Data collection, handling and fitting strategies to optimize accuracy and precision of oxygen uptake kinetics estimation from breath-by-breath measurements.

Phase 2 pulmonary oxygen uptake kinetics (ϕ2 τVO2P) reflect muscle oxygen consumption dynamics and are sensitive to changes in state of training or health. This study identified an unbiased method for data collection, handling and fitting to optimize VO2P kinetics estimation. A validated computational model of VO2P kinetics and a Monte Carlo approach simulated 2 x 10(5) moderate intensity transitions using a distribution of metabolic and circulatory parameters spanning normal health. Effects of averaging (interpolation, binning, stacking or separate fitting of up to 10 transitions) and fitting procedures (bi-exponential fitting, or ϕ2 isolation by time removal, statistical or derivative methods followed by mono-exponential fitting) on accuracy and precision of ϕ2 τVO2P estimation were assessed. The optimal strategy to maximize accuracy and precision of τVO2P estimation was 1-s interpolation of 4 bouts, ensemble averaged, with the first 20 s of exercise data removed. Contradictory to previous advice, we found optimal fitting procedures removed no more than 20 s of ϕ1 data. Averaging method was less critical: interpolation, bin averaging and stacking gave similar results, each with greater accuracy compared to analyzing repeated bouts separately. The optimal procedure resulted in ϕ2 τVO2P estimates for transitions from an unloaded or loaded baseline that averaged 1.97±2.08 and 1.04±2.30 s from true, but were within 2 s of true in only 47-62% of simulations. Optimized 95% confidence intervals for τVO2P ranged from 4.08-4.51 s, suggesting a minimally important difference of ~5 s to determine significant changes in τVO2P during interventional and comparative studies.