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In this work we provide a couple of contributions to the analysis oflongitudinal data collected by smartphones in mobile health applications.First, we propose a novel statistical approach to disentangle personalizedtreatment and "time-of-the-day" effects in observational studies. Under theassumption of no unmeasured confounders, we show how to use conditionalindependence relations in the data in order to determine if a difference inperformance between activity tasks performed before and after the participanthas taken medication, are potentially due to an effect of the medication or toa "time-of-the-day" effect (or still to both). Second, we show that smartphonedata collected from a given study participant can represent a "digitalfingerprint" of the participant, and that classifiers of case/control labels,constructed using longitudinal data, can show artificially improved performancewhen data from each participant is included in both training and test sets. Weillustrate our contributions using data collected during the first 6 months ofthe mPower study.

On the analysis of personalized medication response and classification of case vs control patients in mobile health studies: the mPower case study