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Cited by 0Body Composition Measurement TechniquesSports Performance and TrainingPhysical Education and Training Studies

External validation of bioelectrical impedance analysis equations for body composition against dual-energy X-ray absorptiometry in Mexican recreational runners

Open access
Vázquez-Bautista, Manuel A, Robles-Sardin, Alma E, Reséndiz-Sandoval, Mónica, Navarro-Ibarra, María Jossé, Hernández López, Jesús, Caire-Juvera, Graciela·2025·BIOtecnia
DOI 10.18633/biotecnia.v27.2512
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Abstract

Sports professionals prioritize athlete body composition (BC) due to its relationship to performance. Bioelectrical impedance analysis (BIA) estimates BC using predictive equations, and validating these equations is essential to determine their utility. The aim of this study was to externally validate four bioelectrical impedance equations for predicting body composition in Mexican recreational runners using dual-energy X-ray absorptiometry (DXA) as a reference method. This external validation pilot study followed a comparative, cross-sectional design and included 30 Mexican male recreational runners (aged 38.0 ± 10.7 years). BC was measured using DXA and four BIA equations. A paired t-test was performed to evaluate differences between methods. Equivalent testing, Simple linear regression, and Bland-Altman analysis were carried out to evaluate agreement between methods. Statistical significance was set at p < 0.05. Non-significant differences were found between DXA and predicted values with Macias et al. (2007) and Lukaski and Bolonchuk (1987) equations (p < 0.05). These equations provided equivalence at 5% regions and non-significant bias. In conclusion, Lukaski and Bolonchuk (1987) demonstrate the most accurate equations for the current sample. These equations showed promising results for measuring BC in a cohort of runners; however, caution is advised when applying them to individual tracking.

AI snapshot

Written from the paper’s full text.

TL;DR

This external validation pilot study compared four BIA-based fat-free mass (FFM) equations against DXA in 30 Mexican recreational runners. EQ1 and EQ3 showed better group-level agreement, while EQ2 and EQ4 systematically overestimated FFM.

Key findings

  • EQ3 (Lukaski and Bolonchuk, 1986) was the most accurate and consistent equation: it demonstrated equivalence with DXA in males and the whole sample, with no significant systematic or proportional bias.
  • EQ1 (Macias et al., 2007) demonstrated equivalence with DXA only in the whole sample, with no significant systematic or proportional bias; it rejected equivalence in sex-specific groups.
  • EQ2 and EQ4 significantly overestimated FFM in the whole sample, with systematic biases of -3.52 kg (EQ2) and -3.84 kg (EQ4), and both showed significant proportional bias, meaning overestimation increased at higher FFM values.
  • Correlations between DXA FFM and all equations were moderate-to-strong (r = 0.70 to 0.95, p < 0.01); in the whole sample, R² values were 0.77 for EQ1, 0.89 for EQ2, 0.83 for EQ3, and 0.91 for EQ4.

Methods

  • External validation pilot study with a comparative, cross-sectional design, including a non-representative convenience sample of 30 healthy Mexican recreational runners (12 men, 18 women), aged 18–50 years, recruited in 2018 in northwest Mexico.
  • FFM was measured by DXA (Hologic ASY-05119) as the reference method and predicted using four BIA equations based on BIA and anthropometric variables; BIA was performed with a Quantum II device at 50 kHz.
  • Analysis included paired sample t-tests, equivalence testing with a 5% equivalence region, Pearson correlation, simple linear regression (R² and SEE), and Bland-Altman analysis with tests for systematic and proportional bias; significance was set at p < 0.05.

Limitations

  • The paper states the main limitation is the relatively small sample size, which may limit generalizability and precision, particularly in subgroup analyses.
  • The paper states the sample differed from recreational athlete characteristics reported in other studies, and categorizing athletes is difficult due to lack of standardizing criteria.
  • Evident from the text: the study was a pilot with a non-representative convenience sample, and individual agreement was not accomplished because Bland-Altman limits of agreement were wide (ranging from -7.92 kg to 5.96 kg depending on subgroup), so caution is recommended for individual-level use.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.18633/biotecnia.v27.2512

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

6 of 18 cited sources checked · 2 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Compared with other reference methods, DXA has been reported to overestimate FM.

    “Furthermore, compared with other reference methods, DXA has been reported to overestimate FM (Santos et al., 2010).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Santos et al., 2010 source
  • Findingown data

    Even though sex was not included as a predictive variable in EQ1, R2 differed considerably between sexes.

    “Moreover, even though sex was not included as a predictive variable in EQ1, R 2 differed considerably between sexes.”
    Quote in the paperSubstantial claim
  • Findingown data

    In the whole sample, EQ2 showed a significant proportional bias (R2 = 0.28, p < 0.05), indicating that disagreement increased at higher FFM values.

    “In addition, a significant proportional bias was observed for EQ2 (R 2 = 0.28, p < 0.05), indicating that disagreement increased at higher FFM values.”
    Quote in the paperSubstantial claim
  • Findingown data

    EQ1 demonstrated equivalence with DXA only in the whole sample.

    “Meanwhile, EQ1 demonstrated equivalence with DXA only in the whole sample.”
    Quote in the paperNot a substantial claim
  • Findingown data

    A statistically significant moderate-to-strong relationship was observed between DXA FFM and all equations across all groups (r 0.70 to 0.95; p < 0.01).

    “A statistically significant and moderate-tostrong relationship was observed between DXA FFM and all the equations across all groups (r ranging from 0.70 to 0.95; p < 0.01), indicating a good to excellent linear association.”
    Quote in the paperSubstantial claim
  • May combine two claims. 39
  • Classified as probably not a substantial claim. 13
  • The quote only partly states this claim. 1

  • May combine two claims.

    Interpretationown data

    Using EQ1 in a sex-specific participant cohort may reduce the accuracy of FFM estimations, which could be clinically relevant given that a 5% region of equivalence is a rigorous criterion for evaluating agreement between methods.

    “This suggests that using EQ1 in a sex-specific participant cohort may reduce the accuracy of FFM estimations, which could be clinically relevant, given that a 5% region of equivalence is set as a rigorous criterion for evaluating agreement between methods (Dixon et al., 2018).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Dixon et al., 2018 source

      In Example 1 (page unavailable), Dixon et al. show that a narrower equivalence region imposes a stricter agreement test, which supports describing 5% as rigorous. But the paper does not set a general 5% equivalence criterion: the General principles and Discussion sections say the region should be chosen for the application and its clinical or practical importance. The EQ1 and FFM inference is the citing authors’ own framing, not a finding attributed to Dixon et al. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    In other reports, recreational runners were relatively leaner even when considering the same age range; Nikolaidis et al. (2020) reported a mean %FM of 19.6 ± 4.7% for women and 17.7 ± 4.0% for male participants.

    “However, in other reports, recreational runners were relatively leaner, even when considering the same age range.This was reported by Nikolaidis et al. (2020), with a mean %FM of 19.6 ± 4.7% for women and 17.7 ± 4.0% for male participants.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Nikolaidis et al. (2020) 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

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    In runners, %FM has been observed to fall within 14.9% to 21.0% in females and 7.29% to 11.4% in males.

    “Specifically in runners, the %FM has been observed to fall within the range of 14.9% to 21.0% in females (Piasecki et al., 2018;Carbuhn et al., 2022) and 7.29% to 11.4% in males (Mooses et al., 2013;Carbuhn et al., 2022).”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites Piasecki et al., 2018 source

      Table 1 (Characteristics of controls and athletes, lines 508–509) reports 14.9% mean body fat for eumenorrheic female runners, supporting the lower figure. It reports 10.7% for amenorrheic female runners and does not establish 21.0% as the upper end of the stated range. Piasecki et al. is cited for the female figures, not the male figures. (LLM, conf=0.89)

      Experimental — accuracy not yet measured
    • cites Carbuhn et al., 2022 source
    • cites Mooses et al., 2013 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Another study documented a FM of 16.3 ± 5.6% in male recreational runners with BMI values similar to our sample.

    “Another study documented a FM of 16.3 ± 5.6% in male recreational runners with BMI values similar to our sample (Knechtle and Tanda, 2013).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Knechtle and Tanda, 2013 source

      The Abstract (Methods) identifies the participants as recreational male runners, and Subjects and methods (Anthropometric measurements) says their body-fat percentage was estimated and BMI calculated. The available passages do not give a body-fat mean of 16.3 ± 5.6% or BMI values, so they cannot establish the reported figure or the comparison with the citing authors’ sample (searched: 16.3, 5.6, BMI, Table 2). (LLM, conf=0.78)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingown data

    In the male group, EQ2 and EQ4 showed the highest R2 with DXA FFM (0.91 and 0.90, respectively), and both regression models were statistically significant (p < 0.01).

    “In the male group, EQ2 and EQ4 showed the highest R 2 with DXA FFM (R 2 = 0.91 and 0.90, respectively); both regression models were statistically significant (p < 0.01).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In the whole sample, EQ2 had a significant linear regression with DXA (R2 = 0.89, p < 0.001) and a significant intercept (p < 0.01), suggesting systematic bias between methods.

    “For EQ2, the linear regression was also significant (R 2 = 0.89, p < 0.001), but a significant intercept (p < 0.01) suggests a systematic bias between methods (Fig. 1c).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    A random male participant aged 25 years with BW 63.4 kg and DXA FFM 55.0 kg (13.20% FM) had an EQ3 FFM prediction of 55.40 kg (Δ -0.37 kg).

    “A random male participant aged 25 years old with a BW of 63.4 kg and a DXA FFM of 55.0 kg corresponding to 13.20% FM, resulted in the EQ3 FFM predictions of 55.40 kg (Δ -0.37 kg).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    EQ2 and EQ4 values exhibited significant differences when compared with DXA FFM, rejecting equivalence testing, meaning group differences may be larger than ±2.25 kg of FFM.

    “In contrast, EQ2 and EQ4 values exhibited significant differences when compared with DXA FFM, thereby rejecting equivalence testing, meaning that group differences may be larger than ±2.25 kg of FFM.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In the whole sample, EQ3 presented a significant linear association with DXA (R2 = 0.83, p < 0.001) and a non-significant intercept (p = 0.887), meaning a constant difference between methods across the range.

    “EQ3 presented a significant linear association with DXA (R 2 = 0.83, p < 0.001) (Figure 1e) and a non-significant intercept (p = 0.887), meaning a constant difference between methods across the range.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    EQ3 demonstrated equivalence with DXA in the male group and the whole sample, with differences within ±2.66 kg for males and ±2.25 kg for the whole sample.

    “EQ3 demonstrated equivalence with DXA in the male group and the whole sample meaning that differences between these methods may be within ±2.66 kg for males and ±2.25 kg for the whole sample.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    DXA FFM consistently differed significantly from EQ2 and EQ4 values in both sexes and the whole sample, with EQ2 and EQ4 predicting higher FFM values.

    “DXA FFM consistently differed significantly from the EQ2 and EQ4 values in both sexes and the whole sample, which predictions resulted in higher FFM values.”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Findingown data

    In the female group, the paired sample t-test showed no differences in mean DXA FFM and EQ3.

    “Even though the paired sample t-test showed no differences in mean DXA FFM in the female group, equivalence testing was rejected.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Both EQ2 and EQ4 exhibited significant systematic bias, with an average overestimation of 3.52 kg (EQ2) and 3.84 kg (EQ4) across the whole sample.

    “Both equations also exhibited significant systematic bias, with an average overestimation of 3.52 kg (EQ2) and 3.84 kg (EQ4) across the whole sample.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In the male group, BW, Ht, Ht2/R, and DXA FFM were significantly greater, while R and DXA percentage FM were greater in the female group (p < 0.05).

    “Independent sample t-tests revealed significantly greater values in BW, Ht, Ht 2 /R, and DXA FFM in the male group, while R and DXA percentage FM (%FM) were greater in the female group (p < 0.05).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In the whole sample, EQ1 showed a significant linear association with DXA (R2 = 0.77, p < 0.001) and a non-significant intercept (p = 0.718), indicating consistent differences across the FFM range.

    “Regarding the whole sample analysis (Fig. 1), EQ1 showed a significant linear association with DXA (R 2 = 0.77, p < 0.001) (Fig. 1a), with a non-significant intercept (p = 0.718), indicating consistent differences across the FFM range.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The whole sample improved EQ3 accuracy metrics, with bias slightly reduced.

    “Interestingly, the whole sample improved EQ3 accuracy metrics, given that bias was slightly reduced.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In the whole sample, Bland-Altman analysis for EQ4 confirmed a systematic bias of -3.84 kg (p < 0.05) and a significant proportional bias (R2 = 0.32, p < 0.001), indicating overestimation increased with higher FFM values.

    “Bland-Altman analysis confirmed a systematic bias of -3.84 kg (p < 0.05), and a significant proportional bias (R 2 = 0.32, p < 0.001), indicating overestimation increased with higher FFM values (Fig 1h).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In the female group, EQ2 and EQ4 showed the highest R2 with DXA FFM, and systematic bias for these equations was statistically significant (p < 0.05).

    “A similar behavior was observed in the female group, where EQ2 and EQ4 showed the highest R 2 with DXA FFM, resulting in statistically significant (p < 0.01).Systematic bias for these equations also resulted statistically significant (p < 0.05).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingcitation

    Differences between EQ3 and DXA may not be small in all participants, with differences up to 5.55 kg observed in other participants.

    “However, these differences may not be that small in all participants as differences of up to 5.55 kg were observed, in other participants considered clinically relevant as the %FM increased from 25.10% to 33.48%, thereby resulting in his category shifting from overfat to obese category (Gallagher et al., 2000).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Gallagher et al., 2000 source

  • May combine two claims.

    Findingown data

    In the whole sample, EQ4 showed a significant association with DXA (R2 = 0.91, p < 0.001) and a significant intercept (p < 0.05), suggesting non-constant discrepancies.

    “Lastly, EQ4 showed a significant association (R 2 = 0.91, p < 0.001) (Fig. 1g), but with a significant intercept (p < 0.05), suggesting non-constant discrepancies.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In the whole sample, Bland-Altman analysis for EQ2 confirmed a significant systematic bias of -3.52 kg (p < 0.05), resulting in overestimation of FFM.

    “Bland-Altman, confirmed this with a significant systematic bias of -3.52 kg (p < 0.05) (Fig. 1d), which resulted in an overestimation of FFM.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In the whole sample, Bland-Altman results for EQ3 showed no significant systematic or proportional bias.

    “This was consistent with Bland-Altman results, which showed no significant systematic or proportional bias.(Fig. 1f ).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    EQ1 rejected equivalence testing for each sex group, although the whole sample improved this result.

    “However, considering a more specific analysis, EQ1 rejected the equivalence testing for each sex group, although the whole sample improved this result.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    EQ1 showed improved R2 and lower bias values in the whole sample compared to the subgroups.

    “On the other hand, EQ1 showed improved R 2 and lower bias values in the whole sample compared to the subgroups.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    EQ1 and EQ3 exhibited lower R2 than EQ2 and EQ4, but no significant systematic bias with DXA was observed in either males or females (p > 0.05).

    “On the other hand, although EQ1 and EQ3 exhibited lower R 2 than EQ2 and EQ4, no significant systematic bias with DXA was observed in either males or females (p > 0.05).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In males, Bland-Altman bias for EQ2 and EQ4 was -4.33 kg and -5.11 kg, respectively, both statistically different from zero, indicating systematic bias (p < 0.01).

    “However, the bias observed by the Bland-Altman plot for these equations was -4.33 kg for EQ2 and -5.11 kg for EQ4, both of which resulted statistically different from zero, indicating a systematic bias (p < 0.01).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingnone given

    Participants in the current study presented similarities with findings from previous reports on Ht, BW and BMI, even though recreational athletes may differ from higher-level athletes.

    “Even though, recreational athletes may differ from a higher level athletes, participants in the current study presented similarities with findings from previous reports on Ht, BW and BMI.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Findingnone given

    Our sample differed from recreational athlete characteristics according to other studies.

    “Our sample differed from recreational athlete characteristics according to other studies.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Neither EQ2 nor EQ4 are appropriate for accurately estimating FFM in samples similar to ours.

    “These findings suggest that neither EQ2 nor EQ4 are appropriate for accurately estimating FFM in samples similar to ours.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Since gender is included in EQ2 and EQ4, differences in body composition between males and females may contribute to prediction error.

    “Since gender is a variable included in EQ2 and EQ4, differences in BC in males and females may contribute to prediction error, as numerous equations have included this variable.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    DXA scans were performed by a trained technician.

    “Scans were performed by a trained technician.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    DXA equipment was calibrated according to the manufacturer's instructions every assessment day.

    “DXA equipment was calibrated according to the instructions provided by the manufacturer every assessment day.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    Differences of up to 5.55 kg between EQ3 and DXA were considered clinically relevant because %FM increased from 25.10% to 33.48%, shifting a participant's category from overfat to obese.

    “However, these differences may not be that small in all participants as differences of up to 5.55 kg were observed, in other participants considered clinically relevant as the %FM increased from 25.10% to 33.48%, thereby resulting in his category shifting from overfat to obese category (Gallagher et al., 2000).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Gallagher et al., 2000 source

  • Classified as probably not a substantial claim.

    Premisecitation

    Systematic bias could be found in body composition across different methodologies and populations.

    “Systematic bias could be found in BC across different methodologies and populations (Lohman and Chen, 2005).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Lohman and Chen, 2005 source

  • May combine two claims.

    Premisecitation

    In other studies, a weak relationship between DXA and EQ3 predictions has been reported, particularly among female athletes; Houtkooper et al. (2001) reported an R2 = 0.10 for %FM when comparing DXA using EQ3 in nineteen heptathletes.

    “In other studies, a weak relationship between DXA and EQ3 predictions has been reported, particularly among female athletes.Houtkooper et al. (2001) reported an R 2 = 0.10 for a %FM when comparing DXA using EQ3 in a group of nineteen heptathletes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Houtkooper et al. (2001) source

  • May combine two claims.

    Interpretationreasoning

    Disparities between DXA and EQ1 values may be attributed to differences in BW and %FM, as our sample was leaner than the EQ1 sample, mainly in the female group.

    “Disparities between the DXA and EQ1 values may be attributed to differences in BW and %FM as our sample was leaner than the sample from EQ1 study, mainly in the female group.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The accuracy of predictive equations is influenced by the characteristics of the subjects evaluated, and it is important that these characteristics closely match those involved in the equation development.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    A particularly low FM of 10.31% was reported by Mooses et al. (2013) in recreational athletes, although these authors did not provide explicit criteria for athlete classification.

    “Notably, a particularly low FM of 10.31% was reported by Mooses et al. (2013) in the case of recreational athletes although these authors did not provide explicit criteria for athlete classification.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Mooses et al. (2013) source

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationown data

    The results show the importance of identifying pertinent predictive equations to track and evaluate body composition accurately, and the potential of equations to be used in recreational athletes who may not suit the standard athletic body.

    “Nevertheless, these results show the importance of identifying pertinent predictive equations to track and evaluate accurately BC, and the potential of equations to be used in recreational athletes who may not suit the standard athletic body.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Individual agreement between DXA FFM and predicted values cannot be confirmed, and such discrepancies may be substantial in practice, particularly when body composition estimates are used for personalized training or nutritional decision-making.

    “Given these results, individual agreement between DXA FFM and the predicted values cannot be confirmed, and such discrepancies may be substantial in practice-particularly when BC estimates are used for personalized training or nutritional decision-making.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Differences in sample characteristics could explain the lack of agreement between DXA and predictions of both EQ2 and EQ4, in addition to differences in methods employed for the use of a 4C model in EQ2.

    “Differences in sample characteristics could explain the lack of agreement between DXA and predictions of both equations, in addition to differences in methods employed for the use of a 4C model in EQ2.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The accuracy of predictive equations is influenced by the characteristics of the subjects evaluated, and it is important that these characteristics closely match those involved in the equation development.

  • May combine two claims.

    Interpretationown data

    EQ1 and EQ3 are viable options for predicting FFM in a group of recreational runners, despite the wide 95% limits of agreement that may be considered for individual predictions.

    “We find EQ1 and EQ3 to be viable options for predicting FFM in a group of recreational runners, despite the wide 95% LOA that may be considered for individual predictions.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Considering these results and the findings of the current study, EQ3 may be more suitable for subjects with higher FFM, as both the original sample and the athlete validation sample included individuals who were heavier in terms of FFM than our participants.

    “Considering these results and the findings of the current study, it is likely that EQ3 may be more suitable for subjects with higher FFM, as both the original sample and the athlete validation sample included individuals who were heavier in terms of FFM than our participants.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Equations one and three demonstrated a greater level of accuracy in predicting body composition within the current sample, and Equation three was more consistent between groups of data.

    “Equations one and three demonstrated a greater level of accuracy in predicting BC within the current sample, and Equation three was more consistent between groups of data.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    There is limited information available regarding the EQ3 development sample; its subjects ranged in age from 19 to 50 years and had a mean FFM of 60.2 kg, notably higher than our sample.

    “There is limited information available regarding EQ3 development sample.The subjects ranged in age from 19 to 50 years, and the mean FFM was 60.2 kg, which was notably higher than our sample (Lukaski et al., 1986).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Lukaski et al., 1986 source

  • May combine two claims.

    Premisenone given

    The EQ2 development sample was different from ours: its subjects were considerably younger, larger, and leaner.

    “In the case of EQ2, the sample used in its development was different from ours.These subjects were considerably younger, larger, and leaner.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Compared with DXA, EQ3 demonstrated to be the most consistent equation with fair accuracy, given that most metrics were adequate in males and the whole sample analysis.

    “Compared with DXA, EQ3 demonstrated to be the most consistent equation with a fair accuracy, given that most of the metrics were adequate in males and the whole sample analysis.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    Elite athletes, particularly in endurance sports, are generally expected to be leaner than recreational athletes.

    “It is worth highlighting that elite athletes, particularly in endurance sports, are generally expected to be leaner than recreational athletes.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationnone given

    The assessment method used by these authors was anthropometry, which may add differences in the results.

    “It is important to note, that the assessment method used by these authors was anthropometry which may add differences in the results.”
    Quote in the paperNot a substantial claim
48 of 48

  • The authors state this as an assumption.

    Some characteristics of the EQ3 development sample were comparable to those of the present sample.

    “Nevertheless, due to the fair agreement observed between the DXA and EQ3 values in the present study, we assumed that some of the characteristics in their sample were comparable to ours.”

  • The paper relies on this without support.

    The accuracy of predictive equations is influenced by the characteristics of the subjects evaluated, and it is important that these characteristics closely match those involved in the equation development.

    “Accuracy of predictive equations is influenced by the characteristics of the subjects evaluated, and it is considered important that these characteristics closely match those involved in the equation development.”
2 of 2

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.

  • Height is labeled in centimetres but used at metre-scale values

    Minor

    Table 2 lists male height as 1.69 cm. Its reported Ht2/R value of 56.4 cannot result from 1.69 cm and the reported resistance of 512.9 Ω; it is approximately the value obtained using 169 cm. The height values therefore conflict with their stated unit.

  • EQ3 is labeled as predicting fat mass, but validated as predicting fat-free mass

    Minor

    Table 1 defines EQ3's output as FM (fat mass), while Table 3 reports its predictions in the FFM (fat-free mass) column. These are different body-composition measures, so the equation label conflicts with the reported validation.

2 of 2

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Cholewa, J.M. et al. 2019. Anthropometrical Determinants of deadlift Variant Performance. Journal of Sports Science and Medicine, 18(August): 448–453.

Authors
Vázquez-Bautista, M. A., Robles-Sardin, A. E., Reséndiz-Sandoval, M., Navarro-Ibarra, M. J., Hernández López, J., & Caire-Juvera, G.
Affiliations
Universidad Autónoma de Baja California (MX)
ORCID
Venue
BIOtecnia
Publisher
Universidad de Sonora
Published
2025-10-15
Volume
27
Pages
e2512
ISSN
1665-1456 (ISSN-L)
DOI
10.18633/biotecnia.v27.2512
OpenAlex
W4415219360
Semantic Scholar
a4bbaaa434ef53735445b0a1b6c59a57faf8abf7
Cited by
0
References
29
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
BIOtecnia (publishedVersion, cc-by-nc-sa)
Topics
  • Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
  • Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Physical Education and Training Studies
Keywords
bioelectrical impedance analysis, body composition, external validation, prediction equations, dual-energy X-ray absorptiometry, recreational runners, measurement agreement, Mexican athletes

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