Feasibility of predicting maximal oxygen uptake by using the efficiency factor in healthy men

Conventionally, efficiency is indirectly estimated through a respiratory gas analyser (oxygen, carbon dioxide), which is a complex and rather costly calculation method that is difficult to perform in many situations. Therefore, the present study proposed a modified definition of efficiency, called the efficiency factor (EF) (i.e., the ratio of work to the corresponding exercise intensity), and evaluated the relation between the EF and maximal oxygen uptake ([Formula: see text] ), as well as compared the prediction models established based on the EF. The heart rate (maximal heart rate: 186 ± 6 beats min(−1)), rating of perceived exertion (19 ± 1), and [Formula: see text] (39.0 ± 7.1 mL kg(−1) min(−1)) of 150 healthy men (age: 20 ± 2 years; height: 175.0 ± 6.0 cm; weight: 73.6 ± 10.7 kg; body mass index [BMI]: 24.0 ± 3.0 kg m(−2); percent body fat [PBF]: 17.0 ± 5.7%) were measured during the cardiopulmonary exercise test (CPET). Through multiple linear regression analysis, we established the BMI model using age and BMI as parameters. Additionally, we created the PBF model(HRR) utilizing weight, PBF, and heart rate reserve (HRR) and developed PBF model(EF6) and PBF model(EF7) by incorporating EF6 from the exercise stage 6 and EF7 from the exercise stage 7 during the CPET, respectively. EF6 (r = 0.32, p = 0.001) and EF7 (r = 0.31, p = 0.002) were significantly related to [Formula: see text] . Among the models, the PBF model(EF6) showed the highest accuracy, which could explain 62.6% of the variance in the [Formula: see text] at with a standard error of estimate (SEE) of 4.39 mL kg(−1) min(−1) (%SEE = 11.25%, p < 0.001). These results indicated that the EF is a significant predictor of [Formula: see text] , and compared to the other models, the PBF model(EF6) is the best model for estimating [Formula: see text] .