Factors associated with physical activity engagement among adults with rheumatoid arthritis: A cross‐sectional study
Bibliographic record
Abstract
Abstract Objectives Physical activity (PA) has a number of benefits for rheumatoid arthritis (RA) patients. However, these patients are more physically inactive than the general population. The primary aim of this study was to investigate factors associated with PA engagement among RA patients. The secondary aim was to identify their preference for PA support. Methods There were 96 participants, 76 of whom were female, with a mean age of 56.9 years (range = 34–72 years) and a median RA disease duration of 5 years (interquartile range = 2–12). All patients completed questionnaires assessing demographic status, health status (including cardiovascular disease [CVD] risk and RA disease profile), PA levels and preferences, alongside the perceived benefits of—and barriers to—PA. Hierarchical regressions were carried out to assess the relationship between reported PA levels and both engagement determinants and disease features. Results Forty‐five per cent (n = 44) had low levels (<600 metabolic equivalent‐min/week) of PA. Low level of PA was significantly associated with: CVD risk profile (ηp2 = 0.118, p < 0.002); functional disability (ηp2 = 0.206, p < 0.032); pain (ηp2 = 0.154, p < 0.028); general personal (ηp2 = 0.190, p < 0.001) and arthritis‐specific personal (ηp2 = 0.170, p < 0.001) barriers to PA; age (ηp2 = 0.076, p < 0.026); and sedentary behaviour (ηp2 = 0.275, p < 0.001). Participants displayed a preference for unsupervised (n = 37, 38.5%), low‐intensity (n = 45, 46.9%), indoor home (n = 50, 52.1%) exercises, with no preferences for the diversity of the exercise prescribed (n = 39, 40.6%) or for who provided the exercise counselling (n = 34, 35.4%). Conclusions These results suggest that CVD profile, disability, pain, and general and arthritis‐specific personal barriers are associated with PA levels among RA patients. Intervention development should address these factors to facilitate an increase in PA uptake.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".