The roles of self-efficacy and motivation in the prediction of short- and long-term adherence to exercise among patients with coronary heart disease.
Bibliographic record
Abstract
OBJECTIVES: Poor adherence to regular exercise is a documented challenge among people with heart disease. Identifying key determinants of exercise adherence and distinguishing between the processes driving short- and long-term adherence to regular exercise is a valuable endeavor. The purpose of the present study was to test a model of exercise behavior change, which incorporates motivational orientations and self-efficacy for exercise behavior, in the prediction of short- and long-term exercise adherence. METHOD: Male and female patients (N = 801) hospitalized for coronary heart disease were recruited from 3 tertiary care cardiac centers and followed for a period of 1 year after hospital discharge. A prospective, longitudinal design was used to examine the roles of motivation and self-efficacy (measured at recruitment and at 2 and 6 months after discharge) in the prediction of exercise behavior at 6 and 12 months. Baseline measures of exercise and clinical and demographic covariates were included in the analyses. RESULTS: Structural equation modeling showed that both autonomous motivation and self-efficacy were important determinants of short-term (6-month) exercise behavior regulation, but that only autonomous motivation remained a significant predictor of long-term (12-month) exercise behavior. Self-efficacy partially mediated the relationship between motivation for exercise and 6-month exercise behavior. CONCLUSIONS: This research confirmed the roles of autonomous motivation and self-efficacy in the health behavior change process and emphasized the key function of autonomous motivation in exercise maintenance. Theoretical and cardiac rehabilitation program applications of this research are discussed.
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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 | 0.011 |
| 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 | 0.000 | 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".