ArticleDigital health
Use of voice features from smartphones for monitoring depressive disorders: Scoping review.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 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.
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, 2 syntheses or guidelines pooled it.
- Diagnostic accuracy of traditional and deep learning methods for detecting depression based on speech features: a systematic review and meta-analysis.BMC psychiatry · 2025Pooled it
- Multimodal observable cues in mood, anxiety, and borderline personality disorders: a review of reviews to inform explainable AI in mental health.Frontiers in artificial intelligence · 2025Pooled it
- Screening for depression risk via smartphone narratives with fully fine-tuned WavLM.Scientific reports · 2026Article
- Mood states recognition based on Mandarin speech and deep learning in patients with bipolar disorder.BMC psychiatry · 2025Article
- Cross-Platform Availability of Smartphone Sensors for Depression Indication Systems: Mixed-Methods Umbrella Review.Interactive journal of medical research · 2025Review
- Datasets of Smartphone Modalities for Depression Assessment: A Scoping Review.IEEE transactions on affective computingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Object: This review evaluates the use of smartphone-based voice data for predicting and monitoring depression. Methods: A scoping review was conducted, examining 14 studies from Medline, Scopus, and Web of Science (2010-2023) on voice data collection methods and the use of voice features for minitoring depression. Results: Voice data, especially prosodic features like fundamental frequency and pitch, show promise for predicting depression, though their sole predictive power requires further validation. Integrating voice with multimodal sensor data has been shown to improve accuracy significantly. Conclusion: Smartphone-based voice monitoring offers a promising, noninvasive, and cost-effective approach to depression management. The integration of machine learning with sensor data could significantly enhance mental health monitoring, necessitating further research and longitudinal studies for validation.
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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.