Wearable Tech Accurately Predicts Mood Swings in Bipolar Disorder

Researchers at Brigham and Women’s Hospital have demonstrated that fitness trackers can detect mood episodes in bipolar disorder patients with high accuracy, using noninvasive data and machine learning algorithms. The study found 89.1% accuracy for mania and 80.1% for depression, suggesting potential for real-time monitoring and improved clinical care by alerting healthcare providers to mood changes between appointments. This approach could lead to personalized algorithms for broader patient support without requiring specialized devices or invasive data sharing.