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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

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Department of Anthropology, Faculty of Science, Masaryk University, 611 37 Brno, Czech Republic
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IT4Innovations Centre of Excellence, Brno University of Technology, 612 00 Brno, Czech Republic
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Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX 78712-1415, USA
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School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA
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Department of Anthropology, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 53-114 Wrocław, Poland
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Outpatient Primary Care Pediatric Center, 625 00 Brno, Czech Republic
*
Author to whom correspondence should be addressed.
Academic Editor: Sari A. Acra
Children 2021, 8(10), 934; https://doi.org/10.3390/children8100934
Received: 16 August 2021 / Revised: 4 October 2021 / Accepted: 13 October 2021 / Published: 18 October 2021
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. View Full-Text
Keywords: human growth; growth modelling; functional data analysis; Sitar human growth; growth modelling; functional data analysis; Sitar
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MDPI and ACS Style

Králík, M.; Klíma, O.; Čuta, M.; Malina, R.M.; Kozieł, S.; Polcerová, L.; Škultétyová, A.; Španěl, M.; Kukla, L.; Zemčík, P. 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. Children 2021, 8, 934. https://doi.org/10.3390/children8100934

AMA Style

Králík M, Klíma O, Čuta M, Malina RM, Kozieł S, Polcerová L, Škultétyová A, Španěl M, Kukla L, Zemčík P. 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. Children. 2021; 8(10):934. https://doi.org/10.3390/children8100934

Chicago/Turabian Style

Králík, Miroslav, Ondřej Klíma, Martin Čuta, Robert M. Malina, Sławomir Kozieł, Lenka Polcerová, Anna Škultétyová, Michal Španěl, Lubomír Kukla, and Pavel Zemčík. 2021. "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" Children 8, no. 10: 934. https://doi.org/10.3390/children8100934

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