TY - JOUR
T1 - Estimating Cardiorespiratory Fitness Without Exercise Testing or Physical Activity Status in Healthy Adults
T2 - Regression Model Development and Validation
AU - Sloan, Robert
AU - Visentini-Scarzanella, Marco
AU - Sawada, Susumu
AU - Sui, Xuemei
AU - Myers, Jonathan
N1 - Funding Information:
This research was supported by the Japan Society for the Promotion of Science KAKENHI Grant 19K19437.
Publisher Copyright:
© Robert Sloan, Marco Visentini-Scarzanella, Susumu Sawada, Xuemei Sui, Jonathan Myers.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
KW - EHR
KW - cardiorespiratory
KW - electronic health record
KW - epidemiology
KW - fitness
KW - nonexercise equation
KW - nonexercise estimated cardiorespiratory fitness
KW - physical activity
KW - public health
KW - regression model
KW - surveillance
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U2 - 10.2196/34717
DO - 10.2196/34717
M3 - Review article
C2 - 35793133
AN - SCOPUS:85134360463
SN - 2369-2960
VL - 8
JO - JMIR Public Health and Surveillance
JF - JMIR Public Health and Surveillance
IS - 7
M1 - e34717
ER -
