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The human body gets energy from the three macronutrients: carbohydrates, proteins, and fats. Energy is released when the chemical bonds in the organic compounds present in the food are broken down. The energy content of food is measured in kilocalories (kcal), defined as the amount of heat required to raise the temperature of one kilogram of water by one degree Celsius. This value is determined by measuring the temperature change of the water surrounding a calorimeter after the complete...
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Anyone who has used a microwave oven knows there is energy in electromagnetic waves. Sometimes, this energy is obvious, such as in the summer sun's warmth. At other times, it is subtle, such as the unfelt energy of gamma rays, which can destroy living cells. Electromagnetic waves bring energy into a system through their electric and magnetic fields. These fields can exert forces and move charges in the system and, thus, do work on them. However, there is energy in an electromagnetic wave,...
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Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area.
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Organisms must balance energy intake with the energy required for growth, maintenance and reproduction. These trade-offs result in a variety of survivorship and reproductive strategies, including semelparity and iteroparity. Semelparous species, like annual plants, have only one reproductive episode in their lifetimes and consequently have short lifespans. Iteroparous species, by contrast, have many reproductive events during their lifetimes but have relatively few offspring. These two...
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Methods for calculating dietary energy density in a nationally representative sample.

Jacqueline A Vernarelli1, Diane C Mitchell1, Barbara J Rolls1

  • 1The Pennsylvania State University, Department of Nutritional Sciences, University Park, PA 16802 USA.

Procedia Food Science
|January 17, 2014
PubMed
Summary

Calculating dietary energy density (ED) is complex due to varying methods. This study compares ED calculation approaches using NHANES data to inform a standardized method for consistent dietary research.

Keywords:
National Health and Nutrition Examination Survey, NHANESUSDAWhat We Eat in America, WWEIAenergy density

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Area of Science:

  • Nutrition Science
  • Public Health
  • Dietary Assessment

Background:

  • Dietary energy density (ED) is increasingly linked to health outcomes.
  • Low ED diets are recommended for disease prevention but practical application is challenging.
  • Lack of a standardized method for calculating ED complicates research and recommendations.

Purpose of the Study:

  • To compare and contrast different methods for calculating dietary ED.
  • To provide data informing the selection of a standardized ED calculation method.
  • To enable consistent and reliable dietary ED research.

Main Methods:

  • Utilized 2005-2008 National Health and Nutrition Examination Survey (NHANES) data.
  • Evaluated all consumed items, classifying them as foods or beverages based on USDA codes and consumption context.
  • Calculated mean EDs using different methods, stratified by demographic factors.

Main Results:

  • Demonstrated variability in ED calculations based on methodology.
  • Highlighted the importance of defining foods versus beverages for accurate ED assessment.
  • Presented mean EDs across different calculation methods and population subgroups.

Conclusions:

  • A standardized method for calculating dietary ED can be derived using USDA food codes and NHANES data.
  • This standardized approach will enhance consistency and comparability across dietary studies.
  • The proposed method can be adopted by researchers for future dietary ED analyses.