Statistical analysis
REGRESSION, SECONDARY, COMPLICATIONS
Statistical analyses were conducted using STATA 16 (Stata Corp, College Station, TX, USA). Taking into account a drop-out of 1 centre with an average program size of 10 patients per centre, we assumed that 7 patients per centre would participate in both aerobic fitness tests (due to non-participation, attrition, moving, sickness on the testing day). In case of sickness on the testing day, the stress test (aerobic fitness test) will be repeated on another day. A total number of 10 centres (70 patients per treatment arm) would then provide enough power to detect a true intervention effect of two-thirds of an inter-subject standard deviation at the usual significance level of 0.05 with a probability of 0.8, provided that the standard deviation of the random class effect does not exceed 3% of the inter-subject standard deviation (i.e., corresponding to an intra-class correlation of about 0.03). Therefore, we included 11 treatment centres.Complete-case analyses were performed according to intention to treat. Descriptive statistics were calculated using mean ± SD for continuous variables, or percentage for categorical variables. The analyses of the primary and secondary outcomes were based on a superiority assumption and presented as mean differences with 95% CI and Main baseline characteristic of the participants.The presence of complications and comorbidities are obtained based on the information of the referring physician or the patient.Linear regression analyses were performed to test the difference in outcome at the end of the program between both arms. In the regression models, the dependent variable was represented by baseline to end-of-study change in HbAIn case of significant association between change in outcome and the centre, mixed model analyses were performed. This was the case for aerobic fitness, walking speed, body weight, BMI, lean body mass, fat body mass, HDL, systolic BP. In the mixed models, the centre was the unit of randomization and change in physical fitness, body composition, and cardiometabolic health outcomes as the dependent variables. The predictors were the study arm, age, sex, and educational level. The objective was to check for each outcome if the coefficient related to the program type is significantly different from 0. Statistical level was set at P<0.05 for all analyses.To account for missing values, in a secondary analysis all cases were included in the analysis by using multivariate imputation by chained equation (MICE), with the assumption that the data are missing at random (