Purpose: The purpose of this study was to determine whether the maximal oxygen uptake (VO2max) is attained with the same central and peripheral factors according to the exercise intensity.
Methods: Nine well-trained males performed an incremental exercise test on a cycle ergometer to determine the maximal power associated with VO2max (pVO2max) and maximal cardiac output (Qmax). Two days later, they performed two continuous cycling exercises at 100% (tlim100 = 5 min 12 s +/- 2 min 25 s) and at an intermediate work rate between the lactate threshold and pVO2max (tlimDelta50 +/- 12 min 6 s +/- 3 min 5 s). Heart rate and stroke volume (SV) were measured (by impedance) continuously during all tests. Cardiac output (Q) and arterial-venous O2 difference (a-vO2 diff) were calculated using standard equations.
Results: Repeated measures ANOVA indicated that: 1) maximal heart rate, VE, blood lactate, and VO2 (VO2max) were not different between the three exercises but Q was lower in tlimDelta50 than in the incremental test (24.4 +/- 3.6 L x min(-1) vs 28.4 +/- 4.1 L x min(-1); P < 0.05) due to a lower SV (143 +/- 27 mL x beat(-1) vs 179 +/- 34 mL x beat(-1); P < 0.05), and 2) maximal values of a-vO2 diff were not significantly different between all the exercise protocols but reduced later in tlimDelta50 compared with tlim100 (6 min 58 s +/- 4 min 29 s vs 3 min 6 s +/- 1 min 3 s, P = 0.05). This reduction in a-vO2 diff was correlated with the arterial oxygen desaturation (SaO2 = -15.3 +/- 3.9%) in tlimDelta50 (r = -0.74, P = 0.05).
Conclusion: VO2max was not attained with the same central and peripheral factors in exhaustive exercises, and tlimDelta50 did not elicit the maximal Q. This might be taken into account if the training aim is to enhance the central factors of VO2max using exercise intensities eliciting VO2max but not necessarily Qmax.
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http://dx.doi.org/10.1249/01.mss.0000135977.12456.8f | DOI Listing |
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Department of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Improving energy efficiency is crucial for smart factories that want to meet sustainability goals and operational excellence. This study introduces a novel decision-making framework to optimize energy efficiency in smart manufacturing environments, integrating Intuitionistic Fuzzy Sets (IFS) with Multi-Criteria Decision-Making (MCDM) techniques. The proposed approach addresses key challenges, including reducing carbon footprints, managing operating costs, and adhering to stringent environmental standards.
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