Motivation for Physical Activity as a Key Determinant of Sedentary Behavior Among Postsecondary Students
Bibliographic record
Abstract
It is known that the transition to adulthood represents a critical period of life when acquiring healthy behaviors can influence lifestyle and health throughout adulthood. Given the importance of the consequences of a sedentary lifestyle, identifying influence factors is key to improving healthy behaviors. The objective of this study is to explore the role of postsecondary students’ motivation toward physical activity in the association with their screen time and out-of-school physical activity practice. A total of 1522 postsecondary students (90% were aged 17-20 years) recruited from 17 postsecondary institutions completed the self-reported questionnaire during course time. Multivariate linear regression was used to assess the association between motivation to move including additional predictors of behavior such as intention and tendency to self-activate and self-reported screen time and physical activity controlling for age and sex. Motivation including all 3 motivational variables (interest, utility, competence) was negatively associated with screen time, b = −0.498 (95% CI between −0.635 and −0.361) and positively associated with moderate-to-vigorous physical activity, b = 133.986, (95% CI between 102.129 and 165.843). Of the 3 motivational variables, interest had the strongest negative association with screen time, b = −0.434 (95% CI between −0.551 and −0.317), and the strongest positive association with physical activity, b = 113.671, (95% CI between 86.396 and 140.946). These findings indicate that the motivation of postsecondary students toward physical activity significantly influences their behaviors, including screen time and physical activity engagement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".