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

Digital Health Reviews (1824) Mobile Health (mhealth) (3652) mHealth for Data Collection and Research (1261) Digital Biomarkers and Digital Phenotyping (322) Depression and Mood Disorders; Suicide Prevention (1755) Diagnostic Tools in Mental Health (340) Wearable Devices and Sensors (1212) Fitness Trackers and Smart Pedometers/Accelerometers (693) Anxiety and Stress Disorders (1373)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77331, first published .
Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review

Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review

Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review

Authors of this article:

Hyun Woo Jung1, 2 Author Orcid Image ;   Do Yeon Kim3 Author Orcid Image ;   Ilju Lee2 Author Orcid Image ;   Ok Kim2, 4 Author Orcid Image ;   Seungjin Lee5 Author Orcid Image ;   Sujin Lee5 Author Orcid Image ;   Un Sun Chung6 Author Orcid Image ;   Jae-Hyun Kim7 Author Orcid Image ;   Sehwan Kim8 Author Orcid Image ;   Jung Won Kim9, 10 Author Orcid Image ;   Ah Lahm Shin10, 11 Author Orcid Image ;   Jung Jae Lee1, 2, 12 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

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  1. Yang N, Yang X. Uncovering eHealth Engagement Patterns Through Latent Class Analysis and SHAP: A Data Mining Perspective on Telehealth Access. Information 2026;17(2):215 View
  2. 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