Smart device–assisted telerehabilitation versus conventional rehabilitation after total nee arthroplasty: a systematic review and meta-analysis
Bibliographic record
Abstract
Total knee arthroplasty (TKA) remains the definitive treatment for end-stage knee osteoarthritis (OA). Despite its success, post-operative rehabilitation continues to be challenged by limited access to care, inconsistent patient compliance, and a lack of standardized protocols. In response, smart device-assisted telerehabilitation has gained attention for its capacity to deliver real-time monitoring and individualized feedback. However, its comparative effectiveness relative to traditional rehabilitation approaches remains inconclusive. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) comparing smart device-assisted telerehabilitation to conventional rehabilitation following TKA. Databases searched included PubMed, Web of Science, EMBASE, and Cochrane Library. Key outcomes assessed were pain (Visual Analog Scale (VAS)), functional recovery (Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)), and range of motion (ROM) (knee flexion and extension angles). Meta-analysis was performed using Stata 16.0, with heterogeneity evaluated via the I 2 statistic. Subgroup and sensitivity analyses were also conducted. A total of 22 RCTs encompassing 2,181 participants were included. Overall, there were no significant differences between smart and conventional rehabilitation regarding VAS (SMD = 0.02, 95% CI: − 0.24 to 0.28) and WOMAC scores (SMD = − 0.27, 95% CI: − 0.60 to 0.06). Subgroup analyses revealed that augmented reality (AR) interventions were associated with greater pain reduction (VAS: SMD = − 1.12, 95% CI: − 1.98 to − 0.25), and virtual reality (VR) interventions led to significant functional improvement (WOMAC: SMD = − 0.47, 95% CI: − 0.82 to − 0.13). Furthermore, smart rehabilitation yielded superior outcomes in knee extension angle (SMD = − 0.15, 95% CI: − 0.28 to − 0.02). Sensitivity and publication bias analyses confirmed the stability of the results. Smart device-assisted telerehabilitation is comparable to conventional rehabilitation in overall outcomes after TKA. However, AR and VR technologies demonstrate added value in specific domains of recovery, suggesting that future rehabilitation programs should consider integrating these modalities to enhance effectiveness and personalization of care.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".