Factors influencing the jump momentum - sprint momentum correlation: a data simulation

A Review of the Validity and Reliability of Accelerometer-Based Metrics From Upper Back-Mounted GNSS Player Tracking Systems for Athlete Training Load Monitoring

ABSTRACT

Dawson, L, Beato, M, Devereux, G, and McErlain-Naylor, SA. A review of the validity and reliability of accelerometer-based metrics from upper back-mounted GNSS player tracking systems for athlete training load monitoring. J Strength Cond Res 38(8): e460-e475, 2024-Athlete load monitoring using upper back-mounted global navigation satellite system (GNSS) player tracking is common within many team sports. However, accelerometer-based load monitoring may provide information that cannot be achieved with GNSS alone. This review focuses on the accelerometer-based metrics quantifying the accumulation of accelerations as an estimation of athlete training load, appraising the validity and reliability of accelerometer use in upper back-mounted GNSS player tracking systems, the accelerometer-based metrics, and their potential for application within athlete monitoring. Reliability of GNSS-housed accelerometers and accelerometer-based metrics are dependent on the equipment model, signal processing methods, and the activity being monitored. Furthermore, GNSS unit placement on the upper back may be suboptimal for accelerometer-based estimation of mechanical load. Because there are currently no feasible gold standard comparisons for field-based whole-body biomechanical load, the validity of accelerometer-based load metrics has largely been considered in relation to other measures of training load and exercise intensity. In terms of convergent validity, accelerometer-based metrics (e.g., PlayerLoad, Dynamic Stress Load, Body Load) have correlated, albeit with varying magnitudes and certainty, with measures of internal physiological load, exercise intensity, total distance, collisions and impacts, fatigue, and injury risk and incidence. Currently, comparisons of these metrics should not be made between athletes because of mass or technique differences or between manufacturers because of processing variations. Notable areas for further study include the associations between accelerometer-based metrics and other parts of biomechanical load-adaptation pathways of interest, such as internal biomechanical loads or methods of manipulating these metrics through effective training design.

Momentum-Based Load Prescriptions: Applications to Jump Squat Training

ABSTRACT

Harry, JR, Krzyszkowski, J, Harris, K, Chowning, L, Mackey, E, Bishop, C, and Barker, LA. Momentum-based load prescriptions: Applications to jump squat training. J Strength Cond Res 36(9): 2657-2662, 2022-Velocity-based training is often applied to ballistic exercises, like the barbell jump squat, to improve vertical jump performance. However, determining the ideal training load based on velocity data remains difficult because load prescriptions tend to be limited to subjective velocity loss thresholds, velocity ranges, or both. Using data from jump squats performed with 0, 15, 30, 45, and 60% of the 1-repetition maximum squat, we explored subjective and objective methods to determine the ideal training load. Specifically, we explored takeoff velocity and a related metric only recently discussed in the literature, system momentum (i.e., takeoff velocity multiplied by the mass of the athlete-load system). At the group level, an ideal training load could not be revealed objectively using takeoff velocity. With individual subjects, the process remained challenging using takeoff velocity. Conversely, an ideal training load could be revealed easily and objectively using system momentum at the group average and individual subject levels. System momentum at takeoff is well-suited to assist practitioners seeking to identify appropriate training loads for jump squat training and potentially other ballistic exercises. We suggest a pivot from velocity to system momentum when seeking to objectively establish training loads for the jump squat and related exercises.

Identifying and reporting position-specific countermovement jump outcome and phase characteristics within rugby league

The countermovement jump (CMJ) has been suggested to be an important test of neuromuscular performance for rugby league (RL) players. Identifying force platform-derived CMJ variables that may be more applicable to RL positions (e.g., forwards and backs) has yet to be fully explored in the scientific literature. The aim of this study was to identify RL position-specific CMJ force-time variables. Specifically, we aimed to compare select force-time variables from the countermovement (i.e., the combination of unweighting and braking) and propulsion phases of the CMJ between the global forwards and backs positional groups. We also aimed to compare typical (i.e., jump height) and alternative (i.e., take-off momentum) outcome CMJ variables between positional groups. Finally, we sought to visually present each individual player’s CMJ performance alongside the average data to facilitate the interpretation and reporting of the CMJ performances of RL athletes. Twenty-seven forwards and twenty-seven backs who competed in the senior men’s English RL Championship, performed three CMJs on a force platform at the beginning of the pre-season training period. There were no significant differences in any countermovement or propulsion phase variable between positions with just small effect sizes noted (P ≥0.09, d ≤0.46). Jump height (and so take-off velocity) was significantly greater for backs with moderate effects displayed (P = 0.03, d = 0.60). Take-off momentum (take-off velocity × body mass) was largely and significantly greater for forwards (P<0.01, d = 1.01). There was considerable overlap of individual player’s body mass and CMJ outcome variables across positions, despite significant differences in the mean values attained by each positional group. The results suggest that it may be beneficial for RL practitioners to identify player-specific, or at least position-specific, variables. As a minimum, it may be worthwhile selecting CMJ force-time variables based on what is considered important to individual player’s or small clusters of similar players’ projected successes during RL competition.