Background: All self-report dietary intake data are characterized by measurement error, and validation studies indicate that the estimation of energy intake (EI) is particularly affected.
Methods: Using self-report food frequency and physical activity data from Alberta’s Tomorrow Project participants (n=9,847 men 16,241 women), we compared the revised-Goldberg and the predicted total energy expenditure methods in their ability to identify misreporters of EI. We also compared dietary patterns derived by k-means clustering under different scenarios where misreporters are included in the cluster analysis (Inclusion); excluded prior to completing the cluster analysis (ExBefore); excluded after completing the cluster analysis (ExAfter); and finally, excluded before the cluster analysis but added to the ExBefore cluster solution using the nearest neighbor method (InclusionNN).
Results: The predicted total energy expenditure method identified a significantly higher proportion of participants as EI misreporters compared to the revised-Goldberg method (50% vs. 47%). k-means cluster analysis identified 3 dietary patterns: Healthy, Meats/Pizza and Sweets/Dairy. Among both men and women, participants assigned to dietary patterns changed substantially between ExBefore and ExAfter and also between the Inclusion and InclusionNN scenarios.
Conclusions: Different scenarios used to account for EI misreporters influenced cluster analysis and hence the composition of the dietary patterns.