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Journal of Medical Toxicology

, Volume 12, Issue 3, pp 255–262 | Cite as

Wearable Biosensors to Detect Physiologic Change During Opioid Use

  • Stephanie Carreiro
  • Kelley Wittbold
  • Premananda Indic
  • Hua Fang
  • Jianying Zhang
  • Edward W. Boyer
Original Article

Abstract

Introduction

Opioid analgesic use is a major cause of morbidity and mortality in the US, yet effective treatment programs have a limited ability to detect relapse. The utility of current drug detection methods is often restricted due to their retrospective and subjective nature. Wearable biosensors have the potential to improve detection of relapse by providing objective, real time physiologic data on opioid use that can be used by treating clinicians to augment behavioral interventions.

Methods

Thirty emergency department (ED) patients who were prescribed intravenous opioid medication for acute pain were recruited to wear a wristband biosensor. The biosensor measured electrodermal activity, skin temperature and locomotion data, which was recorded before and after intravenous opioid administration. Hilbert transform analyses combined with paired t-tests were used to compare the biosensor data A) within subjects, before and after administration of opioids; B) between subjects, based on hand dominance, gender, and opioid use history.

Results

Within subjects, a significant decrease in locomotion and increase in skin temperature were consistently detected by the biosensors after opioid administration. A significant change in electrodermal activity was not consistently detected. Between subjects, biometric changes varied with level of opioid use history (heavy vs. nonheavy users), but did not vary with gender or type of opioid. Specifically, heavy users demonstrated a greater decrease in short amplitude movements (i.e. fidgeting movements) compared to non-heavy users.

Conclusion

A wearable biosensor showed a consistent physiologic pattern after ED opioid administration and differences between patterns of heavy and non-heavy opioid users were noted. Potential applications of biosensors to drug addiction treatment and pain management should be studied further.

Keywords

Wearables Opioids Biosensors Biometrics Signal Processing 

Notes

Acknowledgments

This work was generously supported by NIH National Institute on Drug Abuse grant R01DA033769-01 (EWB), the NIH National Institute on Drug Abuse Loan Repayment Program L30 DA038357 (SC), and partly supported by NIH National Institute on Drug Abuse grant 1R01DA033323-01 (JF) and NIH National Center for Advancing Translational Sciences 5UL1TR000161-04 pilot study award (UMass CTCS).

Compliance with Ethical Standards

Funding

NIH NIDA R01DA033769-01, L30 DA038357, NIH NIDA 1R01DA033323-01, and NIH NCATS 5UL1TR000161-04.

Conflicts of Interest

The authors have no conflicts to disclose.

References

  1. 1.
    Center for Disease Control and Prevention. (Last updated January 9, 2015). Prescription overdose in the United States: fact sheet. Retrieved 14 Jan 2015 from http://www.cdc.gov/homeabdrecreationalsafety/overdose/facts.html (2015).
  2. 2.
    Fletcher R, Tam S, Omojola O, Redemske R, Kwan J. Wearable sensor platform and mobile application for use in cognitive behavioral therapy for drug addiction and PTSD. Conf Proc IEEE Eng Med Biol Soc. 2011;1802–5. doi: 10.1109/IEMBS.2011.6090513.
  3. 3.
    Services DOHH. Addressing prescription drug abuse in the United States. 2014, p. 1–36.Google Scholar
  4. 4.
  5. 5.
    Fishman SM, Wilsey B, Yang J, Reisfield GM. Adherence monitoring and drug surveillance in chronic opioid therapy. J Pain Symptom Manag. 2000;20(4):293–307.CrossRefGoogle Scholar
  6. 6.
    Poh M-Z, Swenson NC, Picard RW. A wearable sensor for unobtrusive, long-term assessment of electrodermal activity. IEEE Trans Biomed Eng. 2010;57(5):1243–52.CrossRefPubMedGoogle Scholar
  7. 7.
    Fletcher R, Tam S, Omojola O, Redemske R, Kwan J. Wearable sensor platform and mobile application for use in cognitive behavioral therapy for drug addiction and PTSD. 2011, p. 1–4.Google Scholar
  8. 8.
    Boyer EW, Fletcher R, Fay RJ, Smelson D, Ziedonis D, Picard RW. Preliminary efforts directed toward the detection of craving of illicit substances: the iHeal project. J Med Toxicol. 2012;8(1):5–9.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Boyer EW, Smelson D, Fletcher R, Ziedonis D, Picard RW. Wireless technologies, ubiquitous computing and mobile health: application to drug abuse treatment and compliance with HIV therapies. J Med Toxicol. 2010;6(2):212–6.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Indic P, Murray G, Maggini C, Amore M, Meschi T, Borghi L, et al. Multi-scale motility amplitude associated with suicidal thoughts in major depression. de Erausquin GA, editor. PLoS ONE. 2012;7(6):e38761.Google Scholar
  11. 11.
    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.Google Scholar
  12. 12.
    Hahn S. “The instantaneous complex phase and complex frequency” in Hilbert transforms in signal processing. Boston, MA: Arthech House; 1996. p. 48. Google Scholar
  13. 13.
    Johnson NL, Kotz S, Balakrishnan N. “Order statistics” in continuous univariate distributions. 2nd ed. Wiley series in probability and statistics. New York, NY: Wiley and Sons; 1994. p 10.Google Scholar
  14. 14.
    Johnston DW, Nicholls MER, Shah M, Shields MA. Nature’s experiment? Handedness and early childhood development. Demography. 2009;46(2):281–301.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Center for Substance Abuse Treatment. Appendix B Assessment and Screening Instruments. Rockville, MD: Substance Abuse and Mental Health Services Administration (US); 2004. Available from: http://www.ncbi.nlm.nih.gov/books/NBK64244/
  16. 16.
    Jang E-H, Park B-J, Park M-S, Kim S-H, Sohn J-H. Analysis of physiological signals for recognition of boredom, pain, and surprise emotions. J Phys Anthropol. 2015;34:25.CrossRefGoogle Scholar
  17. 17.
    Kyle BN, McNeil DW. Autonomic arousal and experimentally induced pain: a critical review of the literature. Pain Res Manag. 2014;19(3):159–67.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Hwang SH, Seo S, Yoon HN, Jung, DW, Baek, HJ, Cho, J, et al. Sleep period time estimation based on electrodermal activity. IEEE J Biomed Health Inform. 2015. doi: 10.1109/JBHI.2015.2490480.
  19. 19.
    Sano A, Picard RW, Stickgold R. Quantitative analysis of wrist electrodermal activity during sleep. Int J Psychophysiol. 2014;94(3):382–9.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Fiordelli M, Diviani N, Schulz PJ. Mapping mHealth research: a decade of evolution. J Med Internet Res. JMIR Publications Inc., Toronto, Canada2013;15(5):e95.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Steinhubl SR, Muse ED, Topol EJ. The emerging field of mobile health. Sci Transl Med. Am Assoc Adv Sci. 2015;7(283):283rv3–283rv3.Google Scholar
  22. 22.
    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. J Med Toxicol. 2014;11(1):1–7.Google Scholar

Copyright information

© American College of Medical Toxicology 2016

Authors and Affiliations

  • Stephanie Carreiro
    • 1
  • Kelley Wittbold
    • 1
  • Premananda Indic
    • 2
  • Hua Fang
    • 3
  • Jianying Zhang
    • 3
  • Edward W. Boyer
    • 1
  1. 1.Department of Emergency Medicine, Division of Medical ToxicologyUniversity of Massachusetts Medical SchoolWorcesterUSA
  2. 2.Department of NeurologyUniversity of Massachusetts Medical SchoolWorcesterUSA
  3. 3.Department of Quantitative Health SciencesUniversity of Massachusetts Medical SchoolWorcesterUSA

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