1. Adolescent Social Media Use
2. Life Satisfaction Study
3. Digital Wellbeing Adolescents
4. Social Media Effects Research
5. Youth Online Behavior Analysis

The intricate relationship between social media use and life satisfaction among adolescents has been the subject of extensive debate and research. A 2019 study published in the Proceedings of the National Academy of Sciences (PNAS) sought to provide clarity on this issue by examining large-scale representative panel data to distinguish the individual and collective influences of social media on the well-being of young individuals.

The study, titled “Social media’s enduring effect on adolescent life satisfaction,” DOI: 10.1073/pnas.1902058116, authored by Amy Orben, Tobias Dienlin, and Andrew K. Przybylski, presents a nuanced view, demonstrating that the effects of social media on adolescents are not uniform but rather complex, reciprocal, gender-specific, and dependent on the analytic methods used.

The study’s innovative approach involved analyzing the between-person and within-person relations linking adolescent social media use and well-being, providing insights into how individual differences and changes over time relate to life satisfaction. The main findings suggest that social media use is not a strong predictor of life satisfaction among adolescents. This counters the widespread belief that social media use is directly and negatively impacting the mental health of young people at a population level.

Here, we unpack the study’s methodology, findings, and implications for parents, educators, and policymakers who aim to understand and improve the well-being of adolescents in the digital age.

Methodology and Analytic Approach

The study used longitudinal random-intercept cross-lagged panel models, a robust statistical approach that accounts for individual differences and observes changes within individuals over time. The data were sourced from Understanding Society: The UK Household Longitudinal Study, which provided a rich data set that was representative of the UK adolescent population.

Participants’ life satisfaction was measured along with their social media usage, with analysis accounting for various confounding variables that could influence the relationship between social media use and well-being. The researchers employed a conservative analytic approach, avoiding “the garden of forking paths,” where multiple hypothesis testing can lead to spurious findings.

Key Findings

One of the critical takeaways from the research is that the effect of social media on life satisfaction is small and not uniformly negative as often portrayed in media narratives. It was found that for certain adolescents, the use of social media could have a positive impact, whereas for others, it could indeed be associated with decreased life satisfaction – though these effects are generally minor.

The study also identified that the relationship between social media use and life satisfaction is reciprocal; adolescents who experienced lower life satisfaction may then increase their social media use, possibly as a coping mechanism, and vice versa. Gender differences were also evident, with effects varying significantly between males and females, suggesting that social media’s impact might be more pronounced for one gender over the other.


The nuanced findings of this study challenge the simplistic notion that “social media is bad for teenagers’ mental health” by showing that the reality is far more intricate. Educators, parents, and policymakers should be aware that one-size-fits-all regulations or guidelines for social media use may not be effective. Instead, a personalized approach that considers an individual adolescent’s context and needs is warranted.

For instance, interventions could be designed considering the specific ways in which social media use interacts with life satisfaction for different groups, and education on digital well-being could be tailored to address the unique challenges faced by each gender.

Limitations and Further Research

Despite its strengths, the study is not without limitations. The data are observational, and therefore causal conclusions must be drawn with caution. The self-reported nature of social media use could also introduce bias, as highlighted by Scharkow’s work on the accuracy of self-reported internet use.

Future research could delve deeper into the complex, multidirectional dynamics by utilizing ecological momentary assessment or tracking real-time social media use data. Investigations into how different types of social media content or interaction styles influence adolescents’ well-being are also critical to providing more granular insights.


This study by Orben, Dienlin, and Przybylski offers a comprehensive look at the subtle and intricate ways in which social media use relates to adolescent life satisfaction. By providing evidence to move beyond oversimplified narratives, this research paves the way for more informed discussions and policymaking in the realm of youth digital well-being.

Parents and educators should take these findings into consideration, recognizing that the influence of social media is not monolithic and considering tailored strategies for supporting young people’s well-being in the context of their digital interactions. As social media continues to evolve, ongoing research must adapt accordingly, capturing the nuanced realities of digital life and its implications for the younger generation.


1. Orben, A., Dienlin, T., & Przybylski, A. K. (2019). Social media’s enduring effect on adolescent life satisfaction. Proceedings of the National Academy of Sciences, 116(21), 10226–10228. doi: 10.1073/pnas.1902058116
2. Bell, V., Bishop, D. V. M., & Przybylski, A. K. (2015). The debate over digital technology and young people. BMJ, 351, h3064. doi: 10.1136/bmj.h3064
3. Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. doi: 10.1037/met0000033
4. Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use. Nature Human Behaviour, 3, 173–182. doi: 10.1038/s41562-018-0506-1
5. Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2015). Specification curve: Descriptive and inferential statistics on all reasonable specifications. SSRN Electronic Journal. doi: 10.2139/ssrn.2694998