Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC 0
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC 0
Data sources: ZENODO
DRYAD
Dataset . 2019
License: CC 0
Data sources: Datacite
versions View all 3 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Data from: Transitions in smartphone addiction proneness among children: the effect of gender and use patterns

Authors: Chang, Fong-Ching; Chiang, Jeng-Tung; Lee, Kun-Wei; Hsu, Szu-Yuan;

Data from: Transitions in smartphone addiction proneness among children: the effect of gender and use patterns

Abstract

Objectives This study assessed the incidence of transitions in smartphone addiction proneness (SAP) among children and examined the effects of gender, use patterns (social networking sites (SNSs) use and smartphone gaming) and depression on smartphone addiction transitions.Methods A representative sample of 2,155 children from Taipei completed longitudinal surveys in both 2015 (5th grade) and 2016 (6th grade). Latent transition analysis (LTA) was used to characterize transitions in SAP and to examine the effects of use patterns and depression on SAP transitions among boys and girls. Results LTA identified four latent statuses of SAP: about half of the children were in non-SAP status, one-fifth were in tolerance status, one-sixth were in withdrawal status, and one-seventh were in high-SAP status. Both boys and girls had a higher prevalence of high-SAP and tolerance in 6th grade than in 5th grade, whereas in both grades boys had a higher prevalence of high-SAP and withdrawal, and girls had a higher prevalence of non-SAP and tolerance. Controlling for parents’ education, family structure, and household income, higher use of SNSs by children, increasing use of mobile gaming and higher level of depression were individually associated with increased odds of being in one of the three SAP statuses other than non-SAP. When all three covariates were jointly entered into the model, usage of SNSs and depression remained significant predictors. Conclusion Both boys and girls tended to transition to tolerance or high-SAP statuses, while children’s depression and their usage of SNSs increased the risk of smartphone addiction.

dataTransitions in smartphone addiction proneness among children: The effect of gender and use patterns

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 20
    download downloads 7
  • 20
    views
    7
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
0
Average
Average
Average
20
7
Related to Research communities