  Anxiety disorders are the most common class of psychiatric problems affecting
both children and adults. However, tools to effectively monitor and manage
anxiety are lacking, and comparatively limited research has been applied to
addressing the unique challenges around anxiety. Leveraging passive and
unobtrusive data collection from smartphones could be a viable alternative to
classical methods, allowing for real-time mental health surveillance and
disease management. This paper presents eWellness, an experimental mobile
application designed to track a full-suite of sensor and user-log data off an
individual's device in a continuous and passive manner. We report on an initial
pilot study tracking ten people over the course of a month that showed a nearly
76% success rate at predicting daily anxiety and depression levels based solely
on the passively monitored features.
