Resting Metabolic Rate Measurements Research Articles

Effects of supplemental fish oil on resting metabolic rate, body composition, and salivary cortisol in healthy adults

BackgroundTo determine the effects of supplemental fish oil (FO) on resting metabolic rate (RMR), body composition, and cortisol production in healthy adults.MethodsA total of 44 men and women (34 ± 13y, mean+SD) participated in the study. All testing was performed first thing in the morning following an overnight fast. Baseline measurements of RMR were measured using indirect calorimetry using a facemask, and body composition was measured using air displacement plethysmography. Saliva was collected via passive drool and analyzed for cortisol concentration using ELISA. Following baseline testing, subjects were randomly assigned in a double blind manner to one of two groups: 4 g/d of Safflower Oil (SO); or 4 g/d of FO supplying 1,600 mg/d eicosapentaenoic acid (EPA) and 800 mg/d docosahexaenoic acid (DHA). All tests were repeated following 6 wk of treatment. Pre to post differences were analyzed using a treatment X time repeated measures ANOVA, and correlations were analyzed using Pearson's r.ResultsCompared to the SO group, there was a significant increase in fat free mass following treatment with FO (FO = +0.5 ± 0.5 kg, SO = -0.1 ± 1.2 kg, p = 0.03), a significant reduction in fat mass (FO = -0.5 ± 1.3 kg, SO = +0.2 ± 1.2 kg, p = 0.04), and a tendency for a decrease in body fat percentage (FO = -0.4 ± 1.3% body fat, SO = +0. 3 ± 1.5% body fat, p = 0.08). No significant differences were observed for body mass (FO = 0.0 ± 0.9 kg, SO = +0.2 ± 0.8 kg), RMR (FO = +17 ± 260 kcal, SO = -62 ± 184 kcal) or respiratory exchange ratio (FO = -0.02 ± 0.09, SO = +0.02 ± 0.05). There was a tendency for salivary cortisol to decrease in the FO group (FO = -0.064 ± 0.142 μg/dL, SO = +0.016 ± 0.272 μg/dL, p = 0.11). There was a significant correlation in the FO group between change in cortisol and change in fat free mass (r = -0.504, p = 0.02) and fat mass (r = 0.661, p = 0.001).Conclusion6 wk of supplementation with FO significantly increased lean mass and decreased fat mass. These changes were significantly correlated with a reduction in salivary cortisol following FO treatment.

Acute Effects of A Liquid Dietary Supplement, TAIslimTM Containing A Combination of Indigestible Soluble Dietary Fiber, Phenylalanine, N‐Acetyl‐Tyrosine, Caffeine, Tea Polyphenols and Lycium barbarum on Resting Metabolic Rate: A Randomized, Double‐Blind, Placebo‐Controlled Study

TAIslim is a blend of soluble indigestible fiber, phenylalanine, N‐acetyl‐L‐tyrosine, caffeine, standardized tea polyphenols and Lycium barbarum; some of which are known to improve energy balance. The objective of this randomized, double‐blind, placebo‐controlled clinical study was to determine whether consumption of TAIslim (120 ml/d) for 14 d would have an impact on resting metabolic rate (RMR), anthropometric parameters and subjective feelings of appetite and gastrointestinal conditions. Measurements were taken at baseline and endpoint. A total of 32 healthy adults (age=33.2 y, BMI=27.8 kg/m2) were enrolled. A nutritional beverage (360 kcal) was provided at the completion of the 1st RMR measurement. RMR in a group consuming TAIslim (n=11), a half dose of TAIslim (n=9) and placebo (n=12) was increased by 255, 200 and 150 kcal, about 14%, 11% and 8% of baseline (1,823 kcal), respectively. TAIslim increased RMR to a greater extent than placebo (P < 0.05). Waist circumference and feeling of appetite tended to decrease in the TAIslim group whereas there was no significant difference in the other groups. Other anthropometric parameters and gastrointestinal conditions were not different between groups or between pre‐ and post‐intervention for either group. These results suggest that TAIslim may affect waist circumference by enhancing energy expenditure. A longer term study may clarify potential of body weight control.

Energy metabolism and animal personality

In this paper we show how animal personality could explain some of the large inter‐individual variation in resting metabolic rate (MR) and explore methodological and functional linkages between personality and energetics. Personality will introduce variability in resting MR measures because individuals consistently differ in their stress response, exploration or activity levels, all of which influence MR measurements made with respirometry and the doubly‐labelled water technique. Physiologists try to exclude these behavioural influences from resting MR measurements, but animal personality research indicates that these attempts are unlikely to be successful. For example, because reactive animals “freeze” when submitted to a stress, their MR could be classified as “resting” because of immobility when in fact they are highly stressed with an elevated MR. More importantly, recent research demonstrating that behavioural responses to novel and highly artificial stimuli are correlated with both behaviour and fitness under more natural circumstances calls into question the wisdom of excluding these behavioural influences on MR measurements. The reason that intra‐specific variation in resting MR are so weakly correlated with daily energy expenditure (DEE) and fitness, may be that the latter two measures fully incorporate personality while the former partially excludes its influence. Because activity, exploration, boldness and aggressiveness are energetically costly, personality and metabolism should be correlated and physiological constraints may underlie behavioural syndromes. We show how physiological ecologists can better examine behavioural linkages between personality and metabolism, as required to better understand the physiological correlates of personality and the evolutionary consequences of metabolic variability.

The Measurement of Resting Metabolic Rate in Preschool Children

This study examined the repeatability of measuring resting metabolic rate (RMR) in preschool children and the effect of different calculation protocols. Eleven children (4 females and 7 males) participated in the project. They were recruited through advertisements in local schools and community centers. Resting metabolic rate was measured on 3 occasions over a 2-week period, each after an overnight fast and each lasting approximately 20 to 25 minutes. Results were compared using repeated-measures ANOVA to check for repeatability, and a number of methods of calculating RMR were assessed. Repeatability of RMR measurements was good (coefficient of variation of replicates, 6.8%), with no significant difference between days of measurement. The lowest RMR measurement was obtained when the first 10 minutes were excluded and periods during which large activity was observed were excluded. This measurement was, on average, 4% lower than averaging the measurements after the first 5 minutes, including body movements. This study suggests that RMR can be measured in preschool children and that the best method for calculating RMR in these subjects is to exclude periods when large body movements occur and the first 10 minutes of the measurement period. Only a single measurement of RMR is needed to obtain a reliable estimate.

Measurement of Resting Metabolic Rate (RMR) in a Japanese Population

Measurement of Resting Metabolic Rate (RMR) in a Japanese Population

Validity of Resting Metabolic Rate Prediction Equations in Normal, Overweight and Obese Adults

The assessment of resting metabolic rate (RMR) using prediction equations is often used to estimate energy balance in relation to weight management. These prediction measures may not be generalizable and thus may not be valid across body mass index (BMI) groups. PURPOSE: To compare 4 different RMR prediction equations, Harris-Benedict (HB), Owen (O), Mifflin (M), and World Health Organization (W), with measured RMR (criterion) assessed by a validated RMR analyzer in normal (NO), overweight (OW) and obese (OB) adults. METHODS: One hundred subjects (NO, BMI<25.0 kg/m2, n = 48; OW, 25.0≤BMI<30.0 kg/m2, n=28; OB≥30 kg/m2, n=24) rested for 10 min following a 4-h restriction of food, caffeine and exercise prior to RMR measurement. A two-way ANOVA was used to compare RMR estimated by the 4 prediction equations to the criterion RMR. RESULTS: There were no significant differences for the O and M predicted RMR values compared to the criterion across BMI groups (p>0.05). As indicated in Table 1 the HB equation significantly overestimated RMR compared to criterion for NO and OW groups (p=0.048, p=0.006) but not for the OB group (p=0.576) while the W equation overestimated RMR for the OW group but not for NO or OB groups. The correlations between the criterion and the prediction equations ranged from r = 0.70 to r = 0.76.Table 1: Percent Difference Between the Criterion and the Prediction Equations.CONCLUSIONS: The M and O equations are valid for estimating RMR across BMI groups making them useful in the field for group energy balance assessment. The HB and W equations are not valid for all BMI groups. Although several mean differences between the criterion and predicted RMR values were not statistically different, 24-hr individual estimates differed from the 24-hr criterion RMR by as much as 930 kcal/day.

Combined effect of body position, apparatus and distraction on children's resting metabolic rate

To verify the combined effect of body position, apparatus and distraction on children's resting metabolic rate (RMR). Experiments were carried out on 14 children aged 8-12 (mean age = 10.1 years +/- 1.4). Each participant underwent two test sessions, one week apart under three different situations: a) using mouthpiece and nose-clip (MN) or facemask (FM); b) sitting (SEAT) or lying (LY); and c) TV viewing (TV) or no TV viewing. In the first session, following 20 min rest and watching TV, the protocol was: LY: 20 min stabilization; 10 min using MN and 10 min using FM. Body position was then changed to seated: 20 min stabilization; 10 min using FM; 10 min using MN. In the second session, FM and MN order was changed and participants did not watch TV. Data were analysed according to the eight combinations among the three studied parameters. Repeated measures ANOVA indicated statistically significant differences for VO2 (p =0.01) and RMR (p =0.02), with TVMNSEAT showing higher values than TVFMLY. Bland-Altman analysis showed a bias for VO2, VCO2, respiratory quotient (RQ) and RMR between TVFMLY and TVMNSEAT, respectively, of -17.8+/-14.5 (ml min), -8.8+/-14.5 (ml min), 0.03+/-0.05 and -115.2+/-101.9 (kcal/day). There were no differences in RMR measurements due to body position and apparatus when each variable was isolated. Analyses of distraction in three of four combinations indicated no difference between TV and no TV. Different parameter combinations can result in increased bias and variability, and thereby the reported differences among children's RMR measurement.

Day‐to‐Day Variance in Measurement of Resting Metabolic Rate Using Ventilated‐Hood and Mouthpiece &amp; Nose‐Clip Indirect Calorimetry Systems

To know if the magnitude of change in resting metabolic rate (RMR) observed during an intervention is meaningful, it is imperative to first identify the variability that occurs within individuals from day to day under normal conditions. The 2 most common systems used to measure RMR involve a ventilated hood or a mouthpiece & nose clip to collect expired gases. The variation in measurement using these 2 approaches has not been systematically compared. RMR was measured in 10 healthy adults during 5 separate testing sessions within a 2-week period where usual diet and physical activity were maintained. Each testing session consisted of one measurement of RMR using a ventilated hood system, followed by another using a mouthpiece & nose-clip system. No significant difference in RMR was evident between measurement sessions using either indirect calorimeter. Oxygen consumption and RMR were significantly higher using the mouthpiece & nose-clip system. Average within-individual coefficient of variation for RMR was significantly lower for the ventilated-hood system. RMR measures were consistently lower using the ventilated-hood system by an average of 94.5 +/- 63.3 kcal. Day-to-day variance was between 2% and 4% for both systems. The use of either system is appropriate for assessing RMR in clinical and research settings, but alternating between systems should be undertaken with caution. A change in RMR must be greater than approximately 6% (96 kcal/d; 1.2 kcal/kg/d) or approximately 8% (135 kcal/d; 1.7 kcal/kg/d) when using a ventilated-hood system or a mouthpiece & nose-clip system, respectively, to observe any meaningful intervention-related differences within individuals.

Best Practice Methods to Apply to Measurement of Resting Metabolic Rate in Adults: A Systematic Review

Several factors may alter apparent resting metabolic rate (RMR) during measurement with indirect calorimetry. Likewise, numerous indirect calorimetry measurement protocols have been developed over the years, and the methodology employed could influence test results. As part of a larger project to determine the role of indirect calorimetry in clinical practice, a systematic review of the literature was undertaken to determine the ideal subject condition and test methodology for obtaining reliable measurement of RMR with indirect calorimetry. Food, ethanol, caffeine, and nicotine affect RMR for a variable number of hours after consumption; therefore, intake of these items must be controlled before measurement. Activities of daily living increase metabolic rate, but a short rest (≤20 minutes) before testing is sufficient for the effect to dissipate. Moderate or vigorous physical activity has a longer carryover effect and therefore must be controlled in the hours before a measurement of RMR is attempted. Limited data were found regarding ideal ambient conditions for RMR testing. Measurement duration of 10 minutes with the first 5 minutes deleted and the remaining 5 minutes having a coefficient of variation <10% gave accurate readings of RMR. Individuals preparing for RMR measurement via indirect calorimetry should refrain from eating, consuming ethanol and nicotine, smoking, and engaging in physical activity for varying times before measurement. The test site should be physically comfortable and the individual should have 10 to 20 minutes to rest before measurement commences. A 10-minute test duration with the first 5 minutes discarded and the remaining 5 minutes having a coefficient of variation of <10% will give an accurate measure of RMR.

Adjusted Body Weight, Con: Why Adjust Body Weight in Energy‐Expenditure Calculations?

Evaluation of energy requirements of normal individuals and hospitalized patients is most often accomplished using an energy equation. Energy equations attempt to measure resting metabolic rate (RMR), the largest factor in total daily energy expenditure. Components of most energy equations include height, weight, age, and gender. These factors are related to energy expenditure; however, each factor has individual characteristics that affect energy expenditure. Body weight is a major factor in RMR and total daily energy expenditure. For obese individuals, estimation of energy expenditure may be a challenge due to the increased body weight. Therefore, some equations attempt to minimize the effect of body weight on energy expenditure assessment by adjusting the obese individual's body weight. Data do not support adjustment of body weight in normal individuals. In hospitalized patients, there are several equations that are used to estimate energy expenditure of obese patients, which include adjusting the body weight and modifying the overall energy requirements. Measurement of RMR can obviate the need for estimating energy expenditure. It is important to evaluate any energy-expenditure equation that is used to estimate energy needs in normal people and hospitalized patients before applying it to patient care.

Comparison between Medgem and Deltatrac resting metabolic rate measurements

The primary aims of this trial were to evaluate the reproducibility of a portable handheld calorimeter (Medgem) in a clinical population, and to compare its measures with a calorimeter in typical use with these patients. Cross-sectional clinical validation study. Outpatient Clinical Research Center. A total of 24 stable home nutrition support patients. In random order three measures of resting metabolic rate (RMR) were taken after a 4-h fast, 15 min rest and 2-h abstention from exercise. Two measures were taken with the same Medgem (MG) and one with the traditional calorimeter (Deltatrac). Reproducibility of MG measures and their comparability to a Deltatrac measure were assessed by Bland-Altman analysis, with >+/-250 kcal/day established a priori as a clinically unacceptable error. In addition, disagreement between the two types of measures was defined as greater than 10% difference. The mean difference between two MG measures was -6.8 kcal/day, with limits of agreement between 233 and -247 kcal/day and clinically acceptable. The mean difference between the Deltatrac and mean of two MG measures was -162 kcal/day, with limits of agreement between 577 and -253 kcal/day and clinically unacceptable. In all, 80% of the repeated MG RMR measures agreed within 10%, and the mean MG reading agreed with the Deltatrac in 60% of cases. RMR obtained using the MG calorimeter has an acceptable degree of reproducibility, and is acceptable to patients. The MG measures, however, are frequently lower than traditional measures and require further validation prior to application to practice in this vulnerable patient group.

Simplified Resting Metabolic Rate—Predicting Formulas for Normal‐Sized and Obese Individuals

Resting metabolic rate (RMR) is known to be proportional to body weight and to follow allometric scaling principles. We hypothesized that RMR can be predicted from an allometric formula with weight alone as an independent variable. An allometric, power-law scaling model was fit to RMR measurements obtained from a cohort of patients being treated for weight loss. This, as well as many of the commonly used RMR-predicting formulas, was tested for RMR prediction ability against a large publicly available RMR database. Bland-Altman analysis was used to determine the efficacy of the various RMR-predicting formulas in obese and non-obese subjects. Power law modeling of the RMR-body weight relationship yielded the following RMR-predicting equations: RMR(Women) = 248 x Weight(0.4356) - (5.09 x Age) and RMR(Men) = 293 x Weight(0.4330) - (5.92 x Age). Partial correlation analysis revealed that age significantly contributed to RMR variance and was necessary to include in RMR prediction formulas. The James, allometric, and Harris-Benedict formulas all yielded reasonable RMR predictions for normal sized and obese subjects. A simple power formula relating RMR to body weight can be a reasonable RMR estimator for normal-sized and obese individuals but still requires an age term and separate formulas for men and women for the best possible RMR estimates. The apparent performance of RMR-predicting formulas is highly dependent on the methodology employed to compare the various formulas.

Comparison of Two Systems of Measuring Energy Expenditure

Health care professionals typically use resting metabolic rate (RMR) via indirect calorimetry to determine a person's energy expenditure. Traditional indirect calorimetry measurements involve an expensive, cumbersome piece of equipment that requires careful calibration. The recent development of a handheld indirect calorimeter makes it easier to measure RMR. The purpose of this study was to compare simultaneous measurements of RMR with handheld and traditional indirect calorimeters. Healthy, free-living subjects (n = 50) age 18 years and older were tested simultaneously with both indirect calorimeters. All subjects breathed through the handheld device using a mouthpiece while wearing noseclips to prevent leaks. The handheld indirect calorimetry device was placed inside a canopy. The exhaled gas from the handheld was positioned directly over the inlet to the port delivering gases to the traditional device's mixing chamber. The canopy facilitated the simultaneous collection of all expired gases into the traditional device. During the measurement, oxygen consumption and RMR were continuously recorded on a personal computer. Mean oxygen consumption and RMR did not significantly differ between the two devices, with a mean difference of 0.58 +/- 15.33 mL/min (p = .790) and 4.66 +/- 113.39 kcal/day (p = .773) and an absolute difference of 12.3 +/- 8.99 mL/min and 86.58 +/- 72.32 kcal/day, respectively. Correlation coefficients for oxygen consumption and RMR were 0.945 and 0.941, respectively. No significant difference was found between the measurements of indirect calorimetry with the MedGem (HealtheTech, Golden, CO) device compared with the DeltaTrac device (Datex-Ohmeda, Madison, WI). These findings suggest that the handheld indirect calorimeter may provide an accurate measure of oxygen consumption and RMR measurements for spontaneously breathing subjects.

Comparison of Predictive Equations for Resting Metabolic Rate in Healthy Nonobese and Obese Adults: A Systematic Review

Background An assessment of energy needs is a necessary component in the development and evaluation of a nutrition care plan. The metabolic rate can be measured or estimated by equations, but estimation is by far the more common method. However, predictive equations might generate errors large enough to impact outcome. Therefore, a systematic review of the literature was undertaken to document the accuracy of predictive equations preliminary to deciding on the imperative to measure metabolic rate. Methods As part of a larger project to determine the role of indirect calorimetry in clinical practice, an evidence team identified published articles that examined the validity of various predictive equations for resting metabolic rate (RMR) in nonobese and obese people and also in individuals of various ethnic and age groups. Articles were accepted based on defined criteria and abstracted using evidence analysis tools developed by the American Dietetic Association. Because these equations are applied by dietetics practitioners to individuals, a key inclusion criterion was research reports of individual data. The evidence was systematically evaluated, and a conclusion statement and grade were developed. Results Four prediction equations were identified as the most commonly used in clinical practice (Harris-Benedict, Mifflin-St Jeor, Owen, and World Health Organization/Food and Agriculture Organization/United Nations University [WHO/FAO/UNU]). Of these equations, the Mifflin-St Jeor equation was the most reliable, predicting RMR within 10% of measured in more nonobese and obese individuals than any other equation, and it also had the narrowest error range. No validation work concentrating on individual errors was found for the WHO/FAO/UNU equation. Older adults and US-residing ethnic minorities were underrepresented both in the development of predictive equations and in validation studies. Conclusions The Mifflin-St Jeor equation is more likely than the other equations tested to estimate RMR to within 10% of that measured, but noteworthy errors and limitations exist when it is applied to individuals and possibly when it is generalized to certain age and ethnic groups. RMR estimation errors would be eliminated by valid measurement of RMR with indirect calorimetry, using an evidence-based protocol to minimize measurement error. The Expert Panel advises clinical judgment regarding when to accept estimated RMR using predictive equations in any given individual. Indirect calorimetry may be an important tool when, in the judgment of the clinician, the predictive methods fail an individual in a clinically relevant way. For members of groups that are greatly underrepresented by existing validation studies of predictive equations, a high level of suspicion regarding the accuracy of the equations is warranted.

La mesure du métabolisme de base reste fondamentale pour établir les apports énergétiques conseillés aux patients obèses

Three methods for evaluating the resting metabolic rate (RMR) in obese patients have been compared with the reference technique measurement: indirect calorimetry. The methods were the calculation of RMR by Harris and Benedict's and Black's formulas, and by bioelectrical impedance analysis (BIA). In the same patients, the level of recommended energetic intakes obtained from the dietetic evaluation was compared with the ideal level given by the calorimetric RMR. Bioelectrical impedance analysis and Harris and Benedict's formulas gave mean results non significantly different from indirect calorimetry, but with a high risk of dispersion, and they were consequently unusable in daily practice. Black's formulas significantly underestimated RMR. The dietetic evaluation proposed a too high level of energetic intakes, with a risk for the patients of inefficiency. The calorimetric RMR measurement is still a base for obtaining the level of energetic intakes recommended to obese patients.

Validation of the BodyGem hand-held calorimeter.

To assess the validity and reliability of a hand-held indirect calorimeter. Resting metabolic rate (RMR) was measured on two separate mornings. A heterogeneous sample of 41 healthy adults. RMR using both a metabolic cart (Sensormedics 2900, SM-2900) and a hand-held indirect calorimeter (BodyGem, BG). There were no trial-to-trial differences in RMR measured by the BG (6756+/-163 vs 6697+/-163 kJ/day) or the SM-2900 (6400+/-163 vs 6396+/-167 kJ/day). RMR measured by the BG was significantly higher than that measured by the SM-2900 during both trials. In a sample of 10 subjects, the energy cost of holding the BG in position was determined to be (0.17+/-0.04 kJ/min, or 255+/-84 kJ/day). After applying this adjustment, the differences between systems were no longer significant during trial 1 (mean difference=101+/-67 kJ/day) or trial 2 (46+/-75 kJ/day). In overweight and obese individuals, RMR measured by the BodyGem was more accurate than that estimated by the Harris-Benedict equations. The BodyGem provides valid and reliable measurements of RMR. The BodyGem produces significantly higher values than the Sensor Medics 2900 indirect calorimeter, with the increase largely due to an increased energy demand required to hold the BG in position.

Validation of the MedGem Device for Measurement of Resting Metabolic Rate

Validation of the MedGem Device for Measurement of Resting Metabolic Rate

Validation of a Portable, Indirect Calorimeter for the Measurement of Resting Metabolic Rate

Validation of a Portable, Indirect Calorimeter for the Measurement of Resting Metabolic Rate

Validation of a Portable, Indirect Calorimeter for the Measurement of Resting Metabolic Rate

Validation of a Portable, Indirect Calorimeter for the Measurement of Resting Metabolic Rate

Validation of the MedGem Device for Measurement of Resting Metabolic Rate

Validation of the MedGem Device for Measurement of Resting Metabolic Rate