An Exploratory Study of Aquatic Walking on Symptoms and Functional Limitations in Persons with Knee Osteoarthritis: Part 1
Bibliographic record
Abstract
This paper represents Part 1 of a study that explored the effects of an underwater treadmill (UT) walking program on pain and function in adults with knee osteoarthritis (KOA). The Western Ontario & McMaster Universities Osteoarthritis Index (WOMAC), numerical rating scale (NRS), timed up-and-go (TUG), and 10-m walk were assessed in 6 adults (62.7 ±14.2 years) who participated in an 8-week (3x/wk) UT walking intervention based on the Arthritis Foundation’s Walk With Ease (WWE) program. Walking pace was self-selected, and walking duration of each session was increased from 10 to 45 minutes throughout the study. Knee pain and function were assessed pre-control (PRC), pre-intervention (PRI) and post-intervention (PST). NRS improved from PRC and PRI to PST (p = .03, d = .37). WOMAC subscale scores of pain, (d = .36); stiffness (d = .44); pain during daily activities (d = .41); and total scores (d = .42) improved (p < .05) from PRC to PST. Self-selected walking speed increased concurrently with decreased knee pain (NRS) from PRI to PST. The results support the WWE as a model for an UT walking program for improving knee pain in KOA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".