Modifiable lifestyle factors are associated with lower pain levels in adults with knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: With no cure or effective treatments for osteoarthritis (OA), the need to identify modifiable factors to decrease pain and increase physical function is well recognized. OBJECTIVE: To examine factors that characterize OA patients at different levels of pain, and to investigate the relationships among these factors and pain. METHODS: Details of OA characteristics and lifestyle factors were collected from interviews with healthy adults with knee OA (n=197). The Western Ontario and McMaster Universities Osteoarthritis Index was used to assess pain. Factors were summarized across three pain score categories, and χ(2) and Kruskal-Wallis tests were used to examine differences. Multiple linear regression analysis using a stepwise selection procedure was used to examine associations between lifestyle factors and pain. RESULTS: Multiple linear regression analysis indicated that pain was significantly higher with the use of OA medications and higher body mass index category, and significantly lower with the use of supplements and meeting physical activity guidelines (≥ 150 min/week). Stiffness and physical function scores, bilateral knee OA, body mass index category and OA medication use were significantly higher with increasing pain, whereas self-reported health, servings of fruit, supplement use and meeting physical activity guidelines significantly lower. No significant differences across pain categories were found for sex, age, number of diseases, duration of OA, ever smoked, alcoholic drinks/week, over-the-counter pain medication use, OA supplement use, physical therapy use, servings of vegetables or minutes walked/week. CONCLUSIONS: Healthy weight maintenance, exercise for at least 150 min/week and appropriate use of medications and supplements represent important modifiable factors related to lower knee OA pain.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".