A B S T R A C T ObjectiveEvaluate the use of the Nutrabem (São Paulo, Brasil) mobile application as a tool for measurement of food intake among university students. MethodsCross-sectional study of a random sample of 40 undergraduate students at the Universidade Federal de São Paulo, Campus Baixada Santista. Food intake data were estimated using the Nutrabem app and the 24-hour dietary recall. Intakes of energy, carbohydrates, proteins, lipids, calcium, iron, and vitamin C were calculated. The intake of food groups and diet quality were evaluated by the Diet Quality Index associated with the Digital Food Guide. The agreement between the methods was assessed using the Pearson's correlation coefficient and the Student' t-test. ResultsStrong correlations were observed between energy (0.77), carbohydrates (0.82) and protein (0.83). The groups: poultry, fish, and eggs; beef and pork; refined grains and breads; and fruits and legumes showed strong correlations (between 0.76 and 0.85). There were moderate correlations (0.59 and 0.71) between the groups sugars and sweets; whole grains, tubers and roots, milk and dairy products, animal fats, and the Diet Quality Index associated with the Digital Food Guide scores. Vegetables and leafy greens, nuts, and vegetable oils showed weak correlations (0.31 and 0.43). Homogeneity assessment revealed similarity between the results obtained by both methods (p>0.05). ConclusionThe Nutrabem app can be used as a tool to assess dietary intake among university students since it produces results similar to those obtained by the 24-hour dietary recall method.
Objective: To analyze the dietary intake of university students according to the degree of food processing. Methods: Crosssectional study of a random sample of 40 undergraduate students at the Universidade Federal de São Paulo -campus Baixada Santista (Federal University of São Paulo). Dietary intake was estimated by three non-consecutive 24-hour recalls. Mean intake of energy, carbohydrates, proteins, lipids, calcium, iron, sodium and dietary fiber were calculated. Each food reported was classified according to the degree of processing and organized by food group to evaluate the quality of the diet. Results: The mean energy intake was 1752.27 kcal (SD = 575.26 kcal), being 42.19% from unprocessed or minimally processed foods, 9.71% from processed foods, 7.09% from processed culinary ingredients and 41.01% from ultra-processed foods. There was higher contribution of unprocessed or minimally processed foods for the quotas of protein, iron and dietary fiber, and of the ultra-processed ones for carbohydrates, lipids and sodium. Discussion: Intake of ultraprocessed foods represents almost half of the contribution to the daily energy of university students. Foods of ultra-processed group offer lower contribution of dietary fiber and micronutrients and are most likely to be of high sodium content. Conclusions: It is possible to project that the maintenance of this dietary profile may have negative effects on health, due to the risks associated with high consumption on ultra-processed foods.
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