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13 - Use of Mobile Technology to Understand and Improve Recovery from Alcohol Use Disorder

from Part II - Meso Level

Published online by Cambridge University Press:  23 December 2021

Jalie A. Tucker
Affiliation:
University of Florida
Katie Witkiewitz
Affiliation:
University of New Mexico
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Summary

This chapter reviews how ubiquitous mobile technology can be used to better understand and improve recovery from alcohol use disorder. Distinct applications of both active and passive technology-assisted data collection (i.e., ecological momentary assessment, ambulatory assessment) to assess alcohol use and broader recovery outcomes are described. Previous studies of and future opportunities to use these methods to examine recovery-related processes and mechanisms of behavior change are highlighted. Promising mobile-based interventions or recovery support services examined to date are described, ranging from classic telehealth approaches to sophisticated interventions relying on both self-reported and sensor-based inputs to tailor the timing and content of intervention (i.e., ecological momentary interventions, Just-In-Time Adaptive Interventions). The chapter concludes with discussion of the potential for these interventions to achieve individualized intervention optimization (i.e., personalized treatment, precision medicine).

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Publisher: Cambridge University Press
Print publication year: 2022

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References

Bae, S., Chung, T., Ferreira, D., Dey, A. K., & Suffoletto, B. (2018). Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions. Addictive Behaviors, 83, 4247. https://doi.org/10.1016/j.addbeh.2017.11.039Google Scholar
Bae, S., Ferreira, D., Suffoletto, B., Puyana, J. C., Kurtz, R., Chung, T., & Dey, A. K. (2017). Detecting drinking episodes in young adults using smartphone-based sensorsProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies1(2), 136. https://doi.org/10.1145/3090051Google Scholar
Beckjord, E., & Shiffman, S. (2014). Background for real-time monitoring and intervention related to alcohol use. Alcohol Research: Current Reviews, 36(1), 918. https://pubmed.ncbi.nlm.nih.gov/26258996Google Scholar
Berry, N., Bucci, S., & Lobban, F. (2017). Use of the internet and mobile phones for self-management of severe mental health problems: Qualitative study of staff views. JMIR Mental Health, 4(4), e52. https://doi.org/10.2196/mental.8311Google Scholar
Browne, T., Priester, M. A., Clone, S., Iachini, A., Dehart, D., & Hock, R. (2016). Barriers and facilitators to substance use treatment in the rural south: A qualitative study. Journal of Rural Health, 32(1), 92101. https://doi.org/10.1111/jrh.12129Google Scholar
Bucci, S., Schwannauer, M., & Berry, N. (2019). The digital revolution and its impact on mental health care. Psychology and Psychotherapy: Theory, Research and Practice, 92(2), 277297. https://doi.org/10.1111/papt.12222Google Scholar
Buckman, J. F., Vaschillo, E. G., Fonoberova, M., Mezic, I., & Bates, M. E. (2018). The translational value of psychophysiology methods and mechanisms: Multilevel, dynamic, personalized. Journal of Studies on Alcohol and Drugs, 79, 229238. https://doi.org/10.15288/jsad.2018.79.229Google Scholar
Burke, L. E., Shiffman, S., Music, E., Styn, M. A., Kriska, A., Smailagic, A., Siewiorek, D., Ewing, L. J., Chasens, E., French, B., Mancino, J., Mendez, D., Strollo, P., & Rathbun, S. L. (2017). Ecological momentary assessment in behavioral research: Addressing technological and human participant challenges. Journal of Medical Internet Research, 19(3), e77. https://doi.org/10.2196/jmir.7138CrossRefGoogle ScholarPubMed
Carney, M. A., Tennen, H., Affleck, G., del Boca, F. K., & Kranzler, H. R. (1998). Levels and patterns of alcohol consumption using timeline follow-back, daily diaries, and real time “electronic interviews.Journal of Studies on Alcohol, 59(4), 447-454. https://doi.org/10.15288/jsa.1998.59.447Google Scholar
Carpenter, R. W., Squeglia, L. M., Emery, N. N., & McClure, E. A. (2020). Making pharmacotherapy trials for substance use disorder more efficient: Leveraging real-world data capture to maximize power and expedite the medication development pipeline. Drug and Alcohol Dependence, 209, 107897. https://doi.org/10.1016/j.drugalcdep.2020.107897Google Scholar
Carpenter, R. W., Treloar Padovano, H., Emery, N. N., & Miranda, R. (2019). Rate of alcohol consumption in the daily life of adolescents and emerging adults. Psychopharmacology, 236(11), 31113124. https://doi.org/10.1007/s00213–019-05262-8Google Scholar
Carroll, K. M., Ball, S. A., Martino, S., Nich, C., Babuscio, T. A., Nuro, K. F., Gordon, M. A., Portnoy, G. A., & Rounsaville, B. J. (2008). Computer-assisted delivery of cognitive-behavioral therapy for addiction: A randomized trial of CBT4CBT. American Journal of Psychiatry, 165(7), 881888. https://doi.org/10.1176/appi.ajp.2008.07111835Google Scholar
Colder, C. R., Chassin, L., Lee, M. R., & Villalta, I. K. (2010). Developmental perspectives: Affect and adolescent substance use. In Kassel, J. D. (Ed.), Substance abuse and emotion (pp. 109135). American Psychological Association. https://doi.org/10.1037/12067-005CrossRefGoogle Scholar
Cunningham, J. A., & McCambridge, J. (2012). Is alcohol dependence best viewed as a chronic relapsing disorder? Addiction, 107(1), 612. https://doi.org/10.1111/j.1360-0443.2011.03583.xGoogle Scholar
Curran, P. J., & Bauer, D. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review Of Psychology, 62, 583619. https://doi.org/10.1146/annurev.psych.093008.100356Google Scholar
Danvers, A. F., Sbarra, D. A., & Mehl, M. R. (2020). Understanding personality through patterns of daily socializing: Applying recurrence quantification analysis to naturalistically observed intensive longitudinal social interaction data. European Journal of Personality, 34, 777793. https://doi.org/10.1002/per.2282CrossRefGoogle Scholar
De Vries, L. P., Baselmans, B. M. L., & Bartels, M. (2020). Smartphone-based ecological momentary assessment of well-being: A systematic review and recommendations for future studies. Journal of Happiness Studies, 22, 23612408. https://doi.org/10.1007/s10902–020-00324-7Google Scholar
Dennis, M., Scott, C. K., & Funk, R. (2003). An experimental evaluation of recovery management check-ups (RMC) for people with chronic substance use disorders. Evaluation and Program Planning, 26(3), 339352. https://doi.org/10.1016/S0149–7189(03)00037-5Google Scholar
Ebner-Priemer, U. W., & Trull, T. J. (2009). Ecological momentary assessment of mood disorders and mood dysregulation. Psychological Assessment, 21(4), 463475. https://doi.org/10.1037/a0017075.CrossRefGoogle ScholarPubMed
Eckhoff, R. P., Kizakevich, P. N., Bakalov, V., Zhang, Y., Bryant, S. P., & Hobbs, M. A. (2015). A platform to build mobile health apps: The personal health intervention toolkit (PHIT). JMIR MHealth and UHealth, 3(2), e4202. https://doi.org/10.2196/mhealth.4202CrossRefGoogle ScholarPubMed
Ekholm, O. (2004). Influence of the recall period on self-reported alcohol intake. European Journal of Clinical Nutrition, 58, 6063. https://doi.org/10.1038/sj.ejcn.1601746Google Scholar
Emery, N. N., & Simons, J. S. (2020). The role of affect, emotion management, and attentional bias in young adult drinking: An experience sampling study. Psychopharmacology, 237(5), 15571575. https://doi.org/10.1007/s00213–020-05480-5Google Scholar
Gmel, G., & Daeppen, J.-B. (2007). Recall bias for seven-day recall measurement of alcohol consumption among emergency department patients: Implications for case-crossover designs. Journal of Studies on Alcohol and Drugs, 68(2), 303310. https://doi.org/10.15288/jsad.2007.68.303CrossRefGoogle ScholarPubMed
Gustafson, D. H., McTavish, F. M., Chih, M. Y., Atwood, A. K., Johnson, R. A., Boyle, M. G., Levy, M. S., Driscoll, H., Chisholm, S. M., Dillenburg, L., Isham, A., & Shah, D. (2014). A smartphone application to support recovery from alcoholism: A randomized clinical trial. JAMA Psychiatry, 71(5), 566572. https://doi.org/10.1001/jamapsychiatry.2013.4642CrossRefGoogle ScholarPubMed
Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Multivariate Behavioral Research, 53(6), 820841. https://doi.org/10.1080/00273171.2018.1446819CrossRefGoogle ScholarPubMed
Heron, K. E., & Smyth, J. M. (2010). Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behavior treatments. British Journal of Health Psychology, 15(1), 139. https://doi.org/10.1348/135910709X466063Google Scholar
Kim, J., Marcusson-Clavertz, D., Yoshiuchi, K., & Smyth, J. M. (2019). Potential benefits of integrating ecological momentary assessment data into mHealth care systems. BioPsychoSocial Medicine, 13(1), 19. https://doi.org/10.1186/s13030–019-0160-5Google Scholar
Kizakevich, P. N., Eckhoff, R., Brown, J., Tueller, S. J., Weimer, B., Bell, S., Weeks, A., Hourani, L. L., Spira, J. L., & King, L. A. (2018). PHIT for Duty, a mobile application for stress reduction, sleep improvement, and alcohol moderation. Military Medicine, 183, 353363. https://doi.org/10.1093/milmed/usx157CrossRefGoogle Scholar
Kruse, C. S., Lee, K., Watson, J. B., Lobo, L. G., Stoppelmoor, A. G., & Oyibo, S. E. (2020). Measures of effectiveness, efficiency, and quality of telemedicine in the management of alcohol abuse, addiction, and rehabilitation: Systematic review. Journal of Medical Internet Research, 22(1), 18. https://doi.org/10.2196/13252CrossRefGoogle ScholarPubMed
Lauckner, C., Taylor, E., Patel, D., & Whitmire, A. (2019). The feasibility of using smartphones and mobile breathalyzers to monitor alcohol consumption among people living with HIV/AIDS. Addiction Science & Clinical Practice, 14(1), 43. https://doi.org/10.1186/s13722–019-0174-0CrossRefGoogle ScholarPubMed
Li, R., Dziak, J. J., Tan, X., Huang, L., Wagner, A. T., & Yang, J. (2017). TVEM (time-varying effect model) SAS macro users’ guide (Version 3.1.1). The Methodology Center, Pennsylvania State University. Retrieved from http://methodology.psu.eduGoogle Scholar
Litvin, E. B., Abrantes, A. M., & Brown, R. A. (2013). Computer and mobile technology-based interventions for substance use disorders: An organizing framework. Addictive Behaviors, 38(3), 17471756. https://doi.org/10.1016/j.addbeh.2012.09.003Google Scholar
Lord, S., Moore, S. K., Ramsey, A., Dinauer, S., & Johnson, K. (2016). Implementation of a substance use recovery support mobile phone app in community settings: Qualitative study of clinician and staff perspectives of facilitators and barriers. JMIR Mental Health, 3(2), e24. https://doi.org/10.2196/mental.4927Google Scholar
Luczak, S. E., & Rosen, I. G. (2014). Estimating BrAC from transdermal alcohol concentration data using the BrAC estimator software program. Alcoholism: Clinical and Experimental Research, 38(8), 22432252. http://dx.doi.org/10.1111/acer.12478CrossRefGoogle ScholarPubMed
Marques, P. R., & McKnight, A. S. (2009). Field and laboratory alcohol detection with two types of transdermal devices. Alcoholism: Clinical and Experimental Research, 33(4), 703711. https://doi.org/10.1111/j.1530-0277.2008.00887.xCrossRefGoogle ScholarPubMed
McKnight, A. S., Fell, J. C., & Auld-Owens, A. (2012). Transdermal alcohol monitoring: Case studies (Report No. DOT HS 811 603). US Department of Transportation, National Highway Traffic Safety Administration. www.adtsea.org/webfiles/fnitools/documents/nhtsa-transdermal-alcohol-monitoring.pdfGoogle Scholar
Miller, W. R. (2016). Sacred cows and greener pastures: Reflections from 40 years in addiction research. Alcoholism Treatment Quarterly, 34(1), 92115. https://doi.org/10.1080/07347324.2015.1077637Google Scholar
Miranda, R., Monti, P. M., Ray, L., Treloar, H. R., Reynolds, E. K., Ramirez, J., Chun, T., Gwaltney, C. J., Justus, A., Tidey, J., Blanchard, A., & Magill, M. (2014a). Characterizing subjective responses to alcohol among adolescent problem drinkers. Journal of Abnormal Psychology, 123(1), 117129. https://doi.org/10.1037/a0035328Google Scholar
Miranda, R., Ray, L., Blanchard, A., Reynolds, E. K., Monti, P. M., Chun, T., Justus, A., Swift, R. M., Tidey, J., Gwaltney, C. J., & Ramirez, J. (2014b). Effects of naltrexone on adolescent alcohol cue reactivity and sensitivity: An initial randomized trial. Addiction Biology, 19(5), 941954. https://doi.org/10.1111/adb.12050CrossRefGoogle ScholarPubMed
Moody, L. N., Tegge, A. N., Poe, L. M., Koffarnus, M. N., & Bickel, W. K. (2018). To drink or to drink less? Distinguishing between effects of implementation intentions on decisions to drink and how much to drink in treatment-seeking individuals with alcohol use disorder. Addictive Behaviors, 83, 6471. https://doi.org/10.1016/j.addbeh.2017.11.010Google Scholar
Morgenstern, J., Kuerbis, A., & Muench, F. (2014). Ecological momentary assessment and alcohol use disorder treatment. Alcohol Research: Current Reviews, 36(1), 101110. https://arcr.niaaa.nih.gov/alcohol-research-and-ehealth-technology/ecological-momentary-assessment-and-alcohol-use-disorderGoogle ScholarPubMed
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 52(6), 446462. https://doi.org/10.1007/s12160–016-9830-8Google Scholar
National Institute on Alcohol Abuse and Alcoholism (2020). NIAAA recovery research definitions. www.niaaa.nih.gov/research/niaaa-recovery-from-alcohol-use-disorder/definitionsGoogle Scholar
Neale, J., Vitoratou, S., Finch, E., Lennon, P., Mitcheson, L., Panebianco, D., Rose, D., Strang, J., Wykes, T., & Marsden, J. (2016). Development and validation of “SURE”: A patient reported outcome measure (PROM) for recovery from drug and alcohol dependence. Drug and Alcohol Dependence, 165, 159167. https://doi.org/10.1016/j.drugalcdep.2016.06.006Google Scholar
O’Brien, C. P. (1994). Treatment of alcoholism as a chronic disorder. Alcohol, 11(6), 433437. https://doi.org/10.1016/0741-8329(94)90063-9Google Scholar
Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2016). Bayesian data analysis with the bivariate hierarchical Ornstein-Uhlenbeck process model. Multivariate Behavioral Research, 51(1), 106119. https://doi.org/10.1080/00273171.2015.1110512Google Scholar
Pew Research Center (2019). Mobile fact sheet. www.pewresearch.org/internet/fact-sheet/mobile/Google Scholar
Ramsey, A., Lord, S., Torrey, J., Marsch, L., & Lardiere, M. (2016). Paving the way to successful implementation: Identifying key barriers to use of technology-based therapeutic tools for behavioral health care. Journal of Behavioral Health Services & Research, 43(1), 5470. https://doi.org/10.1007/s11414–014-9436-5Google Scholar
Roos, C. R., Kober, H., Trull, T. J., MacLean, R. R., & Mun, C. J. (2020). Intensive longitudinal methods for studying the role of self-regulation strategies in substance use behavior change. Current Addiction Reports, 7(3), 301316. https://doi.org/10.1007/s40429–020-00329-5Google Scholar
Sakai, J. T., Mikulich‐Gilbertson, S. K., Long, R. J., & Crowley, T. J. (2006). Validity of transdermal alcohol monitoring: Fixed and self‐regulated dosing. Alcoholism: Clinical and Experimental Research, 30(1), 2633. https://doi.org/10.1111/j.1530-0277.2006.00004.xGoogle Scholar
Scott, C. K., & Dennis, M. L. (2009). Results from two randomized clinical trials evaluating the impact of quarterly recovery management check-ups with adult chronic substance users. Addiction, 104(6), 959971. https://doi.org/10.1111/j.1360-0443.2009.02525.xGoogle Scholar
Serre, F., Fatseas, M., Swendsen, J., & Auriacombe, M. (2015). Ecological momentary assessment in the investigation of craving and substance use in daily life: A systematic review. Drug and Alcohol Dependence, 148, 120. https://doi.org/10.1016/j.drugalcdep.2014.12.024CrossRefGoogle ScholarPubMed
Shiffman, S. (2009). Ecological momentary assessment (EMA) in studies of substance use. Psychological Assessment, 21(4), 486497. https://doi.org/10.1037/a0017074Google Scholar
Shiffman, S. (2014). Conceptualizing analyses of ecological momentary assessment data. Nicotine and Tobacco Research, 16(SUPPL2), 7687. https://doi.org/10.1093/ntr/ntt195Google Scholar
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 132. https://doi.org/10.1146/annurev.clinpsy.3.022806.091415Google Scholar
Simons, J. S., Wills, T. A., Emery, N. N., & Marks, R. M. (2015). Quantifying alcohol consumption: Self-report, transdermal assessment, and prediction of dependence symptoms. Addictive Behaviors, 50, 205212. https://doi.org/10.1016/j.addbeh.2015.06.042Google Scholar
Simons, J. S., Wills, T. A., & Neal, D. J. (2014). The many faces of affect: A multilevel model of drinking frequency/quantity and alcohol dependence symptoms among young adults. Journal of Abnormal Psychology, 123(3), 676694. https://doi.org/10.1037/a0036926Google Scholar
Stevenson, B. L., Dvorak, R. D., Kramer, M. P., Peterson, R. S., Dunn, M. E., Leary, A. V., & Pinto, D. (2019). Within- and between-person associations from mood to alcohol consequences: The mediating role of enhancement and coping drinking motives. Journal of Abnormal Psychology, 128(8), 813822. https://doi.org/10.1037/abn0000472Google Scholar
Suffoletto, B., Callaway, C., Kristan, J., Kraemer, K., & Clark, D. B. (2012). Text-message-based drinking assessments and brief interventions for young adults discharged from the emergency department. Alcoholism: Clinical and Experimental Research, 36(3), 552560. https://doi.org/10.1111/j.1530-0277.2011.01646.xGoogle Scholar
Suffoletto, B., Dasgupta, P., Rayuymatiao, B. S., Huber, J., & Flickinger, K. (2020). A preliminary study using smartphone accelerometers to sense gait impairments due to alcohol intoxication. Journal of Studies on Alcohol and Drugs, 81, 505510. https://doi.org/10.15288/jsad.2020.81.505CrossRefGoogle ScholarPubMed
Suffoletto, B., Kristan, J., Callaway, C., Kim, K. H., Chung, T., Monti, P. M., & Clark, D. B. (2014). A text message alcohol intervention for young adult emergency department patients: A randomized clinical trial. Annals of Emergency Medicine, 64(6), 664672. e4. https://doi.org/10.1016/j.annemergmed.2014.06.010CrossRefGoogle ScholarPubMed
Swift, R. (2000). Transdermal alcohol measurement for estimation of blood alcohol concentration. Alcoholism: Clinical and Experimental Research, 24(4), 422423. https://doi.org/10.1111/j.1530-0277.2000.tb02006.xGoogle Scholar
Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151176. https://doi.org/10.1146/annurev-clinpsy-050212-185510Google Scholar
Trull, T. J., & Ebner-Priemer, U. (2014). The role of ambulatory assessment in psychological science. Current Directions in Psychological Science, 23(6), 466470. https://doi.org/10.1177/0963721414550706Google Scholar
Tucker, J. A., Foushee, H. R., Black, B. C., & Roth, D. L. (2007). Agreement between prospective interactive voice response self-monitoring and structured retrospective reports of drinking and contextual variables during natural resolution attempts. Journal of Studies on Alcohol and Drugs, 68(4), 538542. https://doi.org/10.15288/jsad.2007.68.538Google Scholar
Tugade, M. M., Conner, T., & Barrett, L. F. (2007). Assessment of mood. In Ayers, S., Baum, A., McManus, C., Newman, S., Wallston, K., Weinman, J., & West, R. (Eds.), The Cambridge handbook of psychology, health, and medicine (2nd ed.), pp. 278286. Cambridge University Press.Google Scholar
Wang, L., & Miller, L. C. (2020). Just-in-the-moment adaptive interventions (JITAI): A meta-analytical review. Health Communication, 35(12), 15311544. https://doi.org/10.1080/10410236.2019.1652388Google Scholar
Witkiewitz, K., Desai, S. A., Bowen, S., Leigh, B. C., Kirouac, M., & Larimer, M. E. (2014). Development and evaluation of a mobile intervention for heavy drinking and smoking among college students. Psychology of Addictive Behaviors, 28(3), 639650. https://doi.org/10.1037/a0034747Google Scholar
Witkiewitz, K., Montes, K. S., Schwebel, F. J., & Tucker, J. A. (2020). What is recovery? Alcohol Research: Current Reviews40(3), 112. https://doi.org/10.35946/arcr.v40.3.01Google Scholar
Wray, T. B., Merrill, J. E., & Monti, P. M. (2014). Using ecological momentary assessment (EMA) to assess situation-level predictors of alcohol use and alcohol-related consequences. Alcohol Research: Current Reviews, 36(1), 1928. https://arcr.niaaa.nih.gov/alcohol-research-and-ehealth-technology/using-ecological-momentary-assessment-ema-assess-situationGoogle Scholar

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