Screen media activity does not displace other recreational activities among 9-10 year-old youth: a cross-sectional ABCD study®.


Journal

BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562

Informations de publication

Date de publication:
25 Nov 2020
Historique:
received: 09 08 2020
accepted: 15 11 2020
entrez: 26 11 2020
pubmed: 27 11 2020
medline: 24 3 2021
Statut: epublish

Résumé

Screen media is among the most common recreational activities engaged in by children. The displacement hypothesis predicts that increased time spent on screen media activity (SMA) may be at the expense of engagement with other recreational activities, such as sport, music, and art. This study examined associations between non-educational SMA and recreational activity endorsement in 9-10-year-olds, when accounting for other individual (i.e., cognition, psychopathology), interpersonal (i.e., social environment), and sociodemographic characteristics. Participants were 9254 youth from the Adolescent Brain Cognitive Development Study®. Latent factors reflecting SMA, cognition, psychopathology, and social environment were entered as independent variables into logistic mixed models. Sociodemographic covariates included age, sex, race/ethnicity, education, marital status, and household income. Outcome variables included any recreational activity endorsement (of 19 assessed), and specific sport (swimming, soccer, baseball) and hobby (music, art) endorsements. In unadjusted groupwise comparisons, youth who spent more time engaging with SMA were less likely to engage with other recreational activities (ps < .001). However, when variance in cognition, psychopathology, social environment, and sociodemographic covariates were accounted for, most forms of SMA were no longer significantly associated with recreational activity engagement (p > .05). Some marginal effects were observed: for every one SD increase in time spent on games and movies over more social forms of media, youth were at lower odds of engaging in recreational activities (adjusted odds ratio = 0·83, 95% CI 0·76-0·89). Likewise, greater general SMA was associated with lower odds of endorsing group-based sports, including soccer (0·93, 0·88-0·98) and baseball (0·92, 0·86-0·98). Model fit comparisons indicated that sociodemographic characteristics, particularly socio-economic status, explained more variance in rates of recreational activity engagement than SMA and other latent factors. Notably, youth from higher socio-economic families were up to 5·63 (3·83-8·29) times more likely to engage in recreational activities than youth from lower socio-economic backgrounds. Results did not suggest that SMA largely displaces engagement in other recreational activities among 9-10-year-olds. Instead, socio-economic factors greatly contribute to rates of engagement. These findings are important considering recent shifts in time spent on SMA in childhood.

Sections du résumé

BACKGROUND BACKGROUND
Screen media is among the most common recreational activities engaged in by children. The displacement hypothesis predicts that increased time spent on screen media activity (SMA) may be at the expense of engagement with other recreational activities, such as sport, music, and art. This study examined associations between non-educational SMA and recreational activity endorsement in 9-10-year-olds, when accounting for other individual (i.e., cognition, psychopathology), interpersonal (i.e., social environment), and sociodemographic characteristics.
METHODS METHODS
Participants were 9254 youth from the Adolescent Brain Cognitive Development Study®. Latent factors reflecting SMA, cognition, psychopathology, and social environment were entered as independent variables into logistic mixed models. Sociodemographic covariates included age, sex, race/ethnicity, education, marital status, and household income. Outcome variables included any recreational activity endorsement (of 19 assessed), and specific sport (swimming, soccer, baseball) and hobby (music, art) endorsements.
RESULTS RESULTS
In unadjusted groupwise comparisons, youth who spent more time engaging with SMA were less likely to engage with other recreational activities (ps < .001). However, when variance in cognition, psychopathology, social environment, and sociodemographic covariates were accounted for, most forms of SMA were no longer significantly associated with recreational activity engagement (p > .05). Some marginal effects were observed: for every one SD increase in time spent on games and movies over more social forms of media, youth were at lower odds of engaging in recreational activities (adjusted odds ratio = 0·83, 95% CI 0·76-0·89). Likewise, greater general SMA was associated with lower odds of endorsing group-based sports, including soccer (0·93, 0·88-0·98) and baseball (0·92, 0·86-0·98). Model fit comparisons indicated that sociodemographic characteristics, particularly socio-economic status, explained more variance in rates of recreational activity engagement than SMA and other latent factors. Notably, youth from higher socio-economic families were up to 5·63 (3·83-8·29) times more likely to engage in recreational activities than youth from lower socio-economic backgrounds.
CONCLUSIONS CONCLUSIONS
Results did not suggest that SMA largely displaces engagement in other recreational activities among 9-10-year-olds. Instead, socio-economic factors greatly contribute to rates of engagement. These findings are important considering recent shifts in time spent on SMA in childhood.

Identifiants

pubmed: 33238925
doi: 10.1186/s12889-020-09894-w
pii: 10.1186/s12889-020-09894-w
pmc: PMC7687784
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1783

Subventions

Organisme : NIDA NIH HHS
ID : U24 DA041147
Pays : United States
Organisme : NIDA NIH HHS
ID : U01 DA051039
Pays : United States
Organisme : NIDA NIH HHS
ID : U01 DA041093
Pays : United States
Organisme : National Health and Medical Research Council
ID : GNT1169377
Organisme : NIDA NIH HHS
ID : U24 DA041123
Pays : United States
Organisme : NIDA NIH HHS
ID : U01 DA041106
Pays : United States
Organisme : NIDA NIH HHS
ID : U01 DA041148
Pays : United States
Organisme : NIDA NIH HHS
ID : U01 DA041174
Pays : United States
Organisme : NIDA NIH HHS
ID : U01 DA041134
Pays : United States
Organisme : NIAAA NIH HHS
ID : K23 AA025399
Pays : United States
Organisme : NIDA NIH HHS
ID : U01 DA041022
Pays : United States
Organisme : NIDA NIH HHS
ID : U01 DA041025
Pays : United States
Organisme : NIDA NIH HHS
ID : U01 DA050989
Pays : United States

Références

Sports Med. 2006;36(12):1019-30
pubmed: 17123326
Fam Process. 1990 Jun;29(2):199-208; discussion 209-11
pubmed: 2373215
J Adolesc Health. 2013 Apr;52(4):382-92
pubmed: 23299000
BMC Public Health. 2016 May 13;16:399
pubmed: 27178325
Int J Behav Nutr Phys Act. 2016 Aug 22;13:93
pubmed: 27549588
Int J Behav Nutr Phys Act. 2014 Jan 22;11:4
pubmed: 24450617
Pediatrics. 2006 Feb;117(2):417-24
pubmed: 16452361
Int J Behav Nutr Phys Act. 2019 May 16;16(1):44
pubmed: 31097036
J Child Psychol Psychiatry. 1997 Jul;38(5):581-6
pubmed: 9255702
Int J Behav Nutr Phys Act. 2010 May 21;7:46
pubmed: 20492643
J Pediatr Psychol. 2016 May;41(4):429-40
pubmed: 25972373
Psychol Res Behav Manag. 2019 Nov 11;12:1041-1050
pubmed: 31807098
JAMA Pediatr. 2018 Aug 1;172(8):732-740
pubmed: 29889945
PLoS One. 2015 Dec 02;10(12):e0142544
pubmed: 26629688
Dev Cogn Neurosci. 2018 Aug;32:8-15
pubmed: 29706313
Int J Environ Res Public Health. 2019 Jun 12;16(12):
pubmed: 31212786
Behav Genet. 2016 Sep;46(5):665-679
pubmed: 27406597
Dev Cogn Neurosci. 2018 Aug;32:67-79
pubmed: 29525452
Int J Public Health. 2009 Sep;54 Suppl 2:131-9
pubmed: 19639260
Dev Cogn Neurosci. 2018 Aug;32:143-154
pubmed: 28716389
J Adolesc Health. 2013 May;52(5):613-9
pubmed: 23375827
Obes Rev. 2014 Aug;15(8):666-75
pubmed: 24844784
IEEE Trans Neural Netw Learn Syst. 2015 Sep;26(9):2136-47
pubmed: 25532193
BMJ Open. 2019 Jan 3;9(1):e023191
pubmed: 30606703
Child Dev. 1994 Aug;65(4):1120-36
pubmed: 7956469
Int J Behav Nutr Phys Act. 2013 Oct 02;10:113
pubmed: 24088327
Dev Cogn Neurosci. 2018 Aug;32:16-22
pubmed: 29703560
Scand J Med Sci Sports. 2017 Dec;27(12):1902-1912
pubmed: 28106293
J Expo Sci Environ Epidemiol. 2016 Nov;26(6):606-612
pubmed: 27005743
J Adolesc Health. 2013 Mar;52(3):259-70
pubmed: 23299015

Auteurs

Briana Lees (B)

The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Level 6 Jane Foss Russell Building, G02, Camperdown, NSW, 2006, Australia. briana.lees@sydney.edu.au.

Lindsay M Squeglia (LM)

Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Addiction Sciences Division, 171 Ashley Ave, Charleston, SC, 29425, USA.

Florence J Breslin (FJ)

Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA.

Wesley K Thompson (WK)

Division of Biostatistics, Department of Family Medicine and Public Health, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.

Susan F Tapert (SF)

Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.

Martin P Paulus (MP)

Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA.
Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.

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Classifications MeSH