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The effect of personal characteristics on the validity of nutrient intake estimates using a food-frequency questionnaire

Published online by Cambridge University Press:  02 January 2007

Geoffrey C Marks ,
Maria Celia Hughes  and
Jolieke C van der Pols
Geoffrey C Marks*
Affiliation:
School of Population Health, University of Queensland, Herston, Queensland 4006, Australia
Maria Celia Hughes
Affiliation:
Queensland Institute of Medical Research, Herston, Queensland 4029, Australia
Jolieke C van der Pols
Affiliation:
School of Population Health, University of Queensland, Herston, Queensland 4006, Australia Queensland Institute of Medical Research, Herston, Queensland 4029, Australia
*
*Corresponding author: Email [email protected]
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Abstract

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Objective

To assess validity of the Nambour food-frequency questionnaire (FFQ) relative to weighed food records (WFRs), and the extent to which selected demographic, anthropometric and social characteristics explain differences between the two dietary methods.

Design

Inter-method validity study; 129-item FFQ vs. 12 days of WFR over 12 months.

Setting

Community-based Nambour Skin Cancer Prevention Trial.

Subjects

One hundred and fifteen of 168 randomly selected participants in the trial (68% acceptance rate) aged 25–75 years.

Results

Spearman correlations between intakes from the two methods ranged from 0.18 to 0.71 for energy-adjusted values. Differences between FFQ and WFR regressed on personal characteristics were significantly associated with at least one characteristic for 16 of the 21 nutrients. Sex was significantly associated with differences for nine nutrients; body mass index (BMI), presence of any medical condition and age were each significantly associated with differences for three to six nutrients; use of dietary supplements and occupation were associated with differences for one nutrient each. There was no consistency in the direction of the significant associations. Regression models explained from 7% (riboflavin) to 27% (saturated fat) of variation in differences in intakes.

Conclusions

The relative validity of FFQ estimates for many nutrients is quite different for males than for females. Age, BMI, medical condition and level of intake were also associated with relative validity for some nutrients, resulting in the need to adjust intakes estimates for these in modelling diet-disease relationships. Estimates for cholesterol, β-carotene equivalents, retinol equivalents, thiamine, riboflavin and calcium would not benefit from this.

Keywords

Food-frequency questionnaireWeighed diet recordsRelative validityDietary assessment
Type
Research Article
Information
Public Health Nutrition , Volume 9 , Issue 3 , May 2006 , pp. 394 - 402
Copyright
Copyright © The Authors 2006

References

1Block, G. A review of validations of dietary assessment methods. American Journal of Epidemiology 1982; 115(4): 492–505.CrossRefGoogle ScholarPubMed
2Willett, WC. Nutritional Epidemiology, 2nd ed. New York: Oxford University Press, 1998.CrossRefGoogle Scholar
3Cade, J, Thompson, R, Burley, V, Warm, D. Development, validation and utilisation of food-frequency questionnaires - a review. Public Health Nutrition 2002; 5(4): 567–87.CrossRefGoogle ScholarPubMed
4Armstrong, B, White, E, Saracci, R. Principles of Exposure Measurement in Epidemiology, Oxford: Oxford University Press, 1992.CrossRefGoogle Scholar
5Kohlmeier, L, Bellach, B. Exposure assessment error and its handling in nutritional epidemiology. Annual Review of Public Health 1995; 16: 43–59.CrossRefGoogle ScholarPubMed
6Green, A, Battistutta, D, Hart, V, Leslie, D, Marks, G, Williams, G. The Nambour Skin Cancer and Actinic Eye Disease Prevention Trial: design and baseline characteristics of participants. Controlled Clinical Trials 1994; 15(6): 512–22.CrossRefGoogle ScholarPubMed
7Green, A, Williams, G, Neale, R, Hart, V, Leslie, D, Parsons, P. Daily sunscreen application and β-carotene supplementation in prevention of basal-cell and squamous-cell carcinomas of the skin: a randomised controlled trial. Lancet 1999; 354(9183): 723–9.CrossRefGoogle ScholarPubMed
8Ashton, BA, Marks, GC, Battistutta, D, Green, A. the Nambour Study Group (1996) Under-reporting of energy intake in two methods of dietary assessment in the Nambour Trial. Australian Journal of Nutrition and Dietetics 1999; 53: 53–60.Google Scholar
9Willett, WC, Sampson, L, Stampfer, MJ, Rosner, B, Bain, C, Witschi, J. Reproducibility and validity of a semiquantitative food frequency questionnaire. American Journal of Epidemiology 1985; 122(1): 51–65.CrossRefGoogle ScholarPubMed
10Department of Community Services and Health. National Dietary Survey of Adults: 1983. No. 2. Nutrient Intakes. Canberra: Australian Government Publishing Service, 1987.Google Scholar
11Radimer, KL, Harvey, PWJ, Green, A, Orrell, E. Compliance with dietary goals in a Queensland community. Australian Journal of Public Health 1992; 16(3): 277–81.CrossRefGoogle Scholar
12 Food Standards Australia New Zealand. Nuttab95: Nutrient data table for use in Australia [online]. Available at http://www.foodstandards.gov.au/recallssurveillance/foodcompositionprogram/Google Scholar
13Xyris Software. Diet 1: Nutrient Calculation Software, Version 3.1. [computer program]. Brisbane: Xyris Software (Australia) Pty Ltd, 1991.Google Scholar
14Bland, J, Altman, D. Measuring agreement in method comparison studies. Statistical Methods in Medical Research 1999; 8(2): 135–60.CrossRefGoogle ScholarPubMed
15Ludbrook, J. Statistical techniques for comparing measurers and methods of measurement: a critical review. Clinical and Experimental Pharmacology & Physiology 2002; 29(7): 527–36.CrossRefGoogle ScholarPubMed
16SAS Institute. SAS System for Windows, Version Release 8.02. [computer program], Cary, NC: SAS Institute, 1999–2001.Google Scholar
17Hodge, A, Patterson, A, Brown, W, Ireland, P, Giles, G. The Anti-Cancer Council of Victoria FFQ: relative validity of nutrient intakes compared with weighed food records in young to middle-aged women in a study of iron supplementation. Australian and New Zealand Journal of Public Health 2000; 24(6): 576–83.CrossRefGoogle Scholar
18Ambrosini, GL, Mackerras, D, de Klerk, N, Musk, A. Comparison of an Australian food frequency questionnaire with diet records: implications for nutrition surveillance. Public Health Nutrition 2003; 6(4): 415–22.CrossRefGoogle ScholarPubMed
19Nelson, M. The validation of dietary assessment. In: Margetts, B, Nelson, M, eds. Design Concepts in Nutritional Epidemiology. Oxford: Oxford University Press, 1997; 241–72.CrossRefGoogle Scholar
20Rimm, E, Giovannucci, E, Stampfer, MJ, Colditz, GA, Litin, L, Willett, WC. Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals. American Journal of Clinical Nutrition 1992; 135(10): 1114–26.Google ScholarPubMed
21Bingham, SA, Gill, C, Welch, A, Day, K, Cassidy, A, Khaw, KT, et al. Comparison of dietary assessment methods in nutritional epidemiology: weighed records v. 24 h recalls, food frequency questionnaires and estimated-diet records. British Journal of Nutrition 1994; 72(4): 619–43.CrossRefGoogle ScholarPubMed
22Masson, L, McNeill, G, Tomany, J, Simpson, JA, Peace, HS, Wei, L, et al. Statistical approaches for assessing the relative validity of a food-frequency questionnaire: use of correlation coefficients and the kappa statistic. Public Health Nutrition 2003; 6(3): 313–21.CrossRefGoogle ScholarPubMed
23Goldberg, G, Black, A, Jebb, S, Cole, TJ, Murgatroyd, PR, Coward, WA, et al. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. European Journal of Clinical Nutrition 1991; 45(12): 569–81.Google ScholarPubMed
24Bingham, SA, Cassidy, A, Cole, T, Welch, A, Runswick, SA, Black, AE, et al. Validation of weighed records and other methods of dietary assessment using the 24 h urine nitrogen technique and other biomarkers. British Journal of Nutrition 1995; 73(4): 531–50.CrossRefGoogle Scholar
25Subar, AF, Thompson, FE, Kipnis, V, Midthune, D, Hurwitz, P, McNutt, S, et al. Comparative validity of the Block, Willett, and National Cancer Institute food frequency questionnaires. American Journal of Epidemiology 2001; 154(12): 1089–99.CrossRefGoogle Scholar
26Cade, JE, Burley, VJ, Warm, DL, Thompson, RL, Margetts, BM. Food-frequency questionnaires: a review of their design, validation and utilisation. Nutrition Research Reviews 2004; 17: 5–22.CrossRefGoogle ScholarPubMed
27Michels, KB, Welch, AA, Luben, R, Bingham, SA, Day, NE. Measurement of fruit and vegetable consumption with diet questionnaires and implications for analyses and interpretation. American Journal of Epidemiology 2005; 161: 987–94.CrossRefGoogle ScholarPubMed