Relationships Between Internal and External Training Load in Team-Sport Athletes: Evidence for an Individualized Approach

International Journal of Sports Physiology and Performance - Tập 12 Số 2 - Trang 230-234 - 2017
Jonathan Bartlett1,2,3,4,5, Fergus K. O’Connor4,5, Nathan W. Pitchford4,5, Lorena Torres-Ronda4,6, Sam Robertson1,2,3,4,5
1Department of Health and Kinesiology,
2Department of Health and Kinesiology, Exercise and Sport Nutrition Lab, Texas A&M University, College Station, TX.
3Exercise and Sport Nutrition Lab, Texas A&M University, College Station, TX, USA.
4Institute of Sport, Exercise and Active Living (ISEAL), Victoria University, Melbourne, Australia
5Western Bulldogs Football Club, Melbourne, Australia
6Institute of Sport, Exercise & Active Living (ISEAL) College of Sport and Exercise Science Victoria University PO Box 14428, Melbourne, Victoria, Australia 8001

Tóm tắt

Purpose:The aim of this study was to quantify and predict relationships between rating of perceived exertion (RPE) and GPS training-load (TL) variables in professional Australian football (AF) players using group and individualized modeling approaches.Methods:TL data (GPS and RPE) for 41 professional AF players were obtained over a period of 27 wk. A total of 2711 training observations were analyzed with a total of 66 ± 13 sessions/player (range 39–89). Separate generalized estimating equations (GEEs) and artificial-neural-network analyses (ANNs) were conducted to determine the ability to predict RPE from TL variables (ie, session distance, high-speed running [HSR], HSR %, m/min) on a group and individual basis.Results:Prediction error for the individualized ANN (root-mean-square error [RMSE] 1.24 ± 0.41) was lower than the group ANN (RMSE 1.42 ± 0.44), individualized GEE (RMSE 1.58 ± 0.41), and group GEE (RMSE 1.85 ± 0.49). Both the GEE and ANN models determined session distance as the most important predictor of RPE. Furthermore, importance plots generated from the ANN revealed session distance as most predictive of RPE in 36 of the 41 players, whereas HSR was predictive of RPE in just 3 players and m/min was predictive of RPE in just 2 players.Conclusions:This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to TL. These approaches enable further individualization of load monitoring, leading to more accurate training prescription and evaluation.

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International Journal of Sports Physiology and Performance

Tập 12 Số 2

230-234

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