Bipolar disorder affects over 5.7 million Americans. This psychiatric disorder is characterized by recurring episodes of manic and depressive states, both of which make functioning in daily life difficult. Recent advances in signal processing and machine learning have made it possible to detect mood transitions experienced by bipolar patients, enabling passive monitoring of patients’ state and creation of applications that can help patients to more affectively manage their condition.
This project aims to use user-centered design process to develop a mobile-phone application that leverages passive mood tracking to support self-management in bipolar disorder. Through formative work with patients and clinicians and though rapid technology prototyping, we will develop an application that helps bipolar patients to monitor their state, discover triggers for their mood transitions, cope with those transitions, and engage in regular self-care activities like medication use, physical activity, and good sleep hygiene.