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 Experiment Videos

Assembling control models from pulmonary gas exchange dynamics.

G D Swanson1

  • 1Department of Anesthesiology, University of Colorado Health Sciences Center, Denver 80262.

Medicine and Science in Sports and Exercise
|February 1, 1990
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Measuring the statistical probability of dreams?

Alternative therapies in health and medicine·2001
Same author

Triggering of sudden death from cardiac causes by vigorous exertion.

The New England journal of medicine·2001
Same author

Passive smoking and coronary heart disease.

The New England journal of medicine·1999
Same author

Pulmonary training may alter exertional dyspnea and fatigue via an exercise-like training effect of a lowered heart rate.

Advances in experimental medicine and biology·1999
Same author

On analytical methods and inferences for 2 x 2 contingency table data from medical studies.

Computers and biomedical research, an international journal·1991
Same author

Time domain analysis of oxygen uptake during pseudorandom binary sequence exercise tests.

Journal of applied physiology (Bethesda, Md. : 1985)·1991
Same journal

The Impact of Cardiorespiratory Fitness on Cytotoxic T Cell Metabolism and Function.

Medicine and science in sports and exercise·2026
Same journal

Female Athletes Through the Lifespan: Clinical Considerations and a Call for Comprehensive Sports Medicine Healthcare.

Medicine and science in sports and exercise·2026
Same journal

Artificial Intelligence in Exercise Science and Sports Medicine.

Medicine and science in sports and exercise·2026
Same journal

Non-Vigorous Physical Activity Associated with Reduced Hospitalization Risk with or without Diabetes or Peripheral Artery Disease: Study of Latinos.

Medicine and science in sports and exercise·2026
Same journal

One Step Further in Resistance Training Prescription: Do Recent Updates Require Reconsideration?

Medicine and science in sports and exercise·2026
Same journal

Response.

Medicine and science in sports and exercise·2026
See all related articles

This study classifies models based on their intended purpose, aiding in the analysis of pulmonary gas exchange dynamics during exercise. The research demonstrates how these models improve data estimation and experimental design for physiological studies.

Area of Science:

  • Physiology
  • Biomedical Engineering
  • Systems Biology

Background:

  • Models are crucial for understanding complex biological systems by abstracting key features.
  • Classifying models based on their intended purpose (structural, empirical, functional) is essential for their effective application.
  • Understanding pulmonary gas exchange dynamics during exercise requires appropriate modeling approaches.

Purpose of the Study:

  • To classify models based on their intended purpose for studying physiological systems.
  • To illustrate the utility of this model classification for analyzing pulmonary gas exchange dynamic control processes during exercise.
  • To apply the modeling process to estimate breath-by-breath gas exchange data and optimize experimental design.

Main Methods:

  • Classification of models into structural, empirical, and functional types based on purpose.

Related Experiment Videos

  • Application of model classification to the study of pulmonary gas exchange dynamics.
  • Utilizing the model classification to address problems in data estimation, model selection, and experimental design.

    • Main Results:

    • Demonstrated the utility of model classification for studying pulmonary gas exchange dynamics during exercise.
    • Applied the modeling process to estimate breath-by-breath gas exchange data effectively.
    • Showcased the selection of appropriate models for dynamic work rate inputs and the design of dynamic aspects of work rate input.

    Conclusions:

    • Model classification based on intended purpose enhances the study of physiological systems, particularly pulmonary gas exchange.
    • This approach facilitates interaction among experimental data, physiological hypotheses, and experimental design.
    • The demonstrated applications highlight the practical value of this modeling framework in exercise physiology research.