Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

4.3K
Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
4.3K
Diabetes: Management and Pharmacotherapy01:15

Diabetes: Management and Pharmacotherapy

867
The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
Insulin remains the cornerstone of treatment for most patients with type 1 and many...
867
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

4.9K
Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
4.9K
Diabetes: Symptoms, Diagnosis, and Complications01:15

Diabetes: Symptoms, Diagnosis, and Complications

2.1K
For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
2.1K
Carbohydrate Metabolism01:36

Carbohydrate Metabolism

13.8K
Carbohydrates are polymers composed of molecules containing atoms of carbon, hydrogen and oxygen. One gram of carbohydrate can provide four kilo-calories of energy, which makes it the most efficient instant energy source.
Starch accounts for approximately 60% of the carbohydrates consumed by humans. Since amylase enzymes cannot function in the stomach's acidic environment, starch can only be digested in the mouth and small intestine. Simple sugars are found naturally in milk and fruits in...
13.8K
Pathophysiology of Diabetes01:20

Pathophysiology of Diabetes

3.1K
Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility,...
3.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Continuous Glucose Monitoring Metrics for Predicting Adverse Neonatal Outcomes in Individuals Undergoing Gestational Diabetes Screening.

Journal of diabetes science and technology·2026
Same author

Relationship Between Sleep and Meal Timing with Glycemia Parameters in Individuals with Obesity Participating in a Randomized Time-Restricted Eating Study.

Nutrients·2026
Same author

Biological and Social Risk Factors for Predicting Type 2 Diabetes in Youth with Prediabetes: Review of Existing Prediction Models.

Diabetes therapy : research, treatment and education of diabetes and related disorders·2026
Same author

Cross-Sectional Associations Between Hyperglycemia and Hearing Loss at the Year 35 Coronary Artery Risk Development in Young Adults Observational Cohort Study.

American journal of audiology·2026
Same author

Pilot Randomised Controlled Trial of the Feasibility and Acceptability of Family-Based Time Limited Eating to Treat Obesity.

Pediatric obesity·2026
Same author

Integrating the Glycemia Risk Index Into Clinical Practice and Research: A Consensus Report.

Journal of diabetes science and technology·2026

Related Experiment Video

Updated: Jan 15, 2026

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
06:22

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model

Published on: November 29, 2024

1.3K

Managing Exercise-Related Glycemic Events in Type 1 Diabetes: Development and Validation of Predictive Models for a

Sisi Ma1,2, Ryan Coopergard2, Mark Clements3

  • 1Department of Medicine, Medical School, University of Minnesota, Minneapolis, MN, United States.

JMIR Diabetes
|October 10, 2025
PubMed
Summary

Accurate models can now forecast exercise-induced glycemic events in type 1 diabetes using only continuous glucose monitor (CGM) data. This simplifies deployment and reduces user burden for better diabetes self-management.

Keywords:
continuous glucose monitoringdecision support toolexercise-induced glycemic eventshyperglycemiahypoglycemiapredictive modelingtype 1 diabetes

More Related Videos

Improving Strength, Power, Muscle Aerobic Capacity, and Glucose Tolerance through Short-term Progressive Strength Training Among Elderly People
12:59

Improving Strength, Power, Muscle Aerobic Capacity, and Glucose Tolerance through Short-term Progressive Strength Training Among Elderly People

Published on: July 5, 2017

13.0K
A Protocol for Constructing a Rat Wound Model of Type 1 Diabetes
05:18

A Protocol for Constructing a Rat Wound Model of Type 1 Diabetes

Published on: February 17, 2023

5.6K

Related Experiment Videos

Last Updated: Jan 15, 2026

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
06:22

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model

Published on: November 29, 2024

1.3K
Improving Strength, Power, Muscle Aerobic Capacity, and Glucose Tolerance through Short-term Progressive Strength Training Among Elderly People
12:59

Improving Strength, Power, Muscle Aerobic Capacity, and Glucose Tolerance through Short-term Progressive Strength Training Among Elderly People

Published on: July 5, 2017

13.0K
A Protocol for Constructing a Rat Wound Model of Type 1 Diabetes
05:18

A Protocol for Constructing a Rat Wound Model of Type 1 Diabetes

Published on: February 17, 2023

5.6K

Area of Science:

  • Endocrinology
  • Biomedical Engineering
  • Data Science

Background:

  • Exercise is crucial for type 1 diabetes management but often leads to glycemic events, deterring patients.
  • Exercise-induced hyperglycemia and hypoglycemia are significant challenges for individuals with type 1 diabetes.

Purpose of the Study:

  • To develop accurate and easily deployable models for predicting exercise-induced glycemic events in real-world settings.
  • To forecast glycemic events during and after exercise in type 1 diabetes patients.

Main Methods:

  • Analysis of free-living data from the Type 1 Diabetes Exercise Initiative study.
  • Development of predictive models using continuous glucose monitoring (CGM), demographic, clinical, insulin, and diet data.
  • Utilized repeated stratified nested cross-validation for model evaluation and performance estimation.

Main Results:

  • Models incorporating all four data modalities demonstrated excellent predictive performance (AUROCs 0.880-0.992).
  • Models using only CGM data achieved statistically indistinguishable performance, indicating CGM data alone is sufficient.
  • CGM-only models exhibited outstanding calibration and resilience to noisy input.

Conclusions:

  • Successfully developed models to forecast exercise-induced glycemic events using solely CGM data.
  • These models offer excellent predictive performance, calibration, and robustness.
  • The models are easily deployable, require minimal user input, and can be translated into decision support tools.