Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments
Open accessAbstract
Smartphones are capable of passively capturing persons' social interactions, movement patterns, physiological activation, and physical environment. Nevertheless, little research has examined whether momentary anxiety symptoms can be accurately assessed using these methodologies. In this research, we utilize smartphone sensors and personalized deep learning models to predict future anxiety symptoms among a sample reporting clinical anxiety disorder symptoms. Participants (N = 32) with generalized anxiety disorder and/or social anxiety disorder (based on self-report) installed a smartphone application and completed ecological momentary assessment symptoms assessing their anxiety and avoidance symptoms hourly for the course of one week (T = 2007 assessments). During the same period, the smartphone app collected information about physiological activation (heart rate and heart rate variability), exposure to light, social contact, and GPS location. GPS locations were coded to reveal the type of location and the weather information. Personalized deep learning models using the smartphone sensor data were capable of predicting the majority of total variation in anxiety symptoms (R 2 = 0.748) and predicting a large proportion of within-person variation at the hour-by-hour level (mean R 2 = 0.385). These results suggest that personalized deep learning models using smartphone sensor data are capable of accurately predicting future anxiety disorder symptom changes.
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Spending time indoors was associated with anxiety and avoidance symptoms, and this association was significantly higher in individuals with SAD than in those with generalized anxiety disorder.
“Jacobson and Bhattacharya [67] showed that spending time indoors was associated with anxiety and avoidance symptoms, and this association was significantly higher in individuals with SAD than in those with generalized anxiety disorder.”
From Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review (2024) · cites this paper as Jacobson and Bhattacharya [67]
The Results (Variable Importance; page not provided) identify being indoors as a feature associated with anxiety and avoidance symptom severity. They report higher mean variable importance for social anxiety disorder than for generalized anxiety disorder (80% versus 66%), but describe it as a tendency, not a statistically significant difference. The claim’s “significantly higher” wording is therefore unsupported. (LLM, conf=0.90)
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- Authors
- Jacobson, N. C., & Bhattacharya, S.
- Affiliations
- Dartmouth College (US)
- Corresponding author
- Jacobson, Nicholas C.
- Venue
- Behaviour Research and Therapy
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 149
- Pages
- 104013
- ISSN
- 0005-7967 (ISSN-L)
- PMC
- PMC8858490
- OpenAlex
- W4200150286
- Cited by
- 92
- References
- 63
- Citations by year
- 2026: 19 · 2025: 28 · 2024: 21 · 2023: 16 · 2022: 8
- FWCI
- 10.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Behaviour Research and Therapy (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences
- National Institute of Mental Health (1 R01 MH123482-01)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- digital biomarkers, anxiety symptoms, smartphone sensors, ecological momentary assessment, generalized anxiety disorder, social anxiety disorder, heart rate variability, heart rate, GPS positions, light exposure, social contact, weather, within-person variability, symptom prediction
- MeSH terms
- Smartphone, Ecological Momentary Assessment, Deep Learning, Anxiety, Anxiety Disorders, Humans, Biomarkers
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