  Negative affect is a proxy for mental health in adults. By being able to
predict participants' negative affect states unobtrusively, researchers and
clinicians will be better positioned to deliver targeted, just-in-time mental
health interventions via mobile applications. This work attempts to personalize
the passive recognition of negative affect states via group-based modeling of
user behavior patterns captured from mobility, communication, and activity
patterns. Results show that group models outperform generalized models in a
dataset based on two weeks of users' daily lives.
