The impact of strength level on adaptations to combined weightlifting, plyometric, and ballistic training.

The purpose of this investigation was to determine whether the magnitude of adaptation to integrated ballistic training is influenced by initial strength level. Such information is needed to inform resistance training guidelines for both higher- and lower-level athlete populations. To this end, two groups of distinctly different strength levels (stronger: one-repetition-maximum (1RM) squat = 2.01 ± 0.15 kg·BM-1 ; weaker: 1.20 ± 0.20 kg·BM-1 ) completed 10 weeks of resistance training incorporating weightlifting derivatives, plyometric actions, and ballistic exercises. Testing occurred at pre-, mid-, and post-training. Measures included variables derived from the incremental-load jump squat and the 1RM squat, alongside muscle activity (electromyography), and jump mechanics (force-time comparisons throughout the entire movement). The primary outcome variable was peak velocity derived from the unloaded jump squat. It was revealed that the stronger group displayed a greater (P = .05) change in peak velocity at mid-test (baseline: 2.65 ± 0.10 m/s, mid-test: 2.80 ± 0.17 m/s) but not post-test (2.85 ± 0.18 m/s) when compared to the weaker participants (baseline 2.43 ± 0.09, mid-test. 2.47 ± 0.11, post-test: 2.61 ± 0.10 m/s). Different changes occurred between groups in the force-velocity relationship (P = .001-.04) and jump mechanics (P ≤ .05), while only the stronger group displayed increases in muscle activation (P = .05). In conclusion, the magnitude of improvement in peak velocity was significantly influenced by pre-existing strength level in the early stage of training. Changes in the mechanisms underpinning performance were less distinct.

Different Muscle Action Training Protocols on Quadriceps-Hamstrings Neuromuscular Adaptations.

The aim of this study was to compare three specific concentric and eccentric muscle action training protocols on quadriceps-hamstrings neuromuscular adaptations. Forty male volunteers performed 6 weeks of training (two sessions/week) of their dominant and non-dominant legs on an isokinetic dynamometer. They were randomly assigned to one of four groups; concentric quadriceps and concentric hamstrings (CON/CON, n=10), eccentric quadriceps and eccentric hamstrings (ECC/ECC, n=10), concentric quadriceps and eccentric hamstrings (CON/ECC, n=10), or no training (CTRL, n=10). Intensity of training was increased every week by decreasing the angular velocity for concentric and increasing it for eccentric groups in 30°/s increments. Volume of training was increased by adding one set every week. Dominant leg quadriceps and hamstrings muscle thickness, muscle quality, muscle activation, muscle coactivation, and electromechanical delay were tested before and after training. Results revealed that all training groups similarly increased MT of quadriceps and hamstrings compared to control (p<0.05). However, CON/ECC and ECC/ECC training elicited a greater magnitude of change. There were no significant differences between groups for all other neuromuscular variables (p>0.05). These findings suggest that different short-term muscle action isokinetic training protocols elicit similar muscle size increases in hamstrings and quadriceps, but not for other neuromuscular variables. Nevertheless, effect sizes indicate that CON/ECC and ECC/ECC may elicit the greatest magnitude of change in muscle hypertrophy.

Mean Velocity vs. Mean Propulsive Velocity vs. Peak Velocity: Which Variable Determines Bench Press Relative Load With Higher Reliability?

García-Ramos, A, Pestaña-Melero, FL, Pérez-Castilla, A, Rojas, FJ, and Haff, GG. Mean velocity vs. mean propulsive velocity vs. peak velocity: which variable determines bench press relative load with higher reliability? J Strength Cond Res 32(5): 1273-1279, 2018-This study aimed to compare between 3 velocity variables (mean velocity [MV], mean propulsive velocity [MPV], and peak velocity [PV]): (a) the linearity of the load-velocity relationship, (b) the accuracy of general regression equations to predict relative load (%1RM), and (c) the between-session reliability of the velocity attained at each percentage of the 1-repetition maximum (%1RM). The full load-velocity relationship of 30 men was evaluated by means of linear regression models in the concentric-only and eccentric-concentric bench press throw (BPT) variants performed with a Smith machine. The 2 sessions of each BPT variant were performed within the same week separated by 48-72 hours. The main findings were as follows: (a) the MV showed the strongest linearity of the load-velocity relationship (median r = 0.989 for concentric-only BPT and 0.993 for eccentric-concentric BPT), followed by MPV (median r = 0.983 for concentric-only BPT and 0.980 for eccentric-concentric BPT), and finally PV (median r = 0.974 for concentric-only BPT and 0.969 for eccentric-concentric BPT); (b) the accuracy of the general regression equations to predict relative load (%1RM) from movement velocity was higher for MV (SEE = 3.80-4.76%1RM) than for MPV (SEE = 4.91-5.56%1RM) and PV (SEE = 5.36-5.77%1RM); and (c) the PV showed the lowest within-subjects coefficient of variation (3.50%-3.87%), followed by MV (4.05%-4.93%), and finally MPV (5.11%-6.03%). Taken together, these results suggest that the MV could be the most appropriate variable for monitoring the relative load (%1RM) in the BPT exercise performed in a Smith machine.

Relationships Between Different Internal and External Training Load Variables and Elite International Women's Basketball Performance.

PURPOSE: To investigate the relationships between internal and external training load (TL) metrics with elite international women's basketball performance. METHODS: Sessional ratings of perceived exertion, PlayerLoad™/minute, and training duration were collected from 13 elite international-level female basketball athletes (age 29.0 [3.7] y, stature 186.0 [9.8] cm, body mass 77.9 [11.6] kg) during the 18 weeks prior to the International Basketball Federation Olympic qualifying event for the 2016 Rio Olympic Games. Training stress balance, differential load, and the training efficiency index were calculated with 3 different smoothing methods. These TL metrics and their change in the last 21 days prior to competition were examined for their relationship to competition performance as coach ratings of performance. RESULTS: For a number of TL variables, there were consistent significant small to moderate correlations with performance and significant small to large differences between successful and unsuccessful performances. However, these differences were only evident for external TL when using exponentially weighted moving averages to calculate TL. The variable that seemed most sensitive to performance was the change in training efficiency index in the last 21 days prior to competition (performance r = .47-.56, P < .001 and difference between successful and unsuccessful performance P < .001, f2 = 0.305-0.431). CONCLUSIONS: Internal and external TL variables were correlated with performance and distinguished between successful and unsuccessful performances among the same players during international women's basketball games. Manipulating TL in the last 3 weeks prior to competition may be worthwhile for basketball players' performance, especially in internal TL.

The Current State of Subjective Training Load Monitoring-a Practical Perspective and Call to Action.

This commentary delivers a practical perspective on the current state of subjective training load (TL) monitoring, and in particular sessional ratings of perceived exertion, for performance enhancement and injury prevention. Subjective measures may be able to reflect mental fatigue, effort, stress, and motivation. These factors appear to be important moderators of the relationship TL has with performance and injury, and they also seem to differ between open and closed skill sports. As such, mental factors may affect the interaction between TL, performance, and injury in different sports. Further, modeling these interactions may be limited due to the assumption that an independent signal can adequately account for the performance or injury outcomes. An independent signal model does not accurately reflect training environments where multiple stressors (e.g., mechanical, emotional, nutritional) impact adaptations. Common issues with using subjective TL monitoring, including a lack of differentiation between biomechanical, physiological, and cognitive load, may be overcome by considering psychometric measurement best practices, finer graded scales, and differential ratings of perceived exertion. Methods of calculating TL, including different acute and chronic time periods, may also need to be individualized to different sports and potentially different individuals within the same sport. As TL monitoring is predominately a "chronic" decision-making tool, "acute" decision-making tools, e.g., subjective wellness and autonomic nervous system measures, should be combined in a bespoke multivariate model to aid sports coaches. A call to action is presented for future research on key issues associated with TL monitoring that will have relevance for practitioners in an applied setting.