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: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

4.8K
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...
4.8K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

232
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
232
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

221
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
221

You might also read

Related Articles

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

Sort by
Same author

Links between physical activity, time-in-range and glucose predictability in people with type 1 diabetes.

The International journal of artificial organs·2026
Same author

Switched Controllers in Fully Closed Loop Insulin Delivery Systems: Reducing the Trade-Off Between Prandial Control and Safety.

Artificial organs·2025
Same author

Personalized Model Identification for Glucose Dynamics From Clinical Data With Incomplete Inputs.

IEEE transactions on bio-medical engineering·2025
Same author

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

Diabetes technology & therapeutics·2024
Same author

Auto adaptation of closed-loop insulin delivery system using continuous reward functions and incremental discretization.

Computer methods in biomechanics and biomedical engineering·2023
Same author

A Dual-Hormone Multicontroller for Artificial Pancreas Systems.

IEEE journal of biomedical and health informatics·2022

Related Experiment Video

Updated: Jan 6, 2026

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
05:59

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

7.1K

Model based analytical approach for physical activity quantification in people with type 1 diabetes.

Fernando Leonel Da Rosa Jurao1, Emilia Fushimi2, Fabricio Garelli2

  • 1Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales - LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina. leonel.darosajurao@ing.unlp.edu.ar.

Medical & Biological Engineering & Computing
|October 18, 2025
PubMed
Summary

Physical activity management in type 1 diabetes (T1D) is challenging. A new heart rate (HR) model quantifies aerobic and anaerobic exercise, aiding glycemic control for T1D patients.

Keywords:
Dynamic modelExercisePhysical activityType 1 diabetes

More Related Videos

Physical Activity Measurement in Children Accepting Table Tennis Training
06:51

Physical Activity Measurement in Children Accepting Table Tennis Training

Published on: July 27, 2022

2.3K
A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
07:24

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

12.9K

Related Experiment Videos

Last Updated: Jan 6, 2026

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
05:59

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

7.1K
Physical Activity Measurement in Children Accepting Table Tennis Training
06:51

Physical Activity Measurement in Children Accepting Table Tennis Training

Published on: July 27, 2022

2.3K
A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
07:24

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

12.9K

Area of Science:

  • Endocrinology and Exercise Physiology
  • Biomedical Signal Processing

Background:

  • Physical activity (PA) significantly impacts glucose levels in type 1 diabetes (T1D).
  • Effective PA monitoring is crucial for optimizing glycemic control, especially with automated insulin delivery systems.
  • Distinguishing between aerobic and anaerobic PA is key due to their contrasting effects on blood glucose.

Purpose of the Study:

  • To present a novel state-space model utilizing the heart rate (HR) signal.
  • To quantify and differentiate between aerobic and anaerobic physical activity.
  • To enhance glucose management strategies for individuals with T1D during exercise.

Main Methods:

  • Developed a state-space model analyzing HR signal features (mean HR, max HR, fluctuations).
  • The model requires no prior training, offering interpretability and intuitive tuning.
  • Validated the model using two clinical trials: T1DEXI and a pilot study in Argentina.

Main Results:

  • The model successfully quantified and differentiated between aerobic and resistance PA.
  • Demonstrated the model's capacity to distinguish exercise types with contrasting influences on glucose levels.
  • Confirmed the model's applicability in real-world clinical settings.

Conclusions:

  • The developed HR-based state-space model is effective for quantifying and distinguishing aerobic and resistance PA in T1D.
  • This approach offers a promising, explainable, and tunable tool for improving exercise management and glycemic control in T1D.
  • The findings support the integration of this model into future diabetes management technologies.