Conversational Agents in the Treatment of Mental Health Problems: Mixed-Method Systematic Review

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Abstract

Background

The use of conversational agent interventions (including chatbots and robots) in mental health is growing at a fast pace. Recent existing reviews have focused exclusively on a subset of embodied conversational agent interventions despite other modalities aiming to achieve the common goal of improved mental health.

Objective

This study aimed to review the use of conversational agent interventions in the treatment of mental health problems.

Methods

We performed a systematic search using relevant databases (MEDLINE, EMBASE, PsycINFO, Web of Science, and Cochrane library). Studies that reported on an autonomous conversational agent that simulated conversation and reported on a mental health outcome were included.

Results

A total of 13 studies were included in the review. Among them, 4 full-scale randomized controlled trials (RCTs) were included. The rest were feasibility, pilot RCTs and quasi-experimental studies. Interventions were diverse in design and targeted a range of mental health problems using a wide variety of therapeutic orientations. All included studies reported reductions in psychological distress postintervention. Furthermore, 5 controlled studies demonstrated significant reductions in psychological distress compared with inactive control groups. In addition, 3 controlled studies comparing interventions with active control groups failed to demonstrate superior effects. Broader utility in promoting well-being in nonclinical populations was unclear.

Conclusions

The efficacy and acceptability of conversational agent interventions for mental health problems are promising. However, a more robust experimental design is required to demonstrate efficacy and efficiency. A focus on streamlining interventions, demonstrating equivalence to other treatment modalities, and elucidating mechanisms of action has the potential to increase acceptance by users and clinicians and maximize reach.

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Most cited references 25

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Who Sees Human? The Stability and Importance of Individual Differences in Anthropomorphism.

Adam Waytz, John C. Cacioppo, Nicholas Epley (2010)

Anthropomorphism is a far-reaching phenomenon that incorporates ideas from social psychology, cognitive psychology, developmental psychology, and the neurosciences. Although commonly considered to be a relatively universal phenomenon with only limited importance in modern industrialized societies-more cute than critical-our research suggests precisely the opposite. In particular, we provide a measure of stable individual differences in anthropomorphism that predicts three important consequences for everyday life. This research demonstrates that individual differences in anthropomorphism predict the degree of moral care and concern afforded to an agent, the amount of responsibility and trust placed on an agent, and the extent to which an agent serves as a source of social influence on the self. These consequences have implications for disciplines outside of psychology including human-computer interaction, business (marketing and finance), and law. Concluding discussion addresses how understanding anthropomorphism not only informs the burgeoning study of nonpersons, but how it informs classic issues underlying person perception as well.

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Establishing and maintaining long-term human-computer relationships

Rosalind Picard, Timothy W Bickmore (2005)

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An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study

Becky Inkster, Shubhankar Sarda, Vinod Subramanian (2018)

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

Hannah Gaffney:

ORCID: https://orcid.org/0000-0001-9561-2488

School of Health Sciences, Faculty of Biology, Medicine and HealthUniversity of Manchester2nd FloorZochonis BuildingManchester, M13 9PLUnited Kingdom44 161 306 0400hannah.gaffney-2@postgrad.manchester.ac.uk

Journal

Journal ID (nlm-ta): JMIR Ment Health

Journal ID (iso-abbrev): JMIR Ment Health

Journal ID (publisher-id): JMH

Title: JMIR Mental Health

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2368-7959

Publication date Collection: September 2019

Publication date (Electronic): 18 October 2019

Volume: 6

Issue: 10

Electronic Location Identifier: e14166

Affiliations

[1 ] Division of Psychology & Mental Health School of Health Sciences, Faculty of Biology, Medicine and Health University of Manchester Manchester United Kingdom

Author notes

Corresponding Author: Hannah Gaffney hannah.gaffney-2@ 123456postgrad.manchester.ac.uk

Author information

Hannah Gaffney https://orcid.org/0000-0001-9561-2488

Warren Mansell https://orcid.org/0000-0002-5697-1784

Sara Tai https://orcid.org/0000-0002-8316-5796

Article

Publisher ID: v6i10e14166

DOI: 10.2196/14166

PMC ID: 6914342

PubMed ID: 31628789

SO-VID: 41fd6586-a330-424f-8b76-0ad6e915f426

License:

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 Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 27 March 2019

Date revision requested : 7 May 2019

Date revision received : 30 June 2019

Date accepted : 18 July 2019

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