Pain, Anxiety, and Negative Outcome Expectations for Activity: Do Negative Psychological Profiles Differ Between the Inactive and Active?
Bibliographic record
Abstract
OBJECTIVE: Adherence to physical activity at ≥150 minutes/week has proven to offer disease management and health-promoting benefits among adults with arthritis. While highly active people seem undaunted by arthritis pain and are differentiated from the moderately active by adherence-related psychological factors, knowledge about inactive individuals is lacking. This knowledge may identify what to change in order to help inactive people begin and maintain physical activity. The present study examined the planned, self-regulated activity of high, moderate, and inactive individuals to determine if differences existed in negative psychological factors. METHODS: Adults with a medical diagnosis of arthritis completed online measures of physical activity, perceived pain intensity, pain anxiety, and negative disease-related outcome expectations from being active. High active (n = 94), moderately active (n = 77), and inactive (n = 104) groups were identified. RESULTS: A significant multivariate analysis of covariance revealed group differences (P < 0.001). Followup analyses indicated that inactive participants had the most negative psychological profile. Inactive participants reported that negative disease-related outcomes expectancies were more distressing and likely to occur than either group of active participants and expressed greater pain intensity and pain anxiety than the highly active participants (P < 0.05 for all). CONCLUSION: Identifying differences in negative psychological factors aids in the understanding of differential adherence between activity groups and highlights possible factors to change in future intervention and research.
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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 | 0.004 |
| 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 | 0.001 | 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".