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Monitoring what matters:A systematic process for selecting training load measures


Reference:

Williams, S., Trewartha, G., Cross, M. J., Kemp, S. P. T. and Stokes, K. A., 2017. Monitoring what matters:A systematic process for selecting training load measures. International Journal of Sports Physiology and Performance, 12 (Suppl 2), S2 101-106.

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    Official URL:

    http://dx.doi.org/10.1123/ijspp.2016-0337

    Abstract

    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.

    Details

    Item Type Articles
    CreatorsWilliams, S., Trewartha, G., Cross, M. J., Kemp, S. P. T. and Stokes, K. A.
    DOI10.1123/ijspp.2016-0337
    DepartmentsFaculty of Humanities & Social Sciences > Health
    Research CentresEPSRC Centre for Doctoral Training in Statistical Mathematics (SAMBa)
    RefereedYes
    StatusPublished
    ID Code52619

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