Strain energy in the femoral neck during exercise

Martelli, Saulo; Kersh, Mariana E.; Schache, Anthony G.; Pandy, Marcus G.

doi:10.1016/j.jbiomech.2014.03.036

Cited by 49 publications

(68 citation statements)

References 42 publications

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“…Joint angles and torques were calculated using the inverse kinematic, dynamic and static optimization algorithms implemented in OPENSIM [31]. The model yielded joint torques within published values, hip contact forces in agreement with in vivo measurements and muscle force patterns in good qualitative agreement with corresponding EMG recordings [23,25,26].…”

Section: The Gait Modelmentioning

confidence: 73%

Stochastic modelling of muscle recruitment during activity

et al. 2015

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One contribution of 11 to a theme issue 'Multiscale modelling in biomechanics: theoretical, computational and translational challenges'. Muscle forces can be selected from a space of muscle recruitment strategies that produce stable motion and variable muscle and joint forces. However, current optimization methods provide only a single muscle recruitment strategy. We modelled the spectrum of muscle recruitment strategies while walking. The equilibrium equations at the joints, muscle constraints, static optimization solutions and 15-channel electromyography (EMG) recordings for seven walking cycles were taken from earlier studies. The spectrum of muscle forces was calculated using Bayesian statistics and Markov chain Monte Carlo (MCMC) methods, whereas EMG-driven muscle forces were calculated using EMG-driven modelling. We calculated the differences between the spectrum and EMG-driven muscle force for 1-15 input EMGs, and we identified the muscle strategy that best matched the recorded EMG pattern. The best-fit strategy, static optimization solution and EMG-driven force data were compared using correlation analysis. Possible and plausible muscle forces were defined as within physiological boundaries and within EMG boundaries. Possible muscle and joint forces were calculated by constraining the muscle forces between zero and the peak muscle force. Plausible muscle forces were constrained within six selected EMG boundaries. The spectrum to EMG-driven force difference increased from 40 to 108 N for 1-15 EMG inputs. The best-fit muscle strategy better described the EMG-driven pattern (R 2 ¼ 0.94; RMSE ¼ 19 N) than the static optimization solution (R 2 ¼ 0.38; RMSE ¼ 61 N). Possible forces for 27 of 34 muscles varied between zero and the peak muscle force, inducing a peak hip force of 11.3 body-weights. Plausible muscle forces closely matched the selected EMG patterns; no effect of the EMG constraint was observed on the remaining muscle force ranges. The model can be used to study alternative muscle recruitment strategies in both physiological and pathophysiological neuromotor conditions.

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Section: The Gait Modelmentioning

confidence: 73%

Stochastic modelling of muscle recruitment during activity

et al. 2015

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“…Martelli and colleagues [74] performed the first and, to date, only quantitative assessment of femoral neck loading during differing activities using computational methods. Using a lower-limb musculoskeletal model, they reported on the distribution of strain energy and peak tensile strain within the femoral neck during 15 different activities in a single individual.…”

Section: Designing Better Activities For Strengthening Fracture Pronementioning

confidence: 99%

“…Data suggested that one-legged long jump and maximum isokinetic hip extension induced the highest changes in strain energy, while maximal hip extension and knee flexion exercises maximally loaded the thinnest region of the superolateral femoral neck. Also of note, they observed that activities of relatively similar ground reaction forces were capable of producing contrasting levels and distributions of femoral neck strain and strain energy [74]. The latter observation was likely due to the recruitment of different muscles with different geometric arrangements, and suggests that the measurement of ground reaction forces to design and prescribe proximal femur loading programs may be imprecise.…”

Section: Designing Better Activities For Strengthening Fracture Pronementioning

confidence: 99%

“…The data reported by Mantelli and colleagues [74] are limited with regards to the study of a single individual and the application of musculoskeletal loads calculated from a young volunteer to an FE model of the proximal femur from an older individual. However, the contribution highlights the potential of computational musculoskeletal modeling in designing activities that may better strengthen fracture prone regions within the femoral neck.…”

Section: Designing Better Activities For Strengthening Fracture Pronementioning

confidence: 99%

“…(B) Distribution of tensile strain within the femoral neck during hip extension, long jump and knee flexion exercises. Reproduced from Martinelli et al [74] with permission from Elsevier.…”

Section: Figurementioning

confidence: 99%

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Physical Activity for Strengthening Fracture Prone Regions of the Proximal Femur

Fuchs

Kersh

Carballido‐Gamio

et al. 2017

Curr Osteoporos Rep

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Purpose of review Physical activity improves proximal femoral bone health; however, it remains unclear whether changes translate into a reduction in fracture risk. To enhance any fracture-protective effects of physical activity, fracture prone regions within the proximal femur need to be targeted. Recent findings The proximal femur is designed to withstand forces in the weight bearing direction, but less so forces associated with falls in a sideways direction. Sideways falls heighten femoral neck fracture risk by loading the relatively weak superolateral region of femoral neck. Recent studies exploring regional adaptation of the femoral neck to physical activity have identified heterogeneous adaptation, with adaptation principally occurring within inferomedial weight bearing regions and little to no adaptation occurring in the superolateral femoral neck. Summary There is a need to develop novel physical activities that better target and strengthen the superolateral femoral neck within the proximal femur. Design of these activities may be guided by subject-specific musculoskeletal modeling and finite element modeling approaches.

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Functional Data Analysis: An Introduction and Recent Developments

Gertheiss,

Rügamer,

Liew

et al. 2024

Biometrical J

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Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar‐valued or multivariate data, but FDA brings additional challenges due to the high‐ and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands‐on application, the code for these practical examples is made available through a code and data supplement and on GitHub.

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Strain energy in the femoral neck during exercise

Cited by 49 publications

References 42 publications

Stochastic modelling of muscle recruitment during activity

Stochastic modelling of muscle recruitment during activity

Physical Activity for Strengthening Fracture Prone Regions of the Proximal Femur

Functional Data Analysis: An Introduction and Recent Developments

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