ArticleAddiction (Abingdon, England)2023
Development and validation of a model for measuring alcohol consumption from transdermal alcohol content data among college students.
Article in Addiction (Abingdon, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 7 citations in OpenAlex.
- Use of Wearable Transdermal Alcohol Sensors for Monitoring Alcohol Consumption After Detoxification With Contingency Management: Pilot Randomized Feasibility Trial.JMIR human factors · 2025Trial
- A wearable alcohol biosensor: Exploring the accuracy of transdermal drinking detection.Drug and alcohol dependence · 2025Article
- Wearable alcohol monitors for alcohol use data collection among college students: Feasibility and acceptability.Alcohol (Fayetteville, N.Y.) · 2023Article
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Authors and funding
7 authors at 3 institutions in 1 country.
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Abstract
BACKGROUND AND
aimsTransdermal alcohol content (TAC) data collected by wearable alcohol monitors could potentially contribute to alcohol research, but raw data from the devices are challenging to interpret. We aimed to develop and validate a model using TAC data to detect alcohol drinking.
designWe used a model development and validation study design.
settingIndiana, USA
participantsIn March to April 2021, we enrolled 84 college students who reported drinking at least once a week (median age = 20 years, 73% white, 70% female). We observed participants' alcohol drinking behavior for 1 week. MEASUREMENTS: Participants wore BACtrack Skyn monitors (TAC data), provided self-reported drinking start times in real time (smartphone app) and completed daily surveys about their prior day of drinking. We developed a model using signal filtering, peak detection algorithm, regression and hyperparameter optimization. The input was TAC and outputs were alcohol drinking frequency, start time and magnitude. We validated the model using daily surveys (internal validation) and data collected from college students in 2019 (external validation).
findingsParticipants (N = 84) self-reported 213 drinking events. Monitors collected 10 915 hours of TAC. In internal validation, the model had a sensitivity of 70.9% (95% CI = 64.1%-77.0%) and a specificity of 73.9% (68.9%-78.5%) in detecting drinking events. The median absolute time difference between self-reported and model-detected drinking start times was 59 min. Mean absolute error (MAE) for the reported and detected number of drinks was 2.8 drinks. In an exploratory external validation among five participants, number of drinking events, sensitivity, specificity, median time difference and MAE were 15%, 67%, 100%, 45 minutes and 0.9 drinks, respectively. Our model's output was correlated with breath alcohol concentration data (Spearman's correlation [95% CI] = 0.88 [0.77, 0.94]).
conclusionThis study, the largest of its kind to date, developed and validated a model for detecting alcohol drinking using transdermal alcohol content data collected with a new generation of alcohol monitors. The model and its source code are available as Supporting Information (https://osf.io/xngbk).
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