Cited by 429Mental Health Research TopicsDigital Mental Health InterventionsContext-Aware Activity Recognition Systems

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Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.

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Ben-Zeev, Dror, Scherer, Emily A., Wang, Rui, Xie, Haiyi, Campbell, Andrew T.·2015·Psychiatric Rehabilitation Journal

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Abstract

OBJECTIVE: Optimal mental health care is dependent upon sensitive and early detection of mental health problems. We have introduced a state-of-the-art method for the current study for remote behavioral monitoring that transports assessment out of the clinic and into the environments in which individuals negotiate their daily lives. The objective of this study was to examine whether the information captured with multimodal smartphone sensors can serve as behavioral markers for one's mental health. We hypothesized that (a) unobtrusively collected smartphone sensor data would be associated with individuals' daily levels of stress, and (b) sensor data would be associated with changes in depression, stress, and subjective loneliness over time. METHOD: A total of 47 young adults (age range: 19-30 years) were recruited for the study. Individuals were enrolled as a single cohort and participated in the study over a 10-week period. Participants were provided with smartphones embedded with a range of sensors and software that enabled continuous tracking of their geospatial activity (using the Global Positioning System and wireless fidelity), kinesthetic activity (using multiaxial accelerometers), sleep duration (modeled using device-usage data, accelerometer inferences, ambient sound features, and ambient light levels), and time spent proximal to human speech (i.e., speech duration using microphone and speech detection algorithms). Participants completed daily ratings of stress, as well as pre- and postmeasures of depression (Patient Health Questionnaire-9; Spitzer, Kroenke, & Williams, 1999), stress (Perceived Stress Scale; Cohen et al., 1983), and loneliness (Revised UCLA Loneliness Scale; Russell, Peplau, & Cutrona, 1980). RESULTS: Mixed-effects linear modeling showed that sensor-derived geospatial activity (p < .05), sleep duration (p < .05), and variability in geospatial activity (p < .05), were associated with daily stress levels. Penalized functional regression showed associations between changes in depression and sensor-derived speech duration (p < .05), geospatial activity (p < .05), and sleep duration (p < .05). Changes in loneliness were associated with sensor-derived kinesthetic activity (p < .01). CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Smartphones can be harnessed as instruments for unobtrusive monitoring of several behavioral indicators of mental health. Creative leveraging of smartphone sensing could provide novel opportunities for close-to-invisible psychiatric assessment at a scale and efficiency that far exceeds what is currently feasible with existing assessment technologies.

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Authors: Ben-Zeev, D., Scherer, E. A., Wang, R., Xie, H., & Campbell, A. T.
Affiliations: Dartmouth College (US)
ORCID: Scherer, Emily A.
Wang, Rui
Xie, Haiyi
Campbell, Andrew T.
Venue: Psychiatric Rehabilitation Journal
Publisher: American Psychological Association (APA)
Published: 2015-09
Volume: 38
Issue: 3
Pages: 218-226
ISSN: 1095-158X (ISSN-L)
1559-3126
DOI: 10.1037/prj0000130
PMC: PMC4564327
OpenAlex: W2093973716
Cited by: 429
References: 42
Citations by year: 2026: 35 · 2025: 33 · 2024: 36 · 2023: 24 · 2022: 54 · 2021: 55 · 2020: 51 · 2019: 50 · 2018: 43 · 2017: 32 · 2016: 13 · 2015: 2
FWCI: 45.18
Type: journal article
Language: en
Update notices: correction (2015-12-21)
Open access: green
www.ncbi.nlm.nih.gov
Locations: Psychiatric Rehabilitation Journal (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute of Mental Health (R01MH103148)
Topics: Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords: smartphone sensors, mental health assessment, non-intrusive monitoring, daily stress, depression, loneliness, sleep duration, kinesthetic activities, speech duration, young adults, wearable devices, early detection of mental health problems, GPS tracking
MeSH terms: Smartphone, Adult, Mental Disorders, Female, Humans, Male, Telemedicine, Monitoring, Ambulatory, Young Adult

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