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Evaluation of the eNutri automated personalised nutrition advice by users and nutrition professionals in the UK

Nutrition apps have great potential to support people to improve their diets, but few apps give automated validated personalised nutrition advice. A web app capable of delivering automated personalised food-based nutrition advice (eNutri) was developed. The aims of this study were to i) evaluate and...

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Autores principales: Fallaize, Rosalind, Franco, Rodrigo Zenun, Hwang, Faustina, Lovegrove, Julie A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447217/
https://www.ncbi.nlm.nih.gov/pubmed/30943252
http://dx.doi.org/10.1371/journal.pone.0214931
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author Fallaize, Rosalind
Franco, Rodrigo Zenun
Hwang, Faustina
Lovegrove, Julie A.
author_facet Fallaize, Rosalind
Franco, Rodrigo Zenun
Hwang, Faustina
Lovegrove, Julie A.
author_sort Fallaize, Rosalind
collection PubMed
description Nutrition apps have great potential to support people to improve their diets, but few apps give automated validated personalised nutrition advice. A web app capable of delivering automated personalised food-based nutrition advice (eNutri) was developed. The aims of this study were to i) evaluate and optimise the personalised nutrition report provided by the app and ii) compare the personalised food-based advice with nutrition professionals’ standards to aid validation. A study with nutrition professionals (NP) compared the advice provided by the app against professional Registered Dietitians (RD) (n = 16) and Registered Nutritionists (RN) (n = 16) standards. Each NP received two pre-defined scenarios, comprising an individual’s characteristics and dietary intake based on an analysis of a food frequency questionnaire, along with the nutrition food-based advice that was automatically generated by the app for that individual. NPs were asked to use their professional judgment to consider the scenario, provide their three most relevant recommendations for that individual, then consider the app’s advice and rate their level of agreement via 5-star scales (with 5 as complete agreement). NPs were also asked to comment on the eNutri recommendations, scores generated and overall impression. The mean scores for the appropriateness, relevance and suitability of the eNutri diet messages were 3.5, 3.3 and 3.3 respectively.
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spelling pubmed-64472172019-04-17 Evaluation of the eNutri automated personalised nutrition advice by users and nutrition professionals in the UK Fallaize, Rosalind Franco, Rodrigo Zenun Hwang, Faustina Lovegrove, Julie A. PLoS One Research Article Nutrition apps have great potential to support people to improve their diets, but few apps give automated validated personalised nutrition advice. A web app capable of delivering automated personalised food-based nutrition advice (eNutri) was developed. The aims of this study were to i) evaluate and optimise the personalised nutrition report provided by the app and ii) compare the personalised food-based advice with nutrition professionals’ standards to aid validation. A study with nutrition professionals (NP) compared the advice provided by the app against professional Registered Dietitians (RD) (n = 16) and Registered Nutritionists (RN) (n = 16) standards. Each NP received two pre-defined scenarios, comprising an individual’s characteristics and dietary intake based on an analysis of a food frequency questionnaire, along with the nutrition food-based advice that was automatically generated by the app for that individual. NPs were asked to use their professional judgment to consider the scenario, provide their three most relevant recommendations for that individual, then consider the app’s advice and rate their level of agreement via 5-star scales (with 5 as complete agreement). NPs were also asked to comment on the eNutri recommendations, scores generated and overall impression. The mean scores for the appropriateness, relevance and suitability of the eNutri diet messages were 3.5, 3.3 and 3.3 respectively. Public Library of Science 2019-04-03 /pmc/articles/PMC6447217/ /pubmed/30943252 http://dx.doi.org/10.1371/journal.pone.0214931 Text en © 2019 Fallaize et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Fallaize, Rosalind
Franco, Rodrigo Zenun
Hwang, Faustina
Lovegrove, Julie A.
Evaluation of the eNutri automated personalised nutrition advice by users and nutrition professionals in the UK
title Evaluation of the eNutri automated personalised nutrition advice by users and nutrition professionals in the UK
title_full Evaluation of the eNutri automated personalised nutrition advice by users and nutrition professionals in the UK
title_fullStr Evaluation of the eNutri automated personalised nutrition advice by users and nutrition professionals in the UK
title_full_unstemmed Evaluation of the eNutri automated personalised nutrition advice by users and nutrition professionals in the UK
title_short Evaluation of the eNutri automated personalised nutrition advice by users and nutrition professionals in the UK
title_sort evaluation of the enutri automated personalised nutrition advice by users and nutrition professionals in the uk
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447217/
https://www.ncbi.nlm.nih.gov/pubmed/30943252
http://dx.doi.org/10.1371/journal.pone.0214931
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