Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study

Abstract

ObjectiveTo develop a vocal biomarker for fatigue monitoring in people with COVID-19.DesignProspective cohort study.SettingPredi-COVID data between May 2020 and May 2021.ParticipantsA total of 1772 voice recordings were used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphone’s operating system (Android/iOS). The recordings were collected from 296 participants tracked for 2 weeks following SARS-CoV-2 infection.Primary and secondary outcome measuresFour machine learning algorithms (logistic regression, k-nearest neighbours, support vector machine and soft voting classifier) were used to train and derive the fatigue vocal biomarker. The models were evaluated based on the following metrics: area under the curve (AUC), accuracy, F1-score, precision and recall. The Brier score was also used to evaluate the models’ calibrations.ResultsThe final study population included 56% of women and had a mean (±SD) age of 40 (±13) years. Women were more likely to report fatigue (p<0.001). We developed four models for Android female, Android male, iOS female and iOS male users with a weighted AUC of 86%, 82%, 79%, 85% and a mean Brier Score of 0.15, 0.12, 0.17, 0.12, respectively. The vocal biomarker derived from the prediction models successfully discriminated COVID-19 participants with and without fatigue.ConclusionsThis study demonstrates the feasibility of identifying and remotely monitoring fatigue thanks to voice. Vocal biomarkers, digitally integrated into telemedicine technologies, are expected to improve the monitoring of people with COVID-19 or Long-COVID.Trial registration numberNCT04380987.

Automated speech analysis for risk detection of depression, anxiety, insomnia, and fatigue: Algorithm Development and Validation Study

“…We also observed that only the AIS obtained a good performance for age (AIS Age DR=0.77), and, except for PHQ-9 and GAD-7, all DRs were satisfactory or high for sex. Participants are grouped into age categories to allow analysis: [18,30), [30,45), [45,65), >65…”

“…The study of speech biomarkers in mental health holds great potential, offering a non-invasive and easily accessible avenue to capture significant motor, cognitive and behavioral changes due to mental health disorders such as depression and anxiety [10][11][12][13]. Clinical evidence and research studies have increasingly linked specific automated extracted speech features, such as prosody, articulation, and fluency, with various mental health conditions, including depression [10,14], anxiety [15], suicide-risk assessment [16], fatigue [17,18], or sleep deprivation [19]. The complexity of human speech extends beyond the intricate motor coordination involved.…”

Co-Design of a Voice-Based Digital Health Solution to Monitor Persisting Symptoms Related to COVID-19 (UpcomingVoice Study): Protocol for a Mixed Methods Study

“…To date, almost 6000 voice recordings from more than >500 patients with COVID-19 have already been collected in the Predi-COVID study [2]. These voice recordings have been analyzed, and promising vocal biomarker candidates for fatigue, loss of taste and smell, and symptomatic status in people with a COVID-19 infection have already been identified [20][21][22].…”

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

“…Additionally, the COVID-19 pandemic generated increased interest in developing technology to perform remote, minimally invasive respiratory disease diagnostics, and voicederived biomarkers have come to the forefront of this effort. [3][4][5][6][7] Many groups have also found grounds for the analysis of voice for the detection and progression of neurological disorders, such as Parkinson's, Alzheimer's, Huntington's, and dementia. [8][9][10][11][12][13][14][15][16] Additional areas of investigation include pediatric and adult voice disorders, and mental health disorders.…”

“…The vocal biomarkers market was worth USD 1901.1 million in 2021 and is expected to reach USD 5131.8 million in 2028 2 . Additionally, the COVID‐19 pandemic generated increased interest in developing technology to perform remote, minimally invasive respiratory disease diagnostics, and voice‐derived biomarkers have come to the forefront of this effort 3–7 . Many groups have also found grounds for the analysis of voice for the detection and progression of neurological disorders, such as Parkinson's, Alzheimer's, Huntington's, and dementia 8–16 .…”