Facing Multiple Barriers to Exercise: Does Stronger Efficacy Help Individuals with Arthritis?
Bibliographic record
Abstract
BACKGROUND: Research about exercise adherence amongst adults with arthritis has been largely correlational, and theoretically based causal studies are needed. We used an experimental design to test the social cognitive theory premise that high self-efficacy helps to overcome challenging barriers to action. METHODS: Exercising individuals (N = 86; female = 78%; M age = 53; BMI = 27) with differential self-regulatory efficacy for managing salient, non-disease barriers were randomly assigned to many or few barrier conditions. Individuals responded about the strength of their anticipated persistence to continue exercise, and their self-regulatory efficacy to use exercise-enabling coping strategies. RESULTS: In the many barriers condition, higher barriers-efficacy individuals expressed (a) greater persistence (Cohen's d = 0.75 [-0.029, 1.79]) and (b) more confidence in their coping solutions (Cohen's d = 0.65 [-0.30, 1.60]) than lower barriers-efficacy counterparts. CONCLUSION: Experimental support was obtained for the theoretical premise that when facing the greatest barrier challenge, individuals highest in self-regulatory efficacy still view exercise as possible. Findings suggest that identifying lower efficacy exercisers with arthritis to tailor their exercise to increase self-regulatory efficacy might also improve their adherence.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".