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

Machine Learning (2110) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2158) Bipolar Disorders (76) Decision Support for Health Professionals (1560) Wearable Devices and Sensors (1157) mHealth for Data Collection and Research (1210) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1753) Depression and Mood Disorders; Suicide Prevention (1709) Computer-Aided Diagnosis (257)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66277, first published .
Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability Study

Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability Study

Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability Study

Authors of this article:

Chia-Tung Wu1 Author Orcid Image ;   Ming H Hsieh2 Author Orcid Image ;   I-Ming Chen2 Author Orcid Image ;   Lian-Yin Jhao3 Author Orcid Image ;   Ding-Shan Liu4 Author Orcid Image ;   Ssu-Ming Wang5 Author Orcid Image ;   Chia-Ting Wu3 Author Orcid Image ;   Yi-Ling Chien2 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Cochran A, Vineyard J. The Dynamics of Mood in Bipolar Disorder: How Mathematical Models Help Phenotype Individuals, Forecast Mood, and Clarify Underlying Mechanisms. Current Psychiatry Reports 2025;27(12):687 View
  2. Eto A, Mochizuki K, Fukami T, Sakakibara W, Izumi K. Wearable-derived heart rate variability and sleep monitoring as predictors of mood episodes in bipolar disorder: a case report. Frontiers in Psychiatry 2025;16 View