ArticleJMIR medical informatics2022
Personalized Recommendations for Physical Activity e-Coaching (OntoRecoModel): Ontological Modeling.
Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care.BMC health services research · 2023Pooled it
- Contextual recommendation modeling in eCoaching with machine learning, X-AI, and semantic ontology.Frontiers in digital health · 2026Article
- Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer.Scientific reports · 2025Article
- Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology.Scientific reports · 2024Article
- PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data.Briefings in bioinformatics · 2024Article
- Semantic representation and comparative analysis of physical activity sensor observations using MOX2-5 sensor in real and synthetic datasets: a proof-of-concept-study.Scientific reports · 2024Article
- AI and semantic ontology for personalized activity eCoaching in healthy lifestyle recommendations: a meta-heuristic approach.BMC medical informatics and decision making · 2023Article
- Designing a Mobile e-Coaching App for Immigrant Informal Caregivers: Qualitative Study Using the Persuasive System Design Model.JMIR mHealth and uHealth · 2023Article
- Enhancing knowledge of Diabetes self-management and quality of life in people with Diabetes Mellitus by usingJournal of public health research · 2023Article
- An automatic and personalized recommendation modelling in activity eCoaching with deep learning and ontology.Scientific reports · 2023Article
- Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation.Scientific reports · 2022Article
- ProHealth eCoach: user-centered design and development of an eCoach app to promote healthy lifestyle with personalized activity recommendations.BMC health services research · 2022Article
Corrections and comments
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAutomatic 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.
objectiveThis 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.
methodsThe 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.
resultsThe 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.
conclusionsThis study led to the creation of a meaningful ontology to generate and model personalized recommendation messages for physical activity coaching.
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