Association Between 24‐h Movement Behavior, Physical Fitness, and Inhibitory Control in School Adolescents: A Complex Network Analysis
Bibliographic record
Abstract
PURPOSE: To investigate the interrelationships between 24-h movement behaviors, health-related physical fitness, and inhibitory control performance in adolescents. METHODS: This cross-sectional study included 216 Brazilian adolescents (aged 16.7 ± 1.2 years) from a federal public school. Movement behaviors-moderate-to-vigorous physical activity (MVPA), smartphone screen time, sleep duration, and excessive daytime sleepiness-were assessed using the Global School-based Student Health Survey, digital well-being tools, and the Pediatric Daytime Sleepiness Scale. Aerobic capacity was measured using the PACER test, muscular strength by the FitnessGram push-up test, and body composition through body mass index. Inhibitory control was assessed using the Flanker task (E-Prime v3.0). Separate network analyses were performed for congruent and incongruent reaction times (RT). RESULTS: Physically active adolescents had faster RTs than their insufficiently active peers, with physical activity negatively associated with RT in both the congruent (-0.116) and incongruent (-0.125) networks. Aerobic capacity (e.g., expected influence: 0.879-0.902) and muscular strength (expected influence: 1.360-1.384) appeared as central components in both network structures. However, no associations were found between sleep duration, screen time, or excessive daytime sleepiness and inhibitory control. CONCLUSIONS: Adherence to MVPA guidelines was directly associated with improved inhibitory control performance among adolescents. Health-related physical fitness, particularly aerobic capacity and muscular strength, was indirectly associated with inhibitory control. Other movement behaviors were not associated with cognitive performance in this sample.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".