ArticleDrug and alcohol dependence2020
Using machine learning for real-time BAC estimation from a new-generation transdermal biosensor in the laboratory.
Article in Drug and alcohol dependence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05692830 (Towards a Wearable Alcohol Biosensor), which is not on this map. Cited by 39 papers, 2 of them syntheses that pooled it.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Towards a Wearable Alcohol Biosensor: Examining the Accuracy of BAC Estimates From New-Generation Transdermal Technology Using Large-Scale Human Testing and Machine Learning Algorithms
Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Validating transdermal alcohol biosensors: a meta-analysis of associations between blood/breath-based measures and transdermal alcohol sensor output.Addiction (Abingdon, England) · 2022Pooled it
- Accuracy of Wearable Transdermal Alcohol Sensors: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Use of Wearable Transdermal Alcohol Sensors for Monitoring Alcohol Consumption After Detoxification With Contingency Management: Pilot Randomized Feasibility Trial.JMIR human factors · 2025Trial
- 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
- 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
- Using Alcohol and Sleep Sensors to Understand Blackout Risk in Young Adults' Natural Settings (The Lights Out Study): Protocol for an Intensive Longitudinal Pilot Study.JMIR research protocols · 2026Article
- The Role of Artificial Intelligence in Advancing Biosensor Technology: Past, Present, and Future Perspectives.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Exploring associations between drinking contexts and alcohol consumption: An analysis of photographs.Journal of psychopathology and clinical science · 2025Article
- A qualitative exploration of the experiences of transdermal alcohol sensor devices amongst alcohol service practitioners (South London, UK).Addiction research & theory · 2025Article
- A wearable alcohol biosensor: Exploring the accuracy of transdermal drinking detection.Drug and alcohol dependence · 2025Article
- Development of an accelerometer-based wearable sensor approach for alcohol consumption detection.Alcohol, clinical & experimental research · 2024Article
- The impact of alcohol on affiliative verbal behavior: A systematic review and meta-analysis.Alcohol, clinical & experimental research · 2024Review
- A qualitative exploration of the experiences of transdermal alcohol sensor devices amongst people in receipt of treatment for alcohol use disorder.Addictive behaviors reports · 2024Article
- Subjective intoxication predicts alcohol-related consequences at equivalent alcohol concentrations in young adults using ecological momentary assessment and alcohol sensors.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2024Article
- Signal processing and machine learning with transdermal alcohol concentration to predict natural environment alcohol consumption.Experimental and clinical psychopharmacology · 2024Article
- Integrating Alcohol Biosensors With Ecological Momentary Intervention (EMI) for Alcohol Use: a Synthesis of the Latest Literature and Directions for Future Research.Current addiction reports · 2024Article
- REAL-TIME RECURSIVE ESTIMATION OF, AND UNCERTAINTY QUANTIFICATION FOR, BREATH ALCOHOL CONCENTRATION VIA LQ TRACKING CONTROL-BASED INVERSE FILTERING OF TRANSDERMAL ALCOHOL BIOSENSOR SIGNALS.Applied mathematics for modern challenges · 2024Article
- Accuracy of transdermal alcohol monitoring devices in a laboratory setting.Alcohol and alcoholism (Oxford, Oxfordshire) · 2024Article
- Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning.ACS sensors · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundTransdermal biosensors offer a noninvasive, low-cost technology for the assessment of alcohol consumption with broad potential applications in addiction science. Older-generation transdermal devices feature bulky designs and sparse sampling intervals, limiting potential applications for transdermal technology. Recently a new-generation of transdermal device has become available, featuring smartphone connectivity, compact designs, and rapid sampling. Here we present initial laboratory research examining the validity of a new-generation transdermal sensor prototype.
methodsParticipants were young drinkers administered alcohol (target BAC = .08 %) or no-alcohol in the laboratory. Participants wore transdermal sensors while providing repeated breathalyzer (BrAC) readings. We assessed the association between BrAC (measured BrAC for a specific time point) and eBrAC (BrAC estimated based only on transdermal readings collected in the immediately preceding time interval). Extra-Trees machine learning algorithms, incorporating transdermal time series features as predictors, were used to create eBrAC.
resultsFailure rates for the new-generation prototype sensor were high (16 %-34 %). Among participants with useable new-generation sensor data, models demonstrated strong capabilities for separating drinking from non-drinking episodes, and significant (moderate) ability to differentiate BrAC levels within intoxicated participants. Differences between eBrAC and BrAC were 60 % higher for models based on data from old-generation vs new-generation devices. Model comparisons indicated that both time series analysis and machine learning contributed significantly to final model accuracy.
conclusionsResults provide favorable preliminary evidence for the accuracy of real-time BAC estimates from a new-generation sensor. Future research featuring variable alcohol doses and real-world contexts will be required to further validate these devices.
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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.