=Paper=
{{Paper
|id=Vol-2451/paper-09
|storemode=property
|title=Towards Semantic Models for Profiling and Behavior Change in eHealth Applications
|pdfUrl=https://ceur-ws.org/Vol-2451/paper-09.pdf
|volume=Vol-2451
|authors=Jean-Paul Calbimonte,Fabien Dubosson,Michael Ignaz Schumacher
|dblpUrl=https://dblp.org/rec/conf/i-semantics/CalbimonteDS19
}}
==Towards Semantic Models for Profiling and Behavior Change in eHealth Applications==
https://ceur-ws.org/Vol-2451/paper-09.pdf
Towards Semantic Models for Profiling and
Behavior Change in eHealth Applications
Jean-Paul Calbimonte1 , Fabien Dubosson1 , and Michael Schumacher1
eHealth Unit, Institute of Information Systems,
University of Applied Sciences and Arts Western Switzelrand (HES-SO),
Sierre, Switzerland
{firstname.lastname}@hevs.ch
Abstract. Behavior change is a complex process in which people receive
support in order to improve aspects of their behavior, for instance re-
garding their health or lifestyle. Although there exist several theoretical
approaches to model behavior change, including abstractions that can be
applied to different use-cases, these are not easily translated into reusable
components that can be integrated into implementable systems for per-
suasion. This work discusses the need for an ontology-based approach
to modelling interactions in eHealth systems, with the goal of achieving
behavior change. This contribution includes an analysis of current mod-
elling needs in behavior change, specially regarding: stages of change,
motivation & ability factors, plans & actions, argumentation, and do-
main modeling.
1 Introduction
Improving quality of life and reducing health risks are increasingly important
concerns in our society. In different situations, including the presence of chronic
diseases, or the desire to adopt healthier daily habits (e.g. concerning diet, phys-
ical activity, etc.), these improvements are only possible if an effective behaviour
change is also produced. This change can span from small alterations to daily
routine to radical changes in lifestyle. It has been shown that personalized in-
terventions are crucial in order to maximize the efficacy of behaviour change.
Custom and tailored programs are nowadays feasible, thanks to advances in per-
sonal data analytics and personalized digital health. Different models exist to
describe behaviour change strategies [3, 2, 1], and different technological solu-
tions (e-Health, mHealth, serious games, reminders, chatbots, social networks)
have been developed in several use-cases (diabetes, smoke cessation, overweight,
active-ageing, rehabilitation, re-adaptation, etc.).
However, the effort required in order to adapt these models to the appropri-
ate technologies in a given use-case, remains prohibitive and leads to ineffective
or partial implementations, with little or incomplete personalization. As a conse-
quence, there is no clear methodology that allows to effectively model the profile
of a patient, with the goal of using artificial intelligence (AI) techniques to adapt
Copyright ©2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
2 J.-P. Calbimonte et al.
and personalize treatments, recommendations, and other health-related interven-
tions. Therefore, even if different digital solutions and AI techniques have been
shown to provide significant improvement to personalized treatments, it remains
challenging to reuse and apply these methods to other use cases, or to establish
a well-defined workflow for enabling tailored behavior change.
In this paper, we envision an ontology-based approach that establishes in a
systematic way the different elements that can guide the implementation of per-
sonalized behavior change programs, using ontological models as a foundation
layer. In concrete, we address: (i) the modeling of behavior change models them-
selves, i.e. the different states of a user, and the factors and barriers that have an
effect on their actions; (ii) the modeling of arguments that can be used to per-
suade or to influence the user; (iii) the modeling of the interactions with the user,
following agent-based paradigms for autonomous behavior, and negotiation.
Throughout the paper, we use smoking cessation as a running example for
the analysis of semantic modelling needs for behavior change systems.
2 Ontologies for behavior change models
Behavior change is a challenging problem, especially regarding health-related
issues and lifestyle. There are different factors that need to be taken into ac-
count in order to achieve effective outcomes, including the attitude, emotional
issues, social pressure, self-perception, etc. [1]. As each person has a particular
background, context, and circumstances, even if the behavior change goals are
similar, the strategies and techniques need to be personalized.
Fig. 1. I-Change model [3] for behavior change. Different states can be modeled, as well
as the different types of factors that have an influence over the participant’s behavior.
As it can be seen in classic behavior change models, participants may fall un-
der different states, for which intervention may require complete different strate-
gies and approaches. Although different such models exist (e.g. Trans-theoretical
model of stages of change [2], Theory of planned behavior, I-Change model [3]),
they share certain conceptual aspects. For instance the I-Change model (Figure1)
incorporates different stages (similar to the trans-theoretical model), although it
Title Suppressed Due to Excessive Length 3
adds elements, such as predisposing, information, motivation and ability factors,
which have incidence on several aspects of the behavior change process.
As an example, for our smoking cessation use-case, participants may enter a
contemplation state when they consider registering to the program, after real-
izing the benefits of quitting, while considering their own capacity to overcome
addiction. Afterwards, once they enter the program, they may enter a prepa-
ration state in which they become aware of their consumption behavior thanks
to the tracking of cigarettes through a chatbot. This step is crucial as they
consciously learn about their own smoker profiles, which helps detecting which
cigarettes are easily avoidable, what behaviors can be modified, and what ef-
fort is required to attain the desired cessation goals. Furthermore, during the
cessation itself (trial/maintenance), the participants may be in different states
depending on their chosen strategy: e.g., dealing with relapse and/or replacing
smoking with alternative activities.
Using an ontology to represent these states within a behavior change model
can help reusing concepts and relationships shared among them. For instance,
the different intention states (e.g. precontemplation, contemplation, prepara-
tion) can be reused among different models, as well as their relationship with
behavioral states. Moreover, information and awareness factors could feed from
existing ontologies that already provide existing knowledge over a certain sub-
ject (e.g. ontologies for smoking cessation and prevention describing risks, facts,
evidence, etc.). In sum, the modeling of behavior change models would require:
R1.1: Stage modeling: Generic states found in most models provide a high-level
view over a participant’s state with respect to a desired behavior change
program/initiative. From these generic stages, more specific one can be
derived depending on the use case.
R1.2: Modeling of Factors & Barriers: The different factors that my influence
the change of state, as well as the development of a desired behavior
change are numerous, and can be classified in different ways according to
existing models. Motivation, awareness, and ability factors are common
examples.
R1.3: Composition: The combination of different states and factors is desirable,
considering that different models can establish alternative interactions
patterns among them.
R1.4: Triggers: Moving form one stage to the other may happen in different
manners, and often requires to be defined in terms of certain triggers,
events or other type of signaling elements, which should be included in an
ontological model.
3 Modeling plans & actions
Having defined a general behavior change model, the next step is to describe
the plans and actions related to a certain stage of change. These plans refer to
specific activities performed during a program or a treatment, which tend to be
highly domain specific.
4 J.-P. Calbimonte et al.
Ability factors in a behavior change model may include action plans, which
can differ depending on the goals. As an example, in smoking cessation these
action plans may include tracking cigarette consumption, preparing oneself for
the cessation period (e.g., planning distracting actions), or reducing unnecessary
cigarettes. Nevertheless, we can identify the following key elements:
R2.1: Goals: Participants of a health program may define different goals which
may also depend on the stage they are in. For instance, on smoking cessa-
tion, the general goal is to quit smoking entirely. However, there may be
intermediary phases, for instance during preparation or trail, for which the
goal might be to reduce the cigarette consumption, or to at least identify
those cigarettes that can be replaced by other activities. Goals may also
be linked to constraints (e.g., duration) so that they can be evaluated.
R2.2: Planning: Following the goals, a set of activities can be defined according
to a plan. For example, this may translate to monitoring context of con-
sumed cigarettes (reporting mood, need level, circumstances, etc.), adop-
tion of alternative activities replacing smoking (e.g., sports), or changing
certain daily habits.
R2.3: Feedback: During a behavior change program, it is crucial to periodically
assess the situation, in order to check if current measures are effective, or
if amendments must be made to a plan. For instance, in case of relapse, it
may be needed to understand the circumstances of failure. Or conversely,
in case of positive results, how they can be maintained.
R2.4: Personalization: Plans and actions need to be adapted to the specific con-
ditions and context of each participant, to maximize efficacy. People do
not respond in the same way to a treatment or a program, for example in
smoking cessation a participant may struggle more with social smoking,
while others may have troubles dealing with stress. A participant behav-
ioral profile should be modeled, in our case, through ontologies in order
to capture these specificities and patterns.
4 Domain-specific vocabularies
As we mentioned before, domain-specific semantic models are important, in order
to reflect accurately the different aspects relative to a health program. These
ontologies may cover a number of different aspects, some of which we described
throughout this paper. In this section we focus on those aspects that are generally
not extensible to other domains.
Domain specific vocabulary requirements can be summarized as:
R5.1 Profile data: Data models must include domain specific information re-
lated to the user profile. This may include data intrinsic to the user (e.g.
information of the participant history, behavior patterns, self assessment
before, during and after the program, etc.)
R5.2 Health issue data: Ontologies may describe a pathology, a health issue/problem,
including possible complications, relationship with co-morbidities, diagno-
sis, etc.
Title Suppressed Due to Excessive Length 5
R5.3 Treatments/Medication: In some cases certain procedures, treatments and
medications may be part of a behavior change program. In those cases,
existing standards related to these information elements must be incorpo-
rated.
R5.4 Messages and Communication data: Information about a health problem,
motivation and encouragement messages, among others, are fundamental
in order to engage with participants, via different communication means
(e.g. chatbot, facebook, email, etc.) In certain cases these materials are
well known and can be reused or adapted to a degree.
5 Roadmap: Ontology-based behavior change applications
As we have seen, behavior change applications can benefit form existing theories,
represented as behavior and intention states, influenced by different types of fac-
tors and barriers. Using ontology-based models as explained above, can provide
a solid foundation for developing behavior change applications, considering not
only the personal circumstances of a participant, but also the specificities of the
health issue that is being addressed.
In summary, the success of these behavior change applications will require
a combination of these models, and their reuse by agent systems that include
them as part of their knowledge/beliefs/goals. The challenges and future work
include:
(i) The design of vocabularies and ontologies for description, discovery
and exchange within behavior change agents;
(ii) The development of specific domain models that can help enriching
agent-based systems in areas such as physical rehabilitation, medication
adherence, physical activity, sleep training, etc.;
(iii) Agent coordination and negotiation to incorporate computational per-
suasion into the agent execution logic;
(iv) The specification of cooperation protocols for participating agents, find-
ing common problems and targeting community-based interventions;
(v) The implementation of the proposed model, and evaluation on a real
environment with a considerable number of participants;
(vi) Ensuring privacy protection, using different approaches spanning from
obfuscation to anonymity guarantees.
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