ArticleFrontiers in physiology2025
Pre-sleep heart rate variability predicts chronic insomnia and measures of sleep continuity in national-level athletes.
Article in Frontiers in physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial Intelligence and Psychophysiological Monitoring for Integrated Performance Modeling in Elite Soccer: A Scoping Review of Applications, Evidence Gaps, and Translational Challenges.Sports (Basel, Switzerland) · 2026Review
- Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Sleep Disturbances and Sarcopenia in Older Adults: Clinical Associations, Potential Mechanisms, and Management Strategies.International journal of general medicine · 2026Review
- Sleep-timing irregularity moderates the within-participant association between training load and next-morning heart rate variability in university sport participants: a 28-day study.Frontiers in physiology · 2026Article
- Predicting football match outcomes: a multilayer perceptron neural network model based on technical statistics indicators of the FIFA world Cup.Frontiers in sports and active living · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Objective: This study aimed to investigate whether pre-sleep heart rate variability (HRV) could predict chronic insomnia (CI) and sleep quality in male national-level team-based athletes. Methods: A total of 174 athletes participated in this study, including 98 with CI and 76 exhibiting normal sleeping patterns. Pre-sleep HRV was assessed using heart rate chest straps, and sleep quality was evaluated through polysomnography (PSG) before a single night's sleep. Binary logistic regression was first used to predict CI. Multiple linear regression and multi-layer perceptron (MLP) neural network models were then used to predict measures of sleep quality. Results: Binary logistic regression revealed that measures of pre-sleep HRV accurately predict CI ( Conclusion: The present findings support the use of pre-sleep HRV not only to predict CI, but also some sleep continuity measures in national level athletes.
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Registered trials
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