Screen time and manic symptoms in early adolescents: prospective findings from the Adolescent Brain Cognitive Development Study
Bibliographic record
Abstract
PURPOSE: This study aimed to examine prospective associations between screen time and manic symptoms in early adolescents, and the extent to which problematic screen use (characterized by addiction, conflict, relapse, and withdrawal) mediates the association. METHODS: We analyzed prospective cohort data from the Adolescent Brain Cognitive Development Study (N = 9,243; ages 10-11 years in Year 1 in 2017-2019; 48.8% female; 44.0% racial/ethnic minority). Participants reported daily time spent on six different screen subtypes. Linear regression analyses were used to determine associations between typical daily screen time (Year 1; total and subtypes) and manic symptoms (Year 3, 7 Up Mania scale), adjusting for potential confounders. Sleep duration, problematic social media use, and problematic video game use (Year 2) were tested as potential mediators. RESULTS: Adjusting for covariates, overall typical daily screen time in Year 1 was prospectively associated with higher manic symptoms in Year 3 (B = 0.05, 95% CI 0.03, 0.07, p < 0.001), as were four subtypes: social media (B = 0.20, 95% CI 0.09, 0.32, p = 0.001), texting (B = 0.18, 95%CI 0.08, 0.28, p < 0.001), videos (B = 0.14, 95% CI 0.08, 0.19, p < 0.001), and video games (B = 0.09, 95% CI 0.04, 0.14, p = 0.001). Problematic social media use, video game use, and sleep duration in Year 2 were found to be significant partial mediators (47.7%, 58.0%, and 9.0% mediation, respectively). CONCLUSION: Results indicate significant prospective relationships between screen time and manic symptoms in early adolescence and highlight problematic screen use, video game use, and sleep duration as potential mediators. Problematic screen use may be a target for mental health prevention and early intervention efforts among adolescents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".