COVID-19 Misinformation Prophylaxis: Protocol for a Randomized Trial of a Brief Informational Intervention

Jon Agley, Yunyu Xiao, Esi E Thompson, Lilian Golzarri-Arroyo, Jon Agley, Yunyu Xiao, Esi E Thompson, Lilian Golzarri-Arroyo

Abstract

Background: As the COVID-19 pandemic continues to affect life in the United States, the important role of nonpharmaceutical preventive behaviors (such as wearing a face mask) in reducing the risk of infection has become clear. During the pandemic, researchers have observed the rapid proliferation of misinformed or inconsistent narratives about COVID-19. There is growing evidence that such misinformed narratives are associated with various forms of undesirable behavior (eg, burning down cell towers). Furthermore, individuals' adherence to recommended COVID-19 preventive guidelines has been inconsistent, and such mandates have engendered opposition and controversy. Recent research suggests the possibility that trust in science and scientists may be an important thread to weave throughout these seemingly disparate components of the modern public health landscape. Thus, this paper describes the protocol for a randomized trial of a brief, digital intervention designed to increase trust in science.

Objective: The objective of this study is to examine whether exposure to a curated infographic can increase trust in science, reduce the believability of misinformed narratives, and increase the likelihood to engage in preventive behaviors.

Methods: This is a randomized, placebo-controlled, superiority trial comprising 2 parallel groups. A sample of 1000 adults aged ≥18 years who are representative of the population of the United States by gender, race and ethnicity, and age will be randomly assigned (via a 1:1 allocation) to an intervention or a placebo-control arm. The intervention will be a digital infographic with content based on principles of trust in science, developed by a health communications expert. The intervention will then be both pretested and pilot-tested to determine its viability. Study outcomes will include trust in science, a COVID-19 narrative belief latent profile membership, and the likelihood to engage in preventive behaviors, which will be controlled by 8 theoretically selected covariates.

Results: This study was funded in August 2020, approved by the Indiana University Institutional Review Board on September 15, 2020, and prospectively registered with ClinicalTrials.gov.

Conclusions: COVID-19 misinformation prophylaxis is crucial. This proposed experiment investigates the impact of a brief yet actionable intervention that can be easily disseminated to increase individuals' trust in science, with the intention of affecting misinformation believability and, consequently, preventive behavioral intentions.

Trial registration: ClinicalTrials.gov NCT04557241; https://ichgcp.net/clinical-trials-registry/NCT04557241.

International registered report identifier (irrid): PRR1-10.2196/24383.

Keywords: COVID-19; behavior; health information; infodemic; infodemiology; intervention; misinformation; prevention; protocol; trust; trust in science.

Conflict of interest statement

Conflicts of Interest: None declared.

©Jon Agley, Yunyu Xiao, Esi E Thompson, Lilian Golzarri-Arroyo. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 07.12.2020.

Figures

Figure 1
Figure 1
Conceptual framework of study variables.
Figure 2
Figure 2
Study design and workflow.

References

    1. Daily confirmed new cases (7-day moving average) Johns Hopkins University & Medicine - Coronavirus Resource Center. [2020-07-17]. .
    1. COVID-19 United States cases by county. Johns Hopkins University & Medicine - Coronavirus Resource Center. [2020-07-17]. .
    1. Everyone included: social impact of COVID-19. United Nations Department of Economic and Social Affairs - Social Inclusion. [2020-07-18]. .
    1. Social impact COVID-19 events in the United States. Centers for Disease Control and Prevention - CDC COVID Data Tracker. [2020-07-17]. .
    1. Fauci AS, Lane HC, Redfield RR. Covid-19 — Navigating the uncharted. N Engl J Med. 2020 Mar 26;382(13):1268–1269. doi: 10.1056/nejme2002387.
    1. Greenhalgh T, Schmid MB, Czypionka T, Bassler D, Gruer L. Face masks for the public during the covid-19 crisis. BMJ. 2020 Apr 09;369:m1435. doi: 10.1136/bmj.m1435.
    1. Zhang N, Cheng P, Jia W, Dung C, Liu L, Chen W, Lei H, Kan C, Han X, Su B, Xiao S, Qian H, Lin B, Li Y. Impact of intervention methods on COVID-19 transmission in Shenzhen. Build Environ. 2020 Aug;180:107106. doi: 10.1016/j.buildenv.2020.107106.
    1. Gordon A. Dozens of Mississippi lawmakers have coronavirus after weeks of refusing to wear masks. CNN. 2020. Jul 10, [2020-07-11]. .
    1. McGinnis C, Sue T. Airline passengers refuse to wear masks, mayhem ensues. SFGate. 2020. Jun 18, [2020-07-11]. .
    1. Meyersohn N. Stores want shoppers to wear masks, but some customers refuse. KCTV News. 2020. Apr 23, [2020-07-11]. .
    1. Associated Press Church linked to Oregon’s largest outbreak as daily coronavirus count hits record high. Los Angeles Times. 2020. Jun 17, [2020-07-11]. .
    1. Mian A, Khan S. Coronavirus: the spread of misinformation. BMC Med. 2020 Mar 18;18(1):89. doi: 10.1186/s12916-020-01556-3.
    1. Kouzy R, Abi Jaoude Joseph, Kraitem A, El Alam Molly B, Karam Basil, Adib Elio, Zarka Jabra, Traboulsi Cindy, Akl Elie W, Baddour Khalil. Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter. Cureus. 2020 Mar 13;12(3):e7255. doi: 10.7759/cureus.7255.
    1. Brennen JS, Simon FM, Howard PN, Nielsen RK. Types, sources, and claims of COVID-19 misinformation. The Reuters Institute for the Study of Journalism. 2011. Apr 07, [2020-11-30].
    1. Verspoor K. ACL 2020 Workshop on Natural Language Processing for COVID-19 (NLP-COVID) Association for Computational Linguistics; 2020. [2020-07-12]. .
    1. Tso R, Cowling B. Importance of face masks for COVID-19: a call for effective public education. Clin Infect Dis. 2020 Nov 19;71(16):2195–2198. doi: 10.1093/cid/ciaa593.
    1. Agley J, Xiao Y. Misinformation about COVID-19: evidence for differential latent profiles and a strong association with trust in science. Research Square. doi: 10.21203/-35919/v2. Preprint posted online on Oct 21, 2020.
    1. Sutton RM, Douglas KM. Conspiracy theories and the conspiracy mindset: implications for political ideology. Current Opinion in Behavioral Sciences. 2020 Aug;34:118–122. doi: 10.1016/j.cobeha.2020.02.015.
    1. Jasinskaja-Lahti Inga, Jetten J. Unpacking the relationship between religiosity and conspiracy beliefs in Australia. Br J Soc Psychol. 2019 Oct;58(4):938–954. doi: 10.1111/bjso.12314.
    1. Pennycook G, McPhetres J, Bago B, Rand D. Predictors of attitudes and misperceptions about COVID-19 in Canada, the U.K., and the U.S.A. PsyArXiv. Preprint posted online April 14, 2020.
    1. Agley J. Assessing changes in US public trust in science amid the COVID-19 pandemic. Public Health. 2020 Jun;183:122–125. doi: 10.1016/j.puhe.2020.05.004.
    1. Vraga EK, Tully M, Bode L. Empowering users to respond to misinformation about Covid-19. MaC. 2020 Jun 25;8(2):475–479. doi: 10.17645/mac.v8i2.3200.
    1. Koetke J, Schumann K, Porter T. Trust in science increases conservative support for social distancing. OSF Preprints. doi: 10.31219/. Preprint posted online June 24, 2020.
    1. Bennett M. Should I do as I’m told? Trust, experts, and COVID-19. Kennedy Institute of Ethics Journal. 2020;30(3-4):243–263. doi: 10.1353/ken.2020.0014.
    1. Painter M, Qiu T. Political beliefs affect compliance with COVID-19 social distancing orders. SSRN Journal. doi: 10.2139/ssrn.3569098. Preprint posted online July 3, 2020.
    1. Makridis C, Rothwell J. The real cost of political polarization: evidence from the COVID-19 pandemic. SSRN Journal. doi: 10.2139/ssrn.3638373. Preprint posted online Jun 30, 2020.
    1. Simonov A, Sacher S, Dube J, Biswas S. Non-compliance with social distancing during the COVID-19 pandemic. National Bureau of Economic Research. Working Paper Series. 2020 May doi: 10.3386/w27237.
    1. Solomou I, Constantinidou F. Prevalence and predictors of anxiety and depression symptoms during the COVID-19 pandemic and compliance with precautionary measures: age and sex matter. Int J Environ Res Public Health. 2020 Jul 08;17(14) doi: 10.3390/ijerph17144924.
    1. Biddlestone M, Green R, Douglas KM. Cultural orientation, power, belief in conspiracy theories, and intentions to reduce the spread of COVID-19. Br J Soc Psychol. 2020 Jul;59(3):663–673. doi: 10.1111/bjso.12397.
    1. Clark C, Davila A, Regis M, Kraus S. Predictors of COVID-19 voluntary compliance behaviors: an international investigation. Glob Transit. 2020;2:76–82. doi: 10.1016/j.glt.2020.06.003.
    1. Yıldırım M, Güler A. COVID-19 severity, self-efficacy, knowledge, preventive behaviors, and mental health in Turkey. Death Stud. 2020 Jul 16;:1–8. doi: 10.1080/07481187.2020.1793434.
    1. Kigatiira KK. Efficacy of fear appeals on adoption of COVID-19 preventive measures: a case of boda boda riders in Nairobi county, Kenya. Int J Res Granthaalayah. 2020 Jun;8(6):219–228. doi: 10.29121/granthaalayah.v8.i6.2020.533.
    1. Pennycook G, McPhetres J, Zhang Y, Lu J, Rand D. Fighting COVID-19 misinformation on social mediaxperimental evidence for a scalable accuracy-nudge intervention. PsyArXiv Preprints. doi: 10.31234/. Preprint posted online March 17, 2020.
    1. Sumnall HR, Bellis MA. Can health campaigns make people ill? The iatrogenic potential of population-based cannabis prevention. J Epidemiol Community Health. 2007 Nov;61(11):930–1. doi: 10.1136/jech.2007.060277.
    1. Allara E, Ferri M, Bo A, Gasparrini A, Faggiano F. Are mass-media campaigns effective in preventing drug use? A Cochrane systematic review and meta-analysis. BMJ Open. 2015 Sep 03;5(9):e007449. doi: 10.1136/bmjopen-2014-007449.
    1. Lane HC, Fauci AS. Research in the context of a pandemic. N Engl J Med. 2020 Jul 17; doi: 10.1056/NEJMe2024638.
    1. Nadelson L, Jorcyk C, Yang D, Jarratt Smith M, Matson S, Cornell K, Husting V. I just don't trust them: the development and validation of an assessment instrument to measure trust in science and scientists. Sch Sci Math. 2014 Jan 19;114(2):76–86. doi: 10.1111/ssm.12051.
    1. Chambon M, Dalege J, Elberse J, Harreveld Fv. A psychological network approach to factors related to preventive behaviors during pandemics: A European COVID-19 study. PsyArXiv Preprints. doi: 10.31234/. Preprint posted online July 01, 2020.
    1. Shumaker L. Florida sets one-day record with over 15,000 new COVID cases, more than most countries. Reuters. 2020. Jul 12, [2020-07-16]. .
    1. Tanner C. In separate rallies, Utahns protest mask mandate and demand in-person classes. The Salt Lake Tribune. 2020. Jul 15, [2020-07-16].
    1. Hatfield M. Masks off! Protestors claim face mask order unconstitutional. ABC13 Eyewitness News. 2020. Jun 28, [2020-07-16].
    1. Palan S, Schitter C. —A subject pool for online experiments. Journal of Behavioral and Experimental Finance. 2018 Mar;17:22–27. doi: 10.1016/j.jbef.2017.12.004.
    1. Peer E, Brandimarte L, Samat S, Acquisti A. Beyond the Turk: alternative platforms for crowdsourcing behavioral research. Journal of Experimental Social Psychology. 2017 May;70:153–163. doi: 10.1016/j.jesp.2017.01.006.
    1. Representative samples on Prolific. Prolific - Researcher Help Centre. 2019. Mar 5, [2020-07-16].
    1. Prolific Prolific vs. MTurk. Prolific - Researcher Help Centre. [2020-07-19].
    1. Geldsetzer P. Knowledge and perceptions of COVID-19 among the general public in the United States and the United Kingdom: a cross-sectional online survey. Annals of Internal Medicine. 2020 Jul 21;173(2):157–160. doi: 10.7326/m20-0912.
    1. Kim HS, Hodgins DC. Are you for real? Maximizing participant eligibility on Amazon's Mechanical Turk. Addiction. 2020 Oct;115(10):1969–1971. doi: 10.1111/add.15065.
    1. Huang G, Li K, Li H. Show, not tell: The contingency role of infographics versus text in the differential effects of message strategies on optimistic bias. Science Communication. 2019 Nov 19;41(6):732–760. doi: 10.1177/1075547019888659.
    1. Rodríguez Estrada FC, Davis LS. Improving visual communication of science through the incorporation of graphic design theories and practices into science communication. Science Communication. 2014 Dec 26;37(1):140–148. doi: 10.1177/1075547014562914.
    1. Lazard A, Atkinson L. Putting environmental infographics center stage: The role of visuals at the elaboration likelihood model’s critical point of persuasion. Science Communication. 2014 Oct 30;37(1):6–33. doi: 10.1177/1075547014555997.
    1. Dobos AR, Orthia LA, Lamberts R. Does a picture tell a thousand words? The uses of digitally produced, multimodal pictures for communicating information about Alzheimer's disease. Public Underst Sci. 2015 Aug;24(6):712–30. doi: 10.1177/0963662514533623.
    1. Krakow M, Yale R, Jensen J, Carcioppolo N, Ratcliff C. Comparing mediational pathways for narrative- and argument-based messages: believability, counterarguing, and emotional reaction. Human Communication Research. 2018;44(3):299–321. doi: 10.1093/hcr/hqy002.
    1. Herzberg KN, Sheppard SC, Forsyth JP, Credé Marcus, Earleywine M, Eifert GH. The Believability of Anxious Feelings and Thoughts Questionnaire (BAFT): a psychometric evaluation of cognitive fusion in a nonclinical and highly anxious community sample. Psychol Assess. 2012 Dec;24(4):877–91. doi: 10.1037/a0027782.
    1. Lynas M. COVID: Top 10 current conspiarcy theories. Cornell - Alliance for Science. [2020-05-25].
    1. Coronavirus disease (COVID-19) advice for the public: Mythbusters. World Health Organization. [2020-09-07]. .
    1. Kaufman BG, Whitaker R, Mahendraratnam N, Smith VA, McClellan MB. Comparing associations of state reopening strategies with COVID-19 burden. J Gen Intern Med. 2020 Oct 06; doi: 10.1007/s11606-020-06277-0.
    1. Cheng VC, Wong S, Chuang VW, So SY, Chen JH, Sridhar S, To KK, Chan JF, Hung IF, Ho P, Yuen K. The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2. J Infect. 2020 Jul;81(1):107–114. doi: 10.1016/j.jinf.2020.04.024.
    1. Centers for Disease Control and Prevention - Coronavirus Disease 2019 (COVID-19) [2020-07-17]. .
    1. Ajzen I. Theory of Planned Behavior Questionnaire. Measurement Instrument Database for Social Science. [2020-07-17]. .
    1. deBruin WB. Age differences in COVID-19 risk perceptions and mental health: evidence from a national U.S. survey conducted in March 2020. The Journals of Gerontology: Series B. 2020 May 29; doi: 10.1093/geronb/gbaa074.
    1. Wolf EJ, Harrington KM, Clark SL, Miller MW. Sample size requirements for structural equation models: an evaluation of power, bias, and solution propriety. Educ Psychol Meas. 2013 Dec;76(6):913–934. doi: 10.1177/0013164413495237.
    1. Randomizer. QualtricsXM. [2020-08-05].
    1. Graham JW. Missing data analysis: making it work in the real world. Annu Rev Psychol. 2009;60:549–76. doi: 10.1146/annurev.psych.58.110405.085530.
    1. Freedman DA. On the so-called “Huber Sandwich Estimator” and “Robust Standard Errors”. The American Statistician. 2006 Nov;60(4):299–302. doi: 10.1198/000313006x152207.
    1. Tein J, Coxe S, Cham H. Statistical power to detect the correct number of classes in latent profile analysis. Struct Equ Modeling. 2013 Oct 01;20(4):640–657. doi: 10.1080/10705511.2013.824781.
    1. Berlin K, Williams N, Parra G. An introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses. J Pediatr Psychol. 2014 Mar;39(2):174–87. doi: 10.1093/jpepsy/jst084.
    1. Biesanz JC, Falk CF, Savalei V. Assessing mediational models: testing and interval estimation for indirect effects. Multivariate Behav Res. 2010 Aug 06;45(4):661–701. doi: 10.1080/00273171.2010.498292.
    1. Ball P, Maxmen A. The epic battle against coronavirus misinformation and conspiracy theories. Nature. 2020 May;581(7809):371–374. doi: 10.1038/d41586-020-01452-z.
    1. Coronavirus misinformation needs researchers to respond. Nature. 2020 May;581(7809):355–356. doi: 10.1038/d41586-020-01550-y.
    1. Internet/broadband fact sheet. Pew Research Center - Internet & Technology. [2020-08-05].
    1. Vraga EK, Jacobsen KH. Strategies for effective health communication during the coronavirus pandemic and future emerging infectious disease events. World Medical & Health Policy. 2020 Jul 29;12(3):233–241. doi: 10.1002/wmh3.359.
    1. Chandler J, Shapiro D. Conducting clinical research using crowdsourced convenience samples. Annu Rev Clin Psychol. 2016;12:53–81. doi: 10.1146/annurev-clinpsy-021815-093623.
    1. Merz ZC, Lace JW, Eisenstein AM. Examining broad intellectual abilities obtained within an mTurk internet sample. Curr Psychol. 2020 Apr 22; doi: 10.1007/s12144-020-00741-0.
    1. Keith MG, Tay L, Harms PD. Systems perspective of Amazon Mechanical Turk for organizational research: review and recommendations. Front Psychol. 2017;8:1359. doi: 10.3389/fpsyg.2017.01359. doi: 10.3389/fpsyg.2017.01359.

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