Academic journal article Research Quarterly for Exercise and Sport

Running Economy: Comparison of Body Mass Adjustment Methods

Academic journal article Research Quarterly for Exercise and Sport

Running Economy: Comparison of Body Mass Adjustment Methods

Article excerpt

Gender differences have been reported with conflicting results for running economy, defined as submaximal oxygen uptake (V[O.sub.2]submax) for a given speed (Morgan, Martin, & Krahenbuhl, 1989). Daniels and Daniels (1992) compared V[O.sub.2]submax of 20 female and 45 male elite middle- and long-distance runners. Male runners reportedly displayed more economy (lower V[O.sub.2]submax) than their female counterparts at common speeds of 268, 290, and 310 m[multiplied by][min.sup.-1]. Within the same study, maximal oxygen uptake (V[O.sub.2]max)-matched males still had a lower aerobic demand for running than V[O.sub.2]max-matched females at the aforementioned speeds. Bransford and Howley (1977) reported that V[O.sub.2]submax at 200 m[multiplied by][min.sup.-1] for 10 trained males was significantly less than for 10 trained females. They postulated that the differences were attributed to biomechanical and training variables.

Conversely, Daniels, Krahenbuhl, Foster, Gilbert, and Daniels (1977) observed no significant differences in V[O.sub.2]submax with highly trained male and female distance runners at 202, 215, 241, and 268 m[multiplied by][min.sup.-1]. Similar findings were reported for adolescent cross-country runners (Cunningham, 1990a), collegiate cross-country runners (Maksud, Cannistra, & Dublinski, 1976), 24.2 km performance-matched distance runners (Pate, Barnes, & Miller, 1985), and nonelite marathon runners (Wells, Hecht, & Krahenbuhl, 1981).

An explanation for conflicting results in running economy may be in the statistical adjustment methods which may not adequately control for body size differences between genders. Ratio standards of running economy have yielded negative relationships between relative V[O.sub.2]submax (ml[multiplied by][kg.sup.-1][multiplied by][min.sup.-1]) and body mass (Bergh, Sjodin, Forsberg, & Svedenhag, 1991; Williams & Cavanagh, 1986), indicating that the independent effects of body mass may not be completely controlled within and between groups (Nevill, Ramsbottom, & Williams, 1992). Nevill (1994) and Bergh et al. (1991) suggested that an allometric scaling exponent be used to normalize oxygen uptake for body size differences.

Moreover, Winter (1992) suggested that to properly control for body size differences, analysis of covariance (ANCOVA) should be used instead of ratio standards. Unlike ratio standards, ANCOVA does not rely on a y-intercept of zero between the covariate and the criterion variable. Simply, ANCOVA compares groups by adjusting the criterion variable (i.e., absolute V[O.sub.2]submax) for the linear relationship between the covariate (i.e., body mass) and the criterion variable with a regression-based approach (Pedhazur, 1982).

Due to the conflicting running economy results and differing statistical adjustment methods used among studies, the present study was designed to compare absolute V[O.sub.2]submax at 215 m[multiplied by][min.sup.-1] between male and female runners, while controlling body mass with three statistical adjustment methods (ratio standards, allometric scaling, and ANCOVA).


Twelve male and 12 female distance runners between the ages of 18 and 34 years volunteered to participate in this study. Runners were recruited from local track clubs and collegiate track teams. Those selected for the study were averaging at least 24 km[multiplied by][wk.sup.-1] of running training for at least 1 year.

Participants reported to the laboratory rested, without having exercised within 24 hours of the test. All provided written informed consent and completed medical history forms before the start of any testing. A calibrated physician's scale was used to obtain height and weight information for each participant. Participants wore running shoes while their weight was recorded.

Metabolic and ventilatory data for submaximal and maximal testing were collected every 20 s using a calibrated Sensormedics metabolic cart (Sensormedics, Model 2900, Anaheim, CA). …

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