Estimating Cardiorespiratory Fitness Without Exercise Testing or Physical Activity Status in Healthy Adults: Regression Model Development and Validation

Abstract

Background Low cardiorespiratory fitness (CRF) is an independent predictor of morbidity and mortality. Most health care settings use some type of electronic health record (EHR) system. However, many EHRs do not have CRF or physical activity data collected, thereby limiting the types of investigations and analyses that can be done. Objective This study aims to develop a nonexercise equation to estimate and classify CRF (in metabolic equivalent tasks) using variables commonly available in EHRs. Methods Participants were 42,676 healthy adults (female participants: n=9146, 21.4%) from the Aerobics Center Longitudinal Study examined from 1974 to 2005. The nonexercise estimated CRF was based on sex, age, measured BMI, measured resting heart rate, measured resting blood pressure, and smoking status. A maximal treadmill test measured CRF. Results After conducting nonlinear feature augmentation, separate linear regression models were used for male and female participants to calculate correlation and regression coefficients. Cross-classification of actual and estimated CRF was performed using low CRF categories (lowest quintile, lowest quartile, and lowest tertile). The multiple correlation coefficient (R) was 0.70 (mean deviation 1.33) for male participants and 0.65 (mean deviation 1.23) for female participants. The models explained 48.4% (SE estimate 1.70) and 41.9% (SE estimate 1.56) of the variance in CRF for male and female participants, respectively. Correct category classification for low CRF (lowest tertile) was found in 77.2% (n=25,885) of male participants and 74.9% (n=6,850) of female participants. Conclusions The regression models developed in this study provided useful estimation and classification of CRF in a large population of male and female participants. The models may provide a practical method for estimating CRF derived from EHRs for population health research.

Cardiorespiratory Fitness and Its Place in Medicine

“…Thus, for large population-based observational studies, non-exercise estimates generally appear to provide adequate reflections of CRF, although they are somewhat less powerful than directly measured CRF. Nevertheless, these and other studies applying non-exercise estimates of CRF [15,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]56] provide further confirmation of the power of CRF in predicting risk for adverse outcomes.…”

Association between Estimated Cardiorespiratory Fitness and Abnormal Glucose Risk: A Cohort Study

“…A total of 17,954 participants were included in this study ( Supplement S1, Figure S1 ), identified as having measured waist girth and all the eCRF parameters (age, BMI, resting heart rate, blood pressure, smoking status) at baseline. To establish an apparently healthy cohort at baseline, we excluded participants per the American Diabetic Association guidelines with known or diagnosed (fasting) diabetes ( n = 5755), prediabetes ( n = 531), CVD ( n = 90), cancer ( n = 282), abnormal ECG ( n = 503), BMI < 18.5 ( n = 499), age <20 or >90 ( n = 428), chronotropic incompetence ( n = 1253), missing information ( n = 11) [ 12 , 20 , 21 , 22 ]. These inclusion/exclusion criteria ( Supplement S1, Figure S1 ) resulted in 8602 apparently healthy individuals (17.8% women) aged 20 to 81 years old at baseline, followed between 1979 and 2006.…”

“…Weiskopf et al state, “While the prospective collection of data is notoriously expensive and time-consuming, the use of EHRs may allow a medical institution to develop a clinical data repository containing extensive records for large numbers of patients, thereby enabling more efficient retrospective research” [ 11 ]. To overcome the PA data limitation in calculating eCRF from EHRs, Sloan et al recently developed a nuanced eCRF without using PA as an algorithm parameter [ 12 ]. The eCRF algorithm included vital signs commonly found in EHRs (e.g., resting heart rate, systolic blood pressure, diastolic blood pressure) and was compared with measured CRF in 42,676 adults (21.4% females).…”

Association Between Estimated Cardiorespiratory Fitness and Abnormal Glucose Risk: A Cohort Study

“…Weiskopf et al state, "While the prospective collection of data is notoriously expensive and time-consuming, the use of EHRs may allow a medical institution to develop a clinical data repository containing extensive records for large numbers of patients, thereby enabling more efficient retrospective research" [9]. To overcome the PA data limitation in calculating eCRF from EHRs, Sloan et al recently developed a nuanced eCRF without using PA as an algorithm parameter [10]. The eCRF included vital signs commonly found in EHRs (e.g., resting heart rate, systolic blood pressure, diastolic blood pressure) and was compared with measured CRF in 42,676 adults (21.4% female).…”

“…17,954 participants were included in this study, identified as having measured waist girth and eCRF parameters. In line with previous ACLS studies to establish a healthy cohort at baseline, we excluded participants with diabetes (n=5,755), prediabetes (n=531), CVD (n=90), cancer (n=282), abnormal ECG (n= 503), BMI<18.5 (n=499), age <20 or >90 (n=428), chronotropic incompetence (n=1253), missing information (n=11) [10,16,17]. These criteria resulted in 8,602 healthy individuals (17.8% women) aged 20 to 81 years old at baseline from the ACLS followed between 1979 and 2006.…”