JMIR FORMATIVE RESEARCH

Banakou et al

Original Paper

Desensitizing Anxiety Through Imperceptible Change: Feasibility
Study on a Paradigm for Single-Session Exposure Therapy for
Fear of Public Speaking
Domna Banakou1, BSc, MSc, PhD; Tania Johnston2, BSc, MSc, PhD; Alejandro Beacco1, BSc, MSc, PhD; Gizem
Senel1, BSc, MSc, PhD; Mel Slater1, BSc, MSc, MA, DSc
1

Event Lab, Department of Clinical Psychology and Psychobiology, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

2

Systems Neuroscience, August Pi i Sunyer Biomedical Research Institute, Barcelona, Spain

Corresponding Author:
Mel Slater, BSc, MSc, MA, DSc
Event Lab
Department of Clinical Psychology and Psychobiology
Institute of Neurosciences, University of Barcelona
Campus de Mundet Edifici CAVE
Passeig de la Vall d'Hebron 171
Barcelona, 08035
Spain
Phone: 34 93 403 ext 9618
Email: melslater@ub.edu

Abstract
Background: Exposure therapy (ET) for anxiety disorders involves introducing the participant to an anxiety-provoking situation
over several treatment sessions. Each time, the participant is exposed to a higher anxiety-provoking stimulus; for example, in thecase of fear of heights, the participant would successively experience being at a greater height. ET is effective, and its counterpart,virtual reality (VR) exposure therapy (VRET), where VR substitutes real-world exposure, is equally so. However, ET istime-consuming, requiring several sessions.
Objective: This study aimed to compare the results of single-session exposure with those of traditional VRET with regard to
reducing public speaking anxiety.
Methods: We introduced a paradigm concerned with public speaking anxiety where the VR exposure occurred in a single
session while the participant interacted with a virtual therapist. Over time, the therapist transformed into an entire audience withalmost imperceptible changes. We carried out a feasibility study with 45 participants, comparing 3 conditions: single-session
exposure (n=16, 36%); conventional multiple-session exposure (n=14, 31%), where the same content was delivered in successive
segments over 5 sessions; and a control group (n=15, 33%), who interacted with a single virtual character to talk about everyday
matters. A week later, the participants were required to speak on a stage in front of a large audience in VR.
Results: Across most of the series of conventional public speaking anxiety measures, the single-session exposure was at leastas effective in reducing anxiety as the multiple-session exposure, and both these conditions were better than the control condition.
The 12-item Personal Report of Confidence as a Speaker was used to measure public speaking anxiety levels, where higher values
indicated more anxiety. Using a Bayesian model, the posterior probabilities of improvement compared to a high baseline wereat least 1.7 times greater for single- and multiple-session exposures compared to the control group. The State Perceived Index ofCompetence was used as a measure of anticipatory anxiety for speaking on a stage in front of a large audience, where lower
values indicated higher anxiety. The probabilities of improvement were just over 4 times greater for single- and multiple-session
exposures compared to the control group for a low baseline and 489 (single) and 53 (multiple) times greater for a middle baseline.
Conclusions: Overall, the results of this feasibility study show that for moderate public speaking anxiety, the paradigm of
gradual change in a single session is worth following up with further studies with more severe levels of anxiety and a larger
sample size, first with a randomized controlled trial with nonpatients and subsequently, if the outcomes follow those that we havefound, with a full clinical trial with patients.

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Banakou et al(JMIR Form Res 2024;8:e52212) doi: 10.2196/52212
KEYWORDS
exposure therapy; virtual reality; gradual exposure; fear of public speaking; anxiety; change blindness; public speaking;desensitization; anxiety disorder; feasibility study

Introduction
Exposure therapy (ET) for anxiety disorders (and other mental
health conditions) involves the systematic desensitization overtime of patients to feared stimuli or situations. For example,
patients with fear of public speaking might be slowly introduced
to situations of public speaking: in the first session, perhaps toa photograph of an audience, in the next to a video, and thengradually building toward speaking to a live audience. This has
proven over several decades to be an effective therapy [1,2].
Virtual reality (VR) exposure therapy (VRET) uses the same
technique, but VR provides the stimuli, relying on the fact thatpeople tend to respond realistically to events in VR [3] andthereby would exhibit anxiety in response to the feared
situations. For example, in the case of fear of public speaking,a prerequisite for the success of VRET is that people should
exhibit similar anxiety to a virtual audience as they would to areal audience, which has been shown to be the case [4,5]. Thisapproach has been studied and used over the past 3 decades inthe treatment of a variety of phobias and mental health disorders,
including but not limited to social anxiety disorders,claustrophobia [6], panic attacks, posttraumatic stress disorder,
eating disorders [7], and generalized anxiety disorders [8,9].
Early examples can be found in Rothbaum et al [10-12] and in
Difede and Hoffman [13], with a review of this early work in
Krijn et al [14] and a meta-analysis in Powers and Emmelkamp[15], which showed a large effect size for VRET compared tocontrol conditions and no disadvantage compared to in vivo
treatments. This was seen again in a randomized controlled trial(RCT) that specifically considered social anxiety disorders [16].
Moreover, VRET has practical and logistic advantages since
the entire treatment can take place in the office of the clinician,rather than arranging for outside visits for the patients to be
exposed to real-life events (eg, visiting a high floor of a buildingin the case of fear of heights or being exposed to a live audiencein the case of public speaking anxiety). Moreover, there isevidence suggesting that VRET may be preferred to in vivo
therapy [17] by patients with anxiety disorders. Recent
meta-analyses have continued to show that VRET leads totherapeutic outcomes that are at least as successful as in vivo
treatments [18,19], including cases of severe anxiety, obsessive
compulsive, and posttraumatic stress disorders [20] andspecifically in relation to public speaking anxiety [21].
VRET has been extensively researched in the treatment of public
speaking anxiety [16,22-25], where individuals initiallyexperience anxiety, leading to behavioral, cognitive, andphysiological symptoms, when delivering or anticipating thedelivery of a speech in front of an audience. ET for public
speaking anxiety involves gradual exposure of individuals overvarious sessions to situations of public speaking, where at eachsuccessive session, the situation becomes closer to speaking toa live audience. VRET follows the same idea but with virtual
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audiences. VR, in particular, offers greater flexibility at low
cost in the sense that aspects of the stimuli can easily bechanged: audiences can be of different types and be reactive[5], and parameters such as the size of the audience can bechanged [16,26,27], including their appearance and behavior[5,28], as well as the virtual location and context of the speech(eg, classrooms, lecture rooms, conference rooms) [24] and therepresentation of the self [29,30].
ET, whether in vivo or in VR, can be accompanied by cognitive
therapy, where the clinician attempts to help the patient reframe
their anxious thoughts about the situation. For example, Freeman
et al [31] used a simple form of cognitive behavioral ET withVR, where over 5 sessions, patients with fear of heights learned
to overcome their fear. Their task was to move to various levelsof a building accompanied by a virtual therapist and at eachlevel to perform some tasks on a balcony overlooking an atrium
below. Ultimately, they made it to the top floor, and their levelof anxiety was significantly reduced compared to before theexposures and to the control condition.
A meta-analysis by Lim et al [32] found an average of 6 VR
sessions, each lasting around 37 minutes, among effective VRET
interventions. Another meta-study by Hinojo-Lucena et al [33]found that up to 12 sessions are necessary, spread over a week
for effective treatment. This is similar to the finding of Reeves
et al [21], who reported between 5 and 12 sessions in the papers
studied to be effective, while Chesham et al [18] found up to
14 sessions to be effective. Although not in the context of an
ET intervention, Boetje and van Ginkel [34] argued that the
optimum number of VR sessions for reducing public speaking
anxiety cannot be prescribed, since the characteristics of VR
treatments vary among the reviewed studies. Following from
this, Lim et al [32] argued that the number and length of sessions
should be considered as a function of severity of the patient’s
condition.
Here, we introduced a new paradigm based on the idea of VRET
but with a single VR session during which participants advancetoward a situation that would cause greater anxiety, with changes
occurring almost imperceptibly. Participants with fear of public
speaking are engaged in conversation by a virtual counselor,
who explains the issues behind fear of public speaking andencourages the participants to speak about their own public
speaking problems and gives them various exercises to do. The
counselor is represented by a virtual human character, whostands in front of the person, facing them, and talks and gesturesin a natural way. After a while, a copy of the counselor emerges
from behind their virtual body, so there are 2 identical instancesof the counselor, although only the original one continues the
dialogue, while the other one moves off to the side and continues
listening. As time progresses, this division process continues,
where the additional copies emerge. In addition, after a while,the new copies gradually transform into different virtual humancharacters. This process continues mainly in peripheral vision,
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and the changes are imperceptible. Over time, the standing
virtual characters adopt a seated position. By the end of thesession, the participants find themselves speaking in front ofan entire seated audience.
The idea behind why this approach may be effective is toconsider at which point the anxiety provoked by public speaking
would become active. First, the participant speaks to only 1(virtual) person. Then, the number of people becomes 2, but itis the same person—the counselor. However, 2 is not an
audience, nor is 3. If 3 people are not an audience, are 4? If 4
people are all right, does 1 more matter? Generally, if theparticipant is comfortable speaking to n people, thenimperceptibly making the audience n+1 should make no
difference (for n>1). In addition, the changes take place slowly(except for the first), so there is no obvious moment when theparticipant is not speaking to an audience and then is speaking
to an audience. The hypothesis is that people can learn throughthis process that just as it is possible to speak to 1 person withoutanxiety, the gradual transition to an audience should not generateanxiety. There is no point of discontinuity, where at one moment
the participant is suddenly speaking to an audience but a moment
ago was not. The hypothesis is that this learning will carry overto subsequent speeches in front of even larger audiences.
We carried out an experiment to explore the utility of this newparadigm. The participants were not patients but people whoreport some level of fear of public speaking. Hence, this was
not a clinical trial but a feasibility study to assess the efficacyof the paradigm. Hence, the specific aim of this paper was toinvestigate whether the gradual change paradigm might produce
a reduction in the fear of public speaking that is at least as goodas the traditional multiple-session VRET approach.

Methods
Ethical Considerations

Banakou et alprovided written informed consent. All methods were performedin accordance with relevant guidelines and regulations.

Experimental Design
We conducted the experiment with 45 participants using abetween-groups design, with a single factor (exposure) with 3
levels: single-session exposure (n=16, 36%), multiple-session
exposure (n=14, 31%), and control group (n=15, 33%). In the
single-session exposure, participants experienced the scenario
as described in the Introduction section—where 1 counselor
eventually morphed into an entire audience. The
multiple-session exposure group experienced traditional VRET,
with 5 sessions, each with an increasing audience size overapproximately 3 weeks, with 2 sessions per week (Figure 1).
The virtual environment, virtual counselor, and conversation
were identical in these 2 groups. Additionally, there was acontrol group, where participants conversed with agender-matched virtual human who asked general questions of
the participants, such as their name, job, skills, and interests(hobbies), and also talked about themselves (Figure 2A,B). The
purpose of the control group was to check that simple exposure
to any VR where the participant was required to talk would also
be sufficient to reduce anxiety.
At the end of the experiment (single session, multiple sessions
after the end of the fifth session, control group), participants
were asked to return 1 week later, when they would be on astage in front of a large audience in a theatre in VR and would
be required to introduce a performance by the band Dire Straits(Figure 2C,D). This final exposure was for testing. A historic(1980s) band was chosen so that participants would need to
conduct some research to find out more about it. Moreover,since the presentation in front of a large audience was to be
several days later, we could assess anticipatory anxiety.
The experimental scenarios are illustrated in Multimedia
Appendix 1, which shows a movie of the various conditions.

The experiment was approved by the Comissió Bioètica of the
University of Barcelona (IRB00003099), and participants
Figure 1. The scenario. (A) A male participant embodied in a male virtual body with a virtual mirror to his left. The inset shows the person with the
HMD and controllers. (B) Single-session exposure: the virtual counselor talks to the participant, while new copies of him divide and gradually transform
into different virtual human characters. (C) Multiple-session exposure (session 2): the participant is exposed to 3 virtual characters forming the audience.
(D) The full audience in both single- and multiple-session conditions. HMD: head-mounted display.

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Figure 2. Control condition and concert scenario. (A and B) Virtual humans for the control condition. (C) Virtual audience seen from the stage fromthe viewpoint of the participant in front of the microphone prior to introducing the band Dire Straits. (D) View from the audience once the concert
started.

Recruitment
Participants were recruited via advertisements around thecampus of the University of Barcelona and through social media.
Participants had a moderate level of public speaking anxiety.
During recruitment, potential candidates underwent a screening
process using the 12-item Personal Report of Confidence as a
Speaker (PRCS-12) [35], a standard instrument for assessing
public speaking anxiety where greater values indicate moreanxiety, and the Liebowitz Social Anxiety Scale (LSAS) [36]to identify those eligible to participate (see Multimedia
Appendix 2, which also includes details of inclusion andexclusion criteria).

Materials
We used a wide field-of-view stereo head-tracked,
head-mounted display (HMD), through which the participants
were embodied in a life-sized (gender matched) virtual bodyfrom a first-person perspective, spatially coincident with, andvisually substituting their real body. Through upper bodytracking, the virtual body moved synchronously with their own

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body movements. They saw a reflection of the virtual body fromthe neck down in a virtual mirror and when directly looking
toward themselves. Full details of hardware and programming
implementation are given in Multimedia Appendix 3, which
also contains the scripts of the dialogues between virtual
counselors and participants.

Response Variables
There were 2 types of response variables. First, after the VR
exposures, a questionnaire (“VR Questionnaire”) on the levelof body ownership and agency over the virtual body and general
responses to the virtual audience was administered. The
questions are shown in Multimedia Appendix 4; open-ended
questions that were used for sentiment analysis were also
included. Second, we included the response variables directly
related to public speaking anxiety, as shown in Table 1. The“pre” prefix refers to administration of the questionnaire priorto VR exposure, “post” means after the VR exposure and justbefore the VR concert exposure, and “after” refers to after theconcert. Further details of these are given in Multimedia
Appendix 5 [37-40].

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Table 1. Response variables.

a

VariableInterpretation

Study

preIATa, postIAT

•
•
•
•

IAT for fear of negative evaluation
Range: –2 to 2
Negative scores: automatic preference for self/rejected, other/liked
Positive scores: automatic preference for other/rejected, self/liked[37]

prePRCA24b, postPRCA24••

Communication apprehension: level of anxiety triggered by real or anticipated communication
Scores: 24-120 (higher scores indicating more anxiety)[38]

preSPICc, postSPIC,
afterSPIC

•
•

SPIC
Scores: 15-105 (higher scores indicating feeling better about self and considered as more competentperformance)[39]

postSTAId••

Short-form STAI administered before the concert speech
Scores: 8-32 (higher scores indicating more comfort)[40,41]

IAT: Implicit Association Test.

b

PRCA: Personal Report of Communication Apprehension.

c

SPIC: State Perceived Index of Competence.

d

STAI: State-Trait Anxiety Inventory.

Procedures
During the first visit of participants to the VR laboratory, they
were given an information sheet to read, and after they agreed
to continue with the experiment, they were given a consent form
to sign. Next, they completed a series of questionnaires assessing
their fear of public speaking and negative evaluation(prePRCA24, preSPIC, preIAT, and preSTAI, where PRCA
refers to Personal Report of Communication Apprehension,
SPIC refers to State Perceived Index of Competence, IAT refers
to Implicit Association Test, and STAI refers to State-Trait
Anxiety Inventory) that also served as baseline measures, aswell as other demographic data (see Multimedia Appendix 6
for details). Before the experiment started, participants werefitted with a head-mounted display (HMD) and body-tracking
equipment. The view seen through the HMD was calibrated foreach person. Participants in the multiple-session exposure groupreturned approximately 2 days later for the second session andso on until they completed all 5 treatment sessions. After thelast VR exposure, participants completed a post-VR experience
questionnaire. One week after participants’ single (single-session
or control exposure group) or final exposure (multiple-session
exposure group), they returned for a follow-up session, where
they had to give the Dire Straits welcome speech. Before
delivering the speech, they completed the questionnaires
assessing their fear of public speaking and negative evaluation
again (postIAT, postPRCA24, postSPIC, postSTAI). When theyfinished and came out of the virtual environment, they
completed a post-VR questionnaire and a questionnaire on theperceived index of competence (afterSPIC) related to the speech
they had just delivered. Next, they were debriefed about the
purpose of the study and compensated, and they left thelaboratory.

results conformed with earlier studies. Hence, we only
considered these response variables at the descriptive level.
For variables specifically related to anxiety, we used a Bayesianstatistical model detailed later, equivalent to 1-way ANOVA,
for all but 1 response variable and a logistic model for postIAT.
This resulted in posterior distributions for each of the parametersof the model, from which we could compute any probabilities
of interest. The model included all response variables
simultaneously, so there was no issue with multiple comparisonsthat would result in problems in the interpretation of significancelevels in classical null hypothesis testing.
For each response variable (postIAT, postPRCA24, postSPIC,
afterSPIC), the linear predictor, which related the independent
variables to the mean of the response variables, was of the form
Condition + Covariate + (Covariate × Condition) [+ familiarity](1)
where Condition refers to the main effects of control,
multiple-session, and single-session conditions; Covariate refers
to the pre variables (Table 1); Covariate × Condition is theinteraction effect; and the covariate familiarity refers to thescores on a question about the familiarity of participants withthe band Dire Straits. Since the SPIC variables and postSTAI
related to preparation for giving a talk about Dire Straits, we
also included familiarity for these response variables as acovariate based on the results of Figure S4 in Multimedia
Appendix 7, which shows a lower level of familiarity for themultiple-session condition compared with the other conditions.
There was no pre covariate for postSTAI.
The linear predictor for each response variable was thereforeof the form:

Statistical Analysis
We did not make statistical inferences about the questionnairevariables related to the VR experience (eg, body ownership; see
Multimedia Appendix 4) but only wished to check that thehttps://formative.jmir.org/2024/1/e52212

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Banakou et al

This is a standard ANOVA model where:

where ϕ>0 is a scaling parameter, and the mean of the••μ is the grand mean;

distribution is μi. To ensure that
, we used the logit linkfunction between the mean and the linear predictor, with theinverse:•αj is the main effect for condition;
γj is the interaction between the condition and the covariate,
where j=1 (control), 2 (multiple sessions), or 3 (single
session), with α1=γ1=0 so that multiple-session andsingle-session conditions were compared against the control
condition;
Ci is the pre variable as a covariate (eg, preIAT); andFi is familiarity (used for postSPIC, afterSPIC, andpostSTAI).

•••

Since in the case of postSTAI, there was no pre covariate, thecorresponding model was reduced to the main effect together
with Fi.
In the case of all response variables, except for postIAT, weused an ANOVA model with a covariate, so the mean (μi) of
the response variable (yi) was set equal to the linear predictor:
μi=ηi
(3)
In standard ANOVA, the likelihood (ie, the distribution of the
response variable conditional on the parameters) is required tohave a normal distribution. Here, we can be more flexible andlet the likelihood follow a Student t distribution. This has theadvantage that it has a wider dispersion than the normal, thus
allowing for potential outliers. In addition, for high enoughdegrees of freedom, it approximates the normal, and for degrees
of freedom of about >30, it is indistinguishable. Hence, thelikelihood is:(6)This is a standard logistic model.
We used weakly informative prior distributions (ie, proper
probability distributions with wide variance [42,43]). All βj ~normal(mean 0, SD 100); hence, the prior 95% credible intervals(CIs) were –200 to 200. All σ, υ, ϕ ~ Gamma(2,0.1); hence,the 95% prior CIs were 2.4-55.7.
All response variables were in the same overall model, so theparameters were, for example, denoted by μprc, αprc, βprc, γprc,υprc, and σprc in the case of postPRCA24, and the others areshown in Table S4 in Multimedia Appendix 8.
The analysis was carried out using the probabilistic
programming language Stan [44] with the rstan interface in R
(R Foundation for Statistical Computing) [45]. In total, 3000
iterations were used with 4 chains, and all simulations converged
successfully, with all Rhat=1, indicating that the results of thechains were properly mixed.

Results
Participant Statistics

yi ~ Student_t(υ, μi, σ)

The mean age of the 45 participants was 26.1 (SD 7.36) years,
27 (60%) of them identified as female, 14 (31%) as male, andthe remaining 4 (9%) as “other” or “preferred not to say.” Thedetailed demographics are provided in Multimedia Appendix
6.

(4)

Descriptive Statistics for VR Questionnaire Results

where υ>1 is the degrees of freedom parameters, μi is the mean,and σ is the scale parameter. Smaller values of υ and larger
values of σ correspond to greater dispersion with respect to thesymmetric distributions around μi. For larger υ values (>30
approximately), the distribution is equal to the normal
distribution, with mean μi and SD σ.

The median levels of body ownership and agency were highand in line with the results of previous experimental studies (eg,[46]), although body ownership and agency were of peripheral
interest in this study, and the participants only saw their virtual
body in a mirror for a short period prior to the discussion withthe virtual human and prior to introducing the band Dire Straits
in the second phase. Positive responses to the audience weremiddle to high in the single- and multiple-session conditions
but somewhat lower in the control condition. Full details andanalysis of these responses are available in Multimedia
Appendix 7.

In the case of postIAT, this model did not produce a good fit to
the data. This is because IAT is bounded by the values
byconstruction. Hence, we normalized the postIAT and preIAT
values by transforming them to ascale. Next, we used theBeta distribution for the likelihood: Beta was chosen because
the probability density is bound to, and the distribution cantake on many different shapes (symmetric about 0.5, J shaped,
inverse J shaped, uniform, etc) and therefore can adapt to thedata. Hence, in the case of IAT, the model is:yi ~ Beta(ϕμi, ϕ[1 – μi])

Descriptive Statistics for Anxiety-Related Results
Table 2 shows the means and SEs of the response variables thatwere directly related to public speaking from Table 1. It also
shows the effect sizes (Cohen d) for the difference between post
and pre measures and the effect sizes for the post-pre differencescomparing between conditions. The post-pre effect sizes for thecontrol group were all small, except for afterSPIC, which had
a medium effect size. Comparing the single- and(5)https://formative.jmir.org/2024/1/e52212

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Banakou et almultiple-session conditions, the post-pre effect sizes for the
single-session condition were always at least as strong as thosefor the multiple-session condition. In addition, the change in
IAT showed a medium effect size for both conditions, a medium
effect size for afterSPIC in the single-session condition, and asmall effect size for the others. Since postSTAI was only
measured just before the concert speech, there was no post-preeffect size. Most of the effect sizes for comparison between theconditions were small to medium, except for the difference
between the single-session exposure and control conditions,where the effect size was large for afterSPIC.
The effect sizes are crude overall measures and do not consider
the relationship between pre and post measures, nor do they
include the covariate familiarity (how much participants werefamiliar with the band Dire Straits). We now consider thestatistical model that included covariates.

Table 2. Means (SEs) of response variables by condition, paired effect sizes (Cohen d) for differences between post and pre measures, and effect sizes(Cohen d) for post-pre values comparing between conditions.
IATa

PRCA24b

postSPICc

afterSPIC

postSTAId

Mean (SE)0.35 (0.15)

5.38 (2.49)–2.31 (3.00)

5.88 (2.94)

23.2 (1.27)

Cohen d post-pre

0.62

0.26

–0.21

0.58

—e

Mean (SE)0.30 (0.17)

0.00 (3.36)

2.29 (4.77)–5.36 (6.84)

20.7 (0.93)

Cohen d post-pre

0.59

0.00

0.16

–0.32

—

Mean (SE)–0.10 (0.20)

4.93 (2.50)–7.20 (2.31)–11.73 (3.35)

20.6 (1.28)

Cohen d post-pre

–0.18

0.24

–0.35

–0.50

—

Cohen d post-pre (single- vs multiple-session
exposure)

0.075

0.478

–0.306

0.577

0.539

Cohen d post-pre (single-session exposure vscontrol group)

0.639

0.045

0.459

1.423

0.489

Cohen d post-pre (multiple-session exposure
vs control group)

0.564–0.441

0.680

0.317

0.026

Condition
Single-session exposure

Multiple-session exposure

Controla

IAT: Implicit Association Test.

b

PRCA: Personal Report of Communication Apprehension.

c

SPIC: State Perceived Index of Competence.

d

STAI: State-Trait Anxiety Inventory.

e

Not applicable.

Statistical Analysis of Anxiety-Related Results
Summaries of all the posterior distributions of the model’s
parameters are given in Multimedia Appendix 8, with further
details of the results including the model’s goodness of fit andthe availability of data and programs for analysis.
The main concern of interest was to check whether the
single-session condition is at least as effective in contributing
to a diminution of anxiety compared to the multiple-session
condition and whether these are different from the control group.
We considered each response variable in turn, noting that all
CIs were substantially narrower than their priors.

Implicit Association Test
The interaction terms for multiple- and single-session conditions
had CIs mainly in the positive region with high (single session,
0.945) and moderately high (multiple sessions, 0.817)
probabilities of being positive. In other words, greater valuesof preIAT were associated with greater levels of postIAT.
Moreover, the 2 CIs were similar in their range. The coefficient
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different from 0 (probability of being positive=0.458). These
results point to there being no or little difference between themultiple- and single-session exposures with respect to theireffect on IAT, and each of these were different from the control
group. Hence, postIAT increased with preIAT for the multipleand single-session groups. However, for the single-session
group, the probability that the rate of increase (slope) was greaterthan 1 was 0.835, whereas it was 0.601 for the multiple-session
group.

Personal Report of Communication Apprehension
In the control group, there was clearly a positive slope between
postPRCA24 and prePRCA24 (probability=1.000, βprc; see
Table S4 in Multimedia Appendix 8). This slope was reducedin the case of both multiple sessions (probability = 1 – 0.023 =0.977) and the single session (probability = 1 – 0.077 = 0.923).
The CIs for the interaction terms for the multiple- andsingle-session groups were similar. Although both multipleand single-session exposure might have reduced the slopes of
postPRCA24 on prePRCA24, there was no important difference
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between them, and they were both different from the control
group. Hence, both single- and multiple-session interventions
reduce the proportional increase in postPRCA24 with respect
to prePRCA24.

The postSPIC VariableThis was administered prior to the participants presenting theDire Straits welcome speech in front of a virtual audience. In
the control group, postSPIC was positively linearly related to
preSPIC (probability=1.000, coefficient was positive). In themultiple-session group, there was a high main effect (mean
47.78, probability=1.000), but the slope on preSPIC was reduced
compared to the control group by –0.67 (probability of being
negative = 1 – 0.002 = 0.998). The single-session condition also
had a high main effect of 26.29 (probability of being
positive=0.895), and the slope on preSPIC was reduced by amean of –0.33 (probability of being negative = 1 – 0.142 =
0.858). The familiarity variable was positively associated with
postSPIC (probability=0.810), consistent with the likelihoodthat the presentation task would be less stressful for those withprior knowledge of Dire Straits.

The afterSPIC VariableThis was administered after the presentation of the Dire Straits
welcome speech to the audience. As before, the control condition
was positively associated with preSPIC (probability=1.000).
The multiple-session condition had a strong positive main effectof 68.06 (probability of being positive=0.999) but with areduction in the slope on preSPIC by –1.02 (probability of being
negative = 1 – 0.001 = 0.999). The single-session condition also
had a strong positive main effect of 51.42 (probability=0.968)
but decreased in slope on preSPIC by –0.52 (probability = 1 –0.096 = 0.904). There was a high probability (0.970) thatfamiliarity with Dire Straits was positively associated withafterSPIC. For both postSPIC and afterSPIC, there was adifference for both multiple- and single-session exposurecompared with the control group.

The postSTAI Variable
The single- and multiple-session conditions had similar posterior
distributions, and their main effects were greater than those of
the control condition. postSTAI was positively associated withfamiliarity.
From the statistical model, the posterior distributions of each
response variable can be obtained for any values of thecovariates and the familiarity variable using equation 2. Figure
3 shows all the posterior distributions conditional on thecovariates being 10% greater than the lowest-possible value,
the middle value, and 10% lower than the highest-possible value.
Hence, if the range of the variable is xmin-xmax and if d = xmax
– xmin, then these values are xmin + 0.1d, (xmin + xmax)/2,
and xmax – 0.1d, respectively. From Figure 3, we can see thatthe control distributions were clearly different from those of
the multiple- and single-session conditions. Multiple sessions
gave better results than the single session only in the case of
postPRCA24, conditional on prePRCA24 being at the highest
level, as defined earlier, but in this case, both multiple- andsingle-session conditions were superior in their effects than thecontrol condition. For postIAT, postSPIC, afterSPIC, andhttps://formative.jmir.org/2024/1/e52212

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Banakou et al
postSTAI, the single-session condition was always at least as
good as the multiple-session condition.
Table 3 shows probabilities computed from the distributions inFigure 3, particularly the probabilities of improvement. For all
variables except postPRCA24, improvement corresponded to
an increase in the post score compared to the pre score. For
postPRCA24, improvement corresponded to a decrease inscores. For postSTAI, there was no pre score. Formally, theprobabilities are as follows:P(post > x | pre = x, observed data)(7)
where x is the lowest and middle of all variables except
postPRCA24.
For postPRCA24, the probabilities are as follows:P(post < x | pre = x, observed data)(8)
where x is the middle and highest. In the case of postSTAI,
there was no conditioning on pre=x.
For PRCA24, since lower values indicated better outcomes withrespect to public speaking anxiety, we were only interested in
the probability that postPRCA24 (after exposure) was less thanthat when prePRCA24 was equal to the middle and highest
scores before exposure (ie, these indicate improvement). For
all other variables, higher values represented better outcomes,
so we were interested in the probability that the post values
were greater than the pre values for the lowest and middle presettings. Probability ~ lowest means probability > lowest or
probability < lowest, depending on whether the symbol above
the column of probabilities is > or <. For example, theprobability that postPRCA24<110.4 conditional on
prePRCA24=110.4 was 0.958 in the single-session condition.
For postSTAI, there were no pre values, so the probability that
postSTAI>0 was 0.510 in the control condition and 0.900 in
the multiple-session condition. Where relevant in equation 2,
the familiarity variable was set at its median value 3.
From Table 3, we can see that the control condition had theleast probability of improvement in every case but 2 (lowest
IAT and middle PRCA24). Generally, the single-session
condition was only notably worse than the multiple-session
condition in the case of the middle postPRCA24, but in thiscase, the probabilities were in any case low. For afterSPIC, thesingle-session condition had a probability of improvement morethan 9 times greater than that of the multiple-session condition
for the middle score.
We also considered the odds of improvement (ie, ratios ofprobabilities). In the case of postIAT, with the preIAT at the
middle setting, the odds of improvement were 0.257/0.001 =257 for the multiple-session condition compared to the control
condition and 0.631/0.257 = 2.1 for the single-session condition
compared to the multiple-session condition. For postPRCS24,
with the highest prePRCS24, the odds of improvement for themultiple- and single-session conditions over the control
condition were at least 1.7 (0.958/0.577). For postSPIC, theodds of improvement with the middle preSPIC were 27 for the
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JMIR FORMATIVE RESEARCH
multiple-session condition and 24 for the single-session
condition over the control condition. For lowest afterSPIC, themultiple- and single-session conditions had odds of improvementof 4.2 and 4.3, respectively, and in the case of middle afterSPIC,
the multiple-session condition had odds of improvement of 53

Banakou et alover the control condition, while the single-session condition
had odds of improvement of 9 over the multiple-session
condition. For middle postSTAI, the odds of improvement were
1.8 and 1.9 for the multiple- and single-session conditions,
respectively, over the control condition.

Figure 3. Posterior distributions for each response variable using equation 2. The red curves are for the single-session condition, green for themultiple-session condition, and blue for the control condition. For all except postSTAI, these are conditional distributions with the prevariable: lowest(L), middle (M), or highest (H) values. For postSTAI, it is the unconditional distribution. The familiarity variable is set at its median of 3. IAT: Implicit
Association Test; PRCA: Personal Report of Communication Apprehension; SPIC: State Perceived Index of Competence; STAI: State-Trait Anxiety
Inventory.

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Banakou et al

Table 3. Probabilities of response variables showing an improvement in the 3 conditions.
Response variable and conditions

Lowesta

Middlea

Highesta

Probability ~ b lowest Probability ~ middle Probability ~ highest

postIATc

–1.6

0

1.6

>

>

Control

1.000

0.001

Multiple sessions

1.000

0.257

Single session

1.000

0.631

—

<

<

Control

0.116

0.577

Multiple sessions

0.029

0.998

Single session

0.006

0.958

>

>

—

Control

0.006

0.023

Multiple sessions

0.000

0.632

Single session

0.026

0.556

>

>

Control

0.232

0.002

Multiple sessions

1.000

0.107

Single session

0.980

0.978

>

>

Control

1.000

0.510

Multiple sessions

1.000

0.900

Single session

1.000

0.982

postPRCA24ef

postSPIC

afterSPICg

postSTAI

a

33.6

24

24

10.4

72.0

60

60

20

110.4

96

96

29.6

—d

—

—

The values for lowest, middle, and highest are for the pre variates.

b

The symbol ~ stands for > or <, depending on the variable concerned.

c

IAT: Implicit Association Test.

d

Not applicable.

e

PRCA: Personal Report of Communication Apprehension.

f

SPIC: State Perceived Index of Competence.

g

STAI: State-Trait Anxiety Inventory.

Sentiment Analysis of Participant Comments
As mentioned earlier, participants were asked open-ended
questions for comments on their experience at the end of thefirst phase of the study (the treatment: single session, multiple
sessions, or control) and again after the end of the second phase(the test: introduction of the concert 1 week later). Their
responses were used to carry out sentiment analyses to examine
the extent to which sentiment varied across the experimental
conditions. This is described in detail in Multimedia Appendix
9. Sentiment scores for both the treatment and test comments
fell into 2 distinct low- and high-sentiment clusters. Those in
the single- and multiple-session groups were more than twice
as likely to be in the high-sentiment cluster than the
low-sentiment one, whereas those in the control group wereapproximately the same in both. For the test condition, similar
results were found, except that only those in the single-session
group had more than double the likelihood to express highcompared to low sentiment, whereas those in the control grouphttps://formative.jmir.org/2024/1/e52212

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were doubly likely to express low sentiment. It is noteworthy
that those in the control group typically indicated thenervousness they felt during the period before they introduced
the band. With the relatively low sample size, we cannot
conclude that these findings would generalize to the populationof people with fear of public speaking, but they are compatible
with what might be expected.

Discussion
Principal Findings
ET typically requires multiple sessions, with patients being
gradually exposed to increasing levels of anxiety-producing
stimuli. Here, we introduced a new paradigm based on gradualchange within a single session. The participant begins by talking
to a single counselor standing in front of them, but by the endof the session, that single counselor morphs into a seatedaudience. We elicited response variables prior to VRET andthen both before and after participants introduced a music band
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JMIR FORMATIVE RESEARCH

Banakou et alin front of a large virtual theater audience. The single-session
condition was compared with traditional VRET of 5 sessions
and with a control group. The results indicate that single-session
exposure results in outcomes that are at least as favorable as
those in multiple-session exposure on most measures and thatboth single- and multiple-session conditions result in outcomesthat are typically better than those of the control condition.

However, at the medium level of the initial IAT, there is adifferential effect of the 3 methods, with single-session exposure
resulting in a greater probability of higher IAT scores and thecontrol condition being clearly ineffective. This can be seen
even more clearly in the case of both postSPIC (anticipatory
anxiety prior to the concert announcement) and afterSPIC (after
the concert announcement).

Comparison With Prior Work

The idea of single-session ET has been discussed before,
although with a quite different paradigm. A 1-session VRET
for public speaking anxiety was introduced by Lindner et al[49], with strong positive results compared to a waiting-list
group. However, this involved an extensive 3-hour intervention,
including a VR session, followed also by a 4-week in vivotransition program. The paradigm was similar to an earlier one
for treatment of spider phobia, which had similar positive results[50].

The results, as represented by the effect sizes (Table 2), were
mostly moderate. There was a clear distinction between thecontrol group and the treatment groups, and the post-pre effectsizes ranged from 0 to 0.62 (considering absolute values).
However, our results compare well with those of a meta-analysis
reported by Chesham et al [18], where across the 9 studies using
VRET for social anxiety that met their inclusion criteria andthe 13 sets of results within those studies, the post-pre effectsizes (Hedge g) ranged from –0.56 to 1.57 (median 0.486, IQR
0.054-0.604). Considering 6 studies that compared VRET witha waiting group, the overall effect size was 0.82. Similarly, ameta-analysis of 13 papers on social and performance anxiety
was reported by Carl et al [19]. Overall, the median effect size
(Hedge g) was 0.61 (IQR 0.14-0.91). Note that Hedge g and
Cohen d are similar. For comparison only with a waiting-list
group, the median effect size was 0.91 (IQR 0.68-1.33), and forcomparison in vivo, the median was 0.61 (IQR 0.14-0.91). A
recent meta-analysis [32] found similar (in fact, slightly lower)
effect sizes across 92 studies on public speaking anxiety. A
further recent meta-analysis was reported by Reeves et al [21]with higher effect sizes, although breakdowns across individual
studies and measures were not presented. As a further example,
an RCT [23] included 3 groups (cognitive behavioral therapy[CBT], cognitive behavioral therapy in virtual reality [VRCBT],and waiting list) and 12 sessions of ET. There was no difference
between the CBT and VRCBT groups, so they were combined
into 1 treatment group. Across a range of variables, comparisonsof the waiting-list group with the treatment group found effectsizes for anxiety reduction in the range of 0.58-1.00. Good
results were maintained at 1-year follow-up [47]. In a study
comparing in vivo ET (6 sessions) with VRET (4 sessions) anda waiting list [16], effect sizes (Cohen d) were compared for
the VR group and the waiting-list group (posttreatment
controlling for pretreatment) across a number of measures.
When comparing the VR group with the waiting-list group, themedian effect size was 0.74 (IQR 0.63-0.93), and whencomparing in vivo with the waiting-list group, the median effectsize was 0.72 (IQR 0.48-1.00). At 12-month follow-up, themaximum effect size was 0.61. The effect sizes for all thesestudies were across a range of different instruments. In general,
evidence suggests that results from VRET do generalize to reallife [48].
The effect sizes found in our study are in line with findings in
the literature. However, the effect size is a blunt instrument
dealing only with overall average effects. We propose that thetype of analysis illustrated in Figure 3 gives richer information.
Here, we can immediately see the differential effects of thetreatments, depending on the starting state of the participants.
For example, if the IAT is low to start with (greater
self-rejection), all the conditions lead to an improvement.
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ET is thought to be effective due to either habituation throughsystematic desensitization or, alternatively, classical extinction[1,51-53]. In the latter case, the stimulus (an audience) ispresented multiple times but under conditions where the normal
response is avoided through inhibitory control. It is unlikelythat the new paradigm presented here can be explained througheither habituation or extinction. Although there is exposure, it
is implicit rather than explicit (ie, the focus of the participant
is on the discussion with the single individual throughout).
Moreover, the situation, at least to start with, is a bizarre one,with multiple copies of the same individual appearing but taking
no part in the proceedings. We postulate that the positive effectof the treatment occurs through generalization from speaking
to a single individual: the participant implicitly learns thatspeaking to an audience is not different from speaking to anindividual; if the latter is possible without anxiety, then so isthe former.
The model of social anxiety disorder proposed by Clark andWells [54] is useful in understanding the outcome. Although
the model applies to the more general situation, public speaking
anxiety is a specific case of social anxiety. In particular, someof the elements that contribute to instances of social anxiety are
relevant to the gradual and implicit exposure paradigm. One
component of the model is “perceived social danger,” whereindividuals predict their own negative performance. In this case,there is a conversation with an individual, and the required
performance is carefully guided (eg, read out numbers from 1
to 20 or a piece of text, or describe a movie). “Anticipatory
anxiety leading to worry” is where individuals focus on past or
imagined failures and thereby predict their own negative
behavior. This again is unlikely to occur in the situation whereparticipants are talking to a single individual and, moreover,where the conversation is precisely about public speaking
anxiety and where there is counseling about how to prepare fora talk. “Processing of self as a social object” is whereindividuals’ own negative model of themselves is projected
onto others so that they think that others perceive them in thisnegative way. However, the conversation with the single
individual, someone who apparently understands and gives
advice about public speaking anxiety, may militate against this.
The participant is not in a situation of being evaluated but rather
in one where help and advice are available. Overall, we suggest
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JMIR FORMATIVE RESEARCH
that the encounter with the counselor provides an opportunity
for implicit learning that a talk in front of an audience is notfundamentally different from talking with an individual. It isan example of where a single positive experience of talking infront of an audience could generalize to other situations. Usinga different method, it was found by Shadinger et al [55] thatwhen college students were instructed to make positive
affirmative statements about their forthcoming public speaking
performance, this reduced their anticipatory anxiety, suggestingthat a single positive experience can generalize.
Nevertheless, although the conversation is with a single
individual, an audience does gradually emerge, with changes
taking place largely in peripheral vision, with the focus ofattention on the counselor. Suppose that some individuals
experience a degree of change blindness [56,57] where they
were not consciously aware of the growing audience most of
the time. In this case, how could it be possible that the audiencemight have an influence? It has been shown that in change
blindness, when participants do not consciously see the changes,
these, nevertheless, influence their subsequent decision-making[58]. Even if the audience might hardly be noticed during thetime of the conversation, although it is obvious by the end,
participants might still be influenced by the fact that they arehaving a conversation with a growing audience present. Change
blindness has been observed in VR [59,60], and our recent study
shows that it operates with respect to virtual bodies that are
subject to gradual change, even though participants are looking
toward them all the time [61].

Limitations
Although some outcome measures were taken after the
introduction of the band Dire Straits at the virtual concert andhence 7 days after the main VR treatment, there is a need for
longer-term follow-up for this new paradigm. Here, we wereconcerned about providing an initial evaluation, and the positive
results are encouraging to undertake an RCT with a larger
sample and then later a clinical study with longer-term
follow-up. The RCT would also include individuals with ahigher level of public speaking anxiety.

Banakou et al
Consumer VR devices that are entering the market now havebuilt-in eye and facial tracking. Eye tracking especially would
be useful to determine how much participants do indeed pay
attention to the emerging audience, and eye tracking and facial
expression tracking could also provide real-time measures ofthe extent of ongoing anxiety. Furthermore, the method bywhich the audience is introduced can be explored. Our approach
was based on the idea of maintaining the whole exposure as aconversation with a single individual and emphasizing that by
the fact that all new characters emerged from, and were initiallyclones of, that individual. Moreover, we adopted the methodthat the copies would gradually transform into other charactersand eventually sit down. It is possible that similar results might
have been obtained had characters appeared 1 by 1 at different
places in the room. Here again, an audience would graduallyform but not related to the counselor. Our view is that theconnection with the counselor is essential (to maintain the ideathat this was a conversation with an individual and not with agroup), but this would be interesting to study. Anotherpossibility is that the entire audience might gradually become
visible over the course of the conversation, already as different
seated characters. Although this is possible, again our view isthat the morphing of the counselor into an audience is an
essential part of the method.

Conclusion
Our initial results in testing the feasibility of this paradigm areencouraging and worthy of further research. Although we used
the gradual change method in the context of public speaking
anxiety, it could also be used for other anxiety states. For
example, for fear of heights, the participant could be talking toa virtual counselor, initially at the same level, but imperceptibly
the adjacent ground level could move lower and lower until,
ultimately, the participant and counsellor are standing near aprecipice. With, for example, a phobia of spiders, the participant
could interact with a butterfly that gradually morphs into a
spider. In the case of agoraphobia, the participant might starttalking with a counselor in a closed safe space that graduallymorphs into an open shopping area. Further studies of thisparadigm are needed in a variety of situations in order to test
its efficacy.

Acknowledgments
This research was funded by the European Research Council Advanced Grant MoTIVE, Moments in Time in Immersive Virtual
Environments (#742989), and by the Departament de Recerca i Universitats de la Generalitat de Catalunya (AGAUR 2021SGR-01165 - NEUROVIRTUAL), supported by FEDER (Fons Europeu de Desenvolupament Regional). GS was supported by
the “la Caixa” Foundation (ID 100010434) with fellowship code LCF/BQ/DR19/11740007. We thank Francisco Macia and Paul
Casselle for help with the recordings.

Authors' Contributions
DB designed the experiment, implemented the scenarios, carried out the experiment, prepared the data, and wrote the paper. TJ
contributed to the experiment design and reviewed the paper. AB contributed to the implementation of the scenarios and reviewed
the paper. GS contributed to carrying out the experiment and reviewed the paper. MS designed the original concept and theexperiment, carried out the statistical analysis, wrote the paper, and obtained the funding.

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Banakou et al

Conflicts of Interest
The method presented in this paper is the subject of a patent application by the University of Barcelona. MS is a founder of thespin-off company Virtual Bodyworks (also known as kiin.tech), which works in the field of diversity, equity, and inclusion. No
other authors have competing interests.

Multimedia Appendix 1
Movie illustrating the scenario.
[MP4 File (MP4 Video), 104906 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Participant recruitment.
[DOCX File , 21 KB-Multimedia Appendix 2]

Multimedia Appendix 3
Implementation.
[DOCX File , 27 KB-Multimedia Appendix 3]

Multimedia Appendix 4
Virtual reality questionnaire.
[DOCX File , 16 KB-Multimedia Appendix 4]

Multimedia Appendix 5
Response variables.
[DOCX File , 22 KB-Multimedia Appendix 5]

Multimedia Appendix 6
Participant statistics.
[DOCX File , 17 KB-Multimedia Appendix 6]

Multimedia Appendix 7
Virtual reality questionnaire responses.
[DOCX File , 53 KB-Multimedia Appendix 7]

Multimedia Appendix 8
Summary of posterior distributions and goodness of fit.
[DOCX File , 27 KB-Multimedia Appendix 8]

Multimedia Appendix 9
Sentiment analysis.
[DOCX File , 227 KB-Multimedia Appendix 9]

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Abbreviations
CBT: cognitive behavioral therapy
ET: exposure therapy
HMD: head-mounted display
IAT: Implicit Association Test
PRCA: Personal Report of Communication Apprehension
PRCS-12: 12-item Personal Report of Confidence as a Speaker
RCT: randomized controlled trial
SPIC: State Perceived Index of Competence
STAI: State-Trait Anxiety Inventory
VR: virtual reality
VRCBT: cognitive behavioral therapy in virtual reality
VRET: virtual reality exposure therapy

Edited by A Mavragani; submitted 26.08.23; peer-reviewed by A Bustillo; comments to author 23.03.24; revised version received
29.04.24; accepted 08.05.24; published 22.07.24
Please cite as:
Banakou D, Johnston T, Beacco A, Senel G, Slater M
Desensitizing Anxiety Through Imperceptible Change: Feasibility Study on a Paradigm for Single-Session Exposure Therapy for Fearof Public Speaking
JMIR Form Res 2024;8:e52212
URL: https://formative.jmir.org/2024/1/e52212
doi: 10.2196/52212
PMID: 39037760©Domna Banakou, Tania Johnston, Alejandro Beacco, Gizem Senel, Mel Slater. Originally published in JMIR Formative Research(https://formative.jmir.org), 22.07.2024. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete
bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license
information must be included.

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