The informative power of heart rate along with machine learning regression models to predict maximal oxygen consumption and maximal workload capacity

IF 1.1 4区 医学 Q4 ENGINEERING, MECHANICAL
A. Gentilin
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引用次数: 0

Abstract

Prediction of maximal oxygen consumption (VO2max) and maximal workload capacity (MWC) through submaximal exercise tests is an important topic for sports sciences. Numerous studies highlighted the predictive power of submaximal heart rate (HR) and oxygen consumption (VO2) in predicting VO2max and MWC. The challenge is achieving the best possible precision and accuracy by identifying the best predictors and regression models. This project assessed the performance of different indexes along with machine learning regression models to estimate VO2max and MWC. Predictors consisted of biodata (age, weight, and height) along with different combinations of change-scores of HR and VO2 between 0–50 Watts, 50–65 Watts, and 65–80 Watts (Δ0–50, Δ50–65, and Δ65–80, respectively). The use of biodata + HR Δ50–65 + HR Δ65-80 via a Squared Exponential Gaussian Process Regression model resulted in the best performance in predicting VO2max, while the use of biodata + HR Δ0–50 via a Robust Linear Regression model resulted in the best performance in predicting MWC. These results suggest that information provided by HR only during submaximal exercise offers the best predictive mean for estimating VO2max and MWC, while the use of VO2 changes or its addition along with HR changes does not improve predictions. Moreover, different predictors need to be selected for the best estimation of VO2max and MWC. Change-scores refer to absolute value changes, providing information to develop athlete assessment protocols through standardized workloads. These results show practical applicability for sports assessments to be performed indirectly, rapidly, sub-maximally, and through the simple measurement of HR.
心率与机器学习回归模型在预测最大耗氧量和最大工作负荷能力方面的信息能力
通过亚极限运动试验预测最大耗氧量(VO2max)和最大负荷能力(MWC)是体育科学的一个重要课题。大量研究强调了亚最大心率(HR)和耗氧量(VO2)在预测VO2max和MWC方面的预测能力。挑战在于通过确定最佳预测器和回归模型来实现尽可能高的精确度和准确性。本项目利用机器学习回归模型评估了不同指标的性能,以估计VO2max和MWC。预测指标包括生物数据(年龄、体重和身高)以及0-50瓦、50-65瓦和65-80瓦之间HR和VO2变化评分的不同组合(分别为Δ0-50、Δ50-65和Δ65-80)。通过平方指数高斯过程回归模型使用biodata + HR Δ50-65 + HR Δ65-80预测VO2max的效果最好,而使用biodata + HR Δ0-50通过稳健线性回归模型预测MWC的效果最好。这些结果表明,仅在次最大运动期间由HR提供的信息为估计VO2max和MWC提供了最好的预测方法,而使用VO2变化或其随HR变化的增加并不能改善预测。此外,需要选择不同的预测因子以获得最佳的VO2max和MWC估计。变化分数指的是绝对值的变化,通过标准化的工作量为制定运动员评估协议提供信息。这些结果表明,通过简单的人力资源测量,间接、快速、次最大化地进行体育评估具有实际适用性。
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来源期刊
CiteScore
3.50
自引率
20.00%
发文量
51
审稿时长
>12 weeks
期刊介绍: The Journal of Sports Engineering and Technology covers the development of novel sports apparel, footwear, and equipment; and the materials, instrumentation, and processes that make advances in sports possible.
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