2017
DOI: 10.1123/ijspp.2016-0337
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Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures

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 we… Show more

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Cited by 73 publications
(68 citation statements)
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“…, very long (0.7 ≤ r < 0.9), almost perfect (r ≥ 0.9) and perfect (r = 1). In the present study, only indicators with a value higher than 0.7 (positive or negative) were considered to define this component [23].…”
Section: Discussionmentioning
confidence: 99%
“…, very long (0.7 ≤ r < 0.9), almost perfect (r ≥ 0.9) and perfect (r = 1). In the present study, only indicators with a value higher than 0.7 (positive or negative) were considered to define this component [23].…”
Section: Discussionmentioning
confidence: 99%
“…Due to methodological and logistical issues (e.g., participant recording failure and equipment malfunction), it was not possible to collect continuous weekly observations which could have been used to calculate acute:chronic workload ratios or exponentially weighted moving averages (19,43). Future research should aim to assess the week-to-week changes in acute internal and external loads of adolescent rugby union players relative to chronic loads (42).…”
Section: Discussionmentioning
confidence: 99%
“…Many of the training load variables collected were likely to be correlated, thus our prediction problem may suffer from multi-collinearity. Principal component analysis (PCA) is a dimensionality reduction process that reduces a large number of predictor variables to a smaller number of uncorrelated variables [21] and has been employed in studies of training load monitoring [29]. Each multivariate model was trained with unprocessed data and data pre-processed with PCA.…”
Section: Data Pre-processingmentioning
confidence: 99%