Priority Setting of Physical Activity Barriers and Facilitators Among Individuals With Rheumatoid Arthritis: A Nominal Group Technique Study
Bibliographic record
Abstract
OBJECTIVE: To (1) understand key physical activity (PA) determinants for individuals with rheumatoid arthritis (RA), and (2) map determinants using the Behavior Change Wheel (BCW) to guide theory-driven intervention development. METHODS: A patient-centered nominal group technique (NGT) approach was used, with recruitment through purposive sampling from past research participants. After self-reporting demographics and PA, participants attended NGT sessions consisting of idea generation, sharing, and refinement through discussion. Researchers combined PA-determinant lists across sessions using content analysis. Using a survey, participants rated the importance of each determinant from 1 to 9. Theoretical mapping was completed with the most important 2 out of 3 determinants using the BCW. RESULTS: Fourteen individuals participated across 3 sessions. The mean age was 58.1 (SD 13.4) years, with a mean 20.0 (SD 21.8) years with RA. Participants were 86% female, 79% White, and 86% had some university/college education. All participants were physically active, with a mean Godin Leisure-Time Exercise Questionnaire (GLTEQ) leisure score index of 43.3 (SD 18.4; < 14 = insufficiently active, 14-24 = moderately active, > 24 = active). The 22 PA barriers and 25 facilitators fell into 4 categories: personal, social, physical, and environmental. The top 3 barriers (importance: 7.1-7.4/9) were unpredictable fluctuations, fatigue, and lack of RA-specific PA knowledge. The top 3 facilitators (importance: 7.6-7.9/9) were motivation to maintain function, knowledge to tailor PA, and PA self-confidence. Theoretical mapping led to 3 capability-related, 6 opportunity-related, and 4 motivation-related determinants. CONCLUSION: Our study highlighted key PA determinants related to capability, opportunity, and motivation. Interventions may target skills (capability), social influences (opportunity), and beliefs about capabilities (motivation) to increase PA in individuals with RA.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".