Telerehabilitation After Total Knee Arthroplasty: A Narrative Review of Its Effectiveness, Safety, and Access in the Post-COVID Era
Bibliographic record
Abstract
Osteoarthritis (OA) is a leading cause of disability worldwide, affecting primarily older adults. With rising rates of obesity and population aging, the global burden of OA is expected to grow substantially. Total knee arthroplasty (TKA) remains the definitive treatment for end-stage knee OA. However, the growing demand for postoperative rehabilitation has intensified the strain on the physical therapy (PT) workforce. The COVID-19 pandemic accelerated the adoption of telerehabilitation, but evidence regarding its effectiveness, safety, cost-efficiency, and equity after TKA remains scattered. A narrative review of English-language, full-text articles published between January 2018 and May 2025 was performed. PubMed, MEDLINE, and Google Scholar were searched using terms including "telerehabilitation", "virtual physical therapy", and "total knee arthroplasty". Eligible studies were randomized controlled trials (RCTs), cohort studies, case series (n ≥ 10), or systematic reviews/meta-analyses that compared telerehabilitation with conventional in-person rehabilitation after TKA. Outcomes included functional metrics, e.g., Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Oxford Knee Score (OKS), Knee Injury and Osteoarthritis Outcome Score (KOOS), and Knee Society Score (KSS), pain (visual analog scale, VAS), patient satisfaction, adherence, cost/utilization data, and equity indicators. Eight studies (six RCTs, two meta-analyses; n ≈ 2,070) met the inclusion criteria. Interventions included AI-driven apps, video-based therapy, and wearable sensors. Most studies found telerehabilitation to be non-inferior to conventional PT in improving pain, range of motion (ROM), and function. Meta-analyses showed comparable gains in KOOS, ROM, and VAS scores. Cost-effectiveness was favorable in bundled care models, though technology costs were inconsistently reported. No increased adverse events were observed. Digital literacy and broadband access emerged as equity challenges. Telerehabilitation is a safe, effective adjunct to in-person PT following TKA, with potential to expand access and reduce system strain. Hybrid models may offer the most sustainable path forward.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".