Introduction and Objective: In individuals with T1D, increased change in CGM (ΔG) is common during physical activity (PA) due to 3 main factors: impaired endogenous glucose production (ΔGegp), increased insulin-dependent glucose utilization (ΔGid), and increased insulin-independent glucose clearance (ΔGii) by skeletal muscles. To understand how the factors affect ΔG during PA, we identify a data-driven weighted sum model using the 4 most common PAs from the T1DEXI dataset. Methods: To account for glucose dynamics with a scalar value, we used the glucose area under the curve (AUC) of ΔG of each component. We estimated the 3 component weights by minimizing the difference between the sum of the 3 AUCs and the measured total AUC of ΔG. For identification, we used PA sessions with positive net insulin on board (netIOB) at PA onset, where netIOB was defined as the changes in insulin delivery relative to the basal rate. Results: We used 175 sessions of strength training, 201 biking, 123 jogging, and 515 walking. For small netIOB at the PA onset, ΔGii is predominant over the ΔGid, while ΔGii becomes more pronounced for increasing netIOB (Fig. 1). For PA >120 min, both ΔGii and ΔGid heavily contributed to overall ΔG for increasing netIOB. ΔGegp was much smaller than ΔGii or ΔGid. Conclusion: The proposed model constitutes a tool to reproduce real-life scenarios in simulation and is appealing for control design due to its simplicity. E. Aiello: Research Support; Hemsley Charitable Trust. K. Tang: Research Support; Hemsley Charitable Trust. M.P. Dhaliwal: Research Support; Hemsley Charitable Trust. R. Lal: Consultant; Abbott, Biolinq, Capillary Biomedical, Inc, Gluroo, PhysioLogic Devices, Portal Insulin, Sanofi, Tidepool. Advisory Panel; Provention Bio, Inc, Provention Bio, Inc, Microbion, Microbion, Lilly Diabetes. Research Support; Insulet Corporation, Medtronic, Tandem Diabetes Care, Inc, Sinocare Inc. C. Summers: Consultant; Tidepool. M. Connolly: Employee; Tidepool. D.P. Zaharieva: Research Support; Hemsley Charitable Trust. Speaker's Bureau; Dexcom, Inc. Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes. B. Arbiter: Stock/Shareholder; Eli Lilly and Company, Dexcom, Inc. Employee; Tidepool. K. Watson: Employee; Tidepool. M. Friedman: None. L. Figg: None. A.L. Cortes-Navarro: None. I. Balistreri: None. R.S. Kingman: None. B. Suh: None. M.C. Riddell: Advisory Panel; Zucara Therapeutics, embecta. Consultant; Dexcom, Inc., Insulet Corporation, Eli Lilly and Company, Novo Nordisk. Speaker's Bureau; Novo Nordisk, Dexcom, Inc., Sanofi, Eli Lilly and Company. Stock/Shareholder; Zucara Therapeutics. Research Support; Dexcom, Inc., Insulet Corporation, Eli Lilly and Company. Y. Qin: None. The Leona M. & Harry B. Helmsley Charitable Trust Grant (2404-06905)

Introduction and Objective: Developing a data-driven mathematical model to predict the impact of physical activity (PA) on glucose levels in individuals with T1D is non-trivial due to the concurrent insulin and non-insulin-mediated mechanisms. To understand how each mechanism affects the glucose, we performed a sensitivity analysis using the 4 most common PAs from the T1DEXI dataset. Methods: Net insulin on board (netIOB) is the change in insulin delivery relative to the basal rate in the past 4 hours. The dataset was divided into 1) positive (netIOB>0) and 2) negative (netIOB<0) netIOB at the onset of PA. For each PA, we identified a 1st-order linear time series model that predicts the glucose from the heart rate (non-insulin-mediated). Then, we analyzed how parameters change when netIOB varies. Results: We used 65 and 327 sessions of strength training for netIOB>0 and netIOB<0, 70 and 416 biking, 90 and 313 jogging, and 256 and 1072 walking, respectively. The netIOB ranged from -10 U to 10 U. Fig. 1 shows the cutoff between insulin and non-insulin-mediated effects. The parameters remain stable for netIOB<0. The model gain increases with netIOB for netIOB>0. Conclusion: Data with netIOB<0 at PA onset are representative of the non-insulin-mediated glucose uptake due to the muscle contraction based on Fig.1, while data with netIOB>0 are fundamental to identifying insulin-mediated glucose disposal. E. Aiello: Research Support; Hemsley Charitable Trust. K. Tang: Research Support; Hemsley Charitable Trust. M.P. Dhaliwal: Research Support; Hemsley Charitable Trust. R. Lal: Consultant; Abbott, Biolinq, Capillary Biomedical, Inc, Gluroo, PhysioLogic Devices, Portal Insulin, Sanofi, Tidepool. Advisory Panel; Provention Bio, Inc, Provention Bio, Inc, Microbion, Microbion, Lilly Diabetes. Research Support; Insulet Corporation, Medtronic, Tandem Diabetes Care, Inc, Sinocare Inc. C. Summers: Consultant; Tidepool. M. Connolly: Employee; Tidepool. D.P. Zaharieva: Research Support; Hemsley Charitable Trust. Speaker's Bureau; Dexcom, Inc. Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes. B. Arbiter: Stock/Shareholder; Eli Lilly and Company, Dexcom, Inc. Employee; Tidepool. K. Watson: Employee; Tidepool. M. Friedman: None. L. Figg: None. A.L. Cortes-Navarro: None. I. Balistreri: None. R.S. Kingman: None. B. Suh: None. M.C. Riddell: Advisory Panel; Zucara Therapeutics, embecta. Consultant; Dexcom, Inc., Insulet Corporation, Eli Lilly and Company, Novo Nordisk. Speaker's Bureau; Novo Nordisk, Dexcom, Inc., Sanofi, Eli Lilly and Company. Stock/Shareholder; Zucara Therapeutics. Research Support; Dexcom, Inc., Insulet Corporation, Eli Lilly and Company. Y. Qin: None. The Leona M. & Harry B. Helmsley Charitable Trust Grant (2404-06905).

Background: Physical activity (PA) poses significant challenges in glucose management for individuals with type 1 diabetes (T1D). Real-world PA is more frequent than structured PA, but remains underexplored. We analyzed 8171 real-world PA sessions comprising 45 activity types from the Type 1 Diabetes Exercise Initiative, examining hypoglycemia risk correlations with PA-level and population-level factors. Methods: Hypoglycemia risk was measured by change in continuous glucose monitoring (ΔCGM) from PA onset to end, low blood glucose index (LBGI), and hypoglycemia event occurrence. Primary analyses used analysis of variance and Tukey's range test to measure correlations. Secondary analyses compared risk across activity types and categories (aerobic, mixed, and anaerobic). Results: Higher hypoglycemia risk was associated with longer PA duration (median [Interquartile Range (IQR)] ΔCGM -24 [-60, 11] mg/dL for 60-120 min vs. -12 [-31, 5] mg/dL for 15-30 min), lower starting glucose (90% of sessions starting <50 mg/dL had hypoglycemia), and declining glucose rates before PA (all P < 0.05). Carbohydrate (CHO) intake 2-4 h before and during PA was associated with higher hypoglycemia risk (ΔCGM -37 [-67, -14] mg/dL with rescue CHO vs. -15 [-42, 8] mg/dL without, P < 0.05), but this paradoxical effect was explained by higher insulin on board (IoB) and lower starting glucose. Males had larger glucose drops (ΔCGM -20 [-46, 4] mg/dL vs. -16 [-44, 7] mg/dL in females, P < 0.05). Closed-loop users exhibited lower LBGI compared with open-loop users (P < 0.05). Secondary analyses showed significant glycemic variability across activity types (P < 0.05). Aerobic activities caused the greatest glucose drop, followed by mixed and anaerobic (P < 0.05), whereas LBGI differences were nonsignificant (P = 0.32). Conclusions: Real-world PA has a highly variable glycemic impact, with longer duration, lower starting glucose, and higher IoB increasing hypoglycemia risk. Glycemic responses differed significantly by activity type, with aerobic activities resulting in the greatest decline. These findings highlight the need for tailored strategies to mitigate PA-related hypoglycemia in T1D.

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.

Introduction & Objective: We analyzed the unstructured real-world physical activity (PA) sessions from the T1DEXI study to evaluate hypoglycemia risk in terms of change in CGM (ΔCGM) readings and percentage of time below range (%TBR) of T1D participants. Methods: For each session, we calculated the ∆CGM as the difference in glucose readings within 5 mins before and after PA and, %TBR as the percentage of readings <70 mg/dL during PA. ANOVA was used to measure variation in ΔCGM and %TBR associated with activity durations, starting glucose, insulin on board (IOB), and individual factors, i.e. age, sex, and A1c. Results: We analyzed 9,322 sessions, with 46 activity types, for 404 participants (open and closed loop pump users, age 36.6 ± 14.0 yrs, 75.3% female, BMI 25.5 ± 3.9 kg/m2, A1c 6.6 ± 0.7%, T1D duration 18.5 ± 13.0 yrs, total daily dose 40.1 ± 16.2 U), with an average of 24 sessions in 4 weeks. Table 1 shows larger ΔCGM and higher %TBR for increasing activity durations, and IOB (all p<0.01), lower %TBR for increasing starting glucose (p<0.01). %TBR differed significantly for age, A1c, and sex groups (all p<0.03), while ΔCGM only for age (p<0.01). Carbohydrate (CHO) intake correlated with a higher %TBR due to a lower starting glucose for sessions with CHO. Conclusion: Hypoglycemia risk during PA is impacted by population and individual factors, which are important when tailoring glucose management strategies around PA. Disclosure M.P. Dhaliwal: Consultant; Tidepool. K. Tang: Consultant; Tidepool. E.M. Aiello: Other Relationship; Tidepool. D.P. Zaharieva: Research Support; Leona M. and Harry B. Helmsley Charitable Trust. Advisory Panel; Dexcom, Inc. Research Support; Insulet Corporation. Speaker's Bureau; Dexcom, Inc. Research Support; International Society for Pediatric and Adolescent Diabetes. Board Member; Juvenile Diabetes Research Foundation (JDRF). R. Lal: Consultant; Abbott, Adaptyx Biosciences, Biolinq, Capillary Biomedical, Inc., Deep Valley Labs, Gluroo, PhysioLogic Devices, Portal Insulin, Tidepool. Advisory Panel; Lilly Diabetes. C. Summers: Consultant; Tidepool. B. Arbiter: Stock/Shareholder; Eli Lilly and Company. Employee; Tidepool. Stock/Shareholder; Dexcom, Inc. K. Watson: Employee; Tidepool. Other Relationship; Luna Diabetes. Stock/Shareholder; Tandem Diabetes Care, Inc. L. Figg: None. I. Balistreri: None. R.S. Kingman: None. B. Suh: None. Y. Qin: None. Funding The Leona M. & Harry B. Helmsley Charitable Trust (Grant #2404-06905).

Digital decision support and remote patient monitoring may improve outcomes and efficiency, but rarely scale beyond a single institution. Over the last 5 years, the platform Timely Interventions for Diabetes Excellence (TIDE) has been associated with reduced care provider screen time and improved, equitable type 1 diabetes care and outcomes for 268 patients in a heterogeneous population as part of the Teamwork, Targets, Technology, and Tight Control (4T) Study (NCT03968055, NCT04336969). Previous efforts to deploy TIDE at other institutions continue to face delays. In partnership with the diabetes technology non-profit, Tidepool, we developed Tidepool-TIDE, a clinic-agnostic, turnkey solution available to any clinic in the United States. We present how we overcame common technical and operational barriers specific to scaling digital health technology from one site to many. The concepts described are broadly applicable for institutions interested in facilitating broader adoption of digital technology for population-level management of chronic health conditions.

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).

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.

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<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

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.

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 <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 <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<0.001), and a significantly higher post prandial peak glucose (p<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.

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.

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.