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Addressing Challenges in Promoting Healthy Lifestyles: The AI-Chatbot Approach

Authors:
  • Università degli Studi di Trento, Fondazione Bruno Kessler

Abstract and Figures

Healthy lifestyles promotion is the main objective of primary care interventions, starting from the pediatric age, were overweight is nowadays exposing about one third of children to the risk of developing chronic diseases, such as diabetes. Recent years have seen a blast of mHealth apps for health promotion, targeting in particular nutrition and dietary behaviour change. However, reviews show difficulties in the adoption and effective usage of these applications in telemedicine and by the population in general, due to a lack of evidence-based content and strategies provided (e.g., by commercial apps) or lack of sufficient user engagement with the apps. Nutrition apps typically require self-reporting of food intake by the user which is often seen as a burden and a cause of abandonment of the app. However, current wave of research has taken up the challenge of promoting healthy lifestyles with advances in artificial intelligence (AI). This paper focus on AI chatbots as an innovative approach offering more simplicity and facilitating long-term adherence to health promotion interventions. Conversational assistants provide the advantage of being deployed in smartphones and laptops within a wide variety of applications. We will particularly focus on harnessing the power of intelligent chatbot systems to provide behaviour change interventions in telemedicine for healthy lifestyle promotion. We describe an application scenario for an AI-chatbot delivering support to nutrition education that could help to overcome current limitations of similar mHealth solutions provided for healthy lifestyles and contribute to more effective public health interventions in this application domain.
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AddressingChallengesinPromotingHealthyLifestyles:
TheAI-ChatbotApproach
AhmedFadhil1,SilviaGabrielli2
1,2FondazioneBrunoKessler,
1CentroRicercheGPI
ViaSommarive,18-Trento,Italy
+390405,+390461
{fadhil|sgabrielli}@fbk.eu
 
ABSTRACT
Healthy lifestyles promotion is the main objective of primary care
interventions, starting from the pediatric age, were overweight is
nowadays exposing about one third of children to the risk of
developing chronic diseases, such as diabetes. Recent years have
seen a blast of mHealth apps for health promotion, targeting in
particular nutrition and dietary behaviour change. However,
reviews show difficulties in the adoption and effective usage of
these applications in telemedicine and by the population in
general, due to a lack of evidence-based content and strategies
provided (e.g., by commercial apps) or lack of sufficient user
engagement with the apps. Nutrition apps typically require
self-reporting of food intake by the user which is often seen as a
burden and a cause of abandonment of the app. However, current
wave of research has taken up the challenge of promoting healthy
lifestyles with advances in artificial intelligence (AI). This paper
focus on AI chatbots as an innovative approach offering more
simplicity and facilitating long-term adherence to health
promotion interventions. Conversational assistants provide the
advantage of being deployed in smartphones and laptops within a
wide variety of applications. We will particularly focus on
harnessing the power of intelligent chatbot systems to provide
behaviour change interventions in telemedicine for healthy
lifestyle promotion. We describe an application scenario for an
AI-chatbot delivering support to nutrition education that could
help to overcome current limitations of similar mHealth solutions
provided for healthy lifestyles and contribute to more effective
public health interventions in this application domain.
Categories and Subject Descriptors
H.5.m. Information interfaces and presentation (e.g., HCI):
Miscellaneous.
General Terms
Design, Human Factors.
Keywords: Behaviour change interventions, nutrition
education, chatbots, health promotion, sustainability.
1. INTRODUCTION
Today there is an increasing awareness in public health that
preventing unhealthy lifestyles in the population, from the
pediatric to adult life, can bring enormous advantages in reducing
the occurrence of risk conditions, such as obesity that leads to
chronic diseases (e.g., diabetes) which represents a burden for
healthcare system worldwide [1]. Recent years have seen an
increasing offer of mHealth applications providing nutrition
education as well as promoting healthy diet and physical activity.
However, recent reviews show that most apps fail to sustain
long-term users adherence, often due to a lack of evidence-based
strategies and quality of the information provided [2-3-4].
Messaging platforms, such as Facebook messenger, WeChat, and
WhatsApp are quite popular and users tend to spend more time
chatting than using other applications [5]. With the introduction of
services, like Siri and Google Now or Google Allo people are
getting used to interacting with applications designed to support
smart coaching. Recently, Facebook launched their chatbot
platform to host businesses and provide services to customers
directly from their platform through a simple conversation. The
idea of “assistant-as-app” is to create user-virtual assistant
interaction with either voice or natural text messaging to
accomplish increasingly complex tasks with minimal effort.
Notwithstanding, the increasing number of chatbot solutions built
for health and lifestyle are still amateu r and need improvement
with respect to supporting complex behaviours, such as healthy
dietary adherence. To this extend, the existing solutions mainly
targeted food recommendation and physical activity tracking. The
majority of the approaches are focused on rule-based
architectures, where the chatbot intelligence is pre-defined and for
each user action, the bot triggers a response from a predefined list
of answers. Moreover, a few approaches have focused on
combining various profiling factors and techniques to achieve a
sustainable health plan. These factors are related to, for example,
user personality and providing a personalised plan that fits with
their preferences. In addition, sentiment analysis and user
emotional condition are important factors to consider to improve
the effectiveness and quality of user experience with the chatbot.
Integrating the chatbot design with appropriate behaviour change
strategies and techniques (BCTs) can help to effectively deploy
these solutions to support behavioural interventions for healthy
lifestyles and prevention by healthcare providers.
In this paper, we propose a chatbot system to promote healthy and
sustainable eating behaviour as a possible application scenario for
supporting primary care interventions to prevent weight gain in
the adult population. The bot acts as a bi-directional channel
leading to the adoption of more healthy habits regarding diet,
physical activity as well as food purchasing and preparation. The
presented system is unique in the way it collects data about user’s
relevant practices to provide personalised recommendation that
fits with their preferences and profiles. Moreover, since eating
behaviours and practices are strongly correlated with user’s
emotional and choice dynamics, the bot collects data to determine
user intent and perform sentiment analysis, to provide a more
tailored coaching towards a sustainable healthy diet. Finally, our
approach is meant to support behavioural interventions following
1
the ‘Efficiency Model of Support’ [6] where virtual and human
coaching are combined to facilitate users’ transition to healthier
lifestyles in the most efficient way.
2. AI-CHATBOT ARCHITECTURE
FOR HEALTHY LIFESTYLE PROMOTION
Current research shows that most chatbot systems are used to
access information or communicate simple instructions to the
audience. A few studies have addressed the challenge of adapting
conversational interfaces to detect user sentiment and interpret
their intention. The existing studies on chatbot systems for diet
and lifestyle are either providing a way to track meals or provide
recommendation [7]. There is still room to improve these systems
with respect to bot effectiveness and user’s needs. In this work,
we propose a chatbot architecture that combines advanced feature
integration (e.g., machine learning models) to collect and
understand various features about the users. In this way, the bot
can be trained to understand the user state with respect to a given
plan and trigger the right action that can better fit with their
preferences. Moreover, the bot is able to trigger and notify a
healthcare expert (e.g., nutritionist) to facilitate intervention
whenever relevant. The healthcare expert can track user activities
through a web application.
The envisioned chatbot will combine multiple technical and
behavioural aspects to provide the necessary intelligence while
interacting with the user. These technologies include: the anatomy
and finite state machine for the bot design, the framework to
provide rule-based reasoning for the bot, the approach for
sentiment analysis, the behaviour change techniques to understand
user behaviour at a certain stage, and the intent detection
approach.
Chatbot System Anatomy and Finite State
Machine
Chatbot System Anatomy
Conversational agents can be of type voice-enabled or text-based,
and sometimes a combination of the two. Voice enabled can listen
to and respond in spoken language, whereas text-based can read
and respond to typed messages and requests. All conversational
agents rely on a set of core underlying technologies in order to
understand natural language input and human intend, and hence
engage in a human-like conversational. Figure 1 below shows the
key technology steps to be considered when developing
conversational agents.
Figure 1. Conversational Agents System Anatomy.
These above steps can vary depending on the application domain.
However, the order defined above better fits our chatbot scenario.
The Intent Detection categorizes the request into predefined
intents. The intent reflects what the user is trying to say or
achieve, and hence prescribes an action that defines the desired
outcome. The Role Detection assigns predefined categories to
entities of particular type. For example, for the request “I had a
burger for lunch”, the entity “burger” can be labeled food and the
entity “lunch” can be labeled meal time. The Entity Resolution
matches the identified entity with a real world object or concept.
For example, resolving an entity “beef burger” into a burger. The
Question Answering identifies the best answer for the request
based on knowledge base or acquired data. Finally, the Dialogue
Management tracks the context of the conversation and formulates
the appropriate response to the user.
Chatbot System Finite State Machine
The chatbot system finite state machine refers to the number of
states a user has to follow while interacting with the chatbot to
accomplish the action (Figure 2, Table 1). The system follows a
rule-based logic, however it relies on some APIs to add the
intelligence layer on top of the system.
Figure 2. The Finite State Automaton for Health & Lifestyle Chatbot
STATE
DESCRIPTION
Begin
Where the conversation resides.
Presentation
Where the bot provides necessary
information about the possible
functionalities.
Process
The bot provides information to user based
on their request
Options
The bot provides the activities for users
based on their health condition (e.g., user’s
daily diet or physical activity plan).
Feedback
The bot provides a feedback to the user
about their performance.
Suggestion
Provides activities other than the original
one.
Satisfying
When the user successfully fulfils the given
activity and reaches the goal.
2
Unsatisfying
The system will try to provide other
suggestions to the user, if one will work then
it will switch to Satisfying state and
conclude.
External Actor
If changing suggestion won’t work, then
system will request information from the
External Actor state, which will involve a
human actor in the process.
Conclude
Where the last goal-fulfilment task has been
reached.
End
When the bot closes the operation.
Table 1. The Description for Each Finite State.
Chatbot System Architecture
The chatbot follows a rule-based logic to handle various user
requests, and calls API services when providing the response to
such requests. We use Microsoft Bot Framework to provide a
1
scaffolding to host message-handling logic and plumbing to
integrate with various bot client endpoints. This framework
simplifies the task of setting up a server process which listens for
incoming text messages. Since rule-based frameworks provide no
AI capabilities to parse or classify incoming messages, we will
use API services, such as MonkeyLearn to perform more
2
advanced analysis (e.g., Sentiment Analysis and Intent Detection).
For example, if the user types “I feel happy with the current plan”,
the bot can extract the mood of the user with respect to the given
plan, in this case “Positive, with a certain probability”, for the
system architecture see Figure 3.
Figure 3. The Chatbot System High-Level Architecture.
Random Access Navigation (R.A.N.)
To provide as natural as possible interaction, the bot has to be
intelligent enough to detect how each user asks a question. To
achieve this, we followed the framework described by Shane Mac
[8] to provide random navigation for the bot to detect intentions
from user’s requests. This model outperforms fixed decision trees
and can complete more complex tasks while reduce friction [8].
The idea with this model is to detect all parameters required to
1 https://dev.botframework.com/
2 http://monkeylearn.com
perform an intent with a context, allowing the user to change their
mind without going back, and it works seamlessly with web
views. In Figure 4 we provide an illustration of how an R.A.N
model behaves.
Figure 4. The Random Access Navigation Model.
An example of how such model works is provided below
regarding a user’s checking for grocery stores nearby:
I want to go grocery shopping this afternoon in Trento.
• What grocery stores are there in Trento on Friday?
• Show me grocery shops in Trento.
3. BEHAVIORAL INTERVENTION
TECHNOLOGIES
Within the eHealth and mHealth fields there is a subset of
technologies specifically designed to deliver behavior change
support to improve users’ health, and they are called behavioral
intervention technologies (BITs) [9]. Human support has been
integrated into BITs in different ways (text messaging, email,
phone calls provided by supporters with varying expertise
including therapists, nurses, trainees etc.). Integrating human
support, however, requires developed models for providing this
assistance. The Efficiency Model of Support [6] has been
proposed as a framework to guide the actions of supporters
delivering BITs by helping them to effectively manage the
interplay between information and intervention. Efficiency is
defined as the ratio of the outcome of an intervention relative to
the human resources required to deliver it, since each decision
corresponds to supporting that intervention (what, when, how
much, who provides it) represents a trade-off between devoting
additional resources and accruing additional benefits. According
to the model, decisions should be based in the consideration of
why people may fail to benefit from BITs and five categories of
possible failure points are proposed: usability, engagement, fit,
knowledge and implementation. These points should be taken into
consideration as important targets for support.
In the case of a telemedicine intervention for nutrition education
the deployment of a chatbot may lower usability barriers for users,
since conversational agents are considered among the most
intuitive to use kinds of BITs, requiring less learning effort by the
user. A chatbot would also effectively support engagement of the
user by prompting and providing healthy recommendations at the
time and place when food choices are made (e.g., before/during
daily meals, when cooking or shopping for food). By helping
users to unobtrusively keeping track of their food intake over the
week the chatbot can be an ideal solution to help them acquire an
accurate knowledge of healthy nutrition guidelines and to turn this
knowledge into healthy habits and practices of daily life. The
user-chatbot interaction may also provide an opportunity to easily
collect data on user difficulties by following the behavioural
intervention so as to inform healthcare provider (e.g., nutritionist,
pediatricians etc.) about these difficulties and focusing the
3
discussion on the critical points of the intervention for the patient,
thus better personalizing and improving the efficiency of the
support provided.
Chatbots can offer a lot of benefits in the telemedicine domain
both for healthcare providers and patients. For example, by using
chatbots patients’ data logging and assistance can be more
engaging [9]. Conversational agents are effective in making
healthcare topics easier to understand (e.g., physical activity
promotion, hospital discharge instruction, explanation of medical
documents, and family health history-tracking). Bots are effective
in providing nutrition education and primary care services can
harness the simplicity and age friendliness offered by bots in
various health interventions.
The kind of chatbot proposed for this application scenario can
achieve scalability and user engagement with simple message
exchange. We sum up the envisaged support provided to a user
below:
Achieve healthy eating habits: By exchanging text
messages with the bot, user can receive immediate
actionable feedback to improve eating habits.
Be more active: The bot tracks user activities and helps
to acquire healthy habits.
Maintain healthy habits: By providing automation and
coach support, the chatbot can teach users how to stick
to their new habits over time.
Personalised support: The bot can check the user
adherence and encourage relevant activities that fit with
their personal plan.
Track user sentiment: The bot can collect emotional
data about users to understand their mental/mood
condition at a certain stage during their behavior change
process.
Use Case Scenario
To better present the way a chatbot support may fit into a
telemedicine domain, we provide a use case scenario of the
user-chatbot interaction aimed at improving the user’s nutrition
and at implementing more sustainable food choices.
“Sara is 35 years old, she is living and working in Trento- Italy.
She is busy throughout the week days and has no idea where to
get veggies from the closest grocery shop. Moreover, she doesn’t
like to go to city often. Lately, she started having inactive life due
to her job condition and as a result she put on weight. Sometimes,
this condition and her work put her in a lot of stress which affects
her overall life and daily dietary habits.
Sara decides to change this, she consults with our chatbot and
starts using the chatbot application by simply searching the name
of the bot. Initially, the bot asks some questions about Sara’s
preferences and other diet and health related information. Later,
the bot starts to suggest support and recommendations, such as
KM0 grocery shops and optimal diet that goes well with her
condition and preferences. Later, the bot collects data about her
compliance with the suggestions and about her feeling. All these
data are communicated to the healthcare expert who can provide
alternative plans or intervene with Sara whenever needed. Sara is
better able to keep track of her meals and exercise by interacting
regularly with the bot.
The bot analyses her meals and activity, and then sets goal and
checks in periodically to keep her on track. Moreover, Sara
provides information to the bot about her breakfast,
and the bot
explains the healthiness of her meals; by recording information
about her activity, the bot is able to create an updated picture of
Sara’s condition and feed her with proper support.”
4. Design Challenges
There are still several challenges in applying the described chatbot
approach to support BITs in telemedicine. The conversations
supported between chatbot and user generally cannot be very
complex and they require increasing resources when expanding
the chatbot domain focus. This issue might be partially overcome
by future developments in the architectural design of
conversational agents. Some limitations in the interaction with the
bot are also due to the the singular and plural conversational
forms management. For example, if we want the bot to extract
entities, such as “pizza”, we need to provide the plural form as
well, “pizzas”. The synonyms, hypernyms and hyponyms which
are Natural Language Processing and ontology challenges are
among the complex limitations that most chatbots suffer today.
For example, if the user reports soda as a beverage, but the
chatbot only knows specific terms such as coca-cola or pepsi, that
are hyponyms of soda, then it can’t provide a sound reply to the
user request [11]. Other challenges are related to the privacy and
security of the data collected, since when relying on APIs to
perform sentiment analysis and tracking user habits, it’s important
to secure the user data collected.
Chatbots cannot replace humans [12 -13-14], but they can provide
an interesting channel to deliver behavioral interventions
combined to human support. The kinds of information collected
and provided by the chatbot can effectively complement the
support delivered by telemedicine services. This is likely to
increase the quality and efficiency of healthcare resources
deployed. Effective mechanisms enabling fallback in case the bot
is not able to properly manage a certain situation are to be
designed to facilitate human expert intervention. Future research
will need to tackle these challenges by means of empirical testing
in the field of these solutions.
5. CONCLUSION
The biggest advantages of chatbots include being able to reach a
broad audience on messenger apps, as well as the ability to
automate personalised messages. They can also improve
efficiency of resource allocation in the healthcare domain by
offloading clinicians and human operators from tasks that can be
automated.
Chatbots represent an innovative approach to address challenges
in the telecare and prevention domains. If well-designed and
implemented, chatbots can increase users’ engagement and
self-empowerment, by providing a better experience and save
costs for the healthcare system. However, designing an intelligent
chatbot that responds well to user demands is not trivial. The
chatbot ecosystem is moving very fast and new features are being
released every day by the numerous existing platforms. In this
paper we discussed an application scenario regarding a chatbot
system to promote healthy nutrition. The system adapts machine
learning techniques provided by API services to improve the bot
intelligence. We proposed a chatbot approach over mHealth
alternative solutions because it can fit well with usability
requirements that are essential for supporting interaction by
different target user groups. The bot provides a large set of
relevant functionalities ranging from food recommendation to
4
help shopping in a sustainable way. The fact that tech giants like
Google, Facebook, Microsoft, IBM and Amazon are giving
increasing attention to chatbot solutions is a strong indicator that
this technology will play a key role in the future. Currently, there
is an incredible amount of platforms and tools providing
different complexity levels, expressive powers and integration
capabilities. Which one to choose depends on the contextual
design and application scenario considered. Future work is still
needed to advance the Artificial Intelligence techniques required
to obtain intelligent chatbot solutions for the healthcare domain,
especially in terms of machine learning components, sentiment
analysis and intent detection.
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"Anna: A Nutrition-Facts Dialogue System."
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This study analyzed smartphone obesity-management applications developed in Korea and the quality of the information that they provide. Obesity-management smartphone applications were searched using the keywords 'obesity + management,' 'weight + management,' 'weight + loss,' 'weight + exercise,' 'weight + diet,' 'weight + calories,' and 'diet,' with a search application programming interface (provided by Apple) between September 23 and September 27, 2013. These applications were then classified according to their main purpose, type of interventions used, price, type of developer, and user ratings. The information quality of the applications was analyzed using the Silberg scale. In total, 148 smartphone applications for obesity management were found. The main purpose of most of these applications (70.95%) was to provide information regarding weight control. The most frequently used intervention (34.62%) was to provide information on exercise management. More than half of the applications (58.78%) were free of charge. The mean of users' rating of these applications was 3.68 out of 5. The quality of information provided by these applications was evaluated as 4.55 out of 9: specifically, 1.79 out of 3 for authorship, 0.22 out of 2 for attribution, 1.29 out of 2 for disclosure, and 1.25 out of 2 for currency. Only three of the applications (2.88%) had a score on the Silberg scale greater than or equal to 7 points. The findings of this study suggest that the quality of information provided by smartphone applications in the healthcare domain urgently need to be evaluated to prevent users being misinformed by these applications.
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Little is known about how much smartphone apps for weight control adhere to evidence-informed practices. The aim of this study was to review and summarize the content of available weight control apps. Information on content, user rating, and price was extracted from iTunes on September 25, 2009. Apps (n = 204) were coded for adherence to 13 evidence-informed practices for weight control. Latent class analysis was used to identify subgroups of apps based on endorsement practices. Only a small percentage of apps had five or more of the 13 practices (15%). Latent class analysis revealed three main types of apps: diet, physical activity, and weight journals (19%); dietary advice and journals (34%); and weight trackers (46%). User ratings were not associated with apps from these three classes. Many apps have insufficient evidence-informed content. Research is needed that seeks to develop, improve, and evaluate these apps.
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This paper presents a large-scale analysis of contextualized smartphone usage in real life. We introduce two contextual variables that condition the use of smartphone applications, namely places and social context. Our study shows strong dependencies between phone usage and the two contextual cues, which are automatically extracted based on multiple built-in sensors available on the phone. By analyzing continuous data collected on a set of 77 participants from a European country over 9 months of actual usage, our framework automatically reveals key patterns of phone application usage that would traditionally be obtained through manual logging or questionnaire. Our findings contribute to the large-scale understanding of applications and context, bringing out design implications for interfaces on smartphones.