Reliability of 24-Hour Dietary Recalls as a Measure of Diet in African-American Youth

Abstract

Background Although it is a common practice to estimate dietary intake using three random 24-hour dietary recalls, some studies have suggested up to nine may be necessary to reliably estimate usual intake in youth. Given the resulting increase in resources and participant burden, more research is needed to determine if this method is reliable, particularly in African American youth at increased risk for obesity and other chronic diseases. Objective This study estimated the reliability with which 24-hour dietary recalls measure energy, fat, fruit, and vegetable intake in African American youth and examined how reliability changes as a function of the number of recalls. Design Cross-sectional data collection across three studies Participants/setting Participants were African American youth (n=456, 13.28±1.86 years old, 64% girls, 31.45±7.94 body mass index) who completed random 24-hour dietary recalls (67% completed three) conducted by research assistants using the Automated Self-Administered 24-hour Recall system (n=258) or registered dietitians using the Nutrition Data System for Research (n=198). Main outcome measures/statistical analyses Estimates provided by multilevel models were used to calculate the proportion of variance accounted for between individuals and the reliability of means within individuals as a function of the number of recalls. Results Reliability estimates for assessing dietary outcomes using 1-3 recalls ranged from 11-62%. To achieve 80% reliability, the following number of recalls would need to be conducted: 8 for energy intake, 13 for fat intake, 21-32 for fruit intake, and 21-25 for vegetable intake. Conclusions The common practice of assessing dietary intake with three recalls does so with low reliability in African American youth. Until more objective methods for reliably estimating usual intake are developed, researchers who choose to use 24-hour dietary recalls are encouraged to include estimates of the measure's reliability in a priori power calculations for improved decision-making regarding number observations and/or sample size.

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