Why Download Data: The Benefits and Challenges of More Diabetes Data.

IN BRIEF Diabetes care lends itself to interactions centered around data-counting carbohydrate for meals, calculating correction doses, viewing logbooks or device data, and discussing A1C levels-and digital technology has enhanced diabetes care through the improved collection and analysis of data from multiple sources. With these technological advancements have come great improvements in quality of life for people with type 1 diabetes. These technologies allow for more informed and immediate decision-making through better access to blood glucose data and sometimes allow the devices themselves to make decisions, removing the need for patients or clinicians to be involved in decision-making altogether. At the same time, these new technologies bring new challenges for both patients and health care providers, who must now analyze and make sense of more diabetes data.

Use of the Community-Derived Open-Source Automated Insulin Delivery Loop System in Type 2 Diabetes.

Background: No published data are available on the use of the community-derived open-source Loop hybrid closed-loop controller ("Loop") by individuals with type 2 diabetes (T2D). Methods: Through social media postings, we invited individuals with T2D currently using the Loop system to join an observational study. Thirteen responded of whom seven were eligible for the study, were using the Loop algorithm, and provided data. Results: Mean (±standard deviation) age was 61 ± 13 years, and mean body mass index was 31 ± 5 kg/m2. All but one participant were using noninsulin glucose-lowering medications. Self-reported mean hemoglobin A1c decreased from 7.3% ± 1.1% before starting Loop to 6.0% ± 0.5% on Loop. Time in range 70-180 mg/dL increased from 84% to 93%. The amount of time in hypoglycemia was extremely low before and with Loop (time <54 mg/dL was 0.04% ± 0.06% vs. 0.09% ± 0.07%, respectively). No severe hypoglycemia or diabetic ketoacidosis events were reported while using Loop. Conclusion: These data, though limited, suggest that the Loop system is likely to be effective when used by individuals with T2D and should be evaluated in large-scale studies. Clinical Trial Registration numbers: NCT05951569.

Discontinued Use of the Loop Insulin Dosing System: A Mixed-Methods Investigation.

Background: Loop is an open-source automated insulin dosing system that allows users unrivaled control over system settings that affect future glucose prediction. Thousands use Loop, but little is known about those who discontinue. Methods: In a large observational study, 874 Loop participants completed surveys and provided glycemic data, 46 (5.3%) of those self-identified as discontinuing Loop use during the observation window, 45 completed a discontinued use survey, 22 provided system settings data, and 19 participated in semistructured interviews about their discontinuation. Qualitative data were transcribed, coded, and analyzed. Results: Older age and not trusting Loop were associated with discontinued use, although no other demographic or clinical characteristics were significant correlates. The most endorsed reasons were "I decided to try something else" (27.8%) followed by "It just didn't help as much as I thought it would" (22.2%). Qualitative analyses revealed prominent themes centered upon mental and emotional burden and adjusting settings. Other reasons for discontinued use included fear of disapproval of Loop use from diabetes provider, barriers to acquiring component devices, a desire to try new/different technologies, concerns that Loop could not accommodate specific exercise or low insulin dose regimens, and worry about Loop use during pregnancy. It was noted that burdens might be alleviated by enhanced technical assistance and expert guidance. Conclusions: Although the majority of individuals in the Loop observational study continued use, those who discontinued reported similar challenges. Technical support and education specific to setting calculations could expand Loop benefits, alleviate burden, and support sustained use among new Loop users. Clinical Trial Registration: clinicaltrials.gov (NCT03838900).

A Real-World Prospective Study of the Safety and Effectiveness of the Loop Open Source Automated Insulin Delivery System.

Objective: To evaluate the safety and effectiveness of the Loop Do-It-Yourself automated insulin delivery system. Research Design and Methods: A prospective real-world observational study was conducted, which included 558 adults and children (age range 1-71 years, mean HbA1c 6.8% ± 1.0%) who initiated Loop either on their own or with community-developed resources and provided data for 6 months. Results: Mean time-in-range 70-180 mg/dL (TIR) increased from 67% ± 16% at baseline (before starting Loop) to 73% ± 13% during the 6 months (mean change from baseline 6.6%, 95% confidence interval [CI] 5.9%-7.4%; P < 0.001). TIR increased in both adults and children, across the full range of baseline HbA1c, and in participants with both high- and moderate-income levels. Median time <54 mg/dL was 0.40% at baseline and changed by -0.05% (95% CI -0.09% to -0.03%, P < 0.001). Mean HbA1c was 6.8% ± 1.0% at baseline and decreased to 6.5% ± 0.8% after 6 months (mean difference = -0.33%, 95% CI -0.40% to -0.26%, P < 0.001). The incidence rate of reported severe hypoglycemia events was 18.7 per 100 person-years, a reduction from the incidence rate of 181 per 100 person-years during the 3 months before the study. Among the 481 users providing Loop data at 6 months, median continuous glucose monitoring use was 96% (interquartile range [IQR] 91%-98%) and median time Loop modulating basal insulin was at least 83% (IQR 73%-88%). Conclusions: The Loop open source system can be initiated with community-developed resources and used safely and effectively by adults and children with type 1 diabetes.

Pilot Study of a Novel Application for Data Visualization in Type 1 Diabetes.

BACKGROUND: A novel software application, Blip, was created to combine and display diabetes data from multiple devices in a uniform, user-friendly manner. The objective of this study was to test the usability of this application by adults and caregivers of children with type 1 diabetes (T1D). METHODS: Patients (n = 35) and caregivers of children with T1D (n = 30) using an insulin pump for >1 year ± CGM were given access to the software for 3 months. Diabetes management practices and the use of diabetes data were assessed at baseline and at study end, and feedback was gathered in a concluding questionnaire. RESULTS: At baseline, 97% of participants agreed it was important for patients to know how to interpret glucose data. Most felt that clinicians and patients should share the tasks of reviewing data, finding patterns, and making changes to their insulin plans. However, despite valuing shared responsibility, at baseline, 43% of participants never downloaded pump data, and only 9% did so at least once per month. At study end, 72% downloaded data at least once during the 3-month study, and 38% downloaded at least once per month. Regarding the software application, participants liked the central repository of data and the user interface. Suggestions included providing tools for understanding and interpreting glucose patterns, an easier uploading process, and access with mobile devices. CONCLUSIONS: Collaboration between developers and researchers prompted iterative, rapid development of data visualization software and improvements in the uploading process and user interface, which facilitates clinical integration and future clinical studies.

A case study in open source innovation: developing the Tidepool Platform for interoperability in type 1 diabetes management.

OBJECTIVE: Develop a device-agnostic cloud platform to host diabetes device data and catalyze an ecosystem of software innovation for type 1 diabetes (T1D) management. MATERIALS AND METHODS: An interdisciplinary team decided to establish a nonprofit company, Tidepool, and build open-source software. RESULTS: Through a user-centered design process, the authors created a software platform, the Tidepool Platform, to upload and host T1D device data in an integrated, device-agnostic fashion, as well as an application ("app"), Blip, to visualize the data. Tidepool's software utilizes the principles of modular components, modern web design including REST APIs and JavaScript, cloud computing, agile development methodology, and robust privacy and security. DISCUSSION: By consolidating the currently scattered and siloed T1D device data ecosystem into one open platform, Tidepool can improve access to the data and enable new possibilities and efficiencies in T1D clinical care and research. The Tidepool Platform decouples diabetes apps from diabetes devices, allowing software developers to build innovative apps without requiring them to design a unique back-end (e.g., database and security) or unique ways of ingesting device data. It allows people with T1D to choose to use any preferred app regardless of which device(s) they use. CONCLUSION: The authors believe that the Tidepool Platform can solve two current problems in the T1D device landscape: 1) limited access to T1D device data and 2) poor interoperability of data from different devices. If proven effective, Tidepool's open source, cloud model for health data interoperability is applicable to other healthcare use cases.