Effect of aquatic resistance, balance, and proprioception training on lower limb muscle performance in bilateral knee osteoarthritis
Bibliographic record
Abstract
Objectives: Knee osteoarthritis (KOA) leads to persistent pain, joint stiffness, and muscle weakness, significantly limiting physical activity. Treatment options include surgical interventions, non-invasive alternatives, and exercise-based therapies. Land-based training (LBE) strengthens muscles, reduces pain, and improves function. In contrast, aquatic exercise (AQE) offers comfort. The study aimed to investigate the impact of aquatic resistance, balance, and proprioception training on lower limb muscle performance in bilateral KOA patients. Methods: This randomized clinical trial included 290 participants assigned to Groups A (Control group) and B (Interventional group), with 145 participants in each group. Over eight weeks, participants engaged in both LBE and AQE. Visual analog scale (VAS), 1 repetition maximum (RM) leg press test, proprioception, timed up-and-go (TUG) test, 40-m fast-paced walk test (40 mFPWT), and Western Ontario and McMaster Universities Arthritis Index (WOMAC) were utilized to evaluate the results. Results: The results revealed highly significant improvements in both groups’ VAS and WOMAC scores (P = 0.0001). However, when compared to Group A, Group B demonstrated significantly better outcomes in the 1RM leg press test, proprioception, TUG test, and the 40 mFPWT (P = 0.0001). Conclusion: The study found that an eight-week aquatic training program helped alleviate pain and improved lower limb muscle performance in bilateral KOA patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".