Effect of Wii Fit <sup>©</sup> Exercise on Balance of Older Adults with Neurocognitive Disorders: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Exercise is beneficial to maintain balance. Wii Fit©, a video game-based exercise, offers an enjoyable way to exercise and is feasible for older adults with neurocognitive disorders (NCD). OBJECTIVE: To evaluate the effects of Wii Fit© exercise training on the balance of older adults with NCD. METHODS: Systematic review and meta-analysis of randomized control trials using Cochrane collaboration tools. The participants were older adults (60 years and over) with NCD. Balance was measured with Berg Balance Scale (BBS) and Timed Up and Go (TUG). Two reviewers independently searched, selected, extracted data, assessed risk of biases, and determined the quality of evidence. Outcomes were evaluated using Grading of Recommendations Assessment, Development, and Evaluation (GRADE). A meta-analysis was performed. RESULTS: The literature search identified 522 source documents of which titles and abstracts were reviewed for 428 after removing 94 duplicates. The reviewers selected five studies out of 50 after a full text review. The overall effect of Wii Fit© exercise training on BBS was moderate, significant, and clinically meaningful (standardized mean difference [SMD] = 0.5 standard deviation [SD] [95% confidence interval CI] 0.08, 0.84]). No effect was observed with TUG scores (SMD = 0.00 SD [95% CI -0.44, 0.44]). The GRADE quality of evidence was very low. CONCLUSION: Wii Fit© exercise training has a positive effect on balance in older adults with NCD. However, further research with sufficient power is needed to evaluate its effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.057 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".