Development of population-specific prediction equations for bioelectrical impedance analyses in Vietnamese children
Open accessAbstract
There is a need for accurate, inexpensive and field-friendly methods to assess body composition in children. Bioelectrical impedance analysis (BIA) is a promising approach; however, there have been limited validation and use among young children in resource-poor settings. We aim to develop and validate population-specific prediction equations for estimating total fat mass (FM), fat free-mass (FFM) and percentage body fat (PBF) in Vietnamese children (4–7 years) using reactance and resistance from BIA, anthropometric variables and demographic information. We conducted a cross-sectional survey of 120 children. Body composition was measured using dual-energy X-ray absorptiometry (DXA), BIA and anthropometry. To develop prediction equations, we split all data into development (70 %) and validation datasets (30 %). The model performance was evaluated using predicted residual error sum of squares, root mean squared error (RMSE), mean absolute error (MAE) and R2. We identified a top performing model with the least number of parameters (age, sex, weight and resistance index or resistance and height), low RMSE (FM 0·70, FFM 0·74, PBF 3·10), low MAE (FM 0·55, FFM 0·62, PBF 2·49), high R2(FM 0·95, FFM 0·92, PBF 0·82) and the least difference between predicted values and actual values from DXA (FM 0·03 kg or 0·01 sd, FFM 0·06 kg or 0·02 sd, PBF 0·27 % or 0·04 sd). In conclusion, we developed the first valid and highly predictive equations to estimate FM, FFM and PBF in Vietnamese children using BIA. These findings have important implications for future research on the double burden of disease and risks associated with overweight and obesity in young children.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
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 [44]
The Study setting and sample section (page unavailable) describes a convenience sample of 120 healthy children aged 4–7 from one Vietnamese city. The Discussion (page unavailable) cautions that the equations may not generalise to other ethnicities, supporting a concern about applicability. The paper does not assess applicability across 19 studies or assign itself a ‘high’ rating; that classification is not established by this source. (LLM, conf=0.78)
Experimental — accuracy not yet measuredConsidering segmental technology for under 18 years old, five equations are available for estimating FFM in both sexes.
“Considering segmental technology, five equations are available for estimating FFM in both sexes [43–45, 57, 74].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [44]
The Abstract reports FFM prediction equations for Vietnamese boys and girls aged 4–7, and the Measures section describes an eight-electrode BIA device. However, Measures says the equations used total-body resistance and reactance, not segmental measurements. The paper also does not establish that five segmental FFM equations are available across the cited studies. (LLM, conf=0.82)
Experimental — accuracy not yet measuredOut of the 19 equations for under 18 years old, seven were developed using segmental technology.
“Out of the 19 equations, 12 were developed using foot to hand technology [37, 55, 58, 59, 72, 73, 75, 76, 100] and seven equations were developed using segmental technology [43–45, 57, 74].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [44]
The Measures section describes an eight-electrode BIA device with electrodes on both hands and feet, and the Results section reports seven candidate models for fat mass. The paper does not classify its technology as segmental or establish the citing document’s aggregate count of seven segmental equations out of 19; those parts are not supported by this source alone. (LLM, conf=0.76)
Experimental — accuracy not yet measuredRegarding segmental technology for under 18 years old, five equations are suitable for both sexes.
“Regarding segmental technology, five are suitable for both sexes [43–45, 57, 74].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [44]
The remaining 12 studies on subjects under 18 years old were performed in different countries around the world and included both sexes.
“Two of these studies were conducted in the USA, where no female participants were involved [55, 76], while the remaining 12 studies were performed in different countries around the world with both sexes included [37, 43–45, 57–59, 72–75, 100].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [44]
Four predictive equations for under 18 years old were developed with multifrequency devices but at the frequency of 50 kHz.
“All 19 equations were developed at a single frequency of 50 kHz, although four predictive equations were developed with multifrequency devices but at the frequency of 50 kHz [43, 44, 57].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [44]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
Abarca-Gómez, L., Abdeen, Z. A., Hamid, Z. A., Abu-Rmeileh, N. M., Acosta-Cazares, B., Acuin, C., Adams, R. J., Aekplakorn, W., Afsana, K., Aguilar-Salinas, C. A., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)32129-3 · Cited by 7,482 · see citing works
Self-citationPrinciples of nutritional assessment
(1990) · Choice Reviews Online · doi:10.5860/choice.27-6362 · Cited by 2,764 · see citing works
Skinfold equations for estimation of body fatness in children and youth.
Slaughter, M. H., Lohman, T. G., Boileau, R. A., Horswill, C. A., Stillman, R. J., Loan, M. D. V., & Bemben, D. A. (1988) · PubMed · Cited by 2,712 · see citing works
Principles Of Nutritional Assessment
Gibson, R. S. (2005) · doi:10.1093/oso/9780195171693.001.0001 · Cited by 1,887 · 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 · cited 1× · see citing works
“Estimating body composition with BIA is based on the principle that electricity is impeded as it is conducted through a cylinder and passes easily through hydrated tissue, that is, fat-free mass (FFM), yet resisted by tissue with little to no water, fat mass (FM)(23).”
BackgroundPopkin, B. M., Richards, M. K., & Montiero, C. A. (1996) · Journal of Nutrition · doi:10.1093/jn/126.12.3009 · Cited by 506 · see citing works
McLester, C. N., Nickerson, B. S., Kliszczewicz, B. M., & McLester, J. R. (2018) · Journal of Clinical Densitometry · doi:10.1016/j.jocd.2018.10.008 · Cited by 444 · see citing works
Ward, L. C. (2019) · European Journal of Clinical Nutrition · doi:10.1038/s41430-018-0335-3 · Cited by 417 · see citing works
Developmental origins of health and disease: current knowledge and potential mechanisms
Hoffman, D. J., Reynolds, R. M., & Hardy, D. B. (2017) · Nutrition Reviews · doi:10.1093/nutrit/nux053 · Cited by 360 · see citing works
Self-citationRodrı́guez, G., Moreno, L. A., Blay, M. G., Blay, V. A., Fleta, J., Sarría, A., Bueno, M., & the AVENA-Zaragoza Study Group (2005) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1602226 · Cited by 228 · see citing works
Emmett, P. M., & Jones, L. R. (2015) · Nutrition Reviews · doi:10.1093/nutrit/nuv054 · Cited by 185 · see citing works
Kyle, U. G., Earthman, C. P., Pichard, C., & Coss-Bu, J. A. (2015) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2015.86 · Cited by 183 · see citing works
Developmental Origins of Health and Disease – from a small body size at birth to epigenetics
Eriksson, J. G. (2016) · Annals of Medicine · doi:10.1080/07853890.2016.1193786 · Cited by 115 · see citing works
Lohman, T. G., & Going, S. B. (2006) · Food and Nutrition Bulletin · doi:10.1177/15648265060274s512 · Cited by 108 · see citing works
Wan, C. S., Ward, L. C., Halim, J. A., Gow, M. L., Ho, M. M., BRIODY, J. N., Leung, K., Cowell, C. T., & Garnett, S. P. (2014) · BMC Pediatrics · doi:10.1186/1471-2431-14-249 · Cited by 91 · see citing works
Validation of BIA in Obese Children and Adolescents and Re‐evaluation in a Longitudinal Study
Haroun, D., Croker, H. A., Viner, R., Williams, J. E., Darch, T. S., Fewtrell, M. S., Eaton, S. D., & Wells, J. C. K. (2009) · Obesity · doi:10.1038/oby.2009.98 · Cited by 85 · see citing works
Lee, L., Liao, Y.-S., Lu, H.-K., Hsiao, P.-L., Chen, Y.-Y., Ching-Chi, C., & Hsieh, K.-C. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171568 · Cited by 84 · see citing works
Double burden of malnutrition: the Vietnamese perspective.
Khẩn, N. C., & Khoi, H. H. (2008) · PubMed · Cited by 81 · see citing works
Vicente‐Rodríguez, G., Rey‐López, J. P., Mesana, M. I., Poortvliet, E., Ortega, F. B., Polito, A., Nagy, E., Widhalm, K., Sjöström, M., Moreno, L. A., et al. (2012) · Obesity · doi:10.1038/oby.2011.272 · Cited by 79 · see citing works
Deurenberg, P., & Deurenberg‐Yap, M. (2002) · Asia Pacific Journal of Clinical Nutrition · doi:10.1046/j.1440-6047.2002.00258.x · Cited by 76 · see citing works
Liu, A., Byrne, N. M., Kagawa, M., Ma, G., Poh, B. K., Ismail, M. N., Kijboonchoo, K., Nasreddine, L., Trinidad, T. P., & Hills, A. P. (2011) · British Journal Of Nutrition · doi:10.1017/s0007114511001681 · Cited by 60 · see citing works
Ramírez, E., Valencia, M. E., Bourges, H., Espinosa, T. Z., Moya-Camarena, S. Y., Salazar, G., & Alemán-Mateo, H. (2012) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2012.89 · Cited by 59 · see citing works
Biswas, T., Magalhães, R. J. S., Townsend, N. P., Das, S. K., & Mamun, A. A. (2019) · Advances in Nutrition · doi:10.1093/advances/nmz078 · Cited by 57 · see citing works
Liu, A., Byrne, N. M., Ma, G., Nasreddine, L., Trinidad, T. P., Kijboonchoo, K., Ismail, M. N., Kagawa, M., Poh, B. K., & Hills, A. P. (2011) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2011.122 · Cited by 45 · see citing works
Regression equations to estimate percentage body fat in African prepubertal children aged 9 y
Cameron, N., Griffiths, P. L., Wright, M. M., et al. (2004) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/80.1.70 · Cited by 45 · see citing works
Kehoe, S. H., Krishnaveni, G. V., Lubree, H., Wills, A. K., Guntupalli, A. M., Veena, S. R., Bhat, D., Kishore, R., Fall, C. H. D., Yajnik, C. S., et al. (2011) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2011.119 · Cited by 39 · see citing works
Rush, E. C., Bristow, S. M., Plank, L. D., & Rowan, J. (2012) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2012.182 · Cited by 34 · see citing works
Wendel, D., Weber, D. R., Leonard, M. B., Magge, S. N., Kelly, A. T., Stallings, V. A., Pipan, M. E., Stettler, N., & Zemel, B. S. (2016) · Annals of Human Biology · doi:10.3109/03014460.2016.1168867 · Cited by 34 · see citing works
Rachmi, C. N., Li, M., & Baur, L. A. (2019) · PubMed · doi:10.6133/apjcn.062018.02 · Cited by 34 · see citing works
Developmental origins of diabetes—an Indian perspective
Krishnaveni, G. V., & Yajnik, C. S. (2017) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2017.87 · Cited by 33 · see citing works
Estimating total body fat using a skinfold prediction equation in Brazilian children.
Hoffman, D. J., Toro-Ramos, T., Sawaya, A. L., Roberts, S. B., & Rondo, P. (2012) · Annals of Human Biology · doi:10.3109/03014460.2012.660989 · Cited by 30 · see citing works
Self-citationPrediction of percent body fat measurements in Americans 8 years and older
Stevens, J., Ou, F.-S., Cai, J., Heymsfield, S. B., & Truesdale, K. P. (2015) · International Journal of Obesity · doi:10.1038/ijo.2015.231 · Cited by 30 · see citing works
Comparison of Techniques to Evaluate Adiposity in Stunted and Nonstunted Children
Hoffman, D. J., Sawaya, A. L., Martins, P., et al. (2006) · PEDIATRICS · doi:10.1542/peds.2005-0779 · Cited by 28 · see citing works
Self-citationRamírez‐Vélez, R., Tordecilla‐Sanders, A., Correa‐Bautista, J. E., González‐Ruíz, K., González‐Jiménez, E., Triana-Reina, H. R., García‐Hermoso, A., & Schmidt‐RioValle, J. (2017) · American Journal of Human Biology · doi:10.1002/ajhb.23071 · Cited by 28 · see citing works
Ejlerskov, K. T., Jensen, S. M., Christensen, L. B., Ritz, C., Michaelsen, K. F., & Mølgaard, C. (2014) · Scientific Reports · doi:10.1038/srep03889 · Cited by 27 · see citing works
Estimation of fat-free mass in Asian neonates using bioelectrical impedance analysis
Tint, M. T., Ward, L. C., Soh, S., Aris, I. M., Amutha, C., Saw, S., Gluckman, P. D., Godfrey, K. M., Chong, Y. S., Kramer, M. S., et al. (2016) · British Journal Of Nutrition · doi:10.1017/s0007114515005486 · Cited by 27 · see citing works
Stevens, J., Truesdale, K. P., Cai, J., Ou, F.-S., Reynolds, K. R., & Heymsfield, S. B. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.167 · Cited by 23 · see citing works
Huỳnh, G., An, P. L., Viên, N. T., Khanh, T. V., & Vinh, B. Q. (2019) · BioMed Research International · doi:10.1155/2019/1547626 · Cited by 20 · see citing works
Diouf, A., Diongue, O., Mégné, N., Dossou, N. I., Thiam, M., & Wade, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204486 · Cited by 19 · see citing works
Nasreddine, L., Naja, F., Hills, A. P., Youssef, S. K., Chahine, J., & Hwalla, N. C. (2011) · Clinical Nutrition · doi:10.1016/j.clnu.2011.11.008 · Cited by 18 · see citing works
Anthropometric Status among 6–9-Year-Old School Children in Rural Areas in Hai Phong City, Vietnam
Hoang, N. T. D., Orellana, L., Le, T. D., et al. (2018) · Nutrients · doi:10.3390/nu10101431 · Cited by 18 · see citing works
Doña, E., Olveira, C., Palenque, F. J., Porras, N., Dorado, A., Martín‐Valero, R., Godoy, A., Espíldora, F., Contreras, V., & Olveira, G. (2018) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2018.01.013 · Cited by 14 · see citing works
Transition of maternal and child nutrition in Asia
Winichagoon, P. (2015) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/mco.0000000000000158 · Cited by 13 · see citing works
Steinberg, A., Manlhiot, C., Li, P., Metivier, E., Pencharz, P. B., McCrindle, B. W., & Hamilton, J. K. (2019) · Journal of Nutrition · doi:10.1093/jn/nxz063 · Cited by 13 · see citing works
Gonçalves, E. M., Silva, A. M., Santos, D. A., de Lemos‐Marini, S. H. V., Santos, A. O., Mendes-dos-Santos, C. T., de Mello, M. P., & Guerra‐Júnior, G. (2012) · Nutrition · doi:10.1016/j.nut.2011.12.014 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
“All 19 equations were developed at a single frequency of 50 kHz, although four predictive equations were developed with multifrequency devices but at the frequency of 50 kHz [43, 44, 57].”
Background
- Authors
- Nguyen, P. H., Young, M. F., Khuong, L. Q., Ramakrishnan, U., Martorell, R., & Hoffman, D. J.
- Affiliations
- International Food Policy Research Institute (US)
- Emory University (US)
- Venue
- British Journal of Nutrition
- Publisher
- Cambridge University Press (CUP)
- Published
- 2020-07-03
- Volume
- 124
- Issue
- 12
- Pages
- 1345-1352
- ISSN
- 0007-1145 (ISSN-L)
- 1475-2662
- OpenAlex
- W3039896849
- Cited by
- 8
- References
- 46
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 2 · 2020: 1
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- British Journal Of Nutrition (publishedVersion)
- PubMed (publishedVersion)
- IFPRI E-brary (International Food Policy Research Institute) (submittedVersion, other-oa)
- International Food Policy Research Institute (International Food Policy Research Institute) (submittedVersion, other-oa)
- Funders
- Rollins School of Public Health
- Topics
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical and Bioimpedance Tomography
- Keywords
- bioelectrical impedance analysis, body composition, prediction equations, fat mass, fat-free mass, body fat percentage, children, Vietnamese children, reactance, resistance, anthropometry, equation validation, childhood obesity, double burden of disease
- MeSH terms
- Population Health, Clinical Decision Rules, Anthropometry, Body Composition, Child, Child, Preschool, Female, Humans, Male, Predictive Value of Tests, Vietnam, Absorptiometry, Photon, Electric Impedance, Asian People
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
