  In this work we investigate intra-day patterns of activity on a population of
7,261 users of mobile health wearable devices and apps. We show that: (1) using
intra-day step and sleep data recorded from passive trackers significantly
improves classification performance on self-reported chronic conditions related
to mental health and nervous system disorders, (2) Convolutional Neural
Networks achieve top classification performance vs. baseline models when
trained directly on multivariate time series of activity data, and (3) jointly
predicting all condition classes via multi-task learning can be leveraged to
extract features that generalize across data sets and achieve the highest
classification performance.
