Prescribing training intensity and volume is a key problem when designing resistance training programmes. One approach is to base training prescription on the number of repetitions performed at a given percentage of repetition maximum due to the correlation found between these two measures. However, previous research has raised questions as to the accuracy of this method, as the repetitions completed at different percentages of 1RM can differ based upon the characteristics of the athlete. The objective of this study was therefore to evaluate the effect of an athlete's training background on the relationship between the load lifted (as a percentage of one repetition maximum) and the number of repetitions achieved. Eight weightlifters and eight endurance runners each completed a one repetition maximum test on the leg press and completed repetitions to fatigue at 90, 80 and 70% of their one repetition maximum. The endurance runners completed significantly more repetitions than the weightlifters at 70% (39.9 ± 17.6 versus 17.9 ± 2.8; p < 0.05) and 80% (19.8 ± 6.4 versus 11.8 ± 2.7; p < 0.05) of their one repetition maximum but not at 90% (10.8 ± 3.9 versus 7.0 ± 2.1; p > 0.05) of one repetition maximum. These differences could be explained by the contrasting training adaptations demanded by each sport. This study suggests that traditional guidelines may underestimate the potential number of repetitions that can be completed at a given percentage of 1RM, particularly for endurance trained athletes.

Segment-based musculoskeletal models allow the prediction of muscle, ligament, and joint forces without making assumptions regarding joint degrees-of-freedom (DOF). The dataset published for the “Grand Challenge Competition to Predict in vivo Knee Loads” provides directly measured tibiofemoral contact forces for activities of daily living (ADL). For the Sixth Grand Challenge Competition to Predict in vivo Knee Loads, blinded results for “smooth” and “bouncy” gait trials were predicted using a customized patient-specific musculoskeletal model. For an unblinded comparison, the following modifications were made to improve the predictions: further customizations, including modifications to the knee center of rotation; reductions to the maximum allowable muscle forces to represent known loss of strength in knee arthroplasty patients; and a kinematic constraint to the hip joint to address the sensitivity of the segment-based approach to motion tracking artifact. For validation, the improved model was applied to normal gait, squat, and sit-to-stand for three subjects. Comparisons of the predictions with measured contact forces showed that segment-based musculoskeletal models using patient-specific input data can estimate tibiofemoral contact forces with root mean square errors (RMSEs) of 0.48–0.65 times body weight (BW) for normal gait trials. Comparisons between measured and predicted tibiofemoral contact forces yielded an average coefficient of determination of 0.81 and RMSEs of 0.46–1.01 times BW for squatting and 0.70–0.99 times BW for sit-to-stand tasks. This is comparable to the best validations in the literature using alternative models.

Traditional approaches to the biomechanical analysis of movement are joint-based; that is the mechanics of the body are described in terms of the forces and moments acting at the joints, and that muscular forces are considered to create moments about the joints. We have recently shown that segment-based approaches, where the mechanics of the body are described by considering the effect of the muscle, ligament and joint contact forces on the segments themselves, can also prove insightful. We have also previously described a simultaneous, optimization-based, musculoskeletal model of the lower limb. However, this prior model incorporates both joint- and segment-based assumptions. The purpose of this study was therefore to develop an entirely segment-based model of the lower limb and to compare its performance to our previous work. The segment-based model was used to estimate the muscle forces found during vertical jumping, which were in turn compared with the muscular activations that have been found in vertical jumping, by using a Geers' metric to quantify the magnitude and phase errors. The segment-based model was shown to have a similar ability to estimate muscle forces as a model based upon our previous work. In the future, we will evaluate the ability of the segment-based model to be used to provide results with clinical relevance, and compare its performance to joint-based approaches. The segment-based model described in this article is publicly available as a GUI-based M atlab ® application and in the original source code (at www.msksoftware.org.uk ).

The force-vector theory contends that horizontal exercises are more specific to horizontal sports skills. In this context, the focus is on horizontal force production relative to the global coordinate frame. However, according to the principle of dynamic correspondence, the direction of force relative to the athlete is more important, and thus the basis for the force-vector theory is flawed. The purpose of this study was therefore to test the force-vector theory. According to the force-vector theory, hip thrust is a horizontally loaded exercise, and so hip thrust training would be expected to create greater improvements in horizontal jump performance than vertical jump performance. Eleven collegiate female athletes aged 18–24 years completed a 14-week hip thrust training programme. Pre and post testing was used to measure the following: vertical squat jump, vertical countermovement jump, horizontal squat jump, horizontal countermovement jump and hip thrust 3 repetition maximum (3RM). Subjects improved their 3 repetition maximum hip thrust performance by 33.0% (d = 1.399, p < 0.001, η² = 0.784) and their vertical and horizontal jump performance (improvements ranged from 5.4–7.7%; d = 0.371–0.477, p = 0.004, η² = 0.585). However, there were no differences in the magnitude of the improvement between horizontal and vertical jumping (p = 0.561, η² = 0.035). The results of this study are contrary to the predictions of the force-vector theory. Furthermore, this paper concludes with an analysis of the force-vector theory, presenting the mechanical inconsistencies in the theory. Coaches should use the well established principle of dynamic correspondence in order to assess the mechanical similarity of exercises to sports skills.

The role of the biarticular muscles is a topic that has received considerable attention however their function is not well understood. In this paper, we argue that an analysis that is based upon considering the effect of the biarticular muscles on the segments that they span (rather than their effect on joint rotations) can be illuminating. We demonstrate that this understanding is predicated on a consideration of the relative sizes of the moment arms of a biarticular muscle about the two joints that it crosses. The weight of the previous literature suggests that the moment arms of both the biarticular hamstrings and gastrocnemius are smaller at the knee than at the hip or ankle, (respectively). This in turn leads to the conclusion that both biarticular hamstrings and gastrocnemius are extensors of the lower limb. We show that the existence of these biarticular structures lends a degree of flexibility to the motor control strategies available for lower limb extension. In particular, the role of the gastrocnemius and biarticular hamstrings in permitting a large involvement of the quadriceps musculature in closed chain lower limb extension may be more important than is typically portrayed. Finally, the analysis presented in this paper demonstrates the importance of considering the effects of muscles on the body as a whole, not just on the joints they span.