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Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study.
Elbéji, Abir; Zhang, Lu; Higa, Eduardo; Fischer, Aurélie; Despotovic, Vladimir; Nazarov, Petr V; Aguayo, Gloria; Fagherazzi, Guy.
  • Elbéji A; Department of Precision Health, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445 Strassen, Luxembourg.
  • Zhang L; Department of Precision Health, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445 Strassen, Luxembourg.
  • Higa E; Department of Precision Health, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445 Strassen, Luxembourg.
  • Fischer A; Department of Precision Health, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445 Strassen, Luxembourg.
  • Despotovic V; Bioinformatics Platform, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445 Strassen, Luxembourg.
  • Nazarov PV; Bioinformatics Platform, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445 Strassen, Luxembourg.
  • Aguayo G; Department of Precision Health, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445 Strassen, Luxembourg.
  • Fagherazzi G; Department of Precision Health, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445 Strassen, Luxembourg guy.fagherazzi@lih.lu.
BMJ Open ; 12(11): e062463, 2022 11 22.
Article in English | MEDLINE | ID: covidwho-2137736
ABSTRACT

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Limits: Adult / Female / Humans / Male / Middle aged Language: English Journal: BMJ Open Year: 2022 Document Type: Article Affiliation country: Bmjopen-2022-062463

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Limits: Adult / Female / Humans / Male / Middle aged Language: English Journal: BMJ Open Year: 2022 Document Type: Article Affiliation country: Bmjopen-2022-062463