MétaCan
Menu
Back to cohort
Record W2983311422 · doi:10.1186/s12966-019-0862-x

Screen time and problem behaviors in children: exploring the mediating role of sleep duration

2019· article· en· W2983311422 on OpenAlexaff
Michelle Guerrero, Joel D. Barnes, Jean‐Philippe Chaput, Mark S. Tremblay

Bibliographic record

VenueInternational Journal of Behavioral Nutrition and Physical Activity · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of OttawaChildren's Hospital of Eastern Ontario
FundersNational Institute on Drug AbuseNational Institute of Mental HealthNational Institutes of HealthFoundation for the National Institutes of Health
KeywordsScreen timePsychologyVideo gameDevelopmental psychologyDuration (music)MedicineClinical psychologyObesityMultimedia

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.019
GPT teacher head0.299
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations240
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Behavioral Nutrition and Physical ActivitySame topicChild Development and Digital TechnologyFrench-language works237,207