Academic journal article Research Quarterly for Exercise and Sport

An Accurate V[O.Sub.2] Max Nonexercise Regression Model for 18-65-Year-Old Adults

Academic journal article Research Quarterly for Exercise and Sport

An Accurate V[O.Sub.2] Max Nonexercise Regression Model for 18-65-Year-Old Adults

Article excerpt

The purpose of this study was to develop a regression equation to predict maximal oxygen uptake (V[O.sub.2]max) based on nonexercise (N-EX) data. All participants (N = 100), ages 18-65 years, successfully completed a maximal graded exercise test (GXT) to assess V[O.sub.2]max (M = 39. 96 mL x [kg.sup.-1] x [min.sup.-1], SD = 9.54). The N-EX data collected just before the maximal GXT included the participant's age; gender; body mass index (BMI); perceived functional ability (PFA) to walk, jog, or run given distances; and current physical activity (PA-R) level. Multiple linear regression generated the following N-EX prediction equation (R = .93, SEE = 3.45 mL x [kg.sup.-1] x [min.sup.-1], % SEE = 8. 62): V[O.sub.2]max (mL x [kg.sup.-1] x [min.sup.-1]) = 48. 0730 + (6.1779 x gender; women = 0, men = 1) - (0.2463 x age) - (0.6186 x BMI) + (0.7115 x PFA) + (0.6709 x PA-R). Cross validation using PRESS (predicted residual sum of squares) statistics revealed minimal shrinkage ([R.sub.p] = .91 and SE[E.sub.p] = 3.63 mL x [kg.sup.-1] x [min.sup.-1]); thus, this model should yield acceptable accuracy when applied to an independent sample of adults (ages 18-65 years) with a similar cardiorespiratory fitness level. Based on standardized [beta]-weights, the PFA variable (0.41) was the most effective at predicting V[O.sub.2]max followed by age (-0.34), gender (0.33), BMI (-0.27), and PA-R (0.16). This study provides a N-EX regression model that yields relatively accurate results and is a convenient way to predict V[O.sub.2]max in adult men and women.

Key words: cardiorespiratory fitness, exercise testing, maximum oxygen uptake

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Cardiorespiratory fitness (CRF) is the ability to perform dynamic, moderate- to high-intensity exercise using the large muscle groups for long periods of time (American College of Sports Medicine [ACSM], 2000). CRF depends on the respiratory, cardiovascular, and skeletal muscle systems and, therefore, is an important component of health and physical fitness (ACSM). The assessment of CRF is valuable when educating individuals about their overall fitness status, developing exercise programs, and stratifying cardiovascular risk (ACSM).

The standard test for determining CRF is the measurement of maximal oxygen uptake (V[O.sub.2]max) when performing a maximal graded exercise test (GXT; ACSM, 2000). V[Os.ub.2]max is the most accurate way to assess CRF, however, the test requires costly equipment, space to house the equipment, and trained personnel to administer the test. In addition, maximal GXTs are unappealing to some individuals, because the test requires strenuous exercise to the point of volitional exhaustion. Because of this, some older or higher risk individuals should not perform the test without medical supervision (ACSM).

Due to the possible drawbacks of maximal GXTs and the direct measurement of V[O.sub.2]max, submaximal exercise tests are available and provide an acceptable prediction of CRF and V[O.sub.2]max. Submaximal exercise tests use prediction variables, such as age, gender, body mass, exercise pace, and exercise heart rate to predict V[O.sub.2]max (ACSM, 2000). Although not as accurate as maximal GXTs, submaximal exercise tests are easier to perform, require less time and effort to complete, and can be administered at lower costs and reduced risk (ACSM). These tests use a variety of exercise modes, including cycle ergometry, stepping and walking, and jogging or running on a treadmill or track (ACSM, 2000; Bruce, Kusumi, & Hosmer, 1973; Cooper, 1968; Kline, Porcari, Hintermeister, Freedson, & Ward, 1987; Astrand & Ryhming, 1954).

Other methods that do not require exercise are also available to predict V[O.sub.2]max (ACSM, 2000). Nonexercise (N-EX) regression equations provide a convenient estimate of CRF without needing to perform a maximal or submaximal exercise test. This approach is inexpensive, time efficient, and realistic for large groups. …

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