Researchers use fitness tracker data and machine learning to detect bipolar disorder mood swings

Investigators from Brigham and Women’s Hospital, a founding member of the Mass General Brigham healthcare system, evaluated whether data collected from a fitness tracker could be used to accurately detect mood episodes in people with bipolar disorder. Their findings, published in Acta Psychiatrica Scandinavica, indicate that it is possible to detect time intervals when patients with bipolar disorder are experiencing depression or mania with high accuracy using data from fitness tracking devices.

“Most people are walking around with personal digital devices like smartphones and smartwatches that capture day-to-day data that could inform psychiatric treatment. Our goal was to use that data to identify when study participants diagnosed with bipolar disorder were experiencing mood episodes,” said corresponding author Jessica Lipschitz, PhD, an investigator in the Brigham’s Department of Psychiatry. “In the future, our hope is that machine learning algorithms like ours could help patients’ treatment teams respond fast to new or unremitting episodes in order to limit negative impact.”

Bipolar disorder (BD) is a chronic psychiatric disorder characterized by extreme mood swings, including depression, mania, and hypomania followed by periods of remission. Identification and treatment of new and unremitting mood episodes is essential for limiting the impact of BD on patients’ lives. While previous research has indicated that personal digital devices can accurately detect mood episodes, previous studies have not used methods designed for broad application in clinical settings.

As an implementation scientist, Lipschitz, together with colleagues, focused on using methods that could be broadly implemented in clinical practice. Specifically, they used commercially available personal digital devices, limited data filtering, and entirely passively collected and noninvasive data. Applying a new type of machine learning algorithm, they were able to detect clinically significant symptoms of depression with 80.1% accuracy and clinically significant symptoms of mania with 89.1% accuracy.

The researchers note that, “overall, results move the field a step toward personalized algorithms suitable for the full population of patients, rather than only those with high compliance, access to specialized devices, or willingness to share invasive data.” Their next step is to apply these predictive algorithms in routine care where they could be used to improve BD treatment by informing clinicians when their patients are experiencing depressive or manic episodes between scheduled appointments. The researchers have also been working on extending this work to major depressive disorder.