Impact of Telerehabilitation on Rehabilitation Efficacy and Patient Satisfaction After Knee Surgery: Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Postoperative rehabilitation after knee surgery is crucial for functional recovery, but traditional in-person methods can impose burdens on patients, particularly those with mobility limitations or living remotely. Telerehabilitation, leveraging digital platforms, offers a potential alternative, yet its comparative efficacy and acceptability remain debated, especially across surgery types. Objective: This study aims to evaluate whether telerehabilitation improves postoperative rehabilitation satisfaction and efficacy compared to traditional methods for patients undergoing knee joint surgery. Methods: Six databases (Web of Science, PubMed, MEDLINE, ScienceDirect, Embase, and Cochrane Library) were searched from inception to September 27, 2025. Eligibility criteria included randomized controlled trials (RCTs) comparing telerehabilitation with traditional rehabilitation in adult patients undergoing postoperative knee surgery, reporting patient satisfaction and/or efficacy outcomes. Risk of bias was assessed using the Cochrane Risk of Bias 1 tool (developed by the Cochrane Collaboration). Data were synthesized using random-effects meta-analysis with the Hartung-Knapp-Sidik-Jonkman method for CIs, reporting standardized mean differences or mean difference, τ2 (between-study variance), τ (between-study SD), and prediction intervals (PIs) where applicable. Heterogeneity was assessed with τ2, τ, and PIs. Certainty of evidence was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation) criteria. Results: In total, 19 randomized controlled trials were included. Overall, patient satisfaction showed no significant difference between telerehabilitation and traditional rehabilitation (standardized mean difference [SMD] 0.15, 95% CI -0.48 to 0.78; P=.48; τ2=0.30; τ=0.55; PI=-1.17 to 1.47). Subgroup analysis revealed lower satisfaction with synchronous telerehabilitation (k=4 included studies; SMD -0.52, 95% CI -1.02 to -0.02; P=.04; τ2=0.17; τ=0.41) and higher with asynchronous (k=6 included studies; SMD 0.56, 95% CI 0.08-1.03; P=.02; τ2=0.30; τ=0.55). Telerehabilitation showed significant improvements on total Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC; k=4; SMD -0.76, 95% CI -1.38 to -0.14; P=.02; τ2=0.08; τ=0.29; PI=-1.85 to 0.33), Knee Injury and Osteoarthritis Outcome Score (KOOS; k=5; SMD 0.58, 95% CI 0.47-0.70; P=.01; τ2=0; τ=0; PI=0.36-0.80), timed-up-and-go (TUG) test (k=4; mean difference [MD]=-2.73 seconds, 95% CI -4.50 to -0.96; P=.04; τ2=1.14; τ=1.07; PI=-7.17 to 1.72) and knee extension range (k=3; MD=9.64°, 95% CI 6.89-12.39; P=.049; τ2=2.45; τ=1.56; PI=0.60-18.68). Conclusions: The pooled average effects suggest that telerehabilitation is noninferior to traditional care for patient satisfaction on average and may improve pain and function and some objective measures. However, bootstrapped PIs and between-study variability indicate that effects vary by context, so implementation should therefore be individualized with attention to modality, patient digital literacy, and technical support. Targeted trials with standardized measures are recommended to increase certainty and narrow the expected distribution of effects.
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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 | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".