Exercise Management for Young People With Type 1 Diabetes: A Structured Approach to the Exercise Consultation

Predicting Hypoglycemia and Hyperglycemia Risk During and After Activity for Adolescents with Type 1 Diabetes

Objective: To predict hypoglycemia and hyperglycemia risk during and after activity for adolescents with type 1 diabetes (T1D) using real-world data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study. Methods: Adolescents with T1D (n = 225; [mean ± SD] age = 14 ± 2 years; HbA1c = 7.1 ± 1.3%; T1D duration = 5 ± 4 years; 56% using hybrid closed loop), wearing continuous glucose monitors (CGMs), logged 3738 total activities over 10 days. Repeated Measures Random Forest (RMRF) and Repeated Measures Logistic Regression (RMLR) models were used to predict a composite risk of hypoglycemia (<70 mg/dL) and hyperglycemia (>250 mg/dL) within 2 h after starting exercise. Results: RMRF achieved high precision predicting composite risk and was more accurate than RMLR Area under the receiver operating characteristic curve (AUROC 0.737 vs. 0.661; P < 0.001). Activities with minimal composite risk had a starting glucose between 132 and 160 mg/dL and a glucose rate of change at activity start between -0.4 and -1.9 mg/dL/min. Time <70 mg/dL and time >250 mg/dL during the prior 24 h, HbA1c level, and insulin on board at activity start were also predictive. Separate models explored factors at the end of activity; activities with glucose between 128 and 133 mg/dL and glucose rate of change between 0.4 and -0.6 mg/dL/min had minimal composite risk. Conclusions: Physically active adolescents with T1D should aim to start exercise with an interstitial glucose between 130 and 160 mg/dL with a flat or slightly decreasing CGM trend to minimize risk for developing dysglycemia. Incorporating factors such as historical glucose and insulin can improve prediction modeling for the acute glucose responses to exercise.

Digital Gaming and Exercise Among Youth With Type 1 Diabetes: Cross-Sectional Analysis of Data From the Type 1 Diabetes Exercise Initiative Pediatric Study

Background

Regular physical activity and exercise are fundamental components of a healthy lifestyle for youth living with type 1 diabetes (T1D). Yet, few youth living with T1D achieve the daily minimum recommended levels of physical activity. For all youth, regardless of their disease status, minutes of physical activity compete with other daily activities, including digital gaming. There is an emerging area of research exploring whether digital games could be displacing other physical activities and exercise among youth, though, to date, no studies have examined this question in the context of youth living with T1D.

Objective

We examined characteristics of digital gaming versus nondigital gaming (other exercise) sessions and whether youth with T1D who play digital games (gamers) engaged in less other exercise than youth who do not (nongamers), using data from the Type 1 Diabetes Exercise Initiative Pediatric study.

Methods

During a 10-day observation period, youth self-reported exercise sessions, digital gaming sessions, and insulin use. We also collected data from activity wearables, continuous glucose monitors, and insulin pumps (if available).

Results

The sample included 251 youths with T1D (age: mean 14, SD 2 y; self-reported glycated hemoglobin A1c level: mean 7.1%, SD 1.3%), of whom 105 (41.8%) were female. Youth logged 123 digital gaming sessions and 3658 other exercise (nondigital gaming) sessions during the 10-day observation period. Digital gaming sessions lasted longer, and youth had less changes in glucose and lower mean heart rates during these sessions than during other exercise sessions. Youth described a greater percentage of digital gaming sessions as low intensity (82/123, 66.7%) when compared to other exercise sessions (1104/3658, 30.2%). We had 31 youths with T1D who reported at least 1 digital gaming session (gamers) and 220 youths who reported no digital gaming (nongamers). Notably, gamers engaged in a mean of 86 (SD 43) minutes of other exercise per day, which was similar to the minutes of other exercise per day reported by nongamers (mean 80, SD 47 min).

Conclusions

Digital gaming sessions were longer in duration, and youth had less changes in glucose and lower mean heart rates during these sessions when compared to other exercise sessions. Nevertheless, gamers reported similar levels of other exercise per day as nongamers, suggesting that digital gaming may not fully displace other exercise among youth with T1D.

The Acute Effects of Real-World Physical Activity on Glycemia in Adolescents With Type 1 Diabetes: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study

OBJECTIVE

Data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were evaluated to understand glucose changes during activity and identify factors that may influence changes.

RESEARCH DESIGN AND METHODS

In this real-world observational study, adolescents with type 1 diabetes self-reported physical activity, food intake, and insulin dosing (multiple-daily injection users) using a smartphone application. Heart rate and continuous glucose monitoring data were collected, as well as pump data downloads.

RESULTS

Two hundred fifty-one adolescents (age 14 ± 2 years [mean ± SD]; HbA1c 7.1 ± 1.3% [54 ± 14.2 mmol/mol]; 42% female) logged 3,738 activities over ∼10 days of observation. Preactivity glucose was 163 ± 66 mg/dL (9.1 ± 3.7 mmol/L), dropping to 148 ± 66 mg/dL (8.2 ± 3.7 mmol/L) by end of activity; median duration of activity was 40 min (20, 75 [interquartile range]) with a mean and peak heart rate of 109 ± 16 bpm and 130 ± 21 bpm. Drops in glucose were greater in those with lower baseline HbA1c levels (P = 0.002), shorter disease duration (P = 0.02), less hypoglycemia fear (P = 0.04), and a lower BMI (P = 0.05). Event-level predictors of greater drops in glucose included self-classified "noncompetitive" activities, insulin on board >0.05 units/kg body mass, glucose already dropping prior to the activity, preactivity glucose >150 mg/dL (>8.3 mmol/L) and time 70-180 mg/dL >70% in the 24 h before the activity (all P < 0.001).

CONCLUSIONS

Participant-level and activity event-level factors can help predict the magnitude of drop in glucose during real-world physical activity in youth with type 1 diabetes. A better appreciation of these factors may improve decision support tools and self-management strategies to reduce activity-induced dysglycemia in active adolescents living with the disease.

Current Technologies for Managing Type 1 Diabetes Mellitus and Their Impact on Quality of Life-A Narrative Review

Type 1 diabetes mellitus is a chronic autoimmune disease that affects millions of people and generates high healthcare costs due to frequent complications when inappropriately managed. Our paper aimed to review the latest technologies used in T1DM management for better glycemic control and their impact on daily life for people with diabetes. Continuous glucose monitoring systems provide a better understanding of daily glycemic variations for children and adults and can be easily used. These systems diminish diabetes distress and improve diabetes control by decreasing hypoglycemia. Continuous subcutaneous insulin infusions have proven their benefits in selected patients. There is a tendency to use more complex systems, such as hybrid closed-loop systems that can modulate insulin infusion based on glycemic readings and artificial intelligence-based algorithms. It can help people manage the burdens associated with T1DM management, such as fear of hypoglycemia, exercising, and long-term complications. The future is promising and aims to develop more complex ways of automated control of glycemic levels to diminish the distress of individuals living with diabetes.

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).

Physical Activity Management for Youth With Type 1 Diabetes: Supporting Active and Inactive Children

Regular physical activity and exercise are important for youth and essential components of a healthy lifestyle. For youth with type 1 diabetes, regular physical activity can promote cardiovascular fitness, bone health, insulin sensitivity, and glucose management. However, the number of youth with type 1 diabetes who regularly meet minimum physical activity guidelines is low, and many encounter barriers to regular physical activity. Additionally, some health care professionals (HCPs) may be unsure how to approach the topic of exercise with youth and families in a busy clinic setting. This article provides an overview of current physical activity research in youth with type 1 diabetes, a basic description of exercise physiology in type 1 diabetes, and practical strategies for HCPs to conduct effective and individualized exercise consultations for youth with type 1 diabetes.

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

BACKGROUND

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

RESULTS

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

CONCLUSION

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

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Insulin pumps in children - a systematic review

BACKGROUND

Insulin pump therapy is a real breakthrough in managing diabetes Mellitus, particularly in children. It can deliver a tiny amount of insulin and decreases the need for frequent needle injections. It also helps to maintain adequate and optimal glycemic control to reduce the risk of metabolic derangements in different tissues. Children are suitable candidates for pump therapy as they need a more freestyle and proper metabolic control to ensure adequate growth and development. Therefore, children and their caregivers should have proper education and training and understand the proper use of insulin pumps to achieve successful pump therapy. The pump therapy continuously improves to enhance its performance and increase its simulation of the human pancreas. Nonetheless, there is yet a long way to reach the desired goal.

AIM

To review discusses the history of pump development, its indications, types, proper use, special conditions that may enface the children and their families while using the pump, its general care, and its advantages and disadvantages.

METHODS

We conducted comprehensive literature searches of electronic databases until June 30, 2022, related to pump therapy in children and published in the English language.

RESULTS

We included 118 articles concerned with insulin pumps, 61 were reviews, systemic reviews, and meta-analyses, 47 were primary research studies with strong design, and ten were guidelines.

CONCLUSION

The insulin pump provides fewer needles and can provide very tiny insulin doses, a convenient and more flexible way to modify the needed insulin physiologically, like the human pancreas, and can offer adequate and optimal glycemic control to reduce the risk of metabolic derangements in different tissues.

Domesticating the condition: Design lessons gained from a marathon on how to cope with barriers imposed by type 1 diabetes

Through analytical autoethnographic analysis of marathon preparation, this study examines challenges faced by people with Type 1 Diabetes (T1D) who engage in high-performance sports. Autoethnographer and second-person perspectives (T1D runners, family members, and health providers) were collected through introspective activities (autoethnographic diary and in-depth interviews) to understand the T1D runner's coping experience. Six insights involved in T1D self-management were identified and analyzed with reference to related design tools (prototyping, archetyping and journey mapping). Finally, we conclude with a discussion of how endurance physical activity (PA) such as running helps to "domesticate" T1D, a term coined to reflect the difficulties that T1D presents for PA accomplishment and how T1D runners' experiences give them an opportunity to overcome PA barriers promoting physical culture and enriching further health psychology studies.

Association between high levels of physical activity and improved glucose control on active days in youth with type 1 diabetes

BACKGROUND

Sixty minutes per day of at least moderate to vigorous physical activity (MVPA) is recommended for children for a variety of physical and psychological reasons. Adherence to these guidelines is confounded by challenges with glucose control during exercise in type 1 diabetes (T1D).

OBJECTIVES

This study examined the potential association between physical activity level on active days and glucose control in youth with T1D.

METHODS

Blinded continuous glucose monitors (CGM: Abbott Libre Pro) and physical activity data as measured from a body monitor patch (Metria IH1) were collected for up to 3 weeks in youth aged 9-17 years with T1D. The association between physical activity levels, expressed as mean active metabolic equivalent minutes (MET-minutes) per day, with CGM-based mean glucose, percent time in range (TIR: 70-180 mg/dl), % time above range (TAR) and % time below range (TBR) were assessed using a linear regression model adjusted for age, gender, and baseline HbA1c.

RESULTS

Study participants were deemed physically active, as defined by at least 10 min of continuous moderate-to-vigorous activity, on 5.2 ± 1.9 days per week, with a median accumulated physical activity time of 61 [IQR: 37-145] minutes per day. Higher physical activity levels were associated with lower mean glucose levels (r = -0.36; p = 0.02) and lower TAR (r = -0.45; p = 0.002) on active days. Higher activity levels were also associated with greater TIR (r = 0.54; p < 0.001) without being associated with more, or less, TBR.

CONCLUSIONS

Higher amounts of physical activity are associated with improvements in TIR without significantly increasing TBR. These data suggest that youth ages 9-17 years with T1D can benefit from a high level of physical activity without undue fear of hypoglycemia.

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.

Determinants of healthful eating and physical activity among adolescents and young adults with type 1 diabetes in Qatar: A qualitative study

BACKGROUND

In Qatar, as in the rest of the world, the sharp rise in the prevalence of type 1 diabetes (T1D) is a leading cause for concern, in terms associated with morbidity, mortality, and increasing health costs. Besides adhering to medication, the outcome of diabetes management is also dependent on patient adherence to the variable self-care behaviors including healthful eating (HE) and physical activity (PA). Yet, dietary intake and PA in adolescents and young adults (AYAs) with T1D are known to fall short of recommended guidelines. The aim of this study was to develop an in-depth understanding of the behavioral determinants of HE and PA adherence among Arab AYAs within the age range of 17-24 years with T1D attending Hamad General Hospital.

METHODS

Semi-structured, face-to-face individual interviews were conducted with 20 participants. Interviews were based on an integrative health behavior change model, the I-Change model (ICM). All interviews were audio-recorded, transcribed verbatim, and analyzed using the framework method.

RESULTS

More participants reported non-adherence than adherence. Several motivational determinants of adherence to HE and PA were identified. The majority of participants were cognizant of their own behaviors towards HE and PA. Yet, some did not link low adherence to HE and PA with increased risks of health problems resulting from T1D. Facilitators to adherence were identified as being convinced of the advantages of HE and PA, having support and high self-efficacy, a high level of intention, and a good health care system.

CONCLUSION

The suboptimal adherence in AYAs to HE and PA needs more attention. Supportive actions are needed to encourage adherence to a healthy lifestyle to achieve benefits in terms of glycemic control and overall health outcomes, with a special focus on adolescents. Interventions are needed to foster motivation by addressing the relevant determinants in order to promote adherence to these two behaviors in AYAs with T1D.

Effects of exercise training on glycaemic control in youths with type 1 diabetes: a systematic review and meta-analysis of randomised controlled trials

AbstractThe aim of the study is to evaluate whether exercise interventions are associated with improved glycaemic control in children and adolescents with type 1 diabetes mellitus (T1DM), and to examine its relationship with the characteristics of the intervention (i.e., type, intensity, length, and duration of the sessions). Eligible criteria were randomised controlled trials of youth aged 6-18 years with T1DM, participating in an exercise-based intervention where glycaemic control is measured (i.e., glycated haemoglobin [HbA1c]). Pooled effect sizes (Hedges'g) were calculated using random-effects inverse-variance analyses. Fourteen studies enrolling 509 patients were analysed. Effect size was expressed as Hedges' g to correct for possible small sample bias. Overall, HbA1c levels in the exercise group (g=-0.38 95% confidence interval [CI], -0.66 to -0.11; mean difference [MD]=-0.62%) were reduced compared with the control group. Concurrent training (g=-0.63 95%CI, -1.05 to -0.21), high-intensity exercise (g=-0.43 95%CI, -0.83 to -0.03), interventions ≥24 weeks (g=-0.92 95%CI, -1.44 to -0.40), and sessions ≥60 minutes (g=-0.71 95%CI, -1.05 to -0.08) showed larger changes (MD=-0.66% to 1.30%). In conclusion, our study suggests that programs longer than 24 weeks with at least 60 min/session of high-intensity concurrent exercise may serve as a supportive therapy to metabolic control in youth with T1DM.

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.

Protective effects of physical activity against health risks associated with type 1 diabetes: "Health benefits outweigh the risks"

The magnitude of diabetes mellitus (DM) has increased in recent decades, where the number of cases and the proportion of the disease have been gradually increasing over the past few decades. The chronic complications of DM affect many organ systems and account for the majority of morbidity and mortality associated with the disease. The prevalence of type 1 DM (T1DM) is increasing globally, and it has a very significant burden on countries and at an individual level. T1DM is a chronic illness that requires ongoing medical care and patient self-management to prevent complications. This study aims to discuss the health benefits of physical activity (PA) in T1DM patients. The present review article was performed following a comprehensive literature search. The search was conducted using the following electronic databases: "Cochrane Library", Web of Science, PubMed, HINARI, EMBASE, Google for grey literature, Scopus, African journals Online, and Google Scholar for articles published up to June 21, 2021. The present review focused on the effects of PA on many outcomes such as blood glucose (BG) control, physical fitness, endothelial function, insulin sensitivity, well-being, the body defense system, blood lipid profile, insulin resistance, cardiovascular diseases (CVDs), insulin requirements, blood pressure (BP), and mortality. It was found that many studies recommended the use of PA for the effective management of T1DM. PA is a component of comprehensive lifestyle modifications, which is a significant approach for the management of T1DM. It provides several health benefits, such as improving BG control, physical fitness, endothelial function, insulin sensitivity, well-being, and the body defense system. Besides this, it reduces the blood lipid profile, insulin resistance, CVDs, insulin requirements, BP, and mortality. Overall, PA has significant and essential protective effects against the health risks associated with T1DM. Even though PA has several health benefits for patients with T1DM, these patients are not well engaged in PA due to barriers such as a fear of exercise-induced hypoglycemia in particular. However, several effective strategies have been identified to control exercise-induced hypoglycemia in these patients. Finally, the present review concludes that PA should be recommended for the management of patients with T1DM due to its significant health benefits and protective effects against associated health risks. It also provides suggestions for the future direction of research in this field.

Body mass index, basal insulin and glycemic control in children with type 1 diabetes treated with the advanced hybrid closed loop system remain stable - 1-year prospective, observational, two-center study

BACKGROUND

Information on the influence of insulin treatment using advanced hybrid closed loop systems (AHCL) on body weight of young patients with type 1 diabetes (T1D) is scarce. The aim of this study was to observe whether there were any changes in body mass index (BMI) of children and adolescents with T1D treated using the Medtronic Minimed 780G AHCL after 1 year of follow up and to analyze potential associations between these changes and the insulin doses.

MATERIALS AND METHODS

For 50 children and adolescents (age 5.4-16.8 years, 24 (48%) boys, T1D for 3.9 ± 2.56 years) using an AHCL system anthropometric and AHCL data were collected prospectively. BMI Z-scores and two-week AHCL records obtained after AHCL enrollment were compared with data after 6 months and also 1 year after starting AHCL.

RESULTS

The BMI Z-score of the patients at 1 year follow-up did not change from time of AHCL initiation (0.51 ± 2.79 vs 0.57 ± 2.85, p>0.05). There was a slight increase in total daily insulin per kg of body weight (0.67 ± 0.21 U/kg vs 0.80 ± 0.21 U/kg, p <0.001), but the percent of basal insulin was unchanged (34.88 ± 6.91% vs 35.08 ± 6.30%, p>0.05). We observed also no change (AHCL start vs after 1 year) in glycemic control parameters: average sensor glucose (131.36± 11.04 mg/dL vs 132.45 ± 13.42 mg/dL, p>0.05), coefficient of variation (34.99± 5.17% vs 34.06 ± 5.38%, p>0.05), glucose management indicator (6.45 ± 0.26% vs 6.48 ± 0.32%, p>0.05), and time spent in the range of 70-180 mg/dL (79.28 ± 8.12% vs 80.40 ± 8.25%, p>0.05).

CONCLUSION

During the 1 year of follow-up the BMI of children and adolescents with T1D treated with an AHCL system remained stable. Although there was a slight increase in the total daily insulin dose, the percent of basal insulin was unchanged. The patients maintained recommended glycemic control.

Multi-Faceted Influence of Obesity on Type 1 Diabetes in Children - From Disease Pathogenesis to Complications

The prevalence of overweight and obesity among youth patients with diabetes type 1 is increasing. It is estimated, that even up to 35% of young patients with this type of diabetes, considered so far to be characteristic for slim figure, are overweight or even obese. General increase of obesity in children's population complicates differential diagnosis of the type of diabetes in youths. Coexistence of obesity has clinical implications for all stages of diabetes course. It is confirmed that obesity is the risk factor for autoimmune diabetes, and is connected with the earlier onset of diabetes in predisposed patients. Many diabetic patients with obesity present additional risk factors for macroangiopathy, and are recognised to present metabolic syndrome, insulin resistance, and typical for diabetes type 2 - polycystic ovary syndrome, or non-alcoholic fatty liver disease. The prevalence of obesity rises dramatically in adolescence of diabetic child, more often in girls. It has negative impact on metabolic control, glycaemic variability and insulin demand. The risk for microangiopathic complications increases as well. The treatment is difficult and includes not only insulinotherapy and non-pharmacological trials. Recently treatment of insulin resistance with biguanids, and treatment with typical for type 2 new diabetes drugs like GLP-1 analogues, SGLT-2 receptor inhibitors, or even cases of bariatric surgery also has been reported.

Combined Algorithm-Based Adaptations of Insulin Dose and Carbohydrate Intake During Exercise in Children With Type 1 Diabetes: Results From the CAR2DIAB Study

Objectives

To evaluate the evolution of subcutaneous glucose during two sessions of monitored aerobic exercise in children or adolescents with type 1 diabetes after adaptation of insulin doses and carbohydrate intake according to a combined algorithm.

Methods

Twelve patients with type 1 diabetes (15.1 ± 2 years; diabetes duration: 9.5 ± 3.1 years) performed two series of exercise sessions after cardiac evaluation. The first series (TE#1) consisted in a monitored exercise of moderate to vigorous intensity coupled with a bout of maximum effort. The second series of exercises (TE#2) was carried out in real life during exercises categorized and monitored by connected watches. TE#2 sessions were performed after adaptation of insulin doses and fast-acting carbohydrates according to decision algorithms.

Results

Patients did not experience episodes of severe hypoglycemia, symptomatic hyperglycemia, or hyperglycemia associated with ketosis. Analysis of CGM data (15 h) during TE#2 sessions revealed an overall improvement in glycemic average [± standard deviation] (104 ± 14 mg/dl vs. 122 ± 17 mg/dl during TE#1; p < 0.001), associated with a decrease in proportion of hyperglycemia in periods ranging from 4 h to 15 h after performing the exercises. The proportion of hypoglycemia was not changed, except during the TE#2 +4-8 h period, where a significant increase in hypoglycemia <60 mg/dl was observed (25% vs. 6.2%; p = 0.04), yet without concurrent complications.

Conclusion

In our pediatric series, the application of algorithmic adaptations of insulin doses and carbohydrate intake has globally improved glycemic control during 15 h after real-time exercises performed by children and adolescents with type 1 diabetes.

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

In this paper, we propose and describe a robust and flexible modeling framework called MhealthCI based on the Bayesian structural time series, for which we have found to excel at analyzing diverse biosensor data. While Bayesian modeling is often employed in various fields such as finance, marketing, and weather forecasting, it is rarely used in biomedicine, specifically for biosensor and wearable data relating to human health and behavior. We use and apply this framework with the goal of interpreting and quantifying the causal impact of an intervention, a widespread goal of biomedicine. We describe the diversity of data types to which it could apply, provide intuition to its mechanics, collect relevant data in various fields, provide a wrapper tool around well-known R packages that prepares and registers diverse biosensor data to be analyzed, and finally apply the method to showcase its strength in quantifying the impact of interventions.

Association between physical activity level and cardiovascular risk factors in adolescents living with type 1 diabetes mellitus: a cross-sectional study

BACKGROUND

Type 1 diabetes mellitus (T1DM) is associated with an increased risk for cardiovascular disease (CVD) related morbidity and premature mortality. Regular physical activity plays an important role in the primary and secondary prevention of CVD, improving overall health and wellbeing. Previous observational studies have examined the associations between self-reported physical activity and CVD risk factors in largely adult Caucasian populations. However, limited work has evaluated the relationship between objectively measured physical activity and CVD risk factors in other ethnicities, particularly Chinese youth living with T1DM.

METHODS

This cross-sectional study assessed CVD risk factors, physical activity, and aerobic fitness (and their associations) in Chinese youth living with T1DM (n = 48) and peers (n = 19) without T1DM. Primary outcomes included blood pressure, lipid profiles, and physical activity (accelerometry). Statistical differences between groups were determined with chi-square, independent-samples t-tests, or analysis of covariance. The associations between aerobic fitness, daily physical activity variables, and CVD risk factors were assessed with univariate and multivariate linear regression analyses.

RESULTS

Results were summarized using means and standard deviation (SD) for normally distributed variables and medians and 25-75th quartile for non-normally distributed variables. In comparison to peers without diabetes, youth living with T1DM showed higher levels of total cholesterol (3.14 ± 0.67 vs. 4.03 ± 0.81 mmol·L-1, p = 0.001), low-density lipoprotein cholesterol (1.74 ± 0.38 vs. 2.31 ± 0.72 mmol·L-1, p = 0.005), and triglycerides (0.60 ± 0.40 vs. 0.89 ± 0.31 mmol·L-1 p = 0.012), and lower maximal oxygen power (44.43 ± 8.29 vs. 35.48 ± 8.72 mL·kg-1·min-1, p = 0.003), total physical activity counts (451.01 ± 133.52 vs. 346.87 ± 101.97 counts·min-1, p = 0.004), metabolic equivalents (METs) (2.41 ± 0.60 vs. 2.09 ± 0.41 METs, p = 0.033), moderate-to-vigorous intensity physical activity [MVPA: 89.57 (61.00-124.14) vs (53.19 (35.68-63.16) min, p = 0.001], and the percentage of time spent in MVPA [11.91 (7.74-16.22) vs 8.56 (6.18-10.12) %, p = 0.038]. The level of high-density lipoprotein cholesterol was positively associated with METs (β = 0.29, p = 0.030, model R2 = 0.168), and the level of triglycerides was negatively associated with physical activity counts (β = - 0.001, p = 0.018, model R2 = 0.205) and METs (β = - 0.359, p = 0.015, model R2 = 0.208), and positively associated with time spent in sedentary behaviour (β = 0.002, p = 0.041, model R2 = 0.156) in persons living with T1DM.

CONCLUSIONS

Chinese youth with T1DM, despite their young age and short duration of diabetes, present early signs of CVD risk, as well as low physical activity levels and cardiorespiratory fitness compared to apparently healthy peers without diabetes. Regular physical activity is associated with a beneficial cardiovascular profile in T1DM, including improvements in lipid profile. Thus, physical activity participation should be widely promoted in youth living with T1DM.

Responses to Low- and High-Intensity Exercise in Adolescents with Type 1 Diabetes in Relation to Their Level of VO2 Max

The purpose of this study was to investigate the influence of maximal oxygen uptake (VO₂ max) on the glycemic changes during low and high intensity exercises in young type 1 diabetic patients. Twenty boys (age: 14.3 ± 1.6 years; height: 171.0 ± 11.3 cm; weight; 59.5 ± 12.8 kg) were divided into low-fit group (LFG, n = 10) and high-fit group (HFG, n = 10). According to the experimental design, participants performed three physical efforts (VO₂ max test, mixed aerobic-anaerobic effort and aerobic effort) on the cycloergometer, during which real-time glycemia was measured. Mixed aerobic-anaerobic exercise demanded significantly smaller carbohydrate supplementation (0.2 ± 0.2 g/kg during exercise) than the aerobic test session (0.4 ± 0.3 g/kg during exercise). Moreover, patients with higher VO₂ max had lower tendency for glycemic changes during the aerobic effort. The results of the current study suggest that young type 1 diabetic patients should perform different intensity activities using continuous glycemic monitoring system to avoid acute and chronic complications of the disease.

Reproducibility of Glucose Fluctuations Induced by Moderate Intensity Cycling Exercise in Persons with Type 1 Diabetes

AIMS

The purpose was to assess the reproducibility of glucose changes during three sessions of standardized moderate intensity continuous training of cycling on an individual level in people with type 1 diabetes.

METHODS

Twelve adults (six females) with type 1 diabetes performed three test sessions on an ergometer bicycle (30 min, 67% of predicted heart rate) on three different days. The participants were 36.5 (26.6-45.5) (median, IQR) years old, and their HbA1c was 65 ± 15 mmol/mol (mean ± SD). Two hours before the tests, the participants had a standard meal. Interstitial glucose (IG) and capillary glucose (CG) were measured using an iPro2 Medtronic continuous glucose monitor and the Bayer Contour XT-device, respectively. Prior to the test sessions, resting heart rate was measured using a digital blood pressure monitor to estimate the desired intensity of the exercise.

RESULTS

The average within-participant relationship between the average slope in glucose during sessions 2 and 1 was in IG -0.29 (95% CI -1.11; 0.58) and in CG -0.04 (-0.68; 0.77). Between sessions 3 and 2, IG is 0.18 (-0.27; 0.64) and in CG 0.13 (-0.25; 0.55). Between sessions 3 and 1, IG was 0.06 (-0.57; 0.71) and in CG 0.06 (-0.39; 0.52). The results indicate low reproducibility at participant levels and remained unchanged after adjustment for baseline glucose values.

CONCLUSION

On an individual level, the glucose declines during three standardized sessions of PA were not associated with identical responses of the measured IG and CG levels. An overall anticipated decline of glucose concentrations was found in the moderate intensity cycling sessions. This highlights the importance of regular CG measurements during and after physical activity and awareness towards potential exercise-induced hypoglycemia in persons with type 1 diabetes.

People with type 1 diabetes and impaired awareness of hypoglycaemia have a delayed reaction to performing a glucose scan during hypoglycaemia: a prospective observational study

AIMS

Considering that people with type 1 diabetes and impaired awareness of hypoglycaemia (IAH) have a delayed perception of hypoglycaemia, the question arises whether they perform scans later in case of hypoglycaemia than people without IAH. We assessed whether time to performing a scan after reaching hypoglycaemia while using a flash glucose monitoring (flash GM) system is different in people with IAH compared with people without IAH.

METHODS

Ninety-two people with type 1 diabetes [mean (± sd) age 42 ± 14 years, HbA1c 57 ± 9 mmol/mol] using a flash GM system for 3 months were included. Flash GM data were assessed for time until scan after reaching hypoglycaemia level 1 (< 3.9 mmol/l) and level 2 (< 3.0 mmol/l) and compared for type 1 diabetes with vs. without IAH via unpaired t-test/Mann-Whitney U test (P < 0.05).

RESULTS

Significant differences were found only for the delay between reaching hypoglycaemia and scan between people with and without IAH for Gold score [hypoglycaemia level 1: IAH 78 (51-105) min vs. without IAH 63 (42-89) min, P = 0.03; night-time hypoglycaemia level 2: IAH 140 (107-227) min vs. without IAH 96 (41-155) min, P = 0.004] and Pedersen-Bjergaard score [hypoglycaemia level 1: IAH 76 (52-97) min vs. without IAH 54 (38-71) min, P = 0.011; night-time hypoglycaemia level 1: IAH 132 (79-209) min vs. without IAH 89 (59-143) min, P = 0.011; night-time hypoglycaemia level 2: IAH 134 (66-212) min vs. without IAH 80 (37-131) min, P = 0.002). Data are shown as median (i.q.r.).

CONCLUSIONS

Time until scan after reaching hypoglycaemia might be an objective assessment tool for IAH, but needs to be investigated comprehensively in future studies.

Weight Management in Youth with Type 1 Diabetes and Obesity: Challenges and Possible Solutions

PURPOSE OF REVIEW

This review highlights challenges associated with weight management in children and adolescents with type 1 diabetes (T1D). Our purpose is to propose potential solutions to improve weight outcomes in youth with T1D.

RECENT FINDINGS

A common barrier to weight management in T1D is reluctance to engage in exercise for fear of hypoglycemia. Healthcare practitioners generally provide limited guidance for insulin dosing and carbohydrate modifications to maintain stable glycemia during exercise. Adherence to dietary guidelines is associated with improved glycemia; however, youth struggle to meet recommendations. When psychosocial factors are addressed in combination with glucose trends, this often leads to successful T1D management. Newer medications also hold promise to potentially aid in glycemia and weight management, but further research is necessary. Properly addressing physical activity, nutrition, pharmacotherapy, and psychosocial factors while emphasizing weight management may reduce the likelihood of obesity development and its perpetuation in this population.

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

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

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

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

Feasibility and Acceptability of Physical Activity Monitoring as an Educational Tool in Management of Pediatric Type 1 Diabetes

OBJECTIVES

The spontaneous, sporadic and sometimes unpredictable nature of children's physical activity causes fluctuations in blood glucose level and challenges for children with type 1 diabetes. Physical activity monitoring has potential utility. In this study, we explored the perceptions of physical activity monitoring among health-care professionals and assessed the feasibility and acceptability of using it in the management of pediatric type 1 diabetes.

METHODS

Seven health-care professionals from 1 pediatric diabetes centre in the United Kingdom were involved in a focus group. Data were analyzed thematically. Physical activity monitoring using a wrist-worn monitor was tested for feasibility with 13 children aged 7 to 11 years with type 1 diabetes. The primary outcome was feasibility (i.e. recruitment, adherence, data completion, adverse events and acceptability). Secondary measures were glycemic control, parental self-efficacy for diabetes management and parental fear of hypoglycemia.

RESULTS

Health-care professionals valued having an awareness of the level, type and intensity of children's physical activity. They identified unmet training and resource needs that would facilitate them in being able to give physical activity advice to children and families. Recruitment rate was 20%, adherence to the activity monitoring was good and the study completion rate was 62%. No adverse events were reported. Physical activity monitoring was considered acceptable by parents.

CONCLUSIONS

Physical activity monitoring could be a feasible part of routine clinical practice, but further research is needed to understand whether health-care professionals are best placed to implement it and what impact it has on health outcomes.

Can Short Bouts of Exercise ("Exercise Snacks") Improve Body Composition in Adolescents with Type 1 Diabetes? A Feasibility Study

INTRODUCTION

Puberty is associated with a deterioration of blood glucose control in children with type 1 diabetes (T1D). The literature suggests that exercise improves homeostasis in adults with diabetes, but lack of time often precludes the performance of exercise. Besides, in earlier work, supplementation with glutamine, a nonessential amino acid, when administered prior to exercise, decreased overnight post-exercise blood glucose in adolescents with long-standing T1D, suggesting that glutamine increased insulin sensitivity or enhanced tissue glucose uptake. The purpose of the current study therefore was to determine the feasibility of a novel form of exercise ("exercise snacks," that is, short bouts of exercise spread throughout the day) with or without a supplemental amino acid, glutamine, and its impact on blood glucose homeostasis and body composition in adolescents with T1D.

METHODS

Twelve sedentary adolescents with T1D (HbA1c 8.1 ± 0.6%) performed exercise snacks (6 × 1 min of resistance-based activities) 3 times daily for 3 months; in addition, they were randomized to consume a drink containing either placebo or glutamine (0.5 g/kg/day). Continuous glucose monitoring, HbA1c, and dual X-ray absorptiometry were obtained before and after 3 months of each intervention.

RESULTS

Exercise snacks were easy to perform and well tolerated and were associated with a 2.2% loss of body fat mass when both groups were analyzed together (p = 0.015) after 3 months, whereas the change in lean body mass was not significant (p = 0.21). Metabolic control (HbA1c and glucose sensor data) was unchanged as result of the intervention regardless of group, and total daily insulin dose did not decrease.

CONCLUSION

Short bouts of exercise are sustainable over a 3-month period and can improve body composition in adolescents with poorly controlled T1D. Although metabolic control was unchanged as a result of the intervention regardless of group, this was a short-term intervention, hence assessment of metabolic impact will require long-term study.

Improving Clinical Outcomes in Newly Diagnosed Pediatric Type 1 Diabetes: Teamwork, Targets, Technology, and Tight Control-The 4T Study

Many youth with type 1 diabetes (T1D) do not achieve hemoglobin A1c (HbA1c) targets. The mean HbA1c of youth in the USA is higher than much of the developed world. Mean HbA1c in other nations has been successfully modified following benchmarking and quality improvement methods. In this review, we describe the novel 4T approach-teamwork, targets, technology, and tight control-to diabetes management in youth with new-onset T1D. In this program, the diabetes care team (physicians, nurse practitioners, certified diabetes educators, dieticians, social workers, psychologists, and exercise physiologists) work closely to deliver diabetes education from diagnosis. Part of the education curriculum involves early integration of technology, specifically continuous glucose monitoring (CGM), and developing a curriculum around using the CGM to maintain tight control and optimize quality of life.