ArticleEuropean journal of applied physiology2024
Predicting daily recovery during long-term endurance training using machine learning analysis.
Article in European journal of applied physiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.Annals of medicine · 2026Review
- Artificial Intelligence in Sports Medicine: A Decision-Centered Framework for the Future Sports Physician.Diagnostics (Basel, Switzerland) · 2026Review
- Prediction of athlete performance based on a gradient regression model.Scientific reports · 2026Article
- Wearable Biosensing and Machine Learning for Data-Driven Training and Coaching Support.Biosensors · 2026Review
- Artificial intelligence and wearables in sport: performance, injury risk, and wellbeing.Frontiers in artificial intelligence · 2026Review
- Toward a record-eligible sub-2-hour marathon: an updated integrative framework of Physiological, technological, and cognitive determinants.European journal of applied physiology · 2026Review
- Validating Subjective Ratings with Wearable Data for a Nuanced Understanding of Load-Recovery Status in Elite Endurance Athletes.Sports medicine - open · 2025Article
- Construction of the prediction model and analysis of key winning factors in world women's volleyball using gradient boosting decision tree.Scientific reports · 2025Article
- Artificial Intelligence in Endurance Sports: Metabolic, Recovery, and Nutritional Perspectives.Nutrients · 2025Review
- Individual training prescribed by heart rate variability, heart rate and well-being scores in experienced cyclists.Scientific reports · 2025Article
- Identification of Athleticism and Sports Profiles Throughout Machine Learning Applied to Heart Rate Variability.Sports (Basel, Switzerland) · 2025Article
- The long-term mental health benefits of exercise training for physical education students: a comprehensive review of neurobiological, psychological, and social effects.Frontiers in psychiatry · 2025Review
- Mapping HRV in sports science: from monitoring to machine learning.Frontiers in sports and active living · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, sleep, HRV, and subjective well-being measures.
methodsSelf-selected nutrition intake, exercise training, sleep habits, HRV, and subjective well-being of 43 endurance athletes ranging from professional to recreationally trained were monitored daily for 12 weeks (3572 days of tracking). Global and individualized models were constructed using machine learning techniques, with the single best algorithm chosen for each model. The model performance was compared with a baseline intercept-only model.
resultsPrediction error (root mean square error [RMSE]) was lower than baseline for the group models (11.8 vs. 14.1 and 0.22 vs. 0.29 for AM PRS and HRV change, respectively). At the individual level, prediction accuracy outperformed the baseline model but varied greatly across participants (RMSE range 5.5-23.6 and 0.05-0.44 for AM PRS and HRV change, respectively).
conclusionAt the group level, daily recovery measures can be predicted based on commonly measured variables, with a small subset of variables providing most of the predictive power. However, at the individual level, the key variables may vary, and additional data may be needed to improve the prediction accuracy.
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