mHealth for the Detection and Intervention in Adolescent and Young Adult Substance Use Disorder

  • Stephanie Carreiro
  • Peter R. Chai
  • Jennifer Carey
  • Jeffrey Lai
  • David Smelson
  • Edward W. Boyer
Adolescent / Young Adult Addiction (T Chung, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Adolescent / Young Adult Addiction

Abstract

Purpose of Review

The goal of this review is to highlight recent research in mHealth-based approaches to the detection and treatment of substance use disorders in adolescents and young adults.

Recent Findings

The main methods for mHealth-based detection include mobile phone-based self-report tools, GPS tracking, and wearable sensors. Wearables can be used to detect physiologic changes (e.g., heart rate, electrodermal activity) or biochemical contents of analytes (i.e., alcohol in sweat) with reasonable accuracy, but larger studies are needed. Detection methods have been combined with interventions based on mindfulness, education, incentives/goals, and motivation. Few studies have focused specifically on the young adult population, although those that did indicate high rates of utilization and acceptance.

Summary

Research that explores the pairing of advanced detection methods such as wearables with real-time intervention strategies is crucial to realizing the full potential of mHealth in this population.

Keywords

Substance use disorder Technology mHealth Treatment Young adults Wearables 

Notes

Acknowledgments

The authors’ work was generously supported by National Institutes of Health KL2 TR001455-01 (SC) and 1K24DA037109 (EB).

Compliance with Ethical Standards

Conflict of Interest

Stephanie Carreiro has received a grant from RAE Healthcare to investigate the use of wearable sensors for stress and craving during treatment for substance abuse disorder.

Peter R. Chai declares that she has no conflict of interest.

Jennifer Carey declares that she has no conflict of interest.

Jeffrey Lai declares that he has no conflict of interest.

David Smelson declares that he has no conflict of interest.

Edward W. Boyer declares that he has no conflict of interest.

Human and Animal Rights and Informed Consent

All reported studies/experiments with human or animal subjects performed by the authors have been previously published and complied with all applicable ethical standards (including the Helsinki declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).

References

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

  1. 1.
    Substance Abuse and Mental Health Services Administration (US). Key substance use and mental health indicators in the United States: results from the 2016 National Survey on Drug Use and Health (HHS Publication No. SMA 17-5044, NSDUH Series H-52) [Internet]. Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration; 2017. Available from: https://www.samhsa.gov/data.
  2. 2.
    Winters, K. C. Treatment of adolescents with substance use disorders: treatment improvement protocol. Rockville: US Department of Health and Human Services; 1999.Google Scholar
  3. 3.
    Hogue A, Henderson CE, Ozechowski TJ, Robbins MS. Evidence base on outpatient behavioral treatments for adolescent substance use: updates and recommendations 2007-2013. J Clin Child Adolesc Psychol. 2014;43(5):695–720.  https://doi.org/10.1080/15374416.2014.915550.CrossRefPubMedGoogle Scholar
  4. 4.
    Gonzales R, Anglin MD, Beattie R, Ong CA, Glik DC. Understanding recovery barriers: youth perceptions about substance use relapse. Am J Health Behav. 2012;36(5):602–14.  https://doi.org/10.5993/AJHB.36.5.3.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Lenhart A. Teens, Social Media & Technology Overview 2015. http://www.pewinternet.orgteens-social-media-technology.
  6. 6.
    Berrouiguet S, Baca-Garcia E, Brandt S, Walter M, Courtet P. Fundamentals for future mobile-health (mHealth): a systematic review of mobile phone and web-based text messaging in mental health. J Med Internet Res. 2016;18(6):e135.  https://doi.org/10.2196/jmir.5066.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Cunningham JA. Addiction and eHealth. Addiction. 2016;111:389–90.CrossRefPubMedGoogle Scholar
  9. 9.
    Kaner EF, Beyer FR, Garnett C, Crane D, Brown J, Muirhead C, et al. Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community-dwelling populations. Cochrane Database Syst Rev. 2017;9:CD011479.PubMedGoogle Scholar
  10. 10.
    Center for Behavioral Health Statistics and Quality. 2016 national survey on drug use and health: detailed tables. www.samhsa.gov. 2017. p. 1–2889.
  11. 11.
    Cerdá M, Santaella J, Marshall BDL, Kim JH, Martins SS. Nonmedical prescription opioid use in childhood and early adolescence predicts transitions to heroin use in young adulthood: a national study. J Pediatr. 2015;167:605–12.e1–2.CrossRefGoogle Scholar
  12. 12.
    Heitzeg MM, Cope LM, Martz ME, Hardee JE. Neuroimaging risk markers for substance abuse: recent findings on inhibitory control and reward system functioning. Curr Addict Rep. 2015;2(2):91–103.  https://doi.org/10.1007/s40429-015-0048-9.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Evins AE, Green AI, Kane JM, Murray RM. The effect of marijuana use on the risk for schizophrenia. J Clin Psychiatry. 2012;73(11):1463–8.  https://doi.org/10.4088/JCP.12012co1c.CrossRefPubMedGoogle Scholar
  14. 14.
    Scholes-Balog KE, Hemphill SA, Patton GC, Toumbourou JW. Cannabis use and related harms in the transition to young adulthood: a longitudinal study of Australian secondary school students. J Adolesc. 2013;36(3):519–27.  https://doi.org/10.1016/j.adolescence.2013.03.001.CrossRefPubMedGoogle Scholar
  15. 15.
    Hall W. What has research over the past two decades revealed about the adverse health effects of recreational cannabis use? Addiction. 2015;110(1):19–35.  https://doi.org/10.1111/add.12703.CrossRefPubMedGoogle Scholar
  16. 16.
    Rehm J, Manthey J, Struzzo P, Gual A, Wojnar M. Who receives treatment for alcohol use disorders in the European Union? A cross-sectional representative study in primary and specialized health care. Eur Psychiatry. 2015;30(8):885–93.  https://doi.org/10.1016/j.eurpsy.2015.07.012.CrossRefPubMedGoogle Scholar
  17. 17.
    Romer D, Moreno M. Digital media and risks for adolescent substance abuse and problematic gambling. Pediatrics. 2017;140(Supplement 2):S102–6.  https://doi.org/10.1542/peds.2016-1758L.CrossRefPubMedGoogle Scholar
  18. 18.
    Wartella E, Rideout V, Zupancic H, Beaudoin-Ryan L, Lauricella A. Teens, health and technology: a national survey. Center on Media and Human Development, School of Communication, Northwestern University; 2015.Google Scholar
  19. 19.
    Fenner Y, Garland SM, Moore EE, Jayasinghe Y, Fletcher A, Tabrizi SN, et al. Web-based recruiting for health research using a social networking site: an exploratory study. J Med Internet Res. 2012;14(1):e20.  https://doi.org/10.2196/jmir.1978.
  20. 20.
    Dimitrov DV. Medical internet of things and big data in healthcare. Healthc Inform Res. 2016;22(3):156–63.  https://doi.org/10.4258/hir.2016.22.3.156.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Dallery J, Jarvis B, Marsch L, Xie H. Mechanisms of change associated with technology-based interventions for substance use. Drug Alcohol Depend. 2015;150:14–23.  https://doi.org/10.1016/j.drugalcdep.2015.02.036.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    • Bertz, J. W., Epstein, D. H., & Preston, K. L. Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research. Addictive Behaviors; 2017  https://doi.org/10.1016/j.addbeh.2017.11.027. This important review describes the potential interface between mobile EMA with objective measures for SUD treatment.
  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.  https://doi.org/10.1007/s10916-015-0337-9.
  24. 24.
    •• Hossain SM, Ali AA, Rahman M, Ertin E, Epstein D, Kennedy A, et al. Identifying drug (cocaine) intake events from acute physiological response in the presence of free-living physical activity. IPSN NIH Public Access. 2014;2014:71–82. This manuscript describes in detail the derivation of a cocaine detection algorithm for a wearable chest mounted ECG sensor though lab and field testing. Google Scholar
  25. 25.
    Natarajan A, Parate A, Gaiser E, Angarita G, Malison R, Marlin B, et al. Detecting cocaine use with wearable electrocardiogram sensors. UbiComp '13. New York, New York, USA: ACM; 2013. p. 123–32.Google Scholar
  26. 26.
    Leffingwell TR, Cooney NJ, Murphy JG, Luczak S, Rosen G, Dougherty DM, et al. Continuous objective monitoring of alcohol use: twenty-first century measurement using transdermal sensors. Alcohol Clin Exp Res. 2013;37(1):16–22.  https://doi.org/10.1111/j.1530-0277.2012.01869.x.
  27. 27.
    Umasankar Y, Jalal AH, Gonzalez PJ, Chowdhury M, Alfonso A, Bhansali S. Wearable alcohol monitoring device with auto-calibration ability for high chemical specificity. 2016. IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) IEEE; p. 353–8.Google Scholar
  28. 28.
    • Kim J, Jeerapan I, Imani S, Cho TN, Bandodkar A, Cinti S, et al. Noninvasive alcohol monitoring using a wearable tattoo-based iontophoretic-biosensing system. ACS Sens Am Chem Soc. 2016;1:1011–9. This manuscript describes the prototype for a novel transdermal alcohol monitoring sensor. Google Scholar
  29. 29.
    Gamella M, Campuzano S, Manso J, González de Rivera G, López-Colino F, Reviejo AJ, et al. A novel non-invasive electrochemical biosensing device for in situ determination of the alcohol content in blood by monitoring ethanol in sweat. Anal Chim Acta. 2014;806:1–7.  https://doi.org/10.1016/j.aca.2013.09.020.CrossRefPubMedGoogle Scholar
  30. 30.
    Bui AAT, Van Horn JD. NIH BD2K Centers Consortium. Envisioning the future of “big data” biomedicine. J Biomed Inform. 2017;69:115–7.  https://doi.org/10.1016/j.jbi.2017.03.017.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Mohr DC, Zhang M, Schueller SM. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annu Rev Clin Psychol. 2017;13(1):23–47.  https://doi.org/10.1146/annurev-clinpsy-032816-044949.CrossRefPubMedGoogle Scholar
  32. 32.
    Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2016;  https://doi.org/10.1007/s12160-016-9830-8.
  33. 33.
    Swendsen J. Contributions of mobile technologies to addiction research. Dialogues Clin Neurosci. 2016;18:213–21.PubMedPubMedCentralGoogle Scholar
  34. 34.
    Shrier LA, Rhoads AM, Fredette ME, Burke PJ. “Counselor in Your Pocket”: youth and provider perspectives on a mobile motivational intervention for marijuana use. Subst Use Misuse. 2nd ed. 2013;49:134–44.CrossRefGoogle Scholar
  35. 35.
    McClure EA, Acquavita SP, Harding E, Stitzer ML. Utilization of communication technology by patients enrolled in substance abuse treatment. Drug Alcohol Depend. 2013;129(1-2):145–50.  https://doi.org/10.1016/j.drugalcdep.2012.10.003.CrossRefPubMedGoogle Scholar
  36. 36.
    Dahne J, Lejuez CW. Smartphone and mobile application utilization prior to and following treatment among individuals enrolled in residential substance use treatment. J Subst Abus Treat. 2015;58:95–9.  https://doi.org/10.1016/j.jsat.2015.06.017.CrossRefGoogle Scholar
  37. 37.
    Milward J, Day E, Wadsworth E, Strang J, Lynskey M. Mobile phone ownership, usage and readiness to use by patients in drug treatment. Drug Alcohol Depend. 2015;146:111–5.  https://doi.org/10.1016/j.drugalcdep.2014.11.001.CrossRefPubMedGoogle Scholar
  38. 38.
    Burke LE, Shiffman S, Music E, Styn MA, Kriska A, Smailagic A, et al. Ecological momentary assessment in behavioral research: addressing technological and human participant challenges. J Med Internet Res. 2017;19(3):e77.  https://doi.org/10.2196/jmir.7138.
  39. 39.
    • Linas BS, Latkin C, Westergaard RP, Chang LW, Bollinger RC, Genz A, et al. Capturing illicit drug use where and when it happens: an ecological momentary assessment of the social, physical and activity environment of using versus craving illicit drugs. Addiction. 2015;110:315–25. This study describes how active drug users interact with EMA during periods of active use ad craving, and how user annotations can identify relapse patterns and inform intervention strategies. Google Scholar
  40. 40.
    • Carreiro S, Wittbold K, Indic P, Fang H, Zhang J, Boyer EW. Wearable biosensors to detect physiologic change during opioid use. J Med Toxicol. 2016;12:255–62. Springer US; This study describes the detection of opioid use via a wrist-mounted wearable sensor, and the differential responses detected based on participant opioid tolerance. CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Greenfield TK, Bond J, Kerr WC. Biomonitoring for improving alcohol consumption surveys: the new gold standard? Alcohol Res. 2014;36(1):39–45.PubMedPubMedCentralGoogle Scholar
  42. 42.
    •• Natarajan A, Angarita G, Gaiser E, Malison R, Ganesan D, Marlin BM. Domain adaptation methods for improving lab-to-field generalization of cocaine detection using wearable ECG. Proc ACM Int Conf Ubiquitous Comput. New York, New York, USA: ACM Press; 2016;2016:875–85. This manuscript describes mechanisms to translate lab-based substance detection with wearables (in this case cocaine) to field detection. Google Scholar
  43. 43.
    Dennis ML, Scott CK, Funk RR, Nicholson L. A pilot study to examine the feasibility and potential effectiveness of using smartphones to provide recovery support for adolescents. Subst Abus. 2015;36(4):486–92.  https://doi.org/10.1080/08897077.2014.970323.CrossRefPubMedGoogle Scholar
  44. 44.
    Gamito P, Oliveira J, Lopes P, Brito R, Morais D, Silva D, et al. Executive functioning in alcoholics following an mHealth cognitive stimulation program: randomized controlled trial. J Med Internet Res. 2014;16(4):e102.  https://doi.org/10.2196/jmir.2923.
  45. 45.
    Gamito P, Oliveira J, Lopes P, Brito R, Morais D, Cacoete C, et al. Cognitive training through mHealth for individuals with substance use disorder. Methods Inf Med Schattauer Publishers. 2017;56(2):156–61.  https://doi.org/10.3414/ME16-02-0012.CrossRefPubMedGoogle Scholar
  46. 46.
    Bindoff I, de Salas K, Peterson G, Ling T, Lewis I, Wells L, et al. Quittr: the design of a video game to support smoking cessation. JMIR Serious Games. 2016;4(2):e19.  https://doi.org/10.2196/games.6258.
  47. 47.
    •• Gustafson DH, McTavish FM, Chih M-Y, Atwood AK, Johnson RA, Boyle MG, et al. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiatry. 2014;71(5):566–72. This important RCT describes the combination detection/intervention mobile app (ACHESS) in a large population of participants with AUD.  https://doi.org/10.1001/jamapsychiatry.2013.4642.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    • Chih M-Y. Exploring the use patterns of a mobile health application for alcohol addiction before the initial lapse after detoxification. AMIA Annu Symp Proc. 2014;2014:385–94. This manuscript described how AUD participants interact with the ACHESS app (Ref 47) and importantly discusses interaction factors that correlate with positive outcomes. PubMedPubMedCentralGoogle Scholar
  49. 49.
    Gustafson DHS, Landucci G, McTavish F, Kornfield R, Johnson RA, Mares ML, et al. The effect of bundling medication-assisted treatment for opioid addiction with mHealth: study protocol for a randomized clinical trial. Trials. 2016 ed. 2016;17:592.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Dulin PL, Gonzalez VM, Campbell K. Results of a pilot test of a self-administered smartphone-based treatment system for alcohol use disorders: usability and early outcomes. Subst Abus. 2014;35(2):168–75.  https://doi.org/10.1080/08897077.2013.821437.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    • Gonzalez VM, Dulin PL. Comparison of a smartphone app for alcohol use disorders with an Internet-based intervention plus bibliotherapy: a pilot study. J Consult Clin Psychol. 2015;83:335–45. This important RCT describes the combination detection/intervention mobile app (LBMI-A) in a population of participants with AUD. CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Bertholet N, Daeppen J-B, McNeely J, Kushnir V, Cunningham JA. Smartphone application for unhealthy alcohol use: a pilot study. Subst Abus. 2017;38(3):285–91.  https://doi.org/10.1080/08897077.2017.1281860.CrossRefPubMedGoogle Scholar
  53. 53.
    Shrier LA, Rhoads A, Burke P, Walls C, Blood EA. Real-time, contextual intervention using mobile technology to reduce marijuana use among youth: a pilot study. Addict Behav. 2014;39(1):173–80.  https://doi.org/10.1016/j.addbeh.2013.09.028.CrossRefPubMedGoogle Scholar
  54. 54.
    • Attwood S, Parke H, Larsen J, Morton KL. Using a mobile health application to reduce alcohol consumption: a mixed-methods evaluation of the drinkaware track & calculate units application. BMC Public Health. 2017;17:394. This mixed method study evaluates how (and why) individuals who download a commercially available alcohol reduction app, their interaction with the product, and outcomes related to use patterns. CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    • Gajecki M, Berman AH, Sinadinovic K, Rosendahl I, Andersson C. Mobile phone brief intervention applications for risky alcohol use among university students: a randomized controlled study. Addict Sci Clin Pract. 2014;9:11. This large study of university students’ use of a publically available app highlights the potential pitfalls of some well-intentioned interventions: use of the app (which was geared toward responsible alcohol use) was associated with an increase in alcohol intake, specifically among males. CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    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. 2017;112(6):1025–35.  https://doi.org/10.1111/add.13767.CrossRefPubMedGoogle Scholar
  57. 57.
    Capon H, Hall W, Fry C, Carter A. Realising the technological promise of smartphones in addiction research and treatment: an ethical review. Int J Drug Policy. 2016;36:47–57.  https://doi.org/10.1016/j.drugpo.2016.05.013.CrossRefPubMedGoogle Scholar
  58. 58.
    Abroms LC, Lee Westmaas J, Bontemps-Jones J, Ramani R, Mellerson J. A content analysis of popular smartphone apps for smoking cessation. Am J Prev Med. 2013;45(6):732–6.  https://doi.org/10.1016/j.amepre.2013.07.008.CrossRefPubMedGoogle Scholar
  59. 59.
    Penzenstadler L, Chatton A, Van Singer M, Khazaal Y. Quality of smartphone apps related to alcohol use disorder. Eur Addict Res. Karger Publishers. 2016;22:329–38.CrossRefPubMedGoogle Scholar
  60. 60.
    Chai PR. Wearable devices and biosensing: future frontiers. J Med Toxicol. 2016;12:332–4.CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Kazemi DM, Borsari B, Levine MJ, Li S, Lamberson KA, Matta LA. A systematic review of the mHealth interventions to prevent alcohol and substance abuse. J Health Commun. 2017;22(5):413–32.  https://doi.org/10.1080/10810730.2017.1303556.CrossRefPubMedGoogle Scholar
  62. 62.
    Ford JH 2nd, Alagoz E, Dinauer S, Johnson KA, Pe-Romashko K, Gustafson DH. Successful organizational strategies to sustain use of A-CHESS: a mobile intervention for individuals with alcohol use disorders. J Med Internet Res. 2015;17:e201.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Stephanie Carreiro
    • 1
  • Peter R. Chai
    • 2
  • Jennifer Carey
    • 1
  • Jeffrey Lai
    • 1
  • David Smelson
    • 3
  • Edward W. Boyer
    • 2
  1. 1.Department of Emergency Medicine, Division of Medical ToxicologyUniversity of Massachusetts Medical SchoolWorcesterUSA
  2. 2.Brigham and Women’s Hospital, Department of Emergency Medicine, Division of Medical ToxicologyBostonUSA
  3. 3.Department of Psychiatry, Division of Addiction PsychiatryUniversity of Massachusetts Medical SchoolWorcesterUSA

Personalised recommendations