title: Predicting resting energy expenditure in young adults.
abstract: To develop and validate a REE prediction equation for young adults.
  
  
  
  Baseline data from two studies were pooled (N=318; women=52%) and randomly divided into development (n=159) and validation samples (n=159). REE was measured by indirect calorimetry. Stepwise regression was used to develop an equation to predict REE (University of Kansas (KU) equation). The KU equation and 5 additional REE prediction equations used in clinical practice (Mifflin-St. Jeor, Harris-Benedict, Owens, Frankenfield (2 equations)) were evaluated in the validation sample.
  
  
  
  There were no significant differences between predicted and measured REE using the KU equation for either men or women. The Mifflin-St. Jeor equation showed a non-significant mean bias in men; however, mean bias was statistically significant in women. The Harris-Benedict equation significantly over-predicted REE in both men and women. The Owens equation showed a significant mean bias in both men and women. Frankenfield equations #1 and #2 both significantly over-predicted REE in non-obese men and women. We found no significant differences between measured REE and REE predicted by the Frankenfield #2 equations in obese men and women.
  
  
  
  The KU equation, which uses easily assessed characteristics (age, sex, weight) may offer better estimates of REE in young adults compared with the 5 other equations. The KU equation demonstrated adequate prediction accuracy, with approximately equal rates of over and under-prediction. However, enthusiasm for recommending any REE prediction equations evaluated for use in clinical weight management is damped by the highly variable individual prediction error evident with all these equations.
authors.affiliation: University of Kansas Medical Center, Kansas City, KS, USA. Electronic address: ewillis@ku.edu.
authors.forename: Erik A
authors.initials: EA
authors.lastname: Willis
authors.affiliation: Center for Health Outcomes & Prevention Research, Sanford Health, Sioux Falls, SD, USA.
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Herrmann
authors.affiliation: University of Kansas Medical Center, Kansas City, KS, USA.
authors.forename: Lauren T
authors.initials: LT
authors.lastname: Ptomey
authors.affiliation: University of Kansas Medical Center, Kansas City, KS, USA.
authors.forename: Jeffery J
authors.initials: JJ
authors.lastname: Honas
authors.affiliation: University of Kansas Medical Center, Kansas City, KS, USA.
authors.forename: Christopher T
authors.initials: CT
authors.lastname: Bessmer
authors.affiliation: University of Kansas Medical Center, Kansas City, KS, USA.
authors.forename: Joseph E
authors.initials: JE
authors.lastname: Donnelly
authors.affiliation: University of Kansas Medical Center, Kansas City, KS, USA.
authors.forename: Richard A
authors.initials: RA
authors.lastname: Washburn
mesh_terms: D000328:Adult; D000367:Age Factors; D001481:Basal Metabolism; D015982:Bias; D001835:Body Weight; D002153:Calorimetry, Indirect; D004734:Energy Metabolism; D005260:Female; D006801:Humans; D008297:Male; D055641:Mathematical Concepts; D008954:Models, Biological; D009765:Obesity / Q000378:metabolism; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012146:Rest; D012737:Sex Factors; D055815:Young Adult
keywords: Indirect calorimetry; Resting energy expenditure; Weight management; Young adults