Predictors of Problematic Social Media Use in a Nationally Representative Sample of Adolescents in Luxembourg

Social media use has increased substantially over the past decades, especially among adolescents. A proportion of adolescents develop a pattern of problematic social media use (PSMU). Predictors of PSMU are insufficiently understood and researched. This study aims to investigate predictors of PSMU in a nationally representative sample of adolescents in Luxembourg. Data from the Health Behavior in School-aged Children (HBSC) study in Luxembourg were used, in which 8687 students aged 11–18 years old participated. The data were analyzed using hierarchical multiple regression. A range of sociodemo... Mehr ...

Verfasser: Claire van Duin
Andreas Heinz
Helmut Willems
Dokumenttyp: Text
Erscheinungsdatum: 2021
Verlag/Hrsg.: Multidisciplinary Digital Publishing Institute
Schlagwörter: problematic social media use (PSMU) / adolescents / social media / social support / media effects / health behavior in School-aged Children (HBSC) / well-being / differential susceptibility to media effects model / preference for online social interaction
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-29105020
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://doi.org/10.3390/ijerph182211878

Social media use has increased substantially over the past decades, especially among adolescents. A proportion of adolescents develop a pattern of problematic social media use (PSMU). Predictors of PSMU are insufficiently understood and researched. This study aims to investigate predictors of PSMU in a nationally representative sample of adolescents in Luxembourg. Data from the Health Behavior in School-aged Children (HBSC) study in Luxembourg were used, in which 8687 students aged 11–18 years old participated. The data were analyzed using hierarchical multiple regression. A range of sociodemographic, social support, well-being and media use predictors were added to the model in four blocks. The predictors in the final model explained 22.3% of the variance in PSMU. The block of sociodemographic predictors explained the lowest proportion of variance in PSMU compared with the other blocks. Age negatively predicted PSMU. Of the predictors related to social support, cyberbullying perpetration was the strongest predictor of PSMU. Perceived stress and psychosomatic complaints positively predicted PSMU. The intensity of electronic media communication and preference for online social interaction were stronger predictors of PSMU than the other predictors in the model. The results indicate that prevention efforts need to consider the diverse range of predictors related to PSMU.