Torso Shape Improves the Prediction of Body Fat Magnitude and Distribution: Raw data

Summary

This dataset contains raw information obtained from 93 participants in an investigation into whether body shape parameters can improve predictions of body fat and distribution. The data includes anthropmetrics of the torso, arms and legs, body volumes, surface areas, torso shape parameters and body fat percentage and distribution (given as the trunk:peripheral fat ratio) Matlab files are included which process the data and also calculate new metrics that were used in analysis (average arm volume, for example). The statistics and neural network toolboxes are used in calculations.

Keywords:	Anthropmetrics, 3D scanning, body volume, body surface area, body shape, torso shape
Creators:	Choppin, Simon Bullas, Alice Thelwell, Michael https://orcid.org/0000-0003-0145-0452
Academic units:	Faculty of Health and Wellbeing (HWB) > Research Centres > Advanced Wellbeing Research Centre (AWRC) Faculty of Health and Wellbeing (HWB) > Research Centres > Centre for Sports Engineering Research (CSER)
Funders:	Funder Name Grant Number Funder ID Grow Medtech (UKRI) UNSPECIFIED
Publisher of the data:	SHU Research Data Archive (SHURDA)
Publication date:	16 May 2022
Data last accessed:	22 February 2023
DOI:	http://doi.org/10.17032/shu-180035
SHURDA URI:	https://shurda.shu.ac.uk/id/eprint/148
Additional details Additional details Types of data: Dataset Collection period: From To 3 January 2021 31 May 2021 Data collection method: All anthropometric, shape, volume and surface area data was obtained using a SizeStream SS14 full body scanner. Fat proportion data was obtained from a BodPod (Cosmed UK) Fat distribution data was obtained from Tanita MC-780 multifrequency segmental body composition analyser (MC-780MA, Tanita Corp., Tokyo, Japan) Data processing and preparation activities: Data is presented processed (extracted from measurement software, and shape parameters calculated). However, data has not been normalised. Matlab files include normalisation to height or according to allometric principles. Separate allometric scaling parameters are included as a separate data table. Statement on legal, ethical and access issues: All procedures were approved by Sheffield Hallam University Research Ethics Committee, refer-ence number ER31648268. Resource language: English Metadata language: English Depositing User: Simon Choppin Date Deposited: 25 May 2022 12:43 Last Modified: 25 May 2022 12:50