Effectiveness of Active Video Games Used to Augment Physical Therapy for Improving Gross Motor Outcomes of Children with Cerebral Palsy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background:Active video games may be beneficial for improving gross motor outcomes when used to augment traditional physical therapy for children and youth with cerebral palsy (CP). However, their effectiveness for improving gross motor outcomes is unclear. The purpose of this systematic review and meta-analysis was to determine the effectiveness of active video game interventions combined with physical therapy compared to physical therapy alone for improving gross motor outcomes for children with CP. Materials and Methods:MEDLINE, CINAHL, Scopus, EMBASE (Ovid), PsycINFO, and SPORTDiscus databases were searched for relevant literature published prior to January 27, 2023. Eligible studies (a) were published in English, (b) used a randomized study design comparing active video games plus physical therapy to physical therapy alone, (c) included children and/or adolescents with CP (aged 5–18 years), and (d) measured gross motor outcomes. Included articles were assessed for bias (Cochrane risk-of-bias tool—version 2) (RoB-2), outcomes across studies were evaluated for evidence certainty using Grading of Recommendations Assessment, Development, and Evaluation (GRADE), and meta-analyses were conducted on outcomes when at least two studies used the same outcome measure. Results:Twelve articles met the inclusion criteria. Very low certainty evidence supported the use of active video games as an augmentative intervention for improving gross motor function (Z = 3.33; P < 0.001). Meta-analyses focused on other gross motor outcomes (i.e., balance and walking speed/distance) were not statistically significant. Conclusion:Active video games may be beneficial in combination with regular physical therapy for improving gross motor function. However, current evidence is weak, and high-quality research is required.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.004 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".