The 3‐Year Longitudinal Impact of Sedentary Behavior on the Academic Achievement of Secondary School Students
Bibliographic record
Abstract
BACKGROUND: Sedentary behavior is linked to many adverse health outcomes; however, its relationship with academic achievement is less understood. We examined sedentary behavior's impact on academic achievement over 3 years in 4408 adolescents from the COMPASS study. METHODS: Sedentary behavior (screen-based: watching/streaming television shows/movies, video/computer games, surfing the internet; communication-based: texting/messaging/emailing, talking on the phone; and doing homework) and academic achievement (overall math and English marks) were self-reported. RESULTS: Holding time fixed, moving from the lowest quartile (Q1) to Q2, Q3, or Q4 of watching/streaming television shows/movies (Q2: OR = 0.90; 95%CI: 0.84-0.97, Q3: OR = 0.85; 95%CI: 0.74-0.98, Q4: OR = 0.74; 95%CI: 0.64-0.85) or to Q2 of surfing the internet (Q2: OR = 0.87; 95%CI: 0.78-0.97) decreased the likelihood of surpassing English standards. Moving from Q1 to Q2 of communication-based sedentary behavior (OR = 0.90; 95%CI: 0.82-0.99) decreased the likelihood of surpassing math standards. Moving from Q1 to Q4 (OR = 1.31; 95%CI: 1.15-1.50) of watching/streaming television shows/movies increased the likelihood of surpassing math standards. Moving from Q1 to Q4 of doing homework (OR = 1.16; 95%CI: 1.02-1.31) increased the likelihood of surpassing English standards. CONCLUSIONS: Predicting academic achievement from total sedentary behavior is challenging. Targeting specific types of sedentary behavior should be considered for improving math and English achievement.
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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".