Day One Testing
Body Mass and Body Composition. We measured body mass using a force plate (Hawkins Dynamics, ME). Immediately after weighing, we assessed body composition in the seated position using bioelectrical impedance analysis (Omron NBF-306C).
Force Plate Testing. We assessed force-time metrics via a force plate (Hawkins Dynamics, ME). We evaluated force-time output during a countermovement jump (CMJ), rebound jump, and isometric mid-thigh pull. Participants then engaged in a short dynamic warm-up that consisted of five dynamic stretches of 10 yards each and three submaximal sprints of 70%, 80%, and 90% intensity.
Sprint Testing. After a three-minute test period following the last submaximal sprint, the athletes completed two maximum 20-meter sprints, measured with electronic timing gates (VALD Performance, Australia). The athletes were given a 15-meter acceleration zone and instructed to reach full speed before hitting the first timing gate. We used the best of two attempts in the analyses.
Day Two Testing
Three days later, the athletes returned individually for EMG analysis. Before sprinting, we placed electrodes, sensors, attached leads, and foot insoles (Noraxon, AZ). The participants executed two 20-yard flying sprints with four minutes between each repetition. The first sprint served as an MVIC, and we normalized the subsequent sprint to the MVIC. This method was chosen instead of a traditional (prone/supine) isometric contraction as Kyröläinen et al. (2005) found that the traditional isometric contraction is not a good indicator of the activation potential since some muscles recorded amplitudes far greater during sprinting (7).
Most often, but not always, the muscles of interest are the GM, RF, BF, GN, tensor fasciae latae (TFL), and TA. Thus, we used EMG to measure the GM, RF, BF, TFL, GN, and TA for each sprint using the MR Noraxon Software v3.4 (Noraxon USA Inc., Scottsdale, AZ). Standard sEMG electrodes were positioned with a 2-cm spacing along the longitudinal axis of the muscles on the right leg, based on the anatomical reference points and following the SENIAM guidelines for sensor placement. We palpated muscles to identify the surface electrode positioning. We shaved the area of each electrode site using a razor and cleaned it with alcohol-soaked cotton wool. Next, we secured the electrodes with tape to reduce motion artifacts (16;17). We set the acceptable impedance (noise) below ten microvolts (uV). Before data treatment, we applied custom-made digital filtering (Bandpass filter; 20–500 Hz), rectification, and smoothing (Root Mean Square algorithm, 100 ms) to the recorded signal. We calculated the reliability of relative muscle activation patterns using intraclass correlation coefficients (ICC; Version 29, SPSS Statistics, IBM, Armonk, NY, USA). The ICCs were: Swing − 0.03 for GM, 0.80 for TFL, 0.87 for BF, 0.60 for RF, 0.67 for GN, & 0.40 for TA; Stance − 0.83 for GM, 0.79 for TFL, 0.89 for BF, 0.07 for RF, 0.83 for GN, & 0.34 for TA.
We inserted Noraxon myoPRESSURE ™ foot insoles into the participant's footwear to determine the swing and stance phases and the application of force by the feet during the stance phase. We instructed the participants to stand on each leg for three seconds to create average pressure plots. We recorded and analyzed the relative EMG activity of the right leg at touchdown and during the early-swing, mid-swing, and late-swing phases. We recorded but did not analyze the peak EMG to an epoch. We did not delineate the stance phase. When we found significance between groups with the overall swing phase, we parsed apart the swing phases. Specifically, the early swing phase begins when the toes leave the ground and ends one-third through the swing phase. The middle swing begins one-third through the swing phase and ends at approximately two-thirds of the total time to complete the swing phase. The late-swing phase starts about two-thirds of the total time to complete the swing phase and ends at ground contact (4; 5).
Muscular co-contraction, or muscular coactivation, is the simultaneous contraction of agonist and antagonist muscles crossing the same joint (18). We assessed muscular co-contraction as follows:
$$CCI=\frac{EMGS}{EMGL}*\left\{EMGL*\left(EMGS+EMGL\right)\right\}$$
Where CCI = Co-contraction index
Where EMGS = level of activity in the less active muscle
Where EMGL = level of activity in the more active muscle (19)
Statistical Analysis. There was no a priori power analysis for this investigation. We split the athletes dichotomously into two groups using a median split of maximum-speed sprinting velocity during the 20 m sprint obtained during testing on day one. We compared groups using unpaired, two-tailed t-tests or Mann-Whitney tests when data failed (p > 0.05) the Shapiro-Wilk test for normality. We also used a Chi-squared test to compare the proportion of trained adults in each group. Using the EMG data from day two, we compared CCI using mixed effect models with Gait Phase (repeated factor) and Group (Independent factor) separately for the ankle (TA & GN), knee (RF & BF), and hip (GM & TFL) to address our primary hypothesis. We also compared gait phase duration using a mixed effect model with Gait Phase (repeated factor) and Group (Independent factor). Next, we compared muscle activation (expressed as absolute uV & as a percentage of their MVC) using mixed effect models with Gait Phase (repeated factor) and Group (Independent factor) for each of the six muscles examined. We employed Sidak's multiple comparisons test for all mixed effects models for post hoc analyses. Additionally, we used Spearman's correlational analysis to relate 20 m maximum-speed sprint velocity with several variables of interest. We present values as mean ± standard deviation (SD) for data normally distributed or median [interquartile range] for data not normally distributed. We analyzed data using GraphPad Prism (version 10.0 for Windows, GraphPad Software, San Diego, CA, USA).