提高运动后氧气动力学建模与生理界限和手动V·O2_基线输入:一种新的方法
Süleyman Ulupınar1, İzzet İnce2, Cebrail Gençoğlu1
1Faculty of Sports Sciences, Erzurum Technical University, Erzurum, Türkiye.
European journal of sport science
|April 22, 2025
概括
一个新的Python算法改进了运动后氧气消耗 (V ̇O2) 建模,通过结合个别基线V ̇O2.2,改进了运动后氧气消耗 (V ̇O2) 建模. 这提高了恢复动力学的准确性,双指数模型显示出比单指数模型更好的适应性.
科学领域:
- 运动生理学 运动生理学
- 计算生物学 计算生物学
- 运动科学 运动科学 运动科学
背景情况:
- 对运动后氧气消耗 (V·O2) 动力学的准确建模对于理解恢复至关重要.
- 现有的计算工具往往无法将个体的运动前基线V̇O2 (V̇O2_基线) 纳入,限制了模型的精度.
- 用户定义的基线对于将恢复动力学与真正的生理终点对齐至关重要.
研究的目的:
- 开发和验证一个定制的Python算法,用于建模V̇O2动态,并结合用户定义的V̇O2_baseline.
- 为了比较单指数和双指数模型在训练后V·O2分析中的分析性能.
- 评估V̇O2_基线变化对模型参数的影响.
主要方法:
- 22名男性业余足球运动员接受了30岁 Wingate 测试.
- 使用代谢气体分析仪,在运动前,运动期间和运动后进行了连续的V·O2测量.
- 开发了一种定制的Python算法,使用单指数和双指数模型分析V̇O2动力学,并与Origin和GedaeLab进行比较.
主要成果:
- 与单指数模型 (R2 = 0.805 ± 0.078) 相比,双指数模型显示出明显优异的适合性 (R2 = 0.963 ± 0.013).
- 双指数模型在5分钟和15分钟后更准确地近似了运动后的V·O2积分.
- 较高的V·O2_基线值通常会改善单指数模型的适应性,但对双指数模型的影响最小.
结论:
- 开发的Python算法有效地结合了用户定义的V̇O2_baseline,以实现更精确的V̇O2动力学建模.
- 与单指数模型相比,双指数模型提供了更高的准确性,并适合分析运动后的V·O2回收.
- 这种方法增强了运动员V̇O2恢复建模的生理相关性.
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