Fitness Trackers Unveil Mood Swings in Bipolar Disorder

**Investigators at Brigham and Women's Hospital have uncovered a groundbreaking approach to monitoring bipolar disorder using fitness trackers and machine learning.** Bipolar disorder is marked by extreme mood shifts, and timely detection is crucial for effective treatment. By leveraging data from personal digital devices, such as smartphones and smartwatches, researchers aimed to discern mood episodes with precision. This study, published in Acta Psychiatrica Scandinavica, utilized noninvasive data collection and limited data filtering to achieve this, focusing on methods that can be widely applied in clinical environments. The researchers developed a new machine learning algorithm that detected depressive symptoms with 80.1% accuracy and manic symptoms with 89.1% accuracy. The implications of this study are significant as they suggest the possibility of integrating such technology into regular care, potentially allowing clinicians to respond more quickly to mood changes in patients. This could be a step toward personalized psychiatric care, reducing the burden of invasive monitoring and expanding accessibility for all patients, not just those with specialized devices or high compliance. The team is now looking to extend their findings to major depressive disorder, aiming to improve psychiatric treatments further.