Multiple Sclerosis
Conditions
Keywords
Movement analysis, Artificial Intelligence (AI)
Brief summary
Multiple Sclerosis (MS) is a condition that affects the brain and spinal cord, leading to problems with movement, balance, and vision. This project will validate innovative tools developed for monitoring MS progression using advanced movement analysis and retinal imaging technologies. Two computer vision-based applications, Digi Motion and Digi Balance, will measure range of motion, centre of mass, balance, and stability. In parallel, a model will be trained to analyse eye images using Optical Coherence Tomography (OCT)-a non-invasive technique that captures detailed views of retinal structures, including the retinal nerve fibre layer (RNFL), ganglion cell-inner plexiform layer (GC-IPL), and macular thickness, which are key biomarkers of MS-related neuroaxonal loss. These tools will be evaluated in people with MS, comparing results with traditional clinical assessments to determine reliability and validity. By integrating movement and retinal biomarkers, this approach aims to create a comprehensive and personalised method for tracking disease progression.
Detailed description
clinical investigation
Interventions
None listed
Sponsors
University of Exeter
Oxford Brookes University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
* condition has been stable over a period of a month * can walk at least 10 metres independently with or without their walking aids
Exclusion criteria
* pregnancy * allergic to adhesive materials * a condition that precludes safe participation in the assessment as indicated by the referring clinician * insufficient mental capacity to consent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Agreement and reliability between DigiBalance quantitative physical function metrics and standard clinical assessments | Baseline | The researchers are seeking to establish proof of concept through comparison of the data gathered by DigiBalance, with that gathered by the current standard assessment tool, the Expanded Disability Status Scale (EDSS) used to quantify disability in multiple sclerosis (MS). The level of agreement, and therefore the reliability, will be assessed by comparison of the quantitative measures obtained from the DigiBalance digital assessment tool (which captures range of motion, centre of mass displacement, stability index) and corresponding standard clinical assessments of physical function. Additionally the level of agreement between OCT-based retinal measurements (e.g., RNFL thickness, GC IPL thickness, macular thickness) and standard ophthalmic assessments. Agreement will be quantified using Intraclass Correlation Coefficients (ICC) and Bland-Altman limits of agreement. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Feasibility of the practicality of conducting the integrated digital assessment battery within a laboratory setting | Baseline | Assessment of the practicality of conducting the integrated digital assessment battery within a laboratory setting through the success rate. The success rate of completing each component of the assessment will be calculated as follows - the number of participants who completed the assessment divided by the total number of participants to attempt the the assessment. |
| Usability of the interfaces, comfort, and clarity of instructions | Baseline | Qualitative feedback from participants regarding their experience with DigiBalance, and OCT-based methods. This feedback will focus on the usability of the interfaces, comfort, and clarity of instructions, and will be gathered through Patient and Public Involvement and Engagement (PPIE) activities. Feedback will be captured using qualitative measures. |
Countries
United Kingdom
Contacts
PRINCIPAL_INVESTIGATORMae Mansoubi, PhD
University of Exeter
Outcome results
None listed