Screen time and problem behaviors in children: exploring the mediating role of sleep duration
Bibliographic record
Abstract
BACKGROUND: Previous research examining the relationship between screen time (ST) and psychological health outcomes have primarily focused on one type of ST (i.e., television), while little research has considered other types of screens (e.g., videos, movies, social media), screen content (e.g., violent video games), or potential mediating variables. Therefore, the purpose of the present study was to assess ST types and content and their association with problem behaviors, and to determine whether these relationships were mediated by sleep duration. METHODS: Parents and children provided cross-sectional baseline data (2016-18) as part of the Adolescent Brain Cognitive Development study, a broadly US representative sample of 11,875 children aged 9 to 10 years. Parents self-reported their children's emotional and behavioral syndromes via the Child Behavior Checklist and sleep duration using one item from the Parent Sleep Disturbance Scale. Children self-reported their ST behavior, which comprised ST types (television/movies, videos, video games, and social media) and content (mature-rated video games and R-rated movies). RESULTS: Time spent in various ST types was positively associated with problem behaviors: watching television/movies was associated with a 5.9% increase in rule-breaking behavior (incidence rate ratio [IRR] = 1.059), 5% increase in social problems (IRR = 1.050), 4% increase in aggressive behavior (IRR = 1.040), and 3.7% increase in thought problems (IRR = 1.037). Greater time spent playing mature-rated video games was associated with greater somatic complaints (IRR = 1.041), aggressive behavior (IRR = 1.039), and reduced sleep duration (IRR = .938). Sleep duration mediated the relationship between ST (type and content) and problem behaviors, albeit the effect sizes were small. The largest effects were observed between sleep duration and all problem behaviors, with greater sleep duration predicting an 8.8-16.6% decrease in problem behaviors (IRRs ranging from .834 to .905). CONCLUSION: Greater time spent in ST behavior was associated with greater problem behaviors among children. There was strong evidence that longer sleep duration was associated with reduced problem behaviors. While sleep duration mediated the effects of ST on problem behaviors, other potential mediating variables need to be investigated in future research.
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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 | 0.004 |
| 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) | 0.002 | 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".