A Novel Mobile App-Based Neuromuscular Electrical Stimulation Therapy for Improvement of Knee Pain, Stiffness, and Function in Knee Osteoarthritis: A Randomized Trial
Bibliographic record
Abstract
Background Knee osteoarthritis (OA) is a widespread and debilitating disease that continues to plague patients. Over the past decade, neuromuscular electrical stimulation (NMES) therapy has shown promise in alleviating knee OA-related symptoms. This study sought to evaluate the efficacy and safety of a home-based NMES therapy for reduction of pain, stiffness, and function associated with knee OA. Material and methods A randomized, sham-controlled, double-blind, multicenter trial was conducted with 12-week follow-up in 156 knee OA patients receiving either home-based NMES therapy or a modified low-voltage NMES therapy. Outcome measures including knee pain, stiffness, and functionality were collected at baseline through week 12 after the therapy. The primary endpoint was the percentage change from baseline (PCFB) in the Visual Analog Scale (VAS) pain for a patient-nominated physical activity. Secondary endpoints included VAS for general knee pain, Western Ontario and McMaster Universities Osteoarthritis Index, Knee Injury and Osteoarthritis Outcome Score Joint Replacement, and isometric quadriceps strength test. Results A clinically meaningful reduction for VAS Nominated Activity was higher in the per-protocol treatment-compliant NMES group than that in the sham low-voltage NMES group at week 12 (PCFB of 42.8% vs 38.6%, P = .562). This was similarly true for the Western Ontario and McMaster Universities Osteoarthritis Index pain subscale (PCFBs of 36.8% vs 26.6%, P = .038). Similar trends and reductions of pain were observed for VAS General, Knee Injury and Osteoarthritis Outcome Score Joint Replacement Pain subscale, and isometric quadriceps strength. Conclusion Home-based NMES treatment resulted in a clinically meaningful reduction of knee pain, stiffness, and knee functional improvements at week 12 compared with sham NMES treatment.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".