Use of Connected Pen as a Diagnostic Tool to Evaluate Missed Bolus Dosing Behavior in People with Type 1 and Type 2 Diabetes

Impact of Missed and Late Meal Boluses on Glycemic Outcomes in Automated Insulin Delivery-Treated Children and Adolescents with Type 1 Diabetes: A Two-Center, Population-Based Cohort Study

Objective: To evaluate the impact of missed or late meal boluses (MLBs) on glycemic outcomes in children and adolescents with type 1 diabetes using automated insulin delivery (AID) systems. Research Design and Methods: AID-treated (Tandem Control-IQ or Medtronic MiniMed 780G) children and adolescents (aged 6-21 years) from Stanford Medical Center and Steno Diabetes Center Copenhagen with ≥10 days of data were included in this two-center, binational, population-based, retrospective, 1-month cohort study. The primary outcome was the association between the number of algorithm-detected MLBs and time in target glucose range (TIR; 70-180 mg/dL). Results: The study included 189 children and adolescents (48% females with a mean ± standard deviation age of 13 ± 4 years). Overall, the mean number of MLBs per day in the cohort was 2.2 ± 0.9. For each additional MLB per day, TIR decreased by 9.7% points (95% confidence interval [CI] 11.3; 8.1), and compared with the quartile with fewest MLBs (Q1), the quartile with most (Q4) had 22.9% less TIR (95% CI: 27.2; 18.6). The age-, sex-, and treatment modality-adjusted probability of achieving a TIR of >70% in Q4 was 1.4% compared with 74.8% in Q1 (P < 0.001). Conclusions: MLBs significantly impacted glycemic outcomes in AID-treated children and adolescents. The results emphasize the importance of maintaining a focus on bolus behavior to achieve a higher TIR and support the need for further research in technological or behavioral support tools to handle MLBs.

Expert Panel Recommendations for a Standardized Ambulatory Glucose Profile Report for Connected Insulin Pens

Background: Connected insulin pens capture data on insulin dosing/timing and can integrate with continuous glucose monitoring (CGM) devices with essential insulin and glucose metrics combined into a single platform. Standardization of connected insulin pen reports is desirable to enhance clinical utility with a single report. Methods: An international expert panel was convened to develop a standardized connected insulin pen report incorporating insulin and glucose metrics into a single report containing clinically useful information. An extensive literature review and identification of examples of current connected insulin pen reports were performed serving as the basis for creation of a draft of a standardized connected insulin pen report. The expert panel participated in three virtual standardization meetings and online surveys. Results: The Ambulatory Glucose Profile (AGP) Report: Connected Insulin Pen brings all clinically relevant CGM-derived glucose and connected insulin pen metrics into a single simplified two-page report. The first page contains the time in ranges bar, summary of key insulin and glucose metrics, the AGP curve, and detailed basal (long-acting) insulin assessment. The second page contains the bolus (mealtime and correction) insulin assessment periods with information on meal timing, insulin-to-carbohydrate ratio, average bolus insulin dose, and number of days with bolus doses recorded. The report's second page contains daily glucose profiles with an overlay of the timing and amount of basal and bolus insulin administered. Conclusion: The AGP Report: Connected Insulin Pen is a standardized clinically useful report that should be considered by companies developing connected pen technology as part of their system reporting/output.

Association Between Treatment Adherence and Continuous Glucose Monitoring Outcomes in People With Diabetes Using Smart Insulin Pens in a Real-World Setting

OBJECTIVE

To evaluate the association of insulin injection adherence, smart insulin pen engagement, and glycemic control using real-world data from 16 countries from adults self-administering basal insulin degludec and bolus insulin with a smart insulin pen (NovoPen 6 or NovoPen Echo Plus) alongside continuous glucose monitoring (CGM).

RESEARCH DESIGN AND METHODS

Data were aggregated over 14-day periods. Treatment adherence was defined according to the number of missed basal and missed bolus insulin doses and smart pen engagement according to the number of days with data uploads.

RESULTS

Data from 3,945 adults, including 25,157 14-day periods with ≥70% CGM coverage, were analyzed. On average, 0.2 basal and 6.0 bolus insulin doses were missed over 14 days. The estimated probability of missing at least one basal insulin dose over a 14-day period was 17.6% (95% CI 16.5, 18.7). Missing one basal or bolus insulin dose per 14 days was associated with a significant decrease in percentage of time with glucose levels in range (TIR) (3.9-10.0 mmol/L), of -2.8% (95% CI -3.7, -1.8) and -1.7% (-1.8, -1.6), respectively; therefore, missing two basal or four bolus doses would decrease TIR by >5%. Smart pen engagement was associated positively with glycemic outcomes.

CONCLUSIONS

This combined analysis of real-world smart pen and CGM data showed that missing two basal or four bolus insulin doses over a 14-day period would be associated with a clinically relevant decrease in TIR. Smart insulin pens provide valuable insights into treatment injection behaviors.

Future of Time-in-Range Goals in the Era of Advanced Hybrid Closed-Loop Automated Insulin Delivery Systems

The concept of maintaining blood glucose levels within the 70-180 mg/dL range, known as time-in-range, has raised questions regarding its representation of true physiological euglycemia. Some have speculated that focusing on the time spent within the 70-140 mg/dL range, introduced as time in tight range (TITR) through the International Consensus statement, could serve as a more precise metric for assessing normoglycemia in individuals with type 1 diabetes. This article delves into the current status of TITR as an emerging marker and explores how advanced hybrid closed-loop systems may offer a promising avenue for achieving this higher level of glycemic control.

Decreasing the Burden of Carbohydrate Counting and Meal Announcement with Automated Insulin Delivery, Meal Recognition, and Autocorrection Doses: A Case Study

The use of automated insulin delivery (AID) has led to a decrease in the burden of diabetes, allowing for better sleep, decreased anxiety about hypoglycemia, and automatic corrections doses, and meal recognition algorithms have provided "forgiveness" for imprecise carbohydrate (CHO) entries and missed or late meal boluses. We provide a case report and review of the current literature assessing the effect of AID on the burden of meal bolus. The case also demonstrates how sensor and pump data provide insight into insulin bolus behavior, and access to integrated cloud-based data has allowed for virtual patient visits. Glucose sensor metrics provides time in range and time below range, and the sensor-derived glucose management indicator provides an assessment of the long-term risk of complications when a laboratory glycated hemoglobin is not available.

A Three-Step Data-Driven Methodology to Assess Adherence to Basal Insulin Therapy in Patients With Insulin-Treated Type 2 Diabetes

BACKGROUND

While health care providers (HCPs) are generally aware of the challenges concerning insulin adherence in adults with insulin-treated type 2 diabetes (T2D), data guiding identification of insulin nonadherence and understanding of injection patterns have been limited. Hence, the aim of this study was to examine detailed injection data and provide methods for assessing different aspects of basal insulin adherence.

METHOD

Basal insulin data recorded by a connected insulin pen and prescribed doses were collected from 103 insulin-treated patients (aged ≥18 years) with T2D from an ongoing clinical trial (NCT04981808). We categorized the data and analyzed distributions of correct doses, increased doses, reduced doses, and missed doses to quantify adherence. We developed a three-step model evaluating three aspects of adherence (overall adherence, adherence distribution, and dose deviation) offering HCPs a comprehensive assessment approach.

RESULTS

We used data from a connected insulin pen to exemplify the use of the three-step model to evaluate overall, adherence, adherence distribution, and dose deviation using patient cases.

CONCLUSION

The methodology provides HCPs with detailed access to previously limited clinical data on insulin administration, making it possible to identify specific nonadherence behavior which will guide patient-HCP discussions and potentially provide valuable insights for tailoring the most appropriate forms of support.

Personalized Prediction of Change in Fasting Blood Glucose Following Basal Insulin Adjustment in People With Type 2 Diabetes: A Proof-of-Concept Study

AIMS

For people with type 2 diabetes treated with basal insulin, suboptimal glycemic control due to clinical inertia is a common issue. Determining the optimal basal insulin dose can be difficult, as it varies between individuals. Thus, insulin titration can be slow and cautious which may lead to treatment fatigue and non-adherence. A model that predicts changes in fasting blood glucose (FBG) after adjusting basal insulin dose may lead to more optimal titration, reducing some of these challenges.

OBJECTIVE

To predict the change in FBG following adjustment of basal insulin in people with type 2 diabetes using a machine learning framework.

METHODS

A multiple linear regression model was developed based on 786 adults with type 2 diabetes. Data were divided into training (80%) and testing (20%) sets using a ranking approach. Forward feature selection and fivefold cross-validation were used to select features.

RESULTS

Participants had a mean age of approximately 59 years, a mean duration of diabetes of 12 years, and a mean HbA1c at screening of 65 mmol/mol (8.1%). Chosen features were FBG at week 2, basal insulin dose adjustment from week 2 to 7, trial site, hemoglobin level, and alkaline phosphatase level. The model achieved a relative absolute error of 0.67, a Pearson correlation coefficient of 0.74, and a coefficient of determination of 0.55.

CONCLUSIONS

A model using FBG, insulin doses, and blood samples can predict a five-week change in FBG after adjusting the basal insulin dose in people with type 2 diabetes. Implementation of such a model can potentially help optimize titration and improve glycemic control.

First Real-World Experience With Bigfoot Unity: A 6-Month Retrospective Analysis

The Bigfoot Unity Diabetes Management System, a smart pen cap system cleared by the U.S. Food and Drug Administration in May 2021, incorporates continuous glucose monitoring data, real-time glycemic alerts, and clinician-directed dose recommendations. This study analyzed real-world clinical outcomes data for an initial cohort (n = 58, from 13 clinics) managing multiple daily injection insulin therapy using the pen cap system for 6 months. We examined glycemic control, including hypoglycemia events and interaction with and use of the pen cap system. In a cohort mainly consisting of adults with type 2 diabetes and an average age of 62 years, the results demonstrate close adherence to established glycemic targets, including a relatively short amount of time spent in the hypoglycemic range.

Advances in Continuous Glucose Monitoring and Integrated Devices for Management of Diabetes with Insulin-Based Therapy: Improvement in Glycemic Control

Continuous glucose monitoring (CGM) technology has evolved over the past decade with the integration of various devices including insulin pumps, connected insulin pens (CIPs), automated insulin delivery (AID) systems, and virtual platforms. CGM has shown consistent benefits in glycemic outcomes in type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) treated with insulin. Moreover, the combined effect of CGM and education have been shown to improve glycemic outcomes more than CGM alone. Now a CIP is the expected future technology that does not need to be worn all day like insulin pumps and helps to calculate insulin doses with a built-in bolus calculator. Although only a few clinical trials have assessed the effectiveness of CIPs, they consistently show benefits in glycemic outcomes by reducing missed doses of insulin and improving problematic adherence. AID systems and virtual platforms made it possible to achieve target glycosylated hemoglobin in diabetes while minimizing hypoglycemia, which has always been challenging in T1DM. Now fully automatic AID systems and tools for diabetes decisions based on artificial intelligence are in development. These advances in technology could reduce the burden associated with insulin treatment for diabetes.