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dc.contributor.authorLo Siou, Geraldine
dc.contributor.authorAkawung, Alianu K.
dc.contributor.authorSolbak, Nathan M.
dc.contributor.authorMcDonald, Kathryn L.
dc.contributor.authorAl Rajabi, Ala
dc.contributor.authorWhelan, Heather K.
dc.contributor.authorKirkpatrick, Sharon I. 13:18:12 (GMT) 13:18:12 (GMT)
dc.description.abstractBackground: All self-reported 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-reported food frequency and physical activity data from Alberta’s Tomorrow Project participants (n = 9847 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%, p < 0.0001). 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 (Hubert and Arabie’s adjusted Rand Index, Kappa and Cramer’s V statistics < 0.8). Conclusions: Different scenarios used to account for EI misreporters influenced cluster analysis and hence the composition of the dietary patterns. Continued efforts are needed to explore and validate methods and their ability to identify and mitigate the impact of EI misestimation in nutritional epidemiology.en
dc.description.sponsorshipFunder 1, Alberta’s Tomorrow Project is funded by the Alberta Cancer Foundation, the Canadian Partnership Against Cancer || Funder 2, the Alberta Cancer Prevention Legacy Fund (administered by the Government of Alberta) || Funder 3, the University of Toronto || Funder 4, substantial in-kind funding from Alberta Health Services. Although funding has been provided by several organizations, the analyses and interpretation of the data presented in this paper are those of the authors alone.en
dc.publisherBioMed Centralen
dc.relation.ispartofseriesNutrition Journal;
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectAlberta’s tomorrow projecten
dc.subjectCluster analysisen
dc.subjectDietary patternsen
dc.subjectEnergy intakeen
dc.subjectPredicted total energy expenditure methoden
dc.subjectRevised-Goldberg methoden
dc.titleThe effect of different methods to identify, and scenarios used to address energy intake misestimation on dietary patterns derived by cluster analysisen
dcterms.bibliographicCitationLo Siou, G., Akawung, A. K., Solbak, N. M., McDonald, K. L., Al Rajabi, A., Whelan, H. K., & Kirkpatrick, S. I. (2021). The effect of different methods to identify, and scenarios used to address energy intake misestimation on dietary patterns derived by cluster analysis. Nutrition Journal, 20(1), 42.
uws.contributor.affiliation1Faculty of Applied Health Sciencesen
uws.contributor.affiliation2Public Health and Health Systems (School of)en

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