ArticleGeroScience2024
Using voice biomarkers for frailty classification.
Article in GeroScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- The application value of frailty assessment tools in the perioperative period: A narrative review.Journal of anesthesia and translational medicine · 2026Review
- Spontaneous speech enables scalable digital phenotyping of physical functional deficits in aging.npj aging · 2026Article
- Article
- Applying Multiple Machine Learning Models to Classify Mild Cognitive Impairment from Speech in Community-Dwelling Older Adults.Journal of Intelligence · 2026Article
- Using voice and speech data in healthcare: a scoping review of the ethical, legal and social implications.Frontiers in digital health · 2026Review
- A Signal for Voice and Speech Abnormalities in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.Journal of clinical medicine · 2025Article
- Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study.JMIR medical informatics · 2025Article
- Identifying and Estimating Frailty Phenotypes by Vocal Biomarkers: Cross-Sectional Study.Journal of medical Internet research · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinicians use the patient's voice intuitively to evaluate general health and frailty. Voice is an emerging health indicator but has been scarcely studied in the context of frailty. This study explored voice parameters as possible predictors of frailty in older adults. Fifty-three participants over 70 years old were recruited from rehabilitation wards at a tertiary medical center. Participants' frailty was assessed using Rockwood frailty index and they were classified as most-frail (n = 33, 68%) or less-frail (n = 20, 32%). Participants were recorded counting from 1 to 10 and backwards using a smartphone recording application. The following voice biomarkers were derived: peak and average volume, peak/average volume ratio, pauses' total length, and pause length standard deviation. The most-frail group had a higher peak volume/average volume ratio (p = 0.03) and greater variance in lengths of pauses between speech segments (p = 0.002). These parameters indicate greater speech irregularity in the most-frail, compared to the less-frail. The most-frail group also had a longer total duration of pauses (p = 0.02). No statistically significant difference was found in peak and average volume (p = 0.75 and 0.39). Most-frail participants' speech had different characteristics, compared to participants in the less-frail group. This is a first step to developing an AI-based frailty assessment tool that can assist in identifying our most vulnerable patients.
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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.