=Paper=
{{Paper
|id=Vol-2515/paper1
|storemode=property
|title=Extending HeLiS: From Chronic Diseases to Behavior Change
|pdfUrl=https://ceur-ws.org/Vol-2515/paper1.pdf
|volume=Vol-2515
|authors=Mauro Dragoni,Valentina Tamma
|dblpUrl=https://dblp.org/rec/conf/semweb/DragoniT19
}}
==Extending HeLiS: From Chronic Diseases to Behavior Change==
https://ceur-ws.org/Vol-2515/paper1.pdf
Extending HeLiS: From Chronic Diseases to Behavior
Change
Mauro Dragoni and Valentina Tamma
Fondazione Bruno Kessler, Trento, Italy
dragoni@fbk.eu
Abstract. The use of knowledge resources in the digital health domain is a trend-
ing activity significantly grown in the last decade. In this paper, we present two
extensions of HeLiS, an ontology aiming to provide in tandem a representation
of both the food and physical activity domains and the definition of concepts
enabling the monitoring of users’ actions and of their unhealthy behaviors. The
presented extensions focus on two aspects: the modeling of relationships between
food categories and the most common chronic diseases and a top-level layer rep-
resenting the barriers that users may encounter during the self-management of
their lifestyles and/or their chronic diseases. We describe the construction pro-
cess and the main concepts that have been included.
1 Introduction
Chronic diseases, such as heart disease, cancer, and diabetes, are responsible for ap-
proximately 70% of deaths among Europe and U.S. each year and they account for
about 75% of the health spending1 ,2 . Such chronic diseases can be largely preventable
by eating healthy, exercising regularly, avoiding (tobacco) smoking, and receiving pre-
ventive services. Prevention at every stage of life would help people stay healthy, avoid
or delay the onset of diseases, keep diseases they already have from becoming worse or
debilitating; it would also help people lead productive lives and, at the end, reduce the
costs of public health.
People can start their own prevention process by simply monitoring their lifestyles,
in terms of dietary habits and physical activities they do. In order to support this, struc-
tured resources able to combine all information and to support the integration of mon-
itoring facilities have to be developed. Besides the domain knowledge needed for rep-
resenting with a semantic flavor foods’ composition and the effort level of physical
activities for measuring the compliance between a user profile and the rules associated
with it, there are two other aspects enabling the realization of smart platforms. First,
Copyright 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
1
http://www.who.int/nmh/publications/ncd report full en.pdf
2
https://www.cdc.gov/media/releases/2014/p0501-preventable-deaths.html
2 Mauro Dragoni and Valentina Tamma
knowledge representing how much a specific food category can affect the onset or the
worsening of a specific disease. Second, knowledge about the barriers (physical of psy-
chological) that a user may have concerning the adoption of healthy habits.
In this paper, we presents two extensions of the HeLiS ontology [1] 3 represent-
ing the two aspects mentioned above. The first extension provides a conceptual model
representing the risk level of each food categories already defined in HeLiS associated
with the onset or worsening of the most common five chronic diseases (i.e. diabetes,
kidney diseases, cardiovascular diseases, hypertension, and obesity). While, the second
extension, provides an abstract layer of a conceptual model representing the barriers
that a user may encounter during the self-management of his/her lifestyle or of his/her
chronic disease (e.g. knowledge representing why a diabetes patient is not able to check
his/her glycemia constantly).
The relevance of these extensions with respect to the state of the art pivots around
the integrated model representing (i) a fine-grained representation of the links between
food categories and chronic diseases and (ii) a top-level representation of the clinical
(from the psychological perspective) barriers that be exploited for developing more fine-
grained models supporting the realization of behavior change paths. Around these two
extensions, the HeLiS ontology provides a flexible support to rules modeling that can
be used for the reasoning on data provided by users. Besides the conceptual model per
se, the HeLiS ontology represents a valuable resource for the healthcare domain thanks
to the knowledge included into the provided resource.
The remain of the paper is organized as follows. Section 2 provides a brief overview
of the main ontologies concerning the healthy lifestyle domain. In Section 3 we describe
the methodology we followed for modeling the two extensions, while Section 4 shows
the main entities of the conceptual model. Finally, Section 5 concludes the paper.
2 Related Work
We provide in this Section a brief summary of the most relevant work on ontologies
describing both the food and the physical activity domains.
In [2] the authors describe food intake patterns identified by applying new food cat-
egories, in particular: (i) nutrient composition and energy density, (ii) current scientific
evidence of health benefits, and (iii) culinary use of each food. In [3], a process is pre-
sented for a rapid prototyping of a food ontology oriented to the nutritional and health
care domain that is used for sharing existing knowledge. However, unfortunately, this
resource is no longer available.
The contribution presented in [4] discusses the design and development of a food-
oriented ontology-driven system (FOODS), used for food or menu planning in a restau-
rant, clinic/hospital, or at home. FOODS comprises (i) a food ontology, (ii) an expert
system using such an ontology and some knowledge about cooking methods and prices,
and (iii) a user interface suitable for users with different levels of expertise. Its aim is
to support the management of treatment plans for patients affected by type 1 or type
2 diabetes. Instead, the work presented in [5] focuses on the integration of different
3
http://w3id.org/helis
Extending HeLiS: From Chronic Diseases to Behavior Change 3
domain ontologies, like food, health, and nutrition, in order to help personalized infor-
mation systems to retrieve food and health recommendations based on the user’s health
conditions and food preferences. Recently, the work presented in [6] describes an ontol-
ogy modeling the protected names of brands, from the raw materials to the production
process.
A set of ontologies have been proposed that collect information about packaged
food. Examples are Open Food Facts 4 and Food Product Ontology [7]. However, their
focus on categorizing and describing packaged food led to low coverage of concepts
describing food compositions.
Recently, the FoodOn ontology 5 has been released. This ontology represents foods
from a different perspective with respect to the HeLiS ontology. Instead of focusing
on food composition, they aim to realize a food description system that registers food
manufacturers. Indeed, the FoodOn ontology includes, for each product, its origin, the
physical attributes, processing, packaging, dietary uses and geographical origin.
Finally, in [8] the design steps are described, the working mechanism, and the case
of use of the Ontology-Driven Mobile Safe Food Consumption System (FoodWiki)
using semantic matching. This resource aims to address problems similar to the HeLiS
ontology. However, no information about physical activities and their correlation with
food categories are included in the ontology nor the possibility of modeling in a flexible
way rules users should follow and the possible associated violations.
Concerning physical activity, we report two ontologies, both available through the
BioPortal 6 website.
The first one is the SMASH (Semantic Mining of Activity, Social, and Health data) 7
ontology. The goal of the SMASH ontology is to describe concepts correlating physical
activities and social networks. The system developed upon this ontology aims to sustain
weight loss with continued intervention with frequent social contacts. The coverage of
the SMASH ontology is very limited. Indeed, only 18 activities are defined.
The second one is the Ontology of Physical Exercises (OPE) 8 . Here, physical ac-
tivities are modeled from the functional perspective. Thus, exercises are described in
terms of movements, how the different musculoskeletal parts of the human body are
engaged, and which are the expected health outcomes. Also in this case, the coverage
of the physical activity domain is limited because only general categories of activities,
like AerobicExercise or IsotonicExercise, are defined.
3 The HeLiS Ontology
The development of the HeLiS ontology followed the need of providing a knowl-
edge artifact able not only to provide a representation of domains concerning healthier
lifestyles, but, also, to support further activities like, for example, remote medical mon-
itoring and support to behavior change. The two extensions presented in this paper go
4
Open Food Facts. Available online: http://world.openfoodfacts.org/who-we-are
5
http://foodontology.github.io/foodon/
6
http://bioportal.bioontology.org/
7
http://bioportal.bioontology.org/ontologies/SMASHPHYSICAL
8
https://bioportal.bioontology.org/ontologies/OPE
4 Mauro Dragoni and Valentina Tamma
into the same direction by extending the dietary part with information concerning the
risk level of each food category with respect to the onset or worsening of a subset of
chronic diseases and by presenting a representation of barriers that can obstruct persons
to start of behavior change process for improving the quality of their lifestyle.
The process for building these two extensions followed, as happened for the He-
LiS ontology, the METHONTOLOGY [9] methodology. This approach is composed
by seven stages: Specification, Knowledge Acquisition, Conceptualization, Integration,
Implementation, Evaluation, and Documentation. The overall process involved three
knowledge engineers and four domain experts from the Trentino Healthcare Depart-
ment.
The choice of METHONTOLOGY was driven by the necessity of adopting a life-
cycle split in well-defined steps. The development of these HeLiS ontology extensions
required the involvement of the experts in-situ. Thus, the adoption of a methodology
having a clear definition of the tasks to perform was preferred. Other methodologies,
like DILIGENT [10] and NeOn [11], were considered before starting the construction
of the HeLiS ontology. However, the characteristics of such methodologies, like the
emphasis on the decentralized engineering, did not fit our scenario well.
Specification And Knowledge Acquisition. The Specification and Knowledge Acquisi-
tion stages completely overlapped during the building of the two proposed extensions.
The purpose of the HeLiS ontology is two-fold. On the one hand, we want to provide
a detailed and integrated model of the food and diseases domain. On the other hand,
we want to support the definition of behavior change process by taking in account the
barriers that can affect users.
As for the HeLiS ontology, also the presented extensions have been modeled with a
high granularity level. Concerning the representation of associations between food cat-
egories and diseases, we defined the DiseaseRiskLevel concept that allows to link each
food category with their risk level for a specific disease. The risk level is represented by
a datatype property representing such a risk level with an integer value. This knowledge
was acquired directly from a set of focus groups run with domain experts.
While concerning barriers, we defined which are the main type of barriers that we
want to consider for supporting the development of third-party behavior change appli-
cations. The conceptualization of barriers and of the different state of change has been
created by extracting knowledge from domain specific unstructured resources [12].
Conceptualization. The conceptualization of the HeLiS ontology was split into two
steps. The first one was covered by the knowledge acquisition stage, where most of
the terminology is collected and directly modeled into the ontology. Examples are the
diseases and the barriers. While the second step consisted in deciding how to represent,
as classes or as individuals, the information we collected from unstructured resources.
Then, we modeled the properties used for supporting all the requirements.
During this stage we relied on several ontology design patterns (ODP) [13]. How-
ever, in some cases we renamed some properties upon the request of domain experts.
In particular, we exploit the logical patterns Tree and N-Ary Relation, the alignment
pattern Class Equivalence, and the content patterns Parameter, Time Interval, Action,
Classification.
Extending HeLiS: From Chronic Diseases to Behavior Change 5
Integration The integration of both extensions has two objectives: (i) to align them with
a foundational ontology, and (ii) to link it with the Linked Open Data (LOD) cloud. The
first objective was satisfied by aligning the root concepts of both extensions with ones
defined within the DOLCE [14] top-level ontology. While, the second objective was
satisfied by aligning our ontology with the UMLS Knowledge Base 9 since it has been
included within the LOD cloud recently. This way, it may work as a bridge between the
latter and the HeLiS ontology.
Implementation As for the HeLiS ontology, both extensions are represented by us-
ing the RDF/XML 10 language in order to provide a formal representation enabling
the check of inconsistencies, the visualization of ontology structure, and the ease of
maintenance. The editing of the ontology is demanded to the MoKi tool [15], while the
exposure of the ontology is granted by the services available from the HeLiS ontology
website.
Evaluation To evaluate our ontology we adopted the metrics described in [16–20]:
Accuracy, Adaptability, Clarity, Completeness, Consistency/Coherence, and Organiza-
tional fitness.
The overall Accuracy of the extensions has been judged as good. The knowledge of
the domain experts was in-line with the complexity of the use axioms. Indeed, within
the HeLiS ontology there are not very complex axioms. Then, by considering the rep-
resentation of the real world, the evaluators agreed on the correctness of the ontology
in describing the domain.
Concerning the Adaptability of the ontology, the evaluators focused on the possi-
ble extension aspects. They verified that the ontology can be extended and specialized
monotonically since once the two extensions are populated, the ontology does not react
negatively to these changes because its consistency is preserved.
About the Clarity of the ontology, the evaluators agreed with the strategy decided
by the modeling team about using concept labels communicating the intended meaning
of each concept and the use of definitions and descriptions of the main concepts of the
ontology, especially for the root concepts of each branch. Moreover, each definition
has been well documented within the ontology in order to make the meaning of each
concept understandable by who uses the ontology.
The experts agreed about the Completeness of the HeLiS ontology. However, they
distinguished among the TBox and the ABox. Indeed, concerning the TBox, the evalu-
ators agreed about the completeness of the ontology and the lexical representations of
the concepts. While, regarding the ABox, the evaluators highlighted the necessity of in-
cluding further nutritional diseases in the future. This observation is definitely pertinent,
especially, if we consider the possibility of developing end-users applications.
With the introduction of the two extensions, the HeLiS ontology has been judged,
also, Consistent and Coherent. Consistent because no contradictions were found by the
evaluators. Coherent because the evaluators observed little bias between the documen-
tation containing the informal description of the concepts and their formalization.
9
https://www.nlm.nih.gov/research/umls/
10
https://www.w3.org/RDF/
6 Mauro Dragoni and Valentina Tamma
Finally, concerning the Organizational fitness, the HeLiS ontology has been de-
ployed within the organization as a web service in order to make it easily accessible by
the community and potential stakeholders. A focus group has been organized with both
the modeling team and the evaluators for discussing about the adopted methodology,
that was judged appropriate by considering the necessity of working in-situ all together
and of synchronizing the commitments of all the people involved.
Documentation The documentation of the presented extensions of the HeLiS ontol-
ogy has been done from two perspectives. First, during the whole modeling process, a
document has been prepared by the people involved in the construction process. This ac-
tivity was necessary because the development of the extensions and their sustainability
are granted by a public funding program 11 . Thus, all performed steps were documented
and archived within the funding dossier. Second, in order to ease the readiness of the
ontology for users, we provided a different documentation file generated by using the
LODE 12 system and available on the ontology website.
4 Inside The HeLiS Ontology
The full description of HeLiS is provided in [1]. Here, we briefly report which are
the main concepts of the ontology and then we focus on the description of the two
extensions.
4.1 HeLiS Root Concepts
The ontology contains six root concepts: Activity, Food, MonitoringEntity, Nutrient,
TemporalEvent, and UserEvent. Beside these, we also defined the User concept that
does not play the role of superclass of any concept but that is fundamental for associat-
ing specific events with the people did them.
Figure 1 shows a general overview of the ontology with the main concepts.
The Food root concept subsumes two macro-groups of entities descending from
BasicFood and Recipe concepts. Instances of the BasicFood concept describe foods for
which micro-information concerning nutrients (carbohydrates, lipids, proteins, miner-
als, and vitamins) is available, while instances of the Recipe concept describe the com-
position of complex dishes (such as Lasagna) by expressing them as a list of instances
of the RecipeFood concepts. This concept reifies the relationships between each Recipe
individual, the list of BasicFood it contains and the amount of each BasicFood The
Nutrient concept subsumes 81 different type of nutrients properly categorized. Nutri-
ents are instantiated with through individuals describing a specific amount of a nutrient.
Then each BasicFood is linked to all the necessary nutrients’ individuals through the
hasNutrient object property.
The Activity concepts subsumes 21 subclasses representing likewise physical ac-
tivity categories and a total of 856 individuals each one referring to a different kind
11
More details about the sustainability plan of HeLiS are reported in [1]
12
http://www.essepuntato.it/lode
Extending HeLiS: From Chronic Diseases to Behavior Change 7
Fig. 1. Overview of the HeLiS ontology.
of activity. For each activity, we defined datatype properties providing the amount of
calories consumed in one minute for each kilogram of weight and the MET (Metabolic
Equivalent of Task) value expressing the energy cost of the activity.
The TemporalEvent concept defines entities used for representing specific moments
or delimited timespan which the data to analyze refers to.
The UserEvent concept subsumes the conceptualization of information that a user
can provide, i.e. food consumption and performed activities, and also links them with
the possible violation that can be generated after their analysis. Concerning the repre-
sentation of users’ activities and personalized information, we modeled the Consumed-
Food and the PerformedActivity concepts. Both concepts are used as reification of the
fact that a user consumed a specific quantity of a food or performed an activity for a
specific amount of time.
Finally, concepts subsumed by the MonitoringEntity one are responsible for model-
ing the knowledge enabling the monitoring of users’ behaviors. Here, we can appreciate
five concepts exploited at reasoning time for detecting undesired behaviors associated
with users: MonitoringRule, Violation, Profile, Goal, and Interval.
4.2 Food-Disease Extension
The first extension of the HeLiS ontology consisted in adding, to the dietary domain, in-
formation concerning the risk level of food categories with respect to specific diseases.
Figure 2 shows an excerpt of the HeLiS ontology representing the associations repre-
senting the risk level that food categories have with respect to the onset or worsening of
some chronic diseases.
We mentioned early that instances of the BasicFood concept describe foods for
which micro-information of nutrients are available. Moreover, these instances belong
also to subclasses of the BasicFood concept, such as Pasta, Aged Cheese, Eggs, Cold
Cuts and Vegetal Oils. On the other hand, instances of the Recipe concept, describe the
composition of complex dishes (such as Pasta with Carbonara Sauce) by expressing
them as a list of instances of the RecipeFood concepts. This concept reifies the relation-
ships between each Recipe individual, the list of BasicFood it contains and the amount
8 Mauro Dragoni and Valentina Tamma
Fig. 2. Excerpt of the HeLiS ontology including the main concepts (white boxes) and instances
(blue boxes) exploited by our semantic platform. Solid lines are object properties, dashed lines
are RDF core properties
of each BasicFood. Besides this dual classification, instances of both BasicFood and
Recipe concepts are categorized under a more fine-grained structure.
The Disease concept defines the diseases supported by the system such that infor-
mation about the risk level relationship with specific BasicFood is available. Currently,
we instantiate the Disease concept for diabetes, kidney diseases, cardiovascular dis-
eases, hypertension and obesity. Diseases are defined as single individuals instead of
concepts for avoiding the creation of a new individual for each specific disease for
each user. Instances of the DiseaseRiskLevel concept reifies the relationships between
each Disease and BasicFood individuals and with the risk level of a BasicFood for that
Disease. The risk level is represented by a value ranging from 0 (no risk) to 3 (high
risk). For readability we report in Figure 2 only some instances of the DiseaseRiskLevel
concept, e.g., DiseaseRiskLevel-A, DiseaseRiskLevel-B, and DiseasesRiskLevel-C.
4.3 Behavior Change Barriers Extension
The second extensions concerns the modeling of the barriers that a user can encounter
during the maintenance of healthy habits or the self-management of chronic diseases.
This extension is composed by three main parts: (i) the classification of the barriers,
Extending HeLiS: From Chronic Diseases to Behavior Change 9
(ii) the representation of the different state of changes, and (iii) a new taxonomy for
classifying the list of physical activities defined within HeLiS.
The Barrier concept is the root of the barriers classification branch that subsumes
six different kind of barriers. The EnvironmentBarrier refers to the impossibility of
performing an action due to unfavorable climatic conditions, the cost of the equipment
need, the lack of safety, etc.. HealthBarrier concerns the presence of some disease pre-
venting to complete specific action. This concept enables the possibility of importing
external medical knowledge bases (e.g. the UMLS). This way, HeLiS will be connected
with medical knowledge that can be exploited at reasoning time. The PersonalBarrier
concept represents all barriers associated with the real-life situations (e.g. job condi-
tions) that obstruct the performance of specific actions. Then, the PhysicalBarrier and
PsycologicalBarrier concepts are related to hindrances given by physical pains (e.g.
knee injury) or emotional status (e.g. fear) that block a person in performing specific
actions. Finally, the SocialBarrier concept mainly refers to possible lacks of support
from people close to patients (parents, friends, etc.).
The second part consists in the abstract representation of the transtheoretical model.
Such a model is used in psychology for supporting the behavior change process that
a user can perform for changing their lifestyle or habits. Here, we defined the basic
concepts which instances can be linked by the UserStatus concept already defined in
HeLiS and that is used as reification of the status in which a User is during a specific
Timespan. The main concepts we defined are StateOfChange that is the root concept
of this branch, and then the six phases in which a User can be: PreContemplation,
Contemplation, Preparation, Action, Maintenance, and Termination.
Finally, this extension provides a new taxonomy of physically activities defined in
the core of HeLiS. The taxonomy defined within the core of HeLiS classifies physical
activities by type. Differently, this extension provides a classification of physical activi-
ties from two different perspectives: the energetic system generally used for performing
the action (e.g. aerobic or anaerobic), if the activity required flexibility abilities, and the
intensity (or effort) level of each activity. The rationale of this classification is given by
the necessity of defining the relationships between barriers and physical activities. For
instance, in case a user suffers from a asthma, such a HealthBarrier may obstruct the
performance of an OutdoorActivity.
5 Conclusions And Future Work
In this paper, we presented two extensions of the HeLiS ontology concerning (i) the
representation of the risk level of each food categories defined in HeLiS associated
with the onset or worsening of a subset of chronic diseases and (ii) the modeling of
a knowledge layer representing the barriers that a user may encounter during the self-
management of his/her lifestyle or of his/her chronic disease. The knowledge modeled
within the HeLiS ontology combines information extracted from unstructured resources
with the ones collected from domain experts coming from the medical domain. We
described the process we followed to build the ontology and which information we
included.
10 Mauro Dragoni and Valentina Tamma
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