Diabetes Technology Meeting 2021.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.

Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management.

BACKGROUND: Continuous glucose monitoring (CGM) offers multiple data features that can be leveraged to assess glucose management. However, how diabetes healthcare professionals (HCPs) actually assess CGM data and the extent to which they agree in assessing glycemic management are not well understood. METHODS: We asked HCPs to assess ten de-identified CGM datasets (each spanning seven days) and rank order each day by relative glycemic management (from "best" to "worst"). We also asked HCPs to endorse features of CGM data that were important in making such assessments. RESULTS: In the study, 57 HCPs (29 endocrinologists; 28 diabetes educators) participated. Hypoglycemia and glycemic variance were endorsed by nearly all HCPs to be important (91% and 88%, respectively). Time in range and daily lows and highs were endorsed more frequently by educators (all Ps < .05). On average, HCPs endorsed 3.7 of eight data features. Overall, HCPs demonstrated agreement in ranking days by relative glycemic control (Kendall's W = .52, P < .001). Rankings were similar between endocrinologists and educators (R2 = .90, Cohen's kappa = .95, mean absolute error = .4 [all Ps < .05]; Mann-Whitney U = 41, P = .53). CONCLUSIONS: Consensus in the endorsement of certain data features and agreement in assessing glycemic management were observed. While some practice-specific differences in feature endorsement were found, no differences between educators and endocrinologists were observed in assessing glycemic management. Overall, HCPs tended to consider CGM data holistically, in alignment with published recommendations, and made converging assessments regardless of practice.

Digital Diabetes Management Application Improves Glycemic Outcomes in People With Type 1 and Type 2 Diabetes.

BACKGROUND: Diabetes is a chronic condition that requires constant self-management. As a consequence, several software platforms have been developed to facilitate the tracking of diabetes data to improve diabetes management. Our aim was to determine the real-world glycemic benefits of a mobile diabetes management platform used by individuals with type 1 and type 2 diabetes. METHODS: Mobile platform-using (n = 899) and control (n = 900) participants meeting specific minimum data criteria were randomly selected from a database of diabetes users. All results were modeled using different mixed effect generalized linear models, assigning random intercepts for each user, and adjusting the distribution assumption for each outcome. RESULTS: Users of the mobile platform increased their frequency of blood glucose monitoring (+8.8 tests per month, 95% CI [3.4, 14.1], P < .001) and had fewer hyperglycemic events and lower average glucose levels compared to the control group. In addition, a mobile user could expect a 3.5% drop in average BG (-6.4 mg/dL, 95% CI [-2.0, -10.7], P < .001) and a 10.7% decrease in hyperglycemia ( P < .001) after 2 months. CONCLUSION: Users of the mobile platform tested their BG more often and demonstrated greater improvement in blood glucose compared to users who did not use the mobile platform. This supports previous studies indicating that digital technologies can enhance diabetes care in a real-world setting.

Using Remote Communication Technology in Insulin Pump Training: A Feasibility Study.

PURPOSE: This feasibility study was designed to examine if remote communication technology can be used in the technical training of an insulin pump in adults with diabetes who were familiar with insulin pump therapy. METHODS: Surveys were emailed to 69 individuals who purchased an insulin pump and had been trained by the manufacturer's diabetes educators. In consultation with providers, participants were given the choice of receiving training in a face-to-face meeting or via remote communication technology. The survey consisted of 27 questions asking participants' characteristics, device proficiency, confidence, and their satisfaction with the insulin pump and the training method. Differences between the 2 groups were examined using bivariate analyses. RESULTS: There were 17 participants in the remote group and 20 participants in the face-to-face group. Participants had a mean age of 40.9 ± 14.3 years, had diabetes for 24.3 ± 13.8 years, and used an insulin pump for 9.8 ± 4.9 years. The participants in both groups were not statistically different in age, diabetes history, years on insulin pump, device proficiency, confidence, or satisfaction with the training method. The remote group reported less graduate-level education (P < .05) and higher satisfaction scores with the insulin pump training (P < .05). CONCLUSION: Although this study has limitations associated with the small sample size and self-selection bias, the results suggest that remote communication technology may be an effective tool to provide technical training to adults who are familiar with insulin pump therapy. Additional research is required to determine the effectiveness of the remote insulin pump training.

Insulin Pumps and Remote Software Updates: A New Way Forward.

This article discusses the future direction of insulin pump technology and its relationship to the software update process. A user needs analysis revealed that respondents wanted an insulin pump software update process to function much in the same way as smartphone updates. Users of insulin pumps have the same expectations as with other ubiquitous technology such as smartphones, tablets, and laptops. The development of a software update system within a regulated environment that meets the needs of insulin pump users by allowing optional software updates that provide access to pump improvements, feature additions, or access to algorithms that provide therapy-changing technologies is a new way forward for the management of a complicated disease that affects more than 450,000 people using insulin pumps in the United States.

Usability and training differences between two personal insulin pumps.

The purpose of this study was to determine if there were usability and training differences between the Medtronic MiniMed Paradigm Revel Insulin Pump and the Tandem Diabetes Care t:slim Insulin Pump during use by representative users, performing representative tasks, in a simulated use environment. This study utilized a between-subjects experimental design with a total of 72 participants from 5 sites across the United States. Study participants were randomized to either the Revel pump group or the t:slim Pump group. Participants were 18 years of age or older and managed their diabetes using multiple daily insulin injections. Dependent variables included training time, training satisfaction, time on task, task failures, System Usability Scale (SUS) ratings, perceived task difficulty, and a pump survey that measured different aspects of the pumps and training sessions. There was a statistically significant difference in training times and error rates between the t:slim and Revel groups. The training time difference represented a 27% reduction in time to train on the t:slim versus the Revel pump. There was a 65% reduction in participants' use error rates between the t:slim and the Revel group. The t:slim Pump had statistically significant training and usability advantages over the Revel pump. The reduction in training time may have been a result of an optimized information architecture, an intuitive navigational layout, and an easy-to-read screen. The reduction in use errors with the t:slim may have been a result of dynamic error handling and active confirmation screens, which may have prevented programming errors.