bioRxivPhysiologyPreprint
An Empirical Study on Quantifying Physical Energy Expenditure during High-intensity Exercise
yu xingiD, Xiaojun ZhouiD, Jian WangiD, Jun Zhang, Zhaohui WangiD, hao yu
Abstract
In the process of high-intensity exercise (HIE), the accurate evaluation of Physical Energy Expenditure (PEE) is of great significance to many occupational fields. However, due to the limitation of current technology, the quantitative ability of existing PEE evaluation methods in HIE situations is still insufficient. Based on the existing physiological coupling mechanism between cardiovascular parameters and energy metabolism, this study analyzed the dynamic changes of high-frequency blood pressure (BP) and heart rate (HR) during incremental load exercise and high-intensity interval training. The results show that although HR has a nonlinear response to the increase of exercise intensity, its time pattern-when analyzed synergistically with BP fluctuation-can provide reliable metabolic indicators. We used Doubly Labeled Water (DLW) as the gold standard for PEE determination and measured the total energy expenditure (TEE) in three consecutive days. During the monitoring period, high frequency invasive BP and HR monitoring was performed simultaneously, and the time integral value of SBP x HR and its ratio to TEE (i.e. Quotient value) were calculated. By dividing the corresponding SBP x HR integral value during HIE by the above quotient value, the PEE of this exercise can be estimated. Specifically, TEE can be accurately estimated by time integration of the product of SBP and HR. The short-term PEE rate can be solved by the differential equation constructed by the systolic blood pressure(SBP) trend function in this period, and can also be obtained by dividing the PEE in this period by the duration of this period. In this study, based on the results of invasive measurements, a new method for real-time monitoring of HIE using non-invasive cardiovascular parameters during PEE was proposed, providing new ideas and methodological support for applications in related fields.
- assay: imaging
- assay: electrophysiology
- research focus: metabolism & bioenergetics
- approach: statistical model
- Citations
- 0
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