Abstract
The study purpose was to follow a prospective cohort study design to use gait kinematic parameters to identify the risk factors and to develop a statistical model to predict running-related lower limb injuries of sportspersons.
Materials and methods. BTS G-WALK® gait analysis system was used to collect gait kinematic data of 87 subjects from an institute of physical education and sports science.
The subjects were followed for a full academic season after which the researcher inquired about their injury occurrences. Binary logistic regression was used to develop a prediction model to predict lower limb injuries of sportspersons.
Results. The result of the study revealed that increasing Range of Obliquity, Range of Tilt and Range of Rotation were associated with increased likelihood of future running-related lower limb injury. However, the lower Symmetry Index was associated with increase in the likelihood of future running-related lower limb injury.
Conclusions. The study confirmed that it is possible to predict injury, but for practical implication further research is essential with a bigger sample size.
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Description
This study develops a model to predict running-related lower limb injuries using gait kinematic parameters.