## 1 Introduction  
The demand for top-level sports is continuously pushing beyond both physical and mental boundaries. With the advancement of scientific methods and technologies, new possibilities for monitoring athletes' performance are opening. One method that is gaining popularity among both elite and recreational athletes, thanks to its simplicity and accessibility, is heart rate variability (HRV) monitoring [1]. Monitoring HRV provides direct insights into the state of the autonomic nervous system, which is constantly influenced by various psychological and physiological stressors as an indicator of body homeostasis [2]. Additionally, activation of the sympathetic nervous system (SNS) increases HR (heart rate) and decreases HRV, whereas parasympathetic nervous (PNS) activity decreases HRV and increases HR [3]. The interplay between SNS and PNS activity is dynamic; for instance, during stress, a shift from PNS dominance to SNS activation occurs, affecting HRV patterns [4]. Technological developments in the last decade have allowed us to measure HRV using sports watches or smartphones that operate according to the photoplethysmography (PPG) principle [5, 6, 7, 8]. HRV is widely used in sports science and is a valuable tool for monitoringtraining responses, detecting signs of overtraining, and evaluating recovery processes [9]. The athlete's physiological response to exercise is influenced by a training stimulus, and a suitable training load promotes optimum performance improvements [10; 11]. Daily fluctuations in HRV are affected by different stressors and provide valuable insights into the body's current state, including training load, illness, travel, injury, and sleep quality. High-intensity exercise results in a 24-48 h decrease in HRV, while low-intensity exercise results in a 24 h increase in HRV [12; 13]. Several studies confirm that tailoring exercise to daily HRV values increases the effectiveness of an exercise program compared to a traditional approach. The HRV-guided exercise principle recommends low training intensity when HRV is reduced and higher intensity when it reaches typical values. This approach has led to more significant progress in endurance athletes [13; 14; 15]. The most suitable parameters for the general assessment of HRV are standard deviations of NN intervals (SDNNs) and the HRV index [16; 17], while the percentage of successive RR intervals (pNN50) and the root mean square of successive differences (rMSSDs) are most suitable for assessing short-term components of HRV. rMSSD is an HRV time-domain measure which quantifies the amount of variability in measurements of the interbeat interval, which is the time period between successive heartbeats [17]. Additionally, rMSSD is the primary time measure used to estimate the vagally mediated changes reflected in HRV. In sports, rMSSD is most used to monitor HRV, as it is a good indicator of parasympathetic modulation in short-duration measurements (10 s to 1 min) [10; 12]. In addition, rMSSD is a validated and most-used HRV measure in scientific research [10; 13; 18]. Additionally, it is less sensitive compared to respiratory rhythm, and therefore, the athlete can breathe spontaneously during the measurement [12; 19]. In a study performed by [PERSON] et al. [20] involving 138 elite athletes, the average rMSSD was found to be 71.8 ms, with a CI of 67.6 to 76.2 ms. However, the interpretation of HRV measurements is not always straightforward, as it is widely recognized that HRV varies during a training mesocycle based on the training load. Moderate exercise is linked to higher HRV, while intense training results in a decrease [21]. HRV in elite endurance athletes shows a distinct bell-shaped pattern throughout the training periodization cycle, with higher HRV noted during high-volume, low-intensity training phases, followed by a decrease during intensified training and pre-competition tapering [22].