Time Decay in the Performance Benefits From Repeated-Sprint Training in Hypoxia in World-Class Short-Track Speed Skaters

Repeated Sprint Training in Hypoxia Improves Repeated Sprint Ability to Exhaustion Similarly in Active Males and Females

PURPOSE

The aim of this study was to compare the physiological adaptations of males and females to repeated sprint training in hypoxia (RSH).

METHODS

Active males and females completed 7 wk of repeated sprint training in normoxia (RSN; F i O 2 = 0.209, males: n = 11, females: n = 8) or RSH (F i O 2 = 0.146, males: n = 12, females: n = 10). Before (Pre-) and after (Post-) training, a repeated sprint ability (RSA) test was performed (10-s cycle sprints with 20-s recovery between sprints, until exhaustion), and aerobic and anaerobic qualities were evaluated in normoxia.

RESULTS

The number of sprints during RSA increased after training in HYP from 11 to 21 in males and from 8 to 14 in females ( P < 0.001, 95% confidence interval = 5-11), without significant changes after RSN (10 vs 14 and 8 vs 10 in males and females, respectively). No improvements in mean or peak power output were found in either group. Total work during RSA improved after training in all groups (+9 ± 2 kJ, P < 0.001). Tissue saturation index during the repeated sprints was higher in females than males (+10% ± 2%, P < 0.001). The difference in tissue saturation index between the recovery and sprint phases remained unchanged after training. O 2 peak during an incremental exercise test increased in all groups (+3 ± 1 mL·kg -1 ·min -1 , P = 0.039). Mean power output during a Wingate test also increased in both males and females in RSN and RSH (+0.38 ± 0.18 W·kg -1 , P = 0.036). No changes were observed in hematological parameters after training.

CONCLUSIONS

Seven weeks of RSH further increased the number of repeated sprints performed to exhaustion compared with RSN in females, in the same order of magnitude as in males.

Enhancing Short Track Speed Skating Performance through Improved DDQN Tactical Decision Model

This paper studies the tactical decision-making model of short track speed skating based on deep reinforcement learning, so as to improve the competitive performance of corresponding short track speed skaters. Short track speed skating, a traditional discipline in the Winter Olympics since its establishment in 1988, has consistently garnered attention. As artificial intelligence continues to advance, the utilization of deep learning methods to enhance athletes' tactical decision-making capabilities has become increasingly prevalent. Traditional tactical decision techniques often rely on the experience and knowledge of coaches and video analysis methods that require a lot of time and effort. Consequently, this study proposes a scientific simulation environment for short track speed skating, that accurately simulates the physical attributes of the venue, the physiological fitness of the athletes, and the rules of the competition. The Double Deep Q-Network (DDQN) model is enhanced and utilized, with improvements to the reward function and the distinct description of four tactics. This enables agents to learn optimal tactical decisions in various competitive states with a simulation environment. Experimental results demonstrate that this approach effectively enhances the competition performance and physiological fitness allocation of short track speed skaters.