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Review Article

Digital biomarker applications across the spectrum of opioid use disorder

ORCID Icon, ORCID Icon, ORCID Icon, , ORCID Icon & ORCID Icon
Article: 2240375 | Received 06 Mar 2023, Accepted 17 Jul 2023, Published online: 01 Aug 2023

References

  • Adam, F., Dufour, E., & Le Bars, D. (2008). The glycine site-specific NMDA antagonist (+)-HA966 enhances the effect of morphine and reverses morphine tolerance via a spinal mechanism. Neuropharmacology, 54(3), 588–22. https://doi.org/10.1016/j.neuropharm.2007.11.013
  • Ahamad, K., Dong, H., Johnson, C., Hyashi, K., DeBeck, K., Milloy, M. J., & Wood, E. (2019). Factors associated with willingness to wear an electronic overdose detection device. Addiction Science & Clinical Practice, 14(1), 1–5. https://doi.org/10.1186/s13722-019-0153-5
  • Akinosun, A. S., Polson, R., Diaz-Skeete, Y., De Kock, J. H., Carragher, L., Leslie, S., Grindle, M., & Gorely, T. (2021). Digital technology interventions for risk factor modification in patients with cardiovascular disease: Systematic review and meta-analysis. JMIR MHealth and UHealth, 9(3), e21061. https://doi.org/10.2196/21061
  • Babu, K. M., Brent, J., Juurlink, D. N., & Campion, E. W. (2019). Prevention of opioid overdose. New England Journal of Medicine, 380(23), 2246–2255. https://doi.org/10.1056/NEJMra1807054
  • Beaulieu, T., Knight, R., Nolan, S., Quick, O., & Ti, L. (2021). Artificial intelligence interventions focused on opioid use disorders: A review of the gray literature. The American Journal of Drug and Alcohol Abuse, 47(1), 26–42. https://doi.org/10.1080/00952990.2020.1817466
  • Ben-Israel, N., Kliger, M., Zuckerman, G., Katz, Y., & Edry, R. (2013). Monitoring the nociception level: A multi-parameter approach. Journal of Clinical Monitoring and Computing, 27(6), 659–668. https://doi.org/10.1007/s10877-013-9487-9
  • Bent, B., Goldstein, B. A., Kibbe, W. A., & Dunn, J. P. (2020). Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digital Medicine, 3(1), 1–9. https://doi.org/10.1038/s41746-020-0226-6
  • Burma, N. E., Kwok, C. H. T., & Trang, T. (2017). Therapies and mechanisms of opioid withdrawal. Pain Management, 7(6), 455–459. https://doi.org/10.2217/pmt-2017-0028
  • Carpenter, S. M., Menictas, M., Nahum-Shani, I., Wetter, D. W., & Murphy, S. A. (2020). Developments in mobile health just-in-time adaptive interventions for addiction science. Current Addiction Reports, 7(3), 1–11. https://doi.org/10.1007/s40429-020-00322-y
  • Carreiro, S., Chintha, K. K., Shrestha, S., Chapman, B., Smelson, D., & Indic, P. (2020). Wearable sensor-based detection of stress and craving in patients during treatment for substance use disorder: A mixed methods pilot study. Drug and Alcohol Dependence, 209, 107929. https://doi.org/10.1016/j.drugalcdep.2020.107929
  • Carreiro, S., Wittbold, K., Indic, P., Fang, H., Zhang, J., & Boyer, E. W. (2016). Wearable biosensors to detect physiologic change during opioid use. Journal of Medical Toxicology, 12(3), 255–262. https://doi.org/10.1007/s13181-016-0557-5
  • Chai, P. R., Carreiro, S., Innes, B. J., Chapman, B., Schreiber, K. L., Edwards, R. R., Carrico, A. W., & Boyer, E. W. (2017). Oxycodone ingestion patterns in acute fracture pain with digital pills. Anesthesia and Analgesia, 125(6), 2105. https://doi.org/10.1213/ANE.0000000000002574
  • Chai, P. R., Carreiro, S., Innes, B. J., Rosen, R. K., O’Cleirigh, C., Mayer, K. H., & Boyer, E. W. (2017). Digital pills to measure opioid ingestion patterns in emergency department patients with acute fracture pain: A pilot study. Journal of Medical Internet Research, 19(1), e19. https://doi.org/10.2196/jmir.7050
  • Chan, J., Iyer, V., Wang, A., Lyness, A., Kooner, P., Sunshine, J., & Gollakota, S. (2021). Closed-loop wearable naloxone injector system. Scientific Reports, 11(1), 22663. https://doi.org/10.1038/s41598-021-01990-0
  • Chapman, B. P., Lucey, E., Boyer, E. W., Babu, K. M., Smelson, D., & Carreiro, S. (2022). Perceptions on wearable sensor-based interventions for monitoring of opioid therapy: A qualitative study. Frontiers in Digital Health, 4, 212. https://doi.org/10.3389/fdgth.2022.969642
  • Chatterjee, S., Moreno, A., Lizotte, S. L., Akther, S., Ertin, E., Fagundes, C. P., Lam, C., Rehg, J. M., Wan, N., Wetter, D. W., & Kumar, S. (2020). SmokingOpp: Detecting the smoking’Opportunity’Context using mobile sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(1), 1–26. https://doi.org/10.1145/3380987
  • Chintha, K. K., Indic, P., Chapman, B., Boyer, E. W., & Carreiro, S. (2018). 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, Waikoloa, Hawaii.
  • Chu, Y., Zhao, X., Han, J., & Su, Y. (2017). Physiological signal-based method for measurement of pain intensity. Frontiers in Neuroscience, 11, 279. https://doi.org/10.3389/fnins.2017.00279
  • Cicero, T. J., Ellis, M. S., Surratt, H. L., & Kurtz, S. P. (2014). The changing face of heroin use in the United States: A retrospective analysis of the past 50 years. JAMA Psychiatry, 71(7), 821–826. https://doi.org/10.1001/jamapsychiatry.2014.366
  • Cohen, S. P., Christo, P. J., Wang, S., Chen, L., Stojanovic, M. P., Shields, C. H., Brummett, C., & Mao, J. (2008). The effect of opioid dose and treatment duration on the perception of a painful standardized clinical stimulus. Regional Anesthesia & Pain Medicine, 33(3), 199–206. https://doi.org/10.1097/00115550-200805000-00002
  • Colvin, L. A., Bull, F., & Hales, T. G. (2019). Perioperative opioid analgesia—when is enough too much? A review of opioid-induced tolerance and hyperalgesia. The Lancet, 393(10180), 1558–1568. https://doi.org/10.1016/S0140-6736(19)30430-1
  • Corder, G., Tawfik, V. L., Wang, D., Sypek, E. I., Low, S. A., Dickinson, J. R., Sotoudeh, C., Clark, J. D., Barres, B. A., Bohlen, C. J., & Scherrer, G. (2017). Loss of μ opioid receptor signaling in nociceptors, but not microglia, abrogates morphine tolerance without disrupting analgesia. Nature Medicine, 23(2), 164–173. https://doi.org/10.1038/nm.4262
  • Davis-Martin, R. E., Alessi, S. M., & Boudreaux, E. D. (2021). Alcohol use disorder in the age of technology: A review of wearable biosensors in alcohol use disorder treatment. Frontiers in Psychiatry, 12, 246. https://doi.org/10.3389/fpsyt.2021.642813
  • Delgado, M. K., Huang, Y., Meisel, Z., Hennessy, S., Yokell, M., Polsky, D., & Perrone, J. (2018). National variation in opioid prescribing and risk of prolonged use for opioid-naive patients treated in the emergency department for ankle sprains. Annals of Emergency Medicine, 72(4), 389–400. https://doi.org/10.1016/j.annemergmed.2018.06.003
  • Dhowan, B., Lim, J., MacLean, M. D., Berman, A. G., Kim, M. K., Yang, Q., Linnes, J., Lee, C. H., Goergen, C. J., & Lee, H. (2019). Simple minimally-invasive automatic antidote delivery device (A2D2) towards closed-loop reversal of opioid overdose. Journal of Controlled Release, 306, 130–137. https://doi.org/10.1016/j.jconrel.2019.05.041
  • Dumas, E. O., & Pollack, G. M. (2008). Opioid tolerance development: A pharmacokinetic/pharmacodynamic perspective. The AAPS Journal, 10(4), 537–551. https://doi.org/10.1208/s12248-008-9056-1
  • Dundar, N., Kus, A., Gurkan, Y., Toker, K., & Solak, M. (2018). Analgesia nociception index (ani) monitoring in patients with thoracic paravertebral block: A randomized controlled study. Journal of Clinical Monitoring and Computing, 32(3), 481–486. https://doi.org/10.1007/s10877-017-0036-9
  • Edwards, R. R., Dolman, A. J., Michna, E., Katz, J. N., Nedeljkovic, S. S., Janfaza, D., Isaac, Z., Martel, M. O., Jamison, R. N., & Wasan, A. D. (2016). Changes in pain sensitivity and pain modulation during oral opioid treatment: The impact of negative affect. Pain Medicine, 17(10), 1882–1891. https://doi.org/10.1093/pm/pnw010
  • Edwards, R. R., Wasan, A. D., Michna, E., Greenbaum, S., Ross, E., & Jamison, R. N. (2011). Elevated pain sensitivity in chronic pain patients at risk for opioid misuse. The Journal of Pain, 12(9), 953–963. https://doi.org/10.1016/j.jpain.2011.02.357
  • Eisenried, A., Austin, N., Cobb, B., Akhbardeh, A., Carvalho, B., Yeomans, D. C., & Tzabazis, A. Z. (2018). Correlation of changes in hemodynamic response as measured by cerebral optical spectrometry with subjective pain ratings in volunteers and patients: A prospective cohort study. Journal of Pain Research, 11, 1991–1998. https://doi.org/10.2147/JPR.S162839
  • Ertin, E.,Stohs, N., Kumar, S., Raij, A., Al’absi, M., Shah, S. (2011). AutoSense: Unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. Paper presented at: Proceedings of the 9th ACM conference on embedded networked sensor systems, Seattle, WA, USA.
  • Gadhia, S., Richards, G. C., Marriott, T., & Rose, J. (2022). Artificial intelligence and opioid use: A narrative review. BMJ Innovations, (9), 78–96. https://doi.org/10.1136/bmjinnov-2022-000972
  • Goldfine, C., Lai, J. T., Lucey, E., Newcomb, M., & Carreiro, S. (2020). Wearable and wireless mHealth technologies for substance use disorder. Current Addiction Reports, 7(3), 291–300. https://doi.org/10.1007/s40429-020-00318-8
  • Gram, M., Graversen, C., Olesen, A. E., & Drewes, A. M. (2015). Machine learning on encephalographic activity may predict opioid analgesia. European Journal of Pain, 19(10), 1552–1561. https://doi.org/10.1002/ejp.734
  • Gullapalli, B. T., Carreiro, S., Chapman, B. P., Ganesan, D., Sjoquist, J., & Rahman, T. (2021). OpiTrack: A wearable-based clinical opioid use tracker with temporal convolutional attention networks. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(3), 1–29. https://doi.org/10.1145/3478107
  • Gullapalli, B. T., Natarajan, A., Angarita, G. A., Malison, R. T., Ganesan, D., & Rahman, T. (2019). On-body sensing of cocaine craving, euphoria and drug-seeking behavior using cardiac and respiratory signals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(2), 1–31. https://doi.org/10.1145/3328917
  • Hasin, D. S., O’Brien, C. P., Auriacombe, M., Borges, G., Bucholz, K., Budney, A., Compton, W. M., Crowley, T., Ling, W., Petry, N. M., Schuckit, M., & Grant, B. F. (2013). DSM-5 criteria for substance use disorders: Recommendations and rationale. American Journal of Psychiatry, 170(8), 834–851. https://doi.org/10.1176/appi.ajp.2013.12060782
  • Hayhurst, C. J., & Durieux, M. E. (2016). Differential opioid tolerance and opioid-induced hyperalgesia: A clinical reality. Anesthesiology, 124(2), 483–488. https://doi.org/10.1097/ALN.0000000000000963
  • Heit, H. A., & Gourlay, D. L. (2004). Urine drug testing in pain medicine. Journal of Pain and Symptom Management, 27(3), 260–267. https://doi.org/10.1016/j.jpainsymman.2003.07.008
  • Higgins, C., Smith, B. H., & Matthews, K. (2019). Evidence of opioid-induced hyperalgesia in clinical populations after chronic opioid exposure: A systematic review and meta-analysis. British Journal of Anaesthesia, 122(6), e114–e126. https://doi.org/10.1016/j.bja.2018.09.019
  • Imtiaz, M. S., Bandoian, C. V., & Santoro, T. J. (2021). Hypoxia driven opioid targeted automated device for overdose rescue. Scientific Reports, 11(1), 24513. https://doi.org/10.1038/s41598-021-04094-x
  • Jacobson, N. C., & Feng, B. (2022). Digital phenotyping of generalized anxiety disorder: Using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life. Translational Psychiatry, 12(1), 336. https://doi.org/10.1038/s41398-022-02038-1
  • Jacobson, N. C., Summers, B., & Wilhelm, S. (2020). Digital biomarkers of social anxiety severity: Digital phenotyping using passive smartphone sensors. Journal of Medical Internet Research, 22(5), e16875. https://doi.org/10.2196/16875
  • Joly, V., Richebe, P., Guignard, B., Fletcher, D., Maurette, P., Sessler, D., & Chauvin, M. (2005). Remifentanil-induced postoperative hyperalgesia and its prevention with small-dose ketamine. The Journal of the American Society of Anesthesiologists, 103(1), 147–155. https://doi.org/10.1097/00000542-200507000-00022
  • Kanof, P. D., Handelsman, L., Aronson, M. J., Ness, R., Cochrane, K. J., & Rubinstein, K. J. (1992). Clinical characteristics of naloxone-precipitated withdrawal in human opioid-dependent subjects. Journal of Pharmacology and Experimental Therapeutics, 260(1), 355–363.
  • Kanter, K., Gallagher, R., Eweje, F., Lee A, Gordon D, Landy S, Gasior J, Soto-Calderon H, Cronholm, PF, Cocchiaro B, Weimer J. (2021). Willingness to use a wearable device capable of detecting and reversing overdose among people who use opioids in Philadelphia. Harm Reduction Journal, 18(1), 75. https://doi.org/10.1186/s12954-021-00522-3
  • Katz, N., & Fanciullo, G. J. (2002). Role of urine toxicology testing in the management of chronic opioid therapy. The Clinical Journal of Pain, 18(4), S76–S82. https://doi.org/10.1097/00002508-200207001-00009
  • Katz, N. P., Paillard, F. C., & Edwards, R. R. (2015). Review of the performance of quantitative sensory testing methods to detect hyperalgesia in chronic pain patients on long-term opioids. Anesthesiology, 122(3), 677–685. https://doi.org/10.1097/ALN.0000000000000530
  • Kennedy, A. P., Epstein, D. H., Jobes, M. L., Agage, D., Tyburski, M., Phillips, K. A., Ali, A. A., Bari, R., Hossain, S. M., Hovsepian, K., Rahman, M. M., Ertin, E., Kumar, S., & Preston, K. L. (2015). Continuous in-the-field measurement of heart rate: Correlates of drug use, craving, stress, and mood in polydrug users. Drug and Alcohol Dependence, 151, 159–166. https://doi.org/10.1016/j.drugalcdep.2015.03.024
  • Kolodny, A., Courtwright, D. T., Hwang, C. S., Kreiner, P., Eadie, J. L., Clark, T. W., & Alexander, G. C. (2015). The prescription opioid and heroin crisis: A public health approach to an epidemic of addiction. Annual Review of Public Health, 36(1), 559–574. https://doi.org/10.1146/annurev-publhealth-031914-122957
  • Kulman, E.,Chapman, B., Venkatasubramanian, K., Carreiro, S. (2021). Identifying opioid withdrawal using wearable biosensors. Proceedings of the… Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences.
  • Kulman, E., Venkatasubramanian, K., Chapman, B., & Carreiro, S. (2021). Identifying OpioidWithdrawal UsingWearable Biosensors. Proceedings of the Annual Hawaii International Conference on System Sciences, 54, 3583–3592.
  • Lai, J. T., Goldfine, C. E., Chapman, B. P., Taylor, M., Rosen, R., Carreiro, S., & Babu, K. (2021). Nobody wants to be narcan’d: A pilot qualitative analysis of drug users’ perspectives on naloxone. Western Journal of Emergency Medicine, 22(2), 339. https://doi.org/10.5811/westjem.2020.10.48768
  • Lambert, T. P., Gazi, A. H., Harrison, A. B., Gharehbaghi, S., Chan, M., Obideen, M., Alavi, P., Murrah, N., Shallenberger, L., Driggers, E. G., Alvarado Ortega, R., Washington, B., Walton, K. M., Tang, Y.-L., Gupta, R., Nye, J. A., Welsh, J. W., Vaccarino, V. … Bremner, J. D. (2022). Leveraging accelerometry as a prognostic indicator for increase in opioid withdrawal symptoms. Biosensors, 12(11), 924. https://doi.org/10.3390/bios12110924
  • Laulin, J.-P., Maurette, P., Corcuff, J.-B., Rivat, C., Chauvin, M., & Simonnet, G. (2002). The role of ketamine in preventing fentanyl-induced hyperalgesia and subsequent acute morphine tolerance. Anesthesia & Analgesia, 94(5), 1263–1269. https://doi.org/10.1097/00000539-200205000-00040
  • Lee, J., Mawla, I., Kim, J., Loggia, M. L., Ortiz, A., Jung, C., Chan, S.-T., Gerber, J., Schmithorst, V. J., Edwards, R. R., Wasan, A. D., Berna, C., Kong, J., Kaptchuk, T. J., Gollub, R. L., Rosen, B. R., & Napadow, V. (2019). Machine learning–based prediction of clinical pain using multimodal neuroimaging and autonomic metrics. PAIN, 160(3), 550–560. https://doi.org/10.1097/j.pain.0000000000001417
  • Lee, M. C., Wanigasekera, V., & Tracey, I. (2014). Imaging opioid analgesia in the human brain and its potential relevance for understanding opioid use in chronic pain. Neuropharmacology, 84, 123–130. https://doi.org/10.1016/j.neuropharm.2013.06.035
  • Lin, Y., Wang, L., Xiao, Y., Urman, R. D., Dutton, R., & Ramsay, M. (2018). Objective pain measurement based on physiological signals. Proceedings of the International Symposium of Human Factors and Ergonomics in Healthcare, 7(1), 240–247. https://doi.org/10.1177/2327857918071056
  • Loggia, M. L., Juneau, M., & Bushnell, M. C. (2011). Autonomic responses to heat pain: Heart rate, skin conductance, and their relation to verbal ratings and stimulus intensity. PAIN®, 152(3), 592–598. https://doi.org/10.1016/j.pain.2010.11.032
  • Mahmud, M. S.,Fang, H., Wang, H., Carreiro, S., Boyer, E. W. (2018) Automatic detection of opioid intake using wearable biosensor. Proceedings of the 2018 International Conference on Computing, Networking and Communications (ICNC), Maui, Hawaii, USA (pp. 784–788).
  • Marion Lee, M., Sanford Silverman, M., Hans Hansen, M., Vikram Patel, M., & Laxmaiah Manchikanti, M. D. (2011). A comprehensive review of opioid-induced hyperalgesia. Pain Physician, 14(2;3), 145–161. https://doi.org/10.36076/ppj.2011/14/145
  • Marsch, L. A. (2020). Digital health and addiction. Current Opinion in Systems Biology, 20, 1–7. https://doi.org/10.1016/j.coisb.2020.07.004
  • Mattson, C. L., Tanz, L. J., Quinn, K., Kariisa, M., Patel, P., & Davis, N. L. (2021). Trends and geographic patterns in drug and synthetic opioid overdose deaths—United States, 2013–2019. MMWR Morbidity and Mortality Weekly Report, 70(6), 202. https://doi.org/10.15585/mmwr.mm7006a4
  • McCarthy, D. M., Kim, H. S., Hur, S. I., Lank, P. M., Arroyo, C., Opsasnick, L. A., Piserchia, K., Curtis, L. M., Wolf, M. S., & Courtney, D. M. (2021). Patient-reported opioid pill consumption after an ED visit: How many pills are people using? Pain Medicine, 22(2), 292–302. https://doi.org/10.1093/pm/pnaa048
  • McLellan, A. T., Koob, G. F., & Volkow, N. D. (2022). Preaddiction—a missing concept for treating substance use disorders. JAMA Psychiatry, 79(8), 749–751. https://doi.org/10.1001/jamapsychiatry.2022.1652
  • Montag, C., Elhai, J. D., & Dagum, P. (2021). On blurry boundaries when defining digital biomarkers: How much biology needs to be in a digital biomarker? Frontiers in Psychiatry, 2021, 1690. https://doi.org/10.3389/fpsyt.2021.740292
  • Moreno, J. L., Duprey, M. S., Hayes, B. D., Brooks, K., Khalil, S., Wakeman, S. E., Roberts, R. J., Jacobson, J. S., & Devlin, J. W. (2019). Agreement between self-reported psychoactive substance use and urine toxicology results for adults with opioid use disorder admitted to hospital. Toxicology Communications, 3(1), 94–101. https://doi.org/10.1080/24734306.2019.1700339
  • Muhuri, P. K., Gfroerer, J. C., & Davies, M. C. (2013). CBHSQ data review. Center for Behavioral Health Statistics and Quality, SAMHSA, 1, 17. https://img3.reoveme.com/m/25e062e91894208c.pdf
  • Nandakumar, R., Gollakota, S., & Sunshine, J. E. (2019). Opioid overdose detection using smartphones. Science translational medicine. Science Translational Medicine, 11(474). https://doi.org/10.1126/scitranslmed.aau8914
  • Natarajan, A.,Parate, A., Gaiser, E., Angarita G, Malison R, Marlin B, Ganesan D. (2013). Detecting cocaine use with wearable electrocardiogram sensors. Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, Zurich Switzerland.
  • Nuamah, J., Mehta, R., & Sasangohar, F. (2020). Technologies for opioid use disorder management: Mobile app search and scoping review. JMIR MHealth and UHealth, 8(6), e15752. https://doi.org/10.2196/15752
  • Oesterle, T. S., Karpyak, V. M., Coombes, B. J., Athreya, A. P., Breitinger, S. A., Correa da Costa, S., & (Dana) Gerberi, D. J. (2022). Systematic review: Wearable remote monitoring to detect nonalcohol/nonnicotine-related substance use disorder symptoms. The American Journal on Addictions / American Academy of Psychiatrists in Alcoholism & Addictions, 31(6), 535–545. https://doi.org/10.1111/ajad.13341
  • Othman, E., Werner, P., Saxen, F., Fiedler, M.-A., & Al-Hamadi, A. (2022). An automatic system for continuous pain intensity monitoring based on analyzing data from Uni-, Bi-, and Multi-Modality. Sensors, 22(13), 4992. https://doi.org/10.3390/s22134992
  • Pear Theraputics. reSET & reSET-O. (2022). Retrieved December 21, 2022, from https://peartherapeutics.com/products/reset-reset-o/
  • Pergolizzi, J. V., Jr., Raffa, R. B., & Rosenblatt, M. H. (2020). Opioid withdrawal symptoms, a consequence of chronic opioid use and opioid use disorder: Current understanding and approaches to management. Journal of Clinical Pharmacy and Therapeutics, 45(5), 892–903. https://doi.org/10.1111/jcpt.13114
  • Perski, O., Hébert, E. T., Naughton, F., Hekler, E. B., Brown, J., & Businelle, M. S. (2021). Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: A systematic review. Addiction, 117(5), 1220–1241. https://doi.org/10.1111/add.15687
  • Roeckel, L.-A., Le Coz, G.-M., Gavériaux-Ruff, C., & Simonin, F. (2016). Opioid-induced hyperalgesia: Cellular and molecular mechanisms. Neuroscience, 338, 160–182. https://doi.org/10.1016/j.neuroscience.2016.06.029
  • Roth, A. M., Tran, N. K., Cocchiaro, B., Mitchell, A. K., Schwartz, D. G., Hensel, D. J., Ataiants, J., Brenner, J., Yahav, I., & Lankenau, S. E. (2021). Wearable biosensors have the potential to monitor physiological changes associated with opioid overdose among people who use drugs: A proof-of-concept study in a real-world setting. Drug and Alcohol Dependence, 229(Pt A), 109138. https://doi.org/10.1016/j.drugalcdep.2021.109138
  • Rzasa Lynn, R., & Galinkin, J. L. (2018). Naloxone dosage for opioid reversal: Current evidence and clinical implications. Therapeutic Advances in Drug Safety, 9(1), 63–88. https://doi.org/10.1177/2042098617744161
  • Saeed, S. A., & Masters, R. M. (2021). Disparities in health care and the digital divide. Current Psychiatry Reports, 23(9), 1–6. https://doi.org/10.1007/s11920-021-01274-4
  • Salgado García, F. I., Indic, P., Stapp, J., Chintha, K. K., He, Z., Brooks, J. H., Carreiro, S., & Derefinko, K. J. (2022). Using wearable technology to detect prescription opioid self-administration. Pain, 163(2), e357–e367. https://doi.org/10.1097/j.pain.0000000000002375
  • Saxena, M.,Deo, A., Saxena, A. (2021). mHealth for mental health. Proceedings of the International Conference on Innovative Computing and Communications, Delhi, India.
  • Seitsonen, E. R. J., Korhonen, I. K. J., Van Gils, M. J., Huiku, M., Lötjönen, J. M. P., Korttila, K. T., & Yli-Hankala, A. M. (2005). EEG spectral entropy, heart rate, photoplethysmography and motor responses to skin incision during sevoflurane anaesthesia. Acta Anaesthesiologica Scandinavica, 49(3), 284–292. https://doi.org/10.1111/j.1399-6576.2005.00654.x
  • Sober Grid. (2021). Retrieved December 21, 2022, from https://www.sobergrid.com
  • Spire Health. (2022). Technology. Retrieved December 19, 2022, from https://www.spirehealth.com/technology
  • Strayer, R. J., Motov, S. M., & Nelson, L. S. (2017). Something for pain: Responsible opioid use in emergency medicine. The American Journal of Emergency Medicine, 35(2), 337–341. https://doi.org/10.1016/j.ajem.2016.10.043
  • Substance Abuse and Mental Health Services Administration. (2020). Key substance use and mental health indicators in the United States: Results from the 2019 National Survey on Drug Use and Health ( HHS Publication No. PEP20-07-01-001, NSDUH Series H-55). Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. Retrieved from https://www.samhsa.gov/data/
  • Su, D., Liu, Z., Jiang, X., Zhang, F., Yu, W., Ma, H., Wang, C., Wang, Z., Wang, X., Hu, W., Manor, B., Feng, T., & Zhou, J. (2021). Simple smartphone-based assessment of gait characteristics in parkinson disease: Validation study. JMIR MHealth and UHealth, 9(2), e25451. https://doi.org/10.2196/25451
  • Susam, B. T.,Akcakaya, M., Nezamfar, H., Diaz D, Xu X, de Sa, VR, Craig, KD, Huang, JS, Goodwin, MS. (2018). Automated pain assessment using electrodermal activity data and machine learning. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference, Honolulu, Hawaii.
  • Treister, R., Kliger, M., Zuckerman, G., Aryeh, I. G., & Eisenberg, E. (2012). Differentiating between heat pain intensities: The combined effect of multiple autonomic parameters. Pain, 153(9), 1807–1814. https://doi.org/10.1016/j.pain.2012.04.008
  • U.S. Department of Health and Human Services (HHS). (2018).Office of the Surgeon General, Facing Addiction in America: The Surgeon General’s Spotlight on Opioids. HHS. https://addiction.surgeongeneral.gov/sites/default/files/Spotlight-on-Opioids_09192018.pdf
  • Volkow, N. D., & Collins, F. S. (2017). The role of science in addressing the opioid crisis. New England Journal of Medicine, 377(4), 391–394. https://doi.org/10.1056/NEJMsr1706626
  • Wasserman, R. A., Hassett, A. L., Harte, S. E., Goesling, J., Malinoff, H. L., Berland, D. W., Zollars, J., Moser, S. E., & Brummett, C. M. (2015). Pressure pain sensitivity in patients with suspected opioid-induced hyperalgesia. Regional Anesthesia and Pain Medicine, 40(6), 687–693. https://doi.org/10.1097/AAP.0000000000000315
  • Workit Health. (2022). Retrieved December 21, 2022, from https://www.workithealth.com
  • Wright, J. M., Regele, O. B., Kourtis, L. C., Pszenny, S., Sirkar, R., Kovalchick, C., & Jones, G. (2017). Evolution of the digital biomarker ecosystem. Digital Medicine, 3(4), 154. https://doi.org/10.4103/digm.digm_35_17
  • Zahari, Z., Lee, C. S., Ibrahim, M. A., Musa, N., Mohd Yasin, M. A., Lee, Y. Y., Tan, S. C., Mohamad, N., & Ismail, R. (2016). Comparison of pain tolerance between opioid dependent patients on Methadone Maintenance Therapy (MMT) and opioid naive individuals. Journal of Pharmacy & Pharmaceutical Sciences, 19(1), 127–136. https://doi.org/10.18433/J3NS49