A latent growth curve model to estimate electronic screen use patterns amongst adolescents aged 10 to 17 years

Michael Rosenberg; Stephen Houghton; Simon C. Hunter; Corinne Zadow; Trevor Shilton; Lisa Wood; David Lawrence

doi:10.1186/s12889-018-5240-0

A latent growth curve model to estimate electronic screen use patterns amongst adolescents aged 10 to 17 years

Michael Rosenberg^*, Stephen Houghton, Simon C. Hunter, Corinne Zadow, Trevor Shilton, Lisa Wood, David Lawrence

^*Corresponding author for this work

Psychology

Research output: Contribution to journal › Article › peer-review

18 Citations (Scopus)

176 Downloads (Pure)

Abstract

Background: High quality, longitudinal data describing young people’s screen use across a number of distinct forms of screen activity is missing from the literature. This study tracked multiple screen use activities (passive screen use, gaming, social networking, web searching) amongst 10- to 17-year-old adolescents across 24 months.
Methods: This study tracked the screen use of 1,948 Australian students in Grade 5 (n=636), Grade 7 (n=672), and Grade 9 (n=640) for 24 months. At approximately six-month intervals, students reported their total screen time as well as time spent on social networking, passive screen use, gaming, and web use. Patterns of screen use were determined using latent growth curve modelling.
Results: In the Grades 7 and 9 cohorts, girls generally reported more screen use than boys (by approximately one hour a day), though all cohorts of boys reported more gaming. The different forms of screen use were remarkably stable, though specific cohorts showed change for certain forms of screen activity.
Conclusion: These results highlight the diverse nature of adolescent screen use and emphasise the need to consider both grade and sex in future research and policy.

Original language	English
Article number	332 (2018)
Number of pages	10
Journal	BMC Public Health
Volume	18
DOIs	https://doi.org/10.1186/s12889-018-5240-0
Publication status	Published - 7 Mar 2018

Keywords

electronic screen use
guidelines
age factors
internet/statistics & numerical data
social networking
humans
male
models, statistical
video games/statistics & numerical data
time factors
television/statistics & numerical data
adolescent
sex factors
female
students/psychology
adolescent behavior
Australia
child
longitudinal studies

ASJC Scopus subject areas

Public Health, Environmental and Occupational Health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Documents and Links

10.1186/s12889-018-5240-0

Rosenberg, M. et al (2018) A latent growth curve model to estimate electronic screen use patterns amongst adolescents aged 10 to 17 years
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Final published version, 646 KBLicence: CC BY

18 Citations
1 Article

Trajectories of loneliness during adolescence predict subsequent symptoms of depression and positive wellbeing
Hunter, S. C., Seth, R., Houghton, S., Lawrence, D., Zadow, C., Rosenberg, M., Wood, L., Qualter, P. & Shilton, T., May 2024, In: Journal of Youth and Adolescence. 53, 5, p. 1078-1090 13 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
1 Citation (Scopus)

34 Downloads (Pure)

Cite this

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title = "A latent growth curve model to estimate electronic screen use patterns amongst adolescents aged 10 to 17 years",

abstract = "Background: High quality, longitudinal data describing young people{\textquoteright}s screen use across a number of distinct forms of screen activity is missing from the literature. This study tracked multiple screen use activities (passive screen use, gaming, social networking, web searching) amongst 10- to 17-year-old adolescents across 24 months.Methods: This study tracked the screen use of 1,948 Australian students in Grade 5 (n=636), Grade 7 (n=672), and Grade 9 (n=640) for 24 months. At approximately six-month intervals, students reported their total screen time as well as time spent on social networking, passive screen use, gaming, and web use. Patterns of screen use were determined using latent growth curve modelling.Results: In the Grades 7 and 9 cohorts, girls generally reported more screen use than boys (by approximately one hour a day), though all cohorts of boys reported more gaming. The different forms of screen use were remarkably stable, though specific cohorts showed change for certain forms of screen activity.Conclusion: These results highlight the diverse nature of adolescent screen use and emphasise the need to consider both grade and sex in future research and policy.",

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author = "Michael Rosenberg and Stephen Houghton and Hunter, {Simon C.} and Corinne Zadow and Trevor Shilton and Lisa Wood and David Lawrence",

note = "Acceptance from webpage OA article Gold exception applied - published immediate OA in OA journal. ET 9/6/20 ",

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language = "English",

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journal = "BMC Public Health",

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AU - Wood, Lisa

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N2 - Background: High quality, longitudinal data describing young people’s screen use across a number of distinct forms of screen activity is missing from the literature. This study tracked multiple screen use activities (passive screen use, gaming, social networking, web searching) amongst 10- to 17-year-old adolescents across 24 months.Methods: This study tracked the screen use of 1,948 Australian students in Grade 5 (n=636), Grade 7 (n=672), and Grade 9 (n=640) for 24 months. At approximately six-month intervals, students reported their total screen time as well as time spent on social networking, passive screen use, gaming, and web use. Patterns of screen use were determined using latent growth curve modelling.Results: In the Grades 7 and 9 cohorts, girls generally reported more screen use than boys (by approximately one hour a day), though all cohorts of boys reported more gaming. The different forms of screen use were remarkably stable, though specific cohorts showed change for certain forms of screen activity.Conclusion: These results highlight the diverse nature of adolescent screen use and emphasise the need to consider both grade and sex in future research and policy.

AB - Background: High quality, longitudinal data describing young people’s screen use across a number of distinct forms of screen activity is missing from the literature. This study tracked multiple screen use activities (passive screen use, gaming, social networking, web searching) amongst 10- to 17-year-old adolescents across 24 months.Methods: This study tracked the screen use of 1,948 Australian students in Grade 5 (n=636), Grade 7 (n=672), and Grade 9 (n=640) for 24 months. At approximately six-month intervals, students reported their total screen time as well as time spent on social networking, passive screen use, gaming, and web use. Patterns of screen use were determined using latent growth curve modelling.Results: In the Grades 7 and 9 cohorts, girls generally reported more screen use than boys (by approximately one hour a day), though all cohorts of boys reported more gaming. The different forms of screen use were remarkably stable, though specific cohorts showed change for certain forms of screen activity.Conclusion: These results highlight the diverse nature of adolescent screen use and emphasise the need to consider both grade and sex in future research and policy.

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ER -

A latent growth curve model to estimate electronic screen use patterns amongst adolescents aged 10 to 17 years

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Documents and Links

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Research output

Trajectories of loneliness during adolescence predict subsequent symptoms of depression and positive wellbeing

Cite this