Determining the Accuracy and Reliability of Indirect Calorimeters Utilizing the Methanol Combustion Technique

Kaviani, Sepideh; Schoeller, Dale A.; Ravussin, Éric; Melanson, Edward L.; Henes, Sarah T.; Dugas, Lara R.; Dechert, Ronald E.; Mitri, G.; Schoffelen, Paul F.M.; Gubbels, Pim; Tornberg, Åsa; Garland, Stephen; Akkermans, Marco A.; Cooper, Jamie A.

doi:10.1002/ncp.10070

Cited by 32 publications

(42 citation statements)

References 36 publications

(94 reference statements)

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“…The purpose of this study was to evaluate the utility of several practical methods of RMR For this investigation, the ParvoMedics TrueOne® 2400 indirect calorimeter was designated as the reference method of assessment due to its documented accuracy and reliability (Cooper et al 2009;Kaviani et al 2018 D r a f t…”

Section: Discussionmentioning

confidence: 99%

“…The TrueOne® 2400 (ParvoMedics, Sandy, UT, USA) was designated as the reference method, and additional methods were a portable research-grade device (Fitmate TM , Cosmed, Rome, Italy) and a portable consumer-grade device (Breezing® Metabolism Tracker, Breezing, Tempe, AZ, USA). The TrueOne® 2400 was selected as the reference method due to its demonstrated accuracy (Cooper et al 2009;Kaviani et al 2018). A recent study evaluated the accuracy and reliability of 12 indirect calorimeters using methanol combustion (Kaviani et al 2018).…”

Section: Methodsmentioning

confidence: 99%

“…The TrueOne® 2400 was selected as the reference method due to its demonstrated accuracy (Cooper et al 2009;Kaviani et al 2018). A recent study evaluated the accuracy and reliability of 12 indirect calorimeters using methanol combustion (Kaviani et al 2018). Of the 12 devices, two separate TrueOne® 2400 systems were ranked 1 st and 2 nd for CO2 recovery, 2 nd and 5 th for O2 recovery and 2 nd and 4 th for RER accuracy.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Resting metabolic rate in muscular physique athletes: validity of existing methods and development of new prediction equations

Tinsley

Graybeal

Moore

2019

Appl. Physiol. Nutr. Metab.

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Estimation of resting metabolic rate (RMR) is an important step for prescribing an individual’s energy intake. The purpose of this study was to evaluate the validity of portable indirect calorimeters and RMR prediction equations in muscular physique athletes. Twenty-seven males (n = 17; body mass index (BMI): 28.8 ± 2.0 kg/m2; body fat: 12.5% ± 2.7%) and females (n = 10; BMI: 22.8 ± 1.6 kg/m2; body fat: 19.2% ± 3.4%) were evaluated. The reference RMR value was obtained from the ParvoMedics TrueOne 2400 indirect calorimeter, and the Cosmed Fitmate and Breezing Metabolism Tracker provided additional RMR estimates. Existing RMR prediction equations based on body weight (BW) or dual-energy X-ray absorptiometry fat-free mass (FFM) were also evaluated. Errors in RMR estimates were assessed using validity statistics, including t tests with Bonferroni correction, linear regression, and calculation of the standard error of the estimate, total error, and 95% limits of agreement. Additionally, new prediction equations based on BW (RMR (kcal/day) = 24.8 × BW (kg) + 10) and FFM (RMR (kcal/day) = 25.9 × FFM (kg) + 284) were developed using stepwise linear regression and evaluated using leave-one-out cross-validation. Nearly all existing BW- and FFM-based prediction equations, as well as the Breezing Tracker, did not exhibit acceptable validity and typically underestimated RMR. The ten Haaf and Weijs (PLoS ONE, 9: e1084602014 (2014)) and Cunningham (1980) (Am. J. Clin. Nutr. 33: 2372–2374 (1980)) FFM-based equations may produce acceptable RMR estimates, although the Cosmed Fitmate and newly developed BW- and FFM-based equations may be most suitable for RMR estimation in male and female physique athletes. Future research should provide additional external cross-validation of the newly developed equations to refine the ability to predict RMR in physique athletes.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Resting metabolic rate in muscular physique athletes: validity of existing methods and development of new prediction equations

Tinsley

Graybeal

Moore

2019

Appl. Physiol. Nutr. Metab.

View full text Add to dashboard Cite

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“…The QUARK RMR (COSMED) is a metabolic monitoring device operated with an external power supply only and should be connected to a computer, monitor and printer for displaying the RMR and VO 2 readings during the measurement and printing the report. A protocolled methanol burning test (21,22) performed prior to this study by the manufacturer revealed a RQ (the ratio between the produced carbon dioxide and the oxygen consumed) of 0·68 which was within the acceptable range of 0·64-0·69 (21,23) . The QUARK RMR has been validated against the 'gold standard' DELTATRAC in healthy adults (9) and was considered as the reference device in this study.…”

Section: Quark Rmrmentioning

confidence: 76%

“…Table 1 shows the participants' characteristics. A total of sixty-eight participants were included with a median age of 22 years old (IQR [21][22][23][24][25][26][27][28][29][30][31][32]. The majority of the participants were female (72·1 %).…”

Section: Portable Metabolic Monitoring Devicementioning

confidence: 99%

The use of a portable metabolic monitoring device for measuring RMR in healthy adults

et al. 2020

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Objective measurement of resting metabolic rate (RMR) may be important for optimal nutritional care but is hindered by the price and practicality of the metabolic monitoring device. This study compared two metabolic monitoring devices for measuring RMR and oxygen consumption (VO2) and compared the measured RMR with the predicted RMR calculated from equations. RMR was measured using QUARK RMR (reference device) and Fitmate GS (COSMED) in a random order for 30 minutes, each on fasted participants. In total, 68 adults participated (median age 22 years, interquartile range 21-32). Pearson correlation showed that RMR (r=0.86) and VO2 (r=0.86) were highly correlated between the two devices (p<0.05). Intraclass Correlation Coefficients (ICC) showed good relative agreements regarding RMR (ICC=0.84) and VO2 (ICC=0.84) (p<0.05). RMR measured by QUARK RMR was significantly higher (155±180 kcal/day) than Fitmate GS. Equations significantly overpredicted RMR. Accurate RMR (i.e. within ±10% of the RMR measured by QUAR RMR) was found among 38% of the participants for Fitmate GS, and among 46-68% depending on the equations. Bland-Altman analysis showed a low absolute agreement with QUARK RMR at an individual level for both Fitmate GS (limits of agreement (LOA): -198 to +508 kcal/day) and equations (LOA ranged from -473 to +449 kcal/day). In conclusion, both Fitmate GS and predictive equations had low absolute agreements with QUARK RMR at an individual level. Therefore, these limitations should be considered when determining RMR using Fitmate GS or equations.

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Effect of a Low-Fat Vegan Diet on Body Weight, Insulin Sensitivity, Postprandial Metabolism, and Intramyocellular and Hepatocellular Lipid Levels in Overweight Adults

et al. 2020

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IMPORTANCE Excess body weight and insulin resistance lead to type 2 diabetes and other major health problems. There is an urgent need for dietary interventions to address these conditions. OBJECTIVE To measure the effects of a low-fat vegan diet on body weight, insulin resistance, postprandial metabolism, and intramyocellular and hepatocellular lipid levels in overweight adults. DESIGN, SETTING, AND PARTICIPANTS This 16-week randomized clinical trial was conducted between January 2017 and February 2019 in Washington, DC. Of 3115 people who responded to flyers in medical offices and newspaper and radio advertisements, 244 met the participation criteria (age 25 to 75 years; body mass index of 28 to 40) after having been screened by telephone. INTERVENTIONS Participants were randomized in a 1:1 ratio. The intervention group (n = 122) was asked to follow a low-fat vegan diet and the control group (n = 122) to make no diet changes for 16 weeks. MAIN OUTCOMES AND MEASURES At weeks 0 and 16, body weight was assessed using a calibrated scale. Body composition and visceral fat were measured by dual x-ray absorptiometry. Insulin resistance was assessed with the homeostasis model assessment index and the predicted insulin sensitivity index (PREDIM). Thermic effect of food was measured by indirect calorimetry over 3 hours after a standard liquid breakfast (720 kcal). In a subset of participants (n = 44), hepatocellular and intramyocellular lipids were quantified by proton magnetic resonance spectroscopy. Repeated measure analysis of variance was used for statistical analysis. RESULTS Among the 244 participants in the study, 211 (87%) were female, 117 (48%) were White, and the mean (SD) age was 54.4 (11.6) years. Over the 16 weeks, body weight decreased in the intervention group by 5.9 kg (95% CI, 5.0-6.7 kg; P < .001). Thermic effect of food increased in the intervention group by 14.1% (95% CI, 6.5-20.4; P < .001). The homeostasis model assessment index decreased (−1.3; 95% CI, −2.2 to −0.3; P < .001) and PREDIM increased (0.9; 95% CI, 0.5-1.2; P < .001) in the intervention group. Hepatocellular lipid levels decreased in the intervention group by 34.4%, from a mean (SD) of 3.2% (2.9%) to 2.4% (2.2%) (P = .002), and intramyocellular lipid levels decreased by 10.4%, from a mean (SD) of 1.6 (1.1) to 1.5 (1.0) (P = .03). None of these variables changed significantly in the control group over the 16 weeks. The change in PREDIM correlated negatively with the change in body weight (r = −0.43; P < .001). Changes in hepatocellular and intramyocellular lipid levels correlated with changes in insulin resistance (both r = 0.51; P = .01).

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Determining the Accuracy and Reliability of Indirect Calorimeters Utilizing the Methanol Combustion Technique

Cited by 32 publications

References 36 publications

Resting metabolic rate in muscular physique athletes: validity of existing methods and development of new prediction equations

Resting metabolic rate in muscular physique athletes: validity of existing methods and development of new prediction equations

The use of a portable metabolic monitoring device for measuring RMR in healthy adults

Effect of a Low-Fat Vegan Diet on Body Weight, Insulin Sensitivity, Postprandial Metabolism, and Intramyocellular and Hepatocellular Lipid Levels in Overweight Adults

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