Digital N-of-1 Trials and their Application in Experimental Physiology

TL;DR

This paper introduces N-of-1 trials as a novel, efficient study design for experimental physiology that enables individual and population-level inferences, addressing limitations of traditional small-sample studies.

Contribution

It details the key components, design features, statistical analysis, and digital tools for implementing N-of-1 trials in experimental physiology research.

Findings

N-of-1 trials allow valid inference on individual intervention effects.

They can be aggregated for population-level insights more efficiently.

Digital tools facilitate their application in physiology studies.

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. 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.