Spss software version 26.0 for windows

Data analysis was completed using IBM SPSS software Version 26.0 for Windows. Quantitative variables were expressed as mean ± standard deviation. Quantitative variable distributions were assessed using the Kolmogorov-Smirnov test. Differences between quantitative variables were analyzed using Student’s t-test and nonparametric tests (Mann-Whitney) for variables that did not follow a normal distribution. Receiver operating characteristic (ROC) curves were estimated for each muscle mass loss diagnosed method of GLIM. The area under the curve of ROC (AUC) was used to estimate the discriminative ability. The indications for the diagnostic values were as follows: 0.5, none; 0.5 to 0.7, poor; 0.7 to 0.9, moderate; and 0.9 to 1, 2, good. We designed a multivariate logistic regression model. The dependent variable was OH. A multiple logistic regression model included factors identified as potentially significant (p < 0.05) in univariate analysis. A P-value of < 0.05 was considered statistically significant.

Data analyses were performed using the SPSS software version 26.0 for Windows (SPSS Inc., Chicago Illinois, USA). Data are presented as median (interquartile ranges) or mean ± SD and categorical variables as numbers (percentages). Differences in CVH metrics between rural and urban groups were determined using t-test or Mann–Whitney-U-test (depending on data distribution) and Pearson χ²- test (for categorical variables). The impact of living in rural or urban areas on CVH metrics (PA, BMI, systolic BP, diastolic BP, total cholesterol and fasting blood glucose) was assessed using a generalized linear model. Parameters entered in the respective generalized linear model were living in an urban or rural area, age and sex. Logistic regression was used, to assess the association between ideal CVH metrics and living in rural or urban. For this purpose a summary CVH score was calculated by adding all seven ideal CVH metrics from Table 1 (poor = 0, intermediate = 1 and ideal = 2) and entered in the model as quartiles (Quartile 1, CVH score ≤ 9; Quartile 2, CVH score = 10–11; Quartile 3, CVH score = 12; Quartile 4, CVH score = 13–14) with the lowest quartile (Quartile 1) as reference.
Parameters entered in all models were living in an urban or rural area, age and sex. P-values of less than 0.05 were considered statistically significant.

All statistical analyses were performed with SPSS software version 26.0 for Windows. Some data were missing, as one participant cancelled the last training session and did not complete the post-intervention measurement due to a recurrence of LAS after a contact injury. We included the participant who did not complete all 18 sessions in our intention-to-treat analysis. We used a paired t-test to evaluate within-group differences. To evaluate between-group differences, we conducted an analysis of covariance (ANCOVA) to compare the post-intervention scores, using the pre-intervention baseline measures of each outcome variable as covariates. We interpreted Cohen’s d and partial η² effect sizes as either large (≥ 0.80), medium (0.50–0.79) or small (0.20–0.49) (26 (link)). We set statistical significance at P < 0.05.

SPSS software version 26.0 for Windows (SPSS Inc., Chicago, IL, USA) was used for statistical analysis and the data are presented as mean ± standard deviation. Descriptive statistics were used to analyze the general characteristics of participants. A 2 × 2 mixed ANOVA with one within-subject factor (time: pretest and posttest) and one between-subject factor (group: study and control group) was conducted for the effects of the intervention and their interaction. The Shapiro–Wilk test was used to analyze the normality of the demographic data and outcome variables. It showed an abnormal distribution of some data; thus, non-parametric tests were conducted for the analysis of the variables. The Mann–Whitney U-test was used to compare the mean difference in the baseline data between the study and control groups. The Wilcoxon signed-rank test was used to analyze changes over time within groups and the Mann–Whitney U-test was used to compare differences in values between the groups. The significance level for the tests was set at p < 0.05.

The data are presented as mean ± standard deviation. The normality of the data distribution was evaluated using the Shapiro‒Wilk test, and the distinctions between the two groups were analyzed using the t test or Wilcoxon rank-sum test, as deemed suitable. For multiple group comparisons, the appropriate statistical approach involved ANOVA with a Bonferroni correction or the Kruskal-Wallis H test with a Dunn’s correction. All statistical analyses were performed using SPSS software version 26.0 for Windows (IL, USA). A value of P < 0.05 (P-value adjustment in multiple comparison was performed using Bonferroni correction or Dunn’s correction) was considered statistically significant. Reported results were consistently replicated across multiple experiments with all replicates generating similar results: cell experiments: 3 independent experiments, animal experiments: 5 animals per group.