# Monitoring of Training Load and Body Composition in Elite Male Kayakers
## 4 Discussion
After an initial dramatic increase, after approximately 1 min, blood lactate level during maximal efforts reaches a plateau above which there are no further increases. Contributing to the understanding of metabolic validation of blood lactate, a sharp increase in blood lactate levels within 3-4 s of effort among elite sprinters [18] was observed, whereas non-elite sprinters showed a less pronounced rise. The kinetics of lactate remains a dynamic and underexplored area in scientific research. The anaerobic threshold is an important tool for aerobic capacity assessment in training control despite the controversies it involves. A fixed blood lactate threshold of 4 mmol (V4) was chosen to evaluate aerobic endurance during incremental exercise. This threshold correlated well with the maximal lactate steady state although [PERSON] et al. [19] found that a fixed blood lactate of 5.4 mmol\(\cdot\)L\({}^{-1}\) instead of 4 mmol\(\cdot\)L\({}^{-1}\) is a better intensity plateau to achieve the maximal lactate steady state in kayakers. For training control purposes, a fixed value of 4 mmol\(\cdot\)L\({}^{-1}\) is more operative and allows comparison with other studies. In this study, aerobic capacity (V4) increased significantly between M1 and M2 and remained unaltered in M3. This assessment aligns with the planned adaptations throughout the season. Starting from M2, the training focus shifted from solely enhancing aerobic mechanisms to improving competition-specific endurance, emphasizing a significant contribution from anaerobic mechanisms. We acknowledge some shortcomings and potential limitations in our study. Firstly, the absence of a control group could be questioned. However, it is important to note that elite athletes represent a highly specific population with unique attributes. Comparing this group to a control group, particularly non-athletes or even non-elite athletes, would not yield meaningful results. Additionally, the availability of elite athletes is notably limited, making it challenging to establish a control group with comparable characteristics. Furthermore, elite athletes are often preparing for competitive events, and their participation in a control group could interfere with their training routines. In this context, many studies involving elite athletes use pre-intervention data from the same athletes as the control (e.g., M1 in our study). This approach eliminates the need to recruit a separate control group while maintaining the scientific validity of the study. Before implementing the intervention or following a training period, researchers collect baseline data on the athletes' performance or characteristics, which serve as the \"control\". After the intervention or follow-up period, new data are collected and compared to the baseline. Each athlete serves as their own control, eliminating interindividual differences, and ensuring that the results reflect the athletes' responses to a specific intervention or training program. However, these findings are highly specific to the evaluated athletes, potentially limiting their generalizability to other populations. Secondly, confounding factors such as diet, sleep, and stress were not explicitly controlled, which might have partially influenced the outcomes. Nonetheless, these athletes are part of a single national team and are closely monitored by their multidisciplinary staff, which suggests a high level of oversight regarding diet and sleep. Moreover, while this is a longitudinal study, it examined the training effects over part of a single competitive season, leaving it unclear how these changes might persist or evolve across multiple training seasons. Hence, our sample consists of elite athletes from a single national team. Recruiting athletes of this performance caliber is particularly challenging, which is why we opted for a convenient sample. Finally, we did not control the external training load proposed by the coach; we only recorded it.