Competitive swimming events include different distances, from 50 m to 1,500 m. The energy required to swim these distances is supplied by the aerobic and anaerobic systems; the relative contributions of each energy system vary depending on the duration of each race (1). Therefore, improvements in both energy systems are essential to enhance swimming performance, and effective monitoring is key to assessing the impact of training and achieving successful performance (2).

Several reliable parameters are available to monitor aerobic capacity in swimmers, including lactate threshold (3, 4), onset of blood lactate accumulation (5), and critical velocity (4, 6). Measurement of maximal accumulated oxygen uptake (MAOD) is widely used to assess anaerobic capacity in sports and is also used in swimming (7–9). However, MAOD is a time-consuming procedure to measure, and it may not be a fully defensible method to determine anaerobic capacity (10). Therefore, alternative practical methods and indices have been explored (11, 12).

The maximal lactate accumulation rate (ċLa_max) is also an index to access anaerobic capacity. ċLa_max is calculated by dividing the maximum difference in blood lactate (Bla) before and after a maximal sprint by the sprint time, which is reportedly a parameter to evaluate the athlete's glycolytic power (13). Due to its applicability in the training setting, ċLa_max regained popularity and was investigated recently in cycling (14, 15), running (16), and rowing exercises (17). In addition, Quittmann et al. (16) investigated ċLa_max during 100 m sprint running and reported that ċLa_max was significantly correlated with sprint performance parameters, suggesting that it can be used as a sport-specific field test. ċLa_max reportedly differs between handcycling and cycling (15) or cycling and running (18) even when investigated in the same individual, so it has been suggested that ċLa_max is an extremity and movement-specific parameter.

ċLa_max has also been investigated in swimming (19). Sperlich et al. (20) reported that ċLa_max increases with high intensity interval training and decreases with high volume training, and that ċLa_max is sensitive to detecting the change of anaerobic capacity in swimmers. Studies on the relationship between ċLa_max and sprint performance in swimming have yet to reach consensus. Mavroudi et al. (21) reported a significant correlation between 50 m sprint performance and ċLa_max which was investigated at that sprint. On the other hand, Kellar and Wahl (22) reported no significant correlation between ċLa_max assessed by a 20-s sprint test and the average velocity of a 50 m official race. Thus, it is still being determined whether ċLa_max is a genuinely valid measure that can assess anaerobic capacity in swimmers.

Mechanical power is one of the factors that determines maximal swimming velocity (23). Even a semi-tethered swimming test cannot evaluate pure propulsive power, Gonjo et al. (24) reported that the index of the load-velocity profile investigated by semi-tethered swimming can be used to evaluate sprint swimming performance. Their findings revealed that theoretical maximal velocity (V₀), maximal load (L₀), and load-velocity slope were significantly correlated with the mean velocity of a 50 m front crawl trial. Given the potential of these indices as indicators of sprinting ability, understanding the relationship between ċLa_max and these parameters could shed light on whether pure physiological capacity is related to sprinting ability in swimming. If glycolytic power is indeed related to sprint performance parameters, this approach would allow for a more comprehensive understanding of sprint swimming performance, combining both mechanical and physiological aspects.

The purpose of the present study was to investigate the relationship between ċLa_max and parameters related to sprint performance in male competitive swimmers. The primary hypothesis of this study was that ċLa_max negatively correlates with 50 m race time. Secondly, we hypothesized that ċLa_max positively correlates with parameters related to sprint performance (such as V₀ and L₀) estimated by the load-velocity profile. By addressing these research objectives, we provide valuable insights into the use of ċLa_max as an index of anaerobic capacity in competitive swimming.

The measured parameters are shown in Table 2. The average time of the 20 m sprint for the ċLa_max measurement was 11.5 ± 0.4 s, La_pre was 1.6 ± 0.4 mmol/L, La_max was 6.8 ± 1.2 mmol/L, tLa_max was 1.8 ± 0.8 min, and ċLa_max was 0.63 ± 0.14 mmol/L/s. ICC of the twice-measured ċLa_max was 0.913 (95% confidence interval: 0.724–0.976, p < .01). ċLa_max showed significant correlation with t50 (r = −.546, p < .05) and the section time of 0–15 m (r = −.627, p < .01), 15–25 m (r = −.618, p < .01), 25–35 m (r = −.604, p < .05) of the trial. L₀ (r = .837, p < .01), rL₀ (r = .820, p < .01), and the slope (r = .804, p < .01) obtained from the load-velocity profile also significantly correlated with ċLa_max.

The present study explored the relationship between ċLa_max and factors associated with sprint performance in male competitive swimmers. The main findings of this study were that ċLa_max is significantly correlated with 50 m front crawl performance in male swimmers and that a higher glycolytic power contributes to a faster time at the beginning of the sprint race. We were also able to clarify that ċLa_max is significantly related to the theoretical maximal load a swimmer can pull with front crawl swimming.

The time taken for the 20 m sprint to measure ċLa_max was 11.5 ± 0.4 s, which falls within the recommended range of 10–15 s for this parameter (19). In a study by Langley et al. (37), it was found that the 10-s sprint duration produced the highest ċLa_max value when compared to the 15-s and 30-s sprints in cycling tests. Mavroudi et al. (21) also reported higher ċLa_max values when investigating shorter sprints in swimming. Additionally, Quittmann et al. (16) suggested that investigating ċLa_max based on a fixed distance instead of a fixed duration protocol may be more appropriate in field-based settings if the duration variation is minimal. Overall, the 20 m sprint utilized in this study was appropriate and applicable for assessing a swimmer's highest glycolytic power. The ċLa_max value calculated in this study was 0.63 ± 0.14 mmol/L/s, which is similar to the values reported by Sperlich et al. (20) and Mavroudi et al. (21) of 0.64 ± 0.29 and 0.75 ± 0.18 mmol/L/s, respectively. However, in research with young female swimmers, Kellar and Wahl (22) found a lower value of 0.38 ± 0.11 mmol/L/s in their research with young female swimmers. It is worth noting that although ċLa_max was assessed using different methods in these studies, it seems that the value is lower in females compared to males, possibly due to differences in muscle mass (38), which could affect lactate production during exercise (39). Generally, female swimmers tend to have longer completion times for a 20 m sprint compared to male swimmers. Additionally, if lactate production is lower in females, ċLamax values will tend to be lower due to the formula used to calculate this parameter (Equation 1). These factors suggest that the relationship between ċLamax and performance may be gender-specific.

The reliability of the ċLa_max was “excellent” when measured twice within 4 days (ICC = 0.913). This ICC value was slightly higher than those reported in previous investigations on cycling [ICC = 0.904, (40)], running [ICC = 0.907, (16)], and rowing [ICC = 0.85, (17)]. The accuracy and reliability of the Bla measurement device used in this study were evaluated as good according to the methods of Bonaventura et al. (26). It is worth noting that given the small volume of sampled blood (0.3 µl), we took the average Bla value assessed by the two devices to ensure a stable value. The measurement procedure used in this study clarified that ċLa_max could be investigated with high reliability.

Mavroudi et al. (21) studied eight male and four female swimmers and found that a 50 m sprint trial significantly correlated with ċLa_max measured during that swim; however, this result may be affected by including both genders. In the present study of male-only measurements and a 20 m sprint, we also found a significant correlation between ċLa_max and t50. On the other hand, Kellar and Wahl (22) investigated the relationship between ċLa_max and 50 m swim race performance but found no significant relationship. The differences in the measurement protocol may cause different results. Kellar and Wahl (22) investigated ċLa_max from a 20-s sprint test in which the duration is longer than the highest value reportedly observed. The sprint duration should be shorter to assess ċLa_max related to 50 m performance. Moreover, our measurement setting was defined by the fact that the short sprint was executed from a floating position. This execution eliminates the movement of the block start or wall push and the underwater phase, which is completely different from the regular swimming motion. Thus, this indicated that accurately assessing ċLa_max could be achieved by excluding factors other than swimming motion, allowing for the practical evaluation of glycolytic power during swimming. As a result, a 20 m sprint swim solely relying on the swimming motion is an effective way to measure ċLa_max, making it an important parameter to monitor during training.

When examining the relationship between ċLa_max and each section time of the 50 m trial, large correlations were observed at the 0–15 m, 15–25 m, and 25–35 m section times. The contribution ratio of aerobic and anaerobic metabolism is reportedly 30:70 in a 50 m race (1). Therefore, a higher glycolytic power is related to a faster time up to 35 m of the race, while aerobic capacity may be related to the later performance. Therefore, the present study confirmed that ċLa_max is a good performance indicator in the first half of a 50 m front crawl swim, suggesting that glycolytic power is crucial for 50 m sprint performance, and swimmers aiming to start fast in a 50 m race should focus on improving ċLa_max. Research has shown varied effects of different training modalities on ċLamax. Sperlich et al. (20) reported an increase in ċLa_max following high-intensity interval training in junior swimmers. Similarly, Nitzsche et al. (41) observed an increase in ċLa_max with resistance training in male strength-trained volunteers. However, contrasting results were found by Hommel et al. (14), who reported a decrease in ċLa_max after a six-week sprint interval cycling training program. These conflicting findings underscore the need for further research on training interventions specifically aimed at improving glycolytic power in swimmers.

ċLa_max correlated with L₀ and rL₀, indicating the connection between glycolytic power and the ability to exert force in the water. As Maglischo (2) highlighted, the limiting factors of a 50 m race performance are the stroke technique, the rate of anaerobic metabolism, and the amount of creatine phosphate in the working muscle fibers of a swimmer. Therefore, ċLa_max could evaluate the first two factors, making it a valuable parameter for monitoring the swimmer's sprint performance capacity throughout the training season. Coaches and athletes can use ċLa_max and aerobic capacity parameter to plan and prescribe training for performance improvement and to avoid nonfunctional overreaching (19, 29). Our study also proposed a practical procedure to evaluate ċLa_max in swimming.

A very large correlation was found between ċLa_max and L₀, rL₀, and the slope calculated from the load-velocity profile. L₀ represents the theoretical maximal load that swimmers can exert on the water (31). As active drag is approximately proportional to the cube of swimming velocity (42), swimmers need to overcome a large amount of drag during high-velocity swimming. Consequently, a high L₀ is one of the essential abilities for sprinters. rL₀ correlates significantly with 50 m front crawl performance (24). Therefore, the present results indicate that the pure capacity to produce power at a higher rate is related to the higher capacity to apply force to the water, which could be crucial for achieving high sprinting performance. Moreover, the significant correlation between ċLa_max and the load–velocity slope may be affected by the significant relationship between ċLa_max and L₀. Based on our findings, V₀ did not correlate with ċLa_max. As V₀ represents the theoretical maximal velocity, it may be closely associated with the contribution of the alactic energy system. Furthermore, Gonjo et al. (24) suggested that V₀ should not be used to predict the absolute free-swimming speed during a race due to the observed systematic bias between the average swimming velocity of the 50 m front crawl trial and V₀, which may have affected our results.

Beyond evaluating an athlete's glycolytic power, ċLa_max has potential applications in predicting individual pacing strategies. Quittmann et al. (43) suggested that this parameter could be used to predict pacing strategies in 5,000 m running, a concept that may be applicable to sprint swimming races. Swimmers with high ċLa_max values are likely to start races quickly and apply greater force to the water. For these athletes, coaches should focus on strategies that allow for a fast start while minimizing effort, to prevent fatigue at the end of the race. Conversely, swimmers with lower ċLa_max values should avoid an intense burst immediately after the breakout. Instead, they should focus on gradually increasing their effort towards the end of the race. By tailoring race strategies to individual ċLa_max profiles, coaches and swimmers can optimize performance and energy distribution throughout the race. Comparing the relationship between ċLa_max and race performance across multiple competitions may provide valuable insights into whether success or failure in a race is primarily due to physiological or technical factors. This approach could help coaches and athletes differentiate between improvements in anaerobic capacity and enhancements in swimming technique or race strategy. Furthermore, Morais et al. (44) reported that swimmer with faster critical speed was more likely to perform better in middle- or long-distance events than in sprints. By examining critical speed together, it may provide more detailed information to coaches and swimmers including evaluation of aerobic capacity. Longitudinal studies are warranted to fully clarify the practical applicability of ċLa_max in real-world competitive settings. Such research could reveal how this parameter changes over time with training and how these changes correlate with performance improvements. Additionally, it could help establish ċLa_max as a reliable tool for long-term athlete monitoring and performance prediction.

A limitation of the present study is that a constant value of 3 s was used for t_alac to calculate ċLamax. Yang et al. (45) reported that insights into the inter-individual differences in energy and glycolytic metabolism would be altered by using different t_alac values. An alternative procedure to assess t_alac is being explored (46). The actual time of t_alac likely varies among participants, which may influence the precision of ċLa_max calculations. If novel approaches to measure power output during swimming can be developed, it would enable a more accurate evaluation of ċLa_max. Such advancements in measurement techniques could potentially affect the results of the present study and provide more precise insights into the relationship between ċLa_max and swimming performance. However, it still seems complicated to establish a rational method to assess t_alac during swimming exercise; using a constant value may remain a practical procedure to investigate ċLa_max in this sport. Second, Affonso et al. (47) reported an extremely high Bla level of 12.1–17.4 mmol/L after a 5-s short sprint in an elite front crawl swimmer (World Aquatics point = 985). The high ċLa_max in this swimmer suggests that higher glycolytic power may be essential for achieving high sprint performance. However, it should be noted that this relationship may have been observed due to the swimmers having identical mechanical efficiencies. Toussaint (48) examined the propelling efficiency of well-trained competitive swimmers and triathletes, reporting values of 61 ± 6% for swimmers and 44 ± 3% for triathletes. This suggests that, even if two swimmers exert the same glycolytic power, a swimmer with lower mechanical efficiency will be slower. It is essential to consider the implications of our study when applied to swimmers at different performance levels. Several methods exist for investigating propulsive efficiency during sprint swimming, including the MAD (Measuring Active Drag) system (48) and pressure distribution analysis (49). By combining these propulsive efficiency evaluations with ċLa_max assessments, it would be possible to develop a more comprehensive profile of a swimmer's characteristics. This integrated approach could provide valuable information for planning future training strategies. Specifically, it would allow coaches and athletes to determine whether to focus on improving stroke mechanics or enhancing anaerobic power, based on individual strengths and weaknesses identified through these complementary assessments. Such a multifaceted evaluation strategy could lead to more targeted and effective training interventions, potentially optimizing performance improvements in sprint swimming.

The testing procedure of ċLa_max used in the present study was found to be suitable for training settings and to have high reliability. The study clarified that ċLa_max is a parameter associated with the 50 m front crawl performance in male swimmers, and a higher glycolytic power leads to faster times at the beginning of a sprint race. It also indicated that ċLa_max can assess a swimmer's ability to apply force to the water during high-intensity swimming. Therefore, ċLa_max is a good indicator of front crawl sprint performance for well-trained male swimmers.