Statistical analysis
REGRESSION
Baseline characteristics (age, height, weight, waist circumference, physical activity at baseline, gender, ethnicity, percent bicycling and walking) are presented as mean (SD) or median (IQR) or percent (N) as appropriate.We use a categorical multivariate multilevel regression analysis, using a Bayesian estimator, to calculate the odds ratio of belonging to a group other than the control group. The multilevel setup, adjusting for potential clustering effects, was selected because the data is nested (individuals are nested in schools, schools are nested in regions). More specifically this is one solution to deal with the potential dependency that might be present in data that is collected from, for example, the same school. We ran the model with all four outcome variables within the same model.Within the estimated model we adjusted for ethnicity, age, gender, mother’s education, father’s education, and geographical region. Within the multilevel analysis the cluster structure of the data (schools, regions) is specified within the model. In addition, we merged pre-post data and changes of travel habits was the outcome in our analysis. The “No change” group was used as reference level. Cases containing missing data were deleted for each model, meaning that the number of cases vary between models. The results are presented as median log-odds ratio with 95% credibility intervals (95% CI), given by the highest-density interval. The interpretation of the CI is that there is a 95% probability that the odds-ratio falls in this range. The interpretation of the Odds Ratio is that a value above 1 favors the intervention, and below 1 favors the control. All analyses were conducted in R where the models were fitted in Stan through the brms package using 4 chains, 2000 warm-up and 2000 sampling iterations. A description with the R-code used in the analysis is attached as 