Assessing the Feasibility of Large-Scale Digital Sensing for Depression and Anxiety: The Digital Mental Health Study
AuthorsChristopher S Douglas*†,‡, Eliza Congdon*†, Crane Huang, Darsoi Seok†, Zachary D Cohen†,§, Samir Akre†, Veronica Tozzo†, Feiyang Huang†, Danielle Ramo-Larios†, Raphe A Bernier†,¶, Jonathan Flint†, Arash Naeim††, Brunilda Balliu†, Alex AT Bui††, Marian Stewart Bartlett††, Michelle G Craske†, Nelson B Freimer††
Data passively obtained from smartphones and wearables can provide nearly continuous objective information that enables quantification of states and traits across broad physiological, behavioral, and emotional domains impacted in mental health conditions, including depression and anxiety. Widespread application of such digital phenotyping could transform the assessment of depression and anxiety in research and clinical care, but the field has lacked well-powered longitudinal studies demonstrating the utility of this approach. This paper describes the design and implementation of the Digital Mental Health Study (DMHS), which collected up to 12 months of sensor data from iPhone and Apple Watch in more than 4,000 participants, a sample diverse by age, sex at birth, ethnicity, and depression symptom severity. To enable the use of these digital phenotypes to assay the complexity and heterogeneity of depression and anxiety, we designed a protocol of periodic self-report and interview-based scales optimized to assess elements of depression, anxiety, and perceived stress as broadly as possible while minimizing participant measurement burden. We report here the strategies used to recruit and enroll the DMHS sample, the process employed to develop study methods and protocols, and initial findings describing longitudinal symptom trajectories and demonstrating high participant engagement and adherence over a 12-month period.
Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
July 11, 2025research area Health, research area Methods and Algorithmsconference ICML
Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been primarily applied to low-level sensor data, despite behavioral data often being more informative due to their alignment with physiologically relevant timescales and quantities. We develop foundation models of such behavioral signals using over 2.5B hours of wearable…
Earlier this year, Apple hosted the Workshop on Machine Learning for Health. This two-day hybrid event brought together Apple and the academic research community and clinicians to discuss state-of-the-art machine learning (ML) research in health.
Our research in machine learning breaks new ground every day.