Effect of Noninvasive Static Human Data on Maximum Data in Exercise

Yichen Wu and Yining Sun ()
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Yichen Wu: Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Yining Sun: Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China

IJERPH, 2023, vol. 20, issue 2, 1-12

Abstract: Maximum data in exercise (Max-Ex), including maximum heart rate (HR max ), peak oxygen uptake (VO 2pk ), maximum power (MaxP), etc., are frequently used, whether it is for the determination of exercise intensity, the measurement of an athlete’s performance, assessment of recovery from disease, and so on. However, very often this choice does not take into account the targeted individual. We recruited 32 males and 29 females to undergo an incremental graded exercise test (GXT). Therefore, our study seeks to determine variations in Max-Ex, according to the noninvasive static human data (Non-In data). Data showed a significant relationship ( p < 0.001) between body composition and Max-Ex. Of the 41 types of Non-In data we collected in communities, the body composition generally showed high correlation (maximum r = 0.839). 57.5% of the data, of which r > 0.6 were about body composition. The muscle-related body composition data had a greater effect on power, and the fat-related ones had a greater effect on HR max and VO 2pk . For some types of Max-Ex, the older and younger ones showed specific differences. Therefore, these results can be employed to adequately prescribe personalized health promotion programs according to diversity and availability, and have some reference value for other studies using Max-Ex.

Keywords: maximum data in exercise; maximum heart rate; peak oxygen uptake; maximum power; body composition; exercise intensity (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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