Enhancing predictive accuracy for one repetition maximum: Adding phase angle to skeletal muscle mass-based models in healthy young adults

This study aimed to investigate whether incorporating PhA into SMM-based predictive models enhances the accuracy of 1RM estimation for the unilateral chest press (CP) and leg press (LP). We focused on comparing models with and without PhA to assess the added value of PhA.

A cross-sectional study was conducted on 23 healthy participants (aged 21.9 ± 4.7 years, 11 females, 21.1 ± 2.3 kg/m² of body mass index). SMM and PhA were measured using a BIA device (Inbody S10 InBody Co., Ltd., South Korea). The dominant leg or arm SMM was added to half of the trunk SMM, and the whole-body value was used for PhA. 1RM for unilateral CP and LP was measured using standardized testing procedures. Multiple regression analyses were used to develop prediction models, first using SMM alone and then adding PhA to assess its impact on model accuracy. An F-test was conducted to compare the models with SMM alone and those with PhA added. Any p-values less than 0.05 were considered statistically significant.

The 1RM for unilateral CP and LP were 40.5 ± 11.8 kg and 97.0 ± 35.6 kg, respectively. The dominant leg or arm and trunk SMM were 5.3 ± 1.0 kg and 10.5 ± 1.7 kg, respectively. Whole-body PhA was 6.3 ± 0.6°. The predictive accuracy of 1RM using SMM alone for CP and LP were the following: adjusted R² = 0.71, standard error of estimate (SEE) = 6.37 kg for CP, adjusted R² = 0.49, SEE = 25.3 kg for LP. When PhA was added to the SMM-based model, the predictive accuracy improved for CP (adjusted R² = 0.77, SEE = 5.6 kg). For LP, adding PhA to the SMM-based model similarly enhanced the prediction accuracy (adjusted R² = 0.65, SEE = 21.0 kg). The fit of the models significantly improved for both CP and LP (F = 7.23, p = 0.014, F = 10.4, p = 0.004, respectively). These results demonstrate PhA as an additional predictor of 1RM.

The result of this study suggests that adding PhA to SMM-based prediction models can improve the accuracy of 1RM estimations for unilateral CP and LP. PhA may play a critical role in the assessment of muscular strength.

The 1RM prediction is useful for enhancing the safety of resistance training. Our findings contribute to the establishment of a more precise and accurate prediction for 1RM without imposing physical burden.