The participants were free to move around and interact with various objects within the VR environment using 2 hand-held Vive controllers. Surveys assessing depressed mood and anxiety were presented at the start of the paradigm and additional surveys assessing subjective craving and scene relevance were presented between scenes within the headset. A VAS survey was chosen as the in-task measurement of subjective craving owing to its high face-validity, ability to capture the dynamic fluctuations in craving, and low burden on participants, especially over frequently repeated assessment. Survey responses were made by adjusting a slide bar using one of the controllers. Participants were instructed to “Just explore everything around you until the scene changes” and “During the task, we will be measuring what you pay attention to, and we will be asking you to rate your craving level between each scene.” Three Active scenes and three Neutral scenes were developed and included in the final paradigm . The Active scenes include NTP-related cues, while in the Neutral scenes, all cues are neutral. Active cues include ashtrays, lighters, JUUL devices, cigarettes , Puffbars, hookahs, as well as the presence of human models engaged in smoking or vaping behaviors. Neutral cues vary depending on the scene context. All cues are interactable such that the participants are able to pick up, throw, and collide the items with other items in the scene. All scenes include the presence of at least one animated human model. Smoke and vapor effects are incorporated with the animated human models in the Active scenes to increase the immersiveness of the experience.
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,cannabis dryer 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, 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 eye blink 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 microsleep. Using this blink detection definition, the blink count for the majority of the current participants fell within 12-40 blinks per minute,drying weed which appears to align with the consensus of spontaneous blink rates in the literature. 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. Despite consistent declines in rates of cigarette use among adolescents in the last five years, rates of marijuana use have remained constant, with marijuana being the most widely used illicit drug among adolescents . Nationally representative data from Monitoring the Future show rates of conventional cigarette use among 10th graders declining significantly from 9.1% in 2013 to 7.2% in 2014; and among 12th graders from 16.3% in 2013 to 13.6% in 2014 . Rates of marijuana use have remained stable, with 16.6% of 10th graders and 21.2% of 12th graders reporting past 30-day use in 2014 . Blunts have become a common form of marijuana among adolescents, with more than half of 30-day marijuana users also reporting blunt use . Adolescents’ perceptions related to marijuana use have also changed, with the number of youth who perceive significant risk from using marijuana once or twice a week decreasing from 54.6% in 2007 to 39.5% in 2013 . Moreover, 73.3% of 10th graders reported disapproval of occasional marijuana use in 2007, yet 62.9% reported disapproval in 2014 . Social media is a key venue for sharing marijuana-related information and attitudes, particularly among adolescents. For example, between 2012 and 2013, more adolescents than adults tweeted about marijuana, with the majority of these tweets reflecting positive attitudes about marijuana . Social acceptability and perceptions of risks and benefits, including the active sharing of these beliefs on social media, are important predictors of health behavior decision-making . Perceptions of risks generally vary by sex and age, with females and minorities tending to rate perceived risks higher than white males . Additionally, perceptions of risk related to marijuana use are known to be higher among females, non-whites, older adults, and individuals who have a family income between $20,000-49,999 . However, few studies have examined adolescents’ beliefs about specific risks and benefits related to marijuana and blunts, and studies have not examined relationships among adolescents’ perceptions, social acceptability, awareness of social media and actual marijuana use . Understanding these relationships is critical, especially since smoking marijuana places one at risk for a number of the same negative health outcomes and secondhand smoke effects as smoking conventional tobacco cigarettes . Long-term use of marijuana can lead to addiction, with initiation in adolescence associated with higher rates of addiction, negative impacts on brain development, and lower levels of school and lifetime achievement .