Inge K. Stoter

Background: Long conjugate sequence (LCS) resistance training aims to maintain and increase strength and power to enhance sport-specific performance. This study examined (1) how strength and power change during alternating periods of an LCS program, and (2) the relationship between strength, power, and performance in long-track (LT) and short-track (ST) speed skating. Methods: Twenty-three speed skaters (sixteen men, seven women; age = 18.0 [17.0, 19.0], sub-elite to elite performance level) followed a 39-week LCS program alternating strength and power periods. Strength (Isometric Mid-Thigh Pull) and power (Squat Jump) were assessed after each period. Linear mixed-effects models were used to examine the effects of time (baseline and four follow-up assessments), sport (long-track vs. short-track), and sex (male vs. female) on absolute and relative measures of strength and power. Models included fixed effects for time, sport, and sex with all two-way interactions, a random intercept for participants, and a first-order autoregressive covariance structure to account for repeated measures. Model selection followed backward elimination guided primarily by the corrected Akaike Information Criterion (AICc). Kendall’s tau correlations tested associations between strength, power, and competition performances closest to assessments. Results: Change in absolute and relative strength across the season differed by sex (F_{(4, 33.57)} = 2.72, p = 0.046; F_{(4, 31.86)} = 3.50, p = 0.02), with an increase only in male skaters (baseline to Test 4: +406 N ± 115, p = 0.01; +4.37 N/kg ± 1.41, p = 0.03). Absolute power changed (F_{(4, 33.74)} = 3.81, p = 0.01) specifically in the early season (baseline to Test 1: +151 N ± 40, p < 0.01), while relative power remained stable (F_{(4, 53.05)} = 1.94, p = 0.12). Higher absolute strength and power correlated with better LT (n = 12, τ = −0.58–0.67) and ST (n = 9, τ = −0.56–0.89) performance. Yet, relative strength related only to ST performance (n = 9, τ = −0.78–0.89), and relative power chiefly to the first 100 m of a LT performance (n = 12, τ = −0.49). Conclusions: The LCS method is associated with strength development without compromising power. The findings highlight the relationship between resistance training-induced outcomes and speed skating performance.

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.

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.

The aim of this observational study was to examine the differences between training variables as intended by coaches and perceived by junior speed skaters and to explore how these relate to changes in stress and recovery. During a 4-week preparatory period, intended and perceived training intensity (RPE) and duration (min) were monitored for 2 coaches and their 23 speed skaters, respectively. The training load was calculated by multiplying RPE by duration. Changes in perceived stress and recovery were measured using RESTQ-sport questionnaires before and after 4 weeks. Results included 438 intended training sessions and 378 executed sessions of 14 speed skaters. A moderately higher intended (52:37 h) versus perceived duration (45:16 h) was found, as skaters performed fewer training sessions than anticipated (four sessions). Perceived training load was lower than intended for speed skating sessions (−532 ± 545 AU) and strength sessions (−1276 ± 530 AU) due to lower RPE scores for skating (−0.6 ± 0.7) or shorter and fewer training sessions for strength (−04:13 ± 02:06 hh:mm). All training and RESTQ-sport parameters showed large inter-individual variations. Differences between intended–perceived training variables showed large positive correlations with changes in RESTQ-sport, i.e., for the subscale’s success (r = 0.568), physical recovery (r = 0.575), self-regulation (r = 0.598), and personal accomplishment (r = 0.589). To conclude, speed skaters that approach or exceed the coach’s intended training variables demonstrated an increased perception of success, physical recovery, self-regulation, and personal accomplishment.

The Netherlands is highly successful in long track speed skating. To understand how a small country can be great at the international level, we describe how the Dutch organize their talent development programs. We examine how studies based on the Groningen Sport Talent Model (GSTM) provide input for sports practice. In addition, the head coach of the national talent development programs explains how insight in performance development and its underlying, multidimensional performance characteristics (i.e., anthropometrics, physiological, technical, tactical, psychological skills) helps in processes of talent selection and development. Finally, advice is formulated for policy-makers and coaches.

Speed skating is a technical endurance sport. Still, little is known about technical changes in junior speed skaters. Therefore, changes in technique throughout a 1500-m time-trial of elite junior speed skaters is investigated to explore differences between sexes, performance levels and competitive seasons. At (inter)national 1500-m competitions, knee and push-off angles were obtained for 120 elite junior speed skaters (56 female, 64 male, age 17.6±1.1 years) per lap at 250m (lap 1), 650m (lap 2), 1050m (lap 3) and 1450m (lap 4). Additionally, 1500m end-times and lap-times were obtained to divide skaters in faster and slower performance groups and to analyze pacing behavior. Fifteen skaters (8 female, 7 male, age 17.3±1.5 years) were measured again after 1.6±0.6 years. (Repeated measures) ANOVAs were used for statistical analyses (p<0.05). ICC, determined in a pilot study, was 0.55 for knee and 0.76 for push-off angles. Elite junior speed skaters increased their knee angles throughout the race (p<0.005), regardless of sex (p = 0.110) or performance level (p = 0.714). Push-off angles increased from lap 1–3 (p<0.001), in which men showed a larger decay than female skaters (p<0.05), this holds for both performance groups (p = 0.103). Faster skaters had smaller knee and push-off angles than slower skaters (p<0.05). Males showed smaller body angles than females (p<0.001). Faster male and female skaters showed a relative slower start and faster lap 3 compared to slower skaters (p<0.05). Development over competitive seasons showed a shift towards smaller push-off angles (p = 0.038) and less decay in knee angles from lap 2–3 (p = 0.026). The present study shows that technique throughout the 1500m deteriorates. Deterioration in technique is regardless of performance level, even with different pacing behaviors. Differences between sexes were found for push-off angles. The longitudinal development suggests changes in technique towards senior level and highlights the importance of studying juniors separate from seniors.

Sports performance benchmarks useful to select and guide future elites are limited in literature. The present study introduces a method to enable comparison between sports performance of different generations and creates performance benchmarks for the future elites in speed skating. 1500 m Season Best Times (SBT) of Dutch skaters (1043 females, 1812 males, age 13–26 years), who competed in at least six seasons between 1993 and 2013, were corrected for the prevailing world record (WR): rSBT=(SBT/WR)*100%. Regression analyses showed that the calendar year affected SBT (p0.05). Based on rSBT, performance groups were defined: elite (rSBT<110%), sub-elite (110%<rSBT<115%), high-competitive (115%<rSBT<120%), medium-competitive (120%<rSBT125%). Benchmarks were based on the slowest rSBT per age of the elite group. Of the total skaters performing within the elite benchmarks, the elite performance group represented <20% up to age 16 and <50% up to age 21. An out of sample group (n=299) confirmed the usability of the benchmarks. So, by correcting time-trial performance for the prevailing WR, elite performance benchmarks can be made based on multiple generations of elite skaters. The benchmarks can be used to select and guide future elite skaters from age 13–26 years.

Purpose: To provide insight on the development of pacing behavior in junior speed skaters and analyze possible differences between elite, subelite, and nonelite juniors. Methods: Season-best times (SBTs) in the 1500-m and corresponding pacing behavior were obtained longitudinally for 104 Dutch male speed skaters at age 13–14 (U15), 15–16 (U17), and 17–18 (U19) y. Based on their U19 SBT, skaters were divided into elite (n = 17), subelite (n = 64), and nonelite (n = 23) groups. Pacing behavior was analyzed using the 0- to 300-m, 300- to 700-m, 700- to 1100-m, and 1100- to 1500-m times, expressed as a percentage of final time. Mixed analyses of variance were used for statistical analyses. Results: With age, pacing behavior generally developed toward a slower 0- to 300-m and 1100- to 1500-m and a faster midsection relative to final time. While being faster on all sections, the elite were relatively slower on 0- to 300-m (22.1% ± 0.27%) than the subelite and nonelite (21.5% ± 0.44%) (P < .01) but relatively faster on 300- to 700-m (24.6% ± 0.30%) than the nonelite (24.9% ± 0.58%) (P = .002). On 700- to 1100-m, the elite and subelite (26.2% ± 0.25%) were relatively faster than the nonelite (26.5% ± 0.41%) (P = .008). Differences in the development of pacing behavior were found from U17 to U19, with relative 700- to 1100-m times decreasing for the elite and subelite (26.2% ± 0.31% to 26.1% ± 0.27%) but increasing for the nonelite (26.3% ± 0.29% to 26.5% ± 0.41%) (P = .014). Conclusions: Maintaining high speed into 700 to 1100 m, accompanied by a relatively slower start, appears crucial for high performance in 1500-m speed skating. Generally, juniors develop toward this profile, with a more pronounced development toward a relatively faster 700- to 1100-m from U17 to U19 for elite junior speed skaters. The results of the current study indicate the relevance of pacing behavior for talent development.

Purpose: To evaluate pacing behavior and peripheral and central contributions to muscle fatigue in 1500-m speed-skating and cycling time trials when a faster or slower start is instructed.Methods: Nine speed skaters and 9 cyclists, all competing at regional or national level, performed two 1500-m time trials in their sport. Athletes were instructed to start faster than usual in 1 trial and slower in the other. Mean velocity was measured per 100 m. Blood lactate concentrations were measured. Maximal voluntary contraction (MVC), voluntary activation (VA), and potentiated twitch (PT) of the quadriceps muscles were measured to estimate central and peripheral contributions to muscle fatigue. In speed skating, knee, hip, and trunk angles were measured to evaluate technique.Results: Cyclists showed a more explosive start than speed skaters in the fast-start time trial (cyclists performed first 300 m in 24.70 ± 1.73 s, speed skaters in 26.18 ± 0.79 s). Both trials resulted in reduced MVC (12.0% ± 14.5%), VA (2.4% ± 5.0%), and PT (25.4% ± 15.2%). Blood lactate concentrations after the time trial and the decrease in PT were greater in the fast-start than in the slow-start trial. Speed skaters showed higher trunk angles in the fast-start than in the slow-start trial, while knee angles remained similar.Conclusions: Despite similar instructions, behavioral adaptations in pacing differed between the 2 sports, resulting in equal central and peripheral contributions to muscle fatigue in both sports. This provides evidence for the importance of neurophysiological aspects in the regulation of pacing. It also stresses the notion that optimal pacing needs to be studied sport specifically, and coaches should be aware of this.