Healthy lifestyle behaviours are positively and independently associated with academic achievement: An analysis of self-reported data from a nationally representative sample of Canadian early adolescents
Bibliographic record
Abstract
INTRODUCTION: The lifestyle behaviours of early adolescents, including diet, physical activity, sleep, and screen usage, are well established contributors to health. These behaviours have also been shown to be associated with academic achievement. Poor academic achievement can additionally contribute to poorer health over the lifespan. This study aims to characterize the associations between health behaviours and self-reported academic achievement. METHODS: Data from the 2014 Canadian Health Behaviour in School-Aged Children Study (n = 28,608, ages 11-15) were analyzed. Students provided self-report of academic achievement, diet, physical activity, sleep duration, recreational screen time usage, height, weight, and socioeconomic status. Multi-level logistic regression was used to assess the relationship of lifestyle behaviours and body weight status with academic achievement while considering sex, age, and socioeconomic status as potential confounders. RESULTS: All health behaviours exhibited independent associations with academic achievement. Frequent consumption of vegetables and fruits, breakfast and dinner with family and regular physical activity were positively associated with higher levels of academic achievement, while frequent consumption of junk food, not meeting sleep recommendations, and overweight and obesity were negatively associated with high academic achievement. CONCLUSIONS: The present findings demonstrate that lifestyle behaviours are associated with academic achievement, potentially identifying these lifestyle behaviours as effective targets to improve academic achievement in early adolescents. These findings also justify investments in school-based health promotion initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".