Using voice biomarkers for frailty classification

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.

Keywords: Frailty; Machine learning; Speech; Voice biomarkers; Voice recording.

Fig. 1

Voice parameters by frailty category