A Therapeutic Relational Agent for Reducing Problematic Substance Use (Woebot): Development and Usability Study

Judith J Prochaska, Erin A Vogel, Amy Chieng, Matthew Kendra, Michael Baiocchi, Sarah Pajarito, Athena Robinson, Judith J Prochaska, Erin A Vogel, Amy Chieng, Matthew Kendra, Michael Baiocchi, Sarah Pajarito, Athena Robinson

Abstract

Background: Misuse of substances is common, can be serious and costly to society, and often goes untreated due to barriers to accessing care. Woebot is a mental health digital solution informed by cognitive behavioral therapy and built upon an artificial intelligence-driven platform to deliver tailored content to users. In a previous 2-week randomized controlled trial, Woebot alleviated depressive symptoms.

Objective: This study aims to adapt Woebot for the treatment of substance use disorders (W-SUDs) and examine its feasibility, acceptability, and preliminary efficacy.

Methods: American adults (aged 18-65 years) who screened positive for substance misuse without major health contraindications were recruited from online sources and flyers and enrolled between March 27 and May 6, 2020. In a single-group pre/postdesign, all participants received W-SUDs for 8 weeks. W-SUDs provided mood, craving, and pain tracking and modules (psychoeducational lessons and psychotherapeutic tools) using elements of dialectical behavior therapy and motivational interviewing. Paired samples t tests and McNemar nonparametric tests were used to examine within-subject changes from pre- to posttreatment on measures of substance use, confidence, cravings, mood, and pain.

Results: The sample (N=101) had a mean age of 36.8 years (SD 10.0), and 75.2% (76/101) of the participants were female, 78.2% (79/101) were non-Hispanic White, and 72.3% (73/101) were employed. Participants' W-SUDs use averaged 15.7 (SD 14.2) days, 12.1 (SD 8.3) modules, and 600.7 (SD 556.5) sent messages. About 94% (562/598) of all completed psychoeducational lessons were rated positively. From treatment start to end, in-app craving ratings were reduced by half (87/101, 86.1% reporting cravings in the app; odds ratio 0.48, 95% CI 0.32-0.73). Posttreatment assessment completion was 50.5% (51/101), with better retention among those who initially screened higher on substance misuse. From pre- to posttreatment, confidence to resist urges to use substances significantly increased (mean score change +16.9, SD 21.4; P<.001), whereas past month substance use occasions (mean change -9.3, SD 14.1; P<.001) and scores on the Alcohol Use Disorders Identification Test-Concise (mean change -1.3, SD 2.6; P<.001), 10-item Drug Abuse Screening Test (mean change -1.2, SD 2.0; P<.001), Patient Health Questionnaire-8 item (mean change 2.1, SD 5.2; P=.005), Generalized Anxiety Disorder-7 (mean change -2.3, SD 4.7; P=.001), and cravings scale (68.6% vs 47.1% moderate to extreme; P=.01) significantly decreased. Most participants would recommend W-SUDs to a friend (39/51, 76%) and reported receiving the service they desired (41/51, 80%). Fewer felt W-SUDs met most or all of their needs (22/51, 43%).

Conclusions: W-SUDs was feasible to deliver, engaging, and acceptable and was associated with significant improvements in substance use, confidence, cravings, depression, and anxiety. Study attrition was high. Future research will evaluate W-SUDs in a randomized controlled trial with a more diverse sample and with the use of greater study retention strategies.

Trial registration: ClinicalTrials.gov NCT04096001; https://ichgcp.net/clinical-trials-registry/NCT04096001.

Keywords: acceptability; addiction; artificial intelligence; chatbot; conversational agent; craving; feasibility; mobile phone; psychoeducation; psychotherapeutic; substance misuse; treatment.

Conflict of interest statement

Conflicts of Interest: AR and SP are employees of Woebot Health. All other authors declare no conflicts of interest related to this study.

©Judith J Prochaska, Erin A Vogel, Amy Chieng, Matthew Kendra, Michael Baiocchi, Sarah Pajarito, Athena Robinson. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 23.03.2021.

Figures

Figure 1
Figure 1
Study consort diagram. CAGE-AID: Cut down, Annoyed, Guilty, Eye Opener-Adapted to Include Drugs; DTs: delirium tremens; EOT: end of treatment; ETOH: ethyl alcohol; HTN: hypertension; MAT: medication-assisted treatment; OD: overdose; Woebot-SUDs: Woebot for the treatment of substance use disorders.
Figure 2
Figure 2
Sample screenshots of the Woebot for substance use disorders app: a psychoeducational lesson called Misinformation, the core conversational panel (featuring the Lesson Misinformation), and psychotherapeutic skills for behavior change and mood tracking.

References

    1. World Health Organization . Global status report on alcohol and health. Geneva: World Health Organization; 2018.
    1. Institute for health metrics and evaluation. Global burden of disease study 2016 results database. 2017. [2021-02-17]. .
    1. Substance Abuse and Mental Health Services Administration Key substance use and mental health indicators in the United States: results from the 2018 National Survey on Drug Use and Health. Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. 2019. [2021-02-17]. .
    1. Grant BF, Stinson FS, Dawson DA, Chou SP, Dufour MC, Compton W, Pickering RP, Kaplan K. Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders: results from the National Epidemiologic Survey on alcohol and related conditions. Arch Gen Psychiatry. 2004 Aug;61(8):807–16. doi: 10.1001/archpsyc.61.8.807.
    1. Keane H. Facing addiction in America: The Surgeon General's Report on Alcohol, Drugs, and Health U.S. Department of Health and Human Services, Office of the Surgeon General Washington, DC, USA: U.S. Department of Health and Human Services, 2016 382 pp. online (grey literature): Drug Alcohol Rev. 2018 Feb;37(2):282–3. doi: 10.1111/dar.12578.
    1. Giroux I, Goulet A, Mercier J, Jacques C, Bouchard S. Online and mobile interventions for problem gambling, alcohol, and drugs: a systematic review. Front Psychol. 2017;8:954. doi: 10.3389/fpsyg.2017.00954. doi: 10.3389/fpsyg.2017.00954.
    1. Boumparis N, Schulte MHJ, Riper H. Digital mental health for alcohol and substance use disorders. Curr Treat Options Psych. 2019 Nov 26;6(4):352–66. doi: 10.1007/s40501-019-00190-y.
    1. Perrin A, Turner E. Smartphones help blacks, Hispanics bridge some - but not all - digital gaps with whites. Pew Research Center. 2019. [2021-02-17].
    1. Mobile fact sheet. Pew Research Center. 2019. [2021-02-17].
    1. Krebs P, Duncan DT. Health app use among US mobile phone owners: a national survey. JMIR Mhealth Uhealth. 2015 Nov 04;3(4):e101. doi: 10.2196/mhealth.4924.
    1. Smith A. U.S. smartphone use in 2015. Pew Research Center. 2015. [2021-02-17].
    1. Vaidyam AN, Wisniewski H, Halamka JD, Kashavan MS, Torous JB. Chatbots and conversational agents in mental health: a review of the psychiatric landscape. Can J Psychiatry. 2019 Jul;64(7):456–64. doi: 10.1177/0706743719828977.
    1. Baumel A, Muench F, Edan S, Kane JM. Objective user engagement with mental health apps: systematic search and panel-based usage analysis. J Med Internet Res. 2019 Sep 25;21(9) doi: 10.2196/14567.
    1. Torous J, Nicholas J, Larsen ME, Firth J, Christensen H. Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements. Evid Based Ment Health. 2018 Aug;21(3):116–9. doi: 10.1136/eb-2018-102891.
    1. Lucas GM, Gratch J, King A, Morency L. It’s only a computer: Virtual humans increase willingness to disclose. Computers in Human Behavior. 2014 Aug;37:94–100. doi: 10.1016/j.chb.2014.04.043. doi: 10.1016/j.chb.2014.04.043.
    1. Cook JE, Doyle C. Working alliance in online therapy as compared to face-to-face therapy: preliminary results. Cyberpsychol Behav. 2002 Apr;5(2):95–105. doi: 10.1089/109493102753770480.
    1. Berry K, Salter A, Morris R, James S, Bucci S. Assessing therapeutic alliance in the context of mHealth interventions for mental health problems: development of the mobile agnew relationship measure (mARM) questionnaire. J Med Internet Res. 2018 Apr 19;20(4):e90. doi: 10.2196/jmir.8252.
    1. Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment Health. 2017 Jun 06;4(2):e19. doi: 10.2196/mental.7785.
    1. Ly KH, Ly AM, Andersson G. A fully automated conversational agent for promoting mental well-being: A pilot RCT using mixed methods. Internet Interv. 2017 Dec;10:39–46. doi: 10.1016/j.invent.2017.10.002.
    1. Tielman ML, Neerincx MA, Bidarra R, Kybartas B, Brinkman W. A therapy system for post-traumatic stress disorder using a virtual agent and virtual storytelling to reconstruct traumatic memories. J Med Syst. 2017 Aug;41(8):125. doi: 10.1007/s10916-017-0771-y.
    1. Gardiner PM, McCue KD, Negash LM, Cheng T, White LF, Yinusa-Nyahkoon L, Jack BW, Bickmore TW. Engaging women with an embodied conversational agent to deliver mindfulness and lifestyle recommendations: a feasibility randomized control trial. Patient Educ Couns. 2017 Sep;100(9):1720–9. doi: 10.1016/j.pec.2017.04.015.
    1. Bickmore TW, Puskar K, Schlenk EA, Pfeifer LM, Sereika SM. Maintaining reality: relational agents for antipsychotic medication adherence. Interact Comput. 2010 Jul;22(4):276–88. doi: 10.1016/j.intcom.2010.02.001.
    1. Bickmore TW, Mitchell SE, Jack BW, Paasche-Orlow MK, Pfeifer LM, Odonnell J. Response to a relational agent by hospital patients with depressive symptoms. Interact Comput. 2010 Jul 01;22(4):289–98. doi: 10.1016/j.intcom.2009.12.001.
    1. Lucas GM, Rizzo A, Gratch J, Scherer S, Stratou G, Boberg J, Morency L. Reporting mental health symptoms: breaking down barriers to care with virtual human interviewers. Front Robot AI. 2017 Oct 12;4 doi: 10.3389/frobt.2017.00051.
    1. Philip P, Micoulaud-Franchi J, Sagaspe P, Sevin ED, Olive J, Bioulac S, Sauteraud A. Virtual human as a new diagnostic tool, a proof of concept study in the field of major depressive disorders. Sci Rep. 2017 Feb 16;7 doi: 10.1038/srep42656. doi: 10.1038/srep42656.
    1. Tielman ML, Neerincx MA, van Meggelen M, Franken I, Brinkman W. How should a virtual agent present psychoeducation? Influence of verbal and textual presentation on adherence. Technol Health Care. 2017 Dec 04;25(6):1081–96. doi: 10.3233/THC-170899.
    1. Gaffney H, Mansell W, Tai S. Conversational agents in the treatment of mental health problems: mixed-method systematic review. JMIR Ment Health. 2019 Oct 18;6(10) doi: 10.2196/14166.
    1. NIAAA council approves definition of binge drinking. NIAAA Newsletter. 2004. [2021-02-17]. .
    1. Miner AS, Shah N, Bullock KD, Arnow BA, Bailenson J, Hancock J. Key considerations for incorporating conversational AI in psychotherapy. Front Psychiatry. 2019 Oct 18;10:746. doi: 10.3389/fpsyt.2019.00746. doi: 10.3389/fpsyt.2019.00746.
    1. Brown RL, Rounds LA. Conjoint screening questionnaires for alcohol and other drug abuse: criterion validity in a primary care practice. Wis Med J. 1995;94(3):135–40.
    1. Williams C, Wilson P, Morrison J, McMahon A, Walker A, Andrew W, Allan L, McConnachie A, McNeill Y, Tansey L. Guided self-help cognitive behavioural therapy for depression in primary care: a randomised controlled trial. PLoS One. 2013 Jan 11;8(1) doi: 10.1371/journal.pone.0052735.
    1. Salomonsson S, Santoft F, Lindsäter E, Ejeby K, Ingvar M, Öst LG, Lekander M, Ljótsson B, Hedman-Lagerlöf E. Predictors of outcome in guided self-help cognitive behavioural therapy for common mental disorders in primary care. Cogn Behav Ther. 2020 Nov 22;49(6):455–74. doi: 10.1080/16506073.2019.1669701.
    1. Cully JA, Teten AL. A therapist's guide to brief cognitive behavioral therapy. Department of Veterans Affairs, South Central Mental Illness Research, Education, and Clinical Center (MIRECC) 2008. [2021-02-17]. .
    1. Barth J, Munder T, Gerger H, Nüesch E, Trelle S, Znoj H, Jüni P, Cuijpers P. Comparative efficacy of seven psychotherapeutic interventions for patients with depression: a network meta-analysis. PLoS Med. 2013;10(5) doi: 10.1371/journal.pmed.1001454.
    1. Appendix E: Glossary. National Institute for Health and Care Excellence; 2011. National Institute for Health and Care Excellence. 2011. [2021-02-17]. .
    1. Miller WR. Are alcoholism treatments effective? The Project MATCH data: response. BMC Public Health. 2005 Jul 18;5(1):76. doi: 10.1186/1471-2458-5-76.
    1. DiClemente CC, Corno CM, Graydon MM, Wiprovnick AE, Knoblach DJ. Motivational interviewing, enhancement, and brief interventions over the last decade: a review of reviews of efficacy and effectiveness. Psychol Addict Behav. 2017 Dec;31(8):862–87. doi: 10.1037/adb0000318.
    1. Brorson HH, Arnevik AE, Rand-Hendriksen K, Duckert F. Drop-out from addiction treatment: a systematic review of risk factors. Clin Psychol Rev. 2013 Dec;33(8):1010–24. doi: 10.1016/j.cpr.2013.07.007.
    1. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Arch Intern Med. 1998 Sep 14;158(16):1789–95. doi: 10.1001/archinte.158.16.1789.
    1. Skinner HA. The drug abuse screening test. Addictive Behaviors. 1982 Jan;7(4):363–71. doi: 10.1016/0306-4603(82)90005-3. doi: 10.1016/0306-4603(82)90005-3.
    1. Breslin FC, Sobell LC, Sobell MB, Agrawal S. A comparison of a brief and long version of the situational confidence questionnaire. Behav Res Ther. 2000 Dec;38(12):1211–20. doi: 10.1016/s0005-7967(99)00152-7.
    1. Kroenke K, Strine TW, Spitzer RL, Williams JBW, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009 Apr;114(1-3):163–73. doi: 10.1016/j.jad.2008.06.026. doi: 10.1016/j.jad.2008.06.026.
    1. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006 May 22;166(10):1092–7. doi: 10.1001/archinte.166.10.1092.
    1. Briesch AM, Chafouleas SM, Neugebauer SR, Riley-Tillman TC. Assessing influences on intervention implementation: revision of the usage rating profile-intervention. J Sch Psychol. 2013 Feb;51(1):81–96. doi: 10.1016/j.jsp.2012.08.006. doi: 10.1016/j.jsp.2012.08.006.
    1. Larsen DL, Attkisson CC, Hargreaves WA, Nguyen TD. Assessment of client/patient satisfaction: development of a general scale. Eval Prog Plan. 1979 Jan;2(3):197–207. doi: 10.1016/0149-7189(79)90094-6. doi: 10.1016/0149-7189(79)90094-6.
    1. Hatcher RL, Gillaspy JA. Development and validation of a revised short version of the working alliance inventory. Psychoth Res. 2006 Jan;16(1):12–25. doi: 10.1080/10503300500352500.
    1. Cliffe B, Croker A, Denne M, Smith J, Stallard P. Digital cognitive behavioral therapy for insomnia for adolescents with mental health problems: feasibility open trial. JMIR Ment Health. 2020 Mar 03;7(3) doi: 10.2196/14842.
    1. O'Brien H. Why Engagement Matters. Switzerland: Springer; 2016. Theoretical perspectives on user engagement; pp. 1–26.
    1. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2017 Jun;7(2):254–67. doi: 10.1007/s13142-016-0453-1.
    1. Holdener M, Gut A, Angerer A. Applicability of the user engagement scale to mobile health: a survey-based quantitative study. JMIR Mhealth Uhealth. 2020 Jan 03;8(1) doi: 10.2196/13244.
    1. Chien I, Enrique A, Palacios J, Regan T, Keegan D, Carter D, Tschiatschek S, Nori A, Thieme A, Richards D, Doherty G, Belgrave D. A machine learning approach to understanding patterns of engagement with internet-delivered mental health interventions. JAMA Netw Open. 2020 Jul 01;3(7) doi: 10.1001/jamanetworkopen.2020.10791.
    1. Safran JD, Muran JC. The resolution of ruptures in the therapeutic alliance. J Consult Clin Psychol. 1996 Jun;64(3):447–58. doi: 10.1037//0022-006x.64.3.447.
    1. Bordin ES. The generalizability of the psychoanalytic concept of the working alliance. Psychotherapy: Theory, Research & Practice. 1979;16(3):252–60. doi: 10.1037/h0085885.
    1. Horvath AO, Symonds BD. Relation between working alliance and outcome in psychotherapy: a meta-analysis. J Counsel Psychol. 1991;38(2):139–49. doi: 10.1037/0022-0167.38.2.139.
    1. Ruwaard J, Schrieken B, Schrijver M, Broeksteeg J, Dekker J, Vermeulen H, Lange A. Standardized web-based cognitive behavioural therapy of mild to moderate depression: a randomized controlled trial with a long-term follow-up. Cogn Behav Ther. 2009 Dec;38(4):206–21. doi: 10.1080/16506070802408086.
    1. Bickmore T, Gruber A, Picard R. Establishing the computer-patient working alliance in automated health behavior change interventions. Patient Educ Couns. 2005 Oct;59(1):21–30. doi: 10.1016/j.pec.2004.09.008. doi: 10.1016/j.pec.2004.09.008.
    1. Pratap A, Neto EC, Snyder P, Stepnowsky C, Elhadad N, Grant D, Mohebbi MH, Mooney S, Suver C, Wilbanks J, Mangravite L, Heagerty PJ, Areán P, Omberg L. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digit Med. 2020 Feb 17;3(1):21. doi: 10.1038/s41746-020-0224-8. doi: 10.1038/s41746-020-0224-8.
    1. Preventing excessive alcohol consumption: electronic Screening and Brief Intervention (e-SBI) Community Preventive Services Task Force. 2012. [2021-02-17]. .
    1. Rebalancing the ‘COVID-19 effect’ on alcohol sales. [2021-02-17].
    1. Laranjo L, Dunn AG, Tong HL, Kocaballi AB, Chen J, Bashir R, Surian D, Gallego B, Magrabi F, Lau AYS, Coiera E. Conversational agents in healthcare: a systematic review. J Am Med Inform Assoc. 2018 Sep 01;25(9):1248–58. doi: 10.1093/jamia/ocy072.

Source: PubMed

Sponsorer og samarbeidspartnere

Medisinsk tilstand

Legemiddelintervensjoner

3
Abonnere