The Dexcom Community Glucose Monitoring Project is a collaborative, ongoing, primary care–driven public health initiative designed to provide continuous glucose monitoring (CGM) systems to adults with type 2 diabetes who lack health insurance coverage for CGM. After 6 months of program participation, mean A1C decreased by 2.4 ± 1.9% from baseline to 6-month follow-up (from 9.4 ± 1.7 to 7.1 ± 1.2%, P <0.001). There was a clinically meaningful and statistically significant improvement in CGM metrics as well. Greater CGM use in the primary care setting among people with type 2 diabetes may help patients successfully manage their diabetes.

The number of patients with type 2 diabetes treated by primary care providers (PCPs) is expected to grow as the prevalence of type 2 diabetes rises (1,2) and the ongoing shortage of endocrinologists continues (3,4). Approximately 90% of people with type 2 diabetes rely on their PCP for diabetes care (5,6), and this reliance can be magnified in rural, lower-income, and underserved areas (7).

Continuous glucose monitoring (CGM) systems are increasingly used by people with diabetes, including those with type 2 diabetes. Their use is recommended by the American Diabetes Association (ADA) (8) and American Association of Clinical Endocrinology (9). CGM offers several advantages over fingerstick blood glucose monitoring (BGM), namely the continuous data stream of glucose values and the optional alerts that inform users of abnormal glucose concentrations or trends. Multiple studies support the benefits of CGM among people with type 2 diabetes (10–14).

Currently, use of CGM is limited in the primary care setting. In a recent survey of more than 600 PCPs, fewer than 40% reported ever prescribing CGM to their patients, but most were at least somewhat likely to do so in the future (15). PCPs, with their established connections to patients with diabetes, are critical to helping their patients achieve better glycemic management (16). Use of CGM systems and their associated clinician reports and retrospective data analysis tools has been shown to reduce therapeutic inertia (17,18) and empower patients to make healthier choices (19,20).

The Dexcom Community Glucose Monitoring Project is a collaborative, ongoing, PCP-driven public health initiative designed to provide CGM systems at no cost to adults with type 2 diabetes who lack health insurance coverage for CGM. Here, we report on interim changes in A1C and in CGM-based metrics of glycemic control among program participants.

Design and Participants

This real-world study of CGM use in clinical practice was conducted at a primary care practice in collaboration with the local health department. The study was open to residents of Ohio with a diagnosis of type 2 diabetes who were ≥18 years of age, CGM-naive, and willing to wear a CGM sensor. The study targeted uninsured individuals and those without health insurance coverage for CGM. Exclusion criteria included a diagnosis of type 1 diabetes, current or anticipated use of glucocorticoids, pregnancy, history of a severe psychiatric condition, or presence of a medical condition that could make A1C measurement unreliable. There were no exclusion criteria related to A1C or treatment regimen. The study was registered with ClinicalTrials.gov (NCT05351190) and conducted in accordance with the Declaration of Helsinki. The study was approved by the University of Findlay Institutional Review Board, and all participants provided written informed consent.

Procedures

Participants were referred to the study by their primary care doctor at their usual visits. The health department contacted participants and scheduled them for their enrollment visit (typically 1–4 weeks from referral). During the enrollment visit, demographic information, weight, and height were recorded. A point-of-care baseline A1C was performed if >30 days had elapsed from their last visit at their PCP office. Participants also initiated CGM (Dexcom G6; Dexcom, Inc., San Diego, CA), received basic training on CGM use, were instructed to check their CGM before and after meals, and were provided with a 90-day supply of sensors. Participants either downloaded the Dexcom G6 app onto their smartphone for automatic data uploads or were provided a receiver display device if they did not have a compatible smartphone. The receiver connects directly to the CGM sensor and provides the same alert functionality as the phone app, but it differs from the app in that it stores the most recent 30 days of glucose data from the G6 and data can only be obtained via direct download.

At 3 months, participants returned to the public health department for a point-of-care A1C measurement, downloading of their CGM data if a receiver was used, and to receive another 3-month supply of sensors. Because patients visited their PCP throughout the study, medication changes and optimization may have occurred.

Outcome Measurements

The primary outcome was change in A1C from baseline to 6 months. Proportions of patients meeting the ADA treatment target of A1C <7.0% (21) or the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) A1C target of <8.0% at baseline and follow-up were also evaluated (22,23).

CGM data were analyzed for the subset of participants who shared their data with the principal investigator’s Dexcom Clarity Clinic account and met the international consensus for CGM data sufficiency (≥70% [24]) at baseline and at 6 months. CGM metrics included mean glucose, glucose management indicator (GMI), coefficient of variation (CV), time in range (TIR) 70–180 mg/dL, time in tight range (TITR) 70–140 mg/dL, time above range (TAR) >180 mg/dL, TAR 181–250 mg/dL, TAR >250 mg/dL, time below range (TBR) <70 mg/dL, and TBR <54 mg/dL. The proportion of days with CGM use was also assessed. Participants who used a CGM receiver (vs. the app) throughout the 6-month study period were not included in CGM metrics analysis because of the timing of data downloads.

Statistical Analysis

Descriptive statistics, including percentages, means, and SDs, were calculated for participant characteristics and study outcomes. Within-group changes in A1C and CGM metrics were analyzed by paired t tests. Statistical analyses were performed using R, v. 4.2.2, statistical software (R Foundation for Statistical Computing, Vienna, Austria), with statistical significance defined as P <0.05.

A total of 314 patients were referred for enrollment in the study, of whom 237 (75.5%) had baseline and 6-month A1C values and were included in the analysis. Participant baseline characteristics are summarized in Table 1.

Table 1

Baseline Participant Characteristics

DemographicValue
N 237 
Age, years 58.7 ± 11.9 
Female, n (%) 101 (42.6) 
BMI, kg/m2* 35.3 ± 8.2 
A1C, % 9.4 ± 1.7 
Diabetes duration, years 10.7 ± 9.0 
Insulin use, n (%) 101 (42.6) 
DemographicValue
N 237 
Age, years 58.7 ± 11.9 
Female, n (%) 101 (42.6) 
BMI, kg/m2* 35.3 ± 8.2 
A1C, % 9.4 ± 1.7 
Diabetes duration, years 10.7 ± 9.0 
Insulin use, n (%) 101 (42.6) 

Data are presented as mean ± SD unless otherwise indicated.

*

n = 236.

n = 230.

Includes insulin use of any kind (basal, bolus, and/or mixed insulin).

A1C

Study participants’ mean A1C decreased by 2.4 ± 1.9% from baseline to the 6-month follow-up (from 9.4 ± 1.7 to 7.1 ± 1.2%, P <0.001). This A1C improvement was not significantly different between receiver users and app users. In addition, the proportion meeting the ADA A1C target of <7.0% increased from 0.4 to 54.0%, and the proportion meeting the HEDIS A1C target of <8.0% increased from 18.6 to 82.7% (Figure 1A). Overall, those with higher baseline A1C achieved greater reductions in A1C at the 6-month follow-up (Figure 1B).

Figure 1

Change in A1C among participants (N = 237) in the Dexcom Community Glucose Monitoring Project. A) Cumulative distribution of A1C values and proportions meeting ADA treatment target of A1C <7.0% and the HEDIS target of A1C <8.0%. B) Comparison of A1C outcomes at 6 months stratified by baseline A1C.

Figure 1

Change in A1C among participants (N = 237) in the Dexcom Community Glucose Monitoring Project. A) Cumulative distribution of A1C values and proportions meeting ADA treatment target of A1C <7.0% and the HEDIS target of A1C <8.0%. B) Comparison of A1C outcomes at 6 months stratified by baseline A1C.

Close modal

CGM Metrics

A total of 149 participants shared their CGM data via the PCP’s Clarity Clinic account, met the data sufficiency requirement, and were included in CGM data analysis. CGM was worn continuously over the 6-month study period. Improvement was observed for all CGM-based glycemic metrics not meeting target values at baseline (Table 2). Participants had low TBR at baseline and met the level 1 (<4% of time <70 mg/dL) and level 2 (<1% of time <54 mg/dL) consensus targets at baseline (24). Low TBR was maintained at 6 months. Participants experienced a significant reduction in mean glucose and GMI (P = 0.047). TIR 70–180 mg/dL significantly increased by 5.5 ± 28.7% (P = 0.022). The improvement in TIR was attributable to a significant decrease in TAR >180 mg/dL (P = 0.021) and largely occurred as an increase in TITR 70–140 mg/dL.

Table 2

Change in CGM Metrics From Baseline to 6 Months (n = 149*)

ParameterBaselineFollow-UpChangeP
Mean glucose, mg/dL 175.0 ± 38.6 168.4 ± 36.7 −6.6 ± 40.4 0.047 
GMI, % 7.5 ± 0.9 7.3 ± 0.9 −0.2 ± 1.0 0.047 
CV, % 23.1 ± 5.9 23.5 ± 6.1 0.4 ± 4.6 0.268 
Percentage of time in ranges 
TIR 70–180 mg/dL 60.2 ± 28.6 65.6 ± 26.1 5.5 ± 28.7 0.022 
TITR 70–140 mg/dL 30.9 ± 25.6 34.8 ± 26.0 3.9 ± 25.8 0.068 
TAR >180 mg/dL 39.5 ± 28.8 34.0 ± 26.3 −5.5 ± 28.9 0.021 
TAR 181–250 mg/dL 29.0 ± 18.4 25.2 ± 16.6 −3.8 ± 18.3 0.012 
TAR >250 mg/dL 10.5 ± 15.3 8.8 ± 14.3 −1.7 ± 17.4 0.231 
TBR <70 mg/dL 0.3 ± 0.7 0.3 ± 1.3 0.05 ± 1.2 0.613 
TBR <54 mg/dL 0.02 ± 0.1 0.06 ± 0.3 0.03 ± 0.3 0.168 
ParameterBaselineFollow-UpChangeP
Mean glucose, mg/dL 175.0 ± 38.6 168.4 ± 36.7 −6.6 ± 40.4 0.047 
GMI, % 7.5 ± 0.9 7.3 ± 0.9 −0.2 ± 1.0 0.047 
CV, % 23.1 ± 5.9 23.5 ± 6.1 0.4 ± 4.6 0.268 
Percentage of time in ranges 
TIR 70–180 mg/dL 60.2 ± 28.6 65.6 ± 26.1 5.5 ± 28.7 0.022 
TITR 70–140 mg/dL 30.9 ± 25.6 34.8 ± 26.0 3.9 ± 25.8 0.068 
TAR >180 mg/dL 39.5 ± 28.8 34.0 ± 26.3 −5.5 ± 28.9 0.021 
TAR 181–250 mg/dL 29.0 ± 18.4 25.2 ± 16.6 −3.8 ± 18.3 0.012 
TAR >250 mg/dL 10.5 ± 15.3 8.8 ± 14.3 −1.7 ± 17.4 0.231 
TBR <70 mg/dL 0.3 ± 0.7 0.3 ± 1.3 0.05 ± 1.2 0.613 
TBR <54 mg/dL 0.02 ± 0.1 0.06 ± 0.3 0.03 ± 0.3 0.168 

Data are presented as mean ± SD. Bold type indicates statistical significance.

*

Met data sufficiency criteria and elected to share their data through Clarity Clinic account.

This PCP-driven, community-based initiative designed to provide access to CGM for people with type 2 diabetes who lacked insurance coverage for CGM was associated with a clinically meaningful improvement (24) in A1C and TIR after 6 months. Over half of participants met the ADA treatment target of A1C <7.0% (from n = 1 at baseline) and over 80% met the HEDIS target of A1C <8.0%, a greater-than-threefold increase from baseline. Importantly, these results were achieved with minimal training on the use of CGM. This study adds to the growing body of real-world clinical evidence demonstrating the benefit of CGM use in type 2 diabetes and supports broader access to this technology in this population (25).

The A1C improvement of 2.4 ± 1.9% in this study was particularly high compared with studies of similar populations. Randomized controlled trials and retrospective studies of individuals with insulin-treated type 2 diabetes observed A1C changes of −1.1% among CGM users compared with control subjects (adjusted difference −0.4%) (11), −0.41% (adjusted difference −0.4%) (10), and −0.53% (adjusted difference −0.39%) (26). A recent study reported an average A1C change among individuals with type 2 diabetes using basal insulin or noninsulin therapies of −3.0 ± 1.3% (13), a highly significant and clinically meaningful change. In addition, the higher magnitude of glycemic improvement observed among those with the highest baseline A1C has been similarly reported in other studies (27,28). It is well established that proper glycemic control reduces the risks of long-term complications of diabetes such as kidney disease (29), cardiovascular disease (30), and neuropathy (31), as well as acute risks such as hospitalizations (26).

CGM use in this study was near-continuous and likely contributed to the improvement observed in both A1C and TIR. These findings are well correlated (32), and like higher A1C, lower TIR is associated with an increased risk of diabetes complications (33–35). However, it is noteworthy that, while a large improvement was observed in A1C, only moderate improvements in TIR and GMI (a CGM-based estimation of A1C) were observed. Importantly, the changes in TIR in this study were still considered clinically significant based on the International Consensus on Time in Range (24). A recent analysis found that most of the improvements in CGM metrics occur within the first 7 days of unblinded wear (36). Because the current study lacked a blinded run-in period and used 10 days of CGM data, rapid improvements in CGM metrics during the baseline measurement period may have contributed to the discrepancy between A1C and GMI. This proposed explanation is supported by the similar A1C and GMI present at 6 months but not at baseline.

The benefits of CGM for people with type 2 diabetes are multifactorial. It can serve as an educational and motivational tool to encourage lifestyle modification, such as increasing physical activity or making better dietary choices (19,20,37,38). Some patients report that continuous access to their glucose levels and CGM metrics such as TIR is more useful and actionable compared with an A1C measurement (39). The additional data provided by CGM systems, including information about time spent in hyperglycemia, can help reduce therapeutic inertia and facilitate treatment intensification in the primary care setting (17,18). Educating patients on the impact of meeting TIR targets on long-term health and the value of interacting with CGM data may be a powerful way to support behavior change. In fact, a thematic analysis performed by Clark et al. (40) identified six specific attitudinal and behavior changes among a subset of participants in the Dexcom Community Glucose Monitoring Project.

It is important to note that PCPs face multiple challenges in incorporating CGM into routine clinical practice. A recent article (16) described these challenges, which begin with the limited systems of support for PCPs in comparison with those for endocrinologists. This limitation affects individual PCPs’ bandwidth and can reduce the time they have available to, for example, submit prior authorizations. Although prior authorizations can be a fundamental barrier to patients obtaining CGM and PCPs prescribing CGM, payers are increasingly removing this requirement for insulin users and increasing access to CGM via their pharmacy benefit (41–43). In Ohio, prior authorizations for common CGM systems are currently waived for all Medicaid beneficiaries with a diabetes diagnosis, and these patients can obtain their CGM at the pharmacy via the Medicaid pharmacy benefit rather than through their medical benefit from a durable medical equipment supplier (44,45). Finally, user-friendly retrospective data analysis tools such as Clarity Clinic can help facilitate personalized diabetes management discussions during office visits (46), but work remains ongoing to seamlessly integrate CGM data with electronic medical record systems’ software.

In addition to physician support, insurance coverage and low out-of-pocket costs are key to using CGM technology for many patients. Until recently, CGM access through the Centers for Medicare & Medicaid Services was limited to individuals performing four or more BGM tests per day and either using an insulin pump or administering multiple daily injections of insulin. These requirements were removed in April 2023 (47). This decision was made in part because of the beneficial health outcomes that patients with diabetes on less intensive therapy regimens can achieve with the use of CGM (11–13,48–50). These benefits persist across age, education level, and numeracy score (11,51).

Strengths and Limitations

Strengths of this real-world study include the large type 2 diabetes population treated in a clinically relevant primary care setting. Importantly, the study included underserved patients without health insurance or having limited health insurance coverage.

It is possible that the delay in CGM initiation of up to 1 month after study referral, as well as the lack of a blinded run-in period, underestimated the effects of CGM use on CGM metrics such as mean glucose and TIR (36). Conversely, the Hawthorne effect (behavioral changes attributable to study participation) and participant selection bias may have overestimated the effects of CGM use.

Analysis of CGM metrics was also limited to app users because of the data sufficiency and baseline requirements of the study. Study continuation using the Dexcom G7 is expected to avoid this issue because the G7 receiver has a 180-day data storage capacity.

The degree to which patients used the CGM system to monitor their glucose levels and change their behavior also cannot be determined. As is routinely done in clinical practice, the CGM data were used by the health care providers to titrate medications. The favorable reductions seen in A1C levels may have been related to reduced hyperglycemia from medication adjustments and/or lifestyle changes. It is unknown to what degree either factor contributed. Medication management guided by CGM data is ongoing as the study progresses.

Finally, generalizability of these findings is limited by the lack of geographic diversity, the unique design of this PCP-driven study, and the additional support provided by the local public health department.

In this study of people with type 2 diabetes treated in a primary care setting, CGM use was associated with clinically meaningful and statistically significant improvements in A1C and TIR at 6 months. Expanded use of CGM in the primary care setting could help more patients learn about their diabetes, make and sustain lifestyle modifications, and achieve glycemic targets.

Acknowledgments

The authors thank the study participants for their time and their involvement in this study. They also acknowledge the hard-working staff and volunteers at the Hancock County Health Department and at PCP offices who helped facilitate this study. The authors thank William Kose (Blanchard Valley Health System) and Jay Salyer (Endocrinology & Diabetes Specialists of Northwest Ohio) for their advice and intellectual contributions and Ross Wilson and Erika Schuster of Dexcom, Inc., who provided logistical support. Finally, the authors acknowledge the editorial assistance of Courtney Green of Dexcom, Inc.

Funding

Dexcom, Inc., provided study funding and CGM systems at no cost, funded the analysis, and paid for the article processing charges.

Duality of Interest

T.P.G., C.H., J.E.L., and T.C.W. are employees of Dexcom, Inc. No other potential conflicts of interest relevant to this article were reported.

Author Contributions

T.P.G. and T.C.W. conceptualized, designed, and oversaw the study. T.P.G., A.E., L.R., T.B., E.D., J.H., and K.B. conducted the study and interacted with patients. A.E., C.H., and J.E.L. analyzed the data. All authors critically reviewed the manuscript and approved the final version. T.P.G. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation

Portions of the data in this article were presented at the 17th International Conference on Advanced Technologies & Treatments for Diabetes in Florence, Italy, 6–9 March 2024.

1.
Centers for Disease Control and Prevention
.
National Diabetes Statistics Report
. Available from https://www.cdc.gov/diabetes/php/data-research/index.html. Accessed 9 July 2024
2.
International Diabetes Federation
.
IDF Diabetes Atlas 2021. 10th ed
. Available from https://diabetesatlas.org/atlas/tenth-edition. Accessed 30 October 2023
3.
Vigersky
RA
,
Fish
L
,
Hogan
P
, et al
.
The clinical endocrinology workforce: current status and future projections of supply and demand
.
J Clin Endocrinol Metab
2014
;
99
:
3112
3121
4.
Romeo
GR
,
Hirsch
IB
,
Lash
RW
,
Gabbay
RA.
Trends in the endocrinology fellowship recruitment: reasons for concern and possible interventions
.
J Clin Endocrinol Metab
2020
;
105
:
1701
1706
5.
Davidson
JA.
The increasing role of primary care physicians in caring for patients with type 2 diabetes mellitus
.
Mayo Clin Proc
2010
;
85
(
Suppl. 12
):
S3
S4
6.
Shrivastav
M
,
Gibson
W
Jr
,
Shrivastav
R
, et al
.
Type 2 diabetes management in primary care: the role of retrospective, professional continuous glucose monitoring
.
Diabetes Spectr
2018
;
31
:
279
287
7.
Santen
RJ
,
Nass
R
,
Cunningham
C
,
Horton
C
,
Yue
W.
Intensive, telemedicine-based, self-management program for rural, underserved patients with diabetes mellitus: re-entry of retired endocrinologists into practice
.
J Telemed Telecare
2023
;
29
:
153
161
8.
ElSayed
NA
,
Aleppo
G
,
Aroda
VR
, et al.;
American Diabetes Association
.
7. Diabetes technology: Standards of Care in Diabetes—2023
.
Diabetes Care
2023
;
46
(
Suppl
.
1):S111
S127
9.
Samson
SL
,
Vellanki
P
,
Blonde
L
, et al
.
American Association of Clinical Endocrinology consensus statement: comprehensive type 2 diabetes management algorithm—2023 update
.
Endocr Pract
2023
;
29
:
305
340
10.
Karter
AJ
,
Parker
MM
,
Moffet
HH
,
Gilliam
LK
,
Dlott
R.
Association of real-time continuous glucose monitoring with glycemic control and acute metabolic events among patients with insulin-treated diabetes
.
JAMA
2021
;
325
:
2273
2284
11.
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
:
2262
2272
12.
Aronson
R
,
Brown
RE
,
Chu
L
, et al
.
IMpact of flash glucose Monitoring in pEople with type 2 Diabetes Inadequately controlled with non-insulin Antihyperglycaemic ThErapy (IMMEDIATE): a randomized controlled trial
.
Diabetes Obes Metab
2023
;
25
:
1024
1031
13.
Grace
T
,
Salyer
J.
Use of real-time continuous glucose monitoring improves glycemic control and other clinical outcomes in type 2 diabetes patients treated with less intensive therapy
.
Diabetes Technol Ther
2022
;
24
:
26
31
14.
Karter
AJ
,
Parker
MM
,
Moffet
HH
,
Gilliam
LK
,
Dlott
R.
Continuous glucose monitor use prevents glycemic deterioration in insulin-treated patients with type 2 diabetes
.
Diabetes Technol Ther
2022
;
24
:
332
337
15.
Oser
TK
,
Hall
TL
,
Dickinson
LM
, et al
.
Continuous glucose monitoring in primary care: understanding and supporting clinicians' use to enhance diabetes care
.
Ann Fam Med
2022
;
20
:
541
547
16.
Martens
TW.
Roadmap to the effective use of continuous glucose monitoring in primary care
.
Diabetes Spectr
2023
;
36
:
306
314
17.
Martens
TW
,
Parkin
CG.
How use of continuous glucose monitoring can address therapeutic inertia in primary care
.
Postgrad Med
2022
;
134
:
576
588
18.
Gavin
JR
,
Abaniel
RM
,
Virdi
NS.
Therapeutic inertia and delays in insulin intensification in type 2 diabetes: a literature review
.
Diabetes Spectr
2023
;
36
:
379
384
19.
Engler
S
,
Fields
S
,
Leach
W
,
Van Loon
M.
Real-time continuous glucose monitoring as a behavioral intervention tool for T2D: a systematic review
.
J Technol Behav Sci
2022
;
7
:
252
263
20.
Vallis
M
,
Ryan
H
,
Berard
L
, et al
.
How continuous glucose monitoring can motivate self-management: can motivation follow behaviour?
Can J Diabetes
2023
;
47
:
435
444
21.
ElSayed
NA
,
Aleppo
G
,
Aroda
VR
, et al.;
American Diabetes Association
.
6. Glycemic targets: Standards of Care in Diabetes—2023
.
Diabetes Care
2023
;
46
(
Suppl. 1
):
S97
S110
22.
NCQA
.
Comprehensive diabetes care (CDC)
. Available from https://www.ncqa.org/hedis/measures/comprehensive-diabetes-care. Accessed 4 October 2023
23.
U.S. Department of Health and Human Services
.
Healthcare Effectiveness Data and Information Set (HEDIS)
. Available from https://health.gov/healthypeople/objectives-and-data/data-sources-and-methods/data-sources/healthcare-effectiveness-data-and-information-set-hedis. Accessed 4 October 2023
24.
Battelino
T
,
Danne
T
,
Bergenstal
RM
, et al
.
Clinical targets for continuous glucose monitoring data interpretation: recommendations from the International Consensus on Time in Range
.
Diabetes Care
2019
;
42
:
1593
1603
25.
Gregg
EW
,
Patorno
E
,
Karter
AJ
, et al
.
Use of real-world data in population science to improve the prevention and care of diabetes-related outcomes
.
Diabetes Care
2023
;
46
:
1316
1326
26.
Reaven
PD
,
Newell
M
,
Rivas
S
,
Zhou
X
,
Norman
GJ
,
Zhou
JJ.
Initiation of continuous glucose monitoring is linked to improved glycemic control and fewer clinical events in type 1 and type 2 diabetes in the Veterans Health Administration
.
Diabetes Care
2023
;
46
:
854
863
27.
Billings
LK
,
Parkin
CG
,
Price
D.
Baseline glycated hemoglobin values predict the magnitude of glycemic improvement in patients with type 1 and type 2 diabetes: subgroup analyses from the DIAMOND study program
.
Diabetes Technol Ther
2018
;
20
:
561
565
28.
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
:
324
331
29.
de Boer
IH
;
DCCT/EDIC Research Group
.
Kidney disease and related findings in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study
.
Diabetes Care
2014
;
37
:
24
30
30.
Lachin
JM
,
Orchard
TJ
,
Nathan
DM
;
DDCT/EDIC Research Group
.
Update on cardiovascular outcomes at 30 years of the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study
.
Diabetes Care
2014
;
37
:
39
43
31.
Martin
CL
,
Albers
JW
,
Pop-Busui
R;
DCCT/EDIC Research Group
.
Neuropathy and related findings in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study
.
Diabetes Care
2014
;
37
:
31
38
32.
Vigersky
RA
,
McMahon
C.
The relationship of hemoglobin A1C to time-in-range in patients with diabetes
.
Diabetes Technol Ther
2019
;
21
:
81
85
33.
El Malahi
A
,
Van Elsen
M
,
Charleer
S
, et al
.
Relationship between time in range, glycemic variability, HbA1c, and complications in adults with type 1 diabetes mellitus
.
J Clin Endocrinol Metab
2022
;
107
:
e570
e581
34.
Yapanis
M
,
James
S
,
Craig
ME
,
O’Neal
D
,
Ekinci
EI.
Complications of diabetes and metrics of glycemic management derived from continuous glucose monitoring
.
J Clin Endocrinol Metab
2022
;
107
:
e2221
e2236
35.
De Meulemeester
J
,
Charleer
S
,
Visser
MM
,
De Block
C
,
Mathieu
C
,
Gillard
P.
The association of chronic complications with time in tight range and time in range in people with type 1 diabetes: a retrospective cross-sectional real-world study
.
Diabetologia
2024
;
67
:
1527
1335
36.
Raghinaru
D
,
Calhoun
P
,
Bergenstal
RM
,
Beck
RW.
The optimal duration of a run-in period to initiate continuous glucose monitoring for a randomized trial
.
Diabetes Technol Ther
2022
;
24
:
868
872
37.
Farhan
HA
,
Bukhari
K
,
Grewal
N
,
Devarasetty
S
,
Munir
K.
Use of continuous glucose monitor as a motivational device for lifestyle modifications to improve glycaemic control in patients with type 2 diabetes treated with non-insulin therapies
.
BMJ Case Rep
2022
;
15
:
e248579
38.
Fritschi
C
,
Kim
MJ
,
Srimoragot
M
,
Jun
J
,
Sanchez
LE
,
Sharp
LK.
"Something tells me I can't do that no more": experiences with real-time glucose and activity monitoring among underserved Black women with type 2 diabetes
.
Sci Diabetes Self Manag Care
2022
;
48
:
78
86
39.
Runge
AS
,
Kennedy
L
,
Brown
AS
, et al
.
Does time-in-range matter? Perspectives from people with diabetes on the success of current therapies and the drivers of improved outcomes
.
Clin Diabetes
2018
;
36
:
112
119
40.
Clark
TL
,
Polonsky
WH
,
Soriano
EC.
The potential impact of continuous glucose monitoring use on diabetes-related attitudes and behaviors in adults with type 2 diabetes: a qualitative investigation of the patient experience
.
Diabetes Technol Ther. Online ahead of print on
13
May
2024
(doi: 10.1089/dia.2023.0612)
41.
Pangrace
M
,
Dolan
S
,
Grace
T
, et al
.
AMCP Market Insights Health Plan Best Practice: implementing continuous glucose monitoring to improve patient outcomes in diabetes
.
J Manag Care Spec Pharm
2024
;
30
(Suppl. 1a
):
S1
S15
42.
Pathak
S
,
Kearin
K
,
Kahkoska
AR
, et al
.
Impact of expanding access to continuous glucose monitoring systems among insulin users with type 1 or type 2 diabetes
.
Diabetes Technol Ther
2023
;
25
:
169
177
43.
United Healthcare
.
Medicare: continuous glucose monitor pharmacy availability
. Available from https://www.uhcprovider.com/en/resource-library/news/2022/continuous-glucose-monitor-pharmacy-availability.html. Accessed 18 January 2024
44.
Ohio Department of Medicaid
.
Prior authorization requirements
. Available from https://medicaid.ohio.gov/resources-for-providers/billing/prior-authorization-requirements/prior-authorization-requirements. Accessed 21 May 2024
45.
Anthem
.
New guidance on continuous glucose monitors (CGMs) from Ohio Department of Medicaid (ODM)
. Available from https://providernews.anthem.com/ohio/articles/a-message-from-ohio-department-of-medicaid-new-guidance-on-c-17547. Accessed 21 May 2024
46.
Edelman
SV
,
Cavaiola
TS
,
Boeder
S
,
Pettus
J.
Utilizing continuous glucose monitoring in primary care practice: what the numbers mean
.
Prim Care Diabetes
2021
;
15
:
199
207
47.
Centers for Medicare & Medicaid Services
.
Glucose Monitors: L33822
. Available from https://www.cms.gov/medicare-coverage-database/view/lcd.aspx?lcdid=33822&ver=55. Accessed 13 October 2023
48.
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
:
184
189
49.
Shields
S
,
Norman
GJ
,
Thomas
R
,
Ciemins
EL.
HbA1c improvements after initiation of real-time continuous glucose monitoring in primary care patients with type 2 diabetes
.
J Diabetes Sci Technol
2023
;
17
:
1423
1424
50.
Ajjan
RA
,
Battelino
T
,
Cos
X
, et al
.
Continuous glucose monitoring for the routine care of type 2 diabetes mellitus
.
Nat Rev Endocrinol
2024
;
20
:
426
440
51.
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
:
365
374
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.