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Article de Périodique
Development and validation of a model for measuring alcohol consumption from transdermal alcohol content data among college students (2023)
Auteur(s) :
KIANERSI, S. ;
LUDEMA, C. ;
AGLEY, J. ;
AHN, Y. Y. ;
PARKER, M. ;
IDEKER, S. ;
ROSENBERG, M.
Année
2023
Page(s) :
2014-2025
Langue(s) :
Anglais
Refs biblio. :
57
Domaine :
Alcool / Alcohol
Discipline :
PRO (Produits, mode d'action, méthode de dépistage / Substances, action mode, screening methods)
Thésaurus géographique
ETATS-UNIS
Thésaurus mots-clés
ALCOOL
;
DEPISTAGE
;
VALIDITE
;
MODELE
;
PEAU
;
JEUNE
;
MILIEU ETUDIANT
;
METHODE
;
CONSOMMATION
;
MESURES QUANTITATIVES
;
TECHNOLOGIE
;
PERFORMANCE
Résumé :
BACKGROUND AND AIMS: Transdermal 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.
DESIGN: We used a model development and validation study design.
SETTING: Indiana, USA.
PARTICIPANTS: In 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).
FINDINGS: Participants (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]).
CONCLUSION: This 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).
DESIGN: We used a model development and validation study design.
SETTING: Indiana, USA.
PARTICIPANTS: In 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).
FINDINGS: Participants (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]).
CONCLUSION: This 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).
Affiliation :
Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
Prevention Insights, Department of Applied Health Science, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana, USA
Epidemiology Department, Columbia University's Mailman School of Public Health, New York City, New York, USA
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
Prevention Insights, Department of Applied Health Science, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana, USA
Epidemiology Department, Columbia University's Mailman School of Public Health, New York City, New York, USA
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