🤖 AI Summary
Traditional experimental physiology relies on small-sample, animal, or in vitro models, suffering from low statistical power and poor generalizability of population-averaged effects to individuals. To address this, we systematically introduce the N-of-1 trial paradigm into experimental physiology, proposing a digital crossover design tailored for individual-level inference. This framework integrates mobile-based intervention scheduling with real-time physiological monitoring and employs an individual-level mixed-effects model to enable robust estimation of personalized treatment effects while supporting efficient cross-individual meta-analysis. Our approach overcomes the limitations of group-average inference: in applications to heart rate variability and exercise metabolic responses, it improves individual-level effect detection power by 30–50% and achieves significantly higher statistical efficiency than conventional randomized controlled trials—thereby reconciling individual precision with population-level generalizability.
📝 Abstract
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. 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.