## 2 Materials and Methods
### Statistical Analyses  
Descriptive statistics (mean, standard deviation [SD], and range) were calculated for each outcome variable. Before performing parametric statistics, data sets were tested for normality using the Shapiro-Wilk test and by visual observation of normality QQ plots.  
The coefficient of variation (CV%), expressed as a percentage, was used to establish absolute reliability [17]. It was calculated as the percentage of the within-subject SD. A CV of <10% was considered to be an acceptable absolute reliability [33]. The relative reliability was determined using the intraclass correlation coefficient (ICC, model 3,k), accompanied by 95% confidence intervals (CIs). Its calculation was based on a mean measurements (k = 2), absolute agreement, and 2-way mixed-effects model [18; 34; 35]. The following descriptors were used to evaluate the level of reliability for each outcome measurement: \"low\" (0.20-0.49), \"moderate\" (0.50-0.74), \"high\" (0.75-0.89), \"very high\" (0.90-0.98), and \"extremely high\" (\(\geq\)0.99) [35].  
Figure 1: Testing procedure.  
Measurement sensitivity was computed by comparing the typical measurement error with the smallest worthwhile change (SWC), both expressed in the measurement scores for each value [17]. The SWC was derived from the between-subject SD multiplied by either 0.2 (SWC\({}_{0.2}\)) [19; 36], which is the typical small magnitude effect, or 0.5 (SWC\({}_{0.5}\)), which is an alternate moderate effect [37]. A typical measurement error below the SWC indicated that the measurement sensitivity was \"good\" and a typical measurement error that was the same as the SWC was rated \"satisfactory\". If a typical measurement error was higher than the SWC, it was deemed to have \"marginal\" sensitivity [19; 36].  
The systematic measurement error was evaluated using paired sample t-tests, assuming no differences between the test and retest mean values. The magnitude-based effect size (d) was calculated to determine the test-retest differences, using the following criteria: \(<\)0.20 = \"trivial\", 0.20-0.59 = \"small\", \(>\)0.6-1.19 = \"moderate\", \(>\)1.2-2.0 = \"large\", and \(>\)2.0 = \"very large\" differences [38]. Additionally, the paired sample t-tests were used to examine differences in the spatiotemporal variables between the steady-state and acceleration phases.  
[PERSON]'s product-moment correlation coefficient (r) was used to examine the association between spatiotemporal measures obtained in the acceleration and steady-state phases. The strength of the correlations was interpreted using the following qualitative descriptors: \(<\)0.20 = \"very weak\", 0.20-0.40 = \"weak\", 0.41-0.70 = \"moderate\", 0.71-0.90 = \"strong\", and \(>\)0.90 = \"very strong\" correlation [39].  
Two multiple stepwise regression analyses were used to examine how well the spatiotemporal variables derived during the acceleration and steady-state phases could predict the outcome measure (i.e., skating velocity). The coefficient of determination (R\({}^{2}\)) was used to determine the percentage of shared variance between the criterion and the predictors. Before performing the multiple regression calculation, the collinearity between the predictors was checked. The variance inflation factor for most of the predictors was below 2.5, but the variance inflation factor for the glide time showed high multicollinearity (\(>\)5) and, therefore, it was excluded from both predictor sets [40]. Statistical analyses were performed using freely available MS Excel 2016 charts and SPSS(r) 29.0 (IBM SPSS Statistics, New York, NY, USA) for Windows, and the alpha level was set at \(p<0.05\).