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https://doi.org/10.53941/hm.2025.100007
Du, Z., Zhang, F., Ge, Y., Liu, Y., Yu, H., Wang, Y., Dalan, R., & Shen, X. Application of Wearable Devices in Diabetes Management. Health and Metabolism. 2025. doi: https://doi.org/10.53941/hm.2025.100007
Volume 2, Issue 1, 2025
Copyright (c) 2025 by the authors.
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Review

Application of Wearable Devices in Diabetes Management

Zijing Du 1,2,†, Feifan Zhang 1,†, Yifei Ge 1, Yijiang Liu 3, Honghua Yu 2, Yong Wang 4, Rinkoo Dalan 1,5, and Xiaotao Shen 1,3,*

1 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 636921, Singapore

2 Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China

3 School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, 639798, Singapore

4 College of Computing and Data Science, Nanyang Technological University, Singapore, 637616, Singapore

5 Department of Endocrinology, Tan Tock Seng Hospital, Singapore, 308433, Singapore

* Correspondence: xiaotao.shen@ntu.edu.sg

† These authors contributed equally to this work.

Received: 17 December 2024; Revised: 20 January 2025; Accepted: 12 February 2025; Published: 19 February 2025

Abstract: Diabetes mellitus poses a significant global health challenge, impacting hundreds of millions worldwide. Effective management and prevention of complications rely on dynamic, real-time glucose monitoring. This review provides a comprehensive overview of the rapidly evolving landscape of wearable technologies for glucose monitoring and diabetes care, with a focus on cutting-edge advancements and their integration with artificial intelligence (AI) and multi-omics data. We explore diverse glucose monitoring approaches, including continuous glucose monitors (CGMs) and smartwatches, highlighting their contributions to tracking physical activity, food intake, medication adherence, and direct glucose measurements. Our emphasis is placed on the role of AI systems in enabling predictive analytics and personalized care, as well as the integration of wearable data with multi-omics insights—spanning genomics, proteomics, and gut microbiome analyses—to enhance understanding of individual glucose metabolism. Given the challenges of existing methods, such as invasiveness, accuracy, and accessibility, we discuss future directions, including the potential of smart glasses, advanced AI models, and seamless data integration, to revolutionize diabetes management. This review offers valuable insights into how wearable technologies, AI, and multi-source data analysis are shaping the future of precision diabetes care.

Keywords:

diabetes mellitus glucose monitoring wearable devices artificial intelligence multi-omics digital health

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