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Child t-shirt size data set from 3D body scanner anthropometric measurements and a questionnaire.

ABSTRACT: A dataset of a fit assessment study in children is presented. Anthropometric measurements of 113 children were obtained using a 3D body scanner. Children tested a t-shirt of different sizes and a different model for boys and girls, and their fit was assessed by an expert. This expert labeled the fit as 0 (correct), -1 (if the garment was small for that child), or 1 (if the garment was large for that child) in an ordered factor called Size-fit. Moreover, the fit was numerically assessed from 1 (very poor fit) to 10 (perfect fit) in a variable called Expert evaluation. This data set contains the differences between the reference mannequin of the evaluated size and the child?s anthropometric measurements for 27 variables. Besides these variables, in the data set, we can also find the gender, the size evaluated, and the size recommended by the expert, including if an intermediate, but nonexistent size between two consecutive sizes would have been the right size. In total, there are 232 observations. The analysis of these data can be found in Pierola et al. (2016) [2].

SUBMITTER: Pierola A

PROVIDER: S-EPMC5328721 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Child t-shirt size data set from 3D body scanner anthropometric measurements and a questionnaire.

Pierola A A Epifanio I I Alemany S S

Data in brief 20170216

A dataset of a fit assessment study in children is presented. Anthropometric measurements of 113 children were obtained using a 3D body scanner. Children tested a t-shirt of different sizes and a different model for boys and girls, and their fit was assessed by an expert. This expert labeled the fit as 0 (correct), -1 (if the garment was small for that child), or 1 (if the garment was large for that child) in an ordered factor called Size-fit. Moreover, the fit was numerically assessed from 1 (v ...[more]

PMID: 28275665

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Project description:BackgroundManual anthropometric measurements are time-consuming and challenging to perform within acceptable intra- and inter-individual error margins in large studies. Three-dimensional (3D) laser body scanners provide a fast and precise alternative: within a few seconds the system produces a 3D image of the body topography and calculates some 150 standardised body size measurements.ObjectiveThe aim was to enhance the small number of existing validation studies and compare scan and manual techniques based on five selected measurements. We assessed the agreement between two repeated measurements within the two methods, analysed the direct agreement between the two methods, and explored the differences between the techniques when used in regressions assessing the effect of health related determinants on body shape indices.MethodsWe performed two repeated body scans on 123 volunteering young men using a Vitus Smart XXL body scanner. We manually measured height, waist, hip, buttock, and chest circumferences twice for each participant according to the WHO guidelines. The participants also filled in a basic questionnaire.ResultsMean differences between the two scan measurements were smaller than between the two manual measurements, and precision as well as intra-class correlation coefficients were higher. Both techniques were strongly correlated. When comparing means between both techniques we found significant differences: Height was systematically shorter by 2.1 cm, whereas waist, hip and bust circumference measurements were larger in the scans by 1.17-4.37 cm. In consequence, body shape indices also became larger and the prevalence of overweight was greater when calculated from the scans. Between 4.1% and 7.3% of the probands changed risk category from normal to overweight when classified based on the scans. However, when employing regression analyses the two measurement techniques resulted in very similar coefficients, confidence intervals, and p-values.ConclusionFor performing a large number of measurements in a large group of probands in a short time, body scans generally showed good feasibility, reliability, and validity in comparison to manual measurements. The systematic differences between the methods may result from their technical nature (contact vs. non-contact).

Project description:BackgroundPopulation-based surveys collect crucial data on anthropometric measures to track trends in stunting [height-for-age z score (HAZ) < -2SD] and wasting [weight-for-height z score (WHZ) < -2SD] prevalence among young children globally. However, the quality of the anthropometric data varies between surveys, which may affect population-based estimates of malnutrition.ObjectivesWe aimed to develop composite indices of anthropometric data quality for use in multisurvey analysis of child health and nutritional status.MethodsWe used anthropometric data for children 0-59 mo of age from all publicly available Demographic and Health Surveys (DHS) from 2000 onwards. We derived 6 indicators of anthropometric data quality at the survey level, including 1) date of birth completeness, 2) anthropometric measure completeness, 3) digit preference for height and age, 4) difference in mean HAZ by month of birth, 5) proportion of biologically implausible values, and 6) dispersion of HAZ and WHZ distribution. Principal component factor analysis was used to generate a composite index of anthropometric data quality for HAZ and WHZ separately. Surveys were ranked from the highest (best) to the lowest (worst) index values in anthropometric quality across countries and over time.ResultsOf the 145 DHS included, the majority (83 of 145; 57%) were conducted in Sub-Saharan Africa. Surveys were ranked from highest to lowest anthropometric data quality relative to other surveys using the composite index for HAZ. Although slightly higher values in recent DHS suggest potential improvements in anthropometric data quality over time, there continues to be substantial heterogeneity in the quality of anthropometric data across surveys. Results were similar for the WHZ data quality index.ConclusionsA composite index of anthropometric data quality using a parsimonious set of individual indicators can effectively discriminate among surveys with excellent and poor data quality. Such indices can be used to account for variations in anthropometric data quality in multisurvey epidemiologic analyses of child health.

Dataset Information

Child t-shirt size data set from 3D body scanner anthropometric measurements and a questionnaire.

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Child t-shirt size data set from 3D body scanner anthropometric measurements and a questionnaire.

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