Instrumented Static and Dynamic Balance Assessment after Stroke Using Wii Balance Boards: Reliability and Association with Clinical Tests
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The Wii Balance Board (WBB) is a globally accessible device that shows promise as a clinically useful balance assessment tool. Although the WBB has been found to be comparable to a laboratory-grade force platform for obtaining centre of pressure data, it has not been comprehensively studied in clinical populations. The aim of this study was to investigate the measurement properties of tests utilising the WBB in people after stroke. METHODS: Thirty individuals who were more than three months post-stroke and able to stand unsupported were recruited from a single outpatient rehabilitation facility. Participants performed standardised assessments incorporating the WBB and customised software (static stance with eyes open and closed, static weight-bearing asymmetry, dynamic mediolateral weight shifting and dynamic sit-to-stand) in addition to commonly employed clinical tests (10 Metre Walk Test, Timed Up and Go, Step Test and Functional Reach) on two testing occasions one week apart. Test-retest reliability and construct validity of the WBB tests were investigated. RESULTS: All WBB-based outcomes were found to be highly reliable between testing occasions (ICC = 0.82 to 0.98). Correlations were poor to moderate between WBB variables and clinical tests, with the strongest associations observed between task-related activities, such as WBB mediolateral weight shifting and the Step Test. CONCLUSIONS: The WBB, used with customised software, is a reliable and potentially useful tool for the assessment of balance and weight-bearing asymmetry following stroke. Future research is recommended to further investigate validity and responsiveness.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".