Mentally active but not inactive sedentary behaviors are positively related to adolescents’ cognitive-academic achievements, a cross-sectional study — The Cogni-Action Project
Bibliographic record
Abstract
Excessive adolescent sedentary behaviors (SBs) may affect cognitive-academic achievements; however, findings vary according to the SB evaluated and their mental requirements. This study aimed to understand the multivariate association between different SBs and diverse cognitive-academic achievements as a primary analysis. As a secondary one, we differentiated between mentally active and inactive SBs. In this study, 1296 Chilean adolescents (10–14 years old) reported their SB via questionnaires. Cognitive performance was assessed with a neurocognitive battery, and academic achievement was based on school grades. Canonical correlation analysis was performed to determine the mode of covariation (MofC) between two sets of variables. The first set accounted for eight SBs (five considered as “active mentally” and three as “inactive mentally”). The second set accounted for 13 cognitive and academic variables (eight cognitive tasks and five school subjects). Several covariates and a cluster (schools, k = 19) were also included in the analysis. The primary analysis revealed a single significant MofC, with a small canonical relationship (r = 0.22, p = 0.002). This MofC indicated that time spent using computers and engaging in scholarly tasks at home was positively correlated with cognitive processing speed as well as with academic scores in English and History. Secondary analysis revealed two significant modes of covariation. The first confirmed the primary result (r = 0.21, p = 0.001), while the second highlighted the role of time spent playing video games as the sole contributing factor linked to inhibitory control (r = 0.17, p = 0.034). These findings indicate a small positive relationship between certain mentally active SBs and cognitive-academic achievements, emphasizing the need for further comprehensive research to understand these complex relationships.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".