Strategies for online personalised nutrition advice employed in the development of the eNutri web app

Zenun Franco, R., Fallaize, R., Hwang, F. ORCID: https://orcid.org/0000-0002-3243-3869 and Lovegrove, J. A. ORCID: https://orcid.org/0000-0001-7633-9455 (2019) Strategies for online personalised nutrition advice employed in the development of the eNutri web app. Proceedings of the Nutrition Society, 78 (3). pp. 407-417. ISSN 0029-6651 doi: 10.1017/S0029665118002707

Abstract/Summary

The internet has considerable potential to improve health-related food choice at low-cost. Online solutions in this field can be deployed quickly and at very low cost, especially if they are not dependent on bespoke devices or offline processes such as the provision and analysis of biological samples. One key challenge is the automated delivery of personalised dietary advice in a replicable, scalable and inexpensive way, using valid nutrition assessment methods and effective recommendations. We have developed a web-based personalised nutrition system (eNutri) which assesses dietary intake using a validated graphical FFQ and provides personalised food-based dietary advice automatically. Its effectiveness was evaluated during an online randomised controlled trial dietary intervention (EatWellUK study) in which personalised dietary advice was compared with general population recommendations (control) delivered online. The present paper presents a review of literature relevant to this work, and describes the strategies used during the development of the eNutri app. Its design and source code have been made publicly available under a permissive open source license, so that other researchers and organisations can benefit from this work. In a context where personalised diet advice has great potential for health promotion and disease prevention at-scale and yet is not currently being offered in the most popular mobile apps, the strategies and approaches described in the present paper can help to inform and advance the design and development of technologies for personalised nutrition.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/81744
Item Type	Article
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Department of Bio-Engineering Life Sciences > School of Chemistry, Food and Pharmacy > Department of Food and Nutritional Sciences > Human Nutrition Research Group
Publisher	Cambridge University Press
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