  In many mobile health interventions, treatments should only be delivered in a
particular context, for example when a user is currently stressed, walking or
sedentary. Even in an optimal context, concerns about user burden can restrict
which treatments are sent. To diffuse the treatment delivery over times when a
user is in a desired context, it is critical to predict the future number of
times the context will occur. The focus of this paper is on whether
personalization can improve predictions in these settings. Though the variance
between individuals' behavioral patterns suggest that personalization should be
useful, the amount of individual-level data limits its capabilities. Thus, we
investigate several methods which pool data across users to overcome these
deficiencies and find that pooling lowers the overall error rate relative to
both personalized and batch approaches.
