ORIGINAL RESEARCH article
Front. Digit. Health
Sec. Health Informatics
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1689846
This article is part of the Research TopicMachine Learning for Operator Fatigue Detection and Monitoring with Wearable ElectronicsView all 3 articles
Feasibility of Predicting Next-Day Fatigue Levels Using Heart Rate Variability and Activity-Sleep Metrics in People with Post-COVID Fatigue
Provisionally accepted
- 1Newcastle University Faculty of Medical Sciences, Newcastle upon Tyne, United Kingdom
- 2NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne, United Kingdom
- 3Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
- 4Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Abstract: Background: Post-COVID fatigue (pCF) represents a significant burden for many individuals following SARS-CoV-2 infection. The unpredictable nature of fatigue fluctuations impairs daily functioning and quality of life, creating challenges for effective symptom management. Objective: This study investigated the feasibility of developing predictive models to forecast next-day fatigue levels in individuals with pCF, utilizing objective physiological and behavioral features derived from wearable device data. Methods: We analyzed data from 68 participants with pCF who wore an Axivity AX6 device on their non-dominant wrist and a VitalPatch electrocardiogram (ECG) sensor on their chest for up to 21 days while completing fatigue questionnaires every other day. HRV features were extracted from the VitalPatch single-lead ECG signal using the NeuroKit Python package, while activity and sleep features were derived from the Axivity wrist-worn device using the GGIR package. Using a 5-fold cross-validation approach, we trained and evaluated the performances of two machine learning models to predict next-day fatigue levels using Visual Analogue Scale (VAS) fatigue scores: Random Forest and XGBoost . Results: Using five-fold cross-validation, XGBoost outperformed Random Forest in predicting next-day fatigue levels (mean R² = 0.79 ± 0.04 vs. 0.69 ± 0.02; MAE = 3.18 ± 0.63 vs. 6.14 ± 0.96). Predicted and observed fatigue scores were strongly correlated for both models (XGBoost: r = 0.89 ± 0.02; Random Forest: r = 0.86 ± 0.01). Key predictors included heart rate variability features—sample entropy, low-frequency power, and approximate entropy—along with demographic (age, sex) and activity-related (moderate and vigorous duration) factors. These findings underscore the importance of integrating physiological, demographic, and activity data for accurate fatigue prediction. Conclusions: This study demonstrates the feasibility of combining heart rate variability with activity and sleep features to predict next-day fatigue levels in individuals with pCF. Integrating physiological and behavioral data shows promising predictive accuracy and provides insights that could inform future personalized fatigue management strategies.
Keywords: post-COVID syndrome, Fatigue prediction, machine learning, Heart rate variability, accelerometry, Wearable Technology, XGBoost, digital biomarkers
Received: 21 Aug 2025; Accepted: 14 Oct 2025.
Copyright: © 2025 Aboagye, Germann, Baker, Baker and Del Din. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Nana Yaw Aboagye, n.y.aboagye2@newcastle.ac.uk
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.