Statistical analysis
depressive symptoms, pain
CHRONIC ILLNESS, SECONDARY
Data were analysed using SPSS version 25. Descriptive statistics and frequency distributions were calculated for sample characteristics. Chi‐square tests and t‐tests were used to identify any differences in demographics between the experimental and control experimental groups. A Generalized estimating equations (GEE) with the identity link and first‐order autoregressive (AR(1)) working correlation matrix were used to evaluate the effect of the intervention on primary (pain intensity as measured by BPI) and secondary (pain self‐efficacy as measured by PSEQ, pain interference as measured by BPI, depressive symptoms as measured by GDS and loneliness as measured by DJGLS) outcomes. GEE is an extension of generalized linear models that allows for the analysis of repeated measures with unknown covariance structures. GEE uses all available data that participants provide, even if follow‐up data are missing (i.e. intent‐to‐treat analysis). For all models, the main effect of group (experiment group and control group) and time (baseline and post‐intervention) and the Group × Time interaction were evaluated. Models were adjusted for age, gender, sex, marital status, education, living status and presence of chronic illness.GEE models for the entire study sample at both study time points (baseline and post‐intervention) were evaluated. Wald Pairwise comparisons were used to examine the statistical significance Group × Time interactions observed in the GEE analyses to explore the difference in the outcomes between the experimental and control groups at each follow‐up time point. The alpha significance level for all analyses was set at 0.05. No adjustment was made to the alpha to compensate for the number of pairwise comparisons.