» Articles » PMID: 36399378

Time Spent Gaming, Device Type, Addiction Scores, and Well-being of Adolescent English Gamers in the 2021 OxWell Survey: Latent Profile Analysis

Overview
Date 2022 Nov 18
PMID 36399378
Authors
Affiliations
Soon will be listed here.
Abstract

Background: The shift in the last decades to screen-based and increasingly web-based gaming activity has raised concerns about its impact on the development of children and adolescents. Despite decades of research into gaming and related psychosocial effects, the question remains how best to identify what degree or context of gaming may be a cause for concern.

Objective: This study aimed to classify adolescents into gamer profiles based on both gaming behaviors and well-being. Once we distinguished the different gamer profiles, we aimed to explore whether membership to a specific profile could be predicted based on a range of personal characteristics and experiences that could then help identify those at risk.

Methods: We explored gaming and well-being in an adolescent school population (aged 12-18 years) in England as part of the 2021 OxWell student survey. Self-report measures of time spent playing games on computers or consoles, time spent playing games on mobile phones, the Game Addiction Scale, and the Warwick-Edinburgh Mental Well-being Scale were used to classify adolescent heavy gamers (playing games for at least 3.5 hours a day) using latent profile analysis. We used multinomial logistic regression analysis to predict the profile membership based on a range of personal characteristics and experiences.

Results: In total, 12,725 participants answered the OxWell gaming questions. Almost one-third (3970/12,725, 31.2%) indicated that they play games for at least 3.5 hours a day. The correlation between time spent playing video games overall and well-being was not significant (P=.41). The latent profile analysis distinguished 6 profiles of adolescent heavy gamers: adaptive computer gamers (1747/3970, 44%); casual computer gamers (873/3970, 22%); casual phone gamers (595/3970, 15%); unknown device gamers (476/3970, 12%); maladaptive computer gamers (238/3970, 6%); and maladaptive phone gamers (79/3970, 2%). In comparison with adaptive computer gamers, maladaptive phone gamers were mostly female (odds ratio [OR] 0.08, 95% CI 0.03-0.21) and were more likely to have experienced abuse or neglect (OR 3.18, 95% CI 1.34-7.55). Maladaptive computer gamers, who reported gaming both on their mobile phones and on the computer, were mostly male and more likely to report anxiety (OR 2.25, 95% CI 1.23-4.12), aggressive behavior (OR 2.83, 95% CI 1.65-4.88), and web-based gambling (OR 2.18, 95% CI 1.24-3.81).

Conclusions: A substantial number of adolescents are spending ≥3.5 hours gaming each day, with almost 1 in 10 (317/3970, 8%) reporting co-occurring gaming and well-being issues. Long hours gaming using mobile phones, particularly common in female gamers, may signal poorer functioning and indicate a need for additional support. Although increased time gaming might be changing how adolescents spend their free time and might thus have public health implications, it does not seem to relate to co-occurring well-being issues or mental ill-health for the majority of adolescent gamers.

Citing Articles

Influence of parental mediation and social skills on adolescents' use of online video games for escapism: A cross-sectional study.

Commodari E, Consiglio A, Cannata M, La Rosa V J Res Adolesc. 2024; 34(4):1668-1678.

PMID: 39438433 PMC: 11606255. DOI: 10.1111/jora.13034.


Do Patterns of Adolescent Participation in Arts, Culture and Entertainment Activities Predict Later Wellbeing? A Latent Class Analysis.

Thornton E, Petersen K, Marquez J, Humphrey N J Youth Adolesc. 2024; 53(6):1396-1414.

PMID: 38466529 PMC: 11045570. DOI: 10.1007/s10964-024-01950-7.

References
1.
Schoneveld E, Lichtwarck-Aschoff A, Granic I . Preventing Childhood Anxiety Disorders: Is an Applied Game as Effective as a Cognitive Behavioral Therapy-Based Program?. Prev Sci. 2017; 19(2):220-232. PMC: 5801383. DOI: 10.1007/s11121-017-0843-8. View

2.
Lanza S . Latent Class Analysis for Developmental Research. Child Dev Perspect. 2019; 10(1):59-64. PMC: 6914261. DOI: 10.1111/cdep.12163. View

3.
Kuss D, Griffiths M . Online gaming addiction in children and adolescents: A review of empirical research. J Behav Addict. 2015; 1(1):3-22. DOI: 10.1556/JBA.1.2012.1.1. View

4.
Van Rooij A, Schoenmakers T, Vermulst A, Van den Eijnden R, van de Mheen D . Online video game addiction: identification of addicted adolescent gamers. Addiction. 2010; 106(1):205-12. DOI: 10.1111/j.1360-0443.2010.03104.x. View

5.
Myrseth H, Notelaers G . A Latent Class Approach for Classifying the Problem and Disordered Gamers in a Group of Adolescence. Front Psychol. 2018; 9:2273. PMC: 6277857. DOI: 10.3389/fpsyg.2018.02273. View