J. Med. Internet Res. - Harnessing context sensing to develop a mobile intervention for depression.


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CKGROUND: 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.TRIAL REGISTRATION: Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n).

Resumo Limpo

ckground mobil phone sensor can use develop contextawar system automat detect patient requir assist mobil phone can also provid ecolog momentari intervent deliv tailor assist problemat situat howev approach yet use treat major depress disorderobject purpos studi investig technic feasibl function reliabl patient satisfact mobilyz mobil phone internetbas intervent includ ecolog momentari intervent context sensingmethod develop mobil phone applic support architectur machin learn model ie learner predict patient mood emot cognitivemotiv state activ environment context social context base least concurr phone sensor valu eg global posit system ambient light recent call websit includ feedback graph illustr correl patient selfreport state well didact tool teach patient behavior activ concept brief telephon call email clinician use promot adher enrol adult major depress disord singlearm pilot studi receiv mobilyz complet clinic assess weeksresult promis accuraci rate achiev learner predict categor contextu state eg locat state rate scale eg mood predict capabl poor particip satisfi phone applic improv signific selfreport depress symptom betaweek p perprotocol cohen d interview measur depress symptom betaweek p perprotocol cohen d particip also becam less like meet criteria major depress disord diagnosi bweek p perprotocol remiss rate comorbid anxieti symptom also decreas betaweek p perprotocol cohen d conclus mobilyz scalabl feasibl intervent preliminari evid efficaci knowledg first ecolog momentari intervent unipolar depress well one first attempt use context sens identifi mental healthrel state sever lesson learn regard technic function data mine softwar develop process discussedtri registr clinicaltrialsgov nct httpclinicaltrialsgovctshownct archiv webcit httpwwwwebcitationorgcvjphn

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