Allan da Mata Godois1, Christianne de Faria Coelho-Ravagnani2, Raquel Raizel3, Eliseu Verly-Junior4. 1. Nucleus of Physical Fitness, Informatics, Metabolism, Sports and Health, Federal University of Mato Grosso, Cuiabá, Brazil. 2. Department of Physical Education, Faculty of Physical Education, Federal University of Mato Grosso do Sul, Campo Grande, Brazil. 3. Department of Food and Experimental Nutrition, Faculty of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil. 4. Institute of Social Medicine, Department of Epidemiology, Rio de Janeiro State University, Rio de Janeiro, Brazil.
Abstract
AIM: Until recently most dietary survey methods have been developed for non-athletic populations. In this study, we aimed to develop a Food Frequency Questionnaire (FFQ) to assess the regular dietary intake of Brazilian athletes. METHODS: We interviewed 141 athletes (23.36 ± 7.77 years old) for their dietary intake using 24-hour recalls. After grouping conceptually similar food items into 113 food items, percentage contribution analysis and stepwise regression models were used to highlight foods contributing to at least 90% of the between-person variability of key nutrients. RESULTS: The developed FFQ contained 59 foods which were important predictors of the variance in nutrient intake. Vitamin C and pyridoxine had the lowest number of selected foods. Conversely, 13 food items were required to explain the between-person variation for energy, carbohydrate and magnesium intake. CONCLUSIONS: Using a multiple regression analysis we developed a 59-food item questionnaire, which includes culturally specific food items and may represent an important dietary tool to analyse athletic populations. Reproducibility and validity of this FFQ will be verified in future research.
AIM: Until recently most dietary survey methods have been developed for non-athletic populations. In this study, we aimed to develop a Food Frequency Questionnaire (FFQ) to assess the regular dietary intake of Brazilian athletes. METHODS: We interviewed 141 athletes (23.36 ± 7.77 years old) for their dietary intake using 24-hour recalls. After grouping conceptually similar food items into 113 food items, percentage contribution analysis and stepwise regression models were used to highlight foods contributing to at least 90% of the between-person variability of key nutrients. RESULTS: The developed FFQ contained 59 foods which were important predictors of the variance in nutrient intake. Vitamin C and pyridoxine had the lowest number of selected foods. Conversely, 13 food items were required to explain the between-person variation for energy, carbohydrate and magnesium intake. CONCLUSIONS: Using a multiple regression analysis we developed a 59-food item questionnaire, which includes culturally specific food items and may represent an important dietary tool to analyse athletic populations. Reproducibility and validity of this FFQ will be verified in future research.