Project Title

Comparison of Predicted and Measured Resting Metabolic Rate Methods Among CrossFit-Trained Athletes.

Presenters

Faculty Sponsor Name

Yuri Feito

Additional Faculty

Trisha A. VanDusseldorp, Exercise Science, tvanduss@kennesaw.edu Gerald T. Mangine, Exercise Science, gmangine@kennesaw.edu Tiffany A. Esmat, Exercise Science, tesmat@kennesaw.edu

Abstract (300 words maximum)

ABSTRACT: The use of prediction equations and machinery estimation for the assessment of resting metabolic rate (RMR) has grown in popularity. While RMR is crucial when assessing dietary intakes, the accuracy may be dependent on the distinct characteristics of the individual.

PURPOSE: To compare RMR assessed by indirect calorimetry with estimates obtained from three predictive equations for a group of advanced CrossFit-trained athletes.

METHODS: RMR was estimated for six-experienced CrossFit-trained athletes [3 men (27.5 ± 6.5 yrs.; 87.5 ± 5.9 kg; 179.2 ± 2.2 cm), and 3 women (27.7 ± 1.5 yrs.; 67.8 ± 3.3 kg; 168.1 ± 5.3 cm)] using the ParvoMedics 2400 metabolic system (PV) following established protocols. Additionally, RMR was calculated using the Harris-Benedict (HB), Mifflin-St. Jeor (ME) and Nelson (NE) prediction equations. All data is presented as mean ± standard deviation (M ± SD).

RESULTS: Repeated measures analysis of variance revealed significant differences among the four models (F(3)= 7.1, p = 0.003, η2 =0.59), where a greater (p = 0.01) predicted RMR was observed in ME (1646 ± 241 Kcals) was lower compared to HB (1733 ± 271 Kcals, p = 0.01) and ME & NE (1839 ± 322, p = 0.04). No differences were observed between the equations and PV. Moderate intra-class correlations were found PV and HB (ICC = 0.63, 95%CI = -0.10 - 0.94), ME (ICC = 0.52, 95% CI = -0.14-0.91), and NE (ICC = 0.73, 95%CI = 0.07 - 0.96).

CONCLUSION: These results suggest that even though significant differences exist between each of the predictive equations, individually, each equation has good agreement with the values measured by indirect colorimetry.

Project Type

Poster

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Comparison of Predicted and Measured Resting Metabolic Rate Methods Among CrossFit-Trained Athletes.

ABSTRACT: The use of prediction equations and machinery estimation for the assessment of resting metabolic rate (RMR) has grown in popularity. While RMR is crucial when assessing dietary intakes, the accuracy may be dependent on the distinct characteristics of the individual.

PURPOSE: To compare RMR assessed by indirect calorimetry with estimates obtained from three predictive equations for a group of advanced CrossFit-trained athletes.

METHODS: RMR was estimated for six-experienced CrossFit-trained athletes [3 men (27.5 ± 6.5 yrs.; 87.5 ± 5.9 kg; 179.2 ± 2.2 cm), and 3 women (27.7 ± 1.5 yrs.; 67.8 ± 3.3 kg; 168.1 ± 5.3 cm)] using the ParvoMedics 2400 metabolic system (PV) following established protocols. Additionally, RMR was calculated using the Harris-Benedict (HB), Mifflin-St. Jeor (ME) and Nelson (NE) prediction equations. All data is presented as mean ± standard deviation (M ± SD).

RESULTS: Repeated measures analysis of variance revealed significant differences among the four models (F(3)= 7.1, p = 0.003, η2 =0.59), where a greater (p = 0.01) predicted RMR was observed in ME (1646 ± 241 Kcals) was lower compared to HB (1733 ± 271 Kcals, p = 0.01) and ME & NE (1839 ± 322, p = 0.04). No differences were observed between the equations and PV. Moderate intra-class correlations were found PV and HB (ICC = 0.63, 95%CI = -0.10 - 0.94), ME (ICC = 0.52, 95% CI = -0.14-0.91), and NE (ICC = 0.73, 95%CI = 0.07 - 0.96).

CONCLUSION: These results suggest that even though significant differences exist between each of the predictive equations, individually, each equation has good agreement with the values measured by indirect colorimetry.