The use of virtual reality for balance among individuals with chronic stroke: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it