Perceptions About CGMs Among Primary Care Physicians and Endocrinologists

ABSTRACT

Objectives: To identify clinicians’ current practice behaviors, attitudes, and obstacles shaping continuous glucose monitoring (CGM) use.

Study Design: This structured survey asked clinicians about their practice environments, their views on the value of CGM and the importance of comprehensive training and education, their preferred prescribing channels for CGM, and details specific to each practice type.

Methods: Survey participants were selected by PureSpectrum, which used the PureScore system to evaluate participant suitability, collect data, and analyze results. The data show the percentage of respondents in each clinician group who answered each question.

Results: Responses from 101 primary care physicians (PCPs) and 106 endocrinologists were obtained for analysis. Levels of familiarity with the newest CGM technologies were consistent among PCPs and endocrinologists. A large percentage of both PCPs and endocrinologists indicated that concerns about patients’ ability to afford CGM were a main reason for prescribing traditional blood glucose monitoring. The majority of PCPs (69%) and endocrinologists (73%) who prescribe CGM prefer the durable medical equipment (DME) channel. Most PCPs (92%) and endocrinologists (91%) indicated they would likely prescribe CGM through the DME channel more often if those suppliers offered disease education and coaching. A large percentage of endocrinologists (76%) and a smaller percentage of PCPs (45%) reported feeling pressured by health insurers to prescribe through the pharmacy channel.

Conclusion: Clinician prescribing behaviors continue to be shaped by systemic barriers, including insurance pressures and concerns about patient ability/readiness to use CGM technology.

Am J Manag Care. 2026;32(9):In Press

doi:10.37765/ajmc.2026.90010

Takeaway Points

A survey of primary care physicians (PCPs) and endocrinologists identified current practice behaviors, attitudes, and obstacles shaping continuous glucose monitoring (CGM) prescribing practices.

• Most of these physicians are familiar with CGM and reported that it can be transformative for people with diabetes; however, cost concerns prompt most to prescribe traditional blood glucose monitoring instead.
• Although both clinician groups recognize the value of prescribing through durable medical equipment suppliers, most endocrinologists and many PCPs report feeling pressured by health insurers to prescribe CGM through the pharmacy channel.
• Clinician prescribing behaviors continue to be shaped by systemic barriers, including insurance pressures and concerns about patient ability and readiness to use CGM technology.

In 2021, an estimated 38.4 million Americans had diabetes.¹ By 2030, this number is expected to reach 54.9 million, with annual medical and societal costs increasing to more than $622 billion.²

Although early landmark trials demonstrated that achieving and maintaining near-normal glucose levels can prevent the development of many acute and chronic complications of diabetes,^3-6 only 50% of Americans are achieving their blood glucose targets.⁷ Suboptimal status is primarily driven by the failure of clinicians to escalate therapy when glycemic targets are not achieved within 3 to 6 months of treatment initiation, often referred to as therapeutic inertia.⁸ 

Several barriers to therapy intensification have been identified.^9-13 These include the perceptions of people with diabetes (PwDs) of medication efficacy, ability to afford treatment, medication adverse events, regimen complexity, and concerns about hypoglycemia.¹³ Other factors—such as a lack of appropriate education and training and diminished quality of life due to the burden of daily self-management regimens—can lead to nonadherence to prescribed treatment regimens and desired self-management behaviors.¹³

Clinicians are challenged by restrictions on time and resources, lack of training and education, management of PwDs’ comorbidities, concerns about hypoglycemia, and suboptimal adherence.¹³ Some studies show that inaccurate data and/or suboptimal adherence to prescribed glucose testing regimens can hinder a clinician’s ability to make informed treatment decisions, contributing to therapeutic inertia.^14-17 Studies have also shown that adherence to traditional blood glucose monitoring (BGM) is low.^10,18-21

Innovations in glucose monitoring over the past 20 years led to the development of continuous glucose monitoring (CGM). Unlike traditional BGM, CGM sensors automatically transmit a continuous stream of glucose data to the user’s smartphone or handheld reader. The data are displayed in graphical and numerical formats, showing the current glucose level, recent glycemic trends, and trend arrows that indicate the velocity and direction of changes in glucose levels. Most current CGM devices also feature programmable alarms that alert users when glucose is rising above or falling below established thresholds. CGM use has been shown to improve hemoglobin A_1c, reduce time spent in hypoglycemia, and lower the risk of emergency department visits and hospitalizations.^22-42

Although numerous studies have demonstrated the efficacy and safety of CGM use in individuals with insulin-treated and non–insulin-treated diabetes,⁴³ many clinicians are reluctant to prescribe this technology.⁴⁴ We report findings from a survey of primary care physicians (PCPs) and endocrinologists to identify current practices, attitudes, and barriers related to CGM use. The survey was structured to solicit information relevant to 5 specific aims: 1) clinician practice environments; 2) glucose monitoring preferences; 3) perceptions about the role and importance of comprehensive patient education and coaching; 4) channel preferences when prescribing CGM; and 5) information specific to each practice type.

METHODS

The objective of this US study was to identify the challenges and opportunities associated with CGM therapy, disease education, and health coaching among endocrinologists and PCPs who actively treat individuals with diabetes. Eligible respondents were licensed physicians (MD or DO) practicing in the US and were screened by specialty for the respective survey; respondents who did not meet these criteria were excluded. Participants were selected by PureSpectrum, an independent market research firm that engages with several database companies to target respondents in a specific industry or field. PureSpectrum used its proprietary platform, PureScore, to evaluate participants’ appropriateness, gather data, and analyze the results. The PureScore model is an advanced scoring system powered by machine learning and supported by third-party deduplication tools and a team of quality analysts. The model measures individual survey respondent quality by identifying patterns in profiles and behaviors, analyzing responses, and predicting each respondent’s behavior to evaluate their credibility, reliability, and consistency. The more that a respondent deviates from the ideal, the lower their PureScore. The clinicians were surveyed in December 2024 by practice type (endocrinologist or PCP) using structured phone interviews guided by survey questionnaires. Respondents with a failing PureScore were eliminated from the survey. The average length of the interview was less than 10 minutes.

Measures

Demographic and clinical practice characteristics. Participants provided personal demographics (sex, age, provider type) and practice characteristics (location, urbanicity, median income range of postal code).

Aim 1: Clinician practice environment. We used 9 items to obtain additional information about clinicians’ practice environments, including PwDs’ demographics and clinician burden related to providing education, training, and coaching to PwDs.

Aim 2: Glucose monitoring preferences. We used 5 items to better understand clinician attitudes toward CGM in their practices.

Aim 3: Perceptions about patient education and coaching. We used 9 items to gather information on clinicians’ attitudes toward the role of diabetes management education and training.

Aim 4: CGM prescribing behaviors. We used 4 items to learn clinician preferences for prescribing CGM.

Aim 5: Practice-specific perceptions and behaviors. We used 7 items for PCPs to assess their attitudes toward and experiences with referrals to an endocrinologist. We used 8 items to assess endocrinologists’ views on their role within the diabetes care team relevant to PCPs and PwDs’ access to care.

Statistical Analysis

All data are presented as percentages of respondents in each clinician group who answered each question. Additional statistical analysis to determine statistical significance and any correlations was not necessary given the objective of the survey.

RESULTS

Survey responses from 101 PCPs and 106 endocrinologists were obtained for analysis. The PCP cohort showed relatively well-balanced age and sex distribution, whereas the endocrinologist cohort was predominantly male and younger. Geographic location, urbanicity, and PwDs’ income were similar between groups.

Aim 1: Clinician Practice Environment

Fifty-one percent of endocrinologists estimated that 31% to 50% of their patient population had diabetes, whereas 57% of PCPs estimated that 21% to 40% of their patient population had diabetes. Most PCPs’ and endocrinologists’ PwDs resided in urban communities, followed by suburban and rural communities; this pattern was more pronounced among endocrinologists, whose PwD populations were more concentrated in urban areas (65% vs 46% for PCPs) and less so in rural areas (5% vs 14% for PCPs). A large percentage of PCPs and most endocrinologists reported that their PwDs come from underserved communities. Most PCPs and endocrinologists reported an increasing PwD population over the last 2 years, with approximately one-third of all clinicians reporting that it had “significantly increased.” (Table 1). PCPs reported that nearly three-quarters of their PwDs have type 2 diabetes, whereas endocrinologists reported a relatively even percentage of patients with type 1 and type 2 diabetes. The majority also reported observing “signs of despair” in their PwDs.

Many PCPs and most endocrinologists noted feeling overwhelmed by the increased volume of PwDs in their practices, and the majority of clinicians in both groups reported spending more time training and educating themselves to better treat PwDs. This extra time was cited by the majority of both PCPs and endocrinologists as a direct contributor to their professional burnout.

Aim 2: Glucose Monitoring Preferences

Levels of familiarity with the newest CGM technologies were relatively consistent among PCPs and endocrinologists (Table 2). Although both groups tended to agree that use of CGM can be transformative for their PwDs, less than half of PCPs and endocrinologists indicated that they were “most likely” to recommend CGM over BGM—a pattern inconsistent with their stated views on the value of CGM. Notably, a higher percentage of endocrinologists cited CGM complexity and PwDs’ “lack of need” as reasons for not recommending CGM. Both groups indicated that affordability remained a key reason. Additionally, PCPs tended to have lower percentages of insulin-treated PwDs using CGM.

Aim 3: Perceptions About Patient Education and Coaching

Although both groups reported that their offices provide ongoing health or condition education support, almost twice as many PCPs as endocrinologists (35% vs 18%, respectively) reported that PwDs lacked adequate education to properly manage their condition (Table 3). A large majority of clinicians in both groups also reported that without additional education and coaching support between clinic visits, their PwDs would not understand how to use their CGM device and/or interpret their data. The majority of endocrinologists felt that lack of education and coaching support would impact their PwDs’ ability to modify their behavior, whereas the majority of PCPs felt it would lead to poorer health outcomes. Both clinician groups reported that they believe a lack of knowledge about diabetes and self-management education can negatively impact treatment adherence. However, the 2 groups differed on which type of adherence—medication, CGM, or both—they believed was most affected by inadequate education and coaching, with PCPs most often citing CGM adherence and endocrinologists most often citing medication adherence.

Aim 4: CGM Prescribing Behaviors

Most clinicians reported that they would be most likely to prescribe CGM through a durable medical equipment (DME) supplier, particularly if the supplier provided disease-state health education and coaching (Table 4). The majority also felt that where their PwDs obtain their CGM device (DME supplier vs pharmacy) can affect health outcomes (90% of PCPs and 86% of endocrinologists) (Table 4). Nearly half of PCPs (45%) and a large majority of endocrinologists (76%) reported that they have received pressure from health insurers to prescribe CGM through the pharmacy vs a DME supplier.

Aim 5: Items Specific to Practice Type

PCPs. Many PCPs understand that they provide only part of a comprehensive care strategy and view endocrinologists as key members of a PwD’s care team. The majority of PCPs agreed that all PwDs using CGM should see a specialist always or sometimes for at least part of their care (Table 5). PCPs reported that PwDs seek care from them when they are unable to obtain an appointment with a specialist, with 36% reporting that this occurs for most of these patients. In a separate question, 79% of PCPs reported being significantly or somewhat frustrated when this occurs. Nearly half reported that most or all of their PwDs are seeing them for diabetes by default.

When asked to speculate why PwDs are struggling to get appointments, PCPs most commonly cited PwDs prioritizing specialist care for other comorbidities (63%) and an insufficient number of specialists in PwDs' communities (52%). Among the PCPs who indicated frustration with having to fill the gap left by specialists, most reported investing in additional patient resources and/or engaging in partnerships with external vendors and almost half reported having to add staff.

Endocrinologists. The majority of endocrinologists reported that all/nearly all or most PwDs are regularly seeing a PCP as part of their care but that all PwDs should also be seeing a specialist (Table 5). Interestingly, a large percentage of endocrinologists felt that PCPs provide an equal level of care. However, among the 14 endocrinologists who disagreed, 8 cited lack of disease education support as their main reason. Most endocrinologists were accepting new PwDs; however, wait times ranged from “within a week” to “more than 8 weeks,” and most reported that their PwDs have expressed frustration about accessing care from an endocrinologist.

DISCUSSION

Findings from our survey revealed both similarities and distinct differences between the clinician groups. Whereas a high percentage of both PCPs and endocrinologists reported a significant or somewhat significant increase in the volume of PwDs, a lower percentage of PCPs than endocrinologists (23% vs 44%, respectively) estimate that more than 40% of their patients have diabetes. This may suggest underrecognition or underdiagnosis in primary care. Both clinician groups reported spending more time educating or training themselves on treating PwDs than on treating patients with other chronic conditions and that this has contributed to professional burnout.

Although a high percentage of both clinician groups reported that CGM can be “transformative” for PwDs, a higher percentage of PCPs (49%) than endocrinologists (34%) were most likely to recommend CGM to their PwDs. Among those who were unlikely to recommend CGM, the technology’s complexity and affordability were the most common reasons. Although 86% of both PCPs and endocrinologists believe that ongoing health education and coaching support are crucial for therapy adherence and outcomes, they differ somewhat on whether a lack of health education and coaching affects medication adherence more than CGM adherence.

Additionally, nearly twice as many endocrinologists (54%) as PCPs (28%) reported that they believe CGM has little value to PwDs. This level of clinical skepticism among specialists—who are typically the most familiar with diabetes technology—may reflect ongoing concerns around cost-effectiveness, overprescription, or patient burden. It also suggests a potential misalignment between the real-world benefits of CGM and how those benefits are perceived in everyday practice. It may be worth exploring whether this perception is influenced by outdated evidence, workflow limitations, or limited experience with newer, simpler CGM options.

A large majority of PCPs (92%) believe PwDs should see a specialist for at least part of their care. A similarly large majority of PCPs (84%) felt frustrated because most of their PwDs sought diabetes care through primary practice because these PwDs were unable to get an endocrinologist appointment. Similarly, 83% of endocrinologists reported that PwDs had expressed frustration about accessing endocrinology care, with reported appointment wait times ranging from within a week to more than 8 weeks. The majority of both clinician groups indicated they would be most likely to prescribe CGM through a DME supplier if the supplier also provided disease state education and patient coaching.

The pressure to prescribe through the pharmacy channel—felt by 76% of endocrinologists and 45% of PCPs in our survey—stems from efforts to reduce costs by leveraging established pharmacy benefit structures, which can lead to lower administrative costs and more accurate reimbursement.⁴⁵ However, the initial savings realized through the pharmacy channel must be weighed against the total cost when CGM adherence is suboptimal. Although studies have clearly demonstrated the benefits of CGM, outcomes can only improve if PwDs use this technology.^27,44 Previous studies have shown that suboptimal adherence or nonadherence to CGM is associated with adverse health outcomes and increased costs due to less consistent glycemic control.^44,46,47 Moreover, it has been estimated that approximately 25% of CGM supply expenditures are wasted during the first year after an initial order because supplies are purchased but not used.⁴⁸

Allaire et al found that ordering CGM supplies through a DME company that offers disease management education and monitoring also increases adherence to reordering and improves glycemic control while reducing health care costs; mean annual medical costs for adherent PwDs in the pharmacy cohort were 53% higher than in the DME cohort ($10,635 vs $6967; P < .001).⁴⁹

These findings highlight a growing opportunity for DME suppliers—traditionally focused on device fulfillment—to expand their role in chronic disease management. Some DME suppliers have begun incorporating patient education, training, and remote monitoring into their service models. This shift enables the integration of digital health tools, including machine learning (ML), for predictive modeling. Recent studies have shown that predictive modeling is effective at stratifying PwDs by adherence risk, enabling care teams to proactively prioritize outreach and tailor support.^50-53 Such strategies exemplify how artificial intelligence–driven approaches can optimize limited clinical resources while improving patient engagement and long-term outcomes.

Beyond adherence, ML has been applied to predict glycemic control patterns, medication refill behaviors among insulin-naive PwDs, and treatment response based on individualized risk profiles.^51-53 These applications further support the potential for DME suppliers, particularly those integrating clinical services and data analytics, to contribute meaningfully to multidisciplinary diabetes care.

Limitations

Several limitations should be considered. First, the data were collected through structured interviews with a relatively small sample of clinicians (N = 207) and may not reflect the perspectives of all US primary care physicians and endocrinologists. Although efforts were made to ensure geographic and demographic diversity, the sample may still be subject to selection bias based on willingness to participate or availability. Second, responses were self-reported and may be influenced by social desirability or recall bias. Clinicians may have overestimated or underestimated their knowledge, attitudes, or prescribing behaviors related to CGM, diabetes education, and patient engagement. Third, the survey did not include direct patient perspectives, which limits insight into how clinician-reported practices align with patient experiences or outcomes. Fourth, the survey did not capture information about the importance or impact of industry-sponsored education and training programs offered through online platforms or through community health sponsored/supported programs, including church-based wellness ministries. Further studies incorporating patient-reported outcomes would enhance understanding of real-world effectiveness. Fifth, the use of a proprietary sampling method may limit the reproducibility and generalizability of findings. Finally, this study reflects a single time point. Given the rapidly evolving landscape of CGM technology, digital health tools, and payer policies, clinician perspectives may shift over time. These findings provide a timely snapshot of current clinical attitudes and prescribing behaviors; however, as the diabetes care landscape continues to evolve, further longitudinal and mixed-methods research will be important to deepen our understanding and refine future care models.

CONCLUSIONS

Despite strong clinical evidence supporting the benefits of CGM, especially in improving glycemic outcomes,^26,36,37 CGM adoption remains variable across practice settings and therapy types. Findings from this study highlight that both PCPs and endocrinologists recognize CGM as a transformative tool, yet their prescribing behaviors continue to be shaped by systemic barriers, including insurance pressures, concerns about patient readiness, and perceived complexity of CGM technology.

Importantly, the results underscore the central role of patient education and coaching in supporting effective CGM use. Clinicians widely agreed that without ongoing education between visits, PwDs may struggle to interpret CGM data or act on it, leading to reduced adherence, poorer outcomes, and higher downstream costs. The growing interest in digital health programs and DME suppliers that offer education and clinical monitoring services signals a shift toward more integrated care models. When CGM fulfillment is coupled with personalized education and support, clinicians are more likely to prescribe through DME channels, suggesting a clear opportunity to improve alignment between access and outcomes.

Author Affiliations: CCS Medical (CD, SK), St Petersburg, FL; South Piedmont Area Health Education Center (EW), Charlotte, NC; Emory University School of Medicine (JRG), Atlanta, GA; CGParkin Communications, Inc (CGP), Henderson, NV; University of California San Diego (SVE), San Diego, CA; and Taking Control of Your Diabetes (SVE), San Diego, CA.

Source of Funding: CCS Medical provided funding for this study.

Author Disclosures: Dr Wright reports consultancies or paid advisory board participation for Abbott Diabetes Care, AstraZeneca, Bayer AG, Boehringer Ingelheim, Eli Lilly and Company, MannKind Corporation, Merck, Sanofi US, and Voluntis and acting as a speaker for Abbott, Bayer, Boehringer Ingelheim, and Eli Lilly and Company. Dr Dennis and Ms Kohn report employment at CCS Medical. Dr Gavin reports consultancies or paid advisory board participation for Abbott Diabetes Care, Novo Nordisk, Medtronic, and Boehringer Ingelheim. Mr Parkin reports consultancies or paid advisory board participation for Abbott Diabetes Care, CeQur, Dexcom Inc, Tandem Diabetes Care, Sequel Med Tech LLC, CCS Medical, and Roche Diagnostics. Dr Edelman reports consultancies or paid advisory board participation for Senseonics Holdings Inc, Eli Lilly and Company, MannKind Corporation, Novo Nordisk, Sanofi US, Merck, and AstraZeneca.

Authorship Information: Concept and design (EW, CD, SK, JRG, SVE); acquisition of data (CD, SK); analysis and interpretation of data (EW, CD, SK, JRG, CGP, SVE); drafting of the manuscript (EW, CD, SK, JRG, CGP, SVE); critical revision of the manuscript for important intellectual content (EW, CD, SK, JRG, CGP, SVE); obtaining funding (CD, SK); and administrative, technical, or logistic support (CD, SK).

Address Correspondence to: Consuela (Coni) Dennis, DNP, RN, CCS Medical, 161 Fountain Parkway N, St Petersburg, FL 33716. Email: coni.dennis@ccsmed.com.

REFERENCES

1. National Diabetes Statistics Report: estimates of diabetes and its burden in the United States. CDC. November 29, 2023. Accessed May 15, 2025. https://stacks.cdc.gov/view/cdc/148231/cdc_148231_DS1.pdf

2. Rowley WR, Bezold C, Arikan Y, Byrne E, Krohe S. Diabetes 2030: insights from yesterday, today, and future trends. Popul Health Manag. 2017;20(1):6-12. doi:10.1089/pop.2015.0181

3. Nathan DM, Cleary PA, Backlund JY, et al; Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study Research Group. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med. 2005;353(25):2643-2653. doi:10.1056/NEJMoa052187

4. Nathan DM, Genuth S, Lachin J, et al; Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications of insulin-dependent diabetes mellitus. N Engl J Med. 1993;329(14):977-986. doi:10.1056/NEJM199309303291401

5. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998;352(9131):837-853.

6. Holman RR, Paul SK, Bethel A, Matthews DR, Neil HAW. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359(15):1577-1589. doi:10.1056/NEJMoa0806470

7. Fang M, Wang D, Coresh J, Selvin E. Trends in diabetes treatment and control in U.S. adults, 1999-2018. N Engl J Med. 2021;384(23):2219-2228. doi:10.1056/NEJMsa2032271

8. Davies MJ, D’Alessio DA, Fradkin J, et al. Management of hyperglycaemia in type 2 diabetes, 2018. a consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2018;61(12):2461-2498. doi:10.1007/s00125-018-4729-5

9. Zafar A, Stone MA, Davies MJ, Khunti K. Acknowledging and allocating responsibility for clinical inertia in the management of type 2 diabetes in primary care: a qualitative study. Diabet Med. 2015;32(3):407-413. doi:10.1111/dme.12592

10. Peyrot M, Rubin RR, Lauritzen T, Snoek FJ, Matthews DR, Skovlund SE. Psychosocial problems and barriers to improved diabetes management: results of the Cross-National Diabetes Attitudes, Wishes and Needs (DAWN) Study. Diabet Med. 2005;22(10):1379-1385. doi:10.1111/j.1464-5491.2005.01644.x

11. Elgrably F, Costagliola D, Chwalow AJ, Varenne P, Slama G, Tchobroutsky G. Initiation of insulin treatment after 70 years of age: patient status 2 years later. Diabet Med. 1991;8(8):773-777. doi:10.1111/j.1464-5491.1991.tb01699.x

12. Kunt T, Snoek FJ. Barriers to insulin initiation and intensification and how to overcome them. Int J Clin Pract. 2009;(164):6-10. doi:10.1111/j.1742-1241.2009.02176.x

13. Karam SL, Dendy J, Polu S, Blonde L. Overview of therapeutic inertia in diabetes: prevalence, causes, and consequences. Diabetes Spectr. 2020;33(1):8-15. doi:10.2337/ds19-0029

14. Hirsch IB. Blood glucose monitoring technology: translating data into practice. Endocr Pract. 2004;10(1):67-76. doi:10.4158/EP.10.1.67

15. Given JE, O’Kane MJ, Bunting BP, Coates VE. Comparing patient-generated blood glucose diary records with meter memory in diabetes: a systematic review. Diabet Med. 2013;30(8):901-913. doi:10.1111/dme.12130

16. Kazlauskaite R, Soni S, Evans AT, Graham K, Fisher B. Accuracy of self-monitored blood glucose in type 2 diabetes. Diabetes Technol Ther. 2009;11(6):385-392. doi:10.1089/dia.2008.0111

17. Ward JEF, Stetson BA, Mokshagundam SPL. Patient perspectives on self-monitoring of blood glucose: perceived recommendations, behaviors and barriers in a clinic sample of adults with type 2 diabetes. J Diabetes Metab Disord. 2015;14:43. doi:10.1186/s40200-015-0172-z

18. Karter AJ, Ackerson LM, Darbinian JA, et al. Self-monitoring of blood glucose levels and glycemic
control: the Northern California Kaiser Permanente Diabetes registry. Am J Med. 2001;111(1):1-9.
doi:10.1016/s0002-9343(01)00742-2

19. Moström P, Ahlén E, Imberg H, Hansson PO, Lind M. Adherence of self-monitoring of blood glucose in persons with type 1 diabetes in Sweden. BMJ Open Diabetes Res Care. 2017;5(1):e000342. doi:10.1136/bmjdrc-2016-000342

20. Puckrein GA,Hirsch IB, Parkin CG, et al. Assessment of glucose monitoring adherence in Medicare beneficiaries with insulin-treated diabetes. Diabetes Technol Ther. 2023;25(1):31-38. doi:10.1089/dia.2022.0377

21. Siddiqui MH, Khan IA, Moyeen F, Chaudhary KA. Identifying barriers to therapeutic adherence in type 2-diabetes: a complex and multidimensional clinical issue. Asp Biomed Clin Case Rep. 2019;2(1):22-28. doi:10.36502/2019/ASJBCCR.6149

22. Garg SK, Hirsch IB, Repetto E, et al. Impact of continuous glucose monitoring on hospitalizations and glucose control in people with type 2 diabetes: real-world analysis. Diabetes Obes Metab. 2024;26(11):5202-5210. doi:10.1111/dom.15866

23. Hirsch IB, Burugapalli BS, Brandner L, et al. Impact of continuous glucose monitoring on emergency department visits and all-cause hospitalization rates among Medicaid beneficiaries with type 2 diabetes treated with multiple daily insulin or basal insulin therapy. J Manag Care Spec Pharm. 2024;30(suppl 10-b):S21-S29. doi:10.18553/jmcp.2024.30.10-b.s21

24. Davis G, Bailey R, Calhoun P, Price D, Beck RW. Magnitude of glycemic improvement in patients with type 2 diabetes treated with basal insulin: subgroup analyses from the MOBILE study. Diabetes Technol Ther. 2022;24(5):324-331. doi:10.1089/dia.2021.0489

25. Bao S, Bailey R, Calhoun P, Beck RW. Effectiveness of continuous glucose monitoring in older adults with type 2 diabetes treated with basal insulin. Diabetes Technol Ther. 2022;24(5):299-306. doi:10.1089/dia.2021.0494

26. Martens T, Beck RW, Bailey R, et al; MOBILE Study Group. 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(22):2262-2272. doi:10.1001/jama.2021.7444

27. Aleppo G, Beck RW, Bailey R, et al; MOBILE Study Group; Type 2 Diabetes Basal Insulin Users: The Mobile Study (MOBILE) Study Group. The effect of discontinuing continuous glucose monitoring in adults with type 2 diabetes treated with basal insulin. Diabetes Care. 2021;44(12):2729-2737. doi:10.2337/dc21-1304

28. Welsh J, Grace T. Rapid HbA1c reductions in patients with type 2 diabetes in a community setting following initiation of real-time continuous glucose monitoring. Diabetes Technol Ther. 2023;25(suppl 2):A-150. doi:10.1089/dia.2023.2525.abstracts

29. Crawford MA, Chernavvsky DR, Barnard K, et al. 669-P: lower peak glucose and increased time in range (TIR) in a CGM-wearing T2D population not taking fast-acting insulin shows value of real time–CGM (rtCGM) as a behavior change tool. Diabetes. 2022;71(suppl 1):669-P. doi:10.2337/db22-669-P

30. Norman GJ, Paudel ML, Parkin CG, Bancroft T, Lynch PM. Association between real-time continuous glucose monitor use and diabetes-related medical costs for patients with type 2 diabetes. Diabetes Technol Ther. 2022;24(7):520-524. doi:10.1089/dia.2021.0525

31. Shields S, Norman GJ, Ciemins EL. 687-P: changes in HbA1c after initiating real-time continuous glucose monitoring (rtCGM) for primary care patients with type 2 diabetes. Diabetes. 2022;71(suppl 1):687-P. doi:10.2337/db22-687-P

32. Carlson AL, Daniel TD, DeSantis A, et al. Flash glucose monitoring in type 2 diabetes managed with basal insulin in the USA: a retrospective real-world chart review study and meta-analysis. BMJ Open Diabetes Res Care. 2022;10(1):e002590. doi:10.1136/bmjdrc-2021-002590

33. Elliott T, Beca S, Beharry R, Tsoukas MA, Zarruk A, Abitbol A. The impact of flash glucose monitoring on glycated hemoglobin in type 2 diabetes managed with basal insulin in Canada: a retrospective real-world chart review study. Diab Vasc Dis Res. 2021;18(4):14791641211021374. doi:10.1177/14791641211021374

34. Wright EE Jr, Kerr MSD, Reyes IJ, Nabutovsky Y, Miller E. Use of flash continuous glucose monitoring is associated with A1C reduction in people with type 2 diabetes treated with basal insulin or noninsulin therapy. Diabetes Spectr. 2021;34(2):184-189.doi:10.2337/ds20-0069

35. Norman GJ, Paudel ML, Bancroft T, Lynch PM. 77-LB: a retrospective analysis of the association between HbA1c and continuous glucose monitor use for U.S. patients with type 2 diabetes. Diabetes. 2021;70(suppl 1):77-LB. doi:10.2337/db21-77-LB

36. Beck RW, Riddlesworth T, Ruedy K, et al; DIAMOND Study Group. Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections: the DIAMOND randomized clinical trial. JAMA. 2017;317(4):371-378. doi:10.1001/jama.2016.19975

37. Beck RW, Riddlesworth TD, Ruedy K, et al; DIAMOND Study Group. 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(6):365-374. doi:10.7326/M16-2855

38. Haak T, Hanaire H, Ajjan R, Hermanns N, Riveline JP, 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

39. Šoupal J, Petruželková L, Grunberger G, et al. Glycemic outcomes in adults with T1D are impacted more by continuous glucose monitoring than by insulin delivery method: 3 years of follow-up from the COMISAIR Study. Diabetes Care. 2020;43(1):37-43. doi:10.2337/dc19-0888

40. Guerci B, Roussel R, Levrat-Guillen F, et al. Important decrease in hospitalizations for acute diabetes events following FreeStyle Libre System initiation in people with type 2 diabetes on basal insulin therapy in France. Diabetes Technol Ther. 2023;25(1):20-30. doi:10.1089/dia.2022.0271

41. Miller E, Kerr MSD, Roberts GJ, Nabutovsky Y, Wright E. Flash CGM associated with event reduction in nonintensive diabetes therapy. Am J Manag Care. 2021;27(11):e372-e377. doi:10.37765/ajmc.2021.88780

42. Frank JR, Blissett D, Hellmund R, Virdi N. Budget impact of the flash continuous glucose monitoring system in Medicaid diabetes beneficiaries treated with intensive insulin therapy. Diabetes Technol Ther. 2021;23(suppl 3):S36-S44. doi:10.1089/dia.2021.0263

43. Aleppo G, Hirsch IB, Parkin CG, et al. Coverage for continuous glucose monitoring for individuals with type 2 diabetes treated with nonintensive therapies: an evidence-based approach to policymaking. Diabetes Technol Ther. 2023;25(10):741-751. doi:10.1089/dia.2023.0268

44. Nemlekar PM, Hannah KL, Green CR, Norman GJ. Association between adherence, A1C improvement, and type of continuous glucose monitoring system in people with type 1 diabetes or type 2 diabetes treated with intensive insulin therapy. Diabetes Ther. 2024;15(3):639-648. doi:10.1007/s13300-023-01529-8

45. Hurteau LB, Hogue NJ. Pharmacy benefit considerations for continuous glucose monitoring acquisition: Vermont Medicaid experience. J Manag Care Spec Pharm. 2023;29(5):550-556. doi:10.18553/jmcp.2023.29.5.550

46. Bronstone A, Graham C. The potential cost implications of averting severe hypoglycemic events requiring hospitalization in high-risk adults with type 1 diabetes using real-time continuous glucose monitoring. J Diabetes Sci Technol. 2016;10(4):905-913. doi:10.1177/1932296816633233

47. Addala A, Maahs DM, Scheinker D, Chertow S, Leverenz B, Prahalad P. Uninterrupted continuous glucose monitoring access is associated with a decrease in HbA1c in youth with type 1 diabetes and public insurance. Pediatr Diabetes. 2020;21(7):1301-1309. doi:10.1111/pedi.13082

48. 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(6):420-427. doi:10.1089/dia.2017.0435

49. Allaire JC, Dennis C, Masturzo A, Wittlin S. Exploring the impact of device sourcing on real-world adherence and cost implications of continuous glucose monitoring in patients with diabetes: retrospective claims analysis. JMIR Diabetes. 2024;9:e58832. doi:10.2196/58832

50. Mackey R, Kattabomman A, Cantor K, Masturzo A. Improving adherence with continuous glucose monitoring: a risk profile-based digital intervention program. Health Affairs. October 6, 2024. Accessed October 20, 2024. https://web.archive.org/web/20241007014030/https://www.healthaffairs.org/sponsored-content/improving-adherence-with-continuous-glucose-monitoring-a-risk-profile-based-digital-intervention-program

51. Cheng YL, Wu YR, Lin KD, Lin CHR, Lin IM. Using machine learning for the risk factors
classification of glycemic control in type 2 diabetes mellitus. Healthcare (Basel). 2023;11(8):1141.
doi:10.3390/healthcare11081141

52. Thyde DN, Mohebbi A, Bengtsson H, Jensen ML, Mørup M. Machine learning-based adherence detection of type 2 diabetes patients on once-daily basal insulin injections. J Diabetes Sci Technol. 2021;15(1):98-108. doi:10.1177/1932296820912411

53. Zhou X, Luo Y, Huang Q, et al. Differential effect of interventions in patients with prediabetes stratified by a machine learning-based diabetes progression prediction model. Diabetes Obes Metab. 2024;26(1):97-107. doi:10.1111/dom.15291