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Regular exercise is essential to overall cardiovascular health and well-being in people with type 1 diabetes, but exercise can also lead to increased glycemic disturbances. Automated insulin delivery (AID) technology has been shown to modestly improve glycemic time in range (TIR) in adults with type 1 diabetes and significantly improve TIR in youth with type 1 diabetes. Available AID systems still require some user-initiated changes to the settings and, in some cases, significant pre-planning for exercise. Many exercise recommendations for type 1 diabetes were developed initially for people using multiple daily insulin injections or insulin pump therapy. This article highlights recommendations and practical strategies for using AID around exercise in type 1 diabetes.

Background. Managing glycemia around exercise is challenging for individuals with type 1 diabetes (T1D) and their healthcare professionals (HCP). We investigated HCP knowledge and confidence around exercise counseling for youth with T1D worldwide. Objective. To assess HCP familiarity with ISPAD Clinical Practice Consensus Guidelines and confidence to deliver recommendations about T1D and exercise. Methods. A new online survey was developed on strategies and competencies about exercise for youth with T1D, comprising of 64 questions, divided into eight different categories, assessing HCPs perceived exercise knowledge, confidence, training, and barriers to exercise counseling. Results. A total of 125 HCPs mean ± SD age 42 ± 8.2 years (74% female, 73% physicians) completed the survey. The ISPAD exercise guidelines were considered familiar to 68/125 (54%) of responders. Overall, 91/125 (73%) felt confident with giving recommendations about exercise with 47/125 (38%) recommending 45–60 mins/day of physical activity, while 16/125 (13%) recommended >60 mins/day. Several topics related to self-management around exercise were covered by most, but not all responders, and differences were observed in exercise content “confidence” and/or “competence” based on geographic location ( p = 0.048 ). No differences in exercise recommendation dose, confidence, or familiarity with ISPAD guidelines were observed for age, sex, type of HCP, years in practice, or healthcare type. Conclusions. Exercise counseling for youth with T1D remains a challenge in most healthcare settings, globally. In general, the number of physically active minutes per week is under-prescribed for youth with T1D and many HCPs in various settings around the world feel that more professional education is needed to boost confidence around the education of several exercise-related topics.

Disordered eating (DE) in type 1 diabetes (T1D) includes insulin restriction for weight loss with serious complications. Gut microbiota-derived short chain fatty acids (SCFA) may benefit host metabolism but are reduced in T1D. We evaluated the hypothesis that DE and insulin restriction were associated with reduced SCFA-producing gut microbes, SCFA, and intestinal microbial diversity in adults with T1D.We collected stool samples at four timepoints in a hypothesis-generating gut microbiome pilot study ancillary to a weight management pilot in young adults with T1D. 16S ribosomal RNA gene sequencing measured the normalized abundance of SCFA-producing intestinal microbes. Gas-chromatography mass-spectrometry measured SCFA (total, acetate, butyrate, and propionate). The Diabetes Eating Problem Survey-Revised (DEPS-R) assessed DE and insulin restriction. Covariate-adjusted and Bonferroni-corrected generalized estimating equations modeled the associations. COVID-19 interrupted data collection, so models were repeated restricted to pre-COVID-19 data. Data were available for 45 participants at 109 visits, which included 42 participants at 65 visits pre-COVID-19. Participants reported restricting insulin "At least sometimes" at 53.3% of visits. Pre-COVID-19, each 5-point DEPS-R increase was associated with a -0.34 (95% CI -0.56, -0.13, p = 0.07) lower normalized abundance of genus Anaerostipes; and the normalized abundance of Lachnospira genus was -0.94 (95% CI -1.5, -0.42), p = 0.02 lower when insulin restriction was reported "At least sometimes" compared to "Rarely or Never".DE and insulin restriction were associated with a reduced abundance of SCFA-producing gut microbes pre-COVID-19. Additional studies are needed to confirm these associations to inform microbiota-based therapies in T1D.

During the COVID-19 pandemic, fewer in-person clinic visits resulted in fewer point-of-care (POC) HbA1c measurements. In this sub-study, we assessed the performance of alternative glycemic measures that can be obtained remotely, such as HbA1c home kits and Glucose Management Indicator (GMI) values from Dexcom Clarity. Home kit HbA1c (n = 99), GMI, (n = 88), and POC HbA1c (n = 32) were collected from youth with T1D (age 9.7 ± 4.6 years). Bland–Altman analyses and Lin’s concordance correlation coefficient (𝜌c) were used to characterize the agreement between paired HbA1c measures. Both the HbA1c home kit and GMI showed a slight positive bias (mean difference 0.18% and 0.34%, respectively) and strong concordance with POC HbA1c (𝜌c = 0.982 [0.965, 0.991] and 0.823 [0.686, 0.904], respectively). GMI showed a slight positive bias (mean difference 0.28%) and fair concordance (𝜌c = 0.750 [0.658, 0.820]) to the HbA1c home kit. In conclusion, the strong concordance of GMI and home kits to POC A1c measures suggest their utility in telehealth visits assessments. Although these are not candidates for replacement, these measures can facilitate telehealth visits, particularly in the context of other POC HbA1c measurements from an individual.

The benefits of intensive diabetes management have been established by the Diabetes Control and Complications Trial. However, challenges with optimizing glycemic management in youth with type 1 diabetes (T1D) remain across pediatric clinics in the United States. This article will review our Teamwork, Targets, Technology, and Tight Control (4T) study that implements emerging diabetes technology into clinical practice with a team approach to sustain tight glycemic control from the onset of T1D and beyond to optimize clinical outcomes.During the 4T Pilot study and study 1, our team-based approach to intensive target setting, education, and remote data review has led to significant improvements in hemoglobin A1c throughout the first year of T1D diagnosis in youth, as well as family and provider satisfaction.The next steps include refinement of the current 4T study 1, developing a business case, and broader implementation of the 4T study. In study 2, we are including a more pragmatic cadence of remote data review and disseminating exercise education and activity tracking to both English- and Spanish-speaking families. The overall goal is to create and implement a translatable program that can facilitate better outcomes for pediatric clinics across the USA.

Background: This study compared glucose control with fast-acting insulin aspart (FiAsp) versus insulin aspart following moderate-intensity exercise (MIE) and high-intensity exercise (HIE) using a s...

BACKGROUND In the U.S., there are over 37 million people with diabetes (PwD), but only 8,000 endocrinologists. Therefore, many PwD receive care exclusively from primary care providers (PCPs). To democratize knowledge regarding insulin-requiring diabetes through tele-education, Stanford University and the University of Florida developed Project Extension for Community Healthcare Outcomes (ECHO) Diabetes. OBJECTIVE ECHO Diabetes uses a Hub and Spoke model connecting specialists (the “Hub”) with PCPs (the “Spokes”). One-hour, weekly sessions include Hub diabetes didactic presentations and Spoke de-identified case presentations. Lessons learned during these sessions target provider knowledge and confidence surrounding diabetes management and patient care. METHODS Spokes were asked to provide short descriptions of PwD whose diabetes management improved directly or indirectly from their providers’ participation and/or their involvement with a Diabetes Support Coach (DSC). We provide a case series to describe individuals and outcomes. Because this study was not a randomized controlled trial and was a prospective observation of patients with the intervention delivered to providers, the trial is not registered in a public trials registry. RESULTS A case series of 11 PwD was compiled from 10 PCPs and one DSC from California and Florida between 2021 and 2022. The principal impact of ECHO Diabetes is the education amplified from PCPs and DSCs to PwD. In all cases, PwD reported increased engagement and improved diabetes management. Several cases reflected increased access to diabetes technology, improvement in glycemic outcomes, and positive trends in mental health measures. CONCLUSIONS This case series elucidates the potential value of the ECHO Diabetes program to PwD who receive their diabetes care from PCPs. Those matched with a DSC saw clinically significant improvements in HbA1c and mental health outcomes. CLINICALTRIAL Not applicable - Because this study was not a randomized controlled trial and was a prospective observation of patients with the intervention delivered to providers, the trial is not registered in a public trials registry.

Co-management of weight and glycaemia is critical yet challenging in type 1 diabetes (T1D). We evaluated the effect of a hypocaloric low carbohydrate, hypocaloric moderate low fat, and Mediterranean diet without calorie restriction on weight and glycaemia in young adults with T1D and overweight or obesity.We implemented a 9-month Sequential, Multiple Assignment, Randomized Trial pilot among adults aged 19-30 years with T1D for ≥1 year and body mass index 27-39.9 kg/mAdjusted weight and HbA1c (n = 38) changed by -2.7 kg (95% CI -3.8, -1.5, P .0001) and -0.91 percentage points (95% CI -1.5, -0.30, P = .005), respectively, while adjusted body fat percentage remained stable, on average (P = .21). Hypoglycaemia indices remained unchanged following adjustment (n = 28, P .05). Variability in all outcomes, including weight change, was considerable (57.9% were re-randomized primarily due to loss of2% body weight). No outcomes varied by diet.Three months of a diet, irrespective of macronutrient distribution or caloric restriction, resulted in weight loss while improving or maintaining HbA1c levels without increasing hypoglycaemia in adults with T1D.

ContextEarly initiation of continuous glucose monitoring (CGM) is advocated for youth with type 1 diabetes (T1D). Data to guide CGM use on time-in-range (TIR), hypoglycemia, and the role of partial clinical remission (PCR) are limited.ObjectiveOur aims were to assess whether 1) an association between increased TIR and hypoglycemia exists, and 2) how time in hypoglycemia varies by PCR status.MethodsWe analyzed 80 youth who were started on CGM shortly after T1D diagnosis and were followed for up to 1-year post diagnosis. TIR and hypoglycemia rates were determined by CGM data and retrospectively analyzed. PCR was defined as (visit glycated hemoglobin A1c) + (4*units/kg/day) less than 9.ResultsYouth were started on CGM 8.0 (interquartile range, 6.0-13.0) days post diagnosis. Time spent at less than 70 mg/dL remained low despite changes in TIR (highest TIR 74.6 ± 16.7%, 2.4 ± 2.4% hypoglycemia at 1 month post diagnosis; lowest TIR 61.3 ± 20.3%, 2.1 ± 2.7% hypoglycemia at 12 months post diagnosis). No events of severe hypoglycemia occurred. Hypoglycemia was rare and there was minimal difference for PCR vs non-PCR youth (54-70 mg/dL: 1.8% vs 1.2%, P = .04; ConclusionAs TIR gradually decreased over 12 months post diagnosis, hypoglycemia was limited with no episodes of severe hypoglycemia. Hypoglycemia rates did not vary in a clinically meaningful manner by PCR status. With CGM being started earlier, consideration needs to be given to modifying CGM hypoglycemia education, including alarm settings. These data support a trial in the year post diagnosis to determine alarm thresholds for youth who wear CGM.

The prevalence of overweight and obesity in young people with type 1 diabetes (T1D) now parallels that of the general population. Excess adiposity increases the risk of cardiovascular disease, which is already elevated up to 10-fold in T1D, underscoring a compelling need to address weight management as part of routine T1D care. Sustainable weight management requires both diet and physical activity (PA). Diet and PA approaches must be optimized towards the underlying metabolic and behavioral challenges unique to T1D to support glycemic control throughout the day. Diet strategies for people with T1D need to take into consideration glycemic management, metabolic status, clinical goals, personal preferences, and sociocultural considerations. A major barrier to weight management in this high-risk population is the challenge of integrating regular PA with day-to-day management of T1D. Specifically, exercise poses a substantial challenge due to the increased risk of hypoglycemia and/or hyperglycemia. Indeed, about two-thirds of individuals with T1D do not engage in the recommended amount of PA. Hypoglycemia presents a serious health risk, yet prevention and treatment often necessitates the consumption of additional calories, which may prohibit weight loss over time. Exercising safely is a concern and challenge with weight management and maintaining cardiometabolic health for individuals living with T1D and many healthcare professionals. Thus, a tremendous opportunity exists to improve exercise participation and cardiometabolic outcomes in this population. This article will review dietary strategies, the role of combined PA and diet for weight management, current resources for PA and glucose management, barriers to PA adherence in adults with T1D, as well as findings and lessons learned from the Advancing Care for Type 1 Diabetes and Obesity Network (ACT1ON).

The ACT1ON pilot study evaluated the feasibility of three dietary strategies to optimize weight and glycemic management among young adults with T1D and overweight or obesity. As a secondary measure, self-reported physical activity (PA) was collected at baseline, 3-, 6-, and 9-months from 68 young adults with T1D (age 25.5 ± 3.1 years, 72.1% female, HbA1c 7.9 ± 1.8%, BMI 30.4 (27.9 - 33.9)) . Using the Global Physical Activity Questionnaire (GPAQ, n=195) and Previous Day Physical Activity Recalls (PDPAR, n=123) , we estimated weekly minutes of moderate-to-vigorous physical activity (MVPA) . Following the COVID-19 outbreak, a subset of participants wore Garmin Vivosmart4® PA trackers for two weeks at each visit (44 measurements from 27 participants) . Mixed effects regression models assessed the relationship between weekly minutes of MVPA and HbA1c using each PA measure. Median weekly minutes of MVPA were 33% lower following the COVID-19 outbreak compared to pre-pandemic PA levels (p=0.02) per the GPAQ, but not PDPAR (-7.7%, p=0.34) . After adjusting for design, demographic, clinical, and dietary variables, a 1 standard deviation increase in weekly minutes of MVPA (GPAQ) was associated with an absolute increase of 0.27% HbA1c (p>0.001) . A small, statistically non-significant association was observed for PDPAR (β=0.13, p=0.19) ; however, we observed a borderline statistically significant association using the PA tracker data (β=0.231, p=0.08) , despite a smaller sample size (n=44) . These results suggest that among young adults with T1D and overweight and obesity, higher levels of PA may lead to challenges in achieving optimal glycemia. Future work is needed to determine how to best support young adults with T1D and overweight and obesity in attaining both their PA and glycemic management goals. Disclosure F. R. Muntis: None. R. E. Pratley: Other Relationship; Bayer AG, Corcept Therapeutics, Dexcom, Inc., Hanmi Pharm. Co., Ltd., Merck & Co., Inc., Metavention, Novo Nordisk, Pfizer Inc., Poxel SA, Sanofi, Scohia Pharma Inc., Sun Pharmaceutical Industries Ltd. K. Corbin: None. E. J. Mayer-davis: None. D. P. Zaharieva: Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes, Leona M. and Harry B. Helmsley Charitable Trust. D. Igudesman: None. A. Cristello: None. J. Thomas: None. N. Arrizon- ruiz: None. J. Hooper: None. A. Addala: None. J. Crandell: n/a. D. M. Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. Funding National Institutes of Health (1DP3DK113358-01)

We previously examined a type 1 diabetes care model based on the use of a remote patient monitoring (RPM) tool that analyzes CGM data. The care model was associated with improved glucose management and reduced provider effort. We compare the financial feasibility of this care model in value-based care (VBC) and fee-for-service (FFS) settings. We set parameters based on the current deployment of the care model. In the FFS setting, we estimated Medicare reimbursement rates and labor costs for in-person diabetes education. We compared these to costs in a VBC setting where providers make prospective payments for RPM services. We evaluated the financial impact of the care model across a variety of parameters (Fig) . In a FFS setting, we estimated that in-person care incurs net costs of $1per patient. Under a VBC contract, we estimated that prospective payments are offset by reduced labor costs, incurring net costs of $65 per patient (i.e., 49% savings) . These results are sensitive to input parameters (Fig) . We identified financially viable payment models for an algorithm-enabled telemedicine-based care model associated with improved T1D glucose management. The analysis may inform operational planning at the clinic level as well as reimbursement policy design. Disclosure R.Leonard pei: None. P.Dupenloup: None. P.Prahalad: None. R.Johari: None. A.Addala: None. D.P.Zaharieva: Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes, Leona M. and Harry B. Helmsley Charitable Trust. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. D.Scheinker: None. Funding Helmsley Charitable Trust.ISPAD-JDRF Fellowship.R18DK122422.SDRC.LPCH Auxiliaries.The Stanford REDCap platform (http://redcap.stanford.edu) is developed and operated by Stanford Medicine Research IT team. The REDCap platform services at Stanford are subsidized by a) Stanford School of Medicine Research Office, and b) the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085.Stanford Maternal and Child Health Research Institute.

Background: Sequential Multiple Assignment Randomized Trials (SMARTs) efficiently address practical treatment comparison questions and adapt dynamically based on response. They may be useful for development of approaches to co-manage weight and glycemia T1D, which is critical yet challenging. Methods: Our SMART pilot with three diet periods enrolled young adults with T1D (BMI 27-39.9 kg/m²) . Participants were re-randomized after ∼3 months on the hypocaloric Look AHEAD (Low Fat) or Low Carbohydrate (Low Carb) ; or Mediterranean (Med, not calorie restricted) diet if Results: The proportion re-randomized was 57.9% and did not vary by diet. Weight was lost overall but insufficient weight loss was the most common reason for re-randomization for Low Fat and Med. An HbA1c increase ≥0.5% was most common on Med. Low diet acceptability was the most common reason for re-randomization on Low Carb. Conclusions: We achieved safe weight loss among young adults with T1D but observed heterogeneity in reasons for re-randomization by diet, although differences were not statistically significant. A fully-powered efficacy trial may confirm our findings. Disclosure D.Igudesman: None. R.E.Pratley: Other Relationship; Bayer AG, Corcept Therapeutics, Dexcom, Inc., Hanmi Pharm. Co., Ltd., Merck & Co., Inc., Metavention, Novo Nordisk, Pfizer Inc., Poxel SA, Sanofi, Scohia Pharma Inc., Sun Pharmaceutical Industries Ltd. M.R.Kosorok: n/a. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. E.J.Mayer-davis: None. J.Crandell: n/a. T.Pokaprakarn: None. K.Corbin: None. D.P.Zaharieva: Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes, Leona M. and Harry B. Helmsley Charitable Trust. A.Addala: None. J.Thomas: None. A.Casu: None. M.Riddell: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Consultant; Eli Lilly and Company, Jaeb Center for Health Research, Speaker's Bureau; Dexcom, Inc., Eli Lilly and Company, Novo Nordisk. Funding National Institutes of Health 1DP3DK113358

The Omnipod® 5 Automated Insulin Delivery System is a waterproof (IP28) hybrid closed-loop system where the continuous glucose monitor (CGM) communicates directly with the algorithm on the Pod to modulate insulin delivery automatically. However, the system relies on Bluetooth® wireless technology, a radiofrequency with limited underwater transmission ability. Seven frequent swimmers participating in the Omnipod 5 trial reported 63 separate swimming sessions ranging 25-143 min (mean 75±30 min) . CGM-to-Pod wireless communication during swims had a median (IQR) success rate of 17% (5%, 29%) ; however, the communication success rate was 100% across all swims with CGM-to-Pod distance These results suggest that while in-water communication is challenged, Pod to CGM placement strategies can be practiced to better improve the connectivity. Moreover, the system by design remains in automated mode when CGM values are missed and does not revert the user back to manual mode settings. Disclosure M.S.Hughes: Research Support; Insulet Corporation. R.S.Kingman: Research Support; Insulet Corporation. L.Hsu: Research Support; Insulet Corporation, Stock/Shareholder; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc. R.Lal: Advisory Panel; Provention Bio, Inc., Consultant; Abbott Diabetes, Biolinq, Capillary Biomedical, Inc., Deep Valley Labs, Gluroo, Morgan Stanley, Tidepool. B.A.Buckingham: Advisory Panel; Arecor, Lilly Diabetes, Medtronic, Other Relationship; Insulet Corporation, Research Support; Insulet Corporation, Lilly Diabetes, Medtronic. D.P.Zaharieva: Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes, Leona M. and Harry B. Helmsley Charitable Trust. Funding Gift funding from Insulet Corporation

Initiating continuous glucose monitoring (CGM) shortly after Type 1 diabetes diagnosis has glycaemic and quality of life benefits for youth with Type 1 diabetes and their families. The SARS-CoV-2 pandemic led to a rapid shift to virtual delivery of CGM initiation visits. We aimed to understand parents' experiences receiving virtual care to initiate CGM within 30 days of diagnosis.We held focus groups and interviews using a semi-structured interview guide with parents of youth who initiated CGM over telehealth within 30 days of diagnosis during the SARS-CoV-2 pandemic. Questions aimed to explore experiences of starting CGM virtually. Groups and interviews were audio-recorded, transcribed and analysed using thematic analysis.Participants were 16 English-speaking parents (age 43 ± 6 years; 63% female) of 15 youth (age 9 ± 4 years; 47% female; 47% non-Hispanic White, 20% Hispanic, 13% Asian, 7% Black, 13% other). They described multiple benefits of the virtual visit including convenient access to high-quality care; integrating Type 1 diabetes care into daily life; and being in the comfort of home. A minority experienced challenges with virtual care delivery; most preferred the virtual format. Participants expressed that clinics should offer a choice of virtual or in-person to families initiating CGM in the future.Most parents appreciated receiving CGM initiation education via telehealth and felt it should be an option offered to all families. Further efforts can continue to enhance CGM initiation teaching virtually to address identified barriers.

Guidelines for safe exercise strategies exist for both pediatric and adult patients living with type 1 diabetes. The management of type 1 diabetes during exercise is complex, but making insulin dosing adjustments in advance of activity can yield positive outcomes and reduce the likelihood of hypoglycemia. Closed loop (also known as automated insulin delivery) systems are able to partially automate insulin delivery and can assist in exercise and overall management of type 1 diabetes. Current exercise guidelines, however, focus primarily on management strategies for patients using multiple daily injections or open loop insulin pump therapy. Closed loop systems require strategic approaches to type 1 diabetes management, including appropriate timing and duration of exercise targets and carbohydrates around exercise that have yet to be standardized. This review aims to showcase how closed loop technology has evolved over the last decade and summarizes a number of closed loop and exercise studies both in free-living conditions and clinical trials. This review also highlights strategies and approaches for exercise and type 1 diabetes management using closed loop systems. Some differences in closed loop strategies for exercise include the importance of pump suspension if disconnecting during exercise, fewer grams of uncovered carbohydrates before exercise and these should be taken close to exercise onset to avoid a rise in automated insulin delivery. A primary goal for future closed loop systems is to detect exercise without user input, so that patients are not required to preset exercise targets well in advance of activity, as are the current recommendations.

Background: Gestational diabetes mellitus (GDM) management using self-monitoring blood glucose (SMBG) does not normalize pregnancy outcomes. Objective: We aimed to conduct an observational study to...

PURPOSE OF REVIEW: This review highlights challenges associated with weight management in children and adolescents with type 1 diabetes (T1D). Our purpose is to propose potential solutions to improve weight outcomes in youth with T1D. RECENT FINDINGS: A common barrier to weight management in T1D is reluctance to engage in exercise for fear of hypoglycemia. Healthcare practitioners generally provide limited guidance for insulin dosing and carbohydrate modifications to maintain stable glycemia during exercise. Adherence to dietary guidelines is associated with improved glycemia; however, youth struggle to meet recommendations. When psychosocial factors are addressed in combination with glucose trends, this often leads to successful T1D management. Newer medications also hold promise to potentially aid in glycemia and weight management, but further research is necessary. SUMMARY: Properly addressing physical activity, nutrition, pharmacotherapy, and psychosocial factors while emphasizing weight management may reduce the likelihood of obesity development and its perpetuation in this population.

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (ie, before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes.

Evidence suggests that patients with type 1 diabetes (T1D) performing aerobic exercise with their insulin pump connected (pump on) vs pump disconnected (pump off) have an increased risk of hypoglycemia. It has not been examined whether this risk remains during high-intensity exercise. This study compared the effects of pump on (50% basal insulin at exercise onset) vs pump off (0% basal insulin at exercise onset) on glucose concentrations during intermittent high-intensity exercise in adults with T1D and on patients' own perspective of their glycemia.Twelve adults with T1D using insulin pump therapy completed two 40-min intermittent high-intensity exercise bouts. Insulin adjustments included: 1) pump set to 50% of usual basal rate (pump on) or 2) pump suspended (pump off) during exercise, in random order. Blood glucose was recorded every 10 min during exercise and, after providing subjects with an initial reference glucose value before exercise, participants were asked to estimate their glucose during exercise.Glucose levels were higher in pump off (8.1±1.3 mmol/L) vs pump on (7.4±2.1 mmol/L) at exercise start (p0.05), but were similar by the end of exercise (p=0.9). During exercise, hypoglycemia incidence did not differ between conditions (1 of 12 for both). However, the percentage of time in hypoglycemia at 12 h after exercise was 5±8% vs 1±2% for pump on vs pump off, respectively (p=0.3). Participants were better able to estimate their own glucose during pump on vs pump off (rPump on vs pump off at exercise onset showed no significant differences in blood glucose concentrations during 40 min of intermittent high-intensity exercise.

Young adults with type 1 diabetes (T1D) often have difficulty co-managing weight and glycemia. The prevalence of overweight and obesity among individuals with T1D now parallels that of the general population and contributes to dyslipidemia, insulin resistance, and risk for cardiovascular disease. There is a compelling need to develop a program of research designed to optimize two key outcomes–weight management and glycemia–and to address the underlying metabolic processes and behavioral challenges unique to people with T1D. For an intervention addressing these dual outcomes to be effective, it must be appropriate to the unique metabolic phenotype of T1D, and to biological and behavioral responses to glycemia (including hypoglycemia) that relate to weight management. The intervention must also be safe, feasible, and accepted by young adults with T1D. In 2015, we established a consortium called ACT1ON: Advancing Care for Type 1 Diabetes and Obesity Network, a transdisciplinary team of scientists at multiple institutions. The ACT1ON consortium designed a multi-phase study which, in parallel, evaluated the mechanistic aspects of the unique metabolism and energy requirements of individuals with T1D, alongside a rigorous adaptive behavioral intervention to simultaneously facilitate weight management while optimizing glycemia. This manuscript describes the design of our integrative study—comprised of an inpatient mechanistic phase and an outpatient behavioral phase—to generate metabolic, behavioral, feasibility, and acceptability data to support a future, fully powered sequential, multiple assignment, randomized trial to evaluate the best approaches to prevent and treat obesity while co-managing glycemia in people with T1D. Clinicaltrials.gov identifiers: NCT03651622 and NCT03379792. The present study references can be found here: https://clinicaltrials.gov/ct2/show/NCT03651622 https://clinicaltrials.gov/ct2/show/NCT03379792?term=NCT03379792&draw=2&rank=1 Submission Category: “Study Design, Statistical Design, Study Protocols”.

OBJECTIVE: To assess the relationship between mindfulness and glycemia among adolescents with type 1 diabetes (T1D) with suboptimal glycemia, and evaluate the potential mediation by ingestive behaviors, including disordered eating, and impulsivity. METHODS: We used linear mixed models for hemoglobin A1c (HbA1c) and linear regression for continuous glucose monitoring (CGM) to study the relationship of mindfulness [Child and Adolescent Mindfulness Measure (CAMM)] and glycemia in adolescents with T1D from the 18-month Flexible Lifestyles Empowering Change (FLEX) trial. We tested for mediation of the mindfulness-glycemia relationship by ingestive behaviors, including disordered eating (Diabetes Eating Problem Survey – Revised), restrained eating, and emotional eating (Dutch Eating Behavior Questionnaire); and impulsivity (total, attentional, and motor, Barrett Impulsiveness Scale). RESULTS: At baseline, participants (n=152) had a mean age of 14.9 ± 1.1 years and HbA1c of 9.4 ± 1.2% [79±13 mmol/mol]. The majority of adolescents were non-Hispanic white (83.6%), 50.7% were female, and 73.0% used insulin pumps. From adjusted mixed models, a 5-point increase in mindfulness scores was associated with a −0.19% (95%CI −0.29, −0.08, p=0.0006) reduction in HbA1c. We did not find statistically significant associations between mindfulness and CGM metrics. Mediation of the relationship between mindfulness and HbA1c by ingestive behaviors and impulsivity was not found to be statistically significant. CONCLUSIONS: Among adolescents with T1D and suboptimal glycemia, increased mindfulness was associated with lower HbA1c levels. Future studies may consider mindfulness-based interventions as a component of treatment for improving glycemia among adolescents with T1D, though more data are needed to assess feasibility and efficacy.

Context Youth with type 1 diabetes (T1D) do not meet glycated hemoglobin A1c (HbA1c) targets. Objective This work aimed to assess HbA1c outcomes in children with new-onset T1D enrolled in the Teamwork, Targets, Technology and Tight Control (4T) Study. Methods HbA1c levels were compared between the 4T and historical cohorts. HbA1c differences between cohorts were estimated using locally estimated scatter plot smoothing (LOESS). The change from nadir HbA1c (month 4) to 12 months post diagnosis was estimated by cohort using a piecewise mixed-effects regression model accounting for age at diagnosis, sex, ethnicity, and insurance type. We recruited 135 youth with newly diagnosed T1D at Stanford Children’s Health. Starting July 2018, all youth within the first month of T1D diagnosis were offered continuous glucose monitoring (CGM) initiation and remote CGM data review was added in March 2019. The main outcomes measure was HbA1c. Results HbA1c at 6, 9, and 12 months post diagnosis was lower in the 4T cohort than in the historic cohort (–0.54% to –0.52%, and –0.58%, respectively). Within the 4T cohort, HbA1c at 6, 9, and 12 months post diagnosis was lower in those patients with remote monitoring than those without (–0.14%, –0.18% to –0.14%, respectively). Multivariable regression analysis showed that the 4T cohort experienced a significantly lower increase in HbA1c between months 4 and 12 (P Conclusion A technology-enabled, team-based approach to intensified new-onset education involving target setting, CGM initiation, and remote data review statistically significantly decreased HbA1c in youth with T1D 12 months post diagnosis.

Objective: To compare glucose control with hybrid closed loop (HCL) when challenged by moderate-intensity exercise (MIE), high-intensity intermittent exercise (HIE) and resistance exercise (RE) while profiling counter-regulatory hormones, lactate, ketones, and kinetic data in adults with type 1 diabetes. Methods: Open-label multisite randomized crossover trial. Adults with type 1 diabetes undertook 40 min of HIE, MIE, and RE in random order while using HCL (Medtronic 670G) with a temporary target set 2 hours prior to and during exercise and 15g carbohydrates if pre-exercise glucose was Primary outcome was median (IQR) continuous glucose monitoring (CGM) time-in-range (TIR, 70-180 mg/dL) for 14 hours post-exercise commencement. Accelerometer data and venous glucose, ketones, lactate, and counter-regulatory hormones were measured for 280 min post-exercise commencement. Results: Median TIR was 81% [67, 93]%, 91% [80, 94]%, and 80% [73, 89]% for 0-14 hours post-exercise commencement for HIE, MIE and RE, respectively (n=30), with no difference between exercise types (MIE v HIE; p=0.11, MIE v RE p=0.11, HIE v RE p=0.90). Time-below-range was 0% for all exercise bouts. For HIE and RE compared with MIE, there were greater increases respectively in noradrenaline (p=0.01, p=0.004), cortisol (p Conclusions: Under controlled conditions, HCL provided satisfactory glucose control with no difference between exercise type. Lactate, counter-regulatory hormones, and kinetic data differentiate type and intensity of exercise, and their measurement may help inform insulin needs during exercise. However, their potential utility as modulators of insulin dosing will be limited by the pharmacokinetics of subcutaneous insulin delivery.

Objective: To compare glucose control with hybrid closed loop (HCL) when challenged by moderate-intensity exercise (MIE), high-intensity intermittent exercise (HIE) and resistance exercise (RE) while profiling counter-regulatory hormones, lactate, ketones, and kinetic data in adults with type 1 diabetes. Methods: Open-label multisite randomized crossover trial. Adults with type 1 diabetes undertook 40 min of HIE, MIE, and RE in random order while using HCL (Medtronic 670G) with a temporary target set 2 hours prior to and during exercise and 15g carbohydrates if pre-exercise glucose was Primary outcome was median (IQR) continuous glucose monitoring (CGM) time-in-range (TIR, 70-180 mg/dL) for 14 hours post-exercise commencement. Accelerometer data and venous glucose, ketones, lactate, and counter-regulatory hormones were measured for 280 min post-exercise commencement. Results: Median TIR was 81% [67, 93]%, 91% [80, 94]%, and 80% [73, 89]% for 0-14 hours post-exercise commencement for HIE, MIE and RE, respectively (n=30), with no difference between exercise types (MIE v HIE; p=0.11, MIE v RE p=0.11, HIE v RE p=0.90). Time-below-range was 0% for all exercise bouts. For HIE and RE compared with MIE, there were greater increases respectively in noradrenaline (p=0.01, p=0.004), cortisol (p Conclusions: Under controlled conditions, HCL provided satisfactory glucose control with no difference between exercise type. Lactate, counter-regulatory hormones, and kinetic data differentiate type and intensity of exercise, and their measurement may help inform insulin needs during exercise. However, their potential utility as modulators of insulin dosing will be limited by the pharmacokinetics of subcutaneous insulin delivery.

Objective To evaluate glucose control using fast-acting insulin aspart (faster aspart) compared with insulin aspart (IAsp) delivered by the MiniMedä Advanced Hybrid Closed-Loop (AHCL) system in adults with type 1 diabetes. Research Design and Methods In this randomized, open-label, crossover study, participants were assigned to receive faster aspart or IAsp in random-order. Stages 1 and 2 comprised six-weeks in closed-loop; preceded by two-weeks in open-loop. This was followed by Stage 3, whereby participants changed directly back to the insulin formulation used in Stage 1 for one-week in CL. Participants chose their own meals except for two standardized meal tests; a missed and late meal bolus. Primary outcome was % sensor glucose time-in-range (TIR; 70-180mg/dL). Results Twenty-five adults (52% male) were recruited; median (IQR) age was 48 (37, 57) years, and HbA1c 7.0% (6.6, 7.2) (53 [49, 55] mmol/mol). Faster aspart demonstrated greater overall TIR compared with IAsp (82.3% [78.5, 83.7] vs. 79.6% [77.0, 83.4], respectively; mean difference 1.9% [0.5, 3.3]; p=0.007). Four-hour PPG TIR was higher using faster aspart compared with IAsp for all-meals combined (73.6% [69.4, 80.2] vs. 72.1% [64.5, 78.5], respectively; median difference 3.5% [1.0, 7.3]; p=0.003). There was no ketoacidosis or severe hypoglycemia. Conclusions Faster aspart safely improved glucose control compared with IAsp in a well-controlled group of adults with type 1 diabetes using AHCL. The modest improvement mainly related to mealtime glycemia. Whilst the primary outcome demonstrated statistical significance, the clinical impact may be small given an overall difference in TIR of 1.9%.

OBJECTIVE To compare glucose control with hybrid closed-loop (HCL) when challenged by high intensity exercise (HIE), moderate intensity exercise (MIE), and resistance exercise (RE) while profiling counterregulatory hormones, lactate, ketones, and kinetic data in adults with type 1 diabetes. RESEARCH DESIGN AND METHODS This study was an open-label multisite randomized crossover trial. Adults with type 1 diabetes undertook 40 min of HIE, MIE, and RE in random order while using HCL (Medtronic MiniMed 670G) with a temporary target set 2 h prior to and during exercise and 15 g carbohydrates if pre-exercise glucose was RESULTS Median TIR was 81% (67, 93%), 91% (80, 94%), and 80% (73, 89%) for 0–14 h post–exercise commencement for HIE, MIE, and RE, respectively (n = 30), with no difference between exercise types (MIE vs. HIE; P = 0.11, MIE vs. RE, P = 0.11; and HIE vs. RE, P = 0.90). Time-below-range was 0% for all exercise bouts. For HIE and RE compared with MIE, there were greater increases, respectively, in noradrenaline (P = 0.01 and P = 0.004), cortisol (P < 0.001 and P = 0.001), lactate (P ≤ 0.001 and P ≤ 0.001), and heart rate (P = 0.007 and P = 0.015). During HIE compared with MIE, there were greater increases in growth hormone (P = 0.024). CONCLUSIONS Under controlled conditions, HCL provided satisfactory glucose control with no difference between exercise type. Lactate, counterregulatory hormones, and kinetic data differentiate type and intensity of exercise, and their measurement may help inform insulin needs during exercise. However, their potential utility as modulators of insulin dosing will be limited by the pharmacokinetics of subcutaneous insulin delivery.

Background: Real-time continuous glucose monitoring (CGM) devices help detect glycemic excursions associated with exercise, meals, and insulin dosing in patients with type 1 diabetes (T1D). However, the delay between interstitial and blood glucose may result in CGM underestimating the true change in glycemia during activity. The purpose of this study was to examine CGM discrepancies during exercise and the meal postexercise versus self-monitoring of blood glucose (SMBG). Methods: Seventeen adults with T1D using insulin pump therapy and CGM completed 60 min of aerobic exercise on three occasions. A standardized meal was given 30 min postexercise. SMBG was measured during exercise and in recovery using OmniPod® Personal Diabetes Manager (PDM; Insulet, Billerica, MA) with built-in glucose meter (FreeStyle; Abbott Laboratories, Abbott Park, IL), while CGM was measured with Dexcom G4® with 505 algorithm (n = 4) or G5® (n = 13), which were calibrated with subjects' own PDM. Results: SMBG showed a large drop in glycemia during exercise, while CGM showed a lag of 12 ± 11 (mean ± standard deviation) minutes and bias of −7 ± 19 mg/dL/min during activity. Mean absolute relative difference (MARD) for CGM versus SMBG was 13 (6–22)% [median (interquartile range)] during exercise and 8 (5–14)% during mealtime. Clarke error grids showed CGM values were in zones A and B 94%–99% of the time for SMBG. Conclusion: In summary, the drop in CGM lags behind the drop in blood glucose during prolonged aerobic exercise by 12 ± 11 min, and MARD increases to 13 (6–22)% during exercise as well. Therefore, if hypoglycemia is suspected during exercise, individuals should confirm glucose levels with a capillary glucose measurement.

BACKGROUND: Comanagement of glycemia and adiposity is the cornerstone of cardiometabolic risk reduction in type 1 diabetes (T1D), but targets are often not met. The intestinal microbiota and microbiota-derived short-chain fatty acids (SCFAs) influence glycemia and adiposity but have not been sufficiently investigated in longstanding T1D. OBJECTIVES: We evaluated the hypothesis that an increased abundance of SCFA-producing gut microbes, fecal SCFAs, and intestinal microbial diversity were associated with improved glycemia but increased adiposity in young adults with longstanding T1D. METHODS: Participants provided stool samples at ≤4 time points (NCT03651622: https://clinicaltrials.gov/ct2/show/NCT03651622). Sequencing of the 16S ribosomal RNA gene measured abundances of SCFA-producing intestinal microbes. GC-MS measured total and specific SCFAs (acetate, butyrate, propionate). DXA (body fat percentage and percentage lean mass) and anthropometrics (BMI) measured adiposity. Continuous glucose monitoring [percentage of time in range (70-180 mg/dL), above range (>180 mg/dL), and below range (54-69 mg/dL)] and glycated hemoglobin (i.e., HbA1c) assessed glycemia. Adjusted and Bonferroni-corrected generalized estimating equations modeled the associations of SCFA-producing gut microbes, fecal SCFAs, and intestinal microbial diversity with glycemia and adiposity. COVID-19 interrupted data collection, so models were repeated restricted to pre-COVID-19 visits. RESULTS: Data were available for ≤45 participants at 101 visits (including 40 participants at 54 visits pre-COVID-19). Abundance of Eubacterium hallii was associated inversely with BMI (all data). Pre-COVID-19, increased fecal propionate was associated with increased percentage of time above range and reduced percentage of time in target and below range; and abundances of 3 SCFA-producing taxa (Ruminococcus gnavus, Eubacterium ventriosum, and Lachnospira) were associated inversely with body fat percentage, of which two microbes were positively associated with percentage lean mass. Abundance of Anaerostipes was associated with reduced percentage of time in range (all data) and with increased body fat percentage and reduced percentage lean mass (pre-COVID-19). CONCLUSIONS: Unexpectedly, fecal propionate was associated with detriment to glycemia, whereas most SCFA-producing intestinal microbes were associated with benefit to adiposity. Future studies should confirm these associations and determine their potential causal linkages in T1D.This study is registered at clinical.trials.gov (NCT03651622; https://clinicaltrials.gov/ct2/show/NCT03651622).

CGM use is associated with improvements in A1c. We hypothesized that initiation of CGM in the 1st month after T1D diagnosis would improve A1c. Youth with newly diagnosed T1D from July 2018 to May 2020 (pilot cohort, n=122) were offered CGM initiation in the 1st month after T1D diagnosis (119 started CGM). We compared A1c outcomes in the pilot cohort with those diagnosed from 2014-2016 (controls, n=272) who were not offered early CGM. We visualized A1c trajectories using locally estimated scatter plot smoothing (Fig). We and assessed for differences in A1c trajectory by cohort via interaction terms in a linear mixed model adjusted for baseline characteristics (age, sex, race and insurance type). The mean A1c at diagnosis was higher in the pilot cohort (12.2 vs. 10.7%). The median age of diagnosis was 9.5 years [6.8, 13.3], 64% male, 38% non-Hispanic White, and 76% with private insurance in the pilot cohort. In this pilot, 89% initiated CGM in the first 30 days after diagnosis compared to 2% in the control cohort. After adjusting for baseline characteristics, the mean A1c of the pilot cohort was lower at 6 months (-0.81, p = 0.019), 9 months (-1.43, p = 0.013), and 12 months (-2.05, p = 0.012) post-diagnosis compared to the historic cohort. Early initiation of CGM was associated with a lower A1c compared to those in a historic cohort. These data support early initiation of CGM as standard of care in youth with T1D. Disclosure P. Prahalad: None. J. Senaldi: None. A. Freeman: None. A. Addala: None. D. P. Zaharieva: None. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. D. Scheinker: Advisory Panel; Self; Carta Healthcare. M. Desai: None. D. M. Maahs: Advisory Panel; Self; Abbott Diabetes, Dompe, Eli Lilly and Company, Medtronic, Novo Nordisk, Consultant; Self; aditxt. V. Ding: None. B. Leverenz: None. J. Hooper: None. A. Chmielewski: None. B. P. Conrad: Advisory Panel; Self; Abbott Diabetes. J. Leverenz: None. A. Martinez-singh: None. P. Sagan: None. Funding National Institutes of Health (R18DK122422, P30DK116074)

Previous studies have shown that CGM use and frequent contact with the care team is associated with improved A1c. We hypothesized that youth with new onset T1D started on CGM with weekly CGM data review by a CDE would have improved outcomes compared to those on CGM alone. Youth with newly diagnosed T1D from 7/2018 to 3/2019 were started on CGM alone (control, n=46) while those from 3/2019 to 5/2020 were started on CGM with weekly data review by a CDE (GluVue, n=76). Dose adjustments were made via secure messaging. We visualized A1c trajectories using locally estimated scatter plot smoothing (Fig) and assessed for differences in A1c trajectory by GluVue status via cohort*time and cohort*time2 interaction terms in a linear mixed model regression adjusted for baseline characteristics (age, sex, race and insurance type). The mean (SD) A1c at diagnosis was 12.0% (1.8) in the control group and 12.3% (2.3) in the GluVue group. Baseline characteristics of control vs. GluVue: male 25% vs. 39%, Non-Hispanic White 41.3% vs. 35.5%, private insurance 80.4% vs. 73.7%. Mean A1c of the GluVue cohort was lower at 6 months (-0.45%), 9 months (-0.07%), and 12 months (-0.52%) post-diagnosis. A1c trajectories were significantly different between groups via regression analysis (p=0.004). Early initiation of CGM with remote monitoring was associated with lower A1c compared to CGM alone. These data support early CGM initiation plus care team contact in youth with T1D. Disclosure P. Prahalad: None. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. D. Scheinker: Advisory Panel; Self; Carta Healthcare. M. Desai: None. D. M. Maahs: Advisory Panel; Self; Abbott Diabetes, Dompe, Eli Lilly and Company, Medtronic, Novo Nordisk, Consultant; Self; aditxt. V. Ding: None. B. Leverenz: None. J. Hooper: None. J. Leverenz: None. P. Sagan: None. A. Martinez-singh: None. A. Freeman: None. D. P. Zaharieva: None. Funding National Institutes of Health (P30DK116074, R18DK122422)

This pilot and feasibility study aimed to identify dietary strategies to optimize glycemic control and weight management in overweight or obese young adults with T1D. In a sequential multiple assignment randomized trial, participants were randomized to a hypocaloric low-carbohydrate (CHO; 20-25% CHO), hypocaloric moderately low fat (30% energy fat), or Mediterranean (not calorie restricted) diet, with advice to maintain usual physical activity (PA). PA data were collected in a convenience sample of participants from 06/20 to 02/21. Young adults with T1D (n=27, age 25±3 years, 78% female, A1c 7.3±1.2%, BMI 30.7±3.4) wore PA trackers for 2-week periods during diet assignments. Median (IQR) daily steps were 4,931 (3,443-7,363), 5,562 (4,191-9,931), 6,457 (4,394-9,655) for Low CHO, Look AHEAD, and Mediterranean diets, respectively and daily moderate-to-vigorous PA (MVPA) mins were 16 (4-31), 18 (5-31), and 22 (6-32), respectively. Daily steps, active time, or MVPA did not differ by diet (p>0.05). We demonstrate that in a pilot study emphasizing diet and weight loss in young adults with T1D, PA was often below the recommended target of 30 mins MVPA/day (or 150 mins/week). Thus, a larger, adaptive weight management intervention will need to incorporate diet as well as safe and effective PA strategies for overweight and obese young adults with T1D. Disclosure D. P. Zaharieva: None. D. M. Maahs: Advisory Panel; Self; Abbott Diabetes, Dompe, Eli Lilly and Company, Medtronic, Novo Nordisk, Consultant; Self; aditxt. For the act1on study: n/a. F. R. Muntis: None. N. Arrizon- ruiz: None. J. Thomas: None. E. J. Mayer-davis: None. A. Addala: None. M. A. Clements: Consultant; Self; Eli Lilly and Company, Employee; Self; Glooko, Inc., Research Support; Self; Abbott Diabetes, Dexcom, Inc. A. Kollu: None. M. Riddell: Advisory Panel; Self; Zealand Pharma A/S, Consultant; Self; Lilly Diabetes, Research Support; Self; Dexcom, Inc., Insulet Corporation, Speaker’s Bureau; Self; Novo Nordisk Inc., Sanofi, Stock/Shareholder; Self; Zucara Therapeutics Inc. Funding National Institute of Diabetes and Digestive and Kidney Diseases (1DP3DK113358)

Hybrid closed-loop automated insulin delivery systems have been shown to aid glycemic control during exercise in type 1 diabetes, but their performance in the water has not been investigated. Anecdotal patient reports suggest continued function of the Omnipod 5 Automated Insulin Delivery System while swimming, despite reliance on Bluetooth, a radiofrequency with typically limited underwater transmission capability. Three frequent swimmers participating in the Omnipod 5 trial were identified. Omnipod 5 System and glycemic data from their combined 38 separate swimming sessions ranging 30-120 min (mean 48±16 min, total 31 hours) were retrospectively reviewed. Continuous glucose monitor (CGM)-to-Pod wireless communication during swims had a success rate of 58.4%. When sensor communication was lost, mean time without sensor data was 13±10 min (range 5-45 min). Automated mode was used in 74% of swims, of which insulin was automatically suspended in 86%. HypoProtect was activated for 42% of swims. See Table for CGM data. Overall, system communication was reasonable during swims and allowed for frequent insulin suspension. Glycemic control was unchanged across the three time points. These promising initial findings suggest that the Omnipod 5 System may have unique potential for use in water-based activities; further studies to investigate this potential and factors affecting performance are warranted. Disclosure M. S. Hughes: None. R. S. Kingman: Research Support; Self; Insulet Corporation. R. Lal: Consultant; Self; Abbott Diabetes, Biolinq, Capillary Biomedical, Inc., Morgan Stanley, Tidepool. B. A. Buckingham: Advisory Panel; Self; Medtronic, Tolerion, Inc., Research Support; Self; Beta Bionics, Inc., Insulet Corporation, Medtronic, Tandem Diabetes Care. D. P. Zaharieva: None.