Addressing Disparities Using Continuous Glucose Monitors and Remote Patient Monitoring for Youth With Type 1 Diabetes

Abstract

Background: Youth with type 1 diabetes (T1D) and public insurance have lower diabetes technology use. This pilot study assessed the feasibility of a program to support continuous glucose monitor (CGM) use with remote patient monitoring (RPM) to improve glycemia for youth with established T1D and public insurance.

Methods: From August 2020 to June 2023, we provided CGM with RPM support via patient portal messaging for youth with established T1D on public insurance with challenges obtaining consistent CGM supplies. We prospectively collected hemoglobin A_1c (HbA_1c), standard CGM metrics, and diabetes technology use over 12 months.

Results: The cohort included 91 youths with median age at enrollment 14.7 years, duration of diabetes 4.4 years, 33% non-English speakers, and 44% Hispanic. Continuous glucose monitor data were consistently available (≥70%) in 23% of the participants. For the 64% of participants with paired HbA_1c values at enrollment and study end, the median HbA_1c decreased from 9.8% to 9.0% (P < .001). Insulin pump users increased from 31 to 48 and automated insulin delivery users increased from 11 to 38.

Conclusions: We established a program to support CGM use in youth with T1D and barriers to consistent CGM supplies, offering lessons for other clinics to address disparities with team-based, algorithm-enabled, remote T1D care. This real-world pilot and feasibility study noted challenges with low levels of protocol adherence and obtaining complete data in this cohort. Future iterations of the program should explore RPM communication methods that better align with this population's preferences to increase participant engagement.

Keywords: continuous glucose monitors; health disparity; pediatric diabetes; remote patient monitoring; type 1 diabetes.

Figure 1.

Participants with consistent CGM use as defined as ≥ 70% CGM data available during 12-month study period. Time point zero included analysis of CGM data for the initial two weeks after enrollment and subsequent time points examined data availability over four weeks. Cohort with consistent CGM data was highest during the initial two weeks of enrollment, 37/71 (52.1%). Rate of consistent CGM data during the last month of study was 31/71 (43.7%), which does not significantly differ from that at initiation (P = .40).

Figure 2.

Glycemic control over study period as measured by hemoglobin A_1c (HbA_1c) (a), glucose management indicator (GMI) (b), and glucose distribution from continuous glucose monitor (CGM) readings (c). Scatter plot of HbA_1c levels (a) over time with locally estimated scatter plot smoothing (LOESS) show significant reduction over 12 month study period (P < .001). Glucose management indicator was not different at enrollment versus at 1 year (8.6 ± 1.2 vs 8.5 ± 1.3, P = .08) (b). Continuous glucose monitor time in range was not statistically different at enrollment versus at one year (36.5% vs 48.4%, P = .8) (c).

Figure 3.

Glycemic metrics for sub-cohorts with consistent continuous glucose monitor (CGM) data availability (≥ 70%) in red vs inconsistent data availability (< 70%) in blue, including hemoglobin A_1c (HbA_1c) (a) and glucose management indicator (GMI) (b). Sub-cohort with consistent CGM data had lower HbA_1c compared with the sub-cohort without consistent CGM data (HbA_1c 8.7 vs 9.7, P = .02). Similarly, sub-cohort with consistent CGM data had lower GMI compared with the sub-cohort without consistent CGM data (GMI 8.2 vs 8.9, P = .02). Glucose distribution from CGM readings for the sub-cohort with consistent CGM data availability (n = 21) shows no difference at enrollment versus study end (c).