scholarly journals Development of a New Equation for the Prediction of Resting Metabolic Rate in Sri Lankan Adults

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Pathima Fairoosa ◽  
Pulani Lanerolle ◽  
Maduka De Lanerolle-Dias ◽  
V. Pujitha Wickramasinghe ◽  
Indu Waidyatilaka
Keyword(s):  
Metabolic Rate ◽  
Indirect Calorimetry ◽  
Cross Validation ◽  
Resting Metabolic Rate ◽  
Prediction Equation ◽  
Sri Lankan ◽  
Study Objective ◽  
Individual Measurement ◽  
Independent Variables ◽  
New Equation

Resting metabolic rate (RMR) is the key determinant of the energy requirement of an individual. Measurement of RMR by indirect calorimetry is not feasible in field settings and therefore equation-based calculations are used. Since a valid equation is not available for Sri Lankans, it is important to develop a new population-specific equation for field use. The study objective was to develop a new equation for the prediction of RMR in healthy Sri Lankans using a reference method, indirect calorimetry. RMR data were collected from fifty-seven (male 27) adults aged 19 to 60 years. They were randomly assigned to validation (n = 28) and cross-validation (n = 19) groups using the statistical package R (version 3.6.3). Height, weight, and RMR were measured. Multivariable fractional polynomials (MFP) were used to determine explanatory variables and their functional forms for the model. A variable shrinkage method was used to find the best fit predictor coefficients of the equation. The developed equation was cross-validated on an independent group. Weight and sex code (male = 1; female = 0) were identified as reliable independent variables. The new equation developed was RMR (kcal/day) = 284.5 + (13.2 x weight) + (133.0 x sex code). Independent variables of the prediction equation were able to predict 88.5% of the variance. Root mean square error (RMSE) of the prediction equation in validation and cross-validation was 88.11 kcal/day and 79.03 kcal/day, respectively. The equation developed in this study is suitable for predicting RMR in Sri Lankan adults.

Validation of Equations for the Prediction of Resting Metabolic Rate in Sri Lankan Adults

2019 ◽  
Vol 9 (10) ◽  
pp. p9462
Author(s):  
Pathima Fairoosa ◽  
Indu Waidyatilaka ◽  
Maduka de Lanerolle-Dias ◽  
Pujitha Wickramasinghe ◽  
Pulani Lanerolle
Keyword(s):  
Metabolic Rate ◽  
Resting Metabolic Rate ◽  
Sri Lankan

A Generalized Equation for Prediction of VO2peak from a Step Test

2020 ◽  
Author(s):  
Corey A. Selland ◽  
Joshua Kelly ◽  
Kathleen Gums ◽  
Jessica R. Meendering ◽  
Matt Vukovich
Keyword(s):  
Cross Validation ◽  
Prediction Equation ◽  
Step Test ◽  
Abdominal Circumference ◽  
Stepwise Multiple Regression ◽  
Forward Stepwise ◽  
Test Equation ◽  
New Equation ◽  
Pearson Correlations ◽  
Development Group

AbstractThis study aimed to develop an equation to reduce variability of VO2peak prediction from a step test and compare VO2peak prediction from the new equation to the Queen’s College Step Test (QCST). The development group (n=86; 21.7±2 years) was utilized to develop the SDState step test equation to predict relative VO2peak. The cross-validation group (n=99; 21.6±2 years) was used to determine the validity of the SDState step test VO2peak prediction equation. A regression analysis was used to identify the best model to predict VO2peak. Analysis of variance (ANOVA) was further used to determine differences among predicted and measured VO2peak values. Forward stepwise multiple regression identified age, sex, abdominal circumference, and active heart rate at the 3-min mark of the step test to be significant predictors of VO2peak (mL·kg−1·min−1). No differences among measured VO2peak (47.3±7.1 mL·kg−1·min−1) and predicted VO2peak (QCST, 46.9±9.3 mL·kg−1·min−1; SDState 48.3±5.7 mL·kg−1·min−1) were found. Pearson correlations, ICC, SEE, TEE, Bland-Altman plots, and Mountain plots indicate the SDState step test equation provides less variation in the prediction of VO2peak compared to the QCST. The SDState step test equation is effective for predicting VO2peak from the YMCA step test in young, healthy adults.

Percent relative cumulative frequency analysis in indirect calorimetry: application to studies of transgenic mice

2004 ◽  
Vol 82 (12) ◽  
pp. 1075-1083 ◽  
Author(s):  
Marc Riachi ◽  
Jean Himms-Hagen ◽  
Mary-Ellen Harper
Keyword(s):  
Oxygen Consumption ◽  
Energy Expenditure ◽  
Metabolic Rate ◽  
Indirect Calorimetry ◽  
Uncoupling Protein ◽  
Resting Metabolic Rate ◽  
Large Data ◽  
Cumulative Frequency ◽  
And Control ◽  
Deficient Mice

Indirect calorimetry is commonly used in research and clinical settings to assess characteristics of energy expenditure. Respiration chambers in indirect calorimetry allow measurements over long periods of time (e.g., hours to days) and thus the collection of large sets of data. Current methods of data analysis usually involve the extraction of only a selected small proportion of data, most commonly the data that reflects resting metabolic rate. Here, we describe a simple quantitative approach for the analysis of large data sets that is capable of detecting small differences in energy metabolism. We refer to it as the percent relative cumulative frequency (PRCF) approach and have applied it to the study of uncoupling protein-1 (UCP1) deficient and control mice. The approach involves sorting data in ascending order, calculating their cumulative frequency, and expressing the frequencies in the form of percentile curves. Results demonstrate the sensitivity of the PRCF approach for analyses of oxygen consumption ([Formula: see text]02) as well as respiratory exchange ratio data. Statistical comparisons of PRCF curves are based on the 50th percentile values and curve slopes (H values). The application of the PRCF approach revealed that energy expenditure in UCP1-deficient mice housed and studied at room temperature (24 °C) is on average 10% lower (p < 0.0001) than in littermate controls. The gradual acclimation of mice to 12 °C caused a near-doubling of [Formula: see text] in both UCP1-deficient and control mice. At this lower environmental temperature, there were no differences in [Formula: see text] between groups. The latter is likely due to augmented shivering thermogenesis in UCP1-deficient mice compared with controls. With the increased availability of murine models of metabolic disease, indirect calorimetry is increasingly used, and the PRCF approach provides a novel and powerful means for data analysis.Key words: thermogenesis, oxygen consumption, metabolic rate, uncoupling protein, UCP.

scholarly journals Indirect Calorimetry Protocol Development for Measuring Resting Metabolic Rate as a Component of Total Energy Expenditure in Free-Living Postmenopausal Women

2001 ◽  
Vol 131 (8) ◽  
pp. 2215-2218 ◽  
Author(s):  
Neilann K. Horner ◽  
Johanna W. Lampe ◽  
Ruth E. Patterson ◽  
Marian L. Neuhouser ◽  
Shirley A. Beresford ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Metabolic Rate ◽  
Total Energy ◽  
Postmenopausal Women ◽  
Indirect Calorimetry ◽  
Resting Metabolic Rate ◽  
Total Energy Expenditure ◽  
Free Living ◽  
Protocol Development

scholarly journals Resting Metabolic Rate in Elite Rowers and Canoeists: Difference between Indirect Calorimetry and Prediction

2011 ◽  
Vol 58 (3) ◽  
pp. 239-244 ◽  
Author(s):  
Anja Carlsohn ◽  
Friederike Scharhag-Rosenberger ◽  
Michael Cassel ◽  
Frank Mayer
Keyword(s):  
Metabolic Rate ◽  
Indirect Calorimetry ◽  
Resting Metabolic Rate ◽  
Elite Rowers

scholarly journals The Energy Expenditure of Free-Living Physical Activities in Primary Schoolchildren

2016 ◽  
Vol 13 (s1) ◽  
pp. S57-S61 ◽  
Author(s):  
Alison L. Innerd ◽  
Liane B. Azevedo
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Metabolic Rate ◽  
Indirect Calorimetry ◽  
Resting Metabolic Rate ◽  
Physical Activities ◽  
Free Living ◽  
Metabolic Equivalent ◽  
Primary Schoolchildren ◽  
Vigorous Intensity

Background:The aim of this study is to establish the energy expenditure (EE) of a range of child-relevant activities and to compare different methods of estimating activity MET.Methods:27 children (17 boys) aged 9 to 11 years participated. Participants were randomly assigned to 1 of 2 routines of 6 activities ranging from sedentary to vigorous intensity. Indirect calorimetry was used to estimate resting and physical activity EE. Activity metabolic equivalent (MET) was determined using individual resting metabolic rate (RMR), the Harrell-MET and the Schofield equation.Results:Activity EE ranges from 123.7± 35.7 J/min/Kg (playing cards) to 823.1 ± 177.8 J/min/kg (basketball). Individual RMR, the Harrell-MET and the Schofield equation MET prediction were relatively similar at light and moderate but not at vigorous intensity. Schofield equation provided a better comparison with the Compendium of Energy Expenditure for Youth.Conclusion:This information might be advantageous to support the development of a new Compendium of Energy Expenditure for Youth.

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