We examined the neuromuscular adaptations following 3 and 6 weeks of 80 vs. 30% one repetition maximum (1RM) resistance training to failure in the leg extensors. Twenty-six men (age = 23.1 ± 4.7 years) were randomly assigned to a high- (80% 1RM; n = 13) or low-load (30% 1RM; n = 13) resistance training group and completed leg extension resistance training to failure 3 times per week for 6 weeks. Testing was completed at baseline, 3, and 6 weeks of training. During each testing session, ultrasound muscle thickness and echo intensity, 1RM strength, maximal voluntary isometric contraction (MVIC) strength, and contractile properties of the quadriceps femoris were measured. Percent voluntary activation (VA) and electromyographic (EMG) amplitude were measured during MVIC, and during randomly ordered isometric step muscle actions at 10–100% of baseline MVIC. There were similar increases in muscle thickness from Baseline to Week 3 and 6 in the 80 and 30% 1RM groups. However, both 1RM and MVIC strength increased from Baseline to Week 3 and 6 to a greater degree in the 80% than 30% 1RM group. VA during MVIC was also greater in the 80 vs. 30% 1RM group at Week 6, and only training at 80% 1RM elicited a significant increase in EMG amplitude during MVIC. The peak twitch torque to MVIC ratio was also significantly reduced in the 80%, but not 30% 1RM group, at Week 3 and 6. Finally, VA and EMG amplitude were reduced during submaximal torque production as a result of training at 80% 1RM, but not 30% 1RM. Despite eliciting similar hypertrophy, 80% 1RM improved muscle strength more than 30% 1RM, and was accompanied by increases in VA and EMG amplitude during maximal force production. Furthermore, training at 80% 1RM resulted in a decreased neural cost to produce the same relative submaximal torques after training, whereas training at 30% 1RM did not. Therefore, our data suggest that high-load training results in greater neural adaptations that may explain the disparate increases in muscle strength despite similar hypertrophy following high- and low-load training programs.

The results of the present study indicated that there were differences between composite and individual patterns of responses for EMG and MMG parameters during moderate and heavy cycle ergometry at a constant RPE. Thus, composite models did not represent the unique muscle activation strategies exhibited by individual responses when cycling in the moderate and heavy intensity domains when using an RPE-clamp model.

Bergstrom, HC, Housh, TJ, Cochrane-Snyman, KC, Jenkins, NDM, Byrd, MT, Switalla, JR, Schmidt, RJ, and Johnson, GO. A model for identifying intensity zones above critical velocity. J Strength Cond Res 31(12): 3260-3265, 2017-The purpose of this study was to describe the V[Combining Dot Above]O2 responses relative to V[Combining Dot Above]O2peak at 4 different intensities within the severe domain and, based on the V[Combining Dot Above]O2 responses, identify intensity zones above critical velocity (CV). Twelve runners (mean ± SD age = 23.2 ± 3.0 years) performed an incremental treadmill test (ITT) to exhaustion to determine the V[Combining Dot Above]O2peak and velocity associated with V[Combining Dot Above]O2peak (vV[Combining Dot Above]O2peak). Critical velocity was determined from 4 exhaustive, constant velocity, randomly ordered treadmill runs (V1, V2, V3, and V4; V1 = highest, V4 = lowest). The V[Combining Dot Above]O2 responses were recorded during each of the constant velocity runs. Mean differences among V[Combining Dot Above]O2peak values from the ITT and the highest value recorded during the constant velocity runs were examined. The V[Combining Dot Above]O2 values at exhaustion for V1 (3.32 ± 0.10 L·min, p = 0.15) and V2 (3.27 ± 0.91 L·min, p = 0.13) were not significantly different from V[Combining Dot Above]O2peak (3.39 ± 0.96 L·min) from the ITT. The V[Combining Dot Above]O2 values at exhaustion for V3 (3.18 ± 0.88 L·min; p = 0.007) and V4 (3.09 ± 0.86 L·min; p = 0.003), however, were significantly less than the V[Combining Dot Above]O2peak from the ITT. There were intensity-dependent V[Combining Dot Above]O2 responses above CV. Based on these findings, we have hypothesized 3 intensity zones (first severe intensity zone [SIZ1], second severe intensity zone [SIZ2], and extreme intensity zone [EIZ]) within the severe and extreme domains, which are characterized by specific V[Combining Dot Above]O2 responses and may be used to design programs that maximize aerobic performance adaptations.

No previous study has investigated the applications of isolated cannabidiol (CBD) as a recovery aid in untrained human subjects after a bout of exercise-induced muscle damage. Purpose: This study aimed to investigate the effect of CBD oil on perceived muscle soreness, inflammation, and strength performance after eccentric exercise (ECC) of the elbow flexors. Methods: Thirteen untrained men (mean ± SD age, 21.85 ± 2.73 yr) performed 6 sets of 10 maximal ECC isokinetic muscle actions of the elbow flexors as part of a double-blind crossover design. Noninvasive (perceived soreness, arm circumference, hanging joint angle (JA), and peak torque (PT)) measures were taken before and after ECC, and 24, 48, and 72 h after ECC. All subjects completed both the supplement (CBD: 150 mg POST, 24 h, 48 h) and placebo (PLC: POST, 24 h, 48 h) condition separated by 2 wk. Four separate two-way repeated-measures ANOVA (condition [CBD vs PLC] Â time [PRE vs POST vs 24 h vs 48 h vs 72 h]) were used to analyze perceived soreness, arm circumference, JA, and PT. One-way repeated-measures ANOVA were used to decompose significant interactions and main effects. Results: There was no condition-time interaction or main effect of condition (P > 0.05) for perceived soreness, arm circumference, JA, or PT. There were main effects for time for perceived soreness (P = 0.000, η p 2 = 0.71) and JA (P = 0.006, η p 2 = 0.35). Conclusions:The current dose of 150 mg CBD oil at POST, 24 h, and 48 h had no effect on noninvasive markers of muscle damage in the upper extremity. At the current dose and schedule, CBD oil may not be beneficial for untrained men as a recovery aid after exercise-induced muscle damage.

External load has become a common metric for coaches to track the activity profiles of athletes during training and competition.  The advent of wearable technology has made external load monitoring accessible for more coaches.  The purpose of this study was to compare positional (attack, midfield, and defense) and game (first half to second) external loads.  An NCAA Division I women’s lacrosse team was recruited to wear triaxal accelerometers and GPS units during five non-conference games during the 2020 regular season.  The external load metrics evaluated for this study included total distance, sprint distance (> 19 km∙hr-1), number of power plays (> 3 m·s-2), top speed, and PlayerLoad.  Significance was set at p < 0.05.  No significant differences among positions were observed for full game measures (p > 0.05).  A significant main effect for time was observed for sprint distance (midfield; p < 0.001 ) and power plays (midfield; p < 0.001 and defense; p = 0.004).  While no significant differences occurred for activity profiles among positions, high-intensity efforts (sprint distance and power plays) were significantly less in the second half, likely due to fatigue.  Coaches and sports scientists can use this information to manage in-game fatigue through tactics such as strategic substitutions and time-outs, thus preserving the intensity of the activity profiles late in the game.

Verification tests to confirm graded exercise test (GXT) V˙O2max are growing in popularity, but the validity and reliability of such testing in the heat remains unknown. Purpose This study aimed to evaluate the validity and reliability of a verification test to confirm GXT V˙O2max in a hot environment. Methods Twelve recreationally trained cyclists completed a two-test protocol that included a GXT progressing 20 W·min−1 followed by a biphasic supramaximal-load verification test (1 min at 60% increasing to 110% maximal GXT wattage until failure) in a hot environment (39°C, 32% relative humidity). Rest between tests occurred in a thermoneutral room and was anchored to the duration required for gastrointestinal temperature to return to baseline. Results Mean verification test V˙O2max (51.3 ± 8.8 mL·kg−1·min−1) was lower than GXT (55.9 ± 7.6 mL·kg−1·min−1, P = 0.02). Verification tests confirmed GXT V˙O2max in 92% of participants using individual analysis thresholds. Bland–Altman analysis revealed a sizable mean bias (−4.6 ± 4.9 mL·kg−1·min−1) with wide 95% limits of agreement (−14.0 to 5.0 mL·kg−1·min−1) across a range of V˙O2max values. The high coefficient of variation (9.6%) and typical error (±3.48 mL·kg−1·min−1) indicate potential issues of test–retest reliability in the heat. Conclusions Verification testing in a hot condition confirmed GXT V˙O2max in virtually all participants, indicating robust utility. To enhance test–retest reliability in this environment, protocol recommendations for work rate and recovery between tests are provided.

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Stressors related to academic requisites, sport participation and pressure to perform may increase college athlete risk for mental health symptoms (Cox, Ross-Stewart, & Foltz, 2017; Sudano & Miles, 2017; Yang et al., 2007). The purpose of this study was to identify the level of clinically relevant self-reported mental health symptoms in National Collegiate Athletic Association (NCAA) Division III athletes and variations based on sport participation (i.e., men’s or women’s athletics; team or individual sports) over a two-year period. A nonexperimental, trend study design was used. Data analysis included descriptive statistics, chi square test, and multivariate analysis of variance (MANOVA) which used one-way analysis of variance (ANOVA) for follow-up procedures. A MANOVA revealed a significant interaction of gender and sport type for general symptoms [F(1, 564) = 9.583, p = .002] and depression [F(1, 564) = 6.945, p = .009] but not anxiety [F(1, 564) = 3.332, p = .068, ƞ2 = .006]. The project was able to describe mental health symptoms in a population that is not often included in the literature. Knowledge of collegiate athlete mental health prevalence is important because prevention and early intervention is a key component of community-based health programming.