Social norms, advertising and food consumption in schoolchildren: modeling using fuzzy logic

Abstract

Mexico ranks first in childhood obesity in the world, so it is important to identify variables associated with food consumption. The objective of this work was to establish whether the way in which food consumption is modified depending on social food norms and food advertising received by school children. A predictive multivariate study was designed using interval type two fuzzy logic systems (IT2 FLS), and comparing its fit with conventional models, such as multiple linear regression (RLM). We worked with the responses issued by 196 children in a previous study and stored in a database, selecting only those that corresponded to the variables of interest for the study. The social norms to avoid, the number of meals and the purchase of food through food advertising made it possible to predict children’s food consumption through IT2 FLS. In RLM, mealtimes had a greater predictive capacity than the number of meals. The IT2 FLS provided a higher coefficient of determination (R² = 0.649) than that of the RLM (R² = 0.370). Food consumption, being a multicausal and complex phenomenon, can be better predicted by using analysis methods that manage uncertainty more flexibly, as the IT2 FLS does.