Clinical Research Directory
Browse clinical research sites, groups, and studies.
Machine Learning-Guided Training for Elite Athletes (MLGT)
Sponsor: Debre Berhan University
Summary
Plaintext The purpose of this study is to evaluate whether a personalized training protocol driven by machine learning can successfully reduce time-loss sports injuries and enhance athletic performance in elite athletes. During a 9-month competitive sports season, a group of elite athletes was divided into two training
Official title: A Machine Learning-Guided Training Approach to Reduce Injuries and Enhance Performance in Elite Athletes: A Prospective Cohort Evaluation
Key Details
Gender
All
Age Range
18 Years - 35 Years
Study Type
INTERVENTIONAL
Enrollment
120
Start Date
2023-01-01
Completion Date
2023-09-30
Last Updated
2026-07-06
Healthy Volunteers
Yes
Conditions
Interventions
Adaptive Machine Learning Workload Optimization
A personalized, data-driven training intervention where athletic workloads are dynamically adjusted based on predictive modeling. The protocol continuously tracks individual physiological markers, biomechanical data, and workload history to optimize training volume and intensity. This adaptive approach aims to maximize performance gains while minimizing the risk of overtraining and injury during the competitive season.
Locations (2)
Dr. Arefayne
Debre Berhan, Shewa, Ethiopia
M Dessye
Debre Berhan, Shewa, Ethiopia