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.

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.

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.

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.

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Background: Late-meal blousing increases the risk of hyperglycemia following a meal using meter blood glucose data. We used data from the Donors to the Tidepool Big Data Donation project to evaluate real-life bolusing and glycemic excursions using continuous glucose monitoring (CGM). Methods: We analyzed 214,087 meal boluses from 120 subjects, mean age 22 years (range 3-66), average CGM glucose 155mg/dL (range 103-239 mg/dL). We screened for meals meeting the following criteria: carbs of ≥20g, premeal glucose between 70 and 200mg/dL, no glucose &lt;70mg/dL in the hour prior to bolus, no other insulin bolus 2 hours prior and 4 hours post bolus, 95% of CGM readings available 30 min before and 4 hour after the meal bolus. A pre-meal bolus was defined by the glucose rising &lt;0.4mg/dL/min and a late meal boluses by a glucose rise of ≥1mg/dL/min in the 20 minutes prior to the bolus. Results: The post meal bolus had significantly higher CGM glucose levels at the time of bolus (p&lt;0.001), and a significantly higher post prandial peak glucose (p&lt;0.001). There was no statistical difference in the average amount of meal carbs for the two groups. Conclusions: Using integrated pump and CGM data from a large data base, late meal bolusing occurred with 12% of selected meal boluses and was associated with higher CGM values at the time of bolus, and higher peak post prandial CGM values. Disclosure L.M. Norlander: None. E.T. Nykaza: None. B. Arbiter: Employee; Self; Tidepool Project. Stock/Shareholder; Self; Abbott Laboratories, Dexcom, Inc. B.A. Buckingham: Advisory Panel; Self; ConvaTec Inc., Novo Nordisk Inc., Profusa, Inc. Consultant; Self; Medtronic MiniMed, Inc. Research Support; Self; Beta Bionics, ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Medtronic MiniMed, Inc., Tandem Diabetes Care. Other Relationship; Self; Insulet Corporation, Tandem Diabetes Care. R. Lal: Consultant; Self; Abbott.

Loop is a DIY app for automated insulin delivery using an iPhone and commercial continuous glucose monitor (CGM) and insulin pump. An ongoing observational study evaluates glycemic control, adverse event rates, and patient-reported outcomes (PROs) among adults with type 1 diabetes (T1D). This abstract presents PROs data for the first 3 months using Loop. Participants were recruited through online postings and packaging of the Loop RileyLink. Device data were collected via the Tidepool Mobile App, including available CGM data at enrollment. PROs evaluated diabetes distress, technology attitudes and problem solving, hypoglycemia fears and confidence, and sleep quality with validated surveys. There were 290 new users who started Loop within the past 7 days. Complete data were available for 254; 5 stopped using Loop and 31 did not complete PROs at 3 months. Demographic and clinical characteristics are in the Table. t tests and effect sizes showed statistically (p&lt;0.05) and clinically significant (d=0.2-0.6) benefits of less distress and hypoglycemia fear, more confidence in catching hypoglycemia, and better sleep quality. Users were consistent in their report of average ease to start Loop and Loop performing very well day and night. Over 90% would recommend Loop (scores of 4 or 5). This is the first study to systematically measure PROs in people using a DIY system, with new users voicing high satisfaction and across the board positive PRO scores. Disclosure K.K. Hood: Research Support; Self; Dexcom, Inc. Speaker’s Bureau; Self; LifeScan, Inc., MedIQ. J.J. Wong: None. S. Hanes: None. R. Bailey: None. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R. Beck: None. V.R. Barnes-Lomen: None. J.W. Lum: None. B. Arbiter: Employee; Self; Tidepool. Stock/Shareholder; Self; Abbott Laboratories, Dexcom, Inc., Tandem Diabetes Care. D. Naranjo: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust