Gaming patterns and related symptoms in adolescents using cluster analysis: Baseline results from the Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study

Hyunsuk Jeong, Hyeon Woo Yim, Sun Jin Jo, Seung Yup Lee, Hae Kook Lee, Douglas A. Gentile, Hye Jung Son, Hyun ho Han, Yong Sil Kweon, Soo young Bhang, Jung Seok Choi

Research output: Contribution to journalArticle

Abstract

Purpose: The risk and protective factors of Internet gaming disorder (IGD) could vary by individual. The identification of more homogeneous subgroups may lead to better understanding of gaming behaviors and their consequences in adolescents. The purpose of this study was to investigate the prevalence of IGD among the subgroups defined by cluster analysis in adolescents. Methods: A total of 2319 adolescents were enrolled in the Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study at baseline. Self-reported IGD was assessed with a DMS-5 adapted measurement. Smartphone addiction, musculoskeletal discomfort, and dry eye symptoms were evaluated by self-administered questionnaires. Cluster analysis was performed using risk and protective factors of IGD after considering multicollinearity. Results: Three different clusters were identified. Cluster 1 (19.2%) was users with combined potential psychological and social issues. Cluster 2 (32.3%) was users with potential social but no psychological issues. Cluster 3 (45.6%) was users with no potential issues of either a social or psychological nature. Adolescents from both clusters 1 and 2 showed higher degrees of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms than did those from cluster 3. Also compared with adolescents in cluster 3, those in cluster 1 showed statistically higher risks of IGD (aOR:11.9, 95%CI:7.5–19.9), smartphone addiction (aOR:5.4, 95%CI:4.0–7.2), musculoskeletal discomfort (aOR:2.6, 95%CI:2.1–7.4), and dry eye symptoms (aOR:3.8, 95%CI:3.0–4.9). Those in cluster 2 also showed statistically higher risk of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms compared with cluster 3 (aOR:4.5, 95%CI:2.8–7.6; aOR:2.8, 95%CI:2.1–3.7; aOR:1.6, 95%CI:1.3–1.9; and aOR:1.9, 95%CI:1.6–2.4, respectively). Conclusions: Clustering based on the risk and preventive factors of IGD may be suitable for determination of high risk of IGD in adolescents. However, we need to confirm the usefulness and clinical application of the classifications by observing their longitudinal changes.

Original languageEnglish
Article number109105
JournalEnvironmental Research
Volume182
DOIs
StatePublished - Mar 2020

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adolescence
Cluster analysis
Internet
Cluster Analysis
cluster analysis
Smartphones
Psychology
dimethylsulfide

Keywords

  • Adolescents
  • Cluster analysis
  • Internet gaming disorder
  • Protective factor
  • Risk factor

Cite this

Jeong, Hyunsuk ; Yim, Hyeon Woo ; Jo, Sun Jin ; Lee, Seung Yup ; Lee, Hae Kook ; Gentile, Douglas A. ; Son, Hye Jung ; Han, Hyun ho ; Kweon, Yong Sil ; Bhang, Soo young ; Choi, Jung Seok. / Gaming patterns and related symptoms in adolescents using cluster analysis : Baseline results from the Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study. In: Environmental Research. 2020 ; Vol. 182.
@article{cbf80548fb014f558ee6deb9c983d1fc,
title = "Gaming patterns and related symptoms in adolescents using cluster analysis: Baseline results from the Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study",
abstract = "Purpose: The risk and protective factors of Internet gaming disorder (IGD) could vary by individual. The identification of more homogeneous subgroups may lead to better understanding of gaming behaviors and their consequences in adolescents. The purpose of this study was to investigate the prevalence of IGD among the subgroups defined by cluster analysis in adolescents. Methods: A total of 2319 adolescents were enrolled in the Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study at baseline. Self-reported IGD was assessed with a DMS-5 adapted measurement. Smartphone addiction, musculoskeletal discomfort, and dry eye symptoms were evaluated by self-administered questionnaires. Cluster analysis was performed using risk and protective factors of IGD after considering multicollinearity. Results: Three different clusters were identified. Cluster 1 (19.2{\%}) was users with combined potential psychological and social issues. Cluster 2 (32.3{\%}) was users with potential social but no psychological issues. Cluster 3 (45.6{\%}) was users with no potential issues of either a social or psychological nature. Adolescents from both clusters 1 and 2 showed higher degrees of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms than did those from cluster 3. Also compared with adolescents in cluster 3, those in cluster 1 showed statistically higher risks of IGD (aOR:11.9, 95{\%}CI:7.5–19.9), smartphone addiction (aOR:5.4, 95{\%}CI:4.0–7.2), musculoskeletal discomfort (aOR:2.6, 95{\%}CI:2.1–7.4), and dry eye symptoms (aOR:3.8, 95{\%}CI:3.0–4.9). Those in cluster 2 also showed statistically higher risk of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms compared with cluster 3 (aOR:4.5, 95{\%}CI:2.8–7.6; aOR:2.8, 95{\%}CI:2.1–3.7; aOR:1.6, 95{\%}CI:1.3–1.9; and aOR:1.9, 95{\%}CI:1.6–2.4, respectively). Conclusions: Clustering based on the risk and preventive factors of IGD may be suitable for determination of high risk of IGD in adolescents. However, we need to confirm the usefulness and clinical application of the classifications by observing their longitudinal changes.",
keywords = "Adolescents, Cluster analysis, Internet gaming disorder, Protective factor, Risk factor",
author = "Hyunsuk Jeong and Yim, {Hyeon Woo} and Jo, {Sun Jin} and Lee, {Seung Yup} and Lee, {Hae Kook} and Gentile, {Douglas A.} and Son, {Hye Jung} and Han, {Hyun ho} and Kweon, {Yong Sil} and Bhang, {Soo young} and Choi, {Jung Seok}",
year = "2020",
month = "3",
doi = "10.1016/j.envres.2019.109105",
language = "English",
volume = "182",
journal = "Environmental Research",
issn = "0013-9351",
publisher = "Academic Press Inc.",

}

Gaming patterns and related symptoms in adolescents using cluster analysis : Baseline results from the Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study. / Jeong, Hyunsuk; Yim, Hyeon Woo; Jo, Sun Jin; Lee, Seung Yup; Lee, Hae Kook; Gentile, Douglas A.; Son, Hye Jung; Han, Hyun ho; Kweon, Yong Sil; Bhang, Soo young; Choi, Jung Seok.

In: Environmental Research, Vol. 182, 109105, 03.2020.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Gaming patterns and related symptoms in adolescents using cluster analysis

T2 - Baseline results from the Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study

AU - Jeong, Hyunsuk

AU - Yim, Hyeon Woo

AU - Jo, Sun Jin

AU - Lee, Seung Yup

AU - Lee, Hae Kook

AU - Gentile, Douglas A.

AU - Son, Hye Jung

AU - Han, Hyun ho

AU - Kweon, Yong Sil

AU - Bhang, Soo young

AU - Choi, Jung Seok

PY - 2020/3

Y1 - 2020/3

N2 - Purpose: The risk and protective factors of Internet gaming disorder (IGD) could vary by individual. The identification of more homogeneous subgroups may lead to better understanding of gaming behaviors and their consequences in adolescents. The purpose of this study was to investigate the prevalence of IGD among the subgroups defined by cluster analysis in adolescents. Methods: A total of 2319 adolescents were enrolled in the Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study at baseline. Self-reported IGD was assessed with a DMS-5 adapted measurement. Smartphone addiction, musculoskeletal discomfort, and dry eye symptoms were evaluated by self-administered questionnaires. Cluster analysis was performed using risk and protective factors of IGD after considering multicollinearity. Results: Three different clusters were identified. Cluster 1 (19.2%) was users with combined potential psychological and social issues. Cluster 2 (32.3%) was users with potential social but no psychological issues. Cluster 3 (45.6%) was users with no potential issues of either a social or psychological nature. Adolescents from both clusters 1 and 2 showed higher degrees of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms than did those from cluster 3. Also compared with adolescents in cluster 3, those in cluster 1 showed statistically higher risks of IGD (aOR:11.9, 95%CI:7.5–19.9), smartphone addiction (aOR:5.4, 95%CI:4.0–7.2), musculoskeletal discomfort (aOR:2.6, 95%CI:2.1–7.4), and dry eye symptoms (aOR:3.8, 95%CI:3.0–4.9). Those in cluster 2 also showed statistically higher risk of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms compared with cluster 3 (aOR:4.5, 95%CI:2.8–7.6; aOR:2.8, 95%CI:2.1–3.7; aOR:1.6, 95%CI:1.3–1.9; and aOR:1.9, 95%CI:1.6–2.4, respectively). Conclusions: Clustering based on the risk and preventive factors of IGD may be suitable for determination of high risk of IGD in adolescents. However, we need to confirm the usefulness and clinical application of the classifications by observing their longitudinal changes.

AB - Purpose: The risk and protective factors of Internet gaming disorder (IGD) could vary by individual. The identification of more homogeneous subgroups may lead to better understanding of gaming behaviors and their consequences in adolescents. The purpose of this study was to investigate the prevalence of IGD among the subgroups defined by cluster analysis in adolescents. Methods: A total of 2319 adolescents were enrolled in the Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study at baseline. Self-reported IGD was assessed with a DMS-5 adapted measurement. Smartphone addiction, musculoskeletal discomfort, and dry eye symptoms were evaluated by self-administered questionnaires. Cluster analysis was performed using risk and protective factors of IGD after considering multicollinearity. Results: Three different clusters were identified. Cluster 1 (19.2%) was users with combined potential psychological and social issues. Cluster 2 (32.3%) was users with potential social but no psychological issues. Cluster 3 (45.6%) was users with no potential issues of either a social or psychological nature. Adolescents from both clusters 1 and 2 showed higher degrees of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms than did those from cluster 3. Also compared with adolescents in cluster 3, those in cluster 1 showed statistically higher risks of IGD (aOR:11.9, 95%CI:7.5–19.9), smartphone addiction (aOR:5.4, 95%CI:4.0–7.2), musculoskeletal discomfort (aOR:2.6, 95%CI:2.1–7.4), and dry eye symptoms (aOR:3.8, 95%CI:3.0–4.9). Those in cluster 2 also showed statistically higher risk of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms compared with cluster 3 (aOR:4.5, 95%CI:2.8–7.6; aOR:2.8, 95%CI:2.1–3.7; aOR:1.6, 95%CI:1.3–1.9; and aOR:1.9, 95%CI:1.6–2.4, respectively). Conclusions: Clustering based on the risk and preventive factors of IGD may be suitable for determination of high risk of IGD in adolescents. However, we need to confirm the usefulness and clinical application of the classifications by observing their longitudinal changes.

KW - Adolescents

KW - Cluster analysis

KW - Internet gaming disorder

KW - Protective factor

KW - Risk factor

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DO - 10.1016/j.envres.2019.109105

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VL - 182

JO - Environmental Research

JF - Environmental Research

SN - 0013-9351

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