Published online: 05 May 2015.

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1 This article was downloaded by: [University of Akron] On: 05 May 2015, At: 11:45 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Journal of the American College of Nutrition Publication details, including instructions for authors and subscription information: RMR Estimation Model Accuracy Using Air Displacement Plethysmography Derived Body Composition Measures in Young Adults Ronald Otterstetter PhD a, Brian Miller MS ae, Mark Fridline PhD b, Michelle Boltz MS, RD c, Chris Faciana MS a, Kelsey Scanlon MS d & Ronald Mendel PhD d a School of Sport Science & Wellness Education, The University of Akron, Akron, Ohio b Department of Statistics, The University of Akron, Akron, Ohio c School of Nutrition/Dietetics, The University of Akron, Akron, Ohio Click for updates d Department of Human Performance and Sport Business, University of Mount Union, Alliance, Ohio e Department of Health Education and Promotion, Kent State University, Kent, Ohio Published online: 05 May To cite this article: Ronald Otterstetter PhD, Brian Miller MS, Mark Fridline PhD, Michelle Boltz MS, RD, Chris Faciana MS, Kelsey Scanlon MS & Ronald Mendel PhD (2015): RMR Estimation Model Accuracy Using Air Displacement Plethysmography Derived Body Composition Measures in Young Adults, Journal of the American College of Nutrition To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the Content ) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at

2 Original Research RMR Estimation Model Accuracy Using Air Displacement Plethysmography Derived Body Composition Measures in Young Adults Ronald Otterstetter, PhD, Brian Miller, MS, Mark Fridline, PhD, Michelle Boltz, MS, RD, Chris Faciana, MS, Kelsey Scanlon, MS, Ronald Mendel, PhD School of Sport Science & Wellness Education (R.O., B.M., C.F.), Department of Statistics (M.F.), School of Nutrition/Dietetics (M.B.), The University of Akron, Akron, Ohio; Department of Human Performance and Sport Business, University of Mount Union, Alliance, Ohio (K.S., R.M.); Department of Health Education and Promotion, Kent State University, Kent, Ohio (B.M.) Key words: body composition assessment, fat mass, fat-free mass, caloric expenditure INTRODUCTION Objective: Predictive equations derived from regression techniques based on large samples are extensively utilized in estimating resting metabolic rate (RMR). Body composition assessments utilize model equations to estimate RMR. However, the agreement of these predictive models with indirect calorimetery (IC) has come into question. Our aim is to investigate the agreement of RMR estimation models using air displacement plethysmography (ADP) measures against a gas exchange IC system (RMR-C). Methods: Sixty-six participants (25 men, 41 women) completed the study. RMR measurements were obtained from IC and ADP within 10 minutes of one another. IC RMR estimates were tested against 9 other validated models using ADP measures via analysis of variance (ANOVA) techniques with multiple comparisons testing and Bland-Altman analysis. Results: Based on the ANOVA, the Nelson (1992) model underpredicted RMR compared to IC (p < 0.001). The Dore et al. (1982) model was the best predictor of RMR compared to the IC measures (p D 0.907). Discussion: The current RMR estimation model using ADP measures underpredicts total caloric needs. The Dore et al. (1982) model more accurately predicted RMR in the entire sample. Accurate resting metabolic rate (RMR) measurements are necessary for professionals to provide appropriate information regarding energy requirements and macronutrient utilization at rest [1]. Because of its important role in regulating body weight and composition, there is an increased need to provide patients with their baseline metabolic requirements [2]. However, providing patients with an inaccurate RMR might have adverse consequences [3], including body dysmorphism and related disorders [4]. RMR is the energy requirement to sustain vital functions during rest or physical inactivity expressed as kcal/day [3,5,6]. RMR can be measured via indirect calorimetery (IC) or estimated by predictive equations. Portable IC that measures gas exchange provides accurate readouts within 5% of direct calorimetery [1]. The Parvomedics TrueOne 2400 system Ò (CART) is a gas exchange measurement IC system that reliably measures minute ventilation, oxygen consumption, carbon dioxide production, respiratory quotient (RQ), and RMR. When compared to the Douglas Bag method, the accuracy and reliability of the CART system at rest showed no significant difference between methods, indicating their interchangeability or equivalence [7]. In practice the trade-off in measuring body composition and metabolic demands accuracy is often sacrificed for feasibility and convenience [8]. Here, many body composition analysis devices utilize predictive equations to provide an RMR estimation feature. These predictive models are built upon measures from specific samples; thus, the applicability of these models to participants that fall outside of the sample they were derived upon might not be appropriate [9]. Furthermore, many of these Address correspondence to: Ronald Otterstetter, Ph.D., The University of Akron, School of Sport Science and Wellness Education, InfoCision Stadium 317, Akron OH ro5@uakron.edu Journal of the American College of Nutrition, Vol. 0, No. 0, 1 7 (2015) Ó American College of Nutrition Published by Taylor & Francis Group, LLC 1

3 RMR Estimation with Calorimetry and ADP equations employ proxy measures acquired from measurement methods, not from the device for which it was intended, possibly resulting in large variances. When employing regressionbased models, clinicians and practitioners need to consider what sample and what methodology that particular model was derived on to assess whether its estimation is appropriate [3]. Specifically, there are no predictive models used to estimate RMR derived directly from body composition measurements obtained from air displacement plethysmography (ADP) [10]. The Cos Med BOD POD System Ò (BOD POD) uses ADP to determine body volume on the basis of the pressure/volume relationship in tandem with models that estimate body composition derived from body density [11,12]. The BOD POD system has a RMR estimation feature that utilizes the Nelson (1992) model, a cross-validated model that includes both body fat mass (FM) and fat-free mass (FFM) that were not derived from ADP measures [10,13]. Consequently, this model might present with large variances that do not optimally predict RMR for a total population or by sex. There has been limited research concerning the agreement of predictive models in estimating RMR compared to IC methods in young, healthy adults, with most studies focused on critically ill, obese, and/or mentally ill patients [13]. The majority of research in ADP has been based on the accuracy of determining body composition measures, including (FM and FFM, rather than applicability of these measures in estimating RMR. To our knowledge, there exists no research study investigating the BOD POD s accuracy in estimating RMR using the Nelson (1992) model when compared to a criterion method such as IC. The purpose of this study was to investigate the agreement of multiple RMR estimation models using ADP measures with IC RMR estimates. The central hypothesis stated that the current methodology of estimating RMR from the ADP measures using the Nelson (1992) model would result in large variances that are inappropriate for patient recommendations. The aim of this study was to compare multiple measurements of agreement between multiple RMR estimation models using body composition measures obtained from an ADP against IC via CART. MATERIALS/PARTICIPANTS AND METHODS Participants A convenience sample comprising healthy adults (18 30 years old) recruited from a Midwestern university and local surrounding community (n D 66 participants; 41 female d 25 male) gave informed consent to participate in and complete the study. The current study was approved by The University of Akron s Institutional Review Board and University of Mount Union s Institutional Review Board. Testing Procedure The current investigation was a quasi-experimental comparative study with a repeated-measures design which required participants to be tested by two RMR estimation methods, including CART and ADP. The sequencing of each phase was designed to minimize inflation in RMR estimates from additional activity by the participants. All testing took place in the morning. Prior to testing, participants were instructed to: (1) fast 12 hours prior to the test, (2) avoid strenuous exercise 24 hours prior to the test, (3) wear tight-fitting clothing (including the skull cap provided), and (4) remove all jewelry [12,14]. The testing protocol was divided into three phases. The first phase was the metabolic phase, which employed the use of a CART (ParvoMedics TrueOne 2400 Indirect Calorimetery System Ò, Sandy, UT) calibrated to the manufacturer s specifications. The participant was required to lie in the supine position in a quiet room for a total of 60 minutes to assure a resting state while attached to the CART via standard mouthpiece/one way valve apparatus. The first 45 minutes of each test was discarded in order for the subject to reach steady state metabolism. RMR was represented as the average of the last 15 minutes of CART measure extrapolated out to 24 hours. The second phase included collecting anthropometric measurements (height) measured via a stadiometer (Detecto Digital Stadiometer Ò, WebbCity, MO).The final phase employed the use of ADP via the Bod Pod system (Cos Med BOD POD System Ò Life Measurement Instruments, Concord, CA) calibrated to the manufacturer s specifications using a vessel of a standard volume (50 L). The subject entered the chamber wearing tightfitting clothing and a Lycra cap per manufacturer instructions, and data collection took place following the manufacturer s standard procedure. Body densitometry (Db) was estimated using the SIRI (1961) equation to standardize the estimation given the homogeneity of the study sample [12,15]. A maximum of a 10 minute period was allotted between CART and BOD POD measurements. Measurements collected for each subject from the CART include RMR represented as RMR-C, and measurements collected from the BOD POD included FM, FFM, body fat percentage, and body density. RMR was calculated based on the equations in Table 1. The BOD POD system offers a means of estimating RMR which currently uses the Nelson (1992) model due to its inclusion of FM and FFM [12]. Statistical Analysis A repeated-measures one-way ANOVA with a Greenhouse- Geisser correction was performed comparing RMR estimation models accompanied by a Fisher s Least Significant Difference (LSD) multiple comparisons test and 95% confidence intervals of models differences against RMR-C. Interquartile range box plots were used to visually illustrate differences between methods and highlight outliers. Pearson correlations were performed between each estimation method compared to RMR-C. The 2 VOL. 0, NO. 0

4 Table 1. RMR Estimation Models and Derivation Specifics Reference N 1 Equation Sex 2 Body 3 Cunningham (1980) C 21.6(FFM) M/F L/O Dore et al. (1982) C 8.24(wt) C 0.02(FFM) 3.25(age) F L/O Bernstein et al. (1983) C 22(FFM) C 6.4(FM) 2.1(age) M/F O Garrow & Weber (1985) C 24.2(FFM) C 5.8(%fat) F L/O Ravussin et al. (1986) C 21.8(FFM) M/F L/O Owen (1988) C 23.6(FFM) M/F L/O Kashiwazaki (1988) C 24.5(FFM) M/F L/O Mifflin et al. (1989) C 19.7(FFM) M/F L/O Nelson (1992) (FFM) C 4.04(FM) M/F L/O Equations extracted from Cunningham (1991) [26]. 1 Sample size used for derivation. 2 Sex: M D Male; F D Female; Sex used for derivation. 3 Body type: L D Lean; O D Obese; Body type used for derivation. 4 Equation extracted from Nelson et al. (1992) acceptable range for estimation of RMR was set within 10% of the indirect measure to the direct measure [16,26]. Tabulation was performed to sum the total proportion of participants that varied greater than 10% of the RMR-C value. Sample characteristic were expressed as mean standard deviation (SD) with model estimates expressed as mean standard error of the estimate (SEE) with significance set at p < Note that p- values greater than the 0.05 threshold represent agreement between measures. Bland-Altman measures of agreement were performed between RMR-C compared to Nelson (1992) and Dore et al. (1982) [10,17]. Values expressed as mean difference (bias), LLA (lower limits of agreement D mean difference 1.96 SD), and ULA (upper limits of agreement D mean difference C 1.96 SD) [18]. The Bland-Altman analysis compares agreement between two different measurement techniques [18,19]. This procedure plots the difference (or bias) between the two measurement techniques against the averages between the two techniques. The Bland-Altman plot is a useful technique to observe the agreement between the differences in methods and the average magnitude of the measurements. A line of best fit was superimposed within each plot using simple linear regression accompanied by strength of association measure (r) in accordance with Bland and Altman (1999) [19]. Significance was set at p < In accordance with Cohen (1988) [20], a power analysis revealed that a minimum sample size of n D 25 for each sex was required to achieve a power of 0.80, an effect size of 0.25, and significance set at p 0.05 based on the repeated measures ANOVA. All statistical analysis was performed using IBM SPSS version 20, and Bland-Altman plots were created using Minitab version 16 [20]. RESULTS Sample characteristics are displayed in Table 2. Tabulation of RMR estimates that fell outside of 10% of RMR-C are illustrated in Table 3. The Dore et al. (1982) model was the highest performing model with 41%, 44%, and 39% of participants RMR measures within 10% of RMR-C. The Nelson (1992) model performed with 14%, 8%, and 17% of participants RMR within 10% of RMR-C. No other model performed with greater than 10% of participants RMR within range of RMR-C. Box plots of RMR by model are represented in Fig. 1. It is notable that within the female sample, there were numerous outliers identified using the interquartile range method. The analysis of variance revealed significant differences between RMR measures for the total sample and when split by sex (p < 0.001). The multiple comparisons test revealed that the Dore et al. (1982) model did not significantly differ (6 48 kcals) from the RMR-C measure for the total sample (p D 0.907). However, the multiple comparisons test revealed that Dore et al. (1982) significantly overpredicted for females by kcals (p D 0.001) and underpredicted for males by kcals (p D 0.014) when compared to RMR-C. The Nelson (1992) model significantly underpredicted RMR-C by kcals (p < 0.001) for the total sample and by kcals (p < 0.001) for females and kcals (p < 0.001) for males when compared to RMR-C. Results of the repeated measures ANOVA are represented in Table 4. Table 5 presents the data for the Bland-Altman limits of agreement for Nelson (1992) and Dore et al. (1982) compared to RMR-C values. As a whole, the Nelson (1992) model significantly underpredicted RMR when compared to the RMR-C. The Dore et al. (1982) model was the best-performing model. However, for Dore et al. (1982), a significant difference was detected with an overprediction for female participants and an underprediction for male participants, but it still outperformed the Nelson (1992) model. Considering the Bland-Altman limits of agreement, each model performed outside of the 10% acceptable limit when compared to mean RMR-C values. Fig. 1 displays plots expressing a Bland-Altman analysis for Nelson (1992) and Dore et al. (1982) compared to RMR-C for JOURNAL OF THE AMERICAN COLLEGE OF NUTRITION 3

5 Table 2. Sample Characteristics Parameter Total n D 66 Male n D 25 Female n D 41 Age (yr) Height (cm) Weight (kg) BMI (kg/m 2 ) FM (kg) FFM (kg) Body fat % Density (kg/l) RMR-C (kcal/day) Parameters expressed as mean SD 1 Measured via BOD POD body composition estimates. 2 Measured via Parvomedics 2400 Metabolic System. the total sample and split by sex. Notable within all plots was the presence of a negative proportional bias which showed that the difference between estimation models compared to RMR- C increased in both the high and low mean RMR extremes. DISCUSSION The aim of the current study was to investigate the agreement of multiple RMR estimation models using body composition measures obtained from ADP methods compared to RMR estimates via gas exchange IC. There has been limited research concerning the accuracy of predictive models in estimating RMR compared to IC in young, college-aged healthy adults, where studies have primarily focused on critically ill, obese, and/or mentally ill patients [8,13]. Gas exchange IC has been shown to be a reliable RMR measurement method in vivo [21]. However, the availability of this technique might not be cost effective or technically feasible for use by clinicians in everyday practice [9,13]. The issue of accurate estimation of RMR is important because of the use of these results on adjustments of consumer dietary consumption for weight management programs, as one example. As seen in Table 4, the large errors with currently used formulas can lead to severely inaccurate Table 3. Agreement between Estimation Models and RMR-C Model Total Male Female Cunningham (1990) 3% 4% 2% Dore et al. (1982) 41% 44% 39% Bernstein et al. (1983) 3% 4% 2% Garrow & Weber (1985) 2% 4% 0% Ravussin & Bogardus (1989) 3% 4% 2% Owen (1988) 3% 4% 2% Kashiwazaki (1988) 3% 4% 2% Mifflin et al. (1989) 5% 8% 2% Nelson (1992) 14% 8% 17% The above is represented as the percentage of subjects whose agreement between estimation model and RMR-C is less than 10%. Fig. 1. Box plots of estimated RMR by model. 4 VOL. 0, NO. 0

6 Table 4. Results of Repeated Measures ANOVA Model Sample d S.E.M. p-value 95% C.I. Dore et al. (1982) Total ( 90, 101) Male ( 411, 52) Female (63, 237) Nelson (1992) Total <0.001 ( 525, 348) Male <0.001 ( 746, 406) Female <0.001 ( 445, 258) Cunningham (1980) Total <0.001 (1128, 1407) Male <0.001 (1015, 1416) Female <0.001 (1104, 1495) Bernstein et al. (1983) Total <0.001 (1107, 1398) Male <0.001 (997, 1381) Female <0.001 (1083, 1499) Garrow & Weber (1985) Total <0.001 (1373, 1675) Male <0.001 (1291, 1712) Female <0.001 (1324, 1752) Ravussin & Bogardus (1989) Total <0.001 (1041, 1323) Male <0.001 (933, 1336) Female <0.001 (1015, 1409) Owen (1988) Total <0.001 (1049, 1348) Male <0.001 (977, 1397) Female <0.001 (995, 1415) Kashiwazaki (1988) Total <0.001 (1273, 1581) Male <0.001 (1219, 1650) Female <0.001 (1206, 1640) Mifflin et al. (1989) Total <0.001 (812, 1077) Male <0.001 (661, 1045) Female <0.001 (818, 1182) 1 d S.E.M represented. 2 Fisher s LSD Multiple Comparisons Test, Significance (p <0.05). n D 66 (male 25; female 41). recommendations, especially if the RMR is underestimated. This could lead to changes in body composition, and indirectly to cardiovascular disease risk markers. The BOD POD system offers a means of estimating RMR, which currently uses the Nelson (1992) model due to its inclusion of FM and FFM [12]. The Nelson (1992) model was derived using a sample of obese and nonobese males and females (n D 213; 86 male, 127 female) which considered the contribution of FM and FFM to RMR [10]. Nelson et al. Table 5. Limits of Agreement and Correlation with RMR-C Model d 1 ULA 2 LLA 3 r-statistic 4 Nelson (1992) Female Male Dore et al. (1982) Female Male Analysis based on Bland & Altman (1986) with model comparison to RMR-C d, LLA, ULA represented as kcal/day. 1 d D mean difference; expressed as Estimation Model RMR-C. 2 ULA D Bland-Altman d C 2S. 3 LLA D Bland-Altman lower limits of agreement d - 2S. 4 Bland-Altman line-of-best-fit strength of association r-statistic. (1992) compared multiple published linear regression models with FFM as the predictor of RMR. The coefficient of determination, the amount of variance in RMR explained by each model with FFM as its predictor, ranged from ( ). Similarly, Mifflin et al. (1990) found FFM to be the best single predictor of RMR (r 2 D 0.64) when considering age, sex, height, and weight [5]. However, the Nelson (1992) model was not derived using ADP measures but rather hydrodensitometry [10]. Dixon et al. (2005) compared ADP estimates to hydrostatic weighing estimates and found no statistical or practical difference between methods when using lean, young adult participants [22]. According to Hoeger et al. (2011), even though ADP has been shown to agree with hydrostatic weighing in terms of body composition analysis, there is a high risk of error and lack of agreement between methods when sex is not considered [23]. Furthermore, ADP has been shown to be equivocal to hydrostatic weighing and dual x-ray absorptiometry (DXA) techniques in estimating two-compartment body composition including fat and FFM [24]. The results of the current study identified the Dore et al. (1982) model as having the best agreement with the RMR-C for females based upon multiple measurements of agreement for the total sample. Dore et al. (1982) explored the metabolic JOURNAL OF THE AMERICAN COLLEGE OF NUTRITION 5

7 demands of females with different degrees of obesity. This model was derived from a sample of 140 females with varying degrees of obesity. Similar to the Nelson (1992) model, Dore et al. (1982) includes FFM as a predictor of RMR. Like Nelson (1992), this model did not employ ADP measurements in its derivation but rather total body potassium to estimate skeletal muscle mass used interchangeably as FFM [17]. It is interesting to note that this model outperformed a dual sex model considering its derivation primarily based on obese female participants. Based on the multiple comparisons test between the Nelson (1992) and Dore et al. (1982) models compared to RMR-C, the Dore et al. (1982) model was the best overall model when considering RMR estimation using ADP measurements for the total sample. The results of the ANOVA are supported by the means of the plots expressing the Bland-Altman analysis, where both the Dore et al. (1982) and Nelson (1992) models performed similarly for the total population. This supports the notion from Hasson et al. (2011) that when employing a regression-based model, clinicians and practitioners need to consider what sample the model was derived on to assess whether its estimation is appropriate [3]. Notable within the plots expressing a Bland- Altman analysis was the presence of a negative proportional bias where, as the magnitude of each RMR measurement increased, the variance between both estimation models from the RMR-C measurement increased, creating lack of agreement in the high and low extremes of RMR. Furthermore, this negative proportional bias followed a linear trend. Future research is warranted investigating this phenomenon. Limitations The following were considered limitations to the current investigations design. The current study was designed around a convenience sample of young, normal-weight adults. Therefore, generalization of the results is not appropriate. The current investigation utilized the Siri (1961) equation as a general estimate of body fat percentage. However, future investigations should employ population-specific body fat percentage estimation models, such as Schutte for African Americans or Brozek for athletic populations. The investigators did not develop a predictive regression-based model given the limitations in sample size. However, future investigation is warranted in creating a predictive model for estimating RMR based on ADP machine-specific estimates. Future investigation in deriving an ADP-specific RMR estimation model is planned. CONCLUSION In summary, the aim of the current study was to investigate the agreement of multiple pre-existing RMR estimation models with gas exchange IC RMR measurements using body composition measures obtained from ADP. The current protocol using the Nelson (1992) model for RMR estimation with ADP measures underpredicted RMR. The Dore et al. 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