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Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science

  • Mobile Health (K Garrison, Section Editor)
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Abstract

Purpose of Review

Addiction is a serious and prevalent problem across the globe. An important challenge facing intervention science is how to support addiction treatment and recovery while mitigating the associated cost and stigma. A promising solution is the use of mobile health (mHealth) just-in-time adaptive interventions (JITAIs), in which intervention options are delivered in situ via a mobile device when individuals are most in need.

Recent Findings

The present review describes the use of mHealth JITAIs to support addiction treatment and recovery, and provides guidance on when and how the micro-randomized trial (MRT) can be used to optimize a JITAI. We describe the design of five mHealth JITAIs in addiction and three MRT studies, and discuss challenges and future directions.

Summary

This review aims to provide guidance for constructing effective JITAIs to support addiction treatment and recovery.

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References

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

  1. Duncan LG, Mendoza S, Hansen H. Buprenorphine maintenance for opioid dependence in public sector healthcare: benefits and barriers. J Addict Med Ther Sci. 2015;1(2):31–6.

    PubMed  PubMed Central  Google Scholar 

  2. Gwaltney CJ, Shiffman S, Balabanis MH, Paty JA. Dynamic self-efficacy and outcome expectancies: prediction of smoking lapse and relapse. J Abnorm Psychol. 2005;114(4):661–75.

    Article  PubMed  Google Scholar 

  3. Mezinskis JP, Honos-Webb L, Kropp F, Somoza E. The measurement of craving. J Addict Dis. 2001;20(3):67–85.

    Article  CAS  PubMed  Google Scholar 

  4. Nahum-Shani I, Hekler EB, Spruijt-Metz D. Building health behavior models to guide the development of just-in-time adaptive interventions: a pragmatic framework. Health Psychol. 2015 Dec;34(S):1209–19.

    Article  PubMed Central  Google Scholar 

  5. •• Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A. Murphy SA. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2018;52(6):446–62 This is an important article as it lays out the framework that we use for JITAIs in mHealth to provide a better understanding of the implications of providing timely and ecologically sound support for intervention adherence and retention.

    Article  PubMed  Google Scholar 

  6. Pickens RW, Johanson CE. Craving: consensus of status and agenda for future research. Drug Alcohol Depend. 1992 Jun 1;30(2):127–31.

    Article  CAS  PubMed  Google Scholar 

  7. • World Health Organization. mHealth. Use of appropriate digital technologies for public health: report by Director-General. 71st World Health Assembly provisional agenda item. (2017) m 12: A71. This important article provides us with our definition of mHealth.

  8. Whittaker R, McRobbie H, Bullen C, Rodgers A, Gu Y. Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev. 2016;4.

  9. Gustafson DH, McTavish FM, Chih MY, 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.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Brendryen H, Kraft P. Happy ending: a randomized controlled trial of a digital multi-media smoking cessation intervention. Addiction. 2008;103(3):478–84.

    Article  PubMed  Google Scholar 

  11. Tofighi B, Nicholson JM, McNeely J, Muench F, Lee JD. Mobile phone messaging for illicit drug and alcohol dependence: a systematic review of the literature. Drug Alcohol Rev. 2017;36(4):477–91.

    Article  PubMed  Google Scholar 

  12. Penzenstadler L, Chatton A, Van Singer M, Khazaal Y. Quality of smartphone apps related to alcohol use disorder. Eur Addict Res. 2016;22(6):329–38.

    Article  PubMed  Google Scholar 

  13. Weaver ER, Horyniak DR, Jenkinson R, Dietze P, Lim MS. “Let’s get wasted!” and other apps: characteristics, acceptability, and use of alcohol-related smartphone applications. JMIR mHealth and uHealth. 2013;1(1):e9.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Tofighi B, Leonard N, Greco P, Hadavand A, Acosta MC, Lee JD. Technology use patterns among patients enrolled in inpatient detoxification treatment. J Addict Med. 2019;13(4):279–86.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Furnari M, Epstein DH, Phillips KA, Jobes ML, Kowalczyk WJ, Vahabzadeh M, et al. Some of the people, some of the time: field evidence for associations and dissociations between stress and drug use. Psychopharmacology. 2015;232(19):3529–37.

    Article  CAS  PubMed  Google Scholar 

  16. Epstein DH. Let’s agree to agree: a comment on Hogarth (2020), with a plea for not-so-competing theories of addiction. Neuropsychopharmacology. 2020;45(5):715–6.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Spruijt-Metz D, Wen CK, O’Reilly G, Li M, Lee S, Emken BA, et al. Innovations in the use of interactive technology to support weight management. Curr Obes Rep. 2015;4(4):510–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. •• Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, et al. Microrandomized trials: an experimental design for developing just-in-time adaptive interventions. Health Psychol. 2015;34(S):1220 This important article provides justification for the use of MRTs in mHealth.

    Article  PubMed Central  Google Scholar 

  19. Liao P, Klasnja P, Tewari A, Murphy SA. Sample size calculations for micro-randomized trials in mHealth. Stat Med. 2016 May 30;35(12):1944–71.

    Article  PubMed  Google Scholar 

  20. Collins LM. Optimization of behavioral, biobehavioral, and biomedical interventions: the multiphase optimization strategy (MOST): Springer; 2018.

  21. Carver CS, Scheier MF. On the self-regulation of behavior: Cambridge University Press; 2001.

  22. Bandura A. Social cognitive theory of self-regulation. Organ Behav Hum Decis Process. 1991;50(2):248–87.

    Article  Google Scholar 

  23. Oikonomou MT, Arvanitis M, Sokolove RL. Mindfulness training for smoking cessation: a meta-analysis of randomized-controlled trials. J Health Psychol. 2017;22(14):1841–50.

    Article  PubMed  Google Scholar 

  24. Davis JM, Fleming MF, Bonus KA, Baker TB. A pilot study on mindfulness based stress reduction for smokers. BMC Complement Altern Med. 2007;7(1):2.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Maglione MA, Maher AR, Ewing B, Colaiaco B, Newberry S, Kandrack R, et al. Efficacy of mindfulness meditation for smoking cessation: a systematic review and meta-analysis. Addict Behav. 2017;69:27–34.

    Article  PubMed  Google Scholar 

  26. Cropley M, Ussher M, Charitou E. Acute effects of a guided relaxation routine (body scan) on tobacco withdrawal symptoms and cravings in abstinent smokers. Addiction. 2007;102(6):989–93.

    Article  PubMed  Google Scholar 

  27. Black DS. Mindfulness-based interventions: an antidote to suffering in the context of substance use, misuse, and addiction. Subst Use Misuse. 2014 Apr 16;49(5):487–91.

    Article  PubMed  Google Scholar 

  28. Vettese LC, Toneatto T, Stea JN, Nguyen L, Wang JJ. Do mindfulness meditation participants do their homework? And does it make a difference? A review of the empirical evidence. J Cogn Psychother. 2009;23(3):198–225.

    Article  Google Scholar 

  29. Thayer RE, Newman JR, McClain TM. Self-regulation of mood: strategies for changing a bad mood, raising energy, and reducing tension. J Pers Soc Psychol. 1994;67(5):910–25.

    Article  CAS  PubMed  Google Scholar 

  30. Naughton F, Hopewell S, Lathia N, Schalbroeck R, Brown C, Mascolo C, et al. A context-sensing mobile phone app (Q sense) for smoking cessation: a mixed-methods study. JMIR mHealth and uHealth. 2016;4(3):e106.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Businelle MS, Ma P, Kendzor DE, Frank SG, Vidrine DJ, Wetter DW. An ecological momentary intervention for smoking cessation: evaluation of feasibility and effectiveness. J Med Internet Res. 2016;18(12):e321.

    Article  PubMed  PubMed Central  Google Scholar 

  32. • Hébert ET, Ra CK, Alexander AC, Helt A, Moisiuc R, Kendzor DE, et al. A mobile just-in-time adaptive intervention for smoking cessation: pilot randomized controlled trial. J Med Internet Res. 2020;22(3):e16907 This important article provides detail on a pilot RCT for evaluation of the Smart-T2 JITAI package. The results of this pilot RCT suggest that smartphone-based smoking cessation JITAIs may be capable of providing similar outcomes to traditional, in-person counseling.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Eckhoff RP, Kizakevich PN, Bakalov V, Zhang Y, Bryant SP, Hobbs MA. A platform to build mobile health apps: the personal health intervention toolkit (PHIT). JMIR mHealth and uHealth. 2015;3(2):e46.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kizakevich PN, Hubal RC, Brown J, Lyden J, Spira JL, Eckhoff R, et al. PHIT for duty, a mobile approach for psychological health intervention. Annu Rev Cyberther Telemed. 2012;181:268–72.

    Google Scholar 

  35. • Kizakevich PN, Eckhoff R, Brown J, Tueller SJ, Weimer B, Bell S, et al. PHIT for duty, a mobile application for stress reduction, sleep improvement, and alcohol moderation. Mil Med. 2018;183(suppl_1):353–63 This important article provides details on the evaluations of usability and health assessment accuracy for the PHIT for Duty JITAI package.

    Article  PubMed  Google Scholar 

  36. • Spring, B. (2017). Sense2Stop: mobile sensor data to knowledge. In: ClinicalTrials.gov [Internet]. Bethesda (MD): National Library of Medicine (US). 2000- [cited 2019 May 10]. Available at https://clinicaltrials.gov/ct2/show/nct03184389. This important website provides detail on the Sense2Stop clinical trial, a phase of which includes the Sense2Stop MRT described previously.

  37. • Rabbi M, Philyaw-Kotov M, Lee J, Mansour A, Dent L, Wang X, et al. SARA: a mobile app to engage users in health data collection. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers; 2017. p. 781–9. This important article provides further detail on the Alcohol and Drug Use MRT, SARA, described previously.

    Chapter  Google Scholar 

  38. •• Nahum-Shani, B., Wetter, D. W. Novel use of mHealth data to identify states of vulnerability and receptivity to JITAIs. 2018. Grant Funded by NIH/NCI U01 CA229437. This funded grant provides important detail on MARS, our orienting example for MRTs.

  39. Gustafson DH, 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;17(1):592.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile interventions: are our theories up to the task? Transl Behav Med. 2011;1(1):53–71.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Spruijt-Metz D, Nilsen W. Dynamic models of behavior for just-in-time adaptive interventions. IEEE Pervasive Computing. 2014;13(3):13–7.

    Article  Google Scholar 

  42. •• Murphy, S.A., Harvard University. Micro-Randomized Trials. Available from: http://people.seas.harvard.edu/~samurphy/JITAI_MRT/mrts4.html [30 March 2020] This important website gives examples of MRT schematics.

  43. •• Walton A, Nahum-Shani I, Crosby L, Klasnja P, Murphy S. Optimizing digital integrated care via micro-randomized trials. Clin Pharmacol Ther. 2018;104(1):53–8 This important article provides justification for the use of MRTs in mHealth.

  44. Dempsey W, Liao P, Kumar S, Murphy SA. The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments. arXiv preprint arXiv:1711.03587. 2017.

  45. Liao P, Dempsey W, Sarker H, Hossain SM, Al'Absi M, Klasnja P, et al. Just-in-time but not too much: determining treatment timing in mobile health. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2018;2(4):1–21.

    Article  Google Scholar 

  46. Hovsepian K, Al'Absi M, Ertin E, Kamarck T, Nakajima M, Kumar S. cStress: towards a gold standard for continuous stress assessment in the mobile environment. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing 2015 Sep 7 (pp. 493–504).

  47. Yuodelis-Flores C, Ries RK. Addiction and suicide: a review. Am J Addict. 2015;24(2):98–104.

    Article  PubMed  Google Scholar 

  48. Saleheen N, Ali AA, Hossain SM, Sarker H, Chatterjee S, Marlin B, et al. puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing; 2015. p. 999–1010.

    Chapter  Google Scholar 

  49. • Epstein DH, Tyburski M, Kowalczyk WJ, Burgess-Hull AJ, Phillips KA, Curtis BL, et al. Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data. NPJ Digital Med. 2020;3(1):1–2 This important article discusses current limitations to the use of sensor technologies.

    Article  Google Scholar 

  50. Arellano AM, Dai W, Wang S, Jiang X, Ohno-Machado L. Privacy policy and technology in biomedical data science. Annual review of biomedical data science. 2018;1:115–29.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Dehling T, Gao F, Schneider S, Sunyaev A. Exploring the far side of mobile health: information security and privacy of mobile health apps on iOS and Android. JMIR Mhealth Uhealth. 2015;3(1):e8. https://doi.org/10.2196/mhealth.3672.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Papageorgiou A, Strigkos M, Politou E, Alepis E, Solanas A, Patsakis C. Security and privacy analysis of mobile health applications: the alarming state of practice. IEEE Access. 2018 Jan 29;6:9390–403.

    Article  Google Scholar 

  53. SageBionetworks. Privacy Toolkit for Mobile Health, 2019. https://designmanual.sagebionetworks.org/privacy_toolkit.html (accessed on 09/07/2019).

  54. Huckvale K, Prieto JT, Tilney M, Benghozi PJ, Car J. Unaddressed privacy risks in accredited health and wellness apps: a cross-sectional systematic assessment. BMC Med. 2015;13(1):214.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Martínez-Pérez B, De La Torre-Díez I, López-Coronado M. Privacy and security in mobile health apps: a review and recommendations. J Med Syst. 2015;39(1):181.

    Article  PubMed  Google Scholar 

  56. Bojinov H, Michalevsky Y, Nakibly G, Boneh D. Mobile device identification via sensor fingerprinting. arXiv preprint arXiv:1408.1416. 2014 .

  57. Kjeldskov J, Skov MB, Als BS, Høegh RT. Mobile human–computer interaction—mobile HCI 2004. In: Is it worth the hassle? Exploring the added value of evaluating the usability of context-aware mobile systems in the field. Berlin: Springer Heidelberg; 2004. p. 61–73.

    Google Scholar 

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Acknowledgments

This work was supported in part by U.S National Institutes of Health grants R01AA23187 (NIH/NIAAA), P50DA039838 (NIH/NIDA), U01CA229437 (NIH/NCI), R01DA039901 (NIH/NIDA), P30CA042014 (NIH/NCI), UL1TR002538 (NIH/NCATS), and the Huntsman Cancer Foundation.

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Author notes

  1. S.M.C. and M.M. contributed equally to this work. The first two authors are listed in alphabetical order.

Authors and Affiliations

  1. Data-Science for Dynamic Decision-Making Lab (d3lab) at the Institute for Social Research, University of Michigan, 426 Thompson St, Ann Arbor, MI, 48104, USA

    Stephanie M. Carpenter & Inbal Nahum-Shani

  2. Department of Statistics, Harvard University, Science Center, One Oxford Street, Cambridge, MA, 02138-2901, USA

    Marianne Menictas

  3. Department of Population Health Sciences and the Center for Health Outcomes and Population Equity, Huntsman Cancer Institute and the University of Utah, Salt Lake City, UT, USA

    David W. Wetter

  4. Faculty of Arts and Sciences, John A. Paulson School of Engineering and Applied Sciences, and The Radcliffe Institute for Advanced Study, Harvard University, Cambridge, MA, USA

    Susan A. Murphy

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  1. Stephanie M. Carpenter
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  3. Inbal Nahum-Shani
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  4. David W. Wetter
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  5. Susan A. Murphy
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Correspondence to Stephanie M. Carpenter or Marianne Menictas.

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S.M.C., M.M., I.N.-S., D.W.W., and S.A.M. declare that they have no conflicts of interest.

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Carpenter, S.M., Menictas, M., Nahum-Shani, I. et al. Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science. Curr Addict Rep 7, 280–290 (2020). https://doi.org/10.1007/s40429-020-00322-y

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