Funded projects
In December 2024, the Hub awarded funding of over one million pounds to 15 projects collaborative digital health research projects and fellowships.
All funded projects will support the Hub’s aims to increase digital health capability and address unmet health and social care needs across the region. Each proposal also aligns with one or more of the Hub’s four research themes.
Click on a project title to view the full details.
Collaborative research projects
ATmOSPhErE
Lead by Dr Amberly Brigden
Artificial intelligence To Optimise Seizure Prediction to Empower people with Epilepsy: moving from prototype to a minimum viable product
Co-design and loneliness
Lead by Dr Lis Grey
Co-designing technological solutions to loneliness with at-risk populations
D:REACH-HF
Lead by Dr Samantha van Beurden
Improving the usability, accessibility, and inclusivity of digital dyadic cardiac rehabilitation for people living with heart failure
FAL-VITAE
Lead by Dr Genevieve Williams
Falls and Activity Lexicon: using Video and IMU Technology for remote Assessment and Evaluation
PRIDE
Lead by Professor Daniel Gartner
PRIDE: Planning & Resource Integration in Digital Environments – The Case of Frail, Elderly Patients, and Palliative Care
PRISM
Lead by Professor Kenton O’Hara
PRISM: Prioritising Allocation of Scarce Perinatal Pathology Resource with ML-Assisted Placenta Pathology Screening
SCOPE
Lead by Dr Alison Harper
SCOPE: Simulation for Coordination of Orthopaedic Patient Elective Services
SmartADHD
Lead by Dr Anna Price
Co-developing a multilingual AI-powered virtual assistant, to increase engagement with the CareADHD app for young people aged 16-25 with ADHD: reducing health inequalities during transition
Synthetic Data
Lead by Dr Chris McWilliams
Synthetic data generation for privacy-preserving prediction modelling in critical care
Wearable Stress Detection
Lead by Dr Christopher Clarke
Novel Wearable-based Stress Detection Using Earables and Smartwatches
Solo and Twin Fellowship projects
PRADA study
Solo Fellow:
Dr Philip Hamann
PRADA Study: Predicting Rheumatoid Arthritis Disease Activity Study
Predicting readmission risk
Twin Fellows:
Dr Charlotte James
Luke Shaw
Predicting patient readmission risk to support early discharge decisions
Predicting falls
Solo Fellow:
Dr Benjamin Owusu
Predicting falls and hip fractures using routinely collected care data in high-risk populations: Understanding uncertainty quantification and digital twins methods
Tongue-computer interface
Twin Fellows:
Dr Dan Withey
Dr Paul Worgan
Tongue-computer interface for widening computer access
Utilising patient generated health data
Twin Fellows:
Matthew Wragg
Professor Raj Sengupta
Working towards data-driven care: Exploring new methods and new technologies to optimise secondary care clinical dashboards presenting patient-generated health data and predictive analytics