ORIGINAL RESEARCH article
Front. Physiol.
Sec. Exercise Physiology
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1627287
Pre-sleep heart rate variability predicts chronic insomnia and measures of sleep continuity in national-level athletes
Provisionally accepted
- 1Department of Exercise Physiology, Beijing Sport University, Beijing, China
- 2Beijing Xiaomi Mobile Software Co., Ltd., Beijing, China
- 3University of Nottingham, Nottingham, United Kingdom
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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 (R² = 0.902 and 96% accuracy, AUC = 0.997). Multiple linear regression showed that pre-sleep HRV had a moderate predictive capacity for time awake (R² = 0.526, P < 0.001) and sleep efficiency (R² = 0.481, P < 0.001). The multiple linear regression model's predicted values for sleep onset latency (r = 0.459, P < 0.01), sleep efficiency (r = 0.554, P < 0.001), and deep sleep time (r = 0.536, P < 0.001) showed moderate positive correlations with the corresponding actual values, whereas the MLP neural network's predictions were not significantly correlated with the actual values. In contrast, the MLP neural network model was superior at predicting time awake when compared to the multiple linear regression model (MLP: mean absolute percentage error = 0.182 vs Multiple linear regression: mean absolute percentage error = 0.516). Conclusions: 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.
Keywords: Chronic insomnia, Athlete, Heart rate variability, Autonomic Nervous System, linearregression model
Received: 12 May 2025; Accepted: 28 Aug 2025.
Copyright: © 2025 Li, Yu, Lei, Steward and Zhou. 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:
Charles J. Steward, University of Nottingham, Nottingham, United Kingdom
Yue Zhou, Department of Exercise Physiology, Beijing Sport University, Beijing, China
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