Non-exercise Equation Research Articles

Estimating cardiorespiratory fitness in older adults using the international physical activity questionnaire.

Non-exercise estimates of cardiorespiratory fitness hold great utility for epidemiological research and clinical practice. Older adults may yield the greatest benefit from fitness estimates due to limited capacity to undergo strenuous maximal exercise testing, however, few of the previously developed non-exercise equations are suitable for use in older adults. Thus, the current study developed a non-exercise equation for estimating cardiorespiratory fitness in older adults derived from the widely used International Physical Activity Questionnaire (IPAQ). This study was a secondary analysis of baseline data from a randomized controlled trial. Participants were community-dwelling, cognitively unimpaired older adults aged 60-80 years (n = 92). They completed the IPAQ and underwent maximal exercise testing on a cycle ergometer. Stepwise linear regression was used to determine the equation in a randomly selected, sex-balanced, derivation subset of participants (n = 60), and subsequently validated using a second subset of participants (n = 32). The final equation included age, sex, body mass index and leisure time activity from the IPAQ and explained 61% and 55% of the variance in the derivation and validation groups, respectively (standard error of estimates = 3.9, 4.0). Seventy-seven and 81% of the sample fell within ±1SD (5.96 and 6.28 ml·kg-1·min-1) of measured VO2peak for the derivation and validation subgroups. The current equation showed better performance compared to equations from Wier et al. (2006), Jackson et al. (1990), and Schembre & Riebe (2011), although it is acknowledged previous equations were developed for different populations. Using non-exercise, easily accessible measures can yield acceptable estimates of cardiorespiratory fitness in older adults, which should be further validated in other samples and examined in relation to public health outcomes.

Cardiorespiratory fitness and metabolic risk in Chinese population: evidence from a prospective cohort study

BackgroundStudies on the association between estimated cardiorespiratory fitness (CRF) and changes in metabolic risk in the Chinese population are limited. This study aims to examine the associations between CRF and changes in metabolic risk.Subjects and methodsWe included 4,862 and 2,700 participants recruited from 28 provinces in the China Health and Retirement Longitudinal Study (CHARLS) in the baseline (Wave 1) and follow-up (Wave 4) analyses, respectively. CRF was calculated using sex-specific longitudinal non-exercise equations. Metabolic indicators included systolic blood pressure (SBP), diastolic blood pressure (DBP), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and fasting plasma glucose (FPG) levels. The metabolic score was calculated as the number of changes in the above metabolic indicators above the 75th percentile of the distribution of changes (equal to or below the 25th percentile for HDL-C).ResultsIn the baseline analysis, CRF was negatively associated with SBP, DBP, TG, and FPG, and positively correlated with HDL-C after adjusting for age, smoking status, and drinking status (all P < 0.0001) in both males and females. In the follow-up analysis, higher baseline CRF was significantly related to a decrease in SBP, DBP, TG, FPG, and metabolic score (all P < 0.0005), and increased HDL-C (P < 0.0001) after further adjustment for corresponding baseline metabolic indicators. The associations remained significant after stratification by sex, except for the changes in HDL-C levels in females. Furthermore, improved CRF was associated with favorable changes in DBP, TG, HDL-C, FPG, and metabolic scores in all populations and males. Significant associations between changes in CRF and DBP, TG, and FPG levels were found in females.ConclusionHigher baseline CRF and improved CRF were associated with favorable changes in metabolic indicators.

The Validity of the Native Non-sports Equation in Estimating the Maximum Aerobic Capacity of Adolescent Boys

Objectives: The maximum aerobic capacity (VO2max) is the most important indicator of physical fitness, which is closely related to the general health of the body. Considering the problems of laboratory measurement of VO2max, estimating VO2max by non-exercise equations will be one of the alternative solutions. This study aims to evaluate the validity of the native non-exercise equation in estimating VO2max of healthy adolescent boys. Methods: The variables of age, height, weight, and body mass index were measured in 248 healthy boys aged 15 to 18. The subjects' VO2max was measured and predicted by exercise test and non-exercise native equation respectively. To evaluate the validity of the native non-exercise equation, Pearson's correlation, paired t-test, and Bland-Altman's agreement were used. Results: There is a significant correlation between measured and predicted VO2max (R = 0.76, p&lt;0.05). Also, no significant difference was observed between predicted and measured VO2max (43.10 ml/kg/min and 43.38 ml/kg/min respectively) (mean difference=0.28 ml/kg/min, P&gt;0.05). Also, the Bland-Altman graphic diagram indicated agreement between the methods of measuring and estimating VO2max. Conclusion: In the present study, the native non-sports equation had a high validity in estimating VO2max of adolescent boys. It seems that the use of non-sports VO2max equations can be used as a simple, safe, and low-cost tool to evaluate the maximum aerobic capacity of students at a wide level.

Estimated cardiorespiratory fitness and incident risk of cardiovascular disease in China

BackgroundLimited evidence is available on the association between estimated cardiorespiratory fitness (e-CRF) and incidence of cardiovascular disease (CVD) in Chinese population.MethodsA total of 10,507 adults including 5084 men (48.4%) and 5423 (51.6%) women with a median age of 56.0 (25% quantile: 49, 75% quantile 63) years from the China Health and Retirement Longitudinal Study (CHARLS) was recruited in 2011 as baseline. The CVD incident events were followed-up until 2018. e-CRF was calculated from sex-specific longitudinal non-exercise equations and further grouped into quartiles. Cox proportional models were used to calculate hazard ratio (HR) and 95% confidence interval (CI) for incidence risks of CVD, heart disease and stroke.ResultsDuring a median follow-up of 7 years, a total of 1862 CVD, 1409 heart disease and 612 stroke events occurred. In fully adjusted models, each one MET increment of e-CRF was associated with lower risk of CVD (HR = 0.91, 95%CI = 0.85–0.96 for males, HR = 0.87, 95%CI = 0.81–0.94 for females). Compared with the Quartile (Q)1 group, the HRs (95%CI) of the Q2, Q3 and Q4 groups were 0.84 (0.63–1.03), 0.72 (0.57–0.91) and 0.66 (0.51–0.87) for CVD in males. Females had HRs of 0.79 (0.66–0.96) in Q2, 0.71 (0.57–0.88) in Q3 and 0.58 (0.45–0.75) in Q4 for CVD. The associations between e-CRF and heart disease and stroke were slightly weaker than that for CVD in both males and females.ConclusionsHigher e-CRF decreases the incident risk of CVD, heart disease and stroke.

Peak ventilation during treadmill cardiopulmonary exercise testing: applicability analysis of the FRIEND registry prediction equation in a Brazilian healthy sample

Abstract Background Peak ventilation (VEpeak) measured by cardiopulmonary exercise testing (CPX) is a valuable clinical information, especially in patients with pulmonary diseases. Measured values can be compared to maximal voluntary ventilation or to predicted VEpeak calculated by nonexercise equations. However, the FRIEND Registry prediction equation has never been assessed for portability in a Brazilian population sample. Purpose To analyze the applicability of the FRIEND registry prediction equation for VEpeak during treadmill CPX in a Brazilian healthy sample. Methods Cross-sectional study of subjects assessed by CPX in the Brazilian Midwest region between January/2011 to March/2020. The inclusion criteria were CPX performed on treadmill in healthy individuals aged 20 years or older, and with respiratory exchange ratio ≥1.0. The exclusion criteria were any history of cardiovascular or pulmonary disease, presence of cardiovascular risk factors (such as hypertension, diabetes mellitus, current smoking, and obesity) and abnormalities on CPX. Measured VEpeak values were compared with predicted values calculated using age, sex, and height, according to the FRIEND Registry prediction equation. Variables were described as median and interquartile range. Statistical comparisons between measured and predicted VEpeak were performed using Spearman correlation test, Wilcoxon signed rank test, and Blant-Altman agreement analysis. Results During the study period 7,843 CPX were performed and included for study eligibility. After the inclusion and exclusion criteria were applied, 3,544 healthy individuals' assessments were included [1,574 females and 1,970 males, age 39 (33, 47) years]. The measured and predicted VEpeak were, respectively, 76.8 (66.7, 87.4) and 76.2 (68.5, 83.0) L min–1 in females, with significant difference (p=0.004). In males, the measured and predicted VEpeak were, respectively, 123.0 (107.3, 137.3) and 114.8 (107.5, 120.7) L min–1, also with statistical difference (p&amp;lt;0.001). Correlations were significant (p&amp;lt;0.001) for both sexes (r2=0.424 for females and .421 for males) (Figure 1). Average error was lower for females than males, with bias of 1.22 L min–1 (95% limits of agreement: - 26.95 to 29.39) in females and bias of 8.41 L min–1 (95% limits of agreement: −33.04 to 49.87) in males (Figure 2). Conclusion Measured VEpeak median values were greater than predicted median values for both sexes, with lower average error in females than in males. The significant average error in males limits the prediction equation applicability in the Brazilian population. Thus, the development of a new Brazilian prediction equation is necessary to better evaluate the variable response during CPX in Brazil. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

Estimating Cardiorespiratory Fitness Without Exercise Testing or Physical Activity Status in Healthy Adults: Regression Model Development and Validation.

BackgroundLow 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.ObjectiveThis study aims to develop a nonexercise equation to estimate and classify CRF (in metabolic equivalent tasks) using variables commonly available in EHRs.MethodsParticipants 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.ResultsAfter 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.ConclusionsThe 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.

Cross-validation of Non-exercise Estimated Cardiorespiratory Fitness: The NHANES Study

Background: Cardiorespiratory Fitness (CRF) is an independent health predictor of circulatory and respiratory systems and can be estimated using non-exercise equations. However, the accuracy of such equations in a national representative population is unknown. The objective of this study was to cross-validate 11 CRF equations developed by three different researchers using a United States representative population. Methods and Findings: The study included 2470 adult males and females from the National Health and Nutrition Examination Survey (1999 to 2004) with measured CRF (mCRF) available in terms of maximum oxygen consumption (VO2max). The relationships between non-exercise estimated CRFs and measured VO2max were analyzed by examining the Constant Error (CE), Standard Error of Estimate (SEE), correlation coefficient (r) and Root Of Mean Square Error (RMSE). The estimated CRFs from four equations for males and six equations for females were different from mCRF, with CE values ranging from -0.712 (Jurca2) to 0.457 mL/kg/min (Jackson/fat/2level) for males and from -3.722 (Rexhepi2014) to 1.166 mL/kg/min (Jackson/fat/2level) for females (P&lt;0.05 for all). Moreover, SEE, r and RMSE values ranged from 0.036 to 0.079 mL/kg/min, 0.21 to 0.344 mL/kg/min and 2.172 to 2.657 mL/kg/min, respectively. Furthermore, the lowest RMSE values for males (Jackson/fat/5level) and females (Jurca2) represented 20.33% and 21.09% of the mean mCRFs, respectively. Conclusion: Among the 11 equations, Jackson/fat/5level for males and Jurca2 for females provided the most valid non-exercise equations to estimate CRF in a representative US population. Future studies are warranted to develop more accurate equations based on age, gender, race and health status.

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 &lt;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 &lt;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.

Association between Estimated Cardiorespiratory Fitness and Depression among Middle-income Country Adults: Evidence from National Health Survey.

Objective:This study assessed the relationship between cardiorespiratory fitness (CRF) and depression in adults.Methods:A total of 52,611 individuals aged between 18-59 years old were evaluated for symptoms of depression and CRF. The presence of depressive symptoms was self-report through the Patient Health Questionnaire (PHQ-9), and the CRF was predicted from a non-exercise equation. The association between CRF and the presence of depression was determined by crude and multivariable-adjusted logistic regressions.Results:The associations were identified between symptoms of depression and CRF in both unadjusted and adjusted models. After adjusting for age categories, sex, body mass index categories, educational level, marital status, smoking, and alcohol use, the individuals with moderate CRF had 18% lower odds of depression (OR: 0.82, CI 95%: 0.71 – 0.95) compared to individuals with low CRF.Conclusion:Depressive symptoms are inversely related to CRF levels in adults.

Development of a non-exercise equation without physical activity status to estimate cardiorespiratory fitness in Japanese adults

Development of a non-exercise equation without physical activity status to estimate cardiorespiratory fitness in Japanese adults

Prediction of Cardiorespiratory Fitness in Czech Adults: Normative Values and Association with Cardiometabolic Health

Cardiorespiratory fitness (CRF) is a strong independent predictor of morbidity and mortality. However, there is no recent information about the impact of CRF on cardiometabolic risk specifically in Central and Eastern Europe, which are characterized by different biological and social determinants of health. In this cross-sectional study normative CRF values were proposed and the association between CRF and cardiometabolic outcomes was evaluated in an adult Czechian population. In 2054 participants (54.6% females), median age 48 (IQR 19 years), the CRF was predicted from a non-exercise equation. Multivariable-adjusted logistic regressions were carried out to determine the associations. Higher CRF quartiles were associated with lower prevalence of hypertension, type 2 diabetes (T2D) and dyslipidemia. Comparing subjects within the lowest CRF, we see that those within the highest CRF had decreased chances of hypertension (odds ratio (OR) = 0.36; 95% CI: 0.22–0.60); T2D (OR = 0.16; 0.05–0.47), low HDL-c (OR = 0.32; 0.17–0.60), high low-density lipoprotein (OR = 0.33; 0.21–0.53), high triglycerides (OR = 0.13; 0.07–0.81), and high cholesterol (OR = 0.44; 0.29–0.69). There was an inverse association between CRF and cardiometabolic outcomes, supporting the adoption of a non-exercise method to estimate CRF of the Czech population. Therefore, more accurate cardiometabolic studies can be performed incorporating the valuable CRF metric.

PREDICTION OF CARDIORESPIRATORY FITNESS IN THE BRAZILIAN POPULATION AGED 20 TO 59 YEARS: A NON-EXERCISE MODEL APPROACH WITH SELF-REPORTED VARIABLES

ABSTRACT Unlike other more common health-related variables, cardiorespiratory fitness (CRF) is not frequently measured in the general population. This omission characterizes a loss of relevant information. Thus, the objectives of the present study were: a) to characterize the CRF of the Brazilian population aged 20 to 59 years and to develop normative values using a non-exercise equation for predicting maximal oxygen uptake (VO2max), and b) to verify the association between lower levels of CRF and the prevalence of chronic diseases. A total of 32,531 individuals from the National Health Survey (NHS-IBGE-2013) composed the sample. Only self-reported variables were included in the equation of Wier et al. (2006): sex, age, physical activity level, and body mass index. The mean predicted VO2max was 44.6, 39.3, 34.8 and 30.6 ml/kg/min for men, and 34.5, 29.6, 25.4 and 21.1 ml/kg/min for women aged 20-29, 30-39, 40-49 and 50-59 years, respectively. The 20th and 80th percentiles were established as the extremes (very low and very high CRF). Participants with low fitness had a 33% higher chance of cardiovascular disease, an 89% higher chance of diabetes mellitus, and a 67% higher chance of hypertension, regardless of sex, age and presence of obesity, which seem to corroborate the quality of the equation.

Development Of A Non-exercise Equation Without Physical Activity Status To Estimate Cardiorespiratory Fitness In Japanese Adults

Development Of A Non-exercise Equation Without Physical Activity Status To Estimate Cardiorespiratory Fitness In Japanese Adults

Association between type 2 diabetes and non-exercise estimated cardiorespiratory fitness among adults: evidences from a middle-income country

ObjectiveThe objective of this study is to assess the association between type 2 diabetes (T2D) and cardiorespiratory fitness (CRF) estimated through a non-exercise model in a large representative group of Brazilian adults. Study designThe study design of this study is a cross-sectional population-based study. MethodsThe presence of T2D was assessed through self-report in 42,631 individuals aged 20–59 years. The CRF was predicted from a non-exercise equation containing the following data: sex, age, body mass index, and physical activity level. The corresponding metabolic equivalent (MET) value was used to classify participants into the following four groups based on the cutpoints: <6, 6–9, 10–11, and ≥12 METs. Crude and multivariable-adjusted logistic regressions were carried out to determine the association between CRF and the presence of diabetes. ResultsSignificant linear trends were observed in diabetes across different categories of CRF in men. In women, however, linear trends were not observed in T2D across ‘10–11’ and ‘≥12’ categories. A 1-MET increase in estimated CRF was associated with 29% lower odds of diabetes in men and 14% in women after adjusting all the potential confounders (P < 0.001). ConclusionsThe presence of T2D was inversely associated with every CRF level in men and with 6–9 METs when comparing with <6 METs in women, through a non-exercise model in Brazilian adults. This finding suggests that a practical, viable, and low-cost measurement of CRF could be applied in lower income countries to assess the relationship between CRF and T2D. However, new non-exercise models are needed to better detect T2D in women.

Accuracy of Exercise-based Equations for Estimating Cardiorespiratory Fitness.

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. 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. 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%). 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.

Non-exercise Equations For Determining Change In Cardiorespiratory Fitness

CRF is a strong and independent predictor of morbidity and all-cause mortality beyond traditional risk factors. However, CRF is not routinely measured in clinical settings where cost, time, training, and discomfort on behalf of the patient have all been cited as barriers to routine integration. An alternative to measuring CRF is to estimate it using a non-exercise CRF equation. It is currently unknown whether eCRF can be used to estimate change in mCRF following the adoption of regular exercise. PURPOSE: To determine whether change in estimated cardiorespiratory fitness (eCRF) is associated with change in measured CRF (mCRF) independent of exercise amount and intensity over 24 weeks. METHODS: Participants were 163 sedentary adults with abdominal obesity (waist circumference: mean 109.9 (SD; 11.5) cm) randomly assigned to: i) no-exercise control (n=42), ii) low-amount, low-intensity exercise (LALI; n=39), iii) high-amount, low-intensity exercise (HALI; n=51), iv) high-amount, high-intensity exercise (HAHI; n=31). mCRF was measured using a maximal treadmill test at baseline, 8, 16 and 24 weeks. eCRF was calculated using a published non-exercise equation with the following variables: sex, age, waist circumference, resting heart rate, self-selected physical activity. RESULTS: Participants attended 115 of 120 exercise sessions prescribed (96.0 (4.0) % adherence). eCRF change from baseline to 8, 16 and 24 weeks was not different from mCRF change for control, LALI or HALI (P=.03). In HAHI, eCRF change was significantly greater than mCRF change at all time points (P<.001). Change in mCRF and eCRF at 24 weeks were separated into tertiles to determine whether there were systematic differences between the two measures. Tertile scores revealed that for LALI and HALI, eCRF change significantly overestimated the lowest mCRF tertile (P=.001) and underestimated the highest mCRF tertile (P=.003). For HAHI, eCRF change overestimated mCRF within both the lowest and middle tertile (P=.005). CONCLUSION: eCRF change was associated with mCRF change at 24 weeks independent of exercise amount but not intensity. Systematic variation between eCRF and mCRF highlights a possible limitation when using eCRF to follow change in mCRF, specifically, that eCRF does not capture the individual variability of the mCRF response.

A Nonexercise Prediction Equation For Cardiorespiratory Fitness Without The Use Of Physical Activity

Low cardiorespiratory fitness (CRF) is an independent predictor of morbidity and mortality. The majority of healthcare settings use some type of electronic health record system (EHRs). However, many EHRs do not have CRF data collected, thereby limiting the types of investigations and analyses that can be done for research. PURPOSE: To develop a nonexercise equation to estimate and classify CRF (METs) using variables commonly found in EHRs. METHODS: Participants were 41,861 apparently healthy adults (21.4% women) from the Aerobics Center Longitudinal Study examined from 1974 to 2005. Estimated CRF was based on sex, age, measured body mass index, measured resting heart rate, and smoking status. Actual CRF was measured by a maximal treadmill test. RESULTS: After nonlinear feature augmentation was conducted, separate linear regression models were used for male and female patients to calculate Pearson’s correlation and regression coefficients.Cross-classification of actual and estimated CRF was conducted using the lowest 20th percentile as the low-fit category. Correlation coefficients were 0.68 (MD 1.33) and 0.63 (MD 1.23) for men and women respectively. The models explained 46% (SEE 1.69) and 40% (SEE 1.54) variance in CRF for men and women respectively. Correct category classification was found in 84% of men and 80% of women. CONCLUSION: The regression models developed in the present study provided useful estimation and classification of CRF in a large population of men and women. The models may provide a valid method for conducting investigations using CRF data derived from EHRs. Supported by JSPS KAKENHI Grant 19K19437

Design and Validation of Non-Exercise Equations for Estimation of Aerobic Capacity in Iranian Boys

Design and Validation of Non-Exercise Equations for Estimation of Aerobic Capacity in Iranian Boys

Nonexercise Equations for Determining Change in Cardiorespiratory Fitness.

This study aimed to determine whether change in estimated cardiorespiratory fitness (eCRF) is associated with change in measured cardiorespiratory fitness (mCRF) independent of exercise amount and intensity over 24 wk. Participants were 163 sedentary adults with abdominal obesity (mean ± SD waist circumference, 109.9 ± 11.5 cm) randomly assigned to (i) no-exercise control (n = 42); (ii) low-amount, low-intensity exercise (LALI; n = 39); (iii) high-amount, low-intensity exercise (HALI; n = 51); and (iv) high-amount, high-intensity exercise (HAHI; n = 31). mCRF was measured using a maximal treadmill test at baseline, 8, 16, and 24 wk. eCRF was calculated using a published nonexercise equation with the following variables: sex, age, waist circumference, resting heart rate, and self-selected physical activity. Participants attended 115 of 120 exercise sessions prescribed (96.0% ± 4.0% adherence). eCRF change from baseline to 8, 16, and 24 wk was not different from mCRF change for control, LALI, or HALI (P = 0.03). In HAHI, eCRF change was significantly greater than mCRF change at all time points (P < 0.001). Further analysis revealed that change in eCRF systematically overestimated and underestimated small and large changes in mCRF, respectively, in all groups (P < 0.001). eCRF change was associated with mCRF change at 24 wk independent of exercise amount but not intensity. Systematic variation between eCRF and mCRF highlights a possible limitation when using eCRF to follow change in mCRF, specifically that eCRF does not capture the individual variability of the mCRF response.

Agreement between Estimated VO2 max by 6-Minute Walk Test and Non-Exercise Equation in Physiotherapy Students

Agreement between Estimated VO2 max by 6-Minute Walk Test and Non-Exercise Equation in Physiotherapy Students