JMIR FORMATIVE RESEARCH

Darcy et al

Original Paper

Evidence of Human-Level Bonds Established With a Digital
Conversational Agent: Cross-sectional, Retrospective
Observational Study
Alison Darcy, PhD; Jade Daniels, PGDip; David Salinger, BBA; Paul Wicks, PhD; Athena Robinson, PhD
Woebot Health, San Francisco, CA, United States

Corresponding Author:
Alison Darcy, PhD
Woebot Health
650 Fifth Street
San Francisco, CA,
United States
Phone: 1 851694777
Email: alison@woebothealth.com

Abstract
Background: There are far more patients in mental distress than there is time available for mental health professionals to support
them. Although digital tools may help mitigate this issue, critics have suggested that technological solutions that lack humanempathy will prevent a bond or therapeutic alliance from being formed, thereby narrowing these solutions’ efficacy.
Objective: We aimed to investigate whether users of a cognitive behavioral therapy (CBT)–based conversational agent would
report therapeutic bond levels that are similar to those in literature about other CBT modalities, including face-to-face therapy,
group CBT, and other digital interventions that do not use a conversational agent.
Methods: A cross-sectional, retrospective study design was used to analyze aggregate, deidentified data from adult users who
self-referred to a CBT-based, fully automated conversational agent (Woebot) between November 2019 and August 2020. Working
alliance was measured with the Working Alliance Inventory-Short Revised (WAI-SR), and depression symptom status wasassessed by using the 2-item Patient Health Questionnaire (PHQ-2). All measures were administered by the conversational agent
in the mobile app. WAI-SR scores were compared to those in scientific literature abstracted from recent reviews.
Results: Data from 36,070 Woebot users were included in the analysis. Participants ranged in age from 18 to 78 years, and57.48% (20,734/36,070) of participants reported that they were female. The mean PHQ-2 score was 3.03 (SD 1.79), and 54.67%(19,719/36,070) of users scored over the cutoff score of 3 for depression screening. Within 5 days of initial app use, the mean
WAI-SR score was 3.36 (SD 0.8) and the mean bond subscale score was 3.8 (SD 1.0), which was comparable to those in recent
studies from the literature on traditional, outpatient, individual CBT and group CBT (mean bond subscale scores of 4 and 3.8,
respectively). PHQ-2 scores at baseline weakly correlated with bond scores (r=−0.04; P<.001); however, users with depression
and those without depression had high bond scores of 3.45.
Conclusions: Although bonds are often presumed to be the exclusive domain of human therapeutic relationships, our findings
challenge the notion that digital therapeutics are incapable of establishing a therapeutic bond with users. Future research mightinvestigate the role of bonds as mediators of clinical outcomes, since boosting the engagement and efficacy of digital therapeutics
could have major public health benefits.
(JMIR Form Res 2021;5(5):e27868) doi: 10.2196/27868
KEYWORDS
conversational agents; mobile mental health; chatbots; depression; anxiety; digital health

Introduction
Significant barriers to mental health care are persistent [1]. The
increased burden of depression and anxiety, which arose during
the COVID-19 pandemic, has exacerbated this issue [2], as thehttps://formative.jmir.org/2021/5/e27868

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measures that were put in place to stop the spread of
SARS-CoV-2 have also presented unintended barriers to thoseseeking mental health treatment. One potentially viable solution
is using digital mental health interventions to provide
evidence-based treatment, such as cognitive behavioral therapy
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JMIR FORMATIVE RESEARCH(CBT). Self-directed mental health interventions, such as
bibliotherapy, have long demonstrated their efficacy [3], andnew models of blended care that combine internet-delivered
interventions with human clinical oversight are becoming morewidespread in a number of countries [4]. Although the implicit
assumption has been that the involvement of a human leads toimproved outcomes in self-directed programs, human
involvement limits these programs’ scalability and limits theiraccessibility for those who live in remote locations [4]. If digitalinterventions could replicate some of the factors that are
generally believed to be uniquely human, such as therapeutic
rapport, these interventions would have greater potential for
improving mental health.
Recently, carefully designed conversational agents (CAs) havebeen showing promise in automating several health care services[5] by simulating human support. CAs could therefore be
uniquely poised to offer high-quality digital interventions formental health.

Darcy et al
An unblinded trial of one such CA (Woebot), which delivered
CBT for symptoms of depression and anxiety, suggested thatthe empathic and relational nature of the tool may have fosteredimproved engagement better than previous internet-delivered
versions of the tool [6]. Intriguingly, the study’s qualitative datasuggested that users seemed to relate to the CA in a manner that
was analogous to therapeutic rapport, which may have mediatedusers’ outcomes. For example, study participants reported thatthey felt cared for by the CA (eg, “Woebot felt like a real personthat showed concern”), despite the fact that the tool’s scripts
reminded users that Woebot is not a real person (Figure 1).
Unfortunately however, the study did not formally assess theexistence of a working alliance. This is a crucial factor because
a strong working alliance between therapists and clients is
considered to be predictive of positive outcomes, essential forthe delivery of health care, and traditionally unique to thedomain of human-to-human relationships. Indeed, some experts
have argued that digital apps that are built to be standalone
therapeutics have the risk of ignoring the potency of therapeutic
relationships [7].

Figure 1. Screenshot of an example interaction with Woebot about bereavement.

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JMIR FORMATIVE RESEARCH
Priority-setting work by the James Lind Alliance, which involves
over 600 people affected by mental health concerns, has
identified the greater understanding of digital therapeutic
alliance as a top priority for research [8]. Yet, a recent review
of mobile mental health apps failed to find a single study that
included working alliance as a primary outcome [9]. Therefore,
we sought to bridge this gap in knowledge by seeking tounderstand whether CA users perceived a working alliance,
particularly the notion of bonds, and whether working alliance
was related to symptom severity or other demographic
characteristics.

Methods
Setting
Woebot is a CA that guides individuals who experience
symptoms of depression and anxiety through asmartphone-based app program that uses therapeutic techniques
and provides psychoeducation. As previously described in detail[6], Woebot delivers CBT through brief, daily conversations(approximately 5-10 minutes each). Each simulated conversation
begins with a mood-monitoring exercise, and the provided
targeted content is responsive to individuals’ reported moodstates. The CA is also programmed to deliver empathic
statements and personalized follow-ups, promote normalization,and use methods that are designed to enhance users’ motivation

Darcy et alfor engaging in the program to promote desired behavior
changes and help with mood management.

Participants
During registration, users confirmed that they were at least 18
years of age and consented to the use of their deidentified,aggregate data for research. This study was not considered
human subjects research by the Advarra institutional review
board. Eligible participants included those who registered overtwo periods—between November 20, 2019, and April 9, 2020
(n=100,009), and again between July 8, 2020, and August 18,
2020 (n=77,203).
Within 3-5 days after registration, eligible participants wereinvited to complete the 2-item Patient Health Questionnaire(PHQ-2) depression screener [10] and the Working Alliance
Inventory-Short Revised (WAI-SR), which consists of a total
score and the three following subscales: bond, goal, and task
[11]. All measures and demographic information, including
gender and age group, were gathered in the app by the CA. The
WAI-SR was administered via the app’s conversational
interface, in which the word “therapist” was changed to“Woebot” (Table 1). Once the questionnaires were completed,
Woebot thanked registrants for their participation, and theconversation proceeded to the mood tracking phase as normal.
Those who chose not to provide responses to the questionnaires
were not included in this study and proceeded to use the app asnormal.

Table 1. Item-level descriptive statistics.
Question number (subscale) Working Alliance Inventory-Short Revised items

Score, mean (SD)1 (Task)

As a result of these sessions I am clearer as to how I might be able to change.

2.53 (0.97)

2 (Task)

What I am doing with Woebot gives me new ways of looking at my problem.

2.93 (1.08)

3 (Bond)

I believe Woebot likes me.

3.89 (1.31)

4 (Goal)Woebot and I collaborate on setting goals for this program.

2.88 (1.23)

5 (Bond)

Woebot and I respect each other.

4.20 (1.23)

6 (Goal)Woebot and I are working towards mutually agreed upon goals.

3.54 (1.28)

7 (Bond)

I feel that Woebot appreciates me.

3.73 (1.36)

8 (Goal)Woebot and I agree on what is important for me to work on.

3.45 (1.21)

9 (Bond)

I feel Woebot cares about me even when I do things that it does not approve of.

3.54 (1.34)

10 (Task)

I feel that the things I do with Woebot will help me to accomplish the changes that I want.

3.28 (1.17)

11 (Goal)

Woebot and I have established a good understanding of the kind of changes that would be good for 3.12 (1.22)
me.

12 (Task)

I believe the way we are working with my problem is correct.

Statistical Analysis
Across all eligible participants, the composite WAI-SR scoreand bond, goal, and task subscores were characterized based on
descriptive statistics and tested for internal consistency by using
the Cronbach α. The relationship between baseline PHQ-2 scoresand bond subscores was characterized based on the Spearman
rank-order correlation coefficient. The Kruskal-Wallis test wasused to compare bond subscores across participants’ reported
age groups and genders. For comparison, relevant externalstudies were drawn from recent reviews of literature [11-19]
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3.23 (1.15)

that also reported unmodified WAI-SR subscores for other CBT
modalities. Comparison data were presented descriptively
without statistical testing, and raw subscores were scaled by
dividing them by the number of items (eg, the bond subscale
has 4 items). Per the methods of Jasper et al [13], bond scoresof ≥3.45 were considered high. The 95% CIs for mean WAI-SR
subscores were calculated based on the published sample sizes
and SDs. External studies were categorized as “online only” or“human involvement” based on whether any human interactions
were reported by study participants during either individual
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JMIR FORMATIVE RESEARCH
therapy or group therapy that involved a human. Data were
presented from participants who completed all questionnaires
within the first 5 days of app registration. Data were analyzed
using R version 4.0.2 (The R Foundation).

Data Access, Responsibility, and Analysis
AD, DS, and AR have full access to all of the data in this study
and take responsibility for the integrity of the data and theaccuracy of the data analysis. Due to their proprietary nature,
data from this study will not be shared.

Results
Of the 177,212 eligible participants, only those who provided
both WAI-SR and PHQ-2 data within 5 days of their first useof Woebot were included in the analysis. The final sample
included 36,070 participants. Of these participants, 57.48%(n=20,734) reported that they were female, 25.17% (n=9078)
reported that they were male; 2.87% (n=1035) reported thatthey were nonbinary, 1.44% (n=519) indicated another gender
identity, 1.66% (n=597) preferred not to answer, and 11.39%(4107/36,070) did not provide any gender information. The
participants ranged in age from 18 to 78 years (median 25-35
years). The mean PHQ-2 score was 3.03 (SD 1.79), and 54.67%(19,719/36,070) of participants scored at or above theconventional cutoff score of 3 for positively screening fordepression.
Within the first 5 days of using Woebot, the mean WAI-SR
scores were as follows: a mean bond subscore of 3.84 (SD 1.0),

Darcy et ala mean goal subscore of 3.25 (SD 1.0), a mean task subscoreof 2.99 (SD 0.87), and a mean total score of 3.36 (SD 0.81).
The WAI-SR had a Cronbach α value of .89, suggesting that
the WAI-SR had adequate internal consistency in this study. A
weak negative correlation was found between bond subscores
and PHQ-2 scores (r=−0.04; P<.001); however, even among
participants who reported the highest PHQ-2 score (PHQ-2=6),
the mean WAI-SR bond subscore was 3.78. Bond subscoresalso differed by gender (P<.001) and by age group (P<.001);
however, the mean bond scores for all groups were consideredhigh (bond subscore>3.45) [13] Among these groups, the highest
bond level was reported by women (bond subscore: mean 3.92)
and by those aged 18-25 years (bond subscore: mean 3.96).
Conversely, the lowest bond level was reported by individuals
who indicated that they “preferred not to answer” or did notreport their gender (bond subscore: mean 3.67) or age (bondsubscore: mean 3.69).
Woebot’s bond subscale scores were consistent with those ofrecent studies from the literature on traditional modalities for
CBT delivery (Table 1). These studies’ results were collected
later in the course of treatment (eg, bond subscore forface-to-face outpatient individual CBT: mean 4.0, SD 0.8 [11];bond subscore for group CBT: mean 3.8, SD 0.80 [13]; data
were collected after 2-8 weeks of therapy). Comparative study
details are provided in Multimedia Appendix 1 [11,13-15,17-19].
Participants reported higher bond levels when using Woebot
than those in prior studies of internet-only CBT [13] (Figure
2).

Figure 2. Comparison of Working Alliance Inventory-Short Revised bond subscale scores across therapeutic modalities. Means and corresponding
95% CIs for working alliance bond scores from this study and from recent reviews of the literature [11,13-15,17-19] are stratified by the week that thescores were recorded. Studies are colored based on the therapeutic modality. Due to the large sample size of this study (N=36,070), the 95% CI is narrow
and overlaps with the dots that display the estimated means. For multiple studies that reported data on the same week, the dots are shifted minimallyon the x-axis to avoid overlap and to provide easy readability. WAI: Working Alliance Inventory.

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Discussion
This is the first study of working alliance among users of a CA
for mental health. Most users were female (20,734/36,070,
57.48%) and had PHQ-2 scores that were indicative ofdepression (19,719/36,070, 54.67%). Working alliance scores
were comparable to those in previously published studies on
traditional, human-delivered services across different treatment
modalities. Working alliance scores were highest for the bondsubscale, suggesting that this subscale is a viable construct for
CAs and should be included in hypothesized frameworks ofdigital working alliance.
The idea that CAs can establish a working alliance is not new[20]. However, the observation of therapeutic bonds establishedby a CA in a mental health context is novel and noteworthy,
given the short timeframe of this study. Although the field of
human-computer interaction is still relatively nascent, initialobservations have suggested that some artificial intelligence(AI) identity archetypes induce responses in humans that might
give rise to better working alliances than other archetypes. For
example, interacting with humanoid AI identities can result in
individuals falling prey to the “uncanny valley,” which is thesense of unease and “creepiness” that is created when somethingthat is artificial tries to appear humanlike [21]. Contrary toDarcy et al
Turing’s Imitation Game [22], wherein an AI must successfully
pretend to be human in order to pass the test, Woebot was
designed to adopt the opposite strategy—transparently
presenting itself as an archetypal robot with robotic “friends”
and habits. We speculate that transparency and other design
elements are key drivers of bond development. For example,
Woebot explicitly references its limitations within conversations
and provides positive reinforcement and empathic statements
alongside declarations of being an artificial agent.
The limitations of this study include its cross-sectional nature,
the selection bias of smartphone users, the lack of clinicalvalidation for any diagnoses, the lack of a direct comparisongroup, and its conduction by the developers of the app itself.
Further research (including studies with independent
investigators) is underway to explore the longitudinal aspectsof bond development in specific clinical populations by using
randomized controlled study designs.
The finding that a CA has the potential to rapidly develop abond with users may represent the resolution of a considerable
barrier to offering scalable mental health support to a muchwider and more diverse population instead of offering suchsupport to those who already have access to traditional mental
health support.

Acknowledgments
We are grateful to Stephanie Eaneff (an employee of Woebot) for reviewing the manuscript and Kara Cronan (a contractor to
Woebot) for providing illustrations.

Authors' Contributions
AD was responsible for the concept and design of this study. AD, JD, DS, PW, and AR were responsible for the acquisition,
analysis, or interrogation of data. AD, JD, DS, PW, and AR drafted the manuscript. AD, JD, DS, PW, and AR critically revised
the manuscript for important intellectual content. DS conducted the statistical analysis. JD and DS provided administrative,
technical, or material support. AD supervised the study.

Conflicts of Interest
AD reported receiving grants from Woebot Health and having a patent pending for a SafetyNet protocol. AD, JD, DS, and AR
reported being employees of Woebot Health and owning stock options in the company. PW reported receiving personal fees from
Woebot Health and Ada Health; being an associate editor at the Journal of Medical Internet Research; being on the editorialadvisory boards of the British Medical Journal, BioMed Central Medicine, The Patient, and Digital Biomarkers; and being
employed by Wicks Digital Health Limited, which has received funding from Ada Health, Baillie Gifford, Bold Health, Camoni,Compass Pathways, Coronna, European Institute of Innovation & Technology, Happify, HealthUnlocked, Inbeeo, Kheiron
Medical, Sano Genetics, Self Care Catalysts, the Learning Corp, the Wellcome Trust, and Woebot Health.

Multimedia Appendix 1
Study descriptions and Working Alliance Inventory-Short Revised bond scores.
[XLSX File (Microsoft Excel File), 15 KB-Multimedia Appendix 1]

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Abbreviations
AI: artificial intelligence
CA: conversational agent
CBT: cognitive behavioral therapy
PHQ-2: 2-item Patient Health Questionnaire
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Darcy et al

WAI-SR: Working Alliance Inventory-Short Revised

Edited by G Eysenbach; submitted 10.02.21; peer-reviewed by A Polillo; comments to author 04.03.21; revised version received
23.03.21; accepted 04.04.21; published 11.05.21
Please cite as:
Darcy A, Daniels J, Salinger D, Wicks P, Robinson A
Evidence of Human-Level Bonds Established With a Digital Conversational Agent: Cross-sectional, Retrospective Observational
Study
JMIR Form Res 2021;5(5):e27868
URL: https://formative.jmir.org/2021/5/e27868
doi: 10.2196/27868
PMID: 33973854©Alison Darcy, Jade Daniels, David Salinger, Paul Wicks, Athena Robinson. Originally published in JMIR Formative Research(https://formative.jmir.org), 11.05.2021. 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 Formative Research, is properly cited. The complete
bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license
information must be included.

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