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The specificity of training for races is believed to be important for performance development. However, measuring specificity is challenging. This study aimed to develop a method to quantify the specificity of speed skating training for sprint races (i.e., 500 and 1,000 m), and explore the amount of training specificity with a pilot study. On-ice training and races of 10 subelite-to-elite speed skaters were analyzed during 1 season (i.e., 26 weeks). Intensity was mapped using 5 equal zones, between 4 m·s-1 to peak velocity and 50% to peak heart rate. Training specificity was defined as skating in the intensity zone most representative for the race for a similar period as during the race. During the season, eight 500 m races, seven 1,000 m races, and 509 training sessions were analyzed, of which 414 contained heart rate and 375 sessions contained velocity measures. Within-subject analyses were performed. During races, most time was spent in the highest intensity zone (Vz5 and HRz5). In training, the highest velocity zone Vz5 was reached 107 ± 28 times, with 9 ± 3 efforts (0.3 ± 0.1% training) long enough to be considered 500 m specific, 6 ± 5 efforts (0.3 ± 0.3% training) were considered 1,000 m specific. For heart rate, HRz5 was reached 151 ± 89 times in training, 43 ± 33 efforts (1.3 ± 0.9% training) were considered 500 m specific, and 36 ± 23 efforts (3.2 ± 1.7% training) were considered 1,000 m specific. This newly developed method enables the examination of training specificity so that coaches can control whether their intended specificity was reached. It also opens doors to further explore the impact of training specificity on performance development.
Background: Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling. Aim: Using training load data in speed skating, we evaluated how well bivariate KDE plots describe the coupling of internal and external load and differentiate between specific training sessions, compared to training impulse scores or intensity distribution into training zones. Methods: On-ice training sessions of 18 young (sub)elite speed skaters were monitored for velocity and heart rate during 2 consecutive seasons. Training session types were obtained from the coach’s training scheme, including endurance, interval, tempo, and sprint sessions. Differences in training load between session types were assessed using Kruskal–Wallis or Kolmogorov–Smirnov tests for training impulse and KDE scores, respectively. Results: Training impulse scores were not different between training session types, except for extensive endurance sessions. However, all training session types differed when comparing KDEs for heart rate and velocity (both P < .001). In addition, 2D KDE plots of heart rate and velocity provide detailed insights into the (subtle differences in) coupling of internal and external training load that could not be obtained by 2D plots using training zones. Conclusion: 2D KDE plots provide a valuable tool to visualize and inform coaches on the (subtle differences in) coupling of internal and external training load for training sessions. This will help coaches design better training schemes aiming at desired training adaptations.