Bipolar disorder affects more than 5.7 million Americans. This psychiatric condition is characterized by recurring episodes of manic and depressive states, both of which make it difficult for affected persons to function in daily life. 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’ states and the creation of applications that can help patients to more effectively manage their condition. This project will seek to develop technology that will allow those with bipolar disorder to more easily monitor their condition.
The researchers are looking at ways to detect in advance when patients are approaching an inflection point—when they start becoming manic or start being depressed—and allow them to catch it in time to adjust medication and hopefully get them to maintain stability instead of going into a clinical episode.
The application developed for this project will also look at ways to support a bipolar patient’s self-management while he or she is stable. This involves investigating ways to make it easier for patients to follow regimens that include taking medication, participating in physical activity, and getting consistent sleep. These activities have shown to be beneficial in helping bipolar patients remain in a functional state, but it can sometimes be difficult for patients to adhere to such routines. This project will explore various ways to incorporate mobile technology to help patients manage their condition on a day-to-day basis, while at the same time doing some background sensing to see if a clinical episode is approaching.
Another aspect of the study will examine the issue of framing intervention as something other than mental health assistance. Literature on bipolar disorder shows social support systems that allow patients to exchange tips on self-management can be helpful, but patients can be hesitant to participate in these forums. Based on the idea that patients typically value physical health over mental health, researchers will look at ways to integrate physical activities with mental components and introduce a social network into mental health and self-management practices in order to make them less stigmatizing.
Klasnja said the research team is in the beginning stages of qualitative research, using interviews and focus groups with bipolar patients to figure out how they self-manage their condition and what they think they would need to make self-management more effective. Researchers will use qualitative findings to design prototypes for a self-management component and then share the system with patients, make adjustments, and once they develop a more polished product, deploy it with patients to use in daily life. They will then collect data about how patients are using it and revise the system to develop a better version to hopefully incorporate into a larger field research project.