Predictors of Exercise Maintenance 6 Months After Comprehensive Cardiac Rehabilitation
Bibliographic record
Abstract
PURPOSE: The objectives of this study were (1) to assess the effects of a comprehensive education intervention on maintenance of knowledge, exercise behavior, heart-healthy food intake, self-efficacy, and health literacy 6 mo after comprehensive cardiac rehabilitation (CR), and (2) to identify predictors of exercise maintenance 6 mo after comprehensive CR. METHODS: A prospective longitudinal study was conducted to test the effects of a structured educational curriculum in three CR programs in Canada. Participants completed surveys pre-, post-CR and 6 mo post-discharge to assess knowledge, heart-healthy food intake, self-efficacy, and health literacy. Exercise behavior was measured by number of steps/d using a pedometer. RESULTS: One hundred twenty participants completed the final survey. Increases in disease-related knowledge and self-efficacy, as well as behavior changes (increases in exercise and heart-healthy food intake), were achieved in comprehensive CR and sustained 6 mo post-program. Exercise maintenance was predicted by changes in heart-healthy food intake, self-efficacy, health literacy, and exercise-related knowledge. CONCLUSIONS: In this three-site study focusing on patient education for CR patients in Canada, the benefits of an education intervention in maintaining knowledge, exercise, healthy food intake, and self-efficacy were supported.
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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 | 0.003 |
| 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) | 0.001 | 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".