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

Digital Health Reviews (1801) Mobile Health (mhealth) (3599) mHealth for Data Collection and Research (1238) Digital Biomarkers and Digital Phenotyping (313) Depression and Mood Disorders; Suicide Prevention (1742) Diagnostic Tools in Mental Health (337) Wearable Devices and Sensors (1190) Fitness Trackers and Smart Pedometers/Accelerometers (686) Anxiety and Stress Disorders (1363)

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 (1) Tweetations (1) Metrics

Journals

  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