Hostname: page-component-7479d7b7d-rvbq7 Total loading time: 0 Render date: 2024-07-08T13:25:02.884Z Has data issue: false hasContentIssue false
  • English
  • Français

Consistency of food intake factors by different dietary assessment methods and population groups

Published online by Cambridge University Press:  09 March 2007

Per Togo ,
Berit L. Heitmann ,
Thorkild I. A. Sørensen  and
Merete Osler
Per Togo*
Affiliation:
Research Unit for Dietary Studies, Copenhagen University Hospital, Copenhagen, Denmark Copenhagen County Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
Berit L. Heitmann
Affiliation:
Research Unit for Dietary Studies, Copenhagen University Hospital, Copenhagen, Denmark Danish Epidemiology Science Centre at the Institute of Preventive Medicine, Copenhagen University Hospital, Copenhagen, Denmark
Thorkild I. A. Sørensen
Affiliation:
Danish Epidemiology Science Centre at the Institute of Preventive Medicine, Copenhagen University Hospital, Copenhagen, Denmark
Merete Osler
Affiliation:
Institute of Public Health, University of Copenhagen, Copenhagen, Denmark
*
*Corresponding author: Dr Per Togo, present address, Kong Oscars Gade 9, 2.th., DK-2100 Copenhagen Ø, Denmark, fax +45 3332 4240, email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Several studies have used factor analysis to identify food intake patterns in epidemiological studies as an alternative to nutrient-based analyses, but few have validated the factors in a larger population. Our present objectives were: to compare the factor scores based on a food-frequency questionnaire (FFQ) with scores based on a 7 d diet record; to examine the consistency of the factor score correlations across strata of age, BMI, energy intake, education, physical activity and smoking and to compare factors identified in two sub-populations. In 879 men and 927 women, of the total population sample of 3785, scores on food intake factors, three for men (‘green’, sweet’ and ‘traditional’) and two for women (‘green’ and ‘sweet–traditional’), identified in data from the FFQ and the diet record, were compared. The loadings of foods on the factors were very similar and the correlations between the corresponding factor scores, based on the two dietary assessment methods, were: for men ‘green’ 0·61, ‘sweet’ 0·55, ‘traditional’ 0·34; for women, ‘green’ 0·61, ‘sweet–traditional’ 0·57. Stratification did not significantly modify the correlations, with a few inconsistent exceptions. Factors obtained in a different subsample of the population, for which there was only data from the FFQ, were almost identical to the factors found in the subsample, who provided both FFQ and diet record information with regard to food loadings and model fit. In conclusion, the food intake factors identified were reproducible using two different dietary assessment methods and, furthermore, independent of stratification.

Keywords

DietFactor analysisObservational studiesValidation
Type
Research Article
Information
British Journal of Nutrition , Volume 90 , Issue 3 , September 2003 , pp. 667 - 678
Copyright
Copyright © The Nutrition Society 2003

References

Barker, ME, McClean, SI, Thompson, KA & Reid, NG (1990) Dietary behaviours and sociocultural demographics in Northern Ireland. Br J Nutr 64, 319–329.CrossRefGoogle ScholarPubMed
Beaudry, M, Galibois, I & Chaumette, P (1998) Dietary patterns of adults in Quebec and their nutritional adequacy. Can J Public Health 89, 347–351.CrossRefGoogle ScholarPubMed
Fricker, J, Baelde, D, Igoin, A, Huet, JM & Apfelbaum, M (1992) Underreporting of food intake in obese “small eaters”. Appetite 19, 273–283.CrossRefGoogle ScholarPubMed
Gex-Fabry, M, Raymond, L & Jeanneret, O (1988) Multivariate analysis of dietary patterns in 939 Swiss adults: sociodemo-graphic parameters and alcohol consumption profiles. Int J Epidemiol 17, 548–555.CrossRefGoogle Scholar
Heitmann, BL (1993) The influence of fatness, weight change, slimming history and other lifestyle variables on diet reporting in Danish men and women aged 35-65 years. Int J Obes Relat Metab Disord 17, 329–336.Google ScholarPubMed
Hu, FB (2002) Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 13, 3–9.CrossRefGoogle ScholarPubMed
Hu, FB, Rimm, E, Smith, WS, et al. (1999) Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr 69, 243–249.CrossRefGoogle ScholarPubMed
Hu, FB, Rimm, EB, Stampfer, MJ, Ascherio, A, Spiegelman, D & Willett, WC (2000) Prospective study of major dietary patterns and risk of coronary heart disease in men. Am J Clin Nutr 72, 912–921.CrossRefGoogle ScholarPubMed
Jacobson, HN & Stanton, JL (1986) Pattern analysis in nutrition. Clin Nutr 5, 249–253.Google Scholar
Jørgensen, LM (1992) Who completes seven-day food records? Eur J Clin Nutr 46, 735–741.Google ScholarPubMed
Kant, AK (1996) Indexes of overall diet quality: a review. J Am Diet Assoc 96, 785–791.CrossRefGoogle ScholarPubMed
Kleinbaum, DG, Kupper, LL, Muller, KE & Nizam, A (1998) Testing for the equality of two correlations. In Applied Regression Analysis and other Multivariable Methods, 3rd ed., chapters 6 and 7, pp. 99–100. Pacific Grove, CA: Duxbury Press.Google Scholar
Levnedsmiddelstyrelsen (1985) Levnedsmiddeltabeller (The Danish Food Composition Tables), 2nd ed. [A Møller, editor]. Frederikshavn, Denmark: Dafolo forlag, Dafolo A/S.Google Scholar
Loehlin, JC (1998) Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis, 3rd ed. Mahwah, NJ:Lawrence Erlbaum Associates, Inc.Google Scholar
McCann, SE, Weiner, J, Graham, S & Freudenheim, JL (2001) Is principal components analysis necessary to characterise dietary behaviour in studies of diet and disease? Public Health Nutr 4, 903–908.CrossRefGoogle Scholar
Martinez, ME, Marshall, JR & Sechrest, L (1998) Invited commentary: Factor analysis and the search for objectivity (comment). Am J Epidemiol 148, 17–19.CrossRefGoogle Scholar
Maskarinec, G, Novotny, R & Tasaki, K (2000) Dietary patterns are associated with body mass index in multiethnic women. J Nutr 130, 3068–3072.CrossRefGoogle ScholarPubMed
MONICA Data Centre (2000) The WHO MONICA project. http://www.ktl.fi/Monica/Google Scholar
Mullen, K, Williams, R & Hunt, K (2000) Irish descent, religion and food consumption in the west of Scotland. Appetite 34, 47–54.CrossRefGoogle ScholarPubMed
Muthen, LK & Muthen, BO (2001) Mplus User's Guide, Version 2. Los Angeles, CA: Muthen & Muthen. http://www.statmodel. comGoogle Scholar
Mørup, I (1986) Micro-CAMP®: Computer Assisted Menu Planning APS (computer program; no longer available). Herlev, Denmark: Herlev University Hospital.Google Scholar
Quatromoni, PA, Copenhafer, DL, Demissie, S, et al. (2002) The internal validity of a dietary pattern analysis. The Framingham Nutrition Studies. J Epidemiol Community Health 56, 381–388.CrossRefGoogle ScholarPubMed
Randall, DE, Marshall, JR, Brasure, J & Graham, S (1991) Patterns in food use and compliance with NCI dietary guidelines. Nutr Cancer 15, 141–158.CrossRefGoogle ScholarPubMed
Schoeller, DA (1995) Limitations in the assessment of dietary E intake by self-report. Metabolism 44, 18–22.CrossRefGoogle Scholar
Schulze, MB, Hoffmann, K, Kroke, A & Boeing, H (2001) Dietary patterns and their association with food and nutrient intake in the European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam study. Br J Nutr 85, 363–373.CrossRefGoogle ScholarPubMed
Schulze, MB, Hoffmann, K, Kroke, A & Boeing, H (2003) An approach to construct simplified measures of dietary patterns from exploratory factor analysis. Br J Nutr 89, 409–418.CrossRefGoogle ScholarPubMed
Slattery, ML & Boucher, KM (1998) The senior authors' response: Factor analysis as a tool for evaluating eating patterns (comment). Am J Epidemiol 148, 20–21.CrossRefGoogle Scholar
Slattery, ML, Boucher, KM, Caan, BJ, Potter, JD & Ma, KN (1998) Eating patterns and risk of colon cancer. Am J Epidemiol 148, 4–16.CrossRefGoogle ScholarPubMed
Togo, P, Osler, M, Sørensen, TIA & Heitmann, BL (2001) Food intake patterns and body mass index in observational studies. Int J Obes Relat Metab Disord 25, 1741–1751.CrossRefGoogle ScholarPubMed
Tseng, M & DeVillis, R (2000) Correlates of the “western” and “prudent” diet patterns in the US. Ann Epidemiol 10, 481–482.CrossRefGoogle ScholarPubMed
Whichelow, MJ & Prevost, AT (1996) Dietary patterns and their associations with demographic, lifestyle and health variables in a random sample of British adults. Br J Nutr 76, 17–30.CrossRefGoogle Scholar
Willett, WC (1998) Nutritional Epidemiology. 2nd ed. Monographs in Epidemiology and Biostatistics, Vol.30. New York: Oxford University Press, Inc.CrossRefGoogle Scholar
Williams, C, Wiseman, M & Butriss, J (editors) (1999) Food-based dietary guidelines – a staged approach. Conclusions. Br J Nutr 81, Suppl. 2, S153.Google Scholar
World Health Organization (2000) Obesity: Preventing and Managing the Global Epidemic. Report of a WHO consultation, no. 894, p. i-253. Geneva, WHO.Google Scholar