TY - JOUR
T1 - N of 1: optimizing methodology for the detection of individual response variation in resistance training
AU - Robinson, Zac P.
AU - Helms, Eric R.
AU - Trexler, Eric T.
AU - Steele, James
AU - Hall, Michael E.
AU - Huang, Chun-Jung
AU - Zourdos, Michael C.
PY - 2024/6/15
Y1 - 2024/6/15
N2 - Most resistance training research focuses on inference from average intervention effects from observed group-level change scores (i.e., mean change of group A vs group B). However, many practitioners are more interested in training responses (i.e., causal effects of an intervention) on the individual level (i.e., causal effect of intervention A vs intervention B for individual X). To properly examine individual response variation, multiple confounding sources of variation (e.g., random sampling variability, measurement error, biological variability) 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 (i.e., n of 1 trials). 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 predictors of the more favorable intervention, potentially improving exercise prescription. This review outlines the methodology necessary to explore individual response variation to resistance training, predict favorable interventions, and the limitations thereof. [Abstract copyright: © 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.]
AB - Most resistance training research focuses on inference from average intervention effects from observed group-level change scores (i.e., mean change of group A vs group B). However, many practitioners are more interested in training responses (i.e., causal effects of an intervention) on the individual level (i.e., causal effect of intervention A vs intervention B for individual X). To properly examine individual response variation, multiple confounding sources of variation (e.g., random sampling variability, measurement error, biological variability) 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 (i.e., n of 1 trials). 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 predictors of the more favorable intervention, potentially improving exercise prescription. This review outlines the methodology necessary to explore individual response variation to resistance training, predict favorable interventions, and the limitations thereof. [Abstract copyright: © 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.]
U2 - 10.1007/s40279-024-02050-z
DO - 10.1007/s40279-024-02050-z
M3 - Article
SN - 1179-2035
VL - 54
SP - 1979
EP - 1990
JO - Sports Medicine
JF - Sports Medicine
ER -