Impact of the Method Used to Select Gas Exchange Data for Estimating the Resting Metabolic Rate, as Supplied by Breath-by-Breath Metabolic Carts

nutrients Article Impact of the Method Used to Select Gas Exchange Data for Estimating the Resting Metabolic Rate, as Supplied by Breath-by-Breath Metabolic Carts Juan M.A. Alcantara 1,* , Guillermo Sanchez-Delgado 1,2, Francisco J. Amaro-Gahete 1,3 , Jose E. Galgani 4and Jonatan R. Ruiz 1 1 PROFITH “PROmoting FITness and Health Through Physical Activity” Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical and Sports Education, Faculty of Sport Sciences , University of Granada, 18011 Granada, Spain; [email protected] (G.S.-D.); [email protected] (F.J.A.-G.); [email protected] (J.R.R.) 2Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA 3EFFECTS-262 Research Group, Department of Physiology, School of Medicine, University of Granada, 18011 Granada, Spain 4Departamento de Ciencias de la Salud, Carrera de Nutrición y Dietética, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile; Departamento de Nutrición, Diabetes y Metabolismo, Facultad de Medicina, Universidad Pontificia Católica de Chile, 8330023 Santiago, Chile.; [email protected] *Correspondence: [email protected]; Tel.: +34-958-244-353 Received: 23 December 2019; Accepted: 10 February 2020; Published: 14 February 2020   Abstract: The method used to select representative gas exchange data from large datasets influences the resting metabolic rate (RMR) returned. This study determines which of three methods yields the lowest RMR (as recommended for use in human energy balance studies), and in which method the greatest variance in RMR is explained by classical determinants of this variable. A total of 107 young and 74 middle-aged adults underwent a 30 min RMR examination using a breath-by-breath metabolic cart. Three gas exchange data selection methods were used: (i) steady state (SSt) for 3, 4, 5, or 10 min, (ii) a pre-defined time interval (TI), i.e., 6–10, 11–15, 16–20, 21–25, 26–30, 6–25, or 6–30 min, and (iii) “filtering”, setting thresholds depending on the mean RMR value obtained. In both cohorts, the RMRs yielded by the SSt and filtering methods were significantly lower (p<0.021) than those yielded by the TI method. No differences in RMR were seen under the different conditions of the SSt method, or of the filtering method. No differences were seen between the methods in terms of the variance in RMR explained by its classical determinants. In conclusion, the SSt and filtering methods return the lowest RMRs and intra-measurement coefficients of variation when using breath-by-breath metabolic carts. Keywords: resting energy expenditure; CCM express; CPX Ultima CardiO2; indirect calorimetry; macronutrient oxidation 1. Introduction The resting metabolic rate (RMR) is the lowest energy expenditure of a person who is awake [ 1 ], after at least 12 h of fasting, being in physical rest, and in a state of mental relaxation in an ambient environmental temperature; it accounts for some 60–70% of the total daily energy expenditure [ 2 ]. The assessment of RMR is important when studying human energy balance, both in clinical and research settings [ 2 , 3 ]. Indirect calorimetry is the reference method for assessing RMR [ 2 , 4 – 6 ], which is estimated from the consumption of oxygen (VO 2 ) and the production of carbon dioxide (VCO 2 ) [ 6 ]. The measurement of VO 2 and VCO 2 , together with urinary nitrogen excretion, also allows for the Nutrients 2020,12, 487; doi:10.3390/nu12020487 www.mdpi.com/journal/nutrients Nutrients 2020,12, 487 2 of 14 estimation of the nutrient (carbohydrate and fat) oxidation rate [ 7 ]. Indeed, the VCO 2 /VO 2 ratio, i.e., the respiratory quotient (RQ), is an indicator of the relative predominance of fat (FATOx) and carbohydrate (CHOOx) oxidation. The assessment of RMR using indirect calorimetry is normally performed over a 10–30 min period. It is widely assumed that the first 5 min of data recorded should be discarded [ 8 , 9 ]. A short (e.g., 5 min) steady respiratory state period, i.e., a period in which the indirect calorimetry record is markedly stable, then has to be selected from the remaining dataset for estimating the RMR [ 6 , 7 , 10 ]. The assumption that steady state (SSt) methods for gas exchange data selection provide a better estimate of RMR than the other methods available arose from studies performed in ventilated patients [ 10 ]. However, there is no strong evidence that the same can be assumed in healthy, non-ventilated subjects—and indeed different methods have been used. These methods can be grouped into three categories: (i) the selection of an SSt (defined as that providing a coefficient of variance [CV] of <10% for VO 2 , VCO 2 , and minute ventilation [VE], and of <5% for RQ [ 11 ]), (ii) the selection of a pre-defined time interval (TI), without taking the stability of the results obtained into consideration [ 11 ], and (iii) “filtering”, in which data above or below a given RMR threshold are discarded. Both the SSt and TI methods can be used under different time conditions [ 1 ]. Unfortunately, the use of different methods for gas exchange data selection could result in different estimates of RMR and nutrient oxidation rates being made [ 1 , 11 , 12 ]. For instance, in a study involving healthy subjects, Irving et al. [ 1 ] reported RMR estimates made by the SSt and TI methods to differ by some −101 to +121 kcal/day. Certainly, SSt-based RMR estimates are usually lower than those provided by the TI method [ 1 , 11 ]. Given the above definition of RMR [ 1 ], it has been proposed that the lowest estimates obtained by the SSt method should be deemed more accurate than those provided by TI. However, the under-estimation of the homeostatic RMR cannot be ruled out in the SSt method, nor has any study checked whether the filtering methods for data selection provide lower RMR estimates than either SSt or TI. The present work examines whether the SSt, TI, or filtering method yields the lowest RMR value in healthy, non-ventilated subjects, and determines in which method the greatest variance in RMR is explained by the classical determinants of this variable (i.e., body weight, body composition, and sex) [13]. 2. Materials and Methods 2.1. Study Subjects The participants of this retrospective study were 107 young adults (72 women) enrolled in the ACTIBATE study [ 14 ] and 74 middle-aged adults (39 women) enrolled in the FIT-AGEING study [ 15 ]. Detailed information about the methodology of the aforementioned studies can be found elsewhere [ 14 , 15 ]. Briefly, the inclusion criteria were: (i) being physically inactive (<20 min of moderate–vigorous physical activity on <3 days/week), (ii) having a stable body weight (change <3 kg over the last 3 months [ACTIBATE] or <5 kg over the last 5 months [FIT-AGEING]), (iii) not being enrolled in a weight loss program, (iv) not being a smoker, (v) not suffering from an acute or chronic illness, and (vi) not being pregnant. The ACTIBATE study protocol was approved by the Committee for Research Involving Human Subjects at the University of Granada (Reference #924) and the Servicio Andaluz de Salud (Centro de Granada, CEI-Granada), while the FIT-AGEING was approved by the Human Research Ethics Committee of the Junta de Andaluc í a(0838-N-2017). Both studies were performed in accordance with the Declaration of Helsinki (2013 revision) and registered on the clinicaltrials.gov platform (IDs: NCT02365129 for the ACTIBATE study and NCT03334357 for the FIT-AGEING study). Oral and written informed consent was obtained from all the subjects before their enrolment. 2.2. Procedures In both the above studies, subjects underwent a 30 min indirect calorimetry assessment of RMR at rest, early in the morning, following an overnight fast. All subjects were instructed to refrain from Nutrients 2020,12, 487 3 of 14 moderate (24 h) and vigorous physical activity (48 h) before the test day. On the previous evening, all subjects consumed a standardized meal of an egg omelet, boiled rice, and tomato sauce (ad libitum amounts). They were also instructed to avoid physical activity after they woke up, and to come to the research center by car or bus early in the morning, having had no breakfast (ensuring a ~ 12 h fast) . Upon arrival, and after confirming their compliance with these above conditions, body weight and height were measured using a Seca model 799 electronic column scale (seca GmbH & Co. KG, Hamburg, Germany) with subjects barefoot and wearing light clothing. Thereafter, the subjects laid on a bed in the supine position for 20–30 min. Gas exchange data were then recorded by indirect calorimetry for 30 min in a quiet room with dim lighting, controlled at 22–24 ◦ C and 35–45% relative humidity [ 8 ]. During this time the subjects were covered with a bed sheet and instructed to remain silent, stay awake, avoid fidgeting, and to breathe normally. 2.3. Gas Exchange Assessments Gas exchange was recorded using either a CCM Express or a CPX Ultima CardiO2 (two different devices were used only in the middle-aged adults cohort) breath-by-breath metabolic cart (Medical Graphics Corp, St. Paul, MN, USA). Both instruments require the use of a face mask equipped with a Directconnect ™ flow sensor (Medical Graphics Corp, St. Paul, MN, USA). Both determine VCO 2 using a non-dispersive infrared analyzer, and both determine VO 2 using a galvanic fuel cell [ 16 ]. The flow rate was calibrated using a 3 L syringe at the beginning of every test. The gas analyzers were calibrated before each measurement using standard gases according to the manufacturers’ instructions [16]. 2.4. Methods for Gas Exchange Data Selection The collected gas exchange data were processed using MGCDiagnostic ® Breeze Suite 8.1.0.54 SP7 software (Medical Graphics Corp., St. Paul, MN, USA) to yield a data point for each variable for every minute (i.e., the means of all ventilation data (per minute ventilation data—pMVD) for each particular minute). The first 5 min of data were discarded [ 8 , 9 ]; the remaining 25 min period dataset was processed using three different methods to select representative gas exchange data for determining the RMR and nutrient oxidation rate. 2.4.1. Time Interval Method Short TIs of 6–10 min, 11–15 min, 16–20 min, 21–25 min, and 26–30 min, and long TIs of 6–25 min and 6–30 min were established [ 11 ], and the means of the pMVD values for all variables available for these time periods calculated. These processed data were used to calculate the RMR and nutrient oxidation rate (see below for details). 2.4.2. Steady-State Time Method The CVs of VO 2 , VCO 2 , VE, and RQ were calculated for every period of 3, 4, 5, and 10 min (e.g., for the 3 min SSt we processed all the 25 min period datasets and we examined the 6th to 8th min period, the 7th to 9th period, etc.) and the mean CVs for each variable calculated for each time period. The periods selected for the final analyses were those with the lowest mean CV for each (e.g., from the 3 min SSt examined periods, we selected the 7th to 9th) [ 11 ]. The means of the available pMVD values for these time periods were then calculated. These processed data were used to calculate the RMR and nutrient oxidation rate (see below for details). 2.4.3. Filtering Method The pMVD values for VO 2 and VCO 2 for the entire 25 min data collection period—i.e., with no division into SSt or TI periods—were used to calculate the mean 25 min RMR (see below for details). Furthermore, pMVD RMR values were also calculated, discarding either (i) those values <85% or >115% of the mean 25 min RMR (low filter), (ii) <90% or >110% of the mean 25 min RMR (medium filter), Nutrients 2020,12, 487 4 of 14 or (iii) <95% or >105% of the mean 25 min RMR (strong filter). For the minutes that passed these filters, the means were calculated for all pMVD values available. 2.5. Calculating the Resting Metabolic and Nutrient Oxidation Rates Weir’s equation (assuming zero urinary nitrogen excretion) [ 17 ] was used to calculate RMR values from the mean pMVD for VO 2 and VCO 2 obtained with each gas exchange data selection method. The FATOx and CHOOx rates were calculated using Frayn’s stoichiometric equations [ 18 ], also assuming zero urinary nitrogen excretion. Finally, the mean RMR, RQ, VO 2 , VCO 2 , FATOx, and CHOOx were calculated for all gas exchange data selection methods under each different condition. 2.6. Body Composition Body composition was determined by dual energy X-ray absorptiometry using a Discovery Wi device (Hologic, Inc., Bedford, MA, USA). Quality controls, the positioning of participants and analysis of the results were performed according to the manufacturer’s recommendations. 2.7. Statistical analysis Results are presented as mean ± SD unless otherwise stated. All analyses were conducted using the Statistical Package for the Social Sciences v.22.0 (IBM SPSS Statistics, IBM Corporation, Chicago, IL, USA). Significance was set at p<0.05. Repeated-measures analysis of variance (ANOVA) with a post-hoc Bonferroni test was used to detect differences in RMR, RQ, VO 2 , VCO 2 , FATOx, and CHOOx estimates across the methods for gas exchange data selection. The CVs for VO 2 , VCO 2 , RQ, and VE obtained via the different methods were also compared. Two different ANOVA models were used: one with four levels of fixed factors (i.e., short TI, long TI, SSt, and filtering), and one with 14 levels (all methods and their different conditions). The differences between the methods in terms of the variance in RMR explained by its classical determinants (i.e., body weight, body composition [lean and fat masses], and sex) [ 13 ] were examined by either simple linear regression (associations between RMR and body weight) or multiple linear regression (associations between RMR and sex and body weight; RMR and sex and lean and fat masses). 3. Results Table 1provides descriptive data for the subjects in both cohorts. Table 1. Subject descriptive characteristics. Young Adults (n=107) Middle-Aged Adults (n=74) Min Max Percentile 10–90 Min Max Percentile 10–90 Age (years) 22.2 ±2.2 18.2 26.6 19.1–25.2 53.5 ±5.3 45.0 66.0 47.0–61.7 Sex (n%) Women 72, 67 39, 53 35, 47 Men 35, 33 Metabolic cart used (n%) CCM Express 46, 43 61, 57 69.3 ±15.9 167.8 ±8.7 24.5 ±4.4 79.8 ±13.2 41.4 ±9.5 24.0 ±8.9 35.0 ±7.8 0, 0 CPX Ultima CardiO2 74, 100 Body weight (kg) 45.0 118.5 52.2–90.6 75.7 ±15.0 167.8 ±9.8 26.7 ±3.8 95.1 ±11.7 43.5 ±11.7 30.0 ±8.4 39.9 ±9.1 50.6 110.7 57.8–94.6 Height (cm) 148.5 195.1 157.2–180 148.3 189.8 155.8–181.6 BMI (kg/m2)17.2 38.4 19.4–30.9 19.0 38.0 22.0–31.7 Waist circumference (cm) 58.0 125.6 65.0–97.8 68.6 118.7 79.2–107.8 Lean mass (kg) 28.1 66.8 31.0–55.2 22.7 63.6 30.5–59.6 Fat mass (kg) 9.9 51.7 14.6–36.4 14.5 55.8 20.6–40.7 Fat mass (%) 15.3 51.9 26.2–44.4 23.0 59.4 26.7–51.1 Data are presented as mean ±SD unless otherwise stated. Nutrients 2020,12, 487 5 of 14 3.1. Influence of the Gas Exchange Data Selection Method on Estimates of RMR, RQ and Nutrient Oxidation Figure 1shows the RMR and RQ estimates yielded by the gas exchange data selection methods for both the young and middle-aged adults. In the young adults, the short and long TI methods provided higher mean RMR estimates than either the SSt or filtering methods (taking all conditions together; post-hoc Bonferroni p<0.001; Figure 1A). In the middle-aged adults they also provided higher mean RMR estimates than the filtering method (taking all conditions together; post-hoc Bonferroni p<0.001; Figure 1B). No differences were seen between the RMR estimates yielded by the short and long TIs, nor between the SSt and the filtering methods (taking all conditions together) in either the young or the middle-aged adults (all post-hoc Bonferroni p=1.000; Figure 1A,B). For the young adults, the lowest mean RMR values were obtained with the SSt 4 min method (1440 kcal/day; Figure 1E); however, the SSt 4 min method was only statistically different from the TI 6–10 min and the TI 11–15 min. In the middle-aged adults the lowest mean RMR values were provided by the strong-filter method (1493 kcal/day; Figure 1F); the strong-filter method was statistically different from all the different methods, with the exception of the TI 11–15 min, and the SSt 3, 4, and 5 min conditions. Table S1 shows the comparisons (i.e., post-hoc Bonferroni) between the different methods. Similar patterns were observed when analyzing the influence of gas exchange data selection method on VO 2 and VCO 2 estimates (Figure S1). Lastly, the periods in which the SSt were achieved (with the different SSt methods applied) in the young and the middle-aged adults are presented in Figure S2. We observed that ~50% of young and middle-aged adults achieved their SSts (i.e., the one presenting lower mean CV) during the first half of the 30 min indirect calorimetry assessment. On the other hand, we found differences between the first steady state achieved (i.e., the first period in which the CVs of VO 2 <10, VCO 2 <10, VE <10, and RQ <5) and the “best” SSt achieved (i.e., the period with the lowest mean CVs) in RMR estimation in young adults (Figure S3). The RQ estimates yielded by the short TI method were significantly higher than all others in the young adult cohort (all post-hoc Bonferroni p<0.002; Figure 1C) and that filtering method (taking all conditions together) in the middle-aged adults cohort (post-hoc Bonferroni p=0.038; Figure 1D). Moreover, the long TI method provided higher RQ estimates than the SSt and filtering methods (taking all conditions together) in the young adult (both Bonferroni post-hoc p<0.013; Figure 1C). No differences in RQ estimates were seen when comparing the SSt and filtering methods (taking all conditions together) in either the young or the middle-aged adults (post-hoc Bonferroni p=1.000; Figure 1C,D). Furthermore, the long TI and the SSt were not significantly different than either the short TI method or the filtering methods (taking all conditions together) in the middle-aged adults (Figure 1D). The lowest mean RQ values were obtained when using the strong-filter method (0.84) in the young adults (Figure 1G). However, the strong-filter method was only statistically different from the TI 26–30 min and the TI 6–30 min. The lowest mean RQ values were obtained when using the SSt 3 min method (0.80) in the middle-aged adults (Figure 1H). However, no statistical differences were observed. As expected, the data selection methods yielding higher RQ estimates also provided higher CHOOx and lower FATOx estimates, and vice versa (Figure 2). Nutrients 2020,12, 487 6 of 14 Nutrients 2020, 12, 487 6 of 14 Figure 1. Differences among gas exchange data selection methods with respect to resting metabolic rate (RMR) and respiratory quotient (RQ) estimates. Black columns represent short time interval (TI) periods (i.e., the means of the per minute ventilation data (pMVD]) values for all variables available for these time periods, panels A–D; the pMVD values for each short TI period, panels E,F). Light grey columns represent long TI periods (i.e., the means of the pMVD values for all variables available for these time periods, panels A–D; the means of the pMVD values for each long TI period, panels E,F). White columns represent steady state (SSt) periods (i.e., the means of the pMVD values for all variables available for these SSt periods, panels A–D; the means of the pMVD values for each SSt period, panels E,F). Dark grey columns represent filtering methods (i.e., the means of the pMVD values for all variables available for these filtering periods, panels A–D; the means of the pMVD values for each filtering period, panels E,F). p-values come from repeated-measures analysis of variance (ANOVA). Identical indicatory letters highlight differences as determined by post-hoc Bonferroni analysis. Data are presented as mean and standard error of the mean (SEM). Min: minutes; VCO2: production of carbon dioxide; VO2: consumption of oxygen. Figure 1. Differences among gas exchange data selection methods with respect to resting metabolic rate (RMR) and respiratory quotient (RQ) estimates. Black columns represent short time interval (TI) periods (i.e., the means of the per minute ventilation data (pMVD]) values for all variables available for these time periods, panels A – D ; the pMVD values for each short TI period, panels E , F ). Light grey columns represent long TI periods (i.e., the means of the pMVD values for all variables available for these time periods, panels A – D ; the means of the pMVD values for each long TI period, panels E,F ). White columns represent steady state (SSt) periods (i.e., the means of the pMVD values for all variables available for these SSt periods, panels A – D ; the means of the pMVD values for each SSt period, panels E , F ). Dark grey columns represent filtering methods (i.e., the means of the pMVD values for all variables available for these filtering periods, panels A–D; the means of the pMVD values for each filtering period, panels E , F ). p-values come from repeated-measures analysis of variance (ANOVA). Identical indicatory letters highlight differences as determined by post-hoc Bonferroni analysis. Data are presented as mean and standard error of the mean (SEM). Min: minutes; VCO 2 : production of carbon dioxide; VO2: consumption of oxygen. Nutrients 2020,12, 487 7 of 14 Nutrients 2020, 12, 487 7 of 14 The RQ estimates yielded by the short TI method were significantly higher than all others in the young adult cohort (all post-hoc Bonferroni p < 0.002; Figure 1C) and that filtering method (taking all conditions together) in the middle-aged adults cohort (post-hoc Bonferroni p = 0.038; Figure 1D). Moreover, the long TI method provided higher RQ estimates than the SSt and filtering methods (taking all conditions together) in the young adult (both Bonferroni post-hoc p < 0.013; Figure 1C). No differences in RQ estimates were seen when comparing the SSt and filtering methods (taking all conditions together) in either the young or the middle-aged adults (post-hoc Bonferroni p = 1.000; Figure 1C,D). Furthermore, the long TI and the SSt were not significantly different than either the short TI method or the filtering methods (taking all conditions together) in the middle-aged adults (Figure 1D). The lowest mean RQ values were obtained when using the strong-filter method (0.84) in the young adults (Figure 1G). However, the strong-filter method was only statistically different from the TI 26–30 min and the TI 6–30 min. The lowest mean RQ values were obtained when using the SSt 3 min method (0.80) in the middle-aged adults (Figure 1H). However, no statistical differences were observed. As expected, the data selection methods yielding higher RQ estimates also provided higher CHOOx and lower FATOx estimates, and vice versa (Figure 2). Figure 2. Differences among gas exchange data selection methods with respect to fat oxidation (FATOx) and carbohydrate oxidation (CHOOx) rates. Black columns represent short time interval (TI) periods (i.e., the means of the per minute ventilation data [pMVD] values for all variables available for these time periods, panels A – D ; the pMVD values for each short TI period, panels E , F ). Light grey columns represent long TI periods (i.e., the means of the pMVD values for all variables available for these time periods, panels A – D ; the means of the pMVD values for each long TI period, panels E , F ). White columns represent steady state (SSt) periods (i.e., the means of the pMVD values for all variables available for these SSt periods, panels A – D ; the means of the pMVD values for each SSt period, panels E , F ). Dark grey columns represent filtering methods (i.e., the means of the pMVD values for all variables available for these filtering periods, panels A – D ; the means of the pMVD values for each filtering period, panels E , F ). p-values come from repeated-measures analysis of variance (ANOVA). Identical indicatory letters highlight differences as determined by post-hoc Bonferroni analysis. Data are presented as mean and standard error of the mean (SEM). Min: minutes. Nutrients 2020,12, 487 8 of 14 3.2. Differences between the Methods in Terms of the Variance in RMR Explained by Its Classical Determinants The variance in RMR explained by body weight (taking all conditions together) was 36%, 36%, 34% and 38% for the short TI, long TI, SSt, and filtering methods respectively in young adults, and 50%, 51%, 52% and 51% respectively in the middle-aged adults. The most explained variance was obtained using the low-filter method (40%) in young adults and the TI 6–10 min method (54%) in the middle-aged adults. The variance explained increased to 34–45% and 54–68% in the young and middle-aged adults respectively after including subject sex in the model (Table 2). The most explained variance was obtained with the low-filter method in young adults, and both the TI 21–25 min and the medium-filter methods in the middle-aged adults. However, little difference was seen among the methods in terms of the variance explained by the classical determinants of RMR (Table 2). A further model including subject sex, lean, and fat masses increased the variance in RMR explained by ~5% in the young adults, but not in the middle-aged adults (Table 3). The low-filter method explained the greatest variance in RMR in the young adults, and the TI 21–25 min method did so in the middle-aged adults. However, once again, little difference was seen among the methods in terms of the variance explained by the classical determinants of RMR (Table 3). Nutrients 2020,12, 487 9 of 14 Table 2. Variance in resting metabolic rate (RMR) explained by sex and body weight in each of the gas exchange data selection methods. Method of Data Selection Young Adults (n=107) Middle-Aged Adults (n=74) Sex Weight (kg) Sex Weight (kg) Model R2Constant βpβpModel R2Constant βpβp TI 6–10 min 0.44 1232 −171.6 0.001 7.8 <0.001 0.67 1503 −424.0 0.001 8.9 0.001 TI 11–15 min 0.40 1340 −214.4 <0.001 7.3 <0.001 0.67 1616 −418.5 0.001 7.1 0.004 TI 16–20 min 0.40 1300 −214.5 <0.001 7.6 <0.001 0.60 1758 −445.2 0.001 6.2 0.031 TI 21–25 min 0.36 1342 −199.5 0.001 6.7 <0.001 0.68 1935 −507.5 0.001 4.9 0.051 TI 26–30 min 0.34 1208 −155.7 0.009 7.5 <0.001 0.54 1754 −409.8 0.001 5.5 0.058 TI 6–30 min 0.42 1284 −191.2 0.001 7.4 <0.001 0.66 1713 −441.0 0.001 6.5 0.010 TI 6–25 min 0.43 1303 −200.0 <0.001 7.3 <0.001 0.67 1703 −448.8 0.001 6.8 0.008 SSt 3 min 0.39 1245 −194.9 0.001 7.8 <0.001 0.66 1669 −456.7 0.001 7.1 0.008 SSt 4 min 0.39 1169 −167.5 0.004 7.9 <0.001 0.67 1786 −481.1 0.001 6.4 0.016 SSt 5 min 0.36 1301 −207.0 0.001 7.1 <0.001 0.66 1763 −464.1 0.001 6.2 0.016 SSt 10 min 0.41 1232 −182.8 0.001 7.7 <0.001 0.66 1681 −443.8 0.001 7.0 0.008 Low-filter 0.45 1188 −170.6 0.001 8.1 <0.001 0.67 1701 −442.9 0.001 6.6 0.009 Medium-filter 0.43 1209 −170.8 0.001 7.8 <0.001 0.68 1725 −447.5 0.001 6.4 0.010 Strong-filter 0.44 1218 −182.0 0.001 7.7 <0.001 0.67 1684 −431.7 0.001 6.2 0.012 Unstandardized beta and p-values (significant values in bold) from multiple regression analyses, in which sex and body weight were included as independent variables, and the RMR estimates yielded by the different methods of gas exchange data selection were included as dependent variables. Sex: 1 =men; 2 =women.