Over the past year, approximately 10,000 Americans have died by psychostimulant overdose, and over 50% of these deaths were caused by prescription stimulant misuse. A comprehensive approach to detect a drug overdose in the environment where it occurs is imperative to reduce the number of prescription stimulant overdose-related deaths. Teenagers are at the highest risk for prescription stimulant overdose, so this study proposes a multi-factor overdose detection system named Hero which is designed to noninvasively operate within the context of a teen’s life. Hero monitors five factors that indicate stimulant abuse: extreme mood swings, presence of amphetamine metabolite in sweat excreted from the fingertip, heart rate, blood pressure, and respiration rate. An algorithm to detect extreme mood swings in a teen’s outgoing SMS messages was developed by collecting over 3.6 million tweets, creating groups of tweets for euphoria and melancholy using guidelines adapted from DSM-5 criteria, and training six Artificial Intelligence models. These models were used to create a dual-model-based extreme mood swing detection algorithm that was accurate 96% of the time. A biochemical strip, which consisted of a diagnostic measure that changes color when in contact with amphetamine metabolite and a control measure that changes color when the appropriate volume of sweat is excreted, was created. A gold nanoparticle-based diagnostic measure and pH-based control measure were evaluated individually and on the overall strip. The diagnostic measure had an accuracy of 90.62% while the control measure had 84.38% accuracy. Lastly, a vital sign measurement algorithm was built by applying photoplethysmography image processing techniques. A regression model with height, age, and gender features was created to convert heart rate to blood pressure, and the final algorithm had an accuracy of 97.86%. All five of these factors work together to create an accurate and easily integrable system to detect overdoses in real-time and prevent prescription stimulant abuse-related deaths.
References
[1]
National Institute on Drug Abuse (2020) Prescription Stimulants DrugFacts.
http://www.drugabuse.gov/publications/drugfacts/prescription-stimulants
[2]
National Institute on Drug Abuse (2020) What Is the Scope of Prescription Drug Misuse?
http://www.drugabuse.gov/publications/research-reports/misuse-prescription-drugs/what- scope-prescription-drug-misuse
[3]
Prieur, N. (2017) National Adolescent Drug Trends in 2017: Findings Released. Institute for Social Research, University of Michigan.
http://www.monitoringthefuture.org/data/data.html
[4]
Weyandt, L.L., White, T.L., Gudmundsdottir, B.G., Nitenson, A.Z., Rathkey, E.S., De Leon, K.A. and Bjorn, S.A. (2018) Neurocognitive, Autonomic, and Mood Effects of Adderall: A Pilot Study of Healthy College Students. Pharmacy, 6, 58.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165228/
https://doi.org/10.3390/pharmacy6030058
[5]
Center for Disease Control (2018) 2018 Annual Surveillance Report of Drug-Related Risks and Outcomes.
http://www.cdc.gov/drugoverdose/pubs/related-publications.html
[6]
Arria, A.M. and DuPont, R.L. (2010) Nonmedical Prescription Stimulant Use among College Students: Why We Need to Do Something and What We Need to Do. Journal of Addictive Diseases, 29, 417-426.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2951617/
https://doi.org/10.1080/10550887.2010.509273
[7]
Meredith, G., DeLollis, M. and Shad, M.U. (2020) Potential Treatment for Overdose with Synthetic Cannabinoids. Medical Cannabis and Cannabinoids, Karger Publishers. http://www.karger.com/Article/FullText/506635
[8]
National Institute on Drug Abuse (2020) Opioid Overdose Reversal with Naloxone (Narcan, Evzio).
http://www.drugabuse.gov/drug-topics/opioids/opioid-overdose-reversal-naloxone-narcan-evzio
[9]
Facts about Overdose. Learn to Cope—A Support Organization That Offers Education, Resources, Peer Support and Hope for Parents and Family Members Coping with a Loved One Addicted to Heroin, Opioids or Other Drugs.
http://www.learn2cope.org/facts-about-overdose/
[10]
Nandakumar, R., Gollakota, S. and Sunshine, J.E. (2019) Opioid Overdose Detection Using Smartphones. Science Translational Medicine, 11, eaau8914.
https://doi.org/10.1126/scitranslmed.aau8914
[11]
Leyton, M., aan het Rot, M., Booij, L., Baker, G.B., Young, S.N. and Benkelfat, C. (2007) Mood-Elevating Effects of D-Amphetamine and Incentive Salience: The Effect of Acute Dopamine Precursor Depletion. Journal of Psychiatry & Neuroscience, 32, 129-136.
[12]
Baig, A.M. (2018) DARK Side of Amphetamine and Analogues: Pharmacology, Syndromic Manifestation, and Management of Amphetamine Addiction. ACS Chemical Neuroscience, 9, 2299-2303.
https://doi.org/10.1021/acschemneuro.8b00137
[13]
National Institute on Drug Abuse (2020) Drugs and the Brain.
https://www.drugabuse.gov/publications/drugs-brains-behavior-science-addiction/drugs-brain
Kalyani, Gupta, E., Rathee, G. and Chauhan, D.S. (2015) Mood Swing Analyser: A Dynamic Sentiment Detection Approach. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 85, 149-157.
https://www.researchgate.net/publication/279288383_Mood_Swing_Analyser_A_ Dynamic_Sentiment_Detection_Approach
https://doi.org/10.1007/s40010-014-0169-x
[16]
Hudson, M., Stuchinskaya, T., Ramma, S., Patel, J., Sievers, C., Goetz, S., Hines, S., Menzies, E. and Russell, D.A. (2019) Drug Screening Using the Sweat of a Fingerprint: Lateral Flow Detection of Δ9-Tetrahydrocannabinol, Cocaine, Opiates and Amphetamine. Journal of Analytical Toxicology, 43, 88-95.
https://doi.org/10.1093/jat/bky068
[17]
Dring, L.G., Smith, R.L. and Williams, R.T. (1970) The Metabolic Fate of Amphetamine in Man and Other Species. The Biochemical Journal, 116, 425-435.
https://doi.org/10.1042/bj1160425
[18]
Barnes, A.J., Smith, M.L., Kacinko, S.L., Schwilke, E.W., Cone, E.J., Moolchan, E. T. and Huestis, M.A. (2008) Excretion of Methamphetamine and Amphetamine in Human Sweat Following Controlled Oral Methamphetamine Administration. Clinical Chemistry, 54, 172-180. https://doi.org/10.1373/clinchem.2007.092304
[19]
Food and Drug Administration. (2007).
https://www.accessdata.fda.gov/drugsatfda_docs/label/2007/011522s040lbl.pdf
[20]
United States Office on Drugs and Crime (2014) Guidelines for Testing Drugs under International Control in Hair, Sweat, and Oral Fluid.
https://www.unodc.org/documents/scientific/ST_NAR_30_Rev.3_Hair_Sweat_and_Oral_Fluid.pdf
[21]
Yoo, Y.K., Kim, G., Park, D., et al. (2020) Gold Nanoparticles Assisted Sensitivity Improvement of Interdigitated Microelectrodes Biosensor for Amyloid-β Detection in Plasma Sample. Sensors and Actuators B: Chemical, 308, 127710.
https://doi.org/10.1016/j.snb.2020.127710
[22]
Bariya, M., Li, L., Ghattamaneni, R., et al. (2020) Glove-Based Sensors for Multimodal Monitoring of Natural Sweat. Science Advances, 6, eabb8308.
https://doi.org/10.1126/sciadv.abb8308
[23]
Chen, Y.-L., Kuan, W.-H. and Liu, C.-L. (2020) Comparative Study of the Composition of Sweat from Eccrine and Apocrine Sweat Glands during Exercise and in Heat. International Journal of Environmental Research and Public Health, 17, 3377.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7277079/
https://doi.org/10.3390/ijerph17103377
[24]
O’Donnell, J., et al. (2020) Vital Signs: Characteristics of Drug Overdose Deaths Involving Opioids and Stimulants—24 States and the District of Columbia, January-June 2019. MMWR Morbidity and Mortality Weekly Report, 69, 1189-1197.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7470457/
[25]
Khanam, F.-T.-Z., Al-Naji, A. and Chahl, J. (2019) Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review. Applied Sciences, 9, 4474.
http://www.mdpi.com/2076-3417/9/20/4474/htm
https://doi.org/10.3390/app9204474
[26]
Castaneda, D., Esparza, A., Ghamari, M., Soltanpur, C. and Nazeran, H. (2018) A Review on Wearable Photoplethysmography Sensors and Their Potential Future Applications in Health Care. International Journal of Biosensors & Bioelectronics, 4, 195-202. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6426305/
[27]
Lazazzera, R., Belhaj, Y. and Carrault, G. (2019) A New Wearable Device for Blood Pressure Estimation Using Photoplethysmogram. Sensors (Basel), 19, 2557.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603632/
[28]
Dreaden, E.C., Alkilany, A.M., Huang, X.H., Murphy, C.J. and El-Sayed, M.A. (2012) The Golden Age: Gold Nanoparticles for Biomedicine. Chemical Society Reviews, 41, 2740-2779. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876014/
[29]
Castro, W., Oblitas, J., Santa-Cruz, R. and Avila-George, H. (2017) Multilayer Perceptron Architecture Optimization Using Parallel Computing Techniques. PloS One, 12, e0189369. https://doi.org/10.1371/journal.pone.0189369
[30]
Gaind, B., Syal, V. and Padgalwar, S. (2019) Emotion Detection and Analysis on Social Media. Global Journal of Engineering Science and Research, 78-89.
https://arxiv.org/pdf/1901.08458.pdf