Accessibility settings

This paper is in the following e-collection/theme issue:

Digital Health Reviews (1888) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2387) mHealth for Data Collection and Research (1302) Wearable Devices and Sensors (1261) Machine Learning (2332) Transdiagnostic Mental Health Interventions (225) Digital Biomarkers and Digital Phenotyping (344)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77066, first published .
Smartwatch tracking heart rate and brain activity for health monitoring.

Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review

Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review

Authors of this article:

ShiYing Shen1 Author Orcid Image ;   Wenhao Qi1 Author Orcid Image ;   Jianwen Zeng1 Author Orcid Image ;   Sixie Li1 Author Orcid Image ;   Xin Liu1 Author Orcid Image ;   Xiaohong Zhu1 Author Orcid Image ;   Chaoqun Dong1 Author Orcid Image ;   Bin Wang1 Author Orcid Image ;   Yankai Shi1 Author Orcid Image ;   Jiani Yao1 Author Orcid Image ;   Bingsheng Wang1 Author Orcid Image ;   Xiajing Lou1 Author Orcid Image ;   Simin Gu2 Author Orcid Image ;   Pan Li3 Author Orcid Image ;   Jinghua Wang4 Author Orcid Image ;   Guowei Jiang5 Author Orcid Image ;   Shihua Cao1, 6 Author Orcid Image
Article Authors Cited by (28) Tweetations (2) Metrics

Journals

  1. Ghonge M, Shukla V, Pradeep N, Solanki R, Ghonge M, Shukla V, Garg L, Liu H, Tran T. Recent Advances in Multimodal Deep Learning for Stress Prediction: Toward Cycle-Aware and Gender-Sensitive Health Analytics. EPJ Web of Conferences 2025;341:01059 View
  2. Pandian V, Rahimibashar F, Arabfard M, Alhalaiqa F, Vahedian-Azimi A. The role of AI-driven communication in delirium prevention, detection, and care for critically ill ICU patients: A systematic review with inductive thematic synthesis. Intensive and Critical Care Nursing 2026;93:104323 View
  3. Qi L, Liu J. Advancing digital inclusion and cognitive monitoring for older adults. Asian Journal of Psychiatry 2026;117:104839 View
  4. Ha S, Lee T, Seo H, Yoon S, Lee H. A Selective RAG-Enhanced Hybrid ML-LLM Framework for Efficient and Explainable Fatigue Prediction Using Wearable Sensor Data. Bioengineering 2026;13(1):58 View
  5. Bohnert A, Fritsche L, Sen S. Precision Approaches for Scalable Digital and Clinic-Based Interventions in Mental Health. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2026 View
  6. Salomez-Ihl C, Liaigre L, Dala W, Davat A, Barbado M, Chanoine S, Py P, Schmitt D, Defaye P, Bedouch P. Role of the Pharmacist in Supporting the Use of Connected Health Devices: Example of Connected Watches. Pharmacy 2026;14(1):28 View
  7. Partey-Newman V, Baiden I, Phil-Othihiwa S, Dudzilah G. Digital Biomarkers for Early Cognitive Decline: Clinical Promise, Limitations, and Validation Pathways. Journal of Mental Health and Psychology 2026;1(1):10 View
  8. Tolosa E, Ihianle I, Machado P, Yahaya S, Lotfi A. From Patterns to Deviations: Detecting Behavioural Drift for Mental Health Monitoring Using Smartphone and Wearable Data. Electronics 2026;15(4):885 View
  9. Hertlein K, Rajaei A. Integrating Artificial Intelligence in Systemic Therapy: Strategies for Effective Clinical Practice. International Journal of Systemic Therapy 2026:1 View
  10. Busshart L, Petrovic M, Amin R, Hegerl U. Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review. JMIR mHealth and uHealth 2026;14:e70840 View
  11. Dong P, Zhang S, Zheng X, Yuan W, Wu L, Chen Y. Artificial intelligence robots for mental health applications: a scoping review. Psychiatry Research 2026;360:117059 View
  12. Su K, Chen S, Chang J. The digital pandemic in youth: unpacking the algorithmic impact on mental health in an urbanized world. Current Opinion in Psychiatry 2026;39(3):269 View
  13. Xu Y, Zhao Z, Zhu H, Lai S, Jia Y, Liu G. Machine Learning-Empowered Depression Detection With Wearable Skin Electronics: A Review. IEEE Sensors Journal 2026;26(6):7949 View
  14. Zhao Y, Zuo L, Yu D, Pan R, Fu L, Lu Y, Zhang H. Mobile systems for healthcare 5.0: Applications, challenges and opportunities. High-Confidence Computing 2026;6(2):100392 View
  15. Hoshi M, Kozu Y, Okumura S, Endo D, Ito T, Nagata K, Hiranuma H, Furihata R, Sato S, Hirai T, Sakamoto T, Taki H, Akahoshi T, Gon Y, Chin K. Simultaneous estimation of sleep duration and sleep apnea–hypopnea using millimeter-wave radar. Sleep and Biological Rhythms 2026 View
  16. Joseph A, Abraham J. The nexus of mental health, climate change and artificial intelligence: a narrative review of emerging evidence and clinical implications. International Journal of Research in Medical Sciences 2026;14(4):1750 View
  17. Pouchon A, Aloui S, Mayer Dalverny L, Djennaoui L, Guyon-Gardeux C, Godin C, Polosan M. Bipolar disorder relapse detection and prediction using smartwatches. A pilot study for machine learning models using anomaly detection methods. Journal of Affective Disorders 2026;407:121731 View
  18. Lee H, Kang S, Lee S. The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2026;28:e76432 View
  19. Emanuele E, Minoretti P. Monitoring airline pilot mental health: a 3PM framework utilising digital phenotyping and AI. EPMA Journal 2026;17(2):233 View
  20. Paulus M. Current themes of AI in mental health: Actionable evidence and guardrails for mood and anxiety care. Journal of Mood & Anxiety Disorders 2026;14:100181 View
  21. Zhou S, Mohd M, Zakaria L. A Systematic Review of Transformer-Based Models for Depression Detection. Applied Sciences 2026;16(10):5018 View
  22. Sameh A, Nauha L, Seppänen M, Niemelä M, Oussalah M, Korpelainen R, Farrahi V. Digital phenotyping using wearable-determined physical behaviours and machine learning to detect depression and anxiety symptoms in a general population. Psychiatry Research 2026:117242 View
  23. Faurholt-Jepsen M, Hammershøi D, Bruun C, Bardram J, Kessing L. Multimodal digital phenotyping of bipolar disorder subtypes: Differences between bipolar disorder I and II based on passive and active behavioral features from smartphones. Journal of Affective Disorders 2026;411:122027 View
  24. Maimon N, Baruchin G, Grotto I, Intrator N, Molcho L, Zeimer T, Chibotero O, Levi G, Cohen H, Greenstein M, Danino E. Neural dissociation of cognitive effort and physiological arousal: Multimodal single-channel EEG, cortisol, and HRV evidence from an ecologically valid field study. International Journal of Psychophysiology 2026;226:113421 View
  25. Dasgupta A, Xue Y, Tao K. Artificial Intelligence in Public Health: A Systematic Literature Review (Preprint). JMIRx Med 2025 View
  26. Kang J. Just-in-Time Adaptive Interventions in Digital Mental Health Care: A Framework for Psychiatric Conditions. IEEE Access 2026;14:78026 View

Conference Proceedings

  1. Misba M, Yashwanth B, Nikhil S, Vardhan K. 2026 8th International Conference on Intelligent Sustainable Systems (ICISS). Smart Mental Health Band for Students using AIoT and Predictive Algorithms View
  2. Feng K, Sun Z, Lee R, Jiang X, Theng Y, Ding Y. Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization. Personalization Matters: Benchmarking ML, DL and LLMs for Mental Health Forecasting from Smartphone Sensing Data View