The Use of Virtual Reality in Craving Assessment and Cue-Exposure Therapy in Substance Use Disorders
Bibliographic record
Abstract
Craving is recognized as an important diagnosis criterion for substance use disorders (SUDs) and a predictive factor of relapse. Various methods to study craving exist; however, suppressing craving to successfully promote abstinence remains an unmet clinical need in SUDs. One reason is that social and environmental contexts recalling drug and alcohol consumption in the everyday life of patients suffering from SUDs often initiate craving and provoke relapse. Current behavioral therapies for SUDs use the cue-exposure approach to suppress salience of social and environmental contexts that may induce craving. They facilitate learning and cognitive reinforcement of new behavior and entrain craving suppression in the presence of cues related to drug and alcohol consumption. Unfortunately, craving often overweighs behavioral training especially in real social and environmental contexts with peer pressure encouraging the use of substance, such as parties and bars. In this perspective, virtual reality (VR) is gaining interest in the development of cue-reactivity paradigms and practices new skills in treatment. VR enhances ecological validity of traditional craving-induction measurement. In this review, we discuss results from (1) studies using VR and alternative virtual agents in the induction of craving and (2) studies combining cue-exposure therapy with VR in the promotion of abstinence from drugs and alcohol use. They used virtual environments, displaying alcohol and drugs to SUD patients. Moreover, some environments included avatars. Hence, some studies have focused on the social interactions that are associated with drug-seeking behaviors and peer pressure. Findings indicate that VR can successfully increase craving. Studies combining cue-exposure therapy with virtual environment, however, reported mitigated success so far.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".