Effect of a low-intensity, self-management lifestyle intervention on knee pain in community-based young to middle-aged rural women: a cluster randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Knee pain is common with obesity and weight gain being important risk factors. Previous clinical trials have focused on overweight or obese adults with knee pain and osteoarthritis and demonstrated modest effects of intense weight loss programs on reducing knee pain despite very significant weight loss. There has been no lifestyle intervention that targets community-based adults to test its effect on prevention of knee pain. We aimed to determine the effect of a simple low-intensity self-management lifestyle intervention (HeLP-her), proven in randomised controlled trials to improve lifestyle and prevent weight gain, on knee pain in community-based young to middle-aged rural women. METHODS: A 1-year pragmatic, cluster randomised controlled trial was conducted in 649 community-based women (aged 18-50 years) to receive either the HeLP-her program (consisting of one group session, monthly SMS text messages, one phone coaching session, and a program manual) or one general women's health education session. Secondary analyses were performed in 390 women who had knee pain measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at baseline and 12-month follow-up. "Any knee pain" was defined as a WOMAC pain score ≥ 1. Knee pain worsening was defined as an increase in WOMAC pain score over 12 months. RESULTS: (OR 0.28, 95% CI 0.09-0.87, p = 0.03). CONCLUSIONS: In community-based young to middle-aged women, a simple low-intensity lifestyle program reduced the risk of knee pain worsening in those with any knee pain at baseline, particularly in those overweight or obese. Pragmatic lifestyle programs such as HeLP-her may represent a feasible lifestyle intervention to reduce the burden of knee pain in the community. TRIAL REGISTRATION: ACTRN12612000115831 , registered 24 January 2012.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".