RT Journal Article
T1 Core reference ontology for individualized exercise prescription
A1 Liu, Xingyun
A1 Yang, Yin
A1 Zong, Hui
A1 Zhang, Ke
A1 Jiang, Min
A1 Yu, Chunjiang
A1 Chen, Yalan
A1 Bao, Ting
A1 Li, Danting
A1 Wang, Jiao
A1 Tang, Tong
A1 Ren, Shumin
A1 Ruso Beiras, Juan Manuel
A1 Shen, Bairong
K1 Quality of life
K1 Risk factors
K1 Exercise prescription
AB “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
PB Nature
YR 2024
FD 2024-12-18
LK https://hdl.handle.net/10347/44182
UL https://hdl.handle.net/10347/44182
LA eng
NO Liu, X., Yang, Y., Zong, H. et al. Core reference ontology for individualized exercise prescription. Sci Data 11, 1349 (2024). https://doi.org/10.1038/s41597-024-04217-9
NO This work was supported by National Natural Science Foundation of China (Grant No. 32270690 and 32070671)
DS Minerva
RD 24 abr 2026