## 2 Materials and Methods  
The study participant is a top-level athlete and a Slovenian national kayak slalom team member. She competes in women's kayak slalom and kayak cross (K1W and WCSLX) and follows a training regimen of 693 \(\pm\) 22 h per year. During the study period, she was between 25 and 29 years old. Her anthropometric and physiological characteristics were as follows: body height, 169.6 cm; body weight, 63.2 \(\pm\) 0.26 kg; body fat percentage, 12.1 \(\pm\) 0.84%; muscle body mass percentage, 49.1 \(\pm\) 0.56%; BMI, 22 \(\pm\) 0.07; VO\({}_{2}\) max, 58.06 \(\pm\) 2.95 mL-min\({}^{-1}\).kg\({}^{-1}\). HRV was measured using an iPhone 8 and an iPhone X (Apple Inc., Cupertino, CA, USA) and the HRV4 Training app (A.S.M.A. B.V., Aa en Hunze, The Netherlands). HRV4 Training is a commercially available mobile app that is enabled on devices with photoplethysmography (PPG) technology, which allows for non-invasive measurements of HRV and HR at rest [8,30]. Sleep duration was monitored with Apple Watch Series 4 and Series 6 smartwatches. (Apple Inc., Cupertino, CA, USA) [31,32]. Performance tests were conducted on a cycle ergometer twice per year, beginning with an initial load of 60 W and increasing by 15 W every minute until the criteria for determining the maximal oxygen uptake (VO\({}_{2}\) max) were met (Cosmed, citta metropolitana, Italy). The body composition was evaluated using an InBody 720 device (InBody Co., Ltd., Seoul, Republic of Korea). The subject continuously monitored heart rate variability (HRV) from November 2018 to December 2022. Measurements were taken each morning while lying down after waking up. Each measurement lasted one minute, while spontaneous breathing was maintained throughout. At the end of each measurement, the subject completed a standardized questionnaire through a mobile app. The questionnaire assessed physiological and subjective factors, including sleep quality, potential illnesses, injuries, and menstrual cycle phases. Alongside these self-reported variables, we analyzed the impacts of sleep duration and training load on HRV [30]. The acute training load (ATL) was evaluated by multiplying the average training intensity (I), assessed through subjective ratings on the Borg CR-10 scale (0 = minimal effort; 10 = maximal effort), with the total weekly training volume (V), represented by the duration of training sessions. The equation derives from a version of the original [PERSON] equation for quantifying training impulses (TRIMPs), which considers RPE intensity ratings instead of heart rates [33]. \[\text{ATL}=\sum_{i}\frac{\text{V}_{i}\text{-I}_{i}}{\text{n}}\cdot\frac{\text {n}}{100}\sum_{i}\text{V}_{i}\]  
A performance index (PI) was calculated under the guidance of the International Canoe Federation (ICF) using results from international competitions to assess athletic performance. The PI formula includes competition quality, event significance, and achieved results. PI derives as the normalized sum of two indices (PI\({}_{1}\) and PI\({}_{2}\)): the first evaluates results based on the average of the top three overall performances, irrespective of gender or category, while the second explicitly assesses results relative to the average of the top three performances in the women's kayak category. This dual approach offers a more precise evaluation of performance, avoiding over- or underestimations due to smaller or less competitive participants in women's kayak events. \[\text{PI}=\text{PI}_{1}+\text{PI}_{2}\]  
\[\text{PI}_{1}=\frac{\text{phase factor x result (sec)}}{\text{average result of top 3 athletes}}-\text{phase factor}+\text{quality factor}\]  
\[\text{PI}_{2}=\frac{\text{phase factor x result (sec)}}{\text{average result of top 3 K1W}}-\text{phase factor}+\text{quality factor}\]  
Competition quality and event significance are directly related, with event significance increasing alongside the quality of competing athletes. The competition quality factor is calculated based on the average of the ICF ranking points of the top three competitors.