  In this work we provide a couple of contributions to the analysis of
longitudinal data collected by smartphones in mobile health applications.
First, we propose a novel statistical approach to disentangle personalized
treatment and "time-of-the-day" effects in observational studies. Under the
assumption of no unmeasured confounders, we show how to use conditional
independence relations in the data in order to determine if a difference in
performance between activity tasks performed before and after the participant
has taken medication, are potentially due to an effect of the medication or to
a "time-of-the-day" effect (or still to both). Second, we show that smartphone
data collected from a given study participant can represent a "digital
fingerprint" of the participant, and that classifiers of case/control labels,
constructed using longitudinal data, can show artificially improved performance
when data from each participant is included in both training and test sets. We
illustrate our contributions using data collected during the first 6 months of
the mPower study.
