Resting Energy Expenditure For Losing Weight While Pregnancy
REE or RMR is the amount of energy used by the body in a wakeful state while resting. If one were to lie in bed for a 24-hour period that included periods of wakefulness, REE would be the total amount of energy they would expend. Components of REE include blood circulation throughout the body, respiration, body temperature regulation, central nervous system functioning, and other physiological functions. Liver, brain, heart, and kidney tissues use up most of the REE since these are the most metabolically active tissues. Because REE, or RMR, measures energy expenditure in a wakeful and alert state, REE is about 10 percent higher than BEE, or BMR.
REE comprises 60 to 75 percent of TEE, though the actual amount varies within and between individuals. Body composition, body mass, sex, age, height, genetics, hormonal factors, nutritional status, environmental conditions, cigarette, and caffeine use all effect REE. REE can vary within an individual from day to day based on the demands placed upon the body. Some individuals may wish to increase REE to help with weight loss efforts, and while some determinants of REE cannot be altered, there are some modifiable factors. Body composition is one modifiable determinant of RMR. Individuals with greater LBM will have a higher REE because LBM is more metabolically active than FM. That is, LBM uses more energy even when at rest. In fact, even at rest, muscle tissue requires about three times as many calories as fat tissue.
Nutritional status has the potential to alter REE as well. When energy intake is drastically reduced, in efforts to lose weight or during times of illness, the body responds by going into a state of adaptive thermogenesis, commonly known as starvation mode. This process is an evolutionary adaptation by the body to become more efficient with what energy it receives by depressing metabolic rate. The function of this metabolic efficiency is to attempt to attenuate the loss of muscle and FM. Since the body does not know how long the starvation period will last, this response happens quickly. Historically, during periods of famine, energy efficiency was advantageous for survival; however, for the individual looking to lose weight, decreased metabolic rate works against weight loss efforts. In fact, severe caloric restriction has been shown to lower RMR by as much as 18 to 25 percent (Onur et al. 2005; Russell et al. 2001). Depressed metabolic rate will be discussed in more detail in Chapter 8, which addresses weight management.
Some determinants of REE, such as sex and age, cannot be modified. Men have a higher REE than women, primarily because men have a larger amount of lean muscle mass. REE also declines as an individual ages (about 1 to 2 percent per decade). This decrease is partly a result of loss of muscle mass due to lower activity levels, as well as recent research indicating there is an age-dependent increase in markers related to muscle protein breakdown (Tanner et al. 2015). This decline can be mitigated by engaging in resistance exercise throughout the life cycle. Other factors that are somewhat modifiable, although not always practical, such as colder temperatures, higher altitudes, caffeine intake, and smoking, can temporarily increase REE, though their effect is minimal and does not have a lasting effect.
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Measuring Resting Energy Expenditure. Indirect calorimetry. As previously described, measuring REE using indirect calorimetry can provide a reasonably accurate measurement if the correct procedures are followed; however, this method is not always feasible due to either the expenses or access, or both, to the necessary technology. In these cases, other estimates, such as predictive equations, can be used to estimate REE.
Predictive equations. In the absence of an indirect calorimeter, predictive equations can be used to estimate REE. Various characteristics of individuals, including weight, height, sex, age, are included in an equation used to calculate an estimated REE. Predictive equations must be considered a loose estimate because only a few selected factors that affect REE are accounted for. Furthermore, these equations are population specific; that is, they will be most valid when used within the population from which they have been validated. Measurement accuracy will decrease when a specific equation is used on an individual from a different population. Unfortunately, there are insufficient research studies to provide valid equations for all existing populations.
For the general public, there are many equations available to estimate REE. The four most common are the Mifflin St. Jeor equation, the Harris Benedict equation, the Owen equation, and the World Health Organization/Food and Agriculture Organization/United Nations University (WHO/FAO/UNU) equations (see Table 2.1). In a study that compared these four equations to a criterion measurement, a hand-held metabolic calorimeter, it was shown that the Harris Benedict equation best predicted REE across all BMI groups, and that the Mifflin St. Jeor equation best predicted REE among obese populations (Hasson et al. 2011). However, these equations may be less accurate when applied to athletes because they do not take body composition into consideration. Specifically, fat free mass (FFM) including muscle tissue is more metabol-ically active than FM and increases REE (Hall et al. 2012). It is assumed that athletes have a greater percentage of FFM at a given weight, and thus it is expected that they would have a higher REE compared to an individual of the same weight but with less FFM. In fact, research has shown that the amount of FFM an individual has is one of the major determinants of REE regardless of body weight and size (Oshima et al. 2011; Taguchi et al. 2011). Given the importance of FFM as it relates to REE, it is recommended the Cunningham equation be used with athletes because of its inclusion of this component (see Table 2.1). In order to use the Cunningham equation, the amount of FFM must be known. This can be achieved with body composition assessments such as underwater weighing and air displacement plethysmography (the BodPod). These and other body composition assessments will be discussed in Chapter 5.
Note: The Harris-Benedict equation, Mifflin-St. Jeor equation, Owen equation, and WHO/FAO/UNU are four equations that can be used to estimate RMR.
While indirect calorimetry produces the most accurate measurements of REE, technology and expense can limit an athlete’s access to this method. Instead, predictive equations can be used. These calculations are less accurate but may be more practical in some weight loss settings. Many equations are available; however, due to its inclusion of FFM, using the Cunningham is preferred within an athletic population.
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