How Precisely Can Easily Accessible Variables Predict Achilles and Patellar Tendon Forces during Running?

Area of Science:

• Biomechanics
• Sports Medicine
• Running Injury Prevention

Background:

• Patellar and Achilles tendinopathies are common running injuries.
• Predicting cumulative tendon forces may aid injury prevention.
• Accessible data collection outside lab settings is crucial for practical application.

Purpose of the Study:

• To develop algorithms for predicting patellar and Achilles tendon force and impulse during running.
• To utilize easily accessible running data for these predictions.
• To compare the predictive performance of these algorithms against running distance.

Main Methods:

• Collected running data from 24 recreational runners using Xsens suit and Garmin Forerunner 735XT.
• Recorded data at three different running speeds.
• Employed mixed-effects multiple regression to model tendon forces and training load variables.

Main Results:

• Developed twelve algorithms to predict patellar or Achilles tendon peak force and impulse per stride.
• The developed algorithms demonstrated superior predictive performance compared to using running distance alone.

Conclusions:

• Algorithms utilizing accessible running data can effectively predict patellar and Achilles tendon forces and impulses.
• These novel algorithms offer a more accurate approach to monitoring running load for injury prevention than traditional distance metrics.