2.7. Data Analysis 
Very low (<500 kcal) or very high (>7000 kcal) total energy consumption values (The imputation of missing value was obtained from the round that had information. Therefore, those who did not have information in round 1 were imputed by round 2 and those who did not have information in round 2 were imputed by round 1. In this step, 10 possible values were generated from the imputation and we selected the average of these 10 values as the value imputed. When the individual had no value for either round 1 or round 2, the overall mean of observations over that period was used. And, when the imputation value was negative, the positive value was assigned.Then, imputation was performed by the value of the observed round + sociodemographic variable (gender, age, and education). Finally, descriptive data analysis was undertaken and characterized to compare original and the imputed data.After the data were imputed, descriptive statistical analyses were performed, with calculation of medians and interquartile ranges, and percentages to assess the sociodemographic and health characteristics of the participants. Differences between individuals in the IG and CG were evaluated using Pearson’s chi-square and Mann–Whitney’s statistical tests. Normality analyses were carried out. The Wilcoxon statistical test was used to assess the evolution of nutrient consumption of the IG and CG participants at baseline and at reassessment. To analyze the evolution of the adequacy of nutrient intake, the McNemar test was used.The effectiveness of the collective intervention to encourage the consumption of FV was analyzed using the generalized estimation equation model (GEE) in order to verify whether the consumption of nutrients and the adequacy of their consumption, during the intervention, presented the same pattern of behavior according to intervention time and group (IG and CG). For the GEE analysis related to nutrient consumption, an unstructured working correlation matrix for continuous dependent variables was used, and the gamma distribution model with log linkage function was adopted due to the asymmetry of the variables. And the association measure used to present the results was the relative risk with a confidence interval of 95%. As for the GEE analyses, regarding the adequacy of nutrient intake, the interchangeable work correlation matrix was considered and the logistic distribution model was adopted, with the association measure used being the odds ratio with a confidence interval of 95%. Both GEE analyses were adjusted for the variables gender, age, years of study, time of participation in PAS, BMI, and nutrient consumption at baseline.Data analyses were performed using the Data Analysis and Statistical Software (STATA) version 14.0. For the analysis of the GEE Model, the statistical program Statistical Package for the Social Sciences (SPSS) version 20.0 was used. In all analyses, a significance level lower than or equal to 5% was adopted (