Statistical analysis
SE, depression, infection, anxiety
REGRESSION, INFECTION
Descriptive statistics were used to characterize the samples treated in group A and B and to describe the average dose of treatments. We used t-tests or chiPatients were analyzed as randomized, i.e. as intended to treat. The outcomes of both groups were compared using linear regression models separately for both follow-up measurement points (end of rehabilitation and three months after rehabilitation). Baseline parameters of the outcomes were considered as covariates in the models. We report adjusted differences between groups with 95% confidence intervals, as well as adjusted predicted estimates (APE) and standard errors (SE). Our handling of missing data used missing at random assumptions. Missing data were imputed with chained equations [Subgroup analyses were performed to analyze differences between persons with high and low levels of anxiety or depression at baseline (effect modification). We used thresholds of 10 points for the HADS anxiety module and 7 points for the HADS depression module to screen for anxiety or depression at baseline [We additionally tested the effect of the pandemic, and generated a variable that indicated if participants had completed their three-month follow-up questionnaire before or after the German Bundestag declared an epidemic situation of national concern due to SARS-CoV-2 by the end of March 2020. From this point on, many different protective measures were implemented in Germany to interrupt chains of infection (e.g., contact and travel restrictions, postponement of scheduled surgeries). This variable was included as an additional main effect in two supplemental models explaining three-month anxiety and depression.The results of the statistical tests were regarded as significant if the two-sided p-value of a test was less than 0.05. All calculations were performed with Stata SE Version 15.