## 2 Materials and Methods
### Statistics  
Descriptive statistics (mean, standard deviation) were based on 20 barefoot trials and were calculated for 31 kinematic parameters in the sagittal and frontal plane for the hip, knee and ankle joints. Reliability analyses were divided into five parts:  
1. For interobserver reliability, a single measure intraclass correlation coefficient (ICC) was calculated. The number of measures (k) was 60 (\(n\) = 20 barefoot trials from three subjects). 2. For intraobserver reliability, an average measure ICC was calculated. The number of measures (k) was 60. 3. To assess between-laboratory intraobserver reliability, an average measure ICC was calculated and again referenced to the same ICC value classification [21]. The ICC indicated excellent reliability if the value was above 0.75, fair-to-good reliability between 0.40 and 0.75 and poor reliability when less than 0.40. A two-way mixed-effects model (definition: absolute agreement) was used for all calculations. For all ICC values, 95% confidence intervals were reported. 4. To estimate experimental errors of a joint angle, the standard error was calculated (for intra-(o repeated), inter- (oobserver) and between-laboratory intraobserver reliability (oessel(lab))) as described by [PERSON] et al. [10], as well as the magnitude of the interobserver error and its ratio to intra-subject error r (Equations (2)-(4)). \[\mathrm{r}=\sigma^{\mathrm{repeated}}/\sigma^{\mathrm{trial}} \tag{2}\]  
Figure 2: Parameters extracted (No. 1 to 16) out of processed kinematic data for sagittal (**a**) and frontal (**b**) plane of the hip joint, for sagittal (**c**) and frontal (**d**) plane of the knee joint, as well as data for sagittal (**e**) plane of the ankle joint. \[\mathrm{r} =\sigma^{\mathrm{observer}}/\sigma^{\mathrm{trial}} \tag{3}\] \[\mathrm{r} =\sigma^{\mathrm{sesss(lab)}}/\sigma^{\mathrm{trial}} \tag{4}\]
5. A scatter-plot technique suggested by [PERSON] and [PERSON] [22] was used to assess interchangeability (equivalence) of CGA data between laboratories. Calculated differences for joint angles were plotted against their average value for each subject. The interchangeability of CGA data was tested by a bounding criterion defined as the mean \(\pm\) two standard deviations of the measured differences (approximately 95% of all measured values). 6. To evaluate the variability within and between subjects, the standard error of measurement (SEM) was calculated in conjunction with the ICCs, using the following equation from [PERSON] and [PERSON] [23]: \[\mathrm{SEM}=\mathrm{SD}\times\sqrt{(1-\mathrm{ICC})}\] (5) ICC values may be influenced by inter-subject variability of scores, because a large ICC may be reported despite poor trial-to-trial consistency if the inter-subject variability is too high [23; 24]. However, the SEM is not affected by inter-subject variability [24]. 7. Mean differences for multicenter comparisons were tested using variance analysis. A one-factor, univariate general linear model (GLM; dependent variable: hip flexion during stance; independent variable: CGA center; covariate: walking speed) was performed. Prior to inference statistical analyses, all variables were tested for normal distribution (Kolmogorov-Smirnov test). To estimate practical relevance and to quantify the differences between GL1 and GL2, effect sizes (partial eta squared, \(\mathrm{\eta_{P}}^{2}\)[25; 26]) were calculated for the main effects (\(\mathrm{\eta_{P}}^{2}\)) and the mean differences divided by the pooled standard deviations (d). To evaluate effect sizes, d or \(\mathrm{\eta_{P}}^{2}\) were classified, with d \(\geq\) 0.2, d \(\geq\) 0.5, d \(\geq\) 0.8 or \(\mathrm{\eta_{P}}^{2}\)\(\geq\) 0.01, \(\mathrm{\eta_{P}}^{2}\)\(\geq\) 0.06, \(\mathrm{\eta_{P}}^{2}\)\(\geq\) 0.14 indicating small, medium or large effects, respectively [27]. Level of significance was adjusted to \(p\)\(\leq\) 0.002 (0.05/31 = 0.002) for multiple tests by means of a Bonferroni correction. All statistical analyses were performed using SPSS version 25.0 for Windows (SPSS Inc., IBM, Armonk, NY, USA).