TY - JOUR
T1 - Vocal biomarker predicts fatigue in people with COVID-19
T2 - results from the prospective Predi-COVID cohort study
AU - Elbéji, Abir
AU - Zhang, Lu
AU - Higa, Eduardo
AU - Fischer, Aurélie
AU - Despotovic, Vladimir
AU - Nazarov, Petr V
AU - Aguayo, Gloria
AU - Fagherazzi, Guy
N1 - Funding Statement
The Predi-COVID study is supported by the Luxembourg National Research Fund (FNR) (Predi-COVID, grant number 14716273), the André Losch Foundation, and the Luxembourg Institute of Health.
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.
PY - 2022/11/22
Y1 - 2022/11/22
N2 - OBJECTIVE: To develop a vocal biomarker for fatigue monitoring in people with COVID-19. DESIGN: Prospective cohort study. SETTING: Predi-COVID data between May 2020 and May 2021. PARTICIPANTS: A 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 MEASURES: Four 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. RESULTS: The 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. CONCLUSIONS: This 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 NUMBER: NCT04380987.
AB - OBJECTIVE: To develop a vocal biomarker for fatigue monitoring in people with COVID-19. DESIGN: Prospective cohort study. SETTING: Predi-COVID data between May 2020 and May 2021. PARTICIPANTS: A 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 MEASURES: Four 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. RESULTS: The 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. CONCLUSIONS: This 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 NUMBER: NCT04380987.
KW - COVID-19
KW - Health informatics
KW - Public health
UR - http://www.scopus.com/inward/record.url?scp=85142543573&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/36414294
U2 - 10.1136/bmjopen-2022-062463
DO - 10.1136/bmjopen-2022-062463
M3 - Article
C2 - 36414294
SN - 2044-6055
VL - 12
JO - BMJ Open
JF - BMJ Open
IS - 11
M1 - e062463
ER -