EFFECT OF A SIX-WEEK NEUROMUSCULAR TRAINING PROGRAM ON VERTICAL STIFFNESS IN HEALTHY HIGH SCHOOL DISTANCE RUNNERS

Athletes, coaches, and health care teams know that preventing running-related injuries (RRI) and improving running performance are extremely important. Proactive neuromuscular training (NMT) is often included as a complement to running programs for this reason. The purpose of this study was to evaluate the effect of proactive six-week low-intensity NMT focused on proximal hip and thigh muscles on healthy high-school runners’ muscle strength, biomechanical stiffness, peak ground reaction force, cadence, and stride length. The study demonstrates that the NMT increased a runner’s total strength by 10.4% and knee extensor strength by 10.3%, showed no change in stiffness, cadence, or stride length, and showed a decrease in ground reaction force post-program by 1.3%. Results show the multivariable nature of RRI risk, and prompt further, more generalizable, evaluation

Erratum to The Influence of Modeling Separate Neuromuscular Compartments on the Force and Moment Generating Capacities of Muscles of the Feline Hindlimb.

Functional electrical stimulation (FES) has the capacity to regenerate motion for individuals with spinal cord injuries. However, it is not straightforward to determine the stimulation parameters to generate a coordinated movement. Musculoskeletal models can provide a noninvasive simulation environment to estimate muscle force and activation timing sequences for a variety of tasks. Therefore, the purpose of this study was to develop a musculoskeletal model of the feline hindlimb for simulations to determine stimulation parameters for intrafascicular multielectrode stimulation (a method of FES). Additionally, we aimed to explore the differences in modeling neuromuscular compartments compared with representing these muscles as a single line of action. When comparing the modeled neuromuscular compartments of biceps femoris, sartorius, and semimembranosus to representations of these muscles as a single line of action, we observed that modeling the neuromuscular compartments of these three muscles generated different force and moment generating capacities when compared with single muscle representations. Differences as large as 4 N m (∼400% in biceps femoris) were computed between the summed moments of the neuromuscular compartments and the single muscle representations. Therefore, modeling neuromuscular compartments may be necessary to represent physiologically reasonable force and moment generating capacities of the feline hindlimb.</p

The influence of modeling separate neuromuscular compartments on the force and moment generating capacities of muscles of the feline hindlimb

Functional electrical stimulation (FES) has the capacity to regenerate motion for individuals with spinal cord injuries. However, it is not straightforward to determine the stimulation parameters to generate a coordinated movement. Musculoskeletal models can provide a noninvasive simulation environment to estimate muscle force and activation timing sequences for a variety of tasks. Therefore, the purpose of this study was to develop a musculoskeletal model of the feline hindlimb for simulations to determine stimulation parameters for intrafascicular multielectrode stimulation (a method of FES). Additionally, we aimed to explore the differences in modeling neuromuscular compartments compared with representing these muscles as a single line of action. When comparing the modeled neuromuscular compartments of biceps femoris, sartorius, and semimembranosus to representations of these muscles as a single line of action, we observed that modeling the neuromuscular compartments of these three muscles generated different force and moment generating capacities when compared with single muscle representations. Differences as large as 4 N m (∼400% in biceps femoris) were computed between the summed moments of the neuromuscular compartments and the single muscle representations. Therefore, modeling neuromuscular compartments may be necessary to represent physiologically reasonable force and moment generating capacities of the feline hindlimb.</p

Biarticular hip extensor and knee flexor muscle moment arms of the feline hindlimb

Moment arms are important for understanding muscular behavior and for calculating internal muscle forces in musculoskeletal simulations. Biarticular muscles cross two joints and have moment arms that depend on the angle of both joints the muscles cross. The tendon excursion method was used to measure the joint angle-dependence of hamstring (biceps femoris, semimembranosus and semitendinosus) moment arm magnitudes of the feline hindlimb at the knee and hip joints. Knee angle influenced hamstring moment arm magnitudes at the hip joint; compared to a flexed knee joint, the moment arm for semimembranosus posterior at the hip was at most 7.4 mm (25%) larger when the knee was extended. On average, hamstring moment arms at the hip increased by 4.9 mm when the knee was more extended. In contrast, moment arm magnitudes at the knee varied by less than 2.8 mm (mean = 1.6 mm) for all hamstring muscles at the two hip joint angles tested. Thus, hamstring moment arms at the hip were dependent on knee position, while hamstring moment arms at the knee were not as strongly associated with relative hip position. Additionally, the feline hamstring muscle group had a larger mechanical advantage at the hip than at the knee joint.</p

Validated Predictions of Metabolic Energy Consumption for Submaximal Effort Movement

<div><p>Physical performance emerges from complex interactions among many physiological systems that are largely driven by the metabolic energy demanded. Quantifying metabolic demand is an essential step for revealing the many mechanisms of physical performance decrement, but accurate predictive models do not exist. The goal of this study was to investigate if a recently developed model of muscle energetics and force could be extended to reproduce the kinematics, kinetics, and metabolic demand of submaximal effort movement. Upright dynamic knee extension against various levels of ergometer load was simulated. Task energetics were estimated by combining the model of muscle contraction with validated models of lower limb musculotendon paths and segment dynamics. A genetic algorithm was used to compute the muscle excitations that reproduced the movement with the lowest energetic cost, which was determined to be an appropriate criterion for this task. Model predictions of oxygen uptake rate (VO₂) were well within experimental variability for the range over which the model parameters were confidently known. The model's accurate estimates of metabolic demand make it useful for assessing the likelihood and severity of physical performance decrement for a given task as well as investigating underlying physiologic mechanisms.</p></div

Validation of model predictions.

<p>A. Model predictions of energy consumption across dynamic knee extension loads is compared against the rise of pulmonary VO₂ measured experimentally for eighteen subjects [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004911#pcbi.1004911.ref021" target="_blank">21</a>]. Δ Pulmonary VO₂ refers to the increase in steady state rate of oxygen uptake rate by the lungs from rest to exercise. This quantity corresponds to oxygen uptake due to exercise alone (i.e., it does not include the oxygen uptake due to physiological processes contributing to basal oxygen uptake). The model predicts metabolic energy consumption of leg muscles due to exercise (in watts) so to compare it with the Δ Pulmonary VO₂ data reported, the model output of energy consumption of the leg in watts was converted to liters of oxygen uptake per minute [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004911#pcbi.1004911.ref036" target="_blank">36</a>] and then added to the oxygen uptake due to exercise from energy sources other than the leg (Δ pulm VO₂ - Δ leg VO₂; derived from Krustrup et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004911#pcbi.1004911.ref024" target="_blank">24</a>]). B. Model predictions are compared against oxygen uptake of the knee extensors measured for five subjects [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004911#pcbi.1004911.ref022" target="_blank">22</a>]. Knee extensor VO₂ refers to the steady state rate of oxygen uptake by the knee extensor muscles during exercise. This quantity includes oxygen uptake due to exercise as well as the basal oxygen uptake that is measured at rest. The model predicts metabolic energy consumption due to exercise in watts so to compare it with the knee extensor VO₂ data reported, the model output of energy consumption of the knee extensors in watts was converted to liters of oxygen uptake per minute [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004911#pcbi.1004911.ref036" target="_blank">36</a>] and then added to the resting level of knee extensor oxygen uptake obtained from Krustrup et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004911#pcbi.1004911.ref024" target="_blank">24</a>].</p

Sensitivity analysis.

<p>A. Effect of upper limit of knee extension range on pulmonary VO₂ predictions. B. Energetic predictions using nominal, least and most economical musculoskeletal configurations. Mean experimental VO₂ plus/minus one standard deviation is plotted for comparison.</p

Ranges over which musculoskeletal parameters were adjusted for sensitivity analysis.

<p>Range limits for each parameter represent one standard deviation above and below the human subject mean. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004911#pcbi.1004911.s003" target="_blank">S3 Appendix</a> for details.</p

Optimization algorithm.

<p>A genetic algorithm was used to compute the muscle excitation parameters that satisfied the performance criteria. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004911#pcbi.1004911.s002" target="_blank">S2 Appendix</a> for a detailed description of each step.</p