Virtual reality reduces COVID-19 vaccine hesitancy in the wild: a randomized trial

Vaccine hesitancy poses one of the largest threats to global health. Informing people about the collective benefit of vaccination has great potential in increasing vaccination intentions. This research investigates the potential for engaging experiences in immersive virtual reality (VR) to strengthen participants’ understanding of community immunity, and therefore, their intention to get vaccinated. In a pre-registered lab-in-the-field intervention study, participants were recruited in a public park (tested: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n = 232$$\end{document}n=232, analyzed: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n = 222$$\end{document}n=222). They were randomly assigned to experience the collective benefit of community immunity in a gamified immersive virtual reality environment (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{2}{3}$$\end{document}23 of sample), or to receive the same information via text and images (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{1}{3}$$\end{document}13 of sample). Before and after the intervention, participants indicated their intention to take up a hypothetical vaccine for a new COVID-19 strain (0–100 scale) and belief in vaccination as a collective responsibility (1–7 scale). The study employs a crossover design (participants later received a second treatment), but the primary outcome is the effect of the first treatment on vaccination intention. After the VR treatment, for participants with less-than-maximal vaccination intention, intention increases by 9.3 points (95% CI: 7.0 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$11.5,\, p < 0.001$$\end{document}11.5,p<0.001). The text-and-image treatment raises vaccination intention by 3.3 points (difference in effects: 5.8, 95% CI: 2.0 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$9.5,\, p = 0.003$$\end{document}9.5,p=0.003). The VR treatment also increases collective responsibility by 0.82 points (95% CI: 0.37 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.27,\, p < 0.001$$\end{document}1.27,p<0.001). The results suggest that VR interventions are an effective tool for boosting vaccination intention, and that they can be applied “in the wild”—providing a complementary method for vaccine advocacy.


Question wordings
Superscripts indicate whether items are measured pre-treatment (1), after the first treatment (2), and/or after the second treatment (3).

Demographics 1
What is your age?
Have you already been vaccinated against COVID-19?How many times have you used Virtual Reality before?

Vaccination intention 123
Suppose that in the future, a new strain of COVID-19 spreads, and that current vaccines are not effective against this strain.A new vaccine is developed against the new strain.
The new vaccine seems effective and seems to have only mild side effects, but it has been tested on far fewer people.
How likely would you be to get such a vaccine?(0 -would definitely not get it; 100 -would definitely get it)

Collective Responsibility 123
I see COVID-19 vaccination as a collective task against the spread of COVID-19.

Gender 1
At the start of the VR simulation, participants are asked to indicate their gender (male, female, other).
Presence and Embodiment 2 or 3 (Administered once, just after the VR experience) Presence (adapted from [2]): • While I was in the square, I had a sense of "being there".
• I had a sense that I was interacting with other people in the square rather than a computer simulation.
(1 -strongly disagree; 5 -strongly agree) Embodiment (adapted from [3]) I felt as if the virtual body I saw when I looked down was my body.
(1 -strongly disagree; 7 -strongly agree) Media attitudes 3 (order randomized: VR question first or text question first) Next, we would like to hear your thoughts about the Virtual Reality simulation (busy square experience) that you saw in this study.How much do you agree with the following statements?
• It is fun to use a VR simulation to learn about the benefits of vaccines • I would like to receive more health communication through Virtual Reality Next, we would like to hear your thoughts about the text/picture communication (panels with colored dots) that you saw in this study.How much do you agree with the following statements?
• It is fun to use text and pictures to learn about the benefits of vaccines • I would like to receive more health communication through text and pictures

Models for hypothesis testing
The pseudo-R code below shows the models that we use to test the preregistered hypotheses.Models focus on the change in vaccination intention and in collective responsibility after the first treatment, compared to before.The intercepts of these models are used to estimate the effect of the VR treatment.That is, they estimate the size of the change in attitudes after the first treatment, for participants taking the VR treatment as their first treatment.The coefficients of textfirst compare the effect of the VR treatment to the effect of the text treatment.In both models, the sum of the intercept and textfirst coefficient shows the effect of the test-and-image treatment.

Individual treatment effects
Figure S1 shows the individual treatment effects of the text-and-image treatment, VR, and their combination (with either text-and-image or VR coming first).These are measured as vaccine intention after treatment (or after both treatments), minus vaccine intention before treatment.As the Figure illustrates, negative treatment effects are quite rare.Most participants experience modest positive treatment effects (or no effect).

Including participants with maximum scores
Participants with maximum (i.e.100) pre-treatment vaccination intention largely did not decrease.Among these 27 participants, 22 still had maximum vaccination intention after the first treatment.Average vaccination intention for these participants was 93.2 after the first treatment, with the drop being largely driven by a single participant whose intention went down to 0. In the full sample (including these participants), the effect of VR is 6.9 (95% CI: 4.5 to 9.3, p < 0.001).It is stronger than the effect of text and images by 4.1 points (95% CI: −8.2 to −0.08, p < 0.05).This analysis is exploratory and not preregistered.

Figure S1 :
Figure S1: Individual-level change in vaccination intention from pre-treatment measurement, after first treatment (n = 195, first two columns) and after both treatments (n = 189, last two columns), leaving out participants with maximum pre-treatment vaccination intention.Colored dots are participants; colored graphs are distributions of individual treatment effects.Large black dots are average treatment effects with their 95% CIs.