Dr. Jen Symons received the 2018 James M. Wilson Award in recognition of her contributions to equine research. As a mechanical and biomedical engineer, she collaborated with Dr. Sue Stover and other researchers on the study ‘Modelling the effect of race surface and racehorse limb parameters on in silico fetlock motion and propensity for injury.’
At the time of the study, she was part of the Biomedical Engineering Graduate Group at UC Davis. She is now an assistant professor of mechanical and biomedical engineering at the Shiley School of Engineering at the University of Portland. She explains her role in this project as follows:
Previous studies have measured galloping horses on different race surfaces. However, the relationship between race surfaces and racehorse limb motions is complex involving many factors within the horse and race surface, like tendon stiffness and race surface depth. The aim of this study was to consider the effect of changing factors on fetlock motion during gallop, and to determine which factors produce the greatest changes.
We used a computer model of a virtual racehorse galloping on a virtual race surface that has produced consistent limb motions with those measured from actual galloping racehorses on measured race surfaces. This computer model is an economical tool that allows us to gain knowledge of many different factors related to race surfaces and racehorses, without subjecting any animals to research protocols.
Study results indicate that the depth of the upper, softer layer of the race surface has the greatest potential to influence fetlock flexion. Increasing the depth of this layer within the model decreased the degree of simulated fetlock flexion during gallop. Practically, this parameter is related to race surface maintenance, specifically depth of harrowing. Other parameters that produced lesser changes in fetlock motion included lower layer race surface mechanics and racehorse tendon/ligament stiffness. Changes in friction between the hoof and race surface produced the smallest changes in fetlock motion.
These computer model results provide evidence to guide race surface management decisions to reduce the incidence of fetlock injuries in racehorses, particularly through the depth of harrowing race surfaces.