scholarly journals UiAeHo - an OWL-Based Ontology Modeling to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management Targeting Obesity as a Case Study (Preprint)

2020 ◽  
Author(s):  
Ayan Chatterjee ◽  
Andreas Prinz ◽  
Martin Gerdes ◽  
Santiago Martinez
Keyword(s):  
Health Risk ◽  
Risk Prediction ◽  
Situation Awareness ◽  
Web Application ◽  
Healthy Lifestyle ◽  
Personalized Recommendation ◽  
Health And Wellness ◽  
Contextual Data ◽  
Study Case ◽  
Lifestyle Diseases

BACKGROUND Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. But it may produce data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, give situation awareness, help in data integration, and discover inferred knowledge. This “proof of concept (POC)” study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case. OBJECTIVE The aim of this study has been an OWL-based ontology (called the “UiA eHealth Ontology/UiAeHo”) to annotate personal, physiological, behavioral and contextual data from heterogeneous sources (sensor, questionnaire, and interview), and followed by, structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules. METHODS We have developed a Java-based simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of “SSN Ontology”, and “SNOMED-CT” to develop our proposed eHealth ontology. The ontology has been created using Protégé (V. 5.x). Following, we have used the Java-based “Jena Framework” (V. 3.16) for building a semantic web application that includes RDF API, OWL API, native tuple store (TDB), and the SPARQL query engine. The logical and structural consistency of the proposed ontology has been performed with “HermiT 1.4.3.x” ontology reasoner available in Protégé 5.x. RESULTS The proposed ontology has been implemented for the study case “Obesity”. However, it can be extended further for other lifestyle diseases. “UiA eHealth Ontology” has been constructed using 623 logical axioms, 363 declaration axioms, 162 classes, 83 object properties, and 101 data properties. The ontology can be visualized with “Owl Viz”, and the formal representation has been used to infer a participant's health status using the “HermiT” reasoner. In addition, we have developed a Java-based module for ontology verification, that behaves like a rule-based decision support system (DSS) to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Moreover, we have discussed the potential lifestyle recommendation generation plan against adverse behavioral risks. CONCLUSIONS This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive raw, unstructured observations for health and wellness data (e.g., sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.

Logical Representation of Sensor Data, Preferences, and Personalized Activity Recommendations in an eCoach System: An Ontology-based Proof-of-Concept Study (Preprint)

2021 ◽  
Author(s):  
Ayan Chatterjee
Keyword(s):  
Health Risk ◽  
Risk Prediction ◽  
Sparql Query ◽  
Sensor Data ◽  
Personalized Recommendation ◽  
Weather Data ◽  
Health State ◽  
Proof Of Concept ◽  
Logical Representation ◽  
Activity Recommendations

UNSTRUCTURED An automatic electronic coaching (eCoaching) can motivate individuals to lead a healthy lifestyle through early health risk prediction, customized recommendation generation, preference setting (such as, goal setting, response, and interaction), and goal evaluation. Such an eCoach system needs to collect heterogeneous health, wellness, and contextual data, and then convert them into meaningful information for health monitoring, health risk prediction, and the generation of personalized recommendations. However, data from various sources may cause a data compatibility dilemma. The proposed ontology can help in data integration, logical representation of sensory observations and customized suggestions, and discover implied knowledge. This "proof of concept (PoC)" research will help sensors, personal preferences, and recommendation data to be more organized. The research aims to design and develop an OWL-based ontology ("UiA Activity Recommendation Ontology/UiAARO") to annotate activity sensor data, contextual weather data, personal information, personal preferences, and personalized activity recommendations. The ontology was created using Protégé (V. 5.5.0) open-source software. We used the Java-based Jena Framework (V. 3.16) to build a semantic web application, which includes RDF API, OWL API, native tuple storage (TDB), and SPARQL query engine. The HermiT (V. 1.4.3.x) ontology reasoner available in Protégé 5.x has implemented the logical and structural consistency of the proposed ontology. The ontology can be visualized with OWLViz and OntoGraf, and the formal representation has been used to infer the health status of the eCoach participants with a reasoner. We have also developed an ontology verification module that behaves like a rule-based decision making (e.g., health state monitor and prediction), which can evaluate participant’s health state based on the evaluation of SPARQL query results, activity performed and predefined goal. Furthermore, the “UiAARO” has helped to represent the personalized recommendation messages beyond just “String” values, rather more meaningful with object-oriented representation. The scope of the proposed ontology is limited neither to specific sensor data nor only activity recommendations; instead, its scope can be further extended.

scholarly journals From Mediterranean diet to Mediterranean lifestyle: a narrative review

2019 ◽  
Vol 22 (14) ◽  
pp. 2703-2713 ◽  
Author(s):  
Anastasia Diolintzi ◽  
Demosthenes B Panagiotakos ◽  
Labros S Sidossis
Keyword(s):  
English Language ◽  
Healthy Lifestyle ◽  
Scientific Evidence ◽  
Narrative Review ◽  
Special Focus ◽  
Health And Wellness ◽  
Prospective Cohort Studies ◽  
Definition Of ◽  
Meta Analyses ◽  
Lifestyle Diseases

AbstractObjective:To summarize the recent scientific evidence regarding the wellness-promoting capacity of the Mediterranean lifestyle (ML), with a special focus on physical, social and environmental wellness.Design:Narrative review of English-language publications in PubMed, Scopus and Embase, from 1 January 2010 to 31 October 2018.Setting:Prospective cohort studies, interventional studies, meta-analyses and reviews of those investigating the effect of at least one component of the ML on wellness parameters.Participants:General population.Results:Although an explicit definition of ML is missing, compliance with various combinations of its components improves metabolic health and protects against or ameliorates disease state. However, there is heterogeneity in the healthy behaviours that the ML-focused studies include in their design and the way these are assessed. Also, despite that features of the ML could contribute to other wellness dimensions, there are no studies exploring the effect this healthy lifestyle could confer to them.Conclusions:Chronic lifestyle diseases are of multifactorial aetiology and they warrant multifaceted approaches targeting the general way of living. ML, if thoroughly evaluated, can provide a valuable tool to holistically promote health and wellness.

scholarly journals UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data

2021 ◽  
Author(s):  
Chacha Chen ◽  
Junjie Liang ◽  
Fenglong Ma ◽  
Lucas Glass ◽  
Jimeng Sun ◽  
...  
Keyword(s):  
Health Risk ◽  
Risk Prediction

scholarly journals StageNet: Stage-Aware Neural Networks for Health Risk Prediction

2020 ◽  
Author(s):  
Junyi Gao ◽  
Cao Xiao ◽  
Yasha Wang ◽  
Wen Tang ◽  
Lucas M. Glass ◽  
...  
Keyword(s):  
Neural Networks ◽  
Health Risk ◽  
Risk Prediction

scholarly journals Health factor in food consumption

2009 ◽  
pp. 125-132
Author(s):  
Krisztián Lőrinczi
Keyword(s):  
Consumer Behaviour ◽  
Healthy Lifestyle ◽  
Soft Drink ◽  
Soft Drinks ◽  
Mineral Waters ◽  
Purchase Decision ◽  
Carbonated Soft Drinks ◽  
Good Nutrition ◽  
Lifestyle Diseases ◽  
Relevant Factors

Consumer lifestyle and health are relevant factors to understanding consumption preferences. In the last few decades the number of lifestyle diseases has dramatically increased. The main cause for these diseases is the change in lifestyle; including a lack of attention to physical activity and good nutrition. Health and lifestyle are important factors by purchase decision process. In accordance with these, I examine the consumer behaviour toward soft drinks with special regards to healthy lifestyle and the state of health. My examinations can be considered mainly as aqualitative research, which can serve as a basis for further analyses and research, however, the conclusions and experience gained from it are worthy of consideration. I differentiated five soft drink categories: ice tea, carbonated soft drinks, fruit juices, mineral waters, sport and energy drinks and studied the consumer behaviour toward them. The study focuses on the consumption of these and the factors influencing their purchase with special regards to lifestyle.

scholarly journals Health risk prediction models incorporating personality data: Motivation, challenges, and illustration.

2019 ◽  
Vol 10 (1) ◽  
pp. 46-58 ◽  
Author(s):  
Benjamin P. Chapman ◽  
Feng Lin ◽  
Shumita Roy ◽  
Ralph H. B. Benedict ◽  
Jeffrey M. Lyness
Keyword(s):  
Health Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models

Lifestyle Medicine

2021 ◽  
pp. 157-178
Author(s):  
Gia Merlo
Keyword(s):  
Healthy Lifestyle ◽  
Lifestyle Changes ◽  
Medical Community ◽  
Global Public Health ◽  
Lifestyle Medicine ◽  
Public Health Burden ◽  
Obesity And Diabetes ◽  
Managing Stress ◽  
Treatment Of Obesity ◽  
Lifestyle Diseases

This chapter addresses the rise of lifestyle medicine. The impact of chronic diseases on health and quality of life are well-known within the medical community. Preventive medicine has only been partially successful in addressing these problems. For physicians to advocate healthy lifestyle choices for their patients, they must first understand what a healthy lifestyle entails. The Six Pillars of Lifestyle Medicine outline the six main lifestyle changes—healthful eating, increasing physical activity, improving sleep, managing stress, avoiding risky substances, forming and maintaining relationships—that physicians should promote to their patients. The global public health burden of diabetes, obesity, and other lifestyle diseases is increasing at an astounding rate. However, very few training programs have robust educational offerings for physicians on nonpharmacological treatment of obesity and diabetes.

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