• Center on Health Equity and Access
  • Clinical
  • Health Care Cost
  • Health Care Delivery
  • Insurance
  • Policy
  • Technology
  • Value-Based Care

A New Era: Increasing Continuous Glucose Monitoring Use in Type 2 Diabetes

Evidence-Based Diabetes ManagementMarch 2019
Volume 25
Issue 4

Continuous glucose monitors (CGMs) are increasingly accessible and effective for patients with type 2 diabetes (T2D), and even those with prediabetes, as a means for real-time biofeedback and behavior change.

PRECIS: Continuous glucose monitors (CGMs) are increasingly accessible and effective for patients with type 2 diabetes (T2D), and even those with prediabetes, as a means for real-time biofeedback and behavior change.

A convergence of several healthcare megatrends will lead to increasingly common use of CGM in people with T2D and even those with prediabetes: (1) improvements in CGM accuracy, size, and cost; (2) the ability to upload data to the cloud; (3) the availability of digital coaching tools and analytic software, and soon, artificial intelligence, and (4) a shift toward value-based care.

In 2019, estimates put more than 30 million Americans living with T2D and 84 million with prediabetes, and both numbers are rising. Direct US healthcare spending on diabetes, both type 1 diabetes (T1D) and T2D, is currently estimated at $237 billion, with 1 in 4 US healthcare dollars going toward the care of people with diabetes.1 The critical importance of early glycemic control to prevent acute complications and halt disease progression to prevent chronic complications only intensifies as these costs, including the rising costs of insulin, increase.

SMBG and A1C Are Inadequate

The ability for patients and providers to gauge glycemic control in T2D depends on tools that provide incomplete information: self-monitoring of blood glucose (SMBG) data and glycated hemoglobin (A1C). It is challenging to get more than a limited set of SMBG data due to the inconvenience and pain associated with fingersticks, cost of test strips, and unforgiving requirements for specific timing. Even in the best of circumstances, SMBG data can be challenging to interpret. Patients and providers must frequently extrapolate from a single fasting blood glucose (BG) value or from glucose values at scattershot time points without clear temporal relationships to the food, exercise, or other stressors that provide key context. It should come as no surprise that although SMBG remains commonly used in both insulin-treated and noninsulin-treated patients, study results in noninsulin-treated patients have struggled to show efficacy of SMBG in changing patient behavior or reducing A1C.2

While A1C provides a useful measure of overall control, it cannot, either in real time or retrospectively, reveal a person’s specific behaviors and actions to more meaningfully inform patient and provider decisions. An A1C of 7% may underlie either exquisitely stable BG values or mask a roller coaster, coupling dramatic postprandial BG spikes with overly aggressive insulin use and resultant hypoglycemia.

Cheaper and Better CGMs

The first CGM was released by MiniMed (now Medtronic) in 1999. These early systems were rarely used due to cost, painful insertion, bulky size, poor accuracy, and the requirement for numerous fingerstick calibrations. However, as the technology has improved, data have shown improved glycemic control and decreased rates of hypoglycemia in those using CGM, leading both the Endocrine Society and American Diabetes Association to state that CGM use represents standard of care in T1D.3,4 CGM in Americans with T1D is now on an exponential growth curve, rising from 6% in 2011 to 12% in 2014 to 24% in 2016 to 38% in 2018.5

High costs and uncertainty over efficacy and necessity have kept CGM from widespread use in people with T2D. However, the newest CGM models, the Abbott Freestyle Libre and Dexcom G6, have begun to overcome many of these technical barriers to use of CGM systems. The sensors are inserted painlessly, are small enough to fit easily under clothing, can remain in place for 10 to 14 days, and are FDA approved as sufficiently accurate to use in lieu of fingersticks to make insulin-dosing decisions. Overcoming another significant barrier to use, data can now be seamlessly and continuously uploaded wirelessly to the cloud via a user’s smartphone. Of note, the Libre is a flash glucose monitor, requiring the user to scan the sensor to reveal glucose information and recent trends. Although it cannot alert a person to acute hyperglycemia or hypoglycemia in the middle of the night, this is a nonessential feature for the majority of people with T2D. Perhaps most importantly, Abbott has introduced a new, lower-pricing category with Libre, at around $75 to $150 each month for sensors (2 sensors that last 14 days each), translating to $900 to $1800 per year compared with what is typically $3000 to $5000 per year for traditional CGM.

Real-time Biofeedback Enables Behavior Change

CGM affords 2 major benefits over the current standard of SMBG coupled with A1C testing: first, a vast increase in the quantity of blood glucose information, which provides a more comprehensive view of glycemic control. Rather than snapshots in time, continuous information allows us to capture important metrics like time in range, time in hypoglycemia, glucose variability, and many other emerging “glycometrics.” These additional metrics cannot be captured with SMBG, even in the most diligent patients. A CGM recording BG every 5 minutes will record 105,120 BG readings per year compared with between just 1000 to 2000 in a person doing frequent SMBG.

Second is the ability of CGM systems to provide real-time biofeedback. With real-time data now seamlessly available on a user’s mobile device and the internet, easily visible trends and trajectories can help a person understand their own glycemic response in a more meaningful way. Patients can observe which foods and exercises affect them the most. Iterative exposure to this immediate biofeedback allows patients to learn about their own bodies and physiologic responses.

For example, we recently saw a 70-year-old man with T2D and heart disease, with an A1C of 7.5%, who takes metformin but had resisted making any changes to his diet. When he saw his graph of Libre data (Figure 1), he immediately identified the daily morning spike in his glucose level and its source: his daily glass of orange juice and banana. He cut these from his diet and reported an immediate improvement in his glucose levels. Also noteworthy is that had he used traditional fingersticks, he would have been completely unaware of these significant glucose spikes. His postbreakfast CGM scans showed readings of 81, 114, 131, and 99 mg/dL (Figure 2).

Clinical study results demonstrate that CGM in T2D is powerful for behavior change, a critical pillar in management. Patients adhere to exercise recommendations more consistently6,7 and decrease their caloric intake when using CGM systems.7 In addition, patients with T2D using CGMs have less hypoglycemia8 and, importantly, they have A1C reduction without intensification of their existing treatments.9

New Opportunities for Data Analysis and Coaching

Another challenge to date has been the lack of delivery system capacity to review, analyze, and interpret data, and then coach people with T2D based on their day-to-day glucose levels, a constraint which could potentially be magnified with the increased data provided by CGM. However, tech-enabled digital coaching services are emerging to help provide on-demand, accessible support for people with diabetes and prediabetes. Companies like Omada Health, Canary Health, Lark Health, Livongo, and others provide multiple touch points with enrolled patients to use biometric data (eg weight, blood pressure, blood glucose) for coaching and behavior change. Several of these services are already certified by CMS to provide digital diabetes prevention programs (DPP), and the availability of cheaper CGM means they will soon have access to rich, continuous BG data to be able to guide patients in interpreting and acting upon them. This will soon enable a capacity and scale for diabetes coaching that has never before been possible using the traditional care delivery system. The emergence of artificial intelligence tools to aid in data interpretation and even to automate some of the coaching via “chatbot” will only make this more efficient and cheaper.

Cost Implications of CGM Use in Type 2 Diabetes

One study looked at long-term cost-effectiveness for CGM use in people with T2D based on A1C reduction, projecting decreased rates of diabetes associated complications.10 Although we anticipate that A1C reduction through lifestyle changes by CGM users could prevent the addition of costly new medications or dose intensification of existing treatments, more study is needed to test this. This matters: Studies looking at A1C compared with healthcare costs have found significant impacts.11,12 In one case, a 1% or more decrease in A1C was associated with $685 to $950 per year lower total healthcare costs,13 and in another, a 1% increase in A1C was associated with a 7% increase in healthcare costs over the next 3 years.14

There are likely to be cost savings for people switching from frequent SMBG to CGM. Given that a person using 4 test strips a day at a cost of $1.30 per test strip—costs can vary widely from $0.10 to $2.00—is consuming $156 per month in test strips, not to mention other consumables like lancets, the direct cost of CGM might actually be lower in this population in some cases, assuming these patients can largely eliminate their use of test strips. For those using much less frequent SMBG today, such as those not on insulin or with prediabetes, the incremental costs of CGM may seem imposing—but this doesn’t need to be the case. If one were to use a Libre for only 14 days every 3 months, the cost of sensors would be $300 per year, at most, equivalent to about 4 to 5 test strips per week (at $1.30 per strip), and we would argue the CGM would be of substantially higher value. Periodic CGM use enables treatment regimen changes, but more importantly, as seen by Vigersky et al, observations people make and behaviors they change while using CGM result in lower blood glucose levels even after they have stopped using CGM.15 We believe that intermittent CGM use paired with coaching will provide much more impetus for lifestyle change than the current standard of every-3-months A1C with sporadic SMBG.


With rapidly improving CGM technology, wireless data upload, lower-cost CGM devices, and the availability of digital coaching tools, we believe the time is ripe for CGM use in a much broader population, including those with T2D who are on oral medications and those with prediabetes. Although additional studies will need to be done to demonstrate benefit in these populations, costs will likely continue to fall and technology will continue to improve, only further strengthening the value proposition for wider CGM use.


Division of Endocrinology, University of California, San Francisco (TK, AN); UCSF Center for Digital Health Innovation (AN).


Aaron Neinstein, MD

University of California, San Francisco

1700 Owens Street, Suite 541

San Francisco, CA 94158




There are no relevant funding sources.


Dr Neinstein has received research support from Cisco Systems Inc. and The Commonwealth Fund. He has been a consultant to Steady Health, Nokia Growth Partners, WebMD, and Grand Rounds and has received speaking honoraria from Academy Health and Symposia Medicus. He is an uncompensated medical adviser for Tidepool. Dr Kompala has no disclosures.REFERENCES:

  1. American Diabetes Association. Economic costs of diabetes in the US in 2017. Diabetes Care. 2018;41(5):917-928. doi: doi.org/10.2337/dci18-0007.
  2. Malanda UL, Welschen LMC, Riphagen II, Dekker JM, Nijpels G, Bot SDM. Self-monitoring of blood glucose in patients with type 2 diabetes mellitus who are not using insulin. Cochrane Database Syst Rev. 2012;1:CD005060. doi: 10.1002/14651858.CD005060.pub3.
  3. Peters AL, Ahmann AJ, Battelino T, et al. Diabetes technology-continuous subcutaneous insulin infusion therapy and continuous glucose monitoring in adults: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2016;101(11):3922-3937. doi: 10.1210/jc.2016-2534.
  4. American Diabetes Association. Chapter 7: diabetes technology: standards of medical care in diabetes-2019. Diabetes Care. 2019;42(suppl 1):S71-S80. doi: 10.2337/dc19-S007.
  5. Foster NC, Beck RW, Miller KM, et al. State of type 1 diabetes management and outcomes from the T1D exchange in 2016-2018. Diabetes Technol Ther. 2019;21(2):66-72. doi: 10.1089/dia.2018.0384.
  6. Allen NA, Fain JA, Braun B, Chipkin SR. Continuous glucose monitoring in non-insulin-using individuals with type 2 diabetes: acceptability, feasibility, and teaching opportunities. Diabetes Technol Ther. 2009;11(3):151-158. doi: 10.1089/dia.2008.0053.
  7. Taylor PJ, Thompson CH, Brinkworth GD. Effectiveness and acceptability of continuous glucose monitoring for type 2 diabetes management: a narrative review. J Diabetes Investig. 2018;9(4):713-725. doi: 10.1111/jdi.12807.
  8. Haak T, Hanaire H, Ajjan R, Hermanns N, Riveline J-P, Rayman G. Flash glucose-sensing technology as a replacement for blood glucose monitoring for the management of insulin-treated type 2 diabetes: a multicenter, open-label randomized controlled trial. Diabetes Ther. 2017;8(1):55-73. doi: 10.1007/s13300-016-0223-6.
  9. Park C, Le QA. The effectiveness of continuous glucose monitoring in patients with type 2 diabetes: a systematic review of literature and meta-analysis. Diabetes Technol Ther. 2018;20(9):613-621. doi: 10.1089/dia.2018.0177.
  10. Fonda SJ, Graham C, Munakata J, Powers JM, Price D, Vigersky RA. The cost-effectiveness of real-time continuous glucose monitoring (RT-CGM) in type 2 diabetes. J Diabetes Sci Technol. 2016;10(4):898-904. doi: 10.1177/1932296816628547.
  11. Fitch K, Pyenson BS, Iwasaki K. Medical claim cost impact of improved diabetes control for Medicare and commercially insured patients with type 2 diabetes. J Manag Care Pharm. 2013;19(8):609-620, 620a-620-d. doi: 10.18553/jmcp.2013.19.8.609.
  12. Juarez D, Goo R, Tokumaru S, Sentell T, Davis J, Mau M. Association between sustained glycated hemoglobin control and healthcare costs. 2013;5(2):59-64.
  13. Wagner EH. Effect of improved glycemic control on health care costs and utilization. JAMA. 2001;285(2):182-189. doi: 10.1001/jama.285.2.182.
  14. Gilmer TP, O’Connor PJ, Manning WG, Rush WA. The cost to health plans of poor glycemic control. Diabetes Care. 1997;20(12):1847-1853.
  15. Vigersky RA, Fonda SJ, Chellappa M, Walker MS, Ehrhardt NM. Short- and long-term effects of real-time continuous glucose monitoring in patients with type 2 diabetes. Diabetes Care. 2012;35(1):32-38. doi: 10.2337/dc11-1438.
Related Videos
Yael Mauer, MD, MPH
Pregnant Patient | image credit: pressmaster - stock.adobe.com
Diana Isaacs, PharmD
Beau Raymond, MD
Robert Zimmerman, MD
Beau Raymond, MD
Dr Kevin Mallow, PharmD, BCPS, BC-ADM, CDCES
Ian Neeland, MD
Chase D. Hendrickson, MD, MPH
Steven Coca, MD, MS, Icahn School of Medicine, Mount Sinai
Related Content
© 2024 MJH Life Sciences
All rights reserved.