Body Composition Practical

This document discusses the importance of body composition analysis in anthropometric measurement and highlights the limitations of Body Mass Index (BMI) measurements. It also suggests using multiple methods for a comprehensive analysis and considers the implications for healthcare. References for similar comparisons are provided as well.

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BODY COMPOSITION PRACTICAL
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Questions from ‘SCANNNED DOCUMENT’
Why is this?
Body composition analysis is often a more useful method for anthropometric
measurement, for athlete as well as non-athletes due to the incomplete and often incorrect
data presented by Body Mass Index (BMI) measurements. BMI highlights one’s distribution
of body weight as per their unique height and weight, but does not highlight the tissue
composition contributing to the weight. Hence, an individual, who has more lean muscle
mass than fat mass may be displayed as ‘overweight’ by BMI measurements whereas body
composition may highlight him/her as healthy, by providing a detailed measurement of the
tissue composition and body mass within physiological parameters (Gier et al., 2018).
How will you do this? How will you display the data?
In order to display the data, the body composition information from all individuals
may be tabulated based on separate columns highlighting the various methods of body
measurements used. Such data categorization and tabulation will allow for convenient and
simultaneous comparison between similar data obtained from various interventions. For
further ease in displaying, the data can be presented graphically in the form of charts like
graphs of pie diagrams since pictorial data presentation has been known to aid in easy data
analysis, understanding, comprehension and visual aesthetic (Hamilton, Ying & Leskovec,
2017).
What can you say from these comparisons?
From the provided data, the comparisons highlight the differences in results obtained
despite measuring the similar anthropometric measurements. It can observed that body
composition analysis using the Body Stat monitor provides a more comprehensive analysis
since it displays the true adiposity underlying the tissues of an individual. Hence, while other

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techniques such as BMI, bio-impedance and waist-to-hip ratio categorised most individuals
as ‘healthy’ or within the acceptable ranges, the body composition analysis highlight
abnormalities in adiposities. Hence, this suggests that we must use multiple methods for
anthropometric assessments instead of relying on just one for a more comprehensive analysis
(Fosbøl & Zerahn, 2015).
Does this affects how you would rank the different methods?
In terms of accuracy, the obtained results affect rankings resulting in body
composition and bio-impedance analysis methods to be ranked higher since they provide a
more comprehensive anthropometric data. However, such methods are expensive and may
not be feasible for all socio-economic groups hence making body weight and waist hip ratio
measurements easily applicable at the household level. Further, transportation of the Body
Stat equipment may not be feasible for analysis in remote areas due to tis requirements of a
laboratory setting (Ortega et al., 2016).
Are there any implications for healthcare? E.g.: Diagnosis? Selection of
Technique?
Such differences in results obtained signifies that health professionals must not rely
on merely a single method but use multiple methods for anthropometric analysis in patients.
However, prior to administration of the same, health professionals must also consider the cost
of each method and socioeconomic background of the patient since some methods are
expensive (Earthman, 2105).
Can you find any References for similar comparisons?
Similar comparisons were found in the research by Lam et al. (2015), where it was
examined that multiple measurement techniques of waist circumference, body mass index,
waist-to-hip ratio, waist-to-height ratio and body adiposity index are useful in the

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measurement of adiposity and in the prediction of cardiovascular disease risk among
individuals.

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References
Earthman, C. P. (2015). Body composition tools for assessment of adult malnutrition at the
bedside: a tutorial on research considerations and clinical applications. Journal of
Parenteral and Enteral Nutrition, 39(7), 787-822. doi:
https://doi.org/10.1177/0148607115595227.
Fosbøl, M. Ø., & Zerahn, B. (2015). Contemporary methods of body composition
measurement. Clinical physiology and functional imaging, 35(2), 81-97. doi:
https://doi.org/10.1111/cpf.12152.
Gier, A., Khoury, P., Kirk, S., Kist, C., & Siegel, R. (2018). Bmi versus Body Composition
as Measures of Success in a Clinical Pediatric Weight Management Program: 3364
Board# 233 June 2 9. Medicine & Science in Sports & Exercise, 50(5S), 837. doi:
10.1249/01.mss.0000538756.35067.83.
Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Representation learning on graphs:
Methods and applications. arXiv preprint arXiv:1709.05584.
Lam, B. C. C., Koh, G. C. H., Chen, C., Wong, M. T. K., & Fallows, S. J. (2015).
Comparison of body mass index (BMI), body adiposity index (BAI), waist
circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) as
predictors of cardiovascular disease risk factors in an adult population in Singapore.
PloS one, 10(4), e0122985. doi: https://doi.org/10.1371/journal.pone.0122985.
Ortega, F. B., Sui, X., Lavie, C. J., & Blair, S. N. (2016, April). Body mass index, the most
widely used but also widely criticized index: would a criterion standard measure of
total body fat be a better predictor of cardiovascular disease mortality?. In Mayo

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Clinic Proceedings (Vol. 91, No. 4, pp. 443-455). Elsevier. doi:
https://doi.org/10.1016/j.mayocp.2016.01.008.