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COMPLETED
NCT07683091
NA

Machine Learning-Guided Training for Elite Athletes (MLGT)

Sponsor: Debre Berhan University

View on ClinicalTrials.gov

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

Interventions

BEHAVIORAL

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