23/05/2026
https://link.springer.com/article/10.2165/11319670-000000000-00000
Recommendations for Processing Data from Indirect Calorimetry
Based on the prior content, we are now ready to recommend strategies for processing data acquired from indirect calorimetry.
If a time-averaged system has to be used, we recommend no longer than a 30-second time average where the data are aligned to the central time of the interval period, which is 15 seconds, and thereby require time representation of 0, 0.25, 0.75, 1.25, 1.75, 2.25 minutes, etc. We also recommend that exercise physiologists who currently use expired mixing chamber systems, with no choice of other sampling and processing options, strive to equip themselves with software that will support acquisition and data processing as breath averages. While a 30-second average provides reasonable reductions in data variability, it provides unreasonable decreases in data frequency, which will detract from how the data can be used to assess important physiology measurements and trends.
For breath-by-breath systems and averaging systems suited to breath averages, we recommend a 15-breath running average, aligned to the time of the central breath, which is the eighth breath. Although we identified several theoretical problems with a breath average, the alternative of a digital filter requires a degree of mathematical computation and software dependence that simply does not exist in software of all commercial systems of indirect calorimetry. Furthermore, given that the 15-breath average induces minimal data loss (lose initial seven and last seven datapoints), has no data and trend distortion, can be accomplished with the software of many commercial indirect calorimetry systems, and, if not, can be easily applied to datasets with post-acquisition spreadsheet computation, it is a reasonable expectation that all scientists and practitioners can do this data processing.
For scientists able to implement digital filters in their data processing, we recommend a low cut-off frequency digital filter of 0.04 Hz.
Recommendations for Detecting the Highest Value Datapoint
Once the recommended data processing strategies are used, then the task of detecting the peak or maximal value of any variable is simple. The highest, peak or maximal value becomes the highest processed datapoint. Thus, for time-averaged systems, V̇O2max or V̇O2 peak would be the highest 30-second V̇O2 average for the test. For breath-by-breath data, V̇O2max or V̇O2 peak would be the highest 15-breath V̇O2 average for the test. For exercise physiologists who can apply a 0.04 Hz low frequency cut-off digital filter, the largest single datapoint is V̇O2max or V̇O2 peak.
Paper Abstrat given below:
Recommendations for Improved Data Processing from Expired Gas Analysis Indirect Calorimetry
There is currently no universally recommended and accepted method of data processing within the science of indirect calorimetry for either mixing chamber or breath-by-breath systems of expired gas analysis. Exercise physiologists were first surveyed to determine methods used to process oxygen consumption (V̇O2) data, and current attitudes to data processing within the science of indirect calorimetry. Breath-by-breath datasets obtained from indirect calorimetry during incremental exercise were then used to demonstrate the consequences of commonly used time, breath and digital filter post-acquisition data processing strategies. Assessment of the variability in breath-by-breath data was determined using multiple regression based on the independent variables ventilation (VE), and the expired gas fractions for oxygen and carbon dioxide, FEO2 and FECO2, respectively. Based on the results of explanation of variance of the breath-by-breath V̇O2 data, methods of processing to remove variability were proposed for time-averaged, breath averaged and digital filter applications. Among exercise physiologists, the strategy used to remove the variability in sequential V̇O2 measurements varied widely, and consisted of time averages (30 sec [38%], 60 sec [18%], 20 sec [11%], 15 sec [8%]), a moving average of five to 11 breaths (10%), and the middle five of seven breaths (7%). Most respondents indicated that they used multiple criteria to establish maximum V̇O2 (V̇O2max) including: the attainment of age-predicted maximum heart rate (HRmax) [53%], respiratory exchange ratio (RER) >1.10 (49%) or RER >1.15 (27%) and a rating of perceived exertion (RPE) of >17, 18 or 19 (20%). The reasons stated for these strategies included their own beliefs (32%), what they were taught (26%), what they read in research articles (22%), tradition (13%) and the influence of their colleagues (7%). The combination of VE, FEO2 and FECO2 removed 96–98% of V̇O2 breath-by-breath variability in incremental and steady-state exercise V̇O2 data sets, respectively. Correction of residual error in V̇O2 datasets to 10% of the raw variability results from application of a 30-second time average, 15-breath running average, or a 0.04 Hz low cut-off digital filter. Thus, we recommend that once these data processing strategies are used, the peak or maximal value becomes the highest processed datapoint. Exercise physiologists need to agree on, and continually refine through empirical research, a consistent process for analysing data from indirect calorimetry.
There is currently no universally recommended and accepted method of data processing within the science of indirect calorimetry for either mixing chamber or breath-by-breath systems of expired gas analysis. Exercise physiologists were first surveyed to determine methods used to process oxygen consum...