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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, 2(1), 7. 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

References

  1. Xie, J.; Wang, M.; Long, Z.; Ning, H.; Li, J.; Cao, Y.; Liao, Y.; Liu, G.; Wang, F.; Pan, A. Global burden of type 2 diabetes in adolescents and young adults, 1990–2019: Systematic analysis of the Global Burden of Disease Study 2019. BMJ 2022, 379, e072385. doi: 10.1136/bmj-2022-072385
  2. IDF Diabetes Atlas. Available online: https://diabetesatlas.org/ (accessed on 6 December 2021).
  3. Endocrine Society. Diabetes Complications | Endocrine Society. Available online: https://www.endocrine.org/patient-engagement/endocrine-library/diabetes-complications (accessed on 24 January 2022).
  4. Davies, M.J.; D’Alessio, D.A.; Fradkin, J.; Kernan, W.N.; Mathieu, C.; Mingrone, G.; Rossing, P.; Tsapas, A.; Wexler, D.J.; Buse, J.B. Management of Hyperglycemia in Type 2 Diabetes, 2018. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 2018, 41, 2669–2701. doi: 10.2337/dci18-0033
  5. Szymborska-Kajanek, A.; Psurek, A.; Hese, R.; Strojek, K. Self-monitoring of blood glucose in treatment of type 2 diabetes. Diabetes Res. Clin. Pract. 2009, 86, S49–S52. doi: 10.1016/S0168-8227(09)70009-9
  6. Cheng, R.; Taleb, N.; Wu, Z.; Bouchard, D.; Parent, V.; Lalanne-Mistrih, M.L.; Boudreau, V.; Messier, V.; Lacombe, M.J.; Grou, C.; et al. Managing Impending Nonsevere Hypoglycemia with Oral Carbohydrates in Type 1 Diabetes: The REVERSIBLE Trial. Diabetes Care 2024, 47, 476–482. doi: 10.2337/dc23-1328
  7. Zhang, Y.; Yang, Y.; Huang, Q.; Zhang, Q.; Li, M.; Wu, Y. The effectiveness of lifestyle interventions for diabetes remission on patients with type 2 diabetes mellitus: A systematic review and meta-analysis. Worldviews Evid. Based Nurs. 2022, 20, 64–78. doi: 10.1111/wvn.12608
  8. Jenum, A.K.; Brekke, I.; Mdala, I.; Muilwijk, M.; Ramachandran, A.; Kjøllesdal, M.; Andersen, E.; Richardsen, K.R.; Douglas, A.; Cezard, G.; et al. Effects of dietary and physical activity interventions on the risk of type 2 diabetes in South Asians: Meta-analysis of individual participant data from randomised controlled trials. Diabetologia 2019, 62, 1337–1348. doi: 10.1007/s00125-019-4905-2
  9. Lin, M.; Chen, T.; Fan, G. Current status and influential factors associated with adherence to self-monitoring of blood glucose with type 2 diabetes mellitus patients in grassroots communities: A cross-sectional survey based on information-motivation-behavior skills model in China. Front. Endocrinol. 2023, 14, 1111565. doi: 10.3389/fendo.2023.1111565
  10. Seidu, S.; Kunutsor, S.K.; Ajjan, R.A.; Choudhary, P. Efficacy and Safety of Continuous Glucose Monitoring and Intermittently Scanned Continuous Glucose Monitoring in Patients with Type 2 Diabetes: A Systematic Review and Meta-analysis of Interventional Evidence. Diabetes Care 2023, 47, 169–179. doi: 10.2337/dc23-1520
  11. Boland, E.; Monsod, T.; Delucia, M.; Brandt, C.A.; Fernando, S.; Tamborlane, W.V. Limitations of conventional methods of self-monitoring of blood glucose: Lessons learned from 3 days of continuous glucose sensing in pediatric patients with type 1 diabetes. Diabetes Care 2001, 24, 1858–1862. doi: 10.2337/diacare.24.11.1858
  12. Huang, X.; Yao, C.; Huang, S.; Zheng, S.; Liu, Z.; Liu, J.; Wang, J.; Chen, H.J.; Xie, X. Technological Advances of Wearable Device for Continuous Monitoring of In Vivo Glucose. ACS Sens. 2024, 9, 1065–1088.
  13. Yang, J.; Gong, X.; Chen, S.; Zheng, Y.; Peng, L.; Liu, B.; Chen, Z.; Xie, X.; Yi, C.; Jiang, L. Development of Smartphone-Controlled and Microneedle-Based Wearable Continuous Glucose Monitoring System for Home-Care Diabetes Management. ACS Sens. 2023, 8, 1241–1251. doi: 10.1021/acssensors.2c02635
  14. Chakravadhanula, K. A smartphone-based test and predictive models for rapid, non-invasive, and point-of-care monitoring of ocular and cardiovascular complications related to diabetes. Inform. Med. Unlocked 2021, 24, 100485. doi: 10.1016/j.imu.2020.100485
  15. Beck, R.W.; Riddlesworth, T.D.; Ruedy, K.; Ahmann, A.; Haller, S.; Kruger, D.; McGill, J.B.; Polonsky, W.; Price, D.; Aronoff, S.; et al. Continuous Glucose Monitoring Versus Usual Care in Patients with Type 2 Diabetes Receiving Multiple Daily Insulin Injections: A Randomized Trial. Ann. Intern. Med. 2017, 167, 365. doi: 10.7326/M16-2855
  16. Ahmed, A.; Aziz, S.; Abd-alrazaq, A.; Farooq, F.; Sheikh, J. Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review. J. Med. Internet Res. 2022, 24, e36010. doi: 10.2196/36010
  17. Huang, X.; Liang, B.; Huang, S.; Liu, Z.; Yao, C.; Yang, J.; Zheng, S.; Wu, F.; Yue, W.; Wang, J.; et al. Integrated electronic/fluidic microneedle system for glucose sensing and insulin delivery. Theranostics 2024, 14, 1662–1682. doi: 10.7150/thno.92910
  18. Li, Q.-F.; Chen, X.; Wang, H.; Liu, M.; Peng, H.-L. Pt/MXene-Based Flexible Wearable Non-Enzymatic Electrochemical Sensor for Continuous Glucose Detection in Sweat. ACS Appl. Mater. Interfaces 2023, 15, 13290–13298. doi: 10.1021/acsami.2c20543
  19. Zafar, H.; Channa, A.; Jeoti, V.; Stojanović, G.M. Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring. Sensors 2022, 22, 638. doi: 10.3390/s22020638
  20. Heikenfeld, J.; Jajack, A.; Rogers, J.; Gutruf, P.; Tian, L.; Pan, T.; Li, R.; Khine, M.; Kim, J.; Wang, J. Wearable sensors: Modalities, challenges, and prospects. Lab Chip 2018, 18, 217–248. doi: 10.1039/C7LC00914C
  21. Gæde, P.; Vedel, P.; Larsen, N.; Jensen, G.V.; Parving, H.H.; Pedersen, O. Multifactorial Intervention and Cardiovascular Disease in Patients with Type 2 Diabetes. N. Engl. J. Med. 2003, 348, 383–393. doi: 10.1056/NEJMoa021778
  22. Park, S.H.; Yao, J.; Chua, X.H.; Chandran, S.R.; Gardner, D.S.; Khoo, C.M.; Müller-Riemenschneider, F.; Whitton, C.; van Dam, R.M. Diet and Physical Activity as Determinants of Continuously Measured Glucose Levels in Persons at High Risk of Type 2 Diabetes. Nutrients 2022, 14, 366. doi: 10.3390/nu14020366
  23. Jardine, I.R.; Christie, H.E.; Oetsch, K.; Sabag, A.; Kennedy, M.; Meyer, B.J.; Francois, M.E. Physical Activity, but Not Glycaemic Load, Is Associated with Lower Real-Time Glycaemic Control in Free-Living Women with Gestational Diabetes Mellitus. Nutrients 2023, 15, 1974. doi: 10.3390/nu15081974
  24. Vashist, S.K. Non-invasive glucose monitoring technology in diabetes management: A review. Anal. Chim. Acta 2012, 750, 16–27. doi: 10.1016/j.aca.2012.03.043
  25. Montagnana, M.; Caputo, M.; Giavarina, D.; Lippi, G. Overview on self-monitoring of blood glucose. Clin. Chim. Acta 2009, 402, 7–13. doi: 10.1016/j.cca.2009.01.002
  26. Yu, J.; Cho, J.-H.; Lee, S.-H. The era of continuous glucose monitoring and its expanded role in type 2 diabetes. J. Diabetes Investig. 2023, 14, 841–843. doi: 10.1111/jdi.14028
  27. Deiss, D.; Bolinder, J.; Riveline, J.P.; Battelino, T.; Bosi, E.; Tubiana-Rufi, N.; Kerr, D.; Phillip, M. Improved glycemic control in poorly controlled patients with type 1 diabetes using real-time continuous glucose monitoring. Diabetes Care 2006, 29, 2730–2732. doi: 10.2337/dc06-1134
  28. Patton, S.R. Adherence to glycemic monitoring in diabetes. J. Diabetes Sci. Technol. 2015, 9, 668–675. doi: 10.1177/1932296814567709
  29. Carl, C.S.; Jensen, M.M.; Sjøberg, K.A.; Constantin-Teodosiu, D.; Hill, I.R.; Kjøbsted, R.; Greenhaff, P.L.; Wojtaszewski, J.F.; Richter, E.A.; Fritzen, A.M.; et al. Pharmacological activation of PDC flux reverses lipid-induced inhibition of insulin action in muscle during recovery from exercise. Diabetes 2024, 73, 1072–1083. doi: 10.2337/db23-0879
  30. Young, G.M.; Jacobs, P.G.; Tyler, N.S.; Nguyen, T.T.P.; Castle, J.R.; Wilson, L.M.; Branigan, D.; Gabo, V.; Guillot, F.H.; Riddell, M.C.; et al. Quantifying insulin-mediated and noninsulin-mediated changes in glucose dynamics during resistance exercise in type 1 diabetes. Am. J. Physiol.-Endocrinol. Metab. 2023, 325, E192–E206. doi: 10.1152/ajpendo.00298.2022
  31. Ekberg, N.R.; Catrina, S.-B.; Spégel, P. A protein-rich meal provides beneficial glycemic and hormonal responses as compared to meals enriched in carbohydrate, fat or fiber, in individuals with or without type-2 diabetes. Front. Nutr. 2024, 11, 1395745. doi: 10.3389/fnut.2024.1395745
  32. Paramalingam, N.; Keating, B.L.; Chetty, T.; Fournier, P.A.; Soon, W.H.; O’Dea, J.M.; Roberts, A.G.; Horowitz, M.; Jones, T.W.; Davis, E.A. Protein Ingestion in Reducing the Risk of Late-Onset Post-Exercise Hypoglycemia: A Pilot Study in Adolescents and Youth with Type 1 Diabetes. Nutrients 2023, 15, 543. doi: 10.3390/nu15030543
  33. Pickup, J.C.; Hussain, F.; Evans, N.D.; Sachedina, N. In vivo glucose monitoring: The clinical reality and the promise. Biosens. Bioelectron. 2005, 20, 1897–1902. doi: 10.1016/j.bios.2004.08.016
  34. Diez Alvarez, S.; Fellas, A.; Wynne, K.; Santos, D.; Sculley, D.; Acharya, S.; Navathe, P.; Gironès, X.; Coda, A. The Role of Smartwatch Technology in the Provision of Care for Type 1 or 2 Diabetes Mellitus or Gestational Diabetes: Systematic Review. JMIR MHealth UHealth 2024, 12, e54826. doi: 10.2196/54826
  35. Xu, J.; Yan, Z.; Liu, Q. Smartphone-Based Electrochemical Systems for Glucose Monitoring in Biofluids: A Review. Sensors 2022, 22, 5670. doi: 10.3390/s22155670
  36. Daskalaki, E.; Parkinson, A.; Brew-Sam, N.; Hossain, M.Z.; O'Neal, D.; Nolan, C.J.; Suominen, H. The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey. J. Med. Internet Res. 2022, 24, e28901. doi: 10.2196/28901
  37. Makroum, M.A.; Adda, M.; Bouzouane, A.; Ibrahim, H. Machine Learning and Smart Devices for Diabetes Management: Systematic Review. Sensors 2022, 22, 1843. doi: 10.3390/s22051843
  38. Piaseczna, N.; Doniec, R.; Sieciński, S.; Grzegorzek, M.; Tkacz, E. Does glucose affect our vision? A preliminary study using smart glasses. In Proceedings of the 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, Malta, 7–9 December 2023; pp. 113–114. https://doi.org/10.1109/IEEECONF58974.2023.10404616.
  39. Zhu, T.; Li, K.; Herrero, P.; Georgiou, P. Personalized Blood Glucose Prediction for Type 1 Diabetes Using Evidential Deep Learning and Meta-Learning. IEEE Trans. Biomed. Eng. 2023, 70, 193–204. doi: 10.1109/TBME.2022.3187703
  40. Carlson, A.L.; Mullen, D.M.; Bergenstal, R.M. Clinical Use of Continuous Glucose Monitoring in Adults with Type 2 Diabetes. Diabetes Technol. Ther. 2017, 19, S-4–S-11. doi: 10.1089/dia.2017.0024
  41. Hulett, N.A.; Scalzo, R.L.; Reusch JE, B. Glucose Uptake by Skeletal Muscle within the Contexts of Type 2 Diabetes and Exercise: An Integrated Approach. Nutrients 2022, 14, 647. doi: 10.3390/nu14030647
  42. Kido, K.; Egawa, T.; Watanabe, S.; Kawanaka, K.; Treebak, J.T.; Hayashi, T. Fasting potentiates insulin-mediated glucose uptake in rested and prior-contracted rat skeletal muscle. Am. J. Physiol.-Endocrinol. Metab. 2022, 322, E425–E435. doi: 10.1152/ajpendo.00412.2021
  43. Kanaley, J.A.; Colberg, S.R.; Corcoran, M.H.; Malin, S.K.; Rodriguez, N.R.; Crespo, C.J.; Kirwan, J.P.; Zierath, J.R. Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine. Med. Sci. Sports Exerc. 2022, 54, 353–368. doi: 10.1249/MSS.0000000000002800
  44. Hamasaki, H. Efficacy of wearable devices to measure and promote physical activity in the management of diabetes. EMJ Diabetes 2018, 6, 62–69. doi: 10.33590/emjdiabet/10312817
  45. Strain, T.; Wijndaele, K.; Dempsey, P.C.; Sharp, S.J.; Pearce, M.; Jeon, J.; Lindsay, T.; Wareham, N.; Brage, S. Wearable-device-measured physical activity and future health risk. Nat. Med. 2020, 26, 1385–1391. doi: 10.1038/s41591-020-1012-3
  46. Rodriguez-León, C.; Villalonga, C.; Munoz-Torres, M.; Ruiz, J.R.; Banos, O. Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review. JMIR MHealth UHealth 2021, 9, e25138. doi: 10.2196/25138
  47. Chiauzzi, E.; Rodarte, C.; DasMahapatra, P. Patient-centered activity monitoring in the self-management of chronic health conditions. BMC Med. 2015, 13, 77. doi: 10.1186/s12916-015-0319-2
  48. Deichmann, J.; Bachmann, S.; Burckhardt, M.A.; Pfister, M.; Szinnai, G.; Kaltenbach, H.M. New model of glucose-insulin regulation characterizes effects of physical activity and facilitates personalized treatment evaluation in children and adults with type 1 diabetes. PLOS Comput. Biol. 2023, 19, e1010289. doi: 10.1371/journal.pcbi.1010289
  49. Whelan, M.E.; Denton, F.; Bourne, C.L.; Kingsnorth, A.P.; Sherar, L.B.; Orme, M.W.; Esliger, D.W. A digital lifestyle behaviour change intervention for the prevention of type 2 diabetes: A qualitative study exploring intuitive engagement with real-time glucose and physical activity feedback. BMC Public Health 2021, 21, 130. doi: 10.1186/s12889-020-09740-z
  50. Askari, M.R.; Ahmadasas, M.; Shahidehpour, A.; Rashid, M.; Quinn, L.; Park, M.; Cinar, A. Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities. J. Diabetes Sci. Technol. 2023, 17, 1456–1469. doi: 10.1177/19322968231204884
  51. Chang, C.R.; Russell, B.M.; Cyriac, T.; Francois, M.E. Using Continuous Glucose Monitoring to Prescribe a Time to Exercise for Individuals with Type 2 Diabetes. J. Clin. Med. 2023, 12, 3237. doi: 10.3390/jcm12093237
  52. Jung, D.H.; Han, J.W.; Shin, H.; Lim, H.-S. Tailored Meal-Type Food Provision for Diabetes Patients Can Improve Routine Blood Glucose Management in Patients with Type 2 Diabetes: A Crossover Study. Nutrients 2024, 16, 1190. doi: 10.3390/nu16081190
  53. Gonzalez, J.S.; Tanenbaum, M.L.; Commissariat, P.V. Psychosocial factors in medication adherence and diabetes self-management: Implications for research and practice. Am. Psychol. 2016, 71, 539–551. doi: 10.1037/a0040388
  54. Wang, D.D.; Hu, F.B. Precision nutrition for prevention and management of type 2 diabetes. Lancet Diabetes Endocrinol. 2018, 6, 416–426. doi: 10.1016/S2213-8587(18)30037-8
  55. Spanakis, E.G.; Santana, S.; Tsiknakis, M.; Marias, K.; Sakkalis, V.; Teixeira, A.; Janssen, J.H.; De Jong, H.; Tziraki, C. Technology-Based Innovations to Foster Personalized Healthy Lifestyles and Well-Being: A Targeted Review. J. Med. Internet Res. 2016, 18, e128. doi: 10.2196/jmir.4863
  56. Konstantakopoulos, F.S.; Georga, E.I.; Fotiadis, D.I. A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence Systems. IEEE Rev. Biomed. Eng. 2024, 17, 136–152. doi: 10.1109/RBME.2023.3283149
  57. Sujitha, S.; Fathima, S.M.; Kavya, S. Prototyping a Smart Medication Management System with Machine Learning-based Dosage Recommendations. In Proceedings of the 2024 5th International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 18–20 September 2024; pp. 1402–1406. https://doi.org/10.1109/ICOSEC61587.2024.10722123.
  58. Potyok, C.; Simon, B.; Hartveg, Á.; Siket, M.; Dénes-Fazakas, L.; Eigner, G.; Balázs, M.; Kovács, L.; Szilágyi, L. Mobile Application Development for Diabetes Patient. In Proceedings of the 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania, 23–25 May 2024; pp. 000559–000564. https://doi.org/10.1109/SACI60582.2024.10619902.
  59. Vettoretti, M.; Cappon, G.; Facchinetti, A.; Sparacino, G. Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors. Sensors 2020, 20, 3870. doi: 10.3390/s20143870
  60. Huang, X.; Yao, C.; Huang, S.; Zheng, S.; Liu, Z.; Liu, J.; Wang, J.; Chen, H.J.; Xie, X. Technological Advances of Wearable Device for Continuous Monitoring of In Vivo Glucose. ACS Sens. 2024, 9, 1065–1088.
  61. Villena Gonzales, W.; Mobashsher, A.T.; Abbosh, A. The Progress of Glucose Monitoring—A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors. Sensors 2019, 19, 800. doi: 10.3390/s19040800
  62. Massone, P.; Barbieri, M.; Angelucci, A.; Aliverti, A. Technologies for non-invasive and continuous blood glucose monitoring in sports: A Patent Landscape Analysis. In Proceedings of the 2024 IEEE International Workshop on Sport, Technology and Research (STAR) Lecco, Italy, 8–10 July 2024; pp. 5–10. https://doi.org/10.1109/STAR62027.2024.10635992.
  63. Zharkikh, E.; Loktionova, Y.; Dunaev, A. Microcirculatory Dysfunction in Patients with Diabetes Mellitus Detected by a Distributed System of Wearable Laser Doppler Flowmetry Analysers. J. Biophotonics 2024, 17, e202400297. doi: 10.1002/jbio.202400297
  64. Azuma, L.; Natsuaki, R.; Hirose, A. Complex-domain Pulse-wave Synchronous Feature Extraction for Millimeter-wave Adaptive Glucose Concentration Estimation: Proposal and Preliminary Experiments. In Proceedings of the 2023 XXXVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), Sapporo, Japan, 19–26 August 2023; pp. 1–4. https://doi.org/10.23919/URSIGASS57860.2023.10265570.
  65. Sun, Y.; Cano-Garcia, H.; Kallos, E.; O’Brien, F.; Akintonde, A.; Motei, D.E.; Ancu, O.; Mackenzie, R.W.A.; Kosmas, P. Random Forest Analysis of Combined Millimeter-Wave and Near-Infrared Sensing for Noninvasive Glucose Detection. IEEE Sens. J. 2023, 23, 20294–20309. doi: 10.1109/JSEN.2023.3293248
  66. Schwartz, F.L.; Marling, C.R.; Bunescu, R.C. The Promise and Perils of Wearable Physiological Sensors for Diabetes Management. J. Diabetes Sci. Technol. 2018, 12, 587–591. doi: 10.1177/1932296818763228
  67. Yuan, Q.; Fang, H.; Wu, X.; Wu, J.; Luo, X.; Peng, R.; Xu, S.; Yan, S. Self-Adhesive, Biocompatible, Wearable Microfluidics with Erasable Liquid Metal Plasmonic Hotspots for Glucose Detection in Sweat. ACS Appl. Mater. Interfaces 2023, 16, 66810–66818. doi: 10.1021/acsami.3c11746
  68. Saha, T.; Del Caño, R.; Mahato, K.; De la Paz, E.; Chen, C.; Ding, S.; Yin, L.; Wang, J. Wearable Electrochemical Glucose Sensors in Diabetes Management: A Comprehensive Review. Chem. Rev. 2023, 123, 7854–7889. doi: 10.1021/acs.chemrev.3c00078
  69. Nyiramana Mukamurera, P. Advances in Non-Invasive Glucose Monitoring: Challenges, Technologies, and Future Prospects. Res. Output J. Public Health Med. 2024, 3, 1–5. doi: 10.59298/ROJPHM/2024/3315
  70. Wu, C.T.; Wang, S.M.; Su, Y.E.; Hsieh, T.T.; Chen, P.C.; Cheng, Y.C.; Tseng, T.W.; Chang, W.S.; Su, C.S.; Kuo, L.C.; et al. A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine Learning, and Deep Learning. IEEE J. Transl. Eng. Health Med. 2022, 10, 1–14. doi: 10.1109/JTEHM.2022.3207825
  71. Askari, M.R.; Rashid, M.; Sun, X.; Sevil, M.; Shahidehpour, A.; Kawaji, K.; Cinar, A. Detection of Meals and Physical Activity Events From Free-Living Data of People with Diabetes. J. Diabetes Sci. Technol. 2022, 17, 1482–1492. doi: 10.1177/19322968221102183
  72. van den Brink, W.J.; van den Broek, T.J.; Palmisano, S.; Wopereis, S.; de Hoogh, I.M. Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies. Nutrients 2022, 14, 4465. doi: 10.3390/nu14214465
  73. Phillips, N.E.; Collet, T.-H.; Naef, F. Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling. Cell Rep. Methods 2023, 3, 100545. doi: 10.1016/j.crmeth.2023.100545
  74. Ahmed, A.; Aziz, S.; Abd-Alrazaq, A.; Farooq, F.; Househ, M.; Sheikh, J. The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review. J. Med. Internet Res. 2023, 25, e40259. doi: 10.2196/40259
  75. Lee, Y.B.; Kim, G.; Jun, J.E.; Park, H.; Lee, W.J.; Hwang, Y.C.; Kim, J.H. An Integrated Digital Health Care Platform for Diabetes Management with AI-Based Dietary Management: 48-Week Results From a Randomized Controlled Trial. Diabetes Care 2023, 46, 959–966. doi: 10.2337/dc22-1929
  76. Huang, X.; Yao, C.; Huang, S.; Zheng, S.; Liu, Z.; Liu, J.; Wang, J.; Chen, H.J.; Xie, X. Technological Advances of Wearable Device for Continuous Monitoring of In Vivo Glucose. ACS Sens. 2024, 9, 1065–1088. doi: 10.1021/acssensors.3c01947
  77. Dunn, J.; Kidzinski, L.; Runge, R.; Witt, D.; Hicks, J.L.; Schüssler-Fiorenza Rose, S.M.; Li, X.; Bahmani, A.; Delp, S.L.; Hastie, T.; et al. Wearable sensors enable personalized predictions of clinical laboratory measurements. Nat. Med. 2021, 27, 1105–1112. doi: 10.1038/s41591-021-01339-0
  78. Schüssler-Fiorenza Rose, S.M.; Contrepois, K.; Moneghetti, K.J.; Zhou, W.; Mishra, T.; Mataraso, S.; Dagan-Rosenfeld, O.; Ganz, A.B.; Dunn, J.; Hornburg, D.; et al. A longitudinal big data approach for precision health. Nat. Med. 2019, 25, 792–804. doi: 10.1038/s41591-019-0414-6
  79. Das, A.; Mortazavi, B.; Sajjadi, S.; Chaspari, T.; Ruebush, L.E.; Deutz, N.E.; Cote, G.L.; Gutierrez-Osuna, R. Predicting the Macronutrient Composition of Mixed Meals from Dietary Biomarkers in Blood. IEEE J. Biomed. Health Inform. 2022, 26, 2726–2736. doi: 10.1109/JBHI.2021.3134193
  80. McMichael, L.; Johnson, C.; Fanter, R.; Brito, A.; Alarcon, N.; Quintana-Diaz, A.; Schaffner, A.; Jelalian, E.; Wing, R.; Phelan, S.; et al. Identification of Potential Biomarkers for Early Prediction of Gestational Diabetes. Curr. Dev. Nutr. 2020, 4, nzaa049_038. doi: 10.1093/cdn/nzaa049_038
  81. Ismail, L.; Materwala, H. IDMPF: Intelligent diabetes mellitus prediction framework using machine learning. Appl. Comput. Inform. 2021, ahead-of-print. doi: 10.1108/ACI-10-2020-0094
  82. Dzakiyullah, N.R.; Burhanuddin, M.A.; Ikram, R.R.; Ghani, K.A.; Setyonugroho, W. Machine learning methods for diabetes prediction. Int. J. Innov. Technol. Explor. Eng. 2019, 8, 2199–2205.
  83. Wang, X.; Liu, M.; Zhang, Y.; He, S.; Qin, C.; Li, Y.; Lu, T. Deep fusion learning facilitates anatomical therapeutic chemical recognition in drug repurposing and discovery. Brief. Bioinform. 2021, 22, bbab289. doi: 10.1093/bib/bbab289
  84. Alshammary, A.F.; Al-Hakeem, M.M.; Ali Khan, I. Saudi Community-Based Screening Study on Genetic Variants in β-Cell Dysfunction and Its Role in Women with Gestational Diabetes Mellitus. Genes 2023, 14, 924. doi: 10.3390/genes14040924
  85. Kim, H.; Westerman, K.E.; Smith, K.; Chiou, J.; Cole, J.B.; Majarian, T.; von Grotthuss, M.; Kwak, S.H.; Kim, J.; Mercader, J.M.; et al. High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease. Diabetologia 2022, 66, 495–507. doi: 10.1007/s00125-022-05848-6
  86. Madsen, F.M. The dawn of personalized multi-omics: Detecting disease before you know it. Glob. Transl. Med. 2024, 3, 2357. doi: 10.36922/gtm.2357
  87. Fang, H.; E-Lacerda, R.R.; Schertzer, J.D. Obesity promotes a leaky gut, inflammation and pre-diabetes by lowering gut microbiota that metabolise ethanolamine. Gut 2023, 72, 1809–1811. doi: 10.1136/gutjnl-2023-329815
  88. Kolozali, Ş.; White, S.L.; Norris, S.; Fasli, M.; van Heerden, A. Explainable Early Prediction of Gestational Diabetes Biomarkers by Combining Medical Background and Wearable Devices: A Pilot Study with a Cohort Group in South Africa. IEEE J. Biomed. Health Inform. 2024, 28, 1860–1871. doi: 10.1109/JBHI.2024.3361505
  89. Tong, L.; Shi, W.; Isgut, M.; Zhong, Y.; Lais, P.; Gloster, L.; Sun, J.; Swain, A.; Giuste, F.; Wang, M.D. Integrating Multi-Omics Data with EHR for Precision Medicine Using Advanced Artificial Intelligence. IEEE Rev. Biomed. Eng. 2024, 17, 80–97. doi: 10.1109/RBME.2023.3324264
  90. Diedisheim, M.; Pecquet, C.; Julla, J.B.; Carlier, A.; Potier, L.; Hartemann, A.; Jacqueminet, S.; Vidal-Trecan, T.; Gautier, J.F.; Dubois Laforgue, D.; et al. Prevalence and Description of the Skin Reactions Associated with Adhesives in Diabetes Technology Devices in an Adult Population: Results of the CUTADIAB Study. Diabetes Technol. Ther. 2023, 25, 279–286. doi: 10.1089/dia.2022.0513
  91. Hong, Y.J.; Lee, H.; Kim, J.; Lee, M.; Choi, H.J.; Hyeon, T.; Kim, D.H. Multifunctional Wearable System that Integrates Sweat-Based Sensing and Vital-Sign Monitoring to Estimate Pre-/Post-Exercise Glucose Levels. Adv. Funct. Mater. 2018, 28, 1805754. doi: 10.1002/adfm.201805754
  92. Alam, M.M.; Howlader MM, R. High performance nonenzymatic electrochemical sensors via thermally grown Cu native oxides (CuNOx) towards sweat glucose monitoring. The Analyst 2024, 149, 712–728. doi: 10.1039/D3AN01153D
  93. Nakazawa, T.; Morishita, K.; Ienaka, A.; Fujii, T.; Ito, M.; Matsushita, F. Accuracy enhancement of metabolic index-based blood glucose estimation with a screening process for low-quality data. J. Biomed. Opt. 2024, 29, 107001. doi: 10.1117/1.JBO.29.10.107001
  94. Babu, M.; Lautman, Z.; Lin, X.; Sobota MH, B.; Snyder, M.P. Wearable Devices: Implications for Precision Medicine and the Future of Health Care. Annu. Rev. Med. 2024, 75, 401–415. doi: 10.1146/annurev-med-052422-020437
  95. Marengo, L.L.; Barberato-Filho, S. Involvement of Human Volunteers in the Development and Evaluation of Wearable Devices Designed to Improve Medication Adherence: A Scoping Review. Sensors 2023, 23, 3597. doi: 10.3390/s23073597
  96. Wettstein, R.; Sedaghat-Hamedani, F.; Heinze, O.; Amr, A.; Reich, C.; Betz, T.; Kayvanpour, E.; Merzweiler, A.; Büsch, C.; Mohr, I.; et al. A Remote Patient Monitoring System with Feedback Mechanisms Using a Smartwatch: Concept, Implementation, and Evaluation Based on the activeDCM Randomized Controlled Trial. JMIR MHealth UHealth 2024, 12, e58441. doi: 10.2196/58441
  97. Dubosson, F.; Ranvier, J.E.; Bromuri, S.; Calbimonte, J.P.; Ruiz, J.; Schumacher, M. The open D1NAMO dataset: A multi-modal dataset for research on non-invasive type 1 diabetes management. Inform. Med. Unlocked 2018, 13, 92–100. doi: 10.1016/j.imu.2018.09.003
  98. Wang, J.; Coleman, D.C.; Kanter, J.; Ummer, B.; Siminerio, L. Connecting Smartphone and Wearable Fitness Tracker Data with a Nationally Used Electronic Health Record System for Diabetes Education to Facilitate Behavioral Goal Monitoring in Diabetes Care: Protocol for a Pragmatic Multi-Site Randomized Trial. JMIR Res. Protoc. 2018, 7, e10009. doi: 10.2196/10009
  99. Parikh, V.; Mahmud, S.; Agarwal, D.; Li, K.; Guimbretière, F.; Zhang, C. EchoGuide: Active Acoustic Guidance for LLM-Based Eating Event Analysis from Egocentric Videos. In Proceedings of the Proceedings of the 2024 ACM International Symposium on Wearable Computers, Melbourne, VIC, Australia, 5–9 October 2024; pp. 40–47. https://doi.org/10.1145/3675095.3676611.
  100. Xu, S.; Kim, J.; Walter, J.R.; Ghaffari, R.; Rogers, J.A. Translational gaps and opportunities for medical wearables in digital health. Sci. Transl. Med. 2022, 14, eabn6036. doi: 10.1126/scitranslmed.abn6036