Positive affect and exercise self-regulation: An Identity Theory perspective
Bibliographic record
Abstract
As per Identity Theory, individuals reflect on their behaviour (i.e., exercise) and on the extent to which it meets their identity standards. This reflection can lead to different affective responses. The theory posits that negative affect promotes changes in exercise self-regulation and behaviour, with less emphasis on the motivational impact of positive affect (Burke & Stets, 2009). Thus, no past research using Identity Theory has examined the effects of positive affect on future self-regulatory and behavioural outcomes in the exercise domain. The current study examined whether positive affect made a contribution to these outcomes. At Time 1, 129 university students completed measures of affect, exercise intentions and strength of intentions; Time 2 measures assessed intention-behaviour consistency, self-regulatory efficacy, and changes in both exercise intentions and behaviour. Hierarchical multiple regressions revealed that while positive affect regarding one's exercise behaviour was positively related with strength of exercise intentions (? = .230, p = .039; ?R2 = .032) and self-regulatory efficacy for future exercise (? = .375, p = .000; ?R2 = .084), it negatively predicted Time 2 exercise participation (? = -.238, p = .055; ?R2 = .024). These preliminary findings suggest that positive affect may not be inconsequential when predicting future exercise behaviour; rather, it may be associated with a decrease in future exercise. More research is warranted.Acknowledgments: Social Sciences and Humanities Research Council of Canada
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 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".