Impact of Exposure to Health Misinformation on Belief in Health Misinformation: A Meta-Analysis of RCTs

Schmid, Philipp und Bauer, Hannah (2026): Impact of Exposure to Health Misinformation on Belief in Health Misinformation: A Meta-Analysis of RCTs. In: Health Communication, Vol. 41, No. 5: pp. 877-887

Abstract

Health authorities have labeled health misinformation a major global health threat and academicscholars consider exposure to health misinformation a barrier to an individual’s informed decision-making. However, quantitative evaluations of the persuasive impact of exposure to health misinforma-tion produced mixed results. This study uses a meta-analytic approach to analyze the average impact ofhealth misinformation across studies and explore potential moderators of effect size. The analysisincludes randomized controlled trials that compare the impact of exposure to health misinformationon individuals’ belief in misinformation with a neutral control group. The meta-analysis examined 31effect sizes (N = 18,115) from 14 studies that showed that exposure to health misinformation, onaverage, increases individuals’ belief in misinformation, g = 0.24, 95% Confidence Interval [0.10, 0.38].Moderator analyses revealed that exposure to longer health misinformation was associated withstronger effects, g = 0.41, 95% CI [0.27, 0.55], than exposure to shorter statements, g = 0.06, 95% CI[−0.08, 0.20]. The effect size estimates can support fact-checkers in identifying potentially highlypersuasive misinformation and can support researchers in conducting power-analyses and identifyingreasonable smallest effect sizes of interest when studying the impact of health misinformation.

• Item Type: Journal article
• Faculties: Social Sciences > Department of Communications and Media (IfKW)
• Subjects: 300 Social sciences > 380 Commerce, communications and transportation
• ISSN: 1041-0236
• Language: English

References

Abbey, J. D., & Meloy, M. G. (2017). Attention by design: Using attention checks to detect inattentive respondents and improve data quality. Journal of Operations Management, 53(1), 63–70. https://doi.org/10.1016/j.jom.2017.06.001

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Allen, J., Watts, D. J., & Rand, D. G. (2024). Quantifying the impact of misinformation and vaccine-skeptical content on facebook. Science, 384(6699), eadk3451. https://doi.org/10.1126/science.adk3451

Amazeen, M. A. (2019). Practitioner perceptions: Critical junctures and the global emergence and challenges of fact-checking. International Communication Gazette, 81(6–8), 541–561. https://doi.org/10.1177/1748048518817674

Bolsen, T., Palm, R., & Kingsland, J. T. (2020). Framing the origins of COVID-19. Science Communication, 42(5), 562–585. https://doi.org/10.1177/1075547020953603

Bosnjak, M., Fiebach, C. J., Mellor, D., Mueller, S., O’Connor, D. B., Oswald, F. L., & Sokol, R. I. (2022). A template for preregistration of quantitative research in psychology: Report of the joint psychological societies preregistration task force. American Psychologist, 77(4), 602–615. https://doi.org/10.1037/amp0000879

Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752–766. https://doi.org/10.1037/0022-3514.39.5.752

Chan, M. S., & Albarracín, D. (2023). A meta-analysis of correction effects in science-relevant misinformation. Nature Human Behaviour, 7(9), 1514–1525. https://doi.org/10.1038/s41562-023-01623-8

Chen, L., Zhang, Y., Young, R., Wu, X., & Zhu, G. (2021). Effects of vaccine-related conspiracy theories on Chinese young adults’ perceptions of the HPV vaccine: An experimental study. Health Communication, 36(11), 1343–1353. https://doi.org/10.1080/10410236.2020.1751384

Chockalingam, V., Wu, V., Berlinski, N., Chandra, Z., Hu, A., Jones, E., Kramer, J., Li, X., Monfre, T., Ng, Y. S., Sach, M., Smith-Lopez, M., Solomon, S., Sosanya, A., & Nyhan, B. (2021). The limited effects of partisan and consensus messaging in correcting science misperceptions. Research & Politics, 8(2), 1–9. https://doi.org/10.1177/20531680211014980

DeSimone, J. A., Harms, P. D., & DeSimone, A. J. (2015). Best practice recommendations for data screening. Journal of Organizational Behavior, 36(2), 171–181. https://doi.org/10.1002/job.1962

Dixon, G. N., McKeever, B. W., Holton, A. E., Clarke, C., & Eosco, G. (2015). The power of a picture: Overcoming scientific misinformation by communicating weight-of-evidence information with visual exemplars. Journal of Communication, 65(4), 639–659. https://doi.org/10.1111/jcom.12159

Douglas, K. M., Sutton, R. M., & Cichocka, A. (2017). The psychology of conspiracy theories. Current Directions in Psychological Science, 26(6), 538–542. https://doi.org/10.1177/0963721417718261

Douglas, K. M., Uscinski, J. E., Sutton, R. M., Cichocka, A., Nefes, T., Ang, C. S., & Deravi, F. (2019). Understanding conspiracy theories. Political Psychology, 40(S1), 3–35. https://doi.org/10.1111/pops.12568

Ecker, U. K. H., Lewandowsky, S., Cook, J., Schmid, P., Fazio, L. K., Brashier, N., Kendeou, P., Vraga, E. K., & Amazeen, M. A. (2022). The psychological drivers of misinformation belief and its resistance to correction. Nature Reviews Psychology, 1(1), 13–29. https://doi.org/10.1038/s44159-021-00006-y

Edelson, S. M., Reyna, V. F., Singh, A., & Roue, J. E. (2024). The psychology of misinformation across the lifespan. Annual Review of Developmental Psychology, 6(1), 425–454. https://doi.org/10.1146/annurev-devpsych-010923-093547

Egger, M., & Smith, G. D. (1998). Meta-analysis bias in location and selection of studies. BMJ, 316(7124), 61–66. https://doi.org/10.1136/bmj.316.7124.61

Fishbein, M., & Ajzen, I. (1975). Beliefs, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.

Gabis, L. V., Attia, O. L., Goldman, M., Barak, N., Tefera, P., Shefer, S., Shaham, M., & Lerman-Sagie, T. (2022). The myth of vaccination and autism spectrum. European Journal of Paediatric Neurology, 36, 151–158. https://doi.org/10.1016/j.ejpn.2021.12.011

Gratale, S. K., Maloney, E. K., & Cappella, J. N. (2019). Regulating language, not inference: An examination of the potential effectiveness of natural American spirit advertising restrictions. Tobacco Control, 28 (e1), e43–e48. https://doi.org/10.1136/tobaccocontrol-2018-054707

Gratale, S. K., Maloney, E. K., Sangalang, A., & Cappella, J. N. (2018). Influence of natural American spirit advertising on current and former smokers’ perceptions and intentions. Tobacco Control, 27(5), 498–504. https://doi.org/10.1136/tobaccocontrol-2017-053881

Griffin, J. W. (2021). Calculating statistical power for meta-analysis using metapower. The Quantitative Methods for Psychology, 17(1), 24–39. https://doi.org/10.20982/tqmp.17.1.p024 Harff, D., Bollen, C., & Schmuck, D. (2022). Responses to social media influencers’ misinformation about COVID-19: A pre-registered multiple-exposure experiment. Media Psychology, 25(6), 831–850. https://doi.org/10.1080/15213269.2022.2080711

Higgins, J. P., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (2019). Cochrane handbook for systematic reviews of interventions (1st ed.). Wiley. https://doi.org/10.1002/9781119536604

Holford, D., Schmid, P., Fasce, A., & Lewandowsky, S. (2024). The empathetic refutational interview to tackle vaccine misconceptions: Four randomized experiments. Health Psychology, 43(6), 426–437. https://doi.org/10.1037/hea0001354

Jackson, D., & Turner, R. (2017). Power analysis for random-effects meta-analysis. Research Synthesis Methods, 8(3), 290–302. https://doi.org/10.1002/jrsm.1240

Janmohamed, K., Walter, N., Sangngam, N., Hampsher, S., Nyhan, K., De Choudhury, M., & Kumar, N. (2022). Interventions to mitigate vaping misinformation: A meta-analysis. Journal of Health Communication, 27(2), 84–92. https://doi.org/10.1080/10810730.2022.2044941

Jerit, J., & Zhao, Y. (2020). Political misinformation. Annual Review of Political Science, 23(1), 77–94. https://doi.org/10.1146/annurev-polisci-050718-032814

Jolley, D., Douglas, K. M., & Tripp, R. (2014). The effects of anti-vaccine conspiracy theories on vaccination intentions. PLOS ONE, 9(2), e89177. https://doi.org/10.1371/journal.pone.0089177

Konstantopoulos, S. (2011). Fixed effects and variance components estimation in three-level meta-analysis. Research Synthesis Methods, 2(1), 61–76. https://doi.org/10.1002/jrsm.35

Kruglanski, A. W., & Thompson, E. P. (1999). Persuasion by a single route: A view from the unimodel. Psychological Inquiry, 10(2), 83–109. https://doi.org/10.1207/S15327965PL100201

Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108(3), 480–498. https://doi.org/10.1037/0033-2909.108.3.480

Lakens, D., & Ravenzwaaij, D. V. (2022). Sample size justification. Collabra: Psychology, 8(1), 33267. https://doi.org/10.1525/collabra.33267

Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10.1177/2515245918770963

Lewandowsky, S., Ecker, U. K. H., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest, 13(3), 106–131. https://doi.org/10.1177/1529100612451018

Marsh, E. J., & Stanley, M. L. (2021). False beliefs: Byproducts of an adaptive knowledge base? In R. Greifeneder, M. Jaffé & E. J. Newman (Eds.), The psychology of fake news: Accepting, sharing, and correcting misinformation (pp. 131–146). Routledge. https://doi.org/10.4324/9780429295379-10

Murad, M. H., Asi, N., Alsawas, M., & Alahdab, F. (2016). New evidence pyramid. Evidence Based Medicine, 21(4), 125–127. https://doi.org/10.1136/ebmed-2016-110401

Nan, X., Thier, K., & Wang, Y. (2023). Health misinformation: What it is, why people believe it, how to counter it. Annals of the International Communication Association, 47(4), 381–410. https://doi.org/10.1080/23808985.2023.2225489

National Heart, Lung, and Blood Institute. (2021). Study quality assessment tools. U.S. Department of Health and Human Services. https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools

Natoli, E. E., & Marques, M. D. (2021). The antidepressant hoax: Conspiracy theories decrease health-seeking intentions. British Journal of Social Psychology, 60(3), 902–923. https://doi.org/10.1111/bjso.12426

Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600–2606. https://doi.org/10.1073/pnas.1708274114

Office of the Surgeon General. (2021). Confronting health misinformation: The U.S. surgeon general’s advisory on building a healthy information environment. U.S. Department of Health and Human Services. https://www.ncbi.nlm.nih.gov/pubmed/34283416

Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39–50. https://doi.org/10.1016/j.cognition.2018.06.011

Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 19, pp. 123–205). Academic Press. https://doi.org/10.1016/S0065-2601(08)60214-2

Pillai, R. M., & Fazio, L. K. (2021). The effects of repeating false and misleading information on belief. WIREs Cognitive Science, 12(6), e1573. https://doi.org/10.1002/wcs.1573

Porter, E., & Wood, T. J. (2021). The global effectiveness of fact-checking: Evidence from simultaneous experiments in Argentina, Nigeria, South Africa, and the United Kingdom. Proceedings of the National Academy of Sciences, 118(37), e2104235118. https://doi.org/10.1073/pnas.2104235118

Porter, E., & Wood, T. J. (2022). Political misinformation and factual corrections on the facebook news feed: Experimental evidence. The Journal of Politics, 84(3), 1812–1817. https://doi.org/10.1086/719271

Pummerer, L., Böhm, R., Lilleholt, L., Winter, K., Zettler, I., & Sassenberg, K. (2022). Conspiracy theories and their societal effects during the COVID-19 pandemic. Social Psychological and Personality Science, 13(1), 49–59. https://doi.org/10.1177/19485506211000217

Riesthuis, P. (2024). Simulation-based power analyses for the smallest effect size of interest: A confidence-interval approach for minimum-effect and equivalence testing. Advances in Methods and Practices in Psychological Science, 7(2), 25152459241240722. https://doi.org/10.1177/25152459241240722

Rosenstock, I. M. (1974). Historical origins of the health belief model. Health Education Monographs, 2(4), 328–335. https://doi.org/10.1177/109019817400200403 Roth, Y., & Yakobi, O. (2024). Attention! Do we really need attention checks? Journal of Behavioral Decision Making, 37(2), e2377. https://doi.org/10.1002/bdm.2377

Schmid, P., Altay, S., & Scherer, L. D. (2023). The psychological impacts and message features of health misinformation: A systematic review of randomized controlled trials. European Psychologist, 28(3), 162–172. https://doi.org/10.1027/1016-9040/a000494

Schmid, P., & Bauer, H. (2025). Data: Impact of exposure to health misinformation on belief in health misinformation [Dataset]. https://doi.org/10.17605/OSF.IO/U4RTM

Schramme, T. (2023). Health as complete well-being: The WHO definition and beyond. Public Health Ethics, 16(3), 210–218. https://doi.org/10.1093/phe/phad017

Sheeran, P. (2002). Intention-behavior relations: A conceptual and empirical review. European Review of Social Psychology, 12(1), 1–36. https://doi.org/10.1080/14792772143000003

Søe, S. O. (2021). A unified account of information, misinformation, and disinformation. Synthese, 198(6), 5929–5949. https://doi.org/10.1007/s11229-019-02444-x

Sousa-Silva, R. (2022). Fighting the fake: A forensic linguistic analysis to fake news detection. International Journal for the Semiotics of Law -Revue Internationale de Sémiotique Juridique, 35(6), 2409–2433. https://doi.org/10.1007/s11196-022-09901-w

Southwell, B. G., Brennen, J. S. B., Paquin, R., Boudewyns, V., & Zeng, J. (2022). Defining and measuring scientific misinformation. Annals of the American Academy of Political and Social Science, 700(1), 98–111. https://doi.org/10.1177/00027162221084709

Taubert, F., & Schmid, P. (2023). Verschwörungstheorien aus gesundheitspsychologischer Perspektive. In R. Imhoff (Ed.), Die Psychologie der Verschwörungstheorien (pp. 101–118). Hogrefe.

Van Stekelenburg, A., Schaap, G., Veling, H., Van ’t Riet, J., & Buijzen, M. (2022). Scientific-consensus communication about contested science: A preregistered meta-analysis. Psychological Science, 33(12), 1989–2008. https://doi.org/10.1177/09567976221083219

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03

Viechtbauer, W. (2021). Konstantopoulos (2011) three-level meta-analytic model. Metafor-Project.Org. https://www.metaforproject.org/doku.php/analyses:konstantopoulos2011

Vraga, E., Tully, M., & Bode, L. (2021). Assessing the relative merits of news literacy and corrections in responding to misinformation on Twitter. New Media and Society, 24(10), 2354–2371. https://doi.org/10.1177/1461444821998691

Vraga, E. K., & Bode, L. (2020). Defining misinformation and understanding its bounded nature: Using expertise and evidence for describing misinformation. Political Communication, 37(1), 136–144. https://doi.org/10.1080/10584609.2020.1716500

Vraga, E. K., & Bode, L. (2021). Addressing COVID-19 misinformation on social media preemptively and responsively. Emerging Infectious Diseases, 27(2), 396–403. https://doi.org/10.3201/eid2702.203139

Vraga, E. K., Bode, L., & Tully, M. (2022). The effects of a news literacy video and real-time corrections to video misinformation related to sunscreen and skin cancer. Health Communication, 37(13), 1622–1630. https://doi.org/10.1080/10410236.2021.1910165

Walter, N., Brooks, J. J., Saucier, C. J., & Suresh, S. (2021). Evaluating the impact of attempts to correct health misinformation on social media: A meta-analysis. Health Communication, 36(13), 1776–1784. https://doi.org/10.1080/10410236.2020.1794553

World Health Organization. (1948). Constitution of the World Health Organization. https://apps.who.int/gb/bd/PDF/bd47/EN/constitutionen.pdf?ua=1

World Health Organization. (2020). Call for action: Managing the infodemic. https://www.who.int/news/item/11-12-2020-call-for-action-managing-the-infodemic

World Health Organization. (2024). Evaluating the impact of interventions addressing health behaviour: Considerations and tools for policymakers. https://www.who.int/europe/publications/i/item/WHO-EURO-2024-10200-49972-75147

Zabor, E. C., Kaizer, A. M., & Hobbs, B. P. (2020). Randomized controlled trials. Chest, 158(1), S79–S87. https://doi.org/10.1016/j.chest.2020.03.013

Zhou, C., Li, K., & Lu, Y. (2021). Linguistic characteristics and the dissemination of misinformation in social media: The moderating effect of information richness. Information Processing & Management, 58(6), 102679. https://doi.org/10.1016/j.ipm.2021.102679

Zmigrod, L., Burnell, R., & Hameleers, M (2023). The misinformation receptivity framework: Political misinformation and disinformation as cognitive bayesian inference problems. European Psychologist, 28(3), 173–188. https://doi.org/10.1027/10169040/a000498