Statistical Analyses
Pain
ADVERSE EVENTS, SECONDARY
All analyses were done using IBM SPSS Statistics 25. Outcomes were analyzed following the intention-to-treat principle. Descriptive statistics were used for presenting baseline data, adverse events, and feasibility outcomes.For primary efficacy analyses, linear mixed models were used to analyze the effectiveness of the VR intervention at 4 weeks, with “treatment,” “time,” and “treatment time” as fixed effects. A within-person correlation was accounted for by using a repeated measure design. Baseline values were incorporated as a covariate. Possible other covariates were added on a rational basis and only retained when having a significant effect on the model. The best-fitting covariance structure was determined using likelihood ratio tests. Maximum likelihood estimation was used to be able to perform these likelihood ratio tests. For the final model, the restricted maximum likelihood was used, since this is recommended for smaller sample sizes. Residuals were checked for normality. An alpha of 0.05 was considered significant.All time points (postintervention at 4 wk and follow-up at 4 mo) were included in a linear mixed models analysis with repeated measure design. If a significant difference was detected, a pairwise comparison per time point was performed. Pain scores were analyzed using linear mixed models following the same methods. Other secondary outcomes were analyzed using descriptive statistics. When possible differences were observed, delta scores between baseline and at 4 weeks were assessed using 