Femoral neck strain prediction during level walking using a combined musculoskeletal and finite element model approach

Understanding Hip Contact Stress Based on Types of Physical Activity: A Systematic Review

Background and Aims

High contact stresses involving the hip have been shown to increase the risk of developing hip osteoarthritis (OA). Although several risk factors have been identified for OA, a holistic approach to predicting contributed factors toward increased hip contact stresses have not been explored. This study was conducted to comprehensively understand the effects of physical activity on high hip contact stress as predisposing factors of OA.

Methods

The protocol of this systematic review was registered in PROSPERO with registration number CRD42022296638 and conducted based on PRISMA guidelines. Full articles that matched our inclusion criteria were selected using PubMed, Web of Science, and Scopus search engines and keywords such as "hip contact stress," "hip contact force," and/or "hip contact pressure." Category of factors, experimental design, results of the study, and evidence from each article were analyzed.

Results

In total 7972 papers were screened, identified, and reviewed. Two independent authors read the collected fulltext of eligible articles resulting in 21 papers that fulfilled the inclusion criteria of this systematic review.

Conclusion

Types of physical activity (n = 21) have correlation with high hip joint contact stress in various manner. Based on the research findings obtained from various inclusion papers, it can be broadly concluded that the more intense the physical activity, such as running and stair climbing, the greater the impact on the increase in hip contact stress values. However, the reviewed studies vary in their methods. This finding suggested that this area is not well investigated and warrants future research.

Lower limb joint loading during high-impact activities: implication for bone health

Osteoporosis results in low-trauma fractures affecting millions globally, in particular elderly populations. Despite the inclusion of physical activity in fracture prevention strategies, the optimal bone-strengthening exercises remain uncertain, highlighting the need for a deeper understanding of lower limb joint loading dynamics across various exercise types and levels. This study examines lower limb joint loading during high-impact exercises across different intensities. A total of 40 healthy, active participants were recruited (mean ± SD: age of 40.3 ± 13.1 yr; height 1.71 ± 0.08 m; and mass 68.44 ± 11.67 kg). Motion capture data and ground reaction forces of 6 different exercises: a self-selected level of walking, running, countermovement jump, squat jump, unilateral hopping, and bilateral hopping were collected for each participant. Joint reaction forces were estimated using lower body musculoskeletal models developed in OpenSim. Running and hopping increased joint forces compared to walking, notably at the hip (83% and 21%), knee (134% and 94%), and ankle (94% and 77%), while jump exercises reduced hip and ankle loading compared to walking (36% and 19%). Joint loading varied with exercise type and intensity, with running faster increasing forces on all joints, particularly at the hip. Sprinting increased forces at the hip but lowered knee and ankle forces. Higher jumps intensified forces on all joints, while faster hopping reduced forces. The wide variation of lower limb joint loading observed across the exercises tested in this study underscores the importance of implementing diverse exercise routines to optimize overall bone health and strengthen the musculoskeletal structure. Practitioners must therefore ensure that exercise programs include movements that are specifically suitable for their intended purpose.

Multibody dynamics-based musculoskeletal modeling for gait analysis: a systematic review

Beyond qualitative assessment, gait analysis involves the quantitative evaluation of various parameters such as joint kinematics, spatiotemporal metrics, external forces, and muscle activation patterns and forces. Utilizing multibody dynamics-based musculoskeletal (MSK) modeling provides a time and cost-effective non-invasive tool for the prediction of internal joint and muscle forces. Recent advancements in the development of biofidelic MSK models have facilitated their integration into clinical decision-making processes, including quantitative diagnostics, functional assessment of prosthesis and implants, and devising data-driven gait rehabilitation protocols. Through an extensive search and meta-analysis of over 116 studies, this PRISMA-based systematic review provides a comprehensive overview of different existing multibody MSK modeling platforms, including generic templates, methods for personalization to individual subjects, and the solutions used to address statically indeterminate problems. Additionally, it summarizes post-processing techniques and the practical applications of MSK modeling tools. In the field of biomechanics, MSK modeling provides an indispensable tool for simulating and understanding human movement dynamics. However, limitations which remain elusive include the absence of MSK modeling templates based on female anatomy underscores the need for further advancements in this area.

Automatic segmentation of lower limb muscles from MR images of post-menopausal women based on deep learning and data augmentation

Individual muscle segmentation is the process of partitioning medical images into regions representing each muscle. It can be used to isolate spatially structured quantitative muscle characteristics, such as volume, geometry, and the level of fat infiltration. These features are pivotal to measuring the state of muscle functional health and in tracking the response of the body to musculoskeletal and neuromusculoskeletal disorders. The gold standard approach to perform muscle segmentation requires manual processing of large numbers of images and is associated with significant operator repeatability issues and high time requirements. Deep learning-based techniques have been recently suggested to be capable of automating the process, which would catalyse research into the effects of musculoskeletal disorders on the muscular system. In this study, three convolutional neural networks were explored in their capacity to automatically segment twenty-three lower limb muscles from the hips, thigh, and calves from magnetic resonance images. The three neural networks (UNet, Attention UNet, and a novel Spatial Channel UNet) were trained independently with augmented images to segment 6 subjects and were able to segment the muscles with an average Relative Volume Error (RVE) between -8.6% and 2.9%, average Dice Similarity Coefficient (DSC) between 0.70 and 0.84, and average Hausdorff Distance (HD) between 12.2 and 46.5 mm, with performance dependent on both the subject and the network used. The trained convolutional neural networks designed, and data used in this study are openly available for use, either through re-training for other medical images, or application to automatically segment new T1-weighted lower limb magnetic resonance images captured with similar acquisition parameters.

Combined musculoskeletal finite element modeling of femur stress during reactive balance training

The purpose of this study was to determine the material stresses experienced in the femoral neck during the stepping phase of recovery from a forward loss of balance achieved both using release from a static forward lean and rapid treadmill accelerations in 8 older adults. A scalable musculoskeletal model with 23 degrees of freedom and 92 force actuators was used to calculate joint reaction forces. A finite element model of the femur used joint reaction forces calculated by the musculoskeletal model to calculate the material stresses during stepping. Balance recovery from a static forward lean angle had a greater joint contact force and greater maximum tensile stress than a recovery from treadmill induced perturbations both before and after a training session. Hip joint contact loads were found to be large in magnitude, however, all stresses experienced by the bone are less than critical yield stresses for trabecular bone. We suggest that stepping balance recovery is safe for older adults with no obvious loss of bone density or strength and that analyses such as finite element analysis are necessary to understand stresses in the material at the joint level.

Automatic segmentation of skeletal muscles from MR images using modified U-Net and a novel data augmentation approach

Rapid and accurate muscle segmentation is essential for the diagnosis and monitoring of many musculoskeletal diseases. As gold standard, manual annotation suffers from intensive labor and high inter-operator reproducibility errors. In this study, deep learning (DL) based automatic muscle segmentation from MR scans is investigated for post-menopausal women, who normally experience a decline in muscle volume. The performance of four Deep Learning (DL) models was evaluated: U-Net and UNet++ and two modified U-Net networks, which combined feature fusion and attention mechanisms (Feature-Fusion-UNet, FFU, and Attention-Feature-Fusion-UNet, AFFU). The models were tested for automatic segmentation of 16-lower limb muscles from MRI scans of two cohorts of post-menopausal women (11 subjects in PMW-1, 8 subjects in PMW-2; from two different studies so considered independent datasets) and 10 obese post-menopausal women (PMW-OB). Furthermore, a novel data augmentation approach is proposed to enlarge the training dataset. The results were assessed and compared by using the Dice similarity coefficient (DSC), relative volume error (RVE), and Hausdorff distance (HD). The best performance among all four DL models was achieved by AFFU (PMW-1: DSC 0.828 ± 0.079, 1-RVE 0.859 ± 0.122, HD 29.9 mm ± 26.5 mm; PMW-2: DSC 0.833 ± 0.065, 1-RVE 0.873 ± 0.105, HD 25.9 mm ± 27.9 mm; PMW-OB: DSC 0.862 ± 0.048, 1-RVE 0.919 ± 0.076, HD 34.8 mm ± 46.8 mm). Furthermore, the augmentation of data significantly improved the DSC scores of U-Net and AFFU for all 16 tested muscles (between 0.23% and 2.17% (DSC), 1.6%-1.93% (1-RVE), and 9.6%-19.8% (HD) improvement). These findings highlight the feasibility of utilizing DL models for automatic segmentation of muscles in post-menopausal women and indicate that the proposed augmentation method can enhance the performance of models trained on small datasets.

Effect of Muscle Forces on Femur During Level Walking Using a Virtual Population of Older Women

Aging is associated with a greater risk of muscle and bone disorders such as sarcopenia and osteoporosis. These conditions substantially affect one's mobility and quality of life. In the past, muscles and bones are often studied separately using generic or scaled information that are not personal-specific, nor are they representative of the large variations seen in the elderly population. Consequently, the mechanical interaction between the aged muscle and bone is not well understood, especially when carrying out daily activities. This study presents a coupling approach across the body and the organ level, using fully personal-specific musculoskeletal and finite element models in order to study femoral loading during level walking. Variations in lower limb muscle volume/force were examined using a virtual population. These muscle forces were then applied to the finite element model of the femur to study the variations in predicted strains. The study shows that effective coupling across two scales can be carried out to study the muscle-bone interaction in elderly women. The generation of a virtual population is a feasible approach to augment anatomical variations based on a small population that could mimic variations seen in a larger cohort. This is a valuable alternative to overcome the limitation or the need to collect dataset from a large population, which is both time and resource consuming.

Exploration of the synergistic role of cortical thickness asymmetry ("Trabecular Eccentricity" concept) in reducing fracture risk in the human femoral neck and a control bone (Artiodactyl Calcaneus)

The mechanobiology of the human femoral neck is a focus of research for many reasons including studies that aim to curb age-related bone loss that contributes to a near-exponential rate of hip fractures. Many believe that the femoral neck is often loaded in rather simple bending, which causes net tension stress in the upper (superior) femoral neck and net compression stress in its inferior aspect ("T/C paradigm"). This T/C loading regime lacks in vivo proof. The "C/C paradigm" is a plausible alternative simplified load history that is characterized by a gradient of net compression across the entire femoral neck; action of the gluteus medius and external rotators of the hip are important in this context. It is unclear which paradigm is at play in natural loading due to lack of in vivo bone strain data and deficiencies in understanding mechanisms and manifestations of bone adaptation in tension vs. compression. For these reasons, studies of the femoral neck would benefit from being compared to a 'control bone' that has been proven, by strain data, to be habitually loaded in bending. The artiodactyl (sheep and deer) calcaneus model has been shown to be a very suitable control in this context. However, the application of this control in understanding the load history of the femoral neck has only been attempted in two prior studies, which did not examine the interplay between cortical and trabecular bone, or potential load-sharing influences of tendons and ligaments. Our first goal is to compare fracture risk factors of the femoral neck in both paradigms. Our second goal is to compare and contrast the deer calcaneus to the human femoral neck in terms of fracture risk factors in the T/C paradigm (the C/C paradigm is not applicable in the artiodactyl calcaneus due to its highly constrained loading). Our third goal explores interplay between dorsal/compression and plantar/tension regions of the deer calcaneus and the load-sharing roles of a nearby ligament and tendon, with insights for translation to the femoral neck. These goals were achieved by employing the analytical model of Fox and Keaveny (J. Theoretical Biology 2001, 2003) that estimates fracture risk factors of the femoral neck. This model focuses on biomechanical advantages of the asymmetric distribution of cortical bone in the direction of habitual loading. The cortical thickness asymmetry of the femoral neck (thin superior cortex, thick inferior cortex) reflects the superior-inferior placement of trabecular bone (i.e., "trabecular eccentricity," TE). TE helps the femoral neck adapt to typical stresses and strains through load-sharing between superior and inferior cortices. Our goals were evaluated in the context of TE. Results showed the C/C paradigm has lower risk factors for the superior cortex and for the overall femoral neck, which is clinically relevant. TE analyses of the deer calcaneus revealed important synergism in load-sharing between the plantar/tension cortex and adjacent ligament/tendon, which challenges conventional understanding of how this control bone achieves functional adaptation. Comparisons with the control bone also exposed important deficiencies in current understanding of human femoral neck loading and its potential histocompositional adaptations.

Computation of physiological loading induced interstitial fluid motion in muscle standardized femur: Healthy vs. osteoporotic bone

BACKGROUND AND OBJECTIVES

Physiological loading-induced mechanical environments regulate bone modeling and remodeling. Thus, loading-induced normal strain is typically considered a stimulus to osteogenesis. However, several studies noticed new bone formation near the sites of minimal normal strain, e.g., the neutral axis of bending in long bones, which raises a question on how bone mass is maintained near these sites. Secondary mechanical components such as shear strain and interstitial fluid flow also stimulate bone cells and regulate bone mass. However, the osteogenic potential of these components is not well established. Accordingly, the present study estimates the distribution of physiological muscle loading-induced mechanical environments such as normal strain, shear strain, pore pressure, and interstitial fluid flow in long bones.

METHODS

A poroelastic finite element muscle standardized femur (MuscleSF) model is developed to compute the distribution of the mechanical environment as a function of bone porosities associated with osteoporotic and disuse bone loss.

RESULTS

The results indicate the presence of higher shear strain and interstitial fluid motion near the minimal strain sites, i.e., the neutral axis of bending of femoral cross-sections. This suggests that secondary stimuli may maintain the bone mass at these locations. Pore pressure and interstitial fluid motion reduce with the increased porosity associated with bone disorders, possibly resulting in diminished skeletal mechano-sensitivity to exogenous loading.

CONCLUSIONS

These outcomes present a better understanding of mechanical environment-mediated regulation of site-specific bone mass, which can be beneficial in developing prophylactic exercise to prevent bone loss in osteoporosis and muscle disuse.

Potential of Neuromuscular Electrical Stimulation as a Bone Loss Countermeasure in Microgravity

INTRODUCTION: For future long-duration spaceflight missions, additional methods of loading the skeleton may be required to supplement exercise to minimize bone loss. Neuromuscular electrical stimulation (NMES) can elicit muscular contractions that create strain on bone. However, the potential effectiveness of NMES on the proximal femur during disuse is not known.METHODS: We measured the maximum isometric force of NMES-induced contractions of the rectus femoris and the hamstrings of 10 subjects (5 male, 5 female), sitting with the hips and knees at 90 degrees of flexion. We employed 2-D biomechanical models of the knee and hip to estimate the hip joint reaction forces, applied these forces to a generic femur finite element analysis model, and qualitatively compared the peak principal strains of the proximal femoral neck to the peak strains modeled in previous studies for other forms of exercise.RESULTS: The average peak tensile/compressive strains were 1380 ± 719 µε/-2179 ± 1130 µε and 573 ± 345 µε/-900 ± 543 µε for the male and female subjects, respectively. While results varied between studies, the strains achieved during NMES generally were comparable to those achieved during walking or stairs, with some individuals matching higher intensity activities.DISCUSSION: This study demonstrated that isometric NMES contractions of the thigh muscles can create strain in the proximal femoral neck similar to that achieved during low impact activities. While NMES alone will unlikely create a sufficient daily strain stimulus to prevent bone loss, it will likely improve the current spaceflight countermeasures by adding more frequent loading throughout the day.Abitante TJ, Bouxsein ML, Duda KR, Newman DJ. Potential of neuromuscular electrical stimulation as a bone loss countermeasure in microgravity. Aerosp Med Hum Perform. 2022; 93(11):774–782.

Improving the Hip Fracture Risk Prediction with a Statistical Shape-and-Intensity Model of the Proximal Femur

Severe predictions have been made regarding osteoporotic fracture incidence for the next years, with major economic and social impacts in a worldwide greying society. However, the performance of the currently adopted gold standard for fracture risk prediction, the areal Bone Mineral Density (aBMD), remains moderate. To overcome current limitations, the construction of statistical models of the proximal femur, based on three-dimensional shape and intensity (a hallmark of bone density), is here proposed for predicting hip fracture in a Caucasian postmenopausal cohort. Partial Least Square (PLS)-based statistical models of the shape, intensity and their combination were developed, and the corresponding modes and components were identified. Logistic regression models using the first two shape, intensity and shape-intensity PLS components were implemented and tested within a 10-fold cross-validation procedure as predictors of hip fracture. It emerged that (1) intensity components were superior to shape components in stratifying patients according to their fracture status, and that (2) a combination of intensity and shape improved patients risk stratification. The area under the ROC curve was 0.64, 0.85 and 0.92 for the models based on shape, intensity and shape-intensity combination respectively, against a 0.72 value for the aBMD standard approach. Based on these findings, the presented methodology turns out to be promising in tackling the need for an enhanced fracture risk assessment.