Do you want to build muscle? Do you want to lose body fat? Or, do are you satisfied with your current physique and you would like to maintain your weight where it is. Different goals require different calorie intakes. And, to further complicate the matter every person is different. Even if two people are roughly the same age, sex, height, and weight, and they have a similar amount of lean body mass, the could still have very different calorie needs.

No matter if your goal is muscle gain, fat loss, body recomposition, or performance, the information in the article can help you achieve the results you want. Wondering what is TDEE? TDEE stands for Total Daily Energy Expenditure. It is the total number of calories you burn in a given day.

Your TDEE is determined by four key factors:. Basal metabolic rate refers to the number of calories your body burns each day to keep you alive.

BMR does not include physical activity, the process of digestion, or things like walking from one room to another. Basically, BMR is the number of calories your body would expend in a 24 hour period if all you did was lay in bed all day long. This is the absolute bare minimum of calories it takes to ensure your survival.

When we eat food, our body must expend energy to digest the food we eat. This energy expenditure is referred to as the Thermic Effect of Food, and it involves breaking down the protein , carbohydrates, and fat you consume into the individual amino acids, sugars, and fatty acids that are then absorbed and used to by the body to carry out all of its processes including but not limited to building new tissue, synthesizing hormones, producing neurotransmitters, etc.

For example, protein requires more energy to digest than carbohydrates or fat. Non-exercise Activity Thermogenesis NEAT constitutes the number of calories expended during daily movement that is not categorized as structured exercise. NEAT includes activities such as walking the dog, moving from one room to another, or taking the stairs to your office.

NEAT is highly variable from one person to another and can play a rather large or small role in your overall TDEE depending on how physically active your job or daily happenings are. For example, a waitress or construction worker will have a significantly greater NEAT than an office worker who sits at a desk for 8 hours of the day and spends 2 hours commuting to and from work.

Thermic Effect of Activity is the number of calories burned as a result of exercise i. steady-state cardio, resistance training, HIIT, sprints, CrossFit, etc. Similar to NEAT, thermic effect of exercise is highly variable from one person to another or even from one day to another for the same person, as the intensity of training, length of the workout, and training frequency all impact your weekly thermic effect of activity.

Your TDEE is the sum of these four factors, so to put the above parameters into a math equation for simplicity sake, calculating TDEE looks a little something like this:.

When you add all of these numbers together, you get an estimate of the number of calories you need on a daily basis to maintain your current weight. Figuring out your total daily energy expenditure begins with calculating your BMR.

Now, there are a lot of handy calculators readily available on the internet for calculating BMR as well as TDEE. But, the way to truly understand how those fancy calculators work is by understanding the equations powering them.

Researchers have developed a number of models for calculating BMR, and one of the most popular ones is the Harris-Benedict Equation, which takes into account age, height, and weight. Using the Harris-Benedict Equation for men, and plugging the above numbers into the equation gives you:.

So, as a bare minimum to sustain life and ensure longevity, our example male John would need to consume roughly ~ calories. The next step in figuring out TDEE would be to calculate the thermic effect of food as well as the non-exercise and exercise factors.

So, our example guy John needs to consume about calories each day just to maintain his current weight. That is, your actual TDEE could be a little higher or lower than the number you calculate when you use the formula. But, it should be fairly close, and at the very least, it gives you a rough idea of where to start when figuring out a meal plan and setting macronutrient goals.

Once again using John as an example, if he wanted to cut fat, his caloric intake would be:. At a daily calorie deficit, John would lose a little over 1 pound per week, as 1 pound of fat equals approximately calories.

Carbohydrates: The number of calories remaining after protein and fat requirements are met. Therefore, John would consume the following macronutrient profile to lose fat while preserving lean muscle mass:.

Eating at this calorie level should have John losing a little over one pound per week. Now, remember, the TDEE and BMR calculations are estimates.

If however, you find yourself losing more than 2 pounds per week, add calories back into your diet. While it might seem great, losing too much weight too fast typically results in muscle loss as well, which is not what you want in the least. Gaining weight, and preferably muscle, requires consuming more calories than your body expends on a daily basis.

When combined with a structured resistance training program, a caloric surplus provides the essential nutrients needed to optimize performance and build muscle.

For the longest time, it was preached that in order to get big, you had to eat big too. Simply put, the body can synthesize a finite amount of muscle tissue at any given time. This template identifies the fields needed for data entry by the user and inserts factors for the automatic calculation of previously defined energy components.

Factors inserted into the spreadsheet are the EPOC factor of 1. To demonstrate, we use a reference active man of 30 years, 1. After the required data fields are entered, the template spreadsheet automatically calculates the BEE written as follows:.

The next step is the automatic calculation of the impact of each reported physical activity on energy expenditure Δ PAL. This formula, set in the template, is as follows:. After the D PAL is calculated for each physical activity, the physical activity category PAL: sedentary, low active, active, or very active is determined based on the basal activity impact on energy expenditure a factor of 1.

This factor accounts for TEF and postexercise increase in energy expenditure. The PAL is automatically calculated from the sum of D PALi and used to determine the PA based on the following criteria:.

Use of this template is not limited to active individuals; it can also be applied to individuals who have sedentary, low active, or very active lifestyles. The METs entered by the spreadsheet user for each of the physical activities performed will automatically calculate the PAL category necessary to determine the PA and the TEE.

Nor is use of the template limited to Microsoft Excel format. Inherent in the EER predictive equations are some limitations that may affect template spreadsheet calculations and results for PAL. Using either the METs or PAL to calculate energy costs of physical activity may introduce some degree of error.

Likewise, for the PAL there may be differences associated with the increments in energy expenditure associated with physical activity. For most physical activities, these increments are directly proportional to body weight; however, when using the BEE, these increments are proportional to body weight multiplied by 0.

The calculation of the PAL and the resulting PA coefficient is truncated at the very active level and thus does not account for more calories expended and required by exceeding this level of physical activity.

This underestimates energy needs for people who regularly perform vigorous or very vigorous intensity activity for the recommended duration or longer.

The template spreadsheet provides a useful tool and an easy way to calculate TEE and estimated energy requirements for adult men and women based on physical activity level.

Most recently this approach has been used in the MyPyramid Tracker to integrate energy intake from food with energy expenditure from physical activity to determine energy balance status.

Corresponding Author: Shirley Gerrior, Cooperative State Research Education and Extension Service, U. Department of Agriculture, 9th St, SW Waterfront Centre, Rm , Washington, DC Telephone: E-mail: sgerrior csrees. Author Affiliations: WenYen Juan, Peter Basiotis, Center for Nutrition Policy and Promotion, U.

Department of Agriculture, Washington, DC. This table is available as a Microsoft Excel spreadsheet to enable users to calculate their EER based on PAL.

You must have Microsoft Excel to use this table. Download the Excel spreadsheet XLS 17k. The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.

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This is why your estimated kcal needs may vary if you use different equations, apps, or websites. Most prediction equations estimate RMR because it is easier to measure, but some do estimate BMR.

This is important to differentiate because technically RMR and BMR are two different things. Another limitation of prediction equations is that they may not be as accurate for populations historically underrepresented in research.

A study found that common prediction equations overestimated RMR in young Hispanic women 2. Just keep in mind that these equations are guesstimates — not an exact measure of the number of calories that you burn. Some factors can be controlled but others cannot.

If, at any point in your life, you want to gain or lose weight or change your body composition, the first step is to understand factors that influence your resting metabolism. Even though there are many factors that affect RMR, most of them have a very small and temporary effect. The two factors that have the greatest, and potentially long lasting, effect are starvation and loss of lean mass.

To maintain healthy body composition it is important to avoid very low calorie dieting and maintain optimal levels of fat free mass. It is especially important to maintain muscle mass as we age since some of the decline in RMR with age is due to loss of muscle.

This is another reason why strength training is so important. Some people are hesitant to lift weights because they are afraid of getting big bulky muscles.

In fact, it is very difficult for most people to gain big bulky muscles and requires an individual to eat in a calorie surplus and follow a progressive overload strength training program that focuses on hypertrophy. However, people do not need to be concerned that they will automatically bulk up from participating in strength training.

The thermic effect of exercise is the energy required for physical activities. We previously defined physical activity as bodily movement caused by muscle contraction. Energy is required for all muscle contractions, therefore, physical activity increases energy expenditure above basal or resting needs.

Certain factors can increase calorie burning during physical activity, for example, heavier people burn more calories while exercising because they are carrying more weight.

Increasing the intensity, frequency or duration of the activity will also increase calorie burning. As you become more efficient at an activity, calorie burning decreases. This means you need to increase the intensity, frequency, or duration if you want to burn the same number of calories. You are not as efficient as these new sports and will burn calories faster which can help prevent weight gain as you adjust to a new routine.

The normality of data distribution was evaluated using the Shapiro-Wilk or the Kolmogorov-Smirnov tests. The Mann-Whitney U test was used to compare male and female subjects for not normally distributed. Accuracy of the predictive equations at individual and population levels were calculated.

The mean percentage difference between the predicted and measured REE, respectively, was considered a measure of accuracy at group levels.

RMSE was used to better indicate the prediction obtained with this model in our data set. Moreover, statistical analyses were performed by simple linear regression on variables related to the measured REE.

The Bland-Altman plot and analysis was used to compare the REE measured using the indirect calorimeter and calculated using different predictive equations in obese children and adolescents.

Horizontal lines in the Bland-Altman plots of females and males were drawn at the mean difference and at the limits of agreement, which were defined as the mean difference plus and minus 2 times the standard deviation of the differences.

The general characteristics of the participants are presented in Table 2. Females had a significantly higher body fat There was a statistically significant difference in measured REE M: F: Prediction accuracy varied between Prediction accuracy ranged between The comparison of RMSE values of different equations and groups revealed that RMSE was often higher for males than for females.

RMSE was higher in obese individuals compared to the other BMI groups. Moreover, for obese males and females, RMSE was lowest in Müller FFM Figure 1 shows Bland-Altman plots indicating the differences and lower and upper limits of agreement for predicted with different equations and measured REE with indirect calorimetry among individuals with different BMIs.

The Bland-Altman plots indicated that the differences did not show random distribution, and thus were not suitable for application.

On the other hand, the equations showed a relatively random distribution in overweight individuals whereas they were particularly unsuitable for REE estimation in obese populations Fig. Bland-Altman plot of differences in REE measured using the indirect calorimeter and calculated using 14 different predictive equations in children and adolescents with different BMIs.

Dotted lines represent 2 SDs from the mean limits of agreement. continued Bland-Altman plot of differences in REE measured using the indirect calorimeter and calculated using 14 different predictive equations in children and adolescents with different BMIs.

Firstly, REE was accepted as the dependent variable, and all variables presented in Table 2 as independent variables. A univariate regression analysis was performed for each variable. The significant variables were included in the multiple regression analysis model.

In multivariate analysis with backward selection, FFM was the only variable left in the model. The results of multiple regression analysis are presented in Table 6. The equation and results taken from Table 6 are given below.

For female subjects, the lowest RMSE value For male subjects, the highest prediction accuracy In this study, we investigated the accuracy of existing REE estimation equations in children and adolescents 6—18 years with different BMIs and found that the predicted and actual REE values were significantly different and that these equations usually had a high bias.

The difference between predicted and actual REE values and the bias varied according to sex and BMI. To the best of our knowledge, this is the first study of such kind that included Turkish children and adolescents of all BMIs, not only the obese.

Nutritional problems and obesity are common public health problems that affect children and adolescents [ 36 ]. Accurate measurement of REE is essential for nutritional assessment and determining TEE [ 40 ].

TEE is composed of REE, TEF also referred to as diet-induced thermogenesis, DIT , and activity energy expenditure [ 41 ]. Energy expenditure and its specific components can be measured using activity monitors, including direct and indirect calorimetry, isotope dilution mainly the doubly-labeled water technique , 24 h heart rate measurements, and accelerometry, and others [ 42 ].

Indirect calorimetry is the gold standard for estimating REE [ 40 ]. Indirect calorimetry measures energy expenditure, including REE, DIT, and physical activity, based on heat production calculated from the rates of respiratory gas exchange.

However, this method is disadvantageous due to its high cost and impractical nature. Therefore, since the 20th century, researchers have developed equations to estimate REE based on age, sex, race, body weight, height, and BMI [ 38 ].

Predictive equations are used in estimating REE as well as TEE based on the measurements of a large population [ 42 ]. In addition, even though many equations are valid for adults, there are limited data on their validity in predicting energy expenditure REE and TEE in children and adolescents [ 39 , 42 ].

The new prediction equations enable physicians to accurately and sufficiently estimate REE in obese children and adolescents. For validation study, internal validation, not crossover validation, was conducted.

The sample size of the validation study of the new predictive equation was comparable to that of the majority of earlier studies, despite the fact that the study group was not entirely representative of all obese children and adolescents in Turkey [ 39 ]. Harris—Benedict and the Mifflin equations were the most accurate one in predicting RMR among obese and non-obese adults in validation studies [ 18 , 43 ].

In this study, we evaluated the validity of several REE estimation equations and had two major findings: 1 all REE prediction equations had low prediction accuracy and high bias and RMSE, and 2 the differences were non-randomly distributed in the Bland-Altman analysis Tables 3 and 4 ; Fig.

In this study, prediction accuracy was Moreover, prediction accuracy ranged between One study stated that indirect calorimetry is the best option for REE measurement in overweight and obese adolescents and that Molnar equation had the highest accuracy in REE estimation in overweight and obese adolescents aged 12—18 years [ 44 ].

A study on severely obese Canadian adolescents Consistently, a study on black and white children demonstrated that race must be taken into account to accurately estimate REE using gender-specific equations in children [ 46 ].

In this study, the Henry and Molnar equations had the lowest Derumeaux-Burel reported that this equation was sufficient and acceptable for predicting REE in the obese pediatric population by clinicians, but our results did not confirm this proposition, even in obese subjects.

In their validation study, Derumeaux-Burel et al. also indicated that published equations except for the WHO equation contained systematic bias, but our findings also failed to confirm the proposition that the WHO equation does not contain bias [ 35 ].

In this study, RMSE values were higher, i. The literature indicates that sex differences in metabolic physiology and body composition, including body fat mass and FFM, result in differences in REE [ 47 ].

Similar to our results, studies report that even the most accurate equations yield different results for the two genders [ 48 ]. In this study, we also investigated the validity of equations developed specifically for the obese pediatric population, such as the equations described by Tverksya , Schmelzle , Derumeaux-Burel , and Lazzer , and found that their prediction accuracies were low and bias and RMSE values were high compared to the results of normal- and overweight individuals Tables 3 and 4.

Outliers in the Bland-Altman analysis were determined only in obese children and adolescents. As we stated in our study, the new equation seems to be one of the most accurate equations for normal and overweight children and adolescents Fig.

Most equation validation studies focus on obese children and adolescents [ 15 , 38 , 45 ]. Another study of 52 obese Korean children reported that the Molnar, Mifflin-St Jeor, Liu, and Harris-Benedict equations were the most accurate equations [ 15 ].

We ascribe our finding of low accuracy in obese subjects for the equations that were specifically developed for the obese pediatric population to genetic and racial differences.

Since none of the prediction equations were appropriate for the pediatric population, we established a new prediction equation for children and adolescents with different BMIs. Similar to our study, Müller et al. FFM better explains REE than body weight, and body composition is not considered to be particularly important in the pediatric population [ 38 , 49 ].

However, differences in body composition e. For this reason, they often correlate better with REE compared to classical anthropometric measurements such as body weight and height.

This may result in a bias in overweight and obese individuals [ 52 ]. One study suggested that including DXA-derived LBM to REE estimation in boys 11—15 years old and girls 4—10 years old could help prevent systematic error [ 37 ].

However, even the newly developed equation was less accurate in the obese subgroup compared to the other BMI groups. This sheds light on the need to evaluate additional parameters such as advanced segmental analysis in the obese population. As a result, for the estimation of REE is extremely important to determine the most appropriate predictive equation when the direct calorimeter cannot be reached.

However, it is indicated that the REE estimation equations in the clinic are also limited in explaining all individual factors, especially in obese children and adolescents. This study showed that there is a wide variation in the accuracy of predictive equations.

The use of equations in estimating REE in clinical practice is a cheap and fast method, and there is a need for the use of accurate equations.

However, inaccurate REE estimation in clinical intervention may lead to malpractices in medical diet therapy in obese children. For this reason measurement of REE by indirect calorimetry should be preferred, especially in obese children, instead of equations. Larson-Nath C, Goday P. Malnutrition in children with chronic disease.

Nutr Clin Pract. PubMed Google Scholar. Abdullah A. The double burden of undernutrition and overnutrition in developing countries: an update.

Curr Obes Rep. Cheung PC, Cunningham SA, Narayan KV, Kramer MR. Childhood obesity incidence in the United States: a systematic review. Child Obes. PubMed PubMed Central Google Scholar.

De Onis M, Blössner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr. Hohenadel MG, Hollstein T, Thearle M, Reinhardt M, Piaggi P, Salbe AD, et al.

A low resting metabolic rate in late childhood is associated with weight gain in adolescence. CAS PubMed PubMed Central Google Scholar. Lazzer S, Patrizi A, De Col A, Saezza A, Sartorio A. Prediction of basal metabolic rate in obese childreAn and adolescents considering pubertal stages and anthropometric characteristics or body composition.

Eur J Clin Nutr. CAS PubMed Google Scholar. Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc. Siervo M, Bertoli S, Battezzati A, Wells JC, Lara J, Ferraris C, et al.

Accuracy of predictive equations for the measurement of resting energy expenditure in older subjects. Clin Nutr. Hosking J, Metcalf BS, Jeffery AN, Voss LD, Wilkin TJ. Little impact of resting energy expenditure on childhood weight and body composition: a longitudinal study EarlyBird Nutr Res. Gottschlich MM, DeLegge MH, Mattox T, Mueller C, Worthington P.

The ASPEN nutrition support core curriculum: a case-based approach-the adult patient. American Society for Parenteral and Enteral Nutrition Silver Spring, MD; Psota T, Chen K.

Measuring energy expenditure in clinical populations: rewards and challenges. Nielsen S, Hensrud DD, Romanski S, Levine JA, Burguera B, Jensen MD. Body composition and resting energy expenditure in humans: role of fat, fat-free mass and extracellular fluid.

Int J Obes Relat Metab Disord. PMID: Lazzer S, Bedogni G, Lafortuna CL, Marazzi N, Busti C, Galli R, De Col A, Agosti F, Sartorio A. Relationship between basal metabolic rate, gender, age, and body composition in 8, white obese subjects.

Kim M-H, Kim J-H, Kim E-K. Accuracy of predictive equations for resting energy expenditure REE in non-obese and obese korean children and adolescents.

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net …and don't forget to check the FAQ. Your Total Daily Energy Expenditure TDEE is an estimation of how many calories you burn per day when exercise is taken into account.

It is calculated by first figuring out your Basal Metabolic Rate, then multiplying that value by an activity multiplier. Since your BMR represents how many calories your body burns when at rest, it is necessary to adjust the numbers upwards to account for the calories you burn during the day.

As you become more efficient at an activity, calorie burning decreases. This means you need to increase the intensity, frequency, or duration if you want to burn the same number of calories.

You are not as efficient as these new sports and will burn calories faster which can help prevent weight gain as you adjust to a new routine. When it comes to total energy expenditure, the best way to manipulate your energy output to increase, decrease, or maintain weight is by changing your physical activity.

An easy way to estimate calorie burning through physical activity is to use activity factors. This table shows activity factors for various levels of activity.

Once an activity factor is chosen, it can be used to estimate total energy expenditure by multiplying the activity factor by the estimated resting metabolic rate. See Table 3. We will go over some examples of how this is put together in the total energy expenditure section.

Table 3. The energy required for all the enzymatic reactions that take place during food digestion and the absorption and transportation of nutrients is called the thermic effect of food TEF and accounts for about 10 percent of total energy expended per day.

For example, if you eat kcals per day, approximately kcals will be required to digest, absorb, and transport the nutrients in that meal. TEF is the smallest component of energy expenditure so when estimating energy expenditure, emphasis is placed on resting metabolism and physical activity.

As the name implies, total energy expenditure TEE is how much energy your body burns over a 24 hour period. TEE includes both RMR and energy burned for physical activity. When estimating TEE, we simply multiply RMR by an appropriate activity factor.

Because there is a margin of error when estimating RMR and physical activity, we do not factor the thermic effect of food into the TEE calculation. Calculate the total energy expenditure TEE for a lb man with a sedentary lifestyle.

Calculate the total energy expenditure TEE for a lb woman who has an active lifestyle and exercises days a week for an hour. Nutrition and Physical Fitness Copyright © by Angela Harter Alger is licensed under a Creative Commons Attribution 4. Skip to content Most individuals will not have their energy expenditure measured using a metabolic measurement system.

Figure 3. Calculate the RMR for a lb man To convert lb to kg: Divide weight in lb by 2. Kcal burned per hour 68 kg x 0.

To demonstrate, we use a reference active man of 30 years, 1. After the required data fields are entered, the template spreadsheet automatically calculates the BEE written as follows:.

The next step is the automatic calculation of the impact of each reported physical activity on energy expenditure Δ PAL. This formula, set in the template, is as follows:. After the D PAL is calculated for each physical activity, the physical activity category PAL: sedentary, low active, active, or very active is determined based on the basal activity impact on energy expenditure a factor of 1.

This factor accounts for TEF and postexercise increase in energy expenditure. The PAL is automatically calculated from the sum of D PALi and used to determine the PA based on the following criteria:. Use of this template is not limited to active individuals; it can also be applied to individuals who have sedentary, low active, or very active lifestyles.

The METs entered by the spreadsheet user for each of the physical activities performed will automatically calculate the PAL category necessary to determine the PA and the TEE. Nor is use of the template limited to Microsoft Excel format. Inherent in the EER predictive equations are some limitations that may affect template spreadsheet calculations and results for PAL.

Using either the METs or PAL to calculate energy costs of physical activity may introduce some degree of error. Likewise, for the PAL there may be differences associated with the increments in energy expenditure associated with physical activity.

For most physical activities, these increments are directly proportional to body weight; however, when using the BEE, these increments are proportional to body weight multiplied by 0. The calculation of the PAL and the resulting PA coefficient is truncated at the very active level and thus does not account for more calories expended and required by exceeding this level of physical activity.

This underestimates energy needs for people who regularly perform vigorous or very vigorous intensity activity for the recommended duration or longer. The template spreadsheet provides a useful tool and an easy way to calculate TEE and estimated energy requirements for adult men and women based on physical activity level.

Most recently this approach has been used in the MyPyramid Tracker to integrate energy intake from food with energy expenditure from physical activity to determine energy balance status. Corresponding Author: Shirley Gerrior, Cooperative State Research Education and Extension Service, U.

Department of Agriculture, 9th St, SW Waterfront Centre, Rm , Washington, DC Telephone: E-mail: sgerrior csrees. Author Affiliations: WenYen Juan, Peter Basiotis, Center for Nutrition Policy and Promotion, U.

Department of Agriculture, Washington, DC. This table is available as a Microsoft Excel spreadsheet to enable users to calculate their EER based on PAL.

You must have Microsoft Excel to use this table. Download the Excel spreadsheet XLS 17k. The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.

Privacy Policy Accessibility CDC Home Search Health Topics A-Z. View Current Issue Issue Archive Archivo de números en español. Search PCD Emerging Infectious Diseases Journal MMWR.