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PMC7038347	sensors Article Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System Jesus D. Ceron 1 , Christine F. Martindale 2 , Diego M. López 1,* , Felix Kluge 2 and Bjoern M. Eskoﬁer 2,* 1 Telematics Engineering Research Group, Telematics Department, Universidad Del Cauca (Unicauca), Popayán 190002, Colombia; jesusceron@unicauca.edu.co 2 Machine Learning and Data Analytics Lab, Computer Science Department, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany; christine.f.martindale@fau.de (C.F.M.); felix.kluge@fau.de (F.K.) * Correspondence: dmlopez@unicauca.edu.co (D.M.L.); bjoern.eskoﬁer@fau.de (B.M.E.) Received: 27 September 2019; Accepted: 1 November 2019; Published: 24 January 2020 Abstract: The evaluation of trajectory reconstruction of the human body obtained by foot-mounted Inertial Pedestrian Dead-Reckoning (IPDR) methods has usually been carried out in controlled environments, with very few participants and limited to walking. In this study, a pipeline for trajectory reconstruction using a foot-mounted IPDR system is proposed and evaluated in two large datasets containing activities that involve walking, jogging, and running, as well as movements such as side and backward strides, sitting, and standing. First, stride segmentation is addressed using a multi-subsequence Dynamic Time Warping method. Then, detection of Toe-Oﬀ and Mid-Stance is performed by using two new algorithms. Finally, stride length and orientation estimation are performed using a Zero Velocity Update algorithm empowered by a complementary Kalman ﬁlter. As a result, the Toe-Oﬀ detection algorithm reached an F-score between 90% and 100% for activities that do not involve stopping, and between 71% and 78% otherwise. Resulting return position errors were in the range of 0.5% to 8.8% for non-stopping activities and 8.8% to 27.4% otherwise. The proposed pipeline is able to reconstruct indoor trajectories of people performing activities that involve walking, jogging, running, side and backward walking, sitting, and standing. Keywords: trajectory reconstruction; stride segmentation; dynamic time warping; pedestrian dead-reckoning 1. Introduction Indoor positioning systems (IPS) enable the provision of several location-based services such as home monitoring, rehabilitation, navigation for blind and visual impaired people, and ﬁnding and rescuing people/ﬁreﬁghters in emergencies. IPSs can be divided into two approaches: infrastructure-based and infrastructure-free [1,2]. Infrastructure-based IPS require the deployment of devices in the indoor environment to calculate the position of the person. Among the technologies used by this type of IPS are Wi-Fi [3], radio frequency identiﬁcation (RFID) [4], Bluetooth [5], ultra-wide band (UWB) [6], infrared [7], and video cameras [4]. Infrastructure-free IPS do not need the deployment of devices and mainly use dead-reckoning algorithms. Those systems are called inertial pedestrian dead-reckoning (IPDR) because they use body movement information measured by inertial measurement units (IMU) to estimate a person’s position changes based on a previously estimated or known position [2]. The sum of these changes of position allows the reconstruction of the person’s trajectory [2]. An IMU usually consists of a triaxial accelerometer and gyroscope. Although some IMUs Sensors 2020, 20, 651; doi:10.3390/s20030651 www.mdpi.com/journal/sensors Sensors 2020, 20, 651 2 of 20 also incorporate a triaxial magnetometer, alterations of the magnetic ﬁeld indoors make it unreliable for indoor positioning [8]. The advantages of IPDR systems over infrastructure-based systems are generally lower cost, data privacy, and ease of deployment. However, IPDR systems without correction suﬀer from severe drift, as person displacement is often calculated by integrating acceleration data from the accelerometer twice and integrating the rotational angle from the gyroscope. In consequence, intrinsic errors and IMU noise are raised to the third power, making a person’s trajectory reconstruction by direct integration without correction impractical [9–11]. The literature review done in this study is aimed at foot-mounted IMU IPDR systems that only use the accelerometer and/or gyroscope. Foot-mounted IPDRs, together with a zero velocity update (ZUPT) algorithm, have been the most widely and successful method used to mitigate the drift in trajectory reconstruction [9]. We use only the accelerometer and gyroscope because in indoor environments, diﬀerent sources might produce alterations in the magnetic ﬁeld that make the magnetometer readings unreliable for trajectory reconstruction [8]. Most of the foot-mounted IPDR systems that only use accelerometer and gyroscope data are based on trajectory reconstruction during normal walking. Natural movements like avoiding obstacles, sitting, swinging legs, stopping, or performing activities like jumping, jogging, or running have rarely been considered [9,10]. In consequence, the literature review is focused on the foot-mounted IPDR systems that have reconstructed the trajectory of walking, jogging, and/or running activities. Thus, only six studies met the inclusion criteria and are part of the literature review. The foot-mounted IPDR systems are usually evaluated in closed-loop trajectories by measuring the return position error (RPE). The RPE indicates the distance between the ﬁnal position of the person obtained by the system and the actual physical ﬁnal position of the person at the end of the trial [8]. Threshold-based and machine learning-based foot-mounted IPDR approaches have been proposed to deal with walking and running activities [12–16]. Li et al. [12,13] proposed a threshold-based stance-phase detector that consists of one footstep detector and two zero velocity detectors, one for walking and another for running. The evaluation of the system was done with one pedestrian who followed two closed-loop trajectories while walking and running. For the square-shape path (195.7 m), the RPE was 0.24% for walking and 0.42% for running. For the eight-shape path (292.1 m), the RPE was 0.2% for walking and 1.01% for running. An adaptive zero-velocity detector that selects an optimal threshold for zero-velocity detection depending on the movement (walking or running) of the person was proposed by Wagstaﬀ et al. [15]. This system was evaluated by ﬁve people who walked and ran a distance of 130 m in an “L” shaped path. The RPE reported were 1% for walking and 3.24% for running. Considering that zero-velocity detection using machine learning-based IPDR systems is free of threshold-tuning, Wagstaﬀ et al. proposed a method for zero-velocity detection by using a long short-term memory neural network (LSTM) [16]. Five people walked and ran a 220-m “L” shaped path. The RPE in walking was 0.49% and running 0.93%. Similarly, Ren et al. proposed a zero-velocity detection algorithm based on HMM [14]. The system was evaluated by one person in an oval-shaped sports ﬁeld of 422 m. The RPE when walking and running was 0.6% and 1.61%, respectively. The described works have obtained very high precision in the trajectory reconstruction of walking and running. However, the systems were evaluated with very few participants, and the evaluated trajectories involved continuous walking and running activities. Currently, trajectory reconstruction methods in realistic scenarios—with several people, and considering walking, jogging, and running strides—are still missing. Physical activity classiﬁcation and gait event detection are key components of the trajectory reconstruction process using IPDR. Machine learning has played an important role in both topics. In [17] it is shown how diﬀerent machine learning-based algorithms are able to classify diﬀerent physical activities, including standing, sitting, walking, and running. Gait event detection has been performed by using several machine learning algorithms such as deep learning [18], hidden Markov models (HMM) [19,20] and neural networks [21,22]. Sensors 2020, 20, 651 3 of 20 The aim of the present work was to propose a pipeline for trajectory reconstruction using a foot-mounted IPDR system able to reconstruct the trajectories of activities that involve walking, jogging, and running strides as well as natural movements like stopping, standing, sitting, and side-walking. This paper contributes to foot-mounted IPDR systems by (1) comprehensively evaluating the trajectory reconstruction of activities that involve walking, jogging, and running strides including the discrimination of natural activities such as stopping, sitting, and side-walking; and (2) evaluating two algorithms for Toe-oﬀ and Mid-Stance detection during walking, jogging, and running strides adapted from the ones proposed by Barth et al. [23]. The proposed pipeline is able to recognize walking, jogging and running strides and detect the Toe-oﬀ and Mid-Stance events in each of them. With this information, a foot-mounted IPDR system is able to reconstruct the person’s trajectory regardless of their gait speed. This allows the development of new ambient assisted living applications in which indoor tracking is a ground technology as well as the development of new applications for indoor sports. 2. Datasets 2.1. Unicauca Dataset The objective of the Unicauca dataset was to evaluate the trajectory reconstruction of walking, jogging, and running in similar settings as the state-of-the-art methods, which are usually evaluated in close-loop trajectories and the activities performed by the participants include continuous walking, jogging, or running. This dataset was collected at the University of Cauca, Popayán, Colombia. Ten participants (mean age: 30 ± 3 years) walked, jogged, and ran a closed-loop P-shaped path of approximately 150 m (Figure 1) with an IMU attached to the lateral side of the left shoe with a Velcro strap (Figure 2). Sensors 2019, 19, x FOR PEER REVIEW 3 of 20 The aim of the present work was to propose a pipeline for trajectory reconstruction using a foot- mounted IPDR system able to reconstruct the trajectories of activities that involve walking, jogging, and running strides as well as natural movements like stopping, standing, sitting, and side-walking. This paper contributes to foot-mounted IPDR systems by (1) comprehensively evaluating the trajectory reconstruction of activities that involve walking, jogging, and running strides including the discrimination of natural activities such as stopping, sitting, and side-walking; and (2) evaluating two algorithms for Toe-off and Mid-Stance detection during walking, jogging, and running strides adapted from the ones proposed by Barth et al. [23]. The proposed pipeline is able to recognize walking, jogging and running strides and detect the Toe-off and Mid-Stance events in each of them. With this information, a foot-mounted IPDR system is able to reconstruct the person’s trajectory regardless of their gait speed. This allows the development of new ambient assisted living applications in which indoor tracking is a ground technology as well as the development of new applications for indoor sports. 2. Datasets 2.1. Unicauca Dataset The objective of the Unicauca dataset was to evaluate the trajectory reconstruction of walking, jogging, and running in similar settings as the state-of-the-art methods, which are usually evaluated in close-loop trajectories and the activities performed by the participants include continuous walking, jogging, or running. This dataset was collected at the University of Cauca, Popayán, Colombia. Ten participants (mean age: 30 ± 3 years) walked, jogged, and ran a closed-loop P-shaped path of approximately 150 m (Figure 1) with an IMU attached to the lateral side of the left shoe with a Velcro strap (Figure 2). Figure 1. Illustration of the path used for walking, jogging and running in the Unicauca dataset. It is a “P” shaped path. The dotted red line represents the trajectory followed by one person, dotted black lines show outer edges (walls) of the path, and the blue square shows the start and end point of the trajectory. The IMU was a Shimmer3 GSR+ (Shimmer Sensing, Dublin, Ireland). Acceleration (range: ±16 g) and angular velocity (range: ±2000 dps) data were collected at a frequency of 200 Hz. Accelerometer calibration consisted in leaving the sensor still for a few seconds lying on each of its 6 sides on a flat surface. For gyroscope calibration, the sensor is rotated around the three axes. At the beginning of each trial, the participant was asked to remain standing without moving the IMU for at least 10 s for gyroscope bias calculation. Figure 1. Illustration of the path used for walking, jogging and running in the Unicauca dataset. It is a “P” shaped path. The dotted red line represents the trajectory followed by one person, dotted black lines show outer edges (walls) of the path, and the blue square shows the start and end point of the trajectory. The IMU was a Shimmer3 GSR+ (Shimmer Sensing, Dublin, Ireland). Acceleration (range: ±16 g) and angular velocity (range: ±2000 dps) data were collected at a frequency of 200 Hz. Accelerometer calibration consisted in leaving the sensor still for a few seconds lying on each of its 6 sides on a ﬂat surface. For gyroscope calibration, the sensor is rotated around the three axes. At the beginning of each trial, the participant was asked to remain standing without moving the IMU for at least 10 s for gyroscope bias calculation. Sensors 2020, 20, 651 4 of 20 Sensors 2019, 19, x FOR PEER REVIEW 4 of 20 (a) (b) Figure 2. IMU sensor placement and axis alignment. (a) Accelerometer. (b) Gyroscope. 2.2. FAU Dataset The FAU dataset is based on a previous study evaluating a method for smart labeling of cyclic activities [24] and is publicly available at www.activitynet.org. The dataset provides gait data in a relatively natural setting, and its protocol consisted in the execution of 12 different task-driven activities performed in random order for each participant. It includes data from 80 healthy participants with a mean age of 27 ± 6 years. Data were collected from 56 participants at the Friedrich- Alexander University Erlangen-Nürnberg (Germany) and from 24 participants at the University of Ljubljana (Slovenia). In this study, data collected at Slovenia from 20 of the 24 participants (mean age of 28 years) was used as training dataset [25] and data collected in Germany from the 56 participants were used as evaluation dataset. Only the data collected from the IMU worn on the left foot was used for trajectory reconstruction of ten activities (Table 1). Sensor placement and axis alignment are the same used in the Unicauca dataset (Figure 2). The acceleration (range: ±8 g) and angular velocity (range: ±2000 dps) were collected at a frequency of 200 Hz. The on-ground and off-ground phases of each stride are labeled. The accelerometer was calibrated using six static positions and the gyroscope was calibrated using a complete rotation about each of the three axes. Data were acquired in an indoor environment which including chairs and tables (Figure 3). Jogging was described to the participants as “if one would jog for exercise in the evening” and running as “if one is late for a bus”. These instructions were the same used in the Unicauca dataset. Figure 3. Map of the indoor environment used for collecting the FAU dataset. Blue squares represent chairs that denote start/end positions of activities. Black rectangles represent tables, and dotted red lines represent the possible trajectories followed by participants in each activity. Figure 2. IMU sensor placement and axis alignment. (a) Accelerometer. (b) Gyroscope. 2.2. FAU Dataset The FAU dataset is based on a previous study evaluating a method for smart labeling of cyclic activities [24] and is publicly available at www.activitynet.org. The dataset provides gait data in a relatively natural setting, and its protocol consisted in the execution of 12 diﬀerent task-driven activities performed in random order for each participant. It includes data from 80 healthy participants with a mean age of 27 ± 6 years. Data were collected from 56 participants at the Friedrich-Alexander University Erlangen-Nürnberg (Germany) and from 24 participants at the University of Ljubljana (Slovenia). In this study, data collected at Slovenia from 20 of the 24 participants (mean age of 28 years) was used as training dataset [25] and data collected in Germany from the 56 participants were used as evaluation dataset. Only the data collected from the IMU worn on the left foot was used for trajectory reconstruction of ten activities (Table 1). Sensor placement and axis alignment are the same used in the Unicauca dataset (Figure 2). The acceleration (range: ±8 g) and angular velocity (range: ±2000 dps) were collected at a frequency of 200 Hz. The on-ground and oﬀ-ground phases of each stride are labeled. The accelerometer was calibrated using six static positions and the gyroscope was calibrated using a complete rotation about each of the three axes. Data were acquired in an indoor environment which including chairs and tables (Figure 3). Jogging was described to the participants as “if one would jog for exercise in the evening” and running as “if one is late for a bus”. These instructions were the same used in the Unicauca dataset. Sensors 2019, 19, x FOR PEER REVIEW 4 of 20 (a) (b) Figure 2. IMU sensor placement and axis alignment. (a) Accelerometer. (b) Gyroscope. 2.2. FAU Dataset The FAU dataset is based on a previous study evaluating a method for smart labeling of cyclic activities [24] and is publicly available at www.activitynet.org. The dataset provides gait data in a relatively natural setting, and its protocol consisted in the execution of 12 different task-driven activities performed in random order for each participant. It includes data from 80 healthy participants with a mean age of 27 ± 6 years. Data were collected from 56 participants at the Friedrich- Alexander University Erlangen-Nürnberg (Germany) and from 24 participants at the University of Ljubljana (Slovenia). In this study, data collected at Slovenia from 20 of the 24 participants (mean age of 28 years) was used as training dataset [25] and data collected in Germany from the 56 participants were used as evaluation dataset. Only the data collected from the IMU worn on the left foot was used for trajectory reconstruction of ten activities (Table 1). Sensor placement and axis alignment are the same used in the Unicauca dataset (Figure 2). The acceleration (range: ±8 g) and angular velocity (range: ±2000 dps) were collected at a frequency of 200 Hz. The on-ground and off-ground phases of each stride are labeled. The accelerometer was calibrated using six static positions and the gyroscope was calibrated using a complete rotation about each of the three axes. Data were acquired in an indoor environment which including chairs and tables (Figure 3). Jogging was described to the participants as “if one would jog for exercise in the evening” and running as “if one is late for a bus”. These instructions were the same used in the Unicauca dataset. Figure 3. Map of the indoor environment used for collecting the FAU dataset. Blue squares represent chairs that denote start/end positions of activities. Black rectangles represent tables, and dotted red lines represent the possible trajectories followed by participants in each activity. Figure 3. Map of the indoor environment used for collecting the FAU dataset. Blue squares represent chairs that denote start/end positions of activities. Black rectangles represent tables, and dotted red lines represent the possible trajectories followed by participants in each activity. Sensors 2020, 20, 651 5 of 20 Table 1. Activity descriptions and abbreviations, shown with their relevant start and end points as labeled in Figure 3 as well as approximated distances. Activity Description Start/End Position Approximated Distance (m) W-Slalom Walk slalom through 3 tables B→B 31 W-Posters Sign name on 5 posters on the wall C→G 21 W-Tables Perform task at 3 diﬀerent tables while sitting D→D 20 W-Cards Perform task on a table while standing E→E 6 W, J, R-20 Walk, jog, run 2 times 20 m A→A 40 W, J, R-Circuit Walk, jog and run half a circuit each F, G→G,F 43 3. Methods A trajectory reconstruction pipeline was carried out separately for each activity of both datasets (Figure 4). This pipeline is based on previous work by Hannink et al. [26]. A type of activity classiﬁcation step was included. Toe-Oﬀ and Mid-Stance algorithms were modiﬁed in order to deal with non-walking strides as well as a complementary ﬁlter added for stride length and orientation estimation. Sensors 2019, 19, x FOR PEER REVIEW 5 of 20 Table 1. Activity descriptions and abbreviations, shown with their relevant start and end points as labeled in Figure 3 as well as approximated distances. Activity Description Start/End Position Approximated Distance (m) W-Slalom Walk slalom through 3 tables B→B 31 W-Posters Sign name on 5 posters on the wall C→G 21 W-Tables Perform task at 3 different tables while sitting D→D 20 W-Cards Perform task on a table while standing E→E 6 W, J, R-20 Walk, jog, run 2 times 20m A→A 40 W, J, R- Circuit Walk, jog and run half a circuit each F, G→G,F 43 3. Methods A trajectory reconstruction pipeline was carried out separately for each activity of both datasets (Figure 4). This pipeline is based on previous work by Hannink et al. [26]. A type of activity classification step was included. Toe-Off and Mid-Stance algorithms were modified in order to deal with non-walking strides as well as a complementary filter added for stride length and orientation estimation. Figure 4. Pipeline for trajectory reconstruction for each activity. 3.1. Stride Segmentation As shown by Zrenner et al., a threshold-based stride segmentation and a double integration with the ZUPT algorithm performed better than other approaches based on stride time, foot acceleration, and deep learning for calculating stride length in running using a foot-mounted IMU [27]. Thus, multi-dimensional subsequence dynamic time warping (msDTW) and a double integration with ZUPT were used as the stride segmentation and stride length and orientation estimation methods, respectively, in this study [23]. msDTW is used to find a subsequence of continuous signal sequences similar to a given reference pattern. In the context of stride segmentation, that pattern consists of a template of one stride. The stride start was set to the negative peak before the swing phase and stride end to the negative peak at the end of the stance phase (Figure 5a), according to the definition of stride given in [20]. Using that template, msDTW looks for similarities in a movement sequence. msDTW has been shown to be a robust method to segment strides from healthy, geriatric, and Parkinson’s patients using foot- mounted IMUs [28]. 3.1.1. Template Generation A MatLab script was developed for template generation. It included two steps: interpolation and averaging. Interpolation consisted of taking each stride and interpolating it to a fixed duration of 200 samples. After interpolation, the template was obtained by averaging, sample by sample, all the strides. The templates for walking, jogging, and running were built using the 8724, 1688, and 1360 Figure 4. Pipeline for trajectory reconstruction for each activity. 3.1. Stride Segmentation As shown by Zrenner et al., a threshold-based stride segmentation and a double integration with the ZUPT algorithm performed better than other approaches based on stride time, foot acceleration, and deep learning for calculating stride length in running using a foot-mounted IMU [27]. Thus, multi-dimensional subsequence dynamic time warping (msDTW) and a double integration with ZUPT were used as the stride segmentation and stride length and orientation estimation methods, respectively, in this study [23]. msDTW is used to ﬁnd a subsequence of continuous signal sequences similar to a given reference pattern. In the context of stride segmentation, that pattern consists of a template of one stride. The stride start was set to the negative peak before the swing phase and stride end to the negative peak at the end of the stance phase (Figure 5a), according to the deﬁnition of stride given in [20]. Using that template, msDTW looks for similarities in a movement sequence. msDTW has been shown to be a robust method to segment strides from healthy, geriatric, and Parkinson’s patients using foot-mounted IMUs [28]. 3.1.1. Template Generation A MatLab script was developed for template generation. It included two steps: interpolation and averaging. Interpolation consisted of taking each stride and interpolating it to a ﬁxed duration of 200 samples. After interpolation, the template was obtained by averaging, sample by sample, all the strides. The templates for walking, jogging, and running were built using the 8724, 1688, and 1360 walking, jogging, and running strides, respectively, of the training dataset. Unlike other studies, which used only straight strides for building templates [23,28,29], the three templates were built with all the strides of the activities. Thus, both straight and non-straight strides were included in the templates. The swing-phase starts when the foot leaves the ground (Toe-Oﬀ) and ends when the heel strikes the ground (Heel Strike). The portion of the gyroscope z-signal after Heel Strike (HS) describes the Sensors 2020, 20, 651 6 of 20 stance-phase. A Mid-Stance (MS) event is deﬁned as the part of the stance-phase when the signal energy is zero [30]. walking, jogging, and running strides, respectively, of the training dataset. Unlike other studies, which used only straight strides for building templates [23,28,29], the three templates were built with all the strides of the activities. Thus, both straight and non-straight strides were included in the templates. The swing-phase starts when the foot leaves the ground (Toe-Off) and ends when the heel strikes the ground (Heel Strike). The portion of the gyroscope z-signal after Heel Strike (HS) describes the stance-phase. A Mid-Stance (MS) event is defined as the part of the stance-phase when the signal energy is zero [30]. (a) (b) Figure 5. (a) Walking, jogging, and running templates (gyroscope z-axis). (b) Running stride example (gyroscope z-axis). 3.1.2. Classification of Walking, Jogging, and Running Activities In order to automatically select the walking, jogging, or running template that will be used in the stride segmentation process, the machine learning algorithms included in the Matlab Classification Learner app were trained using the activities of the training dataset. A window size of 200 samples (1 s of data) and an overlap of 100 samples were used for feature extraction. The features extracted were velocity (by integrating accelerometer readings), angular velocity (by integrating gyroscope readings) and energy of accelerometer and gyroscope axes. The most frequent value in the result was chosen as the final classification. The evaluation was performed using ten-fold cross- validation. As a result, the highest accuracy (98.1%) was achieved by the SVM classifier with a polynomial kernel function of third-order. 3.1.3. Multi-Subsequence Dynamic Time Warping Implementation The output of the stride segmentation based on msDTW is a set of segments [31]. Each segment describes a possible stride. One issue using these resulting segments for trajectory reconstruction is that often the end of a segment does not coincide with the start of the next segment even for consecutive strides (Figure 6a). The solution to this issue is based on the Toe-Off (TO) detection, which is described in the next section. Using the templates (Figure 5a), the first event detected in each stride is TO. For this reason, TO was defined as the beginning of a stride. For consecutive strides, the end of the stride corresponds with the beginning of the next stride (next TO), resulting in a stride segmentation without “holes” (Figure 6b). The precision and sensitivity of the stride segmentation using msDTW can be tuned using a threshold. The threshold needed to detect a stride indicates the similarity between that stride and the template used, that is, a large threshold indicates a large difference between the template and the segmented stride [23]. Therefore, with a very small threshold, the number of false negatives strides would increase, and a very large threshold would generate false positives strides. Thresholds from 0 to 100 in steps of 5 were tested on the training dataset. As a result, it was found that a fixed threshold Figure 5. (a) Walking, jogging, and running templates (gyroscope z-axis). (b) Running stride example (gyroscope z-axis). 3.1.2. Classiﬁcation of Walking, Jogging, and Running Activities In order to automatically select the walking, jogging, or running template that will be used in the stride segmentation process, the machine learning algorithms included in the Matlab Classiﬁcation Learner app were trained using the activities of the training dataset. A window size of 200 samples (1 s of data) and an overlap of 100 samples were used for feature extraction. The features extracted were velocity (by integrating accelerometer readings), angular velocity (by integrating gyroscope readings) and energy of accelerometer and gyroscope axes. The most frequent value in the result was chosen as the ﬁnal classiﬁcation. The evaluation was performed using ten-fold cross-validation. As a result, the highest accuracy (98.1%) was achieved by the SVM classiﬁer with a polynomial kernel function of third-order. 3.1.3. Multi-Subsequence Dynamic Time Warping Implementation The output of the stride segmentation based on msDTW is a set of segments [31]. Each segment describes a possible stride. One issue using these resulting segments for trajectory reconstruction is that often the end of a segment does not coincide with the start of the next segment even for consecutive strides (Figure 6a). The solution to this issue is based on the Toe-Oﬀ (TO) detection, which is described in the next section. Using the templates (Figure 5a), the ﬁrst event detected in each stride is TO. For this reason, TO was deﬁned as the beginning of a stride. For consecutive strides, the end of the stride corresponds with the beginning of the next stride (next TO), resulting in a stride segmentation without “holes” (Figure 6b). The precision and sensitivity of the stride segmentation using msDTW can be tuned using a threshold. The threshold needed to detect a stride indicates the similarity between that stride and the template used, that is, a large threshold indicates a large diﬀerence between the template and the segmented stride [23]. Therefore, with a very small threshold, the number of false negatives strides would increase, and a very large threshold would generate false positives strides. Thresholds from 0 to 100 in steps of 5 were tested on the training dataset. As a result, it was found that a ﬁxed threshold of 65 maximizes the F-score of the stride segmentation in walking, jogging, and running activities (Figure 7). Sensors 2020, 20, 651 7 of 20 Sensors 2019, 19, x FOR PEER REVIEW 7 of 20 of 65 maximizes the F-score of the stride segmentation in walking, jogging, and running activities (Figure 7). (a) (b) Figure 6. (a) Result of stride segmentation with msDTW. (b) Final stride segmentation with TO detection. Blue vertical lines depict TOs. Light blue rectangles are segmented strides. Figure 6. (a) Result of stride segmentation with msDTW. (b) Final stride segmentation with TO detection. Blue vertical lines depict TOs. Light blue rectangles are segmented strides. Sensors 2019, 19, x FOR PEER REVIEW 7 of 20 of 65 maximizes the F-score of the stride segmentation in walking, jogging, and running activities (Figure 7). (a) (b) Figure 6. (a) Result of stride segmentation with msDTW. (b) Final stride segmentation with TO detection. Blue vertical lines depict TOs. Light blue rectangles are segmented strides. Figure 7. Threshold choice for stride segmentation of walking, jogging, and running strides using msDTW. Sensors 2020, 20, 651 8 of 20 3.2. Toe-Oﬀ and Mid-Stance Detection The previous algorithms for TO and MS detection [31] were modiﬁed in order to improve detection accuracy in jogging and running. These modiﬁcations are described in this section. Both previous and proposed algorithms use the signal of the gyroscope z-axis for TO and MS detection. 3.2.1. To Detection At TO, the gyroscope z-axis describes a zero-crossing because of the ankle joint changes from plantar ﬂexion to a dorsal extension position in the sagittal plane [23]. The algorithm included in [31] for TO detection consists of detecting the ﬁrst zero-crossing in the gyroscope z-axis. Due to the abrupt movements in jogging and running strides, in a few cases, a peak located at the beginning of the stride causes a zero crossing. This would lead to a wrong TO detection (red circle in Figure 8). Consequently, the adapted algorithm for TO detection (Algorithm 1) ﬁnd the maximum peak of the signal and then ﬁnd the nearest zero crossing before it (blue circle in Figure 8). After the detection of all the TOs that belong to the activity, all the portions corresponding from TO to TO are considered as strides (Figure 6b). Considering that the stride time of walking strides is around one second [24], if one TO to TO portion is greater than 2 s (400 samples), only the signal until 1.5 s was taken into account. This often happens because the participant is standing still or sitting. Algorithm 1: Toe-oﬀ (TO) detection algorithm. 1: xMP ← getMaximumPeak(stride) 2: xZC ← getZeroCrossings(stride(1 : xMP)) 3: TO ← getNearestZCtoMP(xZC, xMP) Sensors 2019, 19, x FOR PEER REVIEW 8 of 20 Figure 7. Threshold choice for stride segmentation of walking, jogging, and running strides using msDTW. 3.2. Toe-Off and Mid-Stance Detection The previous algorithms for TO and MS detection [31] were modified in order to improve detection accuracy in jogging and running. These modifications are described in this section. Both previous and proposed algorithms use the signal of the gyroscope z-axis for TO and MS detection. 3.2.1. To Detection At TO, the gyroscope z-axis describes a zero-crossing because of the ankle joint changes from plantar flexion to a dorsal extension position in the sagittal plane [23]. The algorithm included in [31] for TO detection consists of detecting the first zero-crossing in the gyroscope z-axis. Due to the abrupt movements in jogging and running strides, in a few cases, a peak located at the beginning of the stride causes a zero crossing. This would lead to a wrong TO detection (red circle in Figure 8). Consequently, the adapted algorithm for TO detection (Algorithm 1) find the maximum peak of the signal and then find the nearest zero crossing before it (blue circle in Figure 8). After the detection of all the TOs that belong to the activity, all the portions corresponding from TO to TO are considered as strides (Figure 6b). Considering that the stride time of walking strides is around one second [24], if one TO to TO portion is greater than 2 s (400 samples), only the signal until 1.5 s was taken into account. This often happens because the participant is standing still or sitting. Figure 8. Example of TO and MS detection. The red circle and square show a wrong TO and MS detection, respectively, using the previous TO detection algorithm. The blue circle and square show an adequate TO and MS detection, respectively, using the proposed algorithms. Algorithm 1: Toe-off (TO) detection algorithm. 1: xMP ← getMaximumPeak(stride) 2: xZC ← getZeroCrossings(stride(1 : xMP)) 3: TO ← getNearestZCtoMP(xZC, xMP) 3.2.2. Mid-Stance Detection At Mid-Stance (MS) we define that the foot is entirely stationary on the ground [23,28] and its velocity is zero. The gyroscope z-signal is minimal at that moment. As the speed of movement increases from walking to running, the stance-phase time decreases (Figure 5a) making MS detection more difficult [10]. The previous algorithm for MS detection in walking strides consists of calculating the middle of the window with the lowest energy in the full stride’s gyroscope z-signal [23,28,31]. For Figure 8. Example of TO and MS detection. The red circle and square show a wrong TO and MS detection, respectively, using the previous TO detection algorithm. The blue circle and square show an adequate TO and MS detection, respectively, using the proposed algorithms. 3.2.2. Mid-Stance Detection At Mid-Stance (MS) we deﬁne that the foot is entirely stationary on the ground [23,28] and its velocity is zero. The gyroscope z-signal is minimal at that moment. As the speed of movement increases from walking to running, the stance-phase time decreases (Figure 5a) making MS detection more diﬃcult [10]. The previous algorithm for MS detection in walking strides consists of calculating the middle of the window with the lowest energy in the full stride’s gyroscope z-signal [23,28,31]. For jogging and running strides, the MS is often confused with other parts of the signal like the valley just before the HS or the peak before the next TO (red square in Figure 8). Sensors 2020, 20, 651 9 of 20 The adaptation of the MS detection algorithm (Algorithm 2) consisted of (1) taking only the stride portion from HS to 80% of the stride—this portion was chosen taking into account that the stance-phase of walking strides is approximately the last 60% of the stride and for jogging and running strides it is approximately the last 40% of the stride [25]; (2) calculating the middle of the window with the lowest energy within that portion—to this end, a window size of 20 samples (100 ms) and a window overlap of 10 samples (Blue square in Figure 8) are used. Algorithm 2: Mid-Stance (MS) detection algorithm. 1: windowSize ← 20 2: overlap ← 10 3: stride ← interpolateStrideTo200Samples(stride) 4: xMP ← getMaximumPeak(stride) 5: stride ← stride(xMP : 160) 6: xHS ← getMinimumPeak(stride) 7: stride ← stride(xHS : end) 8: MS getMinimumEnergy(stride, windowSize, overlap) 3.2.3. Stride Length and Orientation Estimation The biggest challenge to adequately estimate stride length using IMU data is the signiﬁcant bias derived from the use of IMUs, which leads to large drifts after the double-integration process. For that reason, the ZUPT method was used. Zero-velocity detection was done by evaluating a threshold on the magnitude of the gyroscope rate of turn of each measurement. If the measurement is less than a threshold of 0.6 dps, that measurement is considered as a zero-velocity measurement. It has been proved that this simple approach works properly in walking strides [11,30]. However, this approach does not work correctly in jogging and running strides due to the abrupt signal variations. The solution to this problem is the use of the MS detected previously. Taking into account that the average stance-phase time in running strides is around 100 ms (20 samples), it was empirically found that taking 5 samples to each side of the MS (which corresponds to 50 ms with the sampling frequency used) leads to better zero-velocity detection in jogging and running strides. After zero-velocity detection, a complementary Kalman ﬁlter (CF) was used in order to model the error in velocity and position estimates using the ZUPTs as measurements (see Appendix A for details). When zero-velocity is detected, but the estimated velocity is diﬀerent to zero, the CF adjusts the velocity and the corresponding displacement. The CF used in this work is based on the proposed work by Fischer et al. [11]. Three main parameters have to be set up for CF initialization: accelerometer and gyroscope noise (σa and σw) and the ZUPT detection noise (σv). Accelerometer and gyroscope noise were set to equal value in both datasets (σa = 0.01 m/s2 and σw = 0.01 rad/s). ZUPT detection noise depends on the velocity of the participant. That parameter was established by evaluating from σv = 0.001 m/s to σv = 0.05 m/s in steps of 0.001 m/s for each trajectory performed. The σv chosen was the one that produced the least error in the ﬁnal distance evaluated. The stride length and orientation estimation are obtained using the position increments in each MS event. Stride length, where ∇Pk is the position increment from stride k-1 to stride k, is calculated as follows: SLk = q ∇Pk(x)2 + ∇Pk(y)2, (1) Sensors 2020, 20, 651 10 of 20 4. Results 4.1. Unicauca Dataset 4.1.1. Classiﬁcation of the Type of Activity The accuracy in the activity classiﬁcation was 90%. There were only three misclassiﬁcations: two running activities were classiﬁed as jogging activities and one jogging activity was classiﬁed as a running activity (Figure 9). Sensors 2019, 19, x FOR PEER REVIEW 10 of 20 4. Results 4.1. Unicauca Dataset 4.1.1. Classification of the Type of Activity The accuracy in the activity classification was 90%. There were only three misclassifications: two running activities were classified as jogging activities and one jogging activity was classified as a running activity (Figure 9) Figure 9. Confusion matrix of the classification of the type of activity in the Unicauca dataset. 4.1.2. Toe-Off and Mid-Stance Detection In this dataset, TO and MS were manually labeled. A TO/MS is considered as a true positive (TP) if it is located within 15% of the total number of samples of the stride to the right and left of the TO/MS ground truth. A false positive (FP) occurs when a TO/MS is detected outside this range. A false negative (FN) indicates that a TO/MS for a stride was not detected. Having in mind that 40% and 60% of the stride corresponds to the stance-phase of walking and running strides, respectively [25], the TO detection performance was evaluated in the training dataset using error ranges from 5% to 21% of the total stride in steps of 3% (Figure 10). As a result, 15% was chosen as an acceptable error range for TP calculation. Results of the evaluation of the TO and MS detection using the previous and proposed algorithms are shown in Tables 2 and 3, respectively. Figure 10. TO performance evaluation using error ranges from 5% to 21% in steps of 3%. Figure 9. Confusion matrix of the classiﬁcation of the type of activity in the Unicauca dataset. 4.1.2. Toe-Oﬀ and Mid-Stance Detection In this dataset, TO and MS were manually labeled. A TO/MS is considered as a true positive (TP) if it is located within 15% of the total number of samples of the stride to the right and left of the TO/MS ground truth. A false positive (FP) occurs when a TO/MS is detected outside this range. A false negative (FN) indicates that a TO/MS for a stride was not detected. Having in mind that 40% and 60% of the stride corresponds to the stance-phase of walking and running strides, respectively [25], the TO detection performance was evaluated in the training dataset using error ranges from 5% to 21% of the total stride in steps of 3% (Figure 10). As a result, 15% was chosen as an acceptable error range for TP calculation. Sensors 2019, 19, x FOR PEER REVIEW 10 of 20 4. Results 4.1. Unicauca Dataset 4.1.1. Classification of the Type of Activity The accuracy in the activity classification was 90%. There were only three misclassifications: two running activities were classified as jogging activities and one jogging activity was classified as a running activity (Figure 9) Figure 9. Confusion matrix of the classification of the type of activity in the Unicauca dataset. 4.1.2. Toe-Off and Mid-Stance Detection In this dataset, TO and MS were manually labeled. A TO/MS is considered as a true positive (TP) if it is located within 15% of the total number of samples of the stride to the right and left of the TO/MS ground truth. A false positive (FP) occurs when a TO/MS is detected outside this range. A false negative (FN) indicates that a TO/MS for a stride was not detected. Having in mind that 40% and 60% of the stride corresponds to the stance-phase of walking and running strides, respectively [25], the TO detection performance was evaluated in the training dataset using error ranges from 5% to 21% of the total stride in steps of 3% (Figure 10). As a result, 15% was chosen as an acceptable error range for TP calculation. Results of the evaluation of the TO and MS detection using the previous and proposed algorithms are shown in Tables 2 and 3, respectively. Figure 10. TO performance evaluation using error ranges from 5% to 21% in steps of 3%. Figure 10. TO performance evaluation using error ranges from 5% to 21% in steps of 3%. Results of the evaluation of the TO and MS detection using the previous and proposed algorithms are shown in Tables 2 and 3, respectively. Sensors 2020, 20, 651 11 of 20 Table 2. Averaged results of TO and MS detection for the 10 participants in the Unicauca dataset using the previous TO and MS detection algorithms. Toe-Oﬀ Mid-Stance Activity TO GT TP FP FN F-Score (%) MS GT TP FP FN F-Score (%) Walking 105.5 105.4 0.1 0.1 99.9 104.5 104.4 0.1 0.1 99.9 Jogging 75.4 39.4 37.1 36.2 51.5 74.4 41.2 34.2 33.3 54.9 Running 59.6 21.7 37.5 37.1 36.4 58.6 25.8 31.5 30.7 45.3 TO GT: ground truth TO rate. MS GT: ground truth MS rate. TP: true-positive rate. FP: false-positive rate. FN: false-negative rate. Table 3. Averaged results of TO and MS detection for the 10 participants in the Unicauca dataset using the proposed TO and MS detection algorithms. Toe-Oﬀ Mid-Stance Activity TO GT TP FP FN F-Score (%) MS GT TP FP FN F-Score (%) Walking 105.5 105.5 0 0 100 104.5 104.5 0 0 100 Jogging 75.4 75.2 0.1 0.2 99.8 74.4 74.4 0 0 100 Running 59.6 59.3 0.3 0.2 99.7 58.6 58.5 0.1 0.1 99.8 TO GT: ground truth TO rate. MS GT: ground truth MS rate. TP: true-positive rate. FP: false-positive rate. FN: false-negative rate. A perfect F-score was obtained for TO and MS detection in walking strides. Very few mistakes occurred for jogging and running, but the F-score remains high. 4.1.3. Trajectory Reconstruction Two evaluation measures were used. (1) Return position error (RPE): the distance between the coordinates of the actual ﬁnal point of the activity and the coordinates of the participant’s ﬁnal stride of the corresponding activity. (2) Strides out of trajectory (SOT): All strides of the reconstructed trajectory should be within the boundaries of the corridors represented by black dotted lines (Figure 11). Otherwise, those strides will be counted as out of trajectory. Higher velocity corresponds to more SOT and RPE. Although, on average, 5.7 % of the strides are out of trajectory in the running trial, the RPE remains less than 1.0% (Table 4). Trajectories of the three trials are mostly within the boundaries (Figure 11). Table 4. Average results of trajectory reconstruction for each type of activity performed by the 10 participants using the previous and the proposed TO and MS detection algorithms. Activity SOT RPE [31] New A [31] New A # % # % meters % meters % Walking 1.7 1.6 1.7 1.6 0.8 0.5 0.8 0.5 Jogging 6.6 8.6 2.9 3.8 2.2 1.4 1.4 0.9 Running 5.3 9.2 3.3 5.7 2.6 1.6 1.4 0.9 SOT: strides out of trajectory, RPE: return position error, [31]: previous TO and MS detection algorithms, New A: proposed TO and MS detection algorithms. Sensors 2020, 20, 651 12 of 20 Sensors 2019, 19, x FOR PEER REVIEW 12 of 20 Figure 11. Trajectory reconstruction for the ten participants of the Unicauca dataset in a P shaped path. Black dotted lines show outer edges (walls) of the possible path. Gray lines are the trajectories reconstructed of the ten participants by using the proposed pipeline. 4.2. FAU Dataset 4.2.1. Classification of the Type of Activity The accuracy obtained by the SVM classifier was 93%. Most of the misclassifications occurred when classifying between running and jogging (Figure 12). Figure 12. Confusion matrix of the classification of the type of activity classification in the FAU dataset activities. Figure 11. Trajectory reconstruction for the ten participants of the Unicauca dataset in a P shaped path. Black dotted lines show outer edges (walls) of the possible path. Gray lines are the trajectories reconstructed of the ten participants by using the proposed pipeline. 4.2. FAU Dataset 4.2.1. Classiﬁcation of the Type of Activity The accuracy obtained by the SVM classiﬁer was 93%. Most of the misclassiﬁcations occurred when classifying between running and jogging (Figure 12). Sensors 2019, 19, x FOR PEER REVIEW 12 of 20 Figure 11. Trajectory reconstruction for the ten participants of the Unicauca dataset in a P shaped path. Black dotted lines show outer edges (walls) of the possible path. Gray lines are the trajectories reconstructed of the ten participants by using the proposed pipeline. 4.2. FAU Dataset 4.2.1. Classification of the Type of Activity The accuracy obtained by the SVM classifier was 93%. Most of the misclassifications occurred when classifying between running and jogging (Figure 12). Figure 12. Confusion matrix of the classification of the type of activity classification in the FAU dataset activities. Figure 12. Confusion matrix of the classiﬁcation of the type of activity classiﬁcation in the FAU dataset activities. Sensors 2020, 20, 651 13 of 20 4.2.2. Toe-Oﬀ Detection The last sample of the on-ground phase of each stride was used as ground truth for the evaluation of the TO detection algorithm (Table 5). The same criteria used in the Unicauca dataset for TP, FP, and FN calculations were used. The evaluation was carried out on the data collected from the 56 participants at the Friedrich-Alexander University Erlangen-Nürnberg (Germany) of FAU dataset. Table 5. Average results of TO detection for each type of activity performed by the 56 participants in the FAU dataset using the previous and the proposed TO detection algorithms. Activity TO TP FP FN F-Score (%) [31] New A. [31] New A. [31] New A. [31] New A. W-Slalom 21.5 21.2 21.2 0.8 0.8 0.4 0.3 97 97 W-Posters 13.0 10.6 10.6 3.6 3.5 2.3 2.3 77 78 W-Tables 11.9 9.5 9.5 3.7 3.7 2.4 2.4 75 75 W-Cards 4.33 3.7 3.7 1.6 1.6 2.4 0.6 71 71 W-20 28.4 28.2 28.2 1.3 1.0 0.4 0.2 99 98 J-20 22.3 13.7 21.6 9.7 1.1 8.6 0.7 56 96 R-20 18.4 8.1 17.0 12.3 2.1 10.6 1.3 48 90 W-Circuit 28.2 27.6 27.7 0.7 0.7 0.4 0.3 98 98 J-Circuit 21.9 11.8 21.3 10.8 0.7 10 0.5 49 97 R-Circuit 17.7 7.6 17.3 10.7 0.8 10.2 0.4 40 96 TO: toe-oﬀ rate, TP: true positives rate, FP: false positives rate, FN: false negatives rate, [31]: previous TO and MS detection algorithms, New A: proposed TO and MS detection algorithms. 4.2.3. Body Trajectory Reconstruction For RPE estimation in FAU dataset (Table 6), it is important to note that the start/end activity positions were deﬁned by chairs in the indoor environment. For that reason, the actual positions where the participants started and ﬁnished the activities were not precisely the same as the chairs’ positions since participants began each activity near the corresponding chair and did not necessarily return to the exact point where they started the activity. Based on the videos of the data collection, participants started and ﬁnished the activities within a radius of 1.5 m around the chairs. Light blue and gray rectangles in Figures 13 and 14, respectively, indicate the path where all the strides related to a certain activity should take place. If a stride is out of this path, it is considered as a Stride Out of Trajectory (SOT). A SOT can be caused by the accumulative error of stride lengths and angle calculation of previous strides. These zones were deﬁned taking into account the coordinates of the chairs and tables and the boundaries of the indoor environment. Table 6. Averaged results of trajectory reconstruction of activities performed by the 56 participants in the FAU dataset using the previous and the proposed TO and MS detection algorithms. Activity Activity distance SOT RPE [31] New A. [31] New A. meters # % # % meters % meters % W-Slalom 31 1.1 5.2 1.1 5.2 1.7 5.5 1.7 5.5 W-Posters 21 1.0 8.0 1.0 8.0 1.9 9.0 1.8 8.8 W-Tables 20 3.1 25.9 3.1 25.9 2.8 14.1 2.8 14.1 W-Cards 6 1.3 30.5 1.3 30.5 1.6 27.4 1.6 27.4 W-20 40 3.7 13.1 3.7 13.1 1.7 4.2 1.7 4.2 J-20 40 7.5 34.2 3.9 17.9 5.5 14.2 2.0 5.1 R-20 40 4.1 22.5 3.1 17.0 5.2 13.9 2.5 6.0 W-Circuit 43 4.1 14.4 4.1 14.4 2.9 6.7 3.0 6.7 J-Circuit 43 6.4 30.2 4.5 20.4 14.5 33.7 3.6 8.8 R-Circuit 43 5.1 29.9 3.9 22.4 16.2 37.7 3.7 8.7 SOT: strides out of trajectory, RPE: return position error, [31]: previous TO and MS detection algorithms, New A: proposed TO and MS detection algorithms. Sensors 2020, 20, 651 14 of 20 of activities W-20, J-20, and R-20 describes two straight trajectories, joined by a 180-degree turn. The trajectory reconstruction of W-Slalom allows sight of the area where the tables are located. The W- Posters activity includes non-straight strides, which are well described in the trajectory obtained. Regarding the circuit activities, although most of the strides are inside the activity zones, some trajectories lead towards the outer part of the activity zone. Others lead towards the internal part of the circuit (Figure 13). Figure 13. Trajectory reconstruction of non-circuit activities for all 56 participants of the FAU dataset. Black, blue and orange lines denote R-20, J-20, and W-20, respectively. Red, green, violet and light green lines represent W-Cards, W-Slalom, W-Posters, and W-Tables, respectively. Gray rectangles represent zones where all the strides related to certain activity should take place. Figure 13. Trajectory reconstruction of non-circuit activities for all 56 participants of the FAU dataset. Black, blue and orange lines denote R-20, J-20, and W-20, respectively. Red, green, violet and light green lines represent W-Cards, W-Slalom, W-Posters, and W-Tables, respectively. Gray rectangles represent zones where all the strides related to certain activity should take place. Sensors 2019, 19, x FOR PEER REVIEW 15 of 20 Figure 14. Trajectory reconstruction of circuit activities for all 56 participants of the FAU dataset. Black lines denote the trajectory follows by the participants. Gray zones represent the zone where all the strides should take place. 5. Discussion We have proposed a pipeline for indoor trajectory reconstruction of walking, jogging, and running activities. The proposed pipeline was evaluated with two datasets. The results showed that it is able to reconstruct a person’s trajectory regardless of their gait speed. 5.1. Classification of the Type of Activity It was found that the classification model obtained with the SVM algorithm is able to classify the three types of activities performed: walking, jogging, and running. The classification between jogging and running is the one in which the classifier made more mistakes. This is possibly due to the jogging and running speeds of some participants being similar. The use of personal models to avoid this problem could be promising. 5.2. TO and MS Detection Figure 14. Trajectory reconstruction of circuit activities for all 56 participants of the FAU dataset. Black lines denote the trajectory follows by the participants. Gray zones represent the zone where all the strides should take place. Most of the trajectories were inside the zones (Figures 13 and 14). The trajectory reconstruction of activities W-20, J-20, and R-20 describes two straight trajectories, joined by a 180-degree turn. The trajectory reconstruction of W-Slalom allows sight of the area where the tables are located. The W-Posters activity includes non-straight strides, which are well described in the trajectory obtained. Regarding the circuit activities, although most of the strides are inside the activity zones, some trajectories lead towards the outer part of the activity zone. Others lead towards the internal part of the circuit (Figure 13). Sensors 2020, 20, 651 15 of 20 5. Discussion We have proposed a pipeline for indoor trajectory reconstruction of walking, jogging, and running activities. The proposed pipeline was evaluated with two datasets. The results showed that it is able to reconstruct a person’s trajectory regardless of their gait speed. 5.1. Classiﬁcation of the Type of Activity It was found that the classiﬁcation model obtained with the SVM algorithm is able to classify the three types of activities performed: walking, jogging, and running. The classiﬁcation between jogging and running is the one in which the classiﬁer made more mistakes. This is possibly due to the jogging and running speeds of some participants being similar. The use of personal models to avoid this problem could be promising. 5.2. TO and MS Detection Previous studies focused on the reconstruction of the trajectory during walking and running and do not show results of segmentation or detection of strides [18–22]. The two datasets used in this study allow TO evaluation. In the case of MS detection, ground truth information was not available in the FAU dataset. Therefore, it was not possible to evaluate MS detection in that dataset. However, a high F-score was obtained in the detection of MS in the Unicauca dataset. While the F-score obtained for the proposed TO and MS detection algorithms is similar to that obtained for the previous algorithms for walking activities, the F-score achieved for the proposed TO and MS detection algorithms outperformed that achieved for the previous algorithms for all jogging and running activities. That suggests that the proposed algorithms can detect those gait events in walking, jogging, and running strides. The number of false positives (FP) was always higher than the number of false negatives (FN). This could indicate that the threshold used for stride segmentation with msDTW might have been overestimated, since stride segmentation using a large threshold implies that there is a large diﬀerence between the template used and the segmented strides, leading to the detection of FP strides. However, it was checked that by reducing that threshold, the number of FN increased, causing a decrease in the F-score. Threshold-free methods based on machine learning techniques such as those used by Ren [20] and Wagstaﬀ [22] would make the stride segmentation process straightforward by avoiding setting any threshold. The lowest F-scores are obtained for three walking activities: W-Posters, W-Tables, and W-Cards, which might be due to the fact that those activities involve non-stride movements such as stopping, sitting, lateral and backward steps. This could be because the signal generated for those foot movements is diﬀerent from the walking/running templates. This could be accounted for by using templates generated by those speciﬁc movements, as previously demonstrated in [29], where speciﬁc templates were generated for each speciﬁc activity such as ascending and descending stairs. Unfortunately, the wide range of possible natural foot movements makes this alternative hard to implement. A hierarchical hidden Markov model (hHMM) approach has proved to be a robust method for stride segmentation of walking activities that include non-stride movements in Parkinson’s patients [14] and for stride segmentation of jogging activities [15]. Furthermore, hHMM is a threshold-free approach, therefore it should be explored in order to improve the results obtained for the walking activities that include non-stride movements such as W-Posters, W-Tables, and W-Cards, as well as for stride segmentation of jogging and running activities. 5.3. Trajectory Reconstruction Usually, the foot-mounted IPDR systems have been evaluated in closed-loop trajectories and by measuring the Return Position Error (RPE) [18–22]. The purpose of the Unicauca dataset was, therefore, to provide a starting point to allow a fair comparison with the state-of-the-art papers. Sensors 2020, 20, 651 16 of 20 Sometimes the RPE is small, although the reconstructed trajectory does not ﬁt the actual trajectory performed by the person. That is why we proposed the number of strides out of the trajectory as an additional evaluation metric. The RPEs obtained with the pipeline proposed in this paper for the three trials collected in the Unicauca dataset are less than 1%. The results obtained by the works described in the literature review section are also lower than 1%. As a result of the better detection of TO and MS obtained by using the algorithms proposed in this study, there is also a better trajectory reconstruction since there were fewer strides out of trajectory (SOT) and shorter RPE for jogging and running activities. This demonstrates two things. The ﬁrst is the importance of performing a correct detection of TO and MS for trajectory reconstruction. The second is that if the complementary ﬁlter does not have precise data to perform the ZVUs, it is not capable of modeling errors in speed on its own, even if its parameters were tuned. It has also been demonstrated that by properly detecting TO and MS, the complementary ﬁlter is capable of modeling errors in walking, jogging, and running strides. RPE obtained for trajectories in the FAU dataset are higher than for the Unicauca dataset. It is important to highlight two limitations that the FAU dataset has for trajectory reconstruction. Firstly, the position of the participants at the beginning and end of the activities is not exactly the same. When analyzing the videos of the FAU dataset collection, it was concluded that these positions vary approximately in a radius of one and a half meters, taking as reference the chairs that indicated the start and end of the activities. Therefore, the RPEs calculated have an error of ±1.5 m. This fact should be taken into account for the preparation of the protocol for the collection of a future dataset. Secondly, it was not possible to subtract the gyroscope bias in all activities performed in the FAU dataset, because the activities were performed continuously. A prerequisite for bias computation is that the person stands still for a few seconds for the calculation of the mean of the gyroscope readings and then subtracting it from the entire movement sequence. The number of strides out of trajectory is directly related to the RPE obtained; the more strides out of the acceptable path range, the higher the RPE. When observing the trajectory reconstruction of the activities W-20, J-20, R-20, and W-Circuit, J-Circuit, R-Circuit, it appears that the diﬃculty in trajectory reconstruction increases with stride velocity (from walking to jogging and running). This also occurred in the ﬁve papers described in the literature review section [18–22]. In those papers, the evaluation was performed with very few people. From our study, we can conﬁrm that there is still a gap in trajectory reconstruction using foot-mounted IPDR systems of jogging/running activities regarding the trajectory reconstruction of walking activities. The RPE of the trajectory reconstruction of W-Cards, W-Tables, and W-Posters activities are particularly high, due to the bad detection of TOs. These activities should be treated with special care in future works since they describe movements of daily living activities that happen frequently. The trajectories obtained have a very well-deﬁned shape and could be used for mapping an indoor environment. One important recommendation for future work in the ﬁeld of trajectory reconstruction using IPDR systems is that the datasets collected for evaluation are labeled at activity and stride/step levels, as the FAU dataset used in this paper. Additionally, the participants of the data collection process must start and end precisely at the indicated coordinates. 6. Conclusions In this paper, we have proposed and evaluated a pipeline for trajectory reconstruction of walking, jogging, and running activities using a foot-mounted inertial pedestrian dead-reckoning system. The dynamic time warping method was adapted within this paper to segment walking, jogging, and running strides. Stride length and orientation estimation were performed using a zero velocity update algorithm adapted for walking, jogging, and running strides and empowered by a complementary Kalman ﬁlter. Sensors 2020, 20, 651 17 of 20 The presented results showed that the proposed pipeline provides good trajectory estimations during walking, jogging, and running. TO detection algorithm reached an F-score between 92% and 100% for activities that do not involve stopping, and between 67% and 70% otherwise. Resulting return distance errors were in the range of 0.51% to 8.67% for non-stopping activities and 8.79% to 27.36% otherwise. To the best of the authors’ knowledge, this is the most comprehensive evaluation of a foot-mounted IPDR system regarding the type and number of activities and quantity of people included in the datasets and can serve as a baseline for the comparison of future systems. Future work will be focused on using hidden Markov models in order to improve stride segmentation and fusing symbolic location from an RSSI signal to update the indoor localization when possible. Author Contributions: Conceptualization, J.D.C., C.F.M., D.M.L., F.K. and B.M.E.; Formal analysis, J.D.C. and D.M.L.; Resources, C.F.M., F.K. and B.M.E.; Software, J.D.C.; Writing—original draft, J.D.C. and D.M.L.; Writing—review & editing, C.F.M., F.K. and B.M.E. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), (Call 727-2015). Acknowledgments: Jesus Ceron gratefully acknowledges the support of the Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS) within the national doctoral grants, call 727-2015. Bjoern Eskoﬁer gratefully acknowledges the support of the German Research Foundation (DFG) within the framework of the Heisenberg professorship programme (grant number ES 434/8-1). 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. Appendix A. Complementary Filter The initialization of the Complementary Filter (CF) implies to establish a series of matrices. First, the state of the CF includes the errors in orientation, position, and velocity. (A1) shows the state in an array representation. Each array element is a 1 × 3 array containing the errors in the three-axis. E = h Eo Ep Ev i (A1) The error covariance matrix accumulates the error in orientation, position, and velocity produced in each sample k: Pk = [09x9] (A2) The state transition function is a matrix that is multiplied with the previous state to get the next state, as shown in (A7). ‘S’ is the Skew-symmetric cross-product operator matrix formed from the n-frame accelerations and is the time step equals to 0.005 s, which results from dividing 1 s between the IMU data collection frequency (200Hz). Fk = I3X3 03x3 03x3 03x3 I3X3 I3X3∆t −S∆t 03x3 I3X3 (A3) The process noise covariance matrix is calculated for each sample by multiplying the accelerometer and gyroscope noise by: Qk = h σwx σwy σwz 0 0 0 σax σay σaz ∆t i (A4) Sensors 2020, 20, 651 18 of 20 The uncertainty in velocity during each ZUPT is represented using the measurement noise covariance matrix (A5). It is a diagonal matrix because no correlation in velocity is supposed to exist between axes. R = σ2 vx 0 0 0 σ2 vy 0 0 0 σ2 vz (A5) The measurement function matrix is used to move from the state variables space to the measurement variables states. In this implementation, the measurements are the ZUPTs that is when velocity is supposed to be zero. That way, the measurement function has to contain an identity matrix in the position of the velocity error state as follows: Hk = [(03x3 03x3 I3x3)] (A6) Before running the CF, the gyroscope bias has to be removed. Gyroscope bias is obtained by calculating the mean of the gyroscope readings while IMU is not moving just before the beginning of the activity. The resulting value is subtracted to all gyroscope signals. After gyroscope bias subtraction, the CF is executed. It has two phases: Prediction and update. In the prediction phase, the error covariance matrix (Pk) is propagated using (A7): Pk = FkPk−1FT k + Qk (A7) Only when a sample k is a ZUPT, the Update phase comes into play. In this case, the Kalman gain is calculated with (A8), and with that gain, the error is obtained using (A9). Kk = PkHT(HPkHT + R)−1 (A8) E = h Eo Ep Ev i = KkVk (A9) Finally, the velocity and position estimates are corrected as well as Pk: Vk = Vk − Ev (A10) Posk = Posk − Ep (A11) Pk = (I9x9 − KkH)Pk (A12) References 1. Zheng, L.; Zhou, W.; Tang, W.; Zheng, X.; Peng, A.; Zheng, H. A 3D indoor positioning system based on low-cost MEMS sensors. Simul. Model. Pract. Theory 2016, 65, 45–56. [CrossRef] 2. Susanti, R.M.; Adhinugraha, K.M.; Alamri, S.; Barolli, L.; Taniar, D. Indoor Trajectory Reconstruction Using Mobile Devices. In Proceedings of the IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), Krakow, Poland, 16–18 May 2018. 3. Alariﬁ, A.; Al-Salman, A.; Alsaleh, M.; Alnafessah, A.; Al-Hadhrami, S.; Al-Ammar, M.A.; Al-Khalifa, H.S. Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances. Sensors 2016, 16, 707. 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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/).	Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System.	01-24-2020	Ceron, Jesus D,Martindale, Christine F,López, Diego M,Kluge, Felix,Eskofier, Bjoern M	eng
PMC7538888	Code is deposited in GitHub, see manuscript section “Code availability” for DOI. Sample size was determined by available data during period Jul 2015 to Dec 2018 provided by Polar. This size is by far sufficient, as indicated by smooth distribution functions. Data exclusion criteria were pre-established. Data exclusion was applied to all available data from Polar Flow website. Blinding was not relevant as this is not a clinical research study and differential assessment or treatment cannot occur since individuals uploaded their data to webservice before our study started, without knowing about our analysis. We did not use any personal information of individual besides their sex. All individuals and their activities were linked only by an anonymous ID.	Human running performance from real-world big data.	10-06-2020	Emig, Thorsten,Peltonen, Jussi	eng
PMC5919653	S1 Data set and post‐hoc test results Day 1 Performance time Perceived exertion Participant P1 P2 P3 P4 PE0 PE1 PE2 PE3 PE4 PE5 PE6 PE7 1 629 641 1727 1148 0.5 2 3 5 3 5 4 0 2 544 537 1364 935 0.5 0.5 0.5 5 3 4 3 0 3 917 615 1158 1178 1 3 3 5 5 5.5 6 5 4 720 834 1131 1185 3 5 8 8 6 7 6 4 5 806 1109 1648 1163 3 0.5 7 7 5 5 1 0 6 735 978 860 925 1 3 8 10 9 8 5 0 7 1081 467 1644 1139 0.5 2 7 8 7 7 7 0 8 693 479 884 913 0 0 0.5 10 8 7 6 0 9 440 407 679 701 0.5 1 3 6 4 3 3 2 10 736 695 1096 1587 2 3 5 7 7 5 4 2 11 465 353 778 871 0.5 1 2 8 6 5 3 0.5 12 529 354 664 667 1 1 3 10 8 8 6 1 13 1573 854 1427 1162 2 3 7 8 7 6 7 0.5 14 549 730 686 760 0 0.5 1 6 5 4 4 0 Day 2 1 596 589 1051 1079 0.5 1 3 7 5 6 5 0 2 468 466 790 894 1 2 7 6 5 4 4 0.5 3 548 592 1125 1539 3 4 6 9 6 5 4 1 4 582 614 998 1210 1 3 4 6 5 4 5 1 5 517 455 781 1075 0.5 2 3 6 4 4 3 2 6 423 511 619 770 0 0.5 2 10 9 7 5 0 7 662 608 967 966 0 1 3 9 7 4 3 0 8 613 462 849 926 0 0 0.5 8 7 7 5 0 9 376 325 595 607 0.5 1 4 8 4 4 2 0.5 10 841 546 1009 1097 2 3 6 7 6 5 5 3 11 387 409 586 659 0.5 0.5 3 7 6 5 3 0.5 12 465 327 840 601 1 1 2 9 7 6 4 1 13 561 797 1073 1115 3 4 6 8 8 7 6 1 14 404 316 743 611 0 1 1 7 5 5 4 0 1 Day 1 Cortisol Participant C0 C1 C2 C3 C4 C5 C6 C7 1 2.738 2.760 2.965 3.206 3.207 3.107 2.939 2.906 2 2.481 2.690 2.742 2.924 3.041 2.850 2.735 2.807 3 2.446 2.814 2.891 3.053 2.963 2.850 2.857 2.598 4 2.276 2.404 2.712 2.907 2.958 2.927 2.835 2.634 5 2.300 2.649 2.998 3.126 3.051 3.057 3.025 2.840 6 2.989 3.099 3.109 3.189 3.262 3.189 3.210 3.160 7 3.086 3.021 3.085 3.154 3.160 3.132 3.168 3.108 8 3.008 3.077 3.162 3.222 3.255 3.179 3.182 3.169 9 2.958 3.005 3.005 2.947 2.991 3.144 2.992 3.159 10 3.017 3.054 3.072 3.056 3.207 3.116 3.111 3.089 11 2.496 2.763 2.902 2.923 3.078 2.945 2.933 2.855 12 2.406 2.633 2.779 2.993 3.029 2.869 2.904 2.682 13 2.504 2.635 3.067 2.985 3.034 3.086 2.922 2.884 14 2.568 2.656 2.861 2.882 2.957 2.842 2.797 2.721 Day 2 1 2.612 2.720 2.967 3.004 3.143 2.782 2.832 2.779 2 2.624 2.773 2.787 2.833 2.945 2.804 2.797 2.687 3 2.540 2.640 2.835 2.970 2.976 2.857 2.818 2.614 4 2.387 2.341 2.662 2.690 2.861 2.771 2.711 2.568 5 2.703 2.626 2.584 2.774 2.850 2.812 2.899 2.817 6 2.995 3.047 3.169 3.128 3.176 3.094 3.186 3.119 7 2.961 2.995 3.123 3.034 3.124 2.906 3.064 3.012 8 2.959 2.953 3.033 3.190 3.173 3.208 3.189 3.213 9 2.886 2.897 3.089 2.966 3.138 3.058 3.010 3.103 10 2.979 3.121 3.148 3.226 3.254 3.229 3.231 3.113 11 2.461 2.679 2.900 3.052 3.132 3.012 2.829 2.743 12 2.336 2.636 2.791 2.883 3.030 2.864 2.847 2.823 13 2.521 2.539 2.950 3.029 3.009 3.010 2.916 2.727 14 2.481 2.631 2.700 2.789 2.900 2.805 2.783 2.014 2 Day 1 Chromogranin A Participant CgA0 CgA1 CgA2 CgA3 CgA4 CgA5 CgA6 CgA7 1 4.671 11.166 14.992 204.306 78.050 8.797 3.914 43.287 2 3.515 4.357 6.384 5.442 4.400 5.684 27.355 4.086 3 19.318 8.254 22.145 157.139 135.482 186.204 140.450 70.269 4 326.695 322.969 256.014 327.780 444.586 356.032 332.962 297.086 5 79.720 430.510 418.475 412.865 372.107 424.100 109.699 273.245 6 11.009 146.646 78.207 55.493 270.090 17.091 26.156 9.853 7 7.926 5.256 5.313 6.570 5.570 6.541 7.355 7.969 8 9.296 18.219 35.264 142.977 67.514 64.216 47.542 270.761 9 326.695 386.982 386.982 336.959 244.779 43.330 35.750 314.546 10 11.009 4.257 18.918 13.122 151.028 5.270 9.182 18.047 11 30.882 76.665 309.849 334.489 393.463 380.701 344.625 13.037 12 381.200 38.948 19.689 357.474 384.327 247.962 344.211 410.238 13 5.113 123.447 33.894 19.004 225.049 352.149 74.609 36.306 14 299.385 7.683 65.201 315.731 92.111 429.625 175.083 341.385 Day 2 1 11.024 10.624 4.143 7.041 10.353 49.398 12.094 253.659 2 3.757 5.470 4.143 4.185 7.069 4.285 4.114 7.812 3 32.823 32.138 281.211 108.472 222.951 309.192 85.373 153.298 4 170.187 284.809 311.034 325.281 352.377 334.875 220.324 200.837 5 32.095 317.872 374.533 406.769 417.547 438.390 217.697 59.762 6 81.833 308.536 359.187 390.580 281.097 149.858 320.213 6.784 7 4.014 5.770 4.185 9.667 15.963 17.862 4.228 6.912 8 107.658 214.028 152.185 97.965 118.793 129.186 140.735 189.088 9 406.455 362.770 430.710 285.037 392.022 312.704 312.090 410.152 10 7.912 22.958 76.907 305.666 300.712 17.805 5.185 23.372 11 21.559 398.874 356.831 387.724 390.209 458.819 7.840 13.351 12 227.848 21.688 194.784 367.852 381.686 240.153 331.220 45.300 13 3.929 3.543 20.803 32.266 36.107 197.368 15.178 7.026 14 88.657 265.322 29.782 29.854 89.813 197.725 75.680 381.557 3 Day 1 Memory Visual attention Participant M1 M2 M3 M4 VA0 VA1 VA2 VA3 VA4 VA7 1 8 10 3 4 11 18 14 22 16 25 2 7 8 4 8 13 20 18 22 18 20 3 10 10 9 8 17 26 24 21 22 26 4 7 10 7 7 11 17 22 29 22 24 5 3 4 1 2 15 18 17 19 17 27 6 6 5 4 4 11 15 21 19 20 21 7 7 6 2 4 14 18 24 22 21 20 8 6 7 6 8 11 14 14 19 15 17 9 4 7 4 7 20 17 19 20 23 20 10 3 5 7 9 17 10 13 17 14 20 11 6 5 5 4 24 22 24 23 25 20 12 6 7 7 9 16 21 26 26 26 34 13 4 8 5 3 15 12 20 17 21 22 14 5 5 6 5 17 21 17 21 16 22 Day 2 1 7 8 5 7 19 25 24 28 17 24 2 9 7 5 7 20 24 24 23 24 22 3 11 11 4 8 25 26 23 28 22 27 4 10 9 9 8 26 23 26 31 24 29 5 10 5 5 5 14 23 16 18 18 19 6 5 6 2 5 20 26 24 24 22 25 7 4 4 4 6 23 22 16 25 22 21 8 7 5 6 5 18 21 18 22 24 25 9 9 9 7 10 22 24 22 24 22 22 10 8 7 6 7 20 15 22 13 20 18 11 7 6 6 4 26 23 27 27 25 22 12 6 7 7 7 29 31 27 30 30 31 13 7 8 5 2 17 20 19 21 18 18 14 6 7 3 5 18 25 22 24 22 24 4 Day 1 Attention/mental flexibility Functional psychobiosocial states Participant AMF0 AMF1 AMF2 AMF3 AMF4 AMF7 FPBSS0 FPBSS1 FPBSS2 FPBSS3 FPBSS4 FPBSS5 1 61 58 50 44 55 44 1.57 1.57 1.14 0.86 1.00 2.00 2 60 55 54 56 38 41 1.00 1.14 1.57 3.57 3.14 2.71 3 65 42 38 45 42 39 2.14 2.43 2.14 2.14 2.14 2.43 4 61 61 49 36 62 58 2.57 2.71 2.71 2.57 3.14 2.43 5 70 65 65 52 65 60 1.29 2.00 1.43 1.86 2.29 1.43 6 60 70 64 54 61 47 2.43 2.43 2.29 2.29 2.71 2.29 7 63 55 60 60 49 53 1.86 1.71 1.14 1.14 1.00 1.71 8 55 59 49 44 37 64 2.00 2.14 2.14 2.43 2.71 3.00 9 60 58 50 59 58 48 2.29 2.43 2.43 2.86 2.29 3.14 10 63 60 49 52 47 62 1.14 1.43 0.86 1.00 0.86 0.86 11 63 60 48 44 42 53 2.14 1.71 1.86 1.71 2.14 2.43 12 49 43 37 32 30 30 2.29 2.57 2.43 2.14 2.14 2.14 13 67 69 63 65 61 58 3.29 3.57 3.14 3.14 2.86 3.29 14 66 64 64 66 54 73 3.00 2.14 2.57 2.14 3.29 1.86 Day 2 1 44 37 40 40 35 32 1.71 2.00 2.00 2.00 2.14 2.14 2 43 32 36 36 31 26 0.86 1.14 1.71 2.29 2.29 2.00 3 35 32 54 26 33 28 2.29 2.14 1.43 1.43 1.71 2.29 4 36 34 37 27 34 30 2.57 2.86 2.71 2.43 2.57 2.57 5 60 36 45 38 37 44 1.29 1.71 1.71 1.86 2.14 2.29 6 50 44 45 47 45 38 3.29 3.29 3.00 2.71 2.43 2.86 7 39 38 37 48 35 36 2.57 3.14 3.14 3.14 2.71 2.00 8 39 52 38 40 31 64 2.60 2.14 2.43 2.14 2.86 2.71 9 50 44 32 38 36 33 2.86 2.57 2.71 2.71 2.86 2.43 10 50 44 41 37 44 30 0.71 0.29 0.57 0.43 0.14 0.71 11 62 31 32 26 27 32 2.29 2.43 2.43 2.00 1.86 2.14 12 38 26 23 25 24 24 2.00 1.57 1.71 1.57 1.71 2.29 13 63 47 40 52 40 53 3.71 3.71 3.57 3.29 3.43 3.71 14 61 40 55 53 48 44 3.57 3.71 3.86 3.43 4.57 4.43 5 Day 1 Dysfunctional psychobiosocial states FPBSS6 FPBSS7 Participant DPBSS0 DPBSS1 DPBSS2 DPBSS3 DPBSS4 DPBSS5 DPBSS6 DPBSS7 2.00 2.14 1 0.333 0.500 0.667 1.000 1.333 0.500 0.167 0.000 2.29 1.71 2 0.167 0.167 0.333 0.167 0.167 0.000 0.167 0.167 2.14 2.43 3 0.833 1.000 1.167 1.667 2.000 1.833 1.667 1.500 2.43 2.57 4 0.833 1.000 0.667 0.667 0.667 0.333 0.333 0.667 1.86 2.29 5 1.667 1.500 2.333 1.833 1.000 2.167 2.167 0.500 2.43 3.43 6 1.000 1.333 1.833 1.167 1.333 2.333 2.333 0.000 2.29 2.71 7 0.667 1.167 1.333 2.167 1.167 1.667 0.667 0.000 3.14 3.43 8 0.333 0.667 0.500 1.000 1.000 1.167 1.333 0.833 2.71 2.71 9 0.167 0.000 0.000 0.167 0.167 0.000 0.833 0.333 1.43 1.71 10 2.000 2.333 1.833 2.167 1.833 1.833 1.500 1.167 2.86 2.57 11 0.667 1.000 1.167 1.833 1.000 0.333 0.500 0.500 2.29 2.43 12 0.167 0.833 0.667 1.000 0.500 0.500 0.667 1.000 3.00 2.57 13 0.167 0.333 0.833 0.500 0.833 0.833 0.833 0.500 3.00 2.00 14 0.000 0.000 0.000 0.000 0.167 1.167 0.000 1.333 Day 2 2.00 2.29 1 0.500 0.500 0.333 0.500 0.500 0.500 0.500 0.167 1.71 1.14 2 0.000 0.000 0.167 0.833 0.000 0.000 0.333 0.167 2.14 2.43 3 1.667 1.667 1.500 1.500 1.333 0.500 0.833 0.833 2.71 2.71 4 0.500 1.000 0.667 0.333 0.667 0.833 0.333 0.667 2.00 2.00 5 0.500 0.333 0.167 0.667 0.167 0.000 0.500 0.333 2.71 2.14 6 0.167 0.333 0.333 0.833 0.167 0.500 0.667 1.667 2.71 3.00 7 0.167 0.167 0.667 0.500 0.833 1.167 1.000 0.500 2.43 3.29 8 0.250 0.333 0.333 1.500 0.833 0.667 0.667 0.333 2.57 2.14 9 0.167 0.167 0.167 0.167 0.167 0.167 0.167 0.000 0.71 0.86 10 2.000 2.833 2.500 3.167 3.000 2.833 2.333 2.167 2.14 2.14 11 0.167 0.333 0.167 0.500 1.000 0.667 0.500 0.667 2.29 2.29 12 0.500 0.833 1.500 1.333 1.167 0.667 0.500 0.333 3.29 3.14 13 0.167 0.167 0.333 0.500 0.667 0.667 0.500 0.167 3.57 3.43 14 0.000 0.000 0.000 0.000 0.000 0.000 0.500 0.000 6 Pairwise comparisons Performance time Day 1 (I) Loop (J) Loop Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 97.429 79.172 0.240 ‐73.612 268.469 3 ‐380.643 90.711 0.001 ‐576.613 ‐184.673 4 ‐279.786 74.702 0.002 ‐441.169 ‐118.402 2 3 ‐478.071 97.840 0.000 ‐689.442 ‐266.701 4 ‐377.214 68.475 0.000 ‐525.146 ‐229.282 3 4 100.857 81.248 0.236 ‐74.669 276.383 Performance time Day 2 (I) Loop (J) Loop Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 30.429 33.145 0.375 ‐41.178 102.035 3 ‐327.357 33.702 0.000 ‐400.167 ‐254.548 4 ‐407.571 59.712 0.000 ‐536.572 ‐278.571 2 3 ‐357.786 32.706 0.000 ‐428.442 ‐287.129 4 ‐438.000 52.061 0.000 ‐550.471 ‐325.529 3 4 ‐80.214 43.384 0.087 ‐173.939 13.511 1 Perceived exertion Day1 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐0.714 0.318 0.042 ‐1.400 ‐0.028 3 ‐3.036 0.580 0.000 ‐4.289 ‐1.783 4 ‐6.250 0.536 0.000 ‐7.409 ‐5.091 5 ‐4.821 0.539 0.000 ‐5.985 ‐3.658 6 ‐4.571 0.450 0.000 ‐5.545 ‐3.598 7 ‐3.536 0.566 0.000 ‐4.758 ‐2.314 8 0.036 0.421 0.934 ‐0.873 0.945 2 3 ‐2.321 0.556 0.001 ‐3.523 ‐1.120 4 ‐5.536 0.606 0.000 ‐6.844 ‐4.228 5 ‐4.107 0.558 0.000 ‐5.312 ‐2.902 6 ‐3.857 0.449 0.000 ‐4.827 ‐2.887 7 ‐2.821 0.447 0.000 ‐3.786 ‐1.857 8 0.750 0.358 0.057 ‐0.024 1.524 3 4 ‐3.214 0.752 0.001 ‐4.838 ‐1.590 5 ‐1.786 0.714 0.027 ‐3.329 ‐0.243 6 ‐1.536 0.666 0.038 ‐2.975 ‐0.097 7 ‐0.500 0.784 0.535 ‐2.195 1.195 8 3.071 0.808 0.002 1.325 4.818 4 5 1.429 0.202 0.000 0.992 1.865 6 1.679 0.285 0.000 1.062 2.295 7 2.714 0.518 0.000 1.595 3.834 8 6.286 0.728 0.000 4.714 7.858 5 6 0.250 0.291 0.405 ‐0.378 0.878 7 1.286 0.462 0.016 0.287 2.284 8 4.857 0.688 0.000 3.370 6.344 6 7 1.036 0.365 0.014 0.247 1.824 8 4.607 0.600 0.000 3.310 5.904 7 8 3.571 0.559 0.000 2.363 4.780 2 Perceived exertion Day2 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐0.786 0.155 0.000 ‐1.120 ‐0.451 3 ‐2.679 0.369 0.000 ‐3.476 ‐1.881 4 ‐6.714 0.431 0.000 ‐7.646 ‐5.782 5 ‐5.071 0.450 0.000 ‐6.045 ‐4.098 6 ‐4.286 0.398 0.000 ‐5.146 ‐3.425 7 ‐3.214 0.318 0.000 ‐3.900 ‐2.528 8 0.179 0.249 0.486 ‐0.360 0.717 2 3 ‐1.893 0.336 0.000 ‐2.619 ‐1.166 4 ‐5.929 0.532 0.000 ‐7.077 ‐4.780 5 ‐4.286 0.541 0.000 ‐5.455 ‐3.116 6 ‐3.500 0.508 0.000 ‐4.598 ‐2.402 7 ‐2.429 0.370 0.000 ‐3.228 ‐1.629 8 0.964 0.285 0.005 0.350 1.579 3 4 ‐4.036 0.700 0.000 ‐5.548 ‐2.523 5 ‐2.393 0.707 0.005 ‐3.921 ‐0.864 6 ‐1.607 0.704 0.040 ‐3.127 ‐0.087 7 ‐0.536 0.580 0.373 ‐1.789 0.717 8 2.857 0.467 0.000 1.848 3.866 4 5 1.643 0.269 0.000 1.061 2.225 6 2.429 0.327 0.000 1.723 3.134 7 3.500 0.442 0.000 2.546 4.454 8 6.893 0.472 0.000 5.873 7.913 5 6 0.786 0.261 0.010 0.223 1.349 7 1.857 0.345 0.000 1.111 2.603 8 5.250 0.497 0.000 4.177 6.323 6 7 1.071 0.245 0.001 0.542 1.601 8 4.464 0.437 0.000 3.521 5.408 7 8 3.393 0.360 0.000 2.615 4.171 3 Cortisol Day 1 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐0.142 0.034 0.001 ‐0.215 ‐0.069 3 ‐0.291 0.055 0.000 ‐0.411 ‐0.172 4 ‐0.378 0.067 0.000 ‐0.522 ‐0.234 5 ‐0.423 0.060 0.000 ‐0.553 ‐0.292 6 ‐0.359 0.056 0.000 ‐0.480 ‐0.237 7 ‐0.310 0.054 0.000 ‐0.427 ‐0.193 8 ‐0.238 0.037 0.000 ‐0.319 ‐0.157 2 3 ‐0.149 0.036 0.001 ‐0.227 ‐0.072 4 ‐0.236 0.046 0.000 ‐0.336 ‐0.136 5 ‐0.281 0.042 0.000 ‐0.371 ‐0.191 6 ‐0.217 0.041 0.000 ‐0.306 ‐0.128 7 ‐0.168 0.035 0.000 ‐0.244 ‐0.093 8 ‐0.097 0.030 0.006 ‐0.161 ‐0.032 3 4 ‐0.087 0.028 0.008 ‐0.146 ‐0.027 5 ‐0.132 0.027 0.000 ‐0.190 ‐0.073 6 ‐0.068 0.018 0.003 ‐0.107 ‐0.028 7 ‐0.019 0.020 0.368 ‐0.062 0.025 8 0.053 0.031 0.115 ‐0.015 0.120 4 5 ‐0.045 0.019 0.035 ‐0.086 ‐0.004 6 0.019 0.027 0.482 ‐0.038 0.077 7 0.068 0.026 0.021 0.012 0.124 8 0.140 0.046 0.009 0.040 0.239 5 6 0.064 0.024 0.019 0.012 0.116 7 0.113 0.024 0.000 0.061 0.165 8 0.184 0.038 0.000 0.102 0.267 6 7 0.049 0.020 0.030 0.005 0.092 8 0.120 0.028 0.001 0.061 0.180 7 8 0.072 0.031 0.038 0.005 0.138 4 Cortisol Day 2 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐0.082 0.028 0.011 ‐0.142 ‐0.022 3 ‐0.235 0.042 0.000 ‐0.326 ‐0.145 4 ‐0.294 0.047 0.000 ‐0.397 ‐0.192 5 ‐0.376 0.049 0.000 ‐0.481 ‐0.271 6 ‐0.269 0.047 0.000 ‐0.371 ‐0.168 7 ‐0.262 0.030 0.000 ‐0.326 ‐0.198 8 ‐0.135 0.055 0.029 ‐0.254 ‐0.016 2 3 ‐0.153 0.033 0.000 ‐0.224 ‐0.082 4 ‐0.212 0.037 0.000 ‐0.293 ‐0.132 5 ‐0.294 0.037 0.000 ‐0.373 ‐0.215 6 ‐0.187 0.041 0.001 ‐0.276 ‐0.098 7 ‐0.180 0.028 0.000 ‐0.240 ‐0.119 8 ‐0.053 0.059 0.387 ‐0.180 0.075 3 4 ‐0.059 0.025 0.032 ‐0.112 ‐0.006 5 ‐0.141 0.023 0.000 ‐0.191 ‐0.091 6 ‐0.034 0.034 0.334 ‐0.107 0.039 7 ‐0.027 0.030 0.397 ‐0.092 0.039 8 0.100 0.058 0.106 ‐0.025 0.226 4 5 ‐0.082 0.017 0.000 ‐0.119 ‐0.044 6 0.025 0.022 0.277 ‐0.023 0.074 7 0.033 0.026 0.237 ‐0.024 0.089 8 0.160 0.061 0.022 0.027 0.292 5 6 0.107 0.026 0.001 0.050 0.164 7 0.114 0.029 0.002 0.051 0.177 8 0.241 0.062 0.002 0.107 0.375 6 7 0.007 0.023 0.756 ‐0.042 0.056 8 0.134 0.061 0.045 0.003 0.265 7 8 0.127 0.054 0.034 0.011 0.243 5 Chromogranin A Day 1 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐0.404 1.962 0.840 ‐4.642 3.835 3 ‐0.997 1.875 0.604 ‐5.048 3.055 4 ‐4.300 1.383 0.008 ‐7.289 ‐1.312 5 ‐5.091 1.756 0.012 ‐8.886 ‐1.297 6 ‐3.296 1.993 0.122 ‐7.601 1.010 7 ‐1.417 1.542 0.375 ‐4.749 1.915 8 ‐2.347 1.093 0.051 ‐4.708 0.014 2 3 ‐0.593 0.935 0.537 ‐2.613 1.428 4 ‐3.897 1.828 0.053 ‐7.845 0.052 5 ‐4.688 1.345 0.004 ‐7.594 ‐1.781 6 ‐2.892 2.143 0.200 ‐7.522 1.739 7 ‐1.014 2.085 0.635 ‐5.519 3.491 8 ‐1.944 2.016 0.353 ‐6.298 2.411 3 4 ‐3.304 1.422 0.037 ‐6.376 ‐0.232 5 ‐4.095 1.345 0.009 ‐7.000 ‐1.190 6 ‐2.299 1.928 0.254 ‐6.464 1.865 7 ‐0.421 1.820 0.821 ‐4.354 3.512 8 ‐1.351 1.980 0.507 ‐5.629 2.928 4 5 ‐0.791 1.468 0.599 ‐3.961 2.379 6 1.005 1.675 0.559 ‐2.615 4.624 7 2.883 1.416 0.063 ‐0.176 5.942 8 1.953 1.281 0.151 ‐0.815 4.721 5 6 1.796 1.635 0.292 ‐1.736 5.328 7 3.674 1.325 0.016 0.812 6.535 8 2.744 1.918 0.176 ‐1.400 6.888 6 7 1.878 1.140 0.123 ‐0.584 4.340 8 0.948 1.945 0.634 ‐3.254 5.150 7 8 ‐0.930 1.762 0.607 ‐4.736 2.876 6 Chromogranin A Day 2 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐2.998 1.697 0.101 ‐6.664 0.668 3 ‐4.166 1.578 0.020 ‐7.576 ‐0.756 4 ‐4.660 1.767 0.021 ‐8.477 ‐0.842 5 ‐5.597 1.549 0.003 ‐8.944 ‐2.250 6 ‐5.389 1.627 0.006 ‐8.904 ‐1.875 7 ‐1.747 0.937 0.085 ‐3.771 0.276 8 ‐1.744 1.479 0.259 ‐4.938 1.451 2 3 ‐1.167 1.380 0.413 ‐4.150 1.815 4 ‐1.662 1.702 0.347 ‐5.338 2.015 5 ‐2.599 1.593 0.127 ‐6.040 0.842 6 ‐2.391 1.492 0.133 ‐5.614 0.831 7 1.251 1.755 0.489 ‐2.541 5.042 8 1.254 2.098 0.560 ‐3.277 5.786 3 4 ‐0.494 0.955 0.614 ‐2.557 1.569 5 ‐1.431 0.792 0.094 ‐3.142 0.279 6 ‐1.224 1.198 0.326 ‐3.812 1.365 7 2.418 1.380 0.103 ‐0.564 5.400 8 2.422 2.401 0.332 ‐2.765 7.609 4 5 ‐0.937 0.498 0.082 ‐2.012 0.138 6 ‐0.730 1.591 0.654 ‐4.167 2.708 7 2.912 1.595 0.091 ‐0.533 6.358 8 2.916 2.705 0.301 ‐2.927 8.759 5 6 0.208 1.334 0.879 ‐2.674 3.090 7 3.850 1.491 0.023 0.628 7.072 8 3.853 2.397 0.132 ‐1.325 9.031 6 7 3.642 1.613 0.042 0.158 7.126 8 3.646 2.060 0.100 ‐0.806 8.097 7 8 0.004 1.985 0.999 ‐4.285 4.293 7 Memory Day 1 (I) Loop (J) Loop Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐1.071 0.425 0.026 ‐1.990 ‐0.153 3 0.857 0.653 0.212 ‐0.555 2.269 4 0.000 0.734 1.000 ‐1.585 1.585 2 3 1.929 0.633 0.009 0.561 3.296 4 1.071 0.699 0.149 ‐0.439 2.582 3 4 ‐0.857 0.467 0.089 ‐1.866 0.152 Memory Day 2 (I) Loop (J) Loop Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 0.500 0.454 0.291 ‐0.481 1.481 3 2.286 0.549 0.001 1.100 3.472 4 1.429 0.571 0.027 0.194 2.663 2 3 1.786 0.595 0.010 0.501 3.070 4 0.929 0.518 0.097 ‐0.192 2.049 3 4 ‐0.857 0.553 0.145 ‐2.052 0.338 Visual attention Day 1 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐2.643 1.239 0.052 ‐5.319 0.033 3 ‐4.357 1.269 0.004 ‐7.099 ‐1.615 4 ‐6.071 1.396 0.001 ‐9.088 ‐3.055 5 ‐4.571 1.073 0.001 ‐6.889 ‐2.254 6 ‐7.571 1.540 0.000 ‐10.898 ‐4.245 2 3 ‐1.714 1.056 0.128 ‐3.995 0.567 4 ‐3.429 1.026 0.005 ‐5.644 ‐1.213 5 ‐1.929 1.112 0.106 ‐4.330 0.473 6 ‐4.929 1.220 0.001 ‐7.564 ‐2.293 3 4 ‐1.714 0.986 0.106 ‐3.844 0.415 5 ‐0.214 0.459 0.648 ‐1.206 0.777 6 ‐3.214 1.223 0.021 ‐5.857 ‐0.572 4 5 1.500 0.924 0.129 ‐0.497 3.497 6 ‐1.500 1.088 0.191 ‐3.851 0.851 5 6 ‐3.000 1.186 0.025 ‐5.562 ‐0.438 8 Visual attention Day 2 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐2.214 1.100 0.065 ‐4.591 0.163 3 ‐0.929 0.848 0.293 ‐2.761 0.904 4 ‐2.929 0.946 0.009 ‐4.973 ‐0.885 5 ‐0.929 0.730 0.226 ‐2.505 0.648 6 ‐2.143 0.882 0.030 ‐4.049 ‐0.237 2 3 1.286 1.019 0.229 ‐0.915 3.486 4 ‐0.714 0.854 0.418 ‐2.560 1.132 5 1.286 0.922 0.187 ‐0.707 3.278 6 0.071 0.722 0.923 ‐1.489 1.632 3 4 ‐2.000 1.084 0.088 ‐4.343 0.343 5 0.000 0.914 1.000 ‐1.974 1.974 6 ‐1.214 0.921 0.210 ‐3.204 0.775 4 5 2.000 1.144 0.104 ‐0.470 4.470 6 0.786 0.757 0.318 ‐0.850 2.422 5 6 ‐1.214 0.786 0.146 ‐2.912 0.483 Attention/mental flexibility Day 1 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 3.143 1.955 0.132 ‐1.081 7.367 3 8.786 1.989 0.001 4.488 13.084 4 11.000 2.226 0.000 6.191 15.809 5 11.571 2.301 0.000 6.601 16.542 6 9.500 2.629 0.003 3.821 15.179 2 3 5.643 1.369 0.001 2.685 8.601 4 7.857 2.463 0.007 2.537 13.177 5 8.429 2.005 0.001 4.098 12.759 6 6.357 2.286 0.016 1.418 11.296 3 4 2.214 1.849 0.253 ‐1.781 6.209 5 2.786 2.209 0.230 ‐1.987 7.559 6 0.714 2.590 0.787 ‐4.881 6.310 4 5 0.571 3.063 0.855 ‐6.046 7.188 6 ‐1.500 3.038 0.630 ‐8.063 5.063 5 6 ‐2.071 3.211 0.530 ‐9.009 4.866 9 Attention/mental flexibility Day 2 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 9.500 2.942 0.007 3.144 15.856 3 8.214 3.173 0.022 1.359 15.069 4 9.786 2.798 0.004 3.742 15.829 5 12.143 2.518 0.000 6.704 17.582 6 11.143 3.305 0.005 4.003 18.282 2 3 ‐1.286 2.579 0.626 ‐6.858 4.287 4 0.286 1.920 0.884 ‐3.862 4.433 5 2.643 1.750 0.155 ‐1.137 6.423 6 1.643 1.929 0.410 ‐2.524 5.810 3 4 1.571 2.639 0.562 ‐4.130 7.272 5 3.929 1.659 0.034 0.344 7.513 6 2.929 3.239 0.382 ‐4.068 9.925 4 5 2.357 1.737 0.198 ‐1.396 6.110 6 1.357 2.548 0.603 ‐4.147 6.861 5 6 ‐1.000 3.024 0.746 ‐7.532 5.532 10 Functional psychobiosocial states Day 1 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐0.071 0.099 0.484 ‐0.285 0.143 3 0.082 0.088 0.372 ‐0.109 0.272 4 ‐0.061 0.227 0.792 ‐0.553 0.430 5 ‐0.194 0.202 0.354 ‐0.630 0.242 6 ‐0.194 0.181 0.304 ‐0.586 0.198 7 ‐0.347 0.125 0.016 ‐0.617 ‐0.077 8 ‐0.408 0.175 0.037 ‐0.787 ‐0.030 2 3 0.153 0.092 0.119 ‐0.045 0.351 4 0.010 0.206 0.961 ‐0.434 0.454 5 ‐0.122 0.209 0.567 ‐0.573 0.328 6 ‐0.122 0.171 0.486 ‐0.491 0.246 7 ‐0.276 0.157 0.102 ‐0.614 0.063 8 ‐0.337 0.162 0.058 ‐0.686 0.013 3 4 ‐0.143 0.158 0.384 ‐0.485 0.200 5 ‐0.276 0.142 0.074 ‐0.582 0.030 6 ‐0.276 0.142 0.074 ‐0.582 0.030 7 ‐0.429 0.126 0.005 ‐0.701 ‐0.156 8 ‐0.490 0.180 0.018 ‐0.879 ‐0.101 4 5 ‐0.133 0.121 0.293 ‐0.394 0.129 6 ‐0.133 0.138 0.353 ‐0.430 0.165 7 ‐0.286 0.181 0.138 ‐0.677 0.105 8 ‐0.347 0.236 0.166 ‐0.857 0.163 5 6 0.000 0.186 1.000 ‐0.403 0.403 7 ‐0.153 0.168 0.380 ‐0.517 0.211 8 ‐0.214 0.234 0.377 ‐0.720 0.292 6 7 ‐0.153 0.119 0.222 ‐0.411 0.105 8 ‐0.214 0.169 0.226 ‐0.579 0.150 7 8 ‐0.061 0.133 0.653 ‐0.348 0.226 11 Functional psychobiosocial states Day 2 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐0.029 0.090 0.755 ‐0.222 0.165 3 ‐0.049 0.115 0.677 ‐0.297 0.199 4 0.063 0.159 0.697 ‐0.279 0.406 5 ‐0.080 0.177 0.660 ‐0.462 0.302 6 ‐0.161 0.142 0.277 ‐0.468 0.146 7 ‐0.049 0.107 0.654 ‐0.280 0.182 8 ‐0.049 0.146 0.742 ‐0.364 0.266 2 3 ‐0.020 0.080 0.804 ‐0.194 0.153 4 0.092 0.122 0.466 ‐0.172 0.356 5 ‐0.051 0.156 0.749 ‐0.388 0.286 6 ‐0.133 0.149 0.391 ‐0.455 0.190 7 ‐0.020 0.105 0.848 ‐0.247 0.206 8 ‐0.020 0.156 0.898 ‐0.356 0.316 3 4 0.112 0.071 0.136 ‐0.040 0.265 5 ‐0.031 0.114 0.793 ‐0.277 0.216 6 ‐0.112 0.134 0.418 ‐0.402 0.177 7 0.000 0.090 1.000 ‐0.194 0.194 8 0.000 0.151 1.000 ‐0.327 0.327 4 5 ‐0.143 0.109 0.213 ‐0.378 0.093 6 ‐0.224 0.146 0.147 ‐0.539 0.090 7 ‐0.112 0.096 0.264 ‐0.320 0.095 8 ‐0.112 0.166 0.511 ‐0.471 0.247 5 6 ‐0.082 0.107 0.458 ‐0.312 0.149 7 0.031 0.121 0.804 ‐0.231 0.292 8 0.031 0.166 0.857 ‐0.328 0.390 6 7 0.112 0.094 0.253 ‐0.090 0.315 8 0.112 0.148 0.461 ‐0.207 0.431 7 8 0.000 0.102 1.000 ‐0.219 0.219 12 Dysfunctional psychobiosocial states Day 1 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐0.104 0.054 0.076 ‐0.221 0.013 3 ‐0.160 0.064 0.026 ‐0.298 ‐0.023 4 ‐0.244 0.068 0.003 ‐0.391 ‐0.096 5 ‐0.205 0.071 0.013 ‐0.358 ‐0.052 6 ‐0.192 0.117 0.125 ‐0.445 0.061 7 ‐0.163 0.084 0.075 ‐0.344 0.019 8 0.047 0.156 0.769 ‐0.291 0.385 2 3 ‐0.056 0.043 0.218 ‐0.150 0.038 4 ‐0.140 0.050 0.016 ‐0.248 ‐0.031 5 ‐0.101 0.069 0.168 ‐0.250 0.048 6 ‐0.088 0.110 0.439 ‐0.326 0.150 7 ‐0.059 0.100 0.568 ‐0.275 0.158 8 0.151 0.166 0.380 ‐0.208 0.510 3 4 ‐0.083 0.059 0.181 ‐0.211 0.044 5 ‐0.044 0.074 0.557 ‐0.204 0.115 6 ‐0.032 0.107 0.772 ‐0.263 0.200 7 ‐0.002 0.095 0.981 ‐0.207 0.202 8 0.207 0.181 0.273 ‐0.183 0.598 4 5 0.039 0.064 0.552 ‐0.099 0.176 6 0.052 0.120 0.673 ‐0.207 0.310 7 0.081 0.099 0.429 ‐0.133 0.295 8 0.291 0.174 0.119 ‐0.086 0.667 5 6 0.013 0.095 0.895 ‐0.192 0.218 7 0.042 0.094 0.660 ‐0.160 0.244 8 0.252 0.147 0.111 ‐0.067 0.570 6 7 0.029 0.118 0.807 ‐0.225 0.284 8 0.239 0.168 0.178 ‐0.123 0.601 7 8 0.210 0.162 0.219 ‐0.141 0.560 13 Dysfunctional psychobiosocial states Day 2 (I) Phase (J) Phase Mean difference (I‐J) Std. Error p level 95% CI Lower limit Higher limit 1 2 ‐0.075 0.033 0.041 ‐0.147 ‐0.003 3 ‐0.109 0.060 0.090 ‐0.238 0.019 4 ‐0.269 0.082 0.006 ‐0.447 ‐0.091 5 ‐0.163 0.071 0.040 ‐0.317 ‐0.009 6 ‐0.100 0.100 0.334 ‐0.315 0.115 7 ‐0.200 0.083 0.031 ‐0.379 ‐0.022 8 ‐0.064 0.094 0.506 ‐0.267 0.138 2 3 ‐0.034 0.056 0.551 ‐0.155 0.086 4 ‐0.194 0.086 0.042 ‐0.379 ‐0.008 5 ‐0.088 0.064 0.193 ‐0.226 0.050 6 ‐0.025 0.089 0.786 ‐0.216 0.167 7 ‐0.125 0.093 0.204 ‐0.327 0.077 8 0.011 0.088 0.901 ‐0.179 0.201 3 4 ‐0.160 0.068 0.035 ‐0.306 ‐0.013 5 ‐0.054 0.064 0.418 ‐0.192 0.085 6 0.010 0.080 0.907 ‐0.164 0.183 7 ‐0.091 0.081 0.283 ‐0.266 0.084 8 0.045 0.089 0.620 ‐0.147 0.237 4 5 0.106 0.088 0.250 ‐0.084 0.296 6 0.169 0.104 0.128 ‐0.056 0.394 7 0.069 0.081 0.410 ‐0.105 0.243 8 0.205 0.082 0.027 0.027 0.382 5 6 0.063 0.055 0.272 ‐0.056 0.182 7 ‐0.037 0.090 0.686 ‐0.232 0.158 8 0.099 0.100 0.342 ‐0.118 0.315 6 7 ‐0.101 0.088 0.277 ‐0.292 0.091 8 0.036 0.094 0.710 ‐0.167 0.238 7 8 0.136 0.077 0.102 ‐0.031 0.303 14	Psychophysiological responses of junior orienteers under competitive pressure.	04-26-2018	Robazza, Claudio,Izzicupo, Pascal,D'Amico, Maria Angela,Ghinassi, Barbara,Crippa, Maria Chiara,Di Cecco, Vincenzo,Ruiz, Montse C,Bortoli, Laura,Di Baldassarre, Angela	eng
PMC9794057	1 S2 Table. Qualifications and responsibilities of the expert panel. Qualifications - Possess extensive experience with and knowledge of endurance performance - Interest in the topic Responsibilities - Participate anonymously in three rounds of questionnaires (15 minutes each)	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
PMC9784187	Citation: Sawada, T.; Okawara, H.; Nakashima, D.; Ikeda, K.; Nagahara, J.; Fujitsuka, H.; Hoshino, S.; Maeda, Y.; Katsumata, Y.; Nakamura, M.; et al. Constant Load Pedaling Exercise Combined with Electrical Muscle Stimulation Leads to an Early Increase in Sweat Lactate Levels. Sensors 2022, 22, 9585. https:// doi.org/10.3390/s22249585 Academic Editors: Gian Marco Revel and Sara Casaccia Received: 9 November 2022 Accepted: 6 December 2022 Published: 7 December 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 Constant Load Pedaling Exercise Combined with Electrical Muscle Stimulation Leads to an Early Increase in Sweat Lactate Levels Tomonori Sawada 1 , Hiroki Okawara 1 , Daisuke Nakashima 1,* , Kaito Ikeda 1, Joji Nagahara 1, Haruki Fujitsuka 1, Sosuke Hoshino 1, Yuta Maeda 1, Yoshinori Katsumata 2,3, Masaya Nakamura 1 and Takeo Nagura 1,4 1 Department of Orthopaedic Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan 2 Institute for Integrated Sports Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan 3 Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan 4 Department of Clinical Biomechanics, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan * Correspondence: nakashima@keio.jp; Tel.: +81-3-5363-3812 Abstract: A novel exercise modality combined with electrical muscle stimulation (EMS) has been reported to increase cardiovascular and metabolic responses, such as blood lactate concentration. We aimed to examine the effect of constant load pedaling exercise, combined with EMS, by non-invasively and continuously measuring sweat lactate levels. A total of 22 healthy young men (20.7 ± 0.8 years) performed a constant load pedaling exercise for 20 min at 125% of the pre-measured ventilatory work threshold with (EMS condition) and without (control condition) EMS stimulation. Blood lactate concentration was measured by blood samples obtained from the earlobe every minute. Sweat lactate was monitored in real time using a sensor placed on the forearm. The sweat lactate threshold (sLT) was deﬁned as the point of increase in sweat lactate. sLT occurred signiﬁcantly earlier in the EMS condition than in the control condition. In the single regression analysis, the difference in sLT between the two conditions, as the independent variable, was a signiﬁcant predictor of the difference in blood lactate concentrations at the end of the exercise (p < 0.05, r = −0.52). Sweat lactate measurement may be a noninvasive and simple alternative to blood lactate measurement to determine the effectiveness of exercise combined with EMS. Keywords: sweat lactate; blood lactate; electrical muscle stimulation; exercise 1. Introduction Electrical muscle stimulation (EMS) has been widely used in rehabilitation and sports to assist in exercise therapy or to increase the exercise load, in combination with regular exercise [1–6]. Adding EMS has a signiﬁcant impact on muscle metabolism and induces substantial physiological adaptations [7,8]. Stimulated contractions caused by adding EMS intensively activates anaerobic glycolysis for energy production by phosphocreatine and glycogen degradation, leading to an increase in blood lactate concentration [9–11]. Lactate, which is produced in the glycolytic pathway, is widely recognized as an efﬁcient energy source used by systemic organs [12]. Additionally, it has been reported that lactate increases the peroxisome proliferator-activated receptor-γ coactivator- (PGC-) 1α mRNA expression and mitochondrial biogenesis [13–17]. PGC-1α is the master controller of mi- tochondrial biogenesis and promotes mitochondrial biogenesis through the activation of various transcription factors [18]. Since mitochondrial content is generally considered an indicator of endurance performance [19], increasing mitochondrial biogenesis induced by Sensors 2022, 22, 9585. https://doi.org/10.3390/s22249585 https://www.mdpi.com/journal/sensors Sensors 2022, 22, 9585 2 of 11 exercise training is believed to be an important adaptative event that improves exercise tolerance capacity [20,21]. Further, an exercise-induced increase in PGC-1α mRNA was observed above the ﬁrst lactate threshold, but not below it [22]. Therefore, it is important to determine the EMS intensity that increases the blood lactate levels without fatigue, as compared to exercise without EMS, to establish an efﬁcient program of exercise training combined with EMS. However, measuring the blood lactate levels frequently to determine the appropriate EMS output is not feasible, since it requires invasive blood sampling. Addi- tionally, it is unclear whether the optimal measurement time should be at the moment of puncture, or when the blood is discharged after a time lag of 5–10 s following the puncture. Thus, establishing a noninvasive method of monitoring the dynamics of metabolism during exercise combined with EMS is necessary. To date, noninvasive biosensors of lactate using sweat from the body surface have been reported [23] and have shown that sweat lactate levels increase in conjunction with exercise intensity [24–26]. In a recent study, the point of sweat lactate level elevation during incremental exercise was well correlated with the lactate threshold determined from the blood lactate concentration and the ventilatory threshold (VT), determined using a respiratory gas analyzer in patients with cardiovascular disease, as well as healthy individuals [27]. When this technology is applied to exercise combined with EMS, the noninvasive and continuous monitoring of lactate behavior during exercise could be possible, and increased blood lactate levels may be more easily detected by measuring sweat lactate levels. In particular, it would be suitable for the remote rehabilitation of patients and athletes outside of medical institutions and may enable effective and safe exercise based on daily physical conditions. Thus, the current study aimed to examine whether sweat lactate concentrations could be used to detect increased blood lactate levels during exercise combined with EMS. We hypothesized that the increase in blood lactate concentration during exercise combined with EMS would be predicted, to some extent, by the increase in sweat lactate levels. 2. Materials and Methods 2.1. Participants After recruiting participants from one university starting in May 2021, 22 healthy recreationally trained men (average age, 20.3 years) participated in this study conducted between June and September 2021 (Table 1). The inclusion criteria were (1) age ≥18 years and (2) no medical history of illness or injury, and not currently taking any medication. The exclusion criteria were (1) lower extremity injury or disorder that hinders complete participation in exercise, (2) metabolic, cardiac, respiratory, and psychiatric diseases, and (3) severe skin disease. The study protocol was conducted in compliance with the ethical guidelines for medical and health research involving human subjects and was approved by the Institutional Review Board of our institution (approval number: 20190229). Written informed consent was obtained from the individuals for study participation and publication of the ﬁndings before enrollment. Table 1. Participant characteristics (n = 22). Mean (SD) Range Age (years) 20.7 (0.8) 19–22 Height (cm) 174.0 (5.4) 161.0–184.0 Weight (kg) 66.8 (9.0) 46.8–91.5 BMI (kg/m2) 22.0 (2.2) 18.1–27.0 Body fat ratio (%) 15.4 (4.3) 8.6–23.8 Fat mass (kg) 10.5 (4.0) 4.0–20.1 Lean body mass (kg) 56.4 (6.2) 42.8–71.4 Muscle mass (kg) 53.4 (5.9) 40.5–67.7 Total body water (kg) 40.5 (6.5) 30.3–57.6 Body water (%) 59.7 (4.7) 49.6–65.9 BMI, body mass index. Sensors 2022, 22, 9585 3 of 11 2.2. Experimental Protocol All participants were asked to visit our laboratory three times. For each of the three vis- its, an interval of at least 3–14 days was allowed. In all three sessions, the room temperature was set to the same level and pedaling exercises were performed using an electromagneti- cally braked ergometer (POWER MAX V3 Pro; Konami Sports Co., Ltd., Tokyo, Japan) with a target of 70 rpm. At the ﬁrst visit, prior to an incremental load test, body composition was measured using a multi-frequency body composition meter (MC780A-N; TANITA Cor- poration, Tokyo, Japan). Then, the exercise test was performed, and the VT was determined for each individual with a respiratory gas analyzer using the ventilatory equivalent, excess carbon dioxide, and modiﬁed V-slope methods [28]. Speciﬁcally, following a 2 min rest to stabilize the heart rate and respiration, the participants performed a 4 min warm-up, pedaling at 20 W, and then 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 was measured using a breath-by-breath automated system (Aeromonitor®; Minato Medical Science Co., Ltd., Osaka, Japan). Prior to the second and third visits, the participants were instructed to keep a fast for 3 h prior to the measurements and to refrain from caffeine and alcohol intake and engaging in intensive exercises within 12 h. Partici- pants were also asked to drink 500 mL of water before the exercise. On the second visit, constant load pedaling exercise, without EMS stimulation (control condition, hereinafter called “CR condition”), was performed. To perform the exercise, the sweat lactate level was monitored with a sweat lactate sensor (Grace Imaging Inc., Tokyo, Japan) attached to the left forearm, and a Fitbit Inspire HR (Fitbit Inc., San Francisco, CA, USA) was attached to the left wrist, two-ﬁnger widths above the ulnar styloid process, to measure the heart rate. After an initial 2 min of rest and measurement of sweat and blood lactate levels, a 20 min pedaling exercise was performed at a constant load of 125% of the pre-measured ventilatory work threshold. The loading of VT125% was determined based on a previous study [11] reporting that the difference in blood lactate concentrations between conditions with and without EMS was signiﬁcantly greater in the VT125% exercise loading than in the VT50% and VT75% exercise loading. During the pedaling exercise, the sweat lactate level was measured at 1 Hz, and the blood lactate level was measured (Lactate Pro 2; ARKRAY, Inc., Kyoto, Japan) by drawing blood from the earlobe every minute. The exercise was stopped under the following conditions, even if the duration was <20 min: when the heart rate exceeded 190 bpm; when the examiner judged the exercise to be dangerous to the patient; when the participant requested to stop; when the participant became exhausted; or when it became difﬁcult to maintain the target speed of 70 rpm. In such cases, blood lactate levels were measured at the end of the exercise. At the third visit, the exercise was performed in the same way as the second visit, but with EMS stimulation. In the EMS condition, participants wore a commercially available EMS suit separated into a top and a bottom section (Powersuit; MTG Ltd., Nagoya, Japan) and performed pedaling exercises while seven muscles on each side of the upper and lower trunk were stimulated (biceps brachii, triceps brachii, rectus abdominis, oblique abdominis, gluteus medius, quadriceps femoris, and hamstrings). In accordance with a previous study [11], all electrode pairs were synchronized and biphasic square current pulses with a 100 µs duration were constantly applied at a stimulation frequency of 4 Hz. The EMS intensity was set to a maximum intensity at which each participant did not feel pain or discomfort. The maximal electrical potential and current of this device were 50 V and 4.85 mA, respectively. Sensors 2022, 22, 9585 4 of 11 2.3. Sweat Lactate Threshold Measurements The sweat lactate level was measured using a wearable sensor, which quantiﬁes lactate concentration as a current value because it reacts with sweat lactate and generates an electric current [27]. The current value can be obtained as continuous data within 0.1–80 µA in 0.1-µA increments. After calibration using saline for approximately 3 min, the sensor chip connected to the sensor device was attached to the participants’ dorsal left forearm, which was cleaned with an alcohol-free cloth. Additionally, the data were recorded at a sampling frequency of 1 Hz for mobile applications using a Bluetooth connection. The recorded data were converted to moving average values over 13 s intervals and individually underwent zero correction using the baseline value. Sweat lactate threshold (sLT) was deﬁned as the ﬁrst signiﬁcant increase in the sweat lactate level above baseline, based on graphical plots [27] by three researchers in consultation. 2.4. Statistical Analysis All data are presented as the mean plus standard deviation. For the heart rate and blood lactate concentration data, in a population who performed 20 min of exercise in both conditions, two-way analysis of variance with repeated measures was used to test the main effects under two conditions (CR and EMS) and ﬁve time points (baseline, 5, 10, 15, and 20 min), as well as the interaction effect between the time point and the condition. Bonferroni correction was performed for post hoc pairwise comparison. For sLTs, paired t-tests were conducted on participants for whom sLTs could be deﬁned in order to compare values between the two conditions. Additionally, to predict the increase in blood lactate concentration in the EMS condition relative to the CR condition, a single regression analysis was performed, with the blood lactate concentration change between the two conditions as the dependent variable, and the change in the time to reach sLT between the two conditions as the independent variable. In the regression analysis, the blood lactate concentration was used as the value after 20 min, or at the end of exercise, because the analysis included patients who could not exercise for 20 min. All statistical analyses were performed using SPSS Statistics version 27.0 (IBM Corp., Armonk, NY, USA), with statistical signiﬁcance set at 0.05. 3. Results Representative data of sweat and blood lactate concentrations during a constant load pedaling exercise are shown in Figure 1. Of the 22 participants, 15 completed 20 min of constant load exercise in both conditions, and the remaining 7 had difﬁculty maintaining the target speed of 70 rpm due to fatigue and stopped the exercise before 20 min. Four of the seven participants had difﬁculty completing 20 min in both conditions, two had difﬁculty completing 20 min only in the EMS condition, and one had difﬁculty completing 20 min only in the CR condition. Additionally, detecting sLT was difﬁcult in 2 out of 22 participants, as the sensor did not respond, due to lack of sweating in one participant, and the sensor chip was poorly connected in the other participant. Therefore, only 20 participants were included in the analysis of sLT. Sensors 2022, 22, 9585 5 of 11 Figure 1. Representative data of sweat lactate and blood lactate concentrations during constant load pedaling exercise. EMS, electrical muscle stimulation; CR, without EMS stimulation; bLA, blood lactate; sLA, sweat lactate; sLT, sweat lactate threshold. Figure 2 shows the comparison of the heart rate under the two conditions. As a result, there was a main effect of time point observed [F (4, 56) = 167.831, p < 0.01]. Post hoc test results showed significant differences between BL and other measurement time points (5, 10, 15, and 20 min), between 5 and 10, 5 and 15, and 5 and 20 min. Contrastingly, there was no main effect for condition, and no interaction effect between condition and meas- urement point. Figure 3 also depicts the comparison of blood lactate concentration be- tween the two conditions. For blood lactate concentration, there was a main effect of time point [F (4, 56) = 19.703, p < 0.01] and condition [F (1, 14) = 11.050, p < 0.01] and an interac- tion effect between time point and condition [F (4, 56) = 4.069, p < 0.01]. Post hoc test results showed significant differences between BL and other measurement points (5, 10, 15, and 20 min). Additionally, significant differences were observed between the conditions at 10, 15, and 20 min, with the EMS condition showing an increase in blood lactate concentration compared to the CR condition. -1 0 0 120 240 360 480 600 720 840 960 1080 1200 1320 Time [s] Figure 1. Representative data of sweat lactate and blood lactate concentrations during constant load pedaling exercise. EMS, electrical muscle stimulation; CR, without EMS stimulation; bLA, blood lactate; sLA, sweat lactate; sLT, sweat lactate threshold. Figure 2 shows the comparison of the heart rate under the two conditions. As a result, there was a main effect of time point observed [F (4, 56) = 167.831, p < 0.01]. Post hoc test results showed signiﬁcant differences between BL and other measurement time points (5, 10, 15, and 20 min), between 5 and 10, 5 and 15, and 5 and 20 min. Contrastingly, there was no main effect for condition, and no interaction effect between condition and measurement point. Figure 3 also depicts the comparison of blood lactate concentration between the two conditions. For blood lactate concentration, there was a main effect of time point [F (4, 56) = 19.703, p < 0.01] and condition [F (1, 14) = 11.050, p < 0.01] and an interaction effect between time point and condition [F (4, 56) = 4.069, p < 0.01]. Post hoc test results showed signiﬁcant differences between BL and other measurement points (5, 10, 15, and 20 min). Additionally, signiﬁcant differences were observed between the conditions at 10, 15, and 20 min, with the EMS condition showing an increase in blood lactate concentration compared to the CR condition. Sensors 2022, 22, x FOR PEER REVIEW 5 of 11 Figure 1. Representative data of sweat lactate and blood lactate concentrations during constant load pedaling exercise. EMS, electrical muscle stimulation; CR, without EMS stimulation; bLA, blood lactate; sLA, sweat lactate; sLT, sweat lactate threshold. Figure 2 shows the comparison of the heart rate under the two conditions. As a result, there was a main effect of time point observed [F (4, 56) = 167.831, p < 0.01]. Post hoc test results showed significant differences between BL and other measurement time points (5, 10, 15, and 20 min), between 5 and 10, 5 and 15, and 5 and 20 min. Contrastingly, there was no main effect for condition, and no interaction effect between condition and meas- urement point. Figure 3 also depicts the comparison of blood lactate concentration be- tween the two conditions. For blood lactate concentration, there was a main effect of time point [F (4, 56) = 19.703, p < 0.01] and condition [F (1, 14) = 11.050, p < 0.01] and an interac- tion effect between time point and condition [F (4, 56) = 4.069, p < 0.01]. Post hoc test results showed significant differences between BL and other measurement points (5, 10, 15, and 20 min). Additionally, significant differences were observed between the conditions at 10, 15, and 20 min, with the EMS condition showing an increase in blood lactate concentration compared to the CR condition. Figure 2. Comparison of the heart rate between the EMS and CR conditions (n = 15). †: p < 0.01 signif- icant main effect at time point. EMS, electrical muscle stimulation; CR, without EMS stimulation. -1 0 1 2 3 4 5 6 7 0 0.5 1 1.5 2 2.5 3 3.5 4 0 120 240 360 480 600 720 840 960 1080 1200 1320 Sweat lactate [µA] Blood lactate [mmol/l] Time [s] bLA_CR bLA_EMS sLA_CR sLA_EMS 20 minutes of constant load pedaling exercise sLT 369 [sec] sLT 296 [sec] Figure 2. Comparison of the heart rate between the EMS and CR conditions (n = 15). †: p < 0.01 signiﬁcant main effect at time point. EMS, electrical muscle stimulation; CR, without EMS stimulation. Sensors 2022, 22, 9585 6 of 11 Sensors 2022, 22, x FOR PEER REVIEW 6 of 11 Figure 3. Comparison of blood lactate concentrations between the EMS and CR conditions (n = 15). †: p < 0.01 significant main effect at time point. * p < 0.05, ** p < 0.01 significant difference between conditions. EMS, electrical muscle stimulation; CR, without EMS stimulation. Regarding sLT, it occurred significantly earlier in the EMS condition than in the CR condition (Figure 4, 215.2 ± 74.5 s and 271.8 ± 104.3 s, respectively; p < 0.05). Additionally, in the single regression analysis, the difference in sLT between the two conditions as an independent variable was a significant predictor of the difference in blood lactate concen- trations at the end of the exercise (Figure 5, p < 0.05; r = −0.52). Figure 4. Comparison of sweat lactate threshold between the EMS and CR conditions (n = 20). * p < 0.05. EMS, electrical muscle stimulation; CR, without EMS stimulation. Figure 3. Comparison of blood lactate concentrations between the EMS and CR conditions (n = 15). †: p < 0.01 signiﬁcant main effect at time point. * p < 0.05, ** p < 0.01 signiﬁcant difference between conditions. EMS, electrical muscle stimulation; CR, without EMS stimulation. Regarding sLT, it occurred signiﬁcantly earlier in the EMS condition than in the CR condition (Figure 4, 215.2 ± 74.5 s and 271.8 ± 104.3 s, respectively; p < 0.05). Additionally, in the single regression analysis, the difference in sLT between the two conditions as an independent variable was a signiﬁcant predictor of the difference in blood lactate concentrations at the end of the exercise (Figure 5, p < 0.05; r = −0.52). Sensors 2022, 22, x FOR PEER REVIEW 6 of 11 Figure 3. Comparison of blood lactate concentrations between the EMS and CR conditions (n = 15). †: p < 0.01 significant main effect at time point. * p < 0.05, ** p < 0.01 significant difference between conditions. EMS, electrical muscle stimulation; CR, without EMS stimulation. Regarding sLT, it occurred significantly earlier in the EMS condition than in the CR condition (Figure 4, 215.2 ± 74.5 s and 271.8 ± 104.3 s, respectively; p < 0.05). Additionally, in the single regression analysis, the difference in sLT between the two conditions as an independent variable was a significant predictor of the difference in blood lactate concen- trations at the end of the exercise (Figure 5, p < 0.05; r = −0.52). Figure 4. Comparison of sweat lactate threshold between the EMS and CR conditions (n = 20). * p < 0.05. EMS, electrical muscle stimulation; CR, without EMS stimulation. Figure 4. Comparison of sweat lactate threshold between the EMS and CR conditions (n = 20). * p < 0.05. EMS, electrical muscle stimulation; CR, without EMS stimulation. Sensors 2022, 22, 9585 7 of 11 Sensors 2022, 22, x FOR PEER REVIEW 7 of 11 Figure 5. Relationship between the change in blood lactate concentration at the end of exercise and the change in sweat lactate threshold (n = 20). The amount of change is calculated as the value of the EMS condition minus the value of the CR condition. EMS, electrical muscle stimulation; CR, without EMS stimulation. 4. Discussion The primary result of this study was that the increase in blood lactate concentration due to exercise combined with EMS could be explained, to some extent, by the sLT changes, which can be measured noninvasively. Previous studies have reported that ex- ercise combined with EMS leads to an increase in blood lactate concentration [9–11]. The mechanism of increased blood lactate concentration is considered to be due to the recruit- ment of high-threshold motor units and muscle fibers by the additional use of EMS [29– 32]. Therefore, to accurately assess the load imposed on the skeletal muscle by exercise combined with EMS, it was necessary to frequently measure lactate concentration, which is considered to directly reflect an increase in metabolic rate and glycolytic carbon flow in the skeletal muscles [33]. Additionally, as lactate is also considered a signal molecule that induces mitochondrial neogenesis in skeletal muscle cells [14], it is beneficial to determine the EMS loads that increases blood lactate concentration in each individual. However, measuring the blood lactate concentration was not feasible because it required blood sam- pling. Thus, we focused on the lactate contained in sweat. Sweat lactate measurement has the potential to compensate for the disadvantages of conventional evaluation methods, as it can be measured noninvasively and easily, and this could be demonstrated through exercise combined with EMS. The sweat lactate device that we used is capable of measur- ing sweat lactate continuously and over a long period of time by adjusting the thickness and composition of the topcoat applied to the upper layer of lactate oxidase on the sensor chip [27]. Therefore, it is able to monitor lactate level changes without deactivation of the enzyme in a single measurement. Our results suggest that this device may be applied in the future for setting EMS loads and/or determining the effectiveness of exercise in vari- ous training environments, such as gyms, outdoors, and at home. As for sweat lactate, it reportedly does not reflect the blood lactate levels during ex- ercise [34,35]. While lactate is produced in sweat reflecting exercise intensity, it is influ- enced by the body’s production of lactate, the rate of sweating, and metabolic kinetics in the sweat glands [34,36]. On the other hand, treating sweat lactate as an elevated point during incremental load exercise has been verified in a previous report to be consistent with LT obtained from blood [27]. One possible reason for this measurement consistency is that increased lactate production from the muscle cells reflecting LT may induce a sim- ultaneous increase in sweat lactate values through changes in autonomic balance, Figure 5. Relationship between the change in blood lactate concentration at the end of exercise and the change in sweat lactate threshold (n = 20). The amount of change is calculated as the value of the EMS condition minus the value of the CR condition. EMS, electrical muscle stimulation; CR, without EMS stimulation. 4. Discussion The primary result of this study was that the increase in blood lactate concentration due to exercise combined with EMS could be explained, to some extent, by the sLT changes, which can be measured noninvasively. Previous studies have reported that exercise com- bined with EMS leads to an increase in blood lactate concentration [9–11]. The mechanism of increased blood lactate concentration is considered to be due to the recruitment of high- threshold motor units and muscle ﬁbers by the additional use of EMS [29–32]. Therefore, to accurately assess the load imposed on the skeletal muscle by exercise combined with EMS, it was necessary to frequently measure lactate concentration, which is considered to directly reﬂect an increase in metabolic rate and glycolytic carbon ﬂow in the skeletal muscles [33]. Additionally, as lactate is also considered a signal molecule that induces mitochondrial neogenesis in skeletal muscle cells [14], it is beneﬁcial to determine the EMS loads that increases blood lactate concentration in each individual. However, measuring the blood lactate concentration was not feasible because it required blood sampling. Thus, we focused on the lactate contained in sweat. Sweat lactate measurement has the poten- tial to compensate for the disadvantages of conventional evaluation methods, as it can be measured noninvasively and easily, and this could be demonstrated through exercise combined with EMS. The sweat lactate device that we used is capable of measuring sweat lactate continuously and over a long period of time by adjusting the thickness and compo- sition of the topcoat applied to the upper layer of lactate oxidase on the sensor chip [27]. Therefore, it is able to monitor lactate level changes without deactivation of the enzyme in a single measurement. Our results suggest that this device may be applied in the future for setting EMS loads and/or determining the effectiveness of exercise in various training environments, such as gyms, outdoors, and at home. As for sweat lactate, it reportedly does not reﬂect the blood lactate levels during exer- cise [34,35]. While lactate is produced in sweat reﬂecting exercise intensity, it is inﬂuenced by the body’s production of lactate, the rate of sweating, and metabolic kinetics in the sweat glands [34,36]. On the other hand, treating sweat lactate as an elevated point during incremental load exercise has been veriﬁed in a previous report to be consistent with LT obtained from blood [27]. One possible reason for this measurement consistency is that increased lactate production from the muscle cells reﬂecting LT may induce a simultaneous increase in sweat lactate values through changes in autonomic balance, hormones, acid-base equilibrium, and metabolic dynamics [37,38]. Although the exercise protocol used in the Sensors 2022, 22, 9585 8 of 11 current study was a constant load rather than an incremental load, because of the load above AT (VT125%), the lactate discharged from the sweat glands is thought to strongly reﬂect anaerobic metabolism by the skeletal muscle, in addition to sweat gland metabolism. In such a load setting, blood and sweat lactate concentrations increase in the CR condi- tion itself, which would likely increase further when combined with EMS. In our results for the single regression analysis, R2 was 0.2726, and in terms of correlation coefﬁcient, r = −0.52. Therefore, our results showed a moderate association between the increase in blood lactate at 20 min (or the end of exercise) due to the addition of EMS and the time change to the point of sLT, and the amount of change in the blood lactate concentration between conditions could be explained by the amount of change in the point of sLT. More recently, there have been reports on the development of lactate biosensors that are not affected by sweat secretion rate [39] and on the development of systems that combine colorimetric analysis with deep learning to detect sweat lactate [40]. These techniques may contribute to more accurate sweat lactate measurement during exercise. Moreover, the results of this study suggest that noninvasive skeletal muscle metabolic assessment, which previously required blood sampling, may be possible with sweat lactate. Our results showed that there was no difference in the heart rate between the two con- ditions. A previous study reported a difference between the conditions with and without EMS at 80% VT loading [9], which was inconsistent with the results of this study. One pos- sible reason for the inconsistency in the results could be the difference in the exercise load intensity. Watanabe et al. examined the effects of different voluntary exercise intensities (50%, 75%, 100%, and 125% of VT) on metabolic responses to exercise combined with EMS and reported that the increment in oxygen consumption at 125% of VT was signiﬁcantly lower than those at lower exercise intensities [11], suggesting that the additional recruit- ments of motor units associated with EMS would be attenuated during high-intensity voluntary exercise, especially over the AT. Contrarily, EMS can induce a greater reliance on anaerobic glycolysis for energy production, along with phosphocreatine degradation and lactate formation [41–44]. In this study, the blood lactate concentration was also increased during exercise combined with EMS. Therefore, the constant load exercise combined with EMS at 125% of VT may have further increased the glycolytic metabolism, through large and fatigable fast-twitch motor units with glycolytic ﬁbers. Although future investigations should be conducted to include exercise intensities of 80% VT and 100% VT, as in previous studies, there may be a limited increase in heart rate with the addition of EMS under exercise intensities above AT, in which lactic can accumulate. This study has several limitations. First, all 22 study participants were university students and male, suggesting a population bias. Therefore, future evaluations including participants with different characteristics, such as age, sex, and exercise capacity, would allow for a wider interpretation and application of the results. Second, it was difﬁcult to measure the heart rate using an electrocardiogram at the chest due to wearing a pair of upper and lower full-body suits. Therefore, measurements were taken from the peripheral wrist with a Fitbit instead. Thus, it may be necessary to consider the inﬂuence of mea- surement uncertainty [45,46] as a reason why there was no difference between the two conditions with respect to the heart rate. Third, the EMS load intensity was set based on the subjectivity of everyone; thus, the stimulus intensity differed among participants. Fourth, we cannot eliminate the possibility that the participants were not given any control, such as fasting or prohibiting exercise on the previous day, which may have affected the pattern of blood lactate level changes. Therefore, it is considered that such controls are also necessary to conduct more precise experiments. 5. Conclusions Constant load pedaling exercise combined with EMS resulted in an early increase in sweat lactate levels, which could explain, to some extent, the increase in blood lactate concentrations. Since sweat lactate measurement is noninvasive, continuous, and easy to perform, it may be expected to be used as an alternative to blood lactate measurement Sensors 2022, 22, 9585 9 of 11 for monitoring metabolism during exercise combined with EMS and for determining its effectiveness in the future. Author Contributions: T.S., H.O., D.N. and Y.K. conceived and designed the research; T.S., H.O., K.I., J.N., H.F., S.H. and Y.M. conducted the experiments and analyzed the data; T.S. drafted the manuscript and prepared the tables/ﬁgures; all the authors edited and revised the manuscript; M.N. contributed to the supervision of the research project; D.N. and T.N. fulﬁlled the role of project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by KGRI/loT Healthcare Research Consortium (Grant number 02-066-0008). Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and ethical guidelines for medical and health research involving human participants and was approved by the Institutional Review Board of our institution (approval no. 20190357). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the subjects to publish this paper. Data Availability Statement: The data that support the ﬁndings of this study are available from the corresponding author upon reasonable request. Acknowledgments: We thank Yoshikazu Kikuchi for his assistance with data collection. Conﬂicts of Interest: Daisuke Nakashima is the president of Grace Imaging Inc. and holds shares in this company. This company has no involvement in this research. 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Constant Load Pedaling Exercise Combined with Electrical Muscle Stimulation Leads to an Early Increase in Sweat Lactate Levels.	12-07-2022	Sawada, Tomonori,Okawara, Hiroki,Nakashima, Daisuke,Ikeda, Kaito,Nagahara, Joji,Fujitsuka, Haruki,Hoshino, Sosuke,Maeda, Yuta,Katsumata, Yoshinori,Nakamura, Masaya,Nagura, Takeo	eng
PMC7379642	Supplement Table 9. Standardized proportions with a low VO2max (ml·min-1·kg-1) using different cut-offs, in the total population and by sex. Year <32 ml <28.5 ml <25 ml <21.5 ml <32 ml <28.5 ml <25 ml <21.5 ml <32 ml <28.5 ml <25 ml <21.5 ml 95-97 28,3% 16,5% 7,9% 3,6% 26,3% 14,5% 6,2% 2,5% 27,3% 15,6% 7,1% 3,1% 98-99 37,9% 26,9% 8,7% 2,8% 35,1% 20,3% 11,6% 2,3% 36,5% 23,6% 10,2% 2,5% 00-01 36,7% 20,6% 11,3% 3,2% 38,0% 18,3% 9,7% 5,0% 37,4% 19,4% 10,5% 4,1% 02-03 43,7% 27,3% 14,1% 5,5% 39,7% 27,2% 16,3% 4,0% 41,7% 27,2% 15,2% 4,8% 04-05 43,3% 29,1% 15,6% 6,2% 37,5% 23,3% 9,2% 3,7% 40,4% 26,2% 12,4% 5,0% 06-07 41,0% 26,8% 14,4% 4,7% 38,4% 24,3% 12,1% 4,6% 39,7% 25,5% 13,3% 4,7% 08-09 40,3% 25,5% 14,0% 5,8% 40,5% 26,6% 13,7% 4,7% 40,4% 26,1% 13,9% 5,2% 10-11 43,6% 29,1% 16,8% 6,9% 39,4% 26,1% 13,9% 5,1% 41,5% 27,6% 15,3% 6,0% 12-13 43,2% 29,8% 16,7% 7,3% 43,2% 28,5% 15,6% 6,8% 43,2% 29,1% 16,1% 7,0% 14-15 44,9% 31,4% 18,7% 8,5% 45,8% 30,7% 16,4% 7,0% 45,4% 31,0% 17,5% 7,8% 16-17 45,6% 31,8% 17,2% 6,7% 45,5% 31,4% 18,6% 7,6% 45,6% 31,6% 17,9% 7,2% Women Men Total	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
PMC6647242	Vol.:(0123456789) 1 3 European Journal of Applied Physiology (2019) 119:1865–1874 https://doi.org/10.1007/s00421-019-04175-w ORIGINAL ARTICLE Quantification of aerobic determinants of performance in post‑pubertal adolescent middle‑distance runners Richard C. Blagrove1 · Glyn Howatson2,3 · Charles R. Pedlar4,5,6 · Philip R. Hayes2 Received: 26 March 2019 / Accepted: 7 June 2019 / Published online: 17 June 2019 © The Author(s) 2019 Abstract Purpose The use of oxygen cost ( ̇Oaero) parameters to predict endurance performance has recently been criticized. Instead, it is suggested that aerobic energy cost ( ̇Eaero ) provides greater validity; however, a comparison of these quantification methods has not previously been made. Methods Fifty-six male (n = 34) and female (n = 22) competitive adolescent (17 ± 1 years) middle-distance runners par- ticipated in a sub-maximal and maximal incremental treadmill test. Running economy (RE) was measured at the speed corresponding to lactate turnpoint, and the three speeds prior. Maximal oxygen uptake ( ̇VO2max), speed at ̇VO2max and fraction of ̇VO2max utilized across a range of intensities, and speeds from 0.8, 1.5 and 3 km races were also quantified. RE and fractional utilization were calculated in units of ̇Oaero and ̇Eaero. Results Multiple linear regression models demonstrated no discernible difference in the predictive capability of RE, frac- tional utilization and ̇VO2max when expressed as ̇Oaero or ̇Eaero in both sexes. When plotted as a function of running speed, ̇O aero displayed a stepwise decrease (F = 11.59, p < 0.001) whereas ̇Eaero exhibited a curvilinear response (F = 4.74, p = 0.015). Differences were also evident in the slopes plotted for % ̇VO2max and % ̇Eaeromax against running speed (F = 5.38, p = 0.021). Conclusions Quantifying aerobic determinants of performance in units of ̇Eaero provides no greater validity compared to ̇O aero-based measurement. Although both ̇Eaero and ̇Oaero are sensitive to changes in speed, ̇Eaero provides the more valid reflec- tion of the underlying metabolic cost of running. Physiologists should also be aware of the potential differences between expression of aerobic running intensity based upon % ̇VO2max compared to % ̇Eaeromax. Keywords Running economy · Maximal oxygen uptake · Fractional utilization · Youth Abbreviations ANCOVA Analysis of co-variance ANOVA Analysis of variance CI Confidence interval ̇Eaero Aerobic energy cost ̇Eaeromax Maximal aerobic energy expenditure LTP Lactate turnpoint MDC Minimal detectable change ̇Oaero Oxygen cost RE Running economy RER Respiratory exchange ratio SD Standard deviation sLT Speed corresponding to lactate threshold sLTP Speed corresponding to lactate turnpoint s ̇VO2max Speed at ̇VO2max ̇VO2 Oxygen uptake ̇VO2max Maximal oxygen uptake Communicated by Jean-René Lacour. This article is submitted as part of the Topical Collection on ‘Energetics of Human Locomotion’. * Richard C. Blagrove R.C.Blagrove@lboro.ac.uk 1 School of Sport, Exercise and Health Sciences, Loughborough University, Epinal Way, Loughborough, Leicestershire, UK 2 Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle-upon-Tyne, UK 3 Water Research Group, Northwest University, Potchefstroom, South Africa 4 School of Sport, Health and Applied Science, St Mary’s University, Twickenham, UK 5 Orreco Ltd, National University of Ireland Business Innovation Centre, Galway, Ireland 6 Division of Surgery and Interventional Science, University College London, London, UK 1866 European Journal of Applied Physiology (2019) 119:1865–1874 1 3 Introduction Distance running performance is largely dependent upon aerobic factors, including maximal oxygen uptake ( ̇V O2max), running economy (RE) and the fraction of ̇V O2max utilized over a given distance (Bassett and Howley 2000; Brandon 1995). Although the variability in distance running performance can largely be explained by ̇VO2max in heterogeneous groups of runners, RE and fractional uti- lization are better capable of predicting performance in runners homogenous for ̇VO2max (Conley and Krahenbuhl 1980). Specifically, in middle-distance events, a model that included ̇VO2max and RE, was capable of explaining 96% of the variance in performance in highly trained 800-m and 1500-m runners (Ingham et al. 2008). It has recently been suggested that expressing physiological parameters in terms of aerobic energy cost ( ̇Eaero) provides greater validity for quantifying exercise intensity compared to traditional oxygen cost ( ̇Oaero)-based measurements (Beck et al. 2018); however, these claims have not yet been fully examined with experimental data. The expression of aerobic factors in units of ̇Oaero is limited because this measure does not account for differ- ences in substrate utilization, which can vary substantially between runners operating at the same oxygen uptake ( ̇V O2) (Brooks and Mercier 1994; Fletcher et al. 2009). It has been suggested that RE should, therefore, be quantified as Ėaero, which provides a more accurate reflection of the metabolic cost of exercise (Shaw et al. 2014). Previous reports have confirmed that ̇Eaero provides a more sensitive measure of RE compared to ̇Oaero across range of intensi- ties in highly trained runners (Fletcher et al. 2009; Shaw et al. 2014); however, this has not yet been established in lesser trained populations of runners, such as adolescents. ̇Eaero appears to provide a more reliable measurement of RE compared to ̇Oaero in high-performing adolescent run- ners (Blagrove et al. 2017); however validity-related issues associated with these measures have not previously been scrutinized in this age group. The physiological determinants of performance for adolescents are similar to those of adult runners. A num- ber of investigations have confirmed that ̇VO2max has a moderate–good correlation (r = 0.5–0.9) with performance over 1.5 km (Abe et al. 1998; Almarwaey et al. 2003), 3 km (Abe et al. 1998; Mahon et al. 1996; Unnithan et al. 1995), and 5 km (Abe et al. 1998; Cole et al. 2006; Cun- ningham 1990) in young (10–18 years) groups of runners. Measures of RE quantified in units of ̇VO2 also appear to be related to middle-distance performance (Almarwaey et al. 2003; Mayers and Gutin 1979; Unnithan et al. 1995). Additionally, speed at ̇VO2max (s ̇VO2max) (Abe et al. 1998; Almarwaey et al. 2003; Cole et al. 2006; Cunningham 1990) and fractional utilization calculated in ̇VO2 terms have also been shown to significantly correlate with dis- tance running performance in adolescents (Mahon et al. 1996; Unnithan et al. 1995). Despite these findings, for ̇E aero to possess greater criterion validity compared to ̇Oaero, it should be capable of predicting performance times with greater accuracy. This direct comparative analysis of two different approaches to quantifying aerobic-based determi- nants of performance has not previously been performed and is important for establishing validity of these metrics. Moreover, the method used to partition groups of young participants for differences in body size for variables such as ̇VO2max and RE is also likely to influence findings (Eisenmann et al. 2001). Previous studies have normalized to body mass as a simple ratio (Abe et al. 1998; Almar- waey et al. 2003; Mahon et al. 1996; Unnithan et al. 1995); however, this is unlikely to appropriately partition out the confounding influence of body size (Loftin et al. 2016). It has been proposed that fractional utilization expressed as the ratio between ̇Eaero and maximal aerobic energy expendi- ture ( ̇Eaeromax) at lower intensities (respiratory exchange ratio (RER) < 1.0) provides a numerically lower relative aerobic intensity compared to fractional utilization quantified as % ̇V O2max (Beck et al. 2018). This has important implications for prescription of aerobic exercise intensity and for quantifying the physiological outcomes to training or nutritional interven- tions. Although this difference has been established in elite race walkers (Beck et al. 2018), no papers have attempted to compare these two approaches for other exercise modalities and sub-elite populations. Moreover, small differences in the predictive power of physiological determinants (expressed in ̇Eaero or ̇Oaero terms) on performance times may provide greater deterministic accuracy when combined as part of a multiple- factor regression model. Consequently, the primary purpose of this study was to examine the relationship between physiological variables, quantified as both ̇Eaero and ̇Oaero, and race performances in a group of competitive post-pubertal adolescent middle-distance runners. The secondary aims were to investigate the influence of running speed on RE quantified as both ̇Oaero and ̇Eaero, and examine whether expressing relative aerobic intensity as % ̇V O2max and % ̇Eaeromax produces a different slope of values across a range of speeds. It was hypothesized that ̇Eaero would provide a more valid means of expressing important aerobic performance determinants compared to ̇Oaero. 1867 European Journal of Applied Physiology (2019) 119:1865–1874 1 3 Methods Participants Following institutional level ethical approval and in accord- ance with the Helsinki declaration, 56 competitive male (n = 34) and female (n = 22) middle-distance (0.8–3 km) runners (15–18 years) volunteered to take part in this study. Participant descriptive statistics are displayed in Table 1. All participants possessed at least 2 years of distance running training and racing experience, were familiar with treadmill running and considered middle-distance running to be their main sport. Participants were informed of the requirements and risks associated with the study and thereafter signed consent to participate was obtained from a parent or guard- ian, or the participant themselves if > 18 years. Procedure All trials were conducted in the same laboratory under simi- lar environmental conditions (temperature 16–20 °C; relative humidity, 29–54%; barometric pressure, 746–773 mmHg). Participants were instructed to avoid strenuous exercise in the 48 h preceding the trial, and arrive at least 2 h post- prandial. Upon arrival at the laboratory, stature and sitting height were measured with a stadiometer (SECA GmbH & Co., Hamburg, Germany) to the nearest 0.01 m, and matu- rity offset was predicted for each participant using published formulae (Moore et al. 2015). Body mass was recorded with digital scales (MPMS-230, Marsden Weighing Group, Oxfordshire, UK) to the nearest 0.1 kg. All exercise testing was performed on the same motor- ised treadmill (HP Cosmos Pulsar 4.0, Cosmos Sports & Medical GmbH, Munich, Germany). Throughout the test- ing, participants breathed through a low-dead space mask to monitor expired air via an open-circuit metabolic cart (Oxycon Pro, Erich Jaeger GmbH, Germany). Before test- ing, gas analysers were calibrated with known gas concen- trations (16% O2; 5% CO2) and ventilation measurement with a 3-L syringe. Participants completed a standard- ized warm-up involving a 5-min run at 2 km h−1 slower than the pre-determined start speed for their exercise test. Each test involved a sub-maximal discontinuous incre- mental test followed by an incremental continuous test to volitional exhaustion. The sub-maximal test involved 5–7 × 3-min stages interspersed with 30-s rest periods for extraction of a 20 µL capillary blood sample. The sam- ple was immediately haemolysed in a micro-test tube and tested for blood lactate (Biosen C-Line, EKF Diagnostic, Ebendorfer Chaussee 3, Germany). The start speed of the test was determined using participants’ best race times and published recommendations (Jones 2006). Speed was increased by 1 km h−1 every stage until lactate turn- point (LTP) had been surpassed, which was defined as the speed before a rise of > 1 mMol L−1 compared to the pre- vious stage. The gradient of the treadmill remained at 1% throughout the sub-maximal test (Jones and Doust 1996). Following a 5-min passive recovery, participants ran continuously at the speed corresponding to their LTP (sLTP). At the end of each minute, the treadmill gradi- ent was increased by 1% until volitional exhaustion was reached (typically 6–8 min). Physiological measures Sub‑maximal measures Prior to analysis of expired gases, data were filtered to remove any values that were deemed to represent errant breaths (Lamarra et al. 1987). The absence of a ̇VO2 slow component was verified by calculating the difference between the first 30 s of the final minute and the last 30 s. A difference less than the minimal detectable change (MDC), calculated as standard error of the mean × 1.96 × √ 2, con- firmed a ̇VO2 steady state had been achieved. The final 60 s of each submaximal stage was averaged for ̇VO2, volume of expired CO2 and RER. Updated non-protein quotient equations (Peronnet and Massicotte 1991) and RER val- ues were used to estimate ̇Eaero at each speed. Values for the sLTP and the three speeds prior (sLTP − 1 km.h−1, sLTP − 2 km.h−1, sLTP − 3 km.h−1) were used as the measure of RE, and quantified as both ̇Oaero and ̇Eaero. For each of the four submaximal speeds, the intensity relative to each participants ̇VO2max or ̇Eaeromax was calculated and expressed as a percentage. Fractional utilization at the speed corresponding to lactate threshold (sLT) was also quantified. sLT was defined as the final speed prior to an initial rise (≥ 0.2 mmol L−1) of blood lactate from baseline, which is greater than the typical error of meas- urement at this speed in a similar cohort (10). Table 1 Descriptive characteristics of the study participants ̇VO2max maximal oxygen uptake, sLT speed at lactate threshold, s ̇VO 2max speed at ̇VO2max Measure Males (n = 34) Females (n = 22) Age (year) 17 ± 1 17 ± 1 Stature (m) 1.76 ± 0.06 1.69 ± 0.06 Body mass (kg) 62.5 ± 6.4 52.7 ± 5.8 ̇VO2max (ml kg−1 min−1) 70.1 ± 7.2 61.1 ± 6.4 sLT (km h−1) 13.4 ± 1.5 11.7 ± 1.3 s ̇VO2max (km h−1) 19.2 ± 1.5 17.0 ± 1.5 1868 European Journal of Applied Physiology (2019) 119:1865–1874 1 3 Maximal measures The highest average ̇VO2 attained within a 30-s period dur- ing the maximal test was defined as a participant’s ̇VO2max. Confirmation that ̇VO2max had been attained was identified using an objective procedure (Midgley et al. 2009). A pre- dicted ̇VO2max was calculated using the linear regression line obtained from the ̇VO2 data between + 2 min following the start of the test and − 2 min prior to exhaustion. A pla- teau was confirmed if the difference between the predicted and recorded ̇VO2max values was greater than 0.5 times the regression gradient. ̇Eaeromax was obtained by multiply- ing the ̇VO2max value (expressed in mL min−1) by 21.745 joules. s ̇VO2max was identified by substitution of values into the linear regression equation representing the ̇VO2–speed relationship from the sub-maximal running assessment. Allometric scaling It is well recognised that when expressing variables relative to body size, the use of ratio scaling is inappropriate (Tan- ner 1949). Consequently, when comparing youth perform- ers or individuals of different sexes, allometric expression of variables is more appropriate (Curran-Everett 2013). To obtain allometrically scaled exponents for the population under investigation, body mass and ̇VO2 data were log trans- formed and linear regression lines compared for males and females using an analysis of co-variance (ANCOVA) model. Results revealed homogeneity of regression for the slopes of all variables, thus a common scaling exponent was derived on the logarithmic transformed data sets. The appropriate- ness of the power function was confirmed using an absence of relationships derived from the linear regression correla- tions between body mass and ̇VO2 scaled values. Performance measures Participant’s best times over 0.8, 1.5 and 3 km during com- petitive track races, within 60 days (41 ± 17 days) of labora- tory testing, were converted to running speed as an index of performance. Statistical analysis Data were analysed with IBM SPSS Statistics (v24) and values are displayed as mean ± standard deviation (SD). A p value of < 0.05 was used to denote statistical significance. Normality in distribution of the dependent variables was assessed using the Shapiro–Wilk statistic and homogene- ity of variance with Levene’s test. Data from a number of race distances did not conform to this assumption, thus run- ning speeds were log-transformed prior to further analysis. Normality associated with the standardized residual errors was assessed using probability plots and confirmed objec- tively using the standard residual statistic. Homoscedastic- ity was assessed using scatterplots of the residual errors and predicted values. Several variables displayed multi- collinearity, defined as an r value > 0.7. Speed at LTP and s ̇VO2max were, therefore, analysed as separate independent variables with one-tailed Pearson correlation tests. For each race distance, multiple linear regression models were used to examine the combined influence of predictors expressed in terms of the ̇Oaero measures, and predictors quantified as ̇Eaero. Zero-order correlation statistics were used to interpret the relationship with each variable in the model. To compare the correlation statistics for ̇Oaero-related measures against those expressed as ̇Eaero, a 95% confidence interval (CI) was calculated for each result. Correlation coefficients were interpreted as ≤ 0.30 negligible correlation, 0.31–0.50 low correlation, 0.51–0.70 moderate correlation, 0.71–0.90 high correlation, > 0.90 very high correlation (Hinkle et al. 2003). A one-way repeated measures analysis of variance (ANOVA) was performed to evaluate the differences between ̇Oaero and ̇Eaero across four relative running speeds. Differences between % ̇VO2max and % ̇Eaeromax were assessed using a two-way (measure × speed) ANOVA and the differences between individual relative speeds was analysed using a one-way ANOVA. Bonferroni post hoc adjustments were used to detect any significant differences between individual speeds or measures. Results Performance times for males and females are shown in Table 2. Allometric scaling revealed exponents that approxi- mated three-quarters for ̇VO2 at each speed [sLTP: b = 0.77 (95% CI 0.54–0.99), sLTP − 1 km h−1: b = 0.77 (95% CI 0.54–0.99), sLTP − 2 km h−1: b = 0.78 (95% CI 0.56–0.99), sLTP − 3 km h−1: b = 0.84 (95% CI 0.64–1.05)] and ̇VO2max [b = 0.74 (95% CI 0.48–1.00)]. Applying this power function (b = 0.75) revealed an absence of any significant relationship between body mass and scaled ̇VO2 across the intensities assessed (r ≤ 0.14, p ≥ 0.36). Table 2 shows a high level of similarity between the correlation coefficients for the two methods used to quan- tify aerobic energy expenditure. Multiple-regression analysis revealed that the independent variables of mean RE, fractional utilization at sLT, and ̇VO2max, accounted for > 80% and > 70% of the variance in 3 km perfor- mance in males and females, respectively (p < 0.001). These three variables were also significant predictors of 0.8 km (p < 0.01) and 1.5 km (p < 0.001) performance in males, but were poor predictors of 1.5 km performance in females. s ̇VO2max and sLTP tended to correlate strongly 1869 European Journal of Applied Physiology (2019) 119:1865–1874 1 3 Table 2 Performance time (mean ± standard deviation), coefficients (with 95% CI) of multiple regression, and Pearson correlations for physiological variables and running performance speed in males and females ̇Oaero aerobic oxygen cost, ̇Eaero aerobic energy cost, ̇VO2max maximal oxygen uptake, ̇Eaeromax maximal aerobic energy expenditure, s ̇VO2max speed at ̇VO2max, sLTP speed at lactate turn point a p < 0.05, bp < 0.01, cp < 0.001 A Variables used in multiple regression analysis Distance Time (s) Multiple regression adjusted r2 Mean running economyA Fractional utilization at sLTA ̇VO2maxA s ̇VO2max sLTP ̇Oaero ̇Eaero ̇Oaero ̇Eaero %̇V̇O2max % ̇Eaeromax Males 0.8 km (n = 21) 120.8 ± 8.8 0.40b 0.39b − 0.53a (− 0.78 to − 0.13) − 0.52a (− 0.78 to − 0.12) 0.19 (− 0.26 to 0.57) 0.21 (− 0.25 to 0.59) 0.14 (− 0.31 to 0.55) 0.45 (− 0.01 to 0.72) 0.60b (0.20 to 0.81) 1.5 km (n = 34) 250.8 ± 17.9 0.56c 0.57c − 0.33 (− 0.60 to 0.01) − 0.39a (− 0.64 to − 0.06) − 0.03 (− 0.31 to 0.36) − 0.04 (− 0.29 to 0.38) 0.55b (0.27 to 0.75) 0.75c (0.55 to 0.87) 0.78c (0.60 to 0.89) 3 km (n = 21) 539.5 ± 43.5 0.84c 0.85c − 0.60b (− 0.82 to − 0.23) − 0.63b (− 0.83 to − 0.27) − 0.15 (− 0.52 to 0.33) − 0.14 (− 0.50 to 0.34) 0.77c (0.52 to 0.91) 0.93c (0.77 to 0.97) 0.90c (0.77 to 0.96) Females 0.8 km (n = 16) 136.6 ± 3.7 0.58b 0.44a − 0.66b (− 0.87 to − 0.25) − 0.40 (− 0.74 to 0.13) 0.02 (− 0.49 to 0.50) 0.07 (− 0.43 to 0.55) 0.52a (0.04 to 0.81) − 0.01 (− 0.50 to 0.49) 0.22 (− 0.31 to 0.64) 1.5 km (n = 22) 281.4 ± 11.8 0.10 0.11 − 0.34 (− 0.68 to 0.08) − 0.37 (− 0.69 to 0.05) − 0.29 (− 0.64 to 0.15) − 0.33 (− 0.66 to 0.11) 0.36 (− 0.08 to 0.68) 0.42 (0 to 0.72) 0.55a (0.17 to 0.79) 3 km (n = 16) 622.0 ± 36.3 0.79c 0.73c − 0.57a (− 0.83 to − 0.11) − 0.59a (− 0.84 to − 0.14) − 0.64b (− 0.86 to − 0.22) − 0.66b (− 0.87 to − 0.25) 0.77c (0.45 to 0.92) 0.84c (0.59 to 0.94) 0.85c (0.62 to 0.95) 1870 European Journal of Applied Physiology (2019) 119:1865–1874 1 3 with performance over longer distances but the relation- ships were weaker for 0.8 km in both sexes (Table 2). ANOVA revealed a significant decrease in ̇Oaero as run- ning speed increased (F = 11.59, p < 0.001, Fig. 1). Post hoc analysis revealed significant differences between ̇Oaero at a number of individual speeds (Fig. 1) and two other comparisons (sLTP vs sLTP − 1 km h−1, sLTP − 1 km h−1 vs sLTP − 2 km h−1) approached significance (p = 0.07). A significant effect of running speed was also noted for ̇Eaero (F = 4.74, p = 0.015, Fig. 2). Post hoc inspection identified a difference between sLTP and sLTP − 1 km h−1 (p = 0.02); however, the difference between sLTP and sLTP − 2 km h−1 was close to the threshold of significance (p = 0.06). A significant main effect between the slopes of the lines was detected for % ̇VO2max and % ̇Eaeromax when plotted against relative running speed (F = 5.38, p = 0.021); how- ever, there was an absence of an interaction effect (meas- ure × speed; F = 0.29, p = 0.834). One-way ANOVA analysis was also not able to locate any difference between measures at each relative speed. Discussion The primary aim of this study was to examine the relation- ship between race performances and several important aero- bic variables quantified as both ̇Eaero and ̇Oaero in adolescent Fig. 1 Oxygen cost ( ̇Oaero) for speed at lactate turnpoint (LTP) and the three speeds prior (n = 56). aSignificantly different from speed at LTP (p < 0.01), bsignificantly differ- ent from speed at LTP-1 km h−1 (p = 0.01) Fig. 2 Aerobic energy cost ( ̇E aero) for speed lactate turnpoint (LTP) and the three speeds prior (n = 56). aSignificantly different from speed at LTP (p = 0.02) 1871 European Journal of Applied Physiology (2019) 119:1865–1874 1 3 middle-distance runners. Results indicate that ̇Eaero does not provide a greater level of criterion validity compared to ̇O aero-based measurements in this age group for the middle- distance running events. The study also aimed to investigate the validity of ̇Oaero and ̇Eaero as a means of quantifying RE. Results showed differences in the manner ̇Oaero and ̇Eaero change with increasing running speed, with ̇Oaero displaying a decrease and ̇Eaero a curvilinear response. A further find- ing was that the relationship between relative running speed and the fraction of ̇VO2max or ̇Eaeromax that is accessed also appears to differ, with the difference being greater at lower intensities. These findings provide new insight into the ongoing debate surrounding the most appropriate method of expressing aerobic fitness parameters, which are typically used to evaluate performance, health status and monitor improvement. Results of the multiple regression analysis show that using ̇Eaero to quantify RE and fractional utilization, instead of traditional ̇Oaero, provides no additional value in the pre- diction of middle-distance running performance in adoles- cents. To alter the strength of the relationship between ̇E aero-based determinants and performance, a high-level of inter-individual variability in substrate utilization is required. This was not apparent as coefficient of variation (SD/mean) for the RER values at each relative speed was ~ 4%. The participants used in this study possessed somewhat homog- enous physiological characteristics (Table 1), thus future research could investigate a more heterogeneous sample of runners, who are likely to differ more substantially in terms of their consumption of substrates at the same rela- tive speeds. Similarly, a relatively small range of running speeds (sLTP to sLTP − 3 km h−1) was examined in the present study and measurement stages were relatively short (3 min), which resulted in mean RER values > 0.9. A larger range of speeds and longer sampling duration would pro- duce lower RER values (Van Loon et al. 2001) and may have generated more substantial between-participant vari- ability as maximal rates of lipid oxidation rates are known to occur at ~ 65% ̇VO2max but is dependent upon training status (Achten and Jeukendrup 2004). This would, there- fore, alter the predictive power of variables quantified in ̇E aero units. Nevertheless, it is also questionable that slower running speeds would correlate well with middle-distance performance given the large discrepancy between low-inten- sity running and middle-distance race speed. Although ̇E aero also accounts for the energy yield associated with work performed during sub-maximal exercise compared to ̇Oaero (Shaw et al. 2014), there are numerous other factors that also govern these parameters, including use of stretch-shortening cycle mechanisms, muscle activation in the musculotendi- nous unit, running kinematics and anthropometric factors (Barnes and Kilding 2015). Thus, it may also be the case that the additional metabolic insight, which ̇Eaero provides, is insufficient to alter the predictive capacity of these aerobic parameters on performance. Within study designs that assess participants at more than one point in time, expressing RE as ̇Eaero, rather than ̇Oaero, is likely to provide the most scientifi- cally robust metric (Blagrove et al. 2017; Shaw et al. 2014). To the author’s knowledge, this is the first study to apply a multiple regression model to middle-distance performance in adolescent runners, using acknowledged aerobic deter- minants of performance (Bassett and Howley 2000; Ing- ham et al. 2008). Results showed that a high level (~ 80%, p < 0.001) of inter-individual variability in male and female 3 km performance could be explained by RE, fractional utilization at sLT and ̇VO2max (Table 2). Moreover, ~ 40% (p < 0.01) of the 0.8 km performance variability and 56% (p < 0.001) of male 1.5 km performance could be explained using these same variables. Surprisingly, this regression model could only predict a small (10%) amount of the vari- ability in female 1.5 km performance. This may be due to the homogeneity of the performance times in the female (5%) compared to the male sample (7%) over 1.5 km. Previous studies have shown the importance of ̇VO2max for middle-distance (1.5 km and 3 km) performance in children and adolescent (Abe et al. 1998; Mahon et al. 1996; Unnithan et al. 1995), which is largely confirmed by the results of this investigation (r = 0.55–0.77, p < 0.01). Based upon the non-overlap of the 95% CI with the cor- relation coefficients, it is also apparent that ̇VO2max becomes more important as a determinant of performance as race duration increases, which is in agreement with pre- vious findings in adolescent (Almarwaey et al. 2003) and adult runners (Ingham et al. 2008; Padilla et al. 1992). It is likely that this pattern in results reflects the increasing proportion of ̇VO2max that is attained as race duration increases in middle-distance events (Brandon 1995). This is also the case for s ̇VO2max and sLTP, both of which show high (r > 0.84, p < 0.001) correlations with 3 km per- formance in males and females but weaker correlations at the shorter distances (Table 2). RE, as an independent factor, is not thought to be important for middle-distance running performance (Ingham et al. 2008) despite sev- eral studies observing significant relationships in young runners (Almarwaey et al. 2003; Mayers and Gutin 1979; Unnithan et al. 1995). When RE was expressed as ̇Eaero, it generally showed low–moderate negative relationships (r = − 0.37 to − 0.63) with performance, which did not dif- fer across race distances (Table 2). Relationships were sig- nificant for male participants across all distances (p < 0.05) and females only at 3 km (r = − 0.57, p < 0.05), which is in agreement with the previous findings (Almarwaey et al. 2003). In adolescent middle-distance running, it, therefore, appears that RE influences race performance, but explains 1872 European Journal of Applied Physiology (2019) 119:1865–1874 1 3 a relatively small proportion of inter-individual variability. It is possible that participants who have a low ̇VO2max compensate by possessing better RE (Cunningham 1990). This may explain the low relationship (r = 0.14) between ̇VO2max and 0.8 km performance in males but moderate relationship (r = − 0.52, p < 0.05) between ̇Eaero and per- formance over this distance. Results demonstrate that the RE–speed relationship differed depending upon the strategy used to quantify RE. When expressed as ̇Oaero, running became less meta- bolically expensive as a function of speed (F = 11.59, p < 0.001, Fig. 1), which is in agreement with previous findings (Iaia et al. 2009) but in contrast to others who have shown no change (Fletcher et al. 2009; Shaw et al. 2014) or an increase (Fletcher et al. 2013) in ̇Oaero as speed increases. This discrepancy between findings is likely due to the range of speeds examined in each study and the training status of participants. Similar to the study by Iaia et al. (2009), the speeds selected in the present study rep- resent the upper end of the range over which RE can be measured with high validity (≤ LTP, RER < 1.0), whereas others have utilized a lower range of relative intensities (Fletcher et al. 2009; Shaw et al. 2014). Furthermore, pre- vious studies used highly trained runners (Fletcher et al. 2009; Shaw et al. 2014), who were assessed at faster abso- lute speeds compared to the young runners recruited in the present study. When quantified as ̇Oaero (per unit distance), a faster range of absolute speeds tends to produce a flatter relationship compared to oxygen cost at slower absolute speeds. Conversely, when RE was quantified as ̇Eaero, a sub- tle ‘U-shaped’ profile was apparent across the range of speeds (Fig. 3), with a significant difference noted between sLTP and sLTP − 1 km h−1 (p = 0.02) and a near-sig- nificant difference between sLTP and sLTP − 2 km h−1 (p = 0.06). A curvilinear relationship between ̇Eaero and speed has been observed in a number of studies (Black et al. 2018; Rathkey and Wall‐Scheffler 2017; Steudel- Numbers and Wall-Scheffler 2009; Willcockson and Wall-Scheffler 2012), with the nadir representing the most economical running speed. The least energetically expensive speed (at sLTP − 2 km h−1) in the present study was 13.4 ± 1.7 km h−1, which is similar to the 13 km h−1 (Black et al. 2018) and 12.6 km h−1 (Steudel-Numbers and Wall-Scheffler 2009; Willcockson and Wall-Scheffler 2012) reported previously in similarly trained participants. It is likely that other studies that have observed linear ̇E aero–speed relationships have used a range of speeds that did not capture the lowest point of the curve (Fletcher et al. 2009; Shaw et al. 2014) or used a lesser trained group of runners (Black et al. 2018). Crucially, the trend for an increase in ̇Eaero with faster speeds above sLTP − 2 kmh−1 is in opposition to the rela- tionship demonstrated between ̇Oaero and running speed. ̇E aero represents a theoretically more valid measure of RE as it estimates actual energy turnover, whereas ̇Oaero is sim- ply a measure of the ̇VO2 per unit of running distance. The increase in ̇Eaero as speed progressed from sLTP − 2 km h−1 towards sLTP, therefore, reflects the increase in RER value, indicating an increased reliance on carbohydrate as an energy source. As running speed increases, joint angular velocities are greater and ground contact time reduces, which requires a greater reliance on metabolically ineffi- cient type II muscle fibres (Fletcher and MacIntosh 2017). An increased recruitment of high threshold motor units is likely to be the mechanism that drives the rate of carbohy- drate utilization, and as the energy yield from carbohydrates per mole of O2 is also greater than lipids (Jeukendrup and Wallis 2005), this generates higher ̇Eaero at faster speeds. It is, therefore, recommended that ̇Eaero should be used as a measure of RE as this provides a more valid indicator of the metabolic demand of running compared to ̇Oaero. Fig. 3 Percentage utiliza- tion of maximum oxygen uptake and maximum aerobic energy expenditure across four sub-maximal relative speeds (n = 56). sLTP speed at lactate turnpoint 1873 European Journal of Applied Physiology (2019) 119:1865–1874 1 3 Exercise intensity is often prescribed relative to an individual’s ̇VO2max, thus expression of relative aerobic intensity as a percentage of ̇Eaeromax would be more mean- ingful. However, prescribing exercise intensity on either basis has been criticised due to the heterogeneity at which other important exercise thresholds (critical speed, anaer- obic threshold, lactate threshold) occur (Baldwin et al. 2000; Scharhag-Rosenberger et al. 2010). Therefore, an intensity expressed relative to either ̇VO2max or ̇Eaeromax may represent a severe intensity (relative to critical power) in one individual but provide a steady-state condition for another individual. We attempted to account for this in the present study by comparing the relationship between % ̇VO2max or % ̇Eaeromax across a range of running speeds expressed relative to each individual’s sLTP. A significant main effect method of measurement (% ̇VO2max versus % ̇E aeromax) was detected (F = 5.38, p = 0.021); however, no differences were identified at individual relative speeds. The divergent nature of the gradients (Fig. 3) as relative intensity decreases suggests that at slower relative speeds, the use of % ̇Eaeromax becomes more important. Therefore, it is recommended that if exercise is prescribed based on maximal aerobic values, intensity is expressed as a frac- tion of ̇Eaeromax, rather than ̇VO2max. However, a superior method for prescribing running intensity is to base calcula- tions on sLTP (or a similar objective metabolic threshold), which would reduce inter-individual differences in relative intensity, thus providing a more valid strategy. This study has several limitations that should be acknowledged. First, physiological testing predominantly took place during the pre-season or early competitive rac- ing period, with the duration between a participants’ race performance and laboratory testing typically 3–8 weeks. Although every attempt was made to minimize this time gap, small changes in the physiological profile of partici- pants cannot be discounted, which may have influenced the results. Second, participants performed laboratory testing 2 h post-prandial; however, it is less certain whether this requirement was adhered to prior to races. Participants possessed ≥ 2 years’ racing experience; therefore, it is unlikely that subtle differences in pre-race routine con- found the results to a large extent. Third, middle-distance running performance is limited by anaerobic factors, in addition to the aerobic determinants measured in this study (Thompson 2017). These anaerobic variables were not quantified in this investigation and are likely to explain a large proportion of the variability in performance cur- rently unaccounted for in the regression models. Moreo- ver, investigating the determinants of longer race distances (≥ 5 km), which have a greater reliance on aerobic sources of energy, would also have been of interest in this age group. Conclusions Expression of RE and fractional utilization in terms of ̇E aero rather than ̇Oaero does not appear to alter the ability of these determinants to predict middle-distance running performance in adolescents. RE, fractional utilization at sLT and ̇VO2max accounted for approximately 80% of the variability in 3 km performance in adolescent males and females. These variables could explain less (40–60%) of the variation in performance over shorter race distances and very little over 1.5 km in females. s ̇VO2max and sLTP were confirmed as other important indicators of middle- distance performance in adolescent runners with the strength of relationships tending to be greater over longer distances. Results also indicate markedly different pro- files in the ̇Oaero–speed response compared to ̇Eaero–speed relationship. It is recommended that RE is quantified in ̇Eaero units, which provides a more valid reflection of the metabolic demand of running across a range of speeds. Finally, there were differences observed in the slope of the relationships between running speed and the proportion of ̇VO2max or ̇Eaeromax utilized at each speed, suggesting this should be accounted for if prescribing exercise intensity using this method. Acknowledgements The authors would like to thank the participants and their parents/guardians for the time they committed to this study. The technical support provided by Ian Grant is also greatly appreciated. Author contributions RB and PH conceived and designed research. RB conducted experiments, analyzed data and wrote the manuscript. All authors proof read, contributed to editing, and approved the manuscript. Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecom- mons.org/licenses/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. 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PMC6195805	SDC Figure 1. Group Allocation Assessed for eligibility (n = 37) Excluded (n = 7) Not meeting inclusion criteria (n = 6) Other reasons (n = 1) Randomized (n = 30) Allocated to intervention (n = 15) Received allocated intervention (n = 15) Allocation Enrollment Allocated to intervention (n = 15) Received allocated intervention (n = 15) Follow up Discontinued intervention due to change of location or adherence (n = 2) Discontinued intervention (n =2) due to an injury sustained during the protocol or an injury sustained in a non- invention related event Analysis Analyzed (n = 13) Analyzed (n = 13)	Wrist-worn Accelerometry for Runners: Objective Quantification of Training Load.	[]	Stiles, Victoria H,Pearce, Matthew,Moore, Isabel S,Langford, Joss,Rowlands, Alex V	eng
PMC8306057	International Journal of Environmental Research and Public Health Communication Effects of Acute Hypoxia on Lactate Thresholds and High-Intensity Endurance Performance—A Pilot Study Martin Faulhaber 1,2,, Katharina Gröbner 1, Linda Rausch 1 , Hannes Gatterer 3 and Verena Menz 1 Citation: Faulhaber, M.; Gröbner, K.; Rausch, L.; Gatterer, H.; Menz, V. Effects of Acute Hypoxia on Lactate Thresholds and High-Intensity Endurance Performance—A Pilot Study. Int. J. Environ. Res. Public Health 2021, 18, 7573. https:// doi.org/10.3390/ijerph18147573 Academic Editors: Zbigniew Jastrz˛ebski, Guillermo Felipe López Sánchez, Łukasz Radzimi´nski and Maria Skalska Received: 8 June 2021 Accepted: 13 July 2021 Published: 16 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 Department of Sport Science, University Innsbruck, 6020 Innsbruck, Austria; K.Groebner@student.uibk.ac.at (K.G.); Linda.Rausch@uibk.ac.at (L.R.); Verena.Menz@uibk.ac.at (V.M.) 2 Austrian Society of Alpine and High-Altitude Medicine, 6414 Mieming, Austria 3 Institute of Mountain Emergency Medicine, Eurac Research, 3910 Bolzano, Italy; Hannes.Gatterer@eurac.edu Correspondence: Martin.Faulhaber@uibk.ac.at; Tel.: +43-512-507-45893 Abstract: The present project compared acute hypoxia-induced changes in lactate thresholds (meth- ods according to Mader, Dickhuth and Cheng) with changes in high-intensity endurance performance. Six healthy and well-trained volunteers conducted graded cycle ergometer tests in normoxia and in acute normobaric hypoxia (simulated altitude 3000 m) to determine power output at three lactate thresholds (PMader, PDickhuth, PCheng). Subsequently, participants performed two maximal 30-min cy- cling time trials in normoxia (test 1 for habituation) and one in normobaric hypoxia to determine mean power output (Pmean). PMader, PDickhuth and PCheng decreased signiﬁcantly from normoxia to hypoxia by 18.9 ± 9.6%, 18.4 ± 7.3%, and 11.5 ± 6.0%, whereas Pmean decreased by only 8.3 ± 1.6%. Cor- relation analyses revealed strong and signiﬁcant correlations between Pmean and PMader (r = 0.935), PDickhuth (r = 0.931) and PCheng (r = 0.977) in normoxia and partly weaker signiﬁcant correlations between Pmean and PMader (r = 0.941), PDickhuth (r = 0.869) and PCheng (r = 0.887) in hypoxia. PMader and PCheng did not signiﬁcantly differ from Pmean (p = 0.867 and p = 0.784) in normoxia, whereas this was only the case for PCheng (p = 0.284) in hypoxia. Although investigated in a small and select sample, the results suggest a cautious application of lactate thresholds for exercise intensity prescription in hypoxia. Keywords: anaerobic threshold; high altitude; maximal lactate steady state 1. Introduction Endurance training sessions under hypoxic conditions are part of altitude training concepts for competitive athletes [1], as well as in preventive and therapeutic settings [2,3]. The positive effects of hypoxia application on sport performance and health outcomes have been extensively described in literature, but there are also negative reports [4,5] that should not be ignored, especially since negative health effects (e.g., an increased mechanic stress against the cerebral vessel wall) are also suspected [5]. The determination of exercise intensity zones plays a key role in regulating training adaptations and preventing under or over strain. Anaerobic threshold concepts are very popular to prescribe intensity zones for endurance training; however, it remains unclear whether such concepts are still valid under hypoxic conditions. Despite its practical relevance, scientiﬁc literature dealing with anaerobic threshold concepts in hypoxia is scarce and, for example, ventilatory thresholds seem to be more reduced compared to lactate thresholds [6]. Thus, it remains unclear if intensity zones based on lactate or ventilatory threshold concepts are adequate tools for training prescription in hypoxia. Recently, Weckbach et al. [7] reported that peak power output (Pmax) and power output at different lactate thresholds (LT), derived from incremental exercise testing, were signiﬁcantly reduced in acute hypoxia (2650 m) compared to low altitude. Interestingly, the presented data revealed that the reduction in LT power output was more pronounced compared to Pmax by 20 to 90% depending on the LT concept [7]. However, it remains Int. J. Environ. Res. Public Health 2021, 18, 7573. https://doi.org/10.3390/ijerph18147573 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 7573 2 of 8 unclear whether the reduction in LT also reﬂects a similar decrement in high-intensity endurance performance. If hypoxia-induced changes in LT differ markedly from changes in maximal steady-state performance, the validity of these threshold concepts for exercise prescription under acute hypoxic conditions would be limited. The present pilot study should contribute to ﬁlling this knowledge gap and, therefore, compared different LTs, derived from incremental exercise testing, with high-intensity endurance performance in normoxic and acute hypoxic conditions. Based on a previous study evaluating endurance performance at 3200 m [8] and data of Weckbach et al. at 2650 m [7], we hypothesised that power output at the lactate thresholds would be more impaired by an acute exposure to hypoxia compared to endurance performance. 2. Materials and Methods 2.1. Participants and Study Design Six (three females, three males) students (age: 25 ± 3 years; body height: 176 ± 3 cm; body weight: 71 ± 5 kg; altitude of residence: 677 ± 134 m) from the Department of Sport Science (University of Innsbruck) volunteered to participate in this pilot study. All of them were regularly active for more than 3 h per week including various disciplines (as is common for active sport students), but none of them were competitive endurance athletes. Participants completed a routine health screening using an adapted physical activity readiness questionnaire (PAR-Q) before inclusion in the study. Medical clariﬁcation by a physician was undertaken if the PAR-Q identiﬁed speciﬁc issues that required further investigation. Exclusion criteria were pre-existing acute or chronic diseases, pregnancy or lactation and regular smoking of more than ﬁve cigarettes per day. Participants were informed about the experimental details and gave informed consent before commencing the study. The study was carried out in conformity with the ethical standards laid down in the 1975 Declaration of Helsinki. Since the study was designed as a pilot project the identical protocol of the subsequent main study was approved by the Board for Ethical Questions in Science of the University of Innsbruck, Austria (Certiﬁcate of good standing, 12/2021). The study was designed as a within-subject design (without cross-over). Participants conducted ﬁve cycle ergometer (Cyclus 2, RBM, Leipzig, Germany) tests in a ﬁxed order (Figure 1). Tests 1 and 2 were maximal incremental tests in normoxic (test 1) and hypoxic conditions (test 2). Test 1 and 2 were separated by a recovery period of 7 to 10 days. Tests 3 to 5 were maximal 30-min time trials in normoxic (tests 3 and 4) and hypoxic conditions (test 5), and tests were separated by recovery periods of 2 to 7 days. Test 3 served for habituation and was not included into statistical analyses. Adjustments of the cycle ergometer (e.g., saddle height) were ﬁxed before the ﬁrst test and kept constant for the subsequent tests. All tests took place in the laboratories of the University of Innsbruck (Department of Sport Science, 590 m). Tests under hypoxic conditions were conducted in a normobaric hypoxic chamber (LowOxygen Systems, Berlin, Germany) adjusted at an inspiratory fraction of oxygen of 15.4% corresponding to a simulated altitude of about 3000 m. The hypoxic system provides a high air ﬂow keeping the inspiratory fraction of oxygen constant and to avoid an excessive increase in inspiratory fraction of carbon dioxide as reported in previous studies [9–11]. Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 3 of 8 Figure 1. Experimental design including the sequence of the tests and the breaks between tests. 2.2. Tests and Measurements 2.2.1. Maximal Incremental Tests Participants rested for about 5 min in a sitting position on the cycle ergometer before resting parameters were taken. Workload started at 80 W for female and 100 W for male participants and was increased by 20 W every 3 min until subjective exhaustion. Heart t (M430 P l Vi A t i ) it d Bl d l t t t ti (S Figure 1. Experimental design including the sequence of the tests and the breaks between tests. Int. J. Environ. Res. Public Health 2021, 18, 7573 3 of 8 2.2. Tests and Measurements 2.2.1. Maximal Incremental Tests Participants rested for about 5 min in a sitting position on the cycle ergometer before resting parameters were taken. Workload started at 80 W for female and 100 W for male participants and was increased by 20 W every 3 min until subjective exhaustion. Heart rate (M430, Polar, Vienna, Austria) was monitored. Blood lactate concentrations (Super GL Ambulance, Dr. Müller Gerätebau, Freital, Germany) were analysed from capillary blood samples taken from the hyperaemised earlobe at the end of the resting phase, during the last 30 s of each stage and about 3 min after cessation of the test. Pmax was deﬁned as the last completed work rate plus the fraction of time spent in the ﬁnal uncompleted work rate multiplied by 20 W [12]. Maximal heart rate (HRmax) was deﬁned as the highest 5-s average, and maximal blood lactate concentration (BLAmax) was considered as the value of the last sample about 3 min after test termination. Heart rate and blood lactate values were transferred to an automated software (winlactat, Mesics, Münster, Germany) to determine LTs. In accordance with the study of Weckbach et al. [7], we selected three different methods for the detection of the LT: (a) ﬁxed 4-mmol/L blood lactate concentration according to Mader et al. [13], (b) lactate concentration of 1.5 mmol/L above the minimal lactate equivalent according to Dickhut et al. [14], and (c) maximal perpendicular distance from the blood lactate concentration curve to the line drawn from start- to endpoint (also known as Dmax method) according to Cheng et al. [15]. Outcome parameters of the maximal incremental tests were Pmax, HRmax as well as power output and heart rate at the three LT (PMader, PDickhuth, PCheng and HRMader, HRDickhuth, HRCheng). 2.2.2. Maximal 30-min Time Trials Time trials were conducted as described in detail in previous studies [8,16]. In brief: testing began with warm-up periods of 5 min at 100/150 W (females/males) followed by 5 min at 150/200 W (females/males). Then, the cycle ergometer was shifted to a ﬁxed pedal force so that pedalling at 100 rpm produced about 70% of Pmax (determined by the maximal incremental test in normoxia). Participants were encouraged to choose a maximal pedalling rate that could be maintained for the respective test duration. Heart rate was measured continuously (chest belt, Polar, Austria), and capillary blood samples were taken from the hyperaemised earlobe after 7 and 27 min to determine blood lactate concentrations (BLA7 and BLA27) (Super GL Ambulance, Dr. Müller Gerätebau, Germany). Outcome parameters were mean power output (Pmean) and mean heart rate (HRmean), which were automatically calculated by the software of the ergometer, BLA7 and BLA27. 2.3. Statistics Statistical analyses were performed using SPSS 24.0 (IBM, Vienna, Austria). Data were checked for normal distribution using the Shapiro–Wilk test. Since data were normally distributed (except for BLA7 and BLA27), paired t-tests (Wilcoxon rank tests for BLA7 and BLA27) were used to compare outcome parameters in normoxic versus hypoxic conditions. In the next step, focusing on power output parameters separated for normoxic and hypoxic conditions, Pearson correlation analyses between power output at the LT and Pmean were performed, and potential differences were tested by paired t-tests. In addition, the hypoxia- related reductions in power output at the LTs and in Pmean were compared with paired t-tests to test whether lactate thresholds are affected differently by acute hypoxia compared to high-intensity endurance performance. The level of signiﬁcance was set at p < 0.05. Data are presented as means ± SD. 3. Results The results of the maximal incremental tests revealed a signiﬁcant decrease in Pmax by approximately 12% from normoxia to hypoxia (249 ± 25 versus 221 ± 36 W, p = 0.005). HRmax (189 ± 10 versus 186 ± 8 bpm, p = 0.217) and BLAmax (11.9 ± 2.5 versus 13.1 ± 0.9, p = 0.166) did not signiﬁcantly differ from normoxia to hypoxia. Int. J. Environ. Res. Public Health 2021, 18, 7573 4 of 8 Power output and heart rate at the different LT and parameters of the maximal 30-min time trials are shown in Table 1. Power output decreased for all threshold concepts by about 12 to 19%, whereas heart rate values showed small (0 to 5%) and not signiﬁcant changes from normoxia to hypoxia. Pmean decreased signiﬁcantly from normoxia to hypoxia by approximately 8%, and HRmean was slightly lower in hypoxia compared to normoxia, although not statistically signiﬁcant. Blood lactate concentrations during the time trials did not signiﬁcantly differ between normoxia versus hypoxia (BLA7: 6.1 ± 2.2 versus 6.3 ± 2.2 mmol/L, p = 0.873; BLA27: 8.9 ± 2.5 versus 9.4 ± 2.0 mmol/L, p = 0.385). Table 1. Power output and heart rate at different lactate thresholds and during maximal 30-min time trials in normoxia and hypoxia. Values are means ± SD. p-Values refer to a comparison of normoxia and hypoxia. Normoxia Hypoxia Difference (%) p-Value Lactate thresholds based on stepwise maximal cycle ergometries PMader (W) 194 ± 26 158 ± 33 −18.9 ± 9.6 0.004 HRMader (bpm) 167 ± 8 160 ± 12 −4.5 ± 6.8 0.154 PDickhuth (W) 179 ± 24 146 ± 22 −18.4 ± 7.3 0.001 HRDickhuth (bpm) 161 ± 8 153 ± 16 −5.1 ± 7.0 0.140 PCheng (W) 194 ± 30 172 ± 28 −11.5 ± 6.0 0.005 HRCheng (bpm) 167 ± 9 167 ± 11 +0.3 ± 8.0 0.977 Maximal 30-min time trials Pmean (W) 195 ± 34 179 ± 32 −8.3 ± 1.6 <0.001 HRmean (bpm) 175 ± 12 170 ± 8 −2.7 ± 4.4 0.203 P, power output; HR, hear rate. Lactate thresholds were determined according to the methods of Mader et al. (Mader), Dickhuth et al. (Dickhuth) and Cheng et al. (Cheng) [13–15]. Pmean, mean power output; HRmean, mean heart rate. Correlation analyses for normoxia (Figure 2a) data revealed strong and signiﬁcant correlations for power output at the three LT and Pmean (PMader: r = 0.935, p = 0.006; PDickhuth: r = 0.931, p = 0.007; PCheng: r = 0.977, p = 0.001). Furthermore, PMader and PCheng did not signiﬁcantly differ from Pmean (p = 0.867 and p = 0.784 respectively), whereas PDickhuth was signiﬁcantly lower compared to Pmean (p = 0.045). With respect to hypoxic conditions (Figure 2b), we also found signiﬁcant correlations, but for PDickhuth and PCheng, we found slightly weaker correlations (PMader: r = 0.941, p = 0.005; PDickhuth: r = 0.869, p = 0.024; PCheng: r = 0.887, p = 0.019). PMader and PDickhuth were signiﬁcantly lower compared to Pmean (p = 0.007 and p = 0.005), whereas there was no signiﬁcant difference for PCheng (p = 0.284). Comparing hypoxia-related impairments in power output at the three LT and in Pmean, signiﬁcant differences were found for PMader and PDickhuth but not for PCheng (Figure 3). Int. J. Environ. Res. Public Health 2021, 18, 7573 5 of 8 Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 5 of 8 Figure 2. Association between mean power output during the time trial and power output at three different lactate thresholds (blue = PMader, red = PDickhuth, green PCheng) [11–13]. Data are presented for normoxic (upper panel) und hypoxic conditions (lower panel). (a): normoxic conditions; (b): hypoxic conditions Comparing hypoxia-related impairments in power output at the three LT and in Pmean, significant differences were found for PMader and PDickhuth but not for PCheng (Figure 3). Figure 2. Association between mean power output during the time trial and power output at three different lactate thresholds (blue = PMader, red = PDickhuth, green PCheng) [11–13]. Data are presented for normoxic (upper panel) und hypoxic conditions (lower panel). (a): normoxic conditions; (b): hypoxic conditions. Int. J. Environ. Res. Public Health 2021, 18, 7573 6 of 8 Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 6 of 8 Figure 3. Hypoxia-related changes in power output at the three lactate thresholds (PMader, PDickhuth, PCheng) and in mean power output during the maximal 30-min time trial (Pmean) [13–15]. P-values refer to a comparison to Pmean. Values are means ± SD. 4. Discussion The presented data show that acute hypoxia impaired power output at different LT and Pmean as expected. In accordance with our hypothesis, the results also demonstrate that the reduction in Pmean was clearly lower (approximately 8%) compared to lactate thresholds estimations (12% to 19%). Correlations between power output at the LT and Pmean were found in normoxic as well as in acute hypoxic conditions. However, hypoxia- related decreases in power output at two LT (PMader and PDickhuth) were significantly greater compared to the hypoxia-related decrease in Pmean resulting in significant differences to Pmean in hypoxia. Although LT are often validated for specific exercise protocols, exercise modes and populations, the observed strong correlations in normoxic conditions between power output at the LT (PMader, PDickhuth, PCheng) and Pmean were well documented [17] and in accordance with our findings. The method of Dickhuth et al. [14] was designed to detect the first rise in blood lactate concentration and can be categorised as an aerobic lactate threshold [17]. This observation was also reported in the review article of Faude et al. [17], and, therefore, the significant underestimation of Pmean in our study was not surprising. However, this underestimation of Pmean does not limit the application of this LT in exercise prescription by model-specific intensity zones. Regarding the hypoxia-induced changes in power output, the three LT showed more pronounced reductions compared to Pmean. From a practical point of view, the application of LT-based intensity zones, although fitting in normoxic conditions, can result in an underestimation of endurance capacity and therefore sub-optimal or even ineffective training loads when determined and applied in acute hypoxic conditions. Since the method according to Cheng et al. [15] only slightly underestimated Pmean and, furthermore, showed a strong correlation to Pmean under hypoxic conditions, it seems that exercise intensity prescriptions based on this method may be more robust against hypoxic-related underestimations. Based on the observation that resting lactate concentrations are not influenced by moderate hypoxia (e.g., 3000 m) but are markedly pronounced during exercise at the same absolute intensity level [18,19], the following explanations may be reasonable: LT models, defined by a fixed blood lactate concentration (i.e., 4 mmol/l) or adding a fixed value (i.e., 1.5 mmol/l) to an individual minimum may be directly affected by changes in the absolute lactate values (e.g., under acute hypoxia). Figure 3. Hypoxia-related changes in power output at the three lactate thresholds (PMader, PDickhuth, PCheng) and in mean power output during the maximal 30-min time trial (Pmean) [13–15]. p-Values refer to a comparison to Pmean. Values are means ± SD. 4. Discussion The presented data show that acute hypoxia impaired power output at different LT and Pmean as expected. In accordance with our hypothesis, the results also demonstrate that the reduction in Pmean was clearly lower (approximately 8%) compared to lactate thresholds estimations (12% to 19%). Correlations between power output at the LT and Pmean were found in normoxic as well as in acute hypoxic conditions. However, hypoxia-related decreases in power output at two LT (PMader and PDickhuth) were signiﬁcantly greater compared to the hypoxia-related decrease in Pmean resulting in signiﬁcant differences to Pmean in hypoxia. Although LT are often validated for speciﬁc exercise protocols, exercise modes and populations, the observed strong correlations in normoxic conditions between power output at the LT (PMader, PDickhuth, PCheng) and Pmean were well documented [17] and in accordance with our ﬁndings. The method of Dickhuth et al. [14] was designed to detect the ﬁrst rise in blood lactate concentration and can be categorised as an aerobic lactate threshold [17]. This observation was also reported in the review article of Faude et al. [17], and, therefore, the signiﬁcant underestimation of Pmean in our study was not surprising. However, this underestimation of Pmean does not limit the application of this LT in exercise prescription by model-speciﬁc intensity zones. Regarding the hypoxia-induced changes in power output, the three LT showed more pronounced reductions compared to Pmean. From a practical point of view, the application of LT-based intensity zones, although ﬁtting in normoxic conditions, can result in an underestimation of endurance capacity and therefore sub-optimal or even ineffective training loads when determined and applied in acute hypoxic conditions. Since the method according to Cheng et al. [15] only slightly underestimated Pmean and, furthermore, showed a strong correlation to Pmean under hypoxic conditions, it seems that exercise intensity prescriptions based on this method may be more robust against hypoxic-related underestimations. Based on the observation that resting lactate concentrations are not inﬂuenced by moderate hypoxia (e.g., 3000 m) but are markedly pronounced during exercise at the same absolute intensity level [18,19], the following explanations may be reasonable: LT models, deﬁned by a ﬁxed blood lactate concentration (i.e., 4 mmol/L) or adding a ﬁxed value (i.e., 1.5 mmol/L) to an individual minimum may be directly affected by changes in the absolute lactate values (e.g., under acute hypoxia). In contrast, the LT model by Cheng et al. [15] also considers the shape of the blood lactate curve and thus Int. J. Environ. Res. Public Health 2021, 18, 7573 7 of 8 the individual lactate kinetics, which seems to be a crucial point in evaluating exercise capacity [20]. To the best of our best knowledge, this is the ﬁrst study comparing hypoxia-related changes in lactate thresholds with changes in endurance performance (i.e., 30-min time-trial performance). The inclusion of a ﬁrst time trial for familiarisation improved the validity of the hypoxia-related changes in the second and third time trial because it was shown that time-trial performance improves from a ﬁrst to a second test [16]. The small sample size and the resulting susceptibility for individual outliers represent the main limitation of the present study. For example, the correlation analysis in hypoxia revealed r = 0.887 for PCheng versus Pmean (p = 0.019). However, the exclusion of one person (a statistical borderline outlier) would result in r = 0.959, (p = 0.010, n = 5). In addition, this experiment was conducted in a speciﬁc group of healthy subjects with an above-average ﬁtness level and results cannot be directly transferred either to elite athletes or to speciﬁc patient groups. 5. Conclusions According to the hypothesis, power output at the lactate thresholds were more im- paired by an acute exposure to hypoxia compared to high-intensity endurance performance reaching statistical signiﬁcance for the methods of Mader et al. and Dickhuth et al. In conclusion, the application of LT for exercise intensity prescription in hypoxia, even when determined under such conditions, may be prone to errors. The results of this pilot study should provide a basis for future larger-scale investigations dealing with this topic in different target groups. Author Contributions: Conceptualisation, M.F. and V.M.; methodology, M.F.; formal analysis, K.G. and M.F.; writing—original draft preparation, M.F. and L.R.; writing—review and editing, M.F., V.M. and H.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 according to the guidelines of the Declaration of Helsinki and approved by the Board for Ethical Questions in Science of the University of Innsbruck, Austria (Certiﬁcate of good standing, 12/2021). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data are not publicly available due to ethical considerations on preserving the anonymity of study participants. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Millet, G.P.; Roels, B.; Schmitt, L.; Woorons, X.; Richalet, J.P. Combining Hypoxic Methods for Peak Performance. Sports Med. 2010, 40, 1–25. [CrossRef] [PubMed] 2. Ramos-Campo, D.J.; Girard, O.; Pérez, A.; Rubio-Arias, J.Á. 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Faulhaber, M.; Pocecco, E.; Gatterer, H.; Niedermeier, M.; Huth, M.; Dünnwald, T.; Menz, V.; Bernardi, L.; Burtscher, M. Seven Passive 1-h Hypoxia Exposures Do Not Prevent AMS in Susceptible Individuals. Med. Sci. Sports Exerc. 2016, 48, 2563–2570. [CrossRef] [PubMed] 11. Morawetz, D.; Dünnwald, T.; Faulhaber, M.; Gatterer, H.; Schobersberger, W. Impact of Hyperoxic Preconditioning in Normobaric Hypoxia (3500 m) on Balance Ability in Highly Skilled Skiers: A Randomized, Crossover Study. Int. J. Sports Physiol. Perform. 2019, 14, 934–940. [CrossRef] [PubMed] 12. Stepto, N.K.; Hawley, J.A.; Dennis, S.C.; Hopkins, W.G. Effects of different interval-training programs on cycling time-trial performance. Med. Sci. Sports Exerc. 1999, 31, 736–741. [CrossRef] [PubMed] 13. Mader, A.; Liesen, H.; Heck, H.; Philippi, H.; Rost, R.; Schuerch, P.; Hollmann, W. Zur Beurteilung der sportartspeziﬁschen Ausdauerleistungsfähigkeit im Labor. Sportarzt. Sportmed. 1976, 27, 80–88. 14. Dickhuth, H.H.; Yin, L.; Niess, A.; Röcker, K.; Mayer, F.; Heitkamp, H.C.; Horstmann, T. Ventilatory, lactate-derived and catecholamine thresholds during incremental treadmill running: Relationship and reproducibility. Int. J. Sports Med. 1999, 20, 122–127. [CrossRef] [PubMed] 15. Cheng, B.; Kuipers, H.; Snyder, A.; Keizer, H.; Jeukendrup, A.; Hesselink, M. A New Approach for the Determination of Ventilatory and Lactate Thresholds. Int. J. Sports Med. 1992, 13, 518–522. [CrossRef] [PubMed] 16. Faulhaber, M.; Gatterer, H.; Haider, T.; Patterson, C.; Burtscher, M. Intermittent hypoxia does not affect endurance performance at moderate altitude in well-trained athletes. J. Sports Sci. 2010, 28, 513–519. [CrossRef] [PubMed] 17. Faude, O.; Kindermann, W.; Meyer, T. Lactate Threshold Concepts. Sports Med. 2009, 39, 469–490. [CrossRef] [PubMed] 18. Friedmann, B.; Bauer, T.; Menold, E.; Bärtsch, P. Exercise with the Intensity of the Individual Anaerobic Threshold in Acute Hypoxia. Med. Sci. Sports Exerc. 2004, 36, 1737–1742. [CrossRef] [PubMed] 19. McLellan, T.; Jacobs, I.; Lewis, W. Acute altitude exposure and altered acid-base states. Graefe’s Arch. Clin. Exp. Ophthalmol. 1988, 57, 445–451. [CrossRef] 20. Stegmann, H.; Kindermann, W.; Schnabel, A. Lactate Kinetics and Individual Anaerobic Threshold. Int. J. Sports Med. 1981, 2, 160–165. [CrossRef] [PubMed]	Effects of Acute Hypoxia on Lactate Thresholds and High-Intensity Endurance Performance-A Pilot Study.	07-16-2021	Faulhaber, Martin,Gröbner, Katharina,Rausch, Linda,Gatterer, Hannes,Menz, Verena	eng
PMC5880957	ORIGINAL RESEARCH Validation of a novel wearable, wireless technology to estimate oxygen levels and lactate threshold power in the exercising muscle Parisa Farzam, Zack Starkweather & Maria A. Franceschini Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts Keywords Athletic exercise training, blood lactate, muscle oxygen saturation, near-infrared spectroscopy (NIRS). Correspondence Parisa Farzam, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School 149, 13th Street, Boston, MA 02129. Tel: +1- 617-726-9338 Fax: +1- 617-726-7422 E-mail: pfarzam@mgh.harvard.edu Funding Information This research was sponsored by Dynometrics, Inc. Received: 20 February 2018; Accepted: 23 February 2018 doi: 10.14814/phy2.13664 Physiol Rep, 6 (7), 2018, e13664, https://doi.org/10.14814/phy2.13664 Abstract There is a growing interest in monitoring muscle oxygen saturation (SmO2), which is a localized measure of muscle oxidative metabolism and can be acquired continuously and noninvasively using near-infrared spectroscopy (NIRS) methods. Most NIRS systems are cumbersome, expensive, ﬁber cou- pled devices, with use limited to lab settings. A novel, low cost, wireless, wear- able has been developed for use in athletic training. In this study, we evaluate the advantages and limitations of this new simple continuous-wave (CW) NIRS device with respect to a benchtop, frequency-domain near-infrared spec- troscopy (FDNIRS) system. Oxygen saturation and hemoglobin/myoglobin concentration in the exercising muscles of 17 athletic individuals were mea- sured simultaneously with the two systems, while subjects performed an incre- mental test on a stationary cycle ergometer. In addition, blood lactate concentration was measured at the end of each increment with a lactate ana- lyzer. During exercise, the correlation coefﬁcients of the SmO2 and hemoglo- bin/myoglobin concentrations between the two systems were over 0.70. We also found both systems were insensitive to the presence of thin layers of vary- ing absorption, mimicking different skin colors. Neither system was able to predict the athletes’ lactate threshold power accurately by simply using SmO2 thresholds. Instead, the proprietary software of the wearable device was able to predict the athletes’ lactate threshold power within half of one power incre- ment of the cycling test. These results indicate this novel wearable device may provide a physiological indicator of athlete’s exertion. Introduction Quantifying how muscles respond to physical exercise is of great interest to athletes for improving performance and mitigating the risk of injury. Traditionally, athletes rely on measurements such as heart rate, blood lactate concentration, or maximum oxygen uptake (VO2max). These parameters are used to determine the intensity levels at which athletes should be exerting themselves to maximize athletic performance (Seiler and Kjerland 2006; Bentley et al. 2007; Esteve-Lanao et al. 2007; Goodwin et al. 2007). Although heart rate, blood lactate concentra- tion, and VO2max can help guide an athlete’s training regimen, these measurements are indicative of systemic changes occurring in the body, with no speciﬁc informa- tion about the working muscles. Interest in examining muscle oxygen saturation (SmO2) has been growing due to its ability to provide a localized measurement continuously and noninvasively using near- infrared spectroscopy (NIRS) (Chance et al. 1992; Belar- dinelli et al. 1995; Hamaoka et al. 1996, 2011; Bhambhani et al. 1997; Grassi et al. 1999, 2003; Wang et al. 2006; Soller et al. 2008; Bailey et al. 2009; Ihsan et al. 2013; Racinais et al. 2014; Boone et al. 2016; van der Zwaard et al. 2016; Baker et al. 2017; Hammer et al. 2018; Perrey and Ferrari 2018). NIRS techniques work by delivering 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. 7 \| e13664 Page 1 Physiological Reports ISSN 2051-817X light (in the 650–900 nm wavelength range) into the tis- sue and measuring the diffused light to estimate the absorption and scattering properties of the measured tis- sue volume (Yodh and Chance 1995). The concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the tissue can be estimated from the measured absorp- tion spectrum. Muscle oxygen saturation, also referred as muscle oxygenation (SmO2), is then calculated by taking the ratio of HbO to total hemoglobin concentration (HbT). Previous studies have investigated the HbO, HbR, HbT, or SmO2 trends during exercise to determine if these NIRS parameters provide useful information to guide athletic training (Belardinelli et al. 1995; Bhamb- hani et al. 1997; Grassi et al. 1999, 2003; Wang et al. 2006; Bailey et al. 2009; Hamaoka et al. 2011; Racinais et al. 2014). Correlations have been observed between the threshold power found from VO2max or lactate data and NIRS methods (Belardinelli et al. 1995; Grassi et al. 1999; Wang et al. 2006; van der Zwaard et al. 2016). These NIRS studies developed techniques to ﬁnd an athlete’s threshold power by analyzing the trends in HbO, HbR, HbT, or SmO2 after an incremental power test (Belar- dinelli et al. 1995; Grassi et al. 1999; Wang et al. 2006; van der Zwaard et al. 2016). Other work has been per- formed to understand muscle adaptations throughout an athlete’s training by examining the HbO, HbR, HbT, or SmO2 kinetics during speciﬁc exercise protocols (Bailey et al. 2009; Ihsan et al. 2013). These studies support the incorporation of NIRS technology into athletic training but are limited to the laboratory setting due to the required optical ﬁbers, probes, and larger instruments to obtain the measurements. The noninvasive monitoring of muscle oxygenation could signiﬁcantly beneﬁt from small, wearable, wireless, and accurate devices that can deliver real-time feedback to athletes. Currently, there are a small number of wearable, ﬁber- less, NIRS devices used in the athletic market, such as the Portamon (Artinis Medical System, Einsteinweg, The Netherlands), Moxy Monitor (Fortiori Design, LLC, Hutchinson, MN, USA), and BSX Insight (BSX Athletics, Austin, TX, USA). A review from Perrey and Ferrari (2018) goes into detail about the different studies that have examined the use of these NIRS devices in athletic training and SmO2 monitoring. The Portamon and Moxy devices can be manually strapped on to any muscle group and have been used during a variety of activities, includ- ing cycling, running, and strength training (Perrey and Ferrari 2018). The BSX Insight is designed to be worn on the gastrocnemius muscle within a custom-made com- pression sleeve and differentiates itself by providing ath- letes with their lactate threshold when following a given protocol (Borges and Driller 2016; Driller et al. 2016). These studies on wearable NIRS devices in the athletic community show value in obtaining optical measure- ments during sports, however the validity of measurements from wearables would be enhanced by a direct comparison with a bench-top, ﬁber-based, FDNIRS system. In this study, we compared the SmO2 of the quadriceps muscle group recorded by a wearable, low-cost, continu- ous-wave (CWNIRS) consumer device (Humon Beta, Dynometrics, Inc.) against a benchtop ﬁber-based fre- quency-domain near-infrared spectroscopy (FDNIRS) sys- tem (MetaOx, ISS, Champaign, IL). The FDNIRS system is considered the most robust and reliable reference com- mercially available for comparison of oximeters (Kleiser et al. 2016). Our goal was to examine the accuracy of this wearable device and understand the limitations that arise when using CWNIRS for muscle oxygenation measure- ments. We also investigated the real-time feedback from the Humon Beta device and reported the differences between the optically derived threshold and blood lactate threshold during an incremental cycling test. Methods Study population and measurement protocol Fifteen male and three female athletic subjects performed an incremental step test on the cycle ergometer. For logis- tical reasons, the test was carried out with the MetaOx probe on the right leg rectus femoris and the Humon Beta on the left leg rectus femoris, as illustrated in Fig- ure 1. The rectus femoris was chosen as the muscle to monitor in order to minimize ﬁber movement since this was the area of the leg where the ﬁbers could remain most stable. Each session began with baseline measure- ments for approximately two minutes, where subjects were instructed to stay as still as possible. Following a well-established incremental test protocol (Madden et al. 2013), the subjects began cycling at 30 W for 4 min, and the power was increased in 30 W increments every 4 min until voluntary exhaustion. Voluntary exhaustion was determined when the subject requested to stop or when he/she could no longer maintain the cycling cadence. In the last minute of each power interval, the blood lactate concentration was measured using a handheld lactate ana- lyzer (Lactate Plus meter, Nova Biomedical, Waltham, MA) by averaging three repeated blood samples. The sub- jects were asked to keep a consistent cadence throughout the cycling protocol, which was typically between 80 and 100 rotations per minute (RPM). For each subject, the lactate threshold power was determined as the cycling power at which the athlete’s blood lactate concentration reaches 4 mmol/L (Faude et al. 2009; Madden et al. 2018 \| Vol. 6 \| Iss. 7 \| e13664 Page 2 ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. A New Wearable NIRS to Estimate Lactate Threshold P. Farzam et al. 2013). One male subject was excluded from all data anal- yses because of a Humon Beta malfunction. On this sub- ject, sweat entered into the sensor and corrupted the data, however, we veriﬁed that sweat did not enter on any other Humon Beta device and did not affect any other measurements. Thus, from now on, we just report the values of the 17 subjects. In seven of the subjects, two Humon Beta sensors were worn on the left rectus femoris muscle to examine the heterogeneity of the measurements within the same muscle. The devices were placed 2 cm apart from one another, where one was proximal and the other was distal along the muscle and the variability due to location placement was assessed. We veriﬁed the sepa- ration of the two devices was sufﬁcient to prevent any light leakage between them. Before beginning the cycling on an upright stationary cycle ergometer (Model E3, Kettler USA), basic informa- tion was collected for each subject. This information included age, height, weight, and gender. Body Mass Index (BMI) was calculated as (kg/m2). In addition, the subcutaneous adipose tissue thickness (SCATT) on the rectus femoris of the right quadriceps was measured for each subject using a skinfold caliper before the start of the cycling test. Information about the subjects can be found in Table 1. We also tested the vitals (heart rate, blood pressure, body temperature, arterial oxygen satura- tion) of all subjects to ensure that the subjects were healthy prior to the onset of exercise. The study protocol was reviewed and approved by the Institutional Review Board (IRB) for Partners Healthcare, the Partners Human Research Committee (PHRC). The study method was designed and carried out in accordance with PHRC requirements and the regulations that govern human subjects research. All subjects interested in partici- pating in the study went through a prescreening process to ensure they were eligible. These subjects all exercised more than 3 times per week, each session over 30 min and their exercise routine typically included biking, run- ning, swimming, or rowing. We refer to this population as athletes throughout the text. They were all comfortable exercising at a high intensity for a long period of time. All eligible participants read and signed the approved informed consent form before starting the measurement session. Instrument setup The Humon Beta device (Fig. 1) uses two light sources in the NIR window and three photodetectors to measure the intensity of the light that has propagated through the tis- sue. The sources and detectors are found behind individ- ual polycarbonate windows, which come in contact with the skin of the user. The photodetectors are located at distances of 1.2, 1.8, and 2.4 cm from the light sources. The acquisition rate is set to 4 Hz. The Humon Beta is 6.0 9 5.7 9 1.4 cm in size and has a slight curvature in the plastic case to allow for easy contact with the skin on Figure 1. The schematic of the measurement on an upright stationary ergometer. The top drawing displays the location of Humon wearable on the left leg and MetaOx probe on the right leg. The distribution of the sources and detectors are presented for both the MetaOx probe (source–detector separations: 1.5, 2.0, 2.5, and 3.0 cm) and Humon Beta wearable (source–detector separations: 1.2, 1.8, and 2.4 cm) in the bottom images. Table 1. Subjects demographic. All Males Females Number 17 14 3 Right-footed 15 12 3 Age (years) 31 6 30 6 35 5 Weight (kg) 71 14 75 11 51 3 Height (cm) 176 11 180 7 156 3 BMI (kg/m2) 23 3 23 3 21 2 SCATT (mm) 5 2 5 2 7 1 Details about the subjects’ footedness, age, weight, height, BMI, and subcutaneous adipose tissue thickness (SCATT) are shown, including a gender breakdown for each category. 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. 7 \| e13664 Page 3 P. Farzam et al. A New Wearable NIRS to Estimate Lactate Threshold the quadriceps. The wearable is attached to the athlete’s quadriceps, using a strap that hooks through the device and can be secured around the thigh with a hook-and- loop fastener. The Humon Beta communicates with a smartphone via Bluetooth, and a custom app displays the workout progress in real time. MetaOx is a hybrid device that consists of FDNIRS to measure hemoglobin concentration and tissue oxygena- tion and diffuse correlation spectroscopy (DCS) to mea- sure blood ﬂow (Boas et al. 2016). The details of the system and data analysis are described by Carp et al. (2017). For this work, we only considered the FDNIRS data even though DCS data were also acquired. In sum- mary, the FDNIRS components include 8 lasers in the visible and NIR spectral region (672, 726, 759, 813, 690, 706, 784, and 830 nm) modulated at 110 MHz and 4 photomultiplier tubes detectors (PMT) modulated at 110.005 MHz to achieve heterodyne detection at 5 kHz. The lasers are rapidly multiplexed in sequence and allow the fast measurement (10 Hz) over all wavelengths. The light is delivered to the tissue through a ﬁber optics bun- dle coupled to the probe. The diffused photons are col- lected by the probe’s ﬁber optics bundles located at distances of 1.5, 2.0, 2.5, and 3.0 cm from the source (q), (see Fig. 1) and delivered to the four PMTs. Data analysis The Humon Beta and MetaOx data were coregistered and acquired continuously for the whole duration of the pro- tocol. For MetaOx data analysis, the detected light ampli- tude (AC) and phase shift at four separations and eight wavelengths are calibrated on a silicone tissue mimicking phantom with known optical properties to account for the different gains of the four detectors (Farzam et al. 2013). After calibration, the frequency-domain solution of the photon diffusion equation in the semi-inﬁnite geome- try is used to ﬁt for the wavelength-dependent optical properties of the tissue (Durduran et al. 2010). At each wavelength, a linear ﬁt is performed on (ln(AC(k)q2)) and phase over source-detector distances. Absorption (la) and reduced scattering (ls0) coefﬁcients at each wave- length are calculated from the slopes of the linear ﬁts (Fantini et al. 1995). The measured absorption coefﬁcient is sum of the absorption of tissue chromophores (la (k) = ∑ ei(k)ci), where the wavelength-dependent extinction coefﬁcient, ei(k), of the ith chromophore is obtained from the literature (Prahl), and ci is the concentration of the ith chromophore. The primary muscle chromophores in the near-infrared are water, oxy-, deoxy-hemoglobin, and myoglobin. The muscle tissue water percentage is assumed to be 75% (Franceschini et al. 1997). Since myo- globin cannot be distinguished from hemoglobin due to the spectral overlap (Quaresima et al. 2004), its contribu- tion is combined into the calculated oxy- and deoxy-hemoglobin concentrations. When we refer to hemoglobin throughout the text, we acknowledge that this is a combination of hemoglobin and myoglobin. The total hemoglobin concentration and oxygen saturation are calculated as HbT = HbO + HbR and SmO2 = HbO/ HbT, respectively. The Humon Beta is a CW device and only measures light intensity at three separations and two wavelengths. Hence, to estimate hemoglobin concentration and oxy- genation, it needs to assume a ﬁxed scattering coefﬁcient. The algorithms used to recover hemodynamic parameters were not disclosed and are proprietary to Dynometrics Inc. The HbO, HbR, HbT, and SmO2 results obtained with the two devices were compared to determine the drawbacks of using a limited set of wavelengths and ﬁxed scattering coefﬁcients for a small, inexpensive wireless NIRS device which cannot include as many features as the benchtop frequency-domain system. In addition, to further estimate the error introduced by the simpliﬁcations in the wearable system, we compared full MetaOx results with estimates obtained, using a sub- set of the MetaOx intensity data, which best matches the Humon Beta separations and wavelengths. In this way, we can isolate the errors due to the reduced dataset from contamination arising from other parameters such as dif- ferences in sensor location and differences between left and right leg muscles. To test the effect of superﬁcial thin layers, such as dif- ferent skin tones, on the data collected by the Humon Beta and MetaOx systems, neutral density (ND) ﬁlters were placed over a silicone tissue mimicking phantom with known optical properties and data were collected with the two systems. We evaluated how the presence of these superﬁcial thin layers affect the recovered “effective SmO2”, both with the Humon Beta and the MetaOx. We call the calculated parameter “effective SmO2”, since the phantom is made of silicon and does not contain blood. For the MetaOx we also evaluated how the ND ﬁlter affected the measured intensity and the slope of both phase and intensity versus distance. Moreover, in the 17 subjects we evaluate the effect of subcutaneous adipose tissue on the estimated HbO, HbR, HbT, and SmO2 by analyzing the MetaOx cycling data using only the shorter (1.5 and 2.0 cm) or the larger (2.5 and 3.0 cm) separations. We estimated the difference in the recovered HbO, HbR, HbT, and SmO2 at the two separations throughout the whole cycling test, and the correlation between SmO2 drop and measured SCATT. Finally, we examined the relationship between the mea- sured hemodynamic parameters and the blood lactate measures and tested the accuracy of the Humon Beta for 2018 \| Vol. 6 \| Iss. 7 \| e13664 Page 4 ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. A New Wearable NIRS to Estimate Lactate Threshold P. Farzam et al. predicting the lactate threshold power. The Humon Beta employs a proprietary algorithm to determine the lactate threshold in real time. The data are processed and dis- played on a smartphone application, where in addition to SmO2 values, a corresponding exercise zone is shown. Four zones are used to classify the subjects exercise state: (1) the green zone represents a steady state, (2) the orange zone indicates the athlete is approaching their limit, (3) the red zone shows the athlete has hit/exceeded their limit, and (4) the blue zone means the athlete is in a recovery phase. For this analysis, we excluded the three female subjects since the power increments were too large, which resulted in the lactate threshold power and the end of the exercise to be too close in time. For HbO, HbR, HbT, and SmO2 comparisons between the MetaOx and Humon Beta, we report the Pearson cor- relation coefﬁcient and the relative root mean square error (RMSE). Nonparametric statistical tests were per- formed when comparing features in the SmO2 curves between the MetaOx and Humon Beta. All the data pro- cessing and statistical analyses in this paper are performed using MATLAB (MathWorks, USA), version R2017a, and Statistics and Machine Learning Toolbox, version 11.1. Results Optical and physiological properties of the leg muscle Figure 2 shows the average of optical properties, that is, the absorption coefﬁcient (la) and reduced scattering coefﬁcient (ls0), measured by MetaOx across all the sub- jects over 2 min of baseline before starting to pedal. The error bars indicate the mean over all subjects and 95% conﬁdence interval of the mean. The dashed red line is the ﬁtted spectrum and the gray shaded area indicates the 95% conﬁdence interval of the ﬁtted spectrum. The resulting average oxy- and deoxy-hemoglobin concentra- tions are 37 12 (lmol/L) and 19 6 (lmol/L), respec- tively. For the scattering, the average number density (a) is 8.54 1.4 and the average effective particle size (b) is 0.75 0.24, for the relationship derived from the Mie model (Jacques 2013). The dashed red line is the mean of ﬁtted spectrum and the gray shaded area indicates the 95% conﬁdence inter- val of the mean. SmO2 trend during cycling incremental power test We tested the accuracy of the HbO, HbR, HbT, and SmO2 estimations from the Humon Beta data against the MetaOx measurements. The data from all 17 subjects are included for these calculations. A representative dataset from the incremental power test is shown in Figure 3, where the change of SmO2 over time is presented for both MetaOx (dotted curve) and Humon Beta (solid curve). The vertical lines indicate the time point that the power on the bike was changed and the numbers at the top of the plot between the lines show the power (Watts) at which the subject was cycling. In general, at the begin- ning of the exercise, the muscle oxygen saturation slightly Figure 2. The measured absorption coefﬁcient (la) and reduced scattering coefﬁcient (ls’) of rectus femoris muscle, and their ﬁtted spectrum. The red error bars indicate the mean over all subjects and 95% conﬁdence interval of the mean. The dashed red line is the ﬁtted spectrum and the gray shaded area indicates 95% conﬁdence interval of the ﬁtted spectrum. 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. 7 \| e13664 Page 5 P. Farzam et al. A New Wearable NIRS to Estimate Lactate Threshold increases. As the power increases and the subject under- goes high exertion levels, the oxygen saturation starts to decrease. Finally, SmO2 sharply increases when the subject starts the recovery phase. Figure 4 is a scatter plot of the SmO2 values measured by the two devices in all subjects during cycling. The solid black line in Figure 4 indicates a line with slope of one and zero intercept, which represents the trend if there was a 1:1 relationship between the measured SmO2 by Humon Beta and the MetaOx. The dashed line shows the best linear ﬁt to the data, with an intercept of ~10 and R = 0.74. This linear relationship was found to be signiﬁ- cant (P value < 0.001 for slope and intercept). With respect to the MetaOx, the Humon Beta overestimates (2–3%) SmO2 at low values and underestimates (1%) SmO2 at high values. Quantifying differences between Humon Beta and MetaOx measurements The results of the comparison between Humon Beta and MetaOx, MetaOx-subset and MetaOx, and Humon Beta at two locations are reported in Table 2. In all three cases, the correlation coefﬁcient and the relative RMSE between two measures are reported for HbO, HbR, HbT, and SmO2, averaged over all subjects. The results show a good agreement between Humon Beta and MetaOx data. The differences are larger for HbO, HbR, and HbT (19.4%, 25.7%, and 20.7%, respec- tively) and smaller for SmO2 (3.4%). While the absolute hemoglobin concentration values are different, a strong correlation (correlation coefﬁcient: 0.72-0.86) is present between the time traces measured with the Humon Beta and the MetaOx. The MetaOx-subset assumes the same ﬁxed scattering coefﬁcient and similar wavelengths and separations as the Humon Beta. Table 2 shows that the errors due to the simpliﬁed model results in a small deviation from the full MetaOx data. The average relative RMSE between MetaOx-subset and MetaOx are 14.7%, and 14.7%, 12.8%, for HbO, HbR, and HbT, respectively, and 3.6% for SmO2 (correlation coefﬁcient: 0.83–0.96). To estimate differences due to differences in probe location on the same rectus femoris, the data from the two Humon Betas worn on the same leg (7 subjects) are compared and the results are shown in Table 2, Humon location 1 vs. Humon location 2. Features within the SmO2 curves were also compared between the MetaOx and Humon Beta measurements for the 14 male subjects. Table 3 reports the average SmO2% drop throughout the cycling tests, the average time at which 50% of the recovery occurred, and the average SmO2% overshoot relative to the average starting value during the ﬁrst power increment (30 W). A Wilcoxon rank sum test was performed between the MetaOx and Figure 3. SmO2 results for a representative subject (subject #4) during the incremental cycling test. Humon Beta SmO2 (solid line) and MetaOx SmO2 (dashed line) absolute values are 3–5% different but the lines closely follow each other for the whole duration of the exercise. The vertical lines indicate the different cycling power periods with the power level indicated on the top. Figure 4. : Scatter plot of the SmO2 values measured by Humon Beta (y-axis) and MetaOx (x-axis) during exercise for all subjects. Data were down sampled to 12 sec per point, to simplify the ﬁgure. A strong linear relationship can be observed between the SmO2 measured by both systems. The equation and correlation coefﬁcient of the best linear ﬁt (dashed line) are reported in the ﬁgure. 2018 \| Vol. 6 \| Iss. 7 \| e13664 Page 6 ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. A New Wearable NIRS to Estimate Lactate Threshold P. Farzam et al. Humon Beta data and the p values shown in the ﬁnal row of Table 3 indicate that these values are not signiﬁcantly different between the two systems (P values > 0.05). Determining the lactate threshold power The SmO2 data for the 14 male subjects were analyzed to determine if the lactate threshold power could be found using MetaOx and Humon Beta SmO2 changes. Neither an SmO2 absolute threshold value or relative threshold drop could identify the lactate threshold power accurately. In particular, due to the large variability in SmO2 values across subjects, we could not ﬁnd an absolute threshold value that could work for all subjects. Also, the difference between maximum SmO2 and SmO2 at 4 mmol/L lactate concentration (SmO2-drop) varied a lot across subjects. At best, with either the MetaOx or the Humon Beta, by set- ting the threshold drop equal to the average SmO2-drop across subjects, we obtained an average power difference with respect to the blood lactate measure of about 40 W and a delay of about 5 min. Instead, the Humon Beta proprietary algorithm used to determine the lactate threshold showed good agreements with the blood lactate measurements. Representative cases of the Humon Beta SmO2 with the four exercise zones are shown for subjects one and two in Figure 5. The beginning of the red zone should correspond to the 4 mmol/L lactate concentration threshold. While the Humon Beta algorithms work well for subject #2, there is a relatively large delay on the estimate of lactate power in subject #1. We evaluated the accuracy of this threshold with respect to the blood lactate concentration measures in all 14 male subjects. Table 4 shows the difference between the threshold power found by the blood lactate measurement (interpo- lating the data acquired every 4 min) and the threshold power determined by the Humon Beta proprietary algo- rithm in the 14 male subjects. The time difference between the two thresholds is also reported. Since the power increments during the cycling test are 30 W, the Humon Beta identiﬁed 4 threshold powers correctly and was off by 1 power increment (30 W) in 10 subjects. The absolute average power difference is 21.4 W and the abso- lute average time difference is 2:32 min. Table 2. Results comparisons. Comparison Parameter Correlation coefﬁcient Relative RMSE (%) Humon vs. MetaOx HbO 0.80 0.21 19.4 14.4 HbR 082 0.19 25.7 17.6 HbT 0.72 0.33 20.7 15.6 SmO2 0.86 0.10 3.4 0.9 MetaOx-subset vs. MetaOx HbO 0.89 0.10 14.7 10.9 HbR 0.96 0.08 14.7 9.9 HbT 0.83 0.16 12.8 8.7 SmO2 0.98 0.02 3.6 1.9 Humon loc.1 vs.Humon loc. 2 HbO 0.86 0.15 22.4 15.1 HbR 0.85 0.16 16.4 10.8 HbT 0.92 0.03 20.4 14.6 SmO2 0.87 0.14 3.6 3.0 The average of correlation coefﬁcients and relative root mean square error (RMSE) of HbO, HbR, HbT, and SmO2 over all subjects comparing two different datasets as described in the ﬁrst column. The comparison of Humon Beta at two locations on the muscle is performed on seven subjects, which had two Humon devices on the same muscle. Table 3. SmO2 feature comparison. SmO2 Drop (%) Time to 50% Recovery (sec) SmO2 Overshoot (%) Humon 11.9 5.9 123.4 39.7 8.2 4.9 MetaOx 11.6 5.8 100.8 42.4 6.9 3.7 P value 0.87 0.18 0.60 The SmO2% drop throughout the incremental test (ﬁrst column); The average time to reach 50% of the recovery (middle column), and the average SmO2% overshoot (last column) are reported for the two systems. The p-values indicate there are not signiﬁcant differences between the measured parameters with the two devices. 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. 7 \| e13664 Page 7 P. Farzam et al. A New Wearable NIRS to Estimate Lactate Threshold Inﬂuence of superﬁcial layers on the measured SmO2 Using a silicone tissue mimicking phantom with known optical properties, we veriﬁed that neutral density (ND) ﬁlters minimally affect the recovered effective SmO2 as measured by the Humon Beta and MetaOx (Fig. 6 plots A and B). By placing neutral density ﬁlters of 0.1, 0.2, 0.5, and 0.7 optical densities over the phantom, the FDNIRS light intensity measured by the MetaOx is atten- uated near 100% (Fig. 6C). In the linear ﬁtting of inten- sity, amplitude, and phase versus distance, the thin absorbing layer affects the intercepts while the corre- sponding slopes are minimally affected (Fig. 6D). As a result, the recovered absorption and reduced scattering coefﬁcients of the phantom are not impacted by the pres- ence of the thin superﬁcial absorber. Therefore, the recov- ered effective oxygen saturation (SmO2) is not affected as shown in Figure 6B, where the SmO2 changes with respect to no ﬁlter are less than 2%. Similarly, Figure 6A shows that the SmO2 estimated by the Humon Beta, which also rely on multidistance algorithms, is not affected by the presence of the ND ﬁlters. Finally, to evaluate the effect of subcutaneous adipose tissue on the estimated muscle hemoglobin parameters, we compared MetaOx results at shorter and larger Figure 5. Two representative cases of Humon SmO2 with the estimated zones for (A) subject #2 and (B) subject #1. The measured blood lactate concentration is plotted in the right y-axis (empty circles). The estimate 4 mmol/L blood lactate threshold is indicated by a red star. The vertical lines indicate the different cycling power periods with the power level indicated on the top. Table 4. Comparison of lactate threshold power estimated by blood lactate concentration and Humon Beta proprietary algo- rithm. Subject # Power Difference (W) Time Difference (min:sec) 1 30 4:35 2 0 0:44 4 30 1:06 5 0 1:14 7 0 1:25 8 30 3:06 9 30 1:55 10 30 4:37 11 0 1:25 12 30 1:58 14 30 2:56 15 30 2:34 17 30 4:47 18 30 3:11 Absolute average difference 21.4 14.1 2:32 1:23 Difference between the threshold power found by the blood lac- tate concentration measurements and by the Humon Beta algo- rithm for the 14 male subjects, and the time difference between the two. The positive values indicate power and time delays of the Humon Beta with respect to the blood lactate. The last row reports the absolute average values. 2018 \| Vol. 6 \| Iss. 7 \| e13664 Page 8 ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. A New Wearable NIRS to Estimate Lactate Threshold P. Farzam et al. separations. Using larger separations, we consistently obtained higher hemoglobin concentration. Across all subjects, the difference in total hemoglobin concentration is about 17%. Instead, the difference in SmO2 at the two separations is small (2.8% 1.4%). In addition, we found the SmO2 drop at the end of the exercise to be on average 11.8 5.2% and 12.7 5.5%, using the shorter and larger separations, respectively. By performing a Wil- coxon Rank Sum test, these drops were not found to be signiﬁcantly different (P > 0.05). This indicates that the depth penetration is similar between the shorter and longer separations used. To further assess the effect of the subcutaneous adipose tissue on SmO2, we estimated the correlation between the total SmO2 drop (using all source–detector separations available in each system) and the SCATT in all subjects. As shown in Figure 7, we con- sistently measured smaller SmO2 drops for thicker Figure 6. Effect of a superﬁcial thin absorbing layer on measured optical parameters. Four different neutral density ﬁlters were positioned between the optical probe and a silicone phantom to mimic a thin attenuating superﬁcial layer. Panels (A) and (B) report the difference between the effective hemoglobin saturation (SmO2*, the SmO2 we would have estimated if there was blood in the phantom) measured by Humon Beta and MetaOx with or without ﬁlter. The error bars represent the difference over ﬁve repetitions. Panel (C) shows the strong attenuation of the detected light at 3.0 cm source–detector distance at a representative wavelength (690 nm) in the MetaOx system. Panel (D) shows the corresponding amplitude and phase slope changes with the neutral density ﬁlters. Results are consistent at all wavelengths, independent of the absorbance spectra of the ND ﬁlters. In fact, ND ﬁlters have constant absorption in the visible spectral range, but they attenuate less in the near-infrared range. This difference in attenuation spectra does not affect the effective SmO2 calculated using both red and near-infrared wavelengths, since at each wavelength the effect of the ND on the slopes is negligible. Figure 7. Scatterplot of SmO2 drop during exercise versus subcutaneous adipose tissue. With both devices, for thicker adipose tissue we consistently measured smaller drops in SmO2. 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. 7 \| e13664 Page 9 P. Farzam et al. A New Wearable NIRS to Estimate Lactate Threshold subcutaneous adipose layers (with a correlation coefﬁcient of 0.65). Discussion In this study, we recorded NIRS data from 17 athletes while they were performing an incremental cycling test. The opti- cally measured hemoglobin parameters (HbO, HbR, HbT, and SmO2) responded to muscle physiological changes during exercise. These results exhibit meaningful trends that can provide insights to how muscles respond to exer- tion. The average optical (absorption and reduced scatter- ing coefﬁcients) properties are illustrated in Figure 2. The measured values are consistent with values reported in the literature using similar NIRS methods to measure skeletal muscles (Yu et al. 2005; Gurley et al. 2012; Shang et al. 2012; Mesquita et al. 2013; Baker et al. 2017). Comparison of Humon Beta and MetaOx estimates of SmO2 During exercise, the body supplies a higher level of blood ﬂow to the working muscles to provide oxygen (Yu et al. 2005; Joyner and Casey 2015). In the initial phase of cycling, when the athletes were working at a lower power, there was an overcompensation of blood, thus SmO2 slightly increased (Fig. 3 and Fig. 5). As the subjects approached their lactate threshold power, the SmO2 decreased, indicating that the oxygen consumption in the muscle had exceeded the oxygen supply. When the subjects could no longer continue pedaling at high power, and began the recovery phase, the SmO2 signiﬁcantly increased due to the high blood supply and sudden decrease of mus- cle oxygen consumption. The agreement between the esti- mated SmO2 between the Humon and MetaOx system is consistent across all phases of exercise and all subjects as shown in Table 2 by the high correlation coefﬁcient (0.86). Not only the trends, but also the absolute values on SmO2 are quite similar between the two systems, as shown by the low average RMSE (3.4%). Figure 4 shows that the range of variations of SmO2 measured with the Humon Beta system is smaller than the range measured with the MetaOx. Using the MetaOx subset model, we veriﬁed this difference is not due to the ﬁxed scattering assumption. In fact, the R2 between the two MetaOx models is 0.87 and the range of variation is actually larger by ﬁxing scattering than by cal- culating it. Throughout the duration of the exercise, the scattering coefﬁcients also changed minimally (on average less than 10%). The difference between the Humon and MetaOx estimated SmO2 is probably due to the different consumption of the right and left leg muscles. Eighty-eight percent of our subjects were right-footed and the MetaOx probe was measuring the dominant leg, therefore this may cause some discrepancies between measurements. Bilateral differences have been found by Hesford et al. (2012), who examined asymmetry between the SmO2 of the left and right quadriceps in ice skaters, and found differences depending on the muscle side that was being exerted more. The data from Table 3, supports the similarities between the features of the SmO2 curves measured on the 14 male subjects by the MetaOx and Humon Beta. We investigated the relationship between recovery time and maximum blood lactate concentration, and similar to Chance et al. (1992), we found these variables are uncorrelated. Chance et al. (1992) did ﬁnd, however, that if individuals worked at a fraction of their maximum power level, there was a correlation between the recovery time and blood lactate accumulation. We cannot verify this ﬁnding since in our protocol we only measured recovery to maximum power output. Comparison of Humon Beta and MetaOx estimates of hemoglobin concentration While there was strong agreement between the SmO2 of the two devices, we found larger differences in the hemo- globin concentration values. This result is somehow expected since the assumption of ﬁxed scattering heavily affects the hemoglobin concentrations absolute values (Fantini et al. 1999). Nevertheless, we observed that the model error (ﬁxed scattering, no phase information, only two wavelengths and only three separations vs. full FDNIRS MetaOx data), is smaller than the effect of mus- cle heterogeneity. As reported in Table 2, the RMSE between Humon and MetaOx are similar to the RMSE found comparing two Humon locations and double the RMSE found comparing different MetaOx models. This suggests muscle heterogeneity plays a larger role than dif- ferences between CW and FDNIRS models. The hemoglo- bin concentration spatial distribution throughout the muscle has been previously reported (Hamaoka et al. 2011), and it needs to be considered, when assessing mus- cle physiology with NIRS. Importantly, we found that while hemoglobin content signiﬁcantly differs across loca- tions, its temporal changes during the incremental cycling exercise are uniform across measured parts of the working muscle. In fact, in all our comparisons, we found good correlation coefﬁcients for HbO, HbR, and HbT. Determination of the threshold power On average SmO2 values at exercise onset in the 14 male subjects were 67.8 4.0% for the MetaOx and 65.6 4.9% for the Humon Beta. SmO2 increased during the initial power increments and at the lactate threshold has decreased 6.2 4.6% and 6.3 3.7% from the 2018 \| Vol. 6 \| Iss. 7 \| e13664 Page 10 ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. A New Wearable NIRS to Estimate Lactate Threshold P. Farzam et al. maximum SmO2, measured by the MetaOx and Humon Beta, respectively. Because of large variability across sub- jects, we could not ﬁnd an SmO2 threshold value that could work for all subjects to estimate lactate threshold power (the best attempt found an average power difference of 40 W and a delay of 5 minutes). This is in agreement with previous NIRS works that show how the use of more com- plex analysis techniques of SmO2 in addition to HbR, HbO, and HbT are needed to better assess lactate threshold power from NIRS (Belardinelli et al. 1995; Grassi et al. 1999; Wang et al. 2006; van der Zwaard et al. 2016). The drop in SmO2 was larger when considering the maximum SmO2 measured during the low power intervals as the ref- erence value. SmO2 at rest, before starting pedaling, was on average 3.5% lower than during the ﬁrst power interval (30 W). We believe that in addition to the increase of blood ﬂow with exercise onset which increases SmO2, the muscle contraction and skin tightening during the pedaling play a role here, reducing the contribution of superﬁcial adipose tissue to the measured parameters. For this reason, to avoid this possible measurement artifact, in all our anal- yses, we only considered differences while pedaling, not with respect to rest. The Humon Beta uses a combination of HbO, HbR, HbT, and SmO2 changes to identify the lactate threshold. We tested the agreement between the Humon estimated threshold with the one identiﬁed by the lactate concentra- tion measures and found a close agreement between the two. The Humon Beta’s real-time threshold power on average only differs 21.4 W and less than 3 min from the invasive measure of blood lactate (Table 4). On the seven subjects with two Humon Beta devices, we also veriﬁed the time at which the threshold power was found was on average less than one minute apart between the two loca- tions. The threshold power algorithms are proprietary to Dynometrics Inc. and are being reﬁned as more data is collected in controlled exercise settings like this one. Finally, testing the effects that a light attenuating thin superﬁcial layer has on the NIRS data is crucial to ensure dependable results across a wide range of individuals with different melanin concentration in the skin. It is impor- tant to note that ND ﬁlters have constant absorption in the visible spectral range, but they attenuate less in the near-infrared range. This difference in attenuation spectra does not affect the effective SmO2 calculated using both red and near-infrared wavelengths, since at each wave- length the effect of the ND on the slopes is negligible. Both the MetaOx and the Humon Beta recovered correct effective SmO2 values across all the absorbing ﬁlters used. The reason why the effect of the ﬁlter is negligible is that both the MetaOx and the Humon Beta recover the optical properties from the gradient over distance, not from the absolute intensity values. This ensures conﬁdence that the Humon Beta can measure SmO2 in people of all skin col- ors (Franceschini et al. 1998). Limitations It is important to address that when validating the Humon Beta against the MetaOx, the NIRS data was obtained on different legs of the subjects. While cycling typically engages both legs uniformly, more differences may arise in the data because the two systems were not measuring the exact same location. For the lactate power analysis, we had to exclude the female subjects, since we realized this protocol is not well suited for females. The power increments were too large, which resulted in the lactate threshold power and end of the exercise to be too close in time. We suggest smaller power increments for each step to be used when measur- ing female subjects who have smaller quadriceps com- pared to males. The subcutaneous adipose layer inﬂuences quantiﬁca- tion of hemoglobin parameters, especially at shorter source–detector separations. We veriﬁed that using larger (2.5–3.0 cm) source–detector separations relative to the shorter (1.5–2.0 cm) we obtain an average of ~17% higher hemoglobin concentration over the exercise dura- tion. This is expected because of the larger penetration depth at longer separations and because of the larger hemoglobin concentration of muscle with respect to adi- pose tissue. As a result, in the presence of a subcutaneous adipose tissue layer, we measure higher hemoglobin con- centrations at larger separations. Since fat and muscle have similar oxygenation at rest, the difference in SmO2 at the two separations is small (2.8% 1.4%). We veri- ﬁed that during exercise, the difference in SmO2 at the two sets of distances is constant and did not have statisti- cally signiﬁcant differences in the SmO2 drops by the end of exercise. Nevertheless, at both sets of distances, the presence of a subcutaneous adipose tissue layer affects the measured SmO2 as shown by the correlation between the SmO2 drop and the SCATT in Figure 7. For thicker SCATT layers, there are smaller drops in SmO2 since the volume measured includes less muscle and more fat. For the speciﬁc application in athletes and ﬁt individuals, the SCATT is low, reducing the contamination by the adipose tissue. While we only measured the SCATT over the rec- tus femoris of the right leg, we also assumed that the SCATT is symmetric and that this layer reported in Fig- ure 7 affect similarly the MetaOx and Humon Beta data. In this study, we chose to monitor the rectus femoris rather than the vastus lateralis muscle (which is the com- mon choice in other work examining NIRS measurements in sports (Perrey and Ferrari 2018) to minimize optical ﬁber movement throughout the cycling exercise. Further 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. 7 \| e13664 Page 11 P. Farzam et al. A New Wearable NIRS to Estimate Lactate Threshold work needs to be done to examine the differences in SmO2 measured in multiple muscle groups simultane- ously, using several Humon Beta devices to help under- stand what muscle group is the optimal choice for certain types of activities. It is also crucial to determine if the identiﬁcation of an athletes’ threshold by NIRS is depen- dent on the exact muscle chosen from the many working muscles during a sport. When comparing the NIRS measurements against the lactate threshold, it is relevant to mention that multiple methods exist to ﬁnd the lactate threshold (Faude et al. 2009). By choosing a different threshold identiﬁcation tech- nique (besides 4 mmol/L), a new threshold power may be identiﬁed, which may alter the values in Table 4. We chose to determine the lactate threshold as 4 mmol/L because of the widespread use and established studies that support this method (Heck et al. 1985; Faude et al. 2009). Conclusion In summary, this study validates the performance of a low- cost, wireless, wearable NIRS device against an advanced benchtop device. During the incremental cycling test, the wearable device provides similar results to the more expen- sive FDNIRS technology. We veriﬁed that the assumptions and simpliﬁcations of this CWNIRS system minimally impact the SmO2 quantiﬁcation and the recovery of changes in the hemoglobin concentration trends. The main deviations are accounted for by muscle heterogeneities. While skin color does not affect the results, the main limi- tation, common to all CW and FDNIRS systems, is the reduced sensitivity to muscles in the presence of subcuta- neous adipose tissue. Targeting athletes, who tend to be ﬁt individuals with thin adipose layers, provides a larger drop in SmO2 readings than what can be achieved on people with thicker adipose thicker layers. 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Opt. 10:024027. van der Zwaard, S., R. T. Jaspers, I. J. Blokland, C. Achterberg, J. M. Visser, A. R. den Uil, et al. 2016. Oxygenation threshold derived from near-infrared spectroscopy: reliability and its relationship with the ﬁrst ventilatory threshold. PLoS ONE 11:e0162914. 2018 \| Vol. 6 \| Iss. 7 \| e13664 Page 14 ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. A New Wearable NIRS to Estimate Lactate Threshold P. Farzam et al.	Validation of a novel wearable, wireless technology to estimate oxygen levels and lactate threshold power in the exercising muscle.	[]	Farzam, Parisa,Starkweather, Zack,Franceschini, Maria A	eng
PMC4892259	Supplementary Information Inferring muscle functional roles of the ostrich pelvic limb during walking and running using computer optimization Jeffery W. Rankin, Jonas Rubenson, John R. Hutchinson Supplementary Information – Tables Table S1 – Root mean square (RMS) and peak differences between the experimental and simulated joint angles (degrees) obtained with computed muscle control in walking and running over the entire movement. RMS Error Peak Error RMS Error Peak Error RMS Error Peak Error RMS Error Peak Error Hip Flexion-Extension 0.002 0.061 0.001 0.039 0.007 0.148 0.002 0.044 Hip Ad-Abduction 0.002 0.106 0.001 0.034 0.005 0.164 0.002 0.065 Hip Medial-Lateral Rotation 0.005 0.139 0.003 0.076 0.012 0.347 0.003 0.054 Knee Flexion-Extension 0.006 0.201 0.002 0.047 0.024 0.497 0.002 0.069 Knee Ad-Abduction 0.005 0.130 0.002 0.103 0.014 0.361 0.004 0.071 Knee Medial-Lateral Rotation 0.005 0.236 0.003 0.071 0.010 0.264 0.005 0.122 Ankle Flexion-Extension 0.009 0.325 0.001 0.053 0.051 1.304 0.004 0.093 MTP Flexion-Extension 0.010 0.292 0.007 0.313 0.051 1.543 0.008 0.193 RCMCR RCMCC WCMCR WCMCC Joint Angle (Degrees) Table S2 –Percent of muscle activity (as predicted by the simulations) that occurs during the swing phase. Predictions were calculated by normalizing the entire gait cycle to 100% and then integrating the entire cycle and swing-phase-only portions of the simulated muscle activation patterns. Simulation Percent of Muscle Activity in Swing WSO 13.9 WCMCR 15.6 WCMCC 29.8 RSO 24.4 RCMCR 31.8 RCMCC 38.6 Supplementary Information Inferring muscle functional roles of the ostrich pelvic limb during walking and running using computer optimization Jeffery W. Rankin, Jonas Rubenson, John R. Hutchinson Supplementary Information – Figures Figure S1 – Example fibre excursions of the digital flexor muscles during ostrich running. Due to having short muscle fibres, these muscles undergo extensive fibre lengthening relative to their optimal fibre length during swing. As a result, passive muscle forces generate a large ankle extension moment that cannot be counteracted by the ankle flexors (TCf,TCt, EDL) alone and a reserve torque is required. Shaded area represents stance and the arrow indicates the point of peak ankle reserve actuator torque.	Inferring muscle functional roles of the ostrich pelvic limb during walking and running using computer optimization.	[]	Rankin, Jeffery W,Rubenson, Jonas,Hutchinson, John R	eng
PMC9602838	Citation: Tanous, D.; Motevalli, M.; Wirnitzer, G.; Leitzmann, C.; Rosemann, T.; Knechtle, B.; Wirnitzer, K. Sex Differences in Training Behaviors of 10 km to Ultra-Endurance Runners (Part A)—Results from the NURMI Study (Step 2). Int. J. Environ. Res. Public Health 2022, 19, 13238. https:// doi.org/10.3390/ijerph192013238 Academic Editors: Paul B. Tchounwou, Stacy T. Sims and Christopher T. Minson Received: 18 August 2022 Accepted: 11 October 2022 Published: 14 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 Sex Differences in Training Behaviors of 10 km to Ultra-Endurance Runners (Part A)—Results from the NURMI Study (Step 2) Derrick Tanous 1,2 , Mohamad Motevalli 1,2 , Gerold Wirnitzer 3, Claus Leitzmann 4, Thomas Rosemann 5 , Beat Knechtle 5,6,* and Katharina Wirnitzer 1,2,7 1 Department of Sport Science, University of Innsbruck, 6020 Innsbruck, Austria 2 Department of Research and Development in Teacher Education, University College of Teacher Education, Tyrol, 6010 Innsbruck, Austria 3 adventureV & change2V, 6135 Stans, Austria 4 Institute of Nutrition, University of Gießen, 35390 Gießen, Germany 5 Institute of Primary Care, University of Zurich, 8000 Zurich, Switzerland 6 Medbase St. Gallen Am Vadianplatz, 9000 St. Gallen, Switzerland 7 Research Center Medical Humanities, Leopold-Franzens University of Innsbruck, 6020 Innsbruck, Austria * Correspondence: beat.knechtle@hispeed.ch Abstract: Training for running events is fundamental for successful participation in various running events such as 10 km, half-marathon, marathon, or ultra-marathon distances. Training behaviors are likely based on runner motivations and social constraints, particularly for females. Participants completed a questionnaire following a cross-sectional approach, including questions on sociode- mographics, general training behaviors, and periodization training strategies. The ﬁnal sample included 245 participants (141 females, 104 males), mostly from Germany (72%), Austria (18%), and Switzerland (5%), with a median age of 39 years (IQR 17) and a BMI of 21.7 kg/m2 (IQR 3.5). Males more often trained alone and independently, whereas females were most likely to follow an external resource (p = 0.037). Non-parametric ANOVA revealed signiﬁcant training differences between sexes in daily training mileages and durations at each phase and stage (p < 0.05) as well as in weekly training mileages and durations for general basic training and race-speciﬁc training (p < 0.05). Critical sex differences in training behaviors may arise from physiological differences and social expectations, which may be related to the distances they prefer to race at as well as their motivations for running and racing. This study provides a wide overview of training behaviors for endurance runners or professionals guiding healthy running performance. Keywords: running; marathon; female; motive; recreational athlete; endurance exercise; habit 1. Introduction Running has been well-established as a healthy physical activity for females and males across adulthood [1–3]. While many similar training habits exist between female and male endurance runners, a multitude of factors remain distinguishable between the sexes, including primarily physiological and social differences [4–7]. Numerous studies have investigated sex differences in the training behaviors of endurance runners [8–15]. To the best of the authors’ knowledge, no investigation has analyzed sex-related running training behaviors considering diverse distances, including 10 km (10 km) up to ultra-marathon (UM) distances in one study. Parallel to health beneﬁts, frequent runners often follow various personal motivations for running, primarily for leisure or sports performance [3,4,16]. Females generally report higher running motives for psychological health reasons (body weight concern, a sense Int. J. Environ. Res. Public Health 2022, 19, 13238. https://doi.org/10.3390/ijerph192013238 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 13238 2 of 14 of belonging, life meaning, fulﬁllment/harmony), whereas males are mostly competition- focused [17]. Training provides the structure for progress in meeting the individual’s motivational goals [15,18] independent of sex. Considering the anatomical (e.g., anthropometric, hormonal, maximum muscular strength) [19,20] and societal (e.g., expectations, motives) differences between females and males [6,21], further sex differences in runner training behaviors may arise, including general training (training duration, external resource, other sports participation) and speciﬁc training measures (weekly frequency, weekly and daily mileages and durations, type of training from basic- to competition-speciﬁc, and various intensities) as well as the distances they prefer to run (10 km, half-marathon, marathon/UM) [9,15,17,18]. Regardless of the endurance runner’s motives, athletes typically adhere to periodization strategies when training for running events, including recognizable training phases that may unfold throughout the duration of up to a year before an event [22,23]. Different periods of training (regeneration and transitional period, main preparatory period, competition period) are followed to build and/or maximize beneﬁcial training adaptations, mainly to improve the maximal volume of oxygen consumption in runners to maintain a faster pace for better ﬁnishing times [22–24]. Contrariwise to the sex differences in the training behaviors of endurance runners identiﬁed by the previous literature [8–15], similarities between the sexes are also commonly observed that appear contradictory [8,9,11–14]. However, this occurrence may be due to the previous study designs, the strict inclusion of pre-speciﬁed race distances, and the methodological heterogeneity between the studies at the population level [8–15]. Therefore, this study is the ﬁrst aiming to assess the relationships between training behaviors of female and male recreational endurance runners over 10 km up to UM distances. The present investigation hypothesized that there are critical training differences between the sexes of recreational endurance runners of various distances (half-marathon, marathon/UM, 10 km). 2. Materials and Methods The study protocol of the Nutrition and Running High Mileage (NURMI) Study [25] was approved by the ethics board of St. Gallen, Switzerland on 6 May 2015 (EKSG 14/145). The trial registration number is ISRCTN73074080 (retrospectively registered). Detailed information about the methods of the NURMI Study Step 2 has been described elsewhere [3,4,7,26–30]. The NURMI Study was conducted in three steps following a cross-sectional design. Endurance runners were mainly recruited from Austria, Germany, and Switzerland and were contacted primarily 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, and through personal contacts. The characteristics of the subjects are presented in Table 1. Participants completed an online survey within the NURMI Study Step 2 that was available from February 2015 to December 2015 in German and English at www.nurmi- study.com (accessed on 17 August 2022). Prior to completing the questionnaire on physical and psychological health (including the basic assignment to an area of sports, motivation and aim of running activities, and other sports to balance for running in order to better dif- ferentiate between predominant health, leisure, or sport performance-orientated approach to running), participants were provided with a written description of the procedures and participants gave their informed consent to take part in the study. For complete participation in the study, the following inclusion criteria were required: (1) written informed consent, (2) at least 18 years of age, (3) questionnaire Step 2 completed, and (4) completion of at least a half-marathon distance running event within the past two years. However, as an additional criterion in this study, participants had to (5) select an event as the NURMI running event, including either a half-marathon (HM) or marathon (M) distance that they prepared for and subsequently ﬁnished in Step 3 (main NURMI Study: Step 2 linked to Step 3) [25]. Int. J. Environ. Res. Public Health 2022, 19, 13238 3 of 14 An additional group of 91 highly motivated runners provided accurate and pragmatic answers with plenty of high-quality data and had not successfully participated in either a HM or M but in a 10 km race instead. To avoid an irreversible loss of these valuable data sets, those who met all inclusion criteria but named a 10 km race as their running event were kept as an additional race distance subgroup. To control for measures of (1) running activity (history, motivation, training, racing, etc.) and (2) diet, we included control questions in different survey sections. Incomplete, inconsistent, and conﬂicting data sets were excluded from the data analysis. Moreover, to control for a minimal status of health linked to a minimum level of ﬁtness and further enhance the reliability of data sets, the body mass index (BMI) approach followed the World Health Organization (WHO) [31,32]. With a BMI ≥ 30, other health protective and weight loss strategies alongside running are foremost necessary to safely reduce body weight. Regarding the UM distance, the shortest UM reported was 50 km, and the longest was 160 km. Figure 1 shows the categorization of participants according to sex and race distance subgroups: HM, M/UM, and 10 km (M/UM data were pooled since the marathon distance is within an ultra-marathon). Additionally, the involved reader is kindly referred to the Part B publication for the subsequential linking of training behaviors with race performances [33]. nviron. Res. Public Health 2022, 19, x FOR PEER REVIEW 3 of 15 (5) select an event as the NURMI running event, including either a half-marathon (HM) or marathon (M) distance that they prepared for and subsequently finished in Step 3 (main NURMI Study: Step 2 linked to Step 3) [25]. An additional group of 91 highly motivated runners provided accurate and prag- matic answers with plenty of high-quality data and had not successfully participated in either a HM or M but in a 10 km race instead. To avoid an irreversible loss of these valu- able data sets, those who met all inclusion criteria but named a 10 km race as their running event were kept as an additional race distance subgroup. To control for measures of (1) running activity (history, motivation, training, racing, etc.) and (2) diet, we included control questions in different survey sections. Incomplete, inconsistent, and conflicting data sets were excluded from the data analysis. Moreover, to control for a minimal status of health linked to a minimum level of fitness and further enhance the reliability of data sets, the body mass index (BMI) approach followed the World Health Organization (WHO) [31,32]. With a BMI ≥ 30, other health protective and weight loss strategies alongside running are foremost necessary to safely reduce body weight. Regarding the UM distance, the shortest UM reported was 50 km, and the longest was 160 km. Figure 1 shows the categorization of participants according to sex and race distance subgroups: HM, M/UM, and 10 km (M/UM data were pooled since the marathon distance is within an ultra-marathon). Additionally, the involved reader is kindly referred to the Part B publication for the subsequential linking of training behaviors with race per- formances [33]. Figure 1. Enrollment and Categorization of Participants by Sex. BMI—body mass index. HM—half- marathon. M/UM—marathon/ultra-marathon. 10 km—10 kilometers. Training behaviors of active female and male endurance running participants were described by the following items linked to sex: running motivations (health, leisure, sport performance); preferred time of day and preferred season for running activity (indoor vs. outdoor); training duration for the main event; external training resource followed (none, professional, other); participation in other sports activities to balance running (summer, Figure 1. Enrollment and Categorization of Participants by Sex. BMI—body mass index. HM—half- marathon. M/UM—marathon/ultra-marathon. 10 km—10 kilometers. Training behaviors of active female and male endurance running participants were described by the following items linked to sex: running motivations (health, leisure, sport performance); preferred time of day and preferred season for running activity (indoor vs. outdoor); training duration for the main event; external training resource followed (none, professional, other); participation in other sports activities to balance running (summer, winter); periodized running training (weekly frequency of training, daily and weekly mileages and durations of training (km, hours) related to phase and stage of training, respectively). Int. J. Environ. Res. Public Health 2022, 19, 13238 4 of 14 The statistical software R version 3.6.2 Core Team 2019 (R Foundation for Statistical Computing, Vienna, Austria) performed all statistical analyses. Exploratory analysis was performed by descriptive statistics (median and interquartile range (IQR); mean and standard deviation (SD)). Signiﬁcant differences in running activity (training behaviors) between race distance and sex were calculated by using a non-parametric ANOVA. Chi-square test (χ2; nominal scale) examined the association between variables, and the Wilcoxon test and/or Kruskal– Wallis test (ordinal and metric scale) approximated F distributions with ordinary least squares. Differences in weekly and daily training with the mileages of female and male runners are displayed as box plots. The level of statistical signiﬁcance was set at p ≤ 0.05. 3. Results The survey was completed by 317 endurance runners. A total of 72 participants were excluded from the initial sample due to satisfying the exclusion criteria (i.e., three participants with a BMI ≥ 30) or disagreement with the inclusion criteria. After data clearance, a total of 141 (58%) females and 104 (42%) males (n = 245) comprised the ﬁnal sample with a combined age of 39 (IQR 17) years, a body weight of 65 kg (IQR 14.2), and a BMI of 21.7 kg/m2 (IQR 3.5) from several countries, including Austria (n = 44), Germany (n = 177), Switzerland (n = 13), and some other countries (n = 11: Belgium, Brazil, Canada, Italy, Luxemburg, Netherlands, Poland, Spain, United Kingdom). There were 154 NURMI-Runners (48% female) and 91 runners (74% female) competing at the 10 km distance. The female participants were signiﬁcantly shorter (p < 0.001) and had a lower body weight (p < 0.001) and BMI (p < 0.001) than the males. Regarding the highest academic qualiﬁcation achieved, participants reported a high school diploma or equivalent (53% female), an A Levels or equivalent (58% female), a university degree or higher (59% female), and some provided no answer (64% female). A signiﬁcant difference (p = 0.044) was identiﬁed between the sexes in terms of exercise focus, where females were largely focused on leisure (57% female vs. 50% male), with a smaller proportion of females being sport performance-oriented (30% female vs. 44% male). Participants reported their marital status as single (32% of females vs. 20% of males), married or living with a partner (61% of females vs. 75% of males), or divorced/separated (7% of females vs. 5% of males). Characteristics of participants are further presented in Table 1, and more details on the characteristics of participants are provided in Part B of the sequenced paper [33]. Table 1. Characteristics of Endurance Runners Displayed by Sex. Total Female Male Statistics 100% (245) 58% (141) 42% (104) Age (years) 39 (IQR 17) 37 (IQR 16) 43 (IQR 18) F(1, 243) = 7.03 p = 0.009 † Body Weight (kg) 65 (IQR 14.2) 59.5 (IQR 10.9) 73 (IQR 11.9) F(1, 243) = 191.23 p < 0.001 ‡ Height (m) 1.7 (IQR 0.1) 1.7 (IQR 0.1) 1.8 (IQR 0.1) F(1, 243) = 228.04 p < 0.001 ‡ BMI (kg/m2) 21.7 (IQR 3.5) 20.9 (IQR 3.01) 22.8 (IQR 3.16) F(1, 243) = 28.72 p < 0.001 ‡ Int. J. Environ. Res. Public Health 2022, 19, 13238 5 of 14 Table 1. Cont. Total Female Male Statistics Academic Qualiﬁcation No qualiﬁcation <1% (1) <1% (1) / χ2(4) = 1.96 p = 0.744 High school diploma/ Technical qualiﬁcation/ GCSE or equivalent 34% (83) 31% (44) 38% (39) A Levels or equivalent 22% (53) 22% (31) 21% (22) University degree/ Graduate degree 34% (83) 35% (49) 33% (34) No answer 10% (25) 11% (16) 9% (9) Country of Residence Austria 18% (44) 11% (16) 27% (28) χ2(3) = 11.32 p = 0.010 * Germany 72% (177) 79% (112) 62% (65) Switzerland 5% (13) 4% (6) 7% (7) Other 4% (11) 5% (7) 4% (4) Exercise Focus Health Leisure Sport performance 9% (23) 54% (133) 36% (89) 12% (17) 57% (81) 30% (43) 6% (6) 50% (52) 44% (46) χ2(2) = 6.24 p = 0.044 * Racing Distance HM M/UM 10 km 36% (89) 27% (65) 37% (91) 35% (49) 18% (25) 48% (67) 38% (40) 38% (40) 23% (24) χ2(2) = 19.55 p < 0.001 ‡ Initial Running Motivation Health Leisure 44% (108) 56% (137) 46% (65) 54% (76) 41% (43) 59% (61) χ2(1) = 0.55 p = 0.459 Current Running Motivation Health Leisure Sport performance 19% (47) 46% (113) 35% (85) 21% (30) 48% (67) 31% (44) 16% (17) 44% (46) 39% (41) χ2(2) = 2.06 p = 0.356 Note. , †, or ‡ denote statistical signiﬁcance at the levels p < 0.05, p < 0.01, or p < 0.001, respectively. Results are presented as percentage (%), total numbers, and median (IQR). χ2 statistic calculated by Pearson’s Chi-squared test and F statistic calculated by Kruskal–Wallis test. HM—half-marathon. M/UM—marathon/ultra-marathon. 10 km—10 kilometers. No signiﬁcant differences were found between the sexes for the initial running motiva- tion (p = 0.459) or the current running motivation (p = 0.356). Seasonal running preferences did not differ between females and males, including the preferred indoor (p = 0.312) or outdoor running season (p = 0.727) and the ideal time of day for running, whether indoor (p = 0.419) or outdoor (p = 0.592). From the total sample, most participants preferred out- door running (n = 145; 57% female) in the springtime and most often during the morning (n = 75; 57% female) but had no indoor running preference for a season (n = 178; 54% female) or time of day (n = 168; 54% female). Table 2 displays the general training behaviors for recreation runners for females and males. A signiﬁcant difference was identiﬁed (p = 0.037) in which males were more likely to train alone (84% male vs. 71% female), whereas females were more likely to train under the direction of a professional (24% female vs. 12% male) or another resource (11% female vs. 4% male). Men were signiﬁcantly more likely to participate in fell/trail running (p = 0.021) and ski-touring (p = 0.029); no additional sex-based participation differences were identiﬁed in other sports to balance running: cycling, swimming, rambling, triathlon, skiing, cross-country skiing, or snowboarding. No signiﬁcant differences were observed between training duration for the main event and sex (p = 0.833) shown in Figure 2. Int. J. Environ. Res. Public Health 2022, 19, 13238 6 of 14 Table 2. General Training Behaviors, including Total Duration, Resource, and Other Sports by Sex. Total Female Male Statistics 100% (245) 58% (141) 42% (104) Training Duration for Main Event 1–2 months 3–4 months 4–6 months 7–8 months 9–10 months >12 months 20% (46) 52% (122) 21% (48) 4% (9) 2% (5) 2% (4) 17% (22) 55% (73) 21% (28) 4% (5) 2% (3) 2% (2) 24% (24) 49% (49) 20% (20) 4% (4) 2% (2) 2% (2) χ2(2) = 2.12; p = 0.833 Training Resource for Running Events Alone and independently 76% (179) 71% (94) 84% (85) χ2(2) = 6.57; p = 0.037 Under the direction of a professional 15% (36) 24% (18) 12% (12) Other 8% (19) 11% (15) 4% (4) Other Sports Activities to Balance for Running Summer Cycling 53% (130) 51% (72) 56% (58) χ2(1) = 0.57; p = 0.451 Fell/Trail running 31% (75) 25% (35) 39% (40) χ2(1) = 5.32; p = 0.021 * Swimming 31% (75) 29% (41) 33% (34) χ2(1) = 0.39; p = 0.535 Rambling 31% (75) 34% (47) 27% (28) χ2(1) = 1.13; p = 0.287 Triathlon 19% (46) 17% (24) 21% (22) χ2(1) = 0.69; p = 0.407 Winter Skiing 14% (34) 13% (18) 16% (16) χ2(1) = 0.35; p = 0.552 Cross country skiing 11% (26) 10% (14) 12% (12) χ2(1) = 0.17; p = 0.681 Snowboarding 7% (16) 7% (10) 6% (6) χ2(1) = 0.17; p = 0.682 Ski-touring 4% (9) 1% (2) 7% (7) χ2(1) = 4.79; p = 0.029 * Note. * denotes statistical signiﬁcance at the level p < 0.05. Results are presented as percentage (%) and total numbers. χ2 statistic calculated by Pearson’s Chi-squared test and F statistic calculated by Kruskal–Wallis test. Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 6 of Table 2. General Training Behaviors, including Total Duration, Resource, and Other Sports by S Total Female Male Statistics 100% (245) 58% (141) 42% (104) Training Duration for Main Event 1–2 months 3–4 months 4–6 months 7–8 months 9–10 months >12 months 20% (46) 52% (122) 21% (48) 4% (9) 2% (5) 2% (4) 17% (22) 55% (73) 21% (28) 4% (5) 2% (3) 2% (2) 24% (24) 49% (49) 20% (20) 4% (4) 2% (2) 2% (2) χ2(2) = 2.12; p = 0.833 Training Resource for Running Events Alone and independently 76% (179) 71% (94) 84% (85) χ2(2) = 6.57; p = 0.037 * Under the direction of a professional 15% (36) 24% (18) 12% (12) Other 8% (19) 11% (15) 4% (4) Other Sports Activities to Balance for Running Summer Cycling 53% (130) 51% (72) 56% (58) χ2(1) = 0.57; p = 0.451 Fell/Trail running 31% (75) 25% (35) 39% (40) χ2(1) = 5.32; p = 0.021 * Swimming 31% (75) 29% (41) 33% (34) χ2(1) = 0.39; p = 0.535 Rambling 31% (75) 34% (47) 27% (28) χ2(1) = 1.13; p = 0.287 Triathlon 19% (46) 17% (24) 21% (22) χ2(1) = 0.69; p = 0.407 Winter Skiing 14% (34) 13% (18) 16% (16) χ2(1) = 0.35; p = 0.552 Cross country skiing 11% (26) 10% (14) 12% (12) χ2(1) = 0.17; p = 0.681 Snowboarding 7% (16) 7% (10) 6% (6) χ2(1) = 0.17; p = 0.682 Ski-touring 4% (9) 1% (2) 7% (7) χ2(1) = 4.79; p = 0.029 * Note. * denotes statistical significance at the level p < 0.05. Results are presented as percentage and total numbers. χ2 statistic calculated by Pearson’s Chi-squared test and F statistic calculated Kruskal–Wallis test. Figure 2. Sex-based differences in the prevalence of training duration (displayed in six categori for main events. Data are presented by percentage. Table 3 displays the periodized training phases by sex, including the regenerati stage and transitional period (Phase A), the main preparatory period (Phase B), and t main competition period (Phase C) based on the weekly training frequencies and t weekly and daily training mileages and durations. No significant differences were o served for weekly training frequency and sex, regardless of the phase (p > 0.05). Prepa tory Stage 4 showed the greatest average difference in weekly mileage (+11.4 km/week Figure 2. Sex-based differences in the prevalence of training duration (displayed in six categories) for main events. Data are presented by percentage. Table 3 displays the periodized training phases by sex, including the regeneration stage and transitional period (Phase A), the main preparatory period (Phase B), and the main competition period (Phase C) based on the weekly training frequencies and the weekly and daily training mileages and durations. No signiﬁcant differences were observed for weekly training frequency and sex, regardless of the phase (p > 0.05). Preparatory Stage 4 showed the greatest average difference in weekly mileage (+11.4 km/week for males; p = 0.030) and duration (+1.71 h/day for males; p = 0.032). Signiﬁcant differences were found between the sexes in terms of daily training mileage and daily training duration for every phase and stage (p < 0.05), with a highly signiﬁcant difference identiﬁed in stage 1 (phase B) for daily training mileage (+2.94 km/day for males; p < 0.001) and duration (+0.14 h/day for males; Int. J. Environ. Res. Public Health 2022, 19, 13238 7 of 14 p < 0.001). Figures 3–5 depict the training mileages and weekly training frequency by sex based on the training periodization phases. Table 3. Periodization Training Behavior, including Frequency, Mileages, and Durations Displayed by Sex. Total Female Male Statistics 100% (245) 58% (141) 42% (104) Phase A—Regeneration Stage and Transitional Period Weekly training frequency Weekly training mileage (km) Weekly training duration (hours) 3 (IQR 1) 22.4 ± 18.9 1.21 ± 1.03 2 (IQR 1) 20.6 ± 19 1.12 ± 1.03 3 (IQR 1) 24.6 ± 18.7 1.33 ± 1.02 F(1, 220) = 0.29; p = 0.590 F(1, 220) = 3.77; p = 0.053 F(1, 220) = 3.58; p = 0.060 Daily training mileage (km) Daily training duration (hours) 7.06 ± 5.86 0.26 ± 0.21 6.24 ± 5.33 0.23 ± 0.19 8.12 ± 6.36 0.30 ± 0.23 F(1, 220) = 7.26; p = 0.008 † F(1, 220) = 6.28; p = 0.013 * Phase B—Main Preparatory Period Preparatory Stage 1 (general basic training, mainly at low intensity) Weekly training frequency Weekly training mileage (km) Weekly training duration (hours) 3 (IQR 2) 30.2 ± 24.6 4.63 ± 3.76 3 (IQR 2) 27.2 ± 23.1 4.16 ± 3.54 3 (IQR 2) 34.1 ± 25.9 5.22 ± 3.96 F(1, 220) = 0.01; p = 0.931 F(1, 220) = 4.40; p = 0.037 * F(1, 220) = 4.67; p = 0.032 * Daily training mileage (km) Daily training duration (hours) 8.74 ± 6.61 0.4 ± 0.3 7.46 ± 6.13 0.34 ± 0.28 10.4 ± 6.86 0.48 ± 0.31 F(1, 220) = 14.93; p < 0.001 ‡ F(1, 220) = 14.70; p < 0.001 ‡ Preparatory Stage 2 (speciﬁc basic training, build-up training, low-to-moderate intensity) Weekly training frequency Weekly training mileage (km) Weekly training duration (hours) 3 (IQR 2) 33.5 ± 27.5 4.82 ± 3.96 3 (IQR 2) 31 ± 26 4.46 ± 3.74 3 (IQR 3) 36.7 ± 29.2 5.28 ± 4.2 F(1, 220) = 0.02; p = 0.902 F(1, 220) = 2.15; p = 0.144 F(1, 220) = 2.21; p = 0.138 Daily training mileage (km) 9.42 ± 7.29 0.41 ± 0.32 8.39 ± 7.09 0.36 ± 0.31 10.8 ± 7.37 0.47 ± 0.32 F(1, 220) = 7.91; p = 0.005 † F(1, 220) = 7.85; p = 0.006 † Daily training duration (hours) Preparatory Stage 3 (competition training, intervals, pace, moderate-to-high intensity) Weekly training frequency 4 (IQR 2) 37.1 ± 31.1 5.65 ± 4.73 4 (IQR 1) 34.2 ± 29.5 5.2 ± 4.48 4 (IQR 3) 40.9 ± 32.9 6.22 ± 5 F(1, 220) = 0.04; p = 0.850 F(1, 220) = 2.41; p = 0.122 F(1, 220) = 2.57; p = 0.110 Weekly training mileage (km) Weekly training duration (hours) Daily training mileage (km) 9.98 ± 7.86 0.41 ± 0.32 9.01 ± 7.78 0.37 ± 0.32 11.24 ± 7.82 0.47 ± 0.32 F(1, 220) = 8.37; p = 0.004 † F(1, 220) = 8.20; p = 0.005 † Daily training duration (hours) Preparatory Stage 4 (race-speciﬁc training, test competition, moderate-to-high intensity) Weekly training frequency 4 (IQR 2) 39.5 ± 35.8 5.95 ± 5.39 4 (IQR 1) 34.6 ± 30.6 5.2 ± 4.6 4 (IQR 2) 46 ± 40.9 6.91 ± 6.15 F(1, 220) = 1.49; p = 0.223 F(1, 220) = 4.74; p = 0.030 * F(1, 220) = 4.65; p = 0.032 * Weekly training mileage (km) Weekly training duration (hours) Daily training mileage (km) 10.7 ± 8.31 0.5 ± 0.39 9.47 ± 7.93 0.45 ± 0.37 12.29 ± 8.56 0.58 ± 0.4 F(1, 220) = 10.13; p = 0.002 † F(1, 220) = 9.58; p = 0.002 † Daily training duration (hours) Phase C—Competition Period (incl. tapering and interim race stages) Weekly training frequency 3 (IQR 2) 32.2 ± 27.7 4.41 ± 3.8 3 (IQR 2) 28.8 (24.4) 3.95 (3.33) 4 (IQR 2) 36.6 (31.1) 5.01 (4.26) F(1, 220) = 3.30; p = 0.071 F(1, 220) = 2.94; p = 0.088 F(1, 220) = 2.48; p = 0.117 Weekly training mileage (km) Weekly training duration (hours) Daily training mileage (km) 9.35 ± 8.7 0.41 ± 0.37 8.46 ± 8.33 0.37 ± 0.36 10.5 ± 9.07 0.45 ± 0.39 F(1, 220) = 5.21; p = 0.023 * F(1, 220) = 4.47; p = 0.036 * Daily training duration (hours) Note. , †, or ‡ denote statistical signiﬁcance at the levels p < 0.05, p < 0.01, or p < 0.001, respectively. Results are presented as median (IQR) and mean (SD). F statistic calculated by Kruskal–Wallis test. km—kilometer. Int. J. Environ. Res. Public Health 2022, 19, 13238 8 of 14 Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 8 of 15 Note. , †, or ‡ denote statistical signiﬁcance at the levels p < 0.05, p < 0.01, or p < 0.001, respectively. Results are presented as median (IQR) and mean (SD). F statistic calculated by Kruskal–Wallis test. km—kilometer. Figure 3. Box plots displaying sex differences in weekly training frequencies in different training periodization phases and stages. Figure 4. Box plots displaying sex differences in weekly training mileages (km) in different training periodization phases and stages. Figure 3. Box plots displaying sex differences in weekly training frequencies in different training periodization phases and stages. Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 8 of 15 Note. *, †, or ‡ denote statistical signiﬁcance at the levels p < 0.05, p < 0.01, or p < 0.001, respectively. Results are presented as median (IQR) and mean (SD). F statistic calculated by Kruskal–Wallis test. km—kilometer. Figure 3. Box plots displaying sex differences in weekly training frequencies in different training periodization phases and stages. Figure 4. Box plots displaying sex differences in weekly training mileages (km) in different training periodization phases and stages. Figure 4. Box plots displaying sex differences in weekly training mileages (km) in different training periodization phases and stages. Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 9 of 15 Figure 5. Box plots displaying sex differences in daily training unit (km/unit) in different training periodization phases and stages. 4. Discussion The objective of this study was to investigate sex differences in training behavior among endurance runners; the present investigation was conducted to assess the relation- ships between training behaviors of female and male recreational runners from 10 km up U d h f d f l h d l b d h d Figure 5. Box plots displaying sex differences in daily training unit (km/unit) in different training periodization phases and stages. Int. J. Environ. Res. Public Health 2022, 19, 13238 9 of 14 4. Discussion The objective of this study was to investigate sex differences in training behavior among endurance runners; the present investigation was conducted to assess the relation- ships between training behaviors of female and male recreational runners from 10 km up to UM distances. The main ﬁndings were (1) female runners had a lower body weight and height and thus a lower BMI than the males; (2) a signiﬁcant sex difference was found in main event racing distance; (3) signiﬁcantly more females reported following an external training resource but no sex difference was found in the training duration for the main event; (4) weekly training frequencies were similar for females and males across periodization phases (including all stages of Phase B); (5) various discrepancies were identiﬁed between the sexes for weekly mileages and durations depending on each phase/stage; (6) males were signiﬁcantly more active at all phases/stages considering daily mileage and duration; (7) males had a greater exercise focus on sport performance but no signiﬁcant differences were found between sexes for the initial or current running motivations. While similarities exist between the training behaviors of female and male endurance runners [8–15], the results of the present investigation uphold the hypothesis that there are critical training differences between the sexes of recreational endurance runners of various distances (HM, M/UM, 10 km). Based on the previous literature concerning female and male runner anthropometric differences [12], the present ﬁndings are consistent, indicating that females are generally smaller than their male counterparts. Moreover, the participants’ anthropometrics (body weight, height, BMI), especially of the females, highlight the general knowledge of recre- ational runners being a slim, ﬁt, and healthy population [2–4,28,34]. All the while, only 3 of the initial participants (<0.01% of the sample) reported having obesity and were excluded from this study due to the required WHO-based BMI criteria [31,32]; thus, the typical healthy runner training and race preparation lifestyle is reﬂected by the participants’ BMI within this study [2–4,28,34]. A signiﬁcant difference was found in this study in the proportions of race distance subgroups between the sexes, with the largest proportion of females being 10 km runners and a remarkably larger proportion of males being M or UM runners. Until recent years, female event participation in very long distances (HM, M, or UM) has been trailing that of males [35], and this result may be a reﬂection that this difference between the sexes has not been completely mitigated. Considering that the males were more heavily proportioned among the longer-distance racing subgroups (HM, M/UM), and that the previous litera- ture has shown considerable variation in runner training behaviors based on this factor alone [15], it could be expected that there are signiﬁcant critical sex differences in training behavior of endurance runners. Furthermore, it was found that signiﬁcantly more females were training under the direction of a professional (whether a sport scientist, doctor of sports medicine, or trainer) or followed another resource rather than training alone and independently, which was more common among the males. This ﬁnding could be related to a generally higher level of health consciousness among females, while professional support is well-known to be beneﬁcial for the health of runners and especially injury prevention [3,4,36]. In addition, while participation was similar in most other sports activities to balance running (cycling, swimming, rambling, triathlon, skiing, cross country skiing, and snowboarding), a sex difference for fell/trail running and ski-touring was detected. Regarding the ﬁnding of signiﬁcantly more male participation in fell/trail running, a previous study found the opposite [37]; however, that report only analyzed runners of a speciﬁc trail race, and the current sample was more general [25]. Ski-touring, on the other hand, predominantly takes place in the backcountry with advanced-level terrain and increased avalanche risk; thus, one possible explanation for more male ski-touring participation may be a higher risk- seeking tendency among males [38]. Furthermore, no sex difference in the training duration for the main event was found, which could be considered the overarching periodization scheme [22]. This ﬁnding may be due to the fact that participating in an endurance running Int. J. Environ. Res. Public Health 2022, 19, 13238 10 of 14 event requires a minimal preparatory period to safely complete each event, which is likely dependent upon the event race distance (e.g., 10 km, HM, M, or UM) [15,39]. Regarding the breakdown of the training periodization into three distinct phases (Phase A: Regeneration Stage and Transitional Period; Phase B: Preparatory Period; Phase C: Main Competition Period), no sex difference in the weekly frequencies of training was detected. Therefore, regardless of sex, participants trained regularly throughout each week and training phase, possibly due to the fact that regular exercise is well-accepted as being healthy and provides a plethora of beneﬁts to each individual [34,40]. Within Phase A, no sex differences in weekly training mileage or duration were found, which is likely due to this particular phase having a main focus of recovery to avoid overloading as it comes directly after participating in the previous main event [41]. Therefore, it would be expected that the participants’ characteristics are somewhat unrelated to training in this phase, as the major goal of this phase is proper regeneration and a smooth transition to the main preparatory period [41]. However, signiﬁcant differences between the sexes regarding their daily training mileages and daily training durations in Phase A were found, suggesting that males run a greater distance per day and also spend a greater amount of time running per day than females during this period. This ﬁnding is possibly due to the greater professional advice sought by females, as more resting is highly advisable for achieving maximal recovery during this period, which may be a concern of overuse injury or burnout for males who are seeking less professional support for training [41,42]. Within Phase B, there are four preparatory stages with distinct training characteristics. Weekly training mileages and durations were similar for females and males during Prepara- tory Stages 2 and 3, whereas signiﬁcant sex differences were found for Stages 1 and 4. Considering the training similarities between the sexes in Stage 2 (build-up training, includ- ing speciﬁc basic training, and at a low-to-moderate intensity) and Stage 3 (intervals, pace, speciﬁc competition training, and moderate-to-high intensity), it appears that the training run distance and duration are unrelated to sex. Therefore, these particular types of weekly exercises may be integral to the training plans of endurance runners regardless of sex, which is consistent with previous reports [8,11]. For Stage 1 (general basic training at a low intensity mainly) and Stage 4 (test competition, race-speciﬁc training, and moderate-to-high intensity); however, the discrepancies between the sexes for weekly mileages and durations are likely related to the previously mentioned result of more females racing in the shorter distance (10 km) and more males racing in longer distances (M/UM) [15]. Furthermore, and in connection, both the daily training mileages and daily training durations showed signiﬁcant differences between the sexes across all four stages of Phase B. Considering that runners of longer distances have been shown to run further mileage while training [15], these Phase B mileage differences between the sexes likely arise from the result that the males race over longer distances. Regarding Stage 1, the general basic training at a low in- tensity is dominant in the long-distance runner’s training plan (M/UM) [15,43,44], and the present results show that males run remarkably more during this period with an additional 2.94 km per day (+0.14 h). Previous research has found performance and health beneﬁts to sustained loads of low-intensity training, including left ventricular hypertrophy and the consequential enhancements to stroke volume, maximal volume of oxygen consumption, and thus lower resting heart rate [45]. Indeed, moderate-to-high intensity training at Stage 4 would require extensively longer bouts of mileage and duration for the longer-distance runner to meet their adaptive training thresholds for building mitochondrial density and capillarization [44,46]. Previous research has not found any major link between sex and volume of oxygen consumption training adaptations [46]. For Stage 4, the speciﬁc training types (including test competition and race-speciﬁc training) are less likely to be related to beneﬁcial physical adaptations but are rather mental preparations, which are highly important for the longer-distance runner, as very long distances (M/UM) require extensive durations of focus [47,48]. Therefore, the training durations (weekly or daily) would appear to be dependent upon the mileages (weekly or daily), as increases in the distance load require more time to complete with a speciﬁed running intensity [15]. Int. J. Environ. Res. Public Health 2022, 19, 13238 11 of 14 Results for Phase C, which was considered the main competition period and included tapering and interim race stage/s, show that there was no sex difference for weekly training mileage or duration, which may be due to this phase including tapering, and the consequential drastic reduction in weekly training volume for both females and males compared to the prior phases [49]. However, considering that this phase also included interim race stage/s, which are likely related to the main event race distance, there may be a connection to the signiﬁcant sex differences identiﬁed for daily training mileage and duration [15]. Exercise motivations may underlie much of the participatory behavior in endurance running [3,50]. The present results are in line with previous ﬁndings, that the male exercise focus is more sport performance-oriented than females and that females are more focused on health [50]. Regarding the ﬁnding of leisure as the female’s main exercise focus, this result is somewhat inconsistent with previous literature [50], which suggests that females are primarily health-focused. However, no signiﬁcant sex difference was identiﬁed in terms of the participants’ current running motivations, which could partly explain why there were also some training similarities between the sexes within the current results. Therefore, it is possible that a small fraction of participants considered themselves to be athletes of another main sport instead of running. Causal ascriptions have been suggested as the foundation for developing a theory of motivation and emotion [51]. Therefore, it appears achievement-related projections, which are plausibly socio-bounded, have a major inﬂuence on the exercise focus differences among females and males [50,51]. In our sample, 96% of participants were from Austria, Germany, and Switzerland, suggesting a rather homogenous group in terms of social culture [52]. Those German-speaking countries are reported to have advanced economies even compared to other western nations, and the results indicate no sex difference in academic qualiﬁcation, including a predominantly white-caucasian population with high- income levels, high quality of life, and a high life expectancy [52,53]. Comparable to other studies that follow a cross-sectional design, the presented results include some limitations that should be addressed when interpreting the ﬁndings. The sample size was relatively small, and considering the sex-based approach of the present investigation, there was an unequal distribution of sexes per se (58% females), and also within the race distances, including more females (48% vs. 23% of males) as 10 km runners. However, most of the participants were racing at the HM distance, marathon, or ultra- marathon distance. As the results are based on self-reporting survey methods, over- and under-reporting of answers are possible based on the sociological expectations of the participants’ cultures; however, control questions (e.g., race distance) were used to minimize this effect. Lastly, most participants were from Germany, Austria, and Switzerland, which may limit the interpretation of some of our ﬁndings to Western and European running cultures. While the present investigation includes some limitations, the results have the potential to add light to this speciﬁc gap in the current literature on sex differences in training behaviors of 10 km up to ultra distance recreational runners. Upon careful consideration, the ﬁndings may be particularly beneﬁcial for athletic trainers, physical therapists, coaches, team physicians, exercise physiologists, and endurance runner athletes to reﬁne the vital understanding of planning and applying an optimal, health-based training regimen for successfully running races. Furthermore, the underrepresentation of females in previous athletic samples studied regarding targeted sex-speciﬁc approaches and personalization to training and performance requirements is evident [5,6,22]. In addition, future studies should consider investigating sex differences of endurance runners by controlling for main event race distance associated with runner motives. 5. Conclusions In summary, this is the ﬁrst study aiming to investigate sex differences in training and race preparation behaviors of recreational runners of various distances, including HM, Int. J. Environ. Res. Public Health 2022, 19, 13238 12 of 14 M/UM, and 10 km participants. The results indicate that: female runners are more likely to train with an external resource; male runners train at a higher volume (greater daily mileages and durations at every periodization phase; greater or null weekly mileages and durations at each phase); females exercise with more leisure focus; and males concentrate more on sports performance. It can be concluded that sex differences in training behaviors, which may originate from physiological differences and social expectations, can be related to the distances runners prefer to race and their motivations for running and racing. The results of this study provide a wide overview of the fundamental training behaviors of female and male recreational endurance runners of various distances (HM, M, UM, 10 km) that may be remarkably supportive for carefully designing and implementing a thorough training plan for endurance runners themselves or by their coaches, exercise physiologists, athletic trainers, physical therapists, sport scientists, or sports medicine doctors. Author Contributions: K.W. conceptualized and designed the study and the questionnaires together with B.K. and C.L. K.W. conducted data analysis and D.T. and M.M. provided statistical expertise. D.T., K.W., T.R. and B.K. drafted the manuscript. T.R., M.M., C.L. and K.W. critically reviewed it. G.W. provided technical support and aided in data acquisition and management. 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 protocol is available online via https://springerplus. springeropen.com/articles/10.1186/s40064-016-2126-4 (accessed on 17 August 2022) and was ap- proved 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 with the 1964 Helsinki declaration and its later amendments as of 1996, the Data Security Laws, and good clinical practice guidelines. Study participation was voluntary and could be canceled at any time without the provision of reasons or negative consequences. Informed Consent Statement: Informed consent was obtained from all individual participants included in the study considering the data collected, used, and analyzed exclusively and only in the context of the NURMI Study for scientiﬁc publication. Data Availability Statement: The data sets generated during and/or analyzed during the current study are not publicly available but may be made available upon reasonable request. Subjects will receive a brief summary of the results of the NURMI Study if desired. Acknowledgments: There are no professional relationships with companies or manufacturers who will beneﬁt from the results of the present study. Moreover, this research did not receive any speciﬁc grant or funding from funding agencies in the public, commercial, or non-proﬁt sectors. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Hespanhol Junior, L.C.; Pillay, J.D.; Van Mechelen, W.; Verhagen, E. Meta-Analyses of the Effects of Habitual Running on Indices of Health in Physically Inactive Adults. Sports Med. 2015, 45, 1455–1468. [CrossRef] [PubMed] 2. Lee, D.-C.; Pate, R.R.; Lavie, C.J.; Sui, X.; Church, T.S.; Blair, S.N. Leisure-Time Running Reduces All-Cause and Cardiovascular Mortality Risk. 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PMC8518541	2002 \| Scand J Med Sci Sports. 2021;31:2002–2009. wileyonlinelibrary.com/journal/sms Received: 26 April 2021 \| Revised: 20 June 2021 \| Accepted: 30 June 2021 DOI: 10.1111/sms.14016 O R I G I N A L A R T I C L E The socio- economic impact of running- related injuries: A large prospective cohort study Tjerk S. O. Sleeswijk Visser1,2 \| Marienke van Middelkoop3 \| Tryntsje Fokkema2,4 \| Robert- Jan de Vos1 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. Scandinavian Journal of Medicine & Science In Sports published by John Wiley & Sons Ltd. Trial registration number: NTR number: NL5843 1Department of Orthopedic Surgery and Sports Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands 2Leiden University Medical Center, Leiden, The Netherlands 3Department of General Practice, Erasmus MC University Medical Center, Rotterdam, The Netherlands 4Department of General Practice and Elderly Care Medicine, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands Correspondence Robert- Jan de Vos, Department of Orthopedic Surgery and Sports Medicine, Erasmus MC University Medical Centre, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands. Email: r.devos@erasmusmc.nl Funding information The INSPIRE trial was funded by the Netherlands Organization for Health Research and Development (ZonMW), grant number 536001001 Objectives: To evaluate the impact of running- related injuries (RRIs) on activities of daily living (ADL), work, healthcare utilization, and estimated costs. Design: Prospective cohort study with data from a randomized controlled trial. Methods: Adult recreational runners who registered for a running event (distances 5 to 42 km) were included in this study. Minimum follow- up duration was 3 months (preparation, event participation, and post- race period). Injuries were registered using a standardized definition. Primary outcome measure was a standardized 5- item sur- vey on limitations in ADL. The survey data were categorized to the number of injured runners with complete/moderate/no limitations. This outcome was expressed as the percentage of injured runners with any limitation (complete or moderate limitations amalgamated). Secondary outcomes were work absenteeism, the number of health- care visits per injured runner, and estimated direct medical and indirect costs per participant and per RRI. Results: 1929 runners (mean [SD] age 41 [12] years, 53% men) were included in this study and 883 runners (46%) sustained a RRI during the course of the study. Injured runners reported the highest limitations (% with any limitation) of RRIs dur- ing the first week of injury on sports and leisure activities (70%) and transportation activities (23%). 39% of the injured runners visited a healthcare professional. Work absenteeism due to the RRI was reported in 5% of the injured runners. The total mean estimated costs were €74 per RRI and €35 per participant. Conclusions: Injured runners are mainly limited in their transportation activities and during sports and leisure. While the estimated costs of RRIs are not high when ex- pressed per participant, the absolute costs may be substantial due to the popularity of running. K E Y W O R D S activities of daily living, epidemiology, running, work \| 2003 SLEESWIJK VISSER Et aL. 1 \| INTRODUCTION Physical activity has proven to be a cost- effective way to en- hance overall health and reduce morbidity and mortality.1- 4 Running is an increasingly popular way to improve physical and mental well- being.5- 7 In 2019, close to 2 million people (11% of the Dutch population) performed weekly running ac- tivities in the Netherlands.8 Musculoskeletal injuries are a prominent disadvantage of running, with training errors being frequently suggested to be a major cause of injury.9,10 Most running injuries are due to overuse and are located at the knee, lower leg, ankle, and foot.11,12 The incidence of running- related injuries (RRIs) varies among different populations (eg, cross- country run- ners, novice runners, and long- distance runners) of runners and can be up to 85% in novice runners training for an event.9 Even though RRIs are frequent, not much is known about the impact of these injuries on socio- economic outcome mea- sures. The impact of RRIs on activities of daily living (ADL) has, for example, never been described in literature. Healthcare utilization, direct medical costs, and indirect costs due to absenteeism from work are outcome measures to estimate the impact of a disease.13 A few studies have reported the economic burden of RRIs, which varies between €83 and €174 per RRI and €13 and €105 per participant training for an event.14- 16 These ranges are large and this may be due to the fact that these results were based on small study samples or only novice runners were included.14,15 This makes it dif- ficult to extrapolate these findings to the general recreational running population.16,17 Therefore, it is relevant to evaluate the economic burden of RRIs in a large heterogeneous run- ning population. Knowledge of the social impact, the specific areas affected by RRIs, and the experienced pain and disabil- ity could aid in the design of tailored treatment practices. The magnitude of the economic burden of RRIs is important to know, as it affects the urgency of RRIs in scientific agendas. Therefore, the primary objective of this study is to assess the impact of RRIs on activities of daily living in runners training for an event. Secondary objectives are to evaluate the experienced pain and the effect of RRIs on work absentee- ism, healthcare utilization, and estimated direct and indirect costs. 2 \| METHODS 2.1 \| Study design The study was designed at the Erasmus MC University Medical Centre (Rotterdam, the Netherlands) and was part of a randomized control trial (The INSPIRE trial), which evaluated the effect of an online prevention program on the number of RRIs among recreational runners. A detailed study protocol has been published elsewhere.18 The Medical Ethics Committee of the Erasmus MC University Medical Centre Rotterdam, the Netherlands, approved the study pro- tocol (MEC 2016– 292). The trial was registered before com- mencement (NTR number: NL5843). For the randomized trial, patients in the intervention group had access to an online injury prevention program, whereas the control group did not receive this information. There were no differences in injury proportion between both groups, and therefore, we regarded this study population as a large cohort. The results of this randomized controlled trial have been published elsewhere.19 In the RCT and the current study, an RRI was defined as an injury of the muscles, joints, tendons, and/or bones in the lower back or lower extremities that was caused by run- ning with at least one of the following criteria: (1) the injury caused a reduction in running distance, speed, duration, or frequency for at least 1 week; (2) the injury led to a visit to a medical specialist and/or physiotherapist; and/or (3) med- ication was necessary to reduce symptoms as a result of the injury. 2.2 \| Participants Potentially eligible participants were runners of 18 years or older who registered for one of 3 running events in 2017. These running events included the LadiesRun Rotterdam (5, 7.5 or 10 km), the NN Marathon Rotterdam (10.6 or 42.2 km), and the NN City Pier City The Hague (5, 10 or 21.1 km). If runners expressed their interest to participate during online registration for the event, they were provided with more information, and if still interested, they were as- sessed for eligibility. Participants were included if they met the inclusion criteria (18 years or older, registration at least 2 months before the running event, knowledge of the Dutch language, and access to email). After providing digital in- formed consent, participants could immediately complete the baseline survey. 2.3 \| Procedures Patients were asked to complete an online survey (using the secure application LimeSurvey) on 4 different time points; (i) at baseline (≥2 months before the running event, (ii) 2 weeks before the running event, (iii) 1 day after the running event, and (iv) 1 month after the running event. At baseline, runners were asked to complete questions on demographics (sex, age, length, and weight), training characteristics during the past year (running frequency, duration, and speed), and lifestyle (smoking, alcohol use). The baseline survey also inquired whether the runner had suffered an RRI in the past 12 months. 2004 \| SLEESWIJK VISSER Et aL. The 3 follow- up survey consisted of questions about the cur- rent state of previously reported RRIs, the occurrence of new RRIs, the impact of new RRIs on ADL, and work absentee- ism and health care utilization due to the RRI. Injured run- ners were asked to specify injury location (back, buttock, hip, groin, upper leg, knee, shin, calf, Achilles, ankle, foot, or toe) and injury onset (gradual or acute). 2.4 \| Outcome measures 2.4.1 \| Primary outcome measure Impact of RRIs on activities of daily living (ADL) was meas- ured at all 3 follow- up time points (2 weeks before the run- ning event, 1 day after, and 1 month after the running event), using a 5- item survey. Only participants who sustained one or multiple new RRIs were asked to complete this survey. Injured runners completed this survey only once per follow- up time point, independently of the number of RRIs they sustained. This survey has not been validated but has been used in previous studies on RRIs.20,21 The survey consists of 5 questions on the following dimensions: (1) daily activi- ties (eg, getting up, washing, getting dressed), (2) household activities (eg, cleaning, vacuuming), (3) activities at work/ school, (4) transportation activities (eg, driving, cycling, walking), and (5) sports and leisure activities. Each domain consists of 3 response options: no limitations, moderate limi- tations, and complete limitations. Injured runners were asked to indicate their ability to perform activities of daily living in the first week after the injury. For every follow- up time point, injured runners completed this survey, resulting in an expres- sion of the number (%) of injured runners with complete, moderate, or no limitations per domain. The results of these three separate follow- up time points were combined and ex- pressed as the total number (%) of injured runners with com- plete, moderate, or no limitations per domain. Results were also expressed as the number of injured runners with any limitation (complete or moderate limitations amalgamated). We also compared the impact on ADL in the first week after the injury between RRIs with an acute and gradual onset. 2.4.2 \| Secondary outcome measures Impact on ADL per RRI location We compared the impact on ADL between different injury locations by subdividing the RRIs in 5 clustered injury loca- tions: (1) lower back, (2) buttock/hip/groin, (3) upper leg/ knee, (4) lower leg (shin/Achilles/ankle), and (5) foot/toe.19 If injured runners sustained more than 1 RRI, which origi- nated from different clustered injury locations (eg, if a runner sustained an RRI to the groin and an RRI to the ankle), they were excluded from this part of the analysis. This is because in these cases, it was not possible to adequately assess which RRI specifically led to an impact on ADL. If injured runners sustained multiple RRIs, but these injuries were all located in the same clustered injury location, they were included in this part of the analysis. For every follow- up time point, injured runners completed this survey, resulting in an expression of the number (%) of injured runners with any limitation (com- plete or moderate limitations amalgamated). Injured runners were asked to indicate the amount of pain (on a visual ana- logue scale; VAS 0– 10) during rest and running in the week preceding the completion of the survey. We also compared the mean pain during rest and running between the clustered injury locations and between acute injuries and gradual onset RRIs. Work absenteeism Work absenteeism was assessed by the number of lost days at work/school due to an RRI and was measured at all 3 follow- up time points. Only injured runners were asked to complete this part of the survey. Work absenteeism was expressed as the mean number of days of absence from work per injured runner. Healthcare utilization Injured runners were asked whether they had used health care due to an RRI. Healthcare utilization was assessed by asking the total number of healthcare visits per type of healthcare provider. Healthcare utilization was expressed as the mean healthcare consumption (number of visits) and mean medical costs per injured runner, per type of healthcare provider. Costs The estimated costs were divided into 2 categories: costs from healthcare utilization (direct costs) and costs as a result of ab- senteeism from work (indirect costs). We established produc- tivity costs per hour and the costs per treatment/visit based on a guideline for economic evaluations in health care, published by the Dutch Healthcare Authority.22,23 We determined the di- rect costs by multiplying the total number of visits/treatments with the estimated medical costs for those visits/treatments. The specific costs used for the economic evaluation are pre- sented in Appendix S1. Mean direct and indirect costs due to an RRI were calculated per RRI and per participant (the mean of all participants and not only injured runners). 2.5 \| Statistical analysis Presence of a normal distribution of data was assessed using the Shapiro- Wilk test. Normally distributed data are presented as mean with standard deviation (SD) and non- normally dis- tributed data as median with interquartile range (IQR). We \| 2005 SLEESWIJK VISSER Et aL. presented costs (in €) as mean with standard deviation (SD). Differences in direct, indirect, and total costs between men and women and between acute and gradual onset RRIs were compared using a Mann- Whitney U test. Missing data (par- ticipants who did not complete at least one follow- up sur- vey) were excluded from the analyses for the study purposes described in this manuscript. For the analyses, a p value of <0.05 was considered statistically significant. We used SPSS software (V.24.0.0.1; SPSS) for statistical analysis. 3 \| RESULTS 3.1 \| Participants In total, 2378 participants were included in the randomized control trial of whom 1929 participants (81%) completed at least one follow- up survey and were included in this study. The mean (SD) age was 4212 years with the majority being male (53%). 883 (46%) participants reported at least one RRI during the course of this study. Most injuries (61%) had an acute origin. 714 of the 883 (81%) injured runners completed the surveys for our primary and secondary outcome meas- ures. The participant characteristics are displayed in Table 1. 3.2 \| Primary outcome— Activities of daily living Injured runners reported the highest limitations (any limita- tion) of RRIs during the first week of their injury on sports and leisure activities (70%) and transportation activities (23%). Lower frequencies of limitations were reported for daily activities (10%), household activities (12%), and activi- ties at work/school (9%). Injured runners with acute onset RRIs reported higher limitations (any limitation) of RRIs during the first week of their injury compared to injured runners with gradual onset RRIs on daily activities (11% vs. 6%), household activities (16% vs. 7%), activities at work/school (12% % vs 5%), trans- portation activities (25% vs. 18%), and sports and leisure ac- tivities (75% vs 60%). Figure 1 shows the impact on ADL of acute and gradual onset RRIs. 3.3 \| Secondary outcomes 3.3.1 \| Impact on ADL per RRI location Injured runners with RRIs located at the lower back and lower leg reported higher limitations (any limitation) of RRIs during the first week of their injury compared to the over- all average on household activities (42% and 13% vs. 12%), activities at work/school (25% and 10% vs 9%), transporta- tion activities (42% and 26% vs. 23%), and sports and leisure activities (71% and 75% vs 67%). The impact on ADL per clustered injury location is shown in Figure 2. Mean (SD) pain (VAS 0– 10) score during rest was higher in lower back (4.8 [2.8]) injuries compared to but- tock/hip/groin (3.7 [2.4]), upper leg (3.5 [2.4]), lower leg (3.3 [2.9]), and foot injuries (3.2 [2.4]). Mean pain (VAS 0– 10) score during running was lower in lower back injuries (5.1 [3.1]) compared to buttock/hip/groin (6.0 [2.7]), upper leg (5.9 [2.7]), lower leg (6.0 [2.9]), and foot injuries (6.4 [2.7]). Mean (SD) pain (VAS 0– 10) scores during rest and running were similar for acute injuries (3.7 [2.4] and 5.7 [2.7]) and gradual onset RRIs (3.4 [2.4]) and 6.0 [3.0]). 3.4 \| Work absenteeism Work absenteeism due to an RRI was reported in 5% of the injured runners. Within this group of injured runners, the mean (SD) number of days of absence from work due to an RRI was 3.5 (3.5). 3.5 \| Healthcare utilization 39% of the injured runners visited a healthcare profes- sional and 8% initiated self- care. The mean (SD) number of TABLE 1 Descriptive statistics of participants Characteristics (n = 1929) Mean (SD) Personal characteristics Age (years) 41.9 (12.1) Sex (Male/Female) 1020/909 BMI (kg/m2) 23.6 (2.9) Injury- related factors Injury proportion 883 (45.8%) Injury mechanism (acute/gradual onset); % 61/39 Previous RRI (preceding 12 months) n (%) 994 (51.5%) Reported RRI at baseline (yes) 415 (21.5%) Sports- related factors Running duration (hours/week) 3.1 (3.7) Running experience (years) 6.8 (8.1) Lifestyle- related factors Smoking (yes) (%) 76 (3.9%) Alcohol use (glasses per week) 4.2 (4.8) Days with >30 mins of physical activity (days/ week) 5.8 (2.0) Note: Values are displayed in frequency means (standard deviation). Abbreviations: BMI, body mass index; RRI, Running- related injury; SD, standard deviation. 2006 \| SLEESWIJK VISSER Et aL. healthcare visits was 1.4 (4.5) per injured runner. A visit to a physiotherapist was reported by 32% of the injured run- ners. 4% visited a general practitioner and 2% reported hav- ing visited a medical specialist. 76% of the total number of healthcare visits consisted of physiotherapist visits. Table 2 demonstrates the frequencies of healthcare visits per type of healthcare provider. 3.6 \| Estimated direct and indirect costs The majority (82%) of the estimated total healthcare costs con- sisted of physiotherapy treatments (Table 2). The estimated total healthcare costs were €39 (SD 139) per RRI and €18 (SD 97) per participant, accounting for the entire study population of participants who completed at least one follow- up survey FIGURE 1 Impact on activities of daily living (ADL) during first week of injury (gradual vs. acute onset injuries). Displayed values are percentages of any (moderate and severe) limitation 0 10 20 30 40 50 60 70 80 Daily activites Household activities Activities at work/school Transportation activities Sports/Leisure activities Impact on activities of daily living (ADL) during the first week of injury Gradual Acute FIGURE 2 Impact on activities of daily living (ADL) during first week of injury (specified per injury location). Displayed values are percentages of any (moderate and severe) limitation 0 10 20 30 40 50 60 70 80 Daily activities Household activities Activities at work/school Transportation activities Sports and leisure activities Impact on activities of daily living (ADL) during first week of injury. Lower back Buttock/hip/groin Upper leg/knee Lower leg Foot/toe Overall average TABLE 2 Healthcare utilization and medical costs per injured runner, per type of healthcare provider (n = 714 injured runners) Healthcare provider Patients using health care, no. (%) Mean healthcare consumption (% of all healthcare visits) Mean (SD) Medical costs Primary care (visits) General practitioner 26 (3.6%) 0.06 (8.6%) €2.11 (13.0) Physical therapist 231 (32.4%) 1.2 (76.2%) €39.88 (141.7) Othera 31 (4.3%) 0.1(10.2%) €3.25 (19.4) Secondary care (visits) Medical specialist (eg, Sports medicine physician/orthopedic surgeon) 15 (2.1%) 0.04 (5.0%) €3.48 (27.7) Total 1.4 (100) €48.74 (154.6) Abbreviation: SD, standard deviation. aAnother healthcare provider (eg, masseur, osteopath, podiatrist, alternative healthcare provider). \| 2007 SLEESWIJK VISSER Et aL. (N = 1929). The estimated costs due to absenteeism from work were €35 (SD 267) per RRI and €16 (SD 183) per participant. Total estimated direct and indirect costs were €74 (SD 329) per RRI and €35 (SD 227) per participant (Table 3). Costs from work absenteeism accounted for 48% of the total costs. Estimated direct costs for acute and gradual onset RRIs were €41 (SD 166) and €37 (SD 88), respectively. This difference was not statistically significant (U = 96 950.5, Z = −0.38, p = 0.71). The estimated indirect costs for acute RRIs (mean €59) were significantly higher (U = 92 953.0, Z = −4.2, p = 0.00) than for RRIs with a gradual onset (mean €2). There was no significant difference in total costs for acute RRIs (mean €100) compared to RRIs with a gradual onset (mean €39) (U = 97 563.0, Z = −0.17, p = 0.87). No statistically significant difference in estimated direct and indirect costs was found between males and females (Table 3). 4 \| DISCUSSION This study showed that the largest impact of RRIs is on sports and leisure (70%) and transportation activities (23%), while the impact on other activities of daily living was relatively low. The percentage of injured runners with any limitation in ADL was higher in RRIs located at the lower back and lower leg compared to the other clustered injury locations. Work absen- teeism due to an RRI was reported in 5% of the injured run- ners. The total mean number of healthcare visits was 1.4 per injured runner, and the total mean estimated costs were €74 per RRI and €35 per participant. Acute injuries initially led to more limitations in ADL and higher estimated total costs. 4.1 \| Activities of daily living Activities of daily living were mainly affected in the domains sports and leisure activities and transportation activities. Still approximately 1 in 10 injured runners experienced limita- tions in daily and household activities or activities in work/ school. To better understand the impact of RRIs on activi- ties of daily living, we evaluated this impact for both acute and gradual onset RRIs and for different injury locations. We found that acute onset RRIs and lower back and lower leg in- juries in particular led more frequently to limitations in daily life. This limiting effect only partly correlated with pain score during rest, which was relatively high in lower back injuries. The restricting effect of lower back pain on ADL has been demonstrated in previous studies.24,25 For lower leg injuries such as shin bone, Achilles tendon, and ankle joint injuries, this has never been described using this approach. Healthcare providers can take this into account when educating injured runners with lower back or lower leg injuries about the poten- tial consequences of their injury. This information can also be used by healthcare policy makers in the design of tailored management plans or preventive measures. 4.2 \| Healthcare utilization, work absenteeism, and costs Healthcare utilization mainly consisted of physiotherapy visits, which is in line with existing literature.13- 16 The total costs of RRIs were estimated at €35 per participant and €74 per RRI. We compared these costs to other studies describing the economic burden of RRIs in runners preparing for and participating in a running event. Two studies estimated the economic burden to be around €173 per RRI.14,15 In contrast to our study, only small and selected (trailrunners and run- ners participating in events ≤10 miles) running populations were included. This could have led to a less accurate estima- tion of the economic burden of RRIs in these studies and less generalizability to the overall running population. A large prospective cohort study estimated the cost of RRIs to be €13 per participant.16 However, this study had a follow- up of only Overall Mean (SD) medical costs Mean (SD) indirect costs (absenteeism from paid work) Cost per RRI, total (n = 898) €74.29 (328.6) €38.99 (138.5) €35.29 (266.7) Acute onset (n = 523) €99.75 (420.6) €40.74 (165.6) €59.00 (346.6) Gradual onset (n = 375) €38.78 (100.0) €36.56 (88.0) €2.22 (32.1) p Value 0.87 0.71 0.0 Cost per participant, total (n = 1929) €34.58 (227.7) €18.15 (96.9) €16.43 (182.8) Males (n = 1020) €33.98 (225.7) €20.35 (120.0) €13.63 (169.2) Females (n = 909) €35.25 (230.3) €15.68 (59.3) €19.57 (196.9) p Value 0.48 0.45 0.49 Note: Mann- Whitney U test values comparing costs between acute and gradual onset RRIs and males and females. TABLE 3 Direct and indirect costs per RRI (n = 901) and per participant (n = 1929) 2008 \| SLEESWIJK VISSER Et aL. 6 weeks. This could explain why the estimated costs in the current study are higher as long- standing injuries will have had more impact on our study.16,26,27 Expressed per partici- pant the total costs of RRIs appear to be relatively low. When considering the popularity of running, which is practiced by close to 2 million people in the Netherlands, the absolute costs of RRIs may be substantial. This underlines the need for optimized preventive measures. Previous studies reported contradictory findings on the difference in costs between males and females and between acute and gradual onset RRIs.14- 16 The costs per RRI in the current study were substantially higher for injuries with an acute onset than injuries with a gradual beginning. This dif- ference can be explained by the difference in costs from ab- senteeism from work. The finding of higher indirect costs for acute RRIs is in line with a large study on RRIs among nov- ice runners but in contrast with other studies.14- 16 Overuse injuries are supposed to have more impact over time, which was demonstrated in several studies.16,26,27 If we would have followed the runners for a longer time, costs of overuse inju- ries may have increased. It could be hypothesized that RRIs with an acute onset are accompanied with higher absenteeism from work because the severity of symptoms is higher in the initial phase of the injury, while the severity of injuries with a gradual onset is spread out over time. This might lead to less people being absent from work. We found that injured runners with acute onset RRIs experience more limitations on ADL in the first week of injury, which could support this hypothesis. The difference between both injury groups could also originate from the way we measured indirect costs in this study, as we only included costs from absenteeism from work and did not ask participants about a decrease in work pro- ductivity. A decrease in work productivity could lead to sub- stantial costs, which has been shown in several studies on the impact of overuse injuries.28- 30 It could be that— while acute onset injuries lead to higher absenteeism in the short term— once back at work the work productivity in people who suf- fered this type of injury is back to normal. Subsequently, indirect costs could be similar in both injury groups if they were measured over a longer period of time and measured more accurately (including work productivity measures). 4.3 \| Strengths and limitations This is the first study to report the impact of RRIs on ADL. We were able to show this impact for different injury loca- tions. Furthermore, this prospective cohort study included a large heterogeneous running population, which increases the generalizability of these findings. There are also some limi- tations to this study. We assessed our primary outcome by using a non- validated survey, which could decrease the reli- ability of the results. However, we used an online survey with limited response options, which was completed in the same way by all injured runners at all 3 follow- up time points. This guaranteed the internal consistency of the survey, and it is therefore likely that these results are reliable. Secondly, we asked patients about their limitations in daily life during the first week of injury retrospectively. This could have induced recall bias and may have led to inaccuracy of the results on this specific outcome measure. 4.4 \| Recommendations for future research Future research could focus on the impact of RRIs on qual- ity of life, hereby using validated questionnaires (eg, the EuroQol questionnaire [EQ- 5D]). This will provide more information on the social impact of RRIs and the specific do- mains which are affected. In addition, it would be interesting to perform an economic evaluation of RRIs with the addi- tion of work productivity and a longer follow- up period in order to evaluate if this affects the direct and indirect costs of overuse injuries. Next to this, it would be helpful if more economic evaluations per type of sports are performed to be able to adequately compare the costs of RRIs with different sports. 5 \| PERSPECTIVE This study showed that runners suffering from an RRI are mainly limited in their sports and leisure and transportation activities and these limitations are particularly substantial in lower back and lower leg injuries. The total costs for run- ners training for an event were €74 per RRI and €35 per participant. Even though the estimated costs of RRIs are not high when expressed per participant, the absolute costs may be substantial due to the popularity of running and be- cause long- standing RRIs may further increase the costs with longer follow- up time. Consequently, this study emphasizes the need for optimized preventive measures. CONFLICT OF INTEREST The authors declare there is no conflict of interest. DATA AVAILABILITY STATEMENT The authors can confirm that all relevant data are included in the article and/or its Supplementary Information Files. ORCID Tjerk S. O. Sleeswijk Visser https://orcid. org/0000-0002-4483-1936 Marienke van Middelkoop https://orcid. org/0000-0001-6926-0618 Tryntsje Fokkema https://orcid.org/0000-0002-3767-2770 \| 2009 SLEESWIJK VISSER Et aL. Robert- Jan de Vos https://orcid. org/0000-0003-0372-0188 REFERENCES 1. Hatziandreu EI, Koplan JP, Weinstein MC, Caspersen CJ, Warner KE. A cost- effectiveness analysis of exercise as a health promotion activity. Am J Public Health. 1988;78(11):1417- 1421. 2. Frew EJ, Bhatti M, Win K, et al. Cost- effectiveness of a community- based physical activity programme for adults (Be Active) in the UK: an economic analysis within a natural experiment. Br J Sports Med. 2014;48(3):207- 212. 3. Chakravarty EF, Hubert HB, Lingala VB, Fries JF. Reduced dis- ability and mortality among aging runners: a 21- year longitudinal study. 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Scand J Med Sci Sports. 2021;31:2002– 2009. https://doi.org/10.1111/sms.14016	The socio-economic impact of running-related injuries: A large prospective cohort study.	07-11-2021	Sleeswijk Visser, Tjerk S O,van Middelkoop, Marienke,Fokkema, Tryntsje,de Vos, Robert-Jan	eng
PMC8997730	Citation: Wu, J.; Zhang, L.; Yang, H.; Lu, C.; Jiang, L.; Chen, Y. The Effect of Music Tempo on Fatigue Perception at Different Exercise Intensities. Int. J. Environ. Res. Public Health 2022, 19, 3869. https://doi.org/10.3390/ ijerph19073869 Academic Editors: Mark Reybrouck, Piotr Podlipniak and David Welch Received: 15 February 2022 Accepted: 22 March 2022 Published: 24 March 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 The Effect of Music Tempo on Fatigue Perception at Different Exercise Intensities Jianfeng Wu 1 , Lingyan Zhang 2, Hongchun Yang 1, Chunfu Lu 1,, Lu Jiang 2 and Yuyun Chen 2 1 Industrial Design and Research Institute, Zhejiang University of Technology, Hangzhou 310023, China; jianfw@126.com (J.W.); yhc2016@zjut.edu.cn (H.Y.) 2 School of Design and Architecture, Zhejiang University of Technology, Hangzhou 310023, China; lingyanzh@outlook.com (L.Z.); jl01103149@alibaba-inc.com (L.J.); 2111915006@zjut.edu.cn (Y.C.) Correspondence: luchunfu@126.com Abstract: Background: This study aimed to clarify the effect of music tempo on runners’ perception of fatigue at different exercise intensities and while listening to music of different tempos through running experiments. Methods: This study used a within-subject two-factor experimental design with music tempo (fast music, slow music, no music) and exercise intensity (high intensity, low intensity) as independent variables and the time to fatigue perception (TFP), the difference in heart rate (HR) and the difference in the median frequency (MF) of surface electromyography (sEMG) signals as observation indexes. Eighteen participants completed a total of 108 sets of running experiments. Results: (1) The main effect of music tempo on the TFP was signiﬁcant (p < 0.001). (2) The main effect of exercise intensity on the TFP was signiﬁcant (p < 0.001), and the main effect on the difference in HR was signiﬁcant (p < 0.001). (3) The interaction effect of music tempo and exercise intensity on the TFP was signiﬁcant (p < 0.05). Conclusions: Exercisers’ subjective perception of fatigue was affected by music tempo and the interaction between music tempo and exercise intensity, and exercisers’ objective fatigue perception was inﬂuenced mostly by exercise intensity. The ﬁndings of this study provide guidance for runners’ choice of music at different intensities of exercise. Whether it is low-intensity exercise or high-intensity exercise, listening to fast music while exercising can help runners perform better mentally and physically during their runs. Keywords: running; music tempo; exercise intensity; fatigue perception; heart rate; surface electromyographic signals; median frequency 1. Introduction Runners’ fatigue perception varies while running. Fatigue perception refers to the sub- jective intensity of perception of tension, discomfort and fatigue during physical exercise [1]. Exercise fatigue usually manifests as soreness in muscles, an increased heart rate and de- creased cognitive performance [2,3]. Although the continuous accumulation of fatigue will affect people’s exercise intentions and performance [4] and excessive fatigue will lead to physical injury [5,6], appropriate exercise fatigue can improve physical performance [7]. Therefore, to achieve a better ﬁtness effect, runners usually adjust their exercise intensity continuously according to their subjectively perceived state after perceiving exercise fatigue during exercise [8]. Listening to music to reduce the sense of boredom and fatigue during running and to enhance motivation has become a common behavior during running [9]. In terms of whether music interferes with the perception of fatigue in exercisers, there is a consensus among academics that the effects of music can be explained from three perspectives: emotional regulation, attention diversion and fatigue recovery. Emotion plays an important role in regulating motivation in sports. Music can cause exercisers to experience positive emotions during exercise [10], alleviate their perception of fatigue during running and enhance the pleasure and sense of participation in running [11]. Using parallel information processing theory, Rejeski points out that the bandwidth of human Int. J. Environ. Res. Public Health 2022, 19, 3869. https://doi.org/10.3390/ijerph19073869 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 3869 2 of 18 attention processing narrows during exercise. In the absence of the external stimuli of music, runners may pay more attention to their running behaviors and feelings, thus increasing their perception of fatigue, while in conditions with music, runners shift their attention from unpleasant physical feelings to music, reducing their perception of fatigue and other negative feelings [12]. It has also been pointed out that the tempo of music can inﬂuence the internal movement tempo of the body [13], and music with a good tempo can induce speciﬁc movement patterns [14] and enhance the arousal level of the exerciser, raising the threshold of fatigue perception and speeding up recovery from exercise fatigue [15,16]. Further studies have shown that exercise fatigue perception is inﬂuenced by both music tempo and exercise intensity. Different tempos stimulate different emotional states in listeners, resulting in different effects on runners’ fatigue perception [17]. Exercise intensity is another frequently discussed inﬂuencing factor. It has been argued that music can play a role in reducing the perception of fatigue at different exercise intensities [18]. However, some scholars believe that music only plays a regulatory role at speciﬁc intensities [19]. Subsequently, many scholars have conducted studies on music tempo and exercise in- tensity, trying to ﬁnd the optimal combination of exercise intensity and music tempo for modulating fatigue perception. For example, Maddigan et al. concluded that fast music could improve the performance of exercisers during high-intensity exercise and reduce fatigue perception [20], while Yamamoto et al. found that neither fast music nor slow music changed the average power output of exercisers during high-intensity exercise [11], i.e., different music tempos did not disperse individuals’ perception of fatigue. Some scholars have also discussed how movement perception is inﬂuenced by the interaction of music tempo and movement-related fatigue, but the key lies in the music tempo, the consistency of exercisers’ preference and in the music and movement tempos being synchronous or asynchronous. No study compared music tempo to runner fatigue perception at different intensities of exercise. In addition, some scholars have discussed whether exercisers’ fatigue perception is affected by the interaction between music tempo and exercise intensity, but the studies focused on the consistency of music tempo with exercisers’ preference [21] or the inﬂuence of music tempo being synchronous or asynchronous with exercise tempo [22], and there is no study comparing the effect of music tempo on runners’ fatigue perception at different exercise intensities for the time being. The aforementioned studies provide references for the positive effects of music on exercise fatigue perception, but there is still some space for exploration on how to choose the appropriate tempo of music for different intensities of exercise. For example, how does musical tempo affect fatigue perception of exercisers during different intensities of exercise? Is there an interaction effect between music tempo and exercise intensity on runners’ fatigue perception? All these problems need our attention, and the effects of music tempo on fatigue perception at different exercise intensities still need to be further explored. To clarify the effect of music tempo on runners’ fatigue perception at different exercise intensities, this study conducted experiments on running and analyzed and discussed the changes in runners’ fatigue perception at different exercise intensities and with different music tempo conditions by evaluating runners’ fatigue perception. The results provide guidance for individual ﬁtness practitioners choosing music to listen to during exercise at different intensities. 2. Methods 2.1. Subjects We conducted an a priori analysis of the required sample size in the study using G*power, with the presumption of the presence of a medium effect size of f = 0.25 [23], a statistical test power = 0.8 and a signiﬁcance level α = 0.05. The results of the analysis indicated that a sample size of 18 would be sufﬁcient to achieve a medium effect size interaction effect. To obtain more generalizable research conclusions, the experimental subjects were ordinary college students who did not have regular ﬁtness habits and were not guided by scientiﬁc theories or methodological knowledge of running ﬁtness. They Int. J. Environ. Res. Public Health 2022, 19, 3869 3 of 18 had no diseases of the muscular, skeletal, respiratory or cardiovascular systems. They were not allergic to alcohol and had no cuts or scratches on the thigh muscles. All subjects had no signiﬁcant differences in physical indicators, were between 20 and 30 years old, 170 and 180 cm in height and 55 and 75 kg. A total of 18 healthy males were recruited as experimental subjects after the above rigorous screening. The basic information of the experimental subjects is shown in Table 1. Participants were required to conﬁrm compliance with the following requirements prior to each experiment: (1) Rest at least 8 h the night before each experiment and refrain from participating in any other physical activity other than the exercise tasks of this experiment to prevent cardiopulmonary or muscle function injury or abnormalities. (2) Maintain a previously habitual daily diet and ensure that the experiment is performed at least one hour after the meal, and avoid eating, drinking alcohol or consuming excessive water within one hour prior to the experiment. Table 1. Basic information of the subjects (M ± SD). Number of Subjects Age (Years) Height (cm) Weight (kg) Resting Heart Rate (Beats/min) 18 23.95 ± 1.49 173.78 ± 1.54 64.39 ± 4.55 78.97 ± 8.15 2.2. Experimental Design The experiment was a two-factor within-subject experimental design with the inde- pendent variables being music tempo (fast tempo, slow tempo, no music) and exercise intensity (high intensity, low intensity). The two independent variables led to a total of six experimental protocols. The dependent variables were the time to fatigue perception (TFP), heart rate (HR) and the median frequency (MF) of surface EMG signals. For each of the three music conditions, the experiment was completed for high- and low-intensity running. To prevent interference between different groups of experiments, the interval between each running experiment was 48 h for each subject, and the running experiments for each subject were scheduled at the same time of day (from 9:00 a.m. to 11:00 a.m.) to control for possible effects of diurnal patterns [24]. Fresh air circulation in the room was maintained during the experiment, and the ambient temperature was kept at 23 ± 2 ◦C to reduce the environmental load, promote normal body heat dissipation in the subjects during the exper- iment and to avoid electrode shedding or short circuits caused by excessive sweating. Each participant signed an informed consent form for the experiment before participating. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Industrial Design Institute of Zhejiang University of Technology (protocol code 0903/2021, date of approval 15 September 2021) (Supplementary Materials Files S1 and S2). 2.3. Independent Variable 2.3.1. Music Tempo Music can be divided into fast tempo music and slow tempo music according to the speed of the tempo. The beat of fast tempo music is 150–160 bpm, and the beat is strong. The beat of slow tempo music is 90–100 bpm, and such music is characterized by a narrow range, soft sound and soothing changes [25]. In addition to the requirements of music tempo, this study also followed the following principles of song selection: (1) The songs chosen had a cheerful music style and obvious tempo and were able to induce positive emotions in the subjects. (2) Music without any lyrics was selected to avoid the interference of lyric content. (3) Songs with complex tunes were excluded. According to the principle of music selection, 15 pieces of fast tempo music and 15 pieces of slow tempo music were selected. All subjects who participated in the experiment were invited to score the valence and arousal effect of each piece of music, and the music with higher validity and arousal Int. J. Environ. Res. Public Health 2022, 19, 3869 4 of 18 effect among the fast music and slow music was selected for inclusion in the ﬁnal song list. Finally, 15 pieces of music were obtained for the running experiment. Among them, 9 were slow tempo pieces and 6 were fast tempo music pieces. Details of the selected music are shown in Table 2. Table 2. Details of music selection. Music Tempo Tracks Duration/min Beat/bpm Slow music Falcom Sound Team Jdk 4′23′′ 90 Grass Harvest 3′14′′ 96 The des Alizes 3′40′′ 100 Harunouta 3′03′′ 90 Sakurairo Contrail 2′28′′ 90 Springtime Affair 2′49′′ 95 Regrettably, You Know 2′04′′ 96 A Tiny Sunshine 1′57′′ 99 Sakura Residential Area 2′05′′ 97 Fast music Cigarette Daydreams 3′12′′ 150 Hero 3′34′′ 150 Shanghai Alice Magic Orchestra 3′36′′ 152 Toy War 1′55′′ 160 Where to Jun 3′57′′ 160 Dream Land Days 3′17′′ 155 2.3.2. Exercise Intensity Exercise intensity refers to the amount of exercise an individual can perform per unit of time and is a very important indicator in physical exercise and training. The American College of Sports Medicine suggests that adults should exercise at an exercise intensity of 50–85% of heart rate reserve (%HRR) to improve their cardiopulmonary function [26]. In view of the fact that this study included people who usually exercise less, the exercise intensity during the experiment was within the recommended range of %HRR, with 50–60% HRR for low exercise intensity and 70–80% HRR for high exercise intensity. To facilitate the experiment, the mean treadmill speed was adjusted according to the %HRR interval to explore the relationship between %HRR and exercise intensity and determine the treadmill speed to use for the running experiment [27]. Half of the subjects (7 in total) were randomly selected to participate in the treadmill speed determination experiment. Each subject was required to perform two separate running experiments at 50–60% HRR and 70–80% HRR, respectively. To avoid the effect of fatigue, the two running experiments were separated by more than 24 h. The experimental steps were as follows: (1) the resting HR of the subjects was measured to determine the target HR range; (2) the subjects wore an HR belt and then performed a 3 min warm-up exercise on the treadmill; (3) an incremental running experiment was conducted, in which the subjects started running at a speed of 5.5 km/h and the speed was increased by 0.5 km/h every 2 min; (4) after each subject reached the upper limit of the target HR, the subject ran continuously at that speed and stopped after 5 min. During the 5 min, the change in target HR of the subjects was observed, and the HR of the subjects was kept at the upper limit of the target HR range by increasing or decreasing the speed of the treadmill as appropriate. The target HR was calculated by Equation (1): Target HR = target intensity %HRR × (HRmax − resting HR) + resting HR (1) Based on the average speed of the treadmill while the subjects were in the target HR range, the treadmill speed corresponding to low-intensity exercise (50–60% HRR) for this experiment was 7 km/h, and the treadmill speed corresponding to high-intensity exercise (70–80% HRR) was 9 km/h. Int. J. Environ. Res. Public Health 2022, 19, 3869 5 of 18 2.3.3. Music Tempo with Exercise Intensity The subjects performed constant-load running exercises on a home motorized tread- mill in the general ﬁtness mode at different intensities under the conditions of music or no music. A total of 6 exercise regimens were used for subjects within the 3 × 2 group: no music × low intensity, no music × high intensity, slow tempo × low intensity, slow tempo × high intensity, fast tempo × low intensity, fast tempo × high intensity. To prevent the interference of order effects, the Latin square design method was used to arrange the experimental order in this experiment. 2.4. Dependent Variable 2.4.1. Time to Fatigue Perception In this study, Borg’s scale for rating of perceived exertion (RPE) was used to measure the subjective exertion of exercisers during running. Scores on the RPE scale ranged from 6 (no exertion at all) to 20 (exercise limit), as shown in Table 3. The RPE scale is a subjective assessment of an individual’s perception of muscular exertion, physical tension, discomfort or fatigue during exercise and reﬂects the individual’s perception of his or her fatigue state [28]. When the RPE reaches 15, the exerciser shows shortness of breath and signiﬁcant muscle fatigue. Therefore, to ensure the safety of the experiment, RPE = 15 was chosen as the index for the experiment. Running was stopped when the RPE value reported by the subject reached 15, and the time from the start of running to the time when the RPE reached 15, i.e., the time to fatigue perception, was recorded. Table 3. Borg’s scale for rating of perceived exertion. Score Subjective Exercise Intensity Subjective Exercise Fatigue Score Subjective Exercise Intensity Subjective Exercise Fatigue 6 No exertion at all Not hard at all 14 - 7 Extremely light Extremely relaxed 15 Hard (heavy) Tired 8 - 1 16 - 9 Very light Very relaxed 17 Very hard Very tired 10 - 18 - 11 Light Relaxed 19 Extremely hard Extremely tired 12 - 20 Maximal exertion Trying one’s best 13 Somewhat hard A little tired 1-represents the fatigue status between the two levels. 2.4.2. Instantaneous HR HR can be used to objectively evaluate exercise fatigue and is the easiest indicator to use to assess the intensity of current exercise and exercise fatigue [29]. Exercise fatigue leads to a decrease in the HR regulation function, HR increases with fatigue and HR is dynamic during exercise. In this study, the difference in HR before and after running was used to characterize the degree of fatigue of exercisers. Two heart rate measurements were taken in each set of experiments; the ﬁrst time the subject’s resting HR was measured before the run. The second time was measured during the running process, where the subject was wearing a heart rate belt, and we collected the HR signal of the subject during the whole running process. However, in the data processing, only the heart rate signal of the subject 5 s before the end of the run was selected and averaged as an indicator of the immediate post-run HR. 2.4.3. Surface Electromyography Signal Electromyography (EMG) signals are indicative of biological electrical signals gener- ated by the contraction of human muscles. Surface EMG (sEMG) measures the compre- hensive electrical effect of the conduction of human muscle electrical signals that can be sensed on the skin surface [30]. The sEMG signal is commonly used for the evaluation of neuromuscular function because of its real-time, sensitive and ﬂexible characteristics. It Int. J. Environ. Res. Public Health 2022, 19, 3869 6 of 18 has important practical and research value in the ﬁelds of sports science, clinical medicine, ergonomics, etc. There have been many studies linking sEMG signals to fatigue [31]. The sEMG uses the surface electrode bipolar conduction method, and the derived EMG signal is increased by a signal ampliﬁer and then enters a converter for signal conversion and storage in a computer. It has been shown that the frequency domain index of the sEMG signal is more sensitive to the muscle fatigue state of runners [32]. Frequency domain anal- ysis mainly relies on fast Fourier transformation (FFT) to obtain the frequency spectrum or power spectrum of the EMG signal to reﬂect the variation of the EMG signal in the frequency dimension [33]. The median frequency (MF) is one of the most commonly used indicators in the frequency domain analysis of surface EMG signals, and the MF value is more stable for assessing the muscle fatigue state [34,35]. MF refers to the middle value of muscle ﬁber discharge frequency during skeletal muscle contraction and high frequency discharge is the main expression of excitation of fast muscle ﬁbers, while slow muscle ﬁbers are dominated by low-frequency potential activity [36]. Therefore, the difference in static MF before and after running was used as the index of local muscle fatigue in this study. The formula for MF is shown in Equation (2): MF = 1 2 Z ∞ 0 PSD( f )d f (2) where PSD(f) is the myoelectric power spectral density. Running mainly mobilizes the leg muscle groups; therefore, in this experiment, the sEMG signals of the local muscles in the runners’ legs were collected, and the rectus femoris (RF) and vastus medialis (VM), which display obvious changes in sEMG signals and are very stable during movement changes, were examined [37]. The electrode positions for the RF and VM are shown in Figure 1. Figure 1a shows the electrode positions for the RF, Figure 1b shows the electrode positions for the VM. The black electrode is the reference electrode. 2.4.3. Surface Electromyography Signal Electromyography (EMG) signals are indicative of biological electrical signals gener- ated by the contraction of human muscles. Surface EMG (sEMG) measures the compre- hensive electrical effect of the conduction of human muscle electrical signals that can be sensed on the skin surface [30]. The sEMG signal is commonly used for the evaluation of neuromuscular function because of its real-time, sensitive and flexible characteristics. It has important practical and research value in the fields of sports science, clinical medicine, ergonomics, etc. There have been many studies linking sEMG signals to fatigue [31]. The sEMG uses the surface electrode bipolar conduction method, and the derived EMG signal is increased by a signal amplifier and then enters a converter for signal conversion and storage in a computer. It has been shown that the frequency domain index of the sEMG signal is more sensitive to the muscle fatigue state of runners [32]. Frequency domain analysis mainly relies on fast Fourier transformation (FFT) to obtain the frequency spec- trum or power spectrum of the EMG signal to reflect the variation of the EMG signal in the frequency dimension [33]. The median frequency (MF) is one of the most commonly used indicators in the frequency domain analysis of surface EMG signals, and the MF value is more stable for assessing the muscle fatigue state [34,35]. MF refers to the middle value of muscle fiber discharge frequency during skeletal muscle contraction and high frequency discharge is the main expression of excitation of fast muscle fibers, while slow muscle fibers are dominated by low-frequency potential activity [36]. Therefore, the dif- ference in static MF before and after running was used as the index of local muscle fatigue in this study. The formula for MF is shown in Equation (2): PSD f df   = 0 ( ) 2 1 MF (2) where PSD(f) is the myoelectric power spectral density. Running mainly mobilizes the leg muscle groups; therefore, in this experiment, the sEMG signals of the local muscles in the runners’ legs were collected, and the rectus fem- oris (RF) and vastus medialis (VM), which display obvious changes in sEMG signals and are very stable during movement changes, were examined [37]. The electrode positions for the RF and VM are shown in Figure 1. Figure 1a shows the electrode positions for the RF, Figure 1b shows the electrode positions for the VM. The black electrode is the refer- ence electrode. (a) (b) Figure 1. Muscle and electrode location. (a) Shows the electrode positions for the RF, (b) shows the electrode positions for the VM. The RF and VM sEMG signals of the subject’s dominant leg were collected. The sub- ject was first asked to perform thigh flexion and extension movements to find the target muscle location. Once the target muscle was found, the location of the target muscle was marked [38]. Subsequently, cotton dipped in alcohol was used to clean the skin of surface dirt and remove sweat, sebum and other impurities on the skin surface. The electrodes were pasted after the skin was dry, and surface hairs were removed if necessary [39]. The purpose of taking the above measures was to reduce the impedance effect of the skin, Figure 1. Muscle and electrode location. (a) Shows the electrode positions for the RF, (b) shows the electrode positions for the VM. The RF and VM sEMG signals of the subject’s dominant leg were collected. The subject was ﬁrst asked to perform thigh ﬂexion and extension movements to ﬁnd the target muscle location. Once the target muscle was found, the location of the target muscle was marked [38]. Subsequently, cotton dipped in alcohol was used to clean the skin of surface dirt and remove sweat, sebum and other impurities on the skin surface. The electrodes were pasted after the skin was dry, and surface hairs were removed if necessary [39]. The purpose of taking the above measures was to reduce the impedance effect of the skin, enhance the adhesion of the electrode patch to the skin and obtain the best recording effect. Following the surface EMG for non-invasive assessment of muscles (SENIAM) guidelines, bipolar sEMG electrodes were placed along the longitudinal midline of the muscle (in the direction of the muscle ﬁbers) on the muscle abdomen at the muscle–tendon junction, and the point spacing of the electrode patch was 2–3 cm [40,41]. After placement of the sensor and the reference electrode, a test was performed to determine whether the electrodes were placed properly on the muscle and connected to the equipment so that a reliable Int. J. Environ. Res. Public Health 2022, 19, 3869 7 of 18 sEMG signal could be recorded [42]. EMG signal acquisition software (Acknowledge4.2, Biopac) was connected to the sensor, and then the subject was asked to perform ﬂexion and extension movements. It was then observed whether the corresponding EMG signal in the interface of the acquisition software produced obvious changes. If the signal did not show obvious changes, or the signal showed abnormal values, the muscle selection position was further calibrated, and the electrode patch was checked to determine whether the connection with skin was ﬁrm [38]. Testing continued until the signal was stable. The above steps were followed before each acquisition of sEMG signals. The electrodes used for testing were Ag/AgCl electrocardiographic electrodes. The EMG100c signal ampliﬁer was then secured to the subject’s lower leg with a strap. 2.5. Experimental Apparatus The experimental apparatus included a treadmill, a pair of Bluetooth headphones, a set of barbell pieces with different weights, an HR belt, an RPE scale and an MP150 telemetry physical recorder and its accessories. The treadmill had a rated power of 1100 W and a belt area of 1350 × 480 mm, which was used to provide running conditions with a constant load. Airpod Bluetooth headphones were used to play music at a constant volume of 50% of the maximum volume, or approximately 65 dB. Barbells were hung on the legs before and after running to collect sEMG signals during static muscle exertion. A Polar HR monitor (Polar Electro Oy, Kempele, Finland) with a sampling rate of 125 Hz was used to acquire the real-time HR of the subjects during running. Participants wore the Polar heart rate band on their chest, which synced with their iPhone via Bluetooth. The MP150 telemetry physiological recorder (Biopac Inc., Goleta, CA, USA) was used to acquire sEMG signals at a sampling rate of 2048 Hz, equipped with two EMG100c signal ampliﬁers and several disposable ECG electrode patches (Shanghai Huyou Medical Electrode Co., Ltd., Shangai, China). The electrodes were used to connect the target muscle to the EMG signal ampliﬁer. The bipolar electrode (Ag/AgCl) was attached with a 10 mm diameter gel medium for reducing the impedance between the electrode and the skin. The size of the electrode patches was trimmed to 3 cm before use so that they could meet the electrode patch 2–3 cm spacing distance. 2.6. Experimental Procedure The experimental procedure for this study is shown in Figure 2. Each subject was required to follow the experimental procedure and complete a total of six sets of the running experiment. 2.6.1. Acquisition of Resting HR The HR of the subject was captured and recorded in real-time with an HR band. To exclude the inﬂuence of the initial state among different subjects, the resting HR of the subjects was collected before the experiment began. After the subjects sat in a comfortable position for 5 min, their HR was collected for 1 min, and the average value was calculated as the resting HR of the subjects [28]. 2.6.2. Pre-Run Safety Instructions and Warmup Before the formal experiment started, the subjects were introduced to the experiment contents and procedures in detail and received instructions on the use of the treadmill, correct running posture, a breathing adjustment method that could be used during running and the use of the RPE scale to ensure the safety of the subjects and the smooth operation of the experiment. All subjects underwent a 5-min warmup exercise before the experiment to avoid the occurrence of muscle damage during the experiment. Int. J. Environ. Res. Public Health 2022, 19, 3869 8 of 18 Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 8 Figure 2. Experiment flow chart. 2.6.1. Acquisition of Resting HR The HR of the subject was captured and recorded in real-time with an HR ban exclude the influence of the initial state among different subjects, the resting HR subjects was collected before the experiment began. After the subjects sat in a comfo position for 5 min, their HR was collected for 1 min, and the average value was calcu as the resting HR of the subjects [28]. 2.6.2. Pre-run Safety Instructions and Warmup Before the formal experiment started, the subjects were introduced to the exper contents and procedures in detail and received instructions on the use of the trea correct running posture, a breathing adjustment method that could be used during ning and the use of the RPE scale to ensure the safety of the subjects and the smoot eration of the experiment. All subjects underwent a 5-min warmup exercise befor experiment to avoid the occurrence of muscle damage during the experiment. 2.6.3. Acquisition of sEMG before Running Static sEMG signals of the abovementioned muscles were collected before and running to complement the instability of the sEMG signals during running [28]. A barbell pieces of different weights was prepared for hanging on the dominant leg b and after running to collect sEMG signals during static muscle exertion. Static sEM nals were acquired as follows: the subject’s leg was subjected to a certain percenta weight-bearing and knee extension so that the leg muscles were in a state of exertion then data were recorded for the RF and VM with an MP150 telemetry physical rec for 30 s [43]. In this case, the weight-bearing level was determined based on the weight of each subject: weight-bearing level = body weight (kg) × 10% [44]. The EMG Figure 2. Experiment ﬂow chart. 2.6.3. Acquisition of sEMG before Running Static sEMG signals of the abovementioned muscles were collected before and after running to complement the instability of the sEMG signals during running [28]. A set of barbell pieces of different weights was prepared for hanging on the dominant leg before and after running to collect sEMG signals during static muscle exertion. Static sEMG signals were acquired as follows: the subject’s leg was subjected to a certain percentage of weight-bearing and knee extension so that the leg muscles were in a state of exertion, and then data were recorded for the RF and VM with an MP150 telemetry physical recorder for 30 s [43]. In this case, the weight-bearing level was determined based on the body weight of each subject: weight-bearing level = body weight (kg) × 10% [44]. The EMG amplitudes of the RF and VM on the dominant side of the leg were recorded. To ensure signal stability and data reliability, the middle 20 s of the 30-s static EMG signal were extracted, and the 20-s MF values were calculated with a 2048-point window. The experimental procedure is shown in Figure 3. Figure 3a shows the electrode posi- tioning for the target muscle and the actual situation of wearing and using the instrument; Figure 3b shows the setup for measuring sEMG signals when loading and stretching the knee; and Figure 3c shows the running experimental procedure. 2.6.4. Running Experiment and Data Collection Each runner ran under low-intensity and high-intensity conditions, with no music, fast music and slow music. The subjects started exercising at 7 km/h for the low-intensity exercise condition and at 9 km/h for the high-intensity exercise condition. The subjects were observed and asked about their current fatigue at 1-min intervals, and the RPE values were recorded. The subjects stopped running when their subjective RPE reached 15. The time taken for the RPE value to reach 15 was recorded, and the HR signal was collected for 5 s before the end of the run. Immediately after the subjects stopped running, the weighted knee extension experiment was performed, and the sEMG signals of the RF and VM were Int. J. Environ. Res. Public Health 2022, 19, 3869 9 of 18 collected from the subjects. Throughout the experiment, the experimenter observed the subjects’ movement status and ensured their safety. Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 9 of 18 The experimental procedure is shown in Figure 3. Figure 3a shows the electrode po- sitioning for the target muscle and the actual situation of wearing and using the instru- ment; Figure 3b shows the setup for measuring sEMG signals when loading and stretch- ing the knee; and Figure 3c shows the running experimental procedure. (a) (b) (c) Figure 3. (a) The electrode position for the target muscles and the actual situation of wearing and using the instrument; (b) the setup for measuring sEMG signals when loading and stretching the knee; and (c) the running experimental procedure. 2.6.4. Running Experiment and Data Collection Each runner ran under low-intensity and high-intensity conditions, with no music, fast music and slow music. The subjects started exercising at 7 km/h for the low-intensity exercise condition and at 9 km/h for the high-intensity exercise condition. The subjects were observed and asked about their current fatigue at 1-min intervals, and the RPE val- ues were recorded. The subjects stopped running when their subjective RPE reached 15. The time taken for the RPE value to reach 15 was recorded, and the HR signal was col- lected for 5 s before the end of the run. Immediately after the subjects stopped running, the weighted knee extension experiment was performed, and the sEMG signals of the RF and VM were collected from the subjects. Throughout the experiment, the experimenter observed the subjects’ movement status and ensured their safety. After the experiment, the HR band, electrode pads and other experimental equip- ment were removed, and the subjects were asked if there was any discomfort and were instructed to perform appropriate stretching and recovery exercises to regulate HR recov- ery. Then, the next experiment was scheduled. 2.7. Data Processing and Analysis All results are presented as group means and standard deviations. The normality of the data distribution was confirmed using the Shapiro–Wilk test. To determine the effect of the intervention on the dependent variable, a two-way analysis of variance (ANOVA) for repeated measures was used to calculate between-group differences. If the interaction between music tempo and exercise intensity was significant, Bonferroni post hoc tests were calculated. The effect size was calculated as partial eta squared (η2). The criteria for classifying Cohen d were as follows: small (0 < d < 0.5), medium (0.5 ≤ d < 0.8) and large (d ≥ 0.8) [23]. The significance level was 0.05. All statistical analyses were performed using SPSS 26.0 (SPSS Inc., Chicago, IL, USA). 3. Results 3.1. Examination of the Effects of Music Tempo and Exercise Intensity A two-way repeated-measures ANOVA was conducted for TFP and the differences in HR and MF to determine whether there were main and interaction effects for music Figure 3. (a) The electrode position for the target muscles and the actual situation of wearing and using the instrument; (b) the setup for measuring sEMG signals when loading and stretching the knee; and (c) the running experimental procedure. After the experiment, the HR band, electrode pads and other experimental equipment were removed, and the subjects were asked if there was any discomfort and were instructed to perform appropriate stretching and recovery exercises to regulate HR recovery. Then, the next experiment was scheduled. 2.7. Data Processing and Analysis All results are presented as group means and standard deviations. The normality of the data distribution was conﬁrmed using the Shapiro–Wilk test. To determine the effect of the intervention on the dependent variable, a two-way analysis of variance (ANOVA) for repeated measures was used to calculate between-group differences. If the interaction between music tempo and exercise intensity was signiﬁcant, Bonferroni post hoc tests were calculated. The effect size was calculated as partial eta squared (η2). The criteria for classifying Cohen d were as follows: small (0 < d < 0.5), medium (0.5 ≤ d < 0.8) and large (d ≥ 0.8) [23]. The signiﬁcance level was 0.05. All statistical analyses were performed using SPSS 26.0 (SPSS Inc., Chicago, IL, USA). 3. Results 3.1. Examination of the Effects of Music Tempo and Exercise Intensity A two-way repeated-measures ANOVA was conducted for TFP and the differences in HR and MF to determine whether there were main and interaction effects for music tempo and exercise intensity, and the results are shown in Table 4. The main effect of music tempo on TFP was signiﬁcant (p < 0.001), but the main effects of the differences in HR and MF were not signiﬁcant (p > 0.05). The main effect of exercise intensity on TFP and the difference in HR was signiﬁcant (p < 0.001), but the main effect for the difference in MF was not signiﬁcant (p > 0.05). The interaction effect of music tempo and exercise intensity was signiﬁcant for TFP (p < 0.05) but not for the difference in HR or MF (p > 0.05). Int. J. Environ. Res. Public Health 2022, 19, 3869 10 of 18 Table 4. Examination of the effects of music tempo and exercise intensity. Parameter High Low Music Tempo Exercise Intensity Music Tempo × Exercise Intensity Fast Slow None Fast Slow None p-Value η2 p-Value η2 p-Value η2 TFP (min) 7.18 ± 2.36 6.23 ± 2.44 5.40 ± 1.94 12.68 ± 6.46 10.79 ± 4.86 9.06 ± 4.36 0.000 0.632 0.000 0.540 0.031 0.207 HR difference 1 69.56 ± 10.66 67.39 ± 11.14 71.11 ± 10.18 57.89 ± 10.30 61.83 ± 10.81 62.06 ± 8.95 0.077 0.140 0.000 0.796 0.075 0.141 MFRF difference 2 −0.64 ± 4.98 −2.02 ± 2.95 0.53 ± 4.45 −0.86 ± 6.34 −0.42 ± 6.81 −0.96 ± 5.11 0.672 0.023 0.967 0.000 0.210 0.088 MFVM difference 2 −0.75 ± 5.91 −0.89 ± 3.81 1.01 ± 4.2 0.38 ± 3.81 −1.08 ± 5.6 −0.79 ± 4.41 0.609 0.029 0.670 0.011 0.323 0.064 1 HR difference value is post-run HR−resting HR. 2 MF difference is MF before running−MF after running. RF: rectus femoris. VM: vastus medialis. Int. J. Environ. Res. Public Health 2022, 19, 3869 11 of 18 3.2. Time to Fatigue Perception The effects of music tempo on TFP at different exercise intensities are shown in Figure 4. The combined results show that the TFP of the group with music was signiﬁcantly greater than that of the group without music. Speciﬁcally, in the low-intensity exercise experiment, the TFP in the no music condition (9.06 min) was shorter than the TFP in the slow music condition (10.79 min) and the fast music condition (12.68 min). During high-intensity exercise, the TFP in the no music condition (5.40 min) was shorter than the TFP in the slow tempo music condition (6.23 min) and the TFP in the fast tempo music condition (7.18 min). difference 2 0.64 ± 4.98 2.02 ± 2.95 0.53 ± 4.45 0.86 ± 6.34 0.42 ± 6.81 0.96 ± 5.11 0.672 0.023 0.967 0.000 0.210 0.088 MFVM difference 2 −0.75 ± 5.91 −0.89 ± 3.81 1.01 ± 4.2 0.38 ± 3.81 −1.08 ± 5.6 −0.79 ± 4.41 0.609 0.029 0.670 0.011 0.323 0.064 1 HR difference value is post-run HR−resting HR. 2 MF difference is MF before running−MF after running. RF: rectus femoris. VM: vastus medialis. 3.2. Time to Fatigue Perception The effects of music tempo on TFP at different exercise intensities are shown in Figure 4. The combined results show that the TFP of the group with music was significantly greater than that of the group without music. Specifically, in the low-intensity exercise experiment, the TFP in the no music condition (9.06 min) was shorter than the TFP in the slow music condition (10.79 min) and the fast music condition (12.68 min). During high- intensity exercise, the TFP in the no music condition (5.40 min) was shorter than the TFP in the slow tempo music condition (6.23 min) and the TFP in the fast tempo music condi- tion (7.18 min). Figure 4. Time to fatigue perception for different experimental conditions. Since the interaction effect of music tempo and exercise intensity on the TFP was sig- nificant, a further Bonferroni post hoc test was performed to show the results (Table 5). The results of the Bonferroni post hoc test showed that the difference in the effect on TFP between no music and slow music at different exercise intensities was statistically signif- icant (p < 0.01), the difference in the effect on TFP between no music and fast music was Figure 4. Time to fatigue perception for different experimental conditions. Since the interaction effect of music tempo and exercise intensity on the TFP was signiﬁcant, a further Bonferroni post hoc test was performed to show the results (Table 5). The results of the Bonferroni post hoc test showed that the difference in the effect on TFP between no music and slow music at different exercise intensities was statistically signiﬁcant (p < 0.01), the difference in the effect on TFP between no music and fast music was statistically signiﬁcant (p < 0.001) and the difference in the effect on TFP between slow music and fast music was statistically signiﬁcant (p < 0.001). Table 5. Post hoc test of TFP under different experimental conditions. Exercise Intensity Pairwise Comparisons No Music vs. Slow Music vs. Fast Music vs. Low intensity Slow music: p = 0.004 No music: p = 0.004 No music: p = 0.000 Fast music: p = 0.000 Fast music: p = 0.000 Slow music: p = 0.000 High intensity Slow music: p = 0.000 No music: p = 0.000 No music: p = 0.0000 Fast music: p = 0.000 Fast music: p = 0.000 Slow music: p = 0.000 3.3. HR Changes The effect of music tempo on the differences in HR at different exercise intensities is shown in Figure 5. As seen from the difference in HR between the before and after measurements, the difference in HR in the high-intensity exercise group was higher than that in the low-intensity group overall. This is evident in the following: during low- intensity exercise, the no music group had the highest HR variation (62.06 beats/min), followed by the slow music group (61.83 beats/min) and ﬁnally the fast music group (57.89 beats/min); during high-intensity exercise, the no music group had the highest HR variation (71.11 beats/min), followed by the fast music group (69.56 beats/min) and ﬁnally the slow music group (67.39 beats/min). Int. J. Environ. Res. Public Health 2022, 19, 3869 12 of 18 , g y g p g in the low-intensity group overall. This is evident in the following: during low-intensity exercise, the no music group had the highest HR variation (62.06 beats/min), followed by the slow music group (61.83 beats/min) and finally the fast music group (57.89 beats/min); during high-intensity exercise, the no music group had the highest HR variation (71.11 beats/min), followed by the fast music group (69.56 beats/min) and finally the slow music group (67.39 beats/min). Figure 5. Differences in HR before and after running under different experimental conditions. 3.4. sEMG Changes The results of the MF difference of EMG signals of the RF and VM of the subjects under the six exercise protocols are shown in Figure 6a,b. From the figure, it can be seen that the MF difference values of both the RF and VM have two different directions of pos- itive and negative results, failing to show a regular variation in surface EMG signal changes of the RF and VM. Figure 5. Differences in HR before and after running under different experimental conditions. 3.4. sEMG Changes The results of the MF difference of EMG signals of the RF and VM of the subjects under the six exercise protocols are shown in Figure 6a,b. From the ﬁgure, it can be seen that the MF difference values of both the RF and VM have two different directions of positive and negative results, failing to show a regular variation in surface EMG signal changes of the RF and VM. Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 12 of 18 (a) (b) Figure 6. (a) MF differences of rectus femoris before and after running under different experi- mental conditions; (b) MF differences of vastus medialis before and after running under different experimental conditions. 4. Discussion 4.1. The Effect of Music Tempo on Runners’ Subjective Perception of Fatigue at Different Exer- cise Intensities The TFP results indicate that music can effectively influence runners’ perception of fatigue during running, a finding that is consistent with previous studies [45,46]. The sig- nificant main effect of music tempo (p < 0.001) also confirms the positive effect of music on fatigue perception (0.50 ≤ effect size d < 0.8). The results show that music tempo had a moderate effect on the time of fatigue perception. This result is consistent with Nethery’s study, where listening to music reduced subjective fatigue induced by exercise at different exercise intensities [18]. The mechanism of the effect can be explained by attentional lim- itation theory and selective sensory filtering theory [47,48]. That is, listening to music dur- ing exercise can reduce the excitability of the sympathetic nervous system, thereby reduc- ing subjective fatigue perception and enhancing exercise tolerance. This study further ex- plored the influence of music tempo on the TFP at different exercise intensities. This also suggests that in the absence of musical stimuli, participants may be more focused on their efforts and feel fatigued more quickly. The significant main effect of exercise intensity (p < 0.001) indicates that there was a significant difference between the effects of high and low exercise intensity on runners’ subjective perception of fatigue, and there was a moderate effect of exercise intensity on subjective fatigue perception (0.50 ≤ effect size d < 0.8), which is consistent with common Figure 6. (a) MF differences of rectus femoris before and after running under different experimental conditions; (b) MF differences of vastus medialis before and after running under different experimen- tal conditions. 4. Discussion 4.1. The Effect of Music Tempo on Runners’ Subjective Perception of Fatigue at Different Exercise Intensities The TFP results indicate that music can effectively inﬂuence runners’ perception of fatigue during running, a ﬁnding that is consistent with previous studies [45,46]. The signiﬁcant main effect of music tempo (p < 0.001) also conﬁrms the positive effect of music on fatigue perception (0.50 ≤ effect size d < 0.8). The results show that music tempo had a moderate effect on the time of fatigue perception. This result is consistent with Nethery’s study, where listening to music reduced subjective fatigue induced by exercise at different exercise intensities [18]. The mechanism of the effect can be explained by attentional limitation theory and selective sensory ﬁltering theory [47,48]. That is, listening to music during exercise can reduce the excitability of the sympathetic nervous system, thereby reducing subjective fatigue perception and enhancing exercise tolerance. This study further explored the inﬂuence of music tempo on the TFP at different exercise intensities. This also suggests that in the absence of musical stimuli, participants may be more focused on their efforts and feel fatigued more quickly. Int. J. Environ. Res. Public Health 2022, 19, 3869 13 of 18 The signiﬁcant main effect of exercise intensity (p < 0.001) indicates that there was a signiﬁcant difference between the effects of high and low exercise intensity on runners’ subjective perception of fatigue, and there was a moderate effect of exercise intensity on subjective fatigue perception (0.50 ≤ effect size d < 0.8), which is consistent with common knowledge about the positive correlation between exercise intensity and fatigue perception. Meanwhile, the signiﬁcant interaction effect of music tempo and exercise intensity indicated that the effects of music tempo and exercise intensity (p < 0.05) on runners’ subjective perception of fatigue were inﬂuenced by each other, but the effect is small. However, the effect size result (0 < effect size d < 0.5) shows that music tempo and exercise intensity interacted to a lesser extent. The results of further post hoc analyses indicate that whether in high intensity or low intensity exercise, listening to fast music can prolong the running time and reduce the subjective fatigue of runners compared to listening to slow music. During high-intensity exercise, the effect of fast music and slow music on the TFP is signiﬁcant (p < 0.001), indicating that fast music was to some extent more effective than slow music in reducing fatigue perception. This conclusion is also supported by previous studies, such as Cental’s study, which mentioned that listening to fast music increased overall exercise tolerance and the neuromuscular fatigue threshold [18]. Furthermore, post-experiment interviews with all subjects revealed that listening to fast music resulted in a more uplifted state of mind and a more positive mood, which effectively distracted them and reduced the perception of fatigue. Some subjects also believed that fast music made them unconsciously adjust their running pace, which helped to counteract the perception of fatigue. The combined results of the experiments show that fast music effectively reduced the subjective perception of fatigue at different exercise intensities in runners. 4.2. The Effect of Music Tempo on HR Differential at Different Exercise Intensities The ANOVA results show that the difference in HR before and after running was only affected by the main effect of exercise intensity (p < 0.001) and reached an effect level close to the high intensity (0.50 ≤ effect size d < 0.8). It indicates that HR changes were largely inﬂuenced by the exercise intensity. This ﬁnding indicates that there is a signiﬁcant difference in the magnitude of HR variation in runners at different intensities of exercise, and this ﬁnding is consistent with the common knowledge that there is a strong correlation between the magnitude of HR variation in runners and the intensity of exercise. In addition, a study by Szabo et al. provides evidence for the idea that music not only has an effect on fatigue perception during progressive exercise, but can still provide effects for a period of time at higher intensities, depending on the quality of intrinsic arousal [49]. The main reason is that the greater intensity of exercise causes a greater cardiac load, and the maintenance of cardiac output depends on an increase in HR; the fatigue of the body also increases, resulting in a higher HR immediately after running [50]. However, the main effect of music tempo was not signiﬁcant (p > 0.05), and the level of effect size was small (0 < effect size d < 0.5), indicating that the magnitude of HR change during exercise was inﬂuenced by music tempo to a lesser extent. This ﬁnding is inconsistent with some current studies which indicate that the magnitude of HR variation during exercise is inﬂuenced by music tempo [51]. This ﬁnding is also inconsistent with the signiﬁcant main effect of music tempo on the TFP, probably because the TFP is a subjective index of fatigue that reﬂects runners’ self-evaluation of their physical state and is signiﬁcantly inﬂuenced by mental aspects. In contrast, under control conditions, the HR variation of the subjects mainly depends on subjects’ physical fatigue [52]. As some studies pointed out, the effect of music on fatigue perception would be reﬂected in the shift of runners’ attention to the fatigue state; that is, it would be shown ﬁrst in the shift in perception of mental fatigue and then in the perception of physical fatigue [53,54]. However, the duration of this experiment was determined by the runners’ subjective fatigue, and the runners ended the run when their subjective fatigue reached the level of RPE = 15, which led to the intervention effect of music tempo on the runners’ physical fatigue perception not being fully demonstrated in this experiment. This further led to Int. J. Environ. Res. Public Health 2022, 19, 3869 14 of 18 a nonsigniﬁcant interaction effect between music tempo and exercise intensity (p > 0.05, 0 < effect size d < 0.5). However, there are studies that have come to similar conclusions as the present study, such as Edworthy and Waring and Dyck et al., who also did not ﬁnd a link between music tempo changes and HR changes [17,55]. HR and music tempo can be considered as interacting oscillatory systems that will start at the same period, but this alignment strategy only works at the beginning of the experiment. 4.3. Effect of Music Tempo on the Difference in MF at Different Exercise Intensities The differences in MF for the RF and VM for different exercise regimens varied widely and did not show regular changes. The results show that the main effects of music tempo and exercise intensity on both MFRF difference and MFVM difference were not signiﬁcant (p > 0.05) and that both had small effect sizes (0 < effect size d < 0.5). The interaction effects of music tempo and exercise intensity on MFRF and MFVM differences were also not signiﬁcant (p > 0.05) and both effect sizes were small (0 < effect size d < 0.5). Meanwhile, the experimental results for the differences in MF were negative, which indicates decreased local muscle fatigue after running, which is inconsistent with the results of most previous studies that used the difference in MF as an indicator [56,57]. The reason may be due to the different distribution patterns of fast and slow muscle ﬁbers in different individuals and muscles, which are genetically determined and almost impossible to change later by exercise [58]. In the post-experiment communication with the subjects, we learned that runners may use different force generation methods for different running conditions, which also indirectly inﬂuenced the experimental results. Meanwhile, the ANOVA results also show that the main effects of both exercise intensity and music tempo were not signiﬁcant, indicating that the change in muscle fatigue before and after running was not signiﬁcant. However, in the post-experiment questioning of the subjects, we found that the subjects all had some soreness and swelling in their lower limb muscles after ﬁnishing the running experiment. This phenomenon was not monitored during the experiment, and the sEMG signal changes for the two muscles before and after running were not regular; there were more negative differences in MF, which shows that the MF values in this experiment did not show a decreasing trend but an increasing trend. The reasons for this result in this study may be twofold. (1) The experimental test time was short, and the runners reached the speciﬁed value of subjective fatigue in a short period of time, during which the local muscle tissue continuously recruited fast muscle ﬁbers to participate in the exercise so that the muscle ﬁring frequency increased, and the neural excitability continued during the running process [59,60]. (2) The energy consumption during muscle contraction during running did not exceed the energy recovery during relaxation, and the lactic acid accumulation was not signiﬁcant, so the driving strategy of the central nervous system for the muscles and the conduction speed of the muscle ﬁbers were not affected [61]. Other scholars have also found different trends in MF frequency domain indicators during exercise [62]. Combining the above views to see the limitations of using the decrease in the MF frequency domain index pre- to post-exercise can determine the fatigue of local muscles during dynamic exercise such as running. 4.4. Pratical Applications This study aimed to determine the effect of music tempo on runners’ fatigue perception at different exercise intensities and provided a physiological and psychological explanation of the role of music tempo in resisting fatigue perception by observing time of fatigue perception, heart rate changes and sEMG signal changes during the running experiment. The combined results of the three indicators revealed that the use of music with different tempos at different exercise intensities caused runners to exhibit different mental and physical performances. Speciﬁcally, runners’ TFP was inﬂuenced by the music tempo, and in addition, runners’ TFP was also inﬂuenced by the interaction between music tempo and exercise intensity. The change in HR of runners during running was mainly inﬂuenced by the exercise intensity. Whether it is low-intensity exercise or high-intensity exercise, Int. J. Environ. Res. Public Health 2022, 19, 3869 15 of 18 listening to fast music may mitigate individuals’ perception of fatigue to some extent. This was also pointed out in the study by Centala et al. The effect of listening to fast music during exercise would be better than slow music and could regulate mental emotions and reduce the perception of fatigue during exercise [63]. In addition, this study also provides many practical implications. Running is an endurance sport, and runners will feel fatigue to different degrees during running, es- pecially for many non-professional runners, who often do not persist in running due to insufﬁcient exercise, limited physical strength and lack of perseverance. The results of this study can help runners to develop a more beneﬁcial exercise program. For example, both low-intensity exercisers and high-intensity exercisers are advised to listen to fast music while exercising, which would help them maintain a better mental state, make the exercise process less tedious and better control the physiological state, including heart rate changes. This research could lead to better effects of music on perceived fatigue, allowing runners to have better mental and physical performance while running. 4.5. Limitations There were some limitations to this study. First, the subject population was not subdivided, and none of the runners had undergone professional training. Second, we only examined male subjects. Future studies could use sex as an independent variable to discuss the effects of music tempo on subjects of different sexes and could also include people of all ages and from different occupational backgrounds to make the study results more generalizable. Third, the observation indexes used in this study were relatively limited, and more measurement techniques could be integrated in the future to more accurately assess the fatigue perception status of runners. Fourth, based on this study, future studies could reﬁne the characteristics of the music, such as the cultural background of the music, or use music with lyrics to explore other methods of music intervention during exercise to promote the popularity of the research results. 5. Conclusions In this paper, the effect of music tempo on runners’ subjective and objective fatigue perception at different exercise intensities was investigated through running experiments. The inﬂuence of music on runners’ perception of fatigue was explored, and the interaction between music tempo and exercise intensity was considered. The results of the study showed that (1) there were signiﬁcant main effects and interaction effects of music tempo and exercise intensity on the TFP; (2) in terms of the difference in HR, the main effect of exercise intensity was signiﬁcant, the main effect of music tempo was not signiﬁcant and the interaction effect of music tempo and exercise intensity was not signiﬁcant; and (3) in terms of the difference in the MF of sEMG, the main effect and interaction effect of music tempo and exercise intensity were not signiﬁcant, and the results did not show regular changes. The combined results of the study indicate that fast music can effectively reduce the perception of fatigue of runners during running. The results of this study provide a scientiﬁc basis for ordinary runners to select evidence-based for running, help them effectively overcome their perception of fatigue during exercise and optimally modify their perception of fatigue with music. Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/ijerph19073869/s1, File S1: Ethic reviews; File S2: Informed Consent. Author Contributions: Conceptualization, J.W.; methodology, L.J. and H.Y.; investigation, Y.C.; resources, C.L.; formal analysis, L.J. and L.Z.; writing—original draft preparation, L.J.; writing— review and editing, L.Z.; supervision, J.W. and H.Y. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Key Industrial Projects of Zhejiang Province Science and Technology Department, China, grant number 2019C03124. Int. J. Environ. Res. Public Health 2022, 19, 3869 16 of 18 Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Industrial Design Institute of Zhejiang University of Technology (protocol code 0903/2021, date of approval 15 September 2021). 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[CrossRef]	The Effect of Music Tempo on Fatigue Perception at Different Exercise Intensities.	03-24-2022	Wu, Jianfeng,Zhang, Lingyan,Yang, Hongchun,Lu, Chunfu,Jiang, Lu,Chen, Yuyun	eng
PMC6679305	sensors Article Use of Machine Learning and Wearable Sensors to Predict Energetics and Kinematics of Cutting Maneuvers Matteo Zago 1,2,3,, Chiarella Sforza 4 , Claudia Dolci 4, Marco Tarabini 3,5 and Manuela Galli 1,3 1 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy 2 Fondazione Istituto Farmacologico Filippo Serpero, 20159 Milano, Italy 3 E4Sport Lab, Politecnico di Milano, 20133 Milano, Italy 4 Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, 20133 Milano, Italy 5 Dipartimento di Meccanica, Politecnico di Milano, 20129 Milano, Italy Correspondence: matteo2.zago@polimi.it; Tel.: +39-02-2399-3351 Received: 14 June 2019; Accepted: 9 July 2019; Published: 12 July 2019 Abstract: Changes of directions and cutting maneuvers, including 180-degree turns, are common locomotor actions in team sports, implying high mechanical load. While the mechanics and neurophysiology of turns have been extensively studied in laboratory conditions, modern inertial measurement units allow us to monitor athletes directly on the ﬁeld. In this study, we applied four supervised machine learning techniques (linear regression, support vector regression/machine, boosted decision trees and artiﬁcial neural networks) to predict turn direction, speed (before/after turn) and the related positive/negative mechanical work. Reference values were computed using an optical motion capture system. We collected data from 13 elite female soccer players performing a shuttle run test, wearing a six-axes inertial sensor at the pelvis level. A set of 18 features (predictors) were obtained from accelerometers, gyroscopes and barometer readings. Turn direction classiﬁcation returned good results (accuracy > 98.4%) with all methods. Support vector regression and neural networks obtained the best performance in the estimation of positive/negative mechanical work (coeﬃcient of determination R2 = 0.42–0.43, mean absolute error = 1.14–1.41 J) and running speed before/after the turns (R2 = 0.66–0.69, mean absolute error = 0.15–018 m/s). Although models can be extended to diﬀerent angles, we showed that meaningful information on turn kinematics and energetics can be obtained from inertial units with a data-driven approach. Keywords: supervised learning; changes of direction; IMU; mechanical work 1. Introduction Changes of direction (CoD) and cutting maneuvers are basic locomotor actions in team sports, implying high physiological and mechanical load [1–3]. High-intensity and abrupt sidestepping is the most frequent cause for non-contact ligamentous injuries at the knee level, involving primarily anterior cruciate ligament lesions, and secondarily meniscal or medial collateral ligament strains [4]. The amount of deceleration required in sidestep cutting is related to the angle and speed of approach and has been associated to the likelihood of knee injuries [5,6]. CoDs also have a high associated metabolic cost, impacting on the energetic requirements of exercise [7,8]. The mechanics and neurophysiology of CoDs have been accurately described primarily in laboratory conditions, unveiling joint kinematics and loads as a function of the running angle and technique [1,9–11], foot-landing strategies [12], muscular activations [3,13], and response to Sensors 2019, 19, 3094; doi:10.3390/s19143094 www.mdpi.com/journal/sensors Sensors 2019, 19, 3094 2 of 11 fatigue [14,15]. In addition, we recently proposed an algorithm to estimate the energy cost of running with repeated 180 degree-CoDs [16]: the external mechanical energy associated to the trajectory of a body’s center of mass was combined with the knee ﬂexion angle and ground contacts to provide an estimation of the positive (concentric) and negative (eccentric) muscular work. Although promising, this method was limited by the obtainment of full-body kinematics with optical motion capture systems, being therefore conﬁned to laboratory conditions. Rather, gathering reliable information on the side, speed and energetics of 180-degree sidestep CoDs in realistic on-the-ﬁeld conditions would better help in monitoring the energetic, physiological and mechanical load, as well as to prevent overuse injuries. An emerging trend, quantifying sport actions with body-worn inertial measurements units (IMU), enables the assessment of athletes in ecologic conditions [17,18]. The metrological issues related to the use of wearable sensors for sport performance assessments have been the focal point of diﬀerent research works: even though magneto-inertial technology allows monitoring the performance of athletes of all levels, especially when complemented with a sensor fusion network, there is a need for further research on the ease of use and error compensation to provide coaches and practitioners with informative and concise metrics [19–22]. The use of inertial units also raises technical issues to extract meaningful data from a broad class of signals (acceleration, angular velocity, magnetic ﬁeld orientation, etc.) which are often prone to noise, non-linearities, and measurement inaccuracies. These characteristics might practically limit the usability of results in speciﬁc conditions. In the case of changes of direction, obtaining running speed analytically from one inertial unit and then applying linear equations to estimate the related energy cost in [8] appears practically unfeasible due to inherent biases and drifts. A way to overcome these limitations is to apply machine learning techniques to IMU data. Supervised machine learning algorithms take a known set of input data (called predictors) and know responses and train a model to generate predictions from new data [23]. These techniques have been applied in team sports to quantify movement patterns during training and competition, like physical output and tackling impacts in rugby and Australian football [18,24], player load [25] or deceleration before turns in soccer [26]. However, the estimation of the energetics associated with 180-degree cutting actions in team sports has not been investigated. This study intends to introduce the application of machine learning models to detect direction, speed and external mechanical work associated with 180-degree CoDs, by using only data coming from a single inertial unit. We hypothesize that a unique sensor placed close to the core (pelvis) could capture the key information on athletes’ actions during these tasks. Our complementary aim is to show that the combination of regression analysis technique and easily available sensors can provide coaches and practitioners with a wealth of information about such crucial game actions. 2. Materials and Methods 2.1. Experimental Procedures and Equipment All tests were performed in the morning within two weeks after the end of the regular season. The experimental setting was a full motion capture laboratory equipped with an eight-camera system (sampling frequency: 100 Hz; Smart-Dx, BTS Bioengineering, Milano, Italy). A set of 14 reﬂective markers (diameter: 15 mm) were positioned on the skin in the following anatomical landmarks: tragi, acromia, olecranons; radius styloid processes; greater trochanters; femoral lateral epicondyles; and lateral malleoli (additional markers were added for further biomechanical investigations, but they were not considered in the current study). A six-axes IMU (GaitUp Physilog 5, Lausanne, Switzerland) was ﬁxed to the shorts with a plastic clip close to the sacrum marker. Inertial sensor settings were: sampling frequency 512 Hz, measurement range ±2000 degrees/s (gyroscope) and ±16 g (accelerometer). The x-axis of the sensor reference frame pointed backwards, the y-axis upwards and the z-axis to the subjects’ left. The unit also included a barometer with a sampling frequency of 64 Hz. Sensors 2019, 19, 3094 3 of 11 Ambient temperature was 22–24 ◦C. Participants wore minimal sports clothing and running shoes. They were ﬁrst acquainted with the experimental procedures; after a 10 min warm up supervised by a professional strength and conditioning coach, participants completed a 5 m shuttle-run test (Figure 1) at the average speed of 70% of their maximal aerobic speed, as detailed in [7]. Maximal aerobic speed is the lowest running speed at which the maximum oxygen uptake occurs, and it was estimated with aerobic power tests (Yo-Yo intermittent recovery test [27]) throughout the season. Average running speed was 2.5 ± 0.2 m/s. Athletes had to keep running to exhaustion, i.e., when they could not reach the end lane by the acoustic signal—pacing the shuttle rhythm—for two consecutive times. Sensors 2019, 19, x FOR PEER REVIEW 3 of 11 run test (Figure 1) at the average speed of 70% of their maximal aerobic speed, as detailed in [7]. Maximal aerobic speed is the lowest running speed at which the maximum oxygen uptake occurs, and it was estimated with aerobic power tests (Yo-Yo intermittent recovery test [27]) throughout the season. Average running speed was 2.5 ± 0.2 m/s. Athletes had to keep running to exhaustion, i.e., when they could not reach the end lane by the acoustic signal—pacing the shuttle rhythm—for two consecutive times. Figure 1. Experimental setting and turn (180-degree change of direction) action. In the right picture, the orientation of the sensor reference frame is displayed. 2.2. Study Design and Participants This observational case-series study involved 13 female soccer players (age 23.6 ± 3.3 years, body mass: 59.0 ± 7.3 kg, height: 1.66 ± 0.05 m, body mass index: 21.2 ± 1.9 kg/m2), playing for elite clubs competing in the first and second Italian league. All participants were judged by a medical doctor not to have any restriction to sports practice, had no injuries in the 12 months preceding the test, and signed a written informed consent after a detailed explanation of the aims, benefits and risks of this study. The study was approved by the Institutional Ethics Committee (n. 1/2016) and was conducted according to the Declaration of Helsinki. 2.3. Data Processing and Features Engineering Custom routines were developed within Matlab (v. 2018b, The Mathworks Inc., Natick MA, USA). The three-dimensional coordinates of body center of mass (CoM) were obtained using the segmental centroid method, specifically validated for sports applications [28–30], after applying a low-pass, zero- lag second order Butterworth filter (cut-off frequency: 15 Hz) to the raw marker trajectories. CoD events were easily identified with the peaks of the CoM position in the running direction (CoMx). Mass-specific CoM external energy was computed according to classical physiology texts [31]: 𝐸௘௫௧ = 1 2 𝒗஼௢ெ ଶ + 𝑔ℎ஼௢ெ (1) where vCoM is the norm of instantaneous CoM speed, and hCoM is its vertical height. Positive (negative) mechanical work (W+/−) was then obtained as the sum of positive (negative) changes of Eext [31] in the two second window across the turn. Also, for each turn, we computed CoM approach speed (1 s before the turn, vbefore), and CoM speed during acceleration (1 s after the turn, vafter). These four variables, alongside the side of the pivoting limb (right or left), constitute the set of known targets (responses). Their distribution is illustrated in Figure 2: as negative external work is required to decelerate the CoM before the turn, and positive work is needed to accelerate it back in the new direction, W− was represented relative to vbefore, and W+ to vafter. Figure 1. Experimental setting and turn (180-degree change of direction) action. In the right picture, the orientation of the sensor reference frame is displayed. 2.2. Study Design and Participants This observational case-series study involved 13 female soccer players (age 23.6 ± 3.3 years, body mass: 59.0 ± 7.3 kg, height: 1.66 ± 0.05 m, body mass index: 21.2 ± 1.9 kg/m2), playing for elite clubs competing in the ﬁrst and second Italian league. All participants were judged by a medical doctor not to have any restriction to sports practice, had no injuries in the 12 months preceding the test, and signed a written informed consent after a detailed explanation of the aims, beneﬁts and risks of this study. The study was approved by the Institutional Ethics Committee (n. 1/2016) and was conducted according to the Declaration of Helsinki. 2.3. Data Processing and Features Engineering Custom routines were developed within Matlab (v. 2018b, The Mathworks Inc., Natick, MA, USA). The three-dimensional coordinates of body center of mass (CoM) were obtained using the segmental centroid method, speciﬁcally validated for sports applications [28–30], after applying a low-pass, zero-lag second order Butterworth ﬁlter (cut-oﬀ frequency: 15 Hz) to the raw marker trajectories. CoD events were easily identiﬁed with the peaks of the CoM position in the running direction (CoMx). Mass-speciﬁc CoM external energy was computed according to classical physiology texts [31]: Eext = 1 2v2 CoM + ghCoM (1) where vCoM is the norm of instantaneous CoM speed, and hCoM is its vertical height. Positive (negative) mechanical work (W+/−) was then obtained as the sum of positive (negative) changes of Eext [31] in the two second window across the turn. Also, for each turn, we computed CoM approach speed (1 s before the turn, vbefore), and CoM speed during acceleration (1 s after the turn, vafter). These four variables, alongside the side of the pivoting limb (right or left), constitute the set of known targets (responses). Their distribution is illustrated in Figure 2: as negative external work is required to decelerate the CoM before the turn, and positive work is needed to accelerate it back in the new direction, W− was represented relative to vbefore, and W+ to vafter. Sensors 2019, 19, 3094 4 of 11 Sensors 2019, 19, x FOR PEER REVIEW 4 of 11 Figure 2. Negative (left) and positive (right) external mechanical energy computed in the two second window across the turn as a function of running speed before (left) and after (right) the turn. The distribution of the variables is also displayed on the edges. Eighteen features (predictors) were extracted from IMU data. We first had to detect the same CoD events in the IMU readings: smoothed (Butterworth, zero-lag low-pass filter, fcut = 0.5 Hz) angular velocity around the y (vertical) axis was particularly suitable for this, as it showed clear peaks in correspondence to turns (Figure 3). Matching between events timing was obtained by computing the cross-correlation between the resampled and rectified gyroscope and CoM trajectory and shifting them in time by an offset equal to the lag corresponding to maximum cross-correlation (Figure 3). The average CoD events detection error was 0.001 ± 0.102 s. Likewise for optical data, a two second window across this event was considered for the following computations. Static biases on each channel of the inertial sensors were obtained with a 30 min test with the unit kept still. We then subtracted these values from the accelerometer and gyroscope readings, which were additionally filtered with a fourth order Butterwort filter (cut-off frequency: 128 Hz). Figure 3. Events detection, based on center of mass (CoM) horizontal position (red curve, referred to the origin of the laboratory global reference system) and raw/filtered gyroscope rotation around the vertical axis (gray and blue, respectively). The autocorrelation function between the two allowed us to synchronize the two measurement systems. Figure 2. Negative (left) and positive (right) external mechanical energy computed in the two second window across the turn as a function of running speed before (left) and after (right) the turn. The distribution of the variables is also displayed on the edges. Eighteen features (predictors) were extracted from IMU data. We ﬁrst had to detect the same CoD events in the IMU readings: smoothed (Butterworth, zero-lag low-pass ﬁlter, fcut = 0.5 Hz) angular velocity around the y (vertical) axis was particularly suitable for this, as it showed clear peaks in correspondence to turns (Figure 3). Matching between events timing was obtained by computing the cross-correlation between the resampled and rectiﬁed gyroscope and CoM trajectory and shifting them in time by an oﬀset equal to the lag corresponding to maximum cross-correlation (Figure 3). The average CoD events detection error was 0.001 ± 0.102 s. Likewise for optical data, a two second window across this event was considered for the following computations. Static biases on each channel of the inertial sensors were obtained with a 30 min test with the unit kept still. We then subtracted these values from the accelerometer and gyroscope readings, which were additionally ﬁltered with a fourth order Butterwort ﬁlter (cut-oﬀ frequency: 128 Hz). Sensors 2019, 19, x FOR PEER REVIEW 4 of 11 Figure 2. Negative (left) and positive (right) external mechanical energy computed in the two second window across the turn as a function of running speed before (left) and after (right) the turn. The distribution of the variables is also displayed on the edges. Eighteen features (predictors) were extracted from IMU data. We first had to detect the same CoD events in the IMU readings: smoothed (Butterworth, zero-lag low-pass filter, fcut = 0.5 Hz) angular velocity around the y (vertical) axis was particularly suitable for this, as it showed clear peaks in correspondence to turns (Figure 3). Matching between events timing was obtained by computing the cross-correlation between the resampled and rectified gyroscope and CoM trajectory and shifting them in time by an offset equal to the lag corresponding to maximum cross-correlation (Figure 3). The average CoD events detection error was 0.001 ± 0.102 s. Likewise for optical data, a two second window across this event was considered for the following computations. Static biases on each channel of the inertial sensors were obtained with a 30 min test with the unit kept still. We then subtracted these values from the accelerometer and gyroscope readings, which were additionally filtered with a fourth order Butterwort filter (cut-off frequency: 128 Hz). Figure 3. Events detection, based on center of mass (CoM) horizontal position (red curve, referred to the origin of the laboratory global reference system) and raw/filtered gyroscope rotation around the vertical axis (gray and blue, respectively). The autocorrelation function between the two allowed us to synchronize the two measurement systems. Figure 3. Events detection, based on center of mass (CoM) horizontal position (red curve, referred to the origin of the laboratory global reference system) and raw/ﬁltered gyroscope rotation around the vertical axis (gray and blue, respectively). The autocorrelation function between the two allowed us to synchronize the two measurement systems. Sensors 2019, 19, 3094 5 of 11 The ﬁrst feature F1 was the sum of the root mean square of changes in acceleration and deceleration per second, also known as ’player load’, a metric commonly used to calculate the load or activity level of athletes in team sports [25]: player load = 1023 X t=1 r (ax,t+1 − ax,t)2 + ay,t+1 − ay,t 2 + (az,t+1 − az,t)2 (2) Other accelerometer features were the trapezoidal numerical integral of each axes positive and negative acceleration (F2−7). Features F8−10 were the integral of each gyroscope axis. Features F11−13 and F14−16 were the root mean square, the skewness and the kurtosis of the norm of the accelerometer and gyroscope readings during the two second turn window. Skewness describes the symmetry of the acceleration and angular velocity signal distributions and is given by: skewness = E(x − x)3 σ3 (3) The kurtosis quantiﬁes the extent to which the acceleration and angular velocity signals are peaked or ﬂat with respect to a normal distribution: kurtosis = E(x − x)4 σ4 (4) In Equations (3) and (4), E is the expected value, x is the mean and σ is the standard deviation of the signal in the two second window [32]. These two features quantify the degree of distortion with respect to a normal distribution of acceleration or angular velocity data series: for instance, an abrupt braking action would contain more negative than positive acceleration values, and so it would be highly skewed. The last two features F17 and F18 were obtained from the ﬁltered barometer output (low-pass tenth order Butterworth ﬁlter, cut-oﬀ frequency: 1 Hz) and were the diﬀerence between the mean sea level altitude (computed from ambient pressure) at the CoD event and one second before or after the CoD, respectively. Table 1 provides an overview of features and responses. Before further processing, outliers were removed when examples were outside the variable’s mean ± 3 standard deviations, and the coeﬃcient of variation (CV) was computed for each variable. 2.4. Regression and Classiﬁcation Models For the prediction of W+, W−, vbefore and vafter, we applied four supervised machine learning regression techniques: 1. Multiple linear regression, modeling the linear relationship between predictors and the response (dependent) variables. 2. Support vector regression (SVR): this technique is based on support vector machines (SVM), which in turn construct hyperplanes to deﬁne decision boundaries in a multi-dimensional space. SVR computes the parameter of a function f(x), where x is the matrix of predictors, ﬁtting the input data with the most ε-deviation from the target y (response). As SVR is particularly suited to handle non-linear tasks, in this study we chose a Gaussian kernel. 3. Boosted trees (BT): classiﬁcation or regression models are in the form of a tree structure, which is built top-down from a root node, and involves partitioning data into subsets that contain common features based on the level of information gain, i.e., a decrease in entropy after a dataset is separated [33]. Boosted trees are an extension of decision trees that aggregate an ensemble of decision trees into a unique result, which reduces the chance of overﬁtting. The number of learners (trees) set in this study was 40. Sensors 2019, 19, 3094 6 of 11 4. Artiﬁcial neural networks (ANN): a feedforward network consisting of an input, a hidden and an output layer was designed. Neurons (n = 40) in the hidden layer process the input features in accordance to hyperbolic tangent sigmoid functions. The output layer is a single neuron which returns the estimated (predicted) response. The back-propagation learning algorithm was used to update the weights and biases of the ANN. Input data was split into three subsets: 70% for training, 15% for testing and 15% for validation. Classiﬁcation models to determine turn direction (right or left) matched the previous techniques and included: (1) linear discriminant analysis, (2) SVM, (3) BT, (4) ANN. Table 1. Overview of the ﬁve responses (Ri, where R5 is a categorical variable) and of the 18 features (Fi) used to train the models. Variable Description Unit Mean SD CV Min Max R1 Positive work J/kg 8.49 2.35 0.28 1.18 18.44 R2 Negative work J/kg 8.17 2.73 0.33 1.02 18.91 R3 Speed before turn m/s 2.60 0.38 0.15 1.49 3.69 R4 Speed after turn m/s 2.59 0.33 0.13 1.57 3.44 R5 Turn side cat. - - - F1 Player load A.U. 190.6 62.6 0.33 67.3 407.4 F2 Positive accx integral m/s 325.8 86.0 0.26 125.7 584.0 F3 Positive accy integral m/s 528.00 96.1 0.18 267.4 846.3 F4 Positive accz integral m/s 238.7 67.6 0.28 82.0 435.1 F5 Negative accx integral m/s −328.7 89.4 0.27 −587.1 −107.8 F6 Negative accy integral m/s −508.8 89.2 0.18 −810.1 −263.4 F6 Negative accz integral m/s −251.7 79.0 0.31 −504.5 −93.4 F7 Norm acc RMS m/s2 7.59 3.57 0.47 1.19 21.04 F8 Acceleration skewness - 4.95 1.35 0.27 2.08 9.00 F9 Acceleration kurtosis - 35.8 18.5 0.52 7.6 98.4 F10 Gyroscopex integral rad 9.9·103 1.4·104 1.44 −2.9·104 4.7·104 F11 Gyroscopey integral rad 0.9·103 6.7·104 n.a. −8.5·104 8.6·104 F12 Gyroscopez integral rad −45.3 3.8·104 n.a. −6.0·104 6.1·104 F13 Gyroscope norm RMS rad/s 2.4·103 1.7·103 0.69 0.3·103 7.9·103 F14 Gyroscope skewness - 4.64 1.61 0.35 0.41 9.31 F15 Gyroscope kurtosis - 32.8 20.1 0.61 2.41 104.0 F17 ∆baro, before m −0.39 0.26 0.65 −1.08 0.36 F18 ∆baro, after m 0.41 0.26 0.62 −0.35 1.12 ∆baro: altitude diﬀerence between before/after 1 s. acc: acceleration; A.U. arbitrary units; cat.: categorical variable. CV: coeﬃcient of variation; RMS: root mean square; SD: standard deviation; n.a.: not applicable (bimodal variable, CV > 50). 2.5. Validation To evaluate the predictive accuracy, models 1–3 underwent a standard 10-fold cross-validation procedure: data were randomly partitioned into two sets, the ﬁrst was used for training, while the second was used for validation. This process was repeated 10 times, by randomly selecting the training and validation portions [34]. The means of the 10 classiﬁcation accuracy rates were taken as an unbiased estimate of the model for the complete dataset. Root mean square error (RMSE), mean absolute error and coeﬃcient of determination (R2) were computed as performance metrics for regression models. Classiﬁcation models were evaluated in terms of accuracy, sensitivity, speciﬁcity and area under the receiver operating characteristic curve (AUC). 3. Results Th shuttle run test duration was on average 158.4 ± 65.1 s, with 72 ± 30 turns per participant. Overall, we collected 937 cutting maneuvers. Outlier removal led us to exclude 32 of them. Feature means, standard deviations and ranges are shown in Table 1: while the variability of speed was below Sensors 2019, 19, 3094 7 of 11 15%, that of positive/negative mechanical work reached 33%; the highest variability in predictors was contained by gyroscope integrals and RMS, the lowest in the acceleration integrals. Turn direction was detected with good accuracy (98.4%, Table 2) by linear discriminant analysis (sensitivity = 97.3%, speciﬁcity = 99.6%, AUC = 1.00), while with the other methods (boosted trees, SVM and ANN), we obtained perfect classiﬁcation (accuracy = 100%, AUC = 1.00). The best performance in predicting the mechanical work in decelerations and accelerations was achieved by SVR models, with a moderate R2 and an error of about 15% (Table 2). ANNs best predicted incoming and sprint speed with a moderate to substantial R2, and an error lower than 10% (0.15–0.18 m/s). Feature importance for BT is reported in Figure 4. Sensors 2019, 19, x FOR PEER REVIEW 7 of 11 The best performance in predicting the mechanical work in decelerations and accelerations was achieved by SVR models, with a moderate R2 and an error of about 15% (Table 2). ANNs best predicted incoming and sprint speed with a moderate to substantial R2, and an error lower than 10% (0.15–0.18 m/s). Feature importance for BT is reported in Figure 4. Figure 4. Feature importance returned by boosted trees models when predicting positive/negative mechanical work (Wp and Wn, top), and approach/sprint running speed (vbefore and vafter, bottom). Refer to Table 1 for the description of features Fi. 4. Discussion The combination of accelerometer-, gyroscope- and barometer-based features and relatively simple machine learning models enabled us to estimate key kinematic and energetic characteristics of 180-degree turns with an error of about 15%. Features were obtained from a single pelvis-mounted unit, and no prior calibration procedure was required. In addition, we limited signal processing to a few basic steps, with only standard low-pass filtering being applied to sensor readings: this potentially increases the generalizability to different units and vendors. Table 2. Performance of regression models. In bold, the best model for each response, in terms of root mean square error (RMSE). Response Regression Model R2 RMSE MAE Positive work (J) Multilinear regression 0.36 1.96 1.30 Support vector regression 0.43 1.85 1.14 Boosted trees 0.39 1.91 1.22 Artificial neural network 0.39 2.40 1.80 Figure 4. Feature importance returned by boosted trees models when predicting positive/negative mechanical work (Wp and Wn, top), and approach/sprint running speed (vbefore and vafter, bottom). Refer to Table 1 for the description of features Fi. 4. Discussion The combination of accelerometer-, gyroscope- and barometer-based features and relatively simple machine learning models enabled us to estimate key kinematic and energetic characteristics of 180-degree turns with an error of about 15%. Features were obtained from a single pelvis-mounted unit, and no prior calibration procedure was required. In addition, we limited signal processing to a few basic steps, with only standard low-pass ﬁltering being applied to sensor readings: this potentially increases the generalizability to diﬀerent units and vendors. Sensors 2019, 19, 3094 8 of 11 Table 2. Performance of regression models. In bold, the best model for each response, in terms of root mean square error (RMSE). Response Regression Model R2 RMSE MAE Positive work (J) Multilinear regression 0.36 1.96 1.30 Support vector regression 0.43 1.85 1.14 Boosted trees 0.39 1.91 1.22 Artiﬁcial neural network 0.39 2.40 1.80 Negative work (J) Multilinear regression 0.35 2.27 1.66 Support vector regression 0.42 2.14 1.41 Boosted trees 0.41 2.16 1.49 Artiﬁcial neural network 0.44 2.34 1.84 Speed before (m/s) Multilinear regression 0.53 0.26 0.21 Support vector regression 0.65 0.23 0.17 Boosted trees 0.60 0.24 0.19 Artiﬁcial neural network 0.66 0.23 0.18 Speed after (m/s) Multilinear regression 0.47 0.25 0.20 Support vector regression 0.65 0.20 0.15 Boosted trees 0.62 0.21 0.16 Artiﬁcial neural network 0.69 0.19 0.15 MAE: mean absolute error; R2: coeﬃcient of determination. 4.1. Turn Direction As CoDs and sidestepping maneuvers impose a high muscular and mechanical load on lower limb structures, knowing the intensity and direction of these actions might help in preventing unilateral overloading and in turn potential injuries [2,35]. Turn side (direction) was satisfactorily estimated even with linear discriminant analysis, which makes the adoption of BT, SVM and ANNs redundant for this speciﬁc task. In fact, the combination of gyroscope features F11 (integral of the angular velocity around the pelvic anteroposterior axis) and most importantly F12 (integral around the pelvic mediolateral axis) returned almost perfect classiﬁcations. In other words, pelvis rotation captures the most relevant information about turning direction and could be easily implemented in existing global positioning system (GPS) activity trackers [36]. This sensor location allows for the implementation of smart apparel, that unlike smartwatches or wristbands, can be worn during games. A potential improvement to this model could be the classiﬁcation among multiple classes of directional changes, i.e., 45–90 degree, 90–120 degree and 120–180 degree CoDs. Further, it is likely that the classiﬁcation performance would decrease when positioning the sensor on other less favorable—limited to turn direction classiﬁcation—segments such as feet or shanks. 4.2. Turn Speed and Mechanical Energy Turning speed is related to the energy dissipated during the deceleration (braking) phase, and thus impacts on the risk of ligamentous lesions, especially at the knee level [37]. Once a directional change was detected, the proposed models were successfully able to estimate turn speed with an error below 0.2 m/s, which is comparable to 10 Hz GPS error in common sports applications [38,39]. However, rapid directional changes usually degrade GPS accuracy [38]: obtaining speed during CoDs still represents a challenge for such systems [40], whose weaknesses can be mitigated by adding or integrating data from inertial sensors [36,41]. The best prediction performance of ANNs showed that a linear relationship between predictors and response (i.e., multiple linear regression) was outperformed by non-linear models: ANNs can detect complex nonlinear interactions between inputs and targets [42]. However, the lack of transparency in the mathematical models of ANNs hinders the interpretation of their output. Decision trees showed slightly lower performance, but the model structure of the entire procedure can be followed and Sensors 2019, 19, 3094 9 of 11 interpreted: in BT models, prediction appeared highly dependent on positive mediolateral acceleration (F4) for sprinting speed, and acceleration skewness for approach speed (Figure 4). A similar reasoning applies to Wp and Wn, which were better predicted by SVR with Gaussian kernel. Mechanical energy during a CoD is an indirect measure of its associated metabolic cost. Computed positive/negative external CoM work was in line with previous investigations from diﬀerent groups [8,16], which ensures the predictions were constructed on a solid basis. The prediction error (1.1–1.4 J) can be considered acceptable as it allows us to get a realistic measure of the amount of energy involved in the braking/propulsive actions, which is highly dependent on running speed. 4.3. Limitations and Perspectives Turning event detection was particularly easy in the featured experimental protocol, which involved repeated 180-degree turns with just few running steps in between: further developments should include various angles of approach, and potentially a less homogeneous sample to increase the model’s generalizability. Compared to the reference optical system, CoD events were determined with a variability of about 0.1 s, which is of the same order of magnitude of the stance phase of the pivoting limb (~0.3 s [14]). However, we chose to compute features over a two second span; thus, any overlaps should be in the range of 5% of the whole window. Although data were processed oﬀ-line, once a turn is detected it would be relatively straightforward to apply the regression algorithms to moving windows containing updated data streams. More complex features could be also added to exploit the IMU three-dimensional orientation (i.e., quaternions): however, (i) accurate long-term orientation tracking based on a unique inertial sensor is not trivial and (ii) we intentionally limited feature processing to variables that could be easily computed on a portable device. In addition, the proposed models did not rely on anthropometric information (height, weight, BMI, etc.), that were purposefully excluded from predictions. Even if a more general scope should be adopted for practical on-the-ﬁeld implementations, this paper showed that the extraction of meaningful information on turn kinematics and energetics is highly viable with a data-driven approach using commercially-available units and established regression and classiﬁcation techniques. The current study represents a further step towards the accurate, ecological quantiﬁcation of the key features of changes of direction in team sports. Author Contributions: Conceptualization, M.Z. and C.S.; methodology, M.G. and M.T.; formal analysis, M.Z.; data curation, M.Z.; resources, C.D., C.S. and M.T.; supervision, M.G. and C.S.; project administration, C.D.; writing—original draft preparation, M.Z.; writing—review and editing, M.G., C.S. and M.T. 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A review of analytical techniques for gait data. Part 2: Neural network and wavelet methods. Gait Posture 2001, 13, 102–120. [CrossRef] © 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/).	Use of Machine Learning and Wearable Sensors to Predict Energetics and Kinematics of Cutting Maneuvers.	07-12-2019	Zago, Matteo,Sforza, Chiarella,Dolci, Claudia,Tarabini, Marco,Galli, Manuela	eng
PMC8523042	Sedentary Step length (m) Cadence (steps/s) S4 Fig 0.5 1.0 1.5 2.0 2.5 2 3 4 5 1 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 2 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 3 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 16 (F) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 5 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 6 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 7 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 18 (F) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 9 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 10 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 11 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 17 (F) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 8 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 14 (F) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 19 (F) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 20 (F) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 15 (F) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 12 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 4 (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 13 (M)	Spatiotemporal inflection points in human running: Effects of training level and athletic modality.	10-18-2021	Goto, Yuta,Ogawa, Tetsuya,Kakehata, Gaku,Sazuka, Naoya,Okubo, Atsushi,Wakita, Yoshihiro,Iso, Shigeo,Kanosue, Kazuyuki	eng
PMC5330462	RESEARCH ARTICLE Oxygen uptake kinetics and energy system’s contribution around maximal lactate steady state swimming intensity Jailton Grego´rio Pelarigo1,2,3,4☯, Leandro Machado3,4‡, Ricardo Jorge Fernandes3,4‡, Camila Coelho Greco5‡, João Paulo Vilas-Boas3,4☯ 1 University Catholic Center of Quixada´–UNICATO´ LICA, Quixada´, Ceara´, Brazil, 2 Metropolitan College of Grande Fortaleza–FAMETRO, Fortaleza, Ceara´, Brazil, 3 Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, Porto, Portugal, 4 Porto Biomechanics Laboratory, LABIOMEP, University of Porto, Porto, Portugal, 5 Human Performance Laboratory, Physical Education Department, São Paulo State University, Rio Claro, São Paulo, Brazil ☯ These authors contributed equally to this work. ‡ These authors also contributed equally to this work. jailtongp@hotmail.com Abstract The purpose of this study was to examine the oxygen uptake ( _VO2) kinetics and the energy systems’ contribution at 97.5, 100 and 102.5% of the maximal lactate steady state (MLSS) swimming intensity. Ten elite female swimmers performed three-to-five 30 min submaximal constant swimming bouts at imposed paces for the determination of the swimming velocity (v) at 100%MLSS based on a 7 x 200 m intermittent incremental protocol until voluntary exhaustion to find the v associated at the individual anaerobic threshold. _VO2 kinetics (cardi- odynamic, primary and slow component phases) and the aerobic and anaerobic energy con- tributions were assessed during the continuous exercises, which the former was studied for the beginning and second phase of exercise. Subjects showed similar time delay (TD) (mean = 11.5–14.3 s) and time constant (τp) (mean = 13.8–16.3 s) as a function of v, but reduced amplitude of the primary component for 97.5% (35.7 ± 7.3 mL.kg.min-1) compared to 100 and 102.5%MLSS (41.0 ± 7.0 and 41.3 ± 5.4 mL.kg.min-1, respectively), and τp decreased (mean = 9.6–10.8 s) during the second phase of exercise. Despite the slow com- ponent did not occur for all swimmers at all swim intensities, when observed it tended to increase as a function of v. Moreover, the total energy contribution was almost exclusively aerobic (98–99%) at 97.5, 100 and 102.5%MLSS. We suggest that well-trained endurance swimmers with a fast TD and τp values may be able to adjust faster the physiological requirements to minimize the amplitude of the slow component appearance, parameter associated with the fatigue delay and increase in exhaustion time during performance, how- ever, these fast adjustments were not able to control the progressive fatigue occurred slightly above MLSS, and most of swimmers reached exhaustion before 30min swam. PLOS ONE \| DOI:10.1371/journal.pone.0167263 February 28, 2017 1 / 12 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Pelarigo JG, Machado L, Fernandes RJ, Greco CC, Vilas-Boas JP (2017) Oxygen uptake kinetics and energy system’s contribution around maximal lactate steady state swimming intensity. PLoS ONE 12(2): e0167263. doi:10.1371/journal. pone.0167263 Editor: Juan Sastre, Universitat de Valencia, SPAIN Received: February 16, 2016 Accepted: November 11, 2016 Published: February 28, 2017 Copyright: © 2017 Pelarigo 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 investigation was supported by grants of the Capes Foundation, Ministry of Education of Brazil (BEX: 0536/10-5): JGP and project PTDC/DES/101224/2008 (FCOMP-01- 0124-FEDER-009577): JPVB RF. Competing interests: The authors have declared that no competing interests exist. Introduction An important aspect of aerobic endurance performance is the ability to sustain the highest per- centage of maximal oxygen uptake (% _VO2max) as long as possible. In this sense, coaches and swimmers have used the % _VO2max in different submaximal intensities to control, prescribe and improve sports training [1]. Additionally, scientists have shown that the _VO2 kinetics analysis may help to understand the physiological adjustments produced over time by the ath- letes in several sports, allowing them to maintain a high % _VO2max in a physiological steady- state during aerobic endurance performance [2–4]. Meanwhile, the scientific community has mainly described the _VO2 kinetics in three differ- ent intensity domains during continuous exercise. First, the moderate domain is described as the exercise intensities in which a state steady for _VO2 is achieved within 3 min of constant exercise [5]. Subsequently, the heavy domain is described as the exercise intensities in which _VO2 slow component should be evident, causing a delay on the achievement of the _VO2 steady-state during exercise [2]. Last, the severe domain is described as the exercise intensities in which _VO2 is elevated compared to rest values and continue to increase over time, leading to attain the _VO2max [6, 7]. Maximal lactate steady state (MLSS) is considered one of the main relevant parameters for prescription and improvement of aerobic endurance performance, once it has been assumed as the limit intensity at which, during prolonged and submaximal exercise, the metabolic energy is produced mainly by the aerobic metabolism of pyruvate and glycolysis [8, 9]. More- over, MLSS is identified as the maximal intensity that can be maintained over time without the lactate production exceeding removal more than 1 mmol.L-1, and considered gold-standard method for the evaluation of aerobic capacity [10–12]. Once maximal velocity where a steady-state is found represents a fundamental physiologi- cal border, subtle changes in this intensity could likely modify _VO2 kinetics response. For instance, when the exercise is performed at intensities slightly below MLSS, a physiological steady state is sustained for both blood lactate concentration [La-] and _VO2 as a function of time [6, 7, 13]. On the other hand, at intensities above the MLSS, a significant increase in [La-] and _VO2 is likely to be observed throughout time [3, 7, 8, 12], leading to fatigue and voluntary exhaustion [3, 4, 14]. Moreover, the swimming MLSS determination needs a short time of interruption for the blood collection during the 10th minute of exercise for the analysis of [La- ], and then, a resumption of exercise to complete the test. Thus, it seems to be fundamental to examine the behavior of _VO2 kinetics not only the beginning of exercise, but too after the resumption of exercise throughout exercise to better understanding of the entire process of the swimmer physiological response along the exercise. _VO2 kinetics has been studied in different sports over the last decades [2, 6, 15], and there are relevant number of researches based on [La-] and gas exchange at intensities related to MLSS [8, 13, 14]. However, no study has evaluated _VO2 kinetics at (and around) the MLSS intensity. Thus, our purpose was to examine _VO2 kinetics and the energy systems’ contribu- tion at 97.5, 100 and 102.5%MLSS in swimming. It was hypothesized that at 97.5%MLSS, _VO2 kinetics adjustments may not be so evident such as 100 and 102.5%MLSS. It was further hypothesized that even at the 100%MLSS intensity, swimmers may also have to adjust _VO2 kinetics during the exercise, once this intensity would lead to voluntary exhaustion over time. On the other hand, at the intensity of 102.5%MLSS, _VO2 kinetics may be compromised by fatigue, requiring faster time adjustments for time delay and time constant, and higher _VO2 amplitudes either for primary or slow components compared to lower exercise intensities. We Oxygen kinetics and aerobic endurance performance PLOS ONE \| DOI:10.1371/journal.pone.0167263 February 28, 2017 2 / 12 further intended to assess _VO2 kinetics of the second phase of exercise, starting after the collec- tion of [La-] and resumption of exercise (from 10th min to the exercise end—final exercise), hypothesizing that these parameters could be faster than without previous exercise. Moreover, as MLSS may be maintained for long time period without continuous [La-] accumulation, as well as a submaximal exercise, the energy supply should be mainly supported through the aer- obic system for the swimming intensities of ± 2.5% around MLSS. Material and methods Ten elite female swimmers volunteered and gave written informed consent (or parent/guard- ian when subjects were under 18yrs) to participate in the present study, which was approved by the Ethics Committee of Faculty of Sport from the University of Porto and performed according to the Declaration of Helsinki. The swimmers were (mean ± SD) 17.6 ± 1.9 years of age, 1.70 ± 0.05 m height, 61.3 ± 5.8 kg body mass, 15.5 ± 2.9% body fat mass, and 54.9 ± 6.7 mL.kg.min-1 _VO2max, specialized in middle- and long-distance swimming events. The subjects had, at the least, seven years of experience as competitive swimmers and their mean perfor- mance over a 400m freestyle swim was 88.0 ± 3.4% of the short course word record. The test sessions were performed in a 25 m indoor swimming pool. Air humidity was main- tained nominally between 40–60%, and pool water temperature between 27–28˚C. Swimmers were advised to refrain from intense training at least 24 h before the experimental sessions. The tests were conducted within a seven day period, at the same time of the day (± 2 h), mini- mizing the circadian rhythm effects. Previously to the test sessions, swimmers performed a 1000 m warm-up at low/moderate intensity. The tests were performed in front crawl, with in- water starts and open turns, without relevant underwater glides. A 24 h interval was imposed between all tests. Initially, swimmers performed an intermittent incremental protocol until voluntary exhaustion to find the velocity (v) corresponding to the individual anaerobic threshold (IAnT). The distance covered in each step was 200 m, with v increases of 0.05 m.s-1 and 30 s rest intervals between each swim [16]. According to these authors, the predetermined v of the last step was defined as the currently best expected performance for the subjects’ 400 m front crawl, and then used to define all the v steps for the incremental test. The IAnT was assessed by the relationship between [La-] and v using a curve fitting method, and considered the inter- ception point between linear and exponential regressions to determine the accurate v where [La-] increased exponentially [16, 17]. Subsequently, each swimmer performed three-to-five 30 min submaximal constant swim- ming bouts at imposed paces to determine the highest v where a MLSS was achieved (100% MLSS). The first trial was performed at the v corresponding to IAnT; and, if a steady state or a decrease in [La-] was observed, further subsequent trials with 2.5% higher velocities were per- formed until no [La-] steady state could be maintained [14]. Following this study, if the first trial resulted in a clearly identifiable increase of the [La-], and/or could not be sustained due to exhaustion, further trials were conducted with reduced velocities. MLSS was defined as the [La-] that increased by no more than 1 mmol.l-1 between the 10th and 30th min of the test [9]. Earlobe capillary blood samples (5 μL) were collected: (a) at rest and in the first 30 s after each step of the incremental test, immediately after exhaustion, and at each 2 min of recovery (until the [La-] recovery peak was found); and (b) at rest, 10 and 30th min (or voluntary exhaustion) of each continuous bout (Lactate Pro, Arkray, Inc., Kyoto, Japan). The v was set and maintained using a visual underwater pacer (GBK-Pacer, GBK Electron- ics, Aveiro, Portugal), with lights located each 2.5 m apart by a light strip on the bottom of the pool. Swimmers followed the flashing lights to maintain the predetermined velocities. and Oxygen kinetics and aerobic endurance performance PLOS ONE \| DOI:10.1371/journal.pone.0167263 February 28, 2017 3 / 12 were instructed to keep their heads above each visual signal. Exhaustion was defined when the swimmers remained 5 m behind the lights. _VO2 was measured by a telemetric portable gas analyzer (K4b2, Cosmed, Italy) in both tests, connected to the swimmer by a low hydrodynamic resistance respiratory snorkel and valve system (New AquaTrainer1, Cosmed, Italy). This system has been previously validated [18] and used in similar studies [15]. The device was calibrated for minute ventilation ( _VE) with a calibrated syringe (3 L) and the O2 and CO2 analyzers with standard calibration gases (16% O2 and 5% CO2) before each test. In all tests, _VO2 data were analyzed and errant breaths occurred by swallow water and/or saliva, sighs and coughs were excluded. Afterwards, _VO2 values were measured in mean ± 3 SD and outside values were removed. Subsequently, the breath-by-breath data were linearly interpolated to provide five-by-five s values, and smoothed using three breath averages [15, 19]. Heart rate (HR) was monitored and registered continu- ously by a HR monitor system (Polar Vantage NV, Polar electro Oy, Kempele, Finland) and transferred in real time, through a telemetric signal, to the K4b2 device. The HR values were also averaged every 5 s intervals. The average _VO2 values were analyzed by a nonlinear least squares algorithm to fit the data through MatLab 7.0 Software (MathWorks, Natick, MA). The mathematical model consisted of two (cardiodynamic and primary components) or three (cardiodynamic, primary and slow components) exponential models. An F-Test (p < 0.05) was used to evaluate whether the two or three exponentials models provided the best fit to each data set. _VO2 ðtÞ ¼ _VO2 baseline þ Ac ½1 using multivariate ANOVA and examined by the intensity and previous exercise effects. The v and [La-] values were performed using the univariate ANOVA. All analyses were conducted for repeated measures, complemented with the Bonferroni correction post-hoc test with a sig- nificance level of p < 0.05. Results All swimmers performed 30 min when swimming at 97.5 and 100%MLSS, but eight swimmers were not able to maintain the predetermined v during 30 min at 102.5%MLSS, reaching volun- tary exhaustion at 19.3 ± 4.9 min. The average v and % _VO2max values were different in between the three swim intensities, with 97.5%MLSS slowest and lowest, and 102.5%MLSS fastest and highest (F2,18 = 2560.200, p < 0.001, p 2 = 0.996; F2,18 = 15.538, p < 0.001, p 2 = 0.633, respec- tively) (Table 1). [La-] and HR values for the three swim intensities are also shown in Table 1 with a higher values at 102.5%MLSS compared to 97.5 and 100%MLSS for [La-] (F2,18 = 18.123, p < 0.001, p 2 = 0.668), and at 102.5%MLSS compared to 97.5%MLSS for HR (F2,18 = 7.222, p < 0.005, p 2 = 0.445). _VO2 kinetics parameters are presented in Table 2. Ap tended to increase with the swimming intensity (v) during the initial exercise, but differences were only noticed comparing 100 and 102.5%MLSS to 97.5%MLSS (F2,18 = 8.249, p < 0.05, p 2 = 0.478). Meanwhile, Ap was similar at final exercise for the three swim conditions (F2,18 = 1.167, p = 0.334, p 2 = 0.115). On the other hand, Ap decreased as a function of previous exercise for the three swims bouts. TDp, τp and MRT were similar as function of v at initial exercise and final exercise during the three swim- ming conditions. However, when analyzed the swim bouts as a function of previous exercise, TDp decreased for the 97.5%MLSS, but the values remained similar for 100 and 102.5%MLSS; τp decreased for all swim intensities, and MRT decreased for the 97.5 and 102.5%MLSS, but remained similar for 100%MLSS. The both measured _VO2baseline at initial exercise (F2,18 = 2.389, p = 0.120, p 2 = 0.210) and final exercise (F2,18 = 1.034, p = 0.376, p 2 = 0.103) were similar in between the three swim con- ditions, but _VO2baseline increased as a function of previous exercise (initial to final exercise) for all continuous intensities (F1,9 = 68.311, p < 0.001, p 2 = 0.884). Ac was similar as a function of v for both initial exercise (F2,18 = 0.134, p = 0.876, p 2 = 0.015) and final exercise (F2,18 = 1.974, p = 0.168, p 2 = 0.180). Moreover, at 97.5%MLSS, Ac was lower comparing initial and final exer- cise, but values remained similar for 100 and 102.5%MLSS. As of _VO2 kinetics was observed for all tested swimming intensities and testing phases (ini- tial and final exercise) only in two out of ten subjects. In one subject As was not observed. The As was observed for 6 swimmers during initial exercise and 8 swimmers during final exercise at 97.5%MLSS, for 6 swimmers during initial exercise and 7 swimmers during final exercise at 100%MLSS, and for 9 swimmers during initial exercise and 5 swimmers during final exercise Table 1. Mean (SD) values of swimming velocity (v), blood lactate concentrations ([La-]), heart rate (HR), and percentage of maximal oxygen uptake (%VO2max) are shown at 97.5, 100 and 102.5% of the maximal lactate steady state (MLSS) (N = 10). 97.5%MLSS 100%MLSS 102.5%MLSS v (m.s-1) 1.21 (0.07) 1.24 (0.07)1 1.27 (0.07)1,2 [La-] (mmol.L-1) 1.48 (0.39) 1.89 (0.77) 2.97 (0.87)1,2 HR (beats.min-1) 167.1 (15.0) 173.6 (9.7) 179.3 (9.2)1 % _VO2max (%) 78.9 (8.7) 84.7 (3.8)1 90.9 (4.6)1,2 1,2 Values different from 97.5 and 100%MLSS, respectively (p < 0.05). doi:10.1371/journal.pone.0167263.t001 _ Oxygen kinetics and aerobic endurance performance PLOS ONE \| DOI:10.1371/journal.pone.0167263 February 28, 2017 5 / 12 at 102.5%MLSS. The As values are presented in Table 3. As tended to increase with swimming intensity during initial exercise, but keeping constant during final exercise whatever the inten- sity considered; however no statistical analysis was applied, once the occurrence of the As was apparently chaotic among swimmers both considering swimming intensities and phases of testing (initial and final exercise). The relative energy contribution for each one of the three swim intensity bouts is shown in Fig 1. The aerobic energy contribution decreased (F2,18 = 15.254, p < 0.001, p 2 = 0.629) and the anaerobic energy increased (F2,18 = 15.254, p < 0.001, p 2 = 0.629) at 102.5%MLSS compared to 97.5 and 100%MLSS. Discussion The purposes of this study were to examine the _VO2 kinetics responses during constant-veloc- ity swims at intensities of 97.5, 100 and 102.5%MLSS, the effect of previous exercise on the parameters of _VO2 kinetics, and the contribution of the energetic systems at the three Table 2. Mean (SD) values of VO2 kinetics parameters at velocities of 97.5, 100 and 102.5% of the maximal lactate steady state (MLSS) for the beginning of exercise until the break of swim for blood collection (initial exercise), and the second phase of exercise, starting after blood collec- tion (final exercise) (N = 10). 97.5%MLSS 100%MLSS 102.5%MLSS Initial exercise Final exercise Initial exercise Final exercise Initial exercise Final exercise VO2 baseline (mL.kg-1.min-1) 7.2 (2.1) 16.0 (5.3)a 6.0 (1.0) 17.4 (5.7)a 6.4 (0.8) 18.8 (5.8)a Ac (mL.kg-1.min-1) 16.4 (5.9) 10.4 (4.9)a 16.1 (7.1) 14.2 (5.4) 15.1 (6.5) 14.9 (5.7) Ap (mL.kg-1.min-1) 35.7 (7.3) 26.3 (7.4)a 41.0 (7.0)1 28.3 (5.2)a 41.3 (5.4)1 29.8 (5.5)a TDp (s) 14.3 (5.5) 12.0 (5.3)a 12.4 (8.1) 11.9 (4.9) 11.5 (6.8) 11.1 (4.7) τp (s) 16.3 (5.4) 10.8 (4.7)a 13.8 (4.5) 9.7 (4.5)a 16.0 (5.8) 9.6 (5.3)a MRT (s) 30.6 (5.2) 22.8 (5.4)a 26.2 (6.8) 21.6 (4.6) 27.4 (8.5) 20.7 (5.2)a Statistical analyses were described by intensity and previous exercise effect. 1 Values different from 97.5%MLSS for initial exercise. a Values different from initial exercise (p < 0.05). doi:10.1371/journal.pone.0167263.t002 Table 3. Individual and mean (SD) values of the amplitude of slow component (As) at velocities of 97.5, 100 and 102.5% of the maximal lactate steady state (MLSS) for the beginning of exercise until the break of swim for blood collection (initial exercise), and the second phase of exercise, starting after blood collection (final exercise) (N = 10). As (mL.kg-1.min-1) 97.5%MLSS 100%MLSS 102.5%MLSS swimmer Initial exercise Final exercise Initial exercise Final exercise Initial exercise Final exercise 1 1.7 2.9 2.3 3.8 4.5 1.6 2 2.3 0.7 4.4 0 3.7 0 3 1.1 0 2.6 0 4.4 0 4 0 0 0 0 0 0 5 4.2 0.9 2.8 0.8 2.9 0 6 0 0.8 0 0.8 1.9 2.2 7 0 1.2 0 0.9 7.2 1.1 8 0 1.7 2.8 1.1 4.5 0 9 2.5 1.3 2.6 1.5 6.1 0.8 10 1.4 0 0 1.5 5.1 1.8 Mean (SD) 2.2 (1.1) 1.4 (0.8) 2.9 (0.8) 1.5 (1.1) 4.5 (1.6) 1.5 (0.6) doi:10.1371/journal.pone.0167263.t003 _ Oxygen kinetics and aerobic endurance performance PLOS ONE \| DOI:10.1371/journal.pone.0167263 February 28, 2017 6 / 12 conditions. The main original findings were that increasing exercise intensity resulted in greater primary amplitude of the _VO2 kinetics, in accordance with previous results in running [22]. As demonstrated by other studies [23–25], the previous exercise may increase the ampli- tude of the primary component and accelerate _VO2 kinetics (i.e., MRT) during the subsequent exercise. There was a significant increase in the anaerobic contribution when swimming above MLSS. However, the aerobic energetic system contribution corresponded to ~99% of the total energy demand of the exercise in all exercise conditions analyzed in this study. In sports science, _VO2 kinetics have added the understanding of physiological adjustments over time [2–4], such as muscle metabolism and systemic oxygen transport [26]. Moreover, one of the most relevant exercise intensities in swimming for aerobic training, prescription and evaluation is the v at which MLSS is obtained, being considered the direct and gold-stan- dard method for the evaluation of aerobic capacity [8, 10–12, 14]. Thus, both aspects ( _VO2 kinetics and MLSS) are decisive for the understanding of energy supply and oxidative metabo- lism supporting muscular exercise. Therefore, our purpose was to examine the amplitude and time adjustments of _VO2 kinetics during swims at intensities of 97.5, 100 and 102.5%MLSS, exploring the effects of small prescriptions variations on swimming oxidative physiology. The main findings were: (a) Ap tended to increase with swimming v for the initial phase of exercise, despite differences were only noticed comparing 100 and 102.5%MLSS to 97.5% MLSS. Meanwhile, Ap was similar at the final phase of exercise during the three swim condi- tions. However, Ap decreased as a function of previous exercise for the three swim intensities; (b) TDp, τp and MRT were similar irrespective of v both at initial and final exercise; (c) regard- ing the effect of previous exercise comparing initial and final exercise for the three swimming intensities, TDp decreased for the 97.5%MLSS, but was similar for 100 and 102.5%MLSS, τp decreased for all swim intensities, and MRT decreased for the 97.5 and 102.5%MLSS, but was similar for 100%MLSS; (d) although As was not evident for all swimmers during the three swimming conditions, it tended to increase with intensity during initial exercise, remaining constant during final exercise; (e) Ac was similar both for the initial and final exercise compar- ing the three swim intensities, but was lower during final exercise compared to initial exercise at 97.5%MLSS, and was similar at 100 and 102.5%MLSS; (f) aerobic and anaerobic energy con- tributions were different at 102.5%MLSS compared to lower swim velocities; (g) at the three Fig 1. Mean ± SD of aerobic and anaerobic energy relative contribution values at velocities corresponding to 97.5, 100 and 102.5% of the maximal lactate steady state (MLSS). doi:10.1371/journal.pone.0167263.g001 Oxygen kinetics and aerobic endurance performance PLOS ONE \| DOI:10.1371/journal.pone.0167263 February 28, 2017 7 / 12 swim intensities, the aerobic contribution values were higher than 98% of the total energy input. The _VO2 values in the present study were directly measured breath-by-breath throughout time for the three swim intensities. Subsequently, the _VO2 data were fitted through mathemat- ical modelling as previously applied in swimming for maximal and submaximal exercises [15, 19, 27–30]. Some studies have reported _VO2 kinetics at intensities near the maximal v where a steady state in swimming is found (MLSS) [27–29], however we are unaware of a study that has evaluated and compared _VO2 kinetics at or around the MLSS in swimming. Most of previ- ous studies reported in sports science [2, 15, 19, 27–31] have studied _VO2 kinetics at maximal and submaximal intensities, demonstrating the fundamental role of _VO2 kinetics to under- stand the physiological mechanisms underpinning the dynamics of the aerobic response at dif- ferent exercise intensities. Thus, the understanding of the _VO2 kinetics throughout time may aid the evaluation of aerobic capacity and prescription of specific training sets during these fundamental training intensities around MLSS. The 100%MLSS v values reported in this study are in accordance with those reported in previous ones [13, 14, 32], in spite of the fact that most of the swimmers examined in the previ- ous studies were male when compared with the female subjects of the present study. Despite higher v values at a given relative intensity are expected to be higher for male than female counterparts of similar training level [33], the sex similitude comparing our results with litera- ture could likely be explained by a higher technical and biomechanical proficiency of our female swimmers when compared to the male swimmers of the previous studies. Indeed, the % _VO2max at 100%MLSS (85 ± 4%) observed in the present study for women is similar to previ- ously reported data for men (86.1% _VO2peak) [34], suggesting similar levels of aerobic capacity development, even the _VO2max= _VO2peak being higher in the previous study (mean = ~83 mL. kg-1.min-1) when compared with our results (54.9 ± 6.7 mL.kg-1.min-1). Meanwhile, the mean HR value at 100%MLSS was 174 ± 10 beats.min-1 in the present study, values which were simi- lar to the previous reported in literature [32, 34], as expected by the comparable age of samples. Moreover, the [La-] at 100%MLSS (1.89 ± 0.77 mmol.L-1) in the present study were lower when compared to swimming literature (2.8–3.3 mmol.L-1) [14, 34, 35]. These lower [La-] val- ues may be explained by sex differences for similar levels of aerobic capacity development, with expected lower values for women due to lower body mass and lean muscle mass com- pared to men [36]. Furthermore, women have showed lower testosterone concentration com- pared to men [37] during aerobic endurance exercise [33, 36], suggesting different metabolic contributions between carbohydrates and fat during long-distance exercise [33, 38], and sup- porting comparable lower [La-]. Since the early research on _VO2 kinetics [39] until up to date, the time constant (τ) has been studied in sports science in the attempt to comprehend the physiological adjustments during the non-steady state period at the beginning of exercise due to the increase of metabolic demand. In the present study, the τp values were similar between intensity levels for the initial exercise phase (mean = 15.4 ± 5.2 s) and final exercise phase (mean = 10.0 ± 4.7 s), but the val- ues decreased with previous exercise for the three swim conditions. This is particularly relevant for training practice, underlining the influence of previous exercise on the subsequent meta- bolic dynamics. In all studied exercise intensities, the τp in the present study showed similar values compared than those previously reported in swimming (~15–20 s) [27–29], cycling [40, 41], rowing [42], and running [43, 44]. Thus, those values reported for intensities up to and above the MLSS seem to behave similarly as expected, based on the previous knowledge on the Oxygen kinetics and aerobic endurance performance PLOS ONE \| DOI:10.1371/journal.pone.0167263 February 28, 2017 8 / 12 _VO2 kinetics during different intensity domains for well-trained athletes. Indeed, a faster attainment of a steady state and a reduction in the oxygen deficit are associated to the fatigue delay and increase in exhaustion time, being well trained athletes able to perform at higher intensities with lower requirements of anaerobic energy during the transition from rest to exercise [5]. Hence, the lower τp values reported in this study when compared to previously published ones regarding physiological adaptations induced by aerobic endurance training confirm the highly endurance training status and specialization (endurance athletes) of our swimmers [5, 44]. Partially in contrast with previous literature that showed the existence of the As at these exercise intensities [2, 4, 5], in the present study it has shown to occur chaotically during the three swimming conditions, with very diverse individual occurrence profiles; however, observ- ing the sample data a tendency to As increase as a function of intensity was observed (2.2 ± 1.1, 2.9 ± 0.8 and 4.5 ± 1.6 mL.kg-1.min-1, respectively for 97.5, 100 and 102.5%MLSS), but only during initial exercise, not during the final phase after metabolic adaptation already occurred. Besides, only two swimmers showed As occurrence in all trials both at the initial and final exer- cise phases, and one swimmer did not show any As during all the swimming efforts and phases. It is worthy to emphasize the curiosity of that particular swimmer being a national record holder (800 and 1500m) and the best endurance swimmer of the sample. These partially con- tradictory findings could be explained, at least in part, by the specific physiological adaptations occurred through the highly endurance training status for our swimmers, such as faster τp [44], possible increase in the mitochondrial content of the cell [45], beyond also possible alter- ations in the mitochondrial sensitivity to the respiration regulators [46], and the fact of these endurance athletes might have mainly type I muscle fibers [45]. Thus, our endurance swim- mers with fast _VO2 kinetics would be able to adjust faster the physiological requirements for aerobic performance during the high intensity aerobic exercises, minimizing the As demand. In addition, the appearance of the As is normally explained by a phenomena that may be atten- uated in our very specialized sample, namely the recruitment of type II fibers with fatigue [47], after which the magnitude of As has been correlated with the rise of [La-] [2, 45]. Thereby, the absence of significant As in the present study may be likely explained by the high-level of endurance training of the sample [48]. Moreover, to reinforce the predominance of aerobic energy system during the three swim conditions around MLSS, the present study determined the total energy contribution at each one of the studied exercise intensities. At all swimming intensities up to and above MLSS, the aerobic energy contribution was higher than 98% of the total energy contribution; however there were significant differences between the highest and the lower v regarding aerobic and anaerobic energy contributions. This study was the first study to show the energy contribution during intensities at and around MLSS directly measured breath-by-breath in swimming, which highlights that even at intensities above MLSS; the total energy contribution was mainly and almost exclusively controlled by the oxidative bioenergetics system. Conclusions The present study showed that well-trained endurance swimmers with a fast component of _VO2 kinetics, i.e. an abrupt and fast increase in _VO2 response, and low [La-] may be able to adjust faster the physiological requirements during intensities up to and slightly above MLSS to minimize the appearance of the slow component of _VO2 and reduce the oxygen deficit, both parameters are associated to the fatigue delay and the increase in exhaustion time, key factors to endurance performance. however, these fast adjustments were not able to control the progressive fatigue occurred slightly above MLSS, and most of swimmers reached Oxygen kinetics and aerobic endurance performance PLOS ONE \| DOI:10.1371/journal.pone.0167263 February 28, 2017 9 / 12 exhaustion before 30min swam. Moreover, the data shows that the aerobic energy contribution at intensities up to and slightly above MLSS plays a fundamental role controlling almost exclu- sively the required energy supply. Supporting information S1 File. Values of physiological parameters at 97.5, 100 and 102.5% of the maximal lactate steady state (MLSS) (N = 10). (PDF) S2 File. Values of VO2 kinetics parameters at 97.5, 100 and 102.5% of the maximal lactate steady state (MLSS) (N = 10). (PDF) Acknowledgments This investigation was supported by grants of the Capes Foundation, Ministry of Education of Brazil (BEX: 0536/10-5), and project PTDC/DES/101224/2008 (FCOMP-01-0124-FEDER- 009577). Author Contributions Conceptualization: JGP JPVB CCG RJF. Data curation: JGP LM JPVB CCG RJF. Formal analysis: JGP LM JPVB. Funding acquisition: JPVB RJF. Investigation: JGP JPVB CCG RJF LM. Methodology: JGP JPVB LM CCG RJF. Project administration: JGP JPVB RJF. Resources: JGP RJF JPVB. Software: LM JGP JPVB. Supervision: JGP JPVB CCG RJF LM. Validation: JGP LM JPVB CCG RJF. Visualization: JGP JPVB CCG RJF LM. Writing – original draft: JGP JPVB RJF CCG LM. Writing – review & editing: JGP JPVB CCG RJF LM. References 1. 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PMC8198575	International Journal of Environmental Research and Public Health Article Speed, Change of Direction Speed and Reactive Agility in Adolescent Soccer Players: Age Related Differences Slobodan Andraši´c 1 , Marko Guši´c 2 , Mima Stankovi´c 3 , Draženka Maˇcak 2 , Asim Bradi´c 4, Goran Sporiš 4 and Nebojša Trajkovi´c 3,* Citation: Andraši´c, S.; Guši´c, M.; Stankovi´c, M.; Maˇcak, D.; Bradi´c, A.; Sporiš, G.; Trajkovi´c, N. Speed, Change of Direction Speed and Reactive Agility in Adolescent Soccer Players: Age Related Differences. Int. J. Environ. Res. Public Health 2021, 18, 5883. https://doi.org/10.3390/ ijerph18115883 Academic Editors: Caio Victor Sousa and Samuel da Silva Aguiar Received: 31 March 2021 Accepted: 27 May 2021 Published: 30 May 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 Economics, University of Novi Sad, 24000 Subotica, Serbia; andrasicslobodan@yahoo.com 2 Faculty of Sport and Physical Education, University of Novi Sad, 21000 Novi Sad, Serbia; gusicmarko@yahoo.com (M.G.); macak.md@yahoo.com (D.M.) 3 Faculty of Sport and Physical Education, University of Niš, 18000 Niš, Serbia; mima.stankovic974@gmail.com 4 Faculty of Kinesiology, University of Zagreb, 10000 Zagreb, Croatia; asim.bradic@kif.unizg.hr (A.B.); goran.sporis@kif.unizg.hr (G.S.) * Correspondence: nele_trajce@yahoo.com Abstract: There are a plethora of studies investigating agility in soccer; however, studies have rarely presented the reaction time in differentiating age groups in adolescent soccer players. We investigated age differences in reactive agility, speed, and change of direction speed (CODs), in a group of highly trained adolescent soccer players. A total of 75 adolescent male soccer players (aged 14–19 years) were recruited. The players were grouped based on their age to under 15 (U15; n = 27), under 17 (U17; n = 25), and under 19 (U19; n = 23) players. Players were tested for 5 m, 10 m, and 20 m sprint, CODs speed test, Illinois test, and reactive agility test (total and reaction time). Only the reactive agility test with a live tester (RAT live) and RAT live reaction time (RAT live RT) distinguished U19 from both groups, U17 (RAT live, p < 0.01; RAT RT live, p < 0.01) and U15 (RAT live, p < 0.01; RAT RT live, p < 0.01). Groups did not have different times for 5 m sprint, RAT light and RAT RT light, F = 0.472, 2.691, 1.023, respectively, p > 0.05. Moreover, a signiﬁcantly slower average performance of sprint 20, CODs left and right, and Illinois was also observed in U15 as compared to U17 and U19 (p < 0.05). We can conclude that results in agility tests that include live testers can be a signiﬁcant factor that differentiates between adolescent soccer players considering their age. Keywords: agility; differences; youth; performance; football 1. Introduction The demands of modern soccer have changed signiﬁcantly and increased in recent years. Nowadays, players are required to have more power and to cover greater distances, with more frequent changes in intensity [1–3]. Most high-intensity activities (sprints) take place during decisive moments, such as tackling, offensive and defensive actions, as well as goal-scoring opportunities [4–7]. As soccer is considered to be a sport that requires that attackers evade their opponents’ pressures or tackles, and defenders reduce space on the ﬁeld in order to limit attacking movements or potentially achieve a turnover, having good change of direction speed (CODs) and agility is beneﬁcial [8]. In the last few decades, change of direction speed and reactive agility were considered to be the same skill [9]. However, nowadays, pre-planned agility may be deﬁned as sprints with change of direction, while the reactive agility (RA) is classiﬁed as sprints with directional changes in response to a stimulus [10,11]. Therefore, RA is based on greater levels of motor control, when compared to pre-planned CODs [12]. Reactive agility and CODs are one of the most important skills required for soccer success [13]. Moreover, reactive agility tests (RATs) are able to differentiate the key per- formance indicators presented as the skill levels among soccer players [14]. On the other side, there are still some doubts about the test design and type of stimulus presented in Int. J. Environ. Res. Public Health 2021, 18, 5883. https://doi.org/10.3390/ijerph18115883 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 5883 2 of 7 the existing literature regarding reactive agility tests. In addition, most of the studies only measured total times instead of reaction times. As RATs are designed to evaluate both physical, and technical and cognitive abilities, there is an absolute need to further examine reactive agility assessment in soccer [15]. Some research shows that older adolescent players tend to complete the RATs quicker than younger players due to their higher ﬁtness level, CODs, and anticipatory skill, which makes them play on a much higher level [13]. Several studies investigated the perceptual abilities of higher-level players, and it was shown that there are fundamental perceptual and cognitive differences between them and lower-level players [16–18]. Additionally, Tretloci et al. [19] showed that ﬁeld-based tests including vertical jumps, change of direction speed, and reactive agility can differentiate between under 16 elite and sub-elite soccer players. This was conﬁrmed by Trajkovic et al. [20], showing that the skilled players performed better in reactive agility tests, speed, and CODs compared to amateur players. Moreover, the authors stated that reactive agility tests with live opponent stimuli can be a signiﬁcant factor that differentiates between adolescent soccer players considering their level. At this point, there are studies that determine the difference in physical performance between levels of play. However, there are not many studies that show the difference between age groups in CODs and RA [13,21]. Additionally, the categories used in those studies do not match the categories used here. Moreover, according to the authors’ knowledge, there are no studies that compare light and live stimulus in RATs in soccer players to assess whether these approaches differentiate between different age groups. Therefore, the purpose of this study was to determine possible age-related differences in speed, CODs and reactive agility in a group of trained adolescent soccer players. 2. Materials and Methods 2.1. Subjects A priori, the Gpower 3.1 power analysis software was used to determine that the required sample size is n = 72 given the critical F(69) = 3.13, eta2 = 0.13 p = 0.05, 1 − β = 0.8, and number of groups = 3. A total of 75 adolescent male soccer players (aged 14–19 years), who participate at the highest level of competition in Serbia at their age, were recruited for this research (Table 1). The players were grouped based on their age to under 15 (U15; n = 27), under 17 (U17; n = 25), and under 19 (U19; n = 23) players. Only ﬁeld players were tested, with goalkeepers excluded. Written informed consent was obtained from the players and their parents. Moreover, the ethics board of the Faculty of Sport and Physical Education provided the approval of the research experiment (Ethical Board Approval No: 2019/31). Players were recruited if they had at least 5 years of experience in playing soccer; had a general training history (more than three times per week) in the previous 12 months; were currently training for soccer (more than 7 h per week); and did not have existing medical conditions that would compromise study participation. Table 1. Physical characteristics for U15 (n = 25), U17 (n = 27), and U19 (n = 23). U15 U17 U19 Age 14.7 ± 0.6 16.2 ± 0.7 18.8 ± 0.7 Height 178.06 ± 5.82 180.49 ± 6.56 179.12 ± 5.45 Weight 69.06 ± 10.82 72.89 ± 8.48 70.70 ± 8.22 Experience (years) 5.1 ± 2.7 6.5 ± 2.9 7.3 ± 1.4 Training (min·week-1) 447 ± 126 475 ± 167 487 ± 130 U15—younger than 15 years; U17—younger than 17 years; U19—younger than 19 years; min·week—minutes per week. Int. J. Environ. Res. Public Health 2021, 18, 5883 3 of 7 2.2. Procedure Testing was conducted at the beginning of the annual training season to limit differ- ences in training status between players. All players followed a similar training program under the supervision of their respective coaches. All performance tests were conducted on the same day. Test sessions were undertaken between 09:00 and 13:00 h following at least 8 h of sleep and 48 h of rest. All performance tests were performed on an outdoor facility with artiﬁcial grass in favorable weather conditions (no wind or rain). Before testing, a 20 min standardized warm-up was conducted, which consisted of low-intensity running, acceleration runs, skipping and hopping exercises. Players were familiar with all test procedures. Height and weight measurements were taken in the morning. Height was measured with a ﬁxed stadiometer (+0.1 cm, Holtain Ltd., Crosswell, UK), and body mass with a digital balance (+0.1 kg, ADE Electronic Column Scales, Hamburg, Germany). The same researcher conducted all the measurements. Running speed. The running speed of players was determined using the time to 5, 10 and 20 m using infrared timing gates, 20-m sprint effort with photocell gates (Microgate, Polifemo Radio Light, Bolzano, Italy) placed 0.4 m above the ground, with an accuracy of 0.001 s. The timer was automatically activated as participants crossed the ﬁrst gate at the starting line with split times at 5 m and 10 m. Players were instructed to run as quickly as possible over the 20-m distance from a standing start (crouched start positioned 0.5 m behind the timing lights). Acceleration was evaluated using the time to cover the ﬁrst 5 m of the 20-m test. Participants performed two trials with at least 3 min of rest between them. The best performance of the two tests was used for analysis. Change of direction speed test (CODs). The pre-planned agility test [22] is used to evaluate CODs. Participants were asked to sprint as fast as possible for 5 m through a triggered timing gate (start gate), make a 45◦ cut and sprint 5 m to the left and right through a target gate. In this test, participants knew the cut direction. Running time was recorded using photocell gates (Microgate, Polifemo Radio Light, Bolzano, Italy) placed 0.4 m above the ground, with an accuracy of 0.001 s at the start and ﬁnish gates. The best time of three attempts on the left and right side was considered for further analysis. The reactive agility test (RAT) was performed according to the protocol described previously by Chaouachi et al. [23]. In the current study, the RAT involved a decision- making element provided by a live tester (RAT live) acting as an opponent and light stimuli used instead of testers (RAT light). During RAT live, the tester had 4 options for each condition: preplanned and randomly ordered (i.e., 8 trials). All these conditions were provided to each player in 2 series (5–8 min between sets rest) in a random order. Players were instructed to recognize the cues as soon as possible. Running time was recorded using photocell gates (Microgate, Polifemo Radio Light, Bolzano, Italy) placed 0.4 m above the ground, with an accuracy of 0.001 s. Total time (RAT TT live) and reaction time (RAT RT live) were recorded for each trial, and the best performance was considered for the analysis. The same conditions were used for RAT light, but this time the Witty SEM lights were used instead the testers. When the participants pass the ﬁrst gate, the signal shows right or left direction. The participants must react to the visual signal, change direction and pass the third gate. Similar to RAT live, the total time (RAT TT light) and response movement time (RAT RT light) were recorded for each trial, and the best performance was taken for analysis. Illinois agility test: The length of the ﬁeld is 10 m, while the width (distance between the start and ﬁnish points) is 5 m. Four cones were placed in the center of the testing area at a distance of 3.3 m from one another. Four cones were used to mark the start, ﬁnish and two turning points. The subjects started the test lying face down, with their hands at shoulder level. The trial started on the “go” command, and the subjects began to run as fast as possible. The trial was completed when the players crossed the ﬁnish line without having knocked any cones over. Best time out of three trials was used for analysis [24]. Int. J. Environ. Res. Public Health 2021, 18, 5883 4 of 7 2.3. Statistical Analysis The analysis of the data obtained from the study was performed by SPSS software, version 23.0 (SPSS Inc., Chicago, IL, USA). Data are reported as mean ± SD unless otherwise stated. The Kolmogorov–Smirnov test was conducted to verify if all data met the normality test assumption. Test–retest reliability was assessed for all tests using a one-way intra-class correlation coefﬁcient (ICC) based on average measurements (ICC 1,k). All analyses of variance (ANOVA) were performed on log-transformed data; for the sake of clarity, however, they are reported non-transformed. Age-based comparisons of study outcomes were made with one-way ANOVA (U15, U17, and U19). When ANOVA showed a signiﬁcant group effect, between group differences were allocated by using post hoc Bonferroni tests. Eta squared (η2) is reported as a measure of effect size and deﬁned as small (0.01), medium (0.06), and large (0.14) according to Cohen. The level of signiﬁcance was set at p < 0.05. 3. Results 3.1. Sample Characteristics Table 1 shows the physical characteristics of the players according to age group. Height and weight were similar across the groups (p > 0.05); therefore, adjustments were not performed. 3.2. Study Outcomes in Relation to the Age Group of Soccer Players On average, the U15 needed signiﬁcantly more time than U19 to perform all tests, except sprint 5 m (p = 0.63), RAT light (p = 0.08), and RAT RT light (p = 0.37), which were similar across the age groups. A signiﬁcantly slower average performance of sprint 20, CODs left and right, and Illinois was also observed in U15 as compared to U17 and U19. However, the U17 had a signiﬁcantly slower mean performance only of RAT live and RAT RT live than the U19. Visit Table 2 for detailed results from a one-way analysis of variance. Table 2. Times for 5 m, 10 m sprint, and 20 m sprint, and agility performance for the U15 (n = 25), U17 (n = 27), and U19 (n = 23) soccer players. Outcomes U15 U17 U19 A One-Way ANOVA F(1, 144) η2 Sprint 5 m 1.16 ± 0.23 1.13 ± 0.19 1.11 ± 0.12 0.47 0.02 Sprint 10 m 1.93 ± 0.13 a 1.85 ± 0.18 1.83 ± 0.11 3.28 0.09 Sprint 20 m 3.38 ± 0.23 b 3.18 ± 0.27 3.16 ± 0.31 5.17 ** 0.16 CODs left 2.25 ± 0.15 b 2.15 ± 0.17 2.12 ± 0.21 3.61 * 0.07 CODs right 2.27 ± 0.14 b 2.16 ± 0.19 2.13 ± 0.16 4.87 0.11 Illinois 15.82 ± 0.76 b 15.24 ± 0.53 14.93 ± 0.58 12.47 0.25 RAT light 2.69 ± 0.13 2.61 ± 0.16 2.60 ± 0.19 2.69 0.07 RAT live 2.58 ± 0.10 2.58 ± 0.11 2.48 ± 0.06 c 8.99 0.20 RAT RT light 1.44 ± 0.16 1.41 ± 0.16 1.38 ± 0.11 1.02 0.03 RAT RT live 1.49 ± 0.08 1.38 ± 0.10 1.31 ± 0.14 c 16.27 0.31 Values are mean ± SD. U15—younger than 15 years; U17—younger than 17 years; U19—younger than 19 years; CODs left—change of direction left; CODs right—change of direction right; RAT light—reactive agility test with witty SEM visual signals; RAT RT light—reaction time during RAT live; RAT live—reactive agility test with testers; RAT RT light—reaction time during RAT light; * signiﬁcant age group effect at p ≤ 0.05; ** signiﬁcant age group effect at p ≤ 0.01; a U15 and U19 signiﬁcantly different at p ≤ 0.05; b U15 signiﬁcantly different at p ≤ 0.05; c U19 signiﬁcantly different at p ≤ 0.05. 4. Discussion The present study aimed to determine the difference in several performance indica- tors relevant for soccer performance in adolescent players of different age groups. The main ﬁnding of this study was that the reactive agility test with live testers was able to differentiate U19 players from other age groups. Moreover, the U15 group showed slower average performance in sprint 20, CODs left and right, and Illinois compared to U17 and Int. J. Environ. Res. Public Health 2021, 18, 5883 5 of 7 U19. A possible explanation for these results could be found in the fact that modern soccer training, with frequent changes in tactics based on the characteristics of the opponent, has led to increased adaptability of player roles, especially in young players [9]. Moreover, it is not so uncommon in youth training to change a player’s position in response to different game situations, developing a large range of technical solutions useful for their future soccer-playing career [25]. In the literature, sprinting ability over short (5 m) and longer distances (20 m) is considered to require separate and speciﬁc biomechanical and neuromuscular qualities and, therefore, training techniques [26]. When considering the U15, U17, and U19 players, we found a difference between groups for 10 m and 20 m distance, which is in line with the abovementioned fact. The current ﬁndings [14] show that there is no difference between U17 and U19 for 20 m sprints which is, as well, presented in this study. In addition, we found that U15 players had worse results than U17 and U19 players. Generally, most studies that investigated age difference came to the conclusion that older age groups had better results than younger groups [14,27]. These differences could be associated with the maturity stage of players, which can affect U15 sprint performance more [28] compared to U17 and U19. Moreover, post-pubertal players have accelerated gains in strength supported by rapid gains in muscle mass [29], which may also contribute to the mentioned differences. Our ﬁndings suggest that U19 and U17 players, who performed with similar results, were statistically signiﬁcantly better at CODs left, CODs right and the Illinois test than U15 players. These results are similar to the ones found by [9], where U16 and U18 players gave better results in the Illinois modiﬁed test. Additionally, their study showed that there was no difference in the CODs left and CODs right test between U16 and U18 players, but there were differences found between U14 players and the aforementioned groups. A possible reason for non-existent differences between U19 and U17 players is that the greatest individual differences in biological maturation were found in players 11–16 years old [30]. Contrary to this study [14], Poljskic et al. concluded that U19 players were signiﬁcantly better that U17 players. Taking everything into account, the superiority of U19 players in agility performances may be observed as a direct consequence of their long involvement in systematic soccer training and higher performance level due to the CODs. Our results show that U19 players scored better in the RAT live test than U15 and U17 players. Additionally, we found that U19 players had better results than U15 and U17 in reaction time with live stimuli. Therefore, in the current study, RATs clearly discriminated U19 players from U15 and U17. A possible reason could be found in better anticipatory skills in older adolescent players who have more games and experience behind them. They have better ability to recognize relevant cues of testers, as previously demonstrated for team sport athletes [31,32]. Our results are similar to Fiorilli et al. [13], where U16 and U18 players made better results than U14 in the reactive agility test. Moreover, Poljskic et al. [14] showed that U19 players had better results than U17 in RAG (reactive agility) test. On the other hand, there is a study that found no signiﬁcant difference between juniors (<18) and seniors (>18) in speciﬁc reactive agility tests [33]. However, reactive agility is being developed until the late adolescent age, when it can reach its peak, which could be the reason for the discrepancy in the results. It has been stated recently that intervention programs may have to be different for different age stages [34]. According to our results, we could speculate that modern training is similar for all age categories in adolescent soccer players. Nowadays, the training contains sport-speciﬁc stimuli rather than generic and high-intensity training for physical skills. Soccer players, who have anticipatory expertise and make decisions much faster, are able to recognize and react promptly to a stimulus. Younger players with less experience may need more time to respond to a stimulus before having the proper reaction in the shortest time possible in order to avoid being executed by the opponent. The main limitation of this study is that the attribution of physical ability could be to talent or previous training. In our study, the players were interviewed about their current training load (weekly time) and previous experience (years engaged in soccer). Moreover, Int. J. Environ. Res. Public Health 2021, 18, 5883 6 of 7 they were from the same squad, with the same programs conducted in all categories. Therefore, we could speculate if different approaches to training could contribute to differ- ences in other variables. Future studies should examine players from different teams and academies. Moreover, the maturity level was not introduced and taken into account due to the fact that the majority of studies have focused largely on players 11–16 years of age, where individual differences in biological maturation are perhaps the greatest. Another limitation is the possibility of the circadian rhythm’s inﬂuence on performance [35] due to the time the testing was conducted (9:00 to 13:00 h). 5. Conclusions Reactive agility and COD speed are key skills required for soccer success, based on greater levels of motor control. We found that the reactive agility test with a live tester can be a signiﬁcant factor that differentiates between older and younger adolescent soccer players. Moreover, our ﬁndings prove that ﬁeld-based tests including speed, change of direction speed, and reactive agility are sufﬁciently sensitive to differentiate between a group of adolescent soccer players. Further studies are needed to conﬁrm these results. Author Contributions: Conceptualization, S.A. and N.T.; methodology, M.G.; software, D.M.; vali- dation, S.A. and N.T.; formal analysis, D.M.; investigation, S.A.; resources, G.S.; data curation, S.A., M.G. and N.T.; writing—original draft preparation, S.A., M.S., and N.T.; writing—review and editing, N.T. and D.M.; visualization, A.B. and M.S.; supervision, N.T.; project administration, S.A.; funding acquisition, G.S. 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 approved by the Institutional Review Board of the Faculty of Sport and Physical Education, University of Novi Sad (Ref. No. 30-08-01/2018). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 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[CrossRef] [PubMed]	Speed, Change of Direction Speed and Reactive Agility in Adolescent Soccer Players: Age Related Differences.	05-30-2021	Andrašić, Slobodan,Gušić, Marko,Stanković, Mima,Mačak, Draženka,Bradić, Asim,Sporiš, Goran,Trajković, Nebojša	eng
PMC4552464	RESEARCH ARTICLE Open Access App use, physical activity and healthy lifestyle: a cross sectional study Joan Martine Dallinga1,2, Matthijs Mennes1, Laurence Alpay2†, Harmen Bijwaard2† and Marije Baart de la Faille-Deutekom1,2 Abstract Background: Physical inactivity is a growing public health concern. Use of mobile applications (apps) may be a powerful tool to encourage physical activity and a healthy lifestyle. For instance, apps may be used in the preparation of a running event. However, there is little evidence for the relationship between app use and change in physical activity and health in recreational runners. The aim of this study was to determine the relationship between the use of apps and changes in physical activity, health and lifestyle behaviour, and self-image of short and long distance runners. Methods: A cross sectional study was designed. A random selection of 15,000 runners (of 54,000 participants) of a 16 and 6.4 km recreational run (Dam tot Damloop) in the Netherlands was invited to participate in an online survey two days after the run. Anthropometrics, app use, activity level, preparation for running event, running physical activity (RPA), health and lifestyle, and self-image were addressed. A chi-squared test was conducted to analyse differences between app users and non-app users in baseline characteristics as well as in RPA, healthy lifestyle and perceived health. In addition, a multivariate logistic regression analysis was performed to determine if app use could predict RPA, perceived health and lifestyle, and self-image. Results: Of the 15,000 invited runners, 28 % responded. For both distances, app use was positively related to RPA and feeling healthier (p < 0.05). Also, app use was positively related to feeling better about themselves, feeling like an athlete, motivating others to participate in running, and losing weight (p < 0.01). Furthermore, for 16 km runners app use was positively related to eating healthier, feeling more energetic and reporting a higher chance to maintain sport behaviour (p < 0.05). Conclusions: These results suggest that use of mobile apps has a beneficial role in the preparation of a running event, as it promotes health and physical activity. Further research is now needed to determine a causal relationship between app use and physical and health related behaviour. Background Benefits of physical activity have often been studied and include improved health and reduced mortality rates [1–3]. However, actually becoming physically active is a challenge for many. In the Netherlands research shows that 41 % percent of all adults do not comply with the Dutch Public Health Physical Activity Guideline (at least 30 min of mod- erate to vigorous physical activity during at least 5 days of the week) [4]. Moreover, only 20 % of Dutch adults meet the Strenuous Intensity Physical Activity Guideline of at least three times a week 20 min of vigorous exercise [4]. Physical in- activity is a growing public health concern in the Netherlands as well as in other Western countries. Significant health prob- lems such as increased morbidity and mortality attributable to cardiovascular disease, diabetes, cancers and increased risk of depression may arise if the amount of physical activity in the general population does not increase [5–9]. There is need for innovative ways to promote physical activity and a healthy lifestyle. One promising develop- ment is the use of smartphones during exercise. Use of mobile applications (apps) may be a powerful tool to encourage physical activity and health [10, 11]. Apps are Correspondence: j.m.dallinga@hva.nl †Equal contributors 1School of Sports and Nutrition, Amsterdam University of Applied Sciences, Dr. Meurerlaan 8, 1067 SM Amsterdam, The Netherlands 2Faculty of Health, Sports and Social Work, Inholland University of Applied Sciences, Blijdorplaan 15, 2015 CE Haarlem, The Netherlands © 2015 Dallinga 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. Dallinga et al. BMC Public Health (2015) 15:833 DOI 10.1186/s12889-015-2165-8 accessible, have a large reach, and have multiple function- alities, such as interactive possibilities and feedback op- portunities [12, 13]. Although more than 17,000 health and fitness apps have been developed and are available for the public [12], the literature considering the relationship of app use and health and physical activity is scarce. However, preliminary evidence is promising [11, 14, 15]. Two reviews and one meta-analysis demonstrated positive effects of mobile phone interventions, interventions with mobile technology, and interventions with remote and web interventions in healthy, inactive and overweight indi- viduals [11, 14, 15]. The mobile phone interventions were often combined with additional education, self-reporting of frequency and type of use of the program or telephone calls. The positive effects of these interventions included increased physical activity (expressed by total time, number of occasions of physical activity and energy ex- penditure), cardiovascular fitness and reduced overweight [11, 14, 15]. Small to moderate effect sizes were reported [14, 15]. Nevertheless, in these three reviews few interven- tions were included that used apps. Moreover, in some studies additional interventions were provided next to the mobile phone and app interventions, therefore based on those studies no conclusions can be drawn regarding to the isolated effects of apps on physical activity. Another recent review demonstrated modest effects of app based interventions on physical activity expressed by step count [16]. It should be noted that the apps were often com- bined with external pedometers, small sample sizes were included, small increases in step counts and a short duration of interventions was presented [16]. However, a recent study has shown promising results of the isolated effect of app use [17]. This study demonstrated that use of a Web-based app on lifestyle indicators decreased weight and increased physical activity of people [17]. Moreover, app users presented a higher chance to maintain a healthy lifestyle [17]. In summary, few studies have examined the effect of app use on changes in physical activity and health. In recreational running the use of apps is high and emerging and several apps have been developed to assist individuals in their running exercise. Previous research has shown that recreational running or participation in a running mass event could also be a potential health and physical activity promoting activity [18–20]; Chatton and Kayser showed that participants in a 16 km run were more active than the general population and better in shape [18]. Additionally, in the preparation for a 5 and a 10 km run participants increased physical activity [19, 20]. A majority of participants train in preparation for running events; some of them exercise individually and some of them in a running group [21, 22]. Potentially, app use could assist runners to increase motivation, to increase activity level and set goals during the preparation for a running event. Perhaps the use of apps could assist runners to increase running physical activity and to live and feel healthier. Therefore, the aim of this study was to determine the relationship between the use of apps and changes in physical activity and health and lifestyle behav- iour of short and long distance runners. More specific, we were interested in training volume, alcohol intake, smok- ing behaviour, and lifestyle (e.g. weight loss and eating behaviour). Methods Study design and participants A cross sectional study was designed to analyse the rela- tionship between app use and physical activity, health and lifestyle of recreational runners. On September 21st 2014 the 30th Dam tot Damloop, a running event, was orga- nized in Amsterdam, the Netherlands. The organization of the running event randomly selected and invited 15,000 runners out of 54,410 participants (16 and 6.4 km) to participate in an online survey. Runners of all levels were invited to participate. Participation in the run was either on an individual basis, with a company or for a charity. Inclusion criteria were (a) ≥18 years and (b) signed in- formed consent. Exclusion criteria were (a) participating in both distances or (b) leaving all questions unanswered after informed consent. Two days after participation to the event, an email invi- tation including a link to the online survey was sent to the random selection of participants. After one week, a reminder was sent to the participants who had not responded yet. This online survey was based on a previ- ously developed survey [23], with additional items for this specific running event. An additional file presents the sur- vey questions (see Additional file 1). In the introduction of the survey the purpose of the study was explained and confidentiality was guaranteed. Furthermore, it was ascer- tained that participation was voluntary and that the participant was allowed to quit at any time. Responding to the questionnaire took approximately 15 min. The ethical approval was not required in the Netherlands, however the research was conducted in line with the Helsinki Declaration. Key measures Dependent variables Running physical activity (RPA) was collected. Participants were invited to report on two occasions (before their training phase (baseline) and during training phase) how many kilometres per week they ran (<5 km a week, between 5 and 10, between 10 and 20 a week, between 20 and 30 a week and more than 30 km per week). In addition, the survey included questions regarding health and lifestyle. Alcohol consumption (glasses per week) and frequency of smoking (per day) was asked before their Dallinga et al. BMC Public Health (2015) 15:833 Page 2 of 9 training phase and during training phase. Additionally, participants were requested to indicate whether participa- tion in the run affected their health (no effect, feel much healthier, feel healthier, feel less healthy, feel much less healthy). Moreover, participants indicated if the run influ- enced their body weight, diet, and energy level (totally agree, agree, neutral, disagree, totally disagree). To gain insight in self-image, participants were asked whether the run influenced their perception concerning a healthy lifestyle (totally agree, agree, neutral, disagree, totally dis- agree). Items included were: performing sports is good for me, chance of maintaining physical activity, feeling better about oneself, no change in lifestyle, and feeling tired more often. Potential prediction variables Participants indicated if they used an app or other train- ing tool. Additionally, we collected information about several variables that needed to be controlled for (gen- der, age, body mass index (BMI)). To calculate age, date of birth was asked. Subsequently, age was calculated by subtracting the year of birth from 2014. BMI (kg/m2) was used as a proxy of body composition and calculated as self-reported body weight (kg) divided by the square of height (m). We used the WHO categories for the classification: BMI < 18.5 means underweight, [18.5, 25) equals normal weight, [25, 30) means overweight and ≥ 30 corresponds to obese [24]. In addition, information was collected to determine the participant’s preparation for the event, fitness state, and experience with running/ sports. As an indication, exercise frequency, in number of training sessions per year, was requested [25]. Partici- pants were asked whether they had participated before in this running event (and if so the number of previous participations) to estimate experience with running events. The training period that participants scheduled to prepare for this running event was asked as well. Par- ticipants could choose between categories: no training/ barely, 1–5 weeks, 6–11 weeks, 12 weeks or more, no specific training/train all year and don’t know/no an- swer. Participants indicated self-reported finishing time in hours and minutes as well. Data reduction The difference in RPA between baseline and training phase was calculated. For all participants it was assessed whether the RPA was increased or not. Furthermore, the difference in consumption of alcohol and smoking be- tween baseline and training phase was calculated. Calcula- tions were performed to examine if these two factors were decreased or not. For the outcome of perceived health it was determined whether participants felt healthier or not. Answers on theses concerning healthy lifestyle and self- image were reduced from five to two categories; we calculated if the participants agreed or answered neutral/ disagreed with the theses about these topics. Statistical analysis SPSS version 20.0 was used for all calculations. For both distances, means and standard deviations (SD’s) were calculated for age, BMI and exercise frequency. The data was checked for outliers. For the categorical variables, frequency and percentage were calculated. We used a chi-squared test to determine differences between app users and non-app users in baseline charac- teristics as well as in physical activity, healthy lifestyle and perceived health during training phase. In addition, a multivariate logistic regression analysis was performed to determine if app use could predict changes in RPA, health and lifestyle, and self-image. Outcome variables were ef- fects on RPA (increased, not increased), health (healthier/ not healthier), alcohol consumption (more/not more), smoking (more/not more), eat healthier (agree/disagree), energy level (agree/disagree), performing sports is good for me (agree/disagree), chance of maintaining physical activity (agree/disagree), feeling better about oneself (agree/disagree), no change in lifestyle (agree/ disagree), lose weight (agree/disagree), and feel tired more often (agree/disagree). In these logistic regres- sion analyses, we controlled for age, gender, BMI, kilo- metres per week before preparation and exercise frequency in last year. Separate analyses were per- formed for the 16 and the 6.4 km. The alpha level was set at α ≤ 0.05 a priori. Results Of all invited runners 4307 (28 %) agreed to participate in the survey, of which 2838 runners participated in the 16 km and 1341 in the 6.4 km. Table 1 presents the sub- ject characteristics of male and female 16 and 6.4 km run- ners. Hundred-twelve participants participated in both distances and 507 participants reported too much missing values and were therefore excluded. The type of apps used by participants is shown in Fig. 1. Most participants used Runkeeper (44.4 %) in their preparation. The category ‘other apps” was the second largest app type chosen by participants (16.9 %), these were the apps that were not mentioned in the answer options. Differences app and non-app users Baseline characteristics A significant association was found between app use and gender for both distances. More app users were female (16 km: Chi-squared = 4.90, p = 0.027; 6.4 km: Chi-squared = 9.61, p = 0.002). In addition, app users were significantly younger compared to non-app users (16 km: t = -12.09, df = 2456.56, p < 0.001; 6.4 km: t = −4.24, df = 879.92, p < 0.001) and trained less often in a year (16 km: t = −5.58, df = Dallinga et al. BMC Public Health (2015) 15:833 Page 3 of 9 2542.24, p < 0.001; 6.4 km: t = −2.44, df = 969.84, p = 0.015). In the 6.4 km runners, app use was associated with BMI category (Chi-squared = 7.45, p = 0.024); app users were more often overweight. We found a signifi- cant association between app use and kilometres per week that participants ran before the preparation phase (16 km: Chi-squared = 87.48, p < 0.001; 6.4 km: Chi- squared = 16.10, p = 0.003). In general, it seemed that app users trained fewer kilometres before they had started the preparation for the running event, compared to non-app users. A significant association between app use and duration of training period was found as well (16 km: Chi- squared = 69.36, p < 0.001; 6.4 km: Chi-squared = 30.16, p < 0.001). For the 16 km, there were more app users who trained 12 weeks or more and who did not schedule a spe- cific training period for this event compared to the non- app users. For the 6.4 km, app users trained more often 6 to 11 weeks and 12 weeks or more compared to non-app users, whereas non-app users more often did not train or trained barely compared to app users. Table 1 Subject characteristics of 16 and 6.4 km runners 16 km 6.4 km Males Females Males Females Variable M ± SD M ± SD M ± SD M ± SD Age (years) 42.19 ± 10.73 37.11 ± 10.26 42.01 ± 11.39 36.33 ± 10.31 Training sessions per year (n/year) 120.91 ± 56.81 121.31 ± 55.39 101.17 ± 57.90 99.97 ± 56.08 N (%)a N (%)a N (%)a N (%)a BMI category Underweight 20 (1.0) 46 (2.2) 5 (0.5) 40 (4.1) Normal weight 756 (36.1) 646 (30.9) 97 (10.0) 443 (45.5) Overweight 481 (23.0) 143 (6.8) 140 (14.4) 249 (25.6) Use of app during training Yes 736 (28.5) 543 (21.0) 160 (13.5) 537 (45.3) No 830 (32.1) 477 (18.4) 140 (11.8) 349 (29.4) Duration training period No training/ barely 114 (4.4) 38 (1.5) 37 (3.1) 97 (8.2) 1–5 weeks 129 (5.0) 88 (3.4) 46 (3.9) 100 (8.4) 6–11 weeks 183 (7.1) 125 (4.8) 33 (2.8) 117 (9.9) 12 weeks or more 225 (8.7) 218 (8.4) 38 (3.9) 129 (10.9) No separate training period 909 (35.2) 546 (21.1) 142 (12.0) 433 (36.5) Don’t know/no answer 6 (0.2) 3 (0.1) 5 (0.4) 9 (0.8) Kilometres before < 5 km/week 229 (9.1) 134 (5.3) 86 (7.6) 328 (28.9) 5–10 km/week 318 (12.6) 332 (13.2) 96 (8.5) 314 (27.7) 10–20 km/week 473 (18.8) 307 (12.2) 68 (6.0) 165 (14.5) 20–30 km/week 301 (11.9) 162 (6.4) 25 (2.2) 34 (3.0) > 30 km/week 202 (8.0) 64 (2.5) 12 (1.1) 7 (0.6) aTotal N varies due to missing values .0 10.0 20.0 30.0 40.0 50.0 Get Running-app DtD 2014 app Adidas miCoach App + Renate Wennemars Myasics Strava Endomundo Runtastic Nike + iPod / I Phone app Other RunKeeper Percentage of participants (%) Fig. 1 Apps used in preparation for the 16 and 6.4 km recreational run Dallinga et al. BMC Public Health (2015) 15:833 Page 4 of 9 Outcome variables Table 2 shows the differences be- tween app users and non-app users in RPA, perceived health and lifestyle, and self-image. App users increased more often their RPA, felt healthier, ate healthier (6.4 km no significant difference), felt more energetic, felt that they had a higher chance of maintaining sport behaviour, felt better about themselves, felt more like an athlete, changed their lifestyle, stimulated others to perform sport and lost weight. Predictive ability of app use Table 3 presents results of the logistic regression analyses for each distance, corrected for age, gender, BMI, kilo- metres per week before preparation and frequency of participation in this running event. Logistic regression analyses showed that for both 16 and 6.4 km runners, app use was positively related to RPA and feeling healthier. In addition, the app use was related to feeling better about themselves, feeling more like an athlete, motivating others to participate in running, and losing weight. Also, for the 16 km runners using apps was related to eating healthier, feeling more energetic and reporting a higher chance to maintain sport behaviour. Discussion Our main finding was that app use was positively related to RPA, feeling healthier, changing lifestyle and self-image. Also, use of apps was positively related to stimulating Table 2 Differences between app users and non-app users in RPA, perceived health and lifestyle, and self-image 16 km 6.4 km App use No app use Chi2 P App use No app use Chi2 P N (%) N (%) N (%) N (%) RPA Decreased/same 624 (23.7) 821 (31.1) 55.49 < 0.001 467 (39.1) 369 (30.9) 17.22 < 0.001 Increased 689 (26.1) 504 (19.1) 246 (20.6) 112 (9.4) Perceived health Not healthier 497 (18.2) 722 (27.5) 72.71 < 0.001 294 (23.5) 268 (21.4) 18.36 < 0.001 Healthier 863 (31.6) 646 (23.7) 443 (35.4) 246 (19.7) Smoking behavioura More/equal 164 (43.3) 111 (29.3) 0.11 0.814 91 (52.3) 52 (29.9) 2.16 0.208 Less 64 (16.9) 40 (10.6) 24 (13.8) 7 (4.0) Alcohol consumptionb More/equal 901 (41.5) 897 (41.3) 1.63 0.211 441 (54.4) 296 (36.5) 0.28 0.619 Less 201 (9.3) 173 (8.0) 46 (5.7) 27 (3.3) Eat healthier Agree 496 (18.4) 420 (15.6) 10.71 0.001 221 (18.0) 129 (10.5) 3.76 0.052 Disagree 843 (31.3) 932 (34.6) 502 (40.8) 377 (30.7) Feel more energetic Agree 923 (34.3) 731 (27.2) 65.17 < 0.001 467 (38.1) 281 (22.9) 9.95 0.002 Disagree 412 (15.3) 623 (23.2) 255 (20.8) 223 (18.2) Chance of maintaining sport behaviour Agree 949 (35.3) 868 (32.3) 13.30 < 0.001 538 (44.0) 339 (27.7) 7.33 0.007 Disagree 389 (14.5) 481 (17.9) 183 (15.0) 163 (13.3) I know that performing sport is not my thing Agree 21 (0.8) 28 (1.0) 0.97 0.387 12 (1.0) 14 (1.1) 1.82 0.226 Disagree 1313 (49.1) 1316 (49.1) 711 (58.0) 488 (39.8) Feel better about myself Agree 859 (32.0) 646 (24.1) 74.19 < 0.0001 492 (40.1) 257 (21.0) 37.60 < 0.0001 Disagree 475 (17.7) 703 (26.2) 229 (18.7) 248 (20.2) Feel more like an athlete Agree 605 (22.5) 422 (15.7) 55.40 < 0.0001 343 (28.0) 168 (13.7) 24.68 < 0.0001 Disagree 731 (27.2) 926 (34.5) 377 (30.8) 335 (27.4) Changed lifestyle Agree 913 (34.1) 796 (29.7) 25.01 < 0.0001 502 (40.9) 302 (24.6) 12.76 < 0.001 Disagree 421 (15.7) 550 (20.5) 220 (17.9) 204 (16.6) Stimulating others to perform sport Agree 657 (24.5) 566 (21.1) 14.65 < 0.001 384 (31.3) 217 (17.7) 12.02 0.001 Disagree 676 (25.2) 784 (29.2) 339 (27.6) 287 (23.4) Losing weight Agree 543 (20.2) 399 (14.8) 36.72 < 0.0001 270 (22.0) 125 (10.2) 21.61 < 0.0001 Disagree 794 (29.5) 955 (35.5) 453 (36.9) 380 (30.9) Feel tired more often Agree 97 (3.6) 84 (3.1) 1.17 0.282 52 (4.3) 38 (3.1) 0.08 0.824 Disagree 1237 (46.1) 1266 (47.2) 668 (54.7) 463 (37.9) aThe participants who did not smoke were excluded bThe participants who did not drink alcohol were excluded Dallinga et al. BMC Public Health (2015) 15:833 Page 5 of 9 others to become active. Moreover, app use in 16 km runners was positively related to feeling more energetic, eating healthier and maintaining the sport behaviour. The odds ratios ranged from 1.24 to 1.89. Additionally, for RPA the explained variance was 41 % and 38 % for 16 km and 6.4 km respectively. These findings are of high importance considering that for app users the weekly training volume prior to the preparation phase was lower than non-app users. These results corroborate with the findings of other studies, in which app use seemed to have increased physical activity and a healthy lifestyle [11, 14–17]. In contrast to those studies, the focus in this study was on mobile app use only. It should be noted that we did not analyse the effect of app use, but we examined the use of mobile apps in relation to physical activity, perceived health and self-image. This relationship between app use and perceived health and self-image in the preparation of a running event has not been considered in previous studies. Analysing this relationship is relevant, since it provides insight in innovative and accessible ways to encourage physical activity and a healthier life. Although most results were comparable for 16 and 6.4 km runners, a few differences were found. In 16 km runners, app use was related to eating healthier, feeling more energetic and a higher chance to maintain sport behaviour. The relationships between app use and these variables did not reach significance level in the 6.4 km runners. The “fun run” character of the 6.4 km may be a first explanation for the differences found. Compared to Table 3 Results of multivariate logistic regression with outcome measure RPA, perceived health and lifestyle App use Distance OR (95 % CI)a P R2b RPA 16 km 1.43 (1.16–1.75) 0.001 0.41 6.4 km 1.89 (1.34–2.65) <0.001 0.38 Health 16 km 1.59 (1.33–1.90) <0.0001 0.10 6.4 km 1.33 (1.02–1.73) 0.038 0.10 Alcohol consumption 16 km 1.06 (0.83–1.35) 0.651 0.04 6.4 km 1.57 (0.86–2.85) 0.143 0.03 Smoking behaviour 16 km 1.09 (0.71–1.69) 0.691 0.06 6.4 km 2.06 (0.80–5.30) 0.134 0.05 Eat healthier 16 km 1.24 (1.03–1.48) 0.022 0.02 6.4 km 1.24 (0.93–1.66) 0.150 0.04 Feel more energetic 16 km 1.68 (1.40–2.01) <0.0001 0.08 6.4 km 1.13 (0.99–1.70) 0.055 0.05 I know that performing sport is not my thing 16 km 0.92 (0.44–1.75) 0.701 0.02 6.4 km 0.47 (0.19–1.03) 0.058 0.12 Chance of maintaining sport behaviour 16 km 1.24 (1.03–1.50) 0.021 0.02 6.4 km 1.31 (0.98–1.74) 0.067 0.02 Feel better about myself 16 km 1.75 (1.47–2.09) <0.0001 0.07 6.4 km 1.84 (1.41–2.40) <0.0001 0.07 Feel more like an athlete 16 km 1.69 (1.41–2.01) <0.0001 0.05 6.4 km 1.67 (1.28–2.18) <0.001 0.06 Did not change lifestyle 16 km 0.70 (0.58–0.83) <0.0001 0.02 6.4 km 0.70 (0.53–0.92) 0.010 0.06 Motivated others to participate 16 km 1.43 (1.20–1.69) <0.0001 0.02 6.4 km 1.45 (1.12–1.87) 0.005 0.03 Lost weight 16 km 1.57 (1.31–1.89) <0.0001 0.06 6.4 km 1.72 (1.29–2.30) <0.0001 0.09 Feel tired more often 16 km 1.03 (0.73–1.46) 0.877 0.04 6.4 km 0.70 (0.44–1.12) 0.140 0.03 aControlled for gender, age, BMI, training sessions per year and weekly training distance before training phase bNagelkerke R2 [39] Dallinga et al. BMC Public Health (2015) 15:833 Page 6 of 9 the 16 km run, participation in a 6.4 km run may not require a long preparation phase and lifestyle changes. In addition, we found that in the training phase most 16 km runners trained 10–20 km per week (37.3 %) and 20–30 km per week (27.3 %), whereas the largest part of 6.4 km runners trained 5–10 km per week (42.0 %) and 10–20 km per week (26.3 %). Thus another possible explanation might be that the differences in weekly training distance of 16 and 6.4 km runners combined with a shorter preparation resulted in the inconsistent findings. Previous literature has shown that running improves aerobic fitness and cardiovascular function at rest [26]. In a review, a fairly strong dose–response rela- tionship between weekly training volume and cardiore- spiratory fitness was shown for inactive and healthy middle aged and elderly people [27]. This may explain why the physical fitness of the 16 km runners increased more compared to 6.4 km runners, resulting in a higher perceived energy level. Potentially, there is a link be- tween weekly training volume and eating behaviour as well. To support this suggestion, Williams et al. showed that a larger weekly running distance promoted a health- ier eating pattern [28]. In addition, in that study a rela- tionship was found between weekly running distance and years spend in running, which might provide an explanation for our finding that app use was related to a higher chance to maintain the physical activity of the longer distance runners compared to the shorter dis- tance runners. Previous studies have shown that participating in run- ning events can encourage physical activity [26, 29]. How- ever, maintaining an active lifestyle is difficult for many [30]. Moreover, the gap between intention for being phys- ically active and actually being active is large [31]. In many of behaviour change models, such as the Fogg behaviour model and the attitude, social influence and efficacy (ASE) model, the behavioural intention is assumed to be most important in changing behaviour [32, 33]. It would be interesting to determine the impact of an app on behav- iour determinants such as self-efficacy, attitude and social influence. In addition, given that behaviour change theor- ies (BCTs) are often relatively absent in apps, it would be valuable to find out which of these theories are taken into account in the app [34]. This study showed that the intention to maintain the running behaviour was higher for the app users, therefore app use may assist in decreasing drop-out of running and encouraging physical activity. This is a very interesting finding, since apps were more often used by overweight participants and the participants in the 6.4 km run (who trained less often). For these two groups physical activity may need to be encouraged. Furthermore, a very interest- ing finding was that app users more often encouraged others to engage in running compared to non-app users. This could be explained by the fact that some apps contain features to interact with others, such as following and supporting their activities [13]. This interaction com- bined with the use of social media might motivate others to be more active [35]. These findings suggest that the use of mobile apps can contribute to the promotion of run- ning and prevention of drop-out. Our findings may be related to the new phenomenon of quantified self, which means that people are measuring their health conditions via wearables [36]. This new trend may actually be an underlying element in the findings of this study. Furthermore, when we look at practical implications, we suggest that app use could be an additional stimulus to the training program, because it provides an easy and accessible tool to promote physical activity and a healthy lifestyle. Given that the use of smart phones increases [37, 38], a large amount of individuals can be reached with health and fitness apps. Sport organizations and employers may therefore recommend the use of apps in the preparation of a running event. For instance, large recreational running events often include a business run, in which business teams can compete. The use of apps may encourage employers to train more and live health- ier. This data shows that app use is related to increased physical activity and improved health. Moreover, fre- quency of app use is higher in inexperienced and over- weight participants. We could hypothesize that these group of runners have some comparable characteristics as inactive individuals. Therefore, our results could po- tentially be transferred to inactive individuals. Some limitations of this study need to be addressed. At first, a self-reported, non-validated survey was used. Second, a causality between app use and the outcome variables cannot be determined. It remains unclear what would be the cause and what would be the result; did app use increase physical activity or did physical activity encourage app use. The involvement of other underlying causes should be considered as well. Randomized con- trolled studies need to be performed to determine a causal relationship. The third limitation was that several types of apps were included. The most used app was Runkeeper, but also a number of other apps were used. It would be interesting to find out why people choose certain apps and which features make an app popular. Apps differ in their features and may differ in their effectiveness as well. Therefore, the possibility that the relationships found might be different for each app has to be kept in mind, because the way apps present infor- mation and provide feedback differs. As a fourth limita- tion low explained variances for app use in relation to most of the health and lifestyle outcomes were found. Therefore we have to keep in mind that other factors, such as psychological factors, contributed to the runner’s lifestyle and self-image as well. At last, this study included Dallinga et al. BMC Public Health (2015) 15:833 Page 7 of 9 individuals that were already active and motivated to participate to a running event. However, considering the problem of increased inactivity, it would be even more interesting to conduct research on potential of app use in promoting a healthy lifestyle in inactive individuals includ- ing long-term consequences. Further research is needed to determine which features would need to be included in such an app. Conclusion In conclusion, our results showed that recreational run- ners who used an app are more likely to be more physic- ally active and feel and live healthier. These results suggest that use of mobile apps has a beneficial role in the prepar- ation of a running event, as it promotes health. Further research is now needed to determine a causal relationship between app use and physical and health related out- comes. More specific, a randomized controlled trial (RCT) needs to be developed and conducted. For instance, the effect of one app such as Runkeeper could be examined on weekly training distance and lifestyle. Another example would be to develop and evaluate a physical activity and health promotion app in a group of inactive individuals. To gain insight in long-term effects, a follow-up survey should be included as well. Additional files Additional file 1: Online Survey Dam tot Damloop. This file contains the questions that were stated in the online survey used in this study. The file starts with an introduction in which the purpose is explained and confidentiality is guaranteed. (PDF 239 kb) Competing interests The authors declare that they have no competing interests. Authors’ contributions JD participated in the design of the study, carried out the statistical analyses, interpreted the data and drafted the manuscript. MM was involved in the analysis and interpretation of the data and helped to draft the manuscript. LA was involved in the analysis and interpretation of the data and critically revised the manuscript. HB was involved in the analysis and interpretation of the data and critically revised the manuscript. MB participated in the design of the study, acquisition of data, and analysis and interpretation of these data. In addition, MB critically revised the manuscript. All authors read and approved the manuscript. Also, all authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. 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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 Dallinga et al. BMC Public Health (2015) 15:833 Page 9 of 9	App use, physical activity and healthy lifestyle: a cross sectional study.	08-28-2015	Dallinga, Joan Martine,Mennes, Matthijs,Alpay, Laurence,Bijwaard, Harmen,Baart de la Faille-Deutekom, Marije	eng
PMC6283599	RESEARCH ARTICLE Optimizing beat synchronized running to music Jeska BuhmannID1, Bart Moens1, Edith Van Dyck1, Dobromir Dotov2, Marc Leman1 1 IPEM, Ghent University, Ghent, Belgium, 2 LIVELab, McMaster University, Hamilton, Canada jeska.buhmann@ugent.be Abstract The use of music and specifically tempo-matched music has been shown to affect running performance. But can we maximize the synchronization of movements to music and does maximum synchronization influence kinematics and motivation? In this study, we explore the effect of different types of music-to-movement alignment strategies on phase coher- ence, cadence and motivation. These strategies were compared to a control condition where the music tempo was deliberately not aligned to the running cadence. Results show that without relative phase alignment, a negative mean asynchrony (NMA) of footfall timings with respect to the beats is obtained. This means that footfalls occurred slightly before the beat and that beats were anticipated. Convergence towards this NMA or preferred relative phase angle was facilitated when the first music beat of a new song started close to the step, which means that entrainment occurred. The results also show that using tempo and phase alignment, the relative phase can be manipulated or forced in a certain angle with a high degree of accuracy. Ensuring negative angles larger than NMA (step before beat) results in increased motivation and decreasing cadence. Running in NMA or preferred rela- tive phase angles results in a null effect on cadence. Ensuring a positive phase angle with respect to NMA results in higher motivation and higher cadence. None of the manipulations resulted in change in perceived exhaustion or a change in velocity. Results also indicate that gender plays an important role when using forced phase algorithms: effects were more pronounced for the female population than for the male population. The implementation of the proposed alignment strategies and control of beat timing while running opens possibili- ties optimizing the individual running cadence and motivation. Introduction Sports and exercise activities are generally believed to benefit from music listening. Under par- ticular conditions music has been shown to capture attention, raise spirits, trigger a range of emotions, alter or regulate mood, evoke memories, increase work output, heighten arousal, induce states of higher functioning, reduce inhibitions, and encourage rhythmic movement [1, 2]. Effects of music during exercise can even be enhanced when certain types of music are con- sidered [3, 4], especially when a certain level of synchrony between the musical stimuli and the PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 1 / 21 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Buhmann J, Moens B, Van Dyck E, Dotov D, Leman M (2018) Optimizing beat synchronized running to music. PLoS ONE 13(12): e0208702. https://doi.org/10.1371/journal.pone.0208702 Editor: Ramesh Balasubramaniam, University of California Merced, UNITED STATES Received: February 19, 2018 Accepted: November 21, 2018 Published: December 6, 2018 Copyright: © 2018 Buhmann 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 data files and scripts are available via https://doi.org/10.5281/ zenodo.1467592. Funding: This research has been supported by BeatHealth (contract #610633), a collaborative project funded by the European Commission under the Seventh Framework Programme: https:// cordis.europa.eu/project/rcn/110990_en.html. 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. listener’s movements occurs [5–8]. A much-researched topic concerns the synchrony between music and running, measured by beat and footfall as markers of the rhythm that drives syn- chronized running. However, synchronizing one’s footfall with the musical beat is not a straightforward endeavor. Some people easily synchronize, while others need an instruction to synchronize [9]. Some music facilitates movement (such as groove [10]) and running (such as activating music), while other music is less effective [3, 4]. In short, the use of music and specif- ically tempo-matched music has been shown to affect running performance, but it is an open question whether we can maximize the synchronization of movements to music and whether this maximum synchronization somehow influences kinematics and motivation. In this context, we define synchronization as the stable maintenance over time of the senso- rimotor coupling between beat and footfall. We define entrainment as the process that realizes the sensorimotor coupling, more specifically the process that brings the (perceived) beat and the (performed) footfall towards a stable timing. Rather than through pull and push forces (as in a dynamical system) it is straightforward to assume that prediction error minimization is a mechanism for entrainment [11]. For an in depth explanation of the factors that determine the strength of the sensorimotor coupling and entrainment, see Leman [12]. The tempo of running is expressed in steps per minute (SPM), i.e. cadence, and for music it is expressed in beats per minute (BPM). The tempo of running, or cadence, refers to the fre- quency of steps, whereas speed (expressed in km/h) refers to the total distance travelled in a certain amount of time. In previous studies, the preferred exercise intensity was often mea- sured before the actual experiment and the music tempo was generally rather coarsely matched to a subject’s spontaneous or comfort tempo (e.g., within a 10% range of the assessed cadence) [13, 14]. However, such an approach disregards the fact that the comfort tempo of an individ- ual might be different at the time of the test, or that it might fluctuate during the test period. As a result, the contrast between the tempo of the music and the tempo of the exercise perfor- mance is likely to become too considerable to enable spontaneous entrainment. For that rea- son, we measure comfort tempo in the first part of each running task. In addition, a study by Van Dyck et al. [15] unveiled that uninstructed synchronization of running cadence to musical tempo occurs spontaneously when the tempo of the music does not deviate more than 2.5% from the initial running cadence. This finding highlights that conditions of entrainment yield affordances for sensorimotor adaptation to be effective, provided that the tempo of the music can be matched to the person’s comfort tempo with high accuracy. Other research that addressed the particular relations between entrainment conditions and sensorimotor adapta- tion is, however, scarce [12, 16]. Another aspect that is likely to be important in terms of spontaneously manipulating run- ning cadence is the anticipation effect reported in sensorimotor synchronization studies (SMS). For example, people typically tend to tap a little bit before the auditory stimulus, which is indicated by a slightly negative phase angle of the tap relative to the click, also referred to as negative mean asynchrony (NMA) [11]. They do this, presumably, because the sensing of the tapping has a longer delay than the sensing of the click [17]. Synchronization at brain level therefore means tapping before the click. Being able to continuously manipulate the exact moment of the beat might therefore prove to be a very accurate way to match and influence people’s running capacity. In the present study we focus on very precise sensorimotor (beat-footfall) alignment strate- gies during running. In fact, some of those strategies manipulate only the timing of the beat and it is expected that the timing of the footfall is somehow influenced by this manipulation. Moens et al. [16] describe four different strategies that manipulate sensorimotor alignment. However, these alignment strategies were not yet compared with each other within a single study, nor with the same musical test samples. Therefore, the purpose of this study is to Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 2 / 21 contribute to a high definition sensorimotor alignment strategy that accounts for each person’s sensorimotor ability. In particular, we aim at comparing different beat-footfall alignments using a randomized experimental setup where different strategies for supporting such align- ments are applied. We hypothesized that strategies employing phase alignment to adjust the time of the musical beat relative to the time of the footfall during running would result in the highest level of sensorimotor synchronization and have a bigger effect on kinematics (cadence and speed) compared to strategies using period alignment instead. In addition, we expect that a high degree of synchronization would have an effect on motivation. While our main aim is to discover whether different music-to-movement alignment strate- gies affect kinematics (cadence and speed) and/or motivation in distinct ways, we also want to address possible gender differences in entrainment capacity. The study by Van Dyck et al. [15] unveiled higher levels of entrainment for female compared to male runners. Other research shows that, when people are requested to rate the motivational qualities of musical excerpts, women pay closer attention to the rhythmical qualities of the stimuli compared to their male counterparts [18]. In addition, musical preference seems to affect women differently than men: when female runners listen to highly preferred stimuli, they tend to perform better than when listening to non-preferred music. In comparison, musical preference does not seem to affect the performance of male exercisers [19]. From the above-described findings, we expected to uncover differences between men and women regarding their ability to entrain and synchronize with the beats of the music. Such differences would be particularly relevant in terms of designing a more individualized music-technology approach. Materials and methods Subjects To establish sample size, a power analysis for a repeated-measures design was conducted using GPower 3.1.9.2 [20]. Based on a small effect size (.25) with alpha set at .05 and power at .95, it was estimated that at least 28 participants would be required. In total, 36 healthy, adult partici- pants (19 males) took part in the study. All participants were recreational runners (Mage = 31.22 years; SDage = 8.13 years) and indicated to be capable of running 30 minutes continu- ously. Of all participants, 38.89% were trained in music (Pearson Chi-Square test showed no significant relation between gender and musical background, χ2(1) = 1.22, p = .27). In addi- tion, about half of them (55.56%) reported to generally run without music, 22.22% indicated to usually run with music, and 22.22% ran both with and without musical accompaniment. The study was approved by the Ethics Committee of the Faculty of Arts and Philosophy of Ghent University and was in accordance with the statements of the Declaration of Helsinki. Written informed consent was obtained from all the participants before the start of the experiment. Experimental design Stimuli. A music database consisting of music tracks in the tempo range of 120–200 BPM (the range of natural running cadence) was created. The database included musical stimuli from a previous running experiment [15]. Using the Brunel Music Rating Inventory-2 (BMRI- 2) test [21], all stimuli were rated as highly motivational for running. Additional tracks were selected to ensure complete coverage of the tempo range. In total, 43 tracks with clear beat infor- mation were selected. The tempo stability throughout each entire track was validated and intros lacking clear beats were cut from the stimuli using Audacity (http://audacity.sourceforge.net). BeatRoot [22] was applied to track the beats and tempo of each music track, while Adobe Audi- tion (http://www.adobe.com) was used to normalize perceived loudness and minimize possible Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 3 / 21 imbalances in sound pressure level using the ITU-R BS.1770-3 standard at -23 LUFS which is commonly used in audio broadcasts [23]. Apparatus. Data was collected using a 7" tablet (Panasonic FZ-M1) running Windows 8.1, which was strapped to a backpack. In addition, a pair of sensors, headphones, and a man- agement computer was employed. The tablet operated as the main hub that handled incoming sensor data and provided the musical stimuli. To detect footfall instants, participants were equipped with two iPods (4th generation); one attached at each ankle. Using the Sensor Monitor Pro application on the iPods, data from accelerometers and gyroscopes was streamed wirelessly to the tablet at a sampling rate of 100 Hz. Step timings were extracted from the signal using an approach based on Pappas et al. [24]. Speed measurements were performed using a sonar system (MaxBotix LV-MaxSonar-EZ: MB1010) connected to the tablet through a Teensy 3.1 micro-controller. It detected marker rods of 1.90 m high, placed at a regular interval of 10 m around the running track. Through computation of the time it took to cover each interval, absolute speed was determined. The analogue signal was sampled at 30 Hz and digitized using the Teensy. The wireless connection between the tablet, iPods, and management computer was provided through a Wi-Fi router (TP-Link M5360), firmly strapped to the backpack, ensuring reliable communication between all crucial components. The management computer was applied to initiate the experimental sessions and to monitor sensor data in real-time. Musical tempi were manipulated using a phase vocoder based on the technology of E´lastique Pitch of ZPlane.de [25]. The phase vocoder manipulates tempo in real-time without modifying pitch using a com- bination of frequency and time-domain methods. The system logged all data and calculations in real-time. Music tempo was adapted based on the selected alignment strategy (for the imple- mentation of the music alignment strategies, see Moens et al. [16]). Finally, the aligned music was sent back to the participant using Sennheiser HD60 headphones connected to the tablet. Procedure. All experiments took place in the Flanders Sports Arena of Ghent, Belgium. After being equipped, participants were asked to run on a 200 m running track for five minutes continuously, and this for six consecutive times. In each of the six 5-minute runs, a different alignment strategy was tested and it was ensured that all orders could occur only once. Participants were instructed to run at their own comfortable pace. No information was dis- tributed concerning the purpose of the experiment and all participants ran in solo conditions. After each 5-minute run, participants were allowed to take a break for several minutes in which they rated their perceived exertion (RPE) on the Borg Scale [26]. This way we could determine whether fatigue was influenced by the different music alignment strategies. In addi- tion, they rated the level of physical enjoyment on the 8-item version of the Physical Activity Enjoyment Scale (PACES) [27, 28], a single factor scale to assess the level of enjoyment during a physical activity in adults across exercise modalities. Each of the 5-minute runs started with 25 seconds of silence, followed by five musical excerpts of equal length (55 s) with an original tempo approaching the average cadence of the last seven footsteps. Musical tempo was then manipulated based on the selected alignment strategy, presented in the following section. Conditions or alignment strategies. In the control alignment strategy (S0), music and running performance behave in a completely allochronic fashion (in our case: music is played 20 BPM faster/slower than the assessed running cadence). This strategy is used as a control condition to compare against five other music-to-movement alignment strategies. Two strategies (S1, S2) involve the alignment of the music tempo to the runner’s cadence. The tempo matching occurs either at the beginning of a song only (S1), or continuously throughout the exercise (S2). Tempo-matching alone, however, does not consider the exact matching of the musical beats to the footfall instants; hence it neglects the phase (or the time Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 4 / 21 between musical beat and footfall) while it maintains the period (for the musical beat and foot- fall, independently). In the adaptive tempo conditions, participants were still able to change their timing with respect to the beats as it takes several steps to determine the runners’ cadence. Three strategies (S3, S4, S5) involve the alignment of the music tempo of the runner’s cadence using relative phase angle manipulation. The relative phase angle expresses the footfall-beat time as a segment of the previous beat-period, which is defined as having 360˚. Steps and beats are recurring events, hence cyclic in nature. Hence, the difference in timing between a beat and the nearest footfall can be expressed with a relative phase angle (-180˚ to +180˚), where a 0˚ angle indicates that the beat and footfall instant coincide exactly. Alignment strategies that employ such phase manipulation minimize the relative phase angle between the beat and footfall instant, and therefore, they drive the alignment to any desired relative phase angle. In S3 the relative phase angle is minimized to 0˚ once, at the beginning of the exercise. The tempo of the music is however adapted continuously. The final two strategies (S4, S5) involve continuous relative phase angle adaptation starting at the beginning of the exercise. S4 guides the runner towards a prede- fined phase angle by adjusting the phase and hence tempo of the music at each step, thus at dis- crete timing intervals. In previous research, feedback from participants indicated that such a music adaptation sometimes felt unnatural or forced due to sudden tempo changes. Hence a new strategy is introduced (S5) based on adaptive oscillators [29], which results in continuous relative phase alignment towards a predefined relative phase angle using smoother tempo adaptations. The first experiment in the present study intended that S4 and S5 guided the runner towards a 0˚ relative phase angle, or a perfect synchrony between footfalls and musical beats. However, after the experiment it became clear that an inaccuracy in the calibration led both alignment strategies towards -70˚ instead of 0˚. This resulted in musical beats occurring after the footfall, while they were intended to occur simultaneously. For clarity, we henceforth refer to S4 and S5 as strategies with a configurable relative phase angle. A second (follow-up) experi- ment was then performed to assess the influence of our initially intended S4 [0˚] and S5 [0˚] strategies, which is reported subsequently. Based on the results of the first experiment, we also added an additional condition, which forced a +30˚ angle (beat before the step), and this is also reported later in this paper. In the discussion we consider the results from both experi- ments. A summary of the different strategies is provided in Table 1. Although we are aware of the phenomenon of NMA in SMS studies, little is known about the magnitude of such a NMA for running and whether this is similar for all participants. We therefore decided to use 0˚ as a target relative phase and discuss our results taking into account the knowledge on NMA. Measurements Cadence and speed. We examined the effect of the different alignment strategies on kine- matic parameters such as cadence and speed. Average cadence and speed values during music playback are compared to those in the preceding 25 seconds of silence. The resulting depen- dent variables are expressed in percentages, where zero indicates no difference, while a nega- tive or positive value indicates a respective decrease or increase in cadence or speed compared to the silent part of the condition. Synchronization and phase angles. The level of synchronicity with the music, or rather, the stability and timing of the relation between a runner’s footfall and the musical beat, is typi- cally represented by the resultant vector or R. The length of this vector is a measure of tempo entrainment, ranging from zero to one with one representing perfect entrainment [30]. In addi- tion, the angle of the resultant vector represents the average relative phase angle and reveals whether footfall instants occur before the beat is played (negative phase) or after (positive phase). Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 5 / 21 The resultant vector represents an average of a time period, and is calculated based on a distribu- tion of individual phase angles or timing differences between each step and the closest beat [31]. Each relative phase angle is calculated with the formula below, where St refers to a step at time t. B1 is the time of the beat that occurred before St and B2 is the time of the first beat after St. φ ¼ 360 St Results Synchronization A Friedman’s ANOVA showed a main effect of the strategy on resultant vector length, χ2(5) = 147.705, p < .001. Wilcoxon tests were used to follow up this finding. A Bonferroni correction was applied and so all effects are reported at a .003 level of significance. Results reveal that all six strategies differ with respect to the resultant vector length, except S2 versus S3 and S4 [-70˚] versus S5 [-70˚]. All comparisons are summarized in Table 2. The circular Wheeler-Watson Mardia test was used to verify that all phase angle distribu- tions were significantly different and thus not homogenous (W = 5113.6, p < .001), meaning the alignment strategies had a significant influence on the relative phase angles. Cadence One of the dependent variables of interest is the change in cadence from running in silence to running with music. A 2x6 mixed-design ANOVA test with gender (male, female) as between- subjects variable and condition (S0 to S5 [-70˚]) as within-subjects variable revealed a signifi- cant main effect of the strategy on the change in cadence, F(5,170) = 16.46, p < .001. Contrasts revealed that for S4 [-70˚], F(1, 34) = 17.23, p < .001, r = .58, and S5 [-70˚], F(1, 34) = 29.48, p < .001, r = .68, running cadence decreased significantly more (M = -1.81%, SE = 0.37, and M = -2.15%, SE = 0.34 respectively) compared to S0 (M = -0.53%, SE = 0.27). There was no significant main effect of gender, indicating that on average there were no dif- ferences in change in cadence between male (M = -1.06%, SE = 0.15) and female participants (M = -0.93%, SE = 0.22), F(1,34) < 1, p = .78, r = .05. However, an interaction effect between the strategy and the gender of the participant was observed, F(5, 170) = 4.97, p < .001, indicating that the change in cadence differed between men and women for different strategies. Contrasts were performed, revealing interaction effects between gender x S0 x S4 [-70˚], F(1, 34) = 9.10, p = .005, r = .46, and gender x S0 x S5 [-70˚], F(1, 34) = 6.40, p = .016, r = .40. This indicated that although, for both males and Table 2. Significant differences in phase coherence (resultant vector length R). Comparisons (Mdn) z pa r S0 (0.03) vs. S1 (0.65) -5.232 < .001 -.62 S0 (0.03) vs. S2 (0.80) -5.232 < .001 -.62 S0 (0.03) vs. S3 (0.80) -5.232 < .001 -.62 S0 (0.03) vs. S4 [-70˚] (0.94) -5.232 < .001 -.62 S0 (0.03) vs. S5 [-70˚] (0.94) -5.232 < .001 -.62 S1 (0.65) vs. S2 (0.80) -3.268 .001 -.39 S1 (0.65) vs. S3 (0.80) -3.991 < .001 -.47 S1 (0.65) vs. S4 [-70˚] (0.94) -5.232 < .001 -.62 S1 (0.65) vs. S5 [-70˚] (0.94) -5.232 < .001 -.62 S2 (0.80) vs. S3 (0.80) -0.644 .519 -.08 S2 (0.80) vs. S4 [-70˚] (0.94) -5.185 < .001 -.61 S2 (0.80) vs. S5 [-70˚] (0.94) -5.059 < .001 -.60 S3 (0.80) vs. S4 [-70˚] (0.94) -5.122 < .001 -.60 S3 (0.80) vs. S5 [-70˚] (0.94) -5.001 < .001 -.59 S4 [-70˚] (0.94) vs. S5 [-70˚] (0.94) -0.055 .956 -.01 a p values were calculated with Wilcoxon signed-rank tests comparing all six alignment strategies with each other. https://doi.org/10.1371/journal.pone.0208702.t002 Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 7 / 21 females, cadence decreased substantially during S4 [-70˚] and S5 [-70˚] compared to S0, this decrease was more pronounced for female runners. Speed No main effect of the type of strategy on change in speed was uncovered, F(5, 165) = 1.02, p = .407, nor was there a significant main effect of gender, F(1, 33) = 1.36, p = .25, r = .20. Besides, there was no interaction effect between strategy and gender, F(5, 165) = 2.16, p = .061. Motivation and perceived exertion No significant differences were revealed for perceived exertion (RPE) with a Friedman’s ANOVA, χ2(5) = 6.64, p = .25. Wilcoxon signed-rank tests (comparing each strategy with S0) were performed on the scores of the PACES scale. A Bonferroni correction was applied and so all effects are reported at a .01 level of significance. The motivational scores were higher for S5 [-70˚] (Mdn = 71.38) compared to S0 (Mdn = 67.25), z = -2.662, p = .008, r = -.31. None of the other strategies dis- played significant differences in motivation when compared to the allochronic strategy (S0). Table 3 summarizes the motivational comparisons. The median value for S5 [-70˚] is not that different from S1-4. The reason, however, that ratings for S5 [-70˚] are significantly different from S0, while the other alignment strategies are not, is due to differences in ranking: a higher percentage of the ratings where in favor of S5 [-70˚] over S0 than in the other comparisons. Follow-up experiment The data analysis of the first experiment showed surprising results of S4 and S5, leading to an investigation of the apparatus’ calibration. As we noted earlier, S4 and S5 were initially aimed to obtain a 0˚ phase synchronization. However, the system incorrectly forced a -70˚ relative phase angle. We believed that the non-intended forced -70˚ angle was the main cause of the observed cadence decrease and motivational increase for these strategies. Therefore, it was decided to do a follow-up experiment introducing correct 0˚ strategies. As the procedure and apparatus is almost identical, we only elaborate on the differences and results for cadence and motivation. Materials and methods Tests took place at the same location of the first experiment. We recruited 11 of the initial par- ticipants (6 female, 5 male, Mage = 39.27 years; SDage = 9.82 years) to have a similar population and to be able to compare both experiments. Strategies S0, S1 and S2 were identical to the initial experiment. The -70˚ strategies (S3 [-70˚], S4 [-70˚], S5 [-70˚]) were replaced by their intended counterparts (S3 [0˚] S4 [0˚] and S5 [0˚]), where the beats coincide with the footfalls as initially intended. One additional strategy was added Table 3. Differences in motivation (PACES ratings). Comparisons (Mdn) z pa r S0 (67.25) vs. S1 (69.88) -.718 .487 -.08 S0 (67.25) vs. S2 (73.19) -1.987 .048 -.23 S0 (67.25) vs. S3 (73.31) -1.327 .196 -.15 S0 (67.25) vs. S4 [-70˚] (71.75) -1.581 .109 -.18 S0 (67.25) vs. S5 [-70˚] (71.38) -2.662 .008 -.31 a p values were calculated with Wilcoxon signed-rank tests comparing five alignment strategies with the allochronic control condition (S0). https://doi.org/10.1371/journal.pone.0208702.t003 Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 8 / 21 to explore possible inverse effects of the negative phase angle, namely S5 [+30˚]. This strategy placed the beat slightly before the footfall, implying that the musical beats preceded the runner. For methodological reasons, this S5 [+30˚] strategy occurred at the end of the experiment and was optional, to exclude influences of potential dropouts on our comparison with the earlier experiment. A summary of the different strategies is provided in Table 1. Results All 11 participants completed all seven conditions. In total, two out of 77 trials were invalid due to sensor errors. Given our initial experimental results, this section focuses on phase angles, cadence, and motivation. Phase angles and synchronization. As for the initial experiment, a repeated-measures ANOVA showed a main effect of the strategy on resultant vector length, F(6,54) = 103.794, p < .001. The circular Wheeler-Watson Mardia test was used to verify that all phase angle distri- butions were significantly different and thus not homogeneous (W = 1664.7, p < .001), mean- ing that the alignment strategies had a significant influence on the relative phase angles. Cadence. In order to confirm that the retrieved effect on cadence from the initial experi- ment was due to the phase angle being targeted at -70˚, we expected to find no effects on cadence for S4 and S5 targeted at 0˚. Therefore, as in the initial experiment, a 2x6 mixed- design ANOVA test with gender (male, female) as between-subjects variable and condition (S0 to S5 [0˚]; excluding S5 [+30˚]) as within-subjects variable was performed. Indeed, no main effect of the strategy on the change in cadence was revealed, F(5,40) = 1.210, p = .322. In addition, no significant main effect of gender was observed, F(1,8) = 4.128, p = .077, r = .58, nor an interaction effect between the strategy and the gender of the participant, F(5, 40) = 1.149, p = .351. When we included S5 [+30˚], and performed a 2x7 mixed-design ANOVA, we did find a main effect on cadence, F(6,48) = 3.693, p = .004, and a small but significant gender effect, F (1,8) = 5.412, p = .048, r = .64. No gender x strategy interaction effect was found, F(6,48) = 1.306, p = .273. Concerning the impact on cadence of different target phase angles in the configurable phase angle strategy (S5), additional tests were performed. To compare S5 [-70˚] from our ini- tial experiment with S5 [0˚] from the follow-up experiment an independent samples t-test was executed. This test revealed that the decrease in cadence for S5 [-70˚] was significantly larger (M = -2.15%, SE = 0.34) than for S5 [0˚], (M = -0.09%, SE = 0.51), t(45) = -3.034, p = .004, r = .41. Furthermore, a paired samples t-test was used to compare S5 [+30˚] with S5 [0˚] from the follow-up experiment. Results showed that the change in cadence for S5 [+30˚] was signifi- cantly larger and even positive (M = 0.68%, SE = 0.59) compared to S5 [0˚], (M = -0.40%, SE = 0.44), t(9) = -2.970, p = .016, r = .70. Motivation. Wilcoxon signed-rank tests (comparing each strategy with S0) were per- formed on the scores of the PACES scale. A Bonferroni correction was applied and so all effects are reported at a .008 level of significance. Only the motivational scores for S5 [+30˚] showed a trend towards being significantly higher (Mdn = 44.00) than for S0 (Mdn = 38.00), z = -2.661, p = .008, r = -.57. No other differences were found. Table 4 summarizes the motiva- tional comparisons. Discussion The goal of the present study is whether we maximize the synchronization of perceived musical beat and footfall during running, and whether this maximum synchronization influences kine- matics and motivation. Table 5 summarizes the results, showing the different phase angle Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 9 / 21 distributions for conditions and experiments. We then consider the effects of synchronization, cadence, and motivation. The last section discusses possible gender differences for these effects. Preferred phase angles, synchronization, and phase locking The data from the different alignment strategies reveal differences and preferences in synchro- nization behavior. Fig 1 provides a visual summary of the different strategies that were tested in terms of timing information of the step versus the beat (i.e. the relative phase angle) and the synchronization stability or phase coherence (i.e. resultant vector length). In Fig 1, condition S0 clearly visualizes what the control condition was designed for, i.e. that runners don’t synchronize, nor phase-lock (because they cannot). S0 can thus provide a baseline or comparison for later tests. Since strategy S1, S2, and S3 did not use continuous relative phase angle manipulation, they gave a good idea of the (average over song) relative phase angle when runners ran with music at their (previously measured) preferred tempo. Once the song starts, the tempo remains con- stant (S1), or is continuously adapted to match the runners’ preferred tempo, which may change during the song (S2 and S3). These conditions allowed for self-selected relative phase Table 4. Differences in motivation (PACES ratings) for follow-up experiment. Comparisons (Mdn) z pa r S0 (38.00) vs. S1 (42.00) -1.721 .085 -.37 S0 (38.00) vs. S2 (42.00) -1.736 .083 -.37 S0 (38.00) vs. S3 (41.00) -0.949 .343 -.20 S0 (38.00) vs. S4 [0˚] (41.00) -1.961 .050 -.42 S0 (38.00) vs. S5 [0˚] (42.00) -1.661 .097 -.35 S0 (38.00) vs. S5 [+30˚] (44.00) -2.661 .008 -.57 a p values were calculated with Wilcoxon signed-rank tests comparing six alignment strategies with the allochronic control condition (S0). https://doi.org/10.1371/journal.pone.0208702.t004 Table 5. Circular descriptives of the phase angle distributions for all strategies. Initial experiment data is abbreviated as I.E., while follow-up experiment data is abbre- viated as F.U.E. For each alignment strategy the distribution of relative phase angles is described, using the following parameters: the mean angle ø representing the mean direction, the resultant vector length R and the circular variance CV, angular deviation s (dispersion around the mean), circular skewness b (asymmetry) and the circular kurtosis k (peak load). See [30] for more information. Alignment Strategy S0 S1 S2 S3 S4–70˚ S4 0˚ S5–70˚ S5 0˚ S5 30˚ Short name Allochronic Music Fixed Tempo Continuous Tempo Adaptation Continuous Tempo Adaptation, in phase start Forced Phase Coherence— Algorithmic Forced Phase Coherence— Adaptive Oscillator Forced phase angle start / goal -70˚ 0˚ -70˚ 0˚ -70˚ 0˚ +30˚ Experiment I.E. F.U.E. I.E. F.U.E. I.E. F.U.E. I.E. F.U.E. I.E. F.U.E. I.E. F.U.E. F.U.E. N Steps 26326 7246 26445 7285 26275 7451 26199 6709 25980 7466 25773 7419 6772 N Songs 180 55 180 55 180 55 180 50 180 55 180 55 50 Mean relative phase angle ø 138˚ 165˚ -13.7˚ -17.7˚ -8.04˚ 3.61˚ -22.1˚ -39.2˚ -70.3˚ -1.95˚ -75.5˚ -9.66˚ 17.4˚ Resultant Vector Length j R j 0.0034 0.0085 0.424 0.402 0.196 0.136 0.671 0.667 0.911 0.912 0.925 0.936 0.895 Circular Variance CV 0.997 0.991 0.576 0.598 0.804 0.864 0.329 0.333 0.0891 0.0877 0.0747 0.0645 0.105 Angular Deviation s 1.4 1.4 1.1 1.1 1.3 1.3 0.81 0.82 0.42 0.42 0.39 0.36 0.46 Circular Skewness b 0.00048 -0.0006 0.02 0.03 -0.014 -0.035 -0.031 0.024 -0.0049 0.0074 0.0011 0.02 0.011 Circular Kurtosis k -0.0016 0.0026 0.22 0.19 0.062 0.04 0.46 0.36 0.74 0.74 0.79 0.82 0.74 https://doi.org/10.1371/journal.pone.0208702.t005 Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 10 / 21 angles. Since S2 and S3 used an adaptive tempo strategy, there was less freedom to deviate from the initial relative phase angle between footfall and beat (for S2 this relative phase angle is random, for S3 it is around -70˚ or 0˚ for initial and follow-up experiment). Of particular interest was condition S1, where, due to the fixed tempo of the song, the high- est degree of freedom was implemented with regard to the selection of the relative phase angle. Each song started at a random relative phase angle (random time with respect to footfall), but the distribution ended up with a clear clustering around negative relative phase angles (-14º and -18 º for initial and follow-up experiment respectively). In addition, there was a large spread in resultant vector lengths: in some cases people’s footfalls showed a constant timing relative to the timing of the beats for a song (the dots on or near the circle perimeter, represent- ing a high resultant vector length), in other cases this relation was less stable, or even almost absent (the dots close to the center of the circle, representing a low resultant vector length). This indicates that there was a convergence during the song from the starting angle towards the final angle. The average resulting relative phase angle, as indicated by the arrows in Fig 1 Fig 1. Circular scatter plots of resultant vectors. Each dot represents the resultant vector (both angle and length) per song per participant. A negative angle indicates that during that specific song, the footfall (averaged over the song) occurred before the musical beat (runner is first), while a positive angle indicates that the footfall occurred after the beat (music is first). The distance from the center indicates the resultant vector length. The closer the dot is to the circle perimeter, the higher the phase coherence, i.e. the more steps were taken towards this average phase angle. The arrows indicate the resultant vector length and average phase angle of the complete distribution of songs, thus providing a general overview of all participants’ behavior to all songs, in a specific condition. The color and form of each dot indicate whether the data point was from the initial or the follow-up experiment, which is denoted in the legend of the figures. https://doi.org/10.1371/journal.pone.0208702.g001 Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 11 / 21 and the resultant vector length value (length of the arrow in Fig 1, see also Table 5), show a pre- ferred relative phase angle around -15˚. This confirms the preference for a slight negative mean asynchrony, and that runners prefer the footfalls occurring just prior to the beats. In S2, the tempo of the music was continuously matched to the tempo of the runner. The figure shows high amounts of synchronization (runners consistently keeping their initial rela- tive phase) but with a slight clustering around the same negative relative phase angles. This indicates that phase attraction towards the preferred relative phase angle found in S1 still hap- pened with adaptive tempo conditions, but it is less pronounced and it occurs only when start- ing close to this preferred angle. S3 further elaborated on this finding. The blue dots on Fig 1.S3 indicate data from the fol- low-up experiment, where each song started around 0˚, that is, the beat is perfectly in sync with the footfall. These participants deviated towards -40˚, thus increasing their relative phase angle. For the initial experiment, we discovered a reverse effect: participants started in -70˚ and finished in on average -22˚, thus reducing their relative phase angle. This indicates that there is a ‘phase attractor’ between -20˚ and -40˚, and that when the starting relative phase was close to these angles, participants tended to get phase-locked around their preferred relative phase angle. Note that this attractor effect was not clearly visible in S2 as there was no clear clustering around these relative phase angles. In this condition, the starting relative phase angle was ran- dom. To explain why runners starting in these random relative phase angles were not attracted to their own preferred angle, we refer to a mathematical model which is used in movement coordination: the HKB model [34]. This model shows an attraction basin around the preferred relative phase angle, but if a relative phase angle deviates too far from this preferred relative phase angle, the attraction is not present. This could explain why there are high synchroniza- tion scores, or resultant vector lengths, at all different relative phase angles. Additionally, when participants were eventually attracted towards this preferred angle, another issue arose: since the songs lasted only 55 seconds, it is likely that runners did not have enough time to converge towards their preferred relative phase angle. Fig 1.S2, visualizes this phenomenon with lower resultant vector lengths between -90º and 0˚ (compared to S1), which can be attributed to this time-consuming convergence. A transition from one relative phase angle to a completely dif- ferent relative phase angle results in a wide distribution and thus a lower resultant vector length. We assume that if the song would have been longer, the same clustering as in S1 and S3 might have been discovered in S2 for starting relative phase angles close to the preferred rela- tive phase angles. S1 and S3, both the initial and the follow-up experiments, clearly revealed a negative mean asynchrony (NMA), i.e. the participants showed a tendency to anticipate the beats (from -40˚ to -14˚). This is in accordance with other studies concerning sensorimotor synchronization. On average, the runners put their feet down 40 to 15 ms before they perceive the musical beat. This asynchrony could be explained by the fact that the tactile signal takes longer than the audio signal to reach the brain. To synchronize in the brain, the tactile signal needs to occur before the audio signal, hence the asynchrony of about -15º in relative phase. This is in line with the nerve-conduction hypothesis, where peripheral processing time is dependent on the distance of feedback (tactile or auditory) to the brain [17] and the sensory accumulator model assuming that synchrony is established at the level of central representations [35]. Another group of explanations is based on the onset computation or P-center hypothesis [36]: if an event (e.g., a tap or a footstep) is extended over time, the perceptual center (P-center) differs from the onset of the event. For tapping, it is suggested that rather than the initial surface con- tact, the moment of peak force is the meaningful target in timing control [37]. Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 12 / 21 In terms of attraction towards the NMA or preferred phase angle, we found that our results from S1—S3 are in line with Haken-Kelso-Bunz (HKB) model [34]. This model depicts inter- limb coordination and phase transitions between different states. It shows a strong attraction basin around the 0˚ phase angle and a secondary attractor near 180˚ (antiphase), inducing phase transitions from the current angle towards the closest attractor. The model also shows that the strength of this attraction force decreases with increasing movement frequency, and at higher tempi (such as running at a higher cadence than 150 SPM) the secondary antiphase attractor dis- appears and only relative phases close to 0˚ get attracted. This is remarkably similar to the find- ings of S1 through S3 (although with a rotation towards the NMA or preferred relative phase angle of the participants): (1) if the starting phase differs enough from the NMA, no attraction is present when both systems operate at the same frequency (music is matched to cadence) and (2) when the starting phase is close to the NMA, participants converge towards their NMA. S4 and S5 were designed to keep a constant relation between the moments of the footfall and the beat. This is clearly illustrated in Fig 1 by the concentrations of dots on the circle perimeter near the target phase angle that was implemented in these strategies: -70˚ in the ini- tial experiment (in red), 0˚ (in blue) and +30˚ (in green) in the follow-up experiment. This indicates that the software works as configured, and that we can force users towards a certain relative phase angle. Later sections of the discussion will look into possible kinematic and motivational effects of certain relative phase angles. To summarize, we found that the three conditions allowing self-selected relative phase angles show that the preferred angle does not approximate 0˚, but is slightly negative–following the NMA displayed in sensorimotor synchronization experiments [11, 38]. We also found indica- tions for an attraction force towards this preferred relative phase angle, especially when the initial relative phase was close to the preferred relative phase. When the initial starting relative phase was not near to this attractor, a stable relative phase was kept at this angle. Hence, to reach the preferred self-selected relative phase angle, the starting relative phase is important. If we want to design future music-to-movement alignment strategies that support the runners’ natural and individual interactive behavior with music, not only should the technique be capable of selecting music that matches the preferred tempo of the runner, it should also be able to apply the preferred relative phase angle (as measured for instance with a fixed tempo strategy like S1 and fine-tuned with a strategy similar to S3) as a target relative phase angle in strategies similar to S4 and S5. Effect on cadence The combined results of the initial and the follow-up experiment indicate that strategy S1, S2, and S3 have no significant effect on cadence when compared to the allochronic music strategy (S0). Runners can be phase-locked, i.e. maintain a stable footfall-to-beat relation (seen best in S1, S2 and S3), and in general running with these strategies reflects a natural preference for a slightly negative phase angle relation (NMA). In contrast, our main results do show an effect of the alignment strategy on cadence with S4 and S5. This is further elaborated on with the fol- low-up experiment: the effect appears to be dependent on the target phase angle settings in the strategy. Fig 2 shows the resulting cadence change based on the forced relative phase angle. For this discussion, we group several conditions based on the forced relative phase angle: (0) control or asynchronous music, (1) self-selected relative phase angles, (2) forced at -70˚, (3) forced at 0˚, and (4) forced at +30˚. Fig 2 visualizes the effect of forced relative phase angles on cadence. There was no difference between self-selected angles and the control condition in terms of the cadence change, meaning participants did not show any difference in running behavior in their self-selected angle compared to allochronic music. There was however an effect of 0˚: Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 13 / 21 cadence seemed to increase to a minor extent, while this increase was more pronounced at +30˚. At -70˚, we noticed a clear decrease in cadence. Thus, to have a neutral null-effect, the optimal relative phase angle would be the preferred relative phase angle as found in S1 and S3, similar to our earlier findings on synchronization. The decrease in cadence observed in S4 and S5 at -70º could be explained by the fact that the beats were continuously perceived approximately 70 ms after the moment of the footfall. The participant was therefore constantly urged to lengthen the duration of his or her next step to reach the preferred -20˚ attractor. We theorize that, in order to deal with the perceived sen- sorimotor error, the brain continuously adapts in order to try to minimize the perceived senso- rimotor error (i.e., obtain NMA). Although that error persists (due to our matching algorithm), the entrainment (or brain-driven sensorimotor error minimization) persists as well, and the outcome is a decrease in cadence. Conversely, the introduction of the beat prior to the footfall instant (positive relative phase angle) might rather induce a feeling of ’being late’, which could in turn stimulate the runner to speed up. This was indeed observed in the follow-up experiment, where S5 at +30˚ resulted in an increase in cadence compared to S0. To further explore these results, a correlation analysis was performed to see a potential rela- tion between the relative phase angle and the cadence change. This analysis only considers the data of conditions where phase manipulation was used (S4 [-70˚], S4 [0˚], S5 [-70˚], S5 [0˚] and S5 [+30˚]). A Kolmogorov-Smirnov test indicated that the data were not normally distributed (p < .01), hence a Spearman’s Rho correlation test was performed. We assumed that participants would have tried to reach their preferred relative phase angle around -20˚, hence a one-tailed test was performed as this assumption implies that a negative relative phase angle would result Fig 2. Cadence change compared to initial silence over time. Conditions are grouped based on the forced relative phase angle to improve visual representation. Self- selected relative phase angles include S1 through S3, Forced at -70˚ are S4 [-70˚] and S5 [-70˚], forced at 0˚ include S4 [0˚] and S5 [0˚], and finally forced at +30˚ is S5 [+30˚]. Self-selected relative phase angles induce little to no cadence change compared to the control condition, while the -70˚, 0˚ and +30˚ conditions have an effect over time. Error bars represent 1 SE. The error bars are noticeably higher in the 0˚ and +30˚ conditions, as the amount of participants (N = 11) for the follow-up experiment was lower than the amount of participants in the initial experiment (N = 39). https://doi.org/10.1371/journal.pone.0208702.g002 Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 14 / 21 in a lower cadence and vice versa. We uncovered a positive correlation between the relative phase angle and cadence change (r = .293, p < .001, n = 681), showing that a connection between cadence change and the forced relative phase angle indeed exists. Furthermore, the influence of the relative phase angle appeared to build up over time. Fig 2 shows the evolution of cadence change for different strategies over the five songs. When look- ing at the average cadence change over time, it was evident that the influence was not immedi- ate, but increased over time. The lowering-cadence effect seemed to stop around 2.2% slowdown (similar to the tempo basin found by Van Dyck et al. [15]), while the cadence increasing effect was less pronounced. This shows that the relative phase angle manipulation effect was ’limited’ towards the influence on cadence. Motivation In the initial experiment, as well as in the follow-up experiment, similar results were obtained when comparing the enjoyment ratings of the different strategies to S0. The strategies that did not significantly affect running cadence (S1, S2, S3, S4 [0˚], and S5 [0˚]) were not rated differ- ently from S0. However, in the two conditions that affected cadence most (S5 [-70˚] and S5 [+30˚]) a small but significantly higher enjoyment rate, or a tendency towards such an effect, was observed. According to literature on gaming [39] and exergames [40] the balance between skill and challenge is crucial for being intrinsically self-rewarding. When challenged at a level that the gamer perceives as pleasant, he or she is more likely to have a positive experience. As such, it could be suggested that a small deviation from a preferred cadence, such as realized by continuously manipulating the relative phase angle to a non-preferred phase angle (S5–70˚ and +30˚), might imply a more challenging or rewarding running exercise. A challenge that is not too demanding for the runner’s skills (note that no significant increases in RPE were observed) could result in a more pleasant and rewarding experience than when the runner is not challenged or manipulated to change his or her preferred running behavior. According to Fritz et al. [41] moving in synchrony with music can evoke a sense of agency, or a feeling of ’being in control’. Combined with a certain amount of physical exertion, this sense of agency might contribute to a perceived positivity bias [42]. As such, our results seem to suggest that finding a new phase angle balance induces such feelings of agency. Similar to the skill-chal- lenge balance [39, 40], being in and out of balance with a slightly demanding phase angle seems to more positively impact enjoyment levels compared to maintaining a preferred, less demanding, phase angle. Alternatively, error minimization towards NMA can be seen as a reg- ulatory mechanism to homeostasis (an equilibrium state consisting of predictive, expressive and effort processes) [12]. Fig 3 summarizes our findings concerning the influence of forced relative phase angles. We note 3 regions: the neutral ’self-selected’ or negative mean asynchrony near -20˚ (footfalls slightly before beats), the cadence increasing larger angles between -10˚ and 30˚ (steps simulta- neous or after the beats) and the lower angles between -40˚ and -90˚ (steps noticeably before the beats). The effects of running in these regions are shown in the legend. Gender differences Several studies gave direct and indirect proof or reason to expect differences in how men run, entrain, and synchronize with beats of the music, compared to women [15, 18, 19]. And although no main effect of gender was found on the change in cadence, some interesting inter- action effects were uncovered that are worth discussing. Below, we compare the running behavior of men and women from the perspective of preferred phase angle and synchroniza- tion, cadence, and motivation. Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 15 / 21 Gender differences: Preferred phase angle and synchronization. Analysis showed that there is no significant difference between genders for average phase angles in all different con- ditions. However, in the fixed music condition (S1), where participants had the most freedom to self-select relative phase angles (and converge to their NMA), we noticed a slight difference in distributions for males versus females. The relative phase angle distribution was slightly more asymmetrical and wider than that for females, indicating a larger spread in relative phase angles and a less consistent synchronization. When looking at the phase coherence or synchro- nization of these angles (expressed by the resultant vector length), more pronounced differ- ences emerge: women tend to reach higher synchronization scores than men in the fixed music (S1) condition. Fig 4 shows the distribution of the resultant vector lengths for S1. Analysis with independent t-tests confirmed that there was indeed a significant difference in the resultant vector length for S1 (fixed tempo) for males (M = 0.57, SD = 0.27) compared to females (M = 0.68, SD = 0.27), t(233) = -2.964, p = .003. Females thus obtained higher syn- chronization scores. No other conditions showed such differences; perhaps because these con- ditions (S2-S5) allowed less freedom to self-select phase angles. Using a resultant vector length of .75 as a threshold to determine running in phase coher- ence or not (similar to [4]), we see 52% of songs in phase coherence for women versus 23% for men in the S1 (fixed tempo) condition. Gender differences: Cadence. A study on running to music [15] reported differences between male and female participants: women showed significantly higher levels of tempo entrainment than men. This is in line with research by Karageorghis [18], who found that women pay more attention to the rhythmic characteristics of music than men do. These find- ings make it interesting to look at the before mentioned forced-phase influence on cadence with the gender as a grouping factor. Fig 3. Summary of the effects of running in a forced relative phase angle. https://doi.org/10.1371/journal.pone.0208702.g003 Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 16 / 21 The main results showed an interaction effect between condition (alignment strategy) and gender (F(5, 170) = 4.97, p < .001) for the cadence change, indicating that the change in cadence differed between men and women for the different strategies. More in depth analysis showed a more pronounced change in cadence between the strategies for female runners (see Fig 5). It seems that, compared to their male counterparts, women demonstrated lower levels in cadence change with S4 [-70˚] and S5 [-70˚], and higher ones when running with S1, S2, and S3. A correlation test was performed on the forced relative phase angle conditions to test the significance of the forced phase angle on cadence change. Fig 6 illustrates the dataset. Both male and female distributions were non-normal (p < .01) as determined by the Kolmogorov- Smirnov test. The Spearman’s Rho correlation test showed a difference in correlation coeffi- cients for the different genders. While the male population showed a slightly positive and sig- nificant correlation between relative phase angle and cadence change (r = .124, p < .01, N = 363), the female population demonstrated a much higher correlation coefficient at a higher level of significance (r = .449, p < .001). These results indicate that females might indeed be more susceptible towards the influence of relative phase angles on cadence. Gender differences: Motivation. With respect to gender, our interest was to find out if there are differences in entrainment capacity and effects on cadence. For completeness sake, we also report that no significant differences were observed for the enjoyment ratings (PACES) between men and women: not in general, and not when the data were inspected per strategy. Fig 4. Resultant vector length or phase coherence for all songs in the S1 (fixed music) condition. A high level of phase coherence is observed for most of the women, whereas the flatter distribution of the resultant vector length for men indicates that a higher percentage of men did not maintain a phase-locked running behavior over the duration of a song. https://doi.org/10.1371/journal.pone.0208702.g004 Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 17 / 21 Conclusions Music-to-movement alignment strategies make it possible for everyone to keep running in synchrony with a musical stimulus, without consciously manipulating your own behavior to Fig 5. Cadence change compared to initial silence, split on gender. Conditions are grouped based on the forced relative phase angle to improve visual representation. Self-selected angles include S1 through S3, Forced at -70° are S4 [-70˚] and S5 [-70˚], Forced at 0° include S4 [0˚] and S5 [0˚], and finally Forced at +30° is S5 [+30˚]. Both the control and self-selected phase angles induced little to no cadence change, while the -70˚ and +30˚ conditions had an effect that was more pronounced for females. Running at 0˚ phase angle seemed to have neutral effects for the males but a cadence increasing effect for females. https://doi.org/10.1371/journal.pone.0208702.g005 Fig 6. Correlations between forced relative phase angle and cadence change. The effect is more visible for the female than the male population. https://doi.org/10.1371/journal.pone.0208702.g006 Optimizing beat synchronized running to music PLOS ONE \| https://doi.org/10.1371/journal.pone.0208702 December 6, 2018 18 / 21 align with the music. Our study shows that strategies implementing a continuous relative phase alignment achieve the highest level of synchronization (as intended) and are therefore perfectly capable of spontaneously manipulating running behavior. A change in cadence is in fact induced by deviating the relative phase angle continuously from a preferred phase angle that is typically slightly negative: a more negative phase angle (step before beat) causes a slow- down in cadence and a positive phase angle (step after beat) leads to an increase in cadence. Not only was cadence affected by phase angles that diverged from the runners’ preferred phase angles, also enjoyment levels were affected. Although differences in enjoyment were not very obvious, small, slightly significant increases in enjoyment were reported for the strategies that impacted cadence. Spontaneously guiding runners a little away from their preferred run- ning behavior could add just enough challenge to their exercise, resulting in a more rewarding experience [39, 40]. The general preference for the footfalls to occur prior to the beats suggests that future music alignment strategies could be improved by considering the negative mean asynchrony (NMA) rather than 0˚ as a reference point. A question for future research might be whether the size of the NMA is task- and/or user dependent. For example, is human anticipation of a beat different when running compared to tapping? Are there differences between people and why? With respect to the latter, our results indicate that gender plays a role when using forced phase algorithms: effects of phase coherence and change in cadence were more pronounced for females compared to their male counterparts. Practical applications Music-to-movement alignment strategies enable us to continuously and closely follow a per- son’s behavioral response to music. This is of great value for sports and rehabilitation pro- grams where music-based biofeedback is employed to improve individual performance [43]. In the future, the aim is to further improve our alignment strategies and introduce musical beats either slightly before or after the predicted footfalls. Such strategies could open up possi- bilities to spontaneously (and imperceptibly) optimize cadence and step size [44]. Acknowledgments The authors also wish to thank the Flanders Sports Arena of Ghent, for giving us access to their indoor running track. Author Contributions Conceptualization: Bart Moens, Dobromir Dotov, Marc Leman. Formal analysis: Jeska Buhmann. Funding acquisition: Marc Leman. Investigation: Jeska Buhmann, Edith Van Dyck. Methodology: Jeska Buhmann, Bart Moens, Edith Van Dyck. Project administration: Edith Van Dyck. Software: Bart Moens, Dobromir Dotov. Supervision: Edith Van Dyck. Writing – original draft: Jeska Buhmann. Writing – review & editing: Jeska Buhmann, Bart Moens, Edith Van Dyck, Marc Leman. 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PMC3912221	A Healthy Brain in a Healthy Body: Brain Network Correlates of Physical and Mental Fitness Linda Douw1,2, Dagmar Nieboer3, Bob W. van Dijk4, Cornelis J. Stam4, Jos W. R. Twisk3 Abstract A healthy lifestyle is an important focus in today’s society. The physical benefits of regular exercise are abundantly clear, but physical fitness is also associated with better cognitive performance. How these two factors together relate to characteristics of the brain is still incompletely understood. By applying mathematical concepts from ‘network theory’, insights in the organization and dynamics of brain functioning can be obtained. We test the hypothesis that neural network organization mediates the association between cardio respiratory fitness (i.e. VO2 max) and cognitive functioning. A healthy cohort was studied (n = 219, 113 women, age range 41–44 years). Subjects underwent resting-state eyes-closed magneto- encephalography (MEG). Five artifact-free epochs were analyzed and averaged in six frequency bands (delta-gamma). The phase lag index (PLI) was used as a measure of functional connectivity between all sensors. Modularity analysis was performed, and both within and between-module connectivity of each sensor was calculated. Subjects underwent a maximum oxygen uptake (VO2 max) measurement as an indicator of cardio respiratory fitness. All subjects were tested with a commonly used Dutch intelligence test. Intelligence quotient (IQ) was related to VO2 max. In addition, VO2 max was negatively associated with upper alpha and beta band modularity. Particularly increased intermodular connectivity in the beta band was associated with higher VO2 max and IQ, further indicating a benefit of more global network integration as opposed to local connections. Within-module connectivity showed a spatially varied pattern of correlation, while average connectivity did not show significant results. Mediation analysis was not significant. The occurrence of less modularity in the resting-state is associated with better cardio respiratory fitness, while having increased intermodular connectivity, as opposed to within-module connections, is related to better physical and mental fitness. Citation: Douw L, Nieboer D, van Dijk BW, Stam CJ, Twisk JWR (2014) A Healthy Brain in a Healthy Body: Brain Network Correlates of Physical and Mental Fitness. PLoS ONE 9(2): e88202. doi:10.1371/journal.pone.0088202 Editor: Renaud Lambiotte, University of Namur, Belgium Received October 5, 2013; Accepted January 9, 2014; Published February 3, 2014 Copyright: 2014 Douw 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: This work was funded by grants from the Dairy Foundation, the Netherlands Heart Foundation, the Dutch Prevention Fund, Heineken BV, the Ministry of Public Health, Wellbeing and Sport (VWS), the Scientific Board of Smoking and Health, the VU University and the VU University Medical Center. 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 the following interests: This study was funded in part by Heineken BV. Co-author Prof. CJ Stam is a PLOS ONE Editorial Board member. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials. E-mail: linda.douw@gmail.com Introduction A healthy lifestyle is a major focus in today’s society. Regular exercise and adequate physical fitness have proven to be important for the immune system, metabolism, prevention of infectious disease, skeletal functioning, and risk of cancer [1–6]. In addition to these physical benefits, cardiorespiratory fitness is also related to better cognitive functioning [7]. Several neural factors have been reported to mediate the relationship between mental and physical fitness, including increased neural vascularization [8], increased production of brain derived neurotrophic factor (BDNF; [9]), increased hippocampal volume [10], and higher levels of N- acetylaspartate [11], although none of these mediators fully explain the reported associations. Another framework that has elucidated the neural correlates of the association between cognition and physical fitness is resting- state functional connectivity, as measured with functional mag- netic resonance imaging (fMRI). The resting-state, during which no task is present and alert relaxation is achieved, can be characterized by several highly robust networks [12,13], of which the default mode network (DMN) is the most stable and best studied example [14,15]. This network seems to be the functional backbone of the brain [16], and is implicated in almost all neurological diseases. With respect to cardiorespiratory fitness, higher connectivity within the DMN (as measured by seeding the posterior cingulate cortex and examining its significantly correlat- ed regions) is associated with better fitness level, and DMN connectivity mediates the association between physical fitness and cognitive functioning [17]. Moreover, a one-year aerobic training intervention in older adults improves functional connectivity within several resting-state networks, including the DMN and the fronto-parietal network, which is thought to be important for working memory [18]. Conversely, overweight adults show increased DMN connectivity, which normalizes after a six month exercise program [19]. The important role of functional connec- tivity in the relationship between physical fitness and cognition was confirmed in another study by Voss and colleagues, showing that the association between exercise and connectivity is related to BDNF, insulin-like growth factor type 1 (IGF-1), and vascular PLOS ONE \| www.plosone.org 1 February 2014 \| Volume 9 \| Issue 2 \| e88202 1 Department of Neurology, Vrije Universiteit Medical Center, Amsterdam, The Netherlands, 2 Department of Anatomy and Neurosciences, Vrije Universiteit Medical Center, Amsterdam, The Netherlands, 3 Department of Epidemiology and Biostatistics, Vrije Universiteit Medical Center, Amsterdam, The Netherlands, 4 Department of Clinical Neurophysiology and Magnetoencephalography Center, Vrije Universiteit Medical Center, Amsterdam, The Netherlands, 5 Department of Epidemiology and Biostatistics, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands endothelial growth factor (VEGF), which are markers for neuroplasticity [20]. However, fMRI is an indirect measure of neural functioning, as it measures the slowly operating process of blood oxygenation. Functional connectivity can also be determined frommagnetoen- cephalography (MEG), which is a much more direct measure of neural activity. Furthermore, functional connectivity in general can be used as a starting point for more extensive, higher-order analysis of the entire brain network using graph theory [21–23]. This type of analysis has shown that the brain network is very comparable to many simpler biological and sociological systems [24]. This elegantly theory-governed but still data-driven property has made the application of network theory to the brain a very interesting endeavor. For instance, the brain network is a ‘small- world’, combining local segregation with global integration [24– 27]. Brain network topology is to a large extent genetically determined [28–30] and is disturbed in several neurological diseases [21,31]. The functional brain network also correlates with global cognitive functioning and intelligence [32,33], indicating network theory may add relevant information on neural correlates of functioning above connectivity alone. Important information about the integrity of the (brain) network can be extracted by looking at modularity. Modules are clusters of nodes, or brain areas, that are highly connected to each other, but much less to nodes outside of their own module [34]. In the brain, five to seven modules can be discerned, which correspond to major functional systems [35]. Moreover, the role that specific brain areas play both within their module and in connecting other modules has proven relevant to brain functioning [36–38]. In this study, physical fitness, intelligence, and their neural correlates in terms of network modularityare investigated. We aimed to prove that VO2 max, a measure of cardio respiratory fitness,is related to modular network topology based on MEG in a large group of healthy subjects. Furthermore, we hypothesized that intelligence is associated with physical fitness mediated through brain network topology in terms of modular organization. Materials and Methods Ethics statement This study was approved by the Medical Ethical Institutional Review Board of the VU University Medical Center. All subjects gave written informed consent before participation. This study was carried out in accordance with the Declaration of Helsinki. Subjects All subjects participated in a prospective longitudinal study, originally investigating natural development of growth, health and lifestyle of adolescents, the Amsterdam Growth and Health Longitudinal Study (AGHLS). This cohort study started in 1976 with four annual measurements and continued with an extensive number of assessments with five to seven year time intervals[39]. All participants were born between 1961 and 1965 and were residents of the Netherlands. First- and second-year pupils from two equally large secondary schools were recruited. In 2006, MEG recordings of the remaining 344 participants (who were all between 41 and 44 years old) were obtained, in addition to the health parameters that were investigated at each time-point of the AGHLS [40]. These data are not publicly available at this point. Physical fitness Physical fitness was measured with a maximal running test on a treadmill (Quinton 18–54; Quinton, Bothell, Wash), with a speed of 8 km/h and increasing slope (every 2 minutes) and with direct measurements of oxygen uptake (Ergoanalyzer; Jaeger, Bunnik, the Netherlands). Maximum oxygen consumption (VO2 max) was used as a measure of physical fitness (Kemper, 1995). This measurement was performed approximately six years before MEG recording. Cognitive performance Subjects underwent a cognitive test battery at the time of MEG recording, to assess full-scale intelligence. The test battery administered included the shortened Groninger Intelligence Test (GIT [41]), which is a commonly used Dutch intelligence test. Three subtests of the entire GIT were used, constituting the short version of the test to assess intelligence [42]. Completion of the test took approximately 45 minutes per subject. Magnetoencephalography Magnetic fields were recorded for five minutes while subjects were seated inside a magnetically shielded room (Vacuumsch- melze GmbH, Hanau, Germany), using a 151-channel whole- head MEG system (CTF SystemsInc., Port Coquitlam, BC, Canada). A third-order software gradient was used after online band-pass filtering between 0.25 and 125 Hz. Sample frequency of recording was 625 Hz. Fields were measured during a no-task, eyes-closed condition of five minutes. At the beginning and ending of each recording, the head position relative to the coordinate system of the helmet was recorded by passing small alternating currents through three head position coils attached to the left and right pre-auricular points and the nasion on the subject’s head. For each subject, the first five artifact-free epochs of 4096 samples (6.554 s) were selected by one of the authors [BWvD]. All data analyses were performed using BrainWave [CJS, version 0.9.58, available from http://home.kpn.nl/stam7883/brainwave. html]. Before calculating connectivity and network topology, epochs were band-pass filtered into the commonly used frequency bands delta (0.5–4 Hz), theta (4–8 Hz), lower alpha (8–10 Hz), upper alpha (10–13 Hz), beta (13–30 Hz), and gamma (30– 45 Hz). All further analyses were performed for these bands separately. The average relative power in the six abovementioned frequency bands was calculated in each subject using a fast Fourier transform as described in [43]. Phase Lag Index (PLI) As a measure of functional connectivity, the phase lag index (PLI) was used[44], which calculates the asymmetry of the distribution of (instantaneous) phase differences between two time-series. This asymmetry can be obtained from a time series of phase differences DW (tk), k = 1… N samples: PLI~ vsign sin D tk ð Þ ð Þ ½ w j j The phase difference DQ is defined in the interval [2p, p] and ,. denotes the mean value. Volume conduction causes a zero phase lag between two time-series, but the presence of a consistent, non-zero, phase lag between two time-series reflects true interactions that are unaffected by volume conduction or common sources. Modularity, between and within module connectivity First, to describe modularity in the whole-brain network we used a version of previously described approaches[45], adapted for weighted networks [37,46]: Brain Networks and Physical Fitness PLOS ONE \| www.plosone.org 2 February 2014 \| Volume 9 \| Issue 2 \| e88202 Qw m ~ X m s~1 ls L{ ds 2L 2 where m is the number of modules, ls is the sum of the weights of all links in module s, L is the total sum of all weights in the network, and ds is the sum of the strength of all nodes (i.e. the summed weights per node) in module s. In short, the relation between intra- and intermodular connections determines the strength of each module. This measure describes modularity by summing the relative strength of all the network modules, which takes both within and between module connections into account. A strongly modular network has modularity value close to 1, while modularity is closer to 0 (but not absent) in a random network. Finding the optimal modular organization in a network is a computationally intensive problem. Simulated annealing can overcome part of this issue, and was used in the current study [45]. Initially, each of the N nodes was randomly assigned to one of m possible clusters, where the initial m was taken as the square root of N. At each step, one of the nodes was chosen at random, and assigned a different randomly chosen module number from the interval [1,N]. Modularity was calculated before and after this. The cost C was defined as {Qw m. The new partitioning was preserved with probability p: if the final cost Cf was lower or equal to the initial cost Ci (indicating no added cost when preserving the partition), p was equal to 1. If Cf was higher than Ci, p was calculated as follows: p~ e{ cf { ci T The temperature T was 1 initially, and was lowered once every 100 steps as follows: Tnew = 0.995 Told. In total, the simulated annealing algorithm was run for 106 steps. The partition with the strongest modular organization (highest Q) was identified sepa- rately for each epoch of every person for all the different frequency bands, and subjected to further graph analysis. Once the modular organization in a network has been determined, the topological role of individual nodes can be described in greater detail: nodes can be mainly involved in communication with other nodes in the same module, but can also preferably interact with other modules (see figure1). This aspect is quantified by two properties: the within-module degree (Zi), and the participation coefficient (PC) [45]. The within-module degree measures the connectivity of the node within the module compared to the other nodes in the same module, and thus describes the relative importance within the module. It was defined as follows: zw i ~ kw i mi ð Þ{ k w mi ð Þ skw mi ð Þ Here, mi is the module containing node i, kw i mi ð Þ is the within module strength of node i (the sum of all weights of the links between i and all other nodes in mi), and k w ( mi ) and skw mi ð Þ are the respective mean and standard deviation of the within-module mi degree distribution. The participation coefficient expresses how strongly a node is connected to other modules, and the weighted version is defined as: PCi ~1{ X m[M kw i m ð Þ kw i M is the set of modules, and kw i is the sum of all weights of the links between i and all nodes in module m. The within module degree and the participation coefficient determine the identity of a node in the modular network structure. Statistical analysis Statistical analyses were performed using PASW Statistics package (version 20.0) and Matlab version r2012b. Differences between men and women regarding VO2max, IQ, and average head surface were tested using Student’s t-tests. The association between intelligence and VO2 max was analyzed using a linear regression with IQ as the dependent variable and VO2 max as independent variable, adjusted for sex. The association between VO2 max and modular network topology was investigated with regression analysis in which modularity indices per frequency band were the dependent variables and VO2 max the dependent variable, adjusted for sex, head surface size, and relative power per frequency band. Similar analyses were used to explore average between-module connectivity. Finally, the relationships between VO2 max, network modularity, and intelligence were further studied using mediation analyses. Mediation analysis investigates whether a third parameter underlies an observed relationship between two variables, meaning that the third variable governs the Figure 1. Schematic representation of modularity and modular connectivity. Note. In (a), two modules can be discerned. These modules show high within-module connectivity, but low between- module connectivity. (b) depicts two nodes in the network that are characterized by high within-module connectivity, while (c) shows a node with very high between-module connectivity. doi:10.1371/journal.pone.0088202.g001 Brain Networks and Physical Fitness PLOS ONE \| www.plosone.org 3 February 2014 \| Volume 9 \| Issue 2 \| e88202 association between the other two. In our study, we hypothesized that the association between VO2 max and intelligence is mediated by brain network modularity. This mediation model was tested using the INDIRECT PASW statistics plug-in [47]. Direct and indirect effects between the dependent and indepen- dent variables as well as the mediator were tested with regression analyses (adjusted for significant abovementioned covariates), after which 95% CIs were calculated for the total indirect effects using bootstrapping (5,000 samples) as an unbiased means of testing whether the mediation model was valid. The presence of a mediation effect signifies that instead of having a direct causal effect between the independent variable (VO2 max) and depen- dent variable (IQ), the mediator (modular network topology) plays an important role in the association between these two variables. Results Subject characteristics At this time-point in the AGHLS study, 344 healthy subjects participated. Our strict inspection of artifacts in the MEG recordings caused exclusion of 79 subjects. Fourteen subjects were excluded after examination of their intelligence scores, because they performed well below average (,75). Of the remaining 251 subjects, VO2max measurements were performed in 219 subjects, in whom all subsequent analyses were performed (see table1 for subject characteristics). Subjects were on average 42 years old (range 41–44). With respect to IQ, men and women did not differ (t(217) = 1.005, p = 0.316). Men did have higher VO2 max (t(217) = 14.006, p,0.001) and greater head surface in cm2 (t(217) = 8.979, p,0.001). To ascertain that network topology results were not confounded by head surface size, this variable was used as a covariate in all analyses. Four MEG sensors were malfunctioning at the time of data collection, and these were excluded from further analysis in all subjects. The last VO2 max measurement took place six years before MEG recording, when subjects were approximately 36 years old. In order to investigate whether this gap could induce large changes in physical fitness, we examined data from previous measurements in the AGAHLS cohort. These measurements were performed at 13, 14, 15, 16, 21, 27, 29 and 32 years of age in subgroups of the total cohort (with group sizes varying between 70 and 227 subjects). When looking at the consistency of VO2 max over these time points, there is strong consistency within subjects over time (see supplementary figure1), with an average correlation coefficient R = 0.773 from one time point to the next. When comparing the first adult measurement at 21 years old and the measurement used in the remainder of this study at 36 years old (94 subjects overlapping), the correlation coefficient is 0.791. Furthermore, subjects who experienced major health burdens possibly influenc- ing their lifestyle were excluded, which also ensures the stability and consistency of the VO2 max measurements up to MEG and IQ measurements six years later. Physical fitness, intelligence and brain modularity The previously reported association between physical fitness and intelligence was confirmed: VO2 max was a significant predictor of intelligence in a linear regression model (B = 0.322, 95% CI [0.049 0.594], p = 0.021). We then set out to investigate our hypothesis concerning the association between physical fitness and brain network topology. Lower modularity in the upper alpha and beta bands was related to higher VO2 max (upper alpha band B = 21.81, 95% CI [23.31 20.315], p = 0.018; beta band B = 21.167, 95% CI [21.753 25.81], p = 0.017), adjusted for sex, head surface, and relative power per frequency band (see table2 for results of all frequency bands). In order to confirm that these associations were indeed due to network topology instead of global connectivity levels, we performed an ANOVA with VO2 max as dependent variable and both modularity and average connectivity in the upper alpha and beta bands as independent variables. While the upper alpha and beta connectivity indices did not yield significant results (p = 0.529 and p = 0.869, resp.), modularity indices were signifi- cantly related to VO2 max (p = 0.016 and p = 0.012, resp.). The number of modules in the upper alpha and beta bands was not associated with VO2 max, indicating that it was not the number of modules that mattered, but the connectivity patterns within and between those modules. Modular connectivity We then investigated the associations of between and within module connectivity with VO2 max in these two frequency bands. Results show that in the beta band, higher VO2max was related to increased between-module connectivity (B = 0.674, 95% CI [0.101 1.246], p = 0.021), indicating indeed that physical fitness is Table 1. Subject characteristics. Total group (N = 219) Men (N = 106) Women (N = 113) Mean age in years (SD) 42 (0.7) 42 (0.7) 42 (0.7) IQ score (SD) 108 (13) 109 (13) 108 (13) Head surface in cm2 (SD) 231 (19) 242 (17) 222 (15) VO2max (SD) 46 (8.6) 52 (7.0) 40 (5.5) Note ** = p,.01, significant gender difference. doi:10.1371/journal.pone.0088202.t001 Table 2. Associations between band-specific modularity and VO2 max. B 95% CI (B) p-value Delta band modularity 0.435 [-.080 0.167] 0.487 Theta band modularity 20.390 [21.64 0.086] 0.540 Loweralphamodularity 0.509 [20.121 2.23] 0.561 Upperalphamodularity 21.81 [23.31 23.15] 0.018* Betamodularity 21.17 [21.75 25.81] 0.017* Gamma modularity 211.1 [280.9 58.6] 0.754 Note. * = p,0.05. Sex, relative power in each frequency band, and skull size were entered as covariates in each regression. doi:10.1371/journal.pone.0088202.t002 Brain Networks and Physical Fitness PLOS ONE \| www.plosone.org 4 February 2014 \| Volume 9 \| Issue 2 \| e88202 important for better intermodular integration. Moreover, an ANOVA with both between-module and average connectivity as covariates shows significant results for between-module connec- tivity only (p = 0.039 and p = 0.919, resp.), further underlining the added value of modularity-based connectivity over regular connectivity alone. Figure2a shows significant associations between VO2 max and between-module connectivity per channel for both upper alpha and beta bands, after correcting for the number of tests performed with the false discovery rate (FDR, q,0.05 [48]). These maps confirm the analysis of averaged between-module connectivity, and show positive correlations between VO2 max and between- module connectivity throughout the brain in the beta band, but not the upper alpha band. Due to the nature of the within-module calculation (i.e. within- subject z-score is computed), no global average can be computed for this measure. However, figure2b displays significant within- module connectivity associations with VO2 max in the upper alpha and beta bands, indicating that higher within-module connectivity in the central areas is positively associated with VO2 max, while the within-module connectivity within lateral temporal areas is negatively associated with physical fitness. Modularity and between-module connectivity as VO2 max – IQ mediators Finally, the associations between intelligence, VO2 max, and brain modularity were analyzed using mediation analyses. Our hypothesis was that better physical fitness leads to better cognitive performance and thus higher IQ later, through the mediating effect of brain network modularity (see figure3). This hypothesis was not confirmed. Although separate regressions of the associ- ations between both VO2 max and network characteristics and intelligence were significant, the mediation effects, as evidenced by significance levels and 95% confidence intervals through 5,000 bootstrapping samples, were not (see table3). This indicates that although modularity, VO2 max and IQ are interrelated, the association between VO2max and intelligence is not statistically explained by modularity. Exploratory mediation analyses using different dependent, independent, and mediating variables also did not yield significant results. Discussion Physical fitness and cognitive functioning are related. We show that this relation is also associated with topology of the functional brain network during the resting-state. Decreased upper alpha and beta band modularity were related to higher VO2 max, with higher beta between-module connectivity being associated with better physical fitness. Average functional connectivity did not show this association with VO2 max. The association between Figure 2. Significant sensor-specific associations between modular connectivity and VO2 max. Note. (a) shows an FDR- corrected t-map of significant associations between alpha band and beta band (left and right panel, resp.) between-module connectivity and VO2 max, while (b) shows the same for within-module connectivity. Warm colors indicate positive associations, cool colors refer to negative associations. doi:10.1371/journal.pone.0088202.g002 Figure 3. Graphical representation of hypothesized mediation effect. Note. A mediating effect of brain network topology on the association between VO2 max (maximum oxygen uptake during an effort test) and intelligence quotient (IQ) was hypothesized. doi:10.1371/journal.pone.0088202.g003 Table 3. Mediation analyses of network topology on the association between physical fitness and intelligence. Upper alpha band modularity (total 95% CI [20.042 0.055]) Beta p VO2max - upper alpha modularity 20.216 0.018* VO2max - IQ total 0.187 0.046* VO2max - IQ direct 0.185 0.052 Upper alpha band modularity mediation 20.010 0.885 Beta band modularity (total 95% CI [20.057 0.058]) Beta p VO2max - beta modularity 20.197 0.018* VO2max - IQ total 0.187 0.046* VO2max - IQ direct 0.188 0.049* Beta band modularity mediation 0.004 0.959 Beta band PC (total 95% CI [20.032 0.074]) Beta p VO2max - beta PC 0.206 0.021* VO2max - IQ total 0.187 0.046* VO2max - IQ direct 0.184 0.053 Beta band PC mediation 0.013 0.854 Note. * p,0.05, CI = total confidence interval of indirect effects, based on 5,000 bootstrap samples. Adjusted for sex, head surface, and relative band power. PC = participation coefficient. doi:10.1371/journal.pone.0088202.t003 Brain Networks and Physical Fitness PLOS ONE \| www.plosone.org 5 February 2014 \| Volume 9 \| Issue 2 \| e88202 cardiorespiratory fitness and intelligence was however not statistically mediated by network characteristics. Modularity refers to the extent to which the brain can be subdivided into coherent subsystems. Although such a modular organization is generally beneficial for brain functioning [35], having consistently tight connectivity within modules may be detrimental. Our results show negative correlations between modularity and both mental and physical functioning, indicating that higher levels of within-module connectivity versus between- module connectivity may be related to decreased functioning. An MEG study comparing modularity during several conditions of a working memory task reports decreasing modularity, i.e. increas- ing intermodular communication, as effort increases [49]. A study compiling a large number of task-related fMRI and PET studies also shows the importance of the modular organization of the brain for cognitive functioning [38]. However, how modularity relates to healthy functioning during the resting-state has not been reported. Another resting-state study using modularity reports increased delta and theta band modularity in Alzheimer’s patients when compared to healthy controls, which was related to poorer performance on a fluency task [37]. Furthermore, an fMRI study during task performance did not find changes in overall modularity over consecutive learning sessions, but does report that the flexibility of particular nodes, i.e. the number of times that each node in the network changes its belonging to specific modules, was related to better performance [50]. That is, having a highly dynamic modular structure, as opposed to a fixed modular division, was related to better functioning. These task-based findings concerning network flexibility have recently been replicated, localizing these multi tasking nodes mainly in the fronto-parietal network [51]. Our findings indicate that the resting-state is characterized by lower modularity and increased between-module, possibly long-range connections in brighter and fitter individuals. It would be interesting to investigate the transition from resting-state to any task, which may indicate that the resting-state modular flexibility of the brain network is similar to task-based dynamics. The effects of modularity and between-module connectivity were present in the upper alpha and beta bands. These frequency bands havebeen studied extensively with respect to cognitive tasks, albeit mostly with respect to power and not connectivity or network properties. The (upper) alpha band has been related to attention and working memory [52–55], while the beta band has been implicated in learning, novelty detection, and reward evaluation, indicating that this oscillation might be an important mechanism for directing attention towards a novel stimulus [56– 59]. A previous study used electroencephalography (EEG) to investigate connectivity and network efficiency during a task in active versus sedentary subjects [60]. Results show that in the beta band, active subjects show greater connectivity and network efficiency than sedentary subjects. Similar results were obtained when using coherence as a measure of connectivity, also in the alpha and beta band [61]. None of these studies investigated resting-state network topology. With respect to the previously described study investigating modularity during increasingly difficult cognitive conditions [49], most effects of neural reconfiguration were found in the beta band, which the authors ascribe to the need for higher long-range synchronization, increased intermodular connectivity, and thus loss of modularity in this frequency band during tasks. This hypothesis, as well as our results, are corroborated by computa- tional and animal work, showing that beta oscillations provide excellent support for long-distance synchronization [59,62]. The beta band may speculatively be at the heart of communication between hub areas in the brain, which regulate higher-order functioning of the brain network and therefore relate to intelligence and cardio respiratory fitness, although more studies are needed to confirm this hypothesis. Previous studies have only reported associations between resting-state functional connectivity, cognitive functioning, and physical fitness. Particularly higher connectivity within the default mode network (DMN) has been related to increased cardiorespi- ratory fitness, while DMN connectivity also mediates the association between VO2 max and cognitive functioning [17]. After a 1-year exercise intervention in older adults, both the DMN and the fronto-parietal network show higher connectivity than a control group [18], further building on the causal relationships that might exist between physical fitness and functional connec- tivity. Our results partly corroborate these findings, and indicate that there might be differential associations with particular types of connectivity: in our investigation of a very large cohort of healthy subjects with a direct measurement of neural activity, particularly increased between-module connectivity was related to superior cardiorespiratory fitness and intelligence. Also, our lack of findings with respect to average functional connectivity indicate that network analysis contributes valuable information to the associa- tion between fitness and intelligence, and advocates for investiga- tion of the brain network as a whole instead of only focusing on connectivity between particular spatially determined areas. Although circumstantial evidence is available, the definite direction of the association between increased cardio respiratory fitness, functional brain network organization, and cognition is still uncertain, and our results in a large sample do not support the hypothesis that better physical condition leads to better intelli- gence through brain network topology. Several aerobic interven- tion studies, which usually randomize between an exercise program and a control intervention of for instance light stretching, have reported increased cognitive functioning afterwards, but a number of studies failed to find a cognitive effect of increased cardio respiratory fitness [7,63]. Our study was not aimed at addressing this issue, and mediation analyses were not significant. Additionally, measurement of VO2 max took place approximately six years prior to intelligence testing and MEG recording. Our analysis of VO2 max at previous time points suggests that this measurement is a relatively stable measure of physical fitness, and all subjects with disease burden influencing their lifestyle were excluded. Finally, the presence of associations between VO2 max and intelligence six years later suggest that we are indeed looking at a robust indication of physical fitness. However, we cannot ascertain that this interval between measurements did not influence our results. Future longitudinal studies are needed to shed light on the causal relations between cardiorespiratory fitness, intelligence, and network topology, while investigation of anatom- ical brain connections may also yield further insights into this issue. Increased physical fitness is associated with better functional brain network topology. The step from exercise to functional brain network may be difficult to understand. On a cellular level, better physical fitness has often been associated with increases in BDNF [64,65], and possibly with IGF-1 and VEGF [66]. A recent study suggests that these exercise-induced cellular changes are indeed related to functional connectivity, by comparing BDNF, IGF-1 and VEGF levels in two groups of participants undergoing either an aerobic or non-aerobic intervention [20]. The link between cellular biology and network functioning as measured with MEG has recently also been addressed in a study of protein expression and epilepsy in brain tumor patients [67]. We were able to show a direct association between epilepsy-related protein expression and Brain Networks and Physical Fitness PLOS ONE \| www.plosone.org 6 February 2014 \| Volume 9 \| Issue 2 \| e88202 between-module connectivity of the tumor area, further indicating that these network patterns may be the intermediate between molecules and behavior. Future studies are needed to further explore how cellular changes as a consequence of exercise lead to changes in functional connectivity. Several limitations of the current study should be recognized. First of all, as previously mentioned, measurement of cardiorespi- ratory fitness was performed several years before MEG recording and intelligence testing took place. The influence of the lag between measurements in the current study design on the reported results is unknown. Secondly, this study was performed on the sensor-level, since no anatomical MRI scans (which are necessary to perform accurate source reconstruction in MEG data) were available. This limits the spatial specificity of our results, and prohibits further investigation of specific spatial network proper- ties. Third, the spatial resolution of MEG is limited. Although MEG is much less sensitive to volume conduction and disturbing effects of the skull and scalp than EEG, common sources still pose a serious problem for coupling analysis. However, the phase lag index is a particularly strict measure of functioning connectivity, because it excludes all non-zero lagged correlations [44,68]. In conclusion, we show that functional brain network organi- zation may mediate the association between cardiorespiratory fitness and intelligence. Less tightly connected, more intercon- nected functional modular topology in the upper alpha and particularly beta band may promote long-range connectivity in the resting-state, which relates to both increased physical and mental fitness. Supporting Information Figure S1 Temporal consistency of VO2 max measure- ments in the AGAHLS cohort. Note. (a) depicts the correlations between VO2 max measurements at each neighboring time point in the AGAHLS study, the first two measurements being performed at 13 and 14 years old (YO). The number of overlapping subjects between time points is indicated in paren- theses. In (b), the first adult VO2 max measurement at 21 years old is correlated to the last measurement at 36 years old, which we used in this study (correlation coefficient of 0.791, p,0.001). (TIFF) Acknowledgments The authors would like to thank K. Plugge, P. Ris and N. Sijsma for their help in recording MEG in all subjects. 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PMC9794057	1 S1 Table. CREDES checklist. Items of reporting Reported on page Purpose and rationale. The purpose of the study should be clearly defined and demonstrate the appropriateness of the use of the Delphi technique as a method to achieve the research aim. A rationale for the choice of the Delphi technique as the most suitable method needs to be provided. 4 Expert panel. Criteria for the selection of experts and transparent information on recruitment of the expert panel, sociodemographic details including information on expertise regarding the topic in question, (non)response and response rates over the ongoing iterations should be reported. 5, 8-9 Description of the methods. The methods employed need to be comprehensible; this includes information on preparatory steps (How was available evidence on the topic in question synthesised?), piloting of material and survey instruments, design of the survey instrument(s), the number and design of survey rounds, methods of data analysis, processing and synthesis of experts’ responses to inform the subsequent survey round and methodological decisions taken by the research team throughout the process. 4-7 Procedure. Flow chart to illustrate the stages of the Delphi process, including a preparatory phase, the actual ‘Delphi rounds’, interim steps of data processing and analysis, and concluding steps. Fig 1 Definition and attainment of consensus. It needs to be comprehensible to the reader how consensus was achieved throughout the process, including strategies to deal with non-consensus. 4 Results. Reporting of results for each round separately is highly advisable in order to make the evolving of consensus over the rounds transparent. This includes figures showing the average group response, changes between rounds, as well as any modifications of the survey instrument such as deletion, addition or modification of survey items based on previous rounds. 8-11, Table 2, S5-S7 Tables Discussion of limitations. Reporting should include a critical reflection of potential limitations and their impact of the resulting guidance. 14-15 Adequacy of conclusions. The conclusions should adequately reflect the outcomes of the Delphi study with a view to the scope and applicability of the resulting practice guidance. 16 Publication and dissemination. -	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
PMC4370475	RESEARCH ARTICLE Sprint Conditioning of Junior Soccer Players: Effects of Training Intensity and Technique Supervision Thomas Haugen1,2, Espen Tønnessen1, Øyvind Øksenholt3, Fredrik Lie Haugen3, Gøran Paulsen1,3, Eystein Enoksen3, Stephen Seiler2 1 Norwegian Olympic Sports Program (Olympiatoppen), Sognsveien 228, 0840 Oslo, Norway, 2 Faculty of Health and Sport Sciences, University of Agder, Gimlemoen 25, 4630 Kristiansand, Norway, 3 Norwegian School of Sport Sciences, Sognsveien 220, 0806 Oslo, Norway thomas.haugen@olympiatoppen.no Abstract The aims of the present study were to compare the effects of 1) training at 90 and 100% sprint velocity and 2) supervised versus unsupervised sprint training on soccer-specific physical performance in junior soccer players. Young, male soccer players (17 ±1 yr, 71 ±10 kg, 180 ±6 cm) were randomly assigned to four different treatment conditions over a 7-week intervention period. A control group (CON, n=9) completed regular soccer training according to their teams’ original training plans. Three training groups performed a weekly repeated-sprint training session in addition to their regular soccer training sessions per- formed at A) 100% intensity without supervision (100UNSUP, n=13), B) 90% of maximal sprint velocity with supervision (90SUP, n=10) or C) 90% of maximal sprint velocity without supervision (90UNSUP, n=13). Repetitions x distance for the sprint-training sessions were 15x20 m for 100UNSUP and 30x20 m for 90SUP and 90UNSUP. Single-sprint performance (best time from 15x20 m sprints), repeated-sprint performance (mean time over 15x20 m sprints), countermovement jump and Yo-Yo Intermittent Recovery Level 1 (Yo-Yo IR1) were assessed during pre-training and post-training tests. No significant differences in per- formance outcomes were observed across groups. 90SUP improved Yo-Yo IR1 by a mod- erate margin compared to controls, while all other effect magnitudes were trivial or small. In conclusion, neither weekly sprint training at 90 or 100% velocity, nor supervised sprint train- ing enhanced soccer-specific physical performance in junior soccer players. Introduction The importance of sprinting in professional soccer is well established and the need for speed is clear [1–4]. According to track-and-field statistics [5], trends over time from large retrospective data collections in soccer players [3,4] and the experience of practitioners [6], sprint perfor- mance is resistant to training enhancement. Athletes can spend years training to improve a few hundredths of a second over short distances [5]. Numerous intervention studies have been PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 1 / 13 a11111 OPEN ACCESS Citation: Haugen T, Tønnessen E, Øksenholt Ø, Haugen FL, Paulsen G, Enoksen E, et al. (2015) Sprint Conditioning of Junior Soccer Players: Effects of Training Intensity and Technique Supervision. PLoS ONE 10(3): e0121827. doi:10.1371/journal. pone.0121827 Academic Editor: Oyvind Sandbakk, Norwegian University of Science and Technology, NORWAY Received: November 10, 2014 Accepted: February 4, 2015 Published: March 23, 2015 Copyright: © 2015 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: All relevant data are within the paper. Funding: The authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist. performed over the years in order to enhance soccer-specific sprinting. A recent review reveals that sprinting under assisted, resisted and normal conditions, maximal and explosive strength training, plyometric training and high-intensity running have been investigated in different combinations, but no specific training method has so far emerged as superior [1]. Time effi- ciency is an important constraining aspect of team-sport conditioning and extensive off-field interventions will most likely be rejected by team coaches, independent of intervention efficacy [1]. The term ‘direct supervision’ refers to training situations in which a supervisor or training expert is present at all times [7,8]. The supervisor oversees training activities as they occur and provides direction, instruction, feedback and assistance. The importance of guidance and feed- back during practice is well known in motor skill learning and performance enhancements may happen immediately in such settings [9]. Mazzetti et al. [7] and Coutts et al. [8] concluded that the presence of a training expert was beneficial for maximal strength development over time. To the authors’ knowledge, the effect of supervised sprint-training sessions in soccer players has not been investigated. According to motor skill learning theories, errors increase with the speed of the movement [9]. Technical training of typically rapid or ballistic move- ments should be interfered with by using specific drills, large amount of repetitions and an in- tensity where the athletes are able to control the movements (proper execution not interfered by fatigue). If the movement is slowed down slightly, the same generalized motor program can be used as in the normal-speed version [9]. In contrast, the vast majority of studies involving sprint-training interventions for soccer players make no recommendations other than that sprint velocity should be maximal throughout [1]. Available evidence in endurance and strength training demonstrates that high, but sub-maximal intensity loading effectively stimu- lates adaptation through the interaction between high intensity and larger accumulated work that can be achieved before the onset of fatigue, compared to maximal efforts [10,11]. This makes it tempting to speculate similar effects on sprinting. Anecdotal evidence in support of this is observed in the sprint-training philosophy developed by the athletic sprint pioneer coach Carlo Vittori in the mid-1970s [12]. His successful athletes performed repeated-sprint training sessions with an intensity as low as 90% of maximal sprint speed during initial pre-sea- son conditioning in order to improve sprint endurance (later termed repeated-sprint perfor- mance). Inspection of training diaries reveals that internationally-competing sprinters perform sprint training with varying intensity through all parts of the season (unpublished material, Norwegian Olympic Federation). However, the lowest effective sprinting intensity for stimulat- ing adaptation is so far not established in the research literature. Recently, Haugen et al. [13] observed that repeated 20-m sprints at 90% intensity did not enhance sprint performance dur- ing a soccer season. It was suggested that such training should be performed at other times of the season to avoid training-related constraints due to the high volume of overall soccer condi- tioning. The aims of the present study were therefore to compare the effects of 1) training at 90 and 100% sprint velocity and 2) supervised versus unsupervised sprint training at 90% sprint velocity on soccer-specific physical performance capacities in junior soccer players’ early in pre-season. Materials and Methods Ethics statement This study was conducted in accordance with the declaration of Helsinki. All participants pro- vided written, voluntary informed consent before participation. Written parental consent was also provided for participants < 18 yr old. The human subjects review committee of the Faculty for Health and Sport, University of Agder, approved the study. Repeated-Sprint Training in Soccer Players PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 2 / 13 Experimental approach to the problem In this randomized controlled trial, participants were randomly assigned to four different treat- ment conditions. A control group (CON) completed regular soccer training according to their teams’ original early pre-season training plans. Three training groups performed a weekly re- peated-sprint training session in addition to their regular soccer training sessions, which was performed at A) 100% intensity without supervision (100UNSUP), B) 90% of maximal sprint speed with supervision (90SUP) or C) 90% of maximal sprint speed without supervision (90UNSUP). Based on sample size limitations and motor learning principles identified in the introduction, the present study was not performed with a factorial design (i.e. an additional “100SUP” group). The duration of the intervention period was 7 weeks. To evaluate the treat- ment conditions (independent variables), the following soccer-specific performance tests (pri- mary dependent variables) were assessed prior to and after the intervention period: 15x20 m repeated-sprint, countermovement jump (CMJ) and Yo-Yo Intermittent Recovery 1 (Yo-Yo IR1). To investigate possible mechanistic influences regarding adaptations to sprint training, the following secondary dependent variables were assessed during the 15x20 m repeated-sprint pre- and post-training tests: Heart rate, blood lactate concentration, step length and step rate. Finally, sprint times for all training sessions were assessed for intensity control (90SUP and 90UNSUP) and to examine weekly changes in repeated-sprint performance (100UNSUP). Participants Fifty-two male junior soccer players, aged 16–19 years, volunteered to participate. The athletes were playing in the highest junior division level for four different clubs (n = 6,13,16 and 17) in Norway. Each participant had a minimum two years of soccer-specific conditioning experi- ence. During the intervention period, the participants were requested to refrain from perform- ing any other off-field physical training regimes in terms of speed, strength and/or endurance. All participants were free of injuries prior to preliminary testing. None of the athletes had pre- vious experience with specialized repeated-sprint training. To eliminate the influence of varying overall soccer conditioning, the participants were ini- tially distributed by club and then allocated to one of the four intervention conditions by a co- author not directly involved in testing or the training intervention. The 14 participants ran- domly assigned to each of the three training groups were required to complete at least six out of seven training sessions during the intervention period in addition to all performance tests in order to be included in further analyses. The 10 allocated CON participants were required to perform at least 80% of planned sessions and complete all pre- and post-training tests. We chose a slightly uneven distribution of subjects based on 1) the expectation of increased drop- out risk generally observed in any intervention and 2) the expectation of lower variability of outcome in CON exposed to testing only and an unchanged training routine. One participant each from CON, 100UNSUP and 90SUP dropped out due to illness during training or testing. Two participants from 90SUP and one from 90UNSUP dropped out due to injuries sustained outside the sprint-training intervention. A final player from 90SUP group dropped out due to Achilles tendon strain, possibly associated with the sprint intervention. Thus, 45 of 52 participants completed the study with the following sample sizes (club distribu- tion in brackets): CON = 9 (0,3,3,3), 100UNSUP = 13 (0,4,5,4), 90UNSUP = 13 (1,3,5,4) and 90SUP = 10 (2,3,3,2). Physical and training characteristics of these participants are presented in Table 1. Regular soccer training sessions typically commenced with warm-up activities like short-passing and coordination exercises with the ball, followed by more intensive Repeated-Sprint Training in Soccer Players PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 3 / 13 change-of-direction exercises with and without ball. The main part of the soccer practice con- sisted of small-sided and more full-sized team compositions, ranging from 3 vs. 3 to 7 vs. 11. Testing procedures The pre- and post-training tests were conducted at the Norwegian Olympic Training Centre on two separate days, with two days in between. All participants completed the tests in the same order and at the same time of day. Regarding nutrition, hydration, sleep and physical ac- tivity, the athletes were instructed to prepare as they would for a regular soccer match, includ- ing no high-intensity training the last two days before testing. They were also instructed to use identical footwear and kit for each of the tests. Test day one consisted of CMJ and 15x20 m re- peated- sprint testing. On test day two, the athletes completed the Yo-Yo IR1 test. Prior to test- ing on test-day 1, participants completed a 25 min standardized treadmill warm-up consisting of a 10-min general warm-up at 60–75% of age-predicted maximum heart rate, 3 sets of 4 exer- cise drills (high knees, back kick, sideway and backwards running) and finally 2–3 repetitions of 40-m runs with a progressive increase in speed. Prior to testing on test-day 2, participants warmed up with a 10-min easy jog at 60–75% of age-predicted heart rate followed by the initial 60–90 s of the Yo-Yo IR1 test. CMJ test. Immediately after warm up, each athlete was weighed on a force platform for system calibration before performing three trials of CMJ (vertical jump) separated by 1 min re- covery. The best result for each player was retained for analysis. To isolate leg extensor muscles and minimize technical elements, all jumps were performed with hands placed on the hips. The tests were performed on an AMTI force platform (OR6–5–1, Watertown, USA). Calcula- tion of jump height is formerly described in Haugen et al. [3]. Sprint test. A 15x20 m repeated-sprint test with starts each 60 s was performed directly after the CMJ test. Distance and recovery were chosen in line with mean frequency and typical distance of all-out sprints reported from match analyses [14]. Procedures and equipment are formerly described in Haugen et al. [13]. Best 20-m time was used in order to determine maxi- mal single-sprint capacity, while mean time for the 15 sprints was used to determine repeated- sprint performance. Heart rate was measured continuously during the test (Polar RS400, Kem- pele, Finland). A blood sample was acquired via finger stick to quantify the blood lactate concentration (BLa-) immediately after the last sprint (LactatePro LT-1710, Arkay KDK, Kyoto, Japan). All sprint tests were captured by a video camera (Sony HDR-HC9E)) mounted on a tripod in line with the finish line and 3 m from the sprinter’s running lane. Video recordings were analysed in ProSuite, version 5.5 (Dartfish, Switzerland) to determine step count and derive av- erage step length (SL). For precision, the digital ruler in the analyser window was used to Table 1. Physical and training characteristics at inclusion. Group n Age (yr) BM (kg) Height (cm) Weekly training sessions Games per week (n) Tot. vol. (hwk-1) CON 9 17 ±1 72 ±11 181 ±6 4.4 ±2.3 0.4 ±0.4 6.8 ±3.3 100UNSUP 13 17 ±1 66 ±9* 178 ±6 4.4 ±2.3 0.3 ±0.7 6.6 ±3.8 90UNSUP 13 17 ±1 72 ±6 183 ±5 4.5 ±2.4 0.4 ±1.0 7.0 ±3.5 90SUP 10 17 ±1 72 ±8 178 ±7 4.4 ±1.6 0.4 ±0.9 6.8 ±2.9 Values are mean ± SD. BM = Body mass, Tot. vol. = Total training volume. Training values are based on self-reported weekly averages during the intervention period. There were no signiﬁcant differences among the groups for any of the variables, except for body mass (100UNSUP < 90UNSUP, p = 0.04). doi:10.1371/journal.pone.0121827.t001 Repeated-Sprint Training in Soccer Players PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 4 / 13 interpolate the last step across the finish line. For example; if the 13th and 14th ground contact occurred 0.8 m in front of and 1.2 m beyond the finish line, respectively, the recorded number of steps was registered as 13.4. Mean SL was calculated by dividing the number of steps by the distance (in this case: 20 m13.4–1 = 1.49 m). Mean step rate (SR) was calculated from mean ve- locity and mean SL. Prior to the present study, this measurement method was validated by roll- ing out thin paper at the finish line area in order to measure the distance between the visible spike shoe marks from competitive sprinters. The absolute difference across twenty sprint comparisons never exceeded 0.1 steps. Thus, the maximal margin of error for step counts over 20 m is 0.7–0.8% for athletes using 13–15 steps. Yo-Yo IR1 test. The Yo-Yo IR1 test was performed indoors on artificial turf. Two test leaders supervised the tests. The athletes were divided in small groups that completed the test consecutively, such that each supervisor was responsible for 5 athletes during the test. Set-up and procedures were in line with the guidelines by Krustrup et al. [15], who have reported a test-retest CV < 5%. The test score is reported in total distance covered until exhaustion. Intervention program The training intervention took place from the end of October to mid December, corresponding to early pre-season in the Norwegian soccer annual cycle. The sprint-training sessions were performed at the same time and day for each training group throughout the intervention peri- od and no regular soccer training sessions were performed on the same day as the sprint train- ing took place. Athletes in 100UNSUP performed 15x20 m maximal sprints with starts each 60 s once a week. Groups 90SUP and 90UNSUP performed one weekly training session con- sisting of a larger dose of 30x20 m sprints at 90% of maximal sprint velocity (based on the best 20-m sprint time obtained during the pre-training test) with starts each 60 s. Two sprint-training experts, with extensive national-level coaching experience, supervised the 90SUP group during the intervention. Three key sprint-technical elements and correspond- ing verbal instructions were emphasized during the training sessions: • Optimal upper-body angle relative to the ground during the initial steps in order to create higher horizontal propulsive forces through more effective utilization of hip and knee exten- sors [16,17]. The athletes were instructed to assume a start position with forward lean in the upper body and a lowered centre of gravity and to gradually become more upright through- out the acceleration. • Minimize horizontal braking forces [16]: Athletes with apparently too high braking forces were encouraged to assume a more favourable configuration at the point of ground contact with the foot plant closer to the perpendicular line from the centre of mass. This can be achieved by hitting the ground with a bent knee (relevant during acceleration) or with the centre of mass at a large vertical distance above the ground (relevant during maximal sprinting). • Produce a stiff rebound during ground contact in order to minimize degeneration of hori- zontal propulsive forces [18–20]: Identified “heal runners” were encouraged to pre- activate dorsiflexion muscles prior to foot plant and stiffen the ankle joint during ground contact, al- lowing them to utilize the elasticity in the plantar flexors for greater force development. These instructions were emphasized during the warm up drills. After video analysis of the first training session, the two sprint-training experts prepared an individual capacity profile for all participants in the 90SUP group. Each athlete was presented with one technical task at a time, in accordance with general feedback principles [9]. Players Repeated-Sprint Training in Soccer Players PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 5 / 13 with obvious technical limitations were provided with more verbal instructions than technical- ly well-performing athletes. In the absence of previously published studies, a 1:2 repetition ratio between 100% and 90% sprinting was chosen. Several measurements were assessed in order to compare the two repeat- ed-sprint training sessions used. Firstly, session rated perceived exertion (RPE) was recorded for all athletes after the repeated sprints performed in pre-training testing and the first training session. Written and verbal instructions regarding its use were provided in advance [21]. More- over, heart rate was measured continuously during the first training session for all athletes who ran at 90% sprint intensity, in addition to BLa- immediately after their last sprint. These were compared to corresponding data assessed during pre-training tests. Mean SL and SR for the first sprint-training session were calculated by identical procedures as for the pre- and post- training tests. Finally, all training group athletes performed 3x20 m maximal sprints with starts each 60 s 48 hours after the first training session for a performance recovery check. The mean time for these three sprints was compared with corresponding sprints from the pre- training test. Electronic timing was continuously used to control running speed and adjust intensity ac- cording to each player’s “target time”. Target time for the 90SUP and 90UNSUP participants were derived from the best single-sprint time achieved during preliminary testing by multiply- ing mean velocity over the 20-m distance by 0.9. No feedback other than sprint time informa- tion (for intensity control purposes) was provided by a timekeeper for the 90UNSUP and 100UNSUP groups after each run. Fig. 1 shows intensity distribution for the two 90% groups (90SUP and 90UNSUP pooled together) during all training sessions. More than 90% of all sprints were completed with intensities between 87 and 93% of maximal sprint velocity. All sprints for 100UNSUP during the training sessions were completed with an intensity > 97% (mean ± SD: 98.2 ± 0.8%) when related to the best single-sprint within each training session. Thus, treatment conditions in 90SUP and 90UNSUP were strictly separated and did not over- lap with 100UNSUP. For simplicity, we continue to use the terms “100UNSUP” or “maximal intensity”. Statistical analysis All statistical analyses were carried out using SPSS 17.0 for Windows (SPSS Inc., Chicago, IL, USA). Level of significance was set to p<0.05. The General Linear Model with Repeated Mea- sures followed by Bonferroni adjustment for multiple comparisons was used to examine re- peated-sprint performance development (mean sprint time) for 100UNSUP across tests and training sessions. The same model was used for 90SUP and 90UNSUP (both groups pooled together) to compare effort-related variables in maximal and sub-maximal sprinting. Analy- sis of covariance (ANCOVA) adjusting for the pre-training test value and randomization stratification factor (club) was used to examine within-group and between-group mean changes. The differences were judged by using estimated marginal means (EMM). Bonferroni corrections were used to adjust p-values for multiple testing. Effect magnitudes were calculated and interpreted categorically according to the guidelines by Hopkins et al. [22]. The first 6 sprints from the pre-training test (for all included participants) were used to calculate typical variation for sprint time, SL and SR. Effect size of the within-group changes for mean sprint time were based on mean change and typical variation. The results are ex- pressed as mean ±SD and 95% confidence intervals (95% CIs) were calculated for all measures. Repeated-Sprint Training in Soccer Players PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 6 / 13 Results Table 2 shows effort-related variables for the two repeated-sprint training sessions used in the present intervention. No differences in RPE were observed between the sessions. Mean sprint time for the 3x20-m sprints performed 48 hours after the first training session was not signifi- cantly different when compared to the corresponding pre-training sprint test. Sprinting at 90% velocity was accompanied with reduced HR peak (17%; very large effect; p<0.001), BLa- (55%; large effect; p<0.001) and SR (11%; very large effect; p<0.001) compared to maximal sprinting. Fig 1. Intensity distribution for the sprint training groups during all training sessions. Best sprint from pre-training testing was set as reference (100%) for 90SUP and 90UNSUP, while best sprint within each training session was set as reference (100%) for 100UNSUP. doi:10.1371/journal.pone.0121827.g001 Table 2. Effort-related variables in maximal (100%) and sub-maximal (90%) sprinting. Sprint session 15x20m (100% intensity) 30x20m (90% intensity) Δ sprint time 48 h (s) 0.00 ±0.02 0.01 ±0.02 Session RPE 3.8 ±1.2 4.0 ± 1.1 HR peak (beats min-1) 170 ±10 141 ±10 BLa- (mmolL-1) 4.4 ±1.8 2.0 ±0.7* SL (m) 1.55 ±0.08 1.56 ±0.09 SR (stepss-1) 4.36 ±0.18 3.87 ±0.22* Δ sprint time 48 h = sprint time 48 hours after the ﬁrst training session minus corresponding pre-training sprint test time (mean of ﬁrst 3 sprints for each time point), RPE = rated perceived exertion, HR peak = peak heart rate, BLa- = blood lactate concentration, SL = step length, SR = step rate * = signiﬁcantly different from 100% sprinting (p<0.001). doi:10.1371/journal.pone.0121827.t002 Repeated-Sprint Training in Soccer Players PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 7 / 13 No significant within-group differences for the analyzed performance parameters were ob- served, except that 90SUP improved Yo-Yo IR1 performance from pre- to post-training (258 m; 17,3%; p<0.01). No significant between-group differences were observed (p<0.05). 90SUP improved Yo-Yo IR1 performance by a moderate margin compared to all other groups, while all other between-group differences were small or trivial (Table 3 and Fig. 2). Achievement of best sprint performance was randomly distributed across the 15 sprints in all groups during both pre- and post-training tests. Typical variation for sprint time, SL and SR was 0.025 s (CV 1.0%), 0.028 m (CV 1.8%) and 0.08 stridess-1 (CV 1.9%), respectively. In CON, a variation in mean sprint time of ±0.04 s was observed between the pre- and post-train- ing tests. Corresponding variation for SL and SF was 0.06 m and 0.19 stridess-1, respectively. In 100UNSUP, significant differences from pre- to post-training tests were observed for BLa- (1.5 mmolL-1; 35.7%; p<0.001), SL (-0.04 m; 2.6%; p = 0.020) and SF (0.13 stepss-1; 3.0%; p = 0.019). BLa- increased significantly in 100UNSUP compared to CON from pre- to post-training (p = 0.008) (Table 4). No other within- or between-group differences were Table 3. Between-group changes (mean and 95% CIs) versus controls in physical performance from pre- to post-training. Intervention group Best sprint time (s) Mean sprint time (s) CMJ (cm) Yo-Yo IR1 (m) 100UNSUP -0.03 (-0.07 to 0.00) -0.03 (-0.06 to 0.01) 1.0 (-0.6 to 2.6) -34 (-272 to 205) 90UNSUP -0.03 (-0.07 to 0.01) -0.02 (-0.06 to 0.02) 0.4 (-1.3 to 2.1) -1 (-120 to 117) 90SUP -0.02 (-0.06 to 0.02) -0.03 (-0.07 to 0.01) 1.8 (0.0 to 3.6) 131 (-108 to 369) The differences vs. control group are assessed by estimated marginal mean. Minus (-) indicates lower values post-training compared with the control group (assessed by estimated marginal means). CMJ = countermovement jump, Yo-Yo IR1 = Yo-Yo intermittent recovery level 1. No signiﬁcant between- group differences were observed. doi:10.1371/journal.pone.0121827.t003 Fig 2. Individual changes in 15x20 m mean sprint time from pre- to post-training tests. doi:10.1371/journal.pone.0121827.g002 Repeated-Sprint Training in Soccer Players PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 8 / 13 observed. The change in BLa- within 100UNSUP was moderate while the other effect magni- tudes between- or within-groups were trivial or small. Fig. 3 shows the development of repeated-sprint performance (mean sprint time) for 100UNSUP during the intervention period, including pre- and post-training tests. Weekly changes in group mean values up to 0.05 s were observed. Discussion In the present study, weekly repeated-sprint training sessions performed at maximal or with 90% intensity were not sufficient to improve soccer-specific physical performance in junior soccer players, when compared to a matched control group assumed to maintain a constant training pattern. Moreover, no differences in performance outcomes were observed between supervised and unsupervised sprint-training groups training at 90% maximal sprinting veloci- ty. Apparently, the relative work loads elicited by the current intervention strategies were not sufficient to create appropriate adaptations during the early pre-season soccer period. To the authors’ knowledge, this is the first study to compare the effects of sprint training at 90 vs. 100% sprint intensity or supervised vs. unsupervised sprint training. Our findings con- firm the assumption that sprint performance is resistant to training enhancement, even among junior soccer players during the early pre-season period where total training load is reduced. Since treatment allocation was adjusted for club participation, the current results were not influenced by varying overall soccer conditioning across groups (Table 1). Age distribution was consistent across groups (Table 1) and body mass did not change significantly in any of the groups (Table 4). The moderate group sample sizes may mask possible significant outcomes. However, based on the trivial to moderate effect magnitudes, our findings do not support a rec- ommendation to perform the present training regimes under otherwise identical conditions. Fig 3. 95% confidence intervals of mean sprint time for 100UNSUP during the intervention. doi:10.1371/journal.pone.0121827.g003 Repeated-Sprint Training in Soccer Players PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 9 / 13 Despite the absence of significant differences in the experimental training interventions, the present study may outline directions for future related studies. Effort matched sprint training The two training sessions used were equally rated in terms of session RPE (Table 2). Further- more, recovery status after two days was not different for the maximal and sub-maximal train- ing groups. Based on these observations, we find it reasonable to conclude that the two repeated-sprint training sessions were effort matched. Blood lactate values obtained after re- peated sprints at 90% intensity were below what has been considered “lactate threshold intensi- ty” (2.5–4.0 mmolL-1) in endurance training [23]. In contrast, repeated sprinting at maximal intensity was accompanied with BLa- at or above the typical lactate threshold range described for endurance athletes. Even though BLa- values obtained from sprint and endurance training are not directly comparable, the present data suggest metabolic pathway partitioning differ- ences between 90% and maximal sprinting. Small but meaningful differences in muscle recruit- ment cannot be excluded as contributory to the observed increase in lactate accumulation at maximal sprinting. Effects of training at maximal and sub-maximal intensity The present results revealed only trivial and non-significant changes in soccer-related sprinting from pre- to post-training for 100UNSUP (Table 3, Fig. 2). Previously, Tønnessen et al. [24] observed unaltered sprint velocity over 0–20 m sprint and improved velocity over 20–40 m as a result of weekly repeated 40-m sprints at maximal or near maximal intensity. This suggests that players are more disposed to adaptations over somewhat longer but less soccer-specific sprint distances. Soccer players perform a high number of brief accelerations during training and games [14]. Thus, one could argue that most players have likely maximized their 0–20 m sprint (acceleration) potential during regular soccer conditioning. While sprint performance remained unchanged in 100UNSUP (Table 3), SL and SR changed significantly from pre- to post-training (Table 4). These changes were greater than the observed typical variation. Our findings are somewhat in contrast to previously published studies stating that individual achievement of sprint velocity corresponds to an optimal self-selected step length/step rate ratio [25] and that a different ratio will produce a lower velocity, so-called negative interaction [26]. In the present study, maximal sprint training induced a significant shift in the step length/frequency relationship “selected” by the athletes, with step frequency increasing 3% from 4.33 to 4.46 steps.s-1 and step length declining correspondingly. These changes cannot be ascribed to supervision as this group did not receive sprint technique feedback or instruction. Moreover, 100UNSUP demonstrated an increase in BLa- after 15x20 m maximal sprinting in the post-training test, suggesting possible changes in anaerobic energy release, buffering Table 4. Between group changes (mean and 95% CIs) versus controls for underlying performance variables between pre- and post-training. Intervention group Body mass (kg) HRpeak (beatsmin-1) BLa- (mmolL-1) SL (m) SR (stepss-1) 100UNSUP 0.3 (-0.8 to 1.5) 5 (-1 to 12) 1.9 (0.7 to 3.2)* 0.00 (-0.07 to 0.06) 0.06 (-0.13 to 0.25) 90UNSUP -0.3 (-1.4 to 0.8) 2 (-5 to 8) 1.1 (-0.1 to 2.3) 0.04 (-0.02 to 0.10) -0.09 (-0.28 to 0.10) 90SUP -0.3 (-1.5 to 0.9) 4 (-3 to 11) 1.5 (0.2 to 2.9) 0.03 (-0.03 to 0.10) -0.04 (-0.24 to 0.17) The differences vs. control group are assessed by estimated marginal mean. Minus (-) indicates lower values post-training compared with the control group (assessed by estimated marginal means). HR = heart rate, BLa- = blood lactate concentration, SL = step length, SR = step rate * = signiﬁcantly different (Bonferroni adjusted) from CON (p = 0.01). doi:10.1371/journal.pone.0121827.t004 Repeated-Sprint Training in Soccer Players PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 10 / 13 characteristics or muscle recruitment pattern. However, these possible physiological changes were not accompanied with enhanced performance. Based on both current and previous findings [13], it cannot be concluded that weekly train- ing at 90% velocity is a sufficient sprinting intensity for stimulating adaptation over short sprint distances. Blood lactate and peak heart rate values observed in the present junior soccer players indicate relatively low metabolic stress (Table 2). It is possible that sub-maximal sprint training is more appropriate for typical competitive athletics sprinting distances (100–200 m) compared to 0–20 m accelerations. 20-m sprints are comprised of high to maximal acceleration from a resting state and continuing through the timed distance. In this condition, energy de- mands during the acceleration phase greatly exceed those at peak velocity [17]. The change in kinetic energy (½mv2) is proportional to the square of the change in velocity, such that the 90% sprint condition is associated with a nearly 20% reduction in kinetic energy change (and presumably, muscular energetic demand) compared to maximal sprinting velocity. Due to this non-linearity, a 5% reduction in short sprint velocity during repeated-sprint training over short distances would correspond to 90% workloads in strength training and endurance train- ing and might give a more optimal balance of stress, injury risk reduction and adaptive signal retention. This possibility remains to be explored. Effects of supervised training The present study revealed no significant training effects when supervised and unsupervised sprint training at 90% sprint velocity were compared (Table 3 and Fig. 2). However, the 90SUP group improved Yo-Yo IR1 performance by a moderate margin compared to the other groups. Since Haugen et al. [13] reported unchanged VO2 max after seven weeks of repeated-sprint training at 90% intensity, it is reasonable to assume that locomotion efficiency during high-in- tensity running has improved in 90SUP. The lack of effects on maximal and repeated-sprint performance may have been affected by the possibility that sprint training at 90% sprint speed is below the lowest effective sprinting intensity for stimulating adaptation. Future studies should therefore explore the effect of supervised training with a gradual increase in intensity from sub-maximal to maximal sprint velocity. Another argument for such a gradual increase in velocity also becomes relevant if one assumes that the athletes gradually enhance sprint per- formance over the training period. We chose not to control and adjust for possible sprint per- formance enhancement in 90SUP and 90UNSUP throughout the present intervention period to avoid a mix of different treatment conditions. Theoretically, the lack of effects with supervised sprint training may be due to poor coaching quality such that the athletes were not able to translate the instructions into practice. However, both training experts used in the present study had many years of experience coaching athletics performers on both national and international levels. Mazzetti et al. [7] and Coutts et al. [8] showed that the presence of a training expert was beneficial for maximal strength and power development over time. In contrast to the present study, the training experts in those studies were allowed to adjust the total training load during the interventions. Based on these observa- tions, one could argue that the effect of expert supervision during training is optimized when combined with greater flexibility in the day-to-day training prescription. Training-related constraints Common challenges in applied studies of this nature are related to constraints with overall team conditioning [1,13,27]. Current analyses confirm these constraints, even though the study was conducted early pre-season where total training load is typically reduced. In CON, we observed ±0.04 s absolute individual variation in mean sprint time between pre- and post- Repeated-Sprint Training in Soccer Players PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 11 / 13 training tests. More importantly, weekly changes in group mean values up to 0.05 s (nearly 2%) were observed in 100UNSUP (Fig. 3), despite consistent frequency and volume of games and training sessions during the intervention period (Table 1). This weekly or seasonal variation is considerably higher than the observed typical variability. The present findings emphasize the need for more detailed information about overall conditioning load, accepting that soccer-spe- cific movements (i.e. brief accelerations, high sprinting velocities or changes of directions) are impossible to assess accurately in groups of players with current technology [28,29]. In princi- ple, the present study could have been accomplished in a more controlled experimental envi- ronment, omitting the concurrent soccer training. However, such an approach severely limits the external validity. If improvement of sprinting performance is the primary goal for certain players, future studies should explore the effects of more frequent sprint-training sessions and longer intervention periods, perhaps in combination with other training forms. Future studies should also explore whether it is more effective to structure the players’ weekly soccer training rather than introducing an additional physical conditioning regime. A theoretically perfect conditioning program for certain capabilities may limit other important qualities and vice versa. Coaches and conditioning experts have to balance their training methods and exercises in order to optimize different skills in relation to their contribution to overall soccer perfor- mance. Based on the varying individual responses to each of the performed treatments (Fig. 2) and the absence of evidence supporting the choice of specific training methods at the group level, it is essential to diagnose each individual and develop training interventions that target their key physiological and technical weaknesses. Author Contributions Conceived and designed the experiments: TH ET ØØ FLH EE SS. Performed the experiments: TH ET ØØ FLH EE GP. Analyzed the data: TH ET ØØ FLH GP SS. Contributed reagents/ma- terials/analysis tools: TH ET GP EE SS. Wrote the paper: TH ET ØØ FLH GP EE SS. References 1. Haugen T, Tønnessen E, Hisdal J, Seiler S. 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Harland MJ, Steele JR. Biomechanics of the sprint start. Sports Med. 1997; 23: 11–20. PMID: 9017856 17. di Prampero PE, Fusi S, Sepulcri L, Morin JB, Belli A, Antonutto G. Sprint running: a new energetic ap- proach. J Exp Biol. 2005; 208: 2809–2816. PMID: 16000549 18. Chelly SM, Denis C. Leg power and hopping stiffness: relationship with sprint running performance. Med Sci Sports Exerc. 2001; 33: 326–333. PMID: 11224825 19. Kuitunen S, Komi PV, Kyröläinen H. Knee and ankle joint stiffness in sprint running. Med Sci Sports Exerc. 2002; 34: 166–173. PMID: 11782663 20. Girard O, Micallef JP, Millet GP. Changes in spring-mass model characteristics during repeated running sprints. Eur J Appl Physiol. 2011; 111: 125–134. doi: 10.1007/s00421-010-1638-9 PMID: 20824280 21. Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med Sci Sports Exerc. 1998; 30: 1164–1168. PMID: 9662690 22. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medi- cine and exercise science. Med Sci Sports Exerc. 2009; 41: 3–13. doi: 10.1249/MSS. 0b013e31818cb278 PMID: 19092709 23. Tokmakidis SP, Léger LA, Pilianidis TC. Failure to obtain a unique threshold on the blood lactate con- centration curve during exercise. Eur J Appl Physiol Occup Physiol. 1998; 77: 333–342. PMID: 9562362 24. Tønnessen E, Shalfawi S, Haugen T, Enoksen E. The effect of 40-m repeated sprint training on maxi- mum sprinting speed, repeated sprint endurance, vertical jump and aerobic capacity in young elite male soccer players. J Strength Cond Res. 2011; 25: 2364–2370. doi: 10.1519/JSC. 0b013e3182023a65 PMID: 21869624 25. Salo AI, Bezodis IN, Batterham AM, Kerwin DG. Elite sprinting: are athletes individually step-frequency or step-length reliant? Med Sci Sports Exerc. 2011; 43: 1055–1062. doi: 10.1249/MSS. 0b013e318201f6f8 PMID: 20980924 26. Hunter JP, Marshall RN, McNair PJ. Interaction of step length and step rate during sprint running. Med Sci Sports Exerc. 2004; 36: 261–271. PMID: 14767249 27. Soares-Caldeira LF, de Souza EA, de Freitas VH, de Moraes SM, Leicht AS, Nakamura FY. Effects of additional repeated sprint training during preseason on performance, heart rate variability, and stress symptoms in futsal players: a randomized controlled trial. J Strength Cond Res. 2014; 28: 2815–2826. doi: 10.1519/JSC.0000000000000461 PMID: 24662230 28. Castellano J, Alvarez-Pastor D, Bradley PS. Evaluation of research using computerised tracking sys- tems (Amisco and Prozone) to analyse physical performance in elite soccer: a systematic review. Sports Med. 2014; 44: 701–712. doi: 10.1007/s40279-014-0144-3 PMID: 24510701 29. Cummins C, Orr R, O'Connor H, West C. Global positioning systems (GPS) and microtechnology sen- sors in team sports: a systematic review. Sports Med. 2013; 43: 1025–1042. doi: 10.1007/s40279-013- 0069-2 PMID: 23812857 Repeated-Sprint Training in Soccer Players PLOS ONE \| DOI:10.1371/journal.pone.0121827 March 23, 2015 13 / 13	Sprint conditioning of junior soccer players: effects of training intensity and technique supervision.	03-23-2015	Haugen, Thomas,Tønnessen, Espen,Øksenholt, Øyvind,Haugen, Fredrik Lie,Paulsen, Gøran,Enoksen, Eystein,Seiler, Stephen	eng
PMC7538888	ARTICLE Human running performance from real-world big data Thorsten Emig 1✉ & Jussi Peltonen 2 Wearable exercise trackers provide data that encode information on individual running performance. These data hold great potential for enhancing our understanding of the complex interplay between training and performance. Here we demonstrate feasibility of this idea by applying a previously validated mathematical model to real-world running activities of ≈ 14,000 individuals with ≈ 1.6 million exercise sessions containing duration and distance, with a total distance of ≈ 20 million km. Our model depends on two performance parameters: an aerobic power index and an endurance index. Inclusion of endurance, which describes the decline in sustainable power over duration, offers novel insights into performance: a highly accurate race time prediction and the identiﬁcation of key parameters such as the lactate threshold, commonly used in exercise physiology. Correlations between performance indices and training volume and intensity are quantiﬁed, pointing to an optimal training. Our ﬁndings hint at new ways to quantify and predict athletic performance under real-world conditions. https://doi.org/10.1038/s41467-020-18737-6 OPEN 1 Université Paris-Saclay, CNRS, Laboratoire de Physique Théorique et Modèles Statistiques, 91405 Orsay, France. 2 Polar Electro Oy, Professorintie 5, 90440 Kempele, Finland. ✉email: thorsten.emig@u-psud.fr NATURE COMMUNICATIONS \| (2020) 11:4936 \| https://doi.org/10.1038/s41467-020-18737-6 \| www.nature.com/naturecommunications 1 1234567890():,; S keletal evidence suggests that endurance running may have evolved 2 million years ago1. It probably originated as a hunting skill but has later developed to competition, dating back to ancient Olympic Games ~720 BC2 and exercise form for mass population. Over the years, endurance running has under- gone substantially change. Recent decades have witnessed an ever growing exercising population which uses wearable sensors to bring together astonishing volumes of data for speed, distance, heart rate, accelerations, and more3–5. For example, endurance athletes like runners and cyclists currently upload from GPS enabled sensors more than a billion activities per year world- wide6. In principle, these data provide an exciting opportunity to monitor human physiology noninvasively under real-world conditions outside the laboratory. Measuring the physiological response to physical activity can provide important insights for a variety of populations ranging from elite athletes to recreational exercisers to patients in rehabilitation7,8. However, the analysis of big data sets of large, heterogeneous groups of individuals poses a substantial challenge due to the quality of the data itself9,10, lack of effective theoretical models11, and inﬂuence of environmental factors like weather conditions12,13. The important, robust properties of an individual’s physiology can be overshadowed by details speciﬁc to the conditions of recording. Thus, there is a demand for universal theoretical models that have been validated for noise-free exercise data and can be applied under noisy real- world conditions to derive meaningful physiological and perfor- mance information14. To date, exercise physiologists conventionally use laboratory testing to determine parameters that measure ﬁtness and per- formance potential15. A strength of laboratory testing is that it can distinguish between cardiovascular limit, maximal rate of oxygen consumption (VO2max), neuromuscular effects, and run- ning economy16,17. Together VO2max and running economy determine maximal aerobic speed, which is the slowest speed at which VO2max occurs. Maximal aerobic speed correlates with race speed on shorter distances but alone cannot predict race times for longer distances such as the marathon. Exercise thresholds have been used in exercise testing to quantify metabolism. However, the determination of such thresholds, like the lactate threshold, in the laboratory is somewhat limited. Typical laboratory testing is short-lasting and does not always fully capture time and distance dependent reduction in running economy18,19. For example, only sparse results exist for the endurance limited fractional utilization of maximal aerobic power (MAP) and its dependence on exercise duration20. Moreover laboratory testing is expensive and not available to most of the population. The undeniable fact that the best test of running performance is an actual race and not laboratory tests highlights the need for models speciﬁcally con- structed to extract performance indices of an athlete from their regular exercise performance. For these reasons, models that can utilize data from wearable devices and turn those into meaningful performance parameters may offer a cost effective alternative approach to laboratory testing. However, it must stressed that this type of approach does not elucidate the physiological and bio- mechanical mechanisms that control performance. It is an adjunct to the methods which are already used, providing addi- tional insight into running and the potential training factors inﬂuencing performance and it does not replace the insights that we can gain from laboratory testing. Several empirical and physiological models have been put forward for explaining running world records in terms of a few physiological parameters. The noted physiologist Hill empirically proposed a hyperbola to describe the maximal power output as a function of exercise duration21. Also a purely mechanical approach, based on the runners equation of motion, has been proposed22. These approaches predict that the average racing velocity tends to be a constant value with increasing race distance which contradicts observation. While more recent approaches have combined physiology and observations to propose more realistic logarithmic relations between maximal power output and duration23, these models depend on many parameters that vary among individuals24. Recently we have developed a universal running model which builds on concepts in exercise physiology, depends only a minimal set of key performance indices that are required to predict race performance, contains no additional individual-dependent quantities and has been validated with running world-records14. Here, we show that it is also possible to obtain novel insights into individual’s running performance by applying this model to big exercise datasets. Exercise data are a valuable source of information about individual long-term training protocols. Endurance training leads to a wide spectrum of physiological responses. However, in practice, training is prescribed often only by anecdotal evidence and personal experience. This might be due to a lack of knowl- edge of statistically signiﬁcant correlations between the relevant physiological parameters and training characteristics for large groups of individuals with different ﬁtness status. Here, we demonstrate the feasibility to extract key performance indices from real-world running exercise data recorded with wearable exercise trackers. We apply our method to runners during their training season before a marathon race. Our universal running model characterizes a runner’s performance with two indices that measure (1) endurance (endurance index) and (2) the velocity requiring MAP output (aerobic power index). The main aim of our work is to demonstrate the feasibility of extracting perfor- mance indices from real-world racing results in a big population of runners and to use these indices to predict accurate race times and evaluate the effect and efﬁciency of training. Our approach represents a potentially powerful platform to enlarge dramatically the number of tested subjects in sports science by extending performance index acquisition from conventional laboratory testing to real-world conditions with the aid of mathematical modeling and wearable technology. Results Universal performance model. In previous work we have developed a model that can be used to extract aerobic perfor- mance indices from race data14. To summarize, this model expresses exercise intensity on a relative power scale p, which varies between zero, corresponding to basal metabolic rate, and unity at MAP generation. MAP is expected to correspond to maximal oxygen uptake VO2,max but this analogy needs not to be assumed in our approach. A linear relation p(v) maps running velocity v to relative power with p(vm) = 1 deﬁning vm as an aerobic power index associated with MAP beyond which anaerobic energy supply can yield p > 1 for a short time only. Anaerobic supply contributes to maximal exercise shorter than a crossover time tc which in our model is the longest time over which MAP can be sustained. An important prediction of our model is that the maximal value of the relative power p that a runner can maintain declines logarithmically with duration, with a rate γl, assuming that the durations are longer than tc. This ﬁnding is in agreement with a ﬁnding of A.V. Hill who observed this form of decline in running world records21. For more details on our model, see the “Methods” section. Here, we use this universal, i.e., subject independent model for human running performance, to extract aerobic performance indices from ﬁn- ishing times of runners worldwide by matching them with model predictions14. The analyzed data set comes from an exercise tracking platform that contains precise records of distance and duration (and hence average velocity) of running activities ARTICLE NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-020-18737-6 2 NATURE COMMUNICATIONS \| (2020) 11:4936 \| https://doi.org/10.1038/s41467-020-18737-6 \| www.nature.com/naturecommunications of ≈19K individuals, who ran a total distance of 32M km over a period of 3.5 years. The data were recorded by the individuals with a GPS digital sports watch (V800, Polar Electro Oy, Oulu, Finland)25, and uploaded to the platform. Maximal performance of an individual was measured by the fastest ﬁnishing time for the four most common racing distances 5000 m, 10,000 m, half- marathon (21,097.5 m) and marathon (42,195 m) within a racing season, which is deﬁned as the 180 days preceding the marathon race (see “Methods” section for detection of racing activities). The velocity corresponding to our parameter vm is difﬁcult to measure in laboratory settings since VO2,max can be achieved over a wide range of sub-maximal intensities because of an upward drift of oxygen uptake with exercise duration18,19. In general, our model can determine vm from the crossover of the race–time–distance relation at time tc, and hence is free from this complications. The simplest version of the model assumes a ﬁxed time tc. Model predictions for sub-MAP performances do not depend on this ﬁxed time since other choices lead only to consistently renormalized values for vm and γl (which are then no longer associated strictly with MAP but with a slightly different power). In agreement with the application of our model to running records on both the super- and sub-MAP branches14 and laboratory testings26, we choose tc = 6 min in the following. Combining running economy and the decline of the fractional utilization of maximal power output with race duration, the fastest time T(d) over a distance d is given by the universal expression TðdÞ ¼ tc γl d dc 1 W1 d dc expð1=γlÞ γl h i for d ≥ dc ; ð1Þ where we deﬁned dc = vmtc, and W−1 is a real branch of the Lambert W-function which is deﬁned as the multi-valued inverse of the function w ! w expðwÞ27. W−1(z) is real valued for −1/e ≤ z < 0 which is fulﬁlled for all distances d that we consider (see the “Methods” section for more detail). Note that T(dc) = tc, i.e., dc is the distance that can be maximally raced in the time tc. The condition d ≥ dc is always satisﬁed for the race distances considered here. We note that Eq. (1) is an exact solution of our model. It can be also obtained from earlier descriptions of the energetics of endurance running28–30 when the fractional utilization of MAP is described by our prediction of a slow, logarithmic decay, and a linear increase of the energy cost of running with velocity is assumed. The model parameters, called performance indices, quantify different aspects of performance and provide a unique insight into basic determinants of ﬁtness in a large population of runners over a wide range of exercise capacities and over long time scales. The velocity vm measures combined running economy and MAP and is known to be a better predictor of performance than VO2,max alone31. We deﬁne the endurance index as El ¼ expð0:1=γlÞ, which encodes that 90% of vm can be maintained for an extended time Eltc > tc. The pair of performance indices vm, El is sufﬁcient to account for racing velocity variations for distances from dc (typically one mile in our data set) to the marathon. For example, when analyzing consistent running records of individuals, we found strong evidence that they follow the same universal scaling law of Eq. (1) as running world (or national) records do, with mean errors below 1%14. Here, our model estimates are based on an individual’s fastest times for the four ﬁxed racing distances, 5 k, 10 k, half-marathon, and marathon. Unfortunately, we cannot determine from the available data set if performance was achieved during an actual racing event. For our approach however, it is only required that the recorded performance corresponds to the maximal effort over a given running distance achieved during the racing season. Exercise data. An overview of the data analysis design is provided in Fig. 1. All available subjects and activities in the data set of the exercise tracking platform were grouped by SID and marathon date, combining all individual running activities during the 180 days before the marathon, deﬁning a season. For each season, activities with the fastest time for the four ﬁxed race distances deﬁned a racing season. We imposed the condition that each racing season contains at least two races. If a season contained 30 or more total running activities they were deﬁned as training season. For consistency certain data ﬁlters were applied to all activities and races (see the “Methods” section for more detail). Two variants of racing season were deﬁned, with the marathon included and excluded. A total of ~25,000 racing seasons with the marathon included and ~10,000 racing seasons without the marathon, and ~22,000 training seasons were analyzed (see Table 1 for a summary of the available data and performed analyses). Accuracy of performance prediction. For all individuals, we estimated their performance indices vm and γl for each racing season by matching race events to Eq. (1) by minimizing the relative prediction error for the race times. The probability den- sities of these indices are shown in Fig. 2. For all racing seasons with three and more races (N = 12,309), the mean error between model prediction and actual race time was only 2.0%. This sug- gests that our model captures correctly determinants of aerobic endurance performance. Correlations between performance indices and marathon ﬁnishing times are presented in Fig. 3. To investigate the predictive power of our model in more detail, we applied our model also to the racing season with the marathon performance excluded (see Fig. 4). This allowed us to estimate the marathon ﬁnishing time from the performances on shorter distances only. As a function of performance indices, in the most likely parameter range the model predicted the marathon per- formance with an overall accuracy of better than 10%. Only for very small (or large) endurance El, estimated times tended to be too slow (or fast) which indicates that sub-marathon distances were raced inconsistently, leading to an under (or over) estima- tion of El. Given all the possible uncertainties in marathon racing that are beyond the control of this study (e.g., weather, course proﬁle, and motivation of the athlete), our predictions for the marathon ﬁnishing times are rather satisfying. Maximal velocity for 1 h. Analysis of ~25,000 racing seasons reveals a normally distributed velocity vm and an exponential decay of the probability density for the endurance El (see Fig. 2). Inter- estingly, VO2,max in a study on 450 elite soccer players has also been found to obey a normal distribution32. Note that vm also measures running economy, which varies considerably among individuals and modulates performance24. In exercise physiology, the ability of a runner to maintain a certain effort is often characterized in terms of thresholds, of which a common example is lactate threshold. In our approach, however, there is a continuous relationship between power output and velocity, and the change of this relation with duration appears to be a natural measure for endurance capability. Hence, as a practical measure for endurance, we deﬁne in our model the velocity v1hU ¼ vm½1 0:1 log ð60 min =tcÞ=log ðElÞ that a runner can maintain for 1 h, corresponding to the maximal fractional utilization of MAP for 1 h. While any duration could be chosen here, we used 1 h in analogy to running coaches deﬁning threshold velocity as the effort that can be maintained for about 1 h33. The 1h utilization ratio p1hU = v1hU/vm had been NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-020-18737-6 ARTICLE NATURE COMMUNICATIONS \| (2020) 11:4936 \| https://doi.org/10.1038/s41467-020-18737-6 \| www.nature.com/naturecommunications 3 estimated previously from laboratory measurements and races for a smaller group of 18 male long distance runners to be approximately 0.82 ± 0.0534. Strikingly, our ﬁndings from the running data for ~14,000 subjects corroborate this range without any invasive measurements, as demonstrated in Fig. 2c. Moreover, our obser- vation of exponentially small but ﬁnite probability for larger El explains observed values p1hU ≈0.9 in some well trained long dis- tance runners. We also computed the marathon race time from our model and compared it to the actual marathon time Tm for all racing seasons, see Fig. 3. Our model predicts theoretical curves of constant Tm in the plane of performance indices (shown as dashed lines in Fig. 3a). We found that the actual race times are ordered according to these curves. This shows that our selected physiological proﬁles, computed from sub-marathon and marathon best performances, are highly correlated with Tm. It is important to understand that the position of a marathon performance in the parameter space is determined by all races and hence reﬂects relative importance of the indices vm and El. This demonstrates the crucial importance of taking into account endurance in addition to MAP and running economy when assessing performance of long distance runners. Importance of endurance. Our ﬁndings demonstrate the strong sensitivity of performance to endurance. For example, a runner with a velocity of vm = 5 m s−1 can improve his/her marathon time from 3 h 27 min 38 s to 2 h 53 min 8 s by doubling endur- ance from El = 3 to El = 6 (corresponding to a change in the one- hour utilization from 79 to 87% of VO2max), without any change in VO2,max or running economy. We also ﬁnd that faster runners Exercise tracking platform Raw data Model Training Observations Subjects (N=18,993) completed at least one marathon [SID, gender] Activities (N=2,487,037; d=32,091,664 km) during period of 180 days before M-date, distance ≥ 1km, velocity≤7.8m/s [SID, M-date, time, distance] Athletes (w/o M) (N=6749) at least 2 races/season Athletes (with M) (N=14,304) at least 2 races/season Race seasons (w/o M) [SID, M-date] Race seasons (with M) (Nraces=64,045) (Nraces=21,184) [SID, M-date] Estimate M-time Physiol. param. (w/o M) (NM=9714) [vm, El] (NM=24,858) [vm, El] Physiol. param. (with M) Correlations between Performance and Training Training parameters (NT=21,605) [dtrain, ptrain, TRIMP] Training athletes (N=12,233) athlete has full training season and at least 2 races with mean model error< 5% Training seasons (180 days before M-date) (activities: N=1,616,004; d=19,959,214 km) full season: 30 or more activities [SID, M-date] SID Fig. 1 Flowchart of the exercise data analysis. SID: subject identiﬁer, M: marathon, M-date: date of marathon, d: total running distance, “race season”: fastest times of an athlete for at least two of the distances 5 km, 10 km, half-marathon, and marathon (±3% to account for GPS tolerance), Nraces: total number of races, NM: number of successful model ﬁts, NT: number of analyzed training seasons for which physiological parameters vm, El could be obtained and predicted actual race times within a mean error below 5%, “full training season”: at least 30 activities during the 180 days before M-date. Table 1 Summary of data sets analyzed. Data Available Fit with marathon Fit w/o marathon Training seasonc # Subjects 18,993 14,304 6749 12,233 # Activitiesa (distance ≥ 1 km) 2,487,037 1,616,004 Total distance [km] 32,091,664 19,959,214 Mean distance/activity [km] 12.9 12.4 # Racing eventsb 85,993 64,045 21,184 54,620 # Race/training seasons 24,858 9714 21,605 All data were collected through the PolarFlow web service48. aAfter removal of unrealistic average velocities (faster than world record). bDistances are 5 km, 10 km, half-marathon, and marathon depending on the model ﬁt (w or w/o marathon). cSeasons with # runs ≥ 30. ARTICLE NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-020-18737-6 4 NATURE COMMUNICATIONS \| (2020) 11:4936 \| https://doi.org/10.1038/s41467-020-18737-6 \| www.nature.com/naturecommunications tend to race more consistently over all race distances than slower runners, highlighted by the dependence of the prediction error ΔTm on the marathon ﬁnishing time (see Fig. 4b). For example, within our fastest group of runners with a marathon time below 160 min, the prediction error was typically less than ±2.5%. This observation supports our explanation for the observed uncer- tainty in the endurance parameter El. Correlation with training. Finally, we compared physiological proﬁles to running activities within a training season. There exist a few studies of the relation between training volume and intensity, improvements of aerobic ﬁtness and performance35. For example, it has been stated that running at velocity vm might represent an optimal stimulus for improving endurance36. There is also evidence supporting that a relatively large percentage of low-intensity training over a long period improves performance during highly intense endurance events37,38. It has been argued that running velocity at lactate threshold is the best physiological predictor for distance running performance39. To investigate the effect of training distance and speed, relative to the velocity vm, we selected consistent racing seasons deﬁned by having a mean race time prediction error below 5%. Figure 5a shows that as the total training distance dtrain of the training season increases, vm increases on average linearly, with a weak saturation trend at largest dtrain. Several studies have demon- strated an increased vm due to endurance training35. A faster velocity vm can be achieved by a better running economy and/or an increase in MAP. We hypothesize that longer training distance has generated improved running economy, in agreement with earlier observations in a group of eleven well-trained long 0.0 0.1 0.2 0.3 0.4 0.5 1 3 5 7 9 11 13 Endurance El Probability density b 0.0 0.1 0.2 0.3 0.4 0.5 2 3 4 5 6 7 vm [m s–1] Probability density a 0 1 2 3 4 5 6 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 Relative power p1hU = v1hU/vm Probability density c Fig. 2 Probability density of model parameters. The crossover velocity vm which is the smallest velocity that elicits maximal aerobic power MAP and the endurance El are obtained by applying our model to the fastest performances of a subject for the four distances 5 km, 10 km, Halfmarathon, and Marathon of a racing season. For these distributions, a total of 24,858 racing seasons have been analyzed. a The velocity vm is approximately normally distributed with a mean of 4.4 m s−1. b The probability density for the endurance El resembles an exponential decay. c The probability density for the relative power for 1h utilization (1hU) peaks at about 82% of MAP. b a 12 Endurance El 10 8 6 4 2 12 Endurance El 10 8 6 4 2 2 3 4 5 6 7 6:00 5:30 4:30 5:00 4:00 3:40 3:20 3:00 2:40 2:20 2:00 6:00 5:30 4:30 5:00 4:00 3:40 3:20 3:00 2:40 Velocity vm [m s–1] 2 3 4 5 6 7 Velocity vm [m s–1] Tm [min] 330 40 30 20 10 290 250 210 170 n Fig. 3 Correlation between performance indices and marathon race time (model estimates for 24,504 racing seasons are shown here). a Visualization of the marathon race time Tm in the (vm, El) parameter plane. Performance indices are obtained from individual's best performances during the racing season. Color changes from fast (magenta) to slower (blue) ﬁnishing times (see color legend for time in minutes). Parameter pairs (vm, El) along the dashed curves yield the same marathon race time indicated at the top of the graph (in hh:mm format). b Color coded visualization of the number n of racing seasons analyzed as function of the parameters (vm, El). NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-020-18737-6 ARTICLE NATURE COMMUNICATIONS \| (2020) 11:4936 \| https://doi.org/10.1038/s41467-020-18737-6 \| www.nature.com/naturecommunications 5 ΔTm < −20 % 0.06 0.04 0.02 0.00 −20 to −15 % −15 to −10 % −10 to −5 % −5 to 0 % 0 to 5 % 5 to 10 % 10 to 15 % 15 to 20 % > 20 % Actual Tm [min] Tm < 160 160 < Tm < 180 180 < Tm < 200 200 < Tm < 220 220 < Tm < 240 a b Endurance El Probability density 12 10 8 6 4 2 2 3 4 5 6 7 –20 –15 –10 –5 0 5 10 15 20 Velocity vm [m s–1] ΔTm [%] 6:00 5:30 4:30 5:00 4:00 3:40 3:20 3:00 2:40 Fig. 4 Estimate of Marathon race time from the racing season (for 9410 seasons). a Visualization of the relative difference ΔTm between actual and estimated marathon race time Tm (in percent of race time) as function of crossover velocity vm and endurance El. Magenta (blue) color indicates a faster (slower) than estimated ﬁnish. b Probability density of race time differences color coded according to groups of different race time intervals. 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 3 5 7 9 11 0.5 0.6 0.7 0.8 3 5 7 9 11 5 10 15 20 25 30 Training TRIMP [×103] Endurance El Endurance El Training intensity ptrain = vtrain / vm Training intensity ptrain = vtrain / vm 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 1000 2000 3000 dtrain [km] vm [m s–1] vm [m s–1] a b c d El = 1.83 + 0.101 ptrain e5.19 ptrain Fig. 5 Correlations between performance indices and training characteristics. We have measured the distance and time for each running activity during a training season for a total number of 21,605 seasons. The graphs show the observed relations between performance indices (obtained from a model ﬁt to the racing season) and different measures of training volume and intensity. The magenta line indicates the average, the gray region one standard deviation, and the light magenta and blue shaded areas represent the standard error of the mean and standard deviation, respectively, as obtained from bootstrap resampling with replacement (see “Methods” section for more details). a Increase of crossover velocity vm with total distance dtrain of training runs. b Relation between crossover velocity vm and relative training intensity ptrain ¼ vtrain=vm where vtrain is the average training velocity. c Increase and saturation of endurance El with training impulse (TRIMP). d Exponential growth of endurance El with relative training intensity. ARTICLE NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-020-18737-6 6 NATURE COMMUNICATIONS \| (2020) 11:4936 \| https://doi.org/10.1038/s41467-020-18737-6 \| www.nature.com/naturecommunications distance runners40. Our analysis provides a statistically signiﬁ- cant, quantitative relation between training distance and speed at MAP, vm, for ~22,000 training seasons. Another explanation for this relation could be that ﬁtter runners with a larger MAP and hence higher vm log more kilometer during their training. Unfortunately, we could not measure vm at the beginning and the end of the training season independently from two different racing seasons or time trials. We also found a linear decrease of vm with the mean relative training intensity between 50% and about 90% of vm, as shown in Fig. 5b. Our ﬁndings can be interpreted as faster runners train typically at lower relative intensities which is consistent with high-intensity performance improvement due to low-intensity training. The range of training velocities increases with larger vm which reﬂects a wider range of accessible intensities between minimal (jogging) and maximal speed. For example, a runner with vm = 4 m s−1 typically (within one standard deviation) trains between 64 and 84% of vm or MAP, while a runner with vm = 5 m s−1 trains typically up to 66% of vm so that both runners have an almost identical upper pace ~5 min km−1 for the majority of their runs. Slow runners must train at a relative high intensity if they want to avoid a transition to walking. It is important to realize that these typical ranges do not include fast, high-intensity workouts which account only for a small fraction of total training volume. However, high- intensity sessions involve also resting phases that can reduce the average velocity when timer is not stopped, potentially explaining observed intensities below ~50% of vm. Optimal training impulse. We found strong evidence that combined effect of training volume and intensity, known as TRaining IMPulse (TRIMP)41, enhances endurance only up to a limit. Previously, it was found in recreational long distance run- ners that individual TRIMP correlates with 5000 m and 10,000 m track performances42. We computed TRIMP by summing the TRIMP points of all runs of the training season. For each run, TRIMP points were assigned according to the duration of the run and its relative average velocity v=vm (see “Methods” section for details). We analyzed the quantitative relation between endurance El and total TRIMP of a training season (see Fig. 5c). We observed an initial linear increase of El with TRIMP, a plateau around El = 7.5 ± 2 for TRIMP ~25,000, and a statistically sig- niﬁcant ﬁnal drop which may be due to over-training. This result suggests that there is an optimal TRIMP per training season, and the corresponding maximal endurance enables a close to optimal marathon race time for a given velocity vm (see Fig. 3a). Finally, we probed the deﬁnition of TRIMP itself to determine if it implements the best relation between endurance and training intensity. We found a striking agreement between the exponential dependence of El on vtrain=vm and the original deﬁnition of TRIMP based on the rise of blood lactate with intensity, as demonstrated in Fig. 5d. Our ﬁndings for thousands of runners show that relations between training mode and performance indices that are usually only accessible by invasive and resource- consuming laboratory testing can be obtained reliably from running activity data. Discussion Recent advances in wearable sensor technology have enabled real- time and noninvasive measurement of physiological data during exercise. However, if we are to employ these data to better understand interplay between exercise, performance and human health, we must develop new models that are adapted to extract from the raw data quantities that are most relevant for health and performance assessment. In this work we have taken this approach for long distance running to estimate physiological model indices such as MAP and endurance, and examined their correlations with training volume and intensity by analyzing exercise data of ~14,000 marathon runners worldwide. We found that our recent universal model for a logarithmic relation between fractional utilization of maximal power and exercise duration14 is crucial for going beyond previous approaches which ignored this relation, and for deﬁning a parameter measuring endurance. This is an important complement to physiological testing in the laboratory where the required maximal effort is unpractical to achieve for distances over 20 km. Indeed, our results provide evidence of the possibility to extract precise indicators for performance and ﬁtness status from long-duration real-world exercise tracking data. Using automated digital exercise tracking goes beyond previous outside-lab studies that relied often on frequently inaccurate self-reports of exercise. The probability distributions of the extracted performance indices show large variances, implying that studies with only a few individuals might produce misleading results, missing the large interindividual variability of response to exercise. Our work has also some limitations: For each activity, only total distance and duration was available in the data set. This could lead to biased estimates of the mean velocity, for example due to periods of rest or stopping with the device timer not stopped. For the detected correlations between performance indices and training the direction of any cause–effect relationship remained open: for example, training with a higher total TRIMP might produce better endurance, but higher endurance could also enable runners to follow training modes with a higher TRIMP. To resolve this relationship, additional data ﬁlters need to be devel- oped to select groups of runners with similar initial performance which subsequently follow different training modes. However, the observed correlations can be of practical importance. They can be useful for estimating realistic expectations for a race for less experienced runners from their training intensity and volume. In addition, our observation that endurance peaks at a given training load (TRIMP) should help preventing over-training, i.e., unpro- ductive increase in training that can cause injury and other health problems. It should also be stressed that real-world data always lack the controlled environment of laboratory based testing. For example, the energy cost of running has been measured very accurately in laboratory conditions43–46 and the theoretical approaches derived from these experiments have motivated the development of our model. Our work implies several directions for future research. The combination of effective models and real-world exercise data holds great potential for a change in our theoretical description and understanding of human response to physical activity over longer periods of time, optimal exercise dosing and training, early injury detection and prevention, and elite athlete performance. Approaches similar to ours could be used to develop standards for cardiorespiratory ﬁtness based on the probability distribution of performance indices in populations with certain characteristics. More detailed, time-resolved activity data for heart rate, mechanical power output and others could be integrated in our model to improve accuracy and to extract other performance indices. Further applications of our approach include the detec- tion of the usage of performance enhancers in professional sports, the early identiﬁcation of talented athletes, and even the effect of sports equipment like new running shoe technology on perfor- mance indices47. Methods Exercise tracking platform. Exercise data were obtained from Polar Flow web service48, which is an exercise tracking platform that allows users to upload various exercise data, including running distance and velocity from GPS watches. Meta data and activity data of users are linked anonymously through user identiﬁcation. NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-020-18737-6 ARTICLE NATURE COMMUNICATIONS \| (2020) 11:4936 \| https://doi.org/10.1038/s41467-020-18737-6 \| www.nature.com/naturecommunications 7 Selection of subjects and activities. Users of the exercise tracking platform were selected as subjects for this study under the conditions that they had completed a run over the marathon distance (42,195 m) in the period between 1 Jul 2015 and 31 Dec 2018, and used the same GPS watch (Polar V800) for activity recording to assure comparable accuracy of GPS based distance recording. We analyzed the running data of ~19,000 individuals who completed ~2.5M activities with a total distance of ~32M km (see Table 1 for details). For each individual all running activities in the 180 days before a completed marathon race were grouped together with the marathon race and the groups labeled uniquely by a subject identiﬁer (SID) and the marathon date (M-date). Note that an individual may have have completed multiple marathons during the studied period. For each of those groups, labeled by the pair (SID, M-date), a race season was deﬁned as the fastest runs of all activities over the four race distances 5km, 10km, half-marathon (21,097.5m) and marathon (42,195 m), if distances were available. A tolerance of ±3% was allowed in the distance selection to account for GPS inaccuracy, and average race velocities were determined by assuming the actual race distances (which are more reliable than GPS recordings). We applied conditions that race velocities must increase with decreasing race distance and must be slower than current world record velocities. Inconsistent race seasons were identiﬁed by violation of these conditions and excluded from further analysis. Race seasons were deﬁned both with and without the marathon race included. A valid race season must contain at least two different race distances. For each race season with a successful performance model ﬁt with mean race time error below 5% (see section below) a corresponding training season was deﬁned as all running activities with a total distance ≥1000 m in the 180 days before the marathon. Runs with apparent velocities ≥7.8 m s−1 (world record for 1000 m) were excluded. Only training seasons with 30 or more runs were considered so that runner had trained at least once per week and training seasons with longer interruptions were excluded. Performance model. We mathematically describe running performance by a minimal model based on a relative power scale14. The model is formulated in terms of relative quantities to eliminate irrelevant, subject dependent quantities. The nominal power expenditure P(v) that is required to run at a constant velocity v, the so-called running economy, determines the relative power as pðvÞ ¼ PðvÞ Pb Pm Pb ¼ v vm ; ð2Þ where we introduced a basal power Pb that is obtained by linearly extrapolating the running economy to zero velocity and a crossover power Pm that we expect to be close to the MAP associated with maximal oxygen uptake VO2max. This power Pm deﬁnes a crossover velocity vm that is close to the velocity that permits exercise with maximal time at MAP. For velocities v > vm the energy cost of running cannot be determined from oxygen uptake alone due to anaerobic energy supply. The running performance of an athlete is not only determined by p(v) (which is ﬁxed by running economy and VO2max) but depends crucially on the average power Pmax that can be maximally generated over a duration T over which it can be sustained. To run at the average velocity vmax that can be maximally sustained over the time T, the nominal power P(vmax) = Pmax(T) is required, establishing a relation between vmax and T. It has been shown14 that Pmax(T) can be obtained from a self- consistency relation which states that the time average of the instantaneously utilized power Pmax(T − t) equals the sum of Pmax(T) and a supplemental power. This supplemental power has aerobic and anaerobic contributions and accounts for an upward shift in the power that is required to complete a run with a given average velocity, for example, due to deteriorating running economy or muscle fatigue. The existence of an upward shift has been observed experimentally and it is essential since its absence would yield a duration independent Pmax, which contradicts the fact that a given power cannot be sustained for an arbitrary duration. The solution of the self-consistency equation yields PmaxðTÞ ¼ Pm Pllog T tc for T ≥ tc ; ð3Þ where Pl measures the supplemental power supply and tc is a crossover time scale separating different anaerobic and aerobic forms of supplemental power. It can be shown that for T < tc, Pmax is given by Eq. (3) with Pl replaced by another constant. By inverting PmaxðTÞ and using the power–velocity relation of Eq. (2), we get the maximal time TmaxðvÞ ¼ tc exp½ðvm vÞ=ðγlvmÞ over which an average velocity v can be sustained. Here, the constant γl = Pl/(Pm − Pb) measures endurance El ¼ expð0:1=γlÞ, see main text. The shortest time T(d) for covering a distance d follows from solving T ¼ Tmaxðv ¼ d=TÞ for T, yielding Eq. (1). It is important for the application to a large, inhomogeneous group of subjects that this model is universal in the sense that it only depends on three parameters vm, tc, and γl and does not depend directly on any additional, subject-dependent parameters. Performance data analysis. We tested whether or not meaningful performance indices can be deduced only from the racing performance of individuals, employing the performance model described before. For each racing season, uniquely labeled by a pair (SID, M-date), two model parameters, vm and γl, were computed from Eq. (1) applied to all races in the racing season. In general, the time tc must be obtained from the crossover between anaerobic and aerobic regimes, and hence from races that involve both means of energy supply, i.e., events with ﬁnishing time shorter and longer than tc. Explicit comparison to racing results and laboratory testing has shown that tc = 6 min is a good approximation on average, and this estimate was used in our data analysis14. We numerically minimized the sum of the squared relative differences between the actual race time and the one predicted by Eq. (1). The nonlinear ﬁtting was based on a Levenberg–Marquardt type algorithm with multiple starting values to minimize probability to converge only to local minimum, and with support for lower and upper parameter bounds. Parameter bounds were chosen as 2 m s−1 ≤ vm ≤ 7 m s−1, 0.039 ≤ γl ≤ 0.135 corresponding to 2.1 ≤ El ≤ 13.014. Fits that converged onto these bounds were excluded from further analysis. Training data analysis. To quantify training of individuals during the 180-day period before a marathon, we must establish measures based on duration and distances of activities within the training season. We considered an optimal set of three variables that measure quantity, quality, and a combination of quantity and quality. Training volume was quantiﬁed by total running distance dtrain of a training season. To account for possibly varying physiological adaptions during different training modes, training intensity ptrain ¼ vtrain=vm was measured by the average running velocity vtrain in relation to the characteristic velocity vm that was determined for each race season independently. Finally, the overall training load was evaluated by the TRIMP scale, which is frequently employed in exercise physiology and the design of training. TRIMP is a measure for both volume and intensity of exercise. We assigned to each activity of a training season a TRIMP number using the deﬁnition TRIMP ¼ Ttrainκ1ðv=vmÞ expðκ2v=vmÞ for activity of duration Ttrain and average velocity v with κ1 = 0.64, κ2 = 1.92 for male subjects, and κ1 = 0.86, κ2 = 1.67 for female subjects49. The total training TRIMP number was then obtained by summing the individual TRIMP numbers of all activities within a training season. Usually TRIMP is deﬁned in terms of the average heart rate reserve during exercise which is expected to be well approximated by the ratio v=vm. We are interested in the relation between physiological model parameters vm and El, and training variables. To measure these relations, we grouped training variables into bins of widths Δdtrain = 300 km, Δptrain = 0.025 and ΔTRIMP = 2000. The standard error of the mean and of the standard deviation of vm and El within each bin was estimated by bootstrap resampling with replacement and computa- tion of the standard deviation from 1000 bootstrap replicates. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article. 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Author contributions T.E. designed the study and performed the numerical analysis. T.E. and J.P. wrote the paper. Competing interests The authors declare no competing interests. Additional information Supplementary information is available for this paper at https://doi.org/10.1038/s41467- 020-18737-6. Correspondence and requests for materials should be addressed to T.E. Peer review information Nature Communications thanks Guido Ferretti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Reprints and permission information is available at http://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 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 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 license, visit http://creativecommons.org/ licenses/by/4.0/. © The Author(s) 2020 NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-020-18737-6 ARTICLE NATURE COMMUNICATIONS \| (2020) 11:4936 \| https://doi.org/10.1038/s41467-020-18737-6 \| www.nature.com/naturecommunications 9	Human running performance from real-world big data.	10-06-2020	Emig, Thorsten,Peltonen, Jussi	eng
PMC2346553	The Aerodynamic Signature of Running Spiders Je´roˆ me Casas1, Thomas Steinmann1, Olivier Dangles2,3 1 University of Tours, Institut de Recherches sur la Biologie de l’Insecte, UMR CNRS 6035, 37200 Tours, France, 2 IRD, UR 072, LEGS, UPR 9034, CNRS, 91198 Gif-sur-Yvette, France, 3 Universite´ Paris-Sud 11, 91405 Orsay, France Abstract Many predators display two foraging modes, an ambush strategy and a cruising mode. These foraging strategies have been classically studied in energetic, biomechanical and ecological terms, without considering the role of signals produced by predators and perceived by prey. Wolf spiders are a typical example; they hunt in leaf litter either using an ambush strategy or by moving at high speed, taking over unwary prey. Air flow upstream of running spiders is a source of information for escaping prey, such as crickets and cockroaches. However, air displacement by running arthropods has not been previously examined. Here we show, using digital particle image velocimetry, that running spiders are highly conspicuous aerodynamically, due to substantial air displacement detectable up to several centimetres in front of them. This study explains the bimodal distribution of spider’s foraging modes in terms of sensory ecology and is consistent with the escape distances and speeds of cricket prey. These findings may be relevant to the large and diverse array of arthropod prey- predator interactions in leaf litter. Citation: Casas J, Steinmann T, Dangles O (2008) The Aerodynamic Signature of Running Spiders. PLoS ONE 3(5): e2116. doi:10.1371/journal.pone.0002116 Editor: Andrew Iwaniuk, Smithsonian Institution, United States of America Received January 7, 2008; Accepted March 31, 2008; Published May 7, 2008 Copyright: 2008 Casas 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: This work is part of the research conducted within the Cricket Inspired perCeption and Autonomous Decision Automata (CICADA) project (IST-2001- 34718) and within the Customized Intelligent Life Inspired Arrays (CILIA) project (FP6-IST-016039). These projects are both funded by the European Community under the ‘‘Information Society Technologies-IST’’ Program, Future and emergent Technologies (FET). Competing Interests: The authors have declared that no competing interests exist. E-mail: casas@univ-tours.fr Introduction Many predatory species can switch between foraging modes, usually alternating between an ambush and a cruising mode in water, soil or vegetation. Much care has been taken in evolutionary ecology to evaluate the relative advantages of foraging strategies in terms of energetics, biomechanics, success rate and impact on the ecosystem [1–7]. However, the relationship between the sensory processes involved in signal production by a predator attacking with one of both strategies and the corre- sponding signal perception by its escaping prey is unknown for most systems. The outcome of this relationship is likely to play an important role in defining the most appropriate predatory foraging mode. For instance, wolf spiders pursue their cricket prey on the bare soil and in leaf litter using two attack strategies [8–10]. Spiders attack prey using either an extremely slow-motion approach, corresponding almost to the ambush strategy, or by running over at relatively high speed (up to 40 cm/s, cruising strategy) [10]. Spiders attack at intermediate speeds much less frequently; biotests using a piston mimicking the attack of a spider showed that a cricket’s chances of survival were highest for attacks at intermediate speed (20 cm/s) [10]. Although crickets and many other detritivorous and herbivorous arthropods are sometimes caught unaware by a spider’s fast strike, they often escape with fast movements. Information contained in air signals upstream from running spiders can be used by prey in these fast escape reactions. Indeed, crickets, cockroaches and other orthropteroid insects are equipped with air-flow sensors (filiform hairs) at the rear end of their abdomen [11]. They possess many short hairs, serving as acceleration sensors, and fewer long hairs (velocity sensors) on their cerci [12]. These mechanosensors are among the most sensitive sensors in the animal kingdom, with action potentials triggered by less than one tenth the energy of a photon [13]; indeed, the orthropteroid escape system, and in particular fluid flow sensing using filiform hairs, has maintained textbook-example status over many years [14–17]. Thus, we hypothesised that spiders use the two different hunting strategies to cope with optimal air-flow detection by crickets. One strategy (ambush) substantially reduces the distance at which the prey can perceive the attack, while the other strategy (cruising) reduces the escape probability by over- whelming the prey sensory capabilities. The high speed ensures that the encounter occurs faster than the escape response. The aims of this study were therefore: (1) to quantify the air flow in front of a running spider using digital particle imaging velocimetry (DPIV), and (2) to assess these complex flow patterns in the context of attack and escape strategies by predators and prey. Very little is known about air movements upstream from a running arthropod, limiting potential evaluation of the ecological and evolutionary importance of air-flow sensing for many predator-prey interactions. Near-field fluid movement cues are used by many invertebrate species to obtain information about potential predators, prey or mates, in both terrestrial and aquatic ecosystems. In particular, several recent studies have led to greater understanding of the physics of near-field fluid motion in animal locomotion and sensing in open enclosures. Such technological and conceptual advances have opened up the arena for similar studies on running animals [18–23]. Results We recorded the air flow produced by wolf spiders (Pardosa [lugubris] sp., most likely P. lugubris (Walkenaer)) running in a small wind tunnel (Figure 1). As spiders dislike the intense laser light sheet, we obtained 14 runs from six different individuals with the PLoS ONE \| www.plosone.org 1 May 2008 \| Volume 3 \| Issue 5 \| e2116 horizontal set-up, but only two runs with the vertical set-up. These were not used in the following quantitative analysis, but gave useful information on several other qualitative aspects of the flow, described below. The mean velocity of the spiders recorded in the horizontal set-up was 9.44 cm/s (SD = 65.51; N = 14). This lies within the range of attack speeds observed under unconstrained hunting behaviour [10]. One spider ran at a high speed of 40 cm/ s. This was an outlier in the velocity distribution, and so was not used to calculate the mean. Running spiders displaced air in front of and above their body trunk (Figures 2 & 3). Pockets of high velocity produced by moving legs could be distinguished and substantially extended the region of flow influenced by the spider (Figure 2). Front legs still produce a forward air movement when moving downwards, as they do not move back and forth (Figure 3B and cartoon on Figure 2). The air field within the first centimetre upstream from a spider varies considerably from run to run because it is not possible to synchronise the PIV clock with the leg kinematics. Thus, depending on the exact moment of flow field mapping, a leg may or may not have a large effect on the flow in its near vicinity (see cartoon, Figure 2). This also explains the absence of relationship observed between the air velocity at 6 mm away from the spider and the spider’s body velocity, and our subsequent decision to pool individual runs for a statistical analysis. The air flow upstream from a running spider declines smoothly with distance (Figure 4); a constrained regression, using the function given in (Eq. 2) and the independently measured mean spider’s velocity as a fixed parameter, lead to a good fit over the whole range of distances (R2 = 0.80). Discussion The air field upstream from a running spider is disturbed over a large distance of several body lengths. The need for prey to perceive attacking predators from as large a distance as possible, using the minimal amount of energy, means that this information is of biological importance. Indeed, previous experimental studies on the air flow produced by attacking toads shooting out their toungs [24] and independent theoretical studies [25] suggested that cockroaches may recognise the wind signature of a predator by the low frequency components in the far field. The most sensitive hairs of crickets are the longest ones (.1000 microns), working near the thermal noise level [13]. Electrophysiological studies estimate their minimal threshold at Vthresh= 30 mm/s. Thus, using the expected flow velocity upstream from a running spider from the fitted model, this threshold should be attained at around 3 cm in front of a spider. This distance, obtained using the observed mean speed, will vary as a function of the speed of the spider. Crickets seem to make full use of this information, with their largest escape distances being 2.4 cm in front of a spider and 2.1 cm in front of a piston device mimicking the kinematics of the attack [10]. This is most impressive, given the time taken for processing information in the abdominal terminal ganglion, the insect brain, and from leg movements [26]. Thus, the cricket’s entire escape system, including sensory and locomotive control, is indeed optimised to pick up the slightest air movements by the best sensors. The implications of our results for the foraging modes of spiders are twofold. First, spiders markedly increase their likelihood of successful attacks by launching fast strikes, at the same time decreasing the potential escape time (time between danger perception by a cricket and encounter by a spider) in a non- linear fashion (Figure 5). While low speed movements imply high potential escape times, the distance at which prey can perceive predatory signals is so short that prey are nearly within reach of spiders (ambush strategy). Second, their highest speeds may correspond to the lowest potential prey escape time [26]. Such attack speeds are between 25–35 cm/s, corresponding well with Figure 1. Digital particle image velocity (DPIV) measurements of a running spider. In the horizontal position, the laser light sheet is focussed 3 mm above the floor, at mid-height of the spider, just below the bottom eye row level. The yellow portion represents the camera’s field of view. Spiders were gently triggered to run using a stick inserted through a small hole at the entrance of the wind tunnel. doi:10.1371/journal.pone.0002116.g001 Aerodynamics of Spiders PLoS ONE \| www.plosone.org 2 May 2008 \| Volume 3 \| Issue 5 \| e2116 the higher speeds distribution observed during spider-cricket interactions. Higher hunting speeds are seldom observed, as they do not increase the capture rate but are energetically expensive. Thus, our quantification of air flow upstream from a running predator extends the interpretation of the two foraging modes in terms of sensory processes, beyond the classical description in energetic and biomechanical terms. Future studies dedicated to body and leg kinematics should be prioritised, since our understanding of this subject is substantially poorer than that of wing and leg kinematics in insects, and their influence on the upstream flow. The role of acceleration, body posture and height over the substrate [27,28] as well as the nature of the substrate, aspects which we have neglected here, are also expected to have a major impact on the flow field upstream from the spider. Many other invertebrate predators, including several other arachnid groups, carabid, cincidelid and staphylinid beetles, hunt prey using the same two strategies as those used by spiders. At the same time, many prey living in litter harbour well-developed cerci bearing filiform hairs triggered by slight air movements. These include primitive and modern insects such as bristletails, firebrats, springtails, cockroaches and crickets; indeed, most prey-spider interactions observed today are the same as they were some 400 million years ago [29,30]. For example, cockroaches have been extremely successful and thrive in tropical leaf litter despite strong predator pressure. Our findings demonstrate a significant role of the physical information contained in slight air currents in interspecific interactions among terrestrial arthropods and suggest a tight sensory coevolution between both opponents. Lurking predators may mostly hide and wait for their prey, but the final strike produces conspicuous signals that prey exploit for their survival. Materials and Methods DPIV Our measurement set-up was composed of a sealed glass box (106262 cm), seeded with 0.2 mm oil particles. Oil particles (Di- Ethyl-Hexyl-Sebacat, 0.5 L, TPAS, Dresden, Germany) were generated using an aerosol generator (ATM 230, ACIL, Chatou, France). The laser (NewWave Research Solo PIV 2, Nd:YAG, dual pulsed; Dantec Dynamics A/S, Skovlunde, Denmark) illuminated the flow produced by the spider’s displacement through glass. The laser sheet (width = 17 mm, thickness at focus point = 50 mm) was operated at low power (3 mJ at 532 nm) to minimise glare. A target area (17630 mm) was then imaged onto the CCD array of a digital camera (Photron FastCam X1280 PCI 4K) using a Macro Lens (Nikon, AF Nikkor, 60 mm, f : 2.8). The CCD captured separate image frames (128061024 px). Once a sequence of two light pulses was recorded, the images were divided into small subsections which were cross-correlated with each other using flow map software (Flow Manager 4.4. Dantec Dynamics A/S, Skovlunde, Denmark). The Figure 2. Horizontal flow field and close-up view of the flow around a running spider. The sequence in (A) highlights the pockets of high air-flow velocity created by leg strokes superimposed on the air movements created by the body trunk movement. Neither the tips of the spider’s legs, nor their associated flow patterns, are visible as they are located below the light sheet. The time delay between two images is 500 ms; the spider was running at a speed of 5.7 cm/s. The cartoon, adapted from [9], highlights the relative position of legs to body trunk. An overlay of two images (first image in white, second image in grey) of the moving spider, separated by 500 ms, is shown in (B). The zone of flow velocities above the measurable range is in black. The running speed was 10.5 cm/s. doi:10.1371/journal.pone.0002116.g002 Aerodynamics of Spiders PLoS ONE \| www.plosone.org 3 May 2008 \| Volume 3 \| Issue 5 \| e2116 correlation was achieved using an interrogation area of 32632 pixels, allowing us to obtain valid measurements down to a particle displacement of 0.1 pixels. Using the equation, sV~ sDx Dtime ~ spixdr Dtime ð1Þ with sDx the minimal displacement measurable (m), Dtime = 33 ms, the time separating two image record and dr = 27 mm the spatial resolution, one obtains the lowest detectable speed of 0.082 mm/s, and of 5.4 mm/s for a time interval of 500 ms. Conversely, with the maximal measurable particle displacement of 32 pixels, the maximal detectable speed is 2.62 cm/s for a time interval of 33 ms, and 17.3 cm/s for a time interval of 500 ms. Estimation of spider’s velocity and profile extraction Pardosa (Koch) is the most speciose genus among Holarctic wolf spider genera. Several species groups have been recognized, based on characteristics of the copulatory organs [31]. Based upon identification of mature males from our collecting sites, Pardosa lugubris (Walkenaer) was the most common species. However, this species was recently shown to incorporate distinct cryptic species whose immature individuals are, to date, impossible to differen- tiate (Kronestedt 2007). In our experiments, we used only immature spiders because they naturally spend much of their time hunting for prey and not seeking for partners. The mean body size was 3.6 mm (S.D. = 0.2 mm, N = 6). The body size was obtained by measuring the largest width of the prothorax, to which we added the lengths of coxa and the trochanter, as these three body parts act aerodynamically as a single unit. In the studied spiders, this unit was wider than the abdomen. During a single time interval of 33 ms, a spider travelled a distance of 5 mm when moving at a speed of 15 cm/s. There are therefore no data available on flow velocity for the 5 mm space next to the body surface. The distance from the body for which no information was available was greater for greater speeds. In the horizontal set-up, we took care that the laser light sheet is focused 3 mm above the floor, at mid-height of the spider, just below the bottom eye row level. However, we cannot ascertain that the laser light sheet, which is diverging with an angle of 24u Figure 3. Vertical flow field and close-up view of the flow around a running spider. The sequence in (A) highlights the high air-flow velocity above the spider’s body. The time delay between two images is 500 ms; the spider was running at a speed of 3.7 cm/s. An overlay of two images (first image in white, second image in grey) of a moving spider, separated by 500 ms, is shown in (B). The horizontal component of the air flow in the near vicinity of the legs is always directed forward, as front legs do not move back and forth (see cartoon in Figure 2). The running speed was 21 cm/s. doi:10.1371/journal.pone.0002116.g003 Aerodynamics of Spiders PLoS ONE \| www.plosone.org 4 May 2008 \| Volume 3 \| Issue 5 \| e2116 from the focal point, did not affect the spider, or during the low phase of the body oscillations. Whatever the amount of light spiders did get, it was much below the intensity of the bulk of the laser sheet, as we would otherwise see the eyes within the light sheet. We observed a tendency to avoid the laser light sheet rapidly in the vertical set-up. We recorded 14 runs made by six Pardosa [lugubris] sp. spiders with the horizontal set-up. Measurements were only made when the spider velocity was assumed constant for several centimetres and spiders were running straight. The constant velocity assumption is derived from the measurements in [10] reporting an acceleration phase restricted to one centimetre, followed by a constant velocity. We therefore positioned the field of view of the camera at least 2–3 centimetres away from the entrance of the tunnel. The spider’s velocity was determined by measuring the average velocity of the spider’s body on a run. A run was restricted 5 10 15 20 25 0 1 2 3 4 5 6 7 Distance from body (mm) Velocity (mm/s) Figure 4. Flow velocity upstream of running spiders. The observed speeds (mean and standard deviation; dots and error bars, respectively), and the fit of the statistical function (Eq. 2) are represented. doi:10.1371/journal.pone.0002116.g004 Figure 5. Spider’s attack speed and cricket escape time. The potential escape time for a cricket (red line) is expressed as a function of the spider’s attack speed. At slow attack speeds, the distance at which crickets can perceive spiders is limiting (ambush strategy), whereas at high hunting speeds, the escape time becomes limiting (cruising strategy). The potential escape time is defined as the time interval between predator perception by a cricket and hit by a spider running at a given speed. It is based on the distance, for a given speed, at which the threshold of 30 mm/s for danger perception is attained [13]. The minimal recorded escape time for crickets is around 0.2 ms (horizontal bar, [26]). The distribution of observed attack speeds and the five successful attacks (stars) were obtained from observations of real attacks, at constant speeds, during cricket- spider interactions [10]. doi:10.1371/journal.pone.0002116.g005 Aerodynamics of Spiders PLoS ONE \| www.plosone.org 5 May 2008 \| Volume 3 \| Issue 5 \| e2116 to the pairs of images (varying from one to five pairs) for which the images were of quality high enough for a faithful quantification of air flow. We extracted velocity profiles from the vector fields for each measurement. Profiles were evaluated along the upstream axis. In order to describe the flow velocity as faithfully as possible, we fitted the data with a flexible statistical function: V~Vbody 1 6 A A 2 zx z 1 12 B2 A 2 zx 2 z 1 24 C3 C 2 zx 3 ! ð2Þ With x being the distance to the spider’s body (m), A = 0.0007, B = 20.0011 and C = 0.0179 and Vbody, the spider’s body velocity (0.0944 m/s). Acknowledgments We thank the consortium members of CICADA and CILIA teams for discussions, M. Greenfield, G. Krijnen, C. Lazzari, E. McCauley and J. Mogdans, and two reviewers for comments on the MS. Author Contributions Conceived and designed the experiments: JC TS OD. Performed the experiments: TS. Analyzed the data: JC TS. Contributed reagents/ materials/analysis tools: JC TS. Wrote the paper: JC TS OD. References 1. Schoener TW (1971) Theory of feeding strategies. Ann Rev Ecol. Sys 2: 369–404. 2. Curio E (1976) The ethology of predation. Berlin: Springer Verlag. 250 p. 3. Huey RB, Pianka ER (1981) Ecological consequences of foraging mode. Ecology 62: 991–999. 4. Kramer DL, McLaughlin RL (2001) The behavioural ecology of intermittent locomotion. Am Zool 41: 137–153. 5. Cooper WE (2005) The foraging mode controversy: both continuous variation and clustering of foraging movements occur. J Zool 267: 179–190. 6. Schmitz O (2007) Predator diversity and trophic interactions. Ecology 88: 2415–2426. 7. Schmitz OJ (2008) Effects of predator hunting mode on grassland ecosystem function. Science 319: 952–954. 8. Riechert SE (1992) Spiders as representative ’Sit and wait’ predators. In: Crawley MJ, ed. Natural enemies. London: Blackwell. pp 313–328. 9. Foelix RF (1996) Biology of Spiders. Oxford: Oxford University Press. 330 p. 10. Dangles O, Ory N, Steinmann T, Christides JP, Casas J (2006) Spider’s attack vs. cricket’s escape: velocity modes determine success. Anim Behav 72: 603–610. 11. Edwards JS, Palka J (1974) The cerci and abdominal giant fibres of the house cricket Acheta domesticus. I Anatomy and physiology of normal adults. Proc R Soc Lond B 185: 83–103. 12. Shimozawa T, Kanou M (1984) Varieties of filiform hairs: range fractionation by sensory afferents and cercal interneurons of a cricket. J Comp Physiol A 155: 485–493. 13. Shimozawa T, Murakami J, Kumagai T (2003) Cricket wind receptors: thermal noise for the highest sensitivity known. In: Barth FG, Humphrey JAC, Secomb T, eds. Sensors and sensing in biology and angineering. Berlin: Springer Verlag. pp 145–157. 14. Camhi JM (1984) Neuroethology: Nerve cells and the natural behaviour of animals. Sunderland: Sinauer Associates. 432 p. 15. Tautz J (1989) Medienbewegung in der Sinneswelt der Arthropoden. Stuttgart: Gustav Fischer Verlag. 59 p. 16. Gnatzy W (1996) Digger wasp vs. cricket: neuroethology of a predator-prey interaction. Stuttgart: Gustav Fischer Verlag. 92 p. 17. Dangles O, Casas J, Coolen I (2006) Textbook cricket goes to the field: the ecological scene of the neuroethological play. J Exp Biol 209: 393–398. 18. Dickinson MH, Farely CT, Full RJ, Koehl MAR, Kram R, et al. (2000) How animals move: an integrative view. Science 288: 100–106. 19. Koehl MAR, Koseff JR, Crimaldi JP, Cooper T, McCay M, et al. (2001) Lobster sniffing filters the spatiotemporal information in a turbulent odour plume. Science 294: 1948–1951. 20. Taylor GK, Nudds RL, Thomas ALR (2003) Flying and swimming animals cruise at a strouhal number tuned for high power efficiency. Nature 425: 707–711. 21. Warrick DR, Tobalske BW, Powers DR (2005) Aerodynamics of the hovering hummingbird. Nature 435: 1094–1097. 22. Steinmann T, Casas J, Krijnen G, Dangles O (2006) Air-flow sensitive hairs: boundary layers in oscillatory flows around arthropod appendages. J Exp Biol 209: 4398–4408. 23. Lauder GV, Madden PGA (2008) Advances in comparative physiology from high-speed imaging of animal and fluid motion. Annu Review Physiol; Doi: 10.1146/annurev.physiol.70.113006.100438. 24. Plummer MR, Camhi JM (1981) Discrimination of sensory signals from noise in the escape system of the cockroach: the role of wind acceleration. J Comp Physiol A 142: 347–357. 25. Rinberg D, Davidowitz ÆH (2003) Wind spectra and the response of the cercal system in the cockroach. J Comp Physiol A 189: 867–876. 26. Tauber E, Camhi JF (1995) The wind-evoked escape behaviour of the cricket Gryllus bimaculatus: integration of behavioural elements. J Exp Biol 198: 1895–1907. 27. Full RF, Tu MS (1991) Mechanics of a rapid running insect: two-, four-, and six- legged locomotion. J Exp Biol 156: 215–231. 28. Nishikawa K, Biewener AA, Aerts P, Ahn AN, Chiel HJ, et al. (2007) Neuromechanics: an integrative approach for understanding motor control. Integr Comp Biol 47: 16–54. 29. Labandeira CC (2002) Paleobiology of predators, parasitoids, and parasites: death and accommodation in the fossil record of continental invertebrates. Paleo Soc Papers 8: 211–249. 30. Grimaldi D, Engel MS (2005) Evolution of the insects. Cambridge: Cambridge University Press. 755 p. 31. Kronestedt T (2007) A new species of wolf spider from the Pyrenees, with remarks on other species in the Pardosa pullata-group (Araneae, Lycosidae). Zootaxa 1650: 25–40. Aerodynamics of Spiders PLoS ONE \| www.plosone.org 6 May 2008 \| Volume 3 \| Issue 5 \| e2116	The aerodynamic signature of running spiders.	05-07-2008	Casas, Jérôme,Steinmann, Thomas,Dangles, Olivier	eng
PMC3439386	Alveolar-Membrane Diffusing Capacity Limits Performance in Boston Marathon Qualifiers Kaleen M. Lavin1, Allison M. Straub1, Kathleen A. Uhranowsky1, James M. Smoliga3, Gerald S. Zavorsky1,2* 1 Human Physiology Laboratory, Marywood University, Scranton, Pennsylvania, United States of America, 2 The Commonwealth Medical College, Scranton, Pennsylvania, United States of America, 3 Department of Physical Therapy, High Point University, High Point, North Carolina, United States of America Abstract Purpose: (1) to examine the relation between pulmonary diffusing capacity and marathon finishing time, and (2), to evaluate the accuracy of pulmonary diffusing capacity for nitric oxide (DLNO) in predicting marathon finishing time relative to that of pulmonary diffusing capacity for carbon monoxide (DLCO). Methods: 28 runners [18 males, age = 37 (SD 9) years, body mass = 70 (13) kg, height = 173 (9) cm, percent body fat = 17 (7) %] completed a test battery consisting of measurement of DLNO and DLCO at rest, and a graded exercise test to determine running economy and aerobic capacity prior to the 2011 Steamtown Marathon (Scranton, PA). One to three weeks later, all runners completed the marathon (range: 2:22:38 to 4:48:55). Linear regressions determined the relation between finishing time and a variety of anthropometric characteristics, resting lung function variables, and exercise parameters. Results: In runners meeting Boston Marathon qualification standards, 74% of the variance in marathon finishing time was accounted for by differences in DLNO relative to body surface area (BSA) (SEE = 11.8 min, p,0.01); however, the relation between DLNO or DLCO to finishing time was non-significant in the non-qualifiers (p = 0.14 to 0.46). Whereas both DLCO and DLNO were predictive of finishing time for all finishers, DLNO showed a stronger relation (r2 = 0.30, SEE = 33.4 min, p,0.01) compared to DLCO when considering BSA. Conclusion: DLNO is a performance-limiting factor in only Boston qualifiers. This suggests that alveolar-capillary membrane conductance is a limitation to performance in faster marathoners. Additionally, DLNO/BSA predicts marathon finishing time and aerobic capacity more accurately than DLCO. Citation: Lavin KM, Straub AM, Uhranowsky KA, Smoliga JM, Zavorsky GS (2012) Alveolar-Membrane Diffusing Capacity Limits Performance in Boston Marathon Qualifiers. PLoS ONE 7(9): e44513. doi:10.1371/journal.pone.0044513 Editor: Mauricio Rojas, University of Pittsburgh, United States of America Received March 30, 2012; Accepted August 3, 2012; Published September 11, 2012 Copyright: 2012 Lavin 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: The authors have no funding or support to report. Competing Interests: The authors have declared that no competing interests exist. * E-mail: zavorsky@marywood.edu Introduction In 2010, nearly one half million runners in the United States completed a marathon, representing about 0.2% of the U.S. population over 18 years of age. Many marathoners aspire to qualify for the Boston Marathon, participation in which is restricted to a relatively small percentage of runners by age and gender-graded qualification standards. In 2011, approximately 40,000 runners qualified for the 2012 or 2013 Boston Marathon, representing about 10% of all runners who finished a marathon in the United States. These statistics are readily available to the public online at MarathonGuide.com. Due to its aura and relative popularity, marathon running has been examined in several scientific studies, with particular attention given to the metabolic [1] and physiological [2,3,4,5,6] correlates of running a fast marathon. Although these correlates are multifactorial and widely debated [7], it is well established that aerobic capacity ( _VO2max) is an important determinant of marathon performance. Approximately 40 to 77% of the variance in marathon performance is attributable to _VO2max [5,8,9,10,11]. In addition to aerobic capacity, marathon-specific endurance is related to performance, such that the ability to sustain a higher percentage of _VO2max is correlated with a faster marathon [12]. On average, top marathoners (,136 minutes for men, ,158 minutes for women) run at 85 to 90% _VO2max [5], while those in the 156 to 240 minute range run at approximately 75 to 85% of _VO2max [10,13]. Aerobic capacity is dependent on the integrated function of major organ systems, including the heart, lung, and skeletal muscle [14]. Unlike the heart and skeletal muscle, the lung does not readily adapt to endurance training [15,16], possibly limiting _VO2max [15]. Even in those who are highly aerobically fit, heavy exercise may cause arterial oxygen pressure to drop #80 mm Hg, while the alveolar-to-arterial oxygen pressure difference may increase to $25 mmHg [17]. In this way, arterial oxyhemoglobin saturation is reduced, leading to a decrease in _VO2max and, subsequently, endurance performance [18,19,20]. Recently, alveolar-membrane diffusing capacity (measured at rest) has been shown to be very closely related to _VO2max in fit and PLOS ONE \| www.plosone.org 1 September 2012 \| Volume 7 \| Issue 9 \| e44513 obese individuals [21,22] and to longevity in heart disease patients [23]. Specifically, when measured at rest, pulmonary diffusing capacity for nitric oxide (DLNO, mL NO.min21.mmHg21) – a surrogate for alveolar-membrane diffusing capacity for carbon monoxide (DmCO) [24,25] – has been shown to be related to aerobic capacity in fit men and women, such that for every 1 unit increase in DLNO, _VO2max increases by 0.3 mL O2. kg21. min21 [21]. The ratio of DLNO to DmCO is debated. It has been said that DmCO = DLNO 4 2.42, or, more recently, DmCO = DLNO 4 2.06 to 2.26. As long as the ratio is kept consistent within a study, any percent change in DmCO or pulmonary capillary blood volume is still valid. Furthermore, alveolar membrane conductance is the main pulmonary diffusing capacity component representative of fitness, with the exception of pulmonary capillary blood volume (VC) and the blood transfer conductance (H) for CO (HCO). There are mixed data as to whether DLCO or DLNO is a more valid predictor of aerobic capacity, but overall diffusion capacity does appear to be significantly correlated with aerobic capacity in fit subjects [21,22]. Nitric oxide (NO) has been shown to bind more strongly than CO to hemoglobin [26], leading to a higher value for membrane conductance and a diffusion measurement more reflective of total membrane diffusion. An additional benefit of measuring DLNO simultaneously with DLCO is a reduction in time and effort of the procedure. As there is a relation between DLNO and _VO2max, and between _VO2max and marathon running performance, it follows that DLNO may be related to marathon performance, such that pulmonary diffusing capacity introduces a limitation that influ- ences marathon performance. However, it is likely that this correlation may be observed in only well-trained marathoners, who are more likely to experience pulmonary limitations to oxygen availability. Pulmonary limitations have been observed in elite athletes, and as many as 50% of highly-trained individuals experience low oxygen concentrations in the blood (hypoxemia), potentially due to diffusion limitation [27]. Whereas hypoxemia is most frequently observed at exercise intensities near maximal exertion, it is possible that endurance events such as a marathon place constraints on the working lung muscles regardless of the submaximal speed at which most runners compete. Indeed, Amann et al. [28] found that pulmonary limitations were capable of significantly decreasing performance in a 5 kilometer cycling time trial. This effect is likely intensified during a marathon, which, although run at a slower pace, is more than eight times as long. Another study shows that seasoned runners experience significant decreases in diffusing capacity following completion of a marathon [29], suggesting an important role for the lungs in an event that requires submaximal speed but maximal overall effort. Based on these observations, the purpose of this study was to compare the relation between DLNO and DLCO (indexed to body surface area, BSA) and marathon running performance. It was hypothesized that pulmonary diffusion limitation would exist in faster runners, defined herein as those meeting qualification standards for the Boston Marathon (Boston Qualifiers, BQ). Qualification for this prestigious event is dependent on one’s performance relative to age and gender standards, thus eliminating the concerns associated with grouping subjects based on marathon time alone (e.g. creating a younger or predominantly male group). Furthermore, the Steamtown Marathon is a certified qualification course and frequently qualifies over 20% of its participants for the Boston event. This local event therefore provides the opportunity to study differences between sub-elite and more recreational marathoners using a less ambiguous distinction than running pace. In analyzing these data, the relation between marathon finishing time and DLNO or DLCO in both groups was examined using a linear regression model. No significant relation between these factors was expected in non-qualifiers (non-BQ), whereas qualifiers (BQ) were expected to show a significant correlation between finishing time and DLNO/DLCO. Additionally, it was hypothesized that DLNO would show a stronger relation with marathon finishing time than would DLCO, in agreement with previous findings [21,30]. Methods Twenty-eight endurance trained subjects (18 males, 10 females), reported for preliminary testing 2 to 3 weeks before the 2011 Steamtown Marathon in Scranton, PA. Institutional Review Board-approved informed written consent and a Physical Activity Readiness Questionnaire were obtained from all subjects before participation. Anthropometrics (body mass, height, BSA) and age were obtained, and percent body fat (BF %) was measured using Dual Energy X-ray Absorptiometry (DEXA) (Lunar ProdigyTM, GE Medical Systems, Madison, WI). Pulmonary function tests, consisting of spirometry maneuvers to identify obstructive or restrictive patterns, were conducted according to established guidelines [31]. The maneuver to determine DLNO and DLCO was also performed according to established guidelines [32], with a 5 to 6 second breath-hold [21]. Because this one-step maneuver allows simultaneous measurement of DLNO and DLCO, pulmonary capillary blood volume (VC) was then calculated as follows: DmCO was computed as DLNO 4 2.42. The 1/HCO was determined from Roughton and Forster [33] as (0.73+0.058 ? PAO2) ? (14.6/[Hb]), where alveolar oxygen pressure (PAO2) was 100 mmHg, and the hemoglobin concentra- tion [Hb] was set as 14.6 g. dL21 for males, and 13.4 g. dL21 for females [32]. As such, 1/HCO was 1.310 for males and 1.427 for females. VC was then obtained by solving for it using the following equation [33]: 1 DLCO ~ 1 DmCO z 1 HCO:Vc Reference equations were then used to compare each marathon runner’s lung function to normative data from the standard population [34,35]. After the lung function tests were completed, running economy testing was performed on treadmill at a 2% grade to simulate outdoor running conditions at three different sub-maximal running speeds, each lasting 5 minutes. The treadmill was calibrated before the first subject was tested. Throughout testing, heart rate (HR) was measured using a Polar heart rate monitor (Model S610, Polar Electro USA, Lake Success, NY). Metabolic data were collected using breath-by-breath analysis (Sensormedics Vmax 229D, Viasys, CA). Assuming that steady state exercise had been achieved within the first three minutes, means for metabolic data for the last two minutes of each stage were computed. Running economy for each stage was computed as the _VO2 required to travel one kilometer and expressed as mL O2.min21.km21. Mean running economy for the three speeds was then computed to serve as a measure of overall running economy. The final stage of the running economy test protocol was followed immediately by a graded exercise test, in which treadmill speed was increased by 0.5 mph every minute until volitional exhaustion. Several cardiorespiratory parameters, including max- imum respiratory exchange ratio (RERmax), maximum heart rate DLNO Predicts Marathon Finishing Time PLOS ONE \| www.plosone.org 2 September 2012 \| Volume 7 \| Issue 9 \| e44513 (HRmax), maximum expired ventilation ( _VEmax), and _VO2max were measured, and the treadmill speed at which _VO2max was obtained was recorded (v _VO2max). If this speed was sustainable for less than a full minute, the highest speed sustained for 60 seconds was also recorded. Marathon-specific endurance (%V _VO2max) was calculated by dividing the runner’s mean speed for the Steamtown marathon by v _VO2max, with higher values indicating performance at a greater relative physiologic intensity. Since _VO2 could not be directly measured during the race, average _VO2 for the marathon was calculated from the slope of the regression line between speed and _VO2 for each subject, using the three speeds of the running economy test. Average speed for the marathon was then entered into each subject’s own equation to solve for oxygen consumption. Univariate ANCOVAs or independent t-tests (with a confidence interval of 95%) were used to determine whether differences in finishing time, _VO2max (both absolute, in L.min21, and relative, in mL.kg21.min21), and lung function parameters existed between BQ and non-BQ groups. Age and gender served as covariates for the ANCOVA. Step-wise multiple linear regressions were conducted to determine variables most closely related to finishing time for the entire sample, as well as for each group separately. DLNO and DLCO normalized to body surface area (BSA) were included in regression analyses to account for the effects of body mass and height on lung size. Other predictor variables entered into regression analysis include gender, age, body mass, body fat percentage, _VO2max (L O2. min21), running economy (mL O2. kg21. km21), DLNO, DLCO, and marathon specific endur- ance. To address the significance of diffusing capacity, the relation between DLNO or DLCO and finishing time was further explored in a bivariate regression analysis for both BQ and non-BQ groups. The data were analyzed by SPSS Version 19.0, (SPSS Inc., Chicago, IL). Statistical significance was declared when p,0.05. Results A total of 392 Steamtown Marathon finishers (22% of marathon participants) met qualification standards for the 2012 or 2013 Boston Marathon. Of the 28 subjects in this study, 10 runners (6 males, 4 females) (36%) qualified for Boston. Anthropometric measurements (Table 1) indicated that significant differences exist between BQ’s and non-BQ’s with respect to age and body fat percentage (p,0.05). The graded exercise test to volitional exhaustion lasted 10.1(1.2) minutes including the final 5-min running economy bout. The _VO2max in L O2. min21 from that test was not different between groups; however, relative _VO2max (mL O2. kg21. min21) was significantly different between the two groups (p,0.01) such that BQ’s had a mean (standard deviation) _VO2max about 11(2.5) mL O2. kg21. min21 greater than that of non-BQ’s. There was a non-significant trend (p = 0.08) for body mass to differ between groups. During the graded exercise test, BQ’s attained a maximal treadmill speed 20% faster than non-qualifiers (p,0.05). Addi- tionally, BQ’s completed the marathon at a higher percentage of v _VO2max (7565%) than did non-BQ’s (6764%, p,0.001). For both groups combined, there was a significant bivariate relation between _VO2max (mL O2. kg21. min21) and marathon finishing time (adjusted r2 = 0.47, SEE = 37.5 min, p,0.05). Percent of predicted values for a lung function tests were not significantly different between groups (Table 2). Three subjects in each group had a DLNO greater than the upper limit of normal (ULN). Two BQ’s had a DLCO above the ULN, while 5 non- BQ’s had a DLCO that surpassed the ULN. Chi-square analysis reveals that there is not a significant difference in the proportion of subjects with an abnormally high diffusion capacity (DLCO or DLNO) between groups (data not included). Mean finishing time for all subjects was 220.0 minutes, (range = 142.6 to 288.9). Weight loss from the marathon was comparable between the two groups [1.0 (1.4) kg for BQ’s; 0.9 (0.8) kg for non-BQ’s, p = 0.746]. Ten of the twenty-eight participants (6 males, 4 females) qualified for the Boston Marathon (average time = 180.0+23.1 min); the average time for the remaining 18 (12 males, 6 females) was 242.2+28.3 minutes. Finishing time was significantly faster in BQ’s when controlling for age and gender (p,0.01). Step-wise linear regression determined that finishing time for all subjects was dependent on maximum treadmill speed and specific endurance (adjusted r2 = 0.97, SEE = 6.9 minutes p,0.05); however, 80% of the variance in finishing time is accounted for by differences in maximum treadmill speed sustained for 60 seconds alone (adjusted r2 = 0.80, SEE = 17.7 minutes, p,0.05). In BQ’s, the strongest relation identified was between finishing time and DLNO normalized to BSA. For non-BQ’s, finishing time was best predicted by maximum treadmill velocity sustained for 60 seconds and specific endurance, where 74% of the variance in finishing time is accounted for by differences in maximum treadmill velocity. The relation between DLNO normalized to BSA and finishing time was not significant for non-BQ’s (p = 0.127). A significant difference was found between the correlation coefficients of the two linear regressions (two-tailed z = 22.15, p = 0.03). When regression lines of DLNO normalized to BSA versus finishing time plotted on the same axes, the regressions intersect at a point corresponding to a finishing time of 178.1 minutes (Figure 1), suggesting that the relation between DLNO normalized to BSA and marathon time begins to change around 237 m. min21 pace (6 minutes, 47 seconds per mile). DLCO normalized to BSA was also correlated to finishing time in BQ subjects, to a lesser extent. For non-BQ, no significant relation between the variables is evident (p = 0.46). These regressions intersect at the point corresponding to 184.3 minutes (Figure 2). Discussion The novelty of this study lies in that it shows that the relation between pulmonary factors (measured at rest) and marathon performance may differ between athletes of different skill level. In particular, this study was able to isolate an approximate time point at which the relation between pulmonary diffusing capacity for nitric oxide and marathon finishing time changes for trained endurance athletes, pinpointing a pace at which lung function becomes limiting to performance. Whereas runners qualifying for the Boston Marathon, because of their overall faster pace, are limited by DLNO, non-qualifiers probably experience a more mechanical limitation, such as leg turnover (related to maximum treadmill velocity). The primary purpose of this study was to compare the correlation of DLNO and DLCO to marathon running perfor- mance. The results demonstrate that there was a significant slope (indicating a strong correlation) between DLNO and DLCO (normalized to BSA) versus marathon finishing time only in runners that qualified for the Boston Marathon, with these variables showing a stronger predictive relation to finishing time than either _VO2max or running economy. DLNO was shown to be the strongest predictor of finishing time, such that every 1 mL. min21. mmHg21.m22 increase in DLNO at rest projects that finishing time will decrease by about 1.4 minutes (with a range of 0.8 to 2.1 min). These results strongly suggest that alveolar- capillary membrane conductance may be performance-limiting in DLNO Predicts Marathon Finishing Time PLOS ONE \| www.plosone.org 3 September 2012 \| Volume 7 \| Issue 9 \| e44513 runners that complete a marathon in 3 hours or faster, as shown by the intersection of regression lines for BQ and non-BQ groups (Figure 1 and 2). These figures also demonstrate that DLNO relative to BSA is a more accurate predictor of finishing time than DLCO, as the former correlation shows a larger adjusted r2 and a lower standard error of the estimate. The physiological mechanism closely relating DLNO to marathon performance in BQ’s is speculative, given that these subjects ran at approximately 75% of _VO2max, a value consistent elsewhere in those with similar running abilities [10,13]. Though arterial oxygen pressure and the alveolar-to-arterial oxygen pressure difference was not measured throughout the race in this study, others have shown hypoxemia is not induced in fit athletes running half of a full marathon at ,75% _VO2max [36]. Nonetheless, we suggest that, in well-trained runners, there is a _VO2 threshold at which pulmonary diffusion limits oxygen consumption. In other words, these individuals run the marathon at a speed at which _VO2 is high enough that gas diffusion at the alveolar-capillary membrane becomes a physiological bottleneck, and those with greater alveolar-capillary membrane conductance are able to maintain greater arterial oxygen saturation. Similarly, non-BQ’s likely complete the marathon at a _VO2 at which pulmonary diffusion is not limiting; this may explain the lack of relation between DLNO and performance in this group. Thus, individuals who have superior alveolar-capillary membrane conductance (high DLNO measurements), and yet do not reach a ‘‘heart’’ or ‘‘muscle limitation,’’ (i.e., non-BQ’s) would not have Table 1. Anthropometric Data for Boston Qualifiers and non-Qualifiers. BQ (n = 10) Non-BQ (n = 18) Total (n = 28) ANTHROPOMETRICS Age (yr)* 33 (9) 40 (7) 37 (9) 22–50 29–52 22–52 Weight (kg) 64.1 (11.1) 73.2(13.3) 69.9 (13.2) 42.2–84.5 45.0–103.0 42.2–103.0 Height (cm) 170.7 (9.0) 174.0 (9.2) 172.8 (9.1) 154.0–186.7 156.0–187.0 154.0–187.0 Body Fat (%)* 13.1 (7.12) 19.1 (6.7) 17.0 (7.3) 5.1–24.1 9.0–34.5 5.1–34.5 CARDIOPULMONARY VARIABLES AT MAXIMAL EXERCISE _VO2max (L/min) 3.75 (0.64) 3.56 (0.64) 3.63 (0.64) 2.71–4.75 2.25–4.52 2.25–4.75 _VO2max (mL/kg/min)* 59.4 (8.3) 48.7 (5.0) 52.5 (8.1) 49.1–73.1 38.3–60.4 38.3–73.1 RERmax 1.17 (0.07) 1.16 (0.06) 1.16 (0.06) 1.06–1.30 1.06–1.28 1.06–1.30 VEmax (L/min) 119.91 (21.10) 112.03 (18.46) 114.85 (19.43) 82.6–141.13 81.00–149.30 81.00–149.30 HRmax (bpm) 187 (12) 178 (12) 181 (12) 169–202 152–198 152–202 RUNNING PERFORMANCE Running Economy (mL/kg/km){ 194.5 (13.0) 205.8 (19.1) 202.0 (18.1) 180.8–224.5 172.9–247.6 172.9–247.6 Running Economy (mL/kg/min) 43.2 (5.9) 36.3 (3.7) 38.8 (5.6) 36.0–52.0 30.0–44.2 30.0–52.0 Maximum Treadmill Speed for 60 seconds (m/min)* 316 (34) 266 (31) 284 (40) 271–362 228–316 228–362 Marathon Finishing Time (min) 180.0 (23.1) 242.2 (28.3) 220.0 (40.0) 142.0–203.0 200.4–289.0 142.0–289.0 % _VO2max for Marathon 76.4 (6.9) 74.8 (7.2) 75.3 (7.0) 63.0–82.4 63.0–88.3 63.0–88.3 Specific Endurance (% _V VO2max)* 75.31 (5.44) 66.47 (3.97) 69.62 (6.20) 63.16–81.72 56.26–72.04 56.26–81.72 Data are reported as mean (SD) values and range. denotes significant difference (p,0.05) between BQ and non-BQ subjects. Controlling for age and gender using an ANCOVA did not affect the outcome of statistical analyses. {Running Economy calculated at average speed for group; BQ = 222.2 (30.0) m/min, non-BQ = 177.2 (20.4) m/min. doi:10.1371/journal.pone.0044513.t001 DLNO Predicts Marathon Finishing Time PLOS ONE \| www.plosone.org 4 September 2012 \| Volume 7 \| Issue 9 \| e44513 any performance advantage over other individuals who have lower alveolar-capillary membrane conductance. In fact, studies have demonstrated that marathon running causes a significant drop in pulmonary diffusing capacity [29,37]. About 30% of the drop in DLNO (normalized to BSA) post-exercise is accounted for by marathon finishing time (p = 0.046) [29]. Thus, for every 1 minute improvement in marathon time, DLNO is reduced by 1.2 mL.min21.mmHg21.m22 [29]. The diminished DLNO with marathon running can be expected, since _VO2max accounts for about 40 to 77% of the variance in marathon performance [5,8,9,10,11] and about 40% of the change in DLNO from pre- to post-exercise [38]. Therefore, it is possible that individuals with larger alveolar-capillary membrane conductance at the start of the marathon have a physiological advantage: diffusion impairments that may arise during the race will likely not decrease diffusion capacity to problematic levels. Only one other study to date has examined the association between pulmonary diffusion and marathon finishing time using both DLNO and DLCO as predictors [29]. While Manier and colleagues did not intend to examine this association, the data set was available in the publication. In BQ runners (n = 9, mean finishing time = 177.0615.0 min), the relation between DLNO indexed to BSA and finishing time was present, but it was not as strong as in the current study (adjusted r2 = 0.30, SEE = 12.5 min, p = 0.073). In Manier’s study, for every 1 mL.min21.mmHg21.m22 increase in DLNO at rest, marathon finishing time was 0.8 minutes faster (ranging from 1.8 minutes faster to 0.1 minute slower) [29]. Combining these data with those from the present study suggests that 30 to 74% of the variance in DLNO (mL. min21. mmHg21.m22) at rest is related to marathon finishing time in BQ’s, while no such relation exists in non-BQ’s. Additionally, controlling for age and gender does not affect this correlation in either study separately or collectively [29]. Combin- ing the data from these independent studies further supports that pulmonary diffusing capacity is an important contributor to marathon performance in well-trained runners. It is also important to note that while the BQ group represents a well-trained population and some of the subjects in this study performed at a very high level, international class runners tend to have even higher values of _VO2max and, possibly, an even greater dependence on alveolar-capillary membrane conductance. Although DLNO normalized to BSA was only related to finishing time in BQ’s, DLNO did not significantly differ between BQ and non-BQ whether indexed to BSA (p = 0.078) or not (p = 0.80). DLNO is usually higher in fit subjects [21] and in the present study, DLNO was significantly higher than predicted whether using norms from Zavorsky and colleagues (113% predicted, p,0.01) [34] or Aguilaniu and colleagues (107% predicted, p = 0.011) [39]. Several subjects in both BQ and non- BQ groups had values above the upper limit of normative data for a variety of pulmonary function parameters. As such, we can conclude that BQ and non-BQ were of comparable respiratory fitness. These findings suggest that endurance training itself may Table 2. Pulmonary function measurements for Boston Qualifiers and non-Qualifiers. BQ (n = 10) Non-BQ (n = 18) Total (n = 28) Mean Percent Predicted Mean Percent Predicted Mean Percent Predicted FVC (L) 5.1 (0.9) 110 (8) 5.0 (1.0) 105 (14) 5.0 (0.9) 106 (13)* 3.8–6.4 96–124 3.4–7.2 79–134 3.4–7.2 79–134 FEV1 (L) 4.0 (0.7) 108 (11)* 3.8 (0.7) 100 (11) 3.9 (0.7) 103 (11) 2.9–5.4 96–128 2.5–5.1 86–129 2.5–5.4 86–129 FEV1/FVC 81.2 (10.6) 99 (8) 77.2 (6.3) 96 (9) 78.6 (6.2) 97 (8) 73.4–90.2 89–113 64.9–87.2 77–110 64.9–90.2 77–113 PEF (L) 9.0 (2.2) 102 (11) 10.1 (2.0) 111 (10)* 9.7 (2.1) 108 (11)* 6.0–12.9 81–118 7.0–13.1 97–137 6.0–13.1 81–137 FEF25–75 (L/s) 5.2(1.5) 133 (32)* 4.8 (1.3) 129 (29)* 5.0 (1.3) 131 (29)* 3.2–8.3 91–192 2.7–6.8 84–176 2.7–8.3 84–192 DLCO 34.7 (5.9) 113 (15)* 34.3 (6.6) 114 (14)* 34.3 (6.73) 113 (14)* 25.9–43.4 91–135 22.6–48.6 93–146 22.6–48.6 91–146 DLCO/BSA 20.0 (3.1) – 18.3 (2.6) – 18.9 (2.8) – 15.5–23.9 – 13.6–23.6 – 13.6–23.9 – DLNO 179 (27) 113 (12)* 176 (34) 113 (13)* 175 (35) 113 (13)* 140–212 97–130 124–256 94–149 120–256 94–149 DLNO/BSA 103 (14) – 93 (13) – 97 (14) – 85–123 – 72–124 – 72–124 – VC (mL) 90 (15) 116 (19)* 89 (13) 117 (15)* 90 (14) 117 (16)* 72–113 95–151 64–118 95–144 64–118 95–151 FVC: forced vital capacity; FEV1: forced expiratory volume within 1 sec; FEV1/FVC: fraction of inspired air expired within 1 sec; PEF: peak expiratory flow; FEF25–75: forced expiratory flow during 25–75% of 6-second exhale; DLCO: pulmonary diffusing capacity for carbon monoxide, in mL/min/mmHg; DLCO/BSA: DLCO relative to body surface area, in mL/min/mmHg/m2; DLNO: pulmonary diffusing capacity for nitric oxide, in mL/min/mmHg; DLNO/BSA: DLNO relative to body surface area, in mL/min/ mmHg/m2; VC: pulmonary capillary blood volume. Data are reported as mean (SD) values and range. *Denotes a significant difference in observed parameter relative to predicted (p,0.05). doi:10.1371/journal.pone.0044513.t002 DLNO Predicts Marathon Finishing Time PLOS ONE \| www.plosone.org 5 September 2012 \| Volume 7 \| Issue 9 \| e44513 improve alveolar-capillary membrane conductance above that of untrained individuals, but improvements in DLNO likely plateau well before that of the heart or skeletal muscle. Generally speaking, the lungs become limiting at a running pace of 6:47 minutes per mile or 236 m.min21 (a 3 hour marathon). Therefore, athletes performing at or around this pace should be aware of the potential significance of this limitation and its ramifications for performance. It is unknown whether any specific training practices can be implemented to improve pulmonary diffusion and therefore improve marathon performance. This study is limited by its small sample size; a higher power to detect differences in group means would likely be achieved by recruiting more participants. Nevertheless, it is frowned upon to conduct a post-hoc power analysis after data collection has occurred [40,41], thus we did not perform one. Instead, confidence intervals replace power calculations after a study is completed [40,41]. We have provided confidence intervals in Figures 1 and 2. Addition- ally, small group sizes result in large variances; as such, the standard errors of the estimate for both regressions are large and overlapping, obscuring estimation of a clear range of intersection at which DLNO indexed to BSA begins to predict finishing time. Larger sample sizes would also allow us to divide runners into more specific categories by time, possibly delineating a clearer relation between DLNO indexed to BSA and finishing time with increasing running speed. Additionally, normalization to BSA transforms DLNO into a variable with multiple units, possibly complicating analysis. Mean height and body mass themselves were not significantly different between groups; however, the combination of these variables appears to be important when related to pulmonary variables. It remains possible that BSA introduces variation in the data set due to its relationship with heat dissipation, largely dependent on stature [42]. This might have impacted finishing time, especially in slower runners finishing as the ambient temperature increased on race day, from about 8uC (96% humidity) at the 8:00 AM start to 21uC (51% humidity) at the finish line by 1:00 PM. As pulmonary diffusion has been shown to decrease during long- duration submaximal exercise [29,37], the efficacy of interventions which may counter these negative effects should be explored (e.g., anti-inflammatory drugs or antioxidants). Our results could be strengthened by measuring DLNO immediately after completion of the marathon, with a greater change in DLNO representative of presence of a limitation. Nevertheless, measurement of DLNO at rest may still underestimate severity of diffusion limitation. As such, future studies could also measure DLNO at submaximal speeds during running economy testing and extrapolate these data to marathon race pace, allowing a more accurate estimation of the impact of diffusing capacity on running performance. The genetic basis of DLNO could be further studied to determine how this parameter might change as one adapts to training. Furthermore, longitudinal and interventional studies are recommended to determine if any specific type of training can optimize pulmonary diffusion capacity and therefore improve endurance running performance. More extensive understanding of the relationship outlined in this study will allow us to confirm the validity of diffusing capacity for nitric oxide as a fitness predictor. In conclusion, this study found that DLNO indexed to BSA is a better predictor of marathon finishing time in runners qualifying for the Boston Marathon than are more commonly used variables, such as _VO2max or running economy, but this relation was not observed for non-BQ’s. This suggests that alveolar-capillary membrane conductance can be pulmonary limitation in well-trained runners. Acknowledgments The authors would like to thank the Steamtown Marathon Race Committee, including Assistant Race Director Jim Cummings, for help in recruiting participants for this study. Additionally, we acknowledge the Steamtown Marathon Medical Director, Tim Rowland, MD, for providing available space at the medical triage area at the finish line. Figure 1. Regression showing relation between DLNO/BSA and marathon finishing time. For Boston Qualifiers (solid line), r2 = 0.74, SEE = 11.8, p,0.01, showing a significant correlation. Y-intercept is 198.8+18.7 (95% confidence interval ranges from 155.7 to 242.0); slope of the line is 20.532+0.103, with a 95% confidence interval range of 20.8 to 20.3. For non-qualifiers (dashed line), p = 0.14. Y-intercept is 135.7+26.5 (95% confidence interval ranges from 79.5 to 191.8); the slope of the line is 20.175+0.109, with a confidence interval range of 20.4 to 0.05. The point of intersection for these lines is 178.07 minutes (2:58:11). There is a significant difference between the correlation coefficients of the two regressions (2-tailed z = 22.15, p = 0.03). doi:10.1371/journal.pone.0044513.g001 Figure 2. Regression showing relation between DLCO/BSA and marathon finishing time. For Boston Qualifiers (solid line), r2 = 0.69, SEE = 12.8, p,0.01, showing a significant correlation. Y-intercept of the line is 40.6+4.5, with a 95% confidence interval range of 30.2 to 51.0. The slope of the line is 20.11+0.03, with a confidence interval of 20.18 to 20.06. For non-qualifiers (dashed line), p = 0.172. Y-intercept is 25.7+5.2, confidence interval ranges from 14.6 to 36.8. The slope of the line is 20.03+0.02, with a 95% confidence interval ranging from 20.08 to 0.02. The point of intersection for these lines is 179.22 minutes (2:59:14). The correlation coefficients of these lines are significantly different (2-tailed z = 21.99, p = 0.046). doi:10.1371/journal.pone.0044513.g002 DLNO Predicts Marathon Finishing Time PLOS ONE \| www.plosone.org 6 September 2012 \| Volume 7 \| Issue 9 \| e44513 Author Contributions Conceived and designed the experiments: GSZ JMS. Performed the experiments: KML AMS KAU. Analyzed the data: KML GSZ. Contributed reagents/materials/analysis tools: GSZ. Wrote the paper: KML GSZ AMS. References 1. Rapoport BI (2010) Metabolic factors limiting performance in marathon runners. PLoS Comput Biol 6: e1000960. 2. 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Dennis SC, Noakes TD (1999) Advantages of a smaller bodymass in humans when distance-running in warm, humid conditions. Eur J Appl Physiol Occup Physiol 79: 280–284. DLNO Predicts Marathon Finishing Time PLOS ONE \| www.plosone.org 7 September 2012 \| Volume 7 \| Issue 9 \| e44513	Alveolar-membrane diffusing capacity limits performance in Boston marathon qualifiers.	09-11-2012	Lavin, Kaleen M,Straub, Allison M,Uhranowsky, Kathleen A,Smoliga, James M,Zavorsky, Gerald S	eng
PMC6928244	Physiological Reports. 2019;7:e14313. \| 1 of 7 https://doi.org/10.14814/phy2.14313 wileyonlinelibrary.com/journal/phy2 1 \| INTRODUCTION In 2007 the National Institutes of Health launched the Human Microbiome Project (HMP), an interdisciplinary research initiative seeking to characterize the contribution of human gut microbiota to health and disease (Turnbaugh et al., 2007). Subsequent findings have demonstrated compelling relationships between human gut microbiome composition and many leading causes of death worldwide including car- diovascular disease (Wang et al., 2011), diabetes (Larsen et al., 2010), and cancer (Ahn et al., 2013). Although the gut microbiome is suggested to exhibit exceptional plasticity (Gomez et al., 2019), a detailed understanding of the fac- tors determining human microbiome assembly is lacking (Relman, 2015). DOI: 10.14814/phy2.14313 O R I G I N A L R E S E A R C H Rapid gut microbiome changes in a world-class ultramarathon runner Gregory J. Grosicki1 \| Ryan P. Durk2 \| James R. Bagley2 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. © 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. 1Biodynamics and Human Performance Center, Georgia Southern University, Savannah, Georgia 2San Francisco State University, San Francisco, California Correspondence Gregory J. Grosicki, Biodynamics and Human Performance Center, Georgia Southern University (Armstrong Campus), 11935 Abercorn Street, Savannah, GA, 31419. Email: ggrosicki@georgiasouthern.edu Funding information Rossi Family Foundation Abstract The human gut microbiome is a dynamic ecosystem with prolific health connotations. Physical activity is emerging as a potent regulator of human microbiome composition. This study examined changes in the gut microbiome of a world-class ultramarathon runner before and after competing in the Western States Endurance Run (WSER), a 163 km mountain footrace. Anthropometrics and body composition were assessed and the ultramarathoner's submaximal and maximal performance profiles were eval- uated. Gut microbiome analyses were performed at four time-points: 21 weeks and 2 weeks before and 2 hours and 10 days after WSER. Aerobic power (VO2max) was 4.24 L/min (66.7 ml kg−1 min−1), and running economy (51.1 ml kg−1 min−1 at 268 m/min) and lactate threshold (~83% VO2max) values were comparable to that of highly trained distance runners. Two hours post-race, considerable changes in the ultrarunners’ gut microbiome were observed. Alpha diversity (Shannon Diversity Index) increased from 2.73 to 2.80 and phylum-level bacterial composition (Firmicutes/Bacteroidetes ratio) rose from 4.4 to 14.2. Underlying these macro-level microbial alterations were demonstrable increases in select bacterial genera such as Veillonella (+14,229%) and Streptococcus (+438%) concomitant with reductions in Alloprevotella (−79%) and Subdolingranulum (−50%). To our knowledge, this case study shows the most rapid and pronounced shifts in human gut microbiome compo- sition after acute exercise in the human literature. These findings provide yet another example of how exercise can be a powerful modulator of human health. K E Y W O R D S endurance exercise, gut microbiota, ultramarathon, Veillonella 2 of 7 \| GROSICKI et al. Recently, our group (Durk et al., 2018) and others (Allen et al., 2018; Keohane et al., 2019; Scheiman et al., 2019) have shown a link between physical activity and human microbi- ome composition, aiding in the delineation of a signature mi- crobiome response to exercise training. Increases in bacterial diversity and a proliferation of taxa responsible for the pro- duction of short chain fatty acids, such as butyrate, are among the most pervasively observed microbial alterations with exer- cise (Mailing, Allen, Buford, Fields, & Woods, 2019). While these changes are generally regarded as beneficial to the host, a comprehensive understanding of exercise training-induced microbial modifications and their systemic physiological implications remains to be delineated. Moreover, even less is known regarding the human gut microbiome response to an acute exercise bout. Recently, we had the unique oppor- tunity to track the gut microbiome of a world-class ultra- marathon runner who finished in the top-10 at the Western States Endurance Run, a 163 km mountain footrace featuring ~5,486 m climbing and ~7,010 m of descent. Substantially re- duced splanchnic blood flow over the course of the ~16-hr event in concert with tremendous energetic demands (13,000– 16,000 kcal) (Cuddy, Slivka, Hailes, & Dumke, 2009) pro- vides a unique lens through which to study the plasticity of our microbial inhabitants under extreme conditions. 2 \| METHODS 2.1 \| Participant The participant was a 32 yr old male world-class ultramara- thon runner, studied over the course of a 21-week period dur- ing the 2019 race season. He began running competitively in high school, and in college he was a two-time NCAA Division II Cross-Country All-American. He since raced in the Olympic marathon trials and is a two-time champion at the Javelina Jundred. 2.2 \| Approval and screening This project was approved by the Institutional Review Board at Georgia Southern University. On the day of the first study visit, project objectives and risks and procedures were ver- bally explained to the participant, after which the participant provided written informed consent to participate. A visual rep- resentation of the study methodology is provided in Figure 1. 2.3 \| Training quantification Daily training logs recorded via a Suunto Ambit3 Peak GPS watch provided by the ultramarathoner were utilized to quantify weekly and cumulative training duration, mileage, and pace. 2.4 \| Body composition and bone mineral content Height and body mass were measured on a wall-mounted stadiometer and calibrated digital scale, respectively. Body composition was assessed at 21 and 5 weeks prior to the Western States Endurance Run (WSER) (Figure 1) using dual-energy X-ray absorptiometry (DEXA) (Lunar iDXA, GE Healthcare). The DEXA machine was outfitted with enCORE version 16 and the machine was calibrated im- mediately prior to each scan per manufacturer instructions (laboratory coefficient of variation < 0.07%). Scans were obtained after an overnight fast (>10 hr) and after voiding of the bladder using “standard thickness” mode based on the subject's size characteristics. 2.5 \| Submaximal and maximal oxygen consumption (VO2max) exercise testing Submaximal exercise testing was performed on a slat belt treadmill (4Front, Woodway) 30 min after ingesting a 150 kcal (23 g carbohydrate, 6 g fat, 1 g protein) solid snack (GU Energy Labs). Testing was performed in a tempera- ture-controlled environment (22°C, 63% relative humid- ity and 766.1 mmHg) using a protocol from McMiken and Daniels (1976) to facilitate running economy comparisons to a well-characterized cohort of high-caliber distance runners (Conley & Krahenbuhl, 1980; Costill, Thomason, & Roberts, 1973). Testing stages were 6 min in duration and expired gas was monitored throughout the test via indirect calorimetry (TrueOne2400, Parvo Medics) while heart rate was monitored via wireless telemetry (Polar H10, Polar USA). At the com- pletion of each testing stage, blood lactate was evaluated via finger-stick (Lactate Scout+, EKF Diagnostics). Submaximal testing was performed at a 0% grade using incremental steps in velocity (80, 134, 215, 268, 295 and 311 m/min) and pro- ceeded until blood lactate exceeded 4 mmol/l. Gas values (e.g., O2 consumption, respiratory exchange ratio, etc.) were quantified using the average of two 30 s values during the last minute of each testing stage and rating of perceived exertion (RPE, Borg scale) (Borg, 1970) was assessed in the last 15 s of each stage. Based on a recent report of performance testing in a world-class cyclist (Bell, Furber, Someren, Anton-Solanas, & Swart, 2017), a 15 min rest period was provided between submaximal and maximal (VO2max) treadmill exercise test- ing. The VO2max test was administered in 2-min stages at 0% grade, beginning at 80 m/min and working up to the velocity \| 3 of 7 GROSICKI et al. at lactate threshold (295 m/min). After 2 min at this velocity, the grade was increased to 4% and then another 2% for every 2 min thereafter until volitional exhaustion. Oxygen consump- tion, heart rate and RPE were monitored using the same equip- ment and testing procedures as described above. Attainment of VO2max was verified by (a) achievement of greater than or equal to 90% age-predicted maximal heart rate, (b) a respi- ratory exchange ratio (RER)> 1.1, and (c) a final RPE > 17. 2.6 \| Gut microbiome analyses Gut microbiome analyses were performed at four time-points (21 and 2 weeks pre-WSER, 2 hours and 10 days post-WSER), as shown in Figure 1. Stool samples were self-collected by the ultramarathoner using a commercially available kit (Ubiome Explorer) in accordance with the specifications laid out by the NIH Human Microbiome Project (McInnes, 2010). All samples, besides the 2 hr post sample, were taken at ap- proximately the same time of day before eating or exercis- ing (0800). Following a bowel movement, a sterile swab was used to transfer a small amount of fecal matter into a vial con- taining a lysis and stabilization buffer that preserves the ge- netic material for transport at ambient temperatures. Samples were sent to Ubiome laboratories (Ubiome) (Bik et al., 2018) and lysed by bead-beating prior to DNA extraction in a class 1,000 clean room using a guanidine thiocyanate silica col- umn-based purification method via a liquid-handling robot. PCR amplification of the 16S rRNA genes was performed with primers containing universal primers amplifying the V4 variables region (515F: GTGCCAGCMGCCGCGGTAA and 806R: GGACTACHVGGGTWTCTAAT) (Caporaso et al., 2011). In addition, the primers contained Illumina tags and barcodes. Samples were barcoded with a unique combination of forward and reverse indexes allowing for simultaneous processing of multiple samples. PCR products were pooled, column-purified, and size selected through microfluidic DNA fractionation (Minalla & Dubrow, 2001). Consolidated librar- ies were quantified using quantitative real-time PCR using the Kapa iCycler qPCR kit (Bio-Rad) on a BioRad Myio before loading into the sequencer. Sequencing was performed in a pair-end modality on a NextSeq 500 platform (Illumina) ren- dering 2 × 150 bp pair-end sequences. These DNA sequencing techniques were then used to generate data outputs (.csv file) that provided a comprehensive bacterial taxonomic profile. Shannon diversity index (i.e., alpha diversity) was computed using PAST: Paleontological statistics software package for education and data analysis (version 3.25) (Hammer et al., 2001). 3 \| RESULTS 3.1 \| Training quantification For the 6 weeks prior to the WSER-specific training block, the athlete was running ~115 km per week over mostly flat ground with little strength training. Over the course FIGURE 1 Visual representation of study design. Anthropometric and physiological measurements including cardiorespiratory fitness (treadmill) were first taken 21 weeks prior to Western States Endurance Run (WSER; 163 km mountain footrace from Squaw Valley, CA to Auburn, CA) and are reported in the text as Baseline results. Body composition (scale) and gut microbiome composition (fecal symbol) were intermittently evaluated throughout the observational period. Relevant events prior to the race such as a training camp (tent) and shorter/preparatory races (race flags) have been highlighted for temporal interpretation 4 of 7 \| GROSICKI et al. of the 21-week training block, the participant increased volume slightly (~124 km at an average pace of 201 m/ min) and supplemented with an additional 120 min per week of strength and stretching exercises, accumulating 15,331 total minutes of training time. The largest vol- ume of training consisted of an eight-day training camp in northern California 4–5 weeks prior to WSER (Figure 1), during which the ultramarathon runner accumulated 1,037 total minutes of training time (987 min running) and cov- ered 177 km with 6,016 m elevation gain. To prepare for WSER, the athlete competed in a 50 k trail race at week 4, much of which was on the WSER trail, as well as a 100 k sponsor-endorsed road race. During the 10-day post-race observation period, the athlete refrained from any struc- tured exercise for seven days before beginning light jog- ging (~5:30/km) for 30–60 min duration. 3.2 \| Body composition and bone mineral content Baseline body mass and height were 64.0 kg and 170 cm, respectively, and body fat mass was 14.8%. Regional dis- tribution of baseline body fat mass was 17.2%, 12.8% and 13.7% for the arms, legs and trunk, respectively. Bone min- eral content was 2.6 kg and total body bone mineral den- sity was 1.074 g/cm2 (NHANES/Lunar T-score = −1.3) (WHO Study Group, 1994). After 15 weeks of WSER- specific training, body mass increased slightly (+0.5 kg) and total body fat was reduced to 14.2% due to compo- sitional shifts in the arms and legs (16.5% and 12.7%, re- spectively). Although bone mineral content was identical between visits, bone mineral density improved to 1.086 g/ cm2 (T-score = −1.1), with the most notable changes oc- curring in the spine and pelvis (Table 1). 3.3 \| Submaximal and maximal oxygen consumption (VO2max) exercise testing Submaximal testing lasted 24 min, concluding after comple- tion of a 6-min stage at 311 m/min (~5:10 min/mile) where blood lactate reached 6.1 mmol/l and relative oxygen con- sumption (VO2) was 61.8 ml kg−1 min−1. At velocities of 268 and 295 m/min, VO2 was 51.1 and 55.3 ml kg−1 min−1, running economy values comparable or even superior to those reported by Costill, Thomason and Roberts (51.7 and 59.0 ml kg−1 min−1 at corresponding velocities) in highly trained distance runners (Costill et al., 1973). Maximal exercise testing was terminated upon voli- tional exhaustion (RPE = 20) at 295 m/min at an 8% grade. Maximal oxygen consumption (VO2max) was 4.24 L/min (66.7 ml kg−1 min−1) and maximal heart rate and RER values were 186 bpm and 1.19, respectively. Maximal ventilation was 130.7 L/min. Based on these maximal values, lactate threshold was estimated to occur at ~83% VO2max (RER = 0.89). 3.4 \| Gut microbiome analyses Microbial diversity (Shannon Diversity Index) oscillated throughout the investigation, decreasing following 19-week of highly specific race preparation but then increasing post- event (Figure 2a). Firmicutes/Bacteroidetes ratio, a macro- level indicator of microbial composition, was relatively stable pre-WSER (~5:1), but nearly tripled 2 hr post-race due to a 69% reduction in Bacteroidetes relative abundance (Figure 2b). Meanwhile, the relative proportion of Proteobacteria in- creased by more than fivefold 2 hr post-race, largely owing to a 29-fold increase in Haemophilus (Figure 2c). Other no- table changes in gut microbiome composition post-WSER included the proliferation of Veillonella (+14,229%) and Streptococcus (+438%) genera concomitant with reductions in Alloprevotella (−79%) and Subdolingranulum (−50%). 4 \| DISCUSSION Manipulating the human microbial ecosystem has pro- lific health implications and emerging therapeutic poten- tial (Khanna & Tosh, 2014). To our knowledge, this case study shows the most rapid and pronounced gut microbiome changes after acute exercise in the human literature. These extraordinary microbial dynamics highlight the importance of physical activity in determining human microbiome as- sembly and emphasize yet another way in which human movement can be one of the most powerful modulators of human health. Taxonomic richness (i.e., alpha diversity) is often con- sidered a key indicator of gut microbiome health that is gen- erally thought to increase with exercise training (Estaki et TABLE 1 Regional bone mineral density (g/cm2) in a world- class ultramarathon runner preparing for the Western States 100-mile Endurance Race Baseline Pre-race %Δ Head 1.931 1.910 −1.088 Arms 0.647 0.653 +0.927 Legs 1.264 1.273 +0.712 Trunk 0.890 0.907 +1.910 Ribs 0.739 0.742 +0.406 Spine 0.970 0.992 +2.268 Pelvis 0.984 1.014 +3.048 Total 1.074 1.086 +1.117 \| 5 of 7 GROSICKI et al. al., 2016), as was recently observed throughout an ultra-en- durance rowing race (Keohane et al., 2019). Paradoxically, in this study alpha diversity, depicted as Shannon Diversity Index, decreased following 19 weeks of highly specific race preparation (2.83–2.73). This observation may be attributed to the proliferation of select advantageous bac- terial taxa, such as those involved in butyrate production (e.g., Faecalibacterium, +40% baseline to pre-race), con- comitant with the decline of less relevant microorganisms. In contrast, unpublished observations from our laboratory in a cohort of 28 young recreationally active individuals (~30 years) demonstrated alpha diversity changes of plus or minus 0.05 over a 3-week time period. Post-race, alpha di- versity increased by a similar extent (+0.07) nearly reaching baseline levels (2.80). Of the post-race changes in relative genus abundance, a likely beneficial 143-fold increase in Veillonella (~10% relative abundance post-WSER) was the most pronounced. Indeed, Scheiman et al. (2019) recently proposed an ergogenic role for Veillonella involving lactate recycling after observing a similar, albeit less profound, el- evation (~3% relative abundance) in Veillonella of runners 1–5 days after the Boston Marathon. While an increase in Veillonella abundance was likely a highly favorable adaptation to the 163 km race, other microbial dynamics such as post-race insurgences of Haemophilus, a bacterial genus composed of many sig- nificant pathogenic species (e.g., H. Influenzae) and Streptococcus (genus-level taxon of Group A Streptococcus pyogenes) were also observed. It may be speculated that intestinal proliferation of pathogenic bacterial species plays a role in the increased incidence of infectious epi- sodes observed in endurance athletes following prolonged endurance exercise (Nieman, Johanssen, Lee, & Arabatzis, 1990). Moreover, reductions in butyrate-producing bacteria FIGURE 2 Tracking the microbiome of a world-class ultramarathon runner. (a) Alpha diversity, represented as Shannon Diversity Index (H), of the gut microbiome in a world-class ultramarathon runner measured at Baseline (21 week pre-Western States Endurance Run [WSER; 163 km mountain footrace]), Pre-Race (2 week pre-WSER), Post-Race (2 hr post-WSER), and Recovery (10 days post-WSER) measured via 16S rRNA sequencing. (b) Relative phylum-level gut microbiome composition changes at the same time-points listed above. (c) Relative abundance of bacteria genera over the course of the investigation. Genera comprising ≥5% of total fractional abundance for at least one time-point are individually represented while genera of lower abundance were condensed (Other) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Relative Phylum Abundance (%) Actinobacteria Bacteroidetes Firmicutes Fusobacteria Proteobacteria 2.60 2.65 2.70 2.75 2.80 2.85 2.90 Baseline Pre-Race Post-Race Recovery α-Diversity (Shannon Diversity Index) a b c Pre-Race Post-Race 100 90 80 70 60 50 40 30 20 10 0 Baseline 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Relative Genus Abundance (%) Alloprevotella Anaerostipes Blautia Faecalibacterium Haemophilus Pseudobutyrivibrio Roseburia Streptococcus Subdolingranulum Veillonella Other Recovery 6 of 7 \| GROSICKI et al. (e.g., Subdolingranulum) associated with mucosal integrity likely exacerbate this state of hypervulnerability to infec- tion as well as contributing to elevated levels of circulat- ing inflammation (Gill et al., 2015; Grosicki, Fielding, & Lustgarten, 2018) and gastrointestinal distress (Jeukendrup et al., 2000). Surprisingly however, the athlete did not re- port any significant gastrointestinal complaints during or following the event. Interpreted together, genus-level shifts in gut microbiome composition post-WSER highlight the complexity of interpreting microbiome data and reinforce the importance of avoiding oversimplifying macro-level observations (e.g., F/B ratio and/or alpha diversity) of mi- crobial community structure (Shade, 2017). In conclusion, these data add to a growing body of litera- ture demonstrating the potency of acute exercise in shaping human microbiome composition. Though other factors (e.g., diet, travel, etc.) may have influenced our findings, no purpose- ful changes in diet or feelings of malaise were reported by the ultramarathon runner over the course of the data collection pe- riod. Nonetheless, more research involving carefully structured training studies in both healthy and clinical populations and interdisciplinary research teams is needed to fully understand the complex interaction between physical activity and the gut microbiome. ACKNOWLEDGMENTS We would like to thank the ultramarathon runner for his en- thusiastic participation as well as his coaches, Roxanne Vogel and Magdalena Boulet, for their contributions to his success. We would also like to thank Amanda Fernandez, B.S. (Georgia Southern University), Brett Cross, B.S. (Georgia Southern University), and Gwenaelle Begue, Ph.D. (Sacramento State University) for technical assistance, and Michael Lustgarten, Ph.D. (Tufts University) for analytical insight. CONFLICT OF INTEREST The authors have no conflict to disclose. AUTHOR CONTRIBUTIONS GJG and JRB conceived and designed the research. GJG, RPD, and RJB performed the experiments, analyzed data, interpreted results of experiments, and prepared the figures. GJG drafted the manuscript. GJG, RPD, and JRB edited, re- vised, and approved the final version of the manuscript. ORCID Gregory J. Grosicki https://orcid.org/0000-0001-8929-4903 REFERENCES Ahn, J., Sinha, R., Pei, Z., Dominianni, C., Wu, J., Shi, J., … Yang, L. (2013). Human gut microbiome and risk for colorectal cancer. Journal of the National Cancer Institute, 105, 1907–1911. https :// doi.org/10.1093/jnci/djt300 Allen, J. M., Mailing, L. J., Cohrs, J., Salmonson, C., Fryer, J. D., Nehra, V., … Woods, J. A. (2018). Exercise training-induced modification of the gut microbiota persists after microbiota colonization and at- tenuates the response to chemically-induced colitis in gnotobiotic mice. 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World Health Organization Technical Report Series, 843, 1–129. How to cite this article: Grosicki GJ, Durk RP, Bagley JR. Rapid gut microbiome changes in a world-class ultramarathon runner. Physiol Rep. 2019;7:e14313. https ://doi.org/10.14814/ phy2.14313	Rapid gut microbiome changes in a world-class ultramarathon runner.	[]	Grosicki, Gregory J,Durk, Ryan P,Bagley, James R	eng
PMC10600198	1 Vol.:(0123456789) Scientific Reports \| (2023) 13:18222 \| https://doi.org/10.1038/s41598-023-45064-9 www.nature.com/scientificreports Anthropometric profiles and body composition of male runners at different distances Aleksandra Stachoń , Jadwiga Pietraszewska & Anna Burdukiewicz * Anthropometric parameters are crucial prerequisite to achieve success in professional running sports. However, it is not clear how these parameters are relevant for athletes performing on a less demanding sport level as academic competitions. To help coaches and selectors working on this level, we have explored anthropometric variables and body composition in 68 academic athletes: 26 sprinters, 22 middle distance runners, and 20 long distance runners. Sprinters have a more massive body shape, shorter lower legs in relation to the length of the thigh, broader shoulders and narrower hips, greater musculature and cellular mass. A slender figure, a longer shin, and the greatest subcutaneous fat and extracellular mass characterize long-distance runners. Middle-distance runners are the slimmest, and have a narrow trunk and little subcutaneous fat. Sprinters and long-distance runners are mesomorphic, while middle-distance runners present more mixed mesomorph-ectomorph type. The principal component analysis highlighted the importance of the overall size of the body, limbs musculature and the length of the lower limb together with its segments, and also body fatness. This approach emphasized the morphological distinctiveness of runners at particular distances and allows the use of somatic features as predictors of running performance. Running is the most popular and, at the same time, the simplest form of movement that brings multi-directional benefits to the body, including improving the functioning of the heart, nervous and digestive systems1. The popularization of this sport among amateurs is facilitated by the fact that it can be practiced at various distances, and various technical solutions are available to support training control2,3. In recent years, the popularization of running as a form of physical recreation has resulted in this issue being reflected in numerous scientific works4. Physiological aspects of running performance were studied, and issues related to running economy and energy costs were analyzed5,6. In addition, biomechanical aspects affecting the running economy have been studied7–9. The issue of morphological diversity of runners focused on the characteristics of body proportions, body com- position and somatic structure of competitors of specific distances and the impact of anthropometric features on the results achieved by athletes10. It has been shown that professional athletes (Olympic champions, finalists, and running event participants) are clearly differentiated in age, height and body weight11,12. Sprint champions tend to be heavier than lower-ranked competitors, while distance runners show the opposite trend11,13. A cohort study including elite and leisure runners shows that body composition is a better predictor of running performance than body mass index. Furthermore, fat mass was found to be negatively associated with running speed. High val- ues of the fat-free index had a positive effect on the performance of women, while no such relationship was found for men14. In turn body composition studies of University level male track and field athlete of India have shown that sprinters have the lowest body fat. However, there was no significant difference between middle and long distance runners15. The Sheldon’ typology modified by Heath and Carter16 is often used to assess the physique in sports. That typology allows the assessment of body shape in the form of a somatotype that is understood as a description of the current morphological state of an individual. The somatotype is expressed by three numbers, each of which represents one of the basic components: endomorphy, mesomorphy and ectomorphy17. Endomor- phy is relative fatness, mesomorphy is characterized by relative musculo-skeletal robustness and ectomorphy is relative linearity or slenderness of a physique. Highly trained athletes also differ from the general population of athletes by having less endomorphy and greater mesomorphy. Long distance runners who do mostly aerobic training are less endomorphic and mesomorphic but have more ectomorphy than other athletes16. Similar trends have been reported in the young elite middle and long distance runners18 and Iranian cross-country runners19. However, the cited studies had limitations related to, e.g., the number of studied athletes and their sports level. OPEN Faculty of Physical Education and Sport Sciences, Wroclaw University of Health and Sport Sciences, al. Ignacego Jana Paderewskiego 35, 51-612 Wrocław, Poland. *email: anna.burdukiewicz@awf.wroc.pl 2 Vol:.(1234567890) Scientific Reports \| (2023) 13:18222 \| https://doi.org/10.1038/s41598-023-45064-9 www.nature.com/scientificreports/ As mentioned above, the body morphology of athletes is the result of selection and adaptation of the body to training loads, which differ in individual disciplines20. Running at different distances makes it necessary to use various training methods21. The volume and intensity of training are adapted to the distance the athlete will face. Optimizing selection processes and training methods based on the somatic factors seem to be essential to success in running at particular distances22. Our study aims to determine anthropometric profiles, including body size and proportions, somatotype and body composition of long-, middle- and short-distance runners. Our hypothesis is: the long-, middle- and short-distance runners are diversified in body size and proportions, somatotype as well as in body composition, which may be called as ‘various anthropometric profiles’. Materials and methods Participants Sixty eight male college athletes (age 20.7 ± 2.05 yrs old) participated in the study. This sample included 26 sprint- ers (S), 22 middle distance runners (M) and 20 long distance runners (L). The runners were classified into the S, M or L groups according to their declaration of participation in sprints (200 m and 400 m), middle distances (800 m and 1500 m) and long distances (3000 m, 5000 m, 10,000 m). The athletes were involved in regional and national level competitions and trained at least 4 times a week for 2 h per day. Age and training experience did not significantly differ among the surveyed athletes: sprinters (20.37 ± 1.71 yrs old, 5.16 ± 2.21 years of experi- ence), middle-distance runners (20.31 ± 1.55 yrs old, 5.50 ± 2.46 years of experience), long-distance runners (21.39 ± 2.74 yrs old, 6.75 ± 2.34 years of experience). Academic athletes practice sports in the clubs of the Academic Sports Associations functioning at Wrocław universities. The athletes who fulfilled these criteria inquired for the study. The criterion for inclusion in the study was at least a 3 years of practice and competed at national level, no injuries and no special diets in the period preceding the study. The conditions for exclusion were a break in training and injuries or diseases that prevented the measurements. The research was approved by the Senate’s Research Bioethics Commission of the Wroclaw University of Health and Sport Sciences, Poland [consent number 2/2020], and conducted according to the requirements stipulated in the Declaration of Helsinki. Participants were fully informed about all experimental procedures and written informed consent was obtained from all of them. Measurements and calculations The measurements were carried out taking into account the training periodization, at the beginning of the preparation period and in the Central Research Laboratory of Wroclaw University of Health and Sport Sciences, Poland (Quality Management System Certificate: PN-EN ISO 9001:2015—Certificate Reg. No.: PW-15105-22X). All participants visited the laboratory once and underwent a series of measurements. Measurements were made by experienced anthropometrists in the morning at room temperature about 22-24ºC. Anthropometric measurements were performed following measurement protocols established by the International Society for the Advancement of Kinanthropometry (ISAK). Each anthropometrist took a series of measurements assigned to them and was accompanied by a person recording them. Measurements were taken on the right side of the participant’s body. Anthropometric equipment from GPM Siber Hegner Machinery Ltd. was used. (Zurich, Switzerland): an anthropometer, a sliding caliper—Martin type, a spreading caliper, a skinfold caliper, an anthropometric tape. Body weight was measured using an electronic scale with an accuracy of 0.1 kg (Fawag, Lublin, Poland). Each measure was taken two times by the same investigator. Technical error of measurement was < 3% for skinfolds, and < 1% for breadths, lengths and girths. The mean values were used in the statistical analysis. The results of measurements recommended for monitoring athletes23 were included in the study. Heights, lengths, widths and circumferences were measured to the nearest 0.1 cm: body height, lower limb height to trochanterion point, thigh length between trochanterion and tibiale laterale points, tibia length between tibiale mediale and sphyrion tibiale points, foot length, upper limb length between acromiale and dactylion points, arm length between acromiale and radiale points, forearm length between radiale and stylion points, biacro- mial breadth, biiliocristal breadth, biepicondylar humerus breadth, biepicondylar femur breadth, ankle breadth between malleolare tibiale and malleolare fibulare points. The following girths were measured: chest at the level of the mesosternale point, gluteal at the level of the greatest posterior protuberance of the buttocks, arm flexed, forearm, thigh taken 1 cm below the level of the gluteal fold and calf. Skinfold sites were landmarked at the sub- scapular, abdominal, supraspinale, triceps, forearm, front thigh and medial calf. All sites were then measured using caliper with 10 g × mm−2 constant pressure. The measured features were used to calculate the following indices: body mass index BMI (body mass/body height2 [kg/m2]), lower limb length index (lower limb length/body height), upper limb index (upper limb length/ body height), crural index (tibia length/thigh length), biacromial index (biacromial breadth/body height), biili- ocristal index (biiliocristal breadth/body height), biiliocristal-acromiale index (biiliocristal breadth/biacromial breadth), bone massiveness index (biepicondylar femur breadth + ankle breadth/body height), flexed arm girth index (flexed arm circumference/arm length), forearm girth index (forearm circumference/forearm length), thigh girth index (thigh circumference/thigh length), calf girth index (calf circumference/tibia length). In addition, two indices characterizing subcutaneous fat were calculated: fat distribution index and subcu- taneous fat index. The fat distribution index is the quotient of the sum of skinfolds on limb segments (∑ tri- ceps + forearm + front thigh + medial calf) and trunk (∑ subscapular + supraspinale + abdominal). Subcutaneous fat index (SFI) takes into account trunk and extremity skinfolds and body height (∑ trunk skinfolds + ∑ limbs skinfolds/body height)24. 3 Vol.:(0123456789) Scientific Reports \| (2023) 13:18222 \| https://doi.org/10.1038/s41598-023-45064-9 www.nature.com/scientificreports/ The somatotype of each subject was also determined according to Sheldon’s method, modified by Heath and Carter16. Somatotype Calculation and Analysis software classified the average somatotype of each group and illustrated the outcome in a somatotype chart25. The non-invasive bioelectrical impedance method assessed body composition with tetrapolar version hand- to-foot electrodes (BIA 101 analyzer, Akern, Bodygram 1.31 software). Measurements were made considering the manufacturer’s rules for obtaining correct results. The following features were used in the analysis: fat mass (FM) [kg, %], body cell mass (BCM) [kg, %] and extracellular mass (ECM) [kg, %]. An interview was conducted with the respondents to collect information on the date of birth and training experience, diets and supplements, and the occurrence of injuries. Statistical analysis Statistical analysis was performed using the Statistica 13.3 package (TIBCO Software Inc.). Descriptive statistics were used for the quantitative analysis of the collected data. The Shapiro–Wilk test was used to examine the dis- tributions of the analyzed features. Differentiation in the level of development of the analyzed features between the groups was assessed using the analysis of variance and post-hoc Tukey HSD test for unequal samples. The results in the text and tables are presented in the form of mean and standard deviation. The significance level for all tests and statistical procedures was set at an p value of 0.05. Differences in somatic composition were examined using the SANOVA—Somatotype Analysis of Variance procedure25. A ternary plot was used to examine the relationship between the three components of body com- position (FM, BCM, ECM) in groups of runners. The distribution of individual competitors’ points in the three body composition variables system was assessed using the χ2 test. Principal component (PC) analysis was per- formed, expressing a linear combination of the morphological variables. The analysis was preceded by a Box-Cox transformation, which enabled the principal components to be based on correlations. The number of factors was defined using the Kaiser criterion26. Institutional review board The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the University School of Physical Education in Wrocław, Poland (2/2020). Informed consent Written informed consent has been obtained from the patient(s) to publish this paper. Results Short-distance runners are characterized by significantly higher body weight than competitors from other groups (Table 1). Body height, length of the lower and upper limbs as well as their segments did not show any significant differences between the groups of runners. Among the width features, only the biacromial diameter is signifi- cantly wider in the group of sprinters compared to middle-distance runners. The massiveness of the skeleton assessed by the width of the elbow, knee and inner ankle reaches the highest values among examined sprinters. All the analyzed circumferences of the trunk and limb segments are the largest in the group of short-distance runners. The significantly thickest supraspinale and triceps skinfolds are characteristic of long-distance runners. Also, the sum of skinfolds on the trunk and limb segments is significantly greater in long-distance runners. BMI is significantly higher in the group of sprinters. The smallest massiveness is characteristic of middle- distance runners (Table 2). The general proportions of the length of the upper and lower limbs are similar in all groups of runners, but the proportions of functional segments differ. Sprinters are characterized by a significantly shorter tibia in relation to the length of the thigh compared to other groups of runners. They also have broader shoulders in relation to body height. The highest values of the hip width index characterize long-distance runners. The lowest values of the discussed indicator occurred among sprinters. The relative massiveness of the epiphysis of the lower limb is significantly lower among short- and medium-distance runners. Significantly larger girths of limb segments in relation to their length occurred among sprinters compared to other groups. The distribution of subcutaneous fat on the limbs and trunk expressed by the fat distribution index does not significantly differ between the groups of runners (Table 2). However, among the study participants, there was a tendency to increase the fatness of the limbs in relation to the trunk with the lengthening of the distance. The low- est adiposity of the limbs in relation to the adiposity of the trunk occurs in sprinters, while the highest—applies to the group of long-distance runners. The content of subcutaneous fat in relation to body height, expressed by the subcutaneous fat index, is significantly lower in the groups of short- and medium-distance runners. Endomorphy is significantly lower in groups of short and middle distance runners than in long distance runners (Table 2). On the other hand, the mesomorphic component reaches significantly higher values in the group of short and long distance runners. However, significantly greater ectomorphy is characteristic of middle distance runners compared to sprinters. The average somatotype of middle runners are mesomorph-ectomorph (1.60–3.82–3.81), while sprinters (1.68–4.94–2.90) and long distance runners (2.11–4.72–3.36) are ectomorphic mesomorphs (Fig. 1). The somatotype variance analysis showed a statistically significant difference in the soma- totypes of runners (F = 7.41; p = 0.001). The average values of the fat and extracellular mass percentages do not show statistically significant differences between the groups. In contrast, cell mass is significantly higher in sprinters compared to other groups. Distribu- tions of individual competitors’ points in the system of three body composition variables (Fig. 2) assessed with the χ2 test also show statistically significant differences between groups (χ2 = 31.49, p < 0.05). As a result of the principal components analysis, three principal components were identified, explaining the problem in approximately 74% (Table 3). The first principal component (PC1), which has the greatest part in total 4 Vol:.(1234567890) Scientific Reports \| (2023) 13:18222 \| https://doi.org/10.1038/s41598-023-45064-9 www.nature.com/scientificreports/ variability, is highly correlated with body weight and height, shoulder width and hip width, length of the lower limb and its segments, length of the upper limb, as well as with the muscle circumferences of the arm, forearm, thighs and thighs. Thus, the mentioned variable characterizes the overall size of the body. The second principal component (PC2) is positively correlated with muscle circumferences and negatively correlated with the length of the lower limb, the length of the thigh and the tibia. It can therefore be concluded that PC2 characterizes the massiveness and proportions of the limbs. This principal component divides the participants into two groups: (1) athletes with well-developed musculature, especially of the upper limb, and (2) athletes with a tendency to have longer lower limbs. In the case of the third principal component (PC3), the most diagnostic features are the sum of skinfolds on the trunk and the sum of skinfolds on the limbs. This component characterizes body fatness. Table 4 summarizes the mean principal component scores of the athletes from different distances. It can be concluded that the PC2 and PC3 components are significantly differentiated. The sprinter group’s overall body size (including massiveness and muscularity) is slightly higher. The second principal component PC2 substantially differs between the studied groups. Intergroup differentiation in the light of PC3 is also statistically significant. There is clearly a higher level of development of the analyzed muscle girths in short-distance athletes. Attention is drawn to the distinctiveness of long-distance runners, characterized by greater subcutaneous fat compared to competitors from other groups. Discussion In accordance with the reviewer’s comment, we have introduced in the manuscript the following sentence: The results obtained make it possible to present the detailed somatic characteristics of runners at different dis- tances, confirming the hypothesis that their anthropometric profiles are differentiated. The surveyed men were of similar age and had similar training experience. It is well known that running performance is determined by power output and running efficiency6. The first factor is related to the athlete’s physiological profile, while the second factor characterizes the efficiency in the conversion of power to translocation. It is directly related to the Table 1. Statistical characteristics and inter-group differences of the anthropometric features in short (S), middle (M) and long (L) distance runners (SD—standard deviation; asignificantly different from M; bsignificantly different from L). Group of runners Variable S M L p Mean (SD) Mean (SD) Mean (SD) Body mass [kg] 74.3 (6.56)b 69.2 (7.77) 67.6 (8.99) 0.011 Body height [cm] 180.6 (6.16) 181.3 (6.33) 177.3 (6.91) 0.109 Lower limb length [cm] 95.3 (3.99) 95.9 (4.34) 94.0 (3.89) 0.297 Thigh length [cm] 47.6 (2.17) 47.3 (2.22) 46.7 (2.07) 0.183 Tibia length [cm] 40.2 (1.89) 40.6 (2.13) 40.2 (1.77) 0.219 Foot length [cm] 27.0 (1.35) 26.7 (1.41) 26.6 (1.30) 0.603 Upper limb length [cm] 78.7 (3.02) 79.1 (3.21) 78.1 (3.13) 0.598 Arm length [cm] 33.8 (1.62) 34.0 (1.42) 33.6 (1.44) 0.695 Forearm length [cm] 26.1 (1.13) 26.4 (1.49) 25.8 (1.32) 0.259 Biacromial breadth [cm] 41.5 (1.86)a 40.0 (1.72) 40.7 (2.05) 0.024 Biiliocristal breadth [cm] 28.0 (1.50) 27.3 (1.55) 28.2 (1.92) 0.206 Humerus breadth [cm] 7.1 (0.48) 6.9 (0.37) 7.0 (0.67) 0.361 Femur breadth [cm] 9.8 (0.44) 9.6 (0.36) 9.8 (0.52) 0.185 Ankle breadth [cm] 7.7 (0.37) 7.4 (0.42) 7.5 (0.40) 0.130 Chest girth [cm] 95.3 (3.88) 93.2 (4.41) 93.6 (6.64) 0.333 Gluteal girth [cm] 96.2 (3.83) 94.5 (4.48) 93.6 (4.15) 0.111 Arm flexed girth [cm] 31.2 (1.75)ab 28.9 (1.63) 29.4 (2.37) 0.000 Forearm girth [cm] 25.9 (1.71) 24.8 (1.53) 25.1(1.55) 0.058 Thigh girth [cm] 55.5 (3.01)ab 53.1 (3.10) 51.8 (3.14) 0.001 Calf girth [cm] 37.1 (2.46)a 35.3 (2.65) 35.5 (2.28) 0.022 Subscapular skinfold [mm] 7.8(1.32) 7.1 (1.10) 8.1 (2.19) 0.095 Supraspinale skinfold [mm] 5.7 (1.44)b 5.6 (1.51)b 7.6 (3.47) 0.009 Abdominal skinfold [mm] 6.1(1.75) 6.2 (1.88) 7.2 (2.88) 0.195 Triceps skinfold [mm] 4.4 (1.36)b 4.6 (1.28)b 6.0 (2.56) 0.007 Forearm skinfold [mm] 3.1 (0.59) 3.0 (0.38) 3.3 (0.65) 0.119 Front thigh skinfold [mm] 7.9 (1.06) 7.4 (1.12) 8.2 (1.51) 0.107 Medial calf skinfold [mm] 3.8 (0.83) 3.7 (0.97) 4.4 (1.23) 0.101 ∑ trunk skinfolds 19.6 (3.57 18.9 (3.76)b 22.9 (7.87) 0.038 ∑ limbs skinfolds 17.2 (3.32)b 17.0 (3.21)b 20.3 (5.11) 0.010 5 Vol.:(0123456789) Scientific Reports \| (2023) 13:18222 \| https://doi.org/10.1038/s41598-023-45064-9 www.nature.com/scientificreports/ Table 2. Statistical characteristics and inter-group differences of the anthropometric indices, somatotype’s components and body composition in short (S), middle (M) and long (L) distance runners (SD—standard deviation; asignificantly different from M; bsignificantly different from L). Group S M L p Mean (SD) Mean (SD) Mean (SD) Body proportions Body mass index 22.76 (1.42)ab 20.99 (1.54) 21.44 (1.89) 0.001 Lower limb length index 52.75 (1.31) 52.88 (1.17) 53.00 (1.37) 0.801 Upper limb length index 43.59 (1.02) 43.61 (0.91) 44.06 (1.12) 0.244 Crural index 84.52 (2.12)ab 85.91 (1.89) 86.61 (1.90) 0.002 Biacromial index 22.98 (1.05)a 22.05 (1.02)b 22.97 (1.20) 0.007 Biiliocristal index 15.50 (0.90) 15.06 (0.79)b 15.87 (0.86) 0.012 Biiliocristal-acromiale index 67.48 (3.34) 68.37 (3.95) 69.19 (3.71) 0.294 Bone massiveness index 9.66 (0.39)a 9.37 (0.20)b 9.75 (0.47) 0.002 Flexed arm girth index 92.69 (6.94)ab 85.14 (5.14) 87.69 (6.97) 0.000 Forearm girth index 99.33 (6.62)a 94.13 (7.40) 97.71 (6.75) 0.038 Thigh girth index 116.62 (7.04)b 112.44 (6.67) 111.57 (6.26) 0.026 Calf girth index 92.34 (6.67)a 86.02 (6.01) 87.96 (5.74) 0.002 Fat distribution index 0.89 (0.18) 0.91 (0.18) 0.92 (0.20) 0.823 Subcutaneous fat index 20.44 (3.52)b 19.81 (3.25)b 24.33 (6.23) 0.003 Somatotype components Endomorphy 1.68 (0.38)b 1.60 (0.31)b 2.11 (0.76) 0.004 Mesomorphy 4.94 (1.00)a 3.82 (0.75)b 4.72 (1.12) 0.000 Ectomorphy 2.90 (0.77)a 3.81 (0.80) 3.36 (0.84) 0.001 Body composition % Fat mass 17.06 (3.31) 18.71 (3.57) 18.91 (2.52) 0.095 % Extracellular mass 31.53 (7.27) 32.89 (5.82) 33.22 (3.56) 0.583 % Body cell mass 51.34 (6.56)ab 48.40 (7.45) 48.03 (3.13) 0.035 Figure 1. Mean of somatotypes in groups of male runners (S—short distance, M—middle distance, L—long distance). 6 Vol:.(1234567890) Scientific Reports \| (2023) 13:18222 \| https://doi.org/10.1038/s41598-023-45064-9 www.nature.com/scientificreports/ Figure 2. Distribution of runners in the system of three variables of body composition (○—short distance, Δ— middle distance, □—long distance). Table 3. Principal components loadings and correlations between the components and original variables. Variable PC1 PC2 PC3 Eigenvalues 7.02 2.52 1.59 % total variance 46.82 16.77 10.63 Cumulative eigenvalues 7.02 9.54 11.13 % cumulative 46.82 63.59 74.21 Factor loadings Body mass 0.91 0.27 0.05 Body height 0.84 − 0.35 0.03 Leg length 0.84 − 0.49 − 0.04 Thigh length 0.82 − 0.43 − 0.03 Tibia length 0.73 − 0.54 0.02 Foot length 0.77 − 0.13 0.14 Arm length 0.79 − 0.39 0.06 Biacromial breadth 0.54 0.35 − 0.05 Biiliocristal breadth 0.65 0.06 0.06 Arm flexed girth [cm] 0.66 0.66 − 0.18 Forearm girth [cm] 0.64 0.64 − 0.08 Thigh girth [cm] 0.73 0.44 − 0.10 Calf girth [cm] 0.65 0.42 − 0.23 ∑ trunk skinfolds 0.17 0.30 0.81 ∑ extremities skinfolds 0.03 0.11 0.90 Table 4. Mean principal component scores of the runners from different groups (S—short distance; M— middle distance; L—long distance; asignificantly different from M; bsignificantly different from L). Group S M L p PC1 0.86 − 0.23 − 0.69 0.100 PC2 0.74a − 0.91 0.03 0.001 PC3 − 0.41b − 0.19 0.60 0.010 7 Vol.:(0123456789) Scientific Reports \| (2023) 13:18222 \| https://doi.org/10.1038/s41598-023-45064-9 www.nature.com/scientificreports/ athlete’s biomechanical profile and depends on anthropometric dimensions, limb morphology, and learned and developed movement patterns7,9. As shown in other studies, height and length dimensions were slightly larger in the group of middle-distance runners27. The body height of the athletes from the study groups was similar and oscillated around the value of the 50th percentile (178.7 cm) for the nationwide population28. This suggests that this parameter is not important in the selection process for specific running distances. Body shape assessed by BMI turned out to be slimmer in the middle- and long-distance runners groups than in sprinters, which was also indicated by other researchers29. In previously published studies, differences in body weight were noted depending on the results obtained by runners. The medalists of the Olympic Games in the distances of 100, 200, and 400 m were heavier compared to the finalists and other participants. In turn, the medalists at distances of 5,000 m and 10,000 m and the marathon were lighter than the finalists and other competitors participating in these distances11. High body weight and BMI values in sprinters result from the fact that muscle mass plays a significantly role in their efforts13, which was shown in the currently presented research in the form of a high level of sprinters’ mesomorphy. As previ- ous studies have shown, effective sprinting requires strong deep muscles of the trunk (psoas major, transversus abdominis, and multifidus muscle), which, acting earlier than other muscles, are the basis of limb strength and thus affect the athlete’s sports results30. Also, Tottori et al.31, comparing the cross-sectional areas of the trunk and lower limb muscles in sprinters, found significantly larger psoas major and gluteus maximus muscles than non-runners. In addition, they not only correlated significantly with 100 m sprint performance, but they turned out to be good predictors of top performance over that distance. The upper body and arms also play an important role in running, providing balance and promoting efficient movement. In our research, the transverse dimensions of the trunk (shoulder width, hip width), skeletal mass and limb circumferences were significantly larger in sprinters, which is confirmed in other studies31,32. No such differentiation was found between long- and middle-distance runners, confirming previous observations33,34. The shape of the torso contributes to the locomotor efficiency and energetics of running, influencing the respiratory mechanics and biomechanics of the limbs with its morphology33. Studies of variations in trunk morphology in the context of locomotor ability have shown a relationship between trunk shape and running performance33. According to these results, people with a narrower torso can achieve higher speeds33, which was confirmed by our research. In addition, as other studies have shown34 less thoracic kyphosis and a flattened chest positively affect the mechanics of breathing (chest mobility). On the other hand, increased lordosis has a beneficial effect on the biomechanics of the lower limbs, playing an important role in mitigating the effects of shocks transmitted through the human spine during dynamic activities such as running34. The width of the pelvis also affects the work of the psoas major muscle, affecting its rotation capacity and hip flexors35. The length proportions do not significantly differ between the examined groups of athletes. Slightly higher values of the lower limb length index and upper limb length index characterize long-distance runners, which was confirmed in other studies9,36. In the presented work, it was shown that sprinters are characterized by slightly shorter lower limbs compared to other groups of athletes. Research by other authors shows that long legs are beneficial in sprinters, but only to the optimal level correlating with their height37. If the lower limbs are above this optimal length, they can generate problems producing the high stride frequency that is a prerequisite for good38. In the group of middle-distance runners, slightly longer lower limbs and their segments were demonstrated, as noted by other authors39. Researchers also note that the main difference between long- and short-distance run- ners is stride length, not running pace40. Studies have shown that shorter distance runs require longer strides41,42, which may be reflected in the proportions between the lower limb segments to some extent. Bereket43 noted that the body’s size and proportions affect the energy of locomotion and the speed of move- ment. It was found that taller people with a wider pelvis, having a longer lower leg move at a much higher optimal walking speed at a lower energy cost, which was justified, among others, by the importance of the length of the distal section in heat dissipation44. The analysis of intergroup differences of proportions within the lower limb of runners at various distances carried out in the current study provided interesting results. The relationship between the proximal and distal segments of the lower limb is noteworthy. It was found that long-distance run- ners have a relatively long shin (relative to the thigh), suggesting that long-term intense exercise may promote proportions that favore more efficient heat loss in the lower limb. On the other hand, sprinters have a significantly shorter shin in relation to the length of the thigh (crural index) compared to other groups of competitors. Also, Tomita et al.45 showed that the ratio of tibia length to femur length significantly correlated with running perfor- mance in sprinters, suggesting that this particular morphological factor may play an important role in achieving better running performance in specialized 400 m sprinters. Likewise, the relative slenderness of the thigh and lower leg are significant factors in running economy46. In the presented studies, a significantly more massive skeleton, assessed by the width of the epiphyses, is charac- teristic of long-distance runners, which can be justified by the influence of varied effort47,48. The musculature of the limbs is shaped differently. Significantly larger circumferences of limb segments were found in sprinters compared to other groups of runners. Korhonen et al.49 showed that muscle thickness was a strong predictor of the braking forces generated during sprinting. Similarly, other studies have found that sprinters with higher lean body mass in the lower limbs showed higher mean power in the Wingate test50. Current research has shown that long- and middle-distance runners are characterized by slimmer limb segments compared to sprinters, which has been confirmed in the literature39. The diversified energy cost of running over particular distances affects muscle mass development and body fatness32. In addition, the amount of subcutaneous adipose tissue in different body regions may be of practical importance, as changes in subcutaneous adipose tissue distribution are also associated with changes in running performance51. This enables skinfolds to be used as useful predictors of running performance. As noted by other authors52, also in the presented study, it was found that little subcutaneous fat characterizes groups of short- and middle-distance runners. Thicker skinfolds are characteristic of long-distance runners, which may reflect 8 Vol:.(1234567890) Scientific Reports \| (2023) 13:18222 \| https://doi.org/10.1038/s41598-023-45064-9 www.nature.com/scientificreports/ differences in their metabolism52,53. Subcutaneous adipose tissue is an important and most natural reservoir of energy necessary for athletes to perform long-term efforts54. It also performs important endocrine functions55. The distribution of subcutaneous fat is slightly different in the studied groups of athletes. Limb fatness relative to the trunk is low in sprinters and middle-distance runners, which can be explained by muscle group-specific adipose tissue loss due to systematic training52. Body composition is also an important characteristic of runners. Its basic components, adipose tissue and lean mass with cellular and acellular fractions, strongly correlate with the ability to increase muscle strength, contributing to improved performance and running economy56. Using anthropometric and DXA methods to analyze body composition, it was shown that the low cost of locomotor energy was associated only with param- eters indicating relative slenderness of the body46. In a current BIA study, middle- and long-distance runners have been shown to have a slightly higher percentage of fat and extracellular mass compared to sprinters, reflecting their slimmer physique27. Extracellular mass is known to include connective tissues such as collagen, elastin, skin, tendons and bones57,58. In turn, the body cell mass responsible for metabolism is significantly higher in short-distance runners13. Also, the distribution of individual athletes’ points in the system of three variables of body composition assessed with the χ2 test showed statistically significant differences between groups. Studies by other authors confirm that the differences in the body composition of runners are a consequence of different workloads resulting from the length of the distance covered18,59. The morphological characteristics of runners are supplemented by the somatotypological assessment, which allows for determining the size of endomorphy, mesomorphy and ectomorphy in the body structure16. It should be noted that the athletes are slender with moderate musculature and low body fat, which has also been noted in other studies. SANOVA showed a statistically significant difference in the somatotypes of the tested runners. Middle-distance runners are mesomorph-ectomorph (1.60–3.82–3.81), and sprinters (1.68–4.94–2.90) and long- distance runners (2.11–4.72–3.36) are an ectomorphic mesomorph. The somatotypes of the examined athletes are similar to the somatotypes of Croatian runners over various distances27 and participants of the Olympic Games in 198416. Runners from Croatia presenting a higher sports level (were in the top 15 on the Croatian Athletic Association rank list for the specific event) than the academic runners surveyed in actual research are charac- terized by higher values of endomorphy and ectomorphy (S: 2.0–4.2–3.0; M: 2.1–3.8–3.3; L: 2.6–3.5–3.7), while the mesomorphy in the groups of short- and long-distance runners is slightly lower. In turn, the participants of the 1984 Olympics clearly dominate the size of the mesomorphic component (S: 1.7–5.2–2.8; 400 m runners 1.5–4.6–3.4; M: 1.5–4.3–3.6; L: 1.4–4.2–3.7), which is a consequence of their higher sports level. Using principal components analysis made it possible to isolate three principal components explaining the problem in approximately 74%. The first principal component characterizes the overall size and development of musculature and does not significantly differ between the examined groups of runners. The second component differs significantly between short-distance and middle-distance competitors. It is positively correlated with muscle circumferences and negatively correlated with lower limb length, thigh length and lower leg length, con- firming the observations of other authors indicating the relationship between a more linear body and running economy46,60. The third principal component characterizes the body’s subcutaneous fat in terms of distribution. As previous studies have shown, the anthropometric assessment of athletes should include the assessment of all skinfolds, as the reduction of subcutaneous fat is not the same in all segments. In the case of runners, it concerns mainly the lower limbs52. The morphological distinctiveness of distance runners is expressed in overall body size, muscularity and fat- ness as well as lower limb proportions. These characteristics should be particularly controlled by coaches during the selection process for individual competitions, as well as throughout the training period. The optimization of selection processes and training methods based on these characteristics seems to be an important element for achieving success in running at different distances. Conclusions The conducted analyses indicate the diversification of anthropometric profiles in runners of different distances. Sprinters dominate a more massive body shape, shorter lower legs in relation to the length of the thigh, broader shoulders and narrower hips, greater musculature and cellular mass. Long-distance runners are characterized by a slim figure, a high crural index, a slightly wider pelvis in relation to the width of the shoulders, and the greatest adiposity and extracellular mass. Middle-distance runners are the slimmest, and have a narrow trunk and little subcutaneous fat. The physique of sprinters and long-distance runners is dominated by mesomor- phy, while middle-distance runners are mesomorph-ectomorph. The principal component analysis reduced the multidimensional structure to three variables: overall body size, limbs musculature and the length of the lower limb together with its segments, and body fatness. This approach emphasizes the morphological distinctiveness of runners at particular distances and allows the use of somatic features as predictors of running performance. Our study revealed essential differences between the student athletes in the three running disciplines. How- ever, in the future we plan to link this anthropometric diversity to the other relevant fields as biomechanics to find general success predictors in running. Moreover, we would like to confront our results with a similar study conducted on the cohort of the elite-level runners from the abovementioned disciplines. Therefore, we would be able to provide coaches and selectors with the robust set of success predictors both in the running performed on the elite and non-elite level. This way, future research of the morphological diversity of runners should focus on these somatic features indicated in this manuscript. The future study should be conducted in more homogene- ous groups in terms of sports level, especially groups of high-level athletes and should analyze the importance of indicated features for better performance. 9 Vol.:(0123456789) Scientific Reports \| (2023) 13:18222 \| https://doi.org/10.1038/s41598-023-45064-9 www.nature.com/scientificreports/ Study limitations The small size of the analyzed groups of athletes may limit the interpretation of the results. While the analysis was successful, increasing the sample size would have given a clearer picture of intergroup variability. In addi- tion to that, the competitors were qualified to groups on the basis of a survey concerning the competitions and distances they competed on. However, this division may not be completed, as it happens that athletes change the distance during their career, which may affect the picture of morphological diversity. Interpretation may also be limited by the varied sports’ level of the respondents. 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Also the authors thank all the participants of the survey for their understanding and the time devoted and anonymous reviewers for their helpful comments. Author contributions A.B. designed and conducted the research, obtained the data, statistically analyzed the whole data set, wrote the paper and prepared figures, A.S. and J.P. obtained the data and reviewed the paper. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by Wrocław University of Health and Sport Sciences, Poland, grant number PN/ BK/2020/08. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to A.B. 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. 11 Vol.:(0123456789) Scientific Reports \| (2023) 13:18222 \| https://doi.org/10.1038/s41598-023-45064-9 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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PMC9266034	Citation: Yang, S.-J.; Yang, F.; Gao, Y.; Su, Y.-F.; Sun, W.; Jia, S.-W.; Wang, Y.; Lam, W.-K. Gender and Age Differences in Performance of Over 70,000 Chinese Finishers in the Half- and Full-Marathon Events. Int. J. Environ. Res. Public Health 2022, 19, 7802. https://doi.org/10.3390/ ijerph19137802 Academic Editors: Paul B. Tchounwou and Ukadike Chris Ugbolue Received: 11 May 2022 Accepted: 23 June 2022 Published: 25 June 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 Gender and Age Differences in Performance of Over 70,000 Chinese Finishers in the Half- and Full-Marathon Events San-Jun Yang 1, Fan Yang 1,2,* , Yuan Gao 3, Yan-Feng Su 4, Wei Sun 4, Sheng-Wei Jia 2, Yu Wang 5,* and Wing-Kai Lam 6,* 1 Department of Physical Education and Research, China University of Mining and Technology—Beijing, Beijing 100083, China; 108947@cumtb.edu.cn 2 Li Ning Sports Science Research Center, Li Ning (China) Sports Goods Company Limited, Beijing 101111, China; jiashengwei@li-ning.com.cn 3 School of Physical Education, Yanshan University, Qinhuangdao 066004, China; gaoyuan1107@163.com 4 School of Physical Education and Coaching, Shanghai University of Sport, Shanghai 200438, China; suhui0909@163.com (Y.-F.S.); sunwei1@sus.edu.cn (W.S.) 5 School of Kinesiology and Health, Capital University of Physical Education and Sports, Beijing 100091, China 6 Sports Information and External Affairs Centre, Hong Kong Sports Institute, Sha Tin, Hong Kong * Correspondence: yangfan6@li-ning.com.cn (F.Y.); wangyu@cupes.edu.cn (Y.W.); gilbert.lam@connect.polyu.hk (W.-K.L.); Tel.: +86-189-11326682 (F.Y.); +86-135-01126242 (Y.W.); +86-186-11783188 (W.-K.L.) Abstract: (1) Background: The aim of the present study was to examine the characteristics of over 70,000 long-distance ﬁnishers over the last four years in Chinese half- and full-marathon events; (2) Methods: The available data of all ﬁnishers (n = 73,485; women, n = 17,134; men, n = 56,351) who performed half- and full-marathon events in Hangzhou from 2016 to 2019 were further analyzed for the characteristics of gender, age and average running speed; (3) Results: The total men-to-women ratio was the lowest in the half-marathon event (1.86) and the highest in the full-marathon event (17.42). Faster running performance in males than in females and faster average running speed in short-distance runners were shown. Gender and race distance were observed to have the most signiﬁcant effects on average running speed (p < 0.01). For both male and female ﬁnishers, the slowest running speed was shown in older age groups (p < 0.01) during the full marathon. Our results indicated that the gender difference in performance was attenuated in the longer race distances and older age groups; (4) Conclusions: Understanding the participation and performances across different running distances would provide insights into physiological and biomechanical characteristics for training protocols and sports gear development in different groups. Keywords: gender; men-to-women ratio; marathon; age; running speed 1. Introduction The health beneﬁts of endurance exercise might partially explain the increase in participation in marathon races during the last decades [1]. In recent years, marathon running has been considered a globally popular physical activity that can cater to the various healthy lifestyle needs of urban residents [2,3]. This running boom has gradually spread around the world. Well-known New York, London, Paris, and Berlin marathon events all had between 30,000 and 40,000 ﬁnishers [4]. While distance running used to be a male-dominated sport, today females account for 43% of marathon runners in the USA [5]. Marathon events have developed later in China than in Western countries. The number of marathon events held in China increased from 12 to 53 between 2010 and 2014 [6] to approximately 1100 in 2017 [7], which involved nearly 5 million participants and increased to over 2.2 million participants from 2016 to 2017 [7]. The number further increased to a total of 1900 events in 2019 [1]. The growing popularity of running has inspired a large amount of research on running biomechanics, performance and sport Int. J. Environ. Res. Public Health 2022, 19, 7802. https://doi.org/10.3390/ijerph19137802 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 7802 2 of 9 gears in the past decades [8]. Studies have found that proper pace can effectively reduce the risk of musculoskeletal injury [9], and different running strategies should be used in long-distance running according to gender, age and the event the runner is training for [10]. Moreover, differences in running biomechanics between Chinese men and women were observed, with female runners showing greater range of motion in the hip and knee joints, and a smaller shoe-to-ground angle during the heel touch-down phase. This is believed to be a form of self-regulation that women use to reduce the impact of landing, and men rely more on the performance of their shoes to achieve the purpose of buffering [11]. Following the increase in female participation in distance running [12], investigations into gender differences in running mechanics were intensiﬁed in the Western world [12–14]. The gender difference studies indicated clear differences in females’ body fat and running speed [3], resulting in distinct movement characteristics and injury etiology. Nikolaidis et al. studied the performance and age composition of different genders during marathons, where they found that women achieved their best marathon race time ~5 years earlier in life compared to men. Women’s participation increased disproportionately to men’s participation, leading to an increase in the ratio of men to women [15]. In addition, more and more seniors are joining in marathon races [16]. The sex gap between elite female marathon racers and elite male runners may have reached its limit [17]. The age structure of most male marathon runners is larger and older than that of females (male: 40–44 years; female: 30–34 years) [18]. Although studies have been conducted on anthropometry, physiology and training characteristics have improved our understanding of the predictors of race time [19], as well as age- and gender-related differences in pacing during endurance running [20–23]. While Western counterparts have been extensively analyzed regarding their running characteris- tics, little attention has been paid to the Chinese population. Several articles have shown differences in running between Chinese and Western populations; one study found that, compared with Western women, Chinese women use the medial forefoot more during the push-off phase of running [24]. Western female runners have greater ankle valgus angles than males, while there is no signiﬁcant difference between Chinese females and males [25]. Thus, research ﬁndings obtained from Western runners may not be directly applicable to the Chinese population because of racial differences. The present study examined trends of the men-to-women ratio, number of ﬁnishers and performances by gender and age groups across four years in half- and full-marathon events, respectively. It was expected that there would be a different men-to-women ratio and different performances across different running events. The comparison could help to better understand potential training require- ments for both elite and recreational runners of different age groups, as well as to effectively estimate the demand of running shoes for different gender and age groups. To optimize female performance and health in sport, we need to include women in our analyses in order to better understand peculiarities that may exist in physiology. Therefore, we are happy to enrich the existing pool of knowledge with more data on female participation and performance in marathon racing. Understanding the participation and performance across different running distances (half- and full-marathon events) would provide insights into the physiological and biomechanical characteristics for training protocols for different gender and age groups. 2. Materials and Methods 2.1. Participants and Data Acquisition The complete marathon event data for this study were ofﬁcially obtained from Hangzhou Marathon Organizing Committee (https://www.hzim.org) [26]. The records were collected from all half marathons and full marathons between 2016 and 2019, and were ofﬁcially certiﬁed by the World Athletics Organization. The Hangzhou marathon event included both full and half marathons and runs. Regretfully, the Hangzhou marathon event has been suspended due to the spread of COVID-19 in 2019. The study data included participants who completed the race in the appropriate amount of time. Age and gender Int. J. Environ. Res. Public Health 2022, 19, 7802 3 of 9 information was provided for the period of 4 years. Ultimately, the study included a total of 73,485 participants (male, n = 56,351; female, n = 17,134). 2.2. Procedures Age intervals of ﬁve years were selected to represent age groups among younger and older ﬁnishers in their categories. All runners over 71 years old were placed in one category, as there were only a few male runners in the oldest age group, while the oldest male runner was 74 years old. In total, the ﬁnishers were classiﬁed into 11 age groups; 21–25, 26–30, 31–35, 36–40, 41–45, 45–50, 51–55, 56–60, 61–65, 66–70 and 71+ years. Changes in gender participation are described by the men-to-women ratio (MWR, the quotient of males divided by female completers) [27]. 2.3. Statistical Analysis The ofﬁcial race time (i.e., accurate in seconds) was obtained for all ﬁnishers in both races. The average running speed in km/h was calculated using the ﬁnal race time (h) divided by race distance (km) to allow comparison of performances between two long- distance races. All descriptive statistics were reported as mean and standard deviation. Prior to statistical analyses, data distribution normality was veriﬁed by visual inspection of histograms and QQ plots [18]. To assess age and gender distribution among ﬁnishers in the half- and full-marathon events, a chi-square test (χ2) was performed. Statistical differences in marathon performance between 11 age groups and two events were observed. Meanwhile, their interactions were calculated using a two-way ANOVA, post hoc with Bonferroni-corrected tests, and the signiﬁcance level was controlled at 0.05. All data were organized and summarized using Microsoft Ofﬁce Excel 2019 (Microsoft Corporation, Redmond, WA, USA) and statistical testing was performed using SPSS 20.0 (IBM, Armonk, NY, USA). 3. Results 3.1. Participation by Gender, Race Distance, and Age Group The MWR as well as the total number of male and female ﬁnishers in each age group and race distance are presented in Table 1. Table 1. Distribution of male and female ﬁnishers in each age group and race distance. Half-Marathon Full-Marathon Age Groups Males Females Total MWR Males Females Total MWR 21–25 940 505 1445 1.86 639 158 797 4.04 26–30 3667 2166 5833 1.69 3046 714 3760 4.27 31–35 4381 2063 6444 2.12 5110 1073 6183 4.76 36–40 4360 1712 6072 2.55 6274 1249 7523 5.02 41–45 3514 1538 5052 2.28 6733 1325 8058 5.08 46–50 2820 1404 4224 2.01 6573 1386 7959 4.74 51–55 1428 633 2061 2.26 3569 678 4247 5.26 56–60 618 179 797 3.45 1540 193 1733 7.98 61–65 208 47 255 4.43 502 64 566 7.84 66–70 111 20 131 5.55 209 12 221 17.42 71+ 28 0 28 - 18 0 18 - Total 22,075 10,267 32,342 2.15 34,213 6852 41,065 4.99 MWR = men-to-women ratio. The total MWR was 2.15 and 4.99 in the half and full marathon, respectively. A gender × race distance association in participation was shown (χ2 = 2294.505, p < 0.01, ϕ = 0.177). A gender × age group association in participation was observed in the half marathon (χ2 = 89.091, p < 0.01, ϕ = 0.081) and in the full marathon (χ2 = 53.431, p < 0.01, ϕ = 0.050). Furthermore, a race distance × age group association in participation was Int. J. Environ. Res. Public Health 2022, 19, 7802 4 of 9 shown for male ﬁnishers (χ2 = 1687.054, p < 0.01, ϕ = 0.187) and for female ﬁnishers (χ2 = 600.388, p < 0.01, ϕ = 0.197) of different age groups. In the half marathon, the lowest MWR was observed in the age group of 26–30 years (1.69), whereas the highest MWR was observed in the age group of 36–40 years (6.12). In the full marathon, the lowest MWR was observed in the youngest age group (4.04), whereas the highest MWR was observed in the age group of 66–70 years (17.41). 3.2. Performance (Average Running Speed) 3.2.1. Overall Effects The two-way ANOVA showed significant effects of gender [F (1, 73,365) = 612.757, p < 0.001] and race distance [F (1, 73,365) = 7.914, p < 0.005], as well as age group [F (10, 73,365) = 119.550, p < 0.001]. Moreover, we found significant interactions for age group × race distance [F (10, 73,365) = 3.763, p < 0.001], while no interaction was observed for gender × race distance [F (1, 73,365) = 3.270, p = 0.071] and for gender × age group [F (9, 73,365) = 1.329, p = 0.216]. 3.2.2. Performance by Gender and Race Distance A signiﬁcant effect of gender on average running speed is shown (p < 0.001) in Figure 1, where male ﬁnishers (with the performance of 10.03 ± 1.67 km/h) were faster than female ﬁnishers (with the performance of 9.08 ± 1.27 km/h). In addition, a signiﬁcant effect of race distance on average running speed was observed (p < 0.001). Figure 1 also shows that performance in the full-marathon event (9.95 ± 1.71 km/h) was faster than that in the half-marathon event (9.63 ± 1.53 km/h). 0.177). A gender × age group association in participation was observed in the half mara thon (χ2 = 89.091, p < 0.01, φ = 0.081) and in the full marathon (χ2 = 53.431, p < 0.01, φ = 0.050). Furthermore, a race distance × age group association in participation was shown for male finishers (χ2 = 1687.054, p < 0.01, φ = 0.187) and for female finishers (χ2 = 600.388, p < 0.01, φ = 0.197) of different age groups. In the half marathon, the lowest MWR was observed in the age group of 26–30 years (1.69), whereas the highest MWR was observed in the age group of 36–40 years (6.12). In the full marathon, the lowest MWR was observed in the youngest age group (4.04), whereas the highest MWR was observed in the age group of 66–70 years (17.41). 3.2. Performance (Average Running Speed) 3.2.1. Overall Effects The two-way ANOVA showed significant effects of gender [F (1, 73,365) = 612.757, p < 0.001] and race distance [F (1, 73,365) = 7.914, p < 0.005], as well as age group [F (10, 73,365) = 119.550, p < 0.001]. Moreover, we found significant interactions for age group × race dis- tance [F (10, 73,365) = 3.763, p < 0.001], while no interaction was observed for gender × race distance [F (1, 73,365) = 3.270, p = 0.071] and for gender × age group [F (9, 73,365) = 1.329, p = 0.216]. 3.2.2. Performance by Gender and Race Distance A significant effect of gender on average running speed is shown (p < 0.001) in Figure 1, where male finishers (with the performance of 10.03 ± 1.67 km/h) were faster than fe- male finishers (with the performance of 9.08 ± 1.27 km/h). In addition, a significant effect of race distance on average running speed was observed (p < 0.001). Figure 1 also shows that performance in the full-marathon event (9.95 ± 1.71 km/h) was faster than that in the half-marathon event (9.63 ± 1.53 km/h). No gender × race distance interaction on average running speed was found (p > 0.05), while the gender difference was lower in the half-marathon event (+9.98%) than in the full-marathon event (+10.25%). Half-marathon finishers were slower than full-marathon finishers among females (9.02 ± 1.22 versus 9.17 ± 1.35 km/h, respectively, p < 0.001), as well as among males (9.92 ± 1.57 versus 10.11 ± 1.73 km/h, respectively, p < 0.001). Figure 1. Race speed by race distance and gender. Error bars represent standard deviations. ˆˆ p < 0.001; p < 0.001. No gender × race distance interaction on average running speed was found (p > 0.05), while the gender difference was lower in the half-marathon event (+9.98%) than in the full-marathon event (+10.25%). Half-marathon ﬁnishers were slower than full-marathon ﬁnishers among females (9.02 ± 1.22 versus 9.17 ± 1.35 km/h, respectively, p < 0.001), as well as among males (9.92 ± 1.57 versus 10.11 ± 1.73 km/h, respectively, p < 0.001). Int. J. Environ. Res. Public Health 2022, 19, 7802 5 of 9 3.2.3. Performance by Age Group and Race Distance The age group × race distance interaction had a signiﬁcant effect on average running speed (p < 0.001). Under closer examination of the performance, male ﬁnishers had the fastest average running speed of 10.33 ± 1.60 km/h, while female ﬁnishers had the slowest average running speed of 8.54 ± 1.03 km/h, regardless of the type of event. In the half-marathon event, the fastest male age group was 61–65 years (average running speed of 10.32 ± 1.45 km/h), while the slowest male age group was 26–30 years (average running speed of 9.61 ± 1.57 km/h). In the full-marathon event, the male ﬁnishers had the fastest speed of 10.39 ± 1.62 km/h in the 46–50 age interval and the slowest speed of 9.16 ± 1.18 km/h in the 71+ age interval (Figure 2a). Figure 1. Race speed by race distance and gender. Error bars represent standard deviations. ^^ p < 0.001; p < 0.001. 3.2.3. Performance by Age Group and Race Distance The age group × race distance interaction had a significant effect on average running speed (p < 0.001). Under closer examination of the performance, male finishers had the fastest average running speed of 10.33 ± 1.60 km/h, while female finishers had the slowest average running speed of 8.54 ± 1.03 km/h, regardless of the type of event. In the half-marathon event, the fastest male age group was 61–65 years (average run- ning speed of 10.32 ± 1.45 km/h), while the slowest male age group was 26–30 years (av- erage running speed of 9.61 ± 1.57 km/h). In the full-marathon event, the male finishers had the fastest speed of 10.39 ± 1.62 km/h in the 46–50 age interval and the slowest speed of 9.16 ± 1.18 km/h in the 71+ age interval (Figure 2a). In the half-marathon event, female finishers had the fastest average running speed of 9.61 ± 1.21 km/h in the 66–70 age interval, but the slowest average running speed of 8.65 ± 1.13 km/h in the 26–30 age group. In the full-marathon event, the fastest female finisher was observed in the 46–50 age group (9.43 ± 1.28 km/h), while the slowest female was in the 66–70 age group (8.48 ± 0.91 km/h) (Figure 2b). Figure 2. Race speed by age group and gender in the half-marathon event (a) and in the full-mara- thon event (b). Error bars represent standard deviations. ## p < 0.01; # p < 0.05. ^^ p < 0.01; ^ p < 0.05. 3.2.4. Performance by Gender and Age Group Although the main effect of gender × age group interaction on the average running speed (p = 0.216) was obtained, Bonferroni post hoc comparisons revealed a significant difference (Table 2). In both male and female finishers, there was a significant difference between the age group of 21–25 years and the age group of 36–60 years (p < 0.01), as well as the age group of 61–55 years (p < 0.05). The same difference was found in the age groups of 26–30 years and 31–65 years (p < 0.01). There was also a difference in the average running speed between age groups of 31– 35 years, 36–60 years (p < 0.01) and 61–55 years (p < 0.05). Another significant difference was observed between the 36–40 age interval and the 41–55 age interval (p < 0.01). The performance of the 46–50 age interval showed a significant difference with the 41–45 age interval (p < 0.01) and the 51–55 age interval (p < 0.01). Figure 2. Race speed by age group and gender in the half-marathon event (a) and in the full-marathon event (b). Error bars represent standard deviations. ˆˆ p < 0.01; ˆ p < 0.05. In the half-marathon event, female ﬁnishers had the fastest average running speed of 9.61 ± 1.21 km/h in the 66–70 age interval, but the slowest average running speed of 8.65 ± 1.13 km/h in the 26–30 age group. In the full-marathon event, the fastest female ﬁnisher was observed in the 46–50 age group (9.43 ± 1.28 km/h), while the slowest female was in the 66–70 age group (8.48 ± 0.91 km/h) (Figure 2b). 3.2.4. Performance by Gender and Age Group Although the main effect of gender × age group interaction on the average running speed (p = 0.216) was obtained, Bonferroni post hoc comparisons revealed a signiﬁcant difference (Table 2). In both male and female ﬁnishers, there was a signiﬁcant difference between the age group of 21–25 years and the age group of 36–60 years (p < 0.01), as well as the age group of 61–55 years (p < 0.05). The same difference was found in the age groups of 26–30 years and 31–65 years (p < 0.01). There was also a difference in the average running speed between age groups of 31–35 years, 36–60 years (p < 0.01) and 61–55 years (p < 0.05). Another signiﬁcant difference was observed between the 36–40 age interval and the 41–55 age interval (p < 0.01). The performance of the 46–50 age interval showed a signiﬁcant difference with the 41–45 age interval (p < 0.01) and the 51–55 age interval (p < 0.01). Int. J. Environ. Res. Public Health 2022, 19, 7802 6 of 9 Table 2. Bonferroni post hoc tests of age groups. Age Groups 21–25 26–30 31–35 36–40 41–45 46–50 51–55 56–60 61–65 66–70 71+ 21–25 - - - ** * - - 26–30 - - - - 31–35 - ## - * - - 36–40 ## ## ## - - - - - 41–45 ## ## ## ## - - - - - - 46–50 ## ## ## ## ## - ** - - - - 51–55 ## ## ## ## ## - - - - - 56–60 ## ## ## - - - - - - - - 61–65 # ## # - - - - - - - - 66–70 - - - - - - - - - - - 71+ - - - - - - - - - - - * represent male, # represent female, ** or ## p < 0.01; * or # p < 0.05. 4. Discussion The aim of this study was to assess the age, gender and average speed characteristics of over 70,000 long-distance ﬁnishers over the last four years. We found that the 31–35 and 36–40 age groups had the largest number of male ﬁnishers in the half-marathon event, while the 26–30 and 36–40 age groups had the largest number of female ﬁnishers. These ﬁndings are partly in agreement with previous studies, which reported that, regardless of gender, the largest number of ﬁnishers was found in the 24–34 age group [28]. In addition, a study conducted in Switzerland found that the two largest numbers of full-marathon ﬁnishers were in the age ranges 40–44 and 45–49 in men, but 35–39 and 40–44 in women; this study also reported that most half- marathon participants were 30–34 years old and 45–49 years old in men, but 25–29 years old and 30–34 years old in women [29]. Although there are differences among age groups in Asian and European populations, over 30 years of age appeared to be the dominant age in participants in long-distance running, which may be related to the relationship with physical needs, social inﬂuences and disposable time [30]. The study showed that the largest age group participating in full marathon events was usually older than the half-marathon runners. Such results may be due to older runners being more emotionally stable and responsible than younger people [31]. In terms of the number of ﬁnishers by race distance, there were ~1.3 times more marathon runners than half-marathon runners, which is not in agreement with the previous ﬁndings. The reason for this phenomenon may be that the marathon organizing committee limited the number of half-marathon runners during the registration period. In Switzerland, the total number of half-marathon runners was about 2.6 times higher than the marathon runners between 2000 and 2010 [32]. In Greece, the total number of half-marathon runners was about 3.5 times higher than that in the Oslo Marathon between 2008 and 2018 [28]. This could be explained by the fact that the registered number of half-marathon events is substantially lower than the full-marathon events held in China [33]. It is not difﬁcult to understand the phenomenon that the number of people who ﬁnish the half marathon is higher than the number of people who ﬁnish the full marathon. The demand for physical ﬁtness in the full marathon is undoubtedly higher. Running long distances can lead to dehydration, damage to muscle tissue and an increase in body tem- perature, which must be more difﬁcult to overcome during a full marathon [34]. Moreover, the depletion of glycogen stores in the body causes athletes to “hit the wall (HTW)”; the frequency of HTW among elite and non-elite runners was 51%, with the greatest proportion being found in non-elite runners. Thus, HTW also increases the risk of withdrawal as the distance of the competition increases [35]. Our data conﬁrmed that a higher number of male ﬁnishers than females was also observed in a longer-distance event, which is similar to the “Marathon des Sables” (7-day competition) with a men-to-women ratio (MWR) of 6.76 [3], the “Western States 100-Mile Endurance Run” (161 km) with an MWR of 5.28 [36], or Double Iron Ultra-Triathlon Int. J. Environ. Res. Public Health 2022, 19, 7802 7 of 9 (MWR: 8.96) to Deca Iron Ultra-Triathlon (MWR: 6.94) [37]. However, over the past few years, an increase in women ﬁnishers was observed in half- and full-marathon events, as indicated by the decreased trend of MWR. A plausible explanation for the MWR variation by race distances/intensities might be the consideration of females as relatively “novice” runners compared to males. One study reported that the MWR was decreased from 10.2 in the 1970s to 1.5 in the 2010s in the “New York City Marathon” [38]. Women complete shorter-distance races ﬁrst and then longer-distance races according to their ability and physiological features. The majority opted for the half marathon, resulting in a higher number of female ﬁnishers in the half marathon than in the full marathon. Regarding changes in ﬁnishers by gender and age group, similar trends were shown across all race distances, with older groups having a higher MWR than younger age groups. The current ﬁndings showed faster running performance in males than in females and faster average running speed in shorter race distances. This may be attributable to the innate physiological advantage of male runners and gender differences in training habits, with males having greater body weights and lower body fat percentages in terms of physiol- ogy [3]. The research pointed out that men and women are born with different muscle ﬁber properties, and the advantage of men is that the circumference of muscle ﬁbers is larger than that of women, so they are more powerful [39]. This is the difference between men and women in gene expression in human skeletal muscle [40]. Furthermore, male athletes exhibit higher maximal oxygen uptake (VO2max) and anaerobic thresholds than females in long-distance running [41]; there is a positive correlation between VO2max and thermoreg- ulatory ability [42]. Some articles have pointed out that in the non-competition training phase, men have longer training distances than women and have more weekly training sessions, so the running experience of male runners is higher than that of women [43]; the same goes for training differences for a half marathon [3]. Some researchers have also found that the larger male-to-female ratio in the older group is due to the lower number of female ﬁnishers [44]. Our data also found that the average pace of women in half-marathon events increased with age. In previous research, the slowest pace occurred in the older age groups [22]. One of the reasons for this could be that most young female participants in the half marathon were attempting long-distance running for the ﬁrst time, which makes the pace slower in younger age groups. At the same time, the running speeds of older Chinese women groups were better than Western women. Eastern women are morphologically thinner than Western women [38], thus saving more running economy in long-distance running and are, therefore, faster on average. There were several limitations in the present study. First of all, the role of environ- mental conditions, such as detailed data on temperature, humidity, and wind, was not considered. It is reasonable to involve the effect of these environmental parameters on the endurance performance [45]. Secondly, the number of participants was higher than other studies, as four-year data were accessible. Long-term research should be conducted in future. Thirdly, other endurance events such as 10 km races should be included. 5. Conclusions This study found that the number of female ﬁnishers in the half- and full-marathon races has increased, but, overall, there are still more males than females. A higher number of younger female ﬁnishers participated in both race distances. Moreover, the overall performance of male’s running is better than female’s running, but as the race distance and the age of participants increased, the difference in performance caused by gender gradually weakened. It should be emphasized that the analysis of performance trends is related to changes in MWR based on age group and race distance. Int. J. Environ. Res. Public Health 2022, 19, 7802 8 of 9 Author Contributions: Conceptualization, Y.-F.S., F.Y. and S.-J.Y.; methodology, F.Y. and S.-J.Y. and W.-K.L.; software, F.Y., Y.G. and S.-W.J.; validation, Y.G., S.-J.Y. and W.-K.L.; formal analysis, F.Y.; investigation, S.-J.Y., W.S. and Y.-F.S.; resources, S.-J.Y.; data curation, S.-W.J., S.-J.Y., Y.W. and W.S.; writing—original draft preparation, S.-J.Y.; writing—review and editing, W.-K.L. and F.Y.; visualiza- tion, S.-J.Y. and W.-K.L.; supervision, S.-J.Y. and Y.W.; project administration, F.Y.; funding acquisition, F.Y. and W.-K.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Beijing Technology and Innovation Service Development Research Fund, grant number 154218001. Institutional Review Board Statement: Not applicable. 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. 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. Ren, Z.; Zuo, Y.; Ma, Y.; Zhang, M.; Smith, L.; Yang, L.; Loprinzi, P.D.; Yu, Q.; Zou, L. The Natural Environmental Factors Inﬂuencing the Spatial Distribution of Marathon Event: A Case Study from China. Int. J. Environ. Res. Public Health 2020, 17, 2238. 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PMC9209328	ARTICLE OPEN Rethinking aerobic exercise intensity prescription in adults with spinal cord injury: time to end the use of “moderate to vigorous” intensity? Michael J. Hutchinson 1 and Victoria L. Goosey-Tolfrey 1✉ © The Author(s) 2021 STUDY DESIGN: Cohort study. OBJECTIVES: To investigate and critique different methods for aerobic exercise intensity prescription in adults with spinal cord injury (SCI). SETTING: University laboratory in Loughborough, UK. METHODS: Trained athletes were split into those with paraplegia (PARA; n = 47), tetraplegia (TETRA; n = 20) or alternate health condition (NON-SCI; n = 67). Participants completed a submaximal step test with 3 min stages, followed by graded exercise test to exhaustion. Handcycling, arm crank ergometry or wheelchair propulsion were performed depending on the sport of the participant. Oxygen uptake (V̇O2), heart rate (HR), blood lactate concentration ([BLa]) and ratings of perceived exertion (RPE) on Borg’s RPE scale were measured throughout. Lactate thresholds were identiﬁed according to log-V̇O2 plotted against log-[BLa] (LT1) and 1.5 mmol L−1 greater than LT1 (LT2). These were used to demarcate moderate (<LT1), heavy (>LT1, < LT2) and severe (>LT2) exercise intensity domains. RESULTS: Associations between percentage of peak V̇O2 (%V̇O2peak) and HR (%HRpeak) with RPE differed between PARA and TETRA. At LT1 and LT2, %V̇O2peak and %HRpeak were signiﬁcantly greater in TETRA compared to PARA and NON-SCI (P < 0.05). The variation in %V̇O2peak and %HRpeak at lactate thresholds resulted in large variability in the domain distribution at ﬁxed %V̇O2peak and %HRpeak. CONCLUSIONS: Fixed %V̇O2peak and %HRpeak should not be used for aerobic exercise intensity prescription in adults with SCI as the method does not lead to uniform exercise intensity domain distribution. Spinal Cord (2022) 60:484–490; https://doi.org/10.1038/s41393-021-00733-2 INTRODUCTION For adults with spinal cord injury (SCI), aerobic exercise is beneﬁcial for improving indices of physical [1] and mental [2] health. On this theme, scientiﬁc guidelines published in 2018 describe the dose of aerobic exercise required to improve cardiorespiratory ﬁtness and cardiometabolic health in adults with SCI [3]. Central to the guidelines is information on the frequency (e.g., 3 times per week) and duration (e.g., 30 min) of the exercise, both of which are simple to deﬁne and monitor. The ﬁnal important aspect of the guidelines is the exercise intensity. If aerobic exercise is performed at too low an intensity, without sufﬁcient exercise volume, it will not lead to beneﬁcial physiolo- gical adaptations [4]. Despite this, the guidelines provide no clear prescription of the exercise intensity, other than to say that aerobic exercise should be of a “moderate to vigorous” intensity [3]. The lack of clarity with the exercise intensity terminology is a hindrance to adults with SCI using the guidelines to inform their exercise habits; practitioners actively prescribing exercise training; and researchers investigating the effects of exercise training interventions on markers of health in adults with SCI. There is, therefore, an urgent need to better understand aerobic exercise intensity prescription in adults with SCI. Guidelines for non-disabled adults deﬁne thresholds for ﬁve intensity zones (very light, light, moderate, vigorous, near- maximal/maximal) according to many physiological variables [5]. These variables include percentage maximum oxygen uptake (% V̇O2max) and heart rate (%HRmax), oxygen uptake and heart rate reserve (%V̇O2R, %HRR), and ratings of perceived exertion (RPE) [5]. However, despite the SCI guidelines adopting the “moderate” and “vigorous” descriptives, there is no equivalent resource published for adults with SCI regarding the physiological thresh- olds coinciding with these descriptors. Furthermore, given the physiological consequences of SCI on cardiovascular and respira- tory responses to exercise [6], there is no justiﬁcation for simply adopting the percentage thresholds utilised for non-disabled adults. An alternative approach to exercise intensity prescription is to consider whether different methods result in participants exercis- ing in the same of three exercise domains (moderate, heavy, severe) [7]. This is because of the similar V̇O2 and blood lactate responses between individuals exercising in these domains [7]. Speciﬁcally, the moderate intensity domain (below lactate thresh- old (LT)) is characterised by steady state responses for V̇O2 and blood lactate concentration ([BLa]) [8]. In the heavy intensity Received: 26 July 2021 Revised: 19 November 2021 Accepted: 23 November 2021 Published online: 8 December 2021 1Peter Harrison Centre for Disability Sport, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK. ✉email: V.L.Tolfrey@lboro.ac.uk www.nature.com/sc 1234567890();,: domain (between LT and critical power/speed (CP/CS)) there is a delayed steady state response due to the V̇O2 “slow component”, whilst in the severe domain (above CP/CS) no steady state response is observed [8]. To satisfy the aim of producing a homogenous exercise intensity, the ﬁxed percentage approach is only valid if it is demonstrated that equal relative intensities result in individuals exercising in the same intensity domain [7]. However, in a recent study of non-disabled participants, no ﬁxed %V̇O2max or %HRmax, typically used for exercise prescription, resulted in all participants being in the same intensity domain [9]. This has led to assertions that using ﬁxed %V̇O2max or %HRmax for prescribing exercise intensity is inaccurate and will lead to signiﬁcant inter-individual physiological responses, precluding homogenous exercise inten- sity prescription [7, 9]. Furthermore, with evidence that individual participant %V̇O2R:%HRR relationships diverges from the assumed linear trajectory, there are also questions over how appropriate % V̇O2R and %HRR are for prescribing exercise intensity at the individual level [10]. For adults with SCI there is currently nothing more to inform aerobic exercise intensity prescription than the arbitrary use of “moderate to vigorous” intensity [3]. Furthermore, evidence in non-disabled adults would suggest a need to rethink the traditional use of ﬁxed percentages [7, 9, 10]. Therefore, this study aimed to investigate and critique potential methods for prescribing aerobic exercise intensity in adults with SCI. METHODS This study was performed via a retrospective analysis of athlete data collected in the author’s laboratory. All procedures were approved by the Human participants ethical sub-committee at Loughborough University, and participants provided written, informed consent. Participants Data were available for 134 individuals (male: 98; female: 36). Participants were split into those with paraplegia (PARA), tetraplegia (TETRA), or alternate health condition (NON-SCI), see Table 1. Examples of health conditions for NON-SCI included spina biﬁda, limb deﬁciency, cerebral palsy, and arthrogryposis. Participants were competitive athletes, compet- ing at a national or international level, from one of the following sports: handcycling, para-alpine ski, paratriathlon, wheelchair basketball, wheel- chair rugby or wheelchair tennis. Exercise testing Participants completed a submaximal step test followed by graded exercise test (GXT) to exhaustion. Handcycle (HC) tests were performed in the participants own handcycle attached to a Cyclus 2 ergometer (Avantronic Richter, Leipzig, Germany). For some Paratriathlon, and all para-alpine ski athletes, arm crank ergometry (ACE) was used (Lode Angio, Lode B. V., Groningen, the Netherlands). The ergometer was positioned vertically so the crank axis centre was level with the shoulder, and horizontally to allow slight elbow ﬂexion at the furthest point of the crank cycle. Wheelchair basketball, rugby and tennis players performed a wheelchair propulsion (WCP) test using a motorised treadmill (HP Cosmos, Traunstein, Germany) and their own custom sports wheelchair. Submaximal tests were individualised based on the sport, sex, training status and level of impairment of the participant, with the goal of completing 6-8 stages (average: 6; range: 4–10). HC and ACE tests started at 15–60 W, with 10–20 W increments every 3 min. WCP tests started at 0.7–2.8 m s−1 and were increased by 0.2-0–4 m s−1 every 3 min. V̇O2 (Metalyzer 3B, Cortex, Leipzig, Germany) and HR (RS400, Polar, Kempele, Finland) were continually monitored throughout. The Metalyzer was calibrated before each participant against ambient air and a mix of 15% O2, 5% CO2, with the volume calibrated using a 3 L syringe. RPE was verbally reported in the ﬁnal minute of each stage using Borg’s 6–20 RPE scale [11]. A capillary blood sample from the ear lobe was collected at the end of each stage for measurement of [BLa] (Biosen C-line, EKF Diagnostics, Barleben, Germany). HC and ACE tests were continuous, however, WCP tests were discontinuous as the treadmill needed to be slowed between stages to facilitate blood sampling. For discontinuous tests, the typical interval between stages was 45–60 s. Submaximal tests continued until [BLa] exceeded 4 mmol L−1 or RPE was rated as 17. The RPE criteria was used in TETRA where there may have been blunted lactate responses [12]. Following the submaximal test, participants received 15 min of active recovery or rest before performing a GXT to exhaustion. The starting workload was set to that from the preceding test when [BLa] increased by 0.5 mmol L−1 above rest. Participants performed 1 min at this load, before the exercise intensity were increased in a stepwise manner by 10–20 W min−1 (HC/ACE) or 0.1 m s−1 min−1 (WCP) until participants reached volitional exhaustion. This was deﬁned as an inability to maintain their preferred cadence at the required PO for HC/ACE, or the required speed of the treadmill, despite verbal encouragement. V̇O2 and HR were again monitored throughout, with RPE and [BLa] measured at the end of the test. Data processing V̇O2 and HR data were subjected to a 30 s rolling average, with the greatest of these from the GXT recorded as peak values (V̇O2peak, HRpeak). V̇O2 and HR in the ﬁnal 30 s of each submaximal stage were extracted and calculated as percentages of peak (%V̇O2peak, %HRpeak). Using the submaximal data, the lactate thresholds were identiﬁed as the intersection of the horizontal and ascending sections of the plot of log-[BLa] against log-V̇O2 (LT1) [13], and at [BLa] equal to LT1 plus 1.5 mmol L−1 (LT2) [14]. The inverse of the log-V̇O2 at these points were calculated to give the V̇O2 at LT1 and LT2. HR at LT1 and LT2 was identiﬁed by interpolation of the linear V̇O2:HR relationship for each participant. RPE was modelled against [BLa] using a quadratic function for each participant, with the resultant coefﬁcients used to calculate the RPE at LT1 and LT2 [15]. Exercise intensity domains were deﬁned as moderate (<LT1), heavy (between LT1 and LT2) and severe (>LT2). Statistical analyses Analyses were performed using IBM SPSS Statistics Version 23.0 (IBM Corp., Armonk, NY) and MLWiN Version 3.05 [16]. Data are presented as mean (standard deviation) with statistical signiﬁcance accepted at P < 0.05. Data were checked for normal distribution using the Shapiro Wilk statistic. All individual RPE data points were modelled against the corresponding %V̇O2peak and %HRpeak using dynamic multilevel models with lagged independent variable, whilst accounting for the initial condition. Separate models were created for %V̇O2peak and %HRpeak, which served as the independent variable, with RPE as the dependent variable. Models were multilevel to adjust for the repeated stages performed by each participant and were used due to their ability to characterise group- and individual- level effects [17]. Stage was deﬁned as the ﬁrst, and participant as the second level. Models accounted for the initial condition (e.g., stage (i) = 1) as it was thought that RPE would depend on the %V̇O2peak and %HRpeak when i = 1. The need for them to be dynamic and incorporate a lagged independent variable was required as it was thought RPE at subsequent measurement occasions (when i > 1) would be dependent on %V̇O2peak and %HRpeak for that, as well as the previous, measurement occasion (i.e., i–1). Potential confounding variables were added to the models to assess whether they improved the model ﬁt with ﬁxed effects, or random effects for between- and within-individual variation. Confounding variables were sex (male/female), group (PARA/TETRA/NON-SCI) and exercise mode (ACE/ HC/WCP). The resultant models were used to calculate the %V̇O2peak and % HRpeak corresponding to each value on Borg’s RPE scale. Differences in V̇O2 (L min−1, ml kg−1 min−1, %V̇O2peak), HR (beats·min−1, %HRpeak) and RPE between groups at LT1 and LT2 were assessed via one- way analysis of variance with Bonferroni post-hoc correction for multiple comparisons. Standardised effect sizes (ES) were calculated to describe the magnitude of differences and categorised as trivial (< 0.2), small (0.2–0.6), moderate (0.6–1.2), large (1.2–2.0) and very large (> 2.0) [18]. For each group, the percentage of participants in each intensity domain (moderate, heavy, severe) were calculated at 5% intervals from 35 to 95% V̇O2peak and %HRpeak. RESULTS The associations between RPE and both %V̇O2peak and %HRpeak were not signiﬁcantly affected by sex or exercise mode, so stratiﬁcation based on these variables was not needed. The % M.J. Hutchinson and V.L. Goosey-Tolfrey 485 Spinal Cord (2022) 60:484 – 490 V̇O2peak and %HRpeak coinciding with each rating on Borg’s RPE scale for PARA and TETRA can be found in Table 2. The full RPE models against %V̇O2peak and %HRpeak can be found in the Supplementary Material. RPE and %V̇O2peak model RPE was signiﬁcantly affected by the initial %V̇O2peak (i = 1) (P < 0.01), by %V̇O2peak at subsequent occasions when i > 1 (P < 0.01), as well as by the lagged %V̇O2peak (i.e., i–1) (P < 0.01). Each of these variables also showed signiﬁcant between-individual variation, which was incorporated into the model. There was also an effect of Group at occasions when i > 1 (P = 0.01). TETRA showing signiﬁcantly greater within-individual variation for the effect of % V̇O2peak on RPE compared to PARA and NON-SCI. As such, PARA and NON-SCI remained grouped, as there was no difference between these groups. RPE and %HRpeak model RPE was signiﬁcantly affected by the initial %HRpeak (i = 1) (P < 0.01), by %HRpeak at subsequent occasions when i > 1 (P < 0.01), as well as by the lagged %HRpeak (i.e., i–1) (P < 0.01). These effects were ﬁxed and showed no signiﬁcant between- or within- individual variation. There was a ﬁxed effect for Group, with the association between RPE and %HRpeak being signiﬁcantly different for PARA (P = 0.03). There was no difference between TETRA and NON-SCI, so these remained grouped in this model. Responses at LT1 and LT2 The V̇O2, HR and RPE at LT1 and LT2 are shown in Fig. 1. At LT1 there was a signiﬁcant group effect for absolute (F2 = 7.11, P < 0.01; Fig. 1a) and relative V̇O2 (F2 = 17.65, P < 0.01; Fig. 1c), % V̇O2peak (F2 = 9.86, P < 0.01; Fig. 1e), HR (F2 = 42.79, P < 0.01; Fig. 1i) and %HRpeak (F2 = 7.94, P < 0.01; Fig. 1k). LT1 occurred at a signiﬁcantly smaller absolute and relative V̇O2 in TETRA compared to PARA (ES = 0.86, 1.00) and NON-SCI (ES = 1.00, 1.62). However, %V̇O2peak at LT1 was signiﬁcantly greater in TETRA compared to PARA (ES = 0.96) and NON-SCI (ES = 0.94). Similarly, HR at LT1 was smaller in TETRA compared to PARA (ES = 2.33) and NON-SCI (ES = 2.74), whereas %HRpeak was greater in TETRA compared to PARA (ES = 1.00) and NON-SCI (ES = 0.69). There was no signiﬁcant difference between groups for RPE (F2 = 0.48, P = 0.62) at LT1 (Fig. 1g). There was also a signiﬁcant group effect at LT2 for absolute (F2 = 9.96, P < 0.01; Fig. 1b) and relative V̇O2 (F2 = 19.75, P < 0.01; Table 1. Participant characteristics by group. PARA TETRA NON-SCI Sample size (n) 47 20 67 Sex (M/F) 30/17 18/2 50/17 Age (years) 33 ± 8a 32 ± 7a 27 ± 7 Body mass (kg) 70.9 ± 14.1a 70.8 ± 12.9 64.5 ± 12.8 Neurological level of injury T4-L2 C3-C7 - Injury completeness Complete: 21 Complete: 8 - Incomplete: 23 Incomplete: 4 Unavailable: 3 Unavailable: 8 Time since injury (years) 12 ± 9 12 ± 6 - Peak oxygen uptake (L·min−1) 2.5 ± 0.6b 1.7 ± 0.5 2.6 ± 0.7b (ml·kg−1·min−1) 35.1 ± 8.2b 23.4 ± 5.8 39.9 ± 8.3b Peak heart rate (beats·min−1) 188 ± 9b 134 ± 20 187 ± 10 b Sport (n) Handcycling 8 0 3 Paratriathlon 11 0 8 Para alpine ski 2 1 2 Wheelchair basketball 20 1 31 Wheelchair rugby 0 15 13 Wheelchair tennis 6 3 10 Test mode (n) Arm crank ergometry 11 1 7 Handcycling 10 0 6 Wheelchair propulsion 26 19 54 a: signiﬁcantly greater than NON-SCI; b: signiﬁcantly greater than TETRA, P < 0.05. Table 2. Resultant calculations of percentage peak oxygen uptake and heart rate by group based on multilevel modelling. RPE %V̇O2peak %HRpeak PARA TETRA PARA TETRA 6 16 22 40 43 7 22 28 44 48 8 28 34 49 52 9 34 40 54 57 10 40 46 58 62 11 46 52 63 66 12 52 58 68 71 13 58 64 73 76 14 64 70 77 81 15 70 76 82 85 16 76 82 87 90 17 82 88 91 95 18 88 94 96 99 19 94 100 100 20 100 M.J. Hutchinson and V.L. Goosey-Tolfrey 486 Spinal Cord (2022) 60:484 – 490 Fig. 1d), %V̇O2peak (F2 = 14.80, P < 0.01; Fig. 1f), HR (F2 = 58.99, P < 0.01; Fig. 1i) and %HRpeak (F2 = 6.10, P < 0.01; Fig. 1l). Absolute and relative V̇O2 at LT2 were signiﬁcantly smaller in TETRA compared to PARA (ES = 0.83, 0.97) and NON-SCI (ES = 1.03, 1.72). However, %V̇O2peak at LT2 was signiﬁcantly greater in TETRA compared to PARA (ES = 1.26) and NON-SCI (ES = 1.06). Furthermore, HR at LT2 was signiﬁcantly smaller in TETRA than in PARA (ES = 3.00) and NON-SCI (ES = 3.55), while %HRpeak was signiﬁcantly greater in TETRA compared to PARA (ES = 0.93) and NON-SCI (ES = 0.62). There was no signiﬁcant difference between groups in RPE (F2 = 2.18, P = 0.19) at LT2 (Fig. 1h). Intensity classiﬁcation Thresholds for %V̇O2peak and %HRpeak corresponding with intensity classiﬁcations used in non-disabled exercise guidelines are shown in Table 3. These data suggest there are differences between non-disabled individuals, PARA and TETRA in the thresholds for intensity classiﬁcations. Frequency distribution of individuals within moderate, heavy and severe intensity domains for discrete percentages of %V̇O2peak and %HRpeak are shown in Figs. 2 and 3, respectively. These show that no % V̇O2peak or %HRpeak typically used for exercise prescription purposes leads to all participants being in the same domain, with many %V̇O2peak including participants spread across all three domains. DISCUSSION This study aimed to investigate potential methods of aerobic exercise intensity prescription in adults with SCI. Findings demonstrate that there are differences between PARA and TETRA for the %V̇O2peak and %HRpeak corresponding with the Fig. 1 Group physiological responses at lactate thresholds. a Absolute V̇O2 at LT1. b Absolute V̇O2 at LT2. c Relative V̇O2 at LT1. d Relative V̇O2 at LT2. e %V̇O2peak at LT1. f %V̇O2peak at LT2. g RPE at LT1. h RPE at LT2. i HR at LT1. j HR at LT2. k %HRpeak at LT1. l %HRpeak at LT2. Data are presented at mean (SD) with individual points overlaid. Within each group, numbers refer to the same participant in each ﬁgure. Asterisk (*) indicates signiﬁcantly greater than the identiﬁed group, P < 0.05. Table 3. Classiﬁcation of exercise intensity for individuals with paraplegia and tetraplegia, compared to non-disabled guidelines. Very light (RPE ≤ 8)a Light (RPE 9–11)a Moderate (RPE 12–13)a Vigorous (RPE 14–17)a Near maximal-maximal (RPE ≥ 18)a %V̇O2peak Non-disableda ≤36% V̇O2peak 37–45% V̇O2peak 46–63% V̇O2peak 64–90% V̇O2peak ≥91% V̇O2peak Paraplegia ≤31% V̇O2peak 32–49% V̇O2peak 50–61% V̇O2peak 62–85% V̇O2peak ≥86% V̇O2peak Tetraplegia ≤37% V̇O2peak 38-55% V̇O2peak 56–67% V̇O2peak 68–91% V̇O2peak ≥92% V̇O2peak %HRpeak Non-disableda ≤56% HRpeak 57–63% HRpeak 64-76% HRpeak 77–95% HRpeak ≥96% HRpeak Paraplegia ≤51% HRpeak 52–65% HRpeak 66-75% HRpeak 76–93% HRpeak ≥94% HRpeak Tetraplegia ≤54% HRpeak 55–68% HRpeak 69–78% HRpeak 79–97% HRpeak ≥98% HRpeak a data from Riebe et al. [5]. M.J. Hutchinson and V.L. Goosey-Tolfrey 487 Spinal Cord (2022) 60:484 – 490 descriptions of “moderate” and “vigorous” exercise intensity, as used by the exercise guidelines for adults with SCI [1, 3]. However, the ﬁndings also show that using ﬁxed %V̇O2peak or %HRpeak cannot guarantee a homogenous domain-speciﬁc exercise inten- sity prescription for adults with SCI. Fixed percentages and intensity domains for exercise intensity prescription The ﬁnding of adults with SCI being in different intensity domains, as deﬁned in this study according to LT1 and LT2, despite being at the same %V̇O2peak or %HRpeak supports similar evidence in non- disabled participants [9]. The domain-speciﬁc distribution is also arguably more variable in adults with SCI. For V̇O2peak, Iannetta et al. [9]. report participants in moderate, heavy, and severe domains only at 70% V̇O2peak, whereas in this study this was shown at several ﬁxed percentages, 55-70% V̇O2peak in PARA and 60-70% V̇O2peak in TETRA. It would, therefore, given recent calls to stop using ﬁxed percentages for exercise intensity prescription in non-disabled participants [7, 9, 10], seem appropriate that this is expanded to apply to adults with SCI performing aerobic exercise. This would apply to all adults with SCI, and not just the athletic population utilised in this study. Inter-individual variation will exist in sedentary or low-active participants as much, if not more, than in athletic groups, further limiting the use of ﬁxed percentages for exercise intensity prescription. Instead, more attention should be given to methods that can lead to participants exercising within the same exercise intensity domain, each of which are characterised by distinct V̇O2 kinetic and blood lactate proﬁles [19, 20]. This study utilised LT1 to identify the transition between moderate and heavy intensity domains, in accordance with the literature [19, 20]. However, a limitation within the current study was the use of LT2 to identify the heavy-severe domain transition, due to a lack of evidence supporting this, as well as the number of different methods used to measure and identify LT2 [7, 21]. As such, ﬁrm conclusions cannot be made regarding the heavy-severe domain transition from this study. It would have been more appropriate to measure CP/CS for this purpose [19–21], however, only data from a GXT were available in this study. This highlights an important limitation to the widespread implementation of intensity domain-related exercise prescription. Speciﬁcally, the suitability of different testing protocols for identifying different threshold concepts, as well as data collection and threshold identiﬁcation methods used [21]. This poses the challenge of how to simply prescribe domain-speciﬁc exercise intensity. Data from the present study may support the use of RPE for this purpose, as no difference in RPE was found between groups at LT1 (Fig. 1g) and LT2 (Fig. 1h), in support of previous ﬁndings [15]. Mean RPE at LT1 and LT2 in this study were found to be 11 and 15, respectively, suggesting that these values could be Fig. 2 Distribution of individuals in moderate, heavy, and severe intensity domains at ﬁxed %V̇O2peak. a PARA group. b TETRA group. c NON-SCI group. Fig. 3 Distribution of individuals in moderate, heavy and severe intensity domains at ﬁxed %HRpeak. a PARA group. b TETRA group. c NON-SCI group. M.J. Hutchinson and V.L. Goosey-Tolfrey 488 Spinal Cord (2022) 60:484 – 490 used to guide exercise prescription using a simple and easy to implement method. However, the SD for these values ranged from 1 to 2 units dependent on group. This inter-individual variation serves, therefore, as a limitation to the use of ﬁxed RPE values for exercise intensity prescription and highlights the importance of individualisation in this context. Individualisation, though, poses a further challenge, due to the trade-off between the precision required for research purposes versus the simple messaging for population-level exercise guidelines. Implications for research Our results highlight the need to prescribe exercise intensity in a way that ensures a homogenous intensity domain distribution between participants within both acute and longitudinal study designs. Previously, studies have conducted training interventions with an intensity of either a target range, or ﬁxed value, using variables such as %V̇O2peak, %HRpeak and RPE, e.g. [22–26]., which will have led to signiﬁcant domain heterogeneity. This means the dose of exercise stimulus would not have been controlled between participants. Moving forwards, researchers should identify the domain transitions and prescribe intensity in relation to these. That being said, the authors are not aware of any investigations into CP/CS using participants with SCI, so initial studies need to investigate the validity and reliability of identifying domain transitions in participants in SCI. Furthermore, V̇O2 kinetic responses to exercise in each domain should be investigated in participants with SCI, due to potential differences in V̇O2 kinetics between non-disabled participants and those with SCI [27]. Finally, these investigations must also account for any differences based on the mode of exercise [28]. Our ﬁndings also emphasise the need to individualise the exercise intensity prescription. In non-disabled participants, standardised intensity prescription (e.g., 55–75% V̇O2peak), has been shown to lead to signiﬁcant heterogeneity in responsiveness [29] leading to participants being described as either “responders” or “non-responders” to the intervention. However, an individua- lised exercise prescription (relative to ventilatory threshold) in non-disabled participants resulted in 100% responsiveness, compared to 60% in the standardised intervention [30]. Subse- quently, future exercise research in participants with SCI should individualise the intensity prescription according to intensity domains, whilst also report individual responsiveness to an intervention. This will improve methodological control while also increasing conﬁdence in conclusions made based on the data. Implications for exercise guidelines using “moderate to vigorous” exercise intensity Exercise guidelines must balance the precision required for a speciﬁc intensity prescription, against the need for a simple population-level recommendation. Furthermore, scientiﬁc guide- lines must undergo a knowledge translation process to ensure that the scientiﬁc integrity of the guidelines are maintained, whilst also incorporating the varied needs of all potential end-users [31]. In adults with SCI, the scientiﬁc guidelines [1, 3] recommend performing aerobic exercise at a “moderate to vigorous” intensity, without providing any speciﬁc details on what this means. The same intensity prescription is also used in a community and clinical- practice version of the guidelines [31]. Results from the current study would suggest the scientiﬁc integrity of such an intensity prescription is questionable. Table 3 shows equivalent thresholds for PARA and TETRA for “moderate” and “vigorous” intensity, based on the guidelines for non-disabled adults [5]. Combining these with the intensity domain distributions in Fig. 2, shows participants were spread across moderate, heavy, and severe domains at both “moderate” and “vigorous” intensities. It should also be noted that participants in the present study were competitive athletes, and that responses would likely be even more variable for sedentary or low- active populations. This shows how the use of “moderate” to “vigorous” intensity will not lead to anything close to resembling a uniform exercise intensity prescription between individuals. It also shows how someone expecting to perform “moderate” intensity exercise may actually be much closer to their maximum capacity. This will likely decrease the pleasure the person feels during the exercise, which could ultimately impact on whether they decide to continue doing it [32]. As it is operationally more difﬁcult to deﬁne compared to frequency (e.g., 3 times a week) and duration (e.g., 30 min), it is possible that exercise intensity becomes an ignored piece of exercise guidelines. Perhaps research could seek to understand end-user perceptions of “exercise intensity”, or needs for interpreting and monitoring intensity, before using that informa- tion alongside physiological principles to underpin an evidence- informed intensity prescription. Alternatively, maybe the focus for exercise guidelines should shift from exercise intensity to also acknowledge factors that might help individuals become and stay active, such as their pleasure when performing exercise [32]. CONCLUSION Prescribing a “moderate to vigorous” exercise intensity will not lead to a uniform intensity domain distribution in adults with SCI. Neither will the use of ﬁxed percentages of V̇O2peak or HRpeak, or generic values of RPE, due to inter-individual variation. 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Translating the international scientiﬁc spinal cord injury exercise guidelines into community and clinical practice guidelines: a Canadian evidence-informed resource. Spinal Cord. 2020;58:647–57. 32. Ekkekakis P, Parﬁtt G, Petruzzello SJ. The pleasure and displeasure people feel when they exercise at different intensities: decennial update and progress towards a tripartite rationale for exercise intensity prescription. Sports Med. 2011;41:641–71. ACKNOWLEDGEMENTS We would like to thank the various members of the Peter Harrison Centre for Disability Sport who were involved in the collection of data used in this study. These include Dr John Lenton, Dr Christof Leicht, Dr Tom Paulson, Dr Terri Paulson, Dr Katy Griggs, Dr Ben Stephenson, Dr Ben Stone, Tom O’Brien, and Conor Murphy. AUTHOR CONTRIBUTIONS MJH and VLGT were both responsible for developing the concept of the study and were both involved in the data collection. MJH performed the data analysis, with MJH and VLGT involved in the interpretation of the data. MJH drafted the original version of the manuscript, with VLGT providing comments. MJH and VLGT were both responsible for reviewing and approving the ﬁnal version of the manuscript. FUNDING This work was supported by funding from the Peter Harrison Foundation. STATEMENT OF ETHICS We certify that all applicable institutional regulations concerning the ethical use of human volunteers were followed during the course of this research. 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/s41393-021-00733-2. Correspondence and requests for materials should be addressed to Victoria L. Goosey-Tolfrey. 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To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/. © The Author(s) 2021 M.J. Hutchinson and V.L. Goosey-Tolfrey 490 Spinal Cord (2022) 60:484 – 490	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
PMC7005585	ORIGINAL RESEARCH published: 31 January 2020 doi: 10.3389/fphys.2020.00030 Edited by: Hassane Zouhal, University of Rennes 2, France Reviewed by: Ajmol Ali, Massey University, New Zealand Martin Burtscher, University of Innsbruck, Austria Correspondence: Yifan Lu luyifan@bsu.edu.cn Specialty section: This article was submitted to Exercise Physiology, a section of the journal Frontiers in Physiology Received: 23 October 2019 Accepted: 15 January 2020 Published: 31 January 2020 Citation: Li F, Nie J, Zhang H, Fu F, Yi L, Hopkins W, Liu Y and Lu Y (2020) Effects of Matched Intermittent and Continuous Exercise on Changes of Cardiac Biomarkers in Endurance Runners. Front. Physiol. 11:30. doi: 10.3389/fphys.2020.00030 Effects of Matched Intermittent and Continuous Exercise on Changes of Cardiac Biomarkers in Endurance Runners Feifei Li1,2, Jinlei Nie3, Haifeng Zhang1,4, Frank Fu5, Longyan Yi6, Will Hopkins7, Yang Liu1,4 and Yifan Lu2 1 College of Physical Education, Hebei Normal University, Shijiazhuang, China, 2 College of Sports Medicine and Rehabilitation, Beijing Sport University, Beijing, China, 3 School of Health Sciences and Sports, Macao Polytechnic Institute, Macao, China, 4 Provincial Key Lab of Measurement and Evaluation in Human Movement and Bio-information, Hebei Normal University, Shijiazhuang, China, 5 Dr Stephen Hui Research Centre for Physical Recreation and Wellness, Hong Kong Baptist University, Hong Kong, China, 6 Institute of Sport and Health Sciences, Beijing Sport University, Beijing, China, 7 College of Sport and Exercise Science, Victoria University, Melbourne, VIC, Australia Purpose: Endurance runners training with high-intensity intermittent exercise might experience damage to cardiac muscle. We have therefore compared changes of cardiac biomarkers after workload-matched intermittent and continuous exercise. Methods: Twelve endurance runners [11 males, 1 female; means ± SD ˙VO2max, 62.4 ± 5.4 ml kg−1 min−1; velocity of ˙VO2max (v ˙VO2max), 17.1 ± 1.4 km h−1] completed an intermittent and continuous exercise trial in random order. Intermittent exercise consisted of running at 90% v˙VO2max for 2 min followed by 50% v˙VO2max for 2 min, repeated for 92 min. Continuous exercise was performed at 70% v˙VO2max for 92 min. Blood samples were drawn before and 0, 2, 4, 24, and 48 h after exercise for assay of various cardiac biomarkers. Changes in concentration of biomarkers were averaged for the comparison of intermittent with continuous exercise after adjustment for baseline concentration and exercise intensity expressed as percent of heart-rate reserve (%HRR); magnitudes were assessed by standardization. Results: There were moderate and large increases in high-sensitivity cardiac troponin-I and -T respectively following exercise. The differences between the increases adjusted to the mean intensity of 78 %HRR were trivial, but at 85 %HRR the increases for cardiac troponin-I and -T were moderately higher for intermittent compared with continuous exercise (factor difference, ×/÷90% conﬁdence limits: 3.4, ×/÷1.9 and 2.1, ×/÷1.8 respectively). Differences in the changes in other cardiac biomarkers were trivial. Conclusion: Prolonged intermittent exercise is potentially more damaging to cardiac muscle than continuous exercise of the same average running speed at higher average heart rates in endurance runners. Keywords: high-intensity intermittent exercise, intensity, biomarkers, cardiac troponin, marathon runners Frontiers in Physiology \| www.frontiersin.org 1 January 2020 \| Volume 11 \| Article 30 Li et al. Exercise-Induced Cardiac Biomarkers Elevation INTRODUCTION There is increasing evidence that an acute bout of intense exercise can induce a minor and temporary elevation of cardiac- speciﬁc biomarkers, including various kinds of cardiac troponin, markers diagnostic of myocardial infarction (Thygesen et al., 2012; Eijsvogels et al., 2016). The elevation of cardiac troponin is aﬀected by exercise intensity (Fu et al., 2009), exercise duration (Eijsvogels et al., 2010), baseline level (Legaz-Arrese et al., 2011), training experience of the participants and age (Tian et al., 2012), gender (Kong et al., 2017), cardiovascular risk factors (Vilela et al., 2014), and environment (Li et al., 2016a). A transient increase in myocardial membrane permeability appears to be responsible, but the mechanism is still under debate (Nie et al., 2010, 2016; Li et al., 2016b). Eijsvogels et al. (2016) concluded that exercise intensity was the strongest single predictor of exercise- induced cardiac troponin elevation, and Richardson et al. (2018) found that cardiac troponin was associated with both mean and peak heart rate during exercise. Gresslien and Agewall (2016) hypothesized that there might be a threshold exercise intensity where cardiac troponin release became more marked. Athletes experience transiently high intensities of exercise when they perform interval training to enhance performance but the impact of such exercise on the heart in comparison with continuous exercise is unclear (Billat, 2001; George et al., 2012; Tschakert and Hofmann, 2013). Continuous exercise resulted in a greater cardiac troponin concentration than intermittent in sedentary men in a study by Ranjbar et al. (2017) whereas Nie et al. (2018) reported no diﬀerence between the eﬀects of intermittent and continuous exercise on cardiac troponin changes in sedentary women. In two studies of endurance athletes, intermittent running produced greater increases in cardiac troponin than continuous running, but the exercise intensity and duration were not carefully manipulated and matched (Stewart et al., 2016; Weippert et al., 2016). Other markers of cardiac muscle damage, including N-terminal pro brain natriuretic peptide (NT-pro-BNP) could also provide information on the relative eﬀects of intermittent and continuous exercise (Scherr et al., 2011). The purpose of this study was therefore to compare the changes of cardiac biomarkers after workload-matched high-intensity intermittent and continuous exercise on experienced endurance runners. It was hypothesized that prolonged intermittent exercise potentially induced more perturbations of cardiac biomarkers in endurance runners. MATERIALS AND METHODS Participants After approval of this study by the local Ethical Committee, 12 (11 males and 1 female) endurance runners [means ± standard deviation (SD): age, 23.5 ± 5.5 years; body mass, 63.3 ± 3.9 kg; height, 170.5 ± 5.5 cm; %body fat, 12.6 ± 3.9%; ˙VO2max, 62.4 ± 5.4 ml kg−1 min−1; velocity of ˙VO2max (v ˙VO2max), 17.1 ± 1.4 km h−1; peak heart rate, 192 ± 9 min−1] were recruited from the Department of Physical Education at a local university. They had no history of disease or cardiac symptoms, none were smokers, none were vegetarians or had any special dietary habits and none had taken any drugs or antioxidant supplements in the month before the study. Training history (5.4 ± 3.4 years), training volume (44 ± 25 km week−1), and personal best time in a recent marathon race (186 ± 16 min) were self-reported. An initial medical screening and examination were performed by a team of medical doctors and technicians. All of the participants had normal resting blood pressures and electrocardiographic results. All participants provided their written consent and were fully informed about the purposes, procedures, and potential cardiovascular risks of this study. The study took place in the local sports science research center between October and December in the afternoon. Conditions were similar for each test, with small variations in temperature (20.7 ± 2.3◦C), humidity (49 ± 13%). All of the participants refrained from intense exercise and alcohol 48 h before and after each trial and were allowed to ingest pure water freely during tests. Study Design The design was a randomized-order crossover. On their ﬁrst visit to the laboratory, the runners performed a test for estimation of ˙VO2max and corresponding v ˙VO2max. On two subsequent visits, the runners completed either intermittent or continuous exercise. The order in which each participant completed the two trials was selected at random and separated by at least 7 days, during which they trained normally. Blood samples were drawn pre-exercise (−1.5 h), immediately post-exercise (0 h), and at four later time points (2, 4, 24, and 48 h) for measurement of biomarkers. Determination of the ˙VO2max and v˙VO2max A treadmill (H/p/cosmos Pulsar 4.0, H/p/cosmos Sports and Medical gmbh, Nussdorf-Traunstein, Germany) with a 2% slope was used in the determination of ˙VO2max (Max-1, Physio-Dyne Instrument, NY, United States) and in the exercise trials. After a general 5- to 10-min warm-up with self-set speed, the participants ran at an initial speed of 12 km h−1, which was increased by 1 km h−1 every 3 min without any pause between stages. When the respiratory exchange ratio reached 1.00, the stages were shortened to 2 min. The test stopped either when the increase in oxygen consumption ( ˙VO2) was less than 2.1 ml kg−1 min−1 while the respiratory exchange ratio was greater than or equal to 1.15, or when exhaustion was reached. ˙VO2max was recorded as the highest 30-s average value of the recorded ˙VO2. The corresponding v ˙VO2max was recorded at the minimal speed at which ˙VO2max was reached, as long as this speed was sustained for at least 1 min. Exercise Trials Each participant completed a 1000-m warm-up at their own pace prior to each trial. In the intermittent trial, each bout consisted of a hard run of 90% v ˙VO2max for 2 min, followed by an easy run of 50% v ˙VO2max for 2 min, 23 bouts and 92 min in total. In the continuous trial, participants ran at 70% v ˙VO2max for 92 min. Running velocities for intermittent trial were 15.4 ± 1.3 and 8.4 ± 0.8 km h−1, hard and easy rung respectively; for continuous trial was 12.0 ± 0.9 km h−1. Frontiers in Physiology \| www.frontiersin.org 2 January 2020 \| Volume 11 \| Article 30 Li et al. Exercise-Induced Cardiac Biomarkers Elevation Measurements Heart rate (S810, Polar, Finland) were recorded every 2 min during each exercise trial. Rating of perceived exertion on Borg’s scale (Borg, 1982) was recorded at 30, 32, 58, 60, 90, and 92 min during each trial. Venous blood samples of 5 ml were dropped from the antecubital vein by venous punctures, clotted at room temperature and centrifuged at 3000 × g for 15 min. The separated serum was then drawn and stored at −80◦C for further analysis. These laboratory methods are the standard methods used by the local Hospital Clinical Chemistry Laboratory and have been validated. The high-sensitivity cardiac troponin-I was analyzed using a commercially available high-sensitive immunochemistry STAT assay from Abbott Diagnostics on an Architect i2000SR (Abbott Diagnostics, Chicago, IL, United States) with the detection limit of 1.6 pg ml−1 and 99th percentile of the assay was 26 pg ml−1. The high-sensitivity cardiac troponin-T analysis method was based on the new electrochemiluminescence technology and using Elecsys 2010 automated batch analysers (Roche Diagnostics, Mannheim, Germany). The measurement range was 3–1000 pg ml−1. The 99th percentile cutoﬀ concentration was 14 pg ml−1. NT-pro-BNP was determined by an Elecsys pro-BNP ECLIA on the Modular Analytics E170 analyzer (Roche Diagnostic, Mannheim, Germany) with the analytical range 5–35,000 pg ml−1. The corresponding upper reference limit was 125 pg ml−1. C-reactive protein (CRP) was measured by immunoturbidometric assay from Sekisui Diagnostics (Tokyo, Japan) on an AU2700 analyzer (Olympus Germany, Beckman Coulter, Krefeld, Germany). The cutoﬀ point was set as 3 mg L−1. Serum creatine kinase-MB (CK-MB) and creatine kinase (CK) were detected by DC800 analyzer (Beckman Coulter, Krefeld, Germany) using commercial kit according to electrochemiluminescence technology with cutoﬀ values 25 and 195 U L−1, respectively. Statistical Analysis Raw data were presented as means mean ± SD unless otherwise stated. All variables were log-transformed for analysis then back-transformed to express eﬀects as factors, after adjustment for the modifying eﬀects of the pre-exercise concentration and either exercise intensity expressed as percent of heart- rate reserve (%HRR) or training volume, using a spreadsheet (Hopkins, 2017). Changes between the two modes of exercise were compared for the average of post-exercise 0, 1, 4, and 24 h for all biomarkers except for CRP, which was the average of 24 and 48 h. In the absence of thresholds for acute changes in cardiac biomarkers associated with substantial increased risk of morbidity or mortality in endurance athletes, the magnitudes of the changes were assessed using threshold standardized changes of 0.20, 0.60, 1.20, 2.0, and 4.0 for small, moderate, large, very large, and extremely large, respectively (Hopkins et al., 2009; Hopkins, 2019a). Uncertainty in the estimates of eﬀects are presented as 90% compatibility limits. Probabilistic decisions about true (large-sample) magnitudes accounting for the uncertainty were based on one-sided hypothesis tests of substantial (at least small) eﬀects followed by Bayesian inference. The p-value for rejecting an hypothesis of a substantial eﬀect magnitude of a given sign was the area of the sampling distribution of the eﬀect with substantial values of that sign (Lakens et al., 2018), evaluated via log transformation. Eﬀects were considered decisive with a p-value threshold of <0.05. If an hypothesis of a substantial magnitude of a given sign was rejected, the p-value for the hypothesis of the other sign was interpreted as evidence for that hypothesis, since the p-value corresponds to the posterior probability of the magnitude of the true eﬀect in a reference Bayesian analysis with a minimally informative prior (Hopkins and Batterham, 2016; Hopkins, 2019b). This p-value is reported qualitatively using the following scale: 0.25–0.75, possibly; 0.75–0.95, likely; 0.95–0.995, very likely; >0.995, most likely (Hopkins et al., 2009). If neither hypothesis was rejected, the magnitude of the eﬀect was considered to be unclear, and the magnitude of the eﬀect is shown without a probabilistic qualiﬁer. RESULTS No runners reported any cardiac symptoms during or after the exercises. Heart rate during the intermittent trial was 160 ± 12 min−1 (176 ± 12 min−1 for the hard running and 145 ± 13 min−1 for the easy running); during the continuous trial heart rate was 162 ± 11 min−1. The intensities were 78 ± 6 and 79 ± 5 %HRR for intermittent and continuous trials, respectively, or 83 ± 4 and 84 ± 4 %peak heart rate. Rating of perceived exertion during intermittent and continuous trials was 14 ± 3 and 12 ± 3, respectively. Values for pre-exercise concentration of biomarkers are shown in Table 1, and values for high-sensitivity cardiac troponin-I pre- and post-exercise are shown in Figure 1. The baseline level of high-sensitivity cardiac troponin-I in three runners exceeded the upper reference limit of 26 pg ml−1, and after intermittent and continuous exercises this limit was exceeded by 11 and 10 runners, respectively. The mean and factor SD for the individual averaged changes in concentrations of biomarkers for the intermittent and continuous trials are presented in Table 2. These changes were large for high-sensitivity cardiac troponin-T, moderate for high-sensitivity TABLE 1 \| The pre-exercise concentrations of biomarkers. Raw Back-trans. (mean ± SD) (mean ×/÷ SD) High-sensitivity cardiac troponin I (hs-cTnI, pg ml−1) 70 ± 180 8.6 ×/÷ 6.2 High-sensitivity cardiac troponin T (hs-cTnT, pg ml−1) 8 ± 10 5.3 ×/÷ 2.0 N-terminal pro brain natriuretic peptide (NT-pro-BNP, pg ml−1) 28 ± 26 18 ×/÷ 2.6 C-reactive protein (CRP, mg L−1) 1.1 ± 1.1 0.7 ×/÷ 2.9 Creatine kinase-MB (CK-MB, U L−1) 3.9 ± 2.8 3.3 ×/÷ 1.8 Creatine kinase (CK, U L−1) 350 ± 250 289 ×/÷ 1.9 Back-trans.: back-transformed mean and factor SD of the log-transformed concentrations. Frontiers in Physiology \| www.frontiersin.org 3 January 2020 \| Volume 11 \| Article 30 Li et al. Exercise-Induced Cardiac Biomarkers Elevation FIGURE 1 \| Back-transformed means of log-transformed concentrations of high-sensitivity cardiac troponin I (hs-cTnI) 1.5 h before and 0–48 h after continuous and intermittent exercise. Error bars are standard deviations. cardiac troponin-I and CK-MB, and small for NT-Pro-BNP, CRP, and CK. The individual changes following intermittent and continuous exercise, with regression lines showing prediction by baseline concentration, training volume and exercise intensity, are shown in Figure 2 for one of the biomarkers, high- sensitivity cardiac troponin-I. The ﬁgure shows apparently similar modifying eﬀects of baseline concentration and training volume on high-sensitivity cardiac troponin-I, but a marked diﬀerence in the modifying eﬀect of exercise intensity. Comparisons of the changes in high-sensitivity cardiac troponin-I and in the other biomarkers adjusted to the mean values of the modiﬁers and to approximately 1 SD above the mean values are shown in Table 2. In summary, there were trivial diﬀerences between the two modes of exercise at the mean values of the modiﬁers, although the diﬀerences were not clear for some measures (high-sensitivity cardiac troponin-T, CK-MB, CK). Eﬀects of 1 SD of baseline concentration of the marker and of training volume had at most small observed eﬀects, and true values for the clear eﬀects were at most only possibly substantial. However, intensity approximately 1 SD above the mean of heart-rate reserve (85 %HRR) had clear moderate eﬀects on high-sensitivity cardiac troponin-I and -T, with high likelihoods that the true change with intermittent was greater than that with continuous exercise. With the other biomarkers only CRP showed a clear diﬀerence at 85 %HRR (a small, possibly substantial increase with intermittent compared with continuous), eﬀects on the other biomarkers being trivial or small and unclear. DISCUSSION To our knowledge, this is the ﬁrst study to explore the eﬀect of intermittent exercise on cardiac biomarker concentrations by comparing workload-matched continuous exercise in endurance runners. Our novel ﬁndings suggest that there was little diﬀerence in the eﬀect of exercise mode on cardiac-troponin elevation at typical training intensity, but for runners exercising at higher relative heart rates, prolonged intermittent exercise is potentially more damaging to cardiac muscle than comparable continuous exercise. Referring to the mean values of high sensitivity cardiac troponin-I in Figure 1 and Table 1, there was a similar eﬀect of two forms of exercise. Of course, as markers for diagnosis of acute myocardial injury in clinical settings, the actual magnitude of individual cardiac troponin level itself should be considered. The peak level of high-sensitivity cardiac troponin-I exceeded the upper reference limit of 26 pg ml−1 in most runners after both forms of exercise. Others have noted that cardiac troponin TABLE 2 \| Changes in concentration of biomarkers following continuous and intermittent exercise, with magnitude-based decisions for the comparison of the changes adjusted to mean values of potential modiﬁers and to 1 SD above baseline concentration, to approximately 1 SD above mean Tvol (67 km week−1), and to approximately 1 SD above mean exercise intensity (85% HRR). Factor change scoresa Effect for intermittent/continuousb (mean ×/÷ SD) (mean, ×/÷90CL) and magnitude-based decisionc Continuous Intermittent At mean valuesd At baseline + 1 SDd At 67 km week−1 At 85 %HRR hs-cTnI 4.4 ×/÷ 2.4 4.4 ×/÷ 1.8 1.0, ×/÷1.4 trivial, ↔00 1.7, ×/÷1.7 small, ↑* 1.8, ×/÷2.0 small,↑* 3.4, ×/÷1.9 moderate,↑*** hs-cTnT 3.0 ×/÷ 2.1 3.2 ×/÷ 1.4 1.0, ×/÷1.4 trivial 1.1, ×/÷1.7 trivial, ↔0 ↑* 1.3, ×/÷1.6 trivial, ↔0 ↑* 2.1, ×/÷1.8 moderate,↑** NT-pro-BNP 1.8 ×/÷ 1.3 1.7 ×/÷ 1.2 1.0, ×/÷1.2 trivial, ↔00 1.1, ×/÷1.3 trivial, ↔0 ↑* 0.9, ×/÷1.4 trivial 1.1, ×/÷1.4 trivial CRP 1.3 ×/÷ 1.7 1.6 ×/÷ 1.4 1.2, ×/÷1.3 trivial, ↔0 ↑* 1.1, ×/÷1.4 trivial, ↔0 ↑* 1.1, ×/÷1.3 trivial, ↔00 1.3, ×/÷1.4 small, ↑* CK-MB 1.6 ×/÷ 1.5 1.7 ×/÷ 1.6 1.1, ×/÷1.5 trivial 1.0, ×/÷1.9 trivial 1.0, ×/÷2.0 trivial 1.0, ×/÷1.9 trivial CK 1.4 ×/÷ 1.4 1.5 ×/÷ 1.5 1.0, ×/÷1.4 trivial 1.2, ×/÷1.6 small, ↑ 1.0, ×/÷1.5 trivial 1.1, ×/÷1.7 small, ↑ SD, standard deviation; 90CL, 90% conﬁdence limits as factors; Tvol, training volume, km week−1; %HRR, percent of heart-rate reserve; hs-cTnI, high-sensitivity cardiac troponin I, pg ml−1; hs-cTnT, high-sensitivity cardiac troponin T, pg ml−1; NT-pro-BNP, N-terminal pro brain natriuretic peptide, pg ml−1; CRP, C-reactive protein, mg L−1; CK-MB, creatine kinase-MB, U L−1; CK, creatine kinase, U L−1. aChanges, adjusted to overall mean values of baseline concentration and intensity, are for the mean of 0, 1, 4, and 24 h post-exercise, except for CRP, which is the change for the mean of 24 and 48 h post-exercise. bAll effects are adjusted for baseline concentration. cThe qualitative magnitude shown is derived by standardization for the observed effect. For clear effects, likelihood of a true substantial increase (↑) is indicated as possible; likely; **very likely. Likelihood of a true trivial change (↔) is indicated as follows: 0possible; 00likely. dMeans and SD are shown in Table 1. These effects are adjusted for intensity. Frontiers in Physiology \| www.frontiersin.org 4 January 2020 \| Volume 11 \| Article 30 Li et al. Exercise-Induced Cardiac Biomarkers Elevation FIGURE 2 \| Individual factor changes of high-sensitivity cardiac troponin-I (hs-cTnI) averaged over 0–24 h following intermittent and continuous exercise, with regression lines showing prediction by baseline concentration, training volume and exercise intensity. Horizontal dotted lines indicate no change in concentration; vertical dotted lines indicate mean values of the predictors. often exceeds the upper reference limit following exercise (Vilela et al., 2014; Gresslien and Agewall, 2016). Although strongly needed, there are no agreed thresholds for acute changes in cardiac troponin associated with substantial increased risk of morbidity or mortality in athletes, as the clinical relevance of exercise-induced cardiac troponin is still under debate (Skadberg et al., 2017; Nie, 2018). It has been argued previously that cardiac troponin is not randomly elevated after vigorous exercise but increases in certain “susceptible” individuals (Nie et al., 2011; Tian et al., 2014; Legaz-Arrese et al., 2015). We discuss below the extent to which resting concentration of cardiac troponin and level of training are two characteristics that modify release of cardiac troponin following exercise in our study. Our ﬁndings for the eﬀect of intensity of intermittent exercise are consistent with other studies, reviewed in the Introduction, showing greater elevation of cardiac biomarkers with higher intensity exercise. In particular, it obtained pronounced high-sensitivity cardiac troponin-T elevation after intermittent exercise with average heart rate of 160 min−1, which was similar to the mean heart rate of our runners (Richardson et al., 2018). Stewart et al. (2016) suggested that a heart rate of 145 min−1 was a threshold for the elevation of cardiac troponin. Gresslien and Agewall (2016) and Nie et al. (2018) also presented evidence for a threshold exercise intensity. With the prolonged exercise of trained runners in our study, there is evidence of a threshold heart rate for intermittent exercise, but not for continuous exercise (Figure 2, right-hand scatterplot). The greater increase in cardiac troponin concentrations with intermittent compared with continuous exercise at high relative intensities is obviously due to the higher peak heart rates during the on-phase of each interval; the lower heart rates during the oﬀ-phase do not compensate for the on-phase for high relative intensities, but they more than compensate for low relative intensities, on average. Clearly, however, there are individual diﬀerences in the response to both types of exercise that are not explained by relative exercise intensity across the range of intensities. As shown in Figure 2, the baseline level of cardiac troponin and training volume can explain some of the individual diﬀerences in response to the intermittent and continuous exercise. The three participants with the highest baseline concentrations of cardiac troponin-I experienced absolutely no increase with either form of exercise, so any cardiac pathology represented by the concentration of this marker is apparently not exacerbated by exercise in these participants. In the analysis that included all participants with adjustment for intensity, there was only a small or trivial mean increase with intermittent compared with continuous exercise at high baseline concentrations of either form of cardiac troponin, and the true increases were only possibly substantial. We therefore do not consider that intermittent exercise is a concern for runners with high baseline values of cardiac troponin, at least with this sample: if anything, baseline concentration had a protective eﬀect for exercise- induced increases. There was a similar protective eﬀect for training volume, again with only a possibly small relative mean increase for intermittent compared with continuous exercise for participants with high training volumes. We found small increases in NT-pro-BNP after both forms of exercise, with little diﬀerence between two exercise modes. NT-pro-BNP is a marker of cardiac stress elicited from volume or pressure overload in clinical settings of myocardial injury (Kociol et al., 2010). The magnitude of increase in NT-pro-BNP is primarily dependent on exercise duration as well as basal concentration, but not exercise intensity (Scharhag et al., 2008; Legaz-Arrese et al., 2011; Serrano-Ostariz et al., 2011). There seems a “ceiling eﬀect” that NT-pro-BNP is maximized at a low exercise intensity so that further increase requires accumulation over time (Legaz-Arrese et al., 2011). We also observed small increases in CRP after intermittent and continuous exercise and possible small eﬀect of exercise mode at 85 %HRR. CRP and other inﬂammatory markers have been reported to increase 24– 74 h after endurance exercise (Scherr et al., 2011). Stewart et al. (2016) also observed CRP increased to a greater extent with intermittent exercise accompanying cardiac troponin elevations. Frontiers in Physiology \| www.frontiersin.org 5 January 2020 \| Volume 11 \| Article 30 Li et al. Exercise-Induced Cardiac Biomarkers Elevation Although this marker is not cardiac-speciﬁc, inﬂammatory cytokines related to cardiac injury have been reported to aﬀect the release of cardiac troponin after exercise (La Gerche et al., 2015). Inﬂammation may therefore have contributed to the exercise- induced cardiac troponin elevation via a transient increase in myocardial membrane permeability. Further research is needed to address this issue. LIMITATIONS AND PERSPECTIVE The unclear comparisons of exercise mode and some of the unclear moderating eﬀects for the markers other than cardiac troponin need resolving with a larger sample size, which would also improve the precision of the estimates of the eﬀect with cardiac troponin. Other factors that may contribute to the individual variation of exercise-induced cardiac troponin elevation have not been veriﬁed for the small sample size. Competing at similar intensity, younger age predicts cardiac troponin elevation among endurance runners (Eijsvogels et al., 2015). Sex eﬀect may also inﬂuence the individual variation as one female runner was included in the present study. Exercise- induced cardiac troponin elevation occurred in both male and female runners, lower in female (Kong et al., 2017), but not exactly the case in a meta-analysis (Shave et al., 2007). Furthermore, core temperature, diet and ﬂuid intake, which may have impact on the performance of endurance runners were not monitored between the two trial. Cardiac troponin, a biomarker to diagnosis acute myocardial infarction is moderately to largely increased after typical long hard training in endurance runners. Some runners training with high-intensity intermittent exercise for improved performance may be at increased risk of cardiac stress. Baseline concentration of cardiac biomarkers and training volume have protective eﬀects for cardiac stress of endurance runners participating with high training volumes. Training can be a potentially precondition to better cardiomyocyte tolerance to intense exercise. CONCLUSION Intermittent and continuous exercise at similar mean heart rates have similar mean eﬀects on elevation of cardiac troponins in marathon runners performing long hard training. Elevation of cardiac troponins is greater at higher intensities and less at lower intensities with intermittent but not continuous exercise. Baseline level of cardiac troponin and training volume can explain some of the individual diﬀerences in response to intermittent and continuous exercise. DATA AVAILABILITY STATEMENT All datasets generated for this study are included in the article/supplementary material. ETHICS STATEMENT The studies involving human participants were reviewed and approved by the Beijing Sport University (BSUIRB, 2019003H). The patients/participants provided their written informed consent to participate in this study. AUTHOR CONTRIBUTIONS FL, JN, HZ, FF, LY, and YLu conceived and designed the study. FL, LY, WH, YLi, and YLu performed the experiments and analyzed the data. All authors drafted the manuscript. 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Rep. 6:24614. doi: 10.1038/srep24614 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. The handling Editor and reviewer AA declared their involvement as co-editors in the Research Topic, and conﬁrm the absence of any other collaboration. Copyright © 2020 Li, Nie, Zhang, Fu, Yi, Hopkins, Liu and Lu. 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 \| www.frontiersin.org 7 January 2020 \| Volume 11 \| Article 30	Effects of Matched Intermittent and Continuous Exercise on Changes of Cardiac Biomarkers in Endurance Runners.	01-31-2020	Li, Feifei,Nie, Jinlei,Zhang, Haifeng,Fu, Frank,Yi, Longyan,Hopkins, Will,Liu, Yang,Lu, Yifan	eng
PMC7037282	International Journal of Environmental Research and Public Health Article Amateur Runners’ Commitment: An Analysis of Sociodemographic and Sports Habit Proﬁles David Parra-Camacho 1 , Manuel Alonso Dos Santos 2 and María Huertas González-Serrano 3,* 1 Department of Physical Education and Sports, Faculty of Physical Activity and Sport Sciences, Universitat de València, 46010 Valencia, Spain; david.parra-camacho@uv.es 2 Department of Management, Faculty of Economics and Business Administration, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile; malonso@ucsc.cl 3 Department of Teaching and Learning of Physical Education, Plastic and Music Education, Universidad Católica de Valencia, 46110 Valencia, Spain * Correspondence: mh.gonzalez@ucv.es; Tel.: +34-963-983-707 Received: 14 January 2020; Accepted: 30 January 2020; Published: 2 February 2020 Abstract: The aim of this work is to analyse the commitment to running among urban runners by identifying groups regarding commitment to this sport and by deﬁning their sociodemographic proﬁle and their sports habits. A sample of 1806 participants in popular urban races in the city of Valencia was interviewed using an 11-item questionnaire on commitment to running, sociodemographic characteristics, and sports habits. The psychometric properties of the running-commitment scale allowed for the identiﬁcation of two factors in commitment to running: enthusiasm for running (6 items) and aﬄiction from running (5 items). Subsequently, a cluster analysis combining hierarchical and non-hierarchical methods was performed, identifying three groups of runners: highly committed (n = 650), moderately committed (n = 749), and slightly committed (n = 407). Highly committed runners positively rate all aspects of running enthusiasm (M = 4.15), while moderately committed runners show a more neutral attitude (M = 3.41) and slightly committed runners disagree on these aspects (M = 2.41). Both highly (M = 1.32) and moderately (M = 2.04) committed runners disagree on the aﬄiction-related aspects of running, while slightly committed runners show a trend towards neutrality on some aﬄiction indicators. The variables referring to age, level of studies, sports habits, and running addiction contributed to diﬀerentiating the identiﬁed groups. Keywords: runners; commitment to running; clusters; sports habits; running addiction; urban runners; amateur runners 1. Introduction Today, running has become an immensely popular pastime practised in the public sphere by millions of recreational participants around the world. However, until the 1960s, recreational jogging in streets, parks, or forests was considered an odd activity [1]. An example of the boom in running that has taken place in some Spanish cities can be found in Valencia (Spain), where currently more than 30 popular races are organized every year [2]. Valencia is a city with a strong running tradition, with runners participating in this activity since the beginning of the 19th century [2]. Also, the number of nonprofessional sports clubs oﬀering diﬀerent sports activities has been increasing recently in the main European cities [3,4]. However, both nonprofessional and professional clubs have limited information about the proﬁles of athletes based on their commitment to the activities they oﬀer. The groups of runners are heterogeneous and have diﬀerent subcultures; they could be diﬀerentiated in the frequency of the practice of the sport, in the motives, or in the participation in competitions [5]. For each group, Int. J. Environ. Res. Public Health 2020, 17, 925; doi:10.3390/ijerph17030925 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 925 2 of 19 sports associations and clubs and governmental or nonproﬁt organizations can design communications, activities, and services speciﬁcally targeted at them. Commitment could be a segmentation variable to obtain diﬀerent groups of subjects with diﬀerent motivations, activities, and interests. Commitment is a psychological construct that, from a sporting point of view, represents the desire and willingness to continue practising a sport [6,7]. When associated with positive factors, such as the intrinsic enjoyment of the activity itself; opportunities to participate successfully; personal investments of time, money, and experience in the sport; and social pressures from parents, coaches, peers, etc., commitment increases in tandem with increases in these factors [8]. However, when associated with negative factors, such as alternatives for successful participation in other attractive sports, commitment decreases [8]. Another concept that has been associated with or related to this construct in previous research on commitment to running is the notion of a positive addiction to running, deﬁned by Glasser [9] as enjoyment that increases mental strength and, when lost, produces some type of physical or psychological discomfort [10]. This concept is opposed to that of a negative addiction to continuous running, which can dominate the runner’s life and can produce the unwanted eﬀects of overtraining syndrome [11]: fatigue, decreased performance, and mood disorders. Some studies have observed among endurance sports participants a relationship between commitment to and dependence on exercise [12]. Endurance athletes usually train a considerable number of hours, which some studies have found to have a positive correlation with addiction to running among marathon runners [13,14]. However, most research indicates that commitment to running and negative addiction to running are two diﬀerent concepts predicted by diﬀerent variables and that, although there is a high correlation between them, the two phenomena do not necessarily appear together [15–19]. In this regard, Pargman [20] built on these concepts (positive and negative addiction to running) to describe the existence of two types of runners: addict-dependent runners, who have a positive addiction towards continuous running and perceive joy and happiness in running (if they do not do it, they feel bad), and committed-dedicated runners, who have a negative addiction towards continuous running and a broader, rational, and pragmatic intellectual component that makes them give continuous running a very high priority in their lives without necessarily liking it. Among the ﬁrst contributions on commitment to running, Carmack and Martens [21], who developed and validated the unidimensional commitment to running (CR) scale, equate commitment to running with the concept of positive addiction to continuous running. Subsequently, several studies have used this scale to analyse the characteristics of runners and to learn about their sociodemographic proﬁle and sports habits. Carmack and Martens [21], and Joseph and Robbins [22] explained that the variables that contributed to increasing the commitment to running were related to the time spent on training (number of days of training/week and minutes/training), while Thornton and Scott [23] also highlighted distance covered in kilometres as a predictor variable. Improved personal performance among marathon runners was an important factor in increasing the commitment to running for this proﬁle of runners, while amateur runners emphasized the intrinsic enjoyment of the activity or improved health [24]. In the Spanish context, several investigations have also been carried out among Spanish runners on the psychological variables related to motivation, commitment, and addiction to running [5,19,25–27]. In this regard, it is important to highlight the validation of the Carmack and Martens scale [21] in Spanish marathon runners by Ruiz-Juan and Zarauz [25], who retained 11 indicators from the 12 proposed on the original unidimensional scale that allowed for evaluation of the commitment of the runners. Several studies have analysed the relationships between training variables and sports habits and the commitment and addiction to running in marathon runners [18,19,28]. In addition, the motivation of amateur Spanish runners when participating in this physical activity has also been analysed in various studies [5,26,27]. Int. J. Environ. Res. Public Health 2020, 17, 925 3 of 19 However, studies that segment runners into groups with diﬀerent behaviours towards running are not abundant, and most of the contributions in this regard have focused on identifying groups or on classifying runners according to their motivation to practice this physical activity [5,29,30] rather than according to their commitment to running. Therefore, numerous contributions that analyse diverse psychological variables such as runners’ motivation [17,20,26,27,30–35], addiction [15,19,28,36], or commitment [12,19,21–23,25,37] to running have been made in the study of the running phenomenon. However, many of these studies have focused on long-distance runners, such as half-marathon and marathon runners [19,28,31]. There are few contributions that focus on Spanish urban runners who participate in short-distance races [5,26,27] and that classify runners into clusters according to their commitment to running, identifying groups with diﬀerent sociodemographic characteristics and sports habits. Research on short-distance (versus long-distance) races allows researchers to study a larger universe because more and more people practice sports for health and social reasons but are less concerned about winning long-distance races [38]. This paper analyses the commitment to running among runners who participated in urban races of less than 10 km. These data provide information on the behaviour of this type of urban sportspeople in the context of Spanish short-distance runners. Spain is a country with an established running practice: the percentage of the population who practice running is 30.4% [39]. The aim of this paper is to analyse the commitment to running among urban runners by identifying groups with a greater or lesser commitment to this sport. In addition, the proﬁle of these groups is deﬁned to determine their sociodemographic characteristics and to be able to analyse their sports habits. 2. Materials and Methods 2.1. Sample, Procedures, and Questionnaire In this study, a sample of 1806 runners who participated in the city of Valencia’s urban circuit of popular races during 2015 were interviewed. The circuit has 10 races. The total number of runners in the circuit was 50,038 recurring runners (M = 5004). The sample size used is at least 95% conﬁdence (5% margin of error). The questionnaires were collected after the end of this race circuit during the month of January 2016 by using an online format. This research used a self-supplied online questionnaire because of the diﬃculty of obtaining valid responses among active or tired athletes immediately after the race. The questionnaire was made up of three sections of questions. The ﬁrst included questions of a sociodemographic nature referring to age, gender, occupation, level of studies, and income. The second section contained questions referring to the sports habits of the participants: number of years they have been running, weekly frequency with which they run, preferred distance when participating in a popular race, aﬃliation with a club, whether the club is federated, with whom they usually run, the distance they usually run weekly, the athletic level they consider themselves to have as runners, the number of long-distance races ﬁnished (half marathons and marathons), future intentions regarding participation in urban races, and three items on addiction to running. The three indicators on the intentions of amateur runners were adapted from the scale on behavioural intentions by Zeithaml, Berry, and Parasuraman [40], whereas the three indicators on running addiction were extracted from the Spanish validation by Zarauz and Ruiz-Juan [36] of the scale on running addiction. The adaptation of the scale consisted in translating and adapting the scales to the race under research. The indicators on behavioural intention of amateur runners showed adequate reliability (Cronbach alpha = 0.91, Composite Reliability = 0.92; Average Variance Extracted = 0.79). The indicators of both future intentions and running addiction were assessed with a ﬁve-point Likert scale with the following response options: 1 = strongly disagree, 2 = disagree, 3 = neither disagree nor agree, 4 = agree, and 5 = strongly agree. Int. J. Environ. Res. Public Health 2020, 17, 925 4 of 19 The third section includes indicators related to the participants’ commitment to running. The Ruiz-Juan and Zarauz scale [25] validated in the Spanish context in marathon runners was used to collect participants’ perceptions of their own commitment to running. This scale is composed of 11 indicators evaluated by the interviewees through a Likert scale with the same response options as those of future intentions and running addiction. For this type of research, it was not necessary to get approval from the Committee of Ethics of the university where this study was carried out (University of Valencia). According to the Committee of Ethics and Human Research from this university, it is not necessary to get approval to carry out an opinion survey about a topic or issue, professional status, or satisfaction with certain matters. However, it is obligatory to include a preamble in the survey with presented information about the project (theme and purpose), the beneﬁts that the information collected by the survey may provide, the willingness of the participation, and the anonymous treatment of data (Data Protection Law). It is also compulsory to indicate a contact person to ask for further information and to put one paragraph in which the survey respondent voluntarily accepts participation in the research and gives consent tacitly when responding to the survey. Thus, following these guidelines indicated by the Committee of Ethics and Human Research from the University of Valencia to develop this sort of research, all this information was added at the beginning of the questionnaire. 2.2. Statistical Analysis First, the psychometric properties of the running commitment scale were tested on the sample under study by performing an exploratory factor analysis (EFA) and a conﬁrmatory factor analysis (CFA). The EFA was performed with the FACTOR program following the recommendations of Lloret-Segura et al. [41] using the maximum likelihood (ML) method and oblimin direct rotation. To determine the number of factors, the optimal implementation of the parallel analysis procedure was used [42]. The model ﬁt was observed using the root mean square of the residuals (RMSR) coeﬃcient as well as the goodness-of-ﬁt index (GFI) proposed by Tanaka and Huba [43]. The RMSR should be less than 0.05 [44], and the GFI value should be less than 0.95 [45]. Other indicators that were taken into account were the generalized G-H index (>0.80) to analyse the replicability of the factors derived from the EFA [46]. The measures for sample adequation of Kaiser Meyer Olkin (KMO) were also observed, as was Bartlett’s sphericity test. Items with factorial loads below 0.40 or above this value on two or more factors were eliminated before the next EFA was carried out. After applying the EFA, a CFA was performed on the factorial solution derived from the EFA using robust maximum likelihood estimation (MLR) with the aim of correcting the possible absence of multivariant normality using statistics such as the χ2 of Satorra-Bentler [47]. Thus, for the evaluation of global ﬁt, diﬀerent goodness-of-ﬁt indices recommended in the literature [48], such as the signiﬁcance of the chi-squared value and its robust correction oﬀered by Satorra-Bentler (S-B χ2) [49], were used. In addition, other coeﬃcients that allowed for testing the adequacy of the proposed models, such as the ratio of χ2 and its degrees of freedom (χ2/df) [50], were calculated, with the acceptable values being less than ﬁve [51]. However, these indices are aﬀected by the sample size, so the standardized root mean square residual (SRMR) index was used, where values of 0.06 or less indicate an excellent ﬁt and values of 0.08 or less indicate a good ﬁt [51]. In the same way, the coeﬃcients of the indices of robust goodness-of-ﬁt of the proposed model, the compared ﬁt index (CFI) and the incremental ﬁt index (IFI), were tested. For these indicators, a ﬁt with values above 0.90 is considered good [52]. To ﬁnalize, the root mean square error of approximation (RMSEA) is shown, with a score below 0.08 being considered a good ﬁt [53]. In the evaluation of the reliability of the scale, three measurements were taken into account: the Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE) values for each factor [54]. In addition, convergent validity was tested through the signiﬁcance of the factorial loads in their respective dimensions and the values of the associated t-tests. Additionally, discriminant validity, which has to do with seeing the clear distinction between any pair of constructs, was evaluated using Int. J. Environ. Res. Public Health 2020, 17, 925 5 of 19 the method suggested by Fornell and Larcker [55]. This method conﬁrms discriminant validity if the square root of the AVE value of a determined factor is greater than the correlation coeﬃcients between the factor and any other in the proposed scale. The other criterion to assure discriminant validity is that the correlations between the diﬀerent pairs of factors must be less than 0.85 [48]. After checking the psychometric properties of the running commitment scale, a cluster analysis was performed to identify groups of participants with diﬀerent characteristics according to their running commitment. This analysis was performed using the statistical program SPSS version 24.0 for Windows (IBM, Armonk, NY, USA), with the 11 items of the running commitment scale. Two estimation methods (hierarchical and nonhierarchical) of the cluster solution were combined to optimize the results. The hierarchical cluster analysis was performed using the Ward clustering process and, as a measure of similarity, the Euclidean distance squared. A nonhierarchical analysis was applied to the groups proposed in the previous analysis through the K-means method using as initial centres the means of the variables obtained for each cluster solution of the hierarchical analysis. To deﬁne the characteristics of the group proﬁles and to evaluate predictive validity, ANOVAs and chi-square tests were performed with variables that were not included in the initial analysis. The value of the contingency coeﬃcient (C) was also used to check the intensity of the association and the size of the eﬀect of the related variables. 3. Results 3.1. Sociodemographic Characteristics Table 1 shows the sociodemographic characteristics. The respondents had an average age of 39.48 (SD = 9.21), 74.4% being men and 25.6% being women. In terms of occupation, most are employed (88.4%). The majority was university studies (59.2%) and had an income level of less than 18,000 euros per year (51.3%). Table 1. Sociodemographic characteristics of the sample. Variable Response Option Mean and Percentage Age 39.48 (SD 1 = 9.21) Gender Male 74.4% Female 25.6% Occupation Employee 88.4% Unemployed 6.1% Student 1.2% Other (retired, pensioner, domestic tasks, etc.) 4.3% Level of Studies Primary 6.1% Secondary 6.2% Baccalaureate/ Professional training 28.5% University 59.2% Income Level Less than 12,000 euros 27.0% 12,001–18,000 euros per year 24.3% 18,001–24,000 euros per year 18.2% 24,001–30,000 euros per year 14.2% 30,001–36,000 euros per year 6.9% More than 36,001 euros per year 9.5% 1 SD = Standard Deviation. 3.2. Psychometric Properties of the Commitment to Running Scale First, the properties of the items on the commitment-to-running scale were analysed by checking the corrected correlation item–total values, as well as the mean, standard deviation, asymmetry, and kurtosis values. Table 2 shows the statistics. The mean scores of ﬁve indicators were inverted as indicated by Ruiz-Juan and Zarauz [25]. The values of the item–total corrected correlation coeﬃcients Int. J. Environ. Res. Public Health 2020, 17, 925 6 of 19 were higher than the cutoﬀ point recommended by the literature (≥0.30) [56]. Additionally, the values of asymmetry and kurtosis are acceptable for most variables because they are lower than 3.0 [57] except for item CR7, of which the value exceeded the recommended cutoﬀ point for ensuring a normal distribution of the data. On the other hand, indicators on the intentions of amateur runners showed adequate reliability with a Cronbach alpha of 0.91, CR = 0.92, and AVE = 0.79. Table 2. Mean, standard deviation, corrected item–total correlation, alpha if the item is removed, asymmetry, and kurtosis values of the indicators of the running-commitment scale. Number Items Means (SD) 1 R IT-c 2 α without Item Asymmetry Kurtosis CR1 I am looking forward to running 3.79 (0.93) 0.62 0.79 −0.58 0.21 CR2 Running is drudgery (R) 3.83 (1.12) 0.53 0.80 −0.68 −0.46 CR3 I do not enjoy running (R) 4.34 (0.91) 0.48 0.80 −1.54 2.21 CR4 Running is vitally important to me 3.23 (1.10) 0.59 0.79 −0.23 −0.51 CR5 Life is so much richer as a result of running 3.41 (1.07) 0.56 0.80 −0.47 −0.26 CR6 Running is pleasant 4.09 (0.75) 0.56 0.80 −0.80 1.39 CR7 I dread the thought of running (R) 4.72 (0.68) 0.30 0.82 −3.01 10.20 CR8 I would arrange or change my schedule to meet my need to run 3.29 (1.19) 0.46 0.81 −0.37 −0.68 CR9 I have to force myself to run (R) 3.66 (1.12) 0.42 0.81 −0.46 −0.63 CR10 To miss a day’s run is a sheer relief (R) 4.41 (0.83) 0.39 0.81 −1.45 1.89 CR11 Running is the high point of my day 2.93 (1.15) 0.47 0.81 −0.09 −0.68 1 SD = Standard Deviation; 2 Corrected item–total correlation; (R) = Reverse scoring; 1 = Totally disagree, 2 = Disagree, 3 = Neither agree nor disagree, 4 = Agree, and 5 = Totally agree. After checking the properties of the items, an EFA was carried out for the 11 items related to the running commitment of the interviewed participants. The parallel analysis procedure suggested grouping the indicators into two factors. It was not necessary to eliminate any indicators because all of them had factor loads above 0.40 and no cross-factor loads exceeded this value in the two factors. Table 3 shows the grouping of the indicators into the two factors: enthusiasm for running (6 items) and aﬄiction from running (5 items). Table 3. Rotated factor structure of the commitment-to-running scale of runners participating in popular endurance races, communalities, eigenvalues, and explained variance. Number Items F1 F2 Com. 1 Factor 1: Enthusiasm for running CR1 I am looking forward to running 0.56 0.48 CR4 Running is vitally important to me 0.81 0.64 CR5 Life is so much richer as a result of running 0.78 0.59 CR6 Running is pleasant 0.40 0.37 CR8 I would arrange or change my schedule to meet my need to run 0.62 0.37 CR11 Running is the high point of my day 0.65 0.41 Factor 2: Aﬄiction from running CR2 Running is drudgery 0.57 0.43 CR3 I do not enjoy running 0.67 0.48 CR7 I dread the thought of running 0.67 0.40 CR9 I have to force myself to run 0.56 0.34 CR10 To miss a day’s run is a sheer relief 0.67 0.43 G-H Index 0.85 0.80 Eigenvalue 4.01 2.01 Variance Explained (%) 36.51 18.28 Items 6 5 1 Com. = Communality. Int. J. Environ. Res. Public Health 2020, 17, 925 7 of 19 To check the ﬁt of the model, the RMSR and gamma index or GFI coeﬃcients were analysed, which showed values within the recommended cutoﬀ points: RMSR = 0.03 (<0.05) and GFI = 0.99 (>0.95). In addition, the generalized G-H index showed values higher than 0.80 in the two factors detected by the EFA (0.85 for the running enthusiasm factor and 0.80 for the running aﬄiction factor), indicating the possibility of good replicability of the dimensions in other studies [46]. The variance explained by the 11 items grouped in the two factors was 54.78%. On the other hand, a CFA was derived from the two-factor solution of the EFA and from the unidimensional proposal of the scale presented by the authors of the original scale [21]. The CFA considering the scale as unidimensional did not show a good ﬁt, as seen in the goodness-of-ﬁt indices (χ2 = 2005.81 (df = 44); p < 0.01; SRMR = 0.125; RMSEA = 0.122 (conﬁdence interval (CI) = 0.116–0.128); CFI = 0.73; IFI = 0.73)). However, the factorial solution derived from the EFA by grouping the indicators into two factors did show optimal model ﬁt values. The value of χ2 was signiﬁcant (χ2 = 653.95; df = 43; p < 0.05), although the relationship between the value of χ2 and the degrees of freedom (normed chi-square) was quite high since values below ﬁve are considered acceptable [51]. This is because the value of this statistic is very sensitive to the size of the sample, which could erroneously indicate a poor adjustment of the data [48,58]. For this reason, it is recommended that other goodness-of-ﬁt indices be used [48]: SRMR, RMSEA, CFI, and IFI. These indicators showed a good ﬁt: (SRMR = 0.077; RMSEA = 0.076 (IC = 0.070–0.082); CFI = 0.90; IFI = 0.90)). To analyse reliability, the Cronbach’s alpha, CR, and AVE measurements were observed (see Table 4). The Cronbach’s alpha values were higher than the 0.70 recommended by the literature [54]. This criterion was also fulﬁlled for the CR values [55], with values of 0.83 for the running enthusiasm factor and 0.77 for the running aﬄiction factor. Finally, for the AVE values, it was found that the two factors did not have values higher than the 0.50 recommended by the literature [59]. According to Hatcher [60], when the reliability of the construct is acceptable, a marginally low AVE value can be accepted (see Table 4). Therefore, the decision was made to retain these two factors without combining them into a single factor since the unidimensional solution did not oﬀer a good ﬁt. Table 4. Factorial loads, Cronbach’s alpha, composite reliability, and average variance extracted values from commitment to running scale indicators. Number Items λ α CR 1 AVE 2 Factor 1: Enthusiasm for running 0.82 0.83 0.45 CR1 I am looking forward to running 0.68 CR4 Running is vitally important to me 0.79 CR5 Life is so much richer as a result of running 0.76 CR6 Running is pleasant 0.54 CR8 I would arrange or change my schedule to meet my need to run 0.61 CR11 Running is the high point of my day 0.62 Factor 2: Aﬄiction from running 0.76 0.77 0.40 CR2 Running is drudgery 0.66 CR3 I do not enjoy running 0.69 CR7 I dread the thought of running 0.57 CR9 I have to force myself to run 0.6 CR10 To miss a day’s run is a sheer relief 0.64 1 CR = Composite Reliability; 2 AVE = Average Variance Extracted. To analyse convergent validity, it was found that the values of the t-tests associated with the factorial loads of the items were higher than 1.96 (p < 0.05), ranging from 15.06 to 27.57. Regarding discriminant validity, on the one hand, the correlation between the two factors was less than 0.85 (r = 0.35; p < 0.01). On the other hand, it was found that the square root of the AVE value was higher than the correlations between pairs of factors. This criterion was also complied because the values of the square root of the AVE were √AVE = 0.67 for factor 1 and √AVE = 0.63 for factor 2. Int. J. Environ. Res. Public Health 2020, 17, 925 8 of 19 3.3. Identiﬁcation and Description of Clusters A cluster analysis was conducted to identify groups with diﬀerent characteristics according to their commitment to running among the interviewed runners participating in urban short-distance races. Following the procedure recommended by Hair et al. [54], a hierarchical cluster analysis was ﬁrst performed using Ward’s method of observing the increase in the agglomeration coeﬃcients between clusters two and three, three and four, and four and ﬁve. After observing these coeﬃcients, the solutions of two, three, four, and ﬁve groups were used to apply the second analysis of the k-median clusters by using the initial centres from the hierarchical cluster analysis. It was considered appropriate to contrast all the solutions because there are not many previous studies that identify clusters of amateur runners according to their commitment to running, as mentioned in the theoretical framework. This study used the solution of three clusters because it identiﬁed three groups of urban runners with diﬀerent levels of commitment to running, helping to interpret and identify their sociodemographic proﬁles and sports habits. It is important to note that the choice of an ideal cluster solution depends on the theoretical foundations, common sense, and practical judgement of the researcher [54]. Table 5 shows the mean values (centroids) for each of the 11 variables of the commitment-to-running scale introduced in the analysis and the results of the ANOVA test carried out to conﬁrm the signiﬁcance of the diﬀerences between groups (F statistic and signiﬁcance level). In this table, it can be seen that all the mean scores of the identiﬁed groups present statistically signiﬁcant diﬀerences (p < 0.001). The variables that distinguish the clusters most correspond to the running enthusiasm factor: “Running is vitally important to me” (F = 896.45) and “Life is so much richer as a result of running” (F = 768.53). The item that diﬀerentiates the clusters least is “I dread the thought of running” (F = 47.61). Table 5. Average scores for each variable in the three clusters (obtained through the k-averages method). Number Items 1 = Highly Committed (n = 650) (SD) 1 2 = Moderately Committed (n = 749) (SD) 1 3 = Lowly Committed (n = 407) (SD) 1 F p-Value Factor 1: Enthusiasm for running 4.15 (0.46) 3.41 (0.35) 2.43 (0.41) 2263.89 <0.001 * CR1 I am looking forward to running 4.42 (0.64) 3.73 (0.73) 2.89 (0.87) 543.02 <0.001 * CR4 Running is vitally important to me 4.08 (0.78) 3.16 (0.77) 2.00 (0.80) 896.45 <0.001 * CR5 Life is so much richer as a result of running 4.12 (0.78) 3.47 (0.74) 2.18 (0.87) 768.53 <0.001 * CR6 Running is pleasant 4.51 (0.58) 4.01 (0.59) 3.58 (0.86) 263.28 <0.001 * CR8 I would arrange or change my schedule to meet my need to run 4.09 (0.91) 3.26 (0.93) 2.09 (0.97) 577.43 <0.001 * CR11 Running is the high point of my day 3.71 (0.96) 2.83 (0.90) 1.87 (0.88) 516.87 <0.001 * Factor 2: Aﬄiction from running 1.32 (0.32) 2.04 (0.65) 2.16 (0.68) 388.42 <0.001 * CR2 Running is drudgery 1.46 (0.75) 2.39 (1.01) 2.91 (1.14) 320.47 <0.001 * CR3 I do not enjoy running 1.16 (0.54) 1.89 (0.96) 2.04 (0.93) 192.12 <0.001 * CR7 I dread the thought of running 1.06 (0.33) 1.46 (0.85) 1.31 (0.65) 66.28 <0.001 * CR9 I have to force myself to run 1.73 (0.89) 2.65 (1.03) 2.77 (1.16) 190.17 <0.001 * CR10 To miss a day’s run is a sheer relief 1.20 (0.52) 1.82 (0.89) 1.79 (1.90) 126.35 <0.001 * 1 SD = Standard Deviation; * Statistically signiﬁcant mean diﬀerences p < 0.001. Cluster 1 was labelled “highly committed” (n = 650; 35.99%) because all indicators related to Factor 1 about running enthusiasm present averages close to or above the value of four on the Likert scale, which indicates a high degree of agreement with these statements. This group of runners presents a high commitment to this physical activity because they consider running to be of vital importance for their lives (M = 4.08), they would reorganize or change their schedule to satisfy the need to run (M = 4.09), and they consider their lives to be much richer because they practice this physical activity (M = 4.12). In addition, they show a high degree of agreement about the desire to run (M = 4.42) and enjoy the experience of running (M = 4.51). However, they disagree with the aspects related to factor 2 about a sense of aﬄiction from running. For example, they tend to disagree with items related to a sense of drudgery over the activity (M = 1.46), an absence of enjoyment when running (M = 1.16), Int. J. Environ. Res. Public Health 2020, 17, 925 9 of 19 a feeling of dread over the practice of running continuously (M = 1.06), and a sense of relief when not running for one day (M = 1.20). Cluster 2 was identiﬁed as “moderately committed” (n = 749; 41.47%) and includes a higher proportion of the interviewed runners; this group of runners is characterized by presenting average scores with a tendency towards agreement on some indicators of the dimension of enthusiasm for running but with a more moderate tendency than in cluster 1. Thus, the runners in this group consider the practice of running to be a pleasant activity (M = 4.01) and are willing to practise it (M = 3.73). They also show a positive tendency in considering that running enriches their lives (M = 3.47). However, they do not consider the practice of this physical activity to be essential to their lives (M = 3.16) or to be the high point of their day (M = 2.83). On the other hand, this group also disagrees with most of the negative aspects (aﬄiction for running) associated with a commitment to running. However, the indicator related to the need to force themselves to run shows a score close to the value of three on the scale (M = 2.65), which would indicate some neutrality in the assessments of this group. Cluster 3 was labelled “slightly committed” (n = 407; 22.54%) because they show a tendency to disagree with many aspects related to enthusiasm for running. In this regard, they disagree with the items stating that the practice of this physical activity is of vital importance for them (M = 2.00), that their life is much richer because they practice running (M = 2.18), that the practice of this activity is the high point of their day (M = 1.87), and that they reorganize their schedules to satisfy the need to run (M = 2.09). This group only shows a tendency to agree that running is a pleasant activity (M = 3.58). In the same vein as the other groups, these runners show disagreement with the aspects related to a sense of affliction from running, although there is a tendency towards neutrality in the aspects referring to a sense of drudgery over the practice of this activity (M = 2.91) and a need to force themselves to run (M = 2.77). 3.4. Proﬁle of the Groups Table 6 describes the proﬁle of the runners that make up each group using other independent variables (sociodemographic and sports habits variables) that allow us to ensure the predictive validity of the groups, the percentages for each sociodemographic, and sports habits variable according to the cluster. Statistically signiﬁcant diﬀerences were observed among the groups in the case of the sociodemographic variables related to age (F(2, 1803) = 5.73, p < 0.05) and level of studies (χ2(6) = 18.94, p < 0.01), although the size of the eﬀects (contingency coeﬃcients) presented reduced values (see Table 6). It was also found that there were statistically signiﬁcant diﬀerences in the mean scores among the groups on the indicators related to running addiction: “Some days, even if I do not feel like running, I do it anyway” (F(2, 1803) = 104.78, p ≤ 0.001), “I feel like I need to run at least once every day” (F(2, 1803) = 150.05, p ≤ 0.001), and “I’ve stopped running for at least a week for some other reason than injury” (F(2, 1803) = 46.84, p ≤ 0.001). Additionally, on the indicator related to future intentions to recommend participation in popular running races to others, statistically signiﬁcant diﬀerences in mean group scores were observed (F(2.1803) = 4.72, p < 0.01). The highly committed group is characterized by an average age of 39.51 (SD = 9.33), with a majority percentage of men (76.62%), which is higher than the other groups. It is veriﬁed that there is a greater degree of agreement in this group with indicators related to running addiction, as they show a tendency towards agreement on, for instance, the item stating that, some days, even if they do not feel like running, they go running anyway (M = 3.71), with a signiﬁcantly higher average compared to the other two groups. Signiﬁcantly higher diﬀerences, albeit with a more neutral trend in the average scores, are also observed in this group for the indicator related to the need to run at least once every day (M = 3.04) and lower diﬀerences for the indicator related to having stopped running for at least one week for a reason that was not injury related (M = 3.04). Finally, the runners in this group strongly agree with the items on their future intentions regarding participation, recommendation, and positive comments about running popular races, with slightly higher scores on all these indicators than those of the other two groups. Int. J. Environ. Res. Public Health 2020, 17, 925 10 of 19 Table 6. Characteristics of the diﬀerent groups (clusters). Variable Response Option 1 = Highly Committed (n = 650) 2 = Moderately Committed (n = 749) 3 = Lowly Committed (n = 407) Age * F(2, 1803) = 5.73, p = 0.03 39.51 (SD 1 = 9.33) 40.13 (SD 1 = 9.26) 38.22 (SD 1 = 8.79) Gender χ2(2) = 2.71, p = 0.26 Male 76.62% 73.03% 73.22% Female 23.38% 26.97% 26.78% Occupation χ2(6) = 2.13, p = 0.91 Employee 88.15% 88.79% 87.96% Unemployed 6.92% 5.47% 5.90% Student 1.08% 1.34% 1.23% Other (retired, pensioner, domestic tasks, etc.) 3.85% 4.41% 4.91% Level of studies χ2(6) =18.94, p = 0.004 C 2 = 0.10 Primary 8.46% (3) 5.47% 3.44% Secondary 7.23% 6.14% 4.67% Baccalaureate/Professional training 29.85% 27.64% 27.76% University 54.46% 60.75% 64.13% (1) Income level χ2(10) = 13.71, p = 0.19 Less than 12,000 euros 29.38% 25.90% 25.06% 12,001–18,000 euros per year 25.85% 22.56% 24.82% 18,001–24,000 euros per year 15.54% 19.49% 20.15% 24,001–30,000 euros per year 14.00% 14.69% 13.51% 30,001–36,000 euros per year 7.23% 6.14% 7.86% More than 36,001 euros per year 8.00% 11.21% 8.60% How often you run during the week? * χ2(6) = 290.78, p ≤ 0.001 C 2 = 0.37 Five or more times a week 14.31% (2) (3) 4.01% 1.97% Three to ﬁve times a week 62.46% (2) (3) 51.27% (3) 29.73% Once or twice a week 21.69% 40.32% (1) 51.84% (1) (2) Less frequently 1.54% 4.41% (1) 16.46% (1) (2) Int. J. Environ. Res. Public Health 2020, 17, 925 11 of 19 Table 6. Cont. Variable Response Option 1 = Highly Committed (n = 650) 2 = Moderately Committed (n = 749) 3 = Lowly Committed (n = 407) Preferred distance in popular races * χ2(10) =119.49, p ≤ 0.001 C 2 = 0.25 Less than 7.5 km 25.23% 36.85% (1) 52.58% (1) (2) Between 7.5 km and 10 km 28.15% 30.31% 27.27% Between 10 km and 15 km 27.69% (3) 22.83% (3) 13.51% Between 15 km and 20 km 10.15% (2) (3) 6.14% 4.91% Between 20 km and 30 km 4.62% (3) 2.54% 1.72% More than 30 km 4.15% (2) 1.34% 0.00% How do you usually run? χ2(2) = 0.34, p = 0.84 Alone 62.46% 61.15% 60.93% Accompanied 37.54% 38.85% 39.07% Level you consider you are as a runner * χ2(4) = 133.61, p ≤ 0.001 C 2 = 0.26 High level 8.31% (2) (3) 2.67% 1.47% Intermediate 64.92% (2) (3) 53.14% (3) 39.07% Low level 26.77% 44.19% (1) 59.46% (2) (3) Are you a member of a sports club? χ2(2) = 53.84, p ≤ 0.001 C 2 = 0.17 Yes 48.77% (2) (3) 40.05% (3) 26.04% No 51.23% 59.95% (1) 73.96% (2) (3) Are you sports federated? χ2(2) = 10.47, p = 0.005 C 2 = 0.08 Yes 6.92% (2) (3) 3.60% 3.44% No 93.08% 96.40% (1) 96.56% (1) Distance usually run weekly (kilometres) * F(2, 1803) = 106.76, p ≤ 0.001 35.11 (SD 1 = 18.54) 26.16 (SD 1 = 14.65) 20.58 (SD 1 = 16.01) Years running F(2, 1803) = 7.20, p = 0.01 8.04 (SD 1 = 8.95) 7.32 (SD 1 = 8.20) 6.08 (SD 1 = 6.68) Participation in half marathons * F(2, 1803) = 20.70, p ≤ 0.001 7.99 (SD 1 = 22.17) 4.05 (SD 1 = 10.06) 2.30 (SD 1 = 6.33) Int. J. Environ. Res. Public Health 2020, 17, 925 12 of 19 Table 6. Cont. Variable Response Option 1 = Highly Committed (n = 650) 2 = Moderately Committed (n = 749) 3 = Lowly Committed (n = 407) Participation in marathons * F(2, 1803) = 17.84, p ≤ 0.001 1.38 (SD 1 = 3.77) 0.74 (SD 1 = 2.74) 0.32 (SD 1 = 1.26) Some days, even if I do not feel like running, I do it anyway * F(2, 1803) = 104.78, p ≤ 0.001 3.71 (SD 1 = 1.02) 3.35 (SD 1 = 0.99) 2.76 (SD 1 = 1.13) I feel like I need to run at least once every day * F(2, 1803) = 150.05, p ≤ 0.001 3.04 (SD 1 = 1.20) 2.49 (SD 1 = 1.07) 1.84 (SD 1 = 0.97) I have stopped running for at least a week for another reason that was not an injury * F(2, 1803) = 46.84, p ≤ 0.001 3.04 (SD 1 = 1.44) 3.58 (SD 1 = 1.17) 3.75 (SD 1 = 1.31) Future intentions regarding participation in urban popular races I am willing to continue participating in popular urban races F(2, 1803) = 0.85, p = 0.45 4.42 (SD 1 = 1.03) 4.38 (SD 1 = 0.99) 4.35 (SD 1 = 0.91) I will recommend participation in popular urban races to others F(2, 1803) = 4.72, p = 0.009 4.48 (SD 1 = 3.77) 4.37 (SD 1 = 3.77) 4.32 (SD 1 = 3.77) I will speak well of popular urban races to others F(2, 1803) = 2.04, p = 0.13 4.46 (SD 1 = 0.89) 4.39 (SD 1 = 0.86) 4.37 (SD 1 = 0.80) 1 SD = Standard Deviation; 2 C = Contingency Coeﬃcient; indications of statistically signiﬁcant relationship or statistically signiﬁcant mean diﬀerences: * p < 0.05; p ≤ 0 0.01; * p ≤ 0.001; (1) (2) (3) results are based on bilateral tests with a level of signiﬁcance 0.05. The results table shows for each signiﬁcant pair the key of the group of runners with the proportion of the smallest column below the group of runners with the largest proportion of the column. Int. J. Environ. Res. Public Health 2020, 17, 925 13 of 19 The moderately committed group is characterized by a higher average age of 40.13 (SD = 9.26), signiﬁcantly higher than the slightly committed group, and a majority percentage of men (73.03%). These runners tend towards agreement on the indicator related to having stopped running for at least one week for a reason other than an injury (M = 3.58), with a signiﬁcantly higher average compared to the higher commitment group level. In the same way as the other groups, they strongly agree with the items on their future intentions regarding participation, recommendation, and positive comments on popular races. The third, slightly committed group is characterized by the lowest average age with 38.22 (SD = 8.79) and a majority percentage of men (73.22%). They tend towards agreement on the indicator related to having stopped running for at least one week for a reason that was not injury related (M = 3.75) with a signiﬁcantly higher average compared to the group with greater commitment to the race. Following the trend observed in the other groups, they strongly agree on the item on their future intentions regarding participation, recommendation, and positive comments on popular races. 4. Discussion This study analyses the running commitment of runners participating in urban popular races from the commitment-to-running scale validated with Spanish marathon runners. The analysis of the psychometric properties of the scale showed that the grouping of the indicators into two factors presented a better adjustment to the data collected in this study, identifying two dimensions related to positive aspects (enthusiasm for running) and negative aspects (aﬄiction from running) of the psychological construct of commitment to running. In previous works on the validation of the running-commitment scale, the possibility of grouping the indicators under two factors was assessed but was rejected due to the lack of interpretability and the high degree of uncertainty of the second factor identiﬁed [25]. For this reason, it was decided to test the ﬁt of the scale on a single factor, although the various goodness-of-ﬁt indices showed values distant from those recommended in the literature as acceptable. This study identiﬁed three groups of participants in urban popular races with diﬀerent commitments to running: “highly committed”, “moderately committed”, and “slightly committed”. The denomination of these groups was made from the interpretation of the average scores of the variables on the commitment-to-running scale that allowed us to identify three groups with clearly diﬀerent levels of commitment towards the practice of this physical activity. The sociodemographic variables that contributed to signiﬁcantly diﬀerentiating the identiﬁed groups were age and educational level. However, previous studies that have analysed the commitment to running without segmenting respondents into groups did not ﬁnd signiﬁcant diﬀerences in the items of the running-commitment scale according to age [15,25]. In other studies identifying groups according to their motivations for running, age was found to be a variable that diﬀerentiated the identiﬁed clusters [27,29,30]. The results varied according to gender, with some studies showing diﬀerences in the commitment to running according to this variable [10,19,21,25,37], while other studies, in the same vein as the results of this study, detected no signiﬁcant diﬀerences by gender [15]. It is important to note that the proﬁle of the runners in these studies was runners of long-distance races such as half-marathons and marathons, diﬀerent from the sample analysed in this study. Regarding the level of studies, in the work of Zarauz and Ruiz-Juan [19] on marathon runners, it was observed that a lower level of studies signiﬁcantly predicted the commitment to running. In this study, it was found that there were signiﬁcant disproportions of runners with a higher level of studies among runners with a lower level of commitment to running. In any case, more studies on amateur short-distance runners are needed to determine which sociodemographic variables contribute to diﬀerentiating subgroups of runners according to their commitment to running. On the other hand, variables related to sports habits (training frequency, preferred distance, years of running, number of kilometres run weekly, level at which runners considered themselves to be, participation in long-distance races, and membership of clubs and sports federations) also contributed Int. J. Environ. Res. Public Health 2020, 17, 925 14 of 19 to signiﬁcantly diﬀerentiating the groups identiﬁed, conﬁrming the need to consider runners from a heterogeneous point of view, as observed in other studies [27,29,30,61]. In previous studies, it was found that weekly training frequency, number of years of running, and number of kilometres run were positively related to commitment to running [10,21–23,25]. Similarly, a preference for participating in long-distance running was associated with higher scores on commitment to running [25,37,62]. From the point of view of the variables related to the running-addiction scale, it is worth noting that statistically signiﬁcant diﬀerences were found between the groups, proving the existence of a higher level of involvement in the activity in runners with a high commitment to running. In the theoretical framework, it has been emphasized that previous studies have proved that there is a high correlation between higher values of commitment to running and addiction to this physical activity, although both constructs may be inﬂuenced by diﬀerent variables. In a previous study, it was observed that, in half-marathon runners, the variable that seemed to have most importance with respect to why a half-marathon runner goes from being healthily committed to his sport practice to being pathologically addicted to it is the number of kilometres he runs each week: if it is low, a healthy commitment increases, while if it is high, in men, negative addiction increases [10]. In the present work, although this ﬁnding was not empirically proven, it is observed that the runners in the highly committed group train signiﬁcantly more kilometres per week than the other groups and, in addition, they show a signiﬁcantly higher tendency to agree on statements related to running addiction, such as the item stating that, some days, even if they do not feel like running, they practice the activity anyway. In this sense, the points made by Ruiz-Juan, Zarauz, and Flores-Allende [10] could be conﬁrmed but in runners who participated in urban popular races of reduced distance. In any case, more empirical evidence is needed to conﬁrm the hypothesized relationship between the number of kilometres run and running addiction in amateur short-distance runners. On the other hand, analysis of the groups’ proﬁle veriﬁed that the group denominated as highly committed presents a tendency to agree on most of the indicators related to the factor of enthusiasm to run, indicating a clear commitment through the practice of this activity. In contrast, on the aspects related to the aﬄiction factor from running, they show the inverse tendency, which also corroborates the high commitment of these runners to this physical activity. This is the group with the highest proportion of people with the lowest level of studies, in contrast to the group with the lowest commitment to running, indicating a similar trend to that observed by Zarauz and Ruiz-Juan [19] on the inﬂuence of this variable on the commitment to running. This is a group with sporting habits deﬁned by the constant and regular practice of this physical activity: they tend to run alone, have a training frequency of three or more times per week, display a preference for distances in popular races between 7.5 km and 15 km, are members of sports clubs, have the highest average number of kilometres run weekly, have the greatest number of years practising running, and have the highest average number of long-distance races run. This type of runner has been identiﬁed by Leedy [37] as “committed runners”. Additionally, Carmack and Martens [21] related the high commitment to running to running addiction; these runners were later classed by Pargman [20] as addict-dependent. However, this concept should be considered in a positive sense, that is, as deﬁned by this author, from the perspective that they are participants who show pleasure, enjoyment, and joy in the practice of this sport, as identiﬁed through the average scores observed on the indicators of the running enthusiasm factor. With respect to the group known as moderately committed, a more neutral and moderate attitude is observed in the indicators referring to the enthusiasm for running, highlighting aspects such as the desire to run or pleasure in running while showing a neutral evaluation on items such as the statement that the practice of this physical activity is the high point of the day or of vital importance for their lives. In the case of items on the aﬄiction from running factor, they present a tendency towards disagreement, albeit less pronounced than that of the group with greater commitment. Their sociodemographic proﬁle diﬀers in that they are older than the rest of the groups and have a higher level of education than the most committed group. The sports habits of this group present intermediate levels between the groups with lesser commitment and greater commitment, with the highlights being that they tend to run alone, Int. J. Environ. Res. Public Health 2020, 17, 925 15 of 19 their training frequency is three to ﬁve times per week, they display a preference for short-distance races (less than 10 km), they run a shorter distance each weekly than the highly committed group, and they have fewer years practising this physical activity. On the other hand, regarding the indicators on the running addiction scale, they do not agree that they need to run at least once every day and show a slight tendency towards agreement on the item regarding having stopped running for reasons other than an injury. This runner proﬁle is characterized by a recreational type of orientation and less commitment to the practice of running despite considering it an enjoyable and positive experience for their lives. Previous work on motivation and commitment to running has identiﬁed proﬁles of recreational runners [37], relational runners [5], socializing hedonists [27], and social competitors [29], who may have similarities in terms of their sporting habits and sociodemographic proﬁle. This group could be identiﬁed with a healthier proﬁle from a psychological point of view, since they do not show symptoms of possible running addiction, as could be deduced from the scores of the indicators extracted from the running addiction scale. Finally, the group dubbed slightly committed is the group with the lowest level of commitment to running because it is the group that disagrees most with the indicators related to enthusiasm for running (running is of vital importance to their life; the practice of enriches their life; they reorganize schedules in their daily life; and running is the highlight of the day). Unlike for the other two groups, the only indicator with a positive trend for this group is the fact that they consider running a pleasant activity. In the same way, the other groups disagree with the aspects related to aﬄiction from running. However, the slightly committed group has a certain tendency towards low motivation and considers the activity to be drudgery, as the tendency towards neutrality observed in some scores of the running aﬄiction factor points out. From the point of view of sociodemographic characteristics, this is the youngest group and has the highest level of studies. They are runners who train less frequently (once or twice a week), prefer short-distance races (less than 7.5 km), are less likely to have aﬃliations with sports clubs, run fewer kilometres weekly, and have fewer years practising this sport. There is a lower degree of dependence on the activity, as seen from the scores on the running-addiction indicators, giving a secondary role to the practice of running. 4.1. Practical Implications The objective of this research was to analyze the commitment of runners among urban runners and to segment and classify the runners. Research in this area and on commitment to running is limited, especially among urban short-distance runners. The groups of runners were identiﬁed and described sociodemographic proﬁle and sports habits: highly committed, moderately committed, and slightly committed. The conclusions of the work can be useful for the administration, management, and organization of local sports in terms of knowing the proﬁles of participants in popular races and of developing measures to improve the quality of life of the participants. In this regard, a high commitment to running can imply risks for health and can devolve into addictive behaviours that must be prevented to ensure that a runner is healthily committed to their sport practice rather than pathologically addicted to it. Running is an activity that has experienced an important boom in Spanish cities such as Valencia, requiring the implementation of sports policies and strategic plans that respond to the social demands of this collective. For this reason, it is advisable to oﬀer information and public services (sports, health, psychology, and education) to runners through the organizations and communication initiatives associated with this popular activity. Additionally, the identiﬁcation of diﬀerent proﬁles, characteristics, and habits can beneﬁt sport entities and public organizations to encourage sport activity. 4.2. Limitations and Future Lines of Research This work presents some limitations that should be taken into account when generalizing and extrapolating the results to other populations; for example, the type of sampling used does not allow for generalization of the results to the set of urban amateur runners. In carrying out f more studies Int. J. Environ. Res. Public Health 2020, 17, 925 16 of 19 of diﬀerent populations of amateur runners, eﬀorts should be made to identify subgroups of the population of urban runners in diﬀerent countries and regions with the aim of elaborating typologies of runners at the international level as well as identifying possible diﬀerences according to sociocultural and socioeconomic characteristics. Similarly, the relationship between commitment to running and possible addictions to this physical activity in runners who are starting out in the sport or who participate in shorter-distance races should be explored in greater depth with the aim of preventing future behaviour that could be dangerous to health. 5. Conclusions The aim of this paper is to analyse the commitment to running among urban runners by identifying groups with a greater or lesser commitment to this sport. Three groups of urban amateur runners with diﬀerent levels of commitment to running were identiﬁed: highly committed, moderately committed, and slightly committed. Also, the study identiﬁes two factors within the psychological construct of commitment to running: enthusiasm for running and aﬄiction from running. Sociodemographic variables related to age and educational level contribute to diﬀerentiating the groups of amateur runners according to their commitment to running. It is also found that most of the variables related to sports habits contribute to diﬀerentiating the groups: the frequency with which they go out to run, their preferred distance in popular races, the number of years they have practised running, the number of kilometres run weekly, the level they consider themselves to be at as runners, participation in long-distance races, and membership in clubs and sports federations. Highly committed runners positively value the aspects related to enthusiasm for running and show the reverse trend in the aspects related to the aﬄiction from running. Moderately committed runners are characterized by a moderate attitude towards running enthusiasm and a tendency to disagree on aspects related to running aﬄiction. Slightly committed runners show a low commitment to the practice of running, with a tendency towards disagreement on the indicators related to enthusiasm for running except the one related to considering it a pleasant activity. 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A Typology of Runners: Implications for Marketing Strategies. In Proceedings of the European Sport Management Conference, Heidelberg, Germany, 10–13 September 2008; pp. 321–323. 62. Funk, D.; Jordan, J.; Ridinger, L.; Kaplanidou, K. Capacity of Mass Participant Sport Events for the Development of Activity Commitment and Future Exercise Intention. Leis. Sci. 2011, 33, 250–268. [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/).	Amateur Runners' Commitment: An Analysis of Sociodemographic and Sports Habit Profiles.	02-02-2020	Parra-Camacho, David,Alonso Dos Santos, Manuel,González-Serrano, María Huertas	eng