scholarly journals Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes

2019 ◽  
Vol 13 (4) ◽  
pp. 718-727 ◽  
Author(s):  
Nicole Hobbs ◽  
Iman Hajizadeh ◽  
Mudassir Rashid ◽  
Kamuran Turksoy ◽  
Marc Breton ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Type 1 Diabetes ◽  
Prediction Accuracy ◽  
Artificial Pancreas ◽  
Metabolic State ◽  
Physiological Variables ◽  
Additional Input ◽  
Glucose Prediction

Background: Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the glucose prediction accuracy during exercise. Methods: Predictor-based subspace identification is applied to a dynamic glucose prediction model including heart rate measurements along with variables representing the carbohydrate consumption and insulin boluses. To demonstrate the improvement in prediction ability due to the additional heart rate variable, the performance of the proposed modeling technique is evaluated with (SID-HR) and without heart rate (SID-2) as an additional input using experimental data involving adolescents at ski camp. Furthermore, the performance of the proposed approach is compared to that of the metabolic state observer (MSO) model currently used in the University of Virginia AP algorithm. Results: The addition of heart rate in the subspace-based model (SID-HR) yields a statistically significant improvement in the root-mean-square error compared to the SID-2 model ( P < .001) and the standard MSO ( P < .001). Furthermore, the SID-HR model performed favorably in comparison to the SID-2 and MSO models after accounting for its increased complexity. Conclusions: Directly considering the effects of physical activity levels on glycemic dynamics through the inclusion of heart rate as an additional input variable in the glucose dynamics model improves the glucose prediction accuracy. The proposed methodology could improve exercise-informed model-based predictive control algorithms in artificial pancreas systems.

scholarly journals Prediction of Hypoglycemia During Aerobic Exercise in Adults With Type 1 Diabetes

2019 ◽  
Vol 13 (5) ◽  
pp. 919-927 ◽  
Author(s):  
Ravi Reddy ◽  
Navid Resalat ◽  
Leah M. Wilson ◽  
Jessica R. Castle ◽  
Joseph El Youssef ◽  
...  
Keyword(s):  
Heart Rate ◽  
Type 1 Diabetes ◽  
Aerobic Exercise ◽  
Artificial Pancreas ◽  
Prediction Algorithm ◽  
Exercise Heart Rate ◽  
Pump Therapy ◽  
Validation Data ◽  
Data Set

Background: Fear of exercise related hypoglycemia is a major reason why people with type 1 diabetes (T1D) do not exercise. There is no validated prediction algorithm that can predict hypoglycemia at the start of aerobic exercise. Methods: We have developed and evaluated two separate algorithms to predict hypoglycemia at the start of exercise. Model 1 is a decision tree and model 2 is a random forest model. Both models were trained using a meta-data set based on 154 observations of in-clinic aerobic exercise in 43 adults with T1D from 3 different studies that included participants using sensor augmented pump therapy, automated insulin delivery therapy, and automated insulin and glucagon therapy. Both models were validated using an entirely new validation data set with 90 exercise observations collected from 12 new adults with T1D. Results: Model 1 identified two critical features predictive of hypoglycemia during exercise: heart rate and glucose at the start of exercise. If heart rate was greater than 121 bpm during the first 5 min of exercise and glucose at the start of exercise was less than 182 mg/dL, it predicted hypoglycemia with 79.55% accuracy. Model 2 achieved a higher accuracy of 86.7% using additional features and higher complexity. Conclusions: Models presented here can assist people with T1D to avoid exercise related hypoglycemia. The simple model 1 heuristic can be easily remembered (the 180/120 rule) and model 2 is more complex requiring computational resources, making it suitable for automated artificial pancreas or decision support systems.

scholarly journals Adding Heart Rate Signal to a Control-to-Range Artificial Pancreas System Improves the Protection Against Hypoglycemia During Exercise in Type 1 Diabetes

2014 ◽  
Vol 16 (8) ◽  
pp. 506-511 ◽  
Author(s):  
Marc D. Breton ◽  
Sue A. Brown ◽  
Colleen Hughes Karvetski ◽  
Laura Kollar ◽  
Katarina A. Topchyan ◽  
...  
Keyword(s):  
Heart Rate ◽  
Type 1 Diabetes ◽  
Artificial Pancreas

Impact of physical activity on heart rate variability in children with type 1 diabetes

2007 ◽  
Vol 24 (6) ◽  
pp. 741-747 ◽  
Author(s):  
Su-Ru Chen ◽  
Yann-Jinn Lee ◽  
Hung-Wen Chiu ◽  
Chii Jeng
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Type 1 Diabetes

scholarly journals Comparison of Physical Activity Sensors and Heart Rate Monitoring for Real-Time Activity Detection in Type 1 Diabetes and Control Subjects

2013 ◽  
Vol 15 (9) ◽  
pp. 751-757 ◽  
Author(s):  
Chinmay Manohar ◽  
Derek T. O'Keeffe ◽  
Ling Hinshaw ◽  
Ravi Lingineni ◽  
Shelly K. McCrady-Spitzer ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Type 1 Diabetes ◽  
Real Time ◽  
Activity Detection ◽  
Heart Rate Monitoring ◽  
Time Activity ◽  
Activity Sensors ◽  
And Control

scholarly journals Artificial Pancreas Systems and Physical Activity in Patients with Type 1 Diabetes: Challenges, Adopted Approaches, and Future Perspectives

2019 ◽  
Vol 13 (6) ◽  
pp. 1077-1090 ◽  
Author(s):  
Sémah Tagougui ◽  
Nadine Taleb ◽  
Joséphine Molvau ◽  
Élisabeth Nguyen ◽  
Marie Raffray ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Healthcare Professionals ◽  
Therapeutic Option ◽  
Artificial Pancreas ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Delivery Methods ◽  
Current State

Physical activity is important for patients living with type 1 diabetes (T1D) but limited by the challenges associated with physical activity induced glucose variability. Optimizing glycemic control without increasing the risk of hypoglycemia is still a hurdle despite many advances in insulin formulations, delivery methods, and continuous glucose monitoring systems. In this respect, the artificial pancreas (AP) system is a promising therapeutic option for a safer practice of physical activity in the context of T1D. It is important that healthcare professionals as well as patients acquire the necessary knowledge about how the AP system works, its limits, and how glucose control is regulated during physical activity. This review aims to examine the current state of knowledge on exercise-related glucose variations especially hypoglycemic risk in T1D and to discuss their effects on the use and development of AP systems. Though effective and highly promising, these systems warrant further research for an optimized use around exercise.

A Nonlinear Data-Driven Model of Glucose Dynamics Accounting for Physical Activity for Type 1 Diabetes: An In Silico Study

2016 ◽  
Author(s):  
Jinyu Xie ◽  
Qian Wang
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Clinical Training ◽  
Time Series Data ◽  
Artificial Pancreas ◽  
Data Driven ◽  
Series Data ◽  
Data Sets ◽  
Using Data

Physical activity is an important physiological information which should be taken into account by artificial pancreas to achieve optimal control of blood glucose in Type 1 Diabetes patients. An accurate glucose dynamic model with physical activity as an additional input is highly desirable for the next generation artificial pancreas. In this paper, we present a nonlinear data-driven model that captures both the insulin-independent and -dependent effect of physical activity, especially the prolonged effect of physical activity on insulin sensitivity that can last 24–48 hours post exercise. The model was identified and validated using data sets generated by a physiological glucose-exercise model under a clinical training protocol. Compared to modeling the effect of physical activity as a linear additive term only in a glucose dynamic equation, the proposed nonlinear model showed significant improvement of prediction accuracy in all three metrics, particularly in large prediction horizons (P < 0.05). Further investigation in time-series data indicates that the improvement mainly resulted from the better prediction of glucose around the first meal time after exercise (6 to 8 hours after the meal was taken).

scholarly journals Continuous Glucose Monitoring and Exercise in Type 1 Diabetes: Past, Present and Future

Biosensors ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 73 ◽  
Author(s):  
Shaelyn Houlder ◽  
Jane Yardley
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
Closed Loop ◽  
Artificial Pancreas ◽  
Glucose Monitoring ◽  
Post Exercise ◽  
Exercise Period ◽  
Academic Search

Prior to the widespread use of continuous glucose monitoring (CGM), knowledge of the effects of exercise in type 1 diabetes (T1D) was limited to the exercise period, with few studies having the budget or capacity to monitor participants overnight. Recently, CGM has become a staple of many exercise studies, allowing researchers to observe the otherwise elusive late post-exercise period. We performed a strategic search using PubMed and Academic Search Complete. Studies were included if they involved adults with T1D performing exercise or physical activity, had a sample size greater than 5, and involved the use of CGM. Upon completion of the search protocol, 26 articles were reviewed for inclusion. While outcomes have been variable, CGM use in exercise studies has allowed the assessment of post-exercise (especially nocturnal) trends for different exercise modalities in individuals with T1D. Sensor accuracy is currently considered adequate for exercise, which has been crucial to developing closed-loop and artificial pancreas systems. Until these systems are perfected, CGM continues to provide information about late post-exercise responses, to assist T1D patients in managing their glucose, and to be useful as a tool for teaching individuals with T1D about exercise.

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