Determining day-to-day human variation in indirect calorimetry using Bayesian decision theory

Abstract

New findings: What is the central question of this study? We sought to understand the day-to-day variability of human indirect calorimetry during rest and exercise. Previous work has been unable to separate human day-to-day variability from measurement error and within-trial human variability. We developed models accounting for different levels of human- and machine-level variance and compared the probability density functions using total variation distance. What is the main finding and its importance? After accounting for multiple levels of variance, the average human day-to-day variability of minute ventilation, CO₂ output and O₂ uptake is 4.0, 1.8 and 2.0%, respectively. This is a new method to understand human variability and directly enhances our understanding of human variance during indirect calorimetry.

Abstract: One of the challenges of precision medicine is understanding when serial measurements taken across days are quantifiably different from each other. Previous work examining gas exchange measured by indirect calorimetry has been unable to separate differential measurement error, within-trial human variance and day-to-day human variance effectively in order to ascertain how variable humans are across testing sessions. We used previously published reliability data to construct models of indirect calorimetry variance and compare these models with methods arising from Bayesian decision theory. These models are conditional on the data upon which they are derived and assume that errors conform to a truncated normal distribution. A serial analysis of modelled probability density functions demonstrated that the average human day-to-day variance in minute ventilation ( ${\dot{V}}_E$ ), carbon dioxide output ( ${\dot{V}}_{C{O}_2}$ ) and oxygen uptake ( ${\dot{V}}_{O_2}$ ) was 4.0, 1.8 and 2.0%, respectively. However, the average day-to-day variability masked a wide range of non-linear variance across flow rates, particularly for ${\dot{V}}_E$ . This is the first report isolating day-to-day human variability in indirect calorimetry gas exchange from other sources of variability. This method can be extended to other physiological tools, and an extension of this work facilitates a statistical tool to examine within-trial ${\dot{V}}_{O_2}$ differences, available in a graphical user interface.

Keywords: Bayesian; individual differences; probability; ventilation.