An interventional study of Adaptive Machine Learning Workload Optimization in Athletic Injuries, sponsored by Debre Berhan University. Completed at 2 sites in Ethiopia. Open to participants aged 18 Years to 35 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-07-06.
Sponsored by Debre Berhan University · Not applicable, Interventional, and Prevention
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
This study evaluated the efficacy of an adaptive, machine learning-driven training protocol compared to traditional athletic preparation over a full 9-month competitive sports season. The primary objective was to determine if a dynamic, technology-led approach to training load management could minimize time-loss injuries while concurrently optimizing athletic performance markers.
Participants were elite athletes randomly allocated into two parallel groups:
Throughout the 9-month intervention period, daily tracking was maintained by technical and coaching staff. Data collection focused on the incidence, severity, and duration of all time-loss sports injuries. Concurrently, sport-specific performance parameters were periodically assessed to evaluate physical conditioning and competitive readiness. Statistical analyses were subsequently conducted to compare cumulative injury rates, total days lost to injury, and net performance adaptations between the two cohorts.
258 studies on the registry are indexed under Athletic Injuries; 45 are open to participants now.
This study's enrollment of 120 is above the median of 44 across 164 interventional studies indexed under Athletic Injuries.
Browse Athletic Injuries studies →Debre Berhan University is the lead sponsor of 6 studies on the registry; 2 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Inclusion Criteria:
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.
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Elite adolescent sprinters who followed standard, predetermined high-performance athletic training protocols typical for competitive season preparation. This group received structured training volume and intensity matching standard athletic coaching guidelines, without any machine learning interventions or adaptive workload adjustments.
Behavioral: Adaptive Machine Learning Workload Optimization
Elite adolescent sprinters who received a personalized training protocol dynamically optimized by a machine learning algorithm. The framework evaluated individual biomechanical variables, morning heart rate variability (HRV), sleep quality, and physiological fatigue metrics to adjust training volume and intensity.
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.
Changes in Sprint Performance Time
Sprint performance will be assessed using electronic timing gates to record running times over a specific distance from a stationary start. Lower times indicate improved sprint performance. Measurements will be taken at baseline and at the conclusion of the training intervention period to evaluate the impact of the workload protocols.
Time frame: 12 weeks
Plan to share: No — Individual participant data (IPD) will not be shared publicly to maintain the confidentiality of the elite athletes involved and to protect proprietary training protocols. Aggregated study results and statistical analyses will be available through academic publication.
No publications or documents are linked to this record.
Ran from Jan 2023 to primary completion in Sep 2023, 8 months.
Of 119 non-industry interventional studies with no phase under Athletic Injuries, half reached primary completion within 5 months of starting (the median), counting those still running; a quarter within 2 and three quarters within 14. This study's 8 months is above that.
Counting every sponsor, half of 121 studies reached it within 5 months; a quarter within 2 and three quarters within 14.
How these figures are worked out, and which studies are left out of them.
This study is completed, as verified in Jun 2026. You cannot join it, but the record below documents what was studied.
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Debre Berhan University