Next Article in Journal
Dermatological Manifestations of Diabetes Mellitus and Its Complications
Previous Article in Journal
Diabetes Differentially Alters Glial Cells in Different Brain Regions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diabetology
Volume 6
Issue 3
10.3390/diabetology6030017
diabetology-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

The History, Evolution and Future of Continuous Glucose Monitoring (CGM)

by
Clara Bender
1,
Peter Vestergaard
2,3 and
Simon Lebech Cichosz
1,*
1
Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark
2
Steno Diabetes Center North Denmark, Aalborg University Hospital, 9000 Aalborg, Denmark
3
Department of Endocrinology, Aalborg University Hospital, 9000 Aalborg, Denmark
*
Author to whom correspondence should be addressed.
Diabetology 2025, 6(3), 17; https://doi.org/10.3390/diabetology6030017
Submission received: 8 January 2025 / Revised: 14 February 2025 / Accepted: 26 February 2025 / Published: 3 March 2025
Browse Figures
Versions Notes

Abstract

:
Continuous glucose monitoring (CGM) and flash glucose monitoring (FGM) systems have revolutionized diabetes management by delivering real-time, dynamic insights into blood glucose levels. This article provides a concise overview of the evolution of CGM technology, highlights emerging innovations in the field and explores current and potential future applications (including insulin management, early diagnostics, predictive modeling, diabetes education and integration into automated insulin delivery (AID) systems) of CGM in healthcare.

1. Introduction

Continuous glucose monitoring (CGM) systems (and flash glucose monitoring—FGM) have modernized the management of diabetes by providing real-time, dynamic insights into blood glucose levels [1,2,3,4]. Especially in type 1 diabetes (T1D) management, a 2.5-fold increase in utilization within the last decade has been observed, indicating that almost half of individuals in U.S. with T1D are using CGM [5].
CGM systems offer continuous data on glucose trends, allowing individuals to make more informed decisions about insulin dosing, physical activity, meal planning and improved collaboration between health professionals and patients. CGM technology represents a significant improvement from traditional methods of glucose monitoring, which relied on fingerstick tests, offering a more comprehensive picture of glucose variability throughout the day [6].
In this article, we explore the history of CGM, tracing its development from simple glucose measurement methods to the advanced, wearable devices of today, and discuss the many use cases for CGM.

2. History of CGM

Before the development of blood glucose meters, individuals with diabetes relied on urine glucose testing to estimate their blood sugar levels. This method, used throughout the early 20th century, involved mixing urine with chemical reagents such as Benedict’s solution [7]. However, urine glucose testing provided only a rough estimate of blood glucose levels, since it detected glucose that had already been filtered by the kidneys and was dependent on kidney function and threshold for glucose filtration, which may be lower in, say, pregnancy [8]. The readings were delayed and imprecise, making it difficult to manage diabetes effectively.
In the 1960s and 1970s, the introduction of portable blood glucose meters transformed glucose monitoring. The first meter, developed by Ames, was the Dextrostix system, which involved applying a blood drop to a chemically treated strip [9]. By the late 1970s, the meters had evolved into handheld devices that could read blood glucose levels by measuring the color change on the strip [10]. Although this method was more accurate than urine testing, it still required intermittent testing, usually multiple times per day. As a result, it failed to capture the full picture of glucose fluctuations, particularly overnight or between meals [11]. Dr. Leland Clark discovered glucose biosensing technology in 1965 [12]. This innovation relies on electrodes paired with redox enzymes, typically glucose oxidase or dehydrogenase. Among these, glucose oxidase is favored for its higher specificity to glucose and greater resilience to fluctuations in pH and temperature, making it the predominant choice in commercially available CGMs. The redox reaction generates an electric current with a voltage that correlates to the glucose concentration, which is then converted into a readable glucose measurement.

3. Early Commercial CGM Systems: 1999–2006

The 1990s marked a turning point, with companies beginning to explore more practical and wearable CGM solutions. In 1999, the FDA approved the first commercial CGM device, the MiniMed Continuous Glucose Monitoring System (CGMS) [13]. Although it could only be worn for up to three days and required frequent calibrations, the system provided detailed glucose profiles for healthcare professionals to analyze. It laid the foundation for the development of more user-friendly devices in the years to come. A brief overview of the CGM history is illustrated in Figure 1.
The first CGM system available for home use was introduced by Medtronic in 2004 with the Guardian RT system. This was the first real-time CGM device that provided individuals with diabetes wireless transmitting continuous data on their glucose levels throughout the day. Although it still required frequent calibrations and had limitations in terms of accuracy (e.g., median absolute difference of 21 mg/dL, reported in children [14]), the Guardian RT signaled the start of a new era in diabetes management. In the same period, Dexcom entered the CGM market with its STS (Short-Term Sensor) in 2006. The device had the capability to report glucose values in real time for up to 72 h. It comprised three components: a glucose sensor, a transmitter and a receiver for viewing the glucose values. After subcutaneous insertion, the sensor required a two-hour initialization warm-up period, followed by calibration using a blood glucose meter. The Dexcom STS model also offered programmable alerts, allowing users to set thresholds for high and low glucose levels. For accurate data transmission, the receiver needed to remain within a five-foot range of the transmitter. Although STS was still an early-stage device, it offered improved usability over previous CGM models. The market began to grow as more patients and healthcare providers recognized the benefits of CGM for maintaining tighter glycemic control [15].

4. The Evolution of CGM: 2006–2015

The mid-2000s to early 2010s was a time of rapid advancement in CGM technology, with improvements in both sensor accuracy and user experience [16]. Dexcom introduced its SEVEN system in 2007, extending the wear time of CGM sensors to seven days. This was followed by the SEVEN PLUS in 2009, which offered more accurate readings and additional features such as trend graphs and alarms for high or low blood glucose levels. In 2008, Abbott Laboratories introduced the FreeStyle Navigator CGM system in the United States [17].
At the same time, Medtronic continued to refine its products. The Medtronic MiniMed Paradigm REAL-Time system, released in 2006, integrated CGM with an insulin pump for the first time, enabling more streamlined diabetes management [13]. This integration paved the way for further innovations, particularly in the area of automated insulin delivery systems.
By 2015, CGM technology had become significantly more reliable and user-friendly. Dexcom’s G4 Platinum and Medtronic’s MiniMed 530 G represented major improvements in sensor accuracy and patient comfort. These devices allowed people with diabetes to better understand how their lifestyle, diet and insulin regimens affected their glucose levels, providing more opportunities for proactive management. Moreover, MiniMed 530 G was the first FDA-approved artificial pancreas device system with threshold suspend automation for people with diabetes 16 years of age or older. The system was the first in the U.S. that could automatically stop insulin delivery when sensor glucose values reach a preset level and when the patient did not respond to the alarm [13,18].

5. Modern CGM: 2015–Present

The introduction of the Dexcom G5 in 2015 and the G6 in 2018 marked a major leap forward in CGM technology. With the G6, users no longer needed to perform regular fingerstick calibrations, and the sensor offered a 10-day wear period with high accuracy. The G5/G6 also allowed users to track their glucose levels using smartphone apps, increasing accessibility and convenience [16,18].
Abbott’s FreeStyle Libre system, introduced around 2017, further expanded CGM options. Unlike traditional CGM systems that provided continuous glucose data, the FreeStyle Libre uses “flash” glucose monitoring (FGM). Users scan the sensor, worn on the back of the arm, to obtain a reading. The Libre was designed to last up to 14 days and be more affordable and accessible, offering glucose tracking to a broader population. The sensors were also factory-calibrated, eliminating the need for calibration during usage [17,19].
In 2022, Dexcom’s G7 was FDA-approved for all types of diabetes above 2 years of age, including a better accuracy (8.2% overall mean absolute relative difference [20]), short warm-up period of 30 min and a smaller sensor. In same year, Abbott’s CGM Libre 3 was approved with updated readings every minute, likewise better accuracy (7.9% overall mean absolute relative difference [21]), smaller size and other improvements for ease of use. Table 1 provides a comparison of the latest and most widely used CGM systems from Dexcom, Abbott and Medtronic.
Recent innovations also include closed-loop systems or hybrid artificial pancreas devices, which use CGM data to automatically adjust insulin delivery. Medtronic’s MiniMed 670 G, launched in 2017, was the first such system to be commercially available [13].
Other products are pushing for longer sensor life. In 2018, the FDA approved the Eversense CGM system, developed by Senseonics Inc., for individuals aged 18 and older with diabetes. This system was the first FDA-approved CGM to feature a fully implantable glucose sensor, capable of functioning for up to 90 days [22]. In 2023, Senseonics reported favorable safety and accuracy data for a 365-day sensor, indicating its potential for future commercialization [23]. Furthermore, the CareSens Air by i-SENS inc., has already received CE approval and currently prepares FDA approval for a CGM sensor with readings every 5 min and a 15-day sensor life [24].
In 2024, the Accu-Chek SmartGuide, developed by Hoffmann-La Roche, obtained CE approval for its 14-day glucose sensor and app, designed for individuals aged 18 years and older with type 1 or type 2 diabetes [25]. The device incorporates advanced algorithms capable of predicting the risk of low blood glucose within 30 min, forecasting glucose trends up to two hours ahead and identifying potential nighttime hypoglycemia risks [26].
Also in 2024, the FDA approved Dexcom’s Stelo, the first over-the-counter (OTC) CGM system [27]. An OTC CGM allows people to purchase and use the device without a prescription. The Stelo CGM is designed for individuals aged 18 and older who do not use insulin, including those managing diabetes with oral medications and individuals without diabetes seeking insights into how diet and exercise influence blood glucose levels. Shortly thereafter, the FDA approved Abbott’s Lingo and Libre Rio OTC CGM systems. The Lingo CGM is designed for consumers interested in enhancing their health and wellness through better understanding of their metabolic responses. The Libre Rio is designed for adults with type 2 diabetes who do not use insulin and primarily manage their condition through lifestyle modifications [28].

6. Emerging Technology

CGM continues to experience significant advancements aimed at developing lower-cost, more accurate and user-friendly sensing technologies. While current state-of-the-art CGM systems predominantly rely on invasive measurement techniques, there is growing interest in noninvasive glucose monitoring solutions [29]. An accurate and reliable noninvasive glucose monitoring device would offer substantial benefits in ease of use and accessibility. Furthermore, it could expand applications beyond diabetes management, enabling broader health monitoring, such as pre-diabetes assessment, sports performance optimization and monitoring of other patient groups.
Noninvasive glucose monitoring approaches can be categorized into interstitial fluid-based, radio frequency-based and breath-based methods. Interstitial fluid-based sensors typically analyze glucose levels either by assessing fluid on or beneath the skin using infrared lasers. Radio frequency-based systems penetrate the skin and may directly measure glucose concentrations in blood [29].
Notably, SugarBeat, developed by Nemaura Medical, is a wireless, noninvasive blood glucose monitoring system that employs a disposable skin patch. This patch is paired with a rechargeable transmitter, which detects blood glucose levels and transmits the data to a mobile application at five-minute intervals. The patch is designed for single-day use, with a lifespan of up to 24 h. Notably, SugarBeat has obtained regulatory approval in Europe [30]. Also, Apple has reportedly achieved proof-of-concept for a noninvasive CGM technology, which it aims to integrate into its Apple Watch, underscoring the potential of noninvasive CGM in wearable health monitoring devices [31]. Numerous other companies are actively pursuing the development of noninvasive CGM technologies. However, a truly disruptive product capable of challenging the state-of-the-art invasive systems currently dominated by industry leaders such as Dexcom, Abbott and Medtronic has yet to emerge.

7. CGM Usage

These innovations and new OTC sensors present opportunities to expand the applications of CGM beyond routine diabetes management. Use cases include early diagnostics, predictive modeling, diabetes education and integration into advanced automated insulin delivery (AID) systems, as illustrated in Figure 2. Some of these areas have already been integrated into research, development and clinical care, and others have potential to substantially improve these in the coming years.

8. Predictive Modeling

CGM devices generate time-series data that capture glucose fluctuations with granularity. These data form the foundation for predictive modeling, where advanced algorithms can analyze glucose dynamics to provide actionable insights. Predictive modeling has demonstrated potential in enhancing both individual diabetes management and broader clinical decision-making.
One of the primary applications of predictive modeling in CGM is the development of algorithms capable of forecasting glucose levels, glycemic events or elevated ketone bodies [32,33,34,35,36]. These algorithms employ machine learning (ML) techniques, such as deep neural networks, gradient boosting and support vector machines, to analyze glucose trends in conjunction with other variables [37,38]. By integrating these factors, predictive models can identify patterns that precede glycemic events, offering alerts for impending hypoglycemia or hyperglycemia. In addition to short-term glucose forecasting, predictive modeling can assist with patient risk stratification. By analyzing longitudinal CGM data alongside demographic, clinical and behavioral factors, ML models can identify individuals at heightened risk for adverse outcomes, such as future glycemic control (HbA1c), severe hypoglycemia or glycemic variability [39,40,41]. These insights enable clinicians to implement targeted interventions, such as intensified monitoring or tailored treatment plans, for high-risk patients.
Especially with the latest generation of AI, including foundation models [42], multimodal learning and time-series forecasting architectures [43,44] hold significant promise for also improving CGM-based prediction accuracy, personalization and clinical decision support. Foundation models, such as large-scale deep learning networks pre-trained on diverse health datasets, can potentially leverage vast amounts of glucose dynamics data to enhance predictive accuracy. These models can be fine-tuned for individual patients, enabling more personalized glycemic predictions based on lifestyle, diet and metabolic patterns.

9. Early Diagnostics

CGM has emerged as a potential tool for the early diagnosis of diabetes type 1 or 2 and prediabetes [45,46,47]. In individuals at high risk for type 2 diabetes, CGM can identify abnormal glucose patterns, such as postprandial hyperglycemia or nocturnal hypoglycemia, that might precede overt disease [47]. For type 1 diabetes, CGM can detect glucose irregularities associated with the early stages of autoimmunity, allowing for timely interventions [46]. CGM could also be instrumental in detecting gestational diabetes, where it offers continuous monitoring that surpasses traditional diagnostic methods, such as the oral glucose tolerance test (OGTT), in detecting glucose abnormalities [48]. Artificial intelligence (AI) and CGM also has promise in diagnosing diabetes complications [49,50], such as gastroparesis [51]. Early diagnostic capabilities of CGM can pave the way for interventions that can delay or prevent the progression of diabetes. This early detection is also important in preventing long-term complications such as diabetic retinopathy, neuropathy or diabetic foot ulcers.

10. Diabetes Education

CGM has proven to be a highly effective and interactive educational tool for patients with gestational diabetes, children and type 2 diabetes [52,53,54,55]. By visualizing glucose fluctuations, patients can better understand how factors such as diet, exercise, stress and medications affect their glucose levels. This real-time feedback encourages behavior modifications that promote improved glycemic control. Additionally, CGM serves as a valuable training resource for clinicians, helping them interpret glucose patterns and tailor advice to individual patient needs [56].
CGM fundamentally transforms diabetes education by providing continuous, real-time data that offer a precise understanding of glucose fluctuations throughout the day and night. Unlike traditional methods [57], which rely on static and isolated blood glucose readings, CGM provides a dynamic glucose curve that highlights patterns and trends over time. These dynamic data empower patients to recognize the effects of specific meals or physical activity on their glucose levels, and how they can adjust their management in real time. This kind of feedback helps patients feel more engaged and confident in their ability to manage their condition.
Moreover, CGM helps identify problems that might go unnoticed with traditional monitoring methods. By detecting trends such as frequent hyperglycemia or hypoglycemia, CGM enables early intervention and treatment adjustments, preventing complications like diabetic retinopathy, neuropathy or diabetic foot ulcers. The educational benefits of CGM can not only enhance patient engagement but also potentially improve adherence to treatment regimens, such as exercise in type 2 diabetes [58].

11. Insulin Dosage

Accurate insulin dosing is important in effective diabetes management, especially for individuals with type 1 diabetes. CGM data enable real-time adjustments to both bolus and basal insulin delivery. By capturing glucose trends over time, CGM helps to mitigate glycemic variability during daily activities, such as meals, exercise and periods of stress. Numerous studies have shown the benefits such as reducing HbA1c, hyper-/hypoglycemia, effects on quality of life and the safety of using CGM [59,60,61,62]. The integration of CGM with insulin pumps has further improved glycemic control by allowing for dynamic insulin delivery adjustments based on glucose levels [63].

12. Automated Insulin Delivery Systems

Automated insulin delivery (AID) systems, commonly referred to as artificial pancreas systems, rely on CGM as their central component. These systems integrate CGM data with insulin pumps to automate insulin delivery based on real-time glucose readings [64]. Advanced algorithms that adjust insulin dosing to maintain glucose levels within a target range can minimize the risks of hypoglycemia/hyperglycemia and keep blood glucose in range. Clinical studies have shown significant improvements in glycemic control across different age and groups of patients [65,66,67]. Moreover, AID systems currently in testing aim to improve upon current systems by adding one additional hormone, glucagon, that can be delivered as needed, providing something closer to the endocrine functionality of the pancreas [68,69].

13. Clinical Research

CGM technology has revolutionized clinical diabetes research by providing real-time, continuous glucose data. Unlike traditional metrics like HbA1c [70], CGM captures daily fluctuations and glycemic patterns. These granular data offer deeper insights into diabetes management, enabling more precise evaluations of treatment efficacy and patient outcomes [71,72]. Additionally, CGM is particularly advantageous in scenarios where HbA1c measurements may be unreliable or unreasonable, such as in conditions that affect hemoglobin levels or erythrocyte lifespan [73,74]. Hence, HbA1c reflects average glucose levels over approximately 120 days, aligned with erythrocyte lifespan, making it less informative in newly diagnosed diabetes cases. In such situations, CGM provides critical short-term glucose trends that HbA1c cannot.
CGM-derived metrics such as time-in-range (TIR), time-below-range (TBR), time-above-range (TAR) and glycemic variability (GV) provide actionable insights beyond HbA1c [75]. TIR, for instance, quantifies the percentage of time a patient maintains target glucose levels, a critical marker of effective diabetes control. TBR and TAR highlight periods of hypo- and hyperglycemia, helping to identify risks and refine interventions. Lower GV, an indicator of stable glucose control, correlates with reduced risks of long-term complications.
In clinical trials, CGM facilitates more sensitive endpoints, offering a nuanced view of intervention outcomes. Its real-time data enable precise evaluation of glucose-lowering therapies, lifestyle interventions and behavioral treatments. Beyond diabetes management, CGM has proven invaluable in studying complications such as diabetic foot ulcers. Research links unstable glucose levels and high GV to delayed wound healing and increased risk of infection and amputation. Stabilizing glucose levels not only aids in ulcer prevention but also promotes faster healing, underscoring the importance of CGM data in advancing clinical care strategies [76,77,78].

14. Personalized Treatment

CGM enables the development of personalized treatment strategies that account for individual glucose variability and lifestyle factors [79], which also leads to improved patient autonomy [80]. Healthcare providers can analyze CGM data alongside clinical information to optimize medication regimens, dietary plans and physical activity recommendations, addressing glycemic variability and minimizing extreme glucose excursions. Additionally, integrating CGM with wearable devices and digital health platforms enhances its potential for precision medicine, aligning treatments with the specific needs and preferences of each patient [81].
CGM combined with telemedicine has opened new, interactive opportunities to improve diabetes management. Rather than relying on self-reported data or point measurements, clinicians have direct access to continuous real-time data on the patient’s blood glucose levels. This approach allows for a much more precise and informed dialogue between clinicians and patients. This data-driven interaction helps improve disease management and increases the patient’s understanding of how their actions affect their blood glucose levels. It can also enhance collaboration between the patient and clinician [82], as the patient feels more involved in decision-making and receives personalized advice tailored to their lifestyle [83].
The combination of telemedicine and CGM has been tested in clinical studies and has proven to be an effective method for improving diabetes care [84]. The improvements cover better blood glucose control, higher patient satisfaction and more efficient disease management [85].
Also, CGM is increasingly being explored for use in newborns and young infants, particularly those who are preterm or have unstable glucose levels. CGM provides real-time data on glucose levels, which can help in adjusting treatments promptly and reducing the need for frequent blood tests. This technology is especially valuable in managing neonatal hypoglycemia and hyperglycemia, conditions that can impact neurodevelopment. However, there are still technical challenges to overcome, such as accuracy at low glucose levels and sensor calibration. Despite these challenges, CGM holds promises for improving long-term outcomes in neonatal care [86,87,88].

15. Conclusions

Continuous glucose monitoring has revolutionized the management of diabetes and is now widely acceptable. CGM offers a versatile tool for improving individual care and advancing research. Its applications extend from daily diabetes management and predictive modeling to early diagnosis, education and the development of automated systems. Additionally, CGM has become a basis of personalized medicine and a valuable resource in clinical trials. As CGM technology continues to evolve, its integration with artificial intelligence and other health technologies holds promise for further transforming diabetes care and improving patient outcomes.

Author Contributions

All authors contributed significantly to the development of this manuscript. SLC conceptualized this study and provided the overall direction. S.L.C. and C.B. conducted the literature review and drafted the initial manuscript. P.V. contributed to the analysis and interpretation. All authors critically revised the manuscript for intellectual content and approved the final version. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study did not involve human or animal subjects and did not require institutional ethics approval.

Conflicts of Interest

S.L.C. received funding from i-SENS, Inc. (Seoul, Republic of Korea). This study was conducted independently, and the authors declare that their involvement with i-SENS, Inc. (Seoul, Republic of Korea) did not influence the findings or conclusions of this study.

References

  1. Rodbard, D. Continuous Glucose Monitoring: A Review of Successes, Challenges, and Opportunities. Diabetes Technol. Ther. 2016, 18, S23–S213. [Google Scholar] [CrossRef] [PubMed]
  2. Martens, T.; Beck, R.W.; Bailey, R.; Ruedy, K.J.; Calhoun, P.; Peters, A.L.; Pop-Busui, R.; Philis-Tsimikas, A.; Bao, S.; Umpierrez, G.; et al. Effect of Continuous Glucose Monitoring on Glycemic Control in Patients with Type 2 Diabetes Treated with Basal Insulin: A Randomized Clinical Trial. JAMA 2021, 325, 2262–2272. [Google Scholar] [CrossRef]
  3. 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, S4–S11. [Google Scholar] [CrossRef]
  4. Group TJDRFCGMS. Continuous Glucose Monitoring and Intensive Treatment of Type 1 Diabetes. N. Engl. J. Med. 2008, 359, 1464–1476. [Google Scholar] [CrossRef] [PubMed]
  5. Lacy, M.E.; Lee, K.E.; Atac, O.; Heier, K.; Fowlkes, J.; Kucharska-Newton, A.; Moga, D.C. Patterns and Trends in Continuous Glucose Monitoring Utilization Among Commercially Insured Individuals with Type 1 Diabetes: 2010–2013 to 2016–2019. Clin. Diabetes 2024, 42, 388–397. [Google Scholar] [CrossRef]
  6. Ajjan, R.A. How can we realize the clinical benefits of continuous glucose monitoring? Diabetes Technol. Ther. 2017, 19, S27–S36. [Google Scholar] [CrossRef] [PubMed]
  7. Clarke, S.F.; Foster, J.R. A history of blood glucose meters and their role in self-monitoring of diabetes mellitus. Br. J. Biomed. Sci. 2012, 69, 83–93. [Google Scholar] [CrossRef] [PubMed]
  8. Mortensen, H.; Mølsted-Pedersen, L.; Schmølker, L.; Olesen, H. Reference intervals for urinary glucose in pregnancy. Scand. J. Clin. Lab. Investig. 1984, 44, 409–412. [Google Scholar] [CrossRef] [PubMed]
  9. Rock, J.A.; Gerende, L.J. Dextrostix Method for Determination of Blood Glucose Levels: A Statistical Evaluation. JAMA 1966, 198, 231–236. [Google Scholar] [CrossRef] [PubMed]
  10. Yamada, S. Historical Achievements of Self-Monitoring of Blood Glucose Technology Development in Japan. J. Diabetes Sci. Technol. 2011, 5, 1300. [Google Scholar] [CrossRef]
  11. Kesavadev, J.; Das, A.K.; Manojan, K.K.; Muruganathan, A.; Jagadeesha, A.; Shenoy, M.T.; Sreelakshmi, R.; Basanth, A.; Jothydev, K.; Krishnan, G. History and Evolution of Capillary Glucose Monitoring. Int. J. Diabetes Technol. 2023, 2, 37–42. [Google Scholar] [CrossRef]
  12. Lee, I.; Probst, D.; Klonoff, D.; Sode, K. Continuous glucose monitoring systems—Current status and future perspectives of the flagship technologies in biosensor research. Biosens. Bioelectron. 2021, 181, 113054. [Google Scholar] [CrossRef] [PubMed]
  13. Innovation Milestones|Medtronic. Available online: https://www.medtronicdiabetes.com/about-medtronic-innovation/milestone-timeline (accessed on 22 November 2024).
  14. Diabetes Research in Children Network (DirecNet) Study Group. The accuracy of the Guardian RT continuous glucose monitor in children with type 1 diabetes. Diabetes Technol. Ther. 2008, 10, 266–272. [Google Scholar] [CrossRef] [PubMed]
  15. Olczuk, D.; Priefer, R. A history of continuous glucose monitors (CGMs) in self-monitoring of diabetes mellitus. Diabetes Metab. Syndr. 2018, 12, 181–187. [Google Scholar] [CrossRef]
  16. Didyuk, O.; Econom, N.; Guardia, A.; Livingston, K.; Klueh, U. Continuous Glucose Monitoring Devices: Past, Present, and Future Focus on the History and Evolution of Technological Innovation. J. Diabetes Sci. Technol. 2021, 15, 676–683. [Google Scholar] [CrossRef]
  17. Hirsch, I.B. Introduction: History of Glucose Monitoring. Compendia 2018, 2018, 1. [Google Scholar] [CrossRef]
  18. History of the Dexcom CGM: Revolutionizing Glucose Management Connected in Motion. Available online: https://www.connectedinmotion.ca/blog/history-of-the-dexcom-cgm/ (accessed on 22 November 2024).
  19. FDA Approves First Continuous Glucose Monitoring System for Adults Not Requiring Blood Sample Calibration|FDA. Available online: https://www.fda.gov/news-events/press-announcements/fda-approves-first-continuous-glucose-monitoring-system-adults-not-requiring-blood-sample (accessed on 22 November 2024).
  20. Garg, S.K.; Kipnes, M.; Castorino, K.; Bailey, T.S.; Akturk, H.K.; Welsh, J.B.; Christiansen, M.P.; Balo, A.K.; Brown, S.A.; Reid, J.L.; et al. Accuracy and Safety of Dexcom G7 Continuous Glucose Monitoring in Adults with Diabetes. Diabetes Technol. Ther. 2022, 24, 373–380. [Google Scholar] [CrossRef] [PubMed]
  21. Alva, S.; Brazg, R.; Castorino, K.; Kipnes, M.; Liljenquist, D.R.; Liu, H. Accuracy of the Third Generation of a 14-Day Continuous Glucose Monitoring System. Diabetes Ther. 2023, 14, 767–776. [Google Scholar] [CrossRef] [PubMed]
  22. FDA Approves First Continuous Glucose Monitoring System with a Fully Implantable Glucose Sensor and Compatible Mobile App for Adults with Diabetes|FDA. Available online: https://www.fda.gov/news-events/press-announcements/fda-approves-first-continuous-glucose-monitoring-system-fully-implantable-glucose-sensor-and (accessed on 22 November 2024).
  23. Data Supports Safety and Accuracy of Senseonics 365-Day CGM. Available online: https://www.drugdeliverybusiness.com/data-safety-accuracy-senseonics-365-day-cgm/ (accessed on 22 November 2024).
  24. Kim, K.-S.; Lee, S.-H.; Yoo, W.S.; Park, C.-Y. Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System. Diabetes Technol. Ther. 2024, 26, 222. [Google Scholar] [CrossRef] [PubMed]
  25. Glatzer, T.; Ringemann, C.; Militz, D.; Mueller-Hoffmann, W. Concept and Implementation of a Novel Continuous Glucose Monitoring Solution With Glucose Predictions on Board. J. Diabetes Sci. Technol. 2024, 18, 1004–1008. [Google Scholar] [CrossRef]
  26. Herrero, P.; Andorrà, M.; Babion, N.; Bos, H.; Koehler, M.; Klopfenstein, Y.; Leppäaho, E.; Lustenberger, P.; Peak, A.; Ringemann, C.; et al. Enhancing the Capabilities of Continuous Glucose Monitoring with a Predictive App. J. Diabetes Sci. Technol. 2024, 18, 1014–1026. [Google Scholar] [CrossRef] [PubMed]
  27. FDA Clears First Over-the-Counter Continuous Glucose Monitor|FDA. Available online: https://www.fda.gov/news-events/press-announcements/fda-clears-first-over-counter-continuous-glucose-monitor (accessed on 22 November 2024).
  28. Abbott Receives U.S. FDA Clearance for Two New Over-the-Counter Continuous Glucose Monitoring Systems—10 June 2024. Available online: https://abbott.mediaroom.com/2024-06-10-Abbott-Receives-U-S-FDA-Clearance-for-Two-New-Over-the-Counter-Continuous-Glucose-Monitoring-Systems (accessed on 22 November 2024).
  29. Tang, L.; Chang, S.J.; Chen, C.-J.; Liu, J.-T. Non-Invasive Blood Glucose Monitoring Technology: A Review. Sensors 2020, 20, 6925. [Google Scholar] [CrossRef] [PubMed]
  30. Nemaura Medical Wins CE Mark for SugarBeat CGM—Drug Delivery Business. Available online: https://www.drugdeliverybusiness.com/nemaura-medical-wins-ce-mark-for-sugarbeat-cgm/ (accessed on 25 November 2024).
  31. Apple Watch Creeps Closer to Adding Glucose Tracking: Bloomberg. Available online: https://www.fiercebiotech.com/medtech/apples-long-desired-glucose-tracking-reportedly-proof-concept-stage-bloomberg (accessed on 25 November 2024).
  32. Woldaregay, A.Z.; Årsand, E.; Walderhaug, S.; Albers, D.; Mamykina, L.; Botsis, T.; Hartvigsen, G. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes. Artif. Intell. Med. 2019, 98, 109–134. [Google Scholar] [CrossRef]
  33. Cichosz, S.L.; Jensen, M.H.; Hejlesen, O. Short-term prediction of future continuous glucose monitoring readings in type 1 diabetes: Development and validation of a neural network regression model. Int. J. Med. Inform. 2021, 151, 104472. [Google Scholar] [CrossRef] [PubMed]
  34. Cichosz, S.L.; Kronborg, T.; Jensen, M.H.; Hejlesen, O. Penalty weighted glucose prediction models could lead to better clinically usage. Comput. Biol. Med. 2021, 138, 104865. [Google Scholar] [CrossRef]
  35. Woldaregay, A.Z.; Årsand, E.; Botsis, T.; Albers, D.; Mamykina, L.; Hartvigsen, G. Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes. J. Med. Internet Res. 2019, 21, e11030. [Google Scholar] [CrossRef]
  36. Cichosz, S.L.; Bender, C. Development of Machine Learning Models for the Identification of Elevated Ketone Bodies During Hyperglycemia in Patients with Type 1 Diabetes. Diabetes Technol. Ther. 2024, 26, 403–410. [Google Scholar] [CrossRef] [PubMed]
  37. Fleischer, J.; Hansen, T.K.; Cichosz, S.L. Hypoglycemia event prediction from CGM using ensemble learning. Front. Clin. Diabetes Healthc. 2022, 3, 71. [Google Scholar] [CrossRef] [PubMed]
  38. Cichosz, S.L.; Frystyk, J.; Hejlesen, O.K.; Tarnow, L.; Fleischer, J. A novel algorithm for prediction and detection of hypoglycemia based on continuous glucose monitoring and heart rate variability in patients with type 1 diabetes. J. Diabetes Sci. Technol. 2014, 8, 731–737. [Google Scholar] [CrossRef] [PubMed]
  39. Islam, S.; Qaraqe, M.K.; Belhaouari, S.; Petrovski, G. Long Term HbA1c Prediction Using Multi-Stage CGM Data Analysis. IEEE Sens. J. 2021, 21, 15237–15247. [Google Scholar] [CrossRef]
  40. Cichosz, S.L.; Jensen, M.H.; Olesen, S.S. Development and Validation of a Machine Learning Model to Predict Weekly Risk of Hypoglycemia in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring. Diabetes Technol. Ther. 2024, 26, 457–466. [Google Scholar] [CrossRef] [PubMed]
  41. Cichosz, S.L.; Olesen, S.S.; Jensen, M.H. Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring. J. Diabetes Sci. Technol. 2024. online ahead of print. [Google Scholar] [CrossRef]
  42. Hollmann, N.; Müller, S.; Purucker, L.; Krishnakumar, A.; Körfer, M.; Bin Hoo, S.; Schirrmeister, R.T.; Hutter, F. Accurate predictions on small data with a tabular foundation model. Nature 2025, 637, 319–326. [Google Scholar] [CrossRef] [PubMed]
  43. Shchur, O.; Turkmen, A.C.; Erickson, N.; Shen, H.; Shirkov, A.; Hu, T.; Wang, B. AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting. In Proceedings of the Second International Conference on Automated Machine Learning, PMLR, Potsdam, Germany, 12–15 November 2023; Volume 228. [Google Scholar]
  44. Erickson, N.; Mueller, J.; Shirkov, A.; Zhang, H.; Larroy, P.; Li, M.; Smola, A. AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. arXiv 2020, arXiv:2003.06505. [Google Scholar]
  45. Soliman, A.; DeSanctis, V.; Yassin, M.; Elalaily, R.; Eldarsy, N. Continuous glucose monitoring system and new era of early diagnosis of diabetes in high risk groups. Indian J. Endocrinol. Metab. 2014, 18, 274. [Google Scholar] [CrossRef]
  46. Haynes, A.; Tully, A.; Smith, G.J.; Penno, M.A.; Craig, M.E.; Wentworth, J.M.; Huynh, T.; Colman, P.G.; Soldatos, G.; Anderson, A.J.; et al. Early Dysglycemia Is Detectable Using Continuous Glucose Monitoring in Very Young Children at Risk of Type 1 Diabetes. Diabetes Care 2024, 47, 1750–1756. [Google Scholar] [CrossRef] [PubMed]
  47. Cichosz, S.L.; Kronborg, T.; Laugesen, E.; Hangaard, S.; Fleischer, J.; Hansen, T.K.; Jensen, M.H.; Poulsen, P.L.; Vestergaard, P. From Stability to Variability: Classification of Healthy Individuals, Prediabetes, and Type 2 Diabetes using Glycemic Variability Indices from Continuous Glucose Monitoring Data. Diabetes Technol. Ther. 2024, 27, 34–44. [Google Scholar] [CrossRef]
  48. Di Filippo, D.; Ahmadzai, M.; Chang, M.H.Y.; Horgan, K.; Ong, R.M.; Darling, J.; Akhtar, M.; Henry, A.; Welsh, A. Continuous Glucose Monitoring for the Diagnosis of Gestational Diabetes Mellitus: A Pilot Study. J. Diabetes Res. 2022, 2022, 5142918. [Google Scholar] [CrossRef]
  49. Ayers, A.T.; Ho, C.N.; Kerr, D.; Cichosz, S.L.; Mathioudakis, N.; Wang, M.; Najafi, B.; Moon, S.-J.; Pandey, A.; Klonoff, D.C. Artificial Intelligence to Diagnose Complications of Diabetes. J. Diabetes Sci. Technol. 2024, 19, 246–264. [Google Scholar] [CrossRef]
  50. Cichosz, S.L.; Johansen, M.D.; Hejlesen, O. Toward Big Data Analytics. J. Diabetes Sci. Technol. 2016, 10, 27–34. [Google Scholar] [CrossRef] [PubMed]
  51. Cichosz, S.L.; Hejlesen, O. Classification of Gastroparesis from Glycemic Variability in Type 1 Diabetes: A Proof-of-Concept Study. J. Diabetes Sci. Technol. 2022, 16, 1190–1195. [Google Scholar] [CrossRef]
  52. Phillip, M.; Danne, T.; Shalitin, S.; Buckingham, B.; Laffel, L.; Tamborlane, W.; Battelino, T.; Participants, F.T.C.F. Use of continuous glucose monitoring in children and adolescents (*). Pediatr. Diabetes 2012, 13, 215–228. [Google Scholar] [CrossRef] [PubMed]
  53. Głowińska-Olszewska, B.; Tobiaszewska, M.; Łuczyński, W.; Bossowski, A. Monthly use of a real-time continuous glucose monitoring system as an educational and motivational tool for poorly controlled type 1 diabetes adolescents. Adv. Med. Sci. 2013, 58, 344–352. [Google Scholar] [CrossRef]
  54. Alfadhli, E.; Osman, E.; Basri, T. Use of a real time continuous glucose monitoring system as an educational tool for patients with gestational diabetes. Diabetol. Metab. Syndr. 2016, 8, 48. [Google Scholar] [CrossRef] [PubMed]
  55. Rivera-Ávila, D.A.; Esquivel-Lu, A.I.; Salazar-Lozano, C.R.; Jones, K.; Doubova, S.V. The effects of professional continuous glucose monitoring as an adjuvant educational tool for improving glycemic control in patients with type 2 diabetes. BMC Endocr. Disord. 2021, 21, 79. [Google Scholar] [CrossRef]
  56. Miller, E.M. Using Continuous Glucose Monitoring in Clinical Practice. Clin. Diabetes 2020, 38, 429. [Google Scholar] [CrossRef] [PubMed]
  57. Mensing, C.R.; Norris, S.L. Group Education in Diabetes: Effectiveness and Implementation. Diabetes Spectr. 2003, 16, 96–103. [Google Scholar] [CrossRef]
  58. Bailey, K.J.; Little, J.P.; Jung, M.E. Self-Monitoring Using Continuous Glucose Monitors with Real-Time Feedback Improves Exercise Adherence in Individuals with Impaired Blood Glucose: A Pilot Study. Diabetes Technol. Ther. 2016, 18, 185–193. [Google Scholar] [CrossRef]
  59. Yeh, H.-C.; Brown, T.T.; Maruthur, N.; Ranasinghe, P.; Berger, Z.; Suh, Y.D.; Wilson, L.M.; Haberl, E.B.; Brick, J.; Bass, E.B.; et al. Comparative effectiveness and safety of methods of insulin delivery and glucose monitoring for diabetes mellitus: A systematic review and meta-analysis. Ann. Intern. Med. 2012, 157, 336–347. [Google Scholar] [CrossRef]
  60. Bergenstal, R.M.; Tamborlane, W.V.; Ahmann, A.; Buse, J.B.; Dailey, G.; Davis, S.N.; Joyce, C.; Peoples, T.; Perkins, B.A.; Welsh, J.B.; et al. Effectiveness of Sensor-Augmented Insulin-Pump Therapy in Type 1 Diabetes. N. Engl. J. Med. 2010, 363, 311–320. [Google Scholar] [CrossRef] [PubMed]
  61. Aleppo, G.; Ruedy, K.J.; Riddlesworth, T.D.; Kruger, D.F.; Peters, A.L.; Hirsch, I.; Bergenstal, R.M.; Toschi, E.; Ahmann, A.J.; Shah, V.N.; et al. REPLACE-BG: A Randomized Trial Comparing Continuous Glucose Monitoring With and Without Routine Blood Glucose Monitoring in Adults with Well-Controlled Type 1 Diabetes. Diabetes Care 2017, 40, 538–545. [Google Scholar] [CrossRef] [PubMed]
  62. Battelino, T.; Phillip, M.; Bratina, N.; Nimri, R.; Oskarsson, P.; Bolinder, J. Effect of continuous glucose monitoring on hypoglycemia in type 1 diabetes. Diabetes Care 2011, 34, 795–800. [Google Scholar] [CrossRef]
  63. Riddlesworth, T.D.; Ruedy, K.J.; Kollman, C.; Ahmann, A.J.; Bergenstal, R.M.; Bhargava, A.; Bode, B.W.; Haller, S.; Kruger, D.F.; McGill, J.B.; et al. Effect of initiating use of an insulin pump in adults with type 1 diabetes using multiple daily insulin injections and continuous glucose monitoring (DIAMOND): A multicentre, randomised controlled trial. Lancet Diabetes Endocrinol. 2017, 5, 700–708. [Google Scholar] [CrossRef]
  64. Castle, J.R.; DeVries, J.H.; Kovatchev, B. Future of automated insulin delivery systems. Diabetes Technol. Ther. 2017, 19, S67–S72. [Google Scholar] [CrossRef] [PubMed]
  65. Knoll, C.; Peacock, S.; Wäldchen, M.; Cooper, D.; Aulakh, S.K.; Raile, K.; Hussain, S.; Braune, K. Real-world evidence on clinical outcomes of people with type 1 diabetes using open-source and commercial automated insulin dosing systems: A systematic review. Diabet. Med. 2022, 39, e14741. [Google Scholar] [CrossRef] [PubMed]
  66. Bionic Pancreas Research Group. Multicenter, Randomized Trial of a Bionic Pancreas in Type 1 Diabetes. N. Engl. J. Med. 2022, 387, 1161–1172. [Google Scholar] [CrossRef] [PubMed]
  67. Breton, M.D.; Kanapka, L.G.; Beck, R.W.; Ekhlaspour, L.; Forlenza, G.P.; Cengiz, E.; Schoelwer, M.; Ruedy, K.J.; Jost, E.; Carria, L.; et al. A Randomized Trial of Closed-Loop Control in Children with Type 1 Diabetes. N. Engl. J. Med. 2020, 383, 836–845. [Google Scholar] [CrossRef]
  68. van Bon, A.C.; Blauw, H.; Jansen, T.J.P.; Laverman, G.D.; Urgert, T.; Geessink-Mennink, J.; Mulder, A.H.; Out, M.; Veldman, R.G.; Onvlee, A.J.; et al. Bihormonal fully closed-loop system for the treatment of type 1 diabetes: A real-world multicentre, prospective, single-arm trial in the Netherlands. Lancet Digit. Health 2024, 6, e272–e280. [Google Scholar] [CrossRef]
  69. Wilson, L.M.; Jacobs, P.G.; Castle, J.R. Role of glucagon in automated insulin delivery. Endocrinol. Metab. Clin. N. Am. 2019, 49, 179. [Google Scholar] [CrossRef]
  70. Chehregosha, H.; Khamseh, M.E.; Malek, M.; Hosseinpanah, F.; Ismail-Beigi, F. A View Beyond HbA1c: Role of Continuous Glucose Monitoring. Diabetes Ther. 2019, 10, 853–863. [Google Scholar] [CrossRef]
  71. Battelino, T.; Alexander, C.M.; A Amiel, S.; Arreaza-Rubin, G.; Beck, R.W.; Bergenstal, R.M.; A Buckingham, B.; Carroll, J.; Ceriello, A.; Chow, E.; et al. Continuous glucose monitoring and metrics for clinical trials: An international consensus statement. Lancet Diabetes Endocrinol. 2023, 11, 42–57. [Google Scholar] [CrossRef]
  72. Fox, B.Q.; Benjamin, P.F.; Aqeel, A.; Fitts, E.; Flynn, S.; Levine, B.; Maslak, E.; Milner, R.L.; Ose, B.; Poeschla, M.; et al. Continuous Glucose Monitoring Use in Clinical Trials for On-Market Diabetes Drugs. Clin. Diabetes 2021, 39, 160. [Google Scholar] [CrossRef]
  73. Cichosz, S.L. Beyond A1c: Investigating the Contribution of Red Blood Cell Parameters to Dysglycemia Diagnostics. J. Diabetes Sci. Technol. 2024, 18, 1519–1520. [Google Scholar] [CrossRef] [PubMed]
  74. Loy, S.L.; Lin, J.; Cheung, Y.B.; Sreedharan, A.V.; Chin, X.; Godfrey, K.M.; Tan, K.H.; Shek, L.P.-C.; Chong, Y.S.; Leow, M.K.-S.; et al. Influence of red blood cell indices on HbA1c performance in detecting dysglycaemia in a Singapore preconception cohort study. Sci. Rep. 2021, 11, 20850. [Google Scholar] [CrossRef]
  75. Schnell, O.; Barnard, K.; Bergenstal, R.; Bosi, E.; Garg, S.; Guerci, B.; Haak, T.; Hirsch, I.B.; Ji, L.; Joshi, S.R.; et al. Role of Continuous Glucose Monitoring in Clinical Trials: Recommendations on Reporting. Diabetes Technol. Ther. 2017, 19, 391–399. [Google Scholar] [CrossRef] [PubMed]
  76. Pratley, R.E.; Kanapka, L.G.; Rickels, M.R.; Ahmann, A.; Aleppo, G.; Beck, R.; Bhargava, A.; Bode, B.W.; Carlson, A.; Chaytor, N.S.; et al. Effect of Continuous Glucose Monitoring on Hypoglycemia in Older Adults with Type 1 Diabetes: A Randomized Clinical Trial. JAMA 2020, 323, 2397–2406. [Google Scholar] [CrossRef] [PubMed]
  77. Huang, Z.; Zhang, H.; Huang, Y.; Ye, S.; Ma, Y.; Xin, Y.; Chen, X.; Zhao, S. Association of time in range with postoperative wound healing in patients with diabetic foot ulcers. Int. Wound J. 2021, 19, 1309. [Google Scholar] [CrossRef] [PubMed]
  78. Yin, X.; Zhu, W.; Liu, C.; Yao, H.; You, J.; Chen, Y.; Ying, X.; Li, L. Association of continuous glucose monitoring-derived time in range with major amputation risk in diabetic foot osteomyelitis patients undergoing amputation. Ther. Adv. Endocrinol. Metab. 2022, 13, 20420188221099337. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  79. Reddy, M.; Oliver, N. The role of real-time continuous glucose monitoring in diabetes management and how it should link to integrated personalized diabetes management. Diabetes Obes. Metab. 2024, 26, 46–56. [Google Scholar] [CrossRef] [PubMed]
  80. Lawton, J.; Blackburn, M.; Allen, J.; Campbell, F.; Elleri, D.; Leelarathna, L.; Rankin, D.; Tauschmann, M.; Thabit, H.; Hovorka, R. Patients’ and caregivers’ experiences of using continuous glucose monitoring to support diabetes self-management: Qualitative study. BMC Endocr. Disord. 2018, 18, 12. [Google Scholar] [CrossRef]
  81. Vettoretti, M.; Cappon, G.; Acciaroli, G.; Facchinetti, A.; Sparacino, G. Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications. J. Diabetes Sci. Technol. 2018, 12, 1064–1071. [Google Scholar] [CrossRef] [PubMed]
  82. Lee, M.Y.; Seav, S.M.; Ongwela, L.; Lee, J.J.; Aubyrn, R.; Cao, F.Y.; Kalinsky, A.; Ramos, O.A.; Gu, Y.; Kingston, K.; et al. Empowering Hospitalized Patients With Diabetes: Implementation of a Hospital-wide CGM Policy with EHR-Integrated Validation for Dosing Insulin. Diabetes Care 2024, 47, 1838–1845. [Google Scholar] [CrossRef]
  83. Underwood, P.; Hibben, J.; Gibson, J.; DiNardo, M. Virtual visits and the use of continuous glucose monitoring for diabetes care in the era of COVID-19. J. Am. Assoc. Nurse Pract. 2021, 34, 586. [Google Scholar] [CrossRef] [PubMed]
  84. de Kreutzenberg, S.V. Telemedicine for the Clinical Management of Diabetes; Implications and Considerations After COVID-19 Experience. High Blood Press. Cardiovasc. Prev. 2022, 29, 319. [Google Scholar] [CrossRef]
  85. Reddy, S.; Wu, C.C.; José, A.; Hsieh, J.L.; Rautela, S.D. Personalized Virtual Care Using Continuous Glucose Monitoring in Adults With Type 2 Diabetes Treated with Less Intensive Therapies. Clin. Diabetes 2023, 41, 452. [Google Scholar] [CrossRef] [PubMed]
  86. Shah, R.; McKinlay, C.J.D.; Harding, J.E. Neonatal hypoglycemia: Continuous glucose monitoring. Curr. Opin. Pediatr. 2018, 30, 204. [Google Scholar] [CrossRef] [PubMed]
  87. McKinlay, C.J.; Chase, J.G.; Dickson, J.; Harris, D.L.; Alsweiler, J.M.; Harding, J.E. Continuous glucose monitoring in neonates: A review. Matern. Health Neonatol. Perinatol. 2017, 3, 18. [Google Scholar] [CrossRef] [PubMed]
  88. Beardsall, K.; Thomson, L.; Guy, C.; Iglesias-Platas, I.; van Weissenbruch, M.M.; Bond, S.; Allison, A.; Kim, S.; Petrou, S.; Pantaleo, B.; et al. Real-time continuous glucose monitoring in preterm infants (REACT): An international, open-label, randomised controlled trial. Lancet Child. Adolesc. Health 2021, 5, 265–273. [Google Scholar] [CrossRef]
Diabetology 06 00017 g001
Figure 1. Brief overview of CGM history.
Figure 1. Brief overview of CGM history.
Diabetology 06 00017 g001
Diabetology 06 00017 g002
Figure 2. Brief overview of CGM usage.
Figure 2. Brief overview of CGM usage.
Diabetology 06 00017 g002
Table 1. Comparison of leading continuous glucose monitoring (CGM) systems. This table provides a comparison of the latest and most widely used CGM systems from Dexcom, Abbott and Medtronic, highlighting key features such as sensor lifespan, calibration requirements, accuracy and potential interfering substances. 1 Real-world accuracy may vary due to individual physiological factors and usage conditions. 2 Price estimations: real-world prices may vary due to regional factors, healthcare subsidy and insurance.
Table 1. Comparison of leading continuous glucose monitoring (CGM) systems. This table provides a comparison of the latest and most widely used CGM systems from Dexcom, Abbott and Medtronic, highlighting key features such as sensor lifespan, calibration requirements, accuracy and potential interfering substances. 1 Real-world accuracy may vary due to individual physiological factors and usage conditions. 2 Price estimations: real-world prices may vary due to regional factors, healthcare subsidy and insurance.
Company Dexcom Abbott Medtronic
CGMG6G7Libre 2Libre 3Guardian 4
Approved, years≥2≥2≥4≥4 or 2 (plus)≥7
Sensor life, days10 d10.5 d + 12 h14 d15 d7 d
Warm time120 min30 min60 min60 min120 min
Calibration needednonononono
Pump integrationyesyeslimitedyesyes
CGM data platformClarity—GlookoClarity—GlookoLibre viewLibre viewCarelink
Accuracy (MARD) 19.0%8.2%9.3%7.9%10.6%
Alerts/alarmsYesYesYesYesyes
Potential Interfering SubstancesHydroxyureaHydroxyurea, acetaminophenVitamin C, Salicylic acidVitamin C, Salicylic acidAcetaminophen, hydroxyurea
Price indication 2$$$$$$$$$$
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bender, C.; Vestergaard, P.; Cichosz, S.L. The History, Evolution and Future of Continuous Glucose Monitoring (CGM). Diabetology 2025, 6, 17. https://doi.org/10.3390/diabetology6030017

AMA Style

Bender C, Vestergaard P, Cichosz SL. The History, Evolution and Future of Continuous Glucose Monitoring (CGM). Diabetology. 2025; 6(3):17. https://doi.org/10.3390/diabetology6030017

Chicago/Turabian Style

Bender, Clara, Peter Vestergaard, and Simon Lebech Cichosz. 2025. "The History, Evolution and Future of Continuous Glucose Monitoring (CGM)" Diabetology 6, no. 3: 17. https://doi.org/10.3390/diabetology6030017

APA Style

Bender, C., Vestergaard, P., & Cichosz, S. L. (2025). The History, Evolution and Future of Continuous Glucose Monitoring (CGM). Diabetology, 6(3), 17. https://doi.org/10.3390/diabetology6030017

Article Metrics

Back to TopTop