Statistical analysis
asthma, diabetes
ASTHMA, DIABETES
We employed descriptive statistics to summarize the demographic characteristics of both Head Start Centers and study participants as well as each outcome of interest (parent-reported PA, diet, screen time, and sleep time) measured at each time point (baseline vs. post-intervention). Baseline, post-intervention, and change scores (post-intervention – baseline) for each outcome of interest were compared among the three groups (CBI, CBI + HBI, Control) using the Kruskal–Wallis H test. For each outcome of interest, we used a 3-level (time nested within child, and child nested within center) linear mixed effects model (LMM) to examine the treatment effect. Two random effects were included in the LMM, one to account for the correlation among two measures nested within the same child, and the other to account for the correlation among children nested within the same center. We assumed data were missing at random. In LMM, time (baseline vs. post-intervention), treatment group (CBI vs. CBI + HBI vs. Control), the interaction between time and treatment group, and center size (large vs. small) were fixed design-related predictors that were always kept in the final model, regardless of whether they were statistically significant.We considered the following confounders in the full LMM for each outcome of interest: child’s age at baseline, gender, race/ethnicity, asthma, mother’s education, language spoken most often at home, parent marital status, family history of diabetes, and child’s BMI status at baseline. We employed a backward model selection to remove one non-significant (