Desensitizing anxiety through imperceptible change: feasibility study on a paradigm for single-session exposure therapy for fear of public speaking

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 1Event Lab, Department of Clinical Psychology and Psychobiology, Institute of Neurosciences, University of Barcelona, Barcelona, Spain 2Systems 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: [email protected] 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 the case 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 is time-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 with almost 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 least as 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 were at least 1.7 times greater for singleand multiple-session exposures compared to the control group. The State Perceived Index of Competence 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 singleand 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 have found, with a full clinical trial with patients. JMIR Form Res 2024 | vol. 8 | e52212 | p. 1https://formative.jmir.org/2024/1/e52212 (page number not for citation purposes) Banakou et alJMIR FORMATIVE RESEARCH XSL • FO RenderX (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 over time 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 to a photograph of an audience, in the next to a video, and then gradually 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 that people tend to respond realistically to events in VR [3] and thereby 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 a real audience, which has been shown to be the case [4,5]. This approach has been studied and used over the past 3 decades in the 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 to control 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 building in the case of fear of heights or being exposed to a live audience in the case of public speaking anxiety). Moreover, there is evidence 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 to therapeutic 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] and specifically in relation to public speaking anxiety [21]. VRET has been extensively researched in the treatment of public speaking anxiety [16,22-25], where individuals initially experience anxiety, leading to behavioral, cognitive, and physiological symptoms, when delivering or anticipating the delivery of a speech in front of an audience. ET for public speaking anxiety involves gradual exposure of individuals over various sessions to situations of public speaking, where at each successive session, the situation becomes closer to speaking to a live audience. VRET follows the same idea but with virtual audiences. VR, in particular, offers greater flexibility at low cost in the sense that aspects of the stimuli can easily be changed: audiences can be of different types and be reactive [5], and parameters such as the size of the audience can be changed [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 the representation 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 with VR, where over 5 sessions, patients with fear of heights learned to overcome their fear. Their task was to move to various levels of a building accompanied by a virtual therapist and at each level to perform some tasks on a balcony overlooking an atrium below. Ultimately, they made it to the top floor, and their level of anxiety was significantly reduced compared to before the exposures 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 advance toward 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 and encourages 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, who stands in front of the person, facing them, and talks and gestures in a natural way. After a while, a copy of the counselor emerges from behind their virtual body, so there are 2 identical instances of 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 human characters. This process continues mainly in peripheral vision, JMIR Form Res 2024 | vol. 8 | e52212 | p. 2https://formative.jmir.org/2024/1/e52212 (page number not for citation purposes) Banakou et alJMIR FORMATIVE RESEARCH XSL • FO RenderX and the changes are imperceptible. Over time, the standing virtual characters adopt a seated position. By the end of the session, the participants find themselves speaking in front of an entire seated audience. The idea behind why this approach may be effective is to consider 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 it is 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 the participant is comfortable speaking to n people, then imperceptibly 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 the participant is not speaking to an audience and then is speaking to an audience. The hypothesis is that people can learn through this process that just as it is possible to speak to 1 person without anxiety, the gradual transition to an audience should not generate anxiety. 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 over to subsequent speeches in front of even larger audiences. We carried out an experiment to explore the utility of this new paradigm. The participants were not patients but people who report some level of fear of public speaking. Hence, this was not a clinical trial but a feasibility study to assess the efficacy of the paradigm. Hence, the specific aim of this paper was to investigate whether the gradual change paradigm might produce a reduction in the fear of public speaking that is at least as good as the traditional multiple-session VRET approach. Methods Ethical Considerations The experiment was approved by the Comissió Bioètica of the University of Barcelona (IRB00003099), and participants provided written informed consent. All methods were performed in accordance with relevant guidelines and regulations. Experimental Design We conducted the experiment with 45 participants using a between-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 over approximately 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 a control group, where participants conversed with a gender-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 a stage 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. 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 singleand multiple-session conditions. HMD: head-mounted display. JMIR Form Res 2024 | vol. 8 | e52212 | p. 3https://formative.jmir.org/2024/1/e52212 (page number not for citation purposes) Banakou et alJMIR FORMATIVE RESEARCH XSL • FO RenderX Figure 2. Control condition and concert scenario. (A and B) Virtual humans for the control condition. (C) Virtual audience seen from the stage from the 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 the campus 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 more anxiety, and the Liebowitz Social Anxiety Scale (LSAS) [36] to identify those eligible to participate (see Multimedia Appendix 2, which also includes details of inclusion and exclusion 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 body from a first-person perspective, spatially coincident with, and visually substituting their real body. Through upper body tracking, the virtual body moved synchronously with their own body movements. They saw a reflection of the virtual body from the 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 level of 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 prior to VR exposure, “post” means after the VR exposure and just before the VR concert exposure, and “after” refers to after the concert. Further details of these are given in Multimedia Appendix 5 [37-40]. JMIR Form Res 2024 | vol. 8 | e52212 | p. 4https://formative.jmir.org/2024/1/e52212 (page number not for citation purposes) Banakou et alJMIR FORMATIVE RESEARCH XSL • FO RenderX Table 1. Response variables. StudyInterpretationVariable [37] 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 [38] prePRCA24b, postPRCA24 •Communication apprehension: level of anxiety triggered by real or anticipated communication •Scores: 24-120 (higher scores indicating more anxiety) [39] preSPICc, postSPIC, afterSPIC •SPIC •Scores: 15-105 (higher scores indicating feeling better about self and considered as more competent performance) [40,41] postSTAId•Short-form STAI administered before the concert speech •Scores: 8-32 (higher scores indicating more comfort) aIAT: Implicit Association Test. bPRCA: Personal Report of Communication Apprehension. cSPIC: State Perceived Index of Competence. dSTAI: 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, as well as other demographic data (see Multimedia Appendix 6 for details). Before the experiment started, participants were fitted with a head-mounted display (HMD) and body-tracking equipment. The view seen through the HMD was calibrated for each person. Participants in the multiple-session exposure group returned approximately 2 days later for the second session and so on until they completed all 5 treatment sessions. After the last 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 they finished and came out of the virtual environment, they completed a post-VR questionnaire and a questionnaire on the perceived 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 the laboratory. Statistical Analysis We did not make statistical inferences about the questionnaire variables related to the VR experience (eg, body ownership; see Multimedia Appendix 4) but only wished to check that the 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 Bayesian statistical 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 parameters of 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 comparisons that would result in problems in the interpretation of significance levels 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; Covariaterefers to the pre variables (Table 1); Covariate × Condition is the interaction effect; and the covariate familiarity refers to the scores on a question about the familiarity of participants with the 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 a covariate based on the results of Figure S4 in Multimedia Appendix 7, which shows a lower level of familiarity for the multiple-session condition compared with the other conditions. There was no pre covariate for postSTAI. The linear predictor for each response variable was therefore of the form: (2) JMIR Form Res 2024 | vol. 8 | e52212 | p. 5https://formative.jmir.org/2024/1/e52212 (page number not for citation purposes) Banakou et alJMIR FORMATIVE RESEARCH XSL • FO RenderX This is a standard ANOVA model where: •μ is the grand mean; • •αjis 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 and single-session conditions were compared against the control condition; •Ciis the pre variable as a covariate (eg, preIAT); and •Fiis familiarity (used for postSPIC, afterSPIC, and postSTAI). Since in the case of postSTAI, there was no pre covariate, the corresponding model was reduced to the main effect together with Fi. In the case of all response variables, except for postIAT, we used 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 to have a normal distribution. Here, we can be more flexible and let the likelihood follow a Student tdistribution. This has the advantage that it has a wider dispersion than the normal, thus allowing for potential outliers. In addition, for high enough degrees of freedom, it approximates the normal, and for degrees of freedom of about >30, it is indistinguishable. Hence, the likelihood is: yi ~ Student_t(υ, μi, σ) (4) 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 the symmetric distributions around μi. For larger υ values (>30 approximately), the distribution is equal to the normal distribution, with mean μi and SD σ. 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 by construction. Hence, we normalized the postIAT and preIAT values by transforming them to a scale. Next, we used the Beta distribution for the likelihood: Beta was chosen because the probability density is bound to , and the distribution can take on many different shapes (symmetric about 0.5, J shaped, inverse J shaped, uniform, etc) and therefore can adapt to the data. Hence, in the case of IAT, the model is: yi ~ Beta(ϕμi, ϕ[1 – μi]) (5) where ϕ>0 is a scaling parameter, and the mean of the distribution is μi. To ensure that , we used the logit link function between the mean and the linear predictor, with the inverse: (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 the parameters were, for example, denoted by μprc, αprc, βprc, γprc, υprc, and σprc in the case of postPRCA24, and the others are shown 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 the chains were properly mixed. Results Participant Statistics 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, and the remaining 4 (9%) as “other” or “preferred not to say.” The detailed demographics are provided in Multimedia Appendix 6. Descriptive Statistics for VR Questionnaire Results The median levels of body ownership and agency were high and 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 with the virtual human and prior to introducing the band Dire Straits in the second phase. Positive responses to the audience were middle to high in the singleand multiple-session conditions but somewhat lower in the control condition. Full details and analysis of these responses are available in Multimedia Appendix 7. Descriptive Statistics for Anxiety-Related Results Table 2 shows the means and SEs of the response variables that were 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 differences comparing between conditions. The post-pre effect sizes for the control group were all small, except for afterSPIC, which had a medium effect size. Comparing the singleand JMIR Form Res 2024 | vol. 8 | e52212 | p. 6https://formative.jmir.org/2024/1/e52212 (page number not for citation purposes) Banakou et alJMIR FORMATIVE RESEARCH XSL • FO RenderX multiple-session conditions, the post-pre effect sizes for the single-session condition were always at least as strong as those for 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 a small effect size for the others. Since postSTAI was only measured just before the concert speech, there was no post-pre effect size. Most of the effect sizes for comparison between the conditions 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 were familiar with the band Dire Straits). We now consider the statistical 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. postSTAId afterSPIC postSPICc PRCA24b IATa Condition Single-session exposure 23.2 (1.27)5.88 (2.94)–2.31 (3.00)5.38 (2.49)0.35 (0.15)Mean (SE) —e 0.58–0.210.260.62Cohen d post-pre Multiple-session exposure 20.7 (0.93)–5.36 (6.84)2.29 (4.77)0.00 (3.36)0.30 (0.17)Mean (SE) —–0.320.160.000.59Cohen d post-pre Control 20.6 (1.28)–11.73 (3.35)–7.20 (2.31)4.93 (2.50)–0.10 (0.20)Mean (SE) —–0.50–0.350.24–0.18Cohen d post-pre 0.5390.577–0.3060.4780.075Cohen d post-pre (singlevs multiple-session exposure) 0.4891.4230.4590.0450.639Cohen d post-pre (single-session exposure vs control group) 0.0260.3170.680–0.4410.564Cohen d post-pre (multiple-session exposure vs control group) aIAT: Implicit Association Test. bPRCA: Personal Report of Communication Apprehension. cSPIC: State Perceived Index of Competence. dSTAI: State-Trait Anxiety Inventory. eNot 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 and the 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 multipleand 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 values of preIAT were associated with greater levels of postIAT. Moreover, the 2 CIs were similar in their range. The coefficient of preIAT for the control group showed no evidence of being different from 0 (probability of being positive=0.458). These results point to there being no or little difference between the multipleand single-session exposures with respect to their effect 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 greater than 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 reduced in 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 multipleand single-session groups were similar. Although both multipleand single-session exposure might have reduced the slopes of postPRCA24 on prePRCA24, there was no important difference JMIR Form Res 2024 | vol. 8 | e52212 | p. 7https://formative.jmir.org/2024/1/e52212 (page number not for citation purposes) Banakou et alJMIR FORMATIVE RESEARCH XSL • FO RenderX between them, and they were both different from the control group. Hence, both singleand multiple-session interventions reduce the proportional increase in postPRCA24 with respect to prePRCA24. The postSPIC Variable This was administered prior to the participants presenting the Dire 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 the multiple-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 a mean 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 likelihood that the presentation task would be less stressful for those with prior knowledge of Dire Straits. The afterSPIC Variable This 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 effect of 68.06 (probability of being positive=0.999) but with a reduction 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) that familiarity with Dire Straits was positively associated with afterSPIC. For both postSPIC and afterSPIC, there was a difference for both multipleand single-session exposure compared with the control group. The postSTAI Variable The singleand multiple-session conditions had similar posterior distributions, and their main effects were greater than those of the control condition. postSTAI was positively associated with familiarity. From the statistical model, the posterior distributions of each response variable can be obtained for any values of the covariates and the familiarity variable using equation 2. Figure 3shows all the posterior distributions conditional on the covariates 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 that the control distributions were clearly different from those of the multipleand 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 multipleand single-session conditions were superior in their effects than the control condition. For postIAT, postSPIC, afterSPIC, and postSTAI, the single-session condition was always at least as good as the multiple-session condition. Table 3 shows probabilities computed from the distributions in Figure 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 in scores. For postSTAI, there was no pre score. Formally, the probabilities 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 with respect to public speaking anxiety, we were only interested in the probability that postPRCA24 (after exposure) was less than that 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 pre settings. Probability ~ lowest means probability > lowest or probability < lowest, depending on whether the symbol above the column of probabilities is > or <. For example, the probability 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 the least 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 this case, the probabilities were in any case low. For afterSPIC, the single-session condition had a probability of improvement more than 9 times greater than that of the multiple-session condition for the middle score. We also considered the odds of improvement (ie, ratios of probabilities). 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 the multipleand single-session conditions over the control condition were at least 1.7 (0.958/0.577). For postSPIC, the odds of improvement with the middle preSPIC were 27 for the JMIR Form Res 2024 | vol. 8 | e52212 | p. 8https://formative.jmir.org/2024/1/e52212 (page number not for citation purposes) Banakou et alJMIR FORMATIVE RESEARCH XSL • FO RenderX multiple-session condition and 24 for the single-session condition over the control condition. For lowest afterSPIC, the multipleand single-session conditions had odds of improvement of 4.2 and 4.3, respectively, and in the case of middle afterSPIC, the multiple-session condition had odds of improvement of 53 over 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 multipleand 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 the multiple-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. JMIR Form Res 2024 | vol. 8 | e52212 | p. 9https://formative.jmir.org/2024/1/e52212 (page number not for citation purposes) Banakou et alJMIR FORMATIVE RESEARCH XSL • FO RenderX 55. Shadinger D, Katsion J, Myllykangas S, Case D. 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[FREE Full text] [doi: 10.1016/j.isci.2023.107938] [Medline: 37876610] 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 Fear of 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. JMIR Form Res 2024 | vol. 8 | e52212 | p. 16https://formative.jmir.org/2024/1/e52212 (page number not for citation purposes) Banakou et alJMIR FORMATIVE RESEARCH XSL • FO RenderX