Evidence map›Paper›PMID 35507401›Full record

ArticleJMIR pediatrics and parenting2022

Digital Technology and Media Use by Adolescents: Latent Class Analysis.

Megan A Moreno, Kole Binger, Qianqian Zhao, Jens Eickhoff, Matt Minich, Yalda Tehranian Uhls

Open access · goldAbstract read
In one paragraph

Article in JMIR pediatrics and parenting, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
28.5field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.

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  14. [Family health and technologies: new findings in adolescents].Revista medica del Instituto Mexicano del Seguro Social · 2024
    Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Megan A MorenoUniversity of Wisconsin-Madison, Madison, WI, United States.ORCID https://orcid.org/0000-0003-2767-0787
Kole BingerUniversity of Wisconsin-Madison, Madison, WI, United States.ORCID https://orcid.org/0000-0001-7644-5709
Qianqian ZhaoUniversity of Wisconsin-Madison, Madison, WI, United States.ORCID https://orcid.org/0000-0002-8772-5244
Jens EickhoffUniversity of Wisconsin-Madison, Madison, WI, United States.ORCID https://orcid.org/0000-0003-0525-6462
Matt MinichUniversity of Wisconsin-Madison, Madison, WI, United States.ORCID https://orcid.org/0000-0002-8328-412X
Yalda Tehranian UhlsUniversity of California, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-5172-5541
University of Wisconsin–Madison · USUniversity of California, Los Angeles · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital technology and media use is integral to adolescents' lives and has been associated with both positive and negative health consequences. Previous studies have largely focused on understanding technology behaviors and outcomes within adolescent populations, which can promote assumptions about adolescent technology use as homogeneous. Furthermore, many studies on adolescent technology use have focused on risks and negative outcomes. To better understand adolescent digital technology use, we need new approaches that can assess distinct profiles within study populations and take a balanced approach to understanding the risks and benefits of digital technology use.

objectiveThe purpose of this study was to identify profiles of adolescent technology use within a large study population focusing on four evidence-based constructs: technology ownership and use, parental involvement, health outcomes, and well-being indicators.

methodsAdolescent-parent dyads were recruited for a cross-sectional web-based survey using the Qualtrics (Qualtrics International, Inc) platform and panels. Technology use measures included ownership of devices, social media use frequency, and the Adolescents' Digital Technology Interactions and Importance scale. Parent involvement measures included household media rules, technology-related parenting practices, parent social media use frequency, and the parent-child relationship. Health outcome measures included physical activity, sleep, problematic internet use, and mental health assessments. Well-being indicators included mental wellness, communication, and empathy. We used latent class analysis (LCA) to identify distinct profile groups across the aforementioned 4 critical constructs.

resultsAmong the 3981 adolescent-parent dyads recruited, adolescent participants had a mean age of 15.0 (SD 1.43) years; a total of 46.3% (1842/3981) were female, 67.8% (2701/3981) were White, and 75% (2986/3981) lived in a household with an income above the poverty line. The LCA identified 2 discrete classes. Class 1 was made up of 62.8% (2501/3981) of the participants. Class 1 participants were more likely than Class 2 participants to report family-owned devices, have lower technology importance scores, have household technology rules often centered on content, have positive parent relationships and lower parent social media use, and report better health outcomes and well-being indicators.

conclusionsFindings from this national cross-sectional survey using LCA led to 2 distinct profile groups of adolescent media use and their association with technology use and parent involvement as well as health and well-being outcomes. The two classes included a larger Class 1 (Family-Engaged Adolescents) and a smaller Class 2 (At-Risk Adolescents). The findings of this study can inform interventions to reinforce positive technology use and family support.

Indexed as

adolescentsdigital technologylatent class analysismobile phonesocial media

Identifiers

PMID35507401
PMCPMC9118083
OpenAlexW4225391134

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.