Optimizing research methodology for the detection of individual response variation in resistance training

Zac P. Robinson, Eric R. Helms, Eric T. Trexler, James Steele, Michael E. Hall, Chun-Jung Huang, Michael C. Zourdos

Research output: Working paper/PreprintPreprint


Most resistance training research focuses on group-level outcomes (i.e., group A versus group B). However, many practitioners are more interested in training responses on the individual level (i.e., intervention A versus intervention B for individual X). In order to properly examine individual response variation, multiple confounding sources of variation (e.g., random sampling variation, measurement error, biological variation) must be addressed. Novel study designs where participants complete both interventions and at least one intervention twice can be leveraged to account for these sources of variation. Specifically, the appropriate statistical methods can separate variability into the signal (i.e., participant-by-training interaction) versus the noise (i.e., within-participant variance). This distinction can allow researchers to detect evidence of individual response variation. If evidence of individual response variation exists, researchers can explore potential predictors of the more favorable intervention, thereby improving exercise prescription. This review outlines the methodology necessary to explore individual response variation to resistance training, predict favorable interventions, and the limitations thereof.
Original languageEnglish
PublisherCenter for Open Science
Publication statusPublished - 19 Oct 2023

Publication series

PublisherCenter for Open Science

Cite this