Wearable and Wireless mHealth Technologies for Substance Use Disorder

Abstract

Purpose of Review

The goal of this scoping review is to evaluate the advances in wearable and other wireless mobile health (mHealth) technologies in the treatment of substance use disorders.

Recent Findings

There are a variety of wireless technologies under investigation for the treatment of substance use disorder. Wearable sensors are the most commonly used technology. They can be used to decrease heavy substance use, mitigate factors related to relapse, and monitor for overdose. New technologies pose distinct advantages over traditional therapies by increasing geographic availability and continuously providing feedback and monitoring while remaining relatively non-invasive.

Summary

Wearable and novel technologies are important to the evolving landscape of substance use treatment. As technologies continue to develop and show efficacy, they should be incorporated into multifactorial treatment plans.

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Fig. 1

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.

    SAMHSA Substance abuse and mental health services administration. https://www.samhsa.gov/find-help/atod Accessed 2020.

  2. 2.

    Prevention CfDCa: Opioid overdose: understanding the epidemic https://www.cdc.gov/drugoverdose/epidemic/index.html Accessed.

  3. 3.

    Organization WH. Global status report on alcohol and health 2018. https://www.who.int/substance_abuse/publications/global_alcohol_report/en/ (2018). Accessed. .

  4. 4.

    Keoleian V, Polcin D, Galloway GP. Text messaging for addiction: a review. J Psychoactive Drugs. 2015;47(2):158–76. https://doi.org/10.1080/02791072.2015.1009200.

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Robinson SM, Adinoff B. The mixed message behind “medication-assisted treatment” for substance use disorder. The American journal of drug and alcohol abuse. 2018;44(2):147–50. https://doi.org/10.1080/00952990.2017.1362419.

    PubMed  Google Scholar 

  6. 6.

    International Telecommunication Union (ITU) statistics. https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx Accessed 2020.

  7. 7.

    Shields WC, Omaki E, McDonald EM, Rosenberg R, Aitken M, Stevens MW, et al. Cell phone and computer use among parents visiting an urban pediatric emergency department. Pediatr Emerg Care. 2018;34(12):878–82. https://doi.org/10.1097/pec.0000000000001679.

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Kwon NS, Colucci A, Gulati R, Shawn L, Kasahara Y, El Bakhar A, et al. A survey of the prevalence of cell phones capable of receiving health information among patients presenting to an urban emergency department. The Journal of emergency medicine. 2013;44(4):875–88. https://doi.org/10.1016/j.jemermed.2012.09.041.

    PubMed  Google Scholar 

  9. 9.

    Organization WH: mHealth: new horizons for health through mobile technologies. https://www.who.int/goe/publications/goe_mhealth_web.pdfAccessed. 2011:2020.

  10. 10.

    Organization WH: eHealth. https://www.who.int/ehealth/about/en/ Accessed 2020.

  11. 11.

    Aggarwal M, Borycki EM. Review of mobile apps for prevention and management of opioid-related harm. Studies in health technology and informatics. 2019;257:1–8.

    PubMed  Google Scholar 

  12. 12.

    Sugarman DE, Campbell ANC, Iles BR, Greenfield SF. Technology-based interventions for substance use and comorbid disorders: an examination of the emerging literature. Harvard review of psychiatry. 2017;25(3):123–34. https://doi.org/10.1097/hrp.0000000000000148.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Quanbeck A, Chih MY, Isham A, Gustafson D. Mobile delivery of treatment for alcohol use disorders: a review of the literature. Alcohol research : current reviews. 2014;36(1):111–22.

    Google Scholar 

  14. 14.

    Dougherty DM, Karns TE, Mullen J, Liang Y, Lake SL, Roache JD, et al. Transdermal alcohol concentration data collected during a contingency management program to reduce at-risk drinking. Drug Alcohol Depend. 2015;148:77–84. https://doi.org/10.1016/j.drugalcdep.2014.12.021.

    PubMed  Google Scholar 

  15. 15.

    Barnett NP, Celio MA, Tidey JW, Murphy JG, Colby SM, Swift RM. A preliminary randomized controlled trial of contingency management for alcohol use reduction using a transdermal alcohol sensor. Addiction (Abingdon, England). 2017;112(6):1025–35. https://doi.org/10.1111/add.13767.

  16. 16.

    Mathias CW, Hill-Kapturczak N, Karns-Wright TE, Mullen J, Roache JD, Fell JC, et al. Translating transdermal alcohol monitoring procedures for contingency management among adults recently arrested for DWI. Addict Behav. 2018;83:56–63. https://doi.org/10.1016/j.addbeh.2018.01.033.

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Hamalainen MD, Zetterstrom A, Winkvist M, Soderquist M, Karlberg E, Ohagen P, et al. Real-time monitoring using a breathalyzer-based eHealth system can identify lapse/relapse patterns in alcohol use disorder patients. Alcohol and alcoholism (Oxford, Oxfordshire). 2018;53(4):368–75. https://doi.org/10.1093/alcalc/agy011.

    Google Scholar 

  18. 18.

    Gordon A, Jaffe A, McLellan AT, Richardson G, Skipper G, Sucher M, et al. How should remote clinical monitoring be used to treat alcohol use disorders?: initial findings from an expert round table discussion. J Addict Med. 2017;11(2):145–53. https://doi.org/10.1097/ADM.0000000000000288.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    You CW, Chen YC, Chen CH, Lee CH, Kuo PH, Huang MC, et al. Smartphone-based support system (SoberDiary) coupled with a Bluetooth breathalyser for treatment-seeking alcohol-dependent patients. Addict Behav. 2017;65:174–8. https://doi.org/10.1016/j.addbeh.2016.10.017.

    PubMed  Google Scholar 

  20. 20.

    • Lauckner C, Taylor E, Patel D, Whitmire A. The feasibility of using smartphones and mobile breathalyzers to monitor alcohol consumption among people living with HIV/AIDS. Addiction science & clinical practice. 2019;14(1):43. https://doi.org/10.1186/s13722-019-0174-0This paper found that a mobile phone–linked breathalyzer decreased alcohol consumption, made participants mindful of how much they were drinking, and was well accepted by participants.

    Google Scholar 

  21. 21.

    Leonard NR, Silverman M, Sherpa DP, Naegle M, Kim H, Coffman DL, et al. Mobile health technology using a wearable sensorband for female college students with problem drinking: an acceptability and feasibility study. JMIR mHealth and uHealth. 2017;5(7):e90.

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Kennedy AP, Epstein DH, Jobes ML, Agage D, Tyburski M, Phillips KA, et al. Continuous in-the-field measurement of heart rate: correlates of drug use, craving, stress, and mood in polydrug users. Drug Alcohol Depend. 2015;151:159–66. https://doi.org/10.1016/j.drugalcdep.2015.03.024.

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Carreiro S, Fang H, Zhang J, Wittbold K, Weng S, Mullins R, et al. iMStrong: deployment of a biosensor system to detect cocaine use. J Med Syst. 2015;39(12):186.

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Holtyn AF, Bosworth E, Marsch LA, McLeman B, Meier A, Saunders EC, et al. Towards detecting cocaine use using smartwatches in the NIDA clinical trials network: design, rationale, and methodology. Contemporary clinical trials communications. 2019;15:100392. https://doi.org/10.1016/j.conctc.2019.100392.

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    •• Carreiro S, Chintha KK, Shrestha S, Chapman B, Smelson D, Indic P. Wearable sensor-based detection of stress and craving in patients during treatment for substance use disorder: a mixed methods pilot study. Drug Alcohol Depend. 2020;209(209):107929. https://doi.org/10.1016/j.drugalcdep.2020.107929This study showed the ability of wearable sensors to detect stress and craving events in people who use drugs which is an important step in being able to provide an intervention to prevent relapse.

    PubMed  Google Scholar 

  26. 26.

    Chai PR, Carreiro S, Innes BJ, Rosen RK, O’Cleirigh C, Mayer KH, et al. Digital pills to measure opioid ingestion patterns in emergency department patients with acute fracture pain: a pilot study. J Med Internet Res. 2017;19(1):e19. https://doi.org/10.2196/jmir.7050.

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Chai PR, Carreiro S, Innes BJ, Chapman B, Schreiber KL, Edwards RR, et al. Oxycodone ingestion patterns in acute fracture pain with digital pills. Anesth Analg. 2017;125(6):2105–12. https://doi.org/10.1213/ane.0000000000002574.

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Carreiro S, Smelson D, Ranney M, Horvath KJ, Picard RW, Boudreaux ED, et al. Real-time mobile detection of drug use with wearable biosensors: a pilot study. Journal of medical toxicology : official journal of the American College of Medical Toxicology. 2015;11(1):73–9. https://doi.org/10.1007/s13181-014-0439-7.

    CAS  Google Scholar 

  29. 29.

    Carreiro S, Wittbold K, Indic P, Fang H, Zhang J, Boyer EW. Wearable biosensors to detect physiologic change during opioid use. Journal of medical toxicology : official journal of the American College of Medical Toxicology. 2016;12(3):255–62. https://doi.org/10.1007/s13181-016-0557-5.

    CAS  Google Scholar 

  30. 30.

    • Mahmud MS, Fang H, Wang H, Carreiro S, Boyer E. Automatic detection of opioid intake using wearable biosensor. International conference on computing, networking, and communications : [proceedings] International Conference on Computing, Networking and Communications 2018. 2018:784–8. https://doi.org/10.1109/iccnc.2018.8390334This study was able to detect opioid use with up to 99% accuracy using machine learning algorithms applied to wearable sensor data showing that wearables can be used to detect time of opioid ingestion.

  31. 31.

    Rumbut J, Singh D, Fang H, Wang H, Carreiro S, Boyer E. Poster Abstract: Detecting kratom intoxication in wearable biosensor data. Proceedings - 4th IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 20192019. p. 71–2.

  32. 32.

    •• Nandakumar R, Gollakota S, Sunshine JE. Opioid overdose detection using smartphones. Sci Transl Med. 2019:11 The authors in this study were able to use a contactless sensor system incorporated into a mobile phone to detect respiratory depression in opioid users, which will enable the ability to detect opioid overdoses.

  33. 33.

    •• Chintha KK, Indic P, Chapman B, Boyer EW, Carreiro S. Wearable biosensors to evaluate recurrent opioid toxicity after naloxone administration: a Hilbert transform approach. Proceedings of the Annual Hawaii International Conference on System Sciences Annual Hawaii International Conference on System Sciences 2018. 2018:3247–52 The wearable sensors were able to detect recurrent toxicity after an overdose and thus may be useful to monitor people who are at risk of reccurent opioid overdoses.

  34. 34.

    Dhowan B, Lim J, MacLean MD, Berman AG, Kim MK, Yang Q, et al. Simple minimally-invasive automatic antidote delivery device (A2D2) towards closed-loop reversal of opioid overdose. Journal of controlled release : official journal of the Controlled Release Society. 2019;306:130–7. https://doi.org/10.1016/j.jconrel.2019.05.041.

    CAS  Google Scholar 

  35. 35.

    • Ahamad K, Dong H, Johnson C, Hyashi K, DeBeck K, Milloy MJ, et al. Factors associated with willingness to wear an electronic overdose detection device. Addiction science & clinical practice. 2019;14(1):23. https://doi.org/10.1186/s13722-019-0153-5The importance of this study is that is shows people who use drugs are willing to wear a device, especially after an overdose, if they are on methadone, or have chronic pain.

    Google Scholar 

  36. 36.

    Miranda A, Taca A. Neuromodulation with percutaneous electrical nerve field stimulation is associated with reduction in signs and symptoms of opioid withdrawal: a multisite, retrospective assessment. The American Journal of Drug and Alcohol Abuse. 2018;44(1):56–63. https://doi.org/10.1080/00952990.2017.1295459.

    PubMed  Google Scholar 

  37. 37.

    Metcalf M, Rossie K, Stokes K, Tallman C, Tanner B. Virtual reality cue refusal video game for alcohol and cigarette recovery support: summative study. JMIR serious games. 2018;6(2):e7. https://doi.org/10.2196/games.9231.

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    van Heerden A, Tomlinson M, Skeen S, Parry C, Bryant K, Rotheram-Borus MJ. Innovation at the intersection of alcohol and HIV research. AIDS Behav. 2017;21(Suppl 2):274–8. https://doi.org/10.1007/s10461-017-1926-z.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Pandey PC, Shukla S, Skoog SA, Boehm RD, Narayan RJ. Current advancements in transdermal biosensing and targeted drug delivery. Sensors (Basel, Switzerland). 2019;19(5). doi: https://doi.org/10.3390/s19051028.

  40. 40.

    Portelli AJ, Nasuto SJ. Design and development of non-contact bio-potential electrodes for pervasive health monitoring applications. biosensors. 2017;7(1). https://doi.org/10.3390/bios7010002.

  41. 41.

    Tofighi B, Abrantes A, Stein MD. The role of technology-based interventions for substance use disorders in primary care: a review of the literature. The Medical clinics of North America. 2018;102(4):715–31. https://doi.org/10.1016/j.mcna.2018.02.011.

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Morgenstern J, Kuerbis A, Muench F. Ecological momentary assessment and alcohol use disorder treatment. Alcohol Research: Current Reviews. 2014;36(1):101–10.

    Google Scholar 

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Acknowledgments

The authors would like to acknowledge Catherine W. Carr, MLIS, AHIP, for her contribution as education and clinical services librarian. Dr. Carreiro is generously supported by National Institute on Drug Abuse (K23DA045242).

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Correspondence to Charlotte Goldfine.

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Conflict of Interest

JL reports grants from Alkermes, outside the submitted work. SC reports grant funding by the National Institute on Drug Abuse (K23DA045242 and R44 DA046151). EL, CG, and MN declare no conflicts of interest.

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Goldfine, C., Lai, J.T., Lucey, E. et al. Wearable and Wireless mHealth Technologies for Substance Use Disorder. Curr Addict Rep (2020). https://doi.org/10.1007/s40429-020-00318-8

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Keywords

  • Wearable sensor
  • Biosensor
  • mHealth
  • Substance use disorder