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

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 2weeks 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 summary

This study aimed to develop a vocal biomarker for fatigue monitoring in people with COVID-19. By training AI algorithms, four fatigue prediction models were successfully developed for different users, and it was demonstrated that these models can effectively discriminate between COVID-19 patients with and without fatigue.