The practice of running is a physical activity that receives an increasing adherence around the world, due to factors such as its easy implementation to the routine of the practitioner and low cost besides numerous benefits, such as reduction and control of blood pressure, increase of cardiorespiratory capacity, stimulation to the bony formation, reduction of the stress and improvement of the sleep pattern. However, inadequate biomechanical preparation of the runner may be accompanied by an increase in the prevalence of lower limb injuries, due to factors such as inadequate coordination of the limbs or due to poor performance of the musculoskeletal structures, especially the feet responsible for the absorption of loading and propulsion during running. Studies show that specific training of the musculature of the feet improves the balance, the structure of the arches and its muscular strength, besides preventing the occurrence of joint deformities. Therefore, the ankle-foot complex is key piece during the biomechanical approach of running. However, runners may respond variably to the same therapeutic approach of an exercise program for the intrinsic musculature of the foot because of their variable kinematic patterns of the ankle-foot complex during locomotion. Thus, it is necessary to verify the existence of the possibility of these subjects being classified into distinct homogeneous subgroups, taking into account these kinematic patterns, before the implementation of a therapeutic approach. In addition, it is also important to identify how these subgroups respond to an exercise intervention for the foot muscles in terms of the ankle-foot kinematics. Therefore, this project aims to verify, using an artificial intelligence tool, the possibility of runners being classified into homogeneous subgroups according to their ankle-foot complex kinematic patterns and to analyze the effects of an exercise program for the intrinsic musculature of the foot in kinematic patterns of these different groupings. This is a retrospective cross-sectional study based on data from 50 runners.
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