Title

Reply to Lolli et al

Document Type

Response or Comment

Publication Title

International Journal of Sports Physiology and Performance

Publisher

Human Kinetics

School

School of Medical and Health Sciences

Comments

Coyne, J. O., Nimphius, S., Newton, R. U., & Haff, G. G. (2020). Reply to Lolli et al. International Journal of Sports Physiology and Performance, 15(5), 601-602.

https://doi.org/10.1123/ijspp.2020-0190

Abstract

We thank Lolli et al1 for their comment on our recent article “Does mathematical coupling matter to the acute to chronic workload ratio? A case study from elite sport.”2 In their comment, Lolli et al1 highlighted the potential of psuedoreplication3 in our paper’s statistical analysis. We agree that we had fallen foul of this analytical pitfall and appreciate their identification of this issue. We have since reanalyzed the data using what we consider more appropriate measures for within-subject analysis to address the overall statistical concerns including psuedoreplication. Our analysis now includes examining the repeated-measures correlations4 (as suggested by Lolli et al1) between (1) acute training load (ATL) and coupled and uncoupled chronic training load (CTL) and (2) coupled and uncoupled acute-to-chronic workload ratios (ACWR). These repeated-measures correlations between coupled ATL–CTL and uncoupled ATL–CTL have also been compared against one another with r–z transformations. The results are presented in Table 1. In addition, we extended the statistical revision to evaluate the outcome using linear mixed models (with the athlete and the time point [day] as random intercept) to determine if there are significant differences between coupled and uncoupled CTL and coupled and uncoupled ACWR. Effect sizes of these differences (marginal f2)5 were then calculated and interpreted as trivial ( < 0.02), small (0.02–0.14), medium (0.15–0.34), and large ( > 0.35)6 as presented in Table 2. All statistical analyses were performed using the R statistics package (https://www.r-project.org; R Foundation for Statistical Computing, Vienna, Austria), and our R script has been provided as Supplementary Material (available online).

DOI

10.1123/ijspp.2020-0190

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