title: Harnessing context sensing to develop a mobile intervention for depression.
abstract: 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 (beta(week) = -.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (beta(week) = -.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (b(week) = -.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (beta(week) = -.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.
  
  
  BACKGROUND
  Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n).
authors.affiliation: Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States.
authors.forename: Michelle Nicole
authors.initials: MN
authors.lastname: Burns
authors.forename: Mark
authors.initials: M
authors.lastname: Begale
authors.forename: Jennifer
authors.initials: J
authors.lastname: Duffecy
authors.forename: Darren
authors.initials: D
authors.lastname: Gergle
authors.forename: Chris J
authors.initials: CJ
authors.lastname: Karr
authors.forename: Emily
authors.initials: E
authors.lastname: Giangrande
authors.forename: David C
authors.initials: DC
authors.lastname: Mohr
mesh_terms: D000328:Adult; D040421:Cell Phone; D003071:Cognition; D015928:Cognitive Behavioral Therapy / Q000379:methods*; D003866:Depressive Disorder / Q000523:psychology / Q000628:therapy*; D005240:Feasibility Studies; D005260:Female; D015438:Health Behavior; D006801:Humans; D008297:Male; D010342:Patient Acceptance of Health Care / Q000523:psychology*; D010865:Pilot Projects; D012649:Self Concept; D016896:Treatment Outcome