Non-exercise Prediction Equation Research Articles

Comparison of Cardiorespiratory Fitness Prediction Equations and Generation of New Predictive Model for Patients with Obesity.

Cardiorespiratory fitness (CRF) is a critical marker of overall health and a key predictor of morbidity and mortality, but the existing prediction equations for CRF are primarily derived from general populations and may not be suitable for patients with obesity. Predicted CRF from different non-exercise prediction equations was compared with measured CRF of patients with obesity who underwent maximal cardiopulmonary exercise testing (CPET). Multiple linear regression was used to develop a population-specific non-exercise CRF prediction model for treadmill exercise including age, sex, weight, height and physical activity level as determinants. 660 patients underwent CPET during the study period. Within the entire cohort, R2 values had a range of 0.24-0.46. Predicted CRF was statistically different from measured CRF for 19 included equations. Only 50% of patients were correctly classified into the measured CRF categories according to predicted CRF. A multiple model for CRF prediction (ml/min) was generated (R2 = 0.78) and validated using two cross-validation methods. Most used equations provide inaccurate estimates of CRF in patients with obesity, particularly in cases of severe obesity and low CRF. Therefore, a new prediction equation was developed and validated specifically for patients with obesity, offering a more precise tool for clinical CPET interpretation and risk stratification in this population.

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).

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 . 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. 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. 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.

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.

Assessment of the Relationship Between Executive Function and Cardiorespiratory Fitness in Healthy Older Adults.

Associations between cardiorespiratory fitness and brain health in healthy older adults have been reported using a variety of cardiorespiratory fitness estimates (CRFe). Using commonly used methods to determine CRF, we assessed the relationship between CRFe and executive function performance. Healthy older adults (n = 60, mean age 68 years, 77% women), underwent three CRF tests: a Maximal Graded Exercise Test performed on a cycle ergometer, the Rockport Fitness Walking Test, and a Non-Exercise Prediction Equation. Executive function was assessed by a computerized cognitive assessment using an N-Back task (updating cost) and a Stroop task (interference cost, global and local switch cost). Multiple hierarchical regression analyses were conducted to assess the relationship between different CRFe and executive function performance. Regardless of age and education, cardiorespiratory fitness estimated from the Maximal Graded Exercise Test and the Rockport Fitness Walking Test was significantly associated with the global switch cost. All CRFe were associated with the interference cost. No association was observed between CRFe and local switching costs or the updating costs. In the present study, not all subcomponents of executive function were related to CRFe. Interestingly, the executive functions that were associated with CRFe are those that are known to be the most affected by aging.

Abstract P119: Non-exercise Cardiorespiratory Fitness Prediction Equations: Accuracy Over Time in Apparently Healthy Adults

Introduction: Mortality risk predictions are improved with routine assessment of cardiorespiratory fitness (CRF). Accordingly, an American Heart Association Scientific Statement suggests routine clinical assessment of CRF in apparently healthy adults minimally using non-exercise prediction equations, which can be calculated from common health metrics. However, no study has assessed the ability of non-exercise CRF prediction equations to accurately detect longitudinal changes. Hypothesis: Changes in estimated CRF (eCRF) would be related to directly-measured changes, yet appreciable misclassification would occur at the individual level. Methods: The sample included 987 apparently healthy adults (324 females; mean±SD age 43.1±10.4 years) who completed 2 cardiopulmonary exercise tests (CPX) at least 3 months apart (3.2±5.4 years follow-up). The change in eCRF from 27 distinct non-exercise prediction equations was compared to the change in directly-measured CRF determined from CPX. A change of ≥5% was used to classify participants as having a directional increase or decrease in CRF. Analysis included Pearson product moment correlations, standard error of estimate (SEE) values, the Benjamini-Hochberg procedure to compare eCRF with directly-measured CRF, and chi-squared tests to examine the impact of follow-up time on the percentage of participants correctly identified as having a directional increase or decrease in CRF. Results: The change in eCRF from each equation was correlated to the change in directly-measured CRF ( P <0.001) with R 2 values ranging from 0.06-0.43 and SEE values ranging from 0.9-5.9 ml·kg -1 ·min -1 . For 16 of the 27 equations, the change in eCRF was significantly different from the change in directly-measured CRF. When classifying directional changes, the prediction equations correctly categorized an average of 54% of individuals as having increased, decreased, or no change in CRF. When examining the influence of follow-up time, the average percentage of individuals correctly classified as having a directional increase in CRF was greater when the time between tests was ≤8months (54%) compared to ≥2years (28%). In contrast, the average percentage correctly classified as having a directional decrease in CRF was lower with tests ≤8months apart (8%) compared to ≥2years (73%). Conclusions: As hypothesized, discernible variability was found in the accuracy between non-exercise prediction equations and the ability of equations to accurately assess changes in directly-measured CRF over time. Considering the appreciable error that prediction equations had with detecting even directional changes in CRF, these results suggest eCRF may have limited clinical utility.

Is Less Sedentary Behavior, More Physical Activity, or Higher Fitness Associated with Sleep Quality? A Cross-Sectional Study in Singapore.

Objectives: To examine the independent, joint, and fully combined associations of sedentary behavior (SB), moderate-to-vigorous physical activity (MVPA), and cardiorespiratory fitness (CRF) with the odds of poor sleep quality (SQ). Methods: We performed a secondary data analysis on 757 working adults (male = 345) in Singapore, with an average age of 35.2 years. The Pittsburgh Sleep Quality Index was used to assess SQ. Objectively measured MVPA and SB were each obtained using an accelerometer. A non-exercise prediction equation was used to estimate CRF. Logistic regression models were used to determine associations. Results: In total, 13.2% of the sample (n = 100) was identified as having poor SQ. After adjusting for study covariates, independent analyses revealed a clear inverse association for higher CRF and lower odds of poor SQ (OR = 0.50; 95% CI = 0.28–0.91). SB and MVPA demonstrated no independent associations. Joint associations revealed that odds of having poor SQ for those with low CRF was higher regardless of SB level and was further deteriorated by lower MVPA in the fully combined model. The fully combined model also demonstrated that those with lower SB, higher MVPA, and higher CRF had the lowest odds of having poor SQ (OR = 0.28; 95% CI = 0.10–0.78). Conclusions: Physical activity/exercise training programs that aim to improve CRF may be useful in lowering the odds or poor SQ in working adults.

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

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. 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. 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. 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.

Peak Ventilation Reference Standards from Exercise Testing: From the FRIEND Registry

Cardiopulmonary exercise testing (CPX) provides valuable clinical information, including peak ventilation (V˙Epeak), which has been shown to have diagnostic and prognostic value in the assessment of patients with underlying pulmonary disease. This report provides reference standards for V˙Epeak derived from CPX on treadmills in apparently healthy individuals. Nine laboratories in the United States experienced in CPX administration with established quality control procedures contributed to the Fitness Registry and the Importance of Exercise National Database from 2014 to 2017. Data from 5232 maximal exercise tests from men and women without cardiovascular or pulmonary disease were used to create percentiles of V˙Epeak for both men and women by decade between 20 and 79 yr. Additionally, prediction equations were developed for V˙Epeak using descriptive information. V˙Epeak was found to be significantly different between men and women and across age groups (P < 0.05). The rate of decline in V˙Epeak was 8.0% per decade for both men and women. A stepwise regression model of 70% of the sample revealed that sex, age, and height were significant predictors of V˙Epeak. The equation was cross-validated with data from the remaining 30% of the sample with a final equation developed from the full sample (r = 0.73). Additionally, a linear regression model revealed forced expiratory volume in 1 s significantly predicted V˙Epeak (r = 0.73). Reference standards were developed for V˙Epeak for the United States population. Cardiopulmonary exercise testing laboratories will be able to provide interpretation of V˙Epeak from these age and sex-specific percentile reference values or alternatively can use these nonexercise prediction equations incorporating sex, age, and height or with a single predictor of forced expiratory volume in 1 s.

Estimated Prestroke Peak VO2 Is Related to Circulating IGF-1 Levels During Acute Stroke

Background. Insulin-like growth factor-1 (IGF-1) is neuroprotective after stroke and is regulated by insulin-like binding protein-3 (IGFBP-3). In healthy individuals, exercise and improved aerobic fitness (peak oxygen uptake; peak VO2) increases IGF-1 in circulation. Understanding the relationship between estimated prestroke aerobic fitness and IGF-1 and IGFBP-3 after stroke may provide insight into the benefits of exercise and aerobic fitness on stroke recovery. Objective. The purpose of this study was to determine the relationship of IGF-1 and IGFBP-3 to estimated prestroke peak VO2 in individuals with acute stroke. We hypothesized that (1) estimated prestroke peak VO2 would be related to IGF-1 and IGFBP-3 and (2) individuals with higher than median IGF-1 levels will have higher estimated prestroke peak VO2 compared to those with lower than median levels. Methods. Fifteen individuals with acute stroke had blood sampled within 72 hours of hospital admission. Prestroke peak VO2 was estimated using a nonexercise prediction equation. IGF-1 and IGFBP-3 levels were quantified using enzyme-linked immunoassay. Results. Estimated prestroke peak VO2 was significantly related to circulating IGF-1 levels (r = .60; P = .02) but not IGFBP-3. Individuals with higher than median IGF-1 (117.9 ng/mL) had significantly better estimated aerobic fitness (32.4 ± 6.9 mL kg−1 min−1) than those with lower than median IGF-1 (20.7 ± 7.8 mL kg−1 min−1; P = .03). Conclusions. Improving aerobic fitness prior to stroke may be beneficial by increasing baseline IGF-1 levels. These results set the groundwork for future clinical trials to determine whether high IGF-1 and aerobic fitness are beneficial to stroke recovery by providing neuroprotection and improving function.

Cardiorespiratory Fitness Levels and its Correlates Among Adults with Diabetes

The purpose of this study is to report cardiorespiratory fitness levels among adults with diabetes and report differences by demographic (eg, body mass index) and behavioral (eg, physical activity) variables. Data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES) were used in the analyses. Cardiorespiratory fitness was assessed using a non-exercise prediction equation and through a submaximal treadmill-based test using heart rate extrapolation. Participants completed a questionnaire to assess various demographic and behavior variables. Seventy eight participants met inclusion criteria and constituted the analyzed sample. The majority (55.1%) of participants were estimated as having low or moderate cardiorespiratory fitness. Results showed that for both methods normal weight individuals had greater cardiorespiratory fitness than obese individuals, and for the non-exercise prediction equation, participants who sat during the day and did not walk very much had lower cardiorespiratory fitness (mean = 26.1 mL/kg/min [95% CI: 18.9-33.4]) than those doing heavy work or carrying heavy loads (mean = 34.6 mL/kg/min [95% CI: 30.2-39.1]). These results suggest that obese and inactive adults with diabetes may be at an increased risk for morbidities and mortality associated with low cardiorespiratory fitness. Physical therapists are encouraged to apply evidence-based principles for exercise prescription and physical activity counseling to help patients with diabetes regulate their blood glucose control and improve cardiorespiratory fitness.

Validation of a Nonexercise Prediction Equation of Anaerobic Power

This study examined the validity of estimating anaerobic power in college-aged students using anthropometric data and a paper and pencil test. Peak power (PP) and mean power (MP) were determined for 157 subjects (92 men and 65 women) using a standard Wingate anaerobic test (WAnT) at a resistance of 0.075 and 0.10 kg·body mass for women and men, respectively. Subjects completed previously established paper and pencil tests for assessing aerobic capacity and rated their ability to perform tasks related to anaerobic power, such as their vertical jump height relative to peers. Descriptive statistics were generated, and multiple regression was performed using SAS v9.1 to assess the ability of paper and pencil tests to predict PP and MP from the WAnT. Mean (±SD) age, height, body mass, body mass index, PP, and MP for subjects were 22.1 ± 2.5 years, 175.6 ± 7.5 cm, 78.5 ± 11.4 kg, 25.4 ± 3.0 kg·m, 1015.2 ± 169.7 W, and 784.5 ± 122.1 W and 22.0 ± 3.0 years, 163.6 ± 7.4 cm, 61.1 ± 10.4 kg, 22.8 ± 3.4 kg·m, 593.0 ± 102.4 W, and 478.8 ± 72.8 W, respectively. Mean estimated jump height (EJHt) rating values were 5.8 ± 1.5 and 4.7 ± 1.5 (on a 1-9 Likert-type scale) for men and women, respectively. The following multiple regression models were developed:PP = -34.5 + 249.6 (gender; female = 0, male = 1) + 8.1 (BMkg) + 27.8 (EJHt) (R = 0.82, SEE = 106.6 W);MP = -37.7 + 163.7 (gender) + 6.7 (BMkg) + 22.8 (EJHt) (R = 0.87, SEE = 65.5 W).It was concluded that valid estimates for PP and MP could be obtained from anthropometric data and a single question paper and pencil test asking subjects to estimate relative jumping ability, without the need for performing the Wingate anaerobic cycle test.

Concurrent validity of the non-exercise based VO2max prediction equation using percentage body fat as a variable in asian Indian adults.

BackgroundAerobic capacity (VO2max) is highly dependent upon body composition of an individual and body composition varies with ethnicity. The purpose of this study was to check the concurrent validity of the non-exercise prediction equation developed by Jackson and colleagues (1990) using percentage body fat as a variable in Asian Indian adults.MethodsOne hundred twenty college-aged participants (60 male, 60 female, mean age 22.02 ± 2.29 yrs) successfully completed a maximal graded exercise test (GXT) on a motorized treadmill to assess VO2max. VO2max was then estimated by the non-exercise prediction equation developed by Jackson and colleagues (1990) using percentage body fat. Percentage body fat was calculated by three different models (Sandhu et al’s fat mass equation, Durnin-womersley’s 4 site percentage body fat and Jackson & Pollock’s 4 site percentage body fat) and was used in the above equation. The results of VO2max obtained using “gold standard” treadmill methods were then compared with the three results of VO2max obtained by Jackson et al’s equation (using three different models to calculate percentage body fat) and it was determined which equation is best suited to determine percentage body fat and in turn VO2 max for Indian population.ResultsJackson et al’s prediction equation overpredicts VO2max in Asian Indian subjects who have a lower VO2max (33.41 ± 14.39 ml/kg/min) than those reported in other age matched populations. percentage body fats calculated by the three equations were significantly different and the correlation coefficient (r) between VO2max calculated by Jackson and colleagues (1990) using Sandhu et al’s equation for percentage body fat with VO2 max calculated using treadmill (gold standard) (r = .817) was found slightly more significantly correlated than the other two equations and was not statistically different from the measured value.ConclusionsThis study proves that VO2max equation using Sandhu et al’s model for percentage body fat yields more accurate results than other studied equations in healthy college-aged participants in India.

Association Between Cardiorespiratory Fitness and Hearing Sensitivity

As a follow-up to previous smaller scale studies, the purpose of the present study was to examine the link between cardiorespiratory fitness and hearing sensitivity using a nationally representative U.S. sample of adults. Data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES; U.S. Centers for Disease Control and Prevention [CDC], 2011) were used in the analyses. After exclusions, the final sample included 1,082 NHANES participants ages 20-49 years. Maximum oxygen uptake (VO(2max)) was obtained from an established nonexercise prediction equation and through heart-rate extrapolation during a treadmill-based submaximal test. Audiometry data were objectively measured to obtain estimates of low (LPTA) and high (HPTA) pure-tone frequency average. VO(2max) was not associated with hearing sensitivity when using the heart-rate extrapolation method but was significantly associated with hearing sensitivity (for women) when applying the nonexercise prediction equation for both LPTA and HPTA (p < .01). Women with higher predicted cardiorespiratory fitness were 6% more likely than women with lower predicted cardio-respiratory fitness to have good hearing compared to worse hearing. Cardiorespiratory fitness was associated with hearing sensitivity when using the nonexercise prediction equation to measure VO(2max). Further studies are needed to confirm these findings. Findings suggest a potentially auditory-protective effect of cardiorespiratory fitness.

Validation of a non-exercise prediction equation for cardiorespiratory fitness in the Asian Indian population

The non-exercise based prediction equation is widely used to predict cardiorespiratory fitness (CRF), however it has not been validated for the Asian Indian population. To increase the generalisation of the equation we validated it in Asian Indians. 230 participants aged 40-75 years were recruited after excluding any medical illness. Parameters recorded were body mass index, resting heart rate and a self-reported physical activity score questionnaire. CRF was measured using metabolic equivalent at the final speed and grade of treadmill. CRF was also estimated using the non-exercise based prediction equation. The correlation of measured CRF and that calculated through the equation was 0.769 (P&lt;0.001). Paired t-test yielded t=1.207 with a confidence interval of -0.08-0.34, indicating that the equation was statistically significant for our study population. We conclude that the non-exercise based prediction equation is valid in sedentary Asian Indians in the age group of 40-75 years.

The Accuracy of Non-Exercise Maximal Oxygen Uptake Prediction Equations for Cross Country Athletes

The Accuracy of Non-Exercise Maximal Oxygen Uptake Prediction Equations for Cross Country Athletes

Development Of Non-exercise Prediction Of Peak Oxygen Uptake By The Skeletal Muscle Mass And The Cardiac Dimensions

Previous studies have developed non-exercise prediction equations for peak oxygen uptake (VO2peak) using anthropometric parameters and questionnaires, however it has been suggested that these methods are not valid for certain subject populations. These models do not consider either central circulation and/or peripheral factors associated with VO2peak PURPOSE To develop non-exercise prediction models of VO2peak using cardiac dimensions and regional skeletal muscle (SM) mass, and to investigate the validity of these equations. METHODS Fifty one healthy Japanese men were randomly separated into 2 groups; 40 in the development group and 11 in the validation group. VO2peak was measured during treadmill running using an automated breath-by-breath mass spectrometry system. Left ventricular internal dimension at enddiastole (LVIDD) and at end-systole (LVIDS) were measured using echocardiography with a 2.5 MHz transducer. Stroke volume (SV) was calculated based on the Pombo rule. Muscle thickness (MTH) was measured by B-mode ultrasound (5 MHz scanning head) at 9 sites on anatomical SM belly (lateral forearm, anterior and posterior upper arm, abdomen, subscapula, anterior and posterior thigh, anterior and posterior lower leg). SM mass was predicted by MTH multiple height (Sanada et al 2004). The physical activity of the subjects was evaluated by a questionnaire (PA-R; NASA/ JSC) and a uni-axial accelerometer. RESULTS There were significant correlations between VO2peak (L/min) and the predicted thigh SM mass (r2=0.53, p<0.001), and between the estimated SV (r2=0.55, p<0.001) in the development group. VO2peak was also significantly correlated with body mass (r2=0.31, p<0.001) and PA-R (r2=0.23, p<0.01), but not physical activity using an accelerometer. Stepwise regression analysis selected two variables (estimated SV and thigh SM mass) that predicted VO2peak in the development group, and there was a significant correlation between VO2peak and these variables by multiple regression analysis (r2=0.69, p<0.001). When the VO2peakprediction equations were applied to the validation group, significant correlations were observed between the measured- and the predicted-VO2peak (r2=0.88, p<0.001). The predicted VO2peak was 3.76 ± 0.46 L/min (64.0 ± 3.7 ml/kg/min), and was similar to the measured VO2peak of 3.79 ± 0.47 L/min (63.4 ± 3.1 ml/kg/min). CONCLUSION These results suggest that the non-exercise prediction model for VO2peak in this study is valid for predicting VO2peak in Japanese adults.

Lower reference limit for maximal oxygen uptake in men and women.

Maximal oxygen uptake (VO2max) is an important measure of exercise tolerance and low values may have clinical significance. Our purpose was to develop the necessary statistics--prediction equations and standard errors of estimate (SEE)--so that the lower reference limit for VO2max can be predicted for men and women. The subjects were healthy, non-smoking, sedentary men (n = 115) and women (n = 115) aged 20-70 years who performed 15 W min-1 cycle ergometer exercise tests. Three equations were developed for each gender using multiple linear regression with the non-exercise predictor variables of age and height, age and mass, and age and fat-free mass (FFM). The assumptions of regression analysis were examined and the predicted residual sum of squares (PRESS) method was used to cross-validate each equation. Healthy and diseased individual subject data from the literature were used to externally validate our lower reference limit statistics. The equations developed meet the assumptions of regression analysis and have an accuracy similar to the non-exercise prediction equations in the literature with R2 values of approximately 0.581. The PRESS method revealed that the equations are generalizable, i.e. may be used in future studies without a significant loss of accuracy. The lower reference limit predictions for the healthy and diseased individual subject data from the literature produced few miscategorizations unless the subjects were obese and mass was used as a predictor variable. In conclusion, the equations and SEE generated in this study can be used to predict an accurate and valid VO2max lower reference limit for a given subject.