ArticleFrontiers in digital health2022
Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach.
Article in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Sleep duration, sleep-wake schedules, and social jetlag among corporate employees in Japan: Age- and gender-based comparisons of wearable device data (2019-2022).Preventive medicine reports · 2026Article
- Development and internal validation of a prediction model for sleep apnea syndrome treated with continuous positive airway pressure based on claims and health checkup data linked to personal health records.Sleep & breathing = Schlaf & Atmung · 2026Article
- Article
- Predicting the risk of mental disorders using complete blood count indicators: a machine learning approach.Frontiers in medicine · 2026Article
- The Use of Artificial Intelligence for Personalized Treatment in Psychiatry.Current psychiatry reports · 2025Review
- Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review.Journal of medical Internet research · 2025Article
- Biorhythms derived from consumer wearables predict postoperative complications in children.Science advances · 2025Article
- Predicting stress in first-year college students using sleep data from wearable devices.PLOS digital health · 2024Article
- Predictive model of suicide risk in Colombian university students: quantitative analysis of associated factors.Frontiers in psychiatry · 2024Article
- Wearable neurofeedback acceptance model for students' stress and anxiety management in academic settings.PloS one · 2024Article
- Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy.NPJ digital medicine · 2023Article
- Decreased step count prior to the first visit for MDD treatment: a retrospective, observational, longitudinal cohort study of continuously measured walking activity obtained from smartphones.Frontiers in public health · 2023Observational
- Lifestyle, Demographic and Socio-Economic Determinants of Mental Health Disorders of Employees in the European Countries.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.