Testing a longitudinal integrat ed self-efficacy and self-deter mination theory model for physical activity post-cardiac rehabilitation
Bibliographic record
Abstract
Self-determination theory and self-efficacy theory are prominent theories in the physical activity literature, and studies have begun integrating their concepts. Sweet, Fortier, Strachan and Blanchard (2012) have integrated these two theories in a cross-sectional study. Therefore, this study sought to test a longitudinal integrated model to predict physical activity at the end of a 4-month cardiac rehabilitation program based on theory, research and Sweet et al.'s cross-sectional model. Participants from two cardiac rehabilitation programs (N=109) answered validated self-report questionnaires at baseline, two and four months. Data were analyzed using Amos to assess the path analysis and model fit. Prior to integration, perceived competence and self-efficacy were combined, and labeled as confidence. After controlling for 2-month physical activity and cardiac rehabilitation site, no motivational variables significantly predicted residual change in 4-month physical activity. Although confidence at two months did not predict residual change in 4-month physical activity, it had a strong positive relationship with 2-month physical activity (β=0.30, P<0.001). The overall model retained good fit indices. In conclusion, results diverged from theoretical predictions of physical activity, but self-determination and self-efficacy theory were still partially supported. Because the model had good fit, this study demonstrated that theoretical integration is feasible.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".