PhD Studentship - Developing Remote Monitoring Tools for Assessment of Disease Progression in People Living with Motor Neurone Disease

Project contact: Professor Emma Hudson-Tole

Funding info: Home fees (2022/23) included plus an annual stipend paid at the UKRI rate (£17,668 for 2022/23).

Mode of study: Full time

Eligibility: Only open to home students

Key dates:        

Closing date: 21^st April 2023
Expected start: October 2023

Project summary

We have an exciting multi-disciplinary PhD studentship opportunity funded by the MND Association.

The rapid, debilitating progression of Motor Neurone Disease (MND) makes it challenging to provide optimal care and limits how well the effects of new potential treatments can be evaluated. One barrier to improving these things is a lack of methods to sensitively and unobtrusively measure changes in health and well-being as the disease progresses.

Methods of recording physical behaviour patterns, as we go about daily life activities, are widely available. It is known that physical behaviour measures can indicate the progression and response to treatmentin other neurodegenerative diseases. However, few studies have explored their use in MND. The project will therefore investigate whether MND causes unique changes in physical behaviour patterns.

The project involves working with people living with MND, collecting physical activity data (accelerometery) and mobility and muscle function data from them. A combination of traditional and AI data analysis will then be used to address the key research questions. Improving measurement and knowledge of the effects of MND in the real world will give more information on which personalised care decisions can be based, and new treatments can be thoroughly evaluated.

Specific requirements of the project

We are looking to work with a motivated person with excellent computer science and inter-personal skills on this project.

You should have a minimum of an honours degree at first or upper second class level in a data science or physiological science, with evidence of excellent quantitative data analysis skills. Preference will be given to candidates who have successfully completed an MSc in a data science or physiological sciences topic, particularly those familiar with AI-based data analysis techniques.

You should have a demonstrable interest in healthcare and/or health-related technology.

You should be able to evidence excellent time management and interpersonal skills. You will be working with clinicians, patients and scientific researchers, so there is a particular need for good communication skills.

Full training in collection of all experimental data will be provided. However, experience of collecting data from human participants - particularly muscle function and/or mobility testing would be an advantage (e.g., use of knee/hand-grip dynamometer, timed-up and go testing, physical activity questionnaires).

How to apply

Interested applicants should contact Professor Emma Hodson-Tole for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Sport and Exercise Sciences (or download the PGR application form). You should also complete the PGR thesis proposal (supplementary information) form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest.

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk. Closing date 21^st April 2023. Expected start date October 2023.

Please quote the reference: SciEng-EHT-2023-remote-monitoring-MND

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