Interactive Video Gaming Improves Functional Balance in Poststroke Individuals: Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
The main objective of this study was to evaluate the effects of interactive video games on functional balance and mobility in poststroke individuals. The Health Science databases accessed included Medline via PubMed, LILACS, SciELO, and PEDro. The inclusion criteria were as follows: clinical studies evaluating the use of interactive video games as a treatment to improve functional balance and mobility in individuals poststroke and studies published in the Brazilian Portuguese, English, or Spanish language between 2005 and April 2016. PEDro Scale was used to analyze the methodological quality of the studies. The Berg Balance Scale and Timed Up and Go Test (TUGT) data were evaluated using a meta-analysis, the publication bias was assessed by funnel plots, and the heterogeneity of the studies by I 2 statistic. Eleven studies were included in the final analysis. Functional balance improved in individuals treated using interactive video games (mean difference = 2.24, 95% confidence interval [0.45, 4.04], p = .01), but no improvement was observed in mobility as measured by TUGT. The studies presented low heterogeneity (24%). The mean score on the PEDro Scale was 6.2 ± 1.9. Interactive video games were effective in improving functional balance but did not influence the mobility of individuals poststroke.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".