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The eNutri app – Using diet quality indices to deliver automated personalised nutrition advice

corresponding

ROSALIND FALLAIZE 1,2, MICHELLE WEECH1, RODRIGO ZENUN FRANCO3, ARIANE KEHLBACHER4, FAUSTINA HWANG5*, JULIE LOVEGROVE1
*Corresponding author
1. Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
2. School of Life and Medical Sciences, University of Hertfordshire, Hatfield, United Kingdom
3. Globalyze, Brazil
4. School of Agriculture Policy and Development, University of Reading, United Kingdom
5. Biomedical Engineering Section, School of Biological Sciences, University of Reading, Reading, United Kingdom

Abstract

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.


BACKGROUND

There is mounting evidence that personalising nutritional advice based on a user’s food intake, biomarkers of health, and/or genetic information is more effective at improving adherence to healthy eating guidelines than standard public health messages (1). Healthy diets, typically characterized by high intakes of fruits, vegetables, whole grains, legumes, omega-3 fats and low intakes of refined grains and red and processed meats, are associated with a lower risk of overweight and obesity and cardiovascular diseases (2, 3). Thus, there is great interest in effective tools for improving food intake at a population level. Digital technologies, such as apps, offer the potential to provide tailored advice at-scale with relatively low-cost (vs. face-to-face intervention). However, our review of popular nutrition-related mobile apps revealed that none of the apps reviewed were providing personalised nutrition advice (4). To address this, our research team at the University of Reading have developed a mobile web app capable of delivering automated nutrition advice.

 

OVERVIEW OF THE ENUTRI APP

The online eNutri app was desi ...




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