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Journal Article
Smartphone-Based Digital Phenotyping Enables Robust Detection of High-Risk Depression and Anxiety in Real-World Settings
- Authors
- Ah Young Kim, Seonmin Kim, Jisu Lee, Youngwoong Han, Heon-Jeong Lee, Chul-Hyun Cho
- Issue Date
- 2026-03
- Citation
- Internet Interventions-The Application of Information Technology in Mental and Behavioural Health, v.44, pp.1-10
- ISSN
- 2214-7829
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- Background: Smartphones generate continuous behavioral signals such as mobility and activity patterns, offering scalable opportunities for monitoring mental health in community settings. Digital phenotyping approaches that integrate passive sensing with brief self-report measures may enable early identification of individuals at high risk for depression and anxiety without reliance on additional wearable devices. Method: We prospectively evaluated a smartphone-based digital phenotyping framework in 455 communitydwelling adults in Korea who contributed 28 days of passive Global Positioning System and accelerometer data, daily self-report microsurveys, and weekly PHQ-9/GAD-7 assessments for screening high-risk depression and anxiety. Machine learning models were compared across active-only, passive-only, and combined feature sets. After applying predefined coverage criteria (≥60% passive-data coverage and ≥ 60% corresponding activedata availability), 277 participants were included in the depression cohort and 275 in the anxiety cohort. Results: Passive features capturing mobility, activity regularity, and sleep-related behaviors were derived, and machine learning models were trained using active-only, passive-only, and combined feature sets. For depression, combined models achieved the best performance, with AUCs ranging from 0.77 to 0.83 and APs ranging from 0.86 to 0.91 across classifiers. Similar patterns were observed for anxiety, with AUCs up to 0.86 and APs up to 0.95. Ablation analyses identified robust deployment conditions relevant to clinical screening, including tolerance to missing data and short look-back windows. Discussion: These findings support the practical utility of smartphone-based digital phenotyping pipelines that integrate passive behavioral signals with brief self-reports for scalable screening for high-risk depression and anxiety in real-world environments, and they may inform future just-in-time mental health intervention systems.
- Keyword
- Digital phenotyping, Smartphone sensing, Passive and active data, Depression, Anxiety, High-risk
- KSP Keywords
- Active data, Activity patterns, Behavioral signals, Best performance, Coverage criteria, Data Coverage, Data availability, Global positioning system(GPS), Health Intervention, High risk, Missing data
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
(CC BY NC ND)
