The eNutri app: using diet quality indices to deliver automated personalised nutrition adviceFallaize, 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
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Official URL: https://www.teknoscienze.com/tks_article/the-enutr... Abstract/SummaryPersonalising 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. Download Statistics DownloadsDownloads per month over past year Deposit Details University Staff: Request a correction | Centaur Editors: Update this record |