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. 2001 Jan;101(1):70-80.
doi: 10.1016/S0002-8223(01)00018-9.

Biased over- or under-reporting is characteristic of individuals whether over time or by different assessment methods

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Biased over- or under-reporting is characteristic of individuals whether over time or by different assessment methods

A E Black et al. J Am Diet Assoc. 2001 Jan.

Abstract

Seven studies with repeated measurements of energy intake and/or nitrogen intake were examined to determine whether misreporting is characteristic of some persons or occurs randomly. Four of the studies were validated by doubly labeled water measurements of energy expenditure. Reporting validity was expressed as the ratio of energy intake to energy expenditure. Ratios were consistently below the expected value of 1.0 for some subjects and consistently above 1.0 for others, indicating characteristic reporting validity within subjects. Two year-long studies provided 4 to 12 measurements and a total number of days sufficient to measure individual habitual intake. Subjects mean energy intake to basal metabolic rate (BMR) ratios were < 1.35 in 45% and 47% and < 1.35 at every measurement in 25% of subjects. This indicated persistent underreporting over time, because 1.35 x BMR is the minimum energy expenditure compatible with a normally active lifestyle. Three of the studies used more than 1 assessment method (validated by doubly labeled water and/or urinary nitrogen excretion). There was a tendency for persons determined to be underreporters by 1 method to be also underreporters when tested by other methods. We conclude that biased over- or underreporting is characteristic of some persons. Thus, repeat measurements do not necessarily provide valid measures of individual intake, extreme intakes may reflect under- and overreporting rather than true low or high intakes, and subjects most prone to reporting bias may be repeatedly misclassified in quantiles of the distribution. This presents a challenge to dietitians nutritionists, and statisticians both for the design of surveys and the handling of flawed data.

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