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      Virtual Digital Psychotherapist App–Based Treatment in Patients With Methamphetamine Use Disorder (Echo-APP): Single-Arm Pilot Feasibility and Efficacy Study

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          Abstract

          Background

          Substance use disorder is one of the severe public health problems worldwide. Inequitable resources, discrimination, and physical distances limit patients’ access to medical help. Automated conversational agents have the potential to provide in-home and remote therapy. However, automatic dialogue agents mostly use text and other methods to interact, which affects the interaction experience, treatment immersion, and clinical efficacy.

          Objective

          The aim of this paper is to describe the design and development of Echo-APP, a tablet-based app with the function of a virtual digital psychotherapist, and to conduct a pilot study to explore the feasibility and preliminary efficacy results of Echo-APP for patients with methamphetamine use disorder.

          Methods

          Echo-APP is an assessment and rehabilitation program developed for substance use disorder (SUD) by a team of clinicians, psychotherapists, and computer experts. The program is available for Android tablets. In terms of assessment, the focus is on the core characteristics of SUD, such as mood, impulsivity, treatment motivation, and craving level. In terms of treatment, Echo-APP provides 10 treatment units, involving awareness of addiction, motivation enhancement, emotion regulation, meditation, etc. A total of 47 patients with methamphetamine dependence were eventually enrolled in the pilot study to receive a single session of the Echo-APP–based motivational enhancement treatment. The outcomes were assessed before and after the patients’ treatment, including treatment motivation, craving levels, self-perception on the importance of drug abstinence, and their confidence in stopping the drug use.

          Results

          In the pilot study, scores on the Stages of Change Readiness and Treatment Eagerness Scale and the questionnaire on motivation for abstaining from drugs significantly increased after the Echo-APP–based treatment ( P<.001, Cohen d=–0.60), while craving was reduced ( P=.01, Cohen d=0.38). Patients’ baseline Generalized Anxiety Disorder-7 assessment score ( β=3.57; P<.001; 95% CI 0.80, 2.89) and Barratt Impulsiveness Scale (BIS)–motor impulsiveness score ( β=–2.10; P=.04; 95% CI –0.94, –0.02) were predictive of changes in the patients’ treatment motivation during treatment. Moreover, patients’ baseline Generalized Anxiety Disorder-7 assessment score ( β=–1.607; P=.03; 95% CI –3.08, –0.14), BIS—attentional impulsivity score ( β=–2.43; P=.004; 95% CI –4.03, –0.83), and BIS—nonplanning impulsivity score ( β=2.54; P=.002; 95% CI 0.98, 4.10) were predictive of changes in craving scores during treatment.

          Conclusions

          Echo-APP is a practical, accepted, and promising virtual digital psychotherapist program for patients with methamphetamine dependence. The preliminary findings lay a good foundation for further optimization of the program and the promotion of large-scale randomized controlled clinical studies for SUD.

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          Most cited references38

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          Reliability and validity of the Chinese version of the Patient Health Questionnaire (PHQ-9) in the general population.

          Depression is one of the most common mental illnesses. The reliability and the validity of the Patient Health Questionnaire (PHQ)-9, a depression screening tool, have not been examined in the general population in China. Thus, this study evaluated the reliability and the validity of the Chinese version of the PHQ-9 in detecting major depression in residents of a Chinese community.
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            Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial

            Background Web-based cognitive-behavioral therapeutic (CBT) apps have demonstrated efficacy but are characterized by poor adherence. Conversational agents may offer a convenient, engaging way of getting support at any time. Objective The objective of the study was to determine the feasibility, acceptability, and preliminary efficacy of a fully automated conversational agent to deliver a self-help program for college students who self-identify as having symptoms of anxiety and depression. Methods In an unblinded trial, 70 individuals age 18-28 years were recruited online from a university community social media site and were randomized to receive either 2 weeks (up to 20 sessions) of self-help content derived from CBT principles in a conversational format with a text-based conversational agent (Woebot) (n=34) or were directed to the National Institute of Mental Health ebook, “Depression in College Students,” as an information-only control group (n=36). All participants completed Web-based versions of the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder scale (GAD-7), and the Positive and Negative Affect Scale at baseline and 2-3 weeks later (T2). Results Participants were on average 22.2 years old (SD 2.33), 67% female (47/70), mostly non-Hispanic (93%, 54/58), and Caucasian (79%, 46/58). Participants in the Woebot group engaged with the conversational agent an average of 12.14 (SD 2.23) times over the study period. No significant differences existed between the groups at baseline, and 83% (58/70) of participants provided data at T2 (17% attrition). Intent-to-treat univariate analysis of covariance revealed a significant group difference on depression such that those in the Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F=6.47; P=.01) while those in the information control group did not. In an analysis of completers, participants in both groups significantly reduced anxiety as measured by the GAD-7 (F1,54= 9.24; P=.004). Participants’ comments suggest that process factors were more influential on their acceptability of the program than content factors mirroring traditional therapy. Conclusions Conversational agents appear to be a feasible, engaging, and effective way to deliver CBT.
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              An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study

              Background A World Health Organization 2017 report stated that major depression affects almost 5% of the human population. Major depression is associated with impaired psychosocial functioning and reduced quality of life. Challenges such as shortage of mental health personnel, long waiting times, perceived stigma, and lower government spends pose barriers to the alleviation of mental health problems. Face-to-face psychotherapy alone provides only point-in-time support and cannot scale quickly enough to address this growing global public health challenge. Artificial intelligence (AI)-enabled, empathetic, and evidence-driven conversational mobile app technologies could play an active role in filling this gap by increasing adoption and enabling reach. Although such a technology can help manage these barriers, they should never replace time with a health care professional for more severe mental health problems. However, app technologies could act as a supplementary or intermediate support system. Mobile mental well-being apps need to uphold privacy and foster both short- and long-term positive outcomes. Objective This study aimed to present a preliminary real-world data evaluation of the effectiveness and engagement levels of an AI-enabled, empathetic, text-based conversational mobile mental well-being app, Wysa, on users with self-reported symptoms of depression. Methods In the study, a group of anonymous global users were observed who voluntarily installed the Wysa app, engaged in text-based messaging, and self-reported symptoms of depression using the Patient Health Questionnaire-9. On the basis of the extent of app usage on and between 2 consecutive screening time points, 2 distinct groups of users (high users and low users) emerged. The study used mixed-methods approach to evaluate the impact and engagement levels among these users. The quantitative analysis measured the app impact by comparing the average improvement in symptoms of depression between high and low users. The qualitative analysis measured the app engagement and experience by analyzing in-app user feedback and evaluated the performance of a machine learning classifier to detect user objections during conversations. Results The average mood improvement (ie, difference in pre- and post-self-reported depression scores) between the groups (ie, high vs low users; n=108 and n=21, respectively) revealed that the high users group had significantly higher average improvement (mean 5.84 [SD 6.66]) compared with the low users group (mean 3.52 [SD 6.15]); Mann-Whitney P=.03 and with a moderate effect size of 0.63. Moreover, 67.7% of user-provided feedback responses found the app experience helpful and encouraging. Conclusions The real-world data evaluation findings on the effectiveness and engagement levels of Wysa app on users with self-reported symptoms of depression show promise. However, further work is required to validate these initial findings in much larger samples and across longer periods.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                2023
                31 January 2023
                : 11
                : e40373
                Affiliations
                [1 ] Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Shanghai China
                [2 ] Shanghai Key Laboratory of Psychotic Disorders Shanghai China
                [3 ] Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Chinese Academy of Sciences Shanghai China
                Author notes
                Corresponding Author: Min Zhao drminzhao@ 123456smhc.org.cn
                Author information
                https://orcid.org/0000-0001-6105-8749
                https://orcid.org/0000-0003-3515-0490
                https://orcid.org/0000-0001-8929-4102
                https://orcid.org/0000-0003-4014-1070
                https://orcid.org/0000-0002-5730-2259
                https://orcid.org/0000-0001-8499-2825
                https://orcid.org/0000-0002-1505-3688
                https://orcid.org/0000-0003-0010-4128
                https://orcid.org/0000-0002-9722-8567
                https://orcid.org/0000-0002-4551-043X
                Article
                v11i1e40373
                10.2196/40373
                9929731
                36719727
                2b943a7b-0705-4fc2-a788-835c48719c86
                ©Tianzhen Chen, Liyu Chen, Shuo Li, Jiang Du, Hang Su, Haifeng Jiang, Qianying Wu, Lei Zhang, Jiayi Bao, Min Zhao. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 31.01.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 17 June 2022
                : 8 September 2022
                : 15 November 2022
                : 20 December 2022
                Categories
                Original Paper
                Original Paper

                tablet,android program,substance use disorder,methamphetamine use disorder,digital agent,virtual digital human

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