Spss statistics program version 26

Data were processed and analyzed using the IBM SPSS statistics program, version 26.0 (Thousand Oaks, CA, USA). Categorical variables were presented as number and percent (n%) and were compared by chi-square (chi²) or Fischer’s exact test when appropriate. Quantitative variables were tested for normality using the Kolmogrov-Smirnov test assuming normality at a P value of more than 0.05. The variables were non-normally distributed; data were presented as median and interquartile range (IQR). Mann–Whitney U test was used to compare medians of two groups and the Kruskal–Wallis test was used to compare medians of three groups followed by a post hoc test (Dunn’s test) to detect pair-wise comparison. Correlations between apelin-12 and MetS risk factors were determined using Spearman’s rank-order correlation. Receiver operator characteristic (ROC) curves were created to evaluate the efficiency of serum apelin-12 as a diagnostic biomarker for MetS in obesity groups. The results were expressed as areas under the curves (AUC), 95% confidence intervals (CI), cut-off values of apelin-12, specificity, sensitivity, and accuracy. MedCalc statistical software version 19.6.1 was used for comparing the AUCs of obesity groups. P-value < 0.05 was considered statistically significant.

The data processing, manipulation, and organization were presented utilizing the IBM SPSS statistics program, version 26.0 (Thousand Oaks, CA, USA). The normal distribution of the study data was assessed using the Kolmogorov–Smirnov test. The continuous variables were processed as mean ± standard error (SE) using the one-way ANOVA test. Pairwise comparison between every 2 groups was done using post hoc test (Dunn’s test) for multiple comparisons test. Correlations between the clinical variables were analyzed using the Pearson correlation test. The receiver operating characteristic (ROC) curve was plotted to determine the cut-off point. p ≤ 0.05 was considered significant. The boxplot was carried out by the R programming Language (version 4.0.3) with RStudio open source (version 1.4.1103).

Data are presented as mean ± standard deviation. The assumptions of normality and sphericity were verified using the Shapiro–Wilk and Mauchly tests, respectively. When the sphericity condition was not met, the Greenhouse-Geisser, Huynh-Feldt, and lower bound adjustments were applied. To assess the consistency of the measurements, the intraclass correlation coefficient (ICC) between exercise modalities and intensities was calculated. Values below 0.5 were interpreted as poor reliability, between 0.5 and 0.75, moderate reliability, between 0.75 and 0.9, good reliability, and between 0.90 and 1, excellent reliability (42 ). To examine the possible existence of differences between exercise modalities (r_BIKE vs. v-BIKE vs. s-BIKE vs. ROW vs. ELLIP vs. STAIR vs. TMILL) at a given intensity (RPE 17 vs. MAX INT) in terms of HR frequency, VO2, and EE, the one-way repeated measures ANOVA test was performed, with Bonferrony's test for post hoc comparisons. The effect size was calculated using the partial eta squared parameter (η²_p). Values of η²_p = 0.01 were interpreted as small effect, η²_p = 0.06 medium effect, and η²_p = 0.14 large effect (43 ). The significant level was set at p ≤ 0.05. The statistical analysis was carried out with the IBM SPSS statistics program, version 26 (Chicago, USA).