A NEW METHOD FOR DATA PRESENTATION IN INCREMENTAL CARDIORESPIRATORY EXERCISE TESTING

In incremental cardiopulmonary exercise testing, the averaging of data is usually performed to provide group mean data for statistical purpo ses. They are usually presented as averaged maximum values, or as aver aged data at different exercise levels. However, during incremental ex ercise testing the change in metabolic status may vary between subject s, thus averaging data may not classify the metabolic status accuratel y. We present an averaging method using a segmented ordinal scale base d on individual maximal work performance and the anaerobic threshold ( AT). Individual exercise data are grouped into ten classes ranging fro m unloaded exercise to maximal exercise. The classes are defined in re lation to the AT, resulting in an ordinal scale of four classes for ex ercise data below the AT, one class at the AT and five classes beyond the AT. Resting and unloaded pedalling are treated as separate classes , For evaluation, this method of classification is compared to one bas ed on an absolute scale of oxygen uptake (Cabs) and to another based o n a relative scale in 10% steps of maximal oxygen uptake (Crel). Ten h ealthy male subjects (mean age 23.3 years) performed a ramp cycle ergo meter test. When using the Cabs classification method for mean data av eraging, mean values for performance at high-intensity exercise were c alculated using data from only two of the ten subjects because of vari ations in individual work capacity. In addition, the AT data were dist ributed across four classes, thus anaerobic and aerobic exercise data were mixed. Using the Crel classification method enabled data for all ten subjects to be included in the calculation of every data point, bu t the AT values were still distributed across three classes, resulting in the mixing of anaerobic and aerobic exercise data. However, using the segmented ordinal scale method of classification enabled data from all ten subjects to be included in the calculation of all data points , and it permitted the grouping of the AT values into one class. Thus, this latter method more accurately represents the data of the whole g roup under study and it allows the metabolic status of the subjects to be taken into consideration.