Psychosocial determinants of exercise in individuals post cardiac rehabilitation: Applying the COM_B and TDF models
Bibliographic record
Abstract
Little research has been conducted on the theoretical mechanisms of exercise post cardiac rehabilitation (CR). The Theoretical Domains Framework (TDF), as embedded within the Capability, Opportunity, Motivation, and Behaviour model (COM-B), can provide a theoretical understanding of exercise post-CR. This study aimed at identifying TDF-related psychosocial and environmental variables that were important for predicting moderate to vigorous physical activity (MVPA) in individuals post-CR. Eighty-four individuals (80% men; Mage=60 years, SD=15.83) who completed a CR program answered baseline questionnaires assessing ten TDF domains. Participants wore an accelerometer for 7 days three months after baseline assessments, from total minutes of were computed. Six multiple regressions were conducted to predict MVPA at three months. TDF domains for each COM-B sub-component (e.g., psychological capability) were tested in five separate regressions. A final regression included significant variables from the five regressions to determine the strongest TDF predictor. Decisional balance (? = .28) as part of Capability, streets, walking/cycling, and neighbourhood surroundings (? = .20) for the Opportunity component, negative affect (? = -.09) and identity (? = -.14) within Motivation were significant predictors of MVPA in separate regression models. The final regression explained 26% of the variance in MVPA at three months and intention was the sole significant predictor (? = .33, p< 0.05). Thus, it is important to promote participants' intentions to continue exercise post-CR to help sustained physical activity participation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".