It is commonly stated that individuals respond differently to exercise even when the same exercise intervention is performed. This has led many researchers to conduct exercise interventions and subsequently categorize individuals into different responder categories to determine what causes individuals to respond differently. Some methods by which differential responders are categorized include percentile ranks, standard deviations from the mean, and cluster analyses. Notably, each of these methods will result in the presence of differential responders even in the absence of an exercise intervention, indicating that individuals may be categorized based on the presence of random error as opposed to true differences in the exercise response. Here we propose a method by which differential responders can be classified after accounting for the presence of random error that is quantified from a time-matched control group. Individuals who exceed random error from the mean response of the intervention group can be confidently labelled as high and low responders. Importantly, the number of differential responders will be proportional to the ratio of variance in the exercise and control groups. We provide easy-to-follow steps and examples to demonstrate how this technique can identify differential responders to exercise. We also detail the flaws in other classification methods by demonstrating the number of differential responders who would have been classified using the same data set. Our hope is that this method will help to avoid misclassifying individuals based on random error and, in turn, increase the replicability of differential responder studies.

中文翻译：

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一种停止分析随机误差并开始分析运动差异反应的方法。

通常认为，即使进行相同的运动干预，个体对运动的反应也会有所不同。这导致许多研究人员进行运动干预，随后将个人分为不同的响应者类别，以确定导致个人做出不同响应的原因。区分差分响应者的一些方法包括百分等级，均值的标准差和聚类分析。值得注意的是，即使没有运动干预，这些方法中的每一种都将导致存在差异反应者，这表明可以根据随机误差的存在来对个体进行分类，这与运动反应的真实差异相反。在这里，我们提出了一种方法，在考虑到从时间匹配的对照组量化的随机误差后，可以对差分响应者进行分类。超出干预组平均反应随机误差的个体可以确定地标记为高反应者和低反应者。重要的是，差异反应者的数量将与运动组和对照组的差异比率成正比。我们提供了易于遵循的步骤和示例，以演示该技术如何识别运动中的不同反应者。我们还将通过演示将使用同一数据集进行分类的差分响应者的数量来详细说明其他分类方法中的缺陷。我们希望这种方法将有助于避免基于随机错误对个人进行错误分类，并且