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The relationship between maladaptive personality functioning and problematic technology use in adolescence: A cluster analysis approach.

Simone AmendolaValentina SpensieriGiuseppe Stefano BiusoRita Cerutti
Published in: Scandinavian journal of psychology (2020)
In the last two decades, scientific research has explored the problematic use of internet, videogames and mobile phones. However, there is still little consistent knowledge regarding the co-occurrence of problematic technology use and the role of maladaptive personality characteristics in adolescence. The present study aimed to investigate adolescents' styles of technology use with a cluster analysis approach focusing on personality functioning. The sample comprised 408 Italian adolescents (46.3% males) aged 11 to 18 years (M age  = 13.80; SD = 2.08). Data were collected using the Internet Addiction Test, the Videogame Dependency Scale, the Test of Mobile-Phone Dependence Brief Form and the Personality Inventory for DSM5 Brief Form. Results provided a four-cluster solution based on the co-occurrence of problematic technology use. The four clusters were labeled as follows: cluster 1: "Above average internet and mobile-phone use"; cluster 2: "Below average technology use"; cluster 3: "Above average videogame use"; and cluster 4: "Problematic technology use." Analyses on demographic variables (e.g., gender and age) demonstrated significant differences between the four groups. Adolescents with high levels of problematic technology use reported greater overall personality dysfunction than the other three groups. This finding supported our hypothesis on maladaptive personality functioning in adolescents at risk for addiction. Finally, the Antagonism domain played a specific role in differentiating the severity of adolescents' involvement in technology use. Further studies are needed to confirm our findings and to plan preventive interventions as well as therapeutic treatments.
Keyphrases
  • young adults
  • physical activity
  • healthcare
  • depressive symptoms
  • health information
  • magnetic resonance imaging
  • electronic health record
  • social media
  • deep learning