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Results

   Descriptive statistics for prediction of body fat, by skinfold tests and BIA were obtained using mini-tab, and are displayed in Table 1. See Appendix A for full results
 

Table 1: Descriptive Statistics for Body fat Prediction Data

   The paired T-test between the body fat prediction, by using the D & W(1974) equation, and body fat predicted by BIA showed a T-value = 3.37 and a P-value of 0.005. The paired T-test between the body fat prediction, by using the J & P (1978) equation, and body fat predicted by BIA showed a T-value = -3.98 and a P-value = 0.002. See Appendix B for full analysis.

Fig 1: A Graph to Show x Scores of Three Methods of Body Fat Prediction, and the Standard Error of the Mean

Discussion

   The paired T-test between the body fat prediction, by using the D & W (1974) equation, and body fat predicted by BIA showed a significant difference, that is, the D & W prediction equation over-estimates body fat when compared to BIA. The alternate (Ha) hypothesis may be accepted, and the null (Ho) rejected, due to P<0.005. This indicates that there is a >99% probability that the difference was due to systematic occurrences as opposed to error..

   The paired T-test between the body fat prediction, by using the J & P (1978) equation, and body fat predicted by BIA showed a significant difference. The J & W prediction equation under-estimates body fat when compared to BIA. The alternate (Ha) hypothesis may be accepted, and the null (Ho) rejected, due to P<0.002. This indicates that there is a >99% probability that the difference was due to systematic occurrences as opposed to error.

   The mean values showed that the D & W (1974) equation overestimates body fat, as predicted by BIA, by 3.3% body fat, they also show that the J & W (1978) equation underestimates body fat as predicted by BIA, by 4.7% body fat. Comparison of the mean scores of the D & W (1974) equation, and the J & W (1978) equation shows that there difference of 8% body fat.

   The results are surprising and have important possible practical implications. Assuming that data was valid and there were no major errors, they show the importance of body fat predictions, by doubly indirect methods, being treated as predictions and not measurements. If body fat was calculated, before and after an intervention, by two different methods, then the effectiveness of the intervention wouldn’t be shown. This may have drastic psychological effects if change in body fat was a major goal; for example if an athlete had attempted to reduce body fat, to an ideal weight, before an event and it was measured at the start of the training schedule using the J & P equation. Then after three months of training, body fat was measured using the D & W equation then it may appear that the athlete had maintained or even put on body fat and that the training hadn’t worked. This may decrease motivation and negatively influence performance.

   However, there are several errors that may have occurred during the data collection. According to Reilly et al, before BIA is taken subjects shouldn’t have urinated in the previous 30 minutes, consumed food or drink in the previous 4 hours, exercised in the previous 12 hours or consumed alcohol in the previous 48 hours. Subjects were not informed of the protocol or restrictions until 30 minutes before the tests commenced. Therefore it is unlikely many, if any, of the subjects complied with these restrictions. SkF measurements were taken by different experimenters for each subject. All of the experimenters were very inexperienced and for many it was the first time taking measurements. These two factors leave the SkF measurements very open to error, especially considering that to achieve consistency in measurements an individual needs to have assessed approximately 50 people with varying body fat (McArdle, Katch & Katch, 2001).

References

Clarys, J.P., Martin, A.D., Drinkwater, D.T., & Marfell-Jones M.J. (1987). The skinfold: myth and reality. Journal of Sport Sciences (5), pp. 3-33.

Davis, P.S.W. & Cole, T.J. (1995). Body Composition Techniques in Health and Disease. Cambridge University Press, Cambridge, UK.

Eston, R, & Reilly, T. (2001). Kinanthropometry and Exercise Physiology Laboratory Manual: Tests, Procedures and Data – Volume 1: Anthropometry (Second Edition). Routledge, New York, NY.

Heyward, V. & Stolarczyk, L. (1996), Applied Body Composition Assessment. Human Kinetics, Champaign, IL.

Lukasaki, H.C., Bolonchuk, W.W., Hall, C.B., & Siders, W.A. (1986). Validation of tetrapolar bioelectrical impedance method to assess human body composition. Journal of Applied Physiology (60), pp.1327-1332

McArdle, W., Katch, F. & Katch, V. (2001). Exercise Physiology- Energy, Nutrtion and Human Performance. Lea & Febiger, Philadelphia.

Reilly, T., Maughan, R.J. & Hardy, L. (1996). Bodyfat concensus statement of the steering group of the british Olympic association. Sport & Exercise and Injury (2), p.46-49

Swan, P.D. & McConnell, K.E. (1999). Anthropometry and bioelectrical impedance inconsistently predicts fatness in women with regional adiposity. Medicine & Science For Sport & Exercise (31), pp.1068-1075

Vogel, J.A., & Friedel, K.E. (1992). Body fat assessment in women. Sports Medicine (13), pp245-269

Wagner, D.R. & Heyward, V.H. (1999). Techniques of body composition assessment: A review of laboratory and field methods. Research Quarterly For Exercise and Sport. (70), pp. 135-149

Williams, D.P., Going, S.B., Milliken, L.A., Hall, M.C., & Lohman, T.G. (1994). Practical techniques for assessing body composition in middle-aged and older adults. Medicine & Science For Sport & Exercise(27), pp.776-783
 

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