.2 ± 6.0 28.3 ± 11.2 MIN (n = 8) 27.6 ± 5.2 173.9 ± 7.0 75.4 ± 11.7 26.9 ± 10.7 t-test p = 0.104 p = 0.773 p = 0.262 p = 0.787 3.2. Impact Forces A signiﬁcantly main eﬀect of time on the loading rate was observed (Figure 4; Table 3), which was signiﬁcantly reduced by 22.6% (GR) and 17.2% (MIN) after training (p < 0.001, p = 0.017). The loading rate of the GR group was lower than that of the MIN group after training (p = 0.015). No interaction eﬀect was noted between time × group for any other GRF parameters in this study. Figure 4. Comparison of loading rate between two groups before and after training. Int. J. Environ. Res. Public Health 2020, 17, 818 7 of 13 Table 3. Contrast of ground reaction force (GRF) in diﬀerent shoe conditions before and after training. Parameter GR MIN Pre Post Pre Post FP (BW) 1.78 ± 0.20 N/A 1.83 ± 0.22 2.05 ± 0.47 TFP (ms) 25.50 ± 5.31 N/A 26.20 ± 4.71 28.97 ± 15.75 LR (BW·s−1) 71.62 ± 13.66 # 55.44 ± 25.21 * 74.00 ± 21.42 61.30 ± 32.90 CT (ms) 233.58 ± 20.44 226.35 ± 11.90 243.27 ± 26.65 240.02 ± 26.26 FP: the ﬁrst peak of the touch-down phase; TFP: the instant reaching the FP; BW: body weight; LR: loading rate; CT: ground contact time; #: signiﬁcant diﬀerence from pre- to post-tests. * signiﬁcant diﬀerence between groups at time point, p < 0.005. 3.3. Kinematics A signiﬁcant main eﬀect of time was observed on the foot-strike angle, ankle angle (Figure 5), and angular velocity of hip (p = 0.026, p = 0.011, p = 0.032, respectively) (Table 4). In addition, a signiﬁcant interaction eﬀect between time × group on the foot-strike angle was observed (p = 0.013). After the post-hoc test, the foot-strike angle of the GR group decreased by 10.3◦ after training (p = 0.015), but no diﬀerence was noted in the MIN group (p = 0.753) (Figure 5). The foot-strike angle of the GR group was signiﬁcantly diﬀerent from that of the MIN group in the post-test (p = 0.017). After training, the ankle angle signiﬁcantly decreased by 4.6◦ (GR) and 2.5◦ (MIN) at touchdown, and the maximum angular velocity of hip joint increased by 15.2% (GR) and 25.2% (MIN). There was no interaction eﬀect between time × group for any other kinematic parameters. Figure 5. Comparison of foot-strike angle (left) and ankle angle (right) between two groups before and after training. * signiﬁcant diﬀerence from pre- to post-tests in GR group; #: signiﬁcant diﬀerence between groups at time point, p < 0.005; &: signiﬁcant diﬀerence from pre- to post-tests in MIN group. Table 4. Kinematics changes of hip, knee, and ankle before and after training. Joints Parameter GR MIN Pre Post Pre Post Foot/Ankle θf (deg) 8.07 ± 4.64 ,& −2.21 ± 3.09 # 8.73 ± 6.68 9.27 ± 8.9 θ0 (deg) −0.13 ± 4.29 −4.73 ± 4.79 # 0.61 ± 3.76 * −1.89 ± 5.27 θmax (deg) 17.48 ± 4.76 16.92 ± 4.78 15.91 ± 2.51 16.58 ± 3.38 ω0 (deg·s−1) 289.57 ± 85.81 332.77 ± 103.51 259.22 ± 34.13 267.83 ± 67.03 ωp (deg·s−1) −269.34 ± 90.64 −245.00 ± 60.65 −231.46 ± 66.38 −213.37 ± 44.05 Knee θ0 (deg) −13.56 ± 5.60 −14.24 ± 5.11 −14.17 ± 4.01 −11.80 ± 3.76 θmax (deg) −34.46 ± 2.08 −35.41 ± 4.75 −35.47 ± 2.93 −36.15 ± 4.62 ω0 (deg·s−1) −96.83 ± 35.33 −85.96 ± 51.95 −85.80 ± 39.66 −83.61 ± 57.09 ωp (deg·s−1) 103.52 ± 34.96 109.21 ± 26.37 90.68 ± 40.83 74.21 ± 22.57 Int. J. Environ. Res. Public Health 2020, 17, 818 8 of 13 Table 4. Cont. Hip θ0 (deg) 25.50 ± 5.04 27.36 ± 8.23 29.36 ± 5.36 28.25 ± 7.28 θmax (deg) −11.06 ± 6.59 −10.74 ± 7.30 −7.92 ± 6.03 −9.73 ± 6.71 ω0 (deg·s−1) −82.08 ± 29.88 −60.34 ± 15.15 −64.37 ± 43.64 −62.22 ± 21.88 ωp (deg·s−1) 95.49 ± 39.13 * 109.99 ± 26.54 100.45 ± 26.82 * 125.73 ± 28.51 θf: the angle between foot and ground at initial contact; θ0: the angle at initial contact; θmax: maximum angle; ω0: the peak angular velocity at initial contact; ωp: the peak angular velocity of extension; * signiﬁcant diﬀerence from pre- to post-tests; # signiﬁcant diﬀerence between groups at time point, p < 0.005; &: interaction eﬀect between time × group, p < 0.005. 3.4. Kinetics A signiﬁcant main eﬀect was observed on the peak ankle extension moment (p < 0.001), peak knee extension moment (p = 0.004), and peak power of the hip (p < 0.001) (Figure 6). The peak moment of the knee for the GR and MIN groups signiﬁcantly decreased by 13.4% (GR) and 12.8% (MIN), respectively, after training, and the peak power of the hip for both groups was signiﬁcantly decreased by 38.6% (GR) and 38.2% (MIN). In addition, a signiﬁcant interaction eﬀect was noted on the peak moment of ankle (p = 0.024). Speciﬁcally, the peak moment was increased by 17.8% after training in the GR group (p = 0.001), but no diﬀerence was observed in the MIN group. Figure 6. Comparison of peak moment (left column) and peak power (right column) of hip, knee, and ankle between two groups before and after training. * signiﬁcant diﬀerence from pre- to post-tests. Int. J. Environ. Res. Public Health 2020, 17, 818 9 of 13 3.5. Vertical Stiﬀness A signiﬁcant main eﬀect of time was observed on vertical stiﬀness (p = 0.035). After training, the vertical stiﬀness improved in the GR and MIN groups by 17.2% and 7.1%, respectively (Figure 7). However, no signiﬁcant main eﬀect or interaction was observed on the vertical displacement of CoG and the vertical GRF (Table 5). Figure 7. Comparison of lower limbs stiﬀness between two groups before and after training. * signiﬁcant diﬀerence from pre- to post-tests. Table 5. Vertical GRF when the center of gravity (CoG) was lowest, and the vertical displacement of CoG. GRFi represents the vertical GRF when the CoG was lowest, and ∆y represents the vertical displacement of CoG during centrifugation. Parameter GR MIN Pre Post Pre Post GRFi (BW) 2.61 ± 0.30 2.71 ± 0.31 2.55 ± 0.28 2.60 ± 0.27 ∆y (cm) 5.96 ± 0.90 5.42 ± 0.99 6.21 ± 1.43 5.67 ± 1.47 4. Discussion The purpose of this study was to examine the eﬀect of gait retraining with minimalist shoes on impact forces, joint mechanics, and vertical stiﬀness. Signiﬁcant reduction was found in foot-strike angle and LR. The kinematics and kinetics of the lower extremity joints changed after 12 weeks of gait retraining with the minimalist shoes. Compared with the MIN group, more signiﬁcant changes were noted in the GR group, especially in the strike patterns and kinematics and kinetics characteristics of the ankle joint. These results supported the hypothesis that the participants who received 12 week gait retraining with minimalist shoes would have a lower loading rate and more lower foot strike angle compared to that of those who only used minimalist shoes. 4.1. Impact Forces Gait retraining can signiﬁcantly reduce the impact force, which is considered to be the main cause of lower-limb injuries, and this result supports the ﬁndings of previous studies [2–4]. Seven out of nine participants of the GR group changed to forefoot strike after gait retraining. Previous studies demonstrate that the impact force mainly depends on the eﬀective mass of the lower limbs [11]. The forefoot strike can obviously reduce the eﬀective mass by adjusting the angle between the foot and the ground at initial contact, thus avoiding the high impact force caused by rearfoot strike. The LR, the change in force per unit time, is considered to be a sensitive index to detect the variations in the amount of impact forces during running. In this study, the LR for both groups decreased after training. For the GR group, the change of the strike pattern avoided the peak of impact force, Int. J. Environ. Res. Public Health 2020, 17, 818 10 of 13 thereby reducing the LR. This outcome is similar to the ﬁndings of other related studies [2–4,12,25,26]. By contrast, the MIN showed a signiﬁcantly reduced LR without the change of strike pattern, which may be related to the adaptability of the body after the change of shoe condition [11]. The LR of the GR group was lower than that of the MIN group after training, indicating that the eﬀect of gait retraining may be more signiﬁcant. Gait retraining as an intervention for actively changing the strike pattern may be better matched with minimalist shoes to reduce the LR and avoid the peak of the impact force. 4.2. Kinematics The participants in the GR group preferred to strike with the forefoot after training, as shown by the foot-strike angle reduced by approximately 10.3◦. This preference of forefoot strike in the GR group was the embodiment of the gait retraining [11]. After 12 weeks of training, the participants in the GR group ran similarly to runners who run barefoot. In the MIN group, no change was noted in strike pattern after training. In the study of McCarthy et al. [13], all participants who preferred rearfoot strike became non-rearfoot strike after training with minimalist shoes. Latorre-Roman et al. [18] revealed that a 12 week barefoot running program causes signiﬁcant changes in foot strike patterns with a tendency towards a non-rearfoot strike in long-distance runners. In the study of Hollander et al. [28], habitually shod participants who actively changed from shod to barefoot increased the foot strike index. However, the results in the current study did not show this result, which might have been due to the diﬀerence in training program or the gender, age, and other attributes of the participants. Although only runners in the GR group showed a signiﬁcant change in foot-strike pattern, the two groups exhibited more plantarﬂexion after training. For the GR group, the larger plantarﬂexion angle indicated a trend towards forefoot strike pattern [11,29]. In this study, the variation of the plantarﬂexion angle of the GR group was not larger than the foot-strike angle (10.3◦ vs. 4.8◦). This outcome indicates that the runners in GR may not achieve forefoot strike by simply increasing the plantarﬂexion angle. No signiﬁcant change was observed in knee and hip angles when touching the ground. Apart from increasing the plantarﬂexion angle of ankle, the participants in the GR group might have achieved the forefoot strike pattern by adjusting the position of the body and the forward of the trunk after training instead of simply “running on the toe” [11]. 4.3. Kinetics and Vertical Stiﬀness The peak ankle moment increased signiﬁcantly in the GR group after training, but no signiﬁcant diﬀerence was observed in the MIN group. This outcome may have been due to increasing arm of force changed by the foot strike pattern. Warne et al. reported no signiﬁcant change in the stiﬀness of ankle after gait retraining [12]. Although the peak ankle moment increased, runners maintained the stiﬀness by increasing the range of ankle upon touchdown. In the current study, this phenomenon may have appeared as the increased plantarﬂexion angle. The peak knee extension moment for both groups decreased signiﬁcantly after training. When wearing minimalist shoes, the reduced stiﬀness of the knee may result in reduced moments or increased knee excursion compared with wearing traditional shoes [12], suggesting that the knee is more inclined to soft landing in the case of minimalist shoes. In this study, the peak power of hip of both groups decreased signiﬁcantly. In the study by Williams et al. [30], runners wearing shoes showed a signiﬁcant decrease in the power of hip when using forefoot strike pattern or barefoot running compared with rearfoot strike pattern, and this eﬀect continued after training. Similarly, in the present study, the vertical stiﬀness, which is considered to be related to running economy and energy utilization, increased signiﬁcantly after training in both groups [31]. Gait retraining or using minimalist shoes may improve running performance. The results also showed that the vertical GRF and the displacement of CoG did not change signiﬁcantly after training. Hence, vertical stiﬀness seems better for evaluating the eﬀectiveness of the training. During the intervention, two participants were injured due to non-training factors and they were excluded from the statistics of the proportion of injuries. In other related studies, the proportion of Int. J. Environ. Res. Public Health 2020, 17, 818 11 of 13 injuries in McCarthy’s study was 20–26% [13], and the proportion was 17% for two runners who were injured in the study of Warne et al. [12]. The number of injured in the present study was also two. This outcome suggests that gait retraining is important when running barefoot or wearing minimalist shoes. 4.4. Limitations Firstly, the sample size was relatively small due to the long training period and participant dropout. A large sample size could have increased the statistical power in such a way that additional variables achieved signiﬁcance, especially the variable trends in kinematics but without signiﬁcance. For future research on recreational runners, additional attention should be paid to the training control of the participants due to work travel and other reasons, which are diﬃcult but useful for the sample preservation. In addition, this study focuses on the contrast between two diﬀerent training programs (GR and MIN). Thus, no barefoot and control groups are set up. However, a complete four-group study (combined, control, minimalist, and barefoot groups) is necessary for future research. Secondly, individual diﬀerences in movement learning ability might have led to diﬀerent training eﬀects, especially in the GR group. Moreover, the long-term retention eﬀects caused by retraining changes are unknown. Finally, future investigations, including EMG assessment accompanied with neuro-musculoskeletal adaptations after gait retraining, are warranted. 5. Conclusions The 12 week gait retraining with minimalist shoes converted rearfoot strikers into forefoot strikers. Seven of out nine participants transformed into forefoot strike patterns with a rate of 78%. More importantly, such a combined program, compared to the training with only minimalist shoes, can avoid the peak impact force and decrease the loading rate more eﬀectively, thus providing a potential means of reducing risk of running injury caused by impact forces. Moreover, the increased vertical stiﬀness of lower extremity after gait retraining may improve running economy and corresponding energy utilization. However, these observations also suggest that the sole use of minimalist footwear may have limited eﬀects on reducing running-related impacts. Author Contributions: Conceptualization, W.F.; methodology, Y.Y.; formal analysis, Y.Y., X.Z., Z.L., X.W. and D.Y.; investigation, Y.Y., X.Z., Z.L., X.W. and W.F.; resources, W.F.; data curation, Y.Y.; writing—original draft preparation, Y.Y.; writing—review and editing, W.F.; project administration, W.F.; funding acquisition, W.F. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the National Natural Science Foundation of China (11772201, 11572202); Talent Development Fund of Shanghai Municipal (2018107); the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2019YFF0302100); and the “Dawn” Program of Shanghai Education Commission, China (19SG47). Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Bramble, D.M.; Lieberman, D.E. Endurance running and the evolution of Homo. Nature 2004, 432, 345–352. [CrossRef] [PubMed] 2. Van Gent, R.N.; Siem, D.; van Middelkoop, M.; van Os, A.G.; Bierma-Zeinstra, S.M.; Koes, B.W. Incidence and determinants of lower extremity running injuries in long distance runners: A systematic review. Br. J. Sports Med. 2007, 41, 469–480, discussion 480. [CrossRef] [PubMed] 3. Milner, C.E.; Ferber, R.; Pollard, C.D.; Hamill, J.; Davis, I.S. 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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).	Alterations in Running Biomechanics after 12 Week Gait Retraining with Minimalist Shoes.	01-28-2020	Yang, Yang,Zhang, Xini,Luo, Zhen,Wang, Xi,Ye, Dongqiang,Fu, Weijie	eng
PMC10651037	PLOS ONE Dose response of running on blood biomarkers of wellness in generally healthy individuals --Manuscript Draft-- Manuscript Number: PONE-D-23-25168R1 Article Type: Research Article Full Title: Dose response of running on blood biomarkers of wellness in generally healthy individuals Short Title: Biomarker signature of runners Corresponding Author: Bartosz Nogal InsideTracker Cambridge, MA UNITED STATES Keywords: physical activity, exercise, blood biomarkers, running, generally healthy, mendelian randomization Abstract: Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence supporting its effectiveness. However, less is known about the specific healthspan-promoting effects of exercise on blood biomarkers in the disease-free population. In this work, we examine 23,237 generally healthy individuals who self- report varying weekly running volumes and compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional endurance athletes. We estimate the significance of differences among blood biomarkers for groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, gender, and BMI. We attempt and add insight to our observational dataset analysis via two-sample Mendelian randomization (2S-MR) using large independent datasets. We find that self-reported running volume associates with biomarker signatures of improved wellness, with some serum markers apparently being principally modified by BMI, whereas others show a dose-effect with respect to running volume. We further detect hints of sexually dimorphic serum responses in oxygen transport and hormonal traits, and we also observe a tendency toward pronounced modifications in magnesium status in professional endurance athletes. Thus, our results further characterize blood biomarkers of exercise and metabolic health, particularly regarding dose-effect relationships, and better inform personalized advice for training and performance. Order of Authors: Bartosz Nogal Svetlana Vinogradova Gil Blander Milena Jorge Paul Fabian Ali Torkamani Response to Reviewers: Editor comments: 1. When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. Thank you. We believed we now correctly formatted the manuscript to reflect PLOS formatting requirements for publishing. 2.We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. Thank you - we believe that we now addressed this. We removed all funding information from the manuscript text, amended it to reflect the funding institutions involvement, and moved it to the cover letter, as requested. Please let us know if Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation anything needs further attention. 3. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. We updated our Data Availability statement via the submission system to reflect our opennes to share the minimal dataset upon request from the corresponding author, as well as the url to the public repository where the gwas summary statistics can be found. 4. We note that you have included the phrase “data not shown” in your manuscript.... Thank you. We now addressed this within the text of the manuscript and the “data not shown” no longer appears 5. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Captions for Supporting Information is now included at the end of the manuscript. 6. When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. We believe we have now addressed the style and formatting requirements. Reviewer comments: Reviewer #1: The majority parts of the articles are technically sound. Moreover, the purpose of the study is very sound since it focused on healthy active population. Among the few drawbacks of the study the way the study subjects categorized into groups based on the duration of the activity (>10hours per week and less that 10hours per week is not appropriate. Moreover, the reliability/validity of the information sources in relation to the biomarker tests and lifestyle habits of the study subjects didn't consider the immediate effects of medical services and medication conditions of the respondents at the time of reporting the volume of exercise and biomarker test results. Medical services and lifestyle habits specially all the habits in addition to exercises/running are very important to reach informative decision in this research. So, the above two points need further explanation or modification. Response to Reviewer #1: We appreciate the reviewer's feedback and are pleased that they find the majority of our study technically sound and recognize the importance of our focus on a healthy, active population. We also appreciate the reviewer pointing out an opportunity to improve the clarity around our experimental design as it pertains subject groupings. Regarding the categorization of study subjects, we want to clarify that we actually categorized them into five groups. These groups include professional endurance runners, high volume amateur runners (>10 hours per week), medium volume amateur runners (3-10 hours per week), low volume amateur runners (<3 hours per week), and the sedentary. We now added a sentence starting on line 125 the explicitly states this categorization (“The cohort was divided into five groups:…”). These groupings were determined based on the respondents' self-reported data. We acknowledge the potential influence of medication use on our analysis, and we now address it starting on line 461 (“These factors, such as diet, sleep, and/or medications were not readily ascertained in this free-living cohort…”) and in the Study Limitations section (line 595). We noted that unmeasured confounders such as medications, nutritional supplements, and unreported health conditions may exist. However, given the nature of our cohort, which primarily consists of self-selected, generally healthy individuals, the impact of significant medication use is expected to be limited. We believe that the observed trends in healthier biomarker levels with increased reported running volume support this assertion. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation Furthermore, we recognize the importance of lifestyle habits beyond exercise in influencing our results. To address this, we employed statistical genomics, specifically two-sample Mendelian randomization with physical activity as the exposure. This analysis allowed us to explore other potential habits and behaviors contributing to improved biomarker signatures in physically active runners within our cohort. We kindly refer the reviewer to the "Vigorous physical activity associates with healthier behaviors" section in the results for a detailed examination of this aspect. Notably, our entire cohort is composed of health-conscious individuals within the same health advisory platform, with the primary differentiator being self-reported running activity. We also controlled for key variables such as age, sex, and BMI in our ANOVA analyses. We hope these explanations clarify our approach and address the reviewer's concerns adequately. Reviewer #2: How your data is reliable by using A cross-sectional study design? & How again the Data is reliable by using self-reported running. I understand that Biomarkers are objective measure, but do you think that Self-report is trustworthy? Thank you Response to Reviewer #2: We appreciate the reviewer's questions and concerns regarding the reliability of our runners data, which is largely derived from self-reported exercise habits. Cross-sectional studies inherently have limitations when it comes to establishing causality, and we acknowledge this challenge. To address potential confounding factors, we conducted additional causal analyses, specifically investigating the effects of BMI on the biomarkers under examination to begin to disentangle the relative contributions of known factors. Furthermore, we performed secondary Mendelian randomization (MR) analyses to identify and account for potential confounders in our findings. We kindly invite the reviewer to explore the "Vigorous physical activity associates with healthier behaviors" section in the results for a comprehensive exploration of these confounding aspects. Regarding the reliability of self-reported running activity, we recognize that self-reports can be subject to biases, and individuals may tend to overestimate their exercise commitment. To address this drawback, we added language addressing these limitations in the “Study limitations” section (Line 579: “First, it is generally known that subjects tend to overestimate their commitment to exercise …”). We do note that our study cohort comprises self-selected individuals who are health-conscious and possibly less prone to over-report their running volume. Additionally, the robust increasing trend in baseline levels of muscle damage biomarkers (CK, AST), which are known to be associated with participation in sports and exercise, provides indirect evidence that the different running groups in our study were indeed engaging in increasing volumes of strenuous physical activity. While self-reporting has its limitations, it remains a valuable method for capturing individuals' exercise behaviors in large-scale observational studies. We took measures to mitigate potential biases, and our findings align with established trends in biomarker responses to physical activity. Reviewer #3: Upon a meticulous review of the article in question, I wish to commend the authors for crafting a piece that not only carries immense scientific weight but is also articulated with great clarity. Such insightful work surely merits publication in your distinguished journal. It's admirable how the authors have navigated through a myriad of physiological and biochemical variables (blood biomarkers) across five distinct participant categories and presented their results with lucidity. The experimental framework is robust, the statistical evaluations are apt, and the narrative progresses seamlessly. The references provided are both relevant and adequate. Nevertheless, I'd like to offer a few observations and suggestions: Response: We appreciate the reviewer's positive feedback and kind words about our manuscript. We eagerly await their observations and suggestions should they see further opportunities to improve our work based on our responses to the current suggestions. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation Original Title: “Dose response of running on blood biomarkers of wellness in the generally healthy.” Proposed Title: “Dose-response relationship between running and blood biomarkers of wellness in generally healthy individuals.” Response: Thank you – title has been changed. Page 2, Line 8: The mention of “exposure to sunlight” seems somewhat out of context. Could the authors clarify its relevance or indicate if it has been discussed elsewhere in the article? Response: Thank you for the suggestion, we removed this as we agree it was not relevant in this manuscript. Page 17, Lines 17-18: The text reads: "These observations suggest that elite endurance runners………to their magnesium status." Comments: It would be helpful to clarify whether the professional athletes (PRO) participating in this study are specifically elite endurance runners. Kindly integrate this distinction into the main text if accurate. Response: Thank you for the clarifying suggestion. We included the pro/elite endurance runners clarification within the abstract as well as a section heading (lines 7 and 425) Page 19, Lines 1-2: The assertion: “Indeed whether exercise………..is inconclusive,” needs to be substantiated with a relevant citation. Response: Thank you – citations have been added. Table 1: Please include standard deviation (SD) values. I also recommend expressing exercise duration in terms of "h/week" instead of "hr". Response: Thank you for the catch – units changed to “h/week” and SDs added to Table 1. 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This text is appropriate if the data are owned by a third party and authors do not have permission to share the data. • * typeset Additional data availability information: Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation 1 Dose response of running on blood biomarkers of wellness in generally healthy individuals Bartek Nogal1¶ , Svetlana Vinogradova1¶, Milena Jorge1, Ali Torkamani2,3, Paul Fabian1, Gil Blander1* 1InsideTracker, Cambridge, Massachusetts, United States of America 2The Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA, United States of America 3Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States of America * Corresponding author E-mail: gblander@insidetracker.com (GB) ¶These authors contributed equally to this work Manuscript Click here to access/download;Manuscript;Manuscript.docx 1 Abstract 1 Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence 2 supporting its effectiveness. However, less is known about the specific healthspan-promoting 3 effects of exercise on blood biomarkers in the disease-free population. In this work, we examine 4 23,237 generally healthy individuals who self-report varying weekly running volumes and 5 compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional 6 endurance runners. We estimate the significance of differences among blood biomarkers for 7 groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, 8 gender, and BMI. We attempt and add insight to our observational dataset analysis via two-sample 9 Mendelian randomization (2S-MR) using large independent datasets. We find that self-reported 10 running volume associates with biomarker signatures of improved wellness, with some serum 11 markers apparently being principally modified by BMI, whereas others show a dose-effect with 12 respect to running volume. We further detect hints of sexually dimorphic serum responses in 13 oxygen transport and hormonal traits, and we also observe a tendency toward pronounced 14 modifications in magnesium status in professional endurance athletes. Thus, our results further 15 characterize blood biomarkers of exercise and metabolic health, particularly regarding dose-effect 16 relationships, and better inform personalized advice for training and performance. 17 Introduction 18 Physical inactivity is one of the leading modifiable behavioral causes of death in the US [1]. 19 Worldwide, physical inactivity is estimated to account for about 8.3% of premature mortality, an 20 effect size that is on the same order as smoking and obesity [2]. At the same time, the potent health 21 benefits of exercise have been proven time and time again, with results so consistent across a wide 22 variety of chronic diseases that some posit it can be considered a medical intervention [3-5]. 23 However, since most investigators report the effects of exercise in either diseased populations or 24 athletes [6, 7], there exists a significant gap in knowledge as to the measurable effects of exercise 25 in the generally healthy population who exercise for the purpose of improving their healthspan, 26 which can be projected via established measures such as blood biomarkers [8-11]. 27 It is well established that routine laboratory biomarkers are validated proxies of the state of an 28 individual’s overall metabolic health and other healthpan-related parameters [12]. A large body 29 of evidence supports the effectiveness of exercise in modifying blood biomarkers toward disease 30 mitigation in clinical cohorts as well as athletes, where the effect sizes may be larger [6, 13]. 31 Indeed, it’s been shown that more favorable changes in response to exercise training occur usually 32 in those with more pronounced dyslipidemia [13]. In professional athletes, the sheer volume 33 and/or intensity of physical activity may drive large effects in various hematological, lipid, 34 immune, and endocrine variables [6]. Our aim is to help fill the gap in understanding of the effects 35 of exercise on blood biomarkers in the generally healthy, free-living population. Toward this end, 36 we endeavored to explore the effects of vigorous exercise such as running in apparently healthy, 37 mostly non-athletic cohort to better understand the landscape of blood biomarker modifications 38 expected in the individual who partakes in recreational physical activity for the purpose of 39 maintaining good health. 40 For this purpose, we leveraged the InsideTracker dataset that includes information on self-reported 41 exercise habits combined with blood biomarker and genomics data. We have previously reported 42 on the results of a longitudinal analysis on blood biomarker data from 1032 generally healthy 43 individuals who used our automated, web-based personalized nutrition and lifestyle platform [14]. 44 3 For the purpose of this investigation, we focused on running as the exercise of choice as it is one 45 of the most common (purposeful) physical activity modalities practiced globally by generally 46 healthy individuals and would thus be relevant. Moreover, since this was a cross-sectional study 47 based on self-reported exercise habits, we attempted to increase our capacity to infer intervention 48 effects, as well as tease out potential confounders, by performing 2S-MR in large independent 49 cohorts. 50 Materials and methods 51 Dataset 52 We conducted an observational analysis of data from InsideTracker users. InsideTracker is a 53 direct-to-consumer (DTC) company established in 2009 that markets and sells InsideTracker 54 (insidetracker.com), a personalized lifestyle recommendation platform. The platform provides 55 serum biomarker and genomics testing, and performs integrative analysis of these datasets, 56 combined with activity/sleep tracker data toward biomarker and healthspan optimization (of note, 57 at the time of this analysis, we did not have sufficient users with activity/sleep tracker data to 58 include this data stream in the current study). New users were continuously added to the 59 InsideTracker database from January 2011 to March 2022. 60 61 Recruitment of participants 62 Recruitment of participants aged between 18 and 65 and residing in North America was conducted 63 through company marketing and outreach. Participants were subscribing members to the 64 InsideTracker platform and provided informed consent to have their blood test data and self- 65 reported information used in an anonymized fashion for research purposes. Research was 66 conducted according to guidelines for observational research in tissue samples from human 67 subjects. Eligible participants completed a questionnaire that included age, ethnicity, sex, dietary 68 preferences, physical activity, and other variables. This study employed data from 23,237 69 participants that met our analysis inclusion requirements, namely absence of any chronic disease 70 as determined by questionnaire and metabolic blood biomarkers within normal clinical reference 71 ranges. The platform is not a medical service and does not diagnose or treat medical conditions, 72 so medical history and medication use were not collected. The Institutional Review Board (IRB) 73 determine this work was not subject to a review based on category 4 exemption (“secondary 74 research” with de-identified subjects). 75 Biomarker collection and analysis 76 Blood samples were collected and analyzed by Clinical Laboratory Improvement Amendments 77 (CLIA)–approved, third-party clinical labs (primarily Quest Diagnostics and LabCorp). 78 Participants were instructed to fast for 12 hours prior to the phlebotomy, with the exception of 79 water consumption. Results from the blood analysis were then uploaded to the platform via 80 electronic integration with the CLIA-approved lab. Participants chose a specific blood panel from 81 7 possible offerings, each comprising some subset of the biomarkers available. Due to the variation 82 in blood panels offered, the participant sample size per biomarker is not uniform. 83 Biomarker dataset preparation 84 5 In our raw dataset, occasional outlier values were observed that were deemed implausible (e.g. 85 fasting glucose < 65 mg/dL). To remove anomalous outliers in a systematic way, we used the 86 Interquartile Range (IQR) method of identifying outliers, removing data points which fell below 87 Q1 – 1.5 IQR or above Q3 + 1.5 IQR. The cohort was divided into five groups: professional 88 endurance runners (PRO), high volume amateur (>10 h/week, HVAM ), medium volume amateur 89 (3-10 h/week, MVAM), low volume amateur (<3 h/week, LVAM), and sedentary (SED). 90 Calculation of polygenic scores 91 The variants (SNPs) comprising the polygenic risk scores were derived from publicly available 92 GWAS summary statistics (https://www.ebi.ac.uk/gwas/). Scores were calculated across users by 93 summing the product of effect allele doses weighted by the beta coefficient for each SNP, as 94 reported in the GWAS summary statistics. Variant p-value thresholds were generally chosen based 95 on optimization of respective PGS-blood biomarker correlation in the entire InsideTracker cohort 96 with both blood and genomics datasets (~1000-1500 depending on the blood biomarker at the time 97 of analysis). Genotyping data was derived from a combination of a custom InsideTracker array 98 and third party arrays such as 23andMe and Ancestry. Not all variants for any particular PGS were 99 genotyped on every array; proxies for missing SNPs were extracted via the “LDlinkR” package 100 using the Utah Residents (CEPH) with Northern and Western European ancestry (CEU) population 101 (R2 > 0.8 cut-off). Only results PGSs for which there was sufficient biomarker-genotyping dataset 102 overlap were reported (note that none of the blood biomarker PGSs met this requirement). 103 Blood biomarker analysis with respect to running volume and 104 polygenic scores 105 To estimate significance of differences for blood biomarkers levels among exercise groups, we 106 performed 3-way analysis of variance (ANOVA) analysis adjusting for age, gender, and BMI 107 (type-II analysis-of-variance tables function ANOVA from ‘car’ R package, version 3.0-12). 108 When estimating the effort of reported training volume on biomarkers, we assigned numerical 109 values corresponding to 4 levels of running and performed ANOVA analysis with those levels 110 treating it as an independent variable. P-values were adjusted using the Benjamini & Hochberg 111 method [15]. P-values for interaction plots were calculated with ANOVA including interaction 112 between exercise group and polygenic scores category. When comparing runners (PRO and 113 HVAM combined) versus sedentary individuals, we used propensity score matching method to 114 account for existing covariates (age and gender): we identified 745 sedentary individuals with 115 similar to runners’ age distributions among both males and females. We used ‘MatchIt’ R package 116 (version 4.3.3) implementing nearest neighbor method for matching [16]. 117 Mendelian randomization 118 We attempted to add insight around the causality of exercise vs. BMI differences with respect to 119 serum marker improvement by performing MR analyses on a subset of biomarker observations 120 where BMI featured as a strong covariate and was thus used as the IV in the 2S-MR. Thus, our 121 hypothesis here was that BMI differences were the primary (causal) driver behind the improvement 122 behind some biomarkers. MR uses genetic variants as modifiable exposure (risk factor) proxies 123 to evaluate causal relationships in observational data while reducing the effects of confounders 124 and reverse causation (S1 Fig). These SNPs are used as instrumental variables and must meet 3 125 basic assumptions: (1) they must be robustly associated with the exposure; (2) they must exert 126 their effect on outcome via the exposure, and (3) there must be no unmeasured confounders of the 127 7 associations between the genetic variants and outcome (e.g. horizontal pleiotropy) [17]. 128 Importantly, SNPs are proper randomization instruments because they are determined at birth and 129 thus serve as proxies of long-term exposures and cannot, in general, be modified by the 130 environment. If the 3 above mentioned assumptions hold, MR-estimate effects of exposure on 131 outcomes are not likely to be significantly affected by reverse causation or confounding. In the 132 2S-MR performed here, where GWAS summary statistics are used for both exposure and outcome 133 from independent cohorts, reverse causation and horizontal pleiotropy can readily be assessed, and 134 weak instrument bias and the likelihood of false positive findings are minimized as a result of the 135 much larger samples sizes [17]. Indeed, the bias in the 2S-MR using non-overlapping datasets as 136 performed here is towards the null [17]. Furthermore, to maintain the SNP-exposure associations 137 and linkage disequilibrium (LD) patterns in the non-overlapping populations we used GWAS 138 datasets from the MR-Base platform that were derived from ancestrally similar populations 139 (“ukb”: analysis of UK Biobank phenotypes, and “ieu”: GWAS summary datasets generated by 140 many different European consortia). To perform the analysis we used the R package 141 “TwoSampleMR” that combines the effects sizes of instruments on exposures with those on 142 outcomes via a meta-analysis. We used “TwoSampleMR” package functions for allele 143 harmonization between exposure and outcome datasets, proxy variant substitution when SNPs 144 from exposure were not genotyped in the outcome data (Rsq>0.8 using the 1000G EUR reference 145 data integrated into MR-Base), and clumping to prune instrument SNPs for LD (the R script used 146 for MR analyses is available upon request). We used 5 different MR methods that were included 147 as part of the “TwoSampleMR” package to control for bias inherent to any one technique [18]. 148 For example, the multiplicative random effects inverse variance-weighted (IVW) method is a 149 weighted regression of instrument-outcome effects on instrument-exposure effects with the 150 intercept is set to zero. This method generates a causal estimate of the exposure trait on outcome 151 traits by regressing the, for example, SNP-BMI trait association on the SNP-biomarker measure 152 association, weighted by the inverse of the SNP-biomarker measure association, and constraining 153 the intercept of this regression to zero. This constraint can result in unbalanced horizontal 154 pleiotropy whereby the instruments influence the outcome through causal pathways distinct from 155 that through the exposure (thus violating the second above-mentioned assumption). Such 156 unbalanced horizontal pleiotropy distorts the association between the exposure and the outcome, 157 and the effect estimate from the IVW method can be exaggerated or attenuated. However, 158 unbalanced horizontal pleiotropy can be readily assessed by the MR Egger method (via the MR 159 Egger intercept), which provides a valid MR causal estimate that is adjusted for the presence of 160 such directional pleiotropy, albeit at the cost of statistical efficiency. Finally, to ascertain the 161 directionality of the various causal relationships examined, we also performed each MR analysis 162 in reverse where possible. 163 Results 164 Study population characteristics 165 Table 1 shows the demographic characteristics of the study population. We observed a 166 significant trend toward younger individuals reporting higher running volume, with more than 167 75% of the professional (PRO) group falling between the ages of 18 and 35 (S1 Table). Significant 168 differences were also observed in the distribution of males and females within study groups (Table 169 1). Moreover, higher running volume associated with significantly lower body mass index (BMI). 170 9 Thus, moving forward, combined comparisons of blood biomarkers as they relate to running 171 volume were adjusted for age, gender, and BMI. 172 173 Table 1. Study population demographics 174 Group N Female, % Age, yrs Body mass index, kg/m2 PRO 82 53.7% 33.68 20.15 ± 6.02 HVAM 1103 52.9% 39.48 22.57 ± 9.97 MVAM 6747 54.2% 41.49 23.35 ± 9.76 LVAM 10877 34.2% 41.16 24.72 ± 9.70 SED 4428 48.9% 44.25 27.83 ± 10.70 PRO = Professional, HVAM = high volume amateur (>10 h/week ), MVAM = medium volume 175 amateur (3-10 h/week), LVAM = low volume amateur (<3 h/week), SED = sedentary 176 177 Endurance exercise exhibits a modest association with clusters of 178 blood biomarker features 179 In order to begin to understand the most important variables that may associate with endurance 180 exercise in the form of running, we performed a principal component analysis (PCA), sub-dividing 181 the male and female cohorts into two most divergent groups in terms of exercise volume: 182 PRO/high volume amateur (HVAM) and sedentary (SED) groups. Using propensity matching, 183 PRO and amateur athletes who reported running >10h per week were combined into the PRO- 184 HVAM group to balance out the sample size between the exercising and non-exercising groups. 185 This approach yielded a modest degree of separation, with hematological, inflammation, and lipid 186 features, as well as BMI explaining some of the variance (Fig 1 A through D). We hypothesized 187 that there may more subtle relationships between running volume and the blood biomarker features 188 that contributed to distinguishing the endurance exercise and sedentary groups, thus we next 189 performed ANOVA analyses stratified by running volume as categorized in Table 1. 190 Fig 1. Principal component analysis and variables plots of PRO-HVAM runners and sedentary 191 user blood biomarkers. Females, (A) and (B); males (C) and (D). PRO-HVAM = combined 192 professional and high-volume amateur. Alb = albumin, ALT = alanine transaminase, AST = 193 aspartate aminotransferase, B12 = vitamin B12, Ca = calcium, Chol = total cholesterol, CK = 194 creatine kinase, Cor = cortisol, FE = iron, EOS_PCT = eosinophil percentage, Fer = ferritin, Fol = 195 folate, FT = free testosterone, GGT = gamma-glutamyl transferase, Glu = glucose, Hb = 196 hemoglobin, HCT = hematocrit, HDL = high density lipoprotein, HbA1c = glycated hemoglobin, 197 hsCRP = high-sensitivity C-reactive protein, LDL = low density lipoprotein, LYMPS_PCT = 198 lymphocyte percentage, MCH = mean cell hemoglobin, Mg = magnesium, MONOS_PCT = 199 monocytes percentage, MPV = mean platelet volume, Na = sodium, RBC = red blood cells, 200 RBC_Mg = red blood cell magnesium, RDW = red blood cell distribution width, SHBG = sex 201 hormone binding globulin, Tg = triglycerides, TIBC = total iron binding capacity, WBC = white 202 blood cells 203 Significant trends in glycemic, hematological, blood lipid, and 204 inflammatory serum traits with increasing running volumes 205 Weighted ANOVA analyses adjusted for age, gender, and BMI showed significant differences 206 among groups for multiple blood biomarkers (Table 2 and S2 , Figs 2 and 3). We observed a trend 207 11 toward lower HbA1c, hsCRP, RDW, WBC, ferritin, gamma-glutamyl transferase (GGT), and 208 LDL. HDL, hemoglobin (Hb), transferrin saturation (TS), alanine aminotransferase (ALT), 209 aspartate aminotransferase (AST), vitamin B12, folate, 25-hydroxy vitamin D, and creatine kinase 210 (CK) tended to be higher with increasing reported training volume, particularly in PRO runners 211 (Tables 2 and S2 , Figs 2 and S2 , Fig 3). Hct and Hb were higher only in PRO males, whereas 212 increased running volume associated with upward trend in these biomarkers in females (Fig 3 A 213 and B). Increased running volume was associated with markedly lower Fer in males, whereas 214 female runners did not exhibit varying levels, and SED females showed increased levels (Fig 3 C). 215 The low ferritin observed in male and female runners was not clinically significant. ALT 216 positively associated with running volume in females only (S2 Fig). Serum and RBC magnesium 217 (Mg) were both significantly lower in PRO runners relative to all other groups (Table 2 and Fig 3 218 D and E). Increasing levels of endurance exercise also appeared to be associated with higher sex- 219 hormone binding globulin (SHBG), particularly in PRO male runners (Fig 3 F). 220 221 Table 2. Blood biomarkers significantly different among sedentary individuals and those 222 who partake in running for exercise to various degrees 223 BIOMARKER ANOVA P-VALUE TREND P-VALUE LOWEST MEAN HIGHEST MEAN ALB <1e-16 <0.001 MVAM PRO ALT <1e-16 <1e-16 SED PRO AST <1e-16 <0.001 SED PRO B12 <0.001 <0.001 SED PRO CHOL <0.001 0.005 PRO SED CK <1e-16 <1e-16 SED PRO COR <0.001 0.675 SED PRO FE <0.001 0.119 SED PRO FER <1e-16 <1e-16 MVAM SED FOL <1e-16 <0.001 SED PRO FT <0.001 0.013 SED PRO GGT <1e-16 <0.001 PRO SED GLU 0.087 0.184 PRO SED HB 0.002 <0.001 MVAM PRO HCT 0.053 0.055 MVAM PRO HDL <1e-16 <0.001 SED PRO HBA1C <0.001 0.010 PRO SED HSCRP <0.001 0.176 PRO SED LDL <0.001 0.006 PRO SED MG <0.001 0.276 PRO SED MPV 0.058 0.089 SED HVAM NA <1e-16 0.622 HVAM SED RBC_MG <0.001 0.773 PRO SED RDW <1e-16 0.002 PRO SED SHBG <1e-16 0.004 SED PRO TG <1e-16 <1e-16 PRO SED 13 WBC <1e-16 <1e-16 PRO SED 224 Fig 2. Blood biomarkers associated with running: Inflammation proxies, (A) hsCRP = high- sensitivity C-reactive protein and (B) WBC = white blood cells; blood lipids, (C) HDL = high density lipoprotein (D) LDL = low density lipoprotein, and (E) Tg = triglycerides; glycemia proxies, (F) Glu = glucose and (G) HgbA1c = glycated hemoglobin, and (H) Cor =cortisol Fig 3. Blood biomarkers associated with running: (A and B) Hb (hemoglobin) and Hct (hematocrit) increase with increasing running volume, (C) Fer (ferritin) is reduced with increasing running volume, (D and E) Serum and RBC Mg (red blood cell magnesium) are reduced in professional runners, and (F) SHBG (sex hormone binding globulin) levels increase with increasing running volume in males 225 Endurance exercise correlates with lower BMI across categories of 226 genetic risk 227 Using publicly available GWAS summary statistics, we constructed blood biomarker polygenic 228 risk scores (PGSs) to explore potential genetic risk-mitigating effects of endurance exercise. Since 229 only a subset of the individuals in our cohort were genotyped, we aggregated the groups into 2 230 categories—PRO-HVAM and sedentary—to increase statistical power. This across-group sample 231 size increase generally did not sufficiently power the ANOVA analysis to detect statistically 232 significant trends, though the BMI polygenic risk was suggestively mitigated for both males and 233 female PRO-HVAM runners across categories of genetic risk (Fig 4 B). 234 Fig 4. BMI significantly varied among running groups (A) with some suggestive effects on BMI PGS modification (total number for observations (N) for T1, T2, and T3 were 87, 84, and 100, respectively) (B) T1, T2, and T3 = 1st, and 2nd and 3rd tertials of the polygenic score distribution 235 Increased running volume is associated with lower BMI which may 236 drive biomarker changes 237 We observed a significant downward trend in the BMI with increased running volume for both 238 males and females, and, although some of the biomarker differences between sedentary and 239 exercising individuals remained significant after adjustment for BMI, their significance was 240 attenuated (Fig 4 A). Thus, we hypothesized that BMI may be driving a significant portion of the 241 observed variance in some of the biomarkers across the groups. Thus, to explore causal 242 relationships between weight and biomarker changes, we performed 2S-MR with BMI-associated 243 single-nucleotide polymorphisms (SNPs) as the instrumental variables (IVs) for a subset of the 244 healthspan-related biomarkers where BMI explained a relatively large portion of the variance in 245 our analysis. In general, these blood biomarkers associated with inflammation (hsCRP and 246 RDW), lipid metabolism (Tg and HDL), glycemic control (HbA1c and Glu), as well as Alb and 247 SHBG. We used GWAS summary statistics and found that most of these BMI-blood biomarker 248 relationships examined directionally aligned with our study (except for LDL), and some were 249 indicative of causal relationships in the BMI-biomarker direction even after considering directional 250 15 pleiotropy (S3 Table). We entertained the possibility of reverse causality and thus repeated the 251 2S-MR using each of the biomarker levels as the exposure and BMI as the outcome, and the results 252 were generally not significant (except for WBC – see S4 Table). Of note, to estimate the direct 253 causal effects of running on blood parameters, we attempted to find an instrumental variable for 254 to approximate running as the exposure from publicly available GWAS summary statistics. 255 Toward this end, we found that increasing levels of vigorous physical activity did associate with 256 lower hsCRP, HbA1C, higher HDL, and possibly higher SHBG (although the explained variance 257 (R2) in this exposure was just 0.001009, the F statistic was 37.7, thus meeting the criteria of F > 258 10 for minimizing weak instrument bias) (Figs 5 and S3; S5 Table). 259 Fig 5. Two-sample Mendelian randomization shows that increasing levels of vigorous physical activity such as running is associated with improvement of (A) hsCRP = high-sensitivity C- reactive protein, (B) HDL = high density lipoprotein, and (C) HbA1c = glycated hemoglobin levels 260 Vigorous physical activity associates with healthier behaviors 261 We hypothesized that those who exercise regularly may also partake in other healthful lifestyle 262 habits that may be contributing to more optimal blood biomarker signatures of wellness. However, 263 our dataset did not allow for systematic accounting of other lifestyle habits across all running 264 groups. Thus, we again leveraged the potential of the 2S-MR approach to inform potential 265 confounding associations between modifiable exposures and found that vigorous physical activity 266 such as running is at least suggestively associated with several behaviors associated with improved 267 health (S4 Fig). Our analysis showed that those who participate in increasing levels of vigorous 268 physical activity may be less likely to eat processed meat (IVW p = 0.0000013), sweets (IVW p = 269 0.32), and nap during the day (IVW p = 0.13), while increasing their intake of oily fish (IVW p = 270 0.029), salad/raw vegetable intake (IVW p = 0.00016), and fresh fruit (IVW p = 0.0027) (S6 271 Table). Furthermore, following our assessment of reverse causality, we found evidence for the 272 bidirectionality in the causal relationship between vigorous activity and napping during the day 273 and salad/raw vegetable intake, perhaps suggesting some degree of confounding due to population 274 stratification (S7 Table). The suggestive positive effect of fresh fruit and processed meat intake on 275 vigorous physical activity appeared to violate MR assumption (3) (S1 Fig) (horizontal pleiotropy 276 p-values 0.051 and 0.17, respectively – S5 Fig). 277 Discussion 278 In this report, we describe the variance in wellness-related blood biomarkers among self-reported 279 recreational runners, PRO runners, and individuals who do not report any exercise. Overall, we 280 find that 1) recreational running as an exercise appears to be an effective intervention toward 281 modifying several biomarkers indicative of improved metabolic health, 2) an apparent dose- 282 response relationship between running volume and BMI may itself be responsible for a proportion 283 of the apparent metabolic benefits, and 3) both PRO-level status and gender appear to associate 284 with heterogeneous physiological responses, particularly in iron and magnesium metabolism, as 285 well as some hormonal traits. 286 Self-reported running improves glycemia and lipidemia 287 17 We did not observe distinct clusters corresponding to self-reported high-volume/PRO runners and 288 the sedentary upon dimension reduction. This is, perhaps, not unexpected due, in part, to the self- 289 selected healthspan-oriented nature of our cohort, where even the sedentary subset of individuals 290 tends to exhibit blood biomarker levels in the normal clinical reference ranges. Furthermore, the 291 measurement of running volume via self-report may be vulnerable to overestimation, which may 292 have contributed to the blending of sedentary and exercise groups with respect to the serum 293 markers measured, resulting in only marginal separation between the groups [19, 20]. However, 294 we did observe significant individual blood biomarker variance with respect to reported running 295 volumes when the dataset was subjected to ANOVA, even after adjustment for age, sex, and BMI. 296 From among glycemic control blood biomarkers, we were able to detect a relatively small exercise 297 effect in both fasting glucose and HbA1c in this generally healthy cohort, where the average 298 measures of glycemia were below the prediabetic thresholds in even the sedentary subset of the 299 cohort. Larger exercise intervention effects on metabolic biomarkers may be expected in cohorts 300 that include individuals with more clinically significant baseline values [21]. 301 Similarly, blood lipids improved with higher self-reported running volume, and this result has been 302 reported before in multiple controlled endurance exercise trials [22]. The literature indicates that 303 HDL and Tg are two exercise-modifiable blood lipid biomarkers, with HDL being the most widely 304 reported to be modified by aerobic exercise [23, 24]. Although the mechanism behind this is not 305 entirely clear, it likely involves the modification of lecithin acyltransferase and lipoprotein lipase 306 activities following exercise training [25]. We observed a similar trend in our blood biomarker 307 analysis, with HDL exhibiting an upward trend with increasing reported running volume. While 308 we also found Tg and LDL to decrease with increasing exercise volume, these trends were less 309 pronounced. Reports generally suggest that, in order to reduce LDL more consistently, the 310 intensity of aerobic exercise must be high enough [23]. In the case of Tg, baseline levels may have 311 a significant impact on the exercise intervention effect, with individuals exhibiting higher baselines 312 showing greater improvements [13]. 313 Importantly, these results suggests that exercise has a significant effect on glycemic control and 314 blood lipids even in the self-selected, already healthy individuals who are proactive about 315 preventing cardiometabolic disease. 316 Self-reported running and serum proxies of systemic inflammation 317 Chronic low-grade inflammation is one of the major risk factors for compromised cardiovascular 318 health and metabolic syndrome (MetS). While there is no shortage of inflammation-reducing 319 intervention studies on CVD patients with clinically high levels of metabolic inflammation, there 320 is less emphasis on modifiable lifestyle factors that can help stave off CVD and extend healthspan 321 in the generally healthy individual. Indeed, considering the pathological cardiovascular processes 322 begin shortly after birth, prevention in asymptomatic individuals may be a more appropriate 323 strategy toward decreasing the burden of CVD on the healthcare system [26]. 324 Toward this end, increasing self-reported running volume appeared to associate with improved 325 markers of inflammation, as shown by the lower levels of hsCRP, WBC, as well as ferritin. Of 326 note, while the acute-phase protein, ferritin, is often used in the differential diagnosis of iron 327 deficiency anemia, the biomarker’s specificity appears to depend on the inflammatory state of the 328 individual, as it associates with hsCRP and inflammation more than iron stores, particularly in 329 those with higher BMI [27]. Although serum ferritin and iron is reported to be lower in male and 330 female elite athletes [28], the observed overall negative association of ferritin with increased 331 19 running volume in our cohort may be an indication of lower levels of inflammation rather than 332 compromised iron stores, particularly since the average ferritin level across all groups was above 333 the clinical iron deficiency thresholds. Moreover, increased levels of ferritin have been associated 334 with insulin resistance and lower levels of adiponectin in the general population, both indicators 335 of increased systemic inflammation [29]. Here, exercising groups with lower levels of ferritin also 336 exhibited glycemic and blood lipid traits indicative of improved metabolic states, further 337 supporting ferritin’s role as an inflammation proxy. Finally, Hb, TS and iron tended to be higher 338 in those who run for exercise compared to the SED group (with the TIBC lower), again suggesting 339 that runners, including the PRO group, were iron-sufficient in this cohort. 340 PRO endurance runners exhibit distinct biomarker signatures 341 PRO athletes exhibited lower serum and RBC Mg, which may be indication of the often-reported 342 endurance athlete hypomagnesaemia [30]. While the serum Mg was still within normal clinical 343 reference range for both PRO female and male athletes, RBC Mg, a more sensitive biomarker of 344 Mg status [31], was borderline low in female PRO athletes and might suggest suboptimal dietary 345 intakes and/or much higher volume of running training compared to the other running groups (i.e. 346 >>10h /week). Indeed, this group also had elevated baseline CK and AST, which suggests a much 347 higher training intensity and/or volume. Moreover, PRO level athletes had adequate iron status 348 and serum B12 and folate in the upper quartile of the normal reference range, suggesting that these 349 athletes’ general nutrition status may have been adequate. These observations suggest that elite 350 endurance runners may need to pay particular attention to their magnesium status. 351 Further, we observed higher levels of SHBG in PRO male runners, a biomarker whose levels 352 positively correlate with various indexes of insulin sensitivity [32]. However, since the average 353 SHBG levels in the SED group were not clinically low in both sexes, the observed increase in 354 SHBG levels induced by running in males may be a catabolic response, as cortisol levels in this 355 group were also higher. Indeed, Popovic et al have shown that endurance exercise may increase 356 SHBG, cortisol, and total testosterone levels at the expense of free testosterone levels [33]. This 357 could perhaps in part be explained by higher exercise-induced adiponectin levels, which have been 358 shown to increase SHBG via cAMP kinase (AMPK) activation [34]. However, since our data is 359 observational, we cannot rule out overall energy balance as a significant contributor to SHBG 360 levels. For example, caloric restriction (CR) has been shown to result in higher SHBG and cortisol 361 levels [32]. 362 Finally, regarding the abovementioned PRO group elevated AST and CK biomarkers, evidence 363 suggests that normal reference ranges in both CK and AST in well-recovered athletes should be 364 adjusted up, as training and competition have a profound, non-pathological, impact on the activity 365 of these enzymes [35, 36]. Indeed, the recommendation appears to be not to use reference intervals 366 derived from the general population with hard-training (particularly competitive) athletes [36]. 367 Effect of BMI on blood biomarkers 368 Since the current study is a cross-sectional analysis of self-reported running, we could not rule out 369 the possibility that factors other than exercise were the driving force behind the observed 370 biomarker variance among the groups examined. These factors, such as diet, sleep, and/or 371 medications were not readily ascertained in this free-living cohort at the time of this study, but 372 BMI was readily available to evaluate this biomarker’s potential relative contribution to the 373 observed mean biomarker differences among self-reported runner groups. 374 21 Multiple studies have attempted to uncouple the effects of exercise and BMI reduction on blood 375 biomarker outcomes, with mixed results [37]. For example, it is relatively well-known that acute 376 bouts of exercise improve glucose metabolism, but long-term effects are less well described [38]. 377 Indeed, whether exercise without significant weight-loss is effective toward preventing metabolic 378 disease (and the associated blood biomarker changes) is inconclusive [39-41]. From the literature, 379 it appears that, for endurance exercise to have significant effect on most blood biomarkers, the 380 volume of exercise needs to be very high, and this typically results in significant reduction in 381 weight. Thus, in practice, it is difficult to demonstrably uncouple the effects of significant exercise 382 and the associated weight-loss, and the results may depend on the blood biomarker in question. 383 Indeed, there is evidence that exercise without weight-loss does improve markers of insulin 384 sensitivity but not chronic inflammation, with the latter apparently requiring a reduction in 385 adiposity in the general population [39-41]. 386 In our study of apparently healthy individuals, we observed a downward trend in BMI with 387 increasing self-reported running volume, and, although this study was not longitudinal and we are 388 thus unable to claim weight-loss, our 2S-MR analysis using BMI as the exposure nonetheless 389 suggests this biomarker to be responsible for a significant proportion of the modification of some 390 blood biomarkers. 391 Serum markers of systemic inflammation 392 Through our 2S-MR analyses, we show that BMI is causally associated with markers of systemic 393 inflammation, including RDW, folate, and hsCRP [27, 42, 43]. Similar analyses have reported 394 that genetic variants that associate with higher BMI were associated with higher CRP levels, but 395 not the other way around [44]. The prevailing mechanism proposed to explain this relationship 396 appears to be the pathological nature of overweight/obesity-driven adipose tissue that results in 397 secretion of proinflammatory cytokines such as IL-6 and TNFa, which then stimulate an acute 398 hepatic response, resulting in increased hsCRP levels (among other effects) [45]. Thus, our 2S- 399 MR analyses and those of others [44] would indicate that the primary factor behind the lower 400 systemic inflammation in our cohort may be the exercise-associated lower BMI and not running 401 exercise per se, though the lower hsCRP in runners remained significant after adjustment for BMI 402 in our analysis. 403 Indeed, although a major driver behind reduced systemic inflammation may be a reduction in BMI 404 in the general population, additive effects of other lifestyle factors such as exercise cannot be 405 excluded. For example, a large body of cross-sectional investigations does indicate that physically 406 active individuals exhibit CRP levels that are 19-35% lower than less active individuals, even 407 when adjusted for BMI as was the case in the current analysis [41]. Further, it’s been reported that 408 physical activity at a frequency of as little as 1 day per week is associated with lower CRP in 409 individuals who are otherwise sedentary, while more frequent exercise further reduces 410 inflammation [41]. 411 Significantly, our entire cohort of self-selected apparently healthy individuals did not exhibit 412 clinically high hsCRP, with average BMI also below the overweight thresholds. Because all 413 subjects were voluntarily participating in a personalized wellness platform intended to optimize 414 blood biomarkers that included hsCRP, it is possible that some individuals from across the study 415 groups (both running and sedentary) in our cohort partook in some form of inflammation-reducing 416 dietary and/or lifestyle-based intervention. Thus, that we detected a significant difference in 417 hsCRP between exercising and non-exercising individuals in this self-selected already generally 418 23 healthy cohort may be suggestive of the potential for additional preventative effect of scheduled 419 physical activity on low-grade systemic inflammation in the generally healthy individual. 420 Blood lipids 421 Controlled studies that tightly track exercise and the associated adiposity reduction have reported 422 that body fat reduction (and not improvement in fitness as measured via VO2max) is a predictor of 423 HDL, LDL, and Tg [46]. Similarly, though BMI is an imperfect measure of adiposity, our 2S-MR 424 analysis suggests that this biomarker is causally associated with improved levels of HDL and Tg, 425 though not LDL. This latter finding replicates a report by Hu et al. who, using the Global Lipids 426 Genetics Consortium GWAS summary statistics, applied a network MR approach that revealed 427 causal associations between BMI and blood lipids, where Tg and HDL, but not LDL, were found 428 to trend toward unhealthy levels with increasing adiposity [47]. On the other hand, others 429 implemented a robust BMI genetic risk score and demonstrated a causal association of adiposity 430 with peripheral artery disease and a multiple linear regression showed a strong association with 431 HDL, TC, and LDL, among other metabolic parameters [48]. In our cohort, given the lack of 432 evidence for a causal BMI-LDL association and the overall healthy levels of BMI, the observed a 433 significant improvement in LDL may be a result of marked running intensity and/or volume, 434 possibly combined with the aforementioned additional wellness program intervention variables. 435 Hormonal traits 436 As described above, we observed a trend toward increased plasma cortisol and SHBG in runners, 437 particularly PRO level athletes. The effects on cortisol are consistent with a report by Houmanrd 438 et al, who found male distance runners to exhibit higher levels of baseline cortisol [49]. With 439 respect to the effects of BMI on baseline cortisol levels, this observation is generally supported by 440 our 2S-MR analyses with evidence for a consistent effect of increased cortisol with decreasing 441 BMI. However, this association was suggestive at best, indicating that the higher levels of cortisol 442 exhibited in the PRO runners with significant lower adiposity are not likely to be solely explained 443 by their lower BMI. Indeed, the relationship between BMI and cortisol appears to be complex, 444 with some reports suggesting a U-shaped relationship, where the glucocorticoid’s levels associate 445 negatively up to about a BMI of 30 kg/m2, then exhibiting a positive correlation into obesity 446 phenotypes [50]. MR statistical models generally do not account for such non-linearity and would 447 require a more granular demographical treatment, which is not possible using only GWAS 448 summary statistics data in the context of 2S-MR [17, 51]. 449 Behavioral traits associated with increase physical activity 450 The combination of the body of the literature that describes the effects of endurance training on 451 blood biomarkers, and our own analysis that included markers such as CK and AST, makes us 452 cautiously assured that most of the abovementioned blood biomarker signatures are indeed a result 453 of the interplay between self-reported running and the associated lower BMI. However, as this is 454 a self-report-based analysis and we were unable to track other subject behaviors in this free-living 455 cohort, we acknowledge that multiple behaviors that associate with exercise may be influencing 456 our results. 457 Toward this end, our exploratory 2S-MR analyses revealed potentially causal relationships 458 between vigorous exercise and multiple dietary habits that have been shown to improve the 459 biomarkers we examined. Indeed, diets that avoid processed meat and sweets while providing 460 ample amounts of fresh fruits, as well as oily fish have been shown to be anti-inflammatory, and 461 25 improve glycemic control and dyslipidemia [52, 53]. That physically active individuals are also 462 more likely to make healthier dietary choices adds insight to the potential confounders in ours and 463 others’ observational analyses, and this similar associations have previously been reported [54- 464 56]. For example, using a calculated healthy eating motivation score, Naughton et al. showed that 465 those who partake in more than 2 hours of vigorous physical activity are almost twice as likely to 466 be motivated to eat healthy [56]. Indeed, upon closer examination, the genetic instruments used 467 to approximate vigorous physical activity as the exposure in this work included variants in the 468 genes DPY19L1, CADM2, CTBP2, EXOC4, and FOXO3 [57]. Of these, CADM2 encodes proteins 469 that are involved in neurotransmission in brain regions well known for their involvement in 470 executive function, including motivation, impulse regulation and self-control [58]. Moreover, 471 variants within this locus have been associated with obesity-related traits [59]. Thus, it is likely 472 that the improved metabolic outcomes seen here with our self-reported runners are a composite 473 result of both these individuals exercise and dietary habits. Importantly, the above suggests that a 474 holistic wellness lifestyle approach is in practice the most likely to be most effective toward 475 preventing cardiometabolic disease. Nonetheless, the focus of this work – exercise in the form of 476 running – is known to significantly improve cardiorespiratory fitness (CRF), which has been 477 shown to be an independent predictor of CVD risk and total mortality, outcomes that indeed 478 correlate with dysregulated levels in many of the blood biomarkers examined in this work [7]. 479 Study limitations 480 This study is based on self-reported running and thus has several limitations. First, it is generally 481 known that subjects tend to overestimate their commitment to exercise when self-reporting, 482 although in our cohort is a self-selected health-oriented population that is possibly less likely to 483 over-report their running volume. Furthermore, although the robust increasing trend in baselines 484 for muscle damage biomarkers (CK, AST) that have been shown to be associated with participation 485 in sports and exercise provides indirect evidence that the running groups were indeed participating 486 in increasing volumes of strenuous physical activity, we cannot confirm whether the reported 487 running was performed overground or on a treadmill, which may result in some heterogeneity in 488 physiological responses , nor can we ascertain the actual training volume of PRO-level runners. 489 We also cannot exclude the possibility that the running groups also participated in other forms of 490 exercise (such as strength training) or partook in other wellness program interventions that may 491 have influenced their blood biomarkers and/or BMI via lean muscle accretion. Toward this end, 492 we have attempted to shed light on potential behavioral covariates related to vigorous physical 493 activity via 2S-MR. Finally, while this cohort is generally healthy, we cannot exclude the potential 494 for unmeasured confounders such as medications, nutritional supplements, and unreported health 495 conditions. 496 2S- MR enables the assessment of causal relationships between modifiable traits and is less prone 497 to the so-called “winner’s curse” that more readily affects one-sample MR analyses [17, 51]. 498 Because 2S-MR uses GWAS summary statistics for both exposure and outcome, it is possible to 499 increase statistical power because of the increased sample sizes. However, horizontal pleiotropy 500 is still a concern that can skew the results. Currently, there is no gold standard MR analysis 501 method, thus we used different techniques (IVW, MR-Egger, and median-based estimations – all 502 of which are based on different assumptions and thus biases) to evaluate the consistency among 503 these estimators and only reported associations as ‘causal’ if there was cross-model consistency. 504 Nonetheless, an exposure such as BMI is a complex trait that is composed of multiple sub- 505 phenotypes (such as years of education) that could be driving the causal associations. 506 27 Conclusions 507 Running is one of the most common forms of vigorous exercise practiced globally, thus making it 508 a compelling target of research studies toward understanding its applicability in chronic disease 509 prevention. Our cross-sectional study offers insight into the biomarker signatures of self-reported 510 running in generally healthy individuals that suggest improved insulin sensitivity, blood lipid 511 metabolism, and systemic inflammation. Furthermore, using 2S-MR in independent datasets we 512 provide additional evidence that some biomarkers are readily modified BMI alone, while others 513 appear to respond to the combination of varying exercise and BM 514 I. Our additional bi-directional 2S-MR analyses toward understanding the causal relationships 515 between partaking in vigorous physical activity and other healthy behaviors highlight the inherent 516 challenge in disambiguating exercise intervention effects in cross sectional studies of free-living 517 populations, where healthy behaviors such as exercising and healthy dietary habits co-occur. 518 Overall, our analysis shows that the differences between those who run and the sedentary in our 519 cohort are likely a combination of the specific physiological effects of exercise, the associated 520 changes in BMI, and lifestyle habits associated with those who exercise, such as food choices and 521 baseline activity level. 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Int J Obes (Lond). 733 2018;42(6):1161-76. Epub 20180613. doi: 10.1038/s41366-018-0120-3. PubMed PMID: 734 29899525; PubMed Central PMCID: PMCPMC6195860. 735 58. Arends RM, Pasman JA, Verweij KJH, Derks EM, Gordon SD, Hickie I, et al. 736 Associations between the CADM2 gene, substance use, risky sexual behavior, and self-control: 737 A phenome-wide association study. Addict Biol. 2021;26(6):e13015. Epub 20210218. doi: 738 10.1111/adb.13015. PubMed PMID: 33604983; PubMed Central PMCID: PMCPMC8596397. 739 59. Morris J, Bailey MES, Baldassarre D, Cullen B, de Faire U, Ferguson A, et al. Genetic 740 variation in CADM2 as a link between psychological traits and obesity. Scientific Reports. 741 2019;9(1):7339. doi: 10.1038/s41598-019-43861-9. 742 743 Supporting information 744 33 S1 Table. Number of people in each category by age group. Significant trend toward 745 younger individuals reporting higher running volume, with more than 75% of the elite 746 group falling between the ages of 18 and 35. 747 S2 Table. Full running volume vs. blood biomarker results 748 S3 Table. 2S-MR results with BMI as the exposure and select biomarkers as outcomes. 749 S4 Table. 2S-MR results with BMI with biomarkers as exposures and BMI as outcome to 750 assess reverse causality 751 S5 Table. 2S-MR results with vigorous physical activity as exposure and blood biomarkers 752 as outcomes 753 S6 Table. 2S-MR results with vigorous physical activity as exposure and lifestyle habits as 754 outcomes 755 S7 Table. 2S-MR with healthy/unhealthy dietary habits as exposures and vigorous physical 756 activity as outcome to assess reverse causality 757 S1 Fig. Assumptions of Mendelian randomization 758 S2 Fig. Blood biomarker levels with respect to self-reported running volume and 759 professional athletes 760 S3 Fig. 2S-MR scatter plot showing effects of vigorous physical activity as the exposure on 761 blood biomarkers. 762 S4 Fig. 2S-MR scatter plot showing effects of vigorous physical activity as the exposure 763 dietary habits. 764 S5 Fig. 2S-MR scatter plot showing effects of dietary behaviors as the exposures on vigorous 765 physical activity 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 Fig. 1 Click here to access/download;Figure;Fig1.tif Fig. 2 Click here to access/download;Figure;Fig2.tif Fig. 3 Click here to access/download;Figure;Fig3.tif Fig. 4 Click here to access/download;Figure;Fig4.tif Fig. 5 Click here to access/download;Figure;Fig5.tif Supporting Information Click here to access/download Supporting Information Supplementary_Materials_PONE_rev.pdf Minimum dataset Click here to access/download Supporting Information Dataset.txt 1 Dose response of running on blood biomarkers of wellness in the generally healthy individuals Bartek Nogal PhD1¶ ﾅ, Svetlana Vinogradova PhD1ﾅ¶, Milena Jorge MD,PhD1, Ali Torkamani PhD 2,3, Paul Fabian BSc1, and Gil Blander PhD1* 1InsideTracker, Cambridge, Massachusetts, United States of America. 2The Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA, United States of AmericaUSA. 3Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States of AmericaUSA. * Corresponding author E-mail: gblander@insidetracker.com (GB) ﾅ¶Equal Contribution: These authors contributed equally to this work * Correspondence and reprint requests: Gil Blander, gblander@insdietracker.com Bartek Nogal Formatted: Normal, Line spacing: single Formatted: Indent: Left: 0", Line spacing: single Formatted: Font: Formatted: Indent: Left: 0", Line spacing: single Formatted: Font: Times New Roman Field Code Changed Formatted: Indent: Left: 0" Revised Manuscript with Track Changes 1 bnogal@insidetracker.com 1 Svetlana Vinogradova 2 svinogradova@insidetracker.com 3 Ali Torkamani 4 atorkama@scripps.edu 5 Paul Fabian 6 pfabian@insdietracker.com 7 Running title: Biomarker signature of runners 8 Abstract 9 Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence 10 supporting its effectiveness. However, less is known about the specific healthspan-promoting 11 effects of exercise on blood biomarkers in the disease-free population. In this work, we examine 12 23,237 generally healthy individuals who self-report varying weekly running volumes and 13 compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional 14 endurance athletesrunners. We estimate the significance of differences among blood biomarkers 15 for groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, 16 gender, and BMI. We attempt and add insight to our observational dataset analysis via two-sample 17 Mendelian randomization (2S-MR) using large independent datasets. We find that self-reported 18 Formatted: Font: 18 pt Formatted: Header distance from edge: 0.2", Footer distance from edge: 0.35", Numbering: Continuous running volume associates with biomarker signatures of improved wellness, with some serum 19 markers apparently being principally modified by BMI, whereas others show a dose-effect with 20 respect to running volume. We further detect hints of sexually dimorphic serum responses in 21 oxygen transport and hormonal traits, and we also observe a tendency toward pronounced 22 modifications in magnesium status in professional endurance athletes. Thus, our results further 23 characterize blood biomarkers of exercise and metabolic health, particularly regarding dose-effect 24 relationships, and better inform personalized advice for training and performance. 25 26 27 28 29 Keywords: physical activity, exercise, blood biomarkers, running, generally healthy, mendelian 30 randomization 31 32 33 34 35 36 37 3 38 39 40 41 42 43 44 45 46 1 Introduction 47 Physical inactivity is one of the leading modifiable behavioral causes of death in the US [1]. 48 Worldwide, physical inactivity is estimated to account for about 8.3% of premature mortality, an 49 effect size that is on the same order as smoking and obesity [2]. At the same time, the potent health 50 benefits of exercise have been proven time and time again, with results so consistent across a wide 51 variety of chronic diseases that some posit it can be considered a medical intervention [3-5]. 52 However, since most investigators report the effects of exercise in either diseased populations or 53 athletes [6, 7], there exists a significant gap in knowledge as to the measurable effects of exercise 54 Formatted: Font: 18 pt Formatted: No bullets or numbering in the generally healthy population who exercise for the purpose of improving their healthspan, 55 which can be projected via established measures such as blood biomarkers [8-11]. 56 It is well established that routine laboratory biomarkers are validated proxies of the state of an 57 individual’s overall metabolic health and other healthpan-related parameters [12]. A large body 58 of evidence supports the effectiveness of exercise in modifying blood biomarkers toward disease 59 mitigation in clinical cohorts as well as athletes, where the effect sizes may be larger [6, 13]. 60 Indeed, it’s been shown that more favorable changes in response to exercise training occur usually 61 in those with more pronounced dyslipidemia [13]. In professional athletes, the sheer volume 62 and/or intensity of physical activity may drive large effects in various hematological, lipid, 63 immune, and endocrine variables [6]. Our aim is to help fill the gap in understanding of the effects 64 of exercise on blood biomarkers in the generally healthy, free-living population. Toward this end, 65 we endeavored to explore the effects of vigorous exercise such as running in apparently healthy, 66 mostly non-athletic cohort to better understand the landscape of blood biomarker modifications 67 expected in the individual who partakes in recreational physical activity for the purpose of 68 maintaining good health. 69 For this purpose, we leveraged the InsideTracker dataset that includes information on self-reported 70 exercise habits combined with blood biomarker and genomics data. We have previously reported 71 on the results of a longitudinal analysis on blood biomarker data from 1032 generally healthy 72 individuals who used our automated, web-based personalized nutrition and lifestyle platform [14]. 73 For the purpose of this investigation, we focused on running as the exercise of choice as it is one 74 of the most common (purposeful) physical activity modalities practiced globally by generally 75 healthy individuals and would thus be relevant. Moreover, since this was a cross-sectional study 76 based on self-reported exercise habits, we attempted to increase our capacity to infer intervention 77 5 effects, as well as tease out potential confounders, by performing 2S-MR in large independent 78 cohorts. 79 2 MethodsMaterials and methods 80 2.1 Dataset 81 82 We conducted an observational analysis of data from InsideTracker users. InsideTracker is a 83 direct-to-consumer (DTC) company established in 2009 that markets and sells InsideTracker 84 (insidetracker.com), a personalized lifestyle recommendation platform. The platform provides 85 serum biomarker and genomics testing, and performs integrative analysis of these datasets, 86 combined with activity/sleep tracker data toward biomarker and healthspan optimization (of note, 87 at the time of this analysis, we did not have sufficient users with activity/sleep tracker data to 88 include this data stream in the current study). New users were continuously added to the 89 InsideTracker database from January 2011 to March 2022. 90 91 2.2 Recruitment of participants 92 Recruitment of participants aged between 18 and 65 and residing in North America was conducted 93 through company marketing and outreach. Participants were subscribing members to the 94 InsideTracker platform and provided informed consent to have their blood test data and self- 95 reported information used in an anonymized fashion for research purposes. Research was 96 Formatted: Font: 18 pt Formatted: Font: 18 pt Formatted: Font: 16 pt Formatted: Font: 16 pt Formatted: Font: 16 pt conducted according to guidelines for observational research in tissue samples from human 97 subjects. Eligible participants completed a questionnaire that included age, ethnicity, sex, dietary 98 preferences, physical activity, and exposure to sunlightother variables. This study employed data 99 from 23,237 participants that met our analysis inclusion requirements, namely absence of any 100 chronic disease as determined by questionnaire and metabolic blood biomarkers within normal 101 clinical reference ranges. The platform is not a medical service and does not diagnose or treat 102 medical conditions, so medical history and medication use were not collected. The Institutional 103 Review Board (IRB) determine this work was not subject to a review based on category 4 104 exemption (“secondary research” with de-identified subjects). 105 2.3 Biomarker collection and analysis 106 Blood samples were collected and analyzed by Clinical Laboratory Improvement Amendments 107 (CLIA)–approved, third-party clinical labs (primarily Quest Diagnostics and LabCorp). 108 Participants were instructed to fast for 12 hours prior to the phlebotomy, with the exception of 109 water consumption. Results from the blood analysis were then uploaded to the platform via 110 electronic integration with the CLIA-approved lab. Participants chose a specific blood panel from 111 7 possible offerings, each comprising some subset of the biomarkers available. Due to the variation 112 in blood panels offered, the participant sample size per biomarker is not uniform. 113 2.3 Biomarker dataset preparation 114 115 Formatted: Font: 16 pt Formatted: Font: 16 pt Formatted: Font: 16 pt 7 In our raw dataset, occasional outlier values were observed that were deemed implausible (e.g. 116 fasting glucose < 65 mg/dL). To remove anomalous outliers in a systematic way, we used the 117 Interquartile Range (IQR) method of identifying outliers, removing data points which fell below 118 Q1 – 1.5 IQR or above Q3 + 1.5 IQR. The cohort was divided into five groups: professional 119 endurance runners (PRO), high volume amateur (>10 h/week, HVAM ), medium volume amateur 120 (3-10 h/week, MVAM), low volume amateur (<3 h/week, LVAM), and sedentary (SED). 121 2.4 Calculation of polygenic scores 122 The variants (SNPs) comprising the polygenic risk scores were derived from publicly available 123 GWAS summary statistics (https://www.ebi.ac.uk/gwas/). Scores were calculated across users by 124 summing the product of effect allele doses weighted by the beta coefficient for each SNP, as 125 reported in the GWAS summary statistics. Variant p-value thresholds were generally chosen based 126 on optimization of respective PGS-blood biomarker correlation in the entire InsideTracker cohort 127 with both blood and genomics datasets (~1000-1500 depending on the blood biomarker at the time 128 of analysis). Genotyping data was derived from a combination of a custom InsideTracker array 129 and third party arrays such as 23andMe and Ancestry. Not all variants for any particular PGS were 130 genotyped on every array; proxies for missing SNPs were extracted via the “LDlinkR” package 131 using the Utah Residents (CEPH) with Northern and Western European ancestry (CEU) population 132 (R2 > 0.8 cut-off). Only results PGSs for which there was sufficient biomarker-genotyping dataset 133 overlap were reported (note that none of the blood biomarker PGSs met this requirement). 134 2.5 Blood biomarker analysis with respect to running volume and 135 polygenic scores 136 Formatted: Font: Font color: Text/Background 1 Formatted: Font: 16 pt Formatted: Font: 16 pt To estimate significance of differences for blood biomarkers levels among exercise groups, we 137 performed 3-way analysis of variance (ANOVA) analysis adjusting for age, gender, and BMI 138 (type-II analysis-of-variance tables function ANOVA from ‘car’ R package, version 3.0-12). 139 When estimating the effort of reported training volume on biomarkers, we assigned numerical 140 values corresponding to 4 levels of running and performed ANOVA analysis with those levels 141 treating it as an independent variable. P-values were adjusted using the Benjamini & Hochberg 142 method [15]. P-values for interaction plots were calculated with ANOVA including interaction 143 between exercise group and polygenic scores category. When comparing runners (PRO and 144 HVAM combined) versus sedentary individuals, we used propensity score matching method to 145 account for existing covariates (age and gender): we identified 745 sedentary individuals with 146 similar to runners’ age distributions among both males and females. We used ‘MatchIt’ R package 147 (version 4.3.3) implementing nearest neighbor method for matching [16]. 148 2.6 Mendelian randomization 149 We attempted to add insight around the causality of exercise vs. BMI differences with respect to 150 serum marker improvement by performing MR analyses on a subset of biomarker observations 151 where BMI featured as a strong covariate and was thus used as the IV in the 2S-MR. Thus, our 152 hypothesis here was that BMI differences were the primary (causal) driver behind the improvement 153 behind some biomarkers. MR uses genetic variants as modifiable exposure (risk factor) proxies 154 to evaluate causal relationships in observational data while reducing the effects of confounders 155 and reverse causation (Figure 1SS1 Fig). These SNPs are used as instrumental variables and must 156 meet 3 basic assumptions: (1) they must be robustly associated with the exposure; (2) they must 157 exert their effect on outcome via the exposure, and (3) there must be no unmeasured confounders 158 Formatted: Font: 16 pt 9 of the associations between the genetic variants and outcome (e.g. horizontal pleiotropy) [17]. 159 Importantly, SNPs are proper randomization instruments because they are determined at birth and 160 thus serve as proxies of long-term exposures and cannot, in general, be modified by the 161 environment. If the 3 above mentioned assumptions hold, MR-estimate effects of exposure on 162 outcomes are not likely to be significantly affected by reverse causation or confounding. In the 163 2S-MR performed here, where GWAS summary statistics are used for both exposure and outcome 164 from independent cohorts, reverse causation and horizontal pleiotropy can readily be assessed, and 165 weak instrument bias and the likelihood of false positive findings are minimized as a result of the 166 much larger samples sizes [17]. Indeed, the bias in the 2S-MR using non-overlapping datasets as 167 performed here is towards the null [17]. Furthermore, to maintain the SNP-exposure associations 168 and linkage disequilibrium (LD) patterns in the non-overlapping populations we used GWAS 169 datasets from the MR-Base platform that were derived from ancestrally similar populations 170 (“ukb”: analysis of UK Biobank phenotypes, and “ieu”: GWAS summary datasets generated by 171 many different European consortia). To perform the analysis we used the R package 172 “TwoSampleMR” that combines the effects sizes of instruments on exposures with those on 173 outcomes via a meta-analysis. We used “TwoSampleMR” package functions for allele 174 harmonization between exposure and outcome datasets, proxy variant substitution when SNPs 175 from exposure were not genotyped in the outcome data (Rsq>0.8 using the 1000G EUR reference 176 data integrated into MR-Base), and clumping to prune instrument SNPs for LD (the R script used 177 for MR analyses is available upon request). We used 5 different MR methods that were included 178 as part of the “TwoSampleMR” package to control for bias inherent to any one technique [18]. 179 For example, the multiplicative random effects inverse variance-weighted (IVW) method is a 180 weighted regression of instrument-outcome effects on instrument-exposure effects with the 181 intercept is set to zero. This method generates a causal estimate of the exposure trait on outcome 182 traits by regressing the, for example, SNP-BMI trait association on the SNP-biomarker measure 183 association, weighted by the inverse of the SNP-biomarker measure association, and constraining 184 the intercept of this regression to zero. This constraint can result in unbalanced horizontal 185 pleiotropy whereby the instruments influence the outcome through causal pathways distinct from 186 that through the exposure (thus violating the second above-mentioned assumption). Such 187 unbalanced horizontal pleiotropy distorts the association between the exposure and the outcome, 188 and the effect estimate from the IVW method can be exaggerated or attenuated. However, 189 unbalanced horizontal pleiotropy can be readily assessed by the MR Egger method (via the MR 190 Egger intercept), which provides a valid MR causal estimate that is adjusted for the presence of 191 such directional pleiotropy, albeit at the cost of statistical efficiency. Finally, to ascertain the 192 directionality of the various causal relationships examined, we also performed each MR analysis 193 in reverse where possible. 194 3 Results 195 Study population characteristics 196 Table 1 shows the demographic characteristics of the study population. We observed a 197 significant trend toward younger individuals reporting higher running volume, with more than 198 75% of the professional (PRO) group falling between the ages of 18 and 35 (Table 1SS1 Table). 199 Significant differences were also observed in the distribution of males and females within study 200 groups (Table 1). Moreover, higher running volume associated with significantly lower body mass 201 Formatted: Font: 18 pt Formatted: Font: 18 pt Formatted: Font: 16 pt, Bold, Not Italic 11 index (BMI). Thus, moving forward, combined comparisons of blood biomarkers as they relate 202 to running volume were adjusted for age, gender, and BMI. 203 204 Table 1. Study population demographics 205 Group N Female, % Age, yrs Body mass index, kg/m2 PRO 82 53.7% 33.68 20.15 ± 6.02 HVAM 1103 52.9% 39.48 22.57 ± 9.97 MVAM 6747 54.2% 41.49 23.35 ± 9.76 LVAM 10877 34.2% 41.16 24.72 ± 9.70 SED 4428 48.9% 44.25 27.83 ± 10.70 PRO = Professional, HVAM = high volume amateur (>10 h/week ), MVAM = medium volume 206 amateur (3-10 h/week), LVAM = low volume amateur (<3 h/week), SED = sedentary 207 208 Endurance exercise exhibits a modest association with clusters of 209 blood biomarker features 210 In order to begin to understand the most important variables that may associate with endurance 211 exercise in the form of running, we performed a principal component analysis (PCA), sub-dividing 212 the male and female cohorts into two most divergent groups in terms of exercise volume: 213 PRO/high volume amateur (HVAM) and sedentary (SED) groups. Using propensity matching, 214 PRO and amateur athletes who reported running >10h per week were combined into the PRO- 215 HVAM group to balance out the sample size between the exercising and non-exercising groups. 216 Formatted: Font: (Default) Times New Roman, 12 pt Formatted: Font: (Default) Times New Roman, 12 pt Formatted: Font: (Default) Times New Roman, 12 pt Formatted: Font: (Default) Times New Roman Formatted: Font: (Default) Times New Roman Formatted: Font: (Default) Times New Roman Formatted: Font: (Default) Times New Roman Formatted: Font: (Default) Times New Roman Formatted: Font: (Default) Times New Roman Formatted: Font: (Default) Times New Roman Formatted: Font: (Default) Times New Roman Formatted: Font: (Default) Times New Roman Formatted: Font: (Default) Times New Roman Formatted: Font: (Default) Times New Roman Formatted: Font: (Default) Times New Roman, 12 pt Formatted: Font: 16 pt, Bold, Not Italic Using this approach, we did not observe a significant separation between these groups (data not 217 shown). However, dividing this dataset further into males and femalesThis approach yielded a 218 modest degree of separation, with hematological, inflammation, and lipid features, as well as BMI 219 explaining some of the variance (FigureFig 1 A through D). We hypothesized that there may more 220 subtle relationships between running volume and the blood biomarker features that contributed to 221 distinguishing the endurance exercise and sedentary groups, thus we next performed ANOVA 222 analyses stratified by running volume as categorized in Table 1. 223 Fig 1. Principal component analysis and variables plots of PRO-HVAM runners and sedentary 224 user blood biomarkers. Females, (A) and (B); males (C) and (D). PRO-HVAM = combined 225 professional and high-volume amateur. Alb = albumin, ALT = alanine transaminase, AST = 226 aspartate aminotransferase, B12 = vitamin B12, Ca = calcium, Chol = total cholesterol, CK = 227 creatine kinase, Cor = cortisol, FE = iron, EOS_PCT = eosinophil percentage, Fer = ferritin, Fol = 228 folate, FT = free testosterone, GGT = gamma-glutamyl transferase, Glu = glucose, Hb = 229 hemoglobin, HCT = hematocrit, HDL = high density lipoprotein, HbA1c = glycated hemoglobin, 230 hsCRP = high-sensitivity C-reactive protein, LDL = low density lipoprotein, LYMPS_PCT = 231 lymphocyte percentage, MCH = mean cell hemoglobin, Mg = magnesium, MONOS_PCT = 232 monocytes percentage, MPV = mean platelet volume, Na = sodium, RBC = red blood cells, 233 RBC_Mg = red blood cell magnesium, RDW = red blood cell distribution width, SHBG = sex 234 hormone binding globulin, Tg = triglycerides, TIBC = total iron binding capacity, WBC = white 235 blood cells 236 Significant trends in glycemic, hematological, blood lipid, and 237 inflammatory serum traits with increasing running volumes 238 Formatted: Font: 12 pt Formatted: Font: 16 pt, Bold, Not Italic Formatted: Font: 16 pt, Bold Formatted: Font: 16 pt, Bold, Not Italic 13 Weighted ANOVA analyses adjusted for age, gender, and BMI showed significant differences 239 among groups for multiple blood biomarkers (Table 2 and 2SS2 , FigureFigs 2 and 3). We 240 observed a trend toward lower HbA1c, hsCRP, RDW, WBC, ferritin, gamma-glutamyl transferase 241 (GGT), and LDL. HDL, hemoglobin (Hb), transferrin saturation (TS), alanine aminotransferase 242 (ALT), aspartate aminotransferase (AST), vitamin B12, folate, 25-hydroxy vitamin D, and creatine 243 kinase (CK) tended to be higher with increasing reported training volume, particularly in PRO 244 runners (Tables 2 and 2SS2 , FigureFigs 2 and 2SS2 , FigureFig 3). Hct and Hb were higher only 245 in PRO males, whereas increased running volume associated with upward trend in these 246 biomarkers in females (FigureFig 3 A and B). Increased running volume was associated with 247 markedly lower Fer in males, whereas female runners did not exhibit varying levels, and SED 248 females showed increased levels (FigureFig 3 C). The low ferritin observed in male and female 249 runners was not clinically significant. ALT positively associated with running volume in females 250 only (Figure 2SS2 Fig). Serum and RBC magnesium (Mg) were both significantly lower in PRO 251 runners relative to all other groups (Table 2 and FigureFig 3 D and E). Increasing levels of 252 endurance exercise also appeared to be associated with higher sex-hormone binding globulin 253 (SHBG), particularly in PRO male runners (FigureFig 3 F). 254 255 Table 2. Blood biomarkers significantly different among sedentary individuals and those 256 who partake in running for exercise to various degrees 257 BIOMARKER ANOVA P-VALUE TREND P-VALUE LOWEST MEAN HIGHEST MEAN ALB <1e-16 <0.001 MVAM PRO ALT <1e-16 <1e-16 SED PRO Formatted: Font: (Default) Times New Roman, 12 pt Formatted: Line spacing: single, Don't suppress line numbers Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: (Default) Times New Roman, 12 pt, Bold Formatted: Font: Bold, Font color: Black AST <1e-16 <0.001 SED PRO B12 <0.001 <0.001 SED PRO CHOL <0.001 0.005 PRO SED CK <1e-16 <1e-16 SED PRO COR <0.001 0.675 SED PRO FE <0.001 0.119 SED PRO FER <1e-16 <1e-16 MVAM SED FOL <1e-16 <0.001 SED PRO FT <0.001 0.013 SED PRO GGT <1e-16 <0.001 PRO SED GLU 0.087 0.184 PRO SED HB 0.002 <0.001 MVAM PRO HCT 0.053 0.055 MVAM PRO HDL <1e-16 <0.001 SED PRO HBA1C <0.001 0.010 PRO SED HSCRP <0.001 0.176 PRO SED LDL <0.001 0.006 PRO SED MG <0.001 0.276 PRO SED MPV 0.058 0.089 SED HVAM NA <1e-16 0.622 HVAM SED RBC_MG <0.001 0.773 PRO SED 15 RDW <1e-16 0.002 PRO SED SHBG <1e-16 0.004 SED PRO TG <1e-16 <1e-16 PRO SED WBC <1e-16 <1e-16 PRO SED 258 Fig 2. Blood biomarkers associated with running: Inflammation proxies, (A) hsCRP = high- sensitivity C-reactive protein and (B) WBC = white blood cells; blood lipids, (C) HDL = high density lipoprotein (D) LDL = low density lipoprotein, and (E) Tg = triglycerides; glycemia proxies, (F) Glu = glucose and (G) HgbA1c = glycated hemoglobin, and (H) Cor =cortisol Fig 3. Blood biomarkers associated with running: (A and B) Hb (hemoglobin) and Hct (hematocrit) increase with increasing running volume, (C) Fer (ferritin) is reduced with increasing running volume, (D and E) Serum and RBC Mg (red blood cell magnesium) are reduced in professional runners, and (F) SHBG (sex hormone binding globulin) levels increase with increasing running volume in males 259 Endurance exercise correlates with lower BMI across categories of 260 genetic risk 261 Using publicly available GWAS summary statistics, we constructed blood biomarker polygenic 262 risk scores (PGSs) to explore potential genetic risk-mitigating effects of endurance exercise. Since 263 Formatted: Line spacing: Double Formatted: Font: 12 pt Formatted: Font: 12 pt Formatted: Font: 16 pt, Bold, Not Italic only a subset of the individuals in our cohort were genotyped, we aggregated the groups into 2 264 categories—PRO-HVAM and sedentary—to increase statistical power. This across-group sample 265 size increase generally did not sufficiently power the ANOVA analysis to detect statistically 266 significant trends (data not shown), though the BMI polygenic risk was suggestively mitigated for 267 both males and female PRO-HVAM runners across categories of genetic risk (FigureFig 4 B). 268 Fig 4. BMI significantly varied among running groups (A) with some suggestive effects on BMI PGS modification (total number for observations (N) for T1, T2, and T3 were 87, 84, and 100, respectively) (B) T1, T2, and T3 = 1st, and 2nd and 3rd tertials of the polygenic score distribution 269 Increased running volume is associated with lower BMI which may 270 drive biomarker changes 271 We observed a significant downward trend in the BMI with increased running volume for both 272 males and females, and, although some of the biomarker differences between sedentary and 273 exercising individuals remained significant after adjustment for BMI, their significance was 274 attenuated (FigureFig 4 A, p-value attenuation data not shown). Thus, we hypothesized that BMI 275 may be driving a significant portion of the observed variance in some of the biomarkers across the 276 groups. Thus, to explore causal relationships between weight and biomarker changes, we 277 performed 2S-MR with BMI-associated single-nucleotide polymorphisms (SNPs) as the 278 instrumental variables (IVs) for a subset of the healthspan-related biomarkers where BMI 279 explained a relatively large portion of the variance in our analysis. In general, these blood 280 biomarkers associated with inflammation (hsCRP and RDW), lipid metabolism (Tg and HDL), 281 Formatted: Line spacing: Double Formatted: Font: 12 pt Formatted: Font: 12 pt Formatted: Font: 16 pt, Bold, Not Italic 17 glycemic control (HbA1c and Glu), as well as Alb and SHBG. We used GWAS summary statistics 282 and found that most of these BMI-blood biomarker relationships examined directionally aligned 283 with our study (except for LDL), and some were indicative of causal relationships in the BMI- 284 biomarker direction even after considering directional pleiotropy (Table 3SS3 Table). We 285 entertained the possibility of reverse causality and thus repeated the 2S-MR using each of the 286 biomarker levels as the exposure and BMI as the outcome, and the results were generally not 287 significant (except for WBC – see Table 4SS4 Table). Of note, to estimate the direct causal effects 288 of running on blood parameters, we attempted to find an instrumental variable for to approximate 289 running as the exposure from publicly available GWAS summary statistics. Toward this end, we 290 found that increasing levels of vigorous physical activity did associate with lower hsCRP, HbA1C, 291 higher HDL, and possibly higher SHBG (although the explained variance (R2) in this exposure 292 was just 0.001009, the F statistic was 37.7, thus meeting the criteria of F > 10 for minimizing weak 293 instrument bias) (FigureFigs 5 and 3SS3; Table 5SS5 Table). 294 Fig 5. Two-sample Mendelian randomization shows that increasing levels of vigorous physical activity such as running is associated with improvement of (A) hsCRP = high-sensitivity C- reactive protein, (B) HDL = high density lipoprotein, and (C) HbA1c = glycated hemoglobin levels 295 Vigorous physical activity associates with healthier behaviors 296 We hypothesized that those who exercise regularly may also partake in other healthful lifestyle 297 habits that may be contributing to more optimal blood biomarker signatures of wellness. However, 298 Formatted: Line spacing: Double Formatted: Font: 12 pt Formatted: Font: 12 pt Formatted: Font: 16 pt, Bold, Not Italic Formatted: Font: 16 pt, Bold our dataset did not allow for systematic accounting of other lifestyle habits across all running 299 groups. Thus, we again leveraged the potential of the 2S-MR approach to inform potential 300 confounding associations between modifiable exposures and found that vigorous physical activity 301 such as running is at least suggestively associated with several behaviors associated with improved 302 health (Figure 4SS4 Fig). Our analysis showed that those who participate in increasing levels of 303 vigorous physical activity may be less likely to eat processed meat (IVW p = 0.0000013), sweets 304 (IVW p = 0.32), and nap during the day (IVW p = 0.13), while increasing their intake of oily fish 305 (IVW p = 0.029), salad/raw vegetable intake (IVW p = 0.00016), and fresh fruit (IVW p = 0.0027) 306 (Table 6SS6 Table). Furthermore, following our assessment of reverse causality, we found 307 evidence for the bidirectionality in the causal relationship between vigorous activity and napping 308 during the day and salad/raw vegetable intake, perhaps suggesting some degree of confounding 309 due to population stratification (Table 7SS7 Table). The suggestive positive effect of fresh fruit 310 and processed meat intake on vigorous physical activity appeared to violate MR assumption (3) 311 (Figure 1SS1 Fig) (horizontal pleiotropy p-values 0.051 and 0.17, respectively – Figure 5SS5 Fig). 312 4 Discussion 313 In this report, we describe the variance in wellness-related blood biomarkers among self-reported 314 recreational runners, PRO runners, and individuals who do not report any exercise. Overall, we 315 find that 1) recreational running as an exercise appears to be an effective intervention toward 316 modifying several biomarkers indicative of improved metabolic health, 2) an apparent dose- 317 response relationship between running volume and BMI may itself be responsible for a proportion 318 of the apparent metabolic benefits, and 3) both PRO-level status and gender appear to associate 319 Formatted: Font: 18 pt 19 with heterogeneous physiological responses, particularly in iron and magnesium metabolism, as 320 well as some hormonal traits. 321 4.1 Self-reported running improves glycemia and lipidemia 322 We did not observe distinct clusters corresponding to self-reported high-volume/PRO runners and 323 the sedentary upon dimension reduction. This is, perhaps, not unexpected due, in part, to the self- 324 selected healthspan-oriented nature of our cohort, where even the sedentary subset of individuals 325 tends to exhibit blood biomarker levels in the normal clinical reference ranges. Furthermore, the 326 measurement of running volume via self-report may be vulnerable to overestimation, which may 327 have contributed to the blending of sedentary and exercise groups with respect to the serum 328 markers measured, resulting in only marginal separation between the groups [19, 20]. However, 329 we did observe significant individual blood biomarker variance with respect to reported running 330 volumes when the dataset was subjected to ANOVA, even after adjustment for age, sex, and BMI. 331 From among glycemic control blood biomarkers, we were able to detect a relatively small exercise 332 effect in both fasting glucose and HbA1c in this generally healthy cohort, where the average 333 measures of glycemia were below the prediabetic thresholds in even the sedentary subset of the 334 cohort. Larger exercise intervention effects on metabolic biomarkers may be expected in cohorts 335 that include individuals with more clinically significant baseline values [21]. 336 Similarly, blood lipids improved with higher self-reported running volume, and this result has been 337 reported before in multiple controlled endurance exercise trials [22]. The literature indicates that 338 HDL and Tg are two exercise-modifiable blood lipid biomarkers, with HDL being the most widely 339 reported to be modified by aerobic exercise [23, 24]. Although the mechanism behind this is not 340 Formatted: Font: 16 pt entirely clear, it likely involves the modification of lecithin acyltransferase and lipoprotein lipase 341 activities following exercise training [25]. We observed a similar trend in our blood biomarker 342 analysis, with HDL exhibiting an upward trend with increasing reported running volume. While 343 we also found Tg and LDL to decrease with increasing exercise volume, these trends were less 344 pronounced. Reports generally suggest that, in order to reduce LDL more consistently, the 345 intensity of aerobic exercise must be high enough [23]. In the case of Tg, baseline levels may have 346 a significant impact on the exercise intervention effect, with individuals exhibiting higher baselines 347 showing greater improvements [13]. 348 Importantly, these results suggests that exercise has a significant effect on glycemic control and 349 blood lipids even in the self-selected, already healthy individuals who are proactive about 350 preventing cardiometabolic disease. 351 4.2 Self-reported running and serum proxies of systemic 352 inflammation 353 Chronic low-grade inflammation is one of the major risk factors for compromised cardiovascular 354 health and metabolic syndrome (MetS). While there is no shortage of inflammation-reducing 355 intervention studies on CVD patients with clinically high levels of metabolic inflammation, there 356 is less emphasis on modifiable lifestyle factors that can help stave off CVD and extend healthspan 357 in the generally healthy individual. Indeed, considering the pathological cardiovascular processes 358 begin shortly after birth, prevention in asymptomatic individuals may be a more appropriate 359 strategy toward decreasing the burden of CVD on the healthcare system [26]. 360 Formatted: Font: 16 pt 21 Toward this end, increasing self-reported running volume appeared to associate with improved 361 markers of inflammation, as shown by the lower levels of hsCRP, WBC, as well as ferritin. Of 362 note, while the acute-phase protein, ferritin, is often used in the differential diagnosis of iron 363 deficiency anemia, the biomarker’s specificity appears to depend on the inflammatory state of the 364 individual, as it associates with hsCRP and inflammation more than iron stores, particularly in 365 those with higher BMI [27]. Although serum ferritin and iron is reported to be lower in male and 366 female elite athletes [28], the observed overall negative association of ferritin with increased 367 running volume in our cohort may be an indication of lower levels of inflammation rather than 368 compromised iron stores, particularly since the average ferritin level across all groups was above 369 the clinical iron deficiency thresholds. Moreover, increased levels of ferritin have been associated 370 with insulin resistance and lower levels of adiponectin in the general population, both indicators 371 of increased systemic inflammation [29]. Here, exercising groups with lower levels of ferritin also 372 exhibited glycemic and blood lipid traits indicative of improved metabolic states, further 373 supporting ferritin’s role as an inflammation proxy. Finally, Hb, TS and iron tended to be higher 374 in those who run for exercise compared to the SED group (with the TIBC lower), again suggesting 375 that runners, including the PRO group, were iron-sufficient in this cohort. 376 4.3 PRO athletes endurance runners exhibit distinct biomarker 377 signatures 378 PRO athletes exhibited lower serum and RBC Mg, which may be indication of the often-reported 379 endurance athlete hypomagnesaemia [30]. While the serum Mg was still within normal clinical 380 reference range for both PRO female and male athletes, RBC Mg, a more sensitive biomarker of 381 Mg status [31], was borderline low in female PRO athletes and might suggest suboptimal dietary 382 Formatted: Font: 16 pt intakes and/or much higher volume of running training compared to the other running groups (i.e. 383 >>10h /week). Indeed, this group also had elevated baseline CK and AST, which suggests a much 384 higher training intensity and/or volume. Moreover, PRO level athletes had adequate iron status 385 and serum B12 and folate in the upper quartile of the normal reference range, suggesting that these 386 athletes’ general nutrition status may have been adequate. These observations suggest that elite 387 endurance runners may need to pay particular attention to their magnesium status. 388 Further, we observed higher levels of SHBG in PRO male runners, a biomarker whose levels 389 positively correlate with various indexes of insulin sensitivity [32]. However, since the average 390 SHBG levels in the SED group were not clinically low in both sexes, the observed increase in 391 SHBG levels induced by running in males may be a catabolic response, as cortisol levels in this 392 group were also higher. Indeed, Popovic et al have shown that endurance exercise may increase 393 SHBG, cortisol, and total testosterone levels at the expense of free testosterone levels [33]. This 394 could perhaps in part be explained by higher exercise-induced adiponectin levels, which have been 395 shown to increase SHBG via cAMP kinase (AMPK) activation [34]. However, since our data is 396 observational, we cannot rule out overall energy balance as a significant contributor to SHBG 397 levels. For example, caloric restriction (CR) has been shown to result in higher SHBG and cortisol 398 levels [32]. 399 Finally, regarding the abovementioned PRO group elevated AST and CK biomarkers, evidence 400 suggests that normal reference ranges in both CK and AST in well-recovered athletes should be 401 adjusted up, as training and competition have a profound, non-pathological, impact on the activity 402 of these enzymes [35, 36]. Indeed, the recommendation appears to be not to use reference intervals 403 derived from the general population with hard-training (particularly competitive) athletes [36]. 404 23 4.4 Effect of BMI on blood biomarkers 405 Since the current study is a cross-sectional analysis of self-reported running, we could not rule out 406 the possibility that factors other than exercise were the driving force behind the observed 407 biomarker variance among the groups examined. While These factors, such as diet, sleep, and/or 408 medication medications use were not readily ascertained in this free-living cohort at the time of 409 this study, but BMI was readily available to evaluate this biomarker’s potential relative 410 contribution to the observed mean biomarker differences among self-reported runner groups. 411 Multiple studies have attempted to uncouple the effects of exercise and BMI reduction on blood 412 biomarker outcomes, with mixed results [37]. For example, it is relatively well-known that acute 413 bouts of exercise improve glucose metabolism, but long-term effects are less well described [38]. 414 Indeed, whether exercise without significant weight-loss is effective toward preventing metabolic 415 disease (and the associated blood biomarker changes) is inconclusive [39-41]. From the literature, 416 it appears that, for endurance exercise to have significant effect on most blood biomarkers, the 417 volume of exercise needs to be very high, and this typically results in significant reduction in 418 weight. Thus, in practice, it is difficult to demonstrably uncouple the effects of significant exercise 419 and the associated weight-loss, and the results may depend on the blood biomarker in question. 420 Indeed, there is evidence that exercise without weight-loss does improve markers of insulin 421 sensitivity but not chronic inflammation, with the latter apparently requiring a reduction in 422 adiposity in the general population [39-41]. 423 In our study of apparently healthy individuals, we observed a downward trend in BMI with 424 increasing self-reported running volume, and, although this study was not longitudinal and we are 425 thus unable to claim weight-loss, our 2S-MR analysis using BMI as the exposure nonetheless 426 Formatted: Font: 16 pt suggests this biomarker to be responsible for a significant proportion of the modification of some 427 blood biomarkers. 428 4.5.1 Serum markers of systemic inflammation 429 Through our 2S-MR analyses, we show that BMI is causally associated with markers of systemic 430 inflammation, including RDW, folate, and hsCRP [27, 42, 43]. Similar analyses have reported 431 that genetic variants that associate with higher BMI were associated with higher CRP levels, but 432 not the other way around [44]. The prevailing mechanism proposed to explain this relationship 433 appears to be the pathological nature of overweight/obesity-driven adipose tissue that results in 434 secretion of proinflammatory cytokines such as IL-6 and TNFa, which then stimulate an acute 435 hepatic response, resulting in increased hsCRP levels (among other effects) [45]. Thus, our 2S- 436 MR analyses and those of others [44] would indicate that the primary factor behind the lower 437 systemic inflammation in our cohort may be the exercise-associated lower BMI and not running 438 exercise per se, though the lower hsCRP in runners remained significant after adjustment for BMI 439 in our analysis. 440 Indeed, although a major driver behind reduced systemic inflammation may be a reduction in BMI 441 in the general population, additive effects of other lifestyle factors such as exercise cannot be 442 excluded. For example, a large body of cross-sectional investigations does indicate that physically 443 active individuals exhibit CRP levels that are 19-35% lower than less active individuals, even 444 when adjusted for BMI as was the case in the current analysis [41]. Further, it’s been reported that 445 physical activity at a frequency of as little as 1 day per week is associated with lower CRP in 446 individuals who are otherwise sedentary, while more frequent exercise further reduces 447 inflammation [41]. 448 Formatted: Font: 14 pt 25 Significantly, our entire cohort of self-selected apparently healthy individuals did not exhibit 449 clinically high hsCRP, with average BMI also below the overweight thresholds. Because all 450 subjects were voluntarily participating in a personalized wellness platform intended to optimize 451 blood biomarkers that included hsCRP, it is possible that some individuals from across the study 452 groups (both running and sedentary) in our cohort partook in some form of inflammation-reducing 453 dietary and/or lifestyle-based intervention. Thus, that we detected a significant difference in 454 hsCRP between exercising and non-exercising individuals in this self-selected already generally 455 healthy cohort may be suggestive of the potential for additional preventative effect of scheduled 456 physical activity on low-grade systemic inflammation in the generally healthy individual. 457 4.5.2 Blood lipids 458 Controlled studies that tightly track exercise and the associated adiposity reduction have reported 459 that body fat reduction (and not improvement in fitness as measured via VO2max) is a predictor of 460 HDL, LDL, and Tg [46]. Similarly, though BMI is an imperfect measure of adiposity, our 2S-MR 461 analysis suggests that this biomarker is causally associated with improved levels of HDL and Tg, 462 though not LDL. This latter finding replicates a report by Hu et al. who, using the Global Lipids 463 Genetics Consortium GWAS summary statistics, applied a network MR approach that revealed 464 causal associations between BMI and blood lipids, where Tg and HDL, but not LDL, were found 465 to trend toward unhealthy levels with increasing adiposity [47]. On the other hand, others 466 implemented a robust BMI genetic risk score and demonstrated a causal association of adiposity 467 with peripheral artery disease and a multiple linear regression showed a strong association with 468 HDL, TC, and LDL, among other metabolic parameters [48]. In our cohort, given the lack of 469 evidence for a causal BMI-LDL association and the overall healthy levels of BMI, the observed a 470 Formatted: Font: 14 pt significant improvement in LDL may be a result of marked running intensity and/or volume, 471 possibly combined with the aforementioned additional wellness program intervention variables. 472 4.5.3 Hormonal traits 473 As described above, we observed a trend toward increased plasma cortisol and SHBG in runners, 474 particularly PRO level athletes. The effects on cortisol are consistent with a report by Houmanrd 475 et al, who found male distance runners to exhibit higher levels of baseline cortisol [49]. With 476 respect to the effects of BMI on baseline cortisol levels, this observation is generally supported by 477 our 2S-MR analyses with evidence for a consistent effect of increased cortisol with decreasing 478 BMI. However, this association was suggestive at best, indicating that the higher levels of cortisol 479 exhibited in the PRO runners with significant lower adiposity are not likely to be solely explained 480 by their lower BMI. Indeed, the relationship between BMI and cortisol appears to be complex, 481 with some reports suggesting a U-shaped relationship, where the glucocorticoid’s levels associate 482 negatively up to about a BMI of 30 kg/m2, then exhibiting a positive correlation into obesity 483 phenotypes [50]. MR statistical models generally do not account for such non-linearity and would 484 require a more granular demographical treatment, which is not possible using only GWAS 485 summary statistics data in the context of 2S-MR [17, 51]. 486 4.6 Behavioral traits associated with increase physical activity 487 The combination of the body of the literature that describes the effects of endurance training on 488 blood biomarkers, and our own analysis that included markers such as CK and AST, makes us 489 cautiously assured that most of the abovementioned blood biomarker signatures are indeed a result 490 of the interplay between self-reported running and the associated lower BMI. However, as this is 491 Formatted: Font: 14 pt Formatted: Font: 16 pt 27 a self-report-based analysis and we were unable to track other subject behaviors in this free-living 492 cohort, we acknowledge that multiple behaviors that associate with exercise may be influencing 493 our results. 494 Toward this end, our exploratory 2S-MR analyses revealed potentially causal relationships 495 between vigorous exercise and multiple dietary habits that have been shown to improve the 496 biomarkers we examined. Indeed, diets that avoid processed meat and sweets while providing 497 ample amounts of fresh fruits, as well as oily fish have been shown to be anti-inflammatory, and 498 improve glycemic control and dyslipidemia [52, 53]. That physically active individuals are also 499 more likely to make healthier dietary choices adds insight to the potential confounders in ours and 500 others’ observational analyses, and this similar associations have previously been reported [54- 501 56]. For example, using a calculated healthy eating motivation score, Naughton et al. showed that 502 those who partake in more than 2 hours of vigorous physical activity are almost twice as likely to 503 be motivated to eat healthy [56]. Indeed, upon closer examination, the genetic instruments used 504 to approximate vigorous physical activity as the exposure in this work included variants in the 505 genes DPY19L1, CADM2, CTBP2, EXOC4, and FOXO3 [57]. Of these, CADM2 encodes proteins 506 that are involved in neurotransmission in brain regions well known for their involvement in 507 executive function, including motivation, impulse regulation and self-control [58]. Moreover, 508 variants within this locus have been associated with obesity-related traits [59]. Thus, it is likely 509 that the improved metabolic outcomes seen here with our self-reported runners are a composite 510 result of both these individuals exercise and dietary habits. Importantly, the above suggests that a 511 holistic wellness lifestyle approach is in practice the most likely to be most effective toward 512 preventing cardiometabolic disease. Nonetheless, the focus of this work – exercise in the form of 513 running – is known to significantly improve cardiorespiratory fitness (CRF), which has been 514 shown to be an independent predictor of CVD risk and total mortality, outcomes that indeed 515 correlate with dysregulated levels in many of the blood biomarkers examined in this work [7]. 516 4.7 Study limitations 517 This study is based on self-reported running and thus has several limitations. First, it is generally 518 known that subjects tend to overestimate their commitment to exercise when self-reporting, 519 although in our cohort is a self-selected health-oriented population that is possibly less likely to 520 over-report their running volume. First, it is generally known that subjects tend to overestimate 521 their commitment to exercise when self-reporting, although in our cohort is a self-selected health- 522 oriented population that is possibly less likely to over-report their running volume. Furthermore, 523 although the robust increasing trend in baselines for muscle damage biomarkers (CK, AST) that 524 have been shown to be associated with participation in sports and exercise provides indirect 525 evidence that the running groups were indeed participating in increasing volumes of strenuous 526 physical activity, we cannot confirm whether the reported running was performed overground or 527 on a treadmill, which may result in some heterogeneity in physiological responses , nor can we 528 ascertain the actual training volume of PRO-level runners. We also cannot exclude the possibility 529 that the running groups also participated in other forms of exercise (such as strength training) or 530 partook in other wellness program interventions that may have influenced their blood biomarkers 531 and/or BMI via lean muscle accretion. Toward this end, we have attempted to shed light on 532 potential behavioral covariates related to vigorous physical activity via 2S-MR. Finally, while 533 this cohort is generally healthy, we cannot exclude the potential for unmeasured confounders such 534 as medications, nutritional supplements, and unreported health conditions. 535 Formatted: Font: 16 pt 29 2S- MR enables the assessment of causal relationships between modifiable traits and is less prone 536 to the so-called “winner’s curse” that more readily affects one-sample MR analyses [17, 51]. 537 Because 2S-MR uses GWAS summary statistics for both exposure and outcome, it is possible to 538 increase statistical power because of the increased sample sizes. However, horizontal pleiotropy 539 is still a concern that can skew the results. Currently, there is no gold standard MR analysis 540 method, thus we used different techniques (IVW, MR-Egger, and median-based estimations – all 541 of which are based on different assumptions and thus biases) to evaluate the consistency among 542 these estimators and only reported associations as ‘causal’ if there was cross-model consistency. 543 Nonetheless, an exposure such as BMI is a complex trait that is composed of multiple sub- 544 phenotypes (such as years of education) that could be driving the causal associations. 545 546 5 Conclusions 547 Running is one of the most common forms of vigorous exercise practiced globally, thus making it 548 a compelling target of research studies toward understanding its applicability in chronic disease 549 prevention. Our cross-sectional study offers insight into the biomarker signatures of self-reported 550 running in generally healthy individuals that suggest improved insulin sensitivity, blood lipid 551 metabolism, and systemic inflammation. Furthermore, using 2S-MR in independent datasets we 552 provide additional evidence that some biomarkers are readily modified BMI alone, while others 553 appear to respond to the combination of varying exercise and BM 554 I. Our additional bi-directional 2S-MR analyses toward understanding the causal relationships 555 between partaking in vigorous physical activity and other healthy behaviors highlight the inherent 556 challenge in disambiguating exercise intervention effects in cross sectional studies of free-living 557 Formatted: Justified Formatted: Font: 18 pt Formatted: Font: 18 pt populations, where healthy behaviors such as exercising and healthy dietary habits co-occur. 558 Overall, our analysis shows that the differences between those who run and the sedentary in our 559 cohort are likely a combination of the specific physiological effects of exercise, the associated 560 changes in BMI, and lifestyle habits associated with those who exercise, such as food choices and 561 baseline activity level. Looking ahead, the InsideTracker database is continuously augmented 562 with blood chemistry, genotyping, and activity tracker data, facilitating further investigation of the 563 effects of various exercise modalities on phenotypes related to healthspan, including longitudinal 564 analyses and more granular dose-response dynamics. 565 Data Availability Statement 566 The full set of biomarker change correlations has been made available in the Supplementary 567 Information files. Specific components of the raw dataset are available upon reasonable request 568 from the corresponding author. 2S-MR analysis was performed using publicly available datasets 569 via the TwoSampleMR R package. 570 Ethics statement 571 This study was submitted to The Institutional Review Board (IRB), which determined this work 572 was not subject to a review based on category 4 exemption (“secondary research” with de- 573 identified subjects). 574 Author contributions 575 BN performed the 2S-MR analyses, calculated PGSs, and wrote the manuscript; SV performed 576 blood biomarker and blood biomarker X PGS interaction analysis; PF calculated PGSs; MJ, AT, 577 31 and GB provided guidance. All authors have read and agreed to the published version of the 578 manuscript. 579 Funding 580 InsideTracker was the sole funding source. 581 Conflict of interest 582 InsideTracker is a direct-to-consumer blood biomarker and genomics company providing its 583 users with nutritional and exercise recommendations toward improving wellness. B.N., S.V., 584 P.F., and G.B. are employees of InsideTracker. 585 586 587 588 589 590 Acknowledgments 591 InsideTracker is the sole funding source. We thank Michelle Cawley and Renee Deehan for their 592 assistance with background subject matter research and insightful conversations. 593 594 Formatted: Font: 18 pt 595 596 597 598 599 600 601 602 603 604 605 606 607 References 608 1. Lavie CJ, Ozemek C, Carbone S, Katzmarzyk PT, Blair SN. Sedentary Behavior, 609 Exercise, and Cardiovascular Health. Circ Res. 2019;124(5):799-815. doi: 610 10.1161/CIRCRESAHA.118.312669. PubMed PMID: 30817262. 611 2. Carlson SA, Adams EK, Yang Z, Fulton JE. 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Int J Obes (Lond). 812 2018;42(6):1161-76. Epub 20180613. doi: 10.1038/s41366-018-0120-3. PubMed PMID: 813 29899525; PubMed Central PMCID: PMCPMC6195860. 814 58. Arends RM, Pasman JA, Verweij KJH, Derks EM, Gordon SD, Hickie I, et al. 815 Associations between the CADM2 gene, substance use, risky sexual behavior, and self-control: 816 A phenome-wide association study. Addict Biol. 2021;26(6):e13015. Epub 20210218. doi: 817 10.1111/adb.13015. PubMed PMID: 33604983; PubMed Central PMCID: PMCPMC8596397. 818 59. Morris J, Bailey MES, Baldassarre D, Cullen B, de Faire U, Ferguson A, et al. Genetic 819 variation in CADM2 as a link between psychological traits and obesity. Scientific Reports. 820 2019;9(1):7339. doi: 10.1038/s41598-019-43861-9. 821 822 Supporting information 823 S1 Table. Number of people in each category by age group. Significant trend toward 824 younger individuals reporting higher running volume, with more than 75% of the elite 825 group falling between the ages of 18 and 35. 826 S2 Table. Full running volume vs. blood biomarker results 827 S3 Table. 2S-MR results with BMI as the exposure and select biomarkers as outcomes. 828 S4 Table. 2S-MR results with BMI with biomarkers as exposures and BMI as outcome to 829 assess reverse causality 830 Formatted: Font: Times New Roman, 12 pt, Bold Formatted: Font: Bold Formatted: Font: Bold S5 Table. 2S-MR results with vigorous physical activity as exposure and blood biomarkers 831 as outcomes 832 S6 Table. 2S-MR results with vigorous physical activity as exposure and lifestyle habits as 833 outcomes 834 S7 Table. 2S-MR with healthy/unhealthy dietary habits as exposures and vigorous physical 835 activity as outcome to assess reverse causality 836 S1 Fig. Assumptions of Mendelian randomization 837 S2 Fig. Blood biomarker levels with respect to self-reported running volume and 838 professional athletes 839 S3 Fig. 2S-MR scatter plot showing effects of vigorous physical activity as the exposure on 840 blood biomarkers. 841 S4 Fig. 2S-MR scatter plot showing effects of vigorous physical activity as the exposure 842 dietary habits. 843 S5 Fig. 2S-MR scatter plot showing effects of dietary behaviors as the exposures on vigorous 844 physical activity 845 846 847 Table 1 Study Population Demographics 848 849 Formatted: Line spacing: single, Don't suppress line numbers 39 Group N Female, % Age, yrs Body mass index, kg/m2 PRO 82 53.7% 33.68 20.15 HVAM 1103 52.9% 39.48 22.57 MVAM 6747 54.2% 41.49 23.35 LVAM 10877 34.2% 41.16 24.72 SED 4428 48.9% 44.25 27.83 PRO = Professional, HVAM = high volume amateur (>10 hr), MVAM = medium 850 volume amateur (3-10hr), LVAM = low volume amateur (<3 hr), SED = sedentary 851 852 853 854 855 856 857 858 859 860 Formatted Table Table 2 Blood Biomarkers Significantly Different Among Sedentary 861 Individuals and Those Who Partake in Running for Exercise to 862 Various Degrees 863 BIOMARKER ANOVA P-VALUE TREND P-VALUE LOWEST MEAN HIGHEST MEAN ALB <1e-16 <0.001 MVAM PRO ALT <1e-16 <1e-16 SED PRO AST <1e-16 <0.001 SED PRO B12 <0.001 <0.001 SED PRO CHOL <0.001 0.005 PRO SED CK <1e-16 <1e-16 SED PRO COR <0.001 0.675 SED PRO FE <0.001 0.119 SED PRO FER <1e-16 <1e-16 MVAM SED FOL <1e-16 <0.001 SED PRO FT <0.001 0.013 SED PRO GGT <1e-16 <0.001 PRO SED GLU 0.087 0.184 PRO SED HB 0.002 <0.001 MVAM PRO HCT 0.053 0.055 MVAM PRO HDL <1e-16 <0.001 SED PRO HBA1C <0.001 0.010 PRO SED Formatted Table 41 HSCRP <0.001 0.176 PRO SED LDL <0.001 0.006 PRO SED MG <0.001 0.276 PRO SED MPV 0.058 0.089 SED HVAM NA <1e-16 0.622 HVAM SED RBC_MG <0.001 0.773 PRO SED RDW <1e-16 0.002 PRO SED SHBG <1e-16 0.004 SED PRO TG <1e-16 <1e-16 PRO SED WBC <1e-16 <1e-16 PRO SED 864 865 866 Reviewer #1: The majority parts of the articles are technically sound. Moreover, the purpose of the study is very sound since it focused on healthy active population. Among the few drawbacks of the study the way the study subjects categorized into groups based on the duration of the activity (>10hours per week and less that 10hours per week is not appropriate. Moreover, the reliability/validity of the information sources in relation to the biomarker tests and lifestyle habits of the study subjects didn't consider the immediate effects of medical services and medication conditions of the respondents at the time of reporting the volume of exercise and biomarker test results. Medical services and lifestyle habits specially all the habits in addition to exercises/running are very important to reach informative decision in this research. So, the above two points need further explanation or modification. Response to Reviewer #1: We appreciate the reviewer's feedback and are pleased that they find the majority of our study technically sound and recognize the importance of our focus on a healthy, active population. We also appreciate the reviewer pointing out an opportunity to improve the clarity around our experimental design as it pertains subject groupings. Regarding the categorization of study subjects, we want to clarify that we actually categorized them into five groups. These groups include professional endurance runners, high volume amateur runners (>10 hours per week), medium volume amateur runners (3-10 hours per week), low volume amateur runners (<3 hours per week), and the sedentary. We now added a sentence starting on line 125 the explicitly states this categorization (“The cohort was divided into five groups:…”). These groupings were determined based on the respondents' self- reported data. We acknowledge the potential influence of medication use on our analysis, and we now address it starting on line 461 (“These factors, such as diet, sleep, and/or medications were not readily ascertained in this free-living cohort…”) and in the Study Limitations section (line 595). We noted that unmeasured confounders such as medications, nutritional supplements, and unreported health conditions may exist. However, given the nature of our cohort, which primarily consists of self-selected, generally healthy individuals, the impact of significant medication use is expected to be limited. We believe that the observed trends in healthier biomarker levels with increased reported running volume support this assertion. Furthermore, we recognize the importance of lifestyle habits beyond exercise in influencing our results. To address this, we employed statistical genomics, specifically two-sample Mendelian randomization with physical activity as the exposure. This analysis allowed us to explore other potential habits and behaviors contributing to improved biomarker signatures in physically active runners within our cohort. We kindly refer the reviewer to the "Vigorous physical activity associates with healthier behaviors" section in the results for a detailed examination of this aspect. Notably, our entire cohort is composed of health-conscious individuals within the same health advisory platform, with the primary differentiator being self-reported running activity. We also controlled for key variables such as age, sex, and BMI in our ANOVA analyses. Response to Reviewers We hope these explanations clarify our approach and address the reviewer's concerns adequately. Reviewer #2: How your data is reliable by using A cross-sectional study design? & How again the Data is reliable by using self-reported running. I understand that Biomarkers are objective measure, but do you think that Self-report is trustworthy? Thank you Response to Reviewer #2: We appreciate the reviewer's questions and concerns regarding the reliability of our runners data, which is largely derived from self-reported exercise habits. Cross- sectional studies inherently have limitations when it comes to establishing causality, and we acknowledge this challenge. To address potential confounding factors, we conducted additional causal analyses, specifically investigating the effects of BMI on the biomarkers under examination to begin to disentangle the relative contributions of known factors. Furthermore, we performed secondary Mendelian randomization (MR) analyses to identify and account for potential confounders in our findings. We kindly invite the reviewer to explore the "Vigorous physical activity associates with healthier behaviors" section in the results for a comprehensive exploration of these confounding aspects. Regarding the reliability of self-reported running activity, we recognize that self-reports can be subject to biases, and individuals may tend to overestimate their exercise commitment. To address this drawback, we added language addressing these limitations in the “Study limitations” section (Line 579: “First, it is generally known that subjects tend to overestimate their commitment to exercise …”). We do note that our study cohort comprises self-selected individuals who are health-conscious and possibly less prone to over-report their running volume. Additionally, the robust increasing trend in baseline levels of muscle damage biomarkers (CK, AST), which are known to be associated with participation in sports and exercise, provides indirect evidence that the different running groups in our study were indeed engaging in increasing volumes of strenuous physical activity. While self-reporting has its limitations, it remains a valuable method for capturing individuals' exercise behaviors in large-scale observational studies. We took measures to mitigate potential biases, and our findings align with established trends in biomarker responses to physical activity. Reviewer #3: Upon a meticulous review of the article in question, I wish to commend the authors for crafting a piece that not only carries immense scientific weight but is also articulated with great clarity. Such insightful work surely merits publication in your distinguished journal. It's admirable how the authors have navigated through a myriad of physiological and biochemical variables (blood biomarkers) across five distinct participant categories and presented their results with lucidity. The experimental framework is robust, the statistical evaluations are apt, and the narrative progresses seamlessly. The references provided are both relevant and adequate. Nevertheless, I'd like to offer a few observations and suggestions: Response: We appreciate the reviewer's positive feedback and kind words about our manuscript. We eagerly await their observations and suggestions should they see further opportunities to improve our work based on our responses to the current suggestions. Original Title: “Dose response of running on blood biomarkers of wellness in the generally healthy.” Proposed Title: “Dose-response relationship between running and blood biomarkers of wellness in generally healthy individuals.” Response: Thank you – title has been changed. Page 2, Line 8: The mention of “exposure to sunlight” seems somewhat out of context. Could the authors clarify its relevance or indicate if it has been discussed elsewhere in the article? Response: Thank you for the suggestion, we removed this as we agree it was not relevant in this manuscript. Page 17, Lines 17-18: The text reads: "These observations suggest that elite endurance runners………to their magnesium status." Comments: It would be helpful to clarify whether the professional athletes (PRO) participating in this study are specifically elite endurance runners. Kindly integrate this distinction into the main text if accurate. Response: Thank you for the clarifying suggestion. We included the pro/elite endurance runners clarification within the abstract as well as a section heading (lines 7 and 425) Page 19, Lines 1-2: The assertion: “Indeed whether exercise………..is inconclusive,” needs to be substantiated with a relevant citation. Response: Thank you – citations have been added. Table 1: Please include standard deviation (SD) values. I also recommend expressing exercise duration in terms of "h/week" instead of "hr". Response: Thank you for the catch – units changed to “h/week” and SDs added to Table 1. We are grateful for your valuable feedback, which has contributed to improving the clarity and accuracy of our manuscript.	Dose response of running on blood biomarkers of wellness in generally healthy individuals.	11-15-2023	Nogal, Bartek,Vinogradova, Svetlana,Jorge, Milena,Torkamani, Ali,Fabian, Paul,Blander, Gil	eng
PMC4914003	Original Article Brazilian Cardiorespiratory Fitness Classification Based on Maximum Oxygen Consumption Artur Haddad Herdy1,2,3 and Ananda Caixeta1 Instituto de Cardiologia de Santa Catarina1; Clínica Cardiosport2; Universidade do Sul de Santa Catarina3, Florianópolis, SC – Brazil Mailing Address: Artur Haddad Herdy • Instituto de Cardiologia de Santa Catarina. Rua Newton Ramos 91- 601-A, Centro. Postal Code 88015-395, Florianópolis, SC – Brazil E-mail: arherdy@cardiosport.com.br Manuscript received October 13, 2014; revised manuscript April 30, 2015; accepted June 26, 2015. DOI: 10.5935/abc.20160070 Introduction Cardiopulmonary exercise test (CPET) is considered one of the most complete tools to assess functional aerobic capacity, because it provides an integrated assessment of response to exercise, involving the cardiovascular, pulmonary, hematopoietic, neurophysiological and skeletal muscle systems.1 In clinical practice, it has been widely used to assess cardiac and pulmonary diseases, to stratify the risk of patients with heart failure, and to optimize the prescription Abstract Background: Cardiopulmonary exercise test (CPET) is the most complete tool available to assess functional aerobic capacity (FAC). Maximum oxygen consumption (VO2 max), an important biomarker, reflects the real FAC. Objective: To develop a cardiorespiratory fitness (CRF) classification based on VO2 max in a Brazilian sample of healthy and physically active individuals of both sexes. Methods: We selected 2837 CEPT from 2837 individuals aged 15 to 74 years, distributed as follows: G1 (15 to 24); G2 (25 to 34); G3 (35 to 44); G4 (45 to 54); G5 (55 to 64) and G6 (65 to 74). Good CRF was the mean VO2 max obtained for each group, generating the following subclassification: Very Low (VL): VO2 < 50% of the mean; Low (L): 50% - 80%; Fair (F): 80% - 95%; Good (G): 95% -105%; Excellent (E) > 105%. Results: Men VL < 50% L 50-80% F 80-95% G 95-105% E > 105% G1 < 25.30 25.30-40.48 40.49-48.07 48.08-53.13 > 53.13 G2 < 23.70 23.70-37.92 37.93-45.03 45.04-49.77 > 49.77 G3 < 22.70 22.70-36.32 36.33-43.13 43.14-47.67 > 47.67 G4 < 20.25 20.25-32.40 32.41-38.47 38.48-42.52 > 42.52 G5 < 17.54 17.65-28.24 28.25-33.53 33.54-37.06 > 37.06 G6 < 15 15.00-24.00 24.01-28.50 28.51-31.50 > 31.50 Women G1 < 19.45 19.45-31.12 31.13-36.95 36.96-40.84 > 40.85 G2 < 19.05 19.05-30.48 30.49-36.19 36.20-40.00 > 40.01 G3 < 17.45 17.45-27.92 27.93-33.15 33.16-34.08 > 34.09 G4 < 15.55 15.55-24.88 24.89-29.54 29.55-32.65 > 32.66 G5 < 14.30 14.30-22.88 22.89-27.17 27.18-30.03 > 30.04 G6 < 12.55 12.55-20.08 20.09-23.84 23.85-26.35 > 26.36 Conclusions: This chart stratifies VO2 max measured on a treadmill in a robust Brazilian sample and can be used as an alternative for the real functional evaluation of physically and healthy individuals stratified by age and sex. (Arq Bras Cardiol. 2016; 106(5):389-395) Keywords: Respiratory Function Tests; Exercise; Exercise Test; Oxygen Consumption. 389 Original Article Herdy & Caixeta Cardiorespiratory fitness classification Arq Bras Cardiol. 2016; 106(5):389-395 of physical exercise.2-5 In Brazil, CPET is preferably performed on a treadmill, but, in many countries, a cycle ergometer is preferred. Maximum oxygen consumption (VO2 max) reflects the individual’s maximum capacity to absorb, transport and consume oxygen.2 The major determinants of normal VO2 max are: genetic factors, muscle mass amount, age, sex and body weight.1,2 In practice, VO2 max is considered to be equivalent to the highest VO2 value obtained in peak exertion, which is usually used to classify cardiorespiratory fitness (CRF) in a population. In this study, for practical purposes, we named VO2 peak, which was actually measured, VO2 max. Few studies have provided reference CRF charts for populations, and it is yet to be clarified whether the existing classifications can be extrapolated to other populations. Most published studies have been based on small samples, and the profiles of the populations studied have significantly differed.6,7 The CRF classification charts most used in Brazil are as follows: that of the American Heart Association (AHA), published in 1972 (Table 1), and that by Cooper, of 1987. Brazil does not have a solid and widely used CRF classification for CPET; therefore, this study proposes a classification based on Brazilian population data. Such data, resulting from a recently published study, were used as reference for CPET on a treadmill (ramp protocol) for sedentary and physically active men and women.8 Methods This study’s sample comprised 9,250 CPET performed at a large cardiology referral center in southern Brazil.8 Based on a questionnaire completed during the test, individuals with the following characteristics were excluded from the study: any symptom suggesting disease or pathology; amateur or professional athletes; smokers; users of any medication; obese individuals (body mass index - BMI > 30); and tests with the ratio between the amount of carbon dioxide produced and of oxygen used (respiratory exchange ratio - RER) < 1.1. After applying the exclusion criteria, 3,922 CPET were identified, of which, 2,837 CPET, corresponding to healthy and active individuals, were selected. Those individuals, aged between 15 and 74 years, were of both sexes and different ethnicities, and practiced leisure-time aerobic physical activity for at least 30 minutes a day, three times a week.8 All exercise tests were conducted by cardiologists trained in ergometry and CPET by the Brazilian Society of Cardiology Department of Ergometry and Cardiovascular Rehabilitation. The tests were performed on a treadmill (Inbrasport - ATL™, Brazil, 1999, Software ErgoPC Elite Version 3.3.6.2, Micromed Brazil, 1999), using the ramp protocol. A mixing chamber gas analyzer (MetaLyzer II, CortexTM - Leipzig, Germany, 2004) was used to collect the expired gases. For descriptive statistics, central trend measures, such as means, were used, in addition to dispersion measures (standard deviation). Excel software, Microsoft 2008, was used for statistical analyses and charts. Participants, classified according to sex (female and male), were divided into six age groups between 15 and 74 years as follows: G1 (15 to 24 years); G2 (25 to 34 years); G3 (35 to 44 years); G4 (45 to 54 years); G5 (55 to 64 years); and G6 (65 to 74 years). The CRF classification proposed in this study was based on 2,837 CPET performed in apparently healthy individuals. We arbitrarily adopted as “Good” CRF the mean VO2 max value expressed in mL.kg-1.min-1 obtained in each group, and, taking that value as a reference, we classified CRF as follows: “Very Low” (VO2 value < 50% of the mean); “low” (50-80%); “fair” (80-95%); “good” (95-105%); and “excellent” (> 105%). To internally validate our proposed CRF classification, sedentary individuals of both sexes from the study population sample were assessed, according to previous publication.8 This study was approved by the Ethics Committee in Research of the Instituto de Cardiologia de Santa Catarina. Table 1 – American Heart Association Cardiorespiratory Fitness Chart based on maximum oxygen consumption (VO2 max – mL/kg.min) – 1972 Men Very Low Low Fair Good Excellent Age group 20-29 < 25 25-33 34-42 43-52 ≥ 53 30-39 < 23 23-30 31-38 39-48 ≥ 49 40-49 < 20 20-26 27-35 36-44 ≥ 45 50-59 < 18 18-24 25-33 34-42 ≥ 43 60-69 < 16 16-22 23-30 31-40 ≥ 41 Women Very Low Low Fair Good Excellent Age group 20-29 < 24 24-30 31-37 38-48 ≥ 49 30-39 < 20 20-27 28-33 34-44 ≥ 45 40-49 < 17 17-23 24-30 31-41 ≥ 42 50-59 < 15 15-20 21-27 28-37 ≥ 38 60-69 < 13 13-17 18-23 24-34 ≥ 35 390 Original Article Herdy & Caixeta Cardiorespiratory fitness classification Arq Bras Cardiol. 2016; 106(5):389-395 Table 2 – Distribution of the physically active and sedentary male population according to mean VO2 max (mL/kg.min) and age groups Active men Age (years) 15 – 24 25 – 34 35 – 44 45 – 54 55 – 64 65 – 74 n = 1818 343 597 427 285 134 32 Mean VO2 max (mL/kg.min) 50.6 ± 7.3 47.4 ± 7.4 45.4 ± 6.8 40.5 ± 6.5 35.3 ± 6.2 30 ± 6.1 Sedentary men Age (years) 15 – 24 25 – 34 35 – 44 45 – 54 55 – 64 65 – 74 n = 570 85 188 157 100 30 10 Mean VO2 max (mL/kg.min) 47.4 ± 7.9 41.9 ± 7.2 39.9 ± 6.8 35.6 ± 7.7 30 ± 6.3 23.1 ± 6.3 Table 3 – Distribution of the physically active and sedentary female population according to mean VO2 max (mL/kg.min) and age groups Active women Age (years) 15 – 24 25 – 34 35 – 44 45 – 54 55 – 64 65 – 74 n = 1019 177 300 229 206 81 26 Mean VO2 max (mL/kg.min) 38.9 ± 5.7 38.1 ± 6.6 34.9 ± 5.9 31.1 ± 5.4 28.6 ± 6.1 25.1 ± 4.4 Sedentary women Age (years) 15 – 24 25 – 34 35 – 44 45 – 54 55 – 64 65 – 74 n = 515 85 149 108 108 40 25 Mean VO2 max (mL/kg.min) 35.6 ± 5.7 34.0 ± 4.8 30.0 ± 5.4 27.2 ± 5.0 23.9 ± 4.2 21.3 ± 3.4 Results Tables 2 and 3 show the mean VO2 max values of the original population and the number of CPET performed, stratified by sex and age groups, of physically active and sedentary individuals. The VO2 max levels were higher in the active groups as compared to the sedentary ones, and men had greater VO2 max levels than women did. Tables 4 and 5 show our proposed CRF classification, with five different categories, stratified by sex and age group, of apparently healthy individuals. Table 6 shows the classification of the sedentary population (men and women) from the original sample, considering the new CRF chart proposed in this study. It is worth noting that the CRF of sedentary individuals is always classified as either fair or low. As expected, VO2 max levels dropped throughout the age groups for both sexes (Figures 1 and 2). Discussion We elaborated a CRF classification chart based on VO2 max levels measured during CPET (ramp protocol) performed on an ergometric treadmill, to more accurately classify a solid Brazilian sample of healthy and physically active individuals of both sexes. We chose to base our analysis on data of physically active individuals, who would provide CRF in the “good” category, corresponding to mean CRF values. Not using data of sedentary individuals allowed us to validate our proposed CRF classification chart, observing in which category sedentary individuals would fit. According to our CRF classification chart, we confirmed that the CRF of active men is higher than that of active women of the same age group, and, for both sexes, active individuals had a better CRF as compared to sedentary ones. According to Nunes et al.,7 mean VO2 max values of women are lower than those of men, the mean VO2 max values of the former corresponding to only 70% of those of the latter. The present study showed a mean VO2 max of women corresponding to 76% to 83% of the mean VO2 max of men of the same age group. Sedentary individuals not only had a lower VO2 max as compared to physically active ones, but also a twice higher decrease in VO2 max as age advanced.9,10 Regular exercise practice reduces the VO2 max rate of decrease as compared to a sedentary lifestyle,11 and, the greater the VO2, the greater the protection against cardiovascular events. An increase in aerobic capacity is associated with an increase in survival, as reported by Myers et al.,12 who have demonstrated a significant increase in the relative risk of death from any cause as functional capacity decreased, regardless of the risk factors involved. In addition, those authors have reported a 12%-increase in survival for each 1-MET increase in the CRF level.12 Most CRF classification charts used in clinical practice have been elaborated in other countries and have not been validated for the Brazilian population. Extrapolating those classifications to the Brazilian population can lead to relevant discrepancies. Belli et al.13 have shown significant discrepancies when comparing international charts with Brazilian data. 391 Original Article Herdy & Caixeta Cardiorespiratory fitness classification Arq Bras Cardiol. 2016; 106(5):389-395 Table 4 – Classification of cardiorespiratory fitness based on maximum oxygen consumption (VO2 max – mL/kg.min) for the male sex Age group (years) Very Low Low Fair Good Excellent 15 – 24 < 25.30 25.30 – 40.48 40.49 – 48.07 48.08 – 53.13 > 53.13 25 – 34 < 23.70 23.70 – 37.92 37.93 – 45.03 45.04 – 49.77 > 49.77 35 – 44 < 22.70 22.70 – 36.32 36.33 – 43.13 43.14 – 47.67 > 47.67 45 – 54 < 20.25 20.25 – 32.40 32.41 – 38.47 38.48 – 42.52 > 42.52 55 – 64 < 17.54 17.65 – 28.24 28.25 – 33.53 33.54 – 37.06 > 37.06 65 – 74 < 15 15.00 – 24.00 24.01 – 28.50 28.51 – 31.50 > 31.50 Table 5 – Classification of cardiorespiratory fitness based on maximum oxygen consumption (VO2 max – mL/kg.min) for the female sex Age group (years) Very Low Low Fair Good Excellent 15 – 24 < 19.45 19.45 – 31.12 31.13 – 36.95 36.96 – 40.84 > 40.85 25 – 34 < 19.05 19.05 – 30.48 30.49 – 36.19 36.20 – 40.00 > 40.01 35 – 44 < 17.45 17.45 – 27.92 27.93 – 33.15 33.16 – 34.08 > 34.09 45 – 54 < 15.55 15.55 – 24.88 24.89 – 29.54 29.55 – 32.65 > 32.66 55 – 64 < 14.30 14.30 - 22.88 22.89 – 27.17 27.18 – 30.03 > 30.04 65 – 74 < 12.55 12.55 – 20.08 20.09 – 23.84 23.85 – 26.35 > 26.36 Table 6 – Classification of cardiorespiratory fitness based on maximum oxygen consumption (VO2 max – mL/kg.min) of the male and female sedentary population from the original study and according to the new cardiorespiratory fitness chart proposed in this study Men Age group (years) Very Low Low Fair Good Excellent 15 – 24 VO2 = 47.4 25 – 34 VO2 = 41.9 35 – 44 VO2 = 39.9 45 – 54 VO2 = 35.6 55 – 64 VO2 = 30.0 65 – 74 VO2 = 23.2 Women 15 – 24 VO2 = 35.6 25 – 34 VO2 = 34.0 35 – 44 VO2 = 30.0 45 – 54 VO2 = 27.2 55 – 64 VO2 = 23.9 65 – 74 VO2 = 21.2 Nunes et al.7 have classified CRF into percentiles, similarly to Cooper et al., and have observed a difference in VO2 max when comparing the two charts. VO2 max depends on a frequent and constant physical activity and can be enhanced with treinos.14 However, despite the volume or intensity of the workout raise VO2 max by 10 to 30%, there is also an important genetic influence. Research has shown that genetic inheritance is the main responsible for max VO2 each individual and may be responsible for up to 25% to 50% of the variation in the values of VO2 max, ie, alone accounts for almost half of ACR.15 VO2 max can be measured directly by analyzing the gases expired during CPET, or indirectly, by using calculations. Although some prediction equations provide an acceptable 392 Original Article Herdy & Caixeta Cardiorespiratory fitness classification Arq Bras Cardiol. 2016; 106(5):389-395 Figure 1 – Behavior of maximum oxygen consumption (VO2 max – mL/kg.min) throughout the years in men. Figure 2 – Behavior of maximum oxygen consumption (VO2 max – mL/kg.min) throughout the years in women. association with values obtained via direct measurements, the difference varies, depending on the population studied. The error for one certain individual can be extremely high, ranging from 15% to 20% in some studies, and can even reach or exceed 30%, a high margin of error, considering other measurements in the biological area16. According to data obtained in this study, VO2 max drops with age. That drop in women varies less from one age group to the other as compared to that in men. We observed a higher drop in VO2 max among active women from group 3 to group 4, with a mean of 0.38 mL.kg-1.min-1 per year. Among sedentary women, that drop was sharper 393 Original Article Herdy & Caixeta Cardiorespiratory fitness classification Arq Bras Cardiol. 2016; 106(5):389-395 from group 2 to group 3, with a mean of 0.4 mL.kg-1.min-1 per year. Among both active and sedentary men, however, the VO2 max drop was more marked from group 5 to group 6, with a mean of 0.53 mL.kg-1.min-1 per year among active men, and of 0.69 mL.kg-1.min-1 per year among sedentary men. Nunes et al.7 have shown a VO2 max drop of 0.4 mL.kg-1.min-1 per year among men aged 20 to 60 years. Belli et al.,13 using indirect VO2 max measurement, have evidenced a drop of 20% to 25% per decade in mean VO2 max from the age of 50 years onward, that drop being sharper after the age of 60 years. An approximate drop in VO2 max of 0.4 mL.kg-1.min-1 per year is estimated to occur from the age of 25 years onward, and that VO2 max decline is twice greater in sedentary individuals as compared to physically active ones.8,9 We used the new CRF classification chart to classify sedentary individuals undergoing CPET under the same conditions of the physically active ones from the original population. This would allow us to validate our proposed classification, considering how the VO2 max values of those individuals would fit. Differently from the studies estimating VO2 max indirectly, the direct measurement of VO2 max shows that CRF in sedentary individuals is classified, at the most, as fair, regardless of age and sex (Table 6). From the practical viewpoint, sedentary individuals have decreased tolerance to exertion, and, thus, physical exercise prescription to active and sedentary individuals should differ.17 The CRF chart by Cooper18 and that of the AHA19 (Table 1) are the most commonly used tools to classify CRF in CPET programs in Brazil. However, the literature lacks data concerning sampling methods and sample types used to elaborate the AHA chart. Therefore, the comparison of data obtained in this study with the AHA chart is limited. Our classification comprises a wider age range, from 15 to 74 years, as compared to that of the AHA (20 to 69 years). The VO2 max analysis in both charts evidences, in younger age groups, very similar VO2 max values. However, in the other age groups, a greater difference is observed between our data and the VO2 max values of the AHA chart. Most CRF charts published so far have been elaborated with CPET performed on a cycle ergometer. The VO2 max obtained in tests performed on a treadmill, as opposed to those performed on a bicycle, is approximately 5% to 17% higher (mean of 8%).20,21 The difference is attributed to the amount of active muscle mass involved in the test, which is greater for the inclined treadmill. Another important factor relates to the pedaling effect, which causes localized muscle fatigue by using the large muscle groups of the thigh, and that fatigue can occur before maximum exertion is imposed to the circulatory and respiratory systems, generating a lower VO2 max.2 In our study sample, the age range was wide, including adolescents older than 15 years. We believe that from that age on, individuals already have muscle maturation and performance close to those of young adults under the age of 25 years.22,23 The classification chart proposed should be assessed as an instrument to predict risk for morbidity and mortality, according to each individual’s functional profile. Further studies are required. This study has limitations, such as the lack of standardization of ramp protocols. Individuals classified as physically active practiced different types of activities and sports, making the comparison of the results in different populations difficult. Further studies are required, using the same intensities and inclinations in the protocol ramp and with individuals practicing the same type of aerobic exercise, because that would improve the analysis and comparison of the results. Individuals with hypertension, diabetes or dyslipidemia, those on any type of medication, and those with a BMI greater than 30 (obese) were excluded, making the applicability of that classification in those subgroups uncertain. The Brazilian population is known to be diversified, and, in southern Brazil, the European colonization predominates (smaller percentage of Afrodescendant and Native individuals, as compared to other Brazilian regions). New studies should be developed, including different ethnicities and individuals from other Brazilian regions, aiming at comparing with the classification proposed to verify whether the values differ. Conclusion This is one of the few Brazilian studies to propose a CRF chart with data extracted from a robust population sample, and based on VO2 max measured via CPET on a treadmill. These data can be used for functional capacity classification according to sex and age group and considering different risk profiles. Author contributions Conception and design of the research: Herdy AH e Caixeta A. Acquisition of data: Herdy AH. Analysis and interpretation of the data: Herdy AH e Caixeta A. Statistical analysis: Herdy AH e Caixeta A. Obtaining financing: Herdy AH. Writing of the manuscript: Herdy AH e Caixeta A. Critical revision of the manuscript for intellectual content: Herdy AH e Caixeta A. Potential Conflict of Interest No potential conflict of interest relevant to this article was reported. Sources of Funding There were no external funding sources for this study. Study Association This study is not associated with any thesis or dissertation work. 394 Original Article Herdy & Caixeta Cardiorespiratory fitness classification Arq Bras Cardiol. 2016; 106(5):389-395 1. Wasserman K, Whipp BJ. Exercise physiology in health and disease. Am Rev Resp Dis. 1975;112(2):219-49. 2. Wasserman K. Principles of exercise testing and interpretation: including pathophysiology and clinical applications. 5th ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2012. 3. Meneghelo RS, Araújo CG, Stein R, Mastrocolla LE, Albuquerque PF, Serra SM, et al; Sociedade Brasileira de Cardiologia. III Diretrizes da Sociedade Brasileira de Cardiologia sobre teste ergométrico. Arq Bras Cardiol. 2010;95(5 sup.1):1-26. 4. Herdy AH, López-Jimenez F, Terzic CP, Milani M, Stein R, Carvalho T, et al. South American guidelines for cardiovascular disease prevention and rehabilitation. Arq Bras Cardiol. 2014;103(2 Suppl.1):1-31. 5. Arena R, Sietsema KE. Cardiopulmonary exercise testing in the clinical evaluation of patients with heart an lung disease. Circulation. 2011;123(6):668-80. 6. Koch B, Shaper C, Ittermannn T, Spielhagen T, Dorr M, Volzke H, et al. Reference values for cardiopulmonary exercise testing in health volunteers: the SHIP study. Eur Respir J. 2009;33(2):389-97 7. Nunes RA, Pontes GF, Dantas PM, Fernandes Filho J. Tabela referencial de condicionamento cardiorrespiratório. Fitness & Performance Journal. 2005;4(1):27-33. 8. Herdy AH, Uhlendorf D. Reference values for cardiopulmonary exercise testing for sedentary and active men and women. Arq Bras Cardiol. 2011;96(1):54-9. 9. McArdle WD, Katch FI, Katch VL. Fisiologia do exercício: energia, nutrição e desempenho humano. 3ª. ed. Rio de Janeiro: Guanabara Koogan; 1992. 10. Williams RA. O atleta e a doença cardíaca. Diagnóstico, avaliação e conduta. Rio de Janeiro: Guanabara Koogan; 2002. 11. Rogers MA, Hagberg JM, Martin WH, Ehsani AA, Holloszy JO. Decline in VO2 max with aging in master athletes and sedentary men. J Appl Physiol. 1990;68(5):2195-9. 12. Myers J, Prakash M, Froelicher V, Do D, Partington S, Atwood JE. Exercise capacity and mortality among men referred for exercise testing. N Engl J Med. 2002;346(11):793-801. 13. Belli KC, Calegaro C, Richter CM, Klafke JZ, Stein R, Viecili PR. Cardiorespiratory fitness of a Brazilian regional sample distributed in different tables. Arq Bras Cardiol. 2012;99(3):811-7. Erratum in: Arq Bras Cardiol. 2012;99(4):965. 14. Duscha BD, Slentz CA, Johnson JL, Houmard JA, Bensimhon DR, Knetzger KJ, et al. Effects of exercise training amount and intensity on peak oxygen cosumption in middle-age men and women at risk for cardiovascular disease. Chest. 2005;128(4):2787-93. 15. Bouchard C, Dionne FT, Simoneau AJ, Boulay MR. Genetics of aerobic and anaerobic performances. Exerc Sport Sic Rev. 1992;20:27-58. 16. Araújo CG, Herdy AH, Stein R. Maximum oxygen consumption measurement: valuable biological marker in health and in sickness. Arq Bras Cardiol. 2013;100(4):e51-3. 17. Costa EC, Costa FC, Oliveira GW, e col. Capacidade cardiorrespiratória de mulheres jovens com diferentes níveis de atividade física. Revista Brasileira de Prescrição e Fisiologia do Exercício. 2009;3(14):139-45. 18. Cooper K. The new aerobics. New York: M Evans and Company; 1970. 19. Washington A. Ergometria, reabilitação e cardiologia desportiva. Rio de Janeiro: Revinter; 2011. 20. Astrand PO. Experimental studies of physical working capacity in relation to sex and age. Fiep Bulletin. 1952(2):19-21. 21. Neiderberger M, Bruce RA, Kusumi F, Whitkanack S. Disparities in ventilatory and circulatory responses to bicycle and treadmill exercise. Br Heart J. 1974;36(4):377-82. 22. Rodrigues AN, Perez AJ, Carletti L, Bissoli NS, Abreu GR. Maximum oxygen uptake in adolescents as measured by cardiopulmonary exercise testing: a classification proposal. J Pediatr. 2006;82(6):426-30. 23. Ghorayeb N, Costa RV, Castro I, Daher DJ, Oliveira Filho JA, Oliveira MA, et al; Sociedade Brasileira de Cardiologia. [Guidelines on exercise and sports cardiology from the Brazilian Society of Cardiology and the Brazilian Society of Sports Medicine]. Arq Bras Cardiol. 2013;100(1 Suppl. 2):1-41. References 395	Brazilian Cardiorespiratory Fitness Classification Based on Maximum Oxygen Consumption.	[]	Herdy, Artur Haddad,Caixeta, Ananda	eng
PMC7230843	medicina Article Comparison of Subjective Workout Intensities between Aquatic and Land-based Running in Healthy Young Males: A Pilot Study Chang-Hyung Lee 1, Jun Hwan Choi 2 and Soo-Yeon Kim 1,* 1 Rehabilitation Medicine, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, Korea; aarondoctor@gmail.com 2 Department of Rehabilitation Medicine, Jeju National University Hospital, Jeju National University School of Medicine, Jeju 63241, Korea; miraerojh0728@gmail.com * Correspondence: drkimsy@gmail.com Received: 17 February 2020; Accepted: 26 March 2020; Published: 28 March 2020 Abstract: Background and objectives: Aquatic exercises have demonstrated several advantages over land-based exercise, but only a few studies have compared the workout intensities and eﬃciencies in a stage-speciﬁc manner. This study aimed to investigate workout intensity during aquatic and land-based running, based on the rating of perceived exertion (RPE). Materials and Methods: Twenty healthy young male subjects underwent a land-based running test (LRT) and an aquatic running test (ART), in the form of a cardiopulmonary exercise treadmill test and a shallow-water running test. The seven stages of the ART were composed of 3 minutes each of the Bruce protocol performed during the LRT. In the ART, the participants were instructed to run in a swimming pool with matching RPE to that obtained at each stage of the LRT. Results: Heart rate (HR) during both LRT and ART exhibited a linear relationship (r = 0.997 and 0.996, respectively; p < 0.001). During the initial and middle period, HR was higher in the ART than in the LRT. However, in the ﬁnal period, HR was higher in the LRT than in the ART. Conclusions: In aquatic exercises based on the RPE obtained from the LRT, HR exhibited a linear relationship in both the ART and the LRT. The ART appears to increase cardiac loading more eﬃciently in the initial period and does not increase cardiac loading abruptly at a later period. Although there is no precise, objective, controlled parameter to compare the ART and the LRT, the RPE may be used as a convenient measurement for workout intensity in aquatic running. Keywords: aquatic exercise; cardiorespiratory response; shallow-water running; rating of perceived exertion 1. Introduction Aquatic exercises are popular activities in the context of ﬁtness, therapy, and rehabilitation [1]. Aquatic exercises have several advantages over land-based exercises; they lower risks of injury by reducing musculoskeletal loading [2], provide a sense of comfort, safety and psychological beneﬁt due to freedom from concerns of falling down [3], enable aerobic and resistance exercises to be combined by utilizing the resistance of water [4,5], and, ﬁnally, aid in weight reduction by increasing energy expenditure [6]. Accordingly, aquatic exercises have recently been prescribed for cardiorespiratory ﬁtness, injured athletes, and the elderly [3,7,8]. Aquatic running methods include deep-water running (DWR) and shallow-water running (SWR) [9]. During DWR, participants run while ﬁtted with a buoyant belt or vest across a swimming pool. DWR diﬀers from land running in terms of kinematics and lower limb muscle recruitment [10], due to the absence of a ground support phase and the involvement of water resistance. On the other Medicina 2020, 56, 151; doi:10.3390/medicina56040151 www.mdpi.com/journal/medicina Medicina 2020, 56, 151 2 of 9 hand, during SWR, participants run without any buoyant device and are typically immersed between the waist and xiphoid process. SWR diﬀers from DWR in terms of the presence of ground reaction force, which is lower compared to land running and depends on the depth of immersion. Regardless of water depth, aquatic exercise has been suggested as a good therapeutic method, as it decreases axial loading on the musculoskeletal structure [1,11]. One can exercise safely in water without inﬂicting unnecessary load on the musculoskeletal structure due to weight [7]. However, the eﬃciency of aquatic exercise has not been clearly demonstrated in previous studies. A majority of the studies on this topic have demonstrated that aquatic running is characterized by lower workout intensity than land-based treadmill running, as indicated by lower cardiorespiratory responses such as peak oxygen consumption (VO2, mL/kg/min) and heart rate [3,12,13]. This observation can be explained by several factors such as the absence of, or a low, ground reaction force [14], diﬀerent water temperatures [15,16], no objective measurement method such as self-selected exercise intensity [2], and lack of familiarity with aquatic activities [17]. Unlike other exercises, methods for conventional exercise prescription and the measurement of workout intensity for aquatic exercises are rather limited. The most frequently used method of measuring results from aquatic exercises depends on ratings of perceived exertion (RPE) and heart rate (HR, bpm) [1,5,18]. The former is generally rated on the Borg CR10 Scale [19] in clinics and could be deﬁned as a subjective rating of the intensity of a speciﬁc exercise. Physicians usually prescribe water-based exercises using an appropriate intensity scale of 0 to 10, in accordance with the workout capacity of the patients. However, the measurement of the precise workout intensity in a controlled manner could enable an objective comparison between aquatic and land-based exercises. Although there has been a growth in interest in the use of aquatic exercises for therapy or training, only a few studies have examined means of measuring or controlling the workout intensities associated with aquatic exercises. On the basis of their ﬁndings, and on its strong correlation with heart rate during aquatic running, Wilder et al. suggested a cadence that provided subjects with a rhythm for regular limb movements as a measure of the intensity of aquatic running [18]. In a study on the exercise intensities of water-based activities, Raﬀaelli et al. demonstrated that various workout intensities can be obtained by changing movement frequencies and exercise types, such as jumping, running, and kicking [20]. However, previous studies on aquatic exercises with buoyancy devices or anaerobic exercises showed that these conditions could inﬂuence workout intensities. In addition to the simple comparison of total workout intensity, there are no previous studies demonstrating the diﬀerences in terms of intensity increment. Apparently, the above-mentioned results did not provide a precise comparison between aquatic and land-based exercise. To compare the precise workout intensity between the two methods, the standard protocol for exercise method and stage evaluation should be compared using the same standard measurement methods. In addition, although aquatic exercises have demonstrated several advantages over land-based exercises, only a few studies have compared the workout intensities and eﬃciencies in a stage-speciﬁc manner [3,7,13]. In previous studies of aquatic exercises, aquatic treadmills were used to control walking speed and workout intensities (HR, VO2, and RPE) for comparison with land-based running. However, measuring cardiorespiratory responses, such as VO2,, during running in a swimming pool in clinical practice is challenging. The aim of this study was to assess whether subjective perception reﬂects exercise workout intensity through a comparison between aquatic and land-based running based on the RPE. 2. Materials and Methods Twenty healthy young males with no history of musculoskeletal injury and not undergoing any pharmacological therapy were recruited for this study. To rule out age and sex diﬀerences, we recruited only young males regardless of prior experience in aquatic running. To determine inclusion and exclusion criteria, the participants were asked to respond to a questionnaire. The inclusion criteria were as follows: (1) age ranging from 20 to 39 years (mean, 26.0 ± 7.3 years); (2) stature over 160 cm (mean, 172.1 ± 3.7 cm); (3) body weight ranging from 60 to 75 kg (mean, 66.2 ± 7.3 kg); and (4) body Medicina 2020, 56, 151 3 of 9 mass index (BMI) ranging from 20 to 26 kg/m2 (mean, 22.3 ± 2.7 kg/m2). All subjects received written and oral instructions before the test and provided informed consent. This study was approved on 14 Jan 2014 by the institutional review board (IRB 05-2012-018). 2.1. Experimental Setting for Aquatic Running Test (ART) We suggested an ART protocol for subjective exercise intensity that can be conveniently used in clinical practice. The water level was adjusted to be between the xiphoid process and the jugular notch. No buoyancy device was utilized to prevent any possible inﬂuence on the workout intensities. ART was performed in the form of SWR to gain optimal exercise eﬃciencies with relatively moderate ground reaction force and much lower physical burden compared to the land-based running test (LRT). The workout intensities of LRT were assessed by measuring cardiorespiratory responses such as HR, VO2, and RPE, which provide objective and subjective measures of exercise intensity. Additionally, the workout intensities of ART were assessed by measuring HR and RPE. 2.2. Aquatic Running and Land-Based Running Procedures All 20 subjects participated in two running tests. One was a land-based running test in the form of a cardiopulmonary exercise treadmill test (CPET), and the other was an aquatic running test (Figure 1). Medicina 2020, 56, x FOR PEER REVIEW 3 of 9 stature over 160 cm (mean, 172.1 ± 3.7 cm); (3) body weight ranging from 60 to 75 kg (mean, 66.2 ± 7.3 kg); and (4) body mass index (BMI) ranging from 20 to 26 kg/m2 (mean, 22.3 ± 2.7 kg/m2). All subjects received written and oral instructions before the test and provided informed consent. This study was approved on 14 Jan 2014 by the institutional review board (IRB 05-2012-018). 2.1. Experimental Setting for Aquatic Running Test (ART) We suggested an ART protocol for subjective exercise intensity that can be conveniently used in clinical practice. The water level was adjusted to be between the xiphoid process and the jugular notch. No buoyancy device was utilized to prevent any possible influence on the workout intensities. ART was performed in the form of SWR to gain optimal exercise efficiencies with relatively moderate ground reaction force and much lower physical burden compared to the land-based running test (LRT). The workout intensities of LRT were assessed by measuring cardiorespiratory responses such as HR, VO2, and RPE, which provide objective and subjective measures of exercise intensity. Additionally, the workout intensities of ART were assessed by measuring HR and RPE. 2.2. Aquatic Running and Land-Based Running Procedures All 20 subjects participated in two running tests. One was a land-based running test in the form of a cardiopulmonary exercise treadmill test (CPET), and the other was an aquatic running test (Figure 1). Figure 1. The land-based running test (LRT) (A) and the aquatic running test (ART) (B). LRT was performed on an incline-adjustable treadmill with continuous vital sign and electrocardiographic monitoring. ART was performed in a swimming pool with a water level between the xiphoid-process and the jugular notch, with monitoring of heart rate (HR) using a water-resistant chest-strap transmitter. The LRT was performed before the ART and participants had a rest interval between the two testing sessions of at least 72 hours to maximize performance in each protocol. Each running test included a warm-up exercise for 5 minutes. The peak VO2, HR, and RPE during LRT were measured during the test. LRT was performed on a calibrated, incline-adjustable treadmill (STEX 8100T, TaeHa, Korea) with real-time recording 12-channel electrocardiographic monitoring (Philips Health Care 3000 Minuteman Rd., Andover, MA, USA) and vital sign monitoring based on the Bruce protocol. The Bruce protocol is a standard test in cardiology and comprises multiple exercise stages that each last 3 minutes. At each stage, the gradient and speed of the treadmill are elevated to increase work output, called METs (metabolic equivalent of task). Figure 1. The land-based running test (LRT) (A) and the aquatic running test (ART) (B). LRT was performed on an incline-adjustable treadmill with continuous vital sign and electrocardiographic monitoring. ART was performed in a swimming pool with a water level between the xiphoid-process and the jugular notch, with monitoring of heart rate (HR) using a water-resistant chest-strap transmitter. The LRT was performed before the ART and participants had a rest interval between the two testing sessions of at least 72 hours to maximize performance in each protocol. Each running test included a warm-up exercise for 5 minutes. The peak VO2, HR, and RPE during LRT were measured during the test. LRT was performed on a calibrated, incline-adjustable treadmill (STEX 8100T, TaeHa, Korea) with real-time recording 12-channel electrocardiographic monitoring (Philips Health Care 3000 Minuteman Rd., Andover, MA, USA) and vital sign monitoring based on the Bruce protocol. The Bruce protocol is a standard test in cardiology and comprises multiple exercise stages that each last 3 minutes. At each stage, the gradient and speed of the treadmill are elevated to increase work output, called METs (metabolic equivalent of task). Stage 1 of the Bruce protocol is performed at 1.7 miles per hour and at a 10% gradient. VO2 was determined by analyzing expired air through a breath-by-breath method using a portable telemetric system (Ultima Series™ metabolic stress-testing system, MGC Diagnostics, Saint Paul, Minnesota). For LRT, all the participants were Medicina 2020, 56, 151 4 of 9 instructed to increase their workout intensity on the treadmill test until the achievement of submaximal threshold (80% of maximal heart rate or an RPE of 8~9). The exercise test was terminated on the participant’s request or according to the guidelines of the American College of Sports Medicine [20]. RPE was assessed using a numerical rating scale, the Borg CR10 Scale (0–10). ART was performed in a swimming pool, while heart rate was monitored using a water-resistant chest-strap transmitter (Polar T34, Polar Electro, Inc, Kempele, Oulu, Finland), with the water level reaching between the xiphoid process and the jugular notch (water temperature, 31 ◦C). During ART, all participants were instructed to run by moving their arm back and forth without swimming while their legs continued to run in the pool for 3 minutes in each stage, similar to the Bruce protocol performed in the LRT. To gradually and constantly increase the workout intensity in water, we deﬁned the workout intensity as HR of ART as the matching RPE that was obtained at each stage of LRT. After acquiring all seven stages, the stages were classiﬁed as follows: initial (stages 1 and 2), middle (stages 3 to 5), and ﬁnal (stages 6 and 7). During ART, heart rate was measured at rest and at each stage. 2.3. Statistical Analysis Kolmogorov–Smirnov veriﬁcation was used to prove the normality of the data, which were found to be not normally distributed. A Wilcoxon signed-rank test was used to assess the diﬀerence in heart rate between the ART and the LRT at each stage. Spearman’s correlation was used to analyze the stage-associated diﬀerences. Statistical analyses were performed using SPSS version 21.0 for Windows (SPSS Inc., Chicago, IL, USA). Statistical signiﬁcance was accepted for p values < 0.05 and <0.001 respectively. 3. Results Twenty male subjects completed LRT and ART on separate days. At the end of each exercise, the peak VO2, HR, and RPE in LRT were 43.8 ± 3.9 mL/kg/min, 179.5 ± 9.7 bpm, and 8.70 ± 0.82, respectively. The peak HR for ART at stage 7 were 172.2 ± 4.7 bpm. The ﬁnal workload for the LRT (which is equivalent to stage 7 of ART) was at a speed of 6.0 mph and at an inclination of 22% (Table 1). Table 1. Heart rate (HR), rating of perceived exertion (RPE), and oxygen consumption (VO2) measured at rest state and during the seven stages of the land-based running test (LRT). Stage HR (bpm) RPE VO2 (mL/kg/min) Rest 74.9 ± 9.6 0 3.3 ± 0.8 Stage 1 91.8 ± 10.4 0.3 ± 0.2 8.9 ± 1.0 Stage 2 103.8 ± 8.5 1.2 ± 0.6 13.2 ± 1.3 Stage 3 117.3 ± 9.9 2.5 ± 0.7 18.0 ± 1.4 Stage 4 128.5 ± 9.2 3.7 ± 0.6 24.1 ± 2.4 Stage 5 144.2 ± 10.1 5.1 ± 0.8 30.3 ± 3.9 Stage 6 161.4 ± 11.2 6.8 ± 1.1 36.3 ± 4.3 Stage 7 179.5 ± 9.7 8.7 ± 0.8 43.8 ± 3.9 RPE scores were obtained using the Borg CR10 Scale (0 to 10); Values are presented as mean ± standard deviation (SD); bpm, beats per minute. HR during both LRT and ART exhibited a linear relationship (r = 0.997 and 0.996, respectively; p < 0.001) (Figure 2). During the initial and middle period, HR was higher in ART than LRT; however, in the ﬁnal period, HR was higher in LRT than ART. Statistically signiﬁcant diﬀerences were observed between LRT and ART for HR during stages 2, 3 and 7 (p < 0.05) (Table 2). Medicina 2020, 56, 151 5 of 9 Medicina 2020, 56, x FOR PEER REVIEW 5 of 9 in the final period, HR was higher in LRT than ART. Statistically significant differences were observed between LRT and ART for HR during stages 2, 3 and 7 (p < 0.05) (Table 2). Figure 2. Increase in heart rate (HR) observed during the seven stages of the land-based running test (LRT) (A) and the aquatic running test (ART) (B) from the rest state. Linear relationship between HR and the seven stages in LRT (A) and ART (B) (r = 0.997 and 0.996, respectively, p < 0.001, Spearman’s correlation test); bpm, beats per minute. Table 2. Comparison of heart rate (HR) between land-based running test (LRT) and aqua-based running test (ART) according to stages. Stage HR (bpm) Z-score p Value LRT ART (Mean ± SD) (Median) (Mean ± SD) (Median) Rest 74.9 ± 9.6 77.5 76.1 ±6.6 78.0 −1.029 0.304 Stage 1 91.8 ± 10.4 91.5 93.9 ± 7.7 93.5 −1.188 0.235 Stage 2 103.8 ± 8.5 106.0 112.7 ± 11.1 113.0 −2.298 0.022 * Stage 3 117.3 ± 9.9 116.5 123.4 ± 7.5 124.0 −1.989 0.047 * Stage 4 128.5 ± 9.2 129.0 136.4 ± 8.4 135.5 −1.888 0.059 Stage 5 144.2 ± 10.1 144.0 149.4 ± 7.6 145.5 −1.071 0.284 Stage 6 161.4 ± 11.2 161.0 160.4 ± 6.3 160.0 −0.358 0.721 Stage 7 179.5 ± 9.7 179.5 172.2 ± 4.7 172.5 −2.052 0.040 * * p < 0.05, Wilcoxon signed-rank test; bpm, beats per minute; Z-score using the normal approximation to the binomial distribution. 4. Discussion To prescribe exercise intensity or predict the resulting outcome, precise and controlled exercise protocols should be prepared. Although the beneficial effects of aquatic exercises have been widely accepted, the workout intensity should be considered in exercise prescription for therapy or rehabilitation to obtain optimal positive effects while avoiding possible injury [7,21]. Measurements of total calorie loss or VO2max are useful for acquiring an objective measure of workout intensity. However, in the absence of appropriate respiratory aquatic analysis equipment, obtaining such data is challenging, especially in a swimming-pool-based exercise. Theoretically, subjective parameters used to measure workout intensity have an advantage over objective parameters in reflecting the actual 'perceived intensity' of individuals. Although the same protocol Figure 2. Increase in heart rate (HR) observed during the seven stages of the land-based running test (LRT) (A) and the aquatic running test (ART) (B) from the rest state. Linear relationship between HR and the seven stages in LRT (A) and ART (B) (r = 0.997 and 0.996, respectively, p < 0.001, Spearman’s correlation test); bpm, beats per minute. Table 2. Comparison of heart rate (HR) between land-based running test (LRT) and aqua-based running test (ART) according to stages. Stage HR (bpm) Z-score p Value LRT ART (Mean ± SD) (Median) (Mean ± SD) (Median) Rest 74.9 ± 9.6 77.5 76.1 ±6.6 78.0 −1.029 0.304 Stage 1 91.8 ± 10.4 91.5 93.9 ± 7.7 93.5 −1.188 0.235 Stage 2 103.8 ± 8.5 106.0 112.7 ± 11.1 113.0 −2.298 0.022 * Stage 3 117.3 ± 9.9 116.5 123.4 ± 7.5 124.0 −1.989 0.047 * Stage 4 128.5 ± 9.2 129.0 136.4 ± 8.4 135.5 −1.888 0.059 Stage 5 144.2 ± 10.1 144.0 149.4 ± 7.6 145.5 −1.071 0.284 Stage 6 161.4 ± 11.2 161.0 160.4 ± 6.3 160.0 −0.358 0.721 Stage 7 179.5 ± 9.7 179.5 172.2 ± 4.7 172.5 −2.052 0.040 * * p < 0.05, Wilcoxon signed-rank test; bpm, beats per minute; Z-score using the normal approximation to the binomial distribution. 4. Discussion To prescribe exercise intensity or predict the resulting outcome, precise and controlled exercise protocols should be prepared. Although the beneﬁcial eﬀects of aquatic exercises have been widely accepted, the workout intensity should be considered in exercise prescription for therapy or rehabilitation to obtain optimal positive eﬀects while avoiding possible injury [7,21]. Measurements of total calorie loss or VO2max are useful for acquiring an objective measure of workout intensity. However, in the absence of appropriate respiratory aquatic analysis equipment, obtaining such data is challenging, especially in a swimming-pool-based exercise. Theoretically, subjective parameters used to measure workout intensity have an advantage over objective parameters in reﬂecting the actual ’perceived intensity’ of individuals. Although the same protocol for workout intensity is performed in water, the output (HR in this study) could be inconsistent because exercise output could be strongly inﬂuenced by individual physical characteristics (e.g., running performance in water) [10] and environmental factors (e.g., water temperature, relative depth of water) [16,22]. In this study, we measured the subjective RPE value, and the gradual increase in its intensity in accordance with each stage of the LRT. Due to the viscosity and consistency of water, cadence should not be used Medicina 2020, 56, 151 6 of 9 as the standard measurement method as speed increases. Diﬃculty perceptions in the ART increased with increasing intensity, which corresponded to the RPE in the LRT. HR in both the LRT and the ART with matching RPE exhibited a linear relationship (r = 0.997 and 0.996, respectively; p < 0.001), and there were no statistical diﬀerences, except in stages 2, 3, and 7. Although there is no precise objective parameter compared between the ART and the LRT, the RPE may potentially provide a fundamental, convenient, and meaningful parameter in aqua that reﬂects an “actual perceived workout intensity” measure in individuals. In the present study, HR as exercise workout of ART and LRT were assessed and compared (Table 2). The plots shown in Figure 2 show an increase in HR and during the three deﬁned test periods for both ART and LRT. Initially, ART exhibited higher HR values than LRT during the initial and middle periods. Conversely, HR was lower in ART than LRT during the ﬁnal period (stage 5 to 6). Theoretically, the ground reaction force due to counter dragging force can be controlled in our method, thus enabling exercise intensity to be increased gradually during the course of the test. The maximum kinetic eﬀort was observed during the early stage of the ART, presumably due to the higher metabolic energy required to overcome the drag force on lower limbs to initiate aquatic running [14], and at this stage HRs were higher for the ART than in the LRT. Secondly, the mechanical counterforce corresponding to the water depth should be considered. If a person moves more vigorously in water, more counterforce is imposed, thus leading to an increase in HR and RPE. However, the kinetic eﬀort could not increase exponentially, due to the enhanced counterforce at a later period. Despite the greater eﬀort at a later period, the actual increase in cardiac loading turned out to be small compared to LRT. It was hypothesized that precise measurement of the workout intensity and eﬀectiveness of aquatic exercise in a stage-speciﬁc manner could allow objective comparisons, thus enabling prescription of aquatic exercises in a more objective and safe manner in the clinical setting. In the present study, comparison of the aquatic workout intensity with LRT using RPE as a parameter provided subjective data which could be useful in clinical practice. A higher HR in ART compared to LRT was observed until the middle exercise period, therefore suggesting a higher magnitude of enhancement of workout intensities during the initial and middle period. Aquatic running, a higher-intensity exercise workout that is less physically burdening than land-based running, is likely to be especially advantageous for elderly, obese, and severely ill patients. Consequently, aquatic exercise can be recommended over land exercise for patients with a deconditioned or weakened physical status (for example, patients suﬀering from osteoarthritis or rheumatoid arthritis) [23]. Furthermore, the lower HR during the ﬁnal period of ART relative to LRT provides a safer exercise window. Considering these aspects, ART could be safely recommended in clinical settings, as it is also not as demanding as LRT with regard to its physical burden, especially in patients with a low cardiac ejection fraction below 55% (e.g. heart failure) [24]. There are several possible explanations for the observed workout diﬀerences. The physiological adaptation to water should be considered. In a cross-sectional study, it was demonstrated that subjects who participated in a session of aquatic exercise achieved acute adaptation. For example, as pool water temperature decreased, HR increased rapidly in response to sympathetic nervous system stimulation [16,22]. The increase in peripheral resistance with vasoconstriction is due to the blood being redirected from the periphery to maintain core temperature [25]. It has been shown that immersion at neutral temperature (32 ◦C) lowers HR by 15%, but immersion in cold water (14 ◦C) increases HR by 5% [16]. In the present study, the swimming pool temperature was maintained at 31 ◦C; therefore, we believe that the eﬀect of water temperature on HR was minimal during the early stage of exercise. On the other hand, RPE has been reported to decrease with the depth of immersion [26], and, in another study, high RPE appeared to be positively related to ground reaction force, the amount of drag force on lower limbs [14], and changes in the neuromuscular patterns of active muscles [27]. As ground reaction forces are always lower during shallow water running (SWR) than during land running, musculoskeletal burden and the risk of injury could be reduced during SWR. However, previous studies have demonstrated a decrease in HR with an increase in the water depth [17,26], and that the decline in HR is associated with the inﬂuence of hydrostatic pressure Medicina 2020, 56, 151 7 of 9 and buoyancy on the stroke volume of heart and consequent alteration in blood distribution in the body [18]. In the present study, the water level was adjusted to a level between the xiphoid process and the jugular notch. This depth was more than the speciﬁc level used for SWR but shallower than that used for DWR. Consequently, it was hypothesized that the lower HR observed during the later period of ART compared to LRT was due to the water depth employed in this study. Generally, the water depth in swimming pools is usually at adult chest height, and a similar level was employed in the present study. Apparently, it is hypothesized that our ﬁndings are meaningful, based on the observation that the workout intensities of aquatic exercises reﬂect real situations, and are applicable to aquatic exercises in clinical practice. There are several results of clinical importance in our study. As there was previously no exact guideline on intensity in aquatic therapy, these ﬁndings might suggest a possible guideline for aquatic exercise. Initially, ART can increase the heart rate eﬃciently, which appears to be a good choice for patients in a deconditioned state or unable to walk. Meanwhile HR during the ART rises slower than during the LRT at later stages. This relatively safe ‘window period’ could be safely suggested in patients with cardiac problems. In general, heart rate increases linearly in relation to RPE. However, the heart rate did not increase linearly in later stages of ART. This might reﬂect the increasing viscosity and resistance with the increase in running eﬀort, increasing demand for endurance and muscle fatigue in the water, and hence increasing thermal loss in ART. There are several limitations of this study. First, all study subjects were young male adults; thus, our results are applicable primarily to this population and not to female subjects, the elderly, or patients with cardiac problems. Second, we only considered the partial inﬂuence of cardiorespiratory response on the workout intensity of the ART by measuring HR with controlled RPE, but not VO2, because of the absence of an appropriate respiratory gas analysis system at the swimming pool. In future studies, it will be necessary to conﬁrm whether RPE, a subjective parameter, is a meaningful indicator that can reﬂect and objectively measure exercise intensity in aquatic exercises in various conditions. 5. Conclusions This study demonstrates the workout intensities of aquatic and land-based running based on RPE. The workout diﬃculty perceptions in aquatic running increased with increasing intensity, which corresponded to RPE in the land-based running test. Although there is no precise comparison of controlled cardiorespiratory measurement to compare aquatic and land-based running, RPE may be used as a meaningful measurement for workout intensity in aquatic running. Author Contributions: Writing original draft, C.-H.L.; writing-review & editing, S.-Y.K.; methodology, J.H.C. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI17C2397). Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Barker, A.L.; Talevski, J.; Morello, R.T.; Brand, C.A.; Rahmann, A.E.; Urquhart, D.M. Eﬀectiveness of aquatic exercise for musculoskeletal conditions: A meta-analysis. Arch. Phys. Med. Rehabil. 2014, 95, 1776–1786. [CrossRef] 2. Colado, J.C.; Triplett, N.T. Monitoring the intensity of aquatic resistance exercises with devices that increase the drag force: An update. Strength Cond. J. 2009, 31, 94–100. [CrossRef] 3. 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The physiology of deep-water running. J. Sports Sci. 2003, 21, 959–972. [CrossRef] © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).	Comparison of Subjective Workout Intensities between Aquatic and Land-based Running in Healthy Young Males: A Pilot Study.	03-28-2020	Lee, Chang-Hyung,Choi, Jun Hwan,Kim, Soo-Yeon	eng
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PMC7892879	1 Vol.:(0123456789) Scientific Reports \| (2021) 11:4091 \| https://doi.org/10.1038/s41598-021-83538-w www.nature.com/scientificreports Music‑based biofeedback to reduce tibial shock in over‑ground running: a proof‑of‑concept study Pieter Van den Berghe 1, Valerio Lorenzoni2, Rud Derie 1, Joren Six 2, Joeri Gerlo 1, Marc Leman 2 & Dirk De Clercq 1 Methods to reduce impact in distance runners have been proposed based on real‑time auditory feedback of tibial acceleration. These methods were developed using treadmill running. In this study, we extend these methods to a more natural environment with a proof‑of‑concept. We selected ten runners with high tibial shock. They used a music‑based biofeedback system with headphones in a running session on an athletic track. The feedback consisted of music superimposed with noise coupled to tibial shock. The music was automatically synchronized to the running cadence. The level of noise could be reduced by reducing the momentary level of tibial shock, thereby providing a more pleasant listening experience. The running speed was controlled between the condition without biofeedback and the condition of biofeedback. The results show that tibial shock decreased by 27% or 2.96 g without guided instructions on gait modification in the biofeedback condition. The reduction in tibial shock did not result in a clear increase in the running cadence. The results indicate that a wearable biofeedback system aids in shock reduction during over‑ground running. This paves the way to evaluate and retrain runners in over‑ground running programs that target running with less impact through instantaneous auditory feedback on tibial shock. Real‑time feedback on tibial acceleration during treadmill running. Gait retraining intends to alter a motor pattern that has become habituated over many years1. Gait retraining has been put forward as a method to reduce or treat injuries in distance runners2,3. Various studies have focused on the reduction in tibial shock (i.e., the axial peak tibial acceleration)1,4–7, presumably because the magnitude of the tibial shock has been associated with tibial stress fracture susceptibility. Evidence for this association is provided in female distance runners8. Other case–control studies failed to observe a clear difference in groups of runners with and without a history of tibial stress injury9,10. Nevertheless, gait retraining on a treadmill with the intention of lowering the impact loading has led to fewer running-related injuries (62% lower injury risk) in novice runners2. These run- ners could reduce the maximum instantaneous vertical loading rate of the ground reaction force2, an impact measure that has been correlated with tibial shock during level over-ground running11,12. In several studies4–7, a reduction in tibial shock has been stimulated by providing biofeedback while participants were running on a treadmill (Supplementary information file, supplement 1). For instance, Crowell and colleagues provided bio- feedback that comprised a visual stream of the axial component of tibial acceleration in real-time5. The biofeed- back was shown to the runners using a screen in front of a treadmill during a single session of gait retraining in the laboratory. The concept of real-time biofeedback for lower impact running was further developed by Wood and Kipp, who provided auditory biofeedback in the form of pitched “beeps” scaled relative to a runner’s base- line of peak tibial acceleration13. This simple auditory feedback was found to be equally effective for tibial shock reduction compared to the visual biofeedback during a short run on a treadmill in the laboratory14. Another lab-based study used a combination of visual (traffic lights) and auditory (pitched beeps) feedback modalities in runners screened for high tibial shock7. The authors reported a reduction in tibial shock of 3.28 g or 31% after completing a multi-sessions program of gait retraining on a treadmill7. All these lab-studies demonstrate the effectiveness of biofeedback at reducing tibial shock. Importantly, they pave the way to study lower impact run- ning through such biofeedback in real-world running environments. OPEN 1Biomechanics and Motor Control of Human Movement, Department of Movement and Sports Sciences, Ghent University, 9000 Ghent, Belgium. 2Department of Arts, Music and Theatre Sciences, IPEM, Ghent University, 9000 Ghent, Belgium. email: pieter.vandenberghe@ugent.be 2 Vol:.(1234567890) Scientific Reports \| (2021) 11:4091 \| https://doi.org/10.1038/s41598-021-83538-w www.nature.com/scientificreports/ From pitched beeps to music with superimposed noise. Efforts have been made to apply music- based biofeedback15,16. Music can act as a strong motivator for walking and running17–19, so music may be imple- mented to achieve a pleasant or motivational stimulus. Music-based biofeedback has been explored in people with brain damage after ischemic stroke or traumatic brain injury, to stimulate weight-shift training in patients with impairment in balance function20. Music-based biofeedback has also been proven effective to steer posture parameters while performing a weightlifting task16. There are indications that music can be used as stimulus in a context of reinforcement learning16. The application of music to reduce tibial shock fits well in the context of distance running as about half of the recreational runners regularly train with music18. In short, the development and testing of a wearable music-based biofeedback system will advance the ecological validity of studying run- ners who engage in gait retraining. Based on the above considerations, we have developed a wearable music-based biofeedback system. It consists of a measurement module and a feedback module. The measurement module detects tibial shock and cadence in real-time using accelerometers12. The feedback module generates shock-dependent pink noise which is superim- posed onto synchronized music to stimulate lower impact running12,21. The feedback subsystem has the capabil- ity to synchronize the tempo of the music with the running cadence in real-time, which has been experienced as motivating22. The use of synchronized audio in an exercise program consisting of locomotor activities has improved adherence to physical activity23, emphasizing the idea that interaction with music is empowering24,25. The beats per minute of the music continuously adapt to the steps per minute of the runner21, so the music- based biofeedback system allows for cadence-induced changes if desired by the user. A high momentary level of tibial shock results in a high level of noise. If the runner adopts a self-selected gait adaptation which reduces tibial shock, then the noise level is reduced and the acoustical quality of the music improves. In terms of reinforce- ment learning this creates a punishment/reward dynamic. The whole wearable music-based biofeedback system opens the possibility to test whether runners can reduce the cyclic shock experienced in the lower extremities with the aid of a runner-friendly form of auditory biofeedback. A self‑discovery approach for lower impact running. In previous studies, explicit instructions about running technique have been given to participants with the intention of reducing impact26,27. In these studies27–33 groups of shod runners were asked to substantially increase their running cadence (i.e., steps per minute) or to change to an anterior foot strike pattern6–28.. Besides explicitly imposing a particular change in running tech- nique, a more personalized approach is to let the runner discover his or her own motor strategy of lower impact running with the use of biofeedback as in7,13. In one such preliminary report, Morgan and colleagues observed a systematic increase in running cadence when groups of about ten runners received visual or auditory real-time biofeedback with the intention of reducing the magnitude of an unspecified component of peak tibial accelera- tion on a treadmill14. Aim and hypotheses. We sought (1) to determine the potential of music-based feedback to induce lower impact running in a group of runners who had high tibial shock and (2) to investigate if an eventual shock reduc- tion would be achieved by a clear increase in the running cadence. Runners with high tibial shock experienced shock magnitudes in the highest one-third of the population. A systematic review indicated that feedback on tibial shock has been effective in reducing tibial shock while running on a treadmill34. Consequently, our first hypothesis was that runners with high tibial shock would be able to decrease their level of tibial shock while running over-ground with the use of continuous, real-time, auditory biofeedback on tibial shock at a stable running pace. In a first step toward understanding how runners adapt to real-time auditory biofeedback outside the traditional laboratory, the running cadence was included in the analysis. Therefore, our second hypothesis was that the group of high impact runners would spontaneously increase the running cadence in an attempt to reduce tibial shock at a stable running pace. Methods Participants. For screening purposes, a total of 88 runners were recruited from the Flemish running popu- lation. Analogous to Clansey and Crowell and colleagues4,7, the current study targeted runners experiencing high tibial shock. In this case, the runners with a one-legged averaged value of tibial shock in the highest one- third of the 88 screened runners were contacted to take part in the intervention. The first ten runners who volunteered were selected. An a priori power analysis (GPower; α = 0.05, an effect size of 1.5, paired testing) estimated a required sample size of at least seven (n = 7). The effect size in the power analysis was based on results of a treadmill-based gait retraining program for runners experiencing high tibial shock4. Based on the email response time, the first ten participants who agreed to participate were selected and engaged in the intervention. The sample size is in line with previous studies that offered a single session of real-time feedback on tibial shock to a group of runners6,13,35 (Supplementary information file, supplement 1). The ten selected participants were at least 6 months injury-free and ran in non-minimalist footwear36. They ran at least 15 km/week distributed over at least two sessions at the time of the study. Training habits were questioned (Table 1). All participants signed an informed consent approved by the ethical committee of the Ghent University hospital (Bimetra Number 2015/0864). The methods were carried out following their guidelines and regulations. Informed consent was obtained from the participants to publish the information/image(s) in an online open-access publication. This consent was also obtained from test leaders who might be recognizable in some images. Research design. The quasi-experimental study was unblinded and used a pre-post design without con- trols (Fig. 1). Two over-ground running sessions were completed in the runner’s regular sportswear at a speed of 3.2 ± 0.2 m∙s−1, a common speed range to evaluate endurance running2,6,12,27,38, while instrumented with a 3 Vol.:(0123456789) Scientific Reports \| (2021) 11:4091 \| https://doi.org/10.1038/s41598-021-83538-w www.nature.com/scientificreports/ wearable system that was developed for real-time identification of tibial shock and auditory biofeedback on tibial shock. First, we identified the runners with high tibial shock following a screening session (October 2017—Feb- ruary 2018) in a sports laboratory. Then, a supervised intervention session with auditory biofeedback on tibial shock took place at an indoor track-and-field site (January—March 2018, supplementary video 1). This study employed a within-subjects design to examine changes in tibial shock. The days of individual testing were sup- plemented (digital supplementary file, datasheet). The time required to complete the two sessions was about 2 h (2 · 1 h). The days between the sessions ranged from 59 to 138 (89 ± 28, mean ± SD). Screening session. Set‑up. A test leader instrumented the standing participant with a stand-alone back- pack system. A 7″ tablet (Windows 10) was fixated to a stripped backpack and connected via USB port to a mi- crocontroller (Teensy 3.2, PJRC). The microcontroller was connected to two lightweight, tri-axial accelerometers (LIS331, Sparkfun, Colorado, USA;1000 Hz; ± 24 g) to measure tibial acceleration bilaterally12. The test leader who instrumented the participant was part of a research team with varying experience and expertise level; from a last-year student in sports sciences to a post-doctoral researcher. The tibial skin was pre-stretched by strappal tape at ~ 8 cm above the left and right medial malleolus to minimize unwanted oscillations of the skin in the ver- tical direction during impact7,12. Thereafter, an accelerometer fitted in a shrink socket with a total mass less than 3 grams was firmly attached to the anteromedial aspect of each lower leg by means of non-elastic zinc oxide tape (Supplementary information file, supplement 2 a). The axial axis of the accelerometer was aligned visually with the longitudinal axis of the lower leg before mounting. The tape was tightly fastened by one of the test leaders to the limit of subject tolerance. The applied alignment has been common practice for research involving tibial acceleration in running7,12,37. Procedure. An initial warm-up and familiarization period of five minutes was given along an oval track (circa 32-m length ∙ 5-m width). Participants subsequently ran for circa 20 min. The running speed was monitored on a trial-by-trial basis by timing gates spanning 6-m near the middle of a straight section. The first five satisfactory trials of each foot were collected for processing. Trials were discarded if the running speed fell outside the set boundary of 3.2 ± 0.2 m∙s−1. Table 1. Participants’ characteristics: anthropometrics and self-reported training habits. Variable Mean SD Range Minimum Maximum Body height (m) 1.70 0.07 1.59 1.79 Body mass (kg) 67.7 7.4 56.2 82.1 Age (year) 33 9 24 49 Training volume (km/week) 29 12 15 50 Training speed (m∙s−1) 2.88 0.31 2.36 3.33 Figure 1. Schematic overview of the experimental design involving two running sessions (screening and intervention). A red icon represents a distance runner with high tibial shock. A filled circle indicates a system check and self-selected rest. Tibial shocks were detected in both sessions. The music-based feedback module was activated in the biofeedback condition. 4 Vol:.(1234567890) Scientific Reports \| (2021) 11:4091 \| https://doi.org/10.1038/s41598-021-83538-w www.nature.com/scientificreports/ Data processing. The recorded tibial accelerations were imported for signal processing via custom-built MAT- LAB scripts. Tibial shock magnitudes corresponding to the first five contacts on a force platform were averaged for each foot side and per participant. Unfiltered magnitudes of tibial shock were preferred because the tibial shocks detected by the biofeedback system were derived from the raw signal for the instantaneous auditory bio- feedback. The leg with the highest value was retained. We evaluated the distribution of tibial shock in the group of screened runners and invited the runners who experienced shock magnitudes in the highest one-third of that population. Intervention session. Set‑up. The single-session intervention was supervised and took place at a track and field facility (supplementary video 1). The accelerometers of the wearable system were re-applied to the participant’s lower leg (supplementary information file, supplement 2 b). The manner of attachment of the ac- celerometer in the intervention session was intended to be identical to that of the screening session. The simple mounting technique has resulted in repeatable mean values of the tibial shock between running sessions12. The participant wore an on-ear headphone (HD25-ii, Sennheiser, Wedemark, Germany). A smart music player for real‑time music‑based feedback on tibial shock. A peak detection algorithm repetitively detected the magnitude and timing of tibial shock in each leg12. A custom-built JAVA program operated on the backpack system and detected a peak every time the axial acceleration exceeded 3 g with no higher axial accel- eration value measured in the next 375 ms. This simple algorithm was based on a peak detection algorithm taken from a previous gait retraining study7. The magnitudes and timings were transmitted in real-time through Open Sound Control to a MAX/MSP patch that was built with the intention of providing music-based biofeedback in real-time21. Real-time in this context means with negligible delay. For instance, when a new magnitude of tibial shock was detected, the auditory manipulations were executed in the same stride cycle. The real-time, continuous, auditory biofeedback consisted of commercially available music tracks with super- imposed pink noise of variable loudness (Fig. 2). The loudness of the noise depended on the momentary level of tibial shock of the leg that experienced the greatest mean shock in the baseline measurement. The five last values of that leg’s tibial shock were averaged through a 5-point moving average to account for inherent step-to- step variability in tibial shock7. That momentary level of tibial shock was mapped using an empirically validated fitting to obtain a distinct level of noise loudness21. Six discrete loudness levels (0, 20, 40, 60, 80, 100% of noise) were created for good discretization (supplementary audio, fragment 1)21, thereby accounting for inter-subject differences in the decoding accuracies39. The loudness levels were calculated as a percentage of the root-mean- square amplitude level. So, the upper limit of 100% corresponded to noise with the same amplitude as the root-mean-square amplitude level of the music. Shock values below the target resulted in music only, meaning Figure 2. Schematic representation of the biofeedback system’s main components for continuous biofeedback on tibial shock (axial peak tibial acceleration). An interaction loop of the smart music player that provided the auditory biofeedback in real-time and that continuously accounted for (in)voluntary alterations in the running cadence by aligning the tempo (beats per minute) of the music to the cadence (steps per minute) of the runner. The red horizontal line indicates the baseline tibial shock. The five most recent values of tibial shock are averaged and mapped to a discretized level of noise loudness, which is added to the music playing. 5 Vol.:(0123456789) Scientific Reports \| (2021) 11:4091 \| https://doi.org/10.1038/s41598-021-83538-w www.nature.com/scientificreports/ without pink noise (0% of noise). The target of minus ~ 50% of the baseline tibial shock was taken from previous gait retraining studies4,5,7,14. The running cadence was derived from the timings of the tibial shocks detected during running. We intended to repetitively align the tempo of the music (i.e., the beats per minute) to the running cadence (i.e., the steps per minute) in the biofeedback condition22,40. This real-time synchronization prevents the runner from adjusting his or her cadence to the tempo of the music and is based on the idea that interaction with music is empowering24,25. Music of a preferred genre (pop, rock, electronic dance, swing, world) was chosen by the participant. A music database consisting of seventy-seven tracks with a clear beat in the tempo range of running at sub-maximal speed was created (supplementary information file, supplement 4). Songs with the right tempo were selected by a smart music player that instantaneously and continuously adjusted for a change in the running cadence. Music tempi were manipulated up to ± 4% of the steps per minute without pitch shift21 (supplementary audio, fragment 2). When a change in steps per minute exceeded this tempo shift for eight seconds, another song started playing at a tempo that more closely resembled the altered running cadence. An illustrative audio fragment of a change in a music track was supplemented (supplementary audio, fragment 3). The momentary ratio of the music-to-motion alignment is described by the ratio of the running cadence (steps per minute) to the tempo of the music (beats per minute). The ratio should be close to 1 when the beats per minute of the music are aligned with the steps per minute of the runner. Procedure. Once bilaterally instrumented with the accelerometers and the backpack (Supplementary informa- tion file, supplement 2 b, c), participants ran an initial 4.5 min at ~ 3.2 ± 0.2 m/s. This warm-up period functioned as the no feedback condition wherein no auditory feedback on tibial shock was provided. In the software patch, the baseline tibial shock of the leg exhibiting the highest overall tibial shock was automatically determined for a sequence of 90 s (≈ 1 lap of 289 m) in the middle of the no feedback condition. Before the biofeedback condition started, the runners (i) were familiarized with the different levels of noise loudness by listening to the discrete noise levels going from minimum to maximum and vice versa (supplementary audio, fragment 1); (ii) chose their preferred sound volume; (iii) chose their preferred music genre; (iv) received verbal instructions in mother tongue: “This may be very difficult, but I would like you to try your best to concentrate on the task throughout the entire intervention. Listen carefully to the distorted music. Try to run with the music as clear as possible without any distortion at all. If impossible, keep the music distortion as low as possible by modifying your run- ning technique. The amount of distortion is linked to your tibial shock. The music stops playing when the trial is over.” So each runner was instructed to find a way to run with a lower level of tibial shock. However, to elicit self-discovery strategies, no instructions were given on how to reduce the shock magnitude7,13,14. An illustrative fragment of auditory biofeedback with the different noise levels was supplemented (supplementary audio, frag- ment 4). Biofeedback was provided for 20 min in total with a pause after 10 min. The instructions were repeated during the pause of self-selected duration. The software was configured in such a way that the music and the detection of tibial shock automatically stopped after the set period of time. The runner finished the lap and met the test leader at the checkpoint (Supplementary information file, supplement 2 d). Subsequently, the accelerometers and the backpack were removed from the lower limb. Meanwhile, the runner reported if he or she perceived any difference in the amount of superimposed noise in the biofeedback condition (yes/no). If so, we asked to describe the perceived change in running technique. An estimation of exercise intensity was obtained by asking the runner to give a score (from 1 to 10; from very easy to maximal effort) based on the session rating of perceived exertion scale41. The subject’s score was collected ~ 5 min after the end of the running session. Three participants did not report their level of exertion. Accelerometer data were continuously acquired during the no feedback and biofeedback conditions. Lap times were hand clocked throughout the session to derive the running speed of a lap. Verbal feedback about the running speed was given on a lap-by-lap basis to the runner. Data processing. The proportion of the pink noise generated during the 20-min biofeedback run and the detected tibial shocks were imported for processing using custom-built MATLAB scripts. The tibial shock values of each individual were extracted for a period of 90 s in both the no feedback and biofeedback conditions. The period of the no feedback condition corresponded to the period of the baseline measurement. The tibial shocks belonging to the biofeedback condition were extracted for another period of 90 s at the end of the biofeedback run. Post hoc inspection of all the registered peaks revealed that the peak detection algorithm worked sub- optimally by occasionally detecting false-positive peaks. The values belonging to the falsely identified peaks were post hoc excluded (supplementary information file, supplement 3). The time period at the end of the biofeedback run was chosen for comparison, like that seen in previous research5,6,13,14. We wanted to obtain a representa- tive level of overall tibial shock per participant compared to previous research on gait retraining (i.e., 5 to 20 footfalls) (supplementary information file, supplement 1). Therefore, the values of the tibial shock (g) and the running cadence (steps per minute) of the detected footfalls that belong to the no feedback and the biofeedback conditions were retained for the larger 90 s time period. The analyzed peaks were considered to be indicative of foot–ground contact. The number of analyzed footfalls was respectively 125 ± 10 and 132 ± 7, mean ± SD. Hence, it becomes possible to show the distribution in tibial shock, for example, in someone maximally responding to the music-based biofeedback. The time between sequential tibial shocks was used to derive the steps per minute in order to assess the running cadence. The average running speeds of the no feedback and biofeedback condi- tions were calculated for each participant using the lap times clocked at the indoor track. The running speed was also determined for those laps corresponding to the extracted tibial shocks. For further statistical analysis, the tibial shock, the running cadence and the running speeds were averaged per participant for each condition. Wilcoxon exact signed-rank tests were used for comparison due to the low 6 Vol:.(1234567890) Scientific Reports \| (2021) 11:4091 \| https://doi.org/10.1038/s41598-021-83538-w www.nature.com/scientificreports/ number of participants. Tibial shock, running cadence and running speeds were compared between the no feedback condition and the biofeedback condition. Tibial shock and running cadence were tested one-tailed (p1) because of the directional hypothesis. The Pearson correlation coefficient was calculated post hoc between the session rating of perceived exertion as reported by the runner and the difference in tibial shock. The alpha level was set at 0.05 (SPSS). The effect size rES was calculated by dividing the absolute z-score by the square root of the total number of observations, being rES =\|z\|/√20. Guidelines for rES are that a small effect is 0.1, a medium effect is 0.3, and a large effect is 0.542. The individual metrics can be retrieved online (digital supplementary file, datasheet). The reported values are mean ± SD. Results Tibial shock in the intervention session. Tibial shock was 11.14 ± 1.83 g in the no feedback condition. The individual averages of tibial shock ranged from 8.92 g to 13.71 g between the participants. Tibial shock scores were reduced by 27% to 8.19 ± 1.79 g (p1 = 0.001, z = −2.803, rES = 0.627 (large), mean negative rank = 5.50, absolute range: −0.94 to −7.14 g; relative range: −7 to –53%) in the biofeedback condition (Fig. 3 a), and this without guided instruction on gait modification. 3 5 7 9 11 13 15 17 No feedback Biofeedback hoc ( k g) s laibi T 150 160 170 180 190 200 No feedback Biofeedback Running cadence (steps per minute) a b * Figure 3. (a) The axial peak tibial acceleration representing the tibial shock and the (b) running cadence for the [left] no feedback and [right] biofeedback conditions. Every color is a different participant. The short horizontal line indicates the mean level of the variable of interest in a condition. * indicates p < 0.05. 0 0.05 5 0.1 Least responder Normalized count 0.15 10 0.2 Tibial Shock (g) Average responder 15 20 Greatest responder Figure 4. Histogram of the tibial shock magnitudes in the analysis period (90-s) for the no feedback (dark) and biofeedback (light) conditions in the greatest, average and least responders. The footfalls of each runner in a condition have been normalized to the number of total footfalls in that condition. 7 Vol.:(0123456789) Scientific Reports \| (2021) 11:4091 \| https://doi.org/10.1038/s41598-021-83538-w www.nature.com/scientificreports/ Figure 4 shows the distribution in tibial shock for the average, most and least pronounced responder. While there is an overall decrease in the magnitude of tibial shock in these three runners, few footfalls had a tibial shock that would still be categorized as high. Figure 5 shows the group’s distribution in tibial shock for both conditions. Music‑based biofeedback characteristics. The average momentary ratio of the running cadence to the music tempo was 1.01 ± 0.01 in the 20-min period of biofeedback and 1.02 ± 0.04 in the time period selected for comparison. The noise loudness to the synchronized music varied from zero to maximum on the group level (Fig. 6). This means that tibial shocks did occur both below the target (0% of noise) and above the baseline level of tibial shock (100% of noise). All noise levels were experienced in this group of runners with high tibial shock (Fig. 6). The individual proportions of the noise levels have been supplemented (Supplementary information file, supplement 5). The questioned runners responded quasi-immediately after completing the running session to have perceived a change in noise loudness or quality of the audio during the biofeedback run (digital sup- plementary file, datasheet). Temporospatial characteristics. Figure 3b shows the individual evolution in the running cadence between the conditions of no feedback and biofeedback. The increase of 4 steps per minute or 2.3% in the running cadence was not statistically significant (p1 = 0.065, z = − 1.580, rES = 0.353 (moderate), positive mean rank = + 6.14). The running speed in the 4.5-min no feedback and 20-min biofeedback runs were respectively 3.15 ± 0.12 m∙s−1 and 3.13 ± 0.15 m∙s−1, and did not differ significantly (p = 0.52, z = − 0.71, rES = 0.159 (small)). The respective running speeds for the laps chosen for tibial shock comparison were 3.18 ± 0.15 m∙s−1 and 3.04 ± 0.10 m∙s−1, and did not differ statistically (p = 0.090, z = − 1.72, rES = 0.385 (moderate)). In addition, the running speeds remained within the a priori permitted boundary of ± 0.20 m∙s−1. Perceived exercise intensity. The mean and median scores of the session rating of perceived exertion were respectively 4 (somewhat hard) and 3 (moderate) with individual values ranging from 2 to 9 (digital sup- plementary file, datasheet). In this cohort, the participant reporting the highest rating of perceived exertion also reported the lowest combined training volume and training speed. The perceived exertion did not correlate to the absolute (p = 0.460, r = 0.337) nor relative (p = 0.561, r = 0.268) decreases in tibial shock, suggesting that the attained level of exertion did not influence the achieved reduction in tibial shock. 0 0.05 0.1 0.15 0.2 0.25 4 6 8 10 12 14 16 18 20 22 Tibial shock (g) 0 0.05 0.1 0.15 0.2 0.25 Normalised count Normalised count Figure 5. Histogram of the tibial shock magnitudes in the analysis period for the [upper panel] no feedback condition and the [lower panel] biofeedback condition. Each color represents a participant (n = 10). The number of footfalls within a single bin has been normalized to the total number of detected footfalls in that condition. The solid vertical line indicates the tibial shock averaged for all footfalls in that condition. 8 Vol:.(1234567890) Scientific Reports \| (2021) 11:4091 \| https://doi.org/10.1038/s41598-021-83538-w www.nature.com/scientificreports/ Discussion The purpose of this proof-of-concept study was twofold: (1) to determine if real-time, continuous, music-based feedback on tibial shock helps to reduce the shock magnitude during over-ground running at an instructed and common running speed, and (2) to examine if runners with high tibial shock systematically increase the running cadence in response to the real-time feedback. A single-session intervention was performed at an instructed run- ning speed with pre and post measurements in a screened group of runners. The runners who participated in the intervention session had an averaged value in one of the limbs of at least 9.7 g in tibial shock when screened in the laboratory. A wearable system provided real-time auditory feedback on a modifiable mechanical parameter to stimulate lower impact running in a controlled, indoor training environment. Key implications and discussion regarding the reduction in tibial shock. In support of our first hypothesis, runners with high tibial shock decreased their tibial shock by − 27% or − 2.96 g while running over-ground with the music-based biofeedback. This is the first study performed over-ground in which high impact runners realized shock reduction with the use of unimodal biofeedback. Our findings build on previous research about gait retraining in high impact runners4,7,29, and support the limited literature documenting that self-discovery strategies to achieve shock reduction are effective7,13. For instance, Clansey and colleagues carried out a randomized controlled trial and reported a decrease of 3.28 g in male runners with high tibial shock who completed multiple sessions of continuous real-time feedback on tibial shock at the controlled running speed 3.7 m∙s−17. The decrease in tibial shock we found corresponds to the decrease reported by Clansey and col- leagues in the experimental group, though the present study was performed at the slightly lower running speed of ~ 3.2 m∙s−1 and in a single-session design. The mixed-sex runners in the present study could run a total of 25 min at 3.2 ± 0.2 m∙s−1 and all achieved shock reduction at the end of the biofeedback run. These runners’ shock reduction did not correlate to the reported session rating of perceived exertion. Hence, a substantial reduction in tibial shock is achievable in a heterogeneous group of recreational runners with the aid of a wearable biofeedback system. The participants were informed about the aim of the intervention (i.e., shock reduction) and they were aware of the fact that an auditory element was linked with the tibial shock. However, no explicit instructions about gait modification were given. Key implications and discussion regarding the expected increase in running cadence. The spontaneous self-adaptation in response to the music-based feedback permitted the runners to find their own solution to cover ground with less tibial shock magnitudes, without reducing the running speed. Self-induced changes in running cadence were possible because the music’s tempo was continuously and successfully synchro- nized to the runner’s cadence. Contrary to our second hypothesis, a reduction in tibial shock was not accom- panied by a systematic increase in the running cadence (or a decrease in step length because the running speed remained stable). A preliminary and treadmill-based study has reported a systematic reduction in an unspeci- 0 20 40 60 80 100 Noise level (rms) Tibial shock (%) > 113% 96 - 113% 80 - 95% 65 - 79% 48 - 64% < 48% Distribution in the biofeedback run Figure 6. The proportion of the pink noise generated during the 20-min biofeedback run for the group of high impact runners. Level 0 represents the ‘music only’ category without superimposed noise. The level of noise loudness added to the synchronized music has been subdivided into 5 categories. Each level of noise loudness corresponds to a level of tibial shock relative to the baseline g-value of the runner, which was determined during the no feedback condition. The value corresponding to 100% of tibial shock is identical to the value of tibial shock determined in the no feedback condition. Tibial shock is here synonymous to the axial peak tibial acceleration, rms: root mean square. 9 Vol.:(0123456789) Scientific Reports \| (2021) 11:4091 \| https://doi.org/10.1038/s41598-021-83538-w www.nature.com/scientificreports/ fied component of peak tibial acceleration when providing real-time auditory feedback in response to that peak tibial acceleration, that was accompanied by a systematic increase in running cadence of 2 steps per minute or 1.4%14. In the present study the cadence response between the participants was more variable (Figs. 3, 4). The discrepancy in a systematic change in step frequency between study results highlights the fact that more work is needed to fully understand the motor strategy or strategies for tibial shock reduction. For instance, another way to reduce tibial shock may be a change in the discrete foot strike pattern. An anterior change in foot strike pattern has been found in rearfoot runners with high tibial shock who com- pleted a treadmill-based, multi-week, retraining program by means of visual and auditory biofeedback on tibial shock7. In the current study performed in an over-ground running environment and at a slower running speed, half of our participants claimed to have tried a non-rearfoot strike in the biofeedback condition. Only a single runner declared to have maintained a forefoot strike until the end of the run. Almost all of the participants (9 out of 10) claimed to have performed a rearfoot strike near the end of the biofeedback condition. Based on our observations and on the comments made by the participants, we speculate that the real-time feedback on tibial shock elicits gait alterations with inter-individual differences in kinematic adaptations. Consequently, the gait alterations may influence shock attenuation strategies. A shift from active shock attenuation to more passive mechanisms has, for instance, been proposed as possible adaptation during prolonged running at a submaximal intensity43. When providing biofeedback on the axial peak tibial acceleration, the shock attenuation may rely more heavily on the active mechanisms (e.g., eccentric muscle contractions, changes to joint angles, and modu- lating limb stiffness) than passive deformation of the body tissues. Future research may verify our speculations because 3D kinematics, head nor sacral acceleration were measured. Discussion regarding the targeted reduction in tibial shock using a music‑based approach. We attribute the large effect size obtained in our study to the use of reinforcement. Previous studies that used a manipulation of music to modulate gait parameters have relied on a steering paradigm that is based on reinforce- ment learning25,44 according to which people tend to modify their behavior in order to maximize reward and recursively minimize error (i.e., distance from the target behavior). In this specific case, we sought to reward the runner by providing a way of obtaining maximum acoustic quality of the synchronized music. The rewarding effect of running with only music, thus without superposition of pink noise, occurs if the target is reached. Sur- prisingly, a 50% reduction in tibial shock was reached only for 4.8% of the 20-min biofeedback run. The quote “I heard several noise levels, but I never heard music without noise” of a participant illustrates this finding. Even the greatest responder could not fully supress the level of superimposed noise (i.e., so that only synchronized music would be heard) for the majority of the time (supplementary information file, supplement 5). Many studies on gait retraining with biofeedback aimed to reduce the runner’s baseline value in tibial shock with 50%4–7,14,45. But this relative threshold was difficult to achieve or to maintain in the present study. Accord- ing to our data, a more realistic and relative target for the population of interest seems to be approximately − 30% in tibial shock. Given that some gait adaptations felt unnatural when trying to achieve a 50% reduction in tibial shock, a more feasible target of shock reduction may also counteract the slight discomfort reported by several participants at the end of the run. Nevertheless, more retraining sessions are likely required before the self-discovered gait pattern is perceived as natural. A cohort of runners with high tibial shock namely reported that the new gait pattern felt natural by the end of the sixth retraining session, comprising the instruction to run softer and the use of real-time feedback about tibial acceleration4. Next to feasibility, it is debatable whether an extreme target of − 50% in tibial shock is required to be clinically relevant. Chan and colleagues have executed a randomized controlled trial with one-year follow-up and reported fewer running-related injuries in novice runners who completed a gait retraining program on treadmill2. Even within the multifactorial nature of injury development, their findings are promising to consider gait retraining as a preventive strategy for running-related injuries in distance runners who appear to be at risk for injury2. Their multi-week gait retraining program was performed on an instrumented treadmill with an instruction intended to reduce the vertical impact peak force. The group of runners who engaged in the retraining program could reduce the instantaneous vertical loading rate of the ground reaction force by about 15 to 18%, estimated by manual digitization of the results visualized in Fig. 4 of that publication, and depending on the running speed tested2. Such a reduction in vertical loading rate might be linked with a reduction in tibial shock because of the moderate correlation between the vertical loading rate and the tibial shock during over-ground level running11,12. Multiple lab studies have provided real-time feedback on tibial shock and did report a substantial reduction in tibial shock and in vertical loading rate post-retraining4,7,45. A reduction of about 30% in both tibial shock and vertical loading rate has been achieved by runners with high tibial shock post-retraining in a laboratory setting45. So, a more feasible target of approximately -30% in tibial shock relative to the baseline measurement may still have potential to reduce or to treat running-related injuries in at-risk runners during level over-ground run- ning. The evidence for an association between measures of impact over time and running-related injuries has been conflicting9,10,46–51. Nevertheless, guided usage of a wearable biofeedback system that induces and retains substantial impact-like reduction over time may have clinical implications for injury risk management. Limitations. The self-selected or fixed running speed has been held constant in gait retraining studies that aim to reduce tibial shock4,7,45. The instructed and lap-by-lap monitored speed of 3.2 ± 0.2 m∙s−1 was slightly above the group’s self-reported training pace for their typical distance runs (Table 1). It was still less than the running speed of 3.7 m∙s−1 imposed by Clansey and colleagues7 in male runners during the 20-min retraining sessions. The instructed running speed of the present study may affect results since it influences the absolute magni- tude of impact measures in the time domain, such as tibial shock and the instantaneous vertical loading rate12. 10 Vol:.(1234567890) Scientific Reports \| (2021) 11:4091 \| https://doi.org/10.1038/s41598-021-83538-w www.nature.com/scientificreports/ Nonetheless, Chan and colleagues showed that the vertical loading rate was lowered at multiple running speeds after gait retraining2. Individualization of the instructed speed to the training speed of the participant’s typical distance run may further increase the ecological value of gait retraining. Given that the session rating of perceived exertion indi- cates the exercise intensity41, we estimate that the running session was generally performed near the first ven- tilatory threshold. The average score of 4 on the session rating of perceived exertion scale resembles a physical effort that was “somewhat hard” in this group of mixed-sex runners. Even the participant who reported the highest score of 9 was able to reduce tibial shock. No linear relationship was found between shock reduction and perceived exertion. These results suggest sufficient attention is required for lower impact running with the use of the biofeedback at the instructed speed. This may not be the case at higher exercise intensities, for instance, when the runner needs to cope with maintenance of the running pace during exhaustive runs. The exploration of gait adaptations might affect running economy. Tibial shock reduction has led to more oxygen being consumed whilst running on treadmill in a single session of gait retraining35. In contrast, a multi- sessions program comprising real-time feedback on tibial shock resulted in a clear reduction in tibial shock without affecting the running economy7. Future research may verify the hypothesis of a temporary decrease in running economy in an over-ground setting because oxygen consumption was not measured in the present study. The design of this study does not allow confirmation of whether the synchronised music influences the tibial shock via the biofeedback system. The results can only be attributed to the auditory biofeedback, being the combination of synchronized music and superimposed noise. Besides a positive effect of music to training adherence, there might also be other effects because of the ability of music to distract from a task52. It could be further investigated which kinds of music perform best in a retraining context. In line with previous studies4,7,45, the study was conducted in healthy runners who demonstrated a charac- teristic previously associated with a history of tibial stress fracture in distance runners. Therefore, these findings are not necessarily applicable to injured runners nor to runners with relatively low magnitudes of tibial shock. The selected group of runners had high tibial shock relative to a screened cohort. That inclusion criterion may be a reason for the discrepancy in the absolute reduction of tibial shock (g) between studies with and without a focus on high impact runners only4–7,13,14,35,45. The changes in outcome cannot be fully attributed to the intervention without comparator group. The lack of a control group raises questions about whether the reduction in tibial shock is the result of the continuous real-time feedback or the awareness of the purpose of the feedback (i.e., shock reduction). Verbal information was given to elicit self-discovery strategies without the provision of direct instructions (e.g., “run softer”, “land with a toe-strike”) that may influence tibial accelerations. Although we find it unlikely that explicitly instruct- ing people to “decrease your tibial shock” without clinician or accelerometry guided feedback would result in a substantial shock reduction at the end of a running session, it remains unknown and unexplored. Future directions. The wearable system can instantaneously detect and sonify tibial shock. The next step is to determine the effectiveness of the biofeedback system in an over-ground gait retraining program with a con- trol group. A gait retraining program lasting multiple weeks usually involves fading of the feedback stimulus2,4,45. Analogous to the gradual removal of the continuous and visual stream of tibial acceleration during the last four sessions by Crowell and colleagues4, the continuous auditory feedback may be faded over time to facilitate inter- nalization and persistence of an altered gait pattern. An assessment of motor retraining was beyond the scope of this study, there it normally requires about six to eight sessions to enhance retention of the alterations in the movement pattern4,7,30, but could be incorporated in gait retraining protocols. A possibility to retrain runners in more natural environments eliminates the need of exclusive retraining in laboratory/clinic settings. As such, runners might easily implement the auditory biofeedback-driven approach of retraining, given some technical improvements (e.g., wireless accelerometer connected to a miniaturized pro- cessing device) and adequate speed control. The smart music player might also benefit from a feedback protocol that promotes motor learning in a retraining program consisting of multiple sessions. Conclusion Our experimental study without controls shows that a substantial reduction in tibial shock can be stimulated with the use of continuous music-based biofeedback. 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Sport. https:// doi. org/ 10. 1111/ sms. 13228 (2018). 52. Terry, P. C., Karageorghis, C. I., Curran, M. L., Martin, O. V. & Parsons-Smith, R. L. Effects of music in exercise and sport: a meta- analytic review. Psychol. Bull. 146, 91–117 (2019). Acknowledgements The first two authors shared equal contribution to the presented work. This study was funded by the Research Foundation–Flanders (FWO.3F0.2015.0048.01 and the Methusalem project titled ‘Expressive music interac- tion’) and EU-EFRO-Interreg (Nano4Sports project 0217). The International Society of Biomechanics granted a matching dissertation grant program to P.V.d.B. that supported this research. The authors thank the runners who participated in any part of the study and Topsporthal Vlaanderen for the possibility of conducting a lab- in-the-field test, and we acknowledge the assistance of PhD Bastiaan Breine for partial assistance during data collection, Ing. Davy Spiessens for periodic maintenance of the accelerometers, and MSc Ella Haeck and Maxim Gosseries for devoting a research internship to the present study. Some of the results have been presented at the 2018 congress of the American Society of Biomechanics as part of the doctoral student competition. Author contributions P.V.d.B., M.L., and D.D.C. conceived, designed and coordinated the study. P.V.d.B., J.G, and R.D. collected original data. V.L. and J.S developed the custom software. P.V.d.B., J.G, and R.D., participated in data analysis. P.V.d.B, J.G. and J.S. developed the figures. P.V.d.B initially drafted the manuscript and the other authors provided use- ful suggestions in preparing the final manuscript. All authors reviewed the manuscript and gave approval for publication. Competing interests The authors declare no competing interests. Additional information Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1038/ s41598- 021- 83538-w. Correspondence and requests for materials should be addressed to P.V.d. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. © The Author(s) 2021, corrected publication 2021	Music-based biofeedback to reduce tibial shock in over-ground running: a proof-of-concept study.	02-18-2021	Van den Berghe, Pieter,Lorenzoni, Valerio,Derie, Rud,Six, Joren,Gerlo, Joeri,Leman, Marc,De Clercq, Dirk	eng
PMC8505327	Vol.:(0123456789) 1 3 https://doi.org/10.1007/s00421-021-04780-8 ORIGINAL ARTICLE Steady‑state ̇VO2 above MLSS: evidence that critical speed better represents maximal metabolic steady state in well‑trained runners Rebekah J. Nixon1 · Sascha H. Kranen1 · Anni Vanhatalo1 · Andrew M. Jones1 Received: 1 May 2021 / Accepted: 26 July 2021 © The Author(s) 2021 Abstract The metabolic boundary separating the heavy-intensity and severe-intensity exercise domains is of scientific and practical interest but there is controversy concerning whether the maximal lactate steady state (MLSS) or critical power (synonymous with critical speed, CS) better represents this boundary. We measured the running speeds at MLSS and CS and investigated their ability to discriminate speeds at which ̇VO2 was stable over time from speeds at which a steady-state ̇VO2 could not be established. Ten well-trained male distance runners completed 9–12 constant-speed treadmill tests, including 3–5 runs of up to 30-min duration for the assessment of MLSS and at least 4 runs performed to the limit of tolerance for assessment of CS. The running speeds at CS and MLSS were significantly different (16.4 ± 1.3 vs. 15.2 ± 0.9 km/h, respectively; P < 0.001). Blood lactate concentration was higher and increased with time at a speed 0.5 km/h higher than MLSS compared to MLSS (P < 0.01); however, pulmonary ̇VO2 did not change significantly between 10 and 30 min at either MLSS or MLSS + 0.5 km/h. In contrast, ̇VO2 increased significantly over time and reached ̇VO2 max at end-exercise at a speed ~ 0.4 km/h above CS (P < 0.05) but remained stable at a speed ~ 0.5 km/h below CS. The stability of ̇VO2 at a speed exceeding MLSS suggests that MLSS underestimates the maximal metabolic steady state. These results indicate that CS more closely represents the maximal metabolic steady state when the latter is appropriately defined according to the ability to stabilise pulmonary ̇VO2. Keywords Endurance · Physiology · Oxygen uptake · Performance · Lactate · Threshold Introduction In a pioneering study, Whipp and Wasserman (1972) reported that, for a given individual, exercise at different constant work rates evoked distinctive response profiles for pulmonary O2 uptake ( ̇VO2 ). It is now recognised that, following the initial cardiopulmonary and fundamental phases, ̇VO2 might: (1) reach a rapid (i.e. within ~ 3 min of the onset of exercise) steady state; (2) reach a delayed (within ~ 10–20 min) steady state; or (3) not attain a steady state at all, but rather rise with time until the ̇VO2 max is attained, with task failure occurring shortly thereafter (Jones and Poole 2005; Whipp and Ward 1992). These charac- teristic ̇VO2 response profiles are emblematic of exercise intensity domains which have been termed moderate, heavy and severe (Carter et al. 2002; Poole et al. 1988; Pringle et al. 2003; Wilkerson et al. 2004). These three exercise intensity domains have been considered to be partitioned by the lactate threshold (LT) or gas exchange threshold for the moderate-to-heavy intensity boundary, and the critical power (CP) or maximal lactate steady state (MLSS) for the heavy- to-severe intensity boundary (Jones et al. 2010; Whipp and Wasserman 1972). It has been established that the neuromuscular, metabolic, blood acid–base and pulmonary gas exchange responses dif- fer in the three intensity domains, resulting in differences in the predominant causes of fatigue and the corresponding limitations to exercise performance (Black et al. 2017; Burn- ley et al. 2012; Vanhatalo et al. 2016). The heavy-to-severe intensity boundary is particularly important for endurance exercise in that it will determine whether a particular power output or speed is, or is not, sustainable in a metabolic steady state. Accurately discriminating the boundary between the heavy-intensity and severe-intensity exercise domains is, therefore, of both scientific and practical interest (Burnley Communicated by Philip D. Chilibeck. * Andrew M. Jones a.m.jones@exeter.ac.uk 1 Sport and Health Sciences, University of Exeter, St. Luke’s Campus, Heavitree Road, Exeter EX12LU, UK / Published online: 5 August 2021 European Journal of Applied Physiology (2021) 121:3133–3144 1 3 and Jones 2018; Jones and Vanhatalo 2017; Vanhatalo et al. 2011). As noted above, there are two principal approaches for the determination of the heavy-to-severe exercise intensity boundary. The first of these is based upon the well-known hyperbolic relationship between power (or speed) and time, with the asymptote of this relationship representing CP (or critical speed, CS) and the curvature constant (W´ or D´, for cycling and running, respectively) representing a finite amount of work that can be done, or distance that can be covered, above the CP or CS, respectively (Hughson et al. 1984; Monod and Scherrer 1965; Moritani et al. 1981; Poole et al. 1988). Exercise performed above CP results in the development of a pronounced ̇VO2 ‘slow component’ that does not level off but which eventually drives ̇VO2 to its maximum value; simultaneously, the intramuscular concen- tration of phosphocreatine (PCr) falls and the intramuscular concentrations of inorganic phosphate and H+ rise inexora- bly, to reach values that are presumably limiting to muscle function at the limit of tolerance (Black et al. 2017; Jones et al. 2008; Vanhatalo et al. 2016). Muscle and blood lactate concentrations are elevated and also display non-steady-state behaviour above CP, but are elevated (compared to resting values) but stable over time in the heavy-intensity domain below CP (Black et al. 2017; Vanhatalo et al. 2016). The second approach to determine the heavy-to-severe intensity boundary is based solely upon the blood lactate responses to continuous exercise and the identification of MLSS, with the latter being defined as the highest power or speed that can be sustained without a greater than 1 mM increase in blood lactate concentration between 10 and 30 min of exer- cise (Beneke and von Duvillard 1996; Dekerle et al. 2003; Pringle and Jones 2002; Smith and Jones 2001). CP and MLSS have often been considered to represent the same phenomenon and the terms are frequently used interchangeably. However, small but statistically significant differences between CP and MLSS, with CP being higher, are frequently reported (Dekerle et al. 2003, 2005; Mattioni Maturana et al. 2016; Pringle and Jones 2002; Smith and Jones 2001) and this has led to debate and over which of them should be considered the ‘gold standard’ (Jones et al. 2019a). The rationale underpinning the specific defini- tion of MLSS (i.e. less than a 1 mM increment in blood [lactate] between 10 and 30 min) is obscure and the pro- tocol from which MLSS is derived, it has been argued, is methodologically biased towards an underestimation of the ‘true’ maximal metabolic steady state (Jones et al. 2019a). Moreover, it has been proposed that using changes in blood [lactate] alone as a proxy index of muscle metabolic and respiratory homeostasis lacks precision and that the profile of pulmonary ̇VO2 represents a more reasonable ‘global’ index of physiological conditions which are steady state or non-steady state (Jones et al. 2019a). Nevertheless, it is feasible that modification of the definition of MLSS via dif- ferent permutations of permissible blood [lactate] increment and the time over which lactate accumulates might result in closer agreement between MLSS and CP and enable better approximation of CP from measurements of blood [lactate] derived from submaximal exercise tests. The purpose of the present study was to investigate, in well-trained runners, which of CS and MLSS better repre- sent the highest speed at which ̇VO2 can be stabilised (i.e. maximal metabolic steady state). We hypothesised that: (1) CS would be significantly higher than MLSS; (2) CS would more closely approximate the maximal metabolic steady state, as defined via the behaviour of ̇VO2 ; and (3) modify- ing the definition of MLSS would eliminate the difference between CS and MLSS. Methods Participants Ten well-trained, competitive male athletes (runners n = 7, triathletes n = 3; mean ± SD age 22.8 ± 4.8 years, height 1.80 ± 0.05 m, body mass 73.7 ± 5.8 kg, ̇VO2 peak 63.0 ± 4.0 mL/kg/min) volunteered to participate in this study. The participants gave written informed consent and completed and signed a PAR-Q form as a declaration of their eligibility to take part in the study. Participants had no known medical conditions that would inhibit their ability to perform strenuous exercise or cause them harm while doing so. The study adhered to the principles of the Declaration of Helsinki (2013) and was approved by the University of Exeter Sport and Health Sciences Ethics Committee. Experimental design and general procedures This experiment was designed to investigate the relationship between running speeds at MLSS and CS and to explore which of MLSS and CS provided the better approxima- tion of the maximal metabolic steady state, as defined by the ability to separate running speeds at which ̇VO2 can, or cannot, be stabilised. To this end, following an initial step incremental treadmill test, participants completed a series of constant-speed treadmill runs of up to 30-min duration or to the limit of tolerance for the assessment of MLSS (3–5 runs) and CS (4–6 runs). Tests for determination of MLSS and CS were numbered consecutively from 1. The order of tests was randomised for each participant in a counterbal- anced manner, with five participants starting with a CS test and the other five starting with a MLSS test. The final lab visit involved participants running just above their CS to the limit of tolerance. European Journal of Applied Physiology (2021) 121:3133–3144 3134 1 3 The study had a single blind, counterbalanced and ran- domised design and included 9–12 lab visits. Participants were instructed to avoid strenuous exercise for 24 h before, and caffeine and alcohol consumption for 12 h before, lab visits. Testing was completed at the same time of day (± 2 h) for each participant to minimise any influence of circadian rhythm. All participants were offered a familiarisation visit. Testing was completed within 3–6 weeks, with 2–4 tests per week and at least 24 h between tests. The treadmill (Wood- way PPS 55 Sport, Woodway GmbH, Weil am Rhein, Ger- many) was set to 1% incline for all tests (Jones and Doust 1996). Pulmonary gas exchange was measured during all tests, with participants wearing a facemask connected to a calibrated Jaeger Oxycon Pro breath-by-breath ergospirom- etry system (VIASYS Healthcare GmbH, Hoechberg, Ger- many). Heart rate (HR) was also recorded during all tests (Polar T31 Heart rate strap, Finland). Blood [lactate] was measured during the incremental test and the MLSS deter- mination tests. Blood was collected from a fingertip using a single use lancet. The first drop of blood was wiped using a clean tissue then the blood sample was collected in a cap- illary tube and analysed enzymatically (YSI 2500 Lactate Analyser, YSI, Letchworth, UK). Before all testing sessions, participants completed a standardised warm up which involved running for 5 min at 10 km/h and the opportunity to stretch. For CS predic- tion tests > 18 km/h, participants also completed a 30–60 s ‘strider’ at a speed above 10 km/h but below their LT. Time was recorded for all tests to the nearest second. Timing (Traceable Timer, Fisher Scientific, Loughborough, UK) started as participants let go of the treadmill handrails to transition to the moving belt and stopped when they stepped to the sides of the treadmill to end the test. Initial incremental test On their first visit to the laboratory, the participants com- pleted a multi-stage incremental step test to the limit of tol- erance. The test commenced with 3 min of standing rest during which pulmonary gas exchange, HR and blood [lac- tate] were measured. The treadmill test then commenced at a speed of 10–12 km/h, depending on the ability of the participant. Every 3 min, the participant stopped running by stepping to the side of the treadmill belt for 30 s to facili- tate the collection of a fingertip blood sample. Speed was increased by 1 km/h every 3 min until the participant could not complete a stage or declined the opportunity to start a new stage. The final completed stage was defined as the peak speed attained. ̇VO2 peak was defined as the highest 30 s mean value recorded during the test. The running speeds at LT (i.e. the first increase in blood [lactate] above baseline values of ~ 1 mM) and lactate turnpoint (LTP; i.e. a sudden and sustained second threshold increase in blood [lactate] at ~ 2.5–4 mM) were determined visually from plots of blood [lactate] against running speed by two experienced research- ers (Jones 2007; Jones et al. 2021). Determination of running speed at maximal lactate steady state Participants ran at a constant (but different) speed for up to 30 min on 3–5 occasions (3 runs for four participants, 4 runs for five participants and 5 runs for one participant). Every 5 min they stopped running and stepped to the sides of the treadmill belt for 15 s to enable the collection of a fingertip blood sample. The selection of test speeds was informed by the speed at LTP measured in the incremental test. Speeds used in the tests were subsequently varied by 0.5 km/h and were continued until there was sufficient data to determine MLSS, which was defined as the highest speed which resulted in an increase of blood [lactate] of less than 1 mM between 10 and 30 min (Beneke and von Duvillard 1996; Jones and Doust 1998). If the increase in blood [lac- tate] was ± 0.05 mM above or below 1 mM (an event that occurred in two participants), the test was repeated and the mean of the blood [lactate] at each time point during the two tests was calculated and used in the determination of MLSS. In addition to the conventional definition of MLSS outlined above, different permutations of the criteria for the change in blood [lactate] (i.e., < 1.0 mM, < 1.5 mM and < 2.0 mM) and the time window over which changes in blood [lactate] were measured (i.e. 5–10 min, 5–15 min, 5–20 min, 5–25 min, 5–30 min, 10–15 min, 10–20 min, 10–25 min, 10–30 min, 15–20 min, 15–25 min, 15–30 min, 20–25 min and 20–30 min), were applied to provide a modi- fied MLSS assessment. Determination of critical speed The participants completed constant-speed runs to the limit of tolerance at speeds corresponding to 90%, 95%, 100% and 105% of the peak speed attained in the incremental test such that test duration was ~ 2–15 min. Pulmonary ̇VO2 was monitored throughout the tests to assess whether end-test values exceeded 95% of the peak value determined in the step incremental test. Test durations longer than 15 min were only included in the CS determination if ̇VO2 was greater than 95% of the respective peak value measured in the incre- mental test. CS was subsequently calculated from two lin- ear regression models, the distance–time and speed–1/time models, and the non-linear hyperbolic speed–time model. The standard errors (SE) associated with the CS and Dʹ estimates for each model were calculated using regression analysis. The coefficient of variation (CoV) for each CS and Dʹ estimate was then calculated by expressing the SE as % of the parameter estimate. For a model to be accepted, the European Journal of Applied Physiology (2021) 121:3133–3144 3135 1 3 coefficient of variation (CoV) for the mathematical fit had to be < 5% for CS and < 10% for D´. The output from the model with the lowest error was used in subsequent analysis (Black et al. 2015). If the CoV was too high after the initial four tests, further tests were completed until the CoV criteria were met. In total, five participants completed four tests, two participants completed five tests and three participants completed six tests. Following the assessment of CS and calculation of the 95% confidence intervals (CI) surrounding the CS estimate for each individual, the participants completed a final test just above CS (i.e. CS+ test) in which they ran to the limit of tolerance at the speed representing the upper bound of the 95% CI. The highest running speed that was used in the MLSS assessment but was below the lower bound of the 95% CI for the CS estimate for each participant was identi- fied and defined as CS−. Statistical analysis Analyses were performed using IBM SPSS Statistics 26.0 (Chicago, IL, USA). A two-tailed paired Students t test was used to analyse the difference between the running speeds at MLSS and CS. One-way repeated measures ANOVA were used to assess differences between peak values of ̇VO2 attained during the CS determination trials and the incre- mental test, and also between the running speeds at conven- tional MLSS, modified MLSS and CS. Two-way ANOVA with repeated measures across condition and time (5, 10, 15, 20, 25 and 30 min) was used to assess differences in the blood [lactate] response for running speeds at MLSS and the speed 0.5 km/h above MLSS (termed MLSS+). Two- way ANOVA with repeated measures across condition and time (10 and 30 min < CS and 5 min and end-exercise > CS) was also used to assess differences in the ̇VO2 response for running speeds at MLSS, MLSS+, CS− and CS+. When sphericity was violated, the significance of F-ratios was adjusted using the Greenhouse–Geisser procedure and sig- nificant interaction and main effects were followed up using LSD post hoc tests. Linear regression analysis using Pearson product moment was carried out to determine the relation- ship between CS and D´ and the relationship between the running speeds at conventional MLSS, modified MLSS, and CS. Significance was set at P < 0.05 and results are reported as mean ± SD. Results The LT and LTP occurred at 14.5 ± 1.2 and 16.8 ± 1.0 km/h, respectively, and the peak speed attained in the incremental test was 19.6 ± 1.3 km/h. The ̇VO2 peak achieved during the incremental test was 4.65 ± 0.47 L/min or 63.0 ± 4.0 mL/kg/ min. The times to exhaustion (s) in the CS prediction trials performed at 90%, 95%, 100% and 105% of the peak speed attained in the incremental treadmill test were 801 ± 219, 500 ± 146, 388 ± 112 and 185 ± 33 s. The mean ̇VO2 peak attained during the CS trials (4.92 ± 0.58 L/min) was not significantly different from the peak value achieved during the step incremental test (P = 0.28). The CS and D´ were 16.4 ± 1.3 km/h and 216 ± 79 m, respectively, and the CoV was 0.8 ± 0.7% for CS and 7 ± 3% for D´. Following the calculation of CS and the associated 95% CI, participants completed a final test to the limit of tolerance at a speed just (~ 2.4%) above CS (i.e. at 16.8 ± 1.3 km/h; CS+). The time to the limit of tolerance at this speed was 17.0 ± 4.6 min and there was a strong positive correlation with D´ (r = 0.82, P < 0.01). Comparison of critical speed and running speed at maximal lactate steady state The determination of MLSS is shown for a representa- tive participant in Fig. 1 and the determination of CS is shown in Fig. 2. There was a significant difference between CS and conventionally determined MLSS (16.4 ± 1.3 vs 15.2 ± 1.0 km/h, respectively; P < 0.001). Different permu- tations of the permitted blood [lactate] increase, and the time window over which [lactate] increased, resulted in different estimates for speed at MLSS (Fig. 3). Of all of the 45 per- mutations of blood [lactate] increment and time window that were considered, only the criterion of a < 2.0 mM increase in blood [lactate] between 10 and 20 min produced an MLSS value (15.9 ± 0.9 km/h) that was not significantly different from CS (Fig. 3). The conventional MLSS was significantly correlated with modified MLSS (r = 0.80, P < 0.01) and CS (r = 0.96, P < 0.001) and the modified MLSS was significantly cor- related with CS (r = 0.80, P < 0.01). Behaviour of blood [lactate] and oxygen uptake in the proximity of MLSS and CS There was a significant main effect by time (F = 13.7, P = 0.005) and by condition (F = 38.1, P < 0.001), and a significant interaction effect (F = 14.1, P < 0.001) on blood [lactate] across MLSS and MLSS+ trials. Post hoc tests showed significant differences in blood [lactate] between MLSS and MLSS+ trials at 10, 15, 20, 25 and 30 min (P < 0.05 for all; Fig. 4A). The change in blood [lactate] between 10 and 30 min was significantly greater during the run at MLSS+ compared to the run at MLSS (P < 0.001; Fig. 4A). During the MLSS run, blood [lactate] remained stable (2.1 ± 1.0, 2.3 ± 0.6 and 2.6 ± 1.0 mM at 10, 20 and European Journal of Applied Physiology (2021) 121:3133–3144 3136 1 3 30 min, respectively) whereas, during the MLSS+ run, blood [lactate] increased with time (2.7 ± 1.0, 3.4 ± 1.2 and 4.4 ± 1.3 mM at 10, 20 and 30 min, respectively). There were significant main effects by time (F = 15.9, P = 0.003) and by condition (F = 21.5, P < 0.001), and a significant interaction effect (F = 4.3, P = 0.029) on ̇VO2 for running speeds at MLSS, MLSS+, CS− and CS+. Post hoc analysis revealed that ̇VO2 during MLSS was lower than in MLSS+, CS− and CS+ (P = 0.001, P = 0.004 and P < 0.001, respectively), and ̇VO2 during CS+ was greater than in MLSS, MLSS+ and CS− (P < 0.001, P = 0.012 and P < 0.001, respectively), while there was no difference between MLSS+ and CS− (P = 0.38). ̇VO2 did not change between 10 min and end-exercise during the runs at MLSS, MLSS+ or CS− (P = 0.10, P = 0.26, P = 0.78, respectively) (Fig. 4B and C). During the run at CS+, however, ̇VO2 increased significantly between 5 min and the limit of tol- erance (P < 0.001; Fig. 4C). The ̇VO2 peak measured at the limit of tolerance in the CS+ run (4.67 ± 0.41 L/min) was not significantly different from ̇VO2 peak measured in the step incremental test; however, the end-exercise ̇VO2 in the MLSS, MLSS+ and CS− runs were all significantly lower than ̇VO2 peak measured in the step incremental test (P < 0.05; Fig. 4B and C). Discussion The principal findings of the present study are that: (1) CS is higher than the speed at MLSS; (2) running at a speed 0.5 km/h above MLSS (i.e. MLSS+) results in a significant increase in blood [lactate] between 10 and 30 min, but no significant change in ̇VO2 over the same time frame; (3) run- ning at a speed ~ 0.4 km/h above CS (i.e. CS+), but not at a speed ~ 0.5 km/h below CS (i.e. CS−), results in a significant increase in ̇VO2 over time with peak ̇VO2 attained at the limit of tolerance; and (4) in the current data set, defining the MLSS as a < 2 mM increment in blood [lactate] between 10 and 20 min, as opposed to a < 1 mM increment between 10 and 30 min as per the conventional definition, eliminates the difference between MLSS and CS. These findings are con- sistent with our experimental hypotheses. We interpret the results to indicate that, while MLSS differentiates running speeds for which the blood [lactate] response is steady state vs. non-steady state, it underestimates the maximal meta- bolic steady state as represented by the ability, or inability, to stabilise ̇VO2 during exercise. In contrast, running at a speed just above, but not just below, CS results in a rising ̇VO2 profile until the limit of tolerance is reached, indicating that CS provides a more appropriate representation of maximal metabolic steady state. Consistent with our hypothesis, CS was significantly higher than the speed at MLSS. This finding is in agreement with several other studies which have found CS or CP to be higher than MLSS, both for running and cycling (Dekerle et al. 2003, 2005; Mattioni Maturana et al. 2016; Pringle and Jones 2002). In the present study, CS was ~ 8% higher than MLSS, which is broadly consistent with previous com- parisons: for example, 4% in Smith and Jones (2001), 9% in Pringle and Jones (2002), 16% in Dekerke et al. (2003), 5% in Dekerke et al. (2005) and 1% in Keir et al (2015). While differences in experimental protocol including the number and duration of prediction trials, the sensitivity of MLSS determination (which is a function of the power or speed increments between trials), and the mathematical models used to calculate CP or CS may explain some of the discrep- ancy (Bishop et al. 1998; Black et al. 2015; Mattioni Matu- rana et al. 2018), it is now clear that while CP and MLSS Fig. 1 Maximal lactate steady- state (MLSS) assessment in a representative individual. MLSS was identified as the highest speed where the increase in blood [lactate] did not exceed 1 mM between 10 and 30 min 0 5 10 15 20 25 30 0.0 0.5 1.0 1.5 2.0 2.5 15.0 km/h 15.5 km/h 16.0 km/h Time (min) Blood [lactate] (mmol/L) MLSS European Journal of Applied Physiology (2021) 121:3133–3144 3137 1 3 have historically been considered to represent broadly the same phenomenon, there is limited agreement in practice. The tendency for CP to be higher than MLSS has led to the interpretation that CP overestimates the maximal meta- bolic steady state, with the assumption that MLSS represents the ‘gold standard’ and that a blood [lactate] steady state reflects a ̇VO2 steady state (e.g. Iannetta et al. 2018; Pringle and Jones 2002). However, the physiological rationale for the accepted definition of MLSS (i.e. the highest power or speed at which blood [lactate] does not increase by more than 1 mM between 10 and 30 min of exercise, equivalent to a 0.05 mM increment in blood [lactate] per min) is obscure and apparently arbitrary (Jones et al. 2019a). It has been pointed out that reliance on blood [lactate] alone as a proxy for the existence of muscle metabolic and systemic homeo- stasis is hazardous; that absolute blood [lactate] is influenced by exercise-induced haemoconcentration and modifications to substrate metabolism (Tanaka 1991); and that human, technical and instrument error in the collection and analy- sis of capillary blood samples for [lactate] (Morton et al. 2012; Tanner et al. 2010) at just two discrete time points (10 and 30 min), along with poor day-to-day reproducibility Fig. 2 Pulmonary ̇VO2 responses to four severe-intensity prediction trials at speeds ranging from 17.0 to 21.0 km/h (Panel A), and the critical speed (CS) and D´ estimation in a representative participant using distance–time (Panel B), speed–1/time (Panel C) and speed– time models (Panel D). The model with the lowest sum of coefficients of variation (CoV) for CS and D´ for each participant was selected for analysis. The dashed line indicates the ̇VO2 peak measured in the initial step incremental test in panel A, and the speed-asymptote (CS) in panel D European Journal of Applied Physiology (2021) 121:3133–3144 3138 1 3 of [lactate] during MLSS assessment (Hauser et al. 2013), could result in either false positives or false negatives (Jones et al. 2019a). Moreover, the use of discrete powers or speeds in the MLSS assessment procedure will inevitably result in an underestimation of the actual maximal metabolic steady state (Jones et al. 2019a), the extent of which will depend on the sensitivity of the measurements, with tests typically differing by 20–30 W for cycling and 0.5 or 1.0 km/h for running. Methodological strengths of the present study included that: at least three (and frequently 4 or 5) 30-min trials were used in the assessment of MLSS; the trials were separated by relatively small (0.5 km/h) running speed increments; and, where the increase in blood [lactate] was within 0.05 mM of meeting the criterion of a 1 mM increase (an event that occurred in two participants), the test was repeated and the mean response was used in subsequent analysis. These elements of the study design provide a high level of confidence in the precision of MLSS determination. An important finding in the pre- sent study was that, while MLSS partitioned a running speed at which blood [lactate] did not change between 10 and 30 min from a running speed at which blood [lactate] increased significantly over the same time frame, it did not separate steady state from non-steady-state ̇VO2 responses. Specifically, at the speed immediately (0.5 km/h) above that which was identified as representing MLSS, ̇VO2 did not change significantly between 10 and 30 min. Other recent studies also indicate that MLSS does not reflect the maximum power or speed at which ̇VO2 can be stabilised. For example, Bräuer and Smekal (2020) measured MLSS from 4 to 6 30-min cycle exercise tests in 45 participants and reported stable ̇VO2 over the last 10 min of exercise at both MLSS and at a power output above MLSS. Similarly, Iannetta et al (2018) found that when participants cycled at 10 W above the power output established as representing MLSS, a ̇VO2 steady state was manifest. Such results are insightful because it is known that, in the steady state, pul- monary ̇VO2 closely reflects skeletal muscle ̇VO2 (Grassi et al. 1996; Krustrup et al. 2009). Moreover, pulmonary ̇VO2 and intramuscular [PCr] profiles are closely related both when steady states can be attained and when slow components in the responses are manifest (Rossiter et al. 2002). Collectively, these findings indicate that MLSS, as conventionally defined, does not represent the maxi- mal metabolic steady state, which is more appropriately defined in relation to the ability to stabilise pulmonary ̇VO2 and thus skeletal muscle ̇VO2 and [PCr] (Grassi et al. 1996; Rossiter et al. 2002). In contrast, when the athletes in the present study ran at a speed just above CS (CS+, 16.8 ± 1.3 km/h, calculated according to the 95% CI surrounding the estimate of CS), ̇VO2 increased significantly beyond 5 min, and the end- exercise ̇VO2 was not different from the ̇VO2 peak measured Speed (km/h) 10-30 min 10-20 min 15-30 min 20-30 min 10-30 min 10-20 min 15-30 min 20-30 min 10-30 min 10-20 min 15-30 min 20-30 min >1.0 mM >1.5 mM >2.0 mM * * * * * * * * * * * Fig. 3 Different permutations of the maximal lactate steady state (MLSS) definition including < 1.0, < 1.5 and < 2.0 mM blood [lactate] increase, over time intervals of 10–30 min, 10–20 min, 15–30 min and 20–30 min, and the critical speed (dashed bar). Note that, for clarity, not all of the MLSS permutations are shown. All MLSS permutations were lower than CS (P < 0.05), except for the < 2.0 mM increase in blood [lactate] between 10 and 20 min (MLSS = 15.9 ± 0.9 km/h) European Journal of Applied Physiology (2021) 121:3133–3144 3139 1 3 Fig. 4 Panel A: blood [lactate] responses at the established maximal lactate steady state (MLSS, 15.2 ± 0.9 km/h) and at the speed immedi- ately above MLSS (MLSS+; 15.7 ± 0.9 km/h). Panel B: pulmonary ̇VO2 responses during exercise at MLSS and MLSS+; note that ̇VO2 did not change significantly between 10 and 30 min. Panel C: ̇VO2 at the speeds immediately below CS (CS−, 15.9 ± 0.9 km/h) and above CS (CS+, 16.8 ± 1.3 km/h). ̇VO2 did not change significantly between 10 and 30 min at CS− but increased between 5 min and end-exercise for CS+ (P < 0.05). The dashed line in panels B and C indicates the group mean ̇VO2 peak meas- ured in the step incremental test. Error bars indicate standard deviations. End-exercise ̇VO2 significantly different from ̇VO2 peak measured in the step incremental test (P < 0.05) European Journal of Applied Physiology (2021) 121:3133–3144 3140 1 3 in the maximal incremental test. When the highest speed below CS the athletes ran at (CS−, 15.9 ± 0.9 km/h) was considered, ̇VO2 was not significantly different between 10 and 30 min. Therefore, when the athletes ran at a speed that was ~ 6 s per km slower than CS, ̇VO2 was in steady state and the prescribed 30 min of exercise was completed, whereas when the athletes ran at a speed that was ~ 5 s per km faster than CS, a ̇VO2 steady state could not be achieved and exercise tolerance was limited to ~ 17 min, indicative of exercise within the severe-intensity domain (Black et al. 2017; Poole et al. 1988). These data indicate that CS provides a rather precise demarcation of the highest running speed at which ̇VO2 can be stabilised. This observation is consistent with several pre- vious studies which indicate that CP or CS is the metabolic threshold which partitions severe-intensity exercise, which, by definition, is characterised by an inexorable increase in ̇VO2 to its peak value at the limit of tolerance, from heavy- intensity exercise, during which a ̇VO2 steady state can still be achieved (Jones et al. 2010; Poole et al. 2016; Whipp 1994). It has been established from muscle biopsy studies that these characteristic ̇VO2 profiles during exercise per- formed above and below CP are associated with correspond- ing steady-state or non-steady-state responses in skeletal muscle PCr and lactate concentrations (Black et al. 2017; Vanhatalo et al. 2016). These findings are reinforced by non- invasive 31P-magnetic resonance spectroscopy assessment of the skeletal muscle metabolic responses, which demonstrate striking differences in the profiles of PCr, inorganic phos- phate and pH for exercise performed just above, compared to just below, CP (Jones et al. 2008). These differences in the rates of substrate utilisation and metabolite accumula- tion likely underpin observations that the rate and nature of neuromuscular fatigue development also differ according to the intensity of the exercise task relative to CP (Black et al. 2017; Burnley et al. 2012; Dinyer et al. 2020; Pethick et al. 2020). Finally, it is pertinent to note that simultaneous assessment of the responses of muscle [lactate] and blood [lactate] during heavy-intensity and severe-intensity exercise reveal that the former may be stable while the latter rises (Jones et al. 2019b), suggesting differences in the dynamics of lactate accumulation in the muscle and blood compart- ments. These observations clearly indicate that a maximum blood [lactate] steady state will likely underestimate the maximal metabolic steady state as determined by muscle [lactate], as well as the responses of other muscle ions and metabolites, and ̇VO2. The evaluation of CP or CS is not without its challenges, ideally requiring 3–5 maximal efforts on separate days, although this burden can be alleviated in athletes through the use of recent training or competition data (Jones and Vanhatalo 2017; Karsten et al. 2015; Smyth and Muniz- Pumares 2020). In some situations, estimating CP or CS from submaximal exercise tests may be considered prefer- able to direct assessment. While it is clear from both the present study and from earlier studies that the conventional protocol and criteria for MLSS assessment underestimates the maximal metabolic steady state, it is possible that adjust- ments to these factors might enable a closer approximation of CP or CS. In the present study, we calculated the running speed at MLSS using a variety of permutations of absolute increments in blood [lactate] (e.g. 1.0, 1.5 and 2.0 mM) and the time frame over which such increments were consid- ered. Of these permutations, we found that modifying the criteria to a 2 mM increment in blood [lactate] between 10 and 20 min increased the group mean running speed at MLSS from 15.2 to 15.9 km/h and eliminated the difference between MLSS and CS. This approach does not, however, circumvent other limitations to relying solely on measure- ments of blood [lactate] for the assessment of the maximal metabolic steady state and might be considered to be just as obscure and arbitrary as the conventional definition of MLSS. At the present time, therefore, we favour direct assessment of CP or CS if precision is required in scientific studies or for training prescription. By definition, exercise in the severe-intensity domain should result in the attainment of ̇VO2 peak at or shortly before the limit of tolerance is reached (Hill et al. 2002; Jones et al. 2010; Poole et al. 1988; Whipp 1994). It should be appreci- ated, however, that CP and CS are estimated mathematically from several prediction trials and there will inevitably be some error, both computational and biological (e.g. day- to-day variability), surrounding the estimates (Black et al. 2015; Mattioni Maturana et al. 2018). For this reason, asking participants to exercise to the limit of tolerance exactly at the computed CP or CS can result in wide variability in both the physiological responses and the time to the limit of tolerance due to some participants being below and others being above the CP or CS (Pethick et al. 2020; see Jones et al. 2019a for review). Indeed, the notion of exercising at the CP (or CS) is vacuous because the asymptote of the power–time relationship represents the power that lies exactly between those powers at which W´ is utilised and those powers at which it is not; that is, it defines the threshold separating the heavy-intensity and severe-intensity domains and the inher- ent steady-state or non-steady-state physiological behaviour that defines those domains; and therefore, it is erroneous to define CP as the highest power at which steady-state responses are observed. In the present study, we employed several approaches to minimise the error surrounding the CS estimate including: having the athletes complete at least 4 and up to 6 prediction trials; ensuring that the athletes ran to the limit of tolerance during all prediction trials, as validated by there being no significant difference in ̇VO2 peak achieved in the prediction trials compared to the maximal incremental test; and applying all three standard mathematical models European Journal of Applied Physiology (2021) 121:3133–3144 3141 1 3 and choosing the output from the model with the least error for each individual (Black et al. 2015). Together, these approaches resulted in CoV that were appreciably lower for both CS (0.8 ± 0.7%) and D´ (7 ± 3%) than the degree of error which has been suggested to be acceptable (< 5% for CS and < 10% for D´; Hill 1993). The experimental procedures employed in the present study also ensured that the 95% CI surrounding CS were relatively narrow (i.e. group mean of ± 0.4 km/h). Our study participants, therefore, ran very close to, but very slightly above, their CS to the limit of tolerance as a validation that CS represents the heavy-to-severe exercise intensity bound- ary. It should be acknowledged that, while the group mean CS was estimated to be 16.4 km/h, when the 95% CI is taken into account, the ‘actual’ CS could have occurred anywhere between 16.0 and 16.7 km/h. We took appropriate measures to minimise errors arising from biological factors and math- ematical modelling, but it is important to note that it is not possible to entirely eliminate the error margin surrounding any physiological threshold estimate (Pethick et al. 2020). It should also be appreciated that this range of speeds within which the CS resides represents an error of only ± 4–5 s per km (~ 2%). The time to the limit of tolerance at CS+ (17.0 ± 4.6 min) was closely correlated with the athletes’ D´ (r = 0.82). This indicates that when an exercise task is relativized to ath- letes’ CS values, then the energetic reserve or work capacity above CS becomes an important factor determining exercise tolerance. Within the limitations of the present study (i.e. the error margin surrounding the estimation of CS), these results also reveal that the longest an athlete can run at a con- stant speed and still attain ̇VO2 peak is approximately 17 min. Therefore, assuming an even pace is employed throughout the race to minimise the time taken to complete the distance (Fukuba and Whipp, 1999), it appears likely that athletes will attain ̇VO2 peak during a 5000 m race (since this will be at the lower end of the severe-intensity domain) but not during a 10,000 m race (which is positioned at the upper end of the heavy-intensity domain). These results highlight an important conceptual issue: it is inappropriate to consider peri-CS (or CP) exercise to be ‘fatigueless’, or to be sustain- able indefinitely or for some arbitrary time period such as 60 min; indeed, this might be considered a misinterpreta- tion of the original descriptions of the concept (Monod and Scherrer 1965). Rather, contemporary understanding is (or, at least, in our view, should be) that CS separates exercise domains within which: (1) physiological (including muscle metabolic and cardiorespiratory) responses are differenti- ated by steady-state vs. non-steady-state behaviour; and (2) the predominant determinants of fatigue are altered, with exercise tolerance > CS being predictable as a function of CS and D´. In conclusion, this study affirms that CS occurs at a higher speed than MLSS in well-trained runners. An impor- tant novel finding was that a ̇VO2 steady state was elicited when the athletes ran at a speed which was above MLSS but below CS, whereas a ̇VO2 steady state could not be attained when they ran at a speed which was just above CS. These results indicate that CS, rather than MLSS, provides a better representation of the maximal metabolic steady state. Author contributions AMJ conceived and designed the research. RJN and SHK conducted the experiments. RJN and AV analysed the data. RJN and AMJ wrote the manuscript. All the authors read and approved the manuscript. Declarations Conflict of interests The authors have no conflicts of interest to de- clare. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Beneke R, von Duvillard SP (1996) Determination of maximal lactate steady state response in selected sports events. 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PMC4076256	1 List of abbreviations, deﬁnitions and symbols Abbreviations CoM center of mass SLIP spring loaded inverted pendulum model TD touch down, referring to landing conditions at the swing-stance transition Deﬁnitions Model A reduced-order mathematical description of the physical sys- tem. Here we use the highly reductionist spring-mass model with massless leg. Landing conditions Initial conditions of the CoM (position and velocity) at the be- ginning of stance. These are directly inﬂuenced by the swing- leg trajectory during ﬂight. Passive dynamics Synonymous with intrinsic dynamics—the response of the physical model. Here, the stance dynamics of the model are fully determined by landing conditions and leg stiﬀness. Control policy Active control applied to the model with a speciﬁc target per- formance goal. Here the only applied control is late-swing leg angular trajectory. Peak force control Late-swing leg trajectory optimized to target landing condi- tions for constant peak force of the SLIP model in the drop step (equal to the peak force of the previous step). Impulse control Late-swing leg trajectory optimized to target landing condi- tions for constant axial impulse of the SLIP model in the drop step (equal to the impulse of the previous step). Equilibrium gait control Late-swing leg trajectory optimized to target landing condi- tions for perfect disturbance rejection of the SLIP model in the drop step, resulting in a steady, symmetric gait cycle. 2 Parameters SI Units g gravitational acceleration [m/s2] m body mass [kg] L0 resting leg length [m] BW = mg body weight [N] T = p L0/g periodic time of a pendulum [s] Non-dimensional α leg angle [deg] αPolicy angle of the virtual leg (CoM to foot) predicted by a swing-leg control strategy [deg] αSLIP angle of the virtual leg for the SLIP model during stance [deg] ˙α leg angular velocity [deg/T] ∆ECoM net CoM work [BW L0] Faxial axial leg force [BW] Iaxial axial leg impulse [BW T] Ix fore-aft impulse [BW T] kLeg eﬀective linear leg stiﬀness [BW/L0] L leg length [L0] ˙L leg length velocity [L0/T] r = (x, y)T CoM position [L0] ˙r = ( ˙x, ˙y)T CoM velocity [L0/T] ¨r = (¨x, ¨y)T CoM acceleration [L0/T 2] t time [T]	Swing-leg trajectory of running guinea fowl suggests task-level priority of force regulation rather than disturbance rejection.	06-30-2014	Blum, Yvonne,Vejdani, Hamid R,Birn-Jeffery, Aleksandra V,Hubicki, Christian M,Hurst, Jonathan W,Daley, Monica A	eng
PMC8128237	Dear authors and editor, It is a pleasure the opportunity to act as a reviewer for PLOS ONE. So, thank you very much for it. I have written my comments into two sections, the general comments with the main three ideas, and specific comments. With them, I hope to help the authors to improve the manuscript, encouraging them to perform due to the relevance of this topic. General comments - The lack of a deep literature review makes that introduction and discussion are overviews, but not further contextualization of “state of the art” or discussion of this topic. - I am worried with the sampling rate considered. In sport literature, two systematic reviews have recently highlighted that tracking technologies are limited in high intensity short efforts such as CODs [1,2]. However, the authors considered 20 Hz for data raw and then they down sampling. Are these sampling rates suitable for this analysis? or is it a limitation? - In my opinion, the results section shows a deep analysis, that does not meet with the rest of the article. The results showed: 1. The difference between COD and set angle. 2. Number and duration of COD during matches. 3. Number of CODs per playing position. Regarding to the results 1 and 2, the aim should be: the validity of a new approach to set angles and duration of CODs. These aims are contextualized and fit with the rest of the sections (introduction and discussion), however, the reason to present data per playing position is not contextualized. Why do the authors consider it? Specific comments Title: - Re-consider changing “locomotion” by “set angles and time of CODs”. Abstract: - “CODs” is not defined the first time in it was mentioned. - The “background” of abstract not contextualize the main objective. It should be something like: “Soccer players frequently change of directions (CODs) at various speeds during matches”. However, tracking systems have shown limitations to measure these efforts. Therefore, the aim of the present study was to propose a new approach to measure CODs using a local positioning system (LPS).” Introduction: - The introduction lacks from relevant literature, making non suitable the contextualization of the objective. I suggest: o Paragraph 1: Explain the importance of change of directions. Maybe, different studies performed using PCA are suitable option to highlight the importance of change of directions and other high-intensity short efforts [3]. (The idea has been written, but in my opinion, further information is needed). o Paragraph 2: It should explain: ▪ What are the tracking technologies (the main basis to use the new proposal that the authors presented). ▪ What it has been found about tracking systems and change of directions. ▪ What has it been the main problem of accuracy in this regard (e.g sampling rate). o Paragraph 3: it should explain “the state of the art” about all studies published in this topic (LPS and change of direction). Consider, at least, these references: [4–8]. Following this recently publicized systematic review [1], these all the validity proposals using LPS in sport setting. *In general, the introduction has interesting ideas, but it should be deeply rewritten. Methodology: Separate “participants” from “experimental design”. Participants: Add: - Where do the players selected? What inclusion/exclusion criteria were considered? - Participants characteristics. - See any scale for risk of bias, and follow it. Experimental design: - Line 77-78: CODs in directions determined by 13 set angles. My question is: 3 or 13 set angles? Players´ coordinate data: - Sampling frequency: the authors mentioned 20 Hz (and less with filtering processed). However, the use of 20 Hz has not been enough for high intensity short efforts using LPS. Why do the authors think that in this study could be suitable sampling frequency? Could be a limitation? What was the criteria to use these sampling rates? - The authors mentioned that an LPS was used, but, several factors could affect the outcome that they could not be related with the proposed approach. Therefore, a higher precision about “the use of technology” should be made, mainly to avoid the following principle: “A good experimental design is one in which the only explanation for the change in the dependent variable is due to the treatment applied”. I suggest the use of recently published survey [9]. Results The results are well-conducted, but they have different ways ((i) the difference between COD and set angle, (ii) number and duration of COD during matches, and (iii) number of CODs per playing position)) that are not reflected in the rest of the article. In my opinion, the article should be adapted to performed results (see general comments). Consider explain the three protocols in the abstract. Discussion: - Re-write the discussion, considering the suggested articles for the introduction (paragraph 3). - Reconsider separate a discussion for each of the results presented: (i) the difference between COD and set angle, (ii) number and duration of COD during matches, and (iii) number of CODs per playing position. If all of them are relevant for the aim of this study (see general comments). - Add limitations. Conclusion: See the comments mentioned above. If following the results the aim was different folds, the conclusions should have different folds too. References: A further revision of the literature is needed. Different articles have been published about the use of LPS to assess CODs, and some of them were not considered. Bibliography: 1. Rico-González M, Arcos AL, Clemente FM, Rojas-Valverde D, Pino-Ortega J. Accuracy and Reliability of Local Positioning Systems for Measuring Sport Movement Patterns in Stadium-Scale: A Systematic Review. Applied Sciences. 2020;10:5994. 2. Pino-Ortega J, Oliva-Lozano JM, Gantois P, Nakamura FY, Rico-González M. Comparison of the validity and reliability of local positioning systems against other tracking technologies in team sport: A systematic review. Proc IMechE Part P: J Sports Engineering and Technology. 2021; 3. Oliva-Lozano JM, Rojas-Valverde D, Gómez-Carmona CD, Fortes V, Pino-Ortega J. Impact of contextual variables on the representative external load profile of Spanish professional soccer match-play: A full season study. European Journal of Sport Science. 2020;1–10. 4. Frencken WGP, Lemmink KAPM, Delleman NJ. Soccer-specific accuracy and validity of the local position measurement (LPM) system. Journal of Science and Medicine in Sport. 2010;13:641–5. 5. Ogris G, Leser R, Horsak B, Kornfeind P, Heller M, Baca A. Accuracy of the LPM tracking system considering dynamic position changes. Journal of Sports Sciences. 2012;30:1503–11. 6. Stevens TGA, de Ruiter CJ, van Niel C, van de Rhee R, Beek PJ, Savelsbergh GJP. Measuring Acceleration and Deceleration in Soccer-Specific Movements Using a Local Position Measurement (LPM) System. International Journal of Sports Physiology and Performance. 2014;9:446–56. 7. Linke D, Link D, Lames M. Validation of electronic performance and tracking systems EPTS under field conditions. Ardigò LP, editor. PLOS ONE. 2018;13:e0199519. 8. Luteberget LS, Spencer M, Gilgien M. Validity of the Catapult ClearSky T6 Local Positioning System for Team Sports Specific Drills, in Indoor Conditions. Front Physiol. 2018;9:115. 9. Rico-González M, Arcos AL, Rojas-Valverde D, Clemente FM, Pino-Ortega J. A Survey to Assess the Quality of the Data Obtained by Radio-Frequency Technologies and Microelectromechanical Systems to Measure External Workload and Collective Behavior Variables in Team Sports. Sensors. 2020;16.	A new approach to quantify angles and time of changes-of-direction during soccer matches.	05-17-2021	Kai, Tomohiro,Hirai, Shin,Anbe, Yuhei,Takai, Yohei	eng
PMC10011548	1 Vol.:(0123456789) Scientific Reports \| (2023) 13:4167 \| https://doi.org/10.1038/s41598-023-30798-3 www.nature.com/scientificreports Progressive daily hopping exercise improves running economy in amateur runners: a randomized and controlled trial Tobias Engeroff 1,6, Kristin Kalo 2,6, Ryan Merrifield 3, David Groneberg 4 & Jan Wilke 4,5 This study investigated the effects of a daily plyometric hopping intervention on running economy (RE) in amateur runners. In a randomized, controlled trial, thirty-four amateur runners (29 ± 7 years, 27 males) were allocated to a control or a hopping exercise group. During the six-week study, the exercise group performed 5 min of double-legged hopping exercise daily. To progressively increase loading, the number of hopping bouts (10 s each) was steadily increased while break duration between sets was decreased. Pre- and post-intervention, RE, peak oxygen uptake (VO2peak), and respiratory exchange ratio (RER) were measured during 4-min stages at three running speeds (10, 12, and 14 km/h). ANCOVAs with baseline values and potential cofounders as cofactors were performed to identify differences between groups. ANCOVA revealed an effect of hopping on RE at 12 km/h (df = 1; F = 4.35; p < 0.05; η2 = 0.072) and 14 km/h (df = 1; F = 6.72; p < 0.05; η2 = 0.098), but not at 10 km/h (p > 0.05). Exercise did not affect VO2peak (p > 0.05), but increased RER at 12 km/h (df = 1; F = 4.26; p < 0.05; η2 = 0.059) and 14 km/h (df = 1; F = 36.73; p < 0.001; η2 = 0.520). No difference in RER was observed at 10 km/h (p > 0.05). Daily hopping exercise is effective in improving RE at high running speeds in amateurs and thus can be considered a feasible complementary training program. Clinical trial registration German Register of Clinical Trials (DRKS00017373). Competitive runners are on the constant quest for maximal performance. However, after decades of significant improvements, a trend towards more marginal changes has been observed in several disciplines including endur- ance running1. Consequently, a “marginal gains” approach, which aims at combining multiple small performance improvements in different areas to create a significant advantage, is becoming increasingly popular in amateur and professional competitive sports. Aiming at such marginal gains, amateur runners adopt complementary training approaches such as plyometrics from professional sports with the aim to maximize performance and minimize injury risk2. Running performance is inherently dependent on the efficiency of locomotion, which is often referred to as running economy (RE). How efficient a human moves over the ground is not only influenced by metabolic fac- tors, but also by the quality of movement patterns and the mechanical characteristics of the locomotor system3. The concept of RE takes these metabolic, neural and tissue-specific factors into account. It is defined as the oxygen uptake required per distance at a given running speed and represents one of the key parameters to quantify the ability to transform aerobic capacity into endurance running performance3,4. Studies in both, amateurs5 and elite athletes6, confirmed the relevance of RE as a crucial factor for endurance running performance. Therefore, strategies to improve RE are sought after by coaches, athletes, and sports scientists7. Resistance training, plyometrics, and stretching represent three popular methods used to target RE7. A common facet of these interventions is that they all influence metabolic, biomechanical and neuromuscular OPEN 1Division Health and Performance, Institute of Occupational, Social and Environmental Medicine, Goethe University Frankfurt, Frankfurt Am Main, Germany. 2Department of Sports Medicine, Disease Prevention and Rehabilitation, Johannes Gutenberg University Mainz, Albert-Schweitzer-Straße 22, 55128 Mainz, Germany. 3Department of Sports Medicine and Exercise Physiology, Institute of Sports Sciences, Goethe University Frankfurt, Frankfurt Am Main, Germany. 4Institute of Occupational, Social and Environmental Medicine, Goethe University, Frankfurt, Frankfurt Am Main, Germany. 5Departement of Movement Sciences, University of Klagenfurt, Klagenfurt, Austria. 6These authors contributed equally: Tobias Engeroff and Kristin Kalo. email: kkalo@uni-mainz.de 2 Vol:.(1234567890) Scientific Reports \| (2023) 13:4167 \| https://doi.org/10.1038/s41598-023-30798-3 www.nature.com/scientificreports/ efficiency7. Since simply replacing endurance training by an RE intervention would limit the ability to main- tain or increase maximal aerobic performance, a more promising approach to maximize running performance requires the maintenance of endurance training routines to which additional methods that may improve RE are added (e.g. explosive strength exercises3). Popular contents of such additional training strategies include jumps, hops, or sprints and are suggested to improve muscle/tendon stiffness or to modify movement mechanics and the stretch-shorten cycle (SSC)3. According to the available evidence, the Achilles tendon (AT) has been demonstrated to play a significant role in RE8–10. Kunimasa et al.11 compared a variety of anatomical characteristics of Kenyan and Japanese elite distance runners. The Kenyans, superior in running performance, exhibited higher relative AT lengths and greater AT tendon moment arms. This is of importance because both factors are positively associated with RE9,10. In addi- tion to morphological features, RE is also influenced by the mechanical properties of the AT. Arampatzis et al.8 showed that RE is positively associated with normalized AT stiffness. Following a 14-week resistance exercise intervention, a 7%-increase in plantar flexor strength and a 16%-increase in tendon stiffness resulted in a 4% reduction of oxygen consumption12. In a pioneering study, Kawakami et al.13 found that muscle fibers, contrary to earlier beliefs, act almost iso- metrically during stretch–shortening cycles (SSC). Conversely, the tendon undergoes significant length changes, storing and releasing kinetic energy. While the AT contributes more than 50% of the positive work even at low running speeds of ~ 2 m/s, this proportion increases to about 75% during sprinting14. In view of the accumulat- ing evidence supporting the importance of the AT in RE, numerous studies have investigated the effectiveness of related exercise interventions. Plyometric training, often using reactive jumps, hops or, bounces when applied in the lower limb, is a popular strategy aiming to improve SSC performance. It hence seems particularly suited to trigger morphological and functional adaptation of the tendon. However, so far, only a limited number of studies examined the effect of explosiveness training interventions on RE, and only a few of them used exclu- sively plyometric exercises3. Furthermore, most available trials used one to three sessions per week, but none studied higher frequencies3. This is of importance because it has been shown that collagen production, which is paramount for tendon stiffness, is most effectively triggered by intermittent, progressive loading paradigms with relatively short durations and intervals15,16. Finally, most of the available evidence of exercise interventions and RE focusses on athletes with moderate to high-performance levels17. The present study therefore aimed to investigate the effects of a daily plyometric hopping intervention on RE in amateur runners. We defined amateur runners as persons who compete in sports without striving for financial reward (as opposed to professional athletes), thus do running as a hobby. Methods Study design and survey procedure. A two-arm randomized controlled trial was performed. Active amateur runners were allocated to a hopping exercise (HE) or control (CON) group. Randomization was coun- terbalanced and conducted using BiAS for Windows version 11.10 (Goethe University Frankfurt, Germany). The study was approved by the local ethics committee (Ethikkommission FB 05, Goethe University Frankfurt; reference number: 2018-17b) and registered at the German Register of Clinical Trials (DRKS00017373, date of registry: 03/09/2019). All participants provided written informed consent. Healthy adults were recruited using word of mouth, printed flyers, and social media advertising. To ensure a specific fitness level and thus that participants are able to complete the running protocol, individuals had to be amateur runners with a 10 km time < 55 min and younger than 40 years of age. Exclusion criteria encompassed contraindications for engagement in physical activity (tested by means of the Physical Activity Readiness Ques- tionnaire), severe cardiovascular, metabolic, endocrine, neural, and psychiatric diseases, unhealed orthopaedic injuries and overuse disorders (particularly with regard to the knee and ankle region), local inflammation, pregnancy, self-reported use of supplements containing stimulants and anabolic–androgenic steroids. Intervention. The CON and the HE groups continued their regular exercise regimes. While the CON group did not engage in an additional specific exercise intervention, the HE group completed a six-week plyometric hopping protocol. Each day, the individuals randomized to HE performed a variable amount of double-legged 10-s hopping bouts (Table 1). While total session duration (5 min) was constant, the number of sets (and with this, net training time) was increased weekly in order to ensure safe functional and mechanical adaptation. When hopping, participants were instructed to start with both feet no wider than hip width apart and to hop as high as possible with both legs, keeping the knees extended and aiming to minimize ground contact time. Table 1. Protocol of the hopping intervention. s seconds. Week Sets Set duration [s] Net hopping duration [s] Rest between sets [s] 1 5 10 50 50 2 6 10 60 40 3 8 10 80 30 4 10 10 100 20 5 15 10 150 10 6 15 10 150 10 3 Vol.:(0123456789) Scientific Reports \| (2023) 13:4167 \| https://doi.org/10.1038/s41598-023-30798-3 www.nature.com/scientificreports/ To ensure safe and correct execution, participants received a 1-to-1 explanation by a coach holding a bachelor’s degree in Sports Science, who additionally monitored the first three training sessions of each individual. When later exercising alone, the HE participants provided the instructor with video recordings in order to allow super- vision and, if needed, correction. Moreover, participants completed a hopping diary. If at least 70% of the jumps were completed, the subjects were considered compliant. All participants completed a training diary, documenting weekly running activity (number of sessions, hours per session, pace) as well as other exercises in hours/ week (see Table 2) and (in case of the HE group) adherence to the hopping intervention. Measures. Before and after the intervention period, all exercise tests were performed on an electronically driven treadmill (mercury® med, h/p/cosmos sports & medical gmbh, Traunstein, Germany) without using a safety belt. To reduce intrasubject variability, the time of the day, the test equipment, as well as the type of run- ning shoes worn were standardized for both, baseline and post-exercise tests. Participants were instructed to avoid exercise for 24 h and strenuous exercise for 48 h prior to testing. In addition, participants were asked to eat about 1.5 to 2 h before exercise testing, not to drink alcohol the day before, and not to consume any alcohol or cigarettes on the day of the test. Temperature was controlled by air conditioning and the difference in tempera- ture between the baseline and post measurement did not exceed ± 0.5° Celsius. In accordance with Saunders et al.18, RE was determined by measuring submaximal oxygen uptake (VO2) during 4-min stages at three constant running speeds and an inclination of 0°. After a standardized warm-up of 3 min walking at 5 km/h, participants ran at 10, 12, and 14 km/h, respectively7. Peak oxygen uptake (VO2peak) was determined during a ramp protocol performed 2 min after the last submaximal running stage. For this purpose, speed was increased by 1 km/h every minute from 12 km/h and to 20 km/h, treadmill inclination was increased by 1% until volitional exhaustion19. A breath-by-breath gas analyser was used to monitor gas exchange during exercise testing (Metalyzer, COR- TEX Biophysik GmbH, Leipzig, Germany). Calibration of the gas as well as the flow sensor was performed according to manufacturer recommendations. Meyer et al.20 showed an excellent test–retest reliability for the used system (VO2: 0.969, VCO2: 0.964, VE 0.953). Recorded data was stored and processed using a spiroergometry software (MetaSoft® Studio, CORTEX Biophysik GmbH, Leipzig, Germany). At the end of each running stage, the participants prepared themselves to step off the treadmill by gripping the side handles of the treadmill. Therefore, for each step, the last 10 s of VO2 data was cut off and RE was determined as the V̇O2 collected during the last valid 60 s (i. e., seconds 170 to 230) of each 4-min running stage. Respiratory exchange ratio (RER, VCO2/VO2) was calculated for these 60 s as well. During the ramp protocol, the highest individual V̇O2 recorded over a 30 s period within the testing time (floating mean) was defined as VO2peak. Statistical analysis. All analyses were performed using Jamovi 1.8 (The jamovi project, https:// www. jam- ovi. org) and the significance level was set to α = 0.05. After variance homogeneity was confirmed using Levene´s Test, analyses of covariance (ANCOVA) with baseline values as a cofactor were performed to test for inter- vention effects (between subjects/groups) on running economy and secondary outcomes (VO2peak, respiratory exchange ratio). Sex was analysed as potential confounder. Further confounders (weight, frequency of regular running sessions and other exercises) were tested for between group differences using Kruskal–Wallis Tests. In case of significance, a second ANCOVA for intervention effects on running economy including baseline values for running economy and the potential confounding outcomes was carried out. For the estimates of effect sizes, eta squared (η2) was used and interpreted according to Cohen21: 0.01 (small effect), 0.06 (medium effect) and 0.14 (large effect). Ethics approval and patient consent. The study was conducted according to the ethical guidelines of the Helsinki Declaration and was approved by the local ethical review board (Ethikkommission FB 05, Goethe University Frankfurt), number: 2018-17b. All participants provided written informed consent. Table 2. Description of the sample (mean values and standard deviations). n number, kg kilograms, m meter, h hours, km kilometers. HE group (n = 15) CON group (n = 19) Total (n = 34) Sex 11♂ ♀4 16♂ ♀3 27♂ ♀7 Age [years] 29.1 (7.6) 28.2 (5.9) 28.6 (6.6) Weight [kg] 73.8 (10.4) 78.6 (9.01) 76.5 (9.8) Height [m] 1.78 (0.07) 1.80 (0.07) 1.80 (0.08) Exercise [h/week] 8.0 (3.0) 8.2 (3.4) 8.1 (3.2) Running duration [h/week] 3.3 (2.4) 2.37 (2.2) 2.8 (2.3) Running frequency [n/week] 2.7 (1.5) 1.89 (1.3) 2.2 (1.4) Running experience [years] 6.2 (4.7) 8.21 (5.6) 7.3 (5.3) Running speed [km/h] 10.9 (1.8) 11.15 (1.5) 11.0 (1.6) 4 Vol:.(1234567890) Scientific Reports \| (2023) 13:4167 \| https://doi.org/10.1038/s41598-023-30798-3 www.nature.com/scientificreports/ Results From n = 46 recruited individuals, a total of n = 34 adults (29 ± 7 years, 27 males) completed the study. Overall, n = 12 participants dropped out of our study. Reasons were illness (n = 4), injury (n = 4), lack of time to follow-up (n = 2), and < 70% compliant with the hopping protocol (n = 2). A detailed indication of the dropouts per group is depicted in Fig. 1. Descriptive data of sample characteristics are presented in Table 2. None of the potential confounders includ- ing weight (df = 1; F = 0.0468; p = 0.830; η2 = 0.001), running frequency (df = 1; χ2 = 0.0305; p = 0.861), and general training volume (df = 1; χ2 = 0.8605; p = 0.354) showed significant differences between the two groups. Pre and post values of VO2peak, RE and RER per stage and group are presented in Table 3. Levene´s test indicated variance homogeneity for primary and secondary outcomes. ANCOVA revealed that hopping significantly improves running economy at 12 km/h (df = 1; F = 4.35; p = 0.045; η2 = 0.072) and 14 km/h (df = 1; F = 6.72; p = 0.015; η2 = 0.098) running speed. In contrast, no difference between the HE and CON group was found at a low (10 km/h) running speed (df = 1; F = 3.11; p = 0.088; η2 = 0.043). HE did not lead to higher Figure 1. Flow chart of the study. Table 3. Spiroergometric values (mean values and standard deviations). n number, kg kilograms, h hours, km kilometers, ml milliliters, min minutes, VO2 oxygen intake, RE running economy, RER respiratory exchange ratio, VCO2 carbon dioxide production. HE group (n = 15) CON group (n = 19) Total (n = 34) Pre Post Pre Post Pre Post VO2peak [ml/min/kg] 51.4 (6.0) 51.1 (5.6) 48.26 (4.3) 50.2 (4.5) 49.7 (5.2) 50.6 (5.0) RE (ml/min/kg), 10 km/h 35.2 (2.97) 33.9 (2.95) 34.8 (3.07) 34.8 (2.79) 35.0 (2.99) 34.4 (2.86) RE (ml/min/kg), 12 km/h 41.1 (2.57) 40.2 (2.67) 40.4 (3.35) 41.3 (2.86) 40.7 (3.01) 40.9 (2.79) RE (ml/min/kg), 14 km/h 47.1 (3.04) 46.1 (3.31) 45.3 (3.33) 46.9 (2.79) 46.1 (3.29) 46.5 (3.01) RER (VCO2/VO2), 10 km/h 0.90 (0.04) 0.93 (0.05) 0.92 (0.05) 0.93 (0.05) 0.91 (0.05) 0.93 (0.05) RER (VCO2/VO2), 12 km/h 0.95 (0.04) 0.98 (0.05) 0.97 (0.05) 0.97 (0.05) 0.96 (0.05) 0.98 (0.05) RER (VCO2/VO2), 14 km/h 1.01 (0.06) 1.05 (0.07) 1.02 (0.05) 1.02 (0.05) 1.02 (0.06) 1.03 (0.06) 5 Vol.:(0123456789) Scientific Reports \| (2023) 13:4167 \| https://doi.org/10.1038/s41598-023-30798-3 www.nature.com/scientificreports/ VO2peak values in the HE group compared to the CON group (df = 1; F = 2.83; p = 0.102; η2 = 0.025). However, after six weeks of training, the respiratory exchange ratios during 12 km/h (df = 1; F = 4.26; p = 0.047; η2 = 0.059) and 14 km/h (df = 1; F = 36.73; p < 0.001; η2 = 0.520) running speed were significantly higher in the HE group. RER at 10 km/h running speed remained unchanged (df = 1; F = 0.490; p = 0.489; η2 = 0.011). Sex showed no impact on ANCOVA results. Figure 2 shows group differences in the estimated marginal means of the post-values for RE and RER (generated considering the pre-values) at all three running speeds. Discussion The present study yielded three key findings. Firstly, six weeks of daily hopping exercise improve running econ- omy and increase respiratory exchange ratio at higher running speeds (12 and 14 km/h) in amateur runners. Secondly, maximal aerobic capacity remains unaltered by hopping if regular running and exercise habits are maintained. Our findings are in line with earlier studies examining plyometric interventions in amateur athletes22 and highly trained runners18. They also corroborate the observation of Saunders et al.18 that the effects of plyomet- rics on running economy are more pronounced at higher running speeds. However, in contrast to the previous trials which used three weekly sessions with durations of up to 30 min as well as multiple jump exercises18,22, this study applied short daily bouts (net duration 5 min) consisting of one simple and easy-to-learn hopping exercise only. Our results suggest that regular endurance and concurrent plyometric training can be performed jointly by amateur runners, without complex programs and at a high frequency without leading to adverse events such as overuse injuries or pain. Finally, third, performing a progressive hopping protocol with a high exercise density seems to be safe in amateur athletes as no injury or other side effects related to the interventions were reported. Although the present trial strengthens the evidence that plyometric exercise improves running economy, the relative contributions of factors driving the observed changes are still a matter of debate. Tendon stiffness has been shown to significantly correlate with running economy23. Earlier findings reported that the contribution of AT energy storage to the positive work performed during running increases with locomotion speed14. Our data show significant improvements in RE at 12 and 14 km/h but not at 10 km/h which would provide support for the hypothesis that tendon stiffness was one of the driving factors for improvements in RE. Our hypothesis assumed that repeated hopping would elicit morphological and functional adaptation in the tissue, which in turn improve energy storage capacity. The daily loading paradigm was chosen based on research elucidating optimal loading paradigms for collagenous connective tissues15,16. For instance, Paxton et al.15 used engineered ligaments to study the effect of cyclic stretch on the phosphorylation of the extracellular signal- regulated kinase 1/2 (ERK1/2), an enzyme associated with collagen synthesis. Maximal ERK 1/2 levels were observed after 10 min of cyclic stretch and up to 6 h, cells were refractory to new stretching bouts. As these data suggest that repeated short loading periods may be optimal for tissue adaptation, the authors compared a daily intermittent stretch program (10 min each 6 h) to continuous tissue lengthening and confirmed the superiority of the physiology-specific paradigm. In our trial, tissue loading was of similar duration (8–14 min). Yet, as three training bouts per day with 6-h-intervals are not feasible due to nocturnal sleep, we decided for one exercise Figure 2. Group differences in the estimated marginal means of the post intervention values of RE and RER at three running speeds. Means (dots) and 95% confidence intervals (vertical lines) are displayed. 6 Vol:.(1234567890) Scientific Reports \| (2023) 13:4167 \| https://doi.org/10.1038/s41598-023-30798-3 www.nature.com/scientificreports/ session per day. The intervention was well tolerated and just two participants report pain (shin and ball of the foot), which might be related to the intervention. Upcoming trials may therefore consider further increasing the frequency, e.g., to twice daily. Also, it has to be noted that we did not measure tendon stiffness (e.g., using an instrumented treadmill). Future studies should hence be geared to combine both, assessments of stiffness and RE. In addition to adaptations in the tendon, metabolic factors may have caused the alterations of RE3. One often discussed adaptation is increased aerobic carbohydrate utilisation for oxidative phosphorylation24. A shift in substrate use could have led to better running economy based on a lower oxygen cost for adenosine triphosphate (ATP) synthesis if a greater share of carbohydrates is used instead of fatty acids. Since RER values in our study exceeded 1.0 at higher running speed, another explanation for decreased oxygen uptake might be an increase in anaerobic metabolism5. The assumption that a change in substrate use might have caused alterations in running economy is supported by an increase in respiratory exchange ratios in the hopping training group. Due to the applied method, which is based on oxygen uptake, running economy in our analysis reflects only aerobic energy metabolism. Based on our data, we thus are not able to delineate which of the aforementioned mechanisms led to the increase in RER. Furthermore, we did not control if all participants were able to maintain a metabolic steady state over a larger timeframe than 4 min during all tested running speeds and did not assess running performance using a time trial or a fixed distance. Consequently, further studies are necessary to prove, that differences in oxygen costs of running govern improvements in running performance. A third pathway for hop- ping induced improvements in RE could be based on changes in movement patterns3. Alterations in running mechanics are discussed as a possible mechanism for RE improvements induced by plyometric and explosive resistance training7. However, evidence on specific adaptations is scarce so far and further studies investigating running mechanics are needed. Moreover, we did not investigate the subjective acceptance and feasibility of our hopping protocol to daily (exercise) routines using structured interviews. Our findings have implications for clinical practice and may open new avenues for future research. As indi- cated earlier, previous training paradigms predominantly focussed on complex and more time consuming inter- ventions which are particularly used by professional athletes3. This trial provided first evidence that short daily regimes consisting of just one jump exercise are effective in improving RE in amateurs. Such regimes become more relevant as many people run and compete for fun and social aspects, but also report musculoskeletal pain caused by running25. In addition, the “marginal gains” approach, is becoming increasingly popular in amateur sports2. Therefore, hopping can be considered as short and feasible program for amateur runners to optimize performance and minimize the risk for musculoskeletal pain and injuries. Although direct comparison of a) short daily and b) longer but less frequently applied training approaches are lacking, based on current evidence, the choice could be left to individual preference. In regard to applicability, further studies should analyse if plyometric exercises can be executed in a fatigued and non-fatigued state. As an example, it is unknown whether hopping interventions performed in direct proximity to regular training (immediately before or after) would thwart the beneficial effects achieved by endurance or hopping exercises. Finally, besides endurance runners, athletes in other sports might also benefit from increased RE. Team sports such as basketball or soccer are characterized by periods of moderate intensity running which are disrupted by short bouts of high-intensity actions26. Con- sequently, RE is discussed as a relevant factor for athletic performance26 and training regimes might contribute to enhanced endurance and running speed in team sport athletes. Perspective. This study provides first evidence that 5 min of daily hopping improve RE at moderate and high running speed without compromising maximal aerobic capacity in amateur runners. This is in line with previous studies using less frequent jump exercises with higher duration18,22. The more frequent units nevertheless appear to be feasible and safe. However, the factors that lead to an improvement in RE are still debated. Upcoming trials should focus on comparisons of amateur and elite athletes, further increases of exercise frequency (e.g., twice daily), and the specific mechanisms underlying hopping induced RE improvements, inter alia including altered tendon stiffness and substrate utilisation. Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Received: 25 November 2022; Accepted: 1 March 2023 References 1. Weiss, M., Newman, A., Whitmore, C. & Weiss, S. 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PMC10382573	1 Vol.:(0123456789) Scientific Reports \| (2023) 13:12244 \| https://doi.org/10.1038/s41598-023-38328-x www.nature.com/scientificreports Relationship between biomechanics and energy cost in graded treadmill running Marcel Lemire 1,3, Robin Faricier 2, Alain Dieterlen 1,3, Frédéric Meyer 4,5* & Grégoire P. Millet 4* The objective of this study was to determine whether the relationships between energy cost of running (Cr) and running mechanics during downhill (DR), level (LR) and uphill (UR) running could be related to fitness level. Nineteen athletes performed four experimental tests on an instrumented treadmill: one maximal incremental test in LR, and three randomized running bouts at constant speed (10 km h−1) in LR, UR and DR (± 10% slope). Gas exchange, heart rate and ground reaction forces were collected during steady-state. Subjects were split into two groups using the median Cr for all participants. Contact time, duty factor, and positive external work correlated with Cr during UR (all, p < 0.05), while none of the mechanical variables correlated with Cr during LR and DR. Mechanical differences between the two groups were observed in UR only: contact time and step length were higher in the economical than in the non-economical group (both p < 0.031). This study shows that longer stance duration during UR contributes to lower energy expenditure and Cr (i.e., running economy improvement), which opens the way to optimize specific running training programs. Although complex physiological and biomechanical factors play important roles in level running (LR), the energy cost of running (Cr) appears as one of the three main predictive factors determining performance1. The Cr rep- resents the amount of energy required per unit of kilometer at a given submaximal running velocity allowing to maintain a physiological steady state2. Though, the influence of LR Cr on graded running performance remains unclear3–6. It has been reported that LR cost of locomotion is a poor indictor of performance in short distance trail races3,4 but the importance of energy cost on ultramarathon remains debated5,6. A relationship has been observed between the oxygen cost (amount of oxygen consumed per distance unit) in uphill running (UR) and LR in elite ultra-trail runners7. However, additional specific parameters such as knee extensor muscle endurance and UR Cr may play a role on inclined running performance4. It has been suggested that changes in the running pattern from negative to positive slopes explain the positive linear increase in Cr with positive slope8–11, but not in downhill running (DR), where the relationship between the slopes and Cr has an U-shape with the lowest Cr value at approximately − 10 to − 20% slope9,12. While each individual naturally develops their optimal running pattern (i.e., spatiotemporal parameters of stride, running gait) according to their personal characteristics in order to lower their Cr13,14, it is well known that changing this self-selected running pattern may alter the Cr15–18. Therefore, one may suggest that the most economical runners efficiently adapt their running mechanics to the slope condition. Though, which biomechanical adaptations are associated with a lower Cr remains an open question. According to experimental data, DR involves braking muscle actions of the lower limbs and is considered a predominantly eccentric exercise modality19. In contrast, UR predominantly involves concentric propulsive muscle contractions19. While the physiological adaptations to hilly terrain are currently being widely investigated, the main performance determinants for LR, UR and DR may differ, with a greater contribution of biomechanical parameters in DR performance20. Compared to LR, UR induces a decrease of aerial time and step length, whereas DR increases the aerial time and the step length. However, the contact phase is less affected by slope, leading to an increase of step frequency during UR9,10,21,22. Furthermore, the ratio of positive to negative work is another important biomechanical factor that may explain the slope-dependent variations in Cr9,23. The mechanical positive and negative external works (Wext + and Wext −, respectively) represent the work performed at each step to support the upward and downward OPEN 1Faculty of Sport Sciences, University of Strasbourg, Strasbourg, France. 2School of Kinesiology, The University of Western Ontario, London, ON, Canada. 3Institut de Recherche en Informatique, Mathématiques, Automatique Et Signal, Université de Haute-Alsace, 68070 Mulhouse, France. 4Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland. 5Digital Signal Processing Group, Department of Informatics, University of Oslo, Oslo, Norway. email: fredem@ifi.uio.no; gregoire.millet@unil.ch 2 Vol:.(1234567890) Scientific Reports \| (2023) 13:12244 \| https://doi.org/10.1038/s41598-023-38328-x www.nature.com/scientificreports/ movements of the center of mass of the body (CoM), respectively24. As the downward movement of the CoM decreases with positive slopes, Wext − decreases and the Wext + increases, and conversely with negative slopes the downward movement increases whereas the upward decreases24. Since the energy required to perform Wext − is less than that of Wext + 25, the more positive the slope, the higher the concentric muscle actions for the elevation of the CoM, leading to an increase in energy expenditure8. Conversely, the energy demand in DR is lowered due to the increased part of the eccentric muscle activation and the gravity effect, saving energy8. Nevertheless, the direct relationship between running mechanics and Cr in UR or DR is under investigated. The stride kinematic adaptations in graded running may also influence Cr, as debated in LR13. One study has investigated the relationship between running economy and spatiotemporal running parameters within specific slope conditions in a homogeneous group of well-trained runners and reported correlations between spatiotemporal parameters only in DR12. The Cr in DR was negatively correlated with both step frequency and step length while positively correlated with contact time12. The step length and frequency, and the vertical stiffness were negatively correlated whereas ground contact time was positively correlated with Cr in DR12. Conversely, Lussiana et al.14 showed that minimal shoes reduced contact time and increased aerial phase whatever the slope condition (± 8% slope), while Cr was not affected. Taken together these results tend to show that biomechanical responses may affect Cr in graded running. However, to the best of our knowledge, no study examined these relationships, including ground reaction forces, in a large heterogeneous group. Moreover, the running pattern appears to be dependent on the fitness level in LR. Lower vertical forces were observed during the stance phase in a group of runners with the lowest oxygen consumption for a given speed (~ 13 km h−1)17. The magnitude of peak vertical force determines the work performed by the leg muscles to sup- port the running motion. During incline running, these peak vertical forces have been shown to increase and decrease during DR and UR, respectively21. Nevertheless, to our knowledge, no previous study has attempted to determine whether peak vertical forces are also a factor of Cr in incline running (DR and/or UR). Identification of key running pattern parameters associated with low Cr may have direct practical applications such as developing grade-specific training methods to improve running technique and potentially performance. Thus, it appears interesting to investigate whether specific biomechanical responses can distinguish economical and less economical runners. Therefore, the objectives of this study were, first, to determine if there was a relationship between Cr and mechanical responses associated with the running pattern; and second, to determine if these biomechanical responses were different between two groups of different Cr levels (economical vs. non-economical). Our hypoth- eses were first that Cr values would correlate with their biomechanical responses; and second that biomechanical responses would be different between two groups of different economy levels in each slope condition. Methods Participants. Nineteen volunteer athletes took part in this study (Table 1) and were informed of the benefits and risks of this investigation before giving their written informed consent. They performed between one and five session per week of running training but were not trail specialists. The experiment was previously approved by our Institutional Review Board (CCER-VD 2015-00006) and complied with the Declaration of Helsinki. Experimental setup. All participants completed (1) a level running (0% slope) incremental test to exhaus- tion; and (2) three randomized running bouts at constant velocity (10 km h−1) with different slope conditions, LR, UR (+ 10%) and DR (− 10%). The running speed of 10 km h−1 was selected to ensure that subjects were below the second ventilatory threshold in each slope condition. Participants performed all the sessions on a treadmill (T-170-FMT, Arsalis, Belgium) at the same time of the day with 1 week of recovery allocated. The subjects were instructed to not perform any eccentric and/or strenuous exercises in this time interval. Maximal incremental level running test. The first session was an incremental running test until exhaustion. The test began at 8 km h−1 for 4 min and then the speed increased by 1 km h−1 every min. During each session, V̇O2, carbon dioxide output (V̇CO2), and respiratory exchange ratio (RER) were collected breath- by-breath through a facemask with an open-circuit metabolic cart with rapid O2 and CO2 analyzers (Quark Table 1. Participant characteristics (n = 19). vV̇O2max velocity associated to V̇O2max, VT1 and VT2 V̇O2 at the first and the second ventilatory thresholds, respectively, HRmax maximal heart rate. Age (years) 34 ± 10 Height (cm) 175 ± 10 Body mass (kg) 68.5 ± 12.2 BMI (kg m−2) 22.2 ± 2.3 vV̇O2max (km h−1) 17.3 ± 2.3 V̇O2max (mlO2 kg−1 min−1) 58.3 ± 7.7 VT1 (mlO2 kg−1 min−1) 40.8 ± 4.9 VT2 (mlO2 kg−1 min−1) 54.0 ± 7.3 HRmax (bpm) 179 ± 12 3 Vol.:(0123456789) Scientific Reports \| (2023) 13:12244 \| https://doi.org/10.1038/s41598-023-38328-x www.nature.com/scientificreports/ CPET, Cosmed, Rome, Italy) in order to calculate the Cr. Heart rate (HR) was continuously measured (Polar Electro, Kempele, Finland). The highest V̇O2 value over 30 s during the maximal incremental test represented the V̇O2max. The speed associated with V̇O2max (vV̇O2max) was determined as the speed of the step that elicited V̇O2max 26. The first ventilatory threshold was determined as a breakpoint in the plot of V̇CO2 as a function of V̇O2. At that point, the ventilatory equivalent for O2 (V̇E/V̇O2) increases without an increase in ventilatory equiv- alent for CO2 (V̇E/V̇CO2)27. The second ventilatory threshold was located between the first ventilatory threshold and V̇O2max, when V̇E/V̇CO2 starts to increase while V̇E/V̇O2 continues to rise28. These thresholds were blind assessed by two accustomed experimenters. The average value was kept, and in case of a difference above 30 s, a third experimenter was involved, and the average of the two closest values was used. The rate of perceived exer- tion was obtained by using a designed scale29 to assess the exercise intensity about 30 s after the end of the test. During the second session, after a short warm-up participant performed three randomized constant velocity running bout of 4 min. As for the maximal incremental test, V̇O2, V̇CO2 and RER continuously recorded. Before each session, the O2 and CO2 analyzers were calibrated according to the manufacturer’s instructions. Metabolic power during constant velocity bouts in level, uphill, and downhill running. Mean Cr values were recorded between 3:15 and 3:45 (min:s) of each running bout. The Cr was computed as following12: where Cr is expressed in J kg−1 m−1, ΔV̇O2 for the difference between oxygen consumption at steady-state and oxygen consumption at baseline in mlO2 kg−1 min−1 30, v corresponded to the velocity of the trial (10 km h−1), and E(O2) for O2 energy equivalent determined with RER. As the V̇O2 response is slope-dependent in running31, for each slope condition (i.e., LR, UR and DR), the subjects were arbitrarily divided into two groups (i.e., economi- cal vs. non-economical) based on the absolute Cr median value (2.42, 3.83, and 6.09 J kg−1 m−1 for DR, LR, and UR, respectively), to obtain equal proportion of runners within each group17. Biomechanical data collection and processing. An instrumented treadmill equipped with a three- dimensional force platform sampling 1000 Hz was used in this study. To reduce the noise inherent to the tread- mill’s vibrations, we first applied, a second order stop-band Butterworth filter with edge frequencies set at 25 and 65 Hz, on the vertical ground reaction force signal. The filter configuration was chosen empirically to obtain a satisfactory reduction of the oscillations observed during flight phases while minimizing its widening effect during ground contact time. Further data analysis was conducted using MATLAB software version R2021a (MathWorks Inc., Natick, MA, USA). The instants of initial contact and terminal contact were identified using a threshold of 7% of bodyweight on the filtered vertical ground reaction force signal32, and ~ 80 steps were ana- lyzed for each condition. The contact time (in ms) is the time between initial and terminal contacts of the same leg, the aerial time (in ms) is the time between the terminal contact of one leg and the initial contact of the oppo- site leg. Duty factor (expressed in %) was computed as the ratio between the contact time and the stride time (i.e., contact time + aerial time). The step frequency (in Hz) is the reciprocal of the time required for one step (time between two consecutive initial contacts). The step length (m) is the quotient of the treadmill belt speed divided by step frequency. Peak vertical ground reaction force (GRF) was computed over the entire stance phase. The Wext was determined using the method proposed by Saibene and Minetti33 and is defined as the sum of potential, and horizontal and vertical kinetic works associated with the displacement of the CoM. The Wext − and Wext + represent the work done due to decelerate and accelerate, respectively, the body’s CoM with respect to the envi- ronment. The percentage of negative work is the ratio between the Wext − and the total external work. These data were continuously recorded during 30 s between 3:15 and 3:45 (min:s) of each constant velocity running bouts. Statistical analysis. Jamovi statistical software (Jamovi 1.6.23, Sydney; Australia) was used for all statisti- cal analyses. All variables were examined for normality using a Shapiro–Wilk. A repeated measures ANOVA was performed to compare the effect of the slope’s condition on the Cr and the biomechanical data, after using Mauchly’s test to assess sphericity. Bonferroni’s correction was applied on the alpha level to account for repeated univariate testing. When significant effects were observed, Bonferroni’s post-hoc tests were used to localize the significant differences. For each condition of slope, scale intercept and Pearson’s product–moment correlation coefficients (r) were used to assess the intensity of the relations between Cr and the selected biomechanical vari- ables, with Bonferroni’s multiplicity correction33. A one-way ANOVA was used to compare the biomechanical responses on the treadmill between efficiency groups. For all these analyses, data are expressed as mean ± SD and a p value inferior to 0.05 was considered statistically significant. Ethics approval. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee by our Institutional Review Board (CCER-VD 2015-00006). Consent to participate. Informed consent was obtained from all individual participants included in the study. Results Cost of locomotion and biomechanics. Values of Cr and biomechanical parameters in the different slope conditions are presented in Table 2. The contact time was negatively correlated with the Cr in UR only (r = − 0.54; p = 0.017; Fig. 1). For both UR and LR only, the aerial time was positively correlated (r = 0.54 and r = 0.57, respectively; both p ≤ 0.018; Fig. 1), while the duty factor was negatively correlated with the Cr (r = − 0.50 Cr = V·O2/(v × 1000) × 60 × E(O2) 4 Vol:.(1234567890) Scientific Reports \| (2023) 13:12244 \| https://doi.org/10.1038/s41598-023-38328-x www.nature.com/scientificreports/ and r = − 0.57, respectively; both p ≤ 0.029; Fig. 1). The relative peak force was correlated with the Cr in LR and UR only (r = 0.50 and r = 0.56, respectively; both p ≤ 0.031) but not in DR. Regarding mechanical work param- eters, the Cr correlated to Wext + in UR (r = 0.49; p = 0.035), but none of the mechanical work parameters corre- lates with Cr in DR and UR. In addition, Cr was significantly positively correlated between each slope condition: DR-LR (r = − 0.57; p = 0.011), DR-UR (r = − 0.53; p = 0.020), and LR-UR (r = − 0.72; p < 0.001). Economical and non-economical runners. All running pattern parameters were similar between eco- nomical and non-economical groups in both DR and LR (Table 3). The contact time, step length, and mass- specific peak vertical GRF (Fig. 2A, B, D) were higher while the step frequency (Fig. 2C) was lower in the eco- nomical than in the non-economical group in UR (all p < 0.031). However, aerial time, GRF, or mechanical work values were not different between the two groups in UR (all p > 0.05). Discussion This study provides new insights into biomechanical factors according to the level of metabolic economy of runners on different running slopes. The main findings of the present study are that (1) aerial time, peak verti- cal ground reaction force, positive mechanical work, and duty factor were associated with Cr during LR and UR; while contact time correlated only with Cr during UR; (2) no relationship was observed between Cr and biomechanical responses during DR; and (3) most economical runners tend to have specific running pattern adaptations (i.e., longer contact time and greater step length as well as a lower step frequency) during UR only. These results partially support the hypothesis since specific running pattern responses were characterized by a lower metabolic cost of running on flat or positive slopes but not on negative slope. During UR, Cr positively correlated with several running pattern parameters such as Wext +, aerial time, and mass specific GRF, and negatively correlated with contact time and duty factor. These correlations demonstrate that UR is related to specific running pattern parameters and highlight that the running pattern may influence Cr, especially during UR. Our results revealed that Cr, Wext −, Wext +, duty factor, and step frequency were lower; whereas areal time, step length (in absolute and relative to the height), GRF (expressed in both absolute and rela- tive values), and the percentage of the Wext − were higher during LR than during UR, which is rather consistent with the literature23. Furthermore, as already observed on similar slopes8,9,34, the running economy was reduced when running on the positive slope. Most of the mechanical work performed comes from positive external mechanical work during UR (~ 69% of Wext was provided by Wext + and only ~ 31% by Wext −; Table 2). Comparable distribution on equivalent slope and speed was reported9. These results confirm that UR is primarily a concentric muscle contraction that is energy- consuming25. Indeed, the Wext + represents the amount of mechanical energy spent during the pushing phase to elevate and move forward the CoM24. It has been highlighted that the increase in Wext + was caused by the elevation of CoM related to the upward movement of the body during UR24. The external work is the product of vertical displacement of the CoM and the step frequency. It was observed that lower vertical displacements of the CoM and/or a higher step frequency in LR were associated with better running economy35. Therefore, one could potentially expect a similar relation- ship during UR. However, no correlation was observed between step frequency and Cr, possibly because runners choose their own optimal step frequency and step length, whatever the slope level16, close to their minimal Cr36. Nevertheless, other spatiotemporal parameters such as the contact time and aerial time were correlated with Cr during UR. Contact time was negatively correlated to the Cr, meaning that a longer contact was associated with Table 2. Cost of locomotion and biomechanical parameters in downhill, level and uphill running (n = 19). GRF peak ground reaction force. a p < 0.05 versus downhill running. b p < 0.05 versus level running. Downhill running Level running Uphill running F-STATISTICS Energy cost of running Energy cost (J kg−1 m−1) 2.56 ± 0.51 3.87 ± 0.43a 6.21 ± 0.51ab 674 Running kinematics and GRF Contact time (s) 0.28 ± 0.03 0.29 ± 0.03 0.29 ± 0.03 3.73 Aerial time (s) 0.09 ± 0.03 0.08 ± 0.02a 0.07 ± 0.02ab 25.9 Duty factor (%) 74.9 ± 7.6 78.1 ± 5.8a 81.5 ± 5.2ab 22.6 Step frequency (Hz) 2.66 ± 0.10 2.71 ± 0.12 2.81 ± 0.11ab 26.6 Step length (m) 1.05 ± 0.04 1.03 ± 0.05 0.99 ± 0.04ab 26.2 Relative step length (% of height) 60.0 ± 3.8 58.8 ± 3.8 56.7 ± 3.3ab 25.1 Peak vertical GRF (kN) 1.41 ± 0.20 1.35 ± 0.19a 1.29 ± 0.18ab 21.8 Mass-specific vertical GRF (N kg−1) 20.8 ± 2.1 19.8 ± 1.5a 19.0 ± 1.2ab 22.1 Mechanical work External work (J kg1) 3.06 ± 0.44 2.75 ± 0.29a 2.76 ± 0.19a 17.5 Positive external work (J kg−1) 1.07 ± 0.23 1.42 ± 0.15a 1.91 ± 0.11ab 367 Negative external work (J kg−1) − 1.99 ± 0.22 − 1.34 ± 0.14a − 0.85 ± 0.09ab 737 Percentage negative external work (%) 65.3 ± 2.3 48.5 ± 0.2a 30.8 ± 1.1ab 2270 5 Vol.:(0123456789) Scientific Reports \| (2023) 13:12244 \| https://doi.org/10.1038/s41598-023-38328-x www.nature.com/scientificreports/ a better running economy. This result is in agreement with the observation made by Vernillo et al.37 after an ultramarathon event (330 km with 24,000 m elevation gain). The UR Cr (4 min at 6 km h−1 and + 15% incline) was negatively correlated with contact time, duty factor, and as well as rate of force application (characterized by inverse contact time: tc −1). For instance, shorter contact time reduces the time allowed to generate force into the ground and increases the rate at which the muscle fibers shorten38,39, so more fast muscle fibers or muscle mass should be required for a given applied force37,38. The step frequency depends on the interrelationship Figure 1. Relationships between the cost of locomotion and contact time (A), aerial time (B), duty factor (C), mass-specific peak vertical ground reaction force (D), and positive external mechanical work (E) in different slope conditions (DR downhill running—unfiled circles, LR level running—filled diamonds, UR uphill running—p < 0.05). 6 Vol:.(1234567890) Scientific Reports \| (2023) 13:12244 \| https://doi.org/10.1038/s41598-023-38328-x www.nature.com/scientificreports/ Table 3. Comparison of the cost of locomotion and biomechanical responses for economical versus non- economical runners in the three slope conditions (n = 19). GRF peak ground reaction force. Significant values are in [bold]. Downhill running Level running Uphill running Economical N = 9 Non-economical N = 10 p Economical N = 9 Non-economical N = 10 p Economical N = 9 Non-economical N = 10 p Energy cost of running Energy cost of run- ning (J kg−1 m−1) 2.15 ± 0.20 2.93 ± 0.42 < .001 3.52 ± 0.18 4.20 ± 0.32 < .001 5.82 ± 0.27 6.55 ± 0.42 < .001 Running kinematics and GRF Contact time (s) 0.28 ± 0.03 0.29 ± 0.03 0.697 0.30 ± 0.04 0.28 ± 0.02 0.199 0.30 ± 0.02 0.28 ± 0.02 0.013 Aerial time (s) 0.10 ± 0.03 0.09 ± 0.02 0.621 0.07 ± 0.02 0.09 ± 0.02 0.063 0.06 ± 0.01 0.07 ± 0.02 0.130 Duty factor (%) 74.1 ± 8.9 75.7 ± 6.5 0.654 80.5 ± 5.8 76.0 ± 5.3 0.093 83.7 ± 4.1 79.5 ± 5.5 0.080 Step frequency (Hz) 2.65 ± 0.09 2.66 ± 0.12 0.900 2.71 ± 0.16 2.70 ± 0.09 0.864 2.75 ± 0.10 2.86 ± 0.10 0.026 Step length (m) 1.05 ± 0.04 1.05 ± 0.05 0.932 1.03 ± 0.06 1.03 ± 0.03 0.947 1.01 ± 0.04 0.97 ± 0.03 0.038 Relative step length (% of height) 60.1 ± 2.4 59.8 ± 4.8 0.871 58.3 ± 3.3 59.3 ± 4.4 0.560 56.5 ± 3.5 56.8 ± 3.2 0.854 Peak vertical GRF (kN) 1.43 ± 0.14 1.39 ± 0.2 0.619 1.36 ± 0.16 1.34 ± 0.22 0.875 1.34 ± 0.14 1.25 ± 0.2 0.252 Mass-specific verti- cal GRF (N kg−1) 21.1 ± 2.5 20.5 ± 1.7 0.573 19.2 ± 1.5 20.4 ± 1.4 0.102 18.5 ± 0.9 19.5 ± 1.3 0.075 Mechanical work External work (J kg−1) 3.13 ± 0.47 3.00 ± 0.43 0.537 2.69 ± 0.23 2.81 ± 0.34 0.368 2.71 ± 0.14 2.80 ± 0.23 0.317 Positive external work (J kg−1) 1.11 ± 0.25 1.04 ± 0.22 0.502 1.38 ± 0.12 1.45 ± 0.18 0.363 1.87 ± 0.08 1.93 ± 0.13 0.255 Negative external work (J kg−1) − 2.02 ± 0.23 − 1.97 ± 0.21 − 1.30 ± 0.11 − 1.36 ± 0.17 0.375 − 0.84 ± 0.06 − 0.87 ± 0.11 0.468 Percentage negative external work (%) 64.9 ± 2.5 65.7 ± 2.1 0.448 48.5 ± 0.2 48.5 ± 0.2 0.652 30.8 ± 0.8 30.8 ± 1.3 0.996 Figure 2. Contact time (A), step length (B), step frequency (C) and mass-specific ground reaction force (D) in economical (white box-plots) and non-economical runners (gray box-plots) during uphill running. 7 Vol.:(0123456789) Scientific Reports \| (2023) 13:12244 \| https://doi.org/10.1038/s41598-023-38328-x www.nature.com/scientificreports/ between contact time and step length. Thus, for a given step frequency, increasing the aerial time will shorten the contact time and lead to Cr deterioration, since the metabolic cost of force generation increases as the con- tact time shortens38. In addition, as it exists an inverse relationship between the step frequency and the vertical oscillation of CoM in running35, Furthermore, reducing aerial time and vertical displacement (which will occur together) are the result of less external positive work (resulting in a reduced vertical velocity at takeoff) during UR. However, an excessive increase of the stride frequency during running to reduce mechanical work could be disadvantageous as it causes a Cr raise16. The negative correlation observed between Cr and the duty factor might underline the importance of optimizing energy transfer (from metabolic to mechanics) to reduce energy demands in UR. Altogether, increasing the stance phase and decreasing the aerial time may be an appropriate strategy for improving running economy during UR. As such, patterns of locomotion may play a decisive role in lowering the cost of locomotion by walking compared to running on a steep positive slope40. Indeed, walking pattern is characterized by a longer contact time, a higher duty factor, and a lower stride frequency associated with reduced muscle activation compared to the running pattern on a 30° slope41. The present data showed a relationship between the Cr and GRF. Normalized GRF to body weight was positively correlated with Cr during UR. Since GRF is the result of the forces produced by all the muscles in the vertical direction during the stance phase, an excessive GRF value in this orientation is a waste of energy. Thus, minimizing the vertical GRF seems to be more economical during UR. Increasing step frequency could lower the vertical GRF and might be a useful strategy in UR. Therefore, the adoption of strategies to reduce vertical GRF forces should be incorporated in training programs in order to improve running economy during UR as well as during LR. However, such adjustments must be individually adapted. In the present study, we confirm that negative slope has a significant effect on the running pattern and decreases Cr compared to LR42. Total mechanical work, Wext −, proportion of Wext −, aerial time, and GRF (expressed in absolute and mass-specific values), increased in DR while the Wext + decreased, in agreement with the literature9,23,24,43. However, conversely to UR, downhill Cr was not correlated with any mechanical aspects, suggesting that, at least for the present velocity and slope, there was not a more economical running pattern during DR. These results are not in agreement with previous results12 which reported a significant correlation between Cr and several spatiotemporal parameters such as step length, step frequency, and contact time during DR (− 15%). According to these later results, it was suggested that the ability to store and restitute elastic energy had an important role in DR Cr12. The difference observed in the literature may come from differences in the experimental design and the fitness level of the participants. As observed by Minetti et al.9 more than half (~ 65%) of the total external work is provided by Wext −. The latter represents the work done during the braking phase of the stance phase. During this phase, the knee extensor muscles forcibly lengthen (i.e., eccentric muscle action) under the potential effect of gravity to limit the drop-down of the CoM. From an energetical point of view, this eccentric muscle’s action requires less energy than a concentric muscle contraction25, and part of the potential energy from the vertical oscillation of the CoM is either dissipated as wasted heat (mostly) or stored in the mus- cle–tendon units during the braking phase prior its restitution during the pushing phase. The stretch–shorten- ing cycle is mainly involved during DR16,44 and is known to be less energy consuming than purely concentric actions45, saving energy and reducing the Cr as well45. For moderate negative slopes (~ 15%), the elastic energy stored in the muscle–tendon units can supply almost all the energy demand for the push phase44. However, no correlation was observed between Wext − and the Cr. Runners have their own running style based on ability and experience which implies that a similar Cr from one individual to another can be associated with different biomechanical parameters. Indeed, there was only a relatively small influence of each of the parameters measured on CR, even when the relationship was significant (Fig. 1). Therefore, we have to be cautious when suggesting potential gait modifications for performance enhance- ment. Changing one parameter of the running pattern can alter the overall mechanics and potentially the running economy46. For example, ± 15% changes in preferred step frequency increased Cr by ~ 20% in DR16. Running in negative slopes is demanding for the body, as greater braking force must be applied on the ground to maintain a constant speed, which can generate muscle damage47. Furthermore, since force absorption is less energetically demanding, runners may neglect their running mechanics. A more protective running pattern is privileged by runners based on their experience in DR9. Running on negative slopes where the fear of falling is higher could also exacerbate emotional aspect, when compared to LR and UR. During treadmill running, irrespective of the slope, it is well-known that the mechanics is different than during overground running: the influence of the motion belt that affects both potential and kinetic works remains difficult to be accurately assessed48. Therefore, we have to be cautious for translating the present findings to field running. The present study compared the metabolic and biomechanical responses of economical and non-economical runners at different slopes. Individual Cr values in the three slope conditions were used to split participants into two groups. We showed that the most economical runners remained the same ones, independently of the slope (i.e., in DR, LR or UR). This result is rather consistent with the literature: Willis et al.7 reported a strong cor- relation between Cr measured in LR and in UR (12% slope) in a group of elite ultra-trail runners (6 males and 5 females), while Balducci et al.49 found no correlation between LR and UR (12.5 or 25% slope) in trained trail runners49. Moreover, in the present study, there were differences in biomechanical responses between the two groups, but only in UR economical runners had longer contact time and step length compared to less economical runners, while their step frequency was smaller. Ultimately, the longer duration of the stance phase may allow runners to optimize the direction of propulsive force and the time allowed to apply force to the ground50. Indeed, mass-specific GRF tends to be lower in the economical runners than in the non-economical group (p = 0.070; Table 3), suggesting that lower mass-specific GRF in UR may allow to reduce the metabolic cost of running. A lower vertical force during the stance phase was observed for the group of runners who had the lowest oxygen consumption for a given speed (~ 13 km h−1) on flat terrain17. 8 Vol:.(1234567890) Scientific Reports \| (2023) 13:12244 \| https://doi.org/10.1038/s41598-023-38328-x www.nature.com/scientificreports/ No difference for all the biomechanical variables was observed in DR and LR between the groups of economi- cal and non-economical runners, i.e., with different Cr. This result is rather in line with the literature13,14, and may be partly explained by the heterogeneity of the population in the present study. Less experienced runners tend to have a greater stride to stride variability than experienced runners51. The Cr may be influenced by many other factors, such as anthropometry, flexibility, and joint kinematics but also by physiological differences (e.g., metabolic efficiency) or equipment (e.g., running shoes)18. Runners naturally chose their optimal running pat- tern themselves to minimize their Cr13. Each one having its own specificity, the number of mechanical combina- tions is likely very important. Nevertheless, even if two groups use different running strategies, there were no significant differences in Cr13,14. Conclusions The present study reported that Cr was related to few key running pattern parameters (i.e., contact time, aerial time, mass specific GRF and positive mechanical external work) mainly in UR, but not in DR. Moreover, all run- ning pattern parameters were similar between economical and non-economical runners in DR and LR, but not in UR. Interestingly, the contact time and the step length were longer, whereas the step frequency was lower in the group of economical runners compared to the group of non-economical runners in UR. These results provide interesting insights concerning an optimal running pattern to reduce the cost of locomotion, and consequently improve performance during graded running. In practice, it may be preferable to reduce step frequency, or even to shift to walking, on a positive slope to increase step length and slow down the knee extension during the pro- pulsive phase. On steep slopes, poles could facilitate this mechanism52. Overall, the present study emphasizes that the mechanics of LR and UR are fundamentally different. Future investigations are needed to deepen the knowledge with a heterogeneous population (trained or untrained runners) to improve the training protocols of mountain or trail runners. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Received: 13 October 2022; Accepted: 6 July 2023 References 1. di Prampero, P. E., Atchou, G., Brückner, J.-C. & Moia, C. 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Gait Posture 63, 109–113 (2018). 49. Balducci, P. et al. Comparison of level and graded treadmill tests to evaluate endurance mountain runners. 9 (2016). 50. Kugler, F. & Janshen, L. Body position determines propulsive forces in accelerated running. J. Biomech. 43, 343–348 (2010). 51. Mo, S. & Chow, D. H. K. Stride-to-stride variability and complexity between novice and experienced runners during a prolonged run at anaerobic threshold speed. Gait Posture 64, 7–11 (2018). 52. Giovanelli, N., Sulli, M., Kram, R. & Lazzer, S. Do poles save energy during steep uphill walking?. Eur. J. Appl. Physiol. 119, 1557–1563 (2019). Acknowledgements The authors thank the subjects for their enthusiastic participation in the present study. Author contributions G.P.M. and F.M. participated in the design of the study and contributed to data collection, M.L., A.D. and R.F. contributed to the data reduction/analysis; All authors contributed to the manuscript writing. All authors have read and approved the final version of the manuscript and agree with the order of presentation of the authors. Funding The authors did not receive support from any organization for the submitted work. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to F.M. or G.P.M. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 10 Vol:.(1234567890) Scientific Reports \| (2023) 13:12244 \| https://doi.org/10.1038/s41598-023-38328-x www.nature.com/scientificreports/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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PMC4154741	Is the COL5A1 rs12722 Gene Polymorphism Associated with Running Economy? Roˆ mulo Bertuzzi1,2, Leonardo A. Pasqua2, Saloma˜o Bueno2, Adriano Eduardo Lima-Silva3, Monique Matsuda4, Monica Marquezini1, Paulo H. Saldiva1 1 Laboratory of Experimental Air Pollution, Department of Pathology, School of Medicine, University of Sa˜o Paulo, Sa˜o Paulo, Brazil, 2 Endurance Sports Research Group, School of Physical Education and Sport, University of Sa˜o Paulo, Sa˜o Paulo, Brazil, 3 Sports Science Research Group, Academic Center of Vitoria, Federal University of Pernambuco, Vitoria de Santo Anta˜o, Brazil, 4 Faculty of Medicine, University of Sa˜o Paulo, Sa˜o Paulo, Brazil Abstract The COL5A1 rs12722 polymorphism is considered to be a novel genetic marker for endurance running performance. It has been postulated that COL5A1 rs12722 may influence the elasticity of tendons and the energetic cost of running. To date, there are no experimental data in the literature supporting the relationship between range of motion, running economy, and the COL5A1 rs12722 gene polymorphism. Therefore, the main purpose of the current study was to analyze the influence of the COL5A1rs12722 polymorphism on running economy and range of motion. One hundred and fifty (n = 150) physically active young men performed the following tests: a) a maximal incremental treadmill test, b) two constant-speed running tests (10 kmNh21 and 12 kmNh21) to determine the running economy, and c) a sit-and-reach test to determine the range of motion. All of the subjects were genotyped for the COL5A1 rs12722 single-nucleotide polymorphism. The genotype frequencies were TT = 27.9%, CT = 55.8%, and CC = 16.3%. There were no significant differences between COL5A1 genotypes for running economy measured at 10 kmNh21 (p = 0.232) and 12 kmNh21 (p = 0.259). Similarly, there were no significant differences between COL5A1 genotypes for range of motion (p = 0.337). These findings suggest that the previous relationship reported between COL5A1 rs12722 genotypes and running endurance performance might not be mediated by the energetic cost of running. Citation: Bertuzzi R, Pasqua LA, Bueno S, Lima-Silva AE, Matsuda M, et al. (2014) Is the COL5A1 rs12722 Gene Polymorphism Associated with Running Economy? PLoS ONE 9(9): e106581. doi:10.1371/journal.pone.0106581 Editor: Mikko Lammi, University of Eastern Finland, Finland Received January 30, 2014; Accepted August 8, 2014; Published September 4, 2014 Copyright: 2014 Bertuzzi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Romulo Bertuzzi is supported by CAPES (grant 23038.000486/2011-15). Leonardo A. Pasqua is supported by FAPESP (grant 2010/13913-6). Saloma˜o Bueno is supported by CAPES (grant 1182744). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Email: bertuzzi@usp.br Introduction It is well known that athletic performance is dependent on a multifactorial phenotype resulting from a complex interaction between environmental [1,2] and genetic [3,4] factors. In the past few years, researchers have analyzed the influence of some genes that encode proteins that are involved in metabolic pathways [5] or skeletal muscle structure [4,6], which are important to athletic performance. More recently, Posthumus et al. [7] reported that endurance running performance is associated with a gene, named COL5A1, which encodes a structural protein of the extracellular matrix. These authors observed that the TT genotype was overrepresented in runners with faster performances during the 42.2-km running portion of the Ironman triathlon, suggesting thatCOL5A1 might be a novel genetic marker for endurance running performance. It is believed that the relationship between COL5A1 and endurance running performance might be mediated by the energy storage capacity of the connective tissues [7,8]. COL5A1 encodes the a1 (V) chain of type V collagen [9], which plays a crucial role in the regulation of the size and configuration of other abundant fibrillar collagens supporting many tissues in the body, such as tendons, ligaments, and muscles [10]. A mutation in the COL5A1 gene, which causes haploinsufficiency, results in a 50% reduction of type V collagen, and leads to poorly organized fibrils, decreased tensile strength, and reduced stiffness of connective [9]. It has been demonstrated that a common C to T single nucleotide polymor- phism within the COL5A1 39 untranslated region (rs12722) may alter COL5A1 mRNA stability and thereby reduce the production of type V collagen [11]. Thus, it was hypothesised that individuals with a TT genotype of this variant would have increased type V collagen production and thus favorably altered mechanical properties of tendons, which enhances endurance running ability [7,8]. Running economy (RE), which has been defined as the energy cost or oxygen uptake for a given submaximal running speed [12,13], is considered to be an important determinant of endurance performance [14,15]. It has been indicated that the inter-individual variation in RE is approximately 10–25% between homogeneous subjects for maximal oxygen uptake ( :VO2 max ) [15]. The cause of this variation is not well understood, but it might be partially explained by the energy storage capacity of the tendons [14]. Indeed, elastic energy is generated by the passive stretch of the muscle elastic elements, and this energy is converted to kinetic energy free of metabolic cost during a running bout [16]. Thus, the most economical runners have a higher compliance of the tendons and aponeuroses compared to their less economical PLOS ONE \| www.plosone.org 1 September 2014 \| Volume 9 \| Issue 9 \| e106581 counterparts [14]. Because of this reported association between RE and the energy storage capacity of the tendons, it has been proposed that individuals with a TT genotype would have less extensible tendon structures, resulting in a lower range of motion (ROM) and greater RE when compared with individuals with at least one copy of the C allele [8]. However, to date there are no experimental data in the literature supporting this relationship among RE, ROM, and the COL5A1 rs12722 gene polymorphism. Consequently, it is difficult to determine whether the association previously reported between the COL5A1 gene and endurance performance was due to a reduced running energy cost. Therefore, the main objective of the present study was to analyze the influence of the COL5A1 gene polymorphism on RE. In light of past studies that reported a relation between the COL5A1 gene (rs12722) and endurance running performance [7,8], it was hypothesized that individuals with a TT genotype would have a greater RE and a lower ROM when compared to individuals with the TC and CC genotypes. Materials and Methods Participants One hundred and fifty (n = 150) physically active men (age 25.264.0 years, body mass 77.8613.9 kg, height 173.9621.4 cm, and body fat 13.364.2%) volunteered to participate in this study. All subjects were medication-free, nonsmokers, and free of neuromuscular disorders and cardiovascular dysfunctions. The subjects had been involved in recreational sports in the past year, but they had not engaged in flexibility and strength training for at least six months before the study. The participants received a verbal explanation about the possible benefits, risks, and discomforts associated with the study and signed a written informed consent before participation in the study. The proce- dures were in accordance with the Helsinki declaration of 1975, and the study was approved by the Ethics Committee for Human Studies of the School of Physical Education and Sport of the University of Sa˜o Paulo. Experimental design All of the subjects were required to visit the laboratory on three occasions separated by at least 72 h over a 2-week period. In the first visit, the subjects were asked to perform mouthwashes for genomic DNA extraction, to perform a sit-and-reach test for ROM measurement, and to fill out a short version of the physical activity questionnaire (IPAQ-short version) to estimate their physical activity levels. In the second visit, anthropometric measurements (height, body mass, and body composition) were recorded and a maximal incremental treadmill test was performed. A constant-speed, treadmill running familiarization test was conducted at the end of the first and second visits after a 20-min passive recovery. In the third session, the subjects performed two submaximal constant-speed tests. All of the tests were performed at the same time of day in a controlled-temperature room (20–24uC) and 2–3 h after the last meal. All of the subjects were asked to refrain from any exhaustive or unaccustomed exercise for 48 h preceding the test. They also were instructed to wear standard running shoes and asked from taking off nutritional supplements six months before the experimental period. Anthropometric measurements All of the anthropometric measurements were made according to the procedures described by Norton and Olds [17]. The subjects were weighed using an electronic scale to the nearest 0.1 kg. Height was measured with a stadiometer to the nearest 0.1 cm. Skinfold thickness was measured at seven sites (chest, axilla, triceps, subscapular, abdominal, suprailiac, and thigh) with a Harpenden caliper (West Sussex, UK) to the nearest 0.2 mm. The seven skinfold thickness values were obtained on the right side of the body in a serial fashion, and the median of three values was used for data analysis. When the difference between these three values was higher than 10%, a fourth measurement was obtained. All measurements were made by the same experienced investiga- tor. Body density was estimated using the generalized equation of Jackson and Pollock [18], and body fat was estimated using the equation of Brozek et al. [19]. Maximal incremental treadmill test The subjects performed a maximal incremental test on a motor- driven treadmill (model TK35, CEFISE, Nova Odessa) to determine maximal oxygen uptake ( :VO2 max ). After a 3-min warm-up at 8 km?h21, the velocity was increased by 1 km?h21 every minute until exhaustion. The participants received strong verbal encouragement to ensure attainment of maximal values. Gas exchange was measured breath-by-breath using a gas analyzer (Cortex Metalyzer 3B, Cortex Biophysik, Leipzig, Germany) and was subsequently averaged over 20 s intervals throughout the test. Before each test, the gas analyzer was calibrated according to the recommendations of the manufacturer. Maximal heart rate (HRMAX) was defined as the highest value obtained at the end of the test. Blood samples (25 ml) were collected from the ear lobe one, three, and five minutes after the test to determine the peak blood lactate. Lactate concentrations were measured spectropho- tometrically (EONC, Biotek Instruments, USA) using a wave- length of 546 nm. :VO2 max was determined when two or more of the following criteria were met: an increase in oxygen uptake of less than 2.1 ml?kg21?min21 between two consecutive stages, a respiratory exchange ratio greater than 1.1, and a 610 bpm of the predicted maximal heart rate (i.e., 220-age) [20]. Running economy The subjects performed two constant-speed running tests on a treadmill to determine RE. Because it has been reported that a subject who is economical at a given speed will not be necessarily economical at other speeds [21], we measured the RE at10 km?h21 and 12 km?h21 speeds. These intensities corresponded to 78.866.7% and 89.767.9% of the :VO2 max , respectively. Due the different percentage values required of the :VO2 max , it was assumed that these intensities represented the RE at low (RELW = 10 km?h21) and high (REHG = 12 km?h21) intensities. Before the tests, the participants performed a standardized warm- up consisting of a 5 min run at 8 km?h21 followed by a 5-min of passive recovery. RE was determined by measuring breath-by- breath oxygen uptake for 10 minutes at each running speed. RE was defined by averaging the oxygen uptake values during the last 20 s for each running speed. Recovery time between these two constant-speeds running tests was 10 min. Range of motion Range of motion (ROM) was measured using a sit-and-reach test [22,23]. This test has been used in previous studies investigating the effects of the COL5A1 genotypes on lower limb flexibility because it represents an indirect measure of both hamstring musculotendinous unit length and lumbar ROM [25,26]. The subjects sat with their bare feet pressed against the sit-and-reach box. The knees were extended and the right hand was positioned over the left. Then, the subjects were asked to push a ruler transversally located over the box as far as possible on the COL5A1 and Running Economy PLOS ONE \| www.plosone.org 2 September 2014 \| Volume 9 \| Issue 9 \| e106581 fourth bobbing movement. Three trials were performed, and the best trial was used for statistical analysis. Physical activity level determination Because the RE may be influenced by the training status of the subjects [27], the short version of the International Physical Activity Questionnaire (IPAQ-SV) was used to identify the physical activity level of the participants. This questionnaire was developed with a multi-cultural adaptation and is considered to be one of the most widely utilized instruments due to the quickness of data collection, low operating cost, and non- invasive characteristics [28]. In addition, it has recently been shown that IPAQ-SV outcomes are associated with flexibility and cardiorespiratory fitness levels in healthy men [29]. The participants answered the questionnaire in a classroom setting after a detailed description of the IPAQ-SV. An assistant remained in the classroom setting for eventual doubts. The major aim of the IPAQ-SV is to sum up walking, moderate and vigorous PA and to generate a total PA score for weekly expenditure, expressed in metabolic equivalent task units (METs min/wk). We used the following recommended METs min/wk estimates of the IPAQ-SF: walking PA = 3.3 METs min/wk, moderate PA = 4.0 METs min/wk, vigorous PA = 8.0 METs min/wk. The total PA was calculated assuming: 3.36walking PA+4.06moderate PA+8.06vigorous PA. The PA level was classified as low, moderate or high. Low activity represented individuals who do not meet the criteria for moderate and vigorous intensity categories (,599 METs min/wk). Moderate activity represented moderate or vigorous intensity activities achieving a minimum of at least 600 METs min/wk. High activity represented participants achieving a minimum of at least 3000 METs min/wk (http://www.ipaq.ki.se/scoring.htm). Genotype assessment Cells from the mouthwashes were submitted to an overnight digestion with proteinase K. Nucleotides were separated from the cellular debris by density gradient centrifugation using chloroform. Genomic DNA was then precipitated with isopropyl alcohol, isolated by centrifugation and resuspended in TE buffer. DNA quantification was performed using a spectropho- tometer (NanoDrop, ND 2000, USA), and the concentration was adjusted to 1 mg/mL for subsequent storage in 220uC. COL5A1 rs12722 gene polymorphisms were determined by conventional 2-primer PCR (F: 59-GCAGTCAGCAGCGTGG- GTCTGGTTATCT-39 and R: 59-TTTGGGGTGGCACTTG- CAGCACT GGTCG-39). This assay resulted in the amplifica- tion of a 637-bp fragment of the COL5A1 gene that includes the polymorphic region. The reaction conditions were as follow: initial hold at 94uC for 3 min, 35 cycles of denaturation at 94uC for 60 s, annealing at 53uC for 60 s and extension at 72uC for 60 s, and a final extension step of 8 min at 72uC. The amplified fragment was subsequently digested using BstUI (New England, Biolabs, Beverly, MA, USA), following the supplier’s recommendations. The digested products were then separated on a 3% agarose gel. To ensure proper internal control, for each batch of analysis, we used positive and negative controls from different DNA aliquots that were previously genotyped by the same method, according to recent recommendations for replicating genotype-phenotype association studies [30]. The restriction fragment length polymorphism (RFLP) results were scored by three experienced and independent investigators who were blinded to the participant’s data. Statistical analysis Data normality was assessed using the Kolmogorov-Smirnov test, and all of the variables showed a normal distribution. The results are reported as means and standard deviations (6SD). A X2 test was used to verify that the genotype frequencies were in Hardy-Weinberg equilibrium. The effects of the COL5A1 genotypes in the analyzed variables were tested using a one-way analysis of variance (ANOVA). The significance level was set at p,0.05. All of the statistical analyses were performed using Statistica 8 (StataSoft Inc., Tulsa, OK, USA). Results Genotype distribution and sample characteristics The genotype distribution attained the Hardy-Weinberg Equilibrium, as evidenced by the X2 test. The genotype frequencies were TT = 27.9%, CT = 55.8% and CC = 16.3%. The genotype distribution of the COL5A1 rs12722 gene polymorphism was similar to the distribution reported in the public databases for Caucasian populations (http://opensnp.org/ snps/rs12722). In accordance with the mean values of the IPAQ- SV outcomes, the subjects of the three groups were classified as moderately active [31]. There were no differences in age, anthropometric characteristics, or physical activity levels among the CC, CT, and TT genotypes of the COL5A1 gene (p.0.05) (table 1). Maximal incremental, running economy, and range of motion tests Table 2 shows the mean values of the physiological variables measured during the maximal incremental test. There were no significant differences in :VO2 max , HRMAX, and [La]peak between individuals with different COL5A1 genotypes (p.0.05). Figure 1 shows the results of the constant-speed running and sit- and-reach tests. The RELW (p = 0.232) (panel A), REHG (p = 0.259) (panel B), and ROM (p = 0.337) (panel C) were not Table 1. Anthropometric characteristics and physical activity levels of the subjects in the three genotype groups. CC (n = 24) CT (n = 84) TT (n = 42) P values Age (years) 25.763.7 21.364.2 23.865.0 0.873 Body mass (kg) 80.5610.7 76.169.9 75.2610.4 0.230 Height (cm) 177.865.4 172.666.5 174.265.8 0.243 Body fat (%) 14.363.8 13.264.2 13.964.6 0.584 IPAQ-SV (score) 12916237 13566289 13336301 0.605 Data are means 6 standard deviations. IPAQ-SV: short version of the international physical activity questionnaire. There were no differences between the groups. doi:10.1371/journal.pone.0106581.t001 COL5A1 and Running Economy PLOS ONE \| www.plosone.org 3 September 2014 \| Volume 9 \| Issue 9 \| e106581 statistical different between the CC (RELW = 35.976 3.18 ml?kg21?min21; REHG=40.9864.59 ml?kg21?min21; ROM= 26.1468.34 cm), CT (RELW=36.0362.93 ml?kg21?min21; REHG= 41.8063.36 ml?kg21?min21; ROM=27.8568.22 cm), and TT (RELW=37.6563.37 ml?kg21?min21; REHG=42.6663.79 ml?kg21?min21; ROM = 27.0866.88 cm) COL5A1 genotypes groups. Discussion Previous findings suggested that the COL5A1 gene might be a marker of endurance running performance [24,7]. It has been speculated that the superior running ability of individuals with a TT genotype could be explained by a greater RE when compared with individuals with at least a C allele [8]. However, it is important to notice that these studies did not investigate the relationship between the energetic cost of running and COL5A1- genotypes because the subjects did not perform constant-speed tests to measure the RE. To the best of our knowledge, this is the first study designed to analyze the relationship between a common C to T single-nucleotide polymorphism gene within COL5A1 gene (rs12722) and RE. The major findings of this study show that theCOL5A1 genotypes were not associated with either RE or ROM. It has been proposed that mechanical properties of connective tissues are responsible for converting elastic energy to kinetic energy free of metabolic cost [14]. It was demonstrated that more economical subjects have a higher contractile strength and compliance of the tendons and aponeuroses when compared with their less economical counterparts [14]. Some studies have suggested that the mechanical properties of tendons and ligaments, which largely consists of collagen fibrils, may be genetically determined [32,10]. In particular, the genetic variation in the COL5A1 gene, which encodes type V collagen, may affect the mechanical properties of tendons and ligaments through altering the mechanical properties. It has previously been shown that the COL5A1 gene variant investigated in this study was associated with endurance running ability. Although the specific mechanisms remain largely unknown, we hypothesised in this study that this gene variant improves endurance running ability through an improving running economy. However, the present findings did not confirm the relationship between COL5A1 genotypes and RE. Our results showed no significant differences in RELW between individuals with different COL5A1 genotypes. This finding suggests that the superior performance observed in individuals with a TT genotype in the COL5A1gene (rs12722) cannot be explained by a lower energy cost during running at a low percentage of :VO2 max. In the present study, we considered that a subject who is economical at low relative running intensity might not be economical at high relative running intensity. Our results demonstrated that COL5A1 genotypes were not associated with the energetic cost of running regardless of running intensity. An alternative explanation for the relationship between COL5A1 genotypes and endurance running performance might be the ability to produce force rather than a reduced energetic cost. Recently, Kubo et al. [33] demonstrated that individuals with a T allele had a higher stiffness of the knee extensors compared to those individuals with at least one copy of the C allele. It was previously demonstrated that dynamic muscle actions are positively related to stiffness, possibly due a more effective force transmission from the contractile elements to the bone [34]. In turn, the ability to produce force has been considered an essential determinant of endurance performance without being necessarily related to RE [35,36]. This occurs because the horizontal component of ground reaction force is fundamental for endurance runners to attain high-intensity running speeds [35]. Thus, individuals with a TT genotype may be able to maintain higher running speeds during long-distance events than their counterparts with a CT or CC genotype by applying greater forces to the ground. Nevertheless, this hypothesis should be analyzed with caution because we were unable to obtain ground reaction forces Figure 1. Running economy, range of motion, and COL5A1 (rs12722) genotypes. Panel A: running economy at low intensity, Panel B: running economy at high intensity, Panel C: range of motion. There were no differences between the groups. doi:10.1371/journal.pone.0106581.g001 COL5A1 and Running Economy PLOS ONE \| www.plosone.org 4 September 2014 \| Volume 9 \| Issue 9 \| e106581 in the present study. Thus, further research is needed to examine the underlying mechanisms determining the relationship between COL5A1 genotypes and endurance running performance. It has also been postulated that COL5A1 rs12722 genotypes could alter the elasticity of tendons [10] and contribute to explaining the inter-individual variation in ROM [36] additionally to other polymorphisms (i.e. COL5A1 rs71746744) of this gene [37]. However, conflicting results were reported in the literature regarding the relationship between COL5A1 gene polymorphisms and ROM. Some studies found that the CC genotype was overrepresented in individuals with greater ROM [24], while others found no relationship [25]. In the present study, the ROM values between individuals with CC, CT, and TT genotypes were not significantly different. This result is in agreement with the earlier findings of Brown et al. [25] who found that COL5A1 genotypes in a South African cohort were not associated with ROM in young subjects (,35 years). However, these authors found a positive correlation between COL5A1genotypes and ROM in older subjects (.38 years old) [26]. It is important to note that besides our cohort consisting of younger subjects (25.264.0 years), a significant difference was not detected for age between individuals with different COL5A1 genotypes. Taken together, these findings reinforce the suggestion that COL5A1 rs12722 genotypes might interact with age for ROM in physically active subjects [26]. It is important to acknowledge some of the limitations of the present study. First, the subjects in the present investigate on were characterized as physically active, as evidenced by the IPAQ-SV outcomes. This would imply that our subjects might have a lower capacity for endurance exercise than the trained endurance runners that were previously studied [24,7]. Thus, caution should be exercised in extrapolating these findings for highly-trained endurance runners. Second, the present study was conducted on a relatively small sample size. Therefore, our findings need to be confirmed in a larger cohort of subjects. On the other hand, it is important to notice that in the present study no differences for physical activity levels, age, and anthropometric measurements were observed among the three COL5A1 (rs12722) genotypes (Table 1). In addition, our cohort was composed exclusively of men. This seems to be especially important because training status [38], age [39], body weight [27], and sex [36] all influence RE. Therefore, it is reasonable to assume that most of the potential confounding variables were controlled in the current study. In conclusion, the results of the current study demonstrated that variants within the COL5A1gene were not associated with RE and ROM. This indicates that the previous relationship reported between COL5A1 genotypes and endurance running performance may not be mediated by the energetic cost of running. Therefore, further studies are needed to examine the causal relationship between COL5A1 gene and endurance running performance. Author Contributions Conceived and designed the experiments: RB LAP SB AEL-S PHS. Performed the experiments: RB SB LAP M. Matsuda M. Marquezini. Analyzed the data: M. Matsuda M. Marquezini RB SB LAP. Contributed reagents/materials/analysis tools: M. Matsuda M. Marquezini PHS. Wrote the paper: RB LAP SB AEL-S M. 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Komi PV, Bosco B (1978) Utilization of stored elastic energy in leg extensor muscles by men and women. Med Sci Sports 10: 261–265. 17. Norton Olds (1996) (eds) Anthropometrica. University of New South Wale Press, Sydney. Table 2. Physiological variables measured during the maximal incremental test in the three genotype groups. CC (n = 24) CT (n = 84) TT (n = 42) P values :VO2 max (mL.kg21.min21) 47.165.2 47.466.1 47.665.8 0.966 HRMAX (bpm) 190610 18969 18967 0.940 [La]peak (mmol.L21) 9.963.7 9.463.9 9.664.1 0.781 Data are means 6 standard deviations. :VO2 max: maximal oxygen uptake, HRMAX = maximal heart rate, [La]peak: peak of blood lactate accumulation. There were no differences between the groups. doi:10.1371/journal.pone.0106581.t002 COL5A1 and Running Economy PLOS ONE \| www.plosone.org 5 September 2014 \| Volume 9 \| Issue 9 \| e106581 18. Jackson AS, Pollock ML (1985) Practical assessment of body composition. The Physican and Sportsmedicine. 19: 76–90. 19. Brozek J, Grande F, Anderson J, Keys A (1963) Densitometric analysis of body composition: revision of some quantitative assumptions. Ann NY Acad Sci 110: 113–140. 20. Howley ET, Bassett DR Jr, Welch HG (1995) Criteria for maximal oxygen uptake: review and commentary. Med Sci Sports Exerc 27(9): 1292–301. 21. Kyrolainen H, Kivela R, Koskinen S, McBride J, Andersen JL, et al. (2003) Interrelationships between muscle structure, muscle strength, and running economy. Med Sci Sports Exerc 35(1): 45–9. 22. Samogin Lopes FA, Menegon EM, Franchini E, Tricoli V, de M Bertuzzi RC (2010) Is acute static stretching able to reduce the time to exhaustion at power output corresponding to maximal oxygen uptake? J Strength Cond Res 24(6): 1650–6. 23. Baltaci G, Un N, Tunay V, Besler A, Gerc¸eker S (2003) Comparison of three different sit and reach tests for measurement of hamstring flexibility in female university students. Br J Sports Med 37(1): 59–61. 24. Brown JC, Miller CJ, Posthumus M, Schwellnus MP, Collins M (2011) The COL5A1 gene, ultra-marathon running performance, and range of motion. Int J Sports Physiol Perform 6(4): 485–96. 25. Brown JC, Miller CJ, Schwellnus MP, Collins M (2011) Range of motion measurements diverge with increasing age for COL5A1 genotypes. Scand J Med Sci Sports 21(6): 266–72. 26. Saunders PU, Pyne DB, Telford RD, Hawley JA (2004) Factors affecting running economy in trained distance runners. Sports Med 34(7): 465–85. 27. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, et al. (2003) International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 35: 1381–1395, 2003. 28. Silva-Batista C, Urso RP, Lima Silva AE, Bertuzzi R (2013) Associations between fitness tests and the international physical activity questionnaire-short form in healthy men. J Strength Cond Res 27(12): 3481–7. 29. Fogelholm M, Malmberg J, Suni Santilla M, Kyro¨la¨inen H, Ma¨ntysaari M, et al. (2006) International Physical Activity Questionnaire: Validity against fitness. Med Sci Sports Exerc 38(4): 753–60. 30. Chanock SJ, Thomas G (2007) The devil is in the DNA. Nat Genet 39(3): 283–4. 31. Ficek K, Cieszczyk P, Kaczmarczyk M, Maciejewska-Karłowska A, Sawczuk M, et al. (2013) Gene variants within the COL1A1 gene are associated with reduced anterior cruciate ligament injury in professional soccer players. J Sci Med Sport 16(5): 396–400. 32. Kubo K, Yata H, Tsunoda N (2013) Effect of gene polymorphisms on the mechanical properties of human tendon structures. Springerplus 25(2): 343. 33. Bojsen-Møller J, Magnusson SP, Rasmussen LR, Kjaer M, Aagaard P (2005) Muscle performance during maximal isometric and dynamic contractions is influenced by the stiffness of the tendinous structures. J Appl Physiol 99(3): 986– 94. 34. Nummela A, Kera¨nen T, Mikkelsson LO (2007) Factors related to top running speed and economy. Int J Sports Med 28(8): 655–61. 35. Kyro¨la¨inen H, Belli A, Komi PV (2001) Biomechanical factors affecting running economy. MedSci Sports Exerc 33(8): 1330–7. 36. Collins M, Mokone GG, September AV, van der Merwe L, Schwellnus MP (2009) The COL5A1 genotype is associated with range of motion measurements. Scand J Med Sci Sports 19(6): 803–10. 37. Abrahams S1, Posthumus M, Collins M (2014) A polymorphism in a functional region of the COL5A1 gene: association with ultraendurance-running performance and joint range of motion. Int J Sports Physiol Perform 9(3): 583–90. 38. Bransford DR, Howley ET (1977) Oxygen cost of running in trained and untrained men and women. Med Sci Sports 9(1): 41–4. 39. Arie¨ns GA, van Mechelen W, Kemper HC, Twisk JW (1997) The longitudinal development of running economy in males and females aged between 13 and 27 years: the Amsterdam Growth and Health Study. Eur J Appl Physiol Occup Physiol 76(3): 214–20. COL5A1 and Running Economy PLOS ONE \| www.plosone.org 6 September 2014 \| Volume 9 \| Issue 9 \| e106581	Is the COL5A1 rs12722 gene polymorphism associated with running economy?	09-04-2014	Bertuzzi, Rômulo,Pasqua, Leonardo A,Bueno, Salomão,Lima-Silva, Adriano Eduardo,Matsuda, Monique,Marquezini, Monica,Saldiva, Paulo H	eng
PMC7959157	Footwear designed to enhance energy return improves running economy compared to a minimalist footwear: does it matter for running performance? R.C. Dinato1 00 , R. Cruz1,2 00 , R.A. Azevedo1,3 00 , J.S. Hasegawa1 00 , R.G. Silva1 00 , A.P. Ribeiro4 00 , A.E. Lima-Silva5 00 , and R. Bertuzzi1 00 1Grupo de Estudo em Desempenho Aeróbio, Escola de Educac¸ão Física e Esporte, Universidade de São Paulo, São Paulo, SP, Brasil 2Centro de Desportos, Departamento de Educac¸ão Física, Universidade Federal de Santa Catarina, Florianópolis, SC, Brasil 3Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Canada 4Departamento de Pós-Graduac¸ão em Ciências da Saúde, Laborátorio de Biomecânica e Reabilitac¸ão Musculoesquelética, Universidade de Santo Amaro, São Paulo, SP, Brasil 5Grupo de Pesquisa em Performance Humana, Departamento Acadêmico de Educac¸ão Física, Universidade Tecnológica Federal do Paraná, Curitiba, PR, Brasil Abstract The present study compared the effects of a footwear designed to enhance energy return (thermoplastic polyurethane, TPU) vs minimalist shoes on running economy (RE) and endurance performance. In this counterbalanced and crossover design study, 11 recreational male runners performed two submaximal constant-speed running tests and two 3-km time-trials with the two shoe models. Oxygen uptake was measured during submaximal constant-speed running tests in order to determine the RE at 12 km/h and oxygen cost of running (CTO2) at individual average speed sustained during the 3-km running time-trials wearing either of the two shoes. Our results revealed that RE was improved (2.4%) with TPU shoes compared with minimalist shoes (P=0.01). However, there was no signiﬁcant difference for CTO2 (P=0.61) and running performance (P=0.52) comparing the TPU (710±60 s) and the minimalist (718±63 s) shoe models. These novel ﬁndings demonstrate that shoes with enhanced mechanical energy return (i.e. TPU) produced a lower energy cost of running at low (i.e., 12 km/h) but not at high speeds (i.e., average speed sustained during the 3-km running time-trial, B15 km/h), ultimately resulting in similar running performance compared to the minimalist shoe. Key words: Running shoes; Oxygen uptake; Oxygen cost; Running time-trial; Endurance performance Introduction Running endurance performance has been traditionally associated with several physiological variables, including running economy (RE) (1–5). Individuals with superior RE, deﬁned as the steady-state oxygen uptake at submaximal running speeds (4), are able to sustain higher exercise intensities and/or maintain the same exercise intensity for a longer period of time compared to their counterparts with poorer RE (6). In order to acutely improve RE, previous studies have suggested that footwear characteristics could play a signiﬁcant role on the energy cost of running. For example, minimalist shoes (i.e., with reduced shoe mass and heel drop) results in signiﬁcant improvements in RE (1–4%) compared to conventional shoes (i.e., with ethylene vinyl acetate midsole) (7–9). This enhanced RE with minimalist shoes has been associated with a greater mechanical action of the longitudinal and transverse arches of the foot, which are capable of restoring/ returning approximately 17% of the mechanical energy temporarily stored at each step taken (10), thus acutely improving the RE at submaximal running. Other studies have demonstrated that midsole char- acteristics can also enhance RE (11,12). For instance, Wunsch et al. (13) showed that a leaf spring-structured midsole acutely improved RE (B1%), probably by changes in spatio-temporal variables. More recently, a new midsole material composed by thermoplastic polyurethane (TPU) has been used to enhance energy return during running (14). The TPU appears to reduce oxygen cost of running by increasing the returned mechanical energy from the shoe midsole material. In fact, Sinclair et al. (15) have shown that Correspondence: R. Bertuzzi: <bertuzzi@usp.br> Received September 29, 2020 \| Accepted December 24, 2020 Braz J Med Biol Res \| doi: 10.1590/1414-431X202010693 Brazilian Journal of Medical and Biological Research (2021) 54(5): e10693, http://dx.doi.org/10.1590/1414-431X202010693 ISSN 1414-431X Research Article 1/6 running with a footwear with a midsole composed by TPU exhibits a better RE (4.1%) compared to a con- ventional running shoe. Given that RE contributes to the success in endurance running events, they concluded that footwear with a TPU midsole could lead to better running performance, probably due to the beneﬁcial effects on RE. Despite the remarkable ﬁndings from previous studies showing that both TPU and minimalist shoes can reduce the oxygen cost of running compared with conventional shoes (7,15), it is still unknown whether there is a superior ability between these two models to translate the greater RE into better running performance. This occurs mainly because the current evidence is limited to analyzing the changes in RE (15–17), without necessarily examining whether improved RE translated into better running performance. This is particularly relevant because previous ﬁndings have indicated that improvement in RE does not necessarily result in a better running performance (18). From the practical perspective, this information might be helpful to sports physiologists to better select sport shoes for competition and training. Therefore, the present study aimed to: i) compare the effects of TPU and minimalist shoes on the oxygen cost of running; and ii) analyze the effect of a possible reduced oxygen cost of running mediated by these shoes on running performance. Materials and Methods Participants The sample size required was estimated using the Gpower software (version 3.1.9.2, Germany), with data from a previous investigation that analyzed the effect of different midsole characteristics (TPU vs conventional) on RE (15). A sample size of ﬁve participants was estimated to achieve statistically signiﬁcance in RE, for an expected effect size of 1.92 and power of 0.8 with an alpha level of 0.05. In order to improve statistical power, eleven recreational male runners volunteered to participate in this study. Participants were engaged in local competitions and their best performances in 10-km race times ranged from 35 to 45 min. All participants performed only low- intensity continuous aerobic training (50–70% maximal oxygen uptake, O2max) and were instructed to maintain a similar aerobic training during the experimental period. The exclusion criteria were: i) exhibited a forefoot contact running technique; ii) use of dietary supplement; iii) neuromuscular disorders; and iv) cardiovascular dysfunc- tions. The participants received a verbal explanation about the possible beneﬁts, risks, and discomforts associated with the study and signed a written informed consent before participating in the study. The study was conducted according to the Declaration of Helsinki and approved by the Research Ethics Committee of the School of Physical Education and Sport of the University of São Paulo (protocol number 37502714.8.0000.5391). Experimental design Participants visited the laboratory on four separate occasions at least 48 h apart and within a 4-week period. On the ﬁrst visit, after the anthropometric measurements, the participants performed an incremental test to exhaus- tion in order to determine their O2max wearing their own running shoes. During second and third visits, the participants performed, in a counterbalanced design, two 3-km running time-trials (3-km TT) on an outdoor 400-m track wearing either the TPU or minimalist shoes. On the fourth visit, the oxygen uptake was measured, in a counterbalanced design, during constant-speed running tests performed at 12 km/h (i.e., RE) and at individual average speed sustained during the 3-km TT (i.e., CTO2), wearing either the TPU or minimalist shoes. Between each experimental condition (speed vs shoes), the partici- pant rested for 10 min. Prior to the experimental laboratory and ﬁeld tests, the participants were submitted to a 3-min familiarization run wearing either the TPU or minimalist shoes, as previously described (8). The tests were performed during the preparatory training period, at the same time of the day, and at least 2 h after the last meal. The participants were instructed to record their diet 24 h before the ﬁrst experimental session and to repeat it prior to the subsequent experimental sessions. They were also asked to refrain from any exhaustive or unusual exercise during the experimental period. Anthropometric measurements An experienced investigator performed the anthro- pometric measurements according to the procedures described by Norton and Olds (19). Participants were weighed to the nearest 0.1 kg using an electronic scale (Filizola, model ID 1500, Brazil). Height was measured to the nearest 0.1 cm using a stadiometer. Skinfold thickness was measured to the nearest 0.2 mm at six body sites (triceps brachial, suprailiac, abdominal, chest, subscapular, and anterior thigh) using a Harpenden caliper (West Sussex, UK). The mean of three values was used for further analysis. Body density and body fat were estimated by the equations from Jackson et al. (20) and Brozek et al. (21), respectively. Incremental maximal test The incremental maximal test was performed on a motor-driven treadmill (model TK35, CEFISE, Brazil). All participants were requested to wear their favorite running shoes for this test. After a 3-min warm-up at 8 km/h, the speed was increased by 1 km/h every minute until participants were unable to maintain the required running speed. The subjects received strong verbal encourage- ment to continue as long as possible. Oxygen uptake ( .VO2), carbon dioxide production, and ventilation were measured breath-by-breath using an automatic metabolic cart (Cortex, Metalyzer 3B, Germany) and subsequently averaged over 30-s intervals throughout the test. Before Braz J Med Biol Res \| doi: 10.1590/1414-431X202010693 Footwear, running economy, and endurance performance 2/6 each test, the metabolic cart was calibrated using a 3-L syringe and a standard gas of known O2 (12%) and CO2 (15%) concentrations. The O2max was determined as the average of the oxygen uptake during the last 30 s of the test. Constant-speed tests The constant-speed tests were performed using the same motor-driven treadmill and .VO2 procedures adopted during the maximal incremental test. The subjects per- formed a standardized warm-up, consisting of a 5-min run at 8 km/h followed by a 5-min light stretch. The treadmill speeds were adjusted after warm-up and the subjects ran for 6 min wearing either the TPU or minimalist shoes. Given that previous ﬁndings have shown that an athlete who is energetically economical at a given speed will not necessarily be economical at other speeds (22), the participants performed constant-speed running tests at two distinct speeds wearing either the TPE or minimalist shoes. RE was determined at 12 km/h similar to a previous study (15) and the CTO2 was determined at individual average speed sustained during the 3-km TT (TPU=15.6±0.8 km/h, minimalist=15.4±.6 km/h). The oxygen uptake associated with the RE and CTO2 was measured by averaging the last 30 s from each running constant-speed bout. Recovery time between these constant-speed running tests was 10 min. Running performance The 3-km race is the ofﬁcial track running event that has been used by previous studies that analyzed the determinants of running performance (23,24). The par- ticipants used either the TPU or minimalist shoes during the 3-km TT, in separate sessions (i.e., second and third visits). Running performance was measured as the total time elapsed during the 3-km TT. The time to cover the 3-km TT was registered at each lap (i.e., 400 m) on an outdoor 400-m track with a manual stopwatch (model HS- 1000, Casio, Japan), while the rating of perceived exertion (RPE) was reported by participants at each lap using the Borg 15-point scale (25). Copies of this scale were laminated, reduced to 10 by 5 cm and ﬁxed to the participant’s wrist on the dominant arm. Subjects were instructed to ﬁnish the race as quickly as possible, as in a competitive event. Before the test, the participants warmed up for 10 min at 8 km/h. They were instructed to maintain regular water consumption 24 h before the test and water was provided ad libitum during the entire event. Verbal encouragement was provided during the entire event, but runners were not advised of their lap splits. Ambient temperature and humidity were provided by the Institute of Astronomy, Geophysics and Atmospheric Sciences of the University of São Paulo, Brazil. The mean±SD values for temperature and humidity were 24±2°C and 60±8%, respectively. Experimental footwear The experimental footwear used in the current study consisted of minimalist (Nike Free Run 2, average shoe mass: 275 g, heel drop: 4 mm) and TPU (Adidas Energy BoostTM, average shoe mass: 320 g, heel drop: 10 mm) running shoes. The minimalist model was characterized by a ultraﬂexible sole, lightweight, and no motion control or stability features, as previously described (26). The midsoles of the TPU model were composed of 80 and 20% of TPU and ethylene vinyl acetate, respectively. Shoe sizes ranged from 8–10 (UK system). Both shoes were wrapped with black tape to blind the participants regarding the footwear used in each experimental session. Participants had not used either of these shoes and, therefore, TPU and minimalist shoes were novel to all participants. Statistical analysis A normal data distribution was conﬁrmed by the Shapiro-Wilk test. Data are reported as means±SD. RE, CTO2, and running performance were compared between shoes using paired t-tests. Two-way ANOVA (shoes vs distance) was used to compare running speed distribution and RPE responses throughout the 3-km TT. Effect size (ES) was quantiﬁed using standardized mean differences and deﬁned as trivial (o0.20), small (0.20–0.49), moderate (0.50–0.79), and large (X0.80). A signiﬁcance criterion of Po0.05 was adopted for all analyses. All statistical analyses were performed using the Statistica 8 software package (StataSoft Inc., USA). Results Table 1 presents the main characteristics of the participants. The RE and CTO2 are shown in Figure 1, while running overall performance, running speed dis- tribution, and RPE changes throughout the 3-km TT for each shoe condition are shown in Figure 2. TPU footwear ( .VO2=42.5±2.6 mLkg–1min–1) resulted in better RE (B2.4%) compared to minimalist footwear ( .VO2=43.6± 2.1 mLkg–1min–1) (P=0.01, ES=0.42) (Figure 1A). In contrast, the CTO2 was not signiﬁcantly different (P=0.61, ES=0.18) between TPU ( .VO2=53.1±3.7 mLkg–1min–1) Table 1. Characteristics of the runners that participated in the study. Age (years) 33.1±7.2 Running experience (years) 4.1±2.5 Training volume (km/week) 44.7±14.3 Height (m) 1.74±0.05 Body mass (kg) 70.1±9.9 Maximal oxygen uptake (mLkg-1min-1) 52.1±4.9 Data are reported as means±SD. Braz J Med Biol Res \| doi: 10.1590/1414-431X202010693 Footwear, running economy, and endurance performance 3/6 and minimalist ( .VO2=52.4±3.8 mLkg–1min–1) shoes (Figure 1B). For both experimental conditions, running speed distribution showed a classical U-shaped pacing proﬁle (Figure 2B) with the ﬁrst and last laps faster than other laps, while RPE showed a linear proﬁle (Figure 2C). However, there was a main effect only for distance (Po0.01), without main effects for shoes (P=0.67) and interaction (P=0.75) for running speed distribution. Also, there was a main effect for distance (Po0.01), but not for shoes (P=0.62) and interaction (P=0.38) for RPE. In addition, there was no statistical difference for overall running performance between footwear models (TPU shoe=701±62 s, minimalist shoe=709±61 s, P=0.52, ES=0.18) (Figure 2A). Discussion Based on the assumption that oxygen cost of running is one of the best predictors of endurance performance (27,28) and that RE is acutely improved wearing both minimalist and TPU shoes (29,30), the present study aimed to compare the effects of these distinct models on RE and running performance. The main results of the current study revealed that: i) TPU shoes resulted in better RE (B2.4%), but similar CTO2 compared to the minimalist shoes, and ii) there was no signiﬁcant difference for running parameters (i.e., overall performance, running speed distribution, and RPE responses) between the TPU and minimalist shoes. These ﬁndings suggested that TPU shoes reduced the oxygen cost of running at low (i.e., 12 km/h) but not at high (i.e., individual average speed sustained during the 3-km TT) running speeds, resulting in a similar endurance performance compared with minimalist shoes. Endurance running has become a very popular phys- ical activity with millions of recreational runners starting the activity in the past few years (5). This increased popularity brought attention to the development of different training methods and technologies focused on acute improvement of endurance performance, such as a wide Figure 1. Oxygen uptake during constant-speed tests. A, Running economy at 12 km/h. B, Oxygen cost of running at individual average speed sustained during the 3-km running time- trial. TPU: midsole material composed by thermoplastic polyure- thane. Data are reported as means±SD. *Po0.05 (t-test). Figure 2. A, Overall performance; B, running speed distribution; C, rating of perceived exertion (RPE). TPU: midsole material composed by thermoplastic polyurethane. Data are reported as means±SD. #Po0.05 for main effects for distance (t-test and ANOVA). Braz J Med Biol Res \| doi: 10.1590/1414-431X202010693 Footwear, running economy, and endurance performance 4/6 range of running shoe models commercially available (16,31). Considering previous studies suggesting that the low mass of minimalist models is the main characteristic that affects the energetic cost of running (14,15), in the present study the footwear mass was normalized by adding lead tape to the minimalist shoes in order to reduce the possible inﬂuence on RE, CTO2, and running perfor- mance. Our results showed that the footwear with TPU midsole material resulted in better RE (2.4%) compared to minimalist shoes (Figure 1). In comparison with previous ﬁndings, the changes in RE with TPU shoes was slightly below those reported by Sinclair et al. (14) who compared the TPU and minimalist shoes (B5%), but similar to previous results by Worobets et al. (32) and Sinclair et al. (15), wherein the TPU shoes were able to improve approximately 1–4% of RE compared with conventional shoes. These ﬁndings are in accordance with the energy return advantage attributed to the TPU material, which suggested that RE could be signiﬁcantly improved (15). The mechanisms by which the TPU material could improve RE are not fully understood, but it has been suggested that the TPU material within the midsole would reduce the force needed to push the ground during the propulsion phase, resulting in lower meta- bolic stress in active skeletal muscles of lower limbs (33). Together, these ﬁndings expand the notion that TPU could improve RE by enhanced mechanical energy return (14–16,32), even when compared with minimalist footwear. Even though a growing amount of evidence has shown substantial gains in RE with different footwear models (7–10), there is a lack of information in the literature concerning their acute effects on endurance performance. In the present study, we analyzed the effects of TPU and minimalist shoes on a 3-km TT performance. Our results revealed that running parameters (i.e., overall perfor- mance, running speed distribution, and RPE responses) were not different between TPU and minimalist conditions (Figure 2), despite better RE shown for TPU shoes (Figure 1). The reasons for the similar running parameters despite a better RE with the TPU are not clear, but it could be related to the oxygen cost of running at the average speed at which the 3-km TT was performed. The CTO2 was not signiﬁcantly different between the models, indicating that the TPU was not able to maintain the reduced oxygen cost during high running intensity compared to the minimalist model. This ﬁnding is novel and relevant because previous ﬁndings have demon- strated that improvements in energetic cost of running are more effective to endurance performance if observed at intensities similar to the speed performed in the actual race rather than ﬁxed submaximal constant-speed tests (34), such as the speed chosen for the RE test (i.e., 12 km/h). Therefore, the similar running perfor- mance observed between running shoes could suggest an inability of the TPU material to maintain reduced energetic cost at running speeds similar to those adopted by athletes during 3-km TT. In order to address a ﬁnal conclusion, some limitations of the present study must be highlighted. First, the running performance was determined as the total time to cover a 3-km course, which is relatively short compared to other running events (e.g., 5-, 10-, and 21-km). Thus, given that longer running events are performed at lower relative intensities (closer to 12 km/h), it would be important to compare the effects of the footwear in longer distance running events. Second, we tested only one model of shoes designed to enhance energy return, which was composed by B80% of TPU in its midsole. Perhaps shoes with different percentages of TPU in the midsole (50–100% of TPU) could exacerbate the responses in oxygen cost found in the current study. In conclusion, the ﬁndings of the current study revealed that footwear with TPU midsole material increases RE at low running speed (12 km/h) compared with minimalist shoes. However, the better RE was not evident at the average speed sustained during 3-km TT (B15 km/h), ultimately resulting in a similar running performance compared to minimalist shoes. Therefore, it could be suggested that improved RE observed with the shoe material designed to enhance energy return could be more relevant than the minimalist nature of models for longer distance running events (X5 km). Acknowledgments The authors declare no conﬂict of interest. All costs were supported by a grant from National Council for Scientiﬁc and Technological Development (CNPq, #446337/ 2014-5). References 1. Rusko H, Havu M, Karvinen E. Aerobic performance capacity in athletes. Eur J Appl Physiol Occup Physiol 1978; 38: 151– 159, doi: 10.1007/BF00421531. 2. Tanaka K, Matsuura Y, Kumagai S, Matsuzaka A, Hirakoba K, Asano K. Relationships of anaerobic threshold and onset of blood lactate accumulation with endurance performance. Eur J Appl Physiol Occup Physiol 1983; 52: 51–56, doi: 10.1007/BF00429025. 3. Daniels J, Daniels N. Running economy of elite male and elite female runners. Med Sci Sports Exerc 1992; 24: 483–489, doi: 10.1249/00005768-199204000- 00015. 4. 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PMC4473093	SYSTEMATIC REVIEW Incidence of Running-Related Injuries Per 1000 h of running in Different Types of Runners: A Systematic Review and Meta-Analysis Solvej Videbæk1,2 • Andreas Moeballe Bueno3 • Rasmus Oestergaard Nielsen3 • Sten Rasmussen1,2 Published online: 8 May 2015 The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Background No systematic review has identiﬁed the in- cidence of running-related injuries per 1000 h of running in different types of runners. Objective The purpose of the present review was to systematically search the literature for the incidence of running-related injuries per 1000 h of running in different types of runners, and to include the data in meta-analyses. Data Sources A search of the PubMed, Scopus, SPOR- TDiscus, PEDro and Web of Science databases was conducted. Study Selection Titles, abstracts, and full-text articles were screened by two blinded reviewers to identify prospective cohort studies and randomized controlled trials reporting the incidence of running-related injuries in novice runners, recreational runners, ultra-marathon run- ners, and track and ﬁeld athletes. Study Appraisal and Synthesis Methods Data were ex- tracted from all studies and comprised for further analysis. An adapted scale was applied to assess the risk of bias. Results After screening 815 abstracts, 13 original articles were included in the main analysis. Running-related injuries per 1000 h of running ranged from a minimum of 2.5 in a study of long-distance track and ﬁeld athletes to a maximum of 33.0 in a study of novice runners. The meta-analyses revealed a weighted injury incidence of 17.8 (95 % conﬁ- dence interval [CI] 16.7–19.1) in novice runners and 7.7 (95 % CI 6.9–8.7) in recreational runners. Limitations Heterogeneity in deﬁnitions of injury, deﬁni- tion of type of runner, and outcome measures in the included full-text articles challenged comparison across studies. Conclusion Novice runners seem to face a signiﬁcantly greater risk of injury per 1000 h of running than recre- ational runners. Key Points ‘Injuries per 1000 h of running’ is an important and useful measure of association that enables comparison of the risk of injury across studies. Novice runners are at signiﬁcantly higher risk of injury 17.8 (95 % CI 16.7–19.1) than recreational runners, who sustained 7.7 (95 % CI 6.9–8.7) running-related injuries per 1000 h of running. More studies on ultra-marathon runners and track and ﬁeld athletes are needed in order to calculate weighted estimates. 1 Introduction Running is one of the most popular and accessible sport activities worldwide [1, 2]. It can be performed with minimal equipment, and by a broad variety of people in Electronic supplementary material The online version of this article (doi:10.1007/s40279-015-0333-8) contains supplementary material, which is available to authorized users. & Solvej Videbæk solvejandersen@hotmail.com 1 Department of Orthopaedic Surgery Research Unit, Science and Innovation Center, Aalborg University Hospital, Aarhus University, 18–22 Hobrovej, 9000 Aarhus, Denmark 2 Department of Clinical Medicine, Aalborg University, Aalborg, Denmark 3 Section of Sport Science, Department of Public Health, Faculty of Health Science, Aarhus University, Room 438, Dalgas Avenue 4, 8000 Aarhus, Denmark 123 Sports Med (2015) 45:1017–1026 DOI 10.1007/s40279-015-0333-8 almost every part of the world. In the US, more than 40,000,000 people run [2], and in Denmark and The Netherlands approximately 25 and 12.5 % of the popula- tion, respectively, run on a regular basis [3, 4]. Running-related injuries affect many runners. Unfortu- nately, the exact number of injuries is hard to identify because various studies have provided results on the prevalence and incidence of running-related injuries using different measures of association [5, 6]. To name a few, injuries have been reported as the number of injuries per 1000 km [7, 8]; proportion of injuries in a population [9]; number of injured runners per 100 runners [10]; and number of injured runners per 1000 h of running [11, 12]. The inconsistent use of such measures in the literature makes comparison of injury data difﬁcult across studies. Injuries per 1000 h of running was highlighted by Jakobsen et al. [12] as an important measure of association. They stated that the risk of injury must be related to the time spent running, in order to make the results from dif- ferent studies comparable. This is supported in a review from 2012 by Lopes et al. [13], who emphasize that stan- dardization of the number of injuries per hour of exposure is highly needed in running-related injury research. In a review from 1992, van Mechelen [10] compared the incidence rates of running-related injuries across a few studies presenting such results. The results revealed an injury incidence of 2.5–12.5 injuries per 1000 h of running. Since then, many studies have reported information on running-related injuries in different types of runners per 1000 h of running—for instance, novice runners, recre- ational runners, ultra-marathon runners, and track and ﬁeld athletes. However, no review has been published which systematically searched the literature for studies with in- formation on the incidence of running-related injuries in different types of runners per 1000 h of running. The purpose of the present review was to systematically review the literature for the incidence of running-related injuries in novice runners, recreational runners, ultra- marathon runners, and track and ﬁeld athletes per 1000 h of running. A secondary objective was to compare the injury rates across different types of runners per 1000 h of run- ning and include the data in meta-analyses. 2 Methods 2.1 Search Strategy Five databases (PubMed, Scopus, SPORTDiscus, PEDro and Web of Science) were searched electronically, without restriction on date of publication, to identify studies that included data regarding running-related injury incidences per 1000 h of running. The search was performed in collaboration with a certiﬁed librarian at Aarhus University Library, Denmark. Full details of the electronic search strategy for PubMed are provided in the electronic sup- plementary material (ESM) Appendix S1. Additional studies and trials were identiﬁed by checking references of included full-text articles and published reviews within the running injury thematic. Full-text articles, which were not included after searching the databases, were included afterwards if they, to the authors’ knowledge, had infor- mation about injuries per 1000 h of running. 2.2 Study Selection The screening of eligible studies was performed by two reviewers (SV and AMB), in two steps. In step 1, all ab- stracts were evaluated according to pre-speciﬁed inclusion and exclusion criteria. Inclusion criteria for abstracts con- sisted of the following: subjects were children, novice runners, recreational runners, elite runners, cross-country runners, orienteers, and/or triathletes; the study was based on original research (prospective cohort studies and ran- domized controlled trials); the article was written in Eng- lish or Danish; and the abstract included data regarding running-related injuries per 1000 h of running, or indicated that such data might be available in the full-text article. Exclusion criteria included the following: subjects were military or army recruits; studies in which participants were predominantly exposed to different types of sports other than running; original study designs consisted of cross-sectional studies, case–control studies, case series and case reports; and studies did not include original re- search, for instance reviews. All abstracts were evaluated independently by each of the two reviewers and either included or excluded. In cases of disagreement between the two blinded reviewers (SV and AMB), a third reviewer (RON) made the ﬁnal decision of selection. In step 2, the two reviewers (SV and AMB) read all full texts included in step 1 as well as the full texts of the additional articles identiﬁed in the reference lists. The following criteria were used to ﬁnally include or exclude full-text articles. Inclusion criteria for full-text articles: must include ﬁndings from which it is possible to extract data on running-related injuries per 1000 h of running; articles without data on injuries per 1000 h of running, but containing data on the incidence of injuries per 1000 km. Exclusion criteria: studies in which participants were pre- dominantly exposed to different types of sports other than running and, consequently, running-related injuries could not be distinguished from other sport injuries; if injuries per 1000 h were estimated per leg and not per individual; and data on injuries per 1000 h of running were missing, data on number of events and time at risk were unavailable, 1018 S. Videbæk et al. 123 and the corresponding author was unable to provide these data after being contacted by e-mail. Each reviewer (SV and AMB) processed the articles individually and, in cases of disagreement, they followed a consensus decision-making process. In cases where they did not reach a consensus, a third reviewer (RON) made the ﬁnal judgment. 2.3 Data Collection The study characteristics of the included full-text articles were described to gain insight into the homogeneity of the study populations and deﬁnitions of running-related injuries. The following data were collected: author and year of pub- lication; study design; type of runners; sample size used in the analysis; description of the study population; and deﬁnition of the running-related injury (Table 1). Estimates of the incidence of running-related injuries per 1000 h and per kilometres were extracted from all studies for further analysis. Three studies provided estimates of running-related injuries per 1000 h without 95 % conﬁdence intervals (CIs) and without presenting the raw data needed to calculate these [12, 14, 15]. The corresponding authors were contacted and data were received from two of them [14, 15], which enabled the inclusion of these results in the meta-analyses. The study populations of the included studies were categorized into one of four types of runners: novice run- ners; recreational runners; ultra-marathon runners; and track and ﬁeld athletes. This categorization was made to enable comparison of results across studies. Some studies reported the incidence of running-related injuries per 1000 miles [7, 8, 16] but these results were converted into running-related injuries per 1000 km using an online converter [17]. 2.4 Risk of Bias Assessment The tool used for assessing risk of bias of the included studies was chosen after thorough consideration of the ad- vantages and disadvantages of the available methods for evaluating bias. The studies included both prospective co- hort studies and randomised controlled trials. The main purpose of this review was to measure the incidence of running-related injuries per 1000 h of running. The causes of running-related injuries were not of interest, thus minimizing the importance of methods of randomization for the quality of outcome. Quality assessment by one single tool was therefore possible for both designs. The tool used to assess the risk of bias of the included studies was a version of the Newcastle Ottawa Scale, a tool modiﬁed by Saragiotto et al. [18] to evaluate studies undertaking re- search on runners. The tool contains 11 criteria designed to assess the risk of bias, and uses a star rating system to indicate the quality of a study (see ESM Appendix S2 for a description of each criterion in the original version of the quality assessment tool modiﬁed for runners [18]). Certain modiﬁcations were applied to specify the tool used to assess the risk of bias on the parameter of concern in our review— the incidence of running-related injuries. Three of the 11 criteria were excluded. Item 4 was excluded because an exposed versus non-exposed cohort was irrelevant as long as the total study population was exposed to running; item 7 was excluded because it was linked to item 4; and item 11 concerned the risk of association and was removed because these measures relate to research on associations. In item 3 the wording ‘average runners in the community’ was re- worded to ‘average type of runners researched’, meaning that the article received a star if the study population were representative of the type of runner (novice runners, recreational runners, ultra-marathon runners, or track and ﬁeld athletes) described according to item 1. The criteria adopted to assess risk of bias were (1) description of runners or type of runner; (2) deﬁnition of the running-related in- jury; (3) representativeness of the exposed cohort; (4) ascertainment of exposure; (5) demonstration that outcome of interest was not present at the start of the study; (6) assessment of outcome; (7) was follow-up long enough for outcomes to occur; and (8) adequacy of follow-up of co- horts. The risk of bias assessment was carried out by two researchers (SV and AMB) in a blinded process and, in cases of disagreement, they went through a consensus- making process. Only studies with estimates on injury in- cidence per 1000 h were quality scored since this outcome represented the main analysis. 3 Results A total of 3172 articles were identiﬁed through the data- base searches. Among these articles, 2357 were duplicates, as determined by the reference program RefWorks. Next, 815 titles and abstracts were evaluated in step one of the selection process. Of these, 69 full-text articles were in- cluded and evaluated according to the inclusion and ex- clusion criteria in step two of the selection process, of which 58 were excluded. In Fig. 1, the selection process is visualised in a ﬂowchart. By checking reference lists, one additional study was included [14]. In addition, the authors knew of one article that was not included in the search but in which the relevant information was incorporated [26]. This article was also included. Finally, 13 articles that presented data on running-related injuries per 1000 h of running were included—eight prospective cohort studies and ﬁve randomized controlled trials. Overall, ten studies provided estimates on running-related injuries per 1000 km and these were used for a subanalysis. Incidence of Running-Related Injuries 1019 123 Table 1 Description of studies References, country of origin Study design (follow- up) Study population Baseline characteristics Musculoskeletal injury deﬁnition Novice runners Bovens et al. [25], The Netherlands Prospective cohort study (81 weeks) 73 Novice runners with little or no running experience Age above 20 years. Only volunteers without persisting injuries were accepted. (58 men and 15 women) Any physical complaint developed in relation with running activities and causing restriction in running distance, speed, duration or frequency Bredeweg et al. [24], The Netherlands Randomised controlled trial (9 weeks plus additional 4 weeks for 211 runners) 362 (171?191) All participants had not been running on a regular basis in the previous 12 months Age range 18–65 years. No injury of the lower extremity within the preceding 3 months Any musculoskeletal complaint of the lower extremity or lower back causing restriction of running for at least a week Buist et al. [11], The Netherlands Prospective cohort study (8 weeks) 629 Runners who had signed up for 4-mile running event. 474 novice runners who either restarted running or had no running experience. 155 recreational runners Age above 18 years Any musculoskeletal pain of the lower limb or back causing a restriction of running for at least 1 day Buist et al. [20], The Netherlands Randomized controlled trial (8 and 13 weeks) 486 Novice runners who had not been running on a regular basis in the previous 12 monthsa Age range 18–65 years. No injury of the lower extremity within the preceding 3 months Any self-reported running-related musculoskeletal pain of the lower extremity or back causing a restriction of running for at least 1 week (three scheduled trainings) Nielsen et al. [26], Denmark Prospective cohort study (12 months) 930 Novice runners who had not been running on a regular basis in the previous 12 monthsa Healthy novice runners age range 18–65 years with no injury in the lower extremities or back 3 months preceding baseline investigation. Not participating in other sports for more than 4 h/week Any musculoskeletal complaint of the lower extremity or back causing a restriction of running for at least 1 week Recreational runners Jakobsen et al. [12], Denmark Randomised controlled trial (12 months) 41 Recreational long-distance runners. Had all taken part in marathon races and intended to take part in at least two marathons during the year of investigation 19 Men and 2 women aged 24–56. No runner had any symptoms or objective signs of overuse injury at the start of the investigation Any injury to the musculoskeletal system that was sustained during running and prevented training or competition Malisoux et al. [14], Luxembourg Prospective cohort study (22 weeks) 264 Recreational runners. Mean regularity of runningc in the last 12 months = 9.4–10.8 Healthy participants above 18 years old with any level of ﬁtness A physical pain or complaint located at the lower limbs or lower back region, sustained during or as a result of running practice and impeding planned running activity for at least 1 day Theisen et al. [15], Luxembourg Randomised controlled trial (5 months) 247 Recreational runners Healthy and uninjured leisure-time runners, aged above 18 years. Participants having more than 6 accumulated months of regular trainingb Any ﬁrst-time pain sustained during or as a result of running practice and impeding normal running activity for at least 1 day Van Mechelen et al. [28], The Netherlands Randomised controlled trial (16 weeks) 421 Recreational runners running at least 10 km/week all year-round Healthy, no current injury, not home from work at sick leave, not performing sport as a part of their profession Any injury that occurred as a result of running and caused one or more of the following: (1) the subject had to stop running, (2) the subject could not run on the next occasion, (3) the subject could not go to work the next day, (4) the subject needed medical attention, or (5) the subject suffered from pain or stiffness during 10 subsequent days while running 1020 S. Videbæk et al. 123 Table 1 continued References, country of origin Study design (follow- up) Study population Baseline characteristics Musculoskeletal injury deﬁnition Wen et al. [23], USA Prospective cohort study (32 weeks) MH group:108 recreational runners previously running a mean of 24.94 km/ weekb. However 8.3 % of these were novice runners with no running experience Members of a running group with the purpose to prepare its members to run a marathon Answering yes to having had ‘‘injury or pain’’ to an anatomic part; answering yes to having had to stop training, slow pace, stop intervals, or otherwise having had to modify training; and a ‘‘gradual,’’ versus ‘‘immediate’’, onset of the injury or a self- reported diagnosis that is generally considered an overuse injury Ultra marathon runners Krabak et al. [21], USA Prospective cohort study (7 days) 396 Experienced runners who have completed marathon or ultraendurance events Age range 18–64 years A disability sustained by a study participant during the race, resulting in a medical encounter by the medical staff Track and ﬁeld athletes Bennell et al. [22], Australia Prospective cohort study (12 months) 95 Competitive track and ﬁeld athletes (throwers and walkers excluded) Age range 17–26 years. Training at least three times a week, when uninjured Any musculoskeletal pain or injury that resulted from athletic training and caused alteration of normal training mode, duration, intensity or frequency for 1 week or more Lysholm et al. [19], Sweden Prospective cohort study (12 months) 60 Track and ﬁeld athletes. Sprinters, middle-distance runners and longdistance/marathon runners running in club and competing Previous experience of training (7 h per week or more) varied between 1 and 32 years Any injuries that markedly hampered training or competition for at least 1 week MH mileage-hours a 10km total in all training sessions in the previous 12 months b Miles were converted to km [17] c Regular training (at least once a week) Incidence of Running-Related Injuries 1021 123 The year of publication for the included studies ranged from 1987 to 2014, and the studies represented populations in Australia, Denmark, Luxembourg, Sweden, The Netherlands, and the USA. The follow-up periods ranged from 7 days to 81 weeks. Eight studies used a time-loss deﬁnition of injury; one study deﬁned an injury as a need for medical attention; and the remaining four studies used a mixture of time loss, physical pain, and the need for medical attention in the deﬁnition of injury. Across studies, the primary purpose was to compare the incidence of running-related injuries per 1000 h of running. Five studies reported this estimate in novice runners; ﬁve studies in recreational runners; one study in ultra-marathon runners; and two studies in track and ﬁeld athletes. The estimates ranged from 2.5 [19] to 33.0 [20] running-related injuries per 1000 h of running. Two meta-analyses were performed on the estimates of novice runners and recre- ational runners, respectively. As one article [12] did not provide data to calculate 95 % CIs, estimates from nine studies were included in these quantitative analyses (Fig. 2). The weighted estimates revealed novice runners faced a signiﬁcantly greater injury rate of 17.8 (95 % CI 16.7–19.1) than recreational runners, who sustained 7.7 (95 % CI 6.9–8.7) running-related injuries per 1000 h of running. Ten studies provided estimates of running-related in- juries per 1000 km of running, and these results were pooled in a subanalysis (Table 2). The weighted estimate revealed an injury incidence of 1.07 (95 % CI 1.01–1.13) per 1000 km of running. The risk of bias was assessed for each of the 13 studies presenting an estimate of the incidence of running-related Fig. 1 Flowchart visualizing the selection process of studies in the systematic review 1022 S. Videbæk et al. 123 injuries per 1000 h of running (Table 3). The criteria most frequently awarded with a star were description of runners or type of runners (13/13) and deﬁnition of running-related injury (13/13). The criteria with the least stars awarded comprised ascertainment of exposure (6/13) and assess- ment of outcome (8/13). The average stars awarded to the articles assessed for risk of bias was 6 out of a total of 8 stars, with a maximum of 8 and a minimum of 3. 4 Discussion The present review is the ﬁrst to systematically review the literature on the incidence rate of running-related injuries in different types of runners. The weighted estimate of 17.8 (95 % CI 16.7–19.1) running-related injuries per 1000 h of running in novice runners was signiﬁcantly greater than the incidence rate of 7.7 (95 % CI 6.9–8.7) in recreational runners. One study reported the incidence of running- Fig. 2 Meta-analysis performed on the estimates of running-related injuries per 1000 h in novice runners and recreational runners. aData on standard error or 95 % conﬁdence limits were not reported and the study was therefore not included in the meta-analysis. bData on standard error or 95 % conﬁdence limits were not reported and therefore no meta-analysis was performed on track and ﬁeld athletes. CI conﬁdence intervals Table 2 Running-related injuries per 1000 km of running References Runners (n) Injuries (n) Estimate (RRI per 1000 km) 95 % CI Bennell et al. [22] 95 130 0.58 0.5, 0.7 Bovens et al. [25] 73 174 0.86 0.7, 1.0 Fields et al. [7] 40 17 0.18 0.1, 0.3 Gerlach et al. [8] 86 47 0.22 0.2, 0.3 Jakobsen et al. [12] 41 50 0.62 0.4, 0.9 Krabak et al. [21] 396 217 2.28 2.0, 2.6 Nielsen et al. [27] 58 13 2.85 1.7, 4.9 Nielsen et al. [26] 930 294 1.64 1.5, 1.8 Van Mechelen et al. [28] 421 49 0.44 0.3, 0.6 Wen et al. [23] 108 49 0.76 0.6, 0.9 Weighted estimate 2248 1040 1.07 1.01, 1.13 RRI running-related injuries, km kilometres, CI conﬁdence interval Incidence of Running-Related Injuries 1023 123 related injuries in ultra-marathon runners as 7.2 per 1000 h [21]. In track and ﬁeld athletes, two studies reported the incidences of running-related injuries from 2.5 to 26.3 per 1000 h [19, 22]. In the latter, track and ﬁeld athletes were subdivided into sprinters, middle-distance runners, and long-distance runners, which may be relevant as the re- ported running-related injury incidence per 1000 h was greater in sprinters and middle-distance runners than in long-distance runners [19]. In Fig. 2, a summary of the results in different types of runners is presented. The healthy participant effect may play a role when grouping novice versus recreational run- ners [23]. In novice runners, the ﬁve studies are heteroge- neous since the estimates reported by three of the studies [11, 20, 24] range from 30.1 to 33 and are signiﬁcantly higher than those reported by the two remaining studies [25, 26]. A possible explanation for the discrepancy is the fol- low-up time in the respective studies. The non-injured runners accumulate relatively more exposure time in studies with a long follow-up, while the injured runners are censored. This will mathematically explain the overall de- crease in running-related injuries per 1000 h of running in studies with longer follow-up amongst novice runners. The two studies with the lowest incidence of running-related injuries per 1000 h of running had 81 and 52 weeks of follow-up, while the three studies with the greatest injury incidence had follow-up periods of 8–13 weeks (Table 1). The link between a relatively short follow-up time and a high incidence rate of running-related injuries versus long follow-up time and a lower incidence rate of running-re- lated injuries indicates the possibility that runners classiﬁed as novice runners at the beginning of a study may rea- sonably be classiﬁed as recreational runners as time passes. If novice runners exceed 8–13 weeks without injury, they may well have adapted to running and face a lower injury risk after this period, even though they may spend more time running. Novice runners exceeding 8–13 weeks’ fol- low-up may then be considered as recreational runners instead. Based on this, it may be appropriate to identify a cut-off distinguishing a novice runner from a recreational runner. In contrast, the injury incidences are homogeneous in recreational runners and the weighted estimate is unaf- fected by bias. The strengths of the present review are mainly the sys- tematic search of the literature and the use of meta-analyses to compare the injury incidences. The searches were per- formed thoroughly in ﬁve databases, in cooperation with a certiﬁed librarian. Moreover, all reference lists of the in- cluded full-text articles were checked for additional studies and, to the authors’ knowledge, one article [26] was also able to be included for analysis, although it was not indexed in any of the ﬁve databases searched. Evaluation of the quality of all articles presenting estimates of running-re- lated injuries per 1000 h was accomplished and meta-ana- lyses on these data were conducted. Thus, the present systematic review and meta-analyses represent rigorous evaluations and provide estimates of running-related injury incidences in novice runners, recreational runners, ultra- marathon runners, and track and ﬁeld athletes. The present study has a number of limitations, including differences in deﬁnitions of injury, deﬁnition of type of runner, and outcome measures used. First, deﬁnition of injury varies considerably across studies. Eight studies used time-loss deﬁnitions, but even within this deﬁnition there is a lack of consensus of the amount of time needed to classify time loss from running as a running-related injury. One study did not deﬁne the amount of time [12], some studies used 1 day in their deﬁnition [11, 14, 15], while other studies used 1 week [19, 20, 22, 24]. The only study [21] solely deﬁning injury as the need for medical attention was reporting on ultra-marathon runners, and as these data were collected in real time while the runners participated in Table 3 Risk of bias assessment Criteria for assessing risk of bias 1 2 3 4 5 6 7 8 Novice runners Bovens et al. [25] * * * 0 * * * * Bredeweg et al. [24] RCT * * * 0 * 0 0 0 Buist et al. [11] * * * 0 0 0 0 0 Buist et al. [20] RCT * * * 0 0 0 0 * Nielsen et al. [26] * * * * * * * * Recreational runners Jakobsen et al. [12] RCT * * * 0 * * * 0 Malisoux et al. [14] * * * * * 0 * * Theisen et al. [15] RCT * * 0 * * * * * Van Mechelen et al. [28] RCT * * * 0 * * * 0 Wen et al. [23] * * * 0 0 0 * * Ultra-marathoners Krabak et al. [21] * * * * 0 * 0 * Track and ﬁeld athletes Bennell et al. [22] * * * * * * * * Lysholm et al. [19] * * * * 0 * * 0 Only studies providing estimates of the incidence of running-related injuries per 1000 h were assessed for risk of bias. The criteria adopted to assess risk of bias were: (1) description of runners or type of runner; (2) deﬁnition of the running-related injury; (3) representa- tiveness of the exposed cohort; (4) ascertainment of exposure; (5) demonstration that outcome of interest was not present at start of study; (6) assessment of outcome; (7) was follow-up long enough for outcomes to occur?; (8) adequacy of follow-up of cohorts RCT randomised controlled trial * A study was awarded a star for every criterion it fulﬁlled. The more stars the higher quality 1024 S. Videbæk et al. 123 the ultra-marathon, this method was reasonable. No studies exclusively deﬁned a running-related injury as physical pain alone, but, in four studies, physical pain was incor- porated as part of the injury deﬁnition [16, 22, 25, 28]. Second, runners from the included studies were classiﬁed into four groups according to the type of runner, enabling relevant intergroup comparison. No exact deﬁnition of each category was made, but the baseline characteristics leading to grouping in one of the four types of runners are listed in Table 1. Third, the method of gathering data on exposure time may be questionable. In many studies, runners were asked to self-report their training exposure in web-based running diaries. This approach may lead to training hours or distance being estimated wrongly, possibly because of recall bias and time spent self-reporting [27]. The quality assessment tool accounted for this, and awarded no star when exposure was registered by written self-report (item 5). However, it is questionable whether the risk of bias was, in reality, higher in the study by Bovens et al. [25], which received no star in item 5 because running exposure was collected in diaries, than in the study by Benell et al. [22], in which a star was awarded for a retrospective personal interview completed by one of the researchers at the end of the 12 months of follow-up. Lack of agreement in the way exposure time was calculated was another challenge. In some studies [11, 14, 15, 19, 23–25, 28], exposure time was calculated from the time a participant started the running programme until the time they reported a running-related injury (injured runners) or until the end of the programme (non-injured runners). This way of calculating exposure time was ideal due to that fact that the same runner could only contribute exposure time as long as he had not been injured. Thus, the risk of registering the same injury twice, if re-occurring, was avoided. Additionally, an injured person could not add exposure time after the injury oc- curred, and the number of injuries would be the same as the number of injured runners. Other studies did not mention whether study participants were censored if an injury oc- curred [12, 20, 26]. Further, some studies speciﬁed the premise that the same runner was included and was con- tributing exposure time, if running was resumed after an injury occurrence [21, 22]. Due to the varying ways of calculating exposure time in the included studies, the most appropriate comparison of the incidence of running-related injuries across all included studies was to use the total number of registered injuries instead of the total number of injured runners. This approach made it possible for one runner to ﬁgure twice or more in the pooled count of in- juries. However, it would have been preferable if all studies had used the ideal method of calculating exposure time since this would have meant that one single runner could not accumulate exposure time after a ﬁrst-time injury and have a recurrent injury counted twice. Of the 13 studies providing estimates on running-related injuries per 1000 h of running, not all provided raw data on exact exposure time or 95 % CIs of the reported estimates. Corresponding authors from the respective articles [12, 14, 15, 26] were contacted, and data were received from Malisoux et al. [14], Theisen et al. [15] and Nielsen et al. [26]. Moreover, the estimate of 30.1 running-related in- juries per 1000 h used in the meta-analysis relating to novice runners derives from the complete study population of runners in the prospective study of Buist et al. [11]. Overall, 155 of these 629 runners were described as run- ners already participating in running at baseline, running a mean of 1.2 h per week. Unfortunately, we were unable to obtain data that allowed us to calculate estimates for each of the groups of runners separately. Consequently, we de- cided to include the estimate of 30.1 running-related in- juries per 1000 h in the category of novice runners; therefore, the true incidence of running-related injuries in novice runners might be even higher. The present study constitutes a thorough and fully up- dated literature review presenting data regarding the inci- dence rates of running-related injuries, and outlining relevant key issues, which limit the comparison of studies in running-related injury research. The included meta- analyses form new estimates showing variations in the incidence rates of running-related injuries among different types of runners, and can be used as a starting point in future running-related injury research. 5 Conclusions The reported weighted analysis of running-related injury incidence per 1000 h of running revealed that novice run- ners face a signiﬁcantly greater risk of injury 17.8 (95 % CI 16.7–19.1) than their recreational peers 7.7 (95 % CI 6.9–8.7). Caution is advisable when comparing estimates on the incidence of running-related injuries across studies because of differences in the deﬁnition of injury. Only a few studies reported injury incidences of ultra-marathon runners and track and ﬁeld athletes, and no weighted es- timates were calculated. Acknowledgments No sources of funding were used to assist in the preparation of this review. Solvej Videbæk, Andreas Moeballe Bueno, Rasmus Oestergaard Nielsen and Sten Rasmussen have no potential conﬂicts of interest that are directly relevant to the content of this review. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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PMC8461211	Physiological Reports. 2021;9:e15037. \| 1 of 14 https://doi.org/10.14814/phy2.15037 wileyonlinelibrary.com/journal/phy2 Received: 19 May 2021 \| Revised: 20 August 2021 \| Accepted: 26 August 2021 DOI: 10.14814/phy2.15037 O R I G I N A L A R T I C L E Comparison of constant load exercise intensity for verification of maximal oxygen uptake following a graded exercise test in older adults Ian R. Villanueva1 \| John C. Campbell1 \| Serena M. Medina1 \| Theresa M. Jorgensen1 \| Shannon L. Wilson1 \| Siddhartha S. Angadi2 \| Glenn A. Gaesser1 \| Jared M. Dickinson3 This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society 1Arizona State University, Phoenix, Arizona, USA 2Department of Kinesiology, University of Virginia, Charlottesville, Virginia, USA 3Department of Health Sciences, Central Washington University, Ellensburg, Washington, USA Correspondence Jared M. Dickinson, Department of Health Sciences, Central Washington University, 400 E University Way, Ellensburg, WA 98926, USA. Email: jared.dickinson@cwu.edu Funding information No funding information provided. Abstract Maximal oxygen uptake (VO2max) declines with advancing age and is a predic- tor of morbidity and mortality risk. The purpose here was to assess the utility of constant load tests performed either above or below peak work rate obtained from a graded exercise test for verification of VO2max in older adults. Twenty- two healthy older adults (9M, 13F, 67 ± 6 years, BMI: 26.3 ± 5.1 kg·m−2) par- ticipated in the study. Participants were asked to complete two experimental trials in a randomized, counterbalanced cross- over design. Both trials (cycle er- gometer) consisted of (1) an identical graded exercise test (ramp) and (2) a con- stant load test at either 85% (CL85; n = 22) or 110% (CL110; n = 20) of the peak work rate achieved during the associated ramp (performed 10- min post ramp). No significant differences were observed for peak VO2 (L·min−1) between CL85 (1.86 ± 0.72; p = 0.679) or CL110 (1.79 ± 0.73; p = 0.200) and the associated ramp (Ramp85, 1.85 ± 0.73; Ramp110, 1.85 ± 0.57). Using the study participant's mean coefficient of variation in peak VO2 between the two identical ramp tests (2.9%) to compare individual differences between constant load tests and the associated ramp revealed 19/22 (86%) of participants achieved a peak VO2 during CL85 that was similar or higher versus the ramp, while only 13/20 (65%) of participants achieved a peak VO2 during CL110 that was similar or higher versus the ramp. These data indicate that if a verification of VO2max is warranted when testing older adults, a constant load effort at 85% of ramp peak power may be more likely to verify VO2max as compared to an effort at 110% of ramp peak power. K E Y W O R D S aerobic power, aging, exercise physiology, verification, VO2max, VO2peak 2 of 14 \| VILLANUEVA et al. 1 \| INTRODUCTION Advancing age is associated with a variety of physiologi- cal and biological changes that can contribute to impaired physical function. Of particular interest is the steady de- cline in the maximal rate of oxygen uptake (VO2max) that is well documented during advancing age (Betik & Hepple, 2008; Gries et al., 1985; Kaminsky et al., 2015). Not only is a reduced VO2max in older adults associated with func- tional limitations, such as difficulty with walking, climb- ing stairs, and performing daily activities (Kaminsky et al., 2013; Paterson et al., 1999, 2004; Paterson & Warburton, 2010), but a low VO2max is a powerful independent pre- dictor of cardiovascular disease and all- cause mortality (Imboden et al., 2018; Kokkinos et al., 2010; Myers et al., 2002; Ross et al., 2016). Moreover, the addition of VO2max to other traditional risk factors improves risk stratifica- tion, and inclusion of VO2max to classify morbidity and mortality risk may be particularly powerful for those on the lower end of the VO2max spectrum, such as older adults (Ross et al., 2016). Consequently, developing effec- tive exercise testing strategies that can be used to verify a maximal exercise effort, and thus VO2max, in older adults could have important implications for accurate assess- ment of morbidity and mortality risk in this population. Traditionally, VO2max is often assessed through the use of a graded exercise test, employing either a steady ramp or an incremental step test until volitional exhaustion. In theory, a VO2max is achieved when there is no increase in VO2 with a concomitant increase in power or speed (Day et al., 2003; Hill & Lupton, 1923; Taylor et al., 1955), which is often referred to as a VO2 plateau (Taylor et al., 1955). While sampling rate/interval can influence the oc- currence of a plateau in VO2 (Astorino, 2009), a plateau is not always observed, and in fact has been found to only occur in 17% of VO2max assessments (Day et al., 2003). The absence of an observed VO2 plateau has produced queries as to the validatity of these tests for accurately assessing VO2max (Day et al., 2003; Howley et al., 1995; Midgley & Carroll, 2009; Poole et al., 2008). Consequently, the development of secondary criteria that are predicated on expected values for respiratory exchange ratio, heart rate (HR), and blood lactate, for example, have been used to validate a maximal effort (Howley et al., 1995; Midgley et al., 2007, 2009; Wagner et al., 2020). However, the use of these secondary criteria is problematic (Poole & Jones, 2017) as these criteria can often be achieved at a “submax- imal” effort (Poole et al., 2008). More recently, the use of a secondary constant load test that is performed following a graded exercise test has been implemented as a strategy to verify a maximal effort and VO2max (Costa et al., 2021; Midgley & Carroll, 2009; Poole et al., 2008). While the use of a constant load test to verify VO2max has gained consideration, the intensity at which these constant load tests have been performed is vari- able. For instance, these constant load bouts have been performed at work rates below (Day et al., 2003; Murias et al., 2018; Rossiter et al., 2006; Sedgeman et al., 2013), equal to (Sawyer et al., 2015), or above (Astorino et al., 2009; Barker et al., 2011; Hawkins et al., 2007; Iannetta et al., 2020; Kuffel et al., 2005; Leicht et al., 2013; Midgley et al., 2006; Murias et al., 2018; Nolan et al., 2014; Poole et al., 2008; Rossiter et al., 2006; Scharhag- Rosenberger et al., 2011; Sedgeman et al., 2013; Weatherwax et al., 2016) those achieved during the preceding graded exer- cise test [most previous studies employ constant load tests between 85% and 115% of peak work rate (Astorino et al., 2009; Barker et al., 2011; Dalleck et al., 2012; Hawkins et al., 2007; Iannetta et al., 2020; Kuffel et al., 2005; Leicht et al., 2013; Midgley & Carroll, 2009; Midgley et al., 2006; Murias et al., 2018; Niemela et al., 1980; Nolan et al., 2014; Poole et al., 2008; Rossiter et al., 2006; Sawyer et al., 2015; Scharhag- Rosenberger et al., 2011; Sedgeman et al., 2013; Weatherwax et al., 2016)]. Furthermore, there is lack of agreement on the work rate at which the constant load tests should be performed to best verify a maximal effort (Breda et al., 1985; Iannetta et al., 2020; Poole & Jones, 2017). Specifically, some propose that if a VO2max is to be verified, the constant load effort needs to be conducted at a work rate higher than that achieved during the graded exercise test (Poole & Jones, 2017). On the other hand, recent evidence indicates that a work rate below that achieved during the graded exercise test is more likely to verify a maximal effort (Iannetta et al., 2020). In partic- ular, the use of a “submaximal” constant load work rate to verify VO2max may be more reliable versus the “supra- maximal” work rate when coupled with graded exercise test protocols that are shorter in duration (e.g., steeper ramp protocols) (Iannetta et al., 2020). Consequently, the use of a constant load work rate below the peak work rate achieved during the graded exercise test may be a more reliable strategy to verify VO2max in individuals with a lower VO2max, such as older adults, who may experience shorter graded exercise tests. Therefore, the primary purpose of this study was to em- ploy a cross- over design to assess the utility of a constant load test performed at a work rate below (85%) and a work rate above (110%) the peak work rate achieved during a graded exercise test (ramp) for validating a maximal ef- fort and verifying VO2max in healthy older adults. While comparison of constant load intensities above and below the peak achieved during a ramp test has been previously reported (Murias et al., 2018), to our knowledge no study has employed a randomized, counterbalanced cross- over design. We hypothesized that in healthy older adults, the constant load test below the peak work rate of the ramp \| 3 of 14 VILLANUEVA et al. test would be more likely to verify a maximal effort and VO2max, which would be demonstrated by a greater num- ber of individuals achieving a peak VO2 value during the constant load effort below peak work rate that is similar or higher to the ramp test as compared to the constant load effort above peak work rate. In addition, the randomized cross- over design of the study included the performance of two identical ramp tests by each participant. Therefore, a secondary purpose of this study was to evaluate a second identical ramp test as a strategy to verify VO2max in older adults. 2 \| MATERIALS AND METHODS 2.1 \| Participants Twenty- four healthy older adults volunteered to partici- pate in this study. All participants were between the ages of 60– 80 years and were recruited by advertisement, locally posted flyers, and word of mouth. Participants completed a brief online pre- screening questionnaire to assess general health characteristics which was reviewed by a member of the research team. Following the pre- screening ques- tionnaire, qualified participants were invited to the labo- ratory for a formal informed consent process. Additional screening included a medical history, the Physical Activity Readiness Questionnaire for Everyone (PARQ+), and assessment of resting blood pressure. Participants were excluded if they had uncontrolled hypertension, or any self- reported heart, liver, kidney, blood, or respiratory dis- ease, peripheral vascular disease, diabetes or endocrine disease, active cancer or use of tobacco, self- reported acute or chronic illness, medical/orthopedic conditions preclud- ing exercise, or if they were currently training for an en- durance event (i.e., marathon, triathlon). All participants provided written informed consent prior to participation. Participant characteristics for those that participated in the study are presented in Table 1. This study was approved by the University Institutional Review Board (in compliance with the Declaration of Helsinki, as revised in 1983). 2.2 \| Study design and procedures Participants were studied during two separate experimen- tal trials. The experimental trials were separated on av- erage by 9 days (range, 6– 14 days) and were performed in a randomized, counterbalanced cross- over design at a similar time of day (e.g., morning vs. afternoon). Each ex- perimental trial consisted of a graded exercise ramp test and a constant load test that was performed after 10 min of active rest (pedaling at a work rate no higher than the warm- up) following completion the ramp test. The ramp tests were identical for each experimental trial, however, the visits differed in the work rate at which the subsequent constant load test was performed. During each experimental trial participants reported to the laboratory for testing at least 3 h postprandial and after abstaining from caffeine, alcohol, supplements, and exer- cise for at least 24 h. The participant's height and weight were measured on a calibrated stadiometer and resting blood pressure measures were obtained during each visit (Dinamap® PRO 100 Vital Signs Monitor; GE Healthcare). Participants were equipped with a mouthpiece connected to a standard nonrebreathing valve (Hans Rudolph) for continuous measurement of ventilation and respiratory gas exchange data using a TrueOne 2400 metabolic cart (Parvomedics). A standard calibration was performed before each test per manufacturer recommendations. Participants were also equipped with a chest worn HR mon- itor (Polar, Inc.) to continuously monitor HR. After 2 min of rest, participants performed a standardized warm- up in which the participants pedaled at a cadence of their choice, between 50 and 90 revolutions per minute (RPM), on a sta- tionary cycle ergometer (Ergoline Viasprint 150) at 50 W for males and 40 W for females for 5 min. The chosen RPM was maintained for the remainder of the testing. Ramp test During both experimental trials, participants performed an identical ramp test on a cycle ergometer. Immediately following the warm- up phase (described above), the work rate on the cycle ergometer was increased in a ramp fash- ion corresponding to 20 W·min−1 for males (1 W every 3 s) and 15 W·min−1 for females (1 W every 4 s) until volitional exhaustion. Ratings of perceived exertion (RPE) were as- sessed every 60 s throughout the duration of the ramp test. The test was terminated at volitional exhaustion or if the TABLE 1 Participant characteristics Men (n = 9) Women (n = 13) Total (n = 22) Age, year 69 ± 6 65 ± 6 67 ± 6 Height, cm 172 ± 9 161 ± 5 165 ± 9 Weight, kg 77 ± 18 69 ± 16 72 ± 17 BMI, kg·m−2 26.0 ± 4.1 26.6 ± 5.8 26.3 ± 5.1 Body fat, % 28.1 ± 6.0 37.8 ± 10.5 34.0 ± 10.0 Lean body mass, kg 53 ± 12 39 ± 3 44 ± 10 Data are presented as mean ± SD. Abbreviation: BMI, body mass index; Body fat % is whole body derived from dual energy x- ray absorptiometry. 4 of 14 \| VILLANUEVA et al. participant was unable to maintain his/her RPM despite verbal encouragement. Constant load test During each experimental trial a constant load test was completed following the ramp test, which occurred after 10 min of light active recovery (pedaling at a work rate no higher than the warm- up) on the stationary ergometer. During active recovery, the participants were provided a break from the breathing valve, which was reconnected to the participant at least 3 min prior to the start of the con- stant load test. The constant load test consisted of cycling at a work rate equivalent to either 85% (CL85) or 110% (CL110) of the peak work rate reached during the preced- ing ramp test. Specifically, in a randomized, counterbal- anced cross- over design, participants were randomized to perform either CL85 or CL110 during the first visit, whereas during the second visit the participant completed the constant load test at the other work rate. Participants were instructed to increase cadence as the resistance on the cycle ergometer increased from that during active re- covery to the prescribed intensity. Both constant load tests were performed at a constant work rate until volitional exhaustion. RPE was assessed at the end of the constant load test. The test was terminated at volitional exhaustion (i.e., the participant requesting to stop) or if the partici- pant was unable to maintain his/her RPM despite verbal encouragement. Assessment of body composition During the second visit, participants underwent a dual- energy x- ray absorptiometry (DEXA) whole- body scan (Lunar iDXA, GE Healthcare). The DEXA was per- formed prior to any testing and after voiding the bladder. Participants laid down on the DEXA for 15 min prior to the DEXA to avoid any influence of fluid shifts. A trained and certified radiologist administered the DEXA scan. 2.3 \| Assessment of physiological outcomes All ventilation and gas exchange data were assessed using 10- s average measurements, with O2 and CO2 concentra- tion of expired air derived from samples obtained from a mixing chamber. Peak VO2 values for the ramp tests and constant load tests were taken as the highest three con- secutive 10- s measurements, which were averaged to yield data collected over a 30- s timeframe. Peak RER values were taken as an average of the three 10- s measurements at the same time point as peak VO2. Peak HR for the ramp and constant load tests were taken as the highest recorded HR. Peak power during the ramp was identified as the highest work rate achieved prior to a drop in cadence or volitional exhaustion. Individual data were calculated to determine the percent change of physiological outcomes between the constant load test and the associated ramp, as well as between the ramp during the first visit (Ramp1) compared to the ramp during the second visit (Ramp2). The mean coefficient of variation (CV) for Ramp1 and Ramp2 was used to identify if a similar (within CV) or a higher or lower value (outside CV) for a physiological variable occurred between the constant load test and the associated ramp test and between Ramp1 and Ramp2. 2.4 \| Statistical analysis All data were tested for normality through skewness and kurtosis analyses and visual inspection of the normality plots using SPSS v.24 (IBM). A one- way, repeated meas- ures, analysis of variance (ANOVA) was used to assess differences between the ramp tests and the constant load tests for all outcomes. Pairwise comparisons were per- formed following the ANOVA using a least significant difference (LSD) post hoc analyses adjusted for the fol- lowing two comparisons: constant load test at 85% of peak work rate (CL85) versus the associated ramp (Ramp85); and constant load test at 110% of peak work rate (CL110) versus the associated ramp (Ramp110). Outcome vari- ables obtained from the first (Ramp1) and second ramp test (Ramp2) were compared using a dependent t- test for equivalence. Pearson's correlations were used to de- termine the relationships between variables for the con- stant load test versus ramp and for Ramp1 versus Ramp2. Bland– Altman plots and CVs were used to compare the agreement for all variables between the constant load test and associated ramp test and between Ramp1 and Ramp2. Intraclass correlation coefficients (ICCs) were used to examine the reliability of peak VO2 and peak HR between the constant load test and associated ramp test and between Ramp1 and Ramp2. All comparisons includ- ing a constant load test were made to the ramp performed during the same experimental trial. Pearson's correlations were also used to examine the following relationships within each experimental trial: (1) Difference in peak VO2 (L·min−1) between CL85 and Ramp85 and time to ex- haustion for CL85, (2) Difference in peak VO2 (L·min−1) between CL85 and Ramp85 and time to exhaustion of Ramp85, (3) Difference in peak VO2 (L·min−1) between CL110 and Ramp110 and time to exhaustion of CL110, and (4) Difference in peak VO2 (L·min−1) between CL110 \| 5 of 14 VILLANUEVA et al. and Ramp110 and time to exhaustion during Ramp110. All data were analyzed using SPSS Software (SPSS v24) and significance was set a priori at p ≤ 0.05. All data are presented as means ± SD. 3 \| RESULTS Of the 24 participants who enrolled in the study, two participants were excluded during the screening process (one for high blood pressure, one for underlying medical disease). Two additional participants dropped out of the study after completing the first visit due to circumstances unrelated to the study. Both of these participants only completed the CL85 exercise trial, and these participants were included in the analysis for ramp versus CL85 (e.g., CL85, n = 22; CL110, n = 20). Only participants who com- pleted both trials (n = 20; 67 ± 6 year, 8 males and 12 fe- males) were included in the comparisons between Ramp1 and Ramp2. Participant characteristics are presented in Table 1. In addition, the peak VO2 (mLO2·kg−1·min−1) and peak HR of these participants are expressed relative to age- based reference standards (Kaminsky et al., 2015) in Table 2. 3.1 \| Ramp versus constant load test (group data) Peak VO2 (L·min−1) did not differ (p = 0.679) be- tween Ramp85 (1.85 ± 0.73 L·min−1) and CL85 (1.86 ± 0.72 L·min−1) (CV = 2.07 ± 2.14%) (Table 3, Figures 1a and 2a). Similarly, peak VO2 was not sig- nificantly different (p = 0.200) between Ramp110 (1.85 ± 0.57 L·min−1) and CL110 (1.79 ± 0.73 L·min−1) (CV =3.64% ± 4.47%) (Table 3, Figures 1b and 2b). Intraclass correlations also showed agreement in peak VO2 (L·min−1) between the ramp and constant load test for both CL85 (ICC = 0.997) and CL110 (ICC = 0.979) (Table 3). Time to exhaustion during the ramp and con- stant load tests were examined to determine whether time to exhaustion of the various tests influenced differ- ences in peak VO2 between the constant load test and associated ramp. Time to exhaustion for Ramp85 was not statistically correlated with the difference in peak VO2 between CL85 and Ramp85 (r = 0.17; p = 0.458) (Figure 3a). However, a longer Ramp110 time to exhaustion was negatively associated with the difference in peak VO2 be- tween CL110 and Ramp110 (r = 0.48; p = 0.031) (Figure 3b), indicating that a longer time to exhaustion during the ramp test was associated with a greater likelihood of attaining a lower peak VO2 during CL110 compared to the ramp. Time to exhaustion for the respective constant load test protocols was not statistically correlated with the difference in peak VO2 between CL85 and the associ- ated ramp (r = 0.33; p = 0.134) (Figure 3c) or between CL110 and the associated ramp (r = 0.20; p = 0.393) (Figure 3d). Peak HR did not differ (p = 0.243) between Ramp85 (150 ± 17 bpm) and CL85 (153 ± 17 bpm) (Table 3, Figures 1c and 2c). Similarly, peak HR did not differ (p = 0.085) between Ramp110 (149 ± 16 bpm) and CL110 (146 ± 16 bpm) (Table 3, Figures 1d and 2d). Intraclass correlations showed agreement in peak HR between ramp and constant load test for both CL85 (ICC = 0.950) and CL110 (ICC = 0.906). Peak RER was significantly different (p < 0.01) between Ramp85 (1.17 ± 0.09) and CL85 (1.07 ± 0.08) (Table 3). Similarly, peak RER was significantly different (p < 0.01) between Ramp110 (1.16 ± 0.08) and CL110 (1.03 ± 1.0) (Table 3). Peak RPE did not differ (p = 0.602) between Ramp85 (18.5 ± 1.3) and CL85 (18.3 ± 1.7). Similarly, peak RPE did not differ (p = 0.629) between Ramp110 (18.7 ± 1.0) and CL110 (18.6 ± 1.1). Study participants Reference Percentile Males Peak VO2 (mlO2·kg−1·min−1) 29.8 ± 9.6 (18.5– 49.9) 29.4 ± 7.9 ~50th Peak HR (bpm) 159 ± 17 (135– 186) 158 ± 17 N/A Females Peak VO2 (mLO2·kg−1·min−1) 24.2 ± 10.5 (14.1– 47.9) 20.7 ± 5.0 ~75th Peak HR (bpm) 147 ± 17 (120– 175) 157 ± 17 N/A Study Participant data (9M, 13F, 67 ± 6 years) are presented as mean ± SD (range) from the first visit ramp test. Reference and percentile data are derived from FRIEND for age 60– 69 years (Kaminsky et al., 2015). Abbreviation: bpm, beats per minute. TABLE 2 Study participants relative peak VO2 (mLO2·kg−1·min−1) and heart rate (HR) in comparison to reference standards derived from FRIEND (Kaminsky et al., 2015) 6 of 14 \| VILLANUEVA et al. 3.2 \| Ramp1 versus Ramp2 (group data) Peak VO2 during Ramp1 (1.82 ± 0.72 L·min−1) was not significantly different (p = 0.100) from Ramp2 (1.86 ± 0.81 L·min−1) (CV = 2.90 ± 1.89%) (Table 3). Peak VO2 was also strongly correlated (R2 = 0.987) (p < 0.01) and was in high agreement (ICC = 0.994) between Ramp1 and Ramp2 (Figure 4). Peak HR did not differ (p = 0.115) be- tween Ramp1 (150 ± 17 bpm) and Ramp2 (149 ± 15 bpm) (CV = 2.30 ± 2.06%) (Table 3) and values were strongly correlated (R2 = 0.876) (p < 0.01) and in high agreement (ICC = 0.936) between Ramp1 and Ramp2. RER did not differ (p = 0.348) between Ramp1 (1.16 ± 0.09) and Ramp2 (1.16 ± 0.08) (CV = 3.20 ± 2.05%) (Table 3) and values were correlated (R2 = 0.529) (p < 0.01) (Table 3) and in agreement (ICC = 0.727). Peak power output (W) did not differ between Ramp1 (156 ± 53) and Ramp2 (158 ± 53) (CV = 5.3 ± 5.40%) (Table 3) and values were strongly correlated (R2 = 0.905) (p < 0.01) and in high agreement (ICC = 0.951) between Ramp1 and Ramp2. RPE did not differ (p = 0.481) between Ramp1 (18.5 ± 1.1) and Ramp2 (18.6 ± 1.3) and values were correlated (R2 = 0.480) (p < 0.01). 3.3 \| Individual data We calculated the mean individual participant CV (%) between Ramp1 and Ramp2 to examine individual dif- ferences in physiological variables between the ramps (Ramp1 vs. Ramp2) and between the constant load tests and their associated ramp tests. Using this participant- based CV- derived cut point from Ramp1 and Ramp2 (see Table 3), 68% of participants (15 of 22) achieved a peak VO2 during CL85 that was similar (within 2.9%, CV be- tween Ramp1 and Ramp2 for peak VO2) to the associated TABLE 3 Physiological group and individual responses to the ramp and constant load tests Peak VO2 (L·min−1) Peak HR (bpm) VE (L·min−1) Peak RER Power (W) Time to exhaustion (s) CV (%)a 2.9 2.3 6.3 3.2 5.3 8.0 Ramp 1 versus Ramp 2 Ramp1 1.82 ± 0.72 150 ± 17 76.91 ± 31.69 1.16 ± 0.09 156 ± 53 402 ± 151 Ramp2 1.86 ± 0.81 149 ± 15 79.31 ± 31.84 1.16 ± 0.08 158 ± 53 408 ± 160 Ind. Similarb (9/20) (9/19) (9/20) (7/20) (9/20) (9/20) Ind. Higherb (8/20) (3/19) (6/20) (5/20) (7/20) (6/20) Ind. Lowerb (3/20) (7/19) (5/20) (8/20) (4/20) (5/20) Ramp versus constant load test at 85% Ramp 1.85 ± 0.73 150 ± 17 77.85 ± 30.01 1.17 ± 0.09 158 ± 52 401 ± 142 CL85 1.86 ± 0.72 153 ± 17 80.15 ± 30.20 1.07 ± 0.08* 133 ± 45 185 ± 88 Ind. Similarb (15/22) (11/21) (10/22) (1/22) Ind. Higherb (4/22) (7/21) (8/22) (2/22) Ind. Lowerb (3/22) (3/21) (4/22) (19/22) Ramp versus constant load test at 110% Ramp 1.85 ± 0.57 149 ± 16 78.28 ± 33.63 1.16 ± 0.08 156 ± 54 410 ± 162 CL110 1.79 ± 0.73 146 ± 16 75.83 ± 34.83 1.03 ± 0.10* 170 ± 60 79 ± 62 Ind. Similarb (8/20) (7/19) (9/20) (2/20) Ind. Higherb (5/20) (3/19) (6/20) (0/20) Ind. Lowerb (7/20) (9/19) (5/20) (18/20) Data are presented as mean ± SD. aMean individual participant coefficient of variation (CV) from Ramp1 to Ramp2, presented as percent (%). bNumber of participants with values within the CV (for Ramp1 to Ramp2) between tests (similar), a value that is identified as higher (>CV for Ramp1 to Ramp2) compared to the Ramp (or compared to Ramp 1 for Ramp2 vs. Ramp1) (higher), a value that is identified as lower (>CV for Ramp1 to Ramp2) compared to the Ramp (or compared to Ramp 1 for Ramp2 vs. Ramp1) (lower). HR, heart rate; RER, respiratory exchange ratio; VE, ventilation; Ind. Similar, represents the number of participants that achieved a similar value (within CV) during the constant load test versus the associated ramp or for Ramp2 versus Ramp1; Ind. Higher, represents the number of participants that achieved a higher value (outside CV) during the constant load (CL) test versus the associated ramp or for Ramp2 versus Ramp1; Ind. Lower, represents the number of participants that achieved a lower value (outside CV) during the constant load test versus the associated ramp or for Ramp2 versus Ramp1. p < 0.05 Ramp. \| 7 of 14 VILLANUEVA et al. FIGURE 1 Bland– Altman plots for peak oxygen uptake (VO2, L·min−1) and heart rate (HR). Presented are (a) peak VO2 obtained during the constant load test performed at 85% of ramp peak work rate (CL85) and the associated ramp test (Ramp85), (b) peak VO2 obtained during the constant load test performed at 110% of ramp peak work rate (CL110) and the associated ramp test (Ramp110), (c) peak HR obtained during CL85 and Ramp85, and (d) peak HR obtained during CL110 and Ramp110. Y- axis = constant load test − ramp; x- axis = mean of ramp and constant load test; dotted lines = mean ± 1.96 × SD; dark solid lines = 0 on the y- axis; light solid lines = mean of constant load test − ramp. Filled squares (■) represent male participants and open diamonds (♢) represent female participants. Ramp85 versus CL85, n = 22; Ramp110 versus CL110, n = 20 (a) (c) (d) (b) FIGURE 2 Peak oxygen uptake (VO2, L·min−1) and heart rate (HR) achieved during the ramp (x- axis) and constant load (y- axis) test for each participant. The dotted lines represent the line of identity (y = x). Presented are (a) peak VO2 obtained during the constant load test at 85% of ramp peak work rate (CL85) versus the associated ramp (Ramp85), (b) peak VO2 obtained during the constant load test at 110% of ramp peak work rate (CL110) versus the associated ramp (Ramp110), (c) peak HR obtained during CL85 versus Ramp85, and (d) peak HR obtained during CL110 versus Ramp110. Filled squares (■) represent male participants and open diamonds (♢) represent female participants. Ramp85 versus CL85, n = 22; Ramp110 versus CL110, n = 20 (a) (c) (d) (b) 8 of 14 \| VILLANUEVA et al. ramp peak VO2. Furthermore, 18% of participants (4 of 22) achieved a peak VO2 during CL85 that was >2.9% higher than that achieved during Ramp85, while 14% of partici- pants (3 of 22) achieved a peak VO2 during CL85 that was >2.9% lower than that achieved during Ramp85 (Table 3). In contrast, 40% of participants (8 of 20) achieved a peak VO2 during CL110 that was similar to the associated ramp, 25% of participants (5 of 20) achieved a peak VO2 during CL110 that was >2.9% higher than Ramp110 (Table 3), and 35% of participants (7 of 20) achieved a peak VO2 dur- ing CL110 that was >2.9% lower than Ramp110. Similar results were observed between CL85 and CL110 for peak HR and ventilation (Table 3). When comparing Ramp2 to Ramp1, 45% of participants (9 of 20) achieved a peak VO2 during Ramp2 that was similar to Ramp1, 40% of participants (8 of 20) achieved FIGURE 3 Correlations between time to exhaustion (x- axis) and differences in peak oxygen uptake (VO2, L·min−1) achieved during the constant load and ramp tests. Presented are (a) time to exhaustion during the associated ramp (Ramp85) compared to the difference in peak VO2 obtained during the constant load test at 85% of ramp peak power (CL85) and Ramp85, (b) time to exhaustion during the associated ramp (Ramp110) compared to the difference in peak VO2 obtained during the constant load test at 110% of ramp peak power (CL110) and Ramp110, (c) time to exhaustion during CL85 compared to the difference in peak VO2 obtained during CL85 and Ramp85, and (d) time to exhaustion during CL110 compared to difference in peak VO2 obtained during CL110 and Ramp110. p < 0.05. Filled squares (■) represent male participants and open diamonds (♢) represent female participants. Ramp85 versus CL85, n = 22; Ramp110 versus CL110, n = 20 (a) (b) (c) (d) FIGURE 4 Comparison of peak VO2 values (L·min−1) achieved during the first ramp test (Ramp1) and the second ramp test (Ramp2). Presented are (A) Bland– Altman plot for peak VO2 obtained during Ramp1 and Ramp2 [Y- axis = Ramp2 − Ramp1; x- axis = mean of Ramp1 and Ramp2; dotted lines = mean ± 1.96 × SD; dark solid lines = 0 on the y- axis; light solid lines = mean of Ramp1 − Ramp2] and (b) the relationship between peak VO2 obtained during Ramp1 and Ramp2 [the line represents the line of identity (y = x)]. Filled squares (■) represent male participants and open diamonds (♢) represent female participants (n = 20) (a) (b) \| 9 of 14 VILLANUEVA et al. a peak VO2 during Ramp2 that was identified as higher (>2.9% difference) compared to Ramp1, and 15% (3 of 20) achieved a peak VO2 during Ramp2 that was identified as lower (>2.9% difference) compared to Ramp1 (Table 3). Results for peak HR, VE, and RER are also presented in Table 3. We recognize the lack of consensus on methodologi- cal/statistical approaches for confirming VO2max during a constant load (verification) test (or any secondary test). Therefore, Table 4 provides additional information on individual differences/similarities between tests using ±2 × typical error of the two ramp tests (McCarthy et al., 2021) and a HR of ±2 bpm (Midgley et al., 2006) or ±4 bpm (Midgley et al., 2009) from the peak HR from the ramp tests. In all instances (study CV, ±2 × typical error, HR ±2 or ±4 bpm), when compared to CL110, CL85 had a greater percentage of individuals with a constant load test that was considered similar to or higher than the ramp. 4 \| DISCUSSION To our knowledge this is the first study to employ a rand- omized, counterbalanced cross- over design to evaluate the utility of constant load tests performed above and below ramp- derived peak work rate to serve as a strategy to verify a maximal effort and VO2max in healthy older adults. The primary finding from this investigation is that in healthy older adults, a constant load test performed at a work rate slightly below (85%) peak work rate achieved during a graded exercise test was more likely to verify VO2max as compared to a constant load test performed at a work rate above (110%) that achieved during a graded exercise test. In addition, our data also indicate that while a second identical ramp test could produce a slightly higher peak VO2 in a greater number of individuals as compared to the constant load test at 85% peak work rate, both strategies yield reasonably similar outcomes for verifying VO2max. Relative to younger adults, little attention has been given to the efficacy of a constant load test for verifying a maximal effort and VO2max in older adults (Dalleck et al., 2012; Murias et al., 2018). In this study, we examined to what extent a constant load test performed above (110%) or below (85%) ramp peak work rate could be used to ver- ify VO2max in healthy older adults. We specifically chose these work rates as they represent the range in intensity used in previous studies that used a constant load “verifi- cation” test (Astorino et al., 2009; Barker et al., 2011; Costa et al., 2021; Dalleck et al., 2012; Day et al., 2003; Kuffel et al., 2005; Midgley & Carroll, 2009; Murias et al., 2018; Niemela et al., 1980; Poole et al., 2008; Rossiter et al., 2006; Sawyer et al., 2015; Sedgeman et al., 2013). Consistent with many previous studies, we did not identify “group” differ- ences for peak VO2 achieved between the ramp test and the corresponding constant load test, regardless of inten- sity. However, examination of the individual participant Study CV (±2.9%) 2 × TE (±0.156 L·min−1) Heart rate (±2 bpm) Heart rate (±4 bpm) Ramp 1 versus Ramp 2 Ind. Similara (9/20) (17/20) (8/19) (11/19) Ind. Highera (8/20) (3/20) (2/19) (1/19) Ind. Lowera (3/20) (0/20) (9/19) (7/19) Ramp versus constant load test at 85% Ind. Similara (15/22) (21/22) (10/21) (12/21) Ind. Highera (4/22) (1/22) (7/21) (7/21) Ind. Lowera (3/22) (0/22) (4/21) (2/21) Ramp versus constant load test at 110% Ind. Similara (8/20) (15/20) (8/19) (12/19) Ind. Highera (5/20) (1/20) (6/19) (4/19) Ind. Lowera (7/20) (4/20) (5/19) (3/19) aThe criteria for a similar, higher, or lower value were that the value had to be within or outside (±) the study coefficient of variation (CV), 2 × typical error (TE) (McCarthy et al., 2021), or a heart rate within 2 beats per minute (bpm) (Midgley et al., 2006) or 4 bpm (Midgley et al., 2009) of the peak heart rate achieved during the ramp. Ind. Similar, represents the number of participants that achieved a similar value (within cut points) during the constant load test versus the associated ramp or for Ramp2 versus Ramp1; Ind. Higher, represents the number of participants that achieved a higher value (outside cut point) during the constant load test versus the associated ramp or for Ramp2 versus Ramp1; Ind. Lower, represents the number of participants that achieved a lower value (outside cut point) during the constant load test versus the associated ramp or for Ramp2 versus Ramp1. TABLE 4 Comparison of various individual data “cut points” used in the literature to determine verification of VO2max 10 of 14 \| VILLANUEVA et al. data revealed a greater likelihood for the CL85 test to vali- date a maximal effort and VO2max as compared to CL110. Specifically, only 3 of the 22 participants (~14%) achieved a peak VO2 during the CL85 that was lower (outside the CV of the two ramp tests) than the value achieved during the ramp test. These data indicate that ~86% of the par- ticipants (19 of 22) achieved a peak VO2 during the CL85 that was either similar (15 of 22 participants, within the CV of the two ramp tests) or higher (4 of 22 participants, >CV of the two ramp tests) than that achieved during the associated ramp test. In contrast, 7 of 20 participants (~35%) achieved a peak VO2 during the CL110 test that was lower (>CV of the two ramp tests) than the value achieved during the ramp test, and thus only ~65% achieved a value that was similar (8 of 20 participants) or higher (5 of 20 participants) than the associated ramp test. While we acknowledge previously proposed rationale that the constant load “verification” test should, theoretically, be conducted at a work rate higher than that achieved during the ramp test (e.g., su- pramaximal) (Poole & Jones, 2017), the present results in- dicate that a constant load test performed at a work rate of 110% of ramp peak power may be too high for some older adults as a method to verify a maximal effort and VO2max. Moreover, the greater agreement in VO2peak between the ramp test and CL85 as compared to the ramp test and CL110 is also evident through examination of the limits of agreement and bias presented in the Bland– Altman plots (Figure 1a and 1b), as well as when employing other cut points used in the literature (see Table 4). Collectively, our findings further support (Iannetta et al., 2020) the use of a work rate slightly below peak ramp work rate, as opposed to above, when a constant load test to verity a maximal effort and VO2max in healthy older adults is warranted. Moreover, these results also further support the use of in- dividual data for assessment of VO2max and comparison of constant load “verification” test intensities (Noakes, 2008). As expected, the CL110 test elicited a shorter exercise duration (mean ~79 s [range, 30– 330 s]) compared to CL85 (mean ~185 s [range, 50– 457 s]). Previous research in older adults that used a constant load test at 105% of ramp peak work rate reported mean durations of ~102 s (Murias et al., 2018) and ~150 s (Dalleck et al., 2012). The shorter duration observed during CL110 in this study may be due to the 5% difference in constant load test work rate in participants of approximately the same age (Dalleck et al., 2012; Murias et al., 2018). It is also im- portant to note that the greater likelihood of lower peak VO2 values during CL110 could be the result of a reduced contribution of the slow component of VO2 (Gaesser & Poole, 1996). Specifically, it has been reported that an ex- ercise duration of >3 min is necessary to observe changes in VO2 kinetics that are due to the VO2 slow component (Gaesser & Poole, 1996). However, we did not observe any significant correlations between exercise time of the constant load test and agreement between peak VO2 achieved during the ramp and corresponding constant load test (Figure 3). Interestingly, we did observe that a longer time to exhaustion during Ramp110 (thus, higher peak power) was more likely to result in a lower peak VO2 during CL110. This finding would appear to agree with previous work suggesting that a peak VO2 achieved during a ramp protocol that resulted in a higher peak power was less likely to be validated with a constant load effort above the ramp peak power (Iannetta et al., 2020). To that end, with the exception of one participant who had a history of cycling (highest VO2max), participants were relatively unaccustomed to cycling exercise. Thus, the lower likelihood of verifying VO2max in these older adults when using a constant load test above ramp peak work rate may be due to an inability to tolerate the physio- logical demands of such high work rates for a sufficiently long enough time to elicit VO2max. This may also explain why nearly 50% (9 of 19) of the participants achieved a peak HR during CL110 that was lower (outside the CV of the two ramp tests) than that achieved during the associ- ated ramp. In this study, participants completed two identi- cal ramp assessments approximately 1 week apart (mean = 9 days). We chose this time frame to provide adequate recovery time from the previous test. The mean CV observed for peak VO2 between the two ramp tests is consistent with ranges identified in previous reports (Fielding et al., 1997; Foster et al., 1986; Skinner et al., 1999), and as discussed above, we utilized the mean participant CV (%) from the two identical ramp tests to identify individual differences in physiological variables between ramp and constant load tests. The design of the study also allowed us to examine to what extent a sec- ond ramp test could be used to assess/verify VO2max in older adults. Consistent with previous reports (Foster et al., 1986), we did not observe any significant differ- ences in any physiological variable between the first visit (Ramp1) and the second visit (Ramp2). In addition, using the CV- derived cut point, the number of participants that achieved a similar or higher peak VO2 during Ramp2 compared to Ramp1 (17/20 participants) was similar to that observed when comparing CL85 to the ramp (19/22 participants). However, when compared to the ramp ver- sus constant load test comparisons, more participants achieved a higher peak VO2 during Ramp2 compared to Ramp1 (40%; 8/20 participants). Importantly, these dis- crepancies in peak VO2 achieved during the ramp in the first and second experimental trial did not impact the comparison between the associated ramp and constant \| 11 of 14 VILLANUEVA et al. load tests. Not only was the study counter- balanced, but among participants who completed both trials and achieved a peak VO2 during a constant load test that was different compared to the associated ramp test, there was a similar number of participants who achieved a different (higher or lower) peak VO2 during the constant load test during the first (higher value, n = 5; lower value, n = 4) and during the second experimental trial (higher value, n = 4; lower value, n = 6). Collectively, these data indi- cate that some individuals may not be accustomed to the maximal intensity of exercise, the mode of exercise, or perhaps the breathing apparatus (Poole & Jones, 2012). Moreover, the results of this study indicate that a famil- iarization trial or second ramp could also increase the accuracy of VO2max assessments in some older adults, perhaps for a slightly greater number of individuals as compared to the use of a constant load test. Peak HR was not different during the ramp test and either constant load test intensity. This finding contrasts with the results of a previous study with older adults that found a significantly higher peak HR during a ramp test as compared to a supramaximal verification test (105%) and submaximal (85%) verification test (Murias et al., 2018), al- though the magnitude of difference in that study (Murias et al., 2018) was extremely small (1– 2 bpm). Moreover, similar to VO2max discussed above, individual data in- dicate that a greater number of participants achieved a similar or higher peak HR during CL85 versus the ramp as compared to CL110 versus the ramp (86% vs. 53%). In addition, the individual data and visual inspection of the Bland– Altman plots suggest a greater likelihood for par- ticipants to achieve a lower peak HR during CL110 ver- sus the ramp as compared to CL85. Together with the VO2 data, these peak HR data further support the incorpora- tion of a constant load test performed slightly below peak ramp work rate for verification of maximal values in older adults. We recognize that previous studies have utilized rest periods as short as 3 min and as long as a full week between ramp and constant load verification tests (Astorino et al., 2009; Barker et al., 2011; Dalleck et al., 2012; Day et al., 2003; Hawkins et al., 2007; Kuffel et al., 2005; Leicht et al., 2013; Midgley & Carroll, 2009; Midgley et al., 2006; Murias et al., 2018; Niemela et al., 1980; Nolan et al., 2014; Poole et al., 2008; Rossiter et al., 2006; Sawyer et al., 2015; Scharhag- Rosenberger et al., 2011; Sedgeman et al., 2013; Weatherwax et al., 2016), and thus we cannot extend our findings to situations that may utilize different rest periods between tests. However, we specifically employed a 10- min active rest period between the end of the ramp test and the initia- tion of the constant load test as this timeframe is likely to be more practical for future research and clinical practice as participants would not be required to come back for testing at a later time or date. In addition, it is possible that our findings may have been influenced by the duration of the ramp test (Iannetta et al., 2020). Similarly, some reports indicate that a valid VO2max is achieved with a ramp test of at least 8 min (Buchfuhrer et al., 1983), although this notion has been challenged (Midgley et al., 2008). Finally, we acknowledge that the necessity of verification tests has been questioned (Murias et al., 2018; Wagner et al., 2021), perhaps on the basis that a high percentage of verification tests yield peak VO2 values that are considered similar to the ramp tests. Indeed, if the graded exercise test was a maximal effort, then in theory the ramp and constant load tests should yield similar values. In addition, it is important to note that previous studies (see (Costa et al., 2021)), as well as data from the current investigation, demonstrate that not all ramp tests will yield maximal VO2 values (or values that are similar between the ramp and secondary verification test). Importantly, those ramp efforts that do and do not produce maximal values could not be identified without employing a secondary test to verify the results. Future investigators and/or clinicians will need to determine, for their specific use, the necessity to obtain an accurate measurement of VO2max and to what extent a value requires “verification” using a sin- gle visit or multiple visit approach. In conclusion, these findings have implications for the evaluation of VO2max of older adults in both a re- search and clinical setting. In particular, given the over- whelming data to suggest VO2max/cardiorespiratory fitness is perhaps the most powerful predictor of cardio- vascular disease risk (Kokkinos et al., 2010; Myers et al., 2002; Ross et al., 2016), identifying strategies to obtain an accurate assessment of VO2max in older adults will serve to better identify individuals at risk for cardiovas- cular disease as well as those with increased risk of mor- bidity and mortality. Specifically, our data indicate that when verification of maximal values is warranted in a single testing session, a constant load test performed at 85% of ramp peak power is more likely to verify a max- imal effort and VO2max in older adults as compared to a constant load test at 110% ramp peak power. On the other hand, in situations where multiple participant vis- its are feasible, performing an additional ramp test may also serve to verify VO2max, and could potentially lead to higher values in a slightly greater number of partici- pants. However, the logistics and associated participant burden of recovery times between tests in a single ses- sion and/or multiple visits must be considered in the ap- plication of constant load testing to verify VO2max in the real- world settings (especially clinical environments and clinical populations). 12 of 14 \| VILLANUEVA et al. ACKNOWLEDGMENTS The authors thank the participants for their time. I.R.V., S.S.A., G.A.G., and J.M.D. designed research; I.R.V., J.C.C., S.M.M., T.M.J., S.L.W., S.S.A., G.A.G., and J.M.D. conducted research; I.R.V. and J.M.D. analyzed data and performed statistical analysis; I.R.V. and J.M.D. wrote the manuscript and have primary responsibility for final content; all authors approved the final version of the manuscript. DISCLOSURES The authors have no conflict of interest to declare. ORCID Jared M. Dickinson https://orcid. org/0000-0003-3142-938X REFERENCES Astorino, T. A. (2009). 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[]	Villanueva, Ian R,Campbell, John C,Medina, Serena M,Jorgensen, Theresa M,Wilson, Shannon L,Angadi, Siddhartha S,Gaesser, Glenn A,Dickinson, Jared M	eng
PMC9102981	Citation: King, K.M.; McKay, T.; Thrasher, B.J.; Wintergerst, K.A. Maximal Oxygen Uptake, VO2 Max, Testing Effect on Blood Glucose Level in Adolescents with Type 1 Diabetes Mellitus. Int. J. Environ. Res. Public Health 2022, 19, 5543. https:// doi.org/10.3390/ijerph19095543 Academic Editors: Juan Pablo Rey-López and Paul B. Tchounwou Received: 30 March 2022 Accepted: 30 April 2022 Published: 3 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article Maximal Oxygen Uptake, VO2 Max, Testing Effect on Blood Glucose Level in Adolescents with Type 1 Diabetes Mellitus Kristi M. King 1,* , Timothy McKay 2, Bradly J. Thrasher 2,3 and Kupper A. Wintergerst 2,3 1 Department of Health and Sport Sciences, University of Louisville, Louisville, KY 40292, USA 2 Norton Children’s Hospital, Louisville, KY 40202, USA; timothy.mckay@nortonhealthcare.org (T.M.); bradly.thrasher@louisville.edu (B.J.T.); kupper.wintergerst@louisville.edu (K.A.W.) 3 Wendy Novak Diabetes Center, Pediatric Endocrinology, School of Medicine, University of Louisville, Louisville, KY 40202, USA * Correspondence: kristi.king@louisville.edu Abstract: Assessing maximal oxygen uptake (VO2 max) is generally considered safe when performed properly for most adolescents; however, for adolescents with type 1 diabetes mellitus (T1DM), monitoring glucose levels before and after exercise is critical to maintaining euglycemic ranges. Limited guidance exists for glucose level recommendations for the pediatric population; there- fore, the purpose of this retrospective clinical chart review study was to determine the effects of VO2 max testing on blood glucose levels for adolescents with T1DM. A total of 22 adolescents (mean age = 15.6 ± 1.8 years; male = 13, 59.1%) with a diagnosis of T1DM participated in a Bruce protocol for VO2 max from January 2019 through February 2020. A statistically signiﬁcant reduction in glucose levels between pretest (<30 min, mean = 191.1 mg/dL ± 61.2) and post-test VO2 max (<5 min, mean = 166.7 mg/dL ± 57.9); t(21) = 2.3, p < 0.05) was detected. The results from this current study can help guide health and ﬁtness professionals in formulating glycemic management strategies in preparatory activities prior to exercise testing and during exercise testing. Keywords: maximal oxygen uptake; VO2 max; blood glucose; type 1 diabetes mellitus (T1DM); adolescents; exercise testing; pediatric; clinical exercise 1. Introduction One of the tenets of the sports medicine ﬁeld is to advance and integrate scientiﬁc research to provide educational and practical applications of exercise science and sports medicine. For individuals engaging in physical activity at any level, whether it is recre- ational physical activity or competitive sports, there is clear, scientiﬁcally based guidance regarding exercise testing and prescription for health and ﬁtness professionals to employ with healthy individuals as well as those living with chronic illnesses [1]. One component of health-related physical ﬁtness is cardiorespiratory ﬁtness (CRF), the body’s ability to perform large-muscle, dynamic, moderate-to-vigorous-intensity exercise for prolonged periods of time. Assessing the maximal oxygen uptake (VO2 max) the body is able to use during exercise is an established exercise test for determining CRF and is more predictive of long-term survival than is any traditional risk factor or other measured physiologic parameter [2]. VO2 max testing provides a measurement of the relative amount of oxygen consumption per an amount of work. For example, an improved VO2 may allow one to run longer at the same speed or faster with the same relative effort [3]. The graded exercise test used to elicit VO2 max is aggressive in nature to achieve a maximal response from the participant. Under stress conditions, the hypothalamus controls many hormone secretions to adjust glucose metabolism and energy production. Glucose secretion and uptake are under the control of nervous and hormonal factors such as catecholamines, cortisol, glucagon, growth hormone, and insulin, and all have an immediate impact [4]. Even though exercise testing is generally considered safe when Int. J. Environ. Res. Public Health 2022, 19, 5543. https://doi.org/10.3390/ijerph19095543 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 5543 2 of 7 performed properly for most individuals, maximal- or vigorous-intensity exercise testing does pose some risk [5–8]. Speciﬁcally, for individuals with type 1 diabetes mellitus (T1DM), the risk of hyperglycemia in the initial portion of exercise testing and the risk for hypoglycemia following completion of testing both present themselves. Monitoring glucose levels before and after physical activity is fundamental to maintaining glucose levels in euglycemic ranges during and after exercise [9]. Unfortunately, understanding of safety parameters and the effect of CRF testing on adolescent populations is limited and in need of research [10]. Glucose level recommen- dations have yet to be established for adolescents diagnosed with T1DM who participate in VO2 max exercise testing. Although a decrease in glucose may be expected throughout and immediately after exercise testing, a minimal pretest glucose setpoint has not been established to reduce the risk of hypoglycemia. Therefore, the purpose of this study was to examine the impact of VO2 max testing on blood glucose levels for adolescents with T1DM. 2. Materials and Methods 2.1. Study Design and Setting This cross-sectional, non-interventional, retrospective chart review study was con- ducted at a nationally certiﬁed pediatric diabetes care and academic medical center located in the Southeast region of the United States. At this center, pediatric endocrinologists, registered nurses, registered dieticians, certiﬁed diabetes educators, and clinical exercise physiologists treat pediatric patients diagnosed with T1DM up to 26 years of age. The study was approved by the University Institutional Review board. Retrospective clinical chart reviews were conducted of clinical pediatric sports medicine and physical activity program patients with a diagnosis of T1DM who participated in a Bruce protocol for VO2 max from January 2019 through February 2020. 2.2. Participant Characteristics The baseline characteristics of study participants are displayed in Table 1. Table 1. Characteristics of the Participants, N = 22. Characteristics Mean ± SD or n, % Range (Minimum–Maximum) Age, years 15.6 ± 1.8 13–20 Duration of T1DM diagnosis, years 7.1 ± 4.9 >1–16 Height, centimeters 170.9 ± 8.3 157.3–187.4 Weight, kilograms 67.1 ± 13.4 44.6–107.3 BMI percentile, n = 21 67th percentile ± 17.7 26–99 HbA1c level, n = 21 8.9 ± 1.8 6.1–14.9 Gender Male, n = 13, 59.1% Female, n = 9, 40.9% - Ethnicity Non-Hispanic, n = 20, 90.1% - Race White, n = 17, 77.3% Black or African American, n = 3, 13.6% Unknown or Not Reported, n = 2, 9.1% - Treatment Plan CGM only, n = 2, 9.1% Insulin pump only, n = 4, 18.2% Insulin pump integrated with CGM, n = 9, 40.9% MDI, n = 7, 31.8% Note. Data are presented as mean ± standard deviation (SD) or number of participants (n), percent (%); BMI, body mass index percentile; HbA1c, hemoglobin A1c; CGM, continuous glucose monitor; MDI, multi- ple daily injections. Int. J. Environ. Res. Public Health 2022, 19, 5543 3 of 7 2.3. Measures Socio-demographic, anthropometric, diabetes monitoring and treatment plans, and hemoglobin A1c (HbA1c) levels data were retrieved from patients’ medical records, main- tained in the clinical database, as part of standard practice at each patient’s appointment in the diabetes clinic. The socio-demographic and anthropometric characteristics utilized were participant’s age, date and duration of T1DM diagnosis, ethnicity, race, gender, insurance type, and body mass index (BMI) percentile. Diabetes monitoring was assessed (includ- ing whether the participant used a continuous glucose monitor (CGM)), and the type of treatment plan was recorded. The hemoglobin A1c (HbA1c) level was obtained from that day’s clinical lab measures at check-in. HbA1c, the most prevalent and accessible measure in determining glucose control, was used as an indicator of the average blood glucose levels over the past 3 months. Adolescents managing T1DM should strive for HbA1c levels less than 7%, as an elevated HbA1c level is known to increase the risk for diabetes-related complications [11]. Blood glucose levels and VO2 max data were obtained from sports medicine records collected by clinical exercise physiologists in the sports medicine clinic. 2.4. Preparatory Activities Prior to Exercise Testing Upon registration for an exercise testing appointment, participants were instructed to not eat a heavy meal two hours prior to testing, to maintain their insulin regimen as they would on a regular day, and to dress in exercise attire (e.g., shorts, t-shirt, athletic shoes). Upon arrival to the sports medicine clinic, participants’ blood glucose value was screened by a clinical exercise physiologist. If the blood glucose was >250 mg/dL the clinical exercise physiologist obtained urinary ketones with the next void. If urinary ketones were moderate or large, the participant was excluded from participating in VO2 max testing at that time. If the blood glucose was >300 g/dL and the participant had no or small ketones, the clinical exercise physiologist instructed the participant to give a conservative insulin correction of 50% their calculated correction dose. Approximately 30 min prior to VO2 max testing, a pretest blood glucose sample was taken using a point-of-care glucometer and lancet device. Upon determination that blood glucose levels were in the safe range for physical activity, the clinical exercise physiologist conducted the VO2 max test. Upon completion of the VO2 max test, a post-test glucose level check was conducted within 5 min using the same glucometer for all participants. 2.5. Exercise Testing Procedures A Bruce protocol [12] for VO2 max tests, a valid and reliable measure for assessing cardiorespiratory ﬁtness, was performed on a Woodway ELG treadmill while the partic- ipants were connected to the Parvo Medics metabolic gas exchange analyzer by way of respiratory mask. Participants walked on a treadmill in 3 min bouts, starting at 1.7 mph (45.6 m·min−1) and 10% grade. At each stage, the speed was increased by either 0.8 or 0.9 mph (21.4 or 24.1 m·min−1) and the grade was increased by 2%. This test lasted approximately 10–20 min. If following the graded exercise test the participant’s blood glucose was found to be <70 mg/dL on the glucometer, the participant was treated for hypoglycemia with 15 g of rapid-acting carbohydrate. Blood glucose was then rechecked at 15 min. This process was repeated until their blood glucose was >70 mg/dL. Blood glucose and VO2 max data stored within REDCap on a secure server in the sports medicine program data were linked to the clinical database by the researchers utilizing the patients’ medical record numbers. All clinical data were retrieved from that same-day appointment for each participant. Once data were collected and merged, the full dataset was de-identiﬁed for analysis. 2.6. Data Analysis All statistical analyses were conducted using IBM SPSS 27.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics and frequencies for socio-demographic, anthropometric, diabetes monitoring and treatment plans, HbA1c levels, and pre- (<30 min) and post- Int. J. Environ. Res. Public Health 2022, 19, 5543 4 of 7 test (<5 min) blood glucose levels were calculated. Shapiro–Wilk’s test (p < 0.05) [13,14], histograms, Norman Q–Q plots, and box plots were employed to test the normality of the distribution of the data. A paired-samples t-test was employed to detect differences in blood glucose levels from pretest to post-test. p-Values of <0.05 were considered as statistically signiﬁcant. 3. Results Retrospective VO2 max data were analyzed from a total of 22 adolescents (N = 22; mean age = 15.6 ± 1.8 years; male = 13, 59.1%) (see Table 1). Most of the participants identiﬁed as non-Hispanic (n = 20, 90.9%), and over three-quarters identiﬁed as White (n = 17, 77.3%). Continuous glucose monitors were worn by 13 of the 22 participants (59.1%). Their average HbA1c prior to participating in the VO2 max test was 8.9% ± 1.8. The average BMI, based on age and sex, was in the 67th percentile ± 17.7. The average VO2 max peak was 43.4 mL/kg/min ± 6.4 (See Table 2). Table 2. VO2 max testing measurements, N = 22. Characteristics Mean ± SD Range (Minimum–Maximum) VO2 max, mL/kg/min, n = 21 43.4 ± 6.4 29.3–50.5 Peak HR bpm, n = 19 192.3 ± 21.3 119–212 Glucose, mg/dL pretest, n = 22 191.1 ± 61.1 96–296 Glucose, mg/dL post-test, n = 22 166.7 ± 57.9 83–297 Note. Data are presented as mean ± standard deviation (SD); Peak HR bpm, heart rate beats per minute. Pre- and post-test blood glucose measurements were obtained from 22 participants. The results of a Shapiro–Wilk’s test indicated that the pre- and post-glucose data were normally distributed, and a visual inspection of their histograms, Norman Q–Q plots, and box plots showed that the glucose scores were normally distributed at pretest with a skewness of 0.107 (SE = 0.49) and a kurtosis of −0.868 (SE = 0.95) and at post-test with a skewness of 0.657 (SE = 0.49) and a kurtosis of −0.015 (SE = 0.95). A paired- samples t-test was employed to detect a statistically signiﬁcant reduction in glucose levels between pretest (<30 min, mean = 191.1 mg/dL ± 61.2) and post-test VO2 max (<5 min, mean = 166.7 mg/dL ± 57.9); t(21) = 2.3, p < 0.05). 4. Discussion It is well established that signiﬁcant changes in blood glucose concentration during physical activity can lead to hypoglycemia or hyperglycemia and, if not prevented or treated quickly and properly, can lead to a medical emergency [1,9,15–27]. This current study sought to examine if there was a signiﬁcant drop in blood glucose levels after VO2 max testing, yet it is unique in that it specialized in a pediatric population of adolescents. Results from a recent retrospective study with adults with T1DM (mean age = 32 years, SD ± 13; range 18–65 years) who participated in VO2 max exercise testing using a cycle ergometer did not demonstrate statistically signiﬁcant glucose levels from pretest to post- test [28], which aligned with similar results from other studies [29,30]. The conﬂicting results from the present study may be attributed to differences in the age of participants (and in body composition and hormones) and possibly the modality used during testing. Given that individuals with T1DM are recommended to participate in daily moderate- to-vigorous-intensity physical activity [31], and general guidance for glucose targets as well as nutritional and insulin dose adjustments to protect against exercise-related glucose excursions are available [9,20,21,26,27], fear of activity-related hypoglycemia has been regularly cited as a barrier to physical activity [32,33]. Health care providers wishing to prescribe even modest increases in intensity levels of daily activity, such as walking and/or jogging, or sport participation that may include moderate-to-vigorous-intensity Int. J. Environ. Res. Public Health 2022, 19, 5543 5 of 7 activity to their patients with T1DM may consider VO2 max testing as a ﬁrst step in establishing safety precautions and working toward the adoption and maintenance of an active lifestyle. For example, participation in sports is touted as a beneﬁcial means for adolescents to accumulate physical activity [34,35]. However, caution must be taken if prescribing sport only without the engagement in additional physical activity. This is because many adolescents who participate in a single sport often do not meet sufﬁcient physical activity recommendations. A recent study involving 153 children and adolescents diagnosed with T1DM demonstrated this fact [36]. Although almost two-thirds of the participants reported playing one or more sports in the previous year, they were only physically active for at least one hour or more on an average 3.5 days per week, with less than 8% of the children and adolescents in the study meeting the recommended duration of one hour and frequency of seven days per week of physical activity. The results from this current study may help guide health and ﬁtness professionals in formulating glycemic management strategies in preparatory activities prior to exercise test- ing and during exercise testing. A pre-exercise glucose level of 90–250 mg/dL is suggested in order to prevent symptoms of hypoglycemia and to minimize hyperglycemia [9,11,26]. Considering that the adolescents in this current study experienced a 24.4 mg/dL drop in glucose levels from pretest to post-test, the implications of these results have clinical and practical importance. These results can and should be used to help inform patients and practitioners in clinical care decision making and the formulation of glycemic management strategies. Similar research ﬁndings suggest that patients and clinical care teams under- stand the glycemic changes that occur during progressive exercise so that nutritional and medicinal preparatory routines are safely established [28]. To ensure safe exercise perfor- mance ahead of exercise testing, practitioners, physiologists, and patients should be aware of the interindividual responses to VO2 max testing and treat each case accordingly. With the assistance of a clinical exercise physiologist, physicians can incorporate individualized recommendations for increasing physical activity and/or exercise prescriptions into their clinical practices [37]. Physicians and medical care teams can prescribe physical activity and sport participation when designing treatment plans and refer their patients to qualiﬁed health and ﬁtness professionals such as athletic trainers, strength and conditioning coaches, and physical educators who coach or train adolescent athletes diagnosed with T1DM. These protective measures that are grounded in scientiﬁc evidence [9,11,38] suggest that adolescent patients diagnosed with T1DM can complete maximal exercise testing without fear of inducing hypoglycemia if the necessary safety precautions as described in this study are taken. Limitations and Future Research Various limitations have been identiﬁed in this study. Although all participants were instructed to avoid eating greater than 60 g of carbohydrates prior to exercising, unless hypoglycemic, the study was not controlled for nutrition. Future studies should analyze dietary practices leading into exercise testing. In addition, participants were also instructed to administer insulin per their routine standard of care to create a “real-world” testing situation for this study. Future studies could beneﬁt from more restrictive insulin use parameters. The data in this study were derived from a retrospective chart review of clinical pa- tients who participated in a pre–post VO2 max test at a newly established (2018) clinic serving only pediatric patients diagnosed with T1DM up to 26 years of age from January 2019 until February 2020. In March 2020, non-emergency clinical operations were sus- pended due to COVID-19 safety precautions protocol, and sports medicine programming and study endeavors resumed in August 2021, which further limited our total sample size. At the time of the study, there were no matched control group data available. Next, although the clinic houses the only pediatric endocrinology sports medicine program in the state, the homogeneity of the participants in race and ethnicity does not make the ﬁndings generalizable to adolescents in other areas. Future research will beneﬁt from a Int. J. Environ. Res. Public Health 2022, 19, 5543 6 of 7 more extensive and longitudinal review of the pre- and post-VO2 max testing windows to further understand what variables inﬂuence blood glucose variability 5. Conclusions The results from this retrospective VO2 max testing study on blood glucose levels in adolescents with T1DM can add to the scientiﬁc literature for sports medicine programs that provide clinical care to individuals and their families through patient-centered and community education as well as clinical research. Regardless of sport or physical activity, care is focused on improving the health, safety, and athletic performance of every child and young adult with T1DM. Knowing that a signiﬁcant drop in glucose levels during VO2 max testing may occur with their adolescent patients, health and ﬁtness professionals can discuss and implement preventive glycemic management strategies prior to exercise testing and during exercise testing. Author Contributions: Conceptualization, K.M.K., T.M., B.J.T. and K.A.W.; methodology, K.M.K. and T.M.; formal analysis, K.M.K.; investigation, T.M.; data curation, K.M.K.; writing—original draft preparation, K.M.K.; writing—review and editing, K.M.K., T.M., B.J.T. and K.A.W.; visualization, K.M.K. All authors have read and agreed to the published version of the manuscript. Funding: This study was made possible by generous support from the Christensen Family, the Norton Children’s Hospital Foundation, and the University of Louisville Foundation. Institutional Review Board Statement: This study was approved by the University’s Institutional Review Board on 6-18-2020 (Approval # 20.0506). 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PMC10649254	Citation: van Rassel, C.R.; Ajayi, O.O.; Sales, K.M.; Grifﬁths, J.K.; Fletcher, J.R.; Edwards, W.B.; MacInnis, M.J. Is Running Power a Useful Metric? Quantifying Training Intensity and Aerobic Fitness Using Stryd Running Power Near the Maximal Lactate Steady State. Sensors 2023, 23, 8729. https://doi.org/ 10.3390/s23218729 Academic Editors: Manuel E. Hernandez, Yih-Kuen Jan, Chi-Wen Lung and Ben-Yi Liau Received: 27 September 2023 Revised: 20 October 2023 Accepted: 23 October 2023 Published: 26 October 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sensors Article Is Running Power a Useful Metric? Quantifying Training Intensity and Aerobic Fitness Using Stryd Running Power Near the Maximal Lactate Steady State Cody R. van Rassel 1 , Oluwatimilehin O. Ajayi 1, Kate M. Sales 1, James K. Grifﬁths 1, Jared R. Fletcher 2 , W. Brent Edwards 1 and Martin J. MacInnis 1,* 1 Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada; crvanras@ucalgary.ca (C.R.v.R.) 2 Department of Health and Physical Education, Mount Royal University, Calgary, AB T3E 6K6, Canada * Correspondence: martin.macinnis@ucalgary.ca Abstract: We sought to determine the utility of Stryd, a commercially available inertial measure- ment unit, to quantify running intensity and aerobic ﬁtness. Fifteen (eight male, seven female) runners (age = 30.2 [4.3] years; · VO2max = 54.5 [6.5] mL·kg−1·min−1) performed moderate- and heavy-intensity step transitions, an incremental exercise test, and constant-speed running trials to establish the maximal lactate steady state (MLSS). Stryd running power stability, sensitivity, and reliability were evaluated near the MLSS. Stryd running power was also compared to running speed, · VO2, and metabolic power measures to estimate running mechanical efﬁciency (EFF) and to deter- mine the efﬁcacy of using Stryd to delineate exercise intensities, quantify aerobic ﬁtness, and estimate running economy (RE). Stryd running power was strongly associated with · VO2 (R2 = 0.84; p < 0.001) and running speed at the MLSS (R2 = 0.91; p < 0.001). Stryd running power measures were strongly correlated with RE at the MLSS when combined with metabolic data (R2 = 0.79; p < 0.001) but not in isolation from the metabolic data (R2 = 0.08; p = 0.313). Measures of running EFF near the MLSS were not different across intensities (~21%; p > 0.05). In conclusion, although Stryd could not quantify RE in isolation, it provided a stable, sensitive, and reliable metric that can estimate aerobic ﬁtness, delineate exercise intensities, and approximate the metabolic requirements of running near the MLSS. Keywords: wearable technology; running economy; critical intensity; human performance; inertial measurement unit; treadmill 1. Introduction A consensus regarding an approach to evaluate mechanical running power output (PO) is lacking, resulting in a range of PO values for a given running speed, depending on the method [1,2]. During level running, the working muscles transfer energy to produce and absorb the forces needed to move body segments. As a result, there is no dissipative load external to the body that can be measured to quantify mechanical PO. Instead, running mechanical PO measurements may be derived from “external” or “internal” work per- spectives by evaluating the centre of mass (CoM) or the body segments, respectively [1–3]. Such approaches require sophisticated laboratory assessments of joint kinetics and/or kine- matics based on ground reaction force and motion-capture data. Several methodological challenges also limit the utility of running mechanical PO to approximate the metabolic work rate [1,3], and, in contrast to cycling, where there is a strong relationship between mechanical and metabolic PO [4,5], many factors complicate the relationship between me- chanical and metabolic PO when running [6–8]. Nevertheless, a wearable running device that can quantify and monitor training intensity, analogous to a cycling power meter [9,10], would be useful to guide training and maximize running performance. Sensors 2023, 23, 8729. https://doi.org/10.3390/s23218729 https://www.mdpi.com/journal/sensors Sensors 2023, 23, 8729 2 of 19 Several consumer technologies providing a running power metric have been devel- oped [11,12]. These technologies derive a measurement of mechanical PO using estimates of ground reaction forces, CoM velocity, and/or vertical displacement from global position- ing system (GPS) and/or inertial measurement unit (IMU) sensor data [11–13]. Previously, the Stryd running power device (a portable IMU), has provided the closest relationship with · VO2 when compared to other available commercial devices [11]. Possibly by gen- erating a running power metric based on estimates of horizontal velocity and vertical displacement using acceleration data, it is purported that Stryd power can be used as a proxy for metabolic PO, despite changes in external conditions such as air resistance or gradient [13]. Thus, Stryd running power can theoretically quantify training intensity in a manner analogous to cycling mechanical PO and could be superior to conventional measurement approaches using running speed. Despite evidence of repeatability [11], reliability [14,15], stability during prolonged running [16], and strong linear correlations with running speed [17,18], limited research has investigated the Stryd running metric at stable metabolic work rates relative to exercising thresholds. Thus, to determine the utility of Stryd power to indicate relative exercise intensity and assess running ﬁtness and performance, the relationship between Stryd mechanical power and metabolic power needs to be established using an exercise intensity domain training approach (i.e., evaluating running power metrics during steady-state exercise relative to the gas exchange threshold (GET) and maximal metabolic steady state (MMSS)). Prior to determining whether Stryd running power can monitor training, like cycling power output, in uncontrolled environments (e.g., variable inclines, wind speeds, and surfaces), the primary purpose of the present study was to evaluate the Stryd power metric in a controlled environment (i.e., in situ). Using an exercise intensity domain approach, we assessed the stability, sensitivity, and reliability of Stryd at stable metabolic work rates to (i) determine the efﬁcacy of Stryd running power as a training intensity and running performance metric, (ii) explore the relationship between running power and running economy (RE), (iii) estimate mechanical efﬁciency during constant-speed treadmill running, and (iv) contrast steady-state measurements with measurements derived from incremental exercise. We hypothesized that Stryd running power would be repeatable across two visits, stable during a 30-min run, and sensitive to running speeds near the maximal lactate steady state (MLSS)—a proxy measure of the MMSS. In addition, we hypothesized that Stryd power would be strongly associated with running speed, · VO2, and RE measurements, thereby providing a tool to guide exercise training and assess running ﬁtness. 2. Materials and Methods 2.1. Participants Fifteen (8 male; 7 female) recreationally active or trained/developmental runners [19] (mean [SD]; age = 30.2 [4.3] years; body mass = 68.8 [8.2] kg; height = 173.2 [8.4] cm; · VO2max, 54.5 [6.5] mL·kg−1·min−1) were recruited using convenience sampling. Partic- ipants were included if they were healthy, uninjured, and between 18 and 45 years of age, with recent 10-km performances of ≤50 min and ≤55 min for males and females, respectively. Within the 3 months prior to testing, runners reported exercising an average of 3.5 [1.4] days per week, running an average of 27.7 [17.1] km each week, and having 10-km best performance times of 44.6 [6.5] min. Written informed consent was provided by the runners to participate in the experimental procedures, which were approved by the University of Calgary Conjoint Health Research Ethics Board (REB20-0111) and conducted in accordance with the declaration of Helsinki, except for pre-trial registration. Participants had the option to cease participation at any time during the experimental procedures. Prior to test administration, runners completed the physical activity readiness questionnaire (PAR-Q+) to identify contraindications to exercise testing and to ensure that participants were free of medical conditions and injuries that could interfere with metabolic and car- Sensors 2023, 23, 8729 3 of 19 diorespiratory exercise responses. All runners provided their own lightweight running shoes and wore the same shoes for all testing sessions. 2.2. Experimental Design Runners visited the laboratory for ﬁve to six exercise testing sessions, with a minimum of 48 h between visits. The exercise sessions included: (1) a “Step-Ramp-Step” (SRS) exercise test to determine maximal exercising parameters [20]; (2) a series of 3–4 constant-speed bouts to determine the MLSS; and (3) a repeated trial at the MLSS running speed. Runners were asked to refrain from smoking, eating, or consuming caffeine within 2 h prior to their testing sessions. Runners did not engage in strenuous exercise on the same day as the testing sessions. A manuscript validating the SRS approach to identify the running speed and Stryd running power associated with the MLSS has been published [20]; however, despite the overlap in experimental procedures, the results presented herein are distinct. 2.3. Exercise Protocols 2.3.1. Step-Ramp-Step (SRS) Protocol As described in detail in our previous study [20], runners performed an SRS exercise protocol during their ﬁrst testing visit to establish their maximal exercising values and estimate the running speed associated with the MLSS. This SRS protocol was modiﬁed for treadmill running from a cycle ergometer-based method [21]. Of relevance to the present study, the SRS protocol involved a moderate-intensity step-transition (MOD; 6 min at 1.9 m·s−1, 6 min at 2.4 m·s−1, and 6 min at 1.9 m·s−1); an incremental treadmill running test (an initial speed of 1.9 m·s−1, increasing by ~0.2 m·s−1 (i.e., 0.5 mph) per min, until volitional exhaustion); and a heavy-intensity step transition (HVY; 4 min of treadmill running at 1.9 m·s−1, followed by 12 min of treadmill running at a speed associated with the heavy-intensity exercise domain). The incremental treadmill test immediately preceded the MOD step, but the participants recovered passively for 30 min between the incremental test and the HVY step. The SRS protocol facilitated the identiﬁcation of the MLSS in 2–3 constant-speed trials [20]. 2.3.2. Constant-Speed Treadmill Running—MLSS Determination Following the initial SRS testing visit, runners completed the constant-speed exercise sessions during 4 to 5 separate visits to the lab. These visits consisted of 5 min of treadmill running at 1.9 m·s−1, followed by treadmill running at the predetermined testing speed. During all constant-speed testing visits, participants were encouraged to run until volitional exhaustion, up to a maximum duration of 45 min (excluding warm-up). Data collected beyond 30 min were not used in this study. All runners performed their initial constant- speed test at the running speed estimated to be the MLSS by the SRS protocol. Depending on whether the difference between the 10- and 30-min blood lactate concentrations ([BLa]) was ≤1 mmol·L−1 or >1 mmol·L−1, the subsequent visit was performed at a treadmill speed that was 5% faster or 5% slower, respectively. The MLSS for each runner was identiﬁed as the highest treadmill speed whereby at least 30 min of exercise was performed and the difference between the [Bla] at 10 and 30 min was ≤1 mmol·L−1 [22]. All participants performed constant-speed treadmill running trials at the MLSS, 5% above the MLSS, 5% below the MLSS, and once more at the MLSS. Data analysis was primarily based on data collected at the 10- and 30-min (or at task failure if <30 min) time points. 2.4. Equipment and Measurements 2.4.1. Cardiorespiratory Measurements All exercise sessions were performed on a treadmill (Desmo Pro Evo, Woodway USA Inc., Waukesha, WI, USA) with an incline set to a 1% gradient [23]. Adjustments to treadmill speed could be made in 0.1 mph increments (i.e., ~0.04 m·s−1); however, all running speed data were reported in SI units (i.e., m·s−1). Ventilatory and gas exchange variables were measured using the Quark CPET metabolic cart (COSMED, Rome, Italy), with a mixing Sensors 2023, 23, 8729 4 of 19 chamber (COSMED), facemask (7450 Series V2, Hans-Rudolph, Shawnee, KS, USA), 2-way non-rebreathing valve (Hans-Rudolph), and gas collection hose. The metabolic cart system was calibrated using a 3 L syringe and gas mixture of known composition (5% CO2, 16% O2, and N2 for the balance) prior to each testing visit. For the analysis, 10-s average ventilatory and gas exchange data were used. Heart rates were recorded during all testing sessions using a Polar H10 chest strap (Polar Electro Oy, Kempele, Finland). The · VO2 associated with a disproportionate increase in the rate of carbon dioxide production ( · VCO2) and minute ventilation ( · VE) relative to the increase in · VO2 was used to identify the GET [24]. The · VO2 associated with a second disproportionate increase in · VE and a disproportionate increase in · VE/ · VCO2 relative to the increase in · VO2 was used to identify the respiratory compensation point (RCP) [24,25]. · VO2max was identiﬁed as the highest 30-s average · VO2 achieved during the incremental test. All incremental tests were considered maximal, based on the observation of a · VO2 plateau (deﬁned as a change in · VO2 of less than 150 mL·min−1, despite an increased intensity) or any of the following criteria: maximum HR within 10 bpm of the age-predicted maximal value, a respiratory exchange ratio (RER) greater than 1.15, or [Bla] ≥ 8 mmol·L−1 upon test completion. 2.4.2. Blood Lactate Measurements All [Bla] data were collected using capillary blood drawn from a pinprick of the ﬁnger and analyzed for [Bla] using the Biosen C-Line (EKF Diagnostics, Cardiff, Wales; n = 7) or Lactate Plus (Nova Biomedical, Waltham, MA, USA; n = 8) lactate analyzer. Runners straddled the treadmill (~60–75 s) during [Bla] measurements at 10 and 30 min (or at task failure if <30 min). 2.4.3. Perceptual Responses After familiarization with the scale, the rating of perceived exertion (RPE) was mea- sured every 5 min during constant-speed running, using the Borg RPE scale (6–20) [26]. 2.4.4. Running Power—Stryd Running power measurements were made using the Stryd Summit Running Pod (Stryd, Boulder, CO, USA). The Stryd pod, which is a lightweight (8.0 g) and unobtrusive (4.0 cm in length) wearable sensor (Model v.19, ﬁrmware v.2.1.16, software v.4), was afﬁxed to the runner’s left shoe, approximately equidistant between the runner’s malleoli and the shoe’s toe. A unique Stryd user proﬁle was created for each runner that included their respective height and body mass, which was kept constant for all testing sessions. The iPhone Stryd application (Apple Inc., Cupertino, CA, USA) was used to pair the Stryd device and collect the Stryd running power data during the testing sessions. Running power data were sampled at 1 Hz (see Figure 1). 2.5. Data Analysis 2.5.1. Cardiorespiratory, Running Speed, and Stryd Running Power Data The average · VO2 and running power, measured between minutes 4 and 6 of the MOD step and between minutes 10 and 12 of the HVY step, were calculated from the SRS test. Maximal aerobic speed (MAS) and maximal aerobic power (MAP) were determined as the running speed associated with the highest completed 1-min stage during the incremental test and the average running power during that stage, respectively. Cardiorespiratory and running power data used for analysis from the constant-speed MLSS-determination running trials included the 10- and 30-min · VO2, · VCO2, RER, · VE, HR, and running power measures for running trials 5% below, at, and 5% above MLSS. To align with the timing of [BLa] measurement (i.e., a short pause in running), mean values Sensors 2023, 23, 8729 5 of 19 for · VO2, · VCO2, RER, · VE, HR, and Stryd running power were calculated from the 2 min of data collected prior to the 10-min and the 30-min (or at task failure if <30 min) time points. Although the MLSS is thought to represent the highest intensity at which energy provision is supplied exclusively via oxidative metabolism [27], data collected at 5% above the MLSS were included in the analysis due to the similarly stable · VO2 measurements between the 10- and 30-min values across the three intensities (i.e., differences between 10- and 30-min · VO2 measures were ~50 mL·min−1 at intensities of 5% below, at, and 5% above the MLSS)—with similar ﬁndings previously being reported [28]—and to provide a more comprehensive dataset for the analyses. Sensors 2023, 23, x FOR PEER REVIEW 5 of 20 Figure 1. Example of the running power signal during constant-speed treadmill running at diﬀerent intensities for one participant. Data are shown for the moderate (MOD; 6 min) and heavy (HVY; 12 min) intensity steps, and during 30 min of running at 5% below the maximal lactate steady state (MLSS), at the MLSS, 5% above the MLSS, and during a repeat trial at the MLSS, preceded by run- ning power data recorded for 3–4 min at a running speed of 1.9 m·s−1. Running power data were not collected during the ﬁrst ~1–2 min of each exercise protocol (i.e., warm-up) to allow for synchroni- zation with other measurements. Note that the repeat MLSS trial is obscured by the ﬁrst MLSS trial. 2.5. Data Analysis 2.5.1. Cardiorespiratory, Running Speed, and Stryd Running Power Data The average V̇ O2 and running power, measured between minutes 4 and 6 of the MOD step and between minutes 10 and 12 of the HVY step, were calculated from the SRS test. Maximal aerobic speed (MAS) and maximal aerobic power (MAP) were determined as the running speed associated with the highest completed 1-min stage during the incremental test and the average running power during that stage, respectively. Cardiorespiratory and running power data used for analysis from the constant-speed MLSS-determination running trials included the 10- and 30-min V̇ O2, V̇ CO2, RER, V̇ E, HR, and running power measures for running trials 5% below, at, and 5% above MLSS. To align with the timing of [BLa] measurement (i.e., a short pause in running), mean values for V̇ O2, V̇ CO2, RER, V̇ E, HR, and Stryd running power were calculated from the 2 min of data collected prior to the 10-min and the 30-min (or at task failure if <30 min) time points. Although the MLSS is thought to represent the highest intensity at which energy provi- sion is supplied exclusively via oxidative metabolism [27], data collected at 5% above the MLSS were included in the analysis due to the similarly stable V̇ O2 measurements be- tween the 10- and 30-min values across the three intensities (i.e., diﬀerences between 10- and 30-min V̇ O2 measures were ~50 mL·min−1 at intensities of 5% below, at, and 5% above the MLSS)—with similar ﬁndings previously being reported [28]—and to provide a more comprehensive dataset for the analyses. 2.5.2. Incremental and Constant-Speed V̇ O2–Power and Speed–Power Gains A least-squares linear regression was performed to calculate the V̇ O2–power gain (i.e., the slope of the regression equation) for each participant during both incremental and constant-speed exercise trials, measured separately. This method allowed for the cal- l i f V̇ O i d d d d i i i hi h i ld -5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 0 120 160 200 240 280 320 Time (min) Stryd Power (W) MOD HVY 5% Below MLSS At MLSS 5% Above MLSS Repeat at MLSS Figure 1. Example of the running power signal during constant-speed treadmill running at different intensities for one participant. Data are shown for the moderate (MOD; 6 min) and heavy (HVY; 12 min) intensity steps, and during 30 min of running at 5% below the maximal lactate steady state (MLSS), at the MLSS, 5% above the MLSS, and during a repeat trial at the MLSS, preceded by running power data recorded for 3–4 min at a running speed of 1.9 m·s−1. Running power data were not collected during the ﬁrst ~1–2 min of each exercise protocol (i.e., warm-up) to allow for synchronization with other measurements. Note that the repeat MLSS trial is obscured by the ﬁrst MLSS trial. 2.5.2. Incremental and Constant-Speed · VO2–Power and Speed–Power Gains A least-squares linear regression was performed to calculate the · VO2–power gain (i.e., the slope of the regression equation) for each participant during both incremental and constant-speed exercise trials, measured separately. This method allowed for the calculation of a · VO2–power gain mean and standard deviation in which comparisons could be made between the incremental and constant-speed running tests and to contrast the measurements with cycling data [29]. The “incremental · VO2–power gain” for each runner was calculated as the slope ((mL·min−1)·W−1) of a least-squares linear regression line through the incremental exercise · VO2–power response, from the onset of a systemic rise in · VO2 until test termination or the onset of a plateau, if detected. The “constant-speed · VO2–power gain” for each runner was calculated as the slope ((mL·min−1)·W−1) of a least- squares linear regression line for the steady-state · VO2 and power data from ﬁve constant- speed intensities: the MOD and HVY steps from the SRS-protocol and the constant-speed exercise trials 5% below, at, and 5% above the MLSS. Replacing · VO2 with running speed, these same procedures were used to calculate the “incremental speed–power gain” and the “constant-speed speed–power gain” for each participant. Sensors 2023, 23, 8729 6 of 19 2.5.3. Metabolic Power, Mechanical Power, and Mechanical Efﬁciency Running metabolic power, mechanical power, and mechanical efﬁciency measure- ments were calculated during constant-speed running trials at MOD, HVY, 5% below, at, and 5% above MLSS. Metabolic power was calculated as a gross energy cost per unit of body mass and distance travelled (kJ·kg−1·km−1) using · VO2 and RER [30]. This calcula- tion of metabolic power was used to represent the energy cost of running (i.e., RE) at each respective intensity, providing a reference measure of RE in which all subsequent compar- isons were made. Metabolic power (i.e., StrydMET) was also calculated by expressing the energy cost—using · VO2 and RER [30]—per unit of absolute Stryd power ((J·s−1)·W−1) and per unit of relative Stryd power ((kJ·s−1)·(W·kg−1)−1). StrydMET was calculated in isolation from running speed to provide a metric that characterized the metabolic power requirements per unit of Stryd running power. The units used to describe StrydMET were not simpliﬁed in order to distinguish among related terms and to provide units that clearly described the energy cost of running per unit of absolute and relative Stryd running power. Mechanical power (i.e., StrydMECH) was calculated in isolation from the · VO2 and RER by expressing Stryd running power (W) in units of J·s−1 and by converting mechanical power to an absolute energy cost per unit of the distance travelled (kJ·km−1) and a relative energy cost per unit of distance (kJ·kg−1·km−1). Mechanical efﬁciency (EFF) was calculated as the ratio between StrydMECH (kJ·kg−1·km−1) and metabolic power (kJ·kg−1·km−1), expressed as a percentage. 2.6. Statistical Analysis 2.6.1. General Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, version 26, IBM, Armonk, NY, USA). Linear mixed-effects models were performed us- ing the nlme package (version 3.1-157) in RStudio (version 4.2.0) (R Core Team (2018)). Data visualization was performed using Prism (version 9.5.1 for macOS; GraphPad Software, San Diego, CA, USA). Data are presented as mean [standard deviation (SD)]. Statistical sig- niﬁcance was set at an α level of <0.05. Where appropriate, Bonferroni post hoc tests were used. Test-retest reliability was measured using two-way mixed effects, absolute agree- ment, and single-rater intraclass correlation models wherein reliability was interpreted as poor (ICC < 0.5), moderate (0.5 ≤ ICC < 0.75), good (0.75 ≤ ICC < 0.9), or excellent (ICC ≥ 0.9) [31]. 2.6.2. Stability, Sensitivity, and Reliability Multiple two-way repeated-measure ANOVAs were used to assess stability (the main effect of duration) and sensitivity (the main effect of intensity) of Stryd running power and the physiological and perceptual responses (i.e., · VO2, · VCO2, RER, · VE, HR, [BLa], and RPE) at 10 min and 30 min (or task failure, if <30 min) during constant-speed treadmill running at 5% below, at, and 5% above the MLSS. For the same variables, the two MLSS trials were compared using paired Student’s t tests, intraclass correlations, and Bland–Altman analyses (with 95% limits of agreement) to assess reliability at the 30-min timepoint. Stryd running power stability was further assessed by evaluating the linear association and agreement between the 10- and 30-min running power data for the ﬁrst MLSS trial using a Pearson’s correlation coefﬁcient and Bland–Altman analysis, respectively. 2.6.3. Stryd Running Power—Association with · VO2 and Running Speed Paired Student’s t tests were used to compare the mean · VO2–power gains and · VO2–speed gains between incremental and constant-speed exercise trials. To determine the association between Stryd running power and training intensity, linear mixed-effects models were used to assess the within-individual and between-individual association Sensors 2023, 23, 8729 7 of 19 between running power and · VO2 measurements, and between running power and running speed during the MOD, HVY, MLSS −5%, MLSS, and MLSS +5% running trials. Models included ﬁxed-effects models of absolute running power and relative running power while allowing intercepts as random effects for the participants to account for repeated measure- ments within individuals [32]. Models were estimated using maximum likelihood, model selection was assessed using a chi-squared likelihood ratio test, and model ﬁt was assessed using pseudo-R2 [32]. These analyses were performed for absolute (i.e., W) and relative measures of power (i.e., W·kg−1). The spread of the participants’ intercepts was compared using the · VO2 and absolute power and the · VO2 and relative power relationships and using the speed and absolute power and speed and relative power relationships, employing the Pitman–Morgan test for the homogeneity of variance of paired samples. 2.6.4. Stryd Running Power—Running Economy and Efﬁciency To determine whether Stryd running power provides an indication of RE during constant-speed treadmill running trials at MLSS, Pearson’s correlation coefﬁcients were calculated between metabolic power (kJ·kg−1·km−1) and each of the following variables: absolute StrydMECH (kJ·km−1), relative StrydMECH (kJ·kg−1·km−1), absolute StrydMET ((J·s−1)·W−1), and relative StrydMET ((kJ·s−1)·(W·kg−1)−1). One-way repeated-measures ANOVAs were used to assess the main effect of running intensity on metabolic power, StrydMECH, and StrydMET measurements. Using the 30-min timepoint mean (i.e., 28–30 min) data from the two MLSS trials, the reliability of metabolic power, StrydMECH, and StrydMET were assessed by paired Student’s t tests, intraclass correlations, and Bland–Altman analy- ses (with 95% limits of agreement). One-way repeated-measures ANOVAs were also used to determine whether running intensity affected Stryd-derived assessments of EFF. 2.6.5. Stryd Running Power—Aerobic Fitness To determine whether Stryd running power provides an indication of an athlete’s aerobic ﬁtness during constant-speed treadmill running, Pearson’s correlation coefﬁcients were calculated for the following pairs of variables: · VO2 at MLSS and running power at MLSS, and · VO2 at MLSS and running speed at MLSS. 3. Results 3.1. Participants Table 1 displays the female and male participant characteristics, incremental exercise testing results, and MLSS testing results. All incremental tests were maximal, and the duration of the incremental test portion of the SRS protocol was 12.1 [2.0] min. The measured · VO2 during constant-speed running at MOD and HVY was 91.2 [8.0]% and 92.9 [5.6]% of the · VO2 at GET and RCP, respectively. All runners completed at least 30 min of treadmill running at 5% below the MLSS, at the MLSS, and during the repeat trial at the MLSS; however, seven runners were unable to complete 30 min of running at 5% above the MLSS. 3.2. Stability, Sensitivity, and Reliability of Stryd Running Power The 10- and 30-min running power measurements taken during constant-speed run- ning trials near the MLSS are presented in Figure 2. While the intensity × duration interaction and the main effect of duration were not statistically signiﬁcant for running power, there was a signiﬁcant main effect for running intensity, with signiﬁcant differences between all pairs of intensities (p < 0.001 for all post hoc comparisons; Table 2). Sensors 2023, 23, 8729 8 of 19 Table 1. Participant characteristics, maximal exercise results, and maximal lactate steady-state (MLSS) results. Sex (n) Weight (kg) Maximal Exercising Measurements MLSS Measurements · VO2max (L·min−1) MAS (m·s−1) MAP (W) · VO2 at MLSS (L·min−1) Speed at MLSS (m·s−1) Power at MLSS (W) Female (7) 65.5 [2.9] 3.37 [0.26] 4.29 [0.44] 280 [19] 2.96 [0.17] 3.29 [0.34] 224 [18] Male (8) 71.7 [10.4] 4.10 [0.82] 4.54 [0.44] 330 [58] 3.61 [0.58] 3.43 [0.42] 254 [44] Total (15) 68.8 [8.2] 3.76 [0.71] 4.42 [0.44] 307 [50] 3.30 [0.54] 3.35 [0.37] 240 [37] · VO2max, maximal oxygen uptake; MAS, maximal aerobic speed; MAP, maximal aerobic power. Data are reported as mean [standard deviation]. Sensors 2023, 23, x FOR PEER REVIEW 9 of 20 Figure 2. Running power data near the maximal lactate steady state (MLSS). Panel (A) shows the comparison between the 10-min and 30-min mean running power measurements during treadmill running near the MLSS. Lines representing individual participants, asterisks () indicate statistically signiﬁcant diﬀerences between speeds, and error bars represent one standard deviation. Panel (B) shows the relationship between 10 min and 30 min of running power from the ﬁrst run at the MLSS, and Panel (C) shows the relationship between 30 min of running power from the two separate runs at the MLSS. Panels (D,E) show Bland–Altman plots corresponding to the data in Panels B and C, respectively. In Panels (B–E), squares represent individual data, solid lines represent y=0, dashed lines represent bias, and dotted lines represent 95% limits of agreement. n = 15 for all panels. 5% Below MLSS 5% Above Repeat MLSS 0 150 200 250 300 350 Speed Running Power (W) 10-min 30-min 175 225 275 325 175 225 275 325 10-min Running Power (W) 30-min Running Power (W) Y = 1.01 X − 1.57 R2 = 1.00; P < 0.001 150 200 250 300 350 -5.0 -2.5 0.0 2.5 5.0 Mean of 10-min and 30-min Running Power at MLSS (W) 30-min - 10-min Running Power at MLSS (%) LOA: −1.4 to 1.3% Bias: 0.1% 175 225 275 325 175 225 275 325 First MLSS Trial Running Power (W) Repeat MLSS Trial Running Power (W) Y = 1.00 * X + 0.32 R2 = 0.99; P < 0.001 B D C E A 150 200 250 300 350 -5.0 -2.5 0.0 2.5 5.0 Mean of First and Repeat Trial Running Power at MLSS (W) First - Repeat MLSS Trial Running Power (%) LOA: −3.4 to 2.6% Bias: -0.4% Figure 2. Running power data near the maximal lactate steady state (MLSS). Panel (A) shows the compar- ison between the 10-min and 30-min mean running power measurements during treadmill running near the MLSS. Lines representing individual participants, asterisks () indicate statistically significant differ- ences between speeds, and error bars represent one standard deviation. Panel (B) shows the relationship Sensors 2023, 23, 8729 9 of 19 between 10 min and 30 min of running power from the ﬁrst run at the MLSS, and Panel (C) shows the relationship between 30 min of running power from the two separate runs at the MLSS. Panels (D,E) show Bland–Altman plots corresponding to the data in Panels (B) and (C), respectively. In Panels (B–E), squares represent individual data, solid lines represent y=0, dashed lines represent bias, and dotted lines represent 95% limits of agreement. n = 15 for all panels. Table 2. · VO2 and running power responses to exercise near the maximal lactate steady state (MLSS). 5% below MLSS At MLSS 5% above MLSS ANOVA (DxI, D, I) b 10 min 30 min 10 min 30 min 10 min 30 min a · VO2 (L·min−1) 3.12 [0.55] ¶ 3.16 [0.56] ,¶ 3.26 [0.54] † 3.30 [0.54] ,† 3.41 [0.55] †,¶ 3.46 [0.57] ,†,¶ 0.202, 0.001, <0.001 Running Power (W) 231 [35] ¶ 230 [35] ¶ 239 [36] † 240 [37] † 250 [38] †,¶ 250 [38] †,¶ 0.334, 0.528, <0.001 DxI, duration by intensity interaction; D, duration; I, intensity; · VO2, oxygen uptake. a Or the ﬁnal 2 min if task failure was < 30 min. b p-values are provided for these statistical tests. The * denotes a signiﬁcant difference from the 10-min timepoint at the same intensity (p < 0.05); the † denotes a signiﬁcant difference from 5% below the MLSS (p < 0.05); the ¶ denotes a signiﬁcant difference from the MLSS (p < 0.05). n = 15 for all variables. Data are reported as mean [standard deviation]. The 10- and 30-min running power measurements during the repeat constant-speed running trial at MLSS are reported in Table 3. There was excellent reliability and low bias between the running power measured at two time points within one run at the MLSS and across two runs at the MLSS, without differences between repeated trials at the MLSS (Figure 2; Table 3). Table 3. Reliability of · VO2 and running power responses and metabolic and mechanical power measurements to exercise at the maximal lactate steady state (MLSS). At MLSS (Repeat) a Reliability of Repeated Runs at MLSS (30-min) 10 min 30 min t Test b Bias LOA ICC · VO2 (L·min−1) 3.25 [0.54] 3.26 [0.52] 0.177 0.04 −0.18 to 0.27 0.99 (0.96 to 1.00) Running Power (W) 240 [37] 241 [37] 0.322 −1 −8 to 6 1.00 (0.99 to 1.00) Metabolic Power (kJ·kg−1·km−1) - 4.99 [0.29] 0.249 0.06 −0.30 to 0.42 0.91 (0.74 to 0.97) StrydMECH (kJ·km−1) - 71.9 [9.5] 0.324 −0.28 −2.32 to 1.77 1.00 (0.99 to 1.00) StrydMECH (kJ·kg−1·km−1) - 1.04 [0.03] 0.331 0 −0.04 to 0.03 0.94 (0.82 to 0.98) StrydMET((J·s−1)·W−1) - 4.78 [0.30] 0.153 0.07 −0.29 to 0.44 0.91 (0.73 to 0.97) StrydMET ((kJ·s−1)·(W·kg−1)−1) - 0.33 [0.04] 0.121 0.01 −0.02 to 0.03 0.98 (0.93 to 0.99) LOA, limits of agreement; ICC, intraclass correlation. a See Tables 2 and 4 for data from the ﬁrst MLSS trial. Note that metabolic and mechanical power measures are based on the 30-min time point only. b p-values are provided for these statistical tests. Data are reported as mean [standard deviation]. Table 4. Mean metabolic and mechanical power measures during the moderate-intensity step (MOD) and during constant-speed running 5% below, at, and 5% above the maximal lactate steady state (MLSS). 6-min 30-min ANOVA (p-Value) MOD 5% below MLSS At MLSS 5% above MLSS Metabolic Power (kJ·kg−1·km−1) 4.31 [0.36] ,†,¶ 5.05 [0.35] 5.05 [0.36] 5.07 [0.30] <0.001 StrydMECH (kJ·km−1) 73.8 [9.8] ,†,¶ 72.3 [9.6] ,¶ 71.7 [9.5] 71.1 [9.0] <0.001 StrydMECH (kJ·kg−1·km−1) 1.07 [0.03] ,†,¶ 1.05 [0.03] ¶ 1.04 [0.03] 1.03 [0.03] <0.001 StrydMET ((J·s−1)·W−1) 4.02 [0.29] ,†,¶ 4.82 [0.40] 4.86 [0.34] 4.91 [0.32] <0.001 StrydMET ((kJ·s−1)·(W·kg−1)−1) 0.28 [0.04] ,†,¶ 0.33 [0.05] 0.33 [0.05] 0.34 [0.05] <0.001 Data are based on the moderate-intensity step (MOD) from the “Step-Ramp-Step” protocol or the 30-min timepoint of the indicated trial. * Denotes a signiﬁcant difference between the denoted intensity compared to MLSS (p < 0.05). † Denotes a signiﬁcant difference between the denoted intensity compared to 5% below MLSS (p < 0.05). ¶ Denotes a signiﬁcant difference between the denoted intensity compared to 5% above MLSS (p < 0.05). Data are reported as mean [standard deviation]. n = 15 for all variables. Sensors 2023, 23, 8729 10 of 19 3.3. Physiological and Perceptual Responses The duration × intensity interaction was not signiﬁcant for · VO2; however, there was a main effect of intensity, with signiﬁcant differences across the three running speeds and a main effect of duration, demonstrating 30-min values greater than the 10-min values (p < 0.05 for all post hoc comparisons; Table 2). The · VO2 values measured at two time points within one run at the MLSS had excellent reliability and low bias across two runs at the MLSS, without differences between repeated trials at the MLSS (Table 3). Descriptive data and statistical results for · VCO2, RER, · VE, HR, [BLa], and RPE mea- sured at two time points (10-min and 30-min) for three speeds near the MLSS are reported in the Supplementary Materials (Table S1). 3.4. Stryd Running Power—Association with · VO2 and Running Speed The incremental and constant-speed · VO2–power gains and speed–power gains are reported in Table 5. From the constant-speed running trials, the linear mixed-effects models revealed a strong, positive relationship between absolute running power and · VO2 and between relative power and · VO2 (Table 6; Figure 3). There was signiﬁcant variance between participant intercepts for both models that differed between models (Table 6; Figure 3), providing evidence that the relationship between absolute power and · VO2 was stronger and less variable between the participants than the relationship between relative power and · VO2. Table 5. The · VO2–power gain and speed–power gain calculated from the incremental exercise test and constant-speed running trials. Variable Test p-Value Incremental Constant-Speed Absolute · VO2–power gain ((mL·min−1)·W−1) 11.6 [1.5] 19.8 [3.5] <0.001 Relative · VO2–power gain ((mL·min−1)·(W·kg−1) −1) 810.9 [148.9] 1364.2 [298.7] <0.001 Absolute speed–power gain ((m·s−1)·W−1) 0.015 [0.002] 0.015 [0.002] 0.365 Relative speed–power gain ((m·s−1)·(W·kg−1) −1) 1.05 [0.08] 1.03 [0.07] 0.333 Data are reported as mean [standard deviation]. N = 15 for all variables. Table 6. The within-individual and between-individual association between running power and · VO2 measurements and between running power and running speed during the MOD, HVY, maximal lactate steady state (MLSS) −5%, MLSS, and MLSS +5% running trials. Variable b [95% CI] Statistics SD [95% CI] χ2 Statistics Pitman–Morgan Test Absolute running power and · VO2 18.2 [17.1, 19.3] t(59) = 32.7; p < 0.001; R2 = 0.97 196.9 [130.7, 296.8] χ2(4) = −494.8; p < 0.001 t(13) = −3.08; p = 0.009 Relative running power and · VO2 1246.3 [1150.2, 1342.4] t(59) = 25.6; p < 0.001; R2 = 0.95 414.0 [285.9, 599.6] χ2(4) = 92.3; p < 0.001 Absolute running power and speed 0.015 [0.014, 0.015] t(59) = 37.9; p < 0.001; R2 = 0.97 0.414 [0.288, 0.596] χ2(4) = 125.8; p < 0.001 t(13) = 15.72; p = 0.002 Relative running power and speed 1.01 [0.96, 1.06] t(59) = 42.7; p < 0.001; R2 = 0.97 0.063 [0.039, 0.104] χ2 (4) = 12.7; p < 0.001 b denotes the calculated slope from the linear mixed-effects model. The units of the slope are (mL·min−1)·W−1 and (mL·min−1)·W−1 for absolute and relative running power and · VO2, respectively, and (m·s−1)·W−1 and (m·s−1)·(W·kg−1)−1 for absolute and relative running power and speed, respectively. SD denotes the standard deviation, which is presented in units of mL·min−1 and m·s−1 for · VO2 and speed variables, respectively. Sensors 2023, 23, 8729 11 of 19 Variable p-Value Incremental Constant-Speed Absolute V̇ O2–power gain ((mL·min−1)·W−1) 11.6 [1.5] 19.8 [3.5] <0.001 Relative V̇ O2–power gain ((mL·min−1)·(W·kg−1) −1) 810.9 [148.9] 1364.2 [298.7] <0.001 Absolute speed–power gain ((m·s−1)·W−1) 0.015 [0.002] 0.015 [0.002] 0.365 Relative speed–power gain ((m·s−1)·(W·kg−1) −1) 1.05 [0.08] 1.03 [0.07] 0.333 Data are reported as mean [standard deviation]. N = 15 for all variables. Figure 3. Relationships between absolute and relative running power, running speed, and oxygen uptake (V̇ O2). Panels (A,B) show the relationships between absolute running power and V̇ O2 and between absolute running power and running speed for each participant during the moderate (MOD) and heavy (HVY) intensity steps and constant-speed trials near the maximal lactate steady state (MLSS). Panels (C,D) show the relationships between relative running power and V̇ O2 and between relative running power and running speed for each participant at each running intensity, respectively. Each color represents a single participants set of trials. N = 15 for all panels. 3.5. Stryd Running Power—Association with Running Economy Based on the constant-speed running trials at MOD and near the MLSS, there were signiﬁcant eﬀects of intensity on metabolic power, StrydMECH, and StrydMET measurements (Table 6). The metabolic power and StrydMET measurements were signiﬁcantly lower at MOD compared to measurements 5% below, at, and 5% above the MLSS (p < 0.001 for all pairwise comparisons; Table 6). In contrast, the StrydMECH measurements were signiﬁ- cantly higher at MOD compared to the three higher intensities (p < 0.001 for all compari- sons; Table 6). All variables had excellent reliability for the repeated trials at the MLSS, without signiﬁcant diﬀerences between trials at the MLSS (Table 3). 150 200 250 300 1000 2000 3000 4000 5000 Running Power (W) V̇ O2 (mL·min−1) 2.0 2.5 3.0 3.5 4.0 4.5 1000 2000 3000 4000 5000 Running Power (W·kg−1) V̇ O2 (mL·min−1) 150 200 250 300 2.0 2.5 3.0 3.5 4.0 4.5 Running Power (W) Speed (m·s−1) 2.0 2.5 3.0 3.5 4.0 4.5 2.0 2.5 3.0 3.5 4.0 4.5 Running Power (W·kg−1) Speed (m·s−1) C D A B Figure 3. Relationships between absolute and relative running power, running speed, and oxygen uptake ( · VO2). Panels (A,B) show the relationships between absolute running power and · VO2 and between absolute running power and running speed for each participant during the moderate (MOD) and heavy (HVY) intensity steps and constant-speed trials near the maximal lactate steady state (MLSS). Panels (C,D) show the relationships between relative running power and · VO2 and between relative running power and running speed for each participant at each running intensity, respectively. Each color represents a single participant’s set of trials. N = 15 for all panels. Results were similar when speed was used in place of · VO2; however, the difference in model intercept variances was in the opposite direction, with a stronger and less variable relationship between relative power and speed compared to absolute power and speed (Table 6; Figure 3). 3.5. Stryd Running Power—Association with Running Economy Based on the constant-speed running trials at MOD and near the MLSS, there were signiﬁcant effects of intensity on metabolic power, StrydMECH, and StrydMET measurements (Table 6). The metabolic power and StrydMET measurements were signiﬁcantly lower at MOD compared to measurements 5% below, at, and 5% above the MLSS (p < 0.001 for all pairwise comparisons; Table 6). In contrast, the StrydMECH measurements were signiﬁcantly higher at MOD compared to the three higher intensities (p < 0.001 for all comparisons; Table 6). All variables had excellent reliability for the repeated trials at the MLSS, without signiﬁcant differences between trials at the MLSS (Table 3). Figure 4 depicts the relationships between metabolic power (kJ·kg−1·km−1) and absolute StrydMECH (kJ·km−1), relative StrydMECH (kJ·kg−1·km−1), absolute StrydMET ((J·s−1)·W−1), and relative StrydMET ((kJ·s−1)·(W·kg−1)−1) at the MLSS. Metabolic power (kJ·kg−1·km−1) was not signiﬁcantly correlated with absolute StrydMECH (kJ·km−1) or relative StrydMECH (kJ·kg−1·km−1); however, strong positive and moderately positive correlations were detected between metabolic power (kJ·kg−1·km−1) and absolute StrydMET (J·s−1)·W−1) and relative StrydMET ((kJ·s−1)·(W·kg−1)−1), respectively (Figure 4). The results for other intensities were similar. Sensors 2023, 23, 8729 12 of 19 lute StrydMECH (kJ km ), relative StrydMECH (kJ kg km ), absolute StrydMET ((J s ) W ), and relative StrydMET ((kJ·s−1)·(W·kg−1)−1) at the MLSS. Metabolic power (kJ·kg−1·km−1) was not signiﬁcantly correlated with absolute StrydMECH (kJ·km−1) or relative StrydMECH (kJ·kg−1·km−1); however, strong positive and moderately positive correlations were de- tected between metabolic power (kJ·kg−1·km−1) and absolute StrydMET (J·s−1)·W−1) and rela- tive StrydMET ((kJ·s−1)·(W·kg−1)−1), respectively (Figure 4). The results for other intensities were similar. Figure 4. Relationships between metabolic power and absolute StrydMECH (A), relative StrydMECH (B), absolute StrydMET (C), and relative StrydMET (D) during constant-speed running trials performed at the maximal lactate steady state (MLSS). Circles represent individual data. n = 15 for all panels. 4.0 4.5 5.0 5.5 6.0 45.0 60.0 75.0 90.0 Metabolic Power (kJ·kg−1·km−1) StrydMECH (kJ·km−1) y = 7.5x + 33.9 R2 = 0.079 p = 0.313 4.0 4.5 5.0 5.5 6.0 0.9 1.0 1.1 1.2 Metabolic Power (kJ·kg−1·km−1) StrydMECH (kJ·kg−1·km−1) y = 0.024x + 0.92 R2 = 0.066 p = 0.356 4.0 4.5 5.0 5.5 6.0 4.0 4.5 5.0 5.5 6.0 Metabolic Power (kJ·kg−1·km−1) StrydMET ((J·s−1)·W−1) y = 0.83x + 0.64 R2 = 0.791 p < 0.001 4.0 4.5 5.0 5.5 6.0 0.3 0.3 0.4 0.4 0.5 0.5 Metabolic Power (kJ·kg−1·km−1) StrydMET ((kJ·s−1)·(W·kg−1)−1) y = 0.084x − 0.091 R2 = 0.373 p = 0.016 A B C D Figure 4. Relationships between metabolic power and absolute StrydMECH (A), relative StrydMECH (B), absolute StrydMET (C), and relative StrydMET (D) during constant-speed running trials performed at the maximal lactate steady state (MLSS). Circles represent individual data. n = 15 for all panels. 3.6. Stryd Running Power—Estimates of Mechanical Running Efﬁciency There was a statistically signiﬁcant main effect of running speed for EFF (p < 0.001; Figure 5). Pairwise comparisons revealed that EFF was signiﬁcantly higher at MOD (25.0 [1.8]%) compared to HVY (21.3 [1.2]%), 5% below MLSS (20.9 [1.7]%), MLSS (20.7 [1.4]%), and 5% above MLSS (20.4 [1.4]%) (p < 0.001 for these pairwise comparisons). No other signiﬁcant differences were detected between the EFF measurements. Sensors 2023, 23, x FOR PEER REVIEW 13 of 20 3.6. Stryd Running Power—Estimates of Mechanical Running Eﬃciency There was a statistically signiﬁcant main eﬀect of running speed for EFF (p < 0.001; Figure 5). Pairwise comparisons revealed that EFF was signiﬁcantly higher at MOD (25.0 [1.8]%) compared to HVY (21.3 [1.2]%), 5% below MLSS (20.9 [1.7]%), MLSS (20.7 [1.4]%), and 5% above MLSS (20.4 [1.4]%) (p < 0.001 for these pairwise comparisons). No other signiﬁcant diﬀerences were detected between the EFF measurements. Figure 5. Average running mechanical eﬃciency (EFF) measurements during the moderate- (MOD) and heavy-intensity (HVY) steps, and during constant-speed trials near the maximal lactate steady state (MLSS). The asterisks () indicate statistically signiﬁcant diﬀerences between intensities. Error bars represent one standard deviation. Circles represent individual data. n = 15. 3.7. Stryd Running Power—Association with Aerobic Fitness Absolute running power at the MLSS was strongly correlated with absolute V̇ O2 at the MLSS, moderately correlated with relative V̇ O2 and absolute running speed at the MLSS, and not correlated with relative running speed at the MLSS (Table 1; Figure 6). Relative running power at the MLSS was not correlated with absolute V̇ O2 at the MLSS, but it was strongly correlated with relative V̇ O2 and absolute running speed at the MLSS and moderately correlated with relative running speed at the MLSS (Table 1; Figure 6). MOD HVY Below MLSS Above 0 10 20 30 Condition Mechanical Efficiency (%) Figure 5. Average running mechanical efﬁciency (EFF) measurements during the moderate- (MOD) and heavy-intensity (HVY) steps, and during constant-speed trials near the maximal lactate steady state (MLSS). The asterisks (*) indicate statistically signiﬁcant differences between intensities. Error bars represent one standard deviation. Circles represent individual data. n = 15. 3.7. Stryd Running Power—Association with Aerobic Fitness Absolute running power at the MLSS was strongly correlated with absolute · VO2 at the MLSS, moderately correlated with relative · VO2 and absolute running speed at the MLSS, and not correlated with relative running speed at the MLSS (Table 1; Figure 6). Relative Sensors 2023, 23, 8729 13 of 19 running power at the MLSS was not correlated with absolute · VO2 at the MLSS, but it was strongly correlated with relative · VO2 and absolute running speed at the MLSS and moderately correlated with relative running speed at the MLSS (Table 1; Figure 6). Sensors 2023, 23, x FOR PEER REVIEW 14 of 20 Figure 6. Relationships between absolute and relative running power, running speed, and oxygen uptake (V̇ O2) at the maximal lactate steady state (MLSS). Panels (A–D) show the relationship be- tween V̇ O2 at the MLSS and running power at the MLSS in absolute and relative units. Panels (E–H) show the relationship between running speed at the MLSS and running power at the MLSS in abso- lute and relative units. Individual data are plotted, along with the regression lines. n = 15 for all panels. 4. Discussion The results from this investigation support the use of Stryd in research and applied settings. The Stryd running power metric was stable during 30-min constant-speed 2.5 3.0 3.5 4.0 4.5 150 200 250 300 350 V̇ O2 at MLSS (L·min−1) MLSS Power (W) y = 61.7x + 35.8 R2 = 0.841 p < 0.001 40 45 50 55 60 150 200 250 300 350 V̇ O2 at MLSS (mL·kg−1·min−1) MLSS Power (W) y = 3.9x + 50.9 R2 = 0.280 p = 0.042 2.5 3.0 3.5 4.0 4.5 150 200 250 300 350 Speed at MLSS (m·s−1) MLSS Power (W) y = 51.6x + 66.3 R2 = 0.283 p = 0.041 0.03 0.04 0.05 0.06 0.07 150 200 250 300 350 Speed at MLSS (m·s−1·kg−1) MLSS Power (W) y = −596x + 269 R2 = 0.018 p = 0.638 2.5 3.0 3.5 4.0 4.5 2.5 3.0 3.5 4.0 4.5 V̇ O2 at MLSS (L·min−1) MLSS Power (W·kg−1) y = 0.28x + 2.57 R2 = 0.211 p = 0.085 40 45 50 55 60 2.5 3.0 3.5 4.0 4.5 V̇ O2 at MLSS (mL·kg−1·min−1) MLSS Power (W·kg−1) y = 0.052x + 1.00 R2 = 0.604 p < 0.001 2.5 3.0 3.5 4.0 4.5 2.5 3.0 3.5 4.0 4.5 Speed at MLSS (m·s−1) MLSS Power (W·kg−1) y = 0.83x + 0.71 R2 = 0.905 p < 0.001 0.03 0.04 0.05 0.06 0.07 2.5 3.0 3.5 4.0 4.5 Speed at MLSS (m·s−1·kg−1) MLSS Power (W·kg−1) y = 27.8x + 2.1 R2 = 0.478 p = 0.004 A B C D E F G H Figure 6. Relationships between absolute and relative running power, running speed, and oxygen uptake ( · VO2) at the maximal lactate steady state (MLSS). Panels (A–D) show the relationship between · VO2 at the MLSS and running power at the MLSS in absolute and relative units. Panels (E–H) show the relationship between running speed at the MLSS and running power at the MLSS in absolute and relative units. Individual data are plotted, along with the regression lines. n = 15 for all panels. Sensors 2023, 23, 8729 14 of 19 4. Discussion The results from this investigation support the use of Stryd in research and applied settings. The Stryd running power metric was stable during 30-min constant-speed running trials, repeatable across trials at the MLSS, and sensitive enough to differentiate between trials performed at running speeds of 5% below, at, and 5% above the MLSS threshold. Running power was strongly correlated with running speed and · VO2 during constant- speed exercise relative to the GET and MLSS, supporting its use as a training intensity metric. Furthermore, running power measurements at the MLSS were strongly associated with both the · VO2 and running speed at the MLSS. Although metabolic power was strongly associated with absolute StrydMET, it appears that Stryd power cannot provide an indication of RE in isolation from metabolic data, as the associations between metabolic power and StrydMECH were weak. Despite this ﬁnding, the mechanical running efﬁciency derived using Stryd (i.e., EFF) remained consistent and proportional at various exercise intensities near the MLSS threshold. 4.1. Stability, Sensitivity, and Reliability Mean running power measurements were similar across the 10- and 30-min timepoints during constant-speed running trials at 5% below, at, and 5% above the MLSS. Along with a strong correlation, zero bias, and narrow LOA between time points, these ﬁndings indicate that the Stryd signal remained stable during constant-speed treadmill running. Running power across two runs at the MLSS was also strongly correlated, with a near- zero bias and narrow LOA, indicating the excellent day-to-day reliability of the metric. Furthermore, the Stryd power metric was able to distinguish between exercise intensities near the MLSS. In agreement with our results, previous investigations also reported that Stryd running power was stable during constant-speed running [33], repeatable [11], and sensitive between conditions [34]; however, our investigation is the ﬁrst to evaluate these running power parameters near the MLSS, an important threshold for training programs and ﬁtness assessment [35,36]. In support of the Stryd running power metric results, besides a signiﬁcantly lower RPE measurement during the second compared to the ﬁrst MLSS trial (i.e., 0.8 units on the Borg 6–20 scale), which may indicate increased comfort during testing, the · VO2 (Table 2) and other physiological and perceptual responses to running near the MLSS were also stable, sensitive, and reliable (Table S1). 4.2. Stryd Running Power and Exercise Intensity The strong associations observed between running power, · VO2, and speed support the use of Stryd running power to guide exercise training relative to the exercise intensity domains. Of note, the relationship between Stryd running power and · VO2, considered at the group level, was stronger when running power was expressed in absolute units, whereas the relationship between Stryd running power and speed, at the group level, was stronger when running power was expressed in relative units. In practice, our results suggest that absolute Stryd power may be best used as a metric to approximate the rate of absolute oxygen consumption, while relative running power may be best used to indicate running speed—at least during treadmill running. Due to the varying methodological ap- proaches used to establish · VO2–power relationships in previous research [11,17,18,37–39], it is difﬁcult to make comparisons across studies. A major strength of the current investigation is that exercise intensity domains were delineated and the · VO2 was subsequently evaluated during appropriate durations of constant-speed running before examining the relationship between · VO2 and running power. Exercise in the heavy-intensity domain can result in a slow · VO2 component that delays the attainment of a steady · VO2 measure by ~10–15 min or longer [40]. Thus, without Sensors 2023, 23, 8729 15 of 19 appropriately delineating the exercise intensity domain, it is difﬁcult to discern whether a given absolute work rate or stage duration will produce steady-state exercising condi- tions. The inﬂuence that intensity domain and · VO2 kinetic responses have on subsequent · VO2–power relationships can be highlighted by the substantial difference between the incremental (i.e., 11.6 [1.5] (mL·min−1)·W−1) and constant-speed · VO2–power gains (i.e., 19.8 [3.5] (mL·min−1)·W−1). Of interest, this · VO2–power gain from incremental tread- mill running, measured using Stryd running power, is similar to previously observed · VO2–power gain measured during 15 W·min−1 incremental cycling protocols (i.e., 11.3 [1.2] (mL·min−1)·W−1) [29]. 4.3. Stryd Running Power and Running Fitness Runners with greater MLSS running powers displayed greater · VO2 and running speeds at the MLSS (Figure 6). As the · VO2 and running speed associated with the MLSS are strong predictors of running performance [41,42], at least in samples with broad aerobic ﬁtness ranges, it appears that the Stryd running power metric can be used to indicate ﬁtness in a similar manner to that used for cycling PO from constant-intensity exercise [43,44]; however, in contrast to cycling, where the cycling speed at any given PO is primarily dictated by surface area and aerodynamics [45], body mass has a more substantial inﬂuence on the relationship between Stryd running power and running speed. Thus, while absolute Stryd running power may be used to estimate ﬁtness in terms of absolute · VO2 at the MLSS, in order to evaluate ﬁtness from a speed perspective, it is best to interpret Stryd running power relative to body mass or to only interpret the speed–power relationship relative to the individual. Previous investigations have also reported strong associations between Stryd assessments of critical power (CP) and ﬁtness metrics such as the RCP and · VO2max [38,46], providing further support for the utility of Stryd to quantify running ﬁtness. 4.4. Stryd Running Power, Running Economy, and Mechanical Efﬁciency Although strong associations were observed between absolute running power and · VO2 during constant-speed treadmill running conditions, there was a degree of variabil- ity between the measured · VO2 for a given absolute running power (Figure 3). A large proportion of this variance may be explained by the range of StrydMET requirements for a speciﬁc metabolic power between runners (Figure 4). Indeed, runners with greater absolute StrydMET measurements also exhibited greater metabolic power measures during each constant-speed running intensity test (i.e., MOD and near to the MLSS). Although this ﬁnding may suggest that Stryd can be used as an indication of RE, the strong relationship between running power and running speed likely explains this ﬁnding. Accordingly, when examining the relationship between metabolic power and StrydMECH (i.e., determining whether the Stryd running power metric can be used in isolation from energy expenditure to approximate the RE), there is no indication that StrydMECH is related to RE (i.e., metabolic power), suggesting that this approach cannot distinguish between more and less economi- cal runners. Previous investigations have similarly concluded that Stryd running power metrics may be insufﬁcient for detecting differences in RE between trained runners [46,47] or detecting worsened RE (i.e., increased · VO2 at a given running speed) after purposefully altering running biomechanics [37]. Our Stryd-derived measures of mechanical efﬁciency (~21–25%) are lower than pre- vious estimates of “apparent” running mechanical efﬁciency during level running (e.g., ~50–70%) [6,7,39] but are similar to estimates of gross cycling efﬁciency (e.g., ~20–25%) [4]. Furthermore, in comparison with the up to ~20% difference in previously reported esti- mates of running efﬁciency measurements at various running speeds [6,7,39], the Stryd estimates of running mechanical efﬁciency for level running during MOD and heavy- Sensors 2023, 23, 8729 16 of 19 intensity running were relatively small (i.e., ~4%). Consequently, our results indicate that Stryd-based measures of mechanical running efﬁciency remain relatively stable at various submaximal intensities and that the metabolic requirement per unit of Stryd running power and the metabolic requirement per unit of cycling PO are similar. Despite certain limitations related to the accurate detection of changing RE [37,47] and the quantiﬁcation of running mechanical PO [39], our data suggest that foot-worn running power metrics can still be used to monitor training and quantify running performance. Although these ﬁndings do question the ability of Stryd running power to accurately represent the running mechanical PO, we suggest that a wearable running power device need not evaluate running power in a manner that is true to the deﬁnition of mechanical PO to be useful. Indeed, as the relationship between metabolic demand and measurements of running mechanical PO may vary with running speed, incline, and surface [6–8], a running training tool that provides a consistent and seemingly equivalent evaluation of metabolic demand may be more useful than one that evaluates external work rate, particularly for such applied uses. 4.5. Experimental Considerations Several limitations warrant discussion. Firstly, as all testing was performed on a treadmill with a ﬁxed incline (1%), it remains unknown whether our ﬁndings can be extended to outdoor running conditions under variable running gradients, surfaces, or air resistances. With varying inclines, Stryd has shown evidence of repeatability [11] and strong correlations with · VO2 [11,18,39], but the inﬂuence of variable running gradients and surfaces on metabolic cost requires further investigation. Secondly, it remains unknown whether Stryd power can adjust for changes in air resistance, such as changes in wind speed. For example, changes in air resistance (e.g., wind, drafting, or drag) impact the cycling · VO2–speed relationship [45] without inﬂuencing the · VO2–PO relationship. As Stryd seemingly derives its estimate of running power by quantifying positive changes in vertical displacement and horizontal velocities, whether it can account for the increases in mechanical PO required to overcome greater air resistance is unclear [13]. Despite evidence that Stryd may detect changes in wind speed [48] and has introduced a metric, “Air power”, to adjust running power based on changes in air resistance from increasing or decreasing wind speeds and/or running speeds [49], it remains unknown whether the Stryd power metric– · VO2 relationship is linear in uncontrolled environments. 5. Conclusions A wide variety of internal and external load-monitoring methods have been used in endurance sports, such as running speed and pace, RPE, [BLa], HR, step count, step frequency, and distance [50]; however, none of these variables provide a continuous, instan- taneous, and reliable method to measure training intensity, and imprecise measurements of training stress may negatively affect performance and elevate injury risk. With evidence of stability, reliability, and sensitivity, our study suggests that Stryd’s foot-worn wearable device can be used to monitor training intensity and quantify aerobic ﬁtness. While the impact of variable running gradients, surfaces, and air resistance on the Stryd running power metric still needs to be assessed, our results support the use of Styrd running power to delineate exercise intensity domains, guide training intensity, and assess aerobic ﬁtness during level treadmill running. Supplementary Materials: The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/s23218729/s1, Table S1: Mean physiological and perceptual responses to exercise near the maximal lactate steady state (MLSS), and indices of reliability between two runs at the MLSS. Sensors 2023, 23, 8729 17 of 19 Author Contributions: Conceptualization: C.R.v.R. and M.J.M.; data curation: C.R.v.R. and M.J.M.; formal analysis: C.R.v.R., J.K.G. and M.J.M.; funding acquisition: M.J.M.; investigation: C.R.v.R., O.O.A. and K.M.S.; methodology: all authors; project administration: C.R.v.R. and M.J.M.; resources: M.J.M.; software: M.J.M.; supervision: M.J.M.; validation: C.R.v.R., O.O.A., K.M.S. and M.J.M.; visualization: C.R.v.R., J.K.G. and M.J.M.; writing—original draft: C.R.v.R. and M.J.M.; writing—reviewing and editing: all authors. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by an operating grant from the Natural Sciences and Engineering Research Council of Canada (NSERC; grant number RGPIN-2018-06424) and start-up funding from the Faculty of Kinesiology (University of Calgary) received by M.J.M. C.V.R. was funded by NSERC, the NSERC CREATE Wearable Technology and Collaboration (We-TRAC) Training Program, an Al- berta Innovates Graduate Student Scholarship for Data-Enabled Innovation, and an Alberta Graduate Excellence Scholarship. OOA was also funded by NSERC and the NSERC CREATE We-TRAC. The authors would like to acknowledge the contributions of all participants, students, faculty, and staff, who assisted and made this investigation possible. Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, except for pre-registration of the trial, and was approved by the University of Calgary Conjoint Health Research Ethics Board (REB20-0111, approved 29 July 2020). Informed Consent Statement: Informed consent was obtained from all participants involved in the study. Data Availability Statement: Individual data are shown where possible; however, other data are available from the corresponding author upon reasonable request. Conﬂicts of Interest: M.J.M. has received several foot pods from Stryd for research purposes; how- ever, that equipment was not used in this study. Stryd had no involvement in the conduct, analysis, or reporting of this study, and M.J.M. has no professional relationship with Stryd. All authors declare no competing interests. The funders of this study (NSERC, University of Calgary) had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. References 1. Williams, K.R.; Cavanagh, P.R. A model for the calculation of mechanical power during distance running. J. Biomech. 1983, 16, 115–128. [CrossRef] [PubMed] 2. 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Quantifying Training Intensity and Aerobic Fitness Using Stryd Running Power Near the Maximal Lactate Steady State.	10-26-2023	van Rassel, Cody R,Ajayi, Oluwatimilehin O,Sales, Kate M,Griffiths, James K,Fletcher, Jared R,Edwards, W Brent,MacInnis, Martin J	eng
PMC7552741	sports Article Physiological and Race Pace Characteristics of Medium and Low-Level Athens Marathon Runners Aristides Myrkos 1, Ilias Smilios 1,* , Eleni Maria Kokkinou 1, Evangelos Rousopoulos 2* and Helen Douda 1 1 School of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece; aris7tefaa@gmail.com (A.M.); ekokkino@phyed.duth.gr (E.M.K.); edouda@phyed.duth.gr (H.D.) 2 Ergoscan Physical Performance Evaluation Center, Ionias 110, 17456 Alimos, Greece; vrousso@gmail.com * Correspondence: ismilios@phyed.duth.gr; Tel.: +30-2531039723 Received: 6 July 2020; Accepted: 19 August 2020; Published: 21 August 2020 Abstract: This study examined physiological and race pace characteristics of medium- (ﬁnish time < 240 min) and low-level (ﬁnish time > 240 min) recreational runners who participated in a challenging marathon route with rolling hills, the Athens Authentic Marathon. Fifteen athletes (age: 42 ± 7 years) performed an incremental test, three to nine days before the 2018 Athens Marathon, to determine maximal oxygen uptake (VO2 max), maximal aerobic velocity (MAV), energy cost of running (ECr) and lactate threshold velocity (vLTh), and were analyzed for their pacing during the race. Moderate- (n = 8) compared with low-level (n = 7) runners had higher (p < 0.05) VO2 max (55.6 ± 3.6 vs. 48.9 ± 4.8 mL·kg−1·min−1), MAV (16.5 ± 0.7 vs. 14.4 ± 1.2 km·h−1) and vLTh (11.6 ± 0.8 vs. 9.2 ± 0.7 km·h−1) and lower ECr at 10 km/h (1.137 ± 0.096 vs. 1.232 ± 0.068 kcal·kg−1·km−1). Medium-level runners ran the marathon at a higher percentage of vLTh (105.1 ± 4.7 vs. 93.8 ± 6.2%) and VO2 max (79.7 ± 7.7 vs. 68.8 ± 5.7%). Low-level runners ran at a lower percentage (p < 0.05) of their vLTh in the 21.1–30 km (total ascent/decent: 122 m/5 m) and the 30–42.195 km (total ascent/decent: 32 m/155 m) splits. Moderate-level runners are less aﬀected in their pacing than low-level runners during a marathon route with rolling hills. This could be due to superior physiological characteristics such as VO2 max, ECr, vLTh and fractional utilization of VO2 max. A marathon race pace strategy should be selected individually according to each athlete’s level. Keywords: endurance; aerobic performance; lactate threshold; running economy; maximal oxygen consumption; oxygen fractional utilization; running 1. Introduction Marathon running is one of the most demanding races which requires well-organized mental and physical preparation [1]. Today, marathon races have turned into very large events where thousands of elite, high-level and recreational athletes participate in this 42.195 m race [2–4]. For many years, the physiological demands of a marathon as well as the physiological characteristics of top-class athletes were examined by researchers [5–11]. It is known that the most important parameters to sustain the highest possible running velocity over a marathon are the maximal oxygen uptake (VO2 max), a high fractional utilization of VO2 max and the energy cost of running (ECr) [7,12,13]. These parameters explain 70% of the variance of the average running speed sustained during a marathon race [6,7] and are good indicators of the endurance performance of individuals of diﬀerent ages, genders and disciplines [1]. A typical VO2 max value for male top-class marathoners is about 70–85 mL/kg/min, for low-level athletes around 65 mL/kg/min and for recreational runners about 51–58 mL/kg/min [14–16]. Additionally, oxygen fractional utilization at lactate threshold (LTh) intensity, the point where blood lactate concentrations increase from baseline, is higher for top-class marathoners compared with Sports 2020, 8, 116; doi:10.3390/sports8090116 www.mdpi.com/journal/sports Sports 2020, 8, 116 2 of 10 low-level athletes (65–80% vs. 50–80% of VO2 max, respectively) and is also higher at the lactate turn-point (LTP), the point where an abrupt increase in blood lactate is observed (85–90% vs. 80–85% of VO2 max, respectively) [6,11,12,17,18]. Few studies have examined in more detail the physiological characteristics of recreational marathon runners, with ﬁnishing times >3 h, and how these characteristics aﬀect performance in this group of runners. It was shown that the better the level of recreational marathoners, the higher the VO2 max as well as the velocity and the VO2 at LTh [2,19]. No diﬀerences were observed between the diﬀerent level of runners in the LTh expressed as a percentage of VO2 max and the oxygen cost of running at LTh [2]. Regarding medium- and low-level recreational runners, however, no data exist about the correspondence of race pace on the blood lactate curve, the fractional utilization of VO2 max at race pace and if these diﬀer according to the performance ability of the runners. Most of the studies examined runners who participated in a marathon ran on a ﬂat terrain where they could sustain a relatively stable pace till the end of the race, although the lower the level of the runners, the higher the variability in race pace [20]. The peculiarity of the terrain could be an external factor that may aﬀect the physiological and race pace characteristics of a marathon race. The terrain at one of the most famous and challenging marathons in the world, the Athens Authentic Marathon, is characterized by rolling hills and includes the toughest uphill climb of any major marathon. The total ascent is 317 m (51.2% of the route is uphill), the total descent is 262 m (40.5% of the route is downhill) and the steepest grading ranges from −6.2 to 3.8% [21,22]. It is possible that the diﬃculty of the route may aﬀect diﬀerently the race pace characteristics of medium- and low-level recreational runners. A runner with a faster pace will cross the hill segments in a shorter amount of time compared with a slower runner, altering probably the physiological requirements of the run. Therefore, recreational runners of diﬀerent levels may run the Athens Marathon at a rate corresponding to diﬀerent percentages of aerobic performance parameters. This may lead athletes and coaches to over- or underestimate the potential performance and to the determination of a false race pace strategy. Therefore, it would be useful to examine which are the physiological and race pace characteristics of medium- and low-level recreational athletes participating in the Athens Marathon and if they adopt diﬀerent pace characteristics in relation to their physiological proﬁle. Based on the above, the aim of the present study was to compare the physiological and race pace characteristics of medium- (ﬁnish time < 240 min) and low-level (ﬁnish time > 240 min) recreational runners who participated in the Athens Marathon. 2. Materials and Methods 2.1. Participants Fifteen recreational marathon runners (age: 42 ± 7 years, height: 174.9 ± 6.5 cm and body mass: 72.8 ± 6.9 kg) volunteered to take part in the study. All participants were healthy and ran approximately 1–2 years on a systematic basis with a structured program with an average weekly load of 50–60 km. Based on their ﬁnishing time at the Athens Marathon 2018, athletes were divided into a moderate-level group, with ﬁnishing times < 240 min (n = 8), and a low-level group with ﬁnishing times > 240 min (n = 7). Before the start of the study, the institutional review board committee approved the experimental protocol in accordance with the Helsinki Declaration. 2.2. Maximal Incremental Test Three to nine days before participation in the Athens Marathon 2018 race, participants performed a maximal incremental test on a treadmill (Technogym run race 1200, Italy) for the determination of VO2 max, maximum aerobic velocity (MAV), maximum heart rate (HRmax), the relationship between blood lactate concentration and running velocity, oxygen consumption and running velocity, heart rate and running velocity, and the energy cost of running. Sports 2020, 8, 116 3 of 10 The protocol started at 7 km·h−1 and was increased by 1.5 km·h−1 every 3 min until volitional exhaustion. Treadmill grade was set at 1% throughout the protocol. Gas exchange was measured by the open circuit Douglas bag method as described by Cooke (2009). The subject breathed through a low-resistance 2-way Hans-Rudolph 2700 B valve (Shawnee, OK, USA). The concentrations of CO2 and O2 in the expired air were measured by using the Hi-tech (GIR 250) combined Oxygen and Carbon Dioxide Analyzer. The gas analyzers were calibrated continuously against standardized gases (15.35% O2, 5.08% CO2 and 100% N2). Expired volume was measured by means of a dry gas meter (Harvard) previously calibrated against standard air ﬂow with a 3 L syringe. Barometric pressure and gas temperature were recorded and respiratory gas exchange data for each work load (i.e., VO2, VCO2 and VE) were determined based on the computations described by Cooke [23] when VEatps, FECO2 and FEO2 are known. The highest VO2 value obtained during a 30-sec time period during the incremental exercise test was recorded as the subject’s . VO2max. HR was continuously measured telemetrically (Polar RS400) and the highest 10 sec value was regarded as maximal. The test was considered as maximal when at least 3 of the following criteria were achieved: (a) visual exhaustion of the participants, (b) a plateau in oxygen consumption (<2 mL kg−1·min−1) despite an increase in running velocity, (c) maximal HR higher than 90% of the predicted maximum (220-age) and (d) maximum respiratory exchange ratio > 1.1. MAV was calculated using the following formula: MAV (km·h−1) = Velocity of the last completed stage + (seconds run at last stage/180). 2.3. LTh and LTP Determination At the end of each stage during the incremental test, approximately 0.3 µL of whole blood was collected from the ﬁngertip and immediately analyzed for lactate concentration with a portable analyzer (Lactate Pro 2, Arkray Factory Inc., Koka-Shi, Japan) using an enzymatic-amperometric method. The individual relationships between blood lactate concentrations and running velocities were determined using an exponential model: y = a + b × exp(x/c), where y = lactate concentration, x = running velocity and a, b and c are constants. The LTh and the LTP were identiﬁed as the velocities (km·h−1) at which blood lactate concentrations were increased by 0.3 and 1.5 mmol·L−1 from baseline values, respectively. Furthermore, LTh and LTP were expressed relative to MAV (%MAV) units, and based on the relationship between VO2 and running velocity were also expressed in absolute (mL·kg−1·min−1) and relative (%VO2 max) VO2 max values. 2.4. Energy Cost of Running The gas exchange data (VO2, VCO2) collected during the ﬁnal 30 s of every 3-min stage up to the previous stage from the LTP were used for the calculations of the caloric cost of running. Substrate oxidation rate (g·min−1) was estimated using nonprotein respiratory quotient equations [24]: Fat oxidation (g·min−1) = 1.6947 × VO2 (L·min−1) − 1.7012 × VCO2 (L·min−1) Carbohydrate oxidation (g·min−1) = 4.5851 × VCO2 (L·min−1) − 3.22259 × VO2 (L·min−1) The energy produced from each substrate was calculated by assuming an energy equivalent for 1 g of fat and carbohydrate of 9.75 and 4.07 kcal, respectively [25]. Total ECr was quantiﬁed from the sum of these values and was expressed in kcal·kg−1·km−1. The energy cost of running at 10 km/h and at the velocities corresponding to LTh and marathon race pace were estimated from the relationship between exergy cost and running velocity derived from the incremental test. All the physiological data were analyzed after the completion of the marathon race to avoid any pacing strategies from the participants and their coaches based on the results of testing. 2.5. Route Characteristics and Race Pace Analysis The proﬁle of the Athens Marathon route includes rolling uphills and downhills. More speciﬁcally, when calculated in 450 m intervals, the total ascent, total descent, the percent of uphill distance, the percent of downhill distance and the steepest uphill and downhill, respectively, are for: (a) the total route: 317 m, 262 m, 51.2%, 40.5%, 3.8% and −6.2%, (b) the 0–10 km split: 19 m, 36 m, 36.4%, 50%, 1.3% Sports 2020, 8, 116 4 of 10 and −2.0%, (c) the 10–21.1 km split: 143 m, 66 m, 66.7%, 25%, 3.6% and −6.2%, (d) the 21.1–30 km split: 122 m, 5 m, 95%, 5%, 3.3% and −1.1% and (e) the 30–42.195 km split: 32 m, 155 m, 15.4%, 76.9%, 3.8% and −5.1% [22]. Finishing time and split times for each participant were exported from the oﬃcial results posted on the site of the organization [21]. The average running velocity of each runner was calculated by dividing marathon distance to the time needed to complete the race. Race pace was expressed as a percentage of VO2 max (index of fractional utilization of VO2 max), MAV and the velocities at LTh (vLTh) and LTP (vLTP). To determine the diﬀerences between the two groups in pacing during the race, average running velocities for the distances of 0–10, 10–21.1, 21.1–30 and 30–42.195 km were calculated by dividing the distance of the split to the time to complete the split. For the analysis of the data, mean velocity of each split was expressed as a percentage of the vLTh. The velocity at LTh was selected as a reference point because for the whole sample, average marathon running velocity was equal to vLTh. 2.6. Statistical Analysis All data are presented as means ± SD. Normality of the distribution of the data was examined with the Shapiro–Wilk’s W test. A t-test was used to examine the diﬀerences among the medium-level and the low-level runners in the physiological parameters and race pace characteristics measured. A two-way analysis of variance with repeated measures in the second factor was used to examine the diﬀerences between the two groups in the mean velocity of each running split (0–10, 10–21.1, 21.1–30 and 30–42.195 km). Signiﬁcant diﬀerences between means were located with the Newman–Keuls post hoc test. Pearson product moment correlations were used to determine the association between marathon time and the measured parameters. The statistical signiﬁcance level was set for all tests at p < 0.05. 3. Results 3.1. Physiological Characteristics Medium-level runners had higher (p < 0.05) VO2 max, MAV, LTh (km·h−1), LTh (%MAV), LTh (mL·kg−1·min−1), LTP (km·h−1), LTP (%MAV) and LTP (mL·kg−1·min−1) than the low-level group. There were no signiﬁcant diﬀerences (p > 0.05) between groups at HRmax, LTh (%VO2 max) and LTP (%VO2 max) (Table 1). Medium-level runners had lower ECr at 10 km·h−1 (p = 0.05), at vLTh (p = 0.07) and at marathon race pace (p = 0.09) (Table 1). Table 1. Physiological and race pace characteristics of the medium-level, the low-level and of all runners. Medium-Level Runners Low-Level Runners All Runners p Value between Groups Age (years) 41.00 ± 7.69 42.14 ± 7.20 41.53 ± 7.22 0.36 Body height (m) 175.00 ± 6.44 174.71 ± 7.06 174.87 ± 6.49 0.94 Body mass (kg) 72.50 ± 6.58 73.17 ± 7.91 72.81 ± 6.97 0.86 VO2 max (mL·kg−1·min−1) 55.56 ± 3.62 48.85 ± 4.77 52.43 ± 5.32 0.01 MAV (km·h−1) 16.45 ± 0.74 14.39 ± 1.24 15.49 ± 1.44 0.01 HRmax (b·min−1) 178.25 ± 9.54 183.71 ± 9.25 180.80 ± 9.5 0.28 vLTh (km·h−1) 11.58 ± 0.81 9.22 ± 0.72 10.48 ± 1.42 0.01 vLTP (km·h−1) 13.6 ± 0.87 11.1 ± 0.8 12.43 ± 1.52 0.01 vLT1% (%MAV) 70.37 ± 3.9 64.25 ± 4.15 67.51 ± 5.00 0.01 vLT2% (%MAV) 82.7 ± 4.83 77.37 ± 4.52 80.21 ± 5.29 0.05 Sports 2020, 8, 116 5 of 10 Table 1. Cont. Medium-Level Runners Low-Level Runners All Runners p Value between Groups VO2 LTh (mL·kg−1·min−1) 41.76 ± 1.81 35.11 ± 2.80 38.65 ± 4.10 0.01 VO2 LTP (mL·kg−1·min−1) 48.04 ± 2.41 40.80 ± 3.78 44.66 ± 4.80 0.01 %VO2 LTh (%VO2 max) 75.31 ± 3.64 72.14 ± 5.87 73.83 ± 4.91 0.22 %VO2 LTP (%VO2 max) 86.59 ± 3.90 83.63 ± 4.08 85.21 ± 4.13 0.17 ECr 10 km·h−1 (kcal·kg−1·km−1) 1.137 ± 0.096 1.232 ± 0.068 1.181 ± 0.096 0.05 ECr vLTh (kcal·kg−1·km−1) 1.157 ± 0.079 1.232 ± 0.066 1.192 ± 0.081 0.07 ECr Race Pace (kcal·kg−1·km−1) 1.160 ± 0.083 1.232 ± 0.068 1.194 ± 0.082 0.09 Race pace (km·h−1) 12.14 ± 0.60 8.63 ± 0.64 10.50 ± 1.91 0.01 Race Pace (%MAV) 73.82 ± 2.60 60.11 ± 3.13 67.42 ± 7.59 0.01 Race Pace (%vLTh) 105.08 ± 4.71 93.80 ± 6.20 99.82 ± 7.84 0.01 Race Pace (%vLTP) 89.45 ± 4.47 77.92 ± 6.14 84.07 ± 7.85 0.01 Race Pace (%HRmax) 83.91 ± 5.8 77.41 ± 5.40 80.87 ± 6.37 0.04 Race Pace (%VO2 max) 79.74 ± 7.65 68.80 ± 5.73 74.63 ± 8.68 0.01 HRmax: maximum heart rate, MAV: maximal aerobic velocity, vLTh: velocity (km·h−1) at lactate threshold, vLTP: velocity (km·h−1) at lactate turn-point, ECr: energy cost of running. 3.2. Race Pace Characteristics Medium-level runners had, by design, a lower (p < 0.05) marathon time (209.0 ± 10.4 min, range: 194–225 min) than the low-level runners (289.7 ± 25.1 min, range: 260–328 min). Marathon ﬁnishing time was not related to the number of days between the maximal incremental test and the race day (Figure 1). Medium-level runners had a higher (p < 0.05) race pace expressed as %MAV, %vLTh, %vLTP, %VO2 max and %HRmax (Table 1). Medium- and low-level runners had a similar (p > 0.05) race pace (expressed as %vLTh) at the ﬁrst two running splits (0–10 and 10–21.1 km). However, low-level runners had a lower (p < 0.05) race pace at the last two splits (21.1–30 and 30–42.195 km) compared to the medium-level runners (Figure 2). Sports 2020, 8, x FOR PEER REVIEW 5 of 10 %VO2 LTP (%VO2 max) 86.59 ± 3.90 83.63 ± 4.08 85.21 ± 4.13 0.17 ECr 10 km·h−1 (kcal·kg−1·km−1) 1.137 ± 0.096 1.232 ± 0.068 1.181 ± 0.096 0.05 ECr vLTh (kcal·kg−1·km−1) 1.157 ± 0.079 1.232 ± 0.066 1.192 ± 0.081 0.07 ECr Race Pace (kcal·kg−1·km−1) 1.160 ± 0.083 1.232 ± 0.068 1.194 ± 0.082 0.09 Race pace (km·h−1) 12.14 ± 0.60 8.63 ± 0.64 10.50 ± 1.91 0.01 Race Pace (%MAV) 73.82 ± 2.60 60.11 ± 3.13 67.42 ± 7.59 0.01 Race Pace (%vLTh) 105.08 ± 4.71 93.80 ± 6.20 99.82 ± 7.84 0.01 Race Pace (%vLTP) 89.45 ± 4.47 77.92 ± 6.14 84.07 ± 7.85 0.01 Race Pace (%HRmax) 83.91 ± 5.8 77.41 ± 5.40 80.87 ± 6.37 0.04 Race Pace (%VO2 max) 79.74 ± 7.65 68.80 ± 5.73 74.63 ± 8.68 0.01 HRmax: maximum heart rate, MAV: maximal aerobic velocity, vLTh: velocity (km·h−1) at lactate threshold, vLTP: velocity (km·h−1) at lactate turn-point, ECr: energy cost of running. 3.2. Race Pace Characteristics Medium-level runners had, by design, a lower (p < 0.05) marathon time (209.0 ± 10.4 min, range: 194–225 min) than the low-level runners (289.7 ± 25.1 min, range: 260–328 min). Marathon finishing time was not related to the number of days between the maximal incremental test and the race day (Figure 1). Medium-level runners had a higher (p < 0.05) race pace expressed as %MAV, %vLTh, %vLTP, %VO2 max and %HRmax (Table 1). Medium- and low-level runners had a similar (p > 0.05) race pace (expressed as %vLTh) at the first two running splits (0–10 and 10–21.1 km). However, low- level runners had a lower (p < 0.05) race pace at the last two splits (21.1–30 and 30–42.195 km) compared to the medium-level runners (Figure 2). Figure 1. Plot of marathon finishing time vs. number of days between maximal incremental test and race day for the low-(squares) and the medium-level runners (triangles). Figure 1. Plot of marathon ﬁnishing time vs. number of days between maximal incremental test and race day for the low-(squares) and the medium-level runners (triangles). Sports 2020, 8, 116 6 of 10 Sports 2020, 8, x FOR PEER REVIEW 6 of 10 Figure 2. Race pace, expressed as a percentage of the velocity at lactate threshold (%vLTh), at the running splits of 0–10, 10–21.1, 21.1–30 and 30–42.195 km (total ascent in meters/total decent in meters) of the Athens Marathon, for the low-level, the medium-level and all runners. a: p < 0.05 significant difference between low- and medium-level runners, b: p < 0.05 significantly different from the 0–10 and 10–21.1 km splits for the low-level runners. 3.3. Correlation between Marathon Time and Measured Variables Marathon finish time correlated significantly (p < 0.05) with VO2 max (r = −0,76), MAV (r = −0.88), vLTh (km·h−1; r = −0.91), vLTP (km·h−1; r = −0.88), LTh (%MAV; r = −0.58), LTh (mL·kg−1·min−1; r = −0.86), LTP (mL·kg−1·min−1; r = −0.80), ECr 10 km·h−1 (r = 0.62), ECr vLTh (r = 0.59), ECr race pace (r = 0.55), race pace (%VO2 max; r = −0.62), race pace (%vLTh; r = −0.75), race pace (%vLTP; r = −0.81) and race pace (%MAV; r = −0.90). Marathon finish time did not correlate significantly (p > 0.05) with LTP (%MAV; r = −0.38), LTh (%VO2 max; r = −0.22) and LTP (%VO2 max; r = −0.21). 4. Discussion The purpose of this study was to provide further insight into the physiological and race pace characteristics of medium- and low-level marathon runners with a completion time < 240 min and > 240 min, respectively, of the Athens Authentic Marathon. This marathon race is famous, not only for historical reasons but also for its level of difficulty due to the peculiarity of the terrain. The results of the present study show that recreational medium-level runners compared to lower-level runners have: (a) higher VO2 max, MAV and lactate threshold values in absolute velocity (km·h−1) and VO2 (mL·kg−1·min−1) units, (b) higher lactate threshold in relative velocity units (%MAV), (c) lower energy cost of running at 10 km/h and (d) adopt a race pace corresponding to a higher percentage of their lactate threshold velocity and fractional utilization of VO2 max and show no significant alterations in their pace due to terrain alterations in contrast to the low-level runners to whom the uphill part of the race leads to great reductions in race pace. Previous studies have examined the importance of physiological parameters and race pace characteristics of elite marathoners, but few studies provide data for recreational runners [2,19,20]. Maximal oxygen consumption, a high fractional utilization of VO2 max and the energy cost of running are considered the determinants of endurance performance [26]. Indeed, in the present study the medium-level runners had higher VO2 max than the low-level runners. This agrees with previous reports where the better level marathoners had higher VO2 max than the lower level [2,7,18,19]. The VO2 max values of the medium-level marathoners (55.56 ± 3.62 mL/kg/min) measured in the present Figure 2. Race pace, expressed as a percentage of the velocity at lactate threshold (%vLTh), at the running splits of 0–10, 10–21.1, 21.1–30 and 30–42.195 km (total ascent in meters/total decent in meters) of the Athens Marathon, for the low-level, the medium-level and all runners. a: p < 0.05 signiﬁcant diﬀerence between low- and medium-level runners, b: p < 0.05 signiﬁcantly diﬀerent from the 0–10 and 10–21.1 km splits for the low-level runners. 3.3. Correlation between Marathon Time and Measured Variables Marathon ﬁnish time correlated signiﬁcantly (p < 0.05) with VO2 max (r = −0,76), MAV (r = −0.88), vLTh (km·h−1; r = −0.91), vLTP (km·h−1; r = −0.88), LTh (%MAV; r = −0.58), LTh (mL·kg−1·min−1; r = −0.86), LTP (mL·kg−1·min−1; r = −0.80), ECr 10 km·h−1 (r = 0.62), ECr vLTh (r = 0.59), ECr race pace (r = 0.55), race pace (%VO2 max; r = −0.62), race pace (%vLTh; r = −0.75), race pace (%vLTP; r = −0.81) and race pace (%MAV; r = −0.90). Marathon ﬁnish time did not correlate signiﬁcantly (p > 0.05) with LTP (%MAV; r = −0.38), LTh (%VO2 max; r = −0.22) and LTP (%VO2 max; r = −0.21). 4. Discussion The purpose of this study was to provide further insight into the physiological and race pace characteristics of medium- and low-level marathon runners with a completion time < 240 min and > 240 min, respectively, of the Athens Authentic Marathon. This marathon race is famous, not only for historical reasons but also for its level of diﬃculty due to the peculiarity of the terrain. The results of the present study show that recreational medium-level runners compared to lower-level runners have: (a) higher VO2 max, MAV and lactate threshold values in absolute velocity (km·h−1) and VO2 (mL·kg−1·min−1) units, (b) higher lactate threshold in relative velocity units (%MAV), (c) lower energy cost of running at 10 km/h and (d) adopt a race pace corresponding to a higher percentage of their lactate threshold velocity and fractional utilization of VO2 max and show no signiﬁcant alterations in their pace due to terrain alterations in contrast to the low-level runners to whom the uphill part of the race leads to great reductions in race pace. Previous studies have examined the importance of physiological parameters and race pace characteristics of elite marathoners, but few studies provide data for recreational runners [2,19,20]. Maximal oxygen consumption, a high fractional utilization of VO2 max and the energy cost of running are considered the determinants of endurance performance [26]. Indeed, in the present study the medium-level runners had higher VO2 max than the low-level runners. This agrees with previous reports where the better level marathoners had higher VO2 max than the lower level [2,7,18,19]. Sports 2020, 8, 116 7 of 10 The VO2 max values of the medium-level marathoners (55.56 ± 3.62 mL/kg/min) measured in the present study are approximately the same (55.7 ± 4.8) as those reported by Gordon et al. [2] for athletes who ran the marathon between 3:00 and 3:30 h as in the present study. The same holds even for the low-level runners (VO2 max: 48.85 ± 4.77 mL/kg/min; ﬁnish time: 4:00–5:30 h) of the present study and runners with approximately similar ﬁnishing times in Gordon et al.’s [2] (VO2 max: 46.5 ± 5.2 mL/kg/min; ﬁnish time: >4:30 h) and Chmura et al.’s [14] (VO2 max: 51 ± 2 mL/kg/min; ﬁnish time: 4:17 ± 10.51 min) studies. It appears that certain levels of VO2 max are necessary to achieve certain marathon times regardless of the level of the runner since VO2 max determines the upper limit of aerobic performance. The high correlation between VO2 max and marathon performance which has been previously reported for high-level to elite athletes [7,18,27,28] supports this notion. A large correlation (r = −0.76) between VO2 max and marathon performance was observed as well in the present study for medium- to low-level marathon runners, enriching the limited information available for recreational athletes [2,19]. For most sports scientists, running economy or energy cost of running is a key factor for performance in long distance events and becomes more important as running distance increases [29–32]). In the present study, we examined ECr at a speciﬁc speed (10 km/h) and at the vLTh and we found that medium-level runners had lower ECr than the lower-level runners. This was probably another factor that allowed them to run the marathon at a faster pace. It should be noted that the ECr in the medium-level runners tended to be lower in the race pace as well. This is of importance considering that the medium-level runners sustained a faster running pace. Furthermore, ECr at 10 km/h and at vLTh had large correlations with marathon time (r = 0.62 and r = 0.59, respectively). The results of the current study reveal that running economy is a determinant of performance even for recreational runners with limited training experience and supports the suggestion in the literature that athletes should focus their training on the optimization of this parameter as well [32–34]. Besides the importance of VO2 max and energy cost of running for marathon performance, stronger associations are observed between maximal aerobic velocity and the velocities at the lactate threshold or any point on the blood lactate curve, and endurance performance [35–37]. Similarly, very large correlations were observed in this study between marathon time and velocities at LTh and LTP (r = −0.91 and −0.88) and MAV (r = −0.88) of recreational runners. The velocity at LTh was the stronger single predictor of marathon ﬁnish time. This is not surprising considering that these indexes, when expressed in velocity units, encompass both VO2 max and running economy [37]. When LTh and LTP values were normalized to MAV and VO2 max, the relationship of these parameters with running performance became lower (r = −0.22 to −0.58). This is because the eﬀect of VO2 max and/or running economy was diminished [37]. Medium-level runners had higher LTh values, expressed either in velocity or VO2 units, than the lower-level runners. Even when LTh velocity was normalized to MAV, medium-level runners had a higher LTh, indicating a higher ability of the fat oxidation rate to meet ATP demands and the occurrence of a later increased stimulation of glycolysis and glycogenolysis relative to their maximum performance. This probably reﬂects a greater aerobic capacity and increased buﬀering capacity promoting the ability to achieve higher running velocities due to metabolic and/or locomotor reasons. Many studies declare that the fractional utilization of VO2 max at LTh, LTP and at race pace is one of the most crucial parameters of aerobic performance along with VO2 max and running economy [6,7,31]. Fractional utilization of VO2 max at LTh and LTP did not diﬀer between the two groups in the present study. Similarly, Gordon et al. [2] did not ﬁnd any diﬀerences in fractional utilization of VO2 max at LTh and LTP between recreational runners with diﬀerent marathon ﬁnish times. It could be that adaptations in the utilization of oxygen from working muscles may require a signiﬁcant amount of training load which was not achieved by our runners. In the present study, however, we found that medium-level runners had a higher fractional utilization of VO2 max at marathon race pace. This agrees with previous ﬁndings that in high-level athletes, increased levels of fractional utilization of VO2 max at marathon race pace were associated with faster performance [1,2,7]. Sports 2020, 8, 116 8 of 10 In addition, a positive correlation of fractional utilization at race pace and marathon time was found (r = −0.62), Therefore, our data reveal that even in recreational runners, fractional utilization of VO2 max at marathon race pace appears to be a contributing factor to performance. A main ﬁnding of the present study is that medium-level marathoners ran the marathon distance with an average speed corresponding to higher percentages of vLTh and MAV. The better running economy may allow them to adopt a higher running velocity. Furthermore, the higher LTh and LTP velocities mean that the medium-level runners will cover a given distance at a shorter time. This may allow them to run at a higher point on the blood lactate curve because they can sustain this pace for the time needed to complete the race. On the contrary, the slower LTh and LTP velocities of the low-level runners mean that they need to run for a longer time to complete the race having a lower fractional oxygen utilization. It has been shown that as the duration of an endurance event increases, fractional oxygen utilization decreases [38,39]. The lower running velocity of the low-level runners made them spend more time running the uphill part of the Athens Marathon course. The total ascent from the 21.1st km to the 30th km is about 122 m and almost all this split is uphill. This forced the low-level runners to adopt an even lower velocity during this part of the route. Indeed, the split analysis revealed that the low-level runners were more inﬂuenced by this uphill part than the medium level. It appears that this speciﬁc segment has the greatest impact in the ﬁnish time between diﬀerent levels of athletes and makes the Athens Marathon a totally diﬀerent terrain from other marathons. It is worth noting that even at the last part of the route, which is mostly downhill, low-level runners were not able to increase their speed. Probably, the accumulated fatigue after hours of running may increase even more the stress placed on the musculoskeletal system, besides that induced by the increased eccentric load during downhill running, which prevents an increase in running speed compared to the previous uphill part. Therefore, the peculiarity of the terrain may aﬀect diﬀerently the performance of a marathon runner depending on his/her ability level. This is of importance for coaches and athletes for the determination of the pace strategy to follow when running on a rolling hill terrain. An advantage of the present study is that all recreational runners participated in the same marathon race and not in diﬀerent ones. This makes comparisons between diﬀerent levels of runners more reliable since all of them competed in the same route, on the same day and under the same environmental conditions. In addition, physiological testing was performed at a time point very close to the race day (three–nine days before) providing valid data about the relationship of physiological determinants of endurance performance and actual marathon running performance. Limitations of the present study, though, should also be acknowledged. A larger sample size would have given more valid data about the diﬀerent levels of marathon runners. It was diﬃcult, however, to measure many runners at a time close to the actual race. Furthermore, the energy cost of running at 10 km/h and at the velocity corresponding to LTh was estimated from the relationship between exergy cost and running velocity derived from the incremental test. Measurements at the exact velocities would have given more precise values of the energy cost. Again, the execution of these submaximal measurements would have increased the time of testing and it would not be possible to perform them near the race date. 5. Conclusions The results of the present study enrich the existing literature regarding the physiological proﬁle and the race pace characteristics of recreational marathon runners competing in a diﬃcult route, the Athens Marathon. Medium-level runners (ﬁnish time range: 194–225 min) have higher VO2 max, lactate threshold values, better running economy, greater oxygen fractional utilization at race pace and adopt a faster race pace in relation to their lactate threshold velocity than low-level runners (ﬁnish time range: 260–328 min). Furthermore, medium-level runners show no signiﬁcant alterations in their pace due to terrain alterations in contrast to the low-level runners to whom the uphill part of the race leads to great reductions in race pace. Therefore, slower runners are more inﬂuenced by a hilly terrain and they decrease more their running velocity to complete this part of the race. Thus, careful planning of race pace should be considered so that pacing of the parts before the uphill would Sports 2020, 8, 116 9 of 10 be of such an intensity to avoid a large decrease in running velocity at uphill. Therefore, besides the focus on training for the improvement of important physiological parameters related to endurance performance, it is recommended that the selected race pace strategy be applied individually according to each athlete’s level. Author Contributions: Conceptualization, A.M. and I.S.; methodology, A.M., I.S., E.M.K., E.R. and H.D.; investigation, E.M.K. and E.R.; data analysis, A.M., I.S., E.M.K. and E.R.; writing—original draft preparation, A.M., I.S. and H.D.; writing—review and editing, A.M., I.S. and H.D.; supervision, I.S. and H.D. All authors have read and agreed to the published version of the manuscript. 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Physiological and Race Pace Characteristics of Medium and Low-Level Athens Marathon Runners.	08-21-2020	Myrkos, Aristides,Smilios, Ilias,Kokkinou, Eleni Maria,Rousopoulos, Evangelos,Douda, Helen	eng
PMC7085788	sensors Article Muscle Activation in Middle-Distance Athletes with Compression Stockings Diego Moreno-Pérez 1, Pedro J. Marín 2, Álvaro López-Samanes 3,* , Roberto Cejuela-Anta 4 and Jonathan Esteve-Lanao 5 1 Departament of Education, Research and Evaluation Methods, Comillas Pontiﬁcal University, 28015 Madrid, Spain; dmperez@comillas.edu 2 CYMO Research Institute, 47140 Valladolid, Spain; pjmarin@checkyourmotion.com 3 School of Physiotherapy, Faculty of Health Sciences, Universidad Francisco de Vitoria, 28223 Madrid, Spain 4 Department of Physical Education and Sports, University of Alicante, 03690 Alicante, Spain; roberto.cejuela@ua.es 5 All in Your Mind Training System, Mérida 97134, Mexico; jonathan.esteve@allinyourmind.es * Correspondence: alvaro.lopez@ufv.es; Tel.: +34-91-709-14-00 (ext. 1955) Received: 18 January 2020; Accepted: 24 February 2020; Published: 26 February 2020 Abstract: The aim of this study was to evaluate changes in electromyographic activity with the use of gradual compression stockings (GCSs) on middle-distance endurance athletes’ performance, based on surface electromyography measurement techniques. Sixteen well-trained athletes were recruited (mean ± SD: age 33.4 ± 6.3 years, VO2max 63.7 ± 6.3 mL·kg−1·min−1, maximal aerobic speed 19.7 ± 1.5 km·h). The athletes were divided into two groups and were assigned in a randomized order to their respective groups according to their experience with the use of GCSs. Initially, a maximum oxygen consumption (VO2max) test was performed to standardize the athletes’ running speeds for subsequent tests. Afterward, electromyographic activity, metabolic, and performance variables for each group were measured with surface electromyography. In addition, blood lactate concentration was measured, both with and without GCSs, during 10 min at 3% above VT2 (second ventilatory threshold), all of which were performed on the track. Next, surface electromyography activity was measured during a 1 km run at maximum speed. No signiﬁcant changes were found in electromyography activity, metabolic and performance variables with GCSs use (p > 0.164) in any of the variables measured. Overall, there were no performance beneﬁts when using compression garments against a control condition. Keywords: surface electromyography; compression garment; gradual-elastic compression stockings; muscular fatigue; endurance athletes 1. Introduction Graduated compression stockings (GCSs) are socks that create a compressive pressure around the muscle, bone, and connective tissue, with this pressure higher in the ankle area and gradually decreasing until the knee [1]. In addition, compression garments were originally used to treat deep vein thrombosis [2] and venous insuﬃciencies [1,3]. Thus, several studies have demonstrated an increase in the venous velocity, a reduction of venous pooling, and improvement in venous return in hospital patients who wore GCSs [1,4]. Although there are no signiﬁcant changes in heart rate associated with the use of GCSs in endurance events, [5–8] the interest in the sports sciences ﬁeld in GCS application and commercialization is highly increasing [9]. A decrease in the concentration of metabolites associated with compression garment use may have beneﬁts during submaximal running eﬀorts [10]. Berry and McMurray [11] hypothesized that a Sensors 2020, 20, 1268; doi:10.3390/s20051268 www.mdpi.com/journal/sensors Sensors 2020, 20, 1268 2 of 8 reduced blood lactate concentration associated with the use of compression stockings could be due to greater blood ﬂow removal during exercise with CGSs. In addition, it has been described that GCSs lead to improvements in blood lactate concentration clearance during continuous sports, such as cycling [12]. However, other studies reported no changes in blood lactate concentration with the use of GCSs in endurance eﬀorts [6–8,13]. The diﬀerences between studies could be attributed to the diﬀerent methodologies used [6,7]. Thus, the beneﬁts of using GCSs while running are not entirely clear at the metabolic and cardiovascular levels. Moreover, the possible improvements in muscle recruitment with the use of GCSs during dynamic actions are unknown. One method to measure the ﬁber recruitment is electromyography (EMG). This type of measurement technique comprises the sum of the electrical contributions made by the active motor units (MUs), that are detected by electrodes placed on the skin overlying the muscle. The information extracted from the surface EMG is often considered a global measure of MU activity because of the inability of the traditional (two electrode) recording conﬁguration to detect activity at the level of single MUs, which allows the measurement of the electrical signal during a muscular action [14]. Raez et al. [15] deﬁned EMG as the acquisition, recording, and analysis of electrical activity produced by nerves and muscles through electrode surface electromyography, which is a noninvasive method that allows the evaluation of muscle recruitment during dynamic eﬀorts [16]. Most recent research on compression garments by means of EMG has mainly been focused on the relationship between intramuscular pressure and EMG responses during concentric isokinetic muscle contractions [17]. Likewise, previous research underlined the relationship between the use of compression garments and the perception of lower muscle pain [5,18], greater comfort, and a lower subjective perception of eﬀort (RPE; rating of perceived exertion) [18]. According to Varela-Sanz [13] there seems to be a tendency to run faster with a lower perception of eﬀort. If there are no clear metabolic or cardiovascular beneﬁts, the beneﬁts may be found in a change in muscle recruitment between the thigh muscle and the leg (i.e., triceps surae). The aim of this study is to evaluate changes in electromyographic activity with the use of the use of gradual compression stockings (GCSs) on middle-distance endurance athletes’ performance, based on surface electromyography measurement techniques. We hypothesize an improvement in submaximal (i.e., 10 min at 3% above VT2 (second ventilatory threshold)) and maximal conditions (i.e., 1 km at full speed) with the use of gradual compression stockings compared to control condition. 2. Materials and Methods 2.1. Participants Fourteen male and two female athletes reported to the laboratory three times with 72 h hours between protocols (mean ± SD, age 33.2 ± 7.2 years, VO2max 63.7 ± 6.3 mL·kg−1·min−1, maximal aerobic speed 19.7 ± 1.5 km·h−1, 4 min and 18 s at 1500 mL). All athletes had competed in the Spanish Track and Field Championships, and some of them had won medals at the National Track Veterans’ Championships. Before the beginning of the study, all subjects gave written informed consent in accordance with the Declaration of Helsinki [19]. The protocol was approved by the Ethics Committee of the University. The athletes were randomly assigned to either an experimental group, with GCSs (EXP), or control group without CGSs (CNT). There were no signiﬁcant changes in the descriptive variables between groups (p < 0.050). 2.2. Experimental Design Day 1: A maximum oxygen consumption (VO2max) test was performed in order to deﬁne the subjects’ running speeds for consecutive tests. After a standardized warm-up of 20 min of continuous running on a treadmill (Technogym Run Race 1400 HC, Gambettola, Italy) at 60% of their maximum heart rate and a block of dynamic warm-up [20], subjects performed a VO2max test with a gas analyzer Sensors 2020, 20, 1268 3 of 8 (VO2000, Medical Graphics Corporation, St. Paul, MN, USA). The variables that were measured were oxygen uptake (VO2), pulmonary ventilation (VE), ventilatory equivalents for oxygen (VE·VO2−1) and carbon dioxide (VE·CO2 VE·VO2−1), and end-tidal partial pressure of oxygen (PETO2) and carbon dioxide (PETCO2). VO2max was recorded as the highest VO2 value obtained for any continuous 30 s period during the test. The VT1 was determined using the criteria of an increase in both VE·VO2−1 and PETO2 with no increase in VE·VCO2−1, whereas the VT2 was determined using the criteria of an increase in both VE·VO2−1and VE·VCO2−1 and a decrease in PETCO2 [21]. Two independent observers detected VT1 and VT2. If there was disagreement, a third investigator was consulted. The maximal aerobic speed was associated with the last completed 30 s stage before the exhaustion, which was associated with VO2max [21]. The protocol started with a gradient of 1% at a speed of 10 km·h−1, with increments of 0.3 km·h−1 every 30 s until the maximum exhaustion [21]. The tests were performed in the Exercise Physiology Laboratory of the Universidad Europea de Madrid (i.e., 600 m altitude). All evaluations were performed at the same time of day (i.e., evening, between 7:00 p.m. and 9:00 p.m.) and under similar environmental conditions (i.e., 20–22 ◦C temperature, 60–65% relative humidity) to avoid eﬀects associated with circadian rhythms on performance [22]. Days 2 and 3: Each group had to perform the same training session with compression garments (EXP) and without GCSs (CNT), with a recovery period of 72 h between the two sessions. One group was assigned to use GCSs only on the ﬁrst day, and the other group was assigned to use GCSs only on the second day (the athletes served as their own controls). On the day that GCSs were not used, the athletes used traditional socks. The participants wore GCSs (Medilast Sport, Lleida, Spain) with degressive pressure (15–20 mm Hg at the ankle; 88% Polyamid, 12% Elasthane) from the ankle to the calf area (always under the supervision of a member of the investigators’ team.). The compression was similar to that used in the medical ﬁeld [23]. 2.3. Surface Electromyographic Activity (EMG) EMG was measured according to the electrical activity (EA) recorded with a telemetric system (BTS Pocket EMG, Garbagnate M.se, Italy). The information extracted from the surface EMG give global and, rarely, individual indications of motor units activity [24]. A sampling frequency of 1 kHz was used. Preampliﬁers placed next to the measuring electrode allowed ruling out the inﬂuence of likely movements of the wires on the measurement. Signals from the EMG were band-pass ﬁltered (10–400 Hz), and the root mean square (RMS) was analyzed. Bipolar surface EMG electrodes (Al/AgCl, discs of 10 mm diameter) with an inter-electrode distance of 24 mm were placed on the bellies of the vastus lateralis (VAL), vastus medialis (VAM), rectus femoris (RF), biceps femoris (BF), gastrocnemius (GAM), and soleus (SOL) in accordance with the Surface EMG for Non-invasive Assessment of Muscles [25]. We evaluated EMG during two footraces: (a) 10 min at 3% above VT2 (t > VT2) (i.e., represents a submaximal eﬀort), (b) 1 km at full speed (t1km) (i.e., represents a maximal eﬀort). These runs were performed on the athletics track with a 3 min break in between. All evaluations were performed at the same time of day (i.e., evening, between 7:00 p.m. and 9:00 p.m.) and under similar environmental conditions (i.e., 22–24 ◦C temperature, 55% relative humidity). 2.4. Metabolic, Perceptual and Performance Variables The concentration of blood lactate concentration (mmol/L−1) was measured at t > VT2 with a blood lactate analyzer (Lactate Pro Arkray INIC, Amstelveen, NED). The subjective perception of eﬀort (RPE) was measured using the Borg scale [26]. For the performance variable, a stopwatch was used to measure the time (min) subjects took to run t1km. 2.5. Statistical Analysis The data set obtained was analyzed with the SPSS Statistics 19 software (SPSS Inc., Chicago, IL, USA). T-tests were applied to related samples, both to verify that there were no diﬀerences in Sensors 2020, 20, 1268 4 of 8 matching the subjects and to observe the diﬀerences in the sports performance variables. All data were expressed as mean (M) and standard deviation (SD). Homogeneity of variance was tested with the use of a Kolmogorov–Smirnov test and Lilliefors correction. The level of statistical signiﬁcance was set at p < 0.05. The signiﬁcance level was set at 0.05. Cohen’s formula for eﬀect size (ES) was used and the results were based on the following criteria: trivial (0–0.19), small (0.20–0.49), medium (0.50–0.79), and large (0.80 and greater) [27]. 3. Results 3.1. Metabolic and Perceptual Variables at Submaximal Eﬀorts (t > VT2) According to the metabolic demands and perceptual variables, no statistical diﬀerences were founded in the diﬀerent conditions measured in the study between GCSs and CNT conditions, such as heart rate (182.6 ± 10.1 versus 182.6 ± 10.0, p = 1.000, ES < 0.01, trivial), blood lactate concentration (mmol·L−1) (8.3 ± 2.1 versus 7.9 ± 2.4, p = 0.476, ES = 0.20, small), and RPE (8.5 ± 1.0 versus 9.0 ± 0.6, p = 0.301, ES = 0.33, small). 3.2. Perceptual and Performance Variables at Maximal Eﬀort (t1km) Perceptual and performance variables did not reach statistical signiﬁcance during t1km—GCS versus CNT: RPE (9.9 ± 0.3 versus 10.0 ± 0.0; p = 0.164; ES = 0.39, small), speed (19.2 ± 1.7 versus 19.1 ± 1.7 km·h−1; p = 0.847; ES = 0.00, trivial), % maximal aerobic speed (97.2 ± 3.0 versus 97.0 ± 3.6 km·h−1; p = 0.823; ES = 0.00, trivial). 3.3. Surface Electromyography Muscular activity did not any reach statistical signiﬁcance (Table 1). However, according to eﬀect sizes the electromyographic activity was greater in the calf musculature when not using GCSs while running at submaximal eﬀort, while descriptive changes were observed in eﬀect size (ES). EA was lower in the leg during submaximal eﬀorts (GAM and SOL, ES = 0.10, trivial) compared to in the thigh (VAL, VAM, BF, and RF, ES = 0.24, small) with GCS use versus CNT; leg (ES = 0.25, small) and leg (ES = 0.25, small). Table 1. EMG (electromyography) variables base on wearing or not wearing a graduate compression garment during the t > Vt2 and t1km. EMGrms (mV) GCSs CNT t > VT2 t1km t > VT2 t1km BF 0.320 ± 0.181 0.314 ± 0.196 0.323 ± 0.104 0.304 ± 0.304 RF 0.199 ± 0.058 0.194 ± 0.081 0.220 ± 0.085 0.192 ± 0.094 VAL 0.176 ± 0.075 0.168 ± 0.087 0.195 ± 0.086 0.189 ± 0.088 VAM 0.223 ± 0.053 0.212 ± 0.077 0.212 ± 0.077 0.235 ± 0.064 SOL 0.196 ± 0.116 0.164 ± 0.062 0.296 ± 0.115 0.295 ± 0.141 GAM 0.388 ± 0.289 0.331 ± 0.257 0.268 ± 0.101 0.233 ± 0.092 Abbreviations: vastus lateralis (VAL), vastus medialis (VAM), rectus femoris (RF), biceps femoris (BF), gastrocnemius (GAM), and soleus (SOL). During running maximal eﬀort (i.e., t1km), EA was higher in the leg (GAM and SOL, ES = 0.24, small) compared to in the thigh (VAL, VAM, BF, and RF, ES = 0.11, trivial) with GCS use versus CNT: thigh (ES = 0.17, trivial) and leg (ES = 0.18, trivial). (Table 1) Sensors 2020, 20, 1268 5 of 8 4. Discussion The aim of this study was to evaluate changes in electromyographic activity with the use of the use of gradual compression stockings (GCSs) on endurance athletes’ performance, with the use of electromyography techniques. At the metabolic domain, our results are consistent with previous studies, where GCS use did not aﬀect blood lactate concentration measurement [6–8,13]. Thus, performance variables during t1km reported no beneﬁts from GCS use with regards to the time required to run the speciﬁed distance, and our data are in agreement with studies previously published by Ali [5,7], in which no diﬀerences were found in a 10 km test. Therefore, in our study there were no signiﬁcant changes in the perceptual variables with GCS use, and small eﬀect sizes were found for GCS use during submaximal eﬀorts (t > VT2; ES = 0.33) and maximal eﬀorts (t1km; ES = 0.39). This is consistent with previous studies, where lower muscle pain [5,18], greater comfort [6,7], and a lower subjective perception of eﬀort [13,18,28] were observed. With regard to electromyography activity, it was evaluated by means of changes in amplitude (electromyography amplitude), since it has been described as a variable that provides knowledge on the degree of muscle fatigue [29]. No signiﬁcant changes were found between treatments when measuring EA by surface electromyography, null and small eﬀect sizes were observed (ES = 0.11–0.25), so that muscle activation changed according to whether CGSs were used or not. Thus, in submaximal eﬀorts (t > VT2), the activation of the calf muscles (GAM, SOL) was observed to be less than that of the thigh muscles (VAL, VAM, RF, BF). This would have advantages for performance, as fatigue in the leg muscles frequently tends to limit performance more than that of the thigh muscles, because the gastrocnemius and soleus are the greatest contributors to propulsion and support during submaximal running [30]. According to our data, Lucas-Cuevas AG [31] reported a decrease in the muscular contribution of the GAM using GCSs in the rest situation and at the beginning of submaximal eﬀort in the running race. However, one limitation of this study was that they did not analyze the possible changes in muscle recruitment between the thigh and muscular fatigue between GAM and SOL that we measured in our study. During maximal eﬀorts (t1km), muscle recruitment diﬀers from that of submaximal eﬀorts (t > VT2). The use of GCSs reduces electromyographic activity in the thigh and increases in the gastrocnemius muscles (sum GAM and SOL). If we analyze EA by muscle group in our study during the maximum eﬀort of race (t1km), there was less recruitment using GCSs in both the BF and the RF than where there was no use of GCSs. This could be beneﬁcial since in foot race eﬀorts—as the speed increases, the RF and BF are the muscle groups that increase their muscular contribution the most [32,33]. During the present study, the study subjects realized a protocol for detecting electromyography activity performance, and eﬀort perception variables during two diﬀerent endurance tests. There are a few limitations that need to be addressed. First, it could be that the diﬀerent tests used could not represent the endurance eﬀort made by the athletes in a race. Thus, it could be necessary to gather more studies. Also, the individual data were variable, and the sample size was small and medium. Therefore, the lack of statistical signiﬁcance could be due to a Type II error. Secondly, quantiﬁcation of muscle activity from surface EMG signals is problematic when movement is involved and motion artifacts and other electromagnetic noises may inﬂuence the signal levels. We tried to minimize disturbances by the applied signal processing and ﬁltering routines; still, artifacts may have small impacts on the derived maximum muscle activations. Thirdly, we could not use a multi-channel approach to provide access to a set of physiologically relevant variables on the global muscle level or on the level of single motor units, opening new fronts for the study of muscle fatigue; however, in the present study, we did not have this electromyographic analysis technology. From our point of view, the various ﬁndings reported on previous studies with CGSs may be due to the diﬀerent gradual compression socks used [5–8,12]. Other authors did not specify which type of GCSs they used in their studies; most likely they used stockings with a uniform degree of compression. Sensors 2020, 20, 1268 6 of 8 All the studies conducted to date assessed the potential beneﬁts of using compression means (GCS) during the eﬀorts in the race on metabolic or cardiovascular variables. According to a study developed by Varela-Sanz et al. [13], there seems to be a tendency to run faster with a lower perception of eﬀort. If there are no clear metabolic or cardiovascular beneﬁts, the beneﬁts can be found in a change in muscle recruitment between the thigh and leg muscle (i.e., triceps surae). Only a previous study developed by Lucas Cuevas et al. [31] analyzed muscle fatigue using GCSs by EMG techniques. However, Lucas Cuevas et al. [31] did not compare the possible recruitment changes between the muscles of the thigh; the study was realized with lower performance level athletes and lower intensities (75% maximal speed). The novelty of our study is the analysis of EMG recruitment on thigh muscle in high-level athletes at submaximal/maximal intensities. 5. Conclusions Endurance athletes perform much of their training and competitions at submaximal intensities Contrary to our initial hypothesis, no diﬀerences were reported on any of the variables analyzed on this study in EMG recruitment in well-trained athletes between the diﬀerent conditions (GCSs versus CNT) at submaximal/maximal eﬀorts. Future studies should be developed based on research ﬁndings to conﬁrm our data or even explore other possibilities, such as the analysis of EMG recruitment during the recovery process, that are essential for performance in high-level athletes. Author Contributions: Conceptualization, D.M.-P. and J.E.-L.; methodology, D.M.-P. and J.E.-L.; software, P.J.M.; validation, D.M.-P., P.J.M. and J.E.-L.; formal analysis, D.M.-P. and P.J.M.; investigation, D.M.-P., R.C.-A. and J.E.-L.;; resources, Á.L.-S.; data curation, D.M.-P., Á.L.-S.; writing—original draft preparation, D.M., Á.L.-S. and J.E.-L.; writing—review and editing, D.M., P.J.M., Á.L.-S., R.C.-A., and J.E.-L.; visualization, D.M.-P. and J.E.-L.; supervision, P.J.M. and J.E.-L.; project administration, D.M.-P. and J.E.-L.; funding acquisition, D.M.-P. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: We would like to thank the “Allinyourmind training group” for their participation in this project. We also acknowledge Medilast S. A. (Lleida, Spain), who supplied GCSs for the study participants. 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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).	Muscle Activation in Middle-Distance Athletes with Compression Stockings.	02-26-2020	Moreno-Pérez, Diego,Marín, Pedro J,López-Samanes, Álvaro,Cejuela, Roberto,Esteve-Lanao, Jonathan	eng
PMC8540834	sensors Article The Inﬂuence of Time Winning and Time Losing on Position-Speciﬁc Match Physical Demands in the Top One Spanish Soccer League José C. Ponce-Bordón 1 , Jesús Díaz-García 1,* , Miguel A. López-Gajardo 1 , David Lobo-Triviño 1, Roberto López del Campo 2 , Ricardo Resta 2 and Tomás García-Calvo 1 Citation: Ponce-Bordón, J.C.; Díaz-García, J.; López-Gajardo, M.A.; Lobo-Triviño, D.; López del Campo, R.; Resta, R.; García-Calvo, T. The Inﬂuence of Time Winning and Time Losing on Position-Speciﬁc Match Physical Demands in the Top One Spanish Soccer League. Sensors 2021, 21, 6843. https://doi.org/10.3390/ s21206843 Academic Editor: Angelo Maria Sabatini Received: 17 September 2021 Accepted: 11 October 2021 Published: 14 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Faculty of Sport Sciences, University of Extremadura, Boulevard of the University s/n, 10003 Caceres, Spain; jponcebo@gmail.com (J.C.P.-B.); malopezgajardo@unex.es (M.A.L.-G.); davidlobo123@gmail.com (D.L.-T.); tgarciac@unex.es (T.G.-C.) 2 LaLiga Sport Research Section, 28043 Madrid, Spain; rlopez@laliga.es (R.L.d.C.); rresta@laliga.es (R.R.) * Correspondence: jdiaz@unex.es; Tel.: +31-927-257-460 Abstract: The aim of the present study was to analyze the inﬂuence of time winning and time losing on position-speciﬁc match physical demands with and without ball possession in the top Spanish professional soccer league. All matches played in the First Spanish soccer league over four consecutive seasons (from 2015/16 to 2018/19) were recorded using an optical tracking system (i.e., ChyronHego), and the data were analyzed via Mediacoach®. Total distance (TD), and TD > 21 km·h−1 covered with and without ball possession were analyzed using a Linear Mixed Model, taking into account the contextual variables time winning and losing. Results showed that TD and TD > 21 km·h−1 covered by central midﬁelders (0.01 and 0.005 m/min, respectively), wide midﬁelders (0.02 and 0.01 m/min, respectively), and forwards (0.03 and 0.02 m/min, respectively) signiﬁcantly increased while winning (p < 0.05). By contrast, TD and TD > 21 km·h−1 covered by central defenders (0.01 and 0.008 m/min, respectively) and wide defenders (0.06 and 0.008 m/min, respectively) signiﬁcantly increased while losing (p < 0.05). In addition, for each minute that teams were winning, total distance with ball possession (TDWP) decreased, while, for each minute that teams were losing, TDWP increased. Instead, TDWP > 21 km·h−1 obtained opposite results. Total distance without ball possession increased when teams were winning, and decreased when teams were losing. Therefore, the evolution of scoreline signiﬁcantly inﬂuences tactical–technical and physical demands on soccer matches. Keywords: contextual variables; match running performance; ball possession; positional; professional soccer 1. Introduction Context-related variables are considered the most inﬂuencing variables on match physical demands in soccer [1]. Time–motion analysis research has reported a large amount of information about context-related variables such as match status, match location, and opponent level [2]. Speciﬁcally, it has been previously shown that ﬁnal and partial match status (analyzed by epochs of time—i.e., 15-min periods or half-time) modify match physi- cal demands as well as ball possession [3,4]. However, this method of analysis is probably to do with the interaction of other variables such as the evolution of the match-scoreline [5]. Regardless of ﬁnal match status, the time each team was leading, drawing, or trailing during a match could be different, with matches taking place where a team has won throughout 70 min (i.e., team A scored a goal in the 20th min and team B did not score) or matches where a team has won throughout 1 min (i.e., teams were drawing over the match, and one team scored a goal in the 89th min). Moreover, it is possible that one team that was ahead for a long time would end up losing on the ﬁnal scoreline. Match physical demands Sensors 2021, 21, 6843. https://doi.org/10.3390/s21206843 https://www.mdpi.com/journal/sensors Sensors 2021, 21, 6843 2 of 9 are believed to depend on evolving scoreline (i.e., whether a team is winning or losing) since, when a team is losing, players try to reach their maximal physical capacity in order to draw or win the match [2]. Therefore, this limitation could be solved by taking into account the minutes that teams were ahead and behind during a match separately. According to our knowledge, the inﬂuence of time winning and time losing on position-speciﬁc match physical demands according to the evolution of the scoreline is less known. Match status has been arguably analyzed enough to prove that it inﬂuences soccer teams’ behavior [6]. In this vein, Lago–Peñas [7] reported that losing teams frequently increase their percentage of possession; meanwhile, certain winning teams preferred counterattacking or playing directly. In addition, match status clearly impacts teams playing style [5], and both variables (i.e., match status and playing style) also inﬂuence match physical demands; however, several studies about this topic have drawn the opposite conclusions. For instance, elite Spanish soccer players performed less high-intensity distance (19 km·h−1) when winning than when they were losing, since winning is a comfortable status, it is possible that players assume a ball contention strategy, keeping the game slower, which results in lower speeds [8]. Moreover, Castellano et al. [2] analyzed one Spanish team of LaLiga and they showed that the distances covered at high intensity by the reference team were greater when the result was adverse. Moalla et al. [3] obtained the same results in a study from the Stars League during 2013/14 and 2014/15 seasons. Conversely, during qualifying round matches of the World Cup 2010, drawing teams covered a lower average speed than the winning and losing teams [9]. In this line, variables that determine the intensity of the game (maximum speed and frequency of high-intensity activity) in a professional Brazilian football team were signiﬁcantly lower when the team lost [10], meanwhile greater intensity running distances were observed in matches that the team won as opposed to losing [11]. Therefore, coaches should take into account the match status to analyze the external load implied by the match [12]. However, soccer players could have not been affected by this contextual variable in the same way due to playing style, since playing style changes associated with match status could affect different players differently in ways that are position-speciﬁc [13]. For example, in the German Bundesliga during the 2014/15 season, central defenders and full-backs covered shorter distances at high intensity in won matches than in lost matches (p < 0.01); however, forwards covered signiﬁcantly longer total distance in won matches than in drawn and lost matches (p < 0.05) [14]. Despite these conclusions, position-speciﬁc match physical demands can also vary depending on the evolution of scoreline status. In this vein, when a team was winning, during preseason matches of the 2011/12 season Australian League soccer, the average speed was 4.17% lower than when the team was drawing (p < 0.05) [15]. Regarding player position-speciﬁc data, in the English Premier League, Redwood-Brown et al. [16] found midﬁelders covered more distance at high intensity when level, defenders more when losing, and attackers more when winning. Similarly, losing status increased the total distance covered by defenders from Spanish First soccer league, while attacking players showed the opposite trend [17]. Therefore, these previous studies have suggested that, due to the player position, players perform different tactical roles, and match status and the associated playing style changes could have different inﬂuences on match running performance by positions. The knowledge about the inﬂuence of time that teams were ahead or behind on position-speciﬁc match physical demands could have important practical applications during the competitive season to program the training load in a more strategic way based on physical data [12]. In addition, less is known about the inﬂuence of time which teams spend winning or losing on position-speciﬁc match physical demands. Therefore, the main objective of the present study was to analyze the inﬂuence of time winning and losing on position-speciﬁc match physical demands in the top Spanish soccer league across four seasons (2015/2016–2018/2019). As a secondary objective, the study also aimed to analyze the match physical demands with and without ball possession according to time winning and time losing. Sensors 2021, 21, 6843 3 of 9 Based on previous ﬁndings obtained by the aforementioned studies, the following hypotheses were proposed. Firstly, concerning match physical demands with and without ball possession, it was expected that total distance with ball possession would be less during time winning [7]. Secondly, concerning position-speciﬁc match physical demands, it was expected that total distance would be greater in attackers during time winning and defenders during time losing, based on previous results [16,17]. 2. Materials and Methods 2.1. Participants The sample comprised 36,883 individual match observations of 1037 professional soccer players who competed in the First Spanish professional soccer league (i.e., LaLiga Santander) over four consecutive seasons (from 2015/16 to 2018/19). All players who participated in matches (starters and non-starters) and played 10 min at least were included. Only goalkeepers were excluded. According to previous studies [18], players were divided into ﬁve position-speciﬁc groups: Central Defenders (CD; n = 6787 observations), Wide Defenders (WD; n = 6530 observations); Central Midﬁelders (CM; n = 6826 observations); Wide Midﬁelders (WM; n = 8394 observations); Forwards (FW; n = 8346 observations). Data were provided to the authors by LaLigaTM, and the study received ethical approval from the University of Extremadura; Vice-Rectorate of Research, Transfer and Innovation- Delegation of the Bioethics and Biosafety Commission (Protocol number: 239/2019). 2.2. Procedure and Variables Match physical demands data were collected by an optical tracking system (ChyronHego®; TRACAB, New York, NY, USA). This multi-camera tracking system con- sists of 8 different super 4K-High Dynamic Range cameras situated strategically to follow and track the 22 players on the ﬁeld throughout the match. These cameras ﬁlm from several angles and analyze X and Y coordinates of each player, providing real-time tracking with data recorded at 25 Hz. Mediacoach® is also based on data correction of the semi-automatic video technology (the manual part of the process) [19]. The validity and reliability of the Tracab® video tracking system has been analyzed, reporting average measurement errors of 2% for total distance covered [20–22]. The physical performance variables used for this study were categorized according to the ball possession as follows [23,24]: with possession (WP) and without possession (WOP). The following variables were studied for each of these categories: total distance (m) covered by players (i.e., TD) and total distance covered at more than 21 km·h−1 (i.e., TD > 21 km·h−1). To determine if the scoreline inﬂuenced position-speciﬁc match physical demands, the cumulative time that each team was losing or winning during a match was included in the analysis (not the ﬁnal match result). For example, if team A scored a goal in the 20th min and team B equalized in the ﬁnal minute, team A was classiﬁed as losing for 0 min and winning for 70 min, while team B was classiﬁed as losing for 70 min and winning for 0 min 2.3. Data Analysis All statistical analyses were performed using R-studio [25]. A Linear Mixed Model (LMM) was conducted for each of the physical variables using the lme4 package [26]. This model allows for the analyzing of data with a hierarchical structure in nested units and has demonstrated its ability to cope with unbalanced and repeated-measures data [27]. For example, variables related to the distance covered in matches are nested for players (i.e., each player has a record for every match they have participated in), and players are nested into teams. Also, cumulative times spent winning or losing are nested into matches and these matches can also be nested into teams. This represents a threefold levels structure, where teams are the topmost unit in the hierarchy. A general multilevel-modelling strategy was applied [27], where ﬁxed and random effects had been included in different steps from the simplest to the most complex. First, Sensors 2021, 21, 6843 4 of 9 unconditional models were analyzed exclusively including dependent variables (i.e., dis- tance variables) to check if the grouping variables at levels 2 and 3 (i.e., players and teams) signiﬁcantly affected the intercept (mean) of each dependent variable. These models may be used as baselines for comparing more complex models. Later, different models were performed for each of the dependent variables, setting as ﬁxed effects the position of the players and the time winning/losing. Following the procedure proposed by Heck & Thomas [27], models with different random effects (intercepts and slope) were created for each variable. A model comparison for each step was performed using the Akaike Information Criterion (AIC) [28] and a chi-square likelihood ratio test [29], where a lower value represented a ﬁtter model. Final models presented in Tables 1 and 2 (with random intercept and slope effect) were chosen according to better values of AIC, log-likelihood, and signiﬁcant effect of variables. Maximum Likelihood (ML) estimation for model com- parison and for the ﬁnal model of each physical variable was used, the best model, again, using Restricted Maximum Likelihood (REML) estimation, was reﬁtted. Marginal and conditional R2 metrics [30] for each LMM to provide some measure of effect sizes were reported. Signiﬁcance level was set at p < 0.05. For a suitable interpretation of the results, the time winning/losing was group-mean centered, being centered to the team’s mean in each season. Table 1. Differences by ball possession of position-speciﬁc total distance covered according to the scoreline evolution. CD WD CM WM FW TD (m/min) Intercept 107.30 b, c, d, e 109.90 a, c, d, e 116.10 a, b 115.90 a, b 115.60 a, b, c Slope Time Winning −0.006 c, d, e −0.005 c, d, e 0.01 a, b, d, e 0.02 a, b, c, e 0.03 a, b, c, d Slope Time Lossing 0.01 c, d, e 0.006 c, d, e −0.003 a, b, d, e −0.02 a, b, c, e −0.02 a, b, c, d TDWP (m/min) Intercept 35.93 b, c, d, e 38.41 a, c, d, e 42.21 a, b, e 42.54 a, b, e 43.04 a, b, c, d Slope Time Winning −0.03 −0.04 e −0.04 e −0.04 −0.03 b, c Slope Time Lossing 0.03 0.03 0.03 e 0.02 0.02 c TDWOP (m/min) Intercept 42.50 b, c, d 43.80 a, c, d, e 47.39 a, b, d, e 45.36 a, b, c, e 42.16 b, c, d Slope Time Winning 0.01 c, d, e 0.02 d, e 0.03 a, d, e 0.04 a, b, c 0.01 a, b, c Slope Time Lossing 0.002 d, e −0.003 d, e −0.003 d, e −0.01 a, b, c −0.02 a, b, c Note. CD = Central defenders; WD = Wide defenders; CM = Central midﬁelders; WM = Wide midﬁelders; FW = Forwards; TD = Total distance; TDWP = Total distance with ball possession; TDWOP = Total distance without ball possession; a = signiﬁcant differences compared to central defenders; b = signiﬁcant differences compared to wide defenders; c = signiﬁcant differences compared to central midﬁelders; d = signiﬁcant differences compared to wide midﬁelders; e = signiﬁcant differences compared to forwards. Sensors 2021, 21, 6843 5 of 9 Table 2. Differences by ball possession of position-speciﬁc total distance covered at more than 21 km·h−1 according to the scoreline evolution. CD WD CM WM FW Total distance > 21 km·h−1 (m/min) Intercept 5.74 b, c, d, e 6.68 a, d, e 6.68 a, d, e 7.24 a, b, c, e 7.57 a, b, c, d Slope Time Winning −0.008 c, d, e −0.005 c, d, e 0.005 a, b, d, e 0.01 a, b, c, e 0.02 a, b, c, d Slope Time Lossing 0.008 c, d, e 0.008 c, d, e −0.004 a, b, d, e −0.001 a, b, c, e −0.01 a, b, c, d Total distance with ball possession > 21 km·h−1 (m/min) Intercept 2.21 b, c, d, e 2.81 a, c, d, e 3.14 a, b, d, e 3.57 a, b, c, e 4.01 a, b, c, d Slope Time Winning 0.001 c, d, e −0.001 c, d, e 0.005 a, b, e 0.007 a, b, e 0.01 a, b, c, d Slope Time Lossing −0.001 a, c, d, e 0.001 a, b, d, e −0.006 a, b, c −0.009 a, b, c −0.009 a, c, d, e Total distance without ball possession > 21 km·h−1 (m/min) Intercept 3.54 b, c, d, e 3.80 a, c, d, e 3.37 a, b, e 3.40 a, b, e 3.16 a, b, c, d Slope Time Winning −0.008 b, c, d, e −0.005 a, c, d, e −0.001 a, b, d, e 0.002 a, b, c 0.004 a, b, c Slope Time Lossing −0.009 b, c, d, e 0.007 a, c, d, e 0.002 a, b, d, e −0.001 a, b, c, e −0.002 a, b, c Notes. CD = Central defenders; WD = Wide defenders; CM = Central midﬁelders; WM = Wide midﬁelders; FW = Forwards; a = signiﬁcant differences compared to central defenders; b = signiﬁcant differences compared to wide defenders; c = signiﬁcant differences compared to central midﬁelders; d = signiﬁcant differences compared to wide midﬁelders; e = signiﬁcant differences compared to forwards. 3. Results Firstly, the Wald test and intraclass correlation coefﬁcient (ICC) suggested statistically signiﬁcant variability in the distances covered by players according to time winning and losing (ICC > 0.10); therefore, LMM was justiﬁed for the purpose of the study. Also, AIC suggested that the twofold levels model was the one ﬁtter for this purpose. Secondly, Table 1 shows the differences of TD covered according to ball possession and to the scoreline evolution by player positions. Regardless of scoreline, CM covered signiﬁcantly greater TD than the rest of the players (p < 0.05). TD covered by CD and WD decreased signiﬁcantly with respect to CM, WM, and FW (p < 0.05) for each minute that teams were ahead. By contrast, for each minute that teams were trailing, TD covered by CD and WD increased signiﬁcantly with respect to CM, WM, and FW (p < 0.05). During the match, FW covered signiﬁcantly greater TDWP than CD, WD, CM, and WM (p < 0.05). However, for each minute that teams were ahead, TDWP decreased for all positions. Signiﬁcant differences were found between WD and CM with respect to FW (p < 0.01). On the contrary, for each minute that teams were trailing, TDWP increased for all positions. Signiﬁcant differences between CM and FW were observed (p < 0.05). On the other hand, CM covered TDWOP signiﬁcantly greater than the rest of the players (p < 0.05). However, for each minute that teams were ahead, TDWOP increased for all positions. CM and WM signiﬁcantly increased TDWOP with respect to CD, WD, and FW (p < 0.01). By contrast, for each minute that teams were trailing, TDWOP signiﬁcantly decreased for all positions, except CD (p < 0.05). Thirdly, Table 2 shows the differences by ball possession of TD > 21 km·h−1 according to the scoreline evolution by player positions. Regardless of the scoreline, FW covered TD > 21 km·h−1 signiﬁcantly greater than the rest of the players (p < 0.05). However, for each minute that teams were ahead, CD and WD signiﬁcantly decreased TD > 21 km·h−1 with respect to CM, WM, and FW (p < 0.05). By contrast, for each minute that teams were trailing, CD and WD signiﬁcantly increased TD > 21 km·h−1 with respect to CM, WM, and FW (p < 0.05). During the match, FW covered TD > 21 km·h−1 with ball possession signiﬁcantly greater than CD, WD, CM, and WM (p < 0.05). Moreover, for each minute that teams were Sensors 2021, 21, 6843 6 of 9 ahead, TD > 21 km·h−1 signiﬁcantly increased for all positions, except WD (p < 0.05). By contrast, for each minute that teams were trailing, TD > 21 km·h−1 signiﬁcantly decreased for all positions, except WD (p < 0.05). Finally, WD covered signiﬁcantly greater TD > 21 km·h−1 without ball possession than the rest of the players (p < 0.01). For each minute that teams were ahead, TD > 21 km·h−1 without ball possession covered by WM and FW increased signiﬁcantly, with respect to CD, WD, and CM (p < 0.05). Likewise, for each minute that teams were trailing, WD and CM signiﬁcantly increased TD > 21 km·h−1 without ball possession, with respect to CD, WD and FW (p < 0.05). 4. Discussion The aim of the present study was to analyze the inﬂuence of time winning and losing on position-speciﬁc match physical demands in the top Spanish professional soccer league across four seasons (from 2015/2016 to 2018/2019). Subsequently, match physical demands with and without ball possession according to time winning and losing were examined. The main results showed that TDWP was less while teams were winning, while it was greater while teams were losing. In addition, TDWOP increased while teams were winning, while it decreased while teams were losing. Finally, TD and TD > 21 km·h−1 covered by CM, WD, and FW were greater while teams were winning, while TD and TD > 21 km·h−1 covered by CD and WD were greater while teams were losing. Firstly, it had been hypothesized that TDWP would be less during time winning (Hypothesis 1). Our results showed that TDWP decreased during time winning for all player positions and increased during time losing, therefore Hypothesis 1 was conﬁrmed. These results suggest that during time winning, teams frequently decrease their percentage of possession, which could be associated to defending closer to the goal, counterattacking, or playing directly [7]. It has also been shown that teams that were ahead performed a higher number of defensive actions which, in turn, are related to lower ball possession levels during a match [31]. By contrast, during time losing, these results suggest teams frequently increase their percentage of possession, attacking closer to the other team’s goal [7]. Evidence has reported that successful teams normally have longer possession times than less successful teams [23] or that ball possession might increase in teams that are either losing or trying to tie the match [32]. Therefore, our results reported the need to take into account the evolution of the scoreline. In contrast, TDWP >21 km·h−1 increased during time winning for all player positions and decreased during time losing. A possible reason to explain this result could be the fact that teams adopt an indirect playing style to perform counterattacks [7]. Therefore, players need to execute high-intensity speciﬁc technical and tactical tasks on the pitch when they are in ball possession, such as receiving passes and crosses on the run, followed by dribbling the ball in the opponent’s area to obtain a goal [33]. In fact, it has been demonstrated that high-intensity actions are important within decisive situations in professional football [34]. Furthermore, Yang et al. [24] reported that total sprint distance was signiﬁcantly greater for the best-ranked teams compared to lower-ranked teams, highlighting the importance of sprinting for tactical teamwork that generates offensive actions. A systematic review conducted by Lago–Peñas & Sanromán–Álvarez [35] pointed out that successful teams covered greater high-intensity running distance in ball possession. Therefore, our results suggest that teams perform a greater number of high-intensity actions in ball possession while winning or trying to maintain the advantage; meanwhile, TDWP decreases. During time winning, TDWOP increased for all player positions and decreased dur- ing time losing for all player positions, except CD. One potential reason for this situa- tion could be the fact of ball possession decrease, like Lago-Peñas [7] reported, showing that winning teams preferred counterattacking or playing directly. On the other hand, research has shown that lower-ranked teams covered signiﬁcantly greater TDWOP com- pared with better-ranked teams, which likely represented a greater match time under- taking defensive activities by these teams [24]. Our ﬁndings disagree with those from Sensors 2021, 21, 6843 7 of 9 other studies where ball possession increased when teams were ahead [36]. Similarly, TDWOP > 21 km·h−1 covered by WM and FW signiﬁcantly increased when teams were ahead, and TDWOP > 21 km·h−1 covered by WD and CM increased when teams were behind. This fact could be explained due to that, when the team is not in possession of the ball, the forwards often perform high-intensity activities (high pressing), attempting to recover the lost ball [14,37]. Secondly, it had been hypothesized that TD would be greater in attackers while teams were ahead and in defenders when teams were losing (Hypothesis 2). The results showed that TD and TD > 21 km·h−1 covered by CM, WM, and FW signiﬁcantly increased when teams were ahead (p < 0.05), and TD and TD > 21 km·h−1 covered by CD and WD signiﬁcantly increased when teams were losing (p < 0.05). These ﬁndings are in line with previous studies that found that attackers covered more distance at high intensity when winning and defenders more when losing [16]. Lago–Peñas et al. [17] also reported that losing status increased total distance covered by defenders, while attacking players showed the opposite trend. Thus, it seems to be conﬁrmed that CM, WM, and FW cover greater TD when winning and CD and WD when losing, therefore Hypothesis 2 was accepted. A possible explanation may be due to attackers´ work rate of the opposing team since, when the opposing team is ahead or chasing a goal, attackers maintain a high work rate, implying a defenders´ high work rate [16,38]. In addition, similar results were obtained by Andrzejewski et al. [14], showing that defenders covered shorter distances at high intensity in lost matches, while forwards covered longer total distances in won matches. Another possible reason could be the playing style that the team adopted, for example, a direct style of play when teams are winning can induce higher match intensity in running from the attackers [7,11]. In particular, these ﬁndings indicate that physical demands vary according to position-speciﬁcities and the evolution of the scoreline. 4.1. Study Limitations and Future Directions The present study increases the knowledge about this research topic; however, a number of limitations could be recognized with a view to future research. First, other context-related variables such as match location or opposing team level were not consid- ered. Ball possession percentages of teams have been also not considered, and it would be interesting to analyze this variable when teams are winning or losing with the interaction of match physical demands. Moreover, the comparison between ﬁve players’ positions accord- ing to previous studies was analyzed [18]; however, the existence of more player positions is possible than have been previously analyzed, therefore it would be interesting to conduct a comparison between more player positions. In addition, further research is required, considering several factors such as the playing style, since the player position could depend on the playing style of teams. Finally, research has reported that external load variables such as accelerations and decelerations belong to match physical demands [39], in which case it would be necessary to know the full player´s work rate, including these variables. 4.2. Practical Applications The ﬁndings of this study provide useful information on the variability of match physical demands for practitioners in Spanish professional soccer. In particular, the study extends previous research demonstrating that time that teams were winning or losing inﬂuences both match physical demands and ball possession. This information could help strength and conditioning coaches with personalizing recovery work after match play, according to the different physical efforts performed in matches. Finally, goals scored are the most important of all critical events, therefore the evolution of the scoreline should be taken into account during training sessions to optimize physical aspects of soccer performance. In this vein, it is necessary to know how these situations inﬂuence the player’s capacity to deal with critical events in a match [40]. Sensors 2021, 21, 6843 8 of 9 5. Conclusions The main ﬁndings reported that the evolution of scoreline signiﬁcantly inﬂuences match tactical–technical and physical demands. First, TDWP was less while teams were winning, while it was greater while teams were losing, and TDWOP evolved conversely; therefore, teams modify their playing style and tactical behavior according to the demands of matches. Secondly, attackers covered greater distances when winning, and defenders covered greater distances when losing; therefore, professional soccer players regulate their physical efforts according to the periods of the game. Finally, the inﬂuence of scoreline is reﬂected in changes in the teams and players’ tactical–technical and physical demands as a response to the evolution of match outcome. Author Contributions: Conceptualization, T.G.-C.; methodology, T.G.-C., J.C.P.-B. and J.D.-G.; formal analysis, T.G.-C.; investigation, J.C.P.-B., J.D.-G., M.A.L.-G., D.L.-T., R.L.d.C., R.R. and T.G.-C.; re- sources, R.L.d.C. and R.R.; writing—original draft preparation, J.C.P.-B., J.D.-G. and D.L.-T.; writing— review and editing, M.A.L.-G. and T.G.-C.; funding acquisition, R.L.d.C. and R.R. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the European Regional Development Fund (ERDF), the Government of Extremadura (Department of Economy and Infrastructure) and LaLiga Research and Analysis Sections. 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PMC9685973	Fiedler et al. BMC Research Notes (2022) 15:351 https://doi.org/10.1186/s13104-022-06247-1 RESEARCH NOTE Daytime fluctuations of endurance performance in young soccer players: a randomized cross-over trial Janis Fiedler1* , Stefan Altmann1,2, Hamdi Chtourou3,4, Florian A. Engel5, Rainer Neumann6 and Alexander Woll1 Abstract Objectives: Fluctuations of physical performance and biological responses during a repetitive daily 24-h cycle are known as circadian rhythms. These circadian rhythms can influence the optimal time of day for endurance perfor- mance and related parameters which can be crucial in a variety of sports disciplines. The current study aimed to evaluate the daytime variations in endurance running performance in a 3.000-m field run and endurance running performance, blood lactate levels, and heart rate in an incremental treadmill test in adolescent soccer players. Results: In this study, 15 adolescent male soccer players (age: 18.0 ± 0.6 years) performed a 3.000-m run and an incremental treadmill test at 7:00–8:00 a.m. and 7:00–8:00 p.m. in a randomized cross-over manner. No significant variations after a Bonferroni correction were evident in endurance running performance, perceived exertion, blood lactate levels, and heart rates between the morning and the evening. Here, the largest effect size was observed for maximal blood lactate concentration (9.15 ± 2.18 mmol/l vs. 10.64 ± 2.30 mmol/l, p = .110, ES = 0.67). Therefore, endurance running performance and physiological responses during a field-based 3.000-m run and a laboratory- based test in young male soccer players indicated no evidence for daytime variations. Keywords: Circadian rhythm, Soccer, Aerobic exercise, Endurance, Lactate, Heart rate © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Introduction Circadian rhythms describe periodic changes in physi- ological parameters for an approximately 24-h cycle [1]. They are well established for a range of biological param- eters like core body temperature, heart rate (HR), blood pressure, and different hormones [1] and are also pre- sent in physical performance and related responses [2, 3]. These circadian rhythms are influenced by other parame- ters like age, light hours, sleeping pattern, or type of exer- cise but are overall stable [2]. For coaches and athletes (i.e. soccer players), it might be important to consider circadian rhythms as determinants of exercise capacity as well as performance for the best results in competi- tions [4]. As endurance running performance is related to overall performance in soccer players, and elite play- ers run about 10 km during one game, this motor fitness parameter is of particular interest [5]. Previous research including soccer players found heterogenic results con- cerning the presence of daytime variation for endurance performance and related physiological responses like lac- tate or HR [2, 4, 6–13]. Therefore, this study aimed to examine potential day- time variation (morning vs. evening) in i) endurance running performance during a 3.000-m field run and an incremental treadmill test; and ii) blood lactate concen- tration and HR during the incremental treadmill test. Open Access BMC Research Notes *Correspondence: Janis.fiedler@kit.edu 1 Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131 Karlsruhe, Germany Full list of author information is available at the end of the article Page 2 of 6 Fiedler et al. BMC Research Notes (2022) 15:351 According to the literature, we hypothesized that (i) endurance running performance during the 3.000-m run and the treadmill test would be higher in the even- ing than in the morning [7, 12, 14, 15] and (ii) that both blood lactate levels and HR during the incremental tread- mill test would be different between the morning and evening [4, 16–21]. Material and Methods Participants Fifteen male soccer players (age = 18.0 ± 0.6 years; height = 178.7 ± 5.3 cm; weight = 71.1 ± 6.6 kg), with a regular training volume of three training sessions per week and one soccer match on the weekend, volunteered to participate in this study. Procedures All 15 participants performed a 3.000-m run and an incremental treadmill test (see [22]) on two occasions at two-day times (one in the morning between 7:00 and 8:00 a.m. as well as one in the evening between 7:00 and 8:00 p.m.). Using a cross-over design, the participants were randomly assigned to two groups. Both groups per- formed the 3.000-m run first and the incremental tread- mill test trials second. However, group 1 performed the first trial for both tests (3.000-m run and incremental treadmill test, respectively) in the morning and the sec- ond in the evening, while the timing was switched for group 2. All tests were separated by 36 h. In a first step, all participants performed the two field- based 3.000-m runs on a 400 m running track. The par- ticipants were familiar with the 3.000-m run and were instructed to perform the whole 3.000-m run as fast as possible. Time to completion and ratings of perceived exertion (RPE) [23] were recorded after each trial in order to control for exhaustion criteria [24]. In a second step, all participants performed two labo- ratory-based incremental treadmill tests on a Woodway treadmill (Woodway GmbH, Weil am Rhein, Germany) with a slope of 1%. Each trial started at a running speed of 6 km/h, increasing by 2 km/h every 3 min. After each 3 min-stage, participants rested for 30-s for collection of capillary blood from the earlobe; two participants provided no consent for blood withdrawal and lactate thresholds were estimated using the Ergonizer Software (Ergonizer, Freiburg, Germany). HR was monitored using a Polar system (Polar Electro Oy, Kempele, Fin- land) throughout the whole test. Athletes were instructed to complete as many stages as possible, and the test was finished at volitional exhaustion. Blood lactate concentration for each stage was analyzed utilizing Bio- sen C-Line Sport (EKF-diagnostic GmbH, Barleben, Germany). Data analysis Time to completion and RPE were recorded as param- eters for the 3.000-m run. Regarding the incremental treadmill test, the following measurement points were chosen to measure one or multiple of the following parameters: blood lactate concentration, HR, and run- ning speed (see [25]): • rest: immediately before the beginning of the test in a standing position • individual aerobic threshold (LT): running velocity at which blood lactate concentration begins to rise above baseline levels • individual anaerobic threshold (IAT): running velocity at LT + blood lactate concentration of 1.5 mmol/l • maximal running speed (max): running velocity at the point of volitional exhaustion The following parameters were included for the incre- mental treadmill test: • blood lactate concentration (at rest, LT, IAT, and max) • HR (at rest, LT, IAT, and max) • running speed (LT, IAT, and max) Statistical analysis Because of the cross-over study design, the existence of possible sequencing effects was calculated by perform- ing an independent t-test between the sum scores (day 1 + day 2 group 1 vs day 1 + day 2 group 2) for each parameter in addition to a sufficient washout period [26]. All Data are available in the Additional file 1. Daytime variations in all measured variables were cal- culated using paired t-tests. To correct for multiple test- ing, the results were adapted by multiplying the p-value with the number of comparisons of the parameter follow- ing the Bonferroni correction [27]. In addition, Cohen’s d effect sizes (ES) were calculated to quantify the mag- nitude of differences between the morning and even- ing trials: 0.2 ≤ ES < 0.5 was considered a small effect; 0.5 ≤ ES < 0.8 was considered a moderate effect; ES ≥ 0.8 was considered a large effect [28]. Statistical analyses were performed using SPSS statistical software version 26.0 (SPSS, Inc., Chicago, IL). The level for significance was set a priori to 0.05 after the Bonferroni correction. Page 3 of 6 Fiedler et al. BMC Research Notes (2022) 15:351 Results The investigation of potential sequencing effects, ana- lyzed using an independent t-test, showed no signifi- cant differences between the two groups. For the 3.000-m run, neither time for completion (see Fig. 1a) nor RPE (see Fig. 1b) differed significantly between the morning and evening trials. For the incre- mental treadmill test, no significant differences after the Bonferroni correction were found for blood lac- tate (maximal blood lactate concentration see Fig. 2a) or running speed (maximal running speed see Fig. 2b) between the morning and evening trials (see Table 1 for detailed results). Discussion and conclusion This study aimed to evaluate daytime variation in aerobic endurance performance in a 3.000-m run and an incre- mental treadmill test in young soccer players. Addition- ally, blood lactate concentrations and HR during the incremental treadmill test were analyzed for daytime differences. Hypothesis (i) that aerobic endurance per- formance would be better in the evening than in the morning could not be verified for the 3.000-m run and the incremental treadmill test. Hypothesis (ii) that blood lactate levels and HR during exercise would be higher in the evening could also not be verified. Fig. 1 The individual values of all participants (lines) and the mean value (box) for the parameters a time to completion, and b perceived exertion during the morning and evening trial of the 3.000-m run Fig. 2 The individual values of all participants (lines) and the mean value (box) for a maximal blood lactate concentration, and b maximal running speed during the morning and evening trial of the incremental treadmill test Page 4 of 6 Fiedler et al. BMC Research Notes (2022) 15:351 Aerobic endurance performance in the incremen- tal treadmill test indicated no evidence for differences between the evening and the morning. This is in line with some previous studies in untrained participants [20] and competitive cyclists [29] while others reported increased endurance performance in an incremental cycle ergome- ter test in students [15] and a Yo-Yo intermittent recovery test in young soccer players [12]. While there is a good theoretical basis for performance differences due to hor- monal control of glucose metabolism [13], results from laboratory and field studies yield heterogenic findings. Additionally, no differences in endurance performance and RPE were found for the field test (i.e., 3.000-m run). One possible explanation for the results of the 3.000-m run is that the self-selected pacing is a crucial factor for maximum performance in the 3.000-m run [30]. This is supported by the reported mean RPE which did not reach the range of exhaustion criteria (RPE > 16) in the 3.000-m run, while exhaustion criteria were reached (mean max lactate > 9 mmol/l) [24] in the incremental treadmill test. Furthermore, no evidence for a daytime variation in any physiological parameter was found in our study. Con- trasting, previous studies found higher blood lactate lev- els for various exercises [4, 21]. Additionally, one study reported higher blood lactate levels at rest in the morn- ing compared to the afternoon and evening [20], and another study found no differences in blood lactate levels throughout the day [29]. Reasons for the different results between the aforementioned studies and the results of the present study can be found in different test proce- dure and population. Concerning daytime variations of HR during endurance exercise, the overall results seem to be inconsistent [30]. While some studies reported evi- dence for the presence of daytime variation in HR [16– 18], Chtourou and Souissi described equivocal results for daytime variation of HR in their recent review [30]. Overall, our hypotheses that daytime variations are present in endurance performance and related physi- ological parameters of youth soccer players could not be confirmed by this study. While circadian rhythms are considered an important factor related to physical per- formance and physiological parameters in competitive sports, the importance of circadian rhythms for aerobic endurance performance remains unclear. Limitations Some limitations must be acknowledged concerning this study. First, the use of only two times of the day (i.e., morning and evening) might not be sufficient because the time window for optimal performance differs for each individuum [2]. However, the choice of the selected times of the day in our study did incorporate the optimum time of day for soccer players’ performance between 04:00 Table 1 Results for endurance running performance, blood lactate levels, and heart rate differences between morning and evening Means (standard deviations) and results of the paired t-tests for daytime differences at the incremental treadmill test before the start (rest), at the onset of lactate accumulation (LT), the individual anaerobic threshold (IAT), and immediately after volitional exhaustion (max) and at the end of the 3.000-m run for time to completion (Time) and rating of perceived exertion (RPE). p-values were corrected using the Bonferroni method Incremental treadmill test Parameter Morning Evening Mean difference corrected p-value (original) Cohen’s d (t-value) df Heart rate [1/min] Rest 86.60 (9.68) 85.73 (10.88) 0.87 (10.33) 1.00 (0.750) − 0.09 (0.35) 14 LT 150.93 (12.13) 153.47 (10.29) − 2.53 (9.81) 1.00 (0.334) 0.23 (− 1) 14 IAT 177.47 (7.85) 179.27 (6.31) − 1.80 (4.87) 0.872 (0.174) 0.25 (− 1.43) 14 Max 197.13 (6.29) 198.73 (6.08) − 1.60 (4.21) 0.814 (0.163) 0.26 (− 1.47) 14 Lactate concentration [mmol/l] Rest 0.84 (0.21) 0.83 (0.31) 0.01 (0.31) 1.00 (0.930) − 0.04 (0.09) 12 LT 1.52 (0.67) 1.66 (0.61) − 0.13 (0.40) 1.00 (0.250) 0.22 (− 1.20) 12 IAT 3.02 (0.67) 3.16 (0.61) − 0.14 (0.40) 0.962 (0.241) 0.22 (− 2.51) 12 Max 9.15 (2.18) 10.64 (2.30) − 1.49 (2.15) 0.110 (0.028) 0.67 (− 2.51) 12 Running speed [km/h] LT 8.67 (1.17) 9.00 (1.10) − 3.30 (0.74) 0.429 (0.107) 0.29 (− 1.72) 14 IAT 11.94 (1.28) 12.12 (1.18) − 0.19 (0.68) 1.00 (0.317) 0.15 (− 1.04) 13 Max 15.81 (1.62) 16.31 (1.59) − 0.49 (0.76) 0.100 (0.025) 0.31 (− 2.51) 14 3.000-m test Time [min:sec] 12:59:00 (1:29) 13:06:00 (1:30) − 0:07 (0:22) 0.228 0.08 (− 1.26) 15 RPE 15.31 (1.82) 15.44 (1.09) − 0.13 (1.78) 0.783 0.09 (− 0.28) 15 Page 5 of 6 Fiedler et al. BMC Research Notes (2022) 15:351 p.m. and 08:00 p.m. [6] to compensate for this shortcom- ing. Secondly, the RPE used in the 3.000-m run has not been used in the incremental treadmill test, while blood lactate testing has only been performed during the incre- mental treadmill test and not after the 3.000-m run and therefore limits the interpretation concerning exhaus- tion criteria. Other important factors might be that this study did not control for sleeping patterns, sleep dura- tion, naps, and morning or evening type of participants which is known to influence the circadian rhythm [2, 31]. Here, the relation between the chronotype and the per- formance of athletes at certain daytimes is particularly interesting but evidence in the literature is heterogenic [32, 33]. Finally, a larger sample size would have reduced the beta error and would lead to more robust results. Future studies should address these shortcomings by adding physiological parameters to control for exhaus- tion criteria with parameters like blood lactate, HR, and RPE. Additionally, sleep related variables, and chronotype of participants should be considered. This may enable researchers to distinguish between physio- logical and psychological aspects of aerobic endurance performance and to better determine if and why day- time variations are present for the different outcome parameters. Finally, if studies aim to determine sport- specific (i.e., soccer) daytime variation, a field test rep- resenting the sport-specific requirements seems more appropriate compared to generic endurance tests like the 3.000-m run. Abbreviations ES: Effect size (Cohen’s d); HR: Heart rate; IAT: Individual anaerobic threshold; LT: Individual aerobic threshold; max: Maximal; RPE: Rate of perceived exertion. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s13104- 022- 06247-1. Additional file 1. Exercise data of soccer players. Acknowledgments The authors would like to thank the participants for their enthusiastic participation and the students for their support during data collection. We acknowledge support by the KIT-Publication Fund of the Karlsruhe Institute of Technology. Author contributions Conceptualization, JF, SA, FE; Data curation, JF; Formal analysis, JF; Investi- gation, RN, FE, SA; Methodology, JF, SA, RN, FE, SA; Writing—original draft, JF; Writing—review & editing, SA, HC, FE, RN, and AW All authors read and approved the final manuscript. Funding Open Access funding enabled and organized by Projekt DEAL. No funding was provided for this study. Availability of data and materials All data generated or analysed during this study are included in this published article [and its Additional file 1]. Declarations Ethics approval and consent to participate All participants provided written informed consent before the start. The study was approved by the Institutional Reviewer Board of the Institute of Sport and Sport Science at the Karlsruhe Institute of Technology and all methods were carried out according to the countries guidelines and regulations. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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PMC4388468	RESEARCH ARTICLE The Correlation between Running Economy and Maximal Oxygen Uptake: Cross-Sectional and Longitudinal Relationships in Highly Trained Distance Runners Andrew J. Shaw1,2, Stephen A. Ingham1, Greg Atkinson3, Jonathan P. Folland2 1 English Institute of Sport, Loughborough University, Loughborough, United Kingdom, 2 School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom, 3 Health and Social Care Institute, Teesside University, Middlesbrough, United Kingdom a.shaw@lboro.ac.uk Abstract A positive relationship between running economy and maximal oxygen uptake (V̇O2max) has been postulated in trained athletes, but previous evidence is equivocal and could have been confounded by statistical artefacts. Whether this relationship is preserved in response to running training (changes in running economy and V̇O2max) has yet to be explored. This study examined the relationships of (i) running economy and V̇O2max between runners, and (ii) the changes in running economy and V̇O2max that occur within runners in response to habitual training. 168 trained distance runners (males, n = 98, V̇O2max 73.0 ± 6.3 mLkg-1min-1; females, n = 70, V̇O2max 65.2 ± 5.9 mL kg-1min-1) performed a discontinuous submaximal running test to determine running economy (kcalkm-1). A continuous incre- mental treadmill running test to volitional exhaustion was used to determine V̇O2max 54 par- ticipants (males, n = 27; females, n = 27) also completed at least one follow up assessment. Partial correlation analysis revealed small positive relationships between running economy and V̇O2max (males r = 0.26, females r = 0.25; P<0.006), in addition to moderate positive re- lationships between the changes in running economy and V̇O2max in response to habitual training (r = 0.35; P<0.001). In conclusion, the current investigation demonstrates that only a small to moderate relationship exists between running economy and V̇O2max in highly trained distance runners. With >85% of the variance in these parameters unexplained by this relationship, these findings reaffirm that running economy and V̇O2max are primarily determined independently. Introduction Running economy (RE) and maximal oxygen uptake (V̇O2max) are two of the primary determi- nants of endurance running performance [1–4]. The combination of RE and V̇O2max, defined as the velocity at V̇O2max (vV̇O2max), has been found to account for ~94% of the inter-individual PLOS ONE \| DOI:10.1371/journal.pone.0123101 April 7, 2015 1 / 10 OPEN ACCESS Citation: Shaw AJ, Ingham SA, Atkinson G, Folland JP (2015) The Correlation between Running Economy and Maximal Oxygen Uptake: Cross- Sectional and Longitudinal Relationships in Highly Trained Distance Runners. PLoS ONE 10(4): e0123101. doi:10.1371/journal.pone.0123101 Academic Editor: Oyvind Sandbakk, Norwegian University of Science and Technology, NORWAY Received: October 31, 2014 Accepted: February 27, 2015 Published: April 7, 2015 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: All relevant data are within the paper. Funding: These authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist. variance in running performance over 16.1 km [5]. Consequently, exceptional values of both RE and V̇O2max are considered requirements for success in elite endurance competitions, and en- durance runners strive to improve both parameters through training in order to maximise performance. As the margin of success is extremely small in elite distance running, subtle en- hancements in either parameter could result in substantial performance gains. Therefore, understanding the relationship of RE and V̇O2max both between and within individuals is neces- sary to understand and optimise performance. Within cohorts of trained [6,7] and elite [8] distance runners, it has been suggested that a superior RE, quantified as the submaximal oxygen uptake, is associated with a lower V̇O2max. These findings have been used to postulate that superior economy compensates for a lower V̇O2max in some individual to achieve a similar performance level [3,8,9]. However, these inves- tigations have often been restricted to small sample sizes (<25 participants [3,6,8]), and the va- lidity of their statistical techniques has been questioned due to the expression both variables relative to body mass (i.e. mLkg-1min-1); creating a common divisor that is known to produce spurious correlations [10]. Partial correlation analysis would provide an appropriate method to account for the influence of body mass on both variables whilst avoiding statistical artefacts, however this method has yet to be used to examine the relationship between RE and V̇O2max. Furthermore, studies have solely employed oxygen cost (OC) as a measure of RE, rather than the more valid and comprehensive measurement of energy cost (EC; [11]). Thus, whether a genuine association exists between RE and V̇O2max remains unclear from the limited cross- sectional observations to date. Moreover, the concurrent alterations in RE and V̇O2max that occur within athletes over time with training might further reveal if there is an inherent association between these variables, whilst also informing the optimisation of both variables and thus performance. Previous inves- tigations in well trained athletes have noted enhancements in cycling efficiency following short-term, intensive endurance training, but with no change in V̇O2max evident [9,12,13]. In contrast, a recent investigation reported an association between individual changes in cycling efficiency and V̇O2max in response to endurance training and across a competitive season; de- spite no change in mean group V̇O2max [14]. These preliminary findings highlight the signifi- cance of this relationship for elite endurance athletes, as enhancements in either RE or V̇O2max might only be achievable at the expense of the other variable. However, this previous investiga- tion was limited to measurements of gross efficiency, with no data presented on movement economy. Moreover, analysis of this longitudinal relationship was restricted to observations within small cohorts of athletes, and with responses to run training yet to be explored. The primary aim of the current investigation was to explore the cross-sectional relationship between V̇O2max and RE, quantified as EC (OC data are also presented for comparative pur- poses), within a large cohort of highly trained distance runners. The secondary aim was to ex- amine the longitudinal relationship between the changes in V̇O2max and RE occurring within athletes in response to endurance training. Materials and Methods Overview The cross-sectional investigation involved retrospective analysis of data from 168 healthy en- durance trained athletes with competitive distances ranging from 800m to the marathon (males, n = 98; females, n = 70; Table 1), who undertook testing and monitoring as part of their sport science support from the English Institute of Sport. The following tests were performed after written informed consent was obtained as a part of sports science support provision, with procedures approved by the Internal Review Board of English Institute of Sport. Of the Correlation between Running Economy and V̇O2max PLOS ONE \| DOI:10.1371/journal.pone.0123101 April 7, 2015 2 / 10 participants assessed, 97 (males, n = 57; females, n = 40) were classed as middle distance run- ners, defined by a primary competitive distance3000m [15], with 71 classed as long distance runners (males, n = 41; females, n = 30). During the season following their final visit, athlete’s best performance times in their primary competitive distance were 89.1±6.1% and 91.2±4.4% of the current British record for males and females, respectively. Data were collected from two laboratories, with all tests conducted as part of athlete support services between November 2004 and April 2013. Participants provided informed consent prior to physiological assess- ments, in addition to an athlete agreement providing permission for the use of their data in anonymous retrospective analysis. During each visit to the laboratory, participants completed first submaximal and then maximal running assessments (detailed below). Participants wore appropriate clothing (shorts and a vest or t-shirt) and racing shoes, and laboratory conditions were similar throughout all running assessments (temperature 20.6±1.9°C, relative humidity 45.9±9.8%). As differences in RE and V̇O2max have been noted between sexes [16–18], males and females were analysed separately for cross sectional analyses. The longitudinal aspect of the study was based on 54 participants (males, n = 27; females, n = 27) from amongst the larger cohort of 168 runners, that had completed at least one follow up assessment, with a median trial separation of 203 days (range: 37–2567 days) in order to as- sess within-athlete changes in both RE and V̇O2max over time. The number of repeat assessments in the longitudinal analysis varied between participants, with a median of 3 visits per athlete (range: 2–10 visits), summating to 182 assessments in total. No evidence is currently available re- garding sex differences in the concurrent alterations in RE and V̇O2max in response to habitual endurance training, thus data for males and females were combined for longitudinal analysis. Protocol Submaximal running assessments. Following a warm-up (~10 min at 10–12 kmh-1), participants completed a discontinuous submaximal incremental test consisting of six to nine stages of 3 minutes continuous running, with increments of 1 kmh-1 on a motorised treadmill of known belt speeds (HP cosmos Saturn, Traunstein, Germany) interspersed by 30 s rest peri- ods for blood sampling. As the speeds assessed were typically between 10.5 kmh-1 and 18 kmh-1, treadmill gradient was maintained at 1% throughout submaximal assessments in order to reflect the energetic cost of outdoor running [19]. This protocol has been shown to reliable measures of running economy when quantified as both EC and OC (typical error ~3%; [20]). Moreover, the controlled laboratory environment enabled assessments of EC whilst avoiding the confounding influence of air resistance that is evident during outdoor running as speed Table 1. Physiological and anthropometrical characteristics of athletes within the cross sectional and longitudinal investigations. Cross sectional Longitudinal sub-group Females Males Females Males (n = 70) (n = 98) (n = 27) (n = 27) Age (yrs) 23±4 23±6 23±5 21±3 Body mass (kg) 55.2±4.7 67.1±7.1 55.4±4.3 66.6±6.0 Stature (cm) 169±5 179±7 168±4 179±6 V̇O2max (mLkg-1min-1) 65.2±5.9 73.0±6.3 64.5±4.9 73.6±5.9 vLTP (kmh-1) 15.5±1.2 17.2±1.3 15.7±1.2 17.6±1.1 Running economy (kcalkg-1km-1) 1.15±0.09 1.14±0.09 1.13±0.06 1.13±0.07 V̇O2max, maximal oxygen uptake; vLTP, velocity at lactate turnpoint. doi:10.1371/journal.pone.0123101.t001 Correlation between Running Economy and V̇O2max PLOS ONE \| DOI:10.1371/journal.pone.0123101 April 7, 2015 3 / 10 increases [21]. Recent performance times of participants were used to determine an appropri- ate starting speed to provide ~4 speeds prior to lactate turnpoint (LTP). Increments were continued until blood lactate concentration had risen exponentially, typically defined as an in- crease in blood lactate of ~2 mmolL-1 from the previous stage. HR (s610i, Polar, Finland) and pulmonary gas exchange (detailed below) were monitored throughout the test. Maximal running assessments. V̇O2max was determined by a continuous incremental treadmill running ramp test to volitional exhaustion. After a warm-up, participants initially ran at a speed 2 kmh-1 below the final speed of the submaximal test and at a 1% gradient. Each minute, the incline was increased by 1% until volitional exhaustion. The test duration was typi- cally 6–8 minutes. Measurements Anthropometry. Prior to exercise on laboratory visits, body mass was measured using dig- ital scales (Seca 700, Seca, Hamburg, Germany) to the nearest 0.1 kg. Stature was recorded to the nearest 1 cm using a stadiometer (Harpenden Stadiometer, Holtain Limited, UK). Pulmonary gas exchange. Breath-by-breath gas exchange data was quantified via an auto- mated open circuit metabolic cart (Oxycon Pro, Carefusion, San Diego, USA). Participants breathed through a low dead-space mask, with air sampled at 60 mLmin-1. Prior to each test, two point calibrations of both gas sensors were completed, using a known gas mixture (16% O2, 5% CO2) and ambient air. Ventilatory volume was calibrated using a 3 L (±0.4%) syringe. This system has previously been shown to be a valid apparatus for the determination of oxygen consumption (V̇O2) and carbon dioxide production (V̇CO2) at both low and maximal exercise intensities [22]. As previous data from our laboratory has demonstrated a steady state V̇O2, and V̇CO2 is achieved within the first 2 minutes of each stage for highly trained endurance run- ners [20], mean values from breathe-by-breathe measures over the final 60 seconds of each stage were used to quantify V̇O2, carbon dioxide production V̇CO2, and RER. Blood lactate. A 20μL capillary blood sample was taken from the earlobe for analysis of blood lactate ([La]b) (Biosen C-line, EKF diagnostics, Germany). The LTP was identified via the modified Dmax method [23]. LTP was quantified as the point on the third order polynomi- al curve fitted to the speed-lactate relationship that generated the greatest perpendicular dis- tance to the straight line formed between the stage proceeding an increase in [La]b greater than 0.4 mmol.L-1 (lactate threshold) and the final stage. The four stages prior to LTP were identi- fied for each participant, with an average of these four stages used to quantify OC and EC. Calculation of running economy. V̇O2 and V̇CO2 during the final minute of each sub- maximal stage were used to calculate EC. Updated nonprotein respiratory quotient equations [24] were used to estimate substrate utilisation (gmin-1) during the monitored period. The energy derived from each substrate was then calculated by multiplying fat and carbohydrate usage by 9.75 kcal and 4.07 kcal, respectively, reflecting the mean energy content of the metabolised substrates during moderate to high intensity exercise [25]. EC was quantified as the sum of these values, expressed in kcalkm-1. V̇O2 during the final minute of each submaxi- mal stage was used to determine oxygen cost (OC) in mLkm-1 to enable comparisons to previous investigations. Statistical analyses. Data are presented as mean±SD for all dependant variables. Data analysis was conducted using SPSS for windows (v21; IBM Corporation, Armonk, NY). When an individual visited the laboratory for repeated assessments, an average of the assessments was calculated and used for the cross sectional analysis. Pearsons product-moment coefficients were calculated to assess the relationship between body mass and EC, OC and V̇O2max. As body mass was strongly related to both RE measures (EC, OC) and V̇O2max, partial correlations Correlation between Running Economy and V̇O2max PLOS ONE \| DOI:10.1371/journal.pone.0123101 April 7, 2015 4 / 10 controlling for body mass, and associated 95% confidence intervals (CI), were used to assess the relationship between absolute V̇O2max and both EC and OC. This method removes the in- fluence of body mass on both RE and V̇O2max whilst avoiding spurious correlations created by correlating two variables with a common divisor [26]. For graphical display of these relation- ships, values of EC and V̇O2max adjusted for body mass for each individual were calculated based on individual residuals. This involved summating the individual’s residual, in compari- son to the cohort relationship with body mass (e.g. EC vs body mass), with the group mean for that variable [27]. For the longitudinal analysis, in order to assess any relationships between the changes over time in absolute V̇O2max and the changes in both EC and OC over repeat visits, partial correlation coefficients were calculated using ANCOVA [28]; providing a comprehen- sive model that accounts variations in both body mass and the number of visits per athlete. Cohen's d effect size descriptors (trivial 0.0–0.1, small 0.1–0.3, moderate 0.3–0.5, large 0.5–0.7, very large 0.7–0.9, nearly perfect 0.9–1, perfect 1) were used to infer correlation magnitude [29]. Significance was accepted at P0.05. Results Participant Characteristics Participant characteristics are shown in Table 1. The well trained status of the participants was emphasised by the high V̇O2max and vLTP values for both males and females. Cross-sectional analysis Partial correlation analysis controlling for body mass, revealed small positive relationships be- tween EC and V̇O2max (males r = 0.26, CI 0.07–0.44, P = 0.009; females r = 0.25, CI 0.02–0.46, P = 0.036; Fig 1), and a moderate positive relationship between OC and V̇O2max (males r = 0.33, CI 0.14–0.50, P = 0.001; females r = 0.33, CI 0.10–0.52, P = 0.006). Longitudinal analysis Partial correlation analysis from ANCOVA revealed moderate positive relationships between the changes in EC and V̇O2max over time (r = 0.35; CI 0.19–0.49, P < 0.001; Fig 2), and changes in OC and V̇O2max over time (r = 0.44; CI 0.29–0.57, P < 0.001). Discussion The present investigation explored the cross-sectional and longitudinal relationships between RE and V̇O2max in a large cohort of highly trained distance runners. The major contribution of this study to the field is that only a small to moderate association exists between RE and V̇O2max (R2 ~ 12%) when body mass is appropriately accounted for. With >85% of the vari- ance in these parameters unexplained by this relationship, these findings reaffirm that RE and V̇O2max are primarily determined independently. Cross-sectional analysis revealed a small positive between-participant relationships between V̇O2max and the metabolic cost of running, when quantified as both EC (r ~ 0.25) and OC (r ~ 0.33). These results support the findings of Pate et al. [7], who reported a similar relation- ship (r = 0.29) between submaximal V̇O2 and V̇O2max in a similarly large cohort of habitual distance runners. Conversely, a stronger, moderate positive relationship has been reported be- tween submaximal V̇O2 and V̇O2max in smaller cohorts of elite distance runners (r = 0.59; [8]) and physically active individuals (r = 0.48; [30]). However, all aforementioned investigations are confounded by statistical artefacts that arise when correlating two variables with common divisors [10,26], and thus should be regarded with caution. Within the current study, spurious Correlation between Running Economy and V̇O2max PLOS ONE \| DOI:10.1371/journal.pone.0123101 April 7, 2015 5 / 10 correlations between RE and V̇O2max were avoided by removing the influence of body mass with partial correlations, which enabled the true relationship between these variables to be ex- amined. As a lower metabolic cost is reflective of a more economical runner, our findings con- firm the existence of a small inverse association between RE and V̇O2max in endurance runners. The longitudinal analysis of the relationship between the changes in RE and the changes in V̇O2max within participants in response to training has not previously been documented. Sup- porting the findings from our cross sectional analysis, a moderate positive relationship (r = 0.35) was observed between the changes in EC and V̇O2max over repeated assessments. Fig 1. Scatter plot of energy cost (Kcalkm-1) adjusted for body mass (BM) vs V̇O2max (Lmin-1) adjusted for BM for both females (A; n = 70; r = 0.25; P = 0.036) and males (B; n = 98; r = 0.26; P = 0.009) within the cross-sectional analysis. doi:10.1371/journal.pone.0123101.g001 Correlation between Running Economy and V̇O2max PLOS ONE \| DOI:10.1371/journal.pone.0123101 April 7, 2015 6 / 10 Moreover, these findings support recent observations from competitive road cyclists that highlighted a similar moderate relationship (r = 0.44) between changes in gross efficiency and V̇O2max across a training season [14]. It has been postulated that variations in lipid oxidation rates between individuals might, in part, explain the relationship between OC and V̇O2max that some previous studies have docu- mented; with a higher V̇O2max facilitating greater lipid oxidation and consequently a greater OC during sub-maximal exercise [7]. Whilst OC may be sensitive to lipid oxidation, the calcula- tion of EC includes the RER and thus is insensitive to differences in substrate metabolism. The influence of substrate metabolism could conceivably explain the marginally stronger relation- ship observed between OC and V̇O2max, than EC and V̇O2max, in both the cross sectional (r ~ 0.33 vs r ~ 0.25) and longitudinal observations (r = 0.44 vs r = 0.35). More importantly, a positive relationship was documented between EC and V̇O2max that is clearly independent of variations in lipid metabolism. The mechanisms that underpin the small relationship between EC and V̇O2max remain un- clear. It has been argued that for athletes of a similar, high performance level, there would be an inevitable relationship between EC and V̇O2max in order to produce a similar velocity at V̇O2max [31]. However, we have found no evidence for this possibility, despite all the partici- pants in this study being highly trained and high performing runners, perhaps in part because of the variable performance ability of the athletes. It is also possible that less economical run- ners recruit a larger muscle mass (braking, oscillation etc.) and it is conceivable that this could contribute to a higher V̇O2max. However there is considerable evidence that V̇O2max during whole body exercise such as running is largely dependent on oxygen delivery rather than utili- sation [32], which might question this explanation. Thus, further investigation would be re- quired to identify the factors driving the interdependence of EC and V̇O2max. Though reaching statistical significance, the association between RE and V̇O2max was small. The current study found only ~ 7% (between-participant cross sectional data) and 12% Fig 2. Scatter plot of the changes over time in energy cost (Kcalkm-1) adjusted for body mass (BM) vs the changes over time in V̇O2max (Lmin-1) adjusted for BM (r = 0.35; P < 0.001) within the longitudinal analysis. doi:10.1371/journal.pone.0123101.g002 Correlation between Running Economy and V̇O2max PLOS ONE \| DOI:10.1371/journal.pone.0123101 April 7, 2015 7 / 10 (within-participant longitudinal data) of the variance in RE was explained by V̇O2max. This small association likely reflects the distinct nature of these variables and their physiological determinants. V̇O2max is known to be determined by factors such as cardiac output [33], total haemoglobin mass [34], and mitochondrial capacity [1]. Conversely, RE is thought to be closely associated to multiple biomechanical and anthropometrical factors, including effective storage and re-utilisation of elastic energy [35,36], vertical oscillation [37] and ground contact time [38]. As there are few common determinants of both RE and V̇O2max, adaptations that lead to enhancements in one of these variables are unlikely to directly influence in the opposing variable. In conclusion, the current investigation demonstrates that only a small to moderate rela- tionship exists between running economy and V̇O2max in highly trained distance runners. With >85% of the variance in these parameters unexplained by this relationship, these findings reaffirm that running economy and V̇O2max are primarily determined independently. 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PMC6040767	RESEARCH ARTICLE The effects of beetroot juice supplementation on exercise economy, rating of perceived exertion and running mechanics in elite distance runners: A double-blinded, randomized study Carlos Balsalobre-Ferna´ndez1,2☯, Blanca Romero-Moraleda2,3☯, Rocı´o Cupeiro2‡, Ana Bele´n Peinado2‡, Javier Butragueño2‡, Pedro J. Benito2‡ 1 Department of Physical Education, Sport and Human Movement, Universidad Auto´noma de Madrid, Madrid, Spain, 2 Laboratory of Exercise Physiology Research Group, Department of Health and Human Performance, School of Physical Activity and Sport Sciences-INEF, Universidad Politecnica de Madrid, Madrid, Spain, 3 Faculty of Health, Camilo Jose´ Cela University, Madrid, Spain ☯ These authors contributed equally to this work. ‡ These authors also contributed equally to this work. carlos.balsalobre@icloud.com Abstract Purpose Nitrate-rich beetroot juice supplementation has been extensively used to increase exercise economy in different populations. However, its use in elite distance runners, and its potential effects on biomechanical aspects of running have not been properly investigated. This study aims to analyze the potential effects of 15 days of beetroot juice supplementation on physio- logical, psychological and biomechanical variables in elite runners. Methods Twelve elite middle and long-distance runners (age = 26.3 ± 5.1yrs, VO2Max = 71.8±5.2 mlkg-1min-1) completed an incremental running test to exhaustion on a treadmill before and after a 15-days supplementation period, in which half of the group (EG) consumed a daily nitrate-rich beetroot juice and the other group (PG) consumed a placebo drink. Time to exhaustion (TEx), running economy, vastus lateralis oxygen saturation (SmO2), leg stiffness and rate of perceived exertion (RPE) were measured at 15, 17.1 and 20 km/h during the incremental test. Results Likely to very likely improvements in EG were observed for the RPE (Standardized mean difference (SMD) = -2.17, 90%CI = -3.23, -1.1), SmO2 (SMD = 0.72, 90%CI = 0.03, 1.41) and TEx (SMD = 1.18, 90%CI = -0.14, 2.5) in comparison with PG. No other physiological or biomechanical variable showed substantial improvements after the supplementation period. PLOS ONE \| https://doi.org/10.1371/journal.pone.0200517 July 11, 2018 1 / 10 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Balsalobre-Ferna´ndez C, Romero- Moraleda B, Cupeiro R, Peinado AB, Butragueño J, Benito PJ (2018) The effects of beetroot juice supplementation on exercise economy, rating of perceived exertion and running mechanics in elite distance runners: A double-blinded, randomized study. PLoS ONE 13(7): e0200517. https://doi.org/ 10.1371/journal.pone.0200517 Editor: Gordon Fisher, University of Alabama at Birmingham, UNITED STATES Received: March 15, 2018 Accepted: June 26, 2018 Published: July 11, 2018 Copyright: © 2018 Balsalobre-Ferna´ndez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data are available from figshare (https://doi.org/10.6084/m9.figshare. 5873190.v1). Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Conclusions Fifteen days of nitrate-rich beetroot juice supplementation produced substantial improve- ments in the time to exhaustion in elite runners; however, it didn’t produce meaningful improvements in running economy, VO2Max or mechanical parameters. Introduction Performance at the highest level of competition in endurance events depends on several physi- ological, psychological and biomechanical parameters [1–5]. Even marginal gains in some var- iables might represent a competitive edge to win the race or break records; for instance, wearing energy-recovering shoes have shown to increase running economy by about 4%, which might have helped in the recent first sub-2h attempt on the marathon [6]. Maximal oxy- gen consumption is one of the most relevant physiological variables in distance running per- formance [7,8]; however, in elite athletes other variables seem to influence competitive success to a greater extent [4,9]. Among them, running economy (i.e., the oxygen consumption at a certain pace) has been proposed as one of the most important variables in distance running performance. Theoretically, better running economy means either being able to run at the same pace with a lower exertion or, for the same energy consumption, being able to run faster [9,10]. Therefore, researchers and trainers are focused now on finding strategies to increase economy in different kinds of endurance athletes such as runners, cyclists or swimmers. In this sense, different studies have shown that heavy-loaded resistance training produces a meaningful increase in running economy of athletes at different levels, including elite partici- pants [11,12]. Besides training interventions, different nutritional strategies have also been used to increase distance running performance, from high-carbohydrate to ketogenic diets [13–15]. However, during the last decade one particular supplement has been investigated as a potential beneficial aid for endurance events: beetroot juice [16,17]. Beetroot juice is a high source of nitrate (NO3 -) that has been proposed to increase NO (nitric oxide) availability, a potent signaling molecule that can increase vasodilatation, mitochondrial respiration or glu- cose uptake, among other factors related to exercise performance [17]. Several studies have shown that beetroot juice supplementation for about a week does have an ergogenic effect in moderate trained runners, mainly via an increase in running economy [16,18,19]. However, there is a lack of studies analyzing its potential benefits in elite subjects, who might need a higher dose or exposure than recreational athletes to obtain the desired increases in perfor- mance. A recent study [20] observed that 8 days of beetroot supplementation didn’t increase performance variables in elite 1500m runners; however, whether larger interventions might have benefits in elite populations is unknown. Moreover, to the best of our knowledge, no studies have analyzed the potential effects of beetroot supplementation in mechanical aspects of running such as leg stiffness, a variable that has been linked to running economy in different studies [21–23]. For this, the purpose of this study is to analyze the physiological, psychological and biomechanical effects of beetroot juice supplementation in elite distance runners. Materials & methods Participants Twelve elite middle and long-distance runners, including European medalists and Olympians were recruited from the High Performance Center of Madrid, Spain to join this investigation. Inclusion criteria were as follows: male competitors in national and international events, with Beetroot juice supplementation and exercise economy, rating of perceived exertion and running mechanics PLOS ONE \| https://doi.org/10.1371/journal.pone.0200517 July 11, 2018 2 / 10 personal bests in the urban 10k below 32 min, and below 3min50s. in the 1500m. See details in Table 1. Prior to commencement, all participants provided written informed consent. Participants were instructed to not consume beetroot of any form 2 months before the start of the intervention and to maintain their normal diet throughout the testing period, to follow the same diet for 24 hours prior to each trial, to avoid food and drink in the hour before each trial, and to refrain from strenuous exercise for 24 hours before each trial. None of the participants followed a plant-based (vegetarian or vegan) diet, and they never consumed nitrate-rich beetroot juice in the past. The study protocol complied with the Declaration of Helsinki for Human Experimentation and was approved by the Institutional Review Board of the Universidad Camilo Jose´ Cela. Procedures Beetroot juice supplementation protocol. Participants were randomly assigned to either a placebo (PG) or an experimental (EG) group in a double-blinded manner so neither the ath- letes nor the main researchers could possibly identify who were consuming a nitrate-rich beet- root juice supplementation. See Fig 1. Participants in EG consumed a high-nitrate beetroot juice (6.5-mmol NO3-/70-mL, Beet It Sport; James White Drinks, Ipswich, United Kingdom) for 15 consecutive days with their breakfast meal, while participants in PG consumed a nitrate-free placebo from the same manu- facturer (0.065-mmol NO3-/70-mL Beet It Sport; James White Drinks, Ipswich, United King- dom) for the same amount of time. Placebo drinks had the exact same packaging, color, smell and taste as its nitrate-rich counterpart, so neither the athletes or the researchers could know who were consuming what. Participants were instructed to not use mouth rinse during the supplementation period. Physiological parameters Incremental running test. The maximal graded test was performed with a computerized treadmill (H/P/COSMOS 3PW 4.0, H/P/Cosmos Sports & Medical, Nussdorf-Traunstein, Germany) to determine each subject’s maximal VO2, and to measure running economy at dif- ferent speeds. Expired gases were measured breath-by-breath with the Jaeger Oxycon Pro gas analyser (Erich Jaeger, Viasys Healthcare, Germany). Heart response was continuously moni- tored with a 12-lead ECG. Participants started with 3 minutes warm-up at 10 km/h. Then the protocol continued with 3 steady state steps of 3 min each, at 15, 17.1 and 20 km/h. Subse- quently the speed was increased to 0.2 km/h every 12 seconds until exhaustion. A slope of 1% was set throughout the test [24]. All the tests fulfilled at least 2 of the following criteria [2]: A respiratory exchange ratio (RER) >1.10, a plateau in VO2 (corresponding to a variation of, 100 mlmin-1) despite an increase in exercise intensity and a peak HR>220-age. This test was conducted at baseline and repeated 24-h after the 15th day of supplementation, so there were at least 24h after the last beetroot juice ingestion. O2 saturation measurement. Throughout all the graded tests O2 saturation (SmO2, in %) was registered using near-infrared spectroscopy by placing a wearable device on the belly of the vastus laterallis (Moxy Monitor, Minnesota, USA). In this study, a wearable device was used to measure the muscle oxygenation in the vastus lateralis of dominant leg (along the Table 1. Characteristics of the participants. Personal bests are expressed as range. Age (yrs.) Height (cm) Weight (kg) VO2Max (mlkg-1min-1) PB in urban 10 km (min:s) PB in outdoor 1500m (min:s) Experimental group 27.3±7.8 1.79±0.07 69.2±8.6 69.1±5.3 29:22–31:00 3:32–3:45 Control group 24.2±2.9 1.78±0.04 65.2±2.6 72.3±6.8 28:28–30:54 3:43–3:45 https://doi.org/10.1371/journal.pone.0200517.t001 Beetroot juice supplementation and exercise economy, rating of perceived exertion and running mechanics PLOS ONE \| https://doi.org/10.1371/journal.pone.0200517 July 11, 2018 3 / 10 vertical axis of the thigh, approximately 10–12 cm above the knee joint [25]. The device was wrapped by an elastic tight without occluding the blood flow. Black bandages covered the device to eliminate background light [26]. Biomechanical parameters To measure leg stiffness the validated Runmatic app v2.8 installed on an iPhone 6 with iOS 10.3.3 was used following the procedures described elsewhere [27]. The Runmatic app was used to record leg stiffness during the first minute of each steady state on the incremental test (i.e., at 15, 17.1 and 20 km/h), and mean scores were registered. Psychological parameters At the completion of each steady state step of the incremental running test (i.e., during the last 10 seconds of the 3-min run at 15, 17.1 and 20 km/h) participants were asked to rate their RPE Fig 1. CONSORT flowchart. https://doi.org/10.1371/journal.pone.0200517.g001 Beetroot juice supplementation and exercise economy, rating of perceived exertion and running mechanics PLOS ONE \| https://doi.org/10.1371/journal.pone.0200517 July 11, 2018 4 / 10 in a 1–10 scale. Before starting the incremental test, participants were given instruction to point to a number on a sheet held by a researcher corresponding to their RPE at each step dur- ing the trial. Participants were told that 1 means no exertion at all, and 10 means maximal exertion. Statistical analyses Data is presented as mean with standard deviations. The non-parametric Mann-Whitney’s U statistical test was used to compare the pre-post differences between the experimental and pla- cebo groups. Also, the standardized mean differences (SMD) with the corresponding 90% con- fidence interval (CI) were calculated using a magnitude-based inference approach [28]. The criteria for interpreting the magnitude of the SMD were: trivial (<0.2), small (0.2–0.6), moder- ate (0.6–1.0) and large (>1.0). Probabilities were also determined to establish whether true dif- ferences were lower, similar or higher than the smallest worthwhile change (0.2 x between– participant SD). Quantitative chances of better or worse effects were assessed qualitatively as follows: <1%, almost certainly not; 1–5%, very unlikely; 5–25%, unlikely; 25–75%, possible; 75–95%, likely; 95–99%, very likely; and >99%, almost certain. If the changes of better or worse were both >5%, the true difference was assessed as unclear. Statistical analyses were per- formed using a custom spreadsheet [28] and SPSS 24 for Mac. The level of asymptotic signifi- cance was set as p< 0.05. Results Participants in EG showed unclear improvements in heart rate (SMD = 0.03, 90%CI = -0.36, 0.42) and running economy expressed in VO2 (SMD = 0.36, 90%CI = -0.5, 1.21) in compari- son with PG. Also, changes in the VO2Max did not substantially differ in EG in comparison with PG after the supplementation period (SMD = 0.04, 90%CI = -0.87, 0.95). However, likely to very likely improvements in EG vs. PG were observed for the RPE (SMD = -2.17, 90%CI = -3.23, -1.1), SmO2 (SMD = 0.72, 90%CI = 0.03, 1.41) (in the three steady stages) and time to exhaustion (SMD = 1.18, 90%CI = -0.14, 2.5). Finally, no substantial improvements in leg stiff- ness (SMD = -0.14, 90%CI = -0.44, 0.17) were observed during any of the steady stages of the incremental test as revealed by the low to trivial SMD and the range of the 90%CI. See Fig 2, Table 2 and Table 3 for more details. Discussion The results of our study showed low to trivial improvements in different physiological mea- sures such as running economy at different paces after 15-days of nitrate-rich beetroot juice supplementation in elite distance runners. These results are in line with other experiments conducted with elite distance athletes, in which submaximal exercise economy was not altered after the supplementation period either [20,29]. The supplementation period in our study was about twice larger than other experiments with elite runners; however, more days of ingestion of nitrate-rich beetroot juice supplementation doesn’t seem to boost the adaptations in sub- maximal exercise economy that this supplement has extensively showed to produce in less trained populations [17,18]. A novel contribution from our study was the analysis of the potential benefits of beetroot juice ingestion on different biomechanical variables that has been linked to running perfor- mance in the literature, such as leg stiffness [23,30]. For example, leg stiffness has showed to be negatively associated with running economy, since, in theory, a stiffer lower limb would store a higher amount of elastic energy that would assist in the force production during running and, therefore, it would reduce the cost of the exercise [9,23]. Results in our study showed Beetroot juice supplementation and exercise economy, rating of perceived exertion and running mechanics PLOS ONE \| https://doi.org/10.1371/journal.pone.0200517 July 11, 2018 5 / 10 trivial effects of beetroot juice supplementation in leg stiffness, similar to submaximal running economy. Contrary to the results discussed above, likely to very likely large improvements were observed both in the rate of perceived exertion and the time to exhaustion in the experimental group in comparison with the placebo group after the supplementation period, where athletes who consumed nitrate-rich beetroot juice endured more time before voluntary stopping the incremental test on the treadmill. One potential explanation for this observation is the moder- ate increase in the SmO2 of the vastus lateralis of the athletes from the experimental group in comparison with the control group after the supplementation period. Participants who con- sumed nitrate-rich beetroot supplementation had larger percent of oxygen saturation in their muscles than their counterparts during exercise. That could have limited the accumulation of fatigue-related metabolites and reduce the depletion of PCr, which, in the end, could increase the time to exhaustion. NIRS is used for providing information about performance and oxygen muscle function and to evaluate response before, during and after exercise. In our study, the initial-during-end-exercise SmO2 was greater in experimental group. That finding is related Fig 2. Forest plot with standardized mean differences (SMD) and 90% confidence interval (CI) for the rate of perceived exertion (RPE) and oxygen saturation (SmO2) at different running paces. Lower scores (i.e., to the left in the X-axis) means lower scores in the experimental group. https://doi.org/10.1371/journal.pone.0200517.g002 Beetroot juice supplementation and exercise economy, rating of perceived exertion and running mechanics PLOS ONE \| https://doi.org/10.1371/journal.pone.0200517 July 11, 2018 6 / 10 with recovery capacity and improvements in performance status delaying the fatigue [31]. Then, greater availability of O2 could have impacted RPE and, finally it could have led to the observed increase in the time to exhaustion in the experimental group. For example, several studies have observed that increased oxygen availability via hyperoxia enhances exercise per- formance [32]. Nevertheless, further investigations are needed to better understand the mecha- nisms by which beetroot juice supplementation could reduce RPE and increase time to exhaustion in an incremental test on a treadmill in elite distance runners. Summarizing, results in our study are in line with other investigations that observed no sig- nificant increases in running economy after a supplementation period with nitrate-rich beet- root juice in elite athletes [20,29]; however, the large increase in the time to exhaustion, the reduction in RPE and the increase in the oxygen saturation of the vastus lateralis during exer- cise, observed after 15 consecutive days of nitrate-rich beetroot supplementation provide novel information about the effects of this nutritional aid. Further investigations are required Table 2. Mean values and standard deviations for the experimental and placebo groups before (Pre) and after (Post) the supplementation period. Experimental group Placebo group Pre Post Pre Post Biomechanical parameters Leg stiffness (kN/m) 15 km/h 7.9 ± 0.9 8.6 ± 1.4 8.4 ± 1.2 8.5 ± 1.4 17.1 km/h 8.5 ± 1.2 8.7 ± 1.2 7.9 ± 1.1 8.3 ± 1.3 20 km/h 8.9 ± 0.8 9.2 ± 1.3 7.5 ± 1.0 8.1 ± 0.8 Physiological parameters RE (mlkgmin-1) 15 km/h 50.8 ± 3.5 52.6 ± 2.8 52.3 ± 3.3 53.7 ± 3.8 17.1 km/h 58.3 ± 2.7 60.4 ± 2.3 60.1 ± 3.8 62.1 ± 3.3 20 km/h 67.3 ± 4.4 69.5 ± 2.9 70.5 ± 5.1 71.4 ± 4.8 RER 15 km/h 0.87 ± 0.02 0.83 ± 0.02 0.91 ± 0.02 0.87 ± 0.01 17.1 km/h 0.93 ± 0.02 0.90 ± 0.03 0.96 ± 0.02 0.94 ± 0.02 20 km/h 1.03 ± 0.01 0.97 ± 0.04 1.07 ± 0.03 1.03 ± 0.03 HR (bpm) 15 km/h 147.2 ± 13.7 146.9 ± 11.2 157.3 ± 10.0 151.4 ± 12.4 17.1 km/h 160.6 ± 12.2 157.3 ± 9.2 168.1 ± 8.1 165.9 ± 7.1 20 km/h 164.2 ± 19.1 168.4 ± 11.4 164.6 ± 21.1 177.1 ± 4.6 SmO2 (%) 15 km/h 41.7 ± 7.1 47.7 ± 3.9 46.6 ± 15.5 43.7 ± 5.9 17.1 km/h 34.4 ± 3.5 41.0 ± 2.0 38.2 ± 16.4 37.8 ± 5.2 20 km/h 24.4 ± 2.8 31.0 ± 6.9 26.8 ± 12.1 27.7 ± 4.8 TEx (s) 1173.0 ± 87.1 1269.0 ± 53.6 1251.0 ± 52.6 1230 ± 73.5 VO2Max (mlkgmin-1) 69.1 ± 5.3 70.1 ± 7.0 72.3 ± 6.8 74.9 ± 6.1 Psychological parameters RPE 15 km/h 2.4 ± 0.5 2.3 ± 0.5 2.4 ± 0.5 3.5 ± 0.8 17.1 km/h 3.8 ± 0.8 4.0 ± 0.8 4.4 ± 0.5 5.6 ± 0.8 20 km/h 6.0 ± 0.7 6.0 ± 1.0 7.2 ± 0.4 7.7 ± 1.0 Abbreviations: RE = running economy; VO2Max = maximal oxygen consumption; RER = respiratory exchange ratio; TEx = time to exhaustion; HR = heart rate; SmO2 = saturation of muscle O2; RPE = rate of perceived exertion https://doi.org/10.1371/journal.pone.0200517.t002 Beetroot juice supplementation and exercise economy, rating of perceived exertion and running mechanics PLOS ONE \| https://doi.org/10.1371/journal.pone.0200517 July 11, 2018 7 / 10 to confirm the findings of the present study, as well as to better understand the mechanisms behind these potential benefits for elite distance runners. Practical application and conclusions Fifteen consecutive days of nitrate-rich beetroot juice supplementation didn’t increase running economy, locomotion mechanics or muscular power in elite distance runners. However, time to exhaustion in an incremental test on a treadmill, as well as rate of perceived exertion at different running paces were meaningfully higher and lower in the experimental group in comparison with the placebo group, respectively. These results might be explained in part by a higher muscle oxy- gen saturation observed in the vastus lateralis of the runners from the experimental group in com- parison with the control group. Anyhow, one of the most performance-related variables in distance running (i.e., time to exhaustion) showed large improvements after 15-days of nitrate- rich beetroot juice supplementation. These results could have potential practical application for elite distance runners seeking nutritional strategies to improve their running performance. Supporting information S1 File. CONSORT 2010 checklist of information to include in a randomized controlled trial. (DOC) S2 File. Study protocol as approved by the ethics committee. (PDF) Author Contributions Conceptualization: Carlos Balsalobre-Ferna´ndez, Blanca Romero-Moraleda. Data curation: Carlos Balsalobre-Ferna´ndez, Blanca Romero-Moraleda. Formal analysis: Carlos Balsalobre-Ferna´ndez. Table 3. Pre-post differences on the studied variables in the placebo vs experimental conditions. SMD (90%CI) Chances of being beneficial/trivial/harmful Qualitative inference % of change (experimental vs placebo) P Biomechanical parameters Leg stiffness -0.14 (-0.44, 0.17) 4/61/36 Possibly 2.25 vs 4.64 0.310 Physiological parameters RE 0.36 (-0.5, 1.21) 63/24/13 Unclear 7.07 vs 4.5 0.589 VO2Max 0.04 (-0.87, 0.95) 37/31/32 Unclear 4.41 vs. 3.66 1.000 Av_RER -0.2 (-1.15, 0.74) 50/27/22 Unclear -4.7 vs. -3.6 1.000 TEx (s) 1.18 (-0.14, 2.5) 90/6/4 Likely 8.18 vs 1.6 0.310 Av_HR 0.03 (-0.36, 0.42) 22/64/15 Unclear -0.26 vs. 1.14 0.792 SmO2 0.72 (0.03, 1.41) 91/7/2 Likely 17.8 vs. -2.64 0.167 Psychological parameters RPE -2.17 (-3.23, -1.1) 100/0/0 Very likely -9.0 vs 20.4 0.016 Standardized mean differences (SMD) with 90% Confidence Intervals (CI) express the magnitude of the difference on the pre-post changes between the experimental and placebo groups. Positive values reflect higher increments on the placebo group after the supplementation period, while negative scores reflect lower increments on the experimental group after the supplementation period. P is the asymptotic significance of the Mann-Whiteny’s U test. Abbreviations: RE = running economy; VO2Max = maximal oxygen consumption; Av_RER = average respiratory exchange ratio (whole test); TEx = time to exhaustion; Av_HR = average heart rate (whole test); SmO2 = saturation of muscle O2; RPE = rate of perceived exertion https://doi.org/10.1371/journal.pone.0200517.t003 Beetroot juice supplementation and exercise economy, rating of perceived exertion and running mechanics PLOS ONE \| https://doi.org/10.1371/journal.pone.0200517 July 11, 2018 8 / 10 Investigation: Carlos Balsalobre-Ferna´ndez, Blanca Romero-Moraleda, Rocı´o Cupeiro, Ana Bele´n Peinado, Javier Butragueño, Pedro J. Benito. 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PMC10030110	Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 1 of 20 How human runners regulate footsteps on uneven terrain Nihav Dhawale1,2, Madhusudhan Venkadesan1* 1Department of Mechanical Engineering and Materials Science, Yale University, New Haven, United States; 2National Centre for Biological Sciences, Tata Institute of Fundamental Research, Mumbai, India Abstract Running stably on uneven natural terrain takes skillful control and was critical for human evolution. Even as runners circumnavigate hazardous obstacles such as steep drops, they must contend with uneven ground that is gentler but still destabilizing. We do not know how foot- steps are guided based on the uneven topography of the ground and how those choices influence stability. Therefore, we studied human runners on trail- like undulating uneven terrain and measured their energetics, kinematics, ground forces, and stepping patterns. We find that runners do not selectively step on more level ground areas. Instead, the body’s mechanical response, mediated by the control of leg compliance, helps maintain stability without requiring precise regulation of foot- steps. Furthermore, their overall kinematics and energy consumption on uneven terrain showed little change from flat ground. These findings may explain how runners remain stable on natural terrain while devoting attention to tasks besides guiding footsteps. Editor's evaluation This paper presents fundamental evidence for the control mechanisms used by running humans to maintain stability while running on naturalistically uneven terrain. The authors use a creative and compelling combination of experiments and modeling to analyze running on terrain with mildly stochastic undulating roughness, a condition that resembles natural terrain conditions, such as trail running. The findings suggest that humans use open- loop, intrinsically stable strategies to run on this terrain, and not visually guided foot placement, making an important contribution to under- standing the context- dependent role of vision in human locomotion. Introduction Running on natural terrain is an evolutionarily important human ability (Carrier et al., 1984; Bramble and Lieberman, 2004), which requires the skillful negotiation of uneven ground (Lee and Lishman, 1977; Warren et al., 1986). Part of the challenge is planning a path in real- time that navigates around obstacles or sudden steep drops. Even after finding a path around such hazards, the ground would be uneven. Planning the stepping pattern using detailed information of every bump and dip of the ground is typically infeasible on natural trails because the ground is often covered by foliage or grass. But the seemingly slight unevenness, albeit gentler than large obstacles or drops, could have signif- icant consequences to stability. Mathematical modeling predicts that even slightly uneven ground, with peak- to- valley height variations less than the dorso- plantar foot height, could be severely desta- bilizing unless some form of mitigation strategy is employed to deal with them (Dhawale et al., 2019). In this paper, we investigate how human runners deal with these types of undulating uneven ground. Studies on human walking find that footsteps are visually guided to plan a path through complex, uneven terrain (Matthis et al., 2018; Thomas et al., 2020; Bonnen et al., 2021). Although there are RESEARCH ARTICLE *For correspondence: m.venkadesan@yale.edu Competing interest: The authors declare that no competing interests exist. Funding: See page 17 Received: 04 February 2021 Preprinted: 22 February 2021 Accepted: 21 February 2023 Published: 22 February 2023 Reviewing Editor: Monica A Daley, University of California, Irvine, United States Copyright Dhawale and Venkadesan. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 2 of 20 no similar studies of running on naturalistic uneven terrain, we may expect that vision’s role is multi- fold. For example, in the evolutionary context of persistence hunting (Carrier et al., 1984; Bramble and Lieberman, 2004), vision is needed to track footprints and continuously survey the landscape for prey in addition to dealing with the terrain’s unevenness. The potentially competing demands on visual attention—for stability versus other functional goals—is probably more exacting in running than in walking because of the greater speeds involved and the shorter time available to sense and act. Additional important factors to consider on uneven terrain include dynamic stability (Holmes et al., 2006; Dhawale et al., 2019; Daley and Biewener, 2006; Voloshina and Ferris, 2015), leg safety (Birn- Jeffery et al., 2014), peak force mitigation (Blum et al., 2014), and anticipatory leg adjustments (Birn- Jeffery and Daley, 2012; Müller et al., 2015). However, we presently lack studies of human runners on naturalistic uneven terrain to investigate the role of vision- guided footstep regulation and the subtle regulation of body mechanics for maintaining stability, which motivates the overground running experiments presented in this paper. In addition to vision, the body’s mechanical responses aid stability and are neurally modulated through muscle contractions. These mechanical properties have been studied theoretically, and experimental data have been interpreted, through the lens of models that approximate the runner as a point- like mass on a massless leg, commonly referred to as the spring- legged inverted pendulum (SLIP) model (Seyfarth et al., 2002; Daley et al., 2006; Geyer et al., 2006; Birn- Jeffery et al., 2014; Müller et al., 2016; Seethapathi and Srinivasan, 2019). SLIP models have hypothesized multiple stabilization strategies for terrain with random height variations, several of which have found experi- mental support: higher leg retraction rates (Karssen et al., 2015), wider lateral foot placement (Volos- hina and Ferris, 2015; Mahaki et al., 2019), and the possible use of vision to guide foot placement (Birn- Jeffery and Daley, 2012). But SLIP models do not help understand the effect of slope variations because the ground force is constrained to always point to the center of mass irrespective of whether the foot contacts the ground on a level or sloping region. That is a consequence of the zero moment of inertia about the center of mass for SLIP models. Analyses of models with non- zero moment of inertia show that both height and slope variations are detrimental to stability, with slope being more destabilizing (Dhawale et al., 2019), reminiscent of common experience among runners. Understanding why slope variations degrade stability could generate hypotheses and testable predic- tions for how human runners deal with stability on naturalistic uneven terrain. The mathematical analyses of Dhawale et al., 2019, find that random variations in slope lead to step- to- step fluctuations in the fore- aft ground impulse. For steady forward running, the net forward impulse should be zero for every step. But small step- to- step random variation of the fore- aft ground impulse leads to a gradual accumu- lation of sagittal plane angular momentum, which ultimately destabilizes the runner. However, the rate at which the destabilizing angular momentum builds up depends on where on the terrain the foot lands and how the body responds to landing on the ground, thus suggesting two mitigating strategies. One strategy is to minimize the fore- aft impulse that is experienced at touch down, which has the effect of significantly slowing down the fluctuation- induced build- up of destabilizing angular momentum. This can be achieved by reducing the forward speed of the foot at touchdown via leg retraction and by reducing limb compliance so that the momentum of the rest of the body contributes lesser to the fore- aft impulse. Another strategy is to try and land primarily on local maxima or other flat regions of the terrain so that the destabilizing influence of random slope variations is reduced. The experimental assessment of these two strategies is the topic of this paper. Most past experimental studies of uneven terrain running have used step- like blocks to show how humans and animals deal with height variations on the ground (Daley et al., 2006; Müller et al., 2015). Later work modified the terrain design to use blocks that were narrow enough so that the foot had to span more than one fore- aft block, leading the foot to be randomly tilted during foot flat (Voloshina and Ferris, 2015). Specifically, the blocks were of three different heights (labeled A, B, and C), which leads to six possible height difference pairings (AB, BA, AC, CA, BC, CB). In natural terrain, the variation in slope is continuously graded, which would allow for more variation in the foot flat angle. Moreover, as hypothesized by theoretical analysis (Dhawale et al., 2019), it is not only the foot angle that affects whole body dynamics, but the force direction from the ground also matters. In this regard, the natural terrain may differ from the block design, particularly during initial contact and push- off when only a small region of the foot makes contact with the ground. During that time, the block design would not influence the ground forces like the sloped ground of natural undulating terrain would. Moreover, complex terrain Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 3 of 20 types may be required to capture the range of strategies used to run on naturalistic uneven terrain. This is suggested by studies that examine walking on a variety of outdoor terrain and show that stride variability and energetics significantly depend on terrain complexity (Kowalsky et al., 2021). Undulating uneven terrain have been studied in the context of walking (Kent et al., 2019; Kowalsky et al., 2021), but not running. So there is a need for experiments to study running on undulating terrain with continuously varying slopes to expand the current understanding of how uneven terrain affects stability. In this paper we experimentally assess foot placement patterns, fore- aft ground impulses, stepping kinematics, and metabolic power consumption on undulating uneven terrain whose unevenness is akin to running trails (Figure 1). Methods Protocol and experimental measurements We conducted overground running experiments with nine subjects (eight men, one woman; age 23–45 years, body mass 66.1 ± 8.5 kg, leg length 0.89 ± 0.04 m, reported as mean ± SD). All subjects were able- bodied, ran approximately 30 km per week, and had run at least one half- marathon or marathon within the previous year. Experiments were conducted at the National Centre for Biolog- ical Sciences, Bangalore, India, with informed consent from the volunteers, and IRB approval (TFR:NCB:15_IBSC/2012). Subjects ran back- and- forth on three 24 m long and 0.6 m wide tracks (Figure 2a). In addition to a flat track, we used two custom- made uneven tracks, uneven I and uneven II, which had increasing unevenness. Uneven I and uneven II had peak- to- valley height differences (amplitude) of 18 ± 6 and 28 ± 11 mm (mean ± SD), respectively, and peak- to- peak horizontal separation (wavelength) of 102 ± 45 and 108 ± 52 mm, respectively (Figure 2b, c and d). We recorded kinematics using an 8- camera motion capture system (Vicon Inc., Oxford, UK) at 300 frames per second and measured the ground reaction forces at 600 Hz using two force plates (AMTI Inc., model BP600900) embedded beneath the center of the track. The cameras recorded an approximately 10 m long segment of the center of the track. Breath- by- breath respirometry was also recorded by a mobile gas analyzer (Oxycon Mobile, CareFusion Inc.). A single trial consisted of a 3 min period of standing when the resting metabolic rate was recorded followed by subjects running back- and- forth on the track for at least 8 min and up to 10 min, dictated by VO2 reading equilibration time and the subject’s ability to maintain speed over the course of the Figure 1. Uneven terrain experiments. (a) We conducted human- subject experiments on flat and uneven terrain while recording biomechanical and metabolic data. The reflective markers and the outline of the force plate are digitally exaggerated for clarity. (b) Footsteps were recorded to determine whether terrain geometry influences stepping location, illustrated here by a mean- subtracted contour plot of terrain height for an approximately 6 foot segment of uneven II overlaid with footsteps (location of the heel marker). Blue and red circles represent opposite directions of travel and transparency level differentiates trials. The online version of this article includes the following source data for figure 1: Source data 1. Dimensional mass and leg length of every subject. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 4 of 20 trial. Each subject ran on all three terrains, with the order randomized. We controlled the running speed using a moving light array in 24 m long LED strips laid on either side of the track (Figure 2a). Subjects were instructed to stay within the bounds of a 3 m illuminated segment of the LED strip that traveled at 3 m/s. This speed was chosen as it was comfortable for all subjects and lies within the endurance running speed range for humans (Bramble and Lieberman, 2004). Running speed fluctuated within a trial, however mean speed as well as speed variability were consistent across terrain types (see Results for details). Subjects were provided with standardized, commercially avail- able running shoes. Uneven terrain Terrain unevenness was heuristically specified so that peak- to- valley height variations were approx- imately equal to the height of the malleolus while standing barefoot on level ground, and peak- to- peak horizontal distances were similar to foot length (Figure 2b). Large terrain height variations may elicit obstacle avoidance strategies, which is not the subject of this paper, and peak- to- peak hori- zontal separation longer than the step length may make the slope variation too gentle. Conversely, small height variations that are similar to the heel pad thickness, and peak- to- peak horizontal sepa- ration that is smaller than the foot length, will likely be smoothed out by foot and sole compliance (Venkadesan et al., 2017). The uneven terrains were constructed by Mars Adventures Inc. (Bangalore, India) by laying fiber glass over heuristically created contours. Epoxy was used to harden the fiber glass sheets into a stiff shell which was coated with a slurry of sand and epoxy to create a surface that texturally resembles weathered rock. The width at the ends of the uneven track were broadened to approximately 1 m to allow for runners to change direction while remaining on the terrain. The terrain was then digitized using a dense arrangement of reflective markers that were recorded by the motion capture system. Kinematics Foot kinematics were recorded using fiducial markers that were fixed to the shoes over the calcaneus, second distal metatarsal head, and below the lateral malleolus. Markers were attached to the hip, over Figure 2. Details of the experiment design. (a) Schematic of the running track, camera placement, force plate positions and the LED strip with a 3 m illuminated section. (b) The terrain was designed so that the range of its height distribution h was comparable to ankle height hpp ∼ hf and peak- to- peak distances λ (along the length of the track) were comparable to foot length λ ∼ lf . (c) Histograms of the mean subtracted heights h of the uneven terrain. (d) Histograms of the peak- to- peak separation λ of the uneven terrain. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 5 of 20 the left and right lateral superior iliac spine, and the left and right posterior superior iliac spine. The mean position of the hip markers was used to estimate the center of mass location. Stance is defined as when the heel marker’s forward velocity was minimized and its height was within 15 mm of the marker’s height during standing. The threshold of 15 mm was chosen to account for terrain height variations so that stance may be detected even when the heel lands on a local peak of the uneven terrain. The center of mass forward speed v = dstep/tstep is found from the distance dstep covered by the center of mass in the time duration tstep between consecutive touchdown events. Leg angle at touch- down is defined as the angle between the vertical and the line formed by joining the heel marker to the center of mass. Virtual leg length at touchdown is defined as the distance between the heel marker and the center of mass. Foot length lf is defined as the average distance in the horizontal plane between the toe and heel marker, across all subjects. The center of mass trajectory during stance was fitted with a regression line in the horizontal plane. The step width is twice the distance of nearest approach of the stance foot from the regression line. This definition allows for the runner’s center of mass trajectory to deviate while preserving a definition of step width that is consistent with those previously used (Donelan et al., 2001; Arellano and Kram, 2011). We estimated meander, i.e., the deviation of the center of mass from a straight trajectory, using (d − d0)/d0 , where d is the distance covered by the center of mass in the horizontal plane during a single run across the length of the track and d0 is the length of the straight- line fit to the center of mass trajectory. Foot velocity or center of mass velocity at landing were calculated by fitting a cubic polynomial to the heel marker trajectory or center of mass trajectory, respectively, in a 100 ms window before touchdown, and calculating the time derivative of the fitted polynomial at the moment just prior to touchdown. Leg retraction rate ω is determined using ω = vf/\|\|l\|\| , where vf is the component of the foot’s relative velocity with respect to the center of mass that is perpendicular to the virtual leg vector l (vector joining heel to center of mass). Step width, step length, and virtual leg length at touchdown are normalized by the subject’s leg length, defined as the distance between the greater trochanter and lateral malleolus. To correct for slight angular misalignments between the motion capture reference frame and the long axis of the running track, we align the average CoM trajectory over the entire track length to be parallel to the y- axis of the motion capture reference frame. This correction reflects the experimental observation that the subjects run along the center of the track. Kinetics Force plate data were low- pass filtered using an eighth order, zero- phase, Butterworth filter with a cut- off frequency of 270 Hz. Touchdown on the force plates was defined by a threshold for the vertical force of four standard deviations above the mean unloaded baseline reading. The forward collision impulse, defined as the maximal decelerating fore- aft impulse J∗y , was found by integrating the fore- aft component Fy of the ground reaction force during the deceleration phase as J∗ y = max t t ˆ 0 Fy(τ) dτ . (1) We normalized J∗y by the aerial phase forward momentum mvy , where vy is the forward speed of the center of mass during the aerial phase. Energetics Net metabolic rate is defined as the resting metabolic power consumption subtracted from the power consumption during running and normalized by the runner’s mass. Metabolic power consumption is determined using measurements of the rate of O2 consumption and CO2 consumption using formulae from Brockway, 1987. For running, this is calculated after discarding the first 3 min of the run to elim- inate the effect of transients. The resting metabolic power consumption is calculated after discarding the first minute of the standing period of the trial. Data from each trial were visually inspected to ensure that the rates of O2 consumption and CO2 production had reached a steady state, seen as a plateau in the data trace. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 6 of 20 Shuttle running Of the total track length of 24 m, a 1.2 m turnaround segment was designed at each end to facilitate the subjects to reverse their running direction without stepping off the track. These end segments were 1 m wide, which was broader than the rest of the track that was only 0.6 m wide. The runners would reach the end of the track and turn around promptly. Guiding light bars that controlled the running speed would be half ‘absorbed’ into the end before reversing direction, which allowed for sufficient time for the subjects to turn around while still maintaining the same average speed. The subjects were given, and took, around 0.5 s to turn around. The subjects ran at a steady speed within the capture volume that covers the middle 10 m of the track (see Results for details). The cameras could not capture the ends of the track but the experimenters observed that the subjects stayed within the moving light bar through the 21.6 m long straight portion of the track. The experimental protocol used in this study was tuned through pilot trials involving the authors of this manuscript and two initial subjects. The data from these pilot trial subjects are not part of the reported results in this manuscript. Foot stepping analysis Directed foot placement scheme The runners’ foot landing locations were compared to a Markov chain Monte Carlo (MCMC) model which finds stepping locations with the lowest terrain unevenness subject to constraints of matching experimentally measured stepping kinematics. All participants were heel- strike runners on all terrain types, as judged from the double peak in the vertical ground reaction force profile. Therefore, the stepping model sampled the terrain in rear- foot sized patches, which we define to be 95 mm × 95 mm (dimensions are chosen to be half the size of the foot length, 190 mm). The interquartile range of heights ( hIQR ) in each patch was used as a measure of its unevenness. Starting from an initial position (xi, yi) , the model takes the next step to (xi+1, yi+1) in the following stages: open- loop stage, minimization stage, and a noise process given by, open- loop stage: ˆxi+1 = xi + (−1)isw, ˆyi+1 = yi + (−1)jsl. (2) Minimization stage: (x′ i+1, y′ i+1) = arg min(x,y) t(x, y), x ∈ [ˆxi+1 − σsw,ˆxi+1 + σsw], y ∈ [ˆyi+1 − σsl,ˆyi+1 + σsl]. (3) Noise process: xi+1 = x′ i+1 + ηx, yi+1 = y′ i+1 + ηy, where ηx ∼ vM(1, 0, σsw), ηy ∼ vM(1, 0, σsl). (4) In the open- loop stage, the model takes a step forward and sideways dictated by the experimentally measured mean step length sl and mean step width sw, respectively. The exponent j is either 0 or 1 and keeps track of the direction of travel. The function t(x, y) evaluates the interquartile range of heights of a rear- foot sized patch centered around position (x, y) . In the minimization step, the model conducts a bounded search about (ˆxi+1,ˆyi+1) for the location that minimizes t(x, y) . The search region is defined by the standard deviations of the measured step width σsw and step length σsl . To perform the minimization, a moving rear- foot sized window with step- sizes of σsw/10 along the width of the track and σsl/10 along its length are used to evaluate t(x, y) at various candidate stepping locations within the search region. The step- sizes for translating the moving window were chosen because they were much smaller than typical terrain features and thus the landing location with the lowest unevenness (x′ i+1, y′ i+1) was determined by the terrain properties, not model parameters. To simulate sensorimotor noise, the location of this minimum (x′ i+1, y′ i+1) is perturbed by random variables ηx, ηy . The random variables are drawn from von Mises distributions with κ = 1 , centered about zero, and scaled so that the base of support for the distributions are σsw and σsl , respectively. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 7 of 20 At the ends of the track, the x position of the runner is reset so that the runner is at the center of the track, and the direction of travel is reversed ( j value is toggled). We simulate for 100,000 steps to ensure that reported terrain statistics at footstep locations as well as step length and step width converge, i.e., errors between simulations in these parameters are less than 1% of their mean value. Quantifying foot placement patterns We used a second analysis of footstep patterns that correlated the foot landing probability with terrain unevenness. To perform this analysis, we define a foot placement index to estimate the prob- ability that the runner’s foot lands within a foot- sized patch of the track. To calculate this index, we first divide the terrain into a grid of 0.5 foot lengths × 1.0 foot lengths cells, with the longer side of the cell along the length of the track (Figure 3a). We count the number of footsteps fi,j in each cell ci,j , where i indexes the position of the cell along the length of the track and j indexes the position of cell transverse to the track. The point of landing is determined by the location of the heel marker. Even if the fore- foot crosses over the adjacent cell boundary, the location of the heel marker uniquely specifies the landing cell identity. We also define step length- sized neighborhoods that contain cell ci,j which are one step- length long and as wide as the track. Each such neighborhood has a cumulative footstep count Si that depends on the longitudinal location i of the cell. The average across all such step length- sized neighborhoods that contain cell ci,j is S . This average S is used to normalize each fi,j to yield the foot placement index pi,j according to, pi,j = fi,j S . (5) The index pi,j measures the fraction of times a foot lands in cell ci,j compared to all other cells that are within a step length distance of it (Figure 3b). If runners were perfectly periodic with no variation in footstep location from one run over the terrain to the next, pi,j = 1 for cells on which subjects stepped and pi,j = 0 otherwise. If, however, stepping location was the result of a uniform random process, pi,j would be a constant for every cell of the terrain and equal to the reciprocal of the number of cells in a step- length sized box. Heat maps of the foot placement index pi,j are shown in Figure 3—figure supplement 1. We report the total number of footsteps recorded for each trial in Figure 3—source data 1. To probe foot placement strategies we determine whether the foot placement index pi,j correlates with the median height or the interquartile range of heights within the cell ci,j . Positive correlation with the median height would indicate stepping on local maxima that are flatter than the surrounding, and Figure 3. Foot placement analysis. (a) Red circles denote footstep locations (392 footsteps) in the ‘ x − y ’ plane for a representative trial on uneven II. The grid spacing is 190 mm along the length of the track and 95 mm along its width. Step length s0 is shown for reference. T is the length of the capture volume and W is the width of the track. Note that the x and y axes of this figure are not to the same scale. (b) The probability of landing on a foot- sized region of the track is quantified by the foot placement index Equation 5 shown as a heatmap with the color bar at the top left. The online version of this article includes the following source data and figure supplement(s) for figure 3: Source data 1. Footstep counts for each subject on all terrain. Figure supplement 1. Subject- wise foot placement patterns. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 8 of 20 negative correlation with the interquartile range would indicate stepping on flatter regions with more uniform height. We test this hypothesis through the use of a statistical model described in ‘Statistical analysis and reporting’. Collision model To delineate the relative contributions of joint stiffness and forward foot speed to the fore- aft impulse, we model the impulse due to the foot- ground interaction. In the model, a planar three- link chain represents the foot, shank, and thigh, and a fourth link represents the torso. Following Dempster, 1955, all masses and lengths are expressed as fractions of the body mass and leg length, respec- tively. This model builds upon the leg collision model of Lieberman et al., 2010, by including addi- tional segments representing the thigh and torso and calculating the fore- aft collisional impulse. The collision is assumed to be instantaneous and inelastic, with a point- contact between the leg and the ground. Such collision models are widely used to capture the stance impulse due to ground forces in walking (Donelan et al., 2002; Ruina et al., 2005) and running (Srinivasan and Ruina, 2006; Dhawale et al., 2019). Because the collision is assumed to be instantaneous, only infinite forces contribute to the impulse (Chatterjee and Ruina, 1998; Lieberman et al., 2010). Therefore, to investigate the effect of joint compliance, we model the hinge joints connecting the links as either infinitely compliant or perfectly rigid. The advantage of these contact models is their ability to accurately capture the impulse without the numerous additional parameters needed to represent the complete force- time history when contact occurs between two bodies (Chatterjee and Ruina, 1998). We use experimental data on center of mass velocity and leg retraction rate just prior to landing, along with the leg angle at touchdown, to compute a predicted collisional impulse. Because all our runner’s were heel- strikers, we use foot- strike index s = 0.15 for the collision calculations (Lieberman et al., 2010). The foot- strike index ranges from 0 for heel strikes to 1 for forefoot strikes and encodes the runner’s foot strike pattern. The ratio of the collisional impulse to the measured whole body momentum just prior to landing is calculated for the model at the two joint stiffness extremes and compared with experimental measurements of the normalized fore- aft impulse. By analyzing the colli- sional impulse for these two extremes of joint stiffness, we isolate the contributions to the fore- aft impulse arising from varying the joint stiffness versus varying the forward foot speed at landing. Notation Notation used in this section is as follows. Scalars are denoted by italic symbols (e.g. I for the moment of inertia), vectors by bold, italic symbols ( v for velocity), and points or landmarks in capitalized non- italic symbols (such as center of mass G in Figure 4a). Vectors associated with a point, such as the velocity of center of mass G are written as vG , with the upper- case alphabet in the subscript specifying O E B F C K D mf Ms Mt lf slf Ls Lt O B R1 R2 C a b c d G y z + A θ M H e N R3 Lto D J Figure 4. Model for estimating fore- aft collision impulses from kinematic data. (a) A four- link model of the foot (A–B), shank (B–C), thigh (C–D), and torso (D–N) moving with center of mass velocity v− G and angular velocity Ω− collides with the ground at angle θ . (G) represents the center of mass. Leg length and body mass are obtained from data and scaled according to Dempster, 1955, to obtain segment lengths and masses. Free- body diagrams show all non- zero external impulses: (b) collisional impulse J acting at O, and panels (c, d, e) show reaction impulses R1 , R2 , and R3 acting at B, C, and D, respectively. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 9 of 20 the point in the plane. Moment of inertia variables are subscripted with ‘/A’ representing the moment of inertia computed about point A, such as I/G representing the moment of inertia about the center of mass G. Position vectors are denoted by rA/B which denotes the position of point A with respect to point B. Variables just before the collision with the terrain are denoted by the superscript ‘–’, and just after the collision by the superscript ‘+’. Equations with variables that have no superscript apply throughout stance. Rigid joints Consider the L- shaped bar (Figure 4a) falling with velocity v− G = v− y ˆȷ + v− z ˆk and angular velocity Ω− = ω−ˆı . Upon contact with the ground, the point O on the foot instantly comes to rest and the center of mass translational and angular velocities change to v+ G = v+yˆȷ + v+z ˆk, Ω+ = ω+ˆı . Due to the instantaneous collision assumption, finite forces like the gravitational force do not contribute to the collisional impulse, and the ground reaction force at point O leads to the impulse J (Figure 4b). Angular momentum balance about the contact point O yields the relationship between pre and post collision velocities, MbrG/O × v− G + I/GΩ− = MbrG/O × v+ G + I/GΩ+, (6a) vG = vO + Ω × rG/O, (6b) where v+ O = 0. (6c) The total mass Mb is the sum of the masses of the torso M , thigh Mt , shank Ms , and foot mf. We solve for ω+ in Equation 6a and obtain the post- collision center of mass velocity v+ G using Equation 6b. From this, the collision impulse J and the normalized fore- aft collisional impulse \|J∗y \|/Jb are calculated using, J = Mb(v+ G − v− G ), (7a) J∗y = J .ˆj, (7b) and Jb = Mb(v− G ·ˆȷ). (7c) Compliant joints If the L- bar has compliant joints, then the post- collision velocities for each segment may vary. There- fore, we write additional angular momentum balance equations for each segment to solve for the post- collision state. Since the only non- zero external impulse acting on the shank, thigh, and torso segments is the reaction impulse R1 acting at B (Figure 4c), the only non- zero external impulse on the thigh and torso portion of the leg is the reaction impulse R2 acting at C (Figure 4d), and the only non- zero external impulse acting on the torso portion of the leg is the reaction impulse R3 acting at D (Figure 4e), we write angular momentum balance equations for the entire body and these three segments as MbrG/O × v− G + I/GΩ− = mfrE/O × v+ E + I/EΩ+ E+ MsrF/O × v+ F + I/FΩ+ F + MtrK/O × v+ K + I/KΩ+ K+ MrH/O × v+ H + I/HΩ+ H, (8a) MsrF/B × v− F + MtrK/B × v− K + MrH/B × v− H + (I/F + I/K + IH)Ω− = MsrF/B × v+ F + I/FΩ+ F + MtrK/B × v+ K + I/KΩ+ K+ MrH/B × v+ H + I/HΩ+ H, (8b) MtrK/C × v− K + MrH/C × v− H + (I/K + I/H)Ω− = MtrK/C × v+ K + I/KΩ+ K+ MrH/C × v+ H + I/HΩ+ H, (8c) MrH/D × v− H + I/HΩ− = MrH/D × v+ H + I/HΩ+ H (8d) Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 10 of 20 where I/E, I/F, I/K, I/H are moments of inertia of the foot, shank, thigh, and torso segments, respec- tively, about their centers. The linear and angular velocities of the foot ( vE, ΩE ), shank ( vF, ΩF ), thigh ( vK, ΩK ), and torso ( vH, ΩH ) are related to the velocity of the contact point O as vE = vO + ΩE × rE/O, (9a) vF = vO + ΩE × rB/O + ΩF × rF/B, (9b) vK = vO + ΩE × rB/O + ΩF × rC/B + ΩK × rK/C, (9c) vH = vO + ΩE × rB/O + ΩF × rC/B + ΩK × rD/C + ΩH × rH/D, (9d) where v− O = v− G + Ω− × rO/G, (9e) and v+ O = 0. (9f) Simultaneously solving Equations 8a, b, c, d–9a, b, c, d, e, f yields the post- collision velocities for each segment of the L- bar. From these, we calculate the normalized fore- aft collision impulse for the compliant model using Equation 7a, b, c. Statistical methods Sample size Sample size could refer to the number of subjects or the number of footsteps that were used in the analyses. The number of subjects recruited was informed by typical participant numbers that were used in similar past studies (Donelan et al., 2004; Voloshina and Ferris, 2015; Seethapathi and Srinivasan, 2019). There is an additional consideration for sufficiency of sample numbers for the foot placement analysis. The steps should densely sample the approximately 10 m long central region of the track, where the motion capture system was recording from. The 5262 recorded steps (2526 on uneven I, 2736 on uneven II) are sufficient to densely sample the measurement region assuming a rear- foot sized patch for each step. Statistical analysis and reporting Measures of central tendency (mean or median) and variability (standard deviation or interquartile range) of the distributions of step width, step length, center of mass speed, forward foot speed at landing, fore- aft impulse, virtual leg length at touchdown, leg angle at touchdown, net metabolic rate, and meander are reported for each trial. We use three different linear mixed models to determine (a) whether gait variables vary with terrain type, (b) whether leg angle at touchdown and decelerating fore- aft impulses covary with forward foot speed at touchdown, and (c) whether the foot placement index pi,j (Equation 5) correlates with the median height or the interquartile range of heights within the terrain region at landing. The statistical models are run using the lmerTest package in R (Kuznetsova et al., 2017). We use a linear mixed- model fit by restricted maximum likelihood t- tests with Satterthwaite approximations to degrees of freedom. An ANOVA on the first model tests for the effect of the terrain factor, an ANCOVA on the second model tests for the effect of the terrain factor and the covariate forward foot speed, and an ANCOVA on the third model tests whether the probability of landing on a terrain patch pi,j significantly covaries with the height or unevenness of that terrain patch. Post- hoc pairwise compar- isons, where relevant, are performed using the emmeans package in RStudio with p- values adjusted according to Tukey’s method. A measure of central tendency or variability within a trial is the dependent variable y for the first linear mixed model. There are 27 observations for the dependent variable y corresponding to each trial (nine subjects running on three terrain). Terrain is the fixed factor and subjects are random factors in the model given by yij = (β0 + µj) + βiterraini + ϵij, (10) where i = 1, 2 and j = 1 . . . 9 . The intercept β0 (value of y on flat terrain) and parameters βi for uneven I and uneven II are estimated for this model. The random factor variables µj are assumed to be normally distributed about zero and account for inter- subject variability of the intercept. The model residuals are ϵij which are also assumed to be normally distributed about zero. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 11 of 20 The second linear mixed model uses stepwise data where each step is grouped by subject and terrain type. Each of the 1086 steps in this dataset contains a value for subject number, terrain type, touchdown leg angle, decelerating fore- aft impulse, and forward foot speed at touchdown. The linear model for the dependent variable y (touchdown leg angle or fore- aft impulse) is yij = (β0 + µ1j) + βiterraini + (βf + µ2j + νi)footspeed + ϵij (11) where i = 1, 2 and j = 1 . . . 9 . Like in Equation 10, the model estimates the intercept β0 , i.e., the value of y on flat terrain when foot speed = 0 , βi for terrain factor, and the slope βf for the dependence of y on forward foot speed at touchdown. The variable µ1j account for inter- subject variability of the intercept, and the variables µ2j and νi account for inter- subject and terrain- specific variability of the slope βf , respectively. The residuals ϵij are assumed to be normally distributed. Using a dataset of 5262 steps from all subjects on uneven I and uneven II, we extract 1515 landing probabilities (as detailed in ‘Quantifying foot placement patterns’). To test whether runners aimed for terrain regions with low unevenness, we use a linear mixed model of the form, ykl = (µ1l + ν1k) + (µ2l + ν1k)terr + ϵkl (12) where k = 1, 2 for the two uneven terrain and l = 1 → 9 for the nine subjects. The dependent variable y is the probability of landing in a foot- sized cell pi,j and the independent variable ‘terr’ refers to the median terrain height of the cell or the interquartile range of heights within the cell. The variables µ1l accounts for subject- specific variability in the terrain- specific intercept ν1k . The variables µ2l accounts for subject- specific variability in the terrain- specific slope ν2k . Nondimensionalization Following Alexander and Jayes, 1983, we express lengths in units of leg length ℓ and speed in units of √gℓ , where g is acceleration due to gravity. Statistically significant post- hoc comparisons are addi- tionally reported in dimensional units using g = 9.81 m/s2, and the mean of the measurements across subjects, namely, ℓ = 0.89 m and m = 66.1 kg. Figure 5. Foot placement on uneven terrain. Histogram of the interquartile range of heights ( hIQR ) at footstep locations for the directed sampling scheme (red), experiments (yellow), and the blind sampling scheme (blue) on (a) uneven I (2526 footsteps) and (b) uneven II (2736 footsteps). Note that hIQR varies over a greater range on uneven II. The online version of this article includes the following source data and figure supplement(s) for figure 5: Source data 1. Output of the Markov chain sampling (directed scheme) of the Uneven I terrain. Source data 2. Output of the Markov chain sampling (directed scheme) of the Uneven II terrain. Source data 3. Output of the uniform random sampling (blind scheme) of the Uneven I terrain. Source data 4. Output of the uniform random sampling (blind scheme) of the Uneven II terrain. Source data 5. Subject- wise, per- step data of the terrain height at foot landing locations on the Uneven I terrain. Source data 6. Subject- wise, per- step data of the terrain height at foot landing locations on the Uneven II terrain. Figure supplement 1. Subject- wise foot placement analysis on uneven I. Figure supplement 2. Subject- wise foot placement analysis on uneven II. Figure supplement 3. Subject- wise foot placement analysis. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 12 of 20 Results Foot placement on uneven terrain To test whether real runners prefer to land on flatter patches, the measured footsteps were compared against two extreme models, a null hypothesis of a blind runner and an alternative hypothesis of a directed runner whose footsteps are selectively aimed at level parts of the terrain. The blind scheme uses a uniform random sample of rear- foot sized patches of the terrain to obtain statistics of the terrain at landing locations. The directed scheme preferentially samples more level patches using an MCMC model (‘Directed foot placement scheme’ in Methods). The experimentally measured stepping patterns are the same as the blind scheme on both uneven I and II in terms of the terrain unevenness as quantified by hIQR (human subjects versus blind scheme in Figure 5). However, the directed scheme finds substantially more level landing patches, showing that it was possible for the runners to land on more level ground (directed scheme in Figure 5). These trends are also borne out in a subject- wise analysis (Figure 5—figure supplements 1 and 2). The directed scheme found more level patches and exhibited decreased variability in step length and step width compared with the experimental data. The mean step length and width of the directed scheme are the same as the experimental data on both uneven I and uneven II. However, the stan- dard deviation of step length decreased by 80% on both uneven I and uneven II compared to exper- imental measurements. This corresponds to a change of 0.013 and 0.011 m for the mean subject on uneven I and uneven II, respectively. The standard deviation of step width for the directed scheme decreased by 80% (0.0006 m) on uneven I and by 84% (0.005 m) on uneven II compared to experi- mental measurements. The overall statistics of the terrain location at foot landing may obscure step- to- step dependence of the foot landing on terrain features. A second analysis of correlating foot landing probability pi,j Figure 6. Regulation of fore- aft impulses. (a) The fore- aft impulse J∗y (gray shaded area) is found by integrating the measured fore- aft ground reaction force Fy (black curve) during the deceleration phase. (b) Mean J∗ y mvy for 9 subjects on 3 terrain types. Central red lines denote the median, boxes represent the interquartile range, whiskers extend to 1.5 times the quartile range, and open circles denote outliers. (c) Measured J∗ y mvy (green circles) versus relative forward foot speed at landing (forward foot speed/center of mass speed) for each step recorded on all terrain types (total 1081 steps). The green line is the regression fit for the data. The dark and light gray lines are the predicted fore- aft impulse for the mean stiff and compliant jointed models, respectively. Per step model predictions in Figure 6—figure supplement 1. (d) Measured versus predicted fore- aft impulses for every step. The dotted line represents perfect prediction. The online version of this article includes the following source data and figure supplement(s) for figure 6: Source data 1. Subject- wise, per- step data of fore- aft impulse, foot speed, and touchdown angle. Figure 6 continued on next page Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 13 of 20 with the interquartile range of the terrain heights in the foot- sized cell was consistent with results described above and showed no significance (Table 1). Taken together, these results indicate that the runners did not guide their footsteps toward flatter areas of the terrain. Fore-aft impulses The fore- aft ground reaction force in stance initially decelerates the center of mass before accelerating it forward (Figure 6a). We find that less than 6 ± 1 % (mean ± SD) of the forward momentum is lost during the deceleration phase of stance and there is no dependence on terrain or subject (Figure 6b). The low variability of the fore- aft impulse, just 1% of the forward momentum, suggests that it is tightly regulated across runners, terrain, and steps. The regulation of foot speed is unlikely to be the primary determinant of the low variability in the collision impulse. This is because the dimensionless forward foot speed at touchdown across all terrain varied by nearly 50% of its mean ( 0.4 ± 0.2 , Table 2), whereas fore- aft collision impulses varied only by 17% of its mean. A statistical analysis lends further support and shows that the dimensionless fore- aft impulse depends significantly, but only weakly, on the dimensionless forward foot speed at landing (Table 3, p = 0.001 , slope = 0.01 ± 0.003 ). To further investigate this weak dependence of the retarding impulse on foot speed, we analyzed the mechanics of foot landing and the resultant impulse using a four- link chain model of the leg and torso. The joints are either completely rigid or infinitely compliant when the foot undergoes a rigid, inelastic collision with the ground (‘Collision model’ in Methods). The models at the two extremes of joint stiffness bound the experimental data, with the compliant model underestimating the measured fore- aft impulse while the stiff model overestimates it (Figure 6c, d, and Figure 6— figure supplement 1). This is expected because the muscle contraction needed for weight support and propulsion would induce non- zero but non- infinite stiffness at the joints. Although both models overestimate the dependence of the fore- aft impulse on foot speed, the slope of the compliant model is closest to the measurements (Figure 6c, Figure 6—figure supplement 1). The slope of measured speed- impulse data is 0.01 ± 0.003 ( p = 0.001 , Table 3), closer to compliant model than the stiff model, whose slopes are 0.0203 ± 0.010 ( p < 0.0001 ) and 0.056 ± 0.005 ( p < 0.0001 ), respectively. The measured fore- aft impulse for most steps was below 0.07 (whiskers extend to 1.5 times the interquartile range in Figure 6). The compliant model’s predicted fore- aft impulses show good agreement with measure- ments when the impulse is below 0.07 (measured versus predicted in Figure 6d), and disagree only for the occasional steps when runners experience more severe fore- aft impulses. Unlike the compliant model, the stiff model consistently over- estimates the measured fore- aft impulse over its entire range. Thus, we propose that maintaining low joint stiffness at landing helps maintain low fore- aft impulses despite variations in touchdown foot speed. Source data 2. Per- step data of the measured and predicted fore- aft impulse for the compliant and stiff- leg collision models. Figure supplement 1. Detailed results of the collision analysis. Figure 6 continued Table 1. Correlation between landing probability and terrain unevenness. Details of the ANCOVAs on the linear mixed models from Equation 12 showing denominator degrees of freedom, F- values, and p- values from the dataset of stepping probabilities and terrain height statistics of 1515 recorded pi,j values for all subjects on uneven I and uneven II. Since the foot placement index pi,j values show very little variability (Figure 5—figure supplement 3), the model with the median terrain height was singular. Independent variable DenDF F- value p- Value IQR terrain height 20.6 3.03 0.10 The online version of this article includes the following source data for table 1: Source data 1. Subject- wise statistics of the terrain’s height in heel- sized patches and the probability of stepping in that patch. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 14 of 20 Leg retraction Increased leg retraction rate results in reduced forward foot speed at touchdown, thereby altering the fore- aft impulse (Karssen et al., 2015; Dhawale et al., 2019). The mean non- dimensional forward foot speed at landing is terrain- dependent and lower by 0.17 ± 0.04 ( p = 0.001 ) on uneven I compared to flat ground, and by 0.15 ± 0.04 ( p = 0.002 ) on uneven II compared to flat ground (Figure 7a, Table 2). For the mean subject, these correspond to reductions in forward foot speed of 0.48 ± 0.11 m/s on uneven I and 0.42 ± 0.11 m/s on uneven II compared to flat ground. We find that touchdown angle depends significantly but only weakly on forward foot speed at landing ( p ≈ 0 , slope = 0.07 ± 0.01 rad, Table 3). If the dimensionless forward foot speed at landing Table 2. Kinematic variables on different terrain types reported as mean ± SD, except for meander values which are reported as median ± interquartile range. For each variable, we show details of the ANOVAs performed on the linear model in Equation 10, i.e., the F- value and p- value for the terrain factor. The denominator degrees of freedom for all ANOVAs was 16. Post- hoc comparisons are reported when the ANOVAs reached the significance bound of α = 0.05 . Variable Flat Uneven I Uneven II F- value p- Value Net metabolic rate (W/kg) 13.1 ± 0.5 13.7 ± 0.9 13.7 ± 0.8 2.97 0.08 Median step width (%LL) 3.9 ± 1.9 4.1 ± 1.5 4.7 ± 2.0 4.53 0.03 IQR step width (% LL) 3.9 ± 1.4 4.3 ± 0.9 5.0 ± 1.2 3.65 0.05 Mean step width (%LL) 4.2 ± 1.7 4.7 ± 1.6 5.2 ± 1.7 8.69 0.003 SD step width (% LL) 2.8 ± 0.8 3.4 ± 0.6 3.6 ± 0.6 5.54 0.01 Mean step length (%LL) 128 ± 6 126 ± 9 125 ± 9 1.07 0.37 SD step length (%LL) 6 ± 1 7 ± 4 6 ± 1 0.64 0.54 Mean meander ( ×10−4 ) 3.21 ± 2.59 3.97 ± 1.65 4.88 ± 4.62 1.48 0.25 SD meander ( ×10−4 ) 0.67 ± 0.53 1.33 ± 1.40 1.27 ± 2.78 1.58 0.23 Mean fwd. foot speed (froude num.) 0.53 ± 0.17 0.36 ± 0.10 0.37 ± 0.12 13.08 0.0004 SD fwd. foot speed (froude num.) 0.17 ± 0.05 0.14 ± 0.05 0.18 ± 0.07 1.48 0.26 Mean CoM speed (m/s) 3.24 ± 0.07 3.21 ± 0.07 3.18 ± 0.09 2.32 0.13 SD CoM speed (m/s) 0.11 ± 0.03 0.13 ± 0.04 0.12 ± 0.03 2.00 0.17 Mean touchdown leg length (%LL) 120 ± 5 119 ± 4 119 ± 4 4.28 0.03 SD touchdown leg length (%LL) 1.1 ± 0.7 0.9 ± 0.3 1.3 ± 1.2 1.32 0.29 Mean touchdown leg angle (rad) 0.20 ± 0.02 0.20 ± 0.02 0.21 ± 0.02 3.90 0.04 SD touchdown leg angle (rad) 0.03 ± 0.02 0.02 ± 0.003 0.03 ± 0.02 2.10 0.15 The online version of this article includes the following source data for table 2: Source data 1. Subject- wise, per- step data on foot and leg kinetics and kinematics. Table 3. Details of the ANCOVAs performed on the linear model described in Equation 11 showing the denominator degrees of freedom, F- value and p- value for the fixed terrain factor, and the estimated slopes βf for the fixed forward foot speed effect. Dependent variable Factor DenDF F- value p- Value βf Touchdown leg angle Terrain 193 1.48 0.23 - Fwd. foot speed 38 115.83 <0.0001 0.07±0.01 rad Fore- aft impulse Terrain 79 1.45 0.24 - Fwd. foot speed 78 12.83 0.001 0.01±0.003 Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 15 of 20 varied through its entire observed range from −0.2 to 1.1, it would result in a change in landing angle of 0.08 rad or 5°. Stepping kinematics We find that the median non- dimensional step width is terrain dependent (Figure 7b, Table 2) and increased on uneven II versus flat ground by 0.004 ± 0.001 ( p = 0.03 ). Step width variability, i.e., the interquartile range of step widths within a trial, is also terrain dependent ( p = 0.05 , Figure 7c, Table 2) and greater on uneven II versus level ground by 0.005 ± 0.002 ( p = 0.04 ). For the mean subject, median step width increased by 4 ± 1 mm and the step width variability (IQR) increased by 6 ± 2 mm. Energetics The approximately 5% increase in metabolic power consumption on the uneven terrain compared to flat we measured was not statistically significant ( p = 0.08 , Figure 7d, Table 2). Discussion Our primary finding is that runners do not use visual information about terrain unevenness to guide their footsteps. In addition, the fore- aft collisions that they experience seem almost decoupled from the forward speed with which their foot lands on the ground. Based on the modeling estimate of colli- sional impulses and comparison with measurements, we propose that low joint stiffness underlie the regulation of fore- aft impulses, likely contributing to stability (Dhawale et al., 2019). Taken together, these results suggest that runners rely not on vision- based path planning, but on their body’s passive mechanical response for remaining stable on undulating uneven terrain. Additionally, the changes in step- width kinematics on the uneven versus flat terrain may reflect sensory feedback mediated stepping strategies similar to those reported previously (Seipel and Holmes, 2005; Seethapathi and Srinivasan, 2019), but more work is needed to investigate whether the differences were the result of feedback control or simply the result of variability injected by the terrain’s unevenness. Figure 7. Energetics and stepping kinematics. (a) Box plot of the mean forward foot speed at landing (units of froude number). (b) Box plot of the median step width (normalized to leg length). (c) Box plot of the step width variability. Central red lines denote the median, boxes represent the interquartile range, whiskers extend to 1.5 times the quartile range, and open circles denote outliers. The distribution of step widths within a trial deviated from normality and hence we report the median and the interquartile range of the distribution for each trial (Figure 7—figure supplement 1), instead of the mean and standard deviation as is reported for all other variables. (d) Net metabolic rate normalized to subject mass. Whiskers represent standard deviation across the nine subjects. An ANOVA on the linear mixed model described in Equation 10 was used to determine whether gait measures described above differed between terrain conditions with a significance threshold of 0.05. The online version of this article includes the following source data and figure supplement(s) for figure 7: Source data 1. Subject- wise, per- step data on step width. Figure supplement 1. Subject- wise step width statistics. Figure supplement 2. Representative respirometry data. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 16 of 20 Measurements of fore- aft impulses have not been previously examined in the context of stability. A previous theoretical analysis hypothesized that reducing tangential collisions and maintaining low fore- aft impulses reduces the risk of falling by tumbling in the sagittal- plane (Dhawale et al., 2019). Our data are consistent with this model. We find that only 6 ± 1% of the forward momentum was lost in stance although the forward foot speed at landing varied by nearly 50%. This reduction in variability is surprising because, all else held the same, speed and impulse are expected to be linearly related. This suggests that the fore- aft impulse is tightly regulated by other means. By examining the role of leg joint compliance using model- based analyses of the data, we found that the measured fore- aft impulses were partly consistent with an idealized extreme of zero stiffness in the joints at the point of landing. However, joint stiffness in a real runner cannot be too small because it is needed to withstand the torques for weight support and propulsion. Thus, we propose that the low variability in fore- aft impulses arises from active regulation of joint stiffness. Past studies on running birds (Blum et al., 2014; Birn- Jeffery et al., 2014) provide some hints on why leg compliance, and not foot speed, might be the preferred means to regulate fore- aft impulses. To deal with abrupt changes in terrain height, running birds regulate foot speed and leg retraction rates to maintain consistent leg forces and reduce discomfort or injury risk. Although our terrain has smoothly varying terrain and not the step- like blocks used in the bird studies, our runners may still have encountered sudden height changes because they did not precisely regulate their stepping pattern to avoid uneven terrain areas. Like the running birds, they may have regulated foot speed to mitigate discomfort and high forces. Thus, by employing leg compliance to reduce the fore- aft impulse, the runners could deal with stability independent of foot speed regulation for safety and comfort. However, caution is warranted when comparing our results with these past studies. The bird studies used SLIP models to interpret their findings, but such models are energy conserving and unaf- fected by slope variations that were part of our terrain design. Furthermore, the peak- to- peak height variation of our terrain was less than 6% of the leg length, unlike Blum et al., 2014 and Birn- Jeffery et al., 2014, who used larger step- like obstacles of 10% leg length or more. For example, we see no change in the variability of the leg landing angle between flat and uneven terrain trials (Table 2), which is expected if leg landing angle responded to variations in terrain height (Blum et al., 2014; Birn- Jeffery et al., 2014). So large step- like obstacles probably induce different swing- leg control strategies compared with undulating terrain with smaller height variations. We found variability in step- to- step kinematics that are largely consistent with previous studies on step- like terrain, but with some notable differences. Studies of running birds hypothesize that crouched postures could aid stability on uneven terrain (Blum et al., 2011; Birn- Jeffery and Daley, 2012), as do human- subject data from treadmill running (Voloshina and Ferris, 2015). We find a slight decrease in the virtual leg length at touchdown on the most uneven terrain compared to flat, but the difference was only around 1% of the leg length (Table 2), whose effect on stability would be negligible. We find higher leg retraction rates on uneven terrain, as also reported in running birds (Birn- Jeffery and Daley, 2012; Blum et al., 2014). Leg retraction has been hypothesized to improve running stability in the context of point- mass models by altering leg touchdown angle to aid stability (Seyfarth et al., 2003; Blum et al., 2010). However, we find only a weak dependence between leg retraction rate and leg touchdown angles. Human- subject treadmill experiments report that step width and step length variability increased by 27% and 26%, respectively, and mean step length or step width were the same for flat and uneven terrain (Voloshina and Ferris, 2015). Like those studies, we find 24% greater step width variability on uneven terrain compared to flat, but no significant changes in step length variability (Figure 7b, Table 2). We additionally find that the median step width increased on uneven terrain by 13%. The increase in median step width that we measure could be due to lateral stability challenges of running on relatively more complex terrain with smoothly varying slope and height variations in all directions. Unlike treadmill running studies, we do not find a statistically significant increase in metabolic power consumption on uneven terrain versus flat ground, but the mean increase of around 5% is similar to Voloshina and Ferris, 2015. The acceleration and deceleration when subjects turn around during our overground trials could affect the metabolic energy expenditure. Therefore, caution is warranted in comparing the absolute value of our reported energetics data with other studies on treadmills or unidirectional running. But several aspects of the experimental design allow us to compare the respirometry data between the different terrain types. For every subject, we ensured that Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 17 of 20 the breath- by- breath respirometry data stabilized within the first 3 min and only used the stabilized value for further analyses (‘Energetics’ in Methods). If the transients had dominated the respirometry measurements, the measurements would not have stabilized (Figure 7—figure supplement 2). The use of the moving light bar on either side of the track ensured that the subjects maintained the same speed on all the terrain types. Moreover, the turnaround patches were designed to have the same terrain statistics (flat, uneven I, uneven II) as the rest of the track, thus ensuring that there were no abrupt terrain transitions. This allowed us to control for and mitigate the effects of the turnaround phases when comparing the results between the different terrain types. We find no evidence that subjects used visual information from the terrain geometry to plan footsteps despite predicted advantages to stability (Dhawale et al., 2019). This finding differs from walking studies that highlight the role of vision in guiding step placement on natural, uneven terrain (Matthis et al., 2018; Bonnen et al., 2021). The stochastic stepping model was able to consistently find landing locations with lower unevenness than the human subjects, while matching the measured mean stepping statistics and even reducing step- to- step variability, thus showing that the absence of a foot placement strategy was not due to a lack of feasible landing locations. We speculate that foot placement strategies are used for obstacle avoidance (Matthis and Fajen, 2014) on more complex terrain while our terrains were designed to be continuously undulating and not have large, singular obstacles. While our data suggest that terrain- guided foot placement strategies are not required for stability on gently undulating terrain, it leaves open the possibility that there is a skill- learning component to such foot placement strategies which we could not measure since our volunteers were not experienced trail runners. Further experiments with runners of varying skill levels could test such a hypothesis. Conclusions Footsteps were not directed toward flatter regions of the terrain despite predicted benefits to stability. Instead, we found evidence for a previously uncharacterized control strategy, namely that the body’s stabilizing mechanical response due to low fore- aft impulses was used to mitigate the destabilizing effects of stepping on uneven areas. The limited need for visual attention may explain how runners could employ vision for other functional goals, such as planning a path around large obstacles, or in an evolutionary context, tracking footprints to hunt prey on uneven terrain without falling. Whether other animals employ similar strategies on uneven terrain is presently unknown but data from galloping dogs show that they do not alter their gait on uneven terrain (Wilshin et al., 2020), thus suggesting that other adept runners potentially employ similar principles for stability. We propose that our results could translate to new strategies for reducing the real- time image processing burden in robotic systems, and could also help in training trail runners by emphasizing limber joints when dealing with uneven terrain. Acknowledgements Human Frontier Science Program and Wellcome Trust- DBT Alliance for funding. Additional information Funding Funder Grant reference number Author Human Frontier Science Program RGY0091/2013 Madhusudhan Venkadesan The Wellcome Trust DBT India Alliance Madhusudhan Venkadesan The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. For the purpose of Open Access, the authors have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. Research article Physics of Living Systems \| Neuroscience Dhawale and Venkadesan. eLife 2023;12:e67177. DOI: https:// doi. org/ 10. 7554/ eLife. 67177 18 of 20 Author contributions Nihav Dhawale, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Meth- odology, Writing – original draft, Writing – review and editing; Madhusudhan Venkadesan, Concep- tualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing Author ORCIDs Nihav Dhawale http://orcid.org/0000-0002-5193-9064 Madhusudhan Venkadesan http://orcid.org/0000-0001-5754-7478 Ethics Human subjects: The study was approved by the Institute Ethics Committee (Human Studies) of the National Centre for Biological Sciences, Bengaluru, India (TFR:NCB:15\_IBSC/2012), where the exper- iments were conducted. Informed consent was obtained by the experimenter N. Dhawale and M. Venkadesan, who are the authors of this manuscript. 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PMC5100986	RESEARCH ARTICLE Movement Demands of Elite Under-20s and Senior International Rugby Union Players Daniel J. Cunningham1☯, David A. Shearer2,3☯, Scott Drawer4‡, Ben Pollard4‡, Robin Eager4‡, Neil Taylor4, Christian J. Cook1, Liam P. Kilduff1,3☯* 1 Applied Sport Technology Exercise and Medicine Research Centre (A-STEM), College of Engineering, Swansea University, Swansea, Wales, 2 School of Psychology and Therapeutic Studies, University of South Wales, Rhondda Cynon Taff, Wales, 3 Welsh Institute of Performance Science, College of Engineering, Swansea University, Swansea, Wales, 4 The Rugby Football Union, Greater London, England ☯ These authors contributed equally to this work. ‡ These authors also contributed equally to this work. * l.kilduff@swansea.ac.uk Abstract This study compared the movement demands of elite international Under-20 age grade (U20s) and senior international rugby union players during competitive tournament match play. Forty elite professional players from an U20 and 27 elite professional senior players from international performance squads were monitored using 10Hz global positioning sys- tems (GPS) during 15 (U20s) and 8 (senior) international tournament matches during the 2014 and 2015 seasons. Data on distances, velocities, accelerations, decelerations, high metabolic load (HML) distance and efforts, and number of sprints were derived. Data files from players who played over 60 min (n = 258) were separated firstly into Forwards and Backs, and more specifically into six positional groups; FR–Front Row (prop & hooker), SR–Second Row, BR–Back Row (Flankers & No.8), HB–Half Backs (scrum half & outside half), MF–Midfield (centres), B3 –Back Three (wings & full back) for match analysis. Linear mixed models revealed significant differences between U20 and senior teams in both the forwards and backs. In the forwards the seniors covered greater HML distance (736.4 ± 280.3 vs 701.3 ± 198.7m, p = 0.01) and severe decelerations (2.38 ± 2.2 vs 2.28 ± 1.65, p = 0.05) compared to the U20s, but performed less relative HSR (3.1 ± 1.6 vs 3.2 ± 1.5, p < 0.01), moderate (19.4 ± 10.5 vs 23.6 ± 10.5, p = 0.01) and high accelerations (2.2 ± 1.9 vs 4.3 ± 2.7, p < 0.01) and sprint•min-1 (0.11 ± 0.06 vs 0.11 ± 0.05, p < 0.01). Senior backs cov- ered a greater relative distance (73.3 ± 8.1 vs 69.1 ± 7.6 m•min-1, p < 0.01), greater High Metabolic Load (HML) distance (1138.0 ± 233.5 vs 1060.4 ± 218.1m, p < 0.01), HML efforts (112.7 ± 22.2 vs 98.8 ± 21.7, p < 0.01) and heavy decelerations (9.9 ± 4.3 vs 9.5 ± 4.4, p = 0.04) than the U20s backs. However, the U20s backs performed more relative HSR (7.3 ± 2.1 vs 7.2 ± 2.1, p <0.01) and sprint•min-1 (0.26 ± 0.07 vs 0.25 ± 0.07, p < 0.01). Further investigation highlighted differences between the 6 positional groups of the teams. The positional groups that differed the most on the variables measured were the FR and MF groups, with the U20s FR having higher outputs on HSR, moderate & high accelerations, moderate, high & severe decelerations, HML distance, HML efforts, and sprints•min-1. For the MF group the senior players produced greater values for relative distance covered, PLOS ONE \| DOI:10.1371/journal.pone.0164990 November 8, 2016 1 / 13 a11111 OPEN ACCESS Citation: Cunningham DJ, Shearer DA, Drawer S, Pollard B, Eager R, Taylor N, et al. (2016) Movement Demands of Elite Under-20s and Senior International Rugby Union Players. PLoS ONE 11 (11): e0164990. doi:10.1371/journal. pone.0164990 Editor: Karen Hind, Leeds Beckett University, UNITED KINGDOM Received: March 1, 2016 Accepted: October 4, 2016 Published: November 8, 2016 Copyright: © 2016 Cunningham et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data are available from the Swansea University Ethics Committee for researchers who meet the criteria for access to confidential data. Funding: The author(s) received no specific funding for this work and no authors have any financial or other interest in the products or distributor of the products named in the study. The Rugby Football Union provided support in the form of salaries for authors Scott Drawer, Robin Eager, Ben Pollard and Neil Taylor, but did not have any additional role in the study design, data collection HSR, moderate decelerations, HML distance and sprint•min-1. The BR position group was most similar with the only differences seen on heavy accelerations (U20s higher) and mod- erate decelerations (seniors higher). Findings demonstrate that U20s internationals appear to be an adequate ‘stepping stone’ for preparing players for movement characteristics found senior International rugby, however, the current study highlight for the first time that certain positional groups may require more time to be able to match the movement demands required at a higher playing level than others. Conditioning staff must also bear in mind that the U20s players whilst maintaining or improving match movement capabilities may require to gain substantial mass in some positions to match their senior counterparts. Introduction Rugby union is a high intensity intermittent sport, where periods of intense static exertions, collisions and running at various intensities are interspersed with random periods of lower intensity work and rest [1, 2]. Recent work has characterised the movement demands of senior professional rugby union players [1, 3–7]. There is, however, a lack of literature on movement demands, physical characteristics and match analysis at the highest level of rugby union (inter- national), with only a few studies published on work:rest ratios [8, 9], endocrine response [10, 11], time motion analysis [12] and a recent publication on movement demands [13]. A study by Quarrie et al., [12] reported rugby union players covered on average between 5.5 and 6.3 km per game during 27 international matches observedin their study. Backs generally covered greater distances compared to the forwards, conversely forwards sustained greater contact loads from scrums, rucks and mauls. These researchers utilised video and player tracking soft- ware to quantify distances and contact elements of the game. Unfortunately the current micro- sensor technology appears inadequate in quantifying the collision based elements of the game [14]. A cluster analysis revealed 5 distinct groups of players (e.g. props, second rows, back row, wings & fullback (back 3), centres & fly half)with the authors suggesting that hookers should be grouped with props or second rows and not back row players, which has previously been the case in older time-motion studies [15–17]. Although positional groups covered similar dis- tances during matches, the distances they covered at various speed zones varied considerably and the amount of game time also varied significantly across positions due to tactical substitu- tions [12]. They also suggested that there was a difference in the amount of high speed running (>5m•s-1) performed at international level compared to lower levels of the game and therefore, players hoping to compete at international level need to be conditioned for the increased inten- sity of match play [12]. In addition, there is little in the literature about the elite development pathways (e.g. U20s internationals). With the exception of the work by Lombard et al., [18] on the 10 year physical evolution of South African U20s players, the research of Barr and colleagues [19] reporting speed characteristics of U20s players from a nation outside of the top 10 (i.e. tier 2), and move- ment characteristics in U20 players However, currently no literature exists on how these demands map with those of their senior counterparts. This information would be useful in order to prepare players for the movement demands of senior rugby, whilst minimising the risk of injuries by monitoring playing/training ensuring acute:chronic workloads are appropri- ate [20]. There has been a historical evolution of both senior and junior rugby players from a physical perspective [18, 21] with the rate of increase in body mass particularly large in the last 30–40 Movement Demands of Elite Under-20s and Senior International Rugby Union Players PLOS ONE \| DOI:10.1371/journal.pone.0164990 November 8, 2016 2 / 13 and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. Competing Interests: We have the following interests. Scott Drawer, Robin Eager, Ben Pollard and Neil Taylor are employed by The Rugby Football Union. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors. years [21]. For example, South African U20s players have increased in height (~2.8%), weight (~14%), strength (~51%), muscular endurance (~50%), and improved speed times over 10m (~7%) and 40m (~4%) but have not improved aerobic performance over a 13 year period [18]. This change is most likely due to the amount of training time available since the advent of pro- fessionalism and the training method advances made within the domain of strength and condi- tioning coupled with the desire for larger rugby players in order to gain the upper hand in the collision/contact area of the game. Despite seniors and juniors showing rapid developments in physical characteristics there still appears to be differences between these groups. For example, at the 2015 6 Nations tournament (the highest level of international competition in the north- ern hemisphere) the average weight of a senior English forward was 115.8 ± 8.12 kg compared to 110.3 ± 8.14 kg for an U20s forward. Similarly for the backs 93.8 ± 6.9 vs 89.9 ± 5.7 kg for seniors and U20s (unpublished data). Argus and co-workers [22] also found moderate to very large differences in mass, upper and lower body strength and upper body and lower body power between academy and senior professional southern hemisphere rugby union players. Interestingly Barr et al., [19] found no significant differences between senior and U20s interna- tionals for initial and maximal sprint velocity, however, when initial and maximal sprint momentum was calculated there were significant differences between the groups. These find- ings are further corroborated by the work of Hansen and colleagues [23] who reported signifi- cant differences between elite senior and junior players for mass and measures of strength and power, but not for speed times over 5, 10 and 30m. In rugby league, significant differences in distance travelled during match play between pro- fessional senior and elite junior players have been reported by McClellan & Lovell [24]. Specifi- cally, the mean total distance travelled during professional games (8371 ± 897 m) was significantly greater than that travelled during elite junior (4646 ± 978 m) match-play. Research indicates a progression of physical characteristics between playing levels in rugby lea- gue players, Gabbett [25] outlined the progressive improvement in the physiological capacities (mass, speed, agility and aerobic endurance) of rugby league players as the playing level increases from U13 right the way through to professional level. A similar progression in physi- ological characteristics was reported in an elite English rugby union academy, when Darrall- Jones et al., [26] undertook a comprehensive testing battery with the U16, U18 and U21 acad- emy squads. They reported a progressive increase between the groups on mass, strength, power and momentum, however no differences were found on aerobic endurance or speed times. Recent work by Gannon et al [27] showed improvements in strength and power measures over the course of a season in a professional club environment. The largest improvements were seen in the early to mid-season period with a drop off towards the end of the season but still achiev- ing an overall gain from the start of the season. Neither of these studies made any comparison between age grade players and seniors. A greater understanding of player movement patterns in senior and U20s international rugby union, may give an indication of the positional requirements of performance (as shown in senior professional rugby by Lindsay et al., [3]). Which may also aid in targeting qualities/ physical outputs players need to work on. This will facilitate the planning and implementation of training programmes and development pathways that elicit the required physiological adap- tations specific to individual player needs, whilst ensuring increases in training/playing load are applied appropriately to minimise the risk of injury [20]. Conversely, it could help identify outstanding performers who could be fast tracked into the senior set up. Therefore, assessing how U20s compare to seniors and ascertaining where this development tool sits in the player pathway to senior international honours needs to be addressed. Therefore, the aim of this study was to compare U20s international competitions with senior international competitions based on movement demands recorded using GPS devices. Movement Demands of Elite Under-20s and Senior International Rugby Union Players PLOS ONE \| DOI:10.1371/journal.pone.0164990 November 8, 2016 3 / 13 Methods Elite professional junior players from an U20s international performance squad (n = 43), and elite professional senior players from an international performance squad (n = 27) participated in the study. Prior to providing written informed consent, participants were given information outlining the rationale, potential applications and procedures associated with the study. Ethics approval was granted by the Swansea University Ethics Committee. All players were consid- ered healthy and injury-free at the time of the study and were in full-time training. Players were grouped broadly as forwards and backs and more specificallyin sub units within those groups. With front row (FR), second row (SR) and back row (BR) making up the forwards. Half backs (HB), midfield/centres (MF) and back three (B3) players making up the backs. The U20s players (Table 1) provided a total of 161 GPS files from 15 games from two 6 Nations tournaments (2014 and 2015) and the 2015 Junior World Cup. The senior players (Table 1) provided a total of 97 GPS files from 8 games from the 2014 and 2015 6 Nations tournaments. Previous studies have shown that substitute players display greater work-rates compared to players who start the match, suggesting that these players do not pace their involvement [28]. Therefore, to be included in the analysis players had to complete 60 mins match time [5, 29]. The seniors won all 8 games (5 home, 3 away), the U20 won 8/10 (5 home, 5 away) in the 6 Nations and 4/5 at the Junior World Cup (neutral venues). The average points scored per game was 31.3 and 36, and points conceded 15.4 and 11.4 for senior and U20 respectively. Each player provided at least 1 GPS file with the largest number of files provided by any one player being 11 and 8 in the U20s and seniors, respectively. A total of 79 GPS units were used during the study, units were returned to the manufacturer at the end of each competition for mainte- nance/repair. Procedures All Matches took place between January 2014 and June 2015, each player wore a GPS unit (Viper Pod, STATSport, Belfast, UK) in a bespoke pocket incorporated into their playing jersey on the upper thoracic spine between the scapulae to reduce movement artefacts [30]. The GPS units captured data at a sampling frequency of 10Hz utilising the 4 best available satellites. Recent advancements in GPS technology have made 10 Hz units commercially available, Table 1. Anthropometric Characteristics of Position Groups. Positional Group Team Age (years) M ± SD Height (cm) M ± SD Mass (kg) M ± SD FR Senior 26.1 ± 2.3 185.7 ± 4.2 119.1 ± 5.0 U20 19.5 ± 0.7 184.4 ± 3.0 111.8 ± 5.6 SR Senior 26.4 ± 3.3 199.2 ± 1.6 116.8 ± 4.8 U20 19.7 ± 0.5 199.7 ± 2.3 115.2 ± 4.1 BR Senior 26.0 ± 3.3 190.0 ± 2.6 117.7 ± 10.4 U20 19.9 ± 0.3 187.7 ± 2.7 101.6 ± 3.9 HB Senior 24.2 ± 2.5 179.5 ± 6.0 88.7 ± 4.6 U20 19.6 ± 0.4 176.0 ± 2.1 84.2 ± 4.1 MF Senior 25.7 ± 1.3 190.2 ± 4.1 102.3 ± 6.9 U20 19.5 ± 0.6 183.0 ± 4.9 96.1 ± 6.6 B3 Senior 24.6 ± 3.4 182.6 ± 4.1 91.7 ± 2.1 U20 19.6 ± 0.5 183.7 ± 4.3 89.6 ± 4.9 FR = Front Row (Prop & Hooker), SR = Second Row, BR = Back Row, HB = Half Backs, MF = Midﬁeld/Centres, B3 = Back Three (Wing & Full Back). doi:10.1371/journal.pone.0164990.t001 Movement Demands of Elite Under-20s and Senior International Rugby Union Players PLOS ONE \| DOI:10.1371/journal.pone.0164990 November 8, 2016 4 / 13 which are more accurate for quantifying movement patterns in team sports [31, 32]. For exam- ple, Varley et al., [32] reported that a 10 Hz GPS unit was two to three times more accurate for instantaneous velocity during tasks completed at a range of velocities compared to a criterion measure, 6 times more reliable for measuring maximum instantaneous velocity and had a coef- ficient of variation less than or similar to the calculated smallest worthwhile change [33] during all phases of acceleration/deceleration.More specificallythis brand of GPS devices has been used in team sports to assess movement demands during training and competitive matches [13, 34–39]. In our study, all participants were already familiarized with the devices as part of their day-to-day training and playing practices. Units were activated according to the manufac- turer’s guidelines immediately prior to the pre-match warm-up (~30–60 minutes before kick- off),and to avoid inter-unit variation players wore the same GPS device for each match. Post match, timings from the game were (e.g. kick off, half time, sin binning etc) were entered into the software the raw data files were then processed and data for distance covered, and accelera- tion/deceleration events in pre-set zones were derived automatically by the software (Viper PSA software, STATSports, Belfast, UK). Locomotor Variables The distance relative to playing time (m•min-1), high speed running (HSR) relative to playing time >18.1km•h-1 (the threshold used in numerous rugby GPS studies in both codes; e.g. Aus- tin & Kelly [40] & Jones et al., [4]), number of sprints relative to playing time (sprints•min-1), moderate, high and severe intensity accelerations and decelerations (±2-3m•s-2, ±3-4m•s-2, ±>4m•s-2), high metabolic load distance (HML; defined as distance covered accelerating and decelerating over 2 m•s-2 and/or distance covered >5 m•s-1), and high metabolic load efforts (the number of separate movements/efforts undertaken in producing HML distance). Total time was calculated for ‘playing time’ only, that is, the time the player was on the playing field only, with time off the field (e.g., half time, periods on the bench/sin bin) removed from the data analysis. Time off during match play, such as injury time or video referee, was included in the study, because this was part of the game duration; hence ‘playing time’ may exceed the stan- dard 80 minutes of match play. Data Analysis Linear mixed models were used to examine each dependent variable for the interaction between teams in respect to positional types (forwards and backs) and groups (e.g., front rows, second rows etc.). To allow for the nested design of the data, random intercepts were modelled for participants (individual GPS measures), teams (i.e., U20 vs Senior) and competition (U20 6 Nations 2014, U20 6 Nations 2016, U20 Junior World Cup 2015, Senior 6 Nations 2014, Senior 6 Nations 2015). Attempts were made to also model for random slopes for the same variables but this resulted in over-specified models. Where significant interactions were identified, dif- ferences were interpreted using a combination of estimates of fixed effects, examination of means and 95% confidence intervals (Tables C and D in S1 File). Results Examination of means and standard deviations indicated visible difference between teams as a function of position type (Table 2). Linear mixed models indicated a significant interaction between Team (U20 v Senior) and Positional Type (Forwards and Backs) for M•min-1 (p < .001), HSR m•min-1 (p < .001), Accelerations 2-3m•s-2 (p < .01), Accelerations 3-4m•s-2 (p < .001), Decelerations 3-4m•s-2 (p < .001), Decelerations >4m•s-2 (p < .01), HML Distance (m) (p < .001), Sprint•min-1 (p < .001), and HML Efforts (p < .001). Further examination of fixed Movement Demands of Elite Under-20s and Senior International Rugby Union Players PLOS ONE \| DOI:10.1371/journal.pone.0164990 November 8, 2016 5 / 13 effects for these significant interactions revealed that U20 forwards had significantly higher HSR m•min-1 (p < .001, CI: -3.33 to– 1.08), Accelerations 2-3m•s-2 (p < .01, CI: -9.99 to– 1.46), Accelerations 3-4m•s-2 (p < .001, CI: -4.45 to– 1.72), Decelerations 3-4m•s-2 (p < .05, CI: -3.87 to– 0.44), Sprint•min-1 (p < .001, CI: -.12 to– 0.04), and significantly lower Accelera- tions >4m•s-2 (p < .05, CI: 0.13 to 3.66) and HML Distance (p < .05, CI: -292.12 to– 41.40). For backs, fixed effect revealed U20 players had significantly lower values for M•min-1 (p < .001, CI: 4.52 to 11.34), HML Distance (p < .001, CI: 140.65 to 402.96) HML efforts (p < .001, CI: 10.74 to 34.20) and Decelerations 3-4m•s-2 (p < .05, CI: 0.13 to 3.66), while significantly higher values for HSR m•min-1 (p < .001, CI: 1.15 to 3.51) and Sprint•min-1 (p < .001, CI: 0.04 to 0.13). However, some other variables were close to significance also (Table C in S1 File). Examination of means and standard deviations indicated visible difference between teams as a function of positional groups (Table 3). Linear mixed models indicated a significant inter- action between team (U20 v Senior) and positional groups (e.g., half-back, second rows etc.) for M•min-1 (p < .05), Accelerations 2-3m•s-2 (p < .05), Decelerations 2-3m•s-2 (p < .001), Decelerations 3-4m•s-2 (p> .05), HML Distance (p < .01), and HML efforts (p > .001). Esti- mates of fixed effects were used to indicate differences between specific playing group between teams where interactions occurred.U20 Front Rows scored significantly higher than seniors for HSR m•min-1 (p < .001, CI: -5.15 to -1.61), Accelerations 2-3m•s-2 (p < .001, CI: -21.08 to -8.09), Accelerations 3-4m•s-2 (p < .001, CI: -6.35 to -1.84), Decelerations 2-3m•s-2 (p < .001, CI: -17.78 to -6.79), Decelerations 3-4m•s-2 (p < .001, CI: -7.55 to -2.20), Decelerations >4m•s-2 (p < .01, CI: -4.04 to -0.59), HML Distance (p < .001, CI: -618.18 to -240.92), HML efforts (p < .001, CI: -51.90 to -20.07) and Sprint•min-1 (p < .001, CI: -0.20 to -0.07). U20 Sec- ond rows scored significantly higher for HSR m•min-1 (p < .05, CI: -3.92 to -0.13), Accelera- tions 3-4m•s-2 (p < .01, CI: -5.72 to -0.91), however seniors performed more Sprint•min-1 (p < .001, CI: -0.20 to -0.07). U20 Back rows had significantly less Decelerations 2-3m•s-2 (p < .01, CI: 1.74 to 11.46) but more Accelerations 3-4m•s-2 (p < .05, CI: -4.26 to -0.14). U20 Half back had significantly lower scores for for M•min-1 (p < .001 CI: 6.48 to 17.80), Decelerations 2-3m•s-2 (p < .05, CI: 0.20 to 11.86), HML Distance (p < .05, CI: 56.47 to 503.78) and HML efforts (p < .001, CI: 19.72 to 55.84). U20 Midfield players had significantly lower values for Table 2. Movement Characteristics for Senior and U20s, Forwards and Backs Groups. Position Group Forwards Backs Seniors (n = 15) U20s (n = 21) Seniors (n = 12) U20s (n = 22) GPS Variable M ± SD M ± SD M ± SD M ± SD M•min-1 66.8 ± 7.0 61.5 ± 8.0 73.3 ± 8.1* 69.1 ± 7.6 HSR m•min-1 3.1 ± 1.6 3.2 ± 1.5^ 7.2 ± 2.1 7.3 ± 2.1* HML Distance (m) 736.4 ± 280.3^ 701.3 ± 198.7 1138.0 ± 233.5* 1060.4 ± 218.1 HML Efforts 84.8 ± 30.4 78.8 ± 21.5 112.7 ± 22.2* 98.8 ± 21.7 Accelerations 2-3m•s-2 19.42 ± 10.5* 23.6 ± 8.9^ 26.4 ± 8.4 26.1 ± 10.1 Accelerations 3-4m•s-2 2.2 ± 1.9* 4.3 ± 2.7^ 4.9 ± 3.0* 6.4 ± 4.5 Accelerations >4m•s-2 0.69 ± 0.95 0.47 ± 0.84 1.04 ± 1.22 0.89 ± 1.37 Decelerations 2-3m•s-2 24.56 ± 11.5 25.2 ± 9.3 28.4 ± 7.7* 25.3 ± 9.3 Decelerations 3-4m•s-2 6.4 ± 4.0 7.5 ± 3.5^ 9.9 ± 4.3* 9.5 ± 4.4 Decelerations >4m•s-2 2.38 ± 2.2^ 2.28 ± 1.65 4.39 ± 2.77 4.95 ± 3.0 Sprint•min-1 0.11 ± 0.06 0.11 ± 0.05^ 0.25 ± 0.07 0.26 ± 0.07* ^ = Signiﬁcantly higher than either Senior or U20 forwards counterpart. * = Signiﬁcantly higher than either Senior or U20 backs counterpart. doi:10.1371/journal.pone.0164990.t002 Movement Demands of Elite Under-20s and Senior International Rugby Union Players PLOS ONE \| DOI:10.1371/journal.pone.0164990 November 8, 2016 6 / 13 Table 3. Movement Characteristics Presented by Playing Groups. Position Group FR SR BR HB MF B3 Team M ± SD Team M ± SD Team M ± SD Team M ± SD Team M ± SD Team M ± SD M•min-1 U20s 60.1 ± 7.2 U20s 60.8 ± 5.9 U20s 63.2 ± 9.7 U20s 67.5 ± 9.1* U20s 70.5 ± 6.8* U20s 68.7 ± 7.6* Seniors 61.1 ± 7.9 Seniors 67.6 ± 6.5 Seniors 69.9 ± 4.3 Seniors 77.4 ± 5.6 Seniors 71.9 ± 10.0 Seniors 70.8 ± 7.1 HSR m•min-1 U20s 2.5 ± 1.3* U20s 3.0 ± 1.1* U20s 4.0 ± 1.6 U20s 5.5 ± 2.4 U20s 7.2 ± 1.7* U20s 8.1 ± 1.7* Seniors 1.8 ± 1.1 Seniors 2.9 ± 1.2 Seniors 4.0 ± 1.4 Seniors 6.3 ± 1.6 Seniors 8.0 ± 2.3 Seniors 7.4 ± 2.2 HML Distance (m) U20s 584.9 ± 199.1* U20s 673.3 ± 124.1 U20s 820.6 ± 182.5 U20s 954.8 ± 304.0* U20s 1103.4 ± 168.6* U20s 1069.7 ± 203.0 Seniors 452.3 ± 172.9 Seniors 747.1 ± 227.9 Seniors 911.4 ± 210.2 Seniors 1144.5 ± 175.9 Seniors 1205.4 ± 265.6 Seniors 1076.1 ± 246.0 HML Efforts U20s 66.0 ± 22.6* U20s 77.2 ± 14.5 U20s 90.7 ± 19.0 U20s 99.9 ± 27.2* U20s 105.0 ± 15.9 U20s 93.4 ± 22.4 Seniors 54.6 ± 19.3 Seniors 85.8 ± 26.7 Seniors 103.4 ± 22.4 Seniors 126.3 ± 14.2 Seniors 113.9 ± 23.1 Seniors 99.7 ± 20.5 Accelerations 2-3m•s-2 U20s 17.8 ± 6.6* U20s 22.9 ± 7.4 U20s 29.0 ± 8.5 U20s 23.5 ± 13.6 U20s 27.4 ± 10.3 U20s 26.1 ± 8.1 Seniors 10.0 ± 6.0 Seniors 21.3 ± 9.4 Seniors 24.4 ± 9.5 Seniors 26.8 ± 7.9 Seniors 28.5 ± 9.4 Seniors 24.4 ± 7.9 Accelerations 3-4m•s-2 U20s 3.5 ± 2.4* U20s 3.8 ± 2.1* U20s 5.5 ± 3.1* U20s 4.3 ± 5.4 U20s 5.9 ± 2.8 U20s 7.6 ± 4.9 Seniors 1.1 ± 1.3 Seniors 1.8 ± 1.9 Seniors 3.1 ± 2.0 Seniors 4.8 ±2.9 Seniors 4.0 ± 3.0 Seniors 5.7 ± 3.0 Accelerations >4m•s-2 U20s 0.39 ± 0.75 U20s 0.25 ± 0.53* U20s 0.71 ± 1.04 U20s 0.33 ± 0.49 U20s 0.45 ± 0.78 U20s 1.47 ± 1.73 Seniors 0.50 ± 0.65 Seniors 0.83 ± 1.19 Seniors 0.73 ± 0.98 Seniors 1.19 ± 1.22 Seniors 1.07 ± 1.44 Seniors 0.89 ± 1.08 Decelerations 2-3m•s-2 U20s 21.1 ± 9.0* U20s 24.8 ± 9.8 U20s 28.8 ± 7.9* U20s 24.7 ± 9.9* U20s 28.1 ± 8.9* U20s 23.3 ± 9.1 Seniors 13.0 ± 6.1 Seniors 24.5 ± 10.4 Seniors 32.0 ± 8.4 Seniors 31.1 ± 5.6 Seniors 31.1 ± 8.6 Seniors 23.9 ± 6.7 Decelerations 3-4m•s-2 U20s 6.2 ± 3.7* U20s 8.0 ± 3.0 U20s 8.2 ± 3.4 U20s 6.5 ± 3.6 U20s 11.5 ± 4.1* U20s 9.2 ± 4.3 Seniors 3.5 ± 2.4 Seniors 5.7 ± 2.4 Seniors 8.6 ± 4.3 Seniors 9.4 ± 4.8 Seniors 11.3 ± 4.2 Seniors 9.2 ± 3.9 Decelerations >4m•s-2 U20s 2.2 ± 1.9* U20s 1.6 ± 1.3 U20s 2.9 ± 1.5 U20s 3.0 ± 2.0 U20s 5.2 ± 3.5 U20s 5.5 ± 2.6* Seniors 1.0 ± 1.5 Seniors 2.7 ± 2.2 Seniors 3.1 ± 2.3 Seniors 3.7 ± 2.3 Seniors 4.3 ± 2.7 Seniors 5.1 ± 3.1 Sprint•min-1 U20s 0.09 ± 0.04* U20s 0.10 ± 0.03* U20s 0.14 ± 0.05 U20s 0.18 ± 0.06 U20s 0.27 ± 0.06* U20s 0.29 ± 0.06* Seniors 0.06 ± 0.04 Seniors 0.11 ± 0.04 Seniors 0.14 ± 0.05 Seniors 0.21 ± 0.06 Seniors 0.28 ± 0.07 Seniors 0.27 ± 0.08 FR = Front Row (Prop & Hooker), SR = Second Row, BR = Back Row, HB = Half Backs, MF = Midﬁeld/Centres, B3 = Back Three (Wing & Fullback). * = signiﬁcant difference to Senior counterpart doi:10.1371/journal.pone.0164990.t003 Movement Demands of Elite Under-20s and Senior International Rugby Union Players PLOS ONE \| DOI:10.1371/journal.pone.0164990 November 8, 2016 7 / 13 M•min-1 (p < .05, CI: 1.27 to 12.34), HSR m•min-1 (p < .001, CI: 1.05 to 5.05), Decelerations 2-3m•s-2 (p < .05, CI: 0.21 to 11.73), HML Distance (p < .01, CI: 122.37 to 543.28) and HML efforts (p < .01, CI: 7.07 to 41.74), Sprint•min-1 (p < .001, CI: 0.04 to 0.18), but higher values for Decelerations 3-4m•s-2 (p < .05, CI: 0.24 to 5.99). Finally, U20 Back Three players had sig- nificantly lower values for M•min-1 (p < .05, CI: 1.12 to 10.58), and HML Distance (p < .01, CI: 51.45 to 399.61), but higher values for HSR m•min-1 (p < .001, CI: 0.81 to 4.09), Decelera- tions >4m•s-2 (p < .05, CI: 0.20 to 3.39) and Sprint•min-1 (p < .001, CI: 0.04 to 0.15). Discussion The aim of this study was to compare the locomotor demands of senior international and age grade international (U20) rugby union matches using GPS devices. The current study is the first to present an analysis of movement demands of senior international competition in comparison to the elite junior international competition. The results of the present study increase our under- standing of the movement demands of competition experiencedby players in existing interna- tional rugby union development pathways and determine whether the U20s competition reflects the movement demands of senior match-play. Therefore, the results of the current study may have implications for the design and implementation of physical conditioning programmes in order to prepare players for the movement demands of senior international rugby. In general, the seniors covered greater relative distance for both forwards (66.8 ± 7.1 vs 61.5 ± 8.0m•min-1) and backs (73.3 ± 8.1 vs 69.1 ± 7.6m•min-1), however this was only statisti- cally significant for the backs. The U20s forwards performed more HSR m•min-1 accelerations in zones 2–3 & 3-4m•s-2, decelerations 3-4m•s-2 and sprint•min-1 than the seniors, but less HML distance. In the Backs, the senior group covered more relative distance (m•min-1) per- formed more decelerations 3-4m•s-2, more HML distance & efforts, but the U20s performed more HSR m•min-1 and sprint•min-1. The U20s also had significantly longer match time, which could be due to different substitution strategies or a number of other factors (e.g. more injury stoppages, discipline issues, third match official use). The relative distance values pre- sented in the current study are lower than a recent publication from a southern hemisphere club team [3] (Forwards: 77.3 ± 20.5, Backs: 84.7 ± 10.4m•min-1), and in between values pro- duced by 2 different Pro 12 clubs [5, 7] (Forwards: 60.4 ± 7.8 & 71.6 ± 10.1, Backs: 67.8 ± 8.2 & 81.0 ± 10.2m•min-1). However, when comparing to data published from the Premiership (For- wards: 64.6 IQR 6.3, Backs: 71.1 IQR 11.7 m•min-1) from which the current players are drawn, it appears that U20s international competition is marginally below the movement demands of the Premiership, while senior international competition is higher, in terms of relative distance covered. This may indicate that U20s rugby is preparing players for movement demands in Premiership rugby, which in turn will help prepare for full international matches. However, given the likely variation in tactics/playing styles between the teams and the opposition faced, care must taken when making comparisons [41, 42]. Although generally there are differences between senior and U20s backs and forwards, the number of variables that were significantly different varied across each positional group. There were no significant differences in relative distance covered between U20s and seniors in any forward positional group (front row, second row, back row). There were significant differences in relative distance covered for the half backs (77.37 ± 5.62 vs 67.47 ± 9.10m•min-1), midfield (71.9 ± 10.0 vs 70.5 ± 6.8m•min-1) and back three (70.8 ± 7.1 vs 68.7 ± 7.6m•min-1) with seniors covering greater distances in all cases. The U20s covered greater relative HSR distance in the front & second rows and back three position groups, with the opposite being the case for the midfield group. No differences between seniors and U20s were seen for back row or half backs for HSR. Movement Demands of Elite Under-20s and Senior International Rugby Union Players PLOS ONE \| DOI:10.1371/journal.pone.0164990 November 8, 2016 8 / 13 The front row and midfield groups had the most differences between seniors and U20s. Sig- nificant differences were found between front row groups on HSR (relative), accelerations (2–3 and 3-4m•s-2), decelerations (2–3, 3–4 and >4m•s-2), HML distance, HML efforts, and sprints•min-1 with the U20s having higher outputs on each variable. Conversely for the mid- field group seniors had significantly greater values for relative distance covered, HSR (relative), decelerations (2–3•s-2), HML distance and sprints•min-1. The back row group was most similar with only accelerations (2–3•s-2), decelerations (2–3•s-2) being significantly different. Compar- ing acceleration and deceleration data with previous literature is somewhat problematic as Cunniffe et al. [6] reported no differences between backs and forwards groups, however, a very low sample size was utilised (1 back, 1 forward during 1 game) together with different accelera- tion zones. Jones et al., [5] reported distance covered while in various acceleration and deceler- ation zones for both backs and forwards combined when investigating temporal fatigue in their study, which makes comparison impossible, as the current study used number of acceleration and deceleration events. Owen and co-workers [43] utilised comparable zones and reported number of acceleration/deceleration events, their work supports the current finding that backs are involved in more frequent acceleration and deceleration events. However, the current study appears to have a slightly higher frequency for both backs and forwards, potentially as a result of the level of competition (Super vs International rugby). The number of sprints•min-1 performed was significantly different between U20s and senior forwards (0.11 ± 0.05 vs 0.11 ± 0.06) and U20s and senior backs (0.26 ± 0.07 vs 0.25 ± 0.07). However, this is unlikely to be of practical significance given the low frequency. Backs performed more sprints than the forwards (~x2.5) in both groups. The greatest differ- ence between positional groups was the U20s front row group who performed almost double the amount of their senior counterparts (8.73 ± 4.52 vs 4.71 ± 3.45). This could be due to U20s players being lighter (119.1 ± 5.0 vs 111.8 ± 5.6 kg) and potentially more mobile, or simply a reflection of their physical capabilities. The number of sprints reported in the current study is higher than those reported by Jones and co-workers [5] most likely again to the difference in playing standard (club vs international) or potentially style of play. Overall there were a number of differences between the forwards and backs of the U20s and senior teams. However, when broken down further into positional groups, variations in differ- ences between the two teams in certain positions emerged. The positional groups that appeared most different between the teams on the metrics measured were the front row and midfield groups, with the U20s front row performing more than their senior counterparts on HSR, moderate and heavy accelerations, all decelerations, HML distance HML efforts. As the senior players tend to be heavier and stronger, the static exertions (not measureable by GPS) from scrums have been shown to be greater in the senior international game [44]. This may result in transient fatigue, whereby there is a reduction in high-intensity activity performed immediately following an intense bout, with a subsequent recovery later in performance [45], and account for their lower movement scores. The opposite was true for the midfield group with the seniors producing higher scores for relative distance covered, HSR, moderate decelerations, HML dis- tance and sprints•min-1. Perhaps indicating these players are used more frequently in a more direct, gain-line based game plan. High speed running (HSR) has previously been shown to distinguish between playing levels in a number of sports (e.g. [12, 46, 47]), with the more elite levels covering greater distances in this speed zone. However, in the current study overall the U20s forwards and backs groups, performed more relative HSR than their senior counterparts. This wasn’t the case for each posi- tional group however, both senior and U20 back row and half backs had no differences between them for HSR. Whilst the senior midfield group outperformed the U20s. One potential reason for these discrepancies is that the two groups used here (senior and U20) are not elite and non- Movement Demands of Elite Under-20s and Senior International Rugby Union Players PLOS ONE \| DOI:10.1371/journal.pone.0164990 November 8, 2016 9 / 13 elite as used in studies where HSR has been a distinguishing factor. Both groups could be viewed as ‘elite’, in support of this 8 players from the U20s cohort have already progressed to the senior squads. It is also worth noting U20s players generally weigh less than their senior counterparts so will need to be able to maintain the same movement work load (e.g. distance covered, HSR distance, accelerations, decelerations) whilst increasing in mass to prepare for senior internationals. To our knowledge this is the first study comparing movement demands of U20 and senior International rugby union matches. The data suggests that the movement demands in Under 20s internationals are adequate for preparing players for movement demands reported in International rugby. However, certain positional groups might require more work and/or time to match their senior counterparts than others. Conditioning staff must also bear in mind that the U20s players whilst maintaining or improving match move- ment capabilities may require to gain substantial mass in some positions to match their senior counterparts. Supporting Information S1 File. Table A. Supplementary Movement Characteristicsfor Senior and U20s, Forwards and Backs Groups. Data presented as Mean ± S.D Table B. SupplementaryMovement Char- acteristics Presented by Playing Groups. FR = Front Row (Prop & Hooker), SR = Second Row, BR = Back Row, HB = Half Backs, MF = Midfield/Centres,B3 = Back Three (Wing & Fullback). Data presented as Mean ± S.D. Table C. Estimates of fixed effects for all GPS vari- ables displaying difference for position type between U20s and Seniors Table D. Estimates of fixed effects for all GPS variables displaying difference for position group between U20s and Seniors (DOCX) Author Contributions Conceptualization: DC LK SD RE CC DS BP NT. Data curation: DC LK SD RE CC DS BP NT. Formal analysis: DC LK SD RE BP. Funding acquisition: SD LK. Methodology:DC LK SD RE CC DS BP NT. Project administration: DC LK SD RE CC DS BP NT. Supervision:LK. Writing – original draft: DC LK SD RE CC DS BP NT. Writing – review& editing: DC LK DS. References 1. Cahill N, Lamb K, Worsfold P, Headey R, Murray S. The movement characteristics of English Premier- ship rugby union players. J Sports Sci. 2013; 31(3):229–37. doi: 10.1080/02640414.2012.727456 PMID: 23009129. 2. Roberts SP, Trewartha G, Higgitt RJ, El-Abd J, Stokes KA. The physical demands of elite English rugby union. 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PMC4919094	Supplement: Prediction and Quantiﬁcation of Individual Athletic Performance of Runners Duncan A.J. Blythe ∗ 1,2 and Franz J. Király † 3 1African Institute for Mathematical Sciences, Bagamoyo, Tanzania 2 Bernstein Center for Computational Neuroscience, Berlin, Germany 3 Department of Statistical Science, University College London, United Kingdom Methods The following provides a guideline for reproducing the results. Raw and pre-processed data in MATLAB and CSV formats is available upon request, subject to approval by British Athletics. Complete and documented source code of algorithms and analyses as well as data can be obtained from [1, 2]. Data Source The basis for our analyses is the online database www.thepowerof10.info, which catalogues British individ- uals’ performances achieved in oﬃcially ratiﬁed athletics competitions since 1954, including Olympic athletic events (ﬁeld and non-ﬁeld events), non-Olympic athletic events, cross country events and road races of all distances. With the permission of British Athletics, we obtained an excerpt of the database by automated querying of the freely accessible parts of www.thepowerof10.info, restricted to ten types of running events: 100m, 200m, 400m, 800m, 1500m, the Mile, 5000m (track and road races), 10000m (track and road races), Half- Marathon and Marathon. Other types of running events were available but excluded from the present analyses; the reasons for exclusion were a smaller total of attempts (e.g. 3000m), a diﬀerent population of runners (e.g. 3000m is mainly attempted by younger runners), and varying conditions (steeplechase/ hurdles and cross-country races). The data set consists of two tables: athletes.csv, containing records of individual runners, with ﬁelds: runner ID, gender, date of birth; and events.csv, containing records of individual attempts on running events until August 3, 2013, with ﬁelds: runner ID, event type, date of the attempt, and performance in seconds. Data Cleaning Our excerpt of the database contains (after error and duplication removal) records of 164,746 individuals of both genders, ranging from the amateur to the elite, young to old, and a total of 1,410,789 individual performances for 10 diﬀerent types of events (see previous section). Gender is available for all runners in the database (101,775 male, 62,971 female). The dates of birth of 114,168 runners are missing (recorded as January 1, 1900 in athletes.csv due to particulars of the automated querying); the date of birth of six runners is set to missing due to a recorded age at recorded attempts of eight years or less. For the above runners, a total of 1,417,476 attempts was recorded, out of which 1,410,789 remained in the data set after cleaning: 192,947 over 100m, 194,107 over 200m, 109,430 over 400m, 239,666 over 800m, ∗duncan.blythe@bccn-berlin.de †f.kiraly@ucl.ac.uk 1 176,284 over 1500m, 6,590 at the Mile distance, 96,793 over 5000m (the track and road races), 161,504 over 10000m (on the track and road races), 140,446 for the Half-Marathon and 93,033 for the Marathon. 6,643 duplicate events were removed, and a number of 44 events whose reported performances are better than the oﬃcial world records of their time, or extremely slow. Dates of the attempt were set to missing for 225 of the attempts that recorded January 1, 1901, and one of the attempts that recorded August 20, 2038. Data Preprocessing The events and athletes data sets are collated into (10×164, 746)-tables/matrices of performances, where the 10 columns correspond to events and the 164, 746 rows to individual runners. Rows are indexed increasingly by runner ID, columns by the type of event. Each entry of the table/matrix contains one performance (in seconds) of the runner by which the row is indexed, at the event by which the column is indexed, or a missing value. If the entry contains a performance, the date of that performance is stored as meta-information. We consider two diﬀerent modes of collation, yielding one table/matrix of performances of size (10 × 164, 746) each. In the ﬁrst mode, which in Tables 1 ﬀ. is referenced as “best”, one proceeds as follows. First, for each individual runner, one ﬁnds the best event of each individual, measured by population percentile. Then, for each type of event which was attempted by that runner within a year before or after that best event, the best performance for that type of event is entered into the table. If a certain event was not attempted in this period, it is recorded as missing. For the second mode of collation, which in Tables 1 ﬀ. is referenced as “random”, one proceeds as follows. First, for each individual runner, a calendar year is uniformly randomly selected among the calendar years in which that runner has attempted at least one event. Then, for each type of event which was attempted by that runner within the selected calendar year, the best performance for that type of event is entered into the table. If a certain event was not attempted in the selected calendar year, it is recorded as missing. The ﬁrst collation mode ensures that the data is of high quality: runners are close to optimal ﬁtness, since their best performance was achieved in this time period. Moreover, since ﬁtness was at a high level, it is plausible that the number of injuries incurred was low leading to multiple attempts being made at events – this will lead to higher data quality; indeed it can be observed that the number of attempts per event is higher in this period, eﬀectively decreasing the inﬂuence of noise and the chance that outliers are present after collation. The second collation mode is used to check whether and, if so how strongly, the results depend on the runners being close to optimal ﬁtness. In both cases choosing a narrow time frame ensures that performances are relevant to one another for prediction. Runner-Speciﬁc Summary Statistics For each given runner, several summaries are computed based on the collated matrix. Performance percentiles are computed for each event which a runner attempts in relation to the other runners’ performances on the same event. These column-wise event-speciﬁc percentiles, yield a percentile matrix with the same ﬁlling pattern (pattern of missing entries) as the collated matrix. The preferred distance for a given runner is the geometric mean of the attempted events’ distances. That is, if s1, . . . , sm are the distances for the events which the runner has attempted, then ˜s = (s1 · s2 · . . . · sm)1/m is the preferred distance. The training standard for a given runner is the mean of all performance percentiles in the corresponding row. The no. events for a given runner is the number of events attempted by a runner in the time period of the data considered (best or random). Note that the percentiles yield a mostly physiological description; the preferred distance is a behavioural summary since it describes the type of events the runner attempts. The training standard combines both physiological and behavioural characteristics. Percentiles and training standard depend on the collated matrix. When we consider genders, age or runners who have attempted more than a certain no.event these summary statistics are calculated separately 2 for the subgroup. However, within subgroup these values depend on the entire collated submatrix for that subgroup. Outlier Removal Outliers are removed from the data in both collated matrices. An outlier score for each runner/row is obtained as the diﬀerence of maximum and minimum of all performance percentile of the runner. The ﬁve percent rows/runners with the highest outlier score are removed from the matrix. Prediction: Evaluation and Validation Prediction accuracy is evaluated on row-sub-samples of the collated matrices, deﬁned by (a) a potential subgroup, e.g., given by age or gender, (b) degrees-of-freedom constraints in the prediction methods that require a certain number of entries per row, and (c) a certain range of performance percentiles of runners. The row-sub-samples referred to in the main text and in Tables 1 ﬀ. are obtained by (a) retaining all rows/runners in the subgroup speciﬁed by gender, or age in the best event, (b) retaining all rows/runners with at least no. events or more entries non-missing, and discarding all rows/runners with strictly less than no. events entries non-missing, then (c) retaining all runners in a certain percentile range. The percentiles referred to in (c) are computed as follows: ﬁrst, for each column, in the data retained after step (b), percentiles are computed. Then, for each row/runner, the best of these percentiles is selected as the score over which the overall percentiles are taken. The accuracy of prediction is principally measured empirically in terms of out-of-sample root mean squared error (RMSE) and mean absolute error (MAE), with RMSE, MAE, and standard deviations es- timated from the empirical sample of residuals obtained in 1000 iterations of leave-one-out validation. In selected analyses we measure error in terms of relative RMSE (rRMSE), 1 N P i predictor(i)−predicted(i) predicted(i) 2 and relative MAE (deﬁned analogously) (rMAE). Given the row-sub-sample matrix obtained from (a), (b), (c), prediction and thus we perform leave-one- out validation in two ways: (i) predicting the left-out entry from potentially all remaining entries. In this scenario, the prediction method may have access to the performances of the runner in question which lie in the future of the event to be predicted, though only performances of other events are available; (ii) predicting the left-out entry from all remaining entries of other runners, but only from those events of the runner in question that lie in the past of the event to be predicted. In this task, temporal causality is preserved on the level of the single runner for whom prediction is done; though information about other runners’ results that lie in the future of the event to be predicted may be used. The third option (iii) where predictions are made only from past events has not been studied due to the size of the data set which makes collation of the data set for every single prediction per method and group a computationally extensive task, and due to the potential group-wise sampling bias which would be introduced, skewing the measures of prediction-quality—the population of runners on the older attempts is diﬀerent in many respects from the more recent attempts. We further argue that in the absence of such technical issues, evaluation as in (ii) would be equivalent to (iii); since the performances of two randomly picked runners, no matter how they are related temporally, may be reasonably modelled as statistically independent; positing the contrary would be equivalent to postulating that any given runner’s performance is very likely to be directly inﬂuenced by a large number of other runners’ performance history, which is an assumption which is scientiﬁcally implausible. Given the above, due to equivalence of (ii) and (iii), and the issues occurring in (iii) exclusively, we can conclude that (ii) is preferrable over (iii) from a scientiﬁc and statistical viewpoint. Prediction: Target Outcomes The principal target outcome for the prediction is “performance”, which we present to the prediction methods in three distinct parameterisations. This corresponds to passing not the raw performance matrices obtained in the section “Data Pre-processing” to the prediction methods, but re-parameterized variants where the non-missing entries undergo a univariate variable transform. The three parameterizations of performance considered in our experiments are the following: 3 (a) normalized: performance as the time in which the given runner (row) completes the event in question (column), divided by the average time in which the event in question (column) is completed in the sub- sample; (b) log-time: performance as the natural logarithm of time in seconds in which the given runner (row) completes the event in question (column); (c) speed: performance as the average speed in meters per second, with which the given runner (row) completes the event in question (column). The words in italics indicate which parameterisation is referred to in Table 1. The error measures, RMSE and MAE, are evaluated in the same parameterisation in which prediction is performed. We do not evaluate performance directly in un-normalized time units, as in this representation performances between 100m and the Marathon span 4 orders of magnitude (base-10), which would skew the measures of goodness heavily towards accuracy over the Marathon. Unless stated otherwise, predictions are made in the same parameterisation on which the models are learnt. Prediction: Models and Algorithms In the experiments, a variety of prediction methods are used to perform prediction from the performance data, given as described in “Prediction: Target Outcomes”, evaluated by the measures as described in the section “Prediction: Evaluation and Validation”. In the code available for download, each method is encapsulated as a routine which predicts a missing entry when given the (training entries in the) performance matrix. The methods can be roughly divided in four classes: (1) naive baselines, (2) representatives of the state-of-the-art in prediction of running performance, (3) representatives of the state-of-the-art in matrix completion, and (4) our proposed method and its variants. The naive baselines are: (1.a) mean: predicting the the mean over all performances for the same event within the subgroup considered. (1.b) k-NN: k-nearest neighbours prediction. The parameter k is obtained as the minimizer of out-of-sample RMSE on ﬁve groups of 50 randomly chosen validation data points from the training set, from among k = 1, k = 5, and k = 20. The representatives of the state-of-the-art in predicting running performance are: (2.a) Riegel: The Riegel power law formula with exponent 1.06. (2.b) power law: A power law predictor, as per the Riegel formula, but with the exponent estimated from the data. The exponent is the same for all runners and estimated as the minimizer of the residual sum of squares. (2.c) ind.power law: A power law predictor, as per the Riegel formula, but with the exponent estimated from the data. The exponent may be diﬀerent for each runner and is estimated as the minimizer of the residual sum of squares. (2.d) Purdy: Prediction by calculation of equivalent performances using the Purdy points scheme [3]. Purdy points are calculated by using the measurements given by the Portugese scoring tables which estimate the maximum velocity for a given distance in a straight line, and adjust for the cost of traversing curves and the time required to reach race velocity. The performance with the same number of points as the predicting event is imputed. The representatives of the state-of-the-art in matrix completion are: (3.a) EM: Expectation maximization algorithm assuming a multivariate Gaussian model for the rows of the performance matrix in log-time parameterisation. Missing entries are initialized by the mean of each column. The updates are terminated when the percent increase in log-likelihood is less than 0.1%. For a review of the EM-algorithm see [4]. (3.b) Nuclear Norm: Matrix completion via nuclear norm minimization [5, 6]. The variants of our proposed method are as follows: (4.a-d) LMC rank r: local matrix completion for the low-rank model, with rank r = 1, 2, 3, 4. (4.a) is LMC rank 1, (4.b) is LMC rank 2, and so on. Our algorithm follows the local/entry-wise matrix completion paradigm in [7]. It extends the rank 1 local matrix completion method described in [8] to arbitrary ranks. Our implementation uses: determinants of size (r + 1 × r + 1) as the only circuits; the weighted variance minimization principle in [8]; the linear approximation for the circuit variance outlined in the appendix of [9]; modelling circuits as independent for the co-variance approximation. 4 We further restrict to circuits supported on the event to be predicted and the r log-distance closest events. For the convenience of the reader, we describe the exact way in which the local matrix completion principle is instantiated, in the section “Prediction: Local Matrix Completion” below In the supplementary experiments we also investigate two aggregate predictors to study the potential beneﬁt of using other lengths for prediction: (5.a) bagged power law: bagging the power law predictor with estimated coeﬃcient (2.b) by a weighted average of predictions obtained from diﬀerent events. The weighting procedure is described below. (5.b) bagged LMC rank 2: estimation by LMC rank 2 where determinants can be supported at any three events, not only on the closest ones (as in line 1 of Algorithm 1 below). The ﬁnal, bagged predictor is obtained as a weighted average of LMC rank 2 running on diﬀerent triples of events. The weighting procedure is described below. The averaging weights for (5.a) and (5.b) are both obtained from the Gaussian radial basis function kernel exp Obtaining the Low-Rank Components and Coeﬃcients We obtain three low-rank components f1, . . . , f3 and corresponding coeﬃcients λ1, . . . , λ3 for each runner by considering the data in log-time coordinates. Each component fi is a vector of length 10, with entries corresponding to events. Each coeﬃcient is a scalar, potentially diﬀerent for each runner. To obtain the components and coeﬃcients, we consider the data matrix for the speciﬁc target outcome, sub-sampled to contain the runners who have attempted four or more events and the top 25% percentiles, as described in “Prediction: Evaluation and Validation”. In this data matrix, all missing values are imputed using the rank 3 local matrix completion algorithm, as described in (4.c) of “Prediction: Models and Algo- rithms”, to obtain a complete data matrix M. For this matrix, the singular value decomposition M = USV ⊤ is computed, see [10]. We take the components f2, f3 to be the the 2-th and 3-rd right singular vectors, which are the 2-nd and 3-rd column of V . The component f1 is a re-scaled version of the 1-st column v of V , such that f1(s) ≈ log s, where the natural logarithm is taken. More precisely, f1 := β−1v, where the re-scaling factor β is obtained as the ordinary least-squares regression coeﬃcient of the linear explanatory model v(s) = β log s + c, where s ranges over the ten event distances, which is β = 0.0572. A more detailed study of v and the regression coeﬃcient can be found in supplementary experiment (II.b). The three-number-summary referenced in the main corpus of the manuscript is obtained as follows: for the k-th runner we obtain from the left singular vector the entries Ukj. The second and third score of the three-number-summary are obtained as λ2 = Uk2 and λ3 = Uk3. The individual exponent is λ1 = β · Uj1. The singular value decomposition has the property that the fi and λj are guaranteed to be least-squares estimators for the components and the coeﬃcients in a projection sense. Computation of standard error and signiﬁcance Standard errors for the singular vectors (components of the model of Equation 1) are computed via inde- pendent bootstrap sub-sampling on the rows of the data set (runners). Standard errors for prediction accuracies are obtained by bootstrapping of the predicted performances (1000 per experiment). A method is considered to perform signiﬁcantly better than another when error regions at the 95% conﬁdence level (= mean over repetitions ± 1.96 standard errors) do not intersect. Predictions and three-number-summary for elite runners Performance predictions and three-number-summaries for the selected elite runners in Table 1 and Figure 4 are obtained from their personal best times. The relative standard error of the predicted performances is estimated to be the same as the relative RMSE of predicting time, as reported in Table 1. Calculating a fair race Here we describe the procedure for calculating a fair racing distance with error bars between two runners: runner 1 and runner 2. We ﬁrst calculate predictions for all events. Provided that runner 1 is quicker on some events and runner 2 is quicker on others, then calculating a fair race is feasible. If runner 1 is quicker on shorter events then runner 2 is typically quicker on all longer events beyond a certain distance. In that case, we can ﬁnd the shortest race si whereby runner 2 is predicted to be quicker; then a fair race lies between si and si−1. The performance curves in log-time vs. log-distance of both runners will be locally approximately linear. We thus interpolate the performance curves between log(si) and log(si−1)—the crossing point gives the position of a fair race in log-coordinates. We obtain conﬁdence intervals by repeating this procedure after sampling data points around the estimated performances with standard deviation equal to the RMSE (see Table 1) on the top 25% of runners in log-time. 6 Supplementary Analyses This appendix contains a series of additional experiments supplementing those in the main corpus. It con- tains the following ﬁndings: (I) Validation of the LMC prediction framework. (I.a) Evaluation in terms of MAE. The results in terms of MAE are qualitatively similar to those in RMSE; smaller MAEs indicate the presence of outliers. (I.b) Evaluation in terms of time prediction. The results are qualitatively similar to measuring pre- diction accuracy in RMSE and MAE of log-time. LMC rank 2 has an average error of approximately 2% when predicting the top 25% of male runners. (I.c) Prediction for individual events. LMC outperforms the other predictors on each type of event. The beneﬁt of higher rank is greatest for middle distances. (I.d) Stability w.r.t. the unit measuring performance. LMC performs equally well in predicting (per- formance in time units) when performances are presented in log-time or time normalized by event average. Speed is worse when the rank 2 predictor is used. (I.e) Stability w.r.t. the events used in prediction. LMC performs equally well when predicting from the closest-distance events and when using a bagged version which uses all observed events for prediction. (I.f) Stability w.r.t. the event predicted. LMC performs well both when the predicted event is close to those observed and when the predicted event is further from those observed, in terms of event distance. (I.g) Temporal independence of performances. There are negligible diﬀerences between predictions made only from past events and predictions made from all available events (in the training set). (I.h) Run-time comparisons. LMC is by orders of magnitude the fastest among the matrix completion methods. (II) Validation of the low-rank model. (II.a) Synthetic validation. In a synthetic low-rank model of athletic performance that is a proxy to the real data, the singular components of the model can be correctly recovered by the exact same procedure as on the real data. (II.b) The individual power law component, and the distance/time unit. The ﬁrst singular com- ponent can be explained by a linear model in log-distance (R-square 0.9997) with slope β = 0.0572 ± 0.0003 and intercept c = −0.136 ± 0.003. (II.c) Universality in sub-groups. Quality of prediction, the low-rank model, its rank, and the singular components remain mostly unchanged when considering subgroups male/female, older/younger, elite/amateur. (III) Exploration of the low-rank model. (III.a) Further exploration of the three-number-summary. The three number summary also corre- lates with specialization and training standard. (III.b) Preferred distance vs optimal distance. Most but not all runners prefer to attend the event at which they are predicted to perform best. A notable number of younger runners prefer distances shorter than optimal, and some older runners prefer distances longer than optimal. (IV) Pivoting and phase transitions. The pivoting phenomenon described in Figure 1, right panel, is found in the data for any three close-by distances up to the Mile, with anti-correlation between the shorter and the longer distance. Above 5000m, a change in the shorter of the three distances positively correlates with a change in the longer distance. (I.a) Evaluation in terms of MAE. Table A reports on the goodness of prediction methods in terms of MAE. Compared with the RMSE (Table 1, the MAE tend to be smaller than the RMSE, indicating the presence of outliers. The relative prediction-accuracy of methods when compared to each other is qualita- tively the same. (I.b) Evaluation in terms of time prediction. Tables C and D report on the prediction accuracy of the methods tested in terms the relative RMSE and MAE of predicting time. Relative measures are chosen 7 to avoid bias towards the longer events. The results are qualitatively and quantitatively very similar to the log-time results in Tables 1 and A; this can be explained that mathematically the RMSE and MAE of a logarithm approximate the relative RMSE and MAE well for small values. (I.c) Individual Events. Prediction accuracy of LMC rank 1 and rank 2 on the ten diﬀerent events is displayed in Figure A. The reported prediction accuracy is out-of-sample RMSE in predicting log-time, on the top 25 percentiles of Male runners who have attempted 3 or more events, of events in their best year of performance. The reported RMSE for a given event is the mean over 1000 random prediction samples, standard errors are estimated by the bootstrap. The relative improvement of rank 2 over rank 1 tends to be greater for shorter distances below the Mile. This is in accordance with observation (IV.i) which indicates that the individual exponent is the best descriptor among the three-number summary for longer events, above the Mile. (I.d) Stability w.r.t. the measure of performance. In the main experiment, the LMC model is learnt on the same measure of performance (log-time, speed, normalized) which is predicted. We investigate whether the measure of performance on which the model is learnt inﬂuences the prediction by learning the LMC model on either measure and comparing all predictions using the log-time measure. Table G displays prediction accuracy when the model is learnt in any one of the measures of performance. Here we check the eﬀect of calibration in one coordinates system and testing in another. The reported goodness is out-of-sample RMSE of predicting log-time, on the top 25 percentiles of Male runners who have attempted 3 or more events, of events in their best year of performance. The reported RMSE for a given event is the mean over 1000 random prediction samples, standard errors are estimated by the bootstrap. We ﬁnd that there is no signiﬁcant diﬀerence in prediction goodness when learning the model in log-time coordinates or normalized time coordinates. Learning the model in speed coordinates leads to a signiﬁcantly better prediction than log-time or normalized time when LMC rank 1 is applied, but to a worse prediction with LMC rank 2. As overall prediction with LMC rank 2 is better, log-time or normalized time are the preferable units for predicting performance. (I.e) Stability w.r.t. the event predicted. We consider here the eﬀect of the ratio between the predicted event and the closest predictor. For data of the best 25% of Males in the year of best performance (best), we compute the log-ratio of the closest predicting distance and the predicted distance for Purdy Points, the power law formula and LMC rank 2. See Figure B, where this log ratio is plotted against error. The results show that LMC is far more robust to error for predicting distances far from the predicted distance. (I.f) Stability w.r.t. the events used in prediction. We compare whether we can improve predic- tion by using all events a runner has attempted, by using one of the aggregate predictors (5.a) bagged power law or (5.b) bagged LMC rank 2. The kernel width γ for the aggregate predictors is chosen from −0.001, −0.01, −0.1, −1, −10 as the minimizer of out-of-sample RMSE on ﬁve groups of 50 randomly chosen validation data points from the training set. The validation setting is the same as in the main prediction experiment. Results are displayed in Table H. We ﬁnd that prediction accuracy of (2.b) power law and (5.a) bagged power law is not signiﬁcantly diﬀerent, nor is (4.b) LMC rank 2 signiﬁcantly diﬀerent from (5.b) bagged LMC rank 2 (both p > 0.05; Wilcoxon signed-rank on the absolute residuals). Even though the kernel width selected is in the majority of cases σ = −1 and not σ = −10, the incorporation of all events does not lead to an improvement in prediction accuracy in our aggregation scheme. We ﬁnd there is no signiﬁcant diﬀerence (p > 0.05; Wilcoxon signed-rank on the absolute errors) between the bagged and vanilla LMC for the top 95% of runners. This demonstrates that the relevance of closer events for prediction may be learnt from the data. The same holds for the bagged version of the power law formula. (I.g) Temporal independence of performances. We check here whether the results are aﬀected by using only temporally prior attempts in predicting a runner’s performance, see section “Prediction: Evaluation and Validation” in “Methods”. To this end, we compute out-of-sample RMSEs when predictions are made only from those events. 8 Table B reports out-of-sample RMSE of predicting log-time, on the top 25 percentiles of Male runners who have attempted 3 or more events, of events in their best year of performance. The reported RMSE for a given event is the mean over 1000 random prediction samples, standard errors are estimated by the bootstrap. The results are qualitatively similar to those of Table 1 where all events are used in prediction. (I.h) Run-time comparisons. We compare the run-time cost of a single prediction for the three matrix completion methods LMC, nuclear norm minimiziation, and EM. The other (non-matrix completion) meth- ods are fast or depend only negligibly on the matrix size. We measure run time of LMC rank 3 for completion of a single entry for matrices of 28, 29, . . . , 213 runners, generated as described in (II.a). This is repeated 100 times. For a fair comparison, the nuclear norm minimization algorithm is run with a hyper-parameter already pre-selected by cross validation. The results are displayed in Figure C; LMC is faster by orders of magnitude than nuclear norm and EM and is very robust to the size of the matrix. The reason computation speeds up over the smallest matrix sizes is that 4 × 4 minors, which are required for rank 3 estimation are not available, thus the algorithm must attempt all ranks lower than 3 to ﬁnd suﬃciently many minors. (II.a) Synthetic validation. To validate the assumption of a low-rank generative model, we investigate prediction accuracy and recovery of singular vectors in a synthetic model of athletic performance. Synthetic data for a given number of runners is generated as follows: For each runner, a three-number summary (λ1, λ2, λ3) is generated independently from a Gaussian dis- tribution with the same mean and variance as the three-number-summaries measured on the real data and with uncorrelated entries. Matrices of performances are generated from the model log(t) = λ1f1(s) + λ2f2(s) + λ3f3(s) + η(s) (1) where f1, f2, f3 are the three components estimated from the real data and η(s) is a stationary zero-mean Gaussian white noise process with adjustable variance. We take the components estimated in log-time coordinates from the top 25% of male runners who have attempted at least 4 events as the three components of the model. The distances s are the same ten distances as encountered in the real data. In each experiment the standard deviation of η(s) is set to Std(η) = 0.01, which was shown to be plausible in the previous section. Accuracy of prediction: We synthetically generate a matrix of 1000 runners according to the model of Equation (1), taking as distances the same distances measured on the real data. Missing entries are randomized according to two schemes: (a) 6 (out of 10) uniformly random missing entries per row/runner. (b) per row/runner, four in terms of distance-consecutive entries are non-missing, uniformly at random. We then apply LMC rank 2 and nuclear norm minimization for prediction. This setup is repeated 100 times for ten diﬀerent standard deviations of η between 0.01 and 0.1. The results are displayed in Figure D. LMC performance outperforms nuclear norm; LMC performance is also robust to the pattern of miss- ingness, while nuclear norm minimization is negatively aﬀected by clustering in the rows. RMSE of LMC approaches zero with small noise variance, while RMSE of nuclear norm minimization does not. Comparing the performances with Table 1, an assumption of a noise variance of Std(η) = 0.01 seems plausible. The performance of nuclear norm on the real data is explained by a mix of the sampling schemes (a) and (b). Recovery of model components. We synthetically generate a matrix which has a size and pattern of observed entries identical to the matrix of top 25% of male runners who have attempted at least 4 events in their best year. We set Std(η) = 0.01, which was shown to be plausible in the previous section. We then complete all missing entries of the matrix using LMC rank 3. After this initial step we estimate singular components using SVD, exactly as on the real data. Conﬁdence intervals are estimated by a bootstrap on the rows with 100 iterations. The results are displayed in Figure E. We observe that the ﬁrst two singular components are recovered almost exactly, while the third is a slightly deformed. This is due to the smaller singular value of the third component. 9 (II.b) The individual power law component, and the distance/time unit. We examine linearity of the ﬁrst singular vector v, as listed in Table 1 and as described in methods section “Obtaining the Low-Rank Components and Coeﬃcients”. In an ordinary least squares regression model explaining v by log s and an intercept, we ﬁnd that v ≈ β log s + c with an R-squared of 0.9997 (Table I), where the scaling factor is β = 0.0572 ± 0.0003 and the intercept is c = −0.136 ± 0.003. The intercept corresponds to a choice of unit, the scaling factor to a choice of basis for the logarithm. Thus re-scaling v with β−1, that is, setting f1 := β−1v in the low-rank model, and re-scaling the ﬁrst individual coeﬃcient with β, corresponds to the choice of the natural basis. The residuals of the the linear model appear to be plausibly explained by the second and third singular com- ponent (Table I), though the small number of ﬁtting nodes which is 10 does not allow a for an assessment that is more than qualitative. (II.c) Universality in sub-groups. We repeat the methodology for component estimation described above and obtain the three components in the following sub-groups: female runners, older runners (> 30 years), and amateur runners (25-95 percentile range of training standard). Male runners were considered in the main corpus. For female and older runners, we restrict to the top 95% percentiles of the respective groups for estimation. Figure F displays the estimated components of the low-rank model. The individual power law is found to be unchanged in all groups considered. The second and third component vary between the groups but resemble the components for the male runners. The empirical variance of the second and third component is higher, which may be explained by a slightly reduced consistency in performance, or a reduction in sample size. Whether there is a genuine diﬀerence in form or whether the variation is explained by diﬀerent three- number-summaries in the subgroups cannot be answered from the dataset considered. Table F displays the prediction results in the three subgroups. Prediction accuracy is similar but slightly worse when compared to the male runners. Again this may be explained by reduced consistency in the subgroups’ performances. (III.a) Further exploration of the three-number-summary. Scatter plots of preferred distance and training standard against the runners’ three-number-summaries are displayed in Figure G. The training standard correlates predominantly with the individual exponent (score 1); score 1 vs. standard—r = −0.89 (p ≤ 0.001); score 2 vs. standard—r = 0.22 (p ≤ 0.001); score 3 vs. standard—r = 0.031 (p = 0.07); all correlations are Spearman correlations with signiﬁcance computed using a t-distribution approximation to the correlation coeﬃcient under the null. On the other hand preferred distance is associated with all three numbers in the summary, especially the second; score 1 vs. log(specialization)—r = 0.29 (p ≤ 0.001); score 2 vs. log(specialization)—r = −0.58 (p ≤ 0.001); score 3 vs. log(specialization)—r = −0.14 (p =≤ 0.001); The association between the third score and specialization is non-linear with an optimal value around the middle distances. We stress that low correlation does not imply low predictive power; the whole summary should be considered as a whole, and the LMC predictor is non-linear. Also, we observe that correlations increase when considering only performances over certain distances, see Figure 2. (III.b) Preferred event vs best event. For the top 95% male runners who have attempted 3 or more events, we use LMC rank 2 to compute which percentile they would achieve in each event. We then determine the distance of the event at which they would achieve the best percentile, to which we will refer as the “optimal distance”. Figure H shows for each runner the diﬀerence between their preferred and optimal distance. It can be observed that the large majority of runners prefer to attempt events in the vicinity of their optimal event. There is a group of young runners who attempt events which are shorter than the predicted optimal distance, and a group of old runners attempting events which are longer than optimal. One may hypothesize that both groups could be explained by social phenomena: young runners usually start to train on shorter distances, regardless of their potential over long distances. Older runners may be biased to at- tempting endurance type events. (IV) Pivoting and phase transitions. We look more closely at the pivoting phenomenon illustrated in Figure 1 top right, and the phase transition discussed in observation (V). We consider the top 25% of male runners who have attempted at least 3 events, in their best year. 10 We compute 10 performances of equivalent standard by using LMC rank 1 in log-time coordinates, by setting a benchmark performance over the marathon and sequentially predicting each lower distance (marathon predicts HM, HM predicts 10km etc.). This yields equivalent benchmark performances t1, . . . , t10. We then consider triples of consecutive distances si−1, si, si+1 (excluding the Mile since close in distance to the 1500m) and study the pivoting behaviour on the data set, by performing the analogous prediction displayed in Figure 1. More speciﬁcally, for each triple, we predict the performance on the distance si+1 using LMC rank 2, from the performances over the distances si−1 and si. The prediction is performed in two ways, once with and once without perturbation of the benchmark performance at si−1, which we then compare. Intuitively, this corresponds to comparing the red to the green curve in Figure 1. In mathematical terms: 1. We obtain a prediction bti+1 for the distance si+1 from the benchmark performances ti, ti−1 and consider this as the unperturbed prediction, and 2. We obtain a prediction bti+1 + δ(ϵ) for the distance si+1 from the benchmark performance ti on si and the perturbed performance (1 + ϵ)ti−1 on the distance si−1, considering this as the perturbed prediction. We record these estimates for ϵ = −0.1, 0.09, . . . , 0, 0.01, . . . , 0.1 and calculate the relative change of the perturbed prediction with respect to the unperturbed, which is δi(ϵ)/bti. The results are displayed in Figure I. We ﬁnd that for pivot distances si shorter than 5km, a slower performance on the shorter distance si−2 leads to a faster performance over the longer distance si, insofar as this is predicted by the rank 2 predictor. On the other hand we ﬁnd that for pivot distances greater than or equal to 5km, a faster performance over the shorter distance also implies a faster performance over the longer distance. References [1] Blythe DAJ, Király FJ. Full data to “Prediction and Quantiﬁcation of Individual Athletic Performance of Runners"; 2016. DOI: 10.6084/m9.ﬁgshare.3408202.v1. Available from: https://figshare.com/articles/ thepowerof10/3408202. [2] Blythe DAJ, Király FJ. Full code to “Prediction and Quantiﬁcation of Individual Athletic Performance of Run- ners"; 2016. DOI: 10.6084/m9.ﬁgshare.3408250.v1. Available from: https://figshare.com/articles/Ful_ code_to_Prediction_and_Quantification_of_Individual_Athletic_Performance_of_Runners_/3408250. [3] Purdy JG. Computer generated track and ﬁeld scoring tables: II. Theoretical foundation and development of a model. Medicine and science in sports. 1974;7(2):111–115. [4] Bishop CM, et al. Pattern recognition and machine learning. vol. 4. springer New York; 2006. [5] Candès EJ, Recht B. Exact matrix completion via convex optimization. Foundations of Computational mathe- matics. 2009;9(6):717–772. [6] Tomioka R, Hayashi K, Kashima H. On the extension of trace norm to tensors. In: NIPS Workshop on Tensors, Kernels, and Machine Learning; 2010. p. 7. [7] Király FJ, Theran L, Tomioka R. The algebraic combinatorial approach for low-rank matrix completion. Journal of Machine Learning Research. 2015;. [8] Király FJ, Theran L. Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion. NIPS 2013. 2013;. [9] Blythe DA, Theran L, Kiraly F. Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction. arXiv preprint arXiv:14062864. 2014;. [10] Golub GH, Reinsch C. Singular value decomposition and least squares solutions. Numerische Mathematik. 1970;14(5):403–420. 200m 800m Mile 10km Mar 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 log(distance) RMSE Accuracy by event rank 1 vs. rank 2 rank 1 rank 2 Figure A: The ﬁgure displays the results of prediction by event for the top 25% of male runners who attended ≥ 3 events in their year of best performance. For each event the prediction accuracy of LMC rank 1 (blue) is compared to prediction accuracy in rank 2 (red). RMSE is displayed on the y-axis against distance on the x-axis; the error bars extend two standard deviations of the bootstrapped RMSE either side of the RMSE. 0 1 2 3 0 0.02 0.04 0.06 0.08 0.1 0.12 Absolute Relative Error LMC r2 absolute relative errors log(ratio) [DV] 0 1 2 3 power−law absolute relative errors log(ratio) [DV] 0 1 2 3 Purdy absolute relative errors log(ratio) [DV] Figure B: The ﬁgure displays the absolute log ratio in distance predicted and predicting distance vs. absolute relative error per runner. In each case the log ratio in distance is displayed on the x-axis and the absolute errors of single data points of the y-axis. We see that LMC rank 2 is particularly robust for large ratios in comparison to the power law and Purdy Points. Data is taken from the top 25% of male runners with no. events≥ 3 in the best year. 7 8 9 10 11 12 13 14 0.1 1 10 run time [secs.] log(no.athletes) Run Time Nuclear Norm LMC EM algorithm Figure C: The ﬁgure displays mean run-times for the 3 matrix completion algorithms tested in the paper: Nuclear Norm, EM and LMC (rank 3). Run-times (y-axis) are recorded for completing a single entry in a matrix of size indicated by the x-axis. The averages are over 100 repetitions, standard errors are estimated by the bootstrap. 0 0.02 0.04 0.06 0.08 0.1 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 RMSE in log−time Uniform Missing LMC rank 2 Nuclear Norm 0 0.02 0.04 0.06 0.08 0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Clustered Missing Entries noise−level−nl [log(seconds)] Figure D: LMC and Nuclear Norm prediction accuracy on the synthetic low-rank data. x-axis denotes the noise level (standard deviation of additive noise in log-time coordinates); y-axis is out-of-sample RMSE predicting log-time. Left: prediction performance when (a) the missing entries in each ros are uniform. Right: prediction performance when (b) the observed entries are consecutive. Error bars are one standard deviation, estimated by the bootstrap. 100m 1500m 5km Mar −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 Singular Components Estimated from Complete Performances 100m 1500m 5km Mar −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 log(time) [log(seconds)] Singular Components Estimated from Incomplete Performances Singular Component 1 Singular Component 2 Singular Component 3 distance Figure E: Accuracy of singular component estimation with missing data on synthetic model of performance. x-axis is distance, y-axis is components in log-time. Left: singular components of data generated according to Equation 1 with all data present. Right: singular components of data generated according to Equation 1 with missing entries estimated with LMC rank 3; the observation pattern and number of runners is identical to the real data. The tubes denote one standard deviation estimated by the bootstrap. 100m 1km 10km Mar −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 Older Runners (30−80yrs) component scores log(time) [log(seconds)] Distances 100m 1km 10km Mar −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 Amateur Runners component scores log(time) [log(seconds)] Distances 100m 1km 10km Mar −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 Female Runners component scores log(time) [log(seconds)] Distances Figure F: The three components of the low-rank model in subgroups. Left: for older runners. Middle: for amateur runners = best event below 25th percentile. Right: for female runners. Tubes around the components are one standard deviation, estimated by the bootstrap. The components are the analogous components for the subgroups described as computed in the left-hand panel of Figure 2. Figure G: Scatter plots of training standard vs. three-number-summary (top) and preferred distance vs. three-number-summary. In each case the individual exponents, 2nd and 3rd scores (λ2, λ3) are displayed on the y-axis and the log-preferred distance and training standard on the x-axis. Figure H: Diﬀerence of preferred distance and optimal distance, versus age of the runner, colored by specialization distance. Most runners prefer the distance they are predicted to be best at. There is a mismatch of best and preferred for a group of younger runners who have greater potential over longer distances, and for a group of older runners who’s potential is maximized over shorter distances than attempted. 200m 400m 800m 1500m 5km 10km HM −0.1 −0.05 0 0.05 0.1 f(ε)/ti distance Short Performance peturbations (1−ε)ti−2>ti−2 (1−ε)ti−2<ti−2 Figure I: Pivot phenomenon in the low-rank model. The ﬁgure quantiﬁes the strength and sign of pivoting as in Figure 1, top right, at diﬀerent middle distances si (x-axis). The computations are based on equivalent log-time performances ti−1, ti, ti+1 at consecutive triples si−1, si, si+1 of distances. The y-coordinate indicates the signed relative change of the LMC rank 2 prediction of ti+1 from ti−1 and ti changes, when ti is ﬁxed and ti−1 undergoes a relative change of 1%, 2%, . . . , 10% (red curves, line thickness is proportional to change), or −1%, −2%, . . . , −10% (blue curves, line thickness is proportional to change). For example, the largest peak corresponds to a middle distance of si = 400m. When predicting 800m from 400m and 200m, the predicted log-time ti+1 (= 800m performance) decreases by 8% when ti−1 (= 200m performance) is increased by 10% while ti (= 400m performance) is kept constant. Generic Baselines State of art Performance Predictors State of art Matrix Completion Proposed Method: LMC evaluation percentiles no.events data type r.mean k-NN individual power law riegel power law purdy nuclear norm EM LMC rank 1 LMC rank 2 log time 0-95 3 best 0.1054 0.0421 0.0696 0.0661 0.0654 0.0423 0.1282 0.0387 0.0402 0.0336 ±0.0025 ±0.0014 ±0.0024 ±0.0023 ±0.0023 ±0.0014 ±0.0115 ±0.0013 ±0.0014 ±0.0012 normalized 0-95 3 best 0.1062 0.0441 0.0700 0.0681 0.0674 0.0441 0.0907 0.0400 0.0413 0.0347 ±0.0027 ±0.0018 ±0.0026 ±0.0026 ±0.0025 ±0.0017 ±0.0051 ±0.0016 ±0.0016 ±0.0014 speed 0-95 3 best 0.5463 0.2118 0.3989 0.3640 0.3600 0.2197 2.2846 0.2023 0.2130 0.1716 ±0.0122 ±0.0067 ±0.0145 ±0.0130 ±0.0126 ±0.0070 ±0.8163 ±0.0065 ±0.0072 ±0.0058 log time 0-95 3 random 0.1119 0.0373 0.0655 0.0656 0.0646 0.0411 0.1501 0.0376 0.0385 0.0320 ±0.0026 ±0.0012 ±0.0021 ±0.0021 ±0.0021 ±0.0014 ±0.0131 ±0.0014 ±0.0013 ±0.0011 normalized 0-95 3 random 0.1136 0.0397 0.0660 0.0686 0.0676 0.0438 0.1013 0.0395 0.0405 0.0338 ±0.0029 ±0.0016 ±0.0021 ±0.0025 ±0.0023 ±0.0016 ±0.0055 ±0.0016 ±0.0015 ±0.0013 speed 0-95 3 random 0.5727 0.1825 0.3795 0.3552 0.3497 0.2066 2.5636 0.1913 0.1995 0.1607 ±0.0126 ±0.0060 ±0.0145 ±0.0114 ±0.0112 ±0.0061 ±0.7841 ±0.0065 ±0.0065 ±0.0052 log time 0-95 4 best 0.1014 0.0514 0.0557 0.0551 0.0553 0.0406 0.0716 0.0350 0.0366 0.0310 ±0.0024 ±0.0016 ±0.0017 ±0.0020 ±0.0020 ±0.0013 ±0.0051 ±0.0013 ±0.0013 ±0.0010 log time 0-25 3 best 0.0424 0.0294 0.0559 0.0479 0.0507 0.0310 0.0970 0.0282 0.0300 0.0221 ±0.0012 ±0.0009 ±0.0019 ±0.0015 ±0.0016 ±0.0008 ±0.0092 ±0.0008 ±0.0009 ±0.0007 Table A: Exactly the same table as Table 1 but mean absolute errors reported. Generic Baselines State of art Performance Predictors State of art Matrix Completion Proposed Method: LMC evaluation percentiles no.events data type r.mean k-NN individual power law riegel power law purdy nuclear norm EM LMC rank 1 LMC rank 2 log time 0-95 3 best 0.1398 0.0637 0.1065 0.0100 0.0991 0.0639 0.3624 0.0574 0.0622 0.0545 ±0.0066 ±0.0057 ±0.0054 ±0.0063 ±0.0063 ±0.0066 ±0.0849 ±0.0060 ±0.0054 ±0.0050 normalized 0-95 3 best 0.1483 0.0792 0.1103 0.1051 0.1042 0.0724 0.1769 0.0658 0.0694 0.0620 ±0.0097 ±0.0104 ±0.0068 ±0.0066 ±0.0068 ±0.0097 ±0.0222 ±0.0096 ±0.0078 ±0.0086 speed 0-95 3 best 0.7153 0.3308 0.6553 0.5827 0.5772 0.3383 19.2009 0.3067 0.3410 0.2918 ±0.0349 ±0.0348 ±0.0356 ±0.0368 ±0.0385 ±0.0440 ±9.9799 ±0.0393 ±0.0310 ±0.0321 log time 0-95 3 random 0.1380 0.0544 0.0931 0.0931 0.0919 0.0591 0.4416 0.0561 0.0567 0.0471 ±0.0032 ±0.0027 ±0.0035 ±0.0039 ±0.0038 ±0.0027 ±0.0435 ±0.0031 ±0.0027 ±0.0023 normalized 0-95 3 random 0.1450 0.0623 0.0951 0.1011 0.0998 0.0682 0.2046 0.0634 0.0640 0.0538 ±0.0044 ±0.0037 ±0.0038 ±0.0049 ±0.0049 ±0.0039 ±0.0124 ±0.0041 ±0.0038 ±0.0033 speed 0-95 3 random 0.6935 0.2585 0.5917 0.5052 0.4979 0.2835 24.7206 0.2801 0.2863 0.2261 ±0.0147 ±0.0121 ±0.0329 ±0.0171 ±0.0167 ±0.0134 ±10.7164 ±0.0199 ±0.0121 ±0.0112 log time 0-95 4 best 0.1368 0.0763 0.0823 0.0859 0.0862 0.0620 0.2371 0.0608 0.0599 0.0531 ±0.0075 ±0.0060 ±0.0042 ±0.0060 ±0.0059 ±0.0038 ±0.0423 ±0.0064 ±0.0041 ±0.0040 log time 0-25 3 best 0.0539 0.0425 0.0810 0.0675 0.0710 0.0412 0.2479 0.0358 0.0417 0.0318 ±0.0027 ±0.0030 ±0.0056 ±0.0050 ±0.0051 ±0.0026 ±0.0600 ±0.0022 ±0.0030 ±0.0022 Table B: Prediction only from events which are earlier in time than the performance to be predicted. The table shows out-of- sample RMSE for performance prediction methods on diﬀerent data setups. Predicted performance is of the 25 top percentiles of male runners, in their best year. Standard errors are bootstrap estimates over 1000 repetitions. Legend is as in Table 1. Generic Baselines State of art Performance Predictors State of art Matrix Completion Proposed Method: LMC evaluation percentiles no.events data type r.mean k-NN individual power law riegel power law purdy nuclear norm EM LMC rank 1 LMC rank 2 time 0-95 3 best 0.1295 0.0627 0.0959 0.0973 0.0964 0.0596 0.1785 0.0560 0.0569 0.0499 ±0.0027 ±0.0027 ±0.0035 ±0.0064 ±0.0065 ±0.0025 ±0.0105 ±0.0028 ±0.0023 ±0.0024 time 0-95 3 random 0.1357 0.0535 0.0874 0.0907 0.0895 0.0585 0.1961 0.0544 0.0550 0.0461 ±0.0029 ±0.0022 ±0.0028 ±0.0031 ±0.0031 ±0.0026 ±0.0116 ±0.0025 ±0.0022 ±0.0020 time 0-95 4 best 0.1232 0.0745 0.0750 0.0782 0.0785 0.0566 0.1167 0.0525 0.0522 0.0455 ±0.0025 ±0.0031 ±0.0021 ±0.0027 ±0.0027 ±0.0021 ±0.0084 ±0.0029 ±0.0019 ±0.0019 time 0-25 3 best 0.0559 0.0422 0.0760 0.0668 0.0704 0.0406 0.1579 0.0377 0.0402 0.0302 ±0.0015 ±0.0016 ±0.0025 ±0.0022 ±0.0023 ±0.0012 ±0.0113 ±0.0012 ±0.0014 ±0.0001 Table C: Exactly the same table as Table 1 but relative root mean squared errors reported in terms of time. Models are learnt on the performances in log-time. Generic Baselines State of art Performance Predictors State of art Matrix Completion Proposed Method: LMC evaluation percentiles no.events data type r.mean k-NN individual power law riegel power law purdy nuclear norm EM LMC rank 1 LMC rank 2 time 0-95 3 best 0.1057 0.0424 0.0669 0.0654 0.0647 0.0420 0.0876 0.0384 0.0397 0.0333 ±0.0023 ±0.0015 ±0.0022 ±0.0023 ±0.0024 ±0.0014 ±0.0048 ±0.0013 ±0.0013 ±0.0012 time 0-95 3 random 0.1116 0.0372 0.0635 0.0651 0.0642 0.0410 0.0980 0.0373 0.0381 0.0318 ±0.0024 ±0.0012 ±0.0018 ±0.0019 ±0.0020 ±0.0013 ±0.0055 ±0.0013 ±0.0013 ±0.0011 time 0-95 4 best 0.1006 0.0519 0.0547 0.0540 0.0543 0.0401 0.0605 0.0348 0.0362 0.0307 ±0.0023 ±0.0016 ±0.0016 ±0.0018 ±0.0018 ±0.0013 ±0.0032 ±0.0013 ±0.0012 ±0.0011 time 0-25 3 best 0.0425 0.0296 0.0542 0.0476 0.0504 0.0308 0.0688 0.0280 0.0297 0.0220 ±0.0011 ±0.0001 ±0.0017 ±0.0015 ±0.0016 ±0.0008 ±0.0046 ±0.0008 ±0.0008 ±0.0007 Table D: Exactly the same table as Table 1 but relative mean absolute errors reported in terms of time. Models are learnt on the performances in log-time. no events. r1 r2 r3 r4 3 0.0411 0.0306 — — ±0.0014 ±0.0011 4 0.0446 0.0328 0.0309 — ±0.0016 ±0.0013 ±0.0012 5 0.0518 0.0408 0.0400 0.0408 ±0.0032 ±0.0033 ±0.0034 ±0.0036 Table E: Determination of the true rank of the model. Table displays out-of-sample RMSE for predicting performance with LMC rank 1-4 (columns) Predicted performance is of the 25 top percentiles of male runners, in their best year, who have attempted at least the number of events indicated by the row. The model is learnt on performances in log-time coordinates. Standard errors are bootstrap estimates over 1000 repetitions. The entries where no. events ≥ rank are empty, as LMC rank r needs r + 1 attempted events for leave-one-out-validation. Prediction with LMC rank 3 is always better or equally good compared to using a diﬀerent rank, in terms of out-of-sample prediction accuracy. subgroup RMSE Amateur 0.0305 ±0.0002 Female 0.0305 ±0.0003 Old 0.0326 ±0.0003 Table F: Prediction in three diﬀerent subgroups: amateur runners, female runners, older runners. Table displays out-of-sample RMSE for predicting performance with LMC rank 2. rank log time speed normalized 1 0.0410 0.0376 0.0399 ±0.0014 ±0.0011 ±0.0013 2 0.0304 0.0315 0.0305 ±0.0011 ±0.0011 ±0.0001 Table G: Eﬀect of performance measure in which the LMC model is learnt. The model is learnt on three diﬀerent measures of performance: log-time, time normalized by event mean, speed (columns). The table shows out-of-sample RMSE for predicting log-time performance with LMC rank 1,2. Standard errors are bootstrap estimates over 1000 repetitions. Performance is of the 25 top percentiles of male runners, in their best year of performance. percentiles no.event bagged LMC r2 bagged power-law LMC r2 power-law 0-25 3 0.0310 0.0654 0.0308 0.0666 ±0.0011 ±0.0025 ±0.0011 ±0.0025 0-95 3 0.0529 0.0898 0.0512 0.0948 ±0.0031 ±0.0040 ±0.0028 ±0.0039 0-95 4 0.0480 0.0762 0.0467 0.0825 ±0.0034 ±0.0029 ±0.0021 ±0.0030 Table H: Comparison of prediction using all distances, to prediction using only closest distances. Table displayes out-of-sample RMSE of predicting log-time, for (5.a) the bagged power law and (5.b) the bagged LMC rank 2 predictor, compared with the un- bagged variants, (2.b) and (4.b). Predicted performance is of the 25 top percentiles of male runners, in their best year. Standard errors are bootstrap estimates over 1000 repetitions. The results of the bagging predictors are very similar to the unbagged one. variables β β2 β3 c model 1 log s 0.0572 ± 0.0003 −0.136 ± 0.003 model 2 log s, f2 0.0547 ± 0.0007 −0.017 ± 0.004 −0.115 ± 0.006 model 3 log s, f2, f3 0.0554 ± 0.0007 −0.013 ± 0.004 0.002 ± 0.001 −0.120 ± 0.006 t1 p(X > \|t1\|) t2 p(X > \|t2\|) t3 p(X > \|t3\|) tc p(X > \|tc\|) model 1 168 1.7e-15 -51 2.3e-11 model 2 81 1.1e-12 -3.9 5.9e-3 -21 1.5e-7 model 3 80 2.5e-10 -3.0 2.5e-2 1.8 0.13 -21 7.1e-7 F P (X > F ) RSE R-squared model 1 2.8e+4 1.7e-15 0.0020 0.9997 model 2 3.9e+4 6.6e-15 0.0012 0.9999 model 3 3.4e+4 4.4e-13 0.0011 0.9999 Table I: Explaining the ﬁrst singular component, v. The following explanatory linear models are ﬁtted: v explained by β log s + c (model 1); v explained by β log s + β2f2 + c (model 2); v explained by β log s + β2f2 + β3f3 + c. The β, β2, β3 are the estimated coeﬃcients, ± one standard error. t1, t2, t3 are the t-statistics of β, β2, β3; tc is the t-statistic of c. The F-statistic of the respective model is F , RSE is the residual standard error.	Prediction and Quantification of Individual Athletic Performance of Runners.	06-23-2016	Blythe, Duncan A J,Király, Franz J	eng
PMC4783109	RESEARCH ARTICLE Effects of Heavy Strength Training on Running Performance and Determinants of Running Performance in Female Endurance Athletes Olav Vikmoen1, Truls Raastad2, Olivier Seynnes2, Kristoffer Bergstrøm2, Stian Ellefsen1, Bent R. Rønnestad1 1 Section for Sport Science, Lillehammer University College, Lillehammer, Norway, 2 Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway olav.vikmoen@hil.no Abstract Purpose The purpose of the current study was to investigate the effects of adding strength training to nor- mal endurance training on running performance and running economy in well-trained female athletes. We hypothesized that the added strength training would improve performance and running economy through altered stiffness of the muscle-tendon complex of leg extensors. Methods Nineteen female endurance athletes [maximal oxygen consumption (VO2max): 53±3 mlkg-1min-1, 5.8 h weekly endurance training] were randomly assigned to either normal endurance training (E, n = 8) or normal endurance training combined with strength training (E+S, n = 11). The strength training consisted of four leg exercises [3 x 4–10 repetition maximum (RM)], twice a week for 11 weeks. Muscle strength, 40 min all-out running distance, running performance determinants and patellar tendon stiffness were measured before and after the intervention. Results E+S increased 1RM in leg exercises (40 ± 15%) and maximal jumping height in counter movement jump (6 ± 6%) and squat jump (9 ± 7%, p < 0.05). This was accompanied by increased muscle fiber cross sectional area of both fiber type I (13 ± 7%) and fiber type II (31 ± 20%) in m. vastus lateralis (p < 0.05), with no change in capillary density in m. vastus lateralis or the stiffness of the patellar tendon. Neither E+S nor E changed running economy, fractional utilization of VO2max or VO2max. There were also no change in running distance during a 40 min all-out running test in neither of the groups. Conclusion Adding heavy strength training to endurance training did not affect 40 min all-out running performance or running economy compared to endurance training only. PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 1 / 18 OPEN ACCESS Citation: Vikmoen O, Raastad T, Seynnes O, Bergstrøm K, Ellefsen S, Rønnestad BR (2016) Effects of Heavy Strength Training on Running Performance and Determinants of Running Performance in Female Endurance Athletes. PLoS ONE 11(3): e0150799. doi:10.1371/journal. pone.0150799 Editor: Massimo Sacchetti, University of Rome Foro Italico, ITALY Received: June 16, 2015 Accepted: February 20, 2016 Published: March 8, 2016 Copyright: © 2016 Vikmoen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper. Funding: This work was supported by grant 203961 from the Regional Science Fund—Innlandet of Norway. (http://www.regionaleforskningsfond.no/ prognett-innlandet/Forside/1253953746925). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Introduction The effects of strength training on running performance has been examined in a number of studies with the majority reporting improved running performance [1–6]. However, the litera- ture is far from conclusive, as some studies report no beneficial effect of strength training on running performance [7–10]. Running performance is mainly determined by the maximal oxy- gen consumption (VO2max), fractional utilization of VO2max and running economy [11]. Addi- tion of strength training has neither a negative nor a positive effect on VO2max (e.g., [2, 6, 12]). The effect of combining strength and endurance training on fractional utilization of VO2max has not been directly investigated, but the indirect measure of VO2 at the lactate threshold, expressed as percent of VO2max, seems to be unchanged [2, 12]. Running economy on the other hand seems to be positively affected by strength training (e.g., [2, 6, 12–14]). An improved running performance following strength training is therefore suggested to be mainly related to improved running economy [2, 6]. One of the most frequent proposed mechanisms behind improved running economy after strength training is changes in the stiffness of lower limb muscles and tendons [2, 14, 15]. Dur- ing the first part of the contact phase in the running stride, elastic energy is stored in the mus- cles, tendons and ligaments acting across joints [16]. A partial return of this stored energy during the second part of the contact phase limits the muscle energy expenditure and amplifies the mechanical output of the muscle-tendon complex [16]. Hence, the stiffness of series elastic component, mainly tendons, can affect both the utilization of this elastic energy and the muscle contraction mechanics during the running stride. In fact, stiffer Achilles tendons have been associated with better running economy [17]. Intriguingly, more compliant patellar tendons were associated with better running economy [17], whereas heavy strength training has been shown to increase patellar tendon stiffness [18, 19]. A more compliant patellar tendon may indeed allow the muscle to operate at mechanically efficient lengths and velocities during the contact phase [17]. However, for a given tendon stiffness a stronger muscle would enable larger energy storage. Consequently, heavy strength training might induce changes in muscle and tendon properties with both potential beneficial and negative effects on running economy. It is therefore important to gain insight into the effects of strength training on patellar tendon mechanical properties, and if possible effects induces changes in running economy. However, to our best knowledge, no studies to date have investigated this. Most research on the effects of strength training on running performance are performed with male athletes (e.g., [1, 3, 6, 15]) or a combination of male and female athletes (e.g., [2, 5, 7, 20]). Unfortunately, there is performed a substantial lower volume of research in this area using only female athletes [10, 13]. Therefore, there is a need for more research with female athletes. This is especially true regarding the effect of strength training induced changes in patellar tendon stiffness on running economy since it seems that male and female tendons may react differently to increased loading [21]. Even though strength training may enhance middle to long distance running performance through improved running economy it will also normally increase cross sectional area (CSA) of the muscle fibers [22]. Therefore, it can be speculated that strength training can increase dif- fusion distances from the capillaries to the interior of muscle cells, which will be negative for performance. In untrained individuals there are reports of increased or unchanged numbers of capillaries around each muscle fiber [23, 24] and no change in capillaries per fiber area [24] after strength training. However, as performing endurance training concurrently with strength training may blunt the hypertrophic response (e.g., [25]), and endurance trained athletes have larger numbers of capillaries than untrained [26, 27] these findings may not apply for Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 2 / 18 Competing Interests: The authors have declared that no competing interests exist. endurance athletes. Consequently, there is a need to look closer into the effects of combined strength and endurance training on capillarization and fiber CSA in well-trained endurance athletes. The purpose of this study was to investigate the effects of 11 weeks of heavy strength train- ing on running performance during a 40 min all-out test and running economy in well-trained female endurance athletes. Furthermore, we wanted to assess the effects of the strength training on the mechanical properties of the patellar tendon to elucidate whether this could be related to changes in running performance and running economy. To investigate if strength training would have any effect on capillarization in endurance athletes we measured muscle fiber CSA and capillarization in m. vastus lateralis. We hypothesized that the addition of heavy strength training would result in improved 40 min all-out performance and improved running economy and that these changes would be related to changes in mechanical properties of the patellar tendon, together with no negative effect on capillarization. Materials and Methods Ethical approval The study was approved by the Local Ethics Committee at Lillehammer University College. Written informed consent was obtained from all athletes prior to inclusion, and the study was carried out in accordance with the Declaration of Helsinki. Participants Twenty-eight female endurance athletes active in both cycling and running and that fulfilled at least two of Jeunkedrup et al.’s [28] training and race status descriptions of a well-trained endurance athlete were recruited to this study. None of the athletes had performed systematic strength training for the last 12 months leading up to the study (less than one session per week). The athletes were matched on VO2max and randomly assigned to either adding heavy strength training to the ongoing endurance training (E+S, n = 14) or endurance training only (E, n = 14). During the study, three athletes in E+S left the project for reasons unrelated to the project protocol: one because of an injury, one because of a prolonged period of illness during the last part of the intervention and one because of other medical reasons. In E, six athletes left the study for reasons unrelated to the project protocol (injuries in training, pregnancy and lack of time). Therefore, the final numbers of athletes in E+S and E were 11 and 8, respectively. Experimental overview This study is part of a larger study investigating the effects of heavy strength training on various aspects of endurance performance. Some of the basic characteristics as anthropometrics and endurance training have been reported previously [29]. The strength training program for the E+S group consisted of two strength training sessions per week and lasted for 11 weeks (during the competition period from April to July). Testing before and after the intervention period was performed over four test-days. During pre-tests, day one was utilized to sample muscle biopsies from the right m. vastus lateralis, and measure the mechanical properties of the left patellar tendon. At day two 1RM in one-legged leg press and half squat was measured. Day 3 consisted of an incremental running test for determination of blood lactate profile, a VO2max test and testing of maximal squat jump (SJ) and counter movement jump (CMJ) height. Day 4 consisted of a 40 min all-out running test. There were at least 7 days between day one and two and 3–7 days between the remaining test-days. All tests Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 3 / 18 for each participant were completed within 2–3 weeks. During post tests, athletes in E+S main- tained their strength training with one session per week until all testing were complete. In gen- eral, post-tests were performed in the same order as pre-tests. However, muscle biopsies and patellar tendon measurements were moved to the last test day. The athletes did not perform any systematic periodization so neither the pre tests nor post tests were performed in a particu- lar phase of periodization. Training Endurance training duration and intensity were calculated based on heart rate (HR) record- ings. Endurance training was divided into three HR zones: 1) 60%-82%, 2) 83%-87%, and 3) 88%-100% of maximal HR. The endurance training performed has been previously reported [29]. In brief, there were no significant differences between groups in the mean weekly duration of the endurance training, the distribution of this training within the three intensity zones (across groups values were: zone 1: 3.7 ± 1.6 h, zone 2: 1.1 ± 0.5 h, zone 3: 0.8 ± 0.5) and the numbers of endurance training sessions per week (across groups values were 4.3 ± 1 session week-1). The heavy strength training sessions for the E+S groups targeted leg muscles and were per- formed twice per week during the 11-week intervention period. Adherence to the strength training was high, with E+S athletes completing 21.4 ± 1.0 (range 19–22) of the planned 22 strength-training sessions. The strength-training program was performed as reported in Vik- moen et al. [29]. Briefly, each strength training session consisted of four leg exercises: half squat in a smith machine (Gym 80 International, Gelsenkirchen, Germany), leg press with one leg at a time (Gym 80 International, Gelsenkirchen, Germany), standing one-legged hip flexion in a cable cross machine (Gym 80 International, Gelsenkirchen, Germany), and ankle plantar flexion in a smith machine. For a detailed description of the exercises, see Ronnestad et al. [30]. Three sets were performed per exercise. An investigator supervised the athletes at all workouts during the first two weeks and at least one workout per week thereafter. During weeks one to three, athletes trained with 10RM sets at the first session and 6RM sets at the second session. These alternating loads were adjusted to 8RM and 5RM during weeks four to six, and was fur- ther adjusted to 6RM and 4RM during weeks seven to eleven (Table 1). The numbers of repeti- tions was always the same as the prescribed RM load meaning that the sets were performed until failure, and the athletes adjusted the absolute load as they got stronger to correspond to the prescribed RM load. The athletes were allowed assistance on the last repetition if necessary. Because a proposed mechanisms behind improved running performance after strength training is an increased rate of force development [2] the athletes were instructed to perform the concen- tric phase of the exercises with focus on maximal effort (duration around 1 s) while the eccentric phase was performed more slowly (duration 2–3 s). During each strength training session the athletes consumed a bar containing 15 g protein (Squeezy recovery bar, Squeezy Sports Nutrition, Braunschweig, Germany) to ensure adequate protein intake in conjunction with the strength Table 1. Training loads used during the strength training intervention Week Load session 1 Load session 2 1–3 10RM 6RM 4–6 8RM 5RM 7–11 6RM 4RM RM: Repetition maximum doi:10.1371/journal.pone.0150799.t001 Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 4 / 18 training sessions. The athletes were encouraged to perform the strength training and endurance training on separate days. On days the athletes had to perform both endurance and strength training, they were encouraged to perform strength training in the first session of the day. Strength, jumping and running tests The athletes were instructed to refrain from intense exercise the day preceding testing, to pre- pare for the tests as they would have done for a competition, and to consume the same type of meal before each test. Running tests was performed on a motor driven treadmill (Woodway Desmo Evo, Waukesha, Wisconsin, USA). The inclination of the treadmill was set to 5.3% at all tests. All testing were performed under similar environmental conditions (18–20˚C). 1RM tests. 1RM was tested in one-legged leg press and half squat and the mean value from these two exercises were used for statistical analyses. Prior to the testing day, each athlete was given a supervised familiarization session to learn proper lifting technique, find individual equipment settings and practice SJ and CMJ. During this session, the load was gradually increased to allow estimation of a proper starting point for the 1RM testing. The 1RM tests in both exercises were performed as previously described (Vikmoen et al. 2015). Briefly, testing started with a specific warm-up, consisting of three sets with gradually increasing load (40, 75 and 85% of expected 1RM) and decreasing number of repetitions (10!6!3). The first attempt was performed with a load approximately 5% below the expected 1RM. If a lift was successful, the load was increased by approximately 5%. The test was termi- nated when the athletes failed to lift the load in 2–3 attempts and the highest successful load lifted was noted as 1RM. Athletes were given 3 min rest between lifts. Blood lactate profile. The blood lactate profile tests started with 5 min running at 7 kmh-1, which was subsequently increased every 5 min by 1 kmh-1. Between consecutive 5 min bouts there was a 1 min break, wherein blood was sampled from a finger-tip and analyzed for whole blood lac- tate concentration ([la-]) using a Lactate Pro LT-1710 analyzer (Arcray Inc., Kyoto, Japan), and the rating of perceived exertion (RPE) was recorded. The test was terminated when a [la-] of 4 mmolL-1 or higher was measured. VO2 and HR were measured during the last 3 min of each bout, and mean values were used for statistical analysis. VO2 was measured (30 s sampling time) using a computerized metabolic system with mixing chamber (Oxycon Pro, Erich Jaeger, Hoechberg, Ger- many). The gas analyzers were calibrated with certified calibration gases of known concentrations before every test. The flow turbine (Triple V, Erich Jaeger, Hoechberg, Germany) was calibrated before every test with a 3 l, 5530 series, calibration syringe (Hans Rudolph, Kansas City, USA). HR was recorded using a Polar S610i heart rate monitor (Polar, Kempele, Finland). From this incre- mental running test, the running velocity at 3.5 mmolL-1 [la-] was calculated for each athlete from the relationship between [la-] and running velocity using linear regression between data points. Running economy was determined by the mean VO2 at a running velocity of 10 kmh-1. VO2max. After termination of the blood lactate profile test the athletes ran for 10 min at a freely chosen submaximal workload. The VO2max test was then initiated with 1 min running at 8 kmh-1 and the speed was increased by 1 kmh-1 every minute until exhaustion. The athletes received strong verbal encouragement to run for as long as possible. VO2 was measured contin- uously, and VO2max was calculated as the mean of the two highest 30 s VO2 measurements. The VO2max test was considered valid when two or more of the following criteria were met: a plateau in VO2 was despite increased workload, a respiratory exchange ration above 1.1 and HRpeak ± 10 bpm pf the predicted maximal HR (220-age) [31]. Peak running performance dur- ing the test (Vmax) was calculated as the mean running velocity during the last 2 min of the incremental test. The highest HR recorded during the test was taken as HRpeak and immedi- ately after the test blood [la-] and RPE were recorded. Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 5 / 18 SJ and CMJ. Twenty min after termination of the VO2max test, explosive strength was tested as maximal jumping height in SJ and CMJ. These jumps were performed on a force plate (SG-9, Advanced Mechanical Technologies, Newton, MA, USA, sampling frequency of 1kHz). After 3–5 submaximal warm up jumps, the athletes performed three SJ and three CMJ with 2–3 min rest between jumps. The highest SJ and CMJ were utilized for statistical analyses. Dur- ing all jumps the athletes were instructed to keep their hands placed on their hips and aim for maximal jumping height. The SJ was performed from ~90 degrees knee angle. In this position, they paused for 3 s before jumping. No downward movement was allowed prior to the jump and the force curves were inspected to verify this. During the eccentric phase of the CMJ the athletes were instructed to turn at a knee angle they felt was optimal for achieving maximal jumping height. 40 min all-out test. Prior to the 40 min all-out test, athletes performed 10 min warm up at self-selected submaximal velocities, containing three submaximal sprints performed during the last 2 min. These sprints were standardized from pre to post in each athlete. During the first 5 min of the test, the investigators set the velocity. This individual selected velocity was based on the lactate profile test and corresponded to the velocity at 2.5 mmolL-1 [la-]. Thereafter, run- ning velocity were controlled by the athletes themselves, with instructions to run the greatest distance possible during the 40 min. Measurements of VO2 was made during the last minute of every 5 min section, to allow estimation of performance VO2 and fractional utilization of VO2max,. During this minute, athletes were not allowed to adjust the running velocity. The mean VO2 during this minute was estimated to reflect the mean VO2 during the corresponding 5 min section. During the last 5 min of the test, VO2 was measured continuously as pilot testing showed that athletes performed numerous velocity adjustments during this part of the test. Performance VO2 was calculated as the mean VO2 of all 5 min sections, and fractional utiliza- tion of VO2max was calculated as performance VO2 in percentage of VO2max. During the test, the athletes were allowed to drink water ad libitum. Measurements of the mechanical and material properties of the patellar tendon All the measurements of the mechanical and material properties of the patellar tendon were performed on the left leg and were done as previously described [32]. Briefly, the athletes were seated with a 90° angle in both knee and hip joint in a knee extension apparatus (Knee exten- sion, Gym 2000, Geithus, Norway) instrumented with a force cell (U2A, Hottinger Baldwin Messtechnik GmbH, Darmstadt, Germany). To measure patellar tendon CSA, transversal scans were performed proximally, medially and distally along the tendon length using an B- mode ultrasound apparatus (HD11XE, Phillips, Bothell, WA, USA). Sagittal scanning was used to measure tendon length. To measure tendon force and elongation the ultrasound probe was attached to the left knee with a custom-made device. The athletes performed ramp contractions at a constant rate of 100 Ns-1. To correct for hamstring co-activation when calculating tendon force (see below), a maximal isometric knee flexion were performed after the knee extension test. In addition, EMG data were recorded (TeleMyo 2400 G2 telemetry Systems, Noraxon Inc., Scottsdale, AZ, USA) from the biceps femoris muscle during isometric knee extension and flexion. Patellar tendon force (FPT) was calculated as the force measured in the force cell, cor- rected for hamstring co-activation, internal and external moment arms as follows: FPT ¼ ððFq þ FhÞMeÞ=Mi Where Fq is force measured by the force cell, Fh is estimated hamstrings co-activation force, Mi and Me corresponds to internal and external moment arm respectively. Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 6 / 18 Tendon morphology data were analysed as previously described [32], using an image analy- sis software (ImageJ 1.45s, National Institute of Health, Austin, TE, USA). Tendon elongation data were analyzed using a video analysis software (Tracker Video Analysis and Modeling Tool, Open Source Physics, Douglas Brown, 2012). The patellar apex and the tibia plateau were digitally marked within a common coordinate system. The actual elongation of the tendon was calculated as the change in the distance between coordinates of these anatomical landmarks. To calculate tendon material and mechanical properties force-elongation curves were fitted with a 2nd degree polynomial. All the recordings used in the results had a fit of R2 = 0,92 or higher. Stiffness was calculated as the slope of the force–elongation curve, between 90 and 100% of each athlete’s maximal force. The Young’s modulus was calculated by multiplying the stiffness values by the ratio between the patellar tendon resting length (l0) and mean CSA. Patellar tendon l0 and maximal length (lmax) was used to calculate the patellar tendon strain. Two sets of ultrasound data (from two E+S athletes) had to be discarded because of an insuffi- cient quality to enable analysis. Therefore, the number of athletes included in the data from tendon testing is 9 in E+S and 8 in E. Muscle biopsy sampling Muscle biopsies were sampled from m. vastus lateralis using the Bergström procedure. Athletes were instructed to refrain from physical activity for the last 24h leading up to biopsy sampling. During each biopsy sampling-event, two separate muscle biopsies were retrieved and pooled in a petri dish filled with sterile physiological salt water. An appropriately sized muscle sample (mean wet weight: 29 ± 8 mg) was selected for immunohistochemical analyses and mounted in Tissue-Tek (Sakura Finetek USA, Inc., Torrance, CA, USA) and quickly frozen in isopentane cooled on liquid nitrogen. Muscle samples were stored at– 80°C until later analyses. Immunohistochemistry Cross-sections 8 μm thick were cut using a microtome at −20°C (CM3050; Leica Microsystems GmbH, Wetzlar, Germany) and mounted on microscope slides (Superfrost Plus; Thermo Fisher Scientific, Inc., Waltham, MA, USA). The sections were then air-dried and stored at −80°C. Prior to antibody labelling, muscle sections were blocked in a solution containing 1% BSA (cat. no. A4503; Sigma-Aldrich Corp., St Louis, MO, USA) and 0.05% PBS-T solution (cat. no. 524650; Calbiochem, EMD Biosciences, Inc., San Diego, CA, USA) for 30 min. Then they were incubated overnight at 4°C with antibodies against the capillary marker CD31 (1:200; clone JC70A, M0823; Dako A/S, Glostrup, Denmark), followed by incubation with appropriate secondary antibodies (Alexa Fluor, cat. no. A11005). Following staining, muscle sections were visualized and pictures taken using a high-resolu- tion camera (DP72; Olympus Corp., Tokyo, Japan) mounted on a microscope (BX61; Olympus Corp.) with a fluorescence light source (X-Cite 120PCQ; EXFO Photonic Solutions Inc., Mis- sissauga, Ontario, Canada). These muscle sections were then incubated for 1 hour at room temperature with antibodies against myosin heavy chain type II (1:1000; SC71; gift from Professor S. Schiaffino) and dystro- phin (1:1000; cat. no. ab15277; Abcam Plc), followed by incubation with appropriate secondary antibodies (Alexa Fluor, cat. no. A11005 or A11001; Invitrogen, Inc.). Muscle sections were then covered with a coverslip and glued with ProLong Gold Antifade Reagent with DAPI (cat.no. P36935; Invitrogen Molecular Probes, Eugene, OR,USA) and left to dry overnight at room temperature. Muscle sections were again visualized and new pictures was taken at the exactly same location in the section as the CD31 picture. Between all stages, sections were washed for 3 × 5 min using a 0.05% PBS-T solution. Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 7 / 18 Fiber type distribution, fiber cross-sectional area and capillaries were identified using TEMA software (CheckVision, Hadsund, Denmark). Capillarization was expressed as capillar- ies around each fiber (CAF) and capillaries related to fiber area (CAFA), for type I and type II (IIA and IIX) fibers. Because of technical problems with some analyses, the number of athletes in the immunohistochemistry data is 8 in E+S and 5 in E. Statistical analyses All data in the text, Figs and tables are presented as mean ± standard deviation, unless other- wise stated. Data were analyzed using two-way (group x time) repeated measures ANOVA. Effect sizes (ES) were calculated for key performance and physiological adaptations to elucidate on the practical significance of strength training. ES were calculated as Cohen’s d and the crite- ria to interpret the magnitude were the following: 0–0.2 = trivial, 0.2–0.6 = small, 0.6– 1.2 = moderate, 1.2–2.0 = large and > 2 = very large [33]. Correlations analyses were done using the Pearson product-moment method. Analyses was performed in Excel 2013 (Microsoft Corporation, Redmon, WA, USA). Analyses resulting in p 0.05 were considered statistically significant. Results There were no significant differences between E+S and E in any of the measured variables at baseline. Body mass, maximal leg strength and muscle fiber area Body mass remained unchanged in E+S (from 62.4 ± 5.2 kg to 63.1 ± 5.6 kg) but was slightly reduced in E (from 65.6 ± 8.4 kg to 64.8 ± 8.0 kg, p < 0.05). There was a significant interaction (p < 0.05) between group and time (pre vs post) indicating that the change in body mass was different between the groups. 1RM in the leg exercises increased 40.4 ± 14.7% in E+S (p < 0.01, Fig 1) with no change in E. There was a significant interaction (p < 0.01) between group and time (pre vs post). In addi- tion, the effect size analyses revealed a very large practical effect of E+S compared to E (ES = 3.20). In E+S, CSA of both type I and type II muscle fibers increased in m. vastus lateralis (13.2 ± 6.8% and 30.8 ± 19.6%, respectively, p < 0.01), with no changes occurring in E (Fig 2). E+S had a moderate practical effect on muscle fiber CSA compared to E (ES = 0.83). SJ and CMJ E+S increased SJ and CMJ height by 8.9 ± 6.8% and 5.9 ± 6.0% respectively (p < 0.05) while no changes occurred in E (Fig 1). The effect size analyses revealed a moderate practical effect in favor of E+S in both SJ (ES = 1.06) and CMJ (ES = 0.65). Capillarization None of the groups had any change in CAF or CAFA around neither type I nor type II fibers (Fig 3). Mechanical and material properties of the patellar tendon There were no significant changes in stiffness or Young’s modulus of the patellar tendon in nei- ther E+S nor E (Table 2). The mean CSA of the patellar tendon increased by 5.2 ± 3.6% in E+S (p < 0.01) while no significant changes occurred in E (Table 2). Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 8 / 18 Fig 1. Maximal strength and jumping performance. Individual values (dotted lines) and mean values (solid lines) before (Pre) and after (Post) the intervention period for athletes adding strength training to their normal endurance training (E+S) and athletes performing normal endurance training only (E). a: Squat jump (SJ) height. b: Counter movement jump (CMJ) height. c: Mean 1 repetition maximum (1RM) in half-squat and one- legged leg press (leg exercises). * Different from pre (p < 0.05), # significant interaction between group and time (p < 0.05) doi:10.1371/journal.pone.0150799.g001 Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 9 / 18 VO2max and Vmax Both VO2max and Vmax was unchanged in both groups during the intervention period (Table 3). Running economy and running velocity at 3.5 mmolL-1 [la-] There were no changes in running economy measured at 10 kmh-1 during the blood lactate profile test (Fig 4) or running velocity at 3.5 mmolL-1 [la-] (Fig 4) in neither of the groups. 40 min all-out test There were no change in running distance or performance VO2 during the 40 min all-out test in neither of the groups during the intervention (Fig 4). Fractional utilization of VO2max did not change in E+S (from 85.3 ± 3.9 to 85.3 ± 4.3, Fig 4), but increased in E, going from 83.2 ± 3.1% to 86.0 ± 3.0% (p < 0.05). Before the intervention the performance in the 40 min all-out test correlated with velocity at 3.5 mmolL-1 [la-], VO2max and Vmax (r = 0.65, r = 0. 58, r = 0.79, respectively), but not with running economy (r = -0.24). No significant correlations between changes in these variables and changes in 40 min all-out running distance were observed. Discussion The main results from the current study were that adding heavy strength training to well- trained female athletes`normal endurance training did not affect the mechanical properties of the patellar tendon or running economy. Furthermore, there was no effect on running perfor- mance during a 40 min all-out running test. Strength training had no negative effect on capil- lary density despite increased muscle fiber CSA and muscle strength. Maximal strength and muscle fiber cross sectional area The strength-training program used in the current study was effective in increasing maximal leg strength as shown by an increase in 1RM in the leg exercises. This is in accordance with Fig 2. Muscle fiber cross sectional area. Individual values (dotted lines) and mean values (solid lines) before (Pre) and after (Post) the intervention period for athletes adding strength training to their normal endurance training (E+S, left panel) and athletes performing normal endurance training only (E, right panel). Muscle fiber cross sectional area (CSA) for both type I muscle fibers and type II muscle fibers * Different from pre (p < 0.05) doi:10.1371/journal.pone.0150799.g002 Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 10 / 18 previously observed increases in 1RM in endurance athletes adding heavy strength training to their normal endurance training (e.g., [2, 4, 13]). The results from the current study confirms previous results [2, 34] that a quite large increase in muscular strength can be achived without an increased body mass. This is important for runners since increased body mass can negatively influence running performance. In spite of this, the improved strength seemed to be at least par- tially dependent on increased muscle mass, as evident from the increased muscle fiber CSA. The present muscle hypertrophy is in agreement with other studies using similar strength training protocols in endurance athletes [34–36]. Interestingly, there were no difference in the CSA of the type I and type II fibers in the current athletes, confirming the notion that in endurance athletes the type I fibers may be just as large [37] or even larger [38] than the type II fibers. Fig 3. Capillarization. Individual values (open symbols) and mean values (solid squares) for athletes adding strength training to their normal endurance training (E+S) and athletes performing normal endurance training only (E). a: Percent change in capillaries around each muscle fiber (CAF) for both muscle fiber type I and muscle fiber type II for E+S and E. b: Percent change in capillaries related to fiber area (CAFA) for both muscle fiber type I and muscle fiber type II for E+S and E. doi:10.1371/journal.pone.0150799.g003 Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 11 / 18 SJ and CMJ The current strength training protocol was also effective in increasing leg muscle power, as evi- dent from the increased SJ and CMJ performance. This is in line with previous reports of effects of heavy strength training on jumping ability in untrained participants [39, 40]. However, pre- vious data from endurance athletes are more unclear, as some studies report improved jumping performance [36, 41] whereas others do not [14, 20]. The current study indicate that quite large improvements in jumping ability and explosive strength can be achived with heavy strength traning despite concurrently performing endurance training. Capillarization Eleven weeks of heavy strength training did not affect capillarization expressed as either CAF or CAFA, despite leading to significant muscle fiber hypertrophy. Importantly, this suggest that the potentially negative effect of increased muscle fiber CSA on diffusion distances between blood and inner parts of muscle fibers was counteracted by a non-significant increase in CAF. However, this data should be treated with caution because of the limited sample size. Despite of this, our finding are in line with previous studies in untrained participants that have reported either no change or a slight increase in CAF [23, 24] and no change in CAFA [24] after a period of heavy strength training. Our finding is also in agreement with results reported in elite male cyclists after 16 weeks of heavy strength training [35]. Therefore, it seems like endurance athletes should not be afraid of reduced capillarization when they consider adding heavy strength training to their ongoing endurance training. Mechanical properties of the patellar tendon The lack of changes in mechanical properties of the patellar tendon following heavy strength training is in contrast to most studies, typically reporting increased patellar tendon stiffness, at Table 2. Stiffness, Young’s modulus and mean cross section area (CSA) of the patellar tendon. E+S E Pre Post Pre Post Stiffness (Nmm-1) 2752 ± 402 2483 ± 733 2753 ± 947 2692 ± 697 Young’s Modulus (MPa) 1038 ± 194 925 ± 162 1251 ± 296 1158 ± 273 Mean CSA (mm2) 65.9 ± 7.1 69.2 ± 6.9* 60.3 ± 4.2 59.9 ± 4.4 Stiffness, Young’s modulus and mean cross section area (CSA) of the patellar tendon before (Pre) and after (Post) the intervention period for athletes adding strength training to their normal endurance training (E+S) and athletes performing normal endurance training only (E). Values are mean ± SD * Different from pre (p < 0.05). doi:10.1371/journal.pone.0150799.t002 Table 3. Data from the maximal oxygen consumption test. E+S E Pre Post Pre Post VO2max (mlkg-1min-1) 52.2 ± 2.3 52.7 ± 3.3 54.2 ± 2.9 53.1 ± 1.9 Vmax (kmh-1) 12.8 ± 0.7 13.0 ± 0.9 13.1 ± 0.5 13.3 ± 0.6 HRpeak (beatsmin-1) 193 ± 9 192 ± 9 189 ± 8 187 ± 7 RPE 19 ± 1 20 ± 1 19 ± 1 19 ± 1 [La-1]peak (mmoll-1) 9.7 ± 3.0 8.1 ± 3.8 8.9 ± 2.2 7.7 ± 1.8 Data from the maximal oxygen consumption (VO2max) test before (Pre) and after (Post) the intervention period for athletes adding strength training to their normal endurance training (E+S) and athletes performing normal endurance training only (E). Values are mean ± SD. doi:10.1371/journal.pone.0150799.t003 Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 12 / 18 least in previously untrained participants [19, 42–44]. A possible reason for the discrepancy in results between our study and that by others is that we included female participants while the other studies included males [19, 42–44]. In fact, female tendons have been reported to show a lower rate of new connective tissue formation in response to mechanical loading [21]. Differ- ences in the strength training protocol may also explain the lack of changes in the current study. Indeed, most previous studies reporting increased patellar tendon stiffness following strength training have included heavy knee extension exercise [19, 42, 44] or isometric muscle actions [45]. In the current study, the exercises involved were more complex involving multiple joints that perhaps reduced the absolute mechanical loading on the patellar tendon compared to a pure knee extension exercise. In addition, the athletes were instructed to perform the con- centric phase of the exercises as fast as possible making the time under tension quite low. In contrast to the lack of effect of strength training on patellar tendon properties, it led to increases in its CSA. In line with these findings, some studies on the effect of strength training, yet not all [18], reports an increase in patellar tendon CSA [19, 42]. Without changes in mechanical properties, the tendon hypertrophy measured here suggests that material proper- ties may also have been altered. The lack of change in Young’s modulus following training may highlight the limitation of this parameter based on finite tendon sections to reflect whole ten- don material properties. Interpreting the mechanisms driving tendon hypertrophy extends beyond the scope of the present article. One could speculate that increasing tendon CSA may Fig 4. Determinants of running performance and running performance. Individual values (dotted lines) and mean values (solid lines) before (Pre) and after (Post) the intervention period for athletes adding strength training to their normal endurance training (E+S) and athletes performing normal endurance training only (E). a: Body mass adjusted oxygen consumption at 10 kmh-1. b: Running velocity at 3.5 mmolL-1 [la-] calculated during the blood lactate profile test. c: The fractional utilization of VO2max during the 40 min all-out test. d: Running distance during the 40 min all-out test. * Different from pre (p < 0.05). doi:10.1371/journal.pone.0150799.g004 Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 13 / 18 shield this tissue against damage caused by excessive and/or unusual stresses. Taken together, the present measurements indicate that resistance training triggers an adaptive response in the patellar tendon of female runners, without affecting the mechanical properties of this tissue. Whether this adaptation may affect injury rates or have other effects amongst runners warrants further investigation. VO2max, fractional utilization of VO2max, running velocity at 3.5 mmolL-1 blood [La-], and running economy The lack of change in VO2max after strength training is not surprising and is actually in accor- dance with the current literature (e.g., [6, 12, 46]). Fractional utilization of VO2max measured during the 40 min all-out test did not change in E+S during the course of the study. To our knowledge, this is the first study directly measuring fractional utilization of VO2max in running after addition of heavy strength training in endurance athletes. However, VO2 at lactate thresh- old in percentage of VO2max, is often taken as an indirect measure of fractional utilization of VO2max [11]. The few studies measuring this variable in running reports no effect after addition of heavy strength training [2, 12]. Notably, there was a slight increase in fractional utilization of VO2max in E over the course of the intervention. This was likely due to a combination of two factors; a small but non-significant reduction in VO2max, largely due to one athlete exhibiting a large reduction, and a small but non-significant increase in performance VO2. Surprisingly, we found no effect of heavy strength training on running economy, contrast- ing the majority of previous studies, typically reporting improvements from 3–8% [2, 6, 13, 14]. However, some studies supports the lack of an effect of strength training on running econ- omy [1, 7, 8, 47]. In two of this studies [7, 47] the lack of improved running economy might be because the strength training program only consisted of one session for the legs per week. Supporting the lack of an effect on VO2max, fractional utilization of VO2max and running economy, strength training had no effect on running velocity at 3.5 mmolL-1 blood [La-]. The latter is in accordance with the majority of the current literature which reports no change in velocity at a certain blood [la-1] or ventilatory threshold after adding various forms of strength training to runners’ normal training [2, 7, 12], although exceptions exist [14]. This is quite sur- prising considering that improved running economy in theory should affect the running speed at a certain lactate threshold [11]. Running performance The lack of changes in 40 min all-out performance is not in line with many of the studies in this area where improved running performance have been reported [1, 2, 4–6]. However, no change in performance is in line with the present lack of changes in the important performance determining factors like VO2max, running economy and fractional utilization of VO2max. Since strength training does not affect VO2max and the fractional utilization of VO2max, the mecha- nism for the observations of improved running performance in some other studies seems to be improved running economy [2, 6]. However, not all studies have found strength training to be beneficial for running performance [7, 8, 10, 47], and are in accordance with the current study. Interestingly, these studies do also report no improvements in running economy. Therefore, the lack of improved running performance in the current study is probably because of no changes in running economy. Whilst unclear, the discrepancies in training-induced running performance measures between the current study and that by others may be attributed to methodological differences. In the current study, all running tests were performed at 5.3% inclination. This inclination resulted in a quite low running velocity compared to some other studies. Indeed, changes in Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 14 / 18 running economy after strength training have previously been found to be related to running velocity [46]. However, improvements in running economy after strength training have also been reported at similar velocities [20, 36] and at the same inclination [48] used in the current study. Therefore, the inclination used is probably not the only explanation why no changes in running economy and performance were observed. The fact that this study includes only female athletes while most previous studies include either only males or a mix of both male and female runners this may perhaps explain why no effects of strength training on running economy was observed. However, strength training have been reported to improve running economy in female runners [13] making this explana- tion unlikely. One of the most frequent proposed mechanism for the possible ergogenic effect of strength training on running economy is changes in stiffness of the muscle or tendons in the legs [2, 14]. Despite this speculation, studies have yet to investigate the effect of heavy strength training on patellar tendon mechanical properties in conjunction with running economy. In the current study, the unchanged tendon stiffness and increased strength suggest that more elastic energy may be stored in the patellar tendon during the stance phase, amplifying muscular power out- put and efficiency. However, the lack of changes in running economy do not support this hypothesis, and conclusion cannot be drawn regarding the influence of patellar tendon proper- ties in the present study. Vmax has been reported to be the best laboratory measure to predict performance in various running distances [49] and can actually be considered a measure of running performance [50]. The lack of increased Vmax further indicates that heavy strength training did not lead to improved running performance in the current study. It has previously been reported both improved [6, 36] and no change [14] in Vmax after heavy strength training in trained runners. Conclusion In contrast to our hypothesis, adding heavy strength training to endurance training in well- trained female endurance athletes did not affect running performance measured as running distance during a 40 min all-out test. The lack of effect on performance was probably because the strength training intervention did not improve running economy or changed the mechani- cal properties of the patellar tendon. However, strength training had no negative effect capillary density. Acknowledgments The authors would like to thank the participants for their time and effort. Students Øyvind Trøen, Roger Kristoffersen, Allan Sørgaard Nielsen and Sondre Prestkvern for assistant during the intervention follow-up and data sampling. Author Contributions Conceived and designed the experiments: OV TR OS SE BRR. Performed the experiments: OV TR OS KB SE BRR. Analyzed the data: OV TR OS KB SE BRR. Contributed reagents/materi- als/analysis tools: OV TR OS KB SE BRR. Wrote the paper: OV TR OS KB SE BRR. References 1. Damasceno MV, Lima-Silva AE, Pasqua LA, Tricoli V, Duarte M, Bishop DJ, et al. Effects of resistance training on neuromuscular characteristics and pacing during 10-km running time trial. Eur J Appl Phy- siol. 2015; doi: 10.1007/s00421-015-3130-z Strength Training and Running Performance PLOS ONE \| DOI:10.1371/journal.pone.0150799 March 8, 2016 15 / 18 2. Storen O, Helgerud J, Stoa EM, Hoff J. Maximal strength training improves running economy in dis- tance runners. 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PMC3299590	STUDY PROTOCOL Open Access Effect of running therapy on depression (EFFORT-D). Design of a randomised controlled trial in adult patients [ISRCTN 1894] Frank R Kruisdijk1,3, Ingrid JM Hendriksen2,3, Erwin CPM Tak2,3, Aartjan TF Beekman4,5 and Marijke Hopman-Rock2,3,5 Abstract Background: The societal and personal burden of depressive illness is considerable. Despite the developments in treatment strategies, the effectiveness of both medication and psychotherapy is not ideal. Physical activity, including exercise, is a relatively cheap and non-harmful lifestyle intervention which lacks the side-effects of medication and does not require the introspective ability necessary for most psychotherapies. Several cohort studies and randomised controlled trials (RCTs) have been performed to establish the effect of physical activity on prevention and remission of depressive illness. However, recent meta-analysis’s of all RCTs in this area showed conflicting results. The objective of the present article is to describe the design of a RCT examining the effect of exercise on depressive patients. Methods/Design: The EFFect Of Running Therapy on Depression in adults (EFFORT-D) is a RCT, studying the effectiveness of exercise therapy (running therapy (RT) or Nordic walking (NW)) on depression in adults, in addition to usual care. The study population consists of patients with depressive disorder, Hamilton Rating Scale for Depression (HRSD) ≥ 14, recruited from specialised mental health care. The experimental group receives the exercise intervention besides treatment as usual, the control group receives treatment as usual. The intervention program is a group-based, 1 h session, two times a week for 6 months and of increasing intensity. The control group only performs low intensive non-aerobic exercises. Measurements are performed at inclusion and at 3,6 and 12 months. Primary outcome measure is reduction in depressive symptoms measured by the HRSD. Cardio-respiratory fitness is measured using a sub maximal cycling test, biometric information is gathered and blood samples are collected for metabolic parameters. Also, co-morbidity with pain, anxiety and personality traits is studied, as well as quality of life and cost-effectiveness. Discussion: Exercise in depression can be used as a standalone or as an add-on intervention. In specialised mental health care, chronic forms of depression, co-morbid anxiety or physical complaints and treatment resistance are common. An add-on strategy therefore seems the best choice. This is the first high quality large trial into the effectiveness of exercise as an add-on treatment for depression in adult patients in specialised mental health care. Trial registration: Netherlands Trial Register (NTR): NTR1894 Correspondence: f.kruisdijk@ggzcentraal.nl 1GGZ Centraal Centers for Mental Health, Symfora-Meander Centre for Psychiatry, Utrechtseweg 266, 3800 DB Amersfoort, The Netherlands Full list of author information is available at the end of the article Kruisdijk et al. BMC Public Health 2012, 12:50 http://www.biomedcentral.com/1471-2458/12/50 © 2012 Kruisdijk et al; BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Depression is a common disorder. The lifetime preva- lence of depressive disorders in Dutch adults is 19% [1]. Recurrence of symptoms occurs in an unfavourable and fluctuating course in 44% and a severe chronic course in 32% of the patients. Depressive disorders obviously have negative effects on wellbeing and daily personal and professional functioning. Antidepressants and psychotherapy Treatment frequently includes prescription of antide- pressants. However, the effectiveness of such treatment may be limited because of poor compliance and limited effectiveness. A recent analysis of data of the US Food and Drugs Administration, showed relatively small drug- placebo differences in antidepressant efficacy [2]. Other disadvantages of antidepressants are unpleasant side effects and increased risk of hypertension in depressed patients combined with Diabetes Mellitus type II (DM II) [3,4]. A systematic review of randomised controlled trials into effectiveness and cost-effectiveness of brief psycho- logical treatment for depression showed that some forms, especially cognitive-behavioural based approaches, were beneficial in the treatment of outpati- ents [5]. A meta-analysis, regarding a mostly adult popu- lation, found a favourable outcome for a combination of psychotherapy and pharmacotherapy compared to psy- chological treatment alone for depression in the short- term [6]. A recent meta-analysis examined whether the quality of the studies investigating psychotherapy for adult depression was associated with the effect-size found in these studies [7]. It showed that the effects of psychotherapy for adult depression have been overesti- mated in meta-analytical studies. The authors stated that the effects of psychotherapy are much smaller than is assumed. The traditional treatment strategies, medication and psychotherapy, are still not ideal and a new intervention, exercise, focusing on a different psychopathological mechanism of depression is needed. Exercise Exercise is a potential alternative low-cost therapy, but more studies are needed to define the place of exercise in a stepped care program for depression [8]. Exercise is relatively safe and has less negative side effects than antidepressants. Obviously, exercise has also many bene- ficial effects on physical health [9,10], and is expected to have additional advantages in depressed patients who suffer from a combination of mental and physical pro- blems, such as pain complaints or increased risk of car- diac morbidity. Exercise is suitable for most individuals, participating in an exercise program promotes social integration and successful adaptation can increase self- esteem. Finally, exercise may reduce the negative side effects of antidepressants, such as fatigue, thus increas- ing compliance in medication use. Scientific evidence for the efficacy of exercise Recent reviews and meta-analyses suggest that exercise leads to improvements in depressive symptoms [11,12]. Following the cumulative evidence from prospective cohort studies and RCTs, exercise has proven protective benefits for several aspects of mental health in general and for symptoms of depression in particular. However, study results on the curative effect on a present depres- sion are conflicting. A recent Cochrane meta-analysis [13], updating an earlier systematic review in 2001 by Lawlor and Hopker [14], concluded that the statistically weak findings didn’t support the efficacy of exercise in the treatment of depression. Methodological weaknesses of the trials were identified, including the lack of treat- ment concealment, intention to treat analysis and a clin- ical interview to confirm the diagnosis of depression. Rethorst et al. [15] argue that the earlier mentioned meta-analysis of Lawlor and Hopker suffered from incomplete research data and a lack of moderating vari- able analysis. The meta-analysis by Rethorst et al., with 58 RCTs included, calculated an overall effect size of -0.80 for the effects of exercise on depression. The recommendation of both Lawlor et al. and Rethorst et al. is that further, conclusive research is still needed into the use of exercise for depression. Exercise as an adjunct to recognised treatments, such as psychotherapy and/or medication, should also be studied. Furthermore, follow- up research is needed that examines the sustainability of the effects after exercise therapy has ended. Very recently, Krogh et al. [16] performed a systematic review and meta-analysis in which only 13 trials were selected because of the stringent selection criteria used (i.e. recording clinical depression according to any diag- nostic system, adequate allocation concealment, blinded outcome and intention to treat analysis). Besides an inverse association between duration of intervention and the magnitude of the association of exercise with depression, the effect size was 0.40 for the pooled effect sizes of 13 studies and 0.19 for the selected three high quality design studies. The authors concluded that large high quality design studies are required. The recommen- dations from the three meta-analysis cited above are integrated in the design of the EFFORT-D study. Besides reduction of depressive symptoms, exercise can be useful for other treatment indications, such as cardiovascular condition, metabolic syndrome, pain modulation and the immune system. Depression is Kruisdijk et al. BMC Public Health 2012, 12:50 http://www.biomedcentral.com/1471-2458/12/50 Page 2 of 9 associated with cardiovascular disease. It is an estab- lished risk factor for mortality after acute myocardial infarction. A recent meta-analysis of cohort studies also shows the aetiological and prognostic effects of depres- sion on coronary heart disease (CHD) [17]: the pooled relative risk of depression on future coronary heart dis- ease was 1.80. Another meta-analysis shows an overall relative risk of dying in depressed subjects of 1.80 com- pared to non-depressed subjects, with no major differ- ences between men and women [18]. The increased relative risk on mortality was also found in subclinical forms of depression. These findings suggest that exercise may also be pre- scribed to improve the cardiovascular condition and prevent future heart disease. Depressed patients should also be more closely monitored regarding cardiovascular parameters. The body of evidence showing an association between depression and obesity and metabolic deregulation is growing. Recent meta-analysis of cross-sectional studies in the general population found a significant positive association between depression and obesity, affecting women more than men. A reciprocal risk relation between obesity and depression [19,20], and an U- shaped, non-linear trend in the association between BMI and depression was found [21]. The Dutch NESDA study [22] shows an association between severity of depression and unfavourable cholesterol high-hdl/low- ldl concentration [23]. This tendency for a higher risk of depression on metabolic syndrome, in which various cardiovascular risk factors appear, emphasizes the need of careful screening on this parameters [24], and exer- cise could positively influence these parameters. A recent meta-analysis of studies into cytokines in depressed patients [25] showed that depression and an activation of the immune-system do co-exist. Both tumornecrosisfactor-a (TNF-a) and interleukine-6-con- centration (IL-6) were significantly raised and C-reactive protein (CRP) was linearly associated with several con- ventional risk factors and inflammatory markers [26]. The relevance of CRP however still remains controver- sial [27]. Therefore, measurement of CRP blood levels in an intervention exercise study could contribute to a bet- ter understanding of the clinical importance in relation to heart condition. The reciprocal relation between pain and depression has been reported in several studies where Major Depressive Disorder (MDD) was associated with chronic pain (> 6 months) and nearly 50% of patients had at least one chronic painful physical condition [28,29]. The duration of depressive symptoms was found to be pro- longed in the presence of chronic pain. The therapeutic prognosis of co-morbid pain and depression is poor and the pain-depression association was found to be stronger in men than in women and in older adults compared to younger. Productivity was decreased when depression and pain existed in a co-morbid pattern [30]. Quality of life and cost-effectiveness Systematic evaluation of cost-effectiveness and quality of life, including life-events in the treatment of depression, may contribute to the development of more evidence based care models [31], for instance by disease management. A population sample study showed that, in patients with a remitted MDD, the quality of life was lower than in the general population. A higher depressive severity was associated with a lower quality of life. Ten Doesschate et al. argue that, even in depression in remission, attention is needed for the quality of life, and above that, residual symptoms must be treated aggres- sively to achieve a higher quality of life [32]. Summarized aims and hypothesis of EFFORT-D The main objective of the study is to assess the effec- tiveness of exercise therapy (running therapy (RT) or Nordic walking (NW)), in addition to usual care, on depression in adult patients. We hypothesize that adding exercise therapy to usual care will result in a larger reduction in depressive symptoms (as measured with the Hamilton Rating Scale of Depression, (HRSD)) dur- ing a 6 month treatment program as well as at 6 months follow-up, compared to usual care without exer- cise therapy. Secondary aims are to assess the effectiveness of exer- cise therapy (RT or NW) on the following outcome measures: 1) Cardiovascular and metabolic risk para- meters as fitness (VO2 max, grip strength), BMI, waist circumference, body composition, blood pressure, fasting blood glucose, cholesterol HDL/LDL ratio and CRP, 2) Co-morbid symptoms of anxiety and pain 3) Quality of life, and 4) Cost-effectiveness. To assess whether there are subgroups of patients who show a larger or less effect of exercise therapy on depression, subgroup analysis will be conducted differ- entiated on demographic variables (age, sex) and level and type of depression based on the HRSD, personality traits and level of fitness. Methods/Design EFFORT-D is a randomized controlled trial, designed along the Consort-statement guidelines [33], in which outpatients as well as hospitalised depressed patients will be included and randomized in two groups: a con- trol group or an intervention group (see flow chart in Figure 1). The control group outpatients receive usual care (i.e. anti-depressive medication and/or cognitive and/or interpersonal therapy). The control group Kruisdijk et al. BMC Public Health 2012, 12:50 http://www.biomedcentral.com/1471-2458/12/50 Page 3 of 9 inpatients have their usual treatment program (consist- ing of pharmacotherapy and/or sociotherapy, psy- chotherapy, psycho-education and indicated nonverbal therapies) and are allowed to exercise at low intensity as part of their daily program. The intervention group receives 6 months supervised exercise therapy for 1 h a week and is instructed to train unsupervised for 1 h a week during this period (combined about 40 exercise sessions during the inter- vention period). Both training sessions follow an indivi- dualised intervention protocol and are in addition to the usual care program. Included and randomised patients in the intervention group are invited to take part in Running Therapy (RT). Patients will only be referred to Nordic Walking (NW) in case of clear medical contra-indications against RT, such as muscular-skeleton problems, or if patients have a strong dislike for running which obstructs participation and compliance. The study will run for 27 months, with an 18 months inclusion period during which patients are recruited and randomized. There are four measurements for each patient: at baseline (T0), halfway the 6 month intervention period (T3), at the end of the intervention period (T6), and at follow up, 12 months after baseline (T12). In Table 1 the timetable is shown. Study population The study population consists of adult patients diag- nosed by a clinician with a depression or bipolar disor- der with depressive mood, who are or will be treated in GGZ Centraal Mental Hospitals or Symfora-Meander Hospital. Patients aged between 18-65, with a DSM-IV diagnoses of unipolar, bipolar depression or seasonal depression not responding to light therapy (10 sessions of 1 h), a baseline Hamilton Rating Scale of Depression Psychiatrist diagnosis of depression In -or-outpatients Screening by research-assistant Check inclusion/ exclusion criteria HRSD Biometric values = BV Cycle test = CT Electronic Questionnary = EQ Blood samples = BS Blinded randomisation by random envelopes Control Group Treatment as usual Psychotherapy Medication Lifestyle psychoeducation Intervention Group Treatment as usual Psychotherapy Medication Lifestyle psychoeducation + 20 sessions 1 hr. RT/NW supervised 20 sessions 1 hr. RT/NW individually HR freq. Polar Informed consent + Inclusion in EFF-D T= 0 T = 3 BV,CT,EQ T = 6 BV,CT,EQ,BS T = 12 BV,CT,EQ End of Intervention period Figure 1 Flow chart. Kruisdijk et al. BMC Public Health 2012, 12:50 http://www.biomedcentral.com/1471-2458/12/50 Page 4 of 9 Table 1 Events and time table Intervention period Follow-up period Procedure Source Person No. of items Duration (min.) T0 T3 T6 T12 Written informed consent letter patient X Demographics1 patient file research assistant X Depression Depression history2 patient file research assistant 2 X HRSD3 interview blind rater 17 20 X X X X IDS-SR4 questionnaire patient 30 10 X X X X Bearableness (VAS) questionnaire patient 1 X X X X Metabolic syndrome Length physical test research assistant 1 X Weight physical test research assistant 1 X X X X Waist circumference physical test research assistant 1 X X X X Blood pressure physical test research assistant 1 X X X X Smoking/alchohol intake questionnaire patient 1 X X X X Laboratory assesment6 physical test laboratory 2 X X X X Quality of Life WHO-DAS7 questionnaire patient 36 X X X X Pain GCPS8 questionnaire patient 7 X X X X Bearableness (VAS) questionnaire patient 1 X X X X Anxiety BAI9 questionnaire patient 21 X X X X Cost effectiveness TIC-P10 quest./pnt file pnt/research assistant 26 X X X Euroqol questionnaire patient 5 X X X Subjective health (VAS) questionnaire patient 1 X X X Fitness Submaximal cycle test physical test research assistant 10 X X X X Grip strength physical test research assistant 2 X X X X Exercise intensity (HR)11 physical test exercise instructor during training sessions Personality NEO PI-R12 questionnaire patient 60 X Physical activity SQUASH13 questionnaire patient 12 X X X X Additional Measures LEQ14 questionnaire patient 12 X X X X Compliance registration exercise instructor during training sessions POMS15 questionnaire patient 32 during training sessions Evaluation Intervention16 questionnaire patient X 1: Date of birth, sex, education, ethnicity, income, professional status, living situation, marital status 2: Number and duration of former episodes, duration current episode 3: Hamilton Rating Scale of Depression (or HAMD) 4: Inventory of Depressive Symptomatology - Self Report 6: fasting glucose, triglycerides, total cholesterol, HDL- cholesterol, cholesterol/HDL-ratio, creatinine, MDRD 7: World Health Organisation- Disability Assessment Schedule 8: Graded Chronic Pain Scale 9: Beck Anxiety Inventory 10: Trimbos/iMTA questionnaire for Costs associated with Psychiatric Illness 11: Polar RS 800 CX Run 12: Revised NEO Personality Inventory 13: Short QUestionnaire to ASses Health enhancing physical activity 14: Life Events Questionnaire 15: Profile of Mood State 16: Only for patients who participated in the intervention Kruisdijk et al. BMC Public Health 2012, 12:50 http://www.biomedcentral.com/1471-2458/12/50 Page 5 of 9 (HRSD) score ≥14 and (will be) treated for depression, are included. Criteria for exclusion are: a depression as part of a psychotic disorder, schizophrenia, schizoaffective disor- der or obsessive compulsive disorder, anxiety disorder as primary diagnosis, patients in long stay facilities (including day-care) or with complex pathology and treatment resistant depression (inpatients, treated by protocol more than 6 months with no remission); patients with significant cardiovascular disease or other medical conditions as contra-indication for exercise therapy, walking and/or running such as joint and hip pathology; alcohol/drugs dependence as a primary diag- nosis, pregnancy, high suicide risk with treatment on a closed ward, or already being physically active on a reg- ular basis (2-3 times a week on a high-intensity). Sample size Following earlier RCTs [34] it is expected that patients in the usual care group (controls) will respond with a mean reduction in HRSD of six points. Adding exercise to usual care is expected to result in a decline of at least eight points in HRSD score (thus two extra points). To detect this difference, with an a (two-tailed) of 5% and a power (1-b) of 80%, using two equal groups and a stan- dard deviation of 5 points, 100 patients are needed in each group. Taking 30% drop-out into account, 140 patients have to be included in each group. Procedures and study instruments Names of eligible patients with their registration number of the electronic patient file (EPD) are provided by the diagnosing psychiatrists to the research assistant, who will make a first check on inclusion and exclusion cri- teria, informs the participants and asks them to join the study. Written informed consent will be obtained according to prevailing legal requirements before the start of the study. Eligible patients, HDRS ≥ 14 as pri- mary outcome measure, perform the Åstrand submaxi- mal cycling test, physical measures and fill out the questionnaire, after which the participants are rando- mized. All outcome parameters measured during base- line will be repeated three, six and 12 months after baseline, except for the blood samples (only at T0 and T6). Height is measured according to protocol (Seca 214, Hamburg, Germany) and a bio-impedance scale is used to measure weight and body composition (Omron HBF-510, Omron Healthcare Europe BV, The Nether- lands). Waist circumference is measured twice with a tape measure (Seca 201, Hamburg, Germany) at the midpoint between the lower border of the ribs and the upper border of the pelvis. Systolic and diastolic blood pressure are registered twice at rest, using an electronic blood pressure meter (Omron M6 comfort, Omron Healthcare Europe BV, The Netherlands) with an ade- quate cuff size. Grip-strength is tested according to pro- tocol using a hydraulic hand dynamometer (Jamar J00105, Sammons Preston Rolyan, Bolingbrook, USA). The submaximal Åstrand test [35] will be performed on a stationary bicycle ergometer (Examiner, Lode BV, The Netherlands) and the mean heart rate of the last 2 min of the test will be used to estimate the VO2max. Heart rate during this test is registered by a heart rate monitor (Polar RS 800, Electro Oy, Finland). Questionnaires At each measurement moment the participant will be asked to fill out a digital questionnaire containing the following instruments: Demographics and personal life events Socio-demographic data are collected using standard questions on age, sex, marital status, ethnicity and household composition. Socio-economic variables include highest education and income. Personal history is evaluated by the Life Events Questionnaire (LEQ), a 12-item inventory-type questionnaire in which subjects mark the exposure to negative life events such as unem- ployment, separation from a partner and death of a close family member which have occurred in the past year [36]. Mental and physical health and its consequences The Hamilton Rating Scale of Depression (HRSD) mea- sures depression with a 17-item list performed by trained interviewers [37]., using the Dutch translation of the version of Bech et al. [38], in which the items of depressive symptoms are extensively operationalised and this version is often used in international research [39]. The Inventory of Depressive Symptomatology - Self- Reported (IDS-SR) measures the severity of depression with a 30-item self-report list [40]. It has good respon- siveness to change and is more sensitive for atypical depressive criteria than the HRSD. History of depression is evaluated by a single question into the number and duration of depressive episodes for which treatment was necessary. Bearableness of depression is measured with a visual analogue scale (VAS) ranging from 0 (very unbearable) to 100 (very well bearable). Anxiety is measured by the Beck Anxiety Inventory (BAI), a 21-item multiple-choice self report inventory that measures the severity of gener- alized anxiety and panic symptoms in adults and adoles- cents [41]. Pain complaints are evaluated with the Graded Chronic Pain Scale (GCPS) [42], a 7-item scale measur- ing aspects of pain, physical ability and social interfer- ence, resulting in a 5-class hierarchical scale ranging from 0 (no pain problem) to IV (high disability/severely limiting). Next to the GCPS, bearableness of pain is Kruisdijk et al. BMC Public Health 2012, 12:50 http://www.biomedcentral.com/1471-2458/12/50 Page 6 of 9 evaluated with a VAS-scale ranging from 0 (very unbearable) to 100 (very well bearable). Other secondary outcomes are disability during the last 30-days associated with both physical and mental problems and is measured by a shortened version of the World Health Organisation - Disability Assessment Schedule II (WHO-DAS-II) [43], resulting in a disability score ranging from 0-100 with a higher score reflecting greater disability. Quality of life data are collected using the EQ5D [44], a standardized instrument for describing and valuing health related quality of life. Subjective health is evaluated by a visual analogue scale ranging from 0 (the worst imaginable health condi- tion) to 100 (the best imaginable health condition). Health care use and work productivity are evaluated by the Trimbos/iMTA Questionnaire for Costs asso- ciated with Psychiatric Illness (TIC-P), a 29-item list which focuses on establishing costs related to loss of productivity at work and health care utilization [45]. Variables expected to modify the effect of the inter- vention Personality, as a possible effect modifier, is mea- sured at baseline by the NEO-PI-R, a 60-item validated questionnaire measuring the five domains of personality including neuroticism, extraversion, agreeableness, con- scientiousness and openness to experience [46]. Confounders: Lifestyle behaviours Self-reported level of physical activity is assessed by means of the validated Short QUestionnaire to ASsess Health enhancing physi- cal activity (SQUASH), a 12-item questionnaire which evaluates the frequency and duration of physical activ- ities in the domains of work, domestic and leisure time [47]. Tobacco and alcohol intake is measured with a standard single question on the frequency of use per day. Additional measures in the intervention group The Profile of Mood States (POMS) registers partici- pants’ mood after a running session for in total three times during the intervention period (at the beginning, halfway and at the end). The Dutch shortened version of the POMS [48] consists of 32 items divided over seven subscales including tension, depression, anger, fatigue, vigour, positive and negative affect. At regular intervals participants score their exertion on a Borg-scale ranging from 6 (very, very light) to 20 (very, very severe) [49]. At the end of the intervention period, or when participants dropped out of the RT or NW therapy, a short questionnaire is administered eval- uating their satisfaction and experience with the intervention. Randomisation, blinding and treatment allocation Randomisation takes place at every location separately. This way, every location will have an equal distribution of participants between the intervention and control group. The SPSS random generator (SPSS version 14.0) [50] will be used to allocate patients. Ten closed envel- opes with allocation numbers are presented to the parti- cipants. They choose an envelope, after which the research assistant tells the patients in which arm of the study they are included. Evaluators of the main outcome measure (HRSD) are blinded for group allocation and are trained regularly for inter-rater reliability. All other measures will be evaluated by a research assistant, who is not blinded for group allocation. Exercise intervention The exercise sessions will take place twice a week (40 sessions in total): once a week a supervised group ses- sion is offered and once a week the patient does an individual training, with clear instructions beforehand and an evaluation at the beginning of the next super- vised session. Each supervised session, in which the trainers are working according to a standardized proto- col, lasts one hour, of which 30 min are spent running (RT)/Nordic walking (NW). The remaining time is spent on warming-up and cooling-down. Each patient follows an individualised intervention protocol with a gradually increasing training intensity. The goal is to achieve a 30 min period of continuous running in the last sessions (two times a week 30 min continuous aerobic exercise at least 60% of the maximal heart rate). The NW program follows a comparable progres- sive schedule with increased time spent Nordic walking with high intensity. Intensity in RT as in NW is monitored by the instruc- tor during every supervised session by counting the heart rate and three times during the intervention per- iod by electronic registration (Polar RS 400, Electro Oy, Finland). The control group receives usual care for depression in accordance with the revised Dutch guide- line [51] and are advised to exercise regularly. Hospita- lised and day-care patients in the control group are supposed not to participate in organised high intensity aerobic exercise during the intervention period. Only low-intensity activity psycho-motor therapy is allowed. Compliance and withdrawal In order to improve compliance during the intervention period, a protocol will be followed concerning missed exercise therapy sessions by participants. This protocol includes: 1) active approach by the exercise instructor in case of no show, and 2) encouragement of other partici- pants to contact each other in case of no show. Partici- pants can withdraw at any time for any reason without any consequences. Also, the investigator can decide to withdraw a participant from the study for urgent medi- cal reasons. Participants who withdraw from the inter- vention will be asked the reason(s) for drop-out but will Kruisdijk et al. BMC Public Health 2012, 12:50 http://www.biomedcentral.com/1471-2458/12/50 Page 7 of 9 be retained in the study for the intention to treat analysis. Statistical analyses Comparability of the intervention and control groups will be examined for the baseline measurements. If necessary, analyses will be adjusted for baseline differ- ences. The primary analysis of the data set will be according to the ‘intention to treat’ principle. A second- ary ‘per protocol’ analysis will be done taking into account the level of compliance and the amount of exer- cise during the intervention period. Usual daily physical activity, tobacco smoking and alcohol intake will be treated as confounders. Differences in remission rates (and other categorical outcomes) between the experimental groups are exam- ined by contingency table Chi-square statistics. Differ- ences in mean scores on continuous outcomes (e.g. HRSD) between the intervention groups are examined by analysis-of-variance. Ethical principles and safety The study has been designed and will be carried out in accordance with the principles of the Helsinki Declara- tion (Edinburgh, Scotland Amendment, October 2000). The study protocol has been approved by the Medical ethical committee for mental health (Metigg Kamer Noord). Discussion The aim of this study is to investigate the effectiveness of aerobic exercise therapy (RT or NW) on depression in adult patients in addition to usual care. Also, the effect of physical exercise on frequently existing co-mor- bid diseases or risk factors for such disorders as meta- bolic syndrome is an objective of this study. This is the first well conducted add-on randomised controlled high-quality trial into the effect of aerobic exercise on depression with a correct randomisation procedure, blinded outcome assessment, intention to treat analysis, study into cost-effectiveness, quality of life and long-term follow-up as was advised in earlier publi- cations. EFFORT-D can therefore contribute to stronger evidence for this type of intervention, which can result in more specified recommendations for clinical practice. Another strength of this study is the fact that also severely clinically depressed patients, who are mostly excluded in other studies, will be included. A relatively greater effect of exercise in this subgroup of severely depressed patients is possible. A challenge of the study lies in the motivational techniques needed to exercise with depressed patients, which is proven to be difficult [52] and it will take a serious effort not to exceed the calculated 30% drop-out patients in the intervention group. Because this study is supported by a strong hypothesis and minimal negative side effects are expected, one-tailed statistical analysis is also possible if the large number of included participants can’t be reached within the planned time. Furthermore, the diversity of outcome measures makes it possible to dis- tinguish more explicitly those depressed patients that could benefit most from exercise. Acknowledgements The former “Open Ankh Foundation” (since March 2008 “Zorgcoöperatie Nederland”) funded part of this project, together with the Symfora Group of Mental Hospitals (January 2010 “GGZ Centraal”), and TNO (Netherlands Organisation for Applied Scientific Research). The authors would also like to thank Professor B.W. Penninx and Professor E. de Geus for their contribution in the design discussion and recommendations for biometric diagnostics. Author details 1GGZ Centraal Centers for Mental Health, Symfora-Meander Centre for Psychiatry, Utrechtseweg 266, 3800 DB Amersfoort, The Netherlands. 2TNO Expert Center Life Style, Wassenaarseweg 56, 2333 AL Leiden, The Netherlands. 3Body@Work, Research Center Physical Activity, Work and Health, TNO-VUmc, Van der Boechhorststraat 7, 1081 BT Amsterdam, The Netherlands. 4Department of Psychiatry, VU University Medical Centre, A.J. Ernststraat 887, 1081 HL Amsterdam, The Netherlands. 5The EMGO Institute for Health and Care Research (EMGO+), VU University Medical Centre, Van der Boechhorststraat 7, 1081 BT Amsterdam, The Netherlands. Authors’ contributions FRK is principle investigator, psychiatrist and project leader of the project in GGZ Centraal and Symfora-Meander Hospitals and drafted the manuscript, IH is project leader for the study at Body@Work, designed the study and has been involved in drafting the manuscript, ET was involved in the study design, organizing the data and commented the manuscript, AJB was involved in the study design and revised the manuscript critically and gave approval for publication, MHR was involved in the study design, revised the manuscript critically and gave final approval of the version to be published. Competing interests The authors declare that they have no competing interests. Received: 20 December 2011 Accepted: 19 January 2012 Published: 19 January 2012 References 1. Bijl RV, Ravelli A, van Zessen G: Prevalence of psychiatric disorder in the general population: results of The Netherlands Mental Health Survey and Incidence Study (Nemesis). 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Janca A, Kastrup M, Katschnig H, Lopez-Ibor JJ Jr, Mezzich JE, Sartorius N: The World Health Organization Short Disability Assessment Schedule (WHO DAS-S): a tool for the assessment of difficulties in selected areas of functioning of patients with mental disorders. Soc Psychiatry Psychiatr Epidemiol 1996, 31(6):349-354. 44. EuroQol-Group: EuroQol-a new facility for the measurement of health- related quality of life. Health Policy 1990, 19. 45. Hakkaart-van Roijen L: Manual Trimbos/iMTA questionnaire for costs associated with psychiatric illness. Insitute for Medical Technology Assessment; 2002. 46. The revised NEO Personality Inventory (NEO-PI-R). Edited by: Costa PT, McCrae RR. London: SAGA Publications Ltd.; 2008:. 47. Wendel-Vos GC, Schuit AJ, Saris WH, Kromhout D: Reproducibility and relative validity of the short questionnaire to assess health-enhancing physical activity. J Clin Epidemiol 2003, 56(12):1163-1169. 48. Wald FDM, Mellenbergh GJ: De verkorte versie van de Nederlandse vertaling van de Profile of Mood States POMS. Nederlands Tijdschrift voor de Psychologie 1990, 45:86-90. 49. Borg S: Borg’s Percieved Exertion and Pain scales. Human Kinetics 1998. 50. de Vocht A: SPSS 14 for Windows Utrecht: Bijleveld Press; 2007. 51. Trimbos: Multidisciplinaire Richtlijn Depressie (eerste revisie). Utrecht: Stuurgroep Multidisciplinaire Richtlijn Ontwikkeling 2010. 52. Seime RJ, Vickers KS: The challenges of treating depression with exercise; from evidence to practice. Clin Psychol Sci Pract 2006, 13:194-197. Pre-publication history The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-2458/12/50/prepub doi:10.1186/1471-2458-12-50 Cite this article as: Kruisdijk et al.: Effect of running therapy on depression (EFFORT-D). Design of a randomised controlled trial in adult patients [ISRCTN 1894]. BMC Public Health 2012 12:50. Kruisdijk et al. BMC Public Health 2012, 12:50 http://www.biomedcentral.com/1471-2458/12/50 Page 9 of 9	Effect of running therapy on depression (EFFORT-D). Design of a randomised controlled trial in adult patients [ISRCTN 1894].	01-19-2012	Kruisdijk, Frank R,Hendriksen, Ingrid J M,Tak, Erwin C P M,Beekman, Aartjan T F,Hopman-Rock, Marijke	eng
PMC5015676	original article http://dx.doi.org/10.1590/bjpt-rbf.2014.0165 289 Braz J Phys Ther. 2016 July-Aug; 20(4):289-297 Is heart rate variability a feasible method to determine anaerobic threshold in progressive resistance exercise in coronary artery disease? Milena P. R. Sperling1,2, Rodrigo P. Simões2, Flávia C. R. Caruso2, Renata G. Mendes2, Ross Arena3, Audrey Borghi-Silva1,2,3 ABSTRACT \| Background: Recent studies have shown that the magnitude of the metabolic and autonomic responses during progressive resistance exercise (PRE) is associated with the determination of the anaerobic threshold (AT). AT is an important parameter to determine intensity in dynamic exercise. Objectives: To investigate the metabolic and cardiac autonomic responses during dynamic resistance exercise in patients with Coronary Artery Disease (CAD). Method: Twenty men (age = 63±7 years) with CAD [Left Ventricular Ejection Fraction (LVEF) = 60±10%] underwent a PRE protocol on a leg press until maximal exertion. The protocol began at 10% of One Repetition Maximum Test (1‑RM), with subsequent increases of 10% until maximal exhaustion. Heart Rate Variability (HRV) indices from Poincaré plots (SD1, SD2, SD1/SD2) and time domain (rMSSD and RMSM), and blood lactate were determined at rest and during PRE. Results: Significant alterations in HRV and blood lactate were observed starting at 30% of 1-RM (p<0.05). Bland‑Altman plots revealed a consistent agreement between blood lactate threshold (LT) and rMSSD threshold (rMSSDT) and between LT and SD1 threshold (SD1T). Relative values of 1‑RM in all LT, rMSSDT and SD1T did not differ (29%±5 vs 28%±5 vs 29%±5 Kg, respectively). Conclusion: HRV during PRE could be a feasible noninvasive method of determining AT in CAD patients to plan intensities during cardiac rehabilitation. Keywords: autonomic nervous system; anaerobic threshold; blood lactate; cardiac rehabilitation; cardiac disease; 1‑RM test. BULLET POINTS • Parasympathetic modulation was reduced during lower extremity resistance exercise. • Anaerobic Threshold occurred at ≈30% of 1-RM in patients with CAD. • HRV may prove to be a feasible tool in clinical practice to determine Anaerobic Threshold. • HRV can be safe and appropriate method to determine exercise intensity in patients with CAD. HOW TO CITE THIS ARTICLE Sperling MPR, Simões RP, Caruso FCR, Mendes RG, Arena R, Borghi‑Silva A. Is heart rate variability a feasible method to determine anaerobic threshold in progressive resistance exercise in coronary artery disease? Braz J Phys Ther. 2016 July-Aug; 20(4):289-297 . http://dx.doi.org/10.1590/bjpt‑rbf.2014.0165 1 Interunidades Bioengenharia (EESC/FMRP/IQSC), Universidade de São Paulo (USP), São Carlos, SP, Brazil 2 Laboratório de Fisioterapia Cardiopulmonar, Departamento de Fisioterapia, Universidade Federal de São Carlos (UFSCar), São Carlos, SP, Brazil 3 Integrative Physiology Laboratory, Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago (UIC), Chicago, USA Received: Mar 02, 2015 Revised: Aug 25, 2015 Accepted: Jan 28, 2016 Introduction It is known that the combination of aerobic and resistance exercise for cardiac patients synergistically improves muscular strength and endurance, functional capacity, quality of life, cardiovascular function, metabolism, and cardiovascular risk profile1. In addition, resistance exercise is considered safe for both healthy elderly individuals and cardiac patients1‑4. The magnitude of the cardiovascular and ventilatory responses to exertional demands depends on the type of physical exercise and the intensity of effort1. With respect to exercise intensity, the anaerobic threshold (AT) is defined as a point above a given power value when the production of lactic acid is greater than the capacity for its utilization by body tissues5‑7. The point past which blood lactate concentration increases progressively5 is an important parameter in determining submaximal exercise tolerance. The use of discontinuous protocols to assess functional capacity Sperling MPR, Simões RP, Caruso FCR, Mendes RG, Arena R, Borghi-Silva A 290 Braz J Phys Ther. 2016 July-Aug; 20(4):289-297 and determine AT are potentially advantageous as they reduce the inherent added risks incurred during maximum stress intensities2. The ability of Heart Rate Variability (HRV) to determine changes in blood lactate and AT during resistance and aerobic exercise in healthy individuals has already been investigated8,9. Other studies have also examined the behavior of HRV indices during exercise in diabetic10, heart failure11, and elderly12‑14 cohorts. However, parameters that indicate safe training intensities with resistance exercise, particularly in patients with cardiac conditions, remain unclear. While HRV indices are important predictors of cardiovascular risk and risk of sudden cardiac death and may be used as potential indices of relative risk15, the use of HRV to determine the point of transition between aerobic and anaerobic metabolism (i.e., AT) during incremental resistance exercise in patients with cardiac disease is unknown. Therefore, the objectives of this study were to: 1) evaluate the behavior of HRV and blood lactate; 2) determine the AT during an incremental leg‑press protocol with an incremental percentage of One Repetition Maximum Test (1‑RM); and 3) evaluate the degree of agreement between HRV indices and blood lactate in relation to the AT in a cohort diagnosed with coronary artery disease (CAD). Method Study design and population This is an observational cross‑sectional study involving 20 males with clinically stable CAD (sample of convenience) participating in an outpatient cardiac rehabilitation program. Inclusion criteria consisted of 1) being at least 12 months post an acute event (i.e., myocardial infarction) or 12 months after a surgical or percutaneous revascularization procedure and 2) being clinically stable on a regular pharmacologic regimen. The experimental protocol was approved by the Research Ethics Committee of Centro Universitário de Araraquara, Araraquara, SP, Brazil (n. 1331‑11). All procedures were conducted in accordance with the Declaration of Helsinki. All participants signed an informed consent form. Experimental procedures Subjects did not ingest caffeine or alcohol during the 24‑hour period prior to any of the testing protocols and did not perform any rigorous physical activity during the 48 hours prior to testing. All trials were performed at the same period of the day to avoid any influence of circadian rhythm on cardiovascular variables. The experiments were carried out in a climate‑controlled room (21‑24 °C) with a relative air humidity of 40‑60%. Clinical examination was performed by a physician (cardiologist) before study initiation. This examination consisted of anamnesis and resting 12‑lead electrocardiography. A transthoracic echocardiogram was also performed for all patients. Cardiopulmonary exercise testing – CPX A symptom‑limited incremental exercise test (CPX) was performed on a recumbent cycle ergometer (Corival, Lode, Groningen, The Netherlands) with the collection of gas exchange and ventilatory variables using a calibrated computer‑based exercise system (Oxycon Mobile, JaegerTM, Hoechberg, Germany). The workload (W) was continuously increased in a linear “ramp” pattern of 15 W.min–1. The test finished when subjects reached physical exhaustion or when abnormal test responses warranted test termination16,17. The incremental exercise testing duration was between 8 and 12 minutes18. Peak VO2 was defined as the highest value during the last 15 seconds of exercise and peak respiratory exchange ratio (RER) was the 15‑second averaged VCO2 divided by VO2 at peak exercise16. One Repetition Maximum test – (1-RM - leg press) This test was applied by gradually increasing the resistance until the patients succeeded in performing no more than one repetition on the leg press at a 45 degree angle (Pro‑Fitness, São Paulo, Brazil). The resistance load for 1‑RM was estimated (1‑RM‑E) before the test by multiplying subject body weight by 3.5, based on pilot testing. The details of this test protocol have been described previously13. Discontinuous resistance exercise testing (DRET-leg press) 72 hours after the 1‑RM test, Discontinuous Resistance Exercise Testing (DRET–leg press) was performed, starting at a load of 10% of 1‑RM with subsequent increases of 10% until exhaustion. At each percentage of effort, patients underwent 2 minutes of exercise at a movement rhythm of 12 repetitions/minute, maintaining respiratory cadence. The period between trials was 5 minutes. The details of this test protocol have been described previously13. HRV in resistance exercise in CAD patients 291 Braz J Phys Ther. 2016 July-Aug; 20(4):289-297 Heart Rate (HR) and R‑R intervals were recorded with a wireless HR monitor (Polar S810i, Kempele, Finland) and blood samples (via earlobe puncture) were taken at rest and immediately after the final repetition completed at each load (i.e., % of 1‑RM). Blood samples were analyzed using a YSI 1500 Sports Lactate Analyzer (YSI Inc., Yellow Springs, OH, USA). Measurement of HRV The R‑R intervals were recorded continuously with the wireless HR monitor (Polar S810i) during all exercise protocols. The R‑Ri captured with the monitor can be analyzed with both linear and nonlinear models. After data capture, the signals were transmitted to a receiver and interface connected to a computer for subsequent analysis. The details of this technique have been described previously13. Safety during exercise protocols (CPX, 1-RM–leg press and DRET-leg press) During all exercise protocols, HR was recorded with the HR monitor (Polar S810i) and the ECG was constantly monitored using a USB electrocardiogram (WinCardio, Micromed Biotecnologia, Brasilia, Brazil) to detect any potential arrhythmias or signs of ischemia that would indicate the protocol should be interrupted. Blood pressure (auscultation) and symptoms (muscle fatigue, chest pain, and breathing/ dyspnea), assessed by means of the 10-point modified Borg Scale Rating16,19‑21, were measured and recorded immediately after each effort. The criteria for protocol termination were a systolic blood pressure >200 mmHg, symptoms of lower limb fatigue, angina or shortness of breath, development of any cardiac arrhythmias, or achieving maximum voluntary exhaustion13. Data analysis To evaluate the responses of HR and R‑Ri during DRET–leg press, the first step in the data analysis involved a visual inspection of R‑Ri (ms) distribution in the ECG in order to select the sections corresponding to the final minute of each load (second minute) of resistance exercise maneuvers, as this was considered to be a more stable phase for analysis22‑24. Ectopic beats, arrhythmic events, missing data, and noise effects that might alter the estimation of HRV were excluded15. HRV analysis was carried out using the following linear and nonlinear methods: 1) Linear methods ‑ RMSSD (square root of the mean of the sum of the squares of differences between adjacent RRi divided by the number of RRi minus one, expressed in ms) and RMSM (square root of the sum of the squares of differences of individual values compared to the mean value, divided by the number of RRi in a period for the time domain); and 2) nonlinear method ‑ SD1 (instantaneous R‑R interval variability from Poincaré plots), SD2 (standard deviation of continuous long‑ term R‑R interval variability), and the SD1/SD2 ratio carried out by the Poincaré method of quantitative two‑dimensional vector analysis15. The Poincaré plots were analyzed quantitatively, based on the premise of different temporal effects of changes in vagal and sympathetic modulation of HR on the R‑R intervals without a requirement for a stationary quality of the data24. RR‑interval series were processed using Kubios HRV 2.0 (University of Kuopio, Finland). The details of this technique have been described previously24. Determination of Anaerobic Threshold (AT) in resistance exercise To determine AT, changes in blood lactate curves were generated for each subject and the AT was defined as the exercise intensity at which the blood lactate concentration began to increase exponentially, i.e., breakpoint8,12‑14. To determine the HRV threshold, the rMSSD and SD1 for each stage of exercise were plotted against work rate. This HRV deflection point was defined as the HRV threshold9. The point at which there was an initial decline in indices during exercise, thus indicating vagal withdrawal. The determination of the lactate and HRV threshold occurred through visual inspection of lactate and HRV curves, respectively, by two independent experienced examiners. When there was no agreement between the two evaluators, a third evaluator was called to give the casting vote. Statistical analysis The sample size for the current study was estimated considering previous studies with the same resistance exercise protocol for healthy and elderly13,14 subjects and also different resistance exercise protocols for coronary artery disease22,25. Considering the presence of coronary artery disease, we doubled the sample size (n=20) to increase the chance of having less variability of the resulting data. Initially, we used the Kolmogorov‑Smirnov test to verify the normality of the data and subsequently Sperling MPR, Simões RP, Caruso FCR, Mendes RG, Arena R, Borghi-Silva A 292 Braz J Phys Ther. 2016 July-Aug; 20(4):289-297 one‑way ANOVA with repeated measures was used to analyze the behavior of the HRV indices, blood lactate curves, R‑Ri and RPP responses during the DRET–leg press (at different percentages of 1‑RM), and the different methods of identifying AT (blood lactate curves, rMSSD, and SD1 threshold). When appropriate, post‑hoc analyses were performed using the Tukey test. The degree of agreement between the methods used to determine AT was verified using Bland‑Altman plots26‑28. Data are reported as mean and standard deviation and the significance level was set at 5%. The statistical analysis was carried out using Sigma Plot for Windows version 11.0 (Sigma Plot, San Jose, CA, USA) and MedCalc version 12.6.1.0 (MedCalc Software, Ostend, Belgium). Results Over a one‑year period, 42 patients were assessed for eligibility, 26 were recruited, five did not meet the inclusion criteria, and one was excluded for having an inadequate blood pressure response during CPX. Among the remaining subjects, 20 completed the protocol successfully with no abnormalities that would contraindicate enrollment in the present study and were included in the final analysis. The clinical characteristics of the subjects are summarized in Table 1. All subjects had normal left ventricular systolic function (and mild left ventricle diastolic dysfunction in 45% of the study population) measured by echocardiography. The majority of patients had hypertension, history of smoking, and a family history of CAD. Myocardial infarction was the predominant clinical diagnosis and all patients were NYHA class I. Pharmacologic treatment commonly included antiplatelets, statins, beta‑blockers, ACE inhibitors, and hypoglycemic agents. Mean CPX values indicate this sample had a well‑preserved functional capacity and exerted maximal effort during the exercise test, according to American Heart Association (AHA) standards16. 1‑RM testing and DRET (30% and 50%) responses are summarized in Table 2. Regarding the response to the 1‑RM testing, the criterion for termination for almost all subjects was muscle fatigue (rate of perceived exertion – RPE = 9.2±2.0), with only one test interrupted due to chest pain. No test was interrupted due to ECG alterations or an excessive rise in SBP (>200 mmHg). In relation to the resistance load achieved during 1‑RM, values were similar to those Table 1. Baseline characteristics of the study population. CAD, n=20 Demographics/anthropometrics Age, years 63±7 Height, m 1.7±0.1 Body mass, kg 75.7±12.7 BMI, kg/m2 26.6±2.9 Transthoracic echocardiography LVEF, % 60±10 LV diastolic diameter, cm 5.3±0.6 LV diastolic volume, ml 143±40 Septal thickness, cm 0.9±0.2 Posterior wall thickness, cm 0.9±0.1 Doppler echocardiography LV diastolic function: Normal 11 (55) Mild dysfunction 9 (45) Risk Factors, n (%) Diabetes 5 (25) Hypertension 13 (65) History of smoking 12 (60) Family history of CAD 18 (90) Functional Class (NYHA): I, n (%) 20 (100) History of Myocardial infarction, n (%) 15 (75) Intervention, n (%) CABG 9 (45) PCI 18 (90) Medications, n (%) Antiplatelet (aspirin) 20 (100) Statin 20 (100) Beta‑blocker 14 (70) ACE inhibitor 7 (35) Hypoglycemic 5 (25) CPX Peak VO2, ml.Kg–1.min–1 24±5 PredictiveVO2, ml.Kg–1.min–1 (%) 85±14 Peak workload, W 134±23 Data are presented as mean±SD or number (percentage) of subjects. CAD: coronary artery disease; BMI: body mass index; NYHA: New York Heart Association; CABG: coronary artery bypass grafting; PCI: percutaneous coronary intervention; LVEF: left ventricular ejection fraction; ACE: angiotensin‑converting enzyme; CPX: cardiopulmonary exercise testing; VO2: oxygen uptake; W: watts. Clinical recommendations for Cardiopulmonary Exercise Testing data assessment in specific patient populations16. HRV in resistance exercise in CAD patients 293 Braz J Phys Ther. 2016 July-Aug; 20(4):289-297 stipulated previously during the pilot test (3.5 times the body weight of the patient). Regarding the response to the DRET, the criterion for termination for almost all subjects was muscle fatigue (RPE = 6.6±2.8) or an excessive rise in SBP (>200 mHg) and only one test was interrupted due to chest pain. Figure 1 illustrates the behavior of HRV indices, blood lactate, and R‑Ri and RPP at rest and with the increasing resistance exercise loads through the common maximum load achieved by all patients (i.e., 50% of 1‑RM). Both rMSSD and SD1 indices, which are representative of parasympathetic modulation, demonstrated a significant decrease at peak load compared to resting values, with a significant drop at 30% of 1‑RM (Figure 1A, C) with a parallel significant increase in blood lactate at 30% of 1‑RM (Figure 1B). The SD1/SD2 ratio had a significant decrease from 40% of 1‑RM (Figure 1E). The RMSM and SD2 indices (Figure 1A, C) were significantly increased at 50% of 1‑RM, although there was an increasing trend starting in 30% of 1‑RM, perceived visually. R‑Ri showed a progressive reduction (Figure 1D) and RPP showed a progressive increase concurrently (Figure 1F), reflecting the progressive increase in the intensity of effort at 50% of 1‑RM. The AT was determined for each patient through the analysis of blood lactate curves, rMSSD, and SD1, expressed in both absolute and relative values (Table 3). There were no significant differences in relation to different methods for identifying absolute values in Kg (lactate threshold ‑ LT: 81±19, rMSSD threshold ‑ rMSSDT: 78 ± 14; SD1 threshold ‑ SD1T: 79±13) and relative AT values at ≈30% of 1-RM (29±5; 28±5; 29±5; respectively), as presented in Table 3. The analysis of agreement between methods of determining the AT was carried out using Bland‑Altman plots, considering the blood lactate analyses as the “gold standard“. LT vs. rMSSDT and LT vs. SD1T were plotted. The mean of the differences for identifying AT using the LT and rMSSDT methods was 2.7±20 Kg (Figure 2A), and the mean difference between LT and SD1T was 1.3±19.1 Kg (Figure 2B), demonstrating good agreement. Discussion In this observational cross‑sectional study, we were able to demonstrate that the fall in parasympathetic indices is associated with an increase in blood lactate, starting at ≈30% of 1-RM using a leg-press maneuver. Table 2. Cardiovascular, metabolic, and cardiac autonomic variables obtained during peak 1‑RM testing and DRET (30% and 50% of the 1‑RM testing). CAD, n=20 1-RM testing DRET 30% DRET 50% SBP, mmHg 137±24 156±25 172±23 F=9.07 DBP, mmHg 76±14 87±11 89±18 F=4.47 HR, bpm 95±13 88±11 104a±16 F=6.15 RPE, 0‑10 Lower limb fatigue 9.2±2.0 3.0±2.3 6.6±2.8 F=35.79 Chest pain (angina) −− * F=9.22 Breathing (dyspnea) 5.9±3.2 2.8±2.4 5.9±2.9 Load, Kg 282±46 85±13c 144a,b±23 F=211 Load 1RM/total body mass 3.8±0.9 −− −− Lactate, mlmol.L–1 −− 1.5±0.8 3.4±1.7a F=28.60 HRV indices rMSSD −− 8.6±4.1 7.1±4.7 F=23.61 RMSM −− 15.8±7.3 25.5a±16.0 F=2.53 SD1 −− 6.1±2.9 5.1±3.3 F=23.66 SD2 −− 21.2±10.2 35.5a±22.4 F=3.24 SD1/SD2 −− 0.3±0.2 0.2±0.1 F=21.14 Data are presented as mean ± SD. SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate; 1‑RM: one repetition maximum; DRET: discontinuous resistance exercise testing; RPE: rate of perceived exertion. Only one patient had chest pain. a: difference between DRET 50% and DRET 30%. b: difference between DRET 50% and 1‑RM testing. c: difference between DRET 30% and 1‑RM testing (p value <0.05, one‑way ANOVA with repeated measures). Sperling MPR, Simões RP, Caruso FCR, Mendes RG, Arena R, Borghi-Silva A 294 Braz J Phys Ther. 2016 July-Aug; 20(4):289-297 The good agreement between HRV indices and blood lactate curves may represent the importance and value of HRV in CAD patients for exercise prescription and monitoring. Responses during discontinuous resistance exercise The rMSSD and SD1 indices reflect parasympathetic heart activity15,24,28 and they both demonstrated a significant drop from ≈30% of 1-RM (Figure 1A, C). The total HRV, represented by RMSM and SD2 indices (Figure 1A, C) were significantly increased from ≈50%of 1-RM, although the increasing trend started at ≈30% of 1-RM, observed visually. Lastly, the SD1/SD2 ratio appears stable up to ≈30% of 1‑RM, followed by changes thereafter (Figure 1E). All of these changes indicate a shift in sympathovagal balance towards sympathetic predominance and reduced vagal tone29,30. This increase in sympathetic tone appears to correspond with AT, which in this study, corresponded to ≈30% of 1-RM. Figure 1. Behavior of variables in Discontinuous Resistance Exercise Testing (DRET) in percentage of 1 repetition maximum (1‑RM; x axis), starting from rest until the load in common for all patients (50% of 1‑RM). Data are presented as mean±SD. (A) rMSSD (square root of the difference in the sum of squares between R‑R interval on the recording, divided by the determined time minus one) and RMSM (root mean square of the differences from the mean interval); (B) Blood Lactate; (C) SD1 (standard deviation of instantaneous beat‑to‑beat R‑R interval variability) and SD2 (the standard deviation of continuous long‑term R‑R interval. &: difference in relation to rest. +: difference in relation to 10% of 1‑RM. ‡: difference in relation to 20% of 1‑RM. #: difference in relation to 30% of 1‑RM. •: difference in relation to 40% of 1-RM. : difference in relation to 50% of 1-RM (one-way ANOVA with repeated measures; p<0.05). HRV in resistance exercise in CAD patients 295 Braz J Phys Ther. 2016 July-Aug; 20(4):289-297 Anaerobic threshold determination by HRV and blood lactate The determination of AT through indices of HRV was effective and associated with blood lactate responses in patients with CAD who are receiving standard pharmacological therapy. This is an important topic, since these results can be more representative of the CAD population seen clinically. In this context, Machado et al.25 assessed HRV indices during progressive upper limb exercise in CAD patients and found medications did not influence the HRV response. In the present study, the load corresponding to the AT, considering the blood lactate threshold as a parameter during DRET‑leg press was obtained at ≈30% of the peak load reached during the 1-RM test (Table 3), which is in accordance with other studies in assessing apparently healthy subjects14,31. Figure 2 demonstrates that, although there were agreements among the methods for determining the AT (the mean of the differences was close to zero), the limits of agreement were clinically wide. Other studies have shown the potential use of HRV for the determination of AT/ventilator threshold on a cycle ergometer using rMSSD and SD1 in healthy adults9 and in patients with type‑2 diabetes10. To our knowledge, this is the first study to analyze the behavior of metabolic and autonomic responses during lower limb resistance exercise in CAD patients. The mean CPX values (peak VO2 and predictive VO2) indicate that these patients had a well‑preserved functional capacity and confirm a maximal effort during the exercise test according to AHA standards16. Regarding the response to the DRET, the criteria for interrupting the test was muscle fatigue or excessive rise in SBP (>200 mmHg). All of these patients were included in the data analysis. Study perspectives Our results suggest that HRV may also be considered a useful tool in clinical practice to determine the intensity corresponding to AT. AT was approximately 30% of 1‑RM testing for CAD patients with well‑preserved Table 3. Comparison of relative and absolute resistance values for anaerobic threshold measured with different methods of identification during discontinuous resistance exercise testing (DRET). LT rMSSDT SD1T DRET Absolute values, Kg 81±19 78±14 79±13 p=0.43; F=0.94 Relative values, % 29±5 28±5 29±5 p=0.52; F=0.76 Data are presented as mean±SD. LT: Lactate threshold; rMSSDT: rMSSD threshold; SD1T: SD1 threshold. No significant differences among the three methods of identifying the anaerobic threshold (one‑way ANOVA with repeated measures). Figure 2. Bland‑Altman plot showing the agreement between LT and rMSSDT (A) and LT and SD1T (B). BIAS = mean of the differences among the averages; ± 1.96 SD = 95% limits of agreement. LT = lactate threshold; rMSSDT = rMSSD threshold (rMSSD: square root of the mean of the sum of the squares of differences between adjacent RR‑intervals on the recording, divided by the determined time minus one); SD1T = SD1 threshold (SD1: standard deviation of instantaneous R‑R interval variability). Horizontal lines indicate mean (solid lines) and 95% confidence intervals (dashed lines) of differences between two measurements. Sperling MPR, Simões RP, Caruso FCR, Mendes RG, Arena R, Borghi-Silva A 296 Braz J Phys Ther. 2016 July-Aug; 20(4):289-297 functional capacity. HRV analysis using linear and nonlinear methods could be considered an important method for evaluating and understanding cardiac autonomic modulation in CAD patients during dynamic resistance exercise. In order to establish the correct intensity, it is important to consider that the same exercise may lead to different levels of stress in different patient populations. Several factors, such as body weight, coordination, intention, and perception of the level of effort during resistance exercise, directly interfere with measurements of effort2. Limitations of this study The current study has limitations that should be recognized. RMSM and SD2 HRV indices reflect both sympathetic and parasympathetic influences15,29 and a pure index representative of the sympathetic modulation was not assessed in this study. Moreover, during the exercise protocol with increased load increments every 2 minutes, HRV indices reached steady state in the last minute of each stage of exercise up to AT. However, after AT, this equilibrium condition was not maintained, which is inherent to high exercise intensities. Thus, it is possible that exercise intensities after AT may have affected HRV data capture. Even so, clear trends were apparent in the current investigation. The results found in this study may be protocol‑dependent, considering the duration of each load and rest periods between them. The leg press was chosen because it induces more changes in cardiac, ventilatory, and metabolic parameters, but it is necessary to investigate other kinds of resistance exercise. Once the resistance activity stops, the blood pressure decreases quite rapidly so that measuring by auscultation at the end of exercise would do not give a reliable estimation of the blood pressure during exercise. The evaluation of the blood pressure response was limited to the evaluation of discontinuous blood pressure monitoring, measured at the end of the exercise. However, this is still the most widely used method in clinical practice. Conclusion Our results suggest that parasympathetic modulation was reduced during lower extremity resistance exercise, beginning at AT, which occurred at ≈30% of 1-RM. 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Correspondence Milena Pelosi Rizk Sperling Universidade Federal de São Carlos Departamento de Fisioterapia Laboratório de Fisioterapia Cardiopulmonar Rodovia Washington Luis, Km 235 CEP 13565‑905, São Carlos, SP, Brazil e‑mail: milenasperling@yahoo.com.br	Is heart rate variability a feasible method to determine anaerobic threshold in progressive resistance exercise in coronary artery disease?	06-16-2016	Sperling, Milena P R,Simões, Rodrigo P,Caruso, Flávia C R,Mendes, Renata G,Arena, Ross,Borghi-Silva, Audrey	eng
PMC9518107	Citation: Prieto-González, P.; Sedlacek, J. Effects of Running-Speciﬁc Strength Training, Endurance Training, and Concurrent Training on Recreational Endurance Athletes’ Performance and Selected Anthropometric Parameters. Int. J. Environ. Res. Public Health 2022, 19, 10773. https://doi.org/10.3390/ ijerph191710773 Academic Editors: Jesús Siquier Coll, Ignacio Bartolomé and María Concepción Robles-Gil Received: 29 June 2022 Accepted: 26 August 2022 Published: 29 August 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article Effects of Running-Speciﬁc Strength Training, Endurance Training, and Concurrent Training on Recreational Endurance Athletes’ Performance and Selected Anthropometric Parameters Pablo Prieto-González 1,* and Jaromir Sedlacek 2 1 Health and Physical Education Department, Prince Sultan University, Riyadh 11586, Saudi Arabia 2 Department of Sport Kinanthropology, Faculty of Sports, University of Prešov, 080 01 Prešov, Slovakia * Correspondence: pprieto@psu.edu.sa Abstract: Objective: The present study aimed to verify the effects of running-speciﬁc strength training alone, endurance training alone, and concurrent training on recreational endurance athletes’ performance and selected anthropometric parameters. Method: Thirty male recreational endurance runners were randomly assigned using a blocking technique to either a running-speciﬁc strength training group (RSSTG), an endurance training group (ETG), or a concurrent training group (CTG). RSSTG performed three strength-training sessions per week orientated to running, ETG underwent three endurance sessions per week, and CTG underwent a 3-day-per-week concurrent training program performed on non-consecutive days, alternating the strength and endurance training sessions applied to RSSTG and ETG. The training protocol lasted 12 weeks and was designed using the ATR (Accumulation, Transmutation, Realization) block periodization system. The following assessments were conducted before and after the training protocol: body mass (BM), body mass index (BMI), body fat percentage (BFP), lean mass (LM), countermovement jump (CMJ), 1RM (one- repetition maximum) squat, running economy at 12 and 14 km/h (RE12 and RE14), maximum oxygen consumption (VO2max), and anaerobic threshold (AnT). Results: RSSTG signiﬁcantly improved the results in CMJ, 1RM squat, RE12, and RE14. ETG signiﬁcantly improved in RE12, RE14, VO2max, and AnT. Finally, CTG, obtained signiﬁcant improvements in BFP, LM, CMJ, 1RM squat, RE12, RE14, VO2max, and AnT. RSSTG obtained improvements signiﬁcantly higher than ETG in CMJ, 1RM squat, and RE14. ETG results were signiﬁcantly better than those attained by RSSTG in AnT. Moreover, CTG marks were signiﬁcantly higher than those obtained by ETG in CMJ and RE14. Conclusion: Performing a 12-week concurrent training program integrated into the ATR periodization system effectively improves body composition and performance variables that can be obtained with exclusive running-speciﬁc strength and endurance training in recreational runners aged 30 to 40. Running-speciﬁc strength training enhances maximum and explosive strength and RE, whereas exclusive endurance training improves VO2max, AnT, and RE. Performing concurrent training on non-consecutive days effectively prevents the strength and endurance adaptations attained with single-mode exercise from being attenuated. The ATR periodization system is useful in improving recreational endurance athletes’ performance parameters, especially when performing concurrent training programs. Keywords: concurrent training; endurance training; running-speciﬁc strength training; periodization; recreational runner 1. Introduction Several sports require an adequate levels of strength and endurance to perform at optimum level in competitive events. However, successfully combining endurance and strength training represents the highest complexity in exercise prescription [1]. It has often Int. J. Environ. Res. Public Health 2022, 19, 10773. https://doi.org/10.3390/ijerph191710773 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 10773 2 of 17 been speculated that concurrent training does not generate the same adaptations as single- mode exercise [1]. Even so, the possible mechanisms whereby concurrent training of both ﬁtness components can attenuate strength and endurance adaptations remain unclear [2]. In endurance sports, it has traditionally been thought that cardiovascular capacity is the main limiting factor in sports performance [3]. Therefore, maximum oxygen consump- tion (VO2max) and anaerobic threshold (AnT) have been considered the best indicators to predict athletes’ performance [4]. Nevertheless, in reality, endurance athletes with similar VO2max may perform differently in sports competitions. Hence, VO2max could not be the best indicator to predict their racing performance. Nowadays, running economy (RE) and the evaluations that imply assessing the muscular power exerted or the speed reached by an athlete during the VO2max are considered better sports performance indicators [5–7]. In this way, speciﬁc scientiﬁc evidence indicates that combining endurance and strength train- ing generates additional beneﬁts in terms of athletic performance improvement and injury prevention [8]. These improvements could be related to the following mechanisms [8–13]: (a) Musculotendinous factors: Improved muscle-tendinous stiffness and stretch-shortening cycle properties, conversion of fast-twitch type IIx into more fatigue-resistant type IIa ﬁbers, and delayed activation of less-efﬁcient type II ﬁbers. (b) Neuromuscular factors: Improved neuromuscular function and efﬁciency, improved intramuscular coordination, motor unit recruitment, and ﬁring frequency. (c) Physical ﬁtness components: Improved levels of strength. This allows athletes to apply a lower relative percentage of force, which reduces the contribution of the anaerobic energy system and results in reduced fatigue, maintenance of the required application of strength over a longer period, or appliance of more strength per unit of time. Additionally, peak velocity, speed, maximum aerobic speed, and anaerobic capacity are enhanced. All these enhancements would result in an improved RE. Nevertheless, there are arguments against using concurrent training programs in endurance runners. Vikmoen et al. (2016) and Berryman et al. (2018) [14,15] state that sports requiring high strength levels are opposite in nature to endurance sports in terms of energy metabolism and effort duration. Therefore, developing both ﬁtness components simultaneously would result in potential combative adaptations [14–16]. Actually, the main adaptations produced by endurance and strength training are not only different but also opposed: Endurance training adaptations include oxidative enzyme activity increment, mitochondrial and capillary density increment, maintenance or reduction of ﬁber size, and possibly also ﬁber type transformations (Type II into I), modifying the model of recruitment and reducing the muscle contractile capacity [17]. In contrast, strength training is associated with reduced capillary density, oxidative enzymes, and mitochondrial density, reducing the oxidative muscle capacity [10,15]. In this regard, some studies have revealed a certain degree of incompatibility between endurance and strength training [9,18,19]. Thus, maximum voluntary contraction, rate of force development and some adaptations such as maximal strength and VO2max can be attenuated [20]. Similarly, time to exhaustion and mitochondrial density can be reduced [14]. This phenomenon is known as the interference effect or concurrent training effect [11], and the potential interferences can be chronic and acute [2,11]. The reasons why the interference effect occurs are [1,2,11] the mechanism of muscle ﬁber recruitment used, the transformation of fast twitch muscle ﬁbers into slow twitch muscle ﬁbers, and the functioning of the endocrine system. From the molecular point of view, the simultaneous activation of cellular biomarkers that elicit optimal anabolic and endurance responses is also not possible [11]. Moreover, muscle hypertrophy implies the cross-sectional area increment of muscle ﬁbers, which may increase the distance between the capillaries inside the muscle and could negatively impact performance. Even so, in untrained individuals, there is an increase in (or at least a maintenance of) the number of capillaries surrounding each muscle ﬁber, and the capillaries per ﬁber area do not experience modiﬁcations. However, since performing concurrent strength and endurance Int. J. Environ. Res. Public Health 2022, 19, 10773 3 of 17 training can mitigate the hypertrophic response, and endurance-trained athletes have a greater number of capillaries than untrained athletes, these ﬁndings may not apply to experienced endurance athletes [14]. In line with the existence of arguments for and against the use of concurrent train- ing, some studies have found improvements in sports performance after using strength training in endurance athletes [6,11], whereas no beneﬁcial effect was observed in other research [14,21]. The discrepancies between studies may be due to the development of different types of strength, external variables not directly related to the intervention, the use of different training methods [12,22], or the application of training methods lacking scientiﬁc rigor [23]. As a result, it is essential to continue searching for better strategies to improve athletic performance by implementing strength training sessions on endurance training programs and preventing the interference between both ﬁtness components [11,16]. Future studies should focus on minimizing the interference effect when concurrent training is applied, and training load organization, type, order, and optimal application are crucial to attain this goal. Further research is also needed to understand better the relationship between strength training, anaerobic metabolism, and endurance sports performance. New studies must be longer in duration, since the greatest increases in RE occur after implementing training protocols of more than 24 sessions, and most of the existing studies are shorter [15]. Moreover, valid strength assessments through a range of different velocities must be used, implementing adequate strength training programs over a long-term intervention period, and using multi-joint strength, explosive-strength, or reactive-strength exercises due to their superior functionally [6]. Future studies must also integrate the intervention design into a suitable periodization system and apply sports training principles to synchronize all training contents [20]. In this respect, to the best of our knowledge, at present, only one investigation has been conducted to verify the effect of strength training on endurance runners by using a periodization system [21]. Additionally, athletes’ training programs must be adapted to their personal needs and abilities. Thus, according to the law of diminishing returns, individuals’ ability to attain speciﬁc adaptations will depend on their training level. Less-trained subjects are likely to obtain greater adaptations since they have a greater adaptation reserve. In contrast, well-trained individuals need more demanding training stimuli to attain improvements throughout the training process [22]. Importantly, most existing studies related to concurrent training have focused on verifying the compatibility of simultaneous strength and endurance training. However, very few of them have examined the inﬂuence of strength, and particularly of running- speciﬁc strength training, on endurance performance [24]. Most studies examined the interference of endurance on maximal strength and hypertrophy, but not the opposite [11]. In this context, the utility of strength training remains to be clariﬁed for endurance athletes, and research ﬁndings are often inconclusive [22]. For this reason, it is necessary to continue investigating this topic [17]. In fact, sports scientists have studied new ways to enhance biomechanics, technique, energy production, sports equipment, injury prevention, and recovery. However, concurrent training and the effect of strength training in endurance athletes is a very complex phenomenon that requires new research. 2. Objective The main objective of the present study was to verify the effect of running-speciﬁc strength training, endurance training alone, and concurrent training on physiological performance and selected anthropometric variables in recreational runners. We also aimed to ascertain if 3-day-per-week concurrent training performed on non-consecutive days attenuates the strength, endurance, and anthropometric adaptations compared to strength and endurance training in isolation. Int. J. Environ. Res. Public Health 2022, 19, 10773 4 of 17 3. Materials and Methods A quasi-experimental randomized study was conducted. 3.1. Subjects Thirty male recreational endurance runners participated in the present research. They were assigned into three different groups: a running-speciﬁc strength training group (RSSTG) [age: 34.7 (2.36); height: 1.77 (0.04); weight: 67.05 (5.37); BMI: 21.27 (1.87)], an endurance training group (ETG) [age: 35.1 (2.77); height: 1.78 (0.05); weight: 65.80 (4.38); BMI: 20.68 (1.31)], or a concurrent training group (CTG) [age: 34.3 (2.37); height: 1.76 (0.03); weight: 66.96 (4.48); BMI: 21.48 (1.03)]. RSSTG performed thrice-per-week strength training program orientated to running on non-consecutive days. ETG performed thrice-per-week endurance training on non-consecutive days. Finally, CTG underwent 3-days-per-week concurrent training performed on non-consecutive days. CTG alternated the running- speciﬁc strength training sessions performed by RSSTG and the endurance training sessions underwent by ETG. The inclusion criteria were (a) be an active recreational runner; (b) able to run one km in less than 4:30 min; (c) have practiced running at a recreational level for at least the past ﬁve years before participating in the current research; (d) perform endurance training regularly, with a weekly frequency of not less than three times a week, but no more than ﬁve times a week; (e) have not performed endurance or strength systematic training for the last year leading up to the research’s commencement; (f) non-smoker; (g) do not use nutritional supplementation; (h) do not suffer from chronic diseases or ongoing injuries; (i) aged between 30 and 40 years old. Participants were asked not to modify their dietary habits or lifestyle during the intervention process, and attendance was recorded. Subjects were required to attend at least 90% of the training sessions to be included in the study. Similarly, participants were informed that they could voluntarily withdraw from the study at any time. The research was conducted according to the ethical principles of the Declaration of Helsinki. It was approved by the Ethics Commission of Prešov University (Prešov, Slovakia) (ethical clearance number: 2/2021). Similarly, all subjects who participated in this study were required to submit written informed consent. Previously, they received a verbal and written explanation about the experimental design and the potential risks and beneﬁts of participating in this research. 3.2. Randomization A blocking design was used to avoid any possible bias when the subjects were allocated into three different experimental groups and to ensure the trial’s proper randomization. The blocking factor was the VO2max value obtained through the incremental load test. According to the results obtained in this test, participants were allocated to one of the ten blocks created. Athletes who obtained the best three marks were assigned to block one. Athletes who obtained the fourth, ﬁfth, and sixth-best marks were assigned to block two, and so on. Afterward, each block’s three members were randomly assigned to one of the three different experimental groups. Therefore, there was one member of each block in each experimental group. 3.3. Training Protocol The intervention lasted 12 weeks, and study participants performed three sessions per week on non-consecutive days. The training protocol was designed using the ATR block periodization system. The training intervention was divided into three mesocycles: accumulation (ﬁrst six weeks), transmutation (from week seven to week 10), and realization (weeks 11 and 12). The duration of the training protocol was the minimum necessary to attain the planned adaptations in accordance with the abilities developed in each mesocycle, the sports discipline, and the characteristics of the athletes. The training methods applied to RSSTG are shown in Table 1, and the training methods that ETG underwent are shown in Table 2. Int. J. Environ. Res. Public Health 2022, 19, 10773 5 of 17 Table 1. Training methodology that will be used with RSSTG. Week Training Parameters 1 I: 64% 1RM; S: 4; R: 14; RT: 2”; Ex: Squat, leg curl, calf raise 2 I: 69% 1RM; S: 4; R: 12; RT: 2′; Ex: Squat, leg curl, calf raise 3 I: 69% 1RM; S: 5; R: 12; RTS: 2′; Ex: Squat, leg curl, calf raise 4 I: 69%–69%–75%–75%–80% 1RM; S: 5; R: 12-12-10-10-8; RTS: 3′; Ex: Squat, leg curl, calf raise 5 I: 69%–75%–80%–85% 1RM; S: 4; R: 12-10-8-6; RTS: 3′; Ex: Squat, leg curl, calf raise 6 I: 80% 1RM; S: 4; R: 8; RTS: 3′; Ex: Squat, leg curl, calf raise 7 6S of: Squat (6R at 80% 1RM) + hurdle hops (10R) + 2′30′′ running at 100% of MAS; RTS: 5′ 8 6S of: Squat (5R at 82% 1RM) + hurdle hops (10R) + 2′30′′ running at 100% of MAS; RTS: 5′ 9 6S of: Squat (4R at 84% 1RM) + extended bounds (cover 50 m alternating legs by doing the lowest possible number of strides) + 2′15′′ running at 105% of MAS; RTS: 5′ 10 6S of: = Squat (3R at 86% 1RM) + extended bounds (cover 50 m alternating legs by doing the lowest possible number of strides) + 2′ running at 110% of MAS; RTS: 5′ 11 Uphill running. Di: 200 m; I: 115% of MAS; Incl: 6%; S: 3; R: 5; RTR: 3′; RTS: 10′ 12 Uphill running. Di: 200 m; I: 120% of MAS; Incl: 6%; S: 2; R: 5; RTR: 3′; RTS: 10′ I: intensity; S: sets; R: repetitions; RTS: resting time between sets; RTR: resting time between reps; 1RM: one- repetition maximum; MAS: maximum aerobic speed; Di: distance; Incl: inclination. Table 2. Training methodology that will be used with ETG. Week Training Parameters 1 Fartlek training. Du: 50′; I: 117–162 b.p.m. 2 Fartlek training. Du: 55′; I: 117–162 b.p.m. 3 Fartlek training. Du: 60′; I: 117–162 b.p.m. 4 Continuous training; Du: 55′; I: 135–139 b.p.m. 5 Continuous training; Du: 50′; I: 139–144 b.p.m. 6 Continuous training; Du: 45′; I: 144–149 b.p.m. 7 Extensive interval training (long intervals); I: 159–162 b.p.m.; 10S of 3′; RT: 2′ 8 Extensive interval training (long intervals); I: 162–165 b.p.m.; 10S of 2′30′′; RT: 2′ 9 Extensive interval training (medium intervals). I: 165–168 b.p.m.; 14S of 1′30′′; RT: 2′ 10 Extensive interval training (medium intervals). I: 168–171 b.p.m.; 16S of 1′; RT: 2′ 11 Repetition training. I: 180 b.p.m.; 5R of 3′. RT: 8′ 12 Competition method. I: 100% of competition running pace; 1R; Di: 3.5 km Du: duration; I: intensity; R: repetitions; RT: resting time; b.p.m.: beats per minute; Di: distance. Likewise, CTG underwent 3-day-per-week concurrent training performed on non- consecutive days, alternating the strength and endurance sessions carried out by RSSTG and ETG. All training sessions were supervised by the same researcher: a One Physical Education Bachelor’s Degree holder, expert in sports training, and with more than 20 years of working experience in the sports ﬁeld. Likewise, all training sessions were conducted in the same ﬁtness center to minimize external variables’ inﬂuence avoid compromising the results’ validity and. Int. J. Environ. Res. Public Health 2022, 19, 10773 6 of 17 3.4. Assessments Physiological and selected anthropometric parameters were measured before (pre- test) and after (post-test) applying the 12-week training protocol. The assessments were conducted after a rest period of 48 h, between 5:00 p.m. and 7:00 p.m. The study participants were asked to refrain from ingesting food or beverages three hours before testing. To avoid the learning effect, a theoretical–practical training session was conducted one week before the pre-test. The main researcher explained the testing protocols in detail. Thereupon, the participants practiced the proper technique of execution of all tests to verify the correct functioning of the equipment and procedures used in the assessment. The following two-phase warm-up was performed before conducting the physical and physiological tests: General warm-up: 10 min running at 60% of their theoretical maximum heart rate plus ﬁve minutes of joint mobilization exercises. Speciﬁc warm-up: 2 × 10 vertical jumps, three sets of squats (10 reps at 50% of their estimated 1RM, ﬁve reps at their estimated 70% 1RM, and three reps at their estimated 80% 1RM), and one set of 20 m acceleration. The theoretical 1RM was estimated based on the information collected during the theoretical–practical training session conducted one week before the pre-test. In addition, the subject’s BM, age, and strength training experience were also taken into account. Assessments included in the pre-test and post-test are detailed below: • Body mass (BM) and body mass index (BMI): Both were measured using a Seca digital column scale, model 769 (Hamburg, Germany). Height was measured to the nearest 0.1 cm and body mass to the nearest 0.1 kg. Body mass and body mass index were assessed by the same investigator, and the subjects were in bare feet. • Body fat percentage (BFP): BFP was obtained through the following equation [25]: BFP = [(Σ of abdominal, subscapular, triceps, suprailiac, abdominal, thigh, calf)0.143] + 4.56. The plicometer used to measure the fat folds was a Harpenden Skinfold Caliper, model FG1056 (Sussex, UK). • Lean mass (LM): LM was calculated by using the following formula: LM = Body Mass (kg) − (Fat Mass (Kg) × BFP). • Countermovement jump (CMJ): CMJ was used to assess jumping ability and lower body power due to its high reliability and validity [26]. The test was performed in one indoor gymnasium on a dry and non-slippery surface. The device used was an Optojump-next (Bolzano, Italy) connected to one laptop via USB, and the Microgate software (Optojump software, version 3.01.0001) was also utilized. Participants were not allowed to use the arm swing. Instead, they were required to keep their hands on their hips while performing the test. The test started with a quick countermovement action. When the knees were bent 90◦, athletes initiated the take-off and ﬂight. During the ﬂight, they had to maintain their hips, knees, and ankles extended. Participants were also required to jump vertically. Movements forward, backward, or sideways were not allowed [27]. Each subject had two attempts, with three minutes rest between each jump. • One-repetition maximum (1RM) squat: This test was used to measure lower body maximum strength due to its validity, reliability, and applicability [28,29]. To conduct the test properly, participants kept their trunks naturally upright. The bar was grasped ﬁrmly with both hands, and it was also supported on the shoulders. The test started with knees bent at 90◦ until the performer’s thighs were parallel with the ground. Then, subjects regained the upright position, with the legs fully extended. The number of attempts required to determine the 1RM of each subject was between two and four. Only the attempts correctly performed were registered. The resting time between attempts was three minutes. • Incremental load test: This laboratory test was used to assess the Anaerobic Threshold (AnT), maximal oxygen consumption (VO2max), and running economy (RE). The following protocols were implemented: – Protocol I: The objective was to determine the RE by using one treadmill (Cy- bex 625T, Rosemont, IL, USA), one metabolic gas analyzer (Cosmed Srl, Albano Int. J. Environ. Res. Public Health 2022, 19, 10773 7 of 17 Laziale, Rome, Italy), and one heart rate monitor (Polar H9 BLE Kempele, Finland). The protocol comprised an 8 min submaximal test consisting of two, four-minute stages. That is to say, each stage was four minutes in length to allow for oxy- gen consumption and steady-state heart rate. The ﬁrst stage was performed at 12 km/h, and the second at 14 km/h. These two speeds are within the intensity range established by Barnes and Kilding (2015) to measure RE in recreational athletes [30]. Then, to determine the RE values, the VO2 mean value of the last minute of each stage was recorded [31]. – Protocol II: This test was used to estimate the VO2max and AnT. The treadmill’s initial velocity was set at 2 km/h slower than the subjects’ estimated 4 mmol when the test started. Then, the speed was increased by 0.5 km/h every 30 s until exhaustion [32]. 3.5. Statistical Analysis Data is presented using the format mean SD (standard deviation). The Shapiro–Wilk test was used to contrast the normality of the variables and Levene’s test to verify the homogeneity of variances. Sphericity assumptions were assessed using Mauchly’s test. When those sphericity assumptions were violated, the Greenhouse Gessier correction was applied. To determine the concordance between the pre-test and post-test measurements, the interclass correlation coefﬁcient (ICC) was calculated for all the assessed anthropometric and performance parameters. ICC values were interpreted as follows: ICC ≤ 0.49, poor; 0.50 ≤ ICC < 0.75, moderate; 0.75 ≤ ICC < 0.9, good; ICC ≥ 0.9, excellent [33]. To verify whether there were differences between groups in the baseline, a one-way ANOVA (analysis of variance) test was conducted. To assess the training effects between groups (ETG vs. RSSTG vs. CTG) and within groups (pre-test vs. post-test) on the anthropometric and performance variables, a two-way repeated-measure ANOVA was performed. When statistically signiﬁcant p values were found (group by time interaction effect or signiﬁcant main effects of time or group), a post hoc pairwise comparison was conducted with Bonferroni correction to identify those differences. The effect size was calculated using Cohen’s d. Values of d < 0.2, d = 0.2, d = 0.5, and d = 0.8 were considered as trivial, small, medium, and large effect sizes, respectively [34]. The level of signiﬁcance established was p < 0.05. The statistical analysis of the data was performed using the program IBM SPSS V.26® computing (IBM Corp., Armonk, NY, USA). 4. Results Once the normality and homoscedasticity of the data were veriﬁed, the one-way ANOVA conﬁrmed the absence of signiﬁcant differences between the three experimental groups at the baseline for all the anthropometric and performance variables. Likewise, the ICC values obtained between the pre-test and the post-test in all assessed parameters were higher than 0.9 for the three groups, which indicates an excellent reliability. Then, the two- way repeated-measure ANOVA showed that there was no interaction effect, main effect of time, or group for BM and BMI (see Table 3). Furthermore, the two-way repeated-measure ANOVA revealed the existence of a group-by-time interaction effect for CMJ, 1RM squat, RE12, RE14, VO2max, and AnT. A main effect of time was observed for the BFP, LM, CMJ, 1RM squat, RE12, RE14, VO2max, and AnT. Finally, a main effect of group was found for CMJ and RE14 (see Table 3). Subsequently, the Bonferroni post hoc comparison showed that ST obtained im- provements signiﬁcantly higher than ET enhancements in the following variables: CMJ (p = 0.003; CI95 = 1.81–7.51), 1RM squat (p = 0.035; CI95 = 1.33–9.86), and RE14 (p = 0.046; CI95 = 46.69–5036.38). ET results were signiﬁcantly better than those obtained by ST in AnT (p = 0.04; CI95 = 0.04–1.95). Finally, the improvements obtained by CT were signiﬁcantly higher than those attained by ET in CMJ (p = 0.002; CI95 = 1.21–4.26), and RE14 (p = 0.046; IC95 = 47.47–5035.51). Int. J. Environ. Res. Public Health 2022, 19, 10773 8 of 17 Table 3. Between-subjects comparisons of all the variables assessed: main effect of time, main effect of group, and interaction effect. Variable Main Effect of Time Main Effect of Group Group per Time Interaction Effect F (1–9) p F (2–18) p F (2–18) p BM 0.31 0.591 0.24 0.785 0.55 0.584 BMI 0.30 0.596 0.86 0.440 0.47 0.632 BFP 13.24 0.005 * 0.42 0.662 3.92 0.055 LM 4.35 0.006 * 0.25 0.780 0.92 0.413 CMJ 42.93 <0.001* 7.48 0.004 * 18.62 <0.001 * 1RMsquat 216.38 <0.001 * 1.92 0.175 27.62 <0.001 * RE12 194.51 <0.001 * 0.127 0.882 23.08 <0.001 * RE14 85.14 <0.001 * 20.86 <0.001 * 23.95 <0.001 * VO2max 52.59 <0.001 * 0.12 0.891 8,72 0.002 * AnT 109.84 <0.001 * 1.39 0.275 37.31 <0.001 * Legend: BM: Body mass; BMI: Body mass index; BFP: Body fat percentage; LM: Lean mass; CMJ: Counter- movement jump; 1RM squat: One-repetition maximum squat; AnT: Anaerobic threshold; VO2max: Maximum oxygen consumption; RE: Running economy; p: Level of statistical signiﬁcance; F: Variation between sample means/variation within the samples; : Signiﬁcant improvement between the pre-test and post-test. As for the within-subject comparisons (see Table 4), RSSTG signiﬁcantly improved between the pre- and post-tests in CMJ (p < 0.001; IC95 = 3.42–4.13), 1RM squat (p < 0.001; IC = 5.66–8.73), RE12 (p < 0.001; 5.66–8.73) and RE14 (p = 0.007; F: 0.23–1.11). The effect size of these improvements was small in the case of RE12 and RE14, and large for CMJ and 1RM squat. ETG signiﬁcantly improved between the pre- and post-tests in the following parameters: RE12 (p < 0.001; IC95 = 1.93–2.59), RE14 (p = 0.015; F = 620.65–4464.61), VO2max (p < 0.001; CI95 = 0.51–0.77), and AnT (p < 0.001; CI95 = 262–738). The effect size of these improvements was small in the case of RE14, medium for VO2max, and large for RE12 and AnT. Additionally, CTG signiﬁcantly improved its results between the pre- and the post-tests in the following variables: BFP (p < 0.001; CI95 = 0.354–0.590), LM (p = 0.035; CI95 = 0.5–1.12), CMJ (p < 0.001; CI95 = 1.72–2.47), 1RM squat (p < 0.001; CI95 = 3.10–4.10), RE12 (p = 0.035; CI95 = 1.62–2.56), RE14 (p < 0.001; CI95 = 0.80–1.84), VO2max (p < 0.001; CI95 = 1.57–3.02), and AnT (p < 0.001; 0.84–1.15). The effect size of these improvements was small in the case of RE14, medium for BFP and RE12, and large for the CMJ, 1RM squat, and AnT variables. Table 4. Results obtained by the three experimental groups in the pre- and post-test in all the variables assessed. Variable Group Pre-Test Post-Test Cohen’s d p X SD X SD BM RSSTG 67.11 5.37 67.49 5.32 0.076 0.159 ETG 65.80 4.38 65.62 4.30 0.037 0.330 CTG 66.96 4.48 67.10 4.31 0.066 0.172 BMI RSSTG 21.27 1.87 21.38 1.76 0.066 0.190 ETG 20.68 1.31 20.63 1.34 0.051 0.645 CTG 21.47 1.03 21.52 1.08 0.098 0.171 BFP RSSTG 15.28 1.03 15.04 1.16 0.199 0.152 ETG 15.09 0.95 14.86 0.87 0.233 0.051 CTG 15.18 1.07 14.32 1.15 0.721 <0.001 LM RSSTG 56.77 4.58 57.31 4.71 0.115 0.110 ETG 55.84 3.59 55.81 3.73 0.006 0.108 CTG 56.73 4.29 57.50 4.21 0.141 0.018 * Int. J. Environ. Res. Public Health 2022, 19, 10773 9 of 17 Table 4. Cont. Variable Group Pre-Test Post-Test Cohen’s d p X SD X SD CMJ RSSTG 33.29 1.71 37.07 1.81 2.141 <0.001 * ETG 32.86 1.73 32.42 2.33 0.210 0.446 CTG 33.06 1.41 35.16 1.29 1.551 <0.001 * 1RM squat RSSTG 83.10 4.51 90.30 4.57 1.571 <0.001 * ETG 83.61 2.27 84.71 3.37 0.348 0.168 CTG 82.70 2.75 86.30 2.66 1.321 <0.001 * RE12 RSSTG 41.63 3.07 42.41 3.01 0.272 <0.001 * ETG 40.49 2.65 42.76 2.75 0.838 <0.001 * CTG 41.61 3.08 42.55 2.71 0.783 <0.001 * RE14 RSSTG 49.44 3.41 50.28 3.17 0.253 <0.001 * ETG 48.92 3.13 50.08 3.33 0.346 <0.001 * CTG 48.58 3.12 50.18 3.01 0.431 <0.001 * VO2max RSSTG 60.29 4.18 60.91 4.51 0.138 0.097 ETG 59.02 3.01 60.91 4.27 0.581 <0.001 * CTG 58.67 3.25 60.98 3.36 0.207 <0.001 * AnT RSSTG 14.95 0.68 15.05 0.83 0.131 0.172 ETG 14.90 0.51 16.05 0.83 0.845 <0.001 * CTG 14.65 0.58 15.70 0.42 2.749 <0.001 * Legend: BM: body mass; BMI: body mass index; BFP: body fat percentage; LM: lean mass; CMJ: countermovement jump; 1RM squat: one-repetition maximum squat; AnT: anaerobic threshold; VO2max: maximum oxygen consumption; RE: running economy; p: level of statistical signiﬁcance; *: signiﬁcant improvement between the pre-test and post-test. 5. Discussion 5.1. Anthropometric Parameters None of the three experimental groups presented modiﬁcations to their BM in the present study. Furthermore, the effect sizes of the modiﬁcations produced in this parameter between the baseline and the post-test were trivial in all three cases. These results are consistent with previous recent studies [12,32,35]. Thus, on the one hand, it can be expected that the exclusive practice of endurance training may promote muscular catabolism and increase mitochondrial density and activity. Therefore, these adaptations could reduce body mass and body fat percentage [36]. On the other hand, strength training can potentially increase lean tissue by increasing the release of anabolic hormones such as testosterone and growth hormone [37,38]. However, in the present study, we understand that the absence of signiﬁcant modiﬁcations in BM may be due to the following reasons: Firstly, the duration of the intervention period (12 weeks) could not be long enough to generate signiﬁcant variations in this parameter. Secondly, in RSSTG and CTG, the potential or expected decrease in BM of the participants derived from the slight reduction in fat mass has been hindered by the slight increase in LM. Thus, the net result would be a non-signiﬁcant alteration of athletes’ BM. Likewise, since the study participants were recreational athletes who perform endurance training sessions regularly, to be able to attain signiﬁcant decreases in their body weight, it is plausible that they require not only more extended intervention periods, but also signiﬁcant increases in their weekly training frequency, volume, density, and intensity. Regarding ETG, the absence of a signiﬁcant weight reduction might be related to the participants’ regular practice of endurance training. Therefore, this group may need more intense or prolonged workouts to signiﬁcantly decreases BM. Importantly, there was no increase in lean tissue in ETG but a slight reduction. This means that the slight decrease in lean tissue of the participants included in this group, together with their slight reduction in BFP, was of such small magnitude that it did not cause a signiﬁcant reduction in BM. This is in line with the trivial effect sizes observed in ETG in its reduction of BFP and LM. Int. J. Environ. Res. Public Health 2022, 19, 10773 10 of 17 As for the BMI, there were no signiﬁcant variations in any of the three experimental groups. In the case of RSSTG and CTG, a certain increase in their BMI might be expected due to the strength training practice and its potential anabolic effect. However, the effect size of the BMI increase in both groups was trivial. These results are somewhat surprising, since none of the study participants had previous experience in strength training. Moreover, it must be taken into account that, although the objective of the strength training protocol used in the present research was not designed to produce muscle hypertrophy (see Table 1), strength training is likely to generate certain amount of hypertrophy, particularly in subjects without previous strength training experience [39]. These results are more surprising in the case of RSSTG, since they did not perform endurance training. Thus, the trivial increase in RSSTG suggests that for strength training to generate signiﬁcant increases in BMI, it is necessary to use training methods speciﬁcally aimed at achieving this goal and, probably, a longer intervention period. In ETG, as expected, the BMI did not increase but decreased. However, the reduction was trivial. We understand this slight decrease is in line with the training principle of speciﬁcity since endurance training is more likely to generate reductions in BM and BMI rather than increases [36]. As far as the BFP is concerned, signiﬁcant reductions were only observed in CTG. These results coincide with the study carried out by Eklund et al. (2016) [40]. In con- trast, the BFP remained unaffected in some studies after applying a concurrent training protocol [12,32]. Furthermore, Blagrove et al. (2018c) conducted a systematic review to ana- lyze the effects of adding strength training to the endurance training programs of medium- and long-distance athletes. They observed that BFP is commonly unaffected [41]. In the present study, we understand that only CTG signiﬁcantly improved its BFP due to the strength and endurance training combination. This could be because strength training can increase basal metabolism [39,42], and endurance training may produce a signiﬁcant caloric expenditure [39,42,43]. Regarding ETG, despite the fact that endurance training effectively reduced BFP in some previous studies [38,44], we understand that subjects included in this group did not improve their BFP due to the absence of strength training, which implied that they could not beneﬁt from its potential capacity to increase basal metabolism. On the contrary, we interpret that RSSTG could not signiﬁcantly decrease their BFP since they did not practice endurance training and could not beneﬁt from the signiﬁcant caloric expenditure that endurance training produces. Concerning the LM, only CTG signiﬁcantly increased this parameter after undertaking the 12-week-training protocol. This increase could be related to the following reasons. First, a greater training variability was applied to this group. Second, only CTG signiﬁcantly decreased BFP. Therefore, even though CTG and RSSTG reduced their LM in absolute terms at similar levels and with similar effect sizes, the increase in CTG in relative terms was higher due to its greater reduction in BFP. Third, according to Coffey and Hawley (2017) and Fyfe and Loenneke (2018), the practice of divergent exercise (i.e., strength and endurance) by untrained or recreationally active individuals induced similar increased anabolic signaling in skeletal muscle during the ﬁrst weeks of training [1,22]. Not surprisingly, ETG did not increase the LM. Unlike CTG and RSSTG, this group decreased its LM, but not signiﬁcantly. We consider that this could be related to the potential catabolic effect of aerobic exercise [36]. The LM results of the present study are consistent with those attained by Eklund et al. (2016) and Vikmoen et al. (2020) [40,45]. However, our results only partially coincide with those obtained by Vikmoen et al. (2017) since they observed that both concurrent strength– endurance training and running-speciﬁc strength training are useful for increasing LM [46]. Furthermore, Beattie et al. (2017) did not observe any changes in LM in competitive distance runners, probably because it is more difﬁcult for well-trained subjects to attain adaptations due to their lower reserve of adaptation [32]. 5.2. Performance Variables As expected, ETG did not improve the results in CMJ. This conﬁrms that endurance training does not signiﬁcantly modify the marks obtained by recreational endurance ath- Int. J. Environ. Res. Public Health 2022, 19, 10773 11 of 17 letes in CMJ. In contrast, RSSTG and CTG signiﬁcantly improved their performance in CMJ. In the ﬁrst case, it was expected since RSSTG only performed running-speciﬁc strength training sessions, and the subjects included in the present study had no previous strength training experience. As for CTG, it has been veriﬁed that their improvements in CMJ produced by the strength training performed were not attenuated despite the concomitant strength and endurance training. Additionally, RSSTG and CTG obtained signiﬁcantly better results than ETG in CMJ. This proves that the interference effect does not occur with the weekly training frequency used in the training protocol. The results of the present study are consistent with the ﬁndings obtained by Fyfe, Bishop and Stepto (2014) [47]. These authors state that there is no evidence supporting the interference effect theory. In this regard, Coffey and Hawley (2017) add that despite chronic studies indicating that there is robust evidence supporting that endurance training attenuates strength adaptations when concurrent training protocols are applied, the underlying mechanisms of the mentioned in- terference are unknown. Nevertheless, some studies have veriﬁed that endurance training attenuates improvements in power, speciﬁcally in CMJ when concurrent training protocols are implemented [45,48]. As for 1RM squat, as expected, RSSTG and CTG improved their marks between pre- and post-test, probably because the study participants had no previous strength training experience. Only the marks obtained by RSSTG were signiﬁcantly higher than those attained by ETG in the post-test. This could indicate that a higher frequency of strength training provides additional beneﬁts, since the number of strength sessions performed by RSSTG was higher than the sessions performed by CTG. However, we understand that the interference effect did not occur in CTG, because no signiﬁcant differences between RSSTG and CTG were observed in the post-test in 1RM squat. The present research results are consistent with those obtained by Vikmoen et al. (2016), Vikmoen et al. (2017), and Sousa et al. (2017) [14,46,49]. In all three cases, the utility of concurrent training to improve the 1RM squat was veriﬁed. As far as RE is concerned, the three groups signiﬁcantly improved this parameter at 12 and 14 km/h. The improvements obtained in RE14 by CTG and RSSTG were signiﬁcantly higher than those achieved by ETG. In RSSTG, the improvements in RE could be related to the attainment of certain adaptations [9,10,32,50]: (a) improved musculotendinous stiffness of the lower extremities; (b) improved motor unit recruitment and synchronization patterns; (c) improved intermuscular coordination and neural inhibition; (d) delayed activation of less-efﬁcient type II muscle ﬁbers; (e) conversion of type IIx ﬁbers into fatigue- resistant IIa ﬁbers; (f) facilitation of the optimal application of strength throughout the entire training or competition; (g) reduction of the relative intensity that each particular cycle of effort or sports technique represents for one athlete when overcoming the same resistance; (g) improved ability to perform the same effort with lower oxygen consumption; (h) improved ability to apply the same strength with less muscle mass; (i) improved reuse of elastic energy in each stride. Therefore, attaining all of these physiological adaptations could be the reason why RSSTG obtained signiﬁcant improvements over ETG in RE14. Regarding the results obtained by ETG in RE12 and RE14, we interpreted that the improvements were the result of attaining certain adaptations [51]: (a) improved oxidative capacity, which in turn is associated with better mitochondrial functioning, and leads to a reduction in the use of the oxygen required to perform submaximal intensity efforts; (b) improved buffering capacity of the skeletal muscles and hematological system. As for CTG, we considered they simultaneously beneﬁted from the adaptations that both strength and endurance training provide to improve RE. This circumstance would explain why CTG obtained signiﬁcantly better results than ETG in RE14. Likewise, it is also under- standable that the results obtained by ETG were signiﬁcantly lower than those attained by RSSTG and CTG in RE14 but not in RE12, since runners show greater RE values at race pace [12]. In this regard, considering the characteristics of the athletes included in the present research and their results in AnT (see Table 3), it is understood that their compe- tition race velocity is close to 14 km/h. The results of the present research are consistent Int. J. Environ. Res. Public Health 2022, 19, 10773 12 of 17 with the studies of Beattie et al. (2017), Blagrove et al. (2018b), Giovanelli et al. (2017), and Li et al. (2019) [12,31,32,52]. In all four cases, the practice of concurrent training was effec- tive in improving RE. Additionally, recent systematic reviews and meta-analyses conﬁrmed the efﬁcacy of strength training in improving RE [6,15,41]. In contrast, some studies veriﬁed that the implementation of concurrent training programs was not effective in enhancing RE [14,53]. Regarding the VO2max, although the trainability of this variable could be conditioned by genetic factors [54], ETG and CTG obtained signiﬁcant improvements. Therefore, it can be assumed that these improvements are training-speciﬁc adaptations. Likewise, the fact that the improvements attained by ETG were not signiﬁcantly higher than those achieved by CTG suggests that no interference effect has occurred. The present study results coincide with the research conducted by Patoz et al. (2021) [53]. However, in a systematic review, Blagrove et al. (2018c) veriﬁed that VO2max is typically unaffected after the application of concurrent training programs [41]. Therefore, the discrepancies between studies could occur because the possibility of improving VO2max is genetically conditioned [54]. Moreover, as expected, RSSTG did not improve the VO2max, probably because this group did not perform endurance training sessions. In fact, few studies found signiﬁcant improvements in VO2max after the exclusive practice of strength training. In this regard, Ozaki et al. (2013) conducted a review study to verify the effects of strength training on increasing VO2max, and in only three out of the 17 studies analyzed were signiﬁcant improvements in VO2max registered. They also ascertained that the higher the training level, the more difﬁcult it is to improve the VO2max [55]. Finally, regarding AnT, both ETG and CTG improved this variable. We understand that this improvement resulted from the training methods speciﬁcally designed to enhance the AnT (see Table 2). Likewise, the results attained by ETG were signiﬁcantly better than those achieved by RSSTG. However, the absence of signiﬁcant differences between ETG and CTG reveals that no interference effect has occurred in CTG. Furthermore, as expected, RSSTG did not signiﬁcantly improve the AnT. In this case, we consider that the absence of endurance training explains the non-achievement of signiﬁcant improvements. Moreover, few studies have examined the effects on AnT. Ferrauti et al. (2010) veriﬁed the absence of signiﬁcant differences in AnT between a concurrent and endurance training program in isolation [35]. Likewise, Cragnulini (2016), after conducting one review article, concluded that adding strength training to endurance athletes’ training programs does not have a negative impact on AnT [17]. 5.3. Overall Interpretation of the Results The improvements attained by the three experimental groups are speciﬁc exercise mode adaptations. Thus, RSSTG improved all strength parameters, ETG all the endurance parameters, and CTG strength and endurance parameters, and only the concurrent training program effectively improved body composition in 12 weeks. Additionally, the interference effect did not occur for the strength, endurance, or anthropometric variables. This suggests that the weekly frequency used in the training protocol of the present study prevents the attenuation of adaptations in concurrent training protocols with respect to single-mode strength or endurance exercise. In this regard, Pattison et al. (2020) point out that CMJ is useful for analyzing the interference effect on neuromuscular improvements when performing concurrent training programs [48], and the results obtained in the present research by RSSTG in CMJ were not signiﬁcantly better than those attained by CTG. Furthermore, based on the improvements attained by RSSTG and CTG in 1RM squat and CMJ, and also considering the large effect sizes obtained by both groups, it can be inferred that athletes without previous strength training experience can obtain signiﬁcant improvements in key performance parameters due to their greater reserve of adaptation. In this sense, Fyfe and Loenneke (2018) consider that untrained individuals have a greater capacity to adapt to training stimuli than trained individuals, although their individual genetic potential could also limit the possibility of obtaining improvements [22]. For this Int. J. Environ. Res. Public Health 2022, 19, 10773 13 of 17 reason, Beattie et al. (2014) indicate that, for endurance athletes with lower levels of strength, a general strength training program may be sufﬁcient to improve their maximum strength, explosive strength, and reactive strength [6]. However, athletes with higher strength levels should perform explosive and reactive strength training programs to improve their performance [6,56]. It is also noteworthy that, based on the improvements achieved by the three experi- mental groups after the 12-week training program, the ATR periodization system seems to be adequate to improve the performance of recreational endurance runners, mainly when concurrent training programs are applied. However, it is also possible that intervention periods longer than 12 weeks can be required to attain signiﬁcant improvements in body composition, mainly when a single-mode exercise is used. Regrettably, few studies used concurrent training protocols integrated into periodization systems such as the ATR block periodization system. In this regard, García-Manso et al. (2017) conducted one research with recreational college-age subjects, using one block periodization for nine weeks. They veriﬁed that concurrent and exclusive endurance training effectively improves sports performance. However, no signiﬁcant differences were found between both training proto- cols [21]. Importantly, we must mention future lines of research. Despite several studies exam- ining concurrent training programs’ effects on endurance athletes, many aspects remain unclear. This circumstance is further aggravated by the important methodological differ- ences that exist between studies and their limitations. Thus, future studies might consider the following aspects: (a) The use of concurrent training programs might be oriented to ensure that neuro- muscular adaptations positively impact athlete’s biomechanical and performance parameters [57]. (b) It could be interesting to investigate the effect of different running volumes combined with explosive strength training [58]. (c) Since an eight-week concurrent training program could not be long enough to improve certain performance variables [35], long-term intervention periods might be applied. Li et al. (2019) propose the use of training protocols longer than 16 weeks [31], and Beattie et al. (2014) longer than six months [6]. (d) Although Berryman et al. (2018) found that the beneﬁcial effects of strength training on endurance performance occur regardless of athletes’ level [15], some authors propose conducting studies with different populations. Low et al. (2019) recommend conducting research with women and people with different training status [59], and Li et al. (2019) with groups of senior citizens and women [31]. (e) Future training protocols might be integrated into periodization systems such as the ATR model. In this vein, Berryman et al. (2018) point out that the use of periodization strategies could help clarify the optimal moment to implement strength training activities within the annual training plan [15]. (f) Future studies might include training protocols combining different endurance train- ing methods (i.e., fartlek, continuous training, interval training) [23]. (g) Appropriate training methods and tests to develop and assess strength levels must be used [6]. (h) The number of strength training sessions per week might be between two and three [41]. (i) The training protocols used might be designed in accordance with the sports training principles. Finally, it is necessary to mention the study’s strengths and limitations. As for the strengths, there are two noteworthy aspects. First, as well as including one concurrent and one single-mode endurance training group, one running-speciﬁc strength training group was incorporated. This circumstance (which is not usual in studies conducted with endurance athletes) was useful to verify the adaptations that endurance runners can attain with running-speciﬁc strength training and determine the possible existence of Int. J. Environ. Res. Public Health 2022, 19, 10773 14 of 17 interference effects in CTG. Second, the number of weekly training sessions applied to the three experimental groups was equated. In this regard, it should be noted that in several previous studies, the concurrent group performed two or three additional weekly strength sessions with respect to the endurance training group, which implies using a distinctive number of sessions. As for the limitations, ﬁrst, the sample size was small. A larger sample would have ensured greater representativity. Second, no time-trial test was conducted in the present research since the study participants were not specialized in any speciﬁc distance. Finally, we consider that if the intervention period had been longer, it would have been possible that the three experimental groups had obtained additional improvements, particularly in body composition. Furthermore, signiﬁcant differences between groups could have occurred in more anthropometric and performance variables at the post-test. 6. Conclusions A concurrent training program of 12 weeks integrated into the ATR periodization system is effective in enhancing body composition and selected sports performance param- eters associated with the exclusive practice of strength training (maximum and explosive strength) and endurance training (VO2max and AnT), in addition to RE in recreational runners aged 30–40. The exclusive practice of running-speciﬁc strength training during the same period also using the ATR design improves maximum and explosive strength and RE, while the exclusive practice of endurance training using the ATR model improves endurance parameters (VO2max and AnT) and RE. Thus, concurrent training is the most time-efﬁcient method to attain anthropometric and performance adaptations. Addition- ally, a concurrent training program performed on non-consecutive days did not attenuate the endurance and strength adaptations that can be attained with single-mode exercise. However, it cannot be ruled out that a higher weekly training frequency may generate interference effects. The ATR periodization system improves the performance parameters of recreational endurance athletes, especially when performing concurrent training. Likewise, in recre- ational athletes without previous strength training experience, due to their greater reserve of adaptation, concomitant running-speciﬁc strength and endurance training—in addition to enhancing their body composition and relevant performance variables—produce large improvements in maximum and explosive strength and further enhance RE. 7. Practical Applications • Performing exclusive strength or endurance training allows athletes only to attain speciﬁc exercise mode adaptations. • Undertaking concurrent training programs allows athletes to obtain strength and endurance adaptations. However, to this end, separating the training sessions by at least nine hours is necessary to avoid signiﬁcant interferences, or 24 h to guarantee to a greater extent that the adaptations will not be attenuated [60]. • Concurrent training programs should be integrated into a periodization model to attain greater effectiveness. In this sense, the ATR block periodization system effectively im- proves anthropometric and performance variables in recreational endurance athletes. • Long-term interventions of more than 12 weeks might be used. Intervention periods of 12 weeks are insufﬁcient to attain improvements in anthropometric parameters when single-mode exercise training is used. • Regarding the training load and the type of strength that must be developed, it is nec- essary to adapt the strength training program to the athlete’s objectives, training level, and previous training experience. Thus, a general strength training program might improve sports performance in subjects without previous strength training experience. However, in subjects who did not previously develop their maximum strength, it can be leapfrogging using inappropriate workloads or developing types of strength with lower residual effect (i.e., explosive strength, muscular endurance, reactive strength). Int. J. Environ. Res. Public Health 2022, 19, 10773 15 of 17 It may also reduce their adaptation reserve unnecessarily and limit future improve- ments. By contrast, endurance athletes with previous strength training experience should develop explosive and reactive strength to enhance their performance. Author Contributions: Conceptualization, P.P.-G.; methodology, P.P.-G.; validation, P.P.-G. and J.S.; formal analysis, P.P.-G.; investigation, P.P.-G.; resources, P.P.-G.; data curation, P.P.-G.; writing— original draft preparation, P.P.-G.; writing—review and editing, P.P.-G. and J.S.; supervision, J.S. All authors have read and agreed to the published version of the manuscript. Funding: The authors would like to thank Prince Sultan University, Riyadh, Saudi Arabia, for supporting the article processing charges. Institutional Review Board Statement: The research was conducted according to the ethical princi- ples of the Declaration of Helsinki. It was approved by the Ethics Commission of Prešov University (Slovakia) (ethical clearance number: 2/2021). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Acknowledgments: The authors would like to thank Prince Sultan University, Riyadh, Saudi Arabia, for their support in research. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Coffey, V.G.; Hawley, J.A. Concurrent exercise training: Do opposites distract? J. Physiol. 2017, 595, 2883–2896. 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PMC5408286	ORIGINAL RESEARCH Using a novel data resource to explore heart rate during mountain and road running Andrew Best1 & Barry Braun2 1 Department of Anthropology, University of Massachusetts, Amherst, Massachusetts 2 Department of Health and Exercise Science, Colorado State University, Fort Collins, Colorado Keywords Altitude, cardiac drift, hypoxia, Strava. Correspondence Andrew Best, Department of Anthropology, University of Massachusetts, 217 Machmer Hall, 240 Hicks Way, Amherst, MA. Tel: +1 (860) 882-3678 Fax: +1 413-577-4217 E-mail: abest@umass.edu Funding information No funding information provided. Received: 2 March 2017; Revised: 23 March 2017; Accepted: 23 March 2017 doi: 10.14814/phy2.13256 Physiol Rep, 5 (8), 2017, e13256, doi: 10.14814/phy2.13256 Abstract Online, accessible performance and heart rate data from running competitions are posted publicly or semi-publicly to social media. We tested the efﬁcacy of one such data resource- Strava- as a tool in exercise physiology investigations by exploring heart rate differences in mountain racing and road racing run- ning events. Heart rate and GPS pace data were gathered from Strava activities posted by 111 males aged 21–49, from two mountain races (Mt. Washington Road Race and Pike’s Peak Ascent) and two road race distances (half mara- thon and marathon). Variables of interest included race ﬁnish time, average heart rate, time to complete the ﬁrst half (by distance) of the race, time to complete the second half, average heart rate for both the ﬁrst and second half, estimated maximal heart rate, and competitiveness (ﬁnish time as percentage of winning time). Mountain runners on average showed no change in heart rate in the second versus ﬁrst half of the event, while road racers at the half marathon and marathon distances showed increased second-half heart rate. Mountain runners slowed considerably more in the second half than road runners. Heart rate increases in road races were likely reﬂective of cardiac drift. Altitude and other demands speciﬁc to mountain racing may explain why this was not observed in mountain races. Strava presents enormous untapped opportunity for exercise physiology research, enabling initial inquiry into physiological questions that may then be followed by targeted laboratory studies. Introduction Heart rate and GPS recording devices have become a common training tool for endurance athletes. Thousands post running and cycling activities on social media ser- vices such as Strava (www.strava.com), Movescount (www.movescount.com), and Training Peaks (www.train ingpeaks.com). Of these, Strava has the largest cache of public data (registration not required) and semipublic data (free registration required). Strava enables access to in situ data from thousands of athletic competitions that would require signiﬁcant time and effort to collect through traditional research approaches. Standard data posted in Strava activities include pace and elevation, and sometimes heart rate and age. Some limitations are inherent: potentially relevant information, such as body mass, aerobic capacity, and training history are inaccessi- ble without direct communication with individual ath- letes, which violates Strava’s terms of use. Despite these constraints Strava represents an untapped “big-data” source for exercise physiology research. Here, we explore the efﬁcacy of this novel investigative approach through a comparative study of heart rate proﬁles of road running and mountain running competitions, events for which sufﬁcient data is available on Strava. Mountain running may be broadly deﬁned as running or run/hiking over mountainous terrain and differs from traditional road racing in grade, altitude and terrain. The Mt. Washington Road Race (Gorham, NH) and the Pike’s Peak Ascent (Manitou Springs, CO) are analogous in ª 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 2017 \| Vol. 5 \| Iss. 8 \| e13256 Page 1 Physiological Reports ISSN 2051-817X duration (but not distance) to the half marathon and marathon, respectively, traditional distances for road run- ning competitions. Differences in heart rate and perfor- mance data between these races, gathered from Strava activities, may provide clues to the physiological demands unique to these two disciplines. Thus, a secondary goal of this study is to infer physiological differences between mountain and road running from studying heart rate proﬁles during these events. If identiﬁed, effects of differ- ential biomechanics, cardiovascular physiology, and/or hypoxia could be studied directly in more targeted experi- mental studies. Methods Mountain races sampled include the Mt. Washington Road Race (MWRR, 2009–2016) and the Pike’s Peak Ascent or the ascent split from the Pike’s Peak Marathon (PP, 2012–2016), held the same weekend over the same course but with a return downhill run. Candidate road races were identiﬁed by searching Strava for races with large samples of posted data. Chosen races included the Hartford Half Marathon (2011–2015), the Philadelphia Half Marathon (2011–2015), the Boston Athletic Associa- tion Half Marathon (2015), and the New York City Mara- thon (2015). It was necessary to sample from several half marathons over multiple years to achieve a sufﬁcient sam- ple size, and each of these races was chosen because these race courses are not excessively hilly nor is the second half of the race substantially more difﬁcult than the ﬁrst half. For any race, data were excluded for years where the ambient temperature was excessively warm. Also excluded were Strava activities that showed unrealistic HR proﬁles, such as precipitous changes, long periods of total stasis, or rapid ﬂuctuations, all of which suggest heart rate mon- itor malfunction. Most data were accessed through fully public or semipublic Strava pages (those requiring free site member- ship), but several runners submitted GPX data ﬁles directly after responding to Facebook requests and completing an informed consent document. Per Strava’s request, Strava users were not contacted. Data were anonymized and par- ticipants were assigned study subject ID’s. Heart rate and GPS data from 111 males aged 21–49 (mean 34.5 6.4) were included in this study (see Table 1). Age, race ﬁnish time, winning time, and status of altitude residence (de- ﬁned here as living at 3500’ or higher) were determined by accessing race results posted on race websites. Variables of interest included: race ﬁnish time, age and residence (ob- tained from ofﬁcial race results); average heart rate (HR) throughout the race; time to complete the ﬁrst half (by distance) of the race; time to complete the second half; and average HR for both the ﬁrst and second half. HRmax was estimated using the Tanaka et al. (2001) equation for endurance trained men, 205—(0.6 x age). From these data other measures were calculated, including percentage of winning time (a measure of competitiveness), time differ- ence to complete the ﬁrst versus second half, heart rate difference in beats per minute (bpm) for the ﬁrst versus second half, heart rate difference as a percentage of HRmax for the ﬁrst versus second half, and overall heart rate as a percentage of HRmax. Data were analyzed using SPSS sta- tistical software v. 22 (IBM). T-tests were performed for duration-matched races and ANOVAs were used to exam- ine differences between all test groups. Linear regressions were used to explore relationships between variables of interest. Finally, three additional MWRR competitors for whom heart rate data were not available were included to create a subgroup of four altitude-acclimatized MWRR runners. All study protocol were reviewed and endorsed by the University of Massachusetts Human Subjects Review Board. Table 1. Characteristics of participants in each race, mean SD. All MWRR ½ Marathon Pikes Peak Marathon MWRR altitude subgroup N 111 19 21 21 50 4 Age (years) 34.5 6.4 35.0 5.4 32.9 6.7 37.0 7.5 33.9 5.9 31.8 5.2 Race duration (hr:min:sec) – 1:23:45 0:08:20 1:25:42 0:04:52 2:59:33 0:11:47 2:54:43 0:07:20 1:05:31 0:10:073 % of winning time 136.1 9.5 142.8 13.81,2 138.2 10.1 133.1 8.81 133.9 5.62 111.3 16.63 1Signiﬁcant difference (P < 0.01) between MWRR and Pikes Peak. 2Signiﬁcant difference (P < 0.01) between MWRR and the Marathon. Runners in the shorter races, considered together, were slightly less com- petitive than those in longer races (P < 0.01). MWRR and the ½ marathon were signiﬁcantly shorter in duration than Pikes Peak and the mara- thon (P < 0.0001). 3MWRR altitude subgroup had signiﬁcantly lower % of winning time and race duration than all other groups (P < 0.01). 2017 \| Vol. 5 \| Iss. 8 \| e13256 Page 2 ª 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. Heart Rate during Mountain Running A. Best et al. There were no signiﬁcant differences in age between test groups nor signiﬁcant differences in duration for matched races- MWRR (1:23:45 0:08:20) versus the half mara- thon (1:25:42 0:04:52), and PP (2:59:33 0:11:47) ver- sus the marathon (2:54:43 0:07:20). Only one MWRR participant of 19 lived at altitude (in the initial sample) while only three of 21 PP runners did not. Runners’ ﬁnish times ranged from 112% to 174% of the respective race winning time (mean 136 9.5) and this measure was not different between duration and matched races. MWRR runners, however, were less competitive by this measure than PP and marathon runners (P < 0.01) and sampled runners in the shorter races considered together (MWRR and the ½ marathon) were slightly less competitive than the longer races (PP and the marathon; P < 0.01). Still, overall the runners in this study can be described as recre- ationally competitive. For example, most of the marathon- ers ﬁnished in under 3 h, a common benchmark of competitiveness, and the fastest ran 2 h 34 min, a perfor- mance that would earn a top-10 ﬁnish in most American marathons outside of the major city marathons (Boston, New York, and Chicago). The subgroup of four MWRR runners from altitude was markedly more competitive than the larger study groups: two were race winners and one ﬁn- ished in second place, giving this subgroup an average ﬁn- ish time relative to the winner of 111.3%, signiﬁcantly faster than any test group (P < 0.01). Results Heart rate increased over the second half of both road events: half marathoners saw a 3.7 bpm increase (P < 0.01) and marathoners a 1.8 bpm increase (P < 0.05). There was no change in heart rate in the second versus ﬁrst half of MWRR and HR decreased by 4.4 bpm at PP, though not quite signiﬁcantly (P = 0.056; see Table 2 and Fig. 1). These differences are also reﬂected in percentage estimated HRmax. Heart rate change was highly variable in the PP sample (SD=9.9 bpm) and there was a dramatic outlier whose HR dropped 34 bpm over the second half while slowing about the same as the average PP runner (see Fig. 2). When both mountain races were compared against both road races, the former were found to have signiﬁ- cantly greater slowdown in the second half (P < 0.001) and a signiﬁcantly different second half HR change (P < 0.01; see Table 3). This was true in duration-matched pair com- parisons as well: MWRR had greater second-half slowing and less of a second-half HR increase than the ½ marathon (P < 0.05), while PP had greater second-half slowing and a drop in HR over the second half, as compared with the marathon where HR increased (P < 0.01). The four accli- matized runners comprising the MWRR altitude subgroup had similar second half slowing to other MWRR runners (15.4% 1.0 vs. 11.5% 4.8), signiﬁcantly more than half marathoners and marathoners (P < 0.05) and signiﬁcantly less than PP runners (P < 0.001). Runners in the shorter races (MWRR and the half marathon) displayed higher overall HR (bpm and as percentage estimated HRmax; P < 0.05) and less second-half slowing (P < 0.01). Age was positively, though very weakly, correlated with slowing in the second half when all races were analyzed together (r2=0.054; P < 0.05). No signiﬁcant relationship was found in individual races. Percentage of winning time was positively and weakly correlated with HR for the marathon (r2=0.085; P < 0.05) and inversely correlated with HR change in the second half of MWRR, both in bpm and percent estimated HRmax (r2=0.207; P = 0.05); that is, less competitive runners had a smaller HR increase or larger decrease in the second half at these Table 2. HR and pace results, mean SD. MWRR ½ Marathon Pikes Peak Marathon MWRR altitude subgroup HR (bpm) 168.9 8.3 171.1 7.7 164.1 9.8 166.5 9.3 – HR % estimated max 91.8 4.7 92.4 4.2 89.2 4.7 90.2 5.1 – Second half % slower 11.5 4.81 4.3 8.4 51.2 8.82 5.7 5.4 15.4 1.03 HR change bpm 0.4 4.3 3.7 5.44 4.4 9.95 1.8 6.14 – Change % est. HRmax 0.2 2.3 2.0 2.94 2.4 5.45 1.0 3.3 – 1MWRR slowed more than ½ marathoners (P < 0.01) and marathoners (P = 0.01). 2PP runners slowed more than all other groups (P < 0.0001). 3MWRR altitude runners slowed more than ½ marathoners and marathoners (P < 0.05). 4½ Marathoners’ (P < 0.01) and marathoners’ (P < 0.05) HR increased in the 2nd half. 5PP runners’ HR decrease was signiﬁcantly different from the marathoners’ (P < 0.01) and ½ marathoners’ (P = 0.001) HR increase. ª 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. 2017 \| Vol. 5 \| Iss. 8 \| e13256 Page 3 A. Best et al. Heart Rate during Mountain Running races. Considered all together, runners who slowed more in the second half of their race had less of a HR increase or a greater decrease both in bpm and percent estimated HRmax (r2=0.187 and 0.189, respectively; P < 0.001). This relationship was signiﬁcant only for MWRR runners (r2=0.222 and 0.221, P < 0.05) and marathon runners (r2=0.371 and 0.373, P < 0.001); there was almost no cor- relation at PP or the half marathon. Discussion Our primary objective was to pilot the use of Strava’s publicly and semi-publicly available data in an exercise physiology investigation. The primary strength of this approach is that large quantities of data are available from a variety of competitions and training activities, enabling initial inquiry into questions that would otherwise be dif- ﬁcult to test. Questions dealing with relative performance will likely be more amenable to this approach than inves- tigations of the underlying physiology as performance data are abundant but physiological data from Strava activities are limited. Indeed, heart rate is the only physi- ological measurement available; without knowledge of each subject’s maximal heart rate, VO2-max, blood lactate values and oxygen consumption during the event, physio- logical differences can only be inferred. Predictive equa- tions based on age are routinely used to estimate HRmax, but even the best of these (speciﬁc to endurance-trained subjects and as used in this study) explains only 53% of the variation in HRmax between subjects (Tanaka et al. 2001). We could not control for effects of training history and runners unaccustomed to the speciﬁc demands of prolonged uphill running will surely respond and perform differently than well-prepared competitors, a point that will be discussed further. There are several ways in which Strava’s utility to a researcher could be improved. First, recruiting users to record and submit heart rate and GPS data speciﬁcally for study purposes, which is currently against Strava’s terms of use, could increase sample size and permit col- lection of additional information, including training char- acteristics. However, this would require extensive recruitment, obviating the primary strength of the approach piloted here (easy data access). Second, Strava or other athletic social media services may choose to incorporate a running power feature, allowing uploading of data from running power meters such as Stryd (www. stryd.com), a relatively new device which estimates power in watts; if accuracy is validated this could be a useful measure. Other services dedicated to tracking and analyz- ing athlete data—such as Movescount or Training Peaks, mentioned previously-—may provide additional metrics, but at present these services do not host a public or semipublic data cache as large as Strava’s. Finally, poten- tial physiological differences identiﬁed through this approach could be explored further with targeted labora- tory-based studies where variables such as training his- tory, climate, and terrain could be controlled and direct physiological measurements could be collected. This study is also, to our knowledge, the ﬁrst to demon- strate heart rate differences between mountain running and road running events. Compared to duration-matched road HR difference (bpm) 20 10 0 –10 –20 –30 –40 MWRR 1/2 marathon Pikes peak Marathon Figure 1. HR difference in the second versus ﬁrst half of each race in beat per minute (bpm). Boxplots show ﬁrst, second, and third quartiles, minimum and maximum, and outliers. Second half % slower 80.0 60.0 40.0 20.0 .0 –20.0 HR change (bpm) 20 10 0 –10 –20 –30 –40 Marathon PP 1/2 marathon MWRR Figure 2. Relationship between slowing in the second half and HR change in the second half. Signiﬁcant negative correlation for MWRR (r2=0.224, P < 0.05) and the marathon (r2=0.370, P < 0.0001). 2017 \| Vol. 5 \| Iss. 8 \| e13256 Page 4 ª 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. Heart Rate during Mountain Running A. Best et al. races, participants in mountain races experienced no increase (MWRR) or a reduction (PP) in HR (both absolute and as percentage estimated HRmax) and slowing pace over the second half of the race. The observed increases in HR in the second versus ﬁrst half of the half marathon and marathon are not surprising: cardiac drift, an increase in HR without a concomitant increase in work output (i.e., running speed), has been demonstrated in one-hour (Ekelund 1967; Mognoni et al. 1990) and 4-hour (Dawson et al. 2005) exercise tests and is likely resultant from lower stroke volume concomitant with reduced plasma volume (Hamilton et al. 1991) and lower diastolic function (Dawson et al. 2005). There is little reason to speculate that these factors do not affect runners at MWRR and PP— races of similar duration to the half marathon and marathon, respectively— so maintenance of heart rate in those events must be attributable to a factor sufﬁciently robust that it masks the effect of drift. Three potential explanatory factors, not mutually exclusive, warrant further consideration. Altitude The PP Ascent begins at 2382 m (7814 feet) and ﬁnishes at 4302 m (14 115 feet) while the Mt. Washington Road Race starts at 465 m (1526 feet) and ﬁnishes at 1917 m (6289 feet). The marathon and half marathons included in this study are held near sea level. Performance and car- diac function are undoubtedly impacted by the hypoxia encountered throughout the PP race: acute altitude expo- sure at simulated 4000 m (13 123 feet) increases heart rate and cardiac output during submaximal exercise to compensate for reduced arterial partial pressure of oxygen (PaO2), but maximal HR is reduced slightly, possibly due to reduced oxygen delivery to cardiac tissue (Stenberg et al. 1966) or increased production of epinephrine, which may have additional performance effects as it speeds uptake of glucose into muscle cells (Richardson et al. 1998). Wehrlin and Hallen (2006) found a 1.9 bpm decrease in maximal HR per 1000 m–in keeping with Stenberg et al.’s (1966) result at 4000 m– beginning at least as low as 1000 m, encompassing most of the alti- tudes encountered at MWRR. Thus, perhaps the lack of HR increase observed in mountain races does not reﬂect a reduction in percent HRmax (reduced aerobic effort), but rather lower HRmax resultant from hypoxia opposes cardiac drift and attenuates (MWRR) or completely negates (PP) a rise in heart rate in the second half of the race. Importantly, most participants in the PP Ascent live at and are ostensibly acclimatized to altitude, so many of their physiological responses during the race are not directly comparable with nonacclimatized runners; how- ever, some HR effects persist even after acclimatization (Vogel et al. 1967). Acclimatization also affords these runners a buffer against altitude-induced performance decrements and thus a performance advantage relative to sea-level runners (Mahe et al. 1974; Fulco et al. 2000). Most participants in the Mt. Washington Road Race are sea-level residents and so any hypoxia experienced during the event is novel and acute. Little work has been done to evaluate HR responses to altitudes below 4000 m, but Wehrlin and Hallen’s results (2006) suggest that Mt. Washington’s altitude is sufﬁcient to decrease maximal HR, as has been observed at altitudes equivalent to PP. Regardless, Mt. Washington’s elevation should certainly incur a performance penalty, especially for non-acclimatized runners. Reductions in VO2-max have been observed relative to sea level values beginning at low altitudes: 580 m (Gore et al. 1996, 1997) and even right from sea level (Wehrlin and Hallen 2006). Thus, VO2-max decreases linearly up to 3000 m, an impairment that appears to be more severe for endurance- trained individuals (Lawler et al. 1988; Koistinen et al. 1995). This effect is not resultant from reduced maximal exercise intensity achieved in altitude tests: Wehrlin and Hallen (2006) found that performance, measured as time to exhaustion in running tests at simulated altitudes with speed kept constant, followed an observed 6% VO2-max decrease per 1000 m. As the second half of MWRR ascends to an altitude 730 meters higher than the ﬁrst half, Table 3. HR and pace for mountain versus road races and shorter versus longer races. Mountain races Road races Shorter races Longer races HR (bpm) 166.4 9.3 167.9 9.0 170.0 8.03 165.8 9.4 HR % estimated max 90.5 4.8 90.9 4.9 92.1 4.43 90.0 4.9 Second half % slower 32.4 21.31 5.2 6.4 7.7 7.83 19.1 21.9 HR change bpm 2.1 8.02 2.4 5.9 2.1 5.1 0.0 7.9 Change % est. HRmax 1.1 4.42 1.3 3.2 1.2 2.8 0.0 4.3 1Mountain races had greater second half slowing than road races (P < 0.0001). 2HR change in bpm and as % est. HRmax was signiﬁcantly different in mountain versus road races (P < 0.001). 3Shorter races were characterized by higher HR (bpm and % est. HRmax; P < 0.05) and less second half slowing (P < 0.01) than longer races. ª 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. 2017 \| Vol. 5 \| Iss. 8 \| e13256 Page 5 A. Best et al. Heart Rate during Mountain Running we may predict a 4.3% VO2-max penalty imposed by alti- tude in the second half, explaining part of the observed 11.5% slowing. It is also possible that increasing altitude throughout both mountain races led to a slowing of pace while effort remained relatively unchanged. Enormous slowing at PP (51.2% 8.8) may be explained in part by the more extreme altitude and the increasing difﬁculty of the footing on the racecourse. However, as slowing was not associated with a decrease in HR for PP (r2=0.001), increasing techni- cal difﬁculty alone is unlikely to explain the vast discrep- ancy between PP slowing and marathon slowing (5.7%). MWRR is run on pavement, eliminating terrain as a com- plicating variable, so a direct comparison with the half marathon is easier- and indeed MWRR runners slowed far more than half marathoners (11.5% vs. 4.3%). Slowing pace at MWRR explains only 22% of the variability in HR decrease, so other factors must be operative. Pacing and psychological factors An alternative or additional explanation for lack of HR increase together with slowing pace during mountain races is that runners reduce their effort as the race pro- gresses due to psychological factors, or perhaps runners are not as adept at pacing themselves evenly in these events. This interpretation is supported by a weak but sig- niﬁcant correlation between second-half slowing and HR change at MWRR (r2=0.202; P < 0.001): runners who slo- wed more had a greater decrease, or smaller increase, in HR compared with those who slowed less. However, 80% of the variation in HR change is not explained by slowing pace, and there is almost no correlation at all between these variables for PP (r2=0.001). Additionally, the four altitude-acclimatized MWRR athletes- three of whom are world class mountain runners (one is a world champion)- slowed just as much as their fellow MWRR competitors. This may suggest that altitude is not responsible for the observed slowing (and perhaps HR) effects as altitude acclimatization afforded no buffering against second-half slowing. Alternatively, the fact that highly trained and experienced mountain runners slowed just as much as everyone else may suggest that pacing and psychological factors alone cannot account for slowing and HR changes, as these runners should be expected to be expertly pre- pared for the physical and psychological demands of mountain racing. Also, as acclimatization simply mitigates but does not eliminate altitude-incurred diminishments in aerobic capacity, and VO2-max declines linearly begin- ning from sea level, we may not expect acclimatized run- ners to slow less over the second half but rather to simply experience less of a performance declination overall rela- tive to un-acclimatized athletes. Muscle recruitment and biomechanical factors The road races included in this study climb and descend no more than several hundred feet in total, while MWRR ascends about 4600’ at an average grade of 12% and the PP Ascent climbs about 7800’, also averaging 12% in grade. The biomechanics of uphill running differ signiﬁ- cantly from level running: less eccentric work is per- formed by muscles and tendons, and none above 15% grade (Minetti et al. 1994), contributing to a higher energy cost. At grades steeper than 15% (which are brieﬂy encountered at MWRR and PP) slopes of cost of trans- port for walking and running converge (Minetti et al. 1994), and above 28% grade, walking is more efﬁcient than running (Giovanelli et al. 2016). Of course, in a race, efﬁciency is second to the primary goal of covering the course as quickly as possible. Many participants in mountain racing events, especially the slower racers, employ a mix of running and walking. So different are uphill biomechanics that the traditional deﬁnition of run- ning gait may need to be modiﬁed to encompass locomo- tion lacking a true ﬂight phase, but characterized by a bouncing gait instead of the inverted pendulum motion of walking. Such a gait has been described as “Groucho running” in a study of bent-knee running on a level treadmill (McMahon et al. 1987) and “grounded run- ning” in a study of ostriches (Rubenson et al. 2004), but these terms aptly describe the slower ranges of uphill human running (Giovanelli et al. 2016). How do the incline-speciﬁc biomechanics encountered in mountain racing affect physiology? Balducci et al. (2016) found that ten elite French mountain runners each achieved the same VO2-max, blood lactate concentrations and heart rate in maximal tests on level ground, 12.5% slope, and 25% slope. Further, incline running perfor- mance was poorly predicted by level running perfor- mance, and there was signiﬁcant inter-individual variation in energy cost increase from level to uphill run- ning: moving from 0% to 12.5% incline increased energy cost 50% for some subjects and 104% for others. These results inform the present study by suggesting that, in uphill-trained subjects, (1) uphill racing absent altitude effects should not be characterized by different heart rate proﬁles and aerobic capacities; and (2) uphill running imposes unique challenges and speciﬁc training may have a strong effect on performance. However, this was a short test (<16 min) and the physiology of 1- to 3-h mountain racing may be different; and importantly, the two moun- tain races we sampled do present altitude challenges. The observation that even highly trained mountain runners showed tremendous variation in uphill energy cost sug- gests that this effect may be even stronger in many of the 2017 \| Vol. 5 \| Iss. 8 \| e13256 Page 6 ª 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. Heart Rate during Mountain Running A. Best et al. athletes included in this study. A runner unaccustomed to the muscle recruitment patterns speciﬁc to mountain rac- ing may prematurely exhaust particular muscle groups resulting in reduced performance relative to what he or she can achieve in a traditional road race (for which they are ostensibly better trained). For example, uphill running activates the vastus muscle group and the soleus to a greater extent than level running while other muscle groups are activated less (Gostill et al. 1974; Sloniger et al. 1997). It is possible that a runner unaccustomed to the demands of uphill running may exhaust the vastus and soleus muscles prematurely and be forced to reduce pace and aerobic effort as the race progresses. However, substantial second-half slowing observed in this study’s elite and altitude-acclimatized subgroup—three athletes trained for the speciﬁc demands of mountain running— suggests that this effect may be minimal. A larger sample of elite runners could clarify this point. Heart rate change in the second half of the marathon is more strongly correlated (r2=0.370) with second-half slowing than at MWRR (again, this correlation was inconsequential for the half marathon and PP). Here, per- haps is a good candidate application for the hypothesis of reduced HR due to reduced aerobic output. The eccentric muscle damage and diminishing glycogen stores incurred during a marathon may cause a slowing that is concur- rent with, and causative of, a reduction in aerobic output. A moderate and signiﬁcant correlation between slowing pace and HR decrease supports this explanation. Glyco- gen depletion should also be expected to pose a challenge for PP runners, but as HR change was not associated with changing pace, glycogen depletion can only minimally explain HR and pace changes. Other observed signiﬁcant correlations are most likely specious. Less competitive MWRR runners displayed a greater reduction or smaller increase in HR over the sec- ond half, but competitiveness was not correlated with sec- ond-half slowing at this or any race. The very weak correlation between age and second-half slowing disap- peared when examined for individual races. Less competi- tive marathoners had higher HR, but an r-squared value of 0.085 and a lack of correlation between competitive- ness and any other variable suggest this is a false positive. Finally, the higher HR (as bpm and percentage estimated HRmax) in the shorter-duration events conﬁrms long- established observations that relative intensity is inversely related to exercise duration. In summary, uphill mountain racing does not appear to be characterized by the continually increasing heart rate seen in the half marathon and marathon. Our three hypotheses for this phenomenon could be investigated with a laboratory based study with subjects completing race effort runs on ﬂat and uphill grades. This would control for terrain, altitude, and variability in interindi- vidual responses (each subject could complete both ﬂat and uphill race efforts), and would permit collection of physiological data (VO2, RER, blood lactate, etc.) and training history. Conclusions Strava’s performance and heart rate data are a useful and novel resource for exercise science investigations provided that research questions are carefully articulated in consideration of the strengths and limitations of this approach. Competitors in mountain races slowed more than their counterparts in duration-matched road races. Mountain racing is characterized by a maintained or decreased heart rate in the second versus ﬁrst half of the event, while road racing at the half marathon and mara- thon distances is characterized by an increasing heart rate. It is unclear whether or how altitude or demands speciﬁc to uphill running explain this difference. This study demonstrates how Strava data can be used in an inquiry into a physiological or performance question; results may then be used to inform a targeted labora- tory-based study. Acknowledgment The authors wish to thank the athletes who contributed data directly or indirectly to make this study possible. Conﬂict of Interest The authors declare no conﬂicts of interest. References Balducci, P., M. Clemencon, B. Morel, G. Quiniou, D. Saboul, and C. A. Hautier. 2016. Comparison of level and graded treadmill tests to evaluate endurance mountain runners. J. Sports Sci. Med. 15:239–246. Dawson, E. A., R. Shave, K. George, G. Whyte, D. Ball, D. Gaze, et al. 2005. Cardiac drift during prolonged exercise with echocardiographic evidence of reduced diastolic function of the heart. Eur. J. Appl. Physiol. 94:305–309. Ekelund, L. G. 1967. Circulatory and respiratory adaptation during prolonged exercise. Acta Physiol. Scand. Suppl. 292:1. Fulco, C. S., P. Rock, and A. Cymerman. 2000. Improving athletic performance: is altitude residence or altitude training helpful? Aviat. Space Environ. Med. 71:162–171. Giovanelli, N., A. L. R. Ortiz, K. Henninger, and R. Kram. 2016. Energetics of vertical kilometer foot races; is steeper cheaper? J. Appl. Physiol. 120:370–375. ª 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. 2017 \| Vol. 5 \| Iss. 8 \| e13256 Page 7 A. Best et al. Heart Rate during Mountain Running Gore, C. J., A. G. Hahn, G. C. Scroop, D. B. Watson, K. I. Norton, R. J. Wood, et al. 1996. Increased arterial desaturation in trained cyclists during maximal exercise at 580 m altitude. J. Appl. Physiol. 80:2204–2210. Gore, C. J., S. C. Little, A. G. Hahn, G. C. Scroop, K. I. Norton, P. C. Bourdon, et al. 1997. Reduced performance of male and female athletes at 580 m altitude. Eur. J. Appl. Physiol. Occup. Physiol. 75:136–143. Gostill, D. L., E. Jansson, P. D. Gollnick, and B. Saltin. 1974. Glycogen utilization in leg muscles of men during level and uphill running. Acta Physiol. Scand. 91:475–481. Hamilton, M. T., J. Gonzalez-Alonso, S. J. Montain, and E. F. Coyle. 1991. Fluid replacement and glucose infusion during exercise prevent cardiovascular drift. J. Appl. Physiol. 71:871–877. Koistinen, P., T. Takala, V. Martikkala, and J. Lepp€aluoto. 1995. Aerobic ﬁtness inﬂuences the response of maximal oxygen uptake and lactate threshold in acute hypobaric hypoxia. Int. J. Sports Med. 16:78–81. Lawler, J., S. K. Powers, and D. Thompson. 1988. Linear relationship between VO2-max and VO2-max decrement during exposure to acute hypoxia. J. Appl. Physiol. 64:1486–1492. Mahe, J. T., L. G. Jones, and L. H. Hartley. 1974. Effects of high-altitude exposure on submaximal endurance capacity of men. J. Appl. Physiol. 37:895–898. McMahon, T. A., G. Valian, and E. C. Frederick. 1987. Groucho running. J. Appl. Physiol. 62:2326–2337. Minetti, A. E., L. P. Ardigo, and F. Saibene. 1994. Mechanical determinants of the minimum energy cost of gradient running in humans. J. Exp. Biol. 195:211–225. Mognoni, P., M. D. Sirtori, F. Lorenzelli, and P. Cerretelli. 1990. Physiological responses during prolonged exercise at the power output corresponding to the blood lactate threshold. Eur. J. Appl. Physiol. Occup. Physiol. 60:239–243. Richardson, R. S., E. A. Noyszewski, J. S. Leigh, and P. D. Wagner. 1998. Lactate efﬂux from exercising human skeletal muscle: role of intracellular. J. Appl. Physiol. 85:627–634. Rubenson, J., D. Heliams, D. G. Lloyd, and P. A. Fournier. 2004. Gait selection in the ostrich: mechanical and metabolic characteristics of walking and running with and without an aerial phase. Proc. R. Soc. Lond. B Biol. Sci. 271:1091–1099. Sloniger, M. A., K. J. Cureto, B. M. Prior, and E. M. Evans. 1997. Lower extremity muscle activation during horizontal and uphill running. J. Appl. Physiol. 83:2073–2079. Stenberg, J., B. Ekblom, and R. Messin. 1966. Hemodynamic response to work at simulated altitude, 4000 m. J. Appl. Physiol. 21:1589–1594. Tanaka, H., K. D. Monahan, and D. R. Seals. 2001. Age- predicted maximal heart rate revisited. J. Am. Coll. Cardiol. 37:153–156. Vogel, J. A., J. E. Hansen, and C. W. Harris. 1967. Cardiovascular responses in man during exhaustive work at sea level and high altitude. J. Appl. Physiol. 23:531– 539. Wehrlin, J. P., and J. Hallen. 2006. Linear decrease in VO2- max and performance with increasing altitude in endurance athletes. Eur. J. Appl. Physiol. 96:404–412. 2017 \| Vol. 5 \| Iss. 8 \| e13256 Page 8 ª 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. Heart Rate during Mountain Running A. Best et al.	Using a novel data resource to explore heart rate during mountain and road running.	[]	Best, Andrew,Braun, Barry	eng
PMC10329575	Vol.:(0123456789) 1 3 Journal of Muscle Research and Cell Motility (2023) 44:115–122 https://doi.org/10.1007/s10974-022-09633-1 REVIEW Participation and performance characteristics in half‑marathon run: a brief narrative review Pantelis Theodoros Nikolaidis1 · Beat Knechtle2 Received: 13 April 2022 / Accepted: 17 October 2022 / Published online: 3 November 2022 © The Author(s) 2022 Abstract Half-marathon (HM) is a running sport of increasing popularity in both sexes and in all age groups worldwide during the last years. Many studies have examined several aspects of HM, such as performance and participation trends, sex and age differences, physiological correlates, and training; however, no comprehensive review has ever been contacted to summa- rize the recently accumulated knowledge. Therefore, the aim of the present study was to review all previous research in this sport, focusing on participation and performance aspects. It was shown that HM runners had similar anthropometric and physiological characteristics as full-marathon runners which should be attributed to the affinity of these two races in terms of metabolic demands. Performance in HM was related with superior scores in aerobic capacity (maximal oxygen uptake, anaerobic threshold and running economy) and training characteristics (sport experience, weekly distance, training speed, frequency of sessions and long single endurance run distance), and lower scores in adiposity-related scores (e.g. body mass, body mass index, body fat percentage and skinfold thickness). Considering the popularity of HM race and the lack of many original studies (compared to FM race), this is an exciting field for scientific research with a large potential for practical applications, since the majority of HM runners are amateur runners in need of sex-, age- and performance-tailored exercise prescription. Keywords Aerobic capacity · Anaerobic threshold · Endurance · Exercise · Nutrition · Participation · Running economy Introduction Half-marathon (HM) is a running event of increasing popu- larity in both sexes and in all age groups worldwide during the last years (Bonet et al. 2022). Although full-marathon (FM) is the most popular endurance running distance, most runners participate in HM (Cribari et al. 2013). Many studies have examined several aspects of HM, such as performance and participation trends, sex and age differences, physio- logical correlates, and training,however, no comprehensive review has ever been contacted to summarize the accumu- lated knowledge. Therefore, the aim of the present study is to review all previous research covering all aspects such as epidemiological trends, the role of age and sex, especially focusing on the physiological aspects. Participation Main aspects of participation included the numbers of par- ticipants in HM and whether these numbers would change across calendar years, sex differences in participation (typi- cally examined using the men-to-women ratio) and age across years, whereas these aspects may vary by national- ity. In 2016, more finishers and events were observed in HM than in FM (Fig. 1). Participation trends in HM have been examined with regards to FM in a single country, Switzer- land, where those participating in HM are three times more than those competing in FM races (Anthony et al. 2014). It has been observed in 226,754 HM and 86,419 FMrun- ners competing in Switzerland between 2000 and 2010 that the number of HM increased from 2000 to 2010 for both men (+ 231%) and women (+ 299%), whereas the number of male and female FM runners increased until 2005 only and decreased thereafter (Anthony et al. 2014). A study on * Pantelis Theodoros Nikolaidis pnikolaidis@uniwa.gr 1 School of Health and Caring Sciences, University of West Attica, Ag. Spyridonos, 122 43 Egaleo, Athens, Greece 2 Institute of Primary Care, University of Zurich, Zurich, Switzerland 116 Journal of Muscle Research and Cell Motility (2023) 44:115–122 1 3 508,108 runners (125,894 female and 328,430 male HM and 10,205 female and 43,489 male FM) competing between 1999 and 2014 in all flat HM and FM held in Switzerland showed that the number of women and men increased across years in both HM and FM, and there were 12.3 times more female HM than female FM and 7.5 times more male HM than male FM (Knechtle et al. 2016a). These different pro- portions of women and men competing in HM and FM races indicated that HM was a sport where relatively more women participated compared to FM. The abovementioned prelimi- nary studies (Anthony et al. 2014; Knechtle et al. 2016a) highlighted a larger participation in HM than in FM races, and this trend was more striking in women than in men. An explanation of this discrepancy between sexes might be that women could be considered as more ‘novice’ runners than men, and consequently, should run more HM before enter- ing FM races. The participation and performance in HM may vary by nationality, and it would be interesting to focus on trends concerning East African runners who were considered as experts in long distance running events (Knechtle et al. 2016b).Actually, a study of ~ half million HM and FM run- ners originating from 126 countries and competing between 1999 and 2014 in all road-based HM and FM held in Swit- zerland reported that, in HM, 48 women (0.038%) and 63 men (0.019%) were from Ethiopia and 80 women (0.063%) and 134 men (0.040%) from Kenya, whereas in FM, three women (0.029%) and 15 men (0.034%) were from Ethiopia and two women (0.019%) and 33 men (0.075%) from Kenya (Knechtle et al. 2016b). These findings suggested that the largest percentage of participants in HM is of local origin. In both women and men, the best performance in HM and FM held in Switzerland was achieved by East African run- ners with Ethiopian and Kenyan runners being the youngest in both sexes and formats of race (Knechtle et al. 2016b). These findings showed that East-African runners were the fastest in both HM and FM although they represented the smallest percentage of participants (Knechtle et al. 2016b). This observation was in agreement with an analysis of the world’s best HM runners during 1999–2015, where it was shown that most of them were Kenyans (30% in women and 57% in men) (Nikolaidis et al. 2017). According to the abovementioned effect of nationality, the characteris- tics of HM differed from country to country, e.g. the local people tended to participate more to races taking place in their country than foreigners. Furthermore, the participa- tion and performance may change across years. Actually, a study examined the changes in participation, performance and age of East African runners competing in HM and FM held in Switzerland between 2000 and 2010 indicated that across time, the number of Kenyan and Ethiopian finishers remained stable while the number of Non-African finishers increased for both women and men in both HM and FM (Cribari et al. 2013). This difference in participation trends across years by nationality might be due to the increase of local ‘recreational’ runners, while the number of the most competitive runners coming from abroad would remain sta- ble. To sum up, more runners compete in HM than in FM, and the fastest HM runners are East Africans. Age of peak performance Every sport has its own age of peak performance and thus, it would be important to estimate at which age HM runners achieve their peak performance in order to set long-term training goals. The largest part of the finishers in HM and FM held in Switzerland of both genders was assigned to age group 40–44 years in HM (19.5% of the total number of finishers) and FM (22.0% of finishers) (Anthony et al. 2014). For both HM and FM races, most of the female and male finishers were recorded in age group 40–44 years (Knechtle et al. 2016a; Knechtle and Nikolaidis 2018). In HM, women (41.4 years) were at the same age as men (41.3 years),in FM, women (42.2 years) were at the same age than men (42.1 years),however, women and men FM runners were older than their counterpart HM runners (Kne- chtle et al. 2016a). With regards to the age of peak perfor- mance, it may differ depending on the performance level, i.e. whether all or the top finishers were considered. For instance, in the world’s largest HM race—the Göteborgs- Varvet—U40 was the fastest age group when all finishers were analyzed, whereas U35 were the fastest when the top 10 were considered (Knechtle and Nikolaidis 2018). Moreo- ver, an analysis of the world’s best HM runners indicated Fig. 1 Finishers and events in USA in 2016. Source: http:// www. runni ngusa. org (accessed on 16/9/2017) 117 Journal of Muscle Research and Cell Motility (2023) 44:115–122 1 3 an age of 26–27 years, which was younger than in FM and 100 km ultra-marathon races (Nikolaidis et al. 2017) (Fig. 2). This observation was confirmed by a study using non-linear regression on world records in HM, where the age peak performance was ~ 27 years (Nikolaidis et al. 2018). The effect of age on HM performance differed from other endurance sports (Sterken 2005). It has been shown that age-related losses in endurance performance did not occur before the age of 50 years with mean FM and HM race times being identical for the age groups 20–49 years, whereas age- related performance decreases of the 50–69-year-old sub- jects were only in the range of 2.6–4.4% per decade (Leyk et al. 2007). These results suggested that the majority of older athletes were able to maintain a high degree of physi- cal plasticity supporting the hypothesis that lifestyle factors had considerably stronger influences on functional capacity than the factor age (Leyk et al. 2007). No significant age- related decline in performance appeared before the age of 55 years, whereas only a moderate decline is seen thereafter (Leyk et al. 2010). Performance losses in middle age were mainly due to a sedentary lifestyle, rather than biological aging (Leyk et al. 2010). In summary, the average age of HM runners was 40–44 years, and the age of peak performance was younger than 35–40 years. Performance trends Performance in HM might be examined using either race time or average speed. In HM held in Switzerland, women (10.29 ± 3.03 km/h) were faster than men (10.22 ± 3.06 km/h) as well as in FM, women (14.77 ± 4.13 km/h) were faster than men (14.48 ± 4.07 km/h) (Knechtle et al. 2016a). Slower HM race time by 13% (10% in FM) in women than in men was observed in a study of all and top 10 finishers aged 20–79 years (Leyk et al. 2007). Moreover, a sex dif- ference of ~ 14% was noted in the world best HM runners (Nikolaidis et al. 2017). Sex differences in performance may also be attributed to the different adaptation to long-term exercise between women and men. A study on 16 males and 16 females preparing for a HM revealed a larger increase in the average daily metabolic rate in men than in women sug- gesting exercise stimulates more habitual physical activity and diet-induced thermogenesis in men than in women (Mei- jer et al. 1991). The variation of performance from a race to race seems to depend on competitive experience and attitude toward competing and was found 4.2% for the fastest quartile of men runners in HM with men, slower and younger run- ners presenting more variation (Hopkins and Hewson 2001). Physiological, anthropometric and training correlates of performance were examined in following sections,however, they might differ depending on performance level, consider- ing that in the elite level other factors (e.g. shoe technology) would play an important role (Goss et al. 2022). Physiological correlates of performance Physical fitness is classified as health- (consisting of body composition, aerobic capacity, muscular strength, muscular endurance and flexibility, i.e. components related directly to health) or sport-related (consisting of those components related to sport performance such as speed or reaction time). Based on the relatively long duration of a HM, it is reason- able to assume that performance in this sport relates to maxi- mal oxygen uptake (VO2max), since it relies mostly on the aerobic energy transfer system. One of the oldest studies on HM (Williams and Nute 1983) already identified VO2max and anaerobic threshold as correlates of race time (r = −0.81 and r = −0.88, respectively). In addition, in male recreational runners, HM race time correlated with VO2max (r = −0.64), speed at VO2max (r = −0.84) and anaerobic threshold (r = −0.79) (Alvero-Cruz et al. 2019). These relationships were confirmed in HM runners with asthma, too,actually, the HM pace of asthmatic runners correlated largely with VO2max (r = 0.86) and almost perfectly with running speed Fig. 2 Race time and age of the best runners by race distance and sex. Based on Nikolaidis and colleagues Nikolaidis et al. 2017. Error bars rep- resent standard deviations. HM = half-marathon, FM = full-marathon 118 Journal of Muscle Research and Cell Motility (2023) 44:115–122 1 3 at a blood lactate concentration of 2 mmol.L−1 (r = 0.97) (Freeman et al. 1990). In amateur runners (nine men, age 36 years), HM time was almost perfectly correlated with VO2max (r = 0.91) and speed corresponding to VO2max (r = 0.90) (Santos et al. 2012). Furthermore, a comparison among 400–800 m, 1500–3000 m and HM women runners showed that HM runners had the highest VO2max (Nurme- kivi et al. 1998). In runners, the pace of HM was comparable to the maximal lactate steady state velocity (Legaz-Arrese et al. 2011). Considering the physiological relevance of HM and FM races, previous research examined the relationship of per- formances in these two races (Salinero et al. 2017; Karp 2007; Coyle 2007). A research on 84 male amateur FM run- ners (aged 41.0 years, finish time 226.0 min) showed a very large correlation between FM and HM race time (r = 0.81) (Salinero et al. 2017). In 2004 U.S. Olympic Marathon Tri- als qualifiers (104 men, 151 women), FM performance cor- related to HM performance (Karp 2007). Maintaining the world record pace for the HM in the FM would lead to run a FM in 1:58 h:min (Coyle 2007). In addition to the abovementioned correlation studies, an approach to study determinants of performance in HM is to develop prediction equations of race time based on corre- lates (Pérez et al. 2012; Gómez-Molina et al. 2017), which include usually two steps, first, the development of an equa- tion in a sample of runners, and second, the validation of this equation in another sample. For instance, a study on male runners considered training-related and anthropometric vari- ables, and laboratory data from a graded exercise test (GXT) on a treadmill (VO2max, speed at the anaerobic threshold, peak speed) and biomechanical variables (contact and flight times, step length and step rate) (Gómez-Molina et al. 2017). This study found that HM race time could be predicted to 90.3% by variables related to training and anthropometry, 94.9% by physiological variables, 93.7% by biomechanical parameters and 96.2% by a general equation, and using these equations, the predicted time was significantly correlated with performance (r = 0.78, 0.92, 0.90 and 0.95, respec- tively) (Gómez-Molina et al. 2017). Moreover, HM race speed could be predicted as V21k (km/h) = (V21.085) + (− 0.282bLA2) −0.131, r2 = 0.97, ETE = 0.414 km/h, where V2 was the speed during a test in track at constant pace over 2400 m slightly higher than the competition expected pace and bLA2 blood lactate (Pérez et al. 2012). A research examined the ratio between running speed and heart rate (HR) as predictor for aerobic capacity (based on the assump- tion that lower HR at a given speed is expected for more fit individuals), and subsequently race time in 10 km, HM and FM (Altini et al. 2017). This study showed that the speed to HR ratio provides higher accuracy in aerobic capacity estimation compared to resting HR or no-physiological data, and large correlations between aerobic capacity and race time (r = 0.56–0.61) (Altini et al. 2017). In addition to the comparison with laboratory exercise testing, HM perfor- mance has been investigated with field tests such as Cooper test (Alvero-Cruz et al. 2019; Alvero-Cruz et al. 2020). For instance, Alvero-Cruz and colleagues (Alvero-Cruz et al. 2019; Alvero-Cruz et al. 2020) observed an almost perfect correlation between HM race time and Cooper test distance, and high predictive validity of this test. Another methodological approach that may provide indi- rect information for the identification of correlates of per- formance is to examine the acute physiological responses to a HM by comparing values pre- and post-race (Dressen- dorfer 1991). For instance, runners were tested in a graded exercise test before and after a HM,time to exhaustion (6.0 vs 4.1 min), VO2max (60.0 vs 56.3 ml.kg−1.min−1), peak respiratory exchange ratio (RER; 1.18 vs 1.06), and peak La (9.7 vs 7.8 mmol.L−1) decreased after the HM (Dres- sendorfer 1991). Other studies focus on muscular acute responses to a HM (Boccia et al. 2017 Boccia et al. 2017). For instance, intermittent isometric rapid contractions of the knee extensor muscles were performed the day before and immediately after a HM, where it was observed that HM had a greater negative effect on repeated, rather than on single, attempts of maximal force production (Boccia et al. 2017). In another study of this research group that examined pre- and post-HM race maximum voluntary contractions of knee extensor muscles, it was found that post-race knee extensors showed a decreased strength (−13.9%) and a reduction in EMG amplitude (−13.10%) and in CV (−6.8%, p = 0.032) (Boccia et al. 2017). Moreover, compared to FM, a HM race induced smaller vertical jump height reduction and less self- reported muscle pain suggesting less muscular fatigue in HM than in FM race (Coso et al. 2017). The abovemen- tioned studies (Boccia et al. 2017; Boccia et al. 2017; Coso et al. 2017) highlighted the importance of muscular fitness in addition to aerobic capacity for HM race. In line with the findings of the correlation studies presented in this section, a comparative study among different performance groups highlighted the role of physiological characteristics (Ogueta- Alday et al. 2018). Particularly, Oguea-Alday and colleagues (Ogueta-Alday et al. 2018) found that HM runners with race time 70 min had superior VO2max and running economy than those with race time 80 min, 90 min and 105 min. In summary, performance on HM race would depend mostly on VO2max and other indices of aerobic capacity. Anthropometric and training correlates of performance Performance in HM is not related only to physiological char- acteristics, but also to anthropometry and training habits (Campbell 1985; Friedrich et al. 2014; Knechtle et al. 2012; Zillmann et al. 2013). For instance, a study in university HM 119 Journal of Muscle Research and Cell Motility (2023) 44:115–122 1 3 runners observed that distance run per week and number of weeks training for the event, and body mass index (BMI) were predictors of HM race time (Campbell 1985). In female and male recreational HM runners, HM race time was related to body fat percentage (BF), running speed during training, and BMI was predictor of performance only in men (Frie- drich et al. 2014), whereas elsewhere race time was more strongly associated with anthropometry in women than men (Knechtle et al. 2010). For male HM runners, BMI, BF and speed in running during training were related to race time (Zillmann et al. 2013). Furthermore, in female finishers of the ‘Half Marathon Basel’, race time was largely related to body mass, BMI, BF (r = 0.48–0.60), and could be predicted by the formula ‘166.7 + 1.7midaxilla skinfold – 6.4speed in training’(R2 = 0.71) (Knechtle et al. 2011). In HM, FM and UM master runners (> 35 years old), BF and training charac- teristics, not skeletal muscle mass, were associated with run- ning times (Knechtle et al. 2012). A comparison between HM and FM men runners showed that HM runners were heavier, had longer legs, thicker upper arms, a thicker thigh, a higher sum of skinfold thicknesses, a higher body fat percentage and a higher skeletal muscle mass, fewer years of experience, com- pleted fewer weekly training kilometers, and fewer weekly run- ning hours (Zillmann et al. 2013). The relationship of HM with training characteristics might be explained by exercise-induced chronic cardiometabolic adaptations resulting in improvements in VO2max, a major predictor of performance (Meyler et al. 2021). A comparative study between HM and FM races showed that a fast race time was associated with high weekly train- ing volume (> 32 km) and a long training single distance (> 21 km) in HM, and high weekly training volume (> 65 km) in FM runners (Fokkema et al. 2020). Furthermore, a research among groups different performance groups confirmed the role of anthropometric and training characteristics on HM perfor- mance (Ogueta-Alday et al. 2018), where runners with race time 70 min had more sport experience and weekly running distance, and less body mass, BMI and skinfold thickness than those with race time 80 min, 90 min and 105 min. In summary, performance on HM race would depend mostly on VO2max and other indices of aerobic capacity. To sum up, HM per- formance depended on weekly running distance, number of training units, training running speed, BMI and BF (Campbell 1985; Friedrich et al. 2014; Knechtle et al. 2012; Zillmann et al. 2013). Therefore, a practical advice for recreational run- ners wishing to improve their race time might be to increase training (distance, running speed and frequency) and decrease BMI and BF. Pacing Pacing in sport refers to the conscious or subconscious regulation of performance (e.g. speed during a race) in order to achieve a goal (Thiel et al. 2018; Micklewright 2019). Although pacing is related to performance in endur- ance sports (the less variable the pacing, the faster the race time), little information exist with regards to pac- ing strategies of HM runners (Hanley 2015). The relevant literature has been developed recently (Nikolaidis et al. 2019 Cuk et al. 2019; Cuk et al. 2020; Nikolaidis et al. 2019). A methodological approach to analyze pacing in HM has been to consider speed for intermediate segments, e.g. every 5 km (0–5, 5–10, 10–15 and 15–20 km) and end 1.1 km segments (Hanley 2015). An analysis of all finishers in Ljubljana 2017 HM showed a positive pacing with every segment being slower than its precedent one (Nikolaidis et al. 2019). In elite (finishers in the IAAF World Half Marathon Championships) HM runners, it was observed that the fastest finishers maintained split speed from 5 to 15 km, whereas slower runners decreased speed after 5 km (Hanley 2015). Compared to FM, an analysis of recreational HM runners observed a more even pacing in Ljubljana (Nikolaidis et al. 2019) and Vienna races (Cuk et al. 2020).A research of the pacing in the Great West Run HM showed that RPE scales with the proportion of exercise time that remains inferring that the brain uses a scalar timing mechanism (Faulkner et al. 2008). With regards to the variation of pacing by age, a research in Vienna City HM race revealed a positive pacing strategy, i.e. speed decreased across race, with an end spurt in all age groups and larger variation of speed in the younger age groups (Cuk et al. 2019). Additionally, a more even pacing was observed to relate with high training volume and long single training run (Fokkema et al. 2020). In summary, similarly to other endurance races, pacing was associated with performance in HM race with faster runners adopting a more even pacing than their slower counterparts. Thus, recreational runners would be recommended to maintain running speed during a HM race. Practical applications With regards to the popularity of HM race, the findings of research on this field would concern an increasing number of HM runners. The fact that HM race has been a sport activity recently developed would imply that it could not be ‘covered’ by scientific and professional knowl- edge derived from long distance running – such as 5 km and 10 km – and FM race. This observation highlighted the need to develop further the research on this field. 120 Journal of Muscle Research and Cell Motility (2023) 44:115–122 1 3 In this context, the present review attempted to address fundamental questions on the identity of HM runners (sex, age and nationality), performance trends and cor- relates, to develop practical information for professionals working with runners. For instance, the knowledge of the age of peak performance can aid setting long-term per- formance goals considering the age of runners. Several original research studies were identified that examined correlates of HM performance and enhanced our knowl- edge on this field. Based on these findings, practitioners working with HM runners should aim to optimize aerobic capacity (e.g., VO2max and anaerobic threshold), train- ing indices (e.g., weekly running distance and running speed) and adiposity-related parameters. Although these parameters clearly were related to HM performance in recreational runners that might be considered as an het- erogeneous group, a challenge for future studies would be to examine the variation of their importance depending on performance level. Conclusion In conclusion, performance in HM was related with supe- rior scores in aerobic capacity (maximal oxygen uptake, anaerobic threshold and running economy) and training characteristics (sport experience, weekly distance, training speed, frequency of sessions and long single endurance run distance), and lower scores in adiposity-related scores (e.g. body mass, body mass index, body fat percentage and skinfold thickness) (Fig. 3). Considering the popularity of HM race and the lack of many original studies (compared to FM race), this is an exciting field for scientific research with a large potential for practical applications. Acknowledgements Figure 3 is credited to Dr. Celine Dewas. Author contributions All authors wrote the main manuscript text and prepared figures. All authors reviewed the manuscript. Funding Open access funding provided by HEAL-Link Greece. Fig. 3 Physiological, anthropo- metric and training characteris- tics influencing half-marathon performance 121 Journal of Muscle Research and Cell Motility (2023) 44:115–122 1 3 Data availability All data are available by the corresponding author upon reasonable request. Declarations Conflict of interest The authors declare no conflict of interest. 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PMC6040753	RESEARCH ARTICLE Russians are the fastest 100-km ultra- marathoners in the world Beat Knechtle1,2, Pantelis Theodoros Nikolaidis3, Fabio Valeri2 1 Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland, 2 Institute of Primary Care, University of Zurich, Zurich, Switzerland, 3 Exercise Physiology Laboratory, Nikaia, Greece beat.knechtle@hispeed.ch Abstract Objectives A recent study investigating the top 10 100-km ultra-marathoners by nationality showed that Japanese runners were the fastest worldwide. This selection to top athletes may lead to a selection bias and the aim of this study was to investigate from where the fastest 100-km ultra-marathoners originate by considering all finishers in 100-km ultra-marathons since 1959. Methods We analysed data from 150,710 athletes who finished a 100-km ultra-marathon between 1959 and 2016. To get precise estimates and stable density plots we selected only those nationalities with 900 and more finishes resulting in 24 nationalities. Histograms and density plots were performed to study the distribution of race time. Crude mean, standard deviation, median, interquartile range (IQR), mode, skewness and excess of time for each nationality were computed. A linear regression analysis adjusted by sex, age and year was performed to study the race time between the nationalities. Histograms, density and scatter plots showed that some races seemed to have a time limit of 14 hours. From the complete dataset the finishes with more than 14 hours were removed (truncated dataset) and the same descriptive plots and analysis as for the complete dataset were performed again. In addition to the linear regression a truncated regression was performed with the truncated dataset to allow conclusion for the whole sample. To study a potential difference between races at home and races abroad, an interaction term race site home/abroad with nationality was included in the model. Results Most of the finishes were achieved by runners from Japan, Germany, Switzerland, France, Italy and USA with more than 260’000 (85%) finishes. Runners from Russia and Hungary were the fastest and runners from Hong Kong and China were the slowest finishers. PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 1 / 29 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Knechtle B, Nikolaidis PT, Valeri F (2018) Russians are the fastest 100-km ultra-marathoners in the world. PLoS ONE 13(7): e0199701. https:// doi.org/10.1371/journal.pone.0199701 Editor: Luca Paolo Ardigò, Universita degli Studi di Verona, ITALY Received: November 18, 2017 Accepted: June 12, 2018 Published: July 11, 2018 Copyright: © 2018 Knechtle et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data underlying this study were collected from the ‘Deutsche Ultramarathon Vereinigung’ (DUV) and are freely accessible using the following link: http://statistik. d-u-v.org/geteventlist.php?year=all&dist= 100km&country=all&Submit.x=17&Submit.y= 6&label=&surface=all&sort=1&from=&to=. The authors used the following search criteria: ‘Year’– ‘all’, ‘Distance’–‘100 km’, and ‘Country’–‘All’. This search leads to more than 4,500 races; all race results were manually downloaded by the investigators. The authors did not have any special access privileges. Conclusion In contrast to existing findings investigating the top 10 by nationality, this analysis showed that ultra-marathoners from Russia, not Japan, were the fastest 100-km ultra-marathoners worldwide when considering all races held since 1959. Introduction Ultra-marathon running, such as 100-km race, is a sport of increasing popularity [1]. Particu- larly, the number of finishers in 100-km ultra-marathon running increased exponentially, both for women and men, from 1998 to 2011 [2]. Performance in this sport depends on physi- ological, e.g. peak running velocity during a graded exercise test, anaerobic threshold, maximal oxygen uptake (VO2max) and oxygen uptake (VO2) at 16 km/h [3] and psychological charac- teristics, e.g. cognitive function [4]. Another performance-related characteristic is pacing as it has been shown that faster run- ners exhibit smaller decrease in their speed during a race than slower runners [5]. With regards to their training characteristics, ultra-marathoners have ~8 years of experience in ultra-run- ning and show higher training volume and lower intensity than marathoners [1]. Training characteristics, such as weekly training distance and training pace, are predictors of 100-km ultra-marathon performance [6]. Also, anthropometric characteristics as age, body mass, body mass index and body fat correlate with performance in this sport; however, they might be less important than training characteristics [7, 8]. Furthermore, performance in 100-km ultra-mar- athon running is influenced by nationality and origin of the runners [2]. It is well known that athletes of a certain origin are the fastest in certain sport disciplines. For example in running, the fastest marathoners originate from East Africa, especially from Kenya and Ethiopia [9]. In longer running distances such as 100-km ultra-marathon running, most athletes at world class level originate from Japan [9]. When the top 10n athletes in 100-km ultra-marathon running between 1998 and 2011 were analysed, the 10 best race times were achieved by Japanese runners for both women and men [2]. However, these results [2] might be biased since only the top athletes were considered and not the whole number of athletes competing worldwide, and have not been verified by other studies. The knowledge of the effect of nationality on 100-km ultra-marathon performance would be of great practical importance for professionals working with ultra-marathoners. Therefore, we analysed all 100-km ultra-marathon races held worldwide between 1959 and 2016 with the aim to identify the fastest runners by nationality. Based upon previous findings we hypothesized to confirm that female and male Japanese runners would be the fastest world- wide also when considering all finishers in 100-km ultra-marathon races held since 1959. Materials and methods Ethical approval All procedures used in the study were approved by the Institutional Review Board of Kanton St. Gallen, Switzerland with a waiver of the requirement for informed consent of the partici- pants given the fact that the study involved the analysis of publicly available data. Methods We obtained from the website www.ultra-marathon.org/ of ‘Deutsche Ultramarathon Verei- nigung’ (DUV). DUV has a large data record with race data from all ultra-marathons in The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 2 / 29 Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. http://statistik.d-u-v.org/index.php. By using the link http://statistik.d-u-v.org/ each person can access the publicly available database. We used http://statistik.d-u-v.org/geteventlist.php and inserted in ‘Year’ the term ‘all’, in ‘Distance’ the term ‘100 km’ and in ‘Country’ the term ‘All’ when using the English version of the website. By clicking on ‘Go’, all 100-km ultra-mar- athons held worldwide are presented. This search leads to more than 4,500 races; all race results were manually downloaded by one of the investigators. The original dataset contains the following variables: name, age at race, year of race, sex of finisher, nationality and country, and speed in km per hours. We converted running speed to time in hours by dividing 100 km by speed (km/h). To identify unique finisher we computed date of birth with year of race minus age at race. After cleaning the variable name we identified unique finisher if finisher has the same name, date of birth, sex and nationality. Finishes with missing in age, year, sex and times were removed. Finishes out of the following ranges were removed: date of birth between 1890 and 2000, age between 15 and 100, year between 1950 and 2016, and number if character in nationality and country is 3. After removing the finishes from missing data and outliers we selected only finishes which nationality has equal or more than 900 finishes. This dataset has 24 nationalities and we named it complete dataset. To study the distribution of time we produced histograms and density plots with Gaussian kernel for each of the selected nationality. Also we produced normal distribution for each nationality defined by the crude mean and standard deviation of each nationality to compare with the empirical distribution. Furthermore we computed crude mean, standard deviation, median, interquartile range (IQR), mode, skewness and excess kurtosis of time for each nationality. Excess kurtosis or shortened excess is defined as kurtosis minus 3. An excess of 0 means a Gaussian-like kurtosis (mesokurtic), a positive excess has a slender form of curve (leptokurtic), and negative excess has a broader curve (platykurtic). Due to various kind of distribution of time we decided to cluster the nationalities according to time density. The range of time (h) was segmented in 0 to 7, 7 to 8, 8 to 9, . . .,22 to 23, 23 to 24 and 24. For each of these 19 segments we computed the area under der density curve. With that, we performed an agglomerative hierarchical clustering using the group average clustering to analyse groups of similar distribution of time. To study the time between the nationalities we performed a linear regression analysis adjusting by sex, age and year: time ¼ sex ðyear þ year2Þ þ sex ðage þ age2Þ þ sex nationality ð1Þ We included a quadratic term for age and year and also an interaction term between sex and age and age squared, sex and year and year squared and sex and nationality. This model (1) is based on visual inspection of scatterplots of time against year and time against age for each nationality (Figs 1 and 2). We included the following fitting curve to each panel: a b- spline (solid) of age, respectively, time, and a quadratic term (dashed). Since both curves over- laps for the large range using a quadratic term seems admissible. The variable age and year were centered by the median with median year of 2009 and median age of 44. Reference level of sex was male and reference level of nationality was Australia (AUS). Histograms, density plots (Fig 3) and scatterplots (Figs 1 and 2) show that some races seem to have a time limit: finishers who didn’t reach certain time limit were discarded. To account for this limit we defined a time limit of 14 hours based on the plots and histograms of Japan, Korea and Taiwan. From the complete dataset we removed the finishes which have more than 14 hours (Fig 4) which we called the truncated dataset and produced the same descriptive plots and analysis as for the complete dataset (Figs 5 and 6). Additionally to the linear regression we performed a truncated regression with the truncated dataset to allow conclusion to the whole The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 3 / 29 sample. The estimates from the regressions were used to compute the times of a reference fin- isher: median of age, median of year and male. These times and resulting ranks were compared between the various nationalities. Furthermore, to study if there is a difference between races at home and races on abroad we included in model (1) an interaction term race site (home/abroad) with nationality and sex (2): time ¼ sex ðyear þ year2Þ þ sex ðage þ age2Þ þ sex nationality site ð2Þ Since 64.6% of the finisher had only one race and 16.6% had two races we performed regression analysis without including the cluster effect of finishers. All data processing and analysis were performed with the statistical software R [10]. Trun- cated regression was performed with function truncreg from package truncreg. Results Between 1959 and 2016, a total of 363,924 athletes finished a 100-km ultra-marathon. The vari- able with the highest number of missing data is date of birth. There was no missing in variable sex. Table 1 summarizes the exclusion criteria. Only nationalities with at least 900 finishes were considered to allow precise estimates and robust histogram. To analyse which country have the most missing data in variable date of birth nationalities with at least 1,000 finishes and at least 10% of missing data are listed in Table 2 by descending order of missing data. Malaysia, Korea, Portugal and Great Britain have the most missing data with 70.1%, 60.6%, Fig 1. Scatterplots time against race year for each nationality based on the complete dataset. Year has been jittered. https://doi.org/10.1371/journal.pone.0199701.g001 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 4 / 29 48.1%, 41.5%, respectively. Finally, a total of 150,710 finishers originating from 24 countries with a total of 307,871 finishes could be considered for data analysis. Table 3 presents the number of finishes by origin of the athletes. Most of the finishes were achieved by runners from Japan, Germany, Switzerland, France, Italy and USA with more than 260’000 (85%) finishes. A total of 20 nationalities performed more than 50% of their races at their home country with runners from Japan, Switzerland, Italy and Korea on the top whereas runners from Germany, Great Britain, Belgium and Austria have performed less than 50% of the races abroad (Fig 7). Runners from Finland, Germany, Switzerland, Italy, Netherlands Hungary, Belgium, Austria, France and Russia have an average number of finishes per finisher of more than 2 (Fig 8). A total of 64.6% of the finishers completed only one 100-km ultra-marathon (Table 4). On average, the athletes were 43.7±11.1 years old (Table 5). A total of 88% of the finishers are men and 12% are women (Table 6). From the agglomerative hierarchical clustering 5 groups can be retrieved: • Group 1 with China and Hong Kong which show a wide spread distribution of time. • Group 2 with Russia which has the lowest mode and high excess (3.2). • Group 3 with Korea, Japan and Taiwan which have a very high excess (7.1, 5.8 and 16.8). All have a very step right curve at 18, 14 and 14 hours, respectively. • Group 4 with Czech Republic, Spain, Great Britain, Australia, Switzerland, Italy and USA with low excess and low skewness. Fig 2. Scatterplots time against race age for each nationality based on the complete dataset. Age has been jittered. https://doi.org/10.1371/journal.pone.0199701.g002 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 5 / 29 • Group 5 with Finland, Denmark, Nederland, Belgium, Hungary, Poland, Sweden, France, Canada, Austria and Germany which have a higher skewness (1) compared to group 4 with skewness 1 (Fig 4). Table 7 compares the number of finishes before and after truncation of the data set for each nationality. Hong Kong and China have more than 90% truncated observation, Australia, Czech Republic, Spain, Switzerland and USA have between 50% and 63% truncated observa- tion and all others less than 50%. Table 8 presents the mean, SD, median, interquartiles, mode, skewness and excess of time for each nationality of the complete dataset and Table 9 for the truncated data set. Estimates, standard errors and p-values from models (1) and (2) based on complete and truncated dataset are given in Tables 10–14. These data were used to compute times at the ref- erence sex = male, year of race = 2009 and age = 44 which are presented in Tables 15 and 16 and Figs 9 and 10. The upper panel of Fig 9(A)–9(C) display the time for each nationality and confidence intervals from the multivariable model (1) at reference sex = male, age = 44 and year = 2009. (A) was computed with the complete data set, (B) with the truncated data set and (C) was per- formed by truncated regression and truncated data set. The ranks from the results with com- plete data set show that runners from Russia, Finland, and Hungary were the fastest and Fig 3. Histograms, density plots and normal distributions based on mean and standard deviation for each country. The diagrams are positioned according the hierarchical cluster analysis. Graphs are based on the complete dataset. https://doi.org/10.1371/journal.pone.0199701.g003 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 6 / 29 runners from Hong Kong and China were the slowest finishers. The ranks from the results from the truncated dataset changed to Russia, Hungary, Spain and Great Britain. To visualize changes in ranks between these three methods the estimates were ordered by descending time estimates and the nationality of the estimates were connected with lines. Fig 9 (D) shows the changes between results from linear regression with complete data set and trun- cated data set and Fig 9(E) shows the results from linear regression with the truncated data set and truncated regression. Comparing the regression of complete with truncated data set shows that Russia, Canada, Hong Kong and China hold their position where all other nationalities change their rank. Japan changed from rank 5 to rank 18. Hungary changed from third to sec- ond and Finland from second to fourth. The time for Russia changed from 9.4 h (95%-CI: 9.1– 9.6) to 9.0 h (95%-CI: 8.9–9.1), Hungary from 10.7 h (95%-CI 10.4–11.0) to 9.9 h (95%-CI: 9.7–10.0), Japan from 11.1 h (95%-CI: 10.9–11.3) to 11.4 h (95%-CI: 11.3–11.5) and China from 19.1 h (95%-CI: 18.9–19.3) to 11.9 h (95%-CI: 11.6–12.1). There are only four nationalities which change ranks when ranks from linear regression are compared with ranks from truncated regression both based on truncated dataset (Fig 9E). Fig 10(A) and 10(B) shows changes of ranks between running at home and abroad computed with Fig 4. Scatterplot with excess against skewness. Groups of nation are distinguished by different colours. https://doi.org/10.1371/journal.pone.0199701.g004 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 7 / 29 complete data set, respectively, truncated data set based on model (2). Both show many changes in rank position. Again Russia remains at place 1 at home and abroad with 10.0 h and 8.2 h, respectively, with complete data set and 9.7 h and 7.8 with truncated data set. Japan changed from rank 10 (11.1 h, at home) to rank 20 (14.9 h, abroad) when complete dataset was used and from rank 18 (11.4 h) to 3 (9.2 h) when truncated dataset was used (Table 16). Table 17 shows the mean time of the top 10, 100 and 1000 finishers. Only the fastest finishes of each finisher was considered to define the top. Japan is first at top 10 and top 100 and sec- ond at top 1000 whereas Russia is second at top 10 and top 100 and ninth at top 1000. Table 18 shows the mean time of the top fastest finishes. Russia is first at top 10 and 100 with 5 finisher and 10 finishes and 37 finishers with 100 finishes. Japan is second at top 10 and 100 with 7 fin- ishers and 10 finishes and 50 finishes with 100 finishes. Discussion The aim of this study was to investigate the aspect of nationality of the fastest 100-km ultra- marathons competing between 1959 and 2016 with the hypothesis that the fastest runners would originate from Japan as it has been found for the top 10 runners worldwide competing between 1998 and 2011. However, in contrast to previous findings, athletes from Russia achieved the fastest race times, not athletes from Japan, when all athletes were considered by nationality. A first potential explanation could be the quote of finishes at the origin country. For exam- ple, Russians have ~37% of the finishes outside the origin but Japanese less than 2%. Most probably only the fastest Russian runners compete outside Russia on the fastest races (e.g. Fig 5. Scatterplots time against race year for each nationality based on the truncated dataset. Year has been jittered. https://doi.org/10.1371/journal.pone.0199701.g005 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 8 / 29 World Championships) or the fastest courses (e.g. completely flat course, track races) world- wide. In contrast, Japanese runners competed preferably in races held in Japan where the courses are most probably not fast (i.e. rather hilly courses than flat courses). The present study is, however, not the first to show that Russian athletes are the fastest in an ultra-endur- ance sport. Recently, an analysis of the ‘Engadin Ski Marathon’ showed that Russians were the fastest cross-country skiers [11], which, combined with the findings of the present study, indi- cated a general trend of excellence of Russians in ultra-endurance sports. The two strongest factors which seems influence the population speed are the attitude to participate and rules concerning time limits. Firstly, as the density plots and histograms show it seems that there are countries where also very slow participants were allowed to compete in races and who has been also considered in the ranking. Extreme examples of this kind of com- petitions are athletes from the nationalities China, and Hong Kong, but also Czech Republic, Great Britain, Spain, and Australia. Athletes from other countries like Denmark, Finland, Fig 6. Scatterplots time against race age for each nationality based on the truncated dataset. Age has been jittered. https://doi.org/10.1371/journal.pone.0199701.g006 Table 1. Total number, missing and out of range. Criteria N Finishes N Finisher N Nationality % Finishes % Finisher % Nationality [1] Total 363,924 195,983 128 100 100 100 [2] Exclude missing/incorrect hours 363,923 195,982 128 100 100 100 [3] Exclude missing age/date of birth 318,231 157,190 125 87.4 80.2 97.7 [4] Exclude unclear nationality 318,228 157,187 124 87.4 80.2 96.9 [5] Exclude nation < 900 finishes 307,871 150,710 24 84.6 76.9 18.8 https://doi.org/10.1371/journal.pone.0199701.t001 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 9 / 29 Table 2. Missing data in date of birth and/or age according to nationality. Only nationalities with at least 10% miss- ing are shown. Nationality N Missing Missing (%) MAS 1,507 1,057 70.1 KOR 6,539 3,963 60.6 POR 1,442 693 48.1 GBR 5,834 2,419 41.5 NZL 1,119 452 40.4 FIN 2,359 917 38.9 HKG 2,123 791 37.3 CHN 6,165 1,942 31.5 ESP 5,913 1,854 31.4 TPE 3,843 1,065 27.7 JPN 79,011 17,021 21.5 DEN 1,224 263 21.5 AUS 5,103 1,093 21.4 CAN 3,093 417 13.5 NED 2,261 234 10.3 BEL 2,896 294 10.2 https://doi.org/10.1371/journal.pone.0199701.t002 Table 3. Quantity structure of selected nationalities. Nationality N Finishes N Finisher Race at home (%) Finishes per finisher JPN 61’990 41’081 98.8% 1.51 GER 51’313 18’085 39.7% 2.84 SUI 49’596 17’609 98.8% 2.82 FRA 46’553 22’768 89.6% 2.04 ITA 38’177 14’766 96.2% 2.59 USA 14’356 9’627 91.6% 1.49 POL 5’472 3’112 79.4% 1.76 CHN 4’223 3’069 75.2% 1.38 ESP 4’059 2’785 78.8% 1.46 AUS 4’010 2’437 92.0% 1.65 GBR 3’414 2’301 38.7% 1.48 TPE 2’778 1’955 88.4% 1.42 CAN 2’676 1’401 69.6% 1.91 BEL 2’602 1’163 38.5% 2.24 KOR 2’576 1’486 95.9% 1.73 CZE 2’506 1’289 66.6% 1.94 AUT 2’082 969 19.5% 2.15 NED 2’027 805 62.0% 2.52 RUS 1’852 920 62.6% 2.01 FIN 1’442 479 86.8% 3.01 HKG 1’332 1’070 89.9% 1.24 DEN 960 544 70.6% 1.76 HUN 947 419 65.6% 2.26 SWE 928 570 73.0% 1.63 https://doi.org/10.1371/journal.pone.0199701.t003 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 10 / 29 Sweden, and Russia have a high skewness and excess which means that the bunch is concen- trated over a narrow limit. This may due to attitudes within society (e.g. popularity of sports, policy of furtherance) or socio-economic backgrounds of the individuals that only fast com- petitors participates. It has been suggested also that a successful finish in this sport depends on the motivation to train intensively [7]. Secondly, the density plots of athletes from Japan, Taiwan and Korea show a very steep slope on the right side of the curve. This may due to time limits given by the organizer. These time limits may not only performed in Japan or Taiwan but less frequently also in other coun- tries. This could be the main source of bias which would explain why Japanese were the fastest [1]. To counteract this bias we truncated the dataset and considered only finishes with lower or equal 14 hours. This can cause bias like using top 10 finishers if we would conclude to the whole population. So, we have to consider that using the complete dataset would give bias due to policy rules and if we use the truncated dataset we have selection bias. We used also trun- cated regression to allow conclusion to the whole running population but it seems that too many observations have been truncated which changed the dataset in that way that it changed completely the shape of the original distribution which does not anymore allow conclusion on the complete data set but only on the truncated data set. That’s why the linear regression and truncated regression of the truncated data set gives similar results. Nonetheless, the Fig 7. Percentage of races which takes place at the origin of the finisher. https://doi.org/10.1371/journal.pone.0199701.g007 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 11 / 29 assumption that in Japan is a time limit may be supported by the fact that the race time at home is 11.1 h and on abroad is 14.9 h using model (2) and complete data set. This is an increase of 3.1 hours. For athletes from Russia, the times are 10.0 h and 8.2 h, respectively, a decrease of 1.7 hours. Assuming that only good and the best ultra-marathoners take the effort Fig 8. Average number of finishes. This figure is based on the complete dataset. https://doi.org/10.1371/journal.pone.0199701.g008 Table 4. Distribution of number of finishes per finisher. N Finishes N Finisher Frequency in % 1 97,340 64.6 2 25,032 16.6 3 10,588 7 4 5,561 3.7 5 3,558 2.4 5–9 5,365 3.6 10–19 2,708 1.8 20–39 498 0.3 40–59 51 0 60–79 4 0 80–99 1 0 100–149 2 0 https://doi.org/10.1371/journal.pone.0199701.t004 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 12 / 29 Table 5. Baseline of continuous variables. Variable Mean SD Median IQR Min Max Age 43.7 11.1 44 36–51 15 92 Date of birth 1959 15.3 1960 1949–1970 1891 2000 Year 2003 14 2009 1993–2014 1959 2016 Time 13.7 3.82 12.9 11–15.7 6.17 46.8 https://doi.org/10.1371/journal.pone.0199701.t005 Table 6. Distribution of finishing according to categorical variables. Variable Level N Percent (%) sex Male 271,224 88 Female 36,647 12 nat JPN 61,990 20 GER 51,313 17 SUI 49,596 16 FRA 46,553 15 ITA 38,177 12 USA 14,356 4.7 POL 5,472 1.8 CHN 4,223 1.4 ESP 4,059 1.3 AUS 4,010 1.3 GBR 3,414 1.1 TPE 2,778 0.9 CAN 2,676 0.87 BEL 2,602 0.85 KOR 2,576 0.84 CZE 2,506 0.81 AUT 2,082 0.68 NED 2,027 0.66 RUS 1,852 0.6 FIN 1,442 0.47 HKG 1,332 0.43 DEN 960 0.31 HUN 947 0.31 SWE 928 0.3 country SUI 84,856 28 JPN 61,702 20 FRA 43,751 14 ITA 40,736 13 GER 21,943 7.1 USA 13,770 4.5 POL 4,404 1.4 AUS 4,223 1.4 ESP 3,922 1.3 NED 3,342 1.1 other (49) 28,564 9.3 https://doi.org/10.1371/journal.pone.0199701.t006 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 13 / 29 Table 7. Numbers of finishes before and after truncation and percentage of removed finishes. Nationality N finishes before truncation N finishes after truncation Removed (%) AUS 4,010 1,702 57.6 AUT 2,082 1,417 31.9 BEL 2,602 1,810 30.4 CAN 2,676 1,728 35.4 CHN 4,223 287 93.2 CZE 2,506 940 62.5 DEN 960 826 14 ESP 4,059 1,965 51.6 FIN 1,442 1,324 8.2 FRA 46,553 29,815 36 GBR 3,414 1,871 45.2 GER 51,313 37,412 27.1 HKG 1,332 109 91.8 HUN 947 774 18.3 ITA 38,177 20,021 47.6 JPN 61,990 56,777 8.4 KOR 2,576 1,483 42.4 NED 2,027 1,612 20.5 POL 5,472 2,970 45.7 RUS 1,852 1,648 11 SUI 49,596 21,981 55.7 SWE 928 774 16.6 TPE 2,778 2,286 17.7 USA 14,356 5,819 59.5 https://doi.org/10.1371/journal.pone.0199701.t007 Table 8. Mean, SD, median, interquartiles, mode, skewness and excess of time for each nationality of the complete dataset. Nationality Number of finishes Mean (SD) Median (IQ) Minimum Maximum Mode Skewness Excess AUS 4,010 15.2 (4.17) 15 (12.2–17.8) 6.62 37.9 13.6 0.62 1.256 AUT 2,082 13 (3.69) 12.2 (10.3–15.2) 7.11 27.6 10.8 0.884 0.29 BEL 2,602 12.7 (4.21) 11.8 (9.48–14.8) 6.26 31.7 9.58 1.035 0.88 CAN 2,676 13.8 (4.39) 12.7 (10.7–16) 6.68 35.6 11.6 1.22 1.535 CHN 4,223 20.7 (4.4) 20.9 (17.4–23.9) 6.31 32.3 22.9 -0.086 -0.545 CZE 2,506 16 (5) 16 (11.7–20.3) 6.3 38.2 15.7 0.02 -0.879 DEN 960 11.6 (3.36) 10.7 (9.69–12.3) 6.96 29.8 10.2 2.225 6.055 ESP 4,059 14.8 (5.3) 14.3 (9.96–19.1) 6.33 33.1 9.42 0.377 -0.864 FIN 1,442 11 (2.52) 10.7 (9.49–12.1) 6.51 32.8 10.1 2.068 8.999 FRA 46,553 13.6 (3.88) 12.8 (10.9–15.4) 6.39 36.6 11.7 1.288 2.475 GBR 3,414 13.9 (4.92) 13.4 (9.8–16.8) 6.17 36.4 8.56 0.713 0.222 GER 51,313 12.5 (3.47) 11.7 (9.9–14.4) 6.41 33.9 9.78 1.004 0.728 HKG 1,332 20.1 (4.14) 20 (17.4–23) 8.09 33.4 22.6 -0.072 -0.103 HUN 947 11.2 (3.36) 10.4 (8.82–12.7) 6.53 26.6 9.91 1.46 2.744 ITA 38,177 14 (3.04) 13.8 (11.9–16.1) 6.31 35.7 13.4 0.412 0.489 JPN 61,990 12.4 (2.13) 12.5 (11.2–13.4) 6.23 31.7 12.8 1.262 5.774 KOR 2,576 13.8 (2.76) 13.7 (12.4–14.7) 7.2 28.2 14.5 1.905 7.052 NED 2,027 12 (3.43) 11.2 (9.72–13.4) 6.64 34.2 10.2 1.55 3.787 POL 5,472 14.9 (5.41) 13.4 (10.8–17.3) 6.3 32.4 10.8 1.028 0.292 RUS 1,852 9.92 (3.23) 8.94 (7.56–11.3) 6.31 28.1 7.47 1.644 3.245 SUI 49,596 15.1 (4.03) 14.8 (11.8–18.2) 6.63 33.8 11.8 0.187 -0.89 SWE 928 12 (3.61) 11.4 (9.85–13) 6.38 44.2 11.2 2.226 9.605 TPE 2,778 13.1 (2.7) 12.9 (11.7–13.8) 7.62 46.8 13.6 2.903 16.754 USA 14,356 15.1 (3.96) 14.8 (12.6–17) 6.46 41.8 14.7 1.019 2.522 https://doi.org/10.1371/journal.pone.0199701.t008 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 14 / 29 to go abroad the mean time should decrease which is not the case for Japanese runners. Never- theless, both analyses with the complete and the truncated dataset show that Russian runners were the fastest and athletes from China and Hong Kong were the slowest. All other nationali- ties change their rankings reflecting the distribution of the running time. If we look at the top 10, 100 and 1000 of the fastest finishers, Japanese ultra-marathoners take the first place and the second place, respectively. The rank shifts to the rear the more par- ticipants are included in the data set. It seems that there some very fast Japanese but as the number of participants growths the mean time increase more than in other nationalities. A look at the top 10, 100 of the finishes shows that Russian ultra-marathoners take the first places. This is due to five runners with 10 finishes and 37 runners with 100 finishes. In this case it seems that Russian ultra-marathoners take high ranks since some few runners achieved some very fast finishes. This could be a limit of the linear regression if finishers are not consid- ered in a multilevel regression as random variable. We also performed a linear regression with finisher as random variable and we got similar results as in the linear regression with complete and truncated data set (data not shown). The role of nationality on 100-km ultra-marathon race times highlighted in the present study was in agreement with previous research that identified sports as the most powerful form of national performance [12]. An attempt to use sport to build a sense of national identity has been reported [13]. Either biological or cultural heredity has been identified as a factor associated with the dominance of a nationality in a sport [14]. For instance, certain genes have been identified to relate to endurance performance [15]. In addition, an explanation of the Table 9. Mean, SD, median, interquartiles, mode, skewness and excess of time for each nationality of the truncated dataset. Nationality N finishes Mean (SD) Median (IQ) Minimum Maximum Mode Skewness Excess AUS 1,702 11.4 (1.87) 11.7 (9.93–13) 6.62 14 13.4 -0.492 -0.782 AUT 1,417 10.9 (1.68) 10.9 (9.62–12.2) 7.11 14 10.5 -0.094 -0.896 BEL 1,810 10.4 (1.98) 10.3 (8.79–12.1) 6.26 14 9.58 0.015 -1.025 CAN 1,728 11.1 (1.71) 11.3 (9.84–12.6) 6.68 14 11.6 -0.249 -0.848 CHN 287 12.4 (1.29) 12.8 (11.6–13.4) 6.31 14 13.4 -1.152 1.482 CZE 940 10.6 (2) 10.6 (9.01–12.3) 6.3 14 9.56 -0.054 -1.053 DEN 826 10.5 (1.46) 10.4 (9.53–11.4) 6.96 14 10.1 0.119 -0.473 ESP 1,965 10.1 (1.88) 9.88 (8.66–11.5) 6.33 14 9.59 0.296 -0.801 FIN 1,324 10.5 (1.63) 10.5 (9.38–11.7) 6.51 14 10 0.009 -0.692 FRA 29,815 11.3 (1.64) 11.4 (10.1–12.6) 6.39 14 11.7 -0.352 -0.581 GBR 1,871 10.3 (2.14) 10.2 (8.41–12.1) 6.17 14 8.34 0.086 -1.231 GER 37,412 10.8 (1.69) 10.8 (9.48–12.1) 6.41 14 9.74 -0.027 -0.869 HKG 109 12.4 (1.39) 12.6 (11.4–13.6) 8.09 14 13.4 -0.882 0.019 HUN 774 9.91 (1.79) 9.84 (8.54–11.1) 6.53 14 10.1 0.244 -0.675 ITA 20,021 11.7 (1.6) 12 (10.6–13) 6.31 14 13.5 -0.643 -0.32 JPN 56,777 11.9 (1.5) 12.3 (11–13) 6.23 14 12.8 -0.846 0.155 KOR 1,483 12.3 (1.43) 12.7 (11.5–13.4) 7.2 14 13.5 -1.098 0.72 NED 1,612 10.6 (1.72) 10.5 (9.42–11.8) 6.64 14 10 -0.023 -0.696 POL 2,970 11 (1.64) 10.9 (10–12.2) 6.3 14 10.5 -0.312 -0.127 RUS 1,648 9.04 (1.93) 8.53 (7.39–10.5) 6.31 14 7.23 0.623 -0.647 SUI 21,981 11.3 (1.71) 11.5 (9.95–12.7) 6.63 14 11.7 -0.323 -0.814 SWE 774 10.7 (1.76) 10.9 (9.53–12) 6.38 14 11.3 -0.31 -0.627 TPE 2,286 12.3 (1.31) 12.6 (11.4–13.4) 7.62 14 13.6 -0.74 -0.135 USA 5,819 11.7 (1.79) 12 (10.5–13.1) 6.46 14 13.5 -0.776 -0.245 https://doi.org/10.1371/journal.pone.0199701.t009 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 15 / 29 Table 10. Results from linear regression with complete dataset time = sex×(year+year2)+sex×(age+age2)+sex×nationality and referenced to male, age 44, year 2009 and nationality Australia. Coefficient Standard error P-Value Intercept 13.879 0.0600 0.000 Sex (female) 0.892 0.1254 0.000 Age 0.012 0.0006 0.000 Age squared 0.0033 0.0000 0.000 Year 0.156 0.0013 0.000 Year squared 0.0062 0.0000 0.000 Female×Age 0.022 0.0019 0.000 Female×Age squared -0.0006 0.0001 0.000 Female×Year 0.014 0.0038 0.000 Female×Year squared 0.0016 0.0001 0.000 AUT -1.922 0.0976 0.000 BEL -1.713 0.0905 0.000 CAN -0.976 0.0967 0.000 CHN 5.199 0.0808 0.000 CZE 1.104 0.0927 0.000 DEN -2.806 0.1301 0.000 ESP -0.069 0.0801 0.389 FIN -3.634 0.1126 0.000 FRA -0.898 0.0621 0.000 GBR -0.597 0.0867 0.000 GER -2.075 0.0634 0.000 HKG 4.708 0.1152 0.000 HUN -3.176 0.1334 0.000 ITA -0.378 0.0629 0.000 JPN -2.764 0.0613 0.000 KOR -1.425 0.0895 0.000 NED -2.503 0.0985 0.000 POL 0.220 0.0758 0.004 RUS -4.524 0.1064 0.000 SUI -0.320 0.0642 0.000 SWE -2.678 0.1339 0.000 TPE -1.956 0.0881 0.000 USA 0.079 0.0676 0.244 AUT×Female -0.705 0.2708 0.009 BEL×Female -0.245 0.2660 0.357 CAN×Female 0.330 0.1868 0.077 CHN×Female -0.070 0.1939 0.718 CZE×Female 0.629 0.2280 0.006 DEN×Female -0.638 0.3222 0.048 ESP×Female 1.444 0.2405 0.000 FIN×Female 0.149 0.2622 0.569 FRA×Female -0.038 0.1341 0.778 GBR×Female -1.098 0.1905 0.000 GER×Female -0.441 0.1372 0.001 HKG×Female 0.476 0.2756 0.084 HUN×Female -0.856 0.2987 0.004 ITA×Female -0.418 0.1374 0.002 JPN×Female -0.661 0.1293 0.000 KOR×Female 0.304 0.3260 0.351 NED×Female -0.548 0.2645 0.038 POL×Female 0.473 0.2011 0.019 RUS×Female -0.358 0.2195 0.102 SUI×Female 0.583 0.1439 0.000 SWE×Female -1.207 0.3046 0.000 TPE×Female -0.586 0.2747 0.033 USA×Female 0.028 0.1383 0.840 https://doi.org/10.1371/journal.pone.0199701.t010 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 16 / 29 Table 11. Results from linear regression with truncated dataset time = sex×(year+year2)+sex×(age+age2))+sex×nationality and referenced to male, age 44, year 2009 and nationality Australia. Coefficient Standard error P-Value Intercept 11.110 0.0427 0.000 Sex (female) 0.047 0.0969 0.628 Age 0.021 0.0004 0.000 Age squared 0.0011 0.0000 0.000 Year 0.070 0.0008 0.000 Year squared 0.0024 0.0000 0.000 Female×Age 0.007 0.0013 0.000 Female×Age squared -0.0004 0.0001 0.000 Female×Year 0.015 0.0025 0.000 Female×Year squared 0.0000 0.0001 0.857 AUT -0.319 0.0614 0.000 BEL -0.705 0.0575 0.000 CAN -0.094 0.0615 0.128 CHN 0.758 0.1064 0.000 CZE -0.561 0.0690 0.000 DEN -0.764 0.0730 0.000 ESP -1.073 0.0559 0.000 FIN -0.880 0.0636 0.000 FRA 0.058 0.0438 0.182 GBR -0.740 0.0589 0.000 GER -0.316 0.0441 0.000 HKG 0.787 0.1674 0.000 HUN -1.241 0.0762 0.000 ITA 0.515 0.0443 0.000 JPN 0.261 0.0431 0.000 KOR 0.579 0.0595 0.000 NED -0.553 0.0596 0.000 POL -0.137 0.0522 0.009 RUS -2.124 0.0616 0.000 SUI 0.242 0.0449 0.000 SWE -0.620 0.0759 0.000 TPE 0.678 0.0545 0.000 USA 0.353 0.0484 0.000 AUT×Female 0.222 0.1713 0.195 BEL×Female 0.148 0.1704 0.386 CAN×Female 0.393 0.1282 0.002 CHN×Female 0.127 0.3268 0.698 CZE×Female 0.450 0.1899 0.018 DEN×Female 0.436 0.1792 0.015 ESP×Female 0.693 0.1996 0.001 FIN×Female 0.754 0.1505 0.000 FRA×Female 0.304 0.1015 0.003 GBR×Female -0.434 0.1321 0.001 GER×Female 0.521 0.1015 0.000 HKG×Female -0.137 0.4468 0.759 HUN×Female 0.068 0.1701 0.691 ITA×Female 0.134 0.1040 0.196 JPN×Female 0.164 0.0979 0.094 KOR×Female 0.022 0.2418 0.928 NED×Female 0.385 0.1582 0.015 POL×Female 0.600 0.1476 0.000 RUS×Female 0.534 0.1325 0.000 SUI×Female 0.536 0.1085 0.000 SWE×Female 0.051 0.1715 0.767 TPE×Female -0.158 0.1617 0.327 USA×Female 0.108 0.1081 0.317 https://doi.org/10.1371/journal.pone.0199701.t011 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 17 / 29 Table 12. Results from truncated regression with truncated dataset time = sex×(year+year2)+sex×(age+age2) + sex×nationality and referenced to male, age 44, year 2009 and nationality Australia. Coefficient Standard error P-Value Intercept 11.490 0.067 0.000 Sex (female) 0.003 0.153 0.985 Age 0.038 0.001 0.000 Age squared 0.002 0.000 0.000 Year 0.109 0.001 0.000 Year squared 0.004 0.000 0.000 Female×Age 0.017 0.002 0.000 Female×Age squared -0.000 0.000 0.123 Female×Year 0.035 0.004 0.000 Female×Year squared 0.001 0.000 0.010 AUT -0.443 0.093 0.000 BEL -0.912 0.086 0.000 CAN -0.087 0.095 0.358 CHN 1.672 0.215 0.000 CZE -0.717 0.102 0.000 DEN -0.985 0.107 0.000 ESP -1.343 0.083 0.000 FIN -1.138 0.094 0.000 FRA 0.109 0.069 0.115 GBR -0.935 0.088 0.000 GER -0.405 0.069 0.000 HKG 1.454 0.330 0.000 HUN -1.538 0.109 0.000 ITA 0.856 0.070 0.000 JPN 0.497 0.068 0.000 KOR 1.226 0.106 0.000 NED -0.698 0.090 0.000 POL -0.174 0.081 0.031 RUS -2.464 0.089 0.000 SUI 0.389 0.071 0.000 SWE -0.933 0.112 0.000 TPE 1.390 0.096 0.000 USA 0.614 0.078 0.000 AUT×Female 0.377 0.259 0.146 BEL×Female 0.676 0.255 0.008 CAN×Female 0.625 0.201 0.002 CHN×Female 0.245 0.701 0.726 CZE×Female 0.496 0.284 0.081 DEN×Female 0.672 0.268 0.012 ESP×Female 0.871 0.296 0.003 FIN×Female 0.852 0.232 0.000 FRA×Female 0.570 0.161 0.000 GBR×Female -0.414 0.196 0.034 GER×Female 0.858 0.160 0.000 HKG×Female -0.442 0.839 0.599 HUN×Female 0.267 0.242 0.270 ITA×Female 0.364 0.167 0.029 JPN×Female 0.368 0.156 0.018 KOR×Female 0.074 0.468 0.875 NED×Female 0.613 0.243 0.011 POL×Female 1.219 0.241 0.000 RUS×Female 0.699 0.194 0.000 SUI×Female 0.950 0.173 0.000 SWE×Female 0.311 0.258 0.228 TPE×Female -0.190 0.294 0.517 USA×Female 0.218 0.173 0.208 https://doi.org/10.1371/journal.pone.0199701.t012 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 18 / 29 Table 13. Interaction with race site, results from truncated regression with complete data set time = sex×(year+year2)+sex×(age+age2) + sex×nationality×site and referenced to male, age 44, year 2009 and nationality Austria. Coefficient Standard error P-Value Intercept 14.043 0.0615 0.000 Sex (female) 1.029 0.1296 0.000 Age 0.013 0.0006 0.000 Age squared 0.0032 0.0000 0.000 Year 0.157 0.0013 0.000 Year squared 0.0062 0.0000 0.000 0.157 0.0013 0.000 Female×Age 0.021 0.0019 0.000 Female×Age squared -0.0007 0.0001 0.000 Female×Year 0.009 0.0039 0.015 Female×Year squared 0.0016 0.0001 0.000 AUT -3.621 0.1796 0.000 BEL -3.480 0.1234 0.000 CAN -1.300 0.1076 0.000 CHN 5.201 0.0869 0.000 CZE 2.448 0.1055 0.000 DEN -3.661 0.1475 0.000 ESP -0.399 0.0851 0.000 FIN -3.773 0.1179 0.000 FRA -1.332 0.0637 0.000 GBR -2.477 0.1188 0.000 GER -2.968 0.0673 0.000 HKG 4.780 0.1187 0.000 HUN -3.101 0.1529 0.000 ITA -0.511 0.0643 0.000 JPN -2.961 0.0626 0.000 KOR -1.707 0.0907 0.000 NED -3.523 0.1151 0.000 POL 0.632 0.0801 0.000 RUS -4.060 0.1229 0.000 SUI -0.424 0.0656 0.000 SWE -3.181 0.1519 0.000 TPE -2.170 0.0910 0.000 USA -0.012 0.0693 0.867 AUT×race abroad 3.994 0.2913 0.000 BEL×race abroad 4.684 0.2605 0.000 CAN×race abroad 2.645 0.2770 0.000 CHN×race abroad 1.420 0.2545 0.000 CZE×race abroad -2.328 0.2665 0.000 DEN×race abroad 4.545 0.3366 0.000 ESP×race abroad 2.878 0.2568 0.000 FIN×race abroad 1.972 0.3570 0.000 FRA×race abroad 4.657 0.2278 0.000 GBR×race abroad 4.787 0.2561 0.000 GER×race abroad 3.308 0.2240 0.000 HKG×race abroad -0.484 0.4050 0.232 HUN×race abroad 1.293 0.3396 0.000 ITA×race abroad 1.596 0.2406 0.000 JPN×race abroad 5.831 0.2636 0.000 KOR×race abroad 5.142 0.4134 0.000 NED×race abroad 4.362 0.2723 0.000 (Continued) The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 19 / 29 Table 13. (Continued) Coefficient Standard error P-Value POL×race abroad -0.624 0.2490 0.012 RUS×race abroad 0.267 0.2873 0.353 SUI×race abroad 2.078 0.2632 0.000 SWE×race abroad 3.296 0.3443 0.000 TPE×race abroad 2.539 0.3055 0.000 USA×race abroad 1.300 0.2495 0.000 AUT×female -1.319 0.5884 0.025 BEL×female -0.744 0.4024 0.064 CAN×female 0.792 0.2117 0.000 CHN×female -0.483 0.2139 0.024 CZE×female 0.357 0.2593 0.169 DEN×female -0.611 0.3860 0.113 ESP×female 0.849 0.2644 0.001 FIN×female -0.351 0.2769 0.205 FRA×female -0.068 0.1385 0.626 GBR×female -0.251 0.2474 0.310 GER×female -0.913 0.1526 0.000 HKG×female 0.650 0.2918 0.026 HUN×female -1.180 0.4018 0.003 ITA×female -0.491 0.1413 0.001 JPN×female -0.781 0.1331 0.000 KOR×female -0.311 0.3419 0.363 NED×female -0.730 0.3210 0.023 POL×female 0.025 0.2143 0.906 RUS×female -0.436 0.2716 0.109 SUI×female 0.442 0.1477 0.003 SWE×female -1.042 0.3398 0.002 TPE×female -0.522 0.3117 0.094 USA×female 0.092 0.1427 0.518 AUT× race abroad×female -1.319 0.5884 0.092 BEL× race abroad×female -0.744 0.4024 0.043 CAN× race abroad×female 0.792 0.2117 0.053 CHN× race abroad×female -0.483 0.2139 0.000 CZE× race abroad×female 0.357 0.2593 0.291 DEN× race abroad×female -0.611 0.3860 0.771 ESP× race abroad×female 0.849 0.2644 0.000 FIN× race abroad×female -0.351 0.2769 0.000 FRA× race abroad×female -0.068 0.1385 0.556 GBR× race abroad×female -0.251 0.2474 0.335 GER× race abroad×female -0.913 0.1526 0.004 HKG× race abroad×female 0.650 0.2918 0.982 HUN× race abroad×female -1.180 0.4018 0.030 ITA× race abroad×female -0.491 0.1413 0.356 JPN× race abroad×female -0.781 0.1331 0.004 KOR× race abroad×female -0.311 0.3419 0.011 NED× race abroad×female -0.730 0.3210 0.189 POL× race abroad×female 0.025 0.2143 0.000 RUS× race abroad×female -0.436 0.2716 0.031 SUI× race abroad×female 0.442 0.1477 0.005 SWE× race abroad×female -1.042 0.3398 0.816 TPE× race abroad×female -0.522 0.3117 0.870 USA× race abroad×female 0.092 0.1427 0.005 https://doi.org/10.1371/journal.pone.0199701.t013 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 20 / 29 Table 14. Interaction with race site, results from linear regression with truncated dataset time = sex×year+year2)+sex×(age+age2) + sex×nationality×site and refer- enced to male, age 44, year 2009, site at home and nationality Australia. Coefficient Standard error P-Value Intercept 11.319 0.0447 0.000 Sex (female) 0.139 0.1045 0.182 Age 0.020 0.0004 0.000 Age squared 0.0011 0.0000 0.000 Year 0.068 0.0008 0.000 Year squared 0.0024 0.0000 0.000 Female×Age 0.005 0.0013 0.000 Female×Age squared -0.0004 0.0001 0.000 Female×Year 0.006 0.0025 0.013 Female×Year squared -0.0002 0.0001 0.152 AUT -1.092 0.0953 0.000 BEL -1.228 0.0701 0.000 CAN -0.128 0.0668 0.056 CHN 0.601 0.1196 0.000 CZE -0.314 0.0984 0.001 DEN -0.831 0.0792 0.000 ESP -1.188 0.0601 0.000 FIN -0.994 0.0667 0.000 FRA -0.136 0.0458 0.003 GBR -1.322 0.0737 0.000 GER -1.040 0.0472 0.000 HKG 0.841 0.2060 0.000 HUN -0.986 0.0867 0.000 ITA 0.336 0.0463 0.000 JPN 0.074 0.0451 0.102 KOR 0.432 0.0609 0.000 NED -0.856 0.0658 0.000 POL -0.105 0.0567 0.063 RUS -1.672 0.0706 0.000 SUI 0.001 0.0469 0.986 SWE -0.661 0.0835 0.000 TPE 0.510 0.0568 0.000 USA 0.273 0.0507 0.000 AUT×race abroad 2.484 0.1627 0.000 BEL×race abroad 2.343 0.1501 0.000 CAN×race abroad 0.957 0.1663 0.000 CHN×race abroad 1.571 0.2566 0.000 CZE×race abroad 1.002 0.1705 0.000 DEN×race abroad 1.025 0.1950 0.000 ESP×race abroad 1.297 0.1558 0.000 FIN×race abroad 0.860 0.1944 0.000 FRA×race abroad 1.484 0.1350 0.000 GBR×race abroad 2.418 0.1524 0.000 GER×race abroad 2.584 0.1308 0.000 HKG×race abroad 1.065 0.3543 0.003 HUN×race abroad 0.197 0.1876 0.295 (Continued) The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 21 / 29 Table 14. (Continued) Coefficient Standard error P-Value ITA×race abroad 0.735 0.1427 0.000 JPN×race abroad -0.399 0.1659 0.016 KOR×race abroad 0.012 0.2824 0.965 NED×race abroad 2.018 0.1583 0.000 POL×race abroad 0.864 0.1450 0.000 RUS×race abroad -0.102 0.1583 0.520 SUI×race abroad 0.884 0.1622 0.000 SWE×race abroad 1.021 0.1956 0.000 TPE×race abroad 1.460 0.1714 0.000 USA×race abroad 0.567 0.1488 0.000 AUT×female -0.564 0.3079 0.067 BEL×female 0.207 0.2213 0.349 CAN×female 0.324 0.1445 0.025 CHN×female 0.293 0.3774 0.437 CZE×female -0.191 0.2767 0.489 DEN×female 0.408 0.2037 0.045 ESP×female 0.377 0.2155 0.081 FIN×female 0.567 0.1591 0.000 FRA×female 0.296 0.1089 0.007 GBR×female -0.032 0.1626 0.845 GER×female 0.371 0.1127 0.001 HKG×female 0.798 0.8050 0.321 HUN×female -0.139 0.2253 0.536 ITA×female 0.161 0.1113 0.149 JPN×female 0.144 0.1053 0.171 KOR×female 0.088 0.2539 0.729 NED×female 0.278 0.1813 0.125 POL×female 0.191 0.1639 0.244 RUS×female 0.284 0.1617 0.079 SUI×female 0.454 0.1155 0.000 SWE×female 0.024 0.1890 0.897 TPE×female -0.121 0.1845 0.512 USA×female 0.234 0.1167 0.045 AUT× race abroad×female 0.658 0.4180 0.115 BEL× race abroad×female -0.506 0.3768 0.179 CAN× race abroad×female -0.052 0.3147 0.868 CHN× race abroad×female -0.906 0.7296 0.214 CZE× race abroad×female 0.692 0.4175 0.098 DEN× race abroad×female -0.199 0.4214 0.637 ESP× race abroad×female 1.056 0.5267 0.045 FIN× race abroad×female 0.958 0.4499 0.033 FRA× race abroad×female -1.505 0.2853 0.000 GBR× race abroad×female -1.157 0.3115 0.000 GER× race abroad×female -0.119 0.2609 0.648 HKG× race abroad×female -1.186 0.9940 0.233 HUN× race abroad×female 0.672 0.3816 0.078 ITA× race abroad×female -1.355 0.3009 0.000 (Continued) The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 22 / 29 differences in participation among nationalities might be the differences in their attitudes towards physical activity [16]. Participation in running might be influenced by economic and cultural factors, e.g. those without a migration background are more likely to participate in running than those with a migration background [17]. Another aspect affecting sport Table 14. (Continued) Coefficient Standard error P-Value JPN× race abroad×female -0.214 0.3048 0.482 KOR× race abroad×female 0.116 0.7603 0.878 NED× race abroad×female -0.144 0.3698 0.697 POL× race abroad×female 0.900 0.3611 0.013 RUS× race abroad×female 0.585 0.3127 0.061 SUI× race abroad×female -0.592 0.3508 0.091 SWE× race abroad×female -0.452 0.4260 0.289 TPE× race abroad×female -0.285 0.3870 0.461 USA× race abroad×female -0.878 0.2887 0.002 https://doi.org/10.1371/journal.pone.0199701.t014 Table 15. Comparing times in hours with finishes performed. Times were computed based on the model time = sex×(year+year2)+sex×(age+age2) + sex×nationality and referenced to male, age 44, year 2009 and nationality Australia. In parentheses: 95%-CI. A B % Difference C Data: complete Linear regression with fixed effects Data: truncated at 15 hours Linear regression with fixed effects Between A and B (B-A)/A Data: truncated at 15 hours Truncated linear regression with fixed effects AUS 13.9 (13.8–14.0) 11.1 (11.0–11.2) -20.0% 11.5 (11.4–11.6) AUT 12.0 (11.7–12.2) 10.8 (10.6–10.9) -9.8% 11.0 (10.8–11.3) BEL 12.2 (12.0–12.4) 10.4 (10.3–10.5) -14.5% 10.6 (10.4–10.8) CAN 12.9 (12.7–13.1) 11.0 (10.9–11.2) -14.6% 11.4 (11.2–11.6) CHN 19.1 (18.9–19.3) 11.9 (11.6–12.1) -37.8% 13.2 (12.7–13.6) CZE 15.0 (14.8–15.2) 10.5 (10.4–10.7) -29.6% 10.8 (10.5–11.0) DEN 11.1 (10.8–11.4) 10.3 (10.2–10.5) -6.6% 10.5 (10.3–10.8) ESP 13.8 (13.6–14.0) 10.0 (9.9–10.2) -27.3% 10.1 (9.9–10.4) FIN 10.2 (10.0–10.5) 10.2 (10.1–10.4) -0.1% 10.4 (10.1–10.6) FRA 13.0 (12.8–13.2) 11.2 (11.0–11.3) -14.0% 11.6 (11.4–11.8) GBR 13.3 (13.1–13.5) 10.4 (10.2–10.5) -21.9% 10.6 (10.3–10.8) GER 11.8 (11.6–12.0) 10.8 (10.7–10.9) -8.6% 11.1 (10.9–11.3) HKG 18.6 (18.3–18.8) 11.9 (11.6–12.2) -36.0% 12.9 (12.3–13.6) HUN 10.7 (10.4–11.0) 9.9 (9.7–10.0) -7.8% 10.0 (9.7–10.2) ITA 13.5 (13.3–13.7) 11.6 (11.5–11.7) -13.9% 12.3 (12.2–12.5) JPN 11.1 (10.9–11.3) 11.4 (11.3–11.5) 2.3% 12.0 (11.8–12.2) KOR 12.5 (12.2–12.7) 11.7 (11.5–11.8) -6.1% 12.7 (12.5–13.0) NED 11.4 (11.2–11.6) 10.6 (10.4–10.7) -7.2% 10.8 (10.6–11.0) POL 14.1 (13.9–14.3) 11.0 (10.8–11.1) -22.2% 11.3 (11.1–11.5) RUS 9.4 (9.1–9.6) 9.0 (8.8–9.1) -3.9% 9.0 (8.8–9.2) SUI 13.6 (13.4–13.7) 11.4 (11.2–11.5) -16.3% 11.9 (11.7–12.1) SWE 11.2 (10.9–11.5) 10.5 (10.3–10.7) -6.3% 10.6 (10.3–10.8) TPE 11.9 (11.7–12.1) 11.8 (11.7–11.9) -1.1% 12.9 (12.7–13.1) USA 14.0 (13.8–14.1) 11.5 (11.3–11.6) -17.9% 12.1 (11.9–12.3) https://doi.org/10.1371/journal.pone.0199701.t015 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 23 / 29 performance is doping, for which no accurate rates exist due to its undisclosed practice; how- ever, its prevalence has been estimated as 14–39% in adult elite athletes and has been shown to vary by performance level and nationality [18]. Since it has been shown that the role of nationality might vary by distance in endurance and ultra-endurance running [9], the findings of the present study should not be generalized to other distances. On the other hand, strength of the study was its methodological approach to the research question: first, it used one of the larger samples of 100-km ultra-marathoners ever studied, second, it considered a probably applied time limit barrier by using a truncated data set, third, we compared adjusted times of a reference finisher to compare ranks, and forth, we provide a time distribution classification which helps to interpret the results. We found that ultra-marathoners from Russia were the fastest in this specific ultra-mara- thon distance. Unfortunately, this kind of analysis is not able to explain the reason for this dominance. The role of ethnicity in running is, however, well-known for other running dis- tances. Running best performances are dominated by a few groups of athletes including run- ners with West African ancestry for the sprint distances and East African runners for the long Table 16. Comparing times in hours with finishes performed at home. Times were computed based on the model time = ex×(year+year2)+sex×(age+age2) + sex×natio- nality×site and referenced to male, age 44, year 2009 and nationality Australia. A % Difference between races abroad/at home B % Difference between races at home/on abroad C % Difference between races abroad/at home Data: complete Linear regression with fixed effects Data: truncated at 14 hours Linear regression with fixed effects Data: truncated at 14 hours Truncated linear regression with fixed effects Races at home Races abroad Races at home Races abroad Races at home Races abroad AUS 14.0 12.0 11.3 11.3 9.6 -15.4% 11.7 9.6 -17.8% AUT 10.4 12.4 10.2 10.2 11.0 7.2% 10.5 11.4 9.3% BEL 10.6 13.2 10.1 10.1 10.7 5.9% 10.2 10.9 6.5% CAN 12.7 13.3 11.2 11.2 10.4 -7.1% 11.7 10.6 -9.4% CHN 19.2 18.6 11.9 11.9 11.7 -1.5% 13.1 13.2 0.7% CZE 16.5 12.1 11.0 11.0 10.3 -6.8% 11.3 10.3 -8.8% DEN 10.4 12.9 10.5 10.5 9.8 -6.9% 10.7 9.9 -7.2% ESP 13.6 14.5 10.1 10.1 9.7 -4.4% 10.3 10.0 -3.2% FIN 10.3 10.2 10.3 10.3 9.4 -8.6% 10.4 9.3 -10.6% FRA 12.7 15.3 11.2 11.2 10.9 -2.4% 11.6 11.2 -3.4% GBR 11.6 14.3 10.0 10.0 10.7 6.7% 10.1 10.9 8.1% GER 11.1 12.3 10.3 10.3 11.1 8.1% 10.4 11.5 10.6% HKG 18.8 16.3 12.2 12.2 11.5 -5.6% 13.1 11.2 -14.5% HUN 10.9 10.2 10.3 10.3 8.8 -15.0% 10.5 8.9 -15.3% ITA 13.5 13.1 11.7 11.7 10.6 -8.7% 12.4 10.8 -12.9% JPN 11.1 14.9 11.4 11.4 9.2 -18.8% 12.0 9.4 -21.4% KOR 12.3 15.4 11.8 11.8 10.0 -14.8% 12.8 10.3 -19.4% NED 10.5 12.8 10.5 10.5 10.7 2.6% 10.6 11.0 4.0% POL 14.7 12.0 11.2 11.2 10.3 -7.9% 11.6 10.5 -9.2% RUS 10.0 8.2 9.6 9.6 7.8 -19.2% 9.5 7.9 -17.2% SUI 13.6 13.7 11.3 11.3 10.5 -7.6% 11.8 10.4 -12.3% SWE 10.9 12.1 10.7 10.7 9.9 -6.8% 10.7 9.8 -8.5% TPE 11.9 12.4 11.8 11.8 11.5 -2.4% 12.9 12.2 -5.8% USA 14.0 13.3 11.6 11.6 10.4 -10.2% 12.4 10.6 -14.1% https://doi.org/10.1371/journal.pone.0199701.t016 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 24 / 29 distances [9, 19]. For marathoners, East-African runners from Kenya and Ethiopia dominate since decades running distances up to the marathon [9, 20–22] while for other running dis- tances such as the sprint distances, runners from Jamaica are dominating [23]. For elite Kenyan runners, it is well-known that they are from a distinctive environmental background in terms of geographical distribution, ethnicity and run to school [20]. Interest- ingly, the same findings were reported for elite Ethiopian runners, who also have a distinct environmental background in terms of geographical distribution, ethnicity, and also often run to school [21]. So for both Kenyan and Ethiopian runners both environmental and social fac- tors are important for their athletic success. These aspects are, however, not only for East Afri- can distance runners, but also for sprinters from Jamaica of importance. It has been shown that a higher proportion of middle distance and both jump and throw athletes walked to school and travelled greater distances to school [23]. Although different anthropometric, physiological, biomechanical and training characteristics are of importance for the East African running dominance [22, 24, 25], a strong psychological Fig 9. The upper panel shows the adjusted time for each nationality in ascending order at reference sex = male, age = 44 and year = 2009. (A) is based on linear regression of the complete dataset, (B) on the truncated dataset and (C) on the truncated regression of the truncated dataset. The lower panel with figures (D) and (E) shows the changes in rank from (A) to (B) and (B) to (C). https://doi.org/10.1371/journal.pone.0199701.g009 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 25 / 29 Fig 10. The rank of nationality computed from model 2 (interaction of nationality with races at home/abroad). (A) shows rank changes from races at home to races abroad based on linear regression with complete dataset. (B) shows rank changes from races at home to races abroad based on linear regression with truncated dataset. https://doi.org/10.1371/journal.pone.0199701.g010 Table 17. Mean time of the top 10, 100 and 1000 of the fastest finishers for each nationality. Only the lowest time of a finishes was considered if one finisher had sev- eral finishes. Nationality N of finishers Mean time of top 10 Nationality N of finishers Mean time of top 100 Nationality N of finishers Mean time of top 1000 1 JPN 10 6.41 JPN 100 6.83 GER 1000 7.83 2 RUS 10 6.43 RUS 100 6.86 JPN 1000 7.97 3 FRA 10 6.52 GER 100 7.00 FRA 1000 8.19 4 GBR 10 6.55 FRA 100 7.01 SUI 1000 8.31 5 ESP 10 6.61 SUI 100 7.18 ITA 1000 8.81 6 BEL 10 6.62 GBR 100 7.29 USA 1000 9.46 7 USA 10 6.62 USA 100 7.31 POL 1000 10.24 8 GER 10 6.63 ITA 100 7.38 GBR 1000 10.80 9 POL 10 6.65 ESP 100 7.57 RUS 920 10.82 10 ITA 10 6.67 BEL 100 7.74 FIN 479 10.96 11 SUI 10 6.72 POL 100 7.96 ESP 1000 11.05 12 FIN 10 6.79 NED 100 8.06 TPE 1000 11.42 13 CZE 10 6.81 AUS 100 8.08 AUS 1000 11.60 14 HUN 10 6.81 HUN 100 8.18 DEN 544 11.62 15 NED 10 6.87 AUT 100 8.21 HUN 419 11.62 16 SWE 10 6.89 FIN 100 8.28 CAN 1000 11.86 17 AUS 10 6.95 CZE 100 8.34 NED 805 12.10 18 DEN 10 7.33 CAN 100 8.38 BEL 1000 12.14 19 AUT 10 7.34 SWE 100 8.40 SWE 570 12.18 20 CAN 10 7.36 DEN 100 8.61 KOR 1000 12.37 21 KOR 10 7.68 KOR 100 9.05 AUT 969 12.66 22 TPE 10 7.99 TPE 100 9.26 CZE 1000 14.81 23 CHN 10 8.85 CHN 100 11.14 CHN 1000 15.31 24 HKG 10 9.47 HKG 100 12.41 HKG 1000 19.22 https://doi.org/10.1371/journal.pone.0199701.t017 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 26 / 29 motivation to succeed athletically for the purpose of economic and social advancement is known [26]. Elite Kenyan runners become a competitive athlete due to economic reasons. Typi- cally, Kenyan athletes see athletics as a means of making money to help their families, parents and siblings [20, 27]. Practical applications for athletes and coaches The present study advances existing theoretical knowledge as scientists will improve their understanding of participation and performance trends by nationality in 100-km ultra-mara- thon running which is relatively less studied compared to shorter distances such as sprint and marathon distances. Moreover, coaches and runners can use the findings to optimize their preparation and participation in a 100-km ultra-marathon. Athletes from other countries than the dominating nationalities (i.e. Russia, Hungary) must be aware that they will most probably not be able to reach a world-class level in 100-km ultra-marathon running. Conclusions In summary, in contrast to existing findings investigating the top 10 by nationality, this analy- sis of all runners showed that ultra-marathoners from Russia, not Japan, were the fastest 100-km ultra-marathoners worldwide when considering all races held since 1959. Since we know for the best sprinters and marathoners in the world that specific anthropometric, train- ing and environmental characteristics are prevalent, future studies need to investigate why Russian ultra-marathoners dominate the 100-km ultra-marathon distance. Table 18. Mean time of the top 10, 100 and 1000 of the fastest finishes for each nationality. Nationality N of finishers Mean time of top 10 Nationality N of finishers Mean time of top 100 Nationality N of finishers Mean time of top 1000 1 RUS 5 6.37 RUS 37 6.57 GER 332 7.35 2 JPN 7 6.37 JPN 50 6.68 FRA 358 7.52 3 BEL 3 6.44 FRA 35 6.74 JPN 557 7.60 4 POL 3 6.47 GER 34 6.80 RUS 351 7.70 5 GBR 7 6.47 GBR 30 6.86 SUI 393 7.74 6 ITA 4 6.49 BEL 23 6.88 ITA 362 8.02 7 FRA 7 6.50 ITA 30 6.94 GBR 402 8.54 8 ESP 4 6.50 ESP 28 6.96 USA 543 8.58 9 GER 5 6.54 SUI 47 6.96 ESP 365 8.60 10 CZE 3 6.55 USA 42 6.99 BEL 359 8.87 11 USA 7 6.60 POL 25 7.00 POL 503 9.25 12 SUI 4 6.68 HUN 29 7.25 NED 415 9.49 13 SWE 5 6.68 NED 29 7.32 FIN 358 9.83 14 HUN 5 6.70 CZE 24 7.33 CAN 463 9.94 15 FIN 7 6.72 AUS 42 7.55 AUT 532 10.04 16 NED 4 6.77 FIN 40 7.64 AUS 536 10.14 17 AUS 4 6.81 SWE 47 7.69 CZE 451 10.82 18 CAN 3 6.89 CAN 41 7.80 TPE 753 11.05 19 DEN 7 7.26 AUT 47 7.86 HUN 419 11.18 20 AUT 6 7.28 DEN 47 8.11 DEN 544 11.59 21 KOR 5 7.42 KOR 72 8.76 KOR 625 11.62 22 TPE 9 7.98 TPE 79 9.15 SWE 570 11.96 23 CHN 9 8.85 CHN 84 10.95 CHN 808 14.82 24 HKG 10 9.47 HKG 90 12.21 HKG 846 18.46 https://doi.org/10.1371/journal.pone.0199701.t018 The fastest 100-km ultra-marathoners worldwide PLOS ONE \| https://doi.org/10.1371/journal.pone.0199701 July 11, 2018 27 / 29 Author Contributions Conceptualization: Beat Knechtle, Pantelis Theodoros Nikolaidis. 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PMC9390899	STUDY PROTOCOL Effects of nutritional and hydration strategies during ultramarathon events between finishers and non-finishers: A systematic review protocol James W. NavaltaID1, Victor D. Y. BeckID2, Taylor M. Diaz1, Vernice E. Ollano1 1 Department of Kinesiology and Nutrition Sciences, University of Nevada, Las Vegas, Las Vegas, NV, United States of America, 2 Department of Physical Therapy, University of Nevada, Las Vegas, Las Vegas, NV, United States of America james.navalta@unlv.edu Abstract Ultramarathon running is a sport that is growing in popularity. Competing in an ultramara- thon event is physiologically taxing on the human body, and it should not be surprising that not all individuals who enroll for an event ultimately finish. While many factors can contribute to this phenomenon, it is likely that nutritional and hydration strategies play a large role between finishing and not finishing an ultramarathon. No published paper has systematically reviewed the effects of nutritional and hydration strategies during ultramarathon events between finishers and non-finishers. This paper details our intended protocol with the follow- ing steps that create the flow of the systematic review: 1) Determine the review question and Participant, Intervention, Comparator, Outcome, Study Design (PICOS) criteria; 2) Cre- ate inclusion and exclusion criteria; 3) Create and follow a search strategy; 4) Document sources that are included and excluded according to the pre-determined eligibility criteria; 5) Assess final sources for risk of bias; 6) Extract pertinent data from final full-text articles and synthesize the information; and 7) Disseminate findings of the systematic review. Introduction Ultramarathon running is a sport that is growing in popularity. The oldest ultramarathon event, the Comrades Marathon, began in 1921 covering 89.9 km (55.9 mi), and 17 of the 34 participants did not finish (50% DNF) [1]. Since that time, over 300,000 people have com- pleted the race, which currently caps yearly enrollment at 20,000 participants [2]. Participation in ultramarathon events has risen exponentially since the year 2000, with over a million run- ners participating (1,042,156) [3]. The sport involves running or walking a distance greater than a traditional marathon (42.2 km, or 26.2 mi). The most popular (over three-quarters of a million entrants) ultramarathon distance is 50 km (31.1 mi) as it represents a distance just above the traditional marathon [3]. Participation decreases as the distance gets longer (just over 100,000 entrants for 100km [62.1 mi], and approximately 40,000 for 24 h events) [3]. Competing in an ultramarathon event is physiologically taxing on the human body. Ultra- marathon running has been associated with an increase in cardiac troponin T (a measure of PLOS ONE PLOS ONE \| https://doi.org/10.1371/journal.pone.0272668 August 19, 2022 1 / 8 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Navalta JW, Beck VDY, Diaz TM, Ollano VE (2022) Effects of nutritional and hydration strategies during ultramarathon events between finishers and non-finishers: A systematic review protocol. PLoS ONE 17(8): e0272668. https://doi. org/10.1371/journal.pone.0272668 Editor: Samuel Penna Wanner, Universidade Federal de Minas Gerais, BRAZIL Received: April 8, 2022 Accepted: July 24, 2022 Published: August 19, 2022 Copyright: © 2022 Navalta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: No datasets were generated or analysed during the current study. All relevant data from this study will be made available upon study completion. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. myocardial damage) [4], malondialdehyde and creatine kinase (lipid peroxidation) [5], and oxidative stress [6]. Other reported effects include measures associated with cardiac fatigue such as increased atrial volume [7]. Given these effects it should not be surprising that not all individuals who enroll for an ultramarathon event ultimately finish. It is estimated that between 20–50% of individuals who begin an ultramarathon do not finish [8]. While many fac- tors such as gastrointestinal distress and discomfort can contribute to this phenomenon, it is likely that nutritional and hydration strategies play a large role. Several systematic reviews have been conducted on various ultramarathon running topics including psychology [9], limiting factors [10], long-term health problems [11], and patho- physiology [12]. There has been a 50-year State of the Science offering [13], as well as a Posi- tion Statement on Nutrition specific to training for a single-stage ultramarathon [14]. To our knowledge, no published paper has reviewed the effects of nutritional and hydration strategies during ultramarathon events between finishers and non-finishers. A “systematic review” was selected as the methodology after reading published guidance on review types [15, 16]. The primary aim of the proposed systematic review will be to find and describe the nutritional and hydration strategies between single-stage ultramarathon finishers and non-finishers, and out- comes reported in published literature. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the best practices for performing a system- atic review include publishing the protocol independent of the review to ensure procedural transparency [17]. As our systematic review of ultramarathon nutritional strategies will be, to our knowledge, the first published on the topic, it is especially important to publish our proto- col. This methods paper was written to achieve two objectives: 1) To adhere to the best prac- tices stated in the PRISMA guidelines; and 2) To ensure procedural transparency. Materials and methods Developing the systematic review protocol began with members of the team performing scoping searches in Google Scholar and PubMed. The scoping searches suggested that there were no published systematic reviews of nutritional strategies between finishers and non-finishers of ultramarathon events. The first author consulted peer-reviewed guidance [15, 16] about con- ducting systematic reviews in health fields and has participated in the process previously [18]. The team deliberated and agreed upon the protocol presented in this article. The details of the protocol are available via PROSPERO, an online international prospective register of systematic reviews. The protocol was submitted on February 9, 2022 and registered on March 3, 2022 (PROSPERO ID: 42022308733). The protocol has the following steps that create the flow of the systematic review: 1) Determine the review question and Participant, Intervention, Comparator, Outcome, Study Design (PICOS) criteria; 2) Create inclusion and exclusion criteria; 3) Create and follow a search strategy; 4) Document sources that are included and excluded according to the pre-determined eligibility criteria; 5) Assess final sources for risk of bias; 6) Extract pertinent data from final full-text articles and synthesize the information; and 7) Disseminate findings of the systematic review. An optional part of systematic reviews that will be omitted is a meta-anal- ysis. A meta-analysis will not be performed in this systematic review because the studies are expected to include different ultramarathon distances, sample different populations, and mea- sure different outcomes. Because of differences among these characteristics, the studies will not be homogenous, which is necessary for a meta-analysis [19]. Step 1: Determine the review question and PICOS criteria The first step is to determine the review question and PICOS criteria (Table 1). Because multi- stage ultramarathon events stress the body from a nutritional and hydrational standpoint in a PLOS ONE A systematic review protocol: Nutrition and hydration strategies in ultramarathon finishers and non-finishers PLOS ONE \| https://doi.org/10.1371/journal.pone.0272668 August 19, 2022 2 / 8 much different manner compared to single-stage events, we decided to focus on single-stage events not lasting longer than 24 h. The event can be performed in any location (including indoors on a treadmill). Studies with adults who enrolled in a single-stage ultramarathon between the ages of 18–60 will be relevant. Controlled, uncontrolled, randomized, nonrando- mized, and observational studies will be considered. Information about participants’ charac- teristics, ultrarunning experience, and finisher or non-finisher status will be collected. Step 2: Create inclusion and exclusion criteria The second step is to create inclusion and exclusion criteria, or eligibility criteria. The review question and PICOS criteria form the basis of this systematic review’s eligibility criteria, which aim to be both explicit and succinct. These characteristics provide clarity to the review team members, enabling the exclusion of irrelevant sources during the screening process. Expedi- tious screening is important to the process because teams may be required to screen hundreds to thousands of sources. In consultation of the review question and PICOS criteria, the eligibil- ity criteria were created (Table 2). The inclusion criteria will allow for the inclusion of unpub- lished master’s theses and doctoral dissertations. This decision was made to capture as much data about nutritional and hydrational strategies during a single-stage ultramarathon event as possible. Step 3: Create and follow a search strategy After determining eligibility criteria, the review team will proceed to the third step by creating and following a search strategy (Table 3). Consistent with our PICOS and eligibility criteria, we will search in four databases for all relevant studies, regardless of publication year. The same search combination string will be used in each database (Table 3). The search combina- tion was initially created by the full review team. An iterative process of revision ensued until the search combination returned a manageable number of hits in each database (about 100– 1,000 hits). Table 1. Review question and PICOS table. Review Question Do finishers of single-stage ultramarathon events employ different nutritional and hydration strategies than individuals who do not finish? Population Ultramarathoners of any sex who enrolled in a single-stage ultramarathon event, between the ages of 18–60 years old Intervention Single-stage ultramarathon event not longer than 24 h • The ultramarathon must be performed continuously within 24 h and excludes multi-stage events Comparator Completion of the single-stage ultramarathon event Non completion of the single-stage ultramarathon event (considered as ‘did not finish’ or ‘DNF’) Outcomes Nutrition and hydration strategies employed during the event • Macromolecules (carbohydrates, fats, proteins) • Hydration (fluid type and pacing) • Other supplements (vitamins and minerals) Ultramarathon event characteristics: distance of race, start time, elevation profile, environmental profile (temperature, humidity, windspeed) Participants’ age, sex, body mass, height, years of ultrarunning experience, finisher or non- finisher Setting Any physical environment (indoors, outdoors, urban, rural, built-up, or natural) Study Design Studies with interventions, as well as observational studies • Controlled or uncontrolled • Randomized or nonrandomized https://doi.org/10.1371/journal.pone.0272668.t001 PLOS ONE A systematic review protocol: Nutrition and hydration strategies in ultramarathon finishers and non-finishers PLOS ONE \| https://doi.org/10.1371/journal.pone.0272668 August 19, 2022 3 / 8 With the operational search combination established, each member volunteered for roles. Two members formed Team A, and two members formed Team B. The two members of Team A will search in Google Scholar and SPORTDiscus. The two members of Team B will search in PubMed and Web of Science. This assignment of databases will divide the workload of the search somewhat equally between the teams. The search, screening process, and inclusion pro- cess represents the “search flow.” The search flow will channel an initially broad collection of sources into increasingly smaller collections (Fig 1). The search flow is described in Step 4. Table 2. Eligibility criteria. Participants Adults between the age of 18–60 years old, and any sex, gender, or nationality who has enrolled in a single-stage ultramarathon not lasting longer than 24 h Inclusion Criteria 1. The source is a published article in a peer-reviewed journal or is an unpublished or findable master’s thesis or doctoral dissertation 2. The source is written in English 3. The source reports the findings of an interventional or observational study a. The intervention is any nutritional or hydrational supplement b. At least one reported outcome is finishing or not finishing an ultramarathon event The observation is nutritional, or hydration strategy utilized while performing an ultramarathon event Exclusion Criteria 1. The source is not a published, peer-reviewed journal article or a findable and available master’s thesis or doctoral dissertation 2. The source is written in any language other than English 3. The source reports the findings of an interventional study with an intervention or outcomes irrelevant to this systematic review a. The intervention is an ultramarathon without nutritional or hydration component b. None of the reported outcomes are finishing or not finishing the ultramarathon event https://doi.org/10.1371/journal.pone.0272668.t002 Table 3. Search strategy. Investigators Team A: TD and JN Team B: VB and VO Techniques Search research databases for sources, including them in four stages: 1. Include sources by title 2. Include sources by abstract 3. Include sources by full text 4. Include sources from the reference lists of sources included by full text (journal articles, master’s theses, and doctoral dissertations) Databases Google Scholar, PubMed, and SPORTDiscus, Web of Science Included Types of Literature Published, peer-reviewed journal articles; unpublished and published master’s theses and doctoral dissertations Publication Date Range No limit Intervention Search Terms Outcome Search Terms “Ultramarathon” “24h ultramarathon” “Ultra endurance” “24h race” “Finish” “Completion” “Complete” “DNF” “Dropout” “Nutrition” “Carbohydrate” “Fats” “Protein” “Vitamins” “Minerals” “Hydration” “Electrolytes” “Water” “Fluid” “Supplements” “Supplementation” Search Combination ((ultramarathon) OR (“24h ultramarathon”) OR (“ultra endurance”) OR (“24h race”)) AND ((finish) OR (completion) OR (complete) OR (DNF) OR (“drop out”)) AND ((nutrition) OR (carbohydrate) OR (fats) OR (protein) OR (vitamins) OR (minerals) OR (hydration) OR (electrolytes) OR (water) OR (fluid) OR (supplements) OR (supplementation)) https://doi.org/10.1371/journal.pone.0272668.t003 PLOS ONE A systematic review protocol: Nutrition and hydration strategies in ultramarathon finishers and non-finishers PLOS ONE \| https://doi.org/10.1371/journal.pone.0272668 August 19, 2022 4 / 8 Step 4: Document sources that are included and excluded according to the pre-determined eligibility criteria The fourth step is the application of the systematic review process, the search flow (Fig 2). The search flow is modeled on the 2020 PRISMA statement [17], containing four steps: 1) Identify relevant sources by title, 2) Screen sources by abstract, 3) Assess and include sources by full text, and 4) Include eligible sources from the references of full texts included in the third step. During the search flow, it will be critical to document sources’ inclusion and exclusion clearly [16, 19]. Clear documentation allows the systematic review to be transparent and reproducible. Reproducibility is a hallmark of a systematic review that sets it apart from traditional literature reviews [19]. Fig 1. The search flow funnels sources into smaller collections until the final articles are included. https://doi.org/10.1371/journal.pone.0272668.g001 PLOS ONE A systematic review protocol: Nutrition and hydration strategies in ultramarathon finishers and non-finishers PLOS ONE \| https://doi.org/10.1371/journal.pone.0272668 August 19, 2022 5 / 8 Eligibility criteria will be determined by four individuals (two independent teams of two— Team A and Team B) who will work on selecting the studies. Individuals on the same team will be blinded to the other’s decisions with screening completed independently. The alternate team will resolve any disagreements. To make the search flow reproducible, a specific tool in Google Sheets will be utilized [20]. The tool is a spreadsheet for Teams A and B to coordinate with each other and has four sepa- rate sheets (one for each team member). During the first step of the search flow, members of Team A and B will enter three types of values into the sheet: 1) the number of hits each data- base returns, 2) the number of sources deemed relevant by title, and 3) the number of duplicate sources identified across the databases (identical sources found in the other database). The sheet will automatically sum these values. The sheet represents a precise record of members’ progression through the first step of the search flow. The sheet also helps members record Steps 2–4. Step 5: Assess final sources for risk of bias The fifth step acknowledges that it is important for all systematic reviews to assess the included sources’ risk of bias [16, 19]. Being transparent about the risk of bias allows readers to draw conclusions concerning the quality and strength of evidence for interventions affecting the outcome [16]. This systematic review will assess risk of bias at the study-level using tools spe- cific to the study design. Randomized, parallel trials will be assessed by using the revised Cochrane risk of bias tool for randomized trials (RoB 2) [21]. Non-randomized trials will be assessed by using the risk of bias in non-randomized studies—of interventions (ROBINS-I) tool [22]. The following characteristics will be assessed: deviations from the intended interven- tion, missing outcome data, measurement of the outcome, selection of the reported result. Bias will be evaluated by two independent teams of two individuals. The alternating team will check the others work and settle any disagreements of individual judgements. Fig 2. Search flow. https://doi.org/10.1371/journal.pone.0272668.g002 PLOS ONE A systematic review protocol: Nutrition and hydration strategies in ultramarathon finishers and non-finishers PLOS ONE \| https://doi.org/10.1371/journal.pone.0272668 August 19, 2022 6 / 8 Step 6: Extract pertinent data from final full-text articles and synthesize the information The sixth step is to extract pertinent data from the included full-text articles and write a narra- tive synthesis. The following data will be extracted from study documents: participant charac- teristics of age, body mass, height, years of ultrarunning experience, finisher or non-finisher. Ultramarathon characteristics: distance of race, start time, elevation profile, environmental profile (temperature, humidity, windspeed, start elevation). Nutritional supplementation dur- ing the event: macromolecules (carbohydrates, fats, proteins), hydration (fluid type and pac- ing), and other supplements (vitamins and minerals). We will contact investigators for information that is not provided in the published document. We will report the average differ- ences between finishers and non-finishers for macromolecules (carbohydrates, fats, proteins), hydration (fluid type and pace of ingestion), and other supplements (vitamins and minerals). Once all data are extracted, a narrative synthesis of the data will be written to report the main findings and implications of the systematic review. Step 7: Disseminate the findings of the systematic review The final step is to disseminate the main findings and implications. The intention is to com- plete the systematic review by August 2022 and submit the narrative synthesis for publication in a peer-reviewed academic journal thereafter. Final remarks To our knowledge, the proposed systematic review will be the first to describe the effects of nutrition and hydration between finishers and non-finishers of ultramarathon events as described by the scientific literature. Because of this, the review will fill an important gap in the literature. The protocol is presented here as a best practice [17] and to earn readers’ trust in the review protocol. Additionally, the procedures described here can provide others a framework to conduct their own systematic reviews if so desired. Supporting information S1 Checklist. PRISMA-P (Preferred Reporting Items for Systematic review and Meta- Analysis Protocols) 2015 checklist: Recommended items to address in a systematic review protocol. (DOC) Author Contributions Conceptualization: James W. Navalta, Victor D. Y. Beck, Taylor M. Diaz, Vernice E. Ollano. Writing – original draft: James W. Navalta, Victor D. Y. Beck, Taylor M. Diaz, Vernice E. Ollano. Writing – review & editing: James W. Navalta, Victor D. Y. Beck, Taylor M. Diaz, Vernice E. Ollano. References 1. Comrades Marathon History: Comrades Marathon; Available from: https://www.web.comrades.com/ history/. 2. 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Boland A, Cherry G, Dickson R. Doing a systematic review: A student’s guide. 2nd ed. ed. London, England: SAGE Publications Ltd; 2017. 20. Davis DW, Carrier B, Barrios B, Cruz K, Navalta JW. A protocol and novel tool for systematically review- ing the effects of mindful walking on mental and cardiovascular health. PLoS One. 2021; 16(10): e0258424. https://doi.org/10.1371/journal.pone.0258424 PMID: 34637455 21. Sterne JAC, Savovic J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019; 366:l4898. https://doi.org/10.1136/bmj.l4898 PMID: 31462531 22. Sterne JA, Hernan MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016; 355:i4919. https://doi. org/10.1136/bmj.i4919 PMID: 27733354 PLOS ONE A systematic review protocol: Nutrition and hydration strategies in ultramarathon finishers and non-finishers PLOS ONE \| https://doi.org/10.1371/journal.pone.0272668 August 19, 2022 8 / 8	Effects of nutritional and hydration strategies during ultramarathon events between finishers and non-finishers: A systematic review protocol.	08-19-2022	Navalta, James W,Beck, Victor D Y,Diaz, Taylor M,Ollano, Vernice E	eng
PMC10651037	PLOS ONE Dose response of running on blood biomarkers of wellness in the generally healthy --Manuscript Draft-- Manuscript Number: PONE-D-23-25168 Article Type: Research Article Full Title: Dose response of running on blood biomarkers of wellness in the generally healthy Short Title: Biomarker signature of runners Corresponding Author: Bartosz Nogal InsideTracker Cambridge, MA UNITED STATES Keywords: physical activity, exercise, blood biomarkers, running, generally healthy, mendelian randomization Abstract: Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence supporting its effectiveness. However, less is known about the specific healthspan-promoting effects of exercise on blood biomarkers in the disease-free population. In this work, we examine 23,237 generally healthy individuals who self- report varying weekly running volumes and compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional endurance athletes. We estimate the significance of differences among blood biomarkers for groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, gender, and BMI. We attempt and add insight to our observational dataset analysis via two-sample Mendelian randomization (2S-MR) using large independent datasets. We find that self-reported running volume associates with biomarker signatures of improved wellness, with some serum markers apparently being principally modified by BMI, whereas others show a dose-effect with respect to running volume. We further detect hints of sexually dimorphic serum responses in oxygen transport and hormonal traits, and we also observe a tendency toward pronounced modifications in magnesium status in professional endurance athletes. Thus, our results further characterize blood biomarkers of exercise and metabolic health, particularly regarding dose-effect relationships, and better inform personalized advice for training and performance. Order of Authors: Bartosz Nogal Svetlana Vinogradova Gil Blander Milena Jorge Paul Fabian Ali Torkamani Additional Information: Question Response Financial Disclosure Enter a financial disclosure statement that describes the sources of funding for the work included in this submission. Review the submission guidelines for detailed requirements. View published research articles from PLOS ONE for specific examples. This statement is required for submission and will appear in the published article if InsideTracker was the sole funding source. 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This text is appropriate if the data are owned by a third party and authors do not have permission to share the data. • * typeset Additional data availability information: Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation B Nogal 1 1 Dose response of running on blood biomarkers of wellness in the generally healthy Bartek Nogal PhD1ﾅ, Svetlana Vinogradova PhD1ﾅ, Milena Jorge MD,PhD1, Ali Torkamani PhD 2,3, Paul Fabian BSc1, and Gil Blander PhD1* 1InsideTracker, Cambridge, Massachusetts, United States of America. 2The Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA, USA. 3Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA. ﾅEqual Contribution: These authors contributed equally * Correspondence and reprint requests: Gil Blander, gblander@insdietracker.com Bartek Nogal bnogal@insidetracker.com Svetlana Vinogradova svinogradova@insidetracker.com Ali Torkamani atorkama@scripps.edu Paul Fabian pfabian@insdietracker.com Running title: Biomarker signature of runners Main Text Click here to access/download;Manuscript;Runners_PONE_v1.docx B Nogal 2 Abstract Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence supporting its effectiveness. However, less is known about the specific healthspan-promoting effects of exercise on blood biomarkers in the disease-free population. In this work, we examine 23,237 generally healthy individuals who self-report varying weekly running volumes and compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional endurance athletes. We estimate the significance of differences among blood biomarkers for groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, gender, and BMI. We attempt and add insight to our observational dataset analysis via two-sample Mendelian randomization (2S-MR) using large independent datasets. We find that self-reported running volume associates with biomarker signatures of improved wellness, with some serum markers apparently being principally modified by BMI, whereas others show a dose-effect with respect to running volume. We further detect hints of sexually dimorphic serum responses in oxygen transport and hormonal traits, and we also observe a tendency toward pronounced modifications in magnesium status in professional endurance athletes. Thus, our results further characterize blood biomarkers of exercise and metabolic health, particularly regarding dose-effect relationships, and better inform personalized advice for training and performance. B Nogal 3 3 Keywords: physical activity, exercise, blood biomarkers, running, generally healthy, mendelian randomization B Nogal 4 1 Introduction Physical inactivity is one of the leading modifiable behavioral causes of death in the US 1. Worldwide, physical inactivity is estimated to account for about 8.3% of premature mortality, an effect size that is on the same order as smoking and obesity 2. At the same time, the potent health benefits of exercise have been proven time and time again, with results so consistent across a wide variety of chronic diseases that some posit it can be considered a medical intervention 3-5. However, since most investigators report the effects of exercise in either diseased populations or athletes 6,7, there exists a significant gap in knowledge as to the measurable effects of exercise in the generally healthy population who exercise for the purpose of improving their healthspan, which can be projected via established measures such as blood biomarkers 8-11. It is well established that routine laboratory biomarkers are validated proxies of the state of an individual’s overall metabolic health and other healthpan-related parameters 12. A large body of evidence supports the effectiveness of exercise in modifying blood biomarkers toward disease mitigation in clinical cohorts as well as athletes, where the effect sizes may be larger 6,13. Indeed, it’s been shown that more favorable changes in response to exercise training occur usually in those with more pronounced dyslipidemia 13. In professional athletes, the sheer volume and/or intensity of physical activity may drive large effects in various hematological, lipid, immune, and endocrine variables 6. Our aim is to help fill the gap in understanding of the effects of exercise on blood biomarkers in the generally healthy, free-living population. Toward this end, we endeavored to explore the effects of vigorous exercise such as running in apparently healthy, mostly non-athletic cohort to better understand the landscape of blood biomarker modifications expected in the individual who partakes in recreational physical activity for the purpose of maintaining good health. B Nogal 5 5 For this purpose, we leveraged the InsideTracker dataset that includes information on self-reported exercise habits combined with blood biomarker and genomics data. We have previously reported on the results of a longitudinal analysis on blood biomarker data from 1032 generally healthy individuals who used our automated, web-based personalized nutrition and lifestyle platform 14. For the purpose of this investigation, we focused on running as the exercise of choice as it is one of the most common (purposeful) physical activity modalities practiced globally by generally healthy individuals and would thus be relevant. Moreover, since this was a cross-sectional study based on self-reported exercise habits, we attempted to increase our capacity to infer intervention effects, as well as tease out potential confounders, by performing 2S-MR in large independent cohorts. 2 Methods 2.1 Dataset We conducted an observational analysis of data from InsideTracker users. InsideTracker is a direct-to-consumer (DTC) company established in 2009 that markets and sells InsideTracker (insidetracker.com), a personalized lifestyle recommendation platform. The platform provides serum biomarker and genomics testing, and performs integrative analysis of these datasets, combined with activity/sleep tracker data toward biomarker and healthspan optimization (of note, at the time of this analysis, we did not have sufficient users with activity/sleep tracker data to include this data stream in the current study). New users were continuously added to the InsideTracker database from January 2011 to March 2022. B Nogal 6 2.2 Recruitment of participants Recruitment of participants aged between 18 and 65 and residing in North America was conducted through company marketing and outreach. Participants were subscribing members to the InsideTracker platform and provided informed consent to have their blood test data and self- reported information used in an anonymized fashion for research purposes. Research was conducted according to guidelines for observational research in tissue samples from human subjects. Eligible participants completed a questionnaire that included age, ethnicity, sex, dietary preferences, physical activity, and exposure to sunlight. This study employed data from 23,237 participants that met our analysis inclusion requirements, namely absence of any chronic disease as determined by questionnaire and metabolic blood biomarkers within normal clinical reference ranges. The platform is not a medical service and does not diagnose or treat medical conditions, so medical history and medication use were not collected. The Institutional Review Board (IRB) determine this work was not subject to a review based on category 4 exemption (“secondary research” with de-identified subjects). 2.3 Biomarker collection and analysis Blood samples were collected and analyzed by Clinical Laboratory Improvement Amendments (CLIA)–approved, third-party clinical labs (primarily Quest Diagnostics and LabCorp). Participants were instructed to fast for 12 hours prior to the phlebotomy, with the exception of water consumption. Results from the blood analysis were then uploaded to the platform via electronic integration with the CLIA-approved lab. Participants chose a specific blood panel from 7 possible offerings, each comprising some subset of the biomarkers available. Due to the variation in blood panels offered, the participant sample size per biomarker is not uniform. B Nogal 7 7 2.3 Biomarker dataset preparation In our raw dataset, occasional outlier values were observed that were deemed implausible (e.g. fasting glucose < 65 mg/dL). To remove anomalous outliers in a systematic way, we used the Interquartile Range (IQR) method of identifying outliers, removing data points which fell below Q1 – 1.5 IQR or above Q3 + 1.5 IQR. 2.4 Calculation of polygenic scores The variants (SNPs) comprising the polygenic risk scores were derived from publicly available GWAS summary statistics (https://www.ebi.ac.uk/gwas/). Scores were calculated across users by summing the product of effect allele doses weighted by the beta coefficient for each SNP, as reported in the GWAS summary statistics. Variant p-value thresholds were generally chosen based on optimization of respective PGS-blood biomarker correlation in the entire InsideTracker cohort with both blood and genomics datasets (~1000-1500 depending on the blood biomarker at the time of analysis). Genotyping data was derived from a combination of a custom InsideTracker array and third party arrays such as 23andMe and Ancestry. Not all variants for any particular PGS were genotyped on every array; proxies for missing SNPs were extracted via the “LDlinkR” package using the Utah Residents (CEPH) with Northern and Western European ancestry (CEU) population (R2 > 0.8 cut-off). Only results PGSs for which there was sufficient biomarker-genotyping dataset overlap were reported (note that none of the blood biomarker PGSs met this requirement). 2.5 Blood biomarker analysis with respect to running volume and polygenic scores To estimate significance of differences for blood biomarkers levels among exercise groups, we performed 3-way analysis of variance (ANOVA) analysis adjusting for age, gender, and BMI B Nogal 8 (type-II analysis-of-variance tables function ANOVA from ‘car’ R package, version 3.0-12). When estimating the effort of reported training volume on biomarkers, we assigned numerical values corresponding to 4 levels of running and performed ANOVA analysis with those levels treating it as an independent variable. P-values were adjusted using the Benjamini & Hochberg method 15. P-values for interaction plots were calculated with ANOVA including interaction between exercise group and polygenic scores category. When comparing runners (PRO and HVAM combined) versus sedentary individuals, we used propensity score matching method to account for existing covariates (age and gender): we identified 745 sedentary individuals with similar to runners’ age distributions among both males and females. We used ‘MatchIt’ R package (version 4.3.3) implementing nearest neighbor method for matching 16. 2.6 Mendelian randomization We attempted to add insight around the causality of exercise vs. BMI differences with respect to serum marker improvement by performing MR analyses on a subset of biomarker observations where BMI featured as a strong covariate and was thus used as the IV in the 2S-MR. Thus, our hypothesis here was that BMI differences were the primary (causal) driver behind the improvement behind some biomarkers. MR uses genetic variants as modifiable exposure (risk factor) proxies to evaluate causal relationships in observational data while reducing the effects of confounders and reverse causation (Figure 1S). These SNPs are used as instrumental variables and must meet 3 basic assumptions: (1) they must be robustly associated with the exposure; (2) they must exert their effect on outcome via the exposure, and (3) there must be no unmeasured confounders of the associations between the genetic variants and outcome (e.g. horizontal pleiotropy) 17. Importantly, SNPs are proper randomization instruments because they are determined at birth and thus serve as proxies of long-term exposures and cannot, in general, be modified by the environment. If the 3 B Nogal 9 9 above mentioned assumptions hold, MR-estimate effects of exposure on outcomes are not likely to be significantly affected by reverse causation or confounding. In the 2S-MR performed here, where GWAS summary statistics are used for both exposure and outcome from independent cohorts, reverse causation and horizontal pleiotropy can readily be assessed, and weak instrument bias and the likelihood of false positive findings are minimized as a result of the much larger samples sizes 17. Indeed, the bias in the 2S-MR using non-overlapping datasets as performed here is towards the null 17. Furthermore, to maintain the SNP-exposure associations and linkage disequilibrium (LD) patterns in the non-overlapping populations we used GWAS datasets from the MR-Base platform that were derived from ancestrally similar populations (“ukb”: analysis of UK Biobank phenotypes, and “ieu”: GWAS summary datasets generated by many different European consortia). To perform the analysis we used the R package “TwoSampleMR” that combines the effects sizes of instruments on exposures with those on outcomes via a meta-analysis. We used “TwoSampleMR” package functions for allele harmonization between exposure and outcome datasets, proxy variant substitution when SNPs from exposure were not genotyped in the outcome data (Rsq>0.8 using the 1000G EUR reference data integrated into MR-Base), and clumping to prune instrument SNPs for LD (the R script used for MR analyses is available upon request). We used 5 different MR methods that were included as part of the “TwoSampleMR” package to control for bias inherent to any one technique 18. For example, the multiplicative random effects inverse variance-weighted (IVW) method is a weighted regression of instrument- outcome effects on instrument-exposure effects with the intercept is set to zero. This method generates a causal estimate of the exposure trait on outcome traits by regressing the, for example, SNP-BMI trait association on the SNP-biomarker measure association, weighted by the inverse of the SNP-biomarker measure association, and constraining the intercept of this regression to zero. B Nogal 10 This constraint can result in unbalanced horizontal pleiotropy whereby the instruments influence the outcome through causal pathways distinct from that through the exposure (thus violating the second above-mentioned assumption). Such unbalanced horizontal pleiotropy distorts the association between the exposure and the outcome, and the effect estimate from the IVW method can be exaggerated or attenuated. However, unbalanced horizontal pleiotropy can be readily assessed by the MR Egger method (via the MR Egger intercept), which provides a valid MR causal estimate that is adjusted for the presence of such directional pleiotropy, albeit at the cost of statistical efficiency. Finally, to ascertain the directionality of the various causal relationships examined, we also performed each MR analysis in reverse where possible. 3 Results Study population characteristics Table 1 shows the demographic characteristics of the study population. We observed a significant trend toward younger individuals reporting higher running volume, with more than 75% of the professional (PRO) group falling between the ages of 18 and 35 (Table 1S). Significant differences were also observed in the distribution of males and females within study groups (Table 1). Moreover, higher running volume associated with significantly lower body mass index (BMI). Thus, moving forward, combined comparisons of blood biomarkers as they relate to running volume were adjusted for age, gender, and BMI. Endurance exercise exhibits a modest association with clusters of blood biomarker features In order to begin to understand the most important variables that may associate with endurance exercise in the form of running, we performed a principal component analysis (PCA), dividing the B Nogal 11 11 cohort into two most divergent groups in terms of exercise volume: PRO/high volume amateur (HVAM) and sedentary (SED) groups. Using propensity matching, PRO and amateur athletes who reported running >10h per week were combined into the PRO-HVAM group to balance out the sample size between the exercising and non-exercising groups. Using this approach, we did not observe a significant separation between these groups (data not shown). However, dividing this dataset further into males and females yielded a modest degree of separation, with hematological, inflammation, and lipid features, as well as BMI explaining some of the variance (Figure 1 A through D). We hypothesized that there may more subtle relationships between running volume and the blood biomarker features that contributed to distinguishing the endurance exercise and sedentary groups, thus we next performed ANOVA analyses stratified by running volume as categorized in Table 1. Significant trends in glycemic, hematological, blood lipid, and inflammatory serum traits with increasing running volumes Weighted ANOVA analyses adjusted for age, gender, and BMI showed significant differences among groups for multiple blood biomarkers (Table 2 and 2S, Figures 2 and 3). We observed a trend toward lower HbA1c, hsCRP, RDW, WBC, ferritin, gamma-glutamyl transferase (GGT), and LDL. HDL, hemoglobin (Hb), transferrin saturation (TS), alanine aminotransferase (ALT), aspartate aminotransferase (AST), vitamin B12, folate, 25-hydroxy vitamin D, and creatine kinase (CK) tended to be higher with increasing reported training volume, particularly in PRO runners (Tables 2 and 2S, Figures 2 and 2S, Figure 3). Hct and Hb were higher only in PRO males, whereas increased running volume associated with upward trend in these biomarkers in females (Figure 3 A and B). Increased running volume was associated with markedly lower Fer in males, whereas female runners did not exhibit varying levels, and SED females showed increased levels B Nogal 12 (Figure 3 C). The low ferritin observed in male and female runners was not clinically significant. ALT positively associated with running volume in females only (Figure 2S). Serum and RBC magnesium (Mg) were both significantly lower in PRO runners relative to all other groups (Table 2 and Figure 3 D and E). Increasing levels of endurance exercise also appeared to be associated with higher sex-hormone binding globulin (SHBG), particularly in PRO male runners (Figure 3 F). Endurance exercise correlates with lower BMI across categories of genetic risk Using publicly available GWAS summary statistics, we constructed blood biomarker polygenic risk scores (PGSs) to explore potential genetic risk-mitigating effects of endurance exercise. Since only a subset of the individuals in our cohort were genotyped, we aggregated the groups into 2 categories—PRO-HVAM and sedentary—to increase statistical power. This across-group sample size increase generally did not sufficiently power the ANOVA analysis to detect statistically significant trends (data not shown), though the BMI polygenic risk was suggestively mitigated for both males and female PRO-HVAM runners across categories of genetic risk (Figure 4 B). Increased running volume is associated with lower BMI which may drive biomarker changes We observed a significant downward trend in the BMI with increased running volume for both males and females, and, although some of the biomarker differences between sedentary and exercising individuals remained significant after adjustment for BMI, their significance was attenuated (Figure 4 A, p-value attenuation data not shown). Thus, we hypothesized that BMI may be driving a significant portion of the observed variance in some of the biomarkers across the groups. Thus, to explore causal relationships between weight and biomarker changes, we performed 2S-MR with BMI-associated single-nucleotide polymorphisms (SNPs) as the B Nogal 13 13 instrumental variables (IVs) for a subset of the healthspan-related biomarkers where BMI explained a relatively large portion of the variance in our analysis. In general, these blood biomarkers associated with inflammation (hsCRP and RDW), lipid metabolism (Tg and HDL), glycemic control (HbA1c and Glu), as well as Alb and SHBG. We used GWAS summary statistics and found that most of these BMI-blood biomarker relationships examined directionally aligned with our study (except for LDL), and some were indicative of causal relationships in the BMI- biomarker direction even after considering directional pleiotropy (Table 3S). We entertained the possibility of reverse causality and thus repeated the 2S-MR using each of the biomarker levels as the exposure and BMI as the outcome, and the results were generally not significant (except for WBC – see Table 4S). Of note, to estimate the direct causal effects of running on blood parameters, we attempted to find an instrumental variable for to approximate running as the exposure from publicly available GWAS summary statistics. Toward this end, we found that increasing levels of vigorous physical activity did associate with lower hsCRP, HbA1C, higher HDL, and possibly higher SHBG (although the explained variance (R2) in this exposure was just 0.001009, the F statistic was 37.7, thus meeting the criteria of F > 10 for minimizing weak instrument bias) (Figures 5 and 3S; Table 5S). Vigorous physical activity associates with healthier behaviors We hypothesized that those who exercise regularly may also partake in other healthful lifestyle habits that may be contributing to more optimal blood biomarker signatures of wellness. However, our dataset did not allow for systematic accounting of other lifestyle habits across all running groups. Thus, we again leveraged the potential of the 2S-MR approach to inform potential confounding associations between modifiable exposures and found that vigorous physical activity such as running is at least suggestively associated with several behaviors associated with improved B Nogal 14 health (Figure 4S). Our analysis showed that those who participate in increasing levels of vigorous physical activity may be less likely to eat processed meat (IVW p = 0.0000013), sweets (IVW p = 0.32), and nap during the day (IVW p = 0.13), while increasing their intake of oily fish (IVW p = 0.029), salad/raw vegetable intake (IVW p = 0.00016), and fresh fruit (IVW p = 0.0027) (Table 6S). Furthermore, following our assessment of reverse causality, we found evidence for the bidirectionality in the causal relationship between vigorous activity and napping during the day and salad/raw vegetable intake, perhaps suggesting some degree of confounding due to population stratification (Table 7S). The suggestive positive effect of fresh fruit and processed meat intake on vigorous physical activity appeared to violate MR assumption (3) (Figure 1S) (horizontal pleiotropy p-values 0.051 and 0.17, respectively – Figure 5S). 4 Discussion In this report, we describe the variance in wellness-related blood biomarkers among self-reported recreational runners, PRO runners, and individuals who do not report any exercise. Overall, we find that 1) recreational running as an exercise appears to be an effective intervention toward modifying several biomarkers indicative of improved metabolic health, 2) an apparent dose- response relationship between running volume and BMI may itself be responsible for a proportion of the apparent metabolic benefits, and 3) both PRO-level status and gender appear to associate with heterogeneous physiological responses, particularly in iron and magnesium metabolism, as well as some hormonal traits. 4.1 Self-reported running improves glycemia and lipidemia We did not observe distinct clusters corresponding to self-reported high-volume/PRO runners and the sedentary upon dimension reduction. This is, perhaps, not unexpected due, in part, to the self- B Nogal 15 15 selected healthspan-oriented nature of our cohort, where even the sedentary subset of individuals tends to exhibit blood biomarker levels in the normal clinical reference ranges. Furthermore, the measurement of running volume via self-report may be vulnerable to overestimation, which may have contributed to the blending of sedentary and exercise groups with respect to the serum markers measured, resulting in only marginal separation between the groups 19,20. However, we did observe significant individual blood biomarker variance with respect to reported running volumes when the dataset was subjected to ANOVA, even after adjustment for age, sex, and BMI. From among glycemic control blood biomarkers, we were able to detect a relatively small exercise effect in both fasting glucose and HbA1c in this generally healthy cohort, where the average measures of glycemia were below the prediabetic thresholds in even the sedentary subset of the cohort. Larger exercise intervention effects on metabolic biomarkers may be expected in cohorts that include individuals with more clinically significant baseline values 21. Similarly, blood lipids improved with higher self-reported running volume, and this result has been reported before in multiple controlled endurance exercise trials 22. The literature indicates that HDL and Tg are two exercise-modifiable blood lipid biomarkers, with HDL being the most widely reported to be modified by aerobic exercise 23,24. Although the mechanism behind this is not entirely clear, it likely involves the modification of lecithin acyltransferase and lipoprotein lipase activities following exercise training 25. We observed a similar trend in our blood biomarker analysis, with HDL exhibiting an upward trend with increasing reported running volume. While we also found Tg and LDL to decrease with increasing exercise volume, these trends were less pronounced. Reports generally suggest that, in order to reduce LDL more consistently, the intensity of aerobic exercise must be high enough 23. In the case of Tg, baseline levels may have B Nogal 16 a significant impact on the exercise intervention effect, with individuals exhibiting higher baselines showing greater improvements 13. Importantly, these results suggests that exercise has a significant effect on glycemic control and blood lipids even in the self-selected, already healthy individuals who are proactive about preventing cardiometabolic disease. 4.2 Self-reported running and serum proxies of systemic inflammation Chronic low-grade inflammation is one of the major risk factors for compromised cardiovascular health and metabolic syndrome (MetS). While there is no shortage of inflammation-reducing intervention studies on CVD patients with clinically high levels of metabolic inflammation, there is less emphasis on modifiable lifestyle factors that can help stave off CVD and extend healthspan in the generally healthy individual. Indeed, considering the pathological cardiovascular processes begin shortly after birth, prevention in asymptomatic individuals may be a more appropriate strategy toward decreasing the burden of CVD on the healthcare system 26. Toward this end, increasing self-reported running volume appeared to associate with improved markers of inflammation, as shown by the lower levels of hsCRP, WBC, as well as ferritin. Of note, while the acute-phase protein, ferritin, is often used in the differential diagnosis of iron deficiency anemia, the biomarker’s specificity appears to depend on the inflammatory state of the individual, as it associates with hsCRP and inflammation more than iron stores, particularly in those with higher BMI 27. Although serum ferritin and iron is reported to be lower in male and female elite athletes 28, the observed overall negative association of ferritin with increased running volume in our cohort may be an indication of lower levels of inflammation rather than compromised iron stores, particularly since the average ferritin level across all groups was above B Nogal 17 17 the clinical iron deficiency thresholds. Moreover, increased levels of ferritin have been associated with insulin resistance and lower levels of adiponectin in the general population, both indicators of increased systemic inflammation 29. Here, exercising groups with lower levels of ferritin also exhibited glycemic and blood lipid traits indicative of improved metabolic states, further supporting ferritin’s role as an inflammation proxy. Finally, Hb, TS and iron tended to be higher in those who run for exercise compared to the SED group (with the TIBC lower), again suggesting that runners, including the PRO group, were iron-sufficient in this cohort. 4.3 PRO athletes exhibit distinct biomarker signatures PRO athletes exhibited lower serum and RBC Mg, which may be indication of the often-reported endurance athlete hypomagnesaemia 30. While the serum Mg was still within normal clinical reference range for both PRO female and male athletes, RBC Mg, a more sensitive biomarker of Mg status 31, was borderline low in female PRO athletes and might suggest suboptimal dietary intakes and/or much higher volume of running training compared to the other running groups (i.e. >>10h /week). Indeed, this group also had elevated baseline CK and AST, which suggests a much higher training intensity and/or volume. Moreover, PRO level athletes had adequate iron status and serum B12 and folate in the upper quartile of the normal reference range, suggesting that these athletes’ general nutrition status may have been adequate. These observations suggest that elite endurance runners may need to pay particular attention to their magnesium status. Further, we observed higher levels of SHBG in PRO male runners, a biomarker whose levels positively correlate with various indexes of insulin sensitivity 32. However, since the average SHBG levels in the SED group were not clinically low in both sexes, the observed increase in SHBG levels induced by running in males may be a catabolic response, as cortisol levels in this B Nogal 18 group were also higher. Indeed, Popovic et al have shown that endurance exercise may increase SHBG, cortisol, and total testosterone levels at the expense of free testosterone levels 33. This could perhaps in part be explained by higher exercise-induced adiponectin levels, which have been shown to increase SHBG via cAMP kinase (AMPK) activation 34. However, since our data is observational, we cannot rule out overall energy balance as a significant contributor to SHBG levels. For example, caloric restriction (CR) has been shown to result in higher SHBG and cortisol levels 32. Finally, regarding the abovementioned PRO group elevated AST and CK biomarkers, evidence suggests that normal reference ranges in both CK and AST in well-recovered athletes should be adjusted up, as training and competition have a profound, non-pathological, impact on the activity of these enzymes 35,36. Indeed, the recommendation appears to be not to use reference intervals derived from the general population with hard-training (particularly competitive) athletes 36. 4.4 Effect of BMI on blood biomarkers Since the current study is a cross-sectional analysis of self-reported running, we could not rule out the possibility that factors other than exercise were the driving force behind the observed biomarker variance among the groups examined. While factors such as diet, sleep, and/or medication use were not readily ascertained in this free-living cohort at the time of this study, BMI was readily available to evaluate this biomarker’s potential relative contribution to the observed mean biomarker differences among self-reported runner groups. Multiple studies have attempted to uncouple the effects of exercise and BMI reduction on blood biomarker outcomes, with mixed results 37. For example, it is relatively well-known that acute bouts of exercise improve glucose metabolism, but long-term effects are less well described 38. B Nogal 19 19 Indeed, whether exercise without significant weight-loss is effective toward preventing metabolic disease (and the associated blood biomarker changes) is inconclusive. From the literature, it appears that, for endurance exercise to have significant effect on most blood biomarkers, the volume of exercise needs to be very high, and this typically results in significant reduction in weight. Thus, in practice, it is difficult to demonstrably uncouple the effects of significant exercise and the associated weight-loss, and the results may depend on the blood biomarker in question. Indeed, there is evidence that exercise without weight-loss does improve markers of insulin sensitivity but not chronic inflammation, with the latter apparently requiring a reduction in adiposity in the general population 39-41. In our study of apparently healthy individuals, we observed a downward trend in BMI with increasing self-reported running volume, and, although this study was not longitudinal and we are thus unable to claim weight-loss, our 2S-MR analysis using BMI as the exposure nonetheless suggests this biomarker to be responsible for a significant proportion of the modification of some blood biomarkers. 4.5.1 Serum markers of systemic inflammation Through our 2S-MR analyses, we show that BMI is causally associated with markers of systemic inflammation, including RDW, folate, and hsCRP 27,42,43. Similar analyses have reported that genetic variants that associate with higher BMI were associated with higher CRP levels, but not the other way around 44. The prevailing mechanism proposed to explain this relationship appears to be the pathological nature of overweight/obesity-driven adipose tissue that results in secretion of proinflammatory cytokines such as IL-6 and TNFa, which then stimulate an acute hepatic response, resulting in increased hsCRP levels (among other effects) 45. Thus, our 2S-MR analyses B Nogal 20 and those of others 44 would indicate that the primary factor behind the lower systemic inflammation in our cohort may be the exercise-associated lower BMI and not running exercise per se, though the lower hsCRP in runners remained significant after adjustment for BMI in our analysis. Indeed, although a major driver behind reduced systemic inflammation may be a reduction in BMI in the general population, additive effects of other lifestyle factors such as exercise cannot be excluded. For example, a large body of cross-sectional investigations does indicate that physically active individuals exhibit CRP levels that are 19-35% lower than less active individuals, even when adjusted for BMI as was the case in the current analysis 41. Further, it’s been reported that physical activity at a frequency of as little as 1 day per week is associated with lower CRP in individuals who are otherwise sedentary, while more frequent exercise further reduces inflammation 41. Significantly, our entire cohort of self-selected apparently healthy individuals did not exhibit clinically high hsCRP, with average BMI also below the overweight thresholds. Because all subjects were voluntarily participating in a personalized wellness platform intended to optimize blood biomarkers that included hsCRP, it is possible that some individuals from across the study groups (both running and sedentary) in our cohort partook in some form of inflammation-reducing dietary and/or lifestyle-based intervention. Thus, that we detected a significant difference in hsCRP between exercising and non-exercising individuals in this self-selected already generally healthy cohort may be suggestive of the potential for additional preventative effect of scheduled physical activity on low-grade systemic inflammation in the generally healthy individual. 4.5.2 Blood lipids B Nogal 21 21 Controlled studies that tightly track exercise and the associated adiposity reduction have reported that body fat reduction (and not improvement in fitness as measured via VO2max) is a predictor of HDL, LDL, and Tg 46. Similarly, though BMI is an imperfect measure of adiposity, our 2S-MR analysis suggests that this biomarker is causally associated with improved levels of HDL and Tg, though not LDL. This latter finding replicates a report by Hu et al. who, using the Global Lipids Genetics Consortium GWAS summary statistics, applied a network MR approach that revealed causal associations between BMI and blood lipids, where Tg and HDL, but not LDL, were found to trend toward unhealthy levels with increasing adiposity 47. On the other hand, others implemented a robust BMI genetic risk score and demonstrated a causal association of adiposity with peripheral artery disease and a multiple linear regression showed a strong association with HDL, TC, and LDL, among other metabolic parameters 48. In our cohort, given the lack of evidence for a causal BMI-LDL association and the overall healthy levels of BMI, the observed a significant improvement in LDL may be a result of marked running intensity and/or volume, possibly combined with the aforementioned additional wellness program intervention variables. 4.5.3 Hormonal traits As described above, we observed a trend toward increased plasma cortisol and SHBG in runners, particularly PRO level athletes. The effects on cortisol are consistent with a report by Houmanrd et al, who found male distance runners to exhibit higher levels of baseline cortisol 49. With respect to the effects of BMI on baseline cortisol levels, this observation is generally supported by our 2S- MR analyses with evidence for a consistent effect of increased cortisol with decreasing BMI. However, this association was suggestive at best, indicating that the higher levels of cortisol exhibited in the PRO runners with significant lower adiposity are not likely to be solely explained by their lower BMI. Indeed, the relationship between BMI and cortisol appears to be complex, B Nogal 22 with some reports suggesting a U-shaped relationship, where the glucocorticoid’s levels associate negatively up to about a BMI of 30 kg/m2, then exhibiting a positive correlation into obesity phenotypes 50. MR statistical models generally do not account for such non-linearity and would require a more granular demographical treatment, which is not possible using only GWAS summary statistics data in the context of 2S-MR 17,51. 4.6 Behavioral traits associated with increase physical activity The combination of the body of the literature that describes the effects of endurance training on blood biomarkers, and our own analysis that included markers such as CK and AST, makes us cautiously assured that most of the abovementioned blood biomarker signatures are indeed a result of the interplay between self-reported running and the associated lower BMI. However, as this is a self-report-based analysis and we were unable to track other subject behaviors in this free-living cohort, we acknowledge that multiple behaviors that associate with exercise may be influencing our results. Toward this end, our exploratory 2S-MR analyses revealed potentially causal relationships between vigorous exercise and multiple dietary habits that have been shown to improve the biomarkers we examined. Indeed, diets that avoid processed meat and sweets while providing ample amounts of fresh fruits, as well as oily fish have been shown to be anti-inflammatory, and improve glycemic control and dyslipidemia 52,53. That physically active individuals are also more likely to make healthier dietary choices adds insight to the potential confounders in ours and others’ observational analyses, and this similar associations have previously been reported 54-56. For example, using a calculated healthy eating motivation score, Naughton et al. showed that those who partake in more than 2 hours of vigorous physical activity are almost twice as likely to be B Nogal 23 23 motivated to eat healthy 56. Indeed, upon closer examination, the genetic instruments used to approximate vigorous physical activity as the exposure in this work included variants in the genes DPY19L1, CADM2, CTBP2, EXOC4, and FOXO3 57. Of these, CADM2 encodes proteins that are involved in neurotransmission in brain regions well known for their involvement in executive function, including motivation, impulse regulation and self-control 58. Moreover, variants within this locus have been associated with obesity-related traits 59. Thus, it is likely that the improved metabolic outcomes seen here with our self-reported runners are a composite result of both these individuals exercise and dietary habits. Importantly, the above suggests that a holistic wellness lifestyle approach is in practice the most likely to be most effective toward preventing cardiometabolic disease. Nonetheless, the focus of this work – exercise in the form of running – is known to significantly improve cardiorespiratory fitness (CRF), which has been shown to be an independent predictor of CVD risk and total mortality, outcomes that indeed correlate with dysregulated levels in many of the blood biomarkers examined in this work 7. 4.7 Study limitations This study is based on self-reported running and thus has several limitations. First, it is generally known that subjects tend to overestimate their commitment to exercise when self-reporting, although in our cohort is a self-selected health-oriented population that is possibly less likely to over-report their running volume. Furthermore, although the robust increasing trend in baselines for muscle damage biomarkers (CK, AST) that have been shown to be associated with participation in sports and exercise provides indirect evidence that the running groups were indeed participating in increasing volumes of strenuous physical activity, we cannot confirm whether the reported running was performed overground or on a treadmill, which may result in some heterogeneity in physiological responses , nor can we ascertain the actual training volume of PRO-level runners. B Nogal 24 We also cannot exclude the possibility that the running groups also participated in other forms of exercise (such as strength training) or partook in other wellness program interventions that may have influenced their blood biomarkers and/or BMI via lean muscle accretion. Toward this end, we have attempted to shed light on potential behavioral covariates related to vigorous physical activity via 2S-MR. Finally, while this cohort is generally healthy, we cannot exclude the potential for unmeasured confounders such as medications, nutritional supplements, and unreported health conditions. 2S- MR enables the assessment of causal relationships between modifiable traits and is less prone to the so-called “winner’s curse” that more readily affects one-sample MR analyses 17,51. Because 2S-MR uses GWAS summary statistics for both exposure and outcome, it is possible to increase statistical power because of the increased sample sizes. However, horizontal pleiotropy is still a concern that can skew the results. Currently, there is no gold standard MR analysis method, thus we used different techniques (IVW, MR-Egger, and median-based estimations – all of which are based on different assumptions and thus biases) to evaluate the consistency among these estimators and only reported associations as ‘causal’ if there was cross-model consistency. Nonetheless, an exposure such as BMI is a complex trait that is composed of multiple sub-phenotypes (such as years of education) that could be driving the causal associations. 5 Conclusion Running is one of the most common forms of vigorous exercise practiced globally, thus making it a compelling target of research studies toward understanding its applicability in chronic disease prevention. Our cross-sectional study offers insight into the biomarker signatures of self-reported B Nogal 25 25 running in generally healthy individuals that suggest improved insulin sensitivity, blood lipid metabolism, and systemic inflammation. Furthermore, using 2S-MR in independent datasets we provide additional evidence that some biomarkers are readily modified BMI alone, while others appear to respond to the combination of varying exercise and BM I. Our additional bi-directional 2S-MR analyses toward understanding the causal relationships between partaking in vigorous physical activity and other healthy behaviors highlight the inherent challenge in disambiguating exercise intervention effects in cross sectional studies of free-living populations, where healthy behaviors such as exercising and healthy dietary habits co-occur. Overall, our analysis shows that the differences between those who run and the sedentary in our cohort are likely a combination of the specific physiological effects of exercise, the associated changes in BMI, and lifestyle habits associated with those who exercise, such as food choices and baseline activity level. Looking ahead, the InsideTracker database is continuously augmented with blood chemistry, genotyping, and activity tracker data, facilitating further investigation of the effects of various exercise modalities on phenotypes related to healthspan, including longitudinal analyses and more granular dose-response dynamics. Data Availability Statement The full set of biomarker change correlations has been made available in the Supplementary Information files. Specific components of the raw dataset are available upon reasonable request from the corresponding author. 2S-MR analysis was performed using publicly available datasets via the TwoSampleMR R package. Ethics statement B Nogal 26 This study was submitted to The Institutional Review Board (IRB), which determined this work was not subject to a review based on category 4 exemption (“secondary research” with de- identified subjects). Author contributions BN performed the 2S-MR analyses, calculated PGSs, and wrote the manuscript; SV performed blood biomarker and blood biomarker X PGS interaction analysis; PF calculated PGSs; MJ, AT, and GB provided guidance. All authors have read and agreed to the published version of the manuscript. Funding InsideTracker was the sole funding source. Conflict of interest InsideTracker is a direct-to-consumer blood biomarker and genomics company providing its users with nutritional and exercise recommendations toward improving wellness. B.N., S.V., P.F., and G.B. are employees of InsideTracker. B Nogal 27 27 Acknowledgments InsideTracker is the sole funding source. We thank Michelle Cawley and Renee Deehan for their assistance with background subject matter research and insightful conversations. B Nogal 28 References 1. Lavie CJ, Ozemek C, Carbone S, Katzmarzyk PT, Blair SN. Sedentary Behavior, Exercise, and Cardiovascular Health. Circ Res. 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Scientific Reports. 2019/05/14 2019;9(1):7339. doi:10.1038/s41598-019-43861-9 B Nogal 36 Table 1 Study Population Demographics Group N Female, % Age, yrs Body mass index, kg/m2 PRO 82 53.7% 33.68 20.15 HVAM 1103 52.9% 39.48 22.57 MVAM 6747 54.2% 41.49 23.35 LVAM 10877 34.2% 41.16 24.72 SED 4428 48.9% 44.25 27.83 PRO = Professional, HVAM = high volume amateur (>10 hr), MVAM = medium volume amateur (3-10hr), LVAM = low volume amateur (<3 hr), SED = sedentary B Nogal 37 37 Table 2 Blood Biomarkers Significantly Different Among Sedentary Individuals and Those Who Partake in Running for Exercise to Various Degrees Biomarker ANOVA p-value Trend p-value lowest mean highest mean Alb <1e-16 <0.001 MVAM PRO ALT <1e-16 <1e-16 SED PRO AST <1e-16 <0.001 SED PRO B12 <0.001 <0.001 SED PRO Chol <0.001 0.005 PRO SED CK <1e-16 <1e-16 SED PRO Cor <0.001 0.675 SED PRO FE <0.001 0.119 SED PRO Fer <1e-16 <1e-16 MVAM SED Fol <1e-16 <0.001 SED PRO FT <0.001 0.013 SED PRO GGT <1e-16 <0.001 PRO SED Glu 0.087 0.184 PRO SED Hb 0.002 <0.001 MVAM PRO HCT 0.053 0.055 MVAM PRO HDL <1e-16 <0.001 SED PRO HbA1c <0.001 0.010 PRO SED B Nogal 38 hsCRP <0.001 0.176 PRO SED LDL <0.001 0.006 PRO SED Mg <0.001 0.276 PRO SED MPV 0.058 0.089 SED HVAM Na <1e-16 0.622 HVAM SED RBC_Mg <0.001 0.773 PRO SED RDW <1e-16 0.002 PRO SED SHBG <1e-16 0.004 SED PRO Tg <1e-16 <1e-16 PRO SED WBC <1e-16 <1e-16 PRO SED Figure 1 Click here to access/download;Figure;Figure_1.jpg Figure 2 Click here to access/download;Figure;Figure_2.jpg Figure 3 Click here to access/download;Figure;Figure_3.jpg Figure 4 Click here to access/download;Figure;Figure_4.jpg Figure 4 Click here to access/download;Figure;Figure_5 .jpg Supporting Information Click here to access/download Supporting Information Supplementary_Materials_PONE.pdf	Dose response of running on blood biomarkers of wellness in generally healthy individuals.	11-15-2023	Nogal, Bartek,Vinogradova, Svetlana,Jorge, Milena,Torkamani, Ali,Fabian, Paul,Blander, Gil	eng
PMC8771017	3182 http://journals.tubitak.gov.tr/medical/ Turkish Journal of Medical Sciences Turk J Med Sci (2021) 51: 3182-3193 © TÜBİTAK doi:10.3906/sag-2106-271 Short-term impact of the Covid-19 pandemic on the global and Turkish economy Ömer AÇIKGÖZ, Aslı GÜNAY Department of Economics, Faculty of Political Science, Social Sciences University of Ankara, Ankara, Turkey Correspondence: omeracikgoz63@gmail.com 1. Introduction The Covid-19 pandemic, a new strain discovered in China in December 2019, has killed millions of people and transformed the world forever. It is a historic event since it is not just a health issue; it also has global economic, political, and social dimensions. As of August 2021, the virus had infected more than 205 million people worldwide, resulting in around 4.3 million deaths, and more than 2.4 billion vaccine doses have been administered globally [1]. Besides, the International Monetary Fund (IMF) indicates that the global economy was experiencing its worst crisis with a 3.5% global gross domestic product (GDP) fall in 2020 since the Great Depression of the 1930s [2, 3] compared to an estimated 15% GDP decline in between 1929 and 1932 worldwide [4]. While output in the United States (US) declined by 3.5%, the economy contracted by 6.7% and 4.7% in the euro area and Japan, respectively, in 2020. The pandemic is estimated to have pushed 119–124 million people into poverty in 2020 due to the global economic recession [5]. Many scientific comparisons have been drawn between the Covid-19 pandemic and preceding pandemics (Spanish flu, Asian flu, Hong Kong flu, and swine flu) [6] to demonstrate the magnitude of mortality rate and economic collapse caused by the Covid-19 pandemic. However, considering their worldwide dissemination, nature, intensity, and socioeconomic characteristics, drawing comparisons between them can be difficult. The mortality impact of the Covid-19 pandemic, for example, will be less than the Spanish flu that is estimated to have killed roughly 40 million people globally in 1918 [7, 8]. Furthermore, despite the lack of economic data for analyzing the economic impact of the Spanish flu, it is estimated that GDP and consumption fell by 6% and 8% in the typical country, respectively, and that many businesses, particularly those in the service and entertainment industries, suffered significant amount losses in revenue [8, 9]. On the other hand, existing studies show that the global death number of the Asian flu in 1957 and the Hong Background/aim: The Covid-19 pandemic is one of those rare events that affects everyone on earth and changes our lives. The pandemic, which has killed over four million people worldwide, is putting unprecedented pressure on governments to maintain essential health and social services, as well as keep their economies running, even as the virus threatens people’s daily life on every level. Thus, the purpose of this study is to discuss the short-term economic impact of the pandemic by assessing its costs using official economic data for both the world and Turkey. Furthermore, this research highlights possible economic, social, and political pathways for a postpandemic new world. Materials and methods: This study is a review article that overviews and tracks the economic development of the Covid-19 pandemic from the start, synthesizes and compares current data of reliable institutions, and provides an overall assessment. Results: The pandemic has certainly caused short-term and long-term damage to economies and living standards for many people. Although there are estimates on what this damage is, the exact degree of the damage is still unknown. However, it seems that the recovery will be gradual, long-lasting, and unpredictable due to the unprecedented uncertainty characteristic of the pandemic. Conclusion: Early economic growth projections show that there will be no ordinary recovery for the world economy since short-term countries’ recovery paths are different. It is likely to remain uneven and depend on the effectiveness of the vaccination process, fiscal policy support, public health management, and hard-hit sectors’ growth size in economies. Due to the uncertainty and lack of confidence, governments should ensure an equal and sustainable economic recovery from the Covid-19 pandemic by conducting flexible monetary and fiscal policies. However, without structural reforms, economies can not boost either in the short-term and long-term. Key words: Covid-19, economy, pandemic, Turkey, world Received: 22.06.2021 Accepted/Published Online: 07.08.2021 Final Version: 17.12.2021 Review Article This work is licensed under a Creative Commons Attribution 4.0 International License. AÇIKGÖZ and GÜNAY / Turk J Med Sci 3183 Kong flu in 1968 was around one million people. Moreover, both pandemics had no significant worldwide economic impact [10, 11]. As a result, early indications suggest that the Covid-19 pandemic will have a similar global impact with the Spanish flu rather than others in the end. Covid-19 pandemic is a global crisis, and all countries have been affected by this crisis. Developed as well as emerging and developing countries are experiencing a recession. Countries whose economies substantially rely on tourism and hospitality, travel, and entertainment sector have been particularly hard hit. For example, the global GDP loss from the pandemic crisis could be around 9 trillion dollars over 2020 and 2021, greater than the economies of Japan and Germany combined [2]. Moreover, with inferior health systems, smaller fiscal support, and high debt levels, both emerging and developing countries and low-income countries have faced extra challenges for recovery from that noticeable recession. At the beginning of the Covid-19 pandemic, global uncertainty was at an all-time high, and it continues to be so. Although global economic and policy uncertainty has decreased by about 60% since the onset of the Covid-19 pandemic in the first quarter of 2020, the World Uncertainty Index (WUI) shows that it is still about 50% higher than its historical average from 1996 to 2010 [12]. Hence, the Covid-19 pandemic’s uncertainty is unprecedented since there is a great deal of uncertainty about almost every aspect of the Covid-19 crisis [11]. These are labeled as the virus’ infectiousness and lethality [13], the time required to develop and deploy effective vaccines [14], the duration of social isolation [15, 16], macroeconomic consequences, and government policy responses in both the short-term and long-term [17], the shifts in consumer spending patterns, travel, logistics, new business and working formation [18, 19]. This situation suggests that the global economy will not recover regularly; in other words, the time it takes for each country to recover will most likely vary due to the effectiveness of the vaccination process and public health care measurements implemented by each country. For example, according to the Organisation for Economic Co-operation and Development (OECD), much of Europe will take nearly three years, whereas Korea and the US are likely to recover to prepandemic per capita income levels in roughly 18 months [3]. As a result, the most confusing impact of the Covid-19 pandemic on society, economics, and policies is unprecedented uncertainty since how the pandemic will evolve and end is still ambiguous. The Covid-19 crisis demonstrates that governments, not markets, are the ones that provide much-needed help during the global economic recession. In other words, the Covid-19 pandemic has led to a collapse of the free market phenomena, indicating markets are the only solution mechanism for practically every problem that societies encounter since 1981. Nevertheless, the Covid-19 pandemic has promoted the governments’ intervention rather than the market intervention. Almost every major industry has sought financial aid from the government during the pandemic. Moreover, small enterprises have been pleading for zero-interest loans, tax cuts, and outright cash. As a result, the Covid-19 pandemic has demonstrated that markets alone cannot recover economies in this global crisis, and more market and government collaboration will most likely be new economic policy in the postpandemic world [20]. Already existing disparities and gaps in health and social protection systems between countries have been severely revealed, and in many cases worsened, by the Covid-19 pandemic. Countries with strong health and social protection systems responded better to the crisis by guaranteeing access to health care services, also providing jobs and income security for the neediest, such as informal workers, daily wage earners, self-employed workers, migrants, and the homeless. Countries that do not have robust health and social protection systems, on the other hand, have required international assistance to enable an adequate initial reaction to the pandemic. In this respect, the Covid-19 pandemic presents a chance for countries to prioritize investments in their health and social protection systems and develop them to better deal with future crises [21]. Population mental health has deteriorated significantly since the start of the Covid-19 pandemic. The OECD shows that rates of anxiety and depression increased in 2020 compared to previous years [22]. Economic insecurity, unemployment, lower-income, death fear, domestic violence, mobility restrictions, media exposure about the pandemic, and social isolation have been the main factors that has led to an unprecedented worsening of population mental health during the Covid-19 pandemic [22, 23]. Due to these risk factors, loneliness and individualism are likely to have associated with the pandemic [24]. As individuals are isolated from social life, they have begun to behave more individually than before. People have started to form their own living spaces for conducting their socioeconomic lives based on personal freedoms with the accelerated digitalization in all areas of life. Hence, populations’ social well-being, and their social life and relationships have worsened noticeably during the pandemic. Despite the new positive developments like vaccines, many are still wondering how the postpandemic world will be like. Governments face formidable difficulties in their efforts to safeguard their citizens from the threat of the Covid-19 pandemic. It is recognized that society’s regular functioning cannot be maintained, particularly in light AÇIKGÖZ and GÜNAY / Turk J Med Sci 3184 of the virus’s primary protective measurement, namely confinement. Furthermore, it is acknowledged that the imposed measurements will invariably intrude on rights and freedoms that are an essential feature of a democratic society ruled by law [25]. Countries have no choice but to take extraordinary steps to overcome the pandemic’s unusual situation and save lives, like extensive lockdowns enacted to slow virus transmission and restrict freedom of movement. Such policies may have an unintended impact on people’s lives and security, and access to health care, food, water, sanitation, work, education, and leisure [26]. Furthermore, more security measures may damage democratic principles and fundamental human rights in communities and countries [6]. As a result, the Covid-19 pandemic might lead to a rise in authoritarianism at the global level. Our daily lives have forever changed by the Covid-19 pandemic and the resulting economic crisis. One of the most notable developments has been the acceleration of the movement to digital payments, as customers avoided using cash for fear of spreading the virus, and retailers responded by moving their operations online. For example, in 2019, the overall number of noncash payments in the euro area climbed by 81% to €98 billion from €90.7 billion in 2018, and card payments accounted for 48% of total noncash payments in the euro area in 2019 [27]. In total, the global digital payments industry hit more than $4.7 trillion value in 2019 and increased to $5.4 trillion value in 2020, almost a 16% rise compared with the previous year. The entire sector is expected to continue its impressive growth in 2021, with over $6.6 trillion transaction value. According to the growth rates in 2020, while Europe was the leading digital payments market with the 28.3% growth rate to $1.17 trillion compared to 2019, the US market follows with $1.26 trillion worth of digital payments, 22.6% more than the previous year1. Moreover, a cryptocurrency based on blockchain technology, such as Bitcoin and Ethereum, has gained popularity and traction worldwide as a faster and cheaper way to transmit money across borders during the pandemic. Notably, demand for Bitcoin has been surging globally since the beginning of the Covid-19 pandemic. Despite a significant drop in its value in recent months, the value of Bitcoin increased by more than 300% in 20202. Today, especially emerging economies, are increasingly turning to cryptocurrencies to help them recover from the 1 CPA Practice Advisor (2021). Digital Payments to Hit $6.6 Trillion in 2021, a 40% Jump in Two Years [online]. Website https://www.cpapracticeadvisor. com/accounting-audit/news/21208440/digital-payments-to-hit-66-trillion-in-2021-a-40-jump-in-two-years [accessed 19 June 2021]. 2 Bitcoin.com (2021). Bitcoin Price [online]. Website https://markets.bitcoin.com/crypto/BTC [accessed 10 June 2021]. 3 Oxford Business Group (2021). Economic News [online]. Website https://oxfordbusinessgroup.com/news/can-cryptocurrencies-drive-covid-19- recovery-emerging-markets [accessed 10 June 2021]. 4 CNBC (2021). The Fed this summer will take another step in developing a digital currency [online]. Website https://www.cnbc.com/2021/05/20/the- fed-this-summer-will-take-another-step-ahead-in-developing-a-digital-currency.html [accessed 11 June 2021]. pandemic’s adverse economic impacts3. On the other hand, central bank digital currency (CBDC) and stablecoins have received more attention recently. According to the Bank for International Settlements (BIS) survey, more than 85% of central banks are studying or investigating CBDC; however, many of issuances have yet to be completed [28]. For example, China, the European Central Bank (ECB) and the Federal Reserve are working to build CBDC4 [29]. In contrast to the CBDC, stablecoins are private entities designed to maintain a steady value concerning another asset like a unit of currency and commodity or a basket of assets, unlike cryptocurrencies [30]. Even though the pandemic has highlighted the importance of digital financial services, digital currencies have raised concerns about consumer protection, data privacy, potential cybersecurity risks, disrupting bank lending, and erasing local liquidity from bank deposits [31]. Countries have used big data to combat the Covid-19 pandemic, which enhanced the effectiveness of their efforts in pandemic monitoring, virus tracking, prevention, control, and treatment, as well as resource allocation [32]. However, while using big data to fight the Covid-19 pandemic may improve health care services and their performance, it might also raise other issues related to personal data protection. Before the pandemic, the use of personal data by governments without the permission of individuals was a point of debate; however, it has now become a focal point for human rights violations with the pandemic’s severe measurements. In line with the situations and discussions mentioned above, it is crucial to study the impact of the Covid-19 pandemic on the world and Turkish economies, mainly because of its massive destruction in all areas. While many studies have examined the global economic consequences of the Covid-19 pandemic [33, 34, and 35], some of them investigated the only Turkish economy [36, 37, and 38]. Furthermore, this research might be viewed as an updated version of our previous work, which examined only the early stages of the pandemic [6]. Both studies might be considered complementary, this one investigates the impact of the pandemic on the global and the Turkish economies from the beginning to the present in contrast to the previous one. Hence, the main objective of this study is to evaluate the potential short-term macroeconomic impacts of the Covid-19 pandemic on the world and Turkish economy based on reliable data released by the AÇIKGÖZ and GÜNAY / Turk J Med Sci 3185 IMF, the OECD, the World Bank (WB), Turkish public institutions, and other international institutions and current debate. Analyzing the Covid-19 pandemic impact on the economies in the medium and long term is not our priority since it is not known where the pandemic will evolve. Nevertheless, every data related to the world economy could not be reached due to the dynamic structure of the pandemic since the national data generally has not been published in the middle of the year. Hence, the international economic organizations could not access some countries’ data. In this context, our study focuses on average OECD area data if the world value of the related economic variable does not exist. Since the OECD economies share in world GDP is around 50% with 38 member countries5, it is thought that average OECD data can be used as a proxy for the world economic data in this study. As a result, some basic macroeconomic variables such as growth rate, inflation, interest and unemployment rate, trade volume, fiscal balance, travel and tourism, health spending, and fiscal support are presented to analyze the global and Turkish real sector, financial sector, public sector, labor market, foreign trade, and travel and tourism sector developments in the short-term. Furthermore, based on current data and discussions, some assumptions are made concerning possible changes in the global and Turkish economies. Finally, this study is concluded. 2. Global economic costs of the pandemic until mid-2021 The OECD reveals that global output fell by 3.5% in 2020 due to a sharp decline in global economic activity [3]. Despite new virus outbreaks in several economies in the fourth quarter of 2020, the global economy recovered faster than projected, and global output remained roughly better than an estimated 4.2% contraction [39]. It should be emphasized that sector specialization in different economies has led to variations in countries’ output growth rates. Those most have relied on international travel and tourism sector faced a more considerable GDP loss in 2020. Global GDP growth is forecast to be 5.8% in 2021 and 4.4% in 2022, with global output exceeding the prepandemic level (% 2.7) by mid-2021. However, global income will still be around $3 trillion, roughly equal to the size of the entire French economy, lower by the end of 2022 than forecast before the crisis [3]. Before the Covid-19 pandemic, the travel and tourism sector employed 10.6% of the worldwide workforce (334 million), but 62 million jobs were lost, representing a drop of 18.5% in 2020. In addition, the global GDP contribution of this sector fell from 10.4% ($9.2 trillion) in 2019 to 5.5% 5 Organisation for Economic Co-operation and Development (2021). OECD share in world GDP stable at around 50% in PPP terms in 2017 [online]. Website https://www.oecd.org/sdd/prices-ppp/oecd-share-in-world-gdp-stable-at-around-50-per-cent-in-ppp-terms-in-2017.htm [accessed 21 June 2021]. ($4.7 trillion) in 2020 due to ongoing mobility restrictions. Hence, the loss of travel and tourism sector was almost $4.5 trillion in 2020 [40]. Furthermore, destinations worldwide welcomed one billion fewer international arrivals in 2020 than in the previous year. International arrivals dropped by 74% due to an unprecedented fall in service demand and widespread travel restrictions. Also, the collapse in international travel represents an estimated loss of $1.3 trillion in export revenues, which is more than 11 times the loss recorded during the 2009 global economic crisis [41]. As of 2019, international visitor expenditure totaled $1.7 trillion, accounting for 6.8% of total exports. While domestic visitor expenditure fell by 45%, international visitor expenditure fell by a staggering 69.4% in 2020 [40]. Government retention plans and reduced hours support many jobs, but the potential of job losses and contraction persists without full recovery of the travel and tourism sector. The International Civil Aviation Organization (ICAO) reveals that global passenger traffic declined by 60%, and the revenue loss was nearly $371 billion in 2020 because of the widespread lockdowns, border closures, and travel restrictions worldwide. Moreover, the estimated decline in total world passengers will be between 44% and 49%, and approximately $289 to 323 billion loss of revenues of airlines is projected for the year 2021 compared to 2019 levels [42]. Nevertheless, cargo flights increased 40% in April 2020 in contrast to the fall in passenger traffic. Also, air cargo demand continued to outperform pre-Covid-19 pandemic levels, with demand up 9% in February 2021 compared to February 2019 level [42, 43]. The International Labour Organization (ILO) announces that 8.8% of global working hours were lost relative to the fourth quarter of 2019, equivalent to 255 million full-time jobs in 2020, approximately four times greater than during the global financial crisis in 2009. In total, there were unprecedented global employment losses in 2020 of 114 million jobs relative to 2019. In contrast to previous crises, employment losses in 2020 translated mainly into rising inactivity rather than unemployment, leading to an additional 81 million people shifting to inactivity alongside 33 million additional unemployed. Hence, the unemployment rate rose by 1.1% points to 6.5% around the world in 2020 [44]. Similarly, the unemployment rate increased from 5.4% in 2019 to 7.1% in 2020 in the OECD countries [3]. Before taking into account income support measurements, global labour income in 2020 is estimated to have declined by 8.3%, which amounts to $3.7 trillion, or 4.4% of global GDP [44]. Overall, world trade volumes are expected to increase to 8.2% in 2021, after falling by 8.5% in 2020 despite AÇIKGÖZ and GÜNAY / Turk J Med Sci 3186 ongoing weak services trade due to travel restrictions and lack of travellers’ confidence [3]. Similarly, global merchandise trade volume will likely rise by 8% in 2021 after declining by 5.3% in 2020 [45]. In the third quarter of 2020, shipments of computers and electronic components increased by 11%, while textile shipments increased by 24%, boosted by demand for face masks and other protective equipment compared to the second quarter of 2020. Surprisingly, after growing at a 10% annual rate in the first half of 2020, global pharmaceutical exports fell by 1% in the third quarter of 2020, mainly due to summer stockpiling [5]. Hence, the increased demand for digital technology and medical supplies has boosted global trade above prepandemic levels [3]. The better- than-expected performance at the end of the year can be attributed in part to the November release of additional Covid-19 vaccines, which helped to increase business and consumer confidence. On the other hand, commercial services exports fell by 20% in 2020 due to international travel limitations that impeded the delivery of services that required physical presence or face-to-face interaction [45]. Since oil is a crucial intermediate good, particularly for manufactured products and the energy sector, fluctuations in the oil market have a spillover effect. Because of the general recession of the world economy and the decline in demand for fuels and gasoline due to travel restrictions, the Covid-19 pandemic has had a significant impact on global oil demand. Oil prices fell from $67.3 per barrel in December 2019 to $18.4 per barrel in April 2020, but with a steady downward trend in Covid-19 cases in the second half of 2020, oil prices improved as well, pushing the price of Brent oil to around $50 by December 2020 [5]. Inflation rate in OECD countries was 1.5% in 2020 and is expected to reach 2.7% for 2021. The inflation rate is projected to increase significantly due to the past rise in commodity prices, particularly oil, and some one-off effects of the crisis. A combination of possible negative supply-side effects such as higher operating costs due to containment measurements, disruptions in global supply chains, and a desire to make up for past losses in revenues could push up inflation by more than projected. Upside risks to inflation include further exchange rate depreciation, and food and energy price increases, especially in countries where central bank credibility has already been weakened [3]. Global stock markets have fallen sharply as investors continue to worry about the broader uncertain economic impact of the pandemic. The FTSE, Dow Jones Industrial Average, and the Nikkei all saw massive declines in the first months of the Covid-19 pandemic. Although the major Asian and US stock markets recovered following the announcement of the first vaccine in November, the FTSE 6 BBC (2021). FTSE 100 suffers worst year since financial crisis [online]. Website https://www.bbc.com/news/business-55500103 [accessed 11 June 2021]. is still in negative territory. The FTSE dropped by 14.3% in 2020, its worst performance since 20086. The rise in fiscal deficits has stemmed primarily from the collapse in revenues caused by lower economic activity. In response, central banks in many countries reduced the interest rates to make borrowing cheaper and encourage spending to boost the economy. The longer the pandemic lasts, the greater the challenge is to public finances, and government deficits and debt have risen to unprecedented levels. To calm down markets and encourage spending, central banks have lowered policy rates and purchased government bonds, thereby, facilitating the fiscal responses to the pandemic. The size, composition, and duration of fiscal support have varied across countries, which has influenced its effectiveness. Among economies, the majority of supports was devoted to employment protection and household income support [46]. As a result, the fiscal balance increased from a deficit of 3.1% of GDP in 2019 to 10.8% in 2020 in OECD countries. The estimated deficit value for 2021 is 10.1% due to the continuing fiscal supports [3]. Global additional spending and foregone revenues of governments was 9.2% of 2020 GDP [46]. Along with governments, some international financial institutions provide financial supports to economies to rapid recovery from the Covid- 19 pandemic. For instance, the ECB has introduced a Pandemic Emergency Purchase Programme (PEPP) to support the euro area banks, firms, and households through the Covid-19 crisis. In this context, PEPP increased from €500 billion to €1850 billion in December 2020 [5]. In addition, the IMF is providing financial assistance and debt service relief to member countries, facing the economic impact of the Covid-19 pandemic. Overall, the IMF is currently making about $250 billion, a quarter of its $1 trillion lending capacity, available to member countries [47]. Furthermore, the WB has expected to deploy up to $160 billion over 15 months through June 2021 to support countries’ responses to the Covid-19 crisis [48]. Around the world, an estimated 400 million people do not have access to basic health care services. Nearly 100 million individuals are pushed into extreme poverty each year due to having to pay for their own health care. These numbers have increased with the Covid-19 pandemic, as they will continue to rise as people lose jobs and health insurance costs increase [49]. Before the onset of the Covid-19 pandemic, average health spending as a share of GDP across the OECD countries was around 8.8% [50]. Governments have increased their health spending with additional spending or foregone revenues during the Covid-19 pandemic, which is accounted for 1.2% of 2020 GDP. The IMF projected that while average advanced AÇIKGÖZ and GÜNAY / Turk J Med Sci 3187 economies’ share of health spending in GDP will increase by 2.6% in between 2020 and 2030, this share will be estimated to be 0.5% and 0.1% for emerging market and middle-income countries, and low-income countries, respectively [46]. Hence, it seems that current inequalities in health care services worldwide will continue in the postpandemic period. Briefly, as shown in Figure, global economic output is projected to rise by nearly 6% in this year, an impressive surge after the 3.5% contraction in 2020. While the unemployment rate in OECD countries is estimated to fall from 7.1% in 2020 to 6.5% in 2021, there will be no significant change in the fiscal balance deficit of the OECD area, and it is expected to be around 10% of GDP level 2021. Nevertheless, world trade volumes are projected to increase by close to 8.2% in 2021, falling by 8.5% in 2020. Although inflation in the OECD area declined by 0.4% point to 1.5% in 2020, it is expected to rise 2.7% in 2021 due to the delayed higher commodity prices [3]. 3. Costs of the pandemic on Turkish economy until mid-2021 Despite the OECD’s prediction of a 1.3% decrease in Turkish GDP in December 2020, Turkey demonstrated a more remarkable recovery from the Pandemic in 2020, with a growth rate of 1.8%. Turkey’s GDP is expected to rise at a rate of 5.7% in 2021 before slowing to 3.4% in 2022 in the absence of possible future shocks [3]. Turkey’s GDP increased by 7% in the first quarter of 2021 [51], but the surge in infections which appears to have peaked in May 2021. Therefore, new confinement measures have affected employment, incomes, and private consumption from the second quarter of 2021. The vaccine rollout started fast in February, but the authorities ran into serious procurement issues, causing them to scale down their goals and seek more diverse measurements until June 2021 [3]. Turkey’s overall export and import values were nearly $169.482 billion and $219.397 billion, respectively, resulting in a negative trade balance of $49.915 billion in 2020, which is higher than the $29.512 billion negative trade balance in 2019 [52]. Turkey’s net exports declined by 7.3% in 2020 but it is expected to increase 2.7% in 2021 [3]. In May 2021, exports increased by 65.50%, and imports increased by 54% compared to the previous year’s same month [53]. As of 2020, the top export destinations of Turkey were Germany (9.4%), the US (6%), and Italy (4.7%) and the top import origins were China (10.4%), Germany (9.8%), and Russia (8.1%) [52]. These data show that the supply chain of Turkey does not depend on one country primarily, so the Covid-19 pandemic’s detrimental influence on the manufacturing sector did not persist long in Turkey. For instance, the manufacturing sector in Turkey shrank by 8% in the second quarter of 2020, but then this sector showed a strong rebound in the next quarter with a 28.6% growth [51]. Therefore, the pandemic has not hurt the Turkish manufacturing sector as expected in the end. Turkish current account deficit recorded $1.127 million, indicating a decrease of $1.947 million compared to June of the 2020, bringing the 12-month rolling deficit to $29.674 million [54]. Although Turkey had a current account surplus in 2019 with 0.9% of GDP value, the Covid-19 pandemic widened the current account deficit to 5.2% in 2020 [3]. However, this deterioration was mainly driven by a decrease in the goods trade deficit and an increase in services surplus, which results from the global economic disruptions caused by the Pandemic. The tourism sector has been hardly damaged in Turkey since tourism income fell by 65.1% and declined to $12 billion in 2020 compared to the previous year. In parallel, departing visitors decreased by 69.5% in 2020 compared to Source: [3], [58], [74] and [75] -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 2019 2020 2021 projection 2019 2020 2021 projection OECD Countries Turkey Real GDP growth Unemployment rate Inflation rate Fiscal balance Trade growth Figure. Economic outlook on OECD Countries and Turkey. AÇIKGÖZ and GÜNAY / Turk J Med Sci 3188 2019 and declined to nearly 16 million people [55]. In April 2020, global international passenger capacity experienced an unprecedented 94% reduction for Turkey [42]. The navigational charge losses of Turkey were approximately $210 million in 2020, declined from $363 million in 2019 to $153 million at the end of 2020 [56]. Additional measures to facilitate a strong tourism season in 2021 summer have been implemented in Turkey, including a vaccination process for employees of the travel and tourism sector. Turkey has struggled with the high unemployment rate (13.5%) in the first quarter of 2021 since 2018. The number of unemployed persons increased by 89 thousand to 4 million 277 thousand persons compared to the same quarter of the previous year [57]. It seems that the unemployment rate increased, especially among the blue-collar workers and service sector employees, due to the bankruptcies and closures of factories and small and medium workplaces, who have been probably suffered the major economic losses of the Covid-19 pandemic. This situation has led to income losses for workers because of the weak labour market in Turkey. Moreover, the Consumer Price Index (CPI) of Turkey increased by 16.59% annually in May 2021. On the other hand, transportation with 28.39%, furnishings and household equipment with 21.79%, and health with 19.30% were the main groups where high annual increases were realized [58]. However, the central bank’s management has reiterated its commitment to the 5% inflation target against the 16,59% current inflation rate and persisting inflationary pressures in Turkey [59]. Additionally, unprecedented uncertainty will bring more risks for investors in Turkey. The value of Turkey’s Economic Confidence Index, 99.3 in January 2020, decreased by 3.1 points to 96.2 in January 2021. This fall stemmed from the decline in service and retail trade confidence indices [60]. On the other hand, the Turkish Real Sector Confidence Index increased from 62.30 to 107.40 in April 2021 compared to the same month in the previous year [61]. Therefore, the data indicate that increased confidence will bring better business performance in Turkey soon. The Central Bank of the Republic of Turkey (CBRT) decided to keep the policy interest rate at 19% in May 2021 to improve the financial conditions, which is higher than compared to the same month in the previous year (8.25%) [62]. Furthermore, on March 31, the CBRT introduced a program of outright purchases of sovereign bonds and has substantially increased its liquidity facilities to banks [63]. Today, Turkey’s 5 Years Credit Default Swaps (CDS) premium value is high with 382.62 points on 21 June 2021 but lower than 2020’s maximum value of 643.15. This 7 World Government Bonds (2021). Turkey 5 Years CDS [online]. Website http://www.worldgovernmentbonds.com/cds-historical-data/ [accessed 13 June 2021]. can be interpreted as the Turkish economy is under the pressure of financial risk7 since the higher CDS premium might cause pressure on the Turkish foreign borrowing interest rate to rise. The Turkish authorities predict that the total discretionary fiscal support package will cost 638 billion Turkish liras, 12.7% of GDP, in March 2021 to fight the Covid-19 pandemic. Debt guarantees to businesses and people, loan service deferrals by public banks, tax deferrals for businesses, equity injections into public banks, and a short-term labor program are examples of crucial fiscal measurements in Turkey [63]. In addition, Turkey implemented substantial Value-Added Tax (VAT) cuts for services and a withholding tax reduction for tradespeople. For instance, the VAT on passenger transportation, wedding organizations, residential maintenance and repair, dry cleaning, and tradespeople services like tailoring was reduced from 18% to 8%. Moreover, until mid-May 2021, a nationwide prohibition on layoffs was in effect. Besides, for families with monthly salaries of less than 5.000 Turkish liras ($740), state lenders have proposed a low-interest credit package of up to 10.000 Turkish liras ($1.477). The government also announced that it would pay 60% of the staff salaries of firms forced out of business under the short-term employment allowance program. In addition, the minimum pension was raised to 1.500 Turkish liras ($221) to protect retirees from the pandemic’s harmful consequences, and bonus payments were pushed to earlier dates. The government has recently begun paying 1.000 Turkish liras ($148) to 4.4 million needy families [64]. In the first quarter of 2021, compared to the same quarter the previous year, employee compensation climbed by 16%, while net operational surplus/mixed-income increased by 39.1% [57]. Among G20 emerging economies, Turkey provided the most liquidity support compared to its GDP in response to the Pandemic. Turkey left behind countries, including China, Brazil, India, and South Africa, with a liquidity support to GDP ratio of 9.5%. Brazil followed it with 6.2% and India with 5.2%. This ratio was 1.5% in Russia, and 1.3% in China. Besides, Turkey’s additional spending and foregone revenues were 2.7% of 2020 GDP and lag behind many countries [65]. Public deposit banks began new retail loan campaigns for home buying and consumer spending in June 2021. Also, loans to farmers have been postponed for six months. As part of the government’s Coronavirus Economic Stability Shield program, the Treasury-Backed Credit Guarantee Fund increased from 25 billion Turkish liras ($3.67 million) to 50 billion liras ($7.34 million). The enterprises’ principal and interest payments were postponed for at AÇIKGÖZ and GÜNAY / Turk J Med Sci 3189 least three months, and public lenders refinanced them. Turkey extended repayment periods for certain credit card loans, introduced low-interest credit packages for low- income households, allowed tradespeople to postpone payments without penalty, provided new low-interest loans and credit cards with longer repayment periods for tradespeople, and offered new credit packages for their jobs [66]. Turkey’s health spending was roughly 4.4% of its GDP in 2019, lower than the OECD average of 8.8%. However, health spending in OECD countries increased sharply in 2020 due to the pandemic. According to preliminary estimates, health spending in a group of 16 OECD countries jumped to roughly 9.9% of GDP on average in 2020 [50]. Turkey has also boosted its health spending through additional spending or revenue foregone during pandemic, accounting for 0.3% of GDP in 2020. As a result, IMF estimated that Turkey’s health spending as a share of GDP would rise 0.5% on average until 2030 [46]. Overall, Turkey is the 19th largest economy globally, with a GDP of $761 billion [67]. Turkey was among the few countries to experience positive economic growth in 2020. As shown in Figure, GDP growth is expected to be strong in 2021 with 5.7% if there will be no unprecedented shocks. Turkey’s unemployment rate decreased by 0.6% point to 13.1% in 2020 due to the decline in labor force participation. It is estimated to increase 14% in 2021 due to the weak labour market during the Covid-19 pandemic [3]. Besides, inflation rate increased by 2.8% point to 14.6% in 2020, probably due to inflation expectations and risk premia. Inflation is projected to rise to 16.5% in 2021 in Turkey [68]. Turkey’s net export volume fell from 3.2% in 2019 to -7.3% in 2020, and it will be expected to rise to 2.7% in 2021 as in other countries [3]. On the other hand, Turkey’s fiscal balance deficit rose from 2.9% of GDP in 2019 to 3.4% in 2020. However, the Turkish fiscal balance will be worsened by an estimated 5% deficit in 2021 [68]. 4. Discussion Estimating the real economic consequences of the Covid-19 pandemic is currently difficult due to the Pandemic’s spiral effects on both the national and global economies as a result of increased trade and financial linkages brought on by globalization [69]. In general, the crisis response is organized around four thematic pillars that are aligned with economies’ comparative advantages: saving lives threatened by the pandemic, protecting the poor and vulnerable, assisting in the retention of jobs and businesses, and working to build a more strong recovery 8 Our World in Data (2021). Statistics and Research: Coronavirus (COVID-19) Vaccinations [online]. Website https://ourworldindata.org/covid- vaccinations [accessed 16 August 2021]. 9 Financial Times (2021). Covid-19 looks like a hinge in history [online]. Website https://www.ft.com/content/de643ae8-9527-11ea-899a-f62a20d54625 [accessed 14 June 2021]. [48]. Countries who were fast to vaccinate their populations against the Covid-19 pandemic and manage infections through efficient public health measurements will likely rebound more quickly. However, while vaccination rates in many advanced economies increase, poorer and emerging market countries lag behind [3]. In the short term, global economic recovery will be impossible if equal vaccination distribution between and within countries is not achieved. For example, only 1.2% of people in low-income countries have gotten at least one dose of the Covid-19 vaccination, even though 31.2% of the world’s population has received at least one dose8. This divergence will probably tend to increase economic inequality within countries in the short-term. However, this condition might hurt the world’s social peace in the medium and long-term. It is crucial to highlight that in the early stages of the Covid-19 pandemic, the global leader of the US failed to safeguard its citizens, leaving them unwell and ruined. Millions of Americans have become impoverished and unable to access health care services. The Covid-19 pandemic killed nearly as many Americans than all of the military conflicts of the last 70 years combined9. Despite warnings and considerable advantages, including vast resources, biomedical infrastructures, and scientific skills, the US missed every opportunity to contain the virus. In contrast to many other countries, refused to take effective measurements to reverse the virus’s upward trend. This delay has affected the American economy badly, and the US economy contracted by 3.5% in 2020 [3]. According to the US experience, acting quickly is likely the most critical lesson in determining national outcomes during pandemics. Although increasing vaccine production and distribution is the most substantial current economic policy for boosting economic development, the future of the global economy remains uncertain, and the recovery will be uneven. According to the IMF’s economic predictions, between 90 million and 130 million full-time equivalent jobs will be lost, with global output growth of about 6% in 2021. However, this estimation is subject to vary based on virus evaluations around the world. Thus, the picture is highly unpredictable, with both upside and downside risks making forecasting difficult. Therefore, rebuilding confidence in macroeconomic policy and structural reforms are vital for rebounding the national and world economy [70]. The Covid-19 pandemic demonstrates the importance of public health management. However, many nations’ health systems are overburdened, and expenditures will AÇIKGÖZ and GÜNAY / Turk J Med Sci 3190 be required to improve staff and health care capacity to manage the possibility for Covid-19 renewal and subsequent pandemics. Governments all over the world, on the other hand, have promised billions of dollars towards a Covid-19 vaccine and treatment options; as a result, the market shares of some pharmaceutical companies involved in vaccine development have increased [71]. As a result, it is not incorrect to suggest that pharmaceutical corporations have benefited from the Covid-19 pandemic at most. In addition, several countries supply vials, syringes, needles, and even cool boxes and freezers needed to manufacture, distribute, and administrate vaccines. Hence, mass production, distribution and administering the vaccine supply chain are essential as vaccination [3]. ILO reveals the contrast between massive job losses in hard-hit sectors such as accommodation and food services, arts and culture, retail, and construction and the positive job growth evident in many high-skilled services sectors such as information and communications technology (ICT), and financial and insurance activities [44]. The World Economic Forum’s Future of Jobs Report 2020 projected that technological change is set to displace a range of skills in the labour market while driving greater demand for a new set of core skills such as analytical thinking, creativity, critical thinking, and digital skills [72]. Because of a particular shortfall in digital skills, there is a significant additional disruption in the labour market due to the Covid-19 crisis. Also, only 53.6% of the global population uses the internet, which refers to the digital divide around the world. Hence, the impact of the pandemic should serve as a wake-up call for countries that need to embrace the digitalization process, incentivize companies to move towards digital business models, and invest more in ICT development and digital skills [73]. The Covid-19 pandemic measurements also disrupted global education worldwide, and over 1.6 billion students in more than 190 countries were out of school in months. Two-thirds of an academic year has been missed on average worldwide due to full or partial closures in months, while half of the world’s student population is still affected by closures. The long-term closure of schools poses a great risk for students’ future since they will probably fall below the minimum proficiency levels in both theory and practice. Besides, 24 million children and youth are at risk of dropping out due to the digital divide. Since digital transformation is closely associated with human capital development, more education funding is needed for education recovery in most countries to improve online education services and upgrade curricula for changing world [74]. Also, more alignment is needed between employers and educators to enable students to get the new digital economy skills. However, these reforms will probably bring additional economic costs to the fiscal budget of countries. For many economies, currently, debt affordability is not a risk, but it will be inevitable due to the high amount of income support and government support packages. Vaccine supply brings an extra economic burden to the budget of countries. These additional spendings will bring more taxation on consumers and producers in the future. This situation is likely to deepen the economic crisis in the medium and long-term. Today, international financial institutions like IMF and WB provide financial assistance and debt service relief to member countries facing the economic impact of the Covid-19 pandemic. Nevertheless, whether these aids are given equal or transparent between countries is a separate debate. There will inevitably be paradigm shifts in the economy in the postpandemic period. In contrast to the free market economy, public and private sector cooperations will be essential for economic activities since the Covid-19 pandemic shows that neither governments nor businesses can achieve this complex postpandemic economic transformation alone. In addition, it seems the well- balanced fiscal support and flexible approach need to the economic recovery with the cooperation of the public and private sectors [75]. Today, global viruses will be seen as a threat like a nuclear attack, biological weapon, or global terrorism since their impacts on humans and the world are similarly very destructive, as seen in the Covid-19 pandemic [6]. Furthermore, the pandemic has brought to light another potential global threat, named bioterrorism, because the virus’s terrible impact, which these terrorist groups have acknowledged, has renewed their interest in acquiring, manufacturing, and employing biological weapons [76]. For this reason, countries that could develop a biological defense system or infrastructure for the virus attack will have more power in the postpandemic world. 5. Conclusion Although there were variations in recovery across economies, Turkey was among the few countries with China that showed a solid and positive rebound with 1.8% economic growth during the Covid-19 pandemic in 2020 though the global output declined by 3.5%. Hence, Turkey is apart from its peers due to the rapid recovery resulting from initial better policy responses such as monetary and credit expansion and large liquidity support. From this perspective, it is expected that the world and Turkey’s GDP growth will be 5.8% and 5.7%, respectively, in 2021 without further major shocks. Nevertheless, new confinement measures taken in May 2021 due to the pandemic’s third wave might adversely affect employment, incomes, private consumption, and the travel and tourism sector in Turkey. This unprecedented uncertainty characteristic of the Covid-19 pandemic has likely changed all economic AÇIKGÖZ and GÜNAY / Turk J Med Sci 3191 predictions, and sustainable growth for Turkey might not be possible in the short-term. Global economic output is projected to rise by nearly 6% in 2021, an impressive surge after the 3.5% contraction in 2020, but there will be no ordinary recovery in the world due to the unprecedented economic uncertainty. Also, economic rebound depends on the effectiveness of vaccination programs, public health policies, fiscal polices, and the country’s dependence on a hard-hit sector such as travel and tourism in the short-term. Although the unemployment rate and fiscal balance deficit are projected to fall in 2021 in OECD countries, world trade volumes are estimated to rise. Besides, inflation is expected to increase in the OECD area due to the past rise in commodity and oil prices, and some one-off effects of the crisis. In Turkey, as in other countries, the economic impact of the Covid-19 pandemic has been severe. Turkey’s unemployment rate decreased due to the decline in labor force participation but it is estimated to increase due to the weak labour market during the Covid-19 pandemic. Besides, poverty in Turkey is estimated to have risen by about 1.5 million people as a result of the pandemic [68]. Moreover, the inflation rate has increased, most probably due to the inflation expectations and risk premia. It is obvious that Turkey’s unemployment and inflation rate displayed a different trend in 2020 compared to the OECD economies. Although the tourism sector has been hardly damaged in Turkey, a sharp increase in tourism revenues is expected in 2021 once mass vaccination occurs. As of August 2021, the share of people fully vaccinated against Covid-19 virus is 39%, and the share of people only partly vaccinated against Covid-19 virus is 13% in Turkey, and Turkey ranked 17th in the world10. Although Turkey’s fiscal balance deficit rose, it showed a better performance than other OECD countries. Turkey provided the most liquidity support among G20 emerging economies during the pandemic. However, the Turkish fiscal balance will be worsened because of the extension of fiscal supports. Currently, the actual costs of the fiscal policy supports and vaccination to the Turkish economy have not been yet precise. 10 Our World in Data (2021). Statistics and Research: Coronavirus (COVID-19) Vaccinations [online]. Website https://ourworldindata.org/covid- vaccinations [accessed 16 August 2021]. Today, vaccines are seen as the key to a safe and permanent transition to more typical economic and social conditions. Countries that have been quick to vaccinate their population against the Covid-19 virus and manage to control infections through effective public health strategies have likely displayed more quick recovery. Hence, it seems that the pandemic will provide some economic and political opportunities to some countries if they can manage to control the virus earlier by effective vaccination policy. As a result of the digital transformation in every industry, the world will not be the same in the postpandemic period. Remote working, video- conferencing, online entertainment, e-commerce, and digital currencies, for example, have all grown in popularity since the beginning of the pandemic. Hence, countries should focus on the digitalization process, dissemination of ICT, and digital skills. Supporting students and young people to remain in education and enabling them to acquire digital skills by upgrading their curriculum based on new labour market demand should be a priority for governments. Countries should take urgent measurements to prevent a youth unemployment crisis and promote better mental health for young people. Moreover, cooperation between public and private sectors will probably be a new economic approach rather than a free market. In sum, Turkey must concentrate on new structural reforms to boost the economy, invest in health care, adapt to the new postpandemic world, and achieve a sustainable economy along with the other countries. Overall, trends in other economies suggest that the cost of the Covid-19 pandemic will not be recovered just by attaining a vaccination threshold in the short-term. In other words, vaccination seems to be a beginning rather than an end, and the policy choices governments make today will determine their place in the new world. Nevertheless, global economic recovery will be impossible in the near future if equal vaccination distribution between and within countries is not achieved. References 1. World Health Organization. WHO Coronavirus (Covid-19) Dashboard. Geneva; 13 August 2021. 2. Gopinath G. 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PMC6655540	RESEARCH ARTICLE Sprint mechanical variables in elite athletes: Are force-velocity profiles sport specific or individual? Thomas A. Haugen1, Felix Breitscha¨del1,2, Stephen Seiler3 1 Norwegian Olympic Federation, Oslo, Norway, 2 Norwegian University of Science and Technology, Trondheim, Norway, 3 Faculty of Health and Sport Sciences, University of Agder, Kristiansand, Norway thomas.haugen@olympiatoppen.no Abstract Purpose The main aim of this investigation was to quantify differences in sprint mechanical variables across sports and within each sport. Secondary aims were to quantify sex differences and relationships among the variables. Methods In this cross-sectional study of elite athletes, 235 women (23 ± 5 y and 65 ± 7 kg) and 431 men (23 ± 4 y and 80 ± 12 kg) from 23 different sports (including 128 medalists from World Championships and/or Olympic Games) were tested in a 40-m sprint at the Norwegian Olympic Training Center between 1995 and 2018. These were pre-existing data from quar- terly or semi-annual testing that the athletes performed for training purposes. Anthropomet- ric and speed-time sprint data were used to calculate the theoretical maximal velocity, horizontal force, horizontal power, slope of the force-velocity relationship, maximal ratio of force, and index of force application technique. Results Substantial differences in mechanical profiles were observed across sports. Athletes in sports in which sprinting ability is an important predictor of success (e.g., athletics sprinting, jumping and bobsleigh) produced the highest values for most variables, whereas athletes in sports in which sprinting ability is not as important tended to produce substantially lower val- ues. The sex differences ranged from small to large, depending on variable of interest. Although most of the variables were strongly associated with 10- and 40-m sprint time, con- siderable individual differences in sprint mechanical variables were observed among equally performing athletes. Conclusions Our data from a large sample of elite athletes tested under identical conditions provides a holistic picture of the force-velocity-power profile continuum in athletes. The data indicate PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 1 / 14 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Haugen TA, Breitscha¨del F, Seiler S (2019) Sprint mechanical variables in elite athletes: Are force-velocity profiles sport specific or individual? PLoS ONE 14(7): e0215551. https://doi. org/10.1371/journal.pone.0215551 Editor: Leonardo A. Peyre´-Tartaruga, Universidade Federal do Rio Grande do Sul, BRAZIL Received: January 3, 2019 Accepted: April 3, 2019 Published: July 24, 2019 Copyright: © 2019 Haugen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data underlying this study are deposited at the UiA Open Research Data repository with the following persistent ID: https://dataverse.no/dataset.xhtml?persistentId= doi:10.18710/PJONBM. Researchers interested in verification/replication of the present study can use this data source and are also kindly requested to contact the senior author, Stephen Seiler at Stephen.Seiler@uia.no, or Monica Klungland Torstveit at monica.k.torstveit@uia.no. The data underlying this study are owned by the Norwegian Olympic Federation (NOF). Because there are some unique athletes in the dataset, and their results may that sprint mechanical variables are more individual than sport specific. The values pre- sented in this study could be used by coaches to develop interventions that optimize the training stimulus to the individual athlete. Introduction Running a short distance as fast as possible is a core capacity in many sports. For a sprinter competing in athletics, 100 m and 200 m, this capability alone defines them as performers. In bobsleigh, athletes are required to sprint while moving an external mass. Sprinting capacity is also crucial in most team sports, as the ability to either create or close small gaps can be deci- sive in goal scoring situations. Even in typical endurance sports, explosive acceleration ability (in the context of their slow-twitch dominant peers) can be a medal-winning advantage at the finish of a close race. Accordingly, numerous sprint training studies across a wide range of sports have been performed over the years. Sprinting under assisted, resisted and normal con- ditions, maximal and explosive strength training, plyometric training and high-intensity run- ning have all been investigated in different combinations [1–3]. Although the principle of specificity is clearly present, no training methods have so far emerged as superior. Individual predispositions must therefore be considered when prescribing training programs. Considerable effort has been made over the years to quantify the underlying variables for sprint performance. Seminal works by Fenn & Marsh [4] described the force-velocity (Fv) rela- tionship in isolated frog and cat muscles, a relationship that later was confirmed in humans by Wilkie [5]. Advances in technology have allowed scientists to explore fundamental aspects of sprinting skills more closely, and presently, the physiology and mechanics of sprint running are typically interrogated through macroscopic mechanical variables [6–8]. Samozino et al. [9] have recently developed a simple and practical method for profiling the mechanical capabilities of the neuromuscular system using an inverse dynamic approach applied to the centre-of- mass movement, calculating the step-averaged ground reaction forces in runners’ sagittal plane of motion during accelerated sprinting from only anthropometric and spatiotemporal data. Theoretical maximal velocity (v0), horizontal force (F0), horizontal power (Pmax) and force-velocity profile (i.e., the slope of the force-velocity relationship; SFV) can be calculated from the speed-time curve. Other indices, such as ratio of force (RF) and index of force appli- cation technique (DRF) can also be computed from the same method. RF is a ratio of the step- averaged horizontal component of the ground reaction force to the corresponding resultant force, while DRF expresses the athlete’s ability to maintain a net horizontal force production despite increasing velocity throughout accelerated sprinting [6]. These variables are determi- nant factors for sprint performance, in line with the laws of motion, and provide insights into individual biomechanical limitations [6–8, 10]. A promising application of force-velocity profiling is in the design of individualized sprint training programs. An effective sprint training program should target the main factors that limit the athlete’s performance [11–12]. To help tailor the training program to the individual, the coach could compare test values from the individual to test values that are typical for the sport. An athlete with a velocity value that is low for the sport could then be prescribed more maximal velocity sprinting, whereas an athlete with a horizontal force value that is low for the sport could be prescribed more horizontal strength training [11]. Currently, sprint force-veloc- ity profile data are only available from athletes in a few selected sports; previous studies only analyzed specialist sprinters or athletes from selected team sports [8, 10, 13–19]. It is unclear Sprint mechanical variables in athletes PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 2 / 14 identify them, NOF has determined that the underlying data cannot be shared. Data are therefore available only on request to Stephen. seiler@uia.no for researchers who meet the criteria for access to confidential data. The authors confirm that data would be made available to researchers interested in replication/verification of the present study. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. whether previously measured sprint force-velocity profiles are specific to the sport or specific to the athlete. To individualize a training program for an athlete in a given sport, the coach requires a holistic picture of the force-velocity profile continuum in athletes. Therefore, the aim of the present study was to quantify differences in sprint mechanical variables across many sports and within each sport. Secondary aims were to quantify sex differences and rela- tionships among the variables. Materials and methods Participants The Norwegian Olympic training centre is a standardized testing facility used by a large num- ber of elite athletes from many sports. A database of results from 40-m sprint tests was col- lected from 1995 to 2018, and this database provides a foundation for exploring sprint performance and mechanical properties in athletes. In this cross-sectional study we analyzed sprint tests by 666 athletes from 23 sports. All participants were Norwegian national team ath- letes, i.e., represented Norway in international senior competitions, and 128 of the athletes were medalists from the World Championships and/or Olympic Games. Ethics statement This study was based on pre-existing data from quarterly or semi-annual testing that the ath- letes performed for training purposes, and thus no informed consent was obtained [20]. All data were anonymized to comply with the General Data Protection Regulations of the Euro- pean Union. The study was reviewed by the Norwegian Data Protection Authority and approved by the ethics committee at the Faculty of Health and Sport Sciences, University of Agder (reference number 19/00068). Procedures and data handling All included players were tested in the time period 1995–2018. Tests were performed on a ded- icated indoor 40-m track with 8 mm Mondotrack FTS surface (Mondo, Conshohocken, USA) at the Norwegian Olympic training center in Oslo. A standard warm-up program was com- pleted prior to sprint testing, beginning with a 10–15 min easy jog, followed by 5–6 minutes of sprint specific drill exercises, 2–4 strides with increasing speed and 1–2 trial starts. During test- ing, athletes assumed the starting position and started running on their own initiative after being cleared to start by the test leader. New trials were performed every 3–5 min until a per- formance plateau was observed. In practice, 80% of all players achieved their best performance within two trials. Body mass was assessed immediately prior to or after the sprint test on a sta- tionary force platform (AMTI, model OR6-5-1). Data from a single athlete was only included in one category for each analysis. That category was the player’s affiliation on the day of his/ her best sprint test result. A purpose-built excel spreadsheet developed by Morin & Samozino [21] formed basis for calculations of F0, v0, Pmax, SFV, RFmax and DRF. These calculations were based on best individual sprint test, associated split times and body mass. Temperature and atmospheric pressure were set to 760 mm Hg and 20 ˚C. Two different timing system setups were used. In the time epoch 1995–2011, a 60x60 cm start pad was placed under the track at the start line. The clock was initiated when the front foot stepped off the pad. Split times were recorded with split-beamed timing gates for each 10th m of the sprints. The transmitters where placed 140 cm above ground level, while the reflectors were placed 130 and 150 cm above the floor. Both beams had to be interrupted to trigger each timing gate. The timing setup has been assessed for accuracy and reliability [22]. Sprint mechanical variables in athletes PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 3 / 14 In January 2011, the timing system was upgraded. The split-beamed timing gates were replaced by dual-beamed timing gates, while the start pad was replaced with a single-beamed timing gate located 60 cm in front of the start line and 50 cm above ground level. Rigorous pilot testing was performed before deciding the exact location of the timing gate at start to pro- vide comparable times with the previous setup. Simultaneous comparisons (n = 50) of the old and new timing setup revealed no differences in 40-m sprint times (mean ± SD: 0.00 ± 0.02 s). The dual-beamed timing system has also been assessed for accuracy and reliability [23]. Over- all, typical error (coefficient of variation; CV) was 0.6–2.4% for sprint times, ~1.5% for v0 and RFmax, and 3.5–5.1% for F0, Pmax, SFV and DRF for both timing setups. To ensure valid sprint mechanical values when using split times as input in the method pro- posed by Samozino et al. [9], it is crucial that i) the entire acceleration phase is captured, and ii) time initiation (the time 0) is very close to the first rise of the force production onto the ground [24]. For the current procedures, the body’s center-of-mass was ~ 0.5 m in front of the start line, and possessed a considerable forward momentum, at time triggering. Hence, based on available correction factors [22, 25], 0.5 s was added to all sprint times for converting to “first movement” triggering. All sprint times presented are comparable to starts from blocks and audio signal with reaction time subtracted from the total time. Statistical analysis Data are reported as mean ± SD. Pearson’s R (±90%CL) was used to examine the relationship across variables (after log transformation of physiological data). Correlation values were inter- preted categorically according to the scale outlined by Hopkins et al. [26], meaning that 0.10, 0.30, 0.50, 0.70, 0.90 and 1.0 were thresholds for small, moderate, large, very large, extremely large and perfect, respectively. Magnitudes of differences across category means were assessed by standardization (mean difference divided by the harmonic mean of the SD of the compared groups). The thresholds for assessing the observed difference in means were 0.2, 0.6, 1.2, 2.0 and 4.0 for small, moderate, large, very large and extremely large, respectively [26]. To make inferences about true values of effects, non-clinical magnitude-based inference rather than null-hypothesis significance testing was used [26, 27]. Magnitudes were evaluated mechanisti- cally: if the confidence interval overlapped substantial positive and negative values, the effect was deemed unclear; otherwise effects were deemed clear and shown with the probability that the true effect was substantial using the following scale: 25–75%, possibly; 75–95%, likely; 95– 99.5%, very likely; > 99.5%, most likely [26, 27]. A purpose-built excel spreadsheet for combin- ing outcomes from several subject groups was used to calculate effect magnitudes, confidence limits (CL) and inferences [28]. Women’s performance was defined as 100% for comparisons between male and female athletes (all sport disciplines pooled together). Results To keep the results within reasonable limits, only a summary of the results is presented in this section. However, additional comparisons across category means can be performed by insert- ing data from the supplementary file into Hopkins’ spreadsheet [28]. Table 1 shows age, body mass and sprint performance across the analyzed sports. Overall, athletics sprinting (hereafter referred to as “sprinting” or “sprinters”) produced the fastest sprint times over both the shortest (10 m) and longest (40 m) distances among males, clearly ahead of bobsleigh (mean difference, ±90%CL: 0.02, ±0.03 and 0.09, ±0.09 s; possibly and likely; moderate), athletics jumping (hereafter referred to as “jumping” or “jumpers”) (0.07, ±0.03 and 0.15, ±0.09 s; most likely and very likely; large), soccer (0.11, ±0.02 and 0.38, ±0.07 s; most likely; very large) and all other sports (0.13, ±0.03 to 0.24, ±0.03 and 0.45, ±0.09 to 0.88, Sprint mechanical variables in athletes PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 4 / 14 ±0.09 s; most likely; very large to extremely large). Sprinters also displayed the fastest sprint times over the same distances among women, clearly ahead of jumpers (0.10, ±0.04 and 0.28, ±0.15 s; most likely and very likely; very large), handball (0.15, ±0.03 and 0.52, ±0.11 s; most likely; very large), athletics throwing (0.16, ±0.04 and 0.57, ±0.13 s; most likely; very large) and Table 1. Mean values (± SD) of age, body mass and sprint performance in Norwegian national team athletes (n = 666). Discipline (sex) N Age BM 10 m 20 m 30 m 40 m (y) (kg) (s) (s) (s) (s) Alpine skiing (W) 10 22.6 ± 3.3 66.0 ± 4.2 2.19 ± 0.05 3.62 ± 0.10 4.95 ± 0.15 6.29 ± 0.19 Alpine skiing (M) 13 24.7 ± 3.8 84.4 ± 3.7 2.04 ± 0.05 3.30 ± 0.08 4.48 ± 0.10 5.64 ± 0.13 Athletics jumping (W) 8 20.4 ± 4.9 60.4 ± 2.6 2.10 ± 0.06 3.40 ± 0.10 4.60 ± 0.15 5.79 ± 0.19 Athletics jumping (M) 9 21.8 ± 3.6 77.4 ± 6.8 1.97 ± 0.05 3.16 ± 0.06 4.23 ± 0.09 5.28 ± 0.11 Athletics sprinting (W) 5 19.2 ± 3.0 58.4 ± 2.1 2.00 ± 0.02 3.24 ± 0.04 4.39 ± 0.05 5.51 ± 0.09 Athletics sprinting (M) 8 20.7 ± 3.2 71.8 ± 3.8 1.90 ± 0.03 3.05 ± 0.05 4.09 ± 0.08 5.13 ± 0.11 Athletics throwing (W) 10 20.3 ± 3.6 75.0 ± 6.6 2.16 ± 0.06 3.53 ± 0.09 4.81 ± 0.14 6.08 ± 0.20 Athletics throwing (M) 14 22.4 ± 4.7 100.1 ± 8.8 2.03 ± 0.07 3.30 ± 0.11 4.45 ± 0.15 5.58 ± 0.20 Bandy (W) 13 23.0 ± 5.4 64.1 ± 8.6 2.28 ± 0.07 3.76 ± 0.12 5.18 ± 0.16 6.63 ± 0.21 Bandy (M) 23 19.3 ± 2.7 77.4 ± 8.3 2.09 ± 0.06 3.39 ± 0.09 4.60 ± 0.12 5.80 ± 0.16 Basketball (M) 10 22.6 ± 3.3 86.2 ± 9.5 2.06 ± 0.07 3.36 ± 0.12 4.55 ± 0.17 5.74 ± 0.22 Beach-/volleyball (M) 23 24.9 ± 4.7 87.7 ± 7.7 2.04 ± 0.05 3.34 ± 0.09 4.56 ± 0.13 5.78 ± 0.16 Bobsleigh (M) 9 26.7 ± 1.9 92.8 ± 4.4 1.92 ± 0.03 3.10 ± 0.06 4.17 ± 0.07 5.22 ± 0.09 Combat sports (W) 17 23.7 ± 6.0 60.6 ± 5.7 2.30 ± 0.07 3.78 ± 0.12 5.20 ± 0.19 6.62 ± 0.26 Combat sports (M) 32 22.5 ± 4.2 74.8 ± 11.1 2.08 ± 0.07 3.38 ± 0.11 4.59 ± 0.16 5.80 ± 0.23 Cross-country skiing (W) 8 20.0 ± 3.4 59.9 ± 5.1 2.27 ± 0.10 3.73 ± 0.19 5.12 ± 0.28 6.51 ± 0.37 Cross-country skiing (M) 15 21.9 ± 3.4 74.2 ± 4.4 2.11 ± 0.10 3.44 ± 0.16 4.68 ± 0.23 5.88 ± 0.31 Fencing (W) 5 18.9 ± 1.0 64.6 ± 4.0 2.34 ± 0.06 3.86 ± 0.11 5.30 ± 0.18 6.74 ± 0.25 Fencing (M) 10 21.9 ± 2.0 77.1 ± 6.6 2.14 ± 0.04 3.50 ± 0.07 4.76 ± 0.10 6.01 ± 0.13 Handball (W) 32 25.8 ± 4.6 72.8 ± 6.1 2.15 ± 0.07 3.50 ± 0.13 4.77 ± 0.18 6.03 ± 0.24 Handball (M) 18 23.9 ± 3.6 92.7 ± 9.0 2.03 ± 0.04 3.27 ± 0.07 4.43 ± 0.10 5.58 ± 0.14 Ice hockey (M) 34 24.8 ± 4.6 88.7 ± 7.4 2.03 ± 0.06 3.30 ± 0.10 4.46 ± 0.14 5.62 ± 0.19 Mogul skiing (W) 5 19.4 ± 1.9 64.0 ± 9.1 2.18 ± 0.04 3.57 ± 0.10 4.88 ± 0.16 6.19 ± 0.22 Mogul skiing (M) 14 21.2 ± 3.1 72.9 ± 6.3 2.05 ± 0.04 3.32 ± 0.05 4.52 ± 0.09 5.67 ± 0.12 Nordic combined (M) 22 23.5 ± 4.2 69.6 ± 4.0 2.04 ± 0.05 3.33 ± 0.08 4.51 ± 0.11 5.69 ± 0.16 Ski jumping (W) 11 18.4 ± 4.1 56.3 ± 3.1 2.23 ± 0.04 3.68 ± 0.06 5.05 ± 0.11 6.45 ± 0.15 Ski jumping (M) 28 21.2 ± 3.5 64.3 ± 5.0 2.05 ± 0.07 3.34 ± 0.12 4.55 ± 0.18 5.75 ± 0.25 Snowboard (W) 5 21.7 ± 4.6 59.4 ± 3.4 2.24 ± 0.06 3.73 ± 0.11 5.13 ± 0.17 6.61 ± 0.24 Snowboard (M) 9 21.3 ± 3.1 78.5 ± 7.7 2.05 ± 0.05 3.34 ± 0.09 4.55 ± 0.11 5.76 ± 0.16 Soccer (W) 93 23.8 ± 3.9 64.0 ± 4.9 2.17 ± 0.06 3.55 ± 0.11 4.84 ± 0.16 6.12 ± 0.22 Soccer (M) 57 25.4 ± 4.3 78.7 ± 5.8 2.01 ± 0.05 3.24 ± 0.08 4.39 ± 0.12 5.51 ± 0.16 Speed skating (W) 12 21.4 ± 4.0 68.5 ± 5.8 2.20 ± 0.05 3.60 ± 0.08 4.92 ± 0.12 6.25 ± 0.17 Speed skating (M) 22 22.3 ± 3.7 78.6 ± 8.0 2.10 ± 0.06 3.39 ± 0.12 4.59 ± 0.17 5.78 ± 0.23 Table tennis (M) 13 21.1 ± 4.1 69.5 ± 8.8 2.12 ± 0.06 3.46 ± 0.13 4.71 ± 0.19 5.95 ± 0.27 Telemark skiing (W) 5 18.6 ± 1.7 62.0 ± 1.9 2.26 ± 0.13 3.72 ± 0.23 5.10 ± 0.35 6.49 ± 0.49 Telemark skiing (M) 13 23.3 ± 2.6 82.7 ± 6.3 2.08 ± 0.06 3.39 ± 0.08 4.60 ± 0.11 5.80 ± 0.14 Tennis (W) 7 17.5 ± 1.7 65.6 ± 3.3 2.25 ± 0.07 3.70 ± 0.12 5.08 ± 0.18 6.48 ± 0.24 Tennis (M) 11 20.8 ± 3.3 75.3 ± 4.8 2.07 ± 0.05 3.37 ± 0.07 4.57 ± 0.10 5.78 ± 0.13 Weight-/powerlifting (M) 13 20.6 ± 4.2 87.9 ± 22.2 2.12 ± 0.07 3.45 ± 0.10 4.71 ± 0.17 5.96 ± 0.22 W = women, M = men, BM = body mass. https://doi.org/10.1371/journal.pone.0215551.t001 Sprint mechanical variables in athletes PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 5 / 14 all other sports (0.17, ±0.02 to 0.34, ±0.05 and 0.61, ±0.13 to 1.23, ±0.25 s; most likely; very large to extremely large). The mean sex difference increased from 6.4% for 10-m sprints to 9.3% for 40-m sprints. Fig 1A shows F0 across sports. Bobsleigh and sprinting displayed the greatest F0 values among men (unclear difference between them), clearly ahead of volleyball/beach volleyball (0.4, ±0.3 to 0.5, ±0.3 Nkg-1; likely to very likely; moderate), snowboard (0.4, ±0.4 to 0.5, ±0.4 Nkg-1; likely to very likely; moderate), soccer (0.5, ±0.3 to 0.6, ±0.3 Nkg-1; very likely to most likely; moderate to large) and all other sports (0.5, ±0.3 to 1.3, ±0.3; very likely to most likely; moderate to very large). In women, sprinters exhibited the highest F0 values, clearly ahead of jumping (0.7, ±0.4 Nkg-1; most likely; very large), handball (0.8, ±0.3 Nkg-1; most likely; very large), snowboard (0.9, ±0.4 Nkg-1; very likely; very large), alpine skiing (0.9, ±0.3 Nkg-1; most likely; very large) and all other sports (0.9, ±0.3 Nkg-1 to 1.7, ±0.3 Nkg-1; most likely; very large to extremely large). The sex difference for F0 was 9.3, ±1.2% (most likely; moderate). Fig 1B shows v0 across sports. Sprinters showed the highest values among men, clearly ahead of jumpers (0.4, ±0.4 ms-1; likely; moderate), bobsleigh (0.5, ±0.3 ms-1; very likely; large), soccer (1.1, ±0.3 ms-1; most likely; very large) and all other male sports (1.2, ±0.3 to 2.1, ±0.3 ms-1; most likely; very large to extremely large). Sprinters also displayed superior values among women, clearly better than jumpers (0.4, ±0.3 ms-1; very likely; moderate), handball (1.0, ±0.2 ms-1; most likely; very large), athletics throwing (1.1, ±0.2 ms-1; most likely; very large) and all other female sports (1.1, ±0.2 to 2.0, ±0.4 ms-1; most likely; very large to extremely large). The sex difference for v0 was 11.9, ±1.1% (most likely; large). Fig 2A shows Pmax across sports. In men, sprinters obtained the highest values, clearly ahead of bobsleigh (0.9, ±1.0 Wkg-1; likely; moderate), jumping (2.2, ±1.1 Wkg-1; most likely; large), soccer (3.7, ±0.8 Wkg-1; most likely; very large) and all other male sports (4.2, ±0.9 to 7.2, ±0.9 Wkg-1; most likely; very large to extremely large). Sprinting athletes also displayed the highest Pmax values among women, clearly ahead of jumping (2.4, ±1.1 Wkg-1; most likely; very large), handball (3.6, ±0.8 Wkg-1; most likely; very large), throwers (4.0, ±0.9 Wkg-1; most likely; extremely large) and all other female sports (4.2, ±0.7 to 7.3, ±0.8 Wkg-1; most likely; extremely large). The sex difference for Pmax was 21.9, ±1.1% (most likely; large). Fig 2B shows SFV across sports. Jumpers produced the highest values (i.e., most velocity-ori- ented) among the males, ahead of sprinting specialists (0.02, ±0.04 Ns-1m-1kg-1; unclear; small), speed skating (0.03, ±0.04 Ns-1m-1kg-1; possibly; small) and all other male sports (0.06, ±0.04 to 0.16, ±0.03 Ns-1m-1kg-1; very likely to most likely; moderate to very large. At the other end, volleyball/beach volleyball and snowboard were the most force-oriented disci- plines (unclear difference between them), showing clearly lower SFV values than weight-/ powerlifting (0.03, ±0.04 to 0.04, ±0.04 Ns-1m-1kg-1; likely: small to moderate) and all other male sports (0.03, ±0.04 to 0.16, ±0.03 Ns-1m-1kg-1; likely to most likely; small to very large). Among female athletes, jumpers displayed the most velocity-based SFV values, clearly higher than athletic sprinting (0.04, ±0.05 Ns-1m-1kg-1; likely; moderate) and all other female sports (0.05, ±0.04 to 0.16, ±0.08 Ns-1m-1kg-1; likely to most likely; moderate to very large). Snow- board was the most force-oriented group, ahead of bandy (0.05, ±0.07 Ns-1m-1kg-1; unclear; moderate), ski jumping (0.05, ±0.08 Ns-1m-1kg-1; unclear; moderate) and all other female sports (0.06, ±0.07 to 0.16, ±0.08 Ns-1m-1kg-1; likely to most likely; moderate to very large). The sex difference for SFV was 2.4, ±0.7% (most likely; small). Fig 3A shows RFmax across sports. Sprinters produced the highest percentage values among men, ahead of bobsleigh (0.4, ±0.8%; unclear; small), jumping (1.4, ±0.8%; very likely; large) and all other male sports (2.1, ±0.7 to 4.6, ±0.7%; most likely; large to extremely large). Sprint- ers also displayed the highest values among women, clearly ahead of jumpers (1.7, ±1.1%; very Sprint mechanical variables in athletes PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 6 / 14 Fig 1. Maximal horizontal force (F0) (Panel A) and theoretical maximal velocity (v0) (Panel B) across sports. The sports are ranked according to mean values for men. https://doi.org/10.1371/journal.pone.0215551.g001 Fig 2. Maximal horizontal power (Pmax) (Panel A) and force-velocity slope (SFV) (Panel B) across sports. The sports are ranked according to mean values for men. https://doi.org/10.1371/journal.pone.0215551.g002 Sprint mechanical variables in athletes PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 7 / 14 likely; large) and all other sports (2.9, ±0.9 to 5.9, ±1.2%; most likely; very large to extremely large). Fig 3B shows DRF across sports. Jumpers displayed the highest values, ahead of sprinters (0.2, ±0.4%; unclear; small), speed skating (0.4, ±0.4%; likely; moderate) and all other male sports (0.7, ±0.4 to 1.5, ±0.3%; very likely to most likely; moderate to very large). Among females, jumpers also obtained the highest values, clearly ahead of sprinters (0.2, ±0.4%; unclear; small), handball (0.5, ±0.3%; very likely; moderate) and all other female sports (0.5, ±0.3 to 1.5, ±0.7%; very likely to most likely; moderate to very large). Table 2 shows correlations (±90%CL) across the analyzed variables. The correlations between sprint mechanical variables and sprint times ranged from trivial to perfect. The corre- lations between sprint performance and SFV/DRF increased with increasing sprint distance. Fig 3. Maximal ratio of force (RFmax) (Panel A) and index of force application technique (DRF) (Panel B) across sports. The sports are ranked according to mean values for men. https://doi.org/10.1371/journal.pone.0215551.g003 Table 2. Correlations (±90%CL) across analyzed variables. 10-m time 40-m time F0 v0 Pmax SFV RFmax 40-m time 0.96, ±0.01 F0 -0.89, ±0.01 -0.73, ±0.03 v0 -0.87, ±0.02 -0.97, ±0.01 0.57, ±0.04 Pmax -1.00, ±0.01 -0.97, ±0.01 0.88, ±0.02 0.89, ±0.01 SFV -0.02, ±0.06 -0.30, ±0.06 -0.43, ±0.05 0.50, ±0.05 0.05, ±0.06 RFmax -1.00, ±0.01 -0.96, ±0.01 0.89, ±0.01 0.88, ±0.02 1.00, ±0.01 0.03, ±0.06 DRF -0.15, ±0.06 -0.42, ±0.05 -0.30, ±0.06 0.62, ±0.04 0.19, ±0.06 0.99, ±0.01 0.17, ±0.06 F0 = maximal horizontal force (relative to body mass), v0 = theoretical maximal velocity, Pmax = maximal horizontal power (relative to body mass), SFV = force-velocity slope (relative to body mass), RFmax = ratio of force, DRF = index of force application technique. https://doi.org/10.1371/journal.pone.0215551.t002 Sprint mechanical variables in athletes PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 8 / 14 Discussion To our knowledge, this is the first study to explore and compare underlying physiological and mechanical variables of sprint performance across a wide range of sports. Up to very large and even extremely large differences in sprint mechanical variables were observed across sports. Overall, sports in which sprinting ability is an important predictor of success scored the high- est values for most variables, while sports involving other locomotion modalities than running tended to produce substantially lower values. The current data from a large sample of elite ath- letes tested under identical conditions provides a holistic picture of the Fv profile continuum in sprinting athletes. In the following paragraphs, we will discuss each of the analysed variables more in detail. F0 in the present sample was in the range 7–10 Nkg-1 for men and 6–9 Nkg-1 for women, corresponding to a mean sex difference of 9.3%. Athletic sprinters and bobsleigh contestants were at the upper end of the scale. Previously published studies have shown that world-class male and female sprinters can reach 11 and 10 Nkg-1, respectively [10], representing the cur- rent upper limits for horizontal force production relative to body mass during accelerated sprinting. However, F0, calculated with the simple method outlined by Samozino et al. [9], is larger during resisted sprinting compared to unloaded sprints [14, 15, 29]. Hence, F0 derived from normal sprinting appears not to be a true F0, as the resistance in overcoming body mass inertia appears insufficient for maximal horizontal force-capacity generation. For practition- ers, the importance of F0 is perfectly illustrated by the fact that bobsleigh athletes displayed slightly higher F0-values (and clearly higher body mass) than sprinters despite slightly poorer sprint times across all time splits. Thus, F0 is a particularly crucial measure for athletes who perform brief sprints while moving an external mass. The shorter the distance considered, the higher the correlation between sprint performance and F0 (see Table 2). Cross et al. [17] reported 8.5 ± 1.3 and 8.8 ± 0.4 Nkg-1 for elite rugby union forwards and backs, and 8.1 ± 0.8 and 8.2 ± 1.0 Nkg-1 for corresponding rugby league players. These F0-val- ues are on par with the present male soccer players. Interestingly, although rugby players are generally heavier than soccer players, they do not produce higher F0 when normalized for body mass. As an athlete gets heavier, the energy cost of accelerating that mass also increases, as does the aerodynamic drag associated with pushing that wider frontal area through the air [30]. “Bigger” is therefore not necessarily better for sprinting, at least when there is no external mass to push. Moreover, volleyball/beach volleyball were among the best sports in terms of F0 scores, while weight-/powerlifters produced clearly lower values, despite no substantial group mean differences in body mass. This confirms previous findings that vertically-oriented and heavy strength training of the lower limbs does not necessarily translate to higher horizontal force production during accelerated sprinting [31]. The correlation between v0 and sprint performance was very large for 10-m sprint, and the correlation values increased with increasing sprint distance (see Table 2). V0 was in the range 7.5–11 ms-1 for men and 6–9.5 ms-1 for women, equivalent to a mean sex difference of 11.9%. Not surprisingly, sprinters obtained the highest scores. For comparison, the world’s fastest male and female track sprinters reach peak velocities of ~12 and ~11 ms-1, respectively [10, 32]. The fastest male and female team sport athletes in our material approached/exceeded 10 and 9 ms-1, respectively, in line with previous reports [33–35]. Many practitioners would argue that elite wide receivers, running backs and/or cornerbacks in American football are even faster, but no studies to date have presented comparable data. Metabolic energy turnover and efficient transfer to external power output underlies success- ful performance in many sports. We observed perfect or extremely large correlations between Pmax and sprint performance, depending on distance (Table 2). Sprint time improvement is Sprint mechanical variables in athletes PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 9 / 14 not a linear function of power increase. Indeed, the change in velocity (Δv) is related to the cube of the change in power (ΔP3), such that a 5% increase in velocity would require a nearly 16% increase in power [36]. The present data are consistent with this relationship. We observed Pmax values in the range 13–25 Wkg-1 for men and 11–21 Wkg-1 for women. The mean sex difference observed (21.9%), based on pooled data of all disciplines, corresponds well with data for elite sprinters. Slawinski et al. [10] reported that Pmax in male and female world- class sprinters was 30.3 ± 2.5 and 24.5 ± 4.2 Wkg-1, respectively, typically attained after ~1 s of sprinting. The highest individual values were 36.1 Wkg-1 and 29.3 Wkg-1, representing cur- rent upper limits in humans [37]. Athletes achieve two-to-three times higher Wkg-1 during countermovement jump (CMJ) compared to sprinting [37]. However, because it is not possible to assess a generic anaerobic maximal power, each anaerobic power test must be treated sepa- rately, and comparisons of power values across modalities are meaningless. SFV reflects the athlete’s individual balance between force and velocity capabilities. Morin & Samozino [11] have suggested that athletes with velocity values lower for the sport could be prescribed more maximal velocity sprinting, whereas athletes with horizontal force values lower for the sport could be prescribed more horizontal strength training. The present results show that SFV ranged from -0.75 to -1.10 Ns-1m-1kg-1 for men and -0.80 to -1.15 Ns-1m- 1kg-1 for women, corresponding to a sex difference of 2.4%. The group mean values of national-level sprinters were similar to the SFV values observed in world-class sprinters [10]. In the current study, women displayed lower slope values than men in all the analyzed sports where both sexes were represented. If we follow the approach proposed by Morin & Samozino [11], men should generally perform more force-oriented training than women. To our knowl- edge, no previous studies have recommended differentiated sprint-training programs accord- ing to sex. While young and untrained individuals tend to show improvements irrespective of training methods [38], well-trained senior athletes only achieve annual improvements smaller than typical variation [39]. Sprinting distance must also be considered if training prescription should be based on SFV orientation. The correlation between sprint performance and SFV increased (towards more velocity-oriented FV-profile) with increasing sprint distance (Table 2). This suggests that the longer the sprint distance, the more velocity-oriented training should be prescribed. Force-ori- ented sprint training (e.g., resisted sprints) is likely more appropriate for sports where the ath- letes are required to perform brief sprints while moving an external mass (e.g., bobsleigh). However, macroscopic Fv profiles derived from the simple method provide limited informa- tion about the Fv relationship of the individual muscles involved. The fascicle shortening velocity of the different muscles engaged do not necessarily change with increasing running velocity, and this inconsistent relationship is explained by an augmented contribution from elastic properties with increasing running velocity [40, 41]. Hence, running velocity is not a proxy for muscle contraction velocity, and one cannot use Fv profiles derived from sprint tests to determine training prescriptions for muscles in isolation. RFmax reflects the proportion of the total force production that is directed in the forward direction of motion at sprint start [9]. We observed a perfect correlation between RFmax and Pmax (r = 1.0, Table 2). This is mechanically sound, as Pmax in the simple model corresponds to the peak of the power curve (i.e., the maximal product of horizontal force and velocity), while vertical force corresponds to body mass when averaged over one step. Hence, Pmax and RFmax are two measures of the same capability. Within our material, RFmax ranged from 41 to 52% in men and 37 to 48% in women. Rabita et al. [8] reported 71.6 ± 2.6% in male world-class sprint- ers, but these values are not directly comparable due to methodological differences. In the Rabita et al. paper [8], RF was computed from force platform data, where the y-intercept of the extrapolated linear RF-velocity curve was defined as RFmax, that is; the theoretical maximal Sprint mechanical variables in athletes PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 10 / 14 contribution of anteroposterior force to the total force produced over one contact phase at zero velocity. For the simple method, where mechanical variables are calculated from anthro- pometric and spatiotemporal data, RFmax does not correspond to the extrapolated value at zero velocity, but to the value at 0.5 s (corresponding to the RF-value approximately at the first step). Based on publicly available split time and anthropometric data of world-class sprinters [42], we calculated RFmax values at 0.5 s as high as 56–57% in men and 52–53% in women, using the simple model. However, the maximal possible value of RFmax (100%) is not optimal for sprinting because a certain amount of vertical force is required to work against gravity. DRF expresses the athletes’ ability to maintain a net horizontal force production despite increasing running velocity. The more negative the slope, the faster the loss of net horizontal force during acceleration, and vice versa. In the present dataset, the values ranged from -7 to -10.5% among the men and -7.5 to -11% among the women. For comparisons, Rabita et al. [8] reported −6.4 ± 0.3% for male world-class sprinters. In practical terms, DRF reflects the dis- tance over which athletes are able to accelerate (i.e., distance to peak velocity). Previous research has shown that the duration of the acceleration phase varies as a function of athlete performance level. Team sport athletes [33, 34], students [43] and prepubescent children [44] typically achieve peak velocity at ~ 25–30 m of maximal linear sprinting. National and interna- tional 100-m track sprinters attain peak velocity after 40–50 and 50–80 m of sprinting, respec- tively, but men peak ~20% further in distance than women [10, 32, 45]. The nearly perfect correlation between DRF and SFV (r = 0.99, Table 2) is logical and expected, as the most veloc- ity-oriented athletes are able to accelerate over a longer distance than their more force-ori- ented counterparts. Conclusion In the present study, substantial differences in sprint mechanical properties were observed across sports. Based on these findings, some may argue that the chronic practice of an activity induces different Fv profiles in sprint running over time. However, the large spread within each discipline, in addition to the large overlap across sports, indicate that such variables are more individual than sport specific. Most sprint mechanical variables are strongly correlated with sprint performance level, in line with the laws of motion. Indeed, when split times and anthropometric data form basis for calculations of multiple variables, it is reasonable to expect high correlations among them. Based on these considerations, practitioners may question the practical relevance of such variables, as they are entwined, and in some cases, mere ‘different explanations of the same story’. However, while split time data provide a basis for convenient analysis on the field, sprint mechanical variables may provide deeper insights into individual biomechanical limitations. The values presented here can be used by practitioners to develop individual training interventions. Supporting information S1 File. Excel File containing sample size, group means and SD for all variables across sports. (DOCX) Author Contributions Conceptualization: Thomas A. Haugen, Stephen Seiler. Data curation: Thomas A. Haugen, Felix Breitscha¨del. Sprint mechanical variables in athletes PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 11 / 14 Formal analysis: Thomas A. Haugen, Felix Breitscha¨del. Investigation: Thomas A. Haugen, Felix Breitscha¨del, Stephen Seiler. Methodology: Thomas A. Haugen, Felix Breitscha¨del, Stephen Seiler. Project administration: Thomas A. Haugen, Stephen Seiler. Software: Felix Breitscha¨del. Supervision: Thomas A. Haugen, Stephen Seiler. Writing – original draft: Thomas A. Haugen, Stephen Seiler. Writing – review & editing: Felix Breitscha¨del, Stephen Seiler. References 1. Rumpf MC, Lockie RG, Cronin JB, Jalilvand F. Effect of different sprint training methods on sprint per- formance over various distances: A brief review. 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J Strength Cond Res. 2013; 27(1):116–24. https://doi. org/10.1519/JSC.0b013e31825183ef PMID: 22395270 Sprint mechanical variables in athletes PLOS ONE \| https://doi.org/10.1371/journal.pone.0215551 July 24, 2019 14 / 14	Sprint mechanical variables in elite athletes: Are force-velocity profiles sport specific or individual?	07-24-2019	Haugen, Thomas A,Breitschädel, Felix,Seiler, Stephen	eng
PMC10108008	Received: 19 April 2022 \| Accepted: 16 November 2022 DOI: 10.1111/cpf.12800 O R I G I N A L A R T I C L E Detailed investigation of multiple resting cardiovascular parameters in relation to physical fitness Lars Lind1 \| Karl Michaëlsson2 1Department of Medical Sciences, Uppsala University, Uppsala, Sweden 2Department of Surgical Sciences, Uppsala University, Uppsala, Sweden Correspondence Lars Lind, Department of Medical Sciences, Uppsala University, Uppsala 75185, Sweden. Email: lars.lind@medsci.uu.se Funding information Akademiska Sjukhuset Abstract Objective: Maximal oxygen consumption at an exercise test (VO2‐max) is a commonly used marker of physical fitness. In the present study, we aimed to find independent clinical predictors of VO2‐max by use of multiple measurements of cardiac, respiratory and vascular variables collected while resting. Methods: In the Prospective study of Obesity, Energy and Metabolism (POEM), 420 subjects aged 50 years were investigated regarding endothelial function, arterial compliance, heart rate variability, arterial blood flow and atherosclerosis, left ventricular structure and function, lung function, multiple blood pressure measure- ments, lifestyle habits, body composition and in addition a maximal bicycle exercise test with gas exchange (VO2 and VCO2). Results: When VO2‐max (indexed for lean mass) was used as the dependent variable and the 84 hemodynamic or metabolic variables were used as independent variables in separate sex‐adjusted models, 15 variables showed associations with p < 0.00064 (Bonferroni‐adjusted). Eight independent variables explained 21% of the variance in VO2‐max. Current smoking and pulse wave velocity (PWV) were the two major determinants of VO2‐max (explaining each 7% and 3% of the variance; p < 0.0001 and p = 0.008, respectively). They were in order followed by vital capacity, fat mass, pulse pressure, and high‐density lipoprotein (HDL)‐cholesterol. The relationships were inverse for all these variables, except for vital capacity and HDL. Conclusion: Several metabolic, cardiac, respiratory and vascular variables measured at rest explained together with smoking 21% of the variation in VO2‐max in middle‐ aged individuals. Of those variables, smoking and PWV were the most important. K E Y W O R D S exercise test, physical fitness, pulse wave velocity, smoking, VO2‐max Clin Physiol Funct Imaging. 2023;43:120–127. 120 \| wileyonlinelibrary.com/journal/cpf This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine. 1 \| INTRODUCTION Maximal oxygen consumption at an exercise test (VO2‐max) is commonly used as a marker of physical fitness. VO2‐max has further been shown to be related to all‐cause mortality in a dose response fashion (Kodama, et al., 2009). VO2‐max is generally considered to have a strong genetic component and twin studies report heritability estimates of 0.5–0.7, although fitness is naturally also affected by lifestyle habits (Maes et al., 1996). There are sex differences in VO2‐max, and VO2‐max declines with age (Amara et al., 2000; Serrano‐Sánchez et al., 2010) and increasing body fat (Serrano‐Sánchez et al., 2010). Other important determinants or consequences of low fitness are lung function at rest (forced vital capacity [FVC] and forced expiratory volume at 1 s [FEV1]) (Laukkanen et al., 2009; Mendelson et al., 2016; Nakamura et al., 2004) and smoking (Bernaards et al., 2003; de Borba et al., 2014; Suminski et al., 2009). As reviewed by Rost (1997), cardiac enlargement in athletes was first described by Henshen in 1899 comparing cross‐country skiers with sedentary controls (Henschen, 1899). Later studies have also evaluated total heart size in physical fitness (Bouchard et al., 1977), but in most other studies, the heart size has been divided into left atrial (LA) size, left ventricular (LV) end‐diastolic diameter and LV mass using echo- cardiography to give more detailed information. All of these indices of heart size have been linked to cardiorespiratory fitness (Brinker et al., 2014; Gidding et al., 2010; Lam et al., 2010; Rogers et al., 2020). Regarding other cardiovascular parameters, impaired endothelial vasodilatory capacity (Montero, 2015), increased aortic augmentation index (AIx) (Binder et al., 2006), increased arterial stiffness (Augustine et al., 2016; Boreham et al., 2004; Fernberg et al., 2017), poor LV diastolic function (Brinker et al., 2014), low haemoglobin level (Laukkanen et al., 2009) and carotid artery atherosclerosis (Rauramaa et al., 1995) have all been associated with poor VO2‐max. A major disadvantage with previous studies is that they mainly have investigated a limited number of cardiovascular and lung function parameters in the same individuals. Accordingly, no comprehensive picture of the determinants of VO2‐max has been presented. With the present study, we, therefore, aimed to measure multiple cardiovascular and lung function parameters in the same individuals and to relate those to VO2‐max. We used data from the population‐ based Prospective study of Obesity, Energy and Metabolism (POEM), in which multiple cardiovascular and lung function parameters have been measured in the same individuals at the age of 50. We included all measured cardiovascular and lung function parameters in the analysis to capture as many facets of cardiorespiratory function as possible. The hypothesis tested was that we by this approach could explain a great proportion of the variance in VO2‐max. 2 \| METHODS In a population‐based study of individuals, all aged 50 years, in Uppsala City, Sweden, the POEM (Lind, 2013), a random sample of men and women were invited to participate 1 month following their 50th birthday. The inclusion in the study started in 2012 and was stopped in 2017. The participation rate was 25%, and the inclusion was stopped after 502 participants. The study was approved by the Ethics Committee of the University of Uppsala, and the subjects gave their written informed consent to participate. The participants were asked how many times a week they performed mild (such as walking) and harder (to produce perspiration, like running) exercises for at least 30 min. Based on these data, four groups were defined (see Lind et al., 2021 for details): sedentary (13% of the sample), mild exercise only (24%), some harder exercise (33%) and harder exercise (30%). All individuals were investigated in the morning after an overnight fast. An arterial cannula was inserted in the brachial artery for blood sampling and was later used for regional infusions of vasodilators. Lipid variables and fasting blood glucose were measured by standard laboratory techniques. Height was recorded by a ruler and body weight was measured on a scale (Tanita BC‐418). Thereafter, multiple physiological tests were performed. Endothelial function and arterial compliance/stiffness were both measured by three different techniques: acetylcholine‐mediated increase in forearm blood flow, flow‐mediated vasodilation (FMD) and peripheral artery vasodilation (EndoPath). The carotid arteries were investigated by ultrasound for anatomy (intima‐media thickness and echolucency and blood flow. The myocardial LV was evaluated by ultrasound for LV geometry (LV mass, end‐diastolic volume, wall thickness), systolic (ejection fraction) and diastolic function (isovolu- metric relaxation time, E/A‐ratio, Doppler e′/a′ ratio). Blood pressure was measured by four different techniques (conventional, invasive, derived central pressure, 24 h ambulatory). Basal energy expenditure was measured by indirect calorimetry and heart rate variability (HRV) was recorded for 5 min. Arterial compliance was measured by three techniques (carotid‐femoral pulse wave velocity [PWV], carotid artery distensibility and the stroke volume to pulse pressure ratio). Radial artery pulse wave was recorded for the AIx and reflectance index. Blood flow of the brachial artery was recorded at rest and following 5 min of hyperaemia. Total and regional body fat and lean mass were estimated using dual‐energy X‐ray absorptiometry (DXA; Lunar Prodigy, GE Health- care). To minimize the potential operator bias, all scans were performed in the same room by one experienced nurse. Total fat and lean mass had a precision error of 1.5% and 1.0%, respectively. For analysis, automatic edge detection was always used; however, all scans were thoroughly checked for errors and manually corrected if needed. On a separate day, close to the first investigations, the participants returned to the nonfasted state to evaluate lung function (FVC and FEV1) and to perform a maximal bicycle ergometer test with gas exchange recordings. Also, the recoveries of the heart rate, blood pressure and VO2 and VCO2 during 5 min were recorded. Smoking was identified as current smoking. All the physical investigations have previously been described by Lind and Lampa (2019) and are given in detail in the Supporting Information. LIND AND MICHAËLSSON \| 121 FIGURE 1 (See caption on next page) 122 \| LIND AND MICHAËLSSON 2.1 \| Statistical analysis All variables were checked for a normal distribution, and some variables such as the E/A ratio, serum triglycerides, most HRV vari- ables, were skewed to the right and therefore ln‐transformed to achieve a normal distribution to be used in the models. First, the relationship between VO2‐max and sex was investi- gated by ANOVA (same age of all subjects). Second, the relationships between VO2‐max (adjusted for lean mass) and the 84 hemodynamic or metabolic variables were investigated one by one in sex‐adjusted linear regression models. Third, the relationships between VO2‐max and the 84 hemodynamic or metabolic variables were investigated one by one with sex and fat mass included in the model. Fourth, the interactions between sex and the hemodynamic or metabolic variables were investigated one by one. Fifth, a multiple linear model with VO2‐max as the outcome and sex together with eight other hemodynamic or metabolic variables, which were Bonferroni‐ significant in the initial analyses, as independent variables were evaluated. In this model, variables being closely related (correlation coefficient > 0.3) to other more significant variables, such as FEV1, and several blood pressure and heart rate measurements, were not included in this multiple model due to the risk of co‐linearity. In the second to fourth steps, the relationships between VO2‐max and the 84 hemodynamic or metabolic variables were investigated one by one, and therefore, Bonferroni adjustment for these tests was performed resulting in a critical p‐value of 0.00064. In step five, we regarded p < 0.05 to be significant. STATA14 was used for the calculations (Stata Inc.). 3 \| RESULTS The median and interquartile ranges of measured variables are given in Supporting Information: Table 1. VO2‐max (alone and when adjusted for lean mass) was a normally distributed variable. The mean for unadjusted VO2‐max was 2.79 (SD 0.54) L/min in men and 1.85 (0.40) in women (p < 0.0001). Sex explained 49% of the variation in unadjusted VO2‐max. VO2‐max adjusted for lean mass was 0.46 (SD 0.079) L/min/kg lean mass in men and 0.39 (0.083) in women (p = 0.0043). Sex explained only 1.7% of the variation in VO2‐max after adjustment for lean mass. In the following analysis, VO2‐max adjusted for lean mass was used. Current smoking was reported by 9.8% of the individuals. When VO2‐max was used as the dependent variable and the 84 hemodynamic or metabolic variables were used as indepen- dent variables in separate sex‐adjusted models for each hemo- dynamic or metabolic variable, 15 variables showed associations with p < 0.00064 (Bonferroni adjusted threshold, see Supporting Information: Table 2 and Figure 1 for details). Vital capacity, FEV1 and high‐density lipoprotein (HDL) were positively related to VO2‐max, while the pulse rate, pulse pressure, diastolic night‐ time dipping at 24 h ambulatory recording, BMI, fat mass, triglycerides, office recordings of the pulse rate, diastolic blood pressure and calculated central systolic and diastolic blood pressure all were related to VO2‐max in a negative fashion. All of these variables displayed p < 0.05 when additional adjustment for fat mass was made. No interaction term between sex and any hemodynamic or metabolic variable was significant following adjustment for multiple testing. Together with smoking, eight hemodynamic or metabolic variables being Bonferroni‐significant in the initial analyses explained 21% of the variation in VO2‐max. This held true also after omitting the variable sex, which was included in the first version of the model. In this model with VO2‐max as the outcome, smoking and PWV were the two major determinants of VO2‐max (explaining 7%, p < 0.0001 and explaining 3%, p = 0.008, respectively). They were followed by vital capacity, fat mass, pulse pressure and HDL‐ cholesterol, which all showed p < 0.05 in this multiple model (see Table 1 for details). The relationships were inverse for all these variables, except for vital capacity and HDL. Sex (p = 0.97), triglycerides and the resting heart rate showed p > 0.05. Figure 2 displays some of these main relationships more in detail. 4 \| DISCUSSION The present study showed that smoking and an increased PWV at rest were most closely related to VO2‐max, but lung function, fat mass, pulse pressure and HDL‐cholesterol were also related to this commonly used marker of physical fitness. FIGURE 1 Relationships between hemodynamic and metabolic variables and VO2‐max (adjusted for lean mass) when the hemodynamic and metabolic variables were evaluated one by one. The regression coefficient and 95% CIs are given for the sex‐adjusted analyses. a, atrial contraction transmitral filling velocity; AIx, aortic augmentation index; AMBP, ambulatory monitoring of blood pressure; BMI, body mass index; CI, cardiac index; DBP, diastolic blood pressure; e, early transmitral filling velocity; EDV, endothelium‐dependent vasodilatation; EE, energy expenditure; EF, ejection fraction; EIDV, endothelium‐independent vasodilatation; FEV1, forced expiratory volume at 1 s; FMD, flow‐mediated dilatation; HDL, high‐density lipoprotein; HR, heart rate; HRV, heart rate variability; IM‐GSM, echogenicity of the intima‐media complex; IMT, intima‐media thickness; IVRT, isovolumetric relaxation time; IVS, intraventricular thickness; LA, left atrial diameter; LF, low frequency; LF/HF ratio, low‐frequency/high‐frequency ratio; LVEDD, left ventricular end‐diastolic diameter; LVESD, left ventricular end‐systolic diameter; LVMI, left ventricular mass index; PP, pulse pressure; PW, posterior wall thickness; PWV, pulse wave velocity; RHI, reactive hyperaemia index; RI, reflectance index; RQ, respiratory quote; RWT, relative wall thickness; SBP, systolic blood pressure; SDFV, systolic to diastolic blood flow velocity; SI, stroke index; SV/PP‐ratio, stroke volume to pulse pressure ratio; TPRI, total peripheral resistance index; VC, Vital capacity; VCO2, carbon dioxin production; VO2, oxygen consumption. LIND AND MICHAËLSSON \| 123 4.1 \| Comparison with the literature All of these variables have previously been shown to be related to VO2‐max, as cited in the Introduction section. The novelty of the present study is that we by the measurements of multiple cardiovascular and lung function variables in the same individuals were able to compare these variables in terms of importance and independence from each other. We could not reproduce some other previous findings that endothelial vasodilatory function (FMD) (Montero, 2015), a poor LV diastolic function and a large LV end‐diastolic volume (Brinker et al., 2014), a low haemoglobin level (Laukkanen et al., 2009) and carotid artery atherosclerosis (Rauramaa et al., 1995) were related to VO2‐max. One major advantage of the present study is that we could evaluate the independent contribution of indices reflecting different aspects of physiology in the same model and found that several different physiological pathways are determinants of VO2‐max. This is not a surprise, since it is obvious that the heart, the lungs and the skeletal muscles simultaneously all play important roles in the determination of cardiorespiratory fitness. Given that basic assumption, it was a surprise that no variable reflecting myocardial function or structure was amongst the major identified physiological indices. One explanation for this could be the very strict Bonferroni adjustment applied to compensate for the multiple statistical testing. It could be seen that both the s′ and e′ at TDI, the e′/a′‐ratio at TDI, stroke index, LA diameter (inverse) and relative wall thickness (RWT) (inverse) showed p < 0.05 (p = 0.054 for RWT). Thus, if not using this strict adjustment for multiple testing, we could replicate the findings of others that several myocardial indices are linked to VO2‐max, although other factors might be more important. Only a small part of the variance in VO2‐max could be explained by the evaluated variables despite that a great number of cardiopulmonary variables were assessed. Several factors could explain this finding. First, all variables have a certain lack of precision and variability in measurements that could lower the degree of explained variance, especially when several variables seem to be of importance. Second, all variables were measured at rest. It could be speculated that a better R2 for VO2‐max would be obtained if the variables were measured during exercise instead. Third, certain factors of particular interest were not measured. One such very important feature is the mitochondrial function in the heart and skeletal muscles during exercise. Another could be diffusion capacity in the lungs. Yet another factor is skeletal muscle composition, which is important for endurance capacity (Hall et al., 2021). Fourth, it has been shown that genetic DNA variations both at the global level (Gineviciene et al., 2022), as well as at the mitochondrial level (Vellers et al., 2020), are important determinants of VO2‐max. Fifth, we normalized VO2‐max for lean mass measured at DXA. Most other studies have not performed such rigorous normalization, and if no normalization would have been performed, lean mass in itself would explain 60% of the variance in VO2‐max. 4.2 \| Clinical perspectives Apart from an increase in endurance training, smoking cessation would be the single most important action to improve VO2‐max, as suggested by the present findings. We could not however find any intervention studies to support that assumption. It might also be warranted to reduce arterial stiffness, although the causality is less clear in this case. In a small placebo‐controlled trial in postmyocardial infarction patients, treatment with a combina- tion of a statin and an angiotensin‐receptor blocker reduced PWV (Turk Veselič et al., 2018). In an open trial of the combination of an ACE inhibitor and a calcium channel blocker in patients with hypertension, an improvement in PWV was seen after 12 months (Radchenko et al., 2018). It would be of interest to see if such interventions that improve arterial stiffness would also have an impact on VO2‐max. TABLE 1 Relationships between VO2‐ max (outcome, adjusted for lean mass) and sex and eight hemodynamic or metabolic variables as independent variables Variables related to VO2‐max Beta 95% CI low 95% CI high p Value Sex −0.004335 −0.2405681 0.2318981 0.971 Ambulatory pulse pressure −0.1310065 −0.2354139 −0.0265991 0.014 Smoking −0.2153444 −0.2965848 −0.1341041 0.000 Fat mass −0.1095866 −0.1923139 −0.0268592 0.010 Resting heart rate −0.0134249 −0.1198909 0.093041 0.804 Triglycerides −0.0539096 −0.1287491 0.02093 0.158 Pulse wave velocity −0.112147 −0.1944053 −0.0298887 0.008 HDL 0.0920998 0.0008397 0.1833599 0.048 Resting vital capacity 0.1385489 0.0300567 0.2470411 0.012 Abbreviations: CI, cardiac index; HDL, high‐density lipoprotein; VO2‐max, maximal oxygen consumption. 124 \| LIND AND MICHAËLSSON FIGURE 2 Relationships between VO2‐max (adjusted for lean mass) and variables were found to be of major importance to explain the variation in VO2‐max (adjusted for lean mass). VO2‐max versus current smoking is given in the upper panel. VO2‐max versus pulse wave velocity (PWV) is in the middle panel and VO2‐max versus fat mass is given in the lower panel. LIND AND MICHAËLSSON \| 125 Weight loss might also be a way to increase VO2‐max, and at least in patients with class III obesity (BMI > 40 kg/m2), weight reduction increased VO2‐max (Hakala et al., 1996). 4.3 \| Strengths and limitations The major strength of the present study is the multitude of cardiovascular and lung function variables measured at rest together with VO2‐max in individuals of the same age. Since age is an important determinant of VO2‐max (Amara et al., 2000; Serrano‐ Sánchez et al., 2010), standardization of age would remove the impact of this very important variable on the variance in VO2‐max. Another strength is that we could adjust VO2‐max for lean mass, measured by the gold standard, DXA. As could be seen in our analysis, this standardization removed most of the sex effect on the variation in VO2‐max. This is a cross‐sectional study, and as such causality can never be proven and the directions of relationships are not clear. A limitation of studying a homogeneous sample is that the generalizability is low, so the present results have to be reproduced in samples from other countries with other ethnical groups, as well as in other age groups. 5 \| CONCLUSION Several metabolic, cardiac, respiratory and vascular variables mea- sured at rest explained together with smoking 21% of the variation in VO2‐max in individuals aged 50 years. ACKNOWLEDGEMENTS The study was funded by the University Hospital of Uppsala, Sweden. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. CONFLICT OF INTEREST The authors declare no conflict of interest. DATA AVAILABILITY STATEMENT According to Swedish law, personal health data cannot be made publicly available. Data from this study are however available upon a reasonable request by other researchers. ORCID Lars Lind https://orcid.org/0000-0003-2335-8542 REFERENCES Amara, C.E., Koval, J.J., Johnson, P.J., Paterson, D.H., Winter, E.M. & Cunningham, D.A. (2000) Modelling the influence of fat‐free mass and physical activity on the decline in maximal oxygen uptake with age in older humans. Experimental Physiology, 85, 877–885. Augustine, J.A., Yoon, E.S., Choo, J., Heffernan, K.S. & Jae, S.Y. (2016) The relationship between cardiorespiratory fitness and aortic stiffness in women with central obesity. Journal of Women's Health, 25, 680–686. Bernaards, C.M., Twisk, J.W.R., Van Mechelen, W., Snel, J. & Kemper, H.C.G. (2003) A longitudinal study on smoking in relation- ship to fitness and heart rate response. Medicine and Science in Sports and Exercise, 35, 793–800. 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(2023) Detailed investigation of multiple resting cardiovascular parameters in relation to physical fitness. Clinical Physiology and Functional Imaging, 43, 120–127. https://doi.org/10.1111/cpf.12800 LIND AND MICHAËLSSON \| 127	Detailed investigation of multiple resting cardiovascular parameters in relation to physical fitness.	12-01-2022	Lind, Lars,Michaëlsson, Karl	eng
PMC6901428	Vol.:(0123456789) Sports Medicine (2019) 49 (Suppl 2):S199–S204 https://doi.org/10.1007/s40279-019-01164-z REVIEW ARTICLE Genetic Approaches for Sports Performance: How Far Away Are We? Michael J. Joyner1 Published online: 6 November 2019 © The Author(s) 2019 Abstract Humans vary in their ‘natural ability’ related to sports performance. One facet of natural ability reflects so-called intrinsic ability or the ability to do well with minimal training. A second facet of natural ability is how rapidly an individual adapts to training; this is termed trainability. A third facet is the upper limit achievable after years of prolonged intense training; this represents both intrinsic ability and also trainability. There are other features of natural ability to consider, for example body size, because some events, sports, or positions favor participants of different sizes. In this context, the physiological determinants of elite endurance performance, especially running and cycling, are well known and can be used as a template to discuss these general issues. The key determinants of endurance performance include maximal oxygen uptake ( ̇VO2max) , the lactate threshold, and running economy (efficiency in the case of cycling or other sports). In this article, I use these physi- ological determinants to explore what is known about the genetics of endurance performance. My main conclusion is that at this time there are very few, if any, obvious relationships between these key physiological determinants of performance and DNA sequence variation. Several potential reasons for this lack of relationship will be discussed. Key Points ‘Natural ability’ or talent is a widely appreciated feature of many elements of sports performance. The assumption is that key physiological elements of talent are embedded in, or explained by, interindividual differences in DNA sequence. At this time, interindividual differences in DNA sequence explain only a small fraction of the physiology underpinning sports performance. 1 Introduction Over the past 50 or so years, the key physiological deter- minants of endurance exercise performance have emerged. These include maximal oxygen uptake ( ̇VO2max) , the lactate threshold, and efficiency. In the case of distance run- ning, efficiency is typically referred to as running economy because it is difficult to calculate efficiency in a strict engi- neering context in running humans [1]. By contrast, it is much easier for cycling. Data on these three variables can be modeled to predict performance, and there are field tests that incorporate several of these variables that are also highly predictive of perfor- mance. For example, in the early 1990s I took emerging evidence that humans run the marathon at a pace similar to their running speed at lactate threshold, and calculated a theoretical upper limit, at least at that time, for the ‘fastest’ potential marathon performance by men [2]. This model also reasonably predicted the performance of a given individual. Likewise, so-called velocity at ̇VO2max was shown to be highly correlated with running performance [3]. This latter measure incorporates both ̇VO2max and running economy into one metric. The basic idea underpinning these factors is that they interact in a predictable way. ̇VO2max can be seen as the upper limit of aerobic capacity, the lactate threshold related to the fraction of ̇VO2max that can be sustained for a dura- tion longer than a few minutes, and efficiency or economy related to the actual power output or speed during a race that can be generated at a given V̇O2. Additionally, the physi- ological determinants of ̇VO2max and the lactate threshold * Michael J. Joyner joyner.michael@mayo.edu 1 Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA S200 M. J. Joyner are well understood. Less is known about the physiological determinants of efficiency/economy. The question then is, if the physiological determinants of ̇VO2max and the lactate threshold are well understood, what is known about the con- tribution of DNA variation to these factors? Before I go on, I want to share two sets of assumptions related to the physiology behind ̇VO2max and the lactate threshold. First, for ̇VO2max , the primary physiological determinants under most circumstances in most humans are related to maximum cardiac output and stroke volume, along with red cell mass or total body hemoglobin [4]. In other words, the ability of the heart to pump large quanti- ties of oxygenated blood to the contracting skeletal muscles is absolutely critical. While this is not true in every case and in every circumstance, for example chronic obstructive pulmonary disease (COPD), where the lungs can become limiting, it is true for the vast majority of situations. Second, the lactate threshold reflects, in large part, some combina- tion of skeletal muscle mitochondrial content and function in the contracting skeletal muscles and perhaps capillary density [5, 6]. Efficiency/economy is much more complex and likely sport-specific. It also has an element of the com- petitive medium that needs to be considered. Examples include wind resistance during high-speed cycling versus lower-speed running, or water resistance for sports such as swimming or rowing [1]. Therefore, with this general perspective as a background, I will next try to ask what is known about the genetic con- tributions to the major physiological determinants of endur- ance exercise performance. A key question then is what constitutes ‘genetic’. One approach is to focus on the herit- ability of key traits related to athletic performance. These are typically statistical arguments based on the correlation of a given trait between family members, most notably mono- or dizygotic twins. If the correlation between monozygotic twins is greater than the correlation between dizygotic twins then the interpretation is that this similarity is due primarily to greater similarities in the DNA of monozygotic twins than dizygotic twins [7]. For ̇VO2max , the heritability can be very high for monozygotic twins, consistent with the idea that there is a major genetic component to this variable. Twin (and other family) studies also indicate that there is a sig- nificant genetic component to the increase in ̇VO2max seen with a few months of fitness-type training [8, 9]. While the observations highlighted above suggest there is a strong genetic component to training, specific DNA vari- ants associated with ̇VO2max and how ̇VO2max responds to training have been hard to find. While a number of small effect size variants considered in concert seem related to the rise in ̇VO2max with training, no variants alone or in combination that are clearly linked to canonical biological pathways likely to underpin cardiac output and red cell mass have been identified [10–13]. The issue of limits of genetic ‘causation’ is also part of a general trend in genomic research for complex human traits that has accelerated in recent years following the completion of the human genome project. In the late 1990s and early 2000s, it was generally assumed that a limited number of gene variants would explain much of the risk of develop- ing common non-communicable diseases. The idea was that once these variants were identified, a host of new approaches to diagnosis, prevention, and therapy would emerge. Unfor- tunately, this vision has not been realized and hundreds of gene variants with small effect sizes have been associated with complex non-communicable diseases. Importantly, their role in the diagnosis, prevention, and therapy for these diseases remains obscure. These larger issues related to genomics and complex disease-related traits have been dis- cussed in detail elsewhere [14]. 2 Oxygen Transport Cascade Another way to think about endurance performance is via the so-called oxygen transport cascade (Fig. 1). In this cascade, the path of oxygen from the air to the tissues is considered. Therefore, in addition to cardiac output and red cell mass, factors such as the lung, capillaries, and skeletal muscle are incorporated into this approach. Using this schematic, it is possible to further summarize what is known about DNA- based explanations for differences in other key steps in the oxygen transport cascade. 2.1 The Lung A number of genome-wide association studies (GWAS) have been conducted in an effort to understand the role of DNA variants in lung function. The vast majority of these have focused on lung volumes, and there is little information on diffusing capacity. The take-home message from these stud- ies is that there are a large number of potential common DNA variants that explain a tiny fraction of interindividual differences in lung function. Additionally, when so-called gene scores (composite values for a number of gene vari- ants associated with a given phenotype) are constructed, lung function values in individuals in the highest quartile or quintile versus the lowest quartile or quintile frequently differ by only a few percentage points. These differences typically account for < 0.1 L of a given lung volume and are within the limits of the test–retest validity of spirom- etry [15]. Thus, there is no reason to believe that DNA vari- ants explain any major difference in lung function in elite athletes, or their extremely high ̇VO2max values. Of note, individuals who have spent their entire life at high altitude have increased pulmonary diffusing capacity, but this is an S201 Limitations to DNA Sequence Explanations for Sports Performance adaptive response and is not intrinsic to populations who have lived at high altitude for generations [16]. 2.2 Cardiac Output and Stroke Volume After the lung, the next step in the oxygen transport cascade is cardiac output. The right ventricle of the heart pumps blood through the lungs where it is oxygenated and returned to the left side of the heart, which delivers it to the systemic circulation. A hallmark of elite endurance performance is a high maximum cardiac output driven almost exclusively by a very large stroke volume [17]. To date, no DNA vari- ants have been described that explain the impressive levels of stroke volume and cardiac output in elite athletes. Addi- tionally, no DNA variants have been identified that explain why some people’s ̇VO2max , and presumably cardiac out- put, increases more in response to exercise training than another’s. In the late 1990s and early 2000s, it was thought that differences in the ACE (angiotensin-converting enzyme) genotype might contribute to the high stroke volumes and ̇VO2max values seen in elite athletes, based on the potential for these variants to influence cardiac hypertrophy, but that seems unlikely at this time [18]. Additionally, the genetic contributions to maximum heart rate also appear physiologi- cally trivial—only a few beats per minute [19]. 2.3 Red Cell Mass In addition to cardiac output, red cell mass or total body hemoglobin are also important physiological determinants of ̇VO2max . A high cardiac output that pumps anemic blood will not deliver much oxygen to the periphery. Thus, red cells and hemoglobin are required, together with a high cardiac output, to generate the impressive values seen in elite endurance athletes. At this time, there are no obvious genetic explanations for the high red cell masses seen in elite athletes, and these may be more generally linked to plasma volume expansion with exercise via the so-called critometer concept; in addition, there are examples of individuals with rare variants in their erythropoietin-related systems who have both high hematocrits and high values for ̇VO2max [20, 21]. 2.4 Peripheral Circulation Once the blood leaves the left ventricle and enters the peripheral circulation it is delivered to the tissues. A key determinant of ̇VO2max is the ability to generate very high skeletal muscle blood flows. It is generally accepted that the capacity of skeletal muscle to vasodilate exceeds the ability (at least in humans) of the heart to sustain very high levels of blood flow in a large mass of active skeletal muscles, and also preserve blood pressure [22]. This is known as the ‘sleeping giant hypothesis’. Additionally, endurance exercise training does increase capillary density in the trained skel- etal muscles, and there are also adaptations at the level of the resistance vessels and conducting vessels. As is the case for cardiac output and red cell mass, there is no clear DNA variant-based explanation for interindividual differences in these adaptations, or for the very high level of capillary den- sity that can be seen in some highly trained individuals. It is also interesting to note that pharmacological blockade of vascular endothelial growth factor (VEGF) does not elimi- nate the vascular adaptations in animal models [23]. If at least some training-induced adaptations can occur when a key pathway is blocked, it seems unlikely that there might be a major impact of small effect size gene variants on these responses. 2.5 Mitochondrial Density One of the fundamental adaptations to endurance exercise training is the increase in mitochondrial density seen in trained skeletal muscle. When this was initially observed Fig. 1 Schematic representation of the oxygen transport cascade. The features of the steps in the cascade associated with endurance exer- cise performance are well known, as is how these steps respond to training. The intermediate physiology is also well understood (e.g. the determinants of cardiac output). However, DNA-based explana- tions for the variability of key steps in the oxygen transport cascade have been hard to identify, and, as a result of physiological redun- dancy in adaptive responses, it is unclear whether the search for DNA-based explanations for the key elements of human performance outlined in the text will ever be able to tell a detailed deterministic story S202 M. J. Joyner by John Holloszy in the mid-1960s, it was a revolution- ary finding that initiated the era of exercise biochemistry [24–26]. Subsequent studies in humans showed that highly trained individuals with widely different ̇VO2max values have similar levels of mitochondrial adaptations in their skeletal muscles [6, 27]. As is the case for VEGF above, when knockout animals missing so-called ‘master regula- tors’ for mitochondrial biogenesis are trained, there are still significant mitochondrial adaptations [28]. Again, if at least some training-induced adaptations can occur when a key pathway is blocked or absent, it seems unlikely that there might be a major effect of small effect size gene variants on these responses. While twin studies show that skeletal muscle fiber type is highly heritable, there is ongoing discussion about so- called fiber-type transformation in humans in response to prolonged intense training [29–31]. In this context, a study in a unique set of identical twins highly divergent for physi- cal activity over decades showed that muscle fiber type, especially for ‘slow twitch’ fibers, may be far more plastic than previously demonstrated (see Fig. 2) [32]. 3 Limitations and Potential Objections to This Perspective There are a number of potential limitations to the perspec- tives outlined above. The most obvious is that very large cohorts of subjects (perhaps numbering in the hundreds of thousands) in conjunction with the phenotypes of interest and DNA sequence information are simply not available for the key steps in the oxygen transport cascade discussed in this review. For this sort of cohort to be a reality, beyond a blood test for genotyping, detailed measurements of gas exchange at rest and during submaximal and maximal exercise would be needed. Measurements of cardiac output and red cell mass would also be needed, as would serial measurements of blood lactate during graded exercise. Mus- cle biopsies to assess fiber type, mitochondrial function, and capillary density would also be essential. The financial and logistical barriers to such a research program seem formi- dable to say the least. However, if such a cohort ever did emerge, it seems likely, based on the data from other phenotypes, that very large numbers of variants with very small effect sizes (relative risks of 1.1–1.5 are typically reported) would emerge [33]. Additionally, any rare DNA variants found in smaller case- control-like studies would likely show declining penetrance, and thus explain less of the physiology in any larger cohorts [34]. Importantly, the extent to which these variants would be causally or ‘casually’ associated with the physiological phenotype of interest would be uncertain, as would their overall explanatory power. To address these limitations in the studies of common disease risk, so-called polygenic gene scores have been developed [35]. However, the predictive utility of these scores is questionable for many complex phe- notypes (e.g. obesity, diabetes, hypertension), and the overall genetic contribution to the phenotype of interest is much less than environmental and behavioral influences [36]. A final cautionary note is that for many complex human phenotypes, genetic association studies can have reproduc- ibility issues, and also require diverse ethnic cohorts. The classic example of the reproducibility problem comes from studies of depression where a recent report found essen- tially no significant and reproducible genetic associations for depression [37]. 4 Conclusions The above discussion of the oxygen transport cascade shows that while there is evidence, based on family and twin stud- ies, for a genetic component of ̇VO2max and its trainability, it has been difficult to reconcile these observations with any specific large effect size gene variants or combinations of small effect size variants linked to key physiological path- ways as a whole. Similar comments can be made about peripheral adaptations in skeletal muscle, and the determi- nants of efficiency are almost certainly complicated by bio- mechanical and skill-related factors as much as they are by genetic components. For considerations such as body size, similar observations can be made, and even in the case of ACTN3 variants associated with sprinting or power perfor- mance, the effect sizes are tiny and there are examples of elites with the ‘wrong’ genotype [38, 39]. Additionally, in some sports such as swimming, the ACTN3 genotype does not clearly segregate in sprinters versus endurance athletes [40]. 0% 20% 40% 60% 80% 100% Untrained Twin Trained Twin Type I Fiber % Distribuon Fig. 2 Marked differences in percentage slow-twitch fibers from the vastus lateralis of monozygotic twins aged in their mid-50s who were highly divergent for physical activity. The active twin had been engaged in competitive endurance training and competition for dec- ades [29] S203 Limitations to DNA Sequence Explanations for Sports Performance The obvious question is why? One emerging concept is that there are many potential genetic pathways to a given phenotype [41]. This concept is consistent with ideas that biological redundancy underpins complex multiscale physi- ological responses and adaptations in humans [42]. From an applied perspective, the ideas discussed in this review sug- gest that talent identification on the basis of DNA testing is likely to be of limited value, and that field testing, which is essentially a higher order ‘bioassay’, is likely to remain a key element of talent identification in both the near and foresee- able future [43]. While it is possible that more explanatory DNA-based associations for complex exercise-related traits might emerge if detailed physiological phenotyping of large cohorts of humans is performed, there are many limitations to this perspective. In this context, the advocates of ever- bigger Ns should carefully review the limits of this approach from studies of other complex phenotypes as they make the case for a ‘more is better’ approach to future studies. Acknowledgements This supplement is supported by the Gatorade Sports Science Institute (GSSI). The supplement was guest edited by Lawrence L. Spriet, who attended a meeting of the GSSI Expert Panel in March 2019 and received honoraria from the GSSI, a division of PepsiCo, Inc., for his participation in the meeting. Dr. Spriet received no honorarium for guest editing the supplement. Dr. Spriet suggested peer reviewers for each paper, which were sent to the Sports Medicine Editor-in-Chief for approval, prior to any reviewers being approached. Dr. Spriet provided comments on each paper and made an editorial decision based on comments from the peer reviewers and the Editor- in-Chief. Where decisions were uncertain, Dr. Spriet consulted with the Editor-in-Chief. Compliance with Ethical Standards Funding This article is based on a presentation by Michael Joyner to the GSSI Expert Panel in March 2019. Funding for attendance at that meeting, together with an honorarium for preparation of this article, were provided by the GSSI. Conflict of interest Michael Joyner has no conflicts of interest relevant to the content of this article. Open Access This article is distributed under the terms of the Crea- tive Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribu- tion, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. References 1. Joyner MJ, Coyle EF. Endurance exercise performance: the physi- ology of champions. J Physiol. 2008;586(1):35–44. 2. Joyner MJ. Modeling: optimal marathon performance on the basis of physiological factors. J Appl Physiol. 1991;70(2):683–7. 3. Morgan DW, Baldini FD, Martin PE, Kohrt WM. 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PMC9671438	RESEARCH ARTICLE The validity and reliability of wearable devices for the measurement of vertical oscillation for running Craig P. SmithID, Elliott Fullerton, Liam Walton, Emelia Funnell¤, Dimitrios Pantazis, Heinz Lugo INCUS Performance Ltd., Loughborough, United Kingdom ¤ Current address: Gymshark, Solihull, United Kingdom c.smith@incusperformance.com Abstract Wearable devices are a popular training tool to measure biomechanical performance indica- tors during running, including vertical oscillation (VO). VO is a contributing factor in running economy and injury risk, therefore VO feedback can have a positive impact on running per- formance. The validity and reliability of the VO measurements from wearable devices is cru- cial for them to be an effective training tool. The aims of this study were to test the validity and reliability of VO measurements from wearable devices against video analysis of a single trunk marker. Four wearable devices were compared: the INCUS NOVA, Garmin Heart Rate Monitor-Pro (HRM), Garmin Running Dynamics Pod (RDP), and Stryd Running Power Meter Footpod (Footpod). Fifteen participants completed treadmill running at five different self-selected speeds for one minute at each speed. Each speed interval was completed twice. VO was recorded simultaneously by video and the wearables devices. There was sig- nificant effect of measurement method on VO (p < 0.001), with the NOVA and Footpod underestimating VO compared to video analysis, while the HRM and RDP overestimated. Although there were significant differences in the average VO values, all devices were sig- nificantly correlated with the video analysis (R > = 0.51, p < 0.001). Significant agreement between repeated VO measurements for all devices, revealed the devices to be reliable (ICC > = 0.948, p < 0.001). There was also significant agreement for VO measurements between each device and the video analysis (ICC > = 0.731, p < = 0.001), therefore validat- ing the devices for VO measurement during running. These results demonstrate that wear- able devices are valid and reliable tools to detect changes in VO during running. However, VO measurements varied significantly between the different wearables tested and this should be considered when comparing VO values between devices. Introduction The availability and popularity of wearable sports technology for running has grown exten- sively in recent years [1]. These devices provide users with feedback about a variety of PLOS ONE PLOS ONE \| https://doi.org/10.1371/journal.pone.0277810 November 17, 2022 1 / 12 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Smith CP, Fullerton E, Walton L, Funnell E, Pantazis D, Lugo H (2022) The validity and reliability of wearable devices for the measurement of vertical oscillation for running. PLoS ONE 17(11): e0277810. https://doi.org/10.1371/journal. pone.0277810 Editor: Bernard X. W. Liew, University of Essex, UNITED KINGDOM Received: April 21, 2022 Accepted: November 3, 2022 Published: November 17, 2022 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0277810 Copyright: © 2022 Smith et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. biomechanical information, including running specific metrics such as speed and cadence. The affordability and portable nature of wearable devices make them an attractive method for measuring biomechanical features of running outside of the laboratory for runners [2], researchers [3] and clinicians alike [4]. A running specific metric provided by several wearable devices is vertical oscillation (VO). VO is the vertical displacement of the body during each stride measured at the centre of mass (COM) [5], or proxy positions such as the pelvis [6]. VO has been linked to running economy and injury prevention, with smaller VO of the trunk associated with improved running econ- omy [7, 8] and a reduction in lower-limb injury risk factors such as vertical loading rate [9]. Running technique can be adapted to alter VO [9–11], such as increasing cadence to reduce VO [12]. Providing a runner with real-time visual and auditory feedback that indicates when their vertical oscillation is above or below a target level can allow a runner to manipulate their vertical oscillation as required [13]. Therefore, wearable devices have the potential to provide an accessible method for runners and coaches to obtain and utilise VO feedback for perfor- mance gains. There are a variety of wearable devices currently on the market which provide VO measure- ments for running. These devices commonly utilise an inertial measurement unit (IMU) to record body movement and derive a variety of biomechanical features during running. The position of recording varies between wearables, with some devices positioned on the trunk at the xiphoid process (Garmin Heart Rate Monitors), C7 vertebrae (INCUS NOVA), waistband (Garmin Running Dynamics Pod), or on the dorsum of the foot (Stryd Running Power Meter Footpod). The validity and reliability of VO measurements from wearable devices is essential to determine whether the device can detect changes in VO or whether changes are the result of measurement errors. However, few studies have focused on validating wearable devices for VO measurements. VO recorded from Garmin heart-rate monitors with built in accelerome- ters (HRM) have been compared to a video analysis method and found to be highly agreeable [14, 15], as well as reliable between repeated measures [14]. The manufacturers of the Stryd Running Power Meter Footpod (Footpod) report that the device measures COM VO with a small average error of 3% when compared to a ground reaction forces method for deriving COM VO [16]. These findings provide some evidence that wearable technology can be a valid and reliable tool for measuring VO. However, the validity and reliability of VO for other devices, such as the INCUS NOVA (NOVA) and Garmin Running Dynamics Pod (RDP) has not been reported. Furthermore, it is not understood how VO measurements from different devices compare, especially given they record at different locations on the body. The aim of this study was to test whether wearable devices are reliable and valid tools for the measurement of VO during running by comparing VO measurements from four wearable devices (NOVA, HRM, RDP, and Footpod) to video analysis of a single trunk-based marker. Based on prior research [14–16], it is hypothesised that the wearable devices will provide valid and reliable VO measurements when compared to video analysis. However, because of the dif- ference in body locations between devices, it is hypothesised that the VO measurements will differ between devices, with the device in closest proximity to the trunk marker (NOVA) hav- ing the most accurate VO measurements when compared to the video analysis measurements. Materials and methods Participants Fifteen active runners (run for at least 1 hour per week) without any injury in the last 6 months were recruited (7 females, mean ±SD age = 26.4yrs ±5.5, height = 174.4cm ±9.4, weight = 71.1kg ±9.3). All participants gave written informed consent, and the experiment was PLOS ONE Vertical Oscillation Measured by Wearable Devices for Running PLOS ONE \| https://doi.org/10.1371/journal.pone.0277810 November 17, 2022 2 / 12 Funding: CPS, EF, LW, EF, DP, and HL were funded by Innovate UK (project no. 00106514). https://www.ukri.org/councils/innovate-uk/?_ga=2. 89826907.1149472773.1647884579-1155892482. 1640269449. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: The INCUS NOVA wearable device used in the research article is a license product of INCUS Performance Ltd. CPS, EF, LW, EF, DP, and HL were employees of INCUS Performance Ltd. at the time the research was completed. This does not alter our adherence to PLOS ONE policies on sharing data and materials. conducted in accordance with the Declaration of Helsinki. Ethical approval for this research was obtained from the Loughborough University Ethics Review Committee (ERSC22_27). Apparatus The participants ran indoor on a motorised treadmill (NordicTrack T8.5S, NordicTrack, Utah, USA) in their usual running footwear at a 1% incline with a fan to mimic outdoor run- ning [17] (Fig 1). During running, VO was measured using video analysis and four wearable devices designed to measure VO during running: INCUS NOVA (INUCS Performance Ltd., Loughborough, UK), Garmin HRM (Garmin Ltd., Southampton, UK), Garmin RDP (Garmin Ltd., Southampton, UK), and Stryd Footpod (Stryd, Colorado, US). The wearable devices were worn, and data recorded, as per their instructions. The NOVA was worn in a purpose-built harness positioned towards the top of the spine (C7 vertebrae) and paired with the INCUS mobile application to start and stop data recording. The HRM was fitted using the accompa- nying chest strap, with the device located on the xiphoid process and paired with the Garmin Fig 1. Experimental setup. The illustration shows a participant aboard the treadmill and the positioning of the wearable devices on the body. The INCUS NOVA (NOVA) was worn in a ‘T-Strap’, with the device positioned at C7 vertebrae. The ArUco marker was fixed to the NOVA and video recorded from the rear. The Garmin HRM-Pro chest strap (HRM) was worn with the device positioned at the xiphoid process. The Garmin Running Dynamics Pod (RDP) was clipped to the waistband, aligned with the sagittal plane. The Stryd Running Power Meter Footpod (Footpod) was clipped to the laces of the right trainer. Depictions of the wearable devices are for illustrative purposes only. https://doi.org/10.1371/journal.pone.0277810.g001 PLOS ONE Vertical Oscillation Measured by Wearable Devices for Running PLOS ONE \| https://doi.org/10.1371/journal.pone.0277810 November 17, 2022 3 / 12 Forerunner 945 watch (Garmin Ltd., Southampton, UK) worn by the participant on their right wrist. The RDP was fitted to the rear of the participants waistband aligned with the sagittal plane and paired with a separate Garmin Forerunner 245 watch (Garmin Ltd., Southampton, UK) worn on the participants left wrist. Both watches were set to treadmill mode and the recording of data from both Garmin devices was started/stopped via their corresponding watches. The Footpod was clipped to the lower two laces of the participants right trainer and controlled via the Stryd mobile application. A video analysis system was used to provide a reference to test the validity of the VO mea- surements of the wearable devices. An ArUco marker, a 5 x 5cm square with a black border and an inner black and white binary matrix [18], was fixed to the NOVA device and a digital single-lens reflex camera (Panasonic Lumix DMC-FZ330 Digital, Panasonic, UK) was posi- tioned on a tripod to the rear of the treadmill at the same height as the marker. The marker was video recorded at 200 FPS in 4K. It is possible to accurately measure movements of an ArUco marker <2.2m/s using video recording [19]. Vertical trunk movement during treadmill running was not expected to surpass this velocity limit under the conditions of this study and therefore the video recording of an ArUco marker was deemed a suitable method for measur- ing VO. Protocol Participants self-selected a range of five preferred running speeds of 1km/h increments (e.g., 8–12 km/h) during a five-minute familiarisation period on the treadmill. The participants ran for one-minute intervals at each speed, with a minute of slow walking (1 km/h) between each interval. This was completed twice in two blocks, with three minutes of slow walking between the blocks (Fig 2). Therefore, a total of 10 running intervals (2 blocks x 5 speeds) were com- pleted by each participant. The order of the running speeds was randomised (pseudorandom number generator) within each block and for each participant. Data recording from the wear- able devices were started by the researcher one minute prior to the beginning of the first run- ning interval and recorded continuously throughout the protocol. Video recording of the marker was collected by a researcher during each running interval. Data and statistical analysis The data recording from the NOVA was downloaded via the INCUS mobile application. The data from the watches were downloaded using the Garmin connect software for the HRM and RDP. Footpod data was downloaded using the Stryd Power Center software. Video recordings of the marker positioned on the NOVA device allowed for automatic detection of the trunk (C7 vertebrae) and NOVA’s position throughout each trial. ArUco marker detection was achieved using the open-source library OpenCV [20]. First, using a checkerboard calibration recording [21], any distortion in the camera image was removed from each frame. Each frame was then converted to a grayscale image to achieve accurate marker detection and shorter processing times. The pixel co-ordinates of each corner of the marker were detected and the location of the centre of the marker derived. Using the marker centre location, a pixel to centimetre ratio was calculated using the arclength method to mea- sure the marker perimeter which has a known size (5x5cm). The difference between maximum and minimum position of the marker in the vertical axis was calculated for each stride and converted to centimetres using the pixel to centimetre ratio to derive vertical oscillation. Time synchronisation of VO values between measurement methods was achieved by find- ing the start and end time of each of the ten running intervals within the VO values recorded for each method. For each device the starts of the intervals were clearly visible as a rapid rise in PLOS ONE Vertical Oscillation Measured by Wearable Devices for Running PLOS ONE \| https://doi.org/10.1371/journal.pone.0277810 November 17, 2022 4 / 12 VO, which remained elevated until the end of the interval when VO would then rapidly fall. Therefore, the intervals were defined by the rise and fall of VO above and below the mean VO across the whole protocol for each measurement method. For each interval, the mean and standard deviation of VO was calculated for the middle 30 seconds, and the corresponding running intervals were compared between methods. Differences in average VO (bias and 95% limits of agreement) between the video analysis and the other methods was calculated and a 1 x 5 repeated measure analysis of variance (ANOVA) was used to test for a main effect of measurement method on VO. Post-hoc paired T-tests were then calculated to test for differences between each of the methods, therefore ten comparisons in total. To reduce the likelihood of Type 1 errors when making multiple com- parisons, the alpha level for the paired T-tests was Bonferroni corrected, therefore divided by the number of comparisons (i.e. alpha = 0.05/10). To determine the strength of the relationship between device VO and the video analysis method, repeated measures correlations [22] were calculated between the video analysis VO and each of the wearable devices across all running interval speeds. To determine the reliability of each method, VO measurements during the Fig 2. Running intervals. An example of the running interval profile for a participant with a preferred running speed range of 8–12 km/h. Participants ran for one minute at each selected speed with a one-minute break (walking at 1 km/ h) between each running interval. This was completed twice (Block 1 & 2) with the speed order randomised within each block and a three-minute walking period between the blocks. https://doi.org/10.1371/journal.pone.0277810.g002 PLOS ONE Vertical Oscillation Measured by Wearable Devices for Running PLOS ONE \| https://doi.org/10.1371/journal.pone.0277810 November 17, 2022 5 / 12 participants mid-range selected running speed interval in the first block was compared to the second block using Intraclass Correlation Coefficients (ICC3,1), and the standard error of mea- surement (SEM) was calculated. To determine the validity of the devices, the VO measure- ments recorded for the participants mid-range selected running speed interval were averaged across the two blocks of trials and ICC3,1 were calculated to test the agreement between the devices and video analysis. Statistical analyses were carried out using Python 3.0.0 library Pin- gouin 0.5.0, with the alpha level set at 0.05. Results The mean (±SD) preferred running speed range was 8.9–12.9 km/h ±1.2. The average VO measurements for each device across all running intervals are shown in Fig 3. There was a sig- nificant difference in average VO between the methods of measurement (F(4, 56) = 39.70, p < 0.001). Post-hoc pairwise comparisons between devices (Bonferroni corrected alpha level = 0.005) were all significant (t(14) > = 4.32, p < 0.001) other than between HRM and RDP (t(14) = 0.40, p = 0.692), and NOVA and Footpod (t(14) = 3.16, p = 0.007). Fig 3. Average vertical oscillation for each method. The box plot shows the distribution of VO values for each device. Median VO and quartiles 1 (Q1) and 3 (Q3) are show by the box, while lower and upper bars are Q1–1.5Inter- Quartile Range and Q3 + 1.5Inter-Quartile Range, respectively. There were no outliers above or below the bars for any method. https://doi.org/10.1371/journal.pone.0277810.g003 PLOS ONE Vertical Oscillation Measured by Wearable Devices for Running PLOS ONE \| https://doi.org/10.1371/journal.pone.0277810 November 17, 2022 6 / 12 The Bland-Altman plots (Fig 4), along with the pairwise comparisons reveal that the HRM (10.8cm ±1.5) and RDP (10.7cm ±2.1) overestimated VO compared to the video analysis (9.4cm ±1.8), while the NOVA (8.7cm ±1.7) and Footpod (8.0cm ±1.5) underestimated. Although, there was a difference in the average VO between the devices and video analysis, the NOVA (R = 0.84, p < 0.001), HRM (R = 0.73, p < 0.001), RDP (R = 0.80, p < 0.001), and Footpod (R = 0.51, p < 0.001) were significantly correlated with the video analysis values (Fig 5). To test the reliability of the VO measurements, the VO values for the participants mid- range running speed interval were compared between the first (block 1) and repeated (block 2) measurement. All devices had significant reliability between the repeated measurements (ICC3,1 > = 0.928, F(14,14) > = 26.87, p < 0.001) and standard error of measurements < = 0.5cm (Table 1). The validity of each device when compared to the video analysis was also tested for the mid-range running interval (Table 2). There was significant agreement between all the devices and the video analysis method (ICC3,1 > = 0.731, F(14,14) > = 6.45, p < = 0.001). Fig 4. Bland-Altman plots between video analysis vertical oscillation and wearable devices. Bland-Altman plot for video analysis vertical oscillation values compared to INCUS NOVA (top left), Garmin HRM-Pro chest strap (HRM, top right), Garmin Running Dynamics Pod (RDP, bottom left), and Stryd Running Power Meter Footpod (Footpod, bottom right). Mean bias is indicated by the solid line. Dashed lines indicate 95% Limits of Agreement. All running intervals (n = 10) and participants (n = 15) were included. https://doi.org/10.1371/journal.pone.0277810.g004 PLOS ONE Vertical Oscillation Measured by Wearable Devices for Running PLOS ONE \| https://doi.org/10.1371/journal.pone.0277810 November 17, 2022 7 / 12 Fig 5. Video analysis vertical oscillation versus wearable devices. Scatter plots of the video analysis vertical oscillation measurements (VO) versus VO values from four wearable devices; the INCUS NOVA (top left), Garmin HRM-Pro chest strap (top right), Garmin Running Dynamics Pod positioned on the waistband (bottom left), and the Stryd Running Power Meter Footpod (bottom right). All running intervals (n = 10) and participants (n = 15) are included. The diagonal line represents the line of unity for the video analysis measurements. Repeated measures correlation R values between the video analysis and devices are shown (p < 0.05). https://doi.org/10.1371/journal.pone.0277810.g005 Table 1. Reliability between repeated vertical oscillation measurements. Block 1 Vertical Oscillation (cm) Block 2 Vertical Oscillation (cm) ICC [95% CI] SEM (cm) Video Analysis 9.5 +/- 1.9 9.5 +/- 1.8 0.928 [0.80, 0.98] 0.5 INCUS NOVA 8.7 +/- 1.8 8.8 +/- 1.8 0.956 [0.87, 0.98] 0.4 Garmin HRM-Pro 10.9 +/-1.8 11.1 +/-1.8 0.948 [0.85, 0.98] 0.4 Garmin RDP 10.7 +/- 2.2 10.9 +/- 2.1 0.968 [0.91, 0.99] 0.4 Stryd Footpod 8.2 +/- 1.5 8.2 +/- 1.4 0.954 [0.87, 0.98] 0.3 Mean (±SD) vertical oscillation for the mid-range running speed interval across all participants for the first and second block of trials. ICCs show agreement between the blocks for each method (p < 0.05). Standard Error of Measurement (SEM) indicates the amount of variability between the repeated measures due to measurement error. https://doi.org/10.1371/journal.pone.0277810.t001 PLOS ONE Vertical Oscillation Measured by Wearable Devices for Running PLOS ONE \| https://doi.org/10.1371/journal.pone.0277810 November 17, 2022 8 / 12 Discussion The agreement between the wearable devices and the video analysis reference, along with the high reliability values between repeated measures, indicate that the wearable devices are valid and reliable tools for measuring VO of the trunk during running. As hypothesised, the NOVA measurements had the highest agreement and lowest average bias compared to the video anal- ysis. However, the absolute VO values differed between the devices, with the NOVA and Foot- pod underestimating VO compared to the video analysis, while the RDP and HRM overestimated. All four wearable devices had VO measurements which significantly agreed with the video analysis. However, the strength of the agreement varied between devices. Furthermore, the absolute VO values differed significantly between devices. The largest difference was between the Footpod and HRM, with the average Footpod VO 26% lower than the HRM. Compared to the video analysis, the NOVA had the highest correlation and ICC values of the four devices (R = 0.84, ICC = 0.96), as well as the smallest average bias (0.7cm). The video analysis mea- sured the vertical movement of a marker fixed to the NOVA, therefore measuring VO at the C7 vertebrae. In contrast, the HRM recorded from the xiphoid process, the RDP recorded from the rear of the waistband, and the Footpod from the foot. When compared to video anal- ysis of a marker fixed to the HRM, the HRM has been found to have strong correlation coeffi- cients (ICC > = 0.96) and minimal bias (< = 0.3cm) when compared with video analysis measurements [14, 15], similar to the results for the NOVA in this study. This suggests that a potential reason for the differences in VO found between the NOVA and HRM is that although the devices are measuring VO referenced to the location of the device, real differ- ences in VO between the measurement locations is the explanation for the difference found between these devices. However, on average there was little difference in VO measurements between the HRM and RDP, although these devices record from contrasting trunk locations. This suggests recording location may not be the sole contributor to differences between the trunk-based devices and that the differences are likely due to a combination of both location and the device itself. Further research comparing device VO to video analysis at each device location will help to understand if biomechanical factors contribute to VO measurement dif- ferences when recording at different locations on the trunk. Although positioned on the foot, the Footpod reports to measure VO of COM [16]. VO of the COM is commonly measured using 3D motion capture and a segmental model of the body is applied to locate COM displacements during running [23]. Measuring VO of COM with either video analysis of a single marker [24] or a single IMU [15, 25] has proven difficult, with both methods overestimating COM VO. A linear correction of IMU VO to infer COM VO has been proposed, although this method is susceptible to overfitting on the sample tested and Table 2. Validity of vertical oscillation measurements from wearable devices compared to video analysis. Vertical Oscillation (cm) ICC [95% CI] Bias (cm) 95% Limits of Agreement Video Analysis 9.5 +/- 1.8 INCUS NOVA 8.8 +/- 1.7 0.963 [0.89, 0.99] 0.7 [-0.3, 1.6] Garmin HRM-Pro 11.0 +/-1.8 0.745 [0.39, 0.91] -1.5 [-4.1, 1.1] Garmin RDP 10.8 +/- 2.1 0.858 [0.63, 0.95] -1.3 [-3.4, 0.8] Stryd Footpod 8.2 +/- 1.4 0.731 [0.37, 0.90] 1.3 [-1.1, 3.7] Mean (±SD) vertical oscillation across all participants for the mid-range speed interval. Intraclass correlation coefficients (ICCs) between video analysis and the devices; INCUS NOVA, Garmin HRM-Pro, Garmin Running Dynamics Pod (RDP), and Stryd Running Power Meter Footpod (Footpod) (p < 0.05). Mean bias (±SD) and 95% Limits of Agreement between video analysis values and the devices are shown. https://doi.org/10.1371/journal.pone.0277810.t002 PLOS ONE Vertical Oscillation Measured by Wearable Devices for Running PLOS ONE \| https://doi.org/10.1371/journal.pone.0277810 November 17, 2022 9 / 12 requires validation in different cohorts [15]. The overestimation of COM VO when measured by a single marker or IMU on the trunk may explain why the trunk located devices had higher VO values compared to the Footpod which indirectly measures COM VO from measurements taken at the foot. Although further research is required to understand the mechanisms for the VO differences between devices, this finding demonstrates that caution must be taken when using devices interchangeably. This is an important consideration for users, who may discover significant changes in their VO values when moving from one device to another. An artificial increase in VO measurements could lead a user to unnecessarily adapt their running technique to reduce their VO (e.g., increase cadence) with a negative impact on overall performance. On the other hand, an artificial decrease in VO measurements could result in the user incorrectly believing their VO has improved, preventing them from benefiting from improved running economy [8] and reduced injury risk factors [9] associated with an actual reduction in VO. However, when interpreting the VO feedback from a single device in isolation, this study has found that wearable devices can provide a valid and reliable method for the measurement of VO, which is important for user confidence. The ability to measure VO via a wearable device has the bene- fits of being unobtrusive and affordable compared to the traditional method of video analysis. Therefore, wearable devices provide a broader range of runners the opportunity to incorporate VO feedback into their training. In this study, VO was measured during treadmill running. Running on a treadmill com- pared to overground running could potentially increase VO due to flexion in the treadmill running surface and should be considered when applying the results of treadmill VO studies to outdoor running. Another external factor known to effect VO is running footwear, with evi- dence that running barefoot reduces VO compared to shod running [26]. In this study, partici- pants wore their own choice of running footwear, therefore footwear type was not controlled for. However, the effect of footwear type on VO was likely minimal considering the effect of barefoot running has been reported to be a 7% reduction in VO [26]. Conclusions Wearable devices provide a valid and reliable method for measuring changes in VO during running when compared to a video analysis method. Therefore, such devices give runners an accessible option to track changes in their VO with potential performance and injury related benefits. However, absolute VO values differ between devices, therefore caution must be taken when using devices interchangeably for VO measurements. Supporting information S1 Dataset. (CSV) Acknowledgments The authors would like to thank Emily Codd and Spencer Patmore for their assistance in data collection. Author Contributions Conceptualization: Craig P. Smith, Elliott Fullerton, Liam Walton, Emelia Funnell, Heinz Lugo. PLOS ONE Vertical Oscillation Measured by Wearable Devices for Running PLOS ONE \| https://doi.org/10.1371/journal.pone.0277810 November 17, 2022 10 / 12 Data curation: Craig P. Smith, Elliott Fullerton, Liam Walton, Emelia Funnell. Formal analysis: Craig P. 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PLOS ONE Vertical Oscillation Measured by Wearable Devices for Running PLOS ONE \| https://doi.org/10.1371/journal.pone.0277810 November 17, 2022 12 / 12	The validity and reliability of wearable devices for the measurement of vertical oscillation for running.	11-17-2022	Smith, Craig P,Fullerton, Elliott,Walton, Liam,Funnell, Emelia,Pantazis, Dimitrios,Lugo, Heinz	eng