Does the use of Nintendo Wii Sports™ improve arm function and is it acceptable to patients after stroke? Publication of the Protocol of the Trial of Wii™ in Stroke – TWIST
Bibliographic record
Abstract
INTRODUCTION: Many stroke patients experience loss of arm function requiring rehabilitation, which is expensive, repetitive, and does not always translate into "real life." Nintendo Wii Sports™ (Wii™) may offer task-specific training that is repetitive and motivating. The Trial of Wii™ in Stroke (TWIST) is designed to investigate feasibility, efficacy, and acceptability using Wii™ to improve affected arm function for patients after stroke. METHOD: This is a randomized controlled trial (RCT), incorporating a qualitative study and health economics analysis that compares playing Wii™ versus arm exercises in patients receiving standard rehabilitation in a home setting within 6 months of stroke with a motor deficit of less than 5 on the MRC (Medical Research Council) scale (arm). In this study, we expect to randomize 240 participants. OUTCOME MEASURES: Primary outcome is change in affected arm function at 6 weeks follow-up in intervention and control group using the Action Research Arm Test. Secondary outcomes include occupational performance using the Canadian Occupational Performance Measure, quality of life using the Stroke Impact Scale, cost effectiveness analysis, and a qualitative study investigating factors that influence use of Wii™ for patients and carers. CONCLUSION: TWIST is the first UK RCT assessing the feasibility, cost effectiveness, and acceptability of Wii™ in stroke rehabilitation. The trial has been registered with ISRCTN 06807619 and UK CRN 11030. Results of the study will be published after completion of study in August 2014.
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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 | 0.050 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".