The Design, Development, and Testing of a Virtual Reality Device for Upper Limb Training in People With Multiple Sclerosis: Single-Center Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a common nontraumatic, neurological, disabling disease that often presents with upper limb dysfunction. Exercise training has resulted in improvement for patients; however, there can be a lack of compliance due to access because of location and lack of MS experts. Virtual reality (VR) is a promising technology that can offer exercise therapy/rehabilitation at a distance. This type of remote training can be motivational and effective for patients with MS and can improve range of motion and muscle strength for those with upper limb dysfunction. OBJECTIVE: The aim of this study is to evaluate the safety and feasibility of the XRHealth software and the Oculus Rift Station for patients with MS with upper limb motor dysfunction. METHODS: A single-center, prospective, feasibility study was conducted with patients with MS who had upper limb motor dysfunction. Patients participated in a single 45-minute digital environment session with VR and completed a questionnaire about the quality of the training and fatigability. The clinician also completed a questionnaire to evaluate the suitability and safety of the training. RESULTS: Overall, 30 patients were enrolled between the ages of 20 and 81 years. Patients reported that the training sessions within the digital environment were helpful, challenging, fun, and simple to understand, and that they would be willing to repeat the sessions again. The physical therapist that oversaw the patients reported that the training was suitable for 87% (n=26) of the patients. Anticipated adverse events were fatigue, temporary dizziness, and temporary nausea. The operator complications included that the cable of the head-mounted display interrupted the training (n=2, 7%) and fatigue that caused cessation of the VR training session (n=2, 7%). No serious adverse events were reported. CONCLUSIONS: These preliminary results demonstrated that the use of the XRHealth software and Oculus Rift Station platform is feasible, safe, and engaging for patients, and has the potential to improve the functionality of the upper limbs in patients with MS. This study provides support for future studies of implementing a series of training sessions with virtual reality in a home-based environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".