ReviewFrontiers in psychiatry2023
A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms.
Review in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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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.
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Who cites it
7 citing papers in PubMed.
- Eye-Tracking Phenotypes Across the Depressive Spectrum: Joint Modeling of Diagnostic Group and Symptom Severity.CNS neuroscience & therapeutics · 2026Article
- A systematic exploration of digital biomarkers for the detection of depressive episodes in bipolar disorder.Npj mental health research · 2026Article
- Digital Therapeutics and Blended Care in Major Depressive Disorder: Current Evidence and Future Directions.Advances in experimental medicine and biology · 2026Review
- Tracking mood symptoms across the menstrual cycle in women with depression using ecological momentary assessment and heart rate variability.BMJ mental health · 2025Article
- Oscillatory components of bidirectional cardio-respiratory coupling in depression and suicidal ideation: insights from swarm decomposition and entropy analysis.Frontiers in network physiology · 2025Article
- Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder.Frontiers in artificial intelligence · 2024Article
- Digitally managing depression: A fully remote randomised attention-placebo controlled trial.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Depression is the leading cause of worldwide disability, until now only 3% of patients with major depressive disorder (MDD) experiences full recovery or remission. Different studies have tried to better understand MDD pathophysiology and its resistant forms (TRD), focusing on the identification of candidate biomarkers that would be able to reflect the patients' state and the effects of therapy. Development of digital technologies can generate useful digital biomarkers in a real-world setting. This review aims to focus on the use of digital technologies measuring symptom severity and predicting treatment outcomes for individuals with mood disorders. Methods: Two databases (PubMed and APA PsycINFO) were searched to retrieve papers published from January 1, 2013, to July 30, 2023, on the use of digital devices in persons with MDD. All papers had to meet specific inclusion criteria, which resulted in the inclusion of 12 articles. Results: Research on digital biomarkers confronts four core aspects: (I) predicting diagnostic status, (II) assessing symptom severity and progression, (III) identifying treatment response and (IV) monitoring real-word and ecological validity. Different wearable technologies have been applied to collect physiological, activity/sleep, or subjective data to explore their relationships with depression. Discussion: Depression's stable rates and high relapse risk necessitate innovative approaches. Wearable devices hold promise for continuous monitoring and data collection in real world setting. Conclusion: More studies are needed to translate these digital biomarkers into actionable interventions to improve depression diagnosis, monitoring and management. Future challenges will be the applications of wearable devices routinely in personalized medicine.
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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.