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

Abstract

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: n = 232, analyzed: n = 222). They were randomly assigned to experience the collective benefit of community immunity in a gamified immersive virtual reality environment (2/3 of sample), or to receive the same information via text and images (1/3 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 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 9.5, p = 0.003). The VR treatment also increases collective responsibility by 0.82 points (95% CI: 0.37 to 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.

Full text

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Virtual reality reduces COVID‑19
vaccine hesitancy in the wild:
a randomized trial
Clara Vandeweerdt1,2*, Tiany Luong3, Michael Atchapero1, Aske Mottelson4,
Christian Holz3, Guido Makransky1 & Robert Böhm1,5,6
Vaccine hesitancy poses one of the largest threats to global health. Informing people about the
collective benet 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‑eld intervention study, participants were recruited in a public park
(tested:
n=232
, analyzed:
n=222
). They were randomly assigned to experience the collective
benet of community immunity in a gamied immersive virtual reality environment (
2
3
of sample), or
to receive the same information via text and images (
1
3
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 eect of the rst 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
11.5,
p<
0.001
). The text‑and‑image treatment raises vaccination intention by 3.3 points (dierence
in eects: 5.8, 95% CI: 2.0 to
9.5,
p=
0.003
). The VR treatment also increases collective responsibility
by 0.82 points (95% CI: 0.37 to
1.27,
p<
0.001
). The results suggest that VR interventions are an
eective tool for boosting vaccination intention, and that they can be applied “in the wild”—providing
a complementary method for vaccine advocacy.
Vaccination against most infectious diseases is an individual decision with positive externalities. at is, when
individuals get vaccinated, they not only protect themselves, but typically also limit the probability that they will
transmit the disease to others1. As such, even unvaccinated citizens can be indirectly protected from infection,
known as community immunity or herd immunity1. With regard to the COVID-19 pandemic, it has been esti-
mated that 60–90% of the population needs to be vaccinated (depending, for instance, on the vaccine’s ecacy)
to stop the spread of SARS-CoV-22,3. erefore, vaccine hesitancy—dened as “the delay in acceptance or refusal
of vaccination despite availability of vaccination services”4—is a key obstacle to ending the COVID-19 pandemic.
Vaccine hesitancy is complex and may be aected by several factors5,6: lack of condence (i.e., the tendency
to trust in the safety and eectiveness of vaccines and to trust health authorities and experts who develop,
license, and recommend vaccines), complacency (i.e., low perceived risk of infectious diseases), constraints
(i.e., structural or psychological barriers in daily life that make vaccination dicult or costly), calculation (i.e.,
the degree to which personal costs and benets of vaccination are weighted), lack of collective responsibility
(i.e., the willingness to protect others and eliminate infectious diseases), lack of compliance (i.e., the support for
societal monitoring and sanctioning of people who are not vaccinated), and conspiracy (i.e., conspiracy thinking
and belief in fake news related to vaccination). Accordingly, fully informed vaccination decisions require that
people know and understand the individual costs and benets of a vaccination, as well as its collective benet.
In line with the assumption that people care not only about their own but also about others’ welfare, inform-
ing them about community immunity has sometimes (e.g.,7) but not always (e.g.,8) been shown to increase
vaccination intentions (for a review, see9). Interactive simulations have been particularly eective in increasing
OPEN
1Department of Psychology, University of Copenhagen, Copenhagen, Denmark. 2Department of Political Science,
University of Copenhagen, Copenhagen, Denmark. 3Department of Computer Science, ETH Zürich, Zurich,
Switzerland. 4IT University of Copenhagen, Copenhagen, Denmark. 5Faculty of Psychology, University of Vienna,
Vienna, Austria. 6Copenhagen Center for Social Data Science (SODAS), University of Copenhagen, Copenhagen,
Denmark. *email: clara.vandeweerdt@ifs.ku.dk
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vaccine intentions10–12, potentially because they are more engaging13 and, therefore, increase people’s learning
motivation10,14. In other words, using novel technologies that help people to better understand the collective
benet of vaccination-and the impact that their own vaccination may have on (vulnerable) others—may be a
promising strategy to increase collective responsibility and, in turn, decrease vaccine hesitancy15.
Building on these ndings, in this study we investigate whether vaccination intention is increased by a gami-
ed immersive VR experience showing how community immunity works. Immersive VR is a promising medium
for health communication (cf.16), because compared to other media it facilitates a high level of presence (the
feeling of being in the virtual environment)17 and agency (the psychological experience of controlling one’s own
actions)18, which results in higher levels of enjoyment and engagement19,20. Still, it has only just started to be
tested as a tool for vaccine advocacy, with one study showing no signicant impact on vaccination intentions21,
and a second study nding a noticeable eect22.
In our VR simulation, participants must either try not to infect other non-player characters in a virtual scene,
or try not to get infected by them. All participants play two scenarios—starting with an environment in which
few characters are vaccinated, followed by an environment where many characters are vaccinated. e simulation
thus allows participants to experience community immunity from a rst-person perspective, learning how much
more slowly infection spreads when vaccination rates are high versus low. Moreover, by using gamication in
an immersive VR simulation, participants are likely to be motivated and engaged with the learning content18,23.
We compare the eectiveness of this simulation against a typical information treatment using text and images.
We hypothesized that:
H1. Vaccination intention increases aer the VR treatment.
H2. Vaccination intention increases more aer the VR treatment than aer the text-and-image treatment.
H3. Collective responsibility increases aer the VR treatment.
H4. Collective responsibility increases more aer the VR than than aer the text-and-image treatment.
Method
e design and analysis plan of this randomized control trial was preregistered on 03/06/2021, prior to access-
ing any data. See https:// osf. io/ cjgfe/? view_ only= ac57e a9f e54ed 8bcd9 d3bee 16cc8 a4 for the anonmyized plan.
Registration DOI is https:// doi. o r g/ 10. 17605/ OS F. IO/ WUFXK. Unless otherwise noted, all steps below follow the
pre-registration plan. e full study procedure was approved by the Institutional Review Board at the Psychol-
ogy Department, University of Copenhagen. e study was performed in accordance with the ethical standards
of the Declaration of Helsinki (1964) and its subsequent amendments. Informed consent was obtained from all
participants.
Recruitment. Participants (tested:
n=232
, analyzed:
n=222
) were 207 passersby recruited in a public
park in Copenhagen during the rst weekend in June 2021, plus 15 passersby recruited one week earlier on
campus at the University of Copenhagen. All adults with basic understanding of English were eligible. e
sample size was determined by the number of passersby who agreed to participate during the pre-registered
study period. Respondents (aged 18 to 63) participated in exchange for drinks and snacks. Table1 contains key
descriptive statistics.
Design. Aer lling out a pre-treatment questionnaire,
2
3
of the participants were randomly assigned to the
VR treatment. e other
1
3
were randomly assigned to read a text and see images explaining community immu-
nity. All participants then lled out a post-treatment questionnaire. Finally, all participants also received the
treatment they had not been assigned to initially (VR or text-and-image), and lled out a second post-treatment
questionnaire (crossover design). Figure1 shows the trial prole.
Below, we detail the content of both treatments: the VR simulation and the text-and-image treatment.
Table 1. Characteristics of the analyzed study sample (
n=222
). Continuous variables are summarized as
mean (standard deviation). Last two rows are % of respondents who had maximum values on the outcome
measures before receiving any treatment.
Sample characteristic Result
Age 29.0 (9.1)
% Female 39%
% Vaccinated 16%
Previous VR experience (median) 1–3 times
Pre-treatment vaccination intention 65.8 (25.6)
Pre-treatment collective responsibility 6.0 (1.5)
% Max pre-treatment vaccination intention 12%
% Max pre-treatment collective responsibility 53%
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VR. In the VR treatment, participants wore an Oculus Quest headset for a 5–10 min simulation developed at
SIPLAB (ETH Zurich). ey were embodied as an older character matching their gender. ey were told that
their character is vulnerable to COVID-19.
Participants were randomly assigned to one of two versions of the VR simulation. In the avoid spreading
version, the player character (“avatar”) is already infected and the player must try not to infect others. In the
avoid infecting version, the player character is uninfected and the player must try not to be exposed to infected
characters.
In a rst step, a tutorial showed the mechanics of the game. ere were healthy-and-unvaccinated, infected
(red clothing) and healthy-and-vaccinated (blue clothing) characters in the environment. Infected characters
could spread the disease when coming too close to a healthy-and-unvaccinated character. Close contact was
dened as a 2m radius around the character.
In a second step, participants were tasked with crossing a busy square to reach a marked destination, while
avoiding contact with the other 130 characters in the square. In the rst scenario, they did so in an environment
where 20% of the virtual characters were vaccinated. In the second scenario, they crossed the busy square again,
but with 70% of the characters being vaccinated. Instructions claried that the dierence between the two sce-
narios was the avatars’ vaccination rate.
When participants came into close contact with a character (infecting them or being exposed to their infec-
tion), they were made aware through graphics and haptic feedback (vibrating controllers). A small graph also
helped them see how the disease spread between characters in the square, increasing the count of infected char-
acters as they moved through the scenario. Figure2 shows the square scene and spreading graph.
Text‑and‑image. e alternative treatment, using text and images, displayed the denition of community
immunity by the US Centers for Disease Control and Prevention24, followed by two pictures (adapted from10)
238 participants started
questionnaire
232 randomised
78 assigned
to text-and-image
first
154 assigned to
VR first
145 included in
main analyses
4 dropped out
1 VR sickness
3 unknown
150 completed
post-treatment
questions
3 under 18 years old
3 dropped out
1 did not speak English
1 no informed consent
1unknown
5 not analyzed
3answered w/o completing VR
1 did not speak English
1 under influence of alcohol
78 completed
post-treatment
questions
77 included in
main analyses
1 not analyzed, under
influence of alcohol
2 dropped out, unknown
143 completed
second post-
treatment
questions
4dropped out, unknown
143 included in
exploratory
analyses
73 completed
second post-
treatment
questions
70 included in
exploratory
analyses
3 answered w/o
completing VR
Figure1. Trial prole showing ow of participants into treatment arms and analyses. Because participants
largely self-administered the questionnaires and treatments, needing assistance only to start up the VR
simulation, dropout reasons are sometimes unknown.
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with captions. e pictures represented communities where few or many people are vaccinated. Captions
explained that in a low-vaccination community, many healthy but unvaccinated people are at risk of infection.
In a high-vaccination community, few are at risk.
Both the VR and text-and-image treatment ended with a brief summary, highlighting the takeaway message
(“As you can see, when many people are vaccinated the virus does not spread as fast and it creates a world that is
safer for everyone. You can see the dierence in [...] the low and high vaccination scenarios”). A more detailed
description of both treatments, including video, can be found in the study repository (https:// osf. io/ wufxk/?
view_ only= 56e83 d061c 6d469 637 8d29c 2940a 4a).
Randomization and masking. Simple randomization between treatment orderings (VR rst or text rst)
happened within the Qualtrics survey soware. Random assignment to a version of the VR simulation (avoid
spreading or avoid infection) was tied to participants’ ID numbers (even or uneven), which were allocated con-
secutively to both VR-rst and text-rst participants. Experimenters only assisted participants in starting up
the VR simulation. ey were blind to both the treatment ordering and the VR simulation version that each
participant was assigned to.
Outcome measures. Two key measurements were taken before and aer participants’ rst treatment, as
well as aer their second treatment: First, vaccination intention for a hypothetical new COVID-19 strain was
assessed (0–100 scale; adapted from10). is primary outcome measure was pretested in a pilot study available
in the supplemental material. Second, seeing COVID-19 vaccination as a collective responsibility was assessed
(1–7 scale)5. e supplemental material details the wording of these two items, and all other measures collected
in the study.
All preregistered hypotheses are about the eect of the participants’ rst treatment on the two outcome meas-
ures, either the VR treatment or the text-and-image treatment. e supplemental material section describes the
models used to test these hypotheses; they are simple regressions of rst dierences in the outcome measures
on medium of rst treatment (VR or text).
Results
Figure3 (le panel) illustrates the eect of each treatment on vaccination intention as the dierence between
measurements before and aer the rst treatment. e supplemental material presents the full distribution of
individual treatment eects, as well as analyses that do not exclude maximum-score participants.
Comparing vaccination intention between the pre-treatment and rst post-treatment measure, for partici-
pants who did not already have maximally positive vaccination intention (n = 195), we nd that the VR treat-
ment increased vaccination intention by 9.3 points (95% CI: 7.0 to
11.5, p
<
0.001
). e VR treatment is more
eective than the text-and-image treatment, which only increases vaccination intention by 3.3 points (dierence
in eects: 5.8, 95% CI: 2.0 to
9.5, p
=
0.003
).
Comparing collective responsibility pre- and post-treatment, for participants who did not already score the
maximum on collective responsibility (n = 104), we nd that the VR treatment increases collective responsi-
bility by 0.82 points (95% CI: 0.37 to
1.27, p
<
0.001
). e VR treatment is once again more eective than a
text-and-image treatment, which increases collective responsibility by just 0.43 points, though the dierence is
not signicant (dierence in eects: 0.39, 95% CI: 0.36 to
1.14, p
=
0.275
). e power to detect signicant treat-
ment dierences is lower here, due to the smaller number of “moveable” participants with less-than-maximum
perceptions of collective responsibility.
Figure2. e busy square scene in VR, with feedback graph showing the number of infected, healthy and
vaccinated characters.
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Further, we conducted an exploratory analysis on whether the VR treatment further increases vaccination
intentions aer a text-and-image treatment. Indeed, as shown in the right panel of Fig.3, we nd that for par-
ticipants who experienced the text-and-image treatment rst and did not have maximum pre-treatment vac-
cination intention, the subsequent VR treatment further increased vaccination intention by 6.3 points (95% CI:
4.2 to
8.3, p
<
0.001
). In contrast, for those who received the text-and-image treatment aer the VR treatment,
there was no signicant further increase in vaccination intention (eect: 0.8, 95% CI:
−0.6
to
2.3, p
=
0.309
).
We also explored any potential dierence between the eectiveness of the two versions of the VR treat-
ment: the one where participants avoided spreading COVID-19 and the one where they avoided infection with
COVID-19. ere is no dierence in the eect of these two versions on either vaccination intention (dierence
in eects: 0.2, 95% CI:
−4.2
to
47, p
=
0.912
) or collective responsibility (dierence in eects:
−0.003
, 95% CI:
−0.46
to
0.45, p
=
0.989
).
Finally, we asked all participants who completed the full study (n = 208) whether learning about community
immunity via VR and text/pictures was fun, and whether they would like to receive more health communica-
tions via VR and text/pictures (1–5 scale). Compared to their ratings of text, participants rated VR as more fun
(dierence in means: 0.23, 95% CI: 0.11 to
0.35, p
<
0.001
). ere was no dierence on how much participants
wanted to receive future health communications via the two media (dierence in means:
−0.06
, 95% CI:
−0.18
to
0.05, p
=
0.301
).
Discussion
We provide seminal evidence that a rst-person experience of vaccinations’ collective benets in immersive VR
can increase vaccination intentions. Further, the VR treatment is nearly three times more eective than com-
municating the same content via text and images. As the intended eect of both treatments is quite clear, the
VR treatment’s greater eectiveness shows that its impact cannot be reduced to demand characteristics. is
is further supported by the nding that adding the VR treatment aer the text-and-image treatment further
increased vaccination intention, whereas adding the text-and-image treatment aer the VR treatment did not
provide any further benet.
Our results suggest that, due to the unique type of content it allows for, a VR intervention communicating
about the collective benet of vaccination can go beyond merely providing information. e results t into a
growing literature on the eectiveness of communicating about community immunity on vaccination intentions
(see9 for a review) and builds on the nding that such interventions appear more eective when they are more
engaging, such as via interactive simulations10–12, and when they elicit emotions, such as empathy7. As we dem-
onstrate here, immersive VR is an eective alternative communication medium that can convey the collective
benet of vaccination in a highly engaging and emotional way.
ere are several potential mechanisms for why the VR treatment leads to changes in behavioral intentions
that deserve further investigation in future research. Our participants reported greater fun with the VR treatment
than with the text-and-image treatment. Previous research has shown that immersive VR increases participants’
interest in the content domain25 and enjoyment26, which can increase attention and eort18 to understand a
Figure3. Average eect on vaccination intention of rst treatment (n = 195, le panel) and second treatment
(n = 189, right panel), leaving out participants with maximum pre-treatment vaccination intention. Error bars
are 95% CIs.
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topic. Immersive simulations also induce a sense of presence17 and agency, which are essential for experiencing
embodiment (the feeling of being in and controlling a virtual body)27. is can cause participants to associate
negative and positive emotions with low and high vaccination rates, respectively. All of these features make it
possible to create intense experiences of scenarios from another person’s perspective—increasing empathy with
vulnerable others by allowing users to share their emotional processes28. Such eects are likely to further increase
when elements of gamication are used23, as in the present study. Taken together, there are both cognitive and
emotional features of the VR treatment that are likely inuence behavioral intentions.
e current research has some limitations. Firstly, because is dicult to dierentiate between mechanisms,
it is currently unclear which aspects of the intervention may be modied by practitioners, and which ones must
be kept. In future work, we will further investigate the mechanisms behind the eect of this intervention type,
by developing more versions of the simulation, and measuring more intermediate variables (e.g., empathy).
Secondly, our outcome measures were vaccination intention and collective responsibility. We used established
measures for these constructs and both have been linked to self-reported vaccine uptake6,29. Nevertheless, future
research should investigate the eects on actual vaccination behaviour.
irdly, in contexts with limited funds and technological know-how, VR interventions are less feasible. Still,
headsets have fallen dramatically in price, and they have become easier and more versatile in use—a trend that
is expected to continue, making VR a more accessible option in the future.
Finally, since our sample was composed of passersby in a public park, people who had greater interest in VR
may have been more likely to participate. Further, despite the fact that the study was advertised as a “VR experi-
ment on COVID-19”, with no mention of vaccines or advocacy, it is possible that participants anticipated our
objectives. is means that individuals with strong (anti-)vaccine beliefs may have declined to take part. Indeed,
the strength of our invention lies in motivating the vaccine-hesitant to engage with vaccine-related content, rather
than in persuading strong vaccine deniers.
Despite these limitations of the sample, showing the eectiveness of VR in increasing vaccination intentions
“in the wild” indicates the generalizability of our ndings to non-research settings. Moreover, the fact that VR
attracts a specic audience may be benecial. In fact, most volunteers in our study were younger adults—an age
group that currently has lower COVID-19 vaccine uptake, including in countries where the vaccine is widely
available30,31.
Although we found a substantial change in vaccination intentions, future research could develop an even
more eective VR treatment. For example, subsequent versions may improve the experience’s narrative, or create
more empathy with a main character who is especially vulnerable to the target disease. And while the present
study focused on COVID-19 vaccination intentions, the VR treatment can also easily be adapted to, and tested
for, dierent infectious diseases.
Our research contributes to a potential paradigm shi in health communication generally, and vaccine advo-
cacy in particular. Finding novel methods to reduce vaccine hesitancy is critical4,32,33. Immersive, gamied VR
provides a exible tool to create more engaging and interactive learning experiences—alongside other media
and technology, such as gamied apps and augmented reality. For vaccine and community immunity informa-
tion in particular, it is crucial to reach and engage healthy members of the population (including young adults).
Immersive VR has strong potential to complement more traditional communication channels and, therefore,
contribute to decreasing the threat from infectious diseases.
Data availability
Anonymous individual participant data, plus analysis les a data dictionary with variable descriptions, are avail-
able to anyone from the study repository (https:// osf. io/ wufxk/? view_ only= 56e83 d061c 6d469 637 8d29c 2940a
4a). e repository also includes the study protocol, pre-analysis plan and informed consent form.
Received: 18 August 2021; Accepted: 23 February 2022
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Acknowledgements
is research was funded by the European Institute of Innovation and Technology (EIT Health, Grant No.
210836). Registered at Open Science Foundation, https:// doi. org/ 10. 17605/ OSF. IO/ WUFXK.
Author contributions
All authors were involved in conceiving the study idea and design. C.V. led the study design and implementation,
processed and analyzed data, and draed the paper. T.L. created the Virtual Reality application and processed
data. M.A., A.M., G.M. and R.B. were involved in study implementation. C.V., T.L., M.A., A.M., C.H., G.M. and
R.B. revised the paper.
Funding
e funder of the study (EIT Health) had no role in study design, data collection, data analysis, data interpreta-
tion, or writing of the report.
Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to C.V.
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