Abstract
Longitudinal study, whose objective was to present a better strategy and statistical methods, and demonstrate its use with the data across the 2013-2015 period in schoolchildren aged 7 to 11 years, covered with the same food questionnaire (WebCAAFE) survey in Florianopolis, southern Brazil. Six meals/snacks and 32 foods/beverages yielded 192 possible combinations denominated meal/snack-Specific Food/beverage item (MSFIs). LASSO algorithm (LASSO-logistic regression) was used to determine the MSFIs predictive of overweight/obesity, and then binary (logistic) regression was used to further analyze a subset of these variables. Late breakfast, lunch and dinner were all associated with increased overweight/obesity risk, as was an anticipated lunch. Time-of-day or meal-tagged food/beverage intake result in large number of variables whose predictive patterns regarding weight status can be analyzed by machine learning such as LASSO, which in turn may identify the patterns not amenable to other popular statistical methods such as binary logistic regression.
Key words:
Diet surveys; Machine learning; Overweight; Obesity; Childhood