Evaluation of a cognitive affective model of physical activity behavior
Bibliographic record
Abstract
Background: To empirically evaluate a cognitive affective model of physical activity. This bidirectional, cyclical model hypotheses that executive control processes directly influence habitual engagement in exercise and also directly subserve the exercise-induced affective response to acute exercise associated with future physical activity. Methods: The present study employed a one-week prospective, multi-site design. Participant recruitment and data collection occurred at two separate University sites (one in the United States and the other in Canada). Participants completed a bout of treadmill exercise, with affect and arousal assessed before, during and after the bout of exercise. Subjective and objective measures of executive function were assessed during this visit. Following this laboratory visit, seven days of accelerometry were employed to measure habitual engagement in physical activity. Results: Within our inactive, young adult sample, we observed some evidence of 1) aspects of executive function were associated with more light-intensity physical activity in the future (1-week later) (r = 0.36, 95% CI = -0.03 to 0.66, P = 0.07), 2) aspects of executive function were associated with post-exercise affect (r = -0.39, 95% CI = -0.67 to -0.03, P = 0.03) and forecasted affect (r =0.47, 95% CI = 0.11 to 0.72, P = 0.01), and 3) aspects of acute exercise arousal and affect were associated with current mild-intensity physical activity behavior (r = 0.41, 95% CI = 0.04 to 0.68,P = 0.03). Conclusion: We demonstrate partial support of a cognitive-affective model of physical activity.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".