Promoting Self-efficacy and Outcome Expectations to Enable Adherence to Resistance Training After Cardiac Rehabilitation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Resistance training offers clinical and functional benefits to cardiac patients, yet exercise adherence after cardiac rehabilitation (CR) is problematic. This study examined effects of an intervention targeting self-efficacy, outcome expectations, and adherence to upper-body resistance exercise after CR. PARTICIPANTS AND METHODS: Cardiac patients (N = 40) were randomly allocated to receive either standard exercise recommendations (wait-list control) or an intervention involving a theory-based instructional manual and Thera-Band resistive bands for upper-body resistance exercise. Self-efficacy and outcome expectations were assessed at baseline and 4 weeks later. Participation in resistance exercise was measured at 4 weeks postbaseline and at 3-month follow-up. RESULTS AND CONCLUSIONS: The intervention group reported higher levels of self-efficacy, outcome expectations, and resistance exercise volume compared with the control group at the 4-week follow-up. Adherence differences were sustained at 3-month follow-up, with some support that self-efficacy for adhering to resistance training mediated the effects of the intervention on follow-up exercise training frequency. Findings support the use of a theory-based motivational manual and Thera-Band resistive bands to increase self-efficacy and outcome expectations for, and adherence to, resistance training after 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".