Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA)

The Glucose Management Indicator: Time to Change Course?

Laboratory measurement of hemoglobin A1c (HbA1c) has, for decades, been the standard approach to monitoring glucose control in people with diabetes. Continuous glucose monitoring (CGM) is a revolutionary technology that can also aid in the monitoring of glucose control. However, there is uncertainty in how best to use CGM technology and its resulting data to improve control of glucose and prevent complications of diabetes. The glucose management indicator, or GMI, is an equation used to estimate HbA1c based on CGM mean glucose. GMI was originally proposed to simplify and aid in the interpretation of CGM data and is now provided on all standard summary reports (i.e., average glucose profiles) produced by different CGM manufacturers. This Perspective demonstrates that GMI performs poorly as an estimate of HbA1c and suggests that GMI is a concept that has outlived its usefulness, and it argues that it is preferable to use CGM mean glucose rather than converting glucose to GMI or an estimate of HbA1c. Leaving mean glucose in its raw form is simple and reinforces that glucose and HbA1c are distinct. To reduce patient and provider confusion and optimize glycemic management, mean CGM glucose, not GMI, should be used as a complement to laboratory HbA1c testing in patients using CGM systems.

Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort

AIMS/HYPOTHESIS

Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics.

METHODS

In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l).

RESULTS

A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%).

CONCLUSIONS/INTERPRETATION

Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes.

DATA AVAILABILITY

The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).

Factory-Calibrated Continuous Glucose Monitoring System Accuracy During Exercise in Adolescents With Type 1 Diabetes Mellitus

BACKGROUND

Continuous glucose monitors (CGMs) are widely used for individuals with diabetes mellitus, particularly those with type 1 diabetes (T1D). Advancements in CGM technology allow for glycemic assessment without capillary glucose measurements as many come factory calibrated. However, exercise, an essential component of diabetes care, has been reported to alter accuracy of earlier generation CGM. Considering the importance of physical activity for individuals with T1D and the progression of CGM technology, we aimed to investigate the accuracy of the Dexcom G6 during physical activity.

METHODS

Adolescents (ages 13-20 years) exercised on a treadmill for 40 minutes, with a 10-minute break at minute 20. We obtained paired CGM and glucometer measurements before and every 10 minutes during and after exercise. Accuracy analysis was determined by mean absolute relative difference (MARD), mean absolute difference (MAD), and Clarke Error Grid Analyses.

RESULTS

Mean absolute relative difference and MAD increased during exercise (14%-33% and 24.3-34 mg/dL) but improved after exercise. We noted certain CGM locations produced greater changes in accuracy as MARD and MAD increased markedly when the CGM was on the buttocks (18%-46% and 30-41 mg/dL). We also noted decreased odds of Zone A in the Clarke error grid when the CGM was on the buttocks compared to the abdomen (odds ratio [OR]: 0.146; P = 0.0003; 95% CI = 0.052-0.415).

CONCLUSIONS

This CGM system showed alterations in accuracy during exercise. Our findings additionally suggest interstitial fluid changes in muscles during exercise alter accuracy of CGM; however, additional research is required.

Comparison between a tubeless, on-body automated insulin delivery system and a tubeless, on-body sensor-augmented pump in type 1 diabetes: a multicentre randomised controlled trial

AIMS/HYPOTHESIS

This study compares the efficacy and safety of a tubeless, on-body automated insulin delivery (AID) system with that of a tubeless, on-body sensor-augmented pump (SAP).

METHODS

This multicentre, parallel-group, RCT was conducted at 13 tertiary medical centres in South Korea. Adults aged 19-69 years with type 1 diabetes who had HbA1c levels of <85.8 mmol/mol (<10.0%) were eligible. The participants were assigned at a 1:1 ratio to receive a tubeless, on-body AID system (intervention group) or a tubeless, on-body SAP (control group) for 12 weeks. Stratified block randomisation was conducted by an independent statistician. Blinding was not possible due to the nature of the intervention. The primary outcome was the percentage of time in range (TIR), blood glucose between 3.9 and 10.0 mmol/l, as measured by continuous glucose monitoring. ANCOVAs were conducted with baseline values and study centres as covariates.

RESULTS

A total of 104 participants underwent randomisation, with 53 in the intervention group and 51 in the control group. The mean (±SD) age of the participants was 40±11 years. The mean (±SD) TIR increased from 62.1±17.1% at baseline to 71.5±10.7% over the 12 week trial period in the intervention group and from 64.7±17.0% to 66.9±15.0% in the control group (difference between the adjusted means: 6.5% [95% CI 3.6%, 9.4%], p<0.001). Time below range, time above range, CV and mean glucose levels were also significantly better in the intervention group compared with the control group. HbA1c decreased from 50.9±9.9 mmol/mol (6.8±0.9%) at baseline to 45.9±7.4 mmol/mol (6.4±0.7%) after 12 weeks in the intervention group and from 48.7±9.1 mmol/mol (6.6±0.8%) to 45.7±7.5 mmol/mol (6.3±0.7%) in the control group (difference between the adjusted means: -0.7 mmol/mol [95% CI -2.0, 0.8 mmol/mol] (-0.1% [95% CI -0.2%, 0.1%]), p=0.366). No diabetic ketoacidosis or severe hypoglycaemia events occurred in either group.

CONCLUSIONS/INTERPRETATION

The use of a tubeless, on-body AID system was safe and associated with superior glycaemic profiles, including TIR, time below range, time above range and CV, than the use of a tubeless, on-body SAP.

TRIAL REGISTRATION

Clinical Research Information Service (CRIS) KCT0008398 FUNDING: The study was funded by a grant from the Korea Medical Device Development Fund supported by the Ministry of Science and ICT; the Ministry of Trade, Industry and Energy; the Ministry of Health and Welfare; and the Ministry of Food and Drug Safety (grant number: RS-2020-KD000056).

Accuracy of continuous glucose monitoring during exercise-related hypoglycemia in individuals with type 1 diabetes

Background

Hypoglycemia is common in individuals with type 1 diabetes, especially during exercise. We investigated the accuracy of two different continuous glucose monitoring systems during exercise-related hypoglycemia in an experimental setting.

Materials and methods

Fifteen individuals with type 1 diabetes participated in two separate euglycemic-hypoglycemic clamp days (Clamp-exercise and Clamp-rest) including five phases: 1) baseline euglycemia, 2) plasma glucose (PG) decline ± exercise, 3) 15-minute hypoglycemia ± exercise, 4) 45-minute hypoglycemia, and 5) recovery euglycemia. Interstitial PG levels were measured every five minutes, using Dexcom G6 (DG6) and FreeStyle Libre 1 (FSL1). Yellow Springs Instruments 2900 was used as PG reference method, enabling mean absolute relative difference (MARD) assessment for each phase and Clarke error grid analysis for each day.

Results

Exercise had a negative effect on FSL1 accuracy in phase 2 and 3 compared to rest (ΔMARD = +5.3 percentage points [(95% CI): 1.6, 9.1] and +13.5 percentage points [6.4, 20.5], respectively). In contrast, exercise had a positive effect on DG6 accuracy during phase 2 and 4 compared to rest (ΔMARD = -6.2 percentage points [-11.2, -1.2] and -8.4 percentage points [-12.4, -4.3], respectively). Clarke error grid analysis showed a decrease in clinically acceptable treatment decisions during Clamp-exercise for FSL1 while a contrary increase was observed for DG6.

Conclusion

Physical exercise had clinically relevant impact on the accuracy of the investigated continuous glucose monitoring systems and their ability to accurately detect hypoglycemia.

Exploring Technology's Influence on Health Behaviours and Well-being in Type 1 Diabetes: a Review

PURPOSE OF REVIEW

Maintaining positive health behaviours promotes better health outcomes for people with type 1 diabetes (T1D). However, implementing these behaviours may also lead to additional management burdens and challenges. Diabetes technologies, including continuous glucose monitoring systems, automated insulin delivery systems, and digital platforms, are being rapidly developed and widely used to reduce these burdens. Our aim was to review recent evidence to explore the influence of these technologies on health behaviours and well-being among adults with T1D and discuss future directions.

RECENT FINDINGS

Current evidence, albeit limited, suggests that technologies applied in diabetes self-management education and support (DSME/S), nutrition, physical activity (PA), and psychosocial care areas improved glucose outcomes. They may also increase flexibility in insulin adjustment and eating behaviours, reduce carb counting burden, increase confidence in PA, and reduce mental burden. Technologies have the potential to promote health behaviours changes and well-being for people with T1D. More confirmative studies on their effectiveness and safety are needed to ensure optimal integration in standard care practices.

Diabetes Technology Meeting 2023

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Effect of combined aerobic-resistance training and subsequent detraining on brain-derived neurotrophic factor (BDNF) and depression in women with type 2 diabetes mellitus: A randomized controlled trial

AIMS

In this study, we assessed the effects of a 12-week combined aerobic-resistance training and subsequent detraining on Beck Depression Inventory (BDI) score and mediating role of BDNF and also investigated whether exercise-induced alterations are maintained following a short period of detraining in women with type 2 diabetes (T2D).

MATERIALS AND METHODS

Thirty-four women with T2D were randomly assigned to experimental or control group (age: 60.6 ± 6.3, body mass index (BMI): 30.2 ± 1.3 kg/m2 , HbA1c: 8.09 ± 0.73%). The exercise training comprised of combined aerobic-resistance programme (50%-70% heart rate reserve for aerobic exercise, and 50%-70% 1 repetition maximum for resistance exercise, respectively) performed three sessions per week over 12 weeks. The intervention period was followed by an 8-week detraining period. Data were collected at baseline and also following exercise intervention and detraining. Data were analysed by linear mixed model at p < 0.05.

RESULTS

After 12 weeks of combined exercise training and 8 weeks of detraining, there was a significant difference in BDNF (0.08; 95% confidence interval [CI] = 0.07-0.10; p = 0.001), fasting blood glucose (FBG) (-45.41; CI = -50.83, -39.98; p = 0.001), insulin (-6.47; CI = -7.04, -5.9; p = 0.001), HOMA-IR (-3.76; CI = -4.07, -3.45; p = 0.001) and BDI score (-17.17; CI = -20.29, -14.05; p = 0.001) between the experimental and control group. Multiple mediation analysis indicated that BDNF seems to have a mediating role in exercise-induced improvement of depression (p = 0.04). After the detraining period, BDI score remained unchanged and it showed a significant increase compared to before the start of training (p = 0.001).

CONCLUSIONS

It may be concluded that exercise training improves depression that is likely to be explained by increased BDNF concentration in TD2. In spite of decreased BDNF concentration following an 8-week detraining, depression score was maintained.

The Need to Prioritize Education and Resources to Support Exercise in Type 1 Diabetes: Results of an Australian Survey of Adults With Type 1 Diabetes and Health Providers

OBJECTIVES

Regular exercise is recommended for people with type 1 diabetes (PWD) to improve their health, but many do not meet recommended exercise targets. Educational resources supporting PWD to exercise exist, but their value is unclear. To determine the need for improved exercise resources in Australia, we surveyed adult PWD and health providers (HPs) about their confidence in managing type 1 diabetes (T1D) around exercise, barriers to exercise, and the adequacy of current resources.

METHODS

Australian adult PWD and HPs completed surveys to rate the importance of exercise in T1D management, confidence in managing T1D around exercise, barriers to giving and receiving education, resources used, and what form new resources should take.

RESULTS

Responses were received from 128 PWD and 122 HPs. Both groups considered exercise to be important for diabetes management. PWD cited time constraints (57%) and concern about dysglycemia (43%) as barriers to exercise, and many lacked confidence in managing T1D around exercise. HPs were more confident, but experienced barriers to providing advice, and PWD did not tend to rely on this advice. Instead, 72% of PWD found continuous glucose monitoring most helpful. Both groups desired better resources to support exercise in T1D, with PWD preferring to obtain information through a structured education program and HPs through eLearning.

CONCLUSIONS

Australian HPs and PWD appreciate the importance of exercise in T1D management and express a clear desire for improved educational resources. Our findings provide a basis for developing a comprehensive package of resources for both adult PWD and HPs, to support exercise in PWD.

Design and In Silico Evaluation of an Exercise Decision Support System Using Digital Twin Models

BACKGROUND

Managing glucose levels during exercise is challenging for individuals with type 1 diabetes (T1D) since multiple factors including activity type, duration, intensity and other factors must be considered. Current decision support tools lack personalized recommendations and fail to distinguish between aerobic and resistance exercise. We propose an exercise-aware decision support system (exDSS) that uses digital twins to deliver personalized recommendations to help people with T1D maintain safe glucose levels (70-180 mg/dL) and avoid low glucose (<70 mg/dL) during and after exercise.

METHODS

We evaluated exDSS using various exercise and meal scenarios recorded from a large, free-living study of aerobic and resistance exercise. The model inputs were heart rate, insulin, and meal data. Glucose responses were simulated during and after 30-minute exercise sessions (676 aerobic, 631 resistance) from 247 participants. Glucose outcomes were compared when participants followed exDSS recommendations, clinical guidelines, or did not modify behavior (no intervention).

RESULTS

exDSS significantly improved mean time in range for aerobic (80.2% to 92.3%, P < .0001) and resistance (72.3% to 87.3%, P < .0001) exercises compared with no intervention, and versus clinical guidelines (aerobic: 82.2%, P < .0001; resistance: 80.3%, P < .0001). exDSS reduced time spent in low glucose for both exercise types compared with no intervention (aerobic: 15.1% to 5.1%, P < .0001; resistance: 18.2% to 6.6%, P < .0001) and was comparable with following clinical guidelines (aerobic: 4.5%, resistance: 8.1%, P = N.S.).

CONCLUSIONS

The exDSS tool significantly improved glucose outcomes during and after exercise versus following clinical guidelines and no intervention providing motivation for clinical evaluation of the exDSS system.

Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry

BACKGROUND

Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes.

METHODS

In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine learning approaches to predict glycemic excursions: a classification model and a regression model.

RESULTS

The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone.

CONCLUSIONS

Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.

An overview of diabetes mellitus in pregnant women with obesity

Rates of obesity are increasing world-wide with an estimated 1billion people projected to be obese by 2030 if current trends remain unchanged. Obesity currently considered one of the most significant associated factors of non-communicable diseases poses the greatest threat to health. Diabetes mellitus is an important metabolic disorder closely associated with obesity. It is therefore expected that with the increasing rates of obesity, the rates of diabetes in pregnancy will also be rising. This disorder may pre-date pregnancy (diagnosed or undiagnosed and diagnosed for the first time in pregnancy) or may be of onset in pregnancy. Irrespective of the timing of onset, diabetes in pregnancy is associated with both fetal and maternal complications. Outcomes are much better if control is maximised. Early diagnosis, multidisciplinary care and tailored management with optimum glycaemic control is associated with a significant reduction in not only pregnancy complications but long-term consequences on both the mother and offspring. This review brings together the current understanding of the pathogenesis of the endocrine derangements that are associated with diabetes in pregnancy how screening should be offered and management including pre-pregnancy care and the role of newer agents in management.

Real World Interstitial Glucose Profiles of a Large Cohort of Physically Active Men and Women

The use of continuous glucose monitors (CGMs) in individuals living without diabetes is increasing. The purpose of this study was to profile various CGM metrics around nutritional intake, sleep and exercise in a large cohort of physically active men and women living without any known metabolic disease diagnosis to better understand the normative glycemic response to these common stimuli. A total of 12,504 physically active adults (age 40 ± 11 years, BMI 23.8 ± 3.6 kg/m2; 23% self-identified as women) wore a real-time CGM (Abbott Libre Sense Sport Glucose Biosensor, Abbott, USA) and used a smartphone application (Supersapiens Inc., Atlanta, GA, USA) to log meals, sleep and exercise activities. A total of >1 M exercise events and 274,344 meal events were analyzed. A majority of participants (85%) presented an overall (24 h) average glucose profile between 90 and 110 mg/dL, with the highest glucose levels associated with meals and exercise and the lowest glucose levels associated with sleep. Men had higher mean 24 h glucose levels than women (24 h-men: 100 ± 11 mg/dL, women: 96 ± 10 mg/dL). During exercise, the % time above >140 mg/dL was 10.3 ± 16.7%, while the % time <70 mg/dL was 11.9 ± 11.6%, with the remaining % within the so-called glycemic tight target range (70-140 mg/dL). Average glycemia was also lower for females during exercise and sleep events (p < 0.001). Overall, we see small differences in glucose trends during activity and sleep in females as compared to males and higher levels of both TAR and TBR when these active individuals are undertaking or competing in endurance exercise training and/or competitive events.

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

OBJECTIVE

Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid.

METHODS

Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources.

SIGNIFICANCE

These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Evaluation of Effects of Continuous Glucose Monitoring on Physical Activity Habits and Blood Lipid Levels in Persons With Type 1 Diabetes Managed With Multiple Daily Insulin Injections: An Analysis Based on the GOLD Randomized Trial (GOLD 8)

BACKGROUND

People with type 1 diabetes generally view it easier to exercise when having continuous information of the glucose levels. We evaluated whether patients with type 1 diabetes managed with multiple daily insulin injections (MDI) exercised more after initiating continuous glucose monitoring (CGM) and whether the improved glycemic control and well-being associated with CGM translates into improved blood lipids and markers of inflammation.

METHOD

The GOLD trial was a randomized cross-over trial over 16 months where patients used either CGM or capillary self-monitoring of blood glucose (SMBG) over six months, with a four-month wash-out period between the two treatment periods. We compared grade of physical activity, blood lipids, apolipoproteins, and high-sensitivity C-reactive protein (hsCRP) levels during CGM and SMBG.

RESULTS

There were 116 patients with information of physical activity estimated by the International Physical Activity Questionnaire (IPAQ) during both CGM and SMBG. No changes were found during CGM or SMBG, IPAQ scores 3305 versus 3878 (P = .16). In 136 participants with information of blood lipid levels with no change in lipid-lowering medication during the two treatment periods, HbA1c differed by 4.2 mmol/mol (NGSP 0.39%) between SMBG and CGM treatment (P < .001). No significant changes existed in low-density lipoprotein, high-density lipoprotein, triglycerides, total cholesterol, apolipoprotein A1, apolipoprotein B1, or hsCRP, during CGM and SMBG.

CONCLUSION

Although many patients experience it easier to perform physical activity when monitoring glucose levels with CGM, it does not influence the amount of physical activity in persons with type 1 diabetes. Blood lipids, apolipoprotein, and hsCRP levels were similar during CGM and SMBG.

14. Children and Adolescents: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Effects of a Low-Carbohydrate-High-Protein Pre-Exercise Meal in Type 1 Diabetes-a Randomized Crossover Trial

CONTEXT

Current guidelines for exercise-related glucose management focus on reducing bolus and/or basal insulin doses and considering carbohydrate intake. Yet far less attention has been paid to the potential role of other macronutrients alongside carbohydrates on glucose dynamics around exercise.

OBJECTIVE

To investigate the effects of a low-carbohydrate-high-protein (LCHP) compared with a high-carbohydrate-low-protein (HCLP) pre-exercise meal on the metabolic, hormonal, and physiological responses to exercise in adults with insulin pump-treated type 1 diabetes.

METHODS

Fourteen adults (11 women, 3 men) with insulin pump-treated type 1 diabetes (median [range] HbA1c of 50 [43-59] mmol/mol (6.7% [6.1%-7.5%]), age of 49 [25-65] years, and body mass index of 24.0 [19.3-27.1] kg/m2) completed an unblinded, 2-arm, randomized, crossover study. Participants ingested isocaloric meals that were either LCHP (carbohydrate 21%, protein 52%, fat 27%) or HCLP (carbohydrate 52%, protein 21%, fat 27%) 90 minutes prior to undertaking 45 minutes of cycling at moderate intensity. Meal insulin bolus was dosed according to meal carbohydrate content but reduced by 25%. Basal insulin rates were reduced by 35% from meal ingestion to end of exercise.

RESULTS

Around exercise the coefficient of variability was lower during LCHP (LCHP: 14.5 ± 5.3 vs HCLP: 24.9 ± 7.7%, P = .001). Over exercise, LCHP was associated with a lesser drop (LCHP: Δ-1.49 ± 1.89 vs HCLP: Δ-3.78 ± 1.95 mmol/L, P = .001). Mean insulin concentration was 30% lower during exercise for LCHP compared with HCLP (LCHP: 25.5 ± 11.0 vs HCLP: 36.5 ± 15.9 mU/L, P < .001).

CONCLUSION

Ingesting a LCHP pre-exercise meal lowered plasma glucose variability around exercise and diminished the drop in plasma glucose over exercise.

Road map for personalized exercise medicine in T2DM

The number of patients with type 2 diabetes mellitus (T2DM) is rising at an alarming rate. Regular physical activity and exercise are cornerstones in the therapy of T2DM. While a one-size-fits-all approach fails to account for many between-subject differences, the use of personalized exercise medicine has the potential of optimizing health outcomes. Here, a road map for personalized exercise therapy targeted at patients with T2DM is presented. It considers secondary complications, glucose management, response heterogeneity, and other relevant factors that might influence the effectiveness of exercise as medicine, taking exercise-medication-diet interactions, as well as feasibility and acceptance into account. Furthermore, the potential of artificial intelligence and machine learning-based applications in assisting sports therapists to find appropriate exercise programs is outlined.

A Nutritional Approach to Optimizing Pump Therapy in Type 1 Diabetes Mellitus

Achieving optimal glucose control in individuals with type 1 diabetes (T1DM) continues to pose a significant challenge. While continuous insulin infusion systems have shown promise as an alternative to conventional insulin therapy, there remains a crucial need for greater awareness regarding the necessary adaptations for various special circumstances. Nutritional choices play an essential role in the efficacy of diabetes management and overall health status for patients with T1DM. Factors such as effective carbohydrate counting, assessment of the macronutrient composition of meals, and comprehending the concept of the glycemic index of foods are paramount in making informed pre-meal adjustments when utilizing insulin pumps. Furthermore, the ability to handle such situations as physical exercise, illness, pregnancy, and lactation by making appropriate adjustments in nutrition and pump settings should be cultivated within the patient-practitioner relationship. This review aims to provide healthcare practitioners with practical guidance on optimizing care for individuals living with T1DM. It includes recommendations on carbohydrate counting, managing mixed meals and the glycemic index, addressing exercise-related challenges, coping with illness, and managing nutritional needs during pregnancy and lactation. Additionally, considerations relating to closed-loop systems with regard to nutrition are addressed. By implementing these strategies, healthcare providers can better equip themselves to support individuals with T1DM in achieving improved diabetes management and enhanced quality of life.

An updated algorithm for an effective choice of continuous glucose monitoring for people with insulin-treated diabetes

PURPOSE

Continuous Glucose Monitoring (CGM) is a key tool for insulin-treated people with diabetes (PwD). CGM devices include both real-time CGM (rtCGM) and intermittently scanned CGM (isCGM), which are associated with an improvement of glucose control and less hypoglycemia in clinical trials of people with type 1 and type 2 diabetes.

METHODS

This is an expert position to update a previous algorithm on the most suitable choice of CGM for insulin-treated PwD in light of the recent evidence and clinical practice.

RESULTS

We identified six different clinical scenarios, including type 1 diabetes, type 2 diabetes, pregnancy on intensive insulin therapy, regular physical exercise, new onset of diabetes, and frailty. The use of rtCGM or isCGM is suggested, on the basis of the predominant clinical issue, as suboptimal glucose control or disabling hypoglycemia, regardless of baseline HbA1c or individualized HbA1c target.

CONCLUSION

The present algorithm may help to select the best CGM device based on patients' clinical characteristics, needs and clinical context, offering a further opportunity of a "tailored" therapy for people with insulin-treated diabetes.

Safety and performance of a hybrid closed-loop insulin delivery system with carbohydrate suggestion in adults with type 1 diabetes prone to hypoglycemia

AIMS

To evaluate the safety and performance of a hybrid closed-loop (HCL) system with automatic carbohydrate suggestion in adults with type 1 diabetes (T1D) prone to hypoglycemia.

METHODS

A 32-hour in-hospital pilot study, including a night period, 4 meals and 2 vigorous unannounced 45-minute aerobic sessions, was conducted in 11 adults with T1D prone to hypoglycemia. The primary outcome was the percentage of time in range 70-180 mg/dL (TIR). Main secondary outcomes were time below range < 70 mg/dL (TBR < 70) and < 54 (TBR < 54). Data are presented as median (10th-90th percentile ranges).

RESULTS

The participants, 6 (54.5%) men, were 24 (22-48) years old, and had 22 (9-32) years of T1D duration. All of them regularly used an insulin pump and a continuous glucose monitoring system. The median TIR was 78.7% (75.6-91.2): 92.7% (68.2-100.0) during exercise and recovery period, 79.3% (34.9-100.0) during postprandial period, and 95.4% (66.4-100.0) during overnight period. The TBR < 70 and TBR < 54 were 0.0% (0.0-6.6) and 0.0% (0.0-1.2), respectively. A total of 4 (3-9) 15-g carbohydrate suggestions were administered per person. No severe acute complications occurred during the study.

CONCLUSIONS

The HCL system with automatic carbohydrate suggestion performed well and was safe in this population during challenging conditions in a hospital setting.

A Prospective Study of the Effect of Gastroduodenal Artery Reconstruction on Duodenal Oxygenation and Enzyme Content After Pancreas Transplantation

BACKGROUND

Whole pancreas transplantation provides durable glycemic control and can improve survival rate; however, it can carry an increased risk of surgical complications. One devastating complication is a duodenal leak at the site of enteroenteric anastomosis. The gastroduodenal artery (GDA) supplies blood to the donor duodenum and pancreas but is commonly ligated during procurement. Since we have not had expressive changes in pancreatic back table surgical techniques in the recent decades, we hypothesized whether back table GDA reconstruction, improving perfusion of the donor duodenum and head of the pancreas, could lead to fewer surgical complications in simultaneous pancreas-kidney (SPK) transplants.

MATERIAL AND METHODS

Between 2017 and 2021, we evaluated demographic information, postoperative complications, intraoperative donor duodenum, recipient bowel O2 tissue saturation, and patient morbidity through the Comprehensive Complication Index (CCI®).

RESULTS

A total of 26 patients were included: 13 underwent GDA reconstruction (GDA-R), and 13 had GDA ligation (GDA-L). There were no pancreatic leaks in the GR group compared to 38% (5/13) in the GDA-L group (p = 0.03913). Intraoperative tissue oxygen saturation was higher in the GDA-R group than in the GDA-L (95.18 vs.76.88%, p < 0,001). We observed an increase in transfusion rate in GDA-R (p < 0.05), which did not result in a higher rate of exploration (p = 0.38). CCI® patient morbidity was also significantly lower in the GDA-R group (s < 0.05).

CONCLUSIONS

This study identified improved intraoperative duodenal tissue oxygen saturation in the GDA-R group with an associated reduction in pancreatic leaks and CCI® morbidity risk. A larger prospective multicenter study comparing the two methods is warranted.

Comparative Analysis of Predictive Interstitial Glucose Level Classification Models

BACKGROUND

New methods of continuous glucose monitoring (CGM) provide real-time alerts for hypoglycemia, hyperglycemia, and rapid fluctuations of glucose levels, thereby improving glycemic control, which is especially crucial during meals and physical activity. However, complex CGM systems pose challenges for individuals with diabetes and healthcare professionals, particularly when interpreting rapid glucose level changes, dealing with sensor delays (approximately a 10 min difference between interstitial and plasma glucose readings), and addressing potential malfunctions. The development of advanced predictive glucose level classification models becomes imperative for optimizing insulin dosing and managing daily activities.

METHODS

The aim of this study was to investigate the efficacy of three different predictive models for the glucose level classification: (1) an autoregressive integrated moving average model (ARIMA), (2) logistic regression, and (3) long short-term memory networks (LSTM). The performance of these models was evaluated in predicting hypoglycemia (<70 mg/dL), euglycemia (70-180 mg/dL), and hyperglycemia (>180 mg/dL) classes 15 min and 1 h ahead. More specifically, the confusion matrices were obtained and metrics such as precision, recall, and accuracy were computed for each model at each predictive horizon.

RESULTS

As expected, ARIMA underperformed the other models in predicting hyper- and hypoglycemia classes for both the 15 min and 1 h horizons. For the 15 min forecast horizon, the performance of logistic regression was the highest of all the models for all glycemia classes, with recall rates of 96% for hyper, 91% for norm, and 98% for hypoglycemia. For the 1 h forecast horizon, the LSTM model turned out to be the best for hyper- and hypoglycemia classes, achieving recall values of 85% and 87% respectively.

CONCLUSIONS

Our findings suggest that different models may have varying strengths and weaknesses in predicting glucose level classes, and the choice of model should be carefully considered based on the specific requirements and context of the clinical application. The logistic regression model proved to be more accurate for the next 15 min, particularly in predicting hypoglycemia. However, the LSTM model outperformed logistic regression in predicting glucose level class for the next hour. Future research could explore hybrid models or ensemble approaches that combine the strengths of multiple models to further enhance the accuracy and reliability of glucose predictions.

Investigating sensor location on the effectiveness of continuous glucose monitoring during exercise in a non-diabetic population

The purpose of this investigation was to evaluate whether continuous glucose monitoring (CGM) sensors worn on the active muscle may provide enhanced insight into glucose control in non-diabetic participants during cycling exercise compared to traditional sensor placement on the arm. Data from 9 healthy participants (F:3) was recorded using CGM sensors on the arm (triceps brachii) and leg (vastus medialis) following 100 g glucose ingestion during 30 min experimental visits of: resting control, graded cycling, electrically stimulated quadriceps contractions, and passive whole-body heating. Finger capillary glucose was used to assess sensor accuracy. Under control conditions, the traditional arm sensor better reflected capillary glucose, with a mean absolute relative difference (MARD) of 12.4 ± 9.3% versus 18.3 ± 11.4% in the leg (P = 0.02). For the intended use during exercise, the sensor-site difference was attenuated, with similar MARDs during cycling (arm:15.5 ± 12% versus leg:16.7 ± 10.8%, P = 0.96) and quadriceps stimulation (arm:15.5 ± 14.8% versus leg:13.9 ± 9.5%, P = 0.9). At rest, glucose at the leg was consistently lower than the arm (P = 0.01); whereas, during graded cycling, the leg-glucose was lower only after maximal intensity exercise (P = 0.02). There was no difference between sensors during quadriceps stimulation (P = 0.8). Passive heating caused leg-skin temperature to increase by 3.1 ± 1.8°C versus 1.1 ± 0.72°C at the arm (P = 0.002), elevating MARD in the leg (23.5 ± 16.2%) and lowering glucose in the leg (P < 0.001). At rest, traditional placement of CGM sensors on the arm may best reflect blood glucose; however, during cycling, placement on the leg may offer greater insight to working muscle glucose concentrations, and this is likely due to greater blood-flow rather than muscle contractions.HighlightsWearing a continuous glucose monitoring (CGM) sensor on the arm may better reflect capillary glucose concentrations compared to wearing a sensor on the inner thigh at rest.With passive or active leg-muscle contractions, site-specific differences compared to capillary samples are attenuated; therefore, wearing a CGM sensor on the active-muscle during exercise may provide greater information to non-diabetic athletes regarding glucose flux at the active muscle.Discrepancies in CGM sensors worn at different sites likely primarily reflects differences in blood flow, as passive skin heating caused the largest magnitude difference between arm and leg sensor readings compared to the other experimental conditions (control, electric muscle stimulation, and cycling exercise).

The Effect of Do-It-Yourself Real-Time Continuous Glucose Monitoring on Glycemic Variables and Participant-Reported Outcomes in Adults With Type 1 Diabetes: A Randomized Crossover Trial

AIM

Real-time continuous glucose monitoring (rtCGM) has several advantages over intermittently scanned continuous glucose monitoring (isCGM) but generally comes at a higher cost. Do-it-yourself rtCGM (DIY-rtCGM) potentially has benefits similar to those of rtCGM. This study compared outcomes in adults with type 1 diabetes using DIY-rtCGM versus isCGM.

METHODS

In this crossover trial, adults with type 1 diabetes were randomized to use isCGM or DIY-rtCGM for eight weeks before crossover to use the other device for eight weeks, after a four-week washout period where participants reverted back to isCGM. The primary endpoint was time in range (TIR; 3.9-10 mmol/L). Secondary endpoints included other glycemic control measures, psychosocial outcomes, and sleep quality.

RESULTS

Sixty participants were recruited, and 52 (87%) completed follow-up. Glucose outcomes were similar in the DIY-rtCGM and isCGM groups, including TIR (53.1% vs 51.3%; mean difference -1.7% P = .593), glycosylated hemoglobin (57.0 ± 17.8 vs 61.4 ± 12.2 mmol/L; P = .593), and time in hypoglycemia <3.9 mmol/L (3.9 ± 3.8% vs 3.8 ± 4.0%; P = .947). Hypoglycemia Fear Survey total score (1.17 ± 0.52 vs 0.97 ± 0.54; P = .02) and fear of hypoglycemia score (1.18 ± 0.64 vs 0.97 ± 0.45; P = .02) were significantly higher during DIY-rtCGM versus isCGM. Diabetes Treatment Satisfaction Questionnaire status (DTSQS) score was also higher with DIY-rtCGM versus isCGM (28.7 ± 5.8 vs 26.0 ± 5.8; P = .04), whereas diabetes-related quality of life was slightly lower (DAWN2 Impact of Diabetes score: 3.11 ± 0.4 vs 3.32 ± 0.51; P = .045); sleep quality did not differ between the two groups.

CONCLUSION

Although the use of DIY-rtCGM did not improve glycemic outcomes compared with isCGM, it positively impacted several patient-reported psychosocial variables. DIY-rtCGM potentially provides an alternative, cost-effective rtCGM option.

Relationship between acute glucose variability and cognitive decline in type 2 diabetes: A systematic review and meta-analysis

BACKGROUND

Cognitive decline is one of the most widespread chronic complications of diabetes, which occurs in more than half of the patients with type 2 diabetes (T2DM). Emerging evidences have suggested that glucose variability (GV) is associated with the pathogenesis of diabetic complications. However, the influence of acute GV on cognitive dysfunction in T2DM is still controversial. The aim of the study was to evaluate the association between acute GV and cognitive defect in T2DM, and provide a most recent and comprehensive summary of the evidences in this research field.

METHODS

PubMed, Cochrane library, EMBASE, Web of science, Sinomed, China National Knowledge Infrastructure (CNKI), and Wanfang were searched for articles that reported on the association between acute GV and cognitive impairment in T2DM.

RESULTS

9 eligible studies were included, with a total of 1263 patients with T2DM involved. Results showed that summary Fisher's z value was -0.23 [95%CI (-0.39, -0.06)], suggesting statistical significance (P = 0.006). Summary r value was -0.22 [95%CI (-0.37, -0.06)]. A lower cognitive performance was found in the subjects with greater glucose variation, which has statistical significance. Mean amplitude of glycemic excursions (MAGE) was associated with a higher risk of poor functional outcomes. Fisher's z value was -0.35 [95%CI (-0.43, -0.25)], indicating statistical significance (P = 0.011). Sensitivity analyses by omitting individual studies showed stability of the results.

CONCLUSIONS

Overall, higher acute GV is associated with an increased risk of cognitive impairment in patients with T2DM. Further studies should be required to determine whether targeted intervention of reducing acute GV could prevent cognitive decline.

Quantifying insulin-mediated and noninsulin-mediated changes in glucose dynamics during resistance exercise in type 1 diabetes

Exercise can cause dangerous fluctuations in blood glucose in people living with type 1 diabetes (T1D). Aerobic exercise, for example, can cause acute hypoglycemia secondary to increased insulin-mediated and noninsulin-mediated glucose utilization. Less is known about how resistance exercise (RE) impacts glucose dynamics. Twenty-five people with T1D underwent three sessions of either moderate or high-intensity RE at three insulin infusion rates during a glucose tracer clamp. We calculated time-varying rates of endogenous glucose production (EGP) and glucose disposal (Rd) across all sessions and used linear regression and extrapolation to estimate insulin- and noninsulin-mediated components of glucose utilization. Blood glucose did not change on average during exercise. The area under the curve (AUC) for EGP increased by 1.04 mM during RE (95% CI: 0.65-1.43, P < 0.001) and decreased proportionally to insulin infusion rate (0.003 mM per percent above basal rate, 95% CI: 0.001-0.006, P = 0.003). The AUC for Rd rose by 1.26 mM during RE (95% CI: 0.41-2.10, P = 0.004) and increased proportionally with insulin infusion rate (0.04 mM per percent above basal rate, CI: 0.03-0.04, P < 0.001). No differences were observed between the moderate and high resistance groups. Noninsulin-mediated glucose utilization rose significantly during exercise before returning to baseline roughly 30-min postexercise. Insulin-mediated glucose utilization remained unchanged during exercise sessions. Circulating catecholamines and lactate rose during exercise despite relatively small changes observed in Rd. Results provide an explanation of why RE may pose a lower overall risk for hypoglycemia.NEW & NOTEWORTHY Aerobic exercise is known to cause decreases in blood glucose secondary to increased glucose utilization in people living with type 1 diabetes (T1D). However, less is known about how resistance-type exercise impacts glucose dynamics. Twenty-five participants with T1D performed in-clinic weight-bearing exercises under a glucose clamp. Mathematical modeling of infused glucose tracer allowed for quantification of the rate of hepatic glucose production as well as rates of insulin-mediated and noninsulin-mediated glucose uptake experienced during resistance exercise.

The Type 1 Diabetes and EXercise Initiative: Predicting Hypoglycemia Risk During Exercise for Participants with Type 1 Diabetes Using Repeated Measures Random Forest

Objective: Exercise is known to increase the risk for hypoglycemia in type 1 diabetes (T1D) but predicting when it may occur remains a major challenge. The objective of this study was to develop a hypoglycemia prediction model based on a large real-world study of exercise in T1D. Research Design and Methods: Structured study-specified exercise (aerobic, interval, and resistance training videos) and free-living exercise sessions from the T1D Exercise Initiative study were used to build a model for predicting hypoglycemia, a continuous glucose monitoring value <70 mg/dL, during exercise. Repeated measures random forest (RMRF) and repeated measures logistic regression (RMLR) models were constructed to predict hypoglycemia using predictors at the start of exercise and baseline characteristics. Models were evaluated with area under the receiver operating characteristic curve (AUC) and balanced accuracy. Results: RMRF and RMLR had similar AUC (0.833 vs. 0.825, respectively) and both models had a balanced accuracy of 77%. The probability of hypoglycemia was higher for exercise sessions with lower pre-exercise glucose levels, negative pre-exercise glucose rates of change, greater percent time <70 mg/dL in the 24 h before exercise, and greater pre-exercise bolus insulin-on-board (IOB). Free-living aerobic exercises, walking/hiking, and physical labor had the highest probability of hypoglycemia, while structured exercises had the lowest probability of hypoglycemia. Conclusions: RMRF and RMLR accurately predict hypoglycemia during exercise and identify factors that increase the risk of hypoglycemia. Lower glucose, decreasing levels of glucose before exercise, and greater pre-exercise IOB largely predict hypoglycemia risk in adults with T1D.

Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial

BACKGROUND

Exercise can rapidly drop glucose in people with type 1 diabetes. Ubiquitous wearable fitness sensors are not integrated into automated insulin delivery (AID) systems. We hypothesised that an AID can automate insulin adjustments using real-time wearable fitness data to reduce hypoglycaemia during exercise and free-living conditions compared with an AID not automating use of fitness data.

METHODS

Our study population comprised of individuals (aged 21-50 years) with type 1 diabetes from from the Harold Schnitzer Diabetes Health Center clinic at Oregon Health and Science University, OR, USA, who were enrolled into a 76 h single-centre, two-arm randomised (4-block randomisation), non-blinded crossover study to use (1) an AID that detects exercise, prompts the user, and shuts off insulin during exercise using an exercise-aware adaptive proportional derivative (exAPD) algorithm or (2) an AID that automates insulin adjustments using fitness data in real-time through an exercise-aware model predictive control (exMPC) algorithm. Both algorithms ran on iPancreas comprising commercial glucose sensors, insulin pumps, and smartwatches. Participants executed 1 week run-in on usual therapy followed by exAPD or exMPC for one 12 h primary in-clinic session involving meals, exercise, and activities of daily living, and 2 free-living out-patient days. Primary outcome was time below range (<3·9 mmol/L) during the primary in-clinic session. Secondary outcome measures included mean glucose and time in range (3·9-10 mmol/L). This trial is registered with ClinicalTrials.gov, NCT04771403.

FINDINGS

Between April 13, 2021, and Oct 3, 2022, 27 participants (18 females) were enrolled into the study. There was no significant difference between exMPC (n=24) versus exAPD (n=22) in time below range (mean [SD] 1·3% [2·9] vs 2·5% [7·0]) or time in range (63·2% [23·9] vs 59·4% [23·1]) during the primary in-clinic session. In the 2 h period after start of in-clinic exercise, exMPC had significantly lower mean glucose (7·3 [1·6] vs 8·0 [1·7] mmol/L, p=0·023) and comparable time below range (1·4% [4·2] vs 4·9% [14·4]). Across the 76 h study, both algorithms achieved clinical time in range targets (71·2% [16] and 75·5% [11]) and time below range (1·0% [1·2] and 1·3% [2·2]), significantly lower than run-in period (2·4% [2·4], p=0·0004 vs exMPC; p=0·012 vs exAPD). No adverse events occurred.

INTERPRETATION

AIDs can integrate exercise data from smartwatches to inform insulin dosing and limit hypoglycaemia while improving glucose outcomes. Future AID systems that integrate exercise metrics from wearable fitness sensors may help people living with type 1 diabetes exercise safely by limiting hypoglycaemia.

FUNDING

JDRF Foundation and the Leona M and Harry B Helmsley Charitable Trust, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.

Physical activity patterns in type 1 diabetes in Spain: The SED1 study

AIMS

To describe the physical activity (PA) frequency and intensity in the Spanish type 1 diabetes mellitus (T1D) population and its association with their glycemic control.

METHODS

A cross-sectional observational study was carried out in 75 Spanish public hospitals (the SED1 study). T1D patients over 14years of age self-completed the International Physical Activity Questionnaire (IPAQ) to determine their level of exercise. The relationship between PA frequency and intensity in T1D patients and glycemic control and the diabetes therapeutic education received were analyzed.

RESULTS

A total of 592 patients were evaluable. A 6.8% of the sample performed light PA, 20.9% moderate and 72.3% vigorous. Estimated PA presented a high inter-individual variability. Men consumed more energy (METS) than women, these differences being more noticeable in vigorous METS (2865.80 in men vs 1352.12 in women). Women invested more min/week in the domestic and garden area (639.03 vs 344.39, p = 0,022). A correlation between glycemic control and the METs was not observed.

CONCLUSIONS

The Spanish T1D population performed PA in a higher frequency and intensity than the general population. A relationship between PA and glycemic control couldn´t be shown. However, limitations of the study should be kept in mind to discard a long-term positive influence.

Continuous Glucose Monitoring (CGM) in Sports-A Comparison between a CGM Device and Lab-Based Glucose Analyser under Resting and Exercising Conditions in Athletes

The objective of this pilot study was to compare glucose concentrations in capillary blood (CB) samples analysed in a laboratory by a validated method and glucose concentrations measured in the interstitial fluid (ISF) by continuous glucose monitoring (CGM) under different physical activity levels in a postprandial state in healthy athletes without diabetes. As a physiological shift occurs between glucose concentration from the CB into the ISF, the applicability of CGM in sports, especially during exercise, as well as the comparability of CB and ISF data necessitate an in-depth assessment. Ten subjects (26 ± 4 years, 67 ± 11 kg bodyweight (BW), 11 ± 3 h) were included in the study. Within 14 days, they underwent six tests consisting of (a) two tests resting fasted (HC_Rest/Fast and LC_Rest/Fast), (b) two tests resting with intake of 1 g glucose/kg BW (HC_Rest/Glc and LC_Rest/Glc), (c) running for 60 min at moderate (ModExerc/Glc), and (d) high intensity after intake of 1 g glucose/kg BW (IntExerc/Glc). Data were collected in the morning, following a standardised dinner before test day. Sensor-based glucose concentrations were compared to those determined from capillary blood samples collected at the time of sensor-based analyses and subjected to laboratory glucose measurements. Pearson's r correlation coefficient was highest for Rest/Glc (0.92, p < 0.001) compared to Rest/Fast (0.45, p < 0.001), ModExerc/Glc (0.60, p < 0.001) and IntExerc/Glc (0.70, p < 0.001). Mean absolute relative deviation (MARD) and standard deviation (SD) was smallest for resting fasted and similar between all other conditions (Rest/Fast: 8 ± 6%, Rest/Glc: 17 ± 12%, ModExerc/Glc: 22 ± 24%, IntExerc/Glc: 18 ± 17%). However, Bland-Altman plot analysis showed a higher range between lower and upper limits of agreement (95% confidence interval) of paired data under exercising compared to resting conditions. Under resting fasted conditions, both methods produce similar outcomes. Under resting postprandial and exercising conditions, respectively, there are differences between both methods. Based on the results of this study, the application of CGM in healthy athletes is not recommended without concomitant nutritional or medical advice.

Practical Aspects and Exercise Safety Benefits of Automated Insulin Delivery Systems in Type 1 Diabetes

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.

Is There an Optimal Time of Day for Exercise? A Commentary on When to Exercise for People Living With Type 1 or Type 2 Diabetes

Exercise is a cornerstone of diabetes self-care because of its association with many health benefits. Several studies that have explored the best time of day to exercise to inform clinical recommendations have yielded mixed results. For example, for people with prediabetes or type 2 diabetes, there may be benefits to timing exercise to occur after meals, whereas people with type 1 diabetes may benefit from performing exercise earlier in the day. One common thread is the health benefits of consistent exercise, suggesting that the issue of exercise timing may be secondary to the goal of helping people with diabetes establish an exercise routine that best fits their life.

The associations between physical activity, health-related quality of life, regimen adherence, and glycemic control in adolescents with type 1 diabetes: A cross-sectional study

BACKGROUND

Adolescents with Type 1 Diabetes (T1D) display a greater than two-fold higher risk of developing diabetes-related complications compared with their healthy peers and the risk increases markedly as glycated hemoglobin (HbA1c) increases. The majority of the known factors associated with improved glycemic control in adolescents with T1D are geared toward Western populations. Therefore, this study examined the associations between Physical Activity (PA), Health-Related Quality of Life (HRQoL), and regimen adherence on glycemic control in a Middle Eastern population of adolescents with T1D METHODS: The study utilized a cross-sectional design of Jordanian adolescents (aged 12-18) with T1D (n = 74). Self-reported measures used were the Pediatric Quality of Life-Diabetes Module, the International Physical Activity Questionnaire, and the Summary of Diabetes Self-Care Activities. HbA1c values were obtained from the medical records. Correlation analyses were conducted using Pearson's and Spearman's correlation tests. Multiple regression analyses were conducted to determine if HRQoL, PA, and regimen adherence predict glycemic control.

RESULTS

Only 14.8 % of the participants demonstrated good glycemic control (HbA1c ≤ 7.5 %). Participants with poor control had a statistically significant lower mean PA of MET-minutes/week (3531.9 ± 1356.75 vs. 1619.81 ± 1481.95, p < .001) compared to those with good control. The total sample was found to demonstrate low HRQoL (47.70 ± 10.32). Participants were within the acceptable range of PA (1885.38 ± 1601.13) MET-minutes/week. HbA1c significantly inversely correlated with PA (r = -0.328, p = .010) and regimen adherence (r = -0.299, p = .018). The regression analysis revealed that PA significantly predicted glycemic control (β = -0.367, p < .01) as adherence (β = -0.409, p < .01) and disease duration did (β = 0.444, p < .01).

CONCLUSION

Better glycemic control was significantly associated with higher PA and regimen adherence levels. The correlation between PA and glycemic control depends highly on the level of regimen adherence or arguably, adherence acts as a buffer in the correlation between PA and glycemic control. There was no significant association between glycemic control and HRQoL.

Physical exercise and glycemic management in patients with type 1 diabetes on insulin pump therapy-a cross-sectional study

AIMS

Exercise is an important practice for control in type 1 diabetes (T1D). This study aims to assess de association between exercise and glycemic management in people with T1D and to identify the main barriers to exercise in T1D.

METHODS

We evaluated 95 people with T1D treated with insulin pump therapy. Participants answered a questionnaire about 1) exercise habits, 2) usual adjustments in insulin and food intake with exercise and 3) main barriers to exercise. Continuous glucose monitoring (CGM) was used to evaluate time in range (TIR), time below range (TBR) and time above range (TAR) during the last 60 days before the evaluation. CGM data during, before (2 h before) and after (24 h after) the last bout of exercise was also evaluated.

RESULTS

The mean age was 30.1 ± 12.1 years, and 51.6% were women. Participants that reported practicing exercise (55.8%) had a higher TIR (59.6 ± 16.3 vs. 48.7 ± 15.7, p = 0.012) and a lower TAR (32.6 ± 15.8 vs. 45.4 ± 17.7, p = 0.006). Comparing with the 60 days CGM data, the TBR was lower in the 2 h before exercise (- 1.8 ± 3.8, p = 0.0454) and TAR was lower during (- 16.9 ± 33.6, p = 0.0320) and in the 24 h after (- 8.7 ± 17.2, p = 0.032) the last bout of exercise. The absence of adjustments on insulin and food intake was associated with higher TBR after the exercise (13.44 ± 3.5, p < 0.05). Eating before the exercise and turning off the pump during the exercise were associated with lower TBR after exercise (food booster: - 7.56 ± 3.49, p < 0.05; turning off insulin pump - 8.87 ± 3.52, p < 0.05). The main barriers reported for exercise practicing were fear of hypoglycemia, lack of free time and work schedule.

CONCLUSION

Exercise was associated with better glycemic management in people with T1D. Addressing common barriers may allow a higher adherence to exercise in T1D.

Management of Glycemia during Acute Aerobic and Resistance Training in Patients with Diabetes Type 1: A Croatian Pilot Study

(1) Background: The increased risk of developing hypoglycemia and worsening of glycemic stability during exercise is a major cause of concern for patients with type 1 diabetes mellitus (T1DM). (2) Aim: This pilot study aimed to assess glycemic stability and hypoglycemic episodes during and after aerobic versus resistance exercises using a flash glucose monitoring system in patients with T1DM. (3) Participants and Methods: We conducted a randomized crossover prospective study including 14 adult patients with T1DM. Patients were randomized according to the type of exercise (aerobic vs. resistance) with a recovery period of three days between a change of groups. Glucose stability and hypoglycemic episodes were evaluated during and 24 h after the exercise. Growth hormone (GH), cortisol, and lactate levels were determined at rest, 0, 30, and 60 min post-exercise period. (4) Results: The median age of patients was 53 years, with a median HbA1c of 7.1% and a duration of diabetes of 30 years. During both training sessions, there was a drop in glucose levels immediately after the exercise (0'), followed by an increase at 30' and 60', although the difference was not statistically significant. However, glucose levels significantly decreased from 60' to 24 h in the post-exercise period (p = 0.001) for both types of exercise. Glycemic stability was comparable prior to and after exercise for both training sessions. No differences in the number of hypoglycemic episodes, duration of hypoglycemia, and average glucose level in 24 h post-exercise period were observed between groups. Time to hypoglycemia onset was prolonged after the resistance as opposed to aerobic training (13 vs. 8 h, p = NS). There were no nocturnal hypoglycemic episodes (between 0 and 6 a.m.) after the resistance compared to aerobic exercise (4 vs. 0, p = NS). GH and cortisol responses were similar between the two sessions, while lactate levels were significantly more increased after resistance training. (5) Conclusion: Both exercise regimes induced similar blood glucose responses during and immediately following acute exercise.

Hypoglycemia and glycemic variability of people with type 1 diabetes with lower and higher physical activity loads in free-living conditions using continuous subcutaneous insulin infusion with predictive low-glucose suspend system

INTRODUCTION

Maintaining glycemic control during and after physical activity (PA) is a major challenge in type 1 diabetes (T1D). This study compared the glycemic variability and exercise-related diabetic management strategies of adults with T1D achieving higher and lower PA loads in nighttime-daytime and active- sedentary behavior hours in free-living conditions.

RESEARCH DESIGN AND METHODS

Active adults (n=28) with T1D (ages: 35±10 years; diabetes duration: 21±11 years; body mass index: 24.8±3.4 kg/m²; glycated hemoglobin A1c: 6.9±0.6%) on continuous subcutaneous insulin delivery system with predictive low glucose suspend system and glucose monitoring, performed different types, duration and intensity of PA under free-living conditions, tracked by accelerometer over 14 days. Participants were equally divided into lower load (LL) and higher load (HL) by median of daily counts per minute (61122). Glycemic variability was studied monitoring predefined time in glycemic ranges (time in range (TIR), time above range (TAR) and time below range (TBR)), coefficient of variation (CV) and mean amplitude of glycemic excursions (MAGE). Parameters were studied in defined hours timeframes (nighttime-daytime and active-sedentary behavior). Self-reported diabetes management strategies were analysed during and post-PA.

RESULTS

Higher glycemic variability (CV) was observed in sedentary hours compared with active hours in the LL group (p≤0.05). HL group showed an increment in glycemic variability (MAGE) during nighttime versus daytime (p≤0.05). There were no differences in TIR and TAR across all timeframes between HL and LL groups. The HL group had significantly more TBR during night hours than the LL group (p≤0.05). Both groups showed TBR above recommended values. All participants used fewer post-PA management strategies than during PA (p≤0.05).

CONCLUSION

Active people with T1D are able to maintain glycemic variability, TIR and TAR within recommended values regardless of PA loads. However, the high prevalence of TBR and the less use of post-PA management strategies highlights the potential need to increase awareness on actions to avoid glycemic excursions and hypoglycemia after exercise completion.

Comparison of Insulin Glargine 300 U/mL and Insulin Degludec 100 U/mL Around Spontaneous Exercise Sessions in Adults with Type 1 Diabetes: A Randomized Cross-Over Trial (ULTRAFLEXI-1 Study)

Aims: In the ULTRAFLEXI-1 study, we compared basal insulin Glargine 300 U/mL (IGlar U300) and insulin Degludec 100 U/mL (IDeg U100) for time below range <70 mg/dL (TBR^<70; 3.9 mmol/L) in two different doses (100% and 75% of the regular dose) when used around spontaneous exercise sessions in adults with type 1 diabetes. Methods: A randomized, single-center, four-period, cross-over trial was performed and in each of the four 2-weeks-periods, participants attended six spontaneous 60 min moderate-intensity evening cycle ergometer exercise sessions. The basal insulin administered on the exercise days were IGlar U300 100% or 75% of the regular dose or IDeg U100 100% or 75%, respectively (morning injection). The primary outcome was the TBR^<70 during the 24 h postexercise periods of the six spontaneous exercise sessions in the four trial arms and was analyzed in hierarchical order using the repeated measures linear mixed model. Results: Twenty-five people with type 1 diabetes were enrolled (14 males) with a mean age of 41.4 ± 11.9 years and an HbA_1c of 7.5% ± 0.8% (59 ± 9 mmol/mol). The mean ± standard error of mean TBR^<70 during the 24 h periods following the exercise sessions was 2.71% ± 0.51% for IGlar U300 (100%) and 4.37% ± 0.69% for IDeg U100 (100%) (P = 0.023) as well as 2.28% ± 0.53% for IGlar U300 and 2.55% ± 0.58% for IDeg U100 when using a 75% dose on exercise days (P = 0.720). Time in glucose range^70-180 was the highest in the IDeg U100 (100%) group. Conclusions: TBR^<70 within the first 24 h after spontaneous exercise sessions was significantly lower when receiving IGlar U300 compared to IDeg U100 when a regular basal dose was administered.

Modeling risk of hypoglycemia during and following physical activity in people with type 1 diabetes using explainable mixed-effects machine learning

BACKGROUND

Physical activity (PA) can cause increased hypoglycemia (glucose <70 mg/dL) risk in people with type 1 diabetes (T1D). We modeled the probability of hypoglycemia during and up to 24 h following PA and identified key factors associated with hypoglycemia risk.

METHODS

We leveraged a free-living dataset from Tidepool comprised of glucose measurements, insulin doses, and PA data from 50 individuals with T1D (6448 sessions) for training and validating machine learning models. We also used data from the T1Dexi pilot study that contains glucose management and PA data from 20 individuals with T1D (139 session) for assessing the accuracy of the best performing model on an independent test dataset. We used mixed-effects logistic regression (MELR) and mixed-effects random forest (MERF) to model hypoglycemia risk around PA. We identified risk factors associated with hypoglycemia using odds ratio and partial dependence analysis for the MELR and MERF models, respectively. Prediction accuracy was measured using the area under the receiver operating characteristic curve (AUROC).

RESULTS

The analysis identified risk factors significantly associated with hypoglycemia during and following PA in both MELR and MERF models including glucose and body exposure to insulin at the start of PA, low blood glucose index 24 h prior to PA, and PA intensity and timing. Both models showed overall hypoglycemia risk peaking 1 h after PA and again 5-10 h after PA, which is consistent with the hypoglycemia risk pattern observed in the training dataset. Time following PA impacted hypoglycemia risk differently across different PA types. Accuracy of hypoglycemia prediction using the fixed effects of the MERF model was highest when predicting hypoglycemia during the first hour following the start of PA (AUROCVALIDATION = 0.83 and AUROCTESTING = 0.86) and decreased when predicting hypoglycemia in the 24 h after PA (AUROCVALIDATION = 0.66 and AUROCTESTING = 0.68).

CONCLUSION

Hypoglycemia risk after the start of PA can be modeled using mixed-effects machine learning to identify key risk factors that may be used within decision support and insulin delivery systems. We published the population-level MERF model online for others to use.

Blood glucose response to running or cycling in individuals with type 1 diabetes: A systematic review and meta-analysis

AIMS

The aim of this systematic review and meta-analysis was to assess how running and cycling influence the magnitude of blood glucose (BG) excursions in individuals with type 1 diabetes.

METHODS

A systematic literature search was conducted in EMBASE, PubMed, Cochrane Central Register of Controlled Trials, and ISI Web of Knowledge for publications from January 1950 until February 2021. Parameters included for analysis were population (adults and adolescents), exercise type, intensity, duration and insulin preparation. The meta-analysis was performed to estimate the pooled mean with a 95% confidence interval (CI) of delta BG levels. In addition, sub-group and meta-regression analyses were performed to assess the influence of these parameters on delta BG.

RESULTS

The database search identified 3192 articles of which 69 articles were included in the meta-analysis. Due to crossover designs within articles, 151 different results were included for analysis. Data from 1901 exercise tests of individuals with type 1 diabetes with a mean age of 29 ± 4 years were included. Overall, exercise tests BG decreased by -3.1 mmol/L [-3.4; -2.8] within a mean duration of 46 ± 21 min. The pooled mean decrease in BG for running was -4.1 mmol/L [-4.7; -2.4], whilst the pooled mean decrease in BG for cycling was -2.7 mmol/L [-3.0; -2.4] (p < 0.0001). Overall results can be found in Table S2.

CONCLUSIONS

Running led to a larger decrease in BG in comparison to cycling. Active individuals with type 1 diabetes should be aware that current recommendations for glycaemic management need to be more specific to the mode of exercise.

Exercise in adults with type 1 diabetes mellitus

Regular physical activity improves cardiometabolic and musculoskeletal health, helps with weight management, improves cognitive and psychosocial functioning, and is associated with reduced mortality related to cancer and diabetes mellitus. However, turnover rates of glucose in the blood increase dramatically during exercise, which often results in either hypoglycaemia or hyperglycaemia as well as increased glycaemic variability in individuals with type 1 diabetes mellitus (T1DM). A complex neuroendocrine response to an acute exercise session helps to maintain circulating levels of glucose in a fairly tight range in healthy individuals, while several abnormal physiological processes and limitations of insulin therapy limit the capacity of people with T1DM to exercise in a normoglycaemic state. Knowledge of the acute and chronic effects of exercise and regular physical activity is critical for the formulation of clinical strategies for the management of insulin and nutrition for active patients with T1DM. Emerging diabetes-related technologies, such as continuous glucose monitors, automated insulin delivery systems and the administration of solubilized glucagon, are demonstrating efficacy for preserving glucose homeostasis during and after exercise in this population of patients. This Review highlights the beneficial effects of regular exercise and details the complex endocrine and metabolic responses to different types of exercise for adults with T1DM. An overview of basic clinical strategies for the preservation of glucose homeostasis using emerging technologies is also provided.

"How we do it": A qualitative study of strategies for adopting an exercise routine while living with type 1 diabetes

Introduction

For people living with type 1 diabetes (T1D) the challenge of increasing daily physical activity (PA) is compounded by the increased risks of hypoglycemia and glucose variability. Little information exists on the lived experience of overcoming these barriers and adopting and maintaining an active lifestyle while living with T1D.

Research Design and Methods

We conducted a patient-led qualitative study consisting of semi-structured interviews or focus groups with 22 individuals at least 16 years old living with T1D. We used existing patient co-researcher networks and snowball sampling to obtain a sample of individuals who reported being regularly physically active and had been diagnosed with T1D for at least one year. We used an interpretive description analysis to generate themes and strategies associated with maintaining an active lifestyle while living with T1D. We involved patient co-researchers in study design, data collection, and interpretation.

Results

14 self-identified women and 8 self-identified men (ages 19-62, median age 32 years) completed the study, led by either a researcher, or a patient co-researcher and research assistant regarding their strategies for maintaining an active lifestyle. We identified five themes that facilitate regular sustained PA: (1) Structure and organization are important to adopt safe PA in daily life "I can't do spontaneous exercise. I actually need a couple hours of warning minimum"; (2) Trial and error to learn how their body responds to PA and food "Once you put the time and effort into learning, you will have greater success"; (3) Psychosocial aspects of PA "…because it's not just your body, it's your soul, it's your mind that exercise is for"; (4) Diabetes technology and (5) Education and peer support. Strategies to overcome barriers included (1) Technology; (2) Integrating psychosocial facilitators; (3) Insulin and carbohydrate adjustments; and (4) Planning for exercise.

Conclusions

Living an active lifestyle with T1D is facilitated by dedicated structure and organization of routines, accepting the need for trial and error to understand the personalized glycemic responses to PA and careful use of food to prevent hypoglycemia. These themes could inform clinical practice guidelines or future trials that include PA interventions.

14. Children and Adolescents: Standards of Care in Diabetes-2023

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.