Predicting Exercise from Arthritis Flares and Self-Regulatory Efficacy to Overcome Flare Barriers
Bibliographic record
Abstract
Adults with arthritis struggle to adhere to moderate-vigorous exercise, which is an effective disease self-management strategy. The understanding of theory-based psychosocial factors related to exercise is needed. According to self-efficacy theory, self-regulatory efficacy to overcome challenging barriers may be one such factor. Adults often report that arthritis flares, which involve increases in typical arthritis symptoms (e.g., pain, fatigue), pose a challenge to exercise. However, no research has examined associations between arthritis flares, self-regulatory efficacy to overcome flare barriers, and exercise. The purpose of the study was to examine whether arthritis flares and self-regulatory efficacy to overcome flare barriers predicted weekly moderate-vigorous exercise volume. Ninety adults (Mage = 49.36 ± 16.38 years) with self-reported medically diagnosed arthritis responded to an online survey assessing arthritis flares, self-regulatory efficacy, prior moderate-vigorous exercise, and demographics. A hierarchical multiple regression analysis to predict exercise volume from arthritis flares (step 1) and self-regulatory efficacy to overcome flare barriers (step 2) was significant (R2 adjusted = .14, p < .001). Self-regulatory efficacy was the sole significant predictor in the full model (R2 change = .11, standardized β = .35, p < .001). These findings are the first to illustrate that individuals’ confidence to overcome flare barriers, and not merely the experience of a flare, predict exercise. These findings are important because efficacy beliefs can be changed via theory-based interventions. If future research supports a causal relationship between self-regulatory efficacy to overcome flare barriers and exercise, then an intervention can be designed and tested for improvements in efficacy and, in turn, exercise.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".