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Cross Validation of Fat-Free Mass Prediction Models for Elite Female Gymnasts

Bauer, Patricia W., Pivarnik, James M., Fornetti, Willa C., Jallo, Jennifer J., Nassar, Lawrence·2005·Pediatric Exercise Science

Abstract

The purpose of this investigation was to evaluate three bioelectrical impedance analysis (BIA) prediction models for fat-free mass (FFM) using the U.S. National Women’s Gymnastics team (N = 48; age = 15.8 ± 1.8 years). One model had been developed recently using dual-energy x-ray absorptiometry (DEXA) as the criterion measure, whereas the other two used hydrodensitometry. In this investigation, FFM predictions were compared with measures obtained via DEXA. FFM measured by DEXA averaged 40.5 ± 7.4 kg (± SD), whereas values generated using the three BIA models were within 0.8 kg of this actual measure. Validity coefficients for all models were high (Rxy = .95-98). FFM prediction error was lowest with the model using DEXA as the criterion measure (1.3 kg) compared with the other two (1.9 and 2.4 kg). All BIA models underpredicted FFM in the heaviest girls, and the Lohman and Van Loan et al. models overpredicted FFM in the lightest girls. Whereas prediction error was significantly correlated to the girls’ bone mineral density in all BIA models, this relationship was strongest in the two that were developed using hydrodensitometry.

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Metadata

Authors
Bauer, P. W., Pivarnik, J. M., Fornetti, W. C., Jallo, J. J., & Nassar, L.
Venue
Pediatric Exercise Science
Published
2005-11
Volume
17
Issue
4
Pages
337-344
ISSN
  • 0899-8493
  • 1543-2920
Cited by
7
References
0

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CoChat. (2026). Citation check: Cross Validation of Fat-Free Mass Prediction Models for Elite Female Gymnasts. https://app.cochat.ai/papers/10.1123/pes.17.4.337
The paper
Bauer, P. W., Pivarnik, J. M., Fornetti, W. C., Jallo, J. J., & Nassar, L. (2005). Cross Validation of Fat-Free Mass Prediction Models for Elite Female Gymnasts. Pediatric Exercise Science. https://doi.org/10.1123/pes.17.4.337

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.