All scenes include background music and audio effects consistent with the scene and the participants’interaction. The NTP Cue VR paradigm begins with 3 “test scenes,” which are approximately 3 minutes in duration, depending on participant comfort and abilities with the VR hardware. The first scene is the Practice Room. This is a square room with cubes systematically placed around corners of the room. The participants are asked to gaze at each of the boxes to confirm that the eye-tracking is functioning as intended. Then, the participants are asked to practice using the controllers to teleport to 4 different locations in the room. The second scene is the Practice Slider room, which instructs the participants how to answer the survey questions and provides the opportunity to practice adjusting the slider to answer the scales. The third test scene is the Blink Calibration room. In this scene, the participants are asked to blink 5 times after being prompted by an audio signal. The purpose of this room is to collect pupil diameter data when the participants actively blink to assist with increasing the accuracy of blink detection algorithms. Following the completion of the initial test scenes, the 2 mood surveys are presented, and the 6 scenes are pseudorandomized within scene type such that the general scene order is maintained . The participants are then placed in each scene for 5 minutes. The entire paradigm is approximately 30 minutes in duration.There are 2 types of data recorded within each scene, regular time series and event-based data that is recorded at event onset. Regular time series data are collected at every 10-millisecond interval , independent of the frame time. The following data are recorded periodically: timestamp, raw gaze intersection point,vertical agriculture position and forward direction of the participants’ headset, and pupil diameter and eye openness .
The following events and corresponding timestamps are recorded when they occur: blinks, including number of blinks and the object of gaze at the time of the blink; button presses on the controller, including time, button pressed, and object of interaction ; and object of gaze when eye gaze switches to a new object.However, this raycasting method did not perform well in our experiments, especially for very small objects, owing to the limited precision and accuracy of the eye tracker, microsaccades, etc. Therefore, for small objects of interest, we utilized the G2OM algorithm provided by the Tobii XR SDK, which is a machine learning–based object selection algorithm that aims to improve small object– and fast-moving object–tracking. Based on our testing, this algorithm improved object selection over the naïve method but still lacked selection quality. Thus, to further improve object selection, we introduced an additional mechanism to “lock” the object selection when an object is manipulated such that whenever a participant actively picks up a virtual object, the object selection algorithm will always select the picked object until the participant releases the object. If the participant is not interacting with an object, the G2OM algorithm is employed, or if no small objects are within the field, naïve raycasting is employed. To calculate eye-gaze statistics toward active and neutral cue objects, 4 dictionaries corresponding to 4 different types of objects are initialized prior to the start of participant involvement in the paradigm. These dictionaries are then used to store the cumulating gaze fixation or dwell time durations as values for individual objects belonging to each object and type. When a participant gazes at an object, the object is searched in the dictionary on the basis of its name and type. If the object was encountered before, the current fixation time is added to its cumulative fixation time. If the object had not been encountered before, a new entry is created for the object.
The fixation time is then calculated as the difference between the timestamp of current entry and that of the next line of entry. Following the completion of the paradigm, total fixation time indices are produced, which reflect the sum of values within each dictionary . The mean fixation time indices are also created, which reflect the total fixation time divided by the number of objects gazed at by the participant. Initially, we tested a measurement of eye openness, as calculated by the HTC SRanipal SDK, as an indicator for blink detection. However, given the lack of established thresholds of eye openness for blink detection, we instead chose to rely on estimates of pupil diameter. Consistent with previous studies, an eyeblink is herein defined as complete eyelid closure with the pupil covered for 50-500 milliseconds. For any given time point, we consider a missing pupil diameter reading as a possible complete eyelid closure where the pupil is completely covered by the eyelid. These eye closure durations are blink candidates. If either pupil is covered for less than 50 milliseconds, the candidate is discarded as it is more likely owing to noise or an eye tracker limitation. If either pupil is covered for more than 500 milliseconds, the candidate is also discarded as this is more consistent with a micro-sleep. Using this blink detection definition, the blink count for the majority of the current participants fell within 12-40 blinks per minute, which appears to align with the consensus of spontaneous blink rates in the literature.Following consent procedures, participants undergo an extensive clinical interview and complete several self-report questionnaires covering demographic, psychological health , and substance use domains. The TLFB has high test-retest reliability for intervals ranging from 30 to 360 days prior to the interview date, with an intraclass correlation coefficient=0.92 for “Total number of cigarettes smoked per interval”. Thus, past 90-day NTP use episode count from the TLFB was used in the quantitative analyses presented below.
All study interview and self-report data were collected and managed using REDCap electronic data capture tools hosted at the University of California, San Diego. Participants then undergo the NTP Cue VR paradigm,hydroponic stands which includes repeated assessments of subjective nicotine craving and scene relevance to the individual participant . Upon completion of the paradigm, additional assessments on VR-related outcomes such as VR presence and VR-related simulator or motion sickness are administered. The IPQ total score was calculated using a simple averaging method to obtain a single average perceived presence score ranging 0-100. Similarly, the SSQ was scored in concordance with procedures outlined to assess VR-specific sickness , which involves a simple averaging method to obtain a single average sickness score with a range of 0-100. These analyses include the first 31 participants to complete the study protocol; however, data were missing for some subjects on a subset of indices owing to technological difficulties . Owing to safety restrictions related to COVID-19, no biological verification of abstinence was conducted. Group differences are not being investigated in the present pilot analyses since the goal of this study is to describe the development and general validity of the paradigm and to maximize statistical power. Statistical analyses were conducted using a repeated measures t test or Pearson correlation framework. The threshold of significance was set at P<.05 for all analyses. SPSS Statistics for Windows software was used for all analyses. This report describes our approach to the development of a novel NTP cue VR paradigm designed to simultaneously induce and assess potential eye-based objective correlates of nicotine craving in naturalistic and translatable virtual settings. The preliminary statistical analyses support the potential of this paradigm in its ability to induce subjective craving while instilling a moderate sense of presence in the virtual world and only low levels of VR-related sickness. The preliminary results outline a potential context-specific effect of NTP-related attentional bias and pupil dilation in this pilot sample. Consistent with the literature on attentional bias and pupil dilation, we observed greater Active NTP versus Neutral control cue-related effects in 2 of the 3 Active scenes . The similarity observed in the pattern of effects between attentional bias and pupil dilation provides early evidence of a potential cross-validation of these metrics. No effects were observed for the EBR metric; however, the size of this effect, if present at all, may be smaller than we are currently able to detect with the limited sample. The observed reversal of attentional bias and pupil dilation toward neutral cues in the Driving scene warrants further investigation, given the large effect size. Potential explanations for this include the presence of especially engaging neutral cues in the Driving scene, as a 360° video of a busy city street is presented in the background, which participants report as entertaining to watch. Despite the overall bias toward neutral cues reflected in the global attentional bias metric, and within the Driving scene alone, participants with greater attentional bias toward NTP cues were found to endorse greater NTP use in the previous 90 days.
This effect appears to be driven by the higher-frequency NTP users in our sample and is consistent with the literature supporting the validity of attentional bias as a clinically important indicator of nicotine addiction. Additional analyses are planned to assess direct and indirect relationships between scene eye-related outcomes and relevance to the individual, scene-specific craving level, randomization of scenes, engagement with specific cues, and NTP use groups once more data are collected. This pilot study has several strengths and limitations. Strengths include the development of a cutting-edge VR cue-reactivity task that incorporates the latest technological advances in graphic design to increase translatability to the real-world and simultaneous assessment of multiple potential eye-related indices of cue-reactivity in a 3D virtual environment. Limitations include the absence of biological verification to confirm self-reported NTP use and the inability to investigate NTP use profiles in the analyses owing to limited power. Importantly, given the limited sample size, we caution against over interpretation of our results. It remains unknown whether the absence of significant results, particularly with respect to the correlations between objective eye-related indices and subjective craving ratings, are the result of limited power to detect these relationships or true independence of these indices. However, we believe that the general pattern of scene-related effects on attentional bias and pupil dilation are encouraging and warrant further study. The identification of reliable objective correlates of craving would allow for greater examination of the underlying neurobiological processes involved, and inform new avenues for the development of psychological and pharmacological treatments.PATTERNS OF ALCOHOL intake vary dramatically over the lifespan, with the heaviest drinking and steepest trajectory of increasing alcohol problems typically observed in the mid-teens to mid-20s . In the United States, a person’s first drink is likely to occur at about age 15, and by age 18, 70% of people have consumed alcohol, 35% were ever intoxicated, and 24% admitted to consuming 5 or more drinks on an occasion . It has been estimated that the first drink in the United Kingdom may occur closer to age 14, with 70% of students in that age range having consumed alcohol . Thus, heavy drinking during adolescence may be especially prominent in the United Kingdom, which ranks near the top among 35 European countries regarding several measures of drunkenness . One common alcohol-related adverse consequence is an alcohol-related blackout , defined as not being able to remember parts or entire periods of events that occurred while drinking and awake . Almost 50% of drinkers, including college students, have ever experienced an ARB , as have 80% of individuals with alcohol use disorders . The high rate of blackouts in people with AUDs prompted the inclusion of these phenomena in alcohol questionnaires and interviews , but their prevalence in non-AUD drinkers resulted in their omission from most AUD diagnostic criteria . Blood alcohol concentrations >0.300 g/dl are associated with a 60% rate of ARBs, especially for en bloc events, although fragmentary blackouts are observed with BACs as low as ≥0.06 g/dl . Higher drinking frequencies also relate to blackouts, perhaps reflecting their association with higher quantities . In addition, the rate of ARBs may be higher in individuals consuming other drugs that affect brain functioning . Thus, alcohol quantities and frequencies and the use of substances other than alcohol are important characteristics to consider as predictors of ARBs.