The acute effects of kinesio-taping on movement kinematics and muscle co-activation in rowing athletes.

BACKGROUND: Rowing-related low back disorders may occur from inconsistent technique, high trunk flexion and training volumes, overactivation of paraspinal muscles, and fatigue. OBJECTIVE: To examine if kinesiology tape (KT) affixed to the trunk dorsum affects muscular co-activation and neuromuscular control to limit dangerous rowing movements and associated injuries. METHODS: Participants (n= 18) completed two 2000 m rowing trials under BASELINE and KT conditions. KT was applied to the skin superficial to the paraspinals bilaterally with 60% pre-strain. Participants were instructed to minimize any sensation of tension. Whole body kinematics were obtained using inertial measurement units (IMUs), and surface electromyograms (EMGs) were recorded from trunk and lower extremity. Changes in joint range-of-motion (ROM) and co-activation indices (CAIs) were analyzed for shoulder, lumbar, hip, and knee. RESULTS: Responding participants (n= 5) were identified by reduced maximum lumbar flexion during the KT condition. As expected, significant differences occurred in maximum and minimum lumbar flexion/extension between responders and non-responders to KT. Additionally, there was significant reduction in mean trunk muscle co-activation in both those who did and did not respond to KT through reductions in maximum lumbar flexion. CONCLUSION: KT can be an effective at reducing mean trunk co-activation during a rowing trial in the flexed catch position. Variable responses suggest that further work is necessary to optimize the efficacy of sensory cues derived from KT during rowing movements.

A need for speed: Objectively identifying full-body kinematic and neuromuscular features associated with faster sprint velocities.

Sprinting is multifactorial and dependent on a variety of kinematic, kinetic, and neuromuscular features. A key objective in sprinting is covering a set amount of distance in the shortest amount of time. To achieve this, sprinters are required to coordinate their entire body to achieve a fast sprint velocity. This suggests that a whole-body kinematic and neuromuscular coordinative strategy exists which is associated with improved sprint performance. The purpose of this study was to leverage inertial measurement units (IMUs) and wireless surface electromyography (sEMG) to find coordinative strategies associated with peak over-ground sprint velocity using machine learning. We recruited 40 healthy university age sprint-based athletes from a variety of athletic backgrounds. IMU and sEMG data were used as inputs into a principal components analysis (PCA) to observe major modes of variation (i.e., PC scores). PC scores were then used as inputs into a stepwise multivariate linear regression model to derive associations of each mode of variation with peak sprint velocity. Both the kinematic (R 2 = 0.795) and sEMG data (R 2 = 0.586) produced significant multivariate linear regression models. The PCs that were selected as inputs into the multivariate linear regression model were reconstructed using multi-component reconstruction to produce a representation of the whole-body movement pattern and changes in the sEMG waveform associated with faster sprint velocities. The findings of this work suggest that distinct features are associated with faster sprint velocity. These include the timing of the contralateral arm and leg swing, stance leg kinematics, dynamic trunk extension at toe-off, asymmetry between the right and left swing side leg and a phase shift feature of the posterior chain musculature. These results demonstrate the utility of data-driven frameworks in identifying different coordinative features that are associated with a movement outcome. Using our framework, coaches and biomechanists can make decisions based on objective movement information, which can ultimately improve an athlete's performance.