Harnessing Context Sensing to Develop a Mobile Intervention for Depression

By M. Burns, Mark Begale, J. Duffecy, Darren Gergle, C. Karr, Emily Giangrande, David C. Mohr. Journal of Medical Internet Research, 2011

618 citations recorded by the source corpus.

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

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Citations in the corpus

Behavioral Intervention Technologies: Evidence review and recommendations for future research (2010, unknown): “This application of machine learning is a new and challenging strategy that has been used to detect physical activity, affective state and social context with differing degrees of accuracy (e.g., [50]).”
Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients (2015, background): “Other systems, like [20]–[22] similarly rely on self-reporting, implemented using smartphone applications.”
Realizing the Potential of Behavioral Intervention Technologies (2013, background): “Mobilyze!, a smartphoneapplication-based treatment for depression, collects information about one’s context using embedded sensors to cue relevant and timely intervention content (Burns et al., 2011).”
Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial (2017, background): “The effect size for depression was moderate though smaller than the four published studies [23-26] that describe three other mobile app interventions targeting depression.”
mHealth: a mechanism to deliver more accessible, more effective mental health care. (2014, background): “comprehensive understanding of an individual's behaviour at a given time and have the potential to predict subsequent behaviour (Burns et al., 2011).”
Using Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled Trial (2018, result): “The effect size (Cohen d =0.68) for depression was moderate and greater than previously published studies [10,40-42] that measured the efficacy of using alternative mobile app interventions to relieve symptoms of depression.”
StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones (2014, background): “Mobilyze is an intervention system [13] that uses smartphones to predict self-reported states (e.”
Opportunities and Challenges for Smartphone Applications in Supporting Health Behavior Change: Qualitative Study (2013, background): “For instance, a feasibility study in 8 users of a mobile intervention for depression that included ecological momentary intervention and context sensing found that participants were satisfied with the intervention, despite encountering substantial technical problems [14].”
Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis (2015, background): “In particular, in the pervasive and ubiquitous computing community, several projects have investigated the use of smartphone data for the automatic detection and prediction of psychological states and mental health conditions [22, 32, 23, 6].”
Behavioral Functionality of Mobile Apps in Health Interventions: A Systematic Review of the Literature (2015, background): “Burns et al [22] indicated that users felt the intervention was helpful in understanding negative mood triggers and managing distressing behaviors and thoughts.”
Trust in Automation (2015, background): “For example, context-aware features of smartphones can be leveraged to passively collect data about an individual’s mood, location, activity level, social context, and more (Burns et al., 2011).”
Mapping mHealth Research: A Decade of Evolution (2013, background): “The final sample for the full-text review included 117 [17-133] articles out of the 352 described above.”
Smartphone Sensors for Health Monitoring and Diagnosis (2019, methodology): “However, the prediction accuracy in References [109] and [113] was only slightly better than the chance.”
Smartphone Ownership and Interest in Mobile Applications to Monitor Symptoms of Mental Health Conditions (2014, unknown): “The high cost of supplying patients with smartphones as well as the technical difficulties encountered using new phones has been a limitation of several studies examining smartphone collection of ESM [24].”
Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study (2015, background): “A first study in our group found that phone sensor data could detect social patterns among depressed patients, but this was a small study with only 8 participants [19].”