Quantitative Assessment of Forearm Muscle Architecture Using Dynamic High-Frequency Ultrasound: Determinants of Grip Strength and Predictive Modeling

Quantitative Assessment of Forearm Muscle Architecture Using Dynamic High-Frequency Ultrasound: Determinants of Grip Strength and Predictive Modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantitative Assessment of Forearm Muscle Architecture Using Dynamic High-Frequency Ultrasound: Determinants of Grip Strength and Predictive Modeling Yangkun Chen, Qiyue Li, Ruisi Chen, Jing Tang, Yifu Hou, Lei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9542439/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective The study quantitatively evaluated the relationship between individual forearm muscle architecture and handgrip strength (GS) using dynamic high-frequency ultrasound (US) to develop a multivariate functional predictive model informed by latest international muscle health consensus. Methods Forty-one healthy volunteers (21 females, 20 males; median age 22 years) were enrolled. Maximum GS (standardized isometric dynamometry at 110° elbow flexion) and US-derived muscle thickness (MT) and cross-sectional area (CSA) of seven forearm and hand muscles—palmaris longus (PL), flexor digitorum superficialis (FDS), flexor digitorum profundus (FDP), flexor carpi radialis (FCR), flexor carpi ulnaris (FCU), flexor pollicis longus (FPL), and thenar muscles (TM)—were measured at rest and maximum voluntary contraction (MVC). Surface electromyography (sEMG) from five muscles was recorded simultaneously to assess neural drive. Stepwise multiple linear regression was applied for predictive modeling. Results Significant sexual dimorphism was observed in GS and muscle morphometry (P < 0.05). GS significantly correlated with the MT and CSA of the PL and FCU in both resting and contracted states (P 0.05). The final multiple regression model (R² = 0.682, P < 0.001) identified weight, BMI, FCU thickness at MVC (FCUT2), and PL thickness at MVC as key quantitative predictors. FCUT2 was the most significant sonographic contributor (β = 0.430). Conclusions Dynamic high-frequency US provides a precise, quantitative method for functional muscle assessment. Contractile thickness of the FCU and PL are superior predictors of GS, highlighting their role in wrist stabilization during power grip. This model offers potential for early sarcopenia screening and rehabilitation monitoring consistent with AWGS 2025. High-frequency ultrasonography handgrip strength muscle architecture sarcopenia electromyography predictive modeling AWGS 2025 flexor carpi ulnaris. Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Grip strength (GS) is widely recognized as a "biomarker of aging" and serves as a primary metric for identifying muscle dysfunction and diagnosing sarcopenia ( 1 – 3 ). The diagnostic framework for sarcopenia has undergone significant paradigm shift. Recent consensus updates from the Asian Working Group for Sarcopenia (AWGS 2025) have elevated low muscle strength to the forefront of the diagnostic algorithm, prioritizing it as the primary determinant for screening and diagnosis ( 4 – 6 ). The groundbreaking update has specifically shifted the focus from late-stage disease diagnosis to early "muscle health" promotion, advocating for screening to begin as early as age 50 ( 7 ). This paradigm shift underscores the critical need for precise assessment of muscle function and its morphological correlates. While computed tomography (CT) and magnetic resonance imaging (MRI) are gold standards for assessing muscle mass, their clinical utility is limited by high costs and a "static" nature that fails to capture dynamic contractile changes( 8 – 11 ). Consequently, high-frequency ultrasonography has emerged as a disruptive, cost-effective alternative. Capable of providing real-time, high-resolution visualization, ultrasonography allows for the quantification of architectural parameters such as muscle thickness (MT) and cross-sectional area (CSA) ( 12 ). Numerous validation studies have demonstrated that sonographic measurements of limb muscle mass exhibit high agreement with MRI and dual-energy X-ray absorptiometry (DXA), offering robust reliability and validity in monitoring changes in muscle quality ( 13 – 16 ). Despite the established correlation between overall muscle size and strength, the specific anatomical determinants of grip strength within the complex architecture of the forearm remain incompletely defined. Previous research often overlooks the distinct biomechanical roles of individual muscles, such as the digital flexors (e.g., flexor digitorum superficialis) versus the wrist stabilizers (e.g., flexor carpi ulnaris)( 17 ). Furthermore, while the biomechanical contribution of ulnar-sided hand structures to power grip has been documented ( 18 ), the sonographic morphological characteristics of these muscles during dynamic contraction require further exploration. Alongside morphological parameters, the relationship between bioelectrical activation (measured via surface electromyography, sEMG( 19 )) and physical force output—specifically how it relates to structural "contractile reserve"—remains underexplored( 3 ). This study utilizes dynamic high-frequency US to quantify the architecture of seven specific forearm muscles in both resting and contracted states. By integrating sEMG signals and morphometric indices, we aim to validate US as a quantitative tool for functional hand assessment and establish a multivariate predictive model aligned with the "life-course" approach to muscle health advocated by current international guidelines( 3 ). Methods Study Design and Population A cross-sectional observational study was conducted at a single academic medical center following approval by the local institutional ethics review board. The study recruited 41 healthy volunteers, consisting of 21 females and 20 males aged between 18 and 40 years. This specific age range was selected to establish a baseline of physiological relevance in a population presumably free from age-related primary sarcopenia and myosteatosis, consistent with reference standards used in international sarcopenia research( 4 , 5 ). Inclusion criteria required participants to have intact bilateral forearms and hands and the ability to provide written informed consent. Individuals were excluded if they had a clinical suspicion or prior diagnosis of sarcopenia, severe edema or lymphedema in the upper extremities, neuromuscular disorders affecting hand function (e.g., carpal tunnel syndrome, ulnar neuropathy), or any condition preventing compliance with the maximal voluntary contraction protocol( 20 ). The presence of the Palmaris Longus (PL) was confirmed via clinical examination (Schaffer’s test) and ultrasound screening prior to enrollment. Participants with congenital absence of the PL were excluded from the primary morphometric analysis to ensure the consistency of the predictive model, as the study aimed to quantify the architectural contribution of this specific muscle when present. Clinical Data Collection and Anthropometry Comprehensive clinical characteristics were recorded for each participant to account for confounding variables. Demographic data included age and gender, while body composition was assessed through height, weight, and Body Mass Index (BMI) using calibrated medical-grade instruments. Forearm biometrics were rigorously measured, including forearm length (defined as the distance from the olecranon process to the radial styloid process) and forearm circumference at the point of maximal girth (proximal 30% of forearm length). The proximal 30% landmark was selected as it represents the peak cross-sectional area of the superficial flexor bellies in the majority of adults( 21 ). Circumference measurements were taken under two distinct physiological conditions: resting state (C1) and maximum voluntary contraction (MVC) state (C2) to capture the radial expansion of muscle fibers during sarcomere shortening. All anthropometric values were recorded to the nearest 0.1 cm, with the mean of two consecutive measurements utilized for analysis to minimize error( 16 ). Grip Strength Measurement Protocol Grip strength was quantified using a calibrated handheld electronic dynamometer (Baseline 12–0100). To ensure maximal biomechanical efficiency, this study employed a standardized standing testing protocol. Although the American Society of Hand Therapists (ASHT) generally recommends a seated position, recent biomechanical evidence suggests that a standing posture may induce higher peak grip force by optimizing the kinetic chain engagement through the hip and core stabilizers ( 22 ). Participants stood upright with feet shoulder-width apart and the shoulder adducted. The elbow was flexed to an angle of 110 degrees. The selection of 110 degrees, rather than the traditional 90 degrees, was based on the optimization of the muscle length-tension relationship. Research indicates that the elbow flexors and associated forearm musculature generate optimal isometric torque at joint angles between 90 and 110 degrees, where the flexor tendons possess the most advantageous moment arm( 22 , 23 ). This angle avoids active insufficiency associated with extreme flexion while providing a stable mechanical platform for ulnar deviation, which is critical for generating maximal grip force. The dynamometer handle was adjusted to the individual’s hand size (typically position 2). Participants were instructed to squeeze the device with maximal effort for 3 to 5 seconds. Three trials were performed with a 60-second rest interval between efforts to prevent fatigue. The maximum value (GS max) was recorded for analysis, in accordance with AWGS 2019 recommendations ( 5 ). High-Frequency Ultrasound Assessment Sonographic imaging was performed using a Clover 60 ultrasound system equipped with a high-frequency L12-5 linear array transducer (7–15 MHz). High-frequency linear probes are considered the gold standard for superficial musculoskeletal imaging due to their superior axial resolution, which allows for the clear delineation of thin fascial planes separating muscles like the flexor pollicis longus from the deep flexors. ( 24 ). B-mode settings (gain, depth, and focal zone) were meticulously optimized for musculoskeletal tissue to ensure clear delineation of myofascial planes and to minimize the impact of anisotropy. The frame rate was maintained at approximately 60 Hz, and the dynamic range was set to 70 dB to optimize contrast resolution. The transducer was positioned perpendicular to the long axis of the forearm to obtain transverse cross-sectional views of seven target muscles: palmaris longus (PL), flexor digitorum superficialis (FDS), flexor digitorum profundus (FDP), flexor carpi radialis (FCR), flexor carpi ulnaris (FCU), flexor pollicis longus (FPL), and the thenar muscles (TM). For each muscle (excluding TM, where only thickness was assessed due to irregular geometry), muscle thickness (MT) and cross-sectional area (CSA) were measured under two conditions: full relaxation (Resting) and maximum voluntary contraction (MVC). The contraction state was achieved by having the subject perform a maximum squeeze on the dynamometer while the sonographer held the probe in a fixed position. To ensure measurement reliability and eliminate inter-observer variability, all sonographic examinations were performed by a single experienced sonographer with > 10 years of experience. The reliability of sonographic measurement of muscle architecture has been substantiated in previous studies, with intraclass correlation coefficients (ICCs) for forearm muscle thickness typically exceeding 0.90 ( 25 , 26 ). To eliminate inter-observer variability, all sonographic examinations were performed by a single experienced sonographer. sEMG Data Acquisition Surface electromyography (sEMG) signals were recorded from five forearm flexor muscles, including the palmaris longus (PL), flexor digitorum superficialis (FDS), musculus extensor carpi radialis brevis (ECRB), extensor digitorum (ED) and extensor digiti minimi (EDM). The Trigno™ Wireless EMG System (Delsys Inc., Natick, MA, USA) was utilized for data collection. Sensors were positioned over the most prominent part of the muscle belly for each target muscle, identified through palpation and real-time ultrasound guidance. The raw sEMG signals were sampled at a frequency of 2000 Hz and filtered using a built-in band-pass filter of 20–450 Hz to minimize motion artifacts and high-frequency noise. To elicit maximal muscle activation, subjects were instructed to perform Maximum Voluntary Contractions (MVC). Subjects exerted their maximum possible force as is described in grip strength measurement protocol for a duration of 5 seconds per trial. The sEMG signals and grip force (GF) of five muscles were recorded at the same time. Three trials were conducted with a 60-second rest interval between trials to mitigate the effects of localized muscle fatigue. The sEMG data were processed using the EMGworks® Analysis software. The Root Mean Square (RMS) amplitude was calculated using a 200 ms sliding window to represent the intensity of muscle activation during the MVC tasks. In this study, the absolute RMS values (expressed in V) obtained during the MVC state were directly utilized for statistical analysis, t o preserve gender-based differences in activation potential. For each muscle, the highest RMS amplitude recorded among the three MVC trials was selected for subsequent quantitative comparisons. Statistical Analysis Data management and analysis were conducted using SPSS version 26.0 and R version 4.3.2. Normality of continuous variables was assessed using Shapiro-Wilk tests; data were expressed as mean ± SD (with normal distribution) or median (IQR) (with non-normal distribution) accordingly. Differences between gender groups were analyzed using independent t-tests or Mann-Whitney U tests. Continuous variables with normal distribution were compared using t-tests, while non-normally distributed variables were analyzed using Mann-Whitney U tests. The relationship between GS and clinical/sonographic parameters and the relationship between GF and sEMG signals were all evaluated using Pearson or Spearman correlation coefficients accordingly. Multicollinearity was assessed using the Variance Inflation Factor (VIF), with values exceeding 10 indicating significant redundancy among predictors( 16 ). To identify robust predictors, a stepwise multiple linear regression analysis was performed. The final model's goodness-of-fit was evaluated using the adjusted coefficient of determination (R²), with statistical significance defined as P < 0.05. Results Cohort Characteristics and Functional Dimorphism The study cohort consisted of 41 participants (21 females, 20 males). The descriptive clinical data are summarized in Table 1 . The median age of the cohort was 22 years (IQR: 5), with a range of 18–32 years, confirming a young adult demographic at peak physical capacity, minimizing the confounding effects of subclinical geriatric muscle wasting. There was no statistically significant difference in age between male and female subgroups (P = 0.266), ensuring that age-related variations did not confound the gender comparison. However, significant sexual dimorphism was observed in all other anthropometric and functional metrics. Males exhibited significantly higher values for height, weight, BMI, forearm length, and forearm circumference in both resting (C1) and contracted (C2) states (P < .05). Notably, the functional disparity was pronounced: the average Grip Strength (GS) for the total cohort was 33.6 (15.5) kg. Males demonstrated a markedly higher GS (40.3 ± 8.4 kg) compared to females (27.3 ± 5.1 kg) (P < .001). This represents a nearly 50% higher force output in males, establishing the expected physiological baseline that muscle mass and strength are sexually dimorphic traits( 16 ). Table 1 Descriptive clinical and anthropometric data of participants Parameter Female(n = 21) Male(n = 20) Total(n = 41) P value Age(yrs) 21 ( 7 ) 23 ( 4 ) 22 ( 5 ) 0.266 Height(cm) 162.2 ± 6.0 174.8 ± 6.6 168.3 ± 8.9 <0.001 Weight(kg) 54.2 ± 5.4 68.3 ± 9.5 60( 15 ) <0.001 BMI (kg/m 2 ) 20.6 ± 1.4 22.3 ± 2.6 21.1(2.3) 0.012 C1 a (cm) 21.8(2.2) 25.0(2.6) 23.8(3.4) <0.001 C2(cm) 22.4(2.6) 26.0(2.7) 24.5(3.9) <0.001 Forearm length(cm) 22.1 ± 0.9 24.5 ± 1.2 23.3 ± 1.6 <0.001 GS(kg) 27.3 ± 5.1 40.3 ± 8.4 33.6(15.5) <0.001 Quantitative Ultrasound of Muscle Architecture The ultrasound measurements of muscle thickness (T) and cross-sectional area (CSA) for the seven target muscles are presented in Table 2 . Indicator '1' represents the resting state, and '2' represents the MVC state. The transition from T1 to T2 represents the structural "contractile reserve" of the individual muscle bellies( 11 ). In general, males exhibited relatively larger muscle dimensions than females, aligning with the macroscopic anthropometric data. Notably, while ulnar-sided muscles (FCU and PL) showed profound gender differences in thickness, some radial components like the FDS showed less pronounced dimorphism (P = 0.162). Table 2 Descriptive data of muscles on ultrasonic measurement PLCSA1 b (cm 2 ) Female(n = 21) Male(n = 20) Total(n = 41) P value 0.95 (0.48) 1.28 (0.61) 1.10(0.49) 0.023 PLT1 (cm) 0.680 ± 0.123 0.965 ± 0.255 0.793(0.255) <0.001 FDSCSA1 (cm 2 ) 2.73 ± 0.72 3.08 ± 1.12 2.70(1.18) 0.103 FDST1 (cm) 1.058 ± 0.387 1.280 ± 0.441 1.143 ± 0.423 0.162 FDPCSA1 (cm 2 ) 1.09 (0.99) 1.69 (1.07) 1.31(1.00) 0.115 FDPT1 (cm) 1.509 ± 0.196 1.699 ± 0.215 1.621 ± 0.236 0.003 FCRCSA1 (cm 2 ) 2.38 ± 0.82 2.57 ± 0.61 2.46 ± 0.71 0.420 FCRT1 (cm) 1.066 ± 0.217 1.323 ± 0.226 1.180 ± 0.254 0.001 FCUCSA1 (cm 2 ) 1.29 ± 0.39 1.51 ± 0.44 1.39 ± 0.47 0.179 FCUT1 (cm) 0.790 (0.120) 0.910 (0.230) 0.867 ± 0.156 0.006 FPLCSA1 (cm 2 ) 1.57 ± 0.50 1.96 ± 0.61 1.78 ± 0.57 0.017 FPLT1 (cm) 1.276 ± 0.332 1.385 ± 0.259 1.324 ± 0.303 0.426 TMT1 (cm) 0.940 ± 0.182 1.090 ± 0.300 1.011 ± 0.251 0.120 PLCSA2 (cm 2 ) 0.83 (0.36) 1.13 (0.49) 1.04(0.50) 0.060 PLT2 (cm) 0.687 (0.141) 0.867 (0.287) 0.747(0.230) 0.001 FDSCSA2 (cm 2 ) 2.50 ± 0.75 2.53 ± 0.90 2.53(1.22) 0.378 FDST2 (cm) 1.150 ± 0.416 1.353 ± 0.565 1.249 ± 0.672 0.165 FDPCSA2 (cm 2 ) 0.93(1.06) 1.19(1.22) 1.08(1.00) 0.225 FDPT2 (cm) 1.413 ± 0.200 1.592 ± 0.189 1.519 ± 0.237 0.002 FCRCSA2 (cm 2 ) 2.14 ± 0.78 2.31 ± 0.58 2.22 ± 0.68 0.460 FCRT2 (cm) 1.070 ± 0.272 1.275 ± 0.217 1.163 ± 0.270 0.017 FCUCSA2 (cm 2 ) 1.16 ± 0.33 1.40 ± 0.52 1.33 ± 0.47 0.090 FCUT2 (cm) 0.913 ± 0.161 1.084 ± 0.195 0.986 ± 0.195 0.006 FPLCSA2 (cm 2 ) 1.44 ± 0.50 1.70 ± 0.58 1.57 ± 0.53 0.185 FPLT2 (cm) 1.165(0.560) 1.160(0.260) 1.236 ± 0.263 0.706 TMT2 (cm) 1.430(0.250) 1.570(0.280) 1.520(0.280) 0.078 Electromyography and Activation-Force Divergence Table 3 presents descriptive data of muscles on electromyography measurement. A striking observation is that, apart from grip force, there were no significant statistical differences in the RMS values (neural activation intensity) of these five forearm muscle groups between males and females (P > 0.05). This suggests that while males produce more force, the intensity of their neural drive relative to their structural mass is comparable to females, indicating that the force disparity is primarily structural rather than neuro-regulatory( 27 ). Table 3 Descriptive data of muscles on electromyography measurement GF (kg) Female(n = 21) Male(n = 20) Total(n = 41) P value 25.876 ± 3.9482 37.230 ± 7.0688 30.0000(13.0) <0.001 PL (V) 0.2077 ± 0.0851 0.1919(0.1188) 0.1944(0.1128) 0.535 FDS (V) 0.2147(0.1645) 0.2881(0.2239) 0.2422(0.1689) 0.227 ECRB (V) 0.1866(0.2836) 0.2255(0.1664) 0.2060(0.2036) 0.761 ED (V) 0.1120(0.0672) 0.1317(0.1014) 0.1286(0.0744) 0.566 EDU (V) 0.1815(0.0643) 0.2030(0.1323) 0.1895(0.0943) 0.456 Correlation Matrix and Predictive Modeling Correlation analysis revealed that GS was significantly associated with anthropometric variables (height, weight, BMI, C1, C2, and forearm length; P < 0.05) (Fig. 1 ). In the sonographic indices, the CSA and MT of the PL and FCU were robustly correlated with GS in both physiological states (Table 4 ). Strikingly, the FDS—traditionally viewed as the primary engine of hand flexion—showed no correlation with global GS in either resting or contracted states (r 0.70) ( 16 ). Furthermore, sEMG signals (RMS values) showed no correlation with absolute grip force (P > 0.05), reinforcing the functional-morphological divergence ( Table 5 ) ( 27 ). Table 4 Correlation between GS and ultrasonic measurements of muscles PLCSA1 c Related coefficient(r) P value 0.444 0.004 PLT1 0.591 <0.001 FDSCSA1 0.052 0.749 FDST1 0.015 0.924 FDPCSA1 -0.132 0.410 FDPT1 0.527 <0.001 FCRCSA1 0.195 0.223 FCRT1 0.556 <0.001 FCUCSA1 0.309 0.049 FCUT1 0.604 <0.001 FPLCSA1 0.470 <0.001 FPLT1 0.391 0.017 TMT1 0.430 0.005 PLCSA2 0.510 0.001 PLT2 0.651 <0.001 FDSCSA2 -0.007 0.965 FDST2 0.053 0.741 FDPCSA2 -0.098 0.543 FDPT2 0.543 <0.001 FCRCSA2 0.151 0.345 FCRT2 0.460 0.004 FCUCSA2 0.421 0.006 FCUT2 0.629 <0.001 FPLCSA2 0.238 0.133 FPLT2 0.130 0.436 TMT2 0.421 0.007 Table 5 Correlation between GF and RMS values of muscles PL Related coefficient(r) P value -0.156 0.329 FDS 0.054 0.74 ECRB 0.026 0.87 ED -0.19 0.239 EDU -0.017 0.916 In the multicollinearity test, only the VIF of TMT2 was less than 10 (VIF = 7.525), while all others were greater than 10, indicating multicollinearity. Correlation analysis on each variable was conducted simultaneously. The results were shown in Fig. 2 . The correlation between multiple indices was statistically significant (P 0.70, indicating multicollinearity of the indices too. Due to identified multicollinearity (VIF > 10 for most indices and r > 0.70 in the correlation matrix), stepwise regression was employed to establish a robust multicollinearity model ( Table 6 ) . The final model (Model-4) achieved an R² of 0.682 (P < 0.001). The derived regression equation was: $$\:Strength=8.203+0.567\left(Weight\right)+16.873\left(FCUT2\right)-1.642\left(BMI\right)+8.206\left(PLT2\right)$$ There were four indices included in the model, including weight and BMI in clinical characteristics, as well as FCUT2 and PLT2 in ultrasound indices. The coefficient of FCUT2 was 16.873, which contributed the most to the model. The fundamental assumptions of the multiple linear regression were rigorously tested. A Normal P-P plot of the standardized residuals was generated to assess the normality of the error distribution. As illustrated in Fig. 3 , the observed cumulative probabilities closely aligned with the expected diagonal line, confirming that the residuals are approximately normally distributed and validating the robustness of the derived regression equation. Table 6 Results of multiple linear regression model Model-1 Model-2 Model-3 Model-4 β t β t β t β t Weight 0.645 4.918 0.468 3.731 0.879 4.307 0.792 3.987 FCUT2 0.428 3.413 0.460 3.907 0.430 3.812 BMI -0.499 -2.455 -0.505 -2.162 PLT2 0.239 2.098 F 24.182 21.704 18.683 16.601 R 2 0.416 0.568 0.637 0.682 To illustrate the statistical results more clearly, Fig. 4 shows the difference in thickness of the FCU and PL during resting and MVC states, as viewed from a dynamic ultrasound perspective. During maximum voluntary contraction, the thickness of the two muscles increases significantly. Discussion The most salient finding of this quantitative investigation is the differential contribution of specific forearm muscles to grip strength (GS), challenging the intuitive assumption that the primary digit flexors are the sole determinants of hand force. While previous studies by Abe et al. ( 13 )have demonstrated a general positive correlation between forearm muscle thickness and GS, our granular morphometric analysis reveals that the Flexor Carpi Ulnaris (FCU) and Palmaris Longus (PL)—specifically their thickness during maximal voluntary contraction—are superior predictors compared to the Flexor Digitorum Superficialis (FDS). This aligns with the work of Methot J et al.( 18 ), who reported that ulnar-sided muscle thickness showed stronger correlations with GS than radial musculature . The strong predictive value of the FCU (β = 0.430) likely reflects the biomechanical necessity of wrist stabilization. The FCU, as the most powerful wrist flexor and adductor, provides the essential stable platform for this deviation. This role is supported by the work of Methot et al.( 18 ), who found that restricting ulnar digit function resulted in a disproportionate loss of total grip force (up to 55%), highlighting the critical contribution of the ulnar compartment to global hand function( 28 ). Our results further suggest that ulnar muscle thickness is not just a marker of mass, but a surrogate for the stabilization potential required to anchor the kinetic chain of the forearm. The inclusion of the Palmaris Longus (PL) in our regression model (PLT2) presents an intriguing contrast to existing literature. Several studies, such as those by Cetinus et al.( 29 ) and Sebastin et al.( 30 ), have suggested that the congenital absence of the PL does not significantly diminish grip or pinch strength, making it a preferred donor for tendon transfers( 31 ). However, our data indicates that in individuals where the PL is present, its hypertrophy and contractile thickening (PLT2) significantly contribute to force generation (r = 0.651, P < 0.001). This discrepancy may be explained by the muscle's role in tensing the palmar aponeurosis. By stabilizing the skin and fascial architecture of the palm, the PL minimizes energy dissipation during the squeezing action, ensuring that the force generated by deeper flexors is effectively transmitted to the object being gripped. While the muscle may be phylogenetically regressing, its functional capacity, when present, appears to be synergistic with the wrist stabilizers in optimizing the efficiency of the power grip( 31 ). This suggests that the PL should be viewed not as an "essential" muscle, but as a "performance-enhancing" component of the healthy hand architecture. 9 Unexpectedly, the Flexor Digitorum Superficialis (FDS) showed no significant correlation with GS in this study. This contradicts the findings of Tantipoon et al.( 32 ), who observed a moderate correlation between FDS stiffness and grip force using elastography. This lack of correlation may be attributed to the complex architecture of the FDS at the mid-forearm, where it transitions into multiple discrete tendons, making single-point cross-sectional measurements less representative of its true physiological capacity( 33 ). Furthermore, as suggested by research into “finger force deficit”, neural drive to the FDS may be modulated differently during maximal gross gripping compared to isolated finger flexion. During a power grip, the deeper Flexor Digitorum Profundus (FDP) often becomes the primary driver of distal joint flexion, potentially obscuring a direct morphological-functional relationship for the more superficial FDS in a multi-digit task( 34 ). This reinforces the idea that global grip force is a multi-joint, synergistic action rather than a simple sum of individual flexor outputs. The divergence between sEMG neural activation and absolute force output further highlights that structural architecture, rather than neural drive intensity, is the limiting factor for strength in healthy adults. While individuals can maximize relative motor unit recruitment during MVC, the absolute output remains fundamentally dependent on underlying muscle volume and architecture( 19 ). This is because power grip is a highly synergistic, multi-joint action, the bioelectrical signals from isolated superficial muscles do not adequately surrogate global biomechanical output. Our final predictive algorithm (R 2 = 0.682) integrates these sonographic parameters with BMI and weight to estimate strength. This model's performance is comparable to lower-limb sarcopenia models developed by Chen et al.( 35 ), who used Rectus Femoris parameters to predict muscle mass and function. By validating that ulnar-sided forearm muscle thickness is a robust surrogate for grip strength, this study supports the expansion of ultrasound applications in sarcopenia management as defined by the new AWGS 2025 consensus( 3 ), providing a rapid, non-invasive method to monitor "functional muscle mass" in rehabilitation settings, identifying dynapenia before it manifests as global atrophy Future research should focus on validating this algorithm in geriatric and pathological populations (e.g., patients with rheumatoid arthritis or type 2 diabetes) to establish universal diagnostic thresholds( 36 ). Additionally, the integration of shear-wave elastography (SWE) to measure muscle stiffness alongside B-mode morphometry may provide a more holistic assessment of "muscle quality" (e.g., degree of fibrosis or myosteatosis)( 37 ). Finally, it is essential to acknowledge that this investigation serves as an exploratory pilot study. While our findings provide a compelling proof-of-concept for dynamic ultrasound in muscle assessment, the predictive model was developed within a relatively small, healthy young adult cohort. To fully align with the clinical mandates of the AWGS 2025 consensus—which emphasizes early 'muscle health' screening—future large-scale studies are required to validate these algorithms across a broader age spectrum, particularly in populations aged 50 and above who are at the highest risk for the onset of sarcopenia and dynapenia. Conclusions This investigation validates dynamic high-frequency ultrasound as a robust and highly quantitative method for evaluating functional muscle architecture in the forearm. The dynamic thickness of the Flexor Carpi Ulnaris (FCU) and the Palmaris Longus (PL) during maximal voluntary contraction emerged as the primary sonographic determinants of grip strength, outperforming the primary digital flexors in predictive modeling. The divergence between sEMG neural activation and absolute force output suggests that structural architecture, rather than neural drive, is the limiting factor for strength in healthy adults. The developed multivariate model (R² = 0.682), integrating weight, BMI, and contractile thickness, offers a scientific framework for the early identification of individuals at risk of muscle dysfunction, consistent with the latest AWGS 2025 "Muscle Health" guidelines. These findings could provide a compelling case for the inclusion of individual muscle sonography in routine sarcopenia screening and precision rehabilitation monitoring. Abbreviations ASHT: American Society of Hand Therapists AWGS: Asian Working Group for Sarcopenia BMI: Body Mass Index CSA: Cross-sectional area CT: Computed tomography DXA: Dual-energy X-ray absorptiometry ECRB: Musculus extensor carpi radialis brevis ED: Extensor digitorum EDM: Extensor digiti minimi FCR: Flexor carpi radialis FCU: Flexor carpi ulnaris FDP: Flexor digitorum profundus FDS: Flexor digitorum superficialis FPL: Flexor pollicis longus GF: Grip force GS: Grip strength ICC: Intraclass correlation coefficient IQR: Interquartile range MRI: Magnetic resonance imaging MT: Muscle thickness MVC: Maximum voluntary contraction PL: Palmaris longus RMS: Root mean square SD: Standard deviation sEMG: Surface electromyography SWE: Shear-wave elastography TM: Thenar muscles US: Ultrasound VIF: Variance inflation factor Declarations Ethics approval and consent to participate This study was performed in accordance with the Declaration of Helsinki. Ethical approval was granted by the Ethics Committee for Basic and Clinical Research, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital (Approval No. 2026304). Informed consent was obtained from all individual participants included in the study. All participants were informed of their right to withdraw from the study at any time without any consequences. Consent for Publication Consent for publication has been obtained from all the volunteers involved in this study. Availability of data and materials The quantitative datasets generated and analyzed during the current study—including individual anthropometric measurements, sonographic muscle architecture parameters, and surface electromyography (sEMG) root mean square values—are available from the corresponding author upon reasonable request. The raw imaging and signal data are not publicly available due to institutional privacy and ethical restrictions concerning human participant data. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. As clinical investigators, the authors have disclosed all potential competing interests to the study participants prior to their enrollment. This study was conducted in full compliance with the ethical standards of the institutional research committee. Funding This work was supported by Health Commission of Sichuan Province Medical Science and Technology Program (Grant No. 25CXTD67). Author’s Contributions Yangkun Chen: Data curation (equal); formal analysis (equal); project administration (equal); writing – original draft (lead). Qiyue Li: Data curation (equal); formal analysis (equal); project administration (equal) writing – original draft (equal). Ruisi Chen: Data curation (equal); project administration (equal). Jing Tang: Data curation (equal); ultrasound examination (equal). Lei Wang: Data curation (equal); formal analysis (equal); ultrasound examination (equal); project administration (equal); writing – review and editing (equal). Acknowledgements We would like to express our gratitude to all the volunteers who participated in this study. Additionally, we thank the Department of Ultrasound, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, for providing the necessary imaging facilities and technical support. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work the authors used Google Gemini in order to improve language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. References Bohannon RW. Grip Strength: An Indispensable Biomarker For Older Adults. Clin Interv Aging. 2019;14:1681–91. 10.2147. /CIA.S194543 PubMed PMID: 31631989; PubMed Central PMCID: PMC6778477. Leong DP, Teo KK, Rangarajan S, Lopez-Jaramillo P, Avezum A, Orlandini A, et al. Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet. 2015;386(9990):266–73. 10.1016/S0140-6736 . (14)62000-6 PubMed PMID: 25982160. Won CW, Jung H. 2025 AWGS Sarcopenia Guideline and Wake-up Call for Asian Gerontologists and Geriatricians. Ann Geriatr Med Res. 2025;29(4):421–3. 10.4235/agmr.25 .0207 PubMed PMID: 41486893; PubMed Central PMCID: PMC12766309. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16–31. 10.1093/ageing/afy . 169 PubMed PMID: 30312372; PubMed Central PMCID: PMC6322506. Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020;21(3):300–e3072. 10.1016/j.jamda.2019.12 . .012 PubMed PMID: 32033882. Giovannini S, Brau F, Forino R, Berti A, D’Ignazio F, Loreti C, et al. Sarcopenia: Diagnosis and Management, State of the Art and Contribution of Ultrasound. J Clin Med. 2021;10(23):5552. 10.3390/jcm10235552 . Chen LK, Hsiao FY, Akishita M, Assantachai P, Lee WJ, Lim WS, et al. A focus shift from sarcopenia to muscle health in the Asian Working Group for Sarcopenia 2025 Consensus Update. Nat Aging. 2025;5(11):2164–75. 10.1038/s43587-025- . 01004-y PubMed PMID: 41188603. Buckinx F, Landi F, Cesari M, Fielding RA, Visser M, Engelke K, et al. Pitfalls in the measurement of muscle mass: a need for a reference standard. J Cachexia Sarcopenia Muscle. 2018;9(2):269–78. 10.1002/jcsm.12268 . PubMed PMID: 29349935; PubMed Central PMCID: PMC5879987. Engelke K, Museyko O, Wang L, Laredo JD. Quantitative analysis of skeletal muscle by computed tomography imaging-State of the art. J Orthop Translat. 2018;15:91–103. 10.1016/j.jot.2018.10.004 . PubMed PMID: 30533385; PubMed Central PMCID: PMC6260391. Boutin RD, Yao L, Canter RJ, Lenchik L. Sarcopenia: Current Concepts and Imaging Implications. Am J Roentgenol. 2015;205(3):W255–66. 10.2214/AJR.15.14635 . Rondaij T, Kozjek NR, Jerele C, Jordan T. Is there a place for ultrasound in diagnosing sarcopenia? Radiol Oncol 59(2):153–67. doi:10.2478/raon-2025-0035 PubMed PMID: 40544501; PubMed Central PMCID: PMC12182947. Chen ZT, Li XL, Jin FS, Shi YL, Zhang L, Yin HH, et al. Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study. J Med Internet Res. 2025;27(1):e70545. 10.2196/70545 . Abe T, Fujita E, Thiebaud RS, Loenneke JP, Akamine T. Ultrasound-Derived Forearm Muscle Thickness Is a Powerful Predictor for Estimating DXA-Derived Appendicular Lean Mass in Japanese Older Adults. Ultrasound Med Biol. 2016;42(9):2341–4. 10.1016/j.ultrasmedbio.2016.05.008 . Nijholt W, Scafoglieri A, Jager-Wittenaar H, Hobbelen JSM, van der Schans CP. The reliability and validity of ultrasound to quantify muscles in older adults: a systematic review. J Cachexia Sarcopenia Muscle. 2017;8(5):702–12. 10.1002/jcsm.12210 . PubMed PMID: 28703496; PubMed Central PMCID: PMC5659048. Smerilli G, Castell S, Cipolletta E, Farah S, Carotti M, Salaffi F, Grassi W, Filippucci E. Ultrasound measurement of muscle thickness at the proximal forearm in a rheumatologic setting. Clin Exp Rheumatol; 2020. Aruna R, Sivarajan AA, Madhumitha M, Vasanth CJ. Association of Hand Grip Strength with Ultrasound-derived Forearm Muscle Thickness and Echo Intensity in Young Indian Adults. J Med Ultrasound. 2022;31(3):206–10. 10.4103/jmu.jmu_28_22 . PubMed PMID: 38025016; PubMed Central PMCID: PMC10668901. Abe T, Loenneke JP. Handgrip strength dominance is associated with difference in forearm muscle size. J Phys Ther Sci. 2015;27(7):2147–9. 10.1589/jpts.27 . .2147 PubMed PMID: 26311942; PubMed Central PMCID: PMC4540837. Methot J, Chinchalkar J, Richards SS. Contribution of the Ulnar Digits to Grip Strength. Can J Plast Surg. 2010;18(1):10–4. 10.1177/229255031001800103 . Najjar H, Issa K, Badawe HM, Khraiche ML. Segmental bioimpedance and anthropometry improve machine learning prediction of grip strength in healthy young adults. Front Bioeng Biotechnol. 2026;14:1736894. 10.3389/fbioe.2026.1736894 . PubMed PMID: 41658984. Lung BE, Siwiec RM, Anatomy. Shoulder and Upper Limb, Forearm Flexor Carpi Ulnaris Muscle. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 [cited 2026 Apr 10]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK526051/ PubMed PMID: 30252307. Meza-Valderrama D, Sánchez- Rodríguez D, Perkisas S, Duran X, Bastijns S, Dávalos-Yerovi V, et al. The feasibility and reliability of measuring forearm muscle thickness by ultrasound in a geriatric inpatient setting: a cross-sectional pilot study. BMC Geriatr. 2022;22:137. 10.1186/s12877-022-02811-3 . PubMed PMID: 35177006; PubMed Central PMCID: PMC8855585. Xu ZY, Gao DF, Xu K, Zhou ZQ, Guo YK. The Effect of Posture on Maximum Grip Strength Measurements. J Clin Densitom. 2021;24(4):638–44. 10.1016/j.jocd.2021.01.005 . PubMed PMID: 33526316. Richards LG, Olson B, Palmiter-Thomas P. How forearm position affects grip strength. Am J Occup Ther. 1996;50(2):133–8. 10.5014/ajot.50 . .2.133 PubMed PMID: 8808417. Kissin E. Ultrasonography of the Hand and Wrist. In: Yinh J, Kissin E, DeMarco PJ, Kohler MJ, editors. Clinical Atlas of Musculoskeletal Ultrasound in Rheumatology [Internet]. Cham: Springer Nature Switzerland; 2024 [cited 2026 Feb 2]. pp. 195–239. Available from: https://doi.org/10.1007/978-3-031-63882-4_5 doi:10.1007/978-3-031-63882-4_5. Meza-Valderrama D, Sánchez-Rodríguez D, Perkisas S, Duran X, Bastijns S, Dávalos-Yerovi V, et al. The feasibility and reliability of measuring forearm muscle thickness by ultrasound in a geriatric inpatient setting: a cross-sectional pilot study. BMC Geriatr. 2022;22(1):137. 10.1186/s12877-022-02811-3 . PubMed PMID: 35177006; PubMed Central PMCID: PMC8855585. Giovannini S, Brau F, Forino R, Berti A, D’Ignazio F, Loreti C, et al. Sarcopenia: Diagnosis and Management, State of the Art and Contribution of Ultrasound. J Clin Med. 2021;10(23):5552. 10.3390/jcm10235552 . Najjar H, Issa K, Badawe HM, Khraiche ML. Segmental bioimpedance and anthropometry improve machine learning prediction of grip strength in healthy young adults. Front Bioeng Biotechnol 14:1736894. 10.3389/fbioe.2026.1736894 PubMed PMID: 41658984; PubMed Central PMCID: PMC12872933. Chen ZT, Li XL, Jin FS, Shi YL, Zhang L, Yin HH, et al. Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study. J Med Internet Res. 2025;27(1):e70545. 10.2196/70545 . Cetin A, Genc M, Sevil S, Coban YK. Prevalence of the Palmaris Longus Muscle and its Relationship with Grip and Pinch Strength: A Study in a Turkish Pediatric Population. Hand (New York, N,Y). 2013;8(2):215–20. 10.1007/s11552-013-9509-6 SEBASTIN SJ, LIM AYT, BEE WH, WONG TCM, METHIL BV. Does the Absence of the Palmaris Longus Affect Grip and Pinch Strength? J Hand Surg. 2005;30(4):406–8. 10.1016/J.JHSB.2005.03.011 . Al Risi AM, Al Busaidi S, Al Aufi H, Al Hashmi L, Sirasanagandla SR, Das S. Anatomical Study of the Palmaris Longus Muscle and Its Clinical Importance. Diagnostics. 2025;15(3):304. 10.3390/diagnostics15030304 . Tantipoon P, Praditpod N, Pakleppa M, Li C, Huang Z. Characterization of Flexor Digitorum Superficialis Muscle Stiffness Using Ultrasound Shear Wave Elastography and MyotonPRO: A Cross-Sectional Study Investigating the Correlation between Different Approaches. Appl Sci. 2023;13(11):6384. 10.3390/app13116384 . Ghaffari A, Abouzaki M, Romero Y, Sun A, Seitz A, Langley J et al. Connectome-based predictive modeling of grip strength: a marker of physical frailty. Front Neurosci. 19:1697908. 10.3389/fnins.2025.1697908 PubMed PMID: 41425076; PubMed Central PMCID: PMC12711722. Popp WL, Richner L, Lambercy O, Shirota C, Barry A, Gassert R, et al. Effects of wrist posture and stabilization on precision grip force production and muscle activation patterns. J Neurophysiol. 2023;130(3):596–607. 10.1152/jn.00420.2020 . Chen YL, Liu PT, Chiang HK, Lee SH, Lo YL, Yang YC, et al. Ultrasound Measurement of Rectus Femoris Muscle Parameters for Discriminating Sarcopenia in Community-Dwelling Adults. J Ultrasound Med. 2022;41(9):2269–77. 10.1002/jum.15913 . Anil C, Akaltun MS, Altindag O, Gur A. Reduced forearm muscle thickness and hand strength in patients with rheumatoid arthritis: an ultrasonographic cross-sectional study. Rheumatol Int. 2025;45(10):238. 10.1007/s00296-025-05998-x . PubMed PMID: 40996568. Zhang C, Kang L. Ultrasound Elastography for the Assessment of Sarcopenia. J Clin Med. 2026;15(7):2566. 10.3390/jcm15072566 . Footnotes C1 = resting circumference; C2 = contracted circumference. 1 = resting, 2 = MVC 1 = resting, 2 = MVC Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 30 Apr, 2026 Editor invited by journal 30 Apr, 2026 Editor assigned by journal 28 Apr, 2026 Submission checks completed at journal 28 Apr, 2026 First submitted to journal 27 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9542439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635392759,"identity":"04daffcf-bb80-4bc8-a8b4-1e4a4d104889","order_by":0,"name":"Yangkun Chen","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People’s Hospital (School of Medicine, UESTC)","correspondingAuthor":false,"prefix":"","firstName":"Yangkun","middleName":"","lastName":"Chen","suffix":""},{"id":635392760,"identity":"915c2c83-9db4-4663-afd3-70620de10384","order_by":1,"name":"Qiyue Li","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People’s Hospital (School of Medicine, UESTC)","correspondingAuthor":false,"prefix":"","firstName":"Qiyue","middleName":"","lastName":"Li","suffix":""},{"id":635392762,"identity":"b2cb8be2-04b3-4568-a359-877c542232c8","order_by":2,"name":"Ruisi Chen","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People’s Hospital (School of Medicine, UESTC)","correspondingAuthor":false,"prefix":"","firstName":"Ruisi","middleName":"","lastName":"Chen","suffix":""},{"id":635392763,"identity":"0eadcabe-910a-45a4-a64d-f78ef66ecea8","order_by":3,"name":"Jing Tang","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People’s Hospital (School of Medicine, UESTC)","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Tang","suffix":""},{"id":635392764,"identity":"1b187701-9852-432d-951d-2ce739813334","order_by":4,"name":"Yifu Hou","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People’s Hospital (School of Medicine, UESTC)","correspondingAuthor":false,"prefix":"","firstName":"Yifu","middleName":"","lastName":"Hou","suffix":""},{"id":635392765,"identity":"181bbb2e-2bae-4da1-bc52-ffa2fd422dc0","order_by":5,"name":"Lei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYLCCBwwSDHwgxgcDGzvitCQAtbABacYZBWnJxGphAGth5vlwiLGBkGqD42cPv0iosGBgY28+9tjG4AAzA/vhoxvwajmTl2aRcAboMJ5j6cY5Bnf4GHjS0m7g02J2IMfMILFNor5NIsdMOsfgGTODBI8Zfi3n3wC1/APaIv/GTNrC4DBjA0EtN3KMHyQ2ALUAVUozEKPF/sYbM4aEYyC/pKVJ9hikJbMR8otkf47xhw81dQz87IePSfz4Y2MHYuDVAgRsEqhcAspBgPkDEYpGwSgYBaNgJAMAZ0xDPR6kjNsAAAAASUVORK5CYII=","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People’s Hospital (School of Medicine, UESTC)","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-04-27 13:26:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9542439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9542439/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108879635,"identity":"36784add-50b2-43e0-bd99-ab1b8af3d843","added_by":"auto","created_at":"2026-05-09 16:17:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83092,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationships between GS and quantitative clinical indicators.\u003c/strong\u003e \u003cstrong\u003ea, \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003erelationship between GS and age (P=0.723); \u003cstrong\u003eb, \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003erelationship between GS and height (P \u0026lt; 0.05); \u003cstrong\u003ec,\u003c/strong\u003e The relationship between GS and weight (P \u0026lt; 0.05); \u003cstrong\u003ed, \u003c/strong\u003eThe relationship between GS and BMI (P \u0026lt; 0.05); \u003cstrong\u003ee, \u003c/strong\u003eThe relationship between GS and C1 (P \u0026lt; 0.05); \u003cstrong\u003ef,\u003c/strong\u003e The relationship between GS and C2 (P \u0026lt; 0.05); \u003cstrong\u003eg, \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003erelationships between GS and forearm length (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9542439/v1/d8e5c2b380f2bc07ca2b4a76.png"},{"id":108977241,"identity":"3a1578f5-41f9-4ff2-89c4-d0676882cf4b","added_by":"auto","created_at":"2026-05-11 11:31:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":517351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Matrix among quantitative indicators\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9542439/v1/4fe7665f0d88e04532ac140a.png"},{"id":108976822,"identity":"8f4b9b72-46dd-45a9-96ae-63e06e28545d","added_by":"auto","created_at":"2026-05-11 11:28:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55261,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNormal P-P plot of standardized residuals in multiple linear regression. \u003c/strong\u003eThe plot illustrates the relationship between the actual cumulative probability of the residuals and the expected cumulative probability of a normal distribution. The alignment of the data points along the 45-degree diagonal line indicates that the residuals of the predictive model for grip strength (dependent variable: force) are approximately normally distributed, thereby satisfying the fundamental assumption for the validity of the linear regression analysis\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9542439/v1/865e318749b7093b631bc1c1.png"},{"id":108977206,"identity":"a479942e-5d83-4a15-a0ea-af1bf7d1c0fe","added_by":"auto","created_at":"2026-05-11 11:30:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":556580,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThickness of FCU (yellow line) and PL (green line) in resting and MVC states in ultrasound images. \u003c/strong\u003eDuring maximum voluntary contraction, the thickness of FCU and PL increases significantly.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9542439/v1/f33e9d2d6f71be27035de207.png"},{"id":108979744,"identity":"ed757b4f-a28e-4403-a950-fcbf2a20425d","added_by":"auto","created_at":"2026-05-11 12:01:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1950715,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9542439/v1/9964bce0-a849-417d-a366-3105baa5cb6d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative Assessment of Forearm Muscle Architecture Using Dynamic High-Frequency Ultrasound: Determinants of Grip Strength and Predictive Modeling","fulltext":[{"header":"Background","content":"\u003cp\u003eGrip strength (GS) is widely recognized as a \"biomarker of aging\" and serves as a primary metric for identifying muscle dysfunction and diagnosing sarcopenia (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The diagnostic framework for sarcopenia has undergone significant paradigm shift. Recent consensus updates from the Asian Working Group for Sarcopenia (AWGS 2025) have elevated low muscle strength to the forefront of the diagnostic algorithm, prioritizing it as the primary determinant for screening and diagnosis (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The groundbreaking update has specifically shifted the focus from late-stage disease diagnosis to early \"muscle health\" promotion, advocating for screening to begin as early as age 50 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This paradigm shift underscores the critical need for precise assessment of muscle function and its morphological correlates.\u003c/p\u003e \u003cp\u003eWhile computed tomography (CT) and magnetic resonance imaging (MRI) are gold standards for assessing muscle mass, their clinical utility is limited by high costs and a \"static\" nature that fails to capture dynamic contractile changes(\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Consequently, high-frequency ultrasonography has emerged as a disruptive, cost-effective alternative. Capable of providing real-time, high-resolution visualization, ultrasonography allows for the quantification of architectural parameters such as muscle thickness (MT) and cross-sectional area (CSA) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Numerous validation studies have demonstrated that sonographic measurements of limb muscle mass exhibit high agreement with MRI and dual-energy X-ray absorptiometry (DXA), offering robust reliability and validity in monitoring changes in muscle quality (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the established correlation between overall muscle size and strength, the specific anatomical determinants of grip strength within the complex architecture of the forearm remain incompletely defined. Previous research often overlooks the distinct biomechanical roles of individual muscles, such as the digital flexors (e.g., flexor digitorum superficialis) versus the wrist stabilizers (e.g., flexor carpi ulnaris)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Furthermore, while the biomechanical contribution of ulnar-sided hand structures to power grip has been documented (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), the sonographic morphological characteristics of these muscles during dynamic contraction require further exploration. Alongside morphological parameters, the relationship between bioelectrical activation (measured via surface electromyography, sEMG(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)) and physical force output\u0026mdash;specifically how it relates to structural \"contractile reserve\"\u0026mdash;remains underexplored(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study utilizes dynamic high-frequency US to quantify the architecture of seven specific forearm muscles in both resting and contracted states. By integrating sEMG signals and morphometric indices, we aim to validate US as a quantitative tool for functional hand assessment and establish a multivariate predictive model aligned with the \"life-course\" approach to muscle health advocated by current international guidelines(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003e A cross-sectional observational study was conducted at a single academic medical center following approval by the local institutional ethics review board. The study recruited 41 healthy volunteers, consisting of 21 females and 20 males aged between 18 and 40 years. This specific age range was selected to establish a baseline of physiological relevance in a population presumably free from age-related primary sarcopenia and myosteatosis, consistent with reference standards used in international sarcopenia research(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Inclusion criteria required participants to have intact bilateral forearms and hands and the ability to provide written informed consent. Individuals were excluded if they had a clinical suspicion or prior diagnosis of sarcopenia, severe edema or lymphedema in the upper extremities, neuromuscular disorders affecting hand function (e.g., carpal tunnel syndrome, ulnar neuropathy), or any condition preventing compliance with the maximal voluntary contraction protocol(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The presence of the Palmaris Longus (PL) was confirmed via clinical examination (Schaffer\u0026rsquo;s test) and ultrasound screening prior to enrollment. Participants with congenital absence of the PL were excluded from the primary morphometric analysis to ensure the consistency of the predictive model, as the study aimed to quantify the architectural contribution of this specific muscle when present.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical Data Collection and Anthropometry\u003c/h3\u003e\n\u003cp\u003eComprehensive clinical characteristics were recorded for each participant to account for confounding variables. Demographic data included age and gender, while body composition was assessed through height, weight, and Body Mass Index (BMI) using calibrated medical-grade instruments. Forearm biometrics were rigorously measured, including forearm length (defined as the distance from the olecranon process to the radial styloid process) and forearm circumference at the point of maximal girth (proximal 30% of forearm length). The proximal 30% landmark was selected as it represents the peak cross-sectional area of the superficial flexor bellies in the majority of adults(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Circumference measurements were taken under two distinct physiological conditions: resting state (C1) and maximum voluntary contraction (MVC) state (C2) to capture the radial expansion of muscle fibers during sarcomere shortening. All anthropometric values were recorded to the nearest 0.1 cm, with the mean of two consecutive measurements utilized for analysis to minimize error(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGrip Strength Measurement Protocol\u003c/h3\u003e\n\u003cp\u003eGrip strength was quantified using a calibrated handheld electronic dynamometer (Baseline 12\u0026ndash;0100). To ensure maximal biomechanical efficiency, this study employed a standardized standing testing protocol. Although the American Society of Hand Therapists (ASHT) generally recommends a seated position, recent biomechanical evidence suggests that a standing posture may induce higher peak grip force by optimizing the kinetic chain engagement through the hip and core stabilizers (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticipants stood upright with feet shoulder-width apart and the shoulder adducted. The elbow was flexed to an angle of 110 degrees. The selection of 110 degrees, rather than the traditional 90 degrees, was based on the optimization of the muscle length-tension relationship. Research indicates that the elbow flexors and associated forearm musculature generate optimal isometric torque at joint angles between 90 and 110 degrees, where the flexor tendons possess the most advantageous moment arm(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This angle avoids active insufficiency associated with extreme flexion while providing a stable mechanical platform for ulnar deviation, which is critical for generating maximal grip force. The dynamometer handle was adjusted to the individual\u0026rsquo;s hand size (typically position 2). Participants were instructed to squeeze the device with maximal effort for 3 to 5 seconds. Three trials were performed with a 60-second rest interval between efforts to prevent fatigue. The maximum value (GS max) was recorded for analysis, in accordance with AWGS 2019 recommendations (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eHigh-Frequency Ultrasound Assessment\u003c/h3\u003e\n\u003cp\u003eSonographic imaging was performed using a Clover 60 ultrasound system equipped with a high-frequency L12-5 linear array transducer (7\u0026ndash;15 MHz). High-frequency linear probes are considered the gold standard for superficial musculoskeletal imaging due to their superior axial resolution, which allows for the clear delineation of thin fascial planes separating muscles like the flexor pollicis longus from the deep flexors. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). B-mode settings (gain, depth, and focal zone) were meticulously optimized for musculoskeletal tissue to ensure clear delineation of myofascial planes and to minimize the impact of anisotropy. The frame rate was maintained at approximately 60 Hz, and the dynamic range was set to 70 dB to optimize contrast resolution.\u003c/p\u003e \u003cp\u003eThe transducer was positioned perpendicular to the long axis of the forearm to obtain transverse cross-sectional views of seven target muscles: palmaris longus (PL), flexor digitorum superficialis (FDS), flexor digitorum profundus (FDP), flexor carpi radialis (FCR), flexor carpi ulnaris (FCU), flexor pollicis longus (FPL), and the thenar muscles (TM). For each muscle (excluding TM, where only thickness was assessed due to irregular geometry), muscle thickness (MT) and cross-sectional area (CSA) were measured under two conditions: full relaxation (Resting) and maximum voluntary contraction (MVC). The contraction state was achieved by having the subject perform a maximum squeeze on the dynamometer while the sonographer held the probe in a fixed position. To ensure measurement reliability and eliminate inter-observer variability, all sonographic examinations were performed by a single experienced sonographer with \u0026gt;\u0026thinsp;10 years of experience.\u003c/p\u003e \u003cp\u003eThe reliability of sonographic measurement of muscle architecture has been substantiated in previous studies, with intraclass correlation coefficients (ICCs) for forearm muscle thickness typically exceeding 0.90 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). To eliminate inter-observer variability, all sonographic examinations were performed by a single experienced sonographer.\u003c/p\u003e\n\u003ch3\u003esEMG Data Acquisition\u003c/h3\u003e\n\u003cp\u003eSurface electromyography (sEMG) signals were recorded from five forearm flexor muscles, including the palmaris longus (PL), flexor digitorum superficialis (FDS), musculus extensor carpi radialis brevis (ECRB), extensor digitorum (ED) and extensor digiti minimi (EDM). The Trigno\u0026trade; Wireless EMG System (Delsys Inc., Natick, MA, USA) was utilized for data collection. Sensors were positioned over the most prominent part of the muscle belly for each target muscle, identified through palpation and real-time ultrasound guidance. The raw sEMG signals were sampled at a frequency of 2000 Hz and filtered using a built-in band-pass filter of 20\u0026ndash;450 Hz to minimize motion artifacts and high-frequency noise.\u003c/p\u003e \u003cp\u003eTo elicit maximal muscle activation, subjects were instructed to perform Maximum Voluntary Contractions (MVC). Subjects exerted their maximum possible force as is described in grip strength measurement protocol for a duration of 5 seconds per trial. The sEMG signals and grip force (GF) of five muscles were recorded at the same time. Three trials were conducted with a 60-second rest interval between trials to mitigate the effects of localized muscle fatigue.\u003c/p\u003e \u003cp\u003eThe sEMG data were processed using the EMGworks\u0026reg; Analysis software. The Root Mean Square (RMS) amplitude was calculated using a 200 ms sliding window to represent the intensity of muscle activation during the MVC tasks. In this study, the absolute RMS values (expressed in V) obtained during the MVC state were directly utilized for statistical analysis, \u003cb\u003et\u003c/b\u003eo preserve gender-based differences in activation potential. For each muscle, the highest RMS amplitude recorded among the three MVC trials was selected for subsequent quantitative comparisons.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData management and analysis were conducted using SPSS version 26.0 and R version 4.3.2. Normality of continuous variables was assessed using Shapiro-Wilk tests; data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (with normal distribution) or median (IQR) (with non-normal distribution) accordingly. Differences between gender groups were analyzed using independent t-tests or Mann-Whitney U tests. Continuous variables with normal distribution were compared using t-tests, while non-normally distributed variables were analyzed using Mann-Whitney U tests.\u003c/p\u003e \u003cp\u003eThe relationship between GS and clinical/sonographic parameters and the relationship between GF and sEMG signals were all evaluated using Pearson or Spearman correlation coefficients accordingly. Multicollinearity was assessed using the Variance Inflation Factor (VIF), with values exceeding 10 indicating significant redundancy among predictors(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). To identify robust predictors, a stepwise multiple linear regression analysis was performed. The final model's goodness-of-fit was evaluated using the adjusted coefficient of determination (R\u0026sup2;), with statistical significance defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCohort Characteristics and Functional Dimorphism\u003c/h2\u003e \u003cp\u003eThe study cohort consisted of 41 participants (21 females, 20 males). The descriptive clinical data are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age of the cohort was 22 years (IQR: 5), with a range of 18\u0026ndash;32 years, confirming a young adult demographic at peak physical capacity, minimizing the confounding effects of subclinical geriatric muscle wasting. There was no statistically significant difference in age between male and female subgroups (P\u0026thinsp;=\u0026thinsp;0.266), ensuring that age-related variations did not confound the gender comparison. However, significant sexual dimorphism was observed in all other anthropometric and functional metrics. Males exhibited significantly higher values for height, weight, BMI, forearm length, and forearm circumference in both resting (C1) and contracted (C2) states (P \u0026lt;\u0026thinsp;.05).\u003c/p\u003e \u003cp\u003eNotably, the functional disparity was pronounced: the average Grip Strength (GS) for the total cohort was 33.6 (15.5) kg. Males demonstrated a markedly higher GS (40.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4 kg) compared to females (27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 kg) (P \u0026lt;\u0026thinsp;.001). This represents a nearly 50% higher force output in males, establishing the expected physiological baseline that muscle mass and strength are sexually dimorphic traits(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive clinical and anthropometric data of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.1(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003csup\u003ea\u003c/sup\u003e(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.8(2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.8(3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.4(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0(2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5(3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForearm length(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGS(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.6(15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Ultrasound of Muscle Architecture\u003c/h2\u003e \u003cp\u003eThe ultrasound measurements of muscle thickness (T) and cross-sectional area (CSA) for the seven target muscles are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Indicator '1' represents the resting state, and '2' represents the MVC state. The transition from T1 to T2 represents the structural \"contractile reserve\" of the individual muscle bellies(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In general, males exhibited relatively larger muscle dimensions than females, aligning with the macroscopic anthropometric data. Notably, while ulnar-sided muscles (FCU and PL) showed profound gender differences in thickness, some radial components like the FDS showed less pronounced dimorphism (P\u0026thinsp;=\u0026thinsp;0.162).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive data of muscles on ultrasonic measurement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePLCSA1\u003csup\u003eb\u003c/sup\u003e (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (0.61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10(0.49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT1 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.680\u0026thinsp;\u0026plusmn;\u0026thinsp;0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.965\u0026thinsp;\u0026plusmn;\u0026thinsp;0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.793(0.255)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDSCSA1 (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.70(1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDST1 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.058\u0026thinsp;\u0026plusmn;\u0026thinsp;0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.280\u0026thinsp;\u0026plusmn;\u0026thinsp;0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.143\u0026thinsp;\u0026plusmn;\u0026thinsp;0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDPCSA1 (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69 (1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.31(1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDPT1 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.509\u0026thinsp;\u0026plusmn;\u0026thinsp;0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.699\u0026thinsp;\u0026plusmn;\u0026thinsp;0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.621\u0026thinsp;\u0026plusmn;\u0026thinsp;0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCRCSA1 (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCRT1 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.066\u0026thinsp;\u0026plusmn;\u0026thinsp;0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.323\u0026thinsp;\u0026plusmn;\u0026thinsp;0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.180\u0026thinsp;\u0026plusmn;\u0026thinsp;0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCUCSA1 (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCUT1 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.790 (0.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.910 (0.230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.867\u0026thinsp;\u0026plusmn;\u0026thinsp;0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPLCSA1 (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPLT1 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.276\u0026thinsp;\u0026plusmn;\u0026thinsp;0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.385\u0026thinsp;\u0026plusmn;\u0026thinsp;0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.324\u0026thinsp;\u0026plusmn;\u0026thinsp;0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMT1 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.940\u0026thinsp;\u0026plusmn;\u0026thinsp;0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.090\u0026thinsp;\u0026plusmn;\u0026thinsp;0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.011\u0026thinsp;\u0026plusmn;\u0026thinsp;0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLCSA2 (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04(0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT2 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.687 (0.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.867 (0.287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.747(0.230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDSCSA2 (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.53(1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDST2 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.150\u0026thinsp;\u0026plusmn;\u0026thinsp;0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.353\u0026thinsp;\u0026plusmn;\u0026thinsp;0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.249\u0026thinsp;\u0026plusmn;\u0026thinsp;0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDPCSA2 (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93(1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19(1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08(1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDPT2 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.413\u0026thinsp;\u0026plusmn;\u0026thinsp;0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.592\u0026thinsp;\u0026plusmn;\u0026thinsp;0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.519\u0026thinsp;\u0026plusmn;\u0026thinsp;0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCRCSA2 (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCRT2 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.070\u0026thinsp;\u0026plusmn;\u0026thinsp;0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.275\u0026thinsp;\u0026plusmn;\u0026thinsp;0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.163\u0026thinsp;\u0026plusmn;\u0026thinsp;0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCUCSA2 (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCUT2 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.913\u0026thinsp;\u0026plusmn;\u0026thinsp;0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.084\u0026thinsp;\u0026plusmn;\u0026thinsp;0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.986\u0026thinsp;\u0026plusmn;\u0026thinsp;0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPLCSA2 (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPLT2 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.165(0.560)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.160(0.260)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.236\u0026thinsp;\u0026plusmn;\u0026thinsp;0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMT2 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.430(0.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.570(0.280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.520(0.280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eElectromyography and Activation-Force Divergence\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents descriptive data of muscles on electromyography measurement. A striking observation is that, apart from grip force, there were no significant statistical differences in the RMS values (neural activation intensity) of these five forearm muscle groups between males and females (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This suggests that while males produce more force, the intensity of their neural drive relative to their structural mass is comparable to females, indicating that the force disparity is primarily structural rather than neuro-regulatory(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive data of muscles on electromyography measurement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGF (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.876\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9482\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.230\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0688\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.0000(13.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2077\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1919(0.1188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1944(0.1128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDS (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2147(0.1645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2881(0.2239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2422(0.1689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECRB (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1866(0.2836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2255(0.1664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2060(0.2036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1120(0.0672)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1317(0.1014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1286(0.0744)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDU (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1815(0.0643)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2030(0.1323)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1895(0.0943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Matrix and Predictive Modeling\u003c/h2\u003e \u003cp\u003eCorrelation analysis revealed that GS was significantly associated with anthropometric variables (height, weight, BMI, C1, C2, and forearm length; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the sonographic indices, the CSA and MT of the PL and FCU were robustly correlated with GS in both physiological states (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Strikingly, the FDS\u0026mdash;traditionally viewed as the primary engine of hand flexion\u0026mdash;showed no correlation with global GS in either resting or contracted states (r\u0026thinsp;\u0026lt;\u0026thinsp;0.06, P\u0026thinsp;\u0026gt;\u0026thinsp;0.70) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Furthermore, sEMG signals (RMS values) showed no correlation with absolute grip force (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), reinforcing the functional-morphological divergence \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between GS and ultrasonic measurements of muscles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePLCSA1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelated coefficient(r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDSCSA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDST1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDPCSA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDPT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCRCSA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCRT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCUCSA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCUT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPLCSA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPLT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLCSA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDSCSA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDST2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDPCSA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDPT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCRCSA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCRT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCUCSA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCUT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPLCSA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPLT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between GF and RMS values of muscles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelated coefficient(r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECRB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the multicollinearity test, only the VIF of TMT2 was less than 10 (VIF\u0026thinsp;=\u0026thinsp;7.525), while all others were greater than 10, indicating multicollinearity. Correlation analysis on each variable was conducted simultaneously. The results were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The correlation between multiple indices was statistically significant (P \u0026lt;\u0026thinsp;.05), and there was a situation where r\u0026thinsp;\u0026gt;\u0026thinsp;0.70, indicating multicollinearity of the indices too.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDue to identified multicollinearity (VIF\u0026thinsp;\u0026gt;\u0026thinsp;10 for most indices and r\u0026thinsp;\u0026gt;\u0026thinsp;0.70 in the correlation matrix), stepwise regression was employed to establish a robust multicollinearity model \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The final model (Model-4) achieved an R\u0026sup2; of 0.682 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The derived regression equation was:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Strength=8.203+0.567\\left(Weight\\right)+16.873\\left(FCUT2\\right)-1.642\\left(BMI\\right)+8.206\\left(PLT2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThere were four indices included in the model, including weight and BMI in clinical characteristics, as well as FCUT2 and PLT2 in ultrasound indices. The coefficient of FCUT2 was 16.873, which contributed the most to the model. The fundamental assumptions of the multiple linear regression were rigorously tested. A Normal P-P plot of the standardized residuals was generated to assess the normality of the error distribution. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the observed cumulative probabilities closely aligned with the expected diagonal line, confirming that the residuals are approximately normally distributed and validating the robustness of the derived regression equation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of multiple linear regression model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel-3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel-4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCUT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e24.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e21.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e18.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e16.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo illustrate the statistical results more clearly, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the difference in thickness of the FCU and PL during resting and MVC states, as viewed from a dynamic ultrasound perspective. During maximum voluntary contraction, the thickness of the two muscles increases significantly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe most salient finding of this quantitative investigation is the differential contribution of specific forearm muscles to grip strength (GS), challenging the intuitive assumption that the primary digit flexors are the sole determinants of hand force. While previous studies by Abe et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)have demonstrated a general positive correlation between forearm muscle thickness and GS, our granular morphometric analysis reveals that the Flexor Carpi Ulnaris (FCU) and Palmaris Longus (PL)\u0026mdash;specifically their thickness during maximal voluntary contraction\u0026mdash;are superior predictors compared to the Flexor Digitorum Superficialis (FDS). This aligns with the work of Methot J et al.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), who reported that ulnar-sided muscle thickness showed stronger correlations with GS than radial musculature .\u003c/p\u003e \u003cp\u003eThe strong predictive value of the FCU (β\u0026thinsp;=\u0026thinsp;0.430) likely reflects the biomechanical necessity of wrist stabilization. The FCU, as the most powerful wrist flexor and adductor, provides the essential stable platform for this deviation. This role is supported by the work of Methot et al.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), who found that restricting ulnar digit function resulted in a disproportionate loss of total grip force (up to 55%), highlighting the critical contribution of the ulnar compartment to global hand function(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Our results further suggest that ulnar muscle thickness is not just a marker of mass, but a surrogate for the stabilization potential required to anchor the kinetic chain of the forearm.\u003c/p\u003e \u003cp\u003eThe inclusion of the Palmaris Longus (PL) in our regression model (PLT2) presents an intriguing contrast to existing literature. Several studies, such as those by Cetinus et al.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and Sebastin et al.(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), have suggested that the congenital absence of the PL does not significantly diminish grip or pinch strength, making it a preferred donor for tendon transfers(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). However, our data indicates that in individuals where the PL is present, its hypertrophy and contractile thickening (PLT2) significantly contribute to force generation (r\u0026thinsp;=\u0026thinsp;0.651, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This discrepancy may be explained by the muscle's role in tensing the palmar aponeurosis. By stabilizing the skin and fascial architecture of the palm, the PL minimizes energy dissipation during the squeezing action, ensuring that the force generated by deeper flexors is effectively transmitted to the object being gripped. While the muscle may be phylogenetically regressing, its functional capacity, when present, appears to be synergistic with the wrist stabilizers in optimizing the efficiency of the power grip(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). This suggests that the PL should be viewed not as an \"essential\" muscle, but as a \"performance-enhancing\" component of the healthy hand architecture.\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eUnexpectedly, the Flexor Digitorum Superficialis (FDS) showed no significant correlation with GS in this study. This contradicts the findings of Tantipoon et al.(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), who observed a moderate correlation between FDS stiffness and grip force using elastography. This lack of correlation may be attributed to the complex architecture of the FDS at the mid-forearm, where it transitions into multiple discrete tendons, making single-point cross-sectional measurements less representative of its true physiological capacity(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, as suggested by research into \u0026ldquo;finger force deficit\u0026rdquo;, neural drive to the FDS may be modulated differently during maximal gross gripping compared to isolated finger flexion. During a power grip, the deeper Flexor Digitorum Profundus (FDP) often becomes the primary driver of distal joint flexion, potentially obscuring a direct morphological-functional relationship for the more superficial FDS in a multi-digit task(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). This reinforces the idea that global grip force is a multi-joint, synergistic action rather than a simple sum of individual flexor outputs.\u003c/p\u003e \u003cp\u003eThe divergence between sEMG neural activation and absolute force output further highlights that structural architecture, rather than neural drive intensity, is the limiting factor for strength in healthy adults. While individuals can maximize relative motor unit recruitment during MVC, the absolute output remains fundamentally dependent on underlying muscle volume and architecture(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This is because power grip is a highly synergistic, multi-joint action, the bioelectrical signals from isolated superficial muscles do not adequately surrogate global biomechanical output.\u003c/p\u003e \u003cp\u003eOur final predictive algorithm (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.682) integrates these sonographic parameters with BMI and weight to estimate strength. This model's performance is comparable to lower-limb sarcopenia models developed by Chen et al.(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), who used Rectus Femoris parameters to predict muscle mass and function. By validating that ulnar-sided forearm muscle thickness is a robust surrogate for grip strength, this study supports the expansion of ultrasound applications in sarcopenia management as defined by the new AWGS 2025 consensus(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), providing a rapid, non-invasive method to monitor \"functional muscle mass\" in rehabilitation settings, identifying dynapenia before it manifests as global atrophy\u003c/p\u003e \u003cp\u003eFuture research should focus on validating this algorithm in geriatric and pathological populations (e.g., patients with rheumatoid arthritis or type 2 diabetes) to establish universal diagnostic thresholds(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Additionally, the integration of shear-wave elastography (SWE) to measure muscle stiffness alongside B-mode morphometry may provide a more holistic assessment of \"muscle quality\" (e.g., degree of fibrosis or myosteatosis)(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Finally, it is essential to acknowledge that this investigation serves as an exploratory pilot study. While our findings provide a compelling proof-of-concept for dynamic ultrasound in muscle assessment, the predictive model was developed within a relatively small, healthy young adult cohort. To fully align with the clinical mandates of the AWGS 2025 consensus\u0026mdash;which emphasizes early 'muscle health' screening\u0026mdash;future large-scale studies are required to validate these algorithms across a broader age spectrum, particularly in populations aged 50 and above who are at the highest risk for the onset of sarcopenia and dynapenia.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis investigation validates dynamic high-frequency ultrasound as a robust and highly quantitative method for evaluating functional muscle architecture in the forearm. The dynamic thickness of the Flexor Carpi Ulnaris (FCU) and the Palmaris Longus (PL) during maximal voluntary contraction emerged as the primary sonographic determinants of grip strength, outperforming the primary digital flexors in predictive modeling. The divergence between sEMG neural activation and absolute force output suggests that structural architecture, rather than neural drive, is the limiting factor for strength in healthy adults. The developed multivariate model (R\u0026sup2; = 0.682), integrating weight, BMI, and contractile thickness, offers a scientific framework for the early identification of individuals at risk of muscle dysfunction, consistent with the latest AWGS 2025 \"Muscle Health\" guidelines. These findings could provide a compelling case for the inclusion of individual muscle sonography in routine sarcopenia screening and precision rehabilitation monitoring.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASHT: American Society of Hand Therapists\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAWGS: Asian Working Group for Sarcopenia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCSA: Cross-sectional area\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCT: Computed tomography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDXA: Dual-energy X-ray absorptiometry\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eECRB: Musculus extensor carpi radialis brevis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eED: Extensor digitorum\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEDM: Extensor digiti minimi\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFCR: Flexor carpi radialis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFCU: Flexor carpi ulnaris\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFDP: Flexor digitorum profundus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFDS: Flexor digitorum superficialis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFPL: Flexor pollicis longus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGF: Grip force\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGS: Grip strength\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eICC: Intraclass correlation coefficient\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR: Interquartile range\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMRI: Magnetic resonance imaging\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMT: Muscle thickness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMVC: Maximum voluntary contraction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePL: Palmaris longus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRMS: Root mean square\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSD: Standard deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003esEMG: Surface electromyography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSWE: Shear-wave elastography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTM: Thenar muscles\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUS: Ultrasound\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVIF: Variance inflation factor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in accordance with the Declaration of Helsinki. Ethical approval was granted by the Ethics Committee for Basic and Clinical Research, Sichuan Academy of Medical Sciences \u0026amp; Sichuan Provincial People\u0026rsquo;s Hospital (Approval No. 2026304). Informed consent was obtained from all individual participants included in the study. All participants were informed of their right to withdraw from the study at any time without any consequences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication has been obtained from all the volunteers involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quantitative datasets generated and analyzed during the current study\u0026mdash;including individual anthropometric measurements, sonographic muscle architecture parameters, and surface electromyography (sEMG) root mean square values\u0026mdash;are available from the corresponding author upon reasonable request. The raw imaging and signal data are not publicly available due to institutional privacy and ethical restrictions concerning human participant data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. As clinical investigators, the authors have disclosed all potential competing interests to the study participants prior to their enrollment. This study was conducted in full compliance with the ethical standards of the institutional research committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Health Commission of Sichuan Province Medical Science and Technology Program (Grant No. 25CXTD67).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYangkun Chen: Data curation (equal); formal analysis (equal); project administration (equal); writing\u0026nbsp;\u0026ndash;\u0026nbsp;original draft (lead).\u0026nbsp;Qiyue\u0026nbsp;Li: Data curation (equal); formal analysis (equal); project administration (equal) writing\u0026nbsp;\u0026ndash;\u0026nbsp;original draft (equal).\u0026nbsp;Ruisi Chen: Data curation (equal); project administration (equal).\u0026nbsp;Jing Tang: Data curation (equal); ultrasound examination\u0026nbsp;(equal).\u0026nbsp;Lei Wang: Data curation (equal); formal analysis (equal);\u0026nbsp;ultrasound examination\u0026nbsp;(equal); project administration (equal); writing\u0026nbsp;\u0026ndash;\u0026nbsp;review and editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to all the volunteers who participated in this study. Additionally, we thank the Department of Ultrasound, Sichuan Academy of Medical Sciences \u0026amp; Sichuan Provincial People\u0026rsquo;s Hospital, for providing the necessary imaging facilities and technical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the authors used Google Gemini in order to improve language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBohannon RW. Grip Strength: An Indispensable Biomarker For Older Adults. 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J Clin Med. 2026;15(7):2566. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm15072566\u003c/span\u003e\u003cspan address=\"10.3390/jcm15072566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;C1\u0026thinsp;=\u0026thinsp;resting circumference; C2\u0026thinsp;=\u0026thinsp;contracted circumference.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;\u003cem\u003e1\u0026thinsp;=\u0026thinsp;resting, 2\u0026thinsp;=\u0026thinsp;MVC\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;\u003cem\u003e1\u0026thinsp;=\u0026thinsp;resting, 2\u0026thinsp;=\u0026thinsp;MVC\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"High-frequency ultrasonography, handgrip strength, muscle architecture, sarcopenia, electromyography, predictive modeling, AWGS 2025, flexor carpi ulnaris.","lastPublishedDoi":"10.21203/rs.3.rs-9542439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9542439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe study quantitatively evaluated the relationship between individual forearm muscle architecture and handgrip strength (GS) using dynamic high-frequency ultrasound (US) to develop a multivariate functional predictive model informed by latest international muscle health consensus.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eForty-one healthy volunteers (21 females, 20 males; median age 22 years) were enrolled. Maximum GS (standardized isometric dynamometry at 110\u0026deg; elbow flexion) and US-derived muscle thickness (MT) and cross-sectional area (CSA) of seven forearm and hand muscles\u0026mdash;palmaris longus (PL), flexor digitorum superficialis (FDS), flexor digitorum profundus (FDP), flexor carpi radialis (FCR), flexor carpi ulnaris (FCU), flexor pollicis longus (FPL), and thenar muscles (TM)\u0026mdash;were measured at rest and maximum voluntary contraction (MVC). Surface electromyography (sEMG) from five muscles was recorded simultaneously to assess neural drive. Stepwise multiple linear regression was applied for predictive modeling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSignificant sexual dimorphism was observed in GS and muscle morphometry (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). GS significantly correlated with the MT and CSA of the PL and FCU in both resting and contracted states (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, the primary finger flexor (FDS) and sEMG signals showed no significant correlation with GS (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The final multiple regression model (R\u0026sup2; = 0.682, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) identified weight, BMI, FCU thickness at MVC (FCUT2), and PL thickness at MVC as key quantitative predictors. FCUT2 was the most significant sonographic contributor (β\u0026thinsp;=\u0026thinsp;0.430).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDynamic high-frequency US provides a precise, quantitative method for functional muscle assessment. Contractile thickness of the FCU and PL are superior predictors of GS, highlighting their role in wrist stabilization during power grip. This model offers potential for early sarcopenia screening and rehabilitation monitoring consistent with AWGS 2025.\u003c/p\u003e","manuscriptTitle":"Quantitative Assessment of Forearm Muscle Architecture Using Dynamic High-Frequency Ultrasound: Determinants of Grip Strength and Predictive Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 16:17:11","doi":"10.21203/rs.3.rs-9542439/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"29808549930060295235674130772641684838","date":"2026-05-05T14:53:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142293634431643823232010616524789281689","date":"2026-04-30T17:56:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-30T17:48:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-30T16:43:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-28T14:46:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-28T14:45:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2026-04-27T13:11:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5b44e02-7e57-47a5-a009-6d19e7a87d6d","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"29808549930060295235674130772641684838","date":"2026-05-05T14:53:08+00:00","index":23,"fulltext":""},{"type":"reviewerAgreed","content":"142293634431643823232010616524789281689","date":"2026-04-30T17:56:51+00:00","index":19,"fulltext":""},{"type":"reviewersInvited","content":"8","date":"2026-04-30T17:48:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-30T16:43:03+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T16:17:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 16:17:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9542439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9542439","identity":"rs-9542439","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}