The Effectiveness of Telerehabilitation in Managing Pain, Strength, and Balance in Adult Patients With Knee Osteoarthritis: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is a chronic, degenerative joint disease characterized by pain, stiffness, and functional impairment, significantly affecting mobility and quality of life. Traditional rehabilitation, mainly through in-person physiotherapy, is widely recommended for KOA management. However, access to these services is often limited due to geographic, financial, and mobility constraints. Telerehabilitation has emerged as an alternative, providing remote rehabilitation through digital platforms. Despite its increasing adoption, its effectiveness in improving key functional parameters such as pain, strength, and balance remains uncertain. While previous studies have focused primarily on pain relief and overall functional improvement, a broader assessment of its impact on mobility and fall prevention is needed. OBJECTIVE: This systematic review examines the effectiveness of telerehabilitation in improving pain, strength, and balance in adults with KOA compared with traditional rehabilitation or no intervention. In addition, it evaluates the impact of different telerehabilitation models, such as therapist-guided versus self-managed programs, and explores the feasibility of integrating telerehabilitation as an alternative in KOA management. METHODS: A systematic search of 4 databases (PubMed, PEDro, Cochrane, and Scopus) was conducted to identify randomized controlled trials (RCTs) published from May 2004 to May 2024. Inclusion criteria consisted of adults with KOA, evaluation of telerehabilitation either as a stand-alone intervention or in comparison to traditional rehabilitation or no intervention, and measurement of at least one primary outcome (pain, strength, or balance). A total of 2 independent reviewers assessed the risk of bias using validated tools. Due to variations in intervention programs and assessment methods, a narrative synthesis was performed instead of a meta-analysis. The review followed established guidelines, and data extraction was conducted using appropriate software. RESULTS: A total of 6 RCTs (N=581 participants) met the inclusion criteria. The results indicate that telerehabilitation effectively reduces pain and improves strength and balance, although the extent of benefits varies. Some studies reported similar pain reductions between telerehabilitation and traditional rehabilitation, while others highlighted greater functional improvements in telerehabilitation groups. Therapist-guided telerehabilitation was associated with higher adherence rates and better functional outcomes compared with self-managed programs. The risk of bias assessment showed that most studies were of moderate to good quality, though common issues included selection bias, performance bias, and participant attrition. CONCLUSIONS: Telerehabilitation is a promising alternative for KOA management, especially for individuals facing barriers to in-person therapy. It is effective in reducing pain and improving strength and balance, though its success depends on patient engagement, intervention delivery, and rehabilitation protocols. Therapist-guided programs yield better outcomes than self-managed approaches. Further research is needed to standardize intervention protocols, integrate emerging technologies, and evaluate cost-effectiveness to guide clinical practice and health care policies. TRIAL REGISTRATION: PROSPERO CRD42024564141; https://tinyurl.com/25ykvy7d.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".