Studying individual causal effects of health interventions is of interest whenever intervention effects are heterogeneous between study participants. Conducting N-of-1 trials, which are single-person randomized controlled trials, is the gold standard for their analysis. In this study, we propose to re-analyze existing population-level studies as N-of-1 trials as an alternative, and we use gait as a use case for illustration. Gait data were collected from 16 young and healthy participants under fatigued and non-fatigued, as well as under single-task (only walking) and dual-task (walking while performing a cognitive task) conditions. We first computed standard population-level ANOVA models to evaluate differences in gait parameters (stride length and stride time) across conditions. Then, we estimated the effect of the interventions on gait parameters on the individual level through Bayesian linear mixed models, viewing each participant as their own trial, and compared the results. The results illustrated that while few overall population-level effects were visible, individual-level analyses showed nuanced differences between participants. Baseline values of the gait parameters varied largely among all participants, and the changes induced by fatigue and cognitive task performance were also highly heterogeneous, with some individuals showing effects in opposite direction. These differences between population-level and individual-level analyses were more pronounced for the fatigue intervention compared to the cognitive task intervention. Following our empirical analysis, we discuss re-analyzing population studies through the lens of N-of-1 trials more generally and highlight important considerations and requirements. Our work encourages future studies to investigate individual effects using population-level data.

翻译：研究健康干预措施的个体因果效应在参与者间存在异质性时具有重要意义。进行单病例试验（即单人随机对照试验）是分析个体效应的金标准。本研究提出将现有群体水平研究重新分析为单病例试验的替代方法，并以步态作为应用案例进行说明。我们收集了16名年轻健康参与者在疲劳与非疲劳状态、以及单任务（仅步行）与双任务（步行同时执行认知任务）条件下的步态数据。首先，我们采用标准群体水平方差分析模型评估不同条件下步态参数（步长与步时）的差异。随后，通过贝叶斯线性混合模型，将每位参与者视为自身试验，在个体水平上估计干预措施对步态参数的影响，并对结果进行比较。结果表明，尽管整体群体水平效应不显著，但个体水平分析揭示了参与者间的细微差异：所有参与者的步态参数基线值存在较大变异，且疲劳和认知任务引起的改变也具有高度异质性，部分个体甚至呈现相反方向的变化。与认知任务干预相比，疲劳干预在群体与个体水平分析间的差异更为显著。基于实证分析，我们进一步探讨了从单病例试验视角重新分析群体研究的普适性，并强调关键考量因素与必要条件。本研究鼓励未来研究利用群体水平数据探究个体效应。