# Associations between relative body fat and areal body surface roughness characteristics in 3D photonic body scans-a proof of feasibility

Ritter, Severin; Staub, Kaspar; Eppenberger, Patrick (2021). Associations between relative body fat and areal body surface roughness characteristics in 3D photonic body scans-a proof of feasibility. International Journal of Obesity:Epub ahead of print.

## Abstract

INTRODUCTION

A reliable and accurate estimate of the percentage and distribution of adipose tissue in the human body is essential for evaluating the risk of developing chronic and noncommunicable diseases. A precise and differentiated method, which at the same time is fast, noninvasive, and straightforward to perform, would, therefore, be desirable. We sought a new approach to this research area by linking a person's relative body fat with their body surface's areal roughness characteristics.

MATERIALS AND METHODS

For this feasibility study, we compared areal surface roughness characteristics, assessed from 3D photonic full-body scans of 76 Swiss young men, and compared the results with body impedance-based estimates of relative body fat. We developed an innovative method for characterizing the areal surface roughness distribution of a person's entire body, in a similar approach as it is currently used in geoscience or material science applications. We then performed a statistical analysis using different linear and stepwise regression models.

RESULTS

In a stepwise regression analysis of areal surface roughness frequency tables, a combination of standard deviation, interquartile range, and mode showed the best association with relative body fat (R$^{2}$ = 0.55, p < 0.0001). The best results were achieved by calculating the arithmetic mean height, capable of explaining up to three-quarters of the variance in relative body fat (R$^{2}$ = 0.74, p < 0.001).

DISCUSSION AND CONCLUSION

This study shows that areal surface roughness characteristics assessed from 3D photonic whole-body scans associate well with relative body fat, therefore representing a viable new approach to improve current 3D scanner-based methods for determining body composition and obesity-associated health risks. Further investigations may validate our method with other data or provide a more detailed understanding of the relation between the body's areal surface characteristics and adipose tissue distribution by including larger and more diverse populations or focusing on particular body segments.

## Abstract

INTRODUCTION

A reliable and accurate estimate of the percentage and distribution of adipose tissue in the human body is essential for evaluating the risk of developing chronic and noncommunicable diseases. A precise and differentiated method, which at the same time is fast, noninvasive, and straightforward to perform, would, therefore, be desirable. We sought a new approach to this research area by linking a person's relative body fat with their body surface's areal roughness characteristics.

MATERIALS AND METHODS

For this feasibility study, we compared areal surface roughness characteristics, assessed from 3D photonic full-body scans of 76 Swiss young men, and compared the results with body impedance-based estimates of relative body fat. We developed an innovative method for characterizing the areal surface roughness distribution of a person's entire body, in a similar approach as it is currently used in geoscience or material science applications. We then performed a statistical analysis using different linear and stepwise regression models.

RESULTS

In a stepwise regression analysis of areal surface roughness frequency tables, a combination of standard deviation, interquartile range, and mode showed the best association with relative body fat (R$^{2}$ = 0.55, p < 0.0001). The best results were achieved by calculating the arithmetic mean height, capable of explaining up to three-quarters of the variance in relative body fat (R$^{2}$ = 0.74, p < 0.001).

DISCUSSION AND CONCLUSION

This study shows that areal surface roughness characteristics assessed from 3D photonic whole-body scans associate well with relative body fat, therefore representing a viable new approach to improve current 3D scanner-based methods for determining body composition and obesity-associated health risks. Further investigations may validate our method with other data or provide a more detailed understanding of the relation between the body's areal surface characteristics and adipose tissue distribution by including larger and more diverse populations or focusing on particular body segments.

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