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Selecting informative food items for compiling food-frequency questionnaires: Comparison of procedures

Author: Molag, M.L. · Vries, J.H.M. de · Duif, N. · Ocké, M.C. · Dagnelie, P.C. · Goldbohm, R.A. · Veer, P. van 't
Institution: TNO Kwaliteit van Leven
Source:British Journal of Nutrition, 3, 104, 446-456
Identifier: 409292
Keywords: Health · Leefomgeving en gezondheid · Diet · Epidemiological methods · FFQ · Nutrition assessment · Ascorbic acid · Calcium · Carbohydrate · Fat · Automation · Calcium intake · Carbohydrate intake · Dietary fiber · Fat intake · Food frequency questionnaire · Food intake · Food preference · Human · Intermethod comparison · Nutritional assessment · Vitamin intake · Analysis of variance · Comparative study · Diet · Factual database · Food · Mathematical computing · Middle aged · Questionnaire · Regression analysis · Standard · Statistics · Technique · Adult · Aged · Analysis of Variance · Databases, Factual · Diet · Diet Surveys · Food · Humans · Mathematical Computing · Methods · Middle Aged · Questionnaires · Regression Analysis


The authors automated the selection of foods in a computer system that compiles and processes tailored FFQ. For the selection of food items, several methods are available. The aim of the present study was to compare food lists made by MOM2, which identifies food items with highest between-person variance in intake of the nutrients of interest without taking other items into account, with food lists made by forward regression. The name MOM2 refers to the variance, which is the second moment of the nutrient intake distribution. Food items were selected for the nutrients of interest from 2d of recorded intake in 3524 adults aged 25-65 years. Food lists by 80% MOM2 were compared to those by 80% explained variance for regression on differences between the number and type of food items, and were evaluated on (1) the percentage of explained variance and (2) percentage contribution to population intake computed for the selected items on the food list. MOM2 selected the same food items for Ca, a few more for fat and vitamin C, and a few less for carbohydrates and dietary fibre than forward regression. Food lists by MOM2 based on 80% of variance in intake covered 75-87% of explained variance for different nutrients by regression and contributed 53-75% to total population intake. Concluding, for developing food lists of FFQ, it appears sufficient to select food items based on the contribution to variance in nutrient intake without taking covariance into account. © 2010 The Authors.