Evidence map›Paper›PMID 41550855›Full record

ArticleProceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences2026

Detecting Ethanol Intoxication and Impairment Using Wearable Biosensors.

Eric E Kaczor, Manohar Golleru, Daniel C Carter, Orian B Painter, Kyle J Kelleran, Joshua J Lynch, Brian M Clemency, Lora A Cavuoto, Peter R Chai

Abstract read
In one paragraph

Article in Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Eric E KaczorDepartment of Emergency Medicine, University at Buffalo.
Manohar GolleruDepartment of Industrial and Systems Engineering, University at Buffalo.
Daniel C CarterDepartment of Emergency Medicine, University at Buffalo.
Orian B PainterDepartment of Emergency Medicine, University at Buffalo.
Kyle J KelleranDepartment of Emergency Medicine, University at Buffalo.
Joshua J LynchDepartment of Emergency Medicine, University at Buffalo.
Brian M ClemencyDepartment of Emergency Medicine, University at Buffalo.
Lora A CavuotoDepartment of Industrial and Systems Engineering, University at Buffalo.
Peter R ChaiBrigham and Women's Hospital, Harvard Medical School.

Funding

Smart Steps: A context-aware adherence intervention to improve PrEP adherence among men who have sex with men (MSM) with substance use disorderDP2DA056107 · NIDA · BRIGHAM AND WOMEN'S HOSPITAL · PI CHAI, PETER R · 2022 to 2022
$2.7M
The ANTIDOTE Institute- Advancing New Toxicology Investigators in Drug abuse and Original Translational research EffortsR25DA058490 · NIDA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI STEPHANIE P CARREIRO, Peter R Chai · 2023 to 2026
$1.2M
NIDA NIH HHS DP2 DA056107NIDA NIH HHS R25 DA058490
6 · The paper itself

Abstract

Reliable objective measures of a person's intoxication and impairment from alcohol consumption are not readily available to the public. Wearable biosensors have the potential to provide a ubiquitous on-demand tool to deliver this kind of objective assessment in real world settings. This study evaluated the feasibility of assessing ethanol intoxication in N=28 healthy participants in a police academy's intoxication lab using wrist-worn biosensors to continuously measure heart rate, skin temperature, electrodermal activity, and accelerometry. Participants consumed ad hoc standard alcoholic drinks in a controlled setting and had regular breath alcohol content assessments and underwent standard field sobriety testing. The analysis showed statistically significant changes in each physiologic parameter between the sober and intoxicated periods. An XGBoost model was applied to this data producing machine learning algorithms to identify impairment with an accuracy as high as 0.80. These results demonstrate that it is feasible to assess ethanol intoxication using wrist-worn biosensors.

Indexed as

Alcohol intoxicationBiosensorsImpairmentMachine learning

Identifiers

PMID41550855
PMCPMC12805382

What OpenQuestion holds

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Registered trials

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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.