| EPSRC Reference: |
EP/W003600/1 |
| Title: |
Digital Health: OptiMuscle - Improving health outcomes through the optimization of muscle function |
| Principal Investigator: |
Preece, Professor SJ |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
School of Health and Society |
| Organisation: |
University of Salford |
| Scheme: |
Standard Research - NR1 |
| Starts: |
01 November 2021 |
Ends: |
31 May 2024 |
Value (£): |
404,045
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| EPSRC Research Topic Classifications: |
| Biomechanics & Rehabilitation |
Image & Vision Computing |
| Med.Instrument.Device& Equip. |
Vision & Senses - ICT appl. |
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| EPSRC Industrial Sector Classifications: |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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26 Jan 2021
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Digital Health Sandpit Full Proposals
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Announced
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Summary on Grant Application Form |
Approximately 10% of people in the UK population exhibit some form of dysfunctional breathing. This term describes a range of conditions which are characterised by an impairment in the muscular control of breathing and which can result in breathlessness, hyperventilation and in some cases dizziness. Current clinical assessment techniques and treatments for dysfunctional breathing are low-tech, with clinical management focused around the use of simple breathing exercises. Whilst these exercises do have some effect, we propose that patients would get more benefit from a system which uses biofeedback on muscle patterns to guide breathing re-education. Furthermore, if such a system could be automated, it should be possible to provide breathing re-education to large numbers of people without the need for a clinical specialist to be present. This is important because breathing re-education is often a key part of the clinical management of other health conditions, such as asthma, back pain and anxiety.
This project will deliver a completely new digital health system for the clinical management of dysfunctional breathing. The system will use both visual and auditory biofeedback to communicate abnormal muscle function, guiding patients through a process in which they gradually learn the correct muscular control of breathing. To realise this personalised system, we will develop software which can create an individual avatar of the patient and use this to visualise the actions of the breathing muscle in real-time. To avoid the need to directly measure muscles in a clinical setting, we will develop algorithms which can predict muscle activations from an input of simple sensor data which can be collected in a clinical setting, e.g. inexpensive 3D camera.
To complement the use of visual biofeedback, we will use auditory biofeedback to convey subtle changes in muscle function and help reinforce the learning of new muscle patterns. Our multimodal (visual and audio) biofeedback system will be integrated into a behaviour change intervention, providing patients with the capability, opportunity and motivation to change their muscle-related breathing behaviours. We will develop our new intervention by working closely with patients to understand their views on how the final system should operate. Once created, we will carry out a small trial on people with dysfunctional breathing to understand the future potential. If this testing provides encouraging results, then we will apply for funding for a larger NHS trial.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.salford.ac.uk |