Wearable health technologies are increasingly shaping how we monitor fitness, recovery, and chronic disease risk, yet most devices still rely on indirect measures, such as heart rate or step counts. To advance beyond basic activity tracking, there is growing interest in integrating biomarkers that more directly reflect muscle strength and tissue composition (Daniore et al., 2024). Hand grip strength (HGS) is a simple, non-invasive biomarker that reflects functional capacity and overall health status. Beyond its role in everyday activities, HGS has been consistently linked to chronic disease risk, sarcopenia, cardiovascular and cancer-related mortality, and even cognitive decline (Ho et al., 2019; Vaishya et al., 2024; da Silva et al., 2018). Lower HGS is also associated with poor mental health outcomes including depression, and a reduced quality of life (Fielding et al., 2011; Kwak and Kim, 2019). HGS reflects activity of both extrinsic forearm flexors and intrinsic hand muscles, coordinated with wrist stabilizers. For this reason, HGS is considered a global marker of neuromuscular health, as it integrates signals from multiple muscle groups and the nervous system.

Beyond functional measures such as HGS, segmental bioimpedance (BioZ) analysis characterizes tissue composition, muscle status and fluid balance (Puertas et al., 2021; Nescolarde et al., 2013). BioZ is a non-invasive, safe and versatile technique in which a small alternating current is applied across body tissues to measure electrical impedance and derive physiological parameters (Naranjo-Hernández et al., 2019; Borga et al., 2018; Amini et al., 2018; Campa et al., 2021). A key advantage of BioZ is its ability to deliver real-time, continuous monitoring of cardiovascular and fluid status, enabling early detection of changes such as fluid overload, edema, and cardiac dysfunction (Naranjo-Hernández et al., 2019; Kazory, 2017). This approach has been used to detect sarcopenia, monitor recovery, and evaluate post-surgical swelling, often outperforming conventional anthropometric methods (Konečná et al., 2023; Pichonnaz et al., 2015). Measurement accuracy, however, is influenced by electrode placement and type (Vaishya et al., 2024; Kwak and Kim, 2019; Noh and Park, 2020), skin contact quality (Yang et al., 2022), excitation frequency, and applied pressure (Taji et al., 2018). These factors are critical for translating BioZ into practical wearable systems. Multi-frequency BioZ further enhances diagnostic potential by distinguishing between extracellular and intracellular fluid compartments and assessing cell integrity. Specifically, low-frequency signals (<50 kHz) are limited to extracellular pathways due to cell membrane impedance, while high-frequency signals (>200 kHz) can penetrate cell membranes, enabling assessment of intracellular fluid and cellular integrity (Konečná et al., 2023; Pichonnaz et al., 2015). Recent advances in dry electrodes and strap-based systems highlight the potential for gel-free, continuous monitoring in wearables devices, opening pathways for its broader clinical and consumer applications (Escobar-Linero et al., 2023). Machine learning (ML)-based signal correction complements these advances by enhancing measurement reliability (Nazarzadeh et al., 2024; Zhou et al., 2023; Al-Nabulsi et al., 2024; Lindholm et al., 2018). Despite their diagnostic potential, both HGS and BioZ remain underutilized in routine healthcare and are largely absent from wearable devices. This underuse stems from several challenges, including variability in measurement protocols, lack of clinical standardization, technical barriers for wearable integration, and limited clinician familiarity (Nazarzadeh et al., 2024; Zhou et al., 2023; Al-Nabulsi et al., 2024; Lindholm et al., 2018). Although HGS provides a direct functional measure of muscular strength and BioZ captures underlying physiological and tissue-level properties, their combined integration remains largely under-investigated. To date, few studies have systematically assessed the complementary predictive value of combining HGS and BioZ within ML frameworks, leaving a gap in understanding how these modalities could enhance individualized health monitoring and risk prediction. In the Middle East and North Africa (MENA) region, recent studies have characterized HGS and body composition using BioZ, linking these measures to obesity, sarcopenia and frailty in older adults (Saadeddine et al., 2021; Kreidieh et al., 2020; al-Lababidi et al., 2025). However, data integrating BioZ, HGS, and ML in healthy young adults remain limited. By focusing on this population, the present pilot study establishes a reference framework that can support future comparisons across age groups, clinical conditions, and disease states.

In this work, we present the first integration of segmental BioZ from the wrist and forearm with anthropometry and HGS to predict muscle strength in healthy young adults from the MENA region. By benchmarking multiple linear regression (MLR) against random forest (RF) models, we show that combining localized electrical properties with simple anthropometric measures enhances the prediction of HGS beyond conventional approaches. We further investigate how measurement site and limb geometry influence BioZ behavior, while motivating future investigations into the effects of applied pressure and frequency, generating critical insights for both physiological interpretation and translation into practical wearable systems.

This study investigated the relationship between localized BioZ, anthropometric measurements, and HGS in healthy young adults from the MENA region. The motivation lies in the need for population-specific data, as HGS values vary across ethnic and socioeconomic groups due to genetic, nutritional, and lifestyle differences (Arokiasamy et al., 2021; Wang et al., 2018). To our knowledge, this is the first study in the region to evaluate how segmental BioZ and limb circumference at the wrist and forearm relate to HGS, and to examine their integration with ML models. Our objectives were twofold (1): to evaluate how anthropometric and impedance features are associated with HGS; and (2) investigate how electrode site, frequency, and localized pressure influence impedance, thereby providing guidance for future wearable device design.

Compared to the wrist, the forearm yields more physiologically relevant signals for estimating grip strength (Kamaşak et al., 2023; Seethamma et al., 2019). This likely reflects the forearm’s direct involvement in grip mechanics where the flexor and extensor muscle groups extending from the forearm generate gripping force and stabilize the wrist (Seethamma et al., 2019). Computational modeling further supports this interpretation, demonstrating that electrode placement over muscle regions allows for wider and deeper current penetration and density distributions that would enhance the physiological relevance of BioZ recordings (Salkim and Wu, 2023). The observed negative correlation between forearm circumference and BioZ is consistent with this mechanism, as larger muscle cross-sectional area provides more conductive pathways for current, thereby reducing electrical resistance. Similar findings were reported in young adults, where forearm circumference, height, and body mass were identified as stronger predictors of HGS compared to wrist circumference (Kamaşak et al., 2023; Myles et al., 2025).

ML models integrating anthropometric and localized BioZ features improved HGS estimation compared to anthropometry alone. In baseline models, predictive performance was modest for both MLR and RF regression, indicating that global anthropometric descriptors capture limited variance in muscle strength (Figures 4A,B). Adding segmental forearm BioZ to baseline contributed complementary information alongside height and body mass, suggesting that localized electrical tissue properties provide a more comprehensive representation of grip strength beyond gross body size (Figure 5A). Notably, the strongest overall performance was achieved using a size-normalized forearm BioZ index that integrates both geometric (limb circumference) and electrical tissue properties (BioZ) (Figure 5C). This feature alone outperformed baseline anthropometric models in both linear and nonlinear frameworks, with RF regression achieving the lowest prediction error (RMSE_cv = 6.68 kg) (Table 2). While RF regression showed superior performance (Figure 5C), MLR performed comparably in simpler configurations (Figures 5A,B), indicating that performance gains were driven primarily by physiologically informed feature engineering rather than model complexity alone (Table 2). Although additional regression approaches (regularized and robust linear models) were explored during preliminary analyses, they did not provide performance improvements. We therefore focused on MLR and RF regression as representative linear and nonlinear frameworks that balance interpretability and overfitting risk in small-sample settings. This approach highlights the potential of localized, size-normalized BioZ features as informative inputs for ML-based estimation of muscle strength in healthy young adults, with the focus placed on physiologically informed feature design rather than model benchmarking.

While these findings point toward potential applications in wearable health monitoring, they should be interpreted with caution given the small pilot sample. Rather than claiming immediate translational impact, our results demonstrate proof-of-concept that localized, size-normalized forearm BioZ can enrich predictive models of grip strength being the most accurate and stable across all configurations. With validation in larger and diverse cohorts, such multimodal approaches may support non-invasive monitoring of musculoskeletal health, rehabilitation progress, and early signs of health decline.

In wearable applications, BioZ signals are influenced by dynamic factors such as movement, strap tightness, sweat, and skin-electrode contact variability (Cömert et al., 2013). Our findings highlight how frequency and pressure influence impedance responses. At low frequencies, current travels primarily through extracellular fluid pathways because the cell membranes act as capacitors. Under pressure, low frequency impedance responses were not consistent at the forearm, likely reflecting non-uniform displacement of extracellular fluid and blood. In contrast, high frequency currents penetrate cell membranes and engage both intra- and extracellular pathways. Impedance increased with pressure up to ∼80 mmHg, consistent with extracellular fluid reduction, cell compression, and changes in membrane capacitance (Dodde et al., 2012; Namkoong et al., 2024). Beyond this threshold, a plateau was observed, suggesting tissue compression limits had been reached (Namkoong et al., 2024). These findings indicate that high-frequency BioZ is more sensitive to localized fluid shifts or edema, whereas low-frequency signals provide less consistent information under varying pressure.

At the wrist, electrode configuration strongly influenced signal stability. While the DV placement produced more stable impedance values across pressure levels, it may not be practical for most wearable applications. In contrast, the DD placement exhibited higher baseline impedance and greater sensitivity to pressure-induced variation, suggesting a trade-off between robustness (DV) and responsiveness (DD) that should be carefully considered in wearable device design. In standard watch-style devices, the sensor contacts are limited to one side of the wrist. As a result, electrode placement must conform to a unilateral configuration such as DD, making it the more feasible option despite its higher sensitivity. The wrist’s thinner anatomy and lower muscle density likely explain the narrower impedance range compared with the forearm. Together, these results suggest that practical wearable devices should prioritize high-frequency BioZ measurements with DD electrode placement to maximize both feasibility and physiological relevance.

This study has limitations that should be acknowledged. The sample size was modest (n = 20), which constrains statistical power and limits the generalizability of ML models. In addition, the cohort consisted exclusively of healthy young adults from the MENA region; therefore, the findings may not extend to older populations or individuals with clinical conditions. All measurements were obtained under controlled laboratory conditions, which do not fully replicate the variability of real-world wearable use where factors such as movement, sweat, and strap displacement may degrade signal quality. Finally, ML models presented here should be viewed as exploratory. Larger, more diverse cohorts and external validation studies are required to establish robust predictive accuracy, enhance clinical relevance, and confirm the translational potential of this multimodal approach.

This study demonstrates that segmental forearm bioimpedance (BioZ), when combined with anthropometric measurements, provides a physiologically meaningful and complementary predictor of hand grip strength (HGS) in healthy young adults. By integrating BioZ with traditional anthropometry, we show that multimodal models particularly those using RF regression algorithms achieve higher predictive accuracy than linear models, underscoring the value of nonlinear approaches for capturing complex tissue function relationships. Importantly, our findings highlight the forearm as a superior measurement site compared to the wrist and reveal how factors such as frequency and applied pressure shape BioZ behavior, offering practical guidance for wearable sensor design. While preliminary, these results establish proof-of-concept that coupling BioZ with anthropometry can enrich predictive modeling of muscle strength and inform the development of next-generation wearable systems for continuous, non-invasive monitoring of musculoskeletal health, rehabilitation progress, and early indicators of functional decline.