Relationships between External, Wearable Sensor-Based, and Internal Parameters: A Systematic Review

Micro electro-mechanical systems (MEMS) are used to record training and match play of intermittent team sport athletes. Paired with estimates of internal responses or adaptations to exercise, practitioners gain insight into players’ dose–response relationship which facilitates the prescription of the training stimuli to optimize performance, prevent injuries, and to guide rehabilitation processes. A systematic review on the relationship between external, wearable-based, and internal parameters in team sport athletes, compliant with the PRISMA guidelines, was conducted. The literature research was performed from earliest record to 1 September 2020 using the databases PubMed, Web of Science, CINAHL, and SportDISCUS. A total of 66 full-text articles were reviewed encompassing 1541 athletes. About 109 different relationships between variables have been reviewed. The most investigated relationship across sports was found between (session) rating of perceived exertion ((session-)RPE) and PlayerLoad™ (PL) with, predominantly, moderate to strong associations (r = 0.49–0.84). Relationships between internal parameters and highly dynamic, anaerobic movements were heterogenous. Relationships between average heart rate (HR), Edward’s and Banister’s training impulse (TRIMP) seem to be reflected in parameters of overall activity such as PL and TD for running-intensive team sports. PL may further be suitable to estimate the overall subjective perception. To identify high fine-structured loading—relative to a certain type of sport—more specific measures and devices are needed. Individualization of parameters could be helpful to enhance practicality

The relationship between movement speed and duration during soccer matches.

The relationship between the time duration of movement (t(dur)) and related maximum possible power output has been studied and modeled under many conditions. Inspired by the so-called power profiles known for discontinuous endurance sports like cycling, and the critical power concept of Monod and Scherrer, the aim of this study was to evaluate the numerical characteristics of the function between maximum horizontal movement velocity (HSpeed) and t(dur) in soccer. To evaluate this relationship, GPS data from 38 healthy soccer players and 82 game participations (≥30 min active playtime) were used to select maximum HSpeed for 21 distinct t(dur) values (between 0.3 s and 2,700 s) based on moving medians with an incremental t(dur) window-size. As a result, the relationship between HSpeed and Log(t(dur)) appeared reproducibly as a sigmoidal decay function, and could be fitted to a five-parameter equation with upper and lower asymptotes, and an inflection point, power and decrease rate. Thus, the first three parameters described individual characteristics if evaluated using mixed-model analysis. This study shows for the first time the general numerical relationship between t(dur) and HSpeed in soccer games. In contrast to former descriptions that have evaluated speed against power, HSpeed against t(dur) always yields a sigmoidal shape with a new upper asymptote. The evaluated curve fit potentially describes the maximum moving speed of individual players during the game, and allows for concise interpretations of the functional state of team sports athletes

Relationships between External, Wearable Sensor-Based, and Internal Parameters: A Systematic Review

Micro electro-mechanical systems (MEMS) are used to record training and match play of intermittent team sport athletes. Paired with estimates of internal responses or adaptations to exercise, practitioners gain insight into players’ dose–response relationship which facilitates the prescription of the training stimuli to optimize performance, prevent injuries, and to guide rehabilitation processes. A systematic review on the relationship between external, wearable-based, and internal parameters in team sport athletes, compliant with the PRISMA guidelines, was conducted. The literature research was performed from earliest record to 1 September 2020 using the databases PubMed, Web of Science, CINAHL, and SportDISCUS. A total of 66 full-text articles were reviewed encompassing 1541 athletes. About 109 different relationships between variables have been reviewed. The most investigated relationship across sports was found between (session) rating of perceived exertion ((session-)RPE) and PlayerLoad™ (PL) with, predominantly, moderate to strong associations (r = 0.49–0.84). Relationships between internal parameters and highly dynamic, anaerobic movements were heterogenous. Relationships between average heart rate (HR), Edward’s and Banister’s training impulse (TRIMP) seem to be reflected in parameters of overall activity such as PL and TD for running-intensive team sports. PL may further be suitable to estimate the overall subjective perception. To identify high fine-structured loading—relative to a certain type of sport—more specific measures and devices are needed. Individualization of parameters could be helpful to enhance practicality

Independence of the lower against the upper asymptotes.

<p>A linear (Pearson) correlation between parameters <i>c</i> and <i>d</i> from all individual measurements is given (r² = 0.04).</p

Mean ± SD values for the parameters found when Eq. 8 (S1 Appendix) was used to model the relationship between time duration of movement (<i>t</i>_<i>dur</i>) and the weighted maxima of horizontal moving speed (<i>HSpeed_weighted</i>_<i>max</i>, Eq 7) for all players.

<p>Mean ± SD values for the parameters found when Eq. 8 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181781#pone.0181781.s001" target="_blank">S1 Appendix</a>) was used to model the relationship between time duration of movement (<i>t</i>_<i>dur</i>) and the weighted maxima of horizontal moving speed (<i>HSpeed_weighted</i>_<i>max</i>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181781#pone.0181781.e010" target="_blank">Eq 7</a>) for all players.</p

Mean ± SD values for the parameters found with fittings of Eq. 8, S1 Appendix) to the relationship between time duration of movement (<i>t</i>_<i>dur</i>) and maximum horizontal moving speed (<i>HSpeed</i>_<i>max</i>) for all players.

<p>Mean ± SD values for the parameters found with fittings of Eq. 8, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181781#pone.0181781.s001" target="_blank">S1 Appendix</a>) to the relationship between time duration of movement (<i>t</i>_<i>dur</i>) and maximum horizontal moving speed (<i>HSpeed</i>_<i>max</i>) for all players.</p

Overall evaluation of seven models tested to represent the relationship between maximum horizontal moving speed (<i>HSpeed</i>; <i>y</i>) and time duration of movement (<i>t</i>_<i>dur</i>; <i>x</i>) for every participant and soccer game tested in the study.

<p>Overall evaluation of seven models tested to represent the relationship between maximum horizontal moving speed (<i>HSpeed</i>; <i>y</i>) and time duration of movement (<i>t</i>_<i>dur</i>; <i>x</i>) for every participant and soccer game tested in the study.</p

Players' positions.

<p>Players' positions.</p

Residuals of the seven evaluated fitting equations.

<p>The differences between the measured and the predicted values against the mean between both are shown. Only the Richards' Logistic 5P equation (Eq. 8, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181781#pone.0181781.s001" target="_blank">S1 Appendix</a>) does not display a systematic deviation from the measurements. For further descriptions of the applied equations, see the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181781#pone.0181781.s001" target="_blank">S1 Appendix</a>.</p