Endocrinol Metab.  2020 Sep;35(3):541-548. 10.3803/EnM.2020.675.

Lessons from Use of Continuous Glucose Monitoring Systems in Digital Healthcare

Affiliations
  • 1Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
  • 2Department of Endocrinology and Metabolism, College of Medicine, The Catholic University of Korea, Seoul, Korea

Abstract

We live in a digital world where a variety of wearable medical devices are available. These technologies enable us to measure our health in our daily lives. It is increasingly possible to manage our own health directly through data gathered from these wearable devices. Likewise, healthcare professionals have also been able to indirectly monitor patients’ health. Healthcare professionals have accepted that digital technologies will play an increasingly important role in healthcare. Wearable technologies allow better collection of personal medical data, which healthcare professionals can use to improve the quality of healthcare provided to the public. The use of continuous glucose monitoring systems (CGMS) is the most representative and desirable case in the adoption of digital technology in healthcare. Using the case of CGMS and examining its use from the perspective of healthcare professionals, this paper discusses the necessary adjustments required in clinical practices. There is a need for various stakeholders, such as medical staff, patients, industry partners, and policy-makers, to utilize and harness the potential of digital technology.

Keyword

Blood glucose self-monitoring; Wearable electronic devices; Delivery of health care; Quality of health care

Figure

  • Fig. 1 Scheme of the digital healthcare system.

  • Fig. 2 Schema of regional-based Digital Health Coordinating Center [13,26].


Cited by  3 articles

Towards Telemedicine Adoption in Korea: 10 Practical Recommendations for Physicians
Hun-Sung Kim
J Korean Med Sci. 2021;36(17):e103.    doi: 10.3346/jkms.2021.36.e103.

Lack of Acceptance of Digital Healthcare in the Medical Market: Addressing Old Problems Raised by Various Clinical Professionals and Developing Possible Solutions
Jong Il Park, Hwa Young Lee, Hyunah Kim, Jisan Lee, Jiwon Shinn, Hun-Sung Kim
J Korean Med Sci. 2021;36(37):e253.    doi: 10.3346/jkms.2021.36.e253.

Perceptron: Basic Principles of Deep Neural Networks
Eung-Hee Kim, Hun-Sung Kim
Cardiovasc Prev Pharmacother. 2021;3(3):64-72.    doi: 10.36011/cpp.2021.3.e9.


Reference

1. Karinharju KS, Boughey AM, Tweedy SM, Clanchy KM, Trost SG, Gomersall SR. Validity of the Apple Watch® for monitoring push counts in people using manual wheelchairs. J Spinal Cord Med. 2019; 1–9.
2. Hernando D, Roca S, Sancho J, Alesanco A, Bailon R. Validation of the Apple watch for heart rate variability measurements during relax and mental stress in healthy subjects. Sensors (Basel). 2018; 18:2619.
Article
3. Raja JM, Elsakr C, Roman S, Cave B, Pour-Ghaz I, Nanda A, et al. Apple watch, wearables, and heart rhythm: where do we stand? Ann Transl Med. 2019; 7:417.
Article
4. Kim HS, Cho JH, Yoon KH. New directions in chronic disease management. Endocrinol Metab (Seoul). 2015; 30:159–66.
Article
5. Kim HS, Kim H, Lee S, Lee KH, Kim JH. Current clinical status of telehealth in Korea: categories, scientific basis, and obstacles. Healthc Inform Res. 2015; 21:244–50.
Article
6. Lee J, Kim HS, KIM DJ. Recent technology-driven advancements in cardiovascular disease prevention. Cardiovasc Prev Pharmacother. 2019; 1:43–9.
Article
7. Nittas V, Lun P, Ehrler F, Puhan MA, Mutsch M. Electronic patient-generated health data to facilitate disease prevention and health promotion: scoping review. J Med Internet Res. 2019; 21:e13320.
Article
8. Kim HS, Kim DJ, Yoon KH. Medical big data is not yet available: why we need realism rather than exaggeration. Endocrinol Metab (Seoul). 2019; 34:349–54.
Article
9. Mastrototaro J. The MiniMed continuous glucose monitoring system (CGMS). J Pediatr Endocrinol Metab. 1999; 12(Suppl 3):751–8.
10. Korean Diabetes Association. Treatment guideline for diabetes 2015. Seoul: Korean Diabetes Association;2015.
11. Chetty VT, Almulla A, Odueyungbo A, Thabane L. The effect of continuous subcutaneous glucose monitoring (CGMS) versus intermittent whole blood finger-stick glucose monitoring (SBGM) on hemoglobin A1c (HBA1c) levels in type I diabetic patients: a systematic review. Diabetes Res Clin Pract. 2008; 81:79–87.
Article
12. Wei Q, Sun Z, Yang Y, Yu H, Ding H, Wang S. Effect of a CGMS and SMBG on maternal and neonatal outcomes in gestational diabetes mellitus: a randomized controlled trial. Sci Rep. 2016; 6:19920.
Article
13. Kim HS, Shin JA, Chang JS, Cho JH, Son HY, Yoon KH. Continuous glucose monitoring: current clinical use. Diabetes Metab Res Rev. 2012; 28(Suppl 2):73–8.
Article
14. Anderson SM, Dassau E, Raghinaru D, Lum J, Brown SA, Pinsker JE, et al. The international diabetes closed-loop study: testing artificial pancreas component interoperability. Diabetes Technol Ther. 2019; 21:73–80.
Article
15. Fernandez-Carames TM, Froiz-Miguez I, Blanco-Novoa O, Fraga-Lamas P. Enabling the internet of mobile crowdsourcing health things: a mobile fog computing, blockchain and IOT based continuous glucose monitoring system for diabetes mellitus research and care. Sensors (Basel). 2019; 19:3319.
Article
16. Jung SH, Kim JW, Kang IK, Park CY, Kim YS, Woo JT, et al. Continuous glucose monitoring is needed to detect unrecognized hypoglycemic event in diabetic patients with stroke. J Korean Diabetes. 2002; 3:140–51.
17. Medtronic. The guardian connect CGM [Internet]. Dublin: Medtronic;2018. [cited 2020 Aug 10]. Available from: https://www.medtronicdiabetes.com/products/guardian-connect-continuous-glucose-monitoring-system .
18. Dexcom Inc.. The Dexcom G5® Mobile CGM System [Internet]. San Diego: Dexcom;2019. [cited 2020 Aug 10]. Available from: https://www.dexcom.com/g5-mobile-cgm .
19. Abbott. FreeStyle Libre [Internet]. Chicago: Abbott;2020. [cited 2020 Aug 10]. Available from: https://www.freestylelibre.co.uk/libre/ .
20. Kim HS, Choi W, Baek EK, Kim YA, Yang SJ, Choi IY, et al. Efficacy of the smartphone-based glucose management application stratified by user satisfaction. Diabetes Metab J. 2014; 38:204–10.
Article
21. Basu A, Dube S, Veettil S, Slama M, Kudva YC, Peyser T, et al. Time lag of glucose from intravascular to interstitial compartment in type 1 diabetes. J Diabetes Sci Technol. 2015; 9:63–8.
Article
22. Bergenstal RM. Continuous glucose monitoring: transforming diabetes management step by step. Lancet. 2018; 391:1334–6.
Article
23. Heinemann L. Continuous glucose monitoring (CGM) or blood glucose monitoring (BGM): interactions and implications. J Diabetes Sci Technol. 2018; 12:873–9.
Article
24. Knebel T, Neumiller JJ. Medtronic MiniMed 670G hybrid closed-loop system. Clin Diabetes. 2019; 37:94–5.
Article
25. Mazze RS. Acceptance of FGM or CGM in clinical decision-making and patient preference: where do we go from here? Diabetes Technol Ther. 2017; 19:142–4.
Article
26. Kim HS, Lee KH, Kim H, Kim JH. Using mobile phones in healthcare management for the elderly. Maturitas. 2014; 79:381–8.
Article
27. Kim HS, Hwang Y, Lee JH, Oh HY, Kim YJ, Kwon HY, et al. Future prospects of health management systems using cellular phones. Telemed J E Health. 2014; 20:544–51.
Article
28. Abraham SB, Arunachalam S, Zhong A, Agrawal P, Cohen O, McMahon CM. Improved real-world glycemic control with continuous glucose monitoring system predictive alerts. J Diabetes Sci Technol. 2019; Jul. 4. [Epub]. https://doi.org/10.1177/1932296819859334 .
Article
29. Kim HS. Decision-making in artificial intelligence: is it always correct? J Korean Med Sci. 2020; 35:e1.
Article
30. Kim HS, Yang SJ, Jeong YJ, Kim YE, Hong SW, Cho JH. Satisfaction survey on information technology-based glucose monitoring system targeting diabetes mellitus in private local clinics in Korea. Diabetes Metab J. 2017; 41:213–22.
Article
31. Lim I, Walkup RK, Vannier MW. Rule based artificial intelligence expert system for determination of upper extremity impairment rating. Comput Methods Programs Biomed. 1993; 39:203–11.
Article
32. Ratheau L, Jeandidier N, Moreau F, Sigrist S, Pinget M. How technology has changed diabetes management and what it has failed to achieve. Diabetes Metab. 2011; 37(Suppl 4):S57–64.
Article
33. Reymann MP, Dorschky E, Groh BH, Martindale C, Blank P, Eskofier BM. Blood glucose level prediction based on support vector regression using mobile platforms. Conf Proc IEEE Eng Med Biol Soc. 2016; 2016:2990–3.
Article
34. Seo W, Lee YB, Lee S, Jin SM, Park SM. A machine-learning approach to predict postprandial hypoglycemia. BMC Med Inform Decis Mak. 2019; 19:210.
Article
35. Weisman A, Bai JW, Cardinez M, Kramer CK, Perkins BA. Effect of artificial pancreas systems on glycaemic control in patients with type 1 diabetes: a systematic review and meta-analysis of outpatient randomised controlled trials. Lancet Diabetes Endocrinol. 2017; 5:501–12.
Article
36. Boughton CK, Hovorka R. Is an artificial pancreas (closed-loop system) for type 1 diabetes effective? Diabet Med. 2019; 36:279–86.
Article
37. Yu S, Varughese B, Li Z, Kushner PR. Healthcare resource waste associated with patient nonadherence and early discontinuation of traditional continuous glucose monitoring in real-world settings: a multicountry analysis. Diabetes Technol Ther. 2018; 20:420–7.
Article
38. Kim HS, Sun C, Yang SJ, Sun L, Li F, Choi IY, et al. Randomized, open-label, parallel group study to evaluate the effect of internet-based glucose management system on subjects with diabetes in China. Telemed J E Health. 2016; 22:666–74.
Article
39. Lin YR, Hung CC, Chiu HY, Chang BH, Li BR, Cheng SJ, et al. Noninvasive glucose monitoring with a contact lens and smartphone. Sensors (Basel). 2018; 18:3208.
Article
40. Ascaso FJ, Huerva V. Noninvasive continuous monitoring of tear glucose using glucose-sensing contact lenses. Optom Vis Sci. 2016; 93:426–34.
Article
41. Zhang J, Hodge W, Hutnick C, Wang X. Noninvasive diagnostic devices for diabetes through measuring tear glucose. J Diabetes Sci Technol. 2011; 5:166–72.
Article
42. O’Donnell C, Efron N. Diabetes and contact lens wear. Clin Exp Optom. 2012; 95:328–37.
Article
43. Lee H, Song C, Hong YS, Kim MS, Cho HR, Kang T, et al. Wearable/disposable sweat-based glucose monitoring device with multistage transdermal drug delivery module. Sci Adv. 2017; 3:e1601314.
Article
44. Tankasala D, Linnes JC. Noninvasive glucose detection in exhaled breath condensate. Transl Res. 2019; 213:1–22.
Article
45. Bandodkar AJ, Jia W, Yardimci C, Wang X, Ramirez J, Wang J. Tattoo-based noninvasive glucose monitoring: a proof-of-concept study. Anal Chem. 2015; 87:394–8.
Article
46. Rossen J, Yngve A, Hagstromer M, Brismar K, Ainsworth BE, Iskull C, et al. Physical activity promotion in the primary care setting in pre- and type 2 diabetes: the Sophia step study, an RCT. BMC Public Health. 2015; 15:647.
47. Groat D, Kwon HJ, Grando MA, Cook CB, Thompson B. Comparing real-time self-tracking and device-recorded exercise data in subjects with type 1 diabetes. Appl Clin Inform. 2018; 9:919–26.
Article
Full Text Links
  • ENM
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr