Impact of data averaging strategies on V̇O_2max assessment: Mathematical modeling and reliability

No consensus exists on how to average data to optimize $$ O_2max assessment. Although the $$ O_2max value is reduced with larger averaging blocks, no mathematical procedure is available to account for the effect of the length of the averaging block on $$ O_2max. AIMS: To determine the effect that the number of breaths or seconds included in the averaging block has on the $$ O_2max value and its reproducibility and to develop correction equations to standardize $$ O_2max values obtained with different averaging strategies.

Eighty-four subjects performed duplicate incremental tests to exhaustion (IE) in the cycle ergometer and/or treadmill using two metabolic carts (Vyntus and Vmax N29). Rolling breath averages and fixed time averages were calculated from breath-by-breath data from 6 to 60 breaths or seconds.

$$ O_2max decayed from 6 to 60 breath averages by 10% in low fit ( $$ O_2max  < 40 mL kg^-1  min^-1 ) and 6.7% in trained subjects. The $$ O_2max averaged from a similar number of breaths or seconds was highly concordant (CCC > 0.97). There was a linear-log relationship between the number of breaths or seconds in the averaging block and $$ O_2max (R²  > 0.99, P < 0.001), and specific equations were developed to standardize $$ O_2max values to a fixed number of breaths or seconds. Reproducibility was higher in trained than low-fit subjects and not influenced by the averaging strategy, exercise mode, maximal respiratory rate, or IE protocol.

The $$ O_2max decreases following a linear-log function with the number of breaths or seconds included in the averaging block and can be corrected with specific equations as those developed here.