Longitudinal Algorithms to Estimate Cardiorespiratory Fitness

Abstract

Objectives This study sought to determine the capacity of cardiorespiratory fitness (CRF) algorithms without exercise testing to predict the risk for nonfatal cardiovascular disease (CVD) events and disease-specific mortality.Background Cardiorespiratory fitness (CRF) is not routinely measured, as it requires trained personnel and specialized equipment.Methods Participants were 43,356 adults (21% women) from the Aerobics Center Longitudinal Study, followed up between 1974 and 2003. Estimated CRF was determined on the basis of sex, age, body mass index, waist circumference, resting heart rate, physical activity level, and smoking status. Actual CRF was measured by a maximal treadmill test. Risk reduction per 1-metabolic equivalent increase, discriminative ability (c statistic), and net reclassification improvement were determined.Results During a median follow-up of 14.5 years, 1,934 deaths occurred, 627 due to CVD. In a subsample of 18,095 participants, 1,049 cases of nonfatal CVD events were ascertained. After adjustment for potential confounders, both measured and estimated CRF were inversely associated with risks for all-cause mortality, CVD-related mortality and nonfatal CVD events in men, and all-cause mortality and nonfatal CVD events in women. The risk reduction per 1-metabolic equivalent increase ranged from approximately 10% to 20%. Measured CRF had a slightly better discriminative ability (c statistic) than did estimated CRF, and the net reclassification improvement values in measured CRF versus estimated CRF were 12.3% in men (p < 0.05) and 19.8% in women (p < 0.001).Conclusions These CRF algorithms utilized information routinely collected to obtain an estimate of CRF, which provides a valid indication of health status. In addition to identifying people at risk, this method can provide more appropriate exercise recommendations that reflect initial CRF levels.