Traditionally, studies in experimental physiology have been conducted in small groups of human participants, animal models or cell lines. Identifying optimal study designs that achieve sufficient power for drawing proper statistical inferences to detect group level effects with small sample sizes has been challenging. Moreover, average effects derived from traditional group-level inference do not necessarily apply to individual participants. Here, we introduce N-of-1 trials as an innovative study design that can be used to draw valid statistical inference about the effects of interventions on individual participants and can be aggregated across multiple study participants to provide population-level inferences more efficiently than standard group randomized trials. N-of-1 trials have been used in healthcare settings since the late 1980s, but without large-scale adoption and with few applications in experimental physiology research settings. In this manuscript, we introduce the key components and design features of N-of-1 trials, describe statistical analysis and interpretations of the results, and describe some available digital tools to facilitate their use using examples from experimental physiology.

翻译：传统上，实验生理学研究通常在少量人类参与者、动物模型或细胞系中进行。如何确定最优研究设计以实现足够统计功效，从而从小样本中正确推断组水平效应，一直面临挑战。此外，从传统组水平推断中得出的平均效应并不一定适用于个体参与者。本文介绍N-of-1试验作为一种创新研究设计，可用于对干预措施对个体参与者的效应进行有效统计推断，并可通过跨多个研究参与者的聚合分析，比标准组随机试验更高效地得出群体水平推论。N-of-1试验自20世纪80年代末已在医疗领域得到应用，但尚未大规模推广，且在实验生理学研究中应用有限。本文阐明N-of-1试验的关键组成部分与设计特征，介绍其统计分析方法与结果解读，并基于实验生理学实例描述促进其应用的可用的数字工具。