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. 2025 Dec 5.
doi: 10.1007/s40279-025-02369-1. Online ahead of print.

Impact of an Overload Period on Heart Rate Variability, Sleep Quality, Motivation, and Performance in High-level Swimmers: Use of Explainable Artificial Intelligence (XAI) to Assess Training Load Variations

Diogo D Carvalho  1   2   3 , Márcio Fagundes Goethel  4   5 , Mégane Erblang  6 , João Paulo Vilas-Boas  4   5 , David B Pyne  7 , Ricardo J Fernandes  4   5 , Philippe Lopes  6
Affiliations

Impact of an Overload Period on Heart Rate Variability, Sleep Quality, Motivation, and Performance in High-level Swimmers: Use of Explainable Artificial Intelligence (XAI) to Assess Training Load Variations

Diogo D Carvalho et al. Sports Med. .

Abstract

Background: Understanding the impact of training sessions on physiological, psychological, and immunological responses is crucial for adequate training periodization and preventing negative influences on health, training, and performance.

Objectives: To characterize the responses of heart rate variability (HRV), sleep time and quality, motivation, dry-land strength, and swimming performance to an overload period of three consecutive 7-day cycles (cycles 1, 2, and 3) with different training intensity and volume dynamics. Secondly, to test the capability of HRV to assess daily variation in training loads on the basis of explainable artificial intelligence (XAI) models.

Methods: A total of 14 high-level swimmers (4 males and 10 females, aged 17.5 ± 1.5 years) were monitored via an orthostatic test, Hooper index, sleep questionnaires, and rating of perceived exertion (RPE) of each training session. The self-reported and prescribed training loads were compared. At the beginning of each cycle and at the end of cycle 3, swimmers completed anthropometric testing, countermovement jumps, hand-grip strength tests, and a 5 × 200 m incremental protocol.

Results: High-level swimmers accurately perceived their daily training loads. However, differences between the training and RPE loads emerged on weekends, indicating that physiological and psychological loads have different influences and should be considered simultaneously when characterizing training loads. The overload period was characterized by an increase in both training (27%) and RPE (20%) loads without eliciting a negative effect on sleep quantity and quality. During the overload period, supine (F2.18 = 3.448, η2 = 0.28; p = 0.05) and standing (F2.18 = 3.809, η2 = 0.30; p = 0.04) mean heart rate (HR) increased and supine log root mean square of the successive differences (LnRMSSD; F2.18 = 4.379, η2 = 0.33; p = 0.028) and maximal blood lactate (F3.27 = 3.441, η2 = 0.28; p = 0.03) decreased during and after cycle 3 (respectively). Dry-land and swimming performances were maintained, indicating that the autonomic nervous system appears to be more sensitive (XAI models r2 = 0.91 and 0.9) to changes in acute/short-term training load.

Conclusions: HRV indices, particularly supine RMSSD and mean HR, were the most sensitive markers of training load variation, while sleep, strength and power, and swimming performance remained stable. HRV can be employed as a practical tool for monitoring training responses and managing training loads in competitive swimmers.

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Conflict of interest statement

Declarations. Conflicts of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Consent to participate: Informed consent was obtained from all individual participants included in the study. Ethics approval: This investigation was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Faculty of Sport of University of Porto (CEFADE 28 2019). Data availability statement: The raw data supporting the conclusions of this article will be made available by the corresponding author on request. Author contributions: Conceptualization, DC, DP, PL and RF; methodology, DC, MG, DP, PL and RF; investigation, DC, ME and PL; data curation, DC, MG, ME and PL; formal analysis, DC, MG and JV-B; software, MG; validation, DC and PL; writing-original draft, DC, MG, PL and RF; writing-review and editing, DC, MG, ME, JV-B, DP, PL and RF; visualization, DC, JV-B, DP, PL and RF; supervision, JV-B, DP, PL and RF; project administration, PL and RF; resources, ME and PL; funding acquisition, PL. All authors contributed to the article and approved the submitted version.

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