Integrating Self-Determination Theory and Self-Efficacy Theory to predict 4-month physical activity in cardiac rehabilitation participants
Bibliographic record
Abstract
Although Self-Determination Theory and Self-Efficacy Theory are prominent in the physical activity (PA) literature, there has been a recent call for theory comparison and integration (Noar & Zimmerman, 2005). This study sought to test a longitudinal integrated model of these two theories in order to predict PA at the end of a 4-month cardiac rehabilitation program. Participants (N = 109) answered self-report questionnaires for self-determination and self-efficacy variables as well as for PA over the 4-month period. Data were analyzed using AMOS to assess the path analysis and model fit. Prior to integration, perceived competence and self-efficacy were combined and labelled as confidence, as one factor emerged from a factor analysis. After controlling for 2-month PA and cardiac rehabilitation site, self-determined motivation was the sole predictor of 4-month PA, however this relationship was negative (beta= -.20, p = .01). Although confidence did not predict 4-month PA, it had a strong relationship with 2-month PA (beta= .34, p < .001) and mediated the outcome expectation - PA relationship. Results diverged from theoretical predictions as self-determined motivation negatively predicted PA. In addition, self-efficacy theory variables influenced PA cross-sectionnally rather than longitudinally in this study, suggesting that these construct may have a more proximal impact on behaviour. The contrary nature of these results will be discussed.Acknowledgments: Social Sciences and Humanities Research Council of Canada funded the research project and a doctoral fellowship for Shane N. Sweet
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".