Contemporary screen time modalities and disruptive behavior disorders in children: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Cross-sectional studies have demonstrated associations between screen time and disruptive behavior disorders (conduct disorder and oppositional defiant disorder); however, prospective associations remain unknown. This study's objective was to determine the prospective associations of contemporary screen time modalities with conduct and oppositional defiant disorder in a national cohort of 9-11-year-old children. METHODS: We analyzed data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 11,875). Modified Poisson regression analyses were conducted to estimate the associations between baseline child-reported screen time (total and by modality) and parent-reported conduct or oppositional defiant disorder based on the Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS-5) at 1-year follow-up, adjusting for potential confounders. RESULTS: Participants reported an average of 4 hr of total screen time per day at baseline. Each hour of total screen time per day was prospectively associated with a 7% higher prevalence of conduct disorder (95% CI 1.03-1.11) and a 5% higher prevalence of oppositional defiant disorder (95% CI 1.03-1.08) at 1-year follow-up. Each hour of social media per day was associated with a 62% higher prevalence of conduct disorder (95% CI 1.39-1.87). Each hour of video chat (prevalence ratio [PR] 1.21, 95% CI 1.06-1.37), texting (PR 1.19, 95% CI 1.07-1.33), television/movies (PR 1.17, 95% CI 1.10-1.25), and video games (PR 1.14, 95% CI 1.07-1.21) per day was associated with a higher prevalence of the oppositional defiant disorder. When examining thresholds, exposure to >4 hr of total screen time per day was associated with a higher prevalence of conduct disorder (69%) and oppositional defiant disorder (46%). CONCLUSIONS: Higher screen time was prospectively associated with a higher prevalence of new-onset disruptive behavior disorders. The strongest association was between social media and conduct disorder, indicating that future research and interventions may focus on social media platforms to prevent conduct disorder.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".