Effectiveness of technology-based stroke interventions to improve upper limb functioning in low- and middle-income countries: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Stroke is one of the leading causes of disability worldwide, with low- and middle- income countries (LMICs) representing 69% of stroke incidence. Technology-based interventions offer potential for improving motor function and rehabilitation adherence; however, their impact in LMICs remains unknown. OBJECTIVE: To measure the efficacy of technological interventions compared to conventional physical rehabilitation in improving post- stroke upper limb motor function in LMICs. METHODS: We conducted a systematic review (PROSPERO registration: CRD42020213333) of randomized clinical trials (RCTs) from PubMed, Global Index Medicus, and Physiotherapy Evidence Databases. Studies included stroke survivors receiving technological interventions for upper limb rehabilitation. Effectiveness outcomes included upper limb motor function, performance for activities of daily living, and quality of life. A meta-analysis was performed using mean differences (MD) and 95% confidence intervals (95% CI). Risk of bias was assessed using the Cochrane Collaboration tool for RCTs. RESULTS: Fifty studies were included after the screening phase, comprising a total of 2646 participants. Nine technological interventions were evaluated, including: virtual reality (40%), robotics (22%), telerehabilitation (10%), among others. Meta-analysis showed significant effect of immersive virtual reality on upper limb function using the Fugl-Meyer Scale (MD 5.65; 95% CI 4.88 to 6.43) and on daily activity performance using the Functional Independence Measure (MD 4,82; 95% CI 2,45-7,19). A significant difference was also found between telerehabilitation and conventional therapy using the modified Barthel index (MD of 3.28; 95% CI 0.86 to 5.70). CONCLUSIONS: Immersive virtual reality and telerehabilitation are effective interventions compared to conventional rehabilitation in LMICs.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.003 | 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".