Evidence map›Paper›PMID 41218588›Full record

ArticleJournal of Korean medical science2025

Trends and Determinants of Smart Device Usage Among Children for Six Years: Latent Trajectory Classes Using a Growth Mixture Model.

Saebom Jeon, Sangha Lee, Yunmi Shin

Abstract read
In one paragraph

Article in Journal of Korean medical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Saebom JeonDepartment of Marketing BigData, Mokwon University, Daejeon, Korea.ORCID https://orcid.org/0000-0002-2771-675X
Sangha LeeDepartment of Psychiatry, Ajou University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0002-7042-2052
Yunmi ShinDepartment of Psychiatry, Ajou University School of Medicine, Suwon, Korea. ymshin@ajou.ac.kr.ORCID https://orcid.org/0000-0001-9880-4004

Funding

National Center for Mental Health MHER25B01
6 · The paper itself

Abstract

backgroundThis study investigated the patterns and determinants of smart device usage among children over a period of six years, utilizing latent trajectory class analysis with a growth mixture model (GMM) to identify risk groups based on their screen time trajectories.

methodsThe Kids Cohort for Understanding of internet addiction Risk factors in Early childhood provided data from six waves of surveys conducted from 2018 to 2023, with a final sample size of 313 participants (mostly mothers) who completed all assessments. We used a GMM to analyze six-year patterns of children's smart device usage. Subgroups were identified based on screen time trajectories using GMM, and the impact of sex, age, parental education, family income, and child behavior checklist (CBCL) scores on group membership was assessed with multinomial logistic regression.

resultsThree distinct risk groups were identified: a low-risk group (69.86%) with minimal and stable screen time, a mid-risk group (21.92%) with moderate and occasionally increased screen time, and a high-risk group (8.22%) with consistently high and increasing screen time. Factors such as older age of the child, higher early CBCL scores, lower maternal educational levels, and lower economic status were significantly associated with a higher risk of excessive screen time.

conclusionThis study highlights the significant variations in children's smart device usage patterns influenced by behavioral and socioeconomic factors. The findings suggest the need for targeted interventions that address the specific needs and risks associated with different screen-time trajectories. Effective strategies should consider both individual and familial factors to promote healthier digital habits among children.

Indexed as

Screen TimeSmartphoneBehavior, AddictiveChildChild BehaviorChild, PreschoolFemaleHumansLatent Class AnalysisLogistic ModelsMaleRisk FactorsSocioeconomic FactorsSurveys and QuestionnairesChildrenLatent Class AnalysisSmart Device Usage

Identifiers

PMID41218588
PMCPMC12602144

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LicenceCC BY-NC
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.