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Design and Evaluation of a Mobile Chat App for the Open Source Behavioral Health Intervention Platform MobileCoach

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The open source platform MobileCoach (mobile-coach.eu) has been used for various behavioral health interventions in the public health context. However, so far, MobileCoach is limited to text message-based interactions. That is, participants use error-prone and laborious text-input fields and have to bear the SMS costs. Moreover, MobileCoach does not provide a dedicated chat channel for individual requests beyond the processing capabilities of its chatbot. Intervention designers are also limited to text-based self-report data. In this paper, we thus present a mobile chat app with pre-defined answer options, a dedicated chat channel for patients and health professionals and sensor data integration for the MobileCoach platform. Results of a pretest (N = 11) and preliminary findings of a randomized controlled clinical trial (N = 14) with young patients, who participate in an intervention for the treatment of obesity, are promising with respect to the utility of the chat app.
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Design and Evaluation of a Mobile Chat App
for the Open Source Behavioral Health
Intervention Platform MobileCoach
Tobias Kowatsch
1(&)
, Dirk Volland
2
, Iris Shih
2
, Dominik Rüegger
2
,
Florian Künzler
2
, Filipe Barata
2
, Andreas Filler
1,3
, Dirk Büchter
4
,
Björn Brogle
4
, Katrin Heldt
4
, Pauline Gindrat
5
,
Nathalie Farpour-Lambert
6
, and Dagmar lAllemand
4
1
Institute of Technology Management,
University of St. Gallen, St. Gallen, Switzerland
tobias.kowatsch@unisg.ch
2
Department of Management, Technology and Economics,
ETH Zurich, Zurich, Switzerland
3
Energy Efcient Systems Group, University of Bamberg, Bamberg, Germany
4
Childrens Hospital of Eastern Switzerland, St. Gallen, Switzerland
5
Fondation SportSmile, Nyon, Switzerland
6
Department of Community Medicine, Primary Care and Emergency,
University Hospital of Geneva/University of Geneva, Geneva, Switzerland
Abstract. The open source platform MobileCoach (mobile-coach.eu) has been
used for various behavioral health interventions in the public health context.
However, so far, MobileCoach is limited to text message-based interactions.
That is, participants use error-prone and laborious text-input elds and have to
bear the SMS costs. Moreover, MobileCoach does not provide a dedicated chat
channel for individual requests beyond the processing capabilities of its chatbot.
Intervention designers are also limited to text-based self-report data. In this
paper, we thus present a mobile chat app with pre-dened answer options, a
dedicated chat channel for patients and health professionals and sensor data
integration for the MobileCoach platform. Results of a pretest (N= 11) and
preliminary ndings of a randomized controlled clinical trial (N= 14) with
young patients, who participate in an intervention for the treatment of obesity,
are promising with respect to the utility of the chat app.
Keywords: Health intervention Digital coaching Chat-based interaction
1 Introduction
Non communicable diseases (NCDs) such as heart diseases, asthma, obesity, diabetes
or chronic kidney disease impose the greatest burden on global health [14]. According
to WHOs NCD global monitoring framework, many of these diseases are conse-
quences of adverse health behaviors, for example, harmful use of alcohol and tobacco
or physical inactivity [15]. However, health personnel is strongly limited [2]. Conse-
quently, scalable behavioral health interventions are required.
©Springer International Publishing AG 2017
A. Maedche et al. (Eds.): DESRIST 2017, LNCS 10243, pp. 485489, 2017.
DOI: 10.1007/978-3-319-59144-5_36
Innovative digital health interventions (DHIs) have not only the potential to
improve the efcacy of preventive or therapeutic behavioral health interventions but
also to reduce their costs [1]. With the goal to provide an open source platform that
allows health professionals to design scalable, low-cost and evidence-based DHIs,
MobileCoach (mobile-coach.eu) was developed [5] and evaluated [9].
However, it uses the short message service (SMS) for delivering behavioral health
interventions, and thus comes with various shortcomings as outlined in the next
section. We therefore present in this paper the rst mobile chat app for the Mobile-
Coach platform that addresses these shortcomings and thus, complements existing
communication such as personal exchange, SMS-based, phone-based or video-based
interactions.
The remainder of this paper is structured as follows. Next, we describe the design of
the chat app. Then, the apps signicance to research and practice is outlined. Finally,
we present results from an empirical study with 11 obese children who assessed the
new chat app as the rst target group.
2 Design of the Chat App
Hands-on experience with several MobileCoach-based interventions [79] has revealed
four major shortcomings related to its text messaging approach. First, participants have
to bear the SMS costs which may be an entry barrier if the caregiver does not provide a
monetary compensation. Second, participants are always requested to manually type in
text to answer even Likert-scale type questions. These answers are then parsed by the
MobileCoach, which is error-prone in case the answer does not perfectly t to the
question. Processing these answers is a time-consuming process for the caregiver, too.
Third, participant-initiated requests usually require an individual answer from a care-
giver instead of a scripted answer by a chatbot. A rule-based chatbot does therefore not
always t to the communication needs of the participants. Fourth, text-messaging is
limited to self-report data, i.e. health professionals cannot use objective sensor data
from a smartphone (e.g., accelerometer data used to measure physical activity) or
sensor data from devices connected to that smartphone (e.g., Bluetooth-enabled blood
glucose or peak ow meters) for the design of their DHIs.
Regarding these shortcomings and against the background of smartphone perva-
siveness [4], the following requirements have been dened: (R1) The app must not rely
on the short message service for communication purposes; (R2) the app must imple-
ment pre-dened answer sets for efcient and error-free chat interaction; (R3) the app
must implement a chat channel for individual communication needs that complements
the scalable chatbot channel; (R4) the app must be able to access sensor data from the
smartphone or smartphone-connected devices.
By considering these four requirements, we built a rst mock-up of a mobile app
and evaluated it with six behavioural health experts. As a result of that assessment, a
generic dashboard view was designed. Its purpose is to summarize key statistics of the
envisioned behavioural health interventions for self-monitoring purposes (e.g. steps
achieved per day, intervention progress or goals achieved). Based on this generic
486 T. Kowatsch et al.
mock-up, we implemented a native chat app for Android smartphones for the Mobi-
leCoach platform. Figures 1,2and 3show the graphical user interface of the chat app.
3 Signicance to Research and Practice
The mobile chat app presented in this paper allows behavioral scientists and health
professionals to enrich self-report data with objective sensor data in the everyday life of
their clients. This paves the way for a better understanding of whether psychological/
self-report data and physiological/objective data are rather complement or alternative
measures, a recent research question in the eld of NeuroIS [13].
Moreover, it is by far not clear how to design and frame chatbots for DHIs (e.g., as
an expert or a patient like me) and its interplay with a physicalcaregiver such that
they have a positive effect on the bond between caregiver and their clients and thus,
also on therapeutic outcomes [6]. In contrast to general purpose chat agents such as Siri
(Apple), Alexa (Amazon) or Cortana (Microsoft) and agents with a health focus such as
Florence (getorence.co.uk), Molly (sense.ly) or Lark (lark.com), our chat app allows
full control of personal health data and a generic framework to manipulate the design
and communication style of chatbots in lab and eld settings.
Finally and consistent with the MobileCoach platform, the chat app will be made
open source under the Apache 2.0 license to enable a community-driven design such
that research teams and (business) organizations interested in chat-based digital
coaching approaches do not have to start from scratch but can re-use, revise and
improve the existing code together with the MobileCoach platform.
Fig. 1. Dashboard view, indi-
vidual caregiver chat channel
PathMate and channel with
chatbot Anna
Fig. 2. Chatbot Anna, pre-
dened answer options and
sensor integration; steps are
tracked and used in the chat
Fig. 3. Chat channel with
the caregiver; the PathMate
study team of the childrens
hospital
Mobile Chat App for the Open Source Platform MobileCoach 487
4 Evaluation of the Artifact
Based on prior work demonstrating the acceptance of chat apps by adolescents [11], the
rst test of the novel chat app was conducted in a childrens hospital in December 2016
with 11 patients (age
Mean
= 12.6 years, SD = 2.4; 8 girls), who participated in an
intervention for the treatment of obesity. The goal of this test was (1) to assess
enjoyment, ease of use, usefulness and the intention to use the app [10], and (2) to
identify and address major usability problems with the app [12] prior to a randomized
controlled trial (RCT), in which the efcacy of a chat-based six-month DHI for the
treatment of childhood obesity will be compared to a control group.
First, a chat-based DHI was collaboratively designed by computer scientists,
physicians, a psychotherapist, diet and sport experts. The patients were then asked to
select a chatbot of their liking, i.e. they could choose between a female and male
chatbot (Anna or Lukas). Then, they interacted with the bot for 10 min including
various chat-based photo, physical activity and quiz interactions. The patients were
observed during these interactions by a computer scientist and physician. Afterwards,
patients were asked to ll out a questionnaire to assess the app and to provide quali-
tative feedback on their experience with the app. Similar to prior work [10], we
assessed technology perceptions and behavioral intentions with seven-point Likert
scales anchored from strongly disagree (1) to strongly agree (7). As young patients
deserve special consideration, we used single-item measures to reduce the burden of
evaluation [3].
The descriptive statistics of the evaluation are shown in Table 1. Results indicate
that the chat app was perceived positive regarding all four constructs. A sign test
against the neutral Likert-scale median of 4 supports this observation. Finally, we
found no major usability problems based on the observations and the qualitative
feedback.
First ndings of the aforementioned RCT show that new young patients assigned to
the chat-based DHI (N= 14) completed successfully approx. 61% of the daily inter-
vention tasks over the rst two months. The efcacy of this DHI will be nally
measured by the Body Mass Index after the six-month RCT. We hypothesize that the
chat-based DHI is more effective as the chatbot can provide everyday support on
therapy goals and tasks, thus increasing therapy adherence compared to patients of the
treatment-as-usual control group without everyday support.
Table 1. Descriptive statistics and results of a sign test against the neutral value 4 on a 7-point
Likert-scale (N= 11). Note: Perceived ease of use (PEU), Perceived enjoyment (PEN), Perceived
usefulness (PU) and Intention to use (IU); Signicance */**/*** p < .05 /.01 /.001
# Item Mean Median SD p-value
PEU I found the chat easy to use 6.7 7.0 0.7 ***
PEN I enjoyed chatting 6.2 7.0 1.5 **
PU Chatting with Lukas/Anna could motivate me
to accomplish my intervention tasks
5.9 6.0 1.1 *
IU I could imagine chatting daily that way 5.6 6.0 1.4 *
488 T. Kowatsch et al.
In our future work, we will test chat-based DHIs with older patient populations and
different therapies to assess the degree to which our ndings can be generalized.
Acknowledgements. We would like to thank the CSS Insurance and the Swiss National Science
Foundation for their support through grants 159289 and 162724.
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Mobile Chat App for the Open Source Platform MobileCoach 489
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Background: It is recommended that caregivers receive oral health education and in-person training to improve tooth brushing for young children. To strengthen oral health education before COVID-19, the 21-Day FunDee chatbot with in-person tooth brushing training for caregivers was employed. During the pandemic, however, practical experience was difficult to implement. Therefore, the 30-Day FunDee chatbot was created to extend the coverage of chatbots from 21 to 30 days by incorporating more videos on tooth brushing demonstrations and dialogue. This was a secondary data comparison of two chatbots in similar rural areas of Pattani province, Maikan district (Study I) and Maelan district (Study II). Objective: This study aimed to evaluate the effectiveness and usability of two chatbots, 21-Day FunDee (Study I) and 30-Day FunDee (Study II), based on the protection-motivation theory (PMT). Furthermore, the study explored the feasibility of employing 30-Day FunDee chatbot to increase tooth brushing behaviors for caregivers in oral hygiene care for children aged 6-36 months without in-person training during the COVID-19 pandemic. Methods: A pre-post design was used in both studies. The effectiveness of each chatbot was evaluated among caregivers in terms of oral hygiene practices, knowledge, and oral health care perceptions based on PMT. In Study I, participants received in-person training and a 21-day chatbot course during October 2018 to February 2019. In Study II, participants received only daily chatbot programming for 30 days during December 2021 to February 2022. Data was gathered at baseline of each study and at 30 and 60 days after the start of Study I and Study II, respectively. Only Study I evaluated the plaque score. Open-ended questions in chatbot programs were used to assess the usability of chatbots at the end of their interventions. Only Study II included an in-depth interview. The two studies were compared to determine the feasibility of using the 30-Day FunDee chatbot by an alternative method of in-person training. Results: There were 71 pairs of participants in total, 37 for Study I and 34 for Study II. Both chatbots significantly improved overall knowledge (P<.001; 0.73 (SD 0.21), 0.94 (SD 0.09)) (P=.001; 0.53 (SD 0.26), 0.66 (SD 0.23)), overall oral health care perceptions based on PMT P<.001; 0.58 (SD 0.19), 0.86 (SD 0.16), P<.001; 0.53 (SD 0.26), 0.83 (SD 0.12), and tooth brushing for children by caregivers (P=.02, P=.04) in Study I and Study II, respectively. Only Study I differed statistically significant for frequency of tooth brushing at least twice a day (P=.002) and perceived vulnerability (P=.003; 0.46 (SD 0.51), 0.78 (SD 0.42)). Overall chatbot satisfactions were reported at the highest level at 9.2 (SD 0.9) and 8.6 (SD 1.2) for Study I and Study II, respectively. In Study I, plaque levels differed significantly. (P<0.001; 0.48 (SD 0.33), 0.18 (SD 0.21). Conclusions: This was the first study using a chatbot in oral health education. Two chatbot programs established their effectiveness and usability in promoting oral hygiene care of caregivers for young children. The 30-Day FunDee chatbot showed the possibility to improve tooth brushing skills without requiring in-person training.
Chapter
The social psychology of eating and artificial intelligence can be integrated to develop effective online and personalized interventions to promote sustainable lifestyles that consider individual preferences and orientations. Combining the explanatory power of social psychological models with the predictive power of data-driven artificial intelligence is a promising strategy for effectively promoting sustainable and healthy dietary choices through personalized and automated message-based interventions. Artificial intelligence models can develop automatic interaction systems that adapt to the characteristics of the recipients to enable effective communication. Thanks to these automated systems, communication to promote healthy and sustainable eating habits can address a very large and diverse audience, considering the needs and resources of each individual. All this is done with full awareness of the risks, but also the benefits, that digital communication can bring. Chatbots can provide personalized and empathetic communication that adapts to the user’s motivations and resources, helping to maintain motivation over time.KeywordsArtificial intelligencePredictive powerAutomated communication strategiesChatbotRisks of digital communicationBenefits of digital communication
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