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 was used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphones operating system (Android/iOS). The recordings were collected from 296 participants tracked for two weeks following SARS-CoV-2 infection. primary and secondary outcome measuresFour machine learning algorithms (Logistic regression, k-nearest neighbors, support vector machine, and soft voting classifier) were used to train and derive the fatigue vocal biomarker. A t-test was used to evaluate the distribution of the vocal biomarker between the two classes (Fatigue and No fatigue). 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<.001). We developed four models for Android female, Android male, iOS female, and iOS male users with a weighted AUC of 79%, 85%, 86%, 82%, 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 (t-test P<.001). 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.
