Effect of Manual Data Cleaning on Nutrient Intakes Using the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24)

Abstract

Background: Automated dietary assessment tools such as ASA24® are useful for collecting 24-hour recall data in large-scale studies. Modifications made during manual data cleaning may affect nutrient intakes.

Objectives: We evaluated the effects of modifications made during manual data cleaning on nutrient intakes of interest: energy, carbohydrate, total fat, protein, and fiber.

Methods: Differences in mean intake before and after data cleaning modifications for all recalls and average intakes per subject were analyzed by paired t-tests. The Chi-squared test was used to determine whether unsupervised recalls had more open-ended text responses that required modification than supervised recalls. We characterized food types of text response modifications. Correlations between predictive energy requirements, measured total energy expenditure (TEE), and mean energy intake from raw and modified data were examined.

Results: After excluding 11 recalls with invalidating technical errors, 1499 valid recalls completed by 393 subjects were included in this analysis. We found significant differences before and after modifications for energy, carbohydrate, total fat, and protein intakes for all recalls (P < 0.05). Limiting to modified recalls, there were significant differences for all nutrients of interest, including fiber (P < 0.02). There was not a significantly greater proportion of text responses requiring modification for home compared with supervised recalls (P = 0.271). Predicted energy requirements correlated highly with TEE. There was no significant difference in correlation of mean energy intake with TEE for modified compared with raw data. Mean intake for individual subjects was significantly different for energy, protein, and fat intakes following cleaning modifications (P < 0.001).

Conclusions: Manual modifications can change mean nutrient intakes for an entire cohort and individuals. However, modifications did not significantly affect the correlation of energy intake with predictive requirements and measured expenditure. Investigators can consider their research question and nutrients of interest when deciding to make cleaning modifications.

Keywords: 24-hour recall; ASA24; data cleaning; dietary assessment; total energy expenditure.

Published by Oxford University Press on behalf of the American Society for Nutrition 2021.

Figures

FIGURE 1

Plots of nutrient intakes from food and supplements before and after cleaning modifications for modified recalls (n = 233)

FIGURE 2

Frequency of reasons for text response modifications were classified by food type, whereby inappropriately coded items were changed to a food item that more closely matched the food reported by the respondent. Other reasons for modifications included the quantity of food was inappropriately coded and fat during preparation where a subject did not report oil that was coded.