Why Can't I Stick to my Workout Routine? A Multi-Factor Approach to the Study of Self-Regulation
Bibliographic record
Abstract
It is suspected that deficits in self-regulatory variables such as self-control (SC) – the ability to inhibit impulses, self-motivation (SM) – the ability to mobilize energy, self-efficacy (SE) – confidence in one's abilities, and stress are contributing factors to the high rates of inactivity among Canadians. The majority of research examining this topic adopts a unifactor approach leaving the interactions among these variables unexplored, limiting our understanding of self-regulation and consistent physical activity (PA). The present study aimed to adopt a multifactor approach to explore the interplay among SC, SM, SE, and stress when predicting PA behaviour. At intake, participants completed a baseline questionnaire assessing demographics, trait SE and SM. Over the next two days, participants completed items assessing state SC, SM, SE, stress, and PA duration and intensity. Monte-Carlo simulation power analysis determined the ideal sample size for detecting multiple interactions was 500 (N = 582). The results from the structural equation model revealed that the latent variable state multifactor self-regulation consisting of SC, SM, and SE mediated the relationship between stress and PA. Furthermore, this mediation effect appeared to be moderated by trait SE and SM. These results support the notion of a depletion effect of stress acting on individuals’ state multifactor self-regulation resources resulting in fewer minutes of and lower intensity during physical activity; however, this depletion effect appeared to be buffered by high levels of trait SM and SE.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".