Meeting physical activity recommendations: Self-regulatory efficacy characterizes differential adherence during arthritis flares.
Bibliographic record
Abstract
PURPOSE: Using social-cognitive theory, we examined whether adults who experienced an arthritis flare and met/did not meet the disease-specific public health recommended dose for physical activity differed in their self-regulatory efficacy beliefs, overall pain, and flare-related factors. RESEARCH METHOD/DESIGN: Adults with arthritis (N = 56; M(age) = 49.41 ± 11.56 years) participated in this prospective study. RESULTS: Multivariate analysis of variance comparing groups who met or did not meet the recommended dose (n(met) = 24, ≥ 150 minutes/week vs. n(not met) = 32, < 150 min/week) on efficacy, overall pain, and flare-related factors was significant (p < .01; η(partial)² = .28). People meeting the dose had significantly greater self-regulatory efficacy to overcome arthritis barriers (M(met dose) = 7.33 ± 1.95 vs. M(did not meet dose) = 5.74 ± 2.08, η(partial)² = .14) and to schedule/plan (M(met dose) = 7.27 ± 1.80 vs. M(did not meet dose) = 5.72 ± 1.90, η(partial)² = .15). Overall pain and flare-related factors did not differ (ps > .05). CONCLUSION/IMPLICATION: During flares, individuals with greater self-regulatory efficacy to manage disease barriers and plan their physical activity were more adherent to disease-specific public health activity recommendations. This study was the first to demonstrate differences in social cognitions that characterize adherence to recommended activity among people challenged by arthritis flares. Findings support the theoretical position that self-regulatory efficacy is related to better adherence in the face of challenging disease-related circumstances. The importance of studying individual characteristics of people who succeed in being active despite such obstacles is stressed.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| 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".