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Purpose <p>This study aimed to develop regression equations to estimate (1) absolute whole-body skeletal muscle mass (SMM), (2) SMM normalized for height squared (SMM/height<sup>2</sup>), and (3) SMM normalized for body mass index (SMM/BMI) using simple anthropometric and performance variables.</p> Methods <p>541 participants (6–87&#xa0;years, both sexes) were assessed for SMM using bioelectrical impedance analysis, handgrip strength, normalized rate of force development (nRFD), and countermovement jump (CMJ) height. Hierarchical multiple regression analyses were used to develop predictive equations, and their accuracy was tested in a cross-validation group using linear regression and Bland–Altman analyses.</p> Results <p>The developed equations demonstrated high accuracy for estimating SMM and SMM/height<sup>2</sup> (adjusted R<sup>2</sup> = 0.875–0.964; SEE = 6.4%–7.2%), whereas the accuracy for SMM/BMI was lower (adjusted R<sup>2</sup> = 0.671–0.740; SEE = 14.1%–15.9%) when tested in the cross-validation group. Including CMJ height alongside handgrip strength improved the prediction accuracy, and nRFD contributed significantly to SMM/BMI estimation. Exploratory analyses showed that the association between SMM and performance indices was affected by age and sex, whereas nRFD was not influenced by these factors.</p> Conclusion <p>The SMM can be accurately estimated using simple field-based measures of body size and muscle function. Combining handgrip strength and CMJ height enabled a feasible and size-independent assessment of muscle mass with a wide range of ages in both sexes. Even when only handgrip strength and CMJ height were included as performance variables, the developed equations achieved high predictive accuracy, highlighting their practical applicability in the field and clinical settings.</p>

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Accurate estimation of skeletal muscle mass across the lifespan using simple field tests of strength and power

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Ryoichi Ema

摘要

Purpose

This study aimed to develop regression equations to estimate (1) absolute whole-body skeletal muscle mass (SMM), (2) SMM normalized for height squared (SMM/height2), and (3) SMM normalized for body mass index (SMM/BMI) using simple anthropometric and performance variables.

Methods

541 participants (6–87 years, both sexes) were assessed for SMM using bioelectrical impedance analysis, handgrip strength, normalized rate of force development (nRFD), and countermovement jump (CMJ) height. Hierarchical multiple regression analyses were used to develop predictive equations, and their accuracy was tested in a cross-validation group using linear regression and Bland–Altman analyses.

Results

The developed equations demonstrated high accuracy for estimating SMM and SMM/height2 (adjusted R2 = 0.875–0.964; SEE = 6.4%–7.2%), whereas the accuracy for SMM/BMI was lower (adjusted R2 = 0.671–0.740; SEE = 14.1%–15.9%) when tested in the cross-validation group. Including CMJ height alongside handgrip strength improved the prediction accuracy, and nRFD contributed significantly to SMM/BMI estimation. Exploratory analyses showed that the association between SMM and performance indices was affected by age and sex, whereas nRFD was not influenced by these factors.

Conclusion

The SMM can be accurately estimated using simple field-based measures of body size and muscle function. Combining handgrip strength and CMJ height enabled a feasible and size-independent assessment of muscle mass with a wide range of ages in both sexes. Even when only handgrip strength and CMJ height were included as performance variables, the developed equations achieved high predictive accuracy, highlighting their practical applicability in the field and clinical settings.