Improving the precision of the accumulated oxygen deficit using VO2-power regression points from below and above the lactate threshold
Russell, Aaron, Le Rossignol, P., Snow, Rod and Lo, Sing Kai 2002, Improving the precision of the accumulated oxygen deficit using VO2-power regression points from below and above the lactate threshold, Journal of Exercise Physiology Online, vol. 5, no. 1, pp. 23-31.
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The accumulated oxygen deficit (AOD) method assumes a linear VO<sub>2</sub>-power relationship for exercise intensities increasing from below the lactate threshold (BLT) to above the lactate threshold (ALT). Factors that were likely to effect the linearity of the VO<sub>2</sub>-power regression and the precision of the estimated total energy demand (ETED) were investigated. These included the slow component of VO<sub>2</sub> kinetics (SC), a forced resting y-intercept and exercise intensities BLT and ALT. Criteria for linearity and precision included the Pearson correlation coefficient (PCC) of the VO<sub>2</sub>-power relationship, the length of the 95% confidence interval (95% CI) of the ETED and the standard error of the predicted value (SEP), respectively. Eight trained male and one trained female triathlete completed the required cycling tests to establish the AOD when pedalling at 80 rev/min. The influence of the SC on the linear extrapolation of the ETED was reduced by measuring VO<sub>2</sub> after three min of exercise. Measuring VO<sub>2</sub> at this time provided a new linear extrapolation method consisting of ten regression points spread evenly from BLT and ALT. This method produced an ETED with increased precision compared to using regression equations developed from intensities BLT with no forced y-intercept value; (95%CI (L), 0.70±0.26 versus 1.85±1.10, P<0.01; SEP(L/Watt), 0.07±0.02 versus 0.28±0.17; P<0.01). Including a forced y-intercept value with five regression points either BLT or ALT increased the precision of estimating the total energy demand to the same level as when using 10 regression points, (5 points BLT + y-intercept versus 5 points ALT + y-intercept versus 10 points; 95%CI(l), 0.61±0.32, 0.87±0.40, 0.70±0.26; SEP(L/Watt), 0.07±0.03, 0.08±0.04, 0.07±0.02; p>0.05). The VO<sub>2</sub>-power regression can be designed using a reduced number of regression points... ABSTRACT FROM AUTHOR
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