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.