  Physical activity and sleep play a major role in the prevention and
management of many chronic conditions. It is not a trivial task to understand
their impact on chronic conditions. Currently, data from electronic health
records (EHRs), sleep lab studies, and activity/sleep logs are used. The rapid
increase in the popularity of wearable health devices provides a significant
new data source, making it possible to track the user's lifestyle real-time
through web interfaces, both to consumer as well as their healthcare provider,
potentially. However, at present there is a gap between lifestyle data (e.g.,
sleep, physical activity) and clinical outcomes normally captured in EHRs. This
is a critical barrier for the use of this new source of signal for healthcare
decision making. Applying deep learning to wearables data provides a new
opportunity to overcome this barrier.
  To address the problem of the unavailability of clinical data from a major
fraction of subjects and unrepresentative subject populations, we propose a
novel unsupervised (task-agnostic) time-series representation learning
technique called act2vec. act2vec learns useful features by taking into account
the co-occurrence of activity levels along with periodicity of human activity
patterns. The learned representations are then exploited to boost the
performance of disorder-specific supervised learning models. Furthermore, since
many disorders are often related to each other, a phenomenon referred to as
co-morbidity, we use a multi-task learning framework for exploiting the shared
structure of disorder inducing life-style choices partially captured in the
wearables data. Empirical evaluation using actigraphy data from 4,124 subjects
shows that our proposed method performs and generalizes substantially better
than the conventional time-series symbolic representational methods and
task-specific deep learning models.
