Views of Specialist Clinicians and People With Multiple Sclerosis on Upper Limb Impairment and the Potential Role of Virtual Reality in the Rehabilitation of the Upper Limb in Multiple Sclerosis: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Finding enjoyable and effective long-term approaches to rehabilitation for improving the upper limb (UL) function of people with multiple sclerosis (MS) is challenging. Using virtual reality (VR) could be a solution to this challenge; however, there is a lack of reporting on the views of people with MS and clinicians on VR-based approaches and recommendations for games for rehabilitation. OBJECTIVE: This study aims to identify common UL problems and their related current therapeutic approaches for people with MS, and to explore the opinions of people with MS and specialist clinicians on VR and obtain suggestions for the development and design of VR games. METHODS: Separate focus groups were conducted with people with MS, recruited through the MS Society UK's research network, and clinicians, recruited through the MS Trust Therapists in MS network. A total of 10 people with MS (2 focus groups) and 8 clinicians (5 physiotherapists, 2 occupational therapists, and 1 MS nurse in 2 focus groups) were involved. The focus groups were recorded and transcriptions were analyzed using theme-based content analysis. RESULTS: People with MS commonly reported that their UL problems interfered with activities of daily living and resulted in the loss of meaningful hobbies such as writing. Many people with MS neglected UL exercise and found strategies for adapting to the UL impairments. Similarly, clinicians stated UL rehabilitation was neglected within their service and that it was challenging to find interesting treatment strategies. VR was suggested by both participant groups as a solution, as it was convenient for people with MS to access and it could provide a more engaging and disguised approach to exercise. There were shared concerns with cybersickness and disengagement with using VR approaches. Both groups agreed games should be meaningful and adaptable for users but suggested different VR activities, with clinicians suggesting games directly reflecting activities of daily living and people with MS suggesting more abstract activities. CONCLUSIONS: VR was well received by both people with MS and clinicians for UL rehabilitation. Recommendations were made for the development of VR rehabilitation games which are personalized and customizable for the varying abilities of people with MS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".