Introducing the Tendon Pain Monitoring Dashboard: Revolutionizing Sports Medicine with R Studio
- Carlos Jimenez
- Jul 30, 2024
- 4 min read
I am excited to introduce a project that bridges the gap between data science and sports medicine, specifically in monitoring and managing patellar tendon pain in athletes. The Tendon Pain Monitoring Dashboard is an innovative tool that leverages the power of R Studio to provide sports medicine professionals with actionable insights into athletes’ tendon health.
Inspiration Behind the Dashboard
The inspiration for creating this dashboard came from an insightful article by Daniel Martínez-Silván and Jill Cook, which highlighted the importance of effective monitoring and management of patellar tendinopathy (PT) in competitive athletes. The authors emphasize that the usual methods of capturing injuries often miss out on athletes who continue to train and compete with ongoing symptoms, thereby underestimating the true burden of PT.
The Power of R Studio in Sports Medicine
R Studio proved to be a robust platform for developing this dashboard, offering a comprehensive suite of tools for data analysis, visualization, and interactive application development. Here’s a glimpse into what the Tendon Pain Monitoring Dashboard offers:
Key Features of the Tendon Pain Monitoring Dashboard
1. Add New Athlete: Easily add new athletes to the database with essential details like name, date of birth, and sport.
2. Pre Training Session Assessment: Log pre-training assessments, including pain provocation tests and pain scores, to monitor athletes’ pain levels before training.
3. Post Training Assessment: Assess post-training tendon load, considering factors like tendon load demand, minutes spent, and rate of perceived exertion (RPE).
4. Rehab Builder Exercise: Customize rehabilitation exercises and assess their impact on the tendon load based on specific domains such as muscle contraction type, time under tension, and sports specificity. The tool also allows for easy addition and deletion of exercises to keep the database up-to-date with the latest rehabilitation techniques.
5. Pain Scores Dashboard: Visualize pain scores over time with trend lines and insights to understand the progression of pain levels and make informed decisions.
6. Training Session Dashboard: Monitor the total tendon load for training sessions, providing a detailed overview of the athlete’s load over selected periods.
7. Tensile and Plyometric Load Dashboard: Analyze tensile and plyometric loads to ensure a balanced training regimen, preventing overuse injuries and optimizing performance.
Reactive Coding and Conditional Expressions
One of the powerful features of the Tendon Pain Monitoring Dashboard is its use of reactive coding and conditional expressions. This allows the app to respond dynamically to user inputs, ensuring that the displayed data and visualizations are always up-to-date. For instance, the app recalculates and displays the Exponential Moving Average (EMA) of tendon load based on the latest data entries, providing an accurate and real-time overview of the athlete’s condition.
Exponential Moving Average (EMA)
The dashboard employs EMA to smooth out the tendon load data and highlight trends over a specified period. EMA gives more weight to recent data points, making it particularly useful for monitoring changes in tendon load that reflect the athlete’s current training intensity. This feature helps in identifying patterns that may require intervention to prevent injury.
Linear Regression for Trend Analysis
To analyze trends in tendon load and pain scores, the dashboard utilizes linear regression. By fitting a linear model to the data, the tool can determine the slope of the trend line, indicating whether the load is increasing or decreasing over time. This statistical method provides valuable insights into the athlete’s progress and helps in making data-driven decisions about their training regimen.
Customization for Adding and Deleting Exercises
The customization features of the dashboard allow users to add and delete exercises easily. This flexibility ensures that the rehabilitation protocols can be tailored to the specific needs of each athlete. The tool provides a user-friendly interface for managing exercises, including an option to specify new exercises and delete outdated ones, keeping the database current and relevant.
Martínez-Silván and Cook’s article underscores the critical role of monitoring in managing PT effectively. They stress that tendons have a delayed pain response of 24-48 hours, making daily monitoring essential to adjust training programs accordingly. Effective monitoring includes not just tracking pain levels but also quantifying specific tendon-related training loads and rehabilitation exercises.
They recommend using a comprehensive approach that includes a pain rating score in response to a provocative test, such as a decline single leg squat, performed daily before training. This method helps in identifying the athlete’s pain response and adjusting their training program to prevent exacerbating the condition. Additionally, monitoring should include factors like the type of load, interaction between different training contents, and acute and cumulative fatigue.
The Development Journey
Developing this dashboard using R Studio was a rewarding experience. The combination of Shiny for interactive web applications, Plotly for dynamic visualizations, and SQLite for data management created a powerful tool tailored to the needs of sports medicine professionals.
Conclusion
The Tendon Pain Monitoring Dashboard is a testament to the potential of data science in sports medicine. It provides an innovative solution for monitoring and managing patellar tendon pain, inspired by the work of Martínez-Silván and Cook and powered by the capabilities of R Studio. I invite you to explore the dashboard and see firsthand how data can revolutionize athlete care.
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