Effectiveness of Aquatic Exercise for Obese Patients with Knee Osteoarthritis: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: To design an aquatic exercise (AQE) and land-based exercise (LBE) program to enhance knee function and reduce body fat in patients with obesity and knee osteoarthritis and to investigate the effectiveness of AQE and LBE on body fat, functional fitness, and functional status. SETTING: Outpatient clinic at a Seoul National University Bundang Hospital. PARTICIPANTS: Obese patients with knee osteoarthritis were recruited from patients who visited the rehabilitation, orthopedic surgery, and geriatric outpatient clinics at the hospital. Study participants were limited to those who met the following criteria: body mass index more than 25 kg/m(2), abdominal circumference more than 90 cm (men) or 85 cm (women), clinically diagnosed osteoarthritis with Kellgren-Lawrence scale 2 or higher on radiographic studies, and independent ambulation state. METHODS: Participants were randomly allocated into 3 groups: AQE (n = 26), LBE (n = 25), and the control group (n = 24). Exercise interventions were conducted 3 times a week for 8 weeks. OUTCOME MEASURES: Body fat analysis, brief pain inventory, Western Ontario and McMaster Universities' osteoarthritis index, Short Form-36 questionnaire, and knee isokinetic tests were evaluated to assess changes in body fat composition, pain, physical function, and quality of life before and after the exercise program. RESULTS: Although no significant difference was found in general characteristics among the 3 groups before exercise, body fat proportion in the AQE group decreased significantly (mean +/- SD, from 34.4 +/- 4.7 to 33.3 +/- 4.7; P = .031) after intervention. The body mass index was slightly reduced after intervention, but it was not statistically significant. The AQE group showed significant improvements in pain, disability, and quality of life. Notably, the change in pain interference in the AQE group (mean +/- SD, from 25.8 +/- 15.1 to 18.8 +/- 13.1; P = .009) was greater than that of the LBE group. Both exercise groups showed significant improvements in Western Ontario and McMaster Universities' osteoarthritis index disability compared with the control group. CONCLUSIONS: AQE had an advantage in controlling the interference with activity because of pain. AQE may be an effective tool for patients with obesity who have difficulties with active exercise due to knee osteoarthritis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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) | 0.008 | 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".