Predictors of Problematic Social Media Use in a Nationally Representative Sample of Adolescents in Luxembourg
van Duin C, Heinz A, Heinz A, Willems H
International journal of environmental research and public health · 17 citations
How it was studied
- Design
- Cross-sectional study (classified by our AI screen)
- Studied in
- People
- Main outcome
- Health markers and function
Who paid for it
- Funding
- Independent funding
- University or hospital
- University of Glasgow
- Grants
- University of Glasgow (2017/2018)
Based on 1 listed funder(s) and full-text disclosure statement.
Publication
- Published
- 2021-11-12 · Int J Environ Res Public Health · vol. 18 · issue 22 · p. 11878
- Publisher
- Multidisciplinary Digital Publishing Institute
- Cited
- 34 citations · more than 96% of similar papers · 5.6× the field average
- Impact
- Top 10% most cited in its field
- References
- 55 works
- Access
- Open access (journal) · CC-BY
- Research areas
- Impact of Technology on Adolescents · Child Development and Digital Technology · Social Media and Politics
- Keywords
- social media addiction, adolescents, cyberbullying perpetration, perceived stress, psychosomatic symptoms, social support, online social interaction, sociodemographic predictors, well-being
- MeSH
- humans, social support, students, adolescent, child, luxembourg, social media, cyberbullying
3 authors
From LU
- Claire van Duin · correspondingUniversity of Luxembourg
- Andreas HeinzUniversity of Luxembourg
- Helmut Erich WillemsUniversity of Luxembourg
Abstract
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.
Abstract via Europe PMC. Copyright remains with the authors or publisher (CC BY).
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