A Machine Learning-Guided Training Approach to Reduce Injuries and Enhance Performance in Elite Athletes: A Prospective Cohort Evaluation
Sponsor: Debre Berhan University
Trial Details
| Condition | Athletic Injuries |
| Intervention | Adaptive Machine Learning Workload Optimization |
| Phase | NA |
| Status | Completed |
| Participants | 120 |
| Est. Completion | 30 Sept 2023 |
| Last Updated | 6 July 2026 |
Primary Outcome
Changes in Sprint Performance Time
Eligibility Criteria
Inclusion Criteria
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.
Trial Sites
Countries
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