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

Digital Health Reviews (1758) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2166) Wearable Devices and Sensors (1164) Digital Biomarkers and Digital Phenotyping (297) Transdiagnostic Mental Health Interventions (211) mHealth for Data Collection and Research (1213) Machine Learning (2129)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77066, first published .
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

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 (4) Tweetations (1) Metrics

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  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