Lifestyle Habits Predict Academic Performance in High School Students: The Adolescent Student Academic Performance Longitudinal Study (ASAP)
Bibliographic record
Abstract
This study aimed to determine if lifestyle habits could predict changes in cognitive control and academic performance in high school students using a longitudinal approach. One hundred and eighty-seven grade seventh to ninth students (mean age: 13.1 ± 1.0 years old) completed a 3-year prospective study. Lifestyle habits, cognitive control, and academic performance were assessed every year during the 3-year study. Results show that in female students, screen time measures were negatively correlated with academic performance and cognitive control. Furthermore, changes (Δs) in sleeping habits were associated with Δs in academic performance in both genders, whereas Δs in eating habits and in studying time were correlated with Δs in academic performance only in male students. Moreover, in female students, screen time, social media use, and eating habits measures seem to predict the variance in the Δs of cognitive control measures (r2 between 8.2% and 21.0%), whereas, in male students, studying time, eating, and sleeping habits appear to explain the variance in the Δs of academic performance measures (r2 between 5.9% and 24.8%). In conclusion, results of the present study indicate that lifestyle habits were able to predict Δs in cognitive control and academic performance of high school students during a 3-year period.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".