Validity Of The Acsm Prediction Equation To Term Paper

Length: 6 pages Sources: 1+ Subject: Sports Type: Term Paper Paper: #2080064 Related Topics: Spss, Athletic Training, Testing, Regression Analysis
Excerpt from Term Paper :

Validity of the ACSM prediction equation to estimate submaximal

O2 during cycle ergometry in cyclists and aerobically-trained non-cyclists

Several methods have been developed to estimate oxygen consumption (

O2) during exercise. The American College of Sports Medicine (ACSM) developed equations to predict the energy cost of various activities, including walking, running, and arm and cycle ergometry. The ACSM cycle ergometery equation uses pedal frequency (rpm), distance of flywheel travel (meters), applied resistance to the flywheel (kp), and an estimation of the resting metabolism to predict oxygen cost during submaximal cycle exercise between 50 and 200 watts (W). This equation appears as:

O2 = (kg•m•min-1 x 2 ml•kg•m-1) + (3.5 kg•m•min-1 x M)

where

O2 is in ml•min-1 and M. is the subject's body mass in kg (Franklin, 2000).

The variability in direct

O2 measures has been shown to have a standard error of the estimate of up to 7%; the variability when using the prediction model is even greater (Stanforth et al., 1999). Recent studies have focused on determining the validity of the ACSM prediction equation for cycle ergometry (Lang, Latin, Berg, & Mellion, 1992; Stanforth et al., 1999) In general, these studies have shown the ACSM prediction equation to underestimate the actual

O2 by 0% to 16% at power outputs from 30 to 150 W.

The primary aim of this study was to examine the validity of the ACSM prediction equation for estimating oxygen consumption during submaximal cycling in aerobically trained males. The alternate hypothesis of this study was that the estimated vs. actual

O2 during submaximal cycle ergometry would be similar from 50-200 W. A secondary focus was to analyze and compare the accuracy of the equation between two distinct groups of subjects (trained male cyclists vs. aerobically-trained male non-cyclists).

Methods

This study used a prospective, nonrandomized, noncontrolled study design. A convenience sample of 14 apparently healthy males (26.4 ( 1.2 yr, 179.1 ( 1.8 cm, 79.5 ( 3.4 kg, BMI 24.8 ( 0.9) volunteered for the study. Subject demographics are shown in TABLE 1. Inclusion criteria included: (a) all "no" answers on the PAR-Q questionnaire, (b) no contraindications to exercise as indicated by the responses on the health history questionnaire, (c) informed consent given, and (d) BMI of less than 32 kg•m-2. Seven subjects were trained cyclists, defined as those who had cycled ( 3x/week for ( 30 minutes per training session for at least 6 months prior to testing. Seven subjects were aerobically trained non-cyclists who engaged in aerobic activity ( 3x/week for ( 30 minutes per training session during the previous 6 months. However, the quantity of cycling did not exceed 2x/week or 15 minutes per training session. In addition, no subject in this group had ever cycled more than 50 miles per week for ( 4 consecutive weeks.

Subjects were instructed to avoid food, alcohol, and tobacco for 3 hours prior to testing, and to avoid strenuous physical activity for the 24 hours prior to the test. Each subject's height and weight was recorded. Three-site skinfold tests to estimate body density were performed with a Lange skinfold caliper (Beta Technology, Inc., Santa Cruz, CA). Body fat percentage was estimated using the Siri equation. A cycle ergometer (Monark, Vansbro, Sweden) was used to conduct each test. The seat height was adjusted to allow for 5-10 degrees of knee flexion for each subject.

The subjects pedaled at 65 rpm against no resistance for 1 minute to warm-up. The resistance was increased by 0.75 kg•m•min-1 per stage to yield workloads of 50, 100, 150, and 200 W. For each respective stage. Each stage lasted 3 minutes provided a steady state had been achieved. In order to verify this, the absolute difference in heart rate from the second and third minute of each stage could not exceed 5 beats per minute. When this occurred, the stage continued at the same workload until the heart rate response met the above criteria. Gas exchange variables were analyzed using a SensorMedics gas exchange system (Yorba Linda, CA), which was calibrated prior to each test. Heart rate was monitored with a 3-lead single channel electrocardiogram.

Statistical analyses were performed with SPSS 11.5 (SPSS, Inc., Chicago, IL). Means and...

...

Pearson correlation coefficients were used to show the relationship between the predicted and actual oxygen consumption. A paired samples t-test was used to compare the predicted and actual-test group means. An independent t-test compared

O2 differences in cyclists vs. non-cyclists. Significance was set at ( = 0.05 for all tests. All values are reported as mean ( SE.

Results

The results of this study show that the ACSM cycle ergometry equation underpredicted oxygen consumption values at all but the highest workload, as shown in FIGURE 1. However, significant differences between the predicted and actual values were only noted at 50 W (P < 0.01) and 200 W (P = 0.02), respectively, as shown in FIGURE 2. Pearson correlations between the actual and predicted values ranged from r = 0.07 to r = 0.66, with only the 50 W (r = 0.66, P = 0.01) and 100 W (r = 0.48, P = 0.09) workloads showing a strong relationship.

Descriptive statistics comparing cyclists vs. non-cyclists are shown in TABLE 2. As for the entire group of subjects, the ACSM equation underestimated oxygen consumption at the low workloads and overestimated oxygen consumption at high workloads in each training group. Although the error at any given workload was greater in the non-cyclist group compared to the cyclists, these differences were not statistically significant. However, the prediction equation showed a stronger correlation in the non-cyclists at every workload.

Discussion

The alternate hypothesis of this study was to determine if the ACSM cycle ergometry equation provides a valid estimate of O2 at workloads from 50-200 W. In addition, comparisons were made to determine if training habitus altered the predictive value of the equation. Although there was a tendency for the ACSM prediction equation to underestimate

O2 at workloads from 50-150 W, these differences were not significant. These results are similar to those observed by other investigators (Latin & Berg, 1994; Latin, Berg, Smith, Tolle, & Woodby-Brown, 1993; Stanforth et al., 1999). Latin et al. (Latin & Berg, 1994) compared the accuracy of the ACSM equation in 60 young females. At a workload of 72 W, actual

O2 was underestimated by 15%. From a practical standpoint, however, a 15% error corresponds to a difference of only ~0.5 METS. The range of error in the current study was 7-139 ml•min-1, or 0.03-0.56 METS. Thus, a seemingly large error component may have very little significance from a practical standpoint.

To the author's knowledge, no previous studies have compared the effect of training habitus on O2 prediction. Due to the concept of training specificity, it is reasonable to assume that trained cyclists would be more economical in terms of oxygen consumption in comparison to non-cyclists (Chavarren & Calbet, 1999). In fact, at each workload from 50-150 W, the cyclists had lower

O2 values (42-71 ml•min-1), although not different from non-cyclists. Oxygen consumption difference at 200 W. was less than 0.5% between groups. However, these small differences in efficiency were not great enough to influence the predictive ability of the ACSM cycle ergometry equation. Although the training groups were clearly delimited, the differences in cycling volume may not have great enough to precipitate detectable gains in efficiency, as the training volume in the cycling group was 59 ( 24 miles/week. Also, the frequency of pedaling may have had a negative influence on efficiency for two reasons. First, most trained cyclists prefer a pedaling frequency of 85-110 rpm. The current protocol utilized a frequency of 65 rpm, which most trained cyclists are likely unaccustomed to. Second, Londeree and colleagues (Londeree, Moffitt-Gerstenberger, Padfield, & Lottmann, 1997) demonstrated that slower pedaling frequencies become less efficient at higher resistances. Therefore, comparisons among studies become difficult because of the wide variation (38-120 rpm) (Anton-Kuchly, Roger, & Varene, 1984; Berry, Storsteen, & Woodard, 1993; Coast, Cox, & Welch, 1986; Lang et al., 1992; Latin et al., 1993; Londeree et al., 1997) in pedaling frequency utilized.

Lastly, it has previously been shown that oxygen consumption is nonlinearly related to work rate (Londeree et al., 1997). When

O2 was plotted against work rate and the intercept set to 278 ml•min-1 to represent resting

O2, a second-order polynomial regression line (R2 = 0.956) fit the data better than a linear regression line (R2 = 0.942). Because the ACSM equation assumes linearity with nonlinear responses, inherent error will always be factored into the prediction.

Conclusions

The results of this study suggest that the ACSM cycle ergometry prediction equation is most accurate at moderate workloads (~100-150 W). Furthermore, training status has no effect on the predictive value of the equation. Further studies on the effects of training habitus should be fashioned to include a larger sample with greater distinction in training volume, i.e. cycle ~200 miles/week vs. sedentary. Based on the…

Sources Used in Documents:

References

Anton-Kuchly, B., Roger, P., & Varene, P. (1984). Determinants of increased energy cost of submaximal exercise in obese subjects. J Appl Physiol, 56(1), 18-23.

Berry, M.J., Storsteen, J.A., & Woodard, C.M. (1993). Effects of body mass on exercise efficiency and VO2 during steady-state cycling. Med Sci Sports Exerc, 25(9), 1031-1037.

Chavarren, J., & Calbet, J.A. (1999). Cycling efficiency and pedalling frequency in road cyclists. Eur J. Appl Physiol Occup Physiol, 80(6), 555-563.

Coast, J.R., Cox, R.H., & Welch, H.G. (1986). Optimal pedalling rate in prolonged bouts of cycle ergometry. Med Sci Sports Exerc, 18(2), 225-230.


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