Internet gaming disorder clustering based on personality traits in adolescents, and its relation with comorbid psychological symptoms

In recent years, the evidence regarding Internet Gaming Disorder (IGD) suggests that some personality traits are important risk factors for developing this problem. The heterogeneity involved in problematic online gaming and differences found in the literature regarding the comorbid psychopathology...

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Detalles Bibliográficos
Autores: González-Bueso, Vega, Santamaría, Juan José|||0000-0001-9957-9363, Oliveras, Ignasi|||0000-0002-3082-0355, Fernández Martínez, Daniel|||0000-0002-1076-6697, Montero, Elena|||0000-0002-7371-2135, Baño, Marta|||0000-0002-9916-611X, Jiménez Murcia, Susana|||0000-0002-3596-8033, Del Pino Gutiérrez, Amparo|||0000-0002-2854-9850, Ribas, Joan
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:240497
Acceso en línea:https://ddd.uab.cat/record/240497
https://dx.doi.org/urn:doi:10.3390/ijerph17051516
Access Level:acceso abierto
Palabra clave:Internet gaming disorder
Cluster analysis
Video game
Video game addiction
Personality
Comorbidity
Descripción
Sumario:In recent years, the evidence regarding Internet Gaming Disorder (IGD) suggests that some personality traits are important risk factors for developing this problem. The heterogeneity involved in problematic online gaming and differences found in the literature regarding the comorbid psychopathology associated with the problem could be explained through different types of gamers. Clustering analysis can allow organization of a collection of personality traits into clusters based on similarity. The objectives of this study were: (1) to obtain an empirical classification of IGD patients according to personality variables and (2) to describe the resultant groups in terms of clinical and sociodemographic variables. The sample included 66 IGD adolescent patients who were consecutive referrals at a mental health center in Barcelona, Spain. A Gaussian mixture model cluster analysis was used in order to classify the subjects based on their personality. Two clusters based on personality traits were detected: type I "higher comorbid symptoms" (n = 24), and type II "lower comorbid symptoms" (n = 42). The type I included higher scores in introversive, inhibited, doleful, unruly, forceful, oppositional, self-demeaning and borderline tendency traits, and lower scores in histrionic, egotistic and conforming traits. The type I obtained higher scores on all the Symptom Check List-90 items-Revised, all the State-Trait Anxiety Index scales, and on the DSM-5 IGD criteria. Differences in personality can be useful in determining clusters with different types of dysfunctionality.