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

Machine Learning (2293) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2338) mHealth for Data Collection and Research (1286) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1840) Decision Support for Health Professionals (1680) Depression and Mood Disorders; Suicide Prevention (1793) Wearable Devices and Sensors (1241) Bipolar Disorders (80) Computer-Aided Diagnosis (283)

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 (7) 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
  3. Nair P, D'Souza P. Enhancing Predictive Accuracy of Mood Symptoms Using Wearable Devices and Machine Learning in Bipolar Disorder. JMIR Medical Informatics 2026;14:e92172 View
  4. 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
  5. Bao C. Big Data Approaches to Mood Disorders: Applications of Digital Phenotyping, Personal Sensing, and Multimodal Data Integration. Journal of Education, Humanities and Social Sciences 2026;63:202 View
  6. Zambrano J, Luarte A, Contreras J, Perez J, Yantén-Fuentes L, Prieto M, Lazcano P, Wyneken U, Perez C. Deep learning-based differential diagnosis of major depression and bipolar disorder using microglia-cellular sensors and patient-derived small extracellular vesicles. Scientific Reports 2026;16(1) View
  7. De Francesco S, Archetti D, Baronio C, Demaria C, Boccali A, Crema C, Tura G, Redolfi A. Optimizing Treatment Strategies in the Bipolar Disorder Spectrum with Classical Artificial Intelligence Approaches: A Systematic Review of Performance, Bias, and Clinical Applicability. (Preprint). JMIR Mental Health 2026 View