Retrospectively Estimating Energy Intake and Misreporting From a Qualitative Food Frequency Questionnaire: An Example Using Australian Cohort and National Survey Data.

James P. Goode,Alison J. Venn,Costan G. Magnussen,Wendy H. Oddy,Monique Breslin,Kylie J. Smith,Terence Dwyer,Michelle Kilpatrick

doi:10.3389/fnut.2021.624305

Abstract

Qualitative food frequency questionnaires (Q-FFQ) omit portion size information from dietary assessment. This restricts researchers to consumption frequency data, limiting investigations of dietary composition (i.e., energy-adjusted intakes) and misreporting. To support such researchers, we provide an instructive example of Q-FFQ energy intake estimation that derives typical portion size information from a reference survey population and evaluates misreporting. A sample of 1,919 Childhood Determinants of Adult Health Study (CDAH) participants aged 26–36 years completed a 127-item Q-FFQ. We assumed sex-specific portion sizes for Q-FFQ items using 24-h dietary recall data from the 2011–2012 Australian National Nutrition and Physical Activity Survey (NNPAS) and compiled energy density values primarily using the Australian Food Composition Database. Total energy intake estimation was daily equivalent frequency × portion size (g) × energy density (kJ/g) for each Q-FFQ item, summed. We benchmarked energy intake estimates against a weighted sample of age-matched NNPAS respondents (n = 1,383). Median (interquartile range) energy intake was 9,400 (7,580–11,969) kJ/day in CDAH and 9,055 (6,916–11,825) kJ/day in weighted NNPAS. Median energy intake to basal metabolic rate ratios were 1.43 (1.15–1.78) in CDAH and 1.35 (1.03–1.74) in weighted NNPAS, indicating notable underreporting in both samples, with increased levels of underreporting among the overweight and obese. Using the Goldberg and predicted total energy expenditure methods for classifying misreporting, 65 and 41% of CDAH participants had acceptable/plausible energy intake estimates, respectively. Excluding suspected CDAH misreporters improved the plausibility of energy intake estimates, concordant with expected body weight associations. This process can assist researchers wanting an estimate of energy intake from a Q-FFQ and to evaluate misreporting, broadening the scope of diet–disease investigations that depend on consumption frequency data.

Highlights

Dietary assessment using food frequency questionnaires (FFQ) is common in large-scale epidemiological studies, largely due to practical necessity [1, 2]
We provide an instructive example of qualitative food frequency questionnaire (Q-FFQ) energy intake estimation followed by an assessment of energy misreporting, including the creation of an accompanying portion size and energy density database
The use of three 24-h dietary recalls (24-HDR) and greater portion size estimation assistance in the automated multiple-pass method validation study may partly explain the lower degree of energy underreporting to what we found in Nutrition and Physical Activity Survey (NNPAS)

Summary

Introduction

Dietary assessment using food frequency questionnaires (FFQ) is common in large-scale epidemiological studies, largely due to practical necessity [1, 2]. A semiquantitative FFQ collects consumption frequency and portion size information, facilitating subsequent estimates of nutrient intake when combined with a food composition database [1]. A qualitative (Q)-FFQ omits any reference to portion size, restricting data collection to consumption frequency independent of quantity. Investigators may adopt a QFFQ if deemed sufficient for their research objectives or to reduce respondent burden and simplify data processing [7,8,9]; to estimate nutrient intake, researchers must specify a suitable portion size for each itemised food after the fact [2]. The allocation of researcher-specified portion sizes to existing consumption frequency data appears acceptable for ranking individual food/nutrient intake [10,11,12], with consumption frequency shown to explain the majority of between-person variation in dietary intake [13, 14]. The distinction between “standard” (e.g., as recommended in the Australian Dietary Guidelines) and “typical” (i.e., actual consumption) portion sizes is important [15, 16], and greater clarity is needed in terms of operational definitions and the method of portion size computation

Methods

Results

Discussion

Conclusion