ArticleJournal of medical Internet research2024
Identifying and Estimating Frailty Phenotypes by Vocal Biomarkers: Cross-Sectional Study.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Speech-based digital biomarkers for early etiological stratification of Alzheimer's disease and frontotemporal degeneration: a biomarker-confirmed prospective study.The journal of prevention of Alzheimer's disease · 2026Article
- The application value of frailty assessment tools in the perioperative period: A narrative review.Journal of anesthesia and translational medicine · 2026Review
- Speech as a dynamic biomarker of physical aging: a longitudinal study.GeroScience · 2026Article
- Spontaneous speech enables scalable digital phenotyping of physical functional deficits in aging.npj aging · 2026Article
- Article
- Oral health assessment of end-of-life patients with cancer: Evaluating the diagnostic accuracy of the oral assessment guide and oral health assessment tool in prognosis prediction.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Frailty phenotypes and their association with health consequences: a comparison of different measures.Aging clinical and experimental research · 2024Review
- Consensus-Based Definitions for Vocal Biomarkers: The International VOCAL Initiative.Digital biomarkersArticle
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4 authors.
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Abstract
backgroundResearchers have developed a variety of indices to assess frailty. Recent research indicates that the human voice reflects frailty status. Frailty phenotypes are seldom discussed in the literature on the aging voice.
objectiveThis study aims to examine potential phenotypes of frail older adults and determine their correlation with vocal biomarkers.
methodsParticipants aged ≥60 years who visited the geriatric outpatient clinic of a teaching hospital in central Taiwan between 2020 and 2021 were recruited. We identified 4 frailty phenotypes: energy-based frailty, sarcopenia-based frailty, hybrid-based frailty-energy, and hybrid-based frailty-sarcopenia. Participants were asked to pronounce a sustained vowel "/a/" for approximately 1 second. The speech signals were digitized and analyzed. Four voice parameters-the average number of zero crossings (A1), variations in local peaks and valleys (A2), variations in first and second formant frequencies (A3), and spectral energy ratio (A4)-were used for analyzing changes in voice. Logistic regression was used to elucidate the prediction model.
resultsAmong 277 older adults, an increase in A1 values was associated with a lower likelihood of energy-based frailty (odds ratio [OR] 0.81, 95% CI 0.68-0.96), whereas an increase in A2 values resulted in a higher likelihood of sarcopenia-based frailty (OR 1.34, 95% CI 1.18-1.52). Respondents with larger A3 and A4 values had a higher likelihood of hybrid-based frailty-sarcopenia (OR 1.03, 95% CI 1.002-1.06) and hybrid-based frailty-energy (OR 1.43, 95% CI 1.02-2.01), respectively.
conclusionsVocal biomarkers might be potentially useful in estimating frailty phenotypes. Clinicians can use 2 crucial acoustic parameters, namely A1 and A2, to diagnose a frailty phenotype that is associated with insufficient energy or reduced muscle function. The assessment of A3 and A4 involves a complex frailty phenotype.
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