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Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.
Open accessAbstract
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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“Depression, as one of the leading mental disorders, has been studied using audio sounds [76]. Other mental disorders have also been investigated using audio sensing, including bipolar [66], schizophrenia [77], autism [78]. Stress and mood which are highly correlated to mental health have also been studied using audio sounds [67,76].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [76]
In the citing paper
“The same observations were reproduced in younger adults (aged 19-30 years) [10], showing that changes in daily stress levels in young adults aged 19 to 30 years were associated with changes in geospatial activity, sleep duration (obtained by proxy from phone sensors), and sensor-derived speech duration.”
In the citing paper
“However, contrary to previous findings (19, 67–69), we did not find a significant relationship between the other smartphone features (total distance, location entropy, normalized location entropy and time spent at home) and mental health measures.”
From Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data (2021) · cites this paper as [69]
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Cites
Diagnostic and Statistical Manual of Mental Disorders
Williams, J. B. W., & First, M. B. (2013) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199975839.013.104 · Cited by 109,845
The PHQ-9: Validity of a brief depression severity measure
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2001.016009606.x · Cited by 44,910
A Global Measure of Perceived Stress
Cohen, S., Kamarck, T., & Mermelstein, R. (1983) · Journal of Health and Social Behavior · doi:10.2307/2136404 · Cited by 32,807
Spitzer, R. L. (1999) · JAMA · doi:10.1001/jama.282.18.1737 · Cited by 9,802
Ecological momentary assessment
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.3.022806.091415 · Cited by 6,454
The Revised UCLA Loneliness Scale: concurrent and discriminant validity evidence
Russell, D., Peplau, L. A., & Cutrona, C. E. (1980) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.39.3.472 · Cited by 5,173
Loneliness matters: a theoretical and empirical review of consequences and mechanisms
Hawkley, L. C., & Cacioppo, J. T. (2010) · Annals of Behavioral Medicine · doi:10.1007/s12160-010-9210-8 · Cited by 4,782
Physical Activity and the Prevention of Depression
Mammen, G., & Faulkner, G. (2013) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2013.08.001 · Cited by 1,122
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T. (2014) · Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2632048.2632054 · Cited by 1,116
Self-citationmHealth for mental health: Integrating smartphone technology in behavioral healthcare.
Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011) · Professional Psychology: Research and Practice · doi:10.1037/a0024485 · Cited by 809
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Research works printed but not linked: 2
- Chen Z Lin M Chen F Lane MD Cardone G Wang R Li T Chen Y Choudhury T Campbell AT 2013 Unobtrusive sleep monitoring using Smartphones 7th International ICST Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health) Venice
- Hedeker D Gibbons R 2006 Longitudinal data analysis NJ Wiley-Interscience
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Francesco, S. D., Crema, C., Boccali, A., Demaria, C., Tura, G. B., Martinelli, A., Baronio, C. M., Archetti, D., & Redolfi, A. (2026) · doi:10.2196/preprints.95263
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Ge, H., Wang, J., Wu, D. Y., & Zhou, H. (2026) · Social Science & Medicine · doi:10.1016/j.socscimed.2026.119411
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Kim, E., Park, D.-H., Lee, J., Kim, J., Ko, S., Kim, Y., & Lee, S. Y. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2684306
Guo, J., Frederick, J., Cunningham, L. D., Jacobson, N. C., Fisher, A. J., Pettit, J. W., McMakin, D. L., & Ng, M. Y. (2026) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-026-01464-9
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Lefter, I., & Sano, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33151-0.00016-2
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Yakdan, S. M., Xu, Z., Zhang, J., Arkam, F., Rodebaugh, T. L., Goodin, B. R., Neuman, B. J., Steinmetz, M. P., Kelly, M. P., Bess, S. R., et al. (2026) · Neurosurgery · doi:10.1227/neu.0000000000004195
Fu, C., Su, D., Zhang, J., Pei, S., & Ma, S. (2026) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2026.105143
John, D. T., van Alebeek, H., Pryss, R., Vogel, C., Schobel, J., O’Rourke, T., Helten, A.-K., Apolinário-Hagen, J., & Probst, T. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001716
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Salom, R., Leong, X. Y., Poncet, A., Aradillas, J. C., Rada, Á. P., García, J. J. M., García‐Mieres, H., & Artés-Rodrı́guez, A. (2026) · JMIR Formative Research · doi:10.2196/98935
Zhang, Y., Folarin, A., Zhong, R., Kim, H., Sun, S., Stewart, C., & Dobson, R. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.122643
Naqvi, A., & Naqvi, F. N. (2026) · JMIR mhealth and uhealth · doi:10.2196/103711
Metadata
- Authors
- Ben-Zeev, D., Scherer, E. A., Wang, R., Xie, H., & Campbell, A. T.
- Affiliations
- Dartmouth College (US)
- Venue
- Psychiatric Rehabilitation Journal
- Published
- 2015-09
- Volume
- 38
- Issue
- 3
- Pages
- 218-226
- ISSN
- 1095-158X (ISSN-L)
- 1559-3126
- 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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