  App-based N-of-1 trials offer a scalable experimental design for assessing
the effects of health interventions at an individual level. Their practical
success depends on the strong motivation of participants, which, in turn,
translates into high adherence and reduced loss to follow-up. One way to
maintain participant engagement is by sharing their interim results.
Continuously testing hypotheses during a trial, known as "peeking", can also
lead to shorter, lower-risk trials by detecting strong effects early.
Nevertheless, traditionally, results are only presented upon the trial's
conclusion. In this work, we introduce a potential outcomes framework that
permits interim peeking of the results and enables statistically valid
inferences to be drawn at any point during N-of-1 trials. Our work builds on
the growing literature on valid confidence sequences, which enables
anytime-valid inference with uniform type-1 error guarantees over time. We
propose several causal estimands for treatment effects applicable in an N-of-1
trial and demonstrate, through empirical evaluation, that the proposed approach
results in valid confidence sequences over time. We anticipate that
incorporating anytime-valid inference into clinical trials can significantly
enhance trial participation and empower participants.
