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

Causality in Epidemiology01:21

Causality in Epidemiology

409
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
409
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

2.0K
A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
2.0K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

307
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
307
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

99
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
99
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

285
The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
285
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

1.6K
The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Towards High-Accuracy Athletic Injury Predictions Using a First-Principles Modelling Approach: Theory to (Future) Practice.

Sports medicine (Auckland, N.Z.)·2026
Same author

Developing a Fundamental Theoretical Definition for Athletic Injury: Metaphysics, Logic, and Mathematics.

Sports medicine (Auckland, N.Z.)·2026
Same author

A Conceptual Exploration of Hamstring Muscle-Tendon Functioning during the Late-Swing Phase of Sprinting: The Importance of Evidence-Based Hamstring Training Frameworks.

Sports medicine (Auckland, N.Z.)·2023
Same author

Training Load and Injury: Causal Pathways and Future Directions.

Sports medicine (Auckland, N.Z.)·2021

Related Experiment Video

Updated: Jun 30, 2025

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
11:28

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Published on: May 18, 2015

12.5K

Athletic Injury Research: Frameworks, Models and the Need for Causal Knowledge.

Judd T Kalkhoven1,2

  • 1School of Health Sciences, Western Sydney University, Campbelltown, NSW, Australia. J.Kalkhoven@westernsydney.edu.au.

Sports Medicine (Auckland, N.Z.)
|March 20, 2024
PubMed
Summary
This summary is machine-generated.

This study proposes using causal diagrams to improve athletic injury research. This approach helps identify injury causes and develop effective prevention strategies, overcoming limitations of observational studies.

More Related Videos

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

7.9K
Author Spotlight: Advancing Tendon Research by Developing Mouse Assembloids to Understand Cellular Mechanisms
08:32

Author Spotlight: Advancing Tendon Research by Developing Mouse Assembloids to Understand Cellular Mechanisms

Published on: March 22, 2024

917

Related Experiment Videos

Last Updated: Jun 30, 2025

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
11:28

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Published on: May 18, 2015

12.5K
An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

7.9K
Author Spotlight: Advancing Tendon Research by Developing Mouse Assembloids to Understand Cellular Mechanisms
08:32

Author Spotlight: Advancing Tendon Research by Developing Mouse Assembloids to Understand Cellular Mechanisms

Published on: March 22, 2024

917

Area of Science:

  • Sports Science and Medicine
  • Epidemiology
  • Biostatistics

Background:

  • Applied sports science faces challenges in understanding athletic injury causality and prevention due to limited variable control.
  • Randomized controlled trials are often impractical or unethical, leading to a heavy reliance on observational research.
  • Current observational approaches often lack causal inference tools, hindering knowledge acquisition and effective injury prevention strategy development.

Purpose of the Study:

  • To propose a novel model for investigating athletic injury aetiology and mechanisms.
  • To present a framework for developing and evaluating athletic injury prevention strategies.
  • To address the limitations of current research methodologies in sports science and medicine.

Main Methods:

  • Utilizing causal diagrams, including frameworks, models, and causal directed acyclic graphs (DAGs).
  • Guiding athletic injury research and prevention efforts through structured causal inference.
  • Facilitating the investigation of specific causal links, mechanisms, and assumptions.

Main Results:

  • The proposed causal diagram approach facilitates the investigation of specific causal links and mechanisms.
  • It aids in translating lab-based research into applied sports settings.
  • It guides causal inferences from applied research by establishing robust causal structures.

Conclusions:

  • Causal diagrams offer a solution to challenges in athletic injury research, improving understanding of injury causality.
  • This approach guides the development and adoption of relevant metrics, technologies, and prevention strategies.
  • It encourages the establishment of strong theoretical foundations, minimizing research waste and improving scientific rigor.