"PhD Studentship: Pioneering a virtual-reality-based framework to accurately predict and verify falls risk"

University of East Anglia - School of Health Sciences

Primary supervisor - k.mares@uea.ac.uk

Background:

Falls are common in older people (there are around 3.4 million people in the UK over the age of 65 and 1 in 3 people within this age range will experience a fall). Falls incidence is a significant public health risk leading to injury and hospital emergency admission (during 2023/24, there were 219,155 emergency hospital admissions due to falls). People at risk of falls or recurrent falls should receive a programme of strength and balance exercises delivered by a qualified professional. Access to expert face to face support is limited and therapists rely on the provision of exercise programmes on paper, however adherence to these types of programmes are poor. The NHS 10-year plan advocates for the use of digital technology and technologies such as virtual reality are being used to increase motivation and adherence to exercise programmes without the need for in person contact. This work builds on our existing co-development and implementation work with an industry partner who are expanding their games using augmented and hybrid reality (XR) to include strength and balance exercises for the lower limb.

Through our work we have identified several challenges to the implementation of VR and in particular VR for lower limb exercises which we would look to address through this PhD.

Problem:

• The clinical workforce continues to find implementation of digital technology interventions challenging from both a person and systems perspective.
• There are no clear guidelines for assessing risk of falls when advocating for independent exercise using XR technologies at home.

Work package 1: To carry out a literature review to identify what factors influence clinical decision making and risk management around the prescription of lower limb exercises for falls management.

Work package 2: To use high fidelity simulation with clinicians to identify what factors would influence risk assessment for the prescription of XR technologies in real-time in high-risk falls patients.

Work package 3: To integrate findings from work packages 1 and 2 alongside clinical assessments carried out in XR to develop a risk assessment for the prescription of XR for lower limb balance and strength exercises. To test the robustness of this assessment in a small-scale study.

Impact:

This work will contribute to our related work to develop a training and education package to develop the workforce to support real world implementation of digital technology into the NHS.

It will contribute to the existing work we have commercially with our industry partner to inform the development of their product in future trials and funding applications.

Entry requirements

The minimum entry requirement is 2:1 in Physiotherapy, Occupational Therapy, Sports Therapy.

Mode of study

Full-time

Start date

1 October 2026

Additional Funding Information

This project is fully funded for 3 years. Funding includes tuition fees, an annual tax-free maintenance allowance and a research training support budget.