The Effects of a Community-based Water Exercise Programme on Health Outcomes for Chinese People With Rheumatic Disease
Bibliographic record
Abstract
The purpose of this study was to investigate the effects of a community-based water exercise programme (CBWEP) on physical disability, quality of life, and confidence in performing exercise for people with rheumatic disease. A multiple pretest, within-subject design was used. All subjects participated in a 4-week CBWEP followed by an 8-week maintenance period. Physical disability, pain intensity, quality of life, and level of confidence in performing the exercises were evaluated using a self-administered questionnaire at baseline, pre-class, post-class, and at follow-up. Thirty-one subjects (30 female, 1 male) participated in the study. Repeated measures analysis of variance showed a statistically significant difference in pain intensity (p < 0.001) as well as in six domains of the SF-36 questionnaire: general health (p < 0.001), physical function (p = 0.001), role-physical (p = 0.001), role-emotional (p = 0.001), vitality (p < 0.001), and bodily pain (p = 0.006). There was also an improvement in confidence in performing the exercises (p < 0.001) and exercise participation at the end of the study. A CBWEP and continued maintenance classes may be beneficial for pain reduction and improvement in quality of life. These findings provided evidence for the value of this type of community exercise programme for people with rheumatic disease in Hong Kong.
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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) | 0.000 | 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".