ArticleJMIR mental health2022
Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study.
Article in JMIR mental health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review.Journal of medical Internet research · 2025Pooled it
- A Systematic Review of Location Data for Depression Prediction.International journal of environmental research and public health · 2023Pooled it
- The 'UPIC' cohort: a nationwide prospective study of mental health among adolescents and young adults in Sweden - a study protocol.BMJ open · 2026Article
- Digital Phenotyping of Lifestyle Profiles and Mental Well-Being in German Adults: Prospective Longitudinal Cohort Study.Journal of medical Internet research · 2026Article
- Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems.Current psychiatry reports · 2026Review
- Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026Article
- Circadian rhythm parameters differentiate euthymic, manic and depressive mood states in bipolar disorders - an explorative pilot study.International journal of bipolar disorders · 2025Article
- Multimodal Digital Phenotyping Study in Patients With Major Depressive Episodes and Healthy Controls (Mobile Monitoring of Mood): Observational Longitudinal Study.JMIR mental health · 2025Observational
- Wearables in Chronomedicine and Interpretation of Circadian Health.Diagnostics (Basel, Switzerland) · 2025Review
- Digital biomarkers for non-motor symptoms in Parkinson's disease: the state of the art.NPJ digital medicine · 2024Review
- The Development of Game-Based Digital Mental Health Interventions: Bridging the Paradigms of Health Care and Entertainment.JMIR serious games · 2023Article
- Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study.JMIR mental health · 2023Article
- Developing "MinDag" - an app to capture symptom variation and illness mechanisms in bipolar disorder.Frontiers in medical technology · 2022Article
- Datasets of Smartphone Modalities for Depression Assessment: A Scoping Review.IEEE transactions on affective computingArticle
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMood disorders are burdensome illnesses that often go undetected and untreated. Sensor technologies within smartphones may provide an opportunity for identifying the early changes in circadian rhythm and social support/connectedness that signify the onset of a depressive or manic episode.
objectiveUsing smartphone sensor data, this study investigated the relationship between circadian rhythm, which was determined by GPS data, and symptoms of mental health among a clinical sample of adults diagnosed with major depressive disorder or bipolar disorder.
methodsA total of 121 participants were recruited from a clinical setting to take part in a 10-week observational study. Self-report questionnaires for mental health outcomes, social support, social connectedness, and quality of life were assessed at 6 time points throughout the study period. Participants consented to passively sharing their smartphone GPS data for the duration of the study. Circadian rhythm (ie, regularity of location changes in a 24-hour rhythm) was extracted from GPS mobility patterns at baseline.
resultsAlthough we found no association between circadian rhythm and mental health functioning at baseline, there was a positive association between circadian rhythm and the size of participants' social support networks at baseline (r=0.22; P=.03; R
conclusionsCircadian rhythm, which was extracted from smartphone GPS data, was associated with social support and predicted changes in anxiety in a clinical sample of adults with mood disorders. Larger studies are required for further validations. However, smartphone sensing may have the potential to monitor early symptoms of mood disorders.
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