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A simulation framework to determine optimal strength training and musculoskeletal geometry for sprinting and distance running.

ABSTRACT: Musculoskeletal geometry and muscle volumes vary widely in the population and are intricately linked to the performance of tasks ranging from walking and running to jumping and sprinting. As an alternative to experimental approaches, where it is difficult to isolate factors and establish causal relationships, simulations can be used to independently vary musculoskeletal geometry and muscle volumes, and develop a fundamental understanding. However, our ability to understand how these parameters affect task performance has been limited due to the high computational cost of modelling the necessary complexity of the musculoskeletal system and solving the requisite multi-dimensional optimization problem. For example, sprinting and running are fundamental to many forms of sport, but past research on the relationships between musculoskeletal geometry, muscle volumes, and running performance has been limited to observational studies, which have not established cause-effect relationships, and simulation studies with simplified representations of musculoskeletal geometry. In this study, we developed a novel musculoskeletal simulator that is differentiable with respect to musculoskeletal geometry and muscle volumes. This simulator enabled us to find the optimal body segment dimensions and optimal distribution of added muscle volume for sprinting and marathon running. Our simulation results replicate experimental observations, such as increased muscle mass in sprinters, as well as a mass in the lower end of the healthy BMI range and a higher leg-length-to-height ratio in marathon runners. The simulations also reveal new relationships, for example showing that hip musculature is vital to both sprinting and marathon running. We found hip flexor and extensor moment arms were maximized to optimize sprint and marathon running performance, and hip muscles the main target when we simulated strength training for sprinters. Our simulation results provide insight to inspire future studies to examine optimal strength training. Our simulator can be extended to other athletic tasks, such as jumping, or to non-athletic applications, such as designing interventions to improve mobility in older adults or individuals with movement disorders.

SUBMITTER: Van Wouwe T

PROVIDER: S-EPMC10917303 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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A simulation framework to determine optimal strength training and musculoskeletal geometry for sprinting and distance running.

Van Wouwe Tom T Hicks Jennifer J Delp Scott S Liu Karen C KC

PLoS computational biology 20240223 2

Musculoskeletal geometry and muscle volumes vary widely in the population and are intricately linked to the performance of tasks ranging from walking and running to jumping and sprinting. As an alternative to experimental approaches, where it is difficult to isolate factors and establish causal relationships, simulations can be used to independently vary musculoskeletal geometry and muscle volumes, and develop a fundamental understanding. However, our ability to understand how these parameters a ...[more]

PMID: 38394308

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Project description:ContextRunning-related injuries are common in distance runners. Strength training is used for performance enhancement and injury prevention. However, the association between maximal strength and distance-running biomechanics is unclear.ObjectiveTo determine the relationship between maximal knee- and hip-extensor strength and running biomechanics previously associated with injury risk.DesignCross-sectional study.SettingResearch laboratory.Patients or other participantsA total of 36 collegiate distance runners (26 men, 10 women; age = 20.0 ± 1.5 years, height = 1.74 ± 0.09 m, mass = 61.97 ± 8.26 kg).Main outcome measure(s)Strength was assessed using the 1-repetition maximum (1RM) back squat and maximal voluntary isometric contractions of the knee extensors and hip extensors. Three-dimensional running biomechanics were assessed overground at a self-selected speed. Running variables were the peak instantaneous vertical loading rate; peak forward trunk-lean angle; knee-flexion, internal-rotation, and -abduction angles and internal moments; and hip-extension, internal-rotation, and -adduction angles and internal moments. Separate stepwise linear regression models were used to examine the associations between strength and biomechanical outcomes (ΔR2) after accounting for sex, running speed, and foot-strike index.ResultsGreater 1RM back-squat strength was associated with a larger peak knee-flexion angle (ΔR2 = 0.110, ΔP = .045) and smaller peak knee internal-rotation angle (ΔR2 = 0.127, ΔP = .03) and internal-rotation moment (ΔR2 = 0.129, ΔP = .03) after accounting for sex, speed, and foot-strike index. No associations were found between 1RM back-squat strength and vertical loading rate, trunk lean, or hip kinematics and kinetics. Hip- and knee-extensor maximal voluntary isometric contractions were also not associated with any biomechanical variables.ConclusionsGreater 1RM back-squat strength was weakly associated with a larger peak knee-flexion angle and smaller knee internal-rotation angle and moment in collegiate distance runners. Runners who are weaker in the back-squat exercise may exhibit running biomechanics associated with the development of knee-related injuries.

Project description:This study aimed to provide multiple regression equations taking into account differences in running speed, leg length, and step characteristics to predict kinematics of maximal speed sprinting. Seventy-nine male sprinters performed a maximal effort 60-m sprint, during which they were videoed through the section from the 40- to 50-m mark. From the video images, leg kinematic variables were obtained and used as dependent variables for multiple linear regression equation with predictors of running speed, leg length, step frequency, and swing/support ratio. Multiple regression equations to predict leg kinematics of maximal speed sprinting were successfully obtained. For swing leg kinematics, a significant regression model was obtained to predict thigh angle at the contralateral foot strike, maximal knee flexion and thigh lift angular velocities, and maximal leg backward swing velocity (adjusted R 2 = 0.194-0.378, medium to large effect). For support leg kinematics, a significant regression model was obtained to predict knee flexion and extension angular displacements, maximal knee extension velocity, maximal leg backward swing angular velocity, and the other 13 kinematic variables (adjusted R 2 = 0.134-0.757, medium to large effect). Based on the results, at a given leg length, faster maximal speed sprinting will be accompanied with greater thigh angle at the contralateral foot strike, greater maximal leg backward swing velocity during the swing phase, and smaller knee extension range during the support phase. Longer-legged sprinters will accomplish the same running speed with a greater thigh angle at contralateral foot strike, greater knee flexion range, and smaller maximal leg backward swing velocity during the support phase. At a given running speed and leg length, higher step frequencies will be achieved with a greater thigh angle at contralateral foot strike and smaller knee flexion and extension ranges during the support phase. At a given running speed, leg length and step frequency, a greater swing/support ratio will be accompanied with a greater thigh angle at contralateral foot strike and smaller knee extension angular displacement and velocity during the support phase. The regression equations obtained in this study will be useful for sprinters when trying to improve their maximal speed sprinting motion.

Project description:PurposeLocomotor profiling using anaerobic speed reserve (ASR) enables insights into athletes' physiological and neuromuscular contributing factors and prescription of high-intensity training beyond maximal aerobic speed (MAS). This systematic review aimed to determine the validity and reliability of different methods to assess the characteristics of ASR, i.e., MAS and maximal sprinting speed (MSS).MethodsA comprehensive search of the PubMed and Web of Science databases was conducted according to the PRISMA guidelines. Studies were included if they reported data on validity and/or reliability for methods to assess MAS or MSS.Results58 studies were included with 28 studies referring to MAS and 30 studies to MSS. Regarding MAS, different methods for cardiopulmonary exercise testing yielded different values (four out of seven studies) of MAS (Cohen's d (ES) = 0.83-2.8; Pearson's r/intraclass correlation coefficient (r/ICC) = 0.46-0.85). Criterion validity of different field tests showed heterogeneous results (ES = 0-3.57; r/ICC = 0.40-0.96). Intraday and interday reliability was mostly acceptable for the investigated methods (ICC/r>0.76; CV<16.9%). Regarding MSS, radar and laser measurements (one out of one studies), timing gates (two out of two studies), and video analysis showed mostly good criterion validity (two out of two studies) (ES = 0.02-0.53; r/ICC = 0.93-0.98) and reliability (r/ICC>0.83; CV<2.43%). Criterion validity (ES = 0.02-7.11) and reliability (r/ICC = 0.14-0.97; CV = 0.7-9.77%) for global or local positioning systems (seven out of nine studies) and treadmill sprinting (one out of one studies) was not acceptable in most studies.ConclusionThe criterion validity of incremental field tests or shuttle runs to examine MAS cannot be confirmed. Results on time trials indicate that distances adapted to the participants' sporting background, fitness, or sex might be suitable to estimate MAS. Regarding MSS, only sprints with radar or laser measures, timing gates, or video analysis provide valid and reliable results for linear sprints of 20 to 70 m.

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A simulation framework to determine optimal strength training and musculoskeletal geometry for sprinting and distance running.

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A simulation framework to determine optimal strength training and musculoskeletal geometry for sprinting and distance running.

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