Drafting in long-track speed skating team pursuit on the ice rink.

Drafting is distinctive for team pursuit races in long-track speed skating. This study aims to compare the impact of drafting on physical intensity (heart rate [HR]) and perceived intensity (ratings of perceived exertion [RPE]) per drafting position. Eighteen skilled male (n = 9) and female (n = 9) skaters (20.0 ± 4.8 years) skated three trials, in first, second or third position, with consistent average velocity (F2,10 = 2.30, p = 0.15, ηp2 = 0.32). Differences in HR and RPE (Borg CR-10 scale) were compared within-subjects (three positions) using a repeated-measures ANOVA (p < 0.05). Compared to the first position, HR was lower in the second (benefit 3.2%) and third (benefit 4.7%) position and lower in third compared to second position (benefit 1.5%), observed in 10 skaters (F2,28 = 28.9, p < 0.001, ηp2= 0.67). RPE was lower when comparing second (benefit 18.5%) and third (benefit 16.8%) position to first (F1.3,22.1 = 7.02, p < 0.05, ηp2= 0.29) and similar for third and second positions., observed in 8 skaters. Even though the physical intensity was lower when drafting in third versus second position, the perceived intensity was equal. There were large interindividual differences between skaters. Coaches are advised to adopt a multidimensional, tailored approach when selecting and training skaters for a team pursuit.

Pacing Behavior of Elite Youth Athletes: Analyzing 1500-m Short-Track Speed Skating.

PURPOSE: To gain insight into the development of pacing behavior of youth athletes in 1500-m short-track speed-skating competition. METHODS: Lap times and positioning of elite short-track skaters during the seasons 2011/2012-2015/2016 were analyzed (N = 9715). The participants were grouped into age groups: under 17 (U17), under 19 (U19), under 21 (U21), and senior. The difference between age groups, sexes, and stages of competition within each age group were analyzed through a multivariate analysis of variance (P < .05) of the relative section times (lap time as a percentage of total race time) per lap and by analyzing Kendall tau-b correlations between intermediate positioning and final ranking. RESULTS: The velocity distribution over the race differed between all age groups, explicitly during the first 4 laps (U17: 7.68% [0.80%], U19: 7.77% [0.81%], U21: 7.82% [0.81%], and senior: 7.80% [0.82%]) and laps 12, 13, and 14 (U17: 6.92% [0.14%], U19: 6.83% [0.13%], U21: 6.79% [0.14%], and senior: 6.69% [0.12%]). In all age groups, a difference in velocity distribution was found between the sexes and between finalists and nonfinalists. Positioning data demonstrated that youth skaters showed a higher correlation between intermediate position and final ranking in laps 10, 11, and 12 than seniors. CONCLUSIONS: Youth skaters displayed less conservative pacing behavior than seniors. The pacing behavior of youths, expressed in relative section times and positioning, changed throughout adolescence and came to resemble that of seniors. Pacing behavior and adequately responding to environmental cues in competition could therefore be seen as a self-regulatory skill that is under development throughout adolescence.

Optimal Development of Youth Athletes Toward Elite Athletic Performance: How to Coach Their Motivation, Plan Exercise Training, and Pace the Race.

Elite athletes have invested many years in training and competition to reach the elite level. One very important factor on the road to elite performance is the decision-making process regarding the regulation of effort over time, termed as pacing behavior. The regulation of effort is vital for optimal athletic performance during a single race and over a longer period of time (e.g., a competitive season) as an inadequate regulation could result in a higher risk of injuries, overtraining, and drop-out. Despite this, there is limited knowledge on how young athletes learn and develop the abilities related to pacing. Pacing behavior of athletes develops from childhood throughout adolescence and is thought to be closely connected to physical maturation, the development of pre-frontal cortical related (meta-) cognitive functions, as well as the gathering of experience with exercise tasks. The motivation of an athlete can critically influence how an athlete paces a single race, but also how they distribute their effort over a longer period of time. Coaches are advised to closely monitor the development of pacing behavior during adolescence (e.g., by gathering split times, and related physiological measurement, during training and competition), as well as the underlying factors including physical maturation (meta-) cognitive development and the motivation of young athletes. Furthermore, pacing behavior development could be aided by providing training in which the task, individual, and environment are manipulated. Hereby, presenting athletes with the opportunity to gain experience in situations which closely resemble the perceptual-motor conditions of upcoming competitions.

The Internet: a practical example of the use of new technology in the assessment of vancomycin use in pediatrics. The Pediatric Prevention Network.

BACKGROUND: The rapid emergence of both new infections and new technologies has revolutionized health care during the past 50 years. Increased use of the Internet has enabled health care professionals to educate, interact, and collaborate throughout the world in ways never before possible. Increased use of vancomycin has been associated with the emergence of organisms with decreased susceptibility to vancomycin, such as Enterococcus and staphylococcal species. The purpose of this article is to describe our experience using Internet technology to assess vancomycin use at children's hospitals in the United States. METHODS: A Web-based evaluation was developed and distributed on the Internet to 57 Pediatric Prevention Network hospitals. The evaluation was structured to collect summary statistics on vancomycin use and admissions data by service for 1997 and 1998. RESULTS: Twenty-four hospitals were able to provide archived vancomycin use and patient admissions data; completed evaluations were returned from 15 hospitals (62.5% response rate). Personnel at 6 (40%) hospitals completed the evaluation directly on the Internet. CONCLUSIONS: In our study, Internet technology facilitated a more efficient evaluation of vancomycin use, but fewer than half of the personnel at Pediatric Prevention Network hospitals completed the evaluation directly on the Internet. It is unclear whether personnel at these hospitals were limited in Internet access, support, or understanding. Efforts should be directed to educate health care personnel on the advantages of the Internet. Furthermore, many of the pharmacy databases used in our assessment were not standardized across hospitals nor systematically validated. Understanding that limitations still remain-within the source of the data studied, the health care system sampled, and the Internet tools available-is essential because the Internet offers health care professionals today a tool both to protect patients and to improve quality throughout the world.