Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors
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Abstract
Background: Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier. Objective: This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset. Methods: In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants’ social anxiety symptom severity. Results: The results suggested that these passive sensor data could be utilized to accurately predict participants’ social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect. Conclusions: These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.
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“1% (1/70) of the studies used machine learning techniques [47]”
From Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review (2024) · cites this paper as [47]
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Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study
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Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping
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Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint
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A survey of autonomous monitoring systems in mental health
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Actigraphy monitoring in anxiety disorders: A mini-review of the literature
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Digital Biomarkers in Psychiatric Research: Data Protection Qualifications in a Complex Ecosystem
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Smartphone-Based Digital Phenotyping Across Health Conditions: Scoping Review
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Technological Advances in Clinical Assessment
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- Authors
- Jacobson, N. C., Summers, B., & Wilhelm, S.
- Affiliations
- Dartmouth College (US)
- Harvard University (US)
- Corresponding author
- Jacobson, Nicholas C
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2020-05-29
- Volume
- 22
- Issue
- 5
- Pages
- e16875
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC7293055
- OpenAlex
- W3021953365
- Cited by
- 162
- References
- 45
- Citations by year
- 2026: 23 · 2025: 24 · 2024: 38 · 2023: 31 · 2022: 21 · 2021: 16 · 2020: 9
- FWCI
- 17.40
- 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)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Drug Abuse (P30DA029926)
- 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 › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- social anxiety disorder, digital phenotyping, digital biomarkers, smartphone sensors, passive sensing, accelerometer data, social contact, symptom severity prediction, depression, positive affect, negative affect, machine learning
- MeSH terms
- Smartphone, Phobia, Social, Adolescent, Adult, Female, Humans, Male, Biomarkers, Young Adult
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