Non-exercise Model Research Articles

Accuracy of a Clinical Applicable Method for Prediction of VO2max Using Seismocardiography.

Cardiorespiratory fitness measured as ˙VO2max is considered an important variable in the risk prediction of cardiovascular disease and all-cause mortality. Non-exercise ˙VO2max prediction models are applicable, but lack accuracy. Here a model for the prediction of ˙VO2max using seismocardiography (SCG) was investigated. 97 healthy participants (18-65 yrs., 51 females) underwent measurement of SCG at rest in the supine position combined with demographic data to predict ˙VO2max before performing a graded exercise test (GET) on a cycle ergometer for determination of ˙VO2max using pulmonary gas exchange measurements for comparison. Accuracy assessment revealed no significant difference between SCG and GET ˙VO2max (mean±95% CI; 38.3±1.6 and 39.3±1.6 ml·min-1·kg-1, respectively. P=0.075). Further, a Pearson correlation of r=0.73, a standard error of estimate (SEE) of 5.9 ml·min-1·kg-1, and a coefficient of variation (CV) of 8±1% were found. The SCG ˙VO2max showed higher accuracy, than the non-exercise model based on the FRIENDS study, when this was applied to the present population (bias=-3.7±1.3 ml·min-1·kg-1, p<0.0001. r=0.70. SEE=7.4 ml·min-1·kg-1, and CV=12±2%). The SCG ˙VO2max prediction model is an accurate method for the determination of ˙VO2max in a healthy adult population. However, further investigation on the validity and reliability of the SCG ˙VO2max prediction model in different populations is needed for consideration of clinical applicability.

A Nonexercise Estimate of Cardiorespiratory Fitness Using a Symptom Questionnaire and Clinical Variables.

Cardiorespiratory fitness (CRF) has recently been recognized as a risk factor for mortality, but it is not routinely measured in clinical settings. The purpose of this study was to assess a nonexercise method to estimate CRF (eCRF) and its association with mortality in a clinically referred population. A symptom tool, termed the Veterans Specific Activity Questionnaire (VSAQ), and nonexercise clinical variables were obtained from 1545 clinically referred subjects (60 ± 13 yr), and followed for a mean of 5.6 ± 4.2 yr. The VSAQ along with nonexercise clinical and historical variables was used to develop a multivariate model to predict achieved CRF from maximal exercise testing. Proportional hazards analysis was used to assess the association between measured and eCRF and all-cause mortality. The eCRF model was significantly associated with achieved CRF (multiple R = 0.67, P < .001). Mean achieved CRF from maximal treadmill testing and eCRF were similar (8.6 ± 5.0 metabolic equivalents [METs] vs 8.7 ± 4.7 METs respectively, P = .27). Achieved CRF and eCRF performed similarly for predicting mortality. After full adjustment, each 1 MET higher increment in achieved CRF and eCRF was associated with 19% and 26% reductions in mortality risk, respectively. Compared with the lowest fit group (<5 METs), the highest CRF groups (>11 METs) had 88% and 87% lower risks for mortality for achieved CRF and eCRF, respectively. A multivariable nonexercise model featuring a symptom questionnaire combined with clinical variables that are readily available during a typical clinical encounter had a reasonably strong association with achieved CRF and exhibited prognostic characteristics that were similar to achieved CRF.

A Non-Exercise Model for Predicting Cardiovascular Risks among Apparently Healthy Male Office Workers-Cross-Sectional Analysis: A Pilot Study.

The health condition of working-age males in Poland remains largely associated with long-lasting sick leaves, one of the main reasons of which being cardiovascular diseases (CVD). The aim of this work was to develop a prediction model for FIT Treadmill Score (“FIT” refers to Henry Ford ExercIse Testing (FIT) Project) that only depends on easily accessible somatic data and smoking without the need to perform the exercise test anymore. The study comprised 146 men with a negative cardiological history, aged 26–60, with desk-jobs. By means of regression analysis it was tested to what degree obesity-related indices as well as smoking cigarettes allow for determining the measure level of mortality risk, without the necessity of performing an exercise test. The following independent variables were entered into the linear regression model: age, BMI, Fat%, waist circumference (WC), waist to height ratio (WHtR) as well as smoking. Statistically significant factors were singled out from among them. The obtained model accounts for a significant part (over 87%) of the variability of the mortality risk measure among the tested population. Based on the value of the standardised regression coefficient β, it can be stated that age is the factor that mostly determines the mortality risk measure, followed by the WHtR and smoking. The simplicity of the worked-out model and, resulting from it, the possibility of its common application should enable better health monitoring of working-age men with regard to cardiovascular disease occurrence and, related to it, mortality risk, thereby improving the quality of public health management.

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.

Long-term Changes in Depressive Symptoms and Estimated Cardiorespiratory Fitness and Risk of All-Cause Mortality: The Nord-Trøndelag Health Study

ObjectiveTo assess the independent and combined associations of long-term changes in depressive symptoms (DSs) and estimated cardiorespiratory fitness (eCRF) with all-cause mortality. Participants and MethodsThis is a longitudinal cohort study of 15,217 middle-aged and older individuals attending both the second (from August 15, 1995, through June 18, 1997) and third (from October 3, 2006, through June 25, 2008) health surveys of the Nord-Trøndelag Health Study, Norway, and followed until December 31, 2014. Depressive symptoms were estimated using the validated Hospital Anxiety and Depression Scale, and a validated nonexercise model estimated eCRF. Hazard ratios (HRs) were computed using Cox regression. All-cause mortality was ascertained using the Norwegian Cause of Death Registry. ResultsThe mean age was 63.3±8.9 years, and 7932 (52.1%) were women. During the follow-up period of 7.1±1.1 years, 1157 participants (7.6%) died. Multivariable-adjusted analyses revealed that persistently low DSs were independently associated with a 28% risk reduction of all-cause mortality (HR, 0.72; 95% CI, 0.56-0.92; P=.008) as compared with persistently high DSs. Persistently high eCRF independently predicted a 26% lower risk of death (HR, 0.76; 95% CI, 0.66-0.88; P<.001) relative to low eCRF. Analyses of changes in DSs and eCRF revealed that persistently high eCRF combined with decreased or persistently low DSs decreased mortality risk by 49% (HR, 0.51; 95% CI, 0.28-0.91; P=.02) and 47% (HR, 0.53; 95% CI, 0.37-0.76, P=.001), respectively. ConclusionMaintaining low DSs and high eCRF was independently associated with a lower risk of all-cause mortality. The lowest mortality risk was observed for persistently high eCRF combined with decreased or persistently low DSs. These results emphasize the effect of preventing DSs and maintaining high CRF on long-term mortality risk, which is potentially important for long-term population health.

Cardiorespiratory Fitness, Sedentary Time, and Cardiovascular Risk Factor Clustering.

Prolonged sedentary time (ST) is associated with cardiovascular risk factors (CV-RF) independent of physical activity (PA). Whether a high level of cardiorespiratory fitness (CRF) can modify the deleterious health consequences related to high ST is not known. We performed a cross-sectional study of 12,274 men and 14,209 women (≥20 yr) without known cardiovascular disease. Self-reported ST measurements during a regular day were divided into three sex-specific equally sized groups (≤4, 5 to <7, and ≥7 h·d(-1)). CRF was estimated (eCRF) using a previously validated nonexercise model. Using logistic regression analyses, adjusted odds ratios (OR) and 95% confidence intervals (CI) were estimated for the association of ST with CV-RF clustering and for the potential modifying effect of eCRF. Each hour increase in ST was associated with 5% and 4% greater likelihood of having CV-RF clustering independent of PA in men and women, respectively. Among the participants with higher levels of eCRF, the adjusted OR values associated with ≥7 h·d(-1) of ST were 0.92 (0.56-1.51) for men and 1.16 (0.49-2.74) for women, compared with men and women with low ST (≤4 h·d(-1)) and high eCRF levels. In combined analyses of eCRF, PA, and ST, compared with the reference group of participants meeting the recommendations, ≤4 h·d(-1) of ST and high eCRF, the OR values were 0.63 (0.27-1.44) and 0.65 (0.14-3.07) in fit men and women with ≥7 h·d(-1) of ST, which did not meet the recommendations. Men and women meeting the PA recommendations, but were unfit, had significantly increased odds of having CV-RF clustering across levels of ST. High levels of eCRF abolished the increased odds of having CV-RF clustering associated with high ST, even among those individuals who did not meet the current PA recommendations.

Utility of a Non-Exercise VO2max Prediction Model for Designing Ramp Test Protocols.

This study investigated the validity of determining the final work rates of cycling and walking ramp-incremented maximal cardiopulmonary exercise tests (CPETs) using a non-exercise model to predict maximal oxygen uptake VO2max and the American College of Sports Medicine ACSM's metabolic equations. The validity of using this methodology to elicit the recommended test duration of between 8 and 12 min was then evaluated. First, 83 subjects visited the laboratory once to perform a cycling (n=49) or walking (n=34) CPET to investigate the validity of the methodology. Second, 25 subjects (cycling group: n=13; walking group: n=12) performed a CPET on 2 separate days to test the reliability of CPET outcomes. Observed VO2max was 1.0 ml·kg(-1)·min(-1) lower than predicted in the cycling CPET (P=0.001) and 1.4 ml·kg(-1)·min(-1) lower in the walking CPET (P=0.001). Only one of the 133 conducted CPETs was outside the test duration range of 8-12 min. Test-retest reliability was high for all CPET outcomes, with intraclass correlation coefficients of 0.90 to 0.99. In conclusion, the non-exercise model is a valid and reliable method for establishing the final work rate of cycling and walking CPETs for eliciting test durations of between 8 and 12 min.

Prediction of peak oxygen pulse (O2Ppeak) without exercise testing in older adults

Peak oxygen pulse has been considered a surrogate of cardiovascular function and an independent predictor of all cause mortality. However, O2Ppeak depends on maximal volitional effort which may limit its utility in older subjects. The aim of this study was to develop a model to estimate O2Ppeak without exercise in an elderly sample. This cross-sectional study enrolled 67 community-dwelling older adults (69.4±7.1 years; 41 men) for the non-exercise model development and 30 community-dwelling older adults (67.7±6.4 years; n=30; 17 men) for cross-validation. The non-exercise model was derived through hierarchical regression model and cross-validated by means of PRESS statistics and comparison against an independent sample. Classification accuracy of the model for tertiles of estimated and actual O2Ppeak was tested by gamma (γ) nonparametric correlation. The following prediction equation was generated: −3.416+0.137×weight (kg)+1.226×Veterans Specific Activity Questionnaire (VSAQ) (metabolic equivalents, METs)+1.987×gender (0=women, 1=men)−2.045×β-Blockers use (0=no, 1=yes)−0.044×resting heart rate (HR) (R2=0.83; standard error of estimate (SEE)=1.68mLbeat−1). Correlation in cross-validation group was 0.80 (P<0.001). A high probability was observed for the model to rank the values in the same tertile in validation and cross-validation groups (γ=0.98; γ=0.92, respectively, P<0.05). In conclusion, O2Ppeak can be estimated with reasonable precision without exercise testing, providing an alternative for elder subjects not capable to perform maximal effort.

A Simple Nonexercise Model of Cardiorespiratory Fitness Predicts Long-Term Mortality

Cardiorespiratory fitness (CRF) is a strong predictor of future health, but measurements of CRF are time consuming and involve costly test procedures. We assessed whether a simple, non-exercise-based test of CRF predicted long-term all-cause and cardiovascular disease (CVD) mortality. In this prospective cohort study, we used a previously published nonexercise test to estimate CRF in healthy men (n = 18,348) and women (n = 18,764) from the first HUNT study (1984-1986) in Norway. We used Cox regression to obtain HR for mortality during a mean follow-up of 24 yr. Assessment of model validity was performed by standard procedures of discrimination and calibration. CRF was inversely associated with all-cause and CVD mortality in men and women below 60 yr of age at baseline, after adjustment for confounders. For each MET-higher CRF (MET, approximately 3.5 mL·kg·min), HR for CVD mortality was 21% lower in both men (95% confidence interval (CI), 17%-26%) and women (95% CI, 12%-29%). HR for all-cause mortality was 15% (95% CI, 12%-17%) lower in men and 8% (95% CI, 3%-3%) lower in women for each MET-higher CRF. The ability of the model to discriminate mortality risk among participants below 60 yr was better for CRF (area under the curve (AUC), 0.70-0.77) compared with that for each variable that constituted the model (AUC, 0.55-0.63) and an aggregated sum of z-scores for each variable (AUC, 0.61-0.65). Comparison of observed and predicted risk indicated good model calibration. This method of assessing CRF is feasible and practically useful in primary care for identification of apparently healthy individuals at increased risk of premature CVD disease and all-cause mortality.

A Simple Non-exercise Model Of Cardiorespiratory Fitness Predicts Long-term Mortality

A Simple Non-exercise Model Of Cardiorespiratory Fitness Predicts Long-term Mortality

The Clinical and Nonclinical Values of Nonexercise Estimation of Cardiovascular Endurance in Young Asymptomatic Individuals

Exercise testing is associated with barriers prevent using cardiovascular (CV) endurance (CVE) measure frequently. A recent nonexercise model (NM) is alleged to estimate CVE without exercise. This study examined CVE relationships, using the NM model, with measures of obesity, physical fitness (PF), blood glucose and lipid, and circulation in 188 asymptomatic young (18–40 years) adults. Estimated CVE correlated favorably with measures of PF (r = 0.4 − 0.5) including handgrip strength, distance in 6 munities walking test, and shoulder press, and leg extension strengths, obesity (r = 0.2 − 0.7) including % body fat, body water content, fat mass, muscle mass, BMI, waist and hip circumferences and waist/hip ratio, and circulation (r = 0.2 − 0.3) including blood pressures, blood flow, vascular resistance, and blood (r = 0.2 − 0.5) profile including glucose, total cholesterol, LDL-C, HDL-C, and triglycerides. Additionally, differences (P < 0.05) in examined measures were found between the high, average, and low estimated CVE groups. Obviously the majority of these measures are CV disease risk factors and metabolic syndrome components. These results enhance the NM scientific value, and thus, can be further used in clinical and nonclinical settings.

Estimating V˙O2peak from a Nonexercise Prediction Model

Cardiorespiratory fitness is suggested to be an important marker of cardiovascular risk but is rarely evaluated in health care settings. In the present study, directly measured peak oxygen uptake (V·O 2peak) from a diverse population of 4637 healthy participants were used to develop and cross-validate a new nonexercise regression model of cardiorespiratory fitness for men and women. Multivariable regression analysis was used to develop a nonexercise model of cardiorespiratory fitness for men and women separately with V·O 2peak as the outcome. In the final models, 2067 men (mean age = 48.8 yr) and 2193 women (mean age = 47.9 yr) were included, respectively. Cross-validation of the models was done by standard data splitting procedures with evaluation of constant error and total error of a model developed on one sample and cross-validated on another sample. Age, waist circumference, leisure time physical activity, and resting HR, successively, were the most potent predictors of V·O 2peak for both men and women. Together, 61% and 56% of variance in V·O 2peak, for men and women, respectively, were explained by the full models. SEE was 5.70 and 5.14 for the models including men and women, respectively. The nonexercise regression model developed in the present study was fairly accurate in predicting V·O 2peak in this healthy population of men and women. The model might be generalized to other healthy populations and might be a valid tool for a rough assessment of cardiorespiratory fitness in an outpatient setting.

A New Non Exercise-based VO2max Prediction Equation for Patients with Chronic Low Back Pain

INTRODUCTION The measurement of the maximal oxygen uptake as a parameter of cardiorespiratory fitness is useful in exercise prescription in functional restoration programs but this measurement requires the subject's maximal exertion which is not always possible in patients with chronic low back pain. The purpose of this study was to develop a regression equation to predict maximal oxygen uptake based on non-exercise data in adult patients with chronic low back pain. Cross sectional study in which 70 participants completed a maximal graded exercise test in cycle ergometer to assess maximal oxygen uptake. Patients achieved a mean +/- SD value of VO(2)max of 30.8 (+/-7.7) ml kg(-1) min(-1). The regression model included as data of non-exercise the patient's gender, body mass index and the intensity of physical activity during leisure time. Multiple linear regression analysis generated the following formula (R (2) = 38.3, SEE = 6.08 ml kg(-1) min(-1)): VO(2)max (ml kg(-1) min(-1)) = 35.3377 - 0.475411 x BMI + 0.155232 x PALT + 7.97682 x gender; where BMI = body mass index, PALT = physical activity during leisure time, women = 0, men = 1. The Durbin Watson statistic showed no problems with serial autocorrelation (D-W = 1.86). The Kolmogorov-Smirnov normality test demonstrated that the errors are distributed normally. This study provides a new and relatively precise non-exercise regression model to predict VO(2)max in patients with chronic low back pain.

Non-exercise Model For Predicting Vo2max With Objectively Measured Physical Activity Variable For Japanese Women

Non-exercise Model For Predicting Vo2max With Objectively Measured Physical Activity Variable For Japanese Women

Estimation of maximal oxygen uptake by bioelectrical impedance analysis

Previous non-exercise models for the prediction of maximal oxygen uptake VO(2max) have failed to accurately discriminate cardiorespiratory fitness within large cohorts. The aim of the present study was to evaluate the feasibility of a completely indirect method for predicting VO(2max) that was based on bioelectrical impedance analysis (BIA) in 66 young, healthy fit men and women. Multiple, stepwise regression analysis was used to determine the usefulness of BIA and additional covariates to estimate VO(2max) (ml min(-1)). BIA was highly correlated to VO(2max) (r = 0.914; P < 0.001) and entered the regression equation first. The inclusion of gender and a physical activity rating further improved the model which accounted for 88% of the variance in VO(2max) and resulted in a relative standard error of the estimate (SEE) of 7.2%. Substantial agreement between the methods was confirmed by the fact that nearly all the differences were within +/-2 SD. Furthermore, in contrast to previously published non-exercise models, no trend of a reduction in prediction accuracy with increasing VO(2max) values was apparent. It was concluded that a non-exercise model based on BIA might be a rapid and useful technique to estimate VO(2max), when a direct test does not seem feasible. However, though the present results are useful to determine the viability of the method, further refinement of the BIA approach and its validation in a large, diverse population is needed before it can be applied to the clinical and epidemiological settings.

Waist Girth as a Surrogate for Body Composition in the Non-exercise Model for Estimating VO2max

0604 Jackson, et al demonstrated the significant role of body composition and self-report activity habit (AH) in VO2max by developing two non-exercise models from age, gender AH, %Fat (R = 0.81, SE = 5.35 ml/kg/min) and BMI (R = 0.78, SE = 5.70 ml/kg/min) (Med Sci Sports Exerc 1990;22;863–870). These models were more accurate than sub-maximal treadmill models, but measurement of %Fat and BMI were often found to be cumbersome and inappropriate in the public setting. Like %Fat and BMI, waist girths (WG) are significantly related to fitness, disease and mortality. PURPOSE: to investigate the use of WG as a body composition surrogate for estimating VO2max. METHODS: 2926 subjects (17% women) were measured for VO2max by indirect calorimetry (RER> = 1.1), age (yrs), gender by M = 1, W = 0, AH by the 11 point self-report NASA scale, WG at the umbilicus, %Fat by skin-folds, BMI by weight kg/height m2. Data were randomly divided into validation (N = 2187) and cross-validation (N = 637) samples. The validaiton sample was used to develop three non-exercise models from age, gender AH and either WG, %Fat or BMI. RESULTS: Multiple regression correlations and SE's in ml/kg/min for the models were WG (R = 0.81; SE = 4.77), %Fat (R = 0.81; SE = 4.70), BMI (R = 0.79; SE = 4.99). The PRESS technique was used to cross-validate with no loss of accuracy. An analysis of sub-samples of the validation sample showed the accuracy of the models did not differ across genders and all ranges of age, AH and VO2max. CONCLUSIONS: Comparisons confirmed WG can be used to estimate VO2max with equal or better accuracy than using %Fat or BMI.

Waist Girth as a Surrogate for Body Composition in the Non-exercise Model for Estimating VO2max

Waist Girth as a Surrogate for Body Composition in the Non-exercise Model for Estimating VO2max

Failure of predicted VO2peak to discriminate physical fitness in epidemiological studies

Previous investigators reported that peak oxygen uptake (VO2peak) could be accurately predicted from nonexercise test variables, and that this score would be suitable for categorizing cardiorespiratory fitness (CRF) within epidemiological studies. However, the accuracy of these models has varied considerably. The purposes of this study were: 1) assess the accuracy of predicting VO2peak with a new nonexercise model, and 2) assess the utility of the predicted VO2peak for categorizing CRF in epidemiological studies. Subjects included 2,350 men and women. Cross-validated multiple regression models revealed that age, sex, resting heart rate, body weight, percentage body fat, smoking, and physical activity were significant predictors (P < 0.001) of VO2peak. The multiple regression model for relative VO2peak (ml.kg-1.min-1) had R2 = 0.733 (SEE = 5.38), whereas the model for absolute VO2peak (l.min-1) had R2 = 0.773 (SEE = 0.425). The 95% confidence intervals for the predicted VO2peak were large (+/- 10.6 ml.kg-1.min-1 and +/- 0.833 l.min-1). These results support the notion that VO2peak can be predicted from a multiple regression model devoid of exercise test variables. However, due to the extreme variability in the predicted scores, the regression models were unable to effectively distinguish CRF categories. Therefore, despite statistical success in predicting VO2peak for the nonexercise test regression models, we conclude that such models fail to provide the accuracy needed for categorizing CRF within large epidemiological cohorts where the purpose is to assess mortality risk.

Nonexercise Model Fails to Predict Aerobic Capacity in College Students With High VO2peak

(1994). Nonexercise Model Fails to Predict Aerobic Capacity in College Students With High VO2peak. Research Quarterly for Exercise and Sport: Vol. 65, No. 1, pp. 78-83.