Effectiveness of Sensor-based Rehabilitation in Improving Outcomes in Patients Undergoing Total Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Physical rehabilitation after total knee arthroplasty (TKA) is important for long-term functional recovery. Recently, sensor-based home rehabilitation (SHR) has gained prominence as a promising method that allows monitoring and guidance that is both structured and accessible, compared to traditional methods of physical rehabilitation. Despite the advent of wearable sensor systems, there is a paucity of evidence regarding SHR in the current literature. Thus, this systematic review aimed to evaluate the effect of wearable SHR on post-TKA outcomes. METHODS: We performed a systematic search of three electronic databases from the beginning of record to March 12, 2021. Primary outcomes were patient-reported outcome measures (PROMs) after rehabilitation, including the Knee Injury and Osteoarthritis Outcome Score (KOOS), Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Knee Society Score (KSS). Secondary outcomes were physical activity levels and functional performance including range of motion (ROM) and Timed Up and Go Test (TUG). RESULTS: A total of 16 studies involving 1321 subjects were included. All wearable sensors in our included studies involved a combination of accelerometers, gyroscopes and magnetometers as functional units. These studies reported favourable outcomes for all three PROMs, although the extent of improvement in specific domains varied among studies. Moreover, physical activity in terms of daily steps and time spent on physical activity increased post-rehabilitation. Similarly, there were improvements in ROM and TUG that reflected a favourable post-operative trajectory during rehabilitation. CONCLUSION: SHR is effective for improving subjective and objective outcomes post-TKA. The role of SHR should be evaluated by a dedicated cost-benefit analysis to facilitate its wider adoption in healthcare systems.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".