  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.
