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HAPPI: Harnessing AI for personalised nutrition to promote healthy ageing in older adults

School of Medicine, Dentistry and Biomedical Sciences | PHD
Funding
Funded
Reference Number
SMED-2251-1029
Application Deadline
14 April 2025
Start Date
1 October 2025

Overview

This project is a 4-year PhD project with enhanced training and 3+ month placement, which is funded by UKRI BBSRC through the NI Landscape Partnership in AI for Bioscience (NILAB) Programme, delivered by Queen’s University Belfast and Ulster University. Details of the enhanced training will be available later at qub.ac.uk/nilab/. NILAB aims to bridge the gap between biology and artificial intelligence to accelerate bioscience discovery and foster effective collaboration between academia, industrial partners, and government bodies. NILAB’s mission is to train the next generation of researchers to develop and use AI to uncover the rules of life, addressing challenges in human health, animal welfare, and sustainable food systems.

Undernutrition results in disability, loss of independence, and poor quality of life for older people. Over 1.5 million older UK adults are undernourished costing the NHS billions each year. It is critical to support adequate nutrition for optimal health and well-being in the ageing population. Personalised nutrition holds transformative potential by tailoring dietary interventions to individual needs, but there is limited research in older adults who have unique requirements. Advances in artificial intelligence (AI) technologies offer an unprecedented opportunity to bridge this gap by enabling scalable interventions.

In the ‘HAPPI’ project, the successful PhD student will develop AI personalised nutrition solutions to improve nutritional status and functional abilities in older adults. The student will learn and apply state-of the-art AI techniques to existing large-scale datasets to create personalised nutrition solutions that can reverse and/or prevent undernutrition and improve health and wellbeing. The student will gain experience of working with diverse older adults in the community to design and test a feasible Mobile App to support long-term dietary behaviour change.

The 4-year project provides an exciting opportunity to work with an Internationally recognized scientific team from Queen’s University Belfast to gain expertise in AI and translational public health nutrition and ageing research. There will be several training opportunities to develop knowledge and skills on nutrition and ageing, behaviour change, machine learning and Mobile App development as well as analytical data analysis skills. There is also the opportunity for a short-term placement with Age NI who will actively contribute to the project.

Undernutrition results in disability, loss of independence, and poor quality of life1. Over 1.5 million older UK adults are undernourished2 and the estimated health and social care cost of treating undernutrition is £23.5 billion annually3. It is critical to support adequate nutrition for optimal health and well-being in the ageing population. Personalised nutrition holds transformative potential by tailoring dietary interventions to individual needs, but there is limited research in older adults who have unique requirements. Advances in artificial intelligence (AI) technologies offer an unprecedented opportunity to bridge this gap by enabling scalable and data-driven interventions.

The project aims to develop AI personalised nutrition solutions to improve nutritional status and functional abilities in older adults.

Objectives and methodology:

1: Apply machine learning algorithms to existing datasets and predict dietary changes associated with functional performance. Clustering techniques will identify subgroups based on similarities in diet, cognitive and physical functional performance using data from a dietary trial in older adults at risk of undernutrition. Validation of models will utilise the nationally representative Northern Ireland Cohort of Longitudinal Ageing (NICOLA). Both datasets have comprehensive data on diet, health, lifestyle, socio-demographics and biological markers.

2: Develop AI-driven algorithms to create personalised diet plans that meet nutritional needs. Integration of nutritional recommendations with existing datasets will enable analysis of individual’s dietary habits and health data e.g. via decision trees to determine nutritional choices. Models will prioritise explainability, ensuring that the recommendations are transparent and interpretable.

3: Co-design a 'Healthy Ageing Diet’ mobile application (App) prototype with diverse older adults. Objectives 1 and 2 will inform AI- driven personalised nutrition protype to meet nutritional requirements and tailored dietary counselling to support behaviour change to a healthy ageing diet. To ensure long-term impact, the AI-framework will incorporate behavioural feedback loops to allow for iterative adjustments based on individual progress.

4: Evaluate the Healthy Ageing Diet App. The prototype will be tested by users using an agile development framework and adapted usability scales to determine usability of the AI-driven App.

The project will generate important outcomes including: (i) co-creation of a scalable AI-driven app for personalised nutrition, providing actionable strategies that address the unique needs of older adults; (ii) insights into facilitators of dietary change enabling more targeted interventions, and (iii) predictive ability of achieving dietary change on functional outcomes (physical and cognition) that are critical for healthy ageing.

Funding Information

This project is open to both home and international applicants on a competitive basis. Top ranked students will be offered their choice of projects to which they apply. Funding is available for both domestic and international students with 30% of the funding allocated for international students.

Fees: Queen’s standard
Stipend: £20,780 (2025/2026)

Project Summary
Supervisor

Dr Claire McEvoy

More Information

askmhls@qub.ac.uk

Research Profile


Mode of Study

Full-time: 4 Years


Funding Body
UKRI BBSRC through the NILAB Programme
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