ArticleJMIR mental health2020
Effectiveness of a Smartphone App With a Wearable Activity Tracker in Preventing the Recurrence of Mood Disorders: Prospective Case-Control Study.
Article in JMIR mental health, 2020. 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 28 papers, 7 of them syntheses that pooled it.
What it found
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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
28 citing papers in PubMed, 7 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- Digital phenotyping for mental health conditions: a systematic review of implementation and application.Frontiers in digital health · 2026Pooled it
- Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Barriers and Facilitators of User Engagement With Digital Mental Health Interventions for People With Psychosis or Bipolar Disorder: Systematic Review and Best-Fit Framework Synthesis.JMIR mental health · 2025Pooled it
- Convergence of disciplines: a systematic review of multidisciplinary development approaches in artificial intelligence.Frontiers in digital health · 2025Pooled it
- Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- A Scoping Review on Artificial Intelligence-Supported Interventions for Nonpharmacologic Management of Chronic Rheumatic Diseases.Arthritis care & research · 2026Article
- Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026Article
- From Food to Mood: Psychological and Psychiatric Impact of Diet in Bipolar Disorder.Nutrients · 2025Review
- Links between self-monitoring data collected through smartphones and smartwatches and the individual disease trajectories of adult patients with depressive disorders: Study protocol of a one-year observational trial.Contemporary clinical trials communications · 2025Article
- A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study.Scientific data · 2024Article
- Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features.NPJ digital medicine · 2024Article
- How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- Article
- Daily Self-Monitoring and Feedback of Circadian Rhythm Measures in Major Depression and Bipolar Disorder Using Wearable Devices and Smartphones-The Circadian Rhythm for Mood (CRM®) Trial Protocol: A Randomized Sham Controlled Double-Blind Trial.Psychiatry investigation · 2024Article
- Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis.Frontiers in public health · 2024Article
- The New Emerging Treatment Choice for Major Depressive Disorders: Digital Therapeutics.Advances in experimental medicine and biology · 2024Review
- Telepsychiatry in the Treatment of Major Depressive Disorders.Advances in experimental medicine and biology · 2024Review
- The validity and reliability of the Chinese version of the biological rhythms interview of assessment in neuropsychiatry in the community: a large Chinese college student population.Frontiers in psychiatry · 2024Article
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 at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSmartphones and wearable devices can be used to obtain diverse daily log data related to circadian rhythms. For patients with mood disorders, giving feedback via a smartphone app with appropriate behavioral correction guides could play an important therapeutic role in the real world.
objectiveWe aimed to evaluate the effectiveness of a smartphone app named Circadian Rhythm for Mood (CRM), which was developed to prevent mood episodes based on a machine learning algorithm that uses passive digital phenotype data of circadian rhythm behaviors obtained with a wearable activity tracker. The feedback intervention for the CRM app consisted of a trend report of mood prediction, H-score feedback with behavioral guidance, and an alert system triggered when trending toward a high-risk state.
methodsIn total, 73 patients with a major mood disorder were recruited and allocated in a nonrandomized fashion into 2 groups: the CRM group (14 patients) and the non-CRM group (59 patients). After the data qualification process, 10 subjects in the CRM group and 33 subjects in the non-CRM group were evaluated over 12 months. Both groups were treated in a similar manner. Patients took their usual medications, wore a wrist-worn activity tracker, and checked their eMoodChart daily. Patients in the CRM group were provided with daily feedback on their mood prediction and health scores based on the algorithm. For the CRM group, warning alerts were given when irregular life patterns were observed. However, these alerts were not given to patients in the non-CRM group. Every 3 months, mood episodes that had occurred in the previous 3 months were assessed based on the completed daily eMoodChart for both groups. The clinical course and prognosis, including mood episodes, were evaluated via face-to-face interviews based on the completed daily eMoodChart. For a 1-year prospective period, the number and duration of mood episodes were compared between the CRM and non-CRM groups using a generalized linear model.
resultsThe CRM group had 96.7% fewer total depressive episodes (n/year; exp β=0.033, P=.03), 99.5% shorter depressive episodes (total; exp β=0.005, P<.001), 96.1% shorter manic or hypomanic episodes (exp β=0.039, P<.001), 97.4% fewer total mood episodes (exp β=0.026, P=.008), and 98.9% shorter mood episodes (total; exp β=0.011, P<.001) than the non-CRM group. Positive changes in health behaviors due to the alerts and in wearable device adherence rates were observed in the CRM group.
conclusionsThe CRM app with a wearable activity tracker was found to be effective in preventing and reducing the recurrence of mood disorders, improving prognosis, and promoting better health behaviors. Patients appeared to develop a regular habit of using the CRM app.
trial registrationClinicalTrials.gov NCT03088657; https://clinicaltrials.gov/ct2/show/NCT03088657.
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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.