Effects of Virtual Reality Therapy and Range of Motion Exercise on Range of Motion in Stroke Patients: Meta-Analysis
Bibliographic record
Abstract
Background: Stroke is a disease caused by interference with blood flow in the brain which is still a global problem today. Post-stroke care needs to be done to prevent the worsening of the condition. One of the right interventions that can be done is virtual reality therapy and range of motion exercise. This study aims to examine, analyze and estimate changes in the range of motion of stroke patients with virtual reality therapy and range of motion exercise. Subjects and Method: Metaanalysis was carried out according to the PRISMA flow chart and the PICO model (Population: stroke patients, Intervention: virtual reality therapy and range of motion exercise, Comparison: not performed virtual reality therapy and range of motion exercise Outcome: range of motion). The databases used are Google Scholar, PubMed, and Science Direct. The keywords used (“virtual reality” OR VR) AND (stroke OR CVD) AND (“range of motion exercise” OR ROM OR “motor exercise”) AND (stroke OR CVD) AND “Randomized Control Trial”. The inclusion criteria were full-text articles with RCT studies published in 2012-2022, articles in English, and bivariate and multivariate analysis. Analysis was performed using ReVman 5.3. Results: There were 20 articles with a randomized control trial design originating from Iran, Turkey, China, Egypt, Myanmar, South Korea, Spain, the Netherlands, Italy, Switzerland, and Canada involving 799 people. A meta-analysis of 10 RCT studies concluded that virtual reality therapy increased the range of motion of stroke patients by 2.77 units (SMD= 2.77; 95% CI = 1.29 to 4.24; p<0.001) compared to no virtual reality therapy. In addition, the range of motion exercise intervention can also increase the stroke patient's range of motion by 0.84 units (SMD= 0.84; 95% CI= 0.35 to 1.33; p<0.001) compared to not being given range of motion exercise. Conclusion: Virtual Reality Therapy and Range of Motion exercises can increase the range of motion of stroke patients. Keywords: virtual reality therapy, range of motion exercise, stroke, range of motion, meta-analysis. Correspondence: Septyan Dwi Nugroho. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir Sutami 36A, Surakarta 57126, Central Java. Email: septyandwin@gmail.com. Mobile: 081804418933. Indonesian Journal of Medicine (2023), 08(01): 23-36 https://doi.org/10.26911/theijmed.2023.08.01.03
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.066 |
| Bibliometrics | 0.006 | 0.006 |
| 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".