Exercise, hypoglycemia, and type 1 diabetes

Effect of Interrupting Prolonged Sitting with Frequent Activity Breaks on Postprandial Glycemia and Insulin Sensitivity in Adults with Type 1 Diabetes on Continuous Subcutaneous Insulin Infusion Therapy: A Randomized Crossover Pilot Trial

Objective: This study examined acute effects of interrupting prolonged sitting with short activity breaks on postprandial glucose/insulin responses and estimations of insulin sensitivity in adults with type 1 diabetes (T1D). Method: In a randomized crossover trial, eight adults (age = 46 ± 14 years [mean ± SD], body mass index [BMI] = 27.2 ± 3.8 kg/m2) receiving continuous subcutaneous insulin infusion (CSII) therapy completed two 6-h conditions as follows: uninterrupted sitting (SIT) and sitting interrupted with 3-min bouts of simple resistance activities (SRAs) every 30 min. Basal and bolus insulin were standardized across conditions except in cases of hypoglycemia. Postprandial responses were assessed using incremental area-under-the-curve (iAUC) and total AUC (tAUC) from half-hourly venous sampling. Meal-based insulin sensitivity determined from glucose sensor and insulin pump (SiSP) was assessed from flash continuous glucose monitor and insulin pump data. Outcomes were analyzed using mixed models adjusted for sex, BMI, treatment order, and preprandial values. Results: Glucose iAUC did not differ by condition (SIT: 19.8 ± 3.0 [estimated marginal means ± standard error] vs. SRA: 14.4 ± 3.0 mmol.6 h.L-1; P = 0.086). Despite CSII being standardized between conditions, insulin iAUC was higher in SRA compared to SIT (137.1 ± 22.7 vs. 170.9 ± 22.7 mU.6 h.L-1; P < 0.001). This resulted in a lower glucose response relative to the change in plasma insulin in SRA (tAUCglu/tAUCins: 0.32 ± 0.02 vs. 0.40 ± 0.02 mmol.mU-1; P = 0.03). SiSP was also higher at dinner following the SRA condition, with no between-condition differences at breakfast or lunch. Conclusion: Regularly interrupting prolonged sitting in T1D may increase plasma insulin and improve insulin sensitivity when meals and CSII are standardized. Future studies should explore underlying mechanistic determinants and the applicability of findings to those on multiple daily injections. Trial Registration: Australian and New Zealand Clinical Trial Registry Identifier-ACTRN12618000126213 (www.anzctr.org.au).

Barriers and Facilitators of Self-Management in Older People with Type 1 Diabetes: A Narrative Review Focusing on Cognitive Impairment

Over the past decades, life expectancy of people with type 1 diabetes has increased considerably, which brings potential challenges due to the process of aging. Cognitive aging and dementia, as well as reductions in visual acuity, hearing and dexterity, can influence the frequency and quality of daily self-management activities, including medication taking and insulin dosing, glucose self-monitoring, and healthy eating. This can increase the risk for hypo- and hyperglycemic events, which, in turn, may contribute to cognitive decline. Because there is a gap in understanding the barriers and facilitators of self-management in older adults with type 1 diabetes and the relationship to cognitive functioning, the authors 1) review the available literature on cognitive aging and type 1 diabetes, 2) describe what self-management in later adulthood entails and the cognitive functions required for effective self-management behaviors, 3) analyze the interaction between type 1 diabetes, cognition, aging, and self-management behaviors, and 4) describe the barriers and facilitators for self-management throughout the life span and how they may differ for older people. Potential evidence-based practices that could be developed for older adults with type 1 diabetes are discussed. There is need for further studies that clarify the impact of aging on T1D self-management, ultimately to improve diabetes care and quality of life.

Seasonal differences in physical activity, sedentary behaviour, and sleep patterns in people with type 1 diabetes in Kuwait

AIMS

The main aim of the current study was to measure physical activity, sedentary behaviors and sleep levels across the different seasons in people with type 1 diabetes in Kuwait.

METHODS

A prospective cross-sectional study was conducted from August 2021 to September 2022. Physical activity and sleep metrics were measured over a 7-day period with a wrist-worn accelerometer (GENEActiv). Overall physical activity was measured as a Euclidean Norm Minus One in milli gravitational units (mg). Accelerometer metrics were compared across the seasons and between the sex.

RESULTS

A total of 784 people with type 1 diabetes participated. Mean daily physical activity was 25.2 mg (SD = 7.3). Seasonal differences were seen in overall physical activity (p = 0.05), inactivity (p = 0.04), light activity (p = 0.001), the intensity gradient (p = 0.001) and sleep efficiency (p = 0.02). Poorer metrics were generally seen in Spring and Summer. Overall physical activity, moderate and vigorous physical activity, and inactivity were significantly higher in males compared to females (p ≤ 0.02). Females had a longer sleeping duration (p = 0.02), and higher sleep efficiency (p = 0.04) and light physical activity (p = 0.01). Overall physical activity and the intensity gradient were negatively associated with HbA1c (both p = 0.01).

CONCLUSIONS

Physical activity levels were generally low and sleep poor in people with type 1 diabetes in Kuwait and these varied by sex and season. The current data are useful to target and develop interventions to improve physical activity and glycemic control.

Emerging Diabetes Technologies: Continuous Glucose Monitors/Artificial Pancreases

Over the past decade there have been many advances in diabetes technologies, such as continuous glucose monitors (CGM s), insulin-delivery devices, and hybrid closed loop systems . Now most CGMs (Medtronic-Guardian, Dexcom-G6, and Abbott-Libre-2) have MARD values of < 10%, in contrast to two decades ago when the MARD used to be > 20%. In addition, the majority of the new CGMs do not require calibrations, and the latest CGMs last for 10-14 days. An implantable 6-months CGM by Eversense-3 is now approved in the USA and Europe. Recently, the FDA approved Libre 3 which provides real-time glucose values every minute. Even though it is approved as an iCGM it is not interoperable with automatic-insulin-delivery (AID) systems. The newer CGMs that are likely to be launched in the next few months in the USA include the 10-11 days Dexcom G7 (60% smaller than the existing G6), and the 7-days Medtronic Guardian 4. Most of the newer CGM have several features like automatic initialization, easy insertion, predictive alarms, and alerts. It has also been noticed that an arm insertion site might have better accuracy than abdomen or other sites, like the buttock for kids. Lag time between YSI and different sensors have been reported differently, sometimes it is down to 2-3 min; however, in many instances, it is still 15-20 min, especially when the rate of change of glucose is > 2 mg/min. We believe that in the next decade there will be a significant increase in the number of people who use CGM for their day-to-day diabetes care.

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.

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.

Effects of Aerobic Exercise on Systemic Insulin Degludec Concentrations in People with Type 1 Diabetes

BACKGROUND

There is conflicting evidence on the effect of exercise on systemic insulin concentrations in adults with type 1 diabetes.

METHODS

This prospective single-arm study examined the effect of exercise on systemic insulin degludec (IDeg) concentrations. The study involved 15 male adults with type 1 diabetes (age 30.7 ± 8.0 years, HbA1c 6.9 ± 0.7%) on stable IDeg regimen. Blood samples were collected every 15 minutes at rest, during 60 minutes of cycling (66% VO₂max) and until 90 minutes after exercise termination. IDeg concentrations were quantified using high-resolution mass-spectrometry and analyzed applying generalized estimation equations.

RESULTS

Compared to baseline, systemic IDeg increased during exercise over time (P < .001), with the highest concentrations observed toward the end of the 60-minute exercise (17.9% and 17.6% above baseline after 45 minutes and 60 minutes, respectively). IDeg levels remained elevated until the end of the experiment (14% above baseline at 90 minutes after exercise termination, P < .001).

CONCLUSIONS

A single bout of aerobic exercise increases systemic IDeg exposure in adults on a stable basal IDeg regimen. This finding may have important implications for future hypoglycemia mitigation strategies around physical exercise in IDeg-treated patients.

Prevalence of nocturnal hypoglycemia in free-living conditions in adults with type 1 diabetes: What is the impact of daily physical activity?

Objective

Studies investigating strategies to limit the risk of nocturnal hypoglycemia associated with physical activity (PA) are scarce and have been conducted in standardized, controlled conditions in people with type 1 diabetes (T1D). This study sought to investigate the effect of daily PA level on nocturnal glucose management in free-living conditions while taking into consideration reported mitigation strategies to limit the risk of nocturnal hyoglycemia in people with T1D.

Methods

Data from 25 adults (10 males, 15 females, HbA1c: 7.6 ± 0.8%), 20-60 years old, living with T1D, were collected. One week of continuous glucose monitoring and PA (assessed using an accelerometer) were collected in free-living conditions. Nocturnal glucose values (midnight-6:00 am) following an active day "ACT" and a less active day "L-ACT" were analyzed to assess the time spent within the different glycemic target zones (<3.9 mmol/L; 3.9 - 10.0 mmol/L and >10.0 mmol/L) between conditions. Self-reported data about mitigation strategies applied to reduce the risk of nocturnal hypoglycemia was also analyzed.

Results

Only 44% of participants reported applying a carbohydrate- or insulin-based strategy to limit the risk of nocturnal hypoglycemia on ACT day. Nocturnal hypoglycemia occurrences were comparable on ACT night versus on L-ACT night. Additional post-meal carbohydrate intake was higher on evenings following ACT (27.7 ± 15.6 g, ACT vs. 19.5 ± 11.0 g, L-ACT; P=0.045), but was frequently associated with an insulin bolus (70% of participants). Nocturnal hypoglycemia the night following ACT occurred mostly in people who administrated an additional insulin bolus before midnight (3 out of 5 participants with nocturnal hypoglycemia).

Conclusions

Although people with T1D seem to be aware of the increased risk of nocturnal hypoglycemia associated with PA, the risk associated with additional insulin boluses may not be as clear. Most participants did not report using compensation strategies to reduce the risk of PA related late-onset hypoglycemia which may be because they did not consider habitual PA as something requiring treatment adjustments.

Prevalence of impaired awareness of hypoglycemia and its risk factors among patients with type 1 diabetes in Saudi Arabia

BACKGROUND AND AIMS

To determine the prevalence of impaired awareness of hypoglycemia (IHA) and self-identification of symptoms in patients with type 1 diabetes (T1D).

METHODS

A cross-sectional study was conducted on 242 patients with T1D at the Diabetes Treatment Center, Prince Sultan Military Medical City (PSMMC), Riyadh, Saudi Arabia from May 2021 to September 2021. In addition to the demographic data, patients' HbA1c level was also collected. Awareness and symptoms of hypoglycemia were assessed using two validated questionnaire-based methods, namely the Gold and Edinburgh methods.

RESULTS

The prevalence of IAH among patients with T1D was 62.8% and the presence of IAH was significantly associated with the duration of T1D (p = 0.019). Compared to males, females had significantly higher (p < 0.05) levels of warmth, pounding heart, and inability to concentrate. Compared to unmarried, married patients had significantly higher levels of (p < 0.05) drowsiness, dizziness, and blurred vision. Similarly, compared to school educated, college-educated showed a higher hunger level (p < 0.05). Patients with HbA1c ≥ 7% possess a significantly higher level of drowsiness, dizziness, and hunger. Dizziness, warmth, difficulty speaking, pounding heart, and blurred vision were significantly higher among patients with diabetes duration ≥10 yrs. Nausea was significantly higher among smokers than non-smokers (p < 0.05).

CONCLUSION

The prevalence of IAH is high among patients with T1D in Saudi Arabia. Focused and evidence-based interventions are essential to minimize the hypoglycemia risk among patients with T1D.

Accuracy of Flash Glucose Monitoring During Postprandial Rest and Different Walking Conditions in Overweight or Obese Young Adults

Aims: To investigate the accuracy of FreeStyle Libre^TM flash glucose monitoring (FGM) relevant to plasma glucose (PG) measurements during postprandial rest and different walking conditions in overweight/obese young adults. Methods: Data of 40 overweight/obese participants from two randomized crossover studies were pooled into four trials: (1) sitting (SIT, n = 40); (2) walking continuously for 30 min initiated 20 min before individual postprandial glucose peak (PPGP) (20iP + CONT, n = 40); (3) walking continuously for 30 min initiated at PPGP (iP + CONT, n = 20); and (4) accumulated walking for 30 min initiated 20 min before PPGP (20iP + ACCU, n = 20). Paired FGM and PG were measured 4 h following breakfast. Results: The overall mean absolute relative difference (MARD) between PG and FGM readings was 16.4 ± 8.6% for SIT, 16.2 ± 4.7% for 20iP + CONT, 16.7 ± 12.2% for iP + CONT, and 19.1 ± 6.8% for 20iP + ACCU. The Bland-Altman analysis showed a bias of -1.03 mmol⋅L^-1 in SIT, -0.89 mmol⋅L^-1 in 20iP + CONT, -0.82 mmol⋅L^-1 in iP + CONT, and -1.23 mmol⋅L^-1 in 20iP + ACCU. The Clarke error grid analysis showed that 99.6-100% of the values in all trials fell within zones A and B. Conclusion: Although FGM readings underestimated PG, the FGM accuracy was overall clinically acceptable during postprandial rest and walking in overweight/obese young adults.

Can Resistance Exercise Be a Tool for Healthy Aging in Post-Menopausal Women with Type 1 Diabetes?

Due to improvements in diabetes care, people with type 1 diabetes (T1D) are living longer. Studies show that post-menopausal T1D women have a substantially elevated cardiovascular risk compared to those without T1D. As T1D may also accelerate age-related bone and muscle loss, the risk of frailty may be considerable for T1D women. Exercise and physical activity may be optimal preventative therapies to maintain health and prevent complications in this population: They are associated with improvements in, or maintenance of, cardiovascular health, bone mineral density, and muscle mass in older adults. Resistance exercise, in particular, may provide important protection against age-related frailty, due to its specific effects on bone and muscle. Fear of hypoglycemia can be a barrier to exercise in those with T1D, and resistance exercise may cause less hypoglycemia than aerobic exercise. There are currently no exercise studies involving older, post-menopausal women with T1D. As such, it is unknown whether current guidelines for insulin adjustment/carbohydrate intake for activity are appropriate for this population. This review focuses on existing knowledge about exercise in older adults and considers potential future directions around resistance exercise as a therapeutic intervention for post-menopausal T1D women.

Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions

This paper introduces methods to estimate aspects of physical activity and sedentary behavior from three-axis accelerometer data collected with a wrist-worn device at a sampling rate of 32 [Hz] on adults with type 1 diabetes (T1D) in free-living conditions. In particular, we present two methods able to detect and grade activity based on its intensity and individual fitness as sedentary, mild, moderate or vigorous, and a method that performs activity classification in a supervised learning framework to predict specific user behaviors. Population results for activity level grading show multi-class average accuracy of 99.99%, precision of 98.0 ± 2.2%, recall of 97.9 ± 3.5% and F1 score of 0.9 ± 0.0. As for the specific behavior prediction, our best performing classifier, gave population multi-class average accuracy of 92.43 ± 10.32%, precision of 92.94 ± 9.80%, recall of 92.20 ± 10.16% and F1 score of 92.56 ± 9.94%. Our investigation showed that physical activity and sedentary behavior can be detected, graded and classified with good accuracy and precision from three-axial accelerometer data collected in free-living conditions on people with T1D. This is particularly significant in the context of automated glucose control systems for diabetes, in that the methods we propose have the potential to inform changes in treatment parameters in response to the intensity of physical activity, allowing patients to meet their glycemic targets.

Minimizing the Risk of Exercise-Induced Glucose Fluctuations in People Living With Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion: An Overview of Strategies

Physical activity (PA) is important for individuals living with type 1 diabetes (T1D) due to its various health benefits. Nonetheless, maintaining adequate glycemic control around PA remains a challenge for many individuals living with T1D because of the difficulty in properly managing circulating insulin levels around PA. Although the most common problem is increased incidence of hypoglycemia during and after most types of PA, hyperglycemia can also occur. Accordingly, a large proportion of people living with T1D are sedentary partly due to the fear of PA-associated hypoglycemia. Continuous subcutaneous insulin infusion (CSII) offers a higher precision and flexibility to adjust insulin basal rates and boluses according to the individual's specific needs around PA practice. Indeed, for physically active patients with T1D, CSII can be a preferred option to facilitate glucose regulation. To our knowledge, there are no guidelines to manage exercise-induced hypoglycemia during PA, specifically for individuals living with T1D and using CSII. In this review, we highlight the current state of knowledge on exercise-related glucose variations, especially hypoglycemic risk and its underlying physiology. We also detail the current recommendations for insulin modulations according to the different PA modalities (type, intensity, duration, frequency) in individuals living with T1D using CSII.

Accuracy of the Dexcom G6 Glucose Sensor during Aerobic, Resistance, and Interval Exercise in Adults with Type 1 Diabetes

The accuracy of continuous glucose monitoring (CGM) sensors may be significantly impacted by exercise. We evaluated the impact of three different types of exercise on the accuracy of the Dexcom G6 sensor. Twenty-four adults with type 1 diabetes on multiple daily injections wore a G6 sensor. Participants were randomized to aerobic, resistance, or high intensity interval training (HIIT) exercise. Each participant completed two in-clinic 30-min exercise sessions. The sensors were applied on average 5.3 days prior to the in-clinic visits (range 0.6–9.9). Capillary blood glucose (CBG) measurements with a Contour Next meter were performed before and after exercise as well as every 10 min during exercise. No CGM calibrations were performed. The median absolute relative difference (MARD) and median relative difference (MRD) of the CGM as compared with the reference CBG did not differ significantly from the start of exercise to the end exercise across all exercise types (ranges for aerobic MARD: 8.9 to 13.9% and MRD: −6.4 to 0.5%, resistance MARD: 7.7 to 14.5% and MRD: −8.3 to −2.9%, HIIT MARD: 12.1 to 16.8% and MRD: −14.3 to −9.1%). The accuracy of the no-calibration Dexcom G6 CGM was not significantly impacted by aerobic, resistance, or HIIT exercise.

In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus

In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively administering insulin is an inherently continuous task. Policy gradient algorithms are known to be superior in continuous high-dimensional control tasks. Previously, most of the approaches for automated blood glucose control using reinforcement learning has used a finite set of actions. We use the Trust-Region Policy Optimization algorithm in this work. It represents the state of the art for deep policy gradient algorithms. The experiments are carried out in-silico using the Hovorka model, and stochastic behavior is modeled through simulated carbohydrate counting errors to illustrate the full potential of the framework. Furthermore, we use a model-free approach where no prior information about the patient is given to the algorithm. Our experiments show that the reinforcement learning agent is able to compete with and sometimes outperform state-of-the-art model predictive control in blood glucose regulation.

A Prospective, Randomized Trial Testing Different Regimens of Carbohydrate Administration to Prevent Major Reduction in Plasma Glucose Follwing a Standardized Bout of Moderate Physical Activity in Patients with Type 1 Diabetes

AIM/HYPOTHESIS

It was the aim to prospectively study regimes of "preventive" carbohydrate administration to avoid major reduction in plasma glucose during physical activity.

METHODS

24 patients with type 1 diabetes (age 41±12 years; 11 women, 13 men; BMI 26.5±4.7 kg/m²; HbA_1c 9.1±1.5%; insulin dose 0.64±0.22 IU/kg body weight and day) participated in one experiment without physical activity and in three experiments with a 4 km, 60 min hike starting at 2 p.m.. No "preventive" carbohydrates, 2×10 g or 2×20 g carbohydrates (muesli bars) were taken when starting and after 30 min (randomized order). Plasma glucose was determined.

RESULTS

Within 30 min after starting physical activity, plasma glucose fell by approximately 70 mg/dl, making additional carbohydrate intake necessary in 70% of the subjects. This drop was not prevented by any regimens of "preventive" carbohydrate intake. After the nadir, plasma glucose rose faster after the 2×20 g carbohydrate regime (the largest amount tested; p=0.0036). With "preventive" administration of carbohydrates, significantly (p<0.05) less additional "therapeutic" carbohydrates needed to be administered in 6 h following the initiation of the hike.

CONCLUSIONS/INTERPRETATION

In conclusion, in the setting of 2 h postprandial exercise in type 1 diabetes, preventive carbohydrate supplementation alone will not completely eliminate the risk of brisk falls in plasma glucose concentrations or hypoglycaemic episodes. Else, higher amounts or repeated administration of carbohydrates may be necessary.

Impact of Self-Reported Sitting Time and Transtheoretical Model Based on Exercise Behavior Change on Glycemic and Weight Control in Japanese Adults with Type 1 Diabetes: A Cross-Sectional Study

This cross-sectional study aimed to examine the associations among self-reported sitting time (ST), transtheoretical model (TTM) based on exercise behavior change, and glycemic and weight control in Japanese adults with type 1 diabetes (T1D). Forty-two adults (age, 44.0 (33.3–56.8) years) with uncomplicated T1D answered questions regarding their lifestyles, including ST per day, and TTM using self-administered questionnaires. The glycated hemoglobin (HbA1c) level correlated with age and ST (p < 0.05, p < 0.01, respectively), whereas body mass index correlated with duration of T1D and TTM (p < 0.05, p < 0.01, respectively). Logistic regression analysis showed that poor glycemic control (HbA1c, >7%) was associated with ST (odds ratio, 3.53 (95% confidence interval, 1.54–8.11), p < 0.01). In addition, the cut-off points for quartiles of ST were 4.6, 6.0, and 8.0 h/day, and the HbA1c level in the lowest quartile was 15% lower than that in the highest quartile (p < 0.01). Although further studies with larger samples are needed, these results implied that expanded self-reported ST might be related to poor glycemic control in Japanese T1D adults, most of whom were lean, young and middle-aged, regardless of TTM based on exercise behavior change.

Control-Oriented Model With Intra-Patient Variations for an Artificial Pancreas

In this work, a low-order model designed for glucose regulation in Type 1 Diabetes Mellitus (T1DM) is obtained from the UVA/Padova metabolic simulator. It captures not only the nonlinear behavior of the glucose-insulin system, but also intra-patient variations related to daily insulin sensitivity ( S_I) changes. To overcome the large inter-subject variability, the model can also be personalized based on a priori patient information. The structure is amenable for linear parameter varying (LPV) controller design, and represents the dynamics from the subcutaneous insulin input to the subcutaneous glucose output. The efficacy of this model is evaluated in comparison with a previous control-oriented model which in turn is an improvement of previous models. Both models are compared in terms of their open- and closed-loop differences with respect to the UVA/Padova model. The proposed model outperforms previous T1DM control-oriented models, which could potentially lead to more robust and reliable controllers for glycemia regulation.

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

BACKGROUND

Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia.

METHODS

A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes.

RESULTS

In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68).

CONCLUSION

An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.

Impaired awareness of hypoglycemia in children and adolescents with type 1 diabetes mellitus in north of Jordan

BACKGROUND

Hypoglycemia is a common complication of insulin therapy in patients with Type 1 Diabetes Mellitus (DM). Awareness of hypoglycemic symptoms helps patients to recognize hypoglycemia and initiate self-treatment. Impaired Awareness of Hypoglycemia (IAH) exposes patients to severe hypoglycemia, which could be associated with seizures and unconsciousness. This study aimed to assess IAH, frequency of hypoglycemia, severe hypoglycemia and intensity of hypoglycemic symptoms among children and adolescents with Type 1 DM in North of Jordan.

METHODS

Data were collected from 94 children and adolescents with Type 1 DM. Clarke's and Edinburgh surveys were used to assess IAH and individual symptoms of hypoglycemia, respectively. Frequency of hypoglycemia and other related information were obtained by self-reporting or from medical records.

RESULTS

16.0% of participants were having IAH, 66.0% of participants reported recurrent hypoglycemia (>once/month) and 18.0% of participants developed ≥1 severe hypoglycemia during the previous year. IAH was not associated with age, gender, duration of DM, HbA1c, insulin regimen, adherence to insulin or development of severe hypoglycemia (p-values> 0.05). Instead, IAH was associated with frequency of hypoglycemia during the previous 6 months (p-value< 0.01). Hunger, tiredness, dizziness, drowsiness, inability to concentrate, trembling and weakness were the most common symptoms felt by participants when they develop hypoglycemia. Hunger was the only common symptom that was significantly higher in children compared to adolescent (p-value < 0.01).

CONCLUSIONS

This study has reported low prevalence of IAH in children and adolescents with Type 1 DM in North of Jordan. IAH was more common in subjects with more frequent hypoglycemia.

Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series

Increasingly large observational datasets from healthcare and social media may allow new types of causal inference. However, these data are often missing key variables, increasing the chance of finding spurious causal relationships due to confounding. While methods exist for causal inference with latent variables in static cases, temporal relationships are more challenging, as varying time lags make latent causes more difficult to uncover and approaches often have significantly higher computational complexity. To address this, we make the key observation that while a variable may be latent in one dataset, it may be observed in another, or we may have domain knowledge about its effects. We propose a computationally efficient method that overcomes latent variables by using prior knowledge to reconstruct data for unobserved variables, while remaining robust to cases when the knowledge is wrong or does not apply. On simulated data, our approach outperforms the state of the art with a lower false discovery rate for causal inference. On real-world data from individuals with Type 1 diabetes, we show that our approach can discover causal relationships involving unmeasured meals and exercise.

Enablers and barriers to effective diabetes self-management: A multi-national investigation

OBJECTIVE

The study aimed to identify the common gaps in skills and self-efficacy for diabetes self-management and explore other factors which serve as enablers of, and barriers to, achieving optimal diabetes self-management. The information gathered could provide health professionals with valuable insights to achieving better health outcomes with self-management education and support for diabetes patients.

METHODS

International online survey and telephone interviews were conducted on adults who have type 1 or type 2 diabetes. The survey inquired about their skills and self-efficacy in diabetes self-management, while the interviews assessed other enablers of, and barriers to, diabetes self-management. Surveys were analysed using descriptive and inferential statistics. Interviews were analysed using inductive thematic analysis.

RESULTS

Survey participants (N = 217) had type 1 diabetes (38.2%) or type 2 diabetes (61.8%), with a mean age of 44.56 SD 11.51 and were from 4 continents (Europe, Australia, Asia, America). Identified gaps in diabetes self-management skills included the ability to: recognize and manage the impact of stress on diabetes, exercise planning to avoid hypoglycemia and interpreting blood glucose pattern levels. Self-efficacy for healthy coping with stress and adjusting medications or food intake to reach ideal blood glucose levels were minimal. Sixteen participants were interviewed. Common enablers of diabetes self-management included: (i) the will to prevent the development of diabetes complications and (ii) the use of technological devices. Issues regarding: (i) frustration due to dynamic and chronic nature of diabetes (ii) financial constraints (iii) unrealistic expectations and (iv) work and environment-related factors limited patients' effective self-management of diabetes.

CONCLUSIONS

Educational reinforcement using technological devices such as mobile application has been highlighted as an enabler of diabetes self-management and it could be employed as an intervention to alleviate identified gaps in diabetes self-management. Furthermore, improved approaches that address financial burden, work and environment-related factors as well as diabetes distress are essential for enhancing diabetes self-management.

Outcomes of type 1 diabetes mellitus in pregnancy; effect of excessive gestational weight gain and hyperglycaemia on fetal growth

AIMS

To study pregnancy outcomes in patients with type 1 diabetes mellitus (T1DM) and the factors associated with poor outcomes.

METHODS

A retrospective study of 110 patients with T2DM who attended our diabetes in pregnancy clinic at the Women's Wellness and Research centre, Doha, between March 2015 and December 2016 and 1419 normoglycaemic controls.

RESULTS

There was no difference in age, weight, and BMI between the two groups. The incidence of macrosomia, shoulder dystocia and stillbirth were similar in the two groups while that of pre-term labour, pre-eclampsia, Caesarean section (CS), large for gestational age (LGA), neonatal ICU (NICU) admission and neonatal hypoglycaemia were significantly higher in the T1DM than in the control group. From a multivariate regression analysis, excessive gestational weight gain was associated with increased risk of LGA (OR 4.53; 95% CI [1.42-14.25]). Last trimester HBA1c was associated with increased risk for macrosomia [OR 2.46, 95% CI [1.03-5.86)]; LGA [ OR 3.25, 95% CI [1.65-6.40)]; increased risk for C-section (OR 1.96, 95% CI [1.12-3.45]), and increased risk of NICU admission (OR 2.46, 95% CI [1.04-5.86]). The changes in HBA1C between the first and last trimester HBA1c was associated with a reduction in the risk of LGA [OR 0.46, 95% CI [(0.28-0.75)] CONCLUSION: T1DM in pregnancy is associated with adverse pregnancy outcomes compared to the general population. Reducing gestational weight gain and improving glycaemic control might improve pregnancy outcomes.

Modeling the acute effects of exercise on insulin kinetics in type 1 diabetes

Our objective is to develop a physiology-based model of insulin kinetics to understand how exercise alters insulin concentrations in those with type 1 diabetes (T1D). We reveal the relationship between the insulin absorption rate ([Formula: see text]) from subcutaneous tissue, the insulin delivery rate ([Formula: see text]) to skeletal muscle, and two physiological parameters that characterize the tissue: the perfusion rate (Q) and the capillary permeability surface area (PS), both of which increase during exercise because of capillary recruitment. We compare model predictions to experimental observations from two pump-wearing T1D cohorts [resting subjects ([Formula: see text]) and exercising subjects ([Formula: see text])] who were each given a mixed-meal tolerance test and a bolus of insulin. Using independently measured values of Q and PS from literature, the model predicts that during exercise insulin concentration increases by 30% in plasma and by 60% in skeletal muscle. Predictions reasonably agree with experimental observations from the two cohorts, without the need for parameter estimation by curve fitting. The insulin kinetics model suggests that the increase in surface area associated with exercise-induced capillary recruitment significantly increases [Formula: see text] and [Formula: see text], which explains why insulin concentrations in plasma and skeletal muscle increase during exercise, ultimately enhancing insulin-dependent glucose uptake. Preventing hypoglycemia is of paramount importance in determining the proper insulin dose during exercise. The presented model provides mechanistic insight into how exercise affects insulin kinetics, which could be useful in guiding the design of decision support systems and artificial pancreas control algorithms.

Management of diabetes mellitus in children and adolescents: engaging in physical activity

Regular physical activity is an important component in the management of both type 1 and type 2 diabetes mellitus (T1DM and T2DM), as it has the potential to improve glycemic control, delay cardiovascular complications, and increase overall well-being. Unfortunately, many children and adolescents with diabetes do not partake in regular exercise and physical activity for multiple reasons. This review identifies the barriers to participation from the aspect of the patient, caregiver, and the healthcare provider. The management of physical activity of children and adolescents with diabetes mellitus is unique and requires an understanding of exercise physiology and how it differs in these children and adolescents from those without the condition. These individuals are at risk for important and potentially life threatening complications including, but not limited to, severe or delayed nocturnal hypoglycemia. It is essential to identify these risks as well as, monitor and manage adjustments to carbohydrate intake and insulin dosing through basal-bolus regimen or insulin pump adjustments appropriately before, during, and after the exercise activity. This review discusses these issues and also outlines differences in management between patients with T1DM and T2DM.

Exploration of the Performance of a Hybrid Closed Loop Insulin Delivery Algorithm That Includes Insulin Delivery Limits Designed to Protect Against Hypoglycemia

BACKGROUND

Hypoglycemia remains a risk for closed loop insulin delivery particularly following exercise or if the glucose sensor is inaccurate. The aim of this study was to test whether an algorithm that includes a limit to insulin delivery is effective at protecting against hypoglycemia under those circumstances.

METHODS

An observational study on 8 participants with type 1 diabetes was conducted, where a hybrid closed loop system (HCL) (Medtronic™ 670G) was challenged with hypoglycemic stimuli: exercise and an overreading glucose sensor.

RESULTS

There was no overnight or exercise-induced hypoglycemia during HCL insulin delivery. All daytime hypoglycemia was attributable to postmeal bolused insulin in those participants with a more aggressive carbohydrate factor.

CONCLUSION

HCL systems rely on accurate carbohydrate ratios and carbohydrate counting to avoid hypoglycemia. The algorithm that was tested against moderate exercise and an overreading glucose sensor performed well in terms of hypoglycemia avoidance. Algorithm refinement continues in preparation for long-term outpatient trials.

Strategies used by Patients with Type 1 Diabetes to Avoid Hypoglycemia in a 24×1-Hour Marathon: Comparison with the Amounts of Carbohydrates Estimated by a Customizable Algorithm

OBJECTIVES

The preferred countermeasure to avoid exercise-related hypoglycemia was investigated in a group of patients with type 1 diabetes participating in a stressful event, a 24×1-hour relay marathon. The carbohydrates actually consumed were compared to those estimated for each patient by applying a customizable algorithm, Exercise Carbohydrates Requirement Estimating Software (ECRES), based on patient's usual therapy and diet and on the exercise characteristics.

METHODS

Glycemia was tested at the start, middle and end of the races. Usual therapies and diets and the adopted countermeasures were recorded in detail.

RESULTS

We studied 19 patients who walked/ran 10.4±2.8 km with a heart rate of 167±11 beats per minute. Of the 19 patients, 7 patients reduced the administered insulin (premeal bolus or basal infusion rate). Glycemia fell by the end of the races (p=0.006; median -1.8 mmol⋅L^-1; interquartile range -0.4 mmol⋅L^-1 to -5.3 mmol⋅L^-1), despite 9 patients being hyperglycemic at the start. Of the patients, 14 concluded the race with glycemia on target, and 4 patients were hyperglycemic. Amounts of carbohydrates actually consumed (median 30 g; interquartile range 0 g to 71 g) were not significantly different from those estimated by ECRES (median 38 g; interquartile range 24 g to 68 g), the 2 quantities being significantly related (R=0.64; p=0.003). ECRES estimated lower carbohydrate levels (-13 g) than the amounts actually consumed by the 4 patients who concluded their exercises with hyperglycemia.

CONCLUSIONS

Patients preferred to consume extra carbohydrates to avoid the possible exercise-induced hypoglycemia. ECRES would provide satisfactory estimates of the carbohydrate requirements, even for a stressful condition, and almost equal to the quantities consumed following medical advice.

Randomized trial of a dual-hormone artificial pancreas with dosing adjustment during exercise compared with no adjustment and sensor-augmented pump therapy

AIMS

To test whether adjusting insulin and glucagon in response to exercise within a dual-hormone artificial pancreas (AP) reduces exercise-related hypoglycaemia.

MATERIALS AND METHODS

In random order, 21 adults with type 1 diabetes (T1D) underwent three 22-hour experimental sessions: AP with exercise dosing adjustment (APX); AP with no exercise dosing adjustment (APN); and sensor-augmented pump (SAP) therapy. After an overnight stay and 2 hours after breakfast, participants exercised for 45 minutes at 60% of their maximum heart rate, with no snack given before exercise. During APX, insulin was decreased and glucagon was increased at exercise onset, while during SAP therapy, subjects could adjust dosing before exercise. The two primary outcomes were percentage of time spent in hypoglycaemia (<3.9 mmol/L) and percentage of time spent in euglycaemia (3.9-10 mmol/L) from the start of exercise to the end of the study.

RESULTS

The mean (95% confidence interval) times spent in hypoglycaemia (<3.9 mmol/L) after the start of exercise were 0.3% (-0.1, 0.7) for APX, 3.1% (0.8, 5.3) for APN, and 0.8% (0.1, 1.4) for SAP therapy. There was an absolute difference of 2.8% less time spent in hypoglycaemia for APX versus APN (p = .001) and 0.5% less time spent in hypoglycaemia for APX versus SAP therapy (p = .16). Mean time spent in euglycaemia was similar across the different sessions.

CONCLUSIONS

Adjusting insulin and glucagon delivery at exercise onset within a dual-hormone AP significantly reduces hypoglycaemia compared with no adjustment and performs similarly to SAP therapy when insulin is adjusted before exercise.

Glucagon responses to exercise-induced hypoglycaemia are improved by somatostatin receptor type 2 antagonism in a rat model of diabetes

AIMS/HYPOTHESIS

Regular exercise is at the cornerstone of care in type 1 diabetes. However, relative hyperinsulinaemia and a blunted glucagon response to exercise promote hypoglycaemia. Recently, a selective antagonist of somatostatin receptor 2, PRL-2903, was shown to improve glucagon counterregulation to hypoglycaemia in resting streptozotocin-induced diabetic rats. The aim of this study was to test the efficacy of PRL-2903 in enhancing glucagon counterregulation during repeated hyperinsulinaemic exercise.

METHODS

Diabetic rats performed daily exercise for 1 week and were then exposed to saline (154 mmol/l NaCl) or PRL-2903, 10 mg/kg, before hyperinsulinaemic exercise on two separate occasions spaced 1 day apart. In the following week, animals crossed over to the alternate treatment for a third hyperinsulinaemic exercise protocol.

RESULTS

Liver glycogen content was lower in diabetic rats compared with control rats, despite daily insulin therapy (p < 0.05). Glucagon levels failed to increase during exercise with saline but increased three-to-six fold with PRL-2903 (all p < 0.05). Glucose concentrations tended to be higher during exercise and early recovery with PRL-2903 on both days of treatment; this difference did not achieve statistical significance (p > 0.05).

CONCLUSIONS/INTERPRETATION

PRL-2903 improves glucagon counterregulation during exercise. However, liver glycogen stores or other factors limit the prevention of exercise-induced hypoglycaemia in rats with streptozotocin-induced diabetes.

Incorporating an Exercise Detection, Grading, and Hormone Dosing Algorithm Into the Artificial Pancreas Using Accelerometry and Heart Rate

In this article, we present several important contributions necessary for enabling an artificial endocrine pancreas (AP) system to better respond to exercise events. First, we show how exercise can be automatically detected using body-worn accelerometer and heart rate sensors. During a 22 hour overnight inpatient study, 13 subjects with type 1 diabetes wearing a Zephyr accelerometer and heart rate monitor underwent 45 minutes of mild aerobic treadmill exercise while controlling their glucose levels using sensor-augmented pump therapy. We used the accelerometer and heart rate as inputs into a validated regression model. Using this model, we were able to detect the exercise event with a sensitivity of 97.2% and a specificity of 99.5%. Second, from this same study, we show how patients' glucose declined during the exercise event and we present results from in silico modeling that demonstrate how including an exercise model in the glucoregulatory model improves the estimation of the drop in glucose during exercise. Last, we present an exercise dosing adjustment algorithm and describe parameter tuning and performance using an in silico glucoregulatory model during an exercise event.

Exercise-associated glucose metabolism in individuals with type 1 diabetes mellitus

PURPOSE OF REVIEW

The primary focus of this review is threefold: first, to summarize available knowledge on exercise-associated glucose metabolism in individuals with type 1 diabetes mellitus (T1DM); second, to elucidate physiological mechanisms predisposing to glycemic variations in patients in T1DM; and third, to describe novel approaches derived from physiological perceptions applicable to stabilize exercise-related glycemia in individuals with T1DM.

RECENT FINDINGS

Recent studies corroborate the concept that despite partial differences in counter-regulatory mechanisms individuals with T1DM do not fundamentally differ in their glucose response to exercise when compared with healthy individuals if studies are performed under standardized conditions with insulin and glucose levels held close to physiological ranges. Novel approaches derived from a better understanding of exercise-associated glucose metabolism (e.g., the concept of intermittent high-intensity exercise) may provide alternative ways to master the challenges imposed by exercise to individuals with T1DM.

SUMMARY

Exercise still imposes high demands on patients with T1DM and increases risks for hypoglycemia and hyperglycemia. Deeper insight into the associated metabolic pathways has revealed novel options to stabilize exercise-associated glucose levels in these patients.