A Bibliometric Analysis of Digital Twin Technology Applications in Home Health Monitoring
DOI:
https://doi.org/10.62836/amr.v4i1.524Keywords:
digital twin, home health monitoring, bibliometrics, health managementAbstract
Objective: With population aging and the rising prevalence of chronic diseases and cognitive impairment, the demand for home health monitoring is increasing. This study systematically examines the current status and development trends of digital twin technology in home health monitoring. Methods: Relevant literature published between 2015 and 2025 was retrieved from the Web of Science Core Collection. Following a two-stage screening process, 201 articles were included. Biblioshiny and CiteSpace were employed to conduct bibliometric and visualization analyses, highlighting publication trends, collaboration networks, thematic evolution, and keyword clustering. Results: Findings indicate that research on digital twins in home health monitoring has expanded rapidly since 2016, with major emphases on physiological monitoring, functional assessment, and environmental health interaction. Frequently occurring keywords reflect technological foundations such as “artificial intelligence” and “machine learning”, as well as application hotspots including “rehabilitation” and “virtual reality.” China and the United States emerged as the most productive contributors. Conclusion: Digital twins are driving a paradigm shift in home health monitoring from passive response to proactive intervention. However, challenges remain regarding data standardization, model robustness, and privacy protection. Future work should foster interdisciplinary collaboration and explore integration with traditional Chinese medicine to further enhance the value of digital twins in home health management.
References
Grieves M, Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In Transdisciplinary Perspectives on Complex Systems; Springer: Berlin/Heidelberg, Germany, 2017. https://doi.org/10.1007/978-3-319-38756-7_4.
Katsoulakis E, Wang Q, Wu H, et al. Digital Twins for Health: A Scoping Review. NPJ Digital Medicine 2024; 7(1): 77. https://doi.org/10.1038/s41746-024-01073-0.
Aria M, Cuccurullo C. Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis. Journal of Informetrics 2017; 11(4): 959–975. https://doi.org/10.1016/j.joi.2017.08.007.
Chen, C. CiteSpace II: Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature. Journal of the American Society for Information Science and Technology 2006; 57(3): 359–377. https://doi.org/10.1002/asi.20317.
Gauffriau M, Larsen PO. Counting Methods Are Decisive for Rankings Based on Publication and Citation Studies. Scientometrics 2005; 64(1): 85–93. https://doi.org/10.1007/s11192-005-0239-6.
Abramo G, D’Angelo CA, Rosati F. The Importance of Accounting for the Number of Co-Authors and Their Order When Assessing Research Performance at the Individual Level in the Life Sciences. Journal of Informetrics 2013; 7(1): 198–208. https://doi.org/10.1016/j.joi.2012.11.003.
Jameil AK, Al-Raweshidy H. A Digital Twin Framework for Real-Time Healthcare Monitoring: Leveraging AI and Secure Systems for Enhanced Patient Outcomes. Discover Internet of Things 2025; 5: 37. https://doi.org/10.1007/s43926-025-00135-3.
Wang M, Hu H, Wu S. Opportunities and Challenges of Digital Twin Technology in Healthcare. Chinese Medical Journal 2023; 136(23): 2895–2896. https://doi.org/10.1097/CM9.0000000000002896.
Pellegrino G, Gervasi M, Angelelli M, et al. A Conceptual Framework for Digital Twin in Healthcare: Evidence from a Systematic Meta-Review. Information Systems Frontiers 2024; 27: 7–32. https://doi.org/10.1007/s10796-024-10536-4.
Ringeval M, Etindele Sosso FA, Cousineau M, et al. Advancing Health Care with Digital Twins: Meta-Review of Applications and Implementation Challenges. Journal of Medical Internet Research 2025; 27: e69544. https://doi.org/10.2196/69544.
Dhinakaran D, Raja ES, Ramathilagam A, et al. Ethical and Legal Challenges with IoT in Home Digital Twins. Journal of Information Security Applications 2025; 14: 103409.
Chen X, Zhang Y, Wang Y. Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey. arXiv 2024.
Nadeem M, Kostic S, Dornhöfer M, et al. A comprehensive review of digital twin in healthcare in the scope of simulative health-monitoring. Digital Health 2025; 11: 125. https://doi.org/10.1177/20552076241304078.
Li H, Guo N, Yan L, et al. Research on Digital Twin Modeling and Standardization Requirements and Application. In Proceedings of SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), Wuhan, China, 20–22 December 2023.
Zafar RO, Rybarczyk Y, Borg J. A Systematic Review of Digital Twin Technology for Home Care. ACM Transactions on Computing for Healthcare 2024; 5(4): 20. https://doi.org/10.1145/3681797.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Han Shi, Yuxuan Cui, Zhen Qin, Xiaohan Bian, Jiayue Xu, Shihua Cao
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommerical-NoDerivatives 4.0 International License.
