Effectiveness of SmartGuard Technology in the Prevention of Nocturnal Hypoglycemia After Prolonged Physical Activity

Association of Smartphone-Based Activity Tracking and Nocturnal Hypoglycemia in People With Type 1 Diabetes

BACKGROUND

Nocturnal hypoglycemia (NH) remains a major burden for people with type 1 diabetes (T1D). Daytime physical activity (PA) increases the risk of NH. This pilot study tested whether cumulative daytime PA measured using a smartphone-based step tracker was associated with NH.

METHODS

Adults with T1D for ≥ 5 years (y) on multiple daily insulin or continuous insulin infusion, not using continuous glucose monitoring and HbA1c 6 to 10% wore blinded Freestyle Libre Pro sensors and recorded total daily carbohydrate (TDC) and total daily dose (TDD) of insulin. During this time, daily step count (DSC) was tracked using the smartphone-based Fitbit MobileTrack application. Mixed effects logistic regression was used to estimate the effect of DSC on NH (sensor glucose <70, <54 mg/dl for ≥15 minutes), while adjusting for TDC and TDD of insulin, and treating participants as a random effect.

RESULTS

Twenty-six adults, with 65.4% females, median age 27 years (interquartile range: 26-32) mean body mass index 23.9 kg/m2, median HbA1c 7.6% (7.1-8.1) and mean Gold Score 2.1 (standard deviation 1.0) formed the study population. The median DSC for the whole group was 2867 (1820-4807). There was a significant effect of DSC on NH episodes <70 mg/dl. (odds ratio 1.11 [95% CI: 1.01-1.23, P = .04]. There was no significant effect on NH <54 mg/dl.

CONCLUSION

Daily PA measured by a smartphone-based step tracker was associated with the risk of NH in people with type 1 diabetes.

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.

Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies

Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use.

Nighttime Hypoglycemia in Children with Type 1 Diabetes after one Day of Football Tournament

The aim of the study was to investigate factors related to the occurrence of nighttime hypoglycemia after a football tournament in children with type 1 diabetes mellitus. The multicenter study (GoalDiab study) included 189 children and adolescents with type 1 diabetes mellitus, from 11 diabetes care centers in Poland. Hypoglycemia was defined according to the International Hypoglycemia Study Group Statement. We analyzed the data of 95 participants with completed protocols with regards to nighttime hypoglycemia (82% male), aged 11.6 (9.8-14.2) years, diabetes duration 5.0 (2.0-8.0) years. There were 47 episodes of nighttime Level 1 hypoglycemia (≤3.9 mmol/L). Occurrence of clinically important Level 2 hypoglycemia (<3.0 mmol/L) during a game period was positively associated with nighttime hypoglycemia (≤3.9 mmol/L) incident (Odds Ratio=10.7; 95% Confidence Interval: 1.1-100.2; p=0.04). Using Continuous Glucose Monitoring was negatively associated with the occurrence of nighttime hypoglycemia (≤3.9 mmol/L) compared with using glucose meters or Flash Glucose Monitoring (Odds Ratio=0.31; 95% Confidence Interval: 0.12-0.83; p=0.02). The occurrence of clinically important hypoglycemia related to physical activity is associated with the occurrence of hypoglycemia during the night. Continuous Glucose Monitoring is negatively associated with nighttime hypoglycemia after a day of competition.

Evolution of Insulin Delivery Devices: From Syringes, Pens, and Pumps to DIY Artificial Pancreas

The year 2021 will mark 100 years since the discovery of insulin. Insulin, the first medication to be discovered for diabetes, is still the safest and most potent glucose-lowering therapy. The major challenge of insulin despite its efficacy has been the occurrence of hypoglycemia, which has resulted in sub-optimal dosages being prescribed in the vast majority of patients. Popular devices used for insulin administration are syringes, pens, and pumps. An artificial pancreas (AP) with a closed-loop delivery system with > 95% time in range is believed to soon become a reality. The development of closed-loop delivery systems has gained momentum with recent advances in continuous glucose monitoring (CGM) and computer algorithms. This review discusses the evolution of syringes, disposable, durable pens and connected pens, needles, tethered and patch insulin pumps, bionic pancreas, alternate controller-enabled infusion (ACE) pumps, and do-it-yourself artificial pancreas systems (DIY-APS).

Diabetes Technology and Exercise

Advances in technologies such as glucose monitors, exercise wearables, closed-loop systems, and various smartphone applications are helping many people with diabetes to be more physically active. These technologies are designed to overcome the challenges associated with exercise duration, mode, relative intensity, and absolute intensity, all of which affect glucose homeostasis in people living with diabetes. At present, optimal use of these technologies depends largely on motivation, competence, and adherence to daily diabetes care requirements. This article discusses recent technologies designed to help patients with diabetes to be more physically active, while also trying to improve glucose control around exercise.

Assessment of Safety and Glycemic Control During Football Tournament in Children and Adolescents With Type 1 Diabetes-Results of GoalDiab Study

PURPOSE

To assess glycemic control and safety of children and adolescents with type 1 diabetes participating in a 2-day football tournament.

METHODS

In total, 189 children with type 1 diabetes from 11 diabetes care centers, in Poland, participated in a football tournament in 3 age categories: 7-9 (21.2%), 10-13 (42.9%), and 14-17 (36%) years. Participants were qualified and organized in 23 football teams, played 4 to 6 matches of 30 minutes, and were supervised by a medical team. Data on insulin dose and glycemia were downloaded from personal pumps, glucose meters, continuous glucose monitoring, and flash glucose monitoring systems.

RESULTS

The median level of blood glucose before the matches was 6.78 (4.89-9.39) mmol/L, and after the matches, it was 7.39 (5.5-9.87) mmol/L (P = .001). There were no episodes of severe hypoglycemia or ketoacidosis. The number of episodes of low glucose value (blood glucose ≤3.9 mmol/L) was higher during the tournament versus 30 days before: 1.2 (0-1.5) versus 0.7 (0.3-1.1) event/person/day, P < .001. Lactate levels increased during the matches (2.2 [1.6-4.0] mmol/L to 4.4 [2.6-8.5] mmol/L after the matches, P < .001).

CONCLUSIONS

Large football tournaments can be organized safely for children with type 1 diabetes. For the majority of children, moderate mixed aerobic-anaerobic effort did not adversely affect glycemic results and metabolic safety.

Persistent heterogeneity in diabetes technology reimbursement for children with type 1 diabetes: The SWEET perspective

BACKGROUND

Frequent use of modern diabetes technologies increases the chance for optimal type 1 diabetes (T1D) control. Limited reimbursement influences the access of patients with T1D to these modalities and could worsen their prognosis. We aimed to describe the situation of reimbursement for insulins, glucometers, insulin pumps (CSII) and continuous glucose monitoring (CGM) for children with T1D in European countries participating in the SWEET Project and to compare data from EU countries with data from our previous study in 2009.

METHODS

The study was conducted between March 2017 and August 2017. First, we approached diabetes technology companies with a survey to map the reimbursement of insulins and diabetic devices. The data collected from these companies were then validated by members of the SWEET consortium.

RESULTS

We collected data from 29 European countries, whereas all types of insulins are mostly fully covered, heterogeneity was observed regarding the reimbursement of strips for glucometers (from 90 strips/month to no limit). CSII is readily available in 20 of 29 countries. Seven countries reported significant quota issues or obstacles for CSII prescription, and two countries had no CSII reimbursement. CGM is at least partially reimbursed in 17 of 29 countries. The comparison with the 2009 study showed an increasing availability of CSII and CGM across the EU.

CONCLUSIONS

Although innovative diabetes technology is available, a large proportion of children with T1D still do not benefit from it due to its limited reimbursement.

Excellent Glycemic Control Maintained by Open-Source Hybrid Closed-Loop AndroidAPS During and After Sustained Physical Activity

OBJECTIVE

Officially licensed hybrid closed-loop systems are not currently available worldwide; therefore, open-source systems have become increasingly popular. Our aim was to assess the safety, feasibility, and efficacy of an open-source hybrid closed-loop system (AndroidAPS) versus SmartGuard^® technology for day-and-night glucose control in children under extreme sports conditions.

RESEARCH DESIGN AND METHODS

Twenty-two children (16 girls, 6-15 years of age, median HbA1c 56 ± 9 mmol/mol) were enrolled in this pivotal winter sports camp study. The participants were divided into two groups using either the AndroidAPS or SmartGuard technology. Physical exertion was represented by all-day alpine skiing. The primary endpoints were mean glucose level, time below the threshold of 3.9 mmol/L, and time within the target range of 3.9 to 10 mmol/L.

RESULTS

The children using the AndroidAPS had significantly lower mean glycemia levels (7.2 ± 2.7 vs. 7.7 ± 2.8 mmol/L; 129.6 ± 49 vs. 138.6 ± 50 mg/dL, P < 0.042) than the children using the SmartGuard. The proportion of time below the target (median 5.0% ± 2.5% vs. 3.0% ± 2.3%, P = 0.6) and in the target zone (63% ± 9.5% vs. 63% ± 18%, P = 0.5) did not significantly differ. The AndroidAPS group experienced more frequent malfunctions of the cannula set (median 0.8 ± 0.4 vs. 0.2 ± 0.4, P = 0.02), which could have affected the results. No significant difference was found in the amount of carbohydrates consumed for the prevention and treatment of hypoglycemia [median 40 ± 23 vs. 25 ± 29 g/(patient ·3 days)]. No episodes of severe hypoglycemia or other serious adverse events were noted.

CONCLUSIONS

This pilot study showed that the AndroidAPS system was a safe and feasible alternative to the SmartGuard Technology.

Diabetes and technology in 2030: a utopian or dystopian future?

The ability of an individual living with diabetes to have human-to-human contact with their healthcare provider is not keeping pace with the number of people developing diabetes. From a futurist perspective, however, this dichotomy of diabetes care represents an opportunity for digital healthcare. The focus of technological innovation is unlikely to be the replacement of the multidisciplinary diabetes team but rather the provision of meaningful individual and family support between clinic visits and, on a larger scale, the facilitation of population health management for diabetes. We can also expect to see new therapies, including implantable drug delivery systems, automated closed-loop systems and miniaturized non-invasive glucose monitoring systems. New digital health technologies will create a 'digital diabetes ecosystem' to enhance rather than devolve care from humans. Concerns related to data privacy and ownership will inevitably rise, thus a future for diabetes care relying heavily on technology is not inevitably utopian. Nevertheless, revolutions in the development of novel sensors, accumulation of 'big data', and use of artificial intelligence will provide exciting opportunities for preventing, monitoring and treating diabetes in the near future.