# Towards RehabCoach: Design and Preliminary Evaluation of a Conversational
Agent Supporting Unsupervised Therapy after Stroke

Giada Devittori1, Mehdi Akeddar1, Alexandra Retevoi1, Fabian Schneider2,
Viktoria Cvetkova3, Daria
Dinacci3, Antonella Califfi3, Paolo Rossi3, Claudio Petrillo3, Tobias
Kowatsch2,4,5,6 and Olivier Lambercy1,6 This work was supported by the
Competence Centre for Rehabilitation Engineering and Science (RESC) of ETH
Zurich, and the National Research Foundation, Prime Minister’s Office,
Singapore under its Campus for Research Excellence and Technological
Enterprise (CREATE) program.1Rehabilitation Engineering Laboratory, ETH
Zurich, Switzerland<EMAIL_ADDRESS>of Technology,
Management, and Economics, ETH Zurich, Switzerland3Clinica Hildebrand Centro
di riabilitazione Brissago, Switzerland4Institute for Implementation Science
in Health Care, University of Zurich, Switzerland5School of Medicine,
University of St. Gallen, Switzerland6Future Health Technologies program,
Singapore-ETH Centre, Campus for Research Excellence and Technological
Enterprise (CREATE), Singapore

###### Abstract

Unsupervised therapy after stroke is a promising way to boost therapy dose
without significantly increasing the workload on healthcare professionals.
However, it raises important challenges, such as lower adherence to therapy in
the absence of social interaction with therapists. We present the initial
prototype of RehabCoach, a novel smartphone-based app with conversational
agent to support unsupervised therapy. RehabCoach is designed to increase
patients’ engagement and adherence to therapy and to provide information
(e.g., about stroke, health) in an interactive and user-friendly manner. We
report on the design and usability evaluation of the first prototype of
RehabCoach, assessed by four stroke patients and five healthcare
professionals, who interacted with the app in a single testing session. Task
completion time and success rates were measured for 15 representative tasks,
and participants assessed usability via questionnaires and a semi-structured
interview. Results show that it was feasible for stroke patients to
successfully interact with RehabCoach (task success $\geq$93 %) without
requiring extensive training. Participants positively rated the usability of
RehabCoach (mean mHealth App Usability Questionnaire score: 1.3 for primary
users, 1.4 for healthcare professionals, on a scale from 1 (positive
evaluation) to 7). The feedback collected in this work opens the door to
further enhance RehabCoach as an interactive digital tool to support
unsupervised rehabilitation.

##  I INTRODUCTION

People after a stroke often do not fully recover upper limb function [1],
which negatively impacts independence and quality of life. Increasing the dose
(i.e., task repetitions and therapy time) of upper limb therapy may enhance
functional outcomes or prevent the long-term loss of the improvements gained
during the early rehabilitation phase [2, 3]. However, the current
rehabilitation model, mainly based on one-to-one therapy sessions, and the
limited resources available (e.g., low therapist-to-patient ratio [4]) make
increasing therapy dose challenging.

Unsupervised therapy, defined as patients training without any direct external
supervision, might be a way to increase dose without further straining the
healthcare system. Especially when applied in the home setting, unsupervised
therapy could help decrease the reliance on hospital stays and visits, thus
ensuring access to rehabilitation services even when the healthcare system is
under pressure, as exemplified during the COVID-19 pandemic [5, 6].
Furthermore, studies have shown that unsupervised therapy can be comparable to
standard care [7].

The literature describes a wide range of tools that may support unsupervised
therapy, such as booklets of exercises [8], virtual reality systems [9, 10],
and actuated robots [11, 12]. However, unsupervised therapy raises important
challenges, such as low engagement and motivation to train, poor adherence to
therapy schedule, as well as lack of social interaction and feedback on
progress [13, 14, 7]. Attempts to address these challenges include weekly
communication with a therapist [15] or the implementation of multiuser therapy
exercises [9], which can increase adherence but rely on the presence of
external persons. Integrating a gaming environment into rehabilitation may
also increase motivation [16], but it does not solve the issue of lack of
social interaction.

Digital interventions may play a key role in addressing these gaps [17]. To
explore this, we are proposing RehabCoach, a novel chatbot-based mobile
application designed to complement and support existing methods for
unsupervised therapy. RehabCoach is based on the MobileCoach framework [18,
19, 20], an open-source platform for health interventions that relies on the
establishment of a personal relationship between the user and a digital coach,
mainly through a chat function, to promote long-term compliance with the
intervention [21]. RehabCoach is not meant to deliver therapy directly but
takes advantage of MobileCoach’s digital coaching system, for instance to send
messages to encourage patients to adhere to predefined therapy plans or to
give feedback, instead of only implementing conventional reminders [22, 23].
MobileCoach-based interventions have already been applied and validated in
different populations and applications [24, 25], but never in people after
stroke, who might present additional challenges regarding the use of digital
tools.

Our long-term vision is to use the RehabCoach app in combination with one or
more robotic devices in order to increase patients’ adherence and motivation
to train unsupervised (in the clinic or at home). As an initial step towards
this, we developed a first version of RehabCoach to be presented to primary
(i.e., stroke patients) and secondary (i.e., healthcare professionals) users
in a single evaluation session. This first version is not yet connected to a
rehabilitation device, but still allows users to be involved early in a first
iteration of the RehabCoach app design, to collect data and feedback on
usability to be incorporated into the next version, promoting its acceptance
in the future [26]. Furthermore, testing the app separately from a robotic
device avoids introducing patients to many different technologies at the same
time, limiting potential confounding factors when evaluating usability.

In this paper, we describe the interdisciplinary development and preliminary
usability assessment of RehabCoach. The primary goal is to assess whether
individuals after a stroke can successfully interact with such a digital tool
after minimal instructions. The secondary goal is to collect quantitative and
qualitative feedback on the usability of RehabCoach and gather suggestions for
improvements from both primary and secondary users, which could help drive the
design of future digital interventions supporting rehabilitation after stroke.

##  II METHODS

###  II-A RehabCoach design

The RehabCoach app builds on MobileCoach, an open-source platform for
smartphone-based ecological momentary assessments and health interventions
that focus on health-promoting behavior and psychoeducation [18, 19, 20].
MobileCoach supports the implementation of a chat-based user interface with a
conversational agent and pre-defined answer options. The use of a smartphone-
based conversational agent in individuals after stroke raises additional
challenges, as this population is often not used to technology and may suffer
from cognitive and sensorimotor impairments that could hinder the use of such
an app.

For the first prototype of RehabCoach (version 0), the existing MobileCoach
features previously applied to other populations were combined into a therapy
coaching concept, collaboratively discussed by experts in neurorehabilitation,
computer science, digital health, and information system. Also, existing
literature on mobile apps and means to boost motivation to train for elderly
and stroke patients was consulted and combined with prior experience with
MobileCoach-based interventions in other populations. This resulted in the
definition of a first set of key features relevant to neurorehabilitation and,
in particular, to unsupervised therapy (Figure 1).

Figure 1: Screenshot of the main menu of the RehabCoach app.

Profile: When opening the RehabCoach app for the first time, the patient must
enter some information (e.g., name, ability to type on the phone, ability to
walk) which could then be used to personalize the intervention (i.e., the set
of interactions with RehabCoach aimed at supporting unsupervised therapy). The
key information to enter was discussed and approved by healthcare
professionals. Furthermore, the patient can choose between two avatars both
representing a digital coach.

Chat: The digital coach can send messages to the user via a chat interface.
These messages are designed to increase adherence to pre-defined therapy
schedules, for example, by reminding the user to start a planned training
session. To increase motivation, messages containing feedback about the daily
results can be sent [27]. During the day, the digital coach can also send
messages which are not strictly relevant to therapy but relate, for instance,
to the emotional or physical state of the user (e.g., “How was your day?”).
This aims at strengthening the personal relationship with the user, promoting
long-term interaction with RehabCoach [21], and compensating to a certain
extent for the lack of direct social contact during unsupervised therapy.

Learn: Another detrimental factor to patients’ wellbeing is the lack of
understanding towards stroke and secondary stroke prevention [28]. RehabCoach
offers a learning section where videos on stroke, health, and the importance
of rehabilitation can be accessed. The goal of this section is to increase
patients’ awareness about their disease and health status, which may lead to
higher adherence to therapy, the adoption of a healthier lifestyle, and act as
a secondary stroke prevention intervention [29, 27].

Checklist: The overview of the daily goals (e.g., training sessions) which
have already been achieved or still need to be completed is provided in the
form of a checklist. Clearly stating the daily goals should increase users’
engagement and, thus, adherence to the therapy schedule [30].

“I want to train”: The virtual coach sends messages related to a training
session based on a pre-defined schedule. The “I want to train” feature allows
the user to start a training session spontaneously, in addition to the
training program. Since unsupervised therapy could lead to patients not
training consistently, this feature allows the user of the app to perform a
training session even if they skipped a scheduled one, or to train more.

Figure 2: Excerpts from representative chat-based daily interactions between a
user and RehabCoach. Grey boxes correspond to the messages sent by the virtual
coach, while dark blue boxes are messages sent by the user. Depending on the
question, the user can answer either by choosing between predefined answers
and clicking on the corresponding button (e.g., light blue buttons with
different time options in the planning interaction) or by typing an answer in
a predefined field (e.g., light blue box in the training interaction). While
the planning interaction and the summary interaction are always at a fixed
time (8 am and 7 pm, respectively), the time for the training interaction and
the learning interaction can vary depending on the patient’s answers.

It is envisioned that the features described above are exploited differently
throughout a typical day of using the app to support unsupervised therapy
(Figure 2). The first time the user opens the app, RehabCoach explains how the
different interactions and sessions work (welcome interaction). Then, every
morning, a planning interaction is performed to plan the daily training and
learning sessions at a time suitable for the user, depending on their daily
schedule. Therefore, during this interaction, the number of training sessions
and the time for the training and learning sessions can be set. If the user
does not answer the messages sent by the digital coach during this
interaction, the sessions are set to a default time. At the set time, the
training and learning interactions start with a message from the digital coach
reminding the user to perform the therapy exercises or to watch an informative
video. Users can either confirm that they will start the session or postpone
it. The digital coach then asks for feedback on the session, providing the
opportunity to collect subjective information on the quality of a therapy
session. Finally, in the evening, RehabCoach summarizes the user’s daily
performance (summary interaction). When the app is not open, messages appear
on the smartphone as push notifications. If users do not respond within a
certain time frame (i.e., 10 minutes), the interaction is tagged as
incomplete, and the app proceeds to the next interaction scheduled. All the
answers inputted by the user are transmitted over an encrypted channel and
saved automatically on a secure web server.

###  II-B Preliminary usability evaluation

The goal of this preliminary usability evaluation was to identify potential
technical or accessibility issues and to gather feedback on the app to decide
on its further development before combining it with a rehabilitation device
and conducting a larger feasibility study at home. The study protocol was
approved by the ethics commission of ETH Zurich (2022-N-29). For each
participant, the usability evaluation consisted of a single study session of
about 45 minutes, simulating an entire day of using the app. The evaluation
included individuals after a stroke and healthcare professionals working with
stroke patients. Healthcare professionals were included with the objective to
gain additional feedback on the app, for example, on further features that
could be implemented in upcoming versions. This group would further serve as a
baseline regarding feasibility and performance in the given tasks with the
app. To be included in the study, participants had to be older than 18 years
and sign the informed consent. An additional inclusion criterion for primary
users was a diagnosis of stroke, while healthcare professionals had to
regularly interact with individuals post-stroke. Persons with major cognitive
and/or communication deficits, or major comprehension and/or memory deficits
reported in their clinical records were excluded. The usability evaluation
session consisted of three phases:

Familiarization: The researcher described the aim of the project, answered
eventual questions, and checked that participants understood and signed the
informed consent form. Then, the researcher presented the RehabCoach app on a
test smartphone and explained how to interact with it. This was then followed
by three minutes of familiarization, where the participant could freely
interact with the app and ask questions.

Testing: Participants were asked to perform specific tasks (Figure 3a)
considered essential for correct daily interactions with the app. The time
taken to complete each task and task success were recorded. Task success could
be rated as: successfully completed, completed with an input from the
researcher, completed with an error, or not completed.

Evaluation: Participants were asked to fill in the mHealth App Usability
Questionnaire (MAUQ, [31]) and to rate four custom statements. Both the MAUQ
and the custom statements were rated on a seven-point Likert scale ranging
from 1 (strongly agree, i.e., positive opinion about the app and high
usability) to 7 (strongly disagree), with the possibility of leaving out an
item in case it was not applicable (I don’t know). Items 1-5 of the MAUQ are
specific to ease of use (MAUQ_E), items 6-12 to interface and satisfaction
(MAUQ_I), and items 13-18 to usefulness (MAUQ_U). This was followed by a semi-
structured interview that touched topics related to first impressions about
RehabCoach, likes and dislikes, additional features that participants would
like to have, design, and informative videos.

###  II-C Data analysis

Boxplots were used to represent the time taken to complete the tasks for the
group of primary and secondary users separately. Given the small sample size
of this preliminary study, no statistical testing was reported. Task success
was represented with a heat map to visualize tasks potentially causing more
difficulties. MAUQ scores were calculated as the mean of the scores given to
the single items, excluding the items where participants answered I don’t
know.

##  III RESULTS

Four subacute stroke patients between 45 and 78 years old (mean age: 63.75
years) and five healthcare professionals between 28 and 51 years old (mean
age: 40.2 years) took part in the study. Among the healthcare professionals,
one was a speech therapist, one a neuropsychologist, two were physiotherapists
and one had a double specialty in physiotherapy and neuropsychology.

###  III-A Task success and completion time

All healthcare professionals and two of the primary users could complete all
tasks (Figure 3b). P1 completed the summary interaction (T14) with an error,
as an empty message was sent instead of typing an answer to one of the
questions. During the planning interaction (T10), P2 accepted the proposed
time for the training session (i.e., 2 pm) instead of changing it to 3 pm and
therefore did not successfully complete the task. The input given by the
researcher (n=2) consisted in encouraging participants to carefully look at
one specific section of the app interface to find the icon of interest after
the participants expressed difficulties in finding it. For ten tasks, the time
taken by the primary users was similar to the healthcare professionals
(difference of medians: <6 seconds) (Figure 3c). However, for Tasks 3, 9, 12,
13, and 14, the time taken by the stroke patients was found to be longer
(difference of medians: 14-32 seconds).

Figure 3: Tasks performed by the participants with the RehabCoach app during
the usability evaluation (a), heat map representing task success for both
primary users (P1-P4) and healthcare professionals (H1-H5) (b), and boxplots
of the time taken to complete the different tasks for the group of primary
users and for the one of healthcare professionals (c).

###  III-B MAUQ and custom questionnaire

The mean overall MAUQ score for the primary users was 1.3 (range: 1-1.8),
while for the healthcare professionals it was 1.4 (range: 1-1.8). Mean MAUQ_E
scores were 1.1 and 1.6 for primary users and healthcare professionals,
respectively, MAUQ_I scores were 1.3 and 1.4, and MAUQ_U scores were 1.9 and
1.5. The mean scores assigned to the custom statements are listed in Table I.

TABLE I: Custom statements with mean (range) scores assigned by primary users (P, n = 4) and healthcare professionals (H, n = 5). Possible scores ranged from 1 (strongly agree) to 7 (strongly disagree). Statement | P | H
---|---|---
1: The font size and the colors of the app | 2.3 (1-3) | 2.4 (1-4)
are adequate |  |
2: The size of the icons is adequate | 1 (1-1) | 2.2 (1-4)
3: The questions asked by the virtual coach | 1 (1-1) | 1.8 (1-4)
are easy to understand |  |
4: It is easy to answer the questions of the | 1 (1-1) | 1.4 (1-2)
virtual coach in the chat with the buttons |  |

###  III-C Semi-structured interview

Eight participants specifically stated that RehabCoach is easy to use.
Regarding the chat function, a participant mentioned that it humanizes the
interaction. One patient mentioned that more choices for the avatar of the
digital coach should be provided for increased personalization. Similarly, it
was requested to enhance the chat feature so that users can initiate
interactions themselves, for instance, by asking common questions such as “How
are you?”. One patient mentioned that the empathy of RehabCoach should be
improved, for example, by adding interactions that aim at strengthening the
attachment bond even more, even though they might not be directly linked to
therapy.

Regarding the chat interface, three participants mentioned that they liked the
buttons with predefined answers. Still, two healthcare professionals and one
primary user suggested adding more options, especially when the possible
answers have opposite meanings and do not cover a broad spectrum of possible
responses. For two secondary users more settings should be added to the
personalized profile, especially for the chat background, the font size,
choosing between capital or lowercase letters, and asking for aphasia.
Furthermore, most participants generally liked the idea of having the
informative videos. Two healthcare professionals pointed out that the
information given should be personalized, for example according to the type of
impairment.

##  IV DISCUSSION

Here we present the first prototype of RehabCoach, a coaching app designed to
increase motivation and adherence during unsupervised therapy after stroke,
and its preliminary usability testing. RehabCoach is chatbot-based and thought
of as a scalable social actor to support and later interface with different
devices for unsupervised rehabilitation, depending on the needs of the single
patient. Despite the many existing apps for people after stroke [32], the
potential of chatbot-based apps connected to rehabilitation devices still
needs to be investigated. Our results show that primary users could correctly
interact with the basic features of RehabCoach after only a short
familiarization phase, as demonstrated by high task success and perceived
usability. This is aligned with the findings of Burns et al. [32], who
reviewed 49 articles related to mobile health app interventions for people
after stroke, with most of the articles evaluating feasibility concluding that
the tested digital interventions were feasible.

RehabCoach was perceived as user-friendly and the chat messages were easy to
understand, which is a critical requirement when designing a digital health
intervention for the elderly with potential cognitive impairments, as our
target population. The tested tasks were representative of the expected
interactions during a day of use of RehabCoach and the different features
implemented in this prototype. For most of the tasks, primary users did not
take longer than healthcare professionals to complete them. Two out of the
five tasks that resulted in noticeably longer execution time by the primary
users required typing (T3) or reading long text messages (T9), which is not
surprising as literature shows that elderly read and text slower than young
adults [33, 34]. This suggests that short messages and predefined answers may
be preferred and more suitable for our population. However, in these tasks,
the priority was not to be fast but to correctly interact with the app or
interpret the information. Therefore, the average additional time needed by
primary users to complete all the tasks (+111 seconds in total) is acceptable,
also considering that in the intended use case, this additional time would be
split over the entire day.

Overall, the MAUQ results were very positive despite the currently limited
functionalities of the prototype, which supports the need for developing a
more advanced version of RehabCoach in the future. The results of the semi-
structured interviews will be used to define key features to implement in the
next prototype. For example, while the predefined answers were appreciated as
they provided an easier and faster way to answer than typing, three
participants stated that the offered predefined answers should be further
developed to cover a broader range of possible opinions. Indeed, for most
questions we opted to provide only two possible answers, so as to reduce the
number of options to read and increase usability. Issues with predefined
answer options were also identified in a previous work involving stroke
patients interacting with a chatbot [35].

As the objective was to evaluate as early as possible the feasibility of using
the app in persons with stroke and its usability, the interaction with the app
was limited to a single short session (representing an accelerated day), and
users did not actually engage in the scheduled therapy sessions. This may
influence the perception of the app by the participants. While the number of
participants in this usability evaluation is limited, it still allowed us to
collect essential preliminary data to show feasibility and to improve the app
in the next design iteration, which would be tested in a more extensive study
including more participants undergoing unsupervised rehabilitation sessions
and interacting with the app over multiple days. This will allow defining
whether the positive evaluation of this first prototype can translate into
high motivation to train and adherence to unsupervised therapy. In conclusion,
the positive results gained during this work underline the feasibility of
using chatbot-based interventions in individuals after stroke and open the
door to the use of RehabCoach as a bridging digital tool to support different
devices for unsupervised therapy after stroke.

## ACKNOWLEDGMENT

The authors would like to thank the participants of the usability evaluation
study and the team of the Clinica Hildebrand in Brissago for their support
during this work.

## CONFLICT OF INTEREST

FS and TK are affiliated with the Centre for Digital Health Interventions
(CDHI), a joint initiative of the Institute for Implementation Science in
Health Care, University of Zurich, the Department of Management, Technology,
and Economics at ETH Zurich, and the Institute of Technology Management and
School of Medicine at the University of St.Gallen. CDHI is funded in part by
CSS, a Swiss health insurer, MTIP, a Swiss investor, and Mavie Next, an
Austrian healthcare provider. TK is also a co-founder of Pathmate
Technologies, a university spin-off company that creates and delivers digital
clinical pathways. However, neither CSS, MTIP, Mavie Next nor Pathmate
Technologies were involved in this research.

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