2.5. Analysis
fasting blood glucose
REGRESSION, DISEASE
The sample size was calculated on the basis of the change in body weight, which was the primary outcome parameter of this study. The calculation was conducted using data from a pilot study with a prototype version of the lifestyle program (as described previously [For between-group comparisons, Fisher’s exact test was used for categorical variables, while an independent Multiple linear regression models were used to compare the intervention and control groups regarding changes in total costs (adjusted for covariates). Confounders were identified and considered as potential covariates in the multiple regression models. Firstly, univariate analyses were conducted with the following variables to identify potential covariates: sociodemographic data (sex, age, marital status and education level), blood parameters (total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, fasting blood glucose, HbA1c), vital signs (systolic and diastolic blood pressure and resting heart rate), body weight, waist circumference and BMI, total costs at baseline, smoking status, assignment to IG or CG, alcohol consumption, sleep quality, diagnosed disease and regular medication use were examined. Secondly, AIC-based forward selection was performed to find final models that were statistically significant (general linear F-test: 