About the Project

Nutrition surveys aim to understand how usual diets impact health. The problem with this is that we do not have an accurate tool to assess diet. We rely on people telling us what they have eaten in the last day or month. However, it is difficult to remember what and how much we have eaten. The surveys used also struggle to capture the range of diets in the UK. Often, people we want to talk to about their diets find these methods unsuitable.

There are lots of emerging ways in which we can assess diet. We can use urine and finger-prick blood samples to test for ‘markers’ of food and drinks. The benefit of these is that they give us objective data. We can also use wearable cameras to assess foods and diets. Artificial intelligence software is used to determine the type and amount of food eaten. Additionally, new online tools are making it easier for us to self-report. However, no single tool can accurately measure all aspects of the diet.

Project Aims

The aim of this project is to develop a combined tool to accurately assess diet. To do so, we will determine the optimal combination(s) of these emerging methods. The final tool will be easy-to-use and low cost. The combined tool will capture all aspects of the diet.

The project - led by Aberystwyth University in conjunction with the University of Reading, the MRC Epidemiology Unit at the University of Cambridge and Imperial College London - has major implications for how governments and policy makers assess the success of efforts to improve people’s health and give better dietary advice. It could also help in the monitoring needed to test new treatments for disease, such as the connection between eating patterns and cancer.

Currently diets are measured by people completing complex and time-consuming nutrition surveys themselves and trying to remember what they have eaten, but this can lead to unreliable results. The new five-year project aims to use modern techniques to develop a new way of accurately measuring what people are eating.

In the first stage of the project, which will help help scientists develop new protocols, volunteers will follow set meal plans representative of the UK diet. Under supervised conditions, these volunteers will wear tiny cameras to film what they eat, as well as providing blood and urine samples. Machine learning will be used to analyse the images to measure how accurately the approach can recognise foods eaten by the wearer, compared with the chemical analysis of food intake in their urine and blood. Data from all methods will then be modelled to assess the best combination of techniques to accurately monitor diet in the least invasive way. After the pilot study, there will be a larger remote trial to test the technique’s effectiveness when volunteers are living in their own homes and freely choosing their diet over a period of several weeks.

Throughout the project, we will talk to members of the public to ensure the trials are easy to follow. To ensure uptake of the combined tool, we will hold workshops with key stakeholders. This will include representatives of the nutrition research community and government departments.