Personalized Virtual Care Using Continuous Glucose Monitoring in Adults With Type 2 Diabetes Treated With Less Intensive Therapies Free

Large database analyses have demonstrated strong associations between CGM use and significant reductions in A1C, acute diabetes events, and diabetes- related hospitalizations in people with diabetes treated with basal insulin only or noninsulin medications (38,39). Although these types of studies have limitations, their findings are supported by one randomized controlled trial (40) and smaller observational cohort studies (18,19,23,41).

In a large retrospective database study of 1,034 adults with nonintensively treated type 2 diabetes, Wright et al. (38) reported a significant A1C reduction (from 10.1% to 8.6%, P <0.001) with the use of CGM. Importantly, individuals treated with noninsulin medications showed a greater A1C reduction compared with those treated with basal insulin only (−1.6 vs. −1.1%, respectively, P <0.001).

A similar database analysis by Miller et al. (39) reported significant reductions in acute diabetes events (ADEs) and all-cause hospitalizations (ACHs) among 10,282 adults with nonintensively treated type 2 diabetes using CGM. As reported, the ADE rate decreased from 0.076 to 0.052 events per patient-year (P <0.001). The rate of ACHs decreased from 0.177 to 0.151 events per patient-year (P = 0.002) during the 6 months after acquisition of a CGM system.

In the randomized, controlled MOBILE study, Martens et al. (40) followed 175 adults with type 2 diabetes treated with basal insulin with or without noninsulin medications who were randomized to CGM or BGM. Mean baseline A1C was 9.1%. At 8 months, A1C decreased 1.1% in the CGM group compared with a 0.5% reduction in the BGM group (P = 0.02). The mean percentage of time in the established target glucose range (time in range [TIR]) of 70–180 mg/dL (42) was 59% with CGM versus 43% with BGM. Although neither group achieved the recommended goal of >70% TIR, the difference was significant (P <0.001).

More recently, Grace and Salyer (41) reported findings from a 6-month, prospective, interventional, single-arm study of 38 adults with type 2 diabetes and a mean baseline A1C of 10.1%. Twenty-two (58%) were treated with noninsulin medications, and 16 were treated with basal insulin with or without noninsulin medications. At 6 months, mean A1C decreased to 7.3% (P <0.001), with significant reductions in body weight (−3.1 kg, P = 0.002) and significant increases in TIR, from 57.0% at baseline to 72.2% at study end.

Persistent CGM use (>6 days/week) is generally recommended for individuals treated with intensive insulin regimens (42). However, this level of CGM use may not be necessary for those treated with less intensive regimens.

A recent observational study of 594 adults with type 2 diabetes who were enrolled in the Onduo Virtual Diabetes Care (VDC) clinic found that this cohort achieved significant A1C reductions (mean 0.6%, P <0.001) with intermittent use of CGM supported by coaching and clinical advice delivery via telehealth visits (23). Survey results revealed that 94.7% of respondents agreed or strongly agreed that they were comfortable inserting the sensor via remote training, and the majority agreed or strongly agreed that intermittent use of CGM improved their understanding of the impact of eating (97.0%), increased their diabetes knowledge (95.7%), and helped them improve their diabetes control when not wearing the CGM sensor (79.4%). Additional surveys of the Onduo cohort have found similar glycemic improvements (18,43), and one study by Polonsky et al. (19) showed associations between participation in the VDC and reductions in diabetes-related distress.

Carbon Health designed its endocrinologist-led program to integrate with primary care physicians (PCPs) throughout its primary care and urgent care (UC) network. The program uses virtual telehealth visits, telemonitoring of CGM data, nutrition and physical activity counseling, and support in making other lifestyle changes through a completely virtual platform. A specialized care team of endocrinologists and diabetes care and education specialists (DCESs) interprets the data and creates individualized treatment plans optimized for each patient’s health status, lifestyle, and preferences. The digital delivery of this care provides patients with greater accessibility, with no waiting or travel time involved. The following case examples demonstrate how we used this approach to address the needs of two patients with newly diagnosed type 2 diabetes.

At presentation, patient 1 was a 56-year-old man with a BMI of 31.2 kg/m², weight of 97.8 kg, fasting glucose of 431 mg/dL, and blood pressure of 136/78 mmHg. On 1 November 2021, the patient presented to his PCP with fatigue and polyuria. Laboratory results showed that his A1C was 12.9%. The PCP provided basic education on diabetes management, started the patient on metformin 1,000 mg once daily, and prescribed CGM, which was initiated the next day with an intermittently scanned CGM system.

On 16 November, he returned to his PCP for follow-up. His blood pressure was significantly lower (110/66 mmHg) and his albumin-to-creatinine ratio was normal. The patient explained that he was no longer consuming sugar, had switched to a low-carbohydrate diet (≤35 g at each meal), and was exercising almost daily. He indicated that he did not want to do fingerstick BGM testing and liked being able to see his glucose levels at any time with CGM. His PCP increased his metformin dose to 1,000 mg twice daily; however, the patient declined treatment with an angiotensin receptor blocker for high blood pressure or a statin for elevated lipids.

His CGM glucose profiles revealed notable improvements in glycemic control during the first 4 days of sensor wear (Figure 1A), which corresponded with daily progressive reductions in average glucose of 275, 227, 180, and 167 mg/dL on 2–5 November, respectively. These improvements continued throughout the next week (Figure 1B). During the same period, his TIR increased from 63.2 to 99.4%.

Additional history was obtained when the patient met with the endocrinologist on 30 November 2021 in a telehealth visit. The endocrinologist learned that the patient had been “addicted to soda pop” (drinking six bottles per day) and had his glucose checked annually because, he said, he “knew he would get diabetes some day.” The patient explained that he had given away all of his candy and kept only one can of sugar-sweetened soft drink for possible episodes of low glucose. Additionally, the patient said he had stayed at a health and wellness retreat and met with a nutritionist and physician to learn how to eat more healthfully. The patient also expressed interest in using health-related technologies and wears a sleep and fitness tracking ring, which provides personalized health metrics. Laboratory results obtained in February 2022 revealed that the patient’s A1C had decreased from 12.9% to 6.7%.

At presentation, patient 2 was a 54-year-old man with a BMI of 24.2 kg/m² and weight of 63.5 kg. On 6 April 2021, he presented at a Carbon Health UC clinic complaining of pain in his left hand with cramping and numbness, which had started 3 weeks before the visit. The patient also described symptoms of polyuria and polydipsia. Random glucose was 467 mg/dL. The UC provider scheduled a virtual telehealth visit with the endocrinologist for the next day.

On 7 April, the endocrinologist provided education on the pathophysiology of diabetes and appropriate lifestyle changes and prescribed the patient CGM. Laboratory results showed that the patient’s A1C was 14.5%. The patient was started on metformin 750 mg extended release (ER) once daily and insulin glargine 15 units (0.24 units/kg) once daily to treat the glucotoxicity. A telehealth visit with the DCES was scheduled for the next day.

At that visit, the DCES reviewed proper insulin injection technique, instructed the patient on CGM use and sensor placement, and provided additional nutrition guidance. The patient started an intermittently scanned CGM system on 11 April.

At the follow-up visit 4 days later, the patient reported that he had discontinued the insulin on 11 April. As shown in Figure 2, the patient’s glucose profile showed significant improvement, with 99% of CGM values within the target range.

Throughout April, the DCES kept in touch with the patient through frequent messaging and provided patient-appropriate articles about various aspects of diabetes self-management. At a follow-up visit with the endocrinologist on 1 August, the patient’s A1C was 5.8%, a decrease of 8.7% from his initial visit.

Through use of telehealth visits in conjunction with CGM data, our diabetes program has been effective in improving glycemic outcomes in our patients with type 2 diabetes. Our patients with a baseline A1C >9.0% have achieved an average 4.3% reduction through participation in our program. Our approach allows us to provide comprehensive, personalized diabetes care that is timely and efficient for both patients and our health care team. Whereas the average wait time for an initial nonurgent endocrinologist consultation is 37 days (44), patient 2 was able to meet virtually with an endocrinologist 1 day after being seen by the UC provider. Importantly, our approach facilitates personalized care in a way that allows patients to engage with their self-management.

With patient 1, engagement was immediate. Although initiating metformin effectively lowered his overall glucose levels throughout the day, the notable reductions in glycemic spikes and rapid improvement in TIR suggests that he quickly recognized the causes and effects of his eating pattern and physical activity level and responded appropriately with significant lifestyle changes. Moreover, given his significant improvement in overall diabetes management and his affinity for using health technologies, it is likely that he will persist in using CGM.

With patient 2, we recognized his need for ongoing follow-up, education, and reassurance. We were able to provide this with two telehealth visits with the DCES and frequent messaging during his first month of diabetes care, which reinforced his successes and kept him engaged with his diabetes self-management.

Use of CGM data is an essential component of our approach to care. As we have seen with our patients, CGM facilitates their understanding of diabetes and promotes needed changes in their lifestyle behaviors in addition to providing our team with meaningful information to guide therapy decisions. The patients described here traditionally would have required multiple medications and/or insulin, but with the use of this care model, they achieved optimal glycemic control within 2 weeks with metformin alone by incorporating CGM into their regimens.

Based on our experiences, we believe that CGM should be an integral part of the treatment algorithm for individuals with type 2 diabetes regardless of their treatment regimen. Unfortunately, many individuals with diabetes who are treated with nonintensive therapies are denied access to CGM because of overly restrictive insurance coverage policies. Given the significant and increasing worldwide prevalence of type 2 diabetes, it only makes sense that current and future diabetes technologies be made available to all people with diabetes regardless of type and therapy.

The authors thank Christopher G. Parkin, MS, of CGParkin Communications, Inc., for his assistance in developing this article.

Abbott Diabetes Care provided funding for the development of this article.

S.R. receives speaker fees from Dexcom. J.L.H. receives speaker fees from Dexcom and Eli Lilly. No other potential conflicts of interest relevant to this article were reported.

S.R. and C.C.W. conceptualized the article and contributed to writing, reviewing, and editing the manuscript. All authors curated the data. S.R. is the guarantor of this work and, as such, takes responsibility for the integrity of the data and the accuracy of the information included in the article.