Alcohol Use Disorder in the Age of Technology: A Review of Wearable Biosensors in Alcohol Use Disorder Treatment
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Biosensors enable observation and understanding of latent physiological occurrences otherwise unknown or invasively detected. Wearable biosensors monitoring physiological constructs across a wide variety of mental and physical health conditions have become an important trend in innovative research methodologies. Within substance use research, explorations of biosensor technology commonly focus on identifying physiological indicators of intoxication to increase understanding of addiction etiology and to inform treatment recommendations. In this review, we examine the state of research in this area as it pertains to treatment of alcohol use disorders specifically highlighting the gaps in our current knowledge with recommendations for future research. Annually, alcohol use disorders affect approximately 15 million individuals. A primary focus of existing wearable technology-based research among people with alcohol use disorders is identifying alcohol intoxication. A large benefit of wearable biosensors for this purpose is they provide continuous readings in a passive manner compared with the gold standard measure of blood alcohol content (BAC) traditionally measured intermittently by breathalyzer or blood draw. There are two primary means of measuring intoxication with biosensors: gait and sweat. Gait changes have been measured via smart sensors placed on the wrist, in the shoe, and mobile device sensors in smart phones. Sweat measured by transdermal biosensors detects the presence of alcohol in the blood stream correlating to BAC. Transdermal biosensors have been designed in tattoos/skin patches, shirts, and most commonly, devices worn on the ankle or wrist. Transdermal devices were initially developed to help monitor court-ordered sobriety among offenders with alcohol use disorder. These devices now prove most useful in continuously tracking consumption throughout clinical trials for behavioral treatment modalities. More recent research has started exploring the uses for physical activity trackers and physiological arousal sensors to guide behavioral interventions for relapse prevention. While research has begun to demonstrate wearable devices' utility in reducing alcohol consumption among individuals aiming to cutdown on their drinking, monitoring sustained abstinence in studies exploring contingency management for alcohol use disorders, and facilitating engagement in activity-based treatment interventions, their full potential to further aid in understanding of, and treatment for, alcohol use disorders has yet to be explored.
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Studies have shown a high correlation between the concentrations of alcohol in sweat and blood, which enables continuous noninvasive blood alcohol monitoring by monitoring sweat alcohol concentration.
“Studies have shown a high correlation between the concentrations of alcohol in sweat and blood, which enables continuous noninvasive blood alcohol monitoring to be achieved by monitoring sweat alcohol concentration 91,92 .”
From A review of sampling, energy supply and intelligent monitoring for long-term sweat sensors (2022) · cites this paper as [92]
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(2011) · Cited by 5,203 · see citing works
Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
Mark, G., Fussell, S. R., Lampe, C., Schraefel, M., Hourcade, J., Appert, C., & Wigdor, D. J. (2017) · International Conference on Human Factors in Computing Systems · doi:10.1145/3025453 · Cited by 2,222 · see citing works
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9830-8 · Cited by 2,171 · see citing works
A Meta-Analytic Review of Psychosocial Interventions for Substance Use Disorders
Dutra, L., Stathopoulou, G., Basden, S. L., Leyro, T. M., Powers, M. B., & Otto, M. W. (2008) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2007.06111851 · Cited by 1,287 · see citing works
Boca, F. K. D., & Darkes, J. (2003) · Addiction · doi:10.1046/j.1359-6357.2003.00586.x · Cited by 1,138 · see citing works
Contingency management for treatment of substance use disorders: a meta‐analysis
Prendergast, M., Podus, D., Finney, J., Greenwell, L., & Roll, J. (2006) · Addiction · doi:10.1111/j.1360-0443.2006.01581.x · Cited by 877 · see citing works
Noninvasive Alcohol Monitoring Using a Wearable Tattoo-Based Iontophoretic-Biosensing System
Kim, J., Jeerapan, I., Imani, S., Cho, T. N., Bandodkar, A., Cinti, S., Mercier, P. P., & Wang, J. (2016) · ACS Sensors · doi:10.1021/acssensors.6b00356 · Cited by 599 · see citing works
The Obsessive Compulsive Drinking Scale
Anton, R. F. (1996) · Archives of General Psychiatry · doi:10.1001/archpsyc.1996.01830030047008 · Cited by 507 · see citing works
Talk is cheap: measuring drinking outcomes in clinical trials.
Babor, T., Steinberg, K., Anton, R., & del Boca, F. D. (2000) · Journal of Studies on Alcohol · doi:10.15288/jsa.2000.61.55 · Cited by 493 · see citing works
Baig, M. M., GholamHosseini, H., Moqeem, A. A., Mirza, F., & Lindén, M. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0760-1 · Cited by 351 · see citing works
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Dey, A. K., Chu, H.-H., & Hayes, G. R. (2012) · doi:10.1145/2370216 · Cited by 332 · see citing works
Alcohol use among college students: an international perspective
Karam, E. G., Kypri, K., & Salamoun, M. (2007) · Current Opinion in Psychiatry · doi:10.1097/yco.0b013e3280fa836c · Cited by 325 · see citing works
Advanced Materials for Health Monitoring with Skin‐Based Wearable Devices
Jin, H., Abu‐Raya, Y. S., & Haick, H. (2017) · Advanced Healthcare Materials · doi:10.1002/adhm.201700024 · Cited by 317 · see citing works
Just-in-the-Moment Adaptive Interventions (JITAI): A Meta-Analytical Review
Wang, L., & Miller, L. C. (2020) · Health Communication · doi:10.1080/10410236.2019.1652388 · Cited by 264 · see citing works
Prize-based contingency management for the treatment of substance abusers: a meta-analysis
Benishek, L. A., Dugosh, K. L., Kirby, K. C., Matejkowski, J. C., Clements, N. T., Seymour, B. L., & Festinger, D. S. (2014) · Addiction · doi:10.1111/add.12589 · Cited by 243 · see citing works
Contingency management for alcohol use reduction: A pilot study using a transdermal alcohol sensor
Barnett, N. P., Tidey, J. W., Murphy, J. G., Swift, R. M., & Colby, S. M. (2011) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2011.04.023 · Cited by 145 · see citing works
Leffingwell, T. R., Cooney, N. J., Murphy, J. G., Luczak, S., Rosen, G., Dougherty, D. M., & Barnett, N. P. (2013) · Alcoholism: Clinical and Experimental Research · doi:10.1111/j.1530-0277.2012.01869.x · Cited by 142 · see citing works
Wearable electrochemical alcohol biosensors
Campbell, A. S., Kim, J., & Wang, J. (2018) · Current Opinion in Electrochemistry · doi:10.1016/j.coelec.2018.05.014 · Cited by 140 · see citing works
Alcohol Protracted Withdrawal Syndrome: The Role of Anhedonia
Martinotti, G., Nicola, M. D., Reina, D., Andreoli, S., Focà, F., Cunniff, A. L., Tonioni, F., Bria, P., & Janiri, L. (2008) · Substance Use & Misuse · doi:10.1080/10826080701202429 · Cited by 136 · see citing works
Studies on a Wearable, Electronic, Transdermal Alcohol Sensor
Swift, R. M., Martin, C. S., Swette, L., LaConti, A., & Kackley, N. (1992) · Alcoholism: Clinical and Experimental Research · doi:10.1111/j.1530-0277.1992.tb00668.x · Cited by 114 · see citing works
Barnett, N. P., Celio, M. A., Tidey, J. W., Murphy, J. G., Colby, S. M., & Swift, R. M. (2017) · Addiction · doi:10.1111/add.13767 · Cited by 111 · see citing works
Research perspectives on alcohol craving: an overview
Lowman, C., Hunt, W. A., Litten, R. Z., & Drummond, C. (2000) · Addiction · doi:10.1080/09652140050111636 · Cited by 109 · see citing works
Fang, H., Zhang, Z., Wang, C. J., Daneshmand, M., Wang, C., & Wang, H. (2015) · IEEE Network · doi:10.1109/mnet.2015.7293298 · Cited by 106 · see citing works
Real-Time Mobile Detection of Drug Use with Wearable Biosensors: A Pilot Study
Carreiro, S., Smelson, D. A., Ranney, M. L., Horvath, K. J., Picard, R. W., Boudreaux, E. D., Hayes, R. B., & Boyer, E. W. (2014) · Journal of Medical Toxicology · doi:10.1007/s13181-014-0439-7 · Cited by 98 · see citing works
Self-citationDougherty, D. M., Charles, N. E., Acheson, A., John, S., Furr, R. M., & Hill‐Kapturczak, N. (2012) · Experimental and Clinical Psychopharmacology · doi:10.1037/a0029021 · Cited by 95 · see citing works
Dougherty, D. M., Hill‐Kapturczak, N., Liang, Y., Karns, T. E., Cates, S. E., Lake, S. L., Mullen, J., & Roache, J. D. (2014) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2014.06.039 · Cited by 93 · see citing works
Wrist-worn alcohol biosensors: Strengths, limitations, and future directions
Wang, Y., Fridberg, D. J., Leeman, R. F., Cook, R. L., & Porges, E. C. (2018) · Alcohol · doi:10.1016/j.alcohol.2018.08.013 · Cited by 79 · see citing works
Abrantes, A. M., Blevins, C. E., Battle, C. L., Read, J. P., Gordon, A. L., & Stein, M. D. (2017) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2017.07.006 · Cited by 78 · see citing works
Experiences with SCRAMx alcohol monitoring technology in 100 alcohol treatment outpatients
Alessi, S. M., Barnett, N. P., & Petry, N. M. (2017) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2017.05.031 · Cited by 63 · see citing works
Self-citationSmartphone Inference of Alcohol Consumption Levels from Gait
Arnold, Z., LaRose, D., & Agu, E. (2015) · doi:10.1109/ichi.2015.59 · Cited by 62 · see citing works
Dougherty, D. M., Karns, T. E., Mullen, J., Liang, Y., Lake, S. L., Roache, J. D., & Hill‐Kapturczak, N. (2015) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2014.12.021 · Cited by 59 · see citing works
Kennedy, A. P., Epstein, D., Jobes, M. L., Agage, D. A., Tyburski, M., Phillips, K. A., Ali, A., Bari, R., Hossain, S. M., Hovsepian, K., et al. (2015) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2015.03.024 · Cited by 54 · see citing works
Roll, J. M., Huber, A., Sodano, R., Chudzynski, J. E., Moynier, E., & SHOPTAW, S. J. (2006) · The Psychological Record · doi:10.1007/bf03395538 · Cited by 53 · see citing works
Craving Typology Questionnaire (CTQ): A scale for alcohol craving in normal controls and alcoholics
Martinotti, G., Nicola, M. D., Tedeschi, D., Callea, A., Giannantonio, M. D., & Janiri, L. (2013) · Comprehensive Psychiatry · doi:10.1016/j.comppsych.2013.03.023 · Cited by 51 · see citing works
The Promises and Pitfalls of Digital Technology in Its Application to Alcohol Treatment
Muench, F. J. (2014) · Alcohol research · doi:10.35946/arcr.v36.1.14 · Cited by 50 · see citing works
Phone-based gait analysis to detect alcohol usage
Kao, H.-L., Ho, B.-J., Lin, A. C., & Chu, H.-H. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370354 · Cited by 50 · see citing works
van Egmond, K., Wright, C. C. J., Livingston, M., & Kuntsche, E. N. (2020) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.14432 · Cited by 49 · see citing works
Luczak, S. E., & Rosen, I. G. (2014) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.12478 · Cited by 46 · see citing works
Using contingency management procedures to reduce at-risk drinking in heavy drinkers
Dougherty, D. M., Lake, S. L., Hill-Kapturczak, N., Liang, Y., Karns, T. E., Mullen, J., & Roache, J. D. (2015) · Alcoholism: Clinical and Experimental Research · doi:10.1111/acer.12687 · Cited by 45 · see citing works
Derks, Y. P., Klaassen, R., Westerhof, G. J., Bohlmeijer, E. T., & Noordzij, M. L. (2019) · JMIR mHealth and uHealth · doi:10.2196/13479 · Cited by 45 · see citing works
Alcohol sensors and their potential for improving clinical care
Barnett, N. P. (2014) · Addiction · doi:10.1111/add.12764 · Cited by 43 · see citing works
Leonard, N. R., Silverman, M. K., Sherpa, D., Naegle, M. A., Kim, H., Coffman, D. L., & Ferdschneider, M. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7399 · Cited by 43 · see citing works
Mariakakis, A. T., Parsi, S., Patel, S., & Wobbrock, J. O. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173808 · Cited by 43 · see citing works
Suffoletto, B. P., Gharani, P., Chung, T. A., & Karimi, H. A. (2017) · Gait & Posture · doi:10.1016/j.gaitpost.2017.11.019 · Cited by 42 · see citing works
Kornfield, R., Sarma, P. K., Shah, D. V., Mctavish, F. M., Landucci, G., Pe‐Romashko, K., & Gustafson, D. H. (2018) · Journal of Medical Internet Research · doi:10.2196/10136 · Cited by 35 · see citing works
Alessi, S. M., Barnett, N. P., & Petry, N. M. (2019) · Alcohol · doi:10.1016/j.alcohol.2019.01.008 · Cited by 34 · see citing works
Self-citationHill‐Kapturczak, N., Lake, S. L., Roache, J. D., Cates, S. E., Liang, Y., & Dougherty, D. M. (2014) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.12528 · Cited by 32 · see citing works
Mobile alcohol biosensors and pharmacotherapy development research
Roberts, W., & McKee, S. A. (2018) · Alcohol · doi:10.1016/j.alcohol.2018.07.012 · Cited by 30 · see citing works
Unobtrusive and Continuous Monitoring of Alcohol-impaired Gait Using Smart Shoes
Lee, S. I., Nam, H. S., Garst, J. H., Huang, A., Campion, A., Arnell, M., Ghalehsariand, N., Park, S., Chang, H., Lu, D. C.-T., et al. (2016) · Methods of Information in Medicine · doi:10.3414/me15-02-0008 · Cited by 27 · see citing works
Real-Time Prediction of Blood Alcohol Content Using Smartwatch Sensor Data
Gutiérrez, M., Fast, M. L., Ngu, A. H. H., & Gao, B. J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-29175-8_16 · Cited by 23 · see citing works
Linke, S., Hovsepians, R., Schnebly, B., Godfrey, K. M., Noble, M. L., Strong, D. R., Isgro, M., & Lindamer, L. A. (2019) · Journal of Psychoactive Drugs · doi:10.1080/02791072.2018.1560518 · Cited by 17 · see citing works
An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level
Gharani, P., Suffoletto, B. P., Chung, T. A., & Karimi, H. A. (2017) · Sensors · doi:10.3390/s17122897 · Cited by 17 · see citing works
AlcoWear: Detecting blood alcohol levels from wearables
McAfee, A., Watson, J., Bianchi, B., Aiello, C. J., & Agu, E. (2017) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/uic-atc.2017.8397486 · Cited by 12 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [24]Bremner J Cheung NG Lam QH Huang S. IntoxiGait Deep Learning (Doctoral dissertation). Worcester, MA: Worcester Polytechnic Institute (2018).
Cited by
Sorted by citations
Vo, D.-K., & Trinh, K. T. L. (2024) · Biosensors · doi:10.3390/bios14110560 · Cited by 261 · see citing works
DiMartini, A., Leggio, L., & Singal, A. K. (2022) · The Lancet. Gastroenterology & hepatology · doi:10.1016/s2468-1253(21)00191-6 · Cited by 104 · see citing works
PANI-Based Wearable Electrochemical Sensor for pH Sweat Monitoring
Mazzara, F., Patella, B., D’Agostino, C., Bruno, M. G., Carbone, S., Lopresti, F., Aiello, G., Torino, C., Vilasi, A. D., O'Riordan, A. J., et al. (2021) · Chemosensors · doi:10.3390/chemosensors9070169 · Cited by 91 · see citing works
A review of sampling, energy supply and intelligent monitoring for long-term sweat sensors
Yang, P., Wei, G., Liu, A., Huo, F., & Zhang, Z. (2022) · npj Flexible Electronics · doi:10.1038/s41528-022-00165-9 · Cited by 77 · see citing works
Review of Ethanol Intoxication Sensing Technologies and Techniques
Paprocki, S., Qassem, M., & Kyriacou, P. A. (2022) · Sensors · doi:10.3390/s22186819 · Cited by 44 · see citing works
Lardier, D. T., Coakley, K. E., Holladay, K. R., Amorim, F. T., & Zuhl, M. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.675285 · Cited by 39 · see citing works
Sensing of body fluid hormones using paper-based analytical devices
Kelkar, N., Prabhu, A., Prabhu, A., Nandagopal, M. S. G., & Mani, N. K. (2021) · Microchemical Journal · doi:10.1016/j.microc.2021.107069 · Cited by 36 · see citing works
Bell, L., Garnett, C., Bao, Y., Cheng, Z., Qian, T., Perski, O., Potts, H. W. W., & Williamson, E. (2023) · JMIR mHealth and uHealth · doi:10.2196/38342 · Cited by 35 · see citing works
Fairbairn, C. E., & Bosch, N. (2021) · Addiction · doi:10.1111/add.15523 · Cited by 31 · see citing works
A Review on Flexible Electrochemical Biosensors to Monitor Alcohol in Sweat
Costa, N. G., Antunes, J. C., Paleo, A. J., & Rocha, A. M. A. C. (2022) · Biosensors · doi:10.3390/bios12040252 · Cited by 30 · see citing works
Yu, J., Fairbairn, C. E., Gurrieri, L., & Caumiant, E. P. (2022) · Addiction · doi:10.1111/add.15953 · Cited by 22 · see citing works
Ash, G. I., Gueorguieva, R. V., Barnett, N. P., Wang, W., Robledo, D. S., DeMartini, K. S., Pittman, B. P., Redeker, N. S., O’Malley, S. S., & Fucito, L. M. (2022) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.14804 · Cited by 20 · see citing works
Oesterle, T., Karpyak, V. M., Coombes, B. J., Athreya, A. P., Breitinger, S. A., da Costa, S. C., & Gerberi, D. (2022) · American Journal on Addictions · doi:10.1111/ajad.13341 · Cited by 18 · see citing works
Use of the BACtrack Skyn alcohol biosensor: Practical applications for data collection and analysis
Gunn, R. L., Merrill, J. E., Haines, A. M., Fernandez, M. E., Souza, T., Berey, B. L., Leeman, R. F., Wang, Y., & Barnett, N. P. (2023) · Addiction · doi:10.1111/add.16207 · Cited by 16 · see citing works
Didier, N. A., King, A. C., Polley, E. C., & Fridberg, D. J. (2023) · Experimental and Clinical Psychopharmacology · doi:10.1037/pha0000683 · Cited by 15 · see citing works
Neurobiology and the Treatment of Alcohol Use Disorder: A Review of the Evidence Base
Donato, S., & Ray, L. A. (2023) · Substance Abuse and Rehabilitation · doi:10.2147/sar.s409943 · Cited by 17 · see citing works
Takano, A., Ono, K., Nozawa, K., Sato, M., Onuki, M., Sese, J., Yumoto, Y., Matsushita, S., & Matsumoto, T. (2023) · JMIR Research Protocols · doi:10.2196/44275 · Cited by 11 · see citing works
An Update on the Use of Wearable Devices in Men’s Health
Qureshi, F. M., Golan, R., Ghomeshi, A., & Ramasamy, R. (2023) · The World Journal of Men s Health · doi:10.5534/wjmh.220205 · Cited by 10 · see citing works
Kruse, C. S., Betancourt, J., Madrid, S., Lindsey, C. W., & Wall, V. (2022) · Healthcare · doi:10.3390/healthcare10091672 · Cited by 10 · see citing works
Digital biomarker applications across the spectrum of opioid use disorder
Rigatti, M., Chapman, B. P., Chai, P. R., Smelson, D. A., Babu, K. M., & Carreiro, S. (2023) · Cogent Mental Health · doi:10.1080/28324765.2023.2240375 · Cited by 9 · see citing works
Dobashi, S., Kawaida, K., Saito, G., Owaki, Y., & Yoshimoto, H. (2024) · BMC Public Health · doi:10.1186/s12889-024-17645-4 · Cited by 8 · see citing works
Richards, V. L., Liu, Y., Orr, J., Leeman, R. F., Barnett, N. P., BRYANT, K. J., Cook, R. L., & Wang, Y. (2021) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.14665 · Cited by 9 · see citing works
Richards, V. L., Wang, Y., Porges, E. C., Gullett, J. M., Leeman, R. F., Zhou, Z., Barnett, N. P., & Cook, R. L. (2023) · Experimental and Clinical Psychopharmacology · doi:10.1037/pha0000637 · Cited by 8 · see citing works
Difrancesco, S., van Baardewijk, J. U., Cornelissen, A. S., Varon, C., Hendriks, R. C., & Brouwer, A.-M. (2023) · Frontiers in Network Physiology · doi:10.3389/fnetp.2023.1106650 · Cited by 6 · see citing works
Jeyadevan, A., & Grigg, J. (2024) · Current Addiction Reports · doi:10.1007/s40429-024-00587-7 · Cited by 6 · see citing works
Dobashi, S., Kawaida, K., Saito, G., Owaki, Y., & Yoshimoto, H. (2024) · BMC Medicine · doi:10.1186/s12916-024-03641-3 · Cited by 7 · see citing works
Senapati, A., Acharya, B., & Kar, N. R. (2026) · Nano Trends · doi:10.1016/j.nwnano.2026.100192 · Cited by 4 · see citing works
A systematic review of sensors to combat crime and routes to further sensor development
Cozens, A. E., Johnson, S. D., & Lee, T. (2025) · Frontiers in Chemistry · doi:10.3389/fchem.2025.1568867 · Cited by 4 · see citing works
Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes
Sethuraman, S. C., Kompally, P., Mohanty, S. P., Raju, B. K., & Cruz, M. V. (2022) · SN Computer Science · doi:10.1007/s42979-022-01471-9 · Cited by 4 · see citing works
Buono, F. D., Polonsky, M., Sprong, M. E., Aviles, A., & Cutter, C. J. (2023) · Drug and Alcohol Dependence Reports · doi:10.1016/j.dadr.2023.100202 · Cited by 3 · see citing works
Paprocki, S., Qassem, M., & Kyriacou, P. A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032980 · Cited by 2 · see citing works
Cao, Y., Li, T., Tribby, C. P., & Chang, D. H. F. (2026) · JMIR Research Protocols · doi:10.2196/84919 · Cited by 2 · see citing works
van Gisbergen, M. S., Obermair, K., Iasonna, L., Doicaru, M. M., Rashnoodi, S. R., Campos, F., & Buwalda, V. J. A. (2024) · Springer proceedings in business and economics · doi:10.1007/978-3-031-50559-1_5 · Cited by 2 · see citing works
Zhao, Y., Arora, J., Tao, Y., Miller, D., Adams, A. T., & Choudhury, T. (2024) · JMIR Formative Research · doi:10.2196/54438 · Cited by 2 · see citing works
Casanova, A., Gomis‐Berenguer, A., Brownson, D. A. C., & Iniesta, J. (2024) · Springer eBooks · doi:10.1007/978-981-97-5473-1_9 · Cited by 2 · see citing works
DiMartini, A., Behari, J., Punzi, J., Dunn, M. A., Bataller, R. A., Jakicic, J. M., McNulty, M., Young, R. C., & Dew, M. A. (2024) · Drug and Alcohol Review · doi:10.1111/dar.13978 · Cited by 2 · see citing works
Takano, A., Okuda, K., Sese, J., Ono, K., & Matsumoto, T. (2025) · JMIR Formative Research · doi:10.2196/73790 · Cited by 1 · see citing works
Resting state connectivity in people living with HIV before and after stopping heavy drinking
Gullett, J. M., DeFelice, J. S., Richards, V. L., Porges, E. C., Cohen, R. A., Govind, V., Salan, T., Wang, Y., Zhou, Z., & Cook, R. L. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1102368 · Cited by 2 · see citing works
Richelson, E. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-95702-1.00020-8
Cook, R. L., Richards, V. L., Gullett, J. M., Lerner, B., Zhou, Z., Porges, E. C., Wang, Y., Kahler, C. W., Barnett, N. P., Li, Z., et al. (2023) · JMIR Research Protocols · doi:10.2196/53684
A novel wearable biosensor for real-time alcohol detection
Ushaa, E., Vishal, E., Keerthna, M., & Vivek, E. (2023) · i-manager’s Journal on Electronics Engineering · doi:10.26634/jele.13.4.20192
Jin, Z., Wang, Q., Meng, F., & Xu, J. (2024) · Molecular & cellular biomechanics · doi:10.62617/mcb537
Smartwatch-Based Prediction of Transdermal Alcohol Levels Using Hyperdimensional Computing
Vergés, P., Segura, M. E., Arangott, R., Garcia, A. K., Reynoso, L. G., Nicolau, A. E., Givargis, T., & Gago–Masague, S. (2024) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot62078.2024.10811151
Takano, A., Okuda, K., Sese, J., Ono, K., & Matsumoto, T. (2025) · doi:10.2196/preprints.73790
Applications of Transdermal Alcohol Biosensors
Yao, M., & Rosen, G. (2025) · Biomedical engineering · doi:10.5772/intechopen.1010428
Navarro-Ovando, V., Rijksbaron, J., Dumont, G., & Rivero, C. R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00656-1_14
Dobashi, S., Kawaida, K., Saito, G., Owaki, Y., & Yoshimoto, H. (2024) · Figshare · doi:10.6084/m9.figshare.24980131
Dobashi, S., Kawaida, K., Saito, G., Owaki, Y., & Yoshimoto, H. (2024) · Figshare · doi:10.6084/m9.figshare.27132737
Dobashi, S., Kawaida, K., Saito, G., Owaki, Y., & Yoshimoto, H. (2024) · Figshare · doi:10.6084/m9.figshare.24979639
Advancing Intoxication Detection: A Smartwatch-Based Approach
Segura, M. E., Vergés, P., Ky, R., Arangott, R., Garcia, A. K., Trong, T. D., Hyodo, M., Nicolau, A., Givargis, T., & Gago–Masague, S. (2025) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot64238.2025.11270600
Andersson, K., Handlin, L., Beslic, S. H., Knez, R., Behboudi, A., & Wilhsson, M. (2026) · JMIR Formative Research · doi:10.2196/83592
AI in Substance Use and Addiction Prevention
Saba, S. K., & Leibowitz, G. (2026) · Springer eBooks · doi:10.1007/978-3-032-18443-6_11
Chhaya, O., & Ananth, V. (2026) · Research Square · doi:10.21203/rs.3.rs-11204325/v1
Chhaya, O., & Ananth, V. (2026) · Research Square · doi:10.21203/rs.3.rs-11204325/v2
- Authors
- Davis-Martin, R. E., Alessi, S. M., & Boudreaux, E. D.
- Affiliations
- University of Massachusetts Chan Medical School (US)
- University of Connecticut (US)
- Corresponding author
- Davis-Martin, Rachel E.
- Venue
- Frontiers in Psychiatry
- Publisher
- Frontiers Media SA
- Published
- 2021-03-22
- Volume
- 12
- Pages
- 642813-642813
- ISSN
- 1664-0640 (ISSN-L)
- PMC
- PMC8019775
- OpenAlex
- W3137127954
- Cited by
- 54
- References
- 54
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 13 · 2023: 15 · 2022: 9 · 2021: 5
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychiatry (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- The Journal of the American Medical Association (JAMA) Network (American Medical Association) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (T32 AA007290, P50 AA027055, R01 AA023502)
- Topics
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Health Sciences › Medicine › Pathology and Forensic Medicine › Alcohol Consumption and Health Effects
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- alcoholism, wearable biosensors, gait analysis, blood alcohol concentration, relapse prevention, contingency management, wearable activity trackers, behavior therapy
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