OBJECTIVE:Sex-related discrepancies in mortality risk may be better elucidated and have greater clinical utility if body position and positional changes are considered. Thus, the aim of this study was to determine if supine, standing and the change in supine-to-standing BP have predictive value for all-cause mortality in a sex-dependent manner. METHODS:Participants consisted of 2121 apparently healthy males (51%) and females (49%) without known disease from the Ball State Adult Fitness Longitudinal Lifestyle Study (BALL ST) cohort. Resting supine and standing BP were acquired and the supine-to-standing BP change was calculated. Kaplan-Meier survival curves were created along with Cox proportional hazard models adjusting for traditional risk factors and hypertension medications were performed to assess the association of positional BP and all-cause mortality. RESULTS:Supine and standing systolic BP (SBP) and pulse pressure (PP) in males and females, and supine diastolic BP (DBP) in females predicted mortality in unadjusted models ( P  < 0.05). Supine-to-standing SBP change in females, but not males, predicted mortality in the fully adjusted models ( P  < 0.05), and supine-to-standing DBP change predicted mortality in the unadjusted model only for males ( P  < 0.05). Compared to the low and medium tertiles, the high tertile for change in supine-to-standing SBP ( P  < 0.05) and PP ( P  =0.059) had the lowest survival probability in females, and lowest survival probability for DBP change for males ( P  ≤   0.05). CONCLUSION:The change in supine-to-standing SBP predicts mortality in apparently healthy females independent of traditional risk factors and hypertension medications. Sex-specific differences in positional BP may provide additional diagnostic insight on all-cause mortality risk.

ABSTRACTBACKGROUNDCardiorespiratory fitness (CRF), measured by peak oxygen uptake (VO2peak), is a strong predictor of mortality. Despite its widespread clinical use, current reference equations for VO2peak show distorted calibration in obese individuals. Using data from the Fitness Registry and the Importance of Exercise National Database (FRIEND), we sought to develop novel reference equations for VO2peak better calibrated for overweight/obese individuals - in both males and females, by considering body composition metrics.METHODS AND RESULTSGraded treadmill tests from 6,836 apparently healthy individuals were considered in data analysis. We used the National Health and Nutrition Examination Survey equations to estimate lean body mass (eLBM) and body fat percentage (eBF).Multivariable regression was used to determine sex-specific equations for predicting VO2peak considering age terms, eLBM and eBF. The resultant equations were expressed as VO2peak (male) = 2633.4 + 48.7✕eLBM (kg) - 63.6✕eBF (%) - 0.23✕Age2(R2=0.44) and VO2peak (female) = 1174.9 + 49.4✕eLBM (kg) - 21.7✕eBF (%) - 0.158✕Age2(R2=0.53). These equations were well-calibrated in subgroups based on sex, age and body mass index (BMI), in contrast to the Wasserman equation. In addition, residuals for the percent-predicted VO2peak (ppVO2) were stable over the predicted VO2peak range, with low CRF defined as < 70% ppVO2and average CRF defined between 85-115%.CONCLUSIONSThe derived VO2peak reference equations provided physiologically explainable and were well-calibrated across the spectrum of age, sex and BMI. These equations will yield more accurate VO2peak evaluation, particularly in obese individuals.

Background: Despite the importance of objective measures for prescribing aerobic exercise for mitigating cardiovascular risk in people with coronary artery disease (CAD), no study has examined sex differences in the utility of the cardiopulmonary exercise test (CPET) for developing the exercise prescription. Methods: CPET results from 1352 females and 5875 males with CAD were analysed to determine if there was a sex difference in achieving maximal oxygen uptake ((VO2max)-O-center dot) or an identifiable first ventilatory threshold (VT1). Secondary outcomes were to determine correlates of not achieving (VO2max)-O-center dot or VT1 in all patients and in males and females separately. Results: A greater proportion of males than females achieved (VO2max)-O-center dot or VT1 (89.7% vs 71.3%; P < 0.001) as well as specifically achieving (VO2max)-O-center dot (40.2% vs 26.7%; P < 0.001) and VT1 (88.0% vs 69.2%; P < 0.001). The most influential correlates of not achieving (VO2max)-O-center dot or VT1 were female sex (odds ratio 3.1, 95% confidence interval 2.6-3.7), age > 60 years, tested on treadmill vs cycle, depressive symptoms, and a secondary heart failure diagnosis. At entry to cardiac rehabilitation, these correlates were more prevalent in females than in males. Correlates differed by sex. The threshold for when age affected achieving (VO2max)-O-center dot or VT1 on the cycle CPET was earlier for females (> 50 years of age) than for males (> 70 years of age) with no difference on treadmill (> 80 years of age for both). Conclusions: Although most patients achieved (VO2max)-O-center dot or VT1 on the CPET, females were 3 times less likely than males to achieve (VO2max)-O-center dot or VT1. Strategies to improve utility of CPETs for females, such as alternative exercise test protocols and investigation into underlying mechanisms for effects of depressive symptoms, should be conducted.

PURPOSE:To determine if individuals chronically (>1 yr) prescribed antihypertensive medications have a normal BP response to peak exercise compared with unmedicated individuals. METHODS:Participants included 2555 adults from the Ball State Adult Fitness Longitudinal Lifestyle STudy cohort who performed a peak treadmill exercise test. Participants were divided into groups by sex and antihypertensive medication status. Individuals prescribed antihypertensive medications for >1 yr were included. Exaggerated and blunted SBP within each group was categorized using the Fitness Registry and the Importance of Exercise: A National Database (FRIEND) and absolute criteria as noted by the American Heart Association. RESULTS:The unmedicated group had a greater prevalence ( P < 0.05) of blunted SBP responses, whereas the medicated group had a higher prevalence ( P < 0.05) of exaggerated SBP responses using both the FRIEND and absolute criteria. Peak SBP was higher ( P < 0.01) in medicated compared with unmedicated participants in the overall cohort when controlling for age and sex, but not after controlling for resting SBP ( P = 0.613), risk factors ( P = 0.104), or cardiorespiratory fitness ( P = 0.191). When men and women were assessed independently, peak SBP remained higher in the medicated women after controlling for age and resting SBP ( P = 0.039), but not for men ( P = 0.311). Individuals on beta-blockers had a higher peak SBP even after controlling for age, sex, risk factors, and cardiorespiratory fitness ( P = 0.022). CONCLUSIONS:Individuals on antihypertensive medications have a higher peak SBP response to exercise. Given the prognostic value of exaggerated peak SBP, control of exercise BP should be considered in routine BP assessment and in the treatment of hypertension.

Exercise capacity (EC) is an important predictor of survival in the general population and in subjects with cardiopulmonary disease. Despite its relevance, considering the percent-predicted workload (%pWL) given by current equations may overestimate EC in older adults. Therefore, to improve the reporting of EC in clinical practice, our main objective was to develop workload reference equations (pWL) that better reflect the relation between workload and age. Using the Fitness Registry and the Importance of Exercise National Database (FRIEND), we analyzed a reference group of 6,966 apparently healthy participants and 1,060 participants with heart failure who underwent graded treadmill cardiopulmonary exercise testing. For the first group, the mean age was 44 years (18 to 79); 56.5% of participants were males and 15.4% had obesity. Peak oxygen consumption was 11.6 ± 3.0 METs in males and 8.5 ± 2.4 METs in females. After partition analysis, we first developed sex-specific pWL equations to allow comparisons to a healthy weight reference. For males, pWL (METs) = 14.1−0.9×10−3×age2 and 11.5−0.87×10−3×age2 for females. We used those equations as denominators of %pWL, and based on their distribution, we determined thresholds for EC classification, with average EC defined by the range corresponding to 85% to 115%pWL. Compared with %pWL using current equations, the new equations yielded better-calibrated %pWL across different age ranges. We also derived body mass index-adjusted pWL equations that better assessed EC in subjects with heart failure. In conclusion, the novel pWL equations have the potential to impact the report of EC in practice.

Widening of pulse pressure with age is associated with arterial stiffening and increased risk of cardiovascular disease. High cardiorespiratory fitness (CRF) is associated with lower age-related arterial stiffening but the relationship between CRF and change in pulse pressure over time has not been examined in cohorts that include both men and women. PURPOSE: Examine the relationship between directly measured CRF and change in pulse pressure over time in apparently healthy men and women. METHODS: The sample included 749 normotensive (resting blood pressure < 140/90 mmHg and unmedicated for blood pressure) individuals (476 males, 273 females) with mean age 42 ± 11 yr from the Ball State Adult Fitness Longitudinal Lifestyle Study (BALL ST) who completed a maximal cardiopulmonary exercise test for the assessment of CRF (i.e., VO2max) and cardiometabolic risk factor assessment. Linear regression analysis was performed to assess the relationship between baseline CRF and the change in pulse pressure over time. Models were performed using CRF as VO2max (ml/kg/min) and as percentile based on age and sex-adjusted normative values from the Fitness Registry of the Importance of Exercise National Database (FRIEND). RESULTS: The change in pulse pressure was calculated over a follow-up period of 10.0 ± 9.3 yr. CRF expressed as VO2max was related to the change in pulse pressure in the univariate model (r = -0.123, P < 0.05) and after adjusting for age, sex, follow-up years, and test year (r = -0.117, P < 0.05) but not after further controlling for traditional risk factors (obesity, dyslipidemia, diabetes, physical activity, and smoking) (r = -0.147, P > 0.05). CRF expressed as FRIEND percentile was associated with the change in pulse pressure (r = -0.066, P < 0.05), even after adjusting for age, sex, follow-up years, test years, and traditional risk factors. CONCLUSION: These data suggest the cardioprotective effects of CRF are mediated, at least partially, through reducing arterial stiffening and widening of pulse pressure with aging.

Purpose: Maximal heart rate (HRmax) continues to be an important measure of adequate effort during an exercise test. The aim of this study was to improve the accuracy of HRmax prediction using a machine learning (ML) approach. Methods: We used a sample from the Fitness Registry of the Importance of Exercise National Database, which included 17 325 apparently healthy individuals (81% males) who performed a maximal cardiopulmonary exercise test. Two standard formulas for HRmax prediction were tested: Formula1 = 220 − age (yr), root-mean-squared error (RMSE) 21.9, relative root-mean-squared error (RRMSE) 1.1; and Formula2 = 209.3 − 0.72 × age (yr), RMSE 22.7 and RRMSE 1.1. For ML model prediction, we used age, weight, height, resting HR, and systolic and diastolic blood pressure. The following ML algorithms to predict HRmax were applied: lasso regression (LR), neural networks (NN), support vector machine (SVM) and random forests (RF). An evaluation was performed using cross-validation and by computing the RMSE and RRMSE, Pearson correlation, and Bland-Altman plots. The best predictive model was explained with Shapley Additive Explanations (SHAP). Results: The HRmax for the cohort was 162 ± 20 bpm. All ML models improved HRmax prediction and reduced RMSE and RRMSE compared with Formula1 (LR: 20.2%, NN: 20.4%, SVM: 22.2%, and RF: 24.7%). The predictions of all algorithms significantly correlated with HRmax (r = 0.49, 0.51, 0.54, 0.57, respectively; P < .001). Bland-Altman analysis demonstrated lower bias and 95% CI for all ML models in comparison with standard equations. The SHAP explanation showed a high impact of all selected variables. Conclusions: Machine learning, particularly the RF model, improved prediction of HRmax using readily available measures. This approach should be considered for clinical application to refine HRmax prediction.

Purpose:Nonexercise predictions of peak oxygen uptake (Vo(2peak)) are used clinically, yet current equations were developed from cohorts of apparently healthy individuals and may not be applicable to individuals with cardiovascular disease (CVD). Our purpose was to develop a CVD-specific nonexercise prediction equation for Vo(2peak). Methods:Participants were from the Fitness Registry and Importance of Exercise International Database (FRIEND) with a diagnosis of coronary artery bypass surgery (CABG), myocardial infarction (MI), percutaneous coronary intervention (PCI), or heart failure (HF) who met maximal effort criteria during a cardiopulmonary exercise test (n = 15 997; 83% male; age 63.1 +/- 10.4 yr). The cohort was split into development (n = 12 798) and validation groups (n = 3199). The prediction equation was developed using regression analysis and compared with a previous equation developed on a healthy cohort. Results:Age, sex, height, weight, exercise mode, and CVD diagnosis were all significant predictors of Vo(2peak). The regression equation was:Vo(2peak) (mL center dot kg(-1) center dot min(-1)) = 16.18 - (0.22 x age [yr]) + (3.63 x sex [male = 1; female = 0]) + (0.14 x height [cm]) - (0.12 x weight [kg]) + (3.62 x mode [treadmill = 1; cycle = 0]) - (2.70 x CABG [yes = 1, no = 0]) - (0.31 x MI [yes = 1, no = 0]) + (0.37 x PCI [yes = 1, no = 0]) - (4.47 x HF [yes = 1, no = 0]). Adjusted R-2 = 0.43; SEE = 4.75 mL center dot kg(-1) center dot min(-1).Compared with measured Vo(2peak) in the validation group, percent predicted Vo(2peak) was 141% for the healthy cohort equation and 100% for the CVD-specific equation. Conclusions:The new equation for individuals with CVD had lower error between measured and predicted Vo(2peak) than the healthy cohort equation, suggesting population-specific equations are needed for predicting Vo(2peak); however, errors associated with nonexercise prediction equations suggest Vo(2peak) should be directly measured whenever feasible.

An excessive rise in systolic blood pressure (SBP) during exercise is linked to increased risk of cardiovascular disease (CVD). Hypertensive individuals are more likely to have an exaggerated SBP response to exercise. However, research is limited on the impact of antihypertensive medication use on the exercise blood pressure response. PURPOSE: To determine if individuals on antihypertensive medications (excluding beta-blockers) have a normal blood pressure response to maximal exercise compared to unmedicated individuals. METHODS: Participants included 2,555 apparently healthy adults from the Ball State Adult Fitness Longitudinal Lifestyle STudy (BALL ST) cohort. Participants were divided into groups by sex and antihypertensive medications status (Male medicated, Male unmedicated, Female medicated, Female unmedicated). A 2-way analysis of covariance (Sex x Medication Status) was used to assess peak SBP between groups. A chi-squared test was used to determine the prevalence of exaggerated and blunted responses within each group using the Fitness Registry and the Importance of Exercise: A National Database (FRIEND) and absolute criteria (FRIEND Exaggerated ≥90th percentile, Blunted ≤10th percentile; Absolute exaggerated >210 mmHg for men and > 190 mmHg for women, Blunted <140 mmHg). RESULTS: Peak SBP was higher in medicated compared to unmedicated subjects (p < 0.01) in the overall cohort when controlling for age and resting SBP. When men and women were assessed independently, peak SBP remained higher in the medicated women compared to unmedicated women (p < 0.05), however the difference in peak SBP between medicated and unmedicated men was not significant after controlling for age and resting SBP (p = 0.31). Further, the unmedicated group had a greater prevalence of blunted SBP responses, whereas the medicated group had a higher prevalence of exaggerated SBP responses using both the FRIEND and absolute criteria. CONCLUSION: Individuals on antihypertensive medications had a higher prevalence of an exaggerated peak SBP response than unmedicated. Given the prognostic value of exaggerated peak SBP, individuals on antihypertensive medications should be monitored closely during peak exercise to lower risk of an adverse cardiovascular event.

Cardiorespiratory fitness (CRF) has been associated with future risk of hypertension (HTN); however, this relationship has not been examined in apparently healthy cohorts that include both men and women. PURPOSE: Examine the relationship between directly measured CRF and incident HTN in apparently healthy men and women. METHODS: The sample included 749 individuals (476 males, 273 females) with mean age 42 +/- 11 yr from the Ball State Adult Fitness Longitudinal Lifestyle Study (BALL ST) who completed a maximal cardiopulmonary exercise test for the assessment of CRF (i.e., VO2max) and cardiometabolic risk factor assessment. Cox proportional hazard models were performed to assess the relationship between CRF and incident HTN. Models were performed using CRF as a continuous variable and as a categorical variable based on age and sex-adjusted percentiles from the Fitness Registry and the Importance of Exercise National Database (low CRF: <34th %tile; average CRF: 34th-66th %tile; high CRF: >66th %tile). RESULTS: A total of 212 participants (28% of sample) developed HTN during a follow-up period of 10.0 +/- 9.3 yr. CRF was associated (P < 0.05) with incident HTN (hazard ratio [HR], 95% confidence interval [CI]: 0.977, 0.954-0.982) even after controlling for age, sex, and test year but not after further controlling for traditional risk factors (obesity, dyslipidemia, diabetes, physical activity, and smoking) (HR, 95% CI, 0.983, 0.961-1.006). Further, individuals with average CRF (HR, 95% CI, 0.594, 0.408-0.865, P < 0.05) and high CRF (HR, 95% CI, 0.566, 0.392-0.819, P < 0.05) were less likely to develop HTN compared to those with low CRF. CONCLUSION: These data support the important role of CRF in preventing the development of HTN. Additional large cohort studies are needed to comprehensively assess potential sex differences in these relationships.

Transportation modes that involve physical activity are referred to as active transportation; these include walking and bicycling, as well as the use of public transport. Ecological, cross-sectional, and longitudinal studies have shown that active transportation can help individuals meet physical activity recommendations and reduce their risk of developing obesity. However, transportation policies impact the use of active transportation. Laws, regulations, and rules vary around the world and as a result, the percentage of individuals using active transportation for trips also varies greatly. Not surprisingly, in nations that are more reliant on personal automobiles, obesity rates are far higher compared to those nations where more individuals use walking and bicycling for transportation. Some nations have federal policies that promote walking, cycling, and public transit use and actively discourage the use of personal automobiles. These policies have been found to influence active transportation and as a result, have led to lower obesity rates. This chapter discusses the research on active transport as a means for decreasing the risk of obesity as well as how government policies can increase the use of active transport.

BACKGROUND: Reference standards for ventilatory threshold (VT) have recently been established by the Fitness Registry Importance of Exercise National Database (FRIEND) registry based on age and sex. Based on these values, on average VT occurs at 51-74% of VO2max. The reason for variability in these values is unknown and may be influenced by fitness level. PURPOSE: To examine the impact of fitness level on VT expressed as a percentage of VO2max in apparently healthy men and women of varying fitness levels. METHODS: Participants included 1,784 self-referred male and female participants from the Ball State Adult Fitness Longitudinal Lifestyle STudy (BALL ST) cohort that performed resting health measurements and a maximal cardiopulmonary exercise test (CPET) between 1992 and 2020. Percentage of VT to VO2max was determined by dividing the confirmed VT by the confirmed VO2max derived from the CPET. Fitness level was determined by using the FRIEND registry percentiles with low fit being 33rd percentile, moderate fit 33rd-66th percentile, and high fit >66th percentile. An ANCOVA was performed to determine the differences between fitness levels controlling for age and sex. RESULTS: The mean percentage for VT to VO2max was higher in low fit (65.2 ± 10.4%) than the moderate (61.3 ± 10.9%) and high fit populations (60.8 ± 10.9%) (p < 0.05). CONCLUSION: Low fit individuals have a higher VT when expressed as a percentage of VO2max, and thus likely have a higher range for moderate intensity exercise compared to higher fitness level populations relative to their own exercise capacity. Exercise physiologists should take this information into consideration when prescribing exercise to this population.

Purpose: To evaluate how the changes in directly measured cardiorespiratory fitness (CRF) relate to the changes in metabolic syndrome (MetS) status following 4-6 months of exercise training. Methods: Maximal cardiopulmonary exercise (CPX) tests and MetS risk factors were analyzed prospectively from 336 adults (46% women) aged 45.8 +/- 10.9 years. MetS was defined according to the National Cholesterol Education Program-Adult Treatment Panel III criteria, as updated by the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI). Pearson correlations, chi-squares, and dependent 2-tail t-tests were used to assess the relationship between the change in CRF and the change in MetS risk factors, overall number of MetS risk factors, and a MetS severity score following 4-6 months of participation in a self-referred, community-based exercise program. Results: Overall prevalence of MetS decreased from 23% to 14% following the exercise program (P < 0.05), while CRF improved 15% (4.7 +/- 8.4 mL/kg/min, P < 0.05). Following exercise training, the number of positive risk factors declined from 1.4 +/- 1.3 to 1.2 +/- 1.2 in the overall cohort (P < 0.05). The change in CRF was inversely related to the change in the overall number of MetS risk factors (r = -0.22; P < 0.05) and the MetS severity score (r = -0.28; p < 0.05). Conclusion: This observational cohort study indicates an inverse relationship between the change in CRF and the change in MetS severity following exercise training. These results suggest that participation in a community-based exercise program yields significant improvements in CRF, MetS risk factors, the prevalence of the binary MetS, and the MetS severity score. Improvement in CRF through exercise training should be a primary prevention strategy for MetS.

This study developed a nonexercise prediction equation for peak oxygen uptake (Vo2peak) in individuals with cardiovascular disease. The new equation provided a lower mean error between measured and predicted Vo2peak than an equation developed from a healthy cohort highlighting the importance of using a population-specific equation when predicting Vo2peak. Purpose: Nonexercise predictions of peak oxygen uptake (V˙o2peak) are used clinically, yet current equations were developed from cohorts of apparently healthy individuals and may not be applicable to individuals with cardiovascular disease (CVD). Our purpose was to develop a CVD-specific nonexercise prediction equation for V˙o2peak. Methods: Participants were from the Fitness Registry and Importance of Exercise International Database (FRIEND) with a diagnosis of coronary artery bypass surgery (CABG), myocardial infarction (MI), percutaneous coronary intervention (PCI), or heart failure (HF) who met maximal effort criteria during a cardiopulmonary exercise test (n = 15 997; 83% male; age 63.1 ± 10.4 yr). The cohort was split into development (n = 12 798) and validation groups (n = 3199). The prediction equation was developed using regression analysis and compared with a previous equation developed on a healthy cohort. Results: Age, sex, height, weight, exercise mode, and CVD diagnosis were all significant predictors of V˙o2peak. The regression equation was: V˙o2peak (mL · kg–1 · min–1) = 16.18 – (0.22 × age [yr]) + (3.63 × sex [male = 1; female = 0]) + (0.14 × height [cm]) – (0.12 × weight [kg]) + (3.62 × mode [treadmill = 1; cycle = 0]) – (2.70 × CABG [yes = 1, no = 0]) – (0.31 × MI [yes = 1, no = 0]) + (0.37 × PCI [yes = 1, no = 0]) – (4.47 × HF [yes = 1, no = 0]). Adjusted R2 = 0.43; SEE = 4.75 mL · kg–1 · min–1. Compared with measured V˙o2peak in the validation group, percent predicted V˙o2peak was 141% for the healthy cohort equation and 100% for the CVD-specific equation. Conclusions: The new equation for individuals with CVD had lower error between measured and predicted V˙o2peak than the healthy cohort equation, suggesting population-specific equations are needed for predicting V˙o2peak; however, errors associated with nonexercise prediction equations suggest V˙o2peak should be directly measured whenever feasible.

Background: The association between cardiorespiratory fitness (CRF) and metabolic syndrome (MetSyn) is well established. Additional variables derived from cardiopulmonary exercise testing (CPET) have shown prognostic value in some chronic diseases, however, there is limited information on how cardiopulmonary responses to exercise may be altered in individuals with MetSyn. Thus, the purpose of this study was to examine the association between cardiopulmonary variables derived from CPET and MetSyn. Methods: A cohort of 3181 participants (1714 men, 1467 women), aged 20-79 years, completed CPET and metabolic risk factor assessment between January 1, 1971, and November 1, 2020. Cardiopulmonary variables assessed included CRF defined as the maximum volume of oxygen uptake (VO2max), ventilatory threshold (VO2@VT), oxygen uptake efficiency slope (OUES), the ratio of ventilation to VO2 at peak exercise (peak VE/VO2) and the VE/VCO2slope. MetSyn was defined using the National Cholesterol Education Program/Adult Treatment Panel. Results: VO2max, VO2@VT, and OUES were lower (P < 0.001) and VE/VCO2slope was higher (P < 0.001) in individuals with MetSyn (n = 774), whereas no difference between groups existed for peak VE/VO2. Logistic regression analysis revealed that VO2max [0.91, 0.89-0.93; odds ratio (OR), 95% confidence interval (CI)], VO2@VT (0.91, 0.87-0.95; OR, 95% CI), OUES (0.32, 0.20-0.52; OR, 95% CI), and VE/VCO2slope (1.03, 1.01-1.05 OR, 95% CI) were all associated with the presence of MetSyn (P ≤ 0.001). Conclusion: These results indicate that MetSyn is associated with altered cardiopulomary function that may provide insight into the underlying pathophysiology of MetSyn.

Introduction: The importance of cardiorespiratory fitness (CRF) for stratifying mortality risk and guiding clinical care in patients with cardiovascular disease (CVD) is well-established. An American Heart Association Scientific Statement suggests routine clinical assessment of CRF using non-exercise prediction equations when direct assessment from a cardiopulmonary exercise test is not feasible. However, current prediction equations have been created from cohorts of apparently healthy individuals. Hypothesis: A CVD-specific non-exercise equation would have higher accuracy for predicting CRF compared to an equation developed from a cohort without known CVD. Methods: Participants from the Fitness Registry and Importance of Exercise International Database (FRIEND) with a diagnosis of coronary artery bypass surgery (CABG), myocardial infarction (MI), percutaneous coronary intervention (PCI), or heart failure (HF) who performed a cardiopulmonary exercise test were studied (83% [10,417 of 12,578] male; age 62.7 ± 10.3 years). The cohort (12,578 tests; 49% [6,190] treadmill tests) was split into development (10,062) and validation (2,516) groups. The prediction equation was developed using multiple regression analysis and comparisons were made with a CRF prediction equation developed on an apparently healthy cohort using FRIEND. Results: Age, sex, height, body mass, exercise mode, and CVD diagnosis were all significant predictors of CRF. The regression equation was: CRF (mL/kg/min) = 17.03 – (0.21 * age [years]) + (3.60 * sex [male = 1; female = 0]) + (0.12 * height [cm]) – (0.11 * body mass [kg]) + (3.75 * mode [treadmill = 1; cycle = 0]) – (2.40 * CABG [yes = 1, no = 0]) – (0.29 * MI [yes = 1, no = 0]) + (0.75 * PCI [yes = 1, no = 0]) – (3.90 * HF [yes = 1, no = 0]) (adjusted R 2 = 0.42, SEE = 4.74 mL/kg/min). When compared to measured CRF in the validation group (19.6 ± 6.2 mL/kg/min), predicted CRF was similar for the CVD equation (19.8 ± 4.1 mL/kg/min [101%]) and higher for the healthy cohort equation (28.2 ± 7.0 mL/kg/min [144%]; P <0.05). Significant Pearson correlations were found when using either prediction equation although the correlation when using the CVD equation was higher (r = 0.65) than that for the healthy cohort equation (r = 0.48, P <0.05). Differences between equations were also observed for root mean square error (4.7 and 10.9 mL/kg/min for the CVD and healthy cohort equations, respectively). Conclusions: As hypothesized, the CVD-specific non-exercise equation was a better predictor of CRF in a cohort of individuals with CVD. The new equation for individuals with CVD provided a lower mean error between measured and predicted CRF than an equation developed from an apparently healthy cohort. Thus, population specific equations are needed for predicting CRF; however, the error associated with non-exercise prediction equations suggests CRF should be directly measured whenever feasible.

PURPOSE:Oxygen uptake efficiency slope (OUES), defined as the slope of the linear relationship between oxygen uptake and the semilog transformed ventilation rate measured during an incremental exercise test, may have prognostic utility. The objective of this investigation was to examine the relationship between assessments of OUES and all-cause mortality in a cohort of apparently healthy adults. METHODS:The sample included 2220 apparently healthy adults (48% females) with a mean age of 44.7 ± 12.9 yr who performed cardiopulmonary exercise testing. The OUES was calculated from the entire test, using data from the initial 50% (OUES 50 ) and 75% (OUES 75 ) of test time, and normalized to body surface area. Cox proportional hazard models assessed the relationship between measures of OUES and mortality. Prognostic peak oxygen uptake (V˙ o2peak ) and OUES models were compared using the concordance index. RESULTS:There were 310 deaths (29% females) over a follow-up period of 19.8 ± 11.1 yr. For males, OUES, OUES 75 , and normalized OUES had an inverse association with mortality, even after adjusting for traditional risk factors ( P < .05). For females, only the unadjusted OUES, OUES 75 , and normalized OUES models were associated with mortality ( P < .05). The concordance index values indicated that unadjusted OUES 50 and OUES 75 models had lower discrimination than the unadjusted OUES and V˙ o2peak models ( P < .05). Furthermore, OUES did not complement the fully adjusted V˙ o2peak model ( P ≥ .32). CONCLUSIONS:Assessments of OUES are related to all-cause mortality in males but not in females. These findings suggest that OUES can have prognostic utility in apparently healthy males. Moreover, submaximal determinations of OUES could have value when measuring V˙ o2peak is not feasible.

Cardiorespiratory fitness (CRF) is not only an objective measure of physical activity, but also a useful diagnostic and prognostic health indicator for patients in clinical settings. There is a well-established inverse relationship between cardiorespiratory fitness (CRF) and mortality. However, the effect of CRF on mortality status might be different on subgroups of individuals and could be higher or lower than the estimated average effect of CRF. Thus, the objective of the study is to identify subgroups with higher or lower impact of CRF on mortality status. In addition, we evaluate and compare both tree-based and non-tree-based algorithms for identifying predictive features and subgroups. A penalized logistic regression with least absolute shrinkage and selection operator (LASSO) penalty is performed to identify the features that may be associated with low CRF and all-cause mortality. The algorithms considered are: virtual twins classification (VT(C)), generalized unbiased interaction detection and estimation (GUIDE) classification (Gc), GUIDE sum (Gs), GUIDE interaction (Gi) to find subgroups of participants where CRF exerts positive or negative association with all-cause mortality from the Ball State Adult Fitness Longitudinal Lifestyle Study (BALL ST) data. The overall result suggests that tree-based (VT and GUIDE) methods naturally define subgroups with fewer predictors and the non-tree-based method (logistic-LASSO) fails to find subgroups, only identify predictors that have impact on mortality status. In terms of predictive variable selection and subgroup identification, Gi is the best method compared to other tree-based and non-tree-based algorithms. Our study identifies subgroups that may be benefited from higher CRF.

Purpose: The cardiorespiratory optimal point (COP) is the minimum ventilatory equivalent for oxygen. The COP can be determined during a submaximal incremental exercise test. Reflecting the optimal interaction between the respiratory and cardiovascular systems, COP may have prognostic utility. The aim of this investigation was to determine the relationship between COP and all-cause mortality in a cohort of apparently healthy adults. Methods: The sample included 3160 apparently healthy adults (46% females) with a mean age of 44.0 +/- 12.5 yr who performed a cardiopulmonary exercise test. Cox proportional hazards models were performed to assess the relationship between COP and mortality risk. Prognostic peak oxygen uptake (Vo(2peak)) and COP models were compared using the concordance index. Results: There were 558 deaths (31% females) over a follow-up period of 23.0 +/- 11.9 yr. For males, all Cox proportional hazards models, including the model adjusted for traditional risk factors and Vo(2peak), had a positive association with risk for mortality (P < .05). For females, only the unadjusted COP model was associated with risk for mortality (P < .05). The concordance index values indicated that unadjusted COP models had lower discrimination compared with unadjusted Vo(2peak) models (P < .05) and Vo(2peak) did not complement COP models (P >= .13). Conclusions: Cardiorespiratory optimal point is related to all-cause mortality in males but not females. These findings suggest that a determination of COP can have prognostic utility in apparently healthy males aged 18-85 yr, which may be relevant when a maximal exercise test is not feasible or desirable.