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.
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.
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.
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