Exploring exercise behavior, intention and habit strength relationships
Bibliographic record
Abstract
The purpose of this study was to explore the relevance of integrating exercise habit strength within the framework of the theory of planned behavior. Data were obtained from 538 undergraduate students [mean age=21.19 (SD=2.57); 28.4% males] using validated questionnaires and analyzed using regression analysis and discriminant function analysis. Findings indicated that exercise has both a cognitive and an automatic component and that stronger exercise habits make exercise less intentional, with the intention-exercise relationship nearly three times stronger at lower levels of exercise habit strength than at higher levels. Further, outcome expectancies regarding health and weight management resulting from sufficient exercise did not significantly differ between most profiles that were created from exercise behavior, motivation and habit strength. The results from this study demonstrate the usefulness of incorporating measures of exercise habit strength in order to further our understanding of relevant determinants of exercise behavior. Results also indicate that health outcomes of sufficient exercise are generally well known, implying that persuasive strategies should rather shift in emphasis toward instilling a sense of exercise confidence in various situations. This potentially valuable information may allow for a more thorough understanding of exercise determinants and the development of more effective interventions that target increased exercise levels.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".