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Machine Learning-Guided Training for Elite Athletes (MLGT)

NCT07683091 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 120

Last updated 2026-07-06

No results posted yet for this study

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

Conditions

  • Athletic Injuries

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.

Sponsors & Collaborators

  • Debre Berhan University

    lead OTHER

Principal Investigators

  • Dr. Arefayne M Dessye, PhD · Debre Berhan Univeristy

Study Design

Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
35 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-01-01
Primary Completion
2023-09-30
Completion
2023-09-30

Countries

  • Ethiopia

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07683091 on ClinicalTrials.gov