Evidence map›Paper›PMID 33908674›Full record

ReviewAddiction (Abingdon, England)2021

A new generation of transdermal alcohol biosensing technology: practical applications, machine -learning analytics and questions for future research.

Catharine E Fairbairn, Nigel Bosch

Open access · greenAbstract readReview
In one paragraph

Review in Addiction (Abingdon, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
3.7field-weighted citation impact, top 6% of its field
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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. 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 · 2026
    Article
  3. Article
  4. Review
  5. 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 · 2026
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  7. Review
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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

2 authors at 1 institution in 1 country.

Catharine E FairbairnDepartment of Psychology, University of Illinois, Urbana-Champaign, IL, USA.ORCID 0000-0002-2694-5585
Nigel BoschSchool of Information Sciences and Department of Educational Psychology, University of Illinois, Urbana-Champaign, IL, USA.ORCID 0000-0003-2736-2899
University of Illinois Urbana-Champaign · US

Funding

Examining the Impact of Stress on the Emotionally Reinforcing Properties of Alcohol in Heavy Social Drinkers: A Multimodal Investigation Integrating Laboratory and Ambulatory MethodsR01AA025969 · NIAAA · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Catharine Fairbairn · 2017 to 2026
$3.5M
Towards a Wearable Alcohol Biosensor: Examining the Accuracy of BAC Estimates from New-Generation Transdermal Technology using Large-Scale Human Testing and Machine Learning AlgorithmsR01AA028488 · NIAAA · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Catharine Fairbairn · 2021 to 2026
$2.8M
NIAAA NIH HHS R01 AA025969NIAAA NIH HHS R01 AA028488
6 · The paper itself

Abstract

The use of transdermal alcohol monitors has burgeoned in recent years, now encompassing hundreds of thousands of individuals globally. A new generation of sensors promises to expand the range of applications for transdermal technology exponentially, and advances in machine-learning modeling approaches offer new methods for translating the data produced by transdermal devices. This article provides (1) a review of transdermal sensor research conducted to date, including an analysis of methodological features of past studies potentially key in driving reported sensor performance; (2) updates on methodological developments likely to be transformative for the field of transdermal sensing, including the development of new-generation sensors featuring smartphone integration and rapid sampling capabilities as well as developments in machine-learning analytics suited to data produced by these novel sensors and; (3) an analysis of the expanded range of applications for this new generation of sensor, together with corresponding requirements for sensor accuracy and temporal specificity. We also note questions as yet unanswered and key directions for future research.

Indexed as

Biosensing TechniquesEthanolForecastingHumansMachine LearningSmartphoneEthanolAlcoholbiosensormachine learningtime-series analysistransdermal sensors

Identifiers

PMID33908674
PMCPMC8429066
OpenAlexW3157607001

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.