IN BRIEF In this article, the authors discuss several innovative concepts UnitedHealth Group Research & Development is exploring to help patients manage their type 2 diabetes. The article focuses on efforts to use remote support programs and wearable technology to empower patients to take more active roles in managing their health and to foster more interactive patient-provider conversations. Additionally, the authors reflect on how such efforts could particularly benefit medically underserved communities. They offer observations from claims data about current health outcomes and costs in underserved areas.

The United States is facing a serious shortage in health care services, and that shortage is expected to get worse. In April 2019, the Association of American Medical Colleges predicted a deficit of approximately 21,000–55,000 primary care physicians by 2032 (1), and reports about specialists (1) and nurses (2) are similar. Unsurprisingly, these shortages hit harder in some communities than in others. Many of the hardest-hit communities are medically underserved, facing significant barriers to accessing health care services.

The U.S. government has established specific criteria for identifying medically underserved communities. Because multiple factors contribute to the strain on health care resources, there are several types of designations, each with different criteria. Some of the primary considerations are the number of local providers, the age of the population, local poverty rates, and infant mortality rates. Across the United States, tens of millions of people in both rural and urban areas meet the criteria for some type of health care shortage or underserved designation (3).

Exacerbating this problem are the growing rates of chronic disease across the United States. The incidence of type 2 diabetes in particular has reached epidemic levels; the Centers for Disease Control and Prevention estimates that cases of diagnosed diabetes have nearly quadrupled since 1990 (4), and today, as many as 95% of those diagnoses are for type 2 diabetes (5). Managing this condition through lifestyle changes and medications becomes a lifelong battle for patients, while overloaded health professionals often struggle to provide the ongoing support patients need. Patients from medically underserved communities are at a particular disadvantage in managing chronic conditions because of the difficulties they face in simply accessing care.

However, new technologies can provide a partial solution by creating an increasingly connected world and offering personalized health insights and support. Wearable devices such as continuous glucose monitoring (CGM) systems generate data that can be valuable in care decisions and, importantly, accessed and discussed remotely to guide patients in better self-management. If the health care system at large can effectively harness such devices and the data they generate, it could provide better ongoing care for patients with chronic diseases no matter where they live.

In light of these opportunities, UnitedHealth Group Research & Development (UHG R&D) is exploring ways to provide support for its health plan members with type 2 diabetes that runs parallel to in-clinic care and helps relieve some of the burden on providers and other first-line health care workers. We believe technology could help all patients improve their ability to communicate with providers and to take an active role in managing their own health. We also see significant potential for using technology to overcome some of the access barriers that members of underserved communities face.

To understand how new initiatives at UHG R&D might affect people in medically underserved communities, we conducted a medical claims analysis to get a clearer picture of the challenges people in these communities face. This analysis focused on one specific type of designation: Medically Underserved Areas (MUAs).

The MUA designation comes from the Health Resources & Services Administration (HRSA), an agency of the U.S. Department of Health and Human Services. MUAs are geographical areas that can range in size from a few neighborhoods to multiple adjoining counties. The HRSA designates MUAs using a weighted combination of the following criteria (6):

  • Ratio of primary care providers to the population

  • Proportion of the population below the federal poverty level

  • Proportion of the population ≥65 years of age

  • Local infant mortality rate

To conduct our analysis, we compared medical claims from a sample of Medicare Advantage–enrolled members living in MUAs to those living outside of MUAs. We first identified members as being in or outside of MUAs using data available from the HRSA (7). We then defined our sample population by performing a population-based exact match on age and sex between Medicare Advantage members living in MUAs and those living outside of MUAs. The result was two equal-sized groups and a total sample population of ∼1.7 million members. Using medical claims for 2017, we then examined incidence rates for type 2 diabetes and common comorbidities, patients’ likelihood to incur health care costs, prescription data, specialist visits, and total medical spending.

The first noticeable difference we found was that members residing in MUAs were 13% more likely to have a type 2 diabetes diagnosis than those living outside of MUAs (Table 1). Members with type 2 diabetes living in MUAs also appeared to have slightly worse health outcomes than those living outside of MUAs, as indicated by several points discussed below.

TABLE 1.

Comparison of Health Characteristics Between Populations* Living in and Outside of MUAs in 2017

Entire Sample Population
Sample Population Diagnosed With Type 2 Diabetes
Type 2 Diabetes Diagnosis, %Retinopathy, %Neuropathy, %Nephropathy, %RAF Score
MUA 37.9 15.3 29.1 22.7 1.38 
Non-MUA 33.4 15.0 27.0 21.9 1.33 
Difference, % 13 
Entire Sample Population
Sample Population Diagnosed With Type 2 Diabetes
Type 2 Diabetes Diagnosis, %Retinopathy, %Neuropathy, %Nephropathy, %RAF Score
MUA 37.9 15.3 29.1 22.7 1.38 
Non-MUA 33.4 15.0 27.0 21.9 1.33 
Difference, % 13 
*

Populations consisted of UnitedHealthcare members enrolled in Medicare Advantage plans. See Supplementary Table S1 for International Classification of Diseases, 10th Revision (ICD-10) codes and definitions used in claims analysis.

First, members with type 2 diabetes living in MUAs were more likely to have diabetes-related comorbidities (i.e., retinopathy, neuropathy, and nephropathy [Table 1]). Second, their risk adjustment factor (RAF) scores, which indicate a patients’ likelihood to incur health care costs, were 4% higher (Table 1). RAF is a number defined by the Centers for Medicare & Medicaid Services and is based on a person’s health conditions and demographic factors. A higher RAF predicts a greater cost to maintain an individual’s health, often due to a higher likelihood of health issues.

Additionally, a higher percentage of members in MUAs appeared to have prescriptions for higher-tier diabetes drugs (i.e., insulin and other more advanced medications as opposed to just metformin) than their non-MUA counterparts (Table 2). To calculate these numbers, we examined 2017 prescription fills for our sample population and narrowed our focus to only members who filled prescriptions through their insurance to have enough medication to cover at least 80% of the year (i.e., we identified members who had a proportion of days covered of at least 80% of the year.) We applied this filter so that our numbers represented consistent treatment plans with filled prescriptions; however, this method filtered out members who may have received prescriptions from their providers that they failed to fill or members who may have paid for their prescriptions through means other than their insurance coverage.

TABLE 2.

Comparison of Prescription Fills and Specialist Visits Between Populations* Living in and Outside of MUAs in 2017

Prescriptions Filled to at Least an 80% Supply in 2017
Metformin, %Insulin, %Other Diabetes Medications, %Endocrinologist Visits
MUA 25.3 8.6 22.7 9.5 
Non-MUA 25.8 8.1 20.9 12.2 
Difference, % –2 –22 
Prescriptions Filled to at Least an 80% Supply in 2017
Metformin, %Insulin, %Other Diabetes Medications, %Endocrinologist Visits
MUA 25.3 8.6 22.7 9.5 
Non-MUA 25.8 8.1 20.9 12.2 
Difference, % –2 –22 
*

Populations consist of UnitedHealthcare members with type 2 diabetes enrolled in Medicare Advantage plans. See Supplementary Table S2 for ICD-10 codes and definitions used in claims analysis.

Considers members with diabetes with at least one endocrinologist visit claim in 2017.

Although multiple reasons could explain the medication trends in Table 2, these trends may indicate that members in MUAs are on more complex drug regimens than members living outside of MUAs. Despite this and the other indicators of poorer health outcomes, members with type 2 diabetes in MUAs tended to visit endocrinologists 22% less frequently than those not in an MUA.

After looking at health outcomes and treatment for our sample population, we compared members’ total medical expenditures for the year. We found that spending for members with type 2 diabetes living in MUAs was ∼$450 more per year than for those living outside of MUAs. This number includes only direct medical costs, both those covered by the health plan and those paid by members. If we extrapolate this number to the entire U.S. Medicare population (see Supplementary Materials), we can estimate that the additional health care costs for people with type 2 diabetes living in MUAs compared to those living outside of MUAs was likely between $1 billion and $3 billion in 2017.

Clearly, the issue of MUAs represents a significant burden on the health care system and is a multifaceted problem with no single cause or solution. Widespread efforts from multiple players will be necessary to improve care and reduce costs for both well-served and underserved communities.

Glucose Management Program by UnitedHealthcare

One of UHG R&D’s efforts to improve care for health plan members with type 2 diabetes is a pilot program called the Glucose Management Program (GMP) by UnitedHealthcare (Figure 1). This program is designed to support people with type 2 diabetes so they can better manage their conditions. The baseline component of the GMP is an incentivized activity program to encourage participants to increase their daily physical activity. The GMP also offers CGM paired with remote coaching to select participants—an arm of the program that is continuing to expand. We have found through experience and research that CGM technology has significant potential to transform participants’ confidence in managing and improving their conditions.

FIGURE 1.

Overview of the GMP pathway.

FIGURE 1.

Overview of the GMP pathway.

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UHG R&D built the GMP based on the foundational work of two clinical trials it conducted (8,9), the expertise of its partner clinicians and type 2 diabetes clinical advisory board, and review of the scientific literature. We took particular note of randomized clinical trials indicating that CGM can improve A1C levels (10,11) or at least be equivalent to self-monitoring of blood glucose (SMBG) for patients with type 2 diabetes (12). Furthermore, postprandial activity in particular has been shown to improve glycemic control for people with type 2 diabetes (13,14).

The GMP starts with incentivized physical activity, primarily because it is the simplest part of the program, and lower barriers to entry help facilitate higher engagement. When members first enroll, they receive a Fitbit Charge 2 and can earn $1.50 per day (up to $150 per year) for meeting two basic daily activity goals: reaching 7,500 steps and taking one 10-minute postprandial walk. By providing program participants with financial incentives to move and a wearable device to track their activity, the program aims to shift participants closer to the recommended activity levels of at least 150 minutes of moderate activity per week (13).

Once participants are engaged in the activity program, they are eligible to use a Dexcom CGM system for 8 weeks to learn how food choices and habits can affect glucose levels. This aspect of the GMP is designed to empower participants by providing insights about their own body’s responses to food, medication, and activity in real time, thereby increasing their ability to self-manage glucose levels even after finishing 8 weeks of using CGM technology.

Before participants can receive a prescription for a CGM system, program staff screen their records for any safety and suitability concerns. A nurse then conducts an in-home visit to perform a final eligibility evaluation, provide education on CGM use, and show each participant how to apply the CGM sensor. Participants also receive a toll-free number to call for technical support or in case of emergencies.

Each participant then gets paired with a program coach, who provides educational tips, accountability, and technical support via weekly phone calls and text messages. Over the next 8 weeks, program coaches guide participants through an educational curriculum that emphasizes proper nutrition, physical activity, self-monitoring, and adherence to prescribed medications. Coaches do not provide participants with medical advice, but instead advise participants to continue following their providers’ guidance throughout the GMP and to contact their providers directly regarding medical issues, medication questions, and other medical situations.

The CGM system offers participants the opportunity to gain insight into how their daily choices, routines, and habits affect their glycemic control. Because type 2 diabetes is such a heterogenous condition and glycemic responses to food, medication, and exercise are so individualized (1517), there are few tools besides a CGM system that can provide such powerful educational insights. GMP participants learn what is uniquely best for them, allowing them to personalize their lifestyles based on their situation and physiology. The GMP coaching program includes lessons to help participants see how certain foods or behaviors cause their glucose levels to spike, whereas others do not. Throughout these lessons, program coaches help participants make learning connections and set personal goals, guiding them in a process of self-discovery and empowerment.

Once participants complete their time using CGM technology, they receive a report containing their 8 weeks of glucose data. The coaches strongly encourage participants to share the full reports with their diabetes care providers, and UHG R&D is currently researching other ways to efficiently integrate this information in the health care system, as discussed later in this article.

Program Delivery and Medically Underserved Communities

Earlier iterations of the GMP revealed that there are many obstacles that prevent members from joining the program; accordingly, accessibility is now a design priority. Even the best program cannot make a real impact on health if only few can participate. Although the GMP is still new, thousands of UHG Medicare Advantage members have joined so far, and hundreds have participated in the CGM portion of the program. And, because much of the geography the program covers is classified as medically underserved, mitigating access barriers continues to be key.

To maximize access to the program, the GMP extends invitations to members online, by phone, and by mail and delivers all program materials directly to participants’ homes. Likewise, the use of smartphones, remote coaching via text and phone, in-home nurse visits, and wearable devices helps to overcome other barriers that underserved communities face. Because these methods reach across long distances and do not require participants to travel, issues such as insufficient numbers of local providers and lack of transportation are not as problematic. Furthermore, the GMP runs entirely outside of health clinics, which allows it to augment patient care without adding burden to primary care physicians, endocrinologists, or other diabetes care providers. The GMP also notifies members’ providers by mail that their patients were invited to join the program and again if their patients join. Ideally, the GMP also relieves burden from diabetes care teams by giving participants an additional source of education and support.

Preliminary Lessons Learned From the GMP

The GMP is in the early stages of release and assessment. Preliminary results from the CGM component of the program suggest that participants who have starting A1C values >7.0% experience decreased estimated A1C values and increased time in the target glucose range, which is consistent with the literature on the effects of CGM (10,11). Participant feedback about the program has also been promising.

The results of the GMP are encouraging so far. As work continues for this and similar programs, we will pursue ways to provide patients with the assistance they need to take control of their health. We believe that, if patients have the right tools, information, and support, they will do more to manage their diabetes and take an active role with their health care team.

Alongside its more member-focused efforts, UHG R&D is also investigating better ways to incorporate program data—particularly CGM data—into the care pathway without increasing the burden on providers. We have found one type of CGM report of particular interest: the International Diabetes Center (IDC)’s Ambulatory Glucose Profile (AGP) (Figure 2) (18), which has been endorsed by panels of diabetes care experts (19,20). The AGP report is a standardized summary of CGM data designed for quick interpretation to support decision-making. Most standard diabetes metrics such as SMBG results and A1C tests offer static snapshots of glucose levels, whereas an AGP report based on CGM data gives providers and their patients a dynamic view of glucose levels over multiple weeks.

FIGURE 2.

AGP report with three components. Top panel: Glucose targets and metrics. Middle panel: AGP summary of glucose values from the 10- to 14-day report period, with median (50%) and other percentiles shown as if occurring in a single day (midnight to midnight). Bottom panel: Daily glucose profiles of individual days in the report period, presented in a calendar view. Reprinted with permission from the International Diabetes Center (18).

FIGURE 2.

AGP report with three components. Top panel: Glucose targets and metrics. Middle panel: AGP summary of glucose values from the 10- to 14-day report period, with median (50%) and other percentiles shown as if occurring in a single day (midnight to midnight). Bottom panel: Daily glucose profiles of individual days in the report period, presented in a calendar view. Reprinted with permission from the International Diabetes Center (18).

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In the interest of bringing the AGP and CGM data into the care path, UHG R&D is currently working with Southwest Medical Associates to run a quality improvement (QI) pilot program under the guidance of one of the authors (K.A.K.) and with support from the IDC. The pilot uses AGP reports to interpret data collected from Dexcom CGM systems prescribed to and worn by a small group of patients with type 2 diabetes. So far, it has been found that the AGP facilitates more effective communication with patients about their diabetes and lays solid groundwork for recommending strategies to help patients improve self-management. The AGP also makes it easier for physicians to see what types of medication adjustments patients may need.

Participants in this QI pilot were offered the opportunity to wear CGM systems for 10 days as part of their care. There was no randomization or assigned groups and no A1C requirement for participation. As seen in the preliminary results from the first 26 participants, most patients who participated needed changes to their medication treatment plans (Table 3).

TABLE 3.

Preliminary Results of a QI Pilot Using CGM and AGP to Evaluate Medication Treatment Plans

Patients, nPatients With an A1C <7.0%*, n
Completed 10-day CGM wear 26 
Doctor recommended no Rx changes 
Doctor recommended Rx changes 20 
Patients, nPatients With an A1C <7.0%*, n
Completed 10-day CGM wear 26 
Doctor recommended no Rx changes 
Doctor recommended Rx changes 20 
 Total Occurrences, n Occurrences in Patients With an A1C >7%, n 
Type of Rx Adjustment   
 Rx added 
 Rx removed 
 Rx altered 12 
 Timing adjustment 12 
 Total Occurrences, n Occurrences in Patients With an A1C >7%, n 
Type of Rx Adjustment   
 Rx added 
 Rx removed 
 Rx altered 12 
 Timing adjustment 12 

Some patients received more than one type of adjustment to their treatment plans.

*

Previously measured laboratory value before starting CGM. Rx, medication.

The pilot was still underway at the time of writing of this article. Patients who received significant changes to their medication treatment plans were invited to continue with CGM for 10 more days to confirm the effectiveness of the changes. This process will continue as needed until the provider is satisfied with the treatment plan.

From these experiences and others, we expect more and more diabetes care experts will come to see the AGP as a valuable tool. It will help them understand their patients’ current glycemic trends, identify problem spots, and recommend lifestyle, medication, and other treatment adjustments. As shown in Table 3, even pilot participants with A1C values <7.0% received changes to their medication regimens. Because attaining an A1C <7.0% is a standard goal for nonpregnant adults with type 2 diabetes, it is unlikely that these patients would have received medication adjustments under the normal standard of care, through which providers primarily have access to A1C results to guide therapy decisions. The richer data from the CGM-generated AGP provides an opportunity for improved care, such as reducing hypoglycemia even for patients who are hitting their major glycemic goals. The AGP is also a good tool for helping patients understand their conditions and their providers’ recommendations, which can be foundational to better self-management.

The AGP report also has the potential to improve care for underserved communities, since it can provide rich data while simultaneously sidestepping access barriers. As long as a CGM system can be delivered to a patient’s home, the report can be generated without the need for a visit to the clinic. Once providers and patients understand how to read an AGP, they can also discuss the results over the phone or via other long-distance communication platforms.

We believe that strategic use of CGM systems coupled with a succinct report such as the AGP could significantly improve the current standard of care for type 2 diabetes, and we will continue in our efforts to make these data more available to both providers and patients.

The GMP and the Southwest Medical Associates QI pilot represent two approaches to improving care for people with type 2 diabetes. Both programs are also creating an infrastructure that could help deliver other types of care remotely; this effort may prove particularly important for underserved populations, since it could connect expert care to places and populations that currently lack health resources. For example, once we establish a long-distance connection with members, we could more easily facilitate virtual consultations with dietitians, mental health professionals, endocrinologists, and certified diabetes educators.

It is hoped that the GMP and similar efforts will create a better information infrastructure for providers to access their patients’ real-world data as well, as in the QI pilot described above. GMP participants generate a significant amount of data during their participation, and that information could be useful to providers for improving care decisions. Although there are barriers to obtaining and sharing such data, we believe these barriers can and should be overcome with creative solutions and partnerships. Getting data into providers’ hands could make a significant impact on the quality of care decisions.

The knowledge we gained from the GMP and the QI pilot will continue to guide our future innovations. To drive better care for all health plan members, including those who are underserved, we will continue to focus on solutions that:

  • Empower members to actively manage their conditions by providing them with first-class information that delivers personal, actionable insights into their health

  • Leverage new technology to improve access to care, both by relieving the burden on primary care physicians and by delivering programs remotely to overcome access barriers

  • Scale efficiently to support large numbers of members

  • Integrate with the current health care system to allow for smooth communication among patients, providers, and payers

Although our goal is to support all health plan members, these specific solutions may particularly improve care for underserved areas. The greatest potential of our current and future programs lies in their ability to provide support to our members quickly and seamlessly across large distances. The ultimate delivery framework we envision will be capable of deploying more health care solutions to our underserved member base and their health care teams.

The number of people with type 2 diabetes is predicted to grow significantly in the next several years. This growth will put further strain on the health care system and will likely have a significant effect on underserved communities. It will take innovative solutions—including remote care, new technologies, digital health efforts, increased efficiencies, and more—to solve the problems the health care system faces. UHG R&D is committed to developing and bringing advances in care to members and their providers. In the face of the growing diabetes epidemic, new ideas and collaborations from within and even outside of the health care sector will be essential to provide patients with the care they need.

The authors thank Joe Hafermann, Steve Priem, Katie Hiegel, Dominic Scheck, and Courtney Salvey of UHG R&D for their detailed discussion of and help in preparing this article. We also thank Dr. Richard Bergenstal of the IDC for the use of the AGP report image and for all of his support as a subinvestigator in the QI pilot at Southwest Medical Associates.

C.J.L., S.R.G, B.W.E., J.M.D., C.D.W., J.P.J., and S.R.H. all work for UHG R&D and are stock shareholders in UHG. K.A.K. works for Southwest Medical Associates, which is affiliated with UHG, and is also a stock shareholder in UHG.

The programs and efforts described in this article were developed and run by UHG R&D and other affiliated organizations within UHG.

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of UHG.

C.J.L., J.M.D., and C.D.W. wrote, reviewed, and edited the manuscript. S.R.G. and B.W.E. performed and provided the analysis on the claims data and contributed to the discussion. K.A.K. performed and provided the QI pilot analysis and contributed to the discussion. J.P.J. and S.R.H. reviewed and edited the manuscript and contributed to the discussion. C.J.L. is the guarantor of this work and, as such, had full access to all the data presented and takes responsibility for the integrity of the data and the accuracy of the data analysis.

1.
Association of American Medical Colleges
.
The complexities of physician supply and demand: projections from 2017 to 2032
.
2.
American Association of Colleges of Nursing
.
Nursing shortage fact sheet
.
3.
Health Resources & Services Administration
.
Shortage areas
.
4.
Centers for Disease Control and Prevention
.
Long-term trends in diabetes
.
Available from www.cdc.gov/diabetes/statistics/slides/long_term_trends.pdf. Accessed 10 January 2019
5.
Centers for Disease Control and Prevention
.
National diabetes statistics report, 2017: estimates of diabetes and its burden in the United States
.
6.
Health Resources & Services Administration
.
Medically underserved areas and populations (MUA/Ps)
.
Available from bhw.hrsa.gov/shortage-designation/muap. Accessed 19 January 2019
7.
Health Resources & Services Administration
.
Explore data and maps on HRSA’s health care programs
.
Available from data.hrsa.gov. Accessed 19 January 2019
8.
ClinicalTrials.gov
.
Empowering Medicare patients to self-manage their type 2 diabetes using continuous glucose monitoring (CGM): investigational device pilot. Identification No. NCT03252964
.
9.
Clinical Trials.gov
.
Empowering Medicare patients to self-manage their type 2 diabetes using continuous glucose monitoring (CGM): investigational device pilot (SMA investigational device). Identification No. NCT03290768
.
10.
Beck
RW
,
Riddlesworth
TD
,
Ruedy
K
, et al
.
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
:
365
374
11.
Vigersky
R
,
Fonda
S
,
Chellappa
M
,
Walker
S
,
Ehrhardt
N
.
Short- and long-term effects of real-time continuous glucose monitoring in patients with type 2 diabetes
.
Diabetes Care
2012
;
35
:
32
38
12.
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
:
55
73
13.
Colberg
S
,
Sigal
R
,
Yardley
J
, et al
.
Physical activity/exercise and diabetes: a position statement of the American Diabetes Association
.
Diabetes Care
2016
;
39
:
2065
2079
14.
Colberg
SR
,
Zarrabi
L
,
Bennington
L
, et al
.
Postprandial walking is better for lowering the glycemic effect of dinner than pre-dinner exercise in type 2 diabetic individuals
.
J Am Med Dir Assoc
2009
;
10
:
394
397
15.
Ahlqvist
E
,
Storm
P
,
Karajamaki
A
, et al
.
Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables
.
Lancet Diabetes Endocrinol
2018
;
6
:
361
369
16.
Zeevi
D
,
Korem
T
,
Zmora
N
, et al
.
Personalized nutrition by prediction of glycemic responses
.
Cell
2015
;
163
:
1079
1094
17.
Cantrell
RA
,
Alatorre
CI
,
Davis
EJ
, et al
.
A review of treatment response in type 2 diabetes: assessing the role of patient heterogeneity
.
Diabetes Obes Metab
2010
;
12
:
845
857
18.
International Diabetes Center
.
AGP reports
.
Available from www.agpreport.org/agp/agpreports. Accessed 30 January 2019
19.
Danne
T
,
Nimri
R
,
Battelino
T
, et al
.
International consensus on use of continuous glucose monitoring
.
Diabetes Care
2017
;
40
:
1631
1640
20.
Bergenstal
RM
,
Ahmann
AJ
,
Bailey
T
, et al
.
Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: the Ambulatory Glucose Profile (AGP)
.
Diabetes Technol Ther
2013
;
15
:
198
211