Pacing offers a potential avenue for enhancement of endurance performance. We report here a novel method for characterizing pacing in 800-m freestyle swimming. Websites provided 50-m lap and race times for 192 swims of 20 elite female swimmers between 2000 and 2013. Pacing for each swim was characterized with five parameters derived from a linear model: linear and quadratic coefficients for effect of lap number, reductions from predicted time for first and last laps, and lap-time variability (standard error of the estimate). Race-to-race consistency of the parameters was expressed as intraclass correlation coefficients (ICCs). The average swim was a shallow negative quadratic with slowest time in the eleventh lap. First and last laps were faster by 6.4% and 3.6%, and lap-time variability was ±0.64%. Consistency between swimmers ranged from low-moderate for the linear and quadratic parameters (ICC = 0.29 and 0.36) to high for the last-lap parameter (ICC = 0.62), while consistency for race time was very high (ICC = 0.80). Only ~15% of swimmers had enough swims (~15 or more) to provide reasonable evidence of optimum parameter values in plots of race time vs. each parameter. The modest consistency of most of the pacing parameters and lack of relationships between parameters and performance suggest that swimmers usually compensated for changes in one parameter with changes in another. In conclusion, pacing in 800-m elite female swimmers can be characterized with five parameters, but identifying an optimal pacing profile is generally impractical.

This approach to analysis of pacing may help improve performance in swimmers and other endurance athletes in sports with multiple laps, but data from many competitions are required.

word count: 250 Text-only word count: 3407Number of figures and tables: 2 "Accuracy of Jump-Mat Systems for Measuring Jump Height" by Pueo B et al. International Journal of Sports Physiology and Performance © 2016 Human Kinetics, Inc. AbstractPurpose: Vertical-jump tests are commonly used to evaluate lower-limb power of athletes and non-athletes. Several types of equipment are available for this purpose. Here we compared the error of measurement of two jump-mat systems (Chronojump-Boscosystem and Globus Ergo Tester) with that of a motion-capture system as a criterion. Additionally we determined the modifying effect of foot length on jump height. Methods: Thirty-one young adult males alternated four countermovement jumps with four squat jumps. Mean jump height and standard deviations representing technical error of measurement arising from each device and variability arising from the subjects themselves were estimated with a novel mixed model and evaluated via standardization and magnitude-based inference. Results: The jump-mat systems produced nearly identical measures of jump height (differences in means and in technical errors of measurement 1 mm). Countermovement and squat-jump height were both 13.6 cm higher with motion capture (90% confidence limits ±0.3 cm), but this very large difference was reduced to small unclear differences when adjusted to a foot length of zero. Variability in countermovement and squat-jump height arising from the subjects was small (1.1 and 1.5 cm respectively, 90% confidence limits ±0.3 cm); technical error of motion capture was similar in magnitude (1.7 and 1.6 cm, ±0.3 and ±0.4 cm), while that of the jump mats was similar or smaller (1.2 and 0.3 cm, ±0.5 and ±0.9 cm). Conclusions: The jump-mat systems provide trustworthy measurements for monitoring changes in jump height. Foot length can explain the substantially higher jump height observed with motion capture.

No abstract

This randomized cross-over study examined the effects of typical static and dynamic stretching warm-up protocols on repeated-sprint performance. Thirteen young female handball players performed a 5 min aerobic warm-up followed by one of three stretching protocols for the lower limbs: (1) static stretching, (2) dynamic-ballistic stretching, and (3) no stretching before performing five all-out sprints on a cycle ergometer. Each protocol was performed on a different occasion, separated by 2-3 days. Range of movement (ROM) was also measured before and after the warm-up protocols with a sit-and-reach test. Fixed and random effects of each stretching protocol on repeated sprint performance were estimated with mixed linear modeling and data were evaluated via standardization and magnitude-based inferences. In comparison to no stretching, there were small increases in ROM after dynamic stretching (12.7%, ±0.7%; mean, ±90% confidence limits) and static stretching (19.2%, ±0.9%). There were small increases in the average power across all sprints with dynamic stretching relative to static stretching (3.3%, ±2.4%) and no stretching (3.0%, ±2.4%) and trivial to small increases in the average power in the 1st and 5th trials with dynamic stretching compared to static stretching (3.9%, ±2.6%; 2.6%, ±2.6%, respectively) and no stretching (2.0%, ±2.7%; 4.1%, ±2.8%, respectively). There were also trivial and small decreases in power across all sprints with static relative to dynamic stretching (-1.3%, ±2.8%) and no stretching (-3.5%, ±2.9%). Dynamic stretching improved repeated-sprint performance to a greater extent than static stretching and no stretching.