UNIVERSITY OF HAWAII LIBrARY COMPARISON OF THE BODYGEM, HARRIS BENEDICT PREDICTION EQUATION, AND A METABOLIC CART ON RESTING ENERGY EXPENDITURE

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1 UNIVERSITY OF HAWAII LIBrARY COMPARISON OF THE BODYGEM, HARRIS BENEDICT PREDICTION EQUATION, AND A METABOLIC CART ON RESTING ENERGY EXPENDITURE A THESIS SUBMITIED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAW AI'I IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTERS OF SCIENCE IN KINESIOLOGY AND LEISURE SCIENCE DECEMBER 2006 By KurtC.L. Go Thesis Committee: Ronald Hetzler, Chairperson Iris Kimura Nathan Murata

2 ii We certify that we have read this thesis and that, in our opinion, it is satisfactory in scope and quality for the degree of Master of Science in Kinesiology and Leisure Science. THESIS COMMITIEE /7~ hairperson -

3 ACKNOWLEDGMENTS iii I thank the following individuals for their contributions in the completion of my master's thesis: Dr. Ronald Hetzler for his years of encouragement, guidance, and belief in me. Dr. Iris Kimura for giving me a chance at graduate school, her patience and sense of humor. Dr. Nathan Murata for his time and efforts as a committee member. Dr. Alan Titchenal and Dr. Joannie Dobbs for their inspiration and expertise in the area of sports and human nutrition. Dr. Ronald Aubert for his expertise in statistical analysis. Brian Schroeder for his years of friendship, support, and encouragement. Tomohiro Fujitani for his friendship and hours of assistance in editing this manuscript. Billy Akutagawa for his understanding and support in allowing me the time to complete this manuscript. HealtheTech and staff members for their generous donation of a BodyGem unit, mouthpieces, and technical support during data collection. Also I thank my family for their financial, moral, and loving support. My parents, Dennis and Wilma, and parents-in-law, Judy and David Mikami, for their years of patience, understanding, and belief in me. My grandmother, Rose, and in loving memory of my grandfather, Albert, for their years of inspiration to further my education. Finally, I dedicate this thesis manuscript to my wife Kelly and daughters, Mallory and Meredith, for their continued encouragement, understanding, and love during the past years. I truly appreciate all your patience and faith in me while I spent countless hours in the laboratory and at home in the office. Having the three of you in my life has made the completion of this thesis one of my most memorable accomplishments. Again, thank you everyone!

4 ABSTRACT iv The purpose of this study was to compare measurements of resting energy expenditure (REE) via indirect calorimetry using the BodyGem with measurements from a metabolic cart and the Harris-Benedict equation. Participants were 25 women (12) and men (13) aged years who were tested on one occasion. Results indicated mean REE (kcal day-i) for the metabolic cart, BodyGem, and Harris-Benedict equation were 1,321. 7, 1,280.5, and 1,609.7 kcal'd- I, respectively. Pearson correlation coefficients (r) were significant ranging from r=o.64 to r=o.98 for weight, height, and body mass index compared to REE measurements. There was a significant difference in mean REE between the metabolic cart and Harris-Benedict equation, but no significant difference between the BodyGem and the metabolic cart. Therefore, it was concluded that the BodyGem provided an accurate method to measure REE and the Harris-Benedict equation should not be used if other methods are available.

5 TABLE OF CONTENTS v Acknowledgements... iii Abstraet....iv List of Tables... vi List of Figures... vii Introdnction... 1 Methodology... 4 Participants....4 Protocol Measurements... 5 Anthropometry... 8 Statistical Analysis... 8 Resnlts Discnssion Review ofliteratnre Measuring Energy Expenditure Pretesting and Testing Environment Factors Influencing Resting Energy Expenditure Anthropometry References Appendices Appendix A: Communications Appendix B: Informed Consent Appendix C: Medical History Form Appendix D: Human Subjects Forms Appendix E: Raw Subject Data Appendix F: Descriptive Statistics Appendix G: ANDY A Summary Tables Appendix H: Pearson Correlation Coefficients... 83

6 vi LIST OF TABLES 1. Characteristics of participants (n=25) and stratified by gender (12 women and 13 men) Resting energy expenditure for participants (n=25) and stratified by gender (12 women and 13 men) Pearson correlation coefficients between weight, height, BMI, metabolic cart, BodyGem, and Harris-Benedict equation One-way, ANOV A with repeated measures (within-participant effect) for differences between mean values of REE among three methods Post hoc pair-wise comparisons of mean values for REE between three methods

7 LIST OF FIGURES vii Figure ~ 1. Comparison of mean values of REE by methods... 11

8 INTRODUCTION 1 The ability to accurately measure resting metabolic rate (RMR) is essential in both the study of exercise science and human nutrition. The mechanisms that regulate human energy metabolism are important in determining proper energy balance (i.e., difference between energy intake and expenditure), which contributes to weight management (Segal, Edailo, Blando, & Pi-Sunyer, 1990; Ballor, Harvey-Berino, Ades, Cryan, & Calles-Escandon, 1996; Kraemer, Volek, Clark, Gordon, Incledon, Puhl, et al., 1997). In human energy expenditure, RMR or resting energy expenditure (REE) represents approximately 65% to 75% of total daily energy expenditure (Groff& Gropper, 2000). Thus, REE has been widely studied, since this is the major portion of total daily energy expenditure (Bielinski, Schutz, & Jequier, 1985; LetT, Hill, Yates, Cotsonis, & Heymsfield, 1987; Berke, Gardner, Goran, & Poehlman, 1992; Weststrate, 1993; Ballor et ai., 1996; Gallagher, Belmonte, Deurenberg, Wang, Krasnow, Pi-Sunyer, et ai., 1998; Wilmore, Stanforth, Hudspeth, Gagnon, Daw, Leon, et ai., 1998; niner, Brinkmann, Heller, Bosy-Westphal, & Muller, 2000; Wang, Heshka, Gallagher, Boozer, Kotler, & Heymsfield, 2000; Haugen, Melanson, Tran, Kearney, & Hill, 2003). According to McArdle, Katch, and Katch (1999) direct calorimetry is a method to determine energy expenditure in human metabolism by measuring the amount of heat generated and released by an individual at rest and during exercise. Data are collected while an individual resides within an enclosed chamber with an independent supply of oxygen. However, there are limitations (i.e., types of activities participants can perform) due to structural constraints, cost, and time. Indirect calorimetry, preferably open-circuit spirometry, contrasted with direct calorimetry, is a widely used in the study of human

9 2 energy metabolism, which measures an individual's oxygen consumption. This method of indirect calorimetry requires an individual to breathe ambient air (20.93% oxygen, 0.03% carbon dioxide, and 79.04% nitrogen) while expired gas exchange is monitored and analyzed. This process translates oxygen consumption into calories using calories per liter of oxygen, assuming that there is a mixed-diet and a respiratory exchange ratio (RER) of 0.82, which is the ratio of expired carbon dioxide (VCCh) divided by oxygen consumption (VCh). Therefore, indirect calorimetry via open-circuit spirometry is more practical and economical as compared to direct calorimetry. One method of indirect calorimetry via open-circuit spirometry involves the use of a metabolic cart, which monitors gas concentrations, the volume of respiration, and calculates oxygen consumption (Segal, 1987; Wells & Fuller, 1998). Oxygen consumption is subsequently converted to caloric expenditure. This process is referred to as indirect calorimetry (Segal, 1987; McClave et al., 2003). Although indirect calorimetry is more practical than direct calorimetry, the price range for an indirect calorimetry, open-circuit, system (i.e. metabolic cart) ranges from $25,000 to $50,000. Additionally, many systems used in clinics are not portable and require hours of training. Furthermore, commonly used outpatient protocols for metabolic assessments require about 20 to 30 minutes (Turley, McBride, & Wilmore, 1993). Nutritionists and others involved with weight management commonly use the Harris-Benedict equation to estimate the REE of their clients (Harris & Benedict, 1919; Frankfield, Rowe, Smith & Cooney, 2003). The Harris-Benedict equation has been found to estimate REE with a precision of about ±14% in normal participants, but it did not work well with participants who were malnourished (Roza & Shizgal, 1984). This

10 3 amoldlt of error, 14% of REE, corresponds to about ±280 kcal'd- 1 for an individual with a REE of2000 kcal/d. Errors in estimating REE may directly impact the efficacy of a weight loss or weight maintenance regimen. Therefore, decreasing the error associated with the estimation ofree should improve the ability to effectively prescribe a diet and exercise program. Technological advancement in computerized equipment has led to the development of portable, indirect calorimetry systems (Wideman et al., 1996; McLaughlin, King, Howley, Bassett, & Ainsworth, 2001). In 1998, Healthetech, Inc., (Golden, CO) introduced a prototype handheld indirect calorimeter known as the BodyGem to allied health and fitness professions as a device for measuring REE (Nieman, Trone, & Austin, 2003). Several years later, the BodyGem became commercially available and the initial published study was conducted on the reliability of this device in relation to the Douglas Bag technique (see Appendix A) (Nieman et al., 2003). The cost of this portable handheld indirect calorimeter is about $1,800.00, which is more cost effective than a metabolic cart. Currently, there are three published studies that support the validity and reliability ofree measurements by the BodyGem in adults (Nieman et al., 2003; Melanson et al., 2004; Liou, Chen, ChlDlg, & Chu, 2006). The purpose of this study was to compare measurements ofree via indirect calorimetry using the BodyGem and with measurements obtained using a metabolic cart and the Harris-Benedict equation.

11 METHODOLOGY 4 Participants Participants included 25 adult volunteers (12 women and 13 men) from the general student population of the University of Hawaii at Manoa (UHM). Participants were recruited through conducting brief presentations regarding the requirements of the study to students in health and physical education classes. Prior to participation, participants were required to read and sign an informed consent form (see Appendix B) and complete a medical history form (see Appendix C). On the medical history form, participants self-reported information that included date of birth, present and past medical history, medication history, and physician restrictions. Participants were relatively healthy with no presence of any metabolic diseases (Le. diabetes, hyperlipidemia, hypertension, etc.). Participants reported to the Kinesiology Human Performance Laboratory on one occasion for at least one and one-half hours to complete three different assessments ofree and one assessment of anthropometric measurements. The University of Hawaii's Committee on Human Studies approved the protocol for the current study (see Appendix D). Protocol Participants reported to the laboratory in the mornings on the scheduled day of testing (Haugen et ai., 2003). Participants were instructed to fast for at least 10 hours (Segal, Edai'io, et al., 1990; Berke et al., 1992; Turley et al., 1993; Weststrate. 1993; Reed and Hill, 1996; Kraemer et al., 1997; Haugen, et ai., 2003); only drink water (Dulloo, Geissler, Horton, Collins, and Miller, 1989; Weststrate, Wunnik, Deurenberg, & Hautvast, 1990); and refrain from exercise for at least 24 hours prior to participation in

12 the study (Bielinski et al., 1985; Berke et al., 1992; Turley et al., 1993; Wilmore et al., ). Participants also refrained from smoking prior to assessment (perkins, Epstein, Stiller, Sexton, Fernstrom, Jacob, et al., 1990; Perkins, Epstein, & Pastor, 1990). Participants were given a schedule for the campus shuttle and instructed to use it to go to the laboratory to reduce the amount of activity while traveling to the laboratory (Berke et al., 1992; Turley et al., 1993; Wilmore et al., 1998). Upon arrival, participants were asked to void in the restroom (Berke et al., 1992). Upon returning to the laboratory, participants rested comfortably in a supine semirecumbent position for 30 minutes in a comfortable environment prior to performing any measurements (Segal, 1987; Segal, Edafto, et al., 1990; Berke et al., 1992; Kraemer, et al., 1997; Turleyet al., 1993; Wilmore et al., 1998). Participants were instructed to continue to remain in this supine semi-recumbent position during metabolic assessments. The laboratory was quiet, partially lit, and had a room temperature set at approximately 23' Celsius (Turley et al., 1993; Wilmore et al., 1998) and a relative humidity of approximately 70%. Measurements Resting energy expenditure was measured using the following three methods: (1) a metabolic cart with gas analyzers and computer system; (2) a portable handheld indirect calorimeter; and (3) the Harris-Benedict prediction equation. The first two methods were performed in the laboratory. In the current study, the metabolic measurement cart was the criterion method used in comparing measurements ofree.

13 6 Metabolic Measurement Cart. Open-circuit, indirect calorimetry analysis was performed using an Applied Electrochemistry Oxygen Uptake System OCM-2, Applied Electrochemistry S-3A/1 oxygen analyzer, and an Applied Electrochemistry CD-3A carbon dioxide analyzer (AE! Technologies, Pittsburgh, PA). During metabolic assessments, participants were instructed to breathe through a mouthpiece for 30 minutes while data of oxygen consumption (V0z) and carbon dioxide production (VC02) were collected at 30-second intervals (Segal, 1987). The gas analyzers were calibrated using known standard volume of gases containing 3.00% COz and 17.99% 02 prior to metabolic assessments. The last five minutes of measurements for VOz and VC02 were averaged to calculate the average respiratory exchange ratio (RER), which is the ratio of VCOz to V02 (McClave et al., 2003). The average RER was then used to obtain, from a reference table, the corresponding estimated energy expenditure (kcal) per liter ofvoz (McArdle, Katch, & Katch, 1999). Subsequently, REE in kcal'min- 1 was calculated and later extrapolated to REE in kcal'day"l using 1,440 minutes per day (Daly et al., 1985). This method of calculating REE was on average 0.59% higher (see Appendix E) as compared to using the equation by Weir (1949): Energy expenditure (kcal'day-l) = (3.91 x VOz) + (1.1 x VCOz) Portable Handheld Indirect Calorimeter. The design, protocol, and specifications of the BodyGem (Healthetech, Inc., Gloden, CO) have been described elsewhere in the literature by Nieman, et al. (2003), Melanson, et al. (2004), and Liou, et al. (2006). This portable handheld indirect calorimeter automatically performed a 5- second internal calibration process to initiate flow sensors prior to each test. Participants

14 wore a noise clip and breathed into the unit through a disposable mouthpiece for 7 approximately five to 12 minutes. Test duration depended on the time required for the unit to detect a steady state using proprietary calculations. Upon successful completion of a test, REE in kcal'day'! was displayed on a digital screen. The BodyGem does not have a carbon dioxide sensor. Thus, the unit uses a fixed respiratory quotient of 0.85 and a modified Weir equation to calculate REE. A proprietary sensor measures oxygen concentration during respiration. Other internal settings are programmed for sensor measurements of humidity, temperature, and barometric pressure. BodyGem data were collected prior to and following the previously described metabolic assessment. The average of two measurements was used for statistical analysis (see Appendix E). Previous studies (Melanson, et ai. 2004; Liou, et ai. 2006) have reported that holding the BodyGem results in an increase in REE that should be taken into account. In this study, the BodyGem was supported in place with a rolled-up towel against the neck and over the clavicles so that participants were not required to hold the unit during data collection. Regression Prediction Equation. The Harris-Benedict prediction equation is one of the most practical and widely used regression equations to estimate REE, which was developed using indirect calorimetry (Harris & Benedict, 1919; Lee & Nieman, 1996; Mahan & Escott-Stump, 2000). The use of this regression equation only requires measurements commonly obtained in clinical settings, such as body weight, stature, age, and gender (see Appendix E). This regression equation was based on participants with body weights ranging from 25.0 to kg, stature lsi to 200 cm., and ages ranging from 21 to 70 years (Harris & Benedict, 1919). Initially, the Harris-Benedict equation was developed to predict basal energy expenditure (BEE) in women and men:

15 8 BEE (f) = weight (kg) stature (cm) age (years) BEE (m) = weight (kg) stature (cm) age (years) Anthropometry Body weight and height were recorded in pounds and inches, respectively, using a Cardinal Detecto Certifier scale (Model #442, Webb City, MO). These measurements were converted to metric units and used to show the physical characteristics of participants and as variables in the Harris-Benedict prediction equation for REE (see Appendix E). Body mass index (BMl) was calculated as body weight divided by height squared (kg/m 2 ) (see Appendix E), which is an indirect measure of adiposity commonly used in epidemiological studies (Lee & Nieman, 1996). Body composition was estimated using skinfold measurements (see Appendix E). Lange skinfold calipers were used to take skinfold measurements of the chest, midaxillary, triceps, subscapular, abdomen, suprailiac, and thigh (Nieman, 1999). The Jackson Pollock seven-site formula, generalized regression equations, were used to estimate body density (Jackson & Pollock, 1978; Jackson, Pollock, & Ward, 1980) in men and women. The estimated body density was converted to percent body fat using the 8irl formula (Nieman, 1999). Statistical Analysis Descriptive statistics were generated to characterize data collected on participants (see Appendix F). Also a one-way, repeated measures ANOVA using a general linear model was performed to test for mean differences between measurements ofree (see Appendix G). A significant overall F-statistic was followed by post hoc pair-wise

16 comparisons to determine which means differed significantly from the others. Pearson 9 correlation coefficients (r) were used to determine the relationship between variables including age, weight, height, BMI, body fat, lean mass, and REE measurements (see Appendix H). The analyses were conducted using SAS version 9.1. Statistical significance was set an alpha level ofp < 0.05.

17 RESULTS 10 Descriptive statistics for participants and stratified by gender in the present study are presented in Table 1. The mean age for the participants was 26 years. Mean weight for women and men were 54.7 kg and 79.7 kg, respectively. Mean BMI (kg/m 2 ) for women and men were 22.1 and 26.7, respectively. Mean percentage body fat for women and men were 23.5% and 15.5%, respectively. Resting energy expenditure data for participants and stratified by gender are presented in Table 2. The overall mean REE (kcal'day"l) values for the metabolic cart, BodyGem, and Harris-Benedict equation were 1,321.7,1,280.5, and 1,609.7, respectively, are displayed in Figure 1. Table 1. Characteristics of participants (n=25) and stratified by gender (12 women and 13 men). Variables Age(yIs) Women Men Weight (kg) Women Men Height (em) Women Men BMI (kg/ml) Women Men Body fat (%) Women Men Lean Mass (%) Women Men Mean±Standard Deviation 26.2± ± ± ± ± ± ± ±7.4 I 72.4± ± ± ± ± ± ± ± ± ±6.0 Range (minimum and maximum values)

18 Table 2. Resting energy expenditure for participants (n=2s) and stratified by gender (12 women and 13 men). 11 Resting Energy Expenditure (kcal'day"t) Metabolic cart Women Men BodyGem Women Men Harris-Benedict equation Women Men Mean=Standard Deviation 1,321.7% ,081.0± ,543.9% ,280.5±255.9 I,077.I± ,468.3± ,609.7% ,345.7± ,853.4±233.4 Range (minimum and maximum values) , , , , , , , , , , , , ,325.7 Figure 1. Comparison of mean values ofree by methods. 2,500.00, , ~-;:: 2, =---1.; 11, , ~ Metabolic cart BBodyGem C Harris-Benedict Equation Methods

19 12 Pearson correlation coefficients were generated to assess the relationship between independent variables (weight, height, BMI, age, percent body fat, and lean mass) and the dependent variable (REE) measured with three different methods (metabolic cart, BodyGem, and Harris-Benedict formula). Pearson correlation coefficients indicated significant positive correlation (p < ) between weight (kg), height (cm), and BMI (kg/r02), and all three methods of measuring REE (Table 3). The other independent variables, including age, percent body fat, and lean mass, were not significantly correlated with the three methods of measuring REE. A one-way, repeated measures ANOY A using a general linear model was performed to test for mean differences between measurements ofree. Tests of withinparticipant effects indicated a significant difference (p < 0.05) between mean values of REE (Table 4). Post hoc pair-wise comparisons indicated a significant difference (p < 0.05) between mean REE values when comparing the Harris-Benedict formula and metabolic cart (Table 5). The mean difference in REE values between the metabolic cart and Harris-Benedict formula was -288 kca1s day-1 (p < 0.05), which suggested that on average, the Harris-Benedict formula overestimated REE by 21.8%. There was no significant difference between mean REE values when comparing the BodyGem and the metabolic cart.

20 Table 3. Pearson correlation coefficients between weight, height, BMI, metabolic cart, BodyGem, and Harris-Benedict equation. 13 Weight (kg) Metabolic cart 0.82 BodyGem 0.89 Harris-Benedict equation 0.98 Height (cm) BMI(kgIm') All r values were significant at p < level (2-tailed). Table 4. One-way, ANOV A with repeated measmes (within-participant effect) for differences between mean values ofree among three methods. Source Sum of squares df Mean square F Pvalue REE Error(REE) 1,608, , , Table 5. Post hoc pair-wise comparisons of mean values for REE between three methods. Method Mean REE (kcaj day<i) Method Mean REE (kcaj day i) Metabolic cart 1,321.7 Metabolic cart 1,321.7 BodyGem 1,280.5 Harris-Benedict 1,609.7 Difference (41.2) Difference 287.0' p<o.05, the Harris-Benedict equation significantly over predicted REE.

21 14 DISCUSSION The most important finding of this study was that the BodyGem gives accurate measurements ofree when compared to a metabolic cart. Additionally, the Harris Benedict equation was found to significantly overestimate REE in healthy young adults (p<0.05). The implication of these findings is that nutritionists involved in weight control programs can obtain accurate measurements ofree relatively inexpensively using the BodyGem rather than a metabolic cart. In this study, the Harris-Benedict equation overestimated the REE by a mean of288 kcai day"l. Therefore, if the Harris Benedict equation was used to prescribe an exercise and diet program in an attempt to lose weight, progress may be slower than anticipated. It has also been reported that the Harris-Benedict equation does not work weii for malnourished and overweight individuals (Roza & Shizga1, 1984; Melanson et ai., 2004). In populations that may be malnourished and overweight, directly measuring oxygen consumption using the BodyGem should result in accurate values ofree. No significant differences were found between mean values ofree from the BodyGem and the metabolic cart (p>0.05). This is similar to the findings of Nieman et ai. (2003) who also found no significant difference between mean values ofree obtained using the BodyGem and the Douglas bag method. Niemen et ai. reported that serial samples taken with the BodyGem gave acceptable REE estimates for both obese and non-obese individuals. Melanson et ai. (2004) also found no significant differences in between test measurements ofree from the BodyGem, and no significant difference in REE measurements when comparing the BodyGem with a metabolic cart, after

22 accounting for the energy required in holding the BodyGem. Kretsch et ai. (2004) 15 found no significant differences in estimating energy expenditure from the use of a combination of a physical activity questionnaire and the BodyGem, when compared to measurements obtained from the doubly labeled water method. These authors concluded that reasonable estimates of energy expenditure could be obtained using the questionnaire and BodyGem less expensively. Similarly, Liou et ai. (2006) reported no significant difference between the BodyGem and a metabolic cart when they accounted for the energy requirements from holding the BodyGem and concluded that the BodyGem was a reliable and valid device to measure REE. Therefore, data from the present study and others (above) suggest that the BodyGem is an accurate instrument to measure REE. In the present study, the Harris-Benedict prediction equation significantly overestimatedree by 288 kcal day I, or 21.8 %, as compared to the metabolic cart. Daly, et ai. (1985) found that the Harris-Benedict prediction equation overestimated basal energy expenditure by an average of 12.3%. The authors concluded that the overestimation by the Harris-Benedict equation was possibly related to current technology, various environmental factors, and underlying assumptions. Other authors have also reported that the Harris-Benedict equation systematically overestimates REE (Liou et ai., 2006; Mifflin et ai., 1990). Additionally, Frankfield et ai (2003) found the Harris-Benedict prediction equation significantly overestimated REE in 69% of overweight Participants by more than 10% as measured by open-circuit indirect calorimetry. The authors concluded that the Harris-Benedict prediction equation was inaccurate in predicting REE in obese men with BMI > 50 kg/m 2 and that the clinical application of this equation using adjusted body weight is not appropriate. In contrast,

23 16 application of this equation using adjusted body weight is not appropriate. In contrast, Roza and Shizgal (1984) found the Harris-Benedict prediction equation accurately estimated REE with a precision of about ± 14% in normal weight participants. In summary, the findings of the current study indicate that the BodyGem accurately measured REE in young healthy adult participants. These findings are consistent with the three published studies that support the validity and reliability ofree measurements by the BodyGem (Nieman et al., 2003; Melanson et al., 2004; Liou et al., 2006). Additionally, the Harris-Benedict equation significantly overestimated REE. Both of these findings were consistent with results from previous studies. Therefore, it was concluded that the BodyGem has been shown to provide a relatively affordable method to measure REE and that the Harris-Benedict equation should not be used to estimate REE if other methods, as discussed, are available.

24 REVIEW OF LITERATURE 17 Measuring Energy Expenditure Indirect calorimetry. Leff, Hill, Yates, Cotsonis, and Heymsfiled (1987) investigated the Validity of estimating daily resting energy expenditure (REE) measured in short durations using indirect calorimetry. Participants included two men and twelve women in relatively good health with an average age and body weight of26.8 years and 60.4 kg, respectively. Participants fasted overnight and were instructed to rest one hour in a bed prior to metabolic assessment Metabolic data included oxygen consumption and carbon dioxide production and were collected every hour over an eight-hour period on two different occasions using indirect calorimetry via gas analyzers, a pneumotachograph, and a facemask. Results indicated that the overall mean value for estimated daily REE was not significantly different between days. Also for individuals, results indicated no significant difference between days and that averaging the middle three measurements from the data collected produced the most accurate estimation of daily REE. The authors concluded that errors in estimating daily REE using the first three measurements from the data collected were possibly related to participants becoming familiar with testing apparatus. Thus, the authors suggested the use of a serial collection method, which excludes the initial measurements and uses the subsequent measurements to calculate an average value when estimating daily REE using indirect calorimetry. Segal (1987) investigated measurements ofree using three different methods of indirect calorimetry via open-circuit respirometry. Participants included 8 men and 10 women with mean ages of28 years and 29 years, respectively. Participants fasted

25 overnight and were instructed to rest 30 minutes prior to metabolic assessments. 18 Metabolic data included oxygen consumption, carbon dioxide production, and respiratory exchange ratio. The three methods of open-circuit respirometry included the ventilated canopy, facemask, and mouthpiece with nose clip. The later two methods collected metabolic data in two, five-minute intervals over a 20-minute period, while the ventilated canopy method collected data continuously for 20 minutes. The Weir equation was used to convert metabolic data including oxygen consumption and carbon dioxide production into energy expenditure in kilocalories (kcals). Results indicated no significant difference between the three methods in measuring respiratory exchange ratio, oxygen consumption, and energy expenditure in kcals min- I. Also findings indicated a high reliability between the mask and mouthpiece methods in measuring these variables. The authors concluded that when the mask or mouthpiece methods are performed appropriately, these methods are valid and reliable alternatives to the ventilated canopy method. Wells and Fuller (1998) investigated the reliability and accuracy of the Deltatrac Mk 1 Metabolic Monitor (Datex, Helsinki), an indirect calorimetry system. Three Deltatrac Mk 1 Metablic Monitors were simultaneously infused with carbon dioxide and nitrogen gases intermittently over a seven-hour period. Testing protocols included calibration without gases and with only one gas. Results indicated measurements including expired gases, energy expenditure, and respiratory quotient values were consistent within and between monitors. Also findings indicated measurements were consistent within and between tests on each monitor. The authors concluded that the Deltatrac Mk 1 Metabolic Monitor is a reliable device for indirect calorimetry.

26 Haugen, Melanson, Tran, Kearney, and Hill (2003) investigated the variability 19 of within-session and between-session measurements of REE. Participants included 37 healthy adults with a body mass index (BMl) ranging from 17 kg/m 2 to 34 kg/m 2 Participants were instructed to fast and refrain from exercise for 12 hours prior to metabolic assessments. Metabolic data including oxygen consumption and carbon dioxide production and were collected using indirect calorimetry via ventilated-hood method. Later, oxygen consumption and carbon dioxide production values were converted to REE in kcal'day"l using the Weir equation. Metabolic assessments were conducted twice in the morning and afternoon during two different sessions. Results indicated a significant time effect thus REE in kcal day"l were higher in the afternoon than in the morning. Also, findings indicated that intraclass correlation coefficients for REE measurements in the morning and afternoon between sessions were 0.94 and 0.92, respectively. The authors concluded that REE measurements taken within-session and between-sessions were reliable, and REE measurements in the afternoon were significantly higher than the morning by approximately 5% to 6%. McClave et al. (2003) investigated the criteria for achieving steady state when measuring energy expenditure using indirect calorimetry. Participants included 22 patients who were on mechanical ventilation for respiratory failure and had a mean age of 52.8 years. Metabolic data included oxygen consumption, carbon dioxide production and REE and were collected, using indirect calorimetry via a continuous metabolic monitor. During metabolic assessment, data was collected every minute for the first hour and every 15 minutes for the subsequent 23 hours. Total energy expenditure (TEE) was derived from the metabolic data collected over 24 hours. During the first hour of data

27 collection, three criteria of steady state and four time intervals were defined for 20 measurements of REE. The three criteria of steady state consisted of intervals of 5 consecutive minutes and defined as the percent change in respiratory quotient (RQ), the ratio of carbon dioxide production to oxygen consumption. These criteria were designated as RQ ~1 0%, RQ ~15%, and RQ ~O%. The four time intervals were also defined in minutes as 20 minutes, 30 minutes, 40 minutes, and 60 minutes. Resting energy expenditure (REE) values were determined from these seven data collection periods and later compared to values of estimated TEE for 24 hours. Results indicated that 16 participants met the criterion of RQ ~1 0% and all participants met the RQ ~15%, and RQ ~O% criteria for steady-state. Mean values ofree for the seven data collection periods were also significantly correlated, but were not significantly different than that of TEE for 24 hours. In addition, findings indicated that the highest correlation coefficient between mean values ofree and TEE was observed from achieving the criteria ofrq ~10% (r = 0.943) as compared to the time interval of60 minutes (r = 0.929). The authors concluded that when performing indirect calorimetry methods, allowing for less rigid criteria for steady state, may result in less accurate measurements ofree and estimations TEE for 24 hours. Therefore, authors proposed the use ofrq ~10% within 5 consecutive minutes as a standard criterion for steady-state. Portable indireet calorimetry systems. Wideman et al. (1996) investigated the validity of the Aerosport TEEM 100 (Aerosport, Inc., Ann Arbor, M1), a portable indirect calorimetry system. Twelve participants had a mean age and weight of 25 years and 66.8 kg. respectively. Metabolic data included oxygen consutnption and carbon dioxide production and were collected minute-by-minute simultaneously by a validated open-

28 21 circuit spirometric method and the portable calorimetry device. Metabolic assessments were conducted, while participants performed exercise protocols that bad variable and fixed intensities. The portable calorimetry device weighed 3.3 kg, was operated by a microprocessor, and was fitted with a medium flow pneumotach for this study. Results indicated that when the data sets of both exercise protocols were stratified by absolute oxygen consumption ranges, there existed a significant difference in mean values ofree between the validated system and portable calorimetry device. The portable indirect calorimetry device also overestimated the volume of oxygen consumed and volume of carbon dioxide produced at absolute oxygen consumption levels less than 2.0 I'min"!, and underestimated these variables at all levels greater than 2.5 I min"!. In addition, the portable indirect calorimetty device overestimated values of respiratory exchange ratio at absolute oxygen consumption levels less than 1.5 I min") and underestimated these values at levels greater than 3.0 I min"). The authors concluded that the portable indirect calorimetry device fitted with a medium flow pneumotach had potential use in field assessments of oxygen consumption, but questioned the accuracy in measuring carbon dioxide produced and respiratory exchange ratio. McLaughlin, King, Howley, Bassett, and Ainsworth (2001) investigated the validity of the COSMED K4 b 2 (COSMED s.r.1., Rome, Italy), a portable indirect calorimetry system. Participants included 10 men with a mean age, weight, and percent body fat of27.6 yrs, 75.3 kg, and 9.0%, respectively. Participants wore this portable calorimetry device on their chest and breathed into the device through a rubber facemask: that is attached to a flow meter. This portable calorimetry device uses patented oxygen and carbon dioxide sensors, and proprietary software. Metabolic data included oxygen

29 consumption and carbon dioxide production and were collected using the portable 22 calorimetry device and indirect calorimetry via Douglas Bag method. Metabolic assessments were conducted on two consecutive days. Participants rested for five minutes prior to metabolic assessments, then exercised on a cycle ergometer for five minutes at five workloads that progressed in increments of 50 W. Results indicated no significant differences in values for oxygen consumption and carbon dioxide production at rest between the portable calorimetry device and Douglas Bag method. Findings indicated that the portable calorimetry device reported a significantly lower value of respiratory exchange ratio at rest than the Douglas Bag method. The autbors concluded that the portable indirect calorimetry device was a valid measurement of oxygen consumption. Handheld indireet ealorimeter. Nieman, Trone, and Austin (2003) investigated the accuracy of a new hand-held indirect calorimeter, the BodyGem (HealtheTech Inc., Golden, CO), compared to the Douglas bag method. Participants included 43 women and 20 men with ages ranging from 21 years to 69 years and body mass index (BMI) from 19.1 kg/m 2 to 56.2 kg/m 2 Participants were stratified into three groups based on their BMI. Anthropometric measurements included height, weight, waist-hip circumferences. and skin-fold thickness. Metabolic data included oxygen consumption, carbon dioxide production, and REE in kcal day 1 and were collected using the BodyGem and indirect calorimetry via Douglas Bag method. Metabolic assessments were conducted in the afternoon on two different days over a two-week period. Participants fasted including no beverages containing caffeine for four hours, and refrained from exercise for 24 hours. Participants rested for 10 minutes prior to metabolic assessment. During metabolic

30 assessments, measurements were taken twice using the Douglas Bag method and the 23 BodyGem in a random counter balanced order. The Weir equation was used to calculate REE in kcal day-i. The BodyGem automatically performed a 5 second internal calibration process to initiate flow sensors prior to each test Participants wore a noise clip and breathed into the unit through a disposable mouthpiece for approximately five to 12 minutes, while the device used proprietary calculations to detect a steady state. Upon completion of an assessment, the device displayed REE in kcal day 1 on a digital screen. The BodyGem does not have a carbon dioxide sensor and used a proprietary sensor to measure oxygen concentration during respiration. Thus, the unit uses a fixed respiratory quotient of 0.85 and a modified Weir equation to calculate REE. Other internal settings were programmed for sensor measurements of humidity, temperature, and barometric pressure. Results indicated high within day reliability and correlation coefficients for oxygen consumption when comparing the BodyGem to the Douglas Bag method during metabolic assessments on both days. However, findings indicated no significant differences in estimated oxygen consumption and REE between the BodyGem and Douglas Bag method among all groups of participants. The authors noted that the Harris Benedict equation was used to estimate "near-basal metabolism" in participants, but the current study was not intended to or designed to investigate the differences between this regression equation and the BodyGem and Douglas bag method. The authors concluded that the BodyGem was an accurate and reliable device for measuring REE on obese and non-obese individuals, which is inexpensive and more practical for health professionals and the general public.

31 24 Melanson et ai. (2004) investigated the reliability and validity of the BodyGem (HealtheTech Inc., Golden, CO) compared to a metabolic cart. Participants included 14 men and 27 women. Men had a mean age, body mass index (BMI), and percent body fat of38 years, 25.9 kg/m 2, and 24.8%. Women had a mean age, BMI, and percent body fat of 42 years, 26.2 kg/m 2, and 39.1 %. Metabolic data included REE in kilojoules'day'( that was collected using the BodyGem and the metabolic cart with a canopy system and mouthpiece. Metabolic assessments were conducted on the mornings of two days. Participants were instructed to fast for 12 hours and refrain from physical activity for 24 hours. Participants rested in a supine position for 30 minutes prior to metabolic assessments. The temperature in the testing room ranged from 21 0 Celsius to 24 0 Celsius. Metabolic data were collected for 15 to 20 minutes using the metabolic cart and five to 10 minutes using the BodyGem. Oxygen consumption and carbon dioxide production values obtained from the metabolic cart and converted to REE using the Weir equation. Results indicated no between test differences in measurements ofree when using the metabolic cart and BodyGem. However, findings indicated that the BodyGem produced significantly higher mean value of REE than the metabolic cart, which was partially attributed to the energy expenditure needed to hold the unit during assessments. After adjusting for this additional energy expenditure, there was no significant difference between the BodyGem and metabolic cart. Findings also indicated that the Harris Benedict prediction equations significantly overestimated REE in overweight participants (BMI=25 kg/m 2 to 29.9 kg/m 2 ) as compared to the metabolic cart. The authors concluded that the BodyGem proved to be a valid and reliable device to measure REE.

32 Kretsch et al. (2004) investigated the accuracy of inexpensive methods for 25 estimating energy expenditure compared to doubly labeled water method. Participants included 10 women with a mean age and BMI of 30.8 years and 22.0 kglm 2, respectively. Participants' resting energy expenditure was measured during a fasted stated using a metabolic cart and the BodyGem. Physical activity for 14 days was measured by an accelerometer and activity logs. Energy expenditure was estimated by using a combination of measurements for REE and physical activity. Results indicated no significant differences in estimating energy expenditure from the use of a combination of a physical activity questionnaire and the BodyGem, when compared to measurements obtained from the doubly labeled water method. The authors concluded that these estimates of energy expenditure were reasonable and could be obtained using the questionnaire and BodyGem, which are inexpensive compared to doubly labeled water method. Liou, Chen, Chung, and Chu (2006) investigated the accuracy of the BodyGem for measuring REE in Taiwanese women. Participants included 30 Taiwanese women with a mean age, and BMI of 41.9 years and 24.0 kglm 2, respectively. Participants were stratified into three groups based on their BMI. Anthropometric measurements included height, weight, and bio-impedance analysis. Metabolic data included oxygen consumption, carbon dioxide production, and REE in kca1 day-1 and were taken in the mornings with the BodyGem and a validated metabolic cart. Participants fasted, including no beverages containing caffeine for eight hours, and refrained from exercise for 24 hours. Participants rested for about 30 minutes prior to metabolic assessments. During metabolic assessments, measurements were taken twice using the BodyGem and

33 once using the metabolic cart. The average of the two measurements from the 26 BodyGem was used for statistical analysis. Results indicated that the Harris-Benedict equation overestimated REE by 20.0% in all three BMI categories. Findings indicated a significant correlation between the mean values of REE measured by the BodyGem and metabolic cart. In addition, there was no significant difference found between the BodyGem and a metabolic cart after accounting for the energy requirements from holding the BodyGem during metabolic assessments. The authors concluded that the BodyGem was a more accurate method to estimate REE than the Harris-Benedict equation, and the validity of this handheld device was acceptable when compared to the metabolic cart. Regression prediction equation. Harris and Benedict (1919) investigated the determinants of human basal metabolism in developing regression equations for women and men. Participants included 103 women and 136 men who were relatively healthy. The participants were in a post-absorptive state, defined as fasting for hours and in "complete muscular repose." Results indicated correlations between stature and basal energy expenditure (BEE) in women (r = 0.60) and men (r = 0.80). Partial correlation analysis indicated that stature and body weight were significant independent contributors to the prediction of BEE. The decreases in BEE relating to age were almost the same between women and men when expressed as calories per body weight resulting in 24.5 calories kg-! and 25.7 calories kg-!, respectively. Prediction equations for women and men were developed using regression analysis with BEE as the dependent variable and weight, stature, and age as independent variables. The following BEE prediction equations for women and men, respectively, are reflected below:

34 BEE (t) = weight (kg) stature (em) age (years) 27 BEE (m) = weight (kg) stature (em) age (years) The authors noted that these equations were developed for body weights 25.0 to kg, stature 151 to 200 cm, and age 21 to 70 years. It was concluded that these equations would be appropriate in clinical settings since metabolic measurements were conducted on women, men, athletes, and individuals with disease. Weir (1949) investigated the derivation of a regression equation using indirect calorimetry to calculate energy expenditure with reference to protein metabolism. The liters of carbon dioxide production (VC02) to oxygen consumption (V~) corresponded to a ratio known as the respiratory quotient (RQ). Subsequently, RQ is used to determine the percent of substrate utilization during metabolism. Fat and protein can be estimated using a correction factor for urinary nitrogen. The investigator described in detail the steps in deriving the following abbreviated prediction equation for energy expenditure: The investigator concluded that this equation accurately predicts energy expenditure from the volume of expired gases via indirect calorimetry. Roza and Shizgal (1984) investigated the data previously reported by Harris and Benedict (1919) regarding the relationship between REE, gender, age, and body cell mass (BCM), and the accuracy of the Harris-Benedict equation on normal and malnourished participants. The compiled data included a participant population of 169 women and 168 men. The present study conducted regression analysis on REE in kcal'day"1 and age,

35 height, and weight Regression equations previously published in the literature were 28 used to calculate BCM. Estimating BCM is important since this represents "the total mass ofmetabolica11y active cells." Separate prediction equations were developed from regression analysis on REE, dependent variable, and age and BCM, independent variables. These participants underwent metabolic assessments using indirect calorimetry via the Douglas Bag method and mouthpiece. Measurements of expired gases were collected within an hour. Results indicated significant correlation coefficients r=o.83 and r=o.88 for women and men, respectively, in the previous Harris-Benedict studies. The relationship between REE and BCM were significantly correlated for female and male participants. The derived REE prediction equations using age and BCM as independent variables were significantly correlated for female and male participants with r=o.82 and r=o.86, respectively. Of the 74 patients, 41 patients were classified as malnourished based on sodium/potassium ratio described in the Iitemture. In malnourished patients, REE was predicted by measuring resting oxygen consumption, which had a significant correlation ofr=0.43. The Harris-Benedict equation was found to estimate REE with a precision of about ±14% in normal participants, but the equation did not work well with malnourished participants. The investigators concluded that the Harris-Benedict equation might not be appropriate for malnourished individuals. Daly et al. (1985) investigated the accuracy of the Harris-Benedict regression equation to predict basal energy expenditure (BEE). Participants included 68 women and 59 men with ages ranging from 18 to 67 years, who were divided into two experimental groups. In one group, BEE was measured using direct and indirect calorimetry via direct gradient-layer whole body and pneumotachograph systems, respectively. In the second

36 group, BEE was measured using only indirect calorimetry via a metabolic 29 measurement cart with a facemask. Participants fasted for approximately 12 hours and rested 15 to 30 minutes prior to metabolic assessments. Direct calorimetry measurements were taken 60 to 90 minutes. Indirect calorimetry measurements were taken in twominute intervals until reaching a steady state. Results indicated that BEE using the Harris-Benedict equation was significantly correlated with the pneumotachograph system (r=o.83) and metabolic cart (r=o.82), but significantly overestimated BEE by an average of 10.4% and 12.3%, respectively. The authors concluded that the Harris-Benedict equation overestimated BEE on average by 12.3%, which was possibly related to current technology, various environmental factors, and underlying assumptions. Mifilin et ai., (1990) derived a prediction equation for resting energy expenditure (REE) based on a sample of normal-weight and obese participants. Participants included 247 women and 251 men, who were stratified into five age categories by 10-year intervals. Participants who weighed 119% or less of ideal body weight (lbw) were defined as normal-weight and those who weighed 120% or more ofillw were defined as obese. Mean age of women and men were 44.6 years and 44.4 years, respectively. Resting energy expenditure (REE) was measured using indirect calorimetry via metabolic cart with a canopy hood. Participants fasted and refrained from physical activity and nicotine for 12 hours prior to metabolic assessments. Metabolic assessments were conducted repeatedly until a three-minute steady state was achieved, which lasted approximately 20 minutes. Results of various stepwise multiple regression analysis included a derived prediction equation for REE that included body weight, height, age, and gender, and that could explain 71 % of the variability in REE. This proposed

37 prediction equation was compared to three previously published prediction equations, 30 which included the Harris-Benedict equation. The proposed equation was the most accurate in predicting REE, and the Harris-Benedict equation overestimated REE on average by 5%. The authors concluded that the proposed equation was more accurate in predicting REE in normal and overweight male participants, and that the unexplained variability in predicting REE required further investigation. Gallagher et al. (1998) investigated in vivo approximations of whole body resting energy expenditure (REE), body cell mass (BCM), and fat-free mass (FFM) using magnetic resonance imaging (MR.I), echocardiography, and organ-tissue data referenced from the literature. Participants included five women and eight men with an overall mean age, body mass index, and percent body fat of31.2 years, 22.9 kglm 2, and 17.1%, respectively. Experimental tests included MRI, echocardiography, dual-energy X-ray absorptiometry (DXA), and indirect calorimetry. Authors evaluated three models including REE, BCM, and FFM as functions of organ-tissue masses. The predicted values from these models were compared to values obtained from reference methods in the literature that included indirect calorimetry, total body potassium, and DXA, respectively. During the postabsorptive state, participants were measured for 40 to 60 minutes using indirect calorimeter via ventilated hood, which was inside a respiratory chamber. Resting energy expenditure (REE) was calculated using the Weir equation. Results indicated significant correlations among measurements ofree, FFM, and BCM, and a significant correlation between values ofree, FFM, BCM and body weight. Also there were significant correlations among internal organs, including liver, kidneys and heart, and between these organs and skeletal muscle and adipose tissue. A multiple

38 regression analysis ofree and organ-tissue indicated that the independent variables, 31 including the brain and skeletal muscle, contributed significantly to the prediction and explained variance in REE. The authors concluded that their study was the first in presenting in vivo models, which explained the relationship between human energy expenditure and body composition. Illner, Brinkmann, Heller, Bosy-Westphal, and Muller (2000) investigated the relationship of the metabolically active components off at-free mass (FFM) and organ sizes in the prediction of resting energy expenditure (REE). Participants included 13 women and 13 men of normal weight In female participants, the mean age, weight, and body mass index (BMI) were 24.8 years, 62.8 kg and kglm 2, respectively. In male participants, the mean age, weight, and BMI were 26.2 years, 72.3 kg, and 22.S3 kglm 2, respectively. Participants fasted between 12 to 14 hours the night prior to metabolic assessments. Metabolic assessments were conducted in S-minute intervals over one hour using open-circuit indirect calorimetry via metabolic monitor. The testing site had a humidity of SS% and room temperature of 22 Celsius. Resting energy expenditure was calculated using the Weir equation and four prediction equations previously published in the literature. Body composition assessments included anthropometries, bioelectrical impedance analysis (BIA), dual-energy x-ray absorptiometry (DEXA), and magnetic resonance imaging (MRl). Anthropometries included skinfold and arm circumference measurements. Results indicated significant correlation coefficients between FFMBIAo muscle massdexa. and sum of organsmri. A multiple stepwise regression analysis indicated sum of organsmri and FFMBIA explained 89%of the variance in the prediction ofree. Findings indicated that skeletal muscleoexa and liver massmri were the only

39 independent variables that significantly contributed to the prediction of REE. The 32 authors concluded that the mass of metabolically active components ofree, including skeletal muscle and liver, contributed significantly to predicting of and explaining the variance in REE. Wang et ai., (2000) investigated a new modeling approach to further explain the complex relationship between resting energy expenditure (REE) and fat-free mass (FFM). The authors developed REE-FFM models at two levels of body composition referred to as whole body and tissue/organ body composition levels. This study further investigated the findings of seven previous experimental studies. Current models for REE-body composition relationships have two basic premises. First, REE is attributed only to metabolically active areas that generally consisted of atomic, molecular, cellular, tissue/organ, and whole body. Second, there were observed relationships between REE and metabolically active areas. This study focused on the relationship between REE and FFM at the tissue/organ and whole body levels. All categories in the tissue/organ level were metabolically active. At the whole body level, REE is estimated by the mass and metabolic rate of tissues and organs. Body mass is the only metabolically active area of the whole body level. At the whole body level. authors derived a model with a linear REE and FFM relationship in humans using findings from mammal studies in the literature. A model was also developed at the tissue/organ level to further explain energy expenditure, which was not addressed by the whole body model. The authors cited human studies in the literature that supported the concept of predicting whole body REE at the tissue/organ level. The authors concluded that this study was unique, since the proposed models at the whole body and tissue/organ levels were developed to investigate

40 33 the observed relationship between REE and FFM, which were not previously presented in the literature. It was further concluded that although their findings supported the hypothesis that a linear relation between REE and FFM in humans was similar to the curvilinear relationship in mammals, the proposed models were not appropriate for predicting REE in humans and further studies are warranted. Frankfield, Rowe, Smith and Cooney (2003) investigated the accuracy offour prediction equations for resting energy expenditure (REE) in non-obese and obese individuals. Among the four prediction equations were the Harris-Benedict equation and modified Harrison-Benedict equation, which used adjusted body weight. Participants included 76 men and 101 women who ranged from overweight to obese (BMI 90 kg/m 2 to :;::40 kg/m 2 ) and in ages from 37 to 44 years. Participants were placed in a reclined position during metabolic assessments. Metabolic assessments were conducted for 30 minutes. Metabolic data were collected using open-circuit indirect calorimetry via a canopy. Results indicated that the original Harris-Benedict equation was the second most accurate predict equation for REE, and significantly overestimated REE in 69"10 of overweight participants by more than 10% compared to open-circuit indirect calorimetry. The overestimation ofree in obese participants by the original Harris-Benedict equation was significantly different than the other prediction equations. The modified Harris Benedict equation appreciably underestimated REE in obese participants by more than 10% compared to the original equation. This underestimation in obese participants was consistent with the other prediction equations. The authors concluded that the original Harris-Benedict equation was inaccurate in predicting REE in obese men (BMI>50

41 34 kg/m 2 ) and that the clinical application of the modified Harris-Benedict equation is not appropriate.

42 Pretesting and Testing Environment 35 Berke, Gardner, Goran, and Poehlman (1992) investigated the influence of pretesting environment on resting energy expenditure (REE) in elderly participants during inpatient and outpatient testing conditions. Participants included 10 men and 8 women with an average age of 66 years. Participants participated in a progressive eight-week exercise-training program that included a cycling exercise protocol performed three days a week. Metabolic data included oxygen consumption, carbon dioxide production and respiratory quotient, and were collected using indirect calorimetry via ventilated hood method. The Weir equation was used to calculate REE. For inpatient REE assessments, participants were transported to a research center where they were served an evening meal that provided approximately 1,000 kcal. Upon awaking the next morning, participants were allowed to void, and then returned to their beds for REE assessments that lasted for 45 minutes. Subsequently, 10 days later, participants returned to the same inpatient research center for outpatient REE assessments. Participants were then instructed to fast overnight, use a vehicle for transportation, and refrain from exercise the day prior to assessment. Upon arriving at the research center, participants rested in a supine position for 30 minutes prior to REE assessments that 1asted for 45 minutes. Results indicated that REE values under outpatient conditions were significantly greater than inpatient conditions by 7% and 8% during pre-exercise and post-exercise training periods, respectively. Exercise training significantly increased REE values under outpatient and inpatient conditions when compared to pre-exercise and post-exercise training periods. In addition, fasting values of outpatient and inpatient RQ values significantly decreased when compared to pre-exercise and post-exercise training periods.

43 The authors concluded that outpatient conditions overestimated REE values when 36 compared to inpatient testing conditions, and the similar effect occurred due to the residual effects from exercise training in this population. Turley, McBride, and Wilmore (1993) investigated whether resting energy expenditure (REE) values were different when participants spent the evening at a clinic compared to spending the evening at home prior to metabolic assessment. Participants included six women and four men who were university students. Metabolic data included maximal oxygen consumption, carbon dioxide production, and respiratory exchange ratio and were collected using indirect calorimetry via a metabolic cart and facemask. Other experimental tests included anthropometric measurements and heart rate. Metabolic assessments were randomly conducted on all participants, three for evenings at clinic and three for evenings at home. Participants were instructed to fast for 12 hours, refrain from physical activity for 24 hours, and have at least seven hours of sleep prior to metabolic assessments. When participants spent evenings at home, they were instructed to minimize physical activity upon awakening and promptly drive to the clinic. When participants spent evenings at clinic, they were instructed to use the restroom upon awakening and walk to the testing room about 50 meters from the clinic. Prior to the 30- minute metabolic assessments, participants rested in a semi-recumbent position with the facemask on for approximately 30 minutes in a quiet, dimly lit room with ambient temperature at approximately 22 Celsius. Results indicated no significant difference in mean heart rate and REE values when comparing home and clinic conditions. The authors concluded that REE values were no different than when participants spent the evening at home or spent the evening at the testing clinic prior to assessment Authors

44 recommended that participants adhere to the following criteria prior to metabolic 37 assessments for REE: refrain from physical activity 24 hours; fast for 12 hours; conduct repeated measurements with defined limits; monitor heart rate prior to assessment; and rest with the face mask or ventilated hood prior to assessment Factors InOnencing Resting Energy Expenditure Thermic effects of exercise. Bielinski, Schutz, and Jequier (1985) investigated the residual effects of exercise and consuming a mixed meal during recovery on energy expenditure. Participants were ten relatively healthy men with a mean age of21.8 years. Metabolic data included oxygen consumption, carbon dioxide production, and respiratory quotient (RQ). Metabolic assessments were conducted over two days. On day one (control period), initial baseline measurements were taken prior to participants consuming a mixed meal. Subsequent to participants consuming a mixed meal postprandial resting energy expenditure (REE) was monitored for five hours using indirect calorimetry via a ventilated hood. Then for the next 13 hours, REE was monitored using a respiratory chamber. On day two (treatment period), participants followed the same experimental protocol in addition to walking on a treadmill as the percent grade progressively increased during a three hour period. Participants consumed a mixed meal during the recovery period that was 30 minutes post-exercise. Results indicated that postprandial REE values were significantly greater during the recovery period compared to the control period. Also findings indicated REE values were significantly greater on the morning after the treatment day as compared to the morning of the control period. In addition, postprandial RQ values during the post-exercise period were significantly lower when compared to the control day, and remained lower

45 during the recovery period up to 18 hours. The authors concluded that energy 38 expenditure was elevated four to five hours post-exercise and remained elevated until the next morning. Ballor, Harvey-Berino, Ades, Cryan, and Calles-Escandon (1996) investigated the effects of exercise training on weight management, resting energy expenditure (REE), and fat oxidation subsequent to previous weight loss. Participants included eight men and 10 women with a mean age, percent body fat, and weight loss of 61 years, 45%, and 9 kg, respectively. These participants completed an II-week weight loss program prior to being randomly assigned to one of two, 12-week exercise-training programs. The exercise-training programs consisted of an aerobic-training group and a resistance weight-training group. Each group performed exercise training three days per week. In the aerobic-training group, participants walked on a treadmill at approximately 50% of maximal oxygen uptake, which progressed from 20 minutes to 60 minutes per day. In the resistance weight-training group, participants performed seven exercises for three sets each and progressed from 50% to 80% of their one-repetition maximum. Metabolic data were collected using indirect calorimetry via ventilated hood. The Weir equation was used to calculate REE. Participants fasted for 12 hours, refrained from exercising 36 to 60 hours, and rested in a reclined position prior to metabolic assessment. Metabolic assessments were conducted on two subsequent days. After participants consumed a high-fat meal, post-prandial energy expenditure was measured in 30-minute intervals over a five-hour period. Results indicated that after weight loss, prior to exercise treatments, mean values ofree and fat oxidation were decreased in both experimental groups. In the resistance weight-training group, there was a significant decrease in

46 39 percentage of energy from fat. In the aerobic-training group, there were no changes in REE. Also findings indicated that weight loss, prior to exercise treatments, had no significant effect on post-prandial energy expenditure in both experimental groups. In addition, the resistance weight-training group had a significant increase in total energy expenditure during postprandial energy expenditure, which was significantly different from the aerobic-training group. The authors concluded that the exercise treatments had no significant effects on the reduced REE and post-prandial energy expenditure, which resulted from the previous weight loss program. Kraemer et al. (1997) investigated the effects of diet only and in combination with exercise on physiological and performance changes in overweight women. The participants included 31 women who were relatively healthy, but overweight with BMI ~ 27. Participants were randomly assigned to one of four experimental groups that included control (no treatment); diet only; diet with aerobic-training exercises; and diet with aerobic- and strength-training exercises. Experimental tests on participants were conducted initially for baseline measurements, at six and 12 weeks. Metabolic data included oxygen consumption and carbon dioxide production, and respiratory exchange ratio (RER). Participants fasted for 10 hours and rested in a semi-recumbent position for 30 minutes prior to metabolic assessments. During metabolic assessments for resting energy expenditure (REE) and graded exercise testing, data were collected every minute for 30 minutes and the last 6 minutes of testing, respectively, using indirect calorimetry via gas analyzers and a pneumotachometer. Other experimental tests included antropometric measures, body composition, one repetition maximum (1-RM), anaerobic power, nutritional assessment, and blood profile analysis. Results indicated significant

47 decreases in body weight and percent body fat for all groups at week six and week Also findings indicated that maximal oxygen consumption significantly improved in the diet and aerobic-training group and the diet with aerobic- and strength-training group, but I-RM significantly increased only in the later group. In addition, there was no significant difference in REE, expressed as kcal d- I or kcal. kg FFM"I. day-i, in all experimental groups. The authors concluded that moderate dietary restriction without an exercisetraining program similar outcomes on body composition, REE, and l-rm as compared to dietary restriction with exercise in overweight women. Wilmore et al., (1998) investigated the effects of a 20-week aerobic-training program on resting energy expenditure (REE) in untrained participants. Participants included 40 untrained men and 37 untrained women, who ranged in age from 17 to 63 years, and were recruited from a larger study known as the HERITAGE Family Study. The aerobic-training program consisted of a cycling protocol that progressed in intensity from a heart rate equivalent of 55% to 75% of maximal oxygen uptake (V(hmax) for 30 to 50 minutes per day from baseline to end of week 20. Participants performed the exercise-training program three times per week. Participants fasted for 12 hours and abstained from strenuous physical activity for 36 hours, and rested 30 minutes in a recliner chair in a semi-recumbent position prior to metabolic assessments. Upon awaking at their homes, participants were instructed to limit all physical activity, transport themselves by car to the laboratory, and were limited to walking no more than 100 meters. After arriving at the laboratory, participants were instructed to void and return to a quiet, dimly lit room with a temperature between 22 and 24 Celsius. Metabolic assessments were conducted pre-exercise training on two different days for 30

48 minutes. Metabolic data included oxygen consumption, carbon dioxide, and 41 respiratory exchange mtio and were collected using a metabolic cart with two types of facemasks. Other experimental tests included anthropometric and body composition measurements. Results indicated no significant difference between mean REB values when comparing pre-tmining and post-tmining periods. The authors concluded that the 20-week aerobic endumnce exercise tmining program had no significant effect on REB in this participant population even after controlling for covariants including age, gender, body composition, and pre-exercise training V02max. Thermie effects of food. Segal, Edafio, Blando, and Pi-Sunyer (1990) investigated the effect of caloric load on the thermic effect of food (TEF) in lean and obese men. Participants included II lean and II obese men with an average age and percent body fat of31 years and 32 years, and 15.3% and 32.4%, respectively. Three experimental treatments included resting energy expenditure (REB) after a 12 hour fast; REB after participants consumed a relative meal calculated as 35% of the average REB in (kcai/24 hr); and REB after participants consumed a 720-calorie meal. Metabolic assessments measured participants' responses at various intervals over a three-hour period. Prior to metabolic assessments, participants abstained from exercise for three days, fasted for 12 hours; and rested in the labomtory for 30 minutes. The room tempemture in the Iabomtory was 24 Celsius. Metabolic data included oxygen consumption, carbon dioxide, and respiratory exchange mtio and were collected using indirect calorimetry via a metabolic measurement cart with gas analyzers and a mouthpiece. Later REB was calculated using the Weir equation. Other experimental tests included body composition and oral glucose tolerance. Results indicated that REB

49 was not significantly different between lean and obese participants. The TEF from 42 both meals had a significant greater effect on the lean participants. There was also a significant interaction effect when comparing both meals and groups of participants. AdditionaIIy, the TEF from the 720-caIorie meal had a greater response on lean participants, and TEF from the relative meal had a greater response on obese participants. The authors concluded that TEF from relative and absolute meals had a significantly lower response in obese than lean participants. Weststrate (1993) investigated the methodology for measuring resting energy expenditure (REE) and diet-induced thermogenesis (DIT) in previous studies from January 1986 to August Participants included 49 and 22 normal weight men and women, respectively, and 32 obese women. Metabolic assessments were replicated using indirect calorimetry via a ventilated-hood system. Previous studies were categorized into the following: thermic effects of alcohol; effects of palatability on DIT; effects of dietary intervention on intraindividuai metabolism; effects of diurnai variation in metabolism; and effects of psychological stress on DIT and REE. For women, these categories were similar with the addition of the following: effects of ovular phase in the menstruai cycle; effects of body composition in obesity and metabolism; and body composition on weight management in obese participants. Results indicated mean intraindividuai coefficients of variation (CV) in REE and DIT for male participants were 6.0% and 27.5%, respectively. In female participants, mean intraindividuai CV in REE and DIT were 6.0% and 28.5%, respectively. DiurnaI variations between morning and afternoon and ovular phase of the menstruai cycle had no significant effect on REE. Also findings indicated that dietary intervention did not significantly decrease intraindividuai variability of REE and DIT in

50 43 male and female participants. In male and female participants with lower mean energy intakes, 86% and 83% of the DIT was observed over a two-hour postprandial period, respectively. However, in the male participants, there was a significant difference in postprandial energy expenditure in the third hour and in the female participants a significant difference was observed in the second hour. Authors concluded that an important result of this study was that the intraindividual variance was larger in DIT than in REE, and this large intraindividual variance in DIT could not be explained by methodology or dietary intervention. Reed and Hill (1996) investigated the effect oflength of time in measuring thermic effect of food (TEF) and developed a statistical model to further describe TEF. Participant data included 54 males and 77 females who participated in meal tests and TEF studies from 1988 to Participants' mean weight, fat-free mass, percent body fat, and age were 88.6 kg, 56.5 kg, 35.4%, and 38.1 years, respectively. Mean values for meal size, fat, carbohydrates, and protein were 3953 kj (kilojoules), 36.6%, 48.5%, 15%, respectively. Participants fasted overnight and rested 45 minutes prior to metabolic assessments. Resting energy expenditure assessments were conducted in the mornings for 30 minutes using indirect calorimetry system via ventilated hood. After participants consumed a test meal, metabolic data collected in 10 minutes and every 30 minutes thereafter for six hours. Results indicated a significant mean difference of 15.9 kj h 1 between energy expenditure at six hours and REE at baseline. There was a significant difference in TEF as a percentage of meal size when compared to TEF over six hours and over three, four, and five hours. A proposed statistical model showed a smooth curve over six hours, and multiple-regression analysis indicated a significant positive

51 correlation between meal size, fat-free mass, peak 1EF and total1ef. The authors 44 concluded that the effects of1ef lasted more than six hours and measuring the effects should last at least five hours. Thermic effects of caffeine, alcohol, and nicotine. Dulloo, Geissler, Horton, Collins, and Miller (1989) investigated the effects of caffeine on resting energy expenditure (REE), diet-induced thermogenesis (Ofl), and daily energy expenditure in lean and previously obese participants. Lean participants included six women and three men with a mean age and body mass index (BMI) of24.8 years and 20.6 kglm 2, respectively. Obese participants included six women and three men with a mean age and BMI of 28.2 years and 22.2 kglm 2 Metabolic data were collected using indirect calorimetry via the Douglas Bag method and a mouthpiece. Participants fasted 12 hours and rested in a seated position for 30 minutes prior to metabolic assessments. Subsequently, participants underwent three to four, five-minute baseline assessments for REE. This process was later repeated following one of four experimental treatments in ISO-minute periods over four different days. Experimental treatments included ingesting 100mg of caffeine; a 300 kcalliquid meal; 100mg of caffeine + 300kcalliquid meal; and 200 ml water placebo. Daily energy expenditure was measured for 24 hours in five lean and six previously obese participants using a human respirometer during two separate 12- hour periods for baseline and treatment data. During one assessment, participants ingested 100mg of caffeine every two hours coinciding with meals and between meals. Resting energy expenditure was calculated using the Weir equation. Results indicated that the 100mg of caffeine, similar to a cup of coffee, significantly increased REE by 3% to 4%, and frequent ingestion of 100mg of caffeine increased daily energy expenditure by

52 8% t 11 % in both participant groups over the initial 12-hour period. The authors 45 concluded that caffeine had significant effects on REE and daily energy expenditure, and suggested that the thermogenic response from caffeine be further investigated as a treatment for obesity. Weststrate, Wunnik, Deurenberg, and Hautvast (1990) investigated the effects of alcohol on resting energy expenditure (REE) and diet-induced thermogenesis (DIT). A total of twenty-two males participants were divided into two groups, which participated in two parts of this study. Males participants in part one and part two had mean age and percent body fat values of26.5 yrs and 17.0%, and 27.3 years and 16.0%,respectively. All participants were relatively healthy, of normal weight, and reported similar amounts of cigarette and alcohol consumption. The thermic effect of alcohol was studied in ten participants after oral administration of three different concentrations of 20g of alcohol during three different sessions. The effect of alcohol on DIT was studied in twelve participants after consuming one of two liquid test meals that contained alcohol or a placebo. Metabolic data were collected using indirect calorimetry via a ventilated hood. Metabolic assessments were performed for 90 minutes and 4 hours for thermic effect of alcohol and DIT protocols, respectively. Urine samples were collected to analyze for urea-nitrogen excretion values. Results indicated that REE increased significantly after the participants received the three experimental treatments, but no significant difference in energy expenditure after participants consumed test meals with and without alcohol. The authors concluded that moderate consumption of alcohol of20g resulted in a significant thermic effect on REE, similar to fat and carbohydrates, but did not have a significant effect on DIT even when ingested with a liquid test meal.

53 Perkins, K.A., Epstein, L.H., Stiller, et al. (1990) investigated the acute 46 thermogenic effects of nicotine and a fixed caloric intake of smokers and nonsmokers. Participants included 10 males with age ranging from 18 to 30 years. Male smokers averaged 19.8 cigarettes per day with an average of 5.1 years of experience. Metabolic data were collected using indirect calorimetry via respiratory mask, gas analyzers, and computer system. Experimental treatments of nicotine and placebo were admjnjstered via nasal spray. The test meal had an average caloric intake of259 kcal and consisted of 67% carbohydrates, 10% protein, and 23% fat. Participants were instructed to fast overnight including abstinence from caffeine and exercise, not to smoke, and rest in a reclined position for 15 minutes prior to metabolic assessments. Baseline resting energy expenditure (REE) assessments were performed for 15 minutes. After participants consumed a test meal, treatment of nicotine, placebo, or combination, metabolic assessment was conducted in 20- minute intervals. This protocol was repeated five times over a two-hour period. Results indicated no significant differences in baseline REE values and in effects ofdit between smokers and nonsmokers. For smokers, the increases in REE were higher from the test meal and nicotine treatment when administered individually and not combined in participants. For nonsmokers, there was a significant increase in REE due to nicotine alone. The authors concluded that the combination of the nicotine treatment and test meal had no significant thermic effect when compared to the effects of test meal alone on REE in smokers and nonsmokers. Perkins, Epstein, and Pastor (1990) investigated the energy intake and energy expenditure in female smokers and nonsmokers during a three-week period. During the first week, smokers were allowed to smoke usual amounts of cigarettes. The second

54 week, smokers stopped completely from smoking cigarettes. Then in the third week, 47 participants were allowed to start smoking usual amounts of cigarettes similar to week one. Seven female smokers with mean age, body weight, and smoking history of20.3 years, 59.8 kg, and 17.4 cigarettes/day for 3.6 years. Participants rested 10 minutes and refrained from smoking cigarettes for 30 minutes prior to metabolic assessments. Metabolic assessments were conducted twice during 15 minute periods every weekday. Other experimental measurements included dietary intake and physical activity records. Results indicated that the female participants had a significant increase in REE during weeks two to three. Also findings indicated that caloric intake significantly increased during weeks one and two, and significantly decreased during weeks two and three. These changes in caloric intake were mainly attributed to alcohol consumption. The authors concluded that subsequent to smoking cessation, any relapse resulted in increases in caloric intake and that REE was consistent with the literature. Anthropometry Jackson and Pollock (1978) sought to derive generalized prediction equations for body density in men. A total of 403 male participants were randomly assigned to either a validation group or a cross validation group consisting of 308 and 95 participants, respectively. These participants were studied over a four-year period. The participants in the validation group had a mean age, height, weight, and percent fat of32.6 years, m, 74.8 kg, and 17.7%, respectively. In the cross-validation group participants had a mean age, height, weight, and percent fat of33.3 years, m, 77.6 kg, and 18.7%, respectively. Anthropometric measurements included standing height, body weight, skinfolds, hydrostatic weighing, and waist and forearm circumferences. Body density

55 was calculated using the Brozek formula and subsequently converted to percent body 48 fat using the Siri formula. Results indicated a correlation coefficient ofr=o.98 between body density equations using the sum of three and seven skinfold measurement. Also age, skinfold, and circumference measurements were independent variables that significantly contributed to the prediction of body density. In addition, findings indicated the correlation coefficient between the predicted and actual values of body density was r=o.90. The authors concluded that these derived generalized prediction equations for body density were valid and applicable to heterogeneous groups of men. Jackson, Pollock, and Ward (1980) sought to derive generalized prediction equations for body density in women. A total of 331 female participants were randomly assigned to either a validation group or a cross validation group consisting of249 and 82 participants, respectively. These participants were studied over a six-year period. The participants in the validation group had a mean age, height, weight, and percent fat of years, cm, kg, and 24.09%, respectively. In the cross-validation group participants had a mean age, height, weight, and percent fat of29.94 years, em, kg, and 24.83%, respectively. Participants fasted for at least six hours and abstained from smoking prior to experimental testing. Experimental testing was conducted within seven days prior to and subsequently to menstruation. Anthropometric measurements included standing height, skinfolds, gluteal circumferences, and hydrostatic weighing. Body density was calculated using the Brozek formula and subsequently converted to percent body fat using the Siri formula. Result indicated independent variables, except age in the cross-validation group, were significantly correlated to body density. Also, the sum of skinfold measurement, except age with sum

56 49 of four, and gluteal circumferences were significantly different from zero. In addition, body density equations using the sum of three, four, and seven skinfold measurement had multiple correlation coefficients ranging from r=o.838 to r=o.867. Although when these equations were cross validated the correlation coefficients ranged from r=0.799 to r=o.827, after participants were stratified into age groups, by decades, large standard errors were observed in participants 40 year and older. The authors concluded that these derived genera1ized prediction equations for body density were valid when applied to heterogeneous groups of women, but cautioned the accuracy in those 40 years and older.

57 REFERENCES 50 Ballor, D.L., Harvey-Berino, J.R., Ades, P.A., Cryan, J., & Calles-Escandon, J. (1996). Contrasting effects of resistance and aerobic training on body composition and metabolism after diet-induced weight loss. Metabolism, 45(2), Berke, E. M., Gardner, A. W., Goran, M. I., & Poehlman, E. T. (1992). Resting metabolic rate and the influence of the pretesting environment. American Journal of Clinical Nutrition, 55(3), Bielinski, R., Schutz, Y., & Jequier, E. (1985). Energy metabolism during the postexercise recovery in man. American Journal of Clinical Nutrition, 42(1), Daly, J.M., Heymsfield, S.B., Head, C.A., Harvey, L.P., Nixon, D.W., Katzeff, H., et ai. (1985). Human energy requirements: overestimated by widely used prediction equation. American Journal of Clinical Nutrition, 42(6), Dulloo, A.G., Geissler, C.A., Horton, T., Collins, A., & Miller, D.S. (1989). Normal caffeine consumption: influence on thermogenesis and daily energy expenditure in lean and postobese human volunteers. American Journal of Clinical Nutrition, 49( 1), Frankenfield, D.C., Rowe, W.A, Smith, J.S., & Cooney, R.N. (2003). Validation of several established equations for resting metabolic rate in obese and nonobese people. Journal of the American Dietetic Association, 103(9), Gallagher, D., Belmonte, D., Deurenberg, P., Wang, Z., Krasnow, N., Pi-Sunyer, F.X., et ai. (1998). Organ-tissue mass measurement allows modeling ofree and metabolically active tissue mass. American Journal of Physiology. Endocrinology and Metabolism, 275(2), E249-E258. Groff, J. L., & Gropper S. S (3 m ed). (2000). Advanced nutrition and human metabolism. Body composition and energy apenditure (pp ). Belmont, CA: Wadsworth. Harris, J.A., & Benedict, F.G. (1919). A biometric study of basal metabolism in man. Publication 279. Washington, DC: Carnegie Institution of Washington. Haugen, H.A., Melanson, E.L., Tran, Z.V., Kearney, J.T., & Hill, J.O. (2003). Variability of measured resting metabolic rate. American Journal of Clinical Nutrition, 78(6),

58 51 DIner, K., Brinkmann, G., Heller, M., Bosy-Westphal, A., & Muller, MJ. (2000). Metabolically active components of fat free mass and resting energy expenditure in nonobese adults. American Journal of Physiology. Endocrinology and Metabolism, 278(2), E308-E315. Jackson, A.S. & Pollock, M.L. (1978). Generalized equations for predicting body density of men. British Journal of Nutrition, 40(3), Jackson, A.S., Pollock, M.L., & Ward, A. (1980). Generalized equations for predicting body density of women. Medicine and Science in Sports and Exercise, 12(3), Kraemer, W.J., Volek, J. S., Clark, K. L., Gordon, S. E., Incledon, T., Puhl, S. M., et al. (1997). Physiological adaptations to a weight-loss dietary regiment and exercise programs in women. Journal of Applied PhySiology, 83(1), Kretsch, M.J., Blanton, C.A., Baer, D., Staples, R., Horn, W.F., & Keim, N.L. (2004). Measuring energy expenditure with simple low cost tools [Abstract]. Journal of the American Dietetic Association, 104 (Suppl. 2), 13. Lee, R.D., & Nieman, D.C. (1996). Nutritional Assessment (2 nd ed.). Anthropometry (pp ). Boston: McGraw-Hill. LefT, M.L., Hill, J.O., Yates, A.A., Cotsonis, G.A., & Heymsfield, S.B. (1987). Resting metabolic rete: measurement reliability. Journal of Parenteral and Enteral Nutrition, 11(4), Liou, T.H., Chen, C.M., Chung, W.Y., & Chu, N.F. (2006). Validity and reliability of BodyGem for measuring resting metabolic rete on Taiwanese women. Asia Pacific Journal Clinical Nutrition, 15(3), Mahan, L.K. & Escott-Stump, S. (2004). Kmuse's Food, Nutrition, and Diet Therepy (11 th ed.). Energy (pp ). Philadelphia: W.B. Saunders Company. Melanson, E.L., Coelho, L.B., Tmn, Z.V., Haugen, H.A., Kearney, J.T., & Hill, J.O. (2004). Validation of the BodyGem hand-held calorimeter. International Journal of Obesity and Related Metabolic Disorders, 28(11), McArdle W. D., Katch F. I., & Katch V. L. (1999). Sports and Exercise Nutrition. Measurement of energy: food and physical activity (pp ). Baltimore: Lippincott Williams and Wilkins. McClave, SA, Spain, D.A., Skolnick, J.L., Lowen, C.C, Kieber, M.J., Wickerham, P.S., et al. (2003). Achievement of steady state optimizes results when performing indirect calorimetry. Journal of Parenteral and Enteral Nutrition, 27(1),16-20.

59 52 McLaughlin, J.E., King, G.A., Howley, E.T., Bassett, D.R., & Ainsworth, B.E. (2001). Validation of the COSMED K4 b 2 portable metabolic system. International Journal o/sports Medicine, 22(4), Miftlin, M.D., St Jeor, S.T, Hill, L.A., Scott, B.J., Daugherty, S.A., & Koh, Y.O. (1990). A new predictive equation for resting energy expenditure in healthy individuals. American Journal 0/ Clinical Nutrition, 51 (2), Nieman, D.C. (1999). Exercise Testing and Prescription a Health-Related Approach (4 th ed.). Body composition (pp ). Mountain View, CA: Mayfield Publishing Company. Nieman, D.C., Trone, G.A., & Austin, M.D. (2003). A new handheld device for measuring resting metabolic rate and oxygen consumption. Journal o/the American Dietetic Association, 103(5), Perkins, K.A., Epstein, L.H., Pastor, S. (1990). Changes in energy balance following smoking cessation and resumption of smoking in women. Journal 0/ Consulting and Clinical Psychology, 58(1), Perkins, K.A., Epstein, L.H., Stiller, R.L., Sexton, J.E., Fernstrom, M.H., Jacob, R.G., et al. (1990). Metabolic effects of nicotine after consumption of a meal in smokers and nonsmokers. American Journal 0/ Clinical Nutrition, 52(2), Reed, G.W., & Hill, J.O. (1996). Measuring the thermic effect of food. American Journal o/clinical Nutrition, 63(2), Roza, A.M., & Shizgal, H.M. (1984). The Harris Benedict equation reevaluated: resting energy requirements and the body cell mass. American Journal 0/ Clinical Nutrition, 40(1), Segal, K.R. (1987). Comparison of indirect calorimetric measurements of resting energy expenditure with a ventilated hood, face mask, and mouthpiece. American Journal o/clinical Nutrition, 45(6), Segal, K.R., Edafio, A., Blando, L., & Pi-Sunyer, F.x. (1990). Comparison of thermic effects of constant and relative caloric loads in lean and obese men. American Journal o/clinical Nutrition, 51(1), Turley, K. R., McBride, P. J., & Wilmore, J. H. (1993). Resting metabolic rate measured after participants spent the night at home vs at a clinic. American Journal 0/ Clinical Nutrition, 58(2),

60 Wang, Z., Hesbka, S., D. Gallagher, C. N. Boozer, D. P. Kotler, & S. B. Heymsfield. (2000). Resting energy expenditure-fat-free mass relationship: new insights provided by body composition modeling. American Journal of Physiology: Endocrinology and Metabolism, 279(3), E539-E Weir, J.B. (1949). New methods for calculating metabolic rate with special reference to protein metabolism. Journal of Physiology, 109(1-2), 1-9. Wells, J.C., and Fuller, N.J. (1998). Precision and accuracy in a metabolic monitor for indirect calorimetry. European Journal of Clinical Nutrition, 52(7), Weststrate, J.A. (1993). Resting metabolic rate and diet-induced thermogenesis: a methodological reappraisal. American Journal of Clinical Nutrition, 58(5), Westrate, J.A, Wunnink, I., Deurenberg, P., & Hautvast, J.G. (1990). Alcohol and its acute effects on resting metabolic rate and diet-induced thermogenesis. British Journal of Nutrition, 64(2), Wideman, L. Stoudemire, N.M., Pass, K.A., McGinnes, C.L., Gaesser, G.A., & Weltman, A. (1996). Assessment of the Aerosport TEEM 100 portable metabolic measurement system. Medicine and Science in Sports and Exercise, 28(4), Wilmore, J. H., Stanforth, P. R, Hudspeth, L. A, Gagnon, J., Daw, E. W., Leon, A. S., et ai. (1998). Alterations in resting metabolic rate as a consequence of 20 wk of endurance training: the HERITAGE Family Study. American Journal of Clinical Nutrition, 68(1),

61 APPENDICES 54 Appendix A Communications Communication with Josh Stucker, HeaItheTech. KuI1 KellyGo I'tam: -To: 8oIIjocI: BealtheTech's BodyGe:gd'M is ftot available t.o COllSUl:II:Bru at this time. SodyGem1'M 18 available at sel.eot fitrl.asa and. we1qht loss C!8llte.rs acrgslil the US ami it's quite possible that there ill one near you. We will soon be publlabing a BodyGern Directory that lists those O:t9lUUzat.1OllS where you can obtain a motabollc measurement with Boc:lyG4;rm:. If you would lijce more infotmatlon on how yo\lr fi.tneaa club or weight 1088 centsz: con obtain a Bodyt;.emTM, pl~se c:ontact ow: corporate office in COlorado (303) S26-50BS and ask for a sales tttanager or EHlJI11l supportbhealthetech.cold <maj.ltolsupj)ortbheu.thetec:h.c:om> for a aales representative in your area. Pleaae register for nawa upciate froll! Healthe'rech on our website <httpa/lww.healthetech.com> -----original Maaaaqe----- From. kdd.kam1qqte.net [malltolkmikamisgte.net] Senti Wed1leaday, Octoboc 24, AM TOI Support Subjects Support Inquiry Request From, Namel Kw:t Go '1'lUel lkm1kam18gte.net -y, Address I Kilauea Ave. C1tYI Honolulu State I HI Zip/Post Codes CoUbtryl Phonel F.,,, DesktopOS Verst Pal..mOS Verst Brcwaert Commonta, I would like mora bfoaaatj.od regazd..ing t:ha BoclyGem. ld particular the references reqarding the sc1entific based 8tud1es that validate this equ1}l1xtut. Also how 1& the company IDEO 1twolved witll you company to the BodyGem. Sincerely, Kurt Go IDaoming mail is certified V1.r:us Free.

62 55 Communication with Heather Alexander, HealtheTech. Kurt KellY Go From: sent: To: Subject: HeaIher_~cam) Friday, January 18, 20021:47 PM kmlicsmlcl9te.net I!ocIyGom- _Tech Hi Kurt, Nice to talk to you this week. 'lhanks for sharing yolu protocol with us. I have a few cotmaents that may help you as you move forward. Give IllS a call after you speak with your advisor and we can discuss as needed. 1. Gary indicated that we would be able to prov1cle mouthpieces for the atudy. I am totally supportive of thia- feel. free to call me if you need anything. 2. How will you procedd the output hom tho metabolic cart? Row will steady state be defined? BodyGem currently uses all of the data collected in the at-aaely state calculation. When compadng the BodyGem and metabolic cart: you may want to colleot data for the same length of time on both devices. I can work with Gary to get you BodyGems that will collect ciata for a speo1f1o length of tiitte. My suggestion would be 8-10 minutes. Let me know if this works with your metabolic cart protocol. 3. When we talked you mentioned that you had seen the abstract published by Nieman. The Douglas Ba9 COl!Q)adson works wall for validation. We are also intorested in comparisons to metabolic carts. BeCAuse ow: sensor technology 1& very accurate you may actually find. that BodyGem me88w:6s RMR more accurately thad the metabollc eart. You m1ght consider thinking about these as comparison studiea.rather thad actual valida~on ulals. 'l'hanks, Heat:hel: Alexander MB RD Rea1the'l'ech, Inc. Manager Clinical Research Incoming mail is certified Virus Free. Checked by AVG anti-virus system thttp11iwww.gdsoft.com). Versionl Virus Databasel Release Datel 1/11/02

63 56 Communication with Jay Kearney, HealtheTech. Kwt KeIIyGo Kurt, it was qood to talk to you today. Bss1stance to you. Attached is a paragraph that may be of some S&l1802!'S measured humidity, temperature and baj:'olitgtd.q pressure for use in internal calculations. Ozyqen concentration in the inspired. anc1 up1red airflow is measured by IS proprietary dual cham"l fluorescent quenching sensor. The principle of operation is based on the deactivation of ruthenium in the presence of oxygen. A ruthenium cell 18 excited :by an idtemal light source and fluoresces. 'lh19 reaction is quenched by the presence of oxygen, and the amount of quenching i.e proportional to the concentration of oxygen. 'l'his sensor has til rapid., 50 lnsec response time, and the osyven concantration in the flow path is sampled at 10 Bz. The volume of 11l&Pired and expired air 18 meaaund using ul trason.t.e sensing technology. There 1s a tranedq~r at eocl:l end of the flow tube that amits a BOund pulse. The transmission time from the 88Jld.ing to the receivin.f;j transducel' 18 increased or decreased 1n proportion to the rate and direction of qas flow. Theae sensors work at a rate of 100 Hz. 'lhe 8oc1yGem(tm) ues atandarci metaj)olic fomulas to calculate o~en uptajce. RMIl 18 calculated from oxy;en consumption, a f1ae<l respiratory quotient (RO) of 0.85, and grams of urinary n1trogen calculated fram mean energy and protein intake of 0'.8. malea and females \t8ing a modified Weir eqaatioul RMR (kcol day-l) (3.941 x.02) + ( ) - (2.11 gm urinary nit_).021. in L.day-1, \JIll urinary nit_. ((kcal.day-l x 0.16)/4)/6.25. Previous testing with the BodyGem(tm) using a mechanical metabolic s1mulator cletej:m1necl that the coefficient of variation for repeated tsst1n9 is less than 1.5'. BoclyGem(tID) sensor &paclifications are as follows. pressure *4 mmhljj with a resolution of 0.05 mmrgl temperature tlec, O.OlECI humidity t4.2' rh, 0.01' rbl oxygen to.4-0.8' 02, 0.03% 021 volume %0.5%, /s8c. Jay '1'. mamey, PhD, I'ACSM V.P. Clinical Affaire Realthe'lech, Inc. 523 Park Po1nt Drive, 3rd.. Floor Golden, CO Offico (303) X 328 Home office (119) Cell (120) <:I tlcearney8haalthetecb. colli> Incoming mail is certified. Virus Free. Checked ~y AVG anti v1rus system {httplllwww.v:1soft.com Version: I Virus Database Release Date: 4/17/02

64 57 AppendixB Infonned Consent AGREEMENT TO PARTICIPATE IN Validation of tbe BodyGem on restinl! metabolic rate Kurt Go Graduate Student Principal Investigator Kilauea Avenue Honolulu, HI (hm) (dig. ph.) Ronald Hetzler, Ph.D. Faculty Advisor Department of Kinesiology and Leisure Science College of Education University of Hawaii at Manoa Honolulu, HI The purpose of this research project is to investigate the accuracy of the BodyGem (Healthetech, Colorado), an instrument that measures resting metabolic rate (RMR), against other standard instruments such as a computerized gas analyzer and prediction equations. You will be asked to report to KLS Teaching Laboratory (Stan Sheriff Center, room 100) for a complete assessment of RMR and body composition, which requires at least 1 Va hours (2 hours maximum). However, there is a possibility that you may be asked to report for a second assessment if the need arises. Prior to participation you will be required to read and sign an informed consent and complete a medical history form. You will be asked to refrain from any strenuous physical activity for at least 24 hours or more prior to the day of assessment. Also you will be asked to fast (not eat) for at least 10 hours, only drink water, and refrain from smoking prior to assessment. In order to reduce the amount of activity prior to assessment, you will be asked to obtain a ride or use the campus shuttle (a schedule will be provided) as a means of transportation to the laboratory. Upon arrival you will be asked to walk to the restroom to void yourself. After returning to the laboratory you will be asked to rest comfortably on your back:, on a padded surface, for 30 minutes without falling asleep. Following this rest period, you will wear a nose clip and place a disposable, sterile mouthpiece of the BodyGem into your mouth for approximately 5 to I 0 minutes for a measurement ofrmr. Next, you will place a different sterile mouthpiece attached to a computerized gas analyzer for 30 minutes to obtain another measurement ofrmr. Subsequently, you will take another measurement of RMR using the BodyGem as previously described. Lastly, you will complete the assessment for body composition. First, your height (inches) and body weight (pounds) will be recorded and later used to calculate your RMR using prediction equations and body mass index (BMI), body weight divided by height squared (kg/m 2 ). Second, you will stand while a researcher performs skinfold measurements, using Lange skinfold calipers, at seven different sites of your body. The researcher may need to obtain 2 to 3 measurements at each of the following anatomical landmarks: the chest, below armpit (midaxillary), back of arm (triceps), shoulder blade (subscapular), waist (abdomen), hip

65 58 (suprailiac) and thigh. The 7-site skinfold method estimates percent body fat from generalized prediction equations. There is a possibility that you may experience mild physical discomfort and redness of the skin over the anatomical sites previously described during the skinfold measurements. This is a common reaction as a result of skinfold measurements. By participating in this study you will obtain the results of your assessment for resting metabolic rate and your body composition. This information could be used to assist you in a personal weight management program with the advise of a allied health professional such as a physician or registered dietitian. In the event of any physical injury from the research procedure only immediate and essential medical treatment will be available. You should understand that if you are injured in the course of this research procedure that you alone maybe responsible for the costs of treating your injuries. Your participation is this research project is entirely voluntary. There will be no penalty if you refuse to participate. You are allowed to withdraw at anytime during the investigation without prejudice. Efforts will be made to keep your personal information confidential. The researchers and you will be the only persons: (I) present in the laboratory while tests are being administered; (2) having access to your personal information and recorded values; and (3) your name or identity will not be shown or indicated on any report of this data. We cannot guarantee absolute confidentiality. Your personal information may be disclosed if required by law. I certify that I have read and that I understand the foregoing, that I have been given satisfactory answers to my inquiries conceroing project procedures and other matters and that I have been advised that I am free to withdraw my consent and to discontinue participation in the project or activity at any time without prejudice. I understand that if I am injured in the course of this research procedure, I alone maybe responsible for the costs of treating my injuries. I herewith give my consent to participate in this project with the understanding that such consent does not waive any of my legal rights, nor does it release the principal investigator or the institution or any employee or agent thereof from liability for negligence. Print Name of Participant Date Signature of Participant Date If you cannot obtain satisfactory answers to your questions or have comments or complaints about your treatment in this study, contact: Committee on Human Studies University of Hawaii 2540 Maile Way Honolulu, HI (808)

66 59 AppendixC MEDICAL HISTORY FORM Nwme DmeofBmn Nwmeofronmapenon Relationship Home Phone Work Phone Physician Office Phone Hospital Preference Phone MEDICAL HISTORY Please identify any illness or rondltlon that you have or had that might restrict your participation in physical activity. If you answer yes to any item, please indicate whether any aid requirements are needed. Condition Circle One Circle One or Botb Fainting Spells Headache Convulsion I Epilepsy Asthma High Blood Pressure High Blood Cholesterol Kidney Problems intestinal Disorder Hernia Diabmes Heart Disease Angina Dentalplate Poor Vision Poor Hearing Skin Disorder Metabolic Disorder Stroke Allergies YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO ~~i~ Joint Dislocation YES NO s~i~ Otber YES NO s~i~ PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PAST PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT PRESENT Aid Requmements?

67 Please identify any injury that you have or had that might restrict your participation in physical activity. If you answer yes to any item, please indicate whether any aid requirements are needed. Injnry Circle One Circle One or Both Toes YES NO PAST PRESENT Feet YES NO PAST PRESENT Ankles YES NO PAST PRESENT Lower Legs YES NO PAST PRESENT Knees YES NO PAST PRESENT Thighs YES NO PAST PRESENT Hips YES NO PAST PRESENT Lower Back YES NO PAST PRESENT Upper Back YES NO PAST PRESENT Ribs YES NO PAST PRESENT Abdomen YES NO PAST PRESENT Chest YES NO PAST PRESENT Neck YES NO PAST PRESENT Fingers YES NO PAST PRESENT Hands YES NO PAST PRESENT Wrist YES NO PAST PRESENT Forearms YES NO PAST PRESENT Elbows YES NO PAST PRESENT Upper Arms YES NO PAST PRESENT Shoulders YES NO PAST PRESENT Head YES NO PAST PRESENT specify Other YES NO PAST PRESENT specify Are you currently taking any medication? YES NO If yes, please describe medication, amount and reason taking. 60 Do you have any adverse reaction to medication? YES NO If yes, what medications and what reactions? Has a physician placed any restrictions on your present activity? YES NO If yes, please explain.

68 61 AppendixD Human Subjects Forms App!lratlgn for New Appnm1 of a Study bumug Human SubJl1niwnIityofHawai'i, Qmmritteo..,1fmnan _ (CHS) Spalding HaD 2S2B _ Way. HmoIDJu, Hawai'j TeIfphaao: (808) '*5007 am'~!1~~~7~ TmIDIngIn_ SaI!Ioct-WIIm. whmv..it _ -boo' ' Ormmm. """"""o'p!!rrm!!""""" hl'...!!mpn1m :30-11'308.'" BI\JHMLm! ph - BiD Pm'"! CampJm,,-_ NlA ~~A.;'~'1.ij~m~~~~~~C::i~===1~~&art;;~~~~~~~.~z;n~~::==== Pmposa!SWmIUedtoORS: lxino [ IY... when?----1i!la Pmposa1':(if'loIoIm) N/A I. -)'OIIrprclpllSCd-OoIIIaoolFotimaml_ The _"'this_y 10 to _tile BcxIJGem, pajia!iie 0I111_0I11811fB "'RMR againoi _~!ram8 C8It(AElT~AppIledEleGlo "and '...""""" eq.-.s IncIudlng tile u!ojeoIs (2fI men and 25 _). 18 rear at _. wid be!ram... BltIIe UnIvOisIIJ"'-BI_ Upon o\iiloining pemoi5sion of_tile pdncipie ImIesIIgatorwiD give btf8i <ttbi po," abeoot tile """"""""'*'" to -. Prior to pou1icipaihiii IIU\Jj8cIIo wid be raquiloi! to noad... sign an Informed 0IIIIS0iII and compi8i8 8 _1iIsIDJy faiin. 8u!ojeoIs omost be oeiaiivelyliollluly.freeofany_-'andnallbldnganyll_osuial RIIR. SooIiJ-wiD be _ to report to KLS TOiiCIiIiig L.ooIionitoIy (SIan SIieIIII COidar. """" 100) for a compi8i8 S of RMR and liociy COIlopasI!knl. _ i1iqiijnos Blleasll% hour (2 holds..-..urn). _ possiiiijliy UiaI BiiI1j8CIII may be _ to report for 8 aacund If tile IIOIId arises. 8u!ojeoIswiD be _ to 18ftB1n!ram any _ piiysioailiciivl\y for BlIeasI24 holds at 11lOIII poforto tile day Of 5 108O1L Also subjoiliiiwid be _ to Iaat (nuiaa!) forblleasll0hoools, oniy<llink_. and l8ftb1n!ramamoldng poforto IS 5 helll III onierto_tiie amount of aciivi\y poforto, S S S lieli~ subjoiliii wid be _ to _ ride at... tile campus _ (8_e wid be provided) as means of banspotletion to tile 1alIorIIIory. Upon anivai subjecis wid be _ to waij< to tile IOiIIIaOm to VOId tii<ims8ives. AflBrI'Bllll11lJ1g to tno IalIorIIIory subjecis wid _ to rest oomfortabiy III supine position. on 8 piiiidad _. for 30 failing ~ FollowIng this rest p&i!od. sulij8clllwid p1aoaa..._1iioidiipiaoo of tile BodyGem IntoUialtIllOlllb forapprmdmalaly 8tol0 _ for 8 i1loiisui'8ii18 of RIIR. When this _10 _ a aoij.cailbniiin...,10 COIltpBIecI_ 3- mimdos. A numbarwid be displayed 011 tno digiiai... _ 011 tno RMR III _ porday (24-1iou!). Next, IIU\Jj8cIIowiD p1aoa a /IIOIIIhpI8oo _ to 8 _ iiioiiiliiifiig 0S4 (CipOiMiIIQuII, _ cosjorfmetrj. ~ analyais) for 30 _ to _ anotnor i1loiisui'8ii18 of RIIR. ThIs appoaaius wid be_ usij1g 0 4_011 ~...,. _ II8iII. The _ i1loiisui'8ii180s4 wid it188sii18l11 1 tile oonsumpilon of oxygen (1/00). oxpinid (VCO,). and _ liiiijiljaioiy e bange oatio (RER). AD _1101_' wid be"-poforto and ~ _ 11014S. SUIisIIqJImJIly. suiij8ciii wid _ anotheri1lollsul'8ll18 of RMR usij1g tile EIodyGem as poeviousiy-. L.asIIJ. subjoiliiiwid oompibietlle for bociy COIlijiOSIIIIUL I'ltIII, ~ (InaIoaa) and bociywelghl (pouncis) wid be _ and IaIor used to _ RMR usij1g...-eq.-.s and 1ioCI, mass Index (IIMl). bociy weighidmciodljll halglitsqvarad (IqjIm'). _. subjoiliii wid Bland _ a _ pej10mjs SIdnfold IIiOIiSUJOiii8I usij1g I.ang& SIdnfold osiipois, BlI!lMIIi _ si!os of tile bociy. RogriiSSIuoJ oquaiions are ~ matnocis to _ RMR. one COIIII'IIOIIIJ used equation 10 tile oquaiians: Women: REE a W A Mon: REE-ee W REE (at RMR) 10 porday. W 10 weight III 1IlJograms. 810 _III _ and A Is see. 2. _an_of_1n IhIspoqJect(who, _.-y. leqjbof_-. ftcq1iodoy. eto.) ami tho... dqowid bo~to. Attoohsum:yhim...-lfappliaoWo ExplaIned In ~ above.

69 62 CIIIiIOk -liiiysui!ieoi of)'llllf_ will... _fnim tho foiiowbrg"""!o"'" NlA I ) Mlnom [ ) I'Rpmt W... I ) MImIIIDy DlsabIod I ) _ [ ) _ [ ) PhysI;aIJy DlsabIaI [ )_ a. CImok ad tho _to _ SIIbjocIs dial apply to )'IIIIf JB'OJ=t 1XI I'I1ysImlIl'llUlDa or pain I) Dooc!>IfgD [ ) SIdo_of_ I)... of_ I )... of_ I ) WOIIiOIIIIIsofillDoss I ) PsycbologioaJ pain [ ) Loss ofjqal rfabib [ ) JlJipeaImetdal ~ procuiwus [ ) JlJipeaImetdal_ pi_os [ ) Loss ofpiwoy [ ) Otbor - explain 4. Doscdbo _banism for saii:iy" " 1"8 """ will".. _Ifso:mr badn Is IKlCIIIIngto)'llllfSllbjocls_ "..",,104 '''1'1 WIIIII will".. do 1f1lllCh... risk Is_ ~ can anticipat8 rnlnimai risk In this Sbidy. Tllel81s 8 poss/iiiji\y l1l8i you!1liiy oxparience mild physiasj disoomfo!i and _ ofilia sicin <Nor the _ sit.. praviousiy _ dijiing the ThIs Isa 1IS8 resuiiof SubI le"dng _ pain be _ 10 and 10_ pajtldpallvlllnthe prvjecloraotmlyol"", pt8judioe. In the event of an &JIM "" the pdm8jy InvesIIgatDr and... 1sIng pni!assor 8!8 _In CPR and wlljlmmedia!eiy C8II_"...,... &J1... 1CIes, aciivaiing ems. tbo_ rfsk_ S. liticfij_tho_tbai wiii_to sui!ieoi or to IIIIDldIIdIn geaaa1,...-n oftholddlwlua1'. jwlitipdiw In this prqja:i, '" dial am """"" tho By participating In this study you will obtain the reauits of your absi aanle.1!for resting meiabollcrate and your body composition. ThIs b IfOliil8lkm could be used to assfst you In a personal weight management program with the adviaa of a allied health profasionai such as a physician or ragistared dietitian.

70 63 6. _"'~_"'_)ltbo",~"""'_""'jiiaidi,... "'abioco_iii111)'_ ~_,...wiii IXI SuI!l=(or_)_IlIJIDPIoIo... fiinn&slgos('wlfum fiinn) [ J Om! biieiidgo by PI orpl'liod pmmmoi. _ simple _fiinn rol81' fbmi). BxpIaID _1IIe...m(.)wIIy a fiinn II 001_ ( J OIlIer- oquin 1. Are _ any oilier IocaIIRB's miewiiig this JIIIlPl'Ol'IIXI No [ J Yes. LooaIioD: I qffirm: 0) tjtqt thi1_drug_(8)s1ibmiluldtochsfot'tltlsprojbd dandconjjmwdtobo_and_if dvjnges/n. ClfS.<tpprtmd drug ban mods to _ r=ti1'titiji and CIII'I'81ICYdJau dvjnges _ ban B8I d on thi1 -.and 01) tjtqtthl1 abtmand"'o'_are. _and ofthl1 p1of1o#fl-.:handofasyandobri8k8to homonsllbj_ DaIlr. SIgnod: DaIII: SUpeivIsIIIg_ (n:quiiecilfpl II a _) &IIIlI:QIIOKImIALpO.rlliiI'-... fiiiiimq... Da!eofllDman SuI!l=-TralDIag _01_... 'OiIIa(l3)_fllI_fIIIII UIdIel(l3}_fl.,cda... IO... ~ID.JIIlIcqal 'l1:idieta(l3).fllwdil... pc.'..,..... 'DddI:a(13)./LIIIt..., (Pbea.lt... CII9.. r,...,...,-..ii... )

71 64 OMS No Assurance _or IRS_... "Vsllda1Icn of the ~ on RestinII Me!aboIIa Rate" Kurt Go lij ThIs AssLIranca, on me wuh Department of HaaDh and Human SeMcres, CICMiIt8 this aatm1y. As:suranoa IdenIIfIoati:on no...m=:...121l IRa idenifftaa1ion no.-m- [J -- TIds_..,me"""( ~_~ ~...",,,acti I!y. Assurance IdentifIca!Ion no. IRB IdenfIfIca!iDn rro lf 1J1III/lf;Bb/8} Cl No assumnc:e has been t!iad for thia projecl 1Ida InsUlu!Icm d8dar8s Itta1 It wid provido an Assr.trano8 and CertifIc:sI!cIn of lrb T8'IIew and 8J)prCMd Il!lTldsactl l!y IRS.. _"""... _... any..... _ an peoember 18 2.OQ1 by. CJ FuD lab RlMewor Iil EIIpedIled Rmaw (808) 95&-5007 (808)

72 ~non ~I~ 65 Protection of Human Subjects Assurance IdentlflcatlonllRB CeJtlflcatlonlDaclaratlon of Exemption (Common Rule) 1._,.,.. ti ORIGINAl. [x) ~,.,...,_ Il GRANT Il CONfRA(IT ti FEl.l.OWSHIP ti COOPERATIVEAGREEMENT [) EXEMPTION 4.TIIIe.,_"""", ~oithe BoclyGem an Redng MetabdlcRalu- 1I._...,...,... oilbe_ [XJ UlfiIe""'_orHeamlandHuman_..._ Assurance Iden!i!Ica!Ion No. f>1si8 the expiraifon dat8 (!r1r)hm= 15 2QM IRS Reglsttdon No. rnpqoomteb --- [ ] No assunmgd has been toed for!hl:s InstituUon. ThIs itsutution dedares!bat ltwlll proi4de an Assuranoe end cermjcaiian of lrb mview and [[ Human_..._IIuI......_ {b~. 7. CertifIoa!IGn of trb...(respond 10 one of the fdiaiifng [f',au hay8 an AsIIa8rIce an IDe) [XJ_..."... and_byibeirbin ""'IbeCcmman_andB!IYcftIer by. [lpuiljrbamcwol1(~a:f'irbmcdfda) or (XI Bxped!tadRmewon DBqmntm10 2DC!2 II W..._one. on-... _ -_.,--:-::=_ or... TbeIRB... o:n8r8d by the Comman. Ruta WID be reviewed and apptowid bebe thaj are InIBatBd and tttat appropriate furcher certifgilian WI! be 8UIImi!!ad Fax No. (rrl1hama crxte) 13.EmaD: 14. Nama of 0fIIdaI _H.DondIa CHS~1467 to. Name and Address oilns11ba1on tjniwr8ily of HawaII at Manoa 0fII0a of the ChanoeIIcIr 2444 Dole Street. 8ad'Iman HaD HanoIufu, HI 9EI822 "--' ~==:-.~~~~8-~~~i~~may~tb"i3ga e:~b lj1.. wdfgf'~~~~*~,pj5totmt~ t!tiiblrdmtd:osrepcrttcjcmm:aomccr.room5q32o)t 7, 7 AWIIlIllI,

73 CBB._...,.. ---(CBS) STATUBRBtOR. FORM U_III>afIlawldl,C tl Sj MhaBoB252JI,--Wtoy,P, '..- T 7 p7 t-l Sf 5717 _ 11/26/02 PI(_6C18e)t1l'nrt Go Graduate Student ",,(808) College of Education =-:=.:.m:e;j21oif2& '.eisure science. For... J $ SupaA'S"s ~~ valtdatign of ; '... NIA ApqMda_., " '... v 5 'f4toorb: CIIeok_ Ix! p... wt.t....pona1d, Hetzler Pb D n p ( ' D I I aa ,...,por7ad (11#"""" PrevlaaBCBS o.teapploiecb 12119/01 ~-3802 To.-dltll.' -...., as c :d... ~... JL c ,,.... _a.;-... n L &_pnjod... _1 IINo,_tZle... diiie. kly.. _a...\ leddllle~ ofa...,... See attached *7~-u.. 77 'pie -'2/30/03. cr.... _" IpIZw 66

74 67 L n.ra...,.... _... _ tidi_... ~ ~...-di... _... J.. ~... JnJa:tf ---_.. 1xI-- _....._.,, ~(-~,...v-n.,,~,..,.,,...,... -I.. ciiiipiz5 aq 7 '.,... LfIIID... td dl8lft1c1:dbftwoak:pb:: & mlb.a..,..iot a..a-id -IXI""_.. _ _---..._-... a...,...,._~... ~_a...

75 ft' II 't UNIVER~!I;;Ig:~~t W AI'I,:f~-Q1' "':;B.' ~ ~#WiIe' ' '"i'~:''":; '~~_.~..'-~" 'h. J.~~. "tli'.~;,.';' ;... '.~",~:!lil."",c:. f-~ t:;;,~[j~:u".~ _. '\ -'~-~~...'-Il ii ""~, '.: ~,;"" ~.~. t.,. m. '/'fit." ""';" ~ I, It;' '. ~...,. "r r '--"~';1. \ 'I',rI;'~.,"", 'l,if. ': \-. 1 r:: ~"b ;, ';' '".' 1" 'j -.,.- 0 <I"! l1!.oo....' _ r... t'.. ~. :.,,,,,,.-r...'iibfd. tv'. ~ ~t'~:'. ".:..,; =:--"' ~~, HAS COMPLETED ~i~~r,t:~~'sf?~ WORKSHOP ON "THE PROTEC':f.t~~OF~ RE~4~H SUBJECTS..,,;::..'"..-1l~.n.,'c..'J.'..F.I <.. >:t~ '.~ ~,"'4. _ '. ~li.~ '_ ~-'~.. ~ ~,/ ".t' RESEAR.~~_~~~~~RS",-, ::;a.tl11::_~_~+ t [Ii. Date of workshop: November 27, 2001 ~A-.L~ Edward A. Laws, Ph.D. Interim Vi~ Chan~Dor for Researeh Gnulnate Edncation University ofhawai'l...e go '"

76 69 UNIVERSITY OF HA_AI'I ComatltbIec Humct 8tIdII: MEMORANDUM December 19,2001 TO: FROM: SUBJECI': KurtGoC Principallnvestlgator ", _ ' " Department of Kinesiology esti,leisuie Sct'CiJce (KLS) wi11iamh.dendlel.la\: n ", ' _ Executive 8ecretar)V'I ~ C;;T~.:,. :-..;..;,.'." CHS 114S7-"ValidatiOllilfihe~0IIR.est\DgMoiabolicRate" Your project identified above was reviewed by tho Chalrof~!OoII1l1litied 011 H~ Studie!j through Expedited RevIew procedures. 'Q1e project quiiiu!co ftj~~ review by CPR and 21 CPR , Category(4) Of the DHHS llst'of~.mcwcategorles... ~ This project was approved 011 December 18,2001 for OlIO year. Ifi\l;the a ve cwve~of' your project you intemi to c:iumge the Involvemlmt ofhuibjms ftoliiplllns imlicated in the materials presented for review, prior approvui must be received ~tiw CHSbo1i>\l>~ If tmbdticipated problems arise Involving the risks to subj_ or otiiers; report IDWI!,~ promptly to the CHS, either to iib ChalIpemon or to this omae. 'Tl!is ls RCJlliJed in order that (1) updating of protective measures for humans Involved may be """""'PUshed, and (2) prompt report to DHHS and FDA maybe made by the University if required. Tn accordanoc with tho UDivenri!y policy, you are expected to main1bin, as an essential part of your project records, all records periiiinins to the involvemeot ofhumans in this project, including any smnmarias ofinformation """"")'cd, data, comp1aiol8, correspondence, and any executed forms. These records must be retained for at least three yems from the expiiationlterminatioll date of this study. The CHS appmval period for Ibis pmject win expiie on PrnmnIm Jfyour project continues beyond this date, you must submit. c:ontinuatioll application to the CHS at1east fi>ur woeks prior to the oxpiration of this study. We wish you sw:cess in this eodeavor and are ready to assist you and your project personnel at anytime. Enclosed is your certification for this project. Enclosure ";t;, 2B4O MaCe way, SpakI:ng 2151., HanaIuIu. HawaI'i 96II:lU.303 TGIaphctIe: (8OS) SIIRB08) 1IS6o&m7. FacsImIle: {SO&) W8lI si!b: ~ MEqusI~AotIon JnatIIu!Ion

77 70 UNIVERSITY OF HAWAI'I Caa:mt!I!8e an Haman 8tDdIM MEMORANDUM Dcccmbcr II, 2002 TO: FROM: SUBJl!CT:.'. ~ " :~.. :'.. '.,: '.:if The CHS _wi period for this moiectwm expjreon "'"""""'" If)'Ollfproject continues boyond this date,)'oil IIIlUIISlIbmlt. continuation opp1icat!on to the CHS at 1_ four weeks priorto the Oltjriration of this atady. We wish)'oll... in this endeavor aad "'" ready to _)'OIl aad)'ollf project per!!oiiiio! at aayttmo. Enc1eaed Is )'OlIf certification for this project. Enol...

78 71 AppendixE Raw Subject Data Subject data for metabolic data from metabolic cart and resting energy expenditure (REE) usm~. w ell equation vs conversion. tabl e. METABOLIC CART REE SUBJECT# V02 VC02 WEIREQ. CONVTABLE % (n=25) MfF _(mumin) (mumin) (kcayd) (kcay24hr) Difference 1 M , , M , , M , , M , , M , , M , , M , , M , , M , , M , , M , , M , , M , , F F , , F , , F , , F , , F , , F F , , F , , F F , , F , , Mean , ,

79 Subject data for REE from BodyGem. SUBJECT# (n=25) MJF BGl BG2 BGaVR 1 M 1, , , M 1, , , M 1, , , M 1, , , M 1, , , M 1, , , M 1, , , M 1, , , M 1, , , M 1, , , M 1, , , M 1, , , M 1, , , F F 1, , , F 1, , , F 1, , F 1, , , F 1, , , F 1, , , F F 1, , , F 1, , , F 1, , , F 1, , ,

80 73 Subject data for REE using Harris-Benedict equation (H-B EQ) SUBrnCT# WT HT AGE (n=25) MIF (kit) (em) (yrs) H-BEQ. I M , M , M , M , M , M , M , M , M , M , M , M , M , F , F , F , F , IS F , F , F , F , F , F , F , F ,370.93

81 74 Subject data for weight, height, BMI, and age. Subject# WT HT BMI AGE (n=25) MIF (kg) (em) (kglm2) (yrs) 1 M M M M M M M M M M M M M F F F F F F F F F F F F

82 75 Subject data for skinfold measurements, body density, t body fat, and lean mass. AGE AVERAGEOF3~~ SUM M/F (VIS) CHEST M1DAX TRICEP SUBSCA SUPRA! ABDOMI TInGH 7-8ITES BD %BF %LM 1M % M % M % M % 99.% 6M " M % M % M % M % M % M % M % M % F % F % F % IIF % F % F % F % F % F % F % F % F % Key: MIDAX ~ mldaxiljary. SUBSCA ~ subscapular. SUPRA! ~ supnuliac, ABDOMI ~ abdomlna1, B~body density, %BF ~ percent body tltt %LM ~ percent lean mass (1-0/0BF) Body density calculated usins the formula by Brozek et ai. fercent body fat calcula1<:d usin. the Sm formula.