Improved Function and Reduced Pain after Swimming and Cycling Training in Patients with Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Arthritis and its associated joint pain act as significant barriers for adults attempting to perform land-based physical activity. Swimming can be an ideal form of exercise for patients with arthritis. Yet there is no information on the efficacy of regular swimming exercise involving patients with arthritis. The effect of a swimming exercise intervention on joint pain, stiffness, and physical function was evaluated in patients with osteoarthritis (OA). METHODS: Using a randomized study design, 48 sedentary middle-aged and older adults with OA underwent 3 months of either swimming or cycling exercise training. Supervised exercise training was performed for 45 min/day, 3 days/week at 60-70% heart rate reserve for 12 weeks. The Western Ontario and McMaster Universities Arthritis Index was used to measure joint pain, stiffness, and physical limitation. RESULTS: After the exercise interventions, there were significant reductions in joint pain, stiffness, and physical limitation accompanied by increases in quality of life in both groups (all p < 0.05). Functional capacity as assessed by maximal handgrip strength, isokinetic knee extension and flexion power (15-30% increases), and the distance covered in the 6-min walk test increased (all p < 0.05) in both exercise groups. No differences were observed in the magnitude of improvements between swimming and cycling training. CONCLUSION: Regular swimming exercise reduced joint pain and stiffness associated with OA and improved muscle strength and functional capacity in middle-aged and older adults with OA. Additionally, the benefits of swimming exercise were similar to the more frequently prescribed land-based cycling training. TRIAL REGISTRATION: clinicaltrials.gov NCT01836380.
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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 | 0.000 | 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) | 0.001 | 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".