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Relationships between internal and external training load in team sport athletes: evidence for an individualised approach

Citation

Bartlett, JD and O'Connor, F and Pitchford, N and Torres-Ronda, L, Relationships between internal and external training load in team sport athletes: evidence for an individualised approach, International journal of sports physiology and performance, 12, (2) pp. 230-234. ISSN 1555-0265 (2017) [Refereed Article]


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Copyright 2016 Human Kinetics, Inc.

DOI: doi:10.1123/ijspp.2015-0791

Abstract

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 3989). 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.

Item Details

Item Type:Refereed Article
Keywords:Elite athletes, monitoring, training prescription, RPE, GPS
Research Division:Medical and Health Sciences
Research Group:Human Movement and Sports Science
Research Field:Exercise Physiology
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Medical and Health Sciences
UTAS Author:Pitchford, N (Dr Nathan Pitchford)
ID Code:132229
Year Published:2017
Web of Science® Times Cited:34
Deposited By:Health Sciences
Deposited On:2019-04-30
Last Modified:2019-08-22
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