More Time in Glucose Range During Exercise Days than Sedentary Days in Adults Living with Type 1 Diabetes

Proposed Practical Guidelines to Improve Glycaemic Management by Reducing Glycaemic Variability in People with Type 1 Diabetes Mellitus

INTRODUCTION

For decades, glycaemic variability (GV) was ignored in clinical practice because its precise assessment was challenging and there were no specific recommendations to reduce it. However, the current widespread use of continuous glucose monitoring (CGM) systems has changed this situation. Associations between high GV and risk of hypoglycaemia, onset of macro- and microvascular complications and mortality have been described in type 1 diabetes (T1D). It is therefore important to identify the causes of excessive glycaemic excursions and make recommendations for people with T1D to achieve better glycaemic management by minimising GV in both the short term and the long term.

METHODS

To achieve these aims, a panel comprising four endocrinologists, one diabetes nurse educator and one nutritionist worked together to reach a consensus on the detection of triggers of GV and propose clinical guidelines to reduce GV and improve glycaemic management by reducing the risk of hypoglycaemias.

RESULTS AND CONCLUSIONS

In total, four different areas of interest were identified, in which the insufficient education and/or training of people with T1D could lead to higher GV: physical activity; dietary habits; insulin therapy, especially when pump-based systems are not used; and other causes of GV increase. Practical, easy-to-follow recommendations to reduce GV in daily activities were then issued, with the aim of enabling people with T1D to reduce either hypoglycaemia or hyperglycaemia episodes. By doing this, their quality of life may be improved, and progression of chronic complications may be prevented or delayed.

Online Classification of Unstructured Free-Living Exercise Sessions in People with Type 1 Diabetes

Background: Managing exercise in type 1 diabetes is challenging, in part, because different types of exercises can have diverging effects on glycemia. The aim of this work was to develop a classification model that can classify an exercise event (structured or unstructured) as aerobic, interval, or resistance for the purpose of incorporation into an automated insulin delivery (AID) system. Methods: A long short-term memory network model was developed with real-world data from 30-min structured sessions of at-home exercise (aerobic, resistance, or mixed) using triaxial accelerometer, heart rate, and activity duration information. The detection algorithm was used to classify 15 common free-living and unstructured activities and relate each to exercise-associated change in glucose. Results: A total of 1610 structured exercise sessions were used to train, validate, and test the model. The accuracy for the structured exercise sessions in the testing set was 72% for aerobic, 65% for interval, and 77% for resistance. In addition, we tested the classifier on 3328 unstructured sessions. We validated the session-associated change in glucose against the expected change during exercise for each type. Mean and standard deviation of the change in glucose of -20.8 (40.3) mg/dL were achieved for sessions classified as aerobic, -16.2 (39.0) mg/dL for sessions classified as interval, and -11.6 (38.8) mg/dL for sessions classified as resistance. Conclusions: The proposed algorithm reliably identified physical activity associated with expected change in glucose, which could be integrated into an AID system to manage the exercise disturbance in glycemia according to the predicted class.

The effect of physical activity on glycaemic control in people with type 1 diabetes mellitus: A systematic literature review and meta-analysis

AIMS

Type 1 diabetes mellitus (T1DM) is characterised by insulin deficiency. Due to perceived physical activity (PA)-related hypoglycaemia, a minority of people with T1DM exercise regularly. However, the relationship between T1DM and PA remains poorly understood. Our aim was to summarise the existing literature on the effects of PA on short-term glucose control (glycated haemoglobin or time in range) in people with T1DM.

METHODS

We searched seven electronic databases (PubMed, Embase, Cochrane library, Cinahl, SPORTDiscus, PEDro and Web Of Science) and two sources of the grey literature (ClinicalTrials.gov and ICTRP). All reviews were screened via title/abstract and full text by two independent reviewers (LE and HT), conflicts were solved by a third independent reviewer (DDC). We excluded animal studies, case reports, non-English articles, qualitative studies, conference abstracts and articles without full-text access. A meta-analysis using random effects model was performed to study the effect of PA on haemoglobin A1c (HbA1c) levels in people with T1DM.

RESULTS

We obtained 19,201 unique references across nine different electronic databases. After screening and snowballing, 68 articles were found investigating the effect of PA on glycaemic control in people with T1DM. Overall, HbA1c levels in the PA group (mean difference = 0.29% (0.20%-0.39%)), were lower compared with the control group.

CONCLUSION

An overall small beneficial effect of PA on glycaemic control in people with T1DM was found. Caution is advised when interpreting the results of this meta-analysis, given variations in study type, duration, frequency and intensity of physical activity across included studies.

Factors Influencing Physical Activity Level in Adults With Type 1 Diabetes: A Cross-Sectional Study

OBJECTIVES

Exercise is a recommended component of type 1 diabetes (T1D) treatment because high physical activity levels improve health outcomes. However, many people with T1D do not meet physical activity recommendations. Our aim in this study was to identify factors influencing physical activity levels in people with T1D.

METHODS

This questionnaire-based study included adults with T1D from 1 outpatient clinic in the United Kingdom and 2 clinics in Denmark. Exercise characteristics, motivators, and barriers were assessed. Physical activity level was measured using the Saltin-Grimby Physical Activity Level Scale. Respondents were categorized into 3 activity groups: inactive, light active, and moderate-to-vigorous active.

RESULTS

Of the 332 respondents, 8.4% rated themselves as inactive, 48% as light active, and 43% as moderate-to-vigorous active. Seventy-eight percent of inactive and light active repondents expressed a desire to become more physically active. Fifty-three percent of respondents had received guidance concerning exercise/physical activity from their diabetes team. Being male and having received guidance were associated with a higher physical activity level. The major motivators for exercising/being physically active were improved mental and physical health and glycemic management, whereas the most frequent barriers were busyness with work/private life and lack of motivation. Worries about glucose excursions, costs, lack of knowledge, and health-related reasons were more prevalent barriers in the least active groups.

CONCLUSIONS

This study demonstrated that 78% of inactive and light active respondents reported wishing to become more physically active. Receiving guidance about exercise/physical activity was associated with a higher physical activity level, but only 53% of respondents had received support from their diabetes team.

Design of a Real-Time Physical Activity Detection and Classification Framework for Individuals With Type 1 Diabetes

BACKGROUND

Managing glycemia during and after exercise events in type 1 diabetes (T1D) is challenging since these events can have wide-ranging effects on glycemia depending on the event timing, type, intensity. To this end, advanced physical activity-informed technologies can be beneficial for improving glucose control.

METHODS

We propose a real-time physical activity detection and classification framework, which builds upon random forest models. This module automatically detects exercise sessions and predicts the activity type and intensity from tri-axial accelerometer, heart rate, and continuous glucose monitoring records.

RESULTS

Data from 19 adults with T1D who performed structured sessions of either aerobic, resistance, or high-intensity interval exercise at varying times of day were used to train and test this framework. The exercise onset and completion were both predicted within 1 minute with an average accuracy of 81% and 78%, respectively. Activity type and intensity were identified within 2.38 minutes and from the exercise onset. On participants assigned to the test set, the average accuracy for activity type and intensity classification was 74% and 73%, respectively, if exercise was announced. For unannounced exercise events, the classification accuracy was 65% for the activity type and 70% for its intensity.

CONCLUSIONS

The proposed module showed high performance in detection and classification of exercise in real-time within a minute of exercise onset. Integration of this module into insulin therapy decisions can help facilitate glucose management around physical activity.

Analyzing Diabetes Detection and Classification: A Bibliometric Review (2000-2023)

Bibliometric analysis is a rigorous method to analyze significant quantities of bibliometric data to assess their impact on a particular field. This study used bibliometric analysis to investigate the academic research on diabetes detection and classification from 2000 to 2023. The PRISMA 2020 framework was followed to identify, filter, and select relevant papers. This study used the Web of Science database to determine relevant publications concerning diabetes detection and classification using the keywords "diabetes detection", "diabetes classification", and "diabetes detection and classification". A total of 863 publications were selected for analysis. The research applied two bibliometric techniques: performance analysis and science mapping. Various bibliometric parameters, including publication analysis, trend analysis, citation analysis, and networking analysis, were used to assess the performance of these articles. The analysis findings showed that India, China, and the United States are the top three countries with the highest number of publications and citations on diabetes detection and classification. The most frequently used keywords are machine learning, diabetic retinopathy, and deep learning. Additionally, the study identified "classification", "diagnosis", and "validation" as the prevailing topics for diabetes identification. This research contributes valuable insights into the academic landscape of diabetes detection and classification.

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 ).

Practical considerations for continuous glucose monitoring in elite athletes with type 1 diabetes mellitus: A narrative review

Type 1 diabetes mellitus (T1DM) refers to a metabolic condition where a lack of insulin impairs the usual homeostatic mechanisms to control blood glucose levels. Historically, participation in competitive sport has posed a challenge for those with T1DM, where the dynamic changes in blood glucose during exercise can result in dangerously high (hyperglycaemia) or low blood glucoses (hypoglycaemia) levels. Over the last decade, research and technological development has enhanced the methods of monitoring and managing blood glucose levels, thus reducing the chances of experiencing hyper- or hypoglycaemia during exercise. The introduction of continuous glucose monitoring (CGM) systems means that glucose can be monitored conveniently, without the need for frequent fingerpick glucose checks. CGM devices include a fine sensor inserted under the skin, measuring levels of glucose in the interstitial fluid. Readings can be synchronized to a reader or mobile phone app as often as every 1-5 min. Use of CGM devices is associated with lower HbA1c and a reduction in hypoglycaemic events, promoting overall health and athletic performance. However, there are limitations to CGM, which must be considered when being used by an athlete with T1DM. These limitations can be addressed by individualized education plans, using protective equipment to prevent sensor dislodgement, as well as further research aiming to: (i) account for disparities between CGM and true blood glucose levels during vigorous exercise; (ii) investigate the effects of temperature and altitude on CGM accuracy, and (iii) explore of the sociological impact of CGM use amongst sportspeople without diabetes on those with T1DM.

People with type 1 diabetes exhibit lower exercise capacity compared to a control population with similar physical activity levels

AIMS

We aimed to assess physical activity (PA) levels, adherence to PA guidelines, and fitness capacity in individuals with type 1 diabetes (T1D) and control population.

METHODS

This cross-sectional study included 232 T1D and 248 controls. PA levels (IPAQ-SF questionnaire), adherence to guidelines (>150 min/week of moderate-to-vigorous PA), fitness capacity (VO2max, maximal incremental test on a cycle ergometer and 1RM test) were assessed, along with other clinical variables.

RESULTS

Total PA levels (T1D 2202 ± 1839 vs. controls 2357 ± 2189 METs/min/week), adherence (T1D 53.1 % vs controls 53.2 %), and sedentariness (T1D 27.3 % vs. controls 25.1 %) were similar between groups. However, participants with T1D exhibited significantly lower levels of VO2max (29.1 ± 10.5 vs. 32.5 ± 11.5 mlO2/kg/min, p < 0.001), work capacity (2.73 ± 1.03 vs. 3 ± 10 W/kg of body weight, p = 0.004) and strength capacity (2.29 ± 0.53 vs. 2.41 ± 0.79 kg/kg body weight in 1RM, p = 0.01) than controls, after adjusting for sex and age.

CONCLUSIONS

Individuals with T1D exhibit lower fitness capacity compared to a control population, regardless of age and sex, even when presenting similar levels of total physical activity and adherence to guidelines.

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.

Impact of a 4-week intensive track and field training intervention on glycaemia in adolescents with type 1 diabetes: The ChilDFiT1 study

AIM

To investigate the safety and efficacy of track and field training compared with intensification of insulin treatment only in adolescents with type 1 diabetes (T1D).

MATERIALS AND METHODS

Eighteen adolescents (seven females) with T1D were included (age 15.1 ± 1.1 years, HbA1c 7.3% ± 1.0% [56.3 ± 10.9 mmol/mol]). After a 4-week observational control phase, participants were randomized to either stand-alone intensive glycaemic management (IT; telemedicine or on-site visits, three times/week) or additionally performed track and field exercise (EX; three 60-minute sessions/week) for 4 weeks. Glycaemia was assessed via continuous glucose monitoring during observational control and intervention phases.

RESULTS

Time in range (70-180 mg/dL; 3.9-10.0 mmol/L) significantly improved from the observational control phase to the exercise intervention phase in EX (69% ± 13% vs. 72% ± 11%, P = .049), but not in IT (59% ± 22% vs. 62% ± 16%, P = .399). Time below range 1 (54-69 mg/dL; < 3.9 mmol/L) improved in IT (3.1% ± 1.9% vs. 2.0% ± 0.8%, P = .017) and remained stable in EX (2.0% ± 1.7 vs. 1.9% ± 1.1%, P = .999). The EX group's HbA1c ameliorated preintervention to postintervention (mean difference: ΔHbA1c -0.19% ± 0.17%, P = .042), which was not seen within the IT group (ΔHbA1c -0.16% ± 0.37%, P = .40). Glucose standard deviation was reduced significantly in EX (55 ± 11 vs. 51 ± 10 mg/dL [3.1 ± 0.6 vs. 2.8 ± 0.6 mmol/L], P = .011), but not in IT (70 ± 24 vs. 63 ± 18 mg/dL [3.9 ± 1.3 vs. 3.5 ± 1.0 mmol/L], P = .186).

CONCLUSION

Track and field training combined with intensive glycaemic management improved glycaemia in adolescents with T1D, which was not observed in the non-exercise group.

Analysis of the management and therapeutic performance of diabetes mellitus employing special target

Diabetes mellitus (DM) is a chronic metabolic condition characterized predominantly by hyperglycemia. The most common causes contributing to the pathophysiology of diabetes are insufficient insulin secretion, resistance to insulin's tissue-acting effects, or a combination of both. Over the last 30 years, the global prevalence of diabetes increased from 4% to 6.4%. If no better treatment or cure is found, this amount might climb to 430 million in the coming years. The major factors of the disease's deterioration include age, obesity, and a sedentary lifestyle. Finding new therapies to manage diabetes safely and effectively without jeopardizing patient compliance has always been essential. Among the medications available to manage DM on this journey are glucagon-like peptide-1 agonists, thiazolidinediones, sulphonyl urease, glinides, biguanides, and insulin-targeting receptors discovered more than 10 years ago. Despite the extensive preliminary studies, a few clinical observations suggest this process is still in its early stages. The present review focuses on targets that contribute to insulin regulation and may be employed as targets in treating diabetes since they may be more efficient and secure than current and traditional treatments.

An Overview of Diet and Physical Activity for Healthy Weight in Adolescents and Young Adults with Type 1 Diabetes: Lessons Learned from the ACT1ON Consortium

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

Examining the Acute Glycemic Effects of Different Types of Structured Exercise Sessions in Type 1 Diabetes in a Real-World Setting: The Type 1 Diabetes and Exercise Initiative (T1DEXI)

OBJECTIVE

Maintenance of glycemic control during and after exercise remains a major challenge for individuals with type 1 diabetes. Glycemic responses to exercise may differ by exercise type (aerobic, interval, or resistance), and the effect of activity type on glycemic control after exercise remains unclear.

RESEARCH DESIGN AND METHODS

The Type 1 Diabetes Exercise Initiative (T1DEXI) was a real-world study of at-home exercise. Adult participants were randomly assigned to complete six structured aerobic, interval, or resistance exercise sessions over 4 weeks. Participants self-reported study and nonstudy exercise, food intake, and insulin dosing (multiple daily injection [MDI] users) using a custom smart phone application and provided pump (pump users), heart rate, and continuous glucose monitoring data.

RESULTS

A total of 497 adults with type 1 diabetes (mean age ± SD 37 ± 14 years; mean HbA1c ± SD 6.6 ± 0.8% [49 ± 8.7 mmol/mol]) assigned to structured aerobic (n = 162), interval (n = 165), or resistance (n = 170) exercise were analyzed. The mean (± SD) change in glucose during assigned exercise was -18 ± 39, -14 ± 32, and -9 ± 36 mg/dL for aerobic, interval, and resistance, respectively (P < 0.001), with similar results for closed-loop, standard pump, and MDI users. Time in range 70-180 mg/dL (3.9-10.0 mmol/L) was higher during the 24 h after study exercise when compared with days without exercise (mean ± SD 76 ± 20% vs. 70 ± 23%; P < 0.001).

CONCLUSIONS

Adults with type 1 diabetes experienced the largest drop in glucose level with aerobic exercise, followed by interval and resistance exercise, regardless of insulin delivery modality. Even in adults with well-controlled type 1 diabetes, days with structured exercise sessions contributed to clinically meaningful improvement in glucose time in range but may have slightly increased time below range.

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.

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.

2022: Position of Brazilian Diabetes Society on exercise recommendations for people with type 1 and type 2 diabetes

INTRODUCTION

For individuals diagnosed with diabetes mellitus, the practice of properly oriented physical exercises brings significant benefits to the individual's health and is considered an indispensable tool for metabolic management. The individualization of exercise routines is an essential aspect for therapeutic success, despite the need to consider some general recommendations. This review is an authorized literal translation of the Brazilian Society of Diabetes (SBD) Guidelines 2021-2022, which is based on scientific evidence and provides guidance on physical activities and exercises aimed at individuals with type 1 and 2 diabetes.

METHODS

SBD designated 9 specialists from its "Department of Diabetes, Exercise & Sports" to author chapters on physical activities and exercises directed to individuals with type 1 and 2 diabetes. The aim of these chapters was to highlight recommendations in accordance with Evidence Levels, based on what is described in the literature. These chapters were analyzed by the SBD Central Committee, which is also responsible for the SBD 2021-2022 guidelines. Main clinical inquiries were selected to perform a narrated review by using MEDLINE via PubMed. Top available evidence, such as high-quality clinical trials, large observational studies and meta-analyses related to physical activity and exercise advisory, were analyzed. The adopted MeSh terms were [diabetes], [type 1 diabetes], [type 2 diabetes], [physical activity] [physical exercise].

RESULTS

17 recommendations were defined by the members. For this review, it was considered different Evidence Levels, as well as different Classes of Recommendations. As to Evidence Levels, the following levels were contemplated: Level A) More than one randomized clinical trial or a randomized clinical trial meta-analysis with low heterogeneity. Level B) Meta analysis with observational studies, one randomized clinical trial, sizeable observational studies and sub-groups analysis. Level C) Small non-randomized studies, cross-sectional studies, case control studies, guidelines or experts' opinions. In respect to Recommendation Classes, the following criteria were adopted: I. "Recommended": Meaning there was a consent of more than 90% of the panel; IIa. "Must be considered": meaning there is a general preference of the panel which 70-90% agrees; IIb. "Can be considered". 50-70% agrees; III Not recommended: There is a consensus that the intervention should not be performed.

CONCLUSION

Physical exercise aids on the glycemic control of type 2 diabetes individuals while also decreasing cardiovascular risk in individuals with type 1 and 2 diabetes. Individuals diagnosed with diabetes should perform combined aerobic and resistance exercises in order to manage the disease. In addition, exercises focusing on flexibility and balance should be specially addressed on elderly individuals. Diabetes individuals using insulin as therapeutic treatment should properly monitor glycemia levels before, during and after exercise sessions to minimize health incidents, such as hypoglycemia.

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.

Advancements and future directions in the teamwork, targets, technology, and tight control-the 4T study: improving clinical outcomes in newly diagnosed pediatric type 1 diabetes

PURPOSE OF REVIEW

The benefits of intensive diabetes management have been established by the Diabetes Control and Complications Trial. However, challenges with optimizing glycemic management in youth with type 1 diabetes (T1D) remain across pediatric clinics in the United States. This article will review our Teamwork, Targets, Technology, and Tight Control (4T) study that implements emerging diabetes technology into clinical practice with a team approach to sustain tight glycemic control from the onset of T1D and beyond to optimize clinical outcomes.

RECENT FINDINGS

During the 4T Pilot study and study 1, our team-based approach to intensive target setting, education, and remote data review has led to significant improvements in hemoglobin A1c throughout the first year of T1D diagnosis in youth, as well as family and provider satisfaction.

SUMMARY

The next steps include refinement of the current 4T study 1, developing a business case, and broader implementation of the 4T study. In study 2, we are including a more pragmatic cadence of remote data review and disseminating exercise education and activity tracking to both English- and Spanish-speaking families. The overall goal is to create and implement a translatable program that can facilitate better outcomes for pediatric clinics across the USA.

High Intensity Interval Training reduces hypoglycemic events compared with continuous aerobic training in individuals with type 1 diabetes: HIIT and hypoglycemia in type 1 diabetes

AIMS

to investigate if a High Intensity Interval Training (HIIT) protocol improves glycemic control and fitness capacity, compared to traditional moderate Intensity Continuous Training (MICT) exercise.

METHODS

30 sedentary individuals with type 1 diabetes (T1D) and 26 healthy controls were assigned to a 3-week HIIT or MICT protocol. Blood glucose levels by continuous glucose monitoring system and fitness status were compared before and after the study period.

RESULTS

During workouts, blood glucose levels remained stable in HIIT exercise (+3.2 ± 16.2 mg/dl (p = 0.43)), while decreased in MICT (-27.1 ± 17.5 mg/dl (p < 0.0001)) exercise. In addition, out of the 9 training sessions, HIIT volunteers needed to take carbohydrate supplements to avoid hypoglycemia in 0.56 ± 0.9 sessions, compared to 1.83 ± 0.5 sessions (p < 0.04) in MICT individuals. In the analysis of blood glucose levels between rest and training days (24h-period), training significantly reduced mean glycemic levels in both groups, but the MICT exercise results in an increase in the frequency of hypoglycemic episodes. The response to exercise seems to be attenuated in individuals with T1D, especially in HIIT group.

CONCLUSION

HIIT training results in a greater glycemic stability, with reduction of hypoglycemic episodes.

A Brief Review on the Evolution of Technology in Exercise and Sport in Type 1 Diabetes: Past, Present, and Future

One hundred years ago, insulin was first used to successfully lower blood glucose levels in young people living with what was then called juvenile diabetes. While insulin was not a cure for diabetes, it allowed individuals to resume a near normal life and have some freedom to eat more liberally and gain the strength they needed to live a more active lifestyle. Since then, a number of therapeutic and technical advances have arisen to further improve the health and wellbeing of individuals living with type 1 diabetes, allowing many to participate in sport at the local, regional, national or international level of competition. This review and commentary highlights some of the key advances in diabetes management in sport over the last 100 years since the discovery of insulin.

Quantifying the impact of physical activity on future glucose trends using machine learning

Prevention of hypoglycemia (glucose <70 mg/dL) during aerobic exercise is a major challenge in type 1 diabetes. Providing predictions of glycemic changes during and following exercise can help people with type 1 diabetes avoid hypoglycemia. A unique dataset representing 320 days and 50,000 + time points of glycemic measurements was collected in adults with type 1 diabetes who participated in a 4-arm crossover study evaluating insulin-pump therapies, whereby each participant performed eight identically designed in-clinic exercise studies. We demonstrate that even under highly controlled conditions, there is considerable intra-participant and inter-participant variability in glucose outcomes during and following exercise. Participants with higher aerobic fitness exhibited significantly lower minimum glucose and steeper glucose declines during exercise. Adaptive, personalized machine learning (ML) algorithms were designed to predict exercise-related glucose changes. These algorithms achieved high accuracy in predicting the minimum glucose and hypoglycemia during and following exercise sessions, for all fitness levels.

A Randomized Crossover Trial Comparing Glucose Control During Moderate-Intensity, High-Intensity, and Resistance Exercise With Hybrid Closed-Loop Insulin Delivery While Profiling Potential Additional Signals in Adults With Type 1 Diabetes

OBJECTIVE

To compare glucose control with hybrid closed-loop (HCL) when challenged by high intensity exercise (HIE), moderate intensity exercise (MIE), and resistance exercise (RE) while profiling counterregulatory hormones, lactate, ketones, and kinetic data in adults with type 1 diabetes.

RESEARCH DESIGN AND METHODS

This study was an open-label multisite randomized crossover trial. Adults with type 1 diabetes undertook 40 min of HIE, MIE, and RE in random order while using HCL (Medtronic MiniMed 670G) with a temporary target set 2 h prior to and during exercise and 15 g carbohydrates if pre-exercise glucose was <126 mg/dL to prevent hypoglycemia. Primary outcome was median (interquartile range) continuous glucose monitoring time-in-range (TIR; 70-180 mg/dL) for 14 h post-exercise commencement. Accelerometer data and venous glucose, ketones, lactate, and counterregulatory hormones were measured for 280 min post-exercise commencement.

RESULTS

Median TIR was 81% (67, 93%), 91% (80, 94%), and 80% (73, 89%) for 0-14 h post-exercise commencement for HIE, MIE, and RE, respectively (n = 30), with no difference between exercise types (MIE vs. HIE; P = 0.11, MIE vs. RE, P = 0.11; and HIE vs. RE, P = 0.90). Time-below-range was 0% for all exercise bouts. For HIE and RE compared with MIE, there were greater increases, respectively, in noradrenaline (P = 0.01 and P = 0.004), cortisol (P < 0.001 and P = 0.001), lactate (P ≤ 0.001 and P ≤ 0.001), and heart rate (P = 0.007 and P = 0.015). During HIE compared with MIE, there were greater increases in growth hormone (P = 0.024).

CONCLUSIONS

Under controlled conditions, HCL provided satisfactory glucose control with no difference between exercise type. Lactate, counterregulatory hormones, and kinetic data differentiate type and intensity of exercise, and their measurement may help inform insulin needs during exercise. However, their potential utility as modulators of insulin dosing will be limited by the pharmacokinetics of subcutaneous insulin delivery.

Evaluation of Web-Based and In-Person Methods to Recruit Adults With Type 1 Diabetes for a Mobile Exercise Intervention: Prospective Observational Study

BACKGROUND

Our clinical trial of a mobile exercise intervention for adults 18 to 65 years old with type 1 diabetes (T1D) occurred during COVID-19 social distancing restrictions, prompting us to test web-based recruitment methods previously underexplored for this demographic.

OBJECTIVE

Our objectives for this study were to (1) evaluate the effectiveness and cost of using social media news feed advertisements, a clinic-based approach method, and web-based snowball sampling to reach inadequately active adults with T1D and (2) compare characteristics of enrollees against normative data.

METHODS

Participants were recruited between November 2019 and August 2020. In method #1, Facebook and Instagram news feed advertisements ran for five 1-to-8-day windows targeting adults (18 to 64 years old) in the greater New Haven and Hartford, Connecticut, areas with one or more diabetes-related profile interest. If interested, participants completed a webform so that the research team could contact them for eligibility screening. In method #2, patients 18 to 24 years old with T1D were approached in person at clinical visits in November and December 2019. Those who were interested immediately completed eligibility screening. Older patients could not be approached due to clinic restrictions. In method #3, snowball sampling was conducted by physically active individuals with T1D contacting their peers on Facebook and via email for 48 days, with details to contact the research staff to express interest and complete eligibility screening. Other methods referred participants to the study similarly to snowball sampling.

RESULTS

In method #1, advertisements were displayed to 11,738 unique viewers and attracted 274 clickers (2.33%); 20 participants from this group (7.3%) volunteered, of whom 8 (40%) were eligible. Costs averaged US $1.20 per click and US $95.88 per eligible volunteer. Men had lower click rates than women (1.71% vs 3.17%; P<.001), but their responsiveness and eligibility rates did not differ. In method #2, we approached 40 patients; 32 of these patients (80%) inquired about the study, of whom 20 (63%) volunteered, and 2 of these volunteers (10%) were eligible. Costs including personnel for in-person approaches averaged US $21.01 per inquirer and US $479.79 per eligible volunteer. In method #3, snowball sampling generated 13 inquirers; 12 of these inquirers (92%) volunteered, of whom 8 (67%) were eligible. Incremental costs to attract inquirers were negligible, and total costs averaged US $20.59 per eligible volunteer. Other methods yielded 7 inquirers; 5 of these inquirers (71%) volunteered, of whom 2 (40%) were eligible. Incremental costs to attract inquirers were negligible, and total costs averaged US $34.94 per eligible volunteer. Demographic overrepresentations emerged in the overall cohort (ie, optimal glycemic control, obesity, and low exercise), among those recruited by news feed advertisements (ie, obesity and older age), and among those recruited by snowball sampling (ie, optimal glycemic control and low exercise).

CONCLUSIONS

Web-based advertising and recruitment strategies are a promising means to attract adults with T1D to clinical trials and exercise interventions, with costs comparing favorably to prior trials despite targeting an uncommon condition (ie, T1D) and commitment to an intervention. These strategies should be tailored in future studies to increase access to higher-risk participants.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04204733; https://clinicaltrials.gov/ct2/show/NCT04204733.