Motor performance improvement through virtual reality task is related to fatigue and cognition in people with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: People with multiple sclerosis (MS) who undergo rehabilitation need to perform new motor skills or relearn old motor skills. It is not clear whether people with MS retain the ability to improve motor performance or learning. Furthermore, factors that influence motor performance in people with MS need to be investigated. This study explored motor performance in people with MS using virtual reality (VR). The effect of fatigue and cognitive function on motor performance improvement in people with MS was investigated. METHODS: Twenty MS participants and 20 controls were recruited into the study. To assess motor performance, each participant was asked to perform a VR game for five times (blocks). The main outcome was time to complete the VR game and number of recorded errors. To assess fatigue level and cognitive function, participants were asked to complete the Arabic versions of the Modified Fatigue Impact Scale (MFIS) and the Montréal Cognitive Assessment (MOCA), respectively. RESULTS: MS participants and controls demonstrated a practice-related improvement in performance as shown by the main effect of block for each of the outcome measures (p < .001, time required to complete VR game; p < .001, errors recorded). Strong and significant negative correlations between recorded errors and MOCA (r = .75, p < .001) and between recorded errors and MFIS (r = .55, p = .011) were found in people with MS. CONCLUSIONS: Ability to improve motor performance in people with MS is preserved and related to cognitive function and fatigue impact. Health-care professionals should be made aware of the negative impact of cognitive function and fatigue on motor performance. A multicomponent intervention that targets these factors is advisable. Future research, however, is required to determine the content and potential benefits of such an intervention in the MS population.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".