AbstractObjectives:To highlight the differences between the food list required in a food-frequency questionnaire (FFQ) to rank people by their intake and the food items that contribute to absolute intake, and to discuss possible applications.Methods:We conducted a nutritional survey among 1173 adults using an adapted 24-hour recall questionnaire.Statistical analysis:To develop an FFQ, we analysed the 24-hour recall survey data by performing a stepwise multiple regression after grouping conceptually similar food items into 175 food groups.Results:In total, 126 food groups were included in the developed FFQ in order to explain at least 80% of the variance in the consumption of each of 27 nutrients. The nutrients that were explained by a few food groups were vitamin A (one food group), alcohol (two), β-carotene (two), vitamin E (three) and cholesterol (five). Nutrients that were explained by a large number of food groups were energy (37 food groups), potassium (31), magnesium (31), dietary fibre (30), phosphorus (31) and sodium (29). Using energy intake as an example, soft drinks were the best between-person energy classifiers, while providing only 2.4% of the total energy intake. Wine, seeds and nuts, which contributed highly to the variance, were minor energy contributors. In contrast, milk, sugar, fried chicken/turkey breast or whole chicken/turkey, which explained little of the variation in the population, were major energy contributors.Conclusions:Developing an FFQ on the basis of common foods may not explain the between-person variation required for ranking individual intake in diet–disease studies. Producing lists of ‘discriminating items’ can be a useful application in developing mini-FFQs for selected nutrients.
A new tool for converting food frequency questionnaire data into nutrient and food group values: FETA research methods and availability
