ArticleBMJ open2022
Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study.
Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04380987 (Luxembourg Cohort of Positive Patients for COVID-19), which is not on this map. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Luxembourg Cohort of Positive Patients for COVID-19: a Stratification Study to Predict Patient Prognosis
Who cites it
15 citing papers in PubMed.
- From Reactive to Proactive Healthcare: Synergizing Wearable Biomarkers and Machine Learning in Digital Therapeutics.Bioengineering (Basel, Switzerland) · 2026Review
- Using voice and speech data in healthcare: a scoping review of the ethical, legal and social implications.Frontiers in digital health · 2026Review
- Fatigue: a common but poorly understood symptom in neurological and non-neurological diseases.Nature reviews. Neurology · 2025Review
- The Human Voice as a Digital Health Solution Leveraging Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Review
- Digital Biomarkers and AI for Remote Monitoring of Fatigue Progression in Neurological Disorders: Bridging Mechanisms to Clinical Applications.Brain sciences · 2025Article
- Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.JMIR AI · 2025Review
- Article
- SympCoughNet: symptom assisted audio-based COVID-19 detection.Frontiers in digital health · 2025Article
- Automated Speech Analysis for Risk Detection of Depression, Anxiety, Insomnia, and Fatigue: Algorithm Development and Validation Study.Journal of medical Internet research · 2024Article
- The Prevalence and Associated Factors of Post-COVID-19 Fatigue: A Systematic Review and Meta-Analysis.Cureus · 2024Review
- Digital approaches in post-COVID healthcare: a systematic review of technological innovations in disease management.Biology methods & protocols · 2024Review
- Detecting fatigue in multiple sclerosis through automatic speech analysis.Frontiers in human neuroscience · 2024Article
- 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.JMIR research protocols · 2023Article
- Co-design of a voice-based app to monitor long COVID symptoms with its end-users: A mixed-method study.Digital healthArticle
- Voice Signatures of Momentary Psychological Stress in Real-Life Environments: Results from the Colive Voice Study.Digital biomarkersArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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.
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 NUMBER: NCT04380987.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.