Action Control of Exercise Behavior: Evaluation of Social Cognition, Cross-Behavioral Regulation, and Automaticity
Bibliographic record
Abstract
Intention is considered the proximal determinant of behavior in many popular theories applied to understanding physical activity, yet intention-behavior discordance is high. Thus, an understanding of constructs that facilitate or inhibit the successful translation of intentions into behavior is both timely and important. The action control approach of dividing the intention-behavior relationship into quadrants of successful/unsuccessful intenders has shown utility in the past by demonstrating the magnitude of intention-behavior discordance and allowing for an outcome variable to test predictors. The purpose of this article was to evaluate automaticity and cross-behavioral regulation as predictors of exercise action control, in conjunction with other more standard social cognitive predictors of perceived behavioral control and affective and instrumental attitudes. Participants were a random sample of 263 college students who completed predictor measures at time one, followed by exercise behavior two weeks later. Participants were classified into three intention-behavior profiles: (1) nonintenders (14.1%; n = 31), (2) unsuccessful intenders (35.5%; n = 78), and (3) successful intenders (48.6%; n = 107). Affective attitude, perceived behavioral control, automaticity, and cross-behavioral regulation were predictors of action control. The results demonstrate that automaticity and cross-behavioral regulation, constructs not typically used in intention-based theories, predict intention-behavior discordance.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".