Research article
Assessing Cardiorespiratory Fitness Without Performing Exercise Testing

https://doi.org/10.1016/j.amepre.2005.06.004Get rights and content

Background

Low cardiorespiratory fitness (CRF) is associated with increased risk of chronic diseases and mortality; however, CRF assessment is usually not performed in many healthcare settings. The purpose of this study is to extend previous work on a non–exercise test model to predict CRF from health indicators that are easily obtained.

Methods

Participants were men and women aged 20 to 70 years whose CRF level was quantified with a maximal or submaximal exercise test as part of the National Aeronautics and Space Administration/Johnson Space Center (NASA, n=1863), Aerobics Center Longitudinal Study (ACLS, n=46,190), or Allied Dunbar National Fitness Survey (ADNFS, n=1706). Other variables included gender, age, body mass index, resting heart rate, and self-reported physical activity levels.

Results

All variables used in the multiple linear regression models were independently related to the CRF in each of the study cohorts. The multiple correlation coefficients obtained within NASA, ACLS, and ADNFS participants, respectively, were 0.81, 0.77, and 0.76. The standard error of estimate (SEE) was 1.45, 1.50, and 1.97 metabolic equivalents (METs) (1 MET=3.5 ml O2 uptake · kilograms of body mass−1 · minutes−1), respectively, for the NASA, ACLS, and ADNFS regression models. All regression models demonstrated a high level of cross-validity (0.72<R<0.80). The highest cross-validation coefficients were seen when the NASA regression model was applied to the ACLS and ADNFS cohorts (R=0.76 and R=0.75, respectively).

Conclusions

This study suggests that CRF may be accurately estimated in adults from a non–exercise test model including gender, age, body mass index, resting heart rate, and self-reported physical activity.

Introduction

Low cardiorespiratory fitness (CRF) is associated with adverse metabolic risk factor profiles,1, 2, 3 increased risk of cardiovascular disease, type 2 diabetes, and mortality.4, 5, 6, 7, 8, 9, 10 The strength of association between low CRF and mortality is comparable to that between mortality and conventional health indicators such as body weight, blood pressure, cholesterol level, and smoking.8, 9, 11 Although CRF is an important health indicator, fitness assessment is usually not performed in many healthcare settings.

The decision to measure and evaluate health indicators in most settings is likely influenced by the feasibility and cost of measuring the parameter. Assessments of body weight, blood pressure, cholesterol levels, and smoking habits are relatively easy to obtain, and are routinely obtained and used in patient counseling. Absence of feasible assessment methods and consensus guidelines for interpreting health-related CRF levels may contribute to the lack of fitness evaluation in most settings. Incorporation of CRF into individual risk assessment might be more feasible if simple CRF assessments are available.

The gold standard measure of CRF is maximal oxygen uptake (V̇O2max), typically expressed as follows: milliliters of O2 uptake · kilograms of body mass−1 · minutes−1, or metabolic equivalents (METs), where 1 MET = 3.5 ml O2 uptake · kilograms of body mass−1 · minutes−1.

V̇O2max can be assessed with direct or indirect procedures.12, 13 Direct measures provide the most precise assessment of CRF and are obtained by ventilatory gas analysis at maximal exertion during a graded exercise ergometry test.12, 14 Indirect methods estimate V̇O2max from maximal exercise duration, the peak workload and/or heart rate (HR) responses achieved during submaximal or maximal exercise ergometry, or the amount of time required to walk, jog, or run a specified distance.13, 14 However, both direct and indirect methods of assessing CRF may be impractical for regular use in most settings.

An international group of experts in the areas of physical activity and fitness assessment, epidemiology, preventive medicine, and clinical exercise testing reviewed the precision and feasibility of a variety of methods that might be used to quantify CRF in healthcare settings. Based on the review of the literature and the clinical expertise of these experts, it was concluded that the prediction of CRF from non–exercise test regression models would be most appropriate for widespread use in many healthcare settings if sufficient validity was obtained with this method of assessment. Non–exercise test models estimate V̇O2max from the regression of measured maximal oxygen uptake on independent variables known to be predictive of CRF, such as gender, age, body size, resting HR, and self-reported habitual physical activity levels. This method avoids the burden of exercise testing, while providing a reasonably accurate estimation of CRF.15, 16, 17 The purpose of this report is to extend a previous non–exercise test model15 for estimating CRF. Additional analyses were conducted, including cross-validation studies, in expanded and new data sets.

Section snippets

Methods

Secondary analyses were performed on data previously obtained in three large cohorts of adults. The samples were from the National Aeronautics and Space Administration/Johnson Space Center (NASA; Houston TX), collected from 1971 to 200218, 19; the Aerobics Center Longitudinal Study (ACLS), collected from 1985 to 20009, 10; and the 1990 Allied Dunbar National Fitness Survey (ADNFS).20 Participants provided informed consent to participate in their respective cohort studies.

Results

Demographic and SR-PA data that describe all cohorts are shown in Table 2, Table 3. The men and women in the three samples were similar in age, BMI, and resting HR. The mean CRF of the NASA men was lower than the ACLS and ADNFS men by about 0.59 and 1.63 METs, respectively. The SR-PA profile of the NASA and ADNFS men showed they were more likely to be in the higher SR-PA categories than the ACLS men. The measured mean CRF of the NASA women was lower than in ACLS and ADNFS women, but the

Discussion

CRF is a strong independent predictor of all-cause and cause-specific mortality in asymptomatic individuals as well as in individuals with existing metabolic or cardiovascular disease.8, 9, 11 In spite of having a similar relative and attributable risk of mortality as regularly monitored health indicators,8, 11 feasibility issues limit assessment of CRF in many healthcare settings. The purpose of the current study was to expand previous work15 on a non–exercise test model to predict CRF.

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