JMIR MEDICAL INFORMATICS

Spoladore et al

Original Paper

An Ontology-Based Decision Support System for Tailored ClinicalNutrition Recommendations for Patients With Chronic Obstructive
Pulmonary Disease: Development and Acceptability Study
Daniele Spoladore1,2*, DPhil; Vera Colombo1*, DPhil; Alessia Fumagalli3*, MD, DPhil; Martina Tosi4,5*, RD; Erna
Cecilia Lorenzini4,6*, MD; Marco Sacco1*
1

Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Lecco, Italy2

Department of Pure and Applied Sciences, Computer Science Division, Insubria University, Varese, Italy

3

Unit of Pulmonary Rehabilitation, IRCCS, Italian National Research Center on Aging, Casatenovo, Italy

4

Institute of Agricultural Biology and Biotechnology, National Research Council of Italy, Milan, Italy

5

Department of Health Science, University of Milan, Milan, Italy

6

Department of Biomedical Sciences for Health, Chair of Clinical Pathology, University of Milan, Milan, Italy*

all authors contributed equally

Corresponding Author:
Daniele Spoladore, DPhil
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing
National Research Council of Italy
Via G. Previati 1E
Lecco, 23900
Italy
Phone: 39 03412350202
Email: daniele.spoladore@stiima.cnr.it

Abstract
Background: Chronic obstructive pulmonary disease (COPD) is a chronic condition among the main causes of morbidity andmortality worldwide, representing a burden on health care systems. Scientific literature highlights that nutrition is pivotal in
respiratory inflammatory processes connected to COPD, including exacerbations. Patients with COPD have an increased risk ofdeveloping nutrition-related comorbidities, such as diabetes, cardiovascular diseases, and malnutrition. Moreover, these patients
often manifest sarcopenia and cachexia. Therefore, an adequate nutritional assessment and therapy are essential to help individuals
with COPD in managing the progress of the disease. However, the role of nutrition in pulmonary rehabilitation (PR) programs
is often underestimated due to a lack of resources and dedicated services, mostly because pneumologists may lack the specialized
training for such a discipline.
Objective: This work proposes a novel knowledge-based decision support system to support pneumologists in considering
nutritional aspects in PR. The system provides clinicians with patient-tailored dietary recommendations leveraging expert
knowledge.
Methods: The expert knowledge—acquired from experts and clinical literature—was formalized in domain ontologies andrules, which were developed leveraging the support of Italian clinicians with expertise in the rehabilitation of patients with COPD.
Thus, by following an agile ontology engineering methodology, the relevant formal ontologies were developed to act as a backbone
for an application targeted at pneumologists. The recommendations provided by the decision support system were validated bya group of nutrition experts, whereas the acceptability of such an application in the context of PR was evaluated by pneumologists.
Results: A total of 7 dieticians (mean age 46.60, SD 13.35 years) were interviewed to assess their level of agreement with thedecision support system’s recommendations by evaluating 5 patients’ health conditions. The preliminary results indicate that thesystem performed more than adequately (with an overall average score of 4.23, SD 0.52 out of 5 points), providing meaningful
and safe recommendations in compliance with clinical practice. With regard to the acceptability of the system by lung specialists(mean age 44.71, SD 11.94 years), the usefulness and relevance of the proposed solution were extremely positive—the scoreson each of the perceived usefulness subscales of the technology acceptance model 3 were 4.86 (SD 0.38) out of 5 points, whereas
the score on the intention to use subscale was 4.14 (SD 0.38) out of 5 points.

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Conclusions: Although designed for the Italian clinical context, the proposed system can be adapted for any other national
clinical context by modifying the domain ontologies, thus providing a multidisciplinary approach to the management of patientswith COPD.
(JMIR Med Inform 2024;12:e50980) doi: 10.2196/50980
KEYWORDS
ontology-based decision support system; nutritional recommendation; chronic obstructive pulmonary disease; clinical decision
support system; pulmonary rehabilitation

Introduction
Background
Chronic obstructive pulmonary disease (COPD) is one of theleading causes of morbidity and mortality worldwide. In Italy,
3.2% of the population in 2019 had a diagnosis of COPD, andsuch numbers are expected to increase in the next years due tothe worsening of risk factors [1]. COPD is characterized bychronic inflammation in the lungs and airflow obstruction.
Starting from the respiratory system, COPD has several systemic
effects, including skeletal muscle wasting (which limits exercise
capacity), increased risk of cardiovascular disease, osteoporosis,
depression, and anxiety [2]. Pulmonary rehabilitation (PR) isan evidence-based, nonpharmacological intervention that helps
people with COPD improve their health condition and qualityof life by increasing exercise capacity and reducing symptoms
of dyspnea and fatigue [3]. PR is based on a multidisciplinary
approach and includes several components, among which
nutritional status evaluation is considered relevant.
However, as highlighted recently by the American Thoracic
Society, the scarcity of resources often limits the nutritional
component, which, in some settings, can be accessible only
outside the PR program [4]. Moreover, even though the
influence of nutrition on COPD has been recognized and a
relationship between some nutrients and pulmonary functions
has been identified, PR programs often do not formally include
patient-specific dietary recommendations. Therefore, morestudies are required to drive the implementation of tailorednutritional strategies [5].

The Role of Nutrition in Patients With COPD:
Evidence
The relationship between COPD and nutrition has been
investigated by many authors, who have put together a set ofevidence that underlines the role of nutrients in PR and thedisease exacerbations. The results of the systematic review andmeta-analysis by Collins et al [6] demonstrated that nutritional
support, primarily in the form of oral nutritional supplements,
improves total energy intake, anthropometric measurements,
and grip strength in patients with COPD. This work highlighted
how dietary changes supported protein and energy intake inpatients with COPD, resulting in weight gain and a moderateincrease in muscle strength—which can significantly impact
respiratory muscle strength, walking distance, and quality oflife. Another systematic review [7] found that nutritional
supplementation improves body weight gain in patients with
COPD, especially when malnourished, in terms of bodycomposition, respiratory muscle strength, and quality of life.
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Inadequate nutritional intake, higher daily energy expenditure,weight loss, and fat-free mass (FFM) depletion affect thefunctional capacity of patients with COPD. The progression of
the disease also contributes to an increased respiratory muscle
load, hypoxemia, physical inactivity, and release of
inflammation mediators [8]. In addition, Liu et al [9] explored
the associations among COPD, diet, and inflammation,
identifying a correlation between diet and COPD inflammatory
status. By calculating the Diet Inflammatory Index in COPD,
they observed that COPD was more prevalent in patients witha worse Diet Inflammatory Index.
A systematic review of factors influencing the risk of developing
COPD [10] concluded that the Western diet, characterized by
the consumption of processed meat and alcohol and a low intakeof fruit and vegetables, is associated with the prevalence of
COPD. A cross-sectional study [11] assessed the nutritional
status of outpatients with COPD and found a significant
association between malnutrition and COPD—most of thepatients did not have an adequate daily food intake and showeda lower quality of life, highlighting the need for a more tailorednutritional intervention for managing malnutrition in patientswith COPD with cachexia. In COPD, low FFM and sarcopenia
are predictive factors of mortality [12]. Considering thatliterature data demonstrate that the presence of malnutrition
and, above all, a low FFM index is associated with an increased
risk of mortality, Schols et al [13] identified different nutritionalrisk profiles based on nutritional phenotypes that appear to bepredictors of outcome independent of impaired respiratory
function. These nutritional phenotypes included obesity,
sarcopenic obesity, sarcopenia, and cachexia and required atleast three items for their identification: (1) BMI and body
circumference, (2) bioelectrical impedance analysis, and (3)history of unintentional weight loss. A recent study [14]demonstrated that patients with COPD with sarcopenia andsarcopenic obesity had worse muscle strength than patients withhealthy body weight, claiming that body composition isassociated with physical functions. Patients with COPD withobesity have a higher risk of developing comorbidities such as
diabetes and metabolic and cardiovascular diseases in addition
to typical COPD symptoms.

Research Challenge: Nutritional Status to Prevent
COPD Exacerbation
The evidence reported previously indicates that clinicians anddietitians must evaluate the nutritional status and bodycomposition of patients when giving nutritional advice or dietrecommendations to patients with COPD with obesity to foster
an adequate intake of macro- and micronutrients and control
obesity as well [15].
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Malnutrition plays a pivotal role among the challenges that mustbe faced in treating COPD. The study by Mete et al [16] affirmsthat malnutrition and risk of malnutrition are very frequentconditions among patients with COPD and, together with asignificantly lower BMI, are associated with disease severity,
as highlighted by pulmonary function tests. In clinical practice,
sarcopenia assessment is not part of the standard of care,
implying a loss of personalized treatment that should be essential
to improve health outcomes related to the disease [17]. The
current literature illustrates that screening for sarcopenia andtailored nutrition intervention in patients with COPD could
cost-effectively achieve better health outcomes [18,19].
Functional capacity, respiratory muscle strength, and quality oflife can all be improved through nutritional supplementation
and a better assessment of nutritional status.
The future nutrition challenges require the identification of
specific targets of intervention, considering body composition,
nutritional status, and inflammation in addition to the strictly
clinical aspects. Nowadays, diet is not always an integral partof COPD therapeutic strategy [20]. Moreover, different studies
have highlighted the necessity of new methods to assess
malnutrition and evaluate nutritional status in patients with
COPD, not considering the BMI alone [21] as it misses
important changes in body composition.
This new vision places nutritional intervention as an integral
part of COPD management, not only in the advanced and earlystages but also to prevent the evolution toward respiratory
failure. Creating a decision support system (DSS) that brings
together data and knowledge in the nutritional field to identify
the different nutritional phenotypes and suggest specific dietary
recommendations could be useful to meet these challenges. In
particular, this DSS could become a valuable tool for
pulmonologists to develop nutritional interventions as an integral
part of the COPD therapeutic strategy even in the absence ofspecific resources.

Objective of This Study
Digital health care applications are already used to improve PR
models, mainly for self-management; modification of lifestylefactors; and modification of risk factors, such as smoking
cessation and fostering physical activity [22]. However, to thebest of our knowledge, there are only a few applications focusedon nutritional aspects associated with COPD. This work
leveraged clinical expert knowledge—formalized into domain
ontologies—from the Italian health care context to develop a
DSS to support pneumologists in considering nutritional aspects
in PR to avoid the disease’s exacerbation and provide patientswith tailored dietary guidelines. The application exploiting the
DSS fits the context of a preprototype according to the World
Health Organization (WHO) guidelines for the monitoring andevaluation of digital health interventions [23].

Related Work
Overview
Ontology-based technologies have been adopted for both clinical
DSSs and patient-centered DSSs. Regarding the ontologicalformalization of requisites and tailored suggestions for patientswith COPD in DSSs, no work has tackled this issue in
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PR—except for an early version of the system we propose inthis work [24]. Nonetheless, the use of ontologies to formalize
COPD has been established in some works. Moreover, specificdigital applications for patients with COPD have been developedin the past years. This section presents works relevant to thefields of ontology-based clinical and patient-centered DSSs anddigital applications specifically developed for patients with
COPD.

Ontologies and Ontology-Based Systems for COPD
Management
COPD has been formalized in ontologies or knowledge bases
since the early 2010s. The COPD ontology by Greenberg et al[25] was developed to support COPD longitudinal research andclinical trials, leveraging a preexisting model dedicated torepresenting subpopulations of patients. Cano et al [26]developed a knowledge base devoted to collecting clinicalexperimental data; the COPD Knowledge Base can semanticallymap data to physiological and molecular data to support clinicaldecision-making through a predictive mathematical model.
Ontologies can play a pivotal role in diagnostic systems. In thecase of COPD, Rayner et al [27] developed an ontology for the
early diagnosis of the illness. The ontological model takes
advantage of clinical tests and patients’ data (eg, spirometry,
forced expiratory volume in the first second [FEV1], age, andsmoking status) and tests its robustness against a large data setof patients. The model proved able to categorize patients as“Unlikely COPD,” “Probable COPD,” and “Definite COPD”
cases.
As ontology-enabled reasoning processes are appreciated in thecontext of clinical decision-making, the adoption of formal
models in health care systems aimed at managing chronic
conditions—including systems for patients with COPD—hasalso been established. CHRONIOUS [28] is an open andubiquitous system that exploits ontology-based inferential
reasoning to adapt the platform’s services, including monitoring
patients’ conditions—which is also performed leveraging
wearable sensors. Lasierra et al [29] developed an
ontology-based telemonitoring system aimed at monitoring
patients with chronic diseases at home. The ontology layer(Home Ontology for Integrated Management in Home-Based
Scenarios) represents the patient profile, vital signs, and chronic
conditions (including COPD) to observe the patient’s evolution
and plan activities. Similarly, Ajami and McHeick [30]developed a domain ontology encompassing environmental
features, patient data, clinical status and diseases (including
COPD), and devices to monitor and identify patients’ conditions.
This DSS aimed to foster patients’ adherence to a healthy
lifestyle and identify and avoid possibly dangerous situations.

Digital Applications for Patients With COPD
Digital applications may offer solutions to both clinicians—toease the process of assessment and support clinicaldecisions—and patients—mainly to support them in the
management of the disease. Digital applications currently
available for patients with COPD are mainly telemedicine andtelerehabilitation solutions. Most of them are focused on
educational programs, symptom tracking, behavior change,
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support for medication or treatment, and activity report [31]. In
some cases, as both research prototypes [32] and commercial
products [33], the educational programs include specific sections
on nutrition, in which helpful tips and recommendations areprovided to manage symptoms during meal consumption andhelp maintain a balanced diet. However, such applications do
not provide personalized information tailored to the patient’s
nutritional status. In such cases, the applications targeted athealth care professionals comprise the “clinician side” oftelemedicine platforms (ie, monitoring dashboard allowing for
remote visualization of patients’ data and applications for remoteteleconsulting).
Differently from the aforementioned solutions, the DSS
proposed in this work tackles the role of nutritional therapy in
the PR of patients with COPD—an aspect neglected in existing
DSSs.

Methods
A DSS was developed to tackle the aims described in the
Introduction section and the research challenge described in the
Objective of This Study section, thus supporting pneumologists
in suggesting specific dietary recommendations for patientswith COPD. The DSS leverages expert knowledge in the formof ontologies and, through automatic inference processes, is
expected to provide guidelines to prepare a tailored dietary plan.
The development of the ontology underlying the DSS leveraged
expert clinical knowledge to maximize its acceptability.
Contrary to purely data-driven approaches, ontologies formalize
information to enable a system to perform inferences (based on
the knowledge formalized in the ontology). The inference
process simulates human inference capabilities [34] so that
ontology-based approaches are perceived as transparent. Thus,
ontologies are widely adopted in several artificial intelligenceand health-related applications [35].
However, the development of a domain ontology is not a trivial
task—it is a process that may involve several activities (eg, theacquisition of knowledge, its conceptualization, the survey ofexisting models that can be reused, the development of themodel in a formal language, and the testing of the developed
ontology [36]). The ontology engineered for the proposed DSS
was developed following the Agile, Simplified, andCollaborative Ontology Engineering Methodology (AgiSCOnt)[37] engineering methodology, which involves knowledge
elicitation techniques and domain experts in the development
phase of the ontology. This collaborative ontology engineering
methodology adopts unstructured interviews, scientific literature
surveys, and discussions to elicit the necessary knowledge tominimize the impact of the “knowledge elicitation bottleneck”(ie, it takes longer to gather knowledge from experts anddocumentation than to write the software [38]). Its collaborative
and agile features and validation [37] were the reasons behind
the adoption of AgiSCOnt in this work.
The methodology involves three phases:
1.

Domain analysis and conceptualization, which includes
the identification of the knowledge to be included and the
preparation of competency questions (CQs) [39] that thehttps://medinform.jmir.org/2024/1/e50980

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3.

ontology is expected to answer; it enables theconceptualization of the domain (which results in aconceptual map) and the preliminary identification ofexisting ontological resources that can be reused.
Development and testing, which involves the selection of
the ontological languages to formalize the conceptualization
developed in the previous step and the identification of
ontology design patterns (ie, “micro-ontologies” that canbe reused to model recurrent problems in ontology
engineering [40]). This step results in the prototypizationof the ontology, which undergoes a preliminary test toassess the validity of its inferences.
Ontology use and updating, which includes activities suchas use of the developed ontology in an application, extended
validation, and feedback collection. The following
subsections delve into the engineering process.

The development of this ontology involved the following teammembers: 1 ontologist with experience in modeling using agile
ontology engineering methodologies, 1 biomedical engineer
with previous experience in ontology engineering andknowledge of COPD, 2 senior dieticians with clinical experience
with patients with COPD, and 1 senior pneumologist. The teamwas composed of clinical personnel from universities (dieticians)
and a research and cure center (Scientific Institute for Research,
Hospitalisation and Health Care) with a specialization in COPD
(pneumologist) and yearly experience treating such patients.
The team delved into nutrition and diet’s role in tackling thisdisease, with examples from the literature and clinical trials inwhich the clinical personnel was involved. The discussion wasthen oriented to identify some of the issues that the ontology
was expected to answer (ie, the CQs).

Ethical Considerations
This study does not include human subjects research (no humansubjects experimentation or intervention was conducted) andso does not require institutional review board approval.

Results
This section describes the development of the COPD and
Nutrition domain ontology for the DSS and the ontology-based
application for clinical personnel.

The COPD and Nutrition Domain Ontology
Domain Analysis and Conceptualization
The considerations reported in The Role of Nutrition in Patients
With COPD: Evidence and Research Challenge: Nutritional
Status to Prevent COPD Exacerbation sections were gatheredby the team in this phase. Leveraging the objective (ie, providing
clinical personnel with support in identifying tailored nutritional
recommendations for patients with COPD), the team decided
that the ontology should focus on representing the patients’
health condition and the stage of their COPD. On the basis of
their expertise, clinicians pointed out that the purpose of the
ontology should be to illustrate, for each patient, a tailored
percentage of macro- and micronutrients they are advised toconsume on a daily basis to avoid exacerbations, as well as toprovide nutritional guidance. The summary of the entities and
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expected outputs of the ontology is provided in Textbox 1,

Spoladore et allisting the CQs and their answers.

Textbox 1. The list of competency questions and answers for the Chronic Obstructive Pulmonary Disease (COPD) and Nutrition ontology engineering
process.
What information identifies a patient? What basic information is used to identify the patient? What clinical information is used to identify
the patient?
•A patient is identified by an ID and gender. Each patient is associated with 1 health condition and 1 anthropometric phenotype (defined via BMI
cutoffs). Each patient can be classified as a patient with sarcopenia or cachexia or as a patient without sarcopenia or cachexia.

How is COPD evaluated?
•

COPD is evaluated according to the criteria defined in the gold standard—it can be mild, moderate, severe, and very severe. The criterion to beanalyzed is the forced expiratory volume in the first second.

How is sarcopenia evaluated?
•

The status of sarcopenia is evaluated according to clinical standards (operational definition of sarcopenia). The first criterion comprises low
muscle strength, the second criterion comprises a low muscle quantity or quality, and the third criterion comprises low physical performance.
The presence of the first criterion alone indicates probable sarcopenia, the presence of both the first and second criteria indicates diagnosed
sarcopenia, and the presence of all 3 criteria indicates severe sarcopenia.

How is cachexia evaluated?
•

Cachexia is evaluated by means of biochemical indicators according to the study by Evans et al [41]. Albuminemia, iron transport, and polymerase
chain reaction (PCR) criteria must be copresent to indicate a cachexia diagnosis.

Which data characterize the patient’s health condition? How is the nutritional risk index assessed?
•

Patients’ health condition must indicate the stage of COPD and the nutritional risk index profile characterizing the patient. Each health condition
must illustrate anthropometric measures (current weight, usual weight, height in meters, and BMI), physical performance indicators (hand grip
and gait speed), and biochemical indicators (albuminemia, PCR, resistance, reactance, and iron transport). The nutritional risk index assessment
is performed following clinical standards.

What recommendations are given to clinical personnel?
•

The recommendations provided to clinical personnel indicate (for each patient) the basal metabolic rate and the corrected caloric intake, the daily
macronutrient shares (protein, minimum and maximum share of carbohydrates, minimum and maximum share of fats, minimum and maximum
share of fiber, maximum share of sugar, and maximum share of saturated fats), the amount of cholesterol and sodium, and whether the patient
should increase their caloric intake by means of branched-chain amino acid or energy-protein supplementations.

How are recommendation values calculated?
•

The indications provided in the patient’s recommendation are calculated according to clinical standards and differentiated according to thepatient’s gender, stage of COPD, and anthropometric phenotype.

In this phase, the pneumologist specialized in clinical nutrition
and a clinical dietitian defined the phenotypes and theirnutritional requirements based on anthropometric and clinicalparameters and comorbidities according to national andinternational guidelines. For the definition of eachanthropometric phenotype, COPD stage (defined via FEV1 andforced vital capacity and in particular FEV1–to–forced vital
capacity ratio [42,43]; Table 1) and the presence of sarcopenia
or cachexia were considered. A total of 5 metabolic phenotypes
for patients with COPD were developed (underweight, normal
weight, overweight, first-degree obesity, and second-degree
obesity), and each can be characterized by the presence ofsarcopenia, cachexia, or none of the 2 conditions (Table 2).

Thus, patients were classified according to this formula (Table
3).

Moreover, nutritional risk was assessed by means of thenutritional risk index (NRI) formula [44] as follows:

If the first criterion applied, then the patient was probablesarcopenic; if the first and second criteria applied, then thepatient was diagnosed sarcopenic; if all 3 criteria applied, thenthe patient was severe sarcopenic.

(1)

A similar approach was adopted to identify whether a patientwas cachectic. If a patient’s polymerase chain reaction was >10,https://medinform.jmir.org/2024/1/e50980

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The diagnosis of sarcopenia was based on the analysis of
specific patients’ value indicators. The first one was theappendicular skeletal muscular mass, which is calculated
according to the work by Sergi et al [45]:(2)
Appendicular skeletal muscular mass and other indicators allow
for the classification of patients’ sarcopenic condition according
to the 3 criteria in Table 4 [46].

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the level of iron transport was <150, the level of albuminemia
was <3.5, and the patient was sarcopenic, then they were alsocachectic [41]. As such, it is safe to infer that cachexia is aparticular case of sarcopenia.
Calculation of nutritional recommendations was performed
using the “if-then” type rules produced by clinical personnel
considering reference values reported in scientific literature andnational and international guidelines. The COPD DSS provides
the following nutritional information and recommendations:(1) basal metabolic rate (BMR), (2) total daily energy
requirement (kcal), (3) meal frequency, (4) indication forenergy-protein supplementation (yes or no), (5) indication forbranched-chain amino acid (BCAA) supplementation (yes orno), (6) protein intake (percentage and grams), (7) carbohydrate
intake (percentage and grams), (8) lipid intake (percentage andgrams), (9) sugar intake (maximum percentage and grams), (10)saturated fat intake (maximum percentage and grams), (11)cholesterol intake (maximum milligrams), (12) fiber intake(minimum and maximum grams), (13) sodium intake (maximum
milligrams), and (14) calcium intake (milligrams).
For BMR calculation, Harris-Benedict or Mifflin predictive
equations based on sex, age, weight, and height were considered.
The Harris-Benedict equation was preferred for patients who
were underweight or had normal weight, whereas the Mifflin
equation was used for patients with COPD with overweight orobesity [47-49]. According to the COPD stage, different
correction factors to BMR were applied (Multimedia Appendix
1)—for patients who were underweight, had a normal weight,
were overweight, or had first- or second-class obesity presenting
with COPD at the first or second stage and who were
nonsarcopenic and noncachectic or sarcopenic, a 1.5 correction
factor was used to calculate total daily energy requirement; forthe same categories of patients presenting with COPD at thethird or fourth stage, a correction factor of 1.8 was preferred tocounteract the important energy expenditure due to therespiratory work of these patients. Only for patients withcachexia a 1.8 correction factor was always applied regardlessof BMI and COPD stage.
The impact of physical activity was deemed marginal for thecorrection factor’s definition as patients with COPD are avulnerable population presenting with comorbidities that limit
their capacity to perform physical activity. As seen in thepathological lung mechanics of patients with COPD, dynamic
hyperinflation influences the proper operation of the chest’s
horizontal and vertical diameters that expand during inspiration
due to the activation of the external intercostal muscles and thediaphragmatic contraction. This impairment plays a role in how
much exercise a patient with COPD can tolerate. As shown in
individuals with severe-stage COPD and weight loss related to
COPD, respiratory muscle weakness exacerbates the breathingmechanism. Increased dyspnea and decreased exercise tolerance
are directly related to this respiratory muscle weakening.
Moreover, it has been observed that patients with COPD arecharacterized by higher levels of physical inactivity [50]. As
far as protein requirement was concerned, clinical experts
decided to provide different recommendations according to realor ideal weight or BMI or focusing on FFM considering specificbody composition or COPD stage to give personalized
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recommendations to not compromise health and nutritional
status as well as prevent further decrease in metabolically active
lean mass [12]. For this reason, cachectic phenotype, regardlessof BMI and COPD stage, was always given a high percentage
of protein intake. BCAA was suggested when energy
requirements but not protein requirements were met throughdiet. Differently, protein-caloric supplements were indicatedwhen neither energy nor protein requirements were satisfied
through food intake [51]. Phenotypes characterized by cachexia,
regardless of BMI, underweight sarcopenic and normal-weightphenotype, and COPD stage, were always recommended BCAA
and energy-protein supplementation in consideration of theirhealth status. For the same reason, BCAA supplementation wasalways suggested for sarcopenic phenotypes [52]. As far as
carbohydrate metabolism was concerned, and in line with thelower levels reported in the reference values of nutrients andenergy for the Italian population [13,47], a carbohydrate intakeof 45% to 50% of total daily calories and a maximum sugar
intake of 15% of total calories were indicated except forindividuals with type 2 diabetes mellitus, for whom a maximumof 10% of total calories was indicated to be reached throughsugar intake. A lower recommendation for carbohydrate intake
permits the promotion of protein and lipid intake, which arefunctional for respiratory work and to prevent further weightor lean mass loss. Regarding dietary fiber, it was decided thatproviding a lower indication than that provided by the Livelli
di Assunzione di Riferimento di Nutrienti ed energia (LARN;National Recommended Energy and Nutrient Intake Levels)
was preferred to encourage the intake of energy and protein
foods, especially considering the difficulties met by patientswith COPD in feeding and the early sense of satiety as a resultof high-fiber food intake. Compared to the LARN, a higher
intake of lipids was recommended, especially for phenotypes
presenting with a partial pressure of carbon dioxide of >50mm Hg, a measure of carbon dioxide in arterial or venous blood[53]. For saturated fats, a maximum intake of 10% of total daily
calories was suggested according to LARN guidelines.
Regarding cholesterol intake, a LARN nutritional goal for
prevention, a maximum of 300 mg per day was recommended
except for patients with high cholesterol levels, for whom thetarget was lowered to 200 mg per day. Hypercholesterolemia
was defined as elevated total or low-density lipoproteincholesterol levels or low levels of high-density lipoproteincholesterol. Regarding micronutrients, sodium and calcium
intake was considered. According to the recent WHO report on
sodium intake [54], a maximum intake of <2000 mg per day of
sodium (<5 g per day of salt) was recommended. A slightly
higher recommendation than that of the LARN was given for
calcium to prevent or counteract osteoporosis [55]. Finally,
given the difficulties in feeding and early satiety observed, an
indication for fractioned meals was given to meet energy andnutritional requirements throughout the day with small andfrequent meals.
All the parameters involved in the evaluations presented
previously (ie, FEV1; partial pressure of carbon dioxide;
resistance; reactance; iron transport; albuminemia; polymerase
chain reaction; hand grip; gait speed; and general patient
information such as age, gender, height in meters, current
weight, and usual weight) are usually acquired during patient
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Spoladore et alassessment (such as spirometry and blood tests). AgiSCOnt’s
outputs for the domain analysis phase consisted of theconceptual map reported in Figure 1 and a list of CQs (Textbox
1).

Table 1. The cutoffs identifying the chronic obstructive pulmonary disease (COPD) stages based on the forced expiratory volume in the first second(FEV1) values.
COPD stage number

COPD stage name

FEV1 (%)

I

Mild

≥80

II

Moderate≥50 to <80

III

Severe≥30 to <50

IV

Very severe<30

Table 2. World Health Organization cutoffs for nutritional status categories based on BMI.
Nutritional status

BMI (kg/m2)

Underweight<18.5

Healthy weight≥18.5 to ≤24.9

Overweight (preobesity)≥25.0 to <29.9

Obesity degree I≥30.0 to <34.9

Obesity degree II≥35.0 to <39.9

Obesity degree III≥40

Table 3. The cutoffs identifying the 4 levels of nutritional risk based on the nutritional risk index (NRI).
NRI

Index

Absence of risk

>100

Mild risk≥97.5 to ≤100

Moderate risk

≥83.5 to <97.5

Severe risk

<83.5

Table 4. Criteria for the classification of sarcopenia in patients.

a

Criteria

Cutoff for male patients

Cutoff for female patients

Low muscular strength—hand grip

<27 kg<16 kgLow muscular quantity

ASMMa<20 kg; ASMM/height2<7 kg/m2

ASMM<15 kg; ASMM/height2<5.5 kg/m2

Poor physical performance≤0.8 m/s≤0.8 m/s

ASMM: appendicular skeletal muscular mass.

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Figure 1. An excerpt of the conceptual map developed by the team involved in the ontology engineering process for the clinical chronic obstructive
pulmonary disease (COPD) and nutrition ontology. BCAA: branched-chain amino acid; BMR: basal metabolic rate; NRI: nutritional risk index; PCR:
polymerase chain reaction.

Development and Testing
The development phase adopted the conceptual map and CQs
produced in the previous phase to guide the entire development
process and discuss whether to model some concepts pertaining
to the patients or their health conditions. The ontology editor
Protégé (Stanford Center for Biomedical Informatics Research)[56] that supports Resource Description Framework [57] andWeb Ontology Language [58] with a DL (description logic)
profile [59] was adopted. Clinicians explicitly asked any
significant advancement in the development of TBox and Abox(eg, patient modeling, health condition characterization, andrecommendation modeling) to be illustrated to ensure that
undesired entailments were not modeled in the ontology. Froma reuse perspective, no ontology able to describe theconceptualization reached in this study was found; the only
ontology design pattern reused in this ontology was the one that
relates a copd:Patient to their copd:Health_Condition via the
copd:isInHealthCondition object property [60]. The developed
ontology (prefixed with copd:), discussed in this subsection, isaccessible in Multimedia Appendix 2.
The development started with the identification of concepts that
could be translated into owl:Classes. The concept of copd:Patientis pivotal in this ontology. Each patient is defined by exactly 1
patient ID, is given at least 1 nutritional recommendationindividual, and is characterized by a health condition individual.
Each patient needs to be classified as copd:Female orcopd:Male—disjoint classes—and as copd:Cachectic (or its
complement copd:non-Cachectic) or copd:Sarcopenic (or its
complement copd:non-Sarcopenic). Sarcopenia and cachexia
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are modeled as attributes of the patient and not of their healthcondition. The clinical personnel deemed essential to state thatthese 2 conditions have systemic status; therefore, they
characterize the individual as a whole. The class
copd:Sarcopenic is further detailed into the subclasses
copd:Probable_Sarcopenic, copd:Diagnosed_Sarcopenic, andcopd:Severe_Sarcopenic to reflect the operational definition
standard provided by clinicians (presented in the previous
subsection).
In the same way, the copd:Anthropometric_Phenotypes arecharacteristics of the copd:Patients, and this class lists 5
subclasses for the representation of the phenotypes.
Similarly to copd:Patient, the development of the TBox
pertaining to the patient’s health condition was discussed among
the team members—each health condition is characterized by
an NRI profile, but in general, a copd:Health_Condition is notnecessarily characterized by COPD. The terms adopted in theconceptual map to sketch the relationships among
copd:Health_Condition, copd:Nutritional_Risk_Index_Profile,and copd:COPD_HC were found indicative of the clinicians’
perspective—both NRI and COPD are considered particularattributes of a health condition (ie, there could be healthconditions characterized only by an NRI profile but lacking
COPD).
Therefore,
theclasses
copd:Nutritional_Risk_Index_Profile and copd:COPD_HC weremodeled as rdfs:subclassOf copd:Health_Condition. Thisdecision was also encouraged by the fact that the datatype
properties copd:FEV1 and copd:nutritionalRiskIndex have
copd:Health_Condition as the domain.
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JMIR MEDICAL INFORMATICSThe copd:Nutritional_Risk_Index_Profile and copd:COPD_HC
subclasses are characterized by restrictions that allow for theclassification of individual health conditions whose
copd:nutritionalRiskIndex and copd:FEV1 object values fallunder specific restrictions.
Each copd:Health_Condition is described by a set of datatype
properties, which represent the clinical data elicited in theprevious phase and are required to enable the patient’s
classification and recommendations. Each owl:Individual
belonging to this class also materializes inferred triples relatedtothe
copd:AppendicularSkeletalMuscleMass,thecopd:ResistiveIndex, and the copd:nutritionRiskIndex. Whilethe copd:nutritionalRiskIndex is calculated using semantic web
rule language (SWRL) rules and used to classify each
copd:Health_Condition into one of the NRI’s subclasses, thecopd:ResistiveIndex is a piece of information necessary to
calculate the copd:AppendicularSkeletalMuscleMass (both areinferred as the result of 2 different SWRL rules). Figure 2
illustrates an example of copd:Health_Condition completedwith all its datatype properties (both asserted and inferred).
The ontology makes use of 39 datatype properties and 2 object
properties(copd:isInHealthCondition
andcopd:hasRecommendation)—almost all datatype properties
were used to provide values for the patient’s health conditionand nutritional recommendation.
The ontology also contains 79 SWRL rules, which are largely
used to represent the tuples in Multimedia Appendix 1, depicting
the conditions that determine the shares and amounts of nutrientscharacterizing a patient’s diet (see the full ontology inMultimedia Appendix 2). The equations adopted to calculate
the BMR and the corrected caloric intake were adapted with
SWRL using mathematical built-ins [61]. Taking as an exampleacopd:Overweight,
copd:non-Sarcopenic,
and
copd:non-Cachectic male patient characterized by copd:Stage2
disease, the BMR is inferred through the following rule (foreach male patient not characterized by sarcopenia or cachexia,
calculate the BMR using the Harris-Benedict equation and roundthe result):Male(?p), Overweight(?p), (not (Cachectic))(?p), (not(Sarcopenic))(?p), hasRecommendation(?p, ?rec),isInHealthCondition(?p, ?hc), age(?hc, ?age),currentWeight(?hc, ?kg), height_meters(?hc, ?m),multiply(?a, ?kg, 13.75), multiply(?b, ?m, 5, ?100),
multiply(?c, 6.78, ?age), add(?d, 66.5, ?a, ?b),subtract (?e, ?d, ?c), round(?f, ?e) ->regularRecommendedCaloricIntake(?rec, ?f)Then, the correction is applied (for each male patient not
characterized by sarcopenia or cachexia with stage-1 or stage-2
COPD, correct the BMR calculated using the Harris-Benedict
equation by multiplying it by 1.5):(Normal_Weight or Obesity_1st_Degree or
OverweightorUnderweight)(?p),
hasRecommendation(?p,? re c ) ,isInHealthCondition(?p,?hc),(Stage1orStage2)(?hc),(not(Cachectic))(?p),(nothttps://medinform.jmir.org/2024/1/e50980

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Spoladore et al( S a r c o p e n i c ) ) ( ? p ) ,regularRecommendedCaloricIntake(?rec,?reg),
multiply(?corin, ?reg, 1.5), round(?f, ?corin) ->correctedRecommendedCaloricIntake(?rec, ?f)
The definition of the share of protein that a patient with COPD
needs is calculated by identifying the amount (in grams) ofprotein. With the sole exception of patients with cachexia—who
are given a 25% protein share for clinical reasons—for eachpatient, the daily quantity of protein is calculated according totheir weight (for each patient with normal or overweight status
and not characterized by sarcopenia or cachexia, obtain theamount of protein in grams by multiplying their current weightby 1.2):(Normal_WeightorOverweight)(?p),(not(Cachectic))(?p),(not(Sarcopenic))(?p),
hasRecommendation(?p, ?rec), isInHealthCondition(?p, ?hc), currentWeight (?hc, ?w), multiply (?pgra,?w, 1.2) -> proteinsGrams (?rec, ?pgra)
This amount is then converted into calories, bearing in mind
that 1 protein is equal to 4 kcal, and then the daily protein shareis calculated. This approach also enables the possibility tocorrect the amount of protein for particular classes of patients;for example, dieticians indicated that patients classified as
copd:non-Sarcopenic,
copd:non-Cachectic,
and
copd:Underweight should have their protein share calculated
considering a different BMI (which is set to a higher value tofight their underweight condition and is established at 22.5kg/m2).
As mentioned previously, the ontology provides enough SWRL
rules to model all the information identified by the domain
experts and elicited in Multimedia Appendix 1. As establishedby the development step in AgiSCOnt, the ontology underwent
a test with data from 6 patients provided by clinicians. The test
was divided into 2 steps. The first was dedicated to assessing
whether the ontology provided a correct classification of thepatients (ie, whether it identified copd:Sarcopenic andcopd:Cachectic status for each patient and whether the stage of
COPD and the copd:Nutritional_Risk_Index_Profile werecorrectly inferred). Thus, by querying the ontology using
SPARQL (World Wide Web Consortium) [62], it was possible
to assess the accuracy of the inferences (reported in Table 5).
The pneumologist and dieticians verified the correctness of theclassification for each patient. All 6 individuals representing
patients were found to be correctly classified. The second phase
dealt with the retrieval of nutritional suggestions and theirevaluation by the clinical personnel with the aim of assessing
the validity of the SWRL rules modeled in the COPD and
Nutrition ontology. The ontology was queried using SPARQL
to retrieve all the nutrient minimum and maximum shares andquantities deemed important for patients with COPD (the resultsof the query are reported in Multimedia Appendix 3). Each
copd:Nutritional_Recommendation and its inferred nutrient
values were evaluated by clinical personnel and were found to
be correct—although, for some values such as the
copd:proteinShare and copd:fiberMINamount, a rounding of
the decimal was suggested by dieticians.

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Figure 2. The complete datatype property set for a copd:Health_Condition. The datatype properties with a yellow background represent inferred values.

Table 5. An excerpt of the results retrieved for the query investigating, for each patient, their ID, their status (whether they had sarcopenia or cachexia),
the stage of chronic obstructive pulmonary disease (COPD), and their nutritional risk index profile. For patients characterized by sarcopenia, all thesubclasses of copd:Sarcopenic are illustrated so that clinical personnel can easily see the importance of this condition.

a?id?status?antrPhen?copdstage

?nri

001

copd:Cachectic

copd:Underweight

copd:Stage1

copd:Severe_Risk001

copd:Diagnosed_Sarcopenic

copd:Underweight

copd:Stage1

copd:Severe_Risk001

copd:Female

copd:Underweight

copd:Stage1

copd:Severe_Risk001

copd:Probable_Sarcopenic

copd:Underweight

copd:Stage1

copd:Severe_Risk001

copd:Sarcopenic

copd:Underweight

copd:Stage1

copd:Severe_Risk001

copd:Severe_Sarcopenic

copd:Underweight

copd:Stage1

copd:Severe_Risk

...a

...

...

...

...

BB

copd:Female

copd:Normal_Weight

copd:Stage4

copd:Absence_of_RiskBB

copd:non-Cachetic

copd:Normal_Weight

copd:Stage4

copd:Absence_of_RiskBB

copd:non-Sarcopenic

copd:Normal_Weight

copd:Stage4

copd:Absence_of_RiskCV

copd:Male

copd:Obesity_1st_Degree

copd:Stage3

copd:Mild_RiskCV

copd:non-Cachetic

copd:Obesity_1st_Degree

copd:Stage3

copd:Mild_RiskCV

copd:non-Sarcopenic

copd:Obesity_1st_Degree

copd:Stage3

copd:Mild_Risk

...

...

...

...

...

Data not reported for conciseness.

Ontology Use and Updating
The COPD and Nutrition ontology described in the previous
subsection is a tested prototype able to classify patients properly
and provide clinicians with nutrition-related recommendations.
To support clinical personnel, the ontology-based DSS needs
to be integrated into digital applications, enabling clinicians toaccess its functionalities intuitively and easily (as described in
the following section). This would also enable the assessmentof the usefulness and accuracy of the inferences by leveraging
on clinicians outside the development team.

a new set of dietary recommendations. The application and DSS
communication are based on SPARQL queries running over
the Stardog reasoner. The entire architecture has already beentested in previous work [24,63].
The application is based on a simple and intuitive graphical userinterface (GUI). Its flow is as follows:
1.

The Application for Clinical Personnel
The clinician application was developed to run on a Windows
PC or laptop and allows the lung specialist to obtain an overview
of the patient’s nutritional condition and generate tailored dietary
recommendations. To achieve this, the application is connectedto the DSS (hosted on a semantic repository) with permission
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The clinician logs in to the application either to create a
new patient profile or to modify an existing one. The Patient
Profile (Scheda Paziente) panel (Figure 3A) has several
fields corresponding to the input information needed by the
DSS to represent the user’s health condition. These include
personal data and the results of the patient assessment.
Once the new patient profile is saved, the application sends
a SPARQL query INSERT to upload the new information
into the ontology. The DSS reasons over the new data toobtain the patient’s classification and the nutritional

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3.

4.

recommendations. The output is sent back to the applicationin the form of a JSON file to populate the GUI panels.
The Health Condition (Condizione di Salute) panel, shownin Figure 3B, shows the classification results: metabolic
phenotype, presence of sarcopenia and cachexia,
anthropometric phenotype, COPD stage, and NRI.
The subsequent panel, the Nutritional Recommendations(Indicazioni Nutrizionali), shown in Figure 3C, summarizes

Spoladore et al5.

the nutritional recommendations, indicating basal
metabolism, daily intake, type of diet, suggested BCAA
supplement, percentage, and grams of micro- andmacronutrients.
The clinician can save the recommendations in a PDF fileor print them to share them with the dietician to support
them in preparing a diet for the patient.

Figure 3. The 3 main graphical user interface panels of the clinician application: (A) patient profile, (B) health condition, and (C) nutritional
recommendations.

Preliminary Validation With Clinical Personnel
Overview
Before the implementation in a real use-case scenario, we
performed an expert validation of the DSS as a preliminary but
necessary step. In total, 2 experiments were
performed—considering the multidisciplinary nature of the tool,
it was necessary to validate 2 aspects of the DSS with 2 different
groups of clinicians.
Therefore, each experiment was carried out by a specific groupof clinical experts: (1) a group of nutrition experts validated therecommendations generated by the DSS, and (2) a group of
specialists in respiratory diseases evaluated the acceptability ofthe digital application in clinical practice based on the COPD
DSS.
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Procedure
Participants were recruited through email invitations among thenational experts in nutrition and pulmonology, identified bysearching the literature and professional networks. Once theyexpressed willingness to participate in the study, they signed awritten informed consent form and agreed to the data treatment
according to the General Data Protection Regulation. In total,
2 experimenters scheduled the web-based video calls—one foreach participant—between November 2022 and January 2023.
During the test, the experimenters briefly introduced the aimof the system and the main steps of the validation and recorded
the participants’ answers to brief ad hoc questionnaires andspontaneous comments. Descriptive statistics were calculated
for each variable, and the spontaneous comments were analyzed
and categorized based on their content.

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Experiment 1: Validation of the DSS Recommendations
The first experiment focused on validating the nutritional
recommendations generated by the DSS. A total of 7 nutrition
experts participated in the validation. The experimenters showed
the participants the profiles of 5 real patients and the inferredrecommendations. The patient profiles were obtained from realclinical cases provided by the clinicians involved in the project.
Each profile included the patient ID, age, gender, and healthcondition containing all the clinical parameters needed as input
to the system.
After presenting the patient’s condition, the experimenter
showed the recommendations generated by the DSS, which
included the patient’s inferred classification (metabolic
phenotype, presence of sarcopenia or cachexia, anthropometricphenotype, COPD stage, and NRI) and the nutritional

Spoladore et al
recommendations with information on metabolism andsuggested quantities of macro- and micronutrients. An example
of a patient profile used during the experiment is presented in
Figure 4, whereas all the patient profiles used during theevaluation are available in Multimedia Appendix 3. Participants
were granted up to 15 minutes to observe the presented patient’s
health condition. During this time, participants were free toperform calculations using the data shown, ask questions (ifnecessary) to the experimenter, and consult external sources(eg, books and papers). After the 15-minute period, for eachpatient, we asked the participants to rate on a scale from 1 to 5
how much they agreed with the recommendation; in case of ascore of <5, we asked the participant to provide a brief
explanation. We also collected spontaneous comments thatemerged during the experiment. The maximum duration of theexperiment for each participant was 1 hour and 15 minutes.

Figure 4. An example of a patient profile provided to clinicians (on the left) and the inferences drawn by the ontology-based decision support system(on the right).

Experiment 2: Acceptability Evaluation
The second evaluation focused on assessing the overall system
acceptability, including the DSS and a digital application
working as a GUI for inserting patient data and retrieving
personalized recommendations. A total of 7 lung specialists
agreed to participate in the experiment. The experimenter
explained to each participant the expected process of use of thesystem and the application data flow in a daily clinical routine,
as summarized in Figure 5. The lung specialists performed thepatient assessment; inserted the clinical data to generate thepatient profile on the application GUI, as described in the
Application for Clinical Personnel section; and generated thenutritional recommendations.

Participants were granted 10 minutes to assess the application,
ask the experimenters questions, or ask to be presented with thedata flow again.
After this time, the experimenter administered an ad hoc
questionnaire based on the subscales of the technology
acceptance model by Davis [64] and its subsequent amendments[65] focused on perceived usefulness and intention to use. The
participants had to rate on a scale from 1 to 5 the level ofagreement with the following statements: (1) “I think that the
proposed system is useful for clinicians”; (2) “I think that byusing this application could enhance the treatment of individuals
with COPD”; and (3) “if I had this application at work, I would
use it.”We also asked them to specify at least one reason why theapplication could be useful or not.

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Figure 5. The process of use of the chronic obstructive pulmonary disease decision support system allowing the professionals to generate nutritional
recommendations for a specific patient and some screenshots of the main graphical user interface panels (patient profile, patient classification, andnutritional recommendations).

Results
Experiment 1 Results: Validation of the DSS
Recommendations
A total of 7 experts (mean age 46.60, SD 13.35 years; n=7,
100% female) participated in the first validation phase. They
were all dieticians with an average of 25.57 (SD 11.49) years

of professional experience. The level of agreement with each
recommendation is reported in Table 6 in terms of mean, SD,
and minimum and maximum score.
The comments provided by each participant were analyzed andcategorized according to their content. The categorization andfrequency of the comments and the patient profiles thatoriginated them are reported in Table 7.

Table 6. Validation—level of agreement scores (mean and SD) for each patient profile (BB, FG, LA, TM, and XY2 are the patient IDs) expressed byeach participant; mean and SD for each patient profile.
Scores (1-5)

Values, mean (SD)

BB

5

4

5

5

3

4

5

4.43 (0.79)

FG

5

1

4

2

3

4

4

3.29 (1.38)

LA

5

4

5

5

4

4

4

4.43 (0.53)

TM

5

4

5

5

3

4

5

4.43 (0.79)

XY2

5

4

5

5

4

4

5

4.57 (0.53)

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Table 7. The list of comments provided by clinicians categorized according to their content (category and comment) and linked to the patients thatoriginated them (patient IDs); the frequency of each comment is reported in parentheses.
Category

Comment

Patient ID (frequency)

Quantity“Too high-calorie uptake”

BB (1), FG (6), TM (1), and ANM (1)Quantity“Too high protein uptake”

FG (2) and LA (2)Quantity“Consider reducing simple sugars from 15 to 10%”FG (1), LA (1), TM (2), and ANM (1)Assessment“I suggest including the physical activity level during theassessment and adjust correction factors accordingly”

All (2)

Experiment 2 Results: Acceptability Evaluation
A total of 7 experts (mean age 44.71, SD 11.94 years; n=7,
100% female) participated in the second validation phase. Of
the 7 experts, 5 (71%) were lung specialists, 1 (14%) was aspecialized lung physician, and 1 (14%) was a surgeon of therespiratory system. They had, on average, 14.73 (SD 9.67) years
of professional experience, ranging from a minimum of 1 year
for the specialized physician to 31 years. The acceptability score
for the 3 subscales of perceived usefulness and intention to use
was >4 points out of 5, as reported in Table 8.

Our system was considered useful for clinical practice because(1) it promotes the importance and facilitates the inclusion of
the nutritional aspect in PR, as stated by 43% (3/7) of theparticipants; (2) it quickly provides a complete overview of thepatient’s condition, according to 57% (4/7) of the participants;and (3) it fosters the multidisciplinary collaboration between
lung specialists and dieticians. In addition, 86% (6/7) of theparticipants spontaneously commented on the ease of use of theapplication GUI that allowed the clinician to insert the patient
assessment and obtain the nutritional recommendations.

Table 8. Acceptability—technology acceptance model subscale scores reported by each participant for perceived usefulness (PU1 and PU2) andintention to use (INT); mean and SD are reported for each subscale.
Scores (1-5)

Values, mean (SD)

INT

4

4

4

5

4

4

4

4.86 (0.38)

PU1

5

5

5

5

4

5

5

4.86 (0.38)

PU2

5

5

5

5

5

5

4

4.14 (0.38)

Discussion
Principal Findings
We performed 2 types of evaluation with 2 different experiments
aimed at validating the nutritional recommendations generated
by our system by nutrition experts and assessing the system’s
acceptability by lung specialists. The first validation—performedby 7 nutrition experts—demonstrated that our system is able toprovide meaningful and safe recommendations overall incompliance with clinical practice. In 80% (4/5) of the cases, thelevel of agreement between the human and the “digital” expert
was approximately 4.5 points out of 5. In one case (patient FG),the experts did not completely agree with the recommendations,
reporting a score of 3.29, which is not considered adisagreement. However, such a score was associated with thecase of a critical patient who, in addition to COPD, hadsecond-degree obesity. This is because our system is highly
specialized in treating patients with COPD, who need a higher
energy intake to cope with impaired respiratory functionality.
In such critical cases, the active involvement of the clinician in
the process becomes essential. For instance, the clinician may
adjust the nutritional recommendations for the patient to lose
weight while monitoring them. It is crucial that such a patientdoes not lose muscular mass instead of fat mass.
The comments were positive overall and could be consideredmore as suggestions than criticisms. The presence of small
disagreements among experts (eg, regarding the calculation of
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for maintaining “the clinician in the loop,” as prescribed by the
AgiSCOnt methodology adopted for the development of our
DSS. In fact, although our system provides a useful and easy
way of generating recommendations, each clinical case should
be carefully considered, and slight modifications should bemade by the clinician themselves in person.
The same considerations apply to the spontaneous comments,
summarized in Table 7. The main concerns were about thepercentage of simple sugars, which, for 4 patients, could be
reduced from 15% to 10%. The guidelines indicate 15% as the
maximum value, which should be adjusted by the clinician
based on the percentage of other macronutrients. The othercomments revealed a slight disagreement on the overall energy
and protein intake, which was sometimes considered too high.
However, our system is specifically focused on COPD;
therefore, a higher intake was justified by the need tocompensate for impaired respiratory function. Our group of
experts was representative of the Italian clinical scenario, in
which the influence of COPD on a patient’s nutrition issometimes neglected. Therefore, such a result strengthens therationale of our work, which provides a system able to help
professionals and clinical care facilities, which often lackspecialized services, identify particular needs toward more
personalized and effective care.
The second evaluation demonstrated the acceptability of oursystem by a group of final users (ie, lung specialists involved
daily in the assessment and therapy of patients with COPD).
The usefulness of our system was confirmed and was especially
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related to the possibility of strengthening the consideration ofnutritional aspects as part of PR standard practice. This isconsidered crucial by most specialists and the scientificcommunity; however, due to organizational issues, it is notalways considered [4]. Another crucial aspect that emerged was
related to the importance of a multidisciplinary approach, andour system could especially help ease the cooperation between
lung and nutrition specialists. At the same time, it could help
lung professionals in extending their knowledge by considering
aspects not strictly related to their expertise. Finally, allparticipants expressed their willingness to have such a system
available in their daily clinical routine. They also considered iteasy to use as the GUI was clear and the process was intuitive.

Limitations
This work is not without limitations. First, our system wasdesigned for the Italian context. The DSS is based on thenational nutritional guidelines—it was necessary to follow arecognized standard, which may be different from one country
to another. Similarly, the dietary recommendations are basedon the Italian diet. This was necessary to provide a tool that canbe effectively used by our target users (ie, lung specialists
treating patients with COPD in the Italian health care system).
The DSS’s modularity allows it to overcome such a limitation
easily—the DSS could be adapted to include nutritional
recommendations for other countries. The second limitation isabout the participants of our validation experiments. The firstexperiment was based on the evaluation of 5 patient profiles bya group of nutrition experts. The number of patients examined
was identified as the best compromise between a comprehensive
representation of the clinical context and organizational aspects.
Despite being few, the proposed patient profiles covered mostof the potential real clinical cases. Regarding the acceptability
evaluation, the main limitation resides in the fact thatparticipants were homogeneous in terms of age (most of themwere aged 45-50 years), culture, geographical location, andlanguage (all of them were Italian). Such sociocultural factors
are known to impact digital health technology use [66]; however,as previously stated, our work at this stage is focused on the
Italian scenario, and therefore, our sample can be considered
representative of the final population of target users.

Future WorkAs recently noted, most digital health applications remain
limited to pilot studies—mainly because they fail in the proposed
aims or face significant implementational barriers [67]. Froma digital health application perspective, our experiments aimed
to verify the stability of the developed solution—in line with
the WHO’s guidelines [23]. In particular, the validations verified
the performance consistency, the proposed solution’s overall
feasibility, and the digital tool’s efficacy. Considering the earlystage of the DSS and its application, we need to further
investigate the implementation protocols and to acquirelong-term proof of the efficacy of the tool among pneumologists
and patients with COPD (ie, the acceptability of the tool needs
to be verified with a larger sample of end users and in a realclinical setting so that more pneumologists can provide feedback
regarding the tool’s perceived usefulness, ease of use, andsatisfaction; moreover, the effectiveness of the proposed diets
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Spoladore et alshould be tested with patients with COPD). To achieve theseobjectives, more extensive experimentation with larger samplesof participants (clinical nutritionists, dieticians, andpneumologists) is necessary. Moreover, the involvement ofclinical personnel can support the identification of
implementation protocols suitable for the adoption of the digital
tool in clinical practice. In this way, the application’s level ofmaturity could move from early to mild (according to the WHO[23]), where its effectiveness can be tested in a nonresearch(uncontrolled) setting.
To support the prompt identification of barriers andimplementational challenges, reporting the development of the
COPD DSS within a framework for the definition of digital
health application implementation can be useful. In particular,
the Guidelines and Checklist for the Reporting on Digital Health
Implementations [67], by providing a list of 20 items to bemonitored, can foster the identification of issues in the Technicaldesign phase in the Interoperability and Data management areas.
In this regard, the availability of data and the implementationof the application within the health system are another relevant
node to be investigated. Although the current version of thedigital application is still in its prototypical phase, scaling up
the application to the regional or national level (ie, coverage in
the Guidelines and Checklist for the Reporting on Digital Health
Implementations) is essential to ensure its use in clinicalpractice. Therefore, toward this aim, strategies for collecting
the outputs and making them available in patients’ data (orelectronic health records) need to be investigated. In this regard,scientific literature offers some interesting approaches groundedin the Italian health care system that could be considered tomake the COPD DSS interoperable with existing tools [68-70].
As the COPD DSS leverages ontologies to represent its data,
this technology can be used to achieve semantic interoperabilityof the information [68], moving a step toward the longitudinalcollection of health data about patients [70] while ensuring dataprotection according to the national and European laws [69].
Finally, from an ontological perspective, the domain ontology
regarding COPD presented in this work could benefit frommapping with existing (and larger) biomedical standard
ontologies to increase its shareability (eg, the WHO’s
International Classification of Diseases and International
Classification of Functioning, Disability and Health, as well as
upper biomedical ontologies)—a best practice of ontology
engineering [71]. Moreover, considering that the proposedsystem can be adapted to any other national clinical context by
modifying the domain ontologies, a possible future research
direction consists of involving international clinicians to increase
the knowledge formalized in the ontologies so that it is possible
for the DSS to cover the specific nutritional indications ofdifferent countries.

Conclusions
The role of nutrition in the management of patients with COPD
is often underestimated, although scientific evidence points
toward the important role that diet plays in PR. The nutritional
status of patients with COPD is essential to prevent
exacerbations and avoid comorbidities, but attention to thepatient’s body composition and nutritional status is often
JMIR Med Inform 2024 | vol. 12 | e50980 | p. 15(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
secondary in clinical practice. This may be partially becauselung specialists may lack specialized training in clinicalnutrition, although they recognize the relevance of dietary
recommendations in PR.
An ontology-based DSS was developed to support
pneumologists in considering nutritional aspects. The DSS
formalizes expert knowledge in computable models able to infer
patient-tailored nutritional recommendations, leveraging a setof information to capture the nutritional and physical status of
the patient; therefore, by applying rules, it can support theclassification of patients with COPD and provide tailored
recommendations indicating the percentages and amounts of

Spoladore et al
micro- and macronutrients that should make up their diet. The
domain ontologies act as the backbone of a clinician-dedicatedapplication.
The application was validated to assess the clinical complianceof the DSS’s recommendations and the acceptability of such an
application in clinical practice by lung specialists. For both
validations, the proposed system performed more thanadequately—in particular, pneumologists underlined the rolethat such an application may play in achieving a
multidisciplinary approach in PR. This paper concludes byinvestigating future research directions to implement the COPD
DSS further into a fully-fledged digital health application.

Acknowledgments
The sPATIALS3 (Miglioramento delle Produzioni Agroalimentari e Tecnologie Innovative per un’Alimentazione più Sana,
Sicura e Sostenibile) project is financed by the European Regional Development Fund under the ROP (Regional Operational
Programme) of the Lombardy Region European Regional Development Fund 2014 to 2020 (axis I, “Strengthen technologicalresearch, development and innovation”; action 1.b.1.3, “Support for cooperative R&D activities to develop new sustainable
technologies, products and services”; Call Hub).

Data Availability
All data generated or analyzed during this study are included in this published paper.

Conflicts of Interest
None declared.

Multimedia Appendix 1
A table representing the set of rules for providing phenotype-tailored recommendations.
[DOCX File , 46 KB-Multimedia Appendix 1]

Multimedia Appendix 2
The chronic obstructive pulmonary disease decision support system domain ontology.
[ZIP File (Zip Archive), 24 KB-Multimedia Appendix 2]

Multimedia Appendix 3
A table reporting the nutritional recommendations for each patient used to test the validity of the ontology rule set.
[DOCX File , 31 KB-Multimedia Appendix 3]

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Abbreviations
AgiSCOnt: Agile, Simplified, and Collaborative Ontology Engineering Methodology
BCAA: branched-chain amino acid
BMR: basal metabolic rate
COPD: chronic obstructive pulmonary disease
CQ: competency question
DSS: decision support system
FEV1: forced expiratory volume in the first second
FFM: fat-free mass
GUI: graphical user interface
LARN: Livelli di Assunzione di Riferimento di Nutrienti ed energia
NRI: nutritional risk index
PR: pulmonary rehabilitation
SWRL: semantic web rule language
WHO: World Health Organization

Edited by C Lovis; submitted 18.07.23; peer-reviewed by A AL-Asadi, T Salzmann; comments to author 19.01.24; revised version
received 01.02.24; accepted 23.04.24; published 26.06.24
Please cite as:
Spoladore D, Colombo V, Fumagalli A, Tosi M, Lorenzini EC, Sacco M
An Ontology-Based Decision Support System for Tailored Clinical Nutrition Recommendations for Patients With Chronic Obstructive
Pulmonary Disease: Development and Acceptability StudyJMIR Med Inform 2024;12:e50980
URL: https://medinform.jmir.org/2024/1/e50980
doi: 10.2196/50980
PMID: 38922666©Daniele Spoladore, Vera Colombo, Alessia Fumagalli, Martina Tosi, Erna Cecilia Lorenzini, Marco Sacco. Originally publishedin JMIR Medical Informatics (https://medinform.jmir.org), 26.06.2024. This is an open-access article distributed under the termsof the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
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JMIR Med Inform 2024 | vol. 12 | e50980 | p. 20(page number not for citation purposes)