Improving the well-being of university students through in-class "fit-breaks": A two-part investigation
Bibliographic record
Abstract
Higher levels of sedentary behavior are associated with poor well-being, yet students sit for nearly 70% of their waking hours. Physical activity is an underexplored and emerging classroom tool that can be used to decrease students' classroom sedentary behaviour. This two-part investigation examined implementing Fit-Breaks during a 2-hour lecture on the well-being of university students, compared to a music control condition. Fit-Breaks are 10-minute bouts of easy-to-follow exercises and stretches that are designed to be safe and appropriate for students of all fitness levels within a resource and space-limiting environment. In both studies, lecture sections were randomly assigned to one of the conditions and students completed surveys on well-being at the start and end of the semester. Study 1 focused on global well-being, and study 2 honed in on dimensions of well-being. Based on a repeated measures ANOVA model and significance of p < 0.05, study 1 (N=162) demonstrated a significant interaction effect of break structure and well-being over time, F(1,160) = 5.66. In study 2 (N=380), a repeated-measures MANOVA showed a significant time effect, no group effect, and significant interaction effects across groups and psychological well-being over time for autonomy, F(1,378) = 5.34, p < 0.05 and personal growth, F(1,378) = 5.37, p < 0.05. These findings suggest that even small weekly bursts of physical activity in a real-world setting can positively improve well-being, with potentially strongest effects for autonomy and personal growth.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".