Travis W. Beck

The purpose of the present study was to compare potential changes in the force/surface EMG cross spectrum following concentric versus eccentric exercise. Twenty-six healthy, college-aged men (mean ± SD age = 24.0 ± 3.7 yrs) performed submaximal isometric muscle actions at 60% of the maximum voluntary contraction of the dominant (exercised) and non-dominant (non-exercised) forearm flexors before (PRE) and immediately after (POST) concentric and eccentric exercise. The concentric and eccentric exercise interventions were randomly ordered and performed on days separated by at least 48 h of rest. Both interventions required the subjects to perform six sets of ten maximal muscle actions with the dominant forearm flexors. The surface EMG signal was detected from the biceps brachii of both the dominant and non-dominant limbs during the PRE and POST submaximal contractions. The surface EMG and force signals were selected from the middle six seconds of each contraction. The signals were then analyzed for amplitude and frequency content, and the surface EMG/force cross spectrum was calculated. The total power present in the cross spectrum, as well as the cross spectrum's mean frequency (MNF) were also determined. There were distinct changes in cross spectral power that were dependent on the limb and/or exercise intervention. Cross spectral power increased following both exercise interventions, but the increase was greater for the dominant than the non-dominant limb. In addition, cross spectral MNF increased for both limbs following the concentric exercise, but decreased after the eccentric exercise. These findings indicated that the cross spectral power and MNF responses were dependent on the limb that was exercised and the type of exercise intervention that was performed.

Purpose To evaluate the potential neural cross-over effect between the vastus lateralis muscles in different postural resting positions. Methods Subjects exercised on an upright cycle ergometer, using only their dominate leg, for 2 min at 30 % VO₂ peak. Following this warm-up, subjects then cycled (still using only their dominant leg) for 30 min at 60 % VO₂ peak. After the aerobic phase, subjects cooled down (again, using only their dominant leg) for 2 min at 30 % VO₂ peak. Resting mechanomyography mean frequency was measured prior to and following aerobic exercise. Results There was an approximate 6.3 ± 6.8 and a 10 ± 5.1 % increase (upright sitting position with the subject’s knee joint angle fixed at 180°); an approximate 7 ± 6.6 and a 16.1 ± 6.5 % increase (upright sitting position with the subject’s knee joint angle fixed at 90°); an approximate 0.5 ± 6.8 and 3.7 ± 5.6 % increase (lying supine position with the subject’s knee joint angle fixed at 180°); and an approximately 2 ± 8.3 and 2.5 ± 8.6 % increase (lying supine position with the subject’s knee joint angle fixed at 90°) in normalized mechanomyography mean frequency after aerobic exercise for the dominant and non-dominate vastus lateralis muscles, respectfully. Conclusion There appears to be a statistically significant neural cross-over effect for the vastus lateralis muscle, during three of the four postural resting positions, with the non-dominant vastus lateralis muscle having a greater increase in mechanomyography mean frequency.

Introduction: We examined the etiology of the electromyographic (EMG) spectral shift during dynamic fatigue. Methods: Nineteen subjects (mean ± SD age = 22.4 ± 1.6 years) performed 50 consecutive maximal concentric isokinetic contractions of dominant leg extensors. Surface EMG signals were detected from the vastus lateralis, rectus femoris, and vastus medialis during each contraction, processed with a wavelet analysis, and the resulting spectra were decomposed with a nonparametric spectral decomposition procedure. Results: The results indicated that the decreases in EMG frequency during the 50 contractions were generally due to reductions in high‐frequency power and increases in low‐frequency power. In addition, the spectral shifts were most pronounced for the rectus femoris, followed by the vastus lateralis, and then the vastus medialis. Conclusions: The spectral decomposition procedure is much more sensitive for tracking dynamic fatigue than is EMG mean frequency or median frequency. Muscle Nerve 50: 95–102, 2014

Purpose To examine the effects of low-load knee extensor training to fatigue with and without blood flow restriction (BFR) on calf vascular conductance, calf venous compliance, and peripheral arterial stiffness in middle-aged individuals. Methods Eleven men (55 ± 8 years) and five post-menopausal women (57 ± 5 years) completed 6 weeks of unilateral knee extensor training with one limb exercising with BFR (BFR limb) and the contralateral limb exercising without BFR (free flow, FF limb). Before and after the training, femoral pulse wave velocity (PWV), calf blood flow (normalized as conductance), and calf venous compliance were measured in each limb. Results PWV increased following training in both limbs (main effect of time, p = 0.036; BFR limb 8.9 ± 0.8 vs. 9.5 ± 0.9 m/s, FF limb 9.0 ± 1.2 vs. 9.0 ± 1.1; Pre vs. Post). Calf blood flow increased (p = 0.026) in the FF limb (25.0 ± 7.0 vs. 31.8 ± 12.0 flow/mmHg; Pre vs. Post) but did not change (p = 0.831) in the BFR limb (29.1 ± 11.3 vs. 28.7 ± 11.5 flow/mmHg; Pre vs. Post). Calf venous compliance did not change in either limb following training. Conclusions These results suggest low-load BFR resistance training to fatigue elicits small increases in peripheral arterial stiffness without eliciting concomitant changes in venous compliance. In addition, unlike low-load knee extensor training without BFR, training with BFR did not enhance calf blood flow.

Stical power: (a) the significance level (α), (b) the magnitude or size of the treatment effect (effect size), and (c) the sample size (n). Of these 3 factors, only the sample size can be manipulated by the investigator because the significance level is usually selected before the study, and the effect size is determined by the effectiveness of the treatment. Thus, selection of an appropriate sample size is one of the most important components of research design but is often misunderstood by beginning researchers. The purpose of this tutorial is to describe procedures for estimating sample size for a variety of different experimental designs that are common in strength and conditioning research. Emphasis is placed on selecting an appropriate effect size because this step fully determines sample size when power and the significance level are fixed. There are many different software packages that can be used for sample size estimation. However, I chose to describe the procedures for the G*Power software package (version 3.1.4) because this software is freely downloadable and capable of estimating sample size for many of the different statistical tests used in strength and conditioning research. Furthermore, G*Power provides a number of different auxiliary features that can be useful for researchers when designing studies. It is my hope that the procedures described in this article will be beneficial for researchers in the field of strength and conditioning....

Als in combination with a trend plot to examine changes in EMG intensity patterns during maximal, fatiguing isokinetic muscle actions. Eleven men (mean ± SD age = 22.4 ± 1.1 years) and 7 women (mean ± SD age = 22.7 ± 2.1 years) performed 50 consecutive maximal concentric isokinetic muscle actions of the dominant leg extensors at a velocity of 180°·s−1. During each muscle action, a bipolar surface EMG signal was detected from the vastus lateralis. All signals were then processed with a wavelet analysis designed specifically for EMG signals, which resulted in EMG intensity patterns. The patterns for each subject were then analyzed with a trend plot, which provided information regarding the changes that occurred because of fatigue. The results indicated that for all the 18 subjects, the EMG intensity patterns moved in a predictable manner in pattern space, but the changes to the patterns were different for each subject. These findings reflect the complex changes that occur in the EMG signal during fatigue. These changes cannot be characterized fully with a single amplitude and center frequency parameter and can be useful for athletes and coaches who need to track the fatigue status of individual muscles....