Hence , continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms .
BACKGROUND Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders ( unipolar and bipolar disorders ) .
However , individual studies have reported on different and sometimes contradicting results , and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published .
OBJECTIVE The objectives of this systematic review were to ( 1 ) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and ( 2 ) investigate the strength and statistical significance of these correlations across studies .
The answers to these questions could potentially help identify which objective features have shown most promising results across studies .
Harnessing Context Sensing to Develop a Mobile Intervention for Depression
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 assessment s for 8 weeks . Results Promising accuracy rates ( 60 % to 91 % ) were achieved by learners predicting categorical context ual 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 . Trial Registration Clinical trials.gov NCT01107041 ; http:// clinical trials.gov/ct2/show/NCT01107041 ( Archived by WebCite at http://www.webcitation.org/60CVjPH0n
Hence , continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms .
BACKGROUND Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders ( unipolar and bipolar disorders ) .
However , individual studies have reported on different and sometimes contradicting results , and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published .
OBJECTIVE The objectives of this systematic review were to ( 1 ) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and ( 2 ) investigate the strength and statistical significance of these correlations across studies .
The answers to these questions could potentially help identify which objective features have shown most promising results across studies .