Statistical analysis
An independent team performed all statistical analyses. Independent t-tests and chi-square tests were conducted for continuous and categorical variables, respectively, to examine baseline differences in demographic characteristics and psychological distress and well-being variables between the two groups (gardening group vs. control group). Among the demographic features, marital status was found to be non-equivalent between the groups; therefore, ANCOVA was conducted to re-analyze the treatment effects, considering marital status as a covariate. If the data did not satisfy the assumptions such as normality and homogeneity of variance, it was analyzed using a nonparametric method. Since our data were nested as they were collected from 11 sites, a multilevel analysis was used to compare the changes between the two groups. Multilevel analysis, also known as the linear mixed-effects model, allows random intercepts for individuals and sites for differential clustering. Therefore, individuals (participants) and sites were included as a random effect in our multilevel model. In this model, time (three time points including pre-, mid-, post-test) was included as a within-subject (Level 1) parameter, and individuals (Level 2) and sites (Level 3) were included as between-subject parameters. For effect comparison, the group (gardening vs. control) was set as a moderating variable to examine the interaction effect of the time × group. Group-mean centering was adapted to reduce the risk of multicollinearity and increase the ease of interpretationStatistical analyses were conducted using R software (version 4.2.2). R software was utilized to conduct multilevel analysis, correlation analysis, and calculate effect size using the “nlme”