Starting up or Starting Over: The Role of Intentions to Increase and Maintain the Behavior of Exercise Initiates
Bibliographic record
Abstract
Across various social cognitive theories, behavioral intention is broadly argued to be the most proximal and important predictor of behavior (Ajzen, 1991; Gibbons, Gerrard, Blanton, & Russell, 1998; Rogers, 1983). It seems probable that an intention to increase behavior might be differentially determined from an intention to maintain behavior. Thus, the purpose of the current study was to examine (1) the change in two types of behavioral intention over time and (2) the relationship between intention and the social-cognitive factor mental imagery. Behavioral intention, exercise imagery, and observed exercise behavior was measured in 68 exercise initiates participating in a 12-week exercise program. Results revealed that behavioral intention to increase exercise behavior decreased over the exercise program, whereas intentions to maintain exercise behavior increased. Appearance and technique imagery were found to be significant predictors of intention to increase behavior during the first 6 weeks of the program, and only appearance imagery predicted intention to maintain exercise behavior during the last 6 weeks. These findings suggest that the two types of behavioral intention are distinguishable and may be useful targets for exercise behavior interventions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".