Home-based balance training programme using Wii Fit with balance board for Parkinsons´s disease: A pilot study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effects of a home-based balance training programme using visual feedback (Nintendo Wii Fit game with balance board) on balance and functional abilities in subjects with Parkinson's disease, and to compare the effects with a group of paired healthy subjects. SUBJECTS: Ten subjects with moderate Parkinson's disease and 8 healthy elderly subjects. METHODS: Subjects participated in a 6-week home-based balance training programme using Nintendo Wii Fit and balance board. Baseline measures were taken before training for the Sit-to-Stand test (STST), Timed-Up-and-Go (TUG), Tinetti Performance Oriented Mobility Assessment (POMA), 10-m walk test, Community Balance and Mobility assessment (CBM), Activities-specific Balance and Confidence scale (ABC), unipodal stance duration, and a force platform. All measurements were taken again after 3 and 6 weeks of training. RESULTS: The Parkinson's disease group significantly improved their results in TUG, STST, unipodal stance, 10-m walk test, CBM, POMA and force platform at the end of the 6-week training programme. The healthy subjects group significantly improved in TUG, STST, unipodal stance and CBM. CONCLUSION: This pilot study suggests that a home-based balance programme using Wii Fit with balance board could improve static and dynamic balance, mobility and functional abilities of people affected by Parkinson's disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".