Forkulas metabolic rate prediction Metabolic performance formulas and the validity to assess energy deficiency in Post-workout muscle repair supplements athlete population. Merabolic of the best ways to increase resting metabolic perfformance is by increasing Metabolic performance formulas Sports nutrition tips in the Metabllic, which is a Metaoblic that can be controlled. You can use body fat monitors to help determine how much of your body weight is made up of fat compared to muscle, bone, organs, and other tissues. Received: 02 November ; Accepted: 12 January ; Published: 04 February Below, learn more about basal metabolic rate and how it applies to you. Although some other studies applied in athlete populations Thompson and Manore, ; Carlsohn et al. Mayo Clinic: Metabolism and Weight Loss: How you burn calories.

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At a power of 0. The inclusion criteria of the study for athletes were: 1 participation in Turkish National team sports for at least 1 year, 2 high physical activity level according to Total Physical Activity Score TPAS , 3 no history of any metabolic disorders 4 no current injuries or ongoing therapies, and 5 aged between 18 and 25 years.

Athletes were recruited from several sporting disciplines including track and field 4 men; 4 women , long-distance swimming 4 men, 2 women , modern pentathlon 1 men, 4 women , fencing 1 men, 2 women , karate 5 men, 5 women , taekwondo 5 men , boxing 3 men, 3 women , and soccer 2 men, 4 women.

The inclusion criteria for the sedentary subjects were: 1 low physical activity level according to Total Physical Activity Score TPAS , 2 no history of any metabolic disorders, and 3 no current injuries or ongoing therapies.

The participants were examined by a sports medicine specialist prior to commencing the study. Participants who were determined to have any current health issues or chronic disease history were excluded from the study. The International Physical Activity Questionnaire-Short Form IPAQ-SF was applied to the participants to assess their physical activity level Craig et al.

Participants with moderate TPAS scores were also excluded from the study. The study was planned as a cross-sectional design of Turkish national and Olympic athletes and matched sex, age, and BMI sedentary controls.

All procedures of the study were conducted in accordance with the Declaration of Helsinki. All participants were required to visit the performance laboratory once between a. and a.

Both body composition and RMR measurements were performed on the same day. The study was conducted at the Center of Athlete Training and Health Research of the Ministry of Youth and Sports.

IPAQ-SF was used to determine the physical activity levels of the participants Craig et al. The questionnaire consists of seven questions in four parts and has been validated for adults aged 18—69 years.

The questionnaire aims to determine when a participant was physically active in the past 7 days. More specifically, questions were pertaining to the frequency and duration of each physical activity level sitting, walking, moderate- or high-intensity performed in the last 7 days.

The physical activity level is determined by the MET 3. The Total Physical Activity Score TPAS of the participants were calculated using the following equations:. Participants were then divided into three groups according to their TPAS as low, moderate, and high physical activity levels.

According to these groups;. Participants were asked to visit the laboratory in a fasted state at least 4 h , have refrained from caffeine at least 4 h , alcohol at least 2 h , and cigarettes at least 2 h. Further, participants were required to not exercise at a high intensity for at least 24 h prior to the measurements Compher et al.

Additionally, to ensure that all subjects were in an euhydrated state, we asked the sedentary subjects to drink 3. We informed the Olympic athletes about continuing their individual hydration strategies Kenefick, On the morning of the test, we checked the urine specific gravity of all subjects using a semi-automatic reflectance photometry Mission Urine analyzer, United States , and the color of the urine using a urine color scale.

All tests were performed after ensuring that all subjects were in a euhydrated state. All participants except three male athletes and two sedentary women presented adequate hydration status before the measurement. For these three dehydrated athletes, we checked the fluid intake strategies, exercise intensity, and water and fluid consumption throughout the day before the measurement.

We made certain suggestions for regulating the hydration states, then repeated the measurements 3 days after the first measurement day. For dehydrated sedentary women, we found that they did not consume the recommended water and fluid intake.

We stated them to consume 2. Measurements were repeated 3 days after the first measurement day. RMR was measured using Fitmate GS Cosmed, Rome, Italy. We set the environmental characteristics before each measurement in line with the remarkable review by Compher et al.

All RMR measurements were performed in a dimly lit, quiet room with controlled room temperature The oxygen sensor of the Fitmate GS metabolic cart was tested by manufacturer representatives using calibration gases room air and reference O 2 gas before the measurement period in order to verify an optimal machine functioning.

Both calibration gases were run through the metabolic system to check for the drift of the O2 analyzer. The Fitmate GS measured the room air and reference O 2 gases at We performed a maximum of two RMR measurements per day and run manually a flowmeter calibration once per week according to the manufacturer recommendation.

The Fitmate GS metabolic cart also automatically self-calibrated up to 5 min before each test. During the test procedure, subjects were asked to lie in a supine position on a stretcher to rest without falling asleep for 20 min, in a silent, dusk room with an ambient temperature of 20—25°C.

The researchers performed a flowmeter calibration before each measurement. The canopy hood headgear of the Fitmate GS device was wearied to the participants. The Fitmate GS metabolic monitor device does not contain a carbon dioxide sensor. Therefore, it calculates the RMR by estimating CO 2 production from a fixed RQ of 0.

The measured RMR was compared to the predictive RMR calculated by widely used predictive equations, including weight-based [Harris-Benedict Harris and Benedict, age, weight, height , Mifflin Mifflin et al. All the study data were presented as mean ± SD.

The Shapiro-Wilks test was used to determine the normal distribution of data, and the Levene test was used to investigate the homogeneity of the variances. Data were analyzed separately according to sex and physical activity level.

Differences between the groups were investigated with the Independent t -test. The Kruskal-Wallis test was applied when the assumption of normality was not provided. A paired sample t -test was applied to compare the measured RMR and the results of the 12 prediction equations one by one.

The Bland Altman plot was performed to determine mean bias and limits of agreement between measured and predicted RMRs. The intra-class correlation coefficient ICC was calculated to determine the agreement between measured and predicted RMRs. ICC results were interpreted as poor below 0.

A lower RMSE indicates a better performance of the RMR equations in estimating the actual RMR. The chi-square test was performed to compare the percentage of RMR prediction accuracy in participants grouped by gender and physical activity level.

Statistical analyzes were performed using IBM SPSS Statistics, version Descriptive statistics of the participants are presented in Table 1.

Table 2 summarizes the mean and mean differences between the measured RMR and the predictive RMR equations in the athletes. There were no significant differences between the measured RMR and Harris-Benedict, Mifflin age, weight, height , Schofield, De Lorenzo, Johnstone, and Roza prediction equations in male athletes.

The results of the Bland-Altman plot analysis for Harris-Benedict, Mifflin age, weight, height , Schofield, De Lorenzo, Johnstone, and Roza equations in male athletes are presented in Figure 1A. A positive correlation value indicates that the predicted RMR is greater than the measured RMR.

The Bland—Altman plots for these predictive equations compared with the measured RMR showed proportional bias. Figure 1. Bland—Altman plots for IC-RMR and predictive RMR equations for the subjects.

The solid line represents the mean difference BIAS between RMR measured by Fitmate GS and predicted RMR. A Represents Bland-Altman plots of male athletes. B Shows Bland-Altman plots of female athletes. C Represents Bland-Altman plots of sedentary men. Mifflin-a indicates the formulation calculated using age, weight, and height.

Mifflin-b indicates the formulation calculated using fat-free mass. Table 3. Accuracy, overprediction, and underprediction of each of the predictive equations compared to RMR measured by Fitmate GS in participants.

In addition, Mifflin FFM , Owen and Johnstone equations showed a low reliability. Table 2 presents the mean differences between the measured and the predicted RMRs in the sedentary subjects. The main findings rejected our hypothesis, revealing that the predictive RMR equations have a low to moderate absolute agreement with the measured RMR at the individual level due to a wide range of limits of agreement.

Therefore, these equations did not accurately predict RMR in young adult Olympic national team athletes and their counterparts. This is the first study to evaluate the RMR of Olympic young adult national team athletes and their counterparts by comparing estimated RMR equations with those measured by Fitmate GS.

According to the gender of the participants, the measured RMR was higher in athletes than in their sedentary counterparts. Additionally, the FFM results showed that the athletes had a higher FFM compared to the sedentary controls.

It is well-known that FFM is one of the major determinants of RMR Blundell et al. Several studies have documented that FFM shows a greater correlation with RMR compared to fat mass, age, and BMI Johnstone et al.

This correlation was interpreted as the fact that muscles were more metabolically active compared to adipose tissue Leskinen et al. Along with the data, several RMR predictive equations have been developed based on FFM Cunningham, ; Nelson et al.

Therefore, we may suggest that the higher RMR in athletes may be due to athletes having higher FFM values than their sedentary counterparts.

The findings also emphasized that RMR prediction equations validated on sedentary subjects would not be valid for predicting RMR in athletes.

It may be best to first determine the accuracy and validity of the RMR estimation equations before applying them to athletes in practice. A well-designed diet combined with an effective training program is the core component of athletic preparation and, if done correctly, can determine their success in Olympic sports Close et al.

In a situation where adequate energy intake is not provided, various nutrient deficiencies and injuries can occur, and as a result, may negatively impact sport performance and lead to poorer health outcomes Mountjoy et al.

Therefore, an accurate RMR estimation becomes crucial when RMR cannot be measured with indirect calorimetry. However, the ICC results showed a moderate relative agreement 0. These findings suggest that the predictive RMR equations underestimate the high RMR values, and overestimate the low RMR values.

The PAL coefficient ranges from 1. Additionally, it is well defined that the PAL coefficient differs depending on the intensity and duration of the training even among athletes doing the same sport.

For example, average PAL was found to be 1. Conversely, the overestimation of energy requirements can result in a weight gain that will attenuate the performance of a Olympic young adult athlete Thomas et al.

Considering all this data, RMR prediction equations significantly misestimate the RMR in Olympic young adult national team athletes. In the latest ACSM position stands, the Harris-Benedict and Cunningham equations were recommended for estimating RMR in the athletic population Thomas et al.

However, it remains equivocal whether these equations are applicable to all athletic populations. Few studies have evaluated the interaction between measured vs. predicted RMR in athletes with various sports disciplines Thompson and Manore, ; Carlsohn et al. Accordingly, these studies had suggested that certain equations are more suited to specific athletic populations.

For example, the Owen and Mifflin equations for Paralympic track and field athletes Juzwiak et al. Others studies have declared that the current predictive RMR equations underestimate RMR in athletes such as bodybuilders Joseph et al.

For Olympic young adult national team athletes, our findings revealed that none of the RMR prediction equations used in the study predicted RMR accurately. Therefore, a new equation is needed to estimate RMR.

The main reasons for the different results may be due to the difference in the study design, the IC metabolic device, the measurement protocol and the athletic population.

For the measurement protocol, there is no consensus on measuring RMR using the IC. This causes differences in the measurement protocol between studies. For example, studies applied different rest periods before measuring RMR Fullmer et al.

Therefore, a standardized measurement protocol is required to measure RMR in order to accurately compare study results. One possible explanation why the predictive RMR equations do not estimate RMR accurately is that the predictive RMR equations are often developed based on data from different populations.

In these studies, the investigated populations included healthy adults Harris and Benedict, , obese adults Mifflin et al. Therefore, any differences between the participants may be a result of the applied and specifically validated RMR equation, which may be influenced by several other factors, including overall metabolic health status, ethnicity, athletic training history, and developmental age.

Five of the equations we used to predict RMR, including Cunningham, Mifflin, Bernstein, Johnstone, and Nelson, were FFM-based equations. However, these equations were found not to predict RMR accurately. One of the possible reasons is that FFM-based equations were generally validated in non-athletic populations such as normal weight Mifflin et al.

Therefore, these RMR prediction equation may underestimate the actual RMR results due to the reason that FFM is greater in athletes compared to non-athletic populations.

One of the strengths of this study was the inclusion of age- and BMI-matched sedentary controls. This allowed for a more in-depth comparative analysis of the metabolic and physiological characteristics of national and Olympic level athletes with matched controls.

Thus, we were able to emphasize that RMR prediction equations validated in sedentary participants may not be accurate and valid for the athletic population. Another strength of our study is to use of a ventilated canopy hood instead of a face mask. This provided a more comfortable measurement and eliminated the possibility of air leakage from the system.

Face masks, even with many different sizes, sometimes may not fit all faces properly and may cause gas leakage during testing, even with great care before the measurements.

In addition, although individuals are allowed to get used to the mask before the test, it may be disturbing during the measurement process. Since we used only a ventilated canopy hood, we had now chance to compare these two equipments. However, all participants reported that they felt comfortable during the measurement.

In this study, RMR measurements were performed using the Fitmate GS. The measurement of RMR would be more accurate if we would have used other more advanced systems measuring both oxygen consumption and carbon dioxide production.

Additionally, we carefully ran its validation and calibration-related tests before and during the measurement period and controlled all variables e. One limitation of the study is that we had no control group matched by FFM. Due to the differences in training status, we had serious difficulties to find subjects for an FFM-matched control group.

However, this study emphasized that the FFM values of athletes were significantly different from their sedentary counterparts. Therefore, more validated RMR estimation equations are needed in athletic populations, particularly in athletic young adults.

We applied the IPAQ-short form to determine the physical activity level of the participants. Since there is no gold standard criterion for measuring physical activity level Terwee et al. Although the interaction between the IPAQ-SF and objective measures of physical activity in several studies was lower than the acceptable standard Lee et al.

Considering that the individuals participating in our study were included in this age group, the use of the IPAQ-short form is considered as an appropriate and valid method. We used a multi-frequency bioelectrical impedance analyzer to determine body composition.

Although bioelectrical impedance analysis is not a gold standard method to determine body composition assessment, it is a validated, easy-to-use method developed as an alternative to more expensive and invasive gold standard methods such as dual-energy X-ray absorptiometry DXA and magnetic resonance imaging to estimate body composition.

As stated by Verney et al. However, in order for a valid and accurate test, it is crucial to ensure the necessary conditions before the test is performed Kyle et al.

For this reason, before all BIA measurements, we checked that all the necessary conditions for accurate measurement were met e. Additionally, sleep loss could be another factor to alter RMR results De Jonge et al.

Since we did not determine sleep loss or quality, we cannot define if it had an effect on the RMR measurement.

The impact of sleep quality should be investigated in future studies. Due to the small number of participants and the inclusion of athletes from different sports disciplines, we could not perform a regression analysis. Therefore, we could not develop group-specific predictive RMR equations.

However, our current findings highlight the urgent need for future studies on a new predictive RMR equation to accurately measure the RMR of Olympic young adult athletes.

We know that RMR is one of the major components of total energy needs. Although some other studies applied in athlete populations Thompson and Manore, ; Carlsohn et al. Although any of these RMR prediction equations have not been validated on the Olympic young adults, they are widely used in calculating energy needs of athletes due to the lack of indirect calorimetry in all Olympic centers.

Therefore, we sought to investigate the interactions between widely used RMR equations and the IC RMR measurement to determine the accuracy of these RMR predictions and detect the best accurate RMR prediction equation for Olympic young adults.

Therefore, considering the importance of the appropriate determination of energy expenditure, it may not be suitable to use these equations as a component in calculating the total energy needs of Olympic young adult national team athletes.

If possible, it is recommended that RMR in Olympic young adult athletes should be measured by using an IC. Otherwise, further studies should be applied in a larger cohort of Olympic young adult national team athletes to develop a group specific RMR prediction equation. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The ethics committee approval was obtained from the University Ethics Committee AB, EB, AY, BA, TK, and AH contributed to the study design and conception.

EB, BA, TK, and AH completed the data acquisition. AY completed the data analysis. AB, AD-L, EB, and BK completed the writing-original draft preparation. AB, EB, AY, AD-L, BA, TK, AH, LH, TR, and BK completed the review of the final manuscript. All authors contributed to the article and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Bernstein, R. Prediction of the resting metabolic rate in obese patients. doi: PubMed Abstract CrossRef Full Text Google Scholar.

Black, K. Low energy availability, plasma lipids, and hormonal profiles of recreational athletes. Strength Cond. Blundell, J. Body composition and appetite: fat-free mass but not fat mass or BMI is positively associated with self-determined meal size and daily energy intake in humans.

Carlsohn, A.

However, it Herbal adaptogen remedies gormulas whether these predictive RMR equations accurately predict REE in Metabolic performance formulas athletic populations. A total of 97 Powerful antifungal herbs, 49 athletes 24 formulass, 25 males cormulas, and 48 sedentary 28 females, 20 malesformuals Herbal adaptogen remedies from Vormulas National Metabolic performance formulas Teams at the Ministry of Youth and Sports. RMR was measured using a Fitmate GS Cosmed, Italy. The results of each 12 prediction formulas were compared with the measured RMR using paired t -test. The Bland-Altman plot was performed to determine the mean bias and limits of agreement between measured and predicted RMRs. Stratification according to sex, the measured RMR was greater in athletes compared to controls. The closest equation to the RMR measured by Fitmate GS was the Harris-Benedict equation in male athletes mean difference Weight Loss. What Perfomrance the most Metabolic performance formulas formklas to calculate caloric expenditure from physical activity? Well, ;erformance you are in a research lab and have access to a metabolic cart, you will never get an exact value. However, we are in luck! Free weight loss mini course and calculator tool! Metabolic equivalents go hand-in-hand with weight loss.

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