Behavioral Indicators on a mobile sensing platform predict clinically validated psychiatric symptoms of mood and anxiety disorders
Open accessAbstract
BACKGROUND: There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable. OBJECTIVE: The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform. METHODS: A total of 73 participants (67% [49/73] male, 48% [35/73] non-Hispanic white, 33% [24/73] veteran status) who reported at least one symptom of PTSD or depression completed a 12-week field trial. Behavioral indicators were collected through the noninvasive mobile sensing platform on participants' mobile phones. Clinical symptoms were measured through validated clinical interviews with a licensed clinical social worker. A combination hypothesis and data-driven approach was used to derive key features for modeling symptoms, including the sum of outgoing calls, count of unique numbers texted, absolute distance traveled, dynamic variation of the voice, speaking rate, and voice quality. Participants also reported ease of use and data sharing concerns. RESULTS: Behavioral indicators predicted clinically assessed symptoms of depression and PTSD (cross-validated area under the curve [AUC] for depressed mood=.74, fatigue=.56, interest in activities=.75, and social connectedness=.83). Participants reported comfort sharing individual data with physicians (Mean 3.08, SD 1.22), mental health providers (Mean 3.25, SD 1.39), and medical researchers (Mean 3.03, SD 1.36). CONCLUSIONS: Behavioral indicators passively collected through a mobile sensing platform predicted symptoms of depression and PTSD. The use of mobile sensing platforms can provide clinically validated behavioral indicators in real time; however, further validation of these models and this platform in large clinical samples is needed.
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Mobile phones have been used to monitor stress and mental health.
“Mobile phones have been used to monitor stress and mental health [4-10].”
From Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study (2018) · cites this paper as [10]
The Discussion (Principal Findings; page not available) says the study used a mobile sensing platform to track behavioral indicators of depression and PTSD symptoms, supporting the mental-health part of the claim. The paper does not report monitoring stress itself, so that part is not established by this source. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPassively collected data from mobile phones can be used to monitor and predict real time behavioral indicators of depression and PTSD.
“Indeed, passively collected data from mobile phones can be used to monitor and predict real time behavioral indicators of depression and PTSD175.”
From Circadian rhythm disruption and mental health (2020) · cites this paper as [175]
Preliminary studies suggest that aggregates of smartphone-based passive features are useful in predicting daily mood and presence of depressive symptoms.
“Preliminary studies suggest that aggregates of smartphone-based passive features are useful in predicting daily mood (23) and presence of depressive symptoms (24).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [24]
Prior investigations have primarily focused on detecting presence or absence of depression rather than on assessing its severity, and they have shown overall low accuracy.
“This has resulted in possible reliability problems, since these investigations have primarily focused on detecting presence or absence of depression rather than on assessing its severity, and they have shown overall low accuracy (14–18, 23–27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [24]
Studies conducted with patients with depression have mostly relied on self-report questionnaires to assign diagnoses and/or measure variation in symptoms and train the models.
“Those studies conducted with patients with depression have mostly relied on self-report questionnaires to assign diagnoses and/or measure variation in symptoms and train the models.”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [24]
The finding that activity level was among the most important features is consistent with previous reports.
“is consistent with previous reports (23, 24).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [24]
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- Authors
- Place, S., Blanch-Hartigan, D., Rubin, C., Gorrostieta, C., Mead, C., Kane, J., Marx, B. P., Feast, J., Deckersbach, T., Pentland, A. “., Nierenberg, A., & Azarbayejani, A.
- Affiliations
- Cogito (United States) (US)
- Boston University (US)
- Harvard University (US)
- MIT Media Lab (US)
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2017-03-16
- Volume
- 19
- Issue
- 3
- Pages
- e75
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5374272
- OpenAlex
- W2596241616
- Cited by
- 125
- References
- 36
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 9 · 2023: 7 · 2022: 20 · 2021: 16 · 2020: 20 · 2019: 11 · 2018: 17 · 2017: 3
- FWCI
- 15.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by)
- Funders
- U.S. Department of Defense
- Massachusetts General Hospital
- Defense Advanced Research Projects Agency (N66001)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- mobile sensing, behavioral indicators, post-traumatic stress disorder, depression, mood disorders, anxiety disorders, smartphone sensing, voice analysis, speech rate, fatigue, social connectedness, veterans
- MeSH terms
- Behavior Observation Techniques, Adolescent, Adult, Anxiety Disorders, Depression, Female, Humans, Male, Mental Health, Stress Disorders, Post-Traumatic, Mood Disorders, Young Adult, Mobile Applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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