5 km), end of first lap (23 2 km), time to top of second climb (3

5 km), end of first lap (23.2 km), time to top of second climb (35.7 km) and finish (46.4 km). Throughout the trials, HR and Tre were recorded every 2 min, while self-reports of perception of effort [28], thermal sensation [29], and gastrointestinal comfort

(5-point Likert scale), were recorded at approximately 5-km intervals. On the completion of each time trial, subjects were asked a series of questions related to their effort, motivation, sensation and comfort, as reported previously [11]. Statistical analysis CP-868596 mouse Pre-trial body mass, percentage dehydration, and post-trial subjective ratings were compared between trials (i.e., CON, PC, PC+G) using a one-way analysis of variance (ANOVA). A two-way (trial × time) repeated measures ANOVA was used to examine differences in dependant variables (i.e., rectal temperature, heart rate, urine specific gravity and volume, thermal comfort, stomach fullness and RPE) between trial means at each time point. If a significant main effect was observed, pairwise comparisons were conducted using Newman-Keuls post hoc analysis. These statistical tests were conducted using Statistica for Microsoft

Windows (Version 10; StatSoft, Tulsa, OK) and the data NSC 683864 solubility dmso are presented as means and standard deviations (SD). For these analyses, significance was accepted at P<0.05. The performance data from the three trials were analysed using the magnitude-based inference approach recommended for studies in sports medicine and exercise sciences [30]. A spreadsheet (Microsoft Excel), designed to examine post-only crossover trials, was used

to determine the clinical significance of each treatment Suplatast tosilate (available at newstats.org/xPostOnlyCrossover.xls), as based on guidelines outlined by Hopkins [31]. Performance data are represented by time trial time and power output during the various segments of the course, and are presented as means ± SD. The magnitude of the percentage change in time was interpreted by using PRIMA-1MET values of 0.3, 0.9, 1.6, 2.5 and 4.0 of the within-athlete variation (coefficient of variation) as thresholds for small, moderate, large, very large and extremely large differences in the change in performance time between the trials [30]. These threshold values were also multiplied by an established factor of −2.5 for cycling [32], in order to interpret magnitudes for changes in mean power output. The typical variation (coefficient of variation) for road cycling time trials has been previously established as 1.3% by Paton and Hopkins [33], with the smallest worthwhile change in performance time established at 0.4% [34], which is equivalent to 1.0% in power output. These data are presented with inference about the true value of a precooling treatment effect on simulated cycling time trial performance. In circumstances where the chance (%) of the true value of the statistic being >25% likely to be beneficial (i.e., faster performance time, greater power output), a practical interpretation of risk (benefit:harm) is given.

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