Harnessing consumer wearables for individualized recognition of postpartum depression

Postpartum depression is the most common complication following childbirth, yet it remains significantly underscreened and underdetected, highlighting the urgent need for new strategies to facilitate early detection. Our study demonstrated the feasibility of using individualized (i.e., N-of-1) machine learning models to recognize postpartum depression using digital biomarkers from consumer-grade wearables. Dr. Hurwitz’s research is centered around the vision to enhance personal health by leveraging multi-modal biomedical data for early detection and enhanced monitoring of disease. He believes digital health technologies offer a groundbreaking approach to continuously and longitudinally to monitor human health, elevating precision medicine efforts. Eric Hurwitz, PhD: Postdoctoral Research Associate, Translational and Integrative Sciences Lab, Department of Genetics, UNC Chapel Hill Tuesday, June 3, 2025 The University of Washington is committed to ensuring digital accessibility in our services, programs, and activities. If you encounter accessibility barriers using videos found on this channel, please contact UW Video at uwvideo [at] uw [dot] edu.