Turning Research into Action: A Shiny App for Hamstring Injury Prevention and a New Way to Share Complex Ideas

Carlos Jimenez
Aug 27, 2024
2 min read

Introduction

As a sports medicine provider, my mission is to bridge the gap between the latest research and practical application, especially when it comes to injury prevention. Inspired by the article titled “The Complex Interrelationships of the Risk Factors Leading to Hamstring Injury and Implications for Injury Prevention: A Group Model Building Approach”, I developed a Shiny app designed to help clinicians, coaches, and athletes better understand and mitigate the risks associated with hamstring injuries. Link to the app

A screenshot of a Shiny app interface displaying a “Hamstring Injury Network Graph.” The interface includes three tabs: “Network Graph,” “Tree Structure,” and “Assessment Factors.” The “Network Graph” tab is active, showcasing a slider for adjusting the size of key nodes and several buttons to highlight different injury-related loops such as “Injury Severity and Recovery,” “Psychological Factors and Neuromuscular Coordination,” and “Workload, Fatigue, and Coordination.” The right side of the interface features a complex network graph with nodes of varying sizes and colors connected by lines, representing the relationships between different factors contributing to hamstring injuries. — Hamstring Injury Network Graph Interface

The Inspiration Behind the App

The article highlighted several key factors that contribute to hamstring injuries, such as neuromuscular coordination, workload management, psychological factors, and biomechanical quality. Recognizing the complexity of these interrelated factors, I saw the potential to create a tool that could visualize these relationships and offer actionable insights.

A screenshot of an interactive network graph visualizing the relationships between various factors contributing to hamstring injuries, including nodes representing key factors such as ‘Neuromuscular Coordination’ and ‘High-Speed Running Biomechanics’. The graph shows connected nodes and edges with varying sizes and colors based on user input. — Interactive Network Graph for Hamstring Injury Risk Factors

App Overview

The app I developed uses a combination of data visualization techniques to map out the intricate network of factors associated with hamstring injuries. It integrates advanced plotting libraries like Plotly, igraph, and networkD3, along with dynamic elements powered by Shiny.

Key Features:

1. Network Graph Visualization: This feature allows users to explore the relationships between various factors contributing to hamstring injuries. Nodes represent key factors such as ‘Neuromuscular Coordination,’ ‘Psychological Mood,’ and ‘High-Speed Running Biomechanics,’ while edges highlight the interactions between these factors.

2. Tree Structure: The tree structure offers a hierarchical view of the factors, making it easier to trace the pathways leading to injury. Users can navigate through different levels of the tree to see how each factor contributes to the overall injury risk.

3. Assessment Panels: The app includes detailed assessment panels covering Workload, Running and Strength Biomechanics, Muscle and Core Strength, and Psychological and Compliance factors. Each panel provides specific interventions and strategies to address the identified risks.

4. Dynamic Modals for Detailed Suggestions: When users interact with the network graph, they can access detailed sports medicine suggestions through dynamic modals. These suggestions are tailored to the specific loops (e.g., Injury Severity and Recovery, Psychological Factors) and offer evidence-based strategies for intervention. The ability to hover and show more content helps to bring the focus and attention to the area of interest.

A screenshot of the Assessment Factors tab in the Hamstring Injury Network Graph app. It includes panels for Workload Assessment, Muscle and Core Strength Assessment, Running and Strength Biomechanics, and Psychological and Compliance Assessment, each offering strategies and evaluations for hamstring injury prevention. — Assessment Factors Interface for Hamstring Injury Risk

Practical Applications

The app is designed for use by sports medicine providers, athletic trainers, and rehabilitation specialists. It can serve as a powerful tool in clinical decision-making, helping to identify high-risk athletes and tailor interventions to prevent hamstring injuries.

Conclusion

The development of this app was driven by a desire to translate complex research into practical tools that can make a tangible difference in athletes’ lives. By leveraging cutting-edge technology and evidence-based practices, we can move closer to a future where injuries are not just treated but prevented.