Evaluating the YMCA Move for Health Program in Individuals With Osteoarthritis and Assessing Maintenance During the COVID-19 Pandemic
Bibliographic record
Abstract
Osteoarthritis is the most common condition to co-occur with other chronic health conditions and a broad exercise program on management of chronic conditions may be suitable for this group. This study evaluated the 12-week YMCA Move for Health exercise program among adults with osteoarthritis or with/at risk of chronic health conditions using a mixed-methods study design based on the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) framework. Participants (n = 66) completed the exercise program at the YMCAs in Cambridge, Kitchener, and Waterloo. Assessments included physical function, health-related quality of life, symptoms of arthritis, and physical activity levels and were conducted at baseline (B), postprogram (PP), and 3-month postprogram. Due to interruption by COVID-19, a subgroup of participants completed the 3-month postprogram assessments after the onset of the pandemic. At PP, participants with OA showed significant improvements in level of disability (B = 0.63 ± 0.45 and PP = 0.55 ± 0.47; p = .049), pain (B = 4.3 ± 2.5 and PP = 3.6 ± 2.4; p = .026), fatigue (B = 3.9 ± 3.1 and PP = 2.8 ± 2.6; p = .003), and several domains related to health-related quality of life. Despite interruption by the COVID-19 pandemic and poor maintenance of physical activity levels, nearly all improvements related to level of disability, symptoms of arthritis, and health-related quality of life observed at PP were maintained 3-months postprogram. The Move for Health program proved to be a feasible and effective community program for people with osteoarthritis. Additional supports may be needed to maintain physical activity levels after the program.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".