Abstract
Objective.
To evaluate the effectiveness of continuous glucose monitoring (CGM) compared to point-of-care (POC) glucose testing in reducing HbA1c among adults with diabetes.
Methods.
This single-center, retrospective cohort study included adult patients with type 1 or type 2 diabetes attending Bayhealth Sussex Endocrinology Clinic between January 1, 2023 and December 31, 2024. Controls were POC testers, while the intervention group was CGM users. Data was extracted from Epic electronic medical records via SlicerDicer and chart review. Exclusion criteria included pregnancy, terminal illnesses (i.e. end-stage renal disease, cancer), and medications affecting CGM accuracy (i.e. hydroxyurea, ascorbic acid). The primary endpoint was change in HbA1c from baseline to 3- and 6-month follow-ups. Secondary endpoints included estimated average glucose (eAG) and CGM metrics (time sensor active, time in range (TIR), time above range (TAR)).
Results.
Of 90 patients, 63 used CGM and 27 were POC testers. Baseline characteristics were similar between the groups, though CGM patients had higher comorbidity burden and insulin use. CGM users achieved significantly greater HbA1c reduction than POC testers (-1.8% vs. -1%; p < 0.001). Improvements were most pronounced among patients with baseline HbA1c ≥9% (time sensor active of –2 with p = 0.227, TIR 13.4 with p = 0.43, high TAR –8.7 with p = 0.344, very high TAR 1.9 with p = 0.455) but significant among baseline HbA1c 7-9% (time sensor active of -1.5, TIR –3.6, high TAR 1.9, very high TAR -1, with p < 0.001 for all metrics). CGM compliance maintained high (81.9% at 3 months vs. 75.4% at 6 months, p = 0.021) with notable improvement of time-in-range (TIR) (1.8, p = 0.002).
Conclusion.
CGM use significantly improves glycemic outcomes for patients at-risk of developing HbA1c >9%, which supports broader CGM access for all patients with diabetes.
Introduction
Diabetes is a chronic metabolic condition characterized by persistent hyperglycemia, that, if left untreated, leads to life-threatening complications such as cardiovascular disease, kidney dysfunction, vision impairments, and neuropathy.1 Type 2 diabetes mellitus (T2DM) is the leading cause of diabetes worldwide and a major driver of mortality in the United States.1-3 T2DM arises from insulin resistance and is often influenced by genetic predisposition, elevated body mass index (BMI), and lifestyle factors (i.e. poor diet control and physical inactivity).1
Management modalities for diabetes target lifestyle modification, pharmacotherapy, and blood glucose level monitoring.2 Hemoglobin A1c (HbA1c) is a biomarker that reflects average blood glucose levels over 2-3 months, while point-of-care (POC) glucose testing provides instantaneous blood glucose readings.2 Continuous Glucose Monitoring (CGM) has emerged as a transformative approach to glycemic control given its capabilities for reporting real-time blood glucose data, identifying trends, and providing customizable alerts to prevent adverse health outcomes from hyper- and hypoglycemia.4 Through subcutaneous sensors that transmit to smart devices, CGM empowers patients to make informed decisions about insulin dosing, dietary choices, and overall lifestyle modifications while viewing their live glucose data.3 Unlike HbA1c, CGM is unaffected by conditions that alter red blood cell turnover, such as anemia, chronic kidney disease, and blood loss or transfusion.5
Although studies about CGM show improvement in self-management and glycemic awareness, access remains limited.4,6 Most insurers cover CGM only for insulin-dependent patients, excluding many patients with T2DM or gestational diabetes who might benefit from CGM.1,2 High out-of-pocket costs for CGM components (i.e. receiver/smart device, sensor replacements) further restrict access for socioeconomically disadvantaged populations.7
This study aims to evaluate CGM's effectiveness compared with POC testing in reducing HbA1c levels across a diverse endocrinology clinic population.
Methods
Study Design
We conducted a single-center, retrospective cohort analysis of patients followed for routine diabetes care at our institution’s endocrinology clinic from January 1, 2023 to December 31, 2024. The project was formally reviewed and approved by our institution’s Institutional Review Board (IORG #008401) under protocol reference #BH-139. Patients using POC fingerstick testing served as controls, while CGM users formed the intervention group.
Guided by the study aim, this analysis sought to answer each of the following research questions:
Among adults with type 1 or type 2 diabetes receiving routine endocrinology care, is CGM use associated with greater improvement in HbA1c form baseline to 3- and 6-month follow-up compared with POC fingerstick glucose monitoring?
Does baseline HbA1c severity modify the observed change in glycemic control among CGM users, particularly when comparing patients with less severely uncontrolled diabetes (HbA1c 7-9%) to those with very severely uncontrolled diabetes (HbA1c ≥ 9%)?
Among CGM users with less severely and very severely uncontrolled diabetes, how do CGM-derived measures of glycemic control (i.e. time sensor active, time-in-range (TIR), and time-above-range (TAR)) differ between follow-up visits?
Study Population
Eligible patients were ≥18 years old and diagnosed with type 1 or type 2 diabetes. Pregnancy-related diagnoses were excluded. Inclusion required at least one HbA1c result and three consecutive endocrinology visits during the study period. Both insulin and non-insulin users were included. Other factors excluding patients include inconsistent 3-6 month follow-up, poor HbA1c testing adherence, unclear CGM initiation date or CGM initiation prior to establishing care with the endocrinology clinic, significant comorbidities (end-stage renal disease, active chemotherapy, massive blood transfusion, uncontrolled anemia), >3 hospitalizations within one year, or medications interfering with CGM readability (e.g. hydroxyurea, high-dose acetaminophen, ascorbic acid).8-11 Patients with inconsistent adherence to home fingerstick glucose monitoring were included in the study. Average daily fingersticks were reviewed in relation to estimated average glucose (eAG). A flow chart of the patient selection process and list of the eligibility criteria can be found in Figure 1.
Figure 1. Flow Chart Demonstrating Patient Selection and Inclusion/Exclusion Criteria on Epic SlicerDicer Software.
Data Collection
Patient clinical data and downloaded CGM metrics were recorded onto Epic electronic medical record (EMR) during routine diabetes follow-up visits. Epic SlicerDicer identified 567 potentially eligible patients. Chart reviews were conducted on a randomized subset (n = 190). Baseline demographic data such as age, sex, race, body mass index (BMI), and smoking status were obtained through Epic SlicerDicer. Chart review refined eligibility and captured variables not extractable via SlicerDicer (i.e. physical activity, diet control, medication adherence, financial barriers).
For POC patients, the first three consecutive visits (3–6 months apart) were analyzed. Estimated average glucose (eAG) was derived from HbA1c using the American Diabetes Association (ADA) eAG Conversion Calculator.12 For CGM patients, device data (Freestyle Libre 2/3 (Abbott Diabetes Care Inc.; Abbott Park, IL) or Dexcom G6/G7 (Dexcom, Inc.; San Diego, CA)) were collected at follow-ups. CGM metrics analyzed include time sensor active, TIR, and TAR with categorization per International Consensus Criteria.13,14 Time-below-range was not analyzed given it was documented as 0% for all patients.
Study Endpoints and Assessments
The primary endpoint was the average change in HbA1c from baseline to 3- and 6-month follow-ups. Secondary endpoints included CGM metrics and eAG. Stratification by baseline HbA1c severity was employed: Group A (7-9%; less severely uncontrolled diabetes) and Group B (9-14%; very severely uncontrolled diabetes).15,16
CGM compliance was categorized as low (50-74%), mostly compliant (75-89%), and compliant (>90%) based on sensor usage. Comorbidity conditions was grouped as “Low” (0-3), “Intermediate” (4-6), and “High” (≥7).
Statistical Analysis
Analyses were performed in SPSS with two-tailed significance set at p < 0.05. Linear regression assessed HbA1c changes relative to eAG. Non-parametric Friedman’s Two-way Analysis of Variance (ANOVA) by Ranks test compared HbA1c changes between visits. Descriptive statistics summarized baseline characteristics such as age, sex, race, BMI, number of chronic conditions associated with diabetes, number of glycemic control medications, insulin use status, frequency of daily fingersticks, CGM type, and risk factors for diabetes outcomes. Adjusted p-values were calculated based on the descriptive variables. Secondary analysis compared CGM metrics between visits among Group A and Group B using adjusted linear regression modeling.
Results
Ninety patients were included in the study following EMR chart reviews: 63 CGM users and 27 POC testers. Three patients had T1DM, while the remainder had T2DM.
Baseline Characteristics
Groups were similar in age, sex, and BMI (Table 1). The population was predominantly male (54.4%) and Caucasian (68.9%). Over half (54.4%) had “Intermediate” comorbidity burden, with CGM patients more likely to have “High” burden (22.2% vs. 11.1%). CGM patients used 1.5 times more diabetes medications at baseline but experienced greater medication reductions by the third visit (20.7% vs. 7.4%). Conversely, POC patients had higher rates of medication escalation (44.4% vs. 33.3%). 2/3 of patients were Insulin users, with higher usage in CGM patients (77.8% vs. 40.7%). CGM devices used were Freestyle Libre (69.9%) and Dexcom (30.1%). Minimal exercise (52.2%) and poor diet control (34.4%) were the most frequently reported lifestyle factors.
Table 1. Descriptive Analysis of Baseline Characteristics.
| Variables |
Overall
(n = 90) |
Fingerstick (Control)
(n = 27) |
CGM Users
(n = 63) |
|---|---|---|---|
| Age in years, mean ± SD | 65.5 ± 12.1 | 66.2 ± 10.7 | 65.2 ± 12.8 |
| Sex, n (%) | |||
| Male | 49 (54.4) | 16 (59.3) | 33 (52.4) |
| Female | 41 (45.6) | 11 (40.7) | 30 (47.6) |
| Race, n (%) | |||
| White or Caucasian | 62 (68.9) | 17 (63.0) | 45 (71.4) |
| Black or African American | 25 (27.8) | 8 (29.6) | 17 (27.0) |
| Asian | 1 (1.1) | -- | 1 (1.6) |
| Other | 2 (2.2) | 2 (7.4) | -- |
| Body Mass Index, mean ± SD | 33.1 ± 7.8 | 33.7 ± 9.8 | 32.8 ± 6.9 |
| *# of Comorbid Conditions, mean ± SD | 4.8 ± 1.9 | 4.3 ± 1.6 | 5.0 ± 2.0 |
| By Range, n (%) | |||
| Low (0-3) | 24 (26.7) | 9 (33.3) | 15 (23.8) |
| Intermediate (4-6) | 49 (54.4) | 15 (55.6) | 34 (54.0) |
| High (7+) | 17 (18.9) | 3 (11.1) | 14 (22.2) |
| Medications | |||
| Of Initial Diabetes Medications, mean ± SD | 2.6 ± 1.3 | 2.0 ± 1.0 | 2.9 ± 1.3 |
| Change in Meds from Baseline to 3rd Visit, n (%) | |||
| No change | -- | 13 (48.1) | 29 (46) |
| Reduced by 1 med | -- | 2 (7.4) | 11 (17.5) |
| Reduced by >2 meds | -- | -- | 2 (3.2) |
| Increased by 1 med | -- | 9 (33.3) | 16 (25.4) |
| Increased by >2 meds | -- | 3 (11.1) | 5 (7.9) |
| Insulin Use, n (%) | |||
| Yes | 60 (66.7) | 11 (40.7) | 49 (77.8) |
| No | 30 (33.3) | 16 (59.3) | 14 (22.2) |
| §Frequency of Daily Fingersticks, mean ± SD | |||
| Baseline Visit | -- | 1.5 ± 0.9 | 1.5 ± 1.3 |
| 2nd Visit | -- | 1.4 ± 1.1 | -- |
| 3rd Visit | -- | 1.7 ± 1.1 | -- |
| CGM Type, n (%) | |||
| Dexcom G6 | -- | -- | 7 (11.1) |
| Dexcom G7 | -- | -- | 12 (19.0) |
| Freestyle Libre 2 | -- | -- | 25 (39.7) |
| Freestyle Libre 3 | -- | -- | 19 (30.2) |
| Risk Factors | |||
| Minimal Exercise | 47 (52.2) | 16 (59.3) | 31 (49.2) |
| Poor Diet Control | 31 (34.4) | 11 (40.7) | 20 (31.7) |
| Medication Non-Compliance | 18 (20) | 3 (1.1) | 15 (23.8_ |
| Affordability of Meds | 3 (3.3) | 1 (3.7) | 2 (3.2) |
| Smoking Status | |||
| Heavy Smoker | 1 (1.1) | 1 (3.7) | -- |
| Every Day | 5 (5.6) | 2 (7.4) | 3 (4.8) |
| Former | 41 (45.6) | 15 (55.6) | 26 (41.3) |
| Never | 43 (47.8) | 9 (33.3) | 34 (54.0) |
*Number of comorbid conditions is further stratified into ranges of low (0-3 conditions), medium (4-6 conditions), and high (7+ conditions). §Missing values: Frequency of daily fingersticks for control group (3rd visit, n = 3).
Primary Endpoint Analysis
The average time between visits was variable (baseline to 2nd HbA1c: 3.9 months for POC, 4.5 months for CGM; vs. 2nd to 3rd HbA1c: 4.7 months for POC, 3.8 months for CGM). Across all patients, eAG improved by 29.6 mg/dL (p <0.001), with Group B improving fourfold compared to Group A (-92.2 vs. -20.1 mg/dL; p <0.001) (Table 2). HbA1c reduction was significant for CGM users (p < 0.001) but not for POC testers (p = 0.081). CGM patients achieved an average HbA1c reduction of nearly double compared to POC (-1.8% vs. -1%). Stratification showed larger improvements among Group B patients in both groups (CGM: -4.1%; POC: –3.2%) compared with Group A (CGM: -0.4%; POC: –0.7%).
Table 2. Change in HbA1c Between Control (fingerstick) and Intervention (CGM) Groups from Baseline to 3- and 6-Month Visits .
| Baseline to 2nd visit | 2nd to 3rd visit | Baseline to 3rd visit | p-value | |
|---|---|---|---|---|
| Control Group | ||||
| *Change in eAG vs. HbA1c for Fingerstick Patients, mean ± SD | -18.9 ± 51.9 | -10.6 ± 33.3 | -29.6 ± 58.9 | <0.001 |
| †Initial HbA1c 7-9% | -12.6 ± 15.2 | -7.5 ± 17.1 | -20.1 ± 23.1 | |
| Initial HbA1c ≥9% | -67.1 ± 68 | -25.1 ± 56.1 | -92.2 ± 64.9 | |
| Change in HbA1c for fingerstick patients, mean ± SD | -0.7 ± 1.8 | -0.4 ± 1.2 | -1.0 ± 2.1 | 0.081 |
| Initial HbA1c 7-9% | -0.4 ± 0.5 | -0.3 ± 0.6 | -0.7 ± 0.8 | |
| Initial HbA1c ≥9% | -2.3 ± 2.3 | -0.9 ± 2.0 | -3.2 ± 2.3 | |
| CGM Users | ||||
| Change in HbA1c for CGM patients, mean ± SD | -1.3 ± 1.7 | -0.5 ± 1.8 | -1.8 ± 2.5 | <0.001 |
| Initial HbA1c 7-9% | -0.5 ± 0.8 | 0.1 ± 1.3 | -0.4 ± 1.2 | |
| Initial HbA1c ≥9% | -2.5 ± 1.9 | -1.6 ± 1.6 | -4.1 ± 1.6 |
*Estimated average glucose (eAG) was calculated using American Diabetes Association formula: eAG = 28.7 x HbA1c – 46.7. †Sample sizes for HbA1c stratification are as follows: HbA1c 7-9% (fingerstick, n = 10 and CGM, n = 30) vs. HbA1c ≥9% (fingerstick, n = 8 and CGM, n = 27).
CGM Metrics
Sensor wear time averaged 84%, with 59% of patients ≥90% compliant by the 3rd visit (Table 3). TIR improved by 1.8% (p = 0.002) between 2nd and 3rd visits, driven mainly by Group A. Group B showed a nonsignificant 13.4% increase in TIR (p = 0.43). TAR decreased in the “High” range (-2.1%, p = 0.010) but increased in the “Very High” range (2.7%, p = 0.012) from 2nd to 3rd visits. Group A shifted significantly from “Very High” (-1%, p <0.001) to “High” (1.9%, p <0.001), while Group B showed non-significant variability (“High”: -8.7%, p = 0.344; “Very High”: 1.9%, p = 0.455).
Table 3. CGM Data Interpretation with Relation to Change in HbA1c (n = 63) .
| Glycemic Control | CGM Indicators | 2nd visit | 3rd visit | Average change (2nd to 3rd visit) | p-value (unadjusted) | p-value (adjusted) |
|---|---|---|---|---|---|---|
| Overall | *Time sensor active, mean ± SD | 86.1 ± 13.5 | 84.4 ± 15.1 | -1.7 ± 4.1 |
0.575 |
0.021 |
| Compliant ≥90%, n(%) | 34 (55.7) | 36 (59) | ||||
| Mostly compliant 75-89%, n(%) | 16 (26.2) | 10 (16.4) | ||||
| Low compliance 50-74%, n(%) |
11 (18.0) | 15 (24.6) | ||||
| Time in range, mean ± SD | 60.7 ± 24.4 | 62.2 ± 25.7 | 1.8 ± 38.3 | <0.001 | 0.002 | |
| †Time above range, mean ± SD | ||||||
| “High” | 27.8 ± 16.7 | 25.8 ± 18.4 | -2.1 ± 24.3 | 0.044 | 0.010 | |
| “Very High” | 7.6 ± 12.3 | 9.3 ± 13.5 | 2.7 ± 10 | 0.037 | 0.012 | |
|
Group A Less Severely Uncontrolled HbA1c 7-9% |
Time sensor active, mean ± SD |
87.3 ± 12.1 | 85.9 ± 14 | -1.5 ± 4.5 | 0.398 | <0.001 |
| Compliant ≥90%, n(%) | 18 (62.1) | 19 (65.5) | ||||
| Mostly compliant 75-89%, n(%) | 6 (20.7) | 4 (13.8) | ||||
| Low compliance 50-74%, n(%) |
5 (17.2) | 6 (20.7) | ||||
| Time in range, mean ± SD |
62.4 ± 22.3 | 58.2 ± 28 | -3.6 ± 40.5 | <0.001 | <0.001 | |
| Time above range, mean ± SD | ||||||
| “High” | 27.8 ± 15.7 | 30 ± 20 | 1.9 ± 27.2 | 0.055 | <0.001 | |
| “Very High” | 10.7 ± 15.3 | 9.6 ± 14.6 | -1 ± 11.7 | 0.179 | <0.001 | |
|
Group B Very Severely Uncontrolled HbA1c ≥9% |
Time sensor active, mean ± SD |
85.9 ± 14.8 | 83.9 ± 16.4 | -2 ± 3.8 |
0.48 | 0.227 |
| Compliant ≥90%, n(%) | 14 (53.8) | 15 (57.7) | ||||
| Mostly compliant 75-89%, n(%) | 7 (26.9) | 4 (15.4) | ||||
| Low compliance 50-74%, n(%) |
5 (19.2) | 7 (26.9) | ||||
| Time in range, mean ± SD |
53.4 ± 25.5 | 66.7 ± 22.4 | 13.4 ± 35.2 | 0.361 | 0.43 | |
| Time above range, mean ± SD | ||||||
| “High” | 31.3 ± 17.6 | 22.5 ± 16.2 | -8.7 ± 21.4 | 0.099 | 0.344 | |
| “Very High” | 5 ± 8.4 | 6.9 ± 10.9 | 1.9 ± 4 | 0.967 | 0.455 |
*All time variable averages are reported as percentages. †Time above range (TAR) percentages have been aggregated into qualitative Y/N variables by goal ranges per International Consensus Criteria. The “High” category (BG range 180-250 mg/dL) has a goal of <25%. The “Very High” category (BG >250 mg/dL) has a goal of <5%. §Missing values: Time sensor active (n = 2) in both 2nd and 3rd visit groups, so frequencies are calculated out of n = 61 for overall, n = 29 for HbA1c 7-9%, and n = 26 for HbA1c ≥9%; all other indicators for 3rd visit n = 1 missing, so frequencies are calculated out of n = 62.
Discussion
CGM patients presented with greater disease complexity, higher insulin use, more medications at baseline, and higher rates of medication nonadherence (23.8% vs. 1%), yet achieved superior HbA1c outcomes compared to POC testers. Real-time feedback likely enabled healthier lifestyle decisions, reducing the average medication burden [3,17-18]. In contrast, POC testers had more limited insight into their daily glucose trends, which likely contributed to their need for medication escalation.
CGM data integrity is validated given compliance with sensor wear was high. “Compliant” and “Low compliance” patients both increased, showing a dichotomy in behavior over time. The increase in “Compliant” CGM patients is likely associated with the reduction of diabetes medications by the third visit. Despite higher medication nonadherence reported among CGM users, they achieved better glycemic outcomes, thus underscoring CGM’s behavioral impact.17,18
HbA1c improvements were strongest in patients with very poorly controlled diabetes (Group B), though statistical significance was limited by variability from risk factors (i.e. poor lifestyle habits, inconsistent CGM usage).19,20 Redistribution of hyperglycemia from “Very High” to “High” TAR categories in Group A suggests meaningful risk reduction even without dramatic overall glycemic change. It reinforces the value of CGM for populations in which insulin is often not clinically indicated, whereas Group B’s non-significant findings likely reflect continued compliance issues and reinforces the need for targeted lifestyle interventions in this population.
Study Limitations
Study limitations include reliance on retrospective EMR review, missing HbA1c values, and inconsistent visit intervals. The cohort was predominantly Caucasian, which limited assessment of racial disparities in glycemic management. Limiting the cohort to endocrinology patients likely introduced selection bias toward more severe, complex cases than in primary care. Endocrinology patients have greater access to CGM samples and a larger volume of insulin users who qualify for CGM coverage, which is reflected in the POC group being smaller.21 The POC group was captured at initial endocrinology visits before treatment stabilization, whereas CGM users were well established with endocrinology prior to CGM initiation. Nonadherence to diabetes management overtime among the CGM group assists with neutralizing the effects of this limitation.20 Documentation inconsistencies combining “Very High” and “High” TAR may have skewed TAR analysis. Additionally, acetaminophen use could not be excluded or accurately quantified due to non-prescription acquisition and variable reporting, potentially affecting CGM accuracy.8-11
Public Health Implications
This study serves as a basis for future efforts in diabetes management, healthcare policy, and research. Larger studies should assess long-term outcomes of CGM use compared to POC testing, patient satisfaction, and how CGM use affects daily behavioral patterns. According to the American Academy of Family Physicians, at least 90% of patients in primary care are affected by diabetes.22 As patients seeking diabetes care outside of endocrinology clinics continues to grow, evaluating clinician familiarity with CGM technology, improving medical education to integrate CGM, and assessing CGM adoption and outcomes in primary care is essential.21
Addressing social determinants of health (SDOH) (i.e. low health literacy, limited insurance coverage, financial barriers) is essential for equitable CGM access. Expanding access to diabetes self-management education and support (DSMES) programs, supporting community health worker (CHW) initiatives, and broadening insurance coverage for diabetes resources (i.e. medications, CGM devices, supplies) could play a pivotal role in enhancing diabetes care for underserved populations.2,23,24 Cost-effectiveness analyses comparing CGM to costs from diabetes-related hospitalizations and complications (i.e. amputations, kidney failure, hyper/hypoglycemia episodes) could further justify broader coverage.6
Policy reforms are urgently needed to expand eligibility beyond insulin users.24 The American Diabetes Association (ADA) has highlighted expanding CGM access as a priority for their current diabetes advocacy agenda.25 By addressing these key areas for future diabetes research and advocacy, we can improve glycemic control, reduce disparities in access, and develop equitable, sustainable policies.
Conclusion
CGM use leads to greater improvements in glycemic control compared to POC glucose monitoring as demonstrated by a significant reduction in HbA1c. By enabling real-time feedback, CGM enhances self-management, reduces medication burden, and improves glycemic outcomes. Expanding access to CGM for all patients with diabetes, regardless of insulin use, is essential for optimizing diabetes care.
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