Fat-free Mass Bioelectrical Impedance Analysis Predictive Equation for Athletes using a 4-Compartment Model
Abstract
Bioelectrical impedance analysis equations for fat-free mass prediction in healthy populations exist, nevertheless none accounts for the inter-athlete differences of the chemical composition of the fat-free mass. We aimed to develop a bioimpedance-based model for fat-free mass prediction based on the four-compartment model in a sample of national level athletes; and to cross-validate the new models in a separate cohort of athletes using a 4-compartment model as a criterion. There were 142 highly trained athletes (22.9±5.0 years) evaluated during their respective competitive seasons. Athletes were randomly split into development (n=95) and validation groups (n=47). The criterion method for fat-free mass was the 4-compartment model. Resistance and reactance were obtained with a phase-sensitive 50 kHz bioimpedance device. Athletic impedance-based models were developed (fat-free mass=− 2.261+0.327*Stature2/Resistance+0.525*Weight+5.462*Sex, where stature is in cm, Resistance is in Ω, Weight is in kg, and sex is 0 if female or 1 if male). Cross validation revealed R2 of 0.94, limits of agreement around 10% variability and no trend, as well as a high concordance correlation coefficient. The new equation can be considered valid thus affording practical means to quantify fat-free mass in elite adult athletes.
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In the validation group, routinely used generic athletic BIA equations underestimated FFM and ALST (P < 0.01).
“Train-test validation, performed on the validation group, revealed that generalized formulas for athletes underestimated all the predicted FFM components (p < 0.01)”
To date, only BIA-based predictive equations that include athletes of different sports are available in the literature.
“To date, only BIA-based predictive equations developed that include athletes of different sports are available in literature [32, 33]”
The use of FFM derived by the athletic generalized equation [33] resulted in an overestimation of the percentage of fat mass (15.6% ± 1.3; P < 0.001).
“On the contrary, the use of FFM derived by the athletic generalized equation [33] resulted in an overestimation (15.6% ± 1.3; P < 0.001) of the percentage of fat mass.”
The athletes included in the development of previous predictive models were actively involved in many different sport disciplines.
“The athletes included in the development of the previous predictive models were actively involved in different sport disciplines such as basketball, handball, swimming, triathlon, judo, pentathlon, athletics, tennis, rowing, sailing, karate/taekwondo, boxing, hokey, climbing, rugby, soccer, fencing, motorsports, power lifting, padel, futsal, trail running, korfball, surfing, and gymnastics.”
These groups of athletes presented average values of 47.7 kg for FFM and 29.4 kg for ALST.
“These groups of athletes presented average values of 47.7 kg and 29.4 kg for FFM [33] and ALST [32], respectively.”
Inclusion of athletes from different sports in previous development studies can result in a loss of accuracy when athletes of specific sports are evaluated.
“Similarly, the inclusion of athletes from different sports in the previous development studies [32, 33], even though they may have satisfied an initial request for predictive equations to be applied in the context of sport [20], can result in a loss of accuracy when athletes of specific sports are evaluated.”
A trend was verified between the mean and the difference of methods with large limits of agreement for all selected athletic equations [32,33].
“Concerning agreement analysis, a trend was verified between the mean and the difference of methods with large limits of agreement for all the selected athletic equations [32, 33].”
The use of a generalized equation [33] resulted in an underestimation of FFM and then in an overestimation of fat mass in the soccer players.
“Indeed, the use of a generalized equation [33] resulted in an underestimation of FFM and then in an overestimation of the fat mass in the soccer players.”
The generalized equations [32,33] explained 88-91% of the variance in the reference data.
“Similarly, the generalized equations [32, 33] explained from 88 to 91% of the variance observed in the reference data.”
Four studies on athletes involved Portuguese male and female participants from different sport disciplines.
“Four studies involved Portuguese male and female participants from different sport disciplines [39, 69, 84, 87], while two studies recruited male Brazilian army cadets [85] considered as very active individuals [103] and adolescents from various sport disciplines [104].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [69]
Regarding foot-to-hand technology for athletes, five equations are for estimating FFM in males.
“Regarding foot to hand technology, we found an equation for estimating FM in males [48], five equations for estimating FFM in males [31, 69, 84, 85, 104], and four in females only [51, 62, 69, 104].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [69]
Regarding foot-to-hand technology for athletes, four equations are for estimating FFM in females only.
“Regarding foot to hand technology, we found an equation for estimating FM in males [48], five equations for estimating FFM in males [31, 69, 84, 85, 104], and four in females only [51, 62, 69, 104].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [69]
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“[25], and fat mass (FM) was calculated as the difference between body weight and FFM.”
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“In addition, the FFM prediction equation of Matias et al. (25), which includes bioelectrical resistance, height, weight, and sex, was used to generate FFM, FM, and BFP estimates.”
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“[17], where sex is a binary value where 0 and 1 refer to female and male, respectively.”
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“Arms and legs lean soft tissue (ALST and LLST, respectively), fat-free mass (FFM), fat mass (FM), and percentage of fat mass (FM%) were estimated using a specific equation developed for athletes (26, 27) designed and validated with the same BIA model: 1.”
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Pérez-Castillo, Í. M., Valiño-Marques, A., Chicharro, J. L., Segura-Ortiz, F., Rueda, R., & Bouzamondo, H. (2025) · Sports · doi:10.3390/sports13100348 · Cited by 5 · see citing works
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“Fat-free mass (FFM), TBW, and ECW were obtained by applying the formulas developed by Matias et al. [28,29].”
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“For example, Matias et al. [33] developed a bioimpedance-based model for fat-free mass prediction based on the four-compartment model in a sample of national level athletes that can be used to overcome problems arising when measuring adult elite athletes.”
MethodBarata, C., Reis, J. F., Moncóvio, S. A., Vilares, A., Bento, A. M., Rosa, C. H., Espada, M. C., Matias, C. N., & Monteiro, C. P. (2025) · Applied Sciences · doi:10.3390/app16010353 · Cited by 2 · see citing works
Self-citationMartínez‐Ferrán, M., Berlanga, L. A., Guido, O. B., Duarte, M. M., Vicente-Campos, D., Sánchez-Jorge, S., Morales, C. R., Casla-Barrio, S., Munguía‐Izquierdo, D., & Pareja-Galeano, H. (2023) · Nutrition Research · doi:10.1016/j.nutres.2023.05.005 · Cited by 2 · see citing works
Ribeiro, A. S., Sofiati, S. L., Kassiano, W., Martinho, D. V., do Nascimento, M. A., Avelar, A., Trindade, M. C. D. C., Mayhew, J. L., & Cyrino, E. S. (2024) · Journal of the International Society of Sports Nutrition · doi:10.1080/15502783.2024.2357319 · Cited by 3 · see citing works
Lai, Y.-K., Ho, C.-Y., Huang, A.-C., Lu, H.-K., & Hsieh, K.-C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300911 · Cited by 2 · see citing works
“Among male college basketball players, centers players reflect that their arms and legs have the highest LSTM and FM compared to players in all other positions (with the exception of power forwards) [9].”
BackgroundBody Composition Analysis in Runners Using Bioimpedance Technique
Dammicco, A., & Reina, G. (2025) · IEEE Access · doi:10.1109/access.2025.3635046 · Cited by 1 · see citing works
“Finally, this study presents a further original contribution through a comparative analysis of actual BIA-derived data and commonly used prediction equations (Lukaski-Bolonchuk, Lohman, and Matias [16], [17], [18]).”
Abdelrahman, A. S., & Saad, M. K. (2025) · Journal of Medical Sciences and Interdisciplinary Research · doi:10.51847/jblojwtyn0 · Cited by 1 · see citing works
Matias, C. N., Campa, F., Cavaca, M. L., Paoli, A., & Teixeira, F. J. (2022) · Research Square · doi:10.21203/rs.3.rs-2055824/v1 · Cited by 1 · see citing works
Self-citationVázquez-Bautista, M. A., Robles-Sardin, A. E., Reséndiz-Sandoval, M., Navarro-Ibarra, M. J., Hernández López, J., & Caire-Juvera, G. (2025) · BIOtecnia · doi:10.18633/biotecnia.v27.2512
Matias, C. N., Toselli, S., Monteiro, C. P., & Campa, F. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19105855 · Cited by 1 · see citing works
Self-citationEstimation equation of limb lean soft tissue mass in Asian athletes using bioimpedance analysisPreprint
Lai, Y.-K., Ho, C.-Y., Huang, A.-C., & Hsieh, K.-C. (2022) · Research Square · doi:10.21203/rs.3.rs-1849511/v1
Lai, Y.-K., Ho, C.-Y., Huang, A.-C., et al. (2024) · medRxiv · doi:10.1101/2024.03.07.24303957
Luna, T. B., Bello, J. L. G., Carbonell, A. G., Lafargue, A. L., Ciria, H. M. C., & Montoya, A. D. L. C. R. (2024) · Research Square · doi:10.21203/rs.3.rs-4249139/v1
Mulasso, A., Cusato, J., CUOMO, S., Laspia, S., Mercadante, B., Turiano, E., Chiara, F., Mula, J., Palermiti, A., Manca, A., et al. (2024) · Sport Sciences for Health · doi:10.1007/s11332-024-01174-z
Campa, F., Rossi, A., Martera, G., Trecroci, A., & Bongiovanni, T. (2025) · International Journal of Kinanthropometry · doi:10.34256/ijk2519
Self-citationMicke, F., Huelle, J., D’Hulst, G., Isenmann, E., Geisler, S., & Held, S. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1810477
“…analysis (BIA) data concerning fat mass were inconclusive, possibly due to known limitations in BIA accuracy compared to methods like DEXA or the speci fi c anthropometric protocols used (Matias et al., 2021), the clear reduction in waist circumference suggests positive morphological adaptation.”
Lahlou, Y., Aboukhalaf, A., BOURAQ, M., Oualili, H., JADOUMI, Y. E., Filali, A., Bouqadida, M., Hafid, A., Arifi, K., Makroum, K., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9972847/v1
Farfán-Díaz, Á. (2026) · Revista Multidisciplinar Epistemología de las Ciencias · doi:10.71112/y3e4dd15
- Authors
- Matias, C. N., Campa, F., Santos, D. A., Lukaski, H., Sardinha, L. B., & Silva, A. M.
- Affiliations
- University of Lisbon (PT)
- University of Bologna (IT)
- University of North Dakota (US)
- Corresponding author
- Campa, Francesco
- ORCID
- Venue
- International Journal of Sports Medicine
- Publisher
- Georg Thieme Verlag KG
- Published
- 2020-08-07
- Volume
- 42
- Issue
- 01
- Pages
- 27-32
- ISSN
- 0172-4622 (ISSN-L)
- 1439-3964
- OpenAlex
- W3047806286
- Semantic Scholar
- 467888038a7624f877b69efd8633a5163814c805
- Cited by
- 54
- References
- 34
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 13 · 2023: 9 · 2022: 10 · 2021: 7 · 2020: 3
- FWCI
- 3.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Sports Medicine (publishedVersion)
- PubMed (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical and Bioimpedance Tomography
- Keywords
- fat-free mass, bioelectrical impedance analysis, prediction equations, four-compartment model, athletes, resistance and reactance, cross-validation, elite athletes
- MeSH terms
- Organism Hydration Status, Body Composition, Body Water, Cross-Sectional Studies, Female, Humans, Male, Sports, Reproducibility of Results
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