Abstract
The knowledge of motion dynamics during running activity is crucial to enhance the development of rehabilitation techniques and injury prevention programs. Recent studies investigated the interaction between joints, using several analysis techniques, as cross-correlation, sensitivity analysis, among others. However, the direction of the joints pairing is still not understood. This paper proposes a study of the influence direction pattern in healthy runners by using kinematic data together with partial directed coherence, a frequency approach of Granger causality. The analysis was divided into three anatomical planes, sagittal, frontal, and transverse, and using data from ankle, knee, hip, and trunk segments. Results indicate a predominance of proximal to distal influence during running, reflecting a centralized anatomic source of movements. These findings highlight the necessity of managing proximal joints movements, in addition to motor control and core (trunk and hip) strengthening training to lumbar spine, knee, and ankle injuries prevention and rehabilitation.
Highlights
Running is a popular activity and about 38 million Americans practice this sport regularly (NSGA, 2011)
In recreational and competitive forms, injuries are common and the incidence of musculoskeletal kind ranges from 19.4% to 92.4%, with the knee accounting for 50% of all lower extremity problems
This study was to determine the patterns of directional influence among ankle, knee, hip, and trunk, in the sagittal, frontal, and transverse planes separately in kinematic data from healthy runners
Summary
Running is a popular activity and about 38 million Americans practice this sport regularly (NSGA, 2011). In recreational and competitive forms, injuries are common and the incidence of musculoskeletal kind ranges from 19.4% to 92.4% (van Gent et al, 2007), with the knee accounting for 50% of all lower extremity problems. Considering that these injuries are associated with altered joints movement (Powers, 2003; Chuter and Janse de Jonge, 2012), a thorough understanding of the complex nature of functional movements is important and could improve prevention, training, and rehabilitation. Those studies used several analysis techniques, such as cross-correlation and vector coding (Pohl and Buckley, 2008), sensitivity analysis (Nott et al, 2010), coupling angle and continuous relative phase (Chang et al, 2008), and principal component
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