ArticleFrontiers in sports and active living2025
Using unsupervised machine learning to characterize recovery patterns in elite canoe-kayak athletes across the Olympic training year.
Article in Frontiers in sports and active living, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Combined NY1301 and DDMP on Sleep and Autonomic Function in Summer: A Randomized Clinical Trial.Journal of clinical medicine · 2026Article
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Abstract
Background: Continuous wearable monitoring generates high-volume data, yet methods to translate these streams into actionable recovery insights for elite athletes remain scarce. This study applied a multi-layer, unsupervised machine-learning pipeline to characterize nightly recovery states and season-long physiological phenotypes In Olympic-level French canoe-kayak paddlers. Methods: Seventeen national-team athletes (9 women) were followed for 5,855 nights (≈12 months). Internal load-heart rate, heart-rate 5ariability (HRV), respiratory rate and 30 sleep-architecture variables-was captured with thoracic belts and validated smart rings; external load was logged via an online training platform. After data standardization and validation using multiple indices, K-means clustering was performed. Results: A four-cluster night typology (K0-K3) emerged (Silhouette = 0.52). Sleep quantity and fragmentation indices-time in bed, total sleep duration, light-sleep duration, efficiency, phase count and transitions-explained up to 79 % of between-cluster variance ( Conclusion: Integrated sleep architecture is the dominant discriminator of nightly recovery state, while elevated respiratory rate flags residual metabolic strain. Stable season-long physiological signatures align closely with competitive success, underscoring the value of personalized, ML-driven recovery monitoring in high-performance sport. Athlete profile reveals that exceptional cardiac-autonomic tone can compensate for sub-optimal sleep efficiency in elite performers, suggesting that vagal dominance may be more critical than perfect sleep architecture for competitive success.
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