Data analysis
REGRESSION
The collected data were analyzed using SPSS WIN 25.0, and a two-tailed test was performed at the significance level of 0.05. The normality test of the dependent variable was verified using the Kolmogorov–Smirnov test, and it was found that it was not normally distributed. This study is designed to compare the effects of interventions with repeated measurements, so repeated measures analysis of variance is generally considered, but the normality of the dependent variable is not satisfied and the repeated measures analysis of variance is not appropriate. In consideration of this, the generalized estimating equation (GEE), which is an extension of the generalized linear model, was analyzed. The generalized estimation equation is a method of applying multiple regression analyzes in consideration of the intra-subject correlation of measured values. It can be applied even if the assumption of normality is not satisfied, and it can be analyzed in data containing missing values by considering time as a variable.Descriptive statistics were employed to summarize the general characteristics of the subjects, and the comparability of the three groups was assessed using χ2-test, Fisher’s exact test, or ANOVA, as appropriate. Prior to the intervention, an ANOVA homogeneity test was performed to examine the equality of variance in the dependent variables across the three groups. Disparities among the three groups concerning the time elapsed before and after the implementation of the intervention program were examined utilizing GEE. To assess the distinctions among the mH-L, mH, and control groups at time points T1 and T2, we conducted the Kruskal-Wallis test. Subsequently, a Mann–Whitney test was employed as a 