Use of Glucose Monitoring Devices Among Adults with Diabetes in Germany: Results from Nationwide Surveys Conducted in 2017 and 2021/2022

Abstract

Background:

Devices for continuous glucose monitoring (CGM) have been developed to optimize blood glucose control and liberate people with diabetes from finger-prick glucose measurements. Since 2016, the devices have been reimbursed in Germany for people with diabetes receiving insulin therapy, resulting in their increased use among people with type 1 diabetes (T1D) and type 2 diabetes (T2D). We investigated the prevalence of CGM use and its associated factors among German adults with diabetes in 2017 and 2021/2022.

Methods:

Participants aged 18 years or older with diagnosed diabetes were identified from two nationwide population-based telephone surveys in 2017 (n = 1396) and 2021/2022 (n = 1456). Prevalence and dynamics of CGM use were examined overall and stratified by sociodemographic and diabetes-related characteristics. Factors associated with CGM use were obtained from logistic regression models.

Results:

The overall prevalence of CGM use was 8.2% in 2017 and 16.6% in 2021/2022. An increase in CGM use was observed across all the subgroups except for those without antidiabetic medications. CGM use increased from 31.1% to 75.4% in adults with T1D, from 6.3% to 13.6% in adults with T2D, and from 14.6% to 36.7% in all insulin users. In both surveys, younger age, insulin use, T1D, and reporting hypoglycemia were associated with CGM use. In addition, in 2017, higher education level and absence of obesity were associated with CGM use, whereas in 2021/2022, participation in the diabetes self-management education program and higher self-assessed quality of diabetes care were associated with CGM use.

Conclusion:

Among adults with diabetes in Germany, CGM use increased about twofold within 5 years, irrespective of sociodemographic factors. Educational inequality in CGM use diminished over time. The higher self-rated quality of diabetes care associated with the recent use of CGM provides further evidence to support its use among all adults with diabetes in Germany.

Introduction

Self-monitoring of blood glucose (SMBG) is an effective tool for achieving guideline-based, individualized blood glucose targets, which is critical for reducing the risk of diabetes-related complications and improving quality of life.¹ Therefore, people with diabetes should be trained to measure their own blood glucose levels and interpret the results correctly.^1,2 Conventionally, people with diabetes have to perform up to 10 repeated finger-prick blood glucose tests a day,³ which is painful and particularly burdensome for younger children and elderly adults.

Continuous glucose monitoring (CGM) is a wearable medical device designed to free people with diabetes from the inconvenience of finger-prick blood glucose monitoring.^4,5 There are two types of personal CGMs available, namely, real-time CGM (rtCGM) and intermittent scanned CGM (isCGM, also known as flash glucose monitoring, FGM).⁴ Both methods measure the glucose concentration in the interstitial fluid via a subcutaneous sensor. rtCGM measures and transmits glucose values directly to a reading device every several minutes, whereas isCGM measures glucose levels intermittently and requires a scanning device to read the measured values. Unlike conventional blood glucose meters, which only take spot readings, CGM provides continuous glucose data and trends over time. Thus, immediate actions, such as adjusting the insulin dose, can be taken to prevent hypoglycemia and hyperglycemia. rtCGM allows automated alarms for hypo- and hyperglycemia. In addition, the real-time visual display of glucose levels enables users to link food choices and exercise habits with glucose response, thus contributing to a healthier lifestyle.⁶ The positive effects of CGM use on clinical outcomes superior to those of conventional SMBG have been well documented,⁷ including reductions in glycated hemoglobin A1c (HbA1c),^8

–11 hypoglycemic and diabetic ketoacidosis events,^9,12,13 and hospitalizations for acute diabetes complications.¹⁴ In addition, CGM data can be shared remotely with health care professionals to facilitate treatment adjustments and reduce repeated clinic visits. Furthermore, CGM-derived metrics such as time in range, time above and below range, and glycemic variability have been included in national¹⁵ and international clinical guidelines as important indicators for the assessment of glycemic control and quality of diabetes care.^16
–18 The limitations of using CGM include the inconvenience of wearing the device, replacement of the sensor every 7 − 14 days, and local dermatitis.¹⁹ Despite this, CGM is considered an ideal blood glucose monitoring system and has been increasingly used in individuals on insulin therapy since its introduction to the market. There is now ample evidence to support the use of CGM not only in insulin-dependent people, such as those with type 1 diabetes (T1D), but also in people with type 2 diabetes (T2D), regardless of the treatment regimen.^10,11,20 Currently, rtCGM is the most widely used technology.

Germany is one of the countries with a large diabetes population of about 7 million people.²¹ It is estimated that more than 90% of people with documented diabetes in Germany have T2D, and approximately, 5.5% have T1D.²² While individuals with T1D are completely dependent on insulin therapy, approximately 37% of adults with T2D are treated with insulin.²¹ Despite the large potential for glucose control, CGM use was limited in the earlier years after its market introduction owing to high out-of-pocket costs. Since 2016, when German health insurers started to cover CGM for people on insulin therapy,²³ the use of CGM has increased exponentially not only in people with T1D²⁴ but also in people with T2D receiving insulin therapy.²⁵ While almost all children and adolescents with T1D in Germany now use CGM,²⁴ CGM may still be underutilized in adults with T1D^26
–28 and particularly in adults with T2D.^27,28

To date, few population-based studies have comprehensively examined CGM use over time and its associated factors in adults with diabetes in Germany.²⁷ Understanding the dynamics of CGM use in subgroups over time and the factors associated with CGM use could help to identify barriers and facilitators of CGM use and subsequently improve CGM accessibility and the quality of diabetes care. Against this background, we examined the proportion of CGM use over time, overall and in subgroups, and potential sociodemographic and diabetes-related correlates, using data from two nationwide population-based surveys conducted among adults with diabetes in Germany in 2017 and 2021/2022.

Methods

Study design

The nationwide telephone surveys “Disease Knowledge and Information Needs-Diabetes mellitus 2017” and “German Health Update (GEDA) 2021/2022-Diabetes” were conducted among adults aged ≥18 years between September and November 2017 (2017 survey) and between December 2021 and April 2022 (2021/2022 survey),^21,29 respectively. The study design and sampling methods of both surveys have been previously described in detail elsewhere.^21,29 Both surveys used the established dual-frame method to generate random samples of telephone numbers at the national level, including both landline and cell phone numbers for all potentially reachable private households. Participants in both surveys were interviewed using structured, computer-assisted telephone interviews (CATI). For the 2017 survey, adults with and without diabetes were recruited, whereas for the 2021/2022 survey, only adults with diabetes were targeted. In both surveys, the filter question “Have you ever been diagnosed with diabetes by a physician?” was used to identify individuals with diabetes.

Study population

A total of 1479 and 1503 adults who reported ever having been diagnosed with diabetes completed the CATI in 2017²⁹ and 2021/2022,²¹ respectively. These adults with diabetes were asked further whether they had diabetes in the past 12 months and whether they were currently taking any antidiabetic medication (i.e., insulin, oral agents, and noninsulin injectables). We also asked women who had ever been diagnosed with diabetes whether the diagnosis had occurred during pregnancy. For the present analyses, we included adults with current diabetes, namely those with diabetes in the past 12 months or current antidiabetic medication (n = 1396 in 2017; n = 1456 in 2021/2022, Table 1). Women with gestational diabetes only (n = 40; n = 7) and those who neither reported the presence of diabetes in the past 12 months nor were taking antidiabetic medication (n = 43; n = 40) were excluded.

Table 1.

Descriptive Characteristics of Adults with Diabetes in Nationwide Surveys Conducted in 2017 (n = 1396) and 2021/2022 (n = 1456) in Germany

	2017			2021/2022
	n	%	[95% CI]	n	%	[95% CI]	P
Sociodemographics
Sex
Men	719	50.1	[46.8–53.5]	781	55.0	[51.5–58.4]	0.0497
Women	677	49.9	[46.5–53.2]	675	45.0	[41.6–48.5]
Age group, years
18–64	430	43.7	[40.3–47.1]	330	43.1	[39.4–46.9]	0.828
65+	966	56.3	[52.9–59.7]	1126	56.9	[53.1–60.6]
Urbanicity
Rural area	588	50.3	[46.8–53.8]	600	51.1	[47.4–54.8]	0.749
Cities	675	49.7	[46.2–53.2]	761	48.9	[45.2–52.6]
Education
Primary	395	46.8	[43.4–50.2]	420	50.8	[47.2–54.3]	0.112
Middle/High	999	53.2	[49.8–56.6]	1030	49.2	[45.7–52.8]
Living with partner
Yes	869	62.0	[58.7–65.1]	899	60.2	[56.7–63.6]	0.460
No	525	38.0	[34.9–41.3]	555	39.8	[36.4–43.3]
Disease-related characteristics
Diabetes type
Type 1	68	7.6	[5.5–1.5]	40	5.5	[3.6–8.4]	0.239
Type 2	1312	92.4	[89.5–94.5]	1378	94.5	[91.6–96.4]
Diabetes duration, years
0–4	175	14.8	[12.4–17.5]	257	20.0	[17.3–23.1]	0.015
≥5–14	581	41.8	[38.6–45.1]	521	37.0	[33.5–40.6]
≥15	632	43.4	[40.2–46.7]	659	43.0	[39.5–46.5]
Ever participating in DSME
Yes	1002	73.0	[70.0–75.8]	925	66.1	[62.7–69.4]	0.003
No	394	27.0	[24.2–30.0]	529	33.9	[30.6–37.3]
PACIC
<Mean	768	57.1	[53.6–60.5]	821	53.7	[50.1–57.3]	0.192
≥Mean	515	42.9	[39.5–46.4]	613	46.3	[42.7–49.9]
Treatment pattern
Insulin	665	50.3	[47.0–53.6]	542	41.4	[37.9–45.0]	0.000
Noninsulin only	626	43.1	[39.9–46.4]	760	48.5	[44.9–52.0]
No medication	104	6.6	[5.2–8.3]	147	10.1	[8.2–12.5]
Any diabetes complication
Yes	479	36.3	[33.2–39.6]	374	26.4	[23.5–29.6]	0.000
Obesity
Yes	541	45.5	[42.2–48.8]	540	43.9	[40.4–47.5]	0.540
Hypoglycemia
Yes	490	37.5	[34.3–4.9]	385	29.5	[26.1–33.1]	0.001
CGM use
Yes	107	8.2	[6.6–10.3]	196	16.6	[13.8–19.9]	0.000

P-values for difference in proportion between the two surveys are based on Rao–Scott chi-square test.

Missing values: CGM use (n = 18 in survey 2017; n = 3 in survey 2021/2022), urbanicity (n = 133; n = 95), living with partner (2; 2), education (2; 6), diabetes type (16; 38), diabetes duration (8; 19), ever participating in DSME (0; 2), treatment pattern (1; 7), any diabetes complication (64; 96), obesity (21; 31), hypoglycemia (26; 32), PACIC (113; 22).

DSME, diabetes self-management education; PACIC, patient assessment of chronic illness care; CGM, continuous glucose monitoring; 95% CI, 95% confidence interval.

The 2017 survey was approved by the German Federal Commissioner for Data Protection and Freedom of Information and the Ethics Committee of the Berlin Medical Association (Eth-23/17). The survey in 2021/2022 is subject to strict compliance with the data protection regulations of the EU General Data Protection Regulation and the German Federal Data Protection Act (BDSG); the survey was approved by the Ethics Committee of the Charité-Universitätsmedizin Berlin (EA2/252/21). The participants were fully informed of the study objectives, interview procedures, and pseudonymized data collection and analysis. Verbal informed consent was obtained from all participants before the interviews.

Assessment of CGM use

In the 2017 survey, individuals with diabetes, who indicated blood glucose measurement on at least 1 day to the question of how many days in the past 7 days they measured their blood glucose, were further asked about the method used. Individuals who indicated “yes” to the question “Do you use a meter with a sensor in the subcutaneous fatty issue? This includes CGM or flash systems” were considered as CGM users.

In the 2021/2022 survey, individuals who responded “yes” to the question “Do you-or do family members for you-perform blood glucose self-monitoring?” were further asked “What method of blood glucose monitoring do you use?”. Individuals who selected the answer option “with a sensor in the subcutaneous fatty tissue, including CGM or flash systems” were considered as CGM users.

Assessment of factors potentially related to CGM use

Factors potentially related to CGM use included sociodemographic and diabetes-related characteristics, which were selected based on a literature review^30,31 and availability of data assessed by similar questions in both surveys. The assessment and definition of these variables have been described previously.^21,32

Age was dichotomized into two groups (18–64 vs. ≥65 years) considering the sample size and age distribution of CGM users. Urbanicity was classified as rural area (<20,000 inhabitants) or cities (≥20,000 inhabitants) based on population density. Living with a partner was defined as living with a spouse or partner (yes or no). Educational level was classified as primary, middle, and high educational level^21,32 and dichotomized (primary vs. middle/high) because of the small number of cases in the subgroup with high educational levels.

Individuals likely to have T1D were defined using a previously used algorithm (age at diagnosis <30 years and insulin use immediately after diagnosis and currently).³³ Diabetes duration was calculated by subtracting the age at diagnosis from the current age and was grouped as <5, 5–14, and ≥15 years. Participation in a structured diabetes self-management education (DSME) program was assessed as a dichotomous variable (yes or no). The German version of the diabetes-adapted 9-item Patient Assessment of Chronic Illness Care (PACIC)-DAWN Short Form was used to examine the quality of diabetes care as perceived by participants with diabetes. A higher PACIC score (ranging from 1 to 5) indicates a higher quality of care.^21,32 The mean of the average PACIC score from the two surveys in 2017 and 2021/2022 was used as the cutoff for a dichotomized variable (≥2.43 vs. <2.43). Current treatment of diabetes was grouped as the use of insulin (alone or in combination), exclusive use of noninsulin medications (oral agents and/or noninsulin injectables), and no antidiabetic medication (lifestyle intervention, including exercise and/or diet, no treatment). Diabetes-specific complications included self-reported diabetic retinopathy, nephropathy, neuropathy, diabetic foot, and diabetes-related amputation, aggregated into a dichotomous variable (yes vs. no). Body mass index (BMI) was calculated from the self-reported body height and weight. Obesity was defined as a BMI ≥30 kg/m² (yes vs. no). Hypoglycemia was reported by respondents via the question “Now think about the past 12 months. Have you experienced hypoglycemia during this time?” (yes or no).^21,32

Statistical analyses

Statistical analyses were performed using Stata software (version 17, StataCorp, USA). To ensure the national representativeness of the results, survey-specific weights were calculated to account for differences in the distribution of sociodemographic characteristics (age, sex, education) between the study populations and persons with known diabetes from large nationwide surveys, that is, from GEDA 2012 and GEDA 2019/2020-EHIS, as previously described.^21,29

Descriptive statistics were used to assess the characteristics of the study population in each survey. The prevalence of CGM use, overall and within subgroups, and the distribution of sociodemographic and diabetes-related characteristics were compared between the two surveys using the Rao–Scott chi-square tests. Multivariable logistic regression (MLR) analyses with CGM use as the dependent variable were performed to identify the factors associated with CGM use within each survey. Model 1 included only the sociodemographic variables. Models 2, 3, and 4 included general diabetes characteristics, treatment regimens, and complications, respectively, adjusting for all sociodemographic factors in Model 1. To estimate the adjusted change in CGM use from 2017 to 2021/2022, we pooled data from both surveys and included the survey period (2021/2022 vs. 2017) as an independent variable in the respective models. The adjusted changes in CGM use and 95% confidence intervals were derived from the respective MLR models.

A total of 18 (1.3%) individuals in 2017 and 3 (0.2%) individuals in 2021/2022 had missing information on CGM use. The proportion of missing observations for other variables ranged from 0.04% to 8.0% (footnote of Table 1). Considering the relatively high number of missing observations for some variables, we used multiple imputation by the chained equation (n = 10) for MLR analyses, assuming missing at random. MLR models were run on the 10 imputed datasets, and the estimates were combined to account for variability within and across imputations. A complete case analysis was performed as a sensitivity analysis (Supplementary Table S1). To identify the factors associated with T2D, we repeated all complete case MLR models, excluding individuals with defined T1D (Supplementary Table S2). A P value of <0.05 was considered as significant based on two-tailed tests.

Results

Characteristics of the study populations

The study populations of the two surveys in 2017 and 2021/2022 were similar with respect to the distribution of age groups, urbanicity, educational attainment, and living with a partner. Compared with individuals in the 2017 survey, those in the 2021/2022 survey were more likely to be male (Table 1).

In terms of diabetes-related characteristics, the study population of the two surveys did not differ with respect to the proportions of diabetes types, prevalence of obesity, and PACIC score. Individuals in the 2021/2022 survey were less likely to have participated in the DSME, to use insulin, and to report diabetes complications and hypoglycemic events, but they were more likely to use noninsulin medications and to have no antidiabetic medication than those in the 2017 survey. In addition, individuals in the 2021/2022 survey more often had a duration of diabetes of 0–4 years, but less often had a diabetes duration of 5–14 years than those in the 2017 survey (Table 1).

CGM use prevalence over time

A total of 107 and 196 adults with diabetes were identified as CGM users in the 2017 survey and 2021/2022 survey, respectively. The prevalence of CGM use increased from 8.2% in the 2017 survey to 16.6% in the 2021/2022 survey. Specifically, the prevalence of CGM use increased from 2017 to 2021/2022 among adults with T1D (31.1%–75.4%), adults with T2D (6.3%–13.6%), and all insulin users (14.6%–36.7%). The prevalence of CGM use also increased in all other subgroups, except for those not participating in the DSME, those with a diabetes duration <5 years, those with noninsulin medications only, those without any antidiabetic medication, and those who did not report hypoglycemic events in the past 12 months (Fig. 1).

FIG. 1.

Prevalence (95% CI) of continuous glucose monitoring device use among adults with diabetes in Germany in 2017 (n = 1396) and 2021/2022 (n = 1456)—overall and by sociodemographic and diabetes-related characteristics. 95% CI, 95% confidence interval.

Among persons with T2D on insulin therapy (46.3% in 2017 and 38.4% in 2021/2022 of all persons with T2D), the prevalence of CGM use increased from 11.6% in 2017 to 31.3% in 2021/2022 (data not shown in Fig. 1).

After adjusting for sociodemographic variables, the overall prevalence difference of CGM use from 2017 to 2021/2022 was 8.7 percentage points (model 1). An increase was observed in all subgroups of sociodemographic variables (Model 1), diabetes-related characteristics (Model 2), treatment groups (Model 3, except for the group without antidiabetic medication), and diabetes complications/comorbidities (Model 4) (Table 2).

Table 2.

Unadjusted and Adjusted Changes in the Prevalence of Continuous Glucose Monitoring Device Use Between 2017 and 2021/2022 (in Percentage Points) Among Adults with Diabetes in Germany—Overall and by Sociodemographic and Diabetes-Related Characteristics

	Unadjusted			Adjusted
	Percentage points	95% CI	95% CI	Percentage points	95% CI	95% CI
Model 1
Overall	8.4	4.8	11.9	8.7	5.0	12.4
Sociodemographics
Sex
Men	10.6	5.2	16.0	9.4	5.3	13.5
Women	5.5	1.1	9.8	7.7	4.2	11.2
Age group, years
18–64	10.9	3.7	18.1	11.7	6.3	17.1
65+	6.6	3.8	9.3	6.2	3.7	8.8
Urbanicity
Rural area	10.3	4.7	16.0	9.1	5.1	13.0
Cities	7.0	1.9	12.1	8.3	4.6	12.0
Education
Primary	8.2	3.6	12.8	7.8	4.2	11.5
Middle/high	8.6	3.3	13.9	9.4	5.4	13.4
Living with partner
Yes	8.4	3.5	13.2	9.3	5.3	13.3
No	8.4	3.3	13.4	7.5	3.9	11.1
Model 2
Disease-related characteristics
Diabetes type
Type 1	44.2	19.1	69.4	23.1	13.6	32.6
Type 2	7.2	4.0	10.4	9.8	6.1	13.4
Diabetes duration, years
<5	2.1	−4.5	8.7	7.9	4.1	11.6
≥5–14	9.3	3.3	15.4	9.7	5.7	13.8
≥15	11.0	5.2	16.7	12.4	7.6	17.3
Ever participating in DSME
Yes	11.5	7.0	16.1	11.7	7.3	16.0
No	3.5	−1.8	8.8	7.4	3.9	11.0
PACIC
<Mean	4.5	0.4	8.7	9.4	5.7	13.1
≥Mean	12.6	6.3	18.9	11.6	7.2	15.9
Model 3
Treatment pattern
Insulin	22.0	15.1	28.9	21.1	14.4	27.8
Noninsulin only	0.5	−1.5	2.6	2.4	1.3	3.6
No medication	2.2	—	—	1.6	−0.6	3.8
Model 4
Any diabetes complication
Yes	13.7	6.6	20.8	14.2	9.9	18.5
No	7.1	2.8	11.4	10.8	7.7	14.0
Obesity
Yes	7.6	3.1	12.0	10.6	7.3	14.0
No	9.0	3.6	14.3	13.0	9.4	16.6
Hypoglycemia
Yes	28.4	19.8	37.0	26.3	18.3	34.2
No	1.8	−0.5	4.1	4.5	2.8	6.2

Changes in CGM use in 2017 vs. 2021/2022 and their 95% confidence intervals (95% CIs) were derived from pooled regression models with CGM use as the dependent variable and the survey period as independent variable. Unadjusted changes in CGM use were derived from bivariable regressions. Adjusted changes were derived from multivariable regression models: Model 1 included all sociodemographics. Model 2 = Model 1 + general diabetes characteristics. Model 3 = Model 1 + treatment pattern; Model 4 = Model 1 + complication/comorbidities.

Correlates of CGM use

Both surveys found that age <65 years, T1D, insulin use, and reporting of a hypoglycemic event were associated with CGM use. In addition, a middle/high education level and non-obesity were found to be associated with CGM use in 2017, whereas participation in the DSME and a higher PACIC score were found to be associated with CGM use in 2021/2022 only (Table 3).

Table 3.

Factors Associated with Glucose Monitoring Device Use Among Adults with Diabetes in Germany in 2017 (n = 1396) and 2021/2022 (n = 1456)

	2017				2021/2022
	OR	95% CI	95% CI	P	OR	95% CI	95% CI	P
Model 1
Sex
Women vs. men	1.00	0.60	1.65	0.996	0.73	0.47	1.12	0.153
Age group, years
18–64 vs. 65+	2.30	1.41	3.75	0.001	2.10	1.38	3.19	0.001
Urbanicity
Rural area vs. cities	0.95	0.56	1.60	0.843	1.22	0.76	1.94	0.407
Education
Middle/high vs. primary	1.85	1.02	3.36	0.041	1.29	0.81	2.04	0.287
Living with partner
Yes vs. no	1.55	0.84	2.84	0.157	1.24	0.76	2.02	0.389
Model 2
Diabetes type
Type 1 vs. type 2	3.52	1.60	7.72	0.002	11.64	3.17	42.79	0.000
Diabetes duration, years
≥ 5–14 vs. < 5	1.00	0.40	2.50	0.999	1.45	0.68	3.07	0.336
≥ 15 vs. < 5	1.83	0.73	4.57	0.198	1.94	0.86	4.36	0.109
Ever participating in DSME
Yes vs. no	1.60	0.79	3.27	0.193	2.20	1.08	4.45	0.029
PACIC
≥Mean vs. < mean	1.05	0.63	1.75	0.851	1.79	1.08	2.98	0.024
Model 3
Treatment pattern
Insulin use vs. noninsulin only	7.71	3.60	16.5	0.000	23.6	12.3	45.3	0.000
No medication vs. noninsulin only	—	—	—	—	0.89	0.20	3.97	0.875
Model 4
Any diabetes complication
Yes vs. no	1.46	0.86	2.47	0.159	1.70	0.99	2.92	0.056
Obesity
Yes vs. no	0.50	0.29	0.86	0.012	0.76	0.46	1.25	0.276
Hypoglycemia
Yes vs. no	5.52	3.00	10.1	0.000	15.1	9.08	25.1	0.000

Odds ratios (ORs) and 95% confidence intervals (CIs) were obtained from survey-specific regression models with CGM use as the dependent variable. Model 1: sociodemographic variables only; Model 2 = Model 1 + general diabetes characteristics; Model 3 = Model 1 + treatment pattern; Model 4 = Model 1 + complication/comorbidities.

The results of the correlates of CGM use from the complete case analyses showed no substantial changes in the odds ratios. However, the association with CGM use was no longer significant for education (P = 0.132) in the 2017 survey and PACIC score (P = 0.081) in the 2021/2022 survey and gained statistical significance for diabetes complications in the 2021/2022 survey (P = 0.005, Supplementary Table S1). When adults with T1D were excluded from complete case analyses, the results remained similar to those shown in Supplementary Table S1, with the exception of also gaining significance for diabetes complications in 2017 (P = 0.022) and losing significance for non-obesity in 2017 (P = 0.077) and for age <65 years in 2021/2022 (P = 0.054; Supplementary Table S2).

Discussion

Main findings

Using data from two population-based nationwide surveys, we found that the prevalence of CGM use among adults with diabetes in Germany doubled within about 5 years from 8.2% in 2017 to 16.6% in 2021/2022. CGM use increased from 31.1% to 75.4% in adults with T1D and from 6.3% to 13.6% in adults with T2D. In both surveys, age below 65 years, presence of T1D, insulin use, and reporting hypoglycemia were strongly associated with CGM use. In addition, higher education level and absence of obesity were associated with CGM use in 2017, whereas in 2021/2022, DSME participation and higher self-assessed quality of diabetes care were associated with CGM use.

CGM use in Germany and internationally

In Germany, most of the studies, although not all,^34,35 that investigated CGM use to date have used data from the Diabetes Prospective Follow-up (DPV) registry and mainly focused on individuals with T1D, particularly on children and adolescents^{12,26,36
–38} and, to a lesser extent, on adults ≥18 years^26,35 or adults ≥60 years.²⁷ Before 2016, the use of CGM remained relatively low at 4%–6% for children and adolescents^24,26,38 and at 9.6% for adults with T1D.³⁵ Since 2016, when health insurance companies began covering CGM devices for patients on insulin therapy,²³ the use of CGM in patients with T1D has steadily increased, particularly in children and adolescents. Among patients with T1D aged <26 years, use of CGM jumped from 17.9% in 2016 to 42.0% in 2017, 59.4% in 2018, 70.3% in 2019,³⁷ and 82.9% between 2018 and 2021.³⁶ In 2022, more than 95% of children and adolescents with T1D used CGM.²⁴ However, among adults with T1D in the DPV registry, an increase in CGM use from 4% in 2015 to 30% in 2017 was observed only among those aged 18–26 years, whereas CGM use remained stable at approximately 8%–12% in older adults between 2011 and 2017.²⁶ Among adults aged ≥60 years, CGM use has increased from 28% in 2019 to 45% in 2021.²⁷ Considering both younger and older adults, we found an increase in CGM use among adults with T1D from 31.1% in 2017 to 75.4% in 2021. Interestingly, an investigation conducted among 336 diabetologists across Germany found that 77.3% of their patients with T1D used CGM in 2023,²⁸ similar to our finding of 75.4% in 2021/2022.

In contrast to CGM use in T1D, CGM use in T2D has been less investigated in Germany, and only limited data are available.^27,28 The nationwide survey among diabetologists revealed that 22.4% of their patients with T2D treated in clinical practices/centers were using CGM in 2023.²⁸ Among adults aged ≥60 years with T2D on insulin therapy in the DPV registry, CGM use increased from 10% in 2019 to 18% in 2021.²⁷ In comparison, our finding of 13.6% among adults with T2D in 2021 is slightly lower than the results of the two studies.^27,28 However, it should be noted that only adults with T2D who received insulin therapy were included in the study based on the DPV registry data²⁷ and no detailed information concerning T2D patient characteristics, such as age and sex, was found in the report of diabetologists.²⁸ In our study, less than 40% of individuals with T2D in 2021 were insulin users, and CGM use in individuals without insulin use was low. CGM use in individuals taking only oral/noninsulin injectable agents (2.1% in 2017 and 2.6% in 2021/2022) and in individuals not taking any antidiabetic medication (0 in 2017 and 2.2% in 2021/2022) in our study added new data on CGM use among adults with diabetes in Germany.

The significant increase in CGM use in Germany between 2017 and 2021 could be attributed to several factors. CGM is an advanced technology with benefits for monitoring blood glucose levels. Over the past decade, there has been a significant advancement in the development of accurate and user-friendly CGM products,³⁹ making CGM more acceptable among people with diabetes. In addition, clinical guidelines recommend that individuals with T1D undergoing insulin therapy should be prescribed rtCGM devices for the management of glucose.^15,40 Crucially, a change in reimbursement policies through insurance coverage has made CGM more affordable and accessible.⁴¹ This is reflected in the abrupt and immediate increase in CGM use in 2017, particularly in children with T1D.²⁴

Owing to differences in health care systems and reimbursement policies, the use of CGM varies in international studies. In Italy, a web-based survey of nationwide pediatric and adult diabetes care centers found that less than 2% of all patients with T2D and 40.8% of all patients with T1D (adults: 35%, pediatric patients: 57%) used CGM in 2018.⁴² Using 2015–2018 data from an integrated health care delivery system in Northern California, Karter et al. reported a low CGM use of <1% for adults with T2D, but a high CGM use of >60% for adults with T1D.⁴³ Data from the 2014–2020 Behavioral Risk Factor Surveillance System in the United States showed an increasing trend in yearly CGM use, with proportions ranging from 0.4% in 2014 to 4.1% in 2020 among adults with diabetes.⁴⁴ However, in a cohort of more than 30,000 adults with T2D receiving outpatient diabetes care at a medical center in the United States, 12.7% used CGM during the calendar year 2021.⁴⁵ However, a comparison of our study with other international studies on CGM use is only possible to a limited extent due to methodological differences such as study design, study population, and data collection—apart from differences in health care systems and reimbursement policies. While the results of national population-based health surveys are largely representative of the population, the results of targeted web-based surveys,⁴² patients from primary care,⁴⁵ or regional health care delivery systems⁴³ are often confined to specific patient groups. Similar to the reimbursement policy change in Germany, the reimbursement change of a commercial insurer on CGM use in the United States in 2018 resulted in an immediate 9.5% increase in the use for patients with T1D (from 30.5%) and 2.8% increase in use for patients with T2D treated with insulin (from 6.6%), respectively.⁴⁶ In Australia, following the change in health policy for CGM use in individuals with T1D aged <21 years, CGM uptake increased from 5% presubsidy to 79% after 2 years.⁴⁷ Currently, CGM is a standard therapy with a high rate of use among people with T1D in Germany¹⁵ and Western countries. In contrast, in people with T2D, the use of CGM remains relatively low, suggesting that additional efforts should be made to promote its use, given mounting evidence supporting CGM technology as the standard of care in all people with T2D, including those who are treated with less intensive therapies.^11,48
–50 German diabetologists expected that one in five patients with T2D without insulin therapy and over 70% of patients with T2D with intensified insulin therapy would use a CGM in the next 5 years.²⁸

Correlates of CGM use

It is not an unexpected observation of our study that T1D and insulin therapy were correlated with CGM use. Individuals with T1D on intensive insulin therapy (multiple daily injections or insulin pump therapy) and those on basal insulin need to closely control their blood glucose levels by repeated assessment of glycemic status to avoid hypo- and hyperglycemia. Repeated finger-prick blood tests can be avoided by using CGM, which is particularly beneficial for insulin users. For insulin users and patients with T1D, clinical guidelines strongly recommend CGM.^15,51

In both surveys, we found that CGM use was closely associated with a younger age. This was also the case when individuals with T1D were excluded from the study. Similarly, a study of patients with T2D in a medical center in the Southeastern United States showed that compared with CGM nonusers, CGM users were, on average, approximately 6 years younger.⁴⁵ Younger age as a correlate of CGM use has been consistently reported in prior studies among adults with T1D/T2D^27,44,45 and in adults ≥60 years with T2D.²⁷ Younger adults may be more willing to use and accept new technologies such as CGM. Furthermore, we found that reporting hypoglycemia in the past 12 months was strongly associated with CGM use in both surveys. Severe hypoglycemia is a life-threatening condition that occurs particularly in patients on insulin or specific oral antidiabetic medications (sulfonylurea and glinides). Thus, the prevention of hypoglycemia is an important aspect in the management of diabetes. rtCGM is capable of setting up alarms for high or low glucose levels and notifying the user of impending hypo- or hyperglycemia when glucose levels rise or fall (trend arrows) or reach specified thresholds. Consequently, individuals using CGM are more prone to report hypo- or hyperglycemia, most of which are asymptomatic and need no clinical treatment.⁵² In addition, a history of hypoglycemia may be an indication of CGM use, as adults with diabetes are more likely to be prescribed and reimbursed for CGM if they are unable to notice hypoglycemia reliably (hypoglycemia unawareness).⁵³

In addition to the common factors associated with CGM use in both surveys, higher education and not being obese were identified as determinants of CGM use in 2017, but not in 2021/2022. The differences in the determinants of CGM use in the two surveys likely reflect differences in the characteristics of CGM users and the impact of changes in the reimbursement policy for CGM. In Germany, a higher level of education is usually linked to a higher level of income and is inversely associated with obesity.⁵⁴ The financial burden previously associated with CGM devices in Germany was alleviated by the reimbursement policy of health insurance companies for CGM in 2016,²³ making CGM accessible to a larger group of patients. In children, adolescents, and young adults with T1D aged <26 years in Germany, the effect of area deprivation on CGM use decreased continuously from 2016 to 2018 and disappeared in 2019.³⁷

In 2021/2022, participation in DSME and higher PACIC scores were associated with CGM use. DSME is an integral component of diabetes therapy in Germany. DSME participants acquire knowledge about new technologies for blood glucose monitoring and measurement and are therefore more likely to use a CGM. In addition, specific CGM/FGM training courses, as part of self-management education, are provided in Germany, which are generally required before using CGM for the first time.⁵⁵ One of the major benefits of CGM is that people with diabetes can use glucose trend arrows to identify imminent hypo-/hyperglycemia. This improves the user’s subjective feeling of control over diabetes.⁵⁶ The use of CGM increases overall convenience, flexibility, and understanding of diabetes, as assessed by the Diabetes Treatment Satisfaction Questionnaire.⁵⁷ A higher self-assessed quality of diabetes care associated with CGM use may likely reflect the overall satisfaction with CGM use,^{5,34,58
–60} as well as reduced diabetes distress^61,62 and improved quality of life found in CGM users.

Strengths and limitations

Both surveys included population-based nationwide samples of adults with diabetes and were conducted using standardized methods and largely identical core questionnaires. The results of this study were weighted to national diabetic populations and, thus, can be generalized to the national noninstitutionalized adult population with diabetes in Germany. This study had some limitations. First, selection bias is inevitable in telephone surveys because of the relatively low response rate and the prior exclusion of certain groups of people, such as those with limited German language skills or those without a mobile or landline telephone. Second, owing to the cross-sectional design, the results of this study did not indicate a causal relationship between the factors investigated and CGM use. Third, it was not feasible to obtain blood samples for the measurement of biomarkers, including fasting plasma glucose and glycated HbA1c. Consequently, the potential benefits of CGM, such as achieving individual HbA1c therapy goals, could not be assessed. Furthermore, the type of diabetes was defined based on an algorithm, which may have led to misclassification bias. Clinical data, for example, the type of insulin, which may be associated with CGM use, were not collected in either survey, preventing us from further stratified analysis. Finally, the questions used to collect data on CGM use differed slightly between the two surveys. Therefore, we cannot rule out the possibility that the differences in the outcome variable (CGM use) were partly due to different questions.

Conclusions

The present study provides national epidemiological data on CGM use among adults with diabetes in Germany, indicating a significant increase in CGM use between 2017 and 2021/2022. An increase in CGM use was observed among adults with T1D and T2D and was present across nearly all subgroups irrespective of sociodemographic and diabetes-related characteristics. Factors such as technological advancements, health care professionals following the current recommendations of clinical guidelines,¹⁵ and reimbursement policies may have contributed to this trend. The educational inequality in CGM use found in the earlier survey was no longer observed in 2021/2022, whereas CGM use was associated with a higher self-rated quality of diabetes care, providing further evidence to support CGM use among all adults with diabetes in Germany.

Footnotes

Acknowledgments

The authors would like to thank Dr. Roma Thamm for her support in the scientific activities on the indicator of continuous glucose monitoring as part of the National Diabetes Surveillance Project at the Robert Koch Institute.

Authors’ Contributions

Y.D.: Conceptualization, methodology, formal analysis, writing—original draft, and visualization; J.B.: Conceptualization, methodology, data curation, and writing—review and editing; M.B.: Methodology and writing—review and editing; R.W.H.: Methodology and writing—review and editing; C.H.: Conceptualization, methodology, data curation, writing—review and editing, funding acquisition, and supervision.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This work and the conduction of the surveys “Disease Knowledge and Information Needs-Diabetes mellitus 2017” and “German Health Update (GEDA) 2021/2022-Diabetes” were supported by grants from the German Federal Ministry of Health within the framework of the National Diabetes Surveillance project at the Robert Koch Institute (grant numbers: GE20160358, 2522DIA700, and 2523DIA002).

Supplementary Material

Supplementary Table S1

Supplementary Table S2

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