Real-world Assessment of Multi-Frequency Bioelectrical Impedance Analysis (MFBIA) for Measuring Body Composition in Healthy Physically Active Populations

Real-world Assessment of Multi-Frequency Bioelectrical Impedance Analysis (MFBIA) for Measuring Body Composition in Healthy Physically Active Populations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Real-world Assessment of Multi-Frequency Bioelectrical Impedance Analysis (MFBIA) for Measuring Body Composition in Healthy Physically Active Populations Adam Potter, Leigh Ward, Christopher Chapman, William Tharion, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6728659/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Sep, 2025 Read the published version in European Journal of Clinical Nutrition → Version 1 posted 9 You are reading this latest preprint version Abstract Background: Multi-frequency bioelectrical impedance analysis (MFBIA) methods offer reliable and moderately accurate estimates of body composition in tightly controlled conditions (prandial and hydration status, recent exercise, time of day). Objective: This study examined MFBIA reliability and validity in a real-world environment where these factors were not controlled. Methods: Regional and total body composition estimates by MFBIA (InBody 770) were compared to dual-energy X-ray absorptiometry (DXA) in 1,000 healthy adults (667 men; 333 women), including fat mass (FM), percent body fat (%BF), fat-free mass (FFM), and visceral adipose tissue (VAT). In subsets, reliability was determined from duplicate MFBIA and DXA obtained within one week, and total body water (TBW) was compared to single-frequency BIA (SFBIA). Results: MFBIA demonstrated modest population-level agreement with DXA for total body FM (men, r=0.93, bias -3.7±2.6 kg; women, r=0.96, bias, -1.9±1.8 kg), %BF (men, r=0.89, bias, -4.2±3.0%; women, r=0.92, bias, -2.8±2.6%), and FFM (men, r=0.95, bias, 3.4±2.8 kg; women, r=0.94, bias, 2.0±2.2 kg). Regional correlations were highest for trunk FM (men, r=0.92, CCC=0.86; women r=0.93, CCC=0.93) and lowest for VAT (men, r=0.74, CCC=0.68; women, r=0.74, CCC=0.34). DXA and MFBIA regional and total assessments were highly reliable (DXA, ICC 0.990-0.998) and (MFBIA, ICC 0.987-0.995). TBW by MFBIA and SFBIA showed moderate agreement (men, r=0.73, bias, -1.89±3.31; women, r=0.82, bias, -1.74±2.01). Conclusion: This MFBIA system was shown to have high retest reliability and when compared to laboratory methods, provides a moderately accurate method for measuring TBW and body composition (except for VAT) in uncontrolled conditions. Health sciences/Health care/Weight management Health sciences/Medical research/Translational research body composition BMI DXA body water BIA VAT Figures Figure 1 Figure 2 Figure 3 Introduction Bioelectrical impedance analysis (BIA) has advanced considerably since its first applications to body composition analyses ( 1 , 2 ). The underlying principle of BIA is that impedance (measured in Ω), comprised of the two components resistance ( R ) and reactance ( Xc ) (both in Ω), can be used to assess water content of biological tissues ( 1 , 2 ). Whole body resistance measured at 50 kHz (R50), typically between the hand and foot, expressed relative to height as the resistive index (RI, height 2 /R50), is a good predictor of total body water content (TBW) ( 3 , 4 ). From this, other components of body composition, namely fat mass and fat-free mass (FM, FFM), can be estimated based on the assumption that total body water comprises 73% of the fat-free mass ( 5 – 7 ). While useful at the population level, the assumption of normal hydration at the individual level is not always valid and other factors such as posture, skin temperature, exercise-induced blood flow may change the electrical properties of tissue ( 8 ). Multi-frequency BIA (MFBIA) represents an advance in BIA-based body composition analysis compared to single-frequency BIA (SFBIA). Using measurements at multiple frequencies (e.g., 5, 50, and 250 kHz) provides added information on water compartments since at low frequencies current flows predominantly through the extracellular water space (ECW) while at high frequencies current can cross the cell membrane and flow through both ECW and the intracellular water (ICW), i.e., TBW ( 9 ). With the development of standing octopolar BIA devices, segmental analyses can be easily performed providing regional body composition estimates for arms, legs, and trunk separately and recombined for total body assessments ( 10 , 11 ). Given the expanding role of BIA in weight management programs, performance monitoring, and military readiness assessments, it is important to establish whether current MFBIA systems provide sufficient accuracy for large-scale applications specifically in real-world conditions. The US Army evaluated BIA technology for body fat standard enforcement in 1984 in the largest military body composition study ever conducted and determined that single frequency BIA (SFBIA) available at the time added technological complexity and no advantage over circumference-based body fat estimation ( 6 , 12 ). Recently, Potter et al. reevaluated the suitability of modern MFBIA technologies to replace military height-weight tables and circumference-based predictions of body fat; they concluded that new methods and algorithms may have overcome previous drawbacks with variability in electrode placement, standardization of body position, and influence of biological variables affecting individual measurements ( 13 , 14 ). This also prompted a fresh look at the body composition metrics that are most relevant to health outcomes, sports performance, and military readiness such as trunk or visceral adipose tissue (VAT) and FFM or muscle mass ( 15 ). The present study evaluated the reliability and accuracy of a widely used MFBIA system (InBody 770, InBody Co. Ltd., Seoul, Korea) in uncontrolled free-living conditions, offering critical insights into its feasibility and practicality for routine body composition evaluations beyond controlled laboratory conditions to replace conventional metrics such as body mass index (BMI) or other anthropometric methods such as waist circumference assessments. The primary hypothesis was that MFBIA in uncontrolled field conditions would provide similar reliability and accuracy to that observed in a recent laboratory study with tightly controlled conditions ( 16 ). Additionally, accuracy of MFBIA regional assessments, including arm, leg, trunk, and VAT, was assessed by comparison to dual-energy X-ray absorptiometry (DXA) measurements as a criterion method. In a subset sample, reliability was assessed by repeated measurements within a week, to estimate biological variability. In another subsample, TBW estimates from the MFBIA system were compared to those calculated from resistance at 50 kHz using a conventional tetrapolar arrangement. Methods Participants Study participants included a total of 1,000 healthy active duty US Marines (n = 667 men, n = 333 women). Individuals were recruited from the US National Capital Region (Virginia, Maryland, and Washington, DC), Camp Pendleton, California, and from Camp Butler, Okinawa Japan. Prior to study-related activities, all participants provided written informed consent, and women were provided a rapid pregnancy test to establish absence of detectable pregnancy. Study approval was granted by the US Army Medical Research and Development Command (Fort Detrick, Maryland) and US Marine Corps (Quantico, Virginia) Institutional Review Boards, protocol M10873, approved March 2021. Study Design All participants were assessed for body composition during a single day visit (< 1 h). For retest reliability of measures, a subset of participants (n = 117; 100 men and 17 women) were assessed for all the same measures during a second visit separated by 5–7 days. Each individual wore athletic clothing and was asked to remove all jewelry and/or foreign objects. Measurements were taken of standing height, to the nearest 0.1 cm, using a calibrated stadiometer (Seca, Chino, CA). Weight measurements, provided by the MFBIA device, were checked for recording errors by comparison to weight obtained between systems during the same test session. Whole body composition measures were assessed by dual-energy X-ray absorptiometry (DXA) (iDXA, GE Healthcare, Madison, WI), and by a standing multi-frequency (1, 5, 50, 250, 500 and 1000 kHz) bioelectrical impedance analyzer (MFBIA) (InBody 770, InBody Co. Ltd., Seoul, Korea). Additionally, a subset of the sample (n = 685; 416 men, 269 women) were assessed for total body water (TBW, L) by a single-frequency (50 kHz) bioelectrical impedance analyzer SFBIA (Quantum IV, RJL Systems Inc., Clinton Township, MI) as previously described ( 14 ). Outputs from the MFBIA were compared to the output DXA measurements for whole body values (FM, relative body fat (%BF), and FFM) as well as for regional FM and FFM measurements for the arms (left and right arms combined), legs (left and right legs combined), and trunk. Comparisons of the MFBIA output for TBW were compared to those calculated from the SFBIA system as well as for the main measures of R, Xc , and phase angle ( PhA ) at 50kHz. We note that this is not a perfect comparison as SFBIA was measured with electrodes attached at wrist and ankle, while MFBIA was measured between palms and soles, altering the inter-electrode distance ~ 10 cm between the two systems. SFBIA TBW was calculated using sex-specific equations (17). TBW was compared between the two systems as the primary component of body composition derived from resistance (versus secondary estimates that are derived from assumptions about the normal distribution of water in the FFM, etc.). Statistical Analyses Data were analyzed using R (Version 4.4.1; R Foundation for Statistical Computing; Vienna, Austria) ( 18 ) and reported as mean ± standard deviation (SD) unless specified otherwise. Agreement between MFBIA and DXA as well as between MFBIA and SFBIA for TBW were evaluated based on the bias (mean difference), SD of differences, Lin’s concordance correlation coefficient (CCC), mean absolute percentage error (MAPE), and root mean squared error (RMSE). Bland-Altman analyses were used to show bias and limits of agreement (LoA) within 95% of the measures ( 19 ). Passing and Bablok Regression (PBR) ( 20 ) was used to assess agreement between methods. Using PBR, proportional differences are described by the slope (B 1 ) and systematic differences by the intercept (B 0 ); where B 1 = 1 and B 0 = 0 suggest perfect agreement. Intraclass correlation coefficient (ICC) partitioning out variance components using a linear mixed-effects model with random effects within participants was used to assess device reliability ( 21 , 22 ). Results A sample of 1,000 healthy active duty US Marines (667 men, 333 women) were enrolled in the study. Of the main 1,000, a subset of 117 (100 men, 17 women) had repeat visits used for retest reliability comparisons. Participant characteristics (mean ± SD) were: men, n = 667, age 28.4 ± 7.4 years, height 176.6 ± 7.3 cm, body mass 86.3 ± 11.5 kg, BMI 27.7 ± 3.2 kg/m 2 and women, n = 333, age 27.3 ± 6.8 years, height 162.8 ± 7.2 cm, body mass 67.9 ± 9.5 kg, BMI 25.6 ± 3.0 kg/m 2 . Body composition measures by DXA were 23.1 ± 6.3%BF, VAT 60.1 ± 45.2 cm 2 and 31.7 ± 6.2%BF, VAT 37.1 ± 30.9 cm 2 for men and women respectively. The sample included self-reported race/Hispanic origin categories: Hispanic (29.5%), non-Hispanic white (58.0%), non-Hispanic black (11.1%), non-Hispanic Asian (1.1%), and non-Hispanic “other or multi-racial” (0.3%). More detailed descriptive statistics are shown in Table 1 . Table 1 Participant descriptive statistics Value Description Unit Men Women All Sample n 667 333 1,000 Descriptives Age years 28.37 ± 7.40 27.73 ± 6.76 28.17 ± 7.22 Height cm 176.57 ± 7.31 162.83 ± 7.23 172.27 ± 9.68 Body mass kg 86.25 ± 11.54 67.91 ± 9.51 80.50 ± 13.86 Body mass index kg/m 2 27.66 ± 3.24 25.60 ± 3.00 27.01 ± 3.31 Race Asian, non-Hispanic # (%) 10 1 11 (1.1%) Black, non-Hispanic # (%) 74 37 111 (11.1%) Hispanic # (%) 183 112 295 (29.5%) White, non-Hispanic # (%) 397 183 580 (58%) Other # (%) 3 0 3 (0.3%) Fat * Arm fat kg 2.16 ± 0.73 2.41 ± 0.72 2.24 ± 0.73 Leg fat kg 6.47 ± 2.22 8.37 ± 2.38 7.07 ± 2.44 Trunk fat kg 10.80 ± 4.58 10.19 ± 3.76 10.60 ± 4.35 Total fat kg 20.40 ± 7.22 21.83 ± 6.49 20.84 ± 7.03 Relative body fat %BF 23.12 ± 6.31 31.70 ± 6.18 25.81 ± 7.43 Visceral adipose tissue cm 2 60.12 ± 45.27 37.12 ± 30.87 53.76 ± 43.02 Fat-free * Arm fat-free mass kg 9.47 ± 1.53 5.25 ± 0.94 8.15 ± 2.39 Leg fat-free mass kg 23.20 ± 3.19 15.91 ± 2.27 20.91 ± 4.48 Trunk fat-free mass kg 29.63 ± 3.66 21.26 ± 2.61 27.01 ± 5.14 Total fat-free mass kg 66.46 ± 8.06 46.10 ± 5.56 60.08 ± 11.98 Water ** Total body water L 48.85 ± 6.08 33.75 ± 4.28 42.94 ± 9.16 The MFBIA and DXA whole body and regional data for men and women were compared for precision and accuracy (Table 2 ; Figs. 1 and 2 ). Measures for total FM and FFM had comparable and generally high correlations (total FFM, men r = 0.95, CCC = 0.87 vs. total FM, r = 0.93, CCC = 0.82; women total FFM, r = 0.94, CCC = 0.94 vs. total FM, r = 0.96, CCC = 0.92). Additionally, the total body correlations for both FM and FFM were generally higher than each of their region values individually. Total body water comparisons between SFBIA and MFBIA for the sample were moderately correlated (men, r = 0.73, CCC = 0.82; women, r = 0.82, CCC = 0.84) (Table 2 ; Fig. 2 ). Of the comparisons, VAT had the lowest correlation (men, r = 0.74, CCC = 0.68; women, r = 0.74, CCC = 0.34). Additionally, while there was a moderate correlation in %BF (men, r = 0.89, CCC = 0.73; women, r = 0.92, CCC = 0.84), there was a relatively large negative bias (men, -4.2 ± 3.0; women, -2.8 ± 2.6) confirming a previously observed systematic offset ( 13 , 16 ). Table 2 Men and women assessment for accuracy and precision of multi-frequency bioelectrical impedance analysis (MFBIA) measurements to dual-energy x-ray absorptiometry (DXA). Total body water (TBW) is a comparison between MFBIA and single-frequency bioelectrical impedance analysis (SFBIA) computed from 50 kHz resistance measurements. Variable Sex Bias ± SD LoA r CCC MAPE RMSE (B 1 , B 0 ) FM Arms (kg) Men -0.43 ± 0.73 -1.88–1.01 0.84 0.67 36.78 0.86 1.60, -1.75 Women 0.22 ± 0.72 -1.22–1.67 0.83 0.71 20.94 0.77 1.61, -1.27 Legs (kg) Men 3.20 ± 2.23 -1.16–7.57 0.81 0.49 32.14 2.47 1.68, -1.21 Women 4.29 ± 2.03 0.31–8.27 0.85 0.43 30.81 2.94 1.62, -0.85 Trunk (kg) Men -1.51 ± 1.82 -5.08–2.07 0.92 0.86 18.42 2.36 0.89, -0.30 Women 0.20 ± 1.36 -2.46–2.85 0.93 0.93 11.25 1.37 0.96, 0.52 Total (kg) Men -3.72 ± 2.63 -8.88–1.44 0.93 0.82 20.23 4.56 0.96, -2.77 Women -1.92 ± 1.83 -5.51–1.67 0.96 0.92 10.37 2.65 1.00, -1.96 Relative (%) Men -4.22 ± 2.95 -10.01–1.57 0.89 0.73 19.98 5.15 1.01, -4.46 Women -2.83 ± 2.61 -7.95–2.29 0.92 0.84 10.40 3.85 1.08, -5.47 Visceral (cm 2 ) Men 9.84 ± 30.70 -50.33–70.01 0.74 0.68 70.80 32.21 0.85, 19.80 Women 48.69 ± 23.93 1.79–95.60 0.74 0.34 567.23 54.23 1.28, 39.17 FFM Arms (kg) Men -1.19 ± 0.75 -2.66–0.27 0.88 0.62 12.56 1.41 0.80, 0.71 Women -0.15 ± 0.48 -1.09–0.79 0.86 0.85 7.34 0.50 0.93, 0.18 Legs (kg) Men -3.18 ± 1.84 -6.78–0.43 0.82 0.50 13.59 3.67 0.81, 1.34 Women -1.90 ± 1.19 -4.23–0.43 0.86 0.62 12.14 2.24 0.93, -0.65 Trunk (kg) Men 1.32 ± 1.89 -2.38–5.01 0.86 0.81 6.47 2.30 0.97, 2.14 Women 0.35 ± 1.50 -2.58–3.29 0.84 0.83 5.59 1.54 0.97, 0.82 Total (kg) Men 3.38 ± 2.79 -2.09–8.86 0.95 0.87 5.55 4.39 1.07, -1.20 Women 2.00 ± 2.17 -2.25–6.25 0.94 0.88 4.79 2.95 1.11, -3.41 Water Total (L) Men -1.89 ± 3.31 -4.58–8.37 0.73 0.82 5.93 3.81 1.05, -0.07 Women -1.74 ± 2.01 -2.21–5.69 0.82 0.84 5.80 2.66 1.13, -2.50 Table 2 outlines sex grouped comparisons for body regions between the MFBIA and DXA, as well as between the MFBIA and SFBIA for TBW. Women had higher correlations than men for total FM (women, r = 0.96, CCC = 0.92 vs. men r = 0.93, CCC = 0.82 kg) and very close values for total FFM (women, r = 0.94, CCC = 0.88 vs. men r = 0.95, CCC = 0.87 kg). Both women and men had high correlations for trunk FM (men, r = 0.92, CCC = 0.86; women, r = 0.93, CCC = 0.93 kg) and moderate correlations for FFM (men, r = 0.86, CCC = 0.81; women, r = 0.84, CCC = 0.83 kg). Measures for VAT had the highest relative bias, the lowest correlation, and highest errors for both men (9.84 ± 30.70, r = 0.74, CCC = 0.68, MAPE = 70.8, RMSE = 32.2 cm 2 ) and women (48.69 ± 23.93, r = 0.74, CCC = 0.34, MAPE = 567.23, RMSE = 54.23 cm 2 ). Bland-Altman analyses show negatively skewed LoA for %BF (men − 10.01 to 1.57, women − 7.95 to 2.29%), and wide LoA for VAT (men − 50.33 to 70.01, women 1.79 to 95.60 cm 2 ) (Table 2 ). Generally, the two system raw data outputs are not comparable, as one is taken standing while the other is supine and the measurement locations are not exactly the same. However, along with TBW, Fig. 2 shows comparisons between the SFBIA to the summed segmental data from the MFBIA 50 kHz measures for PhA , R and Xc . The MFBIA system reports segmental PhA values as well as a whole body 50 kHz PhA which are markedly different when compared to SFBIA. Phase angle technically represents the angular difference between the voltage and current, calculated as a ratio of Xc/R , therefore it can be calculated. For this plot in Fig. 2 , shown is the comparison of PhA from the SFBIA to the reported whole body PhA of the MFBIA; while the inset plot shows the average 50 kHz segmental PhA values for the five segments compared to the SFBIA value. Figure 2 also shows a comparison of the SFBIA 50 kHz R and Xc to the summed segmental MFBIA values divided by two for arms and legs plus the trunk value (i.e., \(\:\frac{\text{r}\text{i}\text{g}\text{h}\text{t}\:\text{a}\text{r}\text{m}\:+\:\text{l}\text{e}\text{f}\text{t}\:\text{a}\text{r}\text{m}\:+\:\text{r}\text{i}\text{g}\text{h}\text{t}\:\text{l}\text{e}\text{g}\:+\:\text{l}\text{e}\text{f}\text{t}\:\text{l}\text{e}\text{g}\:}{2}+\text{t}\text{r}\text{u}\text{n}\text{k}\) ). Modified Bland-Altman comparisons are plotted in Fig. 3 and additionally described in Table 2 for bias ± SD of differences, and limits of agreement (LoA) between DXA and MFBIA measurements for both FM and FFM for each main body region (arms, legs, trunk, total). Mean bias between the methods for the total FM and FFM indicate that MFBIA systematically underestimated FM and overestimated FFM compared to DXA (men FM -3.72 ± 2.63 kg, LoA − 8.88–1.44 kg, and FFM 3.38 ± 2.79 kg, LoA − 2.09–8.86 kg; women FM -1.92 ± 1.83 kg, LoA − 5.51–1.67 kg, and FFM 2.00 ± 2.17 kg, LoA − 2.25–6.25 kg). The modified Bland-Altman (MFBIA bias to DXA; Fig. 3 ) shows an increasing positive bias with higher values of arm and leg FM; while in contrast, FFM showed increasing negative bias with higher arm and leg values. Both FM and FFM for the trunk do not have a clear skew positive or negative within values. Graphically, as expected due to propagation of errors, the total values for both FM and FFM have the largest range LoA, as they are the summed values from all regions. Test-retest reliability assessments, collected over a week would reflect both biological and measurement method reliability. Both regional and whole body components measured by MFBIA had exceptional test–retest reliability (ICC ≥ 0.987) (Table 3 ). Table 3 also confirmed the DXA criterion measures to be highly stable over the week (ICC ≥ 0.990). Test-retest reliability was exceptionally high for fat measures by both DXA (ICC 0.990–0.998) and MFBIA (ICC 0.990–0.995). Of FM measurements, the lower reliabilities were seen for legs for both DXA (ICC = 0.990) and MFBIA (ICC = 0.990). Test-retest reliability of FFM measurements was exceptionally high for all compartments and regions by both the DXA (ICC 0.990–0.995) and MFBIA (ICC 0.987–0.994). Additionally, test-retest reliability of TBW as well for intra and extracellular water (ICW, ECW) for the MFBIA system was very high (ICC 0.993–0.996) (Table 3 ). Table 3 Combined men and women assessment for test-retest reliability of dual-energy x-ray absorptiometry (DXA) and multi-frequency bioelectrical impedance analysis (MFBIA) measurements for repeated assessments for subset (n = 117). Region Unit DXA ICC DXA Bias ± SD MFBIA ICC MFBIA Bias ± SD Fat Arms kg 0.990 -0.01 ± 0.15 0.995 0.08 ± 0.43 Legs kg 0.990 -0.03 ± 0.47 0.990 0.13 ± 0.78 Trunk kg 0.997 -0.07 ± 0.48 0.993 0.17 ± 1.48 Relative % 0.996 -0.04 ± 0.95 0.993 0.24 ± 1.28 Visceral cm 2 0.995 -1.04 ± 6.45 0.993 1.18 ± 6.23 Total kg 0.998 -0.04 ± 0.61 0.994 0.41 ± 2.41 Fat-Free Arms kg 0.992 0.04 ± 0.39 0.993 -0.10 ± 0.62 Legs kg 0.994 -0.12 ± 0.83 0.987 -0.16 ± 1.69 Trunk kg 0.990 -0.12 ± 0.93 0.994 -0.33 ± 1.68 Total kg 0.995 -0.16 ± 1.52 0.994 -0.59 ± 4.08 Water Total L N/A N/A 0.995 0.45 ± 2.59 Intracellular L N/A N/A 0.996 0.26 ± 1.57 Extracellular L N/A N/A 0.993 0.18 ± 1.12 Discussion These data validated the use of MFBIA as a practical and reliable method for estimation of whole body and regional FM and FFM composition. Fat was underestimated (18.2% men and 8.8% women) and FFM parameters were slightly overestimated (5.1% men and 4.3% women) as previously reported by us and also reported for other MFBIA devices from other manufacturers (SECA, Tanita, Impedimed) ( 13 , 16 , 23 – 25 ). It has been suggested that segmental analysis may explain, in part, this bias ( 10 ). Previous reports have suggested that greater BMI may be associated with greater bias ( 23 , 24 , 26 ). Several studies have attempted to develop correction factors for body sizes and shapes, and this may be helpful in smaller bodies (e.g., children) but has been less helpful for adults ( 27 , 28 ). We included an analysis of MAPE in order to assess variability of body composition components relative to the total mass or volume of tissue involved; limb fat and fat-free components were markedly higher than trunk and total body (Table 2 ) and the bias increased with mass (Figs. 1 and 3 ). The InBody 770 VAT estimates were similar over the week, but values differed markedly from DXA VAT estimates. Both accuracy and precision were lacking in these comparisons to the criterion measure and in repeated measurements (Table 2 , Fig. 2 ). The apparent basis for the BIA estimation is an electrical resistance that reflects both the truncal cross sectional area (essentially an estimate of the circumference) and the contained visceral fat ( 29 ). However, there remain some unexplained assumptions about the truncal subcutaneous fat layer ( 30 ). The subcutaneous fat component is associated with total adiposity, sex, and age and these suggest potential predictive factors for the estimation of the subcutaneous layer, which could be subtracted from total truncal fat to obtain a VAT estimate. However, is still a high variability among these factors ( 31 ). Matsuzawa demonstrated this large variability in CT-determined VAT and subcutaneous fat between individual sumo wrestlers ( 32 ). In the present data, higher total fat is associated with an increased variability in the BIA-determined VAT. One strategy to improve the BIA VAT assessment might involve the inclusion of some other geometry factors, perhaps even a simple waist circumference ( 27 , 28 ). Defining VAT primarily through electrical properties requires of the trunk tissues further research, and scientific explanation not currently provided with the proprietary algorithms. The early concerns of biological effects on BIA-derived body composition estimates were largely based on the sensitivity of SFBIA (50 kHz) methods to deviations from a consistent 73% hydration of the FFM ( 5 , 33 ). Additionally, as SFBIA is a simple model that seeks to interpret the human from a single cylinder, MFBIA stand-on systems mitigate this simplification by adding more dimensions (added cylinders) to represent the human geometry. The use of more than one frequency and the inclusion of reactance and phase angle with resistance measures could theoretically provide a more robust assessment of water compartments and cell mass less affected by deviations from assumed hydration ( 34 , 35 ). This is a hypothesis that remains to be tested. However, data in this study compared reasonably well to data reported from a previous study with tightly controlled biological variables including hydration status. The previous study involved a small sample of young fit individual soldiers under tightly controlled laboratory conditions ( 16 ). Under these conditions every BIA parameter had a better accuracy and test-retest reliability Another test of this hypothesis is the comparison between the results of single and multi-frequency variability for the same individuals in this study. The MFBIA proprietary algorithm for TBW had a MAPE of 6% overestimation compared to the SFBIA TBW calculated from the 50 kHz resistance measurements (Table 2 , Fig. 2 ). Unfortunately, these data reveal more about the comparison between algorithms and system engineering and less about the influence of hydration and other biological factors. Resistance was overestimated, Xc was underestimated, and PhA was closely aligned for summed segmental measurements obtained from the InBody system compared to the whole body values obtained from the SFBIA system (Fig. 2 ). Another value provided by the MFBIA (InBody) system for “total phase angle” significantly departed from the line of identity; no public information is available about which values are used in the body composition calculations (Fig. 2 ). From these data, we have to conclude again that standardization of test protocols is critical to the reliable application of BIA ( 36 – 38 ). Some of the earlier methodological issues have been resolved through standardization of the human factors design of standing BIA devices that direct users to the correct positioning of arms and legs, removing variation in electrode placement ( 39 ). We report here that everyday variation in prandial and hydration status as well as exercise had relatively little effect on the repeated measurements, but the measurements were not as accurate and reliable as a previous study from this laboratory using tight control of biological variables. This study did not systematically challenge each of the biological variables of hydration, prandial status, recent exercise, and time of day but accepted the real-life variations represented in repeated measurements of a group of healthy fit young men and women going about their daily weekday routines on a military installation. Inquiries into specific biological factors that have the greatest effect on BIA assessments are needed. For example, Tinsley et al. showed an acute effect on BIA measurements for at least the first 10 minutes after bolus water consumption in standing subjects ( 40 ). Another limitation to this study was the use of DXA in lieu of the gold standard but higher radiation and less practical CT scan. The use of DXA for body composition and regional assessments of the components of arms and legs, as well as estimation of VAT, does not represent the gold standard of CT assessment although it has previously been demonstrated to be a reasonable estimate of CT measurements ( 41 ). DXA itself can be influenced at least by large variations in some of the same biological factors we considered, such as hydration status ( 42 ). Our findings support the use of MFBIA systems for field epidemiological studies, military readiness standards, and clinical weight management. For these applications, assuming the use of one single device, reliability of measurement and reproducibility is more important than clinically-mandated accuracy. Despite the underestimation of FM and over-estimation of FFM seen in this and other studies ( 13 , 16 , 23 – 25 ), previous studies have shown that BIA does track changes in body composition ( 43 , 44 ), an important advantage over anthropometric methods which do not adequately track change ( 45 , 46 ). More than 30 years ago, researchers were advocating for the replacement of cruder metrics of body composition such as BMI with BIA ( 47 , 48 ). Three decades later, there is now adequate scientific support to replace BMI with BIA as a better assessment of actual body composition. In real-world conditions (i.e., relatively uncontrolled biological factors) MFBIA can provide a reliable and accurate method for assessing common regional and whole-body FM, FFM, and TBW. Assessments of limb composition are less accurate and reliable. VAT estimation did not match the DXA-estimated VAT and may not be any better than a simple waist circumference. Declarations Funding The authors gratefully acknowledge funding support from the Military Operational Medicine Research Program Restoral funding for modernization of Department of Defense medical readiness standards. Author contributions AWP and KEF were involved in all aspects of this study conception and execution and wrote the first draft of the paper. LCW assisted in data analysis and edited the manuscript. CLC, WJT, and DPL had significant roles in protocol development, methods standardization, data collection, and participated in writing the paper. Acknowledgements First and foremost, the authors thank the Marine men and women that volunteered to participate in this study. We also thank members of the study team for their contributions to coordinating, organizing, and collection of study data, particularly: Lyndsey Nindl, Major Lara Soto, SSG Angie Pazmino, Jason Hancock, Erica Schafer, Juliette Jacques, and LTC Elizabeth Halford. We are indebted to Mr. Brian McGuire (Training and Education Command, US Marine Corps) for his steadfast support throughout this study and to all the Marines who provided logistical support to “make this study happen.” Disclaimer The opinions or assertions contained herein are the private views of the author(s) and are not to be construed as official or reflecting the views of the Army or the Department of Defense. Any citations of commercial organizations and trade names in this report do not constitute an official Department of the Army endorsement of approval of the products or services of these organizations. Author LCW provides consultancy services to ImpediMed Ltd.; all other authors have no conflicts of interest to declare. There was no interaction or any conflict of interest with the commercial vendors of the primary devices used in this study including InBody Co. Ltd, GE HealthCare, or RJL Systems, Inc. This research was supported in part by appointments to the Department of Defense (DOD) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the US Department of Energy (DOE) and the DOD. ORISE is managed by ORAU under DOE contract number DE-SC0014664. All opinions expressed in this paper are the author's and do not necessarily reflect the policies and views of DOD, DOE, or ORAU/ORISE. References Thomasset M. Bioelectric properties of tissue. Impedance measurement in clinical medicine. Significance of curves obtained. Lyon medical. 1962;94:107–18. Ward LC, Müller M. Bioelectrical impedance analysis. European journal of clinical nutrition. 2013;67(1):S1-S. Hoffer EC, Meador CK, Simpson DC. Correlation of whole-body impedance with total body water volume. Journal of applied physiology. 1969;27(4):531–4. Lukaski HC, Johnson PE, Bolonchuk WW, Lykken GI. Assessment of fat-free mass using bioelectrical impedance measurements of the human body. The American journal of clinical nutrition. 1985;41(4):810–7. Pace N, Rathbun EN. Studies on body composition III. The body water and chemically combined nitrogen content in relation to fat content. Journal of Biological Chemistry. 1945;158(3):685–91. Segal KR, Van Loan M, Fitzgerald PI, Hodgdon JA, Van Itallie RB. Lean body mass estimation by bioelectrical impedance analysis: A four-site cross-validation study. American Journal of Clinical Nutrition. 1988;47:7–14. Chumlea WC, Guo SS, Kuczmarski RJ, Flegal KM, Johnson CL, Heymsfield SB, et al. 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Sun SS, Chumlea WC, Heymsfield SB, Lukaski HC, Schoeller D, Friedl K, et al. Development of bioelectrical impedance analysis prediction equations for body composition with the use of a multicomponent model for use in epidemiologic surveys. The American journal of clinical nutrition. 2003;77(2):331–40. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2014. Bland JM, Altman DG. Measuring agreement in method comparison studies. Statistical methods in medical research. 1999;8(2):135–60. Passing H, Bablok W. A new biometrical procedure for testing the equality of measurements from two different analytical methods. Application of linear regression procedures for method comparison studies in clinical chemistry, Part I. 1983. Nakagawa S, Johnson PC, Schielzeth H. The coefficient of determination R 2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface. 2017;14(134):20170213. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. Journal of statistical software. 2015;67:1–48. Feng Q, Bešević J, Conroy M, Omiyale W, Lacey B, Allen N. Comparison of body composition measures assessed by bioelectrical impedance analysis versus dual-energy X-ray absorptiometry in the United Kingdom Biobank. Clinical Nutrition ESPEN. 2024;63:214–25. Day K, Kwok A, Evans A, Mata F, Verdejo-Garcia A, Hart K, et al. Comparison of a bioelectrical impedance device against the reference method dual energy X-ray absorptiometry and anthropometry for the evaluation of body composition in adults. nutrients. 2018;10(10):1469. Tinsley GM, Moore ML, Silva AM, Sardinha LB. Cross-sectional and longitudinal agreement between two multifrequency bioimpedance devices for resistance, reactance, and phase angle values. European journal of clinical nutrition. 2020;74(6):900–11. Deurenberg P. Limitations of the bioelectrical impedance method for the assessment of body fat in severe obesity. The American journal of clinical nutrition. 1996;64(3):449S-52S. Wells JC, Williams JE, Ward LC, Fewtrell MS. Utility of specific bioelectrical impedance vector analysis for the assessment of body composition in children. Clinical Nutrition. 2021;40(3):1147–54. Ward LC, Wells JC, Lyons-Reid J, Tint MT. Individualized body geometry correction factor (KB) for use when predicting body composition from bioimpedance spectroscopy. Physiological Measurement. 2022;43(3):035006. Ryo M, Maeda K, Onda T, Katashima M, Okumiya A, Nishida M, et al. A new simple method for the measurement of visceral fat accumulation by bioelectrical impedance. Diabetes care. 2005;28(2):451–3. Scharfetter H, Schlager T, Stollberger R, Felsberger R, Hutten H, Hinghofer-Szalkay H. Assessing abdominal fatness with local bioimpedance analysis: basics and experimental findings. International Journal of obesity. 2001;25(4):502–11. Chaudry O, Grimm A, Friedberger A, Kemmler W, Uder M, Jakob F, et al. Magnetic resonance imaging and bioelectrical impedance analysis to assess visceral and abdominal adipose tissue. Obesity. 2020;28(2):277–83. Matsuzawa Y, Fujioka S, Tokunaga K, Tarui S. Classification of obesity with respect to morbidity. Proceedings of the Society for Experimental Biology and Medicine. 1992;200(2):197–201. Bioelectrical impedance analysis in body composition measurement: National Institutes of Health Technology Assessment Conference Statement. Am J Clin Nutr. 1996;64(3 Suppl):524s-32s. Gonzalez MC, Barbosa-Silva TG, Bielemann RM, Gallagher D, Heymsfield SB. Phase angle and its determinants in healthy subjects: influence of body composition. The American journal of clinical nutrition. 2016;103(3):712. Lukaski HC, Talluri A. Phase angle as an index of physiological status: validating bioelectrical assessments of hydration and cell mass in health and disease. Reviews in Endocrine and Metabolic Disorders. 2023;24(3):371–9. Brantlov S, Jødal L, Lange A, Rittig S, Ward LC. Standardisation of bioelectrical impedance analysis for the estimation of body composition in healthy paediatric populations: a systematic review. Journal of Medical Engineering & Technology. 2017;41(6):460–79. Brantlov S, Ward LC, Jødal L, Rittig S, Lange A. Critical factors and their impact on bioelectrical impedance analysis in children: a review. Journal of medical engineering & technology. 2017;41(1):22–35. Johnson Stoklossa CA, Forhan M, Padwal RS, Gonzalez MC, Prado CM. Practical considerations for body composition assessment of adults with class II/III obesity using bioelectrical impedance analysis or dual-energy X-ray absorptiometry. Current obesity reports. 2016;5:389–96. Lyons-Reid J, Ward LC, Tint M-T, Kenealy T, Godfrey KM, Chan S-Y, et al. The influence of body position on bioelectrical impedance spectroscopy measurements in young children. Scientific Reports. 2021;11(1):10346. Tinsley GM, Stratton MT, Harty PS, Williams AD, White SJ, Rodriguez C, et al. Influence of acute water ingestion and prolonged standing on raw bioimpedance and subsequent body fluid and composition estimates. Journal of electrical bioimpedance. 2022;13(1):10. Kaul S, Rothney MP, Peters DM, Wacker WK, Davis CE, Shapiro MD, et al. Dual-energy X‐ray absorptiometry for quantification of visceral fat. Obesity. 2012;20(6):1313–8. Friedl KE, Moore RJ, Martinez-Lopez LE, Vogel JA, Askew E, Marchitelli L, et al. Lower limit of body fat in healthy active men. Journal of applied physiology. 1994;77(2):933–40. Kushner RF, Kunigk A, Alspaugh M, Andronis PT, Leitch CA, Schoeller DA. Validation of bioelectrical-impedance analysis as a measurement of change in body composition in obesity. The American journal of clinical nutrition. 1990;52(2):219–23. Antonio J, Kenyon M, Ellerbroek A, Carson C, Burgess V, Tyler-Palmer D, et al. Comparison of Dual-Energy X-Ray Absorptiometry (DXA) versus a Multi-frequency Bioelectrical Impedance (InBody 770) device for body composition assessment after a 4-week hypoenergetic diet. Journal of functional morphology and kinesiology. 2019;4(2):23. Friedl KE, Westphal KA, Marchitelli LJ, Patton JF, Chumlea WC, Shumei SG. Evaluation of anthropometric equations to assess body composition changes in young women. Am J Clin Nutr. 2001;73:268–75. Foulis SA, Friedl KE, Spiering BA, Walker LA, Guerriere KI, Pecorelli VP, et al. Body composition changes during 8 weeks of military training are not accurately captured by circumference-based assessments. Frontiers in Physiology. 2023;14:1183836. Roubenoff R, Dallal GE, Wilson P. Predicting body fatness: the body mass index vs estimation by bioelectrical impedance. American journal of public health. 1995;85(5):726–8. Houtkooper LB, Lohman TG, Going SB, Howell WH. Why bioelectrical impedance analysis should be used for estimating adiposity. The American journal of clinical nutrition. 1996;64(3):436S-48S. Additional Declarations Yes there is potential conflict of interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6728659","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":461684740,"identity":"48024287-50ea-4067-951a-eeacca8d7ed7","order_by":0,"name":"Adam Potter","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYFCCBFQuDwN7D5AqAIkzNh7Aq4WNGaqF5wyQMjAAaWkgSgsDg0QOTAsDAzYt/O3JTzcwtjHk8cv3H3xcwWAnY3Dz7cEHPwz+5DGIHcZqi8SZZ2Y3gFqKJduYmQ3PMCTzGNzOSzbsMTAoZpBOxO6wGwlALdsYEjccY2aTbGA4wCM5O8dMgsfAILEBhxb5G+nfYFrYf4K1zDxj/vMPHi0GN3IQtjCCtPBL8Jgx47PF8MybshuJ/ySAfkk2lmwwSObh58kxlpYxME5sw6FF7nj6thsfztjk8TMffPixocLOno39jOHHNxVyif3S6Q8fYPM+CCQwSCRA3YkkyoZLOVzXKBgFo2AUjAJcAAAdJGMR6Ns6YQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4980-8353","institution":"US Army Research Institute of Environmental Medicine","correspondingAuthor":true,"prefix":"","firstName":"Adam","middleName":"","lastName":"Potter","suffix":""},{"id":461684741,"identity":"6deec3dd-9994-492b-8955-1e0961a4c91f","order_by":1,"name":"Leigh Ward","email":"","orcid":"https://orcid.org/0000-0003-2378-279X","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Leigh","middleName":"","lastName":"Ward","suffix":""},{"id":461684742,"identity":"8b8dd2a4-d56a-47ff-b81b-8cb3fd5ca660","order_by":2,"name":"Christopher Chapman","email":"","orcid":"","institution":"US Army Research Institute of Environmental Medicine","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Chapman","suffix":""},{"id":461684743,"identity":"c19a54d5-0354-4ca2-80af-e47e3a6fe061","order_by":3,"name":"William Tharion","email":"","orcid":"","institution":"US Army Research Institute of Environmental Medicine","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Tharion","suffix":""},{"id":461684744,"identity":"cee25fd7-1f4f-4d40-bd40-29d14aa1f2fc","order_by":4,"name":"David Looney","email":"","orcid":"","institution":"US Army Research Institute of Environmental Medicine","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Looney","suffix":""},{"id":461684745,"identity":"f9bdda58-c324-4c88-9c56-b09032427241","order_by":5,"name":"Karl Friedl","email":"","orcid":"","institution":"US Army Research Institute of Environmental Medicine","correspondingAuthor":false,"prefix":"","firstName":"Karl","middleName":"","lastName":"Friedl","suffix":""}],"badges":[],"createdAt":"2025-05-23 02:40:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6728659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6728659/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41430-025-01664-4","type":"published","date":"2025-09-16T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85561290,"identity":"14e3adde-835e-475d-9a7c-a83c0fa53b59","added_by":"auto","created_at":"2025-06-27 12:58:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1907146,"visible":true,"origin":"","legend":"\u003cp\u003ePassing and Bablok regression comparisons for fat and fat-free mass (FM, FFM) parameters from multi-frequency bioelectrical impedance analysis (MFBIA) measurements to dual-energy X-ray absorptiometry (DXA). Note: men are blue (circles); women are red (triangles), regression lines are dashed.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6728659/v1/d2934b2fb6388ffae91653e3.png"},{"id":85561291,"identity":"1b787d76-0d70-4571-853a-b8f28a984400","added_by":"auto","created_at":"2025-06-27 12:58:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2012454,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of selected estimations and measurements obtained from multi-frequency bioelectrical impedance analysis (MFBIA). Upper panel: MFBIA relative body fat (%BF) and visceral adipose tissue (VAT) measurements compared to dual-energy X-ray absorptiometry (DXA) measurements; MFBIA total body water (TBW) compared to single frequency bioelectrical impedance analysis (SFBIA) calculated TBW. Lower panel: measurements of phase angle, resistance, and reactance from MFBIA (calculated from the summed segmental data) compared to SFBIA. \u003cstrong\u003eNote: \u003c/strong\u003einset graph represents calculated whole body phase angle based on segmental data for MFBIA; men and women are represented by blue circles and red triangles, respectively.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6728659/v1/08402d05dc93788543987d23.png"},{"id":85561384,"identity":"f9036c6e-5e5f-43e8-aef4-0ab7cb307c1b","added_by":"auto","created_at":"2025-06-27 13:06:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2281623,"visible":true,"origin":"","legend":"\u003cp\u003eModified Bland-Altman plots for MFBIA bias compared to DXA for arms, legs, trunk, and whole body fat mass (FM, upper panels) and fat-free mass (FFM, lower panels) assessments. Note: men and women are represented by blue (circles) and red (triangles), respectively, dashed lines represent mean bias, dotted lines represent upper and lower limits of agreement.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6728659/v1/d218b27e65bcadebad9ddc90.png"},{"id":91499665,"identity":"1cf8f5d1-3219-45b0-8523-3642aba8b1dc","added_by":"auto","created_at":"2025-09-17 07:11:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6413796,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6728659/v1/64885e83-2c7f-47b7-8b07-646923a94832.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential conflict of interest.","formattedTitle":"Real-world Assessment of Multi-Frequency Bioelectrical Impedance Analysis (MFBIA) for Measuring Body Composition in Healthy Physically Active Populations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBioelectrical impedance analysis (BIA) has advanced considerably since its first applications to body composition analyses (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The underlying principle of BIA is that impedance (measured in Ω), comprised of the two components resistance (\u003cem\u003eR\u003c/em\u003e) and reactance (\u003cem\u003eXc\u003c/em\u003e) (both in Ω), can be used to assess water content of biological tissues (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Whole body resistance measured at 50 kHz (R50), typically between the hand and foot, expressed relative to height as the resistive index (RI, height\u003csup\u003e2\u003c/sup\u003e/R50), is a good predictor of total body water content (TBW) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). From this, other components of body composition, namely fat mass and fat-free mass (FM, FFM), can be estimated based on the assumption that total body water comprises 73% of the fat-free mass (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). While useful at the population level, the assumption of normal hydration at the individual level is not always valid and other factors such as posture, skin temperature, exercise-induced blood flow may change the electrical properties of tissue (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Multi-frequency BIA (MFBIA) represents an advance in BIA-based body composition analysis compared to single-frequency BIA (SFBIA). Using measurements at multiple frequencies (e.g., 5, 50, and 250 kHz) provides added information on water compartments since at low frequencies current flows predominantly through the extracellular water space (ECW) while at high frequencies current can cross the cell membrane and flow through both ECW and the intracellular water (ICW), i.e., TBW (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). With the development of standing octopolar BIA devices, segmental analyses can be easily performed providing regional body composition estimates for arms, legs, and trunk separately and recombined for total body assessments (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the expanding role of BIA in weight management programs, performance monitoring, and military readiness assessments, it is important to establish whether current MFBIA systems provide sufficient accuracy for large-scale applications specifically in real-world conditions. The US Army evaluated BIA technology for body fat standard enforcement in 1984 in the largest military body composition study ever conducted and determined that single frequency BIA (SFBIA) available at the time added technological complexity and no advantage over circumference-based body fat estimation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Recently, Potter et al. reevaluated the suitability of modern MFBIA technologies to replace military height-weight tables and circumference-based predictions of body fat; they concluded that new methods and algorithms may have overcome previous drawbacks with variability in electrode placement, standardization of body position, and influence of biological variables affecting individual measurements (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This also prompted a fresh look at the body composition metrics that are most relevant to health outcomes, sports performance, and military readiness such as trunk or visceral adipose tissue (VAT) and FFM or muscle mass (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study evaluated the reliability and accuracy of a widely used MFBIA system (InBody 770, InBody Co. Ltd., Seoul, Korea) in uncontrolled free-living conditions, offering critical insights into its feasibility and practicality for routine body composition evaluations beyond controlled laboratory conditions to replace conventional metrics such as body mass index (BMI) or other anthropometric methods such as waist circumference assessments. The primary hypothesis was that MFBIA in uncontrolled field conditions would provide similar reliability and accuracy to that observed in a recent laboratory study with tightly controlled conditions (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Additionally, accuracy of MFBIA regional assessments, including arm, leg, trunk, and VAT, was assessed by comparison to dual-energy X-ray absorptiometry (DXA) measurements as a criterion method. In a subset sample, reliability was assessed by repeated measurements within a week, to estimate biological variability. In another subsample, TBW estimates from the MFBIA system were compared to those calculated from resistance at 50 kHz using a conventional tetrapolar arrangement.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eStudy participants included a total of 1,000 healthy active duty US Marines (n\u0026thinsp;=\u0026thinsp;667 men, n\u0026thinsp;=\u0026thinsp;333 women). Individuals were recruited from the US National Capital Region (Virginia, Maryland, and Washington, DC), Camp Pendleton, California, and from Camp Butler, Okinawa Japan. Prior to study-related activities, all participants provided written informed consent, and women were provided a rapid pregnancy test to establish absence of detectable pregnancy. Study approval was granted by the US Army Medical Research and Development Command (Fort Detrick, Maryland) and US Marine Corps (Quantico, Virginia) Institutional Review Boards, protocol M10873, approved March 2021.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eAll participants were assessed for body composition during a single day visit (\u0026lt;\u0026thinsp;1 h). For retest reliability of measures, a subset of participants (n\u0026thinsp;=\u0026thinsp;117; 100 men and 17 women) were assessed for all the same measures during a second visit separated by 5\u0026ndash;7 days. Each individual wore athletic clothing and was asked to remove all jewelry and/or foreign objects. Measurements were taken of standing height, to the nearest 0.1 cm, using a calibrated stadiometer (Seca, Chino, CA). Weight measurements, provided by the MFBIA device, were checked for recording errors by comparison to weight obtained between systems during the same test session. Whole body composition measures were assessed by dual-energy X-ray absorptiometry (DXA) (iDXA, GE Healthcare, Madison, WI), and by a standing multi-frequency (1, 5, 50, 250, 500 and 1000 kHz) bioelectrical impedance analyzer (MFBIA) (InBody 770, InBody Co. Ltd., Seoul, Korea). Additionally, a subset of the sample (n\u0026thinsp;=\u0026thinsp;685; 416 men, 269 women) were assessed for total body water (TBW, L) by a single-frequency (50 kHz) bioelectrical impedance analyzer SFBIA (Quantum IV, RJL Systems Inc., Clinton Township, MI) as previously described (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOutputs from the MFBIA were compared to the output DXA measurements for whole body values (FM, relative body fat (%BF), and FFM) as well as for regional FM and FFM measurements for the arms (left and right arms combined), legs (left and right legs combined), and trunk. Comparisons of the MFBIA output for TBW were compared to those calculated from the SFBIA system as well as for the main measures of \u003cem\u003eR, Xc\u003c/em\u003e, and phase angle (\u003cem\u003ePhA\u003c/em\u003e) at 50kHz. We note that this is not a perfect comparison as SFBIA was measured with electrodes attached at wrist and ankle, while MFBIA was measured between palms and soles, altering the inter-electrode distance\u0026thinsp;~\u0026thinsp;10 cm between the two systems. SFBIA TBW was calculated using sex-specific equations (17). TBW was compared between the two systems as the primary component of body composition derived from resistance (versus secondary estimates that are derived from assumptions about the normal distribution of water in the FFM, etc.).\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eData were analyzed using R (Version 4.4.1; R Foundation for Statistical Computing; Vienna, Austria) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) unless specified otherwise. Agreement between MFBIA and DXA as well as between MFBIA and SFBIA for TBW were evaluated based on the bias (mean difference), SD of differences, Lin\u0026rsquo;s concordance correlation coefficient (CCC), mean absolute percentage error (MAPE), and root mean squared error (RMSE). Bland-Altman analyses were used to show bias and limits of agreement (LoA) within 95% of the measures (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Passing and Bablok Regression (PBR) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) was used to assess agreement between methods. Using PBR, proportional differences are described by the slope (B\u003csub\u003e1\u003c/sub\u003e) and systematic differences by the intercept (B\u003csub\u003e0\u003c/sub\u003e); where B\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1 and B\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0 suggest perfect agreement. Intraclass correlation coefficient (ICC) partitioning out variance components using a linear mixed-effects model with random effects within participants was used to assess device reliability (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA sample of 1,000 healthy active duty US Marines (667 men, 333 women) were enrolled in the study. Of the main 1,000, a subset of 117 (100 men, 17 women) had repeat visits used for retest reliability comparisons. Participant characteristics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) were: men, n\u0026thinsp;=\u0026thinsp;667, age 28.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4 years, height 176.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3 cm, body mass 86.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5 kg, BMI 27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 kg/m\u003csup\u003e2\u003c/sup\u003e and women, n\u0026thinsp;=\u0026thinsp;333, age 27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 years, height 162.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 cm, body mass 67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5 kg, BMI 25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0 kg/m\u003csup\u003e2\u003c/sup\u003e. Body composition measures by DXA were 23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3%BF, VAT 60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;45.2 cm\u003csup\u003e2\u003c/sup\u003e and 31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2%BF, VAT 37.1\u0026thinsp;\u0026plusmn;\u0026thinsp;30.9 cm\u003csup\u003e2\u003c/sup\u003e for men and women respectively. The sample included self-reported race/Hispanic origin categories: Hispanic (29.5%), non-Hispanic white (58.0%), non-Hispanic black (11.1%), non-Hispanic Asian (1.1%), and non-Hispanic \u0026ldquo;other or multi-racial\u0026rdquo; (0.3%). More detailed descriptive statistics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant descriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDescriptives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyears\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.37\u0026thinsp;\u0026plusmn;\u0026thinsp;7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.73\u0026thinsp;\u0026plusmn;\u0026thinsp;6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176.57\u0026thinsp;\u0026plusmn;\u0026thinsp;7.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e172.27\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.25\u0026thinsp;\u0026plusmn;\u0026thinsp;11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.91\u0026thinsp;\u0026plusmn;\u0026thinsp;9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.50\u0026thinsp;\u0026plusmn;\u0026thinsp;13.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian, non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack, non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e295 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite, non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e580 (58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eFat *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArm fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeg fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.47\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrunk fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.80\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.60\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.40\u0026thinsp;\u0026plusmn;\u0026thinsp;7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.83\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.84\u0026thinsp;\u0026plusmn;\u0026thinsp;7.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelative body fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%BF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.12\u0026thinsp;\u0026plusmn;\u0026thinsp;6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.70\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.81\u0026thinsp;\u0026plusmn;\u0026thinsp;7.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisceral adipose tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.12\u0026thinsp;\u0026plusmn;\u0026thinsp;45.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.12\u0026thinsp;\u0026plusmn;\u0026thinsp;30.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.76\u0026thinsp;\u0026plusmn;\u0026thinsp;43.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFat-free *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArm fat-free mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeg fat-free mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrunk fat-free mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.01\u0026thinsp;\u0026plusmn;\u0026thinsp;5.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal fat-free mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.46\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.08\u0026thinsp;\u0026plusmn;\u0026thinsp;11.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal body water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.85\u0026thinsp;\u0026plusmn;\u0026thinsp;6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.94\u0026thinsp;\u0026plusmn;\u0026thinsp;9.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe MFBIA and DXA whole body and regional data for men and women were compared for precision and accuracy (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Measures for total FM and FFM had comparable and generally high correlations (total FFM, men r\u0026thinsp;=\u0026thinsp;0.95, CCC\u0026thinsp;=\u0026thinsp;0.87 vs. total FM, r\u0026thinsp;=\u0026thinsp;0.93, CCC\u0026thinsp;=\u0026thinsp;0.82; women total FFM, r\u0026thinsp;=\u0026thinsp;0.94, CCC\u0026thinsp;=\u0026thinsp;0.94 vs. total FM, r\u0026thinsp;=\u0026thinsp;0.96, CCC\u0026thinsp;=\u0026thinsp;0.92). Additionally, the total body correlations for both FM and FFM were generally higher than each of their region values individually. Total body water comparisons between SFBIA and MFBIA for the sample were moderately correlated (men, r\u0026thinsp;=\u0026thinsp;0.73, CCC\u0026thinsp;=\u0026thinsp;0.82; women, r\u0026thinsp;=\u0026thinsp;0.82, CCC\u0026thinsp;=\u0026thinsp;0.84) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of the comparisons, VAT had the lowest correlation (men, r\u0026thinsp;=\u0026thinsp;0.74, CCC\u0026thinsp;=\u0026thinsp;0.68; women, r\u0026thinsp;=\u0026thinsp;0.74, CCC\u0026thinsp;=\u0026thinsp;0.34). Additionally, while there was a moderate correlation in %BF (men, r\u0026thinsp;=\u0026thinsp;0.89, CCC\u0026thinsp;=\u0026thinsp;0.73; women, r\u0026thinsp;=\u0026thinsp;0.92, CCC\u0026thinsp;=\u0026thinsp;0.84), there was a relatively large negative bias (men, -4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0; women, -2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6) confirming a previously observed systematic offset (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMen and women assessment for accuracy and precision of multi-frequency bioelectrical impedance analysis (MFBIA) measurements to dual-energy x-ray absorptiometry (DXA). Total body water (TBW) is a comparison between MFBIA and single-frequency bioelectrical impedance analysis (SFBIA) computed from 50 kHz resistance measurements.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBias\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLoA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMAPE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(B\u003csub\u003e1\u003c/sub\u003e, B\u003csub\u003e0\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArms (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.88\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e36.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.60, -1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.22\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.61, -1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLegs (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.16\u0026ndash;7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.68, -1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u0026ndash;8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.62, -0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrunk (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.08\u0026ndash;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.89, -0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.46\u0026ndash;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.96, 0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.88\u0026ndash;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.96, -2.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.51\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00, -1.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRelative (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-10.01\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.01, -4.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.95\u0026ndash;2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.08, -5.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVisceral (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.84\u0026thinsp;\u0026plusmn;\u0026thinsp;30.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-50.33\u0026ndash;70.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e70.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e32.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.85, 19.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e48.69\u0026thinsp;\u0026plusmn;\u0026thinsp;23.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.79\u0026ndash;95.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e567.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e54.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.28, 39.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eFFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArms (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.66\u0026ndash;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.80, 0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.09\u0026ndash;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.93, 0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLegs (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.78\u0026ndash;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.81, 1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.23\u0026ndash;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.93, -0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrunk (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.38\u0026ndash;5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.97, 2.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.58\u0026ndash;3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.97, 0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.09\u0026ndash;8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.07, -1.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.25\u0026ndash;6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.11, -3.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.58\u0026ndash;8.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.05, -0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.21\u0026ndash;5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.13, -2.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e outlines sex grouped comparisons for body regions between the MFBIA and DXA, as well as between the MFBIA and SFBIA for TBW. Women had higher correlations than men for total FM (women, r\u0026thinsp;=\u0026thinsp;0.96, CCC\u0026thinsp;=\u0026thinsp;0.92 vs. men r\u0026thinsp;=\u0026thinsp;0.93, CCC\u0026thinsp;=\u0026thinsp;0.82 kg) and very close values for total FFM (women, r\u0026thinsp;=\u0026thinsp;0.94, CCC\u0026thinsp;=\u0026thinsp;0.88 vs. men r\u0026thinsp;=\u0026thinsp;0.95, CCC\u0026thinsp;=\u0026thinsp;0.87 kg). Both women and men had high correlations for trunk FM (men, r\u0026thinsp;=\u0026thinsp;0.92, CCC\u0026thinsp;=\u0026thinsp;0.86; women, r\u0026thinsp;=\u0026thinsp;0.93, CCC\u0026thinsp;=\u0026thinsp;0.93 kg) and moderate correlations for FFM (men, r\u0026thinsp;=\u0026thinsp;0.86, CCC\u0026thinsp;=\u0026thinsp;0.81; women, r\u0026thinsp;=\u0026thinsp;0.84, CCC\u0026thinsp;=\u0026thinsp;0.83 kg). Measures for VAT had the highest relative bias, the lowest correlation, and highest errors for both men (9.84\u0026thinsp;\u0026plusmn;\u0026thinsp;30.70, r\u0026thinsp;=\u0026thinsp;0.74, CCC\u0026thinsp;=\u0026thinsp;0.68, MAPE\u0026thinsp;=\u0026thinsp;70.8, RMSE\u0026thinsp;=\u0026thinsp;32.2 cm\u003csup\u003e2\u003c/sup\u003e) and women (48.69\u0026thinsp;\u0026plusmn;\u0026thinsp;23.93, r\u0026thinsp;=\u0026thinsp;0.74, CCC\u0026thinsp;=\u0026thinsp;0.34, MAPE\u0026thinsp;=\u0026thinsp;567.23, RMSE\u0026thinsp;=\u0026thinsp;54.23 cm\u003csup\u003e2\u003c/sup\u003e). Bland-Altman analyses show negatively skewed LoA for %BF (men \u0026minus;\u0026thinsp;10.01 to 1.57, women \u0026minus;\u0026thinsp;7.95 to 2.29%), and wide LoA for VAT (men \u0026minus;\u0026thinsp;50.33 to 70.01, women 1.79 to 95.60 cm\u003csup\u003e2\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerally, the two system raw data outputs are not comparable, as one is taken standing while the other is supine and the measurement locations are not exactly the same. However, along with TBW, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows comparisons between the SFBIA to the summed segmental data from the MFBIA 50 kHz measures for \u003cem\u003ePhA\u003c/em\u003e, \u003cem\u003eR\u003c/em\u003e and \u003cem\u003eXc\u003c/em\u003e. The MFBIA system reports segmental \u003cem\u003ePhA\u003c/em\u003e values as well as a whole body 50 kHz \u003cem\u003ePhA\u003c/em\u003e which are markedly different when compared to SFBIA. Phase angle technically represents the angular difference between the voltage and current, calculated as a ratio of \u003cem\u003eXc/R\u003c/em\u003e, therefore it can be calculated. For this plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, shown is the comparison of \u003cem\u003ePhA\u003c/em\u003e from the SFBIA to the reported whole body \u003cem\u003ePhA\u003c/em\u003e of the MFBIA; while the inset plot shows the average 50 kHz segmental \u003cem\u003ePhA\u003c/em\u003e values for the five segments compared to the SFBIA value. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also shows a comparison of the SFBIA 50 kHz \u003cem\u003eR\u003c/em\u003e and \u003cem\u003eXc\u003c/em\u003e to the summed segmental MFBIA values divided by two for arms and legs plus the trunk value (i.e., \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{r}\\text{i}\\text{g}\\text{h}\\text{t}\\:\\text{a}\\text{r}\\text{m}\\:+\\:\\text{l}\\text{e}\\text{f}\\text{t}\\:\\text{a}\\text{r}\\text{m}\\:+\\:\\text{r}\\text{i}\\text{g}\\text{h}\\text{t}\\:\\text{l}\\text{e}\\text{g}\\:+\\:\\text{l}\\text{e}\\text{f}\\text{t}\\:\\text{l}\\text{e}\\text{g}\\:}{2}+\\text{t}\\text{r}\\text{u}\\text{n}\\text{k}\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eModified Bland-Altman comparisons are plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and additionally described in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for bias\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of differences, and limits of agreement (LoA) between DXA and MFBIA measurements for both FM and FFM for each main body region (arms, legs, trunk, total). Mean bias between the methods for the total FM and FFM indicate that MFBIA systematically underestimated FM and overestimated FFM compared to DXA (men FM -3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.63 kg, LoA \u0026minus;\u0026thinsp;8.88\u0026ndash;1.44 kg, and FFM 3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79 kg, LoA \u0026minus;\u0026thinsp;2.09\u0026ndash;8.86 kg; women FM -1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83 kg, LoA \u0026minus;\u0026thinsp;5.51\u0026ndash;1.67 kg, and FFM 2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17 kg, LoA \u0026minus;\u0026thinsp;2.25\u0026ndash;6.25 kg). The modified Bland-Altman (MFBIA bias to DXA; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) shows an increasing positive bias with higher values of arm and leg FM; while in contrast, FFM showed increasing negative bias with higher arm and leg values. Both FM and FFM for the trunk do not have a clear skew positive or negative within values. Graphically, as expected due to propagation of errors, the total values for both FM and FFM have the largest range LoA, as they are the summed values from all regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTest-retest reliability assessments, collected over a week would reflect both biological and measurement method reliability. Both regional and whole body components measured by MFBIA had exceptional test\u0026ndash;retest reliability (ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.987) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e also confirmed the DXA criterion measures to be highly stable over the week (ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.990). Test-retest reliability was exceptionally high for fat measures by both DXA (ICC 0.990\u0026ndash;0.998) and MFBIA (ICC 0.990\u0026ndash;0.995). Of FM measurements, the lower reliabilities were seen for legs for both DXA (ICC\u0026thinsp;=\u0026thinsp;0.990) and MFBIA (ICC\u0026thinsp;=\u0026thinsp;0.990). Test-retest reliability of FFM measurements was exceptionally high for all compartments and regions by both the DXA (ICC 0.990\u0026ndash;0.995) and MFBIA (ICC 0.987\u0026ndash;0.994). Additionally, test-retest reliability of TBW as well for intra and extracellular water (ICW, ECW) for the MFBIA system was very high (ICC 0.993\u0026ndash;0.996) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCombined men and women assessment for test-retest reliability of dual-energy x-ray absorptiometry (DXA) and multi-frequency bioelectrical impedance analysis (MFBIA) measurements for repeated assessments for subset (n\u0026thinsp;=\u0026thinsp;117).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDXA ICC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDXA Bias\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMFBIA ICC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMFBIA Bias\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLegs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrunk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisceral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat-Free\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLegs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrunk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntracellular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtracellular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThese data validated the use of MFBIA as a practical and reliable method for estimation of whole body and regional FM and FFM composition. Fat was underestimated (18.2% men and 8.8% women) and FFM parameters were slightly overestimated (5.1% men and 4.3% women) as previously reported by us and also reported for other MFBIA devices from other manufacturers (SECA, Tanita, Impedimed) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). It has been suggested that segmental analysis may explain, in part, this bias (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Previous reports have suggested that greater BMI may be associated with greater bias (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Several studies have attempted to develop correction factors for body sizes and shapes, and this may be helpful in smaller bodies (e.g., children) but has been less helpful for adults (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). We included an analysis of MAPE in order to assess variability of body composition components relative to the total mass or volume of tissue involved; limb fat and fat-free components were markedly higher than trunk and total body (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and the bias increased with mass (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe InBody 770 VAT estimates were similar over the week, but values differed markedly from DXA VAT estimates. Both accuracy and precision were lacking in these comparisons to the criterion measure and in repeated measurements (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The apparent basis for the BIA estimation is an electrical resistance that reflects both the truncal cross sectional area (essentially an estimate of the circumference) and the contained visceral fat (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, there remain some unexplained assumptions about the truncal subcutaneous fat layer (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The subcutaneous fat component is associated with total adiposity, sex, and age and these suggest potential predictive factors for the estimation of the subcutaneous layer, which could be subtracted from total truncal fat to obtain a VAT estimate. However, is still a high variability among these factors (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Matsuzawa demonstrated this large variability in CT-determined VAT and subcutaneous fat between individual sumo wrestlers (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In the present data, higher total fat is associated with an increased variability in the BIA-determined VAT. One strategy to improve the BIA VAT assessment might involve the inclusion of some other geometry factors, perhaps even a simple waist circumference (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Defining VAT primarily through electrical properties requires of the trunk tissues further research, and scientific explanation not currently provided with the proprietary algorithms.\u003c/p\u003e \u003cp\u003eThe early concerns of biological effects on BIA-derived body composition estimates were largely based on the sensitivity of SFBIA (50 kHz) methods to deviations from a consistent 73% hydration of the FFM (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Additionally, as SFBIA is a simple model that seeks to interpret the human from a single cylinder, MFBIA stand-on systems mitigate this simplification by adding more dimensions (added cylinders) to represent the human geometry. The use of more than one frequency and the inclusion of reactance and phase angle with resistance measures could theoretically provide a more robust assessment of water compartments and cell mass less affected by deviations from assumed hydration (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This is a hypothesis that remains to be tested. However, data in this study compared reasonably well to data reported from a previous study with tightly controlled biological variables including hydration status. The previous study involved a small sample of young fit individual soldiers under tightly controlled laboratory conditions (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Under these conditions every BIA parameter had a better accuracy and test-retest reliability Another test of this hypothesis is the comparison between the results of single and multi-frequency variability for the same individuals in this study. The MFBIA proprietary algorithm for TBW had a MAPE of 6% overestimation compared to the SFBIA TBW calculated from the 50 kHz resistance measurements (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Unfortunately, these data reveal more about the comparison between algorithms and system engineering and less about the influence of hydration and other biological factors. Resistance was overestimated, \u003cem\u003eXc\u003c/em\u003e was underestimated, and \u003cem\u003ePhA\u003c/em\u003e was closely aligned for summed segmental measurements obtained from the InBody system compared to the whole body values obtained from the SFBIA system (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Another value provided by the MFBIA (InBody) system for \u0026ldquo;total phase angle\u0026rdquo; significantly departed from the line of identity; no public information is available about which values are used in the body composition calculations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom these data, we have to conclude again that standardization of test protocols is critical to the reliable application of BIA (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Some of the earlier methodological issues have been resolved through standardization of the human factors design of standing BIA devices that direct users to the correct positioning of arms and legs, removing variation in electrode placement (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). We report here that everyday variation in prandial and hydration status as well as exercise had relatively little effect on the repeated measurements, but the measurements were not as accurate and reliable as a previous study from this laboratory using tight control of biological variables.\u003c/p\u003e \u003cp\u003eThis study did not systematically challenge each of the biological variables of hydration, prandial status, recent exercise, and time of day but accepted the real-life variations represented in repeated measurements of a group of healthy fit young men and women going about their daily weekday routines on a military installation. Inquiries into specific biological factors that have the greatest effect on BIA assessments are needed. For example, Tinsley et al. showed an acute effect on BIA measurements for at least the first 10 minutes after bolus water consumption in standing subjects (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Another limitation to this study was the use of DXA in lieu of the gold standard but higher radiation and less practical CT scan. The use of DXA for body composition and regional assessments of the components of arms and legs, as well as estimation of VAT, does not represent the gold standard of CT assessment although it has previously been demonstrated to be a reasonable estimate of CT measurements (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). DXA itself can be influenced at least by large variations in some of the same biological factors we considered, such as hydration status (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings support the use of MFBIA systems for field epidemiological studies, military readiness standards, and clinical weight management. For these applications, assuming the use of one single device, reliability of measurement and reproducibility is more important than clinically-mandated accuracy. Despite the underestimation of FM and over-estimation of FFM seen in this and other studies (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), previous studies have shown that BIA does track changes in body composition (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), an important advantage over anthropometric methods which do not adequately track change (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). More than 30 years ago, researchers were advocating for the replacement of cruder metrics of body composition such as BMI with BIA (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Three decades later, there is now adequate scientific support to replace BMI with BIA as a better assessment of actual body composition. In real-world conditions (i.e., relatively uncontrolled biological factors) MFBIA can provide a reliable and accurate method for assessing common regional and whole-body FM, FFM, and TBW. Assessments of limb composition are less accurate and reliable. VAT estimation did not match the DXA-estimated VAT and may not be any better than a simple waist circumference.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge funding support from the Military Operational Medicine Research Program Restoral funding for modernization of Department of Defense medical readiness standards. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAWP\u0026nbsp;\u003c/strong\u003eand\u0026nbsp;\u003cstrong\u003eKEF\u003c/strong\u003e were involved in all aspects of this study conception and execution and wrote the first draft of the paper.\u0026nbsp;\u003cstrong\u003eLCW\u0026nbsp;\u003c/strong\u003eassisted in data analysis and edited the manuscript. \u0026nbsp;\u003cstrong\u003eCLC, WJT,\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;DPL\u003c/strong\u003e had significant roles in protocol development, methods standardization, data collection, and participated in writing the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst and foremost, the authors thank the Marine men and women that volunteered to participate in this study. We also thank members of the study team\u0026nbsp;for their contributions to coordinating, organizing, and collection of study data, particularly: Lyndsey Nindl, Major Lara Soto,\u0026nbsp;SSG Angie Pazmino, Jason Hancock, Erica Schafer,\u0026nbsp;Juliette Jacques, and LTC Elizabeth Halford. We are indebted to Mr. Brian McGuire (Training and Education Command, US Marine Corps) for his steadfast support throughout this study and to all the Marines who provided logistical support to “make this study happen.”\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe opinions or assertions contained herein are the private views of the author(s) and are not to be construed as official or reflecting the views of the Army or the Department of Defense. Any citations of commercial organizations and trade names in this report do not constitute an official Department of the Army endorsement of approval of the products or services of these organizations. Author LCW provides consultancy services to ImpediMed Ltd.; all other authors have no conflicts of interest to declare. There was no interaction or any conflict of interest with the commercial vendors of the primary devices used in this study including InBody Co. Ltd, GE HealthCare, or RJL Systems, Inc. \u0026nbsp;This research was supported in part by appointments to the Department of Defense (DOD) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the US Department of Energy (DOE) and the DOD. ORISE is managed by ORAU under DOE contract number DE-SC0014664. All opinions expressed in this paper are the author's and do not necessarily reflect the policies and views of DOD, DOE, or ORAU/ORISE. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThomasset M. Bioelectric properties of tissue. Impedance measurement in clinical medicine. Significance of curves obtained. Lyon medical. 1962;94:107\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWard LC, M\u0026uuml;ller M. Bioelectrical impedance analysis. European journal of clinical nutrition. 2013;67(1):S1-S.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffer EC, Meador CK, Simpson DC. Correlation of whole-body impedance with total body water volume. Journal of applied physiology. 1969;27(4):531\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLukaski HC, Johnson PE, Bolonchuk WW, Lykken GI. 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Predicting body fatness: the body mass index vs estimation by bioelectrical impedance. American journal of public health. 1995;85(5):726\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoutkooper LB, Lohman TG, Going SB, Howell WH. Why bioelectrical impedance analysis should be used for estimating adiposity. The American journal of clinical nutrition. 1996;64(3):436S-48S.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"body composition, BMI, DXA, body water, BIA, VAT","lastPublishedDoi":"10.21203/rs.3.rs-6728659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6728659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Multi-frequency bioelectrical impedance analysis (MFBIA) methods offer reliable and moderately accurate estimates of body composition in tightly controlled conditions (prandial and hydration status, recent exercise, time of day).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study examined MFBIA reliability and validity in a real-world environment where these factors were not controlled.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Regional and total body composition estimates by MFBIA (InBody 770) were compared to dual-energy X-ray absorptiometry (DXA) in 1,000 healthy adults (667 men; 333 women), including fat mass (FM), percent body fat (%BF), fat-free mass (FFM), and visceral adipose tissue (VAT). In subsets, reliability was determined from duplicate MFBIA and DXA obtained within one week, and total body water (TBW) was compared to single-frequency BIA (SFBIA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eMFBIA demonstrated modest population-level agreement with DXA for total body FM (men, r=0.93, bias -3.7±2.6 kg; women, r=0.96, bias, -1.9±1.8 kg), %BF (men, r=0.89, bias, -4.2±3.0%; women, r=0.92, bias, -2.8±2.6%), and FFM (men, r=0.95, bias, 3.4±2.8 kg; women, r=0.94, bias, 2.0±2.2 kg). Regional correlations were highest for trunk FM (men, r=0.92, CCC=0.86; women r=0.93, CCC=0.93) and lowest for VAT (men, r=0.74, CCC=0.68; women, r=0.74, CCC=0.34). DXA and MFBIA regional and total assessments were highly reliable (DXA, ICC 0.990-0.998) and (MFBIA, ICC 0.987-0.995). TBW by MFBIA and SFBIA showed moderate agreement (men, r=0.73, bias, -1.89±3.31; women, r=0.82, bias, -1.74±2.01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This MFBIA system was shown to have high retest reliability and when compared to laboratory methods, provides a moderately accurate method for measuring TBW and body composition (except for VAT) in uncontrolled conditions.\u003c/p\u003e","manuscriptTitle":"Real-world Assessment of Multi-Frequency Bioelectrical Impedance Analysis (MFBIA) for Measuring Body Composition in Healthy Physically Active Populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-27 12:58:25","doi":"10.21203/rs.3.rs-6728659/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-07-02T14:39:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-06-24T10:12:33+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-04T00:13:17+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-05-28T16:18:03+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-05-26T14:13:20+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-05-26T03:27:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-23T09:46:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-23T09:44:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Clinical Nutrition","date":"2025-05-23T02:37:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5b5073e2-d66b-436c-8dcb-18f3e144ed3d","owner":[],"postedDate":"June 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49026402,"name":"Health sciences/Health care/Weight management"},{"id":49026403,"name":"Health sciences/Medical research/Translational research"}],"tags":[],"updatedAt":"2025-09-17T07:11:12+00:00","versionOfRecord":{"articleIdentity":"rs-6728659","link":"https://doi.org/10.1038/s41430-025-01664-4","journal":{"identity":"european-journal-of-clinical-nutrition","isVorOnly":false,"title":"European Journal of Clinical Nutrition"},"publishedOn":"2025-09-16 04:00:00","publishedOnDateReadable":"September 16th, 2025"},"versionCreatedAt":"2025-06-27 12:58:25","video":"","vorDoi":"10.1038/s41430-025-01664-4","vorDoiUrl":"https://doi.org/10.1038/s41430-025-01664-4","workflowStages":[]},"version":"v1","identity":"rs-6728659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6728659","identity":"rs-6728659","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}