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Dale Bickham, Warren Young and Peter Blanch

Objective:

To determine the relationship between lumbopelvic (LP) stabilization strength and pelvic motion during running.

Design:

Runners were assessed for pelvic motion and undertook an LP stabilization strength test.

Participants:

Sixteen elite male middle- and long-distance runners.

interventions:

Pelvis kinematics were assessed while subjects ran at 5 m/s on a treadmill.

Main Outcome Measures:

Angular pelvis displacement was divided into 3 axes of rotation: pelvic tilt, obliquity, and rotation. LP stabilization strength was the capacity to resist increasing static loads applied to each leg and maintain a neutral LP zone. Intercorrelations were calculated for all measures of pelvic motion and LP stabilization strength.

Results:

There were no significant relationships found among any of the variables (P > .05). However, the LP stabilization strength test possessed good interday reliability.

Conclusions:

The relationship between pelvic motion and muscle function should be studied under a variety of other conditions.

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Dean J. McNamara, Tim J. Gabbett, Peter Blanch and Luke Kelly

To date, the monitoring of fast-bowling workloads across training and competition environments has been limited to counting total balls bowled. However, bowling at faster velocities is likely to require greater effort while also placing greater load on the bowler. This study investigated the relationship between prescribed effort and microtechnology outputs in fast bowlers to ascertain whether the technology could provide a more refined measure of workload. Twelve high-performing fast bowlers (mean ± SD age 20.3 ± 2.2 y) participated in the study. Each bowler bowled 6 balls at prescribed bowling intensities of 60%, 70%, 85%, and 100%. The relationships between microtechnology outputs, prescribed intensity, and ball velocity were determined using polynomial regression. Very large relationships were observed between prescribed effort and ball velocity for peak PlayerLoad™ (R = .83 ± .19 and .82 ± .20). The PlayerLoad across lower ranges of prescribed effort exhibited a higher coefficient of variation (CV) (60% = 19.0% [17.0–23.0%]), while the CV at higher ranges of prescribed effort was lower (100% = 7.3% [6.4–8.5%]). Routinely used wearable microtechnology devices offer opportunities to examine workload and intensity in cricket fast bowlers outside the normal metrics reported. They offer a useful tool for prescribing and monitoring bowling intensity and workload in elite fast bowlers.

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David L. Carey, Justin Crow, Kok-Leong Ong, Peter Blanch, Meg E. Morris, Ben J. Dascombe and Kay M. Crossley

Purpose: To investigate whether preseason training plans for Australian football can be computer generated using current training-load guidelines to optimize injury-risk reduction and performance improvement. Methods: A constrained optimization problem was defined for daily total and sprint distance, using the preseason schedule of an elite Australian football team as a template. Maximizing total training volume and maximizing Banister-model-projected performance were both considered optimization objectives. Cumulative workload and acute:chronic workload-ratio constraints were placed on training programs to reflect current guidelines on relative and absolute training loads for injury-risk reduction. Optimization software was then used to generate preseason training plans. Results: The optimization framework was able to generate training plans that satisfied relative and absolute workload constraints. Increasing the off-season chronic training loads enabled the optimization algorithm to prescribe higher amounts of “safe” training and attain higher projected performance levels. Simulations showed that using a Banister-model objective led to plans that included a taper in training load prior to competition to minimize fatigue and maximize projected performance. In contrast, when the objective was to maximize total training volume, more frequent training was prescribed to accumulate as much load as possible. Conclusions: Feasible training plans that maximize projected performance and satisfy injury-risk constraints can be automatically generated by an optimization problem for Australian football. The optimization methods allow for individualized training-plan design and the ability to adapt to changing training objectives and different training-load metrics.