Abstract
ackground: Elite athletes are frequently subjected to high-intensity training regimens, which can result in cumulative physical stress, overtraining, and potential health risks. Monitoring autonomic responses to such load is essential for optimizing performance and preventing maladaptation. Objective: The present study aimed to assess changes in autonomic regulation immediately and two hours after training in athletes, using an integrated framework (combining time- and frequency-domain HRV indices with nonlinear and recurrence quantification analysis). It was investigated how repeated assessments over a 4-month period can reveal cumulative effects and identify athletes at risk. Special attention was paid to identifying signs of excessive fatigue, autonomic imbalance, and cardiovascular stress. Methods: Holter ECGs of 12 athletes (mean age 21±2.22 years; males, athletes participating in competitions) over a 4-month period were recorded before, immediately after, and two hours after high-intensity training, with HRV calculated from 5-min segments. Metrics included HRV and recurrent quantitative analysis. Statistical com- parisons were made between the pre-, post-, and recovery phases to quantify autonomic changes (repeated-measures ANOVA for comparisons across the three states, pairedt-tests for direct two-state contrasts, post hoc analyses with Holm–Bonferroni corrections, and effect size estimatesη 2). Results: Immediately after training, significant decreases in SDNN (↓35%), RMSSD (↓40%), and pNN50 (↓55%), accompanied
HRV and recurrent quantitative analysis. Statistical com- parisons were made between the pre-, post-, and recovery phases to quantify autonomic changes (repeated-measures ANOVA for comparisons across the three states, pairedt-tests for direct two-state contrasts, post hoc analyses with Holm–Bonferroni corrections, and effect size estimatesη 2). Results: Immediately after training, significant decreases in SDNN (↓35%), RMSSD (↓40%), and pNN50 (↓55%), accompanied by increases in LF/HF (↑32%), were observed. DFAα1 and Recurrence Rate increased, indicating reduced complexity and more structured patterns of RR intervals. After two hours of recovery, partial normalization was observed; however, RMSSD (−18% vs. baseline) and HF (−21% vs. baseline) remained suppressed, suggesting incomplete recovery of parasympathetic activity. Indications of overtraining and cardiac risk were found in three athletes. Conclusion: High-intensity training in elite athletes induces pronounced acute autonomic changes and incomplete short-term recovery, potentially increasing fatigue and cardiovascular workload. Longitu- dinal repeated testing highlights differences between well-adapted, fatigued, and at-risk athletes. These findings highlight the need for individualized recovery strategies and ongoing monitoring to optimize adaptation and minimize the risk of overtraining and health complications. Keywords:sports performance monitoring; fractal analysis; recurrence quantification analysis (RQA); detrended fluctuation analysis; sample entropy; fatigue assessment; cardio- vascular stress; overtraining prevention Appl. Sci.2025,15, 10547 https://doi.org/10.3390/app151910547
Appl. Sci.2025,15, 10547 2 of 26 1. Introduction Heart rate variability (HRV) refers to the dynamic fluctuations in the time intervals between consecutive heartbeats [1,2], reflecting the organism’s ability to adapt to internal and external stressors. Higher HRV values are generally associated with greater physiolog- ical resilience and endurance [3]. Short, standardized resting measurements (five-minute recordings) using ECG devices are widely employed as reliable indicators for assessing the body’s responses to physical exertion [4,5]. Heart rate regulation is mediated by the autonomic nervous system (ANS) [6], where the sympathetic branch increases heart rate (HR), while the parasympathetic branch exerts the opposite effect. This interplay results in characteristic psychophysiological changes in HRV [3]. Monitoring HRV provides valuable information on the balance between the two branches of the ANS [7,8]. Previous research has demonstrated that HRV is sensitive to multiple physiological and environmental influences. Dietary factors such as omega-3 fatty acids, polyphenols, and overall nutritional status can modulate HRV both acutely and more chronically [9–11]. HRV is also a validated marker of stress and training status [12]. Furthermore, both ambient temperature [13,14] and body temperature can alter HRV indices [15,16]. During intense physical exercise, the activity of the sympathetic branch predomi- nates, and it has been proven that HRV reflects adaptations to intense physical activity and training load in athletes [17]. The nervous system’s goal is to cope with the stress induced by exercise, which triggers a mechanism to reduce parasympathetic activity, lead- ing to a decrease in many HRV indices, which defines HRV as a measure for optimizing individualized training [18]. Monitoring HRV over time provides insights into autonomic balance and recovery—particularly important in athletes, where training-induced fatigue and adap- tation are critical for managing the training process. For example, endurance athletes typically exhibit faster parasympathetic tone recovery, reflecting better adaptation and fitness level [19]. In athletes, sport-specific changes in ANS regulation can lead to signifi- cant HRV variations [3,20], allowing precise management of training-induced fatigue and fine-tuning of exercise intensity. Bellenger et al., 2021 reports that following functional overreaching, post-exercise RMSSD may increase, potentially reflecting compensatory recovery or even persistent
athletes typically exhibit faster parasympathetic tone recovery, reflecting better adaptation and fitness level [19]. In athletes, sport-specific changes in ANS regulation can lead to signifi- cant HRV variations [3,20], allowing precise management of training-induced fatigue and fine-tuning of exercise intensity. Bellenger et al., 2021 reports that following functional overreaching, post-exercise RMSSD may increase, potentially reflecting compensatory recovery or even persistent fatigue [21]. Such observations facilitate the implementation of strategies for individualized training plans [22–24], competition planning, optimization of training/rest schedules, and overall enhancement of athletic performance. Previous studies have shown that HRV decreases during exercise in proportion to workload intensity, with moderate levels leading to a stable reduction and higher intensities causing minimal additional changes. This can complicate the interpretation of HRV metrics under typical training conditions [25–27]. Nonlinear indices, such as DFAα1 (~0.75), have been proposed to guide exercise intensity regulation [28,29]. Post-exercise HRV recovery generally occurs within one to two hours; however, higher intensity or workload can prolong HRV suppression, even up to 24 h [30–32]. Post-exercise metrics, such as RMSSD, have been associated with improvements in anaerobic endurance [33], while nocturnal HRV monitoring correlates with post-exercise autonomic depression [34]. Tracking individual responses to training loads enables more precise management of the training process and performance prediction, with some evidence suggesting that higher training volumes, rather than intensity, may yield superior adaptations [35]. Although HRV research has primarily focused on endurance sports, there is growing evidence of its applicability in resistance and functional training, where it can support load optimization and recovery [36]. For instance, Sánchez et al. (2024) examined psychological adaptation, training effects, individual differences, recovery, and sleep quality in basketball athletes
Appl. Sci.2025,15, 10547 3 of 26 using HRV [37]. The authors concluded that HRV represents a reliable tool for tailoring training programs to enhance athletes’ performance and development. According to a study on HRV in athletes [38], athletes exhibit lower resting heart rates and higher HRV, indicative of enhanced autonomic regulation. The study highlights critical methodological factors that influence measurements, including body position, time of day, recording duration, baseline heart rate, sex, and age of the subjects. The authors also examine the effects of training intensity, volume, modality, and lifestyle on HRV. Turcu et al. (2023) discussed the use of HRV during or post exercise, as well as its potential for predicting physiological status [39]. During the competitive season, high-intensity training cycles occur that can induce acute autonomic disturbance and, when repeated, may lead to cumulative fatigue, over- training, or potential cardiovascular risk. While the acute autonomic response to single training sessions has been described, less is known about how these responses evolve when athletes are repeatedly exposed to prolonged high-intensity load. Heart rate variability (HRV) analysis, including nonlinear approaches such as detrended fluctuation analysis (DFA) and recurrence quantification analysis (RQA), provides sensitive markers of both acute perturbations and adaptive versus maladaptive trends. HRV is widely used to monitor the autonomic response to exercise, but most available data are from cross-sectional designs or short protocols (pre→post within a single session), often limited to linear measures. Nonlinear and recurrent metrics that capture changes in the structural complexity of the rhythm are less frequently applied in long-term, field-based observations of athletes. At the same time, the early recovery window after exercise (up to 1–2 h) and its incomplete normalization remain poorly characterized on a longitudinal basis. To address these shortcomings, we conducted a 4-month observation with multi- ple measurements before, immediately after, and 2 h after exercise in elite athletes. We combined time- and frequency-based HRV indices with fractal (DFAα1, Hurst, SampEn) and RQA metrics (REC, DET, LAM, TT), which allowed for the differentiation of adaptive versus maladaptive recovery patterns. We show that despite partial normalization by 2 h, sensitive nonlinear and
a 4-month observation with multi- ple measurements before, immediately after, and 2 h after exercise in elite athletes. We combined time- and frequency-based HRV indices with fractal (DFAα1, Hurst, SampEn) and RQA metrics (REC, DET, LAM, TT), which allowed for the differentiation of adaptive versus maladaptive recovery patterns. We show that despite partial normalization by 2 h, sensitive nonlinear and recurrent measures remain aberrant in fatigued athletes (Group 2), and a small subgroup (Group 3) exhibits atypical and potentially risky profiles. Thus, we propose an integrated longitudinal framework for monitoring autonomic regulation and operational decision-making for exercise and recovery. Based on these considerations, our study was guided by the following research ques- tion: What are the acute and short-term recovery dynamics of autonomic regulation in elite athletes undergoing prolonged high-intensity training, and how can nonlinear HRV metrics differentiate between adaptive, maladaptive, and potentially pathological responses over repeated sessions? We hypothesized that while acute autonomic perturbations immediately after training would be present in all athletes, longitudinal monitoring across a four-month period would reveal group-specific differences: well-adapted athletes would restore HRV complexity within two hours, fatigued athletes would show cumulative suppression of nonlinear indices, and a small subgroup would present deviant patterns suggestive of cardiovascular risk. Therefore, the purpose of the present study was to investigate changes in autonomic regulation before, immediately after, and two hours after training sessions, and to assess how repeated measurements over a 4-month period can differentiate between well-adapted, fatigued, and at-risk athletes. In addition, the study explored whether unexpected patterns could emerge, potentially revealing clinical relevance beyond training adaptation.
Appl. Sci.2025,15, 10547 4 of 26 2. Materials and Methods 2.1. Participants and Protocol 2.1.1. Participants Twelve male elite track and field athletes participated in this study. ECG Holter monitoring was performed five days per week, before and after training sessions, over a 4-month period. The specific disciplines of the studied athletes are 200 m sprint, 400 m and long jump; they are from the same sports club (Veliko Tarnovo, Bulgaria) and perform the same training program, which is a decision of the sports club management. The research was conducted in one of the five athletics clubs in the city of Veliko Tarnovo. The observation period is from 1 March 2025 to 30 June 2025. The athletes were aged between 19 and 26 years (mean±SD: 21±2.22 years), with an average height of 1.94±0.28 m. Recordings were obtained immediately before training, immediately after training, and two hours post-training. Inclusion criteria were age over 18 years, normal health status, and absence of cardio- vascular diseases. Participants were instructed to avoid alcohol, caffeine, and smoking for at least 8 h prior to training. All athletes received detailed information regarding the study protocol and provided written informed consent. Participation was voluntary and did not confer any benefits. All athletes were elite competitors participating in national-level competitions. Exclusion criteria: athletes who did not attend training regularly and for this reason, all records were not made for them during the 4-month study period (9 athletes from the initial 21 athletes who started the study). Initially, 21 athletes were included in the study (out of a total of 32 in the research team, who were informed about the objectives of the study and the conditions for its conduct, 6 did not consent due to restrictions on non-consumption of coffee and cigarettes, and 5 are under 18 years of age and do not meet the inclusion criteria); 9 athletes from the initial 21 athletes did not attend training regularly and, accordingly, regular records were not made for them, which is why we excluded them from the study. 2.1.2. Procedure The HRV recordings were carried out during regular training
of coffee and cigarettes, and 5 are under 18 years of age and do not meet the inclusion criteria); 9 athletes from the initial 21 athletes did not attend training regularly and, accordingly, regular records were not made for them, which is why we excluded them from the study. 2.1.2. Procedure The HRV recordings were carried out during regular training sessions under usual training conditions, in order to capture realistic physiological responses to high-intensity exercise. Prior to each measurement, athletes rested in a seated position for at least 10 min to stabilize cardiovascular parameters. They were instructed to avoid caffeine, alcohol, and strenuous exercise for 12 h before testing. Breathing was spontaneous and not exter- nally paced, reflecting natural conditions during training. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Ethics Committee of the Institute of Robotics at Bulgarian Academy of Sciences (protocol code 6 and date: 2 November 2024). Written informed consent was obtained from all participants. A 3-channel Holter ECG system with five electrodes was used for data acquisition. Each ECG recording lasted 10 min and utilized the standard Lead II configuration. Electrode placement was as follows: RA (right clavicle), LA (left clavicle), RL (right lower thorax, ground), LL (left lower thorax), and C (precordial position V5). All ECG Holter devices were applied by the same previously trained research associate (trained by a cardiologist and a sports medicine physician). The recordings were monitored on site by the research team, who ensured compliance with the protocol. Data preprocessing and HRV analysis were performed independently by two researchers with experience in HRV analysis using Kubios software (HRV Scientific 4.1.0) and Python scripts created by the authors. The independent results obtained from the two analyses were compared and it was found that the values obtained were consistent to the second decimal place. All athletes
Appl. Sci.2025,15, 10547 5 of 26 participating in the study performed the same training in terms of duration, sequence of exercises, and intensity. All measurements were performed at the same time of day (10 min before the start of the training session, which started at 4:00 p.m. and 10 min after the end of the training session, which usually ended at 6:00 p.m.). Before each Holter recording, the athletes were asked to rest in a sitting position for 10 min. All participants were young elite athletes of similar age and training status, ensuring a homogeneous sample. Participants were also instructed to refrain from caffeine and alcohol consumption on the day of testing. This minimized confounding factors, and this standardization ensured comparable baseline conditions for all participants by stabilizing heart rate and autonomic tone and reducing variability related to postural changes or residual physical activity. We can assume that there was no influence of circadian effects, since all participating athletes performed their training at the same time of day each time. The same researcher applied the electrodes to the Holter device and two independent analysts processed all files to ensure methodological consistency. The athletes’ training session consisted of a full cycle included the following compo- nents: 5 min of warm-up, 20 min of preparatory running over 400 m, 15 min of tempo running until reaching 160 bpm, and 40 min of athletics-specific exercises using equipment. 2.2. Methods The Holter system allowed continuous ECG recording, from which RR intervals were directly extracted. These time series were analyzed to assess HRV using custom Python scripts, with standard analysis performed on 5-min segments from the Holter recordings [40,41]. To ensure validity, the outputs were cross-checked against Kubios HRV Scientific (v4.1.0, Kubios Oy, Kuopio, Finland), with identical results obtained for all standard parameters. This verification supports the reproducibility and methodological reliability of the custom analysis procedures. Segment length and selection. Each condition (pre-, post-, and 2-h recovery) was recorded for ~10 min in seated rest. HRV indices were computed from a 5-min segment taken from minute 3 to minute 8 of each recording. This choice
with identical results obtained for all standard parameters. This verification supports the reproducibility and methodological reliability of the custom analysis procedures. Segment length and selection. Each condition (pre-, post-, and 2-h recovery) was recorded for ~10 min in seated rest. HRV indices were computed from a 5-min segment taken from minute 3 to minute 8 of each recording. This choice was made to bypass initial stabilization and potential start-up noise, minimize movement or electrode-settling artifacts at the beginning, and capture a steady-state breathing pattern and autonomic tone. When a recording was slightly shorter than 10 min, the 5-min segment was centered on the middle of the trace. As a robustness test, we repeated the analyses on adjacent 5-min windows (minutes 2–7 and 4–9), which produced virtually identical effect sizes and did not alter statistical inferences. The following methods were applied [42]: Time- and Frequency-Domain Methods: • Mean RR (ms): Represents the average duration between two consecutive R-peaks in the ECG (RR intervals). A reduction in Mean RR (increased heart rate) after training indicates sympathetic activation and physiological stress. Prolonged decreases during recovery may reflect incomplete recovery or overtraining. • SDNN (ms): Standard deviation of all NN intervals, representing overall HRV and reflecting both sympathetic and parasympathetic contributions. A sharp post-training decrease may indicate increased load, while slow recovery suggests stress and limited autonomic reserve. • RMSSD (ms): Root mean square of successive differences between NN intervals. A key marker of parasympathetic (vagal) activity. Reduced RMSSD after exercise indicates suppression of parasympathetic activity [43], commonly observed under acute stress. •High-Frequency (HF) Power: Reflects parasympathetic tone, whereas Low-Frequency (LF) power is considered a marker of sympathetic activity [44,45].
Appl. Sci.2025,15, 10547 6 of 26 • LF/HF Ratio: Ratio of LF to HF components, used as an index of sympathovagal bal- ance. An increased LF/HF post-exercise indicates sympathetic dominance; persistently high values suggest stress and insufficient recovery. Nonlinear Methods: • Detrended Fluctuation Analysis (DFAα1,α2) [46]: DFA was applied to RR interval time series to quantify long-term correlations and assess heart rate dynamics. The scaling exponentαwas calculated for short-term (α1) and long-term (α2) intervals to evaluate autonomic regulation. DFA provides insights into the fractal properties of HRV, complementing traditional time- and frequency-domain metrics. Short-term (α1) and long-term (α2) scaling exponents were calculated by plotting the log-log relationship between root-mean-square fluctuationF(n) and window sizen, withα1 derived from small windows andα2 from larger windows. • DFAα1: Measures the short-term fractal structure of heart rate. It is sensitive to changes in autonomic regulation during exercise. Elevated post-exercise values indi- cate loss of “physiological complexity” and increased sympathetic control. • DFAα2: Reflects the long-term fractal structure of HRV. Reduced values are associated with accumulated fatigue, overtraining, and increased stress, while excessively high values may indicate decreased adaptability of autonomic regulation. Fractal Methods: • Sample Entropy (SampEn): Provides an estimate of the complexity and unpredictabil- ity of the heart rate time series [47]. A decrease in SampEn after exercise indicates a more predictable and monotonous rhythm, reflecting physiological stress. Persistently low values during recovery may indicate high levels of fatigue. • Hurst Exponent (H): Measures long-term correlations in the RR interval time series, allowing assessment of persistent trends and overall physiological adaptation. A shift of the Hurst exponent toward or below 0.5 indicates a more chaotic rhythm, associated with fatigue and reduced physiological adaptability. For nonlinear HRV indicators (such as DFAα1, Hurst exponent, SampEn) there are no established and generally accepted reference values in clinical or sports practice. There are individual publications in the literature with indicative data in healthy people or athletes, but there is a lack of consensus and standardization. Therefore, our study makes its contribution in this direction. The literature review shows that DFAα1 values close to 1.0 are regarded as physiologically
are no established and generally accepted reference values in clinical or sports practice. There are individual publications in the literature with indicative data in healthy people or athletes, but there is a lack of consensus and standardization. Therefore, our study makes its contribution in this direction. The literature review shows that DFAα1 values close to 1.0 are regarded as physiologically normal, whereas values <0.75 indicate reduced complexity and sympathetic dominance [29,30]. The Hurst exponent is typically between 0.6 and 0.9 in healthy individuals, with lower values suggesting anti-correlated, less adaptive dynamics [48,49]. Sample entropy is very rarely studied and there is almost no specific data [50]. Recurrence Quantification Analysis [51–54]: Recurrence Plot Analysis Recurrence plots (RPs) are a nonlinear data analysis method used to visualize and quantify the recurrence of states within a dynamical system. In this study, RPs were applied to the time series of RR intervals to detect changes in cardiac dynamics induced by physical exercise. An RP is a two-dimensional representation in which both axes correspond to time indicesiandjof the same signal. Each point (i,j) in the plot is marked when the distance between two state vectorsx iandx jis less than a predefined thresholdε: R i,j=Θ(ε− x i−x j ˙ (1) where
Description
The study assesses autonomic regulation changes in athletes after high-intensity training.