Nicotine Craving and Cue Exposure Therapy by Using Virtual Environments
Bibliographic record
Abstract
Smokers who are exposed to cues associated with smoking show cardiovascular reactivity and an increase in smoking urges as compared to when they are presented with neutral cues. Cue exposure therapy (CET), which refers to the repeated exposure to drug-related cues in order to extinguish this learned association, has increasingly been proposed as a potential treatment of addictive behaviors, including tobacco smoking. The result of our pilot study suggests that a cue elicited using a virtual environment (VE) is more effective than other cue exposure devices. The VE was composed of craving environments (virtual bar) and objects (an alcoholic drink, a packet of cigarettes, a lighter, an ashtray, a glass of beer, and advertising posters) that are likely to trigger craving, a smoking avatar, and an audio environment that included the noisy sound and music of a restaurant. Sixteen late-adolescent males who smoked at least 10 cigarettes a day were recruited to participate in the VE-CET study. The CET virtual bar program consisted of six sessions, and the participants were exposed repeatedly to each session using different questions and procedures. Although the effects of CET did not yield significant reductions in all of the dependent variables, the craving for cigarettes was gradually decreased during the course of the sessions. This tendency was closely related to the reduction in the smoking count between the morning before the experiment and the start of the experiment. Based on these preliminary results, it appears that VE-CET maybe a useful tool to use in treatment programs to help reduce craving in those who are nicotine dependent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".