It has been recognized that use of a single short-term dietary recall, such as the 24 h dietary recall (24HR), is not enough to predict the usual intake. In order to establish the actual association between diet, chronic diseases and associated factors, it is essential to estimate the usual dietary intake. ![]() Since the methods explore different angles of dietary intake, using both approaches simultaneously might yield complementary and useful information. The PCA aims to reduce a large set of correlated variables into a small set of non-correlated variables that contains the same information of the larger one, revealing the underlying structure within diets of the population. ![]() A common technique used is the principal components analysis (PCA), followed by factor analysis. Data-driven approaches are considered important from a public health point of view because they can be used to assist with the development of food-based guidelines. The data-driven approach, on the other hand, provides insight into the dietary behavior of participants, since the evaluation of overall dietary pattern is based on the actual population food intake. Dietary indices are the most common hypothesis-oriented approaches that evaluate the adherence of population intake to nutritional recommendations. The first one, also called “a priori” analysis, is based on previous information (food guides and nutritional recommendations) used to stratify a dietary pattern. Both have different purposes that are useful for deriving meaningful dietary patterns that can be associated or not to a particular health outcome. Currently, two major approaches have been widely used: hypothesis-oriented and data-driven analysis. The use of dietary patterns to assess dietary intake has become increasingly common in nutritional epidemiology studies due to the complexity and multidimensionality of the diet. In this study, using both approaches at the same time provided consistent and complementary information with regard to assessing the overall dietary habits that will be important in order to drive public health programs, and improve their efficiency to monitor and evaluate the dietary patterns of populations. High intakes of sodium, fats and sugars were observed in hypothesis-driven analysis with low total scores for Sodium, Saturated fat and SoFAA (calories from solid fat, alcohol and added sugar) components in agreement, while the data-driven approach showed the intake of several foods/food groups rich in these nutrients, such as butter/margarine, cookies, chocolate powder, whole milk, cheese, processed meat/cold cuts and candies. ![]() In the results, hypothesis-driven analysis showed low scores for Whole grains, Total vegetables, Total fruit and Whole fruits), while, in data-driven analysis, fruits and whole grains were not presented in any pattern. In the data-driven approach, the usual intake of foods/food groups was estimated by the Multiple Source Method. In hypothesis-driven analysis, based on the American National Cancer Institute method, the usual intake of Brazilian Healthy Eating Index Revised components were estimated. Food intake from a cross-sectional survey with 295 adolescents was assessed by 24 h dietary recall (24HR). Since the methods explore different angles of dietary intake, using both approaches simultaneously might yield complementary and useful information thus, we aimed to use both approaches to gain knowledge of adolescents’ dietary patterns. Currently, two main approaches have been widely used to assess dietary patterns: data-driven and hypothesis-driven analysis.
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