2.6. Statistical Analysis
Chi-square tests and simple general linear models were used for frequency and mean comparisons between groups (INT vs. CON) and cohorts (year 1 vs. year 2) at baseline. Primary analyses of the intervention effects were conducted using repeated measures linear mixed models with maximum likelihood estimation, accounting for clustering of children within the center (SAS PROC MIXED). Comparison of covariance structures using Akaike (AIC) and Bayesian information criteria values suggested very little difference for model estimation between covariance types; in the final models, autoregressive (AR1) structures were utilized. For each PA outcome, three models were fit. Models were progressive, with each including additional covariates. Model 1 included accelerometer wear time; Model 2 added the number of days with no outside time (0–2), the number of days with rain (0–2), and the cohort (year 1 or 2); and Model 3 added the child’s age at baseline (years) and the child’s sex (girl or boy). Note that for outcomes that included wear time in the calculation (e.g., % time MVPA), the models did not include wear time. The adjusted least squares means and standard errors for Model 3 are also presented. The percent change (100*[[MEANfu-MEANbl]/MEANbl]) from baseline and the effect size ([INT mean change—CON mean change]/pooled baseline SD) were also computed for each PA outcome.Exploratory analyses were conducted to examine the moderating effects of child sex and child age at baseline for MVPA and Sedentary time. For each, a 3-way (GROUP × TIME × [AGE or SEX] [AGE or SEX]) interaction term was added to Model 3. For child age, the model was run with age as a continuous variable, as well as with age as a 3-level categorical variable. To examine the stability of our results, sensitivity analyses were conducted by fitting models in two sub-samples: (1) removing children who had abnormal days (days with rain or no outside time) and children with very high or low activity estimates (