Journal of medical Internet research · 2024
Identifying and Estimating Frailty Phenotypes by Vocal Biomarkers: Cross-Sectional Study.
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Lin Yu-Chun, Yan Huang-Ting, Lin Chih-Hsueh, Chang Hen-Hong
Аннотация
BACKGROUND: Researchers 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.
OBJECTIVE: This study aims to examine potential phenotypes of frail older adults and determine their correlation with vocal biomarkers.
METHODS: Participants 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.
RESULTS: Among 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.
CONCLUSIONS: Vocal 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.
OBJECTIVE: This study aims to examine potential phenotypes of frail older adults and determine their correlation with vocal biomarkers.
METHODS: Participants 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.
RESULTS: Among 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.
CONCLUSIONS: Vocal 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.