Harnessing context sensing to develop a mobile intervention for depression

Michelle Nicole Burns, Mark Begale, Jennifer Duffecy, Darren Gergle, Chris J. Karr, Emily Giangrande, David C. Mohr*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

450 Scopus citations


Background: Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder. Objective: The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone-and Internet-based intervention including ecological momentary intervention and context sensing. Methods: We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients' mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients' self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks. Results: Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (betaweek= -.82, P < .001, per-protocol Cohen d= 3.43) and interview measures of depressive symptoms (betaweek = -.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (bweek = -.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (betaweek = -.71, P < .001, per-protocol Cohen d = 2.58). Conclusions: Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed.

Original languageEnglish (US)
Pages (from-to)e55
JournalJournal of medical Internet research
Issue number3
StatePublished - 2011


  • Artificial intelligence
  • Behavior therapy
  • Cellular phone
  • Context-aware systems
  • Data mining
  • Depression
  • Mobile health
  • Mobile phone
  • Sensors
  • Telemedicine

ASJC Scopus subject areas

  • Health Informatics


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