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

mHealth for Screening (487) Depression and Mood Disorders; Suicide Prevention (2523) Wearable Devices and Sensors (1662) Validity, Reliability, and Bias (150)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/91479, first published .
Woman checks smartwatch displaying actigraphy data for depressive symptoms detection.

Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and Validation Study

Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and Validation Study

Authors of this article:

Doljinsuren Enkhbayar1 Author Orcid Image ;   Somin Oh1 Author Orcid Image ;   Jinhee Lee2 Author Orcid Image ;   Min-Hyuk Kim2 Author Orcid Image ;   Erdenebayar Urtnasan3 Author Orcid Image ;   Jaehong Key1 Author Orcid Image
Article Authors Cited by (1) Tweetations (1) Metrics

Journals

  1. Pathak B, Shukla A, Agrawal A, Ramasamy R, Dubey P. Hierarchical Transformer for Classifying Depressive-Episode Conditions from Wrist Actigraphy: A Preliminary Participant-Level Study. Information 2026;17(10):955 View