Introduction
A key goal in multidirectional sports is to achieve optimal athletic performance whilst remaining free from injuries. Both performance and injury risk are largely determined by the training load an athlete is exposed to. On the one hand, sufficient training loads (i.e., the volume and intensity of training) are necessary for beneficial physical adaptations, while on the other hand, excessive loads lead to fatigue and subsequent risk of injury (Drew et al., 2016; Eckard et al., 2018). Therefore, training loads are widely measured and monitored in multidirectional sports.
The primary aim of training-load monitoring is to inform training prescription geared to optimise physical adaptations. The relationships between training load and adaptations (also referred to as ‘responses’) are known as load-adaptation pathways. In multidirectional sports the primary focus is on the load-adaptation pathways of two separate systems: the cardiovascular and the musculoskeletal systems (Figure 1) (note: although psychological load and recovery has received increasing attention in recent years, it will not be considered for the purposes of this blog). Physiological load-adaptation pathways describe how repeated skeletal muscle contractions trigger various biochemical responses, primarily in the form of metabolic and cardiorespiratory adaptations (Impellizzeri et al., 2005; MacInnis and Gibala, 2017). Biomechanical load- adaptation pathways describe how repeated exposure of the musculoskeletal system to various forces, trigger mechanobiological tissue responses of the muscles, tendons, ligaments, bones, and cartilage (Bohm et al., 2015; Rosa et al., 2015; Wisdom et al., 2015). Monitoring physiological and biomechanical loads separately is thus preferable to gain a holistic understanding of the distinct adaptive mechanisms that ultimately determine physical fitness and athletic performance (Vanrenterghem et al., 2017).
The extent to which biomechanical loads experienced during multidirectional sports lead to adaptations of the musculoskeletal system are relatively under-investigated and not well understood. A key reason for this lack of understanding is the challenge of quantifying biomechanical loads in field-based contexts. Especially compared to physiological load measurements (e.g., heart rate, blood lactate), quantifying the demands that training puts on the whole musculoskeletal system, its different structures, and individual tissues, remains highly challenging (or even impossible) (Figure 2; Verheul et al., 2020). In this blog, I will 1) describe several important biomechanical load metrics at the different levels of the musculoskeletal system, 2) discuss current methods and challenges for measuring biomechanical loads in vivo, and 3) discuss recent developments and future considerations to enhance field-based biomechanical load monitoring.
Tissue load
During multidirectional sports, the hard and soft tissues of the body are exposed to different mechanical forces. These forces cause mechanical tension within the tissues in the form of stresses (i.e., force over the cross-sectional area of a tissue) and strains (i.e., relative deformation of a tissue) that, together with exercise-induced microdamage and metabolic stress, trigger remodelling and repair mechanisms of the tissue. This process leads to increases in muscle size, strength, fibre lengths, or pennation angles (Krustrup et al., 2010; Nimphius et al., 2012), higher tendon stiffness and tendon-structure enhancements (Mersmann et al., 2017; Stanley et al., 2018), and increased bone mass and mineral density (Fredericson et al., 2007; Helge et al., 2014). Such adaptations are generally considered desirable characteristics for enhanced performance. Excessive exposure to stresses and strains, on the other hand, can outpace repair mechanisms and lead to an accumulation of microdamage and progressive tissue weakening. Over time this can ultimately result in mechanical fatigue and tissue failure, including muscle tears, tendon rupture, or bone fractures (Bertelsen et al., 2017; Edwards, 2018). The optimal amount and intensity of loading varies between different tissue types and depend on aspects such as tissue properties and loading history. Hence, quantifying and monitoring the cumulative tissue-specific stresses and strains during training is desirable.
Measuring tissue-specific loading and its effects is not straightforward. In vitro experiments can be used to examine tissue behaviour following prescribed loading patterns in highly controlled laboratory setups (Loundagin et al., 2018; Wang et al., 2013) (Figure 2). Such experiments can, for example, show after how many load iterations of sport-specific stresses a bone tissue fails (Figure 3; Loundagin et al., 2018). An alternative approach is to use in silico computational modelling to investigate tissue response mechanisms under different mechanical and biological conditions (Amirouche and Bobko, 2015; Smith et al., 2013). Although both approaches can be informative, they require numerous assumptions that negate the natural variations in physiological, structural, and morphological makeup within musculoskeletal tissues. Moreover, during multidirectional sports movements tissues are exposed to a constantly varying combination of non-uniform tensile, compressive, and shear forces. This makes it challenging to directly translate findings from controlled in vitro or in silico studies to the field, beyond understanding the relative tolerances between different tissues.
Structural load
Biomechanical research has extensively investigated loads experienced at a structural level. Musculoskeletal structures, such as muscle-tendon units, joints, or segments, are a combination of various types of tissues. These structures are exposed to forces and moments which are a combination of all the stresses and strains working on their individual tissues. Not surprisingly, then, structural loads are commonly used as proxies (i.e., representative replacements) for loads at a tissue level. For example, knee joint moments are thought to be a good indicator of the loading magnitude and injury risk of the anterior cruciate ligament in the knee (Hewett et al., 2005; Lin et al., 2012), whereas joint or leg stiffness has been used to assess performance-related muscular adaptations in response to training (Verheul et al., 2017) or fatigue (Morin et al., 2011; Oliver et al., 2014). Structural loads are typically measured through musculoskeletal models. However, such models require kinematic (motion-capture systems), kinetic (force platforms) and/or neuromuscular (electromyography) input, the combination of which is yet primarily restricted to laboratories.
Several approaches have been explored to measure structural loads in field-based settings. These can be subdivided into direct and indirect measurements. Examples of direct approaches include surgically implanted force transducers or strain gauges to measure muscle-tendon forces or bone strains (Burr et al., 1996; Fukashiro et al., 1995; Komi et al., 1987). More recently, a wearable shear wave tensiometer device (Figure 4; Martin et al., 2018) has been developed to non-invasively measure tendon forces directly during movement in natural sports environments (Harper et al., 2020; Reiter et al., 2024). Nevertheless, the invasive and temporary nature (implants) and current unavailability for public use (shear wave tensiometers) still hinder the direct measurements and day-to-day monitoring of structural loads in the field.
Challenges with directly measuring structural loads has led to the use of indirect (or surrogate) measurements. One example is the use of tibial accelerations measured from body-worn inertial measurement units (Figure 4) as a field-based indicator of tibial bone loading (Lafortune et al., 1996; Milner et al., 2006). Nevertheless, the relationship between tibial accelerations and the forces, stresses, and strains experienced by the tibia bone remains uncertain (Sheerin et al., 2019; Zandbergen et al., 2023). Another example is the indirect estimation of structural loads from wearable sensor setups in combination with advanced machine-learning methods (David et al., 2024; Hooren et al., 2024; Rasmussen et al., 2023). Although the results of this work are promising, these methods currently remain unavailable for common use. Therefore, despite several options for quantifying structural loads directly or indirectly, their use is still primarily bound to a lab context (Figure 2).
Whole-body load
Besides internal forces acting on individual tissues and structures, the body is exposed to external forces. Ground reaction forces (GRFs; i.e., the force following from foot–ground interactions) especially, are a key driver of muscular action, and are in turn affected by muscle contractions. Hence, GRFs directly contribute to the internal loads experienced by the distinct parts of the musculoskeletal system, and describe the biomechanical load experienced by the musculoskeletal system as a whole. Although associations between GRFs and running performance or injury mechanisms may thus be expected, such relationships remain ambiguous (Gruber, 2023; van der Worp et al., 2015; Zadpoor and Nikooyan, 2011), and GRF is often a poor predictor of the loads experienced at a structural level (Loundagin et al., 2018; Matijevich et al., 2019).
Whilst GRF is unlikely to be sufficiently informative for the prevention or treatment of tissue- or structure-specific pathologies, GRF can still provide a generic indicator of cumulative loading of the total musculoskeletal system. In contrast to tissue- and structure-specific loads, GRFs can be measured relatively easily and non-invasively from force platforms. Unfortunately, force platforms are not suitable for multidirectional sport-specific training and competition environments. Different approaches have been explored to estimate GRF from wearable devices in the field.
Probably the most intuitive method to measure GRF in the field is by using instrumented insoles (Figure 5), which are worn in the shoe to provide a summed measure of the pressure that the foot exerts on the ground. Although pressure insoles can estimate GRF for running and jumping fairly well, their accuracy is comprised for high-intensity movements common to multidirectional sports (Burns et al., 2018; Park et al., 2016; Peebles et al., 2018; Renner et al., 2019; Seiberl et al., 2018). Moreover, practical limitations such as movement restrictions, added mass in the shoe, and discomfort (Ramirez-Bautista et al., 2017), still form a substantial barrier for the use of insoles to monitor GRF on a large scale in the field.
Another method for measuring GRF is by using body accelerations. There is a direct relationship between force and acceleration, as described by Newton’s 2nd law (F=m·a). Based on this relationship, segmental accelerations have been used to indirectly estimate GRF, either from motion capture (Bobbert et al., 1991; Verheul et al., 2024, 2019) or body-worn accelerometers (Gurchiek et al., 2017; Nedergaard et al., 2018; Wundersitz et al., 2013). Especially the use of wearable accelerometers is appealing to enhance field-based load monitoring practices. Despite promising results, however, the successful application of GRF estimations for load monitoring in multidirectional sports remains to be established.
Besides GRF, other accelerometry-derived metrics have been explored to assess whole-body loading. Cumulative acceleration metrics, such as PlayerLoad, New Body Load, Dynamic Stress Load, and Force Load (Barrett et al., 2014; Boyd et al., 2011; Colby et al., 2014; Ehrmann et al., 2016; Gaudino et al., 2014; Page et al., 2015), are particularly common in multidirectional sports. These metrics are typically based on accelerations measured from accelerometers embedded into trunk-worn GPS sensors (Figure 5) and are thought to provide an indication of the cumulative external impacts that the body experiences during training and competition. Nevertheless, the premise underpinning these metrics that accelerations of individual segments appropriately represent the whole-body acceleration is probably not valid (Nedergaard et al., 2017). Moreover, evidence for a relationship between whole-body accelerations and loads acting on a tissue or structural level is currently lacking. Therefore, even if an association between cumulative acceleration metrics and changes in performance or injury risk is observed, this cannot provide an explanation for the underlying mechanisms of the association.
In short, GRF or accelerometry-derived metrics as indicators of whole-body loading are more viable to measure in the field, compared to tissue and structural loads (Figure 2). On the flipside, it is important to consider that their relevance and value for assessing load-adaptation pathways at a structural or tissue level remains questionable.
Moving from lab to field
A big hurdle for understanding biomechanical load-adaptation pathways in multidirectional sports is the difficulty of quantifying biomechanical loads. As we have seen, this is primarily the result of insufficient ways to accurately measure biomechanical information in natural training and competition environment, such as a football or rugby pitch. Nevertheless, recent developments do give reason for optimism.
Kinematic information is becoming increasingly available in field contexts and may be used to gain insights on kinetics too. Examples of recent advances include full-body wireless inertial sensor suits (e.g., Xsens; Roetenberg et al., 2013) or non-invasive markerless motion capture techniques (e.g., Theia3D (Kanko et al., 2021) or OpenCap (Uhlrich et al., 2023)). At a whole-body level, kinematics from inertial-sensor suits have been used to provide GRF estimates during walking (Karatsidis et al., 2017), whereas markerless motion capture can provide estimates of GRFs for various sports movements (Lichtwark et al., 2024; Verheul et al., 2024). At a structural level, inertial and markerless motion capture may be used to non-invasively estimate joint and muscle-tendon forces (Konrath et al., 2019; Uhlrich et al., 2023). These developments show great potential and can make large-scale biomechanical load monitoring at different levels of the musculoskeletal system more accessible soon.
The above-mentioned work primarily relies on conventional mechanical methods and biophysical models. Given the often-limited input information in multidirectional sports, such approaches are not always feasible. An area that has rapidly developed in recent years uses advanced machine-learning approaches to predict biomechanical variables of interest from minimal input information (e.g., one or several acceleration signals). For example, neural network methods have been used to predict GRF (Alcantara et al., 2022; Chen et al., 2025; Johnson et al., 2021; Pogson et al., 2020; Wouda et al., 2018) and forces acting on individual structures (Hooren et al., 2024; Stetter et al., 2019) from body-worn inertial sensors for various multidirectional-sports tasks. These studies show promising results. An important next step is to examine if estimated forces can indeed be monitored longitudinally and associated with musculoskeletal adaptations. If so, this can enable major progress in understanding biomechanical load-adaptation pathways in multidirectional sports.
Practical considerations
To effectively investigate and describe biomechanical load in the field, the relevance and validity of load metrics used should be carefully considered. Body-worn sensor technologies, especially, have opened the door for accessible proxies and estimates of whole-body load indicators. Though promising, the applied researcher or practitioner should keep in mind that their relationship with established markers of tissue or structural load (or their relevance in the context of the adaptive or injury mechanisms) have not always been validated.
Further longitudinal work is required to enhance the understanding of biomechanical load-adaptation pathways in multidirectional sports. For example, changes observed over time in individual load measurements (e.g., due to technique changes in a fatigued state) can be insightful when assessing generic adaptations to training but cannot be used to directly infer on load-adaptation pathways for individual tissues or structures. This highlights the need to carefully investigate field-based metrics alongside measures of tissue and structural adaptations, e.g., from tissue biopsies or ultrasound scanning. Such investigations can be used to establish the relationships between available biomechanical load metrics and the adaptive or injury mechanisms occurring at internal levels.
Summary
In summary, several important points are highlighted in this blog:
- Biomechanical loads and load-adaptations pathways can be assessed at the whole-body, structural, and tissue level of the musculoskeletal system.
- Detailed quantification of biomechanical loads experienced during multidirectional sports, and adaptations at a tissue and structural level, are essential for understanding load-adaptation pathways.
- In contrast to whole-body biomechanical loading, the availability of field-viable structural- or tissue-load measurements is yet limited.
- The value and limitations of biomechanical load metrics at different levels should be critically evaluated, especially alongside potential adaptations.
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