Desenvolvimento de um questionário de frequência alimentar para atletas

• Ano de defesa: 2016
• Autor(a) principal: Godois, Allan da Mata
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Mato Grosso; Brasil; Faculdade de Nutrição (FANUT); UFMT CUC - Cuiabá; Programa de Pós-Graduação em Nutrição, Alimentos e Metabolismo
• Palavras-chave em Português: Inquéritos e questionários; Consumo de alimentos; Análise de regressão; Atletas; CNPQ::CIENCIAS DA SAUDE::NUTRICAO; Surveys and questionnaires; Food consumption; Regression analysis; Athletes
• Link de acesso: http://ri.ufmt.br/handle/1/2127

Food Frequency Questionnaires (FFQ) are widely used in nutritional epidemiology for research usual dietary intake. FFQ are nutrition surveys employed to analyze causal relationships (food-disease) by identifying the influence of diet in the state of disease and nutritional disorders. However, requires the use a questionnaire that takes into consideration the specificity of the target population for greater reliability of its inferences. Objective: To develop a quantitative Food Frequency Questionnaire for athletes. Methods: We studied 141 athletes in the state of Mato Grosso - Brazil who admitted the sport practice for competitive purposes. The sample selection was non-probability for convenience through contact with sports federations, training centers and sports academy. One hundred eleven were male (78,72%). The mean of variable was: age 23.36±7,77years; Body mass= 72.12±16,18kg; Height= 1.73±0,09m; BMI= 23.98±4.09kg/m2; Weekly hours of training= 2.57±1.33H. All athletes were asked to answer two 24-hour recalls (R24hrs). We used multiple regression stepwise analysis to select foods to determine interpersonal variability of energy intake, proteins, lipids, carbohydrates, calcium, iron, potassium, magnesium, zinc, phosphorus, niacin, pyridoxine, and vitamin C. Food items that contributed up to 90% of between-person variability and up to 90% of total energy intake were selected. In total 31 food items were selected and grouped into 11 categories: 1. Cereals, Tubers and Legumes; 2. Pasta, Dough, Pastry and Savory Snacks; 3. Meat and Eggs; 4. Milk and Dairy Products; 5. Vegetable; 6. Fruits and Juices; 7. Sugars and Sweets; 8. Drinks; 9. Oils, Fats and Oil seeds; 10. Mixed group; 11. Supplements. In the next step, it was stipulated the frequency of consumption for monthly, weekly or daily intervals from one to six times. The portion size middle was defined as equivalent to the median (50th percentile). The portions sizes small and large was determined by considering the coefficient of variation of the interquartile range of food consumption. Results: In total, we collected 240 R24hs of which were listed 244 food items and supplements reported. On average, about 7 food items (variation between one and 13) were responsible for discriminating about 90% of the between-person variability and nutrient intake in athletes. The nutrients with the lowest numbers of selected foods were pyridoxine and vitamin C (a food item). For energy, carbohydrate and magnesium it took 13 foods to explain about 90% of between-person variability of nutrient intake. Conclusion: Nutrient intake estimates obtained by instruments that do not consider the different aspect of the athlete's nutrition may provide conflicting information of actual consumption. This study describes the development process of a culturally appropriate FFQ to Mato Grosso athletes from R24hrs survey analysis. After validation, the FFQ developed can be used to estimate nutrient intake in population studies involving athletes.