Core reference ontology for individualized exercise prescription

Xingyun Liu; Yin Yang; Hui Zong; Ke Zhang; Min Jiang; Chunjiang Yu; Yalan Chen; Ting Bao; Danting Li; Jiao Wang; Tong Tang; Shumin Ren; Juan M Ruso; Bairong Shen

doi:10.1038/s41597-024-04217-9

Core reference ontology for individualized exercise prescription

Sci Data. 2024 Dec 18;11(1):1349. doi: 10.1038/s41597-024-04217-9.

Authors

Xingyun Liu^#^{1

2}, Yin Yang^#^{1

3}, Hui Zong¹, Ke Zhang¹, Min Jiang¹, Chunjiang Yu⁴, Yalan Chen⁵, Ting Bao⁶, Danting Li⁶, Jiao Wang^{1

2}, Tong Tang^{1

2}, Shumin Ren^{1

2}, Juan M Ruso⁷, Bairong Shen⁸

Affiliations

¹ Health Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
² Department of Computer Science and Information Technology, University of A Coruña, A Coruña, Spain.
³ Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu, China.
⁴ School of Artificial Intelligence, Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, China.
⁵ Department of Medical Informatics, School of Medicine, Nantong University, Nantong, China.
⁶ Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China.
⁷ Soft Matter and Molecular Biophysics Group, Department of Applied Physics and Institute of Materials (iMATUS), University of Santiago de Compostela, Santiago de Compostela, Spain. juanm.ruso@usc.es.
⁸ Health Management Center, General Practice Medical Center and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China. bairong.shen@scu.edu.cn.

^# Contributed equally.

Abstract

"Exercise is medicine" emphasizes personalized prescriptions for better efficacy. Current guidelines need more support for personalized prescriptions, posing scientific challenges. Facing those challenges, we gathered data from established guidelines, databases, and articles to develop the Exercise Medicine Ontology (EXMO), intending to offer comprehensive support for personalized exercise prescriptions. EXMO was constructed using the Ontology Development 101 methodology, incorporating Open Biological and Biomedical Ontology Foundry principles. EXMO v1.0 comprises 434 classes and 9,732 axioms, encompassing physical activity terms, health status terms, exercise prescription terms, and other related concepts. It has successfully undergone expert evaluation and consistency validation using the ELK and JFact reasoners. EXMO has the potential to provide a much-needed standard for individualized exercise prescription. Beyond prescription standardization, EXMO can also be an excellent tool for supporting databases and recommendation systems. In the future, it could serve as a valuable reference for developing sub-ontologies and facilitating the formation of an ontology network.

Publication types

Dataset

MeSH terms

Biological Ontologies*
Exercise Therapy
Exercise*
Humans
Precision Medicine

Abstract

Publication types

MeSH terms

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