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

NCT ID NCT07683091

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

What the study statuses mean

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Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed This study
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

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.

Study facts

What this study's own registry entry says, in plain language.

Phase

Not a phased trial

Phase numbers describe drug development. The registry uses this when they do not apply, as it does for trials of devices, procedures or behaviour changes, and for observational studies.

Participants

120 people

The number who actually took part.

Started

Jan 2023

Finished

Sep 2023

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Ages

18 to 35 years

Sex

Anyone

Healthy volunteers

Accepted

You do not need to have the condition being studied to take part.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: 1. Must be a competitive, elite-level or sub-elite track and field athlete specializing in short-to-mid distance running events. 2. Aged between 18 and 35 years old. 3. Actively participating in structured athletic training programs for at least 2 years prior to enrollment. 4. Free from any acute musculoskeletal injuries or medical conditions that prevent full participation in high-intensity training protocols. 5. Capable and willing to provide written informed consent to participate in the study. Exclusion Criteria: 1. Current or recent (within the past 3 months) major lower-limb injury or surgery that restricts maximal sprint or aerobic performance. 2\. Concurrent use of performance-enhancing drugs or medications that influence metabolic or cardiovascular responses. 3\. Inability to maintain consistent participation in the designated training protocols due to scheduling conflicts or travel. 4\. Any underlying cardiovascular, respiratory, or systemic condition that creates a health risk during exhaustive exercise testing.

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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

Locations

  • Dr. Arefayne

    Debre Berhan, Shewa, 445, Ethiopia

  • M Dessye

    Debre Berhan, Shewa, 445, Ethiopia

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