James E Peterman1, Mitchell H Whaley2, Matthew P Harber3, Bradley S Fleenor3, Mary T Imboden4, Jonathan Myers5, Ross Arena6, Leonard A Kaminsky1. 1. Fisher Institute of Health and Well-Being, Ball State University, USA. 2. College of Health, Ball State University, USA. 3. Clinical Exercise Physiology Laboratory, Ball State University, USA. 4. Health and Human Performance Department, George Fox University, USA. 5. Division of Cardiology, Veterans Affairs Palo Alto Healthcare System and Stanford University, USA. 6. Department of Physical Therapy and Integrative Physiology Laboratory, University of Illinois at Chicago, USA.
Abstract
AIMS: A recent scientific statement suggests clinicians should routinely assess cardiorespiratory fitness using at least non-exercise prediction equations. However, no study has comprehensively compared the many non-exercise cardiorespiratory fitness prediction equations to directly-measured cardiorespiratory fitness using data from a single cohort. Our purpose was to compare the accuracy of non-exercise prediction equations to directly-measured cardiorespiratory fitness and evaluate their ability to classify an individual's cardiorespiratory fitness. METHODS: The sample included 2529 tests from apparently healthy adults (42% female, aged 45.4 ± 13.1 years (mean±standard deviation). Estimated cardiorespiratory fitness from 28 distinct non-exercise prediction equations was compared with directly-measured cardiorespiratory fitness, determined from a cardiopulmonary exercise test. Analysis included the Benjamini-Hochberg procedure to compare estimated cardiorespiratory fitness with directly-measured cardiorespiratory fitness, Pearson product moment correlations, standard error of estimate values, and the percentage of participants correctly placed into three fitness categories. RESULTS: All of the estimated cardiorespiratory fitness values from the equations were correlated to directly measured cardiorespiratory fitness (p < 0.001) although the R2 values ranged from 0.25-0.70 and the estimated cardiorespiratory fitness values from 27 out of 28 equations were statistically different compared with directly-measured cardiorespiratory fitness. The range of standard error of estimate values was 4.1-6.2 ml·kg-1·min-1. On average, only 52% of participants were correctly classified into the three fitness categories when using estimated cardiorespiratory fitness. CONCLUSION: Differences exist between non-exercise prediction equations, which influences the accuracy of estimated cardiorespiratory fitness. The present analysis can assist researchers and clinicians with choosing a non-exercise prediction equation appropriate for epidemiological or population research. However, the error and misclassification associated with estimated cardiorespiratory fitness suggests future research is needed on the clinical utility of estimated cardiorespiratory fitness. Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: A recent scientific statement suggests clinicians should routinely assess cardiorespiratory fitness using at least non-exercise prediction equations. However, no study has comprehensively compared the many non-exercise cardiorespiratory fitness prediction equations to directly-measured cardiorespiratory fitness using data from a single cohort. Our purpose was to compare the accuracy of non-exercise prediction equations to directly-measured cardiorespiratory fitness and evaluate their ability to classify an individual's cardiorespiratory fitness. METHODS: The sample included 2529 tests from apparently healthy adults (42% female, aged 45.4 ± 13.1 years (mean±standard deviation). Estimated cardiorespiratory fitness from 28 distinct non-exercise prediction equations was compared with directly-measured cardiorespiratory fitness, determined from a cardiopulmonary exercise test. Analysis included the Benjamini-Hochberg procedure to compare estimated cardiorespiratory fitness with directly-measured cardiorespiratory fitness, Pearson product moment correlations, standard error of estimate values, and the percentage of participants correctly placed into three fitness categories. RESULTS: All of the estimated cardiorespiratory fitness values from the equations were correlated to directly measured cardiorespiratory fitness (p < 0.001) although the R2 values ranged from 0.25-0.70 and the estimated cardiorespiratory fitness values from 27 out of 28 equations were statistically different compared with directly-measured cardiorespiratory fitness. The range of standard error of estimate values was 4.1-6.2 ml·kg-1·min-1. On average, only 52% of participants were correctly classified into the three fitness categories when using estimated cardiorespiratory fitness. CONCLUSION: Differences exist between non-exercise prediction equations, which influences the accuracy of estimated cardiorespiratory fitness. The present analysis can assist researchers and clinicians with choosing a non-exercise prediction equation appropriate for epidemiological or population research. However, the error and misclassification associated with estimated cardiorespiratory fitness suggests future research is needed on the clinical utility of estimated cardiorespiratory fitness. Published on behalf of the European Society of Cardiology. All rights reserved.
Authors: James E Peterman; Matthew P Harber; Mary T Imboden; Mitchell H Whaley; Bradley S Fleenor; Jonathan Myers; Ross Arena; W Holmes Finch; Leonard A Kaminsky Journal: J Am Heart Assoc Date: 2020-05-27 Impact factor: 5.501
Authors: Matthew P Harber; McKenzie Metz; James E Peterman; Mitchell H Whaley; Bradley S Fleenor; Leonard A Kaminsky Journal: PLoS One Date: 2020-12-01 Impact factor: 3.240
Authors: Robert A Sloan; Marco V Scarzanella; Yuko Gando; Susumu S Sawada Journal: Int J Environ Res Public Health Date: 2021-11-23 Impact factor: 3.390