Experiences and Behavior During a Virtual Reality Buffet Simulation: A Secondary Analysis of a Ghrelin-Related Pharmacology Randomized Controlled Trial in People with Alcohol Use Disorder

Abstract

Background:

Virtual reality (VR) can deliver standardized, ecologically valid assessments while capturing nuanced psychological and behavioral outcomes. Sensitivity of VR assessments to pharmacological interventions, however, has seldom been tested.

Methods:

We conducted a secondary analysis of a randomized, double-blind, placebo-controlled, crossover study evaluating a growth hormone secretagogue receptor (GHSR, the ghrelin receptor) blocker (PF-5190457) in people (N = 29) with alcohol use disorder. The previously reported primary study results demonstrated that the GHSR blocker did not reduce alcohol cue–elicited craving but did reduce the number of calories selected in a VR buffet food choice assessment. Here, we investigated the impact of the drug on experiences and behavior in the VR buffet.

Results:

Participants reported higher levels of liking the VR, more typicality, and more satisfaction with their food choices under the GHSR blocker than the placebo. We also found that participants who were given the placebo first took longer to make a decision about the food they should eat and were also more likely to go up for a second serving of food when on the placebo. This was significantly less likely for participants who were given the GHSR blocker.

Conclusions:

The present results suggest that the GHSR blocker influenced experiences and behavior in the VR buffet in ways that are conceptually consistent with the known real-world effects of blocking the ghrelin system. These findings support the validity of using VR buffets as proxies for real-world measurements in pharmacology contexts.

Introduction

Virtual reality (VR) has long been recognized as an important tool for conducting behavioral and biomedical research in a variety of domains. The rise of VR as a research tool is primarily due to the suite of methodological benefits it can confer. One of these benefits is reducing the trade-off between internal and external validity in experimental research by presenting constructed environments that are authentic while researchers maintain complete control over the environment and stimuli. Additionally, VR can passively collect information about participant movement and behaviors in a continuous, covert manner and provide the ability to replicate experiments through sharing standardized environments between research teams.

Studies demonstrate that VR-based simulations can serve as a proxy for real-world experiences and behavior in research contexts. Investigators have replicated many established social and perceptual effects in VR settings^1,2 and find that skills learned in VR transfer to improve real-world performance.^3,4 Given this strong foundation for use of VR in biobehavioral research, it is critical to continue evaluating and validating VR tools to expand their methodological scope to encompass broader outcomes and populations.

VR evaluation tools

There is a growing recognition that VR-based assessments can provide new opportunities to rigorously evaluate therapeutic outcomes of drugs, medical devices, and other interventions. Diagnostic and evaluative VR applications allow for focused, standardized, real-time, objective testing in ecologically valid contexts which, compared to self-reported and traditional digital assessments, more closely reflect real-world experiences. Although the field of medical extended reality is still emerging, there are long-standing efforts to develop and validate VR-based tools to support screening, diagnosis, and clinical prediction in mental health,^5,6 cognitive impairment, ⁷ and physical impairment. ⁸ Recent reviews of VR use for neurocognitive assessment report that researchers have validated immersive versions of assessments that, in some cases, outperform existing tests.^9,10 For example, VR-situated cognitive testing tends to be more sophisticated and engender more realistic cognitive load than classical test approaches, therefore better reflecting real-world performance. ¹¹

VR food choice measurement

Dietary oriented assessments are a prominent example of VR use for behavioral measurement. These experiences typically consist of a structured food selection task (e.g., selecting a plate of food for a lunch meal) wherein various components of food choice (e.g., items chosen, calories associated with choices) along with process measures (e.g., order of selections) are used to characterize dietary behavior. These data are often used for evaluating the influence of an experimental stimulus (e.g., an outside educational intervention or configuration of the virtual food environment itself) on behavior. VR dietary assessments have been repeatedly validated to reflect real-world food choice behavior in various contexts. For example, Cheah and colleagues demonstrated that food selections made in a VR cafeteria and a real-world cafeteria, 1 week apart, were well correlated on several nutritional outcomes. ¹² Long and colleagues additionally replicated the effect of portion size on food selection in a VR food choice task. ¹³ These applications are also able to detect nuanced cognitive processes associated with food choice such as variety seeking and psychological state during meal selection.^14,15 VR validation efforts have also replicated known variation in real-world patterns of food choice. For example, parents, when choosing meals for a child in a VR buffet, select higher calorie meals for older (vs. younger) children and for boys (vs. girls). ¹⁶ As such, VR dietary measures can elucidate the influence of real-world factors and experiences on food selection behavior.

Use of VR in clinical pharmacology

Despite VR’s use in other domains, little work has evaluated the influence of pharmacological agents on experiences and behaviors in VR. Muhlberger and colleagues conducted a case–control methylphenidate VR study; while limited by the lack of a placebo-controlled double-blind design, the study demonstrated that performance in a VR classroom–based cognitive test varied significantly between children with attention-deficit/hyperactivity disorder who did versus did not take methylphenidate prior to evaluation. ¹⁷ Additionally, a VR-based evaluation tool was applied to assess recall performance while varying antiepileptic medication load, demonstrating the ability to detect effects of drug load on memory performance. ¹⁸ Despite early studies, use of VR-based assessments of pharmacological outcomes is sparse, with no template for evaluating how VR may measure variations in performance, behavior, and/or experience.

Pharmacological manipulations of the “hunger” hormone ghrelin

A growing body of work in basic and clinical neuropsychopharmacology involves evaluating compounds that aim to alter reward and stress pathways to influence craving and use of addictive substances. Research demonstrates the appetite-regulating gut hormone ghrelin and its receptor, the growth hormone secretagogue receptor (GHSR), are involved in alcohol seeking and intake, with GHSR pharmacological antagonism leading to reductions in the reinforcing properties of alcohol and other alcohol-related outcomes. ¹⁹ Ghrelin is known as the “hunger hormone” and is a key regulator of appetite, food seeking, and metabolism. For example, ghrelin stimulates appetite and has been shown to increase preferences for high-calorie, highly palatable foods.^20,21 Yet, ghrelin’s effects are pleiotropic and extend well beyond hunger and appetite, ²² including effects on a myriad of psychological processes such as mood, cognition, and stress. ²³

Following an initial Phase 1 clinical trial in people with alcohol use disorder (AUD), ²⁴ a Phase 2 clinical trial further evaluated the effects of a GHSR inverse agonist/competitive antagonist, PF-5190457 (hereinafter referred to as the GHSR blocker), on alcohol and food cravings and related outcomes among a treatment-seeking sample of patients with AUD. Participants (N = 42 with 29 completers) entered the trial having already received inpatient, medically supervised treatment for acute alcohol withdrawal. ²⁵ While there was no effect of the GHSR blocker on alcohol cue–elicited craving in the Phase 2 study (while a signal was initially detected in the Phase 1 study), participants chose significantly fewer calories during a VR buffet food choice measurement session under the GHSR blocker than the placebo. ²⁵ Given the established role of the ghrelin/GHSR system in appetite and food seeking, this finding lends credence to the VR food choice measurement’s effective evaluation of downstream influences of pharmacological manipulations.

The current study

Data for the current secondary analysis come from the abovementioned Phase 2 trial of the GHSR blocker in people with AUD. This study aims to characterize additional influences of the GHSR blocker on participants’ experiences and behaviors in the VR buffet. The study was exploratory in nature, with no predefined hypotheses. We broadly expected that the physiological impact of the GHSR blocker would be reflected in differential experience and behavior of participants under the drug (GHSR blocker) versus the placebo, consistent with known influences of blocking ghrelin signaling in real-world contexts.

Method

Participants

Participants enrolled in the parent study were adults with a current diagnosis of AUD seeking treatment for alcohol problems. ²⁵ Additional eligibility criteria relevant to the current study included having a body mass index (BMI) of between 18.5 and 40 kg/m²; normal or corrected-to-normal hearing and vision; and lack of clinically significant history of motion or car sickness or history of vestibular disorders (see Supplementary Data S1 for all inclusion and exclusion criteria). The study was terminated early due to the COVID-19 pandemic. Of the 42 participants in the parent study, the current analysis included only those who participated in two VR buffet sessions (one under the GHSR blocker and one under the placebo, following a randomized, within-subject, crossover design), resulting in a sample of 29 participants in the current analysis. All participants provided written consent before enrollment and were compensated for their time and participation in the study.

Study approval

The study was approved by the National Institutes of Health (NIH) Institutional Review Board and conducted at the NIH Clinical Center in Bethesda, Maryland, USA, under the Food and Drug Administration Investigational New Drug No. 119,365 (clinicaltrials.gov NCT02707055). A Data Safety and Monitoring Board provided independent reviews of the study.

Experimental procedures

For full experimental procedures, see Faulkner et al. (2024). ²⁵ In brief, the parent trial was designed to evaluate the GHSR blocker versus the placebo via a randomized, double-blind, placebo-controlled study where patients received PF-5190457 100 mg b.i.d. (or placebo) in two counterbalanced, within-subject stages with identical procedures during each stage and a washout period between the two. Participants remained hospitalized throughout the trial and participated in AUD standard of care.

The VR buffet was previously validated to align with real-world food choice outcomes in a child feeding context. ²⁶ The VR buffet assessment of food choice occurred on dosing day 4 or 5 during each stage (GHSR blocker and placebo), at approximately 12:30 pm, 30 min following drug administration and preceding a meal. Questionnaires used in the current analysis were administered at study baseline and following each VR buffet assessment. To engage in the VR buffet, participants wore a tethered head-mounted display and made selections using a handheld VR controller. During the VR experiment, participants were introduced to the VR task and equipment and then engaged in a practice session wherein only one food and drink option was available such that participants could acclimate themselves and learn to use the VR buffet. This practice occurred before each VR session. Participants then engaged in the primary assessment session in the VR buffet to select food and drink. They were instructed to make choices as they would for a lunch meal. If participants ran out of space on the first virtual plate, the experimenter asked if they would like a second plate; if so, the VR buffet simulation was restarted, and participants were given an additional empty plate to fill. Food choices were aggregated across instances. Data about food choice and participant behavior in the environment were automatically collected by the VR system for later analyses. A visual of the experiemental proceedure is presented in Figure 1.

FIG. 1.

Simplified experiment procedure. AUD, alcohol use disorder.

Weight status

To investigate the impact of participant-perceived weight status, we assessed self-rated weight using scores of 1 (Underweight) to 4 (Very Overweight). We then conceptually binned the scores (combining “underweight”/“just about right” versus “overweight”/“very overweight”), creating a binary variable with higher and lower weight categorization (n_Lower = 15; n_Higher = 14). We focused on perceived weight as it is a stronger correlate of health behavior and health outcomes than objective BMI.^27,28

Measures

Experience

Presence. Presence, a psychological concept addressing feelings that one is existing in or part of the digital VR environment as opposed to the physical one, was assessed with the average of five items. This scale assessed how involved participants felt in the virtual world, how surrounded they felt by the virtual world, and so on, on a scale of 1 (Not at all) to 5 (Extremely). ²⁹

Other experience variables. We measured perceived Realism, Liking, Typicality, and Satisfaction with one item each on a 1 (Not at all) to 7 (Completely) scale. Realism assessed perceived realism of the buffet scenario. Liking assessed how much participants liked using the virtual buffet overall. Typicality assessed whether the buffet food was perceived as similar to what participants would typically eat. Satisfaction evaluated how satisfied participants were with the meal they prepared.

Behavior and physical symptoms

Simulator Sickness Checklist. We asked participants to rate the degree of experienced symptoms sometimes associated with VR use (cybersickness), including headache, blurred vision, dizziness with eyes open, dizziness with eyes closed, and nausea. Each symptom was measured on a scale of 0 (None) to 3 (Severe). We summed across symptoms for a total scale score. ³⁰

Time to First Selection. The VR system reported the amount of time elapsed from the start of the buffet simulation until the participant selected their first food item. This measure conceptually represents the time participants spent exploring and considering food options before beginning to assemble their meal.

Food Variety. We calculated Food Variety by counting the number of unique food items participants placed on their plate during the VR buffet experience. Conceptually, this measured variety seeking behavior. ¹⁴

Second Plate. We created a variable that indicated whether (or not) the participant chose to use a second plate during the VR buffet experience.

Statistical analysis

Analysis used the Psych Package (version 2.5.6) in R (version R 4.5.1). For each outcome, we ran two sets of mixed analysis of variance. The first set of models included the Drug Condition (GHSR blocker vs. placebo) along with Order (drug first or placebo first) to predict outcomes. We interacted Drug Condition and Order to evaluate whether there was an order effect on the impact of the Drug Condition.

$$\mathit{Variable\hspace{0.5em}of\hspace{0.5em}Interest}=\mathit{Drug\hspace{0.5em}Condition}+\mathit{Order}+\mathit{Drug\hspace{0.5em}Condition}\times \mathit{Order}$$

The second set of models investigated the role of Weight Status on the effect of the Drug Condition while controlling for Order.

$$\mathit{Variable\hspace{0.5em}of\hspace{0.5em}Interest}=\mathit{Drug\hspace{0.5em}Condition}+\mathit{Weight}+\mathit{Order}+\mathit{Drug\hspace{0.5em}Condition}\times \mathit{Weight\hspace{0.5em}Status}$$

In both analyses, Drug Condition was a within-subject variable. Our data contained incomplete pairs of self-report dependent variables. Because of this incompleteness, some models also contained between-subject outcomes for Drug Condition. Models that include these between-subject outcomes are available in Supplementary Tables S1, S2, S3, and S4 for completeness.

To evaluate whether the Drug, Order, or Weight was related to the likelihood that participants went up for a second plate, we ran a general linear mixed-effects logistic regression model, nesting outcomes within participants.

Results

Of the 29 participants, 27.6% were female (n_female = 8), mean age was 51.3 years (range = 26–58), and 58.6% reported being White (n_White = 17), 34.5% Black (n_Black =10), 3.4% Asian (n_Asian =1), and 3.4% unknown race (n_Unknown = 1). Correlations between primary variables are included in Supplementary Table S5.

VR experience outcomes

All VR experience results are illustrated in Table 1. Order interaction results are illustrated in Figure 2, and Weight Status interaction results are illustrated in Figure 3.

Table 1.

Mixed-Effects Models Predicting Experience

	Presence	Realism	Liking	Typicality	Satisfaction
	${\eta }_p^2$	${\eta }_p^2$	${\eta }_p^2$	${\eta }_p^2$	${\eta }_p^2$
Parameter	(F)	(F)	(F)	(F)	(F)
Drug Condition × Order models
Order	0.043 (1.163)	0.009 (0.246)	0.012 (0.325)	0.015 (0.399)	0.001 (0.022)
Drug Condition	0.016 (0.428)	0.018 (0.464)	0.144* (4.375)	0.244** (8.381)	0.153* (4.680)
Drug Condition × Order	0.291** (10.697)	0.001 (0.019)	0.108 (3.161)	0.001 (0.037)	0.074 (2.080)
N	28	28	28	28	28
Drug Condition × Weight Status models controlling for order
Weight Status	0.090 (2.475)	0.084 (2.300)	0.089 (2.443)	0.036 (0.941)	0.068 (1.822)
Order	0.018 (0.451)	0.000 (0.006)	0.001 (0.020)	0.005 (0.134)	0.002 (0.060)
Drug Condition	0.015 (0.388)	0.022 (0.583)	0.148* (4.512)	0.250** (8.680)	0.145* (4.415)
Drug Condition × Weight Status	0.219* (7.288)	0.206* (6.745)	0.135 (4.072)	0.036 (0.964)	0.019 (0.490)
N 1	28	28	28	28	28

¹ Note: *p < 0.05, **p < 0.01, ***p < 0.001.

FIG. 2.

Drug Condition × Order interactions. GHSR, growth hormone secretagogue receptor.

FIG. 3.

Drug Condition × Weight Status interaction.

Presence

Order Interaction. There was no significant main effect of the Drug Condition or Order. There was a significant interaction between Drug Condition and Order, with those who took the placebo first reporting higher Presence than those who took the placebo after the GHSR blocker. Order had little influence on participants under the GHSR blocker.

Weight Status Interaction. There were no significant Order, Weight Status, or Drug Condition main effects. There was a significant interaction between Drug Condition and Weight Status, with those of a lower weight status reporting more Presence than those who reported higher weight status when taking the placebo. Weight status had little influence on Presence under the GHSR blocker.

Realism

Order Interaction. There were no significant main effects or interaction.

Weight Status Interaction. There were no significant main effects. There was a significant interaction between Drug Condition and Weight Status, with those reporting higher weight status also reporting larger differences in Realism between the placebo and the GHSR blocker sessions. Drug Condition had little influence on participants reporting lower weight status.

Liking

Order Interaction. There were no significant Order or interaction effects. There was a significant main effect of Drug Condition, with participants reporting they liked the experience more under the GHSR blocker than the placebo.

Weight Status Interaction. There were no significant Order, Weight Status, or interaction effects. We again found higher liking for the experience under the GHSR blocker than the placebo.

Typicality

Order Interaction. There were no significant Order or interaction effects. There was a significant effect of Drug Condition, with participants reporting they picked more typical foods under the GHSR blocker than the placebo.

Weight Status Interaction. There were no significant Order, Weight Status, or interaction effects. We again found a significant main effect of Drug Condition on Typicality, with participants reporting they picked more typical foods under the GHSR blocker than the placebo.

Satisfaction

Order Interaction. There were no significant Order or interaction effects. There was a significant effect of Drug Condition, with participants reporting they were more satisfied with their choices under the GHSR blocker than the placebo.

Weight Status Interaction. There were no significant Order, Weight Status, or interaction effects. We found a significant main effect of the Drug Condition on Satisfaction, with participants reporting they were more satisfied with their choices under the GHSR blocker than the placebo.

Behavior and symptom outcomes

All behavior and symptom results are illustrated in Table 2. The Order interaction results are illustrated in Figure 4, and Weight Status interaction results are illustrated in Figure 5.

Table 2.

Table of Standardized Effects for Order Interaction Models for Behavior and Symptoms

	SSC	Time to First Food	Food Variety	Second Plate
	${\eta }_p^2$	${\eta }_p^2$	${\eta }_p^2$	Estimate
Parameter	(F)	(F)	(F)	(SE)
Drug Condition × Order models
Order	0.028 (0.759)	0.138* (4.339)	0.001 (0.026)	8.441* (3.898)
Drug Condition	0.036 (0.998)	0.036 (0.995)	0.012 (0.340)	9.241* (4.537)
Drug Condition × Order	0.033 (0.931)	0.224** (7.799)	0.023 (0.634)	−12.039* (5.357)
N	29	29	29
Drug Condition × Weight Status models controlling for order
Weight Status	0.026 (0.681)	0.020 (0.526)	0.057 (1.560)	−0.595 (1.114)
Order	0.018 (0.472)	0.125 (3.716)	0.009 (0.230)	1.303 (1.685)
Drug Condition	0.034 (0.964)	0.029 (0.796)	0.012 (0.336)	−2.798 (2.348)
Drug Condition × Weight Status	0.000 (0.001)	0.030 (0.840)	0.011 (0.313)	1.596 (1.944)
N (within subjects)	29	29	29	58

Note: SSC, Time to First Food, and Food Variety are all evaluated using a repeated measures ANOVA. Second Plate was evaluated using a multilevel logistic regression.

ANOVA, analysis of variance; SE, standard error; SSC, Simulator Sickness Checklist.

*p < 0.05, **p < 0.01, ***p < 0.001.

FIG. 4.

Drug Condition × Order interaction.

FIG. 5.

Drug Condition × Weight Status interaction.

Cybersickness symptoms

Order Interaction. There were no significant main effects or interaction.

Weight Status Interaction. There were no significant main effects or interaction.

Time to first selection

Order Interaction. There were significant Order and interaction effects but no Drug Condition effect. For Order, participants who were assigned to the placebo first took more time selecting food than those who received the GHSR blocker first. This finding was qualified by the interaction effect, wherein those who received the placebo first took more time selecting their first food item under the placebo than under the GHSR blocker. Those who received the GHSR blocker first did not show differences in selection time across Drug Conditions.

Weight Status Interaction. There were no significant main effects or interaction.

Food variety

Order Interaction. There were no significant main effects or interaction.

Weight Status Interaction. There were no significant main effects or interaction.

Went for second plate

Order Interaction. There were significant Drug, Order, and interaction effects. For Drug, when taking the GHSR blocker, participants were more likely to take a second plate. For Order, participants who were assigned to the placebo first were more likely to take a second plate than those who received the drug first. Finally, when participants were given the GHSR blocker and had taken the placebo first, they were less likely to take a second plate. This interaction effect was qualified by an interaction wherein those who received the placebo first were the most likely to get a second plate.

Weight Status Interaction. There were no significant main effects or interaction.

Discussion

The effect of the GHSR blocker on participant experience and behavior in the VR buffet was apparent across several outcomes. Our findings are generally consistent with known roles of ghrelin/GHSR in appetite and food seeking, as well as with the main outcome from the parent study reporting that administration of the GHSR blocker led to a reduction in the number of calories selected in the VR buffet. ²⁷

In the present additional analysis, we focused on VR-related experience and behavior and found that participants, when exposed to the GHSR blocker, reported liking the VR food choice experience more, chose foods more typical of their usual diet, and were more satisfied with chosen foods, indicating generally increased psychological comfort with food choice. Importantly, these outcomes do not signify that participants liked the food more; rather, participants liked the experience more and felt they made more appropriate choices.

The impact of the GHSR blocker on these experience ratings elucidates how the pharmacological impact of the GHSR blocker ultimately shapes higher level experiences around food. This is consistent with other literature on the effects of blocking GHSR on eating experience. For example, drugs blocking GHSR have been associated with reduced craving and hunger along with less food-related stress and cognitive load.^31,32 Presumably, these experiences would distract from the decision-making process inherent in the VR task, as craving itself tends to be associated with increased attentional bias toward food.^31,33 To the extent that these demands compete with decision-making processes, the VR buffet could become less enjoyable and more mentally taxing. Potential associations of ghrelin with impulsive behavior and risk-taking^31,33,34 may also denote how blocking its effects could lead to more thoughtful, considered food choice, as reflected by higher satisfaction ratings under the GHSR blocker. Therefore, we propose that under the placebo condition, participants would be likely to experience more craving and food-related impulses while engaging in the choice task, associated with higher mental load and poorer psychological experiences. In contrast, blocking GHSR may have reduced “food noise,” allowing participants to concentrate on the environment and task, an interpretation in line with the broader emerging concept of ghrelin as a system critically involved in survival-related mechanisms. ³⁵

Order moderated the effect of Drug Condition on Presence such that Presence was lower under the placebo among those who had already experienced the buffet under drug. We believe this is due to an anchoring effect, as participants who initially experienced the VR buffet under the GHSR blocker may have found their subsequent placebo experience more mentally taxing and thus less compelling in comparison.

Weight Status also moderated Presence experiences between the drug conditions. Lower-weight participants reported more Presence than their higher-weight counterparts under the placebo. Importantly, Weight Status did not influence Presence experience under the GHSR blocker. Individuals with higher weight tend to be more mentally preoccupied with and distracted by food cues. ³⁶ It is, therefore, plausible that the VR food environment under the placebo (where hunger and craving are uncontrolled) would engender more stress and concern about food choices, leading to lower Presence among the higher weight group, as they are more susceptible to these effects. This would also indicate that the GHSR blocker reduced preoccupation with food among those in the higher weight group such that their experience was comparable to the lower weight group.

Similar patterns were found for ratings of Realism of the VR buffet, with those of lower weight status reporting higher ratings of Realism under the placebo (but not the GHSR blocker) than those of higher weight status. While experience of cravings is probably “realistic” among higher weight individuals in real-world environments, we suspect this item was interpreted more broadly as an experiential evaluation of the VR buffet, more analogous to Presence. Here, mental preoccupation with food and increased cravings would lead to a less seamless interaction and decision-making experience in the buffet and consequently a more critical evaluation.

In general, participants’ weight status appeared to moderate responses by acting as a proxy for higher susceptibility to heightened food attention, which was viable under the placebo when craving was less controlled. Patterns may also be consistent with higher impression management among those with higher weight, including less willingness to take a second plate.

In evaluating behavior and symptoms, we found significant effects for Time to First Food Selection and Second Plate choice. Those who had their first session under the placebo took longer to begin selecting food than those in all other conditions. When focusing only on those who were completing an unfamiliar task (i.e., using only their first session data), we found patterns consistent with the notion that blocking ghrelin reduced craving and food preoccupation. This was illustrated by finding that, when under the GHSR blocker, participants were less distracted and were therefore ready to begin food selection more quickly. While order was not influential on the second plate choice under the GHSR blocker, it was influential under the placebo. One interpretation relates to individuals’ tendency to serve larger portions or make less “good” food choices. ³⁷ Those who received the placebo in their second session may have felt less need to indulge their desire for larger food servings.

It is also critical to acknowledge outcomes for which there were no effects of the GHSR blocker. Some of these outcomes are not conceptually well linked with known effects of ghrelin blockers, such as symptoms of cybersickness. Furthermore, we did not find any influence of Drug Condition on Food Choice Variety, although conceptually blocking GHSR could have been expected to reduce variety due to reductions in impulsive choice. It is difficult to determine why we did not see this effect within the scope of the current study. However, patterns in second plate selection suggest that the GHSR blocker may have influenced the amount of food chosen rather than the variety.

Taking all the experience findings together, our data suggest that experiences in the VR buffet are sensitive to pharmacological manipulations, including short-term exposure to a medication as in this study. In addition, descriptive results indicate low levels of cybersickness arising from VR Buffet use and relatively high ratings on outcomes such as Liking. These findings further support use of VR measurement approaches of this nature in future pharmacology studies and possibly as a potential tool in the context of medication development efforts. Importantly, findings also suggest need for caution with respect to order effects when using these measures, as there were several instances wherein participants’ status as first-time versus repeat users changed the nature of experiences and behavioral responses.

Limitations

This study did not reach the a priori sample size goal due to interruptions to recruitment during the COVID-19 pandemic. The small sample size is somewhat mitigated by the within-subject nature of the study; however, it meant that we could not meaningfully conduct three-way interactions with Drug Condition, Order, and Weight Status, nor could we include other potentially meaningful predictors. Additional replications using similar experiences and techniques are warranted to corroborate and expand upon our findings.

In addition, this study used secondary data that were not the primary outcome of the parent study, and as such, we were limited in our ability to assess potential drivers of experiential or behavioral differences (e.g., difficulty with making choices, level of cognitive load). Follow-up studies should integrate these metrics to better understand why experiential or behavioral differences occur.

Finally, the parent study did not find a significant effect of the GHSR blocker on alcohol cue–elicited craving, ²⁵ despite an initial signal in an earlier preliminary study. ²⁴ The null alcohol–related findings do not undermine the food-related findings reported in the parent study and reported here. Despite some key overlap in the neurobiology of alcohol- and food-related behaviors, specifics of response to these stimuli may also be divergent. Furthermore, a recent post hoc analysis suggests that the GHSR blocker could in fact reduce alcohol craving but only in individuals who exhibit high alcohol cue response and craving. ³⁸ Regardless, it is critical to keep in mind that ghrelin’s role in appetite and food-seeking behavior is well-established regardless of effects on alcohol craving. Additional studies are necessary to reveal the underlying mechanisms of the GHSR blocker’s effects on experience and behavior in the VR buffet and to further explore the possible role of order effect on these outcomes.

Conclusion

This study provides further evidence for the usefulness and application of the VR buffet for experiential and behavioral assessment in clinical pharmacology studies. Although the VR buffet paradigm is only a simulacrum of a physical buffet, the simulation detected nuanced differences between placebo and the GHSR blocker, reflecting pharmacological antagonism of GHSR, the receptor of the appetite-regulating hormone ghrelin. Two key moderators (order and weight status) also resulted in patterns consistent with our interpretation of pharmacological influence on detection of psychological patterns. In turn, an increased profile of VR tools in clinical pharmacology research can enable sophisticated measurement approaches that probe not only behavioral outcomes but also mechanistic and moderating factors with real-world implications from well-controlled experiments conducted in laboratory settings.

Disclaimer

Pfizer did not have any role in the study design, execution, or interpretation of the results, and this publication does not necessarily represent the official views of Pfizer. The contributions of the NIH authors are considered Works of the United States Government. The findings and conclusions presented in this article are those of the authors and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services.

Authors’ Contributions

M.R.L.: Formal analysis, visualization, writing of original draft, and writing of review and editing. S.P.: Conceptualization, data curation, methodology, visualization, writing of original draft, and writing of review and editing. M.F.: Conceptualization, data curation, investigation, methodology, project administration, and writing of review and editing. L.L.: Conceptualization, data curation, funding acquisition, investigation, methodology, project administration, resources, supervision, and writing of review and editing.

Data Availability

All data are available from the corresponding author upon reasonable request.

Abbreviations Used

ADHD

attention-deficit/hyperactivity disorder

ANOVA

analysis of variance

AUD

alcohol use disorder

b.i.d.

twice daily (bis in die)

BMI

body mass index

CRediT

Contributor Roles Taxonomy

FDA

Food and Drug Administration

GHSR

growth hormone secretagogue receptor

IRB

Institutional Review Board

NCATS

National Center for Advancing Translational Sciences

NHGRI

National Human Genome Research Institute

NIAAA

National Institute on Alcohol Abuse and Alcoholism

NIDA

National Institute on Drug Abuse

NIH

National Institutes of Health

NIMH

National Institute of Mental Health

SE

standard error

SSC

Simulator Sickness Checklist

VR

virtual reality

Cite this article as: Leitao MR, Farokhnia M, Leggio L, Persky S (2026) Experiences and behavior during a virtual reality buffet simulation: A secondary analysis of a ghrelin-related pharmacology randomized controlled trial in people with alcohol use disorder, Journal of Medical Extended Reality, 2026, 00, 29941520261484294, DOI: 10.1177/29941520261484294.