New findings indicate that it is possible to detect when patients with bipolar disorder are experiencing depression or mania with high accuracy using data from fitness tracking devices.
Bipolar disorder is a chronic psychiatric disorder characterised 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 bipolar disorder 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.
For the study, the research team focussed 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 non-invasive data.
Applying a new type of machine learning algorithm, they were able to detect clinically significant symptoms of depression with 80.1 per cent accuracy and clinically significant symptoms of mania with 89.1 per cent accuracy.
“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, investigator at 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.”
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.”
The next step is to apply the predictive algorithms in routine care where they could be used to improve treatment by informing clinicians when their patients are experiencing depressive or manic episodes between scheduled appointments.
The researchers have been working on extending the work to major depressive disorder.
