Does Protection Motivation Theory Explain Exercise Intentions and Behavior During Home-Based Cardiac Rehabilitation?
Bibliographic record
Abstract
OBJECTIVE: Home-based cardiac rehabilitation (CR) programs have been shown to be effective in increasing exercise capacity, which is a significant predictor of longevity for patients with heart disease. However, adherence to these programs has been problematic. Therefore, it is important to identify key theoretical correlates of exercise for these patients that can be used to inform the development of behavioral interventions to help tackle the adherence problem. The purpose of this study was to determine whether protection motivation theory (PMT) explained significant variation in exercise intentions and behavior in patients receiving home-based CR. METHODS: Patients (N = 76) completed a questionnaire that included PMT constructs at the beginning and midpoint (ie, 3 months) of the program and an exercise scale at 3 and 6 months (ie, at the end of the CR program). RESULTS: Path analyses showed that response efficacy was the sole predictor of 3-month (beta = .53) and 6-month (beta = .32) intentions. However, the indirect effect of baseline response efficacy on 3-month exercise behavior through intention was nonsignificant (beta = -.01), whereas it was significant (beta = .11) for 3-month response efficacy on 6-month exercise behavior. Self-efficacy significantly predicted 3-month (beta = .36) and 6-month (beta = .32) exercise behaviors, whereas 3-month intention significantly predicted 6-month exercise behavior (beta = .23). CONCLUSIONS: Coping appraisal variables (ie, response efficacy and self-efficacy) are potentially useful in explaining exercise behavior during home-based CR.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".