Associations between physical activity, screen time, sleep quality and flourishing in university students
Bibliographic record
Abstract
Background: The transition to university is difficult and is often associated with decreased psychological health. Higher physical activity and sleep, and lower screen time are associated with better psychological health; however, few studies have examined all three movement behaviours in one model. The purpose of this study was to examine the association between physical activity, screen time, sleep quality and flourishing after controlling for demographic factors. Methods: First year university students (N = 295; 74.1% female; Mage = 20.49, SD = 5.07) from a psychology participant pool completed self-report questionnaires at one time point. Using hierarchical regression, demographic factors (i.e., age, gender, parents' marital status, mother's and father's highest education and body mass index) were added to the model followed by physical activity, screen time, and sleep quality. Results: Demographic factors accounted for 1% of the variance in flourishing whereas physical activity, screen time, and sleep quality accounted for 15% of the variance. After controlling for demographic variables, physical activity (B = .158, p = .006) and sleep quality (B = .324, p < .001) were significantly related to flourishing. Screen time was not related to flourishing (B = -.063, p = .259). Conclusions: Controlling for demographic factors, better sleep quality and higher physical activity are related to more favourable flourishing in university students. Despite claims of the negative effects of screen time, screen time was unrelated to flourishing in this study. Future research is warranted to examine movement behaviours over time to examine the directionality of these results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".