Literature DB >> 19932866

Variability in results from predicted resting energy needs as compared to measured resting energy expenditure in Korean children.

Jeannine C Lawrence1, Hyun-Mi Lee, Jae-Hee Kim, Eun-Kyung Kim.   

Abstract

Energy needs are influenced by many factors, including ethnicity. Multiple studies have shown that the accuracy of an energy prediction equation varies with the ethnic background of the study population. Therefore, it is crucial to identify the most accurate energy prediction equation to use for a given population. This study compared measured resting energy expenditure to results from commonly-used energy prediction equations to identify the most accurate equation to use for Korean children. Based on previous literature showing wide variation in accuracy of energy prediction equations in different ethnic groups, we hypothesized that results from measured- vs. predicted energy needs would be significantly different in this population. Subjects were 92 South Korean children (38 boys, 54 girls) age 7.7 +/- 2.7 years (mean +/- SD). Measurements included: resting metabolic rate (TrueOne 2400 metabolic cart), weight/height (digital scale/stadiometer); body fat (BIA, Inbody720), blood pressure (sphingomanometer), triceps skinfold thickness (MD-500 skinfold calipers), muscle mass (Heymsfield's formula) and body surface area (Dubois formula) calculations. Resting energy needs were predicted using the Harris-Benedict, WHO/NAO/FAO, Altman and Dittmer, Maffeis, and Schofield-HW equations, and the Dietary Reference Intake recommendations. Measured and predicted energy needs were significantly correlated (P < .001 for all; range R(2) = 0.54-0.56), yet significantly different for all equations studied (P < .05) except the Maffeis and Schofield-HW equations. Differences (means +/- SD) between measured vs. predicted energy needs ranged from 9.5 +/- 123.2 (Schofield-HW) to 199.6 +/- 132.7 (WHO/NAO/FAO) kcal/day, where a value closer to zero indicates increased accuracy of the prediction equation to correspond to measured energy needs. Although results from equations studied were significantly correlated with measured resting energy needs, notable discrepancies existed which, over time, could produce undesirable weight changes in Korean children.

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Year:  2009        PMID: 19932866     DOI: 10.1016/j.nutres.2009.10.017

Source DB:  PubMed          Journal:  Nutr Res        ISSN: 0271-5317            Impact factor:   3.315


  6 in total

1.  Accuracy of predictive equations for resting energy expenditure (REE) in non-obese and obese Korean children and adolescents.

Authors:  Myung-Hee Kim; Jae-Hee Kim; Eun-Kyung Kim
Journal:  Nutr Res Pract       Date:  2012-02-29       Impact factor: 1.926

2.  Quantifying energy expenditure in childhood: utility in managing pediatric metabolic disorders.

Authors:  Laura P E Watson; Katherine S Carr; Michelle C Venables; Carlo L Acerini; Greta Lyons; Carla Moran; Peter R Murgatroyd; Krishna Chatterjee
Journal:  Am J Clin Nutr       Date:  2019-11-01       Impact factor: 7.045

3.  Resting Energy Expenditure Prediction Equations in the Pediatric Population: A Systematic Review.

Authors:  Jimena Fuentes-Servín; Azalia Avila-Nava; Luis E González-Salazar; Oscar A Pérez-González; María Del Carmen Servín-Rodas; Aurora E Serralde-Zuñiga; Isabel Medina-Vera; Martha Guevara-Cruz
Journal:  Front Pediatr       Date:  2021-12-06       Impact factor: 3.418

4.  Acute-Phase Stroke Outcome and Lipids.

Authors:  Osman Serhat Tokgoz; Figen Guney; Ahmet Kaya; Ahmet Bugrul; Esra Eruyar; Huseyin Buyukgol; Abdullah Seyithanoglu; Mehmet Sinan Iyisoy
Journal:  Sisli Etfal Hastan Tip Bul       Date:  2021-12-29

5.  External Validation of Equations to Estimate Resting Energy Expenditure in Critically Ill Children and Adolescents with and without Malnutrition: A Cross-Sectional Study.

Authors:  George Briassoulis; Efrossini Briassouli; Stavroula Ilia; Panagiotis Briassoulis
Journal:  Nutrients       Date:  2022-10-06       Impact factor: 6.706

6.  Accuracy of predictive equations for resting metabolic rate in Korean athletic and non-athletic adolescents.

Authors:  Jae-Hee Kim; Myung-Hee Kim; Gwi-Sun Kim; Ji-Sun Park; Eun-Kyung Kim
Journal:  Nutr Res Pract       Date:  2015-05-22       Impact factor: 1.926

  6 in total

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