Automated Self-Administered 24-H Dietary Assessment Tool (ASA24) recalls for parent proxy-reporting of children’s intake (> 4 years of age): a feasibility study
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AbstractBackgroundRobust measurement of dietary intake in population studies of children is critical to better understand the diet–health nexus. It is unknown whether parent proxy-report of children’s dietary intake through online 24-h recalls is feasible in large cohort studies.ObjectivesThe primary objective of this study was to describe the feasibility of the Automated Self-Administered 24-h Dietary Assessment Tool (ASA24) to measure parent proxy-reported child dietary intake. A secondary objective was to compare intake estimates with those from national surveillance.MethodsParents of children aged 4–15 years participating in the TARGet Kids! research network in Toronto, Canada were invited by email to complete an online ASA24-Canada-2016 recall for their child, with a subsample prompted to complete a second recall about 2 weeks later. Descriptive statistics were reported for ASA24 completion characteristics and intake of several nutrients. Comparisons were made to the 2015 Canadian Community Health Survey (CCHS) 24-h recall data.ResultsA total of 163 parents completed the first recall, and 46 completed the second, reflecting response rates of 35% and 59%, respectively. Seven (4%) first recalls and one (2%) second recall were excluded for ineligibility, missing data, or inadvertent parental self-report. The median number of foods reported on the first recall was 18.0 (interquartile range (IQR) 6.0) and median time to complete was 29.5 min (IQR 17.0). Nutrient intakes for energy, total fat, protein, carbohydrates, fiber, sodium, total sugars, and added sugars were similar across the two recalls and the CCHS.ConclusionsThe ASA24 was found to be feasible for parent proxy-reporting of children’s intake and to yield intake estimates comparable to those from national surveillance, but strategies are needed to increase response rate and support completion to enhance generalizability.
