Improving Reporting of Exercise Capacity Across Age Ranges Using Novel Workload Reference Equations

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.

A Machine Learning Approach to Developing an Accurate Prediction of Maximal Heart Rate During Exercise Testing in Apparently Healthy Adults

PURPOSE

Maximal heart rate (HR max ) continues to be an important measure of adequate effort during an exercise test. The aim of this study was to improve the accuracy of HR max 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 HR max 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 HR max 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 HR max for the cohort was 162 ± 20 bpm. All ML models improved HR max 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 HR max ( 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 HR max using readily available measures. This approach should be considered for clinical application to refine HR max prediction.

A Nonexercise Prediction of Peak Oxygen Uptake for Patients With Cardiovascular Disease: DATA FROM THE FITNESS REGISTRY AND THE IMPORTANCE OF EXERCISE INTERNATIONAL DATABASE (FRIEND)

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 R 2 = 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.

Reference Standards for Peak Rating of Perceived Exertion during Cardiopulmonary Exercise Testing: Data from FRIEND

INTRODUCTION

Peak rating of perceived exertion (RPE) is measured during clinical cardiopulmonary exercise testing (CPX) and is commonly used as a subjective indicator of maximal effort. However, no study to date has reported reference standards or the distribution of peak RPE across a large cohort of apparently healthy individuals.

PURPOSE

This study aimed to determine reference standards for peak RPE when using the 6-20 Borg scale for both treadmill and cycle tests.

METHODS

The analysis included 9551 tests (8821 treadmill, 730 cycle ergometer) from 13 laboratories within the Fitness Registry and Importance of Exercise National Database (FRIEND). Using data from tests conducted January 1, 1980, to January 1, 2021, percentiles of peak RPE for men and women were determined for each decade from 20 to 89 yr of age for treadmill and cycle exercise modes. Two-way ANOVA was used to compare differences in peak RPE values between sexes and across age groups.

RESULTS

There were statistically significant differences in RPE between age groups whether the test was performed on a treadmill or cycle ergometer ( P < 0.05). However, the mean and median RPE for each sex, age group, and test mode were between 18 and 19. In addition, 83% of participants met the traditional RPE criteria of ≥18 for indicating sufficient maximal effort.

CONCLUSIONS

This report provides the first normative reference standards for peak RPE in both male and female individuals performing CPX on a treadmill or cycle ergometer. Furthermore, these reference standards highlight the general consistency of peak RPE responses during CPX.

Cardiorespiratory Optimal Point Is a Submaximal Exercise Test Variable and a Predictor of Mortality Risk: THE BALL STATE ADULT FITNESS LONGITUDINAL LIFESTYLE STUDY (BALL ST)

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 (V˙ o2peak ) 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 V˙ o2peak , 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 V˙ o2peak models ( P < .05) and V˙ o2peak 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.

Cardiorespiratory fitness estimations and their ability to predict all-cause mortality in patients with cardiovascular disease

Background

In cardiac rehabilitation programs, cardiorespiratory fitness is commonly estimated (eCRF) from the maximum workload achieved on a graded exercise test. This study compared four well-established eCRF equations in their ability to predict mortality in patients with cardiovascular disease (CVD).

Methods

A total of 7269 individuals with CVD were studied (81% male; age 59.4 ± 10.3yr). eCRF was calculated using equations from the American College of Sports Medicine, Bruce et al., the Fitness Registry and the Importance of Exercise International Database, and McConnell and Clark. The eCRF from each equation was compared with a RMANOVA. Cox proportional hazard models assessed the relationship between the eCRF equations and mortality risk. The predictive ability of the models was compared using the concordance index.

Results

There were 284 deaths (85% male) over a follow-up period of 5.8 ± 2.8yr. Although differences in eCRF were observed between each equation (P < 0.05), the eCRF from each of the four equations was predictive of mortality (P < 0.05). The concordance index values for each of the models were the same (0.77) indicating similar predictive performance.

Conclusions

The four well-established eCRF equations did not differ in their ability to predict mortality in patients with CVD, indicating any could be used for this purpose. However, the differences in eCRF from each of the equations suggest potential differences in their ability to guide clinical care and should be the focus of future research.

Abstract EP57: A Non-exercise Prediction Of Cardiorespiratory Fitness For Patients With Cardiovascular Disease: Data From The Fitness Registry And The Importance Of Exercise International Database (FRIEND)

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.

Updated Reference Standards for Cardiorespiratory Fitness Measured with Cardiopulmonary Exercise Testing: Data from the Fitness Registry and the Importance of Exercise National Database (FRIEND)

OBJECTIVE

To provide updated reference standards for cardiorespiratory fitness (CRF) for the United States derived from cardiopulmonary exercise (CPX) testing when using a treadmill or cycle ergometer.

PATIENTS AND METHODS

Thirty-four laboratories in the United States contributed data to the Fitness Registry and the Importance of Exercise National Database. Analysis included 22,379 tests (16,278 treadmill and 6101 cycle ergometer) conducted between January 1, 1968, through March 31, 2021, from apparently healthy adults (aged 20 to 89 years). Percentiles of peak oxygen consumption for men and women were determined for each decade from 20 through 89 years of age for treadmill and cycle exercise modes, as well as when defining maximal effort as respiratory exchange ratio (RER) greater than or equal to 1.0 or RER greater than or equal to 1.1.

RESULTS

For both men and women, the 50^th percentile scores for each exercise mode decreased with age and were higher in men across all age groups and higher for treadmill compared with cycle CPX. The average rate of decline per decade over a 6-decade period was 13.5%, 4.0 mLO₂·kg^-1·min^-1 for treadmill CPX and 16.4%, 4.3 mLO₂·kg^-1·min^-1 for cycle CPX. Observationally, the mean peak oxygen consumption was similar whether using an RER criterion of greater than or equal to 1.0 or greater than or equal to 1.1 across the different test modes, ages, and for both sexes. The updated reference standards for treadmill CPX were 1.5 - 4.6 mLO₂·kg^-1·min^-1 lower compared with the previous 2015 standards whereas the updated cycling standards were generally comparable to the original 2017 standards.

CONCLUSION

These updated cardiorespiratory fitness reference standards improve the representativeness of the US population compared with the original standards.

Change in Metabolic Syndrome and Cardiorespiratory Fitness Following Exercise Training - The Ball State Adult Fitness Longitudinal Lifestyle Study (BALL ST)

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.

The importance of healthy lifestyle behaviors in the prevention of cardiovascular disease

Cardiovascular disease (CVD) is the leading cause of death globally. Advancements in the treatment of CVD have reduced mortality rates, yet the global burden of CVD remains high. Considering that CVD is still largely a preventable disease, prioritizing preventative measures through healthy lifestyle (HL) behaviors is necessary to lessen the burden of CVD. HL behaviors, such as regular exercise, healthy eating habits, adequate sleep, and smoking cessation, can influence a number of traditional CVD risk factors as well as a less commonly measured risk factor, cardiorespiratory fitness (CRF). It is important to note that cardiac rehabilitation programs, which traditionally have focused on secondary prevention, also emphasize the importance of making comprehensive HL behavior changes. This review discusses preventative measures to reduce the burden of CVD through an increased uptake and assessment of HL behaviors. An overview of the importance of CRF as a risk factor is discussed along with how to improve CRF and other risk factors through HL behavior interventions. The role of the clinician for promoting HL behaviors to prevent CVD is also reviewed.

Reference Standards for Cardiorespiratory Fitness by Cardiovascular Disease Category and Testing Modality: Data From FRIEND

Background

The importance of cardiorespiratory fitness for stratifying risk and guiding clinical decisions in patients with cardiovascular disease is well‐established. To optimize the clinical value of cardiorespiratory fitness, normative reference standards are essential. The purpose of this report is to extend previous cardiorespiratory fitness normative standards by providing updated cardiorespiratory fitness reference standards according to cardiovascular disease category and testing modality.

Methods and Results

The analysis included 15 045 tests (8079 treadmill, 6966 cycle) from FRIEND (Fitness Registry and the Importance of Exercise National Database). Using data from tests conducted January 1, 1974, through March 1, 2021, percentiles of directly measured peak oxygen consumption (VO_2peak) were determined for each decade from 30 through 89 years of age for men and women with a diagnosis of coronary artery bypass surgery, myocardial infarction, percutaneous coronary intervention, or heart failure. There were significant differences between sex and age groups for VO_2peak (P<0.001). The mean VO_2peak was 23% higher for men compared with women and VO_2peak decreased by a mean of 7% per decade for both sexes. Among each decade, the mean VO_2peak from treadmill tests was 21% higher than the VO_2peak from cycle tests. Differences in VO_2peak were observed among the age groups in both sexes according to cardiovascular disease category.

Conclusions

This report provides normative reference standards by cardiovascular disease category for both men and women performing cardiopulmonary exercise testing on a treadmill or cycle ergometer. These updated and enhanced reference standards can assist with patient risk stratification and guide clinical care.

Associations Between Active Commuting and Cardiovascular Disease in the United States

BACKGROUND

Active commuting is inversely related with cardiovascular disease (CVD) risk factors yet associations with CVD prevalence in the US population are unknown.

METHODS

Aggregate data from national surveys conducted in 2017 provided state-level percentages of adults who have/had coronary heart disease, myocardial infarction, and stroke, and who actively commuted to work. Associations between active commuting and CVD prevalence rates were assessed using Pearson correlations and generalized additive models controlling for covariates.

RESULTS

Significant correlations were observed between active commuting and all CVD rates (r range = -.31 to -.47; P < .05). The generalized additive model analyses for active commuting (walking, cycling, or public transport) in all adults found no relationships with CVD rates; however, a significant curvilinear association was observed for stroke within men. The generalized additive model curves when examining commuting via walking or cycling in all adults demonstrated nuanced, generally negative linear or curvilinear associations between coronary heart disease, myocardial infarction, and stroke.

CONCLUSION

Significant negative correlations were observed between active commuting and prevalence rates of coronary heart disease, myocardial infarction, and stroke. Controlling for covariates influenced these associations and highlights the need for future research to explore the potential of active commuting modes to reduce CVD in the United States.

Peak oxygen pulse and mortality risk in healthy women and men: The Ball State Adult Fitness Longitudinal Lifestyle Study (BALL ST)

Peak oxygen pulse (O₂ pulse_peak) may have predictive utility for health outcomes yet, presently, has only been examined in men and only using a single baseline measure.

PURPOSE

The primary aim of this investigation was to evaluate the relationship between O₂ pulse_peak and all-cause mortality in apparently healthy women and men. A secondary aim was to explore the relationship between longitudinal changes to O₂ pulse_peak and mortality.

METHODS

The sample included 3877 participants (43% women) for the primary aim and 759 participants (32% women) who performed two cardiopulmonary exercise tests ≥1 year apart for the secondary aim. Cox proportional hazard models were performed to determine the relationship between O₂ pulse_peak and mortality. Prognostic peak oxygen consumption (VO_2peak) and O₂ pulse_peak models were compared using the concordance index and Akaike information criterion (AIC).

RESULTS

In the assessment from baseline, there were 730 deaths over a 24.7 ± 11.8 year follow-up period. For men, a single measure of O₂ pulse_peak was inversely associated with risk for mortality (P < 0.05). However, the concordance index and AIC indicated lower discrimination compared to VO_2peak models and O₂ pulse_peak did not provide complementary benefit to VO_2peak models. For women, O₂ pulse_peak was not associated with mortality risk. In the longitudinal analysis, there were 168 deaths over a follow-up of 20.1 ± 11.4 years. Changes to O₂ pulse_peak were not significantly related to mortality in either sex.

CONCLUSIONS

Within an apparently healthy cohort, a single assessment of O₂ pulse_peak is related to all-cause mortality in men but not women. Further, longitudinal changes to O₂ pulse_peak are not predictive of mortality in either sex. These findings suggest O₂ pulse_peak may have limited prognostic utility in healthy individuals, particularly within healthy women.

The V˙E/V˙co2 Slope During Maximal Treadmill Cardiopulmonary Exercise Testing: REFERENCE STANDARDS FROM FRIEND (FITNESS REGISTRY AND THE IMPORTANCE OF EXERCISE: A NATIONAL DATABASE)

PURPOSE

Cardiopulmonary exercise testing (CPX) is the gold standard approach for the assessment of cardiorespiratory fitness (CRF). The primary aim of the current study was to determine reference standards for the minute ventilation/carbon dioxide production (V˙E/V˙co2) slope in a cohort from the "Fitness Registry and the Importance of Exercise: A National Database" (FRIEND) Registry.

METHODS

The current analysis included 2512 tests from 10 CPX laboratories in the United States. Inclusion criteria included CPX data on apparently healthy men and women: (1) age ≥20 yr; and (2) with a symptom-limited exercise test performed on a treadmill. Ventilation and V˙co2 data, from the initiation of exercise to peak, were used to calculate the V˙E/V˙co2 slope via least-squares linear regression. Reference values were determined for men and women by decade of life.

RESULTS

On average, V˙E/V˙co2 slope values were lower in men and increased with age independent of sex. Fiftieth percentile values increased from 27.1 in the second decade to 33.9 in the eighth decade in men and from 28.5 in the second decade to 33.7 in the eighth decade in women. In the overall group, correlations with baseline characteristics and the V˙E/V˙co2 slope were statistically significant (P < .05) although generally weak, particularly for age and body mass index.

CONCLUSION

The results of the current study establish reference values for the V˙E/V˙co2 slope when treadmill testing is performed, and all exercise data are used for the slope calculation. These results may prove useful in enhancing the interpretation of CPX results when assessing CRF.

Accuracy of Exercise-based Equations for Estimating Cardiorespiratory Fitness

Equations are often used to predict cardiorespiratory fitness (CRF) from submaximal or maximal exercise tests. However, no study has comprehensively compared these exercise-based equations with directly measured CRF using data from a single, large cohort.

PURPOSE

This study aimed to compare the accuracy of exercise-based prediction equations with directly measured CRF and evaluate their ability to classify an individual's CRF.

METHODS

The sample included 4871 tests from apparently healthy adults (38% female, age 44.4 ± 12.3 yr (mean ± SD)). Estimated CRF (eCRF) was determined from 2 nonexercise equations, 3 submaximal exercise equations, and 10 maximal exercise equations; all eCRF calculations were then compared with directly measured CRF, determined from a cardiopulmonary exercise test. Analysis included Pearson product-moment correlations, standard error of estimate values, intraclass correlation coefficients, Cohen κ coefficients, and the Benjamini-Hochberg procedure to compare eCRF with directly measured CRF.

RESULTS

All eCRF values from the prediction equations were associated with directly measured CRF (P < 0.01), with intraclass correlation coefficient estimates ranging from 0.07 to 0.89. Although significant agreement was found when using eCRF to categorize participants into fitness tertiles, submaximal exercise equations correctly classified an average of only 51% (range, 37%-58%) and maximal exercise equations correctly classified an average of only 59% (range, 43%-76%).

CONCLUSIONS

Despite significant associations between exercise-based prediction equations and directly measured CRF, the equations had a low degree of accuracy in categorizing participants into fitness tertiles, a key requirement when stratifying risk within a clinical setting. The present analysis highlights the limited accuracy of exercise-based determinations of eCRF and suggests the need to include cardiopulmonary measures with maximal exercise to accurately assess CRF within a clinical setting.

Increasing physical activity in the community setting

Physical activity (PA) is beneficial for both mental and physical health, yet many individuals do not meet PA recommendations. There are a multitude of approaches to increase levels of PA and the role of the community is one area of growing interest. This review discusses the community environment as well as programs within the community and their influence on PA levels. Despite some research limitations, there are clear factors associated with community-based PA. Strategies that improve the built environment along with community-based programs have shown success, although differences between the characteristics of communities can mean strategies to promote PA are not universally effective. Additional research is needed on effective strategies that can be tailored to the characteristics of the community to increase PA. Further, public health interventions and policies should consider the role of the community when aiming to increase PA levels.

Accuracy of Nonexercise Prediction Equations for Assessing Longitudinal Changes to Cardiorespiratory Fitness in Apparently Healthy Adults: BALL ST Cohort

Background

Repeated assessment of cardiorespiratory fitness (CRF) improves mortality risk predictions in apparently healthy adults. Accordingly, the American Heart Association suggests routine clinical assessment of CRF using, at a minimum, nonexercise prediction equations. However, the accuracy of nonexercise prediction equations over time is unknown. Therefore, we compared the ability of nonexercise prediction equations to detect changes in directly measured CRF.

Methods and Results

The sample included 987 apparently healthy adults from the BALL ST (Ball State Adult Fitness Longitudinal Lifestyle Study) cohort (33% women; average age, 43.1±10.4 years) who completed 2 cardiopulmonary exercise tests ≥3 months apart (3.2±5.4 years of follow‐up). The change in estimated CRF (eCRF) from 27 distinct nonexercise prediction equations was compared with the change in directly measured CRF. Analysis included Pearson product moment correlations, SEE values, intraclass correlation coefficient values, Cohen's κ coefficients, γ coefficients, and the Benjamini‐Hochberg procedure to compare eCRF with directly measured CRF. The change in eCRF from 26 of 27 equations was significantly associated to the change in directly measured CRF (P<0.001), with intraclass correlation coefficient values ranging from 0.06 to 0.63. For 16 of the 27 equations, the change in eCRF was significantly different from the change in directly measured CRF. The median percentage of participants correctly classified as having increased, decreased, or no change in CRF was 56% (range, 39%–61%).

Conclusions

Variability was observed in the accuracy between nonexercise prediction equations and the ability of equations to detect changes in CRF. Considering the appreciable error that prediction equations had with detecting even directional changes in CRF, these results suggest eCRF may have limited clinical utility.

Development of Global Reference Standards for Directly Measured Cardiorespiratory Fitness: A Report From the Fitness Registry and Importance of Exercise National Database (FRIEND)

OBJECTIVE

To begin the process of developing global reference standards for adults from directly measured cardiorespiratory fitness (CRF).

METHODS

Percentiles of maximal oxygen consumption (VO₂max) for men and women were determined for each decade from 20 through 79 years of age using International data from the Fitness Registry and Importance of Exercise: A National Database (FRIEND-I) along with previously published data from seven studies. FRIEND-I data from January 1, 2014, through January 1, 2019, included 11,678 maximal treadmill tests from three countries, whereas the previously published reports included 32,329 maximal treadmill tests from six countries.

RESULTS

FRIEND-I data revealed significant differences between sex and age groups for VO₂max (P<0.01). For the 20- to 29-years of age group, the 50^th percentile VO₂max in men and women were 49.5 mLO₂⋅kg^-1⋅min^-1 and 40.6 mLO₂⋅kg^-1⋅min^-1, respectively. VO₂max declined an average of 9% per decade with the 50^th percentile for the 70- to 79-years of age group having a VO₂max of 30.8 mLO₂⋅kg^-1⋅min^-1 in men and 25.0 mLO₂⋅kg^-1⋅min^-1 in women. These results were similar in magnitude and direction to the previously published literature. Within both the FRIEND-I and previously published data there were CRF differences between countries.

CONCLUSION

This report begins to establish global reference standards for CRF. Continued development of FRIEND-I will increase global representation providing an improved ability to identify and stratify CRF risk categories.

Comparison of non-exercise cardiorespiratory fitness prediction equations in apparently healthy adults

AIMS

A recent scientific statement suggests clinicians should routinely assess cardiorespiratory fitness using at least non-exercise prediction equations. However, no study has comprehensively compared the many non-exercise cardiorespiratory fitness prediction equations to directly-measured cardiorespiratory fitness using data from a single cohort. Our purpose was to compare the accuracy of non-exercise prediction equations to directly-measured cardiorespiratory fitness and evaluate their ability to classify an individual's cardiorespiratory fitness.

METHODS

The sample included 2529 tests from apparently healthy adults (42% female, aged 45.4 ± 13.1 years (mean±standard deviation). Estimated cardiorespiratory fitness from 28 distinct non-exercise prediction equations was compared with directly-measured cardiorespiratory fitness, determined from a cardiopulmonary exercise test. Analysis included the Benjamini-Hochberg procedure to compare estimated cardiorespiratory fitness with directly-measured cardiorespiratory fitness, Pearson product moment correlations, standard error of estimate values, and the percentage of participants correctly placed into three fitness categories.

RESULTS

All of the estimated cardiorespiratory fitness values from the equations were correlated to directly measured cardiorespiratory fitness (p < 0.001) although the R2 values ranged from 0.25-0.70 and the estimated cardiorespiratory fitness values from 27 out of 28 equations were statistically different compared with directly-measured cardiorespiratory fitness. The range of standard error of estimate values was 4.1-6.2 ml·kg-1·min-1. On average, only 52% of participants were correctly classified into the three fitness categories when using estimated cardiorespiratory fitness.

CONCLUSION

Differences exist between non-exercise prediction equations, which influences the accuracy of estimated cardiorespiratory fitness. The present analysis can assist researchers and clinicians with choosing a non-exercise prediction equation appropriate for epidemiological or population research. However, the error and misclassification associated with estimated cardiorespiratory fitness suggests future research is needed on the clinical utility of estimated cardiorespiratory fitness.

Estimating Energy Expenditure using Individualized, Power-Specific Gross Efficiencies

Our purpose was to determine if using an individual's power-specific gross efficiency improves the accuracy of estimating energy expenditure from cycling power. 30 subjects performed a graded cycling test to develop 4 gross efficiencies: individual power-specific gross efficiencies, a group mean power-specific gross efficiency, individual fixed gross efficiencies, and a group mean fixed gross efficiency. Energy expenditure was estimated from power using these different gross efficiencies and compared to measured energy expenditure during moderate- and hard-intensity constant-power and 2 variable-power cycling bouts. Estimated energy expenditures using individual or group mean power-specific gross efficiencies were not different from measured energy expenditure across all cycling bouts (p>0.05). To examine the intra-individual variability of the estimates, absolute difference scores (absolute value of estimated minus measured energy expenditure) were compared, where values closer to zero represent more accurate individual estimates. The absolute difference score using individual power-specific gross efficiencies was significantly lower compared to the other gross efficiencies across all cycling bouts (p<0.01). Significant and strong correlations (r≥0.97, p<0.001) were found across all cycling bouts between estimated and measured energy expenditures using individual power-specific gross efficiencies. In conclusion, using an individual's power-specific gross efficiency significantly improves their energy expenditure estimate across different power outputs.

Motor-Driven (Passive) Cycling: A Potential Physical Inactivity Countermeasure?

We have previously shown that motor-driven (passive) stationary cycling elevates energy expenditure (EE).

PURPOSE

This study aimed to quantify how acute passive cycling affects glucose and insulin responses to an oral glucose tolerance test (OGTT) and basic cognition compared with sitting and moderate-intensity active cycling.

METHODS

Twenty-four physically inactive healthy males completed three trials in randomized order involving 30-min conditions of sitting, passive cycling, and moderate-intensity cycling. During each condition, EE was measured, and participants performed cognitive tests. After each condition, a 2-h OGTT was performed.

RESULTS

EE was significantly higher during the cycling conditions compared with sitting (1.36 ± 0.58 and 6.50 ± 1.73 kcal·min greater than sitting for passive and moderate-intensity, respectively). A significant correlation was found between body fat percentage and postsitting OGTT 2-h postplasma glucose (r = 0.30, P < 0.05); thus, participants were divided into lean (n = 11) and nonlean (n = 13) groups. In the nonlean group, compared with sitting, passive cycling lowered 2-h postplasma glucose (7.7 ± 1.3 vs 6.9 ± 1.6 mmol·L, respectively, P < 0.05), and the Matsuda whole-body insulin sensitivity index (WBISI) was higher (2.74 ± 0.86 vs 3.36 ± 1.08, P < 0.05). In addition, passive and moderate-intensity cycling had similar beneficial effects on 2-h postplasma glucose and WBISI. Cognitive performance did not significantly differ between the sitting and passive cycling conditions.

CONCLUSIONS

Two-hour postplasma glucose was lower and WBISI after acute passive cycling was higher in nonlean participants. Given that and the increase in EE without changes in cognitive performance, we propose passive cycling as a promising intervention to counteract some of the deleterious effects of prolonged sitting in the workplace.

Field-measured drag area is a key correlate of level cycling time trial performance

Drag area (A_d) is a primary factor determining aerodynamic resistance during level cycling and is therefore a key determinant of level time trial performance. However, A_d has traditionally been difficult to measure. Our purpose was to determine the value of adding field-measured A_d as a correlate of level cycling time trial performance. In the field, 19 male cyclists performed a level (22.1 km) time trial. Separately, field-determined A_d and rolling resistance were calculated for subjects along with projected frontal area assessed directly (A_P) and indirectly (Est A_P). Also, a graded exercise test was performed to determine \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\dot {V}{O}_{2}$\end{document}V˙O2 peak, lactate threshold (LT), and economy. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\dot {V}{O}_{2}$\end{document}V˙O2 peak (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathrm{l}~\min ^{-1}$\end{document}lmin−1) and power at LT were significantly correlated to power measured during the time trial (r = 0.83 and 0.69, respectively) but were not significantly correlated to performance time (r = − 0.42 and −0.45). The correlation with performance time improved significantly (p < 0.05) when these variables were normalized to A_d. Of note, A_d alone was better correlated to performance time (r = 0.85, p < 0.001) than any combination of non-normalized physiological measure. The best correlate with performance time was field-measured power output during the time trial normalized to A_d (r = − 0.92). A_P only accounted for 54% of the variability in A_d. Accordingly, the correlation to performance time was significantly lower using power normalized to A_P (r = − 0.75) or Est A_P (r = − 0.71). In conclusion, unless normalized to A_d, level time trial performance in the field was not highly correlated to common laboratory measures. Furthermore, our field-measured A_d is easy to determine and was the single best predictor of level time trial performance.

Exercise efficiency of low power output cycling

Exercise efficiency at low power outputs, energetically comparable to daily living activities, can be influenced by homeostatic perturbations (e.g., weight gain/loss). However, an appropriate efficiency calculation for low power outputs used in these studies has not been determined. Fifteen active subjects (seven females, eight males) performed 14, 5-min cycling trials: two types of seated rest (cranks vertical and horizontal), passive (motor-driven) cycling, no-chain cycling, no-load cycling, cycling at low (10, 20, 30, 40 W), and moderate (50, 60, 80, 100, 120 W) power outputs. Mean delta efficiency was 57% for low power outputs compared to 41.3% for moderate power outputs. Means for gross (3.6%) and net (5.7%) efficiencies were low at the lowest power output. At low power outputs, delta and work efficiency values exceeded theoretical values. In conclusion, at low power outputs, none of the common exercise efficiency calculations gave values comparable to theoretical muscle efficiency. However, gross efficiency and the slope and intercept of the metabolic power vs mechanical power output regression provide insights that are still valuable when studying homeostatic perturbations.

Comparison of male and female road cyclists under identical stage race conditions

PURPOSE

To compare the demands of a 6-d stage race using field measures of power output and HR in male (n=8) and female (n=10) competitive cyclists.

METHODS

HR and power output were monitored in males and females competing in separate races on identical courses including a prolog (4 km), four circuit/road races (mean ± SD: 118 ± 23 km), and a criterium (47 km). All subjects participated in laboratory-based exercise testing within 2 wk of the race.

RESULTS

Compared with females, males took 10%, 22%, and 10% less time to complete the prolog, circuit/road races, and criterium, respectively. For males, power output in the prolog, circuit/road races, and criterium averaged 405, 247, and 278 W, respectively. For females, power output averaged 295, 160, and 205 W, respectively. During the prolog, the percent time spent below, at, and above the lactate threshold was 29%, 9%, and 62%, respectively, for males and 24%, 7%, and 69%, respectively, for females. For the circuit/road races, these values were 57%, 10%, and 33%, respectively, for males and 62%, 10%, and 28%, respectively, for females. During the criterium, these values were 51%, 6%, and 43%, respectively, for males, and 50%, 8%, and 42%, respectively, for females.

CONCLUSIONS

Although men had faster finishing times and higher absolute power outputs, no significant difference was found between men and women in their relative power response. These findings suggest that pacing strategy is based on relative exercise responses and not on absolute exercise responses.