Brief ReportUsing Digital Phenotyping to Accurately Detect Depression SeverityJacobson, Nicholas C. MS; Weingarden, Hilary PhD; Wilhelm, Sabine PhDAuthor Information Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. Send reprint requests to Nicholas C. Jacobson, MS, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge St, Suite 2000, Boston, MA 02114. E-mail: firstname.lastname@example.org. The Journal of Nervous and Mental Disease: October 2019 - Volume 207 - Issue 10 - p 893-896 doi: 10.1097/NMD.0000000000001042 Buy Metrics Abstract Development of digital biomarkers holds promise for enabling scalable, time-sensitive, and cost-effective strategies to monitor symptom severity among those with major depressive disorder (MDD). The current study examined the use of passive movement and light data from wearable devices to assess depression severity in 15 patients with MDD. Using over 1 week of movement data, we were able to significantly assess depression severity with high precision for self-reported (r = 0.855; 95% confidence interval [CI], 0.610–0.950; p = 4.95 × 10−5) and clinician-rated (r = 0.604; 95% CI, 0.133–0.894; p = 0.017) symptom severity. Pending replication, the present data suggest that the use of passive wearable sensors to inform healthcare decisions holds considerable promise. Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.