research proposal on impact of social media on youth

Adolescent Social Media Use and Well-Being: A Systematic Review and Thematic Meta-synthesis

• Systematic Review
• Published: 17 April 2021
• Volume 6 , pages 471–492, ( 2021 )

Cite this article

• Michael Shankleman   ORCID: orcid.org/0000-0002-7150-8827 1 ,
• Linda Hammond 1 &
• Fergal W. Jones   ORCID: orcid.org/0000-0001-9459-6631 1  

4353 Accesses

18 Citations

22 Altmetric

Explore all metrics

Qualitative research into adolescents’ experiences of social media use and well-being has the potential to offer rich, nuanced insights, but has yet to be systematically reviewed. The current systematic review identified 19 qualitative studies in which adolescents shared their views and experiences of social media and well-being. A critical appraisal showed that overall study quality was considered relatively high and represented geographically diverse voices across a broad adolescent age range. A thematic meta-synthesis revealed four themes relating to well-being: connections, identity, learning, and emotions. These findings demonstrated the numerous sources of pressures and concerns that adolescents experience, providing important contextual information. The themes appeared related to key developmental processes, namely attachment, identity, attention, and emotional regulation, that provided theoretical links between social media use and well-being. Taken together, the findings suggest that well-being and social media are related by a multifaceted interplay of factors. Suggestions are made that may enhance future research and inform developmentally appropriate social media guidance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Social Media Use and Mental Health among Young Adults

Teenagers, screens and social media: a narrative review of reviews and key studies.

Effects of social media use on desire for cosmetic surgery among young women

Aboujaoude, E., Savage, M. W., Starcevic, V., & Salame, W. O. (2015). Cyberbullying: Review of an old problem gone viral. Journal of Adolescent Health, 57 (1), 10–18. https://doi.org/10.1016/j.jadohealth.2015.04.011 .

Article   Google Scholar  

Allen, J. P., & Tan J. S (2018). The multiple facets of attachment in adolescence. In J. Cassidy & P. R. Shaver (Eds) Handbook of attachment (pp. 399–415). Guilford Press.

Atkins, S., Lewin, S., Smith, H., Engel, M., Fretheim, A., & Volmink, J. (2008). Conducting a meta-ethnography of qualitative literature: Lessons learnt. BMC Medical Research Methodology, 8 , 21. https://doi.org/10.1186/1471-2288-8-21 .

Article   PubMed   PubMed Central   Google Scholar  

Bartsch, A., & Oliver, M. B. (2017). Appreciation of meaningful entertainment experiences and eudaimonic well-being. In L. Reinecke & M. B. Oliver (Eds.), The Routledge handbook of media use and well-being: International perspectives on theory and research on positive media effects (pp. 80–92). Routledge.

Bazarova, N. N., & Choi, Y. H. (2014). Self-disclosure in social media: Extending the functional approach to disclosure motivations and characteristics on social network sites. Journal of Communication, 64 (4), 635–657. https://doi.org/10.1111/jcom.12106 .

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 (2), 77–101. https://doi.org/10.1191/1478088706qp063oa .

Bell, B. T. (2019). “You take fifty photos, delete forty-nine and use one”: A qualitative study of adolescent image-sharing practices on social media. International Journal of Child–Computer Interaction, 20 , 64–71. https://doi.org/10.1016/j.ijcci.2019.03.002 .

Best, P., Manktelow, R., & Taylor, B. (2014). Online communication, social media and adolescent wellbeing: A systematic narrative review. Children and Youth Services Review, 41 , 27–36. https://doi.org/10.1016/j.childyouth.2014.03.001 .

Best, P., Taylor, B., & Manktelow, R. (2015). I’ve 500 friends, but who are my mates? Investigating the influence of online friend networks on adolescent wellbeing. Journal of Public Mental Health, 14 (3), 135–148. https://doi.org/10.1108/JPMH-05-2014-0022 .

Bharucha, J. (2018). Social network use and youth well-being: A study in India. Safer Communities, 17 (2), 119–131. https://doi.org/10.1108/SC-07-2017-0029 .

Blumberg, F. C., Rice, J. L., & Dickmeis, A. (2016). Social media as a venue for emotion regulation among adolescents. In S. Y. Tettegah, Emotions and technology: Communication of feelings for, with, and through digital media. Emotions, technology, and social media (pp. 105–116). Academic.

Bondas, T., & Hall, E. O. (2007). Challenges in approaching metasynthesis research. Qualitative Health Research, 17 (1), 113–121. https://doi.org/10.1177/1049732306295879 .

Article   PubMed   Google Scholar  

Burnette, C. B., Kwitowski, M. A., & Mazzeo, S. E. (2017). “I don’t need people to tell me I’m pretty on social media:” A qualitative study of social media and body image in early adolescent girls. Body Image, 23 , 114–125. https://doi.org/10.1016/j.bodyim.2017.09.001 .

Calancie, O., Ewing, L., Narducci, L. D., Horgan, S., & Khalid-Khan, S. (2017). Exploring how social networking sites impact youth with anxiety: A qualitative study of Facebook stressors among adolescents with an anxiety disorder diagnosis. Cyberpsychology . https://doi.org/10.5817/CP2017-4-2 .

Chua, T. H. H., & Chang, L. (2016). Follow me and like my beautiful selfies: Singapore teenage girls’ engagement in self-presentation and peer comparison on social media. Computers in Human Behavior, 55 , 190–197. https://doi.org/10.1016/j.chb.2015.09.011 .

Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L., & Booth, M. (2020). Does time spent using social media impact mental health?: An eight year longitudinal study. Computers in Human Behavior , 104 , 106160. https://doi.org/10.1016/j.chb.2019.106160

Critical Appraisal Skills Programme (CASP). (2018). CASP Qualitative Checklist . https://casp-uk.net/casp-tools-checklists/ .

Crogan, P., & Kinsley, S. (2012). Paying attention: Towards a critique of the attention economy. Culture Machine . https://tinyurl.com/u28jo47 .

Davis, K. (2012). Friendship 2.0: Adolescents’ experiences of belonging and self-disclosure online. Journal of Adolescence, 35 (6), 1527–1536. https://doi.org/10.1016/j.adolescence.2012.02.013 .

Dickson, K., Richardson, M., Kwan, I., MacDowall, W., Burchett, H., Stansfield, C., Brunton, G., Sutcliffe, K., & Thomas, J. (2018). Screen-based activities and children and young people’s mental health: A Systematic Map of Reviews . EPPI-Centre, Social Science Research Unit, UCL Institute of Education, University College London. http://eppi.ioe.ac.uk/ .

Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a national index. American Psychologist, 55 (1), 34. https://doi.org/10.1037/0003-066X.55.1.34 .

Dowell, E. (2009). Clustering of Internet risk behaviours in a middle school student population. Journal of School Health, 79 , 547–553. https://doi.org/10.1111/j.1746-1561.2009.00447.x .

Duvenage, M., Correia, H., Uink, B., Barber, B. L., Donovan, C. L., & Modecki, K. L. (2020). Technology can sting when reality bites: Adolescents’ frequent online coping is ineffective with momentary stress. Computers in Human Behavior, 102 , 248–259. https://doi.org/10.1016/j.chb.2019.08.024 .

Erfani, S. S., & Abedin, B. (2018). Impacts of the use of social network sites on users’ psychological well-being: A systematic review. Journal of the Association for Information Science and Technology, 69 (7), 900–912. https://doi.org/10.1002/asi.24015 .

Erikson, E. H. (1968). Identity: Youth and crisis . Norton.

Finfgeld-Connett, D. (2010). Generalizability and transferability of meta-synthesis research findings. Journal of Advanced Nursing, 66 , 246–254. https://doi.org/10.1111/j.1365-2648.2009.05250.x .

Gardner, H., & Davis, K. (2013). The app generation: How today’s youth navigate identity, intimacy, and imagination in a digital world . Yale University Press.

Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones and social media. The Internet and Higher Education, 19 , 18–26. https://doi.org/10.1016/j.iheduc.2013.06.002 .

Griffin, A. (2017). Adolescent neurological development and implications for health and well-being. Healthcare, 5 , 62–76. https://doi.org/10.3390/healthcare5040062 .

Article   PubMed Central   Google Scholar  

Goossens, L. (2001). Global versus domain-specific statuses in identity research: A comparison of two self-report measures. Journal of Adolescence, 24 , 681–699. https://doi.org/10.1006/jado.2001.0438 .

Guyer, A. E., Silk, J. S., & Nelson, E. E. (2016). The neurobiology of the emotional adolescent: From the inside out. Neuroscience and Biobehavioral Reviews, 70 , 74–85. https://doi.org/10.1016/j.neubiorev.2016.07.037 .

Haidt, J., & Allen, N. (2020). Scrutinizing the effects of digital technology on mental health. Nature, 578 , 226–227. https://doi.org/10.1038/d41586-020-00296-x .

Harter, S. (1990). Causes, correlates, and the functional role of global self-worth: A life-span perspective. In R. J. Sternberg & J. Kolligan Jr. (Eds.), Competence considered (pp. 67–98). Yale University Press.

Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach . Guilford Publications.

Heffer, T., Good, M., Daly, O., MacDonell, E., & Willoughby, T. (2019). The longitudinal association between social-media use and depressive symptoms among adolescents and young adults: An empirical reply to Twenge et al. (2018). Clinical Psychological Science, 7 (3), 462–470. https://doi.org/10.1177/2167702618812727 .

Jong, S. T., & Drummond, M. J. N. (2016). Hurry up and ‘like’ me: Immediate feedback on social networking sites and the impact on adolescent girls. Asia–Pacific Journal of Health, Sport and Physical Education, 7 (3), 251–267. https://doi.org/10.1080/18377122.2016.1222647 .

Kelly, Y., Zilanawala, A., Booker, C., & Sacker, A. (2018). Social media use and adolescent mental health: Findings from the UK Millennium Cohort Study. EClinicalMedicine, 6 , 59–68. https://doi.org/10.1016/j.eclinm.2018.12.005 .

Keyes, K. M., Gary, D., O’Malley, P. M., Hamilton, A., & Schulenberg, J. (2019). Recent increases in depressive symptoms among US adolescents: Trends from 1991 to 2018. Social Psychiatry and Psychiatric Epidemiology, 54 (8), 987–996. https://doi.org/10.1007/s00127-019-01697-8 .

Lachal, J., Revah-Levy, A., Orri, M., & Moro, M. R. (2017). Metasynthesis: An original method to synthesize qualitative literature in psychiatry. Frontiers in Psychiatry, 8 , 269. https://doi.org/10.3389/fpsyt.2017.00269 .

Landau, A., Eisikovits, Z., & Rafaeli, S. (2019). Coping strategies for youth suffering from online interpersonal rejection. In Proceedings of the 52nd Hawaii international conference on system sciences. http://hdl.handle.net/10125/59656 .

Leary, M. R. (2015). Emotional responses to interpersonal rejection. Dialogues in Clinical Neuroscience, 17 (4), 435. https://doi.org/10.31887/DCNS.2015.17.4/mleary .

Long, H. A., French, D. P., & Brooks, J. M. (2020). Optimising the value of the critical appraisal skills programme (CASP) tool for quality appraisal in qualitative evidence synthesis. Research Methods in Medicine and Health Sciences, 1 (1), 31–42. https://doi.org/10.1177/2632084320947559 .

Liu, M., Wu, L., & Yao, S. (2016). Dose–response association of screen time-based sedentary behavior in children and adolescents and depression: A meta-analysis of observational studies. British Journal of Sports Medicine, 50 (20), 1252–1258. https://doi.org/10.1136/bjsports-2015-095084 .

Lyons-Ruth, K. (1991). Rapprochement or approachment: Mahler’s theory reconsidered from the vantage point of recent research on early attachment relationships. Psychoanalytic Psychology, 8 , 1–23. https://doi.org/10.1037/h0079237 .

MacIsaac, S., Kelly, J., & Gray, S. (2018). ‘She has like 4000 followers!’: The celebrification of self within school social networks. Journal of Youth Studies, 21 (6), 816–835. https://doi.org/10.1080/13676261.2017.1420764 .

Manago, A. M., Taylor, T., & Greenfield, P. M. (2012). Me and my 400 friends: The anatomy of college students’ Facebook networks, their communication patterns, and well-being. Developmental Psychology, 48 (2), 369. https://doi.org/10.1037/a0026338 .

Mihálik, J., Garaj, M., Sakellariou, A., Koronaiou, A., Alexias, G., Nico, M., Nico, M., de Almeida Alves, N., Unt, M., & Taru, M. (2018). Similarity and difference in conceptions of well-being among children and young people in four contrasting European countries. In G. Pollock, J. Ozan, H. Goswami, G. Rees & A. Stasulane (Eds.), Measuring youth well-being (pp. 55–69). Springer.

McQuail, D. (2010). Mass communication theory: An introduction (6 th Ed., pp. 420–430). Sage.

Nabi, R. L., Prestin, A., & So, J. (2013). Facebook friends with (health) benefits? Exploring social network site use and perceptions of social support, stress, and well-being. Cyberpsychology, Behavior, and Social Networking, 16 (10), 721–727. https://doi.org/10.1089/cyber.2012.0521 .

Odgers, C. L., Schueller, S. M., & Ito, M. (2020). Screen time, social media use, and adolescent development. Annual Review of Developmental Psychology, 2 (1), 485–502. https://doi.org/10.1146/annurev-devpsych-121318-084815 .

Oh, H. J., Ozkaya, E., & LaRose, R. (2014). How does online social networking enhance life satisfaction? The relationships among online supportive interaction, affect, perceived social support, sense of community, and life satisfaction. Computers in Human Behavior, 30 , 69–78. https://doi.org/10.1016/j.chb.2013.07.053 .

ONS. (2017). Social networking by age group, 2011 to 2017 . https://tinyurl.com/yc9lhjdk .

Opitz, P. C., Gross, J. J., & Urry, H. L. (2012). Selection, optimization, and compensation in the domain of emotion regulation: Applications to adolescence, older age, and major depressive disorder. Social and Personality Psychology Compass, 6 , 142–155. https://doi.org/10.1111/j.1751-9004.2011.00413.x .

Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use. Nature Human Behaviour , 3 (2), 173–182. https://doi.org/10.1038/s41562-018-0506-1

O’Reilly, M. (2020). Social media and adolescent mental health: The good, the bad and the ugly. Journal of Mental Health . https://doi.org/10.1080/09638237.2020.1714007 Advanced online publication.

O’Reilly, M., Dogra, N., Whiteman, N., Hughes, J., Eruyar, S., & Reilly, P. (2018). Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clinical Child Psychology and Psychiatry, 23 (4), 601–613. https://doi.org/10.1177/1359104518775154 .

Ozan, J., Mierina, I., & Koroleva, I. (2018). A comparative expert survey on measuring and enhancing children and young people’s well-being in Europe. In G. Pollock, J. Ozan, H. Goswami, G. Rees & A. Stasulane (Eds.), Measuring youth well-being. Children’s well-being: Indicators and research (Vol. 19). Springer.

Pajares, F. (2006). Self-efficacy during childhood and adolescence. In T. Urdan & F. Pajares (Eds.), Self-efficacy beliefs of adolescents (pp. 339–367). IAP-Information Age Publishing.

Quoidbach, J., Mikolajczak, M., & Gross, J. J. (2015). Positive interventions: An emotion regulation perspective. Psychology Bulletin, 141 , 655–693. https://doi.org/10.1037/a0038648 .

QSR International Pty Ltd. (2015). NVivo (released in March 2015). https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home .

Radovic, A., Gmelin, T., Stein, B. D., & Miller, E. (2017). Depressed adolescents’ positive and negative use of social media. Journal of Adolescence, 55 , 5–15. https://doi.org/10.1016/j.adolescence.2016.12.002 .

Rideout, V. J., & Robb, M. B. (2019). The common sense census: Media use by tweens and teens . Common Sense Media. https://www.commonsensemedia.org/sites/default/files/uploads/research/2019-census-8-to-18-key-findings-updated.pdf .

Riediger, M., & Klipker, K. (2014). Emotion regulation in adolescence. In J. J. Gross (Ed.), Handbook of emotion regulation (pp. 187–202). The Guilford Press.

Rigby, E., Hagell, A., & Starbuck, L. (2018). What do children and young people tell us about what supports their wellbeing? Evidence from existing research. Health and Wellbeing Alliance. http://www.youngpeopleshealth.org.uk/wp-content/uploads/2019/10/Scoping-paper-CYP-views-on-wellbeing-FINAL.pdf .

Rogers, C. R. (1961). On becoming a person: A therapist’s view of psychotherapy . Houghton Mifflin

Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57 (6), 1069–1081. https://doi.org/10.1037/0022-3514.57.6.1069 .

Scott, H., Biello, S. M., & Woods, H. C. (2019). Identifying drivers for bedtime social media use despite sleep costs: The adolescent perspective. Sleep Health, 6 , 539–545. https://doi.org/10.1016/j.sleh.2019.07.006 .

Singleton, A., Abeles, P., & Smith, I. C. (2016). Online social networking and psychological experiences: The perceptions of young people with mental health difficulties. Computers in Human Behavior, 61 , 394–403. https://doi.org/10.1016/j.chb.2016.03.011 .

Suchert, V., Hanewinkel, R., & Isensee, B. (2015). Sedentary behavior and indicators of mental health in school-aged children and adolescents: A systematic review. Preventive Medicine, 76 , 48–57. https://doi.org/10.1016/j.ypmed.2015.03.026 .

Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8 , 45. https://doi.org/10.1186/1471-2288-8-45 .

Throuvala, M. A., Griffiths, M. D., Rennoldson, M., & Kuss, D. J. (2019). Motivational processes and dysfunctional mechanisms of social media use among adolescents: A qualitative focus group study. Computers in Human Behavior, 93 , 164–175. https://doi.org/10.1016/j.chb.2018.12.012 .

Tong, A., Flemming, K., McInnes, E., Oliver, S., & Craig, J. (2012). Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Medical Research Methodology, 12 , 181. https://doi.org/10.1186/1471-2288-12-181 .

Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6 , 3–17. https://doi.org/10.1177/2167702617723376 .

Twenge, J. M. (2020). Why increases in adolescent depression may be linked to the technological environment. Current Opinion in Psychology, 32 , 89–94. https://doi.org/10.1016/j.copsyc.2019.06.036 .

Twomey, C., & O’Reilly, G. (2017). Associations of self-presentation on Facebook with mental health and personality variables: A systematic review. Cyberpsychology, Behavior, and Social Networking, 20 , 587–595. https://doi.org/10.1089/cyber.2017.0247 .

Vermeulen, A., Vandebosch, H., & Heirman, W. (2018). Shall I call, text, post it online or just tell it face-to-face? How and why Flemish adolescents choose to share their emotions on- or offline. Journal of Children and Media, 12 (1), 81–97. https://doi.org/10.1080/17482798.2017.1386580 .

Vignoles, V. L., Regalia, C., Manzi, C., Golledge, J., & Scabini, E. (2006). Beyond self-esteem: Influence of multiple motives on identity construction. Journal of Personality and Social Psychology, 17 , 251–268. https://doi.org/10.1037/0022-3514.90.2.308 .

Vignoles, V. L. (2011). Identity motives. In S. J. Schwartz, K. Luyckx & V. L. Vignoles (Eds.), Handbook of identity theory and research (pp. 403–432). Springer.

Vorderer, P., & Reinecke, L. (2015). From mood to meaning: The changing model of the user in entertainment research. Communication Theory, 25 (4), 447–453. https://doi.org/10.1111/comt.12082 .

Weinstein, E. (2018). The social media see-saw: Positive and negative influences on adolescents’ affective well-being. New Media and Society, 20 (10), 3597–3623. https://doi.org/10.1177/1461444818755634 .

White, M. (2000). Reflecting Team work as definitional ceremony revisited. In Reflections on narrative practice: essays and interviews. Dulwich Centre Publications.

Download references

Acknowlegement

We extend our gratitude to the authors of the original studies for bringing forth the perspectives of young people.

Preregistration

The review protocol including review question, search strategy, inclusion criteria data extraction, quality assessment, data synthesis was preregistered and is accessible at: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=156922 .

Author information

Authors and affiliations.

Salomons Institute for Applied Psychology, Canterbury Christ Church University, Lucy Fildes Building, 1 Meadow Rd, Royal Tunbridge Wells, TN1 2YG, UK

Michael Shankleman, Linda Hammond & Fergal W. Jones

You can also search for this author in PubMed   Google Scholar

Contributions

MS conceived of the study, participated in its design, coordination, interpretation of the data and drafted the manuscript; LH participated in the design and interpretation of the data; FWJ participated in the design and interpretation of the data. All authors read, helped to draft, and approved the final manuscript.

Corresponding author

Correspondence to Michael Shankleman .

Ethics declarations

Conflict of interest.

The authors report no conflict of interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Shankleman, M., Hammond, L. & Jones, F.W. Adolescent Social Media Use and Well-Being: A Systematic Review and Thematic Meta-synthesis. Adolescent Res Rev 6 , 471–492 (2021). https://doi.org/10.1007/s40894-021-00154-5

Download citation

Received : 14 December 2020

Accepted : 03 April 2021

Published : 17 April 2021

Issue Date : December 2021

DOI : https://doi.org/10.1007/s40894-021-00154-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Adolescents
• Qualitative
• Social media
• Meta-synthesis
• Find a journal
• Publish with us
• Track your research

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Open access
• Published: 23 April 2024

Probing the digital exposome: associations of social media use patterns with youth mental health

• David Pagliaccio   ORCID: orcid.org/0000-0002-1214-1965 1 , 2 ,
• Kate T. Tran 3 , 4 ,
• Elina Visoki 3 , 4 ,
• Grace E. DiDomenico 3 , 4 ,
• Randy P. Auerbach 1 , 2 &
• Ran Barzilay 3 , 4 , 5  

NPP—Digital Psychiatry and Neuroscience volume  2 , Article number:  5 ( 2024 ) Cite this article

323 Accesses

14 Altmetric

Metrics details

• Human behaviour
• Psychiatric disorders
• Risk factors

Recently, the U.S. Surgeon General issued an advisory highlighting the lack of knowledge about the safety of ubiquitous social media use on adolescent mental health. For many youths, social media use can become excessive and can contribute to frequent exposure to adverse peer interactions (e.g., cyberbullying, and hate speech). Nonetheless, social media use is complex, and although there are clear challenges, it also can create critical new avenues for connection, particularly among marginalized youth. In the current project, we leverage a large nationally diverse sample of adolescents from the Adolescent Brain Cognitive Development (ABCD) Study assessed between 2019–2020 ( N  = 10,147, M age  = 12.0, 48% assigned female at birth, 20% Black, 20% Hispanic) to test the associations between specific facets of adolescent social media use (e.g., type of apps used, time spent, addictive patterns of use) and overall mental health. Specifically, a data-driven exposome-wide association was applied to generate digital exposomic risk scores that aggregate the cumulative burden of digital risk exposure. This included general usage, cyberbullying, having secret accounts, problematic/addictive use behavior, and other factors. In validation models, digital exposomic risk explained substantial variance in general child-reported psychopathology, and a history of suicide attempt, over and above sociodemographics, non-social screentime, and non-digital adversity (e.g., abuse, poverty). Furthermore, differences in digital exposomic scores also shed insight into mental health disparities, among youth of color and sexual and gender minority youth. Our work using a data-driven approach supports the notion that digital exposures, in particular social media use, contribute to the mental health burden of US adolescents.

Lay summary

Smartphones and social media are increasingly central to teens’ social lives, leading to concerns about potential effects on mental health. Using a big-data approach, we created composite scores of digital exposures that related to poor mental health in a large national sample of youth. Use of certain apps, cyberbullying, having secret accounts and problematic/addictive social media use cumulatively related to worse outcomes, including the history of suicide attempts, beyond effects of non-social screentime and non-digital adversity.

Similar content being viewed by others

Adults who microdose psychedelics report health related motivations and lower levels of anxiety and depression compared to non-microdosers

Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder

Persistent interaction patterns across social media platforms and over time

Introduction.

The United States Surgeon General and leading pediatric health organizations have declared a national state of emergency regarding youth mental health [ 1 , 2 , 3 ], particularly raising concerns about the potential contributions of social media to mental health [ 4 ]. Spikes in depression and suicide rates have been observed in recent years, especially among youth of color and sexual and gender minority (SGM) adolescents [ 5 , 6 , 7 , 8 ]. Depression and other mental health concerns frequently onset during adolescence [ 9 , 10 , 11 ], which can be an especially stressful developmental period [ 12 ] as well as a critical time for identity and relationship formation [ 13 , 14 , 15 ]. Further research is needed to understand the potential contributions of social media on mental health among youth [ 16 ].

In recent decades, there have been major shifts in the centrality of digital devices to daily life and social relationships, particularly among adolescents. Over 95% of teens in the U.S. own smartphones [ 17 , 18 ]. Smartphones have been increasingly available across income strata [ 17 , 18 ] with nearly all adolescents reporting daily use, and a quarter reporting “almost constant” use [ 19 ]. Accordingly, concerns have been raised in the popular press about the potential negative effects of screentime (i.e., any activity on digital devices) on mental health and development [ 20 , 21 ]. Screentime includes a wide ranges of activities, including passive video watching, texting, games, and social media, as well as educational and school-related activities. Data from the Adolescent Brain Cognitive Development (ABCD) Study shows annual increases in screen time across development (9–12-year-olds) [ 22 ].

Changes in our digital landscape have been particularly rapid regarding social media. Broadly, social media encapsulates digital platforms that help users develop social interaction or online presence [ 23 ]. This definition itself as well as teens’ preferred social media have evolved with the rapid shift from a small set of web-based platforms (e.g., MySpace) to the proliferation of smartphone-based apps (e.g., Instagram, TikTok). Despite increasing use, youth express an ambivalent need to devote time to social media to maintain peer relationships, while not enjoying using social media as much as other activities, e.g., listening to music [ 24 ]. Furthermore, significant sociodemographic differences have been observed; on average, boys tended to report increasing time on games and video watching whereas girls report increases in social activities (e.g., texting, and social media) [ 22 ]. White youth and those from high-income families typically have the greatest access to digital platforms, yet lower-income and youth of color often exhibit more screentime [ 22 , 24 ]. Compared to their heterosexual peer, SGM teens report a greater likelihood of spending 3+ hours of non-school-related screentime daily (up to 85%) [ 5 ].

Given rising rates of mental health issues among adolescents [ 1 , 3 , 25 , 26 ], concerns have been raised about the impact of digital technology. Despite widespread concerns, research has been inconclusive, yielding small or mixed-effects between social media and mental health [ 27 , 28 , 29 , 30 ]. Initial examination of ABCD data suggests only small associations between screentime and mental health [ 31 , 32 , 33 ]. Meta-analyses and large survey studies also suggest small associations between greater child and adolescent use of social media and worse depressive, internalizing, and externalizing symptoms, though substantial heterogeneity is noted [ 27 , 29 , 30 , 34 , 35 ]. This may be, in part, due to less time spent on in-person activities [ 36 ] or factors like social comparison [ 34 , 37 ]. Longitudinal surveys provide mixed or null evidence on the directionality of these effects [ 35 , 38 , 39 , 40 , 41 ]. Cross-sectional data from the 2021 CDC Youth Risk Behavior Survey show that serious consideration of attempting suicide was disproportionately reported among high schoolers reported 3+ hours/day of screentime, covarying race and sex (odds ratio [OR] = 1.68, z  = 10.65, p  < 0.001) [ 5 ]. However, it is not clear whether screentime, per se, is driving the association, or rather the association of screentime with suicidal risk is driven by specific types of use (e.g., adverse social media-related exposures).

Toward addressing this gap, we aimed to test the specificity of social media contributions to youth mental health, over and above general screentime, and non-digital adversity (e.g., abuse, trauma, neighborhood poverty, discrimination) [ 42 ]. We leveraged ABCD data that includes a large sample of diverse youth from across the U.S. We utilized data-driven exposome-wide association study (ExWAS) analyses to test cross-sectional associations of multiple measures of social media use with mental health at age 12. Previous ExWAS have examined environmental and lifestyle factors to explain variance in physical health conditions [ 43 , 44 ] and, more recently, mental health [ 45 , 46 ]. This approach can help advance the field which mostly focuses on single digital exposures in isolation (e.g., cyberbullying, addictive social media use) and can address some challenges of single-exposure studies [ 47 , 48 , 49 ], including multiple comparisons and collinearity. We used ExWAS findings to construct dimensional digital exposomic risk scores that aggregate an individual’s associated mental health risk and apply them for more parsimonious follow-up tests (Fig.  1 ). Specifically, we hypothesized that specific aspects of social media exposure would specifically relate to worse mental health, more so than general screentime, and separable from effects of non-digital adversity [ 42 ]. Furthermore, given known disparities in mental health outcomes by gender, race, and SGM identity [ 5 , 6 , 7 , 8 , 9 , 50 , 51 , 52 , 53 ], we hypothesized differential exposure [ 54 ] based on the exposomic risk scores across these subpopulations (e.g., greater social media exposure among girls than boys) as well as potential differential effects whereby the association between digital exposures and mental health varies by identity (e.g., stronger links between exposure and poor mental health among SGM than non-SGM youth). These analyses will lay the groundwork for future longitudinal and causal analyses.

An overview of the flow of the analytic approach is presented here. The ABCD Study dataset was split into two independent subsamples for training and testing procedures (Step 1). We identified self- and parent-report variables that assessed digital and social media exposures (2A). Associations with mental health symptoms were assessed in separate models (2B). Weighted risk scores were aggregated from the significantly associated variables (2C). These aggregate risk scores were then validated and used in follow-up testing in the independent testing subsample.

Participants

We included ABCD Study participants who completed the 2-year follow-up that included assessment of screentime and social media use ( N  = 10,147) [ 55 , 56 , 57 ]. Data were collected between 2019–2020 and drawn from the ABCD Study’s Data Release 4.0 ( https://doi.org/10.15154/1523041 ). Briefly, the ABCD Study is a collaborative project with the goals of understanding: (a) normal variability and (b) environmental and socioemotional factors that influence brain and cognitive development [ 58 ]. Starting in 2016, ABCD recruited diverse children ( N  = 11,876) ages 9–10 through schools near 21 sites across the US [ 59 ].

Clinical outcomes

Our primary outcome was self-reported youth psychopathology assessed through the Brief Problems Monitor (BPM) [ 60 ]. This measure assesses general functioning and mental health, including items refined from the Child Behavior Checklist (CBCL) [ 61 ]. Specifically, we focused on age- and sex-normed Total Problems T -scores, which reflect a combination of internalizing, externalizing, and attentional problems. In sensitivity analyses, we examined BPM Internalizing T -scores and parent-report CBCL Total Problems T -scores [ 42 ]. Follow-up analyses examined self-reported suicide attempts as a higher severity outcome, based on the computerized Kiddie-Structured Assessment for Affective Disorders and Schizophrenia for DSM-5 (KSADS-5) [ 62 , 63 ].

Digital exposures

ABCD collects youth- and parent-report data on digital experiences, including the Cyber Bullying Questionnaire [ 64 ] and Youth and Parent Screen Time Surveys [ 22 ]. Individual variables were refined for analysis by the co-authors. For example, redundant or branching items and variables with <1% endorsement were removed. Screentime assessments included separate hour and minute response options, which were combined for analysis. Extreme outliers on continuous response variables were removed (e.g., number of social media followers; see Supplement).

We identified 52 digital and social media exposure variables; after cleaning, 41 variables were retained for analysis (Table  S1 ), which captured five broad domains: (1) screentime (i.e., minutes/hours per day by weekend/weekday), (2) parental monitoring (e.g., “ Do you suspect that your child has social media accounts that you are unaware of? ”), (3) apps used (e.g., yes/no has an Instagram account), (4) overuse/addictive patterns of use (e.g., “ I use social media apps so much that it has had a bad effect on my schoolwork or job ”), and (5) peer interaction (e.g., “ I feel connected to others when I am using my phone ”). Total screentime for non-social purposes (e.g., for schoolwork) was extracted as a covariate using 13 items from the Youth/Parent Screen Time Surveys (Supplement).

Statistical analysis

All analyses were conducted in R [ 65 ]. Building on prior ExWAS research [ 42 , 43 , 66 , 67 , 68 ], our analytic plan applied the following steps (Fig.  1 ): (a) the ABCD dataset was split into training ( n  = 5082) and testing ( n  = 5065) subsamples using the ABCD Reproducible Matched Samples (ABCD_3165 collection [ 69 ]), matched across study sites on age, sex, ethnicity, grade, parent education, family income, and family-relatedness; (b) missing digital exposure data was non-parametrically imputed for both subsamples separately ( missForest::RandomForest [ 70 ]; mental health outcome variables were not imputed); (c) collinear (Pearson’s r  > 0.9) exposures in the training sample were removed ( caret::findCorrelation [ 71 ], as in prior work [ 46 ]), (d) each digital exposure was tested as an independent variable in a separate linear-mixed-effects model (LME; lme4::lmer ; [ 72 ]) with BPM- T -scores as the dependent variable in the training sample, with random intercepts for family nested within study site and covariates for age, sex, race (binary variables for identifying as Black and as White), and Hispanic ethnicity, (e) FDR-correction for 41 comparisons was applied to identify significant exposures as risk (coefficient>0) or protective (coefficient<0) factors, and (f) aggregate digital exposomic risk scores were derived for each participant in the testing subsample as the sum of each variable multiplied its coefficient from ExWAS LME models; higher scores indicate greater digital exposomic risk for mental health problems.

In the independent testing sample, successive LME models were used to validate the specific association between aggregate digital exposomic risk scores and BPM Total Problems T -scores, over and above demographics, non-social screentime, and childhood non-digital adversity, calculated in our previous work [ 42 ]. All models included random effects for families nested within the study site as well as fixed-effect covariates for age, sex, race, Hispanic ethnicity, annual household income (ordinal variable, from below $5,000 (1) to above $200,000 (10)), and parent education (data at 1-year assessment, mean of the highest grade or degree that parent(s) completed). We first estimated a model that included demographics (Model-1), then added total non-social screentime (Model-2), and then added a measure of childhood adversity that aggregates environmental burden captured by age 11 [ 42 , 66 ] (Model-3). Last, digital exposomic risk scores were entered to examine the added variance explained by mental health burden (Model-4). Nakagawa marginal R 2 indicated the variance explained by the fixed effects [ 73 ]. All model coefficients and odds ratios are presented with their 95% confidence interval (CI) and adjusted for covariates.

Suicide attempts analyses

To address the potential contribution of digital exposomic risk to suicide attempts, we estimated logistic regression models with self-reported lifetime suicide attempt history (K-SADS) as the dependent variable (instead of BPM- T -score as above).

Disparities in digital exposomic risk across subpopulations

To examine differential exposure, we first compared digital exposomic risk scores across populations in the testing subsample, based on sex, race/ethnicity (groupings for non-Hispanic White, non-Hispanic Black, and Hispanic), and SGM identity [ 74 ]. Non-parametric tests were used with their corresponding effect sizes, specifically Kruskal–Wallis ( \({{\chi }}^{2}\) ) across three race/ethnicity groups and Dunn’s Kruskal–Wallis Multiple Comparisons test with Holm-adjusted p -values for pairwise comparisons across race/ethnicity groups, and Mann–Whitney tests for two-group comparisons (Glass rank biserial coefficient \(\hat{r}\) ). To examine the differential effects of digital exposomic risk across subpopulations, we added interaction terms to the main LME models between exposomic risk scores and sex, race/ethnicity, and SGM identity. Significant interactions suggest that the association between digital exposomic risk and mental health differs across populations. We further parsed significant interactions in stratified subgroup analyses.

Sensitivity analyses

First, we re-examined our main validation model without the removal of outlier values. Similarly, we re-ran our main ExWAS with list-wise deletion rather than multiple imputations. To address possible biases based on outcome measure selection, we re-examined the main validation analyses using self-reports of internalizing symptoms and parent-reported CBCL Total Problems T -scores as outcomes. Furthermore, given the variable nature of digital exposures in this age group, we also examined sensitivity analyses in subsamples of children excluding those (a) who do not have a personal smartphone and (b) do not use social media (see Supplement). Finally, to address unmeasured confounding, we conducted an E -value analysis [ 75 ] on our main Model-4. The E -value approach probes confounding of binary outcomes on a risk-ratio scaling; thus, we conducted a logistic regression (with all covariates in Model-4) with BPM Total Problems T -scores as a binarized outcome comparing the top 10% as high scores against the remaining 90% as the reference.

A summary of demographics, clinical scores, and general screentime is presented in Table  1 , split into training and testing samples. No significant differences were noted between subsamples (all ps > 0.05).

ExWAS (training sample)

Of the 52 digital and social media exposure variables examined, 7 were removed for low endorsement (<1%), and 4 were removed given high collinearity ( r  > |0.9 | ; Fig.  S1 ). Of the 41 remaining variables included in the ExWAS, 35 showed FDR-corrected- p  < 0.05 significant associations with overall mental health in separate models, measured using the self-reported BPM Total Problem T -scores, (Fig.  2 and Table  S1 ). Highly significant risk-related exposures included weekday videogame screentime, having social media accounts secret from one’s parents, addictive social media use, and experiencing cyberbullying (i.e., all showed associations between greater exposure and higher Total Problem scores; Fig.  2b ). Experiencing cyberbullying and having social media accounts secret from one’s parents showed the highest association with worse BPM- T -scores. Having a private (i.e., viewable by friends only) vs. public social media account exhibited a protective association with BPM- T -scores.

Results of ExWAS analysis in the testing subsample are displayed here summarizing variables that exhibited an FDR-corrected significant association with Brief Problems Monitor (BPM) total T -scores. Panel A displays the significance of these associations in a Manhattan plot with p -values from individual linear-mixed-effects models on the y -axis with a log-transformed scale. Variables are arranged into conceptual categories. Panel B shows the magnitude of these associations in a forest plot with the linear-mixed-effects model coefficient and associated 95% confidence interval. Zero indicates no association between exposure and mental health. All variables identified exhibited a positive association such that higher values (or ‘yes’ endorsement) were associated with greater mental health burden. Variables are numbered to match the listing in Table  S1 .

Digital exposomic risk score validation (testing sample)

Following the ExWAS, we calculated an aggregate digital exposomic risk score per participant. To validate the exposomic risk scores in the independent testing sample and determine their specificity, we estimated 4 LME models and examined the variance explained by mental health burden (Table  S2 ; Fig.  S2 ). Demographic variables accounted for minimal variance in BPM- T -scores (1.14%; Model-1). Non-social total screentime was significantly associated with higher BPM- T -scores ( b  = 1.38, 95%CI = [1.19-1.56], t  = 14.46, p  < 0.001, Model-2), and significantly increased variance explained in BPM- T -score to 6.18%. Non-digital childhood adversity was also significantly associated with higher BPM- T -scores ( b  = 1.31, 95%CI = [1.07–1.56], t  = 10.65, p  < 0.001, Model-3) and significantly increased the variance explained in BPM- T -score to 9.07%. Critically, digital exposomic risk scores are significantly associated with higher BPM- T -scores (estimate = 1.78, 95%CI = [1.58–1.98], t  = 17.41, p  < 0.001, Model-4; Table  2 ), over and above these other factors, and significantly increased variance explained in mental health burden to 15.61% (Fig.  S2 ).

Association of digital exposomic risk with suicide attempts

We tested the association of digital exposomic risk scores with youth-reported lifetime suicide attempts (Table  S3 ). Higher digital exposomic scores are significantly associated with higher odds of reporting a prior suicide attempt (OR = 1.76, 95%CI = [1.39–2.23], z  = 4.69, p  < 0.001), while covarying demographics and non-social screentime (OR = 1.10, CI = [0.82–1.47], z  = 0.63, p  = 0.53, Table  S3 ). This association further remained significant when covarying for non-digital childhood adversity (OR = 2.76, CI = [1.88–4.05], z  = 5.18, p  < 0.001; Table  S3 ), which did strongly relate to suicide attempt history.

Disparities in digital exposomic risk

Examination of differential exposure to social media exposomic risk in the testing sample revealed that the digital exposomic risk scores were highest among youth identifying as Non-Hispanic Black (median = 0.38), compared to Non-Hispanic White (median = −0.44) and Hispanic youth (median = 0.01), with Hispanic youth having greater scores than Non-Hispanic White youth ( \({{\chi }}_{{{{{{\rm{Kruskal}}}}}}-{{{{{\rm{Wallis}}}}}}}^{2}\) (2) = 480.90, p  < 0.001; all pairwise Holm-adjusted- p  < 0.001; Fig.  3A ). There were no sex differences in exposomic risk scores (median female = −0.27, male = −0.19, W Mann-Whitney  = 3,124,298, \(\hat{r}\)  = −0.022, p  = 0.17; Fig.  3B ). Youth identifying as SGM had significantly greater exposomic risk scores compared to their peers (median SGM = 0.42, non-SGM = −0.26, W Mann-Whitney  = 1,223,454, \(\hat{r}\)  = 0.33, p  < 0.001; Fig.  3C ).

The top row of figures displays exposomic risk scores in the testing subsample split by A race/ethnicity, B sex, and C sexual and gender minority (SGM) identity. There was significant group difference based on race/ethnicity (non-Hispanic [NH]-Black > Hispanic > NH-White; Kruskal–Wallis p  < 0.001, post hoc pairwise Dunn’s Tests for Multiple Comparisons Holm-adjusted- p  < 0.001 for all comparisons). No sex differences were observed (Mann–Whitney p  = 0.17). SGM youth had greater digital exposomic scores compared to their peers (Mann–Whitney p  < 0.001). Points represent individual youth’s scores along with box-and-whisker plots. The bottom row of figures displays the simple slope (and 95% confidence interval in the shaded region) of the association between digital exposomic risk scores and Brief Problems Monitor (BPM) total T -scores based on D race/ethnicity, E sex, and F SGM identity. Black youth showed a weaker association between digital exposomic risk and BPM scores (digital exposome by Black race interaction p  < 0.001). No significant differential associations were observed based on ethnicity, sex, or SGM identity. *** p  < 0.001.

Examination of differential effects of the association between the digital exposomic risk scores and mental health burden revealed a significant digital exposome-by-Black race interaction (estimate = −0.12, t  = −3.37, p  = 0.001; Fig.  3D ), such that Black youth exhibited weaker association between digital exposomic risk scores and BPM score, with no significant differential associations among Hispanic youth (digital exposome-by-Hispanic ethnicity interaction, p  = 0.47; Fig.  3D ). There was no sex difference in the association between the digital exposomic scores and BPM- T -scores (Fig.  3E , exposure-by-sex interaction; estimate = −0.03, t  = −1.13, p  = 0.26) and no differential associations among youth identifying as SGM (Fig.  3F , exposure-by-SGM interaction; estimate = −0.02, t  = −0.46, p  = 0.65).

Digital exposomic risk scores remained significantly associated with mental health burdens in multiple sensitivity analyses. Specifically, main analysis Model-4 remained the same when not removing potential outliers from the dataset (Table  S4 ) and when excluding children who did not report having a smartphone or those who do not use social media (Tables  S5 and S6 ). Main analyses were confirmed when using list-wise deletion rather than multiple imputations. This sensitivity analysis highlighted 31 variables passing multiple comparisons correction (compared to 35 in the main analysis) in the ExWAS in the training subsample (Table  S1 ). Exposomic risk scores were similarly related to BPM total problem T -scores when not imputing the testing subsample (estimate=1.44, 95%CI = [1.25-1.62], t  = 15.09, p  < 0.001). Furthermore, digital exposomic risk scores were significantly associated with both self-reported BPM Internalizing T -scores (Table  S7 ) and parent-reported CBCL total problem T -scores, though accounting for less variance than in BPM total scores (Table  S8 ). Finally, in E -value analyses, higher digital exposomic risk scores related to higher likelihood of exhibiting high BPM- T -scores (i.e., in the top 10% of scores; OR = 1.95; 95%CI = [1.73–2.20]), covarying for demographics, non-social screen time, and childhood adversity. An unmeasured confounder would have to be associated with 3.3-fold (lower limit of 95%CI = 2.9) increases in both exposome risk scores and likelihood of exhibiting high BPM scores to explain away the observed effect, above and beyond the measured confounders.

Current findings highlight associations between digital exposures and mental health in a large national sample of youth. Furthermore, associations remained significant beyond the effects of general screentime and non-digital childhood adversity, suggesting that the digital exposome adds a specific component to the mental health burden of American youth, consistent with concerns raised by the U.S. Surgeon General [ 4 ]. Social media and other digital exposures are often inter-related and exist within rapidly changing digital landscapes, necessitating analytic methods that do not focus on specific exposures in isolation. Thus, the ExWAS addresses this challenge with data-driven approaches to identify and weigh relative associations of various digital behaviors with mental health. The current results highlight the utility of the ExWAS to develop aggregate risk scores that explained significant variance in mental health burden in independent subsamples of youth. Notably, digital exposomic risk scores are also associated with increased odds of suicide attempts, in contrast to non-social screentime. This suggests that it is not screentime per se that contributes to risk, but rather that the type of digital behavior is critical to consider. We used digital exposomic risk scores further to illuminate disparities in exposure and associations with mental health across sex, race, and SGM identity. Our findings add key insights regarding the association between digital exposures and early adolescent mental health, which is a critical pediatric health problem [ 4 , 16 ]. Taken together, this work can help to develop richer theoretical models to guide the development of prevention and intervention programs.

The ABCD Study provides access to a large, national dataset examining a critical period of development. This allows for a powerful analysis of associations between digital exposures and mental health across the U.S. with an appropriate sample size to pursue independent model testing and validation. First, we began by screening available measures of digital exposures from child- and parent-report in relation to overall mental health severity. Data-driven ExWAS analyses identified a combination of common exposures of smaller effects and rarer exposures of larger effects. For example, 48% of youth in ABCD reported having a public (vs. private) account on their most frequently used social media platform, which related to negative mental health outcomes at a smaller effect size (estimate = 2.13 T -score points higher on the BPM on average). On the other hand, 9% of youth report being the target of cyberbullying, which is associated with larger differences in negative mental health outcomes (estimate = 3.57). This bolsters confidence in the validity of the ExWAS approach as cyberbullying is an established risk factor for depression and suicide in youth [ 76 , 77 , 78 , 79 ].

Examination of individual exposures identified in the ExWAS revealed that different facets of social media contribute to mental health. First, as expected, the subjective feeling that one’s social media use is becoming compulsive and interferes with daily activities (e.g., schoolwork) is related to worse mental health. Endorsement of these feelings and behaviors indicates a clear need for intervention to mitigate problematic use and its underlying causes. Excessive use may include nighttime use, which can impact sleep with potential consequences for mental health [ 80 ], with known implications for suicidal behaviors [ 81 , 82 , 83 ]. Second, parental monitoring of youth social media is also associated with mental health. About 7% of parents suspected that their child had social media account(s) that they were not aware of, with 15% of youth reporting having secret accounts. Both factors are associated with greater youth psychopathology. Although current data did not allow insight into motivations behind keeping secret accounts (e.g., breaking parental rules, accessing mature content), this underscores the importance of developing parent training guidelines to support healthy adolescent social media use.

Our findings begin to provide insights into the association of specific apps with youth mental health, but this must be contextualized within large inter-individual differences and trends over time. In the current sample,16% of youth-reported TikTok as their most used social media app. TikTok became available for download internationally in 2017 and rose to popularity in the U.S. after merging with musical.ly in 2018. Thus, the current data represent a snapshot of TikTok usage during a highly transitional time and its association with youth mental health will need to be monitored in later ABCD data and other future studies. Furthermore, TikTok, Instagram, and Snapchat usage have largely supplanted other platforms for youth [ 17 ]. Few youths ( < 1%) reported Facebook as their most used social media, and thus, this variable was pruned from analyses. It will be important to distinguish types of usage in future work [ 84 , 85 , 86 ], e.g., effects of TikTok and YouTube may be particularly driven by passive scrolling and watching behaviors (vs. more active use or socialization). We did observe a strong but variable association between Tumblr screentime and mental health. This, again, may reflect inter-individual differences and changing trends in usage. Beginning in 2018, Tumblr faced drops in userbase following major changes in content moderation, corporate ownership, and pushback on changes from SGM communities.

Our aggregate weighted digital exposomic risk scores facilitated comprehensive testing of sociodemographic disparities [ 5 , 6 , 7 , 8 , 51 , 52 ]. This approach can be preferable to analyzing individual, correlated exposures in isolation (due to smaller sample sizes and multiple testing). Analyses examined differences in the magnitude of exposure (differential exposure) and associations with mental health (differential effects) across race/ethnicity, sex, and SGM identity. We did not observe differences between males and females in exposure scores nor the association between social media and mental health. Given known sex differences in mental health [ 9 , 50 , 53 ], future work should continue to probe how social media could contribute, particularly across the pubertal transition [ 87 ]. Nonetheless, Black youth and youth who identify as SGM exhibited greater digital exposomic risk scores compared to their peers. Yet, interestingly, Black youth may exhibit weaker associations between social media exposure and mental health. This may be due to various factors, including access to supportive content and communities via social media, greater salience of non-digital risk factors, etc. Clinically, given the crisis around mental health and suicide among Black youth [ 88 , 89 ], our findings may nonetheless suggest that clinicians should be aware of digital risk exposure in these populations. Additionally, whereas SGM youth experienced a greater burden of digital exposomic risk, they did not display differential associations between digital exposomic scores and mental health than non-SGM youth, thus higher differential exposure may contribute to higher mental health burden among SGM youth (rather than differential mechanisms), which does align with some conceptual models of SGM mental health [ 90 ]. Our findings add to previous ABCD analyses showing that SGM youth also report more offline adverse experiences [ 74 ]. Note that the ABCD assessments do not specifically ascertain exposure to SGM minority stress [ 52 , 74 , 90 , 91 ] that may be disproportionally experienced even in digital environments. Additionally, the greater digital exposome burden of SGM youth calls for a deeper examination of the online experiences of LGBTQ+ youth and how this may affect mental health, particularly during identity formation and coming out. In terms of public health, our results call for more research on the causal role of digital exposures in youth mental health and suicide risk, as mitigating exposure to digital stressors is a potentially modifiable risk factor for minoritized groups.

There are several limitations, which may guide future studies. First, the current analyses leveraged cross-sectional ABCD data. Additional social media assessments should be examined from later waves of ABCD in future longitudinal work. Particularly, longitudinal models can help examine the directionality of associations, potential causal effects, and changes in associations over age and puberty. Second, although we removed highly colinear exposure variables, the ExWAS-derived scores do not fully account for collinearity among exposures. The current scores remain interpretable in their construction, but other approaches to modeling the exposome accounting for its correlated structure [ 42 , 92 ] can be examined in the future. Last, ABCD was not designed specifically to interrogate social media and digital exposures, so assessments were limited in scope and depth. Though a diverse range of behaviors were examined, our results highlight areas that can be probed more deeply in future work. Similarly, the available measures relied on self- and parent-report, which can be supplemented by other types of digital phenotyping in the future [ 93 , 94 , 95 ]. Nonetheless, our sensitivity analyses do highlight that exposomic risk is also related to parent-reported psychopathology mitigating potential concerns about shared method variance between adolescent self-reports of exposure and BPM. It will be important to confirm the current results in independent samples to test the robustness of the findings as well as to examine generalizability to other populations that may differ in their access to and relationships with digital exposures.

Additionally, future work should examine exposures that reflect positive or resilience-promoting activities. Critically, social media creates new avenues for youth to establish and maintain social networks [ 19 ], which often do not differ in quality from offline peer relationships [ 96 ]. This became increasingly salient during COVID-19 pandemic lockdowns as digital communication became a positive force and lifeline for many people [ 97 , 98 ]. Furthermore, social media can be highly beneficial to facilitating community building and advocacy work, allowing Black youth to connect across geographic boundaries [ 99 , 100 ]. Similarly, SGM youth can especially benefit from online platforms, including by viewing informative/educational content supporting their identity formation, finding peer support or role models, and navigating the coming out process [ 101 , 102 , 103 ]. Interestingly, prior work does suggest that increased screentime does not displace other types of recreational activities [ 104 ], yet social media remains important for building in-person relationships.

In summary, this work provides the first ExWAS approach to understanding risk factors strictly from the “digital world” in this large national dataset and offers potential inroads for developing public health strategies to support adolescent mental health. This is in line with growing recent concerns about the potential negative effects of social media and online content on mental health, as highlighted by the American Psychological Association for the U.S. Senate Judiciary Committee [ 105 ]. To address these concerns, we must pursue granular parsing of screentime and related behaviors to identify specific and modifiable mechanisms. This must be continually updated and contextualized within rapidly changing digital landscapes. Digital technology will continue to be central to social relationships, and we also cannot discount the potential benefits of digital technologies and social media. Separating nuances in use patterns may be critical, including active vs. passive usage [ 85 , 86 ], public vs. private accounts, and weekday vs. weekend patterns. Understanding the reasons for social media use can also be important, as seeking social connection may relate to problematic outcomes [ 106 ]. As not all platforms are equivalent and a given platform can facilitate a wide range of adolescent behaviors, future work should aim to take dynamic and idiographic approaches grounded in current adolescent lived experiences, and can also apply multi-modal approaches leveraging smartphone sensors or wearables [ 107 , 108 ] to gain comprehensive picture of digital exposures in youth.

Data availability

This study used publicly available data from the Adolescent Brain Cognitive Development (ABCD) Study. Information on how to access ABCD data through the NIMH Data Archive (NDA) is available at https://nda.nih.gov/abcd . Code for all analyses can be found at https://github.com/barzilab1/ABCD_digital_exposome .

McMillan JA, Land ML, Rodday AM, Wills K, Green CM, Leslie LK. Report of a joint association of pediatric program directors–American Board of Pediatrics Workshop: preparing future pediatricians for the mental health crisis. J Pediatr. 2018;201:285–91.

Article   PubMed   Google Scholar  

Hoagwood KE, Kelleher KJ. A Marshall plan for children’s mental health after COVID-19. Psychiatr Serv. 2020;71:1216–7.

Article   PubMed   PubMed Central   Google Scholar  

Bitsko RH, Claussen AH, Lichstein J, Black LI, Jones SE, Danileson ML, et al. Mental health surveillance among children—United States, 2013–2019. MMWR Suppl. 2022;71:1–42.

U.S. DHHS. Advisory on social media and youth mental health: the U.S. Surgeon General’s Advisory. 2023. https://www.hhs.gov/sites/default/files/sg-youth-mental-health-social-media-advisory.pdf . Accessed August 10. U.S. DHHS; 2023.

CDC. Youth risk behavior surveillance data summary & trends report: 2011–2021. CDC; 2023.

Lindsey MA, Sheftall AH, Xiao Y, Joe S. Trends of suicidal behaviors among high school students in the United States: 1991–2017. Pediatrics. 2019;144:e20191187.

Alegria M, Shrout PE, Canino G, Alvarez K, Wang Y, Bird H, et al. The effect of minority status and social context on the development of depression and anxiety: a longitudinal study of Puerto Rican descent youth. World Psychiatry. 2019;18:298–307.

Lee M-J, Liechty JM. Longitudinal associations between immigrant ethnic density, neighborhood processes, and Latino immigrant youth depression. J Immigr Minor Health. 2015;17:983–91.

Avenevoli S, Swendsen J, He J-P, Burstein M, Merikangas KR. Major depression in the national comorbidity survey-adolescent supplement: prevalence, correlates, and treatment. J Am Acad Child Adolesc Psychiatry. 2015;54:37–44.e2.

Merikangas KR, He J-P, Burstein M, Swanson SA, Avenevoli S, Cui L, et al. Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication-Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry. 2010;49:980–9.

McGrath JJ, Al-Hamzawi A, Alonso J, Altwaijri Y, Andrade LH, Bromet EJ, et al. Age of onset and cumulative risk of mental disorders: a cross-national analysis of population surveys from 29 countries. Lancet Psychiatry. 2023;10:668–81.

Casey BJ, Jones RM, Levita L, Libby V, Pattwell SS, Ruberry EJ, et al. The storm and stress of adolescence: insights from human imaging and mouse genetics. Dev Psychobiol. 2010;52:225–35.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Somerville LH. Special issue on the teenage brain: sensitivity to social evaluation. Curr Dir Psychol Sci. 2013;22:121–7.

Sawyer SM, Azzopardi PS, Wickremarathne D, Patton GC. The age of adolescence. Lancet Child Adolesc Health. 2018;2:223–8.

Blakemore S-J. Adolescence and mental health. Lancet. 2019;393:2030–1.

Choukas-Bradley S, Kilic Z, Stout CD, Roberts SR. Perfect storms and double-edged swords: recent advances in research on adolescent social media use and mental health. Adv Psychiatry Behav Health. 2023;3:149–57.

Article   Google Scholar  

Vogels EA, Gelles-Watnick R, Massarat N. Teens, social media and technology 2022. Pew Research Center: Internet. Science & Tech;2022. https://www.pewresearch.org/internet/2022/08/10/teens-social-media-and-technology-2022/ .

Anderson M, Jiang J. Teens, social media & technology 2018. Pew Res Center. 2018;31:1673–89.

Google Scholar  

Lenhart A. Teens, technology and friendships. Washington, DC: Pew Research Center; 2015.

Haidt J, Twenge JM. This is our chance to pull teenagers out of the smartphone trap. NY Times; 2021.

Leonhardt D. On the phone, alone. NY Times; 2022.

Bagot KS, Tomko RL, Marshall AT, Hermann J, Cummins K, Ksinan A, et al. Youth screen use in the ABCD® study. Dev Cogn Neurosci. 2022;57:101150.

van Dijck J. The culture of connectivity: a critical history of social media. New York, NY: Oxford University Press; 2013.

Rideout V. The common sense census: media use by tweens and teens. Los Angeles: Common Sense Media; 2015.

Mojtabai R, Olfson M, Han B. National trends in the prevalence and treatment of depression in adolescents and young adults. Pediatrics. 2016;138:e20161878.

Miron O, Yu K-H, Wilf-Miron R, Kohane IS. Suicide rates among adolescents and young adults in the United States, 2000-2017. JAMA. 2019;321:2362–4.

Odgers CL, Jensen MR. Annual research review: adolescent mental health in the digital age: facts, fears, and future directions. J Child Psychol Psychiatry. 2020;61:336–48.

Baker DA, Algorta GP. The relationship between online social networking and depression: a systematic review of quantitative studies. Cyberpsychol Behav Soc Netw. 2016;19:638–48.

Ivie EJ, Pettitt A, Moses LJ, Allen NB. A meta-analysis of the association between adolescent social media use and depressive symptoms. J Affect Disord. 2020;275:165–74.

Eirich R, McArthur BA, Anhorn C, McGuinness C, Christakis DA, Madigan S. Association of screen time with internalizing and externalizing behavior problems in children 12 years or younger: a systematic review and meta-analysis. JAMA Psychiatry. 2022;79:393–405.

Nagata JM, Cortez CA, Cattle CJ, Ganson KT, Iyer P, Bibbins-Domingo K, et al. Screen time use among US adolescents during the COVID-19 pandemic. JAMA Pediatr. 2022;176:94.

Paulich KN, Ross JM, Lessem JM, Hewitt JK. Screen time and early adolescent mental health, academic, and social outcomes in 9- and 10- year old children: Utilizing the Adolescent Brain Cognitive Development SM (ABCD) Study. PLoS ONE. 2021;16:e0256591.

Paulus MP, Squeglia LM, Bagot K, Jacobus J, Kuplicki R, Breslin FJ, et al. Screen media activity and brain structure in youth: evidence for diverse structural correlation networks from the ABCD study. Neuroimage. 2019;185:140–53.

Yoon S, Kleinman M, Mertz J, Brannick M. Is social network site usage related to depression? A meta-analysis of Facebook–depression relations. J Affect Disord. 2019;248:65–72.

Riehm KE, Feder KA, Tormohlen KN, Crum RM, Young AS, Green KM, et al. Associations between time spent using social media and internalizing and externalizing problems among US youth. JAMA Psychiatry. 2019;76:1266–73.

Twenge JM, Joiner TE, Rogers ML, Martin GN. Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clin Psychol Sci. 2018;6:3–17.

Steers M-LN, Wickham RE, Acitelli LK. Seeing everyone else’s highlight reels: how Facebook usage is linked to depressive symptoms. J Soc Clin Psychol. 2014;33:701–31.

Heffer T, Good M, Daly O, MacDonell E, Willoughby T. The longitudinal association between social-media use and depressive symptoms among adolescents and young adults: an empirical reply to Twenge et al. (2018). Clin Psychol Sci. 2019;7:462–70.

Orben A, Przybylski AK, Blakemore S-J, Kievit RA. Windows of developmental sensitivity to social media. Nat Commun. 2022;13:1649.

Steinsbekk S, Nesi J, Wichstrøm L. Social media behaviors and symptoms of anxiety and depression. A four-wave cohort study from age 10–16 years. Comput Human Behav. 2023;147:107859.

Coyne SM, Rogers AA, Zurcher JD, Stockdale L, Booth M. Does time spent using social media impact mental health?: An eight year longitudinal study. Comput Human Behav. 2020;104:106160.

Moore TM, Visoki E, Argabright ST, Didomenico GE, Sotelo I, Wortzel JD, et al. Modeling environment through a general exposome factor in two independent adolescent cohorts. Exposome. 2022;2:osac010.

Juarez PD, Matthews-Juarez P. Applying an Exposome-Wide (ExWAS) approach to cancer research. Front Oncol. 2018;8:313.

Hosnijeh FS, Nieters A, Vermeulen R. Association between anthropometry and lifestyle factors and risk of B‐cell lymphoma: an exposome‐wide analysis. Int J Cancer. 2021;148:2115–28

Vrijheid M, Fossati S, Maitre L, Márquez S, Roumeliotaki T, Agier L, et al. Early-life environmental exposures and childhood obesity: an exposome-wide approach. Environ Health Perspect. 2020;128:67009.

Article   CAS   PubMed   Google Scholar  

Lin BD, Pries L-K, Sarac HS, van Os J, Rutten BPF, Luykx J, et al. Nongenetic factors associated with psychotic experiences among UK Biobank Participants: exposome-wide analysis and Mendelian randomization analysis. JAMA Psychiatry. 2022;79:857–68.

Wild CP. The exposome: from concept to utility. Int J Epidemiol. 2012;41:24–32.

Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P, et al. The human early-life exposome (HELIX): project rationale and design. Environ Health Perspect. 2014;122:535–44.

Poveda A, Pomares-Millan H, Chen Y, Kurbasic A, Patel CJ, Renström F, et al. Exposome-wide ranking of modifiable risk factors for cardiometabolic disease traits. Sci Rep. 2022;12:4088.

Breslau J, Gilman SE, Stein BD, Ruder T, Gmelin T, Miller E. Sex differences in recent first-onset depression in an epidemiological sample of adolescents. Transl Psychiatry. 2017;7:e1139.

Marshal MP, Dietz LJ, Friedman MS, Stall R, Smith HA, McGinley J, et al. Suicidality and depression disparities between sexual minority and heterosexual youth: a meta-analytic review. J Adolesc Health. 2011;49:115–23.

Fox KR, Choukas-Bradley S, Salk RH, Marshal MP, Thoma BC. Mental health among sexual and gender minority adolescents: examining interactions with race and ethnicity. J Consult Clin Psychol. 2020;88:402–15.

Kessler RC, McGonagle KA, Swartz M, Blazer DG, Nelson CB. Sex and depression in the National Comorbidity Survey. I: lifetime prevalence, chronicity and recurrence. J Affect Disord. 1993;29:85–96.

Nagata JM, Singh G, Sajjad OM, Ganson KT, Testa A, Jackson DB, et al. Social epidemiology of early adolescent problematic screen use in the United States. Pediatr Res. 2022;92:1443–9.

Gonzalez R, Thompson EL, Sanchez M, Morris A, Gonzalez MR, Feldstein Ewing SW, et al. An update on the assessment of culture and environment in the ABCD Study®: Emerging literature and protocol updates over three measurement waves. Dev Cogn Neurosci. 2021;52:101021.

Zucker RA, Gonzalez R, Feldstein Ewing SW, Paulus MP, Arroyo J, Fuligni A, et al. Assessment of culture and environment in the Adolescent Brain and Cognitive Development Study: rationale, description of measures, and early data. Dev Cogn Neurosci. 2018;32:107–20.

Barch DM, Albaugh MD, Avenevoli S, Chang L, Clark DB, Glantz MD, et al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: rationale and description. Dev Cogn Neurosci. 2018;32:55–66.

Volkow ND, Koob GF, Croyle RT, Bianchi DW, Gordon JA, Koroshetz WJ, et al. The conception of the ABCD study: from substance use to a broad NIH collaboration. Dev Cogn Neurosci. 2018;32:4–7.

Garavan H, Bartsch H, Conway K, Decastro A, Goldstein RZ, Heeringa S, et al. Recruiting the ABCD sample: design considerations and procedures. Dev Cogn Neurosci. 2018;32:16–22.

Achenbach TM, McConaughy SH, Ivanova MY, Rescorla LA. Manual for the ASEBA brief problem monitor (BPM). 33. Burlington, VT:ASEBA;2011.

Achenbach TM, Edelbrock C. Child behavior checklist. Burlington (Vt). 1991;7:371–92.

Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997;36:980–8.

Townsend L, Kobak K, Kearney C, Milham M, Andreotti C, Escalera J, et al. Development of three web-based computerized versions of the kiddie schedule for affective disorders and schizophrenia child psychiatric diagnostic interview: preliminary validity data. J Am Acad Child Adolesc Psychiatry. 2020;59:309–25.

Hoffman EA, Clark DB, Orendain N, Hudziak J, Squeglia LM, Dowling GJ. Stress exposures, neurodevelopment and health measures in the ABCD study. Neurobiol Stress. 2019;10:100157.

R Core Team, R. R: A language and environment for statistical computing. R Core Team, R;2013.

Pries L-K, Moore TM, Visoki E, Sotelo I, Barzilay R, Guloksuz S. Estimating the association between exposome and psychosis as well as general psychopathology: results from the ABCD study. Biol Psychiatry Glob Open Sci. 2022;2:283–91.

Barzilay R, Moore TM, Calkins ME, Maliackel L, Jones JD, Boyd RC, et al. Deconstructing the role of the exposome in youth suicidal ideation: Trauma, neighborhood environment, developmental and gender effects. Neurobiol Stress. 2021;14:100314.

Patel CJ. Introduction to environment and exposome-wide association studies: a data-driven method to identify multiple environmental factors associated with phenotypes in human populations. n: Rider CV, Simmons JE, editors. Chemical mixtures and combined chemical and nonchemical stressors. Basel: Springer International Publishing; 2018. p. 129–49.

Feczko E, Conan G, Marek S, Tervo-Clemmens B, Cordova M, Doyle O, et al. Adolescent brain cognitive development (ABCD) community MRI collection and utilities. bioRxiv:2021.07.09.451638v1 [Preprint]. 2021 [cited 2021 Jul 11]: [33 p.]. Available from: https://www.biorxiv.org/content/10.1101/2021.07.09.451638v1 .

Stekhoven DJ, Bühlmann P. MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics. 2012;28:112–8.

Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:1–26.

Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. arXiv [StatCO]. 2014.

Nakagawa S, Johnson PCD, Schielzeth H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J R Soc Interface. 2017;14:20170213.

Gordon JH, Tran KT, Visoki E, Argabright S, DiDomenico GE, Saiegh E, et al. The role of individual discrimination and structural stigma in the mental health of sexual minority youth. J Am Acad Child Adolesc Psychiatry. 2023;5. https://doi.org/10.1016/j.jaac.2023.05.033 .

VanderWeele TJ, Ding P. Sensitivity analysis in observational research: Introducing the E-value. Ann Intern Med. 2017;167:268–74.

Brunstein Klomek A, Marrocco F, Kleinman M, Schonfeld IS, Gould MS. Bullying, depression, and suicidality in adolescents. J Am Acad Child Adolesc Psychiatry. 2007;46:40–9.

Holt MK, Vivolo-Kantor AM, Polanin JR, Holland KM, DeGue S, Matjasko JL, et al. Bullying and suicidal ideation and behaviors: a meta-analysis. Pediatrics. 2015;135:e496–509.

Klomek AB, Sourander A, Gould M. The association of suicide and bullying in childhood to young adulthood: a review of cross-sectional and longitudinal research findings. Can J Psychiatry. 2010;55:282–8.

Ruch DA, Heck KM, Sheftall AH, Fontanella CA, Stevens J, Zhu M, et al. Characteristics and precipitating circumstances of suicide among children aged 5 to 11 years in the United States, 2013-2017. JAMA Netw Open. 2021;4:e2115683.

Nagata JM, Singh G, Yang JH, Smith N, Kiss O, Ganson KT, et al. Bedtime screen use behaviors and sleep outcomes: findings from the Adolescent Brain Cognitive Development (ABCD) Study. Sleep Health; 2023. https://doi.org/10.1016/j.sleh.2023.02.005 .

Bernert RA, Kim JS, Iwata NG, Perlis ML. Sleep disturbances as an evidence-based suicide risk factor. Curr Psychiatry Rep. 2015;17:554.

Pigeon WR, Pinquart M, Conner K. Meta-analysis of sleep disturbance and suicidal thoughts and behaviors. J Clin Psychiatry. 2012;73:e1160–7.

Liu RT, Steele SJ, Hamilton JL, Do QBP, Furbish K, Burke TA, et al. Sleep and suicide: a systematic review and meta-analysis of longitudinal studies. Clin Psychol Rev. 2020;81:101895.

Verduyn P, Gugushvili N, Kross E. The impact of social network sites on mental health: distinguishing active from passive use. World Psychiatry. 2021;20:133–4.

Escobar-Viera CG, Shensa A, Bowman ND, Sidani JE, Knight J, James AE, et al. Passive and active social media use and depressive symptoms among United States adults. Cyberpsychol Behav Soc Netw. 2018;21:437–43.

Frison E, Eggermont S. Exploring the relationships between different types of Facebook use, perceived online social support, and adolescents’ depressed mood. Soc Sci Comput Rev. 2016;34:153–71.

Conley CS, Rudolph KD. The emerging sex difference in adolescent depression: interacting contributions of puberty and peer stress. Dev Psychopathol. 2009;21:593–620.

Sheftall AH, Vakil F, Ruch DA, Boyd RC, Lindsey MA, Bridge JA. Black youth suicide: investigation of current trends and precipitating circumstances. J Am Acad Child Adolesc Psychiatry. 2022;61:662–75.

Gordon J. Addressing the crisis of Black youth suicide. Health. 2020;2019:2018.

Meyer IH. Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychol Bull. 2003;129:674–97.

Frost DM, Meyer IH. Minority stress theory: application, critique, and continued relevance. Curr Opin Psychol. 2023;51:101579.

Barzilay R, Pries L-K, Moore TM, Gur RE, van Os J, Rutten BPF, et al. Exposome and trans-syndromal developmental trajectories toward psychosis. Biol Psychiatry Glob Open Sci. 2022;2:197–205.

Chancellor S, De Choudhury M. Methods in predictive techniques for mental health status on social media: a critical review. NPJ Digit Med. 2020;3:43.

Nguyen VC, Lu N, Kane JM, Birnbaum ML, De Choudhury M. Cross-platform detection of psychiatric hospitalization via social media data: comparison study. JMIR Ment Health. 2022;9:e39747.

Allen NB, Nelson BW, Brent D, Auerbach RP. Short-term prediction of suicidal thoughts and behaviors in adolescents: can recent developments in technology and computational science provide a breakthrough? J Affect Disord. 2019;250:163–9.

Yau JC, Reich SM. Are the qualities of adolescents’ offline friendships present in digital interactions? Adolesc Res Rev. 2018;3:339–55.

Ellis WE, Dumas TM, Forbes LM. Physically isolated but socially connected: psychological adjustment and stress among adolescents during the initial COVID-19 crisis. Can J Behav Sci. 2020;52:177.

Hamilton JL, Dreier MJ, Boyd SI. Social media as a bridge and a window: the changing relationship of adolescents with social media and digital platforms. Curr Opin Psychol. 2023;52:101633.

Klassen S. Black Twitter is gold: why this online community is worthy of study and how to do so respectfully. Interactions. 2022;29:96–98.

Clark MD. To tweet our own cause: a mixed-methods study of the online phenomenon “Black Twitter.” Chapel Hill, NC: The University of North Carolina at Chapel Hill Graduate School; 2014.

Fox J, Ralston R. Queer identity online: Informal learning and teaching experiences of LGBTQ individuals on social media. Comput Human Behav. 2016;65:635–42.

Craig SL, Eaton AD, McInroy LB, Leung VWY, Krishnan S. Can social media participation enhance LGBTQ+ youth well-being? Development of the Social Media Benefits Scale. Social Media + Society. 2021;7:2056305121988931.

Craig SL, McInroy L, McCready LT, Alaggia R. Media: a catalyst for resilience in Lesbian, Gay, Bisexual, Transgender, and Queer Youth. J LGBT Youth. 2015;12:254–75.

Lees B, Squeglia LM, Breslin FJ, Thompson WK, Tapert SF, Paulus MP. Screen media activity does not displace other recreational activities among 9-10 year-old youth: a cross-sectional ABCD study®. BMC Public Health. 2020;20:1783.

APA. APA chief scientist outlines potential harms, benefits of social media for kids. American Psychological Association; 2023. https://www.apa.org/news/press/releases/2023/02/harms-benefits-social-media-kids?utm_source=twitter&utm_medium=social&utm_campaign=apa-press-release-research&utm_content=feb14-prinstein-testimony .

Stockdale LA, Coyne SM. Bored and online: Reasons for using social media, problematic social networking site use, and behavioral outcomes across the transition from adolescence to emerging adulthood. J Adolesc. 2020;79:173–83.

Bagot KS, Matthews SA, Mason M, Squeglia LM, Fowler J, Gray K, et al. Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health. Dev Cogn Neurosci. 2018;32:121–9.

Wade NE, Ortigara JM, Sullivan RM, Tomko RL, Breslin FJ, Baker FC, et al. Passive sensing of preteens’ smartphone use: an Adolescent Brain Cognitive Development (ABCD) Cohort Substudy. JMIR Ment Health. 2021;8:e29426.

Download references

Acknowledgements

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study ( https://abcdstudy.org ), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children ages 9–10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html . A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/ . ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

This study was supported by the National Institute of Mental Health grants R01 MH126181 (DP), U01 MH116923 (RPA), R01 MH135488 (RPA), P50 MH115838 (RB) and R21 MH130797 (RB). The Morgan Stanley Foundation and Tommy Fuss Fund (RPA, DP) and the American Foundation for Suicide Prevention (RB) also supported this research project.

Author information

Authors and affiliations.

Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA

David Pagliaccio & Randy P. Auerbach

Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA

Lifespan Brain Institute of the Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA

Kate T. Tran, Elina Visoki, Grace E. DiDomenico & Ran Barzilay

Children’s Hospital of Philadelphia, Department of Child and Adolescent Psychiatry and Behavioral Sciences, Philadelphia, PA, USA

University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, PA, USA

Ran Barzilay

You can also search for this author in PubMed   Google Scholar

Contributions

DP and RB conceptualized the study and drafted the manuscript. KTT and EV analyzed the data. KTT and GED curated and visualized the data. RPA provided a critical contribution to the interpretation of findings and writing of the manuscript. All authors provided critical revisions that contributed to this manuscript. All authors approved the version to be published.

Corresponding author

Correspondence to Ran Barzilay .

Ethics declarations

Competing interests.

RB serves on the scientific board and reports stock ownership in “Taliaz Health”, with no conflict of interest relevant to this work. RPA is an unpaid scientific advisor for Ksana Health, and he receives equity and payment from Get Sonar, Inc. for his role as a scientific advisor; neither role is a conflict of interest for this manuscript. EV’s spouse is a shareholder and executive in ‘Kidas’, with no conflict of interest relevant to this work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplemental material, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Pagliaccio, D., Tran, K.T., Visoki, E. et al. Probing the digital exposome: associations of social media use patterns with youth mental health. NPP—Digit Psychiatry Neurosci 2 , 5 (2024). https://doi.org/10.1038/s44277-024-00006-9

Download citation

Received : 30 January 2024

Revised : 07 March 2024

Accepted : 22 March 2024

Published : 23 April 2024

DOI : https://doi.org/10.1038/s44277-024-00006-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Assessment of the Impact of Social Media on the Health and Wellbeing of Adolescents and Children

Social media is an important part of the lives of adolescents and children. Increased access to and use of social media has raised concerns among parents, physicians, public health officials, and others about the impact on the mental and physical health and wellbeing of youth. This study will examine the current research and make conclusions about the impact of social media on the mental and physical health and wellbeing of adolescents and children. The study will also explore ways in which product design of social media (e.g. consumer retention strategies, data profiling) impact the mental health and wellbeing of youth.

Publications

Social Media and Adolescent Health

Social media has been fully integrated into the lives of most adolescents in the U.S., raising concerns among parents, physicians, public health officials, and others about its effect on mental and physical health. Over the past year, an ad hoc committee of the National Academies of Sciences, Engineering, and Medicine examined the research and produced this detailed report exploring that effect and laying out recommendations for policymakers, regulators, industry, and others in an effort to maximize the good and minimize the bad. Focus areas include platform design, transparency and accountability, digital media literacy among young people and adults, online harassment, and supporting researchers.

One-Pager for Parents

• Press Release
• Infographic

Video on the Impact of Social Media

Watch this video to learn about the report's findings on the effects of social media on adolescent health and its recommendations for policymakers, regulators, industry, and others.

Infographic: Recommendation Categories at a Glance

This infographic lays out recommendations for policymakers, regulators, industry, and others in the areas of platform design, training and education, addressing online harassment, and supporting research.

This one-pager describes key takeaways from the report for parents concerned about the effects of social media on their children's health.

No projects are underway at this time.

Description

An ad hoc committee of the National Academies of Sciences, Engineering, and Medicine will examine the current research and make conclusions about the impact of social media on the mental and physical health and wellbeing of adolescents and children. The committee will consider the following questions: 1.     In what ways, if any, does social/digital media affect the mental and physical health and wellbeing of adolescents and children (age 13 -18 yrs), including anxiety, depression, addiction and self-efficacy, social isolation, relationship malformation, relationship with their parents, life satisfaction and physical activity?        a.     Do these effects differ between different social/digital media use (e.g. social media vs video streamers)?        b.     Do the effects of social/digital media on adolescents and children differ between different demographics of children (race and ethnicity, gender, socio-economic status)? 2.     In what ways, if any, does the product design of social media (e.g., consumer retention strategies, data profiling, advertising, and others) affect adolescents and children’s physical and mental health and wellbeing? 3.     What consequences, if any, do the effects of social/digital media on adolescents and children’s mental and physical health and wellbeing have for education, social development, family dynamics, and projected economic prospects? 4.     Do new forms of social media (such as 3D social networking) raise novel questions for the health and wellbeing of adolescents and children and their families? The committee should identify what is needed in a research agenda to more fully understand the impact of social media on adolescents, children and their families, as well as the data that would be required in order to comprehensively evaluate the effects of social media products on the mental and physical health and wellbeing of adolescents and children. The committee should also make recommendations for steps that parents, social media companies, and public officials can take to maximize potential benefits and minimize potential harms of social media for adolescents and children.

• Division of Behavioral and Social Sciences and Education
• Division on Engineering and Physical Sciences
• Health and Medicine Division
• Board on Children. Youth. and Families
• Board on Population Health and Public Health Practice
• Computer Science and Telecommunications Board

Consensus Study

• Behavioral and Social Sciences
• Computers and Information Technology
• Health and Medicine

Contact the Public Access Records Office to make an inquiry, request a list of the public access file materials, or obtain a copy of the materials found in the file.

Staff Officer

Past Events

12:00PM - 1:00PM (ET)

December 13, 2023

7:00PM - 7:30PM (ET)

August 30, 2023

10:30AM - 11:00AM (ET)

August 21, 2023

[Closed] Assessment of the Impact of Social Media on the Health and Wellbeing of Adolescents and Children (Pre-Review Check in)

Multiday Event | June 29-30, 2023

[Closed] Assessment of the Impact of Social Media on the Health and Wellbeing of Adolescents and Children , Meeting 5

7:00PM - 8:00PM (ET)

May 23, 2023

Grace Reading

(202) 334-2157

[email protected]

Responsible Staff Officers

• Gillian Buckley  

Additional Project Staff

• Alexis Wojtowicz  

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Teens and social media: Key findings from Pew Research Center surveys

For the latest survey data on social media and tech use among teens, see “ Teens, Social Media, and Technology 2023 .” 

Today’s teens are navigating a digital landscape unlike the one experienced by their predecessors, particularly when it comes to the pervasive presence of social media. In 2022, Pew Research Center fielded an in-depth survey asking American teens – and their parents – about their experiences with and views toward social media . Here are key findings from the survey:

Pew Research Center conducted this study to better understand American teens’ experiences with social media and their parents’ perception of these experiences. For this analysis, we surveyed 1,316 U.S. teens ages 13 to 17, along with one parent from each teen’s household. The survey was conducted online by Ipsos from April 14 to May 4, 2022.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, which is an independent committee of experts that specializes in helping to protect the rights of research participants.

Ipsos invited panelists who were a parent of at least one teen ages 13 to 17 from its KnowledgePanel , a probability-based web panel recruited primarily through national, random sampling of residential addresses, to take this survey. For some of these questions, parents were asked to think about one teen in their household. (If they had multiple teenage children ages 13 to 17 in the household, one was randomly chosen.) This teen was then asked to answer questions as well. The parent portion of the survey is weighted to be representative of U.S. parents of teens ages 13 to 17 by age, gender, race, ethnicity, household income and other categories. The teen portion of the survey is weighted to be representative of U.S. teens ages 13 to 17 who live with parents by age, gender, race, ethnicity, household income and other categories.

Here are the questions used  for this report, along with responses, and its  methodology .

Majorities of teens report ever using YouTube, TikTok, Instagram and Snapchat. YouTube is the platform most commonly used by teens, with 95% of those ages 13 to 17 saying they have ever used it, according to a Center survey conducted April 14-May 4, 2022, that asked about 10 online platforms. Two-thirds of teens report using TikTok, followed by roughly six-in-ten who say they use Instagram (62%) and Snapchat (59%). Much smaller shares of teens say they have ever used Twitter (23%), Twitch (20%), WhatsApp (17%), Reddit (14%) and Tumblr (5%).

Facebook use among teens dropped from 71% in 2014-15 to 32% in 2022. Twitter and Tumblr also experienced declines in teen users during that span, but Instagram and Snapchat saw notable increases.

TikTok use is more common among Black teens and among teen girls. For example, roughly eight-in-ten Black teens (81%) say they use TikTok, compared with 71% of Hispanic teens and 62% of White teens. And Hispanic teens (29%) are more likely than Black (19%) or White teens (10%) to report using WhatsApp. (There were not enough Asian teens in the sample to analyze separately.)

Teens’ use of certain social media platforms also varies by gender. Teen girls are more likely than teen boys to report using TikTok (73% vs. 60%), Instagram (69% vs. 55%) and Snapchat (64% vs. 54%). Boys are more likely than girls to report using YouTube (97% vs. 92%), Twitch (26% vs. 13%) and Reddit (20% vs. 8%).

Majorities of teens use YouTube and TikTok every day, and some report using these sites almost constantly. About three-quarters of teens (77%) say they use YouTube daily, while a smaller majority of teens (58%) say the same about TikTok. About half of teens use Instagram (50%) or Snapchat (51%) at least once a day, while 19% report daily use of Facebook.

Some teens report using these platforms almost constantly. For example, 19% say they use YouTube almost constantly, while 16% and 15% say the same about TikTok and Snapchat, respectively.

More than half of teens say it would be difficult for them to give up social media. About a third of teens (36%) say they spend too much time on social media, while 55% say they spend about the right amount of time there and just 8% say they spend too little time. Girls are more likely than boys to say they spend too much time on social media (41% vs. 31%).

Teens are relatively divided over whether it would be hard or easy for them to give up social media. Some 54% say it would be very or somewhat hard, while 46% say it would be very or somewhat easy.

Girls are more likely than boys to say it would be difficult for them to give up social media (58% vs. 49%). Older teens are also more likely than younger teens to say this: 58% of those ages 15 to 17 say it would be very or somewhat hard to give up social media, compared with 48% of those ages 13 to 14.

Teens are more likely to say social media has had a negative effect on others than on themselves. Some 32% say social media has had a mostly negative effect on people their age, while 9% say this about social media’s effect on themselves.

Conversely, teens are more likely to say these platforms have had a mostly positive impact on their own life than on those of their peers. About a third of teens (32%) say social media has had a mostly positive effect on them personally, while roughly a quarter (24%) say it has been positive for other people their age.

Still, the largest shares of teens say social media has had neither a positive nor negative effect on themselves (59%) or on other teens (45%). These patterns are consistent across demographic groups.

Teens are more likely to report positive than negative experiences in their social media use. Majorities of teens report experiencing each of the four positive experiences asked about: feeling more connected to what is going on in their friends’ lives (80%), like they have a place where they can show their creative side (71%), like they have people who can support them through tough times (67%), and that they are more accepted (58%).

When it comes to negative experiences, 38% of teens say that what they see on social media makes them feel overwhelmed because of all the drama. Roughly three-in-ten say it makes them feel like their friends are leaving them out of things (31%) or feel pressure to post content that will get lots of comments or likes (29%). And 23% say that what they see on social media makes them feel worse about their own life.

There are several gender differences in the experiences teens report having while on social media. Teen girls are more likely than teen boys to say that what they see on social media makes them feel a lot like they have a place to express their creativity or like they have people who can support them. However, girls also report encountering some of the pressures at higher rates than boys. Some 45% of girls say they feel overwhelmed because of all the drama on social media, compared with 32% of boys. Girls are also more likely than boys to say social media has made them feel like their friends are leaving them out of things (37% vs. 24%) or feel worse about their own life (28% vs. 18%).

When it comes to abuse on social media platforms, many teens think criminal charges or permanent bans would help a lot. Half of teens think criminal charges or permanent bans for users who bully or harass others on social media would help a lot to reduce harassment and bullying on these platforms. 

About four-in-ten teens say it would help a lot if social media companies proactively deleted abusive posts or required social media users to use their real names and pictures. Three-in-ten teens say it would help a lot if school districts monitored students’ social media activity for bullying or harassment.

Some teens – especially older girls – avoid posting certain things on social media because of fear of embarrassment or other reasons. Roughly four-in-ten teens say they often or sometimes decide not to post something on social media because they worry people might use it to embarrass them (40%) or because it does not align with how they like to represent themselves on these platforms (38%). A third of teens say they avoid posting certain things out of concern for offending others by what they say, while 27% say they avoid posting things because it could hurt their chances when applying for schools or jobs.

These concerns are more prevalent among older teen girls. For example, roughly half of girls ages 15 to 17 say they often or sometimes decide not to post something on social media because they worry people might use it to embarrass them (50%) or because it doesn’t fit with how they’d like to represent themselves on these sites (51%), compared with smaller shares among younger girls and among boys overall.

Many teens do not feel like they are in the driver’s seat when it comes to controlling what information social media companies collect about them. Six-in-ten teens say they think they have little (40%) or no control (20%) over the personal information that social media companies collect about them. Another 26% aren’t sure how much control they have. Just 14% of teens think they have a lot of control.

Despite many feeling a lack of control, teens are largely unconcerned about companies collecting their information. Only 8% are extremely concerned about the amount of personal information that social media companies might have and 13% are very concerned. Still, 44% of teens say they have little or no concern about how much these companies might know about them.

Only around one-in-five teens think their parents are highly worried about their use of social media. Some 22% of teens think their parents are extremely or very worried about them using social media. But a larger share of teens (41%) think their parents are either not at all (16%) or a little worried (25%) about them using social media. About a quarter of teens (27%) fall more in the middle, saying they think their parents are somewhat worried.

Many teens also believe there is a disconnect between parental perceptions of social media and teens’ lived realities. Some 39% of teens say their experiences on social media are better than parents think, and 27% say their experiences are worse. A third of teens say parents’ views are about right.

Nearly half of parents with teens (46%) are highly worried that their child could be exposed to explicit content on social media. Parents of teens are more likely to be extremely or very concerned about this than about social media causing mental health issues like anxiety, depression or lower self-esteem. Some parents also fret about time management problems for their teen stemming from social media use, such as wasting time on these sites (42%) and being distracted from completing homework (38%).

Note: Here are the questions used  for this report, along with responses, and its  methodology .

CORRECTION (May 17, 2023): In a previous version of this post, the percentages of teens using Instagram and Snapchat daily were transposed in the text. The original chart was correct. This change does not substantively affect the analysis.

• Age & Generations
• Age, Generations & Tech
• Internet & Technology
• Platforms & Services
• Social Media
• Teens & Tech
• Teens & Youth

Emily A. Vogels is a former research associate focusing on internet and technology at Pew Research Center

Risa Gelles-Watnick is a research analyst focusing on internet and technology research at Pew Research Center

How Teens and Parents Approach Screen Time

Who are you the art and science of measuring identity, u.s. centenarian population is projected to quadruple over the next 30 years, older workers are growing in number and earning higher wages, teens, social media and technology 2023, most popular.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Terms & Conditions

Privacy Policy

Cookie Settings

Reprints, Permissions & Use Policy

UNESCO report spotlights harmful effects of social media on young girls

Facebook Twitter Print Email

Digital technologies and algorithm-driven software - especially social media - present high risks of privacy invasion, cyberbullying and distraction from learning to young girls, according to the UN Educational, Scientific and Cultural Organization’s (UNESCO) latest Global Education Monitor (GEM) report released on Thursday .

In an interview with UN News , senior policy analyst from the GEM report team Anna D’Addio said the issue of technology in education was examined through a gender lens.

She said the report highlights progress in the reversal of discrimination against girls over the past two decades, but also exposes the negative impact of technology on girls' education opportunities and outcomes.

Harassment online

“ Girls on social media are much more exposed to different forms of harassment. Cyberbullying is much more frequent among girls than among boys,” Ms. D’Addio said.

“It's something that affects their wellbeing, and their wellbeing is important for learning,” she added.

Guterres stresses internet access

antonioguterres

The report coincides with the UN telecoms agency ( ITU ) led International Girls in ICT Day .  

In a post on his Twitter account, the Secretary-General called for more equipment and support for girls in the information and communications technology (ICT) field, pointing out that fewer women than men have access to the internet and that stands in their way of getting an equal opportunity for work. 

Mental health, body disorders

Based on the GEM report’s findings, social media exposes young girls to a range of unsuitable video material, including sexual content, and the promotion of unhealthy and unrealistic body standards that negatively affect mental health and wellbeing.

It was reported that adolescent girls are twice as likely to feel lonely than boys and suffer from an eating disorder.

“ There is increasing evidence that shows that increased exposure to social media is related to mental health problems, eating disorders and many other issues that condition and distract social media users, and particularly girls, from education which affects their academic achievement,” Ms. D’Addio said.

Instagram has reportedly accounted for 32 per cent of teenage girls' feeling worse about their bodies after consuming the platform’s content, according to a Facebook statistic cited in the report.

The senior policy analyst said social media usage can have positive effects on young girls, especially when used to increase knowledge and raise awareness on social issues.

“I think what is important is…to teach how to use social media and technology,” Ms. D’Addio said.

Girls in STEM

She said the report calls attention to the fact that girls are at a disadvantage in accessing science, technology, engineering and mathematical (STEM) careers, which shows a lack of diversity in the production and development of cutting-edge tech.

Data from the UNESCO Institute for Statistics (IUS) showed that women only make up 35 per cent of tertiary education STEM graduates globally and only hold 25 per cent of science, engineering and ICT jobs.

“There are still too few girls and women that choose…the STEM subjects and work there,” the senior policy analyst said.

She said having more diversity will allow stronger contributions to science and developments without bias.

How does it get better?

The report’s results reveal the need for a greater investment in education and smarter regulation of digital platforms.

Ms. D’Addio said UNESCO is constantly working on remedying the exclusion of girls' access and attainment to education that remains by advocating for policies that make the education system more inclusive and “ promoting laws and regulations that guarantee equal access to education for girls and protect them from discrimination ”.

• information and communication technologies
• Girls Education

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Health Expect
• v.26(1); 2023 Feb

Youth engagement in mental health research: A systematic review

Erin mccabe.

1 Department of Pediatrics, School of Public Policy, University of Calgary, Calgary Alberta, Canada

Mungunzul (Megan) Amarbayan

2 Department of Medicine, School of Public Policy, University of Calgary, Calgary Alberta, Canada

3 Department of Community Health Sciences, University of Calgary, Calgary Alberta, Canada

Justino Mendoza

4 Department of Biology, Faculty of Science and Technology, Mount Royal University, Calgary Alberta, Canada

Syeda Farwa Naqvi

Kalpana thapa bajgain, jennifer d. zwicker.

5 Department of Social Policy and Health, School of Public Policy, University of Calgary, Calgary Alberta, Canada

Maria Santana

6 Department of Pediatrics, Community Health Sciences, University of Calgary, Calgary Alberta, Canada

Associated Data

Data are available on request from the corresponding author.

Introduction

Patient engagement in youth mental health research has the potential to inform research on the interventions, services and policies that will benefit youth. At present, there is little evidence to guide mental health researchers on youth engagement. This systematic review aims to describe the impacts of youth engagement on mental health research and to summarize youth engagement in mental health research.

We searched the following databases: MEDLINE, EMBASE and PsycINFO, using a combination of subject headings, keywords and synonyms for the concepts ‘patient engagement’, ‘youth’ and ‘mental health’. Articles that described engaging youth in mental health research were included. Two reviewers performed the study selection. Study characteristics, research activities performed by youth, impacts of youth engagement, challenges, and facilitators to engagement and recommendations for youth engagement described by authors were extracted. Quality appraisal involved determining the level of engagement of youth and the stage(s) of research where youth were involved.

The database search returned 2836 citations, 151 full‐text articles were screened and 16 articles, representing 14 studies, were selected for inclusion. Youth were involved at nearly all stages of the research cycle, in either advisory or co‐production roles. Youth engagement impacts included enhancing relevant research findings, data collection and analysis and dissemination to academic and stakeholder audiences. Both youth and academic researchers reported personal development across many domains. One negative impact reported was the increase in funding and resources needed for engagement. We produced a list of 35 recommendations under the headings of training, youth researcher composition, strategy, expectations, relationships, meeting approaches and engagement conditions.

Conclusions

This study provides an understanding of the impacts and recommendations of youth engagement in mental health research. The findings from this study may encourage researchers to engage youth in their mental health research and support youth engagement in funding applications.

Patient and Public Contribution

We consulted three youths with experience being engaged in mental health research about the review findings and the discussion. One youth designed a visual representation of the results and provided feedback on the manuscript. All youth's input informed the way the findings were presented and the focus of the discussion.

1. INTRODUCTION

Mental health conditions affect 1.2 million children and youth in Canada and this number is increasing. 1 Five percent of Canadian children aged 5–17 years old report anxiety disorders and 2.1% reported a mood disorder in 2019. 2 This aligns with the findings of a systematic review reporting on the prevalence of these disorders in high‐income countries (5.2% anxiety, 1.8% depressive disorder, 12.7% any mental health disorder). 3 Of the 12.7% of children experiencing a mental health condition, only 44.2% received any services, revealing a large gap in services for children and youth mental health. 3 Emergency department visits for paediatric mental health concerns have increased 61% from 2009 to 2019, 4 which are often the result of a lack of availability of timely appointments in the community. 5 It seems that current mental health services are not meeting the needs of children and youth, suggesting an urgent need to transform mental health services so that effective, accessible services are being provided. 3 , 6 As mental health services undergo a redesign, new innovative ways of implementing and delivering mental health care are being studied. It is important to involve youth in that research to ensure that practices, services, programmes and policies are appropriate, accessible and meet their needs. 7 Using patient engagement in research is one approach to ensuring the youth perspective is integrated into mental health research and innovation.

The Canadian Institute for Health Research (CIHR) defines ‘patient engagement’ as the meaningful and active collaboration of individuals with personal experience of a health issue and their informal caregivers (including family and friends) in governance, priority setting, conducting research and knowledge translation activities. 8 Patient engagement is a close equivalent of the United Kingdom's concept of Patient and Public Involvement. 9 There is a growing acceptance of patient engagement as being essential in health research on the part of researchers, funders and research institutions. The arguments for patient engagement are philosophical (i.e., patients have a right to shape research about their condition), pragmatic (patient input improves the research process and relevancy of outputs) and practical (i.e., increased transparency and accountability for research that is produced by public funds). 10

While patient engagement in adult health research is becoming well‐established, the momentum for youth patient engagement (herein, youth engagement) appears to be lagging (Mawn, 2015). 11 This may be due to system‐level considerations for youth engagement, such as institutional research ethic board approval, issues of consent in youth and a lack of institutional support. 12 , 13 , 14 It may also be due to practical issues such as researchers not feeling competent with youth‐friendly engagement methods, difficulties reaching youth for recruitment and funding issues. 12 Also, the changing interests and developmental needs of youth may make it difficult to sustain engagement partnerships over the entire duration of a research project. 15 Recruiting youth for mental health research may have additional challenges, as youth may have experienced stigma related to mental health in their community or within healthcare settings which may create issues of trust between youth and health researchers, leading to youth being reluctant to engage (Knaak, 2017). 16 Youth may also be hesitant to disclose their mental health condition or may be concerned that their condition may become known to their peers as a consequence of their involvement in research. Furthermore, researchers may perceive youth with mental health conditions as vulnerable, and that research engagement activity may affect their well‐being. 14

Despite these potential barriers, youth engagement is considered a guiding principle in recent efforts to redesign youth mental health services. 17 Youth engagement allows researchers to gain important insights into why youth may not be accessing mental health services, create relevant and responsive interventions and create the conditions that make services accessible to young people. 18 Youth engagement is also a way of recognizing youths' rights for agency and power in shaping mental health services that are for them. 19 Learning about the benefits, successes, challenges and recommendations of researchers with experience with youth engagement in mental health research could help inspire researchers to engage youth in their own mental health research. Furthermore, an understanding of the impacts of youth engagement could support mental health funding applications where youth are engaged as research partners.

To date, the impacts of youth engagement on mental health research and the researchers have not been described. As well, while some recommendations exist about engaging youth in health research, there is little guidance for researchers about youth engagement specific to mental health research. Therefore, the primary purpose of this systematic review was to synthesize the impacts of youth engagement in mental health research. A secondary aim was to describe the challenges and facilitators encountered in mental health studies with youth engagement and to summarize the recommendations for youth engagement in mental health research made by authors.

2.1. Study design

This systematic review follows the meta‐aggregative approach to qualitative synthesis outlined in the JBI Manual for Evidence Synthesis. 20 JBI meta‐aggregative approach seeks to enable generalizable statements to guide practitioners and policymakers. It focuses on producing a synthesis of findings that authentically represent the aggregation of data from primary studies, rather than a more interpretive approach where authors re‐interpret findings from qualitative studies. The protocol for this review was registered with PROSPERO (CRD42022319240). We used the preferred reporting items for systematic review and meta‐analysis (PRISMA) guidelines to report this review. 21 In this review, we distinguish youth co‐researchers from academic researchers by using the terms ‘youth researcher’ and ‘adult researcher’, respectively. We use the term ‘co‐production’ when referring to activities where youth are collaborating with adults or leading the activity, for example, developing recruitment materials. We use the term ‘advise’ to mean that youth researchers provided ideas and feedback on aspects of the project but were not directly involved in those activities. Three youths with experience engaging in youth mental health research were involved in this project.

2.2. Search

We searched MEDLINE, EMBASE and PsycINFO, using a combination of subject headings, keywords and synonyms for the concepts ‘patient engagement’, ‘youth’ and ‘mental health research’. The ‘patient engagement’ concept included participatory action research approaches, which are not always included in definitions of ‘patient engagement’, but were included here because they engage people who bring the collective voice of specific, affected communities to health research. 8 We limited the search to 2000 to the present since patient engagement is a relatively new phenomenon in health research. The ‘mental health research’ concept included mental health, mental health services, as well as clinical diagnostic terms adapted from the Cochrane Common Mental Disorders Group with input from a pediatric psychiatrist. Duplicate citations were removed using automated software and manually by reviewers. Our search strategy is available online as Supporting Information: File  1 .

2.3. Selection

2.3.1. inclusion and exclusion criteria.

We included original research studies where youth were engaged as partners in the research process. We wanted to capture the variations in the approaches to including youth in mental health research, therefore we included a broad age range of youth researchers (8–25 years). To acknowledge that youth may be part of a research team over several years, we included articles where the majority of youth researchers were 25 years or younger. The age of the youth was assessed using the age at which the youth joined the team (where this information was available). Youth researchers could have lived experience with a mental health condition or not. All study contexts were included (i.e., mental health clinical research, mental health services research, community‐based participatory research or health promotion/public health research) and any setting (i.e., inpatient, outpatient, community, schools, residential treatment). We included studies conducted in countries with publicly funded health systems. The study must have described at minimum, one youth research activity and one impact of youth engagement.

We excluded articles that were not peer‐reviewed (e.g., commentaries, theses), those studying youth engagement in a programme of research (rather than a specific research project) and those where youth were engaged only in the stage of developing an intervention (e.g., mental health technology or clinical pathway) but not in research or evaluation of that intervention.

Two reviewers (E. M. and M. A.) screened citations on the title and abstract. The same reviewers reviewed the full text of the articles, comparing them against the inclusion criteria. At both stages, discrepancies between reviewers were resolved through discussion. Inter‐rater reliability was calculated using percent agreement and Cohen's κ . Covidence was used to manage the study selection process.

2.4. Quality appraisal

The focus of this review is on youth engagement within the research studies, and not the specific findings of each study. We felt that assessing the methodological quality of the studies themselves would be less meaningful than assessing the quality of engagement. However, to our knowledge, there are no quality assessment tools available to assess youth engagement as reported in a research article. Therefore, rather than an assessment of quality, we described youth engagement on two dimensions: level of youth engagement, and stages of the research cycle where youth were involved. The description of the level of youth engagement is based on the ‘Types of youth participation’ in INNOVATE Research: Youth Engagement Guidebook for Researchers (2019). These are Participation (i.e., youth are the subject of study), Consultation (i.e., youth provide feedback on research), Partnership (i.e., youth work collaboratively with researchers as equals) and Youth‐led (where every stage of research is driven by youth). Key stages in the research lifecycle are (1) Priority setting and planning; (2) Development of the research proposal; (3) Scientific review; (4) Ethics review; (5) Oversight of a research project; (5) Recruitment of research participants (for some types of research); (6) Data collection; (7) Data analysis and interpretation; (8) Knowledge exchange; (9) Evaluation and quality assurance. 22 One reviewer (E. M.) categorized each study on these two dimensions, with a second reviewer verifying the descriptions (K. T. B.).

2.5. Data extraction and synthesis

Data extracted included study characteristics, characteristics of youth researchers, research activities of youth, as well as the findings of the study that related to youth engagement. We extracted findings about youth engagement for each of the following features: impacts of youth engagement on the research process and researchers, the facilitators and challenges to youth engagement and author recommendations for youth engagement. We used line‐by‐line extraction, from any location in the article, including methods, results, discussion and conclusions. Data extraction was performed by a single researcher (E. M.), with a second researcher cross‐checking the extracted data (K. T. B.). Discrepancies were resolved through discussion.

The findings for each feature were reviewed and descriptively coded. Codes were grouped by similarity in concept by a single reviewer and then combined into categories. One researcher (E. M.) created category descriptions, which were reviewed by one member of the research team (S. R.) and three youth researchers who were consulted.

2.6. Youth engagement in this review

We held a consulting meeting with three youths (ages 19–24, all identify as cis men, all Canadian citizens, one with Chinese and one with Southeast Asian heritage), all with previous experience engaging in mental health research. The aims of the consultation were threefold: to understand whether the way we presented the findings aligned with their experiences as youth engaged in research if they had additional recommendations for youth engagement and which of the findings were most salient to youth engaged in research. The feedback from the consultation informed how we presented the study's results and structured the discussion.

3.1. Search and selection

Figure  1 summarizes the search and selection process. The search retrieved 2838 citations. We removed 672 duplicates and 2166 citations were screened on the title and abstract. The percent agreement between authors was 88.4% (Cohen's κ  = 0.52). The full‐text articles for 148 citations were reviewed, and 132 were excluded, primarily because they were describing co‐design of an intervention or clinical service (43 articles), or youth were participants in the study rather than involved as researchers (34 articles). Sixteen articles were included. The percent agreement between authors was 93.6% (Cohen's κ  = 0.45). Two pairs of articles described the same study, therefore, a total of 14 studies were analysed.

PRISMA diagram for article search and selection process

3.2. Description of studies

Table  1 contains the key characteristics of the articles. The articles were published in four countries: Canada ( n  = 6), the United Kingdom ( n  = 8), Australia ( n  = 1) and Norway ( n  = 1). None of the articles were published before 2014 and most were published between 2020 and 2022 ( n  = 11). In nine articles, a description of youth engagement was embedded within the report on the research project, while seven articles reported directly on the youth engagement aspects of a research project.

Characteristics of articles included in the analysis

The majority of studies engaged youth 16+ years old, with only one study engaging children 9–10 years old. Studies were on mental health services ( n  = 7), clinical research ( n  = 4) and public health ( n  = 3). Studies engaged between 2 and 115 youth. The studies with higher numbers of youth ( n  > 30) were priority‐setting and brainstorming‐type engagement activities. Five studies reported on the racial/ethnic diversity of the youth researchers, while seven reported on the sex or gender of engaged youth. A focus on diversity and inclusion within the research team was present in five studies. Most studies engaged youth with lived experience of mental health conditions (12/14). Five studies used advisory meetings as their only approach to engagement, while two studies engaged youth in specific research activities without conducting formal advisory meetings. Six studies used a combination of both advisory meetings and youth researchers engaging in specific research activities. A variety of models of youth engagement were used (see Table  1 ). Structured research training was provided to youth in five studies.

3.3. Youth engagement

The activities of youth researchers are described in Table  1 . Youth were engaged as advisors and/or actively carried out specific research activities, in some cases leading the activities. Table  2 contains a summary of youth researcher activities, divided by whether the activity was done in a co‐production or advisory role. In four studies, the youth performed an advisory role only. The most common research activities were focusing on the research topic ( n  = 7), co‐analysis of qualitative data ( n  = 7) and dissemination of findings ( n  = 10).

Research activities performed by youth researchers

3.4. Quality appraisal

Youth were engaged at a ‘consultation’ level in five studies, a ‘partnership’ level in eight studies and one study was ‘youth‐led’. In three studies at the partnership level, a hybrid model was used where they had a small number of youth researchers were involved in research activities and a larger advisory committee of youth was consulted at key stages in the research process. This model was used to increase the diversity of the youth perspectives that influenced the research project. Table  3 contains the results of the quality appraisal, that is, the level of engagement of each study, and the stages of research where youth were involved. Seven studies involved youth in almost all stages of research. 23 , 27 , 28 , 29 , 32 , 33 , 34 , 36 , 37 All studies involved youth in some form of quality assurance or evaluation of the research project, with five studies specifically involving youth in evaluating the engagement aspect of the project.

A description of youth engagement by level of engagement and stage of research involvement

a Hybrid model of primary partnership with a small number of co‐researchers, with a larger advisory committee that was consulted for key stages in the research study.

3.5. Impacts of youth engagement

No studies reported a formal impact assessment of youth engagement, although four studies explored the impacts and experiences of youth engagement in research. 15 , 28 , 36 , 37 Table  4 contains a list of the impacts of youth engagement.

Impacts of youth engagement on the research process and researchers

The most common research process impacts of youth engagement reported by authors were (1) the data ( n  = 9), either by shaping the data collection instrument or being actively involved in data collection; (2) the findings from the study ( n  = 9), by youth involvement in the analysis; (3) enhanced knowledge dissemination ( n  = 9), by co‐presenting and advising on knowledge translation strategies. Enhancing the relevancy of research topics was another common impact reported in six studies, and four studies reported that having youth on the research team enhanced the safety and comfort of their research participants. 24 , 27 , 28 , 32 , 36 , 37 One study reported that youth engagement made decision‐making more efficient because youth provided perspectives that made the decision clearer. 32 , 36 Another study reported the opposite, that decision‐making was less efficient, but this was attributed to the adult research team members' intention to create an inclusive environment. 37 Besides the efficiency of decision‐making, other negative impacts included the increased resources required for youth engagement ( n  = 6), and that youth may have unintentionally influenced data collection by asking leading questions or reassuring participants and sharing their own experiences. 27 , 28

Adult researchers reported increasing their knowledge of youth engagement strategies, 15 , 27 , 28 , 32 , 36 , 37 stating that youth engagement broadened their networks and enhanced their understanding of the research findings. 27 , 28 A sense of pride in the youth researchers' development over the course of the project was mentioned in two studies. 15 , 37 In one study, authors reported a greater sense of accountability for their research and thus more motivation to perform high‐quality research, which was described as positive. 15 Related to this, in two studies, a greater sense of responsibility for youth researchers was reported as having a negative impact on adult researchers. 27 , 28 , 31

Youth researchers reported positive findings, feeling empowered and respected, particularly when witnessing their input being acted upon 15 , 23 , 29 and increased confidence in their abilities. 27 , 28 They reported that they gained knowledge about research and mental health, and developed research, project management and communication skills. 15 , 23 , 27 , 28 , 37 A sense of social connectedness and expanded networks were mentioned 15 , 27 , 28 , 37 as well as the research experience being a benefit for their job resumes and applications for postsecondary education and generating income. 37 Figure  2 illustrates the impacts of youth engagement in research.

The impacts of youth engagement in mental health research. The two‐way arrows represent the effects that youth researchers have on the adult researchers and the research process and that also the adult researchers and being engaged in research has an impact on youth.

3.6. Facilitators and challenges to youth engagement

Table  5 describes the challenges and facilitators to meaningful youth engagement reported by the authors. One challenge reported in three studies was the time and effort for relationship‐building within the research team, and this was considered especially important in a mental health context. 15 , 27 , 28 , 37 There were challenges related to the recruitment and retention of youth researchers, and one study mentioned that as youth researchers become more skilled and acculturated to academic research environments, there was a need to monitor whether they were still representing the youth voice. 32 , 36 A final area of challenge related to navigating diverse perspectives and priorities of the research team. For example, adult researchers prioritize rigour versus youth wanting to reassure participants, 27 , 28 managing divergent youth and caregiver perspectives, 32 , 36 and perspectives of youth from different cultural backgrounds. 24 , 32 , 36 , 37

A description of the facilitators and challenges to youth engagement

Relational facilitators of engagement included creating a safe, inclusive space for youth to share perspectives, adult researchers having an awareness of power dynamics and how they are relating with youth, and efforts to build genuine and trusting relationships. Process facilitators included having a dedicated youth engagement coordinator and providing refreshments and compensation for youth researchers.

3.7. Recommendations for youth engagement

Four articles contained recommendations for youth engagement in mental health research, 15 , 27 , 28 , 36 , 37 while other articles contained recommendations embedded within Section  4 . Table  6 contains a summary of recommendations for youth engagement. Recommendations were around training for both youth and adult researchers, the composition of the youth on the research team, processes for engagement, approaches to consultation meetings, agreement between youth and adult researchers about expectations, roles and responsibilities, elements of the relationship between youth and adult researchers and the conditions in which engagement occurs.

Summary of recommendations for youth engagement in mental health research

3.8. Youth engagement in this review

Overall, the youth agreed with the findings of this review. They emphasized that overcoming the power differential between youth and adult researchers, as well as the representation of diverse youth voices was important. Their input resulted in the addition of one new impact, two new challenges, the reorganization of the recommendations section and the addition of concrete examples to some of the recommendations. We also revised the wording of some of the recommendations based on their feedback. One youth (J. M.) produced the visual of the impacts and also contributed to the writing of the manuscript, he is included as a co‐author on this paper.

4. DISCUSSION

Patient engagement research impacts have been conceptualized as both positive or negative, short or long‐term, and are either related to the research process (e.g., research instruments, outcomes measure choice, data collection design, delivery, time, dissemination) or impacts to the people involved (e.g., youth and adult researchers' experiences). 38 Documented impacts of youth engagement on the research process include a positive influence on research design, recruitment, data collection and analysis and dissemination. 39 It has also been reported to increase the youth friendliness and validity of research, the usability of practical tools, accessibility of consent forms and questionnaires and increase media attention. 7 , 39 There were few negative impacts reported, but inexperienced youth facilitators can negatively impact the quality of focus group data, and youth may interpret findings in relation to their own experiences impacting generalizability. 39 Skill development, feeling empowered, confident and valued, as well as enhanced social connectedness, are positive impacts reported by youth engaged in research. 7 , 39 Academic researchers report an increased feeling of commitment to their project, inspiration and pride in their work. 39 In this review, the impacts of youth engagement ranged from enhancing the relevancy of research topics to enhancing dissemination and impact on the health system. This aligns with what has been found in other reviews of youth engagement. 39 , 40 An impact unique to mental health research engagement was the enhanced comfort and emotional safety of research participants resulting from the involvement of youth. In one study, researchers used a pre‐engagement consultation with youth and caregivers to design a distress‐sensitive approach to their recruitment and data collection process, which included holding data collection sessions at community agencies with peer and professional support, providing written materials, giving participants the option of providing written feedback and to separate youth and caregivers. 24 Another study reported that youth completing interviews were able to quickly develop rapport with participants and humanize the interview process for them. This was felt to enhance the emotional safety of participants, for whom talking about mental health may be uncomfortable or stressful. 28

We found that youth researchers reported many personal benefits to being engaged in mental health research, including feeling empowered, a sense of social connectedness, gaining knowledge and skills and enhancing career and education opportunities. 15 , 23 , 28 , 30 , 37 Youth researchers felt that research engagement expanded their professional networks, which was also reported by adult researchers. 28 , 37 The impact on adult researchers of engaging with youth was less often the focus of the studies, however, some impacts were reported such as gaining an appreciation for engagement, increased accountability for their research products and a sense of pride in youth researchers' development. 15 , 28 , 36 , 37 Adult researchers report that youth engagement added more to their responsibilities during research, because of their desire to foster positive engagement experiences for youth, which was viewed as both a positive and a negative impact. 15 , 28 , 31

The negative impacts of youth engagement include the increased time and resources needed for engagement, which is commonly reported across all types of patient engagement studies. 39 , 40 , 41 , 42 , 43 Researchers have reported concerns that youth with some mental health conditions could be vulnerable and engagement could potentially negatively impact their well‐being, whether from experiencing the power imbalance between adults and youths, or perhaps embedding the mental health condition as a part of a youth's identity. 14 , 43 We did not find evidence of these potentially negative impacts in our review, which may be reassuring for mental health researchers. Another potentially negative impact on the research relates to the methodological rigour of the research. Through their involvement in data collection and analysis, youth very commonly impacted data collection and analysis. This was viewed as positive in most cases, though there was some concern expressed about youth introducing bias into data collection and analysis through, for example, asking leading questions or incorporating their own experiences into data analysis. 28 This was viewed by some as a negative impact, but one that could be overcome through training and close supervision. 28 We also found that only one of the studies in this review used quantitative methods, 32 , 36 which could suggest that researchers believe quantitative studies are not suited to engagement or that youth engagement could limit the researchers' choice of methods to answer a particular research question. This was an issue that was also brought up by our youth researchers during the consultation meeting. However, outside of mental health research, youth have been engaged in quantitative research, for example, randomized controlled trials, comparative effectiveness research and measurement instrument development studies, which suggests that youth can be engaged in quantitative mental health research. 40

There were practical challenges encountered by researchers engaging youth in mental health research. The increased resources that are needed for setting up and supporting engagement, recruiting and sustaining youth researchers throughout a project were mentioned across almost all studies Adult researchers also grappled with ethical considerations as well as navigating conflicting priorities of different groups, such as the youth and adult researchers, within youth researchers with different backgrounds and experiences, or between youth and caregivers. 24 , 27 , 28 , 32 , 36 There were also challenges related to the relationship between the adult and youth researchers that needed to be overcome for productive working relationships to develop between youth and adult researchers. These included the inherent power imbalance between youth (as younger, novice researchers) and adults (as older, established researchers) and communication barriers between youth and adults. While these challenges are not unique to youth engagement in mental health research, authors felt that their importance was heightened in a mental health research context, which is a potentially sensitive subject. 15 , 28 , 36 , 37 Authors reported that putting in the time and effort to build trusting and genuine relationships was a successful way to overcome this challenge, as well as the adult researchers practising reflexivity (i.e., being self‐aware, reflecting on the way they relate to youth researchers). This finding aligns with the recent interest in the importance of relationships in patient engagement work. 44 , 45

The findings of this review support the idea that youth are willing and capable of being involved in research activities across the research cycle. Youth were involved, either in an advisory role or performing research activities, at all stages of CIHR's research cycle (i.e., from developing topics to disseminating findings). Studies reported successful youth engagement across all levels of engagement (Collaboration, Partnership, Youth‐led), which differs from some visions of patient engagement, where a partnership or complete control over research is considered the gold standard. This supports the idea put forth by Greenhalgh et al. 10 that a more flexible approach to youth engagement, where the desired outcomes of engagement for the project and the motivations and capabilities of the individuals involved drive the engagement approach, rather than a single framework informing all patient engagement activities.

The recommendations contained in this article will be useful to researchers planning youth engagement in mental health research. They align well with the practical recommendations for youth engagement in health research put forth by Hawke et al. 7 The recommendations from our review that might be considered unique to a mental health research context, such as creating a safe space for open discussion, accommodating emotional and mental needs, are incorporated in Hawke and colleagues' recommendations. The youth researchers we consulted in this review agreed with all the recommendations in the review. They emphasized the importance of overcoming power imbalances, which was a common theme among the articles in our review. They also felt that representation of diverse youth voices, in terms of ethnicity, race, gender and sexual identity and degree of experience in research was important. Related to this, they felt that adult researchers engaging with youth in a mental health context should have training in trauma‐informed approaches, as well as cultural competence. Although this was not a recommendation in any of the articles in this review study, it is supported by Shimmin and colleagues' argument that patient engagement should be underpinned by trauma‐informed approaches, as well as a recommendation in INNOVATE Research . 46 , 47 This may be especially true in a mental health context, where typically youth researchers are seeking to help shape a research project because of their experiential knowledge of mental health or mental health services. These experiences may co‐occur with traumatic experiences and asking the youth to share their experiences may be retraumatizing or cause them significant distress. 47

4.1. Strengths, limitations and future directions

A strength of this review is the rigorous study search and selection strategy, and our focus on describing patient engagement in lieu of a traditional quality appraisal, which would have been less informative for this study. Also, we used an established method for aggregating qualitative findings.

A limitation of this review is the degree of youth engagement in the project. Youth were involved at the later stages of the review but were not involved in the conception or design of the review, which may limit the relevancy of this review for youth involved in research.

Also, as this is a relatively new field, the terminology used in the field of patient engagement varies across geographic settings. Though we made an effort to be comprehensive in our search strategy, there is the possibility that we missed some studies due to variability in terminology. As well, since this review relied upon authors' reporting on engagement activities, it is likely that some activities and impacts were missed, especially in studies where engagement was not the focus of the article.

One final limitation in this review is the possibility of a bias in our findings towards more positive engagement impacts. This could be due to adult researchers' position of power exerting control (intentionally or unintentionally) over what is reported in the manuscript leading to underreporting of negative experiences or impacts of youth engagement. Also, the inclusion criteria for this review included a requirement that authors reported on at least one activity and one impact on youth engagement. This may have created led to a positive bias in our findings because researchers who report more extensively about engagement may also have been more measured in their approaches to youth engagement, leading to positive engagement experiences for the research team. Similarly, due to the power imbalance between adult and youth researchers, youth researchers may be reluctant to report the negative impacts or experiences during the project. Finally, youth researchers could have experienced negative impacts in studies where youth engagement was minimally reported or where youth engagement was not evaluated. Therefore, our findings should be interpreted with some caution.

The impacts described in the articles were mostly proximal (e.g., effects of youth engagement on the research process), with some intermediate (e.g., skill development of researchers). However, the long‐term impacts of youth engagement, such as impacts on patient outcomes, were not reported. As previously discussed, none of the studies described a formal assessment of the impacts of youth engagement. This unfortunately limits the extent of the evidence for youth engagement in mental health research and also suggests a need for more formal evaluations of youth engagement in future projects. While impact assessment is complex and requires more resources, it is nevertheless important to lend credibility to the argument that patient engagement in research is worth the return on investment. To overcome the positive bias described above, these evaluations could be led by youth, giving them more power to openly report on engagement impacts.

5. CONCLUSION

The overall purpose of this systematic review was to synthesize the impacts of youth engagement on mental health research. We aggregated the reported impacts of youth engagement across research studies and described how youth were being engaged in research, challenges and facilitators to engagement. The recommendations for youth engagement in mental health research contained in this article can be applied by researchers who are planning to engage youth in mental health research. This study provides an understanding of youth engagement in mental health research that may encourage researchers to engage youth in their mental health research. It will also be useful in supporting requests for funding for youth engagement.

AUTHOR CONTRIBUTIONS

Erin McCabe conceptualized and designed the study, search and selected articles performed data extraction and analysis and wrote most of the manuscript. Mungunzul (Megan) Amarbayan contributed to the study design, and article selection, and critically reviewed the manuscript. Sarah Rabi assisted with youth researcher consultations, and wrote parts of and critically reviewed the manuscript. Justino Mendoza contributed to the analysis and interpretation of results, developed a figure and critically reviewed the manuscript. Syeda Farwa Naqvi assisted with youth researcher consultations and critically reviewed the manuscript. Kalpana Thapa Bajgain performed data extraction and critically reviewed the manuscript. Jennifer D. Zwicker contributed to the study design, and data interpretation and critically reviewed the manuscript. Maria Santana conceptualized the study, contributed to study design and data interpretation and critically reviewed the manuscript. All authors reviewed and approved the final manuscript.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

Supporting information

Supplementary information.

ACKNOWLEDGEMENTS

We would like to acknowledge the support of Heather Ganshorn, Health Sciences Librarian and our youth researchers Eric Au, Thomas Tri and Justino Mendoza for their insights into this project. This project was supported by the Alberta Children's Hospital Foundation and Kids Brain Health Network. Jennifer D. Zwicker was supported by a Tier II Canada Research Chair in Disability Policy for Children and Youth.

McCabe E, Amarbayan M(Megan), Rabi S, et al. Youth engagement in mental health research: a systematic review . Health Expect . 2023; 26 :30‐50. 10.1111/hex.13650 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

DATA AVAILABILITY STATEMENT