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.

大多数阻力训练研究侧重于根据观察到的组水平变化分数（即 A 组与 B 组的平均变化）的平均干预效果进行推断。然而，许多从业者对个体水平上的训练反应（即干预的因果效应）更感兴趣（即干预 A 与干预 B 对个体 X 的因果效应）。为了正确检查个体反应变化，必须解决多种混杂的变化来源（例如随机抽样变异性、测量误差、生物变异性）。参与者完成两项干预措施并且至少一项干预措施两次的新颖研究设计可以用来解释这些变异来源（即 1 项试验中的n 项）。具体来说，适当的统计方法可以将变异性分离为信号（即，参与者与训练之间的交互）与噪声（即，参与者内的方差）。这种区别可以让研究人员发现个体反应差异的证据。如果存在个体反应差异的证据，研究人员可以探索更有利的干预措施的预测因素，从而有可能改善运动处方。这篇综述概述了探索个体对阻力训练的反应变化、预测有利的干预措施及其局限性所必需的方法。