Estimating Growth in Height from Limited Longitudinal Growth Data Using Full-Curves Training Dataset: A Comparison of Two Procedures of Curve Optimization-Functional Principal Component Analysis and SITAR

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU142972" target="_blank" >RIV/00216305:26230/21:PU142972 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14310/21:00119867

Result on the web

<a href="https://www.mdpi.com/2227-9067/8/10/934" target="_blank" >https://www.mdpi.com/2227-9067/8/10/934</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/children8100934" target="_blank" >10.3390/children8100934</a>

Result language

angličtina

Original language name

Estimating Growth in Height from Limited Longitudinal Growth Data Using Full-Curves Training Dataset: A Comparison of Two Procedures of Curve Optimization-Functional Principal Component Analysis and SITAR

Original language description

A variety of models are available for the estimation of parameters of the human growth curve. Several have been widely and successfully used with longitudinal data that are reasonably complete. On the other hand, the modeling of data for a limited number of observation points is problematic and requires the interpolation of the interval between points and often an extrapolation of the growth trajectory beyond the range of empirical limits (prediction). This study tested a new approach for fitting a relatively limited number of longitudinal data using the normal variation of human empirical growth curves. First, functional principal components analysis was done for curve phase and amplitude using complete and dense data sets for a reference sample (Brno Growth Study). Subsequently, artificial curves were generated with a combination of 12 of the principal components and applied for fitting to the newly analyzed data with the Levenberg-Marquardt optimization algorithm. The approach was tested on seven 5-points/year longitudinal data samples of adolescents extracted from the reference sample. The samples differed in their distance from the mean age at peak velocity for the sample and were tested by a permutation leave-one-out approach. The results indicated the potential of this method for growth modeling as a user-friendly application for practical applications in pediatrics, auxology and youth sport.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30209 - Paediatrics

Project

<a href="/en/project/TL01000394" target="_blank" >TL01000394: Computer-Aided Analysis and Prediction of the Child Growth and Development</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Children-Basel

ISSN

2227-9067

e-ISSN

—

Volume of the periodical

8

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

21

Pages from-to

934-955

UT code for WoS article

000716165700001

EID of the result in the Scopus database

2-s2.0-85117604063