A pilot study on objective quantification and anatomical modelling of in vivo head and neck positions commonly applied in training and competition of sport horses.

This research focuses on quantifying in vivo head and neck positions (HNP) of sport horses and creating a model to estimate the load on their cervical vertebrae. The purpose of this research is to better understand these positions, potentially improving equine welfare during training and competition.

Objective and Methodology

The purpose of the study was twofold:

• To empirically quantify the head and neck positions (HNP) of sport horses that occur during training and competition.
• To develop a model to estimate the corresponding physical load exerted on the horses’ cervical vertebrae, using the biomechanical concept of nuchal ligament loading.

The study employed markers glued at specific points on seven Warmblood breed horses while they were filmed walking in straight lines. These points represented the diverse range of HNPs encountered during training and competition. The researchers then used two-dimensional video frames to determine the specific angles and distances characterizing the five HNPs.

Results of the Study

The study found significant variations in the angles and distances among the five HNPs. This finding was especially noticeable for the line between C1 and T6 with the horizontal and the line connecting the facial crest (CF) and C1 with the vertical. The differences were also significant in the vertical distance from CF to the lateral styloid process of the radius (PS).

Furthermore, according to the data, the largest load appeared to be at the origin of C2 for all HNPs (except for ‘hyperextended’ HNP5). The ‘hyperflexed’ HNP4 showed the greatest loading values on the nuchal ligament origins at all locations.

Conclusions and Implications

The study concluded that head and neck positions could in fact be quantified within a plane based on defined anatomical landmarks and home-video captured images.

Most importantly, the proposed modeling could eventually be employed to estimate loadings for individual horses, enabling the ergonomic optimization of their respective HNPs. This could markedly improve equine welfare by minimizing overstraining during training and competition. However, potential implementation of these conclusions would necessitate further research.