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      Harnessing Context Sensing to Develop a Mobile Intervention for Depression

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          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.

          Trial Registration

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

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          Most cited references 51

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          Ensemble Methods in Machine Learning

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            Activity Recognition from User-Annotated Acceleration Data

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              Excess mortality in depression: a meta-analysis of community studies.

              Although most studies examining the relationship between depression and mortality indicate that there is excess mortality in depressed subjects, this is not confirmed in all studies. Furthermore, it has been hypothesized that mortality rates in depressed men are higher than in depressed women. Finally, it is not clear if the increased mortality rates exist only in major depression or also in subclinical depression. A meta-analysis was conducted to examine these questions. A total of 25 studies with 106,628 subjects, of whom 6416 were depressed, were examined. Both univariate and multivariate analyses were conducted. The overall relative risk (RR) of dying in depressed subjects was 1.81 (95% CI: 1.58-2.07) compared to non-depressed subjects. No major differences were found between men and women, although the RR was somewhat larger in men. The RR in subclinical depression was no smaller than the RR in clinical depression. Only RRs of mortality were examined, which were not corrected for important confounding variables, such as chronic illnesses, or life-style. In the selected studies important differences existed between study characteristics and populations. The number of comparisons was relatively small. There is an increased risk of mortality in depression. An important finding of this study is that the increased risk not only exists in major depression, but also in subclinical forms of depression. In many cases, depression should be considered as a life-threatening disorder.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                Gunther Eysenbach (JMIR Publications Inc., Toronto, Canada )
                1438-8871
                Jul-Sep 2011
                12 August 2011
                : 13
                : 3
                Affiliations
                3simpleAudacious Software Chicago, ILUnited States
                2simpleDepartment of Communication Studies simpleNorthwestern University Evanston, ILUnited States
                1simpleDepartment of Preventive Medicine simpleFeinberg School of Medicine simpleNorthwestern University Chicago, ILUnited States
                Article
                v13i3e55
                10.2196/jmir.1838
                3222181
                21840837
                ©Michelle Nicole Burns, Mark Begale, Jennifer Duffecy, Darren Gergle, Chris J Karr, Emily Giangrande, David C Mohr. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 12.08.2011.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

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