NIATx-TI versus typical product training on e-health technology implementation: a clustered randomized controlled trial study protocol

Veronica M White, Todd Molfenter, David H Gustafson, Julie Horst, Rachelle Greller, David H Gustafson Jr, Jee-Seon Kim, Eric Preuss, Olivia Cody, Praan Pisitthakarm, Alexander Toy, Veronica M White, Todd Molfenter, David H Gustafson, Julie Horst, Rachelle Greller, David H Gustafson Jr, Jee-Seon Kim, Eric Preuss, Olivia Cody, Praan Pisitthakarm, Alexander Toy

Abstract

Background: Substance use disorders (SUDs) lead to tens-of-thousands of overdose deaths and other forms of preventable deaths in the USA each year. This results in over $500 billion per year in societal and economic costs as well as a considerable amount of grief for loved ones of affected individuals. Despite these health and societal consequences, only a small percentage of people seek treatment for SUDs, and the majority of those that seek help fail to achieve long-term sobriety. E-health applications in healthcare have proven to be effective at sustaining treatment and reaching patients traditional treatment pathways would have missed. However, e-health adoption and sustainment rates in healthcare are poor, especially in the SUD treatment sector. Implementation engineering can address this gap in the e-health field by augmenting existing implementation models, which explain organizational and individual e-health behaviors retrospectively, with prospective resources that can guide implementation.

Methods: This cluster randomized control trial is designed to test two implementation strategies at adopting an evidence-based mobile e-health technology for SUD treatment. The proposed e-health implementation model is the Network for the Improvement of Addiction Treatment-Technology Implementation (NIATx-TI) Framework. This project, based in Iowa, will compare a control condition (using a typical software product training approach that includes in-person staff training followed by access to on-line support) to software implementation utilizing NIATx-TI, which includes change management training, followed by coaching on how to implement and use the mobile application. While e-health spans many modalities and health disciplines, this project will focus on implementing the Addiction Comprehensive Health Enhancement Support System (A-CHESS), an evidence-based SUD treatment recovery app framework. This trial will be conducted in Iowa at 46 organizational sites within 12 SUD treatment agencies. The control arm consists of 23 individual treatment sites based at five organizations, and the intervention arm consists of 23 individual SUD treatment sites based at seven organizations DISCUSSION: This study addresses an issue of substantial public health significance: enhancing the uptake of the growing inventory of patient-centered evidence-based addiction treatment e-health technologies.

Trial registration: ClinicalTrials.gov , NCT03954184 . Posted 17 May 2019.

Keywords: Coaching; Evidence-based practice implementation; Mobile technology; Substance use disorder treatment; Technology implementation model.

Conflict of interest statement

Todd Molfenter is a faculty member at CHESS. In addition to his academic affiliation, Dr. Molfenter has a less than .1% ownership with CHESS Health, the organization responsible for making the A-CHESS addiction recovery app commercially available to the public. Dr. Molfenter has worked extensively with his institution to manage any conflicts of interest. An external advisory board approved all survey instruments applied, and the individuals who will conduct the data collection and interpretation for this study will have no affiliation with CHESS Health. Also, parts of the NIATx organizational change model used in part of this trial were developed by the Center for Health Enhancement System Studies (CHESS) at the University of Wisconsin–Madison, where Dr. Molfenter is a faculty member. Dr. Molfenter is also affiliated with the NIATx Foundation, the organization responsible for making the NIATx organizational change model available to the public. For this scenario, Dr. Molfenter also has an institutionally approved plan to manage potential conflicts of interest. The individuals who will conduct the data collection and interpretation for this study manuscript will have no affiliation with the NIATx Foundation.

David Gustafson is a part-owner of CHESS Health, devoted to marketing information technologies to agencies that deliver addiction treatment. He is also on the board of directors of the not-for-profit NIATx Foundation, as well as a small consulting company doing business as David H. Gustafson and Associates. These relationships do not carry with them any restrictions on publication, and any associated intellectual property will be disclosed and processed according to his institution’s policies.

Figures

Fig. 1
Fig. 1
Consort diagram: RISE-Iowa study recruitment
Fig. 2
Fig. 2
Product training and NIATx-TI framework overview

References

    1. Stahre M, Roeber J, Kanny D, Brewer RD, Zhang X. Contribution of excessive alcohol consumption to deaths and years of potential life lost in the United States. Prev Chronic Dis. 2014;11:E109. doi: 10.5888/pcd11.130293.
    1. National Drug Intelligence Center (NDIC) The economic impact of illicit drug use on American society. Washington, DC: United States Department of Justice; 2011.
    1. Hedegaard H, Miniño AM, Warner M. Drug overdose deaths in the United States, 1999–2017. NCHS data brief no. 329. National Center for Health Statistics: Hyattsville, MD; 2018.
    1. Council of Economic Advisers (CEA). The underestimated cost of the opioid crisis. 2017. Available from: .
    1. Center for Behavioral Health Statistics and Quality. Behavioral health trends in the United States: results from the 2014 National Survey on Drug Use and Health. HHS Publication No. SMA 15-4927, NSDUH Series H-50. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2015. Available from: .
    1. Miller WR, Walters ST, Bennett ME. How effective is alcoholism treatment in the United States? J Stud Alcohol. 2001;62(2):211–220. doi: 10.15288/jsa.2001.62.211.
    1. Budney AJ, Marsch LA, Bickel WK. Computerized therapies: towards an addiction treatment technology test. In: El-Guebaly N, Carra G, Galanter M, editors. Textbook of addiction treatment: international perspectives. Berlin: Springer-Verlag; 2015. pp. 987–1006.
    1. Pew Research Center. Smartphone ownership is growing rapidly around the world, but not always equally. 2019, Feb 5. Available from: .
    1. Lindhiem O, Bennett CB, Rosen D, Silk J. Mobile technology boosts the effectiveness of psychotherapy and behavioral interventions: a meta-analysis. Behav Modif. 2015;39(6):785–804. doi: 10.1177/0145445515595198.
    1. Ramsey A, Gerke D, Proctor E. Implementation of technology-based alcohol interventions in primary care: a systematic review. Addiction Health Services Researchers Conference; Marina Del Ray, CA; 2015.
    1. Firth J, Torous J, Nicholas J, Carney R, Rosenbaum S, Sarris J. Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. J Affect Disord. 2017;218:15–22. doi: 10.1016/j.jad.2017.04.046.
    1. Elbert NJ, van Os-Medendorp H, van Renselaar W, Ekeland AG, Hakkaart-van Roijen L, Raat H, et al. Effectiveness and cost-effectiveness of ehealth interventions in somatic diseases: a systematic review of systematic reviews and meta-analyses. J Med Internet Res. 2014;16(4):e110. doi: 10.2196/jmir.2790.
    1. Ho C, Severn M. E-therapy interventions for the treatments of substance use disorders and other addictions: a review of clinical effectiveness [internet] Canadian Agency for Drugs and Technology in Health: Ottawa, Canada; 2018.
    1. Kaner EFS, 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 of Systematic Reviews [Internet]. 2017;(9). Available from: .
    1. Miller EA. Solving the disjuncture between research and practice: telehealth trends in the 21st century. Health Policy (New York) 2007;82(2):133–141. doi: 10.1016/j.healthpol.2006.09.011.
    1. Zanaboni P, Lettieri E. Institutionalizing telemedicine applications: the challenge of legitimizing decision-making. J Med Internet Res. 2011;13(3).
    1. Hebert MA, Korabek B, Scott RE. Moving research into practice: a decision framework for integrating home telehealth into chronic illness care. Int J Med Inform. 2006;75(12):786–794. doi: 10.1016/j.ijmedinf.2006.05.041.
    1. Hester RK, Delaney HD, Campbell W, Handmaker N. A web application for moderation training: initial results of a randomized clinical trial. J Subst Abus Treat. 2009;37(3):266–276. doi: 10.1016/j.jsat.2009.03.001.
    1. Squires DD, Hester RK. Using technical innovations in clinical practice: the Drinker’s check-up software program. J Clin Psychol. 2004;60(2):159–169. doi: 10.1002/jclp.10242.
    1. Marsch LA, Guarino H, Acosta M, Aponte-Melendez Y, Cleland C, Grabinski M, et al. Web-based behavioral treatment for substance use disorders as a partial replacement of standard methadone maintenance treatment. J Subst Abus Treat. 2014;46(1):43–51. doi: 10.1016/j.jsat.2013.08.012.
    1. Carroll K, Ball S, Martino S, Nich C, Babuscio T, Nuro K, et al. Computer-assisted delivery of cognitive-behavioral therapy for addiction: a randomized trial of CBT4CBT. Am J Psychiatry. 2008;165(7):881–888. doi: 10.1176/appi.ajp.2008.07111835.
    1. 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):566–572. doi: 10.1001/jamapsychiatry.2013.4642.
    1. Molfenter T, Capoccia VA, Boyle MG, Sherbeck CK. The readiness of addiction treatment agencies for health care reform. Subst Abuse Treat Prev Policy. 2012;7(1):1–8. doi: 10.1186/1747-597X-7-16.
    1. Patterson Silver Wolf DA. A COVID-19 level overreaction is needed for substance use disorder treatment: the future is mobile. Los Angeles, CA: SAGE Publications; 2020.
    1. Ornell F, Moura HF, Scherer JN, Pechansky F, Kessler FHP, von Diemen L. The COVID-19 pandemic and its impact on substance use: implications for prevention and treatment. Psychiatry Res. 2020;289:113096. doi: 10.1016/j.psychres.2020.113096.
    1. Iyengar K, Upadhyaya GK, Vaishya R, Jain V. COVID-19 and applications of smartphone technology in the current pandemic. Diabetes Metab Syndr. 2020;14(5):733–737. doi: 10.1016/j.dsx.2020.05.033.
    1. Venkatesh V, Davis FD, Morris MG. Dead or alive? The development, trajectory and future of technology adoption research. J Assoc Info Sys. 2007;8(4):267.
    1. Oliveira T, Martins MF, editors. Information technology adoption models at firm level: review of literature. European Conference on Information Management and Evaluation. Academic Conferences International Limited; 2010.
    1. Holden RJ, Karsh B-T. The technology acceptance model: its past and its future in health care. J Biomed Inform. 2010;43(1):159–172. doi: 10.1016/j.jbi.2009.07.002.
    1. Novak LL, Holden RJ, Anders SH, Hong JY, Karsh B-T. Using a sociotechnical framework to understand adaptations in health IT implementation. Int J Med Inform. 2013;82(12):e331–ee44. doi: 10.1016/j.ijmedinf.2013.01.009.
    1. Khoja S, Scott RE, Casebeer AL, Mohsin M, Ishaq AFM, Gilani S. E-health readiness assessment tools for healthcare institutions in developing countries. Telemed J E Health. 2007;13(4):425–432. doi: 10.1089/tmj.2006.0064.
    1. Brooks E, Turvey C, Augusterfer EF. Provider barriers to telemental health: obstacles overcome, obstacles remaining. Telemed J E Health. 2013;19(6):433–437. doi: 10.1089/tmj.2013.0068.
    1. Jeyaraj A, Rottman JW, Lacity MC. A review of the predictors, linkages, and biases in IT innovation adoption research. J Info Tech. 2006;21(1):1–23. doi: 10.1057/palgrave.jit.2000056.
    1. van Gemert-Pijnen JE, Nijland N, van Limburg M, Ossebaard HC, Kelders SM, Eysenbach G, et al. A holistic framework to improve the uptake and impact of eHealth technologies. J Med Internet Res. 2011;13(4):e111. doi: 10.2196/jmir.1672.
    1. Kukafka R, Johnson SB, Linfante A, Allegrante JP. Grounding a new information technology implementation framework in behavioral science: a systematic analysis of the literature on IT use. J Biomed Inform. 2003;36(3):218–227. doi: 10.1016/j.jbi.2003.09.002.
    1. Hutzschenreuter T, Kleindienst I. Strategy-process research: what have we learned and what is still to be explored. J Manage. 2006;32(5):673–720.
    1. Porter ME. How competitive forces shape strategy. Harv Bus Rev. 1979;137.
    1. Mintzberg H, Waters JA. Of strategies, deliberate and emergent. Strateg Manage J. 1985;6(3):257–272. doi: 10.1002/smj.4250060306.
    1. Finger S, Dixon JR. A review of research in mechanical engineering design. Part I: descriptive, prescriptive, and computer-based models of design processes. Res Eng Des. 1989;1(1):51–67. doi: 10.1007/BF01580003.
    1. Tsang EW. Organizational learning and the learning organization: a dichotomy between descriptive and prescriptive research. Hum Relat. 1997;50(1):73–89.
    1. Bryson JM. Strategic planning for public and nonprofit organizations: a guide to strengthening and sustaining organizational achievement. Hoboken, NJ: John Wiley & Sons; 2011.
    1. Molfenter TD, Boyle MG, Holloway D, Zwick J. Trends in telemedicine use in addiction treatment. Addict Sci Clin Pract. 2015;10(1):14. doi: 10.1186/s13722-015-0035-4.
    1. 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(8):e201. doi: 10.2196/jmir.3965.
    1. Johnson K, Richards S, Chih MY, Moon TJ, Curtis H, Gustafson DH. A pilot test of a mobile app for drug court participants. Subst Abuse. 2016;10:1–7.
    1. 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):486–492. doi: 10.1080/08897077.2014.970323.
    1. Gustafson DH, Sainfort F, Eichler M, Adams L, Bisognano M, Steudel H. Developing and testing a model to predict outcomes of organizational change. Health Serv Res. 2003;38(2):751–776. doi: 10.1111/1475-6773.00143.
    1. Quanbeck A, Gustafson DH, Marsch LA, Chih M-Y, Kornfield R, McTavish F, et al. Implementing a mobile health system to integrate the treatment of addiction into primary care: a hybrid implementation-effectiveness study. J Med Internet Res. 2018;20(1):e37. doi: 10.2196/jmir.8928.
    1. Muroff J, Robinson W, Chassler D, López LM, Lundgren L, Guauque C, et al. An outcome study of the CASA-CHESS smartphone relapse prevention tool for Latinx Spanish-speakers with substance use disorders. Substance Use Misuse. 2019;54(9):1438–1449. doi: 10.1080/10826084.2019.1585457.
    1. Scott CK, Dennis ML, Gustafson DH. Using ecological momentary assessments to predict relapse after adult substance use treatment. Addict Behav. 2018;82:72–78. doi: 10.1016/j.addbeh.2018.02.025.
    1. Johnston DC, Mathews WD, Maus A, Gustafson DH. Using smartphones to improve treatment retention among impoverished substance-using Appalachian women: a naturalistic study. Subst Abuse. 2019;13:1178221819861377.
    1. James R. McKay, David H. Gustafson, Megan Ivey, Fiona McTavish, Kevin G. Lynch, Klaren Pe-Romashko, et al. Efficacy of telephone and automated smartphone remote continuing care for alcohol use disorder. Manuscript in preparation..
    1. Gustafson DH, Hawkins RP, Boberg EW, McTavish F, Owens B, Wise M, et al. CHESS: 10 years of research and development in consumer health informatics for broad populations, including the underserved. Int J Med Inform. 2002;65(3):169–177. doi: 10.1016/S1386-5056(02)00048-5.
    1. Gustafson DH, McTavish FM, Stengle W, Ballard D, Hawkins R, Shaw BR, et al. Use and impact of eHealth system by low-income women with breast cancer. J Health Commun. 2005;10(S1):195–218. doi: 10.1080/10810730500263257.
    1. Gustafson DH, Hawkins R, McTavish F, Pingree S, Chen WC, Volrathongchai K, et al. Internet-based interactive support for cancer patients: are integrated systems better? J Commun. 2008;58(2):238–257. doi: 10.1111/j.1460-2466.2008.00383.x.
    1. Gustafson DH, Hawkins R, Boberg E, Pingree S, Serlin RE, Graziano F, et al. Impact of a patient-centered, computer-based health information/support system. Am J Prev Med. 1999;16(1):1–9. doi: 10.1016/S0749-3797(98)00108-1.
    1. Patten CA, Croghan IT, Meis TM, Decker PA, Pingree S, Colligan RC, et al. Randomized clinical trial of an internet-based versus brief office intervention for adolescent smoking cessation. Patient Educ Couns. 2006;64(1-3):249–258. doi: 10.1016/j.pec.2006.03.001.
    1. Japuntich SJ, Zehner ME, Smith SS, Jorenby DE, Valdez JA, Fiore MC, et al. Smoking cessation via the internet: a randomized clinical trial of an internet intervention as adjuvant treatment in a smoking cessation intervention. Nicotine Tob Res. 2006;8(Suppl 1):S59–S67. doi: 10.1080/14622200601047900.
    1. Gustafson DH, McTavish FM, Schubert CJ, Johnson RA. The effect of a computer-based intervention on adult children of alcoholics. J Addict Med. 2012;6(1):24–28. doi: 10.1097/ADM.0b013e31822b80ca.
    1. Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68–78. doi: 10.1037/0003-066X.55.1.68.
    1. Marlatt GA, George WH. Relapse prevention: introduction and overview of the model. Br J Addict. 1984;79(3):261–273. doi: 10.1111/j.1360-0443.1984.tb00274.x.
    1. Mathews D. Evaluation consultant. Evaluation report: combatting addiction with technology for pregnant Appalachian women using smartphones. Hazard, KY: Kentucky River Community Care, Inc.; 2014.
    1. Chih MY, Patton T, McTavish FM, Isham AJ, Judkins-Fisher CL, Atwood AK, et al. Predictive modeling of addiction lapses in a mobile health application. J Subst Abus Treat. 2014;46(1):29–35. doi: 10.1016/j.jsat.2013.08.004.
    1. Zandieh SO, Yoon-Flannery K, Kuperman GJ, Langsam DJ, Hyman D, Kaushal R. Challenges to EHR implementation in electronic- versus paper-based office practices. J Gen Intern Med. 2008;23(6):755–761. doi: 10.1007/s11606-008-0573-5.
    1. Terry AL, Thorpe CF, Giles G, Brown JB, Harris SB, Reid GJ, et al. Implementing electronic health records: key factors in primary care. Can Fam Physician. 2008;54(5):730–736.
    1. Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999;89(9):1322–1327. doi: 10.2105/AJPH.89.9.1322.
    1. Klein KJ, Conn AB, Sorra JS. Implementing computerized technology: an organizational analysis. J Appl Psychol. 2001;86(5):811–824. doi: 10.1037/0021-9010.86.5.811.
    1. Rosenthal R, Rubin DB. A simple, general purpose display of magnitude of experimental effect. J Educ Psychol. 1982;74(2):166. doi: 10.1037/0022-0663.74.2.166.
    1. Hsieh FY, Lavori PW, Cohen HJ, Feussner JR. An overview of variance inflation factors for sample-size calculation. Eval Health Prof. 2003;26(3):239–257. doi: 10.1177/0163278703255230.
    1. Simpson DD, Joe GW, Broome KM, Hiller ML, Knight K, Rowan-Szal GA. Program diversity and treatment retention rates in the drug abuse treatment outcome study (DATOS) Psychol Addict Behav. 1997;11(4):279. doi: 10.1037/0893-164X.11.4.279.
    1. Hubbard RL, Craddock SG, Anderson J. Overview of 5-year followup outcomes in the drug abuse treatment outcome studies (DATOS) J Subst Abus Treat. 2003;25(3):125–134. doi: 10.1016/S0740-5472(03)00130-2.
    1. Knight DK, Broome KM, Simpson DD, Flynn PM. Program structure and counselor-client contact in outpatient substance abuse treatment. Health Serv Res. 2008;43(2):616–634. doi: 10.1111/j.1475-6773.2007.00778.x.
    1. Substance Abuse and Mental Health Services Administration (SAMHSA). The N-SSATS report: trends in the use of methadone and buprenorphine at substance abuse treatment facilities: 2003 to 2011. Rockville, MD: Center for Behavioral Health Statistics and Quality. p. 2013.
    1. Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality . National admissions to substance abuse treatment services. BHSIS series S-71, HHS publication no. (SMA) 14-4850. Rockville, MD: Substance Abuse Mental Health Services Administration; 2014. Treatment episode data set (TEDS): 2002-2012.
    1. Kim JS, Anderson CJ, Keller B. Multilevel analysis of assessment data. In: Rutkowski L, von Davier M, Rutkowski D, editors. Handbook of international large-scale assessment: background, technical issues, and methods of data analysis. Boca Raton, FL: CRC Press; 2013. pp. 389–424.
    1. Singer JD, Willett JB. Applied longitudinal data analysis: modeling change and event occurrence. New York, NY: Oxford University Press; 2003.
    1. MacKinnon DP. Introduction to statistical mediation analysis. New York, NY: Lawrence Erlbaum Associates; 2008.
    1. Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychol Methods. 2010;15(4):309–334. doi: 10.1037/a0020761.
    1. Tingley D, Yamamoto T, Hirose K, Keele L, Imai K. Mediation: R package for causal mediation analysis. J Stat Softw. 2014;59(5):1–39. doi: 10.18637/jss.v059.i05.
    1. Damschroder LJ, Lowery JC. Evaluation of a large-scale weight management program using the consolidated framework for implementation research (CFIR) Implement Sci. 2013;8:51. doi: 10.1186/1748-5908-8-51.
    1. Lipari RN, Park-Lee E, Van Horn S. America’s need for and receipt of substance use treatment in 2015. The CBHSQ report. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2016.
    1. Lipari RN, Van Horn SL. Trends in substance use disorders among adults aged 18 or older. The CBHSQ report. Substance Abuse and Mental Health Services Administration: Rockville, MD; 2017.
    1. Rogers EM. Diffusion of innovations. New York: Simon and Schuster; 2010.
    1. Damschroder LJ, Hagedorn HJ. A guiding framework and approach for implementation research in substance use disorders treatment. Psychol Addict Behav. 2011;25(2):194. doi: 10.1037/a0022284.
    1. Van de Ven AH. Problem solving, planning, and innovation. Part I. test of the program planning model. Hum Relat. 1980;33(10):711–740. doi: 10.1177/001872678003301003.

Source: PubMed

Sponsors et collaborateurs

Les conditions médicales

Interventions en matière de drogue

3
S'abonner