Task Demand Changes Motor Control Strategies in Vertical Jumping

Is there a relationship between isometric hamstrings-to-quadriceps torque ratio and athletes' plyometric performance?

BACKGROUND

The application of the modified reactive strength index (RSImod) and isometric hamstrings to quadriceps (H:Q) torque ratio to monitoring the athletes' performance is well established, but their relationship to each other still remains unknown. Therefore, the purpose of this investigation was to clarify whether there is a relationship between RSImod and the isometric H:Q torque ratio.

METHODS

Twenty-one male recreational athletes (age, 24.89 ± 4.46 years; weight, 74.11 ± 8.66 kg; height, 179.78 ± 6.76 cm) volunteered to participate in this research. Their isometric H:Q torque ratio via hand-held dynamometer and jumping performance variables during the stop jump (STJ), double leg-drop vertical jump (DL-DVJ), and single leg-drop vertical jump (SL-DVJ) tasks were measured. Also, the RSImod was calculated by dividing the vertical jump height by the time to take-off. Pearson correlation coefficients were used to determine the relationship between the RSImod and jumping performance variables.

RESULTS

The result showed a significant strong positive relationship between the H:Q torque ratio and STJ performance based on the RSImod (p = .027, r = .724). Although, there was a moderate positive relationship between the H:Q torque ratio and jumping height of the STJ task, but it wasn't statistically significant (p = .096, r = .588). Also, no significant relationship was found between the H:Q torque ratio and all jumping performance variables of the DL-DVJ and SL-DVJ tasks (p ≥ .05).

CONCLUSION

The current study exhibited that the isometric H:Q torque ratio correlates to STJ performance based on RSImod but not to SL-DVJ and DL-DVJ. Notably, the difference in results between jumping tasks can be attributed to the complexity of the movement, which means that the RSImod is probably related to other factors. Therefore, the isometric H:Q torque ratio used to monitor the athletes' performance couldn't independently represent the jumping performance that is determined by RSImod.

Training for "Worst-Case" Scenarios in Sidestepping: Unifying Strength and Conditioning and Perception-Action Approaches

Sidesteps can impose high demands on the knee joint and lead to non-contact anterior cruciate ligament (ACL) injuries. Understanding how different constraints shape an athlete's movement strategy and the associated joint demands can help design training interventions to increase injury resilience. Motor capacities, such as muscular strength and power, act as boundaries for the safe execution of perceptual-motor skills and co-determine the emergence of unique movement strategies. Increasing single- and multi-joint strength enables a broader solution space for movement strategies and increases load tolerance. Manipulating task constraints during sidesteps can be used in the training process to systematically expose athletes to increasing demands (on the knee joint or any joint or structure) in preparation for "worst-case" scenarios. In particular, the type and timing of information available influence the preparation time, subsequently affecting the movement strategy and the associated magnitude of external knee joint loading (e.g., knee valgus moment). While an athlete's perceptual-cognitive skills contribute to the preparation time during in situ scenarios, attempts to further improve those skills with the aim of increasing athlete preparation time prior to "worst-case" scenarios are yet to demonstrate conclusive evidence of transfer to on-field situations. Therefore, in the current article, we reflect on the impact of different interacting constraints that influence the execution of sidesteps during in situ scenarios and impose high demands on the knee joint. Subsequently, we discuss how an integrated perspective, drawing on knowledge and perspectives from strength and conditioning and perception-action, may enhance an athlete's ability to withstand "worst-case" scenarios and adapt to perform varied movement executions when sidestepping.

Performance Level and Strike Type during Ground and Pound Determine Impact Characteristics and Net Force Variability

The evaluation of strike impact is important for optimal training, conditioning and tactical use. Therefore, the aim of this study was to evaluate ground and pound strikes, in terms of net force variability, across genders and performance levels. Eighty-one participants, professional men (n = 8, 37 ± 6 years, 195 ± 7 cm, 113 ± 27 kg), advanced men (n = 47, 26 ± 8 years, 180 ± 7 cm, 76 ± 11 kg), and advanced women (n = 26, 21 ± 1 years, 167 ± 6 cm, 61 ± 7 kg) performed three strikes from a kneeling position into a force plate on the ground. The elbow strike resulted in the highest impulse and the palm strike in the highest peak force for all three categories. These results support the recommendation that has previously been made to teach the palm strike to beginners and advanced tactical and combat athletes. The direct punch and elbow strike net force were characterized by a double peak curve, where the first peak variability explained 70.2-84% of the net force. The second peak was pronounced in professional men during elbow strikes, which explained 16% of net force variability. The strike type determines the impact net force and its characteristics, where palm strike is typical by highest peak impact tolerance and elbow strike by double force peak with high net force impulse.

GAPSO-Optimized Fuzzy PID Controller for Electric-Driven Seeding

To improve the seeding motor control performance of electric-driven seeding (EDS), a genetic particle swarm optimization (GAPSO)-optimized fuzzy PID control strategy for electric-driven seeding was designed. Since the parameters of the fuzzy controller were difficult to determine, two quantization factors were applied to the input of the fuzzy controller, and three scaling factors were introduced into the output of fuzzy controller. Genetic algorithm (GA) and particle swarm optimization (PSO) were combined into GAPSO by a genetic screening method. GAPSO was introduced to optimize the initial values of the two quantization factors, three scaling factors, and three characteristic functions before updating. The simulation results showed that the maximum overshoot of the GAPSO-based fuzzy PID controller system was 0.071%, settling time was 0.408 s, and steady-state error was 3.0693 × 10^-5, which indicated the excellent control performance of the proposed strategy. Results of the field experiment showed that the EDS had better performance than the ground wheel chain sprocket seeding (GCSS). With a seeder operating speed of 6km/h, the average qualified index (I_q) was 95.83%, the average multiple index (I_mult) was 1.11%, the average missing index (I_miss) was 3.23%, and the average precision index (I_p) was 14.64%. The research results provide a reference for the parameter tuning mode of the fuzzy PID controller for EDS.

A Proposed Framework to Describe Movement Variability within Sporting Tasks: A Scoping Review

Movement variability is defined as the normal variations in motor performance across multiple repetitions of a task. However, the term "movement variability" can mean different things depending on context, and when used by itself does not capture the specifics of what has been investigated. Within sport, complex movements are performed repeatedly under a variety of different constraints (e.g. different situations, presence of defenders, time pressure). Movement variability has implications for sport performance and injury risk management. Given the importance of movement variability, it is important to understand the terms used to measure and describe it. This broad term of "movement variability" does not specify the different types of movement variability that are currently being assessed in the sporting literature. We conducted a scoping review (1) to assess the current terms and definitions used to describe movement variability within sporting tasks and (2) to utilise the results of the review for a proposed framework that distinguishes and defines the different types of movement variability within sporting tasks. To be considered eligible, sources must have assessed a sporting movement or skill and had at least one quantifiable measure of movement variability. A total of 43 peer-reviewed journal article sources were included in the scoping review. A total of 280 terms relating to movement variability terminology were extracted using a data-charting form jointly developed by two reviewers. One source out of 43 (2%) supplied definitions for all types of movement variability discussed. Moreover, 169 of 280 terms (60%) were undefined in the source material. Our proposed theoretical framework explains three types of movement variability: strategic, execution, and outcome. Strategic variability describes the different approaches or methods of movement used to complete a task. Execution variability describes the intentional and unintentional adjustments of the body between repetitions within the same strategy. Outcome variability describes the differences in the result or product of a movement. These types emerged from broader frameworks in motor control and were adapted to fit the movement variability needs in sports literature. By providing specific terms with explicit definitions, our proposed framework can ensure like-to-like comparisons of previous terms used in the literature. The practical goal of this framework is to aid athletes, coaches, and support staff to gain a better understanding of how the different types of movement variability within sporting tasks contribute to performance. The framework may allow training methods to be tailored to optimise the specific aspects of movement variability that contribute to success. This review was retrospectively registered using the Open Science Framework (OSF) Registries ( https://osf.io/q73fd ).

Principal Component Analysis can Be Used to Discriminate Between Elite and Sub-Elite Kicking Performance

Contemporary descriptions of motor control suggest that variability in movement can be indicative of skilled or unskilled performance. Here we used principal component analysis to study the kicking performance of elite and sub-elite soldiers who were highly familiar with the skill in order to compare the variability in the first and second principal components. The subjects kicked a force plate under a range of loaded conditions, and their movement was recorded using optical motion capture. The first principal component explained >92% of the variability across all kinematic variables when analyzed separately for each condition, and both groups and explained more of the variation in the movement of the elite group. There was more variation in the loading coefficient of the first principal component for the sub-elite group. In contrast, for the second principal component, there was more variation in the loading coefficient for the elite group, and the relative magnitude of the variation was greater than for the first principal component for both groups. These results suggest that the first principal component represented the most fundamental movement pattern, and there was less variation in this mode for the elite group. In addition, more of the variability was explained by the hip than the knee angle entered when both variables were entered into the same PCA, which suggests that the movement is driven by the hip.

Differences in Motor Control Strategies of Jumping Tasks, as Revealed by Group and Individual Analysis

The aim of this study was to investigate the motor control strategies adopted when performing two jumping tasks with different task demands when analysed at an individual and group level. Twenty-two healthy individuals performed two jumping tasks: jumping without the use of an arm swing (CMJnas) and jumping starting in a plantar flexed position with the use of an arm swing (PF). Principal component analysis (PCA) was performed using hip, knee and ankle joint moment data on individual (PCAi) and group data (PCAc). The results demonstrate that a greater number of PCs are required to explain the majority of variance within the dataset in the PF condition at both an individual and group level, compared to CMJnas condition. Although common control strategies were observed between the two jumping conditions, differences in the organisation of the movement (PC loading coefficients) were observed. Results from the group analysis did not completely reflect the individual strategies used to perform each jumping task and highlight the value in performing individual analysis to determine emergent control strategies.