ArticleNPJ digital medicine2024
Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03088657 (Mood Disorder Cohort Research Consortium), which is not on this map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Mood Disorder Cohort Research Consortium (MDCRC) in Korea
Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital Markers for Passive Remote Monitoring of Bipolar Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- Review
- Essential Information About Chronobiology and Chronotherapy for the Optimal Care of People With Bipolar Disorders: An Expert Consensus.Bipolar disorders · 2026Article
- Genetic architecture of a Circadian Imbalance Index: genome-wide association, phenome-wide association, and Mendelian randomisation analyses.EBioMedicine · 2026Article
- Smartphone-based digital phenotyping for detection of high-risk depression and anxiety in Korean community settings.Internet interventions · 2026Article
- Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning.JMIR mHealth and uHealth · 2026Article
- The future of psychiatry: Reclaiming relevance in an era of technological and systemic transformation.The Australian and New Zealand journal of psychiatry · 2026Article
- Interpretable depressive symptoms screening via statistical reasoning-augmented large language models using wearable and environmental data.Scientific reports · 2026Article
- Agreement between smartphone-based mobile sensing and actigraphy sleep metrics in young people with bipolar disorder.medRxiv : the preprint server for health sciences · 2026Article
- Circadian Phase Estimation From Ambulatory Wearables With Particle Filtering: Accuracy Depends on Initialization, Recording Duration, and Light Exposure.Journal of biological rhythms · 2026Article
- Full-day sleep pattern analysis in common mental disorders: Leveraging highly discrepant recordings from two consumer tracking devices.PloS one · 2026Article
- Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026Article
- Circadian Rhythm Traits Matter More Than Shift Work Demands in the Sleep-Depression-Safety Behavior Pathway Among Shift-Working Nurses.Journal of nursing management · 2026Article
- Federated foundation models for psychiatry: a new paradigm for diagnosis, prognosis, and treatment of mood disorders.Frontiers in psychiatry · 2026Article
- The Role of Chronopsychiatry in Precision Medicine: Circadian Dysregulation as Diagnostic Marker and Therapeutic Target.Current sleep medicine reports · 2026Review
- Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications.Frontiers in digital health · 2026Review
- Reconceptualizing bipolar disorder through an integrative biological lens: synthesis, convergence, and emerging directions.Frontiers in integrative neuroscience · 2026Review
- Article
- The Dynamics of Mood in Bipolar Disorder: How Mathematical Models Help Phenotype Individuals, Forecast Mood, and Clarify Underlying Mechanisms.Current psychiatry reports · 2025Review
- A user-driven consent platform for health data sharing in digital health applications.NPJ digital medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Wearable devices enable passive collection of sleep, heart rate, and step-count data, offering potential for mood episode prediction in mood disorder patients. However, current models often require various data types, limiting real-world application. Here, we develop models that predict future episodes using only sleep-wake data, easily gathered through smartphones and wearables when trained on an individual's sleep-wake history and past mood episodes. Using mathematical modeling to longitudinal data from 168 patients (587 days average clinical follow-up, 267 days wearable data), we derived 36 sleep and circadian rhythm features. These features enabled accurate next-day predictions for depressive, manic, and hypomanic episodes (AUCs: 0.80, 0.98, 0.95). Notably, daily circadian phase shifts were the most significant predictors: delays linked to depressive episodes, advances to manic episodes. This prospective observational cohort study (ClinicalTrials.gov: NCT03088657, 2017-3-23) shows sleep-wake data, combined with prior mood episode history, can effectively predict mood episodes, enhancing mood disorder management.
Identifiers
What OpenQuestion holds
Registered trials
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.