Increased recreational screen time and time to fall asleep are associated with worse academic performance in Canadian undergraduates
Bibliographic record
Abstract
Academic success is a primary goal for students and academic institutions. Previous studies have investigated the impacts of physical activity, screen time, and sleep but not sedentary time on academic performance. It is unclear which lifestyle behaviours are most influential on academic performance. The study purposes were to 1) determine how achieving each component of the 24-hr movement guidelines independently impacted the academic performance of Canadian undergraduates, and 2) explore which lifestyle behaviours were most influential to academic performance. A cross-sectional quantitative survey was open to any undergraduate student in Canada. Respondents (n = 411, 335 females, mean±standard deviation: 22.08 ± 3.66 years) completed a survey about their lifestyle behaviours. Sedentary time (Pearson’s correlation: R = 0.13), recreational screen time (R=-0.14), and time-to-sleep (R=-0.18) were correlated (all p < 0.01) to academic performance. Sex (F = 4.62), age (R=-0.16), and BMI (R=-0.17) were included as covariates (all p < 0.03). Regression analysis identified several lifestyle behaviours were associated with academic performance (model: R2 = 0.13; P < 0.001), including sedentary time (β = 0.38; 16.80% weight), leisure screen time (β=-0.41; 16.90% weight), and time-to-sleep (β=-0.05; 19.00% weight; all, p < 0.007) when controlling for sex (β=-2.86; 19.10% weight), age (β=-1.68; 11.80% weight), and BMI (β=-2.54: 16.40% weight) as covariates. Students who are more sedentary performed well academically because academic obligations (e.g., studying) are commonly conducted in sedentary postures. Reducing recreational screen time, particularly before bedtime, may be an effective strategy for increasing academic performance. Encouraging students to engage in less leisure sedentary time on screen could be effective for improving academic performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".