Mindful attention to alcohol can reduce cravings in the moment and consumption in daily life
Bibliographic record
Abstract
Developing interventions to change health behaviors—especially those targeting cross-cutting health risk factors like alcohol use—is a public health priority. In this study, we used a translational neuroscience approach to evaluate the underlying mechanisms and individual differences in a mindful distancing intervention designed to reduce alcohol consumption among college students. We combined functional neuroimaging and machine learning to develop a brain-based predictive model (a “neural signature”) of mindful distancing. This model allowed us to track moment-to-moment variation in how participants implemented the strategy, as well as differences between individuals. Students completed a mindful distancing task involving alcohol cues during fMRI scanning. They then completed a 28-day, smartphone-based, experience sampling intervention. In the laboratory, mindfully attending to alcohol decreased craving, particularly among people who more strongly expressed the mindful distancing signature. In daily life, the mindful distancing intervention increased mindful responses to alcohol and decreased subsequent alcohol consumption through two distinct pathways: mindful responses directly influenced alcohol consumption and indirectly influenced it by reducing cravings for alcohol. Individuals with stronger expression of the neural signature experienced the greatest benefits from the intervention. These findings extend theoretical models of how mindfulness-based emotion regulation strategies impact alcohol use in emerging adults without alcohol use disorders. They also demonstrate the potential of using neural signatures to evaluate health behavior change interventions within a translational neuroscience framework.
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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 | 0.002 |
| 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".