Adolescents' Intense and Problematic Social Media Use and Their Well-Being in 29 Countries

Abstract

Purpose: This study examined (1) whether intense and problematic social media use (SMU) were independently associated with adolescent well-being; (2) whether these associations varied by the country-level prevalence of intense and problematic SMU; and (3) whether differences in the country-level prevalence of intense and problematic SMU were related to differences in mobile Internet access.

Methods: Individual-level data came from 154,981 adolescents (mean_age = 13.5) from 29 countries that participated in the 2017/2018 Health Behaviour in School-aged Children (HBSC) survey. Intense SMU was measured by the time spent on social media, whereas problematic SMU was defined by symptoms of addiction to social media. Mental (life satisfaction and psychological complaints), school (school satisfaction and perceived school pressure), and social (family support and friend support) well-being were assessed. Country-level data came from aggregated individual-level data and data from the Organisation for Economic Co-operation and Development (OECD) on Internet access.

Results: Two-level regression analyses indicated that in countries with a lower prevalence of intense SMU, intense users reported lower levels of life satisfaction and family support and more psychological complaints than nonintense users. In contrast, in countries with a higher prevalence of intense SMU, intense users reported higher levels of family support and life satisfaction than nonintense users, and similar levels of psychological complaints. In all countries, intense users reported more friend support than nonintense users. The findings regarding problematic SMU were more consistent: In all countries, problematic users reported lower well-being on all domains than nonproblematic users. Observed differences in country-level prevalence rates of intense and problematic SMU could not be explained by mobile Internet access.

Conclusions: Adolescents reporting problematic SMU are particularly at risk of lower well-being. In many countries, intense SMU may be a normative adolescent behavior that contributes positively to specific domains of their well-being.

Keywords: Adolescents; Cross-national research; HBSC; Problematic social media use; Social media use; Well-being.

Figure 1

Analytical model. SMU = social media use; FAS = family affluence; Subscripts i and j denote individuals (i) in countries (j); Black circles denote random slopes (S1 and S2); Black square denotes random intercept; White squares denote observed variables; White circles denote latent variables; Grey arrows denote estimates that were added for control purposes. The analytical model was applied to all six well-being measures.

Figure 2

Associations between intense SMU and well-being. SMU = social media use; B = unstandardized coefficient; M = mean; ∗p < .05, ∗∗p < .01, ∗∗∗p < .001. Left (A): dots denote average estimated associations between intense SMU and the well-being outcomes, horizontal lines through the dots denote their 95% prediction interval. Right (B): diagonal lines represent the estimated associations of intense SMU and the well-being outcomes by country-level prevalence of intense SMU. Cross-level interactions were reported when they improved model fit and when they were significant at p < .05. All estimates were derived from multilevel regression models (Table A1).

Figure 3

Associations between problematic SMU and well-being. SMU = social media use; B = unstandardized coefficient; M = mean; ∗p < .05, ∗∗p < .01, ∗∗∗p < .001. Left (A): dots denote average estimated associations between problematic SMU and the well-being outcomes, horizontal lines through the dots denote their 95% prediction interval. Right (B): diagonal lines represent the estimated associations of problematic SMU and the well-being outcomes by country-level prevalence of problematic SMU. Cross-level interactions were reported when they improved model fit and when they were significant at p < .05. All estimates were derived from multilevel regression models (Table A1).