Continuous Glucose Monitoring Users Research Articles

A Nutrition-Focused Approach During Continuous Glucose Monitoring Initiation in People With Type 2 Diabetes: Using a Theoretical Framework to Unite Continuous Glucose Monitoring and Food Choices.

Continuous glucose monitoring (CGM) has transformed diabetes care, yet opportunities for further innovations still exist. Some research suggests CGM could be an ideal tool to guide food choices and other healthy lifestyle behaviors, especially for people with type 2 diabetes (T2D). Behavior change theories can be used to understand and describe how CGM users make food-related decisions, which could ultimately lead to the design of more tailored and effective interventions. In this commentary, we describe what it looks like to use the behavior change wheel-a theory-based intervention development framework-to design an intervention for people with T2D who will use CGM data to guide food choices aligned with evidence-based nutrition recommendations. Such frameworks may be beneficial when designing or evaluating future technology-focused behavior change interventions.

Clinical Effectiveness of Continuous Glucose Monitoring in Pregnancies Affected by Type 1 Diabetes.

Background: Continuous glucose monitoring (CGM) improves neonatal outcomes in type 1 diabetes pregnancies; however, its effectiveness has not been assessed in a real-world setting in the United States. Objective: The Triple C Study aimed to examine the clinical effectiveness, assessed through maternal glucose control and gestational health outcomes, of CGM use compared with self-monitoring of blood glucose (SMBG) in pregnancies associated with type 1 diabetes in a real-world setting. Research Design and Methods: We retrospectively identified 160 type 1 diabetes pregnancies at the Barbara Davis Center for Diabetes managed with CGM therapy (n = 109) or SMBG (n = 51) over a 6.5-year period (2014-2020). Obstetric care was provided at multiple practices. CGM use was defined as ≥60% wear in the second and third trimesters of pregnancy. Data were obtained from the electronic medical record system, hospital records, and vital statistics departments (Colorado and Wyoming). We used Student's t-test for continuous variables and chi-square test for categorical variables to compare outcomes between groups. Results: The CGM group had more participants meeting trimester-specific hemoglobin A1C (HbA1c) goals throughout pregnancy and postpartum (P < 0.01 in each time period). The CGM group had fewer participants never meeting HbA1c goals in any trimester than the SMBG group (P < 0.001). There were no significant differences in neonatal outcomes between groups, other than for macrosomia (12.8% CGM vs. 29.4% SMBG, P = 0.01). Infants of CGM users required a neonatal intensive care unit admission less often (52.9% CGM vs. 68.3% SMBG, P = 0.0989). Conclusions: CGM use was associated with improved maternal glucose levels in a diverse real-world cohort.

Comparing Continuous Glucose Monitoring and Blood Glucose Monitoring in Adults With Inadequately Controlled, Insulin-Treated Type 2 Diabetes (Steno2tech Study): A 12-Month, Single-Center, Randomized Controlled Trial.

To compare the 12-month effects of continuous glucose monitoring (CGM) versus blood glucose monitoring (BGM) in adults with insulin-treated type 2 diabetes. This is a single-center, parallel, open-label, randomized controlled trial including adults with inadequately controlled, insulin-treated type 2 diabetes from the outpatient clinic at Steno Diabetes Center Copenhagen, Denmark. Inclusion criteria were ≥18 years of age, insulin-treated type 2 diabetes, and HbA1c ≥ 7.5% (58 mmol/mol). Participants were randomly assigned (1:1) to 12 months of either CGM or BGM. All participants received a diabetes self-management education course and were followed by their usual healthcare providers. Primary outcome was between-group differences in change in time in range (TIR) 3.9-10.0 mmol/L, assessed at baseline, after 6 and 12 months by blinded CGM. The prespecified secondary outcomes were differences in change in several other glycemic, metabolic, and participant-reported outcomes. The 76 participants had a median baseline HbA1c of 8.3 (7.8, 9.1)% (67 [62-76] mmol/mol), and 61.8% were male. Compared with BGM, CGM usage was associated with significantly greater improvements in TIR (between-group difference 15.2%, 95% CI 4.6;25.9), HbA1c (-0.9%, -1.4; -0.3 [-9.4 mmol/mol, -15.2; -3.5]), total daily insulin dose (-10.6 units/day, -19.9; -1.3), weight (-3.3 kg, -5.5; -1.1), BMI (-1.1 kg/m2, -1.8; -0.3), and greater self-rated diabetes-related health, well-being, satisfaction, and health behavior. In adults with inadequately controlled insulin-treated type 2 diabetes, the 12-month impact of CGM was superior to BGM in improving glucose control and other crucial health parameters. The findings support the use of CGM in the insulin-treated subgroup of type 2 diabetes.

Is It Time to Move Beyond TIR to TITR? Real-World Data from Over 20,000 Users of Continuous Glucose Monitoring in Patients with Type 1 and Type 2 Diabetes.

The growing use of continuous glucose monitoring (CGM) has been supported by expert consensus and clinical guidelines on glycemic management in diabetes with time in range (TIR 70-180 mg/dL) representing a key CGM-derived glucose metric. Time in tight range (TITR) has also been proposed for clinical use, spanning largely normal glucose levels of 70-140 mg/dL. However, keeping such narrow glucose ranges can be challenging, and understanding the factors modulating TITR can help achieve these tight glycemic targets. Our real-life study aimed to evaluate the relationship between average glucose (AG) and TIR/TITR in a large cohort (n = 22,006) of CGM users, divided into four groups: self-identified as having type 1 diabetes (T1D) treated with insulin using multiple daily injections (MDI) or pumps; type 2 diabetes (T2D) on MDI or insulin pumps; T2D on basal insulin only; and T2D not on insulin treatment. The T2D groups, regardless of treatment type, displayed the highest TIR and TITR values, associated with lowest glycemic variability measured as glucose coefficient of variation (CV; 23-30%). The T1D group showed the lowest TIR and TITR, associated with the highest CVs (36-38%). Overall, higher CV was associated with lower TIR and TITR for AG values below 180 and 140 mg/dL, respectively, with the reverse holding true for AG values above these thresholds. The discordance between AG and TIR/TITR was less pronounced in T2D compared with T1D, attributed to lower CV in the former group. It was also observed that TITR has advantages over TIR for assessing glycemia status and progress toward more stringent A1C, particularly when approaching normal glucose levels. The data detail how CV affects the AG relationship with TIR/TITR, which has implications for CGM interpretation. In many instances TITR, rather than TIR, may be preferable to employ once AG falls below 140 mg/dL and near-normal glucose levels are required clinically.

The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns.

Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find interpreting the large volume of CGM data to be overwhelming and complex. To address this challenge, an efficient, intelligent method for detecting and classifying discernable patterns in CGM data was desired. We developed an automated artificial intelligence (AI)-driven method to detect and classify different discernable CGM patterns which called "CGM events." We trained different models using 60 days of CGM data from 27 individuals with diabetes from a publicly available data set and then evaluated model performance using separate test data from the same group. Each event is classified according to clinical significance based on three parameters: (1) glucose category at or near the beginning of the CGM event; (2) a calculated severity score that encompasses both signal shape and temporal characteristics (e.g., how high the CGM curve goes (measured in mg/dL) and how long it stays above target (as established by published consensus guidelines); and (3) the glucose category at or near the end of the event. The system accurately detected and classified events from actual CGM data. This was also validated with expert diabetes clinicians. Advanced pattern recognition methods can be used to detect and classify CGM events of interest and may be used to provide actionable insights and self-management support to CGM users and decision support to the clinicians caring for them.

Limitations of 14-Day Continuous Glucose Monitoring Sampling for Assessment of Hypoglycemia and Glycemic Variability in Type 1 Diabetes.

Continuous glucose monitoring (CGM) has become the standard of care in diabetes management with the recent advances in technology and accessibility in the last decade. An International Consensus was established to define CGM metrics and its goals in diabetes care. The 2019 International Consensus suggested 14 days of CGM sampling for the assessment of CGM metrics stating the limitations that may occur for hypoglycemia and glycemic variability metrics. Since then, several studies assessed the correlation between CGM metrics and duration of the sampling period. This review summarized the studies that investigated the relationship between 14-day CGM sampling to 90-day CGM data in >70% CGM users for all CGM metrics and highlighted possible solutions for more accurate CGM sampling durations in type 1 diabetes (T1D). Accumulating evidence showed that 14-day CGM sampling correlates well with 90-day CGM data for mean glucose, time in 70-180 mg/dL, and hyperglycemia metrics; however, it correlates weakly for hypoglycemia and glycemic variability metrics. In the studies included in this review, in adults with T1D, minimum sampling duration was 14 days for mean glucose, time in 70-180 mg/dL, and time in hyperglycemia (>180 and >250 mg/dL); however, minimum sampling duration varied between 21 to 30 days for time <70 mg/dL, 30 to 35 days for time <54 mg/dL, and 28 to 35 days for coefficient of variation. Longer than 14 days of CGM, sampling was required to properly assess hypoglycemia and glycemic variability in T1D.

The impact of socioeconomic factors, social determinants, and ethnicity on the utilization of glucose sensor technology among persons with diabetes mellitus: a narrative review.

Continuous glucose monitoring (CGM) usage has been shown to improve disease outcomes in people living with diabetes by facilitating better glycemic management. However, previous research has suggested that access to these devices can be influenced by nonmedical factors such as socioeconomic status and ethnicity. It is critical that equitable access to CGM devices is ensured as people from those groups experience poorer diabetes-related health outcomes. In this narrative review, we provide an overview of the various healthcare systems worldwide and how socioeconomic status, social context, and ethnicity shape device usage and the associated health outcomes. In general, we found that having a lower socioeconomic status and belonging to an ethnic minority group negatively impact CGM usage. While financial means proved to be an important mediator in this process, it was not the sole driver as disparities persisted even after adjustment for factors such as income and insurance status. Recommendations to increase CGM usage for people of a lower socioeconomic status and ethnic minorities include increasing the availability of financial, administrative, and educational support, for both patients and healthcare providers. However, recommendations will vary due to local country-specific circumstances, such as reimbursement criteria and healthcare ecosystems.

Personalized nutrition and machine-learning: Exploring the scope of continuous glucose monitoring in healthy individuals in uncontrolled settings

Machine-learning models, when combined with continuous glucose monitoring (CGM), can help effectively analyze extensive datasets of glycemic responses to food. A total of 3,296 90-minute-long CGM meal responses from 927 healthy individuals were used to train/test a machine-learning model (XGBRegressor). The model input were the individual's anthropometric characteristics, macronutrients, and features related to the 24-hour CGM trace preceding the meal. The model output consisted in the parameters of a bell-shaped equation, used to analytically describe the glycemic response to food. To interpret the machine-learning model, the Shapley's values method was employed. A multi-linear regression model was used to study the impact of food macronutrients on the magnitude of the glycemic response. The machine-learning model was able to predict the magnitude of the glycemic response with a root mean square error of 13.2 ± 9.5 mg/dL, and a correlation coefficient of r = 0.48 (p < 0.001) but suffered from a systematic bias (r = 0.83, p < 0.001). The Shapley's values revealed that age brings a positive contribution to the magnitude of the glycemic response beyond 40 years. The multi-linear model (R2 = 0.14, p < 0.001) highlighted the positive and the negative impact of the carbohydrates (β = 0.263, p < 0.001) and fat (β = −0.108, p < 0.001) on the magnitude of the glycemic response, respectively. The maximum attainable accuracy in predicting the glycemic response to food using this machine-learning model may be inherently limited by the uncontrolled nature of the adopted dataset. Nevertheless, this model holds significant educational value for CGM users, as it facilitates comprehension of the intricate relationships between individual characteristics, meal composition, and glucose levels.

A Pragmatic Rethinking of Glucose Monitoring for the Incarcerated

Objective Using continuous glucose monitoring (CGM) improves diabetes-related outcomes in the community, yet the fingerstick blood glucose monitoring (BGM) method is the norm in prisons. The purpose of this study was to investigate the safety and patient perceptions of CGM in the carcerate environment, quantify changes in A1C after initiating CGM, and investigate rates of emergency department (ED) visits for diabetes-related complications comparing CGM users to patients using BGM. Study design This pragmatic longitudinal analysis was conducted in two parts. A pilot program was carried out at a single women’s prison. A CGM program was initiated at men’s facilities within a single U.S. state, where A1C change and ED visit rates were investigated. Methods Interested patients at an appropriate security level were invited to use CGM. Pre- and post-CGM surveys of glucose monitoring perceptions were administered during the pilot program. Security and perceptions were analyzed descriptively. A1C change was assessed using a t test. Fisher exact test, Barnard exact test, and post-hoc power analysis were applied to ED visits. Results Security was not disrupted. Patient perceptions of glucose monitoring improved with CGM use (n = 6). A1C declined by 0.60% with a medium effect size (Cohen’s d −0.45, n = 42). Power to detect a difference in ED visits was low; however, no CGM patients had an ED visit (n = 758). Conclusion It is safe and efficacious to replace BGM with CGM in prisons when patients are engaged in their care and are at a security level allowing CGM devices in their cells.

Overcoming Barriers to Diabetes Management in Young Adults with Type 1 Diabetes by Leveraging Telehealth: A Pilot Study

ObjectiveThe LIFT-YA (leveraging intensive follow-up treatment in young adults) quality improvement program was developed to address clinical and social barriers in young adults (YA) with type 1 diabetes (T1D), using telehealth visits to promote clinic attendance and improve diabetes care. MethodsLIFT-YA enrolled YA aged 18-30 with T1D and HbA1c >8% (64 mmol/mol) who had established adult care in our diabetes clinic. The 6-month, 7-visit hybrid program was facilitated by a case manager serving as the liaison between participants and the care team. The primary end-points were within-group and between-group changes from the baseline in HbA1c at the last visit and adoption of continuous glucose monitoring (CGM). ResultsOf the 57 eligible YA, 24 were enrolled and 33 were unable to participate (UTP). Thirteen of the enrolled participants attended at least 4/7 visits (“completers”, C), whereas 11 were noncompleters (NC). HbA1c at the end of the program was significantly lower in the C versus UTP group [median −1.0; IQR (−0.6, −2.5) vs −0.25 (0.2, −1.0) in UTP; P < .05]. The percentage of CGM users significantly increased by 70% in the C group (P < .05), but did not change in the NC and UTP groups. Limited access to telehealth and the high cost of frequent visits were the main hurdles preventing enrollment into or completion of the program. ConclusionsThe LIFT-YA pathway was associated with a significant HbA1c reduction and an increase in the adoption of CGM. Policy changes are necessary to expand access to LIFT-YA and other programs for high-risk YA with T1D in underserved communities and across all backgrounds.

SAT107 Assessing Continuous Glucose Monitor Alarm Use By Families Of Children With Diabetes

Abstract Disclosure: V.S. Ochs: None. S.E. Woerner: Employee; Self; Lilly USA, LLC. A. Ustyol: None. L.A. DiMeglio: Advisory Board Member; Self; Merck, Abata. Consulting Fee; Self; Vertex. Background: Continuous glucose monitors (CGM) offer customizable alarms which alert persons with diabetes and their caregivers of current or pending glycemic changes. However, there has been little work studying real-world CGM alert setting use in large clinical populations. We sought to understand CGM alarm use through report analyses. Methods: After IRB approval we analyzed data from two-week CGM reports obtained clinically to determine common settings and compared those to target guidelines. Results: CGM download data from 150 children using a Dexcom® G6 were analyzed (median age 14y, (range: 1-19), 89% white, 9.5% black, and 1.5% Asian, 47% female, 51% pump users). Median A1c was 7.8% (range: 5.4-15). Median CGM glucose was 190 (range: 56-374). Average time in range was 47.7% (range: 3.4-99.1).A Low Alarm alert was set for 131 participants (87%) with a median sensor glucose alert threshold of 74 mg/dL (range: 60-100). The High Alarm was used by 109 children (73%) with a median threshold of 272 mg/dL (range: 120-400). The Signal Loss alarm was used by 103 (69%) participants with a median time until notification of 20 min (range: 20-240). Low Repeat and High Repeat alarms were used by only 50 (33%) and 36 (24%) of children, respectively. Rise Rate and Fall Rate alarms were used by 18 (12%) and 34 (23%). There was a significant difference between the median High alarm cutoff of 272 mg/dL and a recommended standard (e.g. PantherProgram.org) of 250 mg/dL (p=0.0016). The same was true with the median Low alarm cutoff of 74 mg/dL and a recommended 70 mg/dL (p&amp;lt;0.0001). There was significant difference between the median High Repeat notification time of 1 hour and the recommended 2 hours (p&amp;lt;0.0001). Pump users were 2.2x more likely to use High Repeat alarms compared to injection users (95% CI: 1.18-4.15, p&amp;lt;0.01). The same was true with the Low Repeat alarm being 1.8x more likely to be used by pumpers compared to injection users (95% CI: 1.12-2.99, p=0.01). There were no significant differences in Low, High, Rise Rate, Fall Rate, Urgent Low Soon, Urgent Low Soon Repeat, or Signal Loss alarms.When the group was divided based on age, above 12 and &amp;lt;=12, younger CGM users were 1.2 times more likely to use the Low alarm compared to older users (95% CI: 1.03-1.30). Also, younger CGM users were more likely to use the Rise Rate (RR=3.6, 95% CI:1.37-9.67), Low Repeat (RR=1.7, 95% CI:1.09-2.71), Signal Loss (RR=1.3, 95% CI:1.04-1.60), Urgent Low Soon (RR=1.2, 95% CI:1.07-1.41), and Urgent Low Soon Repeat (RR=1.2, 95% CI:1.05-1.38) alarms. There were no significant differences in High, High Repeat, or Fall Rate alarm settings. Conclusions: The wide variability of alarm settings used by patients indicates likely educational gaps in CGM onboarding and use. Ensuring CGM alarm best practices will help children with diabetes and their caregivers get needed real-time glycemic data while minimizing alarm fatigue. Presentation: Saturday, June 17, 2023

THU130 Long Term Tracking Of HbA1c Levels Across The Life Span In Type 1 Diabetes: From Infants To Young Adults

Abstract Disclosure: S. Kim: None. K. Song: None. M. Lee: None. J. Seo: None. H. Chae: None. H. Kim: None. A. Kwon: None. Objectives HbA1c is the most important monitoring value and generally used in diabetes. However, HbA1c may change across the life span according to growth and puberty. This study explores the long-term tracking of HbA1c from infants to young adults diagnosed with Type 1 Diabetes mellitus (T1DM) and analyzes the effects of HbA1c according to life span on complications. Method This retrospective study, included 289 patients (M, 43.0%; mean first visit age, 7.39±3.94 years old; mean followed up periods, 4.32±3.15 years) diagnosed with T1DM. The total of 6368 HbA1c values were analyzed. The distribution of HbA1c values according to age and life period was evaluated. In addition, the differences in mean HbA1c values according to sex and the use of continuous glucose monitoring (CGM) were analyzed. Furthermore, it was analyzed which HbA1c of life period has the most influence on occurrence of complications. Result The level of HbA1c was relatively high at the age of 1- to 2-years, and then decreased between 4 to 5-years. After 5 years of age, HbA1C gradually escalated and peaked at the age of 12- to 15-years, during puberty. Especially for girls, HbA1c increased dramatically during puberty between the age of 10- to 13-years, which was consistent with the physiologic insulin resistance. During in childhood to young adulthood, the mean HbA1c of CGM users was lower than that of non-users. The higher mean HbA1c during all ages affected the occurrence of complications. Intriguingly, the HbA1c level during adolescence was important for the occurrence of diabetic retinopathy. Conclusion HbA1c level change across life span, and peaked at pubertal stage, which consistent with physiologic insulin resistance during puberty. In addition, good glycemic control in adolescence may reduce the occurrence of diabetic retinopathy. Therefore, using CGM in adolescence may be helpful for glycemic control and prevention of diabetic complications. Presentation: Thursday, June 15, 2023

Minimum Sampling Duration for Continuous Glucose Monitoring Metrics to Achieve Representative Glycemic Outcomes in Suboptimal Continuous Glucose Monitor Use.

Two weeks of continuous glucose monitoring (CGM) sampling with >70% CGM use is recommended to accurately reflect 90 days of glycemic metrics. However, minimum sampling duration for CGM use <70% is not well studied. We investigated the minimum duration of CGM sampling required for each CGM metric to achieve representative glycemic outcomes for <70% CGM use over 90 days. Ninety days of CGM data were collected in 336 real-life CGM users with type 1 diabetes. CGM data were grouped in 5% increments of CGM use (45%-95%) over 90 days. For each CGM metric and each CGM use category, the correlation between the summary statistic calculated using each sampling period and all 90 days of data was determined using the squared value of the Spearmen correlation coefficient (R2). For CGM use 45% to 95% over 90 days, minimum sampling period is 14 days for mean glucose, time in range (70-180 mg/dL), time >180 mg/dL, and time >250 mg/dL; 28 days for coefficient of variation, and 35 days for time <54 mg/dL. For time <70 mg/dL, 28 days is sufficient between 45 and 80% CGM use, while 21 days is required >80% CGM use. We defined minimum sampling durations for all CGM metrics in suboptimal CGM use. CGM sampling of at least 14 days is required for >45% CGM use over 90 days to sufficiently reflect most of the CGM metrics. Assessment of hypoglycemia and coefficient of variation require a longer sampling period regardless of CGM use duration.

Using Continuous Glucose Monitoring Values for Bolus Size Calculation in Smart Multiple Daily Injection Systems: No Negative Impact on Post-bolus Glycemic Outcomes Found in Real-World Data.

Recent evidence shows that it may be safe to estimate bolus sizes based on continuous glucose monitoring (CGM) rather than blood glucose (BG) values using glycemic trend-adjusted bolus calculators. Users may already be doing this in the real world, though it is unclear whether this is safe or effective for calculators not employing trend adjustment. We assessed real-world data from a smart multiple daily injections (MDIs) device users with a CGM system, hypothesizing that four-hour post-bolus outcomes using CGM values are not inferior to those using BG values. Our data set included 184 users and spanned 18 months with 79 000 bolus observations. We tested differences using logistic regression predicting CGM or BG value usage based on outcomes and confirmed initial results using a mixed model regression accounting for within-subject correlations. Comparing four-hour outcomes for bolus events using CGM and BG values revealed no differences using our initial approach (P > .183). This finding was confirmed by our mixed model regression approach in all cases (P > .199), except for times below range outcomes. Higher times below range were predictive of lower odds of CGM-based bolus calculations (OR = 0.987, P < .0001 and OR = 0.987, P = .0276, for time below 70 and 54 mg/dL, respectively). We found no differences in four-hour post-bolus glycemic outcomes when using CGM or BG except for time below range, which showed evidence of being lower for CGM. Though preliminary, our results confirm prior findings showing non-inferiority of using CGM values for bolus calculation compared with BG usage in the real world.

Cost analysis of self-monitoring blood glucose in nonintensively managed type 2 diabetes.

To compare health care resource utilization (HCRU) and costs between self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) users in adults with nonintensively managed type 2 diabetes (T2D). Retrospective analysis of the MarketScan Databases. Adults with T2D using SMBG or initiating CGM between January 2018 and March 2019 were eligible for inclusion. Inclusion criteria were (1) 2 consecutive claims for T2D or 1 claim for T2D and a claim for glucose-lowering therapy, (2) at least 1 pharmacy claim for SMBG strips or CGM sensors, and (3) continuous enrollment for 1 year before and after the index date. Individuals with evidence of CGM in the preindex period, pregnancy, use of rapid-acting insulin or glucagon, type 1 diabetes, gestational diabetes, or secondary diabetes at any time during the study period were excluded. SMBG and CGM patients were matched using propensity score, and all-cause HCRU and costs during a 1-year period were compared. A total of 3498 patients were included in each matched cohort. The per-patient per-year (PPPY) all-cause cost was $20,542 in CGM users vs $19,349 in SMBG users (P < .001). The PPPY cost of glucose-lowering medication was $6312 in CGM users vs $5606 in SMBG users (P < .001). No significant differences in the number of emergency department visits or hospitalizations were observed, but CGM users had more all-cause outpatient visits and office visits with an endocrinologist. In adults with nonintensively managed T2D, SMBG appears to be less costly than CGM and is associated with lower pharmacy costs.

Continuous Glucose Monitoring vs. Capillary Blood Glucose in Hospitalized Type 2 Diabetes Patients.

The emergence of continuous glucose monitoring devices revolutionized the monitoring of diabetes, allowing real-time measurement of interstitial glucose levels. These devices are especially important for people with diabetes treated with insulin therapy and have been extensively studied in outpatient settings. In hospitalized patients, studies using continuous glucose monitoring have focused mainly on evaluating its accuracy and feasibility, but the results were unclear on whether continuous glucose monitoring was superior to capillary blood glucose in improving glycemic control and further research is needed to support the use of these devices in hospitalized patients with diabetes. The primary endpoint of this study was to assess the increase in time-in-range (glycemic readings between 100-180 mg/dL) in hospitalized patients with continuous glucose monitoring, compared to capillary blood glucose. The secondary endpoints included the assessment of reductions in hypoglycemia incidence, mean glucose levels, and glucose coefficient of variation. Additionally, we assessed the intervention's impact on reducing the length of hospital stay, mortality rates, and incidence of inpatient infections. This was a retrospective, cohort study of 60 hospitalized patients with type 2 diabetes, divided into two groups of 30 individuals each: an intervention group monitored through continuous glucose monitoring and a control group using capillary blood glucose. Both groups were comparable in terms of demographic and clinical characteristics. Continuous glucose monitoring users had a higher number of readings per day (six vs. four, p < 0.001), in-range readings (53.5% vs. 35%, p = 0.027), fewer above-range readings (25.5% vs. 56.5%, p = 0.003), particularly above 250 mg/dL (5% vs. 27.5%, p = 0.001), with no difference in the percentage of hypoglycemia occurence (1% vs. 0%, p = 0.107). Lower mean glucose (161.9 mg/dL vs. 206.5 mg/dL, p < 0.001) was also observed in this group. No difference was observed in mortality, length of stay, or in infection rate (p = 1.000, p = 0.455, and p = 0.606, respectively). This retrospective study supports the use of continuous glucose monitoring in optimizing glycemic control in hospitalized patients with type 2 diabetes on intensive insulin therapy. These findings suggest that continuous glucose monitoring can improve time-in-range and prevent hyperglycemia.

Importance of continuous glucose monitoring in the treatment of diabetes mellitus

Background: Emerging evidence suggests that controlling both glycemic variability and hemoglobin A1c is necessary to prevent complications associated with diabetes mellitus. Hence, continuous glucose monitoring (CGM) is crucial for effectively managing diabetes.Current Concepts: There are two primary types of CGM. Retrospective CGM only allows the reviewal of glycemic data after the monitoring period, whereas personal CGM allows real-time monitoring. Personal CGM can be further categorized into real-time CGM and intermittently scanned CGM. To interpret CGM data, time in range (TIR) is considered the standard parameter. A TIR of 70–180 mg/dL for more than 70% of the period has been established as a typical target for both type 1 and type 2 diabetes. Other parameters such as time below range, time above range, coefficient of variation, and glucose management indicator should also be reviewed. Importantly, numerous clinical studies have demonstrated the efficacy of CGM in both type 1 and type 2 diabetes.Discussion and Conclusion: A wealth of clinical evidence supports the application of CGM in diabetes, confirming its effectiveness across various treatment stages. CGM has emerged as a compelling therapeutic option in instances when other treatment choices remain limited. With a growing body of clinical evidence, the widespread adoption of CGM in diabetes management appears inevitable. However, challenges related to user comfort, cost, the need for extensive data interpretation, and necessary system improvements remain unaddressed. Further research is required to validate the appropriate usage and frequency of CGM through costeffectiveness analyses.

Changes in the glycaemia risk index and its association with other continuous glucose monitoring metrics after initiation of an automated insulin delivery system in adults with type 1 diabetes.

To evaluate the glycaemia risk index (GRI) and its association with other continuous glucose monitoring (CGM) metrics after initiation of an automated insulin delivery (AID) system in patients with type 1 diabetes (T1D). Up to 90 days of CGM data before and after initiation of an AID system from 185 CGM users with T1D were collected. GRI and other CGM metrics were calculated using cgmanalysis R software and were analysed for 24 hours, for both night-time and daytime. GRI values were assigned to five GRI zones: zone A (0-20), B (21-40), C (41-60), D (61-80) and E (81-100). Compared with baseline, GRI and its components decreased significantly after AID initiation (GRI: 48.7 ± 21.8 vs. 29 ± 13; hypoglycaemia component: 2.7 ± 2.8 vs. 1.6 ± 1.7; hyperglycaemia component: 25.3 ± 14.5 vs. 15 ± 8.5; P < .001 for all). The GRI was inversely correlated with time in range before (r = -0.962) and after (r = -0.961) AID initiation (P < .001 for both). GRI was correlated with time above range (before: r = 0.906; after = 0.910; P < .001 for both), but not with time below range (P > .05). All CGM metrics improved after AID initiation during 24 hours, for both daytime and night-time (P < .001 for all). Metrics improved significantly more during night-time than daytime (P < .01). GRI was highly correlated with various CGM metrics above, but not below target range, both before and after AID initiation.

Supporting the 'lived expertise' of older adults with type 1 diabetes: An applied focus group analysis to characterize barriers, facilitators, and strategies for self-management in a growing and understudied population.

There is a growing number of older adults (≥65 years) who live with type 1 diabetes. We qualitatively explored experiences and perspectives regarding type 1 diabetes self-management and treatment decisions among older adults, focusing on adopting care advances such as continuous glucose monitoring (CGM). Among a clinic-based sample of older adults ≥65 years with type 1 diabetes, we conducted a series of literature and expert informed focus groups with structured discussion activities. Groups were transcribed followed by inductive coding, theme identification, and inference verification. Medical records and surveys added clinical information. Twenty nine older adults (age 73.4 ± 4.5 years; 86% CGM users) and four caregivers (age 73.3 ± 2.9 years) participated. Participants were 58% female and 82% non-Hispanic White. Analysis revealed themes related to attitudes, behaviours, and experiences, as well as interpersonal and contextual factors that shape self-management and outcomes. These factors and their interactions drive variability in diabetes outcomes and optimal treatment strategies between individuals as well as within individuals over time (i.e. with ageing). Participants proposed strategies to address these factors: regular, holistic needs assessments to match people with effective self-care approaches and adapt them over the lifespan; longitudinal support (e.g., education, tactical help, sharing and validating experiences); tailored education and skills training; and leveraging of caregivers, family, and peers as resources. Our study of what influences self-management decisions and technology adoption among older adults with type 1 diabetes underscores the importance of ongoing assessments to address dynamic age-specific needs, as well as individualized multi-faceted support that integrates peers and caregivers.

935-P: Racial and Ethnic Disparities in the Application of Continuous Glucose Monitoring in Real-World Clinical Settings

Background: The use of continuous glucose monitoring (CGM) has improved glycemic control. Disparity in CGM use has been reported; however, the utilization of CGM across a health system in real-world clinical settings remains unknown. We aimed to characterize patients who use CGM and compare them to patients with diabetes who do not use CGM in a large health system. Methods: We retrospectively reviewed the electronic health record (EHR) of patients who received care at NYU Langone Health from 2015. CGM users were those whose EHR records had at least one uploaded CGM report, while CGM non-users were those who had at least one outpatient encounter in which a diabetes diagnosis was documented and who did not have any CGM files uploaded into Epic. Results: We identified 10,515 CGM users and 108,395 CGM non-users. CGM use significantly increased in patients with type 2 diabetes (T2D) after 2019, with higher use of CGM in patients with T2D than those with type 1 diabetes (T1D) (63.6% vs. 36.4%; p&amp;lt;.001). The rate of CGM use was significantly different across racial/ethnic groups: 9.9% of Whites, 7.8% Hispanics/Latinos, 7.5% African Americans, 7.2% Asian, and 7.1% Native Americans. When comparing CGM user among racial groups, CGM user was higher (than non-use) among Whites (60.4% vs 53.1%) and Filipinos (0.68% vs 0.55%), but less frequent among African Americans (13.0% vs 15.6%), Chinese (0.71% vs 1.7%), and Koreans (0.19% vs 0.45%) (p&amp;lt;.001). Regarding the ethnicity, more CGM users were Salvadoran (0.83% vs 0.45%), while less were Puerto Rican (3.3% vs 3.6%), Dominican (1.2% vs 1.4%), and Mexican (0.55 vs 1.4) (p&amp;lt;.001). Conclusions: Using a large real-world clinical setting dataset, our study identified disparity in the CGM use in patients with T1D compared to T2D. Further we noted racial/ethnic disparities in the CGM use and heterogeneity within Hispanics and Asian Americans. Researchers and clinicians should consider interventions to reduce disparities in CGM prescription patterns. Disclosure Y.Zheng: None. E.Iturrate: None. B.Wu: None. J.Fletcher: None. G.D.Melkus: None. S.B.Johnson: None. Funding National Institutes of Health (P30DK111022)