Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial

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Abstract

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 (F _1,54= 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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Most cited references 19

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Harnessing Context Sensing to Develop a Mobile Intervention for Depression

Michelle Burns, Mark Begale, Jennifer Duffecy … (2011)

Background Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder. Objective The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing. Methods We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients’ mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients’ self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks. Results Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (betaweek = –.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (betaweek = –.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (bweek = –.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (betaweek = –.71, P < .001, per-protocol Cohen d = 2.58). Conclusions Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed. Trial Registration Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n)

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Persistence of mental health problems and needs in a college student population.

Kara Zivin, Ezra Golberstein, Daniel Eisenberg … (2009)

Cross-sectional studies indicate a high prevalence of mental health problems among college students, but there are fewer longitudinal data on these problems and related help-seeking behavior. We conducted a baseline web-based survey of students attending a large public university in fall 2005 and a two-year follow-up survey in fall 2007. We used brief screening instruments to measure symptoms of mental disorders (anxiety, depression, eating disorders), as well as self-injury and suicidal ideation. We estimated the persistence of these mental health problems between the two time points, and determined to what extent students with mental health problems perceived a need for or used mental health services (medication or therapy). We conducted logistic regression analyses examining how baseline predictors were associated with mental health and help-seeking two years later. Over half of students suffered from at least one mental health problem at baseline or follow-up. Among students with at least one mental health problem at baseline, 60% had at least one mental health problem two years later. Among students with a mental health problem at both time points, fewer than half received treatment between those time points. Mental health problems are based on self-report to brief screens, and the sample is from a single university. These findings indicate that mental disorders are prevalent and persistent in a student population. While the majority of students with probable disorders are aware of the need for treatment, most of these students do not receive treatment, even over a two-year period.

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It’s only a computer: Virtual humans increase willingness to disclose

Gale Lucas, Jonathan Gratch, Aisha King … (2014)

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Author and article information

Contributors

Alison Darcy:

Woebot Labs Inc.55 Fair AvenueSan Francisco, CA, 94110United Statesalison@woebot.io

ORCID: http://orcid.org/0000-0002-5082-7685

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: Apr-Jun 2017

Publication date (Electronic): 06 June 2017

Volume: 4

Issue: 2

Electronic Location Identifier: e19

Affiliations

[1] ¹Stanford School of Medicine Department of Psychiatry and Behavioral Sciences Stanford, CAUnited States

[2] ²Woebot Labs Inc. San Francisco, CAUnited States

Author notes

Corresponding Author: Alison Darcy alison@ 123456woebot.io

Author information

Kathleen Kara Fitzpatrick http://orcid.org/0000-0003-4702-4440

Alison Darcy http://orcid.org/0000-0002-5082-7685

Molly Vierhile http://orcid.org/0000-0003-3381-3510

Article

Publisher ID: v4i2e19

DOI: 10.2196/mental.7785

PMC ID: 5478797

PubMed ID: 28588005

SO-VID: 20e4231f-d39c-41ff-9b5b-3669f755df9f

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 : 29 March 2017

Date revision requested : 12 April 2017

Date revision received : 5 May 2017

Date accepted : 22 May 2017

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