Abstract
Background:
Alcohol use disorder (AUD) is a chronic relapsing illness that causes significant morbidity. Craving is a key factor responsible for relapse in AUD. Cue exposure (CE) has attempted as an assessment and therapeutic procedure in AUD. Virtual reality (VR) has allowed for efficient conducting of CE. Limited research exists on VR for AUD in Indian settings.
Aim:
This study intended to develop (using open-source tools) and validate eight VR environments for alcohol CE for Indian scenarios. We also determined the predictive variables associated with alcohol craving upon exposure to these environments.
Methods:
A cross-sectional study was conducted among 44 detoxified male inpatients with AUD. Eight alcohol-cue and two neutral VR environments were developed. Craving before and after exposure and the sense of presence in VR environments were evaluated. Sociodemographic, psychological, and alcohol consumption characteristics were examined as possible predicting variables.
Results:
Alcohol-cue environments significantly increased craving postexposure compared to neutral environments. Age, duration of AUD, baseline craving, severity of AUD, and sense of presence significantly correlated with the increase in craving postexposure. Multiple linear regression analyses showed baseline craving, physical, and self-presence were determinants of craving in alcohol cue environments.
Conclusion:
The developed environments induced craving, although the effect size was moderate, and there was heterogeneity in response. Baseline craving and sense of presence were key predictors of craving. These variables can help ease the identification of those who can benefit from VR CE. Environments are being made available for other researchers to stimulate further research.
Keywords: Alcohol use disorder, craving, cue exposure, sense of presence, virtual reality
INTRODUCTION
Alcohol use disorder (AUD) is a significant public health problem with a global lifetime prevalence of 8.6%.[1] In India, approximately 29 million individuals are dependent users (or a prevalence of 2.7%).[2]
Craving is a key factor in both development and persistence of AUD and is often a factor in relapse.[3] Replicating real-life craving experiences in controlled laboratory settings is difficult and has been conventionally done using cue exposure.[4] This involves exposure to stimuli linked with alcohol consumption, which is anticipated to trigger both psychological (subjective craving) and physiological responses (cue reactivity) in vulnerable individuals. Cue reactivity is considered a significant trigger for relapse.[5,6,7,8] Nonetheless, traditional cue-reactivity methods often lack ecological validity and only slightly over half the participants exhibit adequate response.[3,9]
Virtual reality (VR) has been investigated as an alternative to traditional paradigms.[10,11] VR enables creation of immersive, multisensory, and personalized environments that can mimic real-world scenarios.[12,13] Studies investigating VR for AUD have demonstrated that relevant environments induce both subjective craving and cue reactivity in vulnerable individuals.[14,15,16] Individuals with AUD experience heightened craving in alcohol-related VR environments compared to neutral ones.[17,18,19] Heavy drinkers experience greater craving in cue VR environments compared to light drinkers.[20] Repeated exposures to VR environments have been utilized to reduce craving over time.[14,21]
The effectiveness of VR is shaped by users’ cultural backgrounds and expectations.[22] Factors impacting the efficacy of VR include the type and intensity of alcohol-related cues, the presence of and interaction with virtual characters, individual characteristics, the degree of realism, and the sense of presence in the environment.[3,12,16,23] These implicate the necessity of developing of culturally relevant VR environments to enhance users’ feeling of “being there”. Such environments can enhance both perceived realism and the sense of presence, leading to more impactful VR experiences.[24] Research on VR in India is limited, and to the best of our knowledge, no publicly available VR environments exist for AUD. In this study, we developed and validated a series of VR environments depicting Indian alcohol consumption scenarios. The objective of the study was to develop representative alcohol cue environments (suited for Indian alcohol consumption scenarios) using open-source methods and compare the magnitude of cue-induced craving before and after exposure to VR cues. We hypothesized that these environments will enhance subjective craving among those with AUD and studied baseline factors determining this craving response.
METHODS
The study used a within-subjects experimental design with control conditions. The Institutional Human Ethics Committee of the institute approved the study. Male patients aged between 18 and 65 years, fulfilling the diagnosis of AUD as per DSM 5 criteria, with no other comorbid psychiatric diagnoses or substance use other than tobacco use disorder, who had completed inpatient detoxification and were not in withdrawal (Clinical institute withdrawal assessment for alcohol scale-Revised (CIWA-Ar) <7) were included in the study. Written informed consent was obtained prior to participation. The entire procedure included baseline data collection and exposure to all the VR environments in a single session lasting about 3–4 hours. Participants who had any concomitant sensory or neurological disorders (i.e., refractory error, headaches, seizures, etc.) or used anticraving agents during the recruitment/detoxification were excluded. A total of 44 participants were identified during the study period. Data were collected from the participants between the months of December, 2023 and May, 2024 and included sociodemographic information, alcohol use history, and baseline craving in the past week (assessed through the Penn alcohol craving scale (PACS)). The PACS consists of 5 items, each scored from 0 to 6, assessing alcohol craving at the time of assessment. The total score ranges from 0 to 30, with higher scores indicating greater craving intensity.[25]
Development of VR environments (Instruments)
Hardware: The VR equipment consisted of a Meta Quest 2 head-mounted display (HMD), a resolution of 1,832 × 1920 pixels per eye, a 90 Hz refresh rate, and 104° field of view, with 6 degrees of freedom and hand-mounted controllers to control the display. The VR environments were developed with the help of an Insta360 X3 camera. The camera was capable of taking 360° photos in a single shot at a resolution of 72 megapixels per image.
Development of VR environments (Software)
The captured images were edited for artifacts and processed into 360° skybox images using the accompanying Insta360 STUDIO 2023 version 4.9.1 software. The processed images were uploaded to an online repository (https://www.360cities.net/) for viewing in the HMD. The images were viewed in VR mode using the built-in browser of the device. Sample images of the VR images are shown in Figure 1.
Figure 1.
Equirectangular images of VR environments used in the study (clockwise from top left) 1. Neutral environment depicting a park, 2. Bottles of alcohol in a living room, 3. Bottle of beer and snacks in a restaurant, and 4. Bar serving alcohol
Development of VR environments (Procedure)
Although VR has emerged as a tool for understanding and addressing behavioral issues, including substance use disorders, almost all researchers have relied on custom-built VR systems.[17,19,23,26] However, these setups often come with significant drawbacks, such as high costs and limited scalability. We explored an alternative approach—that leverages commercially available hardware and software to create immersive VR environments for AUD research.
Ten virtual environments (eight containing alcohol cues and two neutral environments) were developed on the basis of previous studies about common situations and specific cues that produce a craving for alcohol. Eight environments were chosen to maximize the number of validated environments coming out of the study. The alcohol cue environments depicted either environments simulating the consumption of alcohol or environments simulating alcohol shops and the purchase of alcohol. Photographic content from similar environments has been shown to induce craving for alcohol in published studies; no additional content validation was done in this study. The specific environments were as follows: a living room with alcohol and snacks on the table, a hotel room with alcohol and snacks on the counter, an empty room with alcohol and snacks on the floor, a party venue with an alcohol counter, a bar where alcohol is served, an aisle of a large format alcohol shop, a courtyard with an alcohol shop, and a rack in a room containing multiple bottles of alcohol. The neutral environments were a living room and an empty park.
The process involved photographing the ten environments described previously using the Insta360 camera. These 360-degree photos capture the entire environment in a single click, and care was taken not to photograph or capture any potential moving object or person as stillness can potentially increase the uncanny valley. The raw photos are then processed using the Insta360 software. They were transformed into skybox images. These skybox images provided panoramic depiction of the alcohol consumption scenes. The skybox images seamlessly stitch the images into a curvature that envelops the viewer, creating a sense of immersion, and allowed participants to explore the environment as if they were physically present.
The skybox images were then imported to SimLab VR studio (https://www.simlab-soft.com/3d-products/vr-studio.aspx) where they were transformed into a VR environment. The environments were finally displayed on the Meta Quest 2 hardware. To avoid using sideloaded software on the Quest 2, we hosted the environments in an online repository. The repository served as gateway to the VR environments and also allow the additional advantage of being made available other researchers to create and add further environments.
Study procedure
The baseline assessment included sociodemographic and alcohol consumption details along with craving in the past week (using the Penn alcohol craving scale). After baseline assessment, participants reported their subjective craving for alcohol at that time on a ten-point Likert scale. Participants were given instructions on how to use the VR equipment and became familiar with the VR technology. They were asked to view each VR environment from any angle desired and were free to move around if they wanted to. To view the VR environments, the participants wore the headset in an empty ten ft. by ten ft. area designated as the virtual “play area.” The VR device automatically stopped displaying the environment if the participants tended to exit this area. However, during the study, no session had to be terminated because the participant tended to exit the “play area.” Participants will be allowed to explore the VR environments for 3 minutes. The participants again reported their subjective craving for alcohol at that time. A washout period of 15 minutes was provided before participants were exposed to the next VR environment to allow any residual craving to subside. The eight alcohol and two neutral environments were presented randomly to all study participants. After experiencing the environments, participants also reported their evaluation of each of the VR environments using the Multimodal Presence Scale (MPS). The MPS assesses presence in VR environments across three dimensions: Physical, Social, and Self-presence. It consists of 15 items, rated on a 5-point scale. The total score ranges from 15 to 75, and higher scores indicate a stronger sense of presence.[27,28]
After all the VR environments were experienced, a therapist in the Addiction Medicine Clinic performed a debriefing interview with the aim of minimizing cravings and stopping any alcohol consumption because of residual craving from experiencing the VR environments. The participants were discharged from the hospital only after completing treatment (including starting long-term pharmacotherapy) and after they subjectively did not report any alcohol cravings.
Statistical analysis
Statistical analyses were performed using SPSS 25.0 (SPSS Inc, 2017). All the analyses set the alpha level for statistical significance at 0.05. A post-hoc correction of alpha was not considered as there was significant loss of statistical power. The comparisons made between sociodemographic, cue-exposure craving and presence variables were a preliminary analysis to justify their inclusion in the regression model. This regression model was used for final testing of the impact of sociodemographic and presence variable on cue-induced craving. Descriptive statistics were used to characterize the cohort at baseline, including sociodemographic, psychological, and consumption characteristics. Next, associations were tested for a relationship between sociodemographic, psychological, and consumption variables (age, gender, education, socioeconomic status, AUD severity, intensity of alcohol craving experienced during the previous week) and the scores in the following quantitative variables: (1) the intensity of alcohol craving during VR exposure and (2) PR of the virtual environments. Independent t-test and analyses of variance (ANOVA) were used for none (with two or more than two categories), and Pearson correlation was used for quantitative variables. Previous studies have demonstrated that sociodemographic, substance use, and presence variables significantly modulate the responses to VR environments.[23] Similarly, studies have demonstrated the consumption variables such as AUD severity and abstinence duration also predict response to VR environments, though results have been mixed.[29]
RESULTS
Forty-four persons with AUD completed the study. The Supplementary Material Table 1 show the participants’ sociodemographic and clinical characteristics at baseline.
Supplementary Material Table 1.
Socio-demographic characteristics of participants at baseline and their categorisation with severity of alcohol use
| Characteristic | Variables | All Participants, n=44, Mean (SD)/n (%) | Moderate AUD Participant, n=21, Mean (SD)/n (%) | Severe AUD Participants, n=23, Mean (SD)/n (%) | T (P) /χ2 (P)/ |
|---|---|---|---|---|---|
| Age | 43.3 (13.2) | 42.3 (12.8) | 44.2 (13.9) | 0.47 (0.64) | |
| Gender | Male | 44 (100) | 21 (47.8) | 23 (52.2) | |
| Education | Up to middle School | 5 (11.3%) | 2 | 3 | 0.89(0.91)^ |
| Secondary School | 17 (38.6%) | 8 | 9 | ||
| Higher secondary | 19 (43.1%) | 9 | 10 | ||
| Graduate and above | 3 (6.8%) | 2 | 1 | ||
| Religion | Hindu | 38 (86.3%) | 16 | 22 | 0.15 (0.22)^ |
| Muslim | 4 (9.1%) | 3 | 1 | ||
| Christian | 2 (4.5%) | 2 | 0 | ||
| Employment status | Employed | 33 (75.0%) | 18 | 15 | 2.4 (0.11) |
| Unemployed | 11 (25.0%) | 3 | 8 | ||
| Socioeconomic status | Upper Class | 3 (6.8%) | 2 | 1 | 0.67 (0.54)^ |
| Upper middle class | 6 (13.6%) | 4 | 2 | ||
| Lower middle class | 14 (31.8%) | 7 | 10 | ||
| Lower class | 21 (47.7%) | 8 | 10 | ||
| Age of 1st drink (in years) | 20.4 (3.1) | 20.2 (3.4) | 20.6 (2.8) | 0.43 (0.67) | |
| Duration of AUD (in years) | 14.7 (9.6) | 12.8 (8.4) | 16.2 (10.4) | 1.18 (0.24) | |
| Estimated number of units per 24 hours in the week prior to admission | 15.8 (8.1) | 8.4 (4.2) | 22.5 (3.8) | 11.7 (<0.01)# | |
| Days since last drink (at time of assessment) | 11.3 (4.5) | 10.8 (4.9) | 11.7 (4.1) | 0.66 (0.51) | |
| Family history | Yes | 28 (63.6%) | 13 | 15 | 0.05 (0.82) |
| No | 16 (36.3%) | 8 | 8 | ||
| Baseline craving as per PACS | 11.7 (2.7) |
10.1 (2.3) | 15.3 (2.7) | 6.8 (<0.01)# |
t=Students t-test statistic, χ2=Chi-square statistic, #=Significance at P<0.01, ^=Freeman Halton extension of Fisher exact test has been done for cell frequencies <5 and tables greater than 2*2
Cue-exposure craving was defined as the increase in subjective craving after exposure to the alcohol cue or neutral environments. Table 1 shows the comparison between the categorical sociodemographic and alcohol consumption variables with cue-exposure craving, physical presence, social presence, and self-presence. The comparison between the continuous sociodemographic and alcohol consumption variables with cue-exposure craving and presence variables is demonstrated in Table 2. Employment status was significantly associated with presence. Severity of AUD was significantly associated with cue-exposure craving. Additionally, age, duration of AUD, and baseline craving were correlated significantly with cue-exposure craving. Baseline craving was also significantly associated with all the presence variables. We conducted this correlation analysis to determine the variables to be used in the regression analysis done subsequently.
Table 1.
Relationship of categorical sociodemographic and alcohol consumption variables (n=44) with cue-exposure craving, physical presence, social presence, and self-presence
| Characteristic | Variables | n (%) | Cue-exposure craving, | Physical presence, | Social presence, | Self-presence, |
|---|---|---|---|---|---|---|
| F (P) | F (P) | F (P) | F (P) | |||
| Education | Up to middle School | 5 (11.3%) | 1.21 (0.84) | 1.68 (0.52) | 1.72 (0.66) | 1.64 (0.56) |
| Secondary School | 17 (38.6%) | |||||
| Higher secondary | 19 (43.1%) | |||||
| Graduate and above | 3 (6.8%) | |||||
| Religion | Hindu | 38 (86.3%) | 1.18 (0.84) | 1.14 (0.82) | 1.26 (0.76) | 1.22 (0.78) |
| Muslim | 4 (9.1%) | |||||
| Christian | 2 (4.5%) | |||||
| Socioeconomic status | Upper Class | 3 (6.8%) | 1.12 (0.86) | 1.12 (0.78) | 1.16 (0.74) | 1.28 (0.80) |
| Upper middle class | 6 (13.6%) | |||||
| Lower middle class | 14 (31.8%) | |||||
| Lower class | 21 (47.7%) | |||||
|
| ||||||
| Characteristic | Variables | n (%) | t (P) | t (P) | t (P) | t (P) |
|
| ||||||
| Employment status | Employed | 33 (75.0%) | 2.17 (0.74) | 2.42 (0.03)* | 2.58 (0.02)* | 2.52 (0.02)* |
| Unemployed | 11 (25.0%) | |||||
| Severity of AUD (DSM-5) | Moderate | 21 (47.7%) | 2.75 (0.01)# | 1.14 (0.41) | 1.35 (0.34) | 1.21 (0.38) |
| Severe | 23 (52.2%) | |||||
| Family history | Yes | 28 (63.6%) | 1.54 (0.64) | 1.72 (0.58) | 1.66 (0.60) | 1.48 (0.70) |
| No | 16 (36.3%) | |||||
t=Students t-test statistic, F=ANOVA F statistic, *=Significance at P<0.05, #=Significance at P<0.01
Table 2.
Correlation of continuous sociodemographic and baseline variables with cue-exposure craving, physical presence, social presence, and self-presence
| Characteristic (Continuous variables) | Mean (SD) | Cue-exposure craving (n=44) | Physical presence (n=44) | Social presence (n=44) | Self-presence (n=44) |
|---|---|---|---|---|---|
| r (P) | r (P) | r (P) | r (P) | ||
| Age | 43.3 (13.2) | 0.43 (0.03)* | 0.22 (0.10) | 0.24 (0.09) | 0.20 (0.12) |
| Age of 1st drink (in years) | 20.4 (3.1) | 0.16 (0.28) | 0.14 (0.31) | 0.12 (0.44) | 0.08 (0.64) |
| Duration of AUD (in years) | 14.7 (9.6) | 0.47 (0.02)* | 0.24 (0.09) | 0.26 (0.09) | 0.28 (0.08) |
| Estimated number of units per 24 hours in the week prior to admission. | 15.8 (8.1) | 0.24 (0.12) | 0.12 (0.44) | 0.14 (0.36) | 0.18 (0.42) |
| Days since last drink | 11.3 (4.5) | 0.24 (0.14) | 0.34 (0.06) | 0.28 (0.08) | 0.32 (0.06) |
| Baseline craving as per PACS | 11.7 (2.7) | 0.56 (<0.01)# | 0.52 (<0.01)# | 0.60 (<0.01)# | 0.54 (<0.01)# |
r=Pearson correlation co-efficient, *=Significance at P<0.05, #=Significance at P<0.01
Table 3 shows the subjective craving before and after exposure to the alcohol and neutral environments. Alcohol cue environments significantly increased the subjective craving compared to neutral cue environments. The response of the participants across alcohol cue environments was not uniform. The median number of environments for which a participant reported an increase in craving was 4 (IQR: 3). This indicates each participant as many as half the environments were unable to induce craving. Chi-square analysis did not indicate that a particular environment had a greater propensity to induce craving for (χ2 = 2.17, P = 0.15). Paired t-tests [Table 4] indicate that there was a significant difference between pre- and postexposure craving for alcohol cue environments but not for neutral cue environments with a modest effect size (Cohen’s d = 0.41).
Table 3.
Pre- and postexposure craving scores for all participants for alcohol and neutral environments
| Group | Pre-exposure craving, n=44, Mean (SD) | t (P) | Postexposure craving, n=44, Mean (SD) | t (P) | Difference in craving, n=44, Mean (SD) | t (P) |
|---|---|---|---|---|---|---|
| Alcohol Environments | 2.85 (1.76) | 0.64 (0.52) | 3.20 (1.95) | 0.41 (0.67) | 0.34 (0.47) | 4.07 (<0.01)# |
| Neutral Environments | 3.09 (1.62) | 3.04 (1.84) | -0.05 (0.35) |
t=Students t-test statistic, #Significance at P<0.01
Table 4.
Paired samples t-test for pre- and postexposure craving for alcohol and neutral environments
| Mean difference (SD) | 95% Confidence Interval | t (P) | Cohen’s d | 95% CI | |
|---|---|---|---|---|---|
| Alcohol Cue Pre vs post | -0.34 (0.47) | -0.38 - -0.05 | -2.76 (<0.01)# | 0.41 | 0.10 – 0.72 |
| Neutral Cue Pre vs Post | -0.06 (0.35) | -0.17 – 0.04 | -1.23 (0.22) | 0.18 | 0.11 – 0.48 |
t=Students t-test statistic, CI=Confidence interval, #Significance at P<0.01
There was a significant difference in the patient’s increased craving when participants were grouped according to the severity of AUD. There was also a significant difference in the patient’s responses to the MPS when participants were grouped according to employment status. Pearson correlation indicated a significant association of age, duration of AUD, and baseline craving with the increase in craving after exposure to alcohol cue environments. There was also a significant correlation of baseline craving with each of the subdomains of the MPS.
A multiple regression analysis was conducted to determine the predictors of cue-exposure craving to these VR environments. Table 5 presents the results from this multiple regression analysis. Baseline sociodemographic, AUD severity, and presence variables were used as independents variables, and baseline craving was used as a covariate in this analysis. The multiple regression model statistically significantly predicted cue-exposure craving, and these variables accounted for 38% of the explained variability. Baseline craving, physical presence, and self-presence were found to be statistically significant predictors of cue-exposure craving.
Table 5.
Multiple regression analyses of cue-exposure craving with baseline sociodemographic and alcohol consumption variables
| Unstandardized B (S.E) | 95% C.I. for Unstandardised B | β | t (P) | |
|---|---|---|---|---|
| Age | 0.35 (0.12) | -0.17 – 1.11 | 0.51 | 0.72 (0.47) |
| Severity of AUD | 0.27 (0.17) | -0.06 – 0.60 | 0.42 | 0.69 (0.08) |
| Duration of AUD | 0.37 (0.27) | -0.15 – 0.89 | 0.39 | 1.24 (0.19) |
| Baseline craving | 0.47 (0.33) | 0.11 – 0.58 | 0.61 | 5.21 (<0.01)# |
| Physical Presence | 0.51 (0.11) | 0.29 – 0.72 | 0.52 | 12.21 (<0.01)# |
| Social Presence | 0.27 (0.28) | -0.27 – 0.81 | 0.49 | 2.14 (0.06) |
| Self-Presence | 0.58 (0.16) | 0.26 – 0.89 | 0.51 | 14.25 (<0.01)# |
DISCUSSION
Almost all studies and research groups studying VR for alcohol have developed lab-specific VR systems—custom-built environments tailored to requirements and budgetary constraints of the specific labs. These setups are challenging to develop and have significant drawbacks. VR hardware is now widely available for general public use. Therefore, we decided to use a pragmatic approach and leverage commercially available hardware and software to create VR environments that are both accessible and clinically relevant.
The first objective of this study was to assess the ability of the eight developed alcohol-related VR environments to induce craving in persons with AUD. There was a significant increase in craving after exposure to cue environments compared to neutral ones. However, there was significant heterogeneity in individual responses. This indicates that persons with AUD experience an increase in craving when exposed to some particular alcohol-related VR environments but not for a neutral environment. These results are similar to previous research on using VR for AUD and other substance use disorders.[17,18,19]
The intensity of induced craving was moderate. Intensity of craving depends on expectations and level of arousal.[30] Like our study, other studies have also reported that the intensity of craving differs across environments.[26] Unlike previous studies, we did not find any particular environment with a significantly higher probability of inducing craving. Unfamiliarity with VR technology and lower expectations of experiencing a real-world drinking situation (leading to lower arousal) could be the reason for the moderate effect size in our study.
Ecological momentary assessment studies have also established the importance of context in craving and substance use behavior.[31,32] For VR environments to induce craving for alcohol, they must reflect the usual alcohol consumption scenario. The moderate effect size may also indicate the need for additional VR environments that are more nuanced to Indian cultural situations.[33]
The second objective of this study was to examine the sociodemographic, alcohol consumption, psychological, and environment-related variables that predicted this increase in alcohol craving. The increase in craving correlated significantly with the severity and duration of alcohol use, age, baseline craving, and sense of presence. Previous research has demonstrated that heavy alcohol consumers experience more craving after exposure to alcohol-related VR environments compared to occasional drinkers.[3] In our regression model, only baseline craving, physical presence, and self-presence significantly determined craving.
The importance of presence as a determining factor for cue-induced craving in VR environments has been highlighted across studies. Perceived realism has been the single most important determinant of craving in VR environments.[23] Presence is the experience of “being there” in the sense of attentional allocation to the virtual environment and is closely related to the perceived realism or the subjective degree of reality of the depicted environment.[34] Presence is determined by multiple factors, including the technological sophistication and the educational status of the participant.[35] In our study, there was a significant difference in the sense of presence between employed and unemployed participants. However, further research is required to evaluate the determinants of presence in the Indian population.
Overall, our study aimed to develop VR environments for AUD that are suitable for the Indian population. The developed environments were able to induce craving, albeit moderately. Baseline craving and sense of physical or social presence were determinants of the induced craving.
The highlight of this study is the use of commercially available hardware and software and use of an open-source approach in creating these VR environments. The environments created in this study are being made available to researchers across India and globally through your journal under a CC BY-NC-SA 4.0 licence. Researchers may contact the corresponding author for a link to download, recreate, and make derivative works of the VR environments. We hope that this would democratize VR research in this country and lead to wider adoption and replicability of the research. Additionally, commercially available hardware and software are designed with scalability in mind, and using this open-source framework, we hope that clinicians will be able to scale the development and translational use of the VR environments.
By opting for off-the-shelf hardware—such as consumer-grade VR headsets—and readily available software, we have been able to sidestep the financial hurdles that often come with the development of these systems. Traditional lab-developed VR systems can be prohibitively expensive, and we are expectant that the setup described in the study will be particularly cost-effective. This cost-effectiveness is particularly crucial in resource-constrained settings like India. An additional advantage of using consumer grade software is their resemblance to real-world scenarios. Consumer VR headsets mimic what patients might encounter outside the lab and therefore have better ecological validity. Lab-specific VR systems often require extensive informed consent processes due to their experimental nature and may make participants feel apprehensive. With commercial hardware, participants are likely more comfortable. Ethical approval and recruitment become easier allowing for faster deployment. In summary, our approach reduces the gap between research and practical implementation and can make VR interventions more feasible, relevant, and impactful.
Some limitations of the present study should be mentioned. First, the participants in the study all underwent inpatient detoxification. They are, therefore, more likely to represent those with more severe AUD and, therefore, may not be representative of all persons suffering from AUD. However, moderate and severe AUD were equally represented among the study participants, and the results can be generalized to this group. Additionally, these patients were likely to be more motivated than usual. This could have also had a bearing on the participants’ reporting of craving. The DSM-5 criteria were used to classify the severity of dependence and are dichotomous in nature. This may have precluded us from further understanding the nuanced nature of severity and cue reactivity.
Second, and despite the environments being presented in random order for all the participants and allowing for a washout period between exposure to the environments, it is possible, due to the nature of craving, that there is some cumulative effect from the beginning to the end of each experiment. An interparticipant (between-subjects) design or different sessions for each environment in an intraparticipant (within-subjects) design could have addressed this effect, but the time and effort involved would have increased significantly, and this approach was determined to be unfeasible. To mitigate this somewhat, we chose to use the change in craving before and after exposure as an indicator of the effects of the VR environments. Third, we did not use any psychophysiological assessment to determine the external validity of the VR environments. The decision to omit psychophysiological assessments was driven by the lack of consistency of these assessments (especially galvanic skin response and skin temperature) and the technological limitations of integrating these assessments with the hardware. Specifically, we choose to conduct our study on widely available commercial hardware so that other researchers can successfully use the environments. Finally, there has been a demonstrated intersubject variability in responses to VR environments and cue reactivity implying that these finding may not be replicated in larger studies.[9]
Despite these limitations, our study, to the best of our knowledge, was the first study aimed at developing VR environments contextual to India. These environments were capable of inducing a craving for alcohol. However, for VR to be a valuable tool in either craving induction or relapse prevention, additional environments must be developed depicting more scenarios. The environments in our study can be freely used by other researchers to develop modules using VR for cue exposure and relapse prevention sessions modules using the CC BY-NC-SA 4.0 licence mentioned above. The database can also be expanded with additional environments and aims to address an existing gap in validated VR environments for alcohol use.
Declaration regarding the use of generative AI
The author(s) attest that there was no use of generative artificial intelligence (AI) technology in the generation of text, figures, or other informational content of this manuscript.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
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