Athletic bioimpedance-based equations underestimate fat free mass components in male elite soccer players: development and validation of new soccer-specific predictive models
Open accessAbstract
Background: Bioelectrical impedance analysis (BIA) is a rapid and user-friendly technique for assessing body composition in sports. Currently, no sport-specific predictive equations are available, and the utilization of generalized formulas can introduce systematic bias. The objectives of this study were as follows: (i) to develop and validate new predictive models for estimating fat-free mass (FFM) components in male elite soccer players; (ii) to evaluate the accuracy of existing predictive equations. Methods: A total of 102 male elite soccer players (mean age 24.7 ± 5.7 years), participating in the Italian first league, underwent assessments during the first half of the in-season period and were randomly divided into development and validation groups. Bioelectrical resistance (R) and reactance (Xc), representing the bioimpedance components, were measured using a foot-to-hand BIA device at a single frequency of 50 kHz. Dual-energy X-ray absorptiometry was employed to acquire reference data for FFM, lean soft tissue (LST), and appendicular lean soft tissue (ALST). The validation of the newly developed predictive equations was conducted through regression analysis, Bland–Altman tests, and the area under the curves (AUC) of regression receiver operating characteristic (RROC) curves. Results: Developed models were: FFM = − 7.729 + (body mass × 0.686) + (stature 2/R × 0.227) + (Xc × 0.086) + (age × 0.058), R 2 = 0.97, Standard error of estimation (SEE) = 1.0 kg; LST = − 8.929 + (body mass × 0.635) + (stature 2/R × 0.244) + (Xc × 0.093) + (age × 0.048), R 2 = 0.96, SEE = 0.9 kg; ALST = − 24.068 + (body mass × 0.347) + (stature 2/R × 0.308) + (Xc × 0.152), R 2 = 0.88, SEE = 1.4 kg. Train-test validation, performed on the validation group, revealed that generalized formulas for athletes underestimated all the predicted FFM components (p < 0.01), while the new predictive models showed no mean bias (p > 0.05), with R 2 values ranging from 0.83 to 0.91, and no trend (p > 0.05). The AUC scores of the RROC curves indicated an accuracy of 0.92, 0.92, and 0.74 for FFM, LST, and ALST, respectively. Conclusions: The utilization of generalized predictive equations leads to an underestimation of FFM and ALST in elite soccer players. The newly developed soccer-specific formulas enable valid estimations of body composition while preserving the portability of a field-based method.
AI snapshot
Written from the paper’s full text.
TL;DR
New soccer-specific BIA equations were developed and validated in 102 male elite Italian Serie A soccer players to estimate fat-free mass (FFM), lean soft tissue (LST), and appendicular lean soft tissue (ALST). The new models showed high accuracy and no mean bias, whereas generalized athletic BIA equations underestimated FFM components.
Key findings
- Developed models: FFM = −7.729 + (body mass × 0.686) + (stature²/R × 0.227) + (Xc × 0.086) + (age × 0.058), R² = 0.97, SEE = 1.0 kg; LST = −8.929 + (body mass × 0.635) + (stature²/R × 0.244) + (Xc × 0.093) + (age × 0.048), R² = 0.96, SEE = 0.9 kg; ALST = −24.068 + (body mass × 0.347) + (stature²/R × 0.308) + (Xc × 0.152), R² = 0.88, SEE = 1.4 kg.
- In train-test validation, generalized formulas for athletes underestimated all predicted FFM components (p < 0.01), while the new soccer-specific models showed no mean bias (p > 0.05), with R² values ranging from 0.83 to 0.91 and no trend (p > 0.05).
- RROC AUC scores were 0.92 for FFM, 0.92 for LST, and 0.74 for ALST.
- Using the new FFM model, predicted vs reference fat mass percentage showed no mean difference (12.3% ± 0.8; P = 0.954), whereas the generalized athletic equation overestimated fat mass percentage (15.6% ± 1.3; P < 0.001).
Methods
- Cross-sectional study of 102 male elite soccer players (mean age 24.7 ± 5.7 years) from the Italian first division (Serie A), assessed during the first half of the in-season period and randomly divided into development (N = 73) and validation (N = 29) groups.
- Foot-to-hand BIA at 50 kHz measured resistance (R) and reactance (Xc); DXA provided reference FFM, LST, and ALST.
- Backward stepwise linear regression developed models; validation used regression analysis, Bland–Altman tests, concordance correlation coefficient, and RROC AUC.
Limitations
- The paper states DXA is not the state-of-the-art method for fat and FFM, which should be assessed with air plethysmography and dilution techniques in a four-compartment model.
- The cross-sectional design prevents assessing predicted FFM components over time; longitudinal and interventional studies are needed.
- Findings cannot be generalized to other sports, females, sub-elite or adolescent players, or BIA measures from different technologies or sampling frequencies.
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Claims the paper makes
19 of 50 cited sources checked · 8 supported · 0 flagged · accuracy not yet measured
- Premisecitation
The use of predictive equations developed in the general population results in an underestimation of FFM components when applied to athletes.
“Particularly, the use of predictive equations developed in the general population results in an underestimation of FFM components when applied to athletes [22].”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [22] Coratella G, Campa F, Matias CN, Toselli S, Koury JC, Andreoli A, et al. Generalized bioelectric impedance-based equations underestimate body fluids in athletes. Scand J Med Sci Sports. 2021. https://doi.org/10.1111/sms.14033. source
The Discussion (page unavailable) reports that generalized equations generally underestimated total body water and extracellular water in athletes. These are FFM components, but the paper does not establish underestimation of FFM components more broadly, and it reports exceptions for some equations and athlete groups. (LLM, conf=0.84)
Experimental — accuracy not yet measured
- Premisecitation
Soccer players present different bioelectrical characteristics compared to other athletes.
“As result of these peculiarities, recent studies [24, 27] have shown that soccer players present different bioelectrical characteristics compared to other athletes [28] and this could make it necessary to develop dedicated predictive equations.”
Quote in the paperSubstantial claim3 of 3 sources checked- cites [24] Mala L, Maly T, Zahalka F, Bunc V, Kaplan A, Jebavy R, et al. Body composition of elite female players in five different sports games. J Hum Kinet. 2015;45:207–15. https://doi.org/10.1515/hukin-2015-0021. source
The Conclusion (page unavailable) reports that elite female soccer players differed from players of at least one other sport in 8 of 10 body-composition variables measured using bioimpedance. It does not establish that soccer players generally have different bioelectrical characteristics: the measured phase angle did not differ significantly between groups, and the study does not report a comparison of raw electrical characteristics. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [27] Campa F, Silva AM, Talluri J, Matias CN, Badicu G, Toselli S. Somatotype and bioimpedance vector analysis: a new target zone for male athletes. Sustainability. 2020. https://doi.org/10.3390/su12114365. source
- cites [28] Santos DA. Reference values for body composition and anthropometric measurements in athletes. PLoS ONE. 2014. https://doi.org/10.1371/journal.pone.0097846. source
The Results tables (page not available) compare soccer players with athletes in other sports on body-composition and anthropometric measures. The Methods list weight, height, anthropometry and DXA, but the passages found when searching for bioelectrical impedance, resistance, reactance and phase angle do not address whether soccer players differ in bioelectrical characteristics. (LLM, conf=0.76)
Experimental — accuracy not yet measured
- Premisecitation
Loss of accuracy of BIA predictive equations may occur when anthropometric characteristics differ between the development study subjects and the subjects on whom the formulas are applied.
“This loss of accuracy may happen when anthropometric characteristics differ among the subjects involved in the development studies and the subjects on which these formulas are applied [11].”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [11] Campa F, Coratella G. Athlete or non-athlete? This is the question in body composition. Front Physiol. 2021. https://doi.org/10.3389/fphys.2021.814572. source
- Premisecitation
Underestimation of FFM components from generalized equations was already shown when equations for the normal population were applied to athletes.
“The underestimation of FFM components from generalized equations was already showed when equations for the normal population were applied on athletes [22].”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [22] Coratella G, Campa F, Matias CN, Toselli S, Koury JC, Andreoli A, et al. Generalized bioelectric impedance-based equations underestimate body fluids in athletes. Scand J Med Sci Sports. 2021. https://doi.org/10.1111/sms.14033. source
- Premisecitation
Lean soft tissues are more sensitive to variations due to detraining periods where a rapid decrease can be experimented.
“but with respect to body fat, lean soft tissues are more sensitive to variations due to detraining periods where a rapid decrease can be experimented [50].”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [50] Campa F, Bongiovanni T, Trecroci A, Rossi A, Greco G, Pasta G, et al. Effects of the COVID-19 lockdown on body composition and bioelectrical phase angle in serie A Soccer players: a comparison of two consecutive seasons. Biology. 2021. https://doi.org/10.3390/biology10111175. source
The Discussion (page unavailable) reports that muscle mass fell markedly during the detraining-like lockdown while fat-mass percentage remained unchanged. This supports the comparison for muscle mass in the soccer players studied, but the paper does not establish that lean soft tissues generally decrease rapidly during detraining. (LLM, conf=0.83)
Experimental — accuracy not yet measured
- Premisecitation
Smaller parts of FFM such as LST and ALST are closely related to skeletal muscle components and particularly informative of strength and power expression in elite soccer players.
“As already mentioned, smaller parts of the FFM such as LST and ALST are closely related to skeletal muscle components and particularly informative of strength and power expression in elite soccer players [43].”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [43] Bongiovanni T, Tinsley G, Martera G, Orlandi C, Genovesi F, Puleo G, et al. Regional lean soft tissue and intracellular water are associated with changes in lower-body neuromuscular performance: a pilot study in elite soccer players. Eur J Investig Heal Psychol Educ. 2022;12:882–92. https://doi.org/10.3390/ejihpe12080064. source
- Findingown data
The developed BIA model for ALST in male elite soccer players was ALST = −24.068 + (body mass × 0.347) + (stature2/R × 0.308) + (Xc × 0.152), with R2 = 0.88 and SEE = 1.4 kg.
“ALST = − 24.068 + (body mass × 0.347) + (stature2/R × 0.308) + (Xc × 0.152), R2 = 0.88, SEE = 1.4 kg”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Findingnone given
Approximately 90% of the soccer players' body mass was determined by FFM components.
“approximately 90% of the soccer players’ body mass was determined by FFM components”
Quote in the paperNot a substantial claim - Findingown data
The AUC of RROC curves for the new LST equation was 0.92.
“AUC score of RROC shows an outstanding accuracy of FFM (0.92) and LST (0.92) formulas, while an acceptable one for ALST (0.74), as presented in Fig. 3.”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Findingown data
The AUC of RROC curves for the new FFM equation was 0.92.
“AUC score of RROC shows an outstanding accuracy of FFM (0.92) and LST (0.92) formulas, while an acceptable one for ALST (0.74), as presented in Fig. 3.”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Findingown data
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.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [32] Sardinha LB, Correia IR, Magalhães JP, Júdice PB, Silva AM, Hetherington-Rauth M. Development and validation of BIA prediction equations of upper and lower limb lean soft tissue in athletes. Eur J Clin Nutr. 2020;74:1646–52. https://doi.org/10.1038/s41430-020-0666-8. source
- cites [33] Matias CN, Campa F, Santos DA, Lukaski H, Sardinha LB, Silva AM. Fat-free mass bioelectrical impedance analysis predictive equation for athletes using a 4-compartment model. Int J Sports Med. 2021;42:27–32. https://doi.org/10.1055/a-1179-6236. source
- Findingreasoning
In all three final models, body mass was the best predictor.
“In all the three final models, body mass resulted as the best predictor”
Quote in the paperNot a substantial claim - Findingown data
The developed BIA model for LST in male elite soccer players was LST = −8.929 + (body mass × 0.635) + (stature2/R × 0.244) + (Xc × 0.093) + (age × 0.048), with R2 = 0.96 and SEE = 0.9 kg.
“LST = − 8.929 + (body mass × 0.635) + (stature2/R × 0.244) + (Xc × 0.093) + (age × 0.048), R2 = 0.96, SEE = 0.9 kg”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Findingown data
In the validation group, the newly developed soccer-specific BIA equations showed no significant trend in Bland-Altman analysis (P > 0.05).
“and no trend (p > 0.05)”
- Also stated in: § Results · plain
“there were no significant association between the differences and the means of the BIA- and DXA-derived variables”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Also stated in: § Results · plain
- Findingown data
The AUC of RROC curves for the new ALST equation was 0.74.
“AUC score of RROC shows an outstanding accuracy of FFM (0.92) and LST (0.92) formulas, while an acceptable one for ALST (0.74), as presented in Fig. 3.”
Quote in the paperNot a substantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Findingown data
The mean impedance vectors with their 95% confidence ellipses for the development and validation groups fell within the 50% tolerance ellipses of the reference male elite soccer population.
“The mean vectors with their 95% confidence ellipses fell within the 50% tolerance ellipses of the reference male elite soccer population (Fig. 1).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [29] Micheli ML, Pagani L, Marella M, Gulisano M, Piccoli A, Angelini F, et al. Bioimpedance and impedance vector patterns as predictors of league level in male soccer players. Int J Sports Physiol Perform. 2014;9:532–9. https://doi.org/10.1123/ijspp.2013-0119. source
- Findingown data
The developed BIA model for FFM in male elite soccer players was FFM = −7.729 + (body mass × 0.686) + (stature2/R × 0.227) + (Xc × 0.086) + (age × 0.058), with R2 = 0.97 and SEE = 1.0 kg.
“Developed models were: FFM = − 7.729 + (body mass × 0.686) + (stature2/R × 0.227) + (Xc × 0.086) + (age × 0.058), R2 = 0.97, Standard error of estimation (SEE) = 1.0 kg”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Findingown data
The resistance index (stature2/resistance) was the second predictor variable in all developed models.
“the resistance index (calculated as stature2/resistance) appeared as the second predictor variable in all the developed models”
Quote in the paperNot a substantial claim - Findingown data
Using the new predictive models, BIA-based estimation of FFM, LST, and ALST were highly correlated (R2 0.83-0.91) with DXA reference values.
“Using the new predictive models, BIA-based estimation of FFM, LST, and ALST were highly correlated (R2 ranged from 0.83 to 0.91) with the reference values obtained from DXA.”
Quote in the paperSubstantial claim - Findingown data
In the validation group, the newly developed soccer-specific BIA equations had R2 values ranging from 0.83 to 0.91.
“with R2 values ranging from 0.83 to 0.91”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Findingown data
No mean differences between predicted and reference percentage of fat mass (12.3% ± 0.8; P = 0.954) were observed when the new soccer-specific model was used for assessing FFM.
“No mean differences between the predicted and reference percentage of fat mass (12.3% ± 0.8; P = 0.954) were observed when the new soccer-specific model was used for assessing FFM.”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Findingown data
In the validation group, the newly developed soccer-specific BIA equations showed no mean bias compared with DXA (P > 0.05).
“the new predictive models showed no mean bias (p > 0.05)”
- Also stated in: § Results · plain
“No differences between methods were observed for the newly developed sport-specific equations (P > 0.01)”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Also stated in: § Results · plain
- Findingown data
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)”
- Also stated in: § Results · plain
“routinely used generic athletic equations underestimated (P < 0.01) FFM and ALST (Fig. 2).”
- Also stated in: § Discussion · plain
“The use of generalized predictive equations resulted in an underestimation of FFM and ALST in the soccer players.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [33] Matias CN, Campa F, Santos DA, Lukaski H, Sardinha LB, Silva AM. Fat-free mass bioelectrical impedance analysis predictive equation for athletes using a 4-compartment model. Int J Sports Med. 2021;42:27–32. https://doi.org/10.1055/a-1179-6236. source
- cites [32] Sardinha LB, Correia IR, Magalhães JP, Júdice PB, Silva AM, Hetherington-Rauth M. Development and validation of BIA prediction equations of upper and lower limb lean soft tissue in athletes. Eur J Clin Nutr. 2020;74:1646–52. https://doi.org/10.1038/s41430-020-0666-8. source
- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Also stated in: § Results · plain
- Findingown data
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.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [33] Matias CN, Campa F, Santos DA, Lukaski H, Sardinha LB, Silva AM. Fat-free mass bioelectrical impedance analysis predictive equation for athletes using a 4-compartment model. Int J Sports Med. 2021;42:27–32. https://doi.org/10.1055/a-1179-6236. source
- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Findingown data
A moderate to substantial strength of agreement (CCC > 0.90) was observed between the new equations and reference methods for estimating FFM components.
“A moderate to substantial strength of agreement between the methods was observed in estimating FFM components using the new equations (CCC > 0.90)”
Quote in the paperSubstantial claim - Findingown data
Generalized BIA-based equations for athletes result in an underestimation of FFM components when applied to elite soccer players.
“This investigation shows that generalized BIA-based equations for athletes result in an underestimation of FFM components when applied to elite soccer players.”
Quote in the paperSubstantial claim - Findingown data
A poor agreement was found between methods when former generalized equations were applied.
“while a poor agreement was found between the methods when former generalized equations were applied.”
Quote in the paperSubstantial claim - Findingown data
The use of generalized athletic equations on soccer players with higher FFM and ALST values led to systematic underestimation (higher 95% LoA = 0.1 kg for FFM and −6.1 kg for ALST).
“whereas when applied on soccer players with higher values of FFM and ALST led to a systematic underestimation of the aforementioned body mass components (higher 95% LoA = 0.1 kg and −6.1 kg, for FFM and ALST respectively).”
Quote in the paperSubstantial claim - Findingown data
The use of generalized athletic equations on soccer players with lower FFM values resulted in overestimations (lower 95% LoA = −5.6 kg for FFM and −11.3 kg for ALST).
“the use of generalized athletic equations on soccer players with lower FFM values resulted in overestimations (lower 95% LoA = −5.6 kg and −11.3 kg, for FFM and ALST respectively)”
Quote in the paperSubstantial claim - Findingcitation
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].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [32] Sardinha LB, Correia IR, Magalhães JP, Júdice PB, Silva AM, Hetherington-Rauth M. Development and validation of BIA prediction equations of upper and lower limb lean soft tissue in athletes. Eur J Clin Nutr. 2020;74:1646–52. https://doi.org/10.1038/s41430-020-0666-8. source
- cites [33] Matias CN, Campa F, Santos DA, Lukaski H, Sardinha LB, Silva AM. Fat-free mass bioelectrical impedance analysis predictive equation for athletes using a 4-compartment model. Int J Sports Med. 2021;42:27–32. https://doi.org/10.1055/a-1179-6236. source
- Findingown data
The standard error of estimation was lower for all new equations (1.38-1.73 kg) than for generalized models.
“In comparison with the generalized models, the standard error of estimation was lower for all the new equations, ranging from 1.38 to 1.73 kg.”
Quote in the paperSubstantial claim - Findingown data
Body composition features of elite soccer players have not gone through a changing trend in the last decade, as individual and mean bioimpedance vectors fell within the 50% reference tolerance ellipse.
“This comparison showed that body composition features have not gone through a changing trend in the last decade, since individual, as well as mean bioimpedance vectors of the soccer players fell within the 50% reference tolerance ellipse.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [29] Micheli ML, Pagani L, Marella M, Gulisano M, Piccoli A, Angelini F, et al. Bioimpedance and impedance vector patterns as predictors of league level in male soccer players. Int J Sports Physiol Perform. 2014;9:532–9. https://doi.org/10.1123/ijspp.2013-0119. source
- Findingreasoning
Morphologic characteristics appeared to be independent of the players' positions.
“However, morphologic characteristics appeared to be independent of the players positions”
Quote in the paperSubstantial claim - Interpretationown data
The newly developed soccer-specific formulas provide more accurate estimations of body composition than generalized formulas in elite soccer players.
“In contrast, the new soccer-specific formulas provided more accurate estimations of body composition.”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Rests on an assumption: The sample of Italian Serie A male soccer players is representative of male elite soccer players generally.
- Interpretationown data
The use of generalized predictive equations leads to an underestimation of FFM and ALST in elite soccer players.
“The utilization of generalized predictive equations leads to an underestimation of FFM and ALST in elite soccer players.”
- Also stated in: § Discussion · plain
“The use of generalized predictive equations resulted in an underestimation of FFM and ALST in the soccer players.”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Rests on an assumption: The sample of Italian Serie A male soccer players is representative of male elite soccer players generally.
- Also stated in: § Discussion · plain
- Study design and conductreasoning
The sample size of 102 participants was sufficient for assuring an adequate power analysis in models development.
“our sample size was sufficient for assuring an adequate power analysis in models development.”
Quote in the paperSubstantial claim - Interpretationown data
The new predictive equations presented in this study allow, for the first time, to obtain valid estimation of body composition using BIA-based procedures specifically developed for elite soccer players.
“The new predictive equations presented in this study allows, for the first time, to obtain valid estimation of body composition using BIA-based procedures specifically developed for elite soccer players.”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Rests on an assumption: The sample of Italian Serie A male soccer players is representative of male elite soccer players generally.
- Rests on an assumption: No other study has validated BIA prediction equations for estimating FFM components in soccer players.
- Premisecitation
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]”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [32] Sardinha LB, Correia IR, Magalhães JP, Júdice PB, Silva AM, Hetherington-Rauth M. Development and validation of BIA prediction equations of upper and lower limb lean soft tissue in athletes. Eur J Clin Nutr. 2020;74:1646–52. https://doi.org/10.1038/s41430-020-0666-8. source
- cites [33] Matias CN, Campa F, Santos DA, Lukaski H, Sardinha LB, Silva AM. Fat-free mass bioelectrical impedance analysis predictive equation for athletes using a 4-compartment model. Int J Sports Med. 2021;42:27–32. https://doi.org/10.1055/a-1179-6236. source
- Study design and conductown data
No differences were observed for the bioelectrical features between the development and validation groups (T = 3.2, F = 1.6, P = 0.207, Mahalanobis distance = 0.39).
“Table 1 and Fig. 1 present the general characteristics for the developmental and validation samples, with no differences observed for the bioelectrical features between the two groups (T = 3.2, F = 1.6, P = 0.207, Mahalanobis distance = 0.39).”
Quote in the paperSubstantial claim - Interpretationown data
Based on the AUC results, all the new formulas could be considered valid for the prediction purpose.
“Based on the AUC results all the formulas could be considered valid for the prediction purpose.”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Study design and conductown data
No interactions were found between independent variables.
“No interactions were found between independent variables; therefore, we used the whole sample in the model development.”
Quote in the paperSubstantial claim - Interpretationown data
High accuracy in FFM estimation may allow calculation of fat mass as the difference between body mass and FFM without possible overestimations.
“The findings of the present study showed that a high accuracy in the FFM estimation may allow to calculate the fat mass as the difference between body mass and FFM without possible overestimations.”
Quote in the paperSubstantial claim - Interpretationreasoning
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.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [32] Sardinha LB, Correia IR, Magalhães JP, Júdice PB, Silva AM, Hetherington-Rauth M. Development and validation of BIA prediction equations of upper and lower limb lean soft tissue in athletes. Eur J Clin Nutr. 2020;74:1646–52. https://doi.org/10.1038/s41430-020-0666-8. source
- cites [33] Matias CN, Campa F, Santos DA, Lukaski H, Sardinha LB, Silva AM. Fat-free mass bioelectrical impedance analysis predictive equation for athletes using a 4-compartment model. Int J Sports Med. 2021;42:27–32. https://doi.org/10.1055/a-1179-6236. source
- cites [20] Moon JR. Body composition in athletes and sports nutrition: an examination of the bioimpedance analysis technique. Eur J Clin Nutr. 2013;67:S54. https://doi.org/10.1038/ejcn.2012.165. source
- Interpretationreasoning
These limitations highlight the specificity of BIA equations concerning each technology and population.
“These limitations highlight the specificity of the BIA equations concerning each technology and population.”
Quote in the paperSubstantial claim - Study design and conductreasoning
This cross-sectional design prevents assessing predicted FFM components over time.
“Second, this cross-sectional design prevents from assessing predicted FFM components over time.”
Quote in the paperSubstantial claim - Interpretationreasoning
Bias in the estimation of FFM components may occur when assessing groups with different body composition features than those on which predictive BIA-based models are developed.
“On these bases, bias in the estimation of FFM components may occur when assessing group of subjects with different body composition features than the ones on which predictive BIA-based models are developed.”
Quote in the paperSubstantial claim - Interpretationnone given
The new developed and validated soccer-specific predictive equations are the first to provide practitioners the possibility to achieve high accuracy using BIA in the context of soccer.
“The new developed and validated soccer-specific predictive equations are the first to provide practitioners the possibility to achieve high accuracy using BIA in the context of soccer.”
Quote in the paperNot a substantial claim - Interpretationcitation
The underestimation could be due to different body composition features, such as lower FFM values that discriminate the general population from athletes.
“This could be due to different body composition features, such as lower FFM values that discriminate the general population with respect to the athletic one [42].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [42] Campa F, Matias CN, Teixeira FJ, Reis JF, Valamatos MJ, Coratella G, et al. Comparison of generalized and athletic bioimpedance-based predictive equations for estimating fat-free mass in resistance-trained exercisers. Nutrition. 2022;102: 111694. https://doi.org/10.1016/j.nut.2022.111694. source
- Premisecitation
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.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [32] Sardinha LB, Correia IR, Magalhães JP, Júdice PB, Silva AM, Hetherington-Rauth M. Development and validation of BIA prediction equations of upper and lower limb lean soft tissue in athletes. Eur J Clin Nutr. 2020;74:1646–52. https://doi.org/10.1038/s41430-020-0666-8. source
- cites [33] Matias CN, Campa F, Santos DA, Lukaski H, Sardinha LB, Silva AM. Fat-free mass bioelectrical impedance analysis predictive equation for athletes using a 4-compartment model. Int J Sports Med. 2021;42:27–32. https://doi.org/10.1055/a-1179-6236. source
- Classified as probably not a substantial claim. 21
- May combine two claims. 17
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationThe resistance index being the second predictor is due to the conductive properties of soft tissues.
“this is the result of the well-known conductive properties of soft tissues [5]”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [5] Lukaski H, Raymond-Pope CJ. New frontiers of body composition in sport. Int J Sports Med. 2021. https://doi.org/10.1055/a-1373-5881. source
Probably not a substantial claim: may not pass S2 (checkable).
PremisecitationIt is crucial to use BIA-based equations that have been developed and validated in a similar population as the one being assessed.
“Recent studies showed that it is crucial to use BIA-based equations that have been developed and validated in a similar population as the one being assessed [20, 21].”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [20] Moon JR. Body composition in athletes and sports nutrition: an examination of the bioimpedance analysis technique. Eur J Clin Nutr. 2013;67:S54. https://doi.org/10.1038/ejcn.2012.165. source
- cites [21] Campa F, Matias CN, Moro T, Cerullo G, Casolo A, Teixeira FJ, et al. Methods over materials: the need for sport-specific equations to accurately predict fat mass using bioimpedance analysis or anthropometry. Nutrients. 2023. https://doi.org/10.3390/nu15020278. source
May combine two claims.
PremisecitationSoccer players are on average shorter and lighter than volleyball or basketball players, resulting in lower FFM and different morphology.
“For example, soccer players are on average shorter and lighter than volleyball or basketball players, resulting in a lower FFM as well as a different morphology [24, 25].”
Quote in the paperSubstantial claim2 of 2 sources checked- cites [24] Mala L, Maly T, Zahalka F, Bunc V, Kaplan A, Jebavy R, et al. Body composition of elite female players in five different sports games. J Hum Kinet. 2015;45:207–15. https://doi.org/10.1515/hukin-2015-0021. source
The Discussion reports that elite female soccer players had significantly lower fat-free mass than volleyball and basketball players, and links that difference to their lower height and body mass. The Results also report height and body-mass differences between those groups. The claim generalizes beyond elite female players and presents the proposed explanation as a definite cause, whereas the paper says the differences could be caused by height. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [25] Campa F, Silva AM, Matias CN, Monteiro CP, Paoli A, Nunes JP, et al. Body water content and morphological characteristics modify bioimpedance vector patterns in volleyball, soccer, and rugby players. Int J Environ Res Public Health. 2020. https://doi.org/10.3390/ijerph17186604. source
The Results report different mean somatotype components for soccer and volleyball players, offering some support for a difference in morphology. Our copy does not report that soccer players were shorter or lighter, or had lower fat-free mass, than volleyball players; the study did not include basketball players. (LLM, conf=0.81)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
InterpretationcitationALST reduction could be expected during transition periods and therefore closely monitored in soccer players.
“Regarding the ALST, its reduction could be expected during transition periods and therefore closely monitored in soccer players [50].”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [50] Campa F, Bongiovanni T, Trecroci A, Rossi A, Greco G, Pasta G, et al. Effects of the COVID-19 lockdown on body composition and bioelectrical phase angle in serie A Soccer players: a comparison of two consecutive seasons. Biology. 2021. https://doi.org/10.3390/biology10111175. source
The Abstract and Conclusions report reduced muscle mass during the lockdown-related detraining period and recommend monitoring muscle mass in soccer players during detraining. The Introduction also discusses body-composition changes during off-season breaks. However, the study measured estimated muscle mass, not ALST specifically, and did not directly test ordinary transition periods. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationBIVA provides soft tissue classification (under, normal, over) and ranking (more or less than before intervention) by comparing an individual vector to a reference population.
“the characteristic and innovative aspect of BIVA is that it provides soft tissue classification (under, normal, and over) and ranking (more or less than before intervention), comparing the position of an individual vector to a reference population [5].”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [5] Lukaski H, Raymond-Pope CJ. New frontiers of body composition in sport. Int J Sports Med. 2021. https://doi.org/10.1055/a-1373-5881. source
May combine two claims.
FindingcitationThe participants of the present study had higher FFM and ALST values than previous groups, in line with previous studies on soccer players.
“In contrast, the participants of the present study had higher FFM and ALST values, which are in line with previous studies on soccer players [29, 43].”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [29] Micheli ML, Pagani L, Marella M, Gulisano M, Piccoli A, Angelini F, et al. Bioimpedance and impedance vector patterns as predictors of league level in male soccer players. Int J Sports Physiol Perform. 2014;9:532–9. https://doi.org/10.1123/ijspp.2013-0119. source
- cites [43] Bongiovanni T, Tinsley G, Martera G, Orlandi C, Genovesi F, Puleo G, et al. Regional lean soft tissue and intracellular water are associated with changes in lower-body neuromuscular performance: a pilot study in elite soccer players. Eur J Investig Heal Psychol Educ. 2022;12:882–92. https://doi.org/10.3390/ejihpe12080064. source
May combine two claims.
Study design and conductcitationThe present findings cannot be generalized to other sport disciplines, females, sub-elite or adolescent players, and cannot be extended to BIA measures from different technologies or sampling frequencies due to lack of agreement between bioelectrical impedance analysers.
“Lastly, the present findings cannot be generalized to other sport disciplines, females, sub-elite or adolescent players, and they cannot be extended to BIA-measures obtained from different technologies or sampling frequencies due to the lack of agreement between bioelectrical impedance analysers [53, 54].”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [53] Dellinger JR, Johnson BA, Benavides ML, Moore ML, Stratton MT, Harty PS, et al. Agreement of bioelectrical resistance, reactance, and phase angle values from supine and standing bioimpedance analyzers. Physiol Meas. 2021. https://doi.org/10.1088/1361-6579/abe6fa. source
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured - cites [54] Stratton MT, Smith RW, Harty PS, Rodriguez C, Johnson BA, Dellinger JR, et al. Longitudinal agreement of four bioimpedance analyzers for detecting changes in raw bioimpedance during purposeful weight gain with resistance training. Eur J Clin Nutr. 2021;75:1060–8. https://doi.org/10.1038/s41430-020-00811-3. source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationProfessional soccer players present fat mass values ranging from 11 to 14%, depending on the assessment method.
“Professional soccer players present fat mass values ranging from 11 to 14%, depending on the assessment method [19].”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [19] Sebastiá-Rico J, Soriano JM, González-Gálvez N, Martínez-Sanz JM. Body composition of male professional soccer players using different measurement methods: a systematic review and meta-analysis. Nutrients. 2023. https://doi.org/10.3390/nu15051160. source
The Results report an overall mean fat-mass percentage of 11.85%, and the Discussion reports a DXA mean of 13.46%; both fall within the claimed range. But the Results also report a 10.19% mean for anthropometric equations, so 11–14% does not capture the full method-dependent range reported for male professional players. Page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationRecent studies highlighted that body composition differs according to gender, competitive levels, and age groups rather than playing position in soccer players.
“Indeed, recent studies highlighted how body composition differs according to gender, competitive levels, and age groups rather than the playing position in soccer players [44, 45].”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [44] Slimani M, Nikolaidis PT. Anthropometric and physiological characteristics of male soccer players according to their competitive level, playing position and age group: a systematic review. J Sports Med Phys Fitness. 2019;59:141–63. https://doi.org/10.23736/S0022-4707.17.07950-6. source
- cites [45] Mascherini G, Castizo-Olier J, Irurtia A, Petri C, Galanti G. Differences between the sexes in athletes’ body composition and lower limb bioimpedance values. Muscles Ligaments Tendons J. 2017;7:573–81. https://doi.org/10.11138/mltj/2017.7.4.573. source
The Discussion reports differences in body composition between male and female elite soccer players. The Methods and Discussion indicate that players were matched by age and competed at the same elite level; the paper does not establish differences by age group or competitive level, or compare those factors with playing position. (LLM, conf=0.89)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
FindingcitationAnthropometric dimensions of elite soccer players revealed a profile characterized by high musculoskeletal and loglinear components, with a low percentage of body fat.
“In line with previous studies, anthropometric dimensions of elite soccer players revealed a profile characterized by high musculoskeletal as well as loglinear components, with a low percentage of body fat [25, 44].”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [25] Campa F, Silva AM, Matias CN, Monteiro CP, Paoli A, Nunes JP, et al. Body water content and morphological characteristics modify bioimpedance vector patterns in volleyball, soccer, and rugby players. Int J Environ Res Public Health. 2020. https://doi.org/10.3390/ijerph17186604. source
The Results report that the soccer players’ average somatotype was ectomorphic mesomorph, with relatively high mesomorphy and ectomorphy and low endomorphy. This supports the muscular and linear profile, but the paper does not report a body-fat percentage for the soccer players; low endomorphy alone does not establish that part of the claim. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [44] Slimani M, Nikolaidis PT. Anthropometric and physiological characteristics of male soccer players according to their competitive level, playing position and age group: a systematic review. J Sports Med Phys Fitness. 2019;59:141–63. https://doi.org/10.23736/S0022-4707.17.07950-6. source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
FindingcitationSomatotype was assessed among participants and three morphologies (balanced mesomorph, ectomorphic mesomorph, and mesomorph-ectomorph) were identified according to the Heath and Carter method.
“Somatotype was also assessed among the participants and three different morphologies (i.e., balanced mesomorph, ectomorphic mesomorph, and mesomorph-ectomorph) were identified according to the Heath and Carter method [34].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [34] Carter JEL. The heath-carter anthropometric somatotype. San Diego: San Diego State University; 2002.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataIndividual bioimpedance vectors were distributed within the BIVA soccer-specific references, with some vectors positioned out of the 95% tolerance ellipse.
“Considering the individual vectors, they were distributed within the BIVA soccer-specific references [29], with some vectors positioned out of the 95% tolerance ellipse.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [29] Micheli ML, Pagani L, Marella M, Gulisano M, Piccoli A, Angelini F, et al. Bioimpedance and impedance vector patterns as predictors of league level in male soccer players. Int J Sports Physiol Perform. 2014;9:532–9. https://doi.org/10.1123/ijspp.2013-0119. source
May combine two claims.
Findingown dataThe 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.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [33] Matias CN, Campa F, Santos DA, Lukaski H, Sardinha LB, Silva AM. Fat-free mass bioelectrical impedance analysis predictive equation for athletes using a 4-compartment model. Int J Sports Med. 2021;42:27–32. https://doi.org/10.1055/a-1179-6236. source
Classified as probably not a substantial claim.
Study design and conductcitationUrine-specific gravity ≤ 1.020 was used to assure that participants were in a hydrated state.
“Urine-specific gravity has been determined to assure that participants were in a hydrated state, defined as the value of urine-specific gravity ≤ 1.020 [36].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [36] Armstrong LE. Assessing hydration status: the elusive gold standard. J Am Coll Nutr. 2007;26:575S-584S. source
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe DXA scanner was calibrated daily prior to scanning using a calibration block according to manufacturer indications.
“The scanner was calibrated daily prior to scanning, according to manufacturer indications using a calibration block.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataTwo-thirds of participants were randomly assigned to the train set and one-third to the test group.
“2/3 of the participants were randomly assigned (using random.org) to the train set, while the remaining participants (1/3) to the test group.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenA sample size of 77 participants was calculated considering a type 1 error of 5% and a power of 80%.
“Given that a sample size of 77 participants was calculated considering a type 1 error of 5% and a power of 80%”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe newly developed soccer-specific formulas enable valid estimations of body composition while preserving the portability of a field-based method.
“The newly developed soccer-specific formulas enable valid estimations of body composition while preserving the portability of a field-based method.”
Quote in the paperSubstantial claim- Rests on an assumption: DXA provides accurate reference measurements of FFM, LST, and ALST.
- Rests on an assumption: The sample of Italian Serie A male soccer players is representative of male elite soccer players generally.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenNormality of residuals and homogeneity of variance were tested during model development.
“During model development, normality of residuals and homogeneity of variance were tested.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe test-retest coefficient of variation on duplicate measurements of R and Xc was 0.3% and 0.9%, respectively.
“The precision of the bioelectrical device was assessed before each test session; the test– retest coefficient of variation (CV% = standard deviation/mean × 100%) on duplicate measurements of R and Xc was 0.3% and 0.9%, respectively.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThe applicability of the new equations may extend to elite soccer players worldwide, given that participants originated from diverse territories reflecting the typical composition of high-level soccer teams.
“Conversely, the applicability of the new equations may extend to elite soccer players worldwide, given that the participants in this study originated from diverse territories, reflecting the typical composition of high-level soccer teams.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationcitationUnderestimation of FFM may result in a hypoproteic diet and impairment of maintenance of skeletal muscle structures, given that athletes are commonly advised to consume protein intakes of ∼2.3-3.1 g per kg of FFM during the preparatory phase.
“Considering that during the preparatory phase athletes are commonly advised to consume protein intakes in the range of ∼2.3–3.1 g for each kg of FFM, its underestimation may result in a hypoproteic diet and in an impairment of the maintenance of the skeletal muscle structures [12].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [12] Murphy CH, Hector AJ, Phillips SM. Considerations for protein intake in managing weight loss in athletes. Eur J Sport Sci. 2015;15:21–8. https://doi.org/10.1080/17461391.2014.936325. source
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningVectors out of the 95% tolerance ellipse may represent subjects with a lower fluid content with respect to the average soccer population.
“According to the BIVA basics, such as vectors may represent subjects with a lower fluid content with respect to the average soccer population.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationDXA is not considered the state-of-the-art method for evaluating fat and FFM; it should be employed with air plethysmography and dilution techniques according to a four-compartment body composition model.
“although DXA represents one of the most accurate technique for assessing lean soft tissues it is not considered the state-of-the-art method for evaluating fat and FFM, for which it should be employed in conjunction with air plethysmography and dilution techniques according to a four-compartment body composition model [14].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] Silva AM. Structural and functional body components in athletic health and performance phenotypes. Eur J Clin Nutr. 2019;73:215–24. https://doi.org/10.1038/s41430-018-0321-9. source
- Rests on an assumption: DXA is one of the most accurate techniques for assessing lean soft tissues.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningA low total body water may depend on a low body mass, so long vectors should not always be related to a hypohydration state.
“Since a low total body water may depend by a low body mass, long vectors should not be always related to a hypohydration state.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationown dataThe independence of morphologic characteristics from positions may reflect the profile of the new millennium soccer player, where body dimensions can be similar among roles.
“and this may reflect the profile of the new millennium soccer player, where body dimensions can be similar among roles.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningThe new equations allow managing training and nutritional strategies aiming to enhance body composition and soccer performance, while maintaining the portability of BIA.
“This allows to manage training and nutritional strategies aiming to enhance body composition and soccer performance, while maintaining the portability of the BIA.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationThese 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.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [33] Matias CN, Campa F, Santos DA, Lukaski H, Sardinha LB, Silva AM. Fat-free mass bioelectrical impedance analysis predictive equation for athletes using a 4-compartment model. Int J Sports Med. 2021;42:27–32. https://doi.org/10.1055/a-1179-6236. source
- cites [32] Sardinha LB, Correia IR, Magalhães JP, Júdice PB, Silva AM, Hetherington-Rauth M. Development and validation of BIA prediction equations of upper and lower limb lean soft tissue in athletes. Eur J Clin Nutr. 2020;74:1646–52. https://doi.org/10.1038/s41430-020-0666-8. source
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationcitationValid estimations of LST and ALST can be useful for monitoring the response of training regimes across the different phases of the season or during return-to-play after an injury.
“Therefore, valid estimations of LST and ALST can be useful for monitor the response of training regimes across the different phases of the season or during the return-to-play after an injury [52].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [52] Nescolarde L, Yanguas J, Lukaski H, Rodas G, Rosell-Ferrer J. Localized BIA identifies structural and pathophysiological changes in soft tissue after post-traumatic injuries in soccer players. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3743–6. https://doi.org/10.1109/EMBC.2014.6944437. source
The paper relies on this without support.
DXA provides accurate reference measurements of FFM, LST, and ALST.
“DXA was performed to derive whole-body measures of FFM, fat mass, LST, and ALST.”
The paper relies on this without support.
The sample of Italian Serie A male soccer players is representative of male elite soccer players generally.
“A total of 102 male soccer players (age 24.7 ± 5.7 yrs) from the first Italian division (Serie A) were included in this cross-sectional study.”
“The utilization of generalized predictive equations leads to an underestimation of FFM and ALST in elite soccer players.”
The paper relies on this without support.
No other study has validated BIA prediction equations for estimating FFM components in soccer players.
“no other study has validated BIA prediction equations for estimating FFM components in soccer players.”
The paper relies on this without support.
DXA is one of the most accurate techniques for assessing lean soft tissues.
“although DXA represents one of the most accurate technique for assessing lean soft tissues”
The paper relies on this without support.
There are currently no specific reference values for elite soccer players.
“To our knowledge, there are currently no specific reference values for elite soccer players”
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?
Two studies on athletes were conducted on elite male soccer players in Taiwan and Italy.
“One study was conducted on USA collegiate female athletes from different sports [62], one on Greek elite female dancers [51], and two studies on elite male soccer players in Taiwan [88] and Italy [31].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [31]
Out of the 19 available equations for athletes, 15 were developed with foot-to-hand technology.
“Out of the 19 available equations, 15 were developed with foot to hand technology [31, 39, 48, 51, 62, 69, 84, 85, 87, 104], while four equations were developed using segmental technology [88].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [31]
Of the five LSM equations for athletes, two are suitable for males only.
“Moreover, we retrieved one equation for estimating TBW [39], one for estimating ECW [39] and five for estimating LSM [31, 87], where three of them are suitable for both sexes [39, 87], and two in male only [31].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [31]
Considering foot-to-hand technology for athletes, 12 equations are suitable for males.
“Considering the foot to hand technology, 12 equations are suitable for male [31, 39, 48, 69, 84, 85, 87, 104] and eight for female people only [39, 51, 62, 69, 87, 104].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [31]
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 [31]
Five equations for athletes are for estimating LSM.
“Moreover, we retrieved one equation for estimating TBW [39], one for estimating ECW [39] and five for estimating LSM [31, 87], where three of them are suitable for both sexes [39, 87], and two in male only [31].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [31]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Body-mass VIF changes between models with identical predictors
MinorThe FFM and LST models use the same development group and the same four predictors. A predictor's variance inflation factor does not depend on the outcome, so body mass cannot have VIFs of both 1.48 and 3.42.
“| BM (kg) | 0.686 | 0.774 | | | 1.48 |”
“| BM (kg) | 0.635 | 0.751 | | | 3.42 |”
Negative ALST limit is labeled underestimation despite the stated sign convention
MinorThe paragraph associates a negative lower limit of agreement for ALST (−11.3 kg) with overestimation, but calls its negative upper limit (−6.1 kg) underestimation. Under the sign convention used for the lower limit, the upper limit also indicates overestimation, contradicting the conclusion about ALST at higher values.
“whereas when applied on soccer players with higher values of FFM and ALST led to a systematic underestimation of the aforementioned body mass components (higher 95% LoA = 0.1 kg and −6.1 kg, for FFM and ALST respectively).”
“the use of generalized athletic equations on soccer players with lower FFM values resulted in overestimations (lower 95% LoA = −5.6 kg and −11.3 kg, for FFM and ALST respectively)”
Overall mean age is below both subgroup means
MinorThe abstract reports a mean age of 24.7 years, but the development and validation groups are the entire sample and have mean ages of 25.2 and 25.9 years. The overall mean cannot be lower than both subgroup means.
“A total of 102 male elite soccer players (mean age 24.7 ± 5.7 years)”
“| Age (yrs) | 25.2 ± 5.2 | 25.9 ± 6.7 |”
Individual vectors cannot be both within 50% and outside 95% ellipses
MinorThe discussion says individual vectors fell within the 50% reference tolerance ellipse, then says some individual vectors were outside the 95% tolerance ellipse. Vectors inside the 50% ellipse cannot also be outside the wider 95% ellipse.
“since individual, as well as mean bioimpedance vectors of the soccer players fell within the 50% reference tolerance ellipse.”
“Considering the individual vectors, they were distributed within the BIVA soccer-specific references [29], with some vectors positioned out of the 95% tolerance ellipse.”
Lean-soft-tissue SEE differs between prose and table
MinorThe prose gives the LST model's SEE as 0.9 kg, while Table 2 gives 0.99 kg. Rounded to one decimal place, 0.99 kg is 1.0 kg, not 0.9 kg.
“LST = − 8.929 + (body mass × 0.635) + (stature2/R × 0.244) + (Xc × 0.093) + (age × 0.048), R2 = 0.96, SEE = 0.9 kg”
“| Lean soft tissue (kg) | | | 0.96 | 0.99 | |”
Cites
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785 · cited 1× · see citing works
“Using Lin’s approach [37] the concordance correlation coefficient (CCC) was calculated and interpreted as suggested by McBride [38] (almost perfect > 0.99; substantial > 0.95–0.99; moderate = 0.90–0.95; and poor < 0.90).”
MethodInfluentialReceiver Operating Characteristic Curve in Diagnostic Test Assessment
Mandrekar, J. N. (2010) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e3181ec173d · Cited by 4,506 · cited 1× · see citing works
“In general, an AUC of lower than 0.7 suggests no discrimination, from 0.7 to 0.8 is considered acceptable, from 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding [41].”
BackgroundInfluentialAssessing Hydration Status: The Elusive Gold Standard
Armstrong, L. E. (2007) · Journal of the American College of Nutrition · doi:10.1080/07315724.2007.10719661 · Cited by 643 · cited 1× · see citing works
“Urine-specific gravity has been determined to assure that participants were in a hydrated state, defined as the value of urine-specific gravity ≤ 1.020 [36].”
BackgroundInfluentialWard, L. C. (2019) · European Journal of Clinical Nutrition · doi:10.1038/s41430-018-0335-3 · Cited by 417 · cited 1× · see citing works
“…represents a BIA modality, uses a wider range of frequencies to derive the bioelectrical measures, involving them in nonlinear mathematical models to estimate intra-and extracellular resistance values, possibly countering some of the limitations relate to the use of BIA-based predictive models [7].”
BackgroundZiv, G., & Lidor, R. (2009) · Sports Medicine · doi:10.2165/00007256-200939070-00003 · Cited by 399 · 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
(2005) · doi:10.5040/9781492596950 · Cited by 322 · see citing works
Campa, F., Toselli, S., Mazzilli, M., Gobbo, L. A., & Coratella, G. (2021) · Nutrients · doi:10.3390/nu13051620 · Cited by 315 · cited 4× · see citing works
“…were instructed to remove all objects containing metal and to stay in a supine position during the measurements, isolated from the ground and electrical conductors, with legs abducted at 45°, shoulders abducted at 30° relative to the body midline, and hands pronated (Campa, Toselli, et al., [17]).”
MethodInfluentialSelf-citationReference Values for Body Composition and Anthropometric Measurements in Athletes
Santos, D. A., Dawson, J. A., Matias, C. N., Rocha, P. M., Minderico, C. S., Allison, D. B., Sardinha, L. B., & Silva, A. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097846 · Cited by 282 · cited 1× · see citing works
“As result of these peculiarities, recent studies [24, 27] have shown that soccer players present different bioelectrical characteristics compared to other athletes [28] and this could make it necessary to develop dedicated predictive equations.”
BackgroundBland, M., Altman, D. G., & Warner, D. S. (2011) · Anesthesiology · doi:10.1097/aln.0b013e31823d7784 · Cited by 249 · cited 1× · see citing works
“Agreement between BIA-based models and the criterion procedure was determined using the Bland–Altman method [39], including the analysis of the correlation between the mean and the difference of the methods and an estimate of the 95% limits of agreement (LoA).”
MethodMoon, J. R. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2012.165 · Cited by 237 · cited 2× · see citing works
“Recent studies showed that it is crucial to use BIA-based equations that have been developed and validated in a similar population as the one being assessed [20, 21].”
BackgroundMethodSlimani, M., & Nikolaidis, P. T. (2018) · The Journal of Sports Medicine and Physical Fitness · doi:10.23736/s0022-4707.17.07950-6 · Cited by 218 · cited 3× · see citing works
“Indeed, recent studies highlighted how body composition differs according to gender, competitive levels, and age groups rather than the playing position in soccer players [44, 45].”
BackgroundResultMarini, E., Campa, F., Buffa, R., Stagi, S., Matias, C. N., Toselli, S., Sardinha, L. B., & Silva, A. M. (2019) · Clinical Nutrition · doi:10.1016/j.clnu.2019.02.016 · Cited by 186 · cited 1× · see citing works
“Starting from the relationships between bioelectrical properties and FFM components [9, 10], it has been possible to implement a wide range of BIA-based predictive models over the years [6].”
MethodSelf-citationThe Transition Period in Soccer: A Window of Opportunity
Silva, J. R., Brito, J., Akenhead, R., & Nassis, G. P. (2015) · Sports Medicine · doi:10.1007/s40279-015-0419-3 · Cited by 177 · cited 1× · see citing works
“Furthermore, among the body composition parameters that can be quantified with BIA there is also the fat mass, a component that negatively affects the soccer performance [44] and that is scrupulously monitored during the transaction periods [51].”
BackgroundHernández‐Orallo, J. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.06.014 · Cited by 153 · cited 1× · see citing works
“The Area Under the Curves (AUC) of Regression Receiver Operating Characteristic (RROC) curve [40] to understand the validity of the new formulas.”
New Frontiers of Body Composition in Sport
Lukaski, H., & Raymond-Pope, C. J. (2021) · International Journal of Sports Medicine · doi:10.1055/a-1373-5881 · Cited by 149 · cited 5× · see citing works
“However, since its application is limited due to high costs and radiation exposure, alternative methods such as the bioelectrical impedance analysis (BIA) are often utilized for routine assessments [5].”
BackgroundMethodInfluentialNiebauer, J., Börjesson, M., Carré, F., Caselli, S., Palatini, P., Quattrini, F. M., Serratosa, L., Adami, P. E., Biffi, A., Preßler, A., et al. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy511 · Cited by 121 · cited 1× · see citing works
“In terms of body composition, each sport may have peculiar features as result of different game demands [14, 23].”
BackgroundCampa, F., Gobbo, L. A., Stagi, S., Cyrino, L. T., Toselli, S., Marini, E., & Coratella, G. (2022) · European Journal of Applied Physiology · doi:10.1007/s00421-021-04879-y · Cited by 115 · cited 2× · see citing works
“Based on four different technologies (i.e., hand to hand, leg to leg, foot to hand, and segmental), many BIA devices have been developed through the years, working at a wide range of sampling frequencies [6].”
BackgroundMethodSelf-citationStructural and functional body components in athletic health and performance phenotypes
Silva, A. M. (2019) · European Journal of Clinical Nutrition · doi:10.1038/s41430-018-0321-9 · Cited by 107 · cited 4× · see citing works
“…of the most accurate technique for assessing lean soft tissues it is not considered the state-of-the-art method for evaluating fat and FFM, for which it should be employed in conjunction with air plethysmography and dilution techniques according to a four-compartment body composition model [14].”
BackgroundMethodInfluentialBioimpedance and Impedance Vector Patterns as Predictors of League Level in Male Soccer Players
Micheli, M. L., Pagani, L., Marella, M., Gulisano, M., Piccoli, A., Angelini, F., Burtscher, M., & Gatterer, H. (2014) · International Journal of Sports Physiology and Performance · doi:10.1123/ijspp.2013-0119 · Cited by 106 · cited 5× · see citing works
“Bioelectrical characteristics of the soccer players involved in the present study where initially compared with the reference data provided on elite soccer players in 2014 [29].”
BackgroundMethodResultInfluentialQuarrie, K. L., Handcock, P. J., Waller, A. E., et al. (1995) · British Journal of Sports Medicine · doi:10.1136/bjsm.29.4.263 · Cited by 101 · cited 1× · see citing works
“This could be the case with rugby defenders where high body fat could be useful during tackles or with the role of libero in vol-leyball or playmaker in basketball where a shorter stature might be found [47–49].”
BackgroundBody Composition of Elite Female Players in Five Different Sports Games
Mala, L., Maly, T., Zahalka, F., Bunc, V., Kaplan, A., Jebavy, R., & Tuma, M. (2015) · Journal of Human Kinetics · doi:10.1515/hukin-2015-0021 · Cited by 96 · cited 2× · see citing works
“For example, soccer players are on average shorter and lighter than volleyball or basketball players, resulting in a lower FFM as well as a different morphology [24, 25].”
BackgroundCoratella, G., Beato, M., Milanese, C., Longo, S., Limonta, E., Rampichini, S., Cè, E., Bisconti, A. V., Schena, F. F., & Esposito, F. (2018) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000002463 · Cited by 94 · cited 1× · see citing works
“In addition, since LST and ALST refer to muscles and their associate structures [14], their assessment is particularly informative in soccer players, given its relation to strength and power expressions [15, 16].”
BackgroundFrancisco, R., Matias, C. N., Santos, D. A., Campa, F., Minderico, C. S., Rocha, P. M., Heymsfield, S. B., Lukaski, H. C., Sardinha, L. B., & Silva, A. M. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17030759 · Cited by 89 · cited 1× · see citing works
“Starting from the relationships between bioelectrical properties and FFM components [9, 10], it has been possible to implement a wide range of BIA-based predictive models over the years [6].”
MethodSelf-citationMilić, M., Grgantov, Z., Chamari, K., Ardigò, L. P., Bianco, A., & Padulo, J. (2017) · Biology of Sport · doi:10.5114/biolsport.2017.63382 · Cited by 86 · see citing works
Detrimental Effects of the Off-Season in Soccer Players: A Systematic Review and Meta-analysis
Clemente, F. M., Ramírez‐Campillo, R., & Sarmento, H. (2021) · Sports Medicine · doi:10.1007/s40279-020-01407-4 · Cited by 74 · see citing works
Segmental bioelectrical impedance analysis
LORENZO, A. D., & Andréoli, A. (2003) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/00075197-200309000-00008 · Cited by 70 · cited 1× · see citing works
“Although the simplest way to analyse body composition is to consider body mass as the sum of fat mass and fat-free mass (FFM), the quantification of smallest FFM components allows for a higher accuracy through a compartmental-based assessment [3].”
MethodCampa, F., Coratella, G., Cerullo, G., Stagi, S., Paoli, S., Marini, S., Grigoletto, A., Moroni, A., Petri, C., Andreoli, A., et al. (2023) · Clinical Nutrition · doi:10.1016/j.clnu.2023.07.025 · Cited by 69 · see citing works
Self-citationSebastiá-Rico, J., Soriano, J. M., González-Gálvez, N., & Martínez-Sanz, J. M. (2023) · Nutrients · doi:10.3390/nu15051160 · Cited by 66 · cited 2× · see citing works
“Professional soccer players present fat mass values ranging from 11 to 14%, depending on the assessment method [19].”
BackgroundPaoli, A., Mancin, L., Caprio, M., Monti, E., Narici, M. V., Cenci, L., Piccini, F., Pincella, M., Grigoletto, D., & Marcolin, G. (2021) · Journal of the International Society of Sports Nutrition · doi:10.1186/s12970-021-00459-9 · Cited by 63 · see citing works
Self-citationBody Composition Variables by Sport and Sport-Position in Elite Collegiate Athletes
Fields, J. B., Merrigan, J. J., White, J. B., & Jones, M. T. (2018) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000002865 · Cited by 59 · cited 3× · see citing works
“As aforementioned, since each sport implies different bioelectrical and body composition characteristics [14, 18], our hypothesis was that the use of sport-specific instead of generalized equations would result in more accurate estimations of FFM, LST, and ALST in elite soccer players.”
BackgroundMatias, C. N., Campa, F., Santos, D. A., Lukaski, H., Sardinha, L. B., & Silva, A. M. (2021) · International Journal of Sports Medicine · doi:10.1055/a-1179-6236 · Cited by 54 · see citing works
Self-citationConsiderations for protein intake in managing weight loss in athletes
Murphy, C. H., Hector, A. J., & Phillips, S. M. (2015) · European Journal of Sport Science · doi:10.1080/17461391.2014.936325 · Cited by 52 · cited 2× · see citing works
“Considering that during the preparatory phase athletes are commonly advised to consume protein intakes in the range of ∼ 2.3–3.1 g for each kg of FFM, its underestimation may result in a hypoproteic diet and in an impairment of the maintenance of the skeletal muscle structures [12].”
BackgroundMethodInfluentialDellinger, J. R., Johnson, B. A., Benavides, M. L., Moore, M. L., Stratton, M. T., Harty, P. S., Siedler, M. R., & Tinsley, G. M. (2021) · Physiological Measurement · doi:10.1088/1361-6579/abe6fa · Cited by 52 · cited 1× · see citing works
“…the present findings cannot be generalized to other sport disciplines, females, sub-elite or adolescent players, and they cannot be extended to BIA-measures obtained from different technologies or sampling frequencies due to the lack of agreement between bioelectrical impedance analysers [53, 54].”
BackgroundUse of Bioelectrical Impedance: General Principles and Overview
Stahn, A. C., Terblanche, E., & Gunga, H. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1788-1_3 · Cited by 49 · cited 3× · see citing works
“Bioelec-trical R represents the opposition offered by the body to the flow of an alternating electrical current through the conductive volume and is inversely related to the water and electrolyte content of tissues [8].”
BackgroundInfluentialStratton, M. T., Smith, R. W., Harty, P. S., Rodriguez, C., Johnson, B. A., Dellinger, J. R., Williams, A. D., White, S. J., Benavides, M. L., & Tinsley, G. M. (2021) · European Journal of Clinical Nutrition · doi:10.1038/s41430-020-00811-3 · Cited by 46 · cited 1× · see citing works
“…the present findings cannot be generalized to other sport disciplines, females, sub-elite or adolescent players, and they cannot be extended to BIA-measures obtained from different technologies or sampling frequencies due to the lack of agreement between bioelectrical impedance analysers [53, 54].”
BackgroundGeneralized bioelectric impedance‐based equations underestimate body fluids in athletes
Coratella, G., Campa, F., Matias, C. N., Toselli, S., Koury, J. C., Andreoli, A., Sardinha, L. B., & Silva, A. M. (2021) · Scandinavian Journal of Medicine & Science in Sports · doi:10.1111/sms.14033 · Cited by 45 · cited 2× · see citing works
“The underestimation of FFM components from generalized equations was already showed when equations for the normal population were applied on athletes [22].”
BackgroundSelf-citationDifferences between the sexes in athletes� body composition and lower limb bioimpedance values
Mascherini, G. (2017) · Muscles, Ligaments and Tendons Journal · doi:10.11138/mltj/2017.7.4.573 · Cited by 43 · see citing works
The Heath-Carter Anthropometric Somatotype
(2007) · Exercise physiology/CRC series in exercise physiology · doi:10.1201/9781420008784.pt5 · Cited by 41 · see citing works
Somatotype and Bioimpedance Vector Analysis: A New Target Zone for Male Athletes
Campa, F., Silva, A. M., Talluri, J., Matias, C. N., Badicu, G., & Toselli, S. (2020) · Sustainability · doi:10.3390/su12114365 · Cited by 39 · see citing works
Self-citationBongiovanni, T., Mascherini, G., Genovesi, F., Pasta, G., Iaia, F. M., Trecroci, A., Ventimiglia, M., Alberti, G., & Campa, F. (2020) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk5040073 · Cited by 39 · cited 1× · see citing works
“Purposely, bioelectrical references for soccer players were provided through the years [29, 30].”
BackgroundSelf-citationSardinha, L. B., Correia, I. R., Magalhães, J. P., Júdice, P. B., Silva, A. M., & Hetherington-Rauth, M. (2020) · European Journal of Clinical Nutrition · doi:10.1038/s41430-020-0666-8 · Cited by 38 · cited 6× · see citing works
“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…”
BackgroundMethodInfluentialCategorizing 10 Sports According to Bone and Soft Tissue Profiles in Adolescents
Agostinete, R. R., Fernandes, R. A., Narciso, P. H., et al. (2020) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002420 · Cited by 36 · cited 1× · see citing works
“Considering the influence of FFM components on physical performance, soccer players may be impacted by a high legs LST since they must repeatedly lift their body mass against gravity to perform high-speed sprints with changes of direction [15, 26].”
BackgroundAthlete or Non-athlete? This Is the Question in Body Composition
Campa, F., & Coratella, G. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.814572 · Cited by 35 · cited 2× · see citing works
“Once developed, these formulas can be applied to individuals within the same population to estimate body composition parameters without the need for more invasive or time-consuming methods [11].”
BackgroundSelf-citationCampa, F., Gatterer, H., Lukaski, H., & Toselli, S. (2019) · International Journal of Sports Physiology and Performance · doi:10.1123/ijspp.2018-0676 · Cited by 33 · cited 1× · see citing works
“After cleaning the skin with isopropyl alcohol, two adhesive electrodes (Biatrodes Akern Srl, Fire-nze, Italy) were applied on the surface of the right hand and two on the right foot, according to the guidelines for athletes [17, 35].”
MethodSelf-citationCampa, F., Silva, A. M., Matias, C. N., Monteiro, C. P., Paoli, A., Nunes, J. P., Talluri, J., Lukaski, H., & Toselli, S. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17186604 · Cited by 33 · cited 3× · see citing works
“In line with previous studies, anthropometric dimensions of elite soccer players revealed a profile characterized by high musculoskeletal as well as loglinear components, with a low percentage of body fat [25, 44].”
BackgroundMethodResultSelf-citationCampa, F., Matias, C. N., Moro, T., Cerullo, G., Casolo, A., Teixeira, F. J., & Paoli, A. (2023) · Nutrients · doi:10.3390/nu15020278 · Cited by 33 · cited 1× · see citing works
“Recent studies showed that it is crucial to use BIA-based equations that have been developed and validated in a similar population as the one being assessed [20, 21].”
MethodSelf-citationCampa, F., Bongiovanni, T., Trecroci, A., Rossi, A., Greco, G., Pasta, G., & Coratella, G. (2021) · Biology · doi:10.3390/biology10111175 · Cited by 20 · cited 2× · see citing works
“Regarding the ALST, its reduction could be expected during transition periods and therefore closely monitored in soccer players [50].”
BackgroundSelf-citationNescolarde, L., Yanguas, J., Lukaski, H., Rodas, G., & Rosell-Ferrer, J. (2014) · 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc.2014.6944437 · Cited by 15 · cited 1× · see citing works
“Therefore, valid estimations of LST and ALST can be useful for monitor the response of training regimes across the different phases of the season or during the return-to-play after an injury [52].”
BackgroundBongiovanni, T., Rossi, A., Iaia, F. M., Alberti, G., Pasta, G., & Trecroci, A. (2021) · The Journal of Sports Medicine and Physical Fitness · doi:10.23736/s0022-4707.21.12911-1 · Cited by 14 · cited 2× · see citing works
“In addition, since LST and ALST refer to muscles and their associate structures [14], their assessment is particularly informative in soccer players, given its relation to strength and power expressions [15, 16].”
BackgroundSelf-citationBongiovanni, T., Tinsley, G., Martera, G., Orlandi, C., Genovesi, F., Puleo, G., Rossi, A., & Trecroci, A. (2022) · European Journal of Investigation in Health, Psychology and Education · doi:10.3390/ejihpe12080064 · Cited by 11 · cited 2× · see citing works
“In contrast, the participants of the present study had higher FFM and ALST values, which are in line with previous studies on soccer players [29, 43].”
BackgroundResultSelf-citationCampa, F., Matias, C. N., Teixeira, F. J., Reis, J. F., Valamatos, M. J., Coratella, G., & Monteiro, C. P. (2022) · Nutrition · doi:10.1016/j.nut.2022.111694 · Cited by 9 · cited 1× · see citing works
“This could be due to different body composition features, such as lower FFM values that discriminate the general population with respect to the athletic one [42].”
BackgroundSelf-citationRamos, I. E., Coelho, G. M. D. O., Lanzillotti, H. S., Marini, E., & Koury, J. C. (2022) · International Journal of Sport Nutrition and Exercise Metabolism · doi:10.1123/ijsnem.2021-0301 · Cited by 5 · cited 1× · see citing works
“Nowadays, BIA is widely used in soccer to person-alize dietary intervention by calculating the necessary protein intake [12] starting from the FFM or using it for evaluating the energy expenditure [13].”
Method
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
“Out of the 19 available equations, 15 were developed with foot to hand technology [31, 39, 48, 51, 62, 69, 84, 85, 87, 104], while four equations were developed using segmental technology [88].”
BackgroundMethodInfluentialSelf-citationISAK-Based Anthropometric Standards for Elite Male and Female Soccer Players
Petri, C., Campa, F., Holway, F. E., Pengue, L., & Suarez-Arrones, L. J. (2024) · Sports · doi:10.3390/sports12030069 · Cited by 55 · see citing works
Self-citationPaoli, A., & Campa, F. (2024) · Current Obesity Reports · doi:10.1007/s13679-024-00573-0 · Cited by 24 · see citing works
“To date, a wide range of predictive equations is available in the literature, especially for the parameters that were most considered in the 72 studies, such as FM, FFM, fluids, SMM, BCM, and ALSM [140–149].”
BackgroundSelf-citationPetri, C., Pengue, L., Bartolini, A., Pistolesi, D., & Suarez-Arrones, L. J. (2024) · Nutrients · doi:10.3390/nu16030334 · Cited by 14 · see citing works
Kampo, D., Závodná, E., & Vondra, V. (. (2025) · Physiological Research · doi:10.33549/physiolres.935758 · Cited by 5 · see citing works
“Nevertheless, DXA remains one of the most widely accepted reference methods and continues to play a crucial role in the validation of BIA prediction equations across diverse demographic groups [25,28,30].”
Haynes, H. K., Tinsley, G. M., Swafford, S. H., Compton, A. T., Moore, J., Donahue, P. T., & Graybeal, A. J. (2024) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000005039 · Cited by 5 · see citing works
Bioelectrical Impedance Analysis in Professional and Semi-Professional Football: A Scoping Review
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
“This evidence supports the validation of specific tolerance ellipses for elite male football players [14,22].”
InfluentialBaroncini, G., Campa, F., Tarabini, P. C., Sala, A., Boldrini, L., Mazzoni, S., & Paoli, A. (2025) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-025-01115-4 · Cited by 3 · see citing works
Self-citationHolway, F. E., Campa, F., Petri, C., et al. (2024) · International Journal of Kinanthropometry · doi:10.34256/ijk2418 · Cited by 3 · see citing works
“…marked differences in positional roles regarding game-specific tasks and physical demands, which consequently lead to variations in body morphology (Campa et al., 2023, 2020). or instance, physiological capacity is closely related to player position: midfielders and fullbacks exhibit the largest…”
BackgroundSelf-citationPapadakis, Z. (2025) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk10030266 · Cited by 3 · see citing works
“BIA is susceptible to hydration status and may provide different absolute values compared to gold-standard methods like DEXA, potentially underestimating BF% and overestimating LBM in some populations [18,31,80].”
Farfán-Díaz, Á. (2026) · Revista Multidisciplinar Epistemología de las Ciencias · doi:10.71112/y3e4dd15
Differences in Body Composition in Football Players by Playing Position
Tasevski, Z., & Gontarev, S. (2026) · RESEARCH IN PHYSICAL EDUCATION SPORT AND HEALTH · doi:10.2478/rpesh-2026-0005
“…and widely used field-based method that, despite its advantages, is less precise than reference techniques such as dual-energy X-ray absorptiometry (DXA) and may be influenced by prediction equations and measurement conditions (Campa et al., 2023; Sebastiá-Rico et al., 2023; Sutton et al., 2009).”
Cattem, M. V. D. O., Coelho, G. M. O., dos Santos, G. G., & Koury, J. C. (2026) · Nutrition · doi:10.1016/j.nut.2026.113435
Ponce-Bordón, J. C., Polo-Tejada, J., Lobo-Triviño, D., et al. (2026) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk11040410
- Authors
- Campa, F., Bongiovanni, T., Rossi, A., Cerullo, G., Casolo, A., Martera, G., Trecroci, A., Moro, T., & Paoli, A.
- Affiliations
- University of Padua (IT)
- University of Bologna (IT)
- University of Pisa (IT)
- University of Milan (IT)
- Corresponding author
- Casolo, Andrea
- ORCID
- Venue
- Journal of Translational Medicine
- Published
- 2023-12-15
- Volume
- 21
- Issue
- 1
- Pages
- 912-912
- ISSN
- 1479-5876 (ISSN-L)
- PMC
- PMC10722788
- OpenAlex
- W4389788180
- Cited by
- 16
- References
- 53
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 7
- FWCI
- 2.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Translational Medicine (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Università degli Studi di Padova
- 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
- bioelectrical impedance analysis, fat-free mass, soccer players, prediction equations, body composition, bioimpedance, reactance, resistance
- MeSH terms
- Adult, Body Composition, Humans, Male, Regression Analysis, Soccer, Reproducibility of Results
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.
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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 ›
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