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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. ispartof: PEDIATRIC DIABETES vol:21 issue:8 pages:1375-1393 ispartof: location:Denmark status: published

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.

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.

OBJECTIVE To investigate physiological responses to cardiopulmonary exercise (CPX) testing in adults with type 1 diabetes compared with age-, sex-, and BMI-matched control participants without type 1 diabetes. RESEARCH DESIGN AND METHODS We compared results from CPX tests on a cycle ergometer in individuals with type 1 diabetes and control participants without type 1 diabetes. Parameters were peak and threshold variables of VO2, heart rate, and power output. Differences between groups were investigated through restricted maximum likelihood modeling and post hoc tests. Differences between groups were explained by stepwise linear regressions (P < 0.05). RESULTS Among 303 individuals with type 1 diabetes (age 33 [interquartile range 22; 43] years, 93 females, BMI 23.6 [22; 26] kg/m2, HbA1c 6.9% [6.2; 7.7%] [52 (44; 61) mmol/mol]), VO2peak (32.55 [26.49; 38.72] vs. 42.67 ± 10.44 mL/kg/min), peak heart rate (179 [170; 187] vs. 184 [175; 191] beats/min), and peak power (216 [171; 253] vs. 245 [200; 300] W) were lower compared with 308 control participants without type 1 diabetes (all P < 0.001). Individuals with type 1 diabetes displayed an impaired degree and direction of the heart rate-to-performance curve compared with control participants without type 1 diabetes (0.07 [−0.75; 1.09] vs. 0.66 [−0.28; 1.45]; P < 0.001). None of the exercise physiological responses were associated with HbA1c in individuals with type 1 diabetes. CONCLUSIONS Individuals with type 1 diabetes show altered responses to CPX testing, which cannot be explained by HbA1c. Intriguingly, the participants in our cohort were people with recent-onset type 1 diabetes; heart rate dynamics were altered during CPX testing.

Regular exercise is important for health, fitness and longevity in people living with type 1 diabetes, and many individuals seek to train and compete while living with the condition. Muscle, liver and glycogen metabolism can be normal in athletes with diabetes with good overall glucose management, and exercise performance can be facilitated by modifications to insulin dose and nutrition. However, maintaining normal glucose levels during training, travel and competition can be a major challenge for athletes living with type 1 diabetes. Some athletes have low-to-moderate levels of carbohydrate intake during training and rest days but tend to benefit, from both a glucose and performance perspective, from high rates of carbohydrate feeding during long-distance events. This review highlights the unique metabolic responses to various types of exercise in athletes living with type 1 diabetes. Graphical abstract Electronic supplementary material The online version of this article (10.1007/s00125-020-05183-8) contains a slideset of the figures for download, which is available to authorised users.

Regular physical activity is associated with many health benefits for people with type 1 diabetes, including improved quality of life, increased vigor, enhanced insulin sensitivity, and protection against cardiovascular disease and other diabetes-related complications (1). Despite its benefits, exercise can aggravate dysglycemia because it causes major changes to glucose production and utilization rates (2). For example, mild to intense aerobic exercise (e.g., walking, cycling, jogging, and most individual and team sports) increases the risk of hypoglycemia during the activity and in recovery because of impaired rates of glucose production, whereas very intense aerobic exercise (>80% of maximal aerobic capacity) and anaerobic exercise (e.g., sprinting and heavy weightlifting) can cause glucose levels to rise because of reduced rates of glucose disposal (3,4). Numerous strategies have been developed to help limit hypoglycemia during exercise in individuals with type 1 diabetes. One of the main reasons hypoglycemia occurs is the inability to naturally reduce insulin levels at the onset of exercise (1). Strategies to help limit hypoglycemia include exercising in the fasted state (5), reducing the insulin for the meal before exercise (6,7), interrupting basal insulin infusion for patients on insulin pump therapy (8–10), and increasing carbohydrate intake (11–14). Continuous glucose monitoring (CGM) can also help to prevent hypoglycemia in people with type 1 diabetes (15). In contrast, very little has been done to develop strategies for exercise-associated hyperglycemia, even though the mechanisms for this effect are largely established (16). The inability to naturally raise insulin levels after intense exercise to combat a rise in catecholamines is the main reason why post-exercise hyperglycemia occurs (17), although excursions associated with aggressive insulin reductions or excessive carbohydrate intake also likely bear some blame (18). In instances of exercise-associated hyperglycemia caused by intense exercise, insulin concentrations …

Exercise causes glycemic disturbances in individuals with type 1 diabetes (T1D). Continuous moderate-intensity aerobic exercise (CON) generally lowers blood glucose (BG) levels and often leads to hypoglycemia. In comparison, circuit-based exercise (CIRC) may attenuate the drop in BG. The goal of this study is to contrast the effects of basal insulin suspension at the onset of two different forms of exercise (CON vs. CIRC).Twelve individuals (six men and six women) with T1D on insulin pump therapy were recruited for the study. All participants completed a maximal aerobic fitness test and two 40-min exercise sessions, consisting of either continuous treadmill walking or a circuit workout. Basal insulin infusion was stopped at the onset of exercise and resumed in recovery. After providing an initial reference value, volunteers were blinded to their [BG] and were asked to estimate their levels during exercise.Oxygen consumption (47.5 ± 7.5 vs. 54.5 ± 13.5 mL·kgDespite a lower intensity of exercise, with full basal insulin suspension at the start of exercise, CON results in a larger drop in BG vs. CIRC. These findings have implications for single hormone-based artificial pancreas development for exercise. While this study does not negate the importance of frequent capillary BG monitoring during exercise, it does suggest that if persons are knowledgeable about their pre-exercise BG levels, they can accurately perceive the changes in BG during CON, but not during CIRC.

Regular physical activity (PA) promotes numerous health benefits for people living with type 1 diabetes (T1D). However, PA also complicates blood glucose control. Factors affecting blood glucose fluctuations during PA include activity type, intensity and duration as well as the amount of insulin and food in the body at the time of the activity. To maintain equilibrium with blood glucose concentrations during PA, the rate of glucose appearance (Ra) to disappearance (Rd) in the bloodstream must be balanced. In nondiabetics, there is a rise in glucagon and a reduction in insulin release at the onset of mild to moderate aerobic PA. During intense aerobic -anaerobic work, insulin release first decreases and then rises rapidly in early recovery to offset a more dramatic increase in counterregulatory hormones and metabolites. An “exercise smart” artificial pancreas (AP) must be capable of sensing glucose and perhaps other physiological responses to various types and intensities of PA. The emergence of this new technology may benefit active persons with T1D who are prone to hypo and hyperglycemia.

The blockade of glucocorticoid (GC) action through antagonism of the glucocorticoid receptor II (GRII) has been used to minimize the undesirable effects of chronically elevated GC levels. Mifepristone (RU486) is known to competitively block GRII action, but not exclusively, as it antagonizes the progesterone receptor. A number of new selective GRII antagonists have been developed, but limited testing has been completed in animal models of overt type 2 diabetes mellitus. Therefore, two selective GRII antagonists (C113176 and C108297) were tested to determine their effects in our model of GC-induced rapid-onset diabetes (ROD). Male Sprague-Dawley rats (∼ six weeks of age) were placed on a high-fat diet (60%), surgically implanted with pellets containing corticosterone (CORT) or wax (control) and divided into five treatment groups. Each group was treated with either a GRII antagonist or vehicle for 14 days after surgery: CORT pellets (400 mg/rat) + antagonists (80 mg/kg/day); CORT pellets + drug vehicle; and wax pellets (control) + drug vehicle. After 10 days of CORT treatment, body mass gain was increased with RU486 (by ∼20% from baseline) and maintained with C113176 administration, whereas rats given C108297 had similar body mass loss (∼15%) to ROD animals. Fasting glycemia was elevated in the ROD animals (>20 mM), normalized completely in animals treated with RU486 (6.2±0.1 mM, p