Evidence map›Paper›PMID 39001012›Full record

ArticleSensors (Basel, Switzerland)2024

Wearable Alcohol Monitoring Device for the Data-Driven Transcutaneous Alcohol Diffusion Model.

Ahmed Hasnain Jalal, Sepehr Arbabi, Mohammad A Ahad, Fahmida Alam, Md Ashfaq Ahmed

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Interpol review of forensic toxicology, 2023-2025.Forensic science international. Synergy · 2026
    Review
  2. Review
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

5 authors.

Ahmed Hasnain JalalDepartment of Electrical and Computer Engineering, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA.ORCID 0000-0003-1857-1621
Sepehr ArbabiDepartment of Chemical Engineering, University of Texas Permian Basin, Odessa, TX 79762, USA.
Mohammad A AhadDepartment of Electrical and Computer Engineering, Georgia Southern University, Statesboro, GA 30458, USA.
Fahmida AlamDepartment of Electrical and Computer Engineering, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA.
Md Ashfaq AhmedBaptist Health South Florida, Miami, FL 33186, USA.

Funding

University of Texas STARs Award# P5011032
6 · The paper itself

Abstract

Wearable alcohol monitoring devices demand noninvasive, real-time measurement of blood alcohol content (BAC) reliably and continuously. A few commercial devices are available to determine BAC noninvasively by detecting transcutaneous diffused alcohol. However, they suffer from a lack of accuracy and reliability in the determination of BAC in real time due to the complex scenario of the human skin for transcutaneous alcohol diffusion and numerous factors (e.g., skin thickness, kinetics of alcohol, body weight, age, sex, metabolism rate, etc.). In this work, a transcutaneous alcohol diffusion model has been developed from real-time captured data from human wrists to better understand the kinetics of diffused alcohol from blood to different skin epidermis layers. Such a model will be a footprint to determine a base computational model in larger studies. Eight anonymous volunteers participated in this pilot study. A laboratory-built wearable blood alcohol content (BAC) monitoring device collected all the data to develop this diffusion model. The proton exchange membrane fuel cell (PEMFC) sensor was fabricated and integrated with an nRF51822 microcontroller, LMP91000 miniaturized potentiostat, 2.4 GHz transceiver supporting Bluetooth low energy (BLE), and all the necessary electronic components to build this wearable BAC monitoring device. The %BAC data in real time were collected using this device from these volunteers' wrists and stored in the end device (e.g., smartphone). From the captured data, we demonstrate how the volatile alcohol concentration on the skin varies over time by comparing the alcohol concentration in the initial stage (= 10 min) and later time (= 100 min). We also compare the experimental results with the outputs of three different input profiles: piecewise linear, exponential linear, and Hoerl, to optimize the developed diffusion model. Our results demonstrate that the exponential linear function best fits the experimental data compared to the piecewise linear and Hoerl functions. Moreover, we have studied the impact of skin epidermis thickness within ±20% and demonstrate that a 20% decrease in this thickness results in faster dynamics compared to thicker skin. The model clearly shows how the diffusion front changes within a skin epidermis layer with time. We further verified that 60 min was roughly the time to reach the maximum concentration,

Indexed as

Blood Alcohol ContentSkinWearable Electronic DevicesAdultDiffusionEthanolFemaleHumansMaleMonitoring, PhysiologicBlood Alcohol ContentEthanolBACdiffusion modelepidermisexponential linearPEMFC sensortranscutaneous

Identifiers

PMID39001012
PMCPMC11244443

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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.