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Inter-and intra-individual variability in food intake of elderly people in Perugia (Italy)

Published online by Cambridge University Press:  09 March 2007

R. Borrelli ,
M. S. Simonetti  and
F. Fidanza
R. Borrelli
Affiliation:
Institute of Nutrition, University of Perugia, Perugia, Italy National Cancer Institute‘Fondazione G. Pascale’, Naples, Italy
M. S. Simonetti
Affiliation:
Institute of Nutrition, University of Perugia, Perugia, Italy
F. Fidanza
Affiliation:
Institute of Nutrition, University of Perugia, Perugia, Italy
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Abstract

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It has been shown that each individual has a considerable day-to-day variation (intra-individual variation) in his or her level of food consumption. A large intra-individual variation has adverse effects on the reliability of research studies. The effect of the intra-individual variation can be minimized by taking food intake records over several days. An increase in the number of days entails higher costs, and this could limit the practicability of the study. In the recent literature on the methodology of dietary surveys, there is a growing interest in the estimation of the number of days required to conduct a reliable dietary survey. Recent developments in statistical theory allow the problem of large intra-individual variability to be overcome. These new statistical techniques require knowledge of the intra- and inter-individual variability and the appropriate adjustment of the statistical results. The aim of the present study was to evaluate the number of days of recorded intake required to obtain an estimate of the components of variance (the inter-individual and the intra-individual variability) focusing on foods rather than nutrients.

Keywords

Components of varianceDietary surveysElderly
Type
Dietary Intake of Individuals
Information
British Journal of Nutrition , Volume 68 , Issue 1 , July 1992 , pp. 3 - 10
Copyright
Copyright © The Nutrition Society 1992

References

REFERENCES

Armitage, P. & Berry, G. (1987). Statistical Methods in Medical Research, 2nd ed. Oxford: Blackwell Scientific Publications.Google Scholar
Basiotis, P. P., Welsh, S. O., Cronin, F. J., Kelsay, J. L. & Mertz, W. (1987). Number of days of food intake records required to estimate individual and group nutrient intake by men and women as determined from one year's daily dietary records. Journal of Nutrition 117, 1638–1641.CrossRefGoogle Scholar
Beaton, G. H. (1988). Nutrient intake and population data. Proceedings of the Nutrition Society 47, 63–78.CrossRefGoogle ScholarPubMed
Bingham, S. A. (1987). The dietary assessment of individuals; methods, accuracy, new techniques and recommendations. Nutrition Abstracts and Reviews 57, 705–742.Google Scholar
Black, A. E., Cole, T. J., Wiles, S. J. & Winter, F. (1983). Daily variation in food intake in infants from 2 to 18 months. Human Nutrition: Applied Nutrition 37A, 448–458.Google Scholar
Borrelli, R., Cole, T. J., Di Biase, G. & Contaldo, F. (1989). Some statistical considerations on dietary assessment methods. European Journal of Clinical Nutrition 43, 453–463.Google ScholarPubMed
Cochran, W. G. (1968). Errors of measurement in statistics. Technometrics 10, 637–666.CrossRefGoogle Scholar
Fidanza, F., Coli, R., Damiani, P., Fiorini, D., Maurizi, A. & Simonetti, M. S. (1988). Nutritional status of a group of institutionalized elderly people in Perugia (Italy). International Journal of Vitamin and Nutrition Research 58, 85–90.Google ScholarPubMed
Liu, K. (1988). Measurement error and its impact on partial correlation coefficient and multiple linear regression analyses. American Journal of Epidemiology 12, 864–874.CrossRefGoogle Scholar
Liu, K. (1989). Consideration of and compensation for intraindividual variability in nutrient intake. In Epidemiology, Nutrition and Health, pp. 87–97 [Kohlmeier, L. and Helsing, H., editors]. London: Smith-Gordon.Google Scholar
Liu, K., Stamler, J., Dyer, A., McKeever, J. & McKeever, P. (1978). Statistical methods to assess and minimize the role of intraindividual variability in obscuring the relationship between dietary lipid and serum cholesterol. Journal of Chronic Diseases 31, 399–418.CrossRefGoogle ScholarPubMed
Marr, J. W. & Heady, J. A. (1986). Within- and between-person variation in dietary surveys: number of days needed to classify individuals. Human Nutrition: Applied Nutrition 40A, 347–364.Google Scholar
National Research Council (1986). Nutrient Adequacy: Assessment Using Food Consumption Surveys. Report of the Food and Nutrition Board. Washington, DC: National Academy of Sciences.Google Scholar
Nelson, M., Black, A. E., Morris, J. A. & Cole, T. J. (1989). Between- and within-person variation in nutrient intake from infancy to old age: estimating the number of days required to rank dietary intakes with desired precision. American Journal of Clinical Nutrition 50, 155–167.CrossRefGoogle Scholar
Rosner, B., Spiegelman, D. & Willett, W. C. (1990). Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error. American Journal of Epidemiology 132, 734–745.CrossRefGoogle ScholarPubMed
Rosner, B. & Willett, W. C. (1988). Interval estimates for correlation coefficient corrected for within-person variation: implications for study design and hypothesis testing. American Journal of Epidemiology 127, 377–386.CrossRefGoogle ScholarPubMed