The use of virtual reality for balance among individuals with chronic stroke: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) is becoming a popular alternative to traditional upper and lower limb rehabilitation following a stroke. OBJECTIVE: To conduct a systematic review and meta-analysis on the effectiveness of VR interventions for improving balance in a chronic stroke (≥6 months) population. DATA SOURCES: A literature search of Pubmed, Scopus, CINAHL, Embase, Psycinfo, and Web of Science databases was conducted. STUDY SELECTION: English randomized controlled trials published up to September 2015 assessing balance with VR in chronic stroke participants. DATA EXTRACTION: Mean and standard deviations from outcome measures were extracted. Pooled standard mean differences ± standard error were calculated for the Berg Balance Scale (BBS) and the Timed Up and Go test (TUG). RESULTS: Wii Fit balance board (n = 7), treadmill training and VR (n = 7), and postural training using VR (n = 6). Significant improvements were found for VR interventions evaluating the BBS (n = 12; MD = 2.94 ± 0.57; p < 0.001) and TUG (n = 13; MD = 2.49 ± 0.57; p < 0.001). Sub-analyses revealed postural VR interventions had a significant effect on BBS (n = 5) and TUG (n = 3) scores (BBS: MD = 3.82 ± 0.79; p < 0.001 and TUG: MD = 3.74 ± 0.97; p < 0.001). VR and treadmill training (n = 5) had a significant effect on TUG scores (MD = 2.15 ± 0.89, p = 0.016). CONCLUSION: Wii Fit balance board may not be effective, although further confirmatory studies are necessary. Results should be interpreted with caution due to differences in therapy intensities and effect sizes within the included studies.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".