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Altuhaifa F, Al Tuhaifa D. Developing an Ontology Representing Fall Risk Management Domain Knowledge. J Med Syst 2024; 48:47. [PMID: 38662184 PMCID: PMC11045586 DOI: 10.1007/s10916-024-02062-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 04/04/2024] [Indexed: 04/26/2024]
Abstract
Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults.
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Affiliation(s)
- Fatimah Altuhaifa
- School of Computing and Information Technology, University of Wollongong, Northfields Ave, Wollongong, NSW, 2522, Australia.
- Saudi Arabia Ministry of Higher Education, Riyadh, Saudi Arabia.
| | - Dalal Al Tuhaifa
- Microbiology laboratory department, Maternity and Children's Hospital, Al Imam Ali Ibn Abi Talib St, Al Muraikabat, Dammam, 32253, Saudi Arabia.
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2
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Song J, Jin DL, Song TM, Lee SH. Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5753. [PMID: 37174270 PMCID: PMC10178337 DOI: 10.3390/ijerph20095753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/09/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023]
Abstract
COVID-19 is a respiratory infectious disease that first reported in Wuhan, China, in December 2019. With COVID-19 spreading to patients worldwide, the WHO declared it a pandemic on 11 March 2020. This study collected 1,746,347 tweets from the Korean-language version of Twitter between February and May 2020 to explore future signals of COVID-19 and present response strategies for information diffusion. To explore future signals, we analyzed the term frequency and document frequency of key factors occurring in the tweets, analyzing the degree of visibility and degree of diffusion. Depression, digestive symptoms, inspection, diagnosis kits, and stay home obesity had high frequencies. The increase in the degree of visibility was higher than the median value, indicating that the signal became stronger with time. The degree of visibility of the mean word frequency was high for disinfectant, healthcare, and mask. However, the increase in the degree of visibility was lower than the median value, indicating that the signal grew weaker with time. Infodemic had a higher degree of diffusion mean word frequency. However, the mean degree of diffusion increase rate was lower than the median value, indicating that the signal grew weaker over time. As the general flow of signal progression is latent signal → weak signal → strong signal → strong signal with lower increase rate, it is necessary to obtain active response strategies for stay home, inspection, obesity, digestive symptoms, online shopping, and asymptomatic.
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Affiliation(s)
- Juyoung Song
- Criminal Justice, Pennsylvania State University, Schuylkill, PA 17972, USA
| | - Dal-Lae Jin
- Department of Public Health, Graduate School of Korea University & Transdisciplinary Major in Learning Health Systems, Korea University, Seoul 02841, Republic of Korea
| | - Tae Min Song
- School of Industry and Environment, Gachon University, Seoul 13120, Republic of Korea
| | - Sang Ho Lee
- CEO for HealthMax Co., Ltd., Seoul 06078, Republic of Korea
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3
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Chatterjee A, Prinz A. Personalized Recommendations for Physical Activity e-Coaching (OntoRecoModel): Ontological Modeling. JMIR Med Inform 2022; 10:e33847. [PMID: 35737439 PMCID: PMC9282669 DOI: 10.2196/33847] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 03/05/2022] [Accepted: 04/21/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Automatic e-coaching may motivate individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Multiple studies have reported on uninterrupted and automatic monitoring of behavioral aspects (such as sedentary time, amount, and type of physical activity); however, e-coaching and personalized feedback techniques are still in a nascent stage. Current intelligent coaching strategies are mostly based on the handcrafted string messages that rarely individualize to each user's needs, context, and preferences. Therefore, more realistic, flexible, practical, sophisticated, and engaging strategies are needed to model personalized recommendations. OBJECTIVE This study aims to design and develop an ontology to model personalized recommendation message intent, components (such as suggestion, feedback, argument, and follow-ups), and contents (such as spatial and temporal content and objects relevant to perform the recommended activities). A reasoning technique will help to discover implied knowledge from the proposed ontology. Furthermore, recommendation messages can be classified into different categories in the proposed ontology. METHODS The ontology was created using Protégé (version 5.5.0) open-source software. We used the Java-based Jena Framework (version 3.16) to build a semantic web application as a proof of concept, which included Resource Description Framework application programming interface, World Wide Web Consortium Web Ontology Language application programming interface, native tuple database, and SPARQL Protocol and Resource Description Framework Query Language query engine. The HermiT (version 1.4.3.x) ontology reasoner available in Protégé 5.x implemented the logical and structural consistency of the proposed ontology. To verify the proposed ontology model, we simulated data for 8 test cases. The personalized recommendation messages were generated based on the processing of personal activity data in combination with contextual weather data and personal preference data. The developed ontology was processed using a query engine against a rule base to generate personalized recommendations. RESULTS The proposed ontology was implemented in automatic activity coaching to generate and deliver meaningful, personalized lifestyle recommendations. The ontology can be visualized using OWLViz and OntoGraf. In addition, we developed an ontology verification module that behaves similar to a rule-based decision support system to analyze the generation and delivery of personalized recommendation messages following a logical structure. CONCLUSIONS This study led to the creation of a meaningful ontology to generate and model personalized recommendation messages for physical activity coaching.
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Affiliation(s)
- Ayan Chatterjee
- Department of Information and Communication Technology, Center for eHealth, University of Agder, Grimstad, Norway
| | - Andreas Prinz
- Department of Information and Communication Technology, Center for eHealth, University of Agder, Grimstad, Norway
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4
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Chen Y, Ji M, Wu Y, Wang Q, Deng Y, Liu Y, Wu F, Liu M, Guo Y, Fu Z, Zheng X. An Intelligent Individualized Cardiovascular App for Risk Elimination (iCARE) for Individuals With Coronary Heart Disease: Development and Usability Testing Analysis. JMIR Mhealth Uhealth 2021; 9:e26439. [PMID: 34898449 PMCID: PMC8713096 DOI: 10.2196/26439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 03/18/2021] [Accepted: 10/05/2021] [Indexed: 11/24/2022] Open
Abstract
Background Death and disability from coronary heart disease (CHD) can be largely reduced by improving risk factor management. However, adhering to evidence-based recommendations is challenging and requires interventions at the level of the patient, provider, and health system. Objective The aim of this study was to develop an Intelligent Individualized Cardiovascular App for Risk Elimination (iCARE) to facilitate adherence to health behaviors and preventive medications, and to test the usability of iCARE. Methods We developed iCARE based on a user-centered design approach, which included 4 phases: (1) function design, (2) iterative design, (3) expert inspections and walkthroughs of the prototypes, and (4) usability testing with end users. The usability testing of iCARE included 2 stages: stage I, which included a task analysis and a usability evaluation (January to March 2019) of the iCARE patient app using the modified Health Information Technology Usability Survey (Health-ITUES); and stage II (June 2020), which used the Health-ITUES among end users who used the app for 6 months. The end users were individuals with a confirmed diagnosis of CHD from 2 university-affiliated hospitals in Beijing, China. Results iCARE consists of a patient app, a care provider app, and a cloud platform. It has a set of algorithms that trigger tailored feedback and can send individualized interventions based on data from initial assessment and health monitoring via manual entry or wearable devices. For stage I usability testing, 88 hospitalized patients (72% [63/88] male; mean age 60 [SD 9.9] years) with CHD were included in the study. The mean score of the usability testing was 90.1 (interquartile range 83.3-99.0). Among enrolled participants, 90% (79/88) were satisfied with iCARE; 94% (83/88) and 82% (72/88) reported that iCARE was useful and easy to use, respectively. For stage II usability testing, 61 individuals with CHD (85% [52/61] male; mean age 53 [SD 8.2] years) who were from an intervention arm and used iCARE for at least six months were included. The mean total score on usability testing based on the questionnaire was 89.0 (interquartile distance: 77.0-99.5). Among enrolled participants, 89% (54/61) were satisfied with the use of iCARE, 93% (57/61) perceived it as useful, and 70% (43/61) as easy to use. Conclusions This study developed an intelligent, individualized, evidence-based, and theory-driven app (iCARE) to improve patients’ adherence to health behaviors and medication management. iCARE was identified to be highly acceptable, useful, and easy to use among individuals with a diagnosis of CHD. Trial Registration Chinese Clinical Trial Registry ChiCTR-INR-16010242; https://tinyurl.com/2p8bkrew
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Affiliation(s)
- Yuling Chen
- School of Nursing, Capital Medical University, Beijing, China
| | - Meihua Ji
- School of Nursing, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ying Wu
- School of Nursing, Capital Medical University, Beijing, China
| | - Qingyu Wang
- School of Nursing, Capital Medical University, Beijing, China
| | - Ying Deng
- School of Nursing, Capital Medical University, Beijing, China
| | - Yong Liu
- Along Technology Inc, Beijing, China
| | - Fangqin Wu
- School of Nursing, Capital Medical University, Beijing, China
| | - Mingxuan Liu
- School of Nursing, Capital Medical University, Beijing, China
| | - Yiqiang Guo
- School of Nursing, Capital Medical University, Beijing, China
| | - Ziyuan Fu
- School of Nursing, Capital Medical University, Beijing, China
| | - Xiaoying Zheng
- The Asia-Pacific Economic Cooperation Health Science Academy, Peking University, Beijing, China.,Institute of Population Research, Peking University, Beijing, China
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Chatterjee A, Prinz A, Gerdes M, Martinez S. An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study. J Med Internet Res 2021; 23:e24656. [PMID: 33835031 PMCID: PMC8065560 DOI: 10.2196/24656] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 01/26/2021] [Accepted: 02/08/2021] [Indexed: 12/11/2022] Open
Abstract
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 early 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. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This “proof-of-concept” 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 is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), 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 simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of “Semantic Sensor Network Ontology” and “Systematized Nomenclature of Medicine—Clinical Terms” to develop our proposed eHealth ontology. The ontology has been created using Protégé (version 5.x). We have used the Java-based “Jena Framework” (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the “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 to other lifestyle diseases. “UiA eHealth Ontology” has been constructed using logical axioms, declaration axioms, classes, object properties, and 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. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we 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 (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.
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Affiliation(s)
- Ayan Chatterjee
- Department of Information and Communication Technologies, Centre for e-Health, University of Agder, Grimstad, Norway
| | - Andreas Prinz
- Department of Information and Communication Technologies, Centre for e-Health, University of Agder, Grimstad, Norway
| | - Martin Gerdes
- Department of Information and Communication Technologies, Centre for e-Health, University of Agder, Grimstad, Norway
| | - Santiago Martinez
- Department of Health and Nursing Science, Centre for e-Health, University of Agder, Grimstad, Norway
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Westra BL, Lytle ,KS, Whittenburg L, Adams M, Ali S, Furukawa M, Hartleben S, Hook M, Johnson S, Collins Rossetti S, Settergren T(T. A refined methodology for validation of information models derived from flowsheet data and applied to a genitourinary case. J Am Med Inform Assoc 2020; 27:1732-1740. [PMID: 32940673 PMCID: PMC7671628 DOI: 10.1093/jamia/ocaa166] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 04/14/2020] [Accepted: 07/17/2020] [Indexed: 11/14/2022] Open
Abstract
Use of electronic health record data is expanding to support quality improvement and research; however, this requires standardization of the data and validation within and across organizations. Information models (IMs) are created to standardize data elements into a logical organization that includes data elements, definitions, data types, values, and relationships. To be generalizable, these models need to be validated across organizations. The purpose of this case report is to describe a refined methodology for validation of flowsheet IMs and apply the revised process to a genitourinary IM created in one organization. The refined IM process, adding evidence and input from experts, produced a clinically relevant and evidence-based model of genitourinary care. The refined IM process provides a foundation for optimizing electronic health records with comparable nurse sensitive data that can add to common data models for continuity of care and ongoing use for quality improvement and research.
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Affiliation(s)
- Bonnie L Westra
- School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA
| | - , Kay S Lytle
- Health System Nursing and Duke Health Technology Solutions, Duke University Health System, Durham, North Carolina, USA
| | | | | | - Samira Ali
- School of Nursing, Wilkes University, Wilkes-Barre, Pennsylvania, USA
| | - Meg Furukawa
- Department of Information Services and Solutions, UCLA Health, Los Angeles, California, USA
| | | | - Mary Hook
- Center for Nursing Practice and Research, Advocate Aurora Health Care, Milwaukee, Wisconsin, USA
| | - Steve Johnson
- Institute of Health Informatics and School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA
| | - Sarah Collins Rossetti
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- School of Nursing, Columbia University, New York, New York, USA
| | - Tess (Theresa) Settergren
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- School of Nursing, Columbia University, New York, New York, USA
- Independent Consultant
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7
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Jing X, Hardiker NR, Kay S, Gao Y. Identifying Principles for the Construction of an Ontology-Based Knowledge Base: A Case Study Approach. JMIR Med Inform 2018; 6:e52. [PMID: 30578220 PMCID: PMC6320437 DOI: 10.2196/medinform.9979] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 04/25/2018] [Accepted: 07/19/2018] [Indexed: 11/13/2022] Open
Abstract
Background Ontologies are key enabling technologies for the Semantic Web. The Web Ontology Language (OWL) is a semantic markup language for publishing and sharing ontologies. Objective The supply of customizable, computable, and formally represented molecular genetics information and health information, via electronic health record (EHR) interfaces, can play a critical role in achieving precision medicine. In this study, we used cystic fibrosis as an example to build an Ontology-based Knowledge Base prototype on Cystic Fibrobis (OntoKBCF) to supply such information via an EHR prototype. In addition, we elaborate on the construction and representation principles, approaches, applications, and representation challenges that we faced in the construction of OntoKBCF. The principles and approaches can be referenced and applied in constructing other ontology-based domain knowledge bases. Methods First, we defined the scope of OntoKBCF according to possible clinical information needs about cystic fibrosis on both a molecular level and a clinical phenotype level. We then selected the knowledge sources to be represented in OntoKBCF. We utilized top-to-bottom content analysis and bottom-up construction to build OntoKBCF. Protégé-OWL was used to construct OntoKBCF. The construction principles included (1) to use existing basic terms as much as possible; (2) to use intersection and combination in representations; (3) to represent as many different types of facts as possible; and (4) to provide 2-5 examples for each type. HermiT 1.3.8.413 within Protégé-5.1.0 was used to check the consistency of OntoKBCF. Results OntoKBCF was constructed successfully, with the inclusion of 408 classes, 35 properties, and 113 equivalent classes. OntoKBCF includes both atomic concepts (such as amino acid) and complex concepts (such as “adolescent female cystic fibrosis patient”) and their descriptions. We demonstrated that OntoKBCF could make customizable molecular and health information available automatically and usable via an EHR prototype. The main challenges include the provision of a more comprehensive account of different patient groups as well as the representation of uncertain knowledge, ambiguous concepts, and negative statements and more complicated and detailed molecular mechanisms or pathway information about cystic fibrosis. Conclusions Although cystic fibrosis is just one example, based on the current structure of OntoKBCF, it should be relatively straightforward to extend the prototype to cover different topics. Moreover, the principles underpinning its development could be reused for building alternative human monogenetic diseases knowledge bases.
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Affiliation(s)
- Xia Jing
- Department of Social and Public Health, College of Health Sciences and Professions, Ohio University, Athens, OH, United States
| | - Nicholas R Hardiker
- School of Human and Health Sciences, University of Huddersfield, Huddersfield, United Kingdom
| | | | - Yongsheng Gao
- International Health Terminology Standards Development Organization, London, United Kingdom
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8
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Win KT, Roberts MRH, Oinas-Kukkonen H. Persuasive system features in computer-mediated lifestyle modification interventions for physical activity. Inform Health Soc Care 2018; 44:376-404. [PMID: 30351975 DOI: 10.1080/17538157.2018.1511565] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Objective: Increasing physical activity has been identified as one of the most important factors in lifestyle modification. Previous studies have reported the effectiveness of using the Internet in motivating behavioral modifications of physical activities. The aim of this study is to identify the persuasive system features most frequently used in computer-mediated physical activities in the current literature.Materials and Methods: In this review, intervention studies were identified through a structured computerized search of PubMed, PsychInfo, and Web of Science. The results of the search were analyzed using the persuasive systems design (PSD) features identified by Oinas-Kukkonen and Harjumaa (2009).Results: Thirty-eight articles were reviewed, and the features of the physical activity interventions described were mapped to the identified facets of PSD. The PSD features used most often by researchers in the studies considered in this research included tailoring, tunneling, reminders, trustworthiness, and expertise. The effectiveness of the interventions described in the studies was also compared. The stage of change theory was applied in several intervention studies, and the importance of stage of change has been identified in effectiveness of persuasion toward physical activity.
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Merlo G, Chiazzese G, Taibi D, Chifari A. Development and Validation of a Functional Behavioural Assessment Ontology to Support Behavioural Health Interventions. JMIR Med Inform 2018; 6:e37. [PMID: 29853438 PMCID: PMC6002668 DOI: 10.2196/medinform.7799] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 09/05/2017] [Accepted: 03/14/2018] [Indexed: 11/22/2022] Open
Abstract
Background In the cognitive-behavioral approach, Functional Behavioural Assessment is one of the most effective methods to identify the variables that determine a problem behavior. In this context, the use of modern technologies can encourage the collection and sharing of behavioral patterns, effective intervention strategies, and statistical evidence about antecedents and consequences of clusters of problem behaviors, encouraging the designing of function-based interventions. Objective The paper describes the development and validation process used to design a specific Functional Behavioural Assessment Ontology (FBA-Ontology). The FBA-Ontology is a semantic representation of the variables that intervene in a behavioral observation process, facilitating the systematic collection of behavioral data, the consequential planning of treatment strategies and, indirectly, the scientific advancement in this field of study. Methods The ontology has been developed deducing concepts and relationships of the ontology from a gold standard and then performing a machine-based validation and a human-based assessment to validate the Functional Behavioural Assessment Ontology. These validation and verification processes were aimed to verify how much the ontology is conceptually well founded and semantically and syntactically correct. Results The Pellet reasoner checked the logical consistency and the integrity of classes and properties defined in the ontology, not detecting any violation of constraints in the ontology definition. To assess whether the ontology definition is coherent with the knowledge domain, human evaluation of the ontology was performed asking 84 people to fill in a questionnaire composed by 13 questions assessing concepts, relations between concepts, and concepts’ attributes. The response rate for the survey was 29/84 (34.52%). The domain experts confirmed that the concepts, the attributes, and the relationships between concepts defined in the FBA-Ontology are valid and well represent the Functional Behavioural Assessment process. Conclusions The new ontology developed could be a useful tool to design new evidence-based systems in the Behavioral Interventions practices, encouraging the link with other Linked Open Data datasets and repositories to provide users with new models of eHealth focused on the management of problem behaviors. Therefore, new research is needed to develop and implement innovative strategies to improve the poor reproducibility and translatability of basic research findings in the field of behavioral assessment.
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Affiliation(s)
- Gianluca Merlo
- Istituto per le Tecnologie Didattiche, Consiglio Nazionale delle Ricerche, Palermo, Italy
| | - Giuseppe Chiazzese
- Istituto per le Tecnologie Didattiche, Consiglio Nazionale delle Ricerche, Palermo, Italy
| | - Davide Taibi
- Istituto per le Tecnologie Didattiche, Consiglio Nazionale delle Ricerche, Palermo, Italy
| | - Antonella Chifari
- Istituto per le Tecnologie Didattiche, Consiglio Nazionale delle Ricerche, Palermo, Italy
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10
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A System Model for Personalized Medication Management (MyMediMan)—The Consumers’ Point of View. INFORMATION 2018. [DOI: 10.3390/info9040069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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11
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Kim AR, Park HA, Song TM. Development and Evaluation of an Obesity Ontology for Social Big Data Analysis. Healthc Inform Res 2017; 23:159-168. [PMID: 28875050 PMCID: PMC5572519 DOI: 10.4258/hir.2017.23.3.159] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 06/18/2017] [Accepted: 06/28/2017] [Indexed: 11/23/2022] Open
Abstract
Objectives The aim of this study was to develop and evaluate an obesity ontology as a framework for collecting and analyzing unstructured obesity-related social media posts. Methods The obesity ontology was developed according to the ‘Ontology Development 101’. The coverage rate of the developed ontology was examined by mapping concepts and terms of the ontology with concepts and terms extracted from obesity-related Twitter postings. The structure and representative ability of the ontology was evaluated by nurse experts. We applied the ontology to the density analysis of keywords related to obesity types and management strategies and to the sentiment analysis of obesity and diet using social big data. Results The developed obesity ontology was represented by 8 superclasses and 124 subordinate classes. The superclasses comprised ‘risk factors,’ ‘types,’ ‘symptoms,’ ‘complications,’ ‘assessment,’ ‘diagnosis,’ ‘management strategies,’ and ‘settings.’ The coverage rate of the ontology was 100% for the concepts and 87.8% for the terms. The evaluation scores for representative ability were higher than 4.0 out of 5.0 for all of the evaluation items. The density analysis of keywords revealed that the top-two posted types of obesity were abdomen and thigh, and the top-three posted management strategies were diet, exercise, and dietary supplements or drug therapy. Positive expressions of obesity-related postings has increased annually in the sentiment analysis. Conclusions It was found that the developed obesity ontology was useful to identify the most frequently used terms on obesity and opinions and emotions toward obesity posted by the geneal population on social media.
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Affiliation(s)
- Ae Ran Kim
- College of Nursing & Systems Biomedical Informatics Research Center, Seoul National University, Seoul, Korea.,Department of Nursing, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyeoun-Ae Park
- College of Nursing & Systems Biomedical Informatics Research Center, Seoul National University, Seoul, Korea.,Research Institute of Nursing Science, Seoul National University, Seoul, Korea
| | - Tae-Min Song
- Korea Institute for Health and Social Affairs, Sejong, Korea
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12
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Win KT, Hassan NM, Oinas-Kukkonen H, Probst Y. Online Patient Education for Chronic Disease Management: Consumer Perspectives. J Med Syst 2016; 40:88. [PMID: 26846749 DOI: 10.1007/s10916-016-0438-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Accepted: 01/07/2016] [Indexed: 01/27/2023]
Abstract
Patient education plays an important role in chronic disease management. The aim of this study is to identify patients' preferences in regard to the design features of effective online patient education (OPE) and the benefits. A review of the existing literature was conducted in order to identify the benefits of OPE and its essential design features. These design features were empirically tested by conducting survey with patients and caregivers. Reliability analysis, construct validity and regression analysis were performed for data analysis. The results identified patient-tailored information, interactivity, content credibility, clear presentation of content, use of multimedia and interpretability as the essential design features of online patient education websites for chronic disease management.
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Affiliation(s)
- Khin Than Win
- Faculty of Engineering and Information Science, University of Wollongong, Wollongong, Australia.
| | - Naffisah Mohd Hassan
- Faculty of Engineering and Information Science, University of Wollongong, Wollongong, Australia.
| | - Harri Oinas-Kukkonen
- Department of Information Processing Science, University of Oulu, Oulu, Finland.
| | - Yasmine Probst
- Faculty of Engineering and Information Science, University of Wollongong, Wollongong, Australia.
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Kang H, Park HA. A Mobile App for Hypertension Management Based on Clinical Practice Guidelines: Development and Deployment. JMIR Mhealth Uhealth 2016; 4:e12. [PMID: 26839283 PMCID: PMC4756253 DOI: 10.2196/mhealth.4966] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Revised: 09/08/2015] [Accepted: 10/09/2015] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Hypertension is a chronic and lifestyle-related disease that requires continuous preventive care. Although there are many evidence-based clinical practice guidelines (CPGs) for hypertension management, applying them to daily management can be difficult for patients with hypertension. A mobile app, based on CPGs, could help patients with hypertension manage their disease. OBJECTIVE To develop a mobile app for hypertension management based on CPGs and evaluate its effectiveness in patients with hypertension with respect to perceived usefulness, user satisfaction, and medication adherence. METHODS The hypertension management app (HMA) was developed according to the Web-Roadmap methodology, which includes planning, analysis, design, implementation, and evaluation phases. The HMA was provided to individuals (N=38) with hypertension. Medication adherence was measured before and after using the HMA for 4 weeks. The perceived usefulness and user satisfaction were surveyed in the patients who completed the medication adherence survey. RESULTS Of the 38 study participants, 29 (76%) participated in medical adherence assessment. Medication adherence, as measured by the Modified Morisky Scale, was significantly improved in these patients after they had used the HMA (P=.001). The perceived usefulness score was 3.7 out of 5. The user satisfaction scores, with respect to using the HMA for blood pressure recording, medication recording, data sending, alerting, recommending, and educating about medication were 4.3, 3.8, 3.1, 3.2, 3.4, and 3.8 out of 5, respectively, in the 19 patients. CONCLUSIONS This study showed that a mobile app for hypertension management based on CPGs is effective at improving medication adherence.
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Affiliation(s)
- Hannah Kang
- Systems Biomedical Informatics Research Center, College of Nursing, Seoul National University, Seoul, Republic Of Korea
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Diniz IA, Cavalcante RB, Otoni A, da Mata LRF. Perception of primary healthcare management nurses on the nursing process. Rev Bras Enferm 2015. [PMID: 26222162 DOI: 10.1590/0034-7167.2015680204i] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVE this qualitative study aimed to analyze the perceptions of primary health care management nurses on the nursing process. METHOD data were collected through interviews and analyzed by the Content Analysis proposed by Bardin's theoretical framework. RESULTS managers recognize the importance of the nursing process, although its implementation was not a priority at the time of the interviews. A conceptual difficulty and a lack of understanding that the implementation of the care methodology should be a cross-departmental action in the local healthcare management were clearly observed. CONCLUSION managers should have their perspectives broadened concerning the relevance of the nursing process and the professional training. The active participation of legislative nursing bodies, local healthcare management and the federal government may open the way for the effective implementation of the nursing process.
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Affiliation(s)
- Ieda Aparecida Diniz
- Programa de Residência Multiprofissional em Atenção Básica/Saúde da Família, Universidade Federal de São João Del Rei, Divinópolis, MG, Brasil
| | | | - Alba Otoni
- Curso de Enfermagem, Universidade Federal de São João Del Rei, Divinópolis, MG, Brasil
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Myneni S, Amith M, Geng Y, Tao C. Towards an Ontology-driven Framework to Enable Development of Personalized mHealth Solutions for Cancer Survivors' Engagement in Healthy Living. Stud Health Technol Inform 2015; 216:113-7. [PMID: 26262021 PMCID: PMC4946640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Adolescent and Young Adult (AYA) cancer survivors manage an array of health-related issues. Survivorship Care Plans (SCPs) have the potential to empower these young survivors by providing information regarding treatment summary, late-effects of cancer therapies, healthy lifestyle guidance, coping with work-life-health balance, and follow-up care. However, current mHealth infrastructure used to deliver SCPs has been limited in terms of flexibility, engagement, and reusability. The objective of this study is to develop an ontology-driven survivor engagement framework to facilitate rapid development of mobile apps that are targeted, extensible, and engaging. The major components include ontology models, patient engagement features, and behavioral intervention technologies. We apply the proposed framework to characterize individual building blocks ("survivor digilegos"), which form the basis for mHealth tools that address user needs across the cancer care continuum. Results indicate that the framework (a) allows identification of AYA survivorship components, (b) facilitates infusion of engagement elements, and (c) integrates behavior change constructs into the design architecture of survivorship applications. Implications for design of patient-engaging chronic disease management solutions are discussed.
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