Prediction of Adaptive Self‐Regulatory Responses to Arthritis Pain Anxiety in Exercising Adults: Does Pain Acceptance Matter?
Bibliographic record
Abstract
BACKGROUND: Exercising for ≥ 150 min/week is a recommended strategy for self-managing arthritis. However, exercise nonadherence is a problem. Arthritis pain anxiety may interfere with regular exercise. According to the fear-avoidance model, individuals may confront their pain anxiety by using adaptive self-regulatory responses (eg, changing exercise type or duration). Furthermore, the anxiety-self-regulatory responses relationship may vary as a function of individuals' pain acceptance levels. OBJECTIVES: To investigate pain acceptance as a moderator of the pain anxiety-adaptive self-regulatory responses relationship. The secondary objective was to examine whether groups of patients who differed in meeting exercise recommendations also differed in pain-related and self-regulatory responses. METHODS: Adults (mean [± SD] age 49.75 ± 13.88 years) with medically diagnosed arthritis completed online measures of arthritis pain-related variables and self-regulatory responses at baseline, and exercise participation two weeks later. Individuals meeting (n=87) and not meeting (n=49) exercise recommendations were identified. RESULTS: Hierarchical multiple regression analysis revealed that pain acceptance moderated the anxiety-adaptive self-regulatory responses relationship. When pain anxiety was lower, greater pain acceptance was associated with less frequent use of adaptive responses. When anxiety was higher, adaptive responses were used regardless of pain acceptance level. MANOVA findings revealed that participants meeting the recommended exercise dose reported significantly lower pain and pain anxiety, and greater pain acceptance (P<0.05) than those not meeting the dose. CONCLUSIONS: Greater pain acceptance may help individuals to focus their efforts to adapt to their pain anxiety only when it is higher, leaving self-regulatory capacity to cope with additional challenges to exercise adherence (eg, busy schedule).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".