  Unhealthy behaviors, e.g., physical inactivity and unhealthful food choice,
are the primary healthcare cost drivers in developed countries. Pervasive
computational, sensing, and communication technology provided by smartphones
and smartwatches have made it possible to support individuals in their everyday
lives to develop healthier lifestyles. In this paper, we propose an exercise
recommendation system that also predicts individual success rates. The system,
consisting of two inter-connected recurrent neural networks (RNNs), uses the
history of workouts to recommend the next workout activity for each individual.
The system then predicts the probability of successful completion of the
predicted activity by the individual. The prediction accuracy of this
interconnected-RNN model is assessed on previously published data from a
four-week mobile health experiment and is shown to improve upon previous
predictions from a computational cognitive model.
