Investigating the role of self‐control beliefs in predicting exercise behaviour: A longitudinal study
Bibliographic record
Abstract
BACKGROUND: Engaging in exercise behaviour regularly requires a repeated investment of resources to reap the health benefits. An individual's self-control resources, when performing a behaviour can be perceived as being recharged or depleted. The investigation on how self-control beliefs resources predict exercise behaviour is very limited in the literature. The purpose of this study was to understand how self-control beliefs predict exercise behaviour across time in a physical activity model. METHODS: Participants (N = 161) were a sample of adults recruited across twelve gyms and recreation centres in a large city. Participants completed surveys across five months. Data were analysed using a multilevel structural equation model with participants (level 2) nested within time (level 1). RESULTS: Behaviour was found to be a function of intention, habit, and planning. Specifically, planning moderated the intention-behaviour relationship, where those who scored higher on planning engaged in more exercise. Self-control beliefs functioned as a proximal predictor of autonomous motivation and predicted habit, and intention when accounting for total effects. CONCLUSIONS: Self-control beliefs played a pivotal role in supporting recognized exercise determinants. Exercise-focussed interventions that help participants strengthen their beliefs as recharging and reduce depletion beliefs could be beneficial for promoting regular exercise.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".