NCT07575802已完成不适用
Lower Limb Muscle Strength and Power Predict Exercise-Induced Prefrontal Hemodynamic Response: An Interpretable Machine Learning Study Using fNIRS Data
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 256
- 试验地点
- 1
研究概览
简要总结
This study aims to investigate the predictive value of lower limb muscle strength and explosive power on exercise-induced prefrontal hemodynamic responses. Using an interpretable machine learning framework (GCAT-Net), the research analyzes how various physical performance indicators-such as isokinetic muscle strength, 1RM leg press, and vertical jump metrics-can predict oxygenated hemoglobin (ΔHbO) changes in the bilateral dorsolateral prefrontal cortex (DLPFC) during moderate-intensity cycling.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Basic Science
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 60 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Healthy adults aged between 18.00 and 60.00 years.
- •Must be able to perform standardized lower-limb physical assessments, including isokinetic strength testing and explosive power tests (e.g., CMJ, SJ, and 30m sprints).
- •Capability to complete a 6-minute aerobic cycling task at a moderate intensity (60% VO_2max).
- •Physically cleared for exercise as determined by a Physical Activity Readiness Questionnaire (PAR-Q).
- •Willingness to undergo functional near-infrared spectroscopy (fNIRS) monitoring with a Scalp Coupling Index (SCI) ≥ 0.7.
排除标准
- •History of musculoskeletal injuries to the lower limbs within the past 6 months that would impede maximum voluntary contraction (MVC) or high-intensity efforts.
- •Known cardiovascular, respiratory, or neurological conditions that contraindicate maximal exercise testing (VO_2max ramp protocol).
- •Body Mass Index (BMI) or health status that prevents the safe completion of high-impact explosive power tests like the 30cm drop jump (RSI assessment).
- •Presence of excessive motion artifacts or poor fNIRS signal quality, specifically a Signal-to-Noise Ratio (SNR) < 5 dB or motion artifact frame ratio ≥$ 10%.
研究者
Linjun Liu
Doctoral Researcher
Lincoln University College
研究点 (1)
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