ReviewCureus2024
A Comprehensive Review on Harnessing Wearable Technology for Enhanced Depression Treatment.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Veteran Monitoring Initiative for Noninvasive Physiology and Depression (V-MIND) Exploring Physical Activity and Mental Health in UK Veterans: Protocol for an Observational Digital Phenotyping Study.JMIR research protocols · 2026Article
- Digital Therapeutics and Blended Care in Major Depressive Disorder: Current Evidence and Future Directions.Advances in experimental medicine and biology · 2026Review
- Advancing mental health diagnostics: a review on the role of smartphones, wearable devices, and artificial intelligence in depression and anxiety detection.Biomedical engineering letters · 2025Review
- Telehealth and Pharmacotherapy: The Role of Synchronous and Novel Asynchronous Digital Health Tools in Psychiatry.Pharmaceutical medicine · 2025Review
- Use of a Wearable Self-Tracking Instrument by Refugees With Complex Posttraumatic Stress Disorder: A Qualitative Study of Psychotherapeutic Mediation and Engagement.JMIR formative research · 2025Article
- Young adults' perceptions of digital health technology for improving exercise: A qualitative study.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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression is a prevalent and debilitating mental health disorder that significantly impacts individuals, families, and societies worldwide. Despite advancements in treatment, challenges remain in effectively managing and monitoring depressive symptoms. Wearable technology, which encompasses devices that can monitor physiological and behavioral parameters in real time, offers promising new avenues for enhancing depression treatment. This comprehensive review explores the potential of wearable technology in managing and treating depression. It examines how wearables can monitor depressive symptoms, improve patient engagement and adherence to treatment plans, and provide valuable data for personalized treatment strategies. The review covers the integration of wearable technology in clinical settings, the role of wearables in remote monitoring and telemedicine, and the ethical and privacy considerations associated with their use. Additionally, it highlights case studies and pilot programs demonstrating the practical applications and outcomes of wearable technology interventions. Future directions and innovations are discussed, identifying potential advancements and challenges in this emerging field. This review aims to inform healthcare professionals, researchers, and policymakers about the opportunities and challenges of integrating wearable technology into depression treatment, ultimately contributing to improved mental healthcare outcomes.
Indexed as
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.