Key result
Exercise-based prediction equations correctly classify only ~59% of individuals into fitness tertiles.
Why the study?
Equations are often used to predict cardiorespiratory fitness from exercise tests, but no study had comprehensively compared these exercise-based equations with directly measured CRF in a single, large cohort.
Do exercise-based prediction equations accurately estimate cardiorespiratory fitness compared to direct measurement in healthy adults?
Observational (n=4,871)
Do exercise-based prediction equations accurately estimate cardiorespiratory fitness compared to direct measurement in healthy adults?
p-value: p=<0.01
Exercise-based equations for estimating cardiorespiratory fitness have limited accuracy in categorizing individuals into fitness tertiles, highlighting the need for direct cardiopulmonary measures in clinical settings.
Low accuracy of exercise-based equations cautions against their use for fitness categorization; leaves open validation of refined models against direct measures.
Equations are often used to predict cardiorespiratory fitness (CRF) from submaximal or maximal exercise tests. However, no study has comprehensively compared these exercise-based equations with directly measured CRF using data from a single, large cohort. PURPOSE: This study aimed to compare the accuracy of exercise-based prediction equations with directly measured CRF and evaluate their ability to classify an individual's CRF. METHODS: The sample included 4871 tests from apparently healthy adults (38% female, age 44.4 ± 12.3 yr (mean ± SD)). Estimated CRF (eCRF) was determined from 2 nonexercise equations, 3 submaximal exercise equations, and 10 maximal exercise equations; all eCRF calculations were then compared with directly measured CRF, determined from a cardiopulmonary exercise test. Analysis included Pearson product-moment correlations, standard error of estimate values, intraclass correlation coefficients, Cohen κ coefficients, and the Benjamini-Hochberg procedure to compare eCRF with directly measured CRF. RESULTS: All eCRF values from the prediction equations were associated with directly measured CRF (P < 0.01), with intraclass correlation coefficient estimates ranging from 0.07 to 0.89. Although significant agreement was found when using eCRF to categorize participants into fitness tertiles, submaximal exercise equations correctly classified an average of only 51% (range, 37%-58%) and maximal exercise equations correctly classified an average of only 59% (range, 43%-76%). CONCLUSIONS: Despite significant associations between exercise-based prediction equations and directly measured CRF, the equations had a low degree of accuracy in categorizing participants into fitness tertiles, a key requirement when stratifying risk within a clinical setting. The present analysis highlights the limited accuracy of exercise-based determinations of eCRF and suggests the need to include cardiopulmonary measures with maximal exercise to accurately assess CRF within a clinical setting.
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Peterman et al. (2020) conducted an observational in Apparently healthy adults (n=4,871). Exercise-based prediction equations vs. Directly measured cardiorespiratory fitness was evaluated on Accuracy in categorizing participants into fitness tertiles (p=<0.01). Exercise-based prediction equations had low accuracy in categorizing participants into fitness tertiles, correctly classifying only 51% (submaximal) and 59% (maximal) of individuals.
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