Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures
Click name to view affiliation
Search for other papers by Sean Williams in
Current site
Google Scholar
PubMed
Search for other papers by Grant Trewartha in
Current site
Google Scholar
PubMed
Search for other papers by Matthew J. Cross in
Current site
Google Scholar
PubMed
Search for other papers by Simon P.T. Kemp in
Current site
Google Scholar
PubMed
Search for other papers by Keith A. Stokes in
Current site
Google Scholar
PubMed
Purpose:
Numerous derivative measures can be calculated from the simple session rating of perceived exertion (sRPE), a tool for monitoring training loads (eg, acute:chronic workload and cumulative loads). The challenge from a practitioner’s perspective is to decide which measures to calculate and monitor in athletes for injury-prevention purposes. The aim of the current study was to outline a systematic process of data reduction and variable selection for such training-load measures.
Methods:
Training loads were collected from 173 professional rugby union players during the 2013–14 English Premiership season, using the sRPE method, with injuries reported via an established surveillance system. Ten derivative measures of sRPE training load were identified from existing literature and subjected to principal-component analysis. A representative measure from each component was selected by identifying the variable that explained the largest amount of variance in injury risk from univariate generalized linear mixed-effects models.
Results:
Three principal components were extracted, explaining 57%, 24%, and 9% of the variance. The training-load measures that were highly loaded on component 1 represented measures of the cumulative load placed on players, component 2 was associated with measures of changes in load, and component 3 represented a measure of acute load. Four-week cumulative load, acute:chronic workload, and daily training load were selected as the representative measures for each component.
Conclusions:
The process outlined in the current study enables practitioners to monitor the most parsimonious set of variables while still retaining the variation and distinct aspects of “load” in the data.
Williams, Trewartha, and Stokes are with the Dept for Health, University of Bath, Bath, UK. Cross and Kemp are with Rugby Football Union, Twickenham, UK.
© 2023 Human Kinetics