ArticleExperimental and clinical psychopharmacology2024
Signal processing and machine learning with transdermal alcohol concentration to predict natural environment alcohol consumption.
Article in Experimental and clinical psychopharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
17 citing papers in PubMed.
- Use of Wearable Transdermal Alcohol Sensors for Monitoring Alcohol Consumption After Detoxification With Contingency Management: Pilot Randomized Feasibility Trial.JMIR human factors · 2025Trial
- Daily-Level Drinking Motives as Predictors of Transdermal Alcohol Concentration and Alcohol-Induced Blackouts Among Young Adults.Alcohol, clinical & experimental research · 2026Article
- Applying Artificial Intelligence to Expand the Measurement Tool Kit in Clinical-Psychological Science: Moving Beyond Self-Reports.Clinical psychological science : a journal of the Association for Psychological Science · 2026Article
- Alcohol consumption dynamics and day-to-day experiences of alcohol-related problems: Combining transdermal alcohol concentration sensors and daily self-reports in young adults' natural settings.Experimental and clinical psychopharmacology · 2026Article
- Exploring neural markers of incentive salience and real-world drinking among individuals with alcohol use disorder.Physiology & behavior · 2026Article
- Objective Assessment in Clinical Psychological Science: Progress in Wearable Alcohol Biosensors.Annual review of clinical psychology · 2026Review
- Daily-level associations between protective behavioral strategies, transdermal alcohol concentration, and alcohol-induced blackouts among young adults.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Common and unique latent transition analysis (CULTA) as a way to examine the trait-state dynamics of alcohol intoxication.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2025Article
- Effects of nicotine-alcohol co-use on alcohol-induced blackouts and other alcohol-related consequences in college students.Psychopharmacology · 2025Article
- Development of an alcohol biosensor non-wear algorithm: laboratory-based machine learning and field-based deployment.Scientific reports · 2025Article
- Profiles of transdermal alcohol concentration and their prediction of negative and positive alcohol-related consequences in young adults' natural settings.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2025Article
- Profiles of alcohol intoxication and their associated risks in young adults' natural settings: A multilevel latent profile analysis applied to daily transdermal alcohol concentration data.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2025Article
- Serious harm reduction protective behavioral strategies reduce consequences associated with alcohol-induced blackouts in college students.Addictive behaviors · 2025Article
- A wearable alcohol biosensor: Exploring the accuracy of transdermal drinking detection.Drug and alcohol dependence · 2025Article
- Examining the Impact of Simultaneous Alcohol and Cannabis Use on Alcohol Consumption and Consequences: Protocol for an Observational Ambulatory Assessment Study in Young Adults.JMIR research protocols · 2024Article
- Transdermal alcohol concentration features predict alcohol-induced blackouts in college students.Alcohol, clinical & experimental research · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Wrist-worn alcohol biosensors continuously and discreetly record transdermal alcohol concentration (TAC) and may allow alcohol researchers to monitor alcohol consumption in participants' natural environments. However, the field lacks established methods for signal processing and detecting alcohol events using these devices. We developed software that streamlines analysis of raw data (TAC, temperature, and motion) from a wrist-worn alcohol biosensor (BACtrack Skyn) through a signal processing and machine learning pipeline: biologically implausible skin surface temperature readings (< 28°C) were screened for potential device removal and TAC artifacts were corrected, features that describe TAC (e.g., rise duration) were calculated and used to train models (random forest and logistic regression) that predict self-reported alcohol consumption, and model performances were measured and summarized in autogenerated reports. The software was tested using 60 Skyn data sets recorded during 30 alcohol drinking episodes and 30 nonalcohol drinking episodes. Participants (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.