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Cited by 477Diabetes Management and ResearchElectrochemical sensors and biosensorsHyperglycemia and glycemic control in critically ill and hospitalized patients

The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.

Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications

Open access
Cappon, Giacomo, Vettoretti, Martina, Sparacino, Giovanni, Facchinetti, Andrea·2019·Diabetes & Metabolism Journal
DOI 10.4093/dmj.2019.0121PMC PMC6712232
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Abstract

By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.

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Crossref · 10.4093/dmj.2019.0121

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Figures

Fig. 1 (A) Representative blood glucose (BG) monitoring data obtainable with self-monitoring of blood glucose (SMBG; in green) and with continuous glucose monitoring (CGM; in blue). Dotted circles denote hyperglycemic and hypoglycemic episodes that, using only SMBG measurements, are not detectable. (B) Assessment of the accuracy of a CGM sensor can be performed by comparing Yellow Spring Instruments Inc. (YSI) measurements (red stars) versus Dexcom G4 Platinum CGM (black solid line) measurements. For example, mean absolute relative difference can be calculated as the average ratio between the absolute difference between the CGM measurements and the YSI over the YSI.
Fig. 1 (A) Representative blood glucose (BG) monitoring data obtainable with self-monitoring of blood glucose (SMBG; in green) and with continuous glucose monitoring (CGM; in blue). Dotted circles denote hyperglycemic and hypoglycemic episodes that, using only SMBG measurements, are not detectable. (B) Assessment of the accuracy of a CGM sensor can be performed by comparing Yellow Spring Instruments Inc. (YSI) measurements (red stars) versus Dexcom G4 Platinum CGM (black solid line) measurements. For example, mean absolute relative difference can be calculated as the average ratio between the absolute difference between the CGM measurements and the YSI over the YSI.CC BY-NC · PubMed Central
Fig. 2 Accuracy evolution of state-of-the-art CGM systems through years. From the left: Medtronic Enlite, Abbott Freestyle Navigator, Dexcom G4 Platinum, Abbott Freestyle Libre, Dexcom G4 Platinum with 505 software, Senseonics Eversense, Dexcom G5, Dexcom G6. MARD, mean absolute relative difference; SMBG, self-monitoring of blood glucose.
Fig. 2 Accuracy evolution of state-of-the-art CGM systems through years. From the left: Medtronic Enlite, Abbott Freestyle Navigator, Dexcom G4 Platinum, Abbott Freestyle Libre, Dexcom G4 Platinum with 505 software, Senseonics Eversense, Dexcom G5, Dexcom G6. MARD, mean absolute relative difference; SMBG, self-monitoring of blood glucose.CC BY-NC · PubMed Central

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Research works printed but not linked: 2

  1. Gashgari, 2016, Stud Health Technol Inform, 226, 41
  2. Herrero, 2016, PEPPER: Patient empowerment through predictive personalized decision support, 8

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Metadata

Authors
Cappon, G., Vettoretti, M., Sparacino, G., & Facchinetti, A.
Affiliations
University of Padua (IT)
Corresponding author
Facchinetti, Andrea
ORCID
Venue
Diabetes & Metabolism Journal
Publisher
Korean Diabetes Association
Published
2019
Volume
43
Issue
4
Pages
383
ISSN
  • 2233-6079 (ISSN-L)
  • 2233-6087
DOI
10.4093/dmj.2019.0121
PMC
PMC6712232
OpenAlex
W2964434942
Cited by
477
References
81
Citations by year
2026: 70 · 2025: 105 · 2024: 65 · 2023: 81 · 2022: 65 · 2021: 48 · 2020: 38 · 2019: 4
FWCI
5.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Diabetes & Metabolism Journal (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by-nc)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Physical Sciences › Chemistry › Analytical Chemistry › Electrochemical sensors and biosensors
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Hyperglycemia and glycemic control in critically ill and hospitalized patients
Keywords
continuous glucose monitoring, diabetes management, blood glucose monitoring, hypoglycemia, glycemic variability, insulin dose, insulin infusion, decision support, wearable sensors
MeSH terms
Wearable Electronic Devices, Adolescent, Adult, Algorithms, Blood Glucose, Child, Child, Preschool, Decision Support Systems, Management, Diabetes Mellitus, Female, Humans, Hyperglycemia, Hypoglycemia, Insulin Infusion Systems, Male, Blood Glucose Self-Monitoring, Young Adult

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