Social media use and health risk behaviours in young people: systematic review and meta-analysis
Open accessAbstract
Objectives: To examine the association between social media use and health risk behaviours in adolescents (defined as those 10-19 years). Design: Systematic review and meta-analysis. Data sources: EMBASE, Medline, APA PsycINFO, SocINDEX, CINAHL, SSRN, SocArXic, PsyArXiv, medRxiv, and Google Scholar (1 January 1997 to 6 June 2022). Methods: Health risk behaviours were defined as use of alcohol, drugs, tobacco, electronic nicotine delivery systems, unhealthy dietary behaviour, inadequate physical activity, gambling, and anti-social, sexual risk, and multiple risk behaviours. Included studies reported a social media variable (ie, time spent, frequency of use, exposure to health risk behaviour content, or other social media activities) and one or more relevant outcomes. Screening and risk of bias assessments were completed independently by two reviewers. Synthesis without meta-analysis based on effect direction and random-effects meta-analyses was used. Effect modification was explored using meta-regression and stratification. Certainty of evidence was assessed using GRADE (Grading of Recommendations, Assessment, Development and Evaluations). Results: Of 17 077 studies screened, 126 were included (73 included in meta-analyses). The final sample included 1 431 534 adolescents (mean age 15.0 years). Synthesis without meta-analysis indicated harmful associations between social media and all health risk behaviours in most included studies, except inadequate physical activity where beneficial associations were reported in 63.6% of studies. Frequent ( v infrequent) social media use was associated with increased alcohol consumption (odds ratio 1.48 (95% confidence interval 1.35 to 1.62); n=383 068), drug use (1.28 (1.05 to 1.56); n=117 646), tobacco use (1.85, 1.49 to 2.30; n=424 326), sexual risk behaviours (1.77 (1.48 to 2.12); n=47 280), anti-social behaviour (1.73 (1.44 to 2.06); n=54 993), multiple risk behaviours (1.75 (1.30 to 2.35); n=43 571), and gambling (2.84 (2.04 to 3.97); n=26 537). Exposure to content showcasing health risk behaviours on social media ( v no exposure) was associated with increased odds of use of electronic nicotine delivery systems (1.73 (1.34 to 2.23); n=721 322), unhealthy dietary behaviours (2.48 (2.08 to 2.97); n=9892), and alcohol consumption (2.43 (1.25 to 4.71); n=14 731). For alcohol consumption, stronger associations were identified for exposure to user generated content (3.21 (2.37 to 4.33)) versus marketer generated content (2.12 (1.06 to 4.24)). For time spent on social media, use for at least 2 h per day ( v <2 h) increased odds of alcohol consumption (2.12 (1.53 to 2.95); n=12 390). GRADE certainty was moderate for unhealthy dietary behaviour, low for alcohol use, and very low for other investigated outcomes. Conclusions: Social media use is associated with adverse health risk behaviours in young people, but further high quality research is needed to establish causality, understand effects on health inequalities, and determine which aspects of social media are most harmful. Study registration: PROSPERO, CRD42020179766.
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Increased social media engagement is associated with greater likelihood of engaging in health-risk behaviors, including alcohol and drug consumption.
“A systematic review and meta-analysis found that increased social media engagement is associated with a greater likelihood of engaging in health-risk behaviors, including alcohol and drug consumption [3].”
From The Impact of Social Media & Technology on Child and Adolescent Mental Health (2025) · cites this paper as [3]
Approximately 24.4% of adolescents meet criteria for social media addiction.
“Recent studies indicate that approximately 24.4% of adolescents meet the criteria for social media addiction [3].”
From The Impact of Social Media & Technology on Child and Adolescent Mental Health (2025) · cites this paper as [3]
The Abstract Results and Results sections report associations between social media use and health risk behaviours, not the prevalence of social media addiction. Searches for “24.4%” and adolescent social media addiction prevalence found no passage in our copy supporting the claimed figure. (LLM, conf=0.76)
Experimental — accuracy not yet measuredCompulsive engagement with digital platforms is associated with increased symptoms of anxiety, depression, and attention disorders.
“This compulsive engagement with digital platforms has been associated with increased symptoms of anxiety, depression, and attention disorders, raising concerns about the long-term consequences of excessive screen time.”
From The Impact of Social Media & Technology on Child and Adolescent Mental Health (2025) · cites this paper as [3]
The Methods (Study inclusion and exclusion criteria; page unavailable) define the review’s outcomes as health risk behaviours, not anxiety, depression, or attention disorders. The Introduction mentions depression as a possible consequence of alcohol or drug use, but does not link it to compulsive digital-platform engagement. Searches for anxiety, depression, attention disorders, and problematic use found no passage establishing the claimed association. (LLM, conf=0.76)
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The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions
Sterne, J. A., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., Henry, D., Altman, D. G., Ansari, M. T., Boutron, I., et al. (2016) · BMJ · doi:10.1136/bmj.i4919 · Cited by 20,329 · see citing works
Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline
Campbell, M., McKenzie, J. E., Sowden, A., Katikireddi, S. V., Brennan, S. E., Ellis, S., Hartmann-Boyce, J., Ryan, R., Shepperd, S., Thomas, J., et al. (2020) · BMJ · doi:10.1136/bmj.l6890 · Cited by 4,628 · see citing works
Self-citationSawyer, S. M., Azzopardi, P. S., Wickremarathne, D., & Patton, G. C. (2018) · The Lancet Child & Adolescent Health · doi:10.1016/s2352-4642(18)30022-1 · Cited by 3,666 · see citing works
Peters, J. L., Sutton, A. J., Jones, D. R., Abrams, K. R., & Rushton, L. (2008) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2007.11.010 · Cited by 2,053 · see citing works
Keles, B., McCrae, N., & Grealish, A. (2020) · International Journal of Adolescence and Youth · doi:10.1080/02673843.2019.1590851 · Cited by 1,909 · see citing works
Methods to estimate the between‐study variance and its uncertainty in meta‐analysis
Veroniki, A. A., Jackson, D., Viechtbauer, W., Bender, R., Bowden, J., Knapp, G., Kuss, O., Higgins, J. P., Langan, D., & Salanti, G. (2016) · Research Synthesis Methods · doi:10.1002/jrsm.1164 · Cited by 1,685 · see citing works
Stiglic, N., & Viner, R. M. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-023191 · Cited by 1,259 · see citing works
The appropriateness of asymmetry tests for publication bias in meta-analyses: A large survey
Ioannidis, J. P. A., & Trikalinos, T. A. (2007) · Canadian Medical Association Journal · doi:10.1503/cmaj.060410 · Cited by 1,052 · see citing works
Mokdad, A. H., Forouzanfar, M. H., Daoud, F., Mokdad, A. A., El Bcheraoui, C., Moradi-Lakeh, M., Kyu, H. H., Barber, R. M., Wagner, J., Cercy, K., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)00648-6 · Cited by 984 · see citing works
Social media use and adolescent mental health: Findings from the UK Millennium Cohort Study
Kelly, Y., Zilanawala, A., Booker, C., & Sacker, A. (2018) · EClinicalMedicine · doi:10.1016/j.eclinm.2018.12.005 · Cited by 740 · see citing works
Marino, C., Gini, G., Vieno, A., & Spada, M. M. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.10.007 · Cited by 658 · see citing works
Social media use and risky behaviors in adolescents: A meta-analysis
Vannucci, A., Simpson, E. G., Gagnon, S., & Ohannessian, C. M. (2020) · Journal of Adolescence · doi:10.1016/j.adolescence.2020.01.014 · Cited by 410 · cited 4× · see citing works
“…and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.”
BackgroundMethodInfluentialChild Social Media Influencers and Unhealthy Food Product Placement
Alruwaily, A., Mangold, C., Greene, T., Arshonsky, J., Cassidy, O., Pomeranz, J. L., & Bragg, M. (2020) · Pediatrics · doi:10.1542/peds.2019-4057 · Cited by 114 · see citing works
Boniel-Nissim, M., van den Eijnden, R. J. J. M., Furstova, J., Marino, C., Lahti, H., Inchley, J., Šmigelskas, K., Vieno, A., & Badura, P. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107144 · Cited by 96 · see citing works
Flagged first, then by citations
Research works printed but not linked: 46
- [3]Gebremeskel RH Sessoms K Krehnbrink M Haney CJ Coyne-Beasley T. Social media use and adolescent risk taking behavior. J Adolesc Health 2014; 54: S46- 7. 10.1016/j.jadohealth.2013.10.106. · DOI
- [4]Chou WY Hunt YM Beckjord EB Moser RP Hesse BW. Social media use in the United States: implications for health communication. J Med Internet Res 2009; 11: e48. 10.2196/jmir.1249. 19945947 PMC2802563 · DOI · Paper page
- [5]Aichner T Grünfelder M Maurer O Jegeni D. Twenty-five years of social media: A review of social media applications and definitions from 1994 to 2019. Cyberpsychol Behav Soc Netw 2021; 24: 215- 22. 10.1089/cyber.2020.0134. 33847527 PMC8064945 · DOI
- [8]Hamm MP Shulhan J Williams G Milne A Scott SD Hartling L. A systematic review of the use and effectiveness of social media in child health. BMC Pediatr 2014; 14: 138. 10.1186/1471-2431-14-138. 24886048 PMC4047773 · DOI
- [9]Goodyear VA Wood G Skinner B Thompson JL. The effect of social media interventions on physical activity and dietary behaviours in young people and adults: a systematic review. Int J Behav Nutr Phys Act 2021; 18: 72. 10.1186/s12966-021-01138-3. 34090469 PMC8180076 · DOI
- [12]Moreno MA Whitehill JM. Influence of social media on alcohol use in adolescents and young adults. Alcohol Res 2014; 36: 91- 100. 26259003 PMC4432862
- [13]Qutteina Y Hallez L Mennes N De Backer C Smits T. What do adolescents see on social media? A diary study of food marketing images on social media. Front Psychol 2019; 10: 2637. 10.3389/fpsyg.2019.02637. 31824391 PMC6883917 · DOI
- [14]Winpenny EM Marteau TM Nolte E. Exposure of children and adolescents to alcohol marketing on social media websites. Alcohol 2014; 49: 154- 9. 10.1093/alcalc/agt174. 24293506 PMC3932831 · DOI
- [16]Sacks G Looi ESY. The advertising policies of major social media platforms overlook the imperative to restrict the exposure of children and adolescents to the promotion of unhealthy foods and beverages. Int J Environ Res Public Health 2020; 17: 4172. 10.3390/ijerph17114172. 32545343 PMC7312784 · DOI · Paper page
- [17]Moreno MA Parks M Richardson LP. What are adolescents showing the world about their health risk behaviors on MySpace? MedGenMed 2007; 9: 9. 18311359 PMC2234280
- [18]Laestadius LI Wahl MM. Mobilizing social media users to become advertisers: Corporate hashtag campaigns as a public health concern. Digit Health 2017; 3: 2055207617710802. 10.1177/2055207617710802. 29942600 PMC6001194 · DOI
- [19]Jackson KM Janssen T Gabrielli J. Media/marketing influences on adolescent and young adult substance abuse. Curr Addict Rep 2018; 5: 146- 57. 10.1007/s40429-018-0199-6. 30393590 PMC6208350 · DOI · Paper page
- [21]Moreno MA Briner LR Williams A Walker L Christakis DA. Real use or “real cool”: adolescents speak out about displayed alcohol references on social networking websites. J Adolesc Health 2009; 45: 420- 2. 10.1016/j.jadohealth.2009.04.015. 19766949 · DOI
- [22]Litt DM Stock ML. Adolescent alcohol-related risk cognitions: the roles of social norms and social networking sites. Psychol Addict Behav 2011; 25: 708- 13. 10.1037/a0024226. 21644803 · DOI
- [23]Huang GC Unger JB Soto D. Peer influences: the impact of online and offline friendship networks on adolescent smoking and alcohol use. J Adolesc Health 2014; 54: 508- 14. 10.1016/j.jadohealth.2013.07.001. 24012065 PMC4694047 · DOI
- [24]Papasolomou I Melanthiou Y. Social media: Marketing public relations’ new best friend. J Promot Manage 2012; 18: 319- 28. 10.1080/10496491.2012.696458. · DOI
- [25]Sawyer SM Afifi RA Bearinger LH. Adolescence: a foundation for future health. Lancet 2012; 379: 1630- 40. 10.1016/S0140-6736(12)60072-5. 22538178 · DOI · Paper page
- [26]Nesi J Rothenberg WA Hussong AM Jackson KM. Friends’ alcohol-related social networking site activity predicts escalations in adolescent drinking: Mediation by peer norms. J Adolesc Health 2017; 60: 641- 7. 10.1016/j.jadohealth.2017.01.009. 28325545 PMC6402495 · DOI
- [28]Murray CJL Aravkin AY Zheng P GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396: 1223- 49. 10.1016/S0140-6736(20)30752-2. 33069327 PMC7566194 · DOI · Paper page
- [29]Gopinath B Flood VM Burlutsky G Mitchell P. Combined influence of health behaviors on total and cause-specific mortality. Arch Intern Med 2010; 170: 1605- 7. 10.1001/archinternmed.2010.303. 20876415 · DOI
- [31]Otto C Kaman A Erhart M. Risk and resource factors of antisocial behaviour in children and adolescents: results of the longitudinal BELLA study. Child Adolesc Psychiatry Ment Health 2021; 15: 61. 10.1186/s13034-021-00412-3. 34686200 PMC8539834 · DOI
- [32]Curtis BL Lookatch SJ Ramo DE McKay JR Feinn RS Kranzler HR. Meta-analysis of the association of alcohol-related social media use with alcohol consumption and alcohol-related problems in adolescents and young adults. Alcohol Clin Exp Res 2018; 42: 978- 86. 10.1111/acer.13642. 29786874 PMC5984178 · DOI
- [33]Frost RL Rickwood DJ. A systematic review of the mental health outcomes associated with Facebook use. Comput Human Behav 2017; 76: 576- 600. 10.1016/j.chb.2017.08.001. · DOI
- [34]Orben A. Teenagers, screens and social media: a narrative review of reviews and key studies. Soc Psychiatry Psychiatr Epidemiol 2020; 55: 407- 14. 10.1007/s00127-019-01825-4. 31925481 · DOI · Paper page
- [35]Lanthier-Labonté S Dufour M Milot DM Loslier J. Is problematic Internet use associated with alcohol and cannabis use among youth? A systematic review. Addict Behav 2020; 106: 106331. 32151892
- [36]Fs A Khani A Daud F. A systematic review of immersive social media activities and risk factors for sexual boundary violations among adolescents. IIUM Med J Malaysia. 2021; 20: 159- 70. 10.31436/imjm.v20i1.1766. · DOI
- [38]Livingstone S Nandi A Banaji S Stoilova M. Young adolescents and digital media: Uses, risks and opportunities in low- and middle-income countries: A rapid evidence review. Gender and Adolescence Global Evidence, 2017.
- [39]Diderichsen F Evans T Whithead M. The social basis of disparities in health. In: Evans T Whitehead M Diderichsen F Bhuiya A Wirth M, eds. Challenging inequities in health: From ethics to action. Oxford University Press, 2001, 10.1093/acprof:oso/9780195137408.003.0002. · DOI
- [45]Purba AK, Henery PH, Thomson RM, Pearce A, Henderson M KS. Does social media influence adolescent engagement in health risk behaviours? A protocol for a systematic review and meta-analysis. PROSPERO 2020 CRD42020179766.
- [50]Moreno MA Parks MR Zimmerman FJ Brito TE Christakis DA. Display of health risk behaviors on MySpace by adolescents: prevalence and associations. Arch Pediatr Adolesc Med 2009; 163: 27- 34. 10.1001/archpediatrics.2008.528 19124700 · DOI
- [51]Sloan L Quan-Haase A. The SAGE handbook of social media research methods. SAGE Publications Ltd, 2017.
- [53]Aburahmah L Al Rawi H Izz Y Syed L. Online social gaming and social networking sites. Procedia Comput Sci 2016; 82: 72- 9. 10.1016/j.procs.2016.04.011. · DOI
- [54]Kaakinen M Sirola A Savolainen I Oksanen A. Young people and gambling content in social media: An experimental insight. Drug Alcohol Rev 2020; 39: 152- 61. 10.1111/dar.13010. 31815340 · DOI
- [58]MacArthur G Caldwell DM Redmore J. Individual-, family-, and school-level interventions targeting multiple risk behaviours in young people. Cochrane Database Syst Rev 2018; 10: CD009927. 10.1002/14651858.CD009927.pub2. 30288738 PMC6517301 · DOI
- [59]Guyatt GH Oxman AD Schünemann HJ Tugwell P Knottnerus A. GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology. J Clin Epidemiol 2011; 64: 380- 2. 10.1016/j.jclinepi.2010.09.011. 21185693 · DOI · Paper page
- [61]Veritas Health Innovation. Covidence Systematic Review Software. Veritas Health Innovation, 2020.
- [65]Boon MH Thomson H. The effect direction plot revisited: application of the 2019 Cochrane Handbook guidance on alternative synthesis methods. Res Synth Methods 2021; 12: 29- 33. 10.1002/jrsm.1458. 32979023 PMC7821279 · DOI
- [74]Harden KP Kretsch N Mann FD. Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking. Dev Cogn Neurosci 2017; 25: 221- 34. 10.1016/j.dcn.2016.12.007. 28082127 PMC6886471 · DOI · Paper page
- [79]McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 2012; 22: 276- 82. 10.11613/BM.2012.031 23092060 PMC3900052 · DOI · Paper page
- [80]Ioannidis JPA Patsopoulos NA Rothstein HR. Reasons or excuses for avoiding meta-analysis in forest plots. BMJ 2008; 336: 1413- 1415. 10.1136/bmj.a117 18566080 PMC2432114 · DOI · Paper page
- [82]De Jans S Spielvogel I Naderer B Hudders L. Digital food marketing to children: How an influencer’s lifestyle can stimulate healthy food choices among children. Appetite 2021; 162: 105182. 10.1016/j.appet.2021.105182 33667499 · DOI
- [83]Folkvord F de Bruijne M. The effect of the promotion of vegetables by a social influencer on adolescents’ subsequent vegetable intake: A pilot study. Int J Environ Res Public Health 2020; 17: 2243. 10.3390/ijerph17072243. 32225032 PMC7177819 · DOI
- [84]Chau MM Burgermaster M Mamykina L. The use of social media in nutrition interventions for adolescents and young adults-A systematic review. Int J Med Inform 2018; 120: 77- 91. 10.1016/j.ijmedinf.2018.10.001. 30409348 PMC6983924 · DOI
- [85]Günther L Schleberger S Pischke CR. Effectiveness of social media-based interventions for the promotion of physical activity: Scoping review. Int J Environ Res Public Health 2021; 18: 13018. 10.3390/ijerph182413018. 34948628 PMC8702047 · DOI · Paper page
- [88]Carah N Brodmerkel S. Alcohol marketing in the era of digital media platforms. J Stud Alcohol Drugs 2021; 82: 18- 27. 10.15288/jsad.2021.82.18. 33573719 · DOI
- [92]Geusens F Beullens K. Strategic self-presentation or authentic communication? Predicting adolescents’ alcohol references on social media. J Stud Alcohol Drugs 2017; 78: 124- 33. 10.15288/jsad.2017.78.124. 27936372 · DOI
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The Impact of Social Media & Technology on Child and Adolescent Mental Health
Masri-zada, T., Martirosyan, S., Abdou, A., Barbar, R., Kades, S., Makki, H., Haley, G., & K Agrawal, D. (2025) · Journal of Psychiatry and Psychiatric Disorders · doi:10.26502/jppd.2572-519x0242 · Cited by 45 · see citing works
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Le Blanc-Brillon, J., Fortin, J.-S., Lafrance, L., & Hétu, S. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1597241 · Cited by 16 · see citing works
- Authors
- Purba, A. K., Thomson, R. M., Henery, P. M., Pearce, A., Henderson, M., & Katikireddi, S. V.
- Affiliations
- University of Strathclyde (GB)
- Corresponding author
- Purba, Amrit Kaur
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2023-11-29
- Volume
- 383
- Pages
- e073552
- ISSN
- 1756-1833 (ISSN-L)
- PMC
- PMC10685288
- OpenAlex
- W4389154931
- Cited by
- 158
- References
- 15
- Citations by year
- 2026: 75 · 2025: 54 · 2024: 26 · 2023: 2 · 2022: 1
- FWCI
- 7.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- BMJ (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by)
- Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (acceptedVersion, cc-by)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Chief Scientist Office
- Wellcome Trust
- Medical Research Council
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Health Sciences › Health Professions › General Health Professions › Adolescent Health and Behaviors
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Keywords
- social media use, health risk behaviors, adolescents, alcohol consumption, drug use, tobacco use, electronic cigarettes, sexual risk behavior, antisocial behavior, multiple risk behaviors, gambling, physical activity, dietary behaviour, social media exposure, screen time, health policy, health communication, health promotion
- MeSH terms
- Health Risk Behaviors, Adolescent, Alcohol Drinking, Diet, Humans, Exercise, Social Media
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