The eNutri app: using diet quality indices to deliver automated personalised nutrition advice

Fallaize, R., Weech, M. ORCID: https://orcid.org/0000-0003-1738-877X, Zenun Franco, R., Kehlbacher, A., Hwang, F. ORCID: https://orcid.org/0000-0002-3243-3869 and Lovegrove, J. ORCID: https://orcid.org/0000-0001-7633-9455 (2020) The eNutri app: using diet quality indices to deliver automated personalised nutrition advice. Agro Food Industry Hi-Tech, 31 (2). ISSN 1722-6996

Abstract/Summary

Personalising nutrition advice using digital technologies, such as web-apps, offers great potential to improve users’ adherence to healthy eating guidelines. However, commercial offerings currently lack decision engines capable of delivering personalised nutrition advice. This article outlines the core concepts, content and features of the novel eNutri app, developed by researchers at the University of Reading. Uniquely, the app identifies and recommends food-based modifications that would be most beneficial for an individual taking into account both their current diet quality and their individual preferences.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/92698
Refereed	Yes
Divisions	Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR) Life Sciences > School of Biological Sciences > Department of Bio-Engineering Interdisciplinary Research Centres (IDRCs) > Institute for Food, Nutrition and Health (IFNH) Life Sciences > School of Agriculture, Policy and Development > Department of Agri-Food Economics & Marketing Life Sciences > School of Chemistry, Food and Pharmacy > Department of Food and Nutritional Sciences > Human Nutrition Research Group
Publisher	Tks
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