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Paper presented at the Persuasive Embodied Agents for Behavior Change (PEACH2017)
Workshop, co-located with the 17th International Conference on Intelligent Virtual
Agents (IVA 2017), Stockholm, Sweden.
Text-based Healthcare Chatbots Supporting Patient and
Health Professional Teams: Preliminary Results of a
Randomized Controlled Trial on Childhood Obesity
Tobias Kowatsch1(*), Marcia Nißen2, Chen-Hsuan Iris Shih2, Dominik Rüegger2, Dirk
Volland1, Andreas Filler1, Florian Künzler2, Filipe Barata2, Severin Haug3, Dirk
Büchter4, Björn Brogle4, Katrin Heldt4, Pauline Gindrat5, Nathalie Farpour-Lambert6
& Dagmar l’Allemand4
1 University of St. Gallen, Institute of Technology Management, St.Gallen, Switzerland
tobias.kowatsch@unisg.ch
2 ETH Zurich, Department of Management, Technology and Economics, Zurich, Switzerland
3 Swiss Research Institute for Public Health and Addiction, Zurich University,
Zurich, Switzerland
4 Children’s Hospital of Eastern Switzerland, St. Gallen, Switzerland
5 Fondation SportSmile, Nyon, Switzerland
6 University Hospital of Geneva / University of Geneva, Department of Community Medicine,
Primary Care and Emergency, Geneva, Switzerland
Abstract. Health professionals have limited resources and are not able to person-
ally monitor and support patients in their everyday life. Against this background
and due to the increasing number of self-service channels and digital health in-
terventions, we investigate how text-based healthcare chatbots (THCB) can be
designed to effectively support patients and health professionals in therapeutic
settings beyond on-site consultations. We present an open source THCB system
and how the THCP was designed for a childhood obesity intervention. Prelimi-
nary results with 15 patients indicate promising results with respect to interven-
tion adherence (ca. 13.000 conversational turns over the course of 4 months or
ca. 8 per day and patient), scalability of the THCB approach (ca. 99.5% of all
conversational turns were THCB-driven) and over-average scores on perceived
enjoyment and attachment bond between patient and THCB. Future work is dis-
cussed.
Keywords: Human-computer Interaction, Chatbot, Conversational Agent, In-
terpersonal Closeness, Attachment Bond, Counseling Psychology.
1 Introduction
Technology-based self-service channels [41] and digital health interventions [1, 31]
have the potential to support patients in their everyday life and health professionals
likewise. Although there are scalable self-service channels in the form of digital voice
assistants and chatbots offered by Apple (Siri), Amazon (Alexa), Google (Assistant),
Microsoft (Cortana) or Samsung (Bixby), they cannot (yet) be applied in healthcare
2 Kowatsch et al.
settings due to their lack of domain knowledge [32]. Thus, chatbots with a health focus,
for example, Florence (getflorence.co.uk), Molly (sense.ly), Lark (lark.com), koko
(itskoko.com) and various other messaging services, have recently gained interest in
academia and industry with both inconclusive [20] and promising results related to user
acceptance [5], working alliance [4] and treatment success [9, 14].
Text-based messaging services are “cheap, fast, democratic and popular” [15] and,
especially for young people, the preferred way of communication [44]. We are therefore
interested in effective designs of text-based healthcare chatbots (THCB) that focus on
linguistic cues and a limited set of visual cues, i.e. usually only a small still image
representing the agent, in contrast to embodied conversational agents discussed in prior
work [4, 5, 8, 10, 11, 34]. And indeed, the efficacy of THCB approaches has already
been shown [see 43 for an overview]. It is, however, open how to design THCB that
support both patients and health professionals in therapeutic settings beyond on-site
consultations as depicted in Fig. 1 and Fig. 2. We see THCBs as digital health coaches
that use not only self-reports to infer the health condition of patients but also sensor
information from everyday objects (e.g. from a smartphone [21], a car [19] or a PC
mouse [16, 24-26, 42, 45]) and medical devices (e.g. a blood glucose meter [39]).
Fig. 1. A text-based healthcare chatbot using health data from everyday objects and self-reports
from patients (sensing) to guide both patients and health professionals (support).
To address our research question, we propose and describe the design of a THCB and
provide preliminary results of a clinical study as a part of an ongoing randomized con-
trolled trial (RCT), in which the THCB was used to support not only young patients
with obesity in reaching their daily intervention goals but also to support health profes-
sionals that were responsible for these teenagers.
The remainder of this paper is structured as follows. Next, we describe the open source
behavioral intervention platform MobileCoach for THCBs and how it supports the
© University of St. Gallen & ETH Zurich & CSS
Slide 1| Dr. Tobias Kowatsch | Kantonsspital Aarau | May 2 | 2017
Physical & Digital Coaching
Health Professional
Support
with respect to goals,
tasks and emotions
Patient
Tex t-based
Healthcare
Chatbot
Health Institution
Hospital
Pharmacy
Family
Doctor
...
Car
PC Mouse
Smartphone …
Everyday Life of a Patient
Family
Work
Leisure
Wearable
Wearable
Medical Device Sensing
Text-based Healthcare Chatbots for Patient & Health Professionals 3
communication between patients and health professionals. We then describe the RCT
and report first empirical results with respect to intervention adherence of the patient,
scalability of the THCB, perceived enjoyment and attachment bond, a relationship qual-
ity of the working alliance inventory [17], between patient and THCB. We conclude
this work with an outlook of future research.
2 Text-based Healthcare Chatbot
The THCB is part of the open source behavioral intervention platform MobileCoach
www.mobile-coach.eu [12, 27]. It has already been evaluated in the public health con-
text [14, 37] and provides a modular architecture and rule engine for the design of fully-
automated digital health interventions. It also supports the implementation of RCTs and
micro-randomized trials [22]. A mobile chat app has been recently introduced as a new
chat client for MobileCoach [27]. An overview of the chat app’s user interface is pro-
vided in Fig. 2.
Fig. 2. Overview of the chat app’s user interface. Note: Text-based Healthcare Chatbot (THCB)
The app allows the integration of visual THCB cues in a dedicated chat channel. This
channel also provides pre-defined answer options for efficient chat interactions com-
pared to traditional text-messaging systems. Moreover, we have implemented a second
chat channel for patient and health professional communication like WhatsApp or
iMessage and situations in which the THCB is unable to support patients in an auto-
mated way. For example, this channel can be used if a physician wants to motivate a
patient in addition to the THCB or to ask patients to perform an ad-hoc task instead of
missed intervention tasks such as “Dear John, why not go outside, run around about
Pre-
defined
answer
options
(e.g. Likert
or pictorial
scales,
photos),
text or
sensor
input
Study&Team
Study&Name
Anna
Health
Professional &
THCB (Anna)
chat channels
Dashboard
View
4 Kowatsch et al.
5000 steps, make a selfie afterwards and send the picture back to me?” or because it
was snowing the last night such as “Dear John, why not go outside, make a snowman,
take a picture of it and send it back to me?” With that chat channel, patients are also
able to get in direct contact with their health professionals, for example, to ask for help
regarding an intervention task. Finally, MobileCoach uses a rule-based system to allow
the THCB to automatically send notifications to health professionals or other individu-
als supporting the intervention like parents, sisters or peers of the patient. For example,
if quality of life scores or other critical health states show a clear negative trend, which
has been sensed either via chat-based answers fed back to the THCB or via smartphone
sensors (e.g. no physical activity during the last 5 days), then the THCB can inform the
physician via e-mail or SMS about that event. That is, health professionals do not have
to actively monitor the conversational turns between the THCB and their patients but
can design rules that trigger notifications that are relevant to them in an automated
fashion. Finally, a dashboard view is integrated into the app to indicate general and
automated feedback with respect to a digital health intervention, for example, to indi-
cate the intervention progress, the number of goals achieved, average steps made or
points earned.
3 Preliminary Evaluation
Against the background of the previous two sections, we now describe how a concrete
THCB-based intervention for obese teenagers has been implemented with Mo-
bileCoach and present preliminary empirical results.
3.1 Design of the Text-based Healthcare Chatbot
The first THCB implementation based on MobileCoach and the novel chat app was
collaboratively designed by computer scientists, physicians, a psychotherapist, diet and
sport experts for a technology-supported intervention targeting childhood obesity. Lin-
guistic and visual THCB characteristics were informed by the assumption that interper-
sonal closeness is positively related to attachment bond between patient and THCB [2,
17, 20, 38]. We therefore framed the THCB to represent a peer of the patient instead of
an abstract entity such as the Google Assistant. There is a female and male version of
the THCB, named Anna and Lukas, respectively. To clearly communicate the artificial
character of the THCB and thus, not tricking patients in any way that they may interact
with a real person, a comic profile image of an ordinary-looking teenager was used [33,
40] as depicted in Fig. 1 and Fig. 2 for the female version of the TCBH. For both task-
related and social-interaction-related talk in terms of verbal cues [36], we used appro-
priate informal greetings and farewells [7, 28], the first name of the patient as the form
of address [28], lay language [46] and the personal “Du” as T-V-distinction [29] used
in German-speaking countries. Additionally, we used emoticons as quasi-nonverbal
cues [47] and empathic feedback as relational cues [6, 23, 30]. The THCB also engaged
in small-talk [3] on a non-regular basis and expressed happiness to see the patient and
chat with him or her [4, 35].
Text-based Healthcare Chatbots for Patient & Health Professionals 5
3.2 Study Design, Intervention Details and Evaluation Measures
After a first pretest with 11 obese children in 2016, in which the THCB was positively
evaluated [27] and the approval by the cantonal ethics committee, the study was started
in January 2017. The study is still ongoing and has the objective to assess the efficacy
of the THCB-based 5.5-month intervention compared to a treatment-as-usual control
group without THCB support. In this 1-year trial, patients of the THCB group see their
physicians four times during the intervention: at baseline, twice during the intervention
and after the 5.5-month intervention. In addition, there are 2 telephone contacts during
the intervention and follow up visits at 9 and 12 months. The primary medical outcome
is the reduction of the sex- and age-adjusted body mass index standard deviation score
(BMI-SDS) at the 1-year follow-up. Depending on the degree of challenge and therapy
achievement, 1 out of 3 patients of both groups could win a smartphone after the inter-
vention.
The THCB was introduced to the patients of the intervention group as an artificial
assistant at baseline by the physicians and a dedicated study smartphone, a Samsung S6
with the chat app pre-installed, was handed out to the patients for the duration of the
5.5-month intervention. Physicians explained to their patients that they could choose
between Anna or Lukas and that the corresponding THCB will come up with a chal-
lenge every day, i.e. the active intervention ingredients like doing a relaxation exercise
(stress management module), counting steps as daily goal (physical activity module),
taking photos of meals (diet module) or answering entertaining quiz questions (health
literacy and entertainment module). Moreover, patients were told that the physicians
could monitor the conversational turns between them and the THCB and that the results
of the challenges would be reviewed in the next consultation hour.
Among other assessment instruments, we measured intervention adherence by the
number of conversational turns per day in the chat app and the percentage of challenges
that have been successfully completed. Scalability of the THCB was measured by the
ratio of conversational turns in the manual chat channel vs. conversational turns in the
THCB channel. Finally, we adopted the short version [13] of the attachment bond scale
of the working alliance inventory [17] to measure the emotional and social relationship
between patient and THCB. We further adopted one item of the perceived enjoyment
scale [18] which was anchored on a 7-point Likert scale ranging from strongly disagree
(1) to strongly agree (7). Intervention adherence and scalability were measured during
the time of the intervention while the self-report instruments were employed after the
5.5-month intervention.
3.3 Interim Results of the THCB-based Intervention Group
We now present interim THCB-based results of the intervention group. For the adher-
ence and scalability measures we report results of the first 4 months and for 15 patients
(Agemean = 14.2, SD = 2.6, range 11.9 – 17.0). During the intervention 2 patients
dropped out for medical reasons and did not fill out the questionnaire at the end of the
intervention.
Results on intervention adherence are depicted in Fig. 3 and indicate that after 4
months, almost 70% of the patients had at least 4 conversational turns with the THCB
6 Kowatsch et al.
per day, i.e. the average number of turns to accept a challenge. Moreover, almost 40%
of the daily challenges are completed successfully in month 4.
Fig. 3. Percentage of 15 patients with at least 4 conversational turns with the THCB per day on
average (blue line chart on top) and percentage of successfully completed challenges (red bar
chart on the bottom).
Results related to the scalability of the THCB are shown in Fig 4. Overall, 12.994 con-
versational turns have been recorded over the first 4 months and 15 patients. This results
in approximately 8 conversational turns per day and patient.
Fig. 4. Distribution of 12.994 conversational turns between patient and (a) health professionals
(blue, top) and (b) THCB (pink, bottom) for the first four months of the intervention.
But only in the first month, 3.4% of the conversational turns took place in the manual
chat channel with the study team and physicians. That was mainly because of technical
issues and questions that were discussed over the first couple of weeks. As a result, less
than 0.5% of all conversational turns took place in the manual chat channel in the fourth
month indicating that the monitoring and support of the patients in their everyday life
was mainly driven by interactions with the THCB. The health professionals were only
© University of St. Gallen & ETH Zurich & CSS
Slide 1| Dr. Tobias Kowatsch | St.Gallen | June 30, 2017
99,52%
99,21%
98,53%
96,51%
0,48%
0,79%
1,47%
3,49%
Month 4
Month 3
Month 2
Month 1
Text-based Healthcare Chatbots for Patient & Health Professionals 7
informed in cases, when there was no interaction between THCB and patients for three
consecutive days, for example, when patients did not use the study smartphone for a
longer period of time or were on holidays abroad and had no Internet access. On aver-
age, these notifications were triggered 4.1 times per patient and month (SD = 10.7).
Finally, the descriptive statistics related to perceived enjoyment and attachment bond
are listed in Table 1. The 13 remaining patients indicated, that they enjoyed the THCB
chat with Lukas and Anna. Moreover, attachment bond of the working alliance inven-
tory between patients and the THCBs indicates also a high degree of social and emo-
tional relationship at the end of the intervention.
Table 1. Descriptive statistics for perceived enjoyment and attachment bond (N=13). Note:
Alpha = Cronbach’s Alpha; Perceived enjoyment was anchored from strongly disagree (1) to
strongly agree (7), attachment bond items from never (1) to always (7)
Construct
Items (Alpha)
Item / Example
Mean
SD
Perceived enjoyment
1 (N/A)
I enjoyed chatting with ___.
5.54
1.56
Attachment bond
4 (0.67)
I believe ___ liked me.
5.50
1.03
4 Summary and Future Work
In this work, we described one concrete instance of a text-based healthcare chatbot
(THCB) system that was designed to support patients and health professionals likewise.
Interim analysis of the intervention group from an ongoing RCT indicate that the im-
plemented THCB, which took over the role of a peer character, engaged patients over
four months to a remarkable extent. Moreover, more than 99.5% of the conversational
turns were driven by the THCB which underlines their scalability of THCBs. Patients’
perceptions regarding enjoyment and attachment bond with the THCB were also found
to be good.
In our future work, we will assess additional measures (e.g. perceived interpersonal
closeness between THCB and patient or incentive-based motivation) and medical out-
comes of the RCT and compare them with the control group. We will also adapt the
THCB to various other digital health interventions, for example, for individuals with
asthma or diabetes or for substance abuse treatments. We are finally interested in in-
vestigating the health economic effects of THCBs in the context of various other non-
communicable diseases to increase the efficiency of integrated care models and clinical
pathways. Here, it is of utmost interest to identify ways to reallocate the limited re-
sources of health professionals to those and only those patients that need personal, face-
to-face care and where THCBs are likely to fail.
Acknowledgements: We would like to thank the CSS Insurance and the Swiss National
Science Foundation for their support through grants 159289 and 162724.
8 Kowatsch et al.
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