Prediction of fat-free mass by bioimpedance analysis in migrant Asian Indian men and women: a cross validation study
Abstract
Background Bioimpedance analysis (BIA) is a fast and convenient field technique for estimation of total body fat-free mass (FFM). However, bioimpedance-based prediction equations have been developed in predominantly white populations and little information is available on their usefulness in Asian Indian populations. Objective To develop a prediction equation for FFM based on BIA measurements applicable to a migrant population of Asian Indians and to investigate the predictive accuracy of published BIA-based equations in this ethnic group. Design FFM was measured by dual-energy X-ray absorptiometry in 211 healthy adults (110 men, 101 women; age ranges 19-74 year) of Asian Indian ethnicity and used as the reference measure to develop prediction equations based on single-frequency BIA measurements of resistance and reactance. A cross-validation technique was applied. Predictive accuracy of published BIA-based equations was assessed in this sample. Results Sex-specific equations developed in the entire group included height2/resistance and body weight as predictors (R2=0.84 and 0.70 and standard errors of estimate of 2.8 and 2.0 kg for males and females, respectively; CV: 6%). Of published equations examined, one predicted FFM satisfactorily in men with nonsignificant bias and may be applicable to Asian Indian populations. None of the published equations tested performed satisfactorily in women. Conclusions Bioimpedance-based equations for predicting FFM developed specifically in Asian Indians are recommended for field studies designed to measure body composition of this ethnic group.
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Concerns with applicability were high in 19 studies.
“Concerns with applicability were high in 19 studies [37, 42–59] and unclear [41, 50, 60–69] in 12 studies due to the restricted sample size.”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [47]
Out of the 26 available equations for adults, 22 were developed with foot-to-hand technology.
“Out of the 26 available equations, 22 were developed with foot to hand technology [34, 42, 47, 50, 52, 54, 61, 66, 71, 77, 78, 102], and four with segmental technology”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [47]
Regarding foot-to-hand technology for adults, five equations are suitable for estimating FFM in males.
“Regarding foot-to-hand technology, five equations are suitable for estimating FFM in males [47, 52, 66, 71, 78] and six in females [34, 47, 50, 52, 66, 71].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [47]
Concerning foot-to-hand technology for adults, 17 equations are available for both sexes.
“Concerning foot to hand technology, 17 equations are available for both sexes [42, 47, 52, 54, 61, 66, 71, 77, 102], while one is suitable for males [78] and two are suitable for females only [34, 50].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [47]
Regarding foot-to-hand technology for adults, six equations are suitable for estimating FFM in females.
“Regarding foot-to-hand technology, five equations are suitable for estimating FFM in males [47, 52, 66, 71, 78] and six in females [34, 47, 50, 52, 66, 71].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [47]
Cites
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965 · see citing works
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · see citing works
Beckett, A. H., Rowland, M., & TURNER, P. (1965) · The Lancet · doi:10.1016/s0140-6736(65)91037-8 · Cited by 16,917 · see citing works
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785 · see citing works
Applied Linear Regression Models
(1997) · Technometrics · doi:10.1080/00401706.1997.10485142 · Cited by 4,638 · see citing works
Bioelectrical impedance analysis?part I: review of principles and methods
KYLE, U. (2004) · Clinical Nutrition · doi:10.1016/j.clnu.2004.06.004 · Cited by 3,200 · see citing works
Mallows, C. L. (1995) · Technometrics · doi:10.1080/00401706.1995.10484370 · Cited by 2,083 · see citing works
Mallows, C. L. (1973) · Technometrics · doi:10.2307/1267380 · Cited by 1,978 · see citing works
Validation of tetrapolar bioelectrical impedance method to assess human body composition
Lukaski, H. C., Bolonchuk, W. W., Hall, C. B., & Siders, W. A. (1986) · Journal of Applied Physiology · doi:10.1152/jappl.1986.60.4.1327 · Cited by 1,376 · see citing works
Lean body mass estimation by bioelectrical impedance analysis: a four-site cross-validation study
Segal, K., Van Loan, M., Fitzgerald, P., Hodgdon, A., & Van Itallie, T. (1988) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/47.1.7 · Cited by 881 · see citing works
Applied Linear Regression Models
Lambrinos, J., Neter, J., Wasserman, W., & Kutner, M. (1984) · Technometrics · doi:10.2307/1269508 · Cited by 876 · see citing works
Roche, A. F., Heymsfield, S. B., & Lohman, T. G. (2005) · Medical Entomology and Zoology · Cited by 743 · see citing works
Sun, S. S., Chumlea, W. C., Heymsfield, S. B., Lukaski, H. C., Schoeller, D., Friedl, K., Kuczmarski, R. J., Flegal, K. M., Johnson, C. L., & Hubbard, V. S. (2003) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/77.2.331 · Cited by 701 · see citing works
Single prediction equation for bioelectrical impedance analysis in adults aged 20–94 years
Kyle, U. G., Genton, L., Karsegard, L., Slosman, D. O., & Pichard, C. (2001) · Nutrition · doi:10.1016/s0899-9007(00)00553-0 · Cited by 573 · see citing works
Is the Impedance index (ht2/R) significant in predicting total body water?
Kushner, R., Schoeller, D., Fjeld, C., & Danford, L. (1992) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/56.5.835 · Cited by 374 · see citing works
Deurenberg, P., Kooy, K. V. D., Leenen, R. C. A., Weststrate, J. A., & Seidell, J. C. (1991) · PubMed · Cited by 347 · see citing works
Komiya, S., Mitsuzono, R., & Ube, M. (1995) · 体力科學 · doi:10.7600/jspfsm1949.44.211 · Cited by 256 · see citing works
The impact of body build on the relationship between body mass index and percent body fat
Deurenberg, P., Yap, M., Wang, J., Lin, F., & Schmidt, G. (1999) · International Journal of Obesity · doi:10.1038/sj.ijo.0800868 · Cited by 208 · see citing works
Bioelectrical Impedance and Body Composition: Present Status and Future Directions
Chumlea, W. C., & Guo, S. S. (2009) · Nutrition Reviews · doi:10.1111/j.1753-4887.1994.tb01404.x · Cited by 194 · see citing works
Dual-energy X-ray absorptiometry and body composition
Plank, L. D. (2005) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/01.mco.0000165010.31826.3d · Cited by 184 · see citing works
Self-citationRush, E. C., Plank, L. D., Chandu, V., Laulu, M. S., Simmons, D. S., Swinburn, B. A., & Yajnik, C. S. (2004) · PubMed · Cited by 172 · see citing works
Self-citationIs a Re-evaluation of Who Body Mass Index Cut-off Values Needed? the Case of Asians in Singapore
Deurenberg‐Yap, M., & Deurenberg, P. (2003) · Nutrition Reviews · doi:10.1301/nr.2003.may.s80-s87 · Cited by 107 · see citing works
Body composition in Pima Indians: validation of bioelectrical resistance
Rising, R., Swinburn, B., Larson, K., & Ravussin, E. (1991) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/53.3.594 · Cited by 77 · see citing works
Body fat measurement in Indian men: comparison of three methods based on a two-compartment model
Bhat, D., Yajnik, C. S., Sayyad, M. G., Raut, K. N., Lubree, H., Rege, S., Chougule, S., Shetty, P. S., Yudkin, J. S., & Kurpad, A. V. (2005) · International Journal of Obesity · doi:10.1038/sj.ijo.0802953 · Cited by 57 · see citing works
Validation of expedient methods for measuring body composition in Indian adults.
Kuriyan, R., Petracchi, C., Ferro-Luzzi, A., Shetty, P. S., & Kurpad, A. V. (1998) · PubMed · Cited by 56 · see citing works
Flagged first, then by citations
1 of the 26 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Mallows CL . Some comments on Cp. Technometrics 1973; 15: 661–675.
Cited by
Sorted by citations
Assessment methods in human body composition
Lee, S. Y., & Gallagher, D. (2008) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/mco.0b013e32830b5f23 · Cited by 694 · see citing works
“Validity of BIA is also influenced by sex, age, disease state, race or ethnicity [7], and level of fatness in which TBW and relative ECW are greater in obese individuals compared with normal-weight individuals; and type of BIA system in which SF-BIA was found to have better absolute agreement than multifrequency BIA w…”
ResultBosy‐Westphal, A., Later, W., Hitze, B., Sato, T., Kossel, E., Glüer, C., Heller, M., & Müller, M. J. (2008) · Obesity Facts · doi:10.1159/000176061 · Cited by 265 · see citing works
Hayashida, I., Tanimoto, Y., Takahashi, Y., Kusabiraki, T., & Tamaki, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111810 · Cited by 180 · see citing works
“Further, the results of the present study were consistent with those of previous studies using different measurement techniques, indicating the high reproducibility of BIA results [22], [23].”
ResultPredicted fat mass and lean mass in relation to all‐cause and cause‐specific mortality
Liu, M., Zhang, Z., Zhou, C., Ye, Z., He, P., Zhang, Y., Li, H., Liu, C., & Qin, X. (2022) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12921 · Cited by 100 · see citing works
“However, the validity of BIA can be influenced by age, sex, race, body structure, disease state, and hydration status.(20,23,24) Indeed, predicted fat mass and lean mass can be calculated using the validated sex-specific anthropometric prediction equations developed by the NHANES based on individual age, race, height,…”
MethodBerker, D. S., Koparal, S., Işık, S., Paşaoğlu, L., Aydın, Y., Erol, H. K., Delibaşı, T., & Güler, S. (2009) · Diagnostic and Interventional Radiology · doi:10.4261/1305-3825.dir.2749-09.1 · Cited by 93 · see citing works
The importance of prediction model validation and assessment in obesity and nutrition research
Ivanescu, A. E., Li, P., George, B. J., Brown, A. W., Keith, S. W., Raju, D. A., & Allison, D. B. (2015) · International Journal of Obesity · doi:10.1038/ijo.2015.214 · Cited by 90 · see citing works
Campa, F., Coratella, G., Cerullo, G., Noriega, Z., Francisco, R., Charrier, D., Irurtia, A., Lukaski, H., Silva, A. M., & Paoli, A. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05272-x · Cited by 80 · see citing works
“A total of 14,912 males, 17,362 females, and 534 subjects without any sex designated were included in 14 studies [34, 42, 47, 50, 52, 54, 61, 66, 71, 77–82] aimed to develop predictive equations for adults (from 18 to 65 years old) from the general population.”
BackgroundMethodInfluentialYoshida, D., Shimada, H., Park, H., Anan, Y., Ito, T., Harada, A., & Suzuki, T. (2014) · Geriatrics & Gerontology International · doi:10.1111/ggi.12177 · Cited by 76 · see citing works
The Role of Skeletal Muscle in Development of Nonalcoholic Fatty Liver Disease
Moon, J. S., Yoon, J. S., Won, K. C., & Lee, H. W. (2013) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2013.37.4.278 · Cited by 75 · see citing works
“BIA showed good correlation with the dual energy X-ray absorptiometry method and was also validated in studies of Asians including the Korean population [36,39].”
MethodKesavachandran, C. N., Bihari, V., & Mathur, N. (2012) · The Indian Journal of Medical Research · doi:10.4103/0971-5916.93427 · Cited by 57 · see citing works
Kumar, K. R. V., Ranganath, V., Naik, R., Banu, S., & Nichani, A. (2014) · Journal of Periodontal Research · doi:10.1111/jre.12172 · Cited by 45 · see citing works
“Ethnic differences in fat distribution, muscularity, bone mass and leg length may contribute to ethnicspecific relationships between body fatness and BMI (73).”
BackgroundHyeoijin, K., Kim, C.-H., Kim, D.-W., Park, M., Park, H. S., Min, S.-S., Han, S., Yee, J.-Y., Chung, S., & Kim, C. (2011) · Nutrition Research and Practice · doi:10.4162/nrp.2011.5.3.246 · Cited by 44 · see citing works
“The rates of residual scores for other BIA models ranged from 34-59% [33-35].”
BackgroundKolt, G. S., Schofield, G., Rush, E., Oliver, M., & Chadha, N. K. (2007) · PubMed · Cited by 44 · see citing works
Self-citationAglago, E. K., Menchawy, I. E., Kari, K. E., Hamdouchi, A. E., Barkat, A., Bengueddour, R., Haloui, N. E., Mokhtar, N., & Aguenaou, H. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2013.125 · Cited by 38 · see citing works
Epidemiology, assessment, and management of excess abdominal fat in persons with HIV infection.
Moyle, G. J., Moutschen, M. P., Martínez, E., Domingo, P., Guaraldi, G., Raffi, F., Behrens, G. M. N., & Reiss, P. (2010) · PubMed · Cited by 30 · see citing works
Wu, C.-S., Chen, Y.-Y., Chuang, C.-L., Chiang, L.-M., Dwyer, G. B., Hsu, Y., Huang, A.-C., Lai, C.-L., & Hsieh, K.-C. (2015) · Nutrition Journal · doi:10.1186/s12937-015-0041-0 · Cited by 29 · see citing works
“The allsubjects predictive model also showed results similar to the G1 and G2 models in terms of the correlation coefficient, SEE, and CV, thus validating the accuracy of the predictive model.”
BackgroundMethodInfluentialMeasuring Visceral and Hepatic Fat in Clinical Practice and Clinical Research
Naboush, A., & Hamdy, O. (2013) · Endocrine Practice · doi:10.4158/ep12331.or · Cited by 27 · see citing works
Objective Data Assessment (ODA) Methods as Nutritional Assessment Tools
Hamada, Y. (2015) · The Journal of Medical Investigation · doi:10.2152/jmi.62.119 · Cited by 23 · see citing works
de Freitas, W. R., Ilias, E. J., Kassab, P., Cordts, R. M., Porto, P. G., Rodrigues, F. C. M., Taha, M. I. A., Carrara, P., Aguiar, I. D. C., de Oliveira, L. V. F., et al. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/843253 · Cited by 23 · see citing works
Bioelectrical Impedance among Rural Bangladeshi Women during Pregnancy and in the Postpartum Period
Shaikh, S., Schulze, K. J., Ali, H., et al. (2011) · Journal of Health Population and Nutrition · doi:10.3329/jhpn.v29i3.7871 · Cited by 23 · see citing works
Eng, P. C., Teo, A. E. D., Leow, M. K. S., Tai, E. S., & Khoo, C. M. (2025) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.16601 · Cited by 22 · see citing works
“44,45 Therefore, it is necessary to consider ethnicity-specific calibration or interpretation of anthropometric and impedance-derived indices, particularly in multi-ethnic populations where the relationship between surface measurements and internal fat depots can vary.”
Hanyuda, A., Lee, D. H., Ogino, S., Wu, K., & Giovannucci, E. L. (2019) · International Journal of Cancer · doi:10.1002/ijc.32553 · Cited by 20 · see citing works
Regional and total body bioelectrical impedance analysis compared with DXA in Icelandic elderly
Ramel, A., Geirsdóttir, Ó. G., Arnarson, A., & Þórsdóttir, I. (2011) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2011.65 · Cited by 19 · see citing works
“In general, validity of BIA is influenced by several factors, for example, gender, age, race, ethnicity or disturbances of fluid distribution (Rush et al., 2006; Tattersall, 2009).”
BackgroundMethodBody Fat Measurements in Singaporean Adults Using Four Methods
Bi, X., Loo, Y. T., & Henry, C. J. (2018) · Nutrients · doi:10.3390/nu10030303 · Cited by 17 · see citing works
“Although several studies have validated BIA against DEXA [13,14], the validity is affected by gender, age, disease state, level of fatness and ethnic backgrounds [15].”
BackgroundShaikh, S., Schulze, K. J., Kurpad, A., Ali, H., Shamim, A. A., Mehra, S., Wu, L. S.-F., Rashid, M., Labrique, A. B., Christian, P., et al. (2013) · British Journal of Nutrition · doi:10.1017/s0007114512001687 · Cited by 10 · see citing works
“age over 30 % among women of Asian descent((12,15,29,30)),”
Jayawardena, R., Weerasinghe, K., Nanayakkara, I., Madhujith, T., Hills, A. P., & Kalupahana, N. S. (2025) · Trials · doi:10.1186/s13063-024-08683-9 · Cited by 10 · see citing works
“%) will be calculated using resistance and reactance values applied to an ethnicity-specific prediction equation designed for Asians [28].”
Asian Indian adolescents from Guadeloupe are fatter than their island counterparts
Sinnapah, S., Antoine-Jonville, S., & Hue, O. (2009) · British Journal of Nutrition · doi:10.1017/s000711450999122x · Cited by 8 · see citing works
“Indeed, the equations initially published for Caucasians are not necessarily applicable to Asian Indian men and women((25)).”
BackgroundJung, C., Cho, Y. Y., Choi, D., Kim, B., Jung, S.-H., Kim, C., & Mok, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-74390-9 · Cited by 8 · see citing works
“The reliability of BIA is influenced by several factors such as hydration status, physical condition, and placement of electrode and its validity is also influenced by sex, age, population, ethnicity, and the level of fatness 56,57 .”
BackgroundSooriyaarachchi, P., Jayawardena, R., Pavey, T., & King, N. A. (2022) · Trials · doi:10.1186/s13063-022-06784-x · Cited by 7 · see citing works
“To calculate fat-free mass and BF%, the resistance and reactance values will be applied to an ethnic-specific prediction equation developed for Asians [25].”
MethodMachine learning-based equations for improved body composition estimation in Indian adults
Birk, N. W., Kulkarni, B., Bhogadi, S., Aggarwal, A., Walia, G. K., Gupta, V., Rani, U., Mahajan, H., Kinra, S., & Mallinson, P. A. C. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000671 · Cited by 7 · see citing works
“…for fat-free mass based on a sample of Black and White individuals by Sun et al reported RMSE of 3.9 kg for males and 2.9 kg for females [6], while the equations developed by Rush et al for estimating fat-free mass in an Asian Indian population report an RMSE of 2.13 in the study sample [18].”
InfluentialSöğüt, M., Altunsoy, K., & Varela‐Silva, M. I. (2018) · Anthropological Review · doi:10.2478/anre-2018-0015 · Cited by 5 · see citing works
“It is portable, noninvasive, relatively inexpensive, quick, and useful in large-scale studies of populations from different ages and body sizes (Phillips et al. 2003; Lee and Gallagher 2008; Rush et al. 2006; Karelis et al. 2013).”
BackgroundEcuaciones antropométricas para el cálculo de grasa corporal en adultos jóvenes
Ramos‐Jiménez, A., Hernández‐Torres, R. P., & Murguía‐Romero, M. (2018) · Archivos Latinoamericanos de Nutrición · doi:10.37527/2018.68.2.002 · Cited by 7 · see citing works
“Este método de determinación ha sido validado contra DEXA y métodos de dilución mostrando alta correlación entre ellos (R2 = 0.75 - 0.91) (18,19).”
BackgroundInfluentialBanik, S. D., & Das, S. (2014) · HOMO · doi:10.1016/j.jchb.2014.10.002 · Cited by 4 · see citing works
Body composition monitoring through bioelectrical impedance analysis
Chetan, M. (2015) · International Symposium on Advanced Topics in Electrical Engineering · doi:10.1109/atee.2015.7133826 · Cited by 3 · see citing works
“not the least, normal hydration of the subject’s body, which is the main requirement of the 2C model [2,19].”
BackgroundSooriyaarachchi, P., Jayawardena, R., Pavey, T., & King, N. A. (2024) · Nutrition & Metabolism · doi:10.1186/s12986-024-00799-8 · Cited by 3 · see citing works
“Fat-free mass was calculated using the sex-specific equations developed based on BIA measurements applicable to Asian Indian populations [22].”
MethodObesity and Overweight Surveillance in England: what is measured and where are the gaps?
Townsend, N. P. (2009) · Bristol Research (University of Bristol) · Cited by 3 · see citing works
Determination of Body Fat Percentage by Body State Devices and Dual Energy X-Ray Absorptiometry
Mostafa, R. A., Negm, D., & ElSawy, N. A. (2019) · Asian Journal of Medicine and Health · doi:10.9734/ajmah/2019/v17i230163 · Cited by 3 · see citing works
“In addition, The BIA has a lot of other advantages stripped-down participant participation needed and safety (not counseled for participants with a pacemaker), so creating it engaging for largescale studies [28].”
BackgroundCossio‐Bolaños, M., Espinoza, R. V., Sulla-Torres, J. A., Urra-Albornoz, C., Sanchez‐Macedo, L., de Arruda, M., Alvear-Vasquez, F., de Lázari, E. C., & Gómez‐Campos, R. (2025) · Diagnostics · doi:10.3390/diagnostics15232982 · Cited by 2 · see citing works
“In recent years, thousands of studies have focused on validating anthropometric equations to predict LBM in various populations and age groups [16–18].”
Sooriyaarachchi, P., Jayawardena, R., Pavey, T., & King, N. A. (2022) · Research Square · doi:10.21203/rs.3.rs-1415732/v1 · Cited by 2 · see citing works
Tesamorelin for the treatment of excess abdominal fat in HIV-infected individuals with lipodystrophy
Guaraldi, G., Stentarelli, C., & Faluz, J. (2013) · Clinical Investigation · doi:10.4155/cli.13.66 · Cited by 1 · see citing works
Barry, D. (2020) · Elsevier eBooks · doi:10.1016/b978-0-323-43044-9.00217-x · Cited by 1 · see citing works
The Effect of Shiftwork on Body Composition
Sooriyaarachchi, P., Jayawardena, R., Pavey, T., & King, N. A. (2022) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000002692 · Cited by 1 · see citing works
Khademi, M., & Jahanlou, A. S. (2017) · مجله پزشکی بالینی ابن سینا · doi:10.21859/hums-24017 · Cited by 1 · see citing works
Behavior of a New Equation using Bio- Impedancemetry to Predict Body Fat in Chilean Adults
Schifferli, I. (2017) · Obesity & Control Therapies Open Access · doi:10.15226/2374-8354/4/2/00135 · Cited by 1 · see citing works
“Following the recommendations at the international level, the most appropriate to achieve assessments of the body composition of a population in an accurate and free of bias; when using the BIA methodology; Is the construction of prediction equations; That can respond to the particular characteristics of that populati…”
MethodMachine learning-based equations for improved body composition estimation in Indian adultsPreprint
Birk, N. W., Kulkarni, B., Bhogadi, S., et al. (2024) · medRxiv · doi:10.1101/2024.10.17.24315678 · Cited by 1 · see citing works
Preventing Vitamin B12 deficiency in South Asian women of childbearing age - the VitB12 study
Mearns, G. J. (2012) · Tuwhera (Auckland University of Technology)
Women of childbearing age: dietary patterns and vitamin B12 status
Xin, L. (2008) · Tuwhera (Auckland University of Technology)
Design and Implementation of Human Body Composition Information Management System Based on Android
Liu, B., Li, X., Zhao, H., et al. (2014) · Journal of Software · doi:10.4304/jsw.9.10.2713-2720
“, between subjects and the reference population data used to generate the BIA algorithm [12-15].”
MethodHeinrich, J., & München, L.-M.-U. (2012)
Obesity- Related Health Risk: A Trajectory Based Approach
Matveev, R. D. (2013) · YorkSpace (York University)
Investigations of the Mechanisms of Diabetes Remission: A Focus on Adaptive Thermogenesis
Corley, B. (2019) · Otago University Research Archive (University of Otago)
Accurate Methods Of Determining Body Composition Of Senior Patients
Pîslaru, A. I., Ilie, A. C., Sandu, I. A., & Alexa, I. D. (2019) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb47216.2019.8969916
“Besides, BIA is influenced by age, gender, diseases, race, but also by the fat level [13].”
BackgroundA comparison of body composition measurements in college students using three assessment devices
Mora, J. C., Chen, Z., & Escalante, G. (2020) · Journal of Kinesiology & Wellness · doi:10.56980/jkw.v9i.73
“Hence, limitations of the impedancebased devices include sex, age, disease state, race and/or ethnicity (Rush et al., 2006).”
BackgroundMeasurement of body composition and its significance in treatment of oncological patients
Pavlović, M., Linarić, P., Vazdar, L., Vuger, A. T., Jakšić, P., & Šeparović, R. (2024) · Libri Oncologici Croatian Journal of Oncology · doi:10.20471/lo.2023.51.02-03.16
“When used in a population with different charac - teristics, these methods could yield wrong results with an average error of up to 8%(22,23).”
MethodÜtük, B., Gökçe, İ., Çiçek, N., & Alpay, H. (2025) · Pediatric Nephrology · doi:10.1007/s00467-025-06970-4
“Moreover, even though BIA is a non-invasive approach that offers significant advantages in assessing hypervolemia, the measurements may be influenced by various factors [25]; thus, repetitive measurements may be required.”
Jayawardena, R., Weerasinghe, K., Nanayakkara, I., Madhujith, T., Hills, A. P., & Kalupahana, N. S. (2026) · Journal of the International Society of Sports Nutrition · doi:10.1080/15502783.2026.2683613
“Fat-free mass (FFM) was calculated using sex-specific equations developed by Rush et al. for Asian Indians [33], incorporating height, total body weight, and BIA variables such as reactance and resistance.”
- Authors
- Rush, E. C., Chandu, V, & Plank, L. D.
- Affiliations
- Auckland University of Technology (NZ)
- University of Auckland (NZ)
- Corresponding author
- Rush, E C
- ORCID
- Venue
- International Journal of Obesity
- Published
- 2006-01-24
- Volume
- 30
- Issue
- 7
- Pages
- 1125-1131
- ISSN
- 0307-0565 (ISSN-L)
- 1476-5497
- OpenAlex
- W1986416781
- Cited by
- 65
- References
- 26
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 7 · 2022: 9 · 2020: 2 · 2019: 4 · 2018: 3 · 2017: 2 · 2015: 4 · 2014: 6 · 2013: 5 · 2012: 4
- FWCI
- 1.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- International Journal of Obesity (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical and Bioimpedance Tomography
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Keywords
- fat-free mass, bioelectrical impedance analysis, prediction equations, body composition, ethnic differences, resistance and reactance, cross-validation, dual-energy X-ray absorptiometry
- MeSH terms
- Adipose Tissue, Adult, Aged, Anthropometry, Asia, Western, Body Composition, Female, Humans, Male
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