Clinical trial Recruiting at 1 site Observational Ages 15 to 19 Men only

Multimodal Machine Learning to Predict When Elite Youth Football Players Might Need Training Changes

Official title: Machine Learning-Based Short-Term Prediction of Reduced Participation Within the Competitive Microcycle in Elite Youth Football Players Using Multimodal Data

Run by Palacky University · NCT07870460 · Data updated 2026-11-12

Researchers are testing whether combining wearable and performance data can predict, within 24–72 hours, if an elite youth player will have to modify training or miss time due to health issues.

More Fatigue Recovery trials

Quick facts

Study typeObservational
Ages15 Years to 19 Years
SexMen only
Enrollment30 (estimated)
SponsorPalacky University
Academic / other
Locations1 site (Olomouc)
StatusRecruiting
StartedJuly 15, 2026
Main results expectedJune 30, 2027
Study endsJune 30, 2028
Registry record updatedOctober 15, 2026
Trial IDNCT07870460 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Elite youth football players in the U16 or U19 squads of SK Sigma Olomouc, with at least three years of academy training and high sensor-wearing compliance.

Probably not a fit

Players with low adherence to monitoring or with conditions limiting wearables may not benefit from the predictive models.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This is a prospective, longitudinal observational study conducted over one full season at an elite youth academy in Czechia. The study links continuous multimodal monitoring data—external movement from Catapult sensors and nocturnal autonomic signals from WHOOP—with staff-adjudicated daily participation outcomes to model short-term risk of reduced participation. The cohort includes about 30 male players aged approximately 16 to 19 years, generating thousands of player-days of data. Primary analysis focuses on one-step hazard of transitioning from full participation to a lower participation category within 24, 48, or 72 hours, with inter-rater reliability assessed for outcome adjudication. The study uses eight predictor domains, including maturation, anthropometry, sleep, and physiological metrics, to build machine learning models that tolerate missing or variable compliance.

Why it matters

Potential benefit: If successful, the approach could enable early identification of fatigue or health-related risks to help tailor training loads and reduce unplanned absences.

How similar studies have gone: Previous work in sports analytics has shown promise for multimodal data in predicting performance and injury risk, but this specific model for short-term participation changes is novel in this setting.

Who can join: the full rules

Show full inclusion / exclusion criteria
Inclusion Criteria:

* Registered player in the U16 or U19 squad of an elite football academy (SK Sigma Olomouc).
* At least three years of structured football training within an organized academy programme.
* Regular active participation in microcycle team training and competitive league matches.
* Written informed assent by the player and written informed consent by a parent or legal guardian (for minor participants).
* Monitoring compliance rate \> 80% during the initial 14-day passive run-in period.

Exclusion Criteria:

* Pre-existing severe musculoskeletal injury, orthopedic surgery, or neurological disorder preventing full participation at baseline.
* Known cardiovascular, metabolic, or systemic disease contraindicating maximal athletic exertion.
* Inability or refusal to wear continuous monitoring sensors (Catapult GNSS unit during training, WHOOP strap during sleep).
* Prolonged absence from the squad due to non-medical administrative reasons (transfer, academic leave, disciplinary suspension).

Where it's running

  • SK Sigma Olomouc Academy & AC Baluo, Palacký University Olomouc
    Olomouc, Czechia
    Recruiting

Study contacts

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.