Defining the Threshold: A Dose-Response Meta-Analysis of Daily Screen Time and Adverse Behavioral Outcomes in Children and Adolescents
Bibliographic record
Abstract
The pervasive integration of digital media into the lives of children and adolescents has generated significant concern regarding its impact on developmental health. While associations between high levels of screen time and negative outcomes are frequently reported, the precise dose-response relationship remains poorly quantified, leaving clinicians and parents without evidence-based thresholds for guidance. This study aimed to quantitatively synthesize the evidence linking daily screen time duration to the risk of adverse behavioral outcomes in youth. Following PRISMA guidelines, a systematic search of PubMed, Embase, PsycINFO, and Scopus was conducted through February 2025. Observational studies that reported quantifiable measures of daily screen time and validated assessments of behavioral outcomes in individuals aged 3-18 years were included. Two reviewers independently performed study selection, data extraction, and risk of bias assessment using the Newcastle-Ottawa Scale (NOS). A two-stage, random-effects dose-response meta-analysis using restricted cubic splines was employed to model the non-linear association between screen time (in hours/day) and the odds of adverse behavioral outcomes. From an initial 4,891 records, 7 key studies comprising 46,882 participants were included in the quantitative synthesis. The dose-response analysis revealed a significant, non-linear relationship. Compared to 30 minutes of daily screen time, the pooled odds ratio (OR) for adverse behavioral outcomes was minimal at 1 hour/day (OR 1.05; 95% CI, 0.97-1.14) but began to increase significantly thereafter. The risk became more pronounced at 2 hours/day (OR 1.31; 95% CI, 1.17-1.47), rose substantially at 4 hours/day (OR 1.82; 95% CI, 1.60-2.07), and continued to climb at 6 hours/day (OR 2.55; 95% CI, 2.15-3.03). The association was stronger in preschool-aged children compared to adolescents. In conclusion, this focused meta-analysis provides quantitative evidence for a dose-dependent association between daily screen time and behavioral problems in youth, with a notable increase in risk observed beyond two hours per day. These findings provide an evidence-based foundation for clinical guidance and public health recommendations aimed at mitigating the behavioral risks of excessive digital media exposure during critical developmental periods.
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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 | 0.077 | 0.127 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.061 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".