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-pure-test.eprints-hosting.org/id/eprint/92698 |
| Official URL | https://www.teknoscienze.com/tks_article/the-enutr... |
| Refereed | Yes |
| Divisions | Central Services Life Sciences > School of Chemistry, Food and Pharmacy > Department of Food and Nutritional Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering |
| Download/View statistics | View download statistics for this item |
University Staff: Request a correction | Centaur Editors: Update this record
Download
Download