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Non-Nutritive (Artificial) Sweetener Knowledge among University Students
This study determined non-nutritive sweetener (NNS; artificial sweetener) depth of knowledge among university health and science students. An online survey was delivered to 1248 science students and completed by 493 respondents (19.0 ± 2.2 years old), evaluating ability to provide an NNS description...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6769725/ https://www.ncbi.nlm.nih.gov/pubmed/31547360 http://dx.doi.org/10.3390/nu11092201 |
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author | Wilson, Ted Murray, Breanna Price, Tamara Atherton, Denzel Hooks, Tisha |
author_facet | Wilson, Ted Murray, Breanna Price, Tamara Atherton, Denzel Hooks, Tisha |
author_sort | Wilson, Ted |
collection | PubMed |
description | This study determined non-nutritive sweetener (NNS; artificial sweetener) depth of knowledge among university health and science students. An online survey was delivered to 1248 science students and completed by 493 respondents (19.0 ± 2.2 years old), evaluating ability to provide an NNS description/definition, examples of NNS from memory, and evaluate NNS word familiarity with a click-drag-box to identify six NNS by chemical name (CN) and six NNS by trade name (TN), relative to six decoy NNS, six caloric sweeteners, and six food items (mean ± standard deviation). NNS definitions contained 1.1 ± 1.1 of four previously defined elements suggestive of knowledge depth, with highest scores among self-described non-NNS users and food ingredient label users. Knowledge depth was not correlated with gender, age, American College Test score, or history of weight loss attempts. Without prompting, respondents could name 0.9 ± 1.1 NNS from memory, with highest scores among self-described non-NNS users (1.4 ± 0.8) and food ingredient label users (1.4 ± 0.8). NNS example memory was not correlated with gender, age, ACT score, or history of weight loss attempts. With the click-drag-box exercise, NNS were correctly identified 4.9 ± 1.0 times by TN and significantly less by CN (3.9 ± 1.9 times). Decoy NNS were incorrectly identified as being a real NNS 4.7 ± 1.3 times, while caloric sweeteners and food items were incorrectly identified as NNS 1.7 ± 1.7 times and 1.0 ± 1.5 times, (TN and Decoy NNS > CN > caloric sweetener and food item). NNS knowledge among university students may be inadequate for understanding what NNS are, if they consume NNS, or whether NNS are important for dietary health. |
format | Online Article Text |
id | pubmed-6769725 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-67697252019-10-30 Non-Nutritive (Artificial) Sweetener Knowledge among University Students Wilson, Ted Murray, Breanna Price, Tamara Atherton, Denzel Hooks, Tisha Nutrients Communication This study determined non-nutritive sweetener (NNS; artificial sweetener) depth of knowledge among university health and science students. An online survey was delivered to 1248 science students and completed by 493 respondents (19.0 ± 2.2 years old), evaluating ability to provide an NNS description/definition, examples of NNS from memory, and evaluate NNS word familiarity with a click-drag-box to identify six NNS by chemical name (CN) and six NNS by trade name (TN), relative to six decoy NNS, six caloric sweeteners, and six food items (mean ± standard deviation). NNS definitions contained 1.1 ± 1.1 of four previously defined elements suggestive of knowledge depth, with highest scores among self-described non-NNS users and food ingredient label users. Knowledge depth was not correlated with gender, age, American College Test score, or history of weight loss attempts. Without prompting, respondents could name 0.9 ± 1.1 NNS from memory, with highest scores among self-described non-NNS users (1.4 ± 0.8) and food ingredient label users (1.4 ± 0.8). NNS example memory was not correlated with gender, age, ACT score, or history of weight loss attempts. With the click-drag-box exercise, NNS were correctly identified 4.9 ± 1.0 times by TN and significantly less by CN (3.9 ± 1.9 times). Decoy NNS were incorrectly identified as being a real NNS 4.7 ± 1.3 times, while caloric sweeteners and food items were incorrectly identified as NNS 1.7 ± 1.7 times and 1.0 ± 1.5 times, (TN and Decoy NNS > CN > caloric sweetener and food item). NNS knowledge among university students may be inadequate for understanding what NNS are, if they consume NNS, or whether NNS are important for dietary health. MDPI 2019-09-12 /pmc/articles/PMC6769725/ /pubmed/31547360 http://dx.doi.org/10.3390/nu11092201 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Wilson, Ted Murray, Breanna Price, Tamara Atherton, Denzel Hooks, Tisha Non-Nutritive (Artificial) Sweetener Knowledge among University Students |
title | Non-Nutritive (Artificial) Sweetener Knowledge among University Students |
title_full | Non-Nutritive (Artificial) Sweetener Knowledge among University Students |
title_fullStr | Non-Nutritive (Artificial) Sweetener Knowledge among University Students |
title_full_unstemmed | Non-Nutritive (Artificial) Sweetener Knowledge among University Students |
title_short | Non-Nutritive (Artificial) Sweetener Knowledge among University Students |
title_sort | non-nutritive (artificial) sweetener knowledge among university students |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6769725/ https://www.ncbi.nlm.nih.gov/pubmed/31547360 http://dx.doi.org/10.3390/nu11092201 |
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