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Sports injury biomechanics: Success stories, challenges and opportunities

I have just spent part of the Southern Hemisphere winter chasing the sun in the UK and Europe – a combination of work and play! Along the way I attended the annual conferences of the International Society of Biomechanics in Sport (ISBS) in Liverpool and the European College of Sport Science (ECSS) in Sevilla. I’ve listed the sessions that I took part in here. This post is a summary of a presentation I gave during a session at ECSS that was arranged by the International Society of Biomechanics (ISB) to celebrate their 50th anniversary.

Model of injury causation

The behaviour of biological tissues under load adheres to the principles of material mechanics. When tissue is loaded, internal resistance develops in response to the external load. This is known as stress and can cause strain, which is deformation of the tissue. If the applied load causes strain that exceeds the elastic limit – the point beyond which the tissue can no longer return to its original form - it will result in irreversible damage. If the ultimate stress is reached, the tissue will fully rupture. So, in the most simplistic terms, we can think about the mechanism of any sports injury being due to the applied load exceeding the tissue’s capacity to withstand that load.

This ‘inciting event’ is incorporated in comprehensive models of injury causation along with factors that are more distant from the injury mechanism. These include intrinsic risk factors that predispose an athlete to injury and exposure to extrinsic risk factors that make them susceptible when an inciting event occurs. A complete explanation of the injury mechanism describes the playing situation, player/opponent behaviour and whole body and joint level biomechanics. This knowledge forms the foundation of biomechanically informed injury risk reduction programmes.

Success stories

Programmes based on knowledge of the joint and whole body biomechanical mechanisms of injury have been shown to be able to successfully reduce the incidence of non-contact anterior cruciate ligament (ACL) rupture. This type of injury typically occurs during a side step cutting manoeuvre (playing situation), when an athlete is changing direction to avoid an opponent (player behaviour), affecting the plant leg during the change of direction step (gross biomechanical description).

The summation of a large body of work completed by many laboratories over several decades has identified the detailed, joint-level biomechanical description, whereby the combination of flexion, valgus and internal rotation external joint moments during the weight acceptance phase cause strain that may exceed the ligament’s load tolerance. These knee joint loads are increased by the trunk leaning away from the direction of travel, dynamic knee valgus, a more extended knee joint, and rear foot strike postures*. Also, forces produced by the muscles acting at the hip, knee and ankle can increase the support to the knee joint to counter the applied loads. Having established the mechanism of injury and biomechanical risk factors, one can now design physical interventions that specifically aim to address these modifiable features.

* Read more about the relationship between knee joint loads, side stepping technique and change of direction performance here.

The efficacy of the intervention can be enhanced by targeting athletes who are most at risk. Athlete screening is therefore recommended to support decisions about who may most benefit from interventions. Screening has become somewhat of a controversial topic of late, but the rationale for it remains sound if (i) there is a strong relationship between the modifiable biomechanical risk factor and ACL load and/or injury rates, (ii) the measure demonstrates good reliability, and (iii) the screening protocol improves the cost-effectiveness of the injury prevention programme compared to blanket application of the programme to all athletes.

A vital component of injury prevention strategies that is often overlooked is the adoption of the intervention by the athletes it is intended to help. A framework for ACL injury prevention therefore firstly includes implementation of the training intervention in an “ideal” scenario, where there is a high level of control. This must be followed by implementation in a real-world context, with modifications potentially needed to account for sporting and individual needs. Finally, the efficacy of widespread implementation of programmes across sporting communities needs to be evaluated, to fully address the injury problem.


Numerous trials have implemented ACL injury prevention programmes around the world with good success, in particular when the level of monitoring and control is fairly high. However, we currently face certain challenges in order to produce similar results with a wider reach across different populations and in other types of injuries.

Inverse dynamics and musculoskeletal modelling techniques are suitable to estimate joint loads and have provided a strong evidence base to understand injury mechanisms, such as in the ACL example discussed here. However, their use does have some limitations in that they are largely limited to the laboratory, which compromises the ecological validity of the performance of many sports tasks and prevents in-field monitoring. Because of these limitations, researchers have sought simpler measures as a proxy for joint or segment load. For example, ground reaction force (GRF) is widely used in studies on running injury mechanisms and is often assumed to represent tibial bone load. More recently, shin-mounted accelerometers have been used to measure tibial acceleration as a proposed proxy measure of tibial force. However, both approaches are flawed as they ignore the substantial contribution of muscle contractions to the load experienced by the bone. So, it remains a challenge to find accessible ways of estimating load that reduce the time, cost of testing and can be done in a field setting.

Another challenge appears on the other side of the “injury equation”, where intrinsic risk factors represent the individual’s capacity to withstand load. Tissue geometry and material properties, for example, vary widely between individuals and influence the stress and strain that occur in response to load. So, in prospective injury studies, it can be difficult to tease out the mechanism of injury as this may be washed out by the presence of intrinsic factors that vary between individuals. These factors are therefore generally not incorporated into biomechanical models, studies of injury mechanisms or real-world intervention programmes.


Opportunities to move the field forward stem from advances in technology and data science. Following several years of development work, we are now starting to see tangible potential in wearable devices and video-based analyses to provide valid estimates of joint load.

For example, a recent publication by the research group at Vanderbilt University’s Department of Mechanical Engineering has provided the first empirical evidence for a tool to monitor tibial bone force during running, using multiple input data sources (a pressure sensing insole and a shoe-mounted inertial measurement unit) and machine learning. Perhaps not surprisingly, the force under the foot and the foot’s angle were identified as the critical signals required for the algorithm to predict bone force – this is similar to the inputs used to calculate kinetics in the laboratory!

Tools are also being developed to derive joint loads from video footage. The biomechanics group at the University of Western Australia have done so by capitalising on a vast database of side stepping trials in the laboratory from their past research on ACL injury mechanisms. First, they were able to predict knee joint loads from a small number of marker trajectories, and more recently have been enlarging 2D video datasets from these 3D motion capture resources. Another very exciting development in this space, by researchers from Stanford University, is the recent launch of a web-based platform that facilitates 3D analysis from two smartphone videos. The app computes kinematics and kinetics using deep learning and musculoskeletal simulation.

These technological advances provide the potential for an enormous reduction in the cost and time of biomechanical testing and analysis. This promise should be accompanied by a warning for practitioners and researchers, though. The usefulness of such tools depends on good quality input data and still requires a sound research question or clinical need as the starting point – just because you can measure something, doesn’t mean that you should!


Bahr, R. and Krosshaug, T. Understanding injury mechanisms: a key component of preventing injuries in sport. British Journal of Sports Medicine, 2015. DOI: 10.1136/bjsm.2005.018341.

Donnelly, C.J., et al. An anterior cruciate ligament injury prevention framework: incorporating the recent evidence. Research in Sports Medicine, 2012. DOI: 10.1080/15438627.2012.680989.

Elstub, J., et al. Tibial bone forces can be monitored using shoe-worn wearable sensors during running. Journal of Sports Sciences, 2022. DOI: 10.1080/02640414.2022.2107816.

Johnson, W., et al. On-field player workload exposure and knee injury risk monitoring via

deep learning. Journal of Biomechanics, 2019. DOI: 10.1016/j.jbiomech.2019.07.002

Matijevich, E., et al. Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: Implications for science, sport and wearable tech. Plos One, 2019. DOI: 10.1371/journal.pone.0210000.

Mundt, M., et al. Synthesising 2D Video from 3D Motion Data for Machine Learning Applications. Sensors, 2022. DOI: 10.3390/s22176522

Ulrich, S., et al. OpenCap: 3D human movement dynamics from smartphone videos, 2022. DOI: 10.1101/2022.07.07.499061.

Weir, G. Anterior cruciate ligament injury prevention in sport: biomechanically informed approaches. Sports Biomechanics, 2021. DOI: 10.1080/14763141.2021.2016925.


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