Why Train Together When Racing Is Performed Alone?:Drafting in Long-Track Speed Skating

PURPOSE: In long-track speed skating, drafting is a commonly used phenomenon in training; however, it is not allowed in time-trial races. In speed skating, limited research is available on the physical and psychological impact of drafting. The aim of this study was to determine the influence of "skating alone," "leading," or "drafting" on physical intensity (heart rate and blood lactate) and perceived intensity (perceived exertion) of speed skaters.METHODS: Twenty-two national-level long-track speed skaters with a mean age of 19.3 (2.6) years skated 5 laps, with similar external intensity in 3 different conditions: skating alone, leading, or drafting. Repeated-measures analysis of variance showed differences between the 3 conditions, heart rate (F2,36 = 10.546, P &lt; .001), lactate (F2,36 = 12.711, P &lt; .001), and rating of perceived exertion (F2,36 = 5.759, P &lt; .01).RESULTS: Heart rate and lactate concentration were significantly lower (P &lt; .001) when drafting compared with leading (heart rate Δ = 7 [8] beats·min-1, 4.0% [4.7%]; lactate Δ = 2.3 [2.3] mmol/L, 28.2% [29.9%]) or skating alone (heart rate Δ = 8 [7.1] beats·min-1, 4.6% [3.9%]; lactate Δ = 2.8 [2.5] mmol/L, 33.6% [23.6%]). Rating of perceived exertion was significantly lower (P &lt; .01) when drafting (Δ = 0.8 [1.0], 16.5% [20.9%]) or leading (Δ = 0.5 [0.9], 7.7% [20.5%]) versus skating alone.CONCLUSIONS: With similar external intensity, physical intensity, as well as perceived intensity, is reduced when drafting in comparison with skating alone. A key finding of this study is the psychological effect: Skating alone was shown to be more demanding than leading, whereas leading and drafting were perceived to be similar in terms of perceived exertion. Knowledge about the reduction of internal intensity for a drafting skater compared with leading or skating alone can be used by coaches and trainers to optimize training conditions.</p

A New Submaximal Rowing Test to Predict 2,000-m Rowing Ergometer Performance

Otter, RTA, Brink, MS, Lamberts, RP, and Lemmink, KAPM. A new submaximal rowing test to predict 2,000-m rowing ergometer performance. J Strength Cond Res 29(9): 2426-2433, 2015-The purpose of this study was to assess predictive value of a new submaximal rowing test (SmRT) on 2,000-m ergometer rowing time-trial performance in competitive rowers. In addition, the reliability of the SmRT was investigated. Twenty-four competitive male rowers participated in this study. After determining individual HRmax, all rowers performed an SmRT followed by a 2,000-m rowing ergometer time trial. In addition, the SmRT was performed 4 times (2 days in between) to determine the reliability. The SmRT consists of two 6-minute stages of rowing at 70 and 80% HRmax, followed by a 3-minute stage at 90% HRmax. Power was captured during the 3 stages, and 60 seconds of heart rate recovery (HRR60s) was measured directly after the third stage. Results showed that predictive value of power during the SmRT on 2,000-m rowing time also increased with stages. CVTEE% is 2.4, 1.9, and 1.3%. Pearson correlations (95% confidence interval [95% CI]) were -0.73 (-0.88 to -0.45), -0.80 (-0.94 to -0.67), and -0.93 (-0.97 to -0.84). 2,000-m rowing time and HRR60s showed no relationship. Reliability of power during the SmRT improved with the increasing intensity of the stages. The coefficient of variation (CVTEM%) was 9.2, 5.6, and 0.4%. Intraclass correlation coefficients (ICC) and 95% CI were 0.91 (0.78-0.97), 0.92 (0.81-0.97), and 0.99 (0.97-1.00). The CVTEM% and ICC of HRR60s were 8.1% and 0.93 (0.82-0.98). In conclusion, the data of this study shows that the SmRT is a reliable test that it is able to accurately predict 2,000-m rowing time on an ergometer. The SmRT is a practical and valuable submaximal test for rowers, which can potentially assist with monitoring, fine-tuning and optimizing training prescription in rowers

Introducing a Method to Quantify the Specificity of Training for Races in Speed Skating

Roete, AJ, Stoter, IK, Lamberts, RP, Elferink-Gemser, MT, and Otter, RTA. Introducing a method to quantify the specificity of training for races in speed skating. J Strength Cond Res 36(7): 1998-2004, 2022-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 center dot 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

Capturing the Complex Relationship Between Internal and External Training Load: A Data-Driven Approach

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

Introducing a Method to Quantify the Specificity of Training for Races in Speed Skating

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

Co-Operative Design of a Coach Dashboard for Training Monitoring and Feedback

Athlete development depends on many factors that need to be balanced by the coach. The amount of data collected grows with the development of sensor technology. To make data-informed decisions for training prescription of their athletes, coaches could be supported by feedback through a coach dashboard. The aim of this paper is to describe the design of a coach dashboard based on scientific knowledge, user requirements, and (sensor) data to support decision making of coaches for athlete development in cyclic sports. The design process involved collaboration with coaches, embedded scientists, researchers, and IT professionals. A classic design thinking process was used to structure the research activities in five phases: empathise, define, ideate, prototype, and test phases. To understand the user requirements of coaches, a survey (n = 38), interviews (n = 8) and focus-group sessions (n = 4) were held. Design principles were adopted into mock-ups, prototypes, and the final coach dashboard. Designing a coach dashboard using the co-operative research design helped to gain deep insights into the specific user requirements of coaches in their daily training practice. Integrating these requirements, scientific knowledge, and functionalities in the final coach dashboard allows the coach to make data-informed decisions on training prescription and optimise athlete development