AI coach: machine learning aims to slash sports injuries and boost sprint times

NCT ID NCT07683091

Completed Disease control Sponsor: Debre Berhan University Source: ClinicalTrials.gov ↗

First seen Jul 06, 2026 · Last updated Jul 07, 2026 · Updated 1 time

Summary

This study tests whether a personalized training program guided by machine learning can reduce time-loss injuries and improve performance in elite track and field athletes. Over a 9-month season, 120 athletes aged 18–35 will either follow the adaptive, data-driven plan or a standard training regimen. The goal is to see if smart workload adjustments can keep athletes healthier and faster.

What this could mean

Our plain-language read of the trial. This is informational only — not medical advice or a prediction.

Active substance: Adaptive Machine Learning Workload Optimization
What this could lead to: If successful, this approach could help elite athletes train smarter, reducing injury risk while improving speed and performance.
What could go wrong: This is a single study with 120 athletes, so results may not apply broadly. The machine learning model's predictions may not always prevent injuries.

This is an AI summary of the original study and may miss details. Read our disclaimer.

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Conditions

The condition(s) this trial relates to.

Athletic Injuries injury prevention target

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

ATHLETIC INJURIES

Contacts and locations

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Locations

Dr. Arefayne

Debre Berhan, Shewa, 445, Ethiopia
M Dessye

Debre Berhan, Shewa, 445, Ethiopia

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