Data Analysis
All analyses were performed using IBM SPSS Statistics 22.0. Intention-to-treat analyses were conducted for the participants’ results. The missing data from the failed follow-up participants were imputed using ‘multiple imputation’ (Jakobsen, Gluud, Wetterslev, & Winkel, 2017). Continuous variables are presented as mean and standard deviation (SD), while categorical variables are presented as absolute numbers and proportions. The data were assessed for distributional assumptions, and approximate normality and homogeneity of variance were confirmed. Baseline differences between groups were assessed using the independentt-test for continuous variables and the chi-square test for categorical variables. A repeated measures analysis of variance (ANOVA) was conducted to compare changes over time in outcomes between groups, with a 2 (condition) by 3 (time) design.
The Bonferroni method was employed to make multiple adjustments for post hoc pairwise comparisons and to correct for multiplicity in the reportedP values. Changes between intervals and effect sizes were reported along with the corresponding 95% confidence intervals (CI). All tests were two-tailed, and P <.05 indicated statistical significance.