A Machine Learning-Guided Training Approach to Reduce Injuries and Enhance Performance in Elite Athletes: A Prospective Cohort Evaluation
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 120
- 试验地点
- 2
- 主要终点
- Changes in Sprint Performance Time
研究概览
简要总结
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
详细描述
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:
- The Experimental Group, which underwent training regimens dynamically adjusted using a machine learning algorithm that analyzed individual biomechanical data and historical workload parameters to optimize training volume and intensity.
- The Control Group, which followed standard, predetermined high-performance athletic training protocols typical for competitive season preparation.
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Prevention
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 35 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Must be a competitive, elite-level or sub-elite track and field athlete specializing in short-to-mid distance running events.
- •Aged between 18 and 35 years old.
- •Actively participating in structured athletic training programs for at least 2 years prior to enrollment.
- •Free from any acute musculoskeletal injuries or medical conditions that prevent full participation in high-intensity training protocols.
- •Capable and willing to provide written informed consent to participate in the study.
排除标准
- •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.
结局指标
主要结局
Changes in Sprint Performance Time
时间窗: 12 weeks
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.
次要结局
未报告次要终点
研究者
Dr. Arefayne Mesfen Dessye
Assistant Professor
Debre Berhan University