BACKGROUND: Non-exercise algorithms are cost-effective methods to estimate cardiorespiratory fitness (CRF) in healthcare settings. The limitation of current non-exercise models is that they were developed with cross-sectional data. PURPOSE: To extend the non-exercise research by developing algorithms for men and women using longitudinal data on indicators available in healthcare settings. METHODS: The sample included 1325 women (aged 20-78 years) and 10,040 men (aged 20-86 years) who completed two to 21 maximal treadmill tests between 1977 and 2005. The data were analyzed in 2011 and 2012. The dependent variable was CRF measured by treadmill test. The independent variables were age; body composition (percentage fat or BMI); waist circumference; self-reported physical activity; resting heart rate; and smoking behavior. RESULTS: Linear mixed-models regression showed that all variables were independently related to CRF. There was a positive association between CRF and physical activity. Higher levels of body composition were linked to lower CRF. High resting heart rate and smoking resulted in lower estimates of CRF. The error estimates of the percentage fat algorithms were as follows: women, 1.41 METs (95% CI=1.35, 1.47); and men, METs 1.54 (95% CI=1.51, 1.55). The BMI models were somewhat less accurate: women, METs 1.51 (95% CI=1.45, 1.58); and men, 1.66 METs (95% CI=1.63, 1.68). CONCLUSIONS: These results showed that the CRF of women and men can be estimated from easily obtained health indicators. The longitudinal non-exercise algorithms provide models to accurately estimate CRF changes associated with aging and provide cost-effective algorithms to track CRF over time with health indicators available in healthcare settings.
BACKGROUND: Non-exercise algorithms are cost-effective methods to estimate cardiorespiratory fitness (CRF) in healthcare settings. The limitation of current non-exercise models is that they were developed with cross-sectional data. PURPOSE: To extend the non-exercise research by developing algorithms for men and women using longitudinal data on indicators available in healthcare settings. METHODS: The sample included 1325 women (aged 20-78 years) and 10,040 men (aged 20-86 years) who completed two to 21 maximal treadmill tests between 1977 and 2005. The data were analyzed in 2011 and 2012. The dependent variable was CRF measured by treadmill test. The independent variables were age; body composition (percentage fat or BMI); waist circumference; self-reported physical activity; resting heart rate; and smoking behavior. RESULTS: Linear mixed-models regression showed that all variables were independently related to CRF. There was a positive association between CRF and physical activity. Higher levels of body composition were linked to lower CRF. High resting heart rate and smoking resulted in lower estimates of CRF. The error estimates of the percentage fat algorithms were as follows: women, 1.41 METs (95% CI=1.35, 1.47); and men, METs 1.54 (95% CI=1.51, 1.55). The BMI models were somewhat less accurate: women, METs 1.51 (95% CI=1.45, 1.58); and men, 1.66 METs (95% CI=1.63, 1.68). CONCLUSIONS: These results showed that the CRF of women and men can be estimated from easily obtained health indicators. The longitudinal non-exercise algorithms provide models to accurately estimate CRF changes associated with aging and provide cost-effective algorithms to track CRF over time with health indicators available in healthcare settings.
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