Statistical analysis
Categorical variables were expressed as numbers and percentages, and Chi Square test was used to compare the groups. Continuous variables were summarized as mean and standard deviation and as median and quartiles (Q1–Q3) where appropriate. The normality of the distribution for continuous variables was confirmed with the Kolmogorov–Smirnov test. For comparison of continuous variables between two groups, Mann–Whitney U test was used and Friedman test was used for time-dependent measures. Spearman correlation test was used between 2 continuous variables. The F1-LD-F1 design was used for longitudinal data analysis because the data were not normally distributed. The F1-LD-F1 model generates an ANOVA-type statistic for group, time, and group-time interaction. The F1-LD-F1 model, in addition to the ANOVA-type statistic, provides relative treatment effects (RTE) as a unitless measure of effect size. The RTE has values ranging from 0 to 1 and can be interpreted as follows: An RTE of 0.25 for a specific subgroup indicates that the probability of a randomly selected person from this subgroup outperforming a randomly selected person from the entire dataset is estimated to be 25%. The probability of a randomly selected person from this subgroup scoring lower than a randomly selected person from the entire dataset, on the other hand, is estimated to be 75%. An RTE of 0.50 indicates that there is no tendency for a higher or lower score in any subgroup. All statistical analyses were carried out using IBM SPSS 20 (Armonk, NY: IBM Corp.) and RStudio (RStudio Team, 2020). The npar LD package was used in RStudio for the F1-LD-F1 design. The statistical level of significance for all tests was considered to be 0.05.