Larry T. Wier

Prediction of functional aerobic capacity without exercise testing

The purpose of this study was to develop functional aerobic capacity prediction models without using exercise tests (N-Ex) and to compare the accuracy with Astrand single-stage submaximal prediction methods. The data of 2,009 subjects (9.7% female) were randomly divided into validation (N = 1,543) and cross-validation (N = 466) samples. The validation sample was used to develop two N-Ex models to estimate VO2peak. Gender, age, body composition, and self-report activity were used to develop two N-Ex prediction models. One model estimated percent fat from skinfolds (N-Ex %fat) and the other used body mass index (N-Ex BMI) to represent body composition. The multiple correlations for the developed models were R = 0.81 (SE = 5.3 ml.kg-1.min-1) and R = 0.78 (SE = 5.6 ml.kg-1.min-1). This accuracy was confirmed when applied to the cross-validation sample. The N-Ex models were more accurate than what was obtained from VO2peak estimated from the Astrand prediction models. The SEs of the Astrand models ranged from 5.5-9.7 ml.kg-1.min-1. The N-Ex models were cross-validated on 59 men on hypertensive medication and 71 men who were found to have a positive exercise ECG. The SEs of the N-Ex models ranged from 4.6-5.4 ml.kg-1.min-1 with these subjects.(ABSTRACT TRUNCATED AT 250 WORDS)

Changes in aerobic power of men, ages 25-70 yr.

This study quantified and compared the cross-sectional and longitudinal influence of age, self-report physical activity (SR-PA), and body composition (%fat) on the decline of maximal aerobic power (VO2peak). The cross-sectional sample consisted of 1,499 healthy men ages 25-70 yr. The 156 men of the longitudinal sample were from the same population and examined twice, the mean time between tests was 4.1 (+/- 1.2) yr. Peak oxygen uptake was determined by indirect calorimetry during a maximal treadmill exercise test. The zero-order correlations between VO2peak and %fat (r = -0.62) and SR-PA (r = 0.58) were significantly (P < 0.05) higher that the age correlation (r = -0.45). Linear regression defined the cross-sectional age-related decline in VO2peak at 0.46 ml.kg-1.min-1.yr-1. Multiple regression analysis (R = 0.79) showed that nearly 50% of this cross-sectional decline was due to %fat and SR-PA, adding these lifestyle variables to the multiple regression model reduced the age regression weight to -0.26 ml.kg-1.min-1.yr-1. Statistically controlling for time differences between tests, general linear models analysis showed that longitudinal changes in aerobic power were due to independent changes in %fat and SR-PA, confirming the cross-sectional results.

Changes in aerobic power of women, ages 20-64 yr

This study quantified and compared the cross-sectional and longitudinal influence of age, self-report physical activity (SR-PA), and body composition (%fat) on the decline of maximal aerobic power (VO2peak) of women. The cross-sectional sample consisted of 409 healthy women, ages 20-64 yr. The 43 women of the longitudinal sample were from the same population and examined twice, the mean time between tests was 3.7 (+/-2.2) yr. Peak oxygen uptake was determined by indirect calorimetry during a maximal treadmill test. The zero-order correlation of -0.742 between VO2peak and %fat was significantly (P < 0.05) higher then the SR-PA (r = 0.626) and age correlations (r = -0.633). Linear regression defined the cross-sectional age-related decline in VO2peak at 0.537 ml.kg-1.min-1.yr-1. Multiple regression analysis (R = 0.851) showed that adding %fat and SR-PA and their interaction to the regression model reduced the age regression weight of -0.537, to -0.265 ml.kg-1.min-1.yr-1. Statistically controlling for time differences between tests, general linear models analysis showed that longitudinal changes in aerobic power were due to independent changes in %fat and SR-PA, confirming the cross-sectional results. These findings are consistent with mens data from the same lab showing that about 50% of the cross-sectional age-related decline in VO2peak was due to %fat and SR-PA.

Determining the amount of physical activity needed for long-term weight control

OBJECTIVE: To evaluate prospectively the influence of habitual physical activity on body weight of men and women and to develop a model that defines the role of physical activity on longitudinal weight change.DESIGN AND SETTING: Occupational cohort study conducted for a mean of 5.5 y.SUBJECTS: A total of 496 (341 male and 155 female) NASA/Johnson Space Center employees who completed the 3 month education component of the employee health-related fitness program and remained involved for a minimum of 2 y.MEASUREMENTS: Body weights were measured at baseline (T1) and follow-up (T2), and habitual physical activity was obtained from the mean of multiple ratings of the 11-point (0–10) NASA Activity Scale (NAS) recorded quarterly between T1 and T2. Other measures included age, gender, VO2 max obtained from maximal treadmill testing, body mass index (BMI), and body fat percentage.RESULTS: Multiple regression demonstrated that mean NAS, T1 weight, aging and gender all influence long-term T2 weight. T1 age was significant for the men only. Independently, each increase in mean NAS significantly (P<0.01) reduced T2 weight in men (b=−0.91 kg; 95% CI:−1.4 to−0.42 kg) and women (b=−2.14 kg; 95% CI:−2.93 to−1.35 kg). Mean NAS had a greater effect on T2 weight as T1 weight increased, and the relationship was dose-dependent.CONCLUSIONS: Habitual physical activity is a significant source of long-term weight change. The use of self-reported activity level is helpful in predicting long-term weight changes and may be used by health care professionals when counseling patients about the value of physical activity for weight control.

Development of normative values for resting and exercise rate pressure product.

PURPOSE The purpose of this study was to develop multivariate models to quantify resting, submaximal, and maximal rate pressure products (RPP). METHODS A validation sample (N = 1623) was randomly selected from a clinically healthy population, and four cross-validation samples were randomly selected from a clinical cohort. The cross-validation samples were patients who had a negative exercise ECG with (Neg-Med, N = 179) and without cardiovascular drug (Neg-NoMed, N = 350), and patients who had a positive exercise ECG with (Pos-Med, N = 60) and without cardiovascular drug (Pos-NoMed, N = 75). Men made up 83% of the validation sample (mean age = 44.2+/-8.7) and women 17% (mean age = 39.7+/-10.1). The validation sample was used to develop multiple regression equations to quantify resting, submaximal, and maximal RPP. RESULTS Results indicated that gender, body mass index (BMI), and physical activity level (Ex-code) were significantly related with resting RPP. Gender, age, BMI, and Ex-code were significantly related with maximal RPP. Gender, age, BMI, Ex-code, and percent of maximal heart rate at submaximal exercise (%HRmax) were significantly related with submaximal RPP. The multiple correlations for the resting, submaximal, and maximal models were 0.29 (SE = 16.75 beats x min(-1) x mm Hg), 0.87 (SE = 29.04 beats x min(-1) x mm Hg), and 0.31 (SE = 42.41 beats x min(-1) x mm Hg), respectively. The accuracy of the models was confirmed when applied to the Neg-NoMed and Pos-NoMed samples but not the Neg-Med and Pos-Med samples. This result suggest that the regression models developed from this study can be generalized to other populations where patients were not taking cardiovascular medication. Microcomputer programs were suggested to evaluate RPP at rest, maximal exercise, and submaximal exercise. CONCLUSION Normative RPP for resting and exercise relies on multiple fitness parameters. Practical regression models are developed and can be applied to patients without cardiovascular medication.

Factors affecting compliance in the NASA/Johnson space center fitness programme

This paper has 2 general purposes. The first is to summarise the results of the 2 NASA/JSC retrospective studies. Results of these studies provided the foundation for a follow-up study, which was designed to develop risk profiles predicting the level and time-course of compliance from baseline data. Quite simply, the second purpose was to determine if those most likely to maintain or discontinue the exercice practice could be identified at enrolment

Multivariate Model for Defining Changes in Maximal Physical Working Capacity of Men, Ages 25 to 70 Years

The purpose of this study was to develop a multivariate model with cross-sectional data that defined the decline in VO2max over time, and cross-validate the model with longitudinal data. The cross-sectional sample consisted of 1,608 healthy men who ranged in age from 25 to 70 years. VO2max was directly measured during a maximum Bruce treadmill stress test. Regression analysis showed that the cross-sectional age and VO2max relationship was linear, r = 0.45 and the age decline in VO2max was 0.48 ml/kg/min/year. Multiple regression developed the multivariate model from age, percent body fat (%fat), self-report physical activity (SR-PA), and the interaction of SR-PA and %fat (R = 0.793). Accounting for the variance in percent body fat and exercise habits decreased the influence of age on the decline of VO2max to just −0.27 ml/kg/min/year. This showed that much of decline in maximal physical working capacity was due to physical activity level and percent body fat, not aging. The multivariate equation was applied to the data of the longitudinal sample of 156 men who had been tested twice (Mean AgeΔ = 3.1 ± 1.2 years). The correlation between the measured and estimated change in VO2max over time (ΔVO2max) was 0.75. The results of the study showed that changes in body composition and exercise habits had more of an influence on changes in maximal physical working capacity than aging. The developed model provides a useful way to quantify the changes in physical working capacity with aging.