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Calorie labeling and consumer estimation of calories purchased

BACKGROUND: Studies rarely find fewer calories purchased following calorie labeling implementation. However, few studies consider whether estimates of the number of calories purchased improved following calorie labeling legislation. FINDINGS: Researchers surveyed customers and collected purchase rec...

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Autores principales: Taksler, Glen B, Elbel, Brian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4104464/
https://www.ncbi.nlm.nih.gov/pubmed/25015547
http://dx.doi.org/10.1186/s12966-014-0091-2
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author Taksler, Glen B
Elbel, Brian
author_facet Taksler, Glen B
Elbel, Brian
author_sort Taksler, Glen B
collection PubMed
description BACKGROUND: Studies rarely find fewer calories purchased following calorie labeling implementation. However, few studies consider whether estimates of the number of calories purchased improved following calorie labeling legislation. FINDINGS: Researchers surveyed customers and collected purchase receipts at fast food restaurants in the United States cities of Philadelphia (which implemented calorie labeling policies) and Baltimore (a matched comparison city) in December 2009 (pre-implementation) and June 2010 (post-implementation). A difference-in-difference design was used to examine the difference between estimated and actual calories purchased, and the odds of underestimating calories. Participants in both cities, both pre- and post-calorie labeling, tended to underestimate calories purchased, by an average 216–409 calories. Adjusted difference-in-differences in estimated-actual calories were significant for individuals who ordered small meals and those with some college education (accuracy in Philadelphia improved by 78 and 231 calories, respectively, relative to Baltimore, p = 0.03-0.04). However, categorical accuracy was similar; the adjusted odds ratio [AOR] for underestimation by >100 calories was 0.90 (p = 0.48) in difference-in-difference models. Accuracy was most improved for subjects with a BA or higher education (AOR = 0.25, p < 0.001) and for individuals ordering small meals (AOR = 0.54, p = 0.001). Accuracy worsened for females (AOR = 1.38, p < 0.001) and for individuals ordering large meals (AOR = 1.27, p = 0.028). CONCLUSIONS: We concluded that the odds of underestimating calories varied by subgroup, suggesting that at some level, consumers may incorporate labeling information.
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spelling pubmed-41044642014-07-31 Calorie labeling and consumer estimation of calories purchased Taksler, Glen B Elbel, Brian Int J Behav Nutr Phys Act Short Paper BACKGROUND: Studies rarely find fewer calories purchased following calorie labeling implementation. However, few studies consider whether estimates of the number of calories purchased improved following calorie labeling legislation. FINDINGS: Researchers surveyed customers and collected purchase receipts at fast food restaurants in the United States cities of Philadelphia (which implemented calorie labeling policies) and Baltimore (a matched comparison city) in December 2009 (pre-implementation) and June 2010 (post-implementation). A difference-in-difference design was used to examine the difference between estimated and actual calories purchased, and the odds of underestimating calories. Participants in both cities, both pre- and post-calorie labeling, tended to underestimate calories purchased, by an average 216–409 calories. Adjusted difference-in-differences in estimated-actual calories were significant for individuals who ordered small meals and those with some college education (accuracy in Philadelphia improved by 78 and 231 calories, respectively, relative to Baltimore, p = 0.03-0.04). However, categorical accuracy was similar; the adjusted odds ratio [AOR] for underestimation by >100 calories was 0.90 (p = 0.48) in difference-in-difference models. Accuracy was most improved for subjects with a BA or higher education (AOR = 0.25, p < 0.001) and for individuals ordering small meals (AOR = 0.54, p = 0.001). Accuracy worsened for females (AOR = 1.38, p < 0.001) and for individuals ordering large meals (AOR = 1.27, p = 0.028). CONCLUSIONS: We concluded that the odds of underestimating calories varied by subgroup, suggesting that at some level, consumers may incorporate labeling information. BioMed Central 2014-07-12 /pmc/articles/PMC4104464/ /pubmed/25015547 http://dx.doi.org/10.1186/s12966-014-0091-2 Text en Copyright © 2014 Taksler and Elbel; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Short Paper
Taksler, Glen B
Elbel, Brian
Calorie labeling and consumer estimation of calories purchased
title Calorie labeling and consumer estimation of calories purchased
title_full Calorie labeling and consumer estimation of calories purchased
title_fullStr Calorie labeling and consumer estimation of calories purchased
title_full_unstemmed Calorie labeling and consumer estimation of calories purchased
title_short Calorie labeling and consumer estimation of calories purchased
title_sort calorie labeling and consumer estimation of calories purchased
topic Short Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4104464/
https://www.ncbi.nlm.nih.gov/pubmed/25015547
http://dx.doi.org/10.1186/s12966-014-0091-2
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