The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Comparison of segmental multifrequency bioelectrical impedance analysis with dual‐energy X ‐ray absorptiometry for the assessment of body composition in a community‐dwelling older population
Abstract
Aim: The purpose of the present study was to examine the agreement of segmental multifrequency bioelectrical impedance analysis ( SMF‐BIA ) for the assessment of whole‐body and appendicular fat mass ( FM ) and lean soft tissue mass ( LSTM ) compared with dual‐energy X ‐ray absorptiometry ( DXA ) in a community‐dwelling J apanese older population. Methods: The study population included 551 community‐dwelling J apanese older adults (241 men and 310 women) aged between 65 and 87 years. Agreement between SMF‐BIA and DXA for whole‐body and appendicular body composition was assessed using simple linear regression and B land– A ltman analysis. Results: High coefficients of determination ( R 2 ) were observed for whole‐body FM ( R 2 = 0.91, standard error of estimate [ SEE ] = 1.4 kg in men and R 2 = 0.94, SEE = 1.2 kg in women) between SMF‐BIA and DXA . The R 2 coefficient for whole‐body LSTM was higher in men ( R 2 = 0.88, SEE = 1.9 kg) than in women ( R 2 = 0.83, SEE = 1.5 kg). There was systematic bias with overestimation of whole‐body FM and underestimation of whole‐body LSTM by SMF‐BIA . Proportional bias was noted for measurement of whole‐body FM and LSTM in both men and women, but there was no proportional bias between the two methods for measurement of appendicular LSTM ( r = 0.05, P = 0.428 and r = −0.10, P = 0.070 for men and women, respectively). Conclusion: SMF‐BIA is a good alternative for estimating the whole‐body and appendicular FM and LSTM in a community‐dwelling J apanese older population, although it overestimated FM and underestimated LSTM when validated against DXA . Geriatr Gerontol Int 2015; 15: 1013–1022.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Several studies have indicated comparable outcomes between DXA and MF-BIA (26–29).”
From Transforming body composition with semaglutide in adults with obesity and type 2 diabetes mellitus (2024) · cites this paper as [27]
Citations
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
Beckett, A. H., Rowland, M., & TURNER, P. (1965) · The Lancet · doi:10.1016/s0140-6736(65)91037-8 · Cited by 16,917
Epidemiology of Sarcopenia among the Elderly in New Mexico
Baumgartner, R. N., Koehler, K. M., Gallagher, D., Romero, L., Heymsfield, S. B., Ross, R. R., Garry, P. J., & Lindeman, R. D. (1998) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a009520 · Cited by 4,004
Janssen, I., Heymsfield, S. B., & Ross, R. (2002) · Journal of the American Geriatrics Society · doi:10.1046/j.1532-5415.2002.50216.x · Cited by 3,139
Bioelectrical impedance analysis—part II: utilization in clinical practice
Kyle, U. G., Bosaeus, I., De Lorenzo, A. D., Deurenberg, P., Elia, M., Manuel Gómez, J., Lilienthal Heitmann, B., Kent-Smith, L., Melchior, J.-C., Pirlich, M., et al. (2004) · Clinical Nutrition · doi:10.1016/j.clnu.2004.09.012 · Cited by 2,350
Appendicular skeletal muscle mass: effects of age, gender, and ethnicity
Gallagher, D., Visser, M., De Meersman, R. E., Sepúlveda, D., Baumgartner, R. N., Pierson, R. N., Harris, T., & Heymsfield, S. B. (1997) · Journal of Applied Physiology · doi:10.1152/jappl.1997.83.1.229 · Cited by 966
Hughes, V. A., Frontera, W. R., Roubenoff, R., Evans, W. J., & Singh, M. A. F. (2002) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/76.2.473 · Cited by 804
Ling, C. H. Y., de Craen, A. J. M., Slagboom, P. E., Gunn, D. A., Stokkel, M. P. M., Westendorp, R. G. J., & Maier, A. B. (2011) · Clinical Nutrition · doi:10.1016/j.clnu.2011.04.001 · Cited by 646
Body composition estimates from NHANES III bioelectrical impedance data
Chumlea, W., Guo, S., Kuczmarski, R., Flegal, K., Johnson, C., Heymsfield, S., Lukaski, H., Friedl, K., & Hubbard, V. (2002) · International Journal of Obesity · doi:10.1038/sj.ijo.0802167 · Cited by 507
Advances in body composition assessment
Boileau, R. A. (1993) · Cadernos de Saúde Pública · doi:10.1590/s0102-311x1993000500016 · Cited by 449
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–221 of 221
Yoon, M.-J., Song, K., Ji, Y., Choi, S., Lee, H., Jung, J. Y., Song, S., Kim, J. Y., Kim, M., Won, C. W., et al. (2025) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-025-01773-y · Cited by 1
Self-citationLee, D.-J., Choi, D. K., Kim, J.-G., Kim, W., & Do, H.-K. (2026) · PeerJ · doi:10.7717/peerj.20820 · Cited by 2
Sugimoto, H., Taga, M., Fukushima, K., Akashi, Y., & Momose, K. (2025) · Journal of Physical Therapy Science · doi:10.1589/jpts.37.77 · Cited by 1
Kim, S., Shin, H. E., Kim, M., & Won, C. W. (2025) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2025.105778 · Cited by 1
Self-citationアルツハイマー病高齢者における認知症重症度別, 身体組成・栄養指標に関する検討
本川, 佳., 弥生, 田., 洋子, 菅., 洋子, 細., 枝広, あ., 大輔, 高., 浩彦, 平., & 裕, 渡. (2017) · 静脈経腸栄養 · doi:10.11244/jspen.32.851
Clinical Usability of Home Body Composition Meters
Sadakiyo, K., SASAKI, T., Sugita, Y., MAEDA, S., Ayano, Y. U. S. A., Watanabe, M., SADAKIYO, H., Ishizaka, M., & Akira, K. U. B. O. (2018) · Rigakuryoho Kagaku · doi:10.1589/rika.33.151
Estimation of Visceral Fat Area Using an Eight-Polar Bioelectrical Impedance Analyzer
Nakanishi, Y., Suzuki, Y., Sakurai, S., Fujita, S., Takaya, M., & Sakuraba, K. (2019) · doi:10.34363/jocp.49.5_175
Kitgo, M., Seino, S., Shinkai, S., Nofuji, Y., Yokoyama, Y., Abe, T., Taniguchi, Y., Amano, H., Murayama, H., Kitamura, A., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2945230/v1
Self-citationDifferences in Functional Capacity between Oncologic and Non-Oncologic Populations: Reference Values
Mojas, E., Santisteban, A., Muñoz‐Pérez, I., Larrínaga-Undabarrena, A., Arietaleanizbeaskoa, M. S., Mendizabal-Gallastegui, N., Gonzalo Grandes, O., Cacicedo, J., & Río, X. (2024) · Healthcare · doi:10.3390/healthcare12030318
Iida, H., Sekiyama, T., Hashimoto, Y., Matsushita, J., Shindo, A., Okada, H., Murata, H., & Fukui, M. (2024) · Diabetology International · doi:10.1007/s13340-024-00766-x
Kudaka, K., Sato, K., Nakayama, Y., Koike, M., & Ogawa, T. (2024) · Neurological Research · doi:10.1080/01616412.2024.2423587
Wang, X., Yang, Y., Yin, L., Fang, Y., Mei, Q., & Niu, K. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1663996
Zhou, T., Ye, J., Luo, L., Wang, W., Feng, S., Dong, Z., Zhuo, S., & Zhong, B. (2023) · Figshare · doi:10.6084/m9.figshare.24873000
Shibata, Y., Sudo, T., Tazuma, S., Sada, H., Tanimine, N., Shimada, N., Tazawa, H., Suzuki, T., Onoe, T., Shimizu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.24987774
Taniguchi, M., Ikezoe, T., Tsuboyama, T., Tabara, Y., Matsuda, F., & Ichihashi, N. (2021) · Figshare · doi:10.6084/m9.figshare.16599731
Kobayashi, G., Hashimoto, Y., Yamamoto, S., Hamaguchi, M., & Fukui, M. (2026) · Diabetology International · doi:10.1007/s13340-026-00887-5
Martins, A. D., Batalha, N. M. P., Fernandes, O., Oliveira, R. F. S., Gonçalves, B. V., & Brito, J. P. (2026) · Sport Sciences for Health · doi:10.1007/s11332-026-01715-8
Supalak, R., Doungchan, N., Sespheng, A., Yuenyongchaiwat, K., Scholand, S. J., & Punsawad, C. (2026) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph23081040
Tongdee, P., Kupittayanant, S., Marungrueng, K., Ongsricharoenbhorn, S., & Nimkuntod, P. (2026) · Healthcare · doi:10.3390/healthcare14162663
Tomita, R., Sasaki, E., Ishíbashi, K., Kura, R., Ishibashi, H., Kimura, Y., Fujimoto, K., Tamada, Y., Murashita, K., & Ishibashi, Y. (2026) · Osteoarthritis and Cartilage Open · doi:10.1016/j.ocarto.2026.100874
Seko, T., Himuro, N., Koyama, M., Nakata, K., Akasaka, H., Mori, M., Ogawa, S., Miura, S., & Ohnishi, H. (2026) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2026.106458
Metadata
- Authors
- Kim, M., Shinkai, S., Murayama, H., & Mori, S.
- Affiliations
- Tokyo Metropolitan Institute of Gerontology (JP)
- Tokyo Metropolitan Geriatric Hospital (JP)
- Corresponding author
- Kim, Miji
- Venue
- Geriatrics & Gerontology International
- Publisher
- Wiley
- Published
- 2014-10-27
- Volume
- 15
- Issue
- 8
- Pages
- 1013-1022
- ISSN
- 1444-1586 (ISSN-L)
- 1447-0594
- OpenAlex
- W1579246632
- Cited by
- 221
- References
- 37
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 36 · 2023: 19 · 2022: 37 · 2021: 32 · 2020: 26 · 2019: 15 · 2018: 17 · 2017: 8 · 2016: 8 · 2015: 3
- FWCI
- 4.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Geriatrics and gerontology international/Geriatrics & gerontology international (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (24792611)
- Topics
- Health Sciences › Medicine › Physiology (medical) › Body Composition Measurement Techniques
- Health Sciences › Medicine › Physiology (medical) › Nutrition and Health in Aging
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Bone health and osteoporosis research
- Keywords
- dual-energy X-ray absorptiometry, body composition, fat mass, lean soft tissue mass, appendicular lean mass, older adults, community-dwelling older adults, measurement agreement, validation
- MeSH terms
- Age Factors, Aged, Aged, 80 and over, Body Composition, Female, Humans, Japan, Male, Obesity, Prospective Studies, Residence Characteristics, Sensitivity and Specificity, Sex Factors, Cohort Studies, Absorptiometry, Photon, Geriatric Assessment, Body Mass Index, Linear Models, Electric Impedance, Risk Assessment, Sarcopenia, Independent Living
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review