Application of Bioelectrical Impedance Analysis to Elderly Populations
Abstract
BACKGROUND: Bioelectrical impedance analysis (BIA) can potentially be used to estimate body composition in large populations studied at multiple sites. However, it is not clear whether age-specific BIA equations are necessary for accurate application of BIA to research on elderly subjects. METHODS: We compared a published equation designed to predict fat-free mass (FFM) that had been derived in a young healthy population (mean age 27 y; mean BMI 23.9 kg/m2), with equations that we developed for the elderly by using data from 455 participants in the Framingham Heart Study (78 Y; 27.3 kg/m2), using dual-energy x-ray absorptiometry (DXA) as a reference technique. The BIA equations were then compared in an independent sample of 283 participants in the New Mexico Aging Process Study (76 y, 25.5 kg/m2), who also underwent BIA and DXA. RESULTS: When the young-population equation was applied to Framingham, it caused an overestimation of FFM in heavier subjects that was eliminated by use of the age-specific equation. However, when the two equations were tested in the New Mexico population, the published equation gave estimates of FFM that were closer to DXA than the Framingham equations did. CONCLUSIONS: The accuracy of a BIA equation depends on the body composition of the population of the population and the validation method rather than on age per se. Application of BIA to elderly populations requires uniform validation procedures in the actual study population, rather than reliance on age-specific equations.
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Risk of bias was unclear in six studies.
“Risk of bias was high in two [34, 35] and unclear in six [36–41] studies due to the low to moderate (R2 < 80%) coefficient of determination (R2) resulted from the multiple regression models.”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [36]
Out of the 26 available equations for elderly, 23 were developed with foot-to-hand technology.
“Out of the 26 available equations, 23 were developed with foot to hand technology [36, 38, 46, 49, 53, 63, 67, 89–93, 95, 97], while three equations were developed using segmental technology [68, 94].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [36]
Two equations for elderly are for assessing FFM in males.
“Two different equations are available to determine FM in males and females [53], while there are two equations for assessing FFM in males [36, 49], three in females only [36, 49, 97], and five suitable for both sexes [89, 91].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [36]
Concerning foot-to-hand technology for elderly, four predictive equations are suitable for females only.
“Concerning foot-to-hand technology, three predictive equations were suitable for males [36, 49, 53] and four for females only [36, 49, 53, 97], while thirteen equations suitable for both sexes [38, 46, 63, 67, 89–93, 95].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [36]
The selected elderly studies encompassed Central and Southern American, European, and Asian male and female participants.
“The selected studies encompassed Central[36, 38, 49] and Southern American [46, 67], European [53, 63, 89–91, 93, 95, 97], and Asian [68, 94] male and female participants.”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [36]
Concerning foot-to-hand technology for elderly, three predictive equations are suitable for males.
“Concerning foot-to-hand technology, three predictive equations were suitable for males [36, 49, 53] and four for females only [36, 49, 53, 97], while thirteen equations suitable for both sexes [38, 46, 63, 67, 89–93, 95].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [36]
Three equations for elderly are for assessing FFM in females only.
“Two different equations are available to determine FM in males and females [53], while there are two equations for assessing FFM in males [36, 49], three in females only [36, 49, 97], and five suitable for both sexes [89, 91].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [36]
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“Third, some studies have noted inaccuracies and little within-subject variation when using bioelectrical impedance analysis to assess fat-free mass in the elderly (36, 37).”
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“Briefly, we chose a formula that had been developed using subjects whose ethnicity, age and BMI distribution (10) were similar to our own study.”
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“FFM was calculated using an equation validated in healthy elderly persons (22).”
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“4 kg [19-24].”
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Self-citationHida, T., Imagama, S., Ando, K., Kobayashi, K., Muramoto, A., Ito, K., Ishikawa, Y., Tsushima, M., Nishida, Y., Ishiguro, N., et al. (2017) · Modern Rheumatology · doi:10.1080/14397595.2017.1349058 · Cited by 84 · see citing works
“Therefore, the BIA is a simple, easy, and non-invasive method for measuring muscle mass [19].”
MethodTager, I. B., Haight, T., Sternfeld, B., Yu, Z., & van Der Laan, M. (2004) · Epidemiology · doi:10.1097/01.ede.0000128401.55545.c6 · Cited by 84 · see citing works
“However, we conducted a validation substudy to generate our own regressions, which agreed extremely well with other published data in the elderly.(62) Finally, the validity of marginal structural model estimates depend heavily on the validity of the assumption of no unmeasured confounders.”
ResultYeh, S., Wu, S.-Y., Levine, D. M., Parker, T. S., Olson, J., Stevens, M. R., & Schuster, M. W. (2001) · The Journals of Gerontology Series A · doi:10.1093/gerona/56.1.m48 · Cited by 81 · see citing works
“There will be some gain in accuracy and precision by using BIA equations designed for the elderly population, such as those of Deurenberg and colleagues (38) or Roubenoff and colleagues (39).”
MethodCampa, 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
“Concerning foot-to-hand technology, three predictive equations were suitable for males [36, 49, 53] and four for females only [36, 49, 53, 97], while thirteen equations suitable for both sexes [38, 46, 63, 67, 89–93 , 95].”
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
Migration of the bioelectrical impedance vector in healthy elderly subjects
Buffa, R., Floris, G., & Marini, E. (2003) · Nutrition · doi:10.1016/s0899-9007(03)00180-1 · Cited by 78 · see citing works
Managing Sarcopenia and Its Related-Fractures to Improve Quality of Life in Geriatric Populations
Hida, T. (2014) · Aging and Disease · doi:10.14336/ad.2014.0500226 · Cited by 75 · see citing works
“The best feature of BIA is the portability of the equipment used, making it suitable for epidemiological examination for community-dwelling people [30, 31].”
BackgroundDey, D. K., Bosaeus, I., Lissner, L., & Steen, B. (2003) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1601625 · Cited by 75 · see citing works
“The PRESS (predicted residual sum of squares) statistic was calculated for cross-validation of the BIA prediction equation (Guo et al, 1996; Roubenoff et al, 1997).”
BackgroundMethodResultInfluentialHofmann, M., Halper, B., Oesen, S., Franzke, B., Stuparits, P., Tschan, H., Bachl, N., Strasser, E.-M., Quittan, M., Ploder, M., et al. (2015) · Experimental Gerontology · doi:10.1016/j.exger.2015.02.008 · Cited by 74 · see citing works
Szlejf, C., Suemoto, C. K., Brunoni, A. R., Viana, M. C., Moreno, A. B., Matos, S. M. A., Lotufo, P. A., & Benseñor, I. M. (2018) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2018.09.020 · Cited by 73 · see citing works
Whole-body protein turnover in the healthy elderly
Morais, J., Gougeon, R., Pencharz, P. B., Jones, P. J. H., Ross, R., & Marliss, E. (1997) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/66.4.880 · Cited by 73 · see citing works
Papachristou, E., Ramsay, S. E., LENNON, L. T., Papacosta, O., Iliffe, S., Whincup, P. H., & Wannamethee, S. G. (2015) · BMC Geriatrics · doi:10.1186/s12877-015-0169-y · Cited by 73 · see citing works
“BIA has been shown to be influenced by the variability in fatfree mass (FFM) hydration [37, 38], however studies in selected populations have shown that BIA assessments are comparable to the ones obtained by DXA [39, 40] and that it is one of the most simple and efficient ways to obtain body-composition characteristic…”
Resultde Vos, N. J., Singh, N. A., Ross, D. A., Stavrinos, T. M., Orr, R., & Singh, M. A. F. (2008) · Journal of Aging and Physical Activity · doi:10.1123/japa.16.4.393 · Cited by 72 · see citing works
“Fasting body weight, height, and bioelectrical-impedance estimates of fat and fat-free mass (FFM) were determined using standard procedures (BIA-101; RJL Systems, Detroit, MI; Lukaski, Bolunchuk, Hall, & Siders, 1986; Roubenoff et al., 1997).”
MethodBody composition and knee cartilage properties in healthy, community-based adults
Wang, Y., Wluka, A. E., English, D. R., Teichtahl, A. J., Giles, G. G., O’Sullivan, R., & Cicuttini, F. (2007) · Annals of the Rheumatic Diseases · doi:10.1136/ard.2006.064352 · Cited by 72 · see citing works
“We used bioimpedance analysis to estimate non-adipose mass, hereafter termed fat-free mass (FFM), as 9.1536 + (0.4273 6 height 2 /resistance) + (0.1926 6 weight) + (0.0667 6 reactance) for men, and 7.7435 + (0.4542 6 height 2 /resistance) + (0.119 6 weight) + (0.0455 6 reactance) for women.”
InfluentialKyle, U. G., Genton, L., & Pichard, C. (2002) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/00075197-200207000-00012 · Cited by 70 · see citing works
Sousa, N., Mendes, R., Abrantes, C., Sampaio, J., & Oliveira, J. (2013) · Experimental Gerontology · doi:10.1016/j.exger.2013.04.008 · Cited by 68 · see citing works
“This instrument has been shown to reliably evaluate body composition in the elderly population (Roubenoff et al., 1997).”
A large infrapatellar fat pad protects against knee pain and lateral tibial cartilage volume loss
Teichtahl, A. J., Wulidasari, E., Brady, S. R. E., Wang, Y., Wluka, A. E., Ding, C., Giles, G. G., & Cicuttini, F. (2015) · Arthritis Research & Therapy · doi:10.1186/s13075-015-0831-y · Cited by 67 · see citing works
“0455 × reactance) for female subjects [14].”
＜Editors' Choice＞ Ultrasound measurement of thigh muscle thickness for assessment of sarcopenia.
Hida, T., Ando, K., Kobayashi, K., Ito, K., Tsushima, M., Kobayakawa, T., Morozumi, M., Tanaka, S. A., Machino, M., Ota, K., et al. (2018) · Europe PMC (PubMed Central) · doi:10.18999/nagjms.80.4.519 · Cited by 68 · see citing works
Serum Creatinine and Functional Limitation in Elderly Persons
Odden, M. C., Shlipak, M. G., & Tager, I. B. (2009) · The Journals of Gerontology Series A · doi:10.1093/gerona/gln037 · Cited by 66 · see citing works
Similarities in Acquired Factors Related to Postmenopausal Osteoporosis and Sarcopenia
Sirola, J. P., & Kröger, H. (2011) · Journal of Osteoporosis · doi:10.4061/2011/536735 · Cited by 65 · see citing works
“[30] R.”
MethodMuscoloskeletal aging, sarcopenia and cancer
Colloca, G. A., Capua, B. D., Bellieni, A., Cesari, M., Marzetti, E., Valentini, V., & Calvani, R. (2018) · Journal of Geriatric Oncology · doi:10.1016/j.jgo.2018.11.007 · Cited by 64 · see citing works
Body Size and Composition and the Risk of Lymphohematopoietic Malignancies
MacInnis, R. J., English, D. R., Hopper, J. L., & Giles, G. G. (2005) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/dji209 · Cited by 62 · see citing works
Kyle, U. G., Genton, L., Mentha, G., Nicod, L. P., Slosman, D. O., & Pichard, C. (2001) · Journal of Parenteral and Enteral Nutrition · doi:10.1177/014860710102500245 · Cited by 60 · see citing works
Moon, J. R., Stout, J. R., Smith-Ryan, A. E., Kendall, K. L., Fukuda, D. H., Cramer, J. T., & Moon, S. E. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2012.163 · Cited by 59 · see citing works
Roubenoff, R., Hughes, V. A., Dallal, G. E., Nelson, M. E., Morganti, C., Kehayias, J. J., Singh, M. A. F., & Roberts, S. (2000) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/55.12.m757 · Cited by 55 · see citing works
Self-citationSlee, A., Birch, D., & Stokoe, D. (2014) · Nutrition · doi:10.1016/j.nut.2014.06.002 · Cited by 57 · see citing works
“2 [24]”
Tanaka, S. A., Ando, K., Kobayashi, K., et al. (2018) · Archives of Osteoporosis · doi:10.1007/s11657-018-0450-8 · Cited by 57 · see citing works
Yoshimura, N., Muraki, S., Oka, H., Iidaka, T., Kodama, R., Horii, C., Kawaguchi, H., Nakamura, K., Akune, T., & Tanaka, S. (2018) · Osteoporosis International · doi:10.1007/s00198-018-4596-4 · Cited by 56 · see citing works
Low muscle mass in older men: The role of lifestyle, diet and cardiovascular risk factors
Atkins, J. L., Whincup, P. H., Morris, R. W., & Wannamethee, S. G. (2014) · The Journal of nutrition, health and aging · doi:10.1007/s12603-013-0336-9 · Cited by 55 · see citing works
“this could be explained by the greater inaccuracy of BiA in assessment of FFMi in older people, predominantly due to fat-free mass hydration variability (41, 42).”
Bulut, E. A., Soysal, P., Dokuzlar, Ö., Koçyiğit, S. E., Aydın, A. E., Yavuz, İ., & Işık, A. T. (2020) · Aging Clinical and Experimental Research · doi:10.1007/s40520-019-01448-4 · Cited by 54 · see citing works
“Measurements may be affected by some situations, including hydration status [34].”
BackgroundLaviano, A., Gori, C. S., & Rianda, S. (2014) · Advances in food and nutrition research · doi:10.1016/b978-0-12-800270-4.00003-1 · Cited by 53 · see citing works
Harris, T. B. (2002) · American Journal of Epidemiology · doi:10.1093/aje/kwf024 · Cited by 51 · see citing works
Self-citationMölzer, C., Wallner, M., Kern, C., Tosevska, A., Schwarz, U., Zadnikar, R., Doberer, D., Marculescu, R., & Wagner, K. (2016) · Scientific Reports · doi:10.1038/srep30051 · Cited by 50 · see citing works
Body size and composition and risk of rectal cancer (Australia)
MacInnis, R. J., English, D. R., Haydon, A. M., Hopper, J. L., Gertig, D. M., & Giles, G. G. (2006) · Cancer Causes & Control · doi:10.1007/s10552-006-0074-y · Cited by 51 · see citing works
“The algorithm we chose to compute fat-free mass and fat mass was developed using subjects of similar ethnicity, age, and BMI distribution to our own [18].”
MethodHaapala, I., Hirvonen, A., Niskanen, L., Uusitupa, M., Kröger, H., Alhava, E., & Nissinen, A. (2002) · Clinical Physiology and Functional Imaging · doi:10.1046/j.1475-097x.2002.00447.x · Cited by 46 · see citing works
“In the present study, the BIA (Kyle et al., 2001) and BIA (Roubenoff et al., 1997) equations, which include reactance as a predictor variable, produced estimations which were significantly different from the FFMDXA estimates and in which the error increased with the average FFM.”
BackgroundMethodResultInfluentialDittmar, M., & Reber, H. (2001) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.2001.281.5.e1005 · Cited by 45 · see citing works
“So far, age-specific predictive BIA equations for elderly groups have been developed for estimating fat-free mass (2, 15, 30, 38), whereas validation studies for estimating BCM are still, to the authors’ knowledge, missing.”
MethodSarcopenia and cachexia: molecular mechanisms and therapeutic interventions
Wang, T., Zhou, D., & Hong, Z. (2025) · MedComm · doi:10.1002/mco2.70030 · Cited by 46 · see citing works
Teichtahl, A. J., Wluka, A. E., Wang, Y., Hanna, F. S., English, D. R., Giles, G. G., & Cicuttini, F. (2008) · Annals of the Rheumatic Diseases · doi:10.1136/ard.2008.093310 · Cited by 45 · see citing works
Influence of Adiposity in the Blunted Whole-Body Protein Anabolic Response to Insulin With Aging
Chevalier, S., Gougeon, R., Choong, N., Lamarche, M., & Morais, J. A. (2006) · The Journals of Gerontology Series A · doi:10.1093/gerona/61.2.156 · Cited by 45 · see citing works
“FFM was assessed by bioelectrical impedance analysis using the RJL-101A Systems (Detroit, MI) instrument and using specific equations validated for the elderly (15) and younger participants (16).”
MethodSmartphone-Based Bioelectrical Impedance Analysis Devices for Daily Obesity Management
Choi, A., Kim, J. J., Jo, S., Jee, J. H., Heymsfield, S. B., Bhagat, Y. A., Kim, I.-S., & Cho, J. (2015) · Sensors · doi:10.3390/s150922151 · Cited by 44 · see citing works
“Many reports emphasize that children and elderly people have different body composition compared to healthy adults [24–26].”
BackgroundRelation of Lean Body Mass to Health-Related Quality of Life in Persons With HIV
Wilson, I. B., Roubenoff, R., Knox, T. A., et al. (2000) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/00126334-200006010-00008 · Cited by 44 · see citing works
Self-citationThe Longitudinal Relationship Between Body Composition and Patella Cartilage in Healthy Adults
Teichtahl, A. J., Wang, Y., Wluka, A. E., Szramka, M., English, D. R., Giles, G. G., O’Sullivan, R., & Cicuttini, F. (2008) · Obesity · doi:10.1038/oby.2007.37 · Cited by 43 · see citing works
- Authors
- Roubenoff, R., Baumgartner, R. N., Harris, T. B., Dallal, G. E., Hannan, M. T., Economos, C. D., Stauber, P. M., Wilson, P. W. F., & Kiel, D. P.
- Affiliations
- Tufts University (US)
- University of New Mexico (US)
- National Institutes of Health (US)
- Boston University (US)
- Framingham Heart Study (US)
- Hebrew SeniorLife (US)
- ORCID
- Venue
- The Journals of Gerontology Series A: Biological Sciences and Medical Sciences
- Publisher
- Oxford University Press (OUP)
- Published
- 1997-05-01
- Volume
- 52A
- Issue
- 3
- Pages
- M129-M136
- ISSN
- 1079-5006 (ISSN-L)
- 1758-535X
- OpenAlex
- W1986223929
- Cited by
- 261
- References
- 11
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 4 · 2023: 6 · 2022: 6 · 2021: 3 · 2020: 7 · 2019: 15 · 2018: 21 · 2017: 8 · 2016: 15 · 2015: 13 · 2014: 18 · 2013: 12 · 2012: 12
- FWCI
- 1.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journals of Gerontology Series A (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Health Sciences › Medicine › Physiology › Nutrition and Health in Aging
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical and Bioimpedance Tomography
- Keywords
- bioelectrical impedance analysis, body composition, fat-free mass, older adults, equation validation, dual-energy X-ray absorptiometry
- MeSH terms
- Aged, Aging, Body Composition, Evaluation Studies as Topic, Humans, Models, Biological, Absorptiometry, Photon, Electric Impedance
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