27, 28 and 29 The impact of adding sEMG to a prediction equation for muscle force that already includes a measure of muscle size was less than expected. this website Hahn30 used sEMG to predict isokinetic knee torque using a multiple linear regression. An equation containing limb position, height, body mass and sEMG produced R2 values of 0.67–0.71. Similarly, Youn and Kim 31 used sEMG from the biceps brachii and brachioradialis for elbow flexion prediction and found correlations of 0.90 and above between observed and predicted forces. One possible reason that

sEMG had a greater contribution to the prediction of muscle strength in the aforementioned studies may be the inclusion of activity from multiple muscles, including antagonistic co-activation. Joint torque is the product of a multiple muscle system and we only included sEMG activity from the primary agonist. Praagman and colleagues32 observed sEMG of elbow flexors and extensors during static contractions at varying joint angles and pronation–supination positions. They found that joint angle, moment arm, and muscle length influenced the EMG amplitude. Similarly, Brookham and colleagues33 found that these same variables, and the load applied to the joint, influenced the amount of co-activation MAPK inhibitor present during isometric contractions. The inclusion of sEMG from multiple muscles at different joint angles may be beneficial for

the prediction of muscle strength. However, in agreement with the current findings, Hahn30 reported that the primary force predictors for knee torque were the position of the limb, body mass and body height, followed secondarily by sEMG. Anthropometrics provides a strong prediction equation for the estimation of isometric elbow flexion strength using multiple linear regression. While muscle activation, as measured by RMS sEMG activity, accounted for a significant (p < 0.05) amount of variance in most prediction equations, its contribution was comparable to the

use of an additional anthropometric variable. Therefore, Methisazone the hypothesis that muscle activation would improve the prediction equation more than anthropometrics alone cannot be entirely accepted. It was found that the strongest prediction equation for both males and females included BW, forearm length, and elbow circumference. This study was supported by the Natural Sciences and Engineering Research Council of Canada. This work is dedicated to the memory of Dr. Walter Kroll. “
“Ankle ligament sprain is the most common sports injury,1, 2, 3 and 4 accounting for 15% of all sport injuries in 15 National Collegiate Athletic Association sports.4 Among the ankle ligament injuries, lateral ankle sprain is the most common type and typically caused by excessive inversion, particularly when the ankle is in a plantarflexed position.