Muscle recruitment strategies can reduce joint loading during level walking

Weight-Bearing Physical Activity, Lower-Limb Muscle Mass, and Risk of Knee Osteoarthritis

Importance

It has been demonstrated that total physical activity is not associated with risk of osteoarthritis. However, the association of different types of physical activity with incident knee osteoarthritis remains unclear.

Objective

To determine whether weight-bearing recreational physical activities are associated with increased risk of incident knee osteoarthritis.

Design, Setting, and Participants

This prospective cohort study used data from the Rotterdam Study (1996 to 2009), including participants with knee x-ray measurements at baseline and follow-up examinations. Participants with knee osteoarthritis at baseline were excluded. Residents aged 45 years and older of the Ommoord district in the city of Rotterdam in The Netherlands were invited to join the Rotterdam Study (78% response rate). Analysis was conducted in June 2023.

Exposure

Total, weight-bearing, and non-weight-bearing recreational physical activities collected by questionnaires at baseline.

Main Outcomes and Measures

Incident radiographic knee osteoarthritis measured by knee x-ray was the primary outcome, and incident symptomatic knee osteoarthritis defined by x-ray and knee pain questionnaire was the secondary outcome. The association of different types of recreational physical activity with radiographic knee osteoarthritis was examined using logistic regression within generalized estimating equation framework after adjusting for potential confounders. A prespecified stratification analysis was planned on the basis of lower-limb muscle mass index (LMI) tertiles, measured by dual-energy x-ray absorptiometry.

Results

A total of 5003 individuals (2804 women [56.0%]; mean [SD] age, 64.5 [7.9] years) were included. The knee osteoarthritis incident rate was 8.4% (793 of 9483 knees) for a mean (SD) follow-up time of 6.33 (2.46) years. Higher weight-bearing activity was associated with increased odds of incident knee osteoarthritis (odds ratio [OR], 1.22; 95% CI, 1.10-1.35; P < .001), but non-weight-bearing activity was not (OR, 1.04; 95% CI, 0.95-1.15; P = .37). In the analysis stratified by LMI tertiles, the association of weight-bearing activity with incident osteoarthritis was found only among 431 patients in the lowest LMI tertile (OR, 1.53; 95% CI, 1.15-2.04; P = .003), but not among patients in the middle or high LMI tertile.

Conclusions and Relevance

The findings of this study suggest that weight-bearing activity is associated with incident knee osteoarthritis in people with low levels of lower-limb muscle mass, which might be a promising avenue for tailored advice for physical activity.

A framework based on subject-specific musculoskeletal models and Monte Carlo simulations to personalize muscle coordination retraining

Excessive loads at lower limb joints can lead to pain and degenerative diseases. Altering joint loads with muscle coordination retraining might help to treat or prevent clinical symptoms in a non-invasive way. Knowing how much muscle coordination retraining can reduce joint loads and which muscles have the biggest impact on joint loads is crucial for personalized gait retraining. We introduced a simulation framework to quantify the potential of muscle coordination retraining to reduce joint loads for an individuum. Furthermore, the proposed framework enables to pinpoint muscles, which alterations have the highest likelihood to reduce joint loads. Simulations were performed based on three-dimensional motion capture data of five healthy adolescents (femoral torsion 10°-29°, tibial torsion 19°-38°) and five patients with idiopathic torsional deformities at the femur and/or tibia (femoral torsion 18°-52°, tibial torsion 3°-50°). For each participant, a musculoskeletal model was modified to match the femoral and tibial geometry obtained from magnetic resonance images. Each participant's model and the corresponding motion capture data were used as input for a Monte Carlo analysis to investigate how different muscle coordination strategies influence joint loads. OpenSim was used to run 10,000 simulations for each participant. Root-mean-square of muscle forces and peak joint contact forces were compared between simulations. Depending on the participant, altering muscle coordination led to a maximum reduction in hip, knee, patellofemoral and ankle joint loads between 5 and 18%, 4% and 45%, 16% and 36%, and 2% and 6%, respectively. In some but not all participants reducing joint loads at one joint increased joint loads at other joints. The required alteration in muscle forces to achieve a reduction in joint loads showed a large variability between participants. The potential of muscle coordination retraining to reduce joint loads depends on the person's musculoskeletal geometry and gait pattern and therefore showed a large variability between participants, which highlights the usefulness and importance of the proposed framework to personalize gait retraining.

Influence of femoral anteversion angle and neck-shaft angle on muscle forces and joint loading during walking

Femoral deformities, e.g. increased or decreased femoral anteversion (AVA) and neck-shaft angle (NSA), can lead to pathological gait patterns, altered joint loads, and degenerative joint diseases. The mechanism how femoral geometry influences muscle forces and joint load during walking is still not fully understood. The objective of our study was to investigate the influence of femoral AVA and NSA on muscle forces and joint loads during walking. We conducted a comprehensive musculoskeletal modelling study based on three-dimensional motion capture data of a healthy person with a typical gait pattern. We created 25 musculoskeletal models with a variety of NSA (93°-153°) and AVA (-12°-48°). For each model we calculated moment arms, muscle forces, muscle moments, co-contraction indices and joint loads using OpenSim. Multiple regression analyses were used to predict muscle activations, muscle moments, co-contraction indices, and joint contact forces based on the femoral geometry. We found a significant increase in co-contraction of hip and knee joint spanning muscles in models with increasing AVA and NSA, which led to a substantial increase in hip and knee joint contact forces. Decreased AVA and NSA had a minor impact on muscle and joint contact forces. Large AVA lead to increases in both knee and hip contact forces. Large NSA (153°) combined with large AVA (48°) led to increases in hip joint contact forces by five times body weight. Low NSA (108° and 93°) combined with large AVA (48°) led to two-fold increases in the second peak of the knee contact forces. Increased joint contact forces in models with increased AVA and NSA were linked to changes in hip muscle moment arms and compensatory increases in hip and knee muscle forces. Knowing the influence of femoral geometry on muscle forces and joint loads can help clinicians to improve treatment strategies in patients with femoral deformities.

Modeling Human Suboptimal Control: A Review

This review paper provides an overview of the approaches to model neuromuscular control, focusing on methods to identify nonoptimal control strategies typical of populations with neuromuscular disorders or children. Where possible, the authors tightened the description of the methods to the mechanisms behind the underlying biomechanical and physiological rationale. They start by describing the first and most simplified approach, the reductionist approach, which splits the role of the nervous and musculoskeletal systems. Static optimization and dynamic optimization methods and electromyography-based approaches are summarized to highlight their limitations and understand (the need for) their developments over time. Then, the authors look at the more recent stochastic approach, introduced to explore the space of plausible neural solutions, thus implementing the uncontrolled manifold theory, according to which the central nervous system only controls specific motions and tasks to limit energy consumption while allowing for some degree of adaptability to perturbations. Finally, they explore the literature covering the explicit modeling of the coupling between the nervous system (acting as controller) and the musculoskeletal system (the actuator), which may be employed to overcome the split characterizing the reductionist approach.

Neuromuscular control: from a biomechanist's perspective

Understanding neural control of movement necessitates a collaborative approach between many disciplines, including biomechanics, neuroscience, and motor control. Biomechanics grounds us to the laws of physics that our musculoskeletal system must obey. Neuroscience reveals the inner workings of our nervous system that functions to control our body. Motor control investigates the coordinated motor behaviours we display when interacting with our environment. The combined efforts across the many disciplines aimed at understanding human movement has resulted in a rich and rapidly growing body of literature overflowing with theories, models, and experimental paradigms. As a result, gathering knowledge and drawing connections between the overlapping but seemingly disparate fields can be an overwhelming endeavour. This review paper evolved as a need for us to learn of the diverse perspectives underlying current understanding of neuromuscular control. The purpose of our review paper is to integrate ideas from biomechanics, neuroscience, and motor control to better understand how we voluntarily control our muscles. As biomechanists, we approach this paper starting from a biomechanical modelling framework. We first define the theoretical solutions (i.e., muscle activity patterns) that an individual could feasibly use to complete a motor task. The theoretical solutions will be compared to experimental findings and reveal that individuals display structured muscle activity patterns that do not span the entire theoretical solution space. Prevalent neuromuscular control theories will be discussed in length, highlighting optimality, probabilistic principles, and neuromechanical constraints, that may guide individuals to families of muscle activity solutions within what is theoretically possible. Our intention is for this paper to serve as a primer for the neuromuscular control scientific community by introducing and integrating many of the ideas common across disciplines today, as well as inspire future work to improve the representation of neural control in biomechanical models.

Musculotendon Parameters in Lower Limb Models: Simplifications, Uncertainties, and Muscle Force Estimation Sensitivity

Musculotendon parameters are key factors in the Hill-type muscle contraction dynamics, determining the muscle force estimation accuracy of a musculoskeletal model. Their values are mostly derived from muscle architecture datasets, whose emergence has been a major impetus for model development. However, it is often not clear if such parameter update indeed improves simulation accuracy. Our goal is to explain to model users how these parameters are derived and how accurate they are, as well as to what extent errors in parameter values might influence force estimation. We examine in detail the derivation of musculotendon parameters in six muscle architecture datasets and four prominent OpenSim models of the lower limb, and then identify simplifications which could add uncertainties to the derived parameter values. Finally, we analyze the sensitivity of muscle force estimation to these parameters both numerically and analytically. Nine typical simplifications in parameter derivation are identified. Partial derivatives of the Hill-type contraction dynamics are derived. Tendon slack length is determined as the musculotendon parameter that muscle force estimation is most sensitive to, whereas pennation angle is the least impactful. Anatomical measurements alone are not enough to calibrate musculotendon parameters, and the improvement on muscle force estimation accuracy will be limited if the source muscle architecture datasets are the only main update. Model users may check if a dataset or model is free of concerning factors for their research or application requirements. The derived partial derivatives may be used as the gradient for musculotendon parameter calibration. For model development, we demonstrate that it is more promising to focus on other model parameters or components and seek alternative strategies to further increase simulation accuracy.

Ten steps to becoming a musculoskeletal simulation expert: A half-century of progress and outlook for the future

Over the past half-century, musculoskeletal simulations have deepened our knowledge of human and animal movement. This article outlines ten steps to becoming a musculoskeletal simulation expert so you can contribute to the next half-century of technical innovation and scientific discovery. We advocate looking to the past, present, and future to harness the power of simulations that seek to understand and improve mobility. Instead of presenting a comprehensive literature review, we articulate a set of ideas intended to help researchers use simulations effectively and responsibly by understanding the work on which today's musculoskeletal simulations are built, following established modeling and simulation principles, and branching out in new directions.

On the estimation of hip joint loads through musculoskeletal modeling

Noninvasive estimation of joint loads is still an open challenge in biomechanics. Although musculoskeletal modeling represents a solid resource, multiple improvements are still necessary to obtain accurate predictions of joint loads and to translate such potential into practical utility. The present study, focused on the hip joint, is aimed at reviewing the state-of-the-art literature on the estimation of hip joint reaction forces through musculoskeletal modeling. Our literature inspection, based on well-defined selection criteria, returned seventeen works, which were compared in terms of methods and results. Deviations between predicted and in vivo measured hip joint loads, taken from the OrthoLoad database, were assessed through quantitative deviation indices. Despite the numerous modeling and computational improvements made over the last two decades, predicted hip joint loads still deviate from their experimental counterparts and typically overestimate them. Several critical aspects have emerged that affect muscle force estimation, hence joint loads. Among them, the physical fidelity of the musculoskeletal model, with its parameters and geometry, plays a crucial role. Also, predicted joint loads are markedly affected by the selected muscle recruitment strategy, which reflects the underlying motor control policy. Practical guidelines for researchers interested in noninvasive estimation of hip joint loads are also provided.

Muscle coordination retraining inspired by musculoskeletal simulations reduces knee contact force

Humans typically coordinate their muscles to meet movement objectives like minimizing energy expenditure. In the presence of pathology, new objectives gain importance, like reducing loading in an osteoarthritic joint, but people often do not change their muscle coordination patterns to meet these new objectives. Here we use musculoskeletal simulations to identify simple changes in coordination that can be taught using electromyographic biofeedback, achieving the therapeutic goal of reducing joint loading. Our simulations predicted that changing the relative activation of two redundant ankle plantarflexor muscles—the gastrocnemius and soleus—could reduce knee contact force during walking, but it was unclear whether humans could re-coordinate redundant muscles during a complex task like walking. Our experiments showed that after a single session of walking with biofeedback of summary measures of plantarflexor muscle activation, healthy individuals reduced the ratio of gastrocnemius-to-soleus muscle activation by 25 ± 15% (p = 0.004, paired t test, n = 10). Participants who walked with this “gastrocnemius avoidance” gait pattern reduced late-stance knee contact force by 12 ± 12% (p = 0.029, paired t test, n = 8). Simulation-informed coordination retraining could be a promising treatment for knee osteoarthritis and a powerful tool for optimizing coordination for a variety of rehabilitation and performance applications.

Altering the strength of the muscles crossing the lower limb joints only affects knee joint reaction forces

BACKGROUND

Generic musculoskeletal models based on literature data are often used to estimate joint reaction forces (JRFs) that otherwise could only be measured invasively. Estimated JRFs are sensitive to changes in maximum isometric force (F_iso) of the muscles, but these are normally simply scaled using a multiplicative coefficient. The impact of varying F_iso, or strength, of muscles crossing each lower limb joint on estimated JRFs has not been systematically explored in musculoskeletal models of the lower limb.

RESEARCH QUESTION

How do alterations in the strength of joint-crossing muscles influence the lower limb JRF magnitudes computed through a generic musculoskeletal model?

METHODS

By modifying F_iso of muscles crossing hip, knee, ankle, or all joints at once up to ± 40% in 10% increments, thirty-two models were created to simulate the gait of a patient with an instrumented tibial prosthesis (5^th Grand Challenge dataset). A standard workflow (inverse kinematics, static optimization, joint reaction analysis) was utilized to calculate JRFs. Both alterations in JRF magnitudes due to joint crossing muscles' strength modifications and their accuracy against in vivo knee loading measurements were quantified.

RESULTS

The knee JRF was the most sensitive force to changes in the joint-crossing muscles' strength (variations ranging from -37.9 ± 0.5% to +37.9 ± 3.2%), while the hip and ankle JRFs were almost unaffected (maximum variation: +6.1%). Reducing the strength of knee and ankle-crossing muscles and intensifying the strength of hip-crossing muscles lowered the knee JRF. The knee JRF was best estimated (peak error: 0.42 ± 0.15 body weight, root mean squared error: 0.37 ± 0.06 body weight, coefficient of determination: 0.76 ± 0.10) by the model with -40% weakened knee-crossing muscles.

SIGNIFICANCE

Altering strengths mainly affects knee JRF estimated with generic musculoskeletal models, suggesting that personalization of strength of joint-crossing muscles is required for accurate knee JRF estimations. Rehabilitation regimes meant to strengthen muscles crossing a joint should be carefully designed to avoid undesired effects on the other joints.

The panniculus carnosus muscle: a missing link in the chronicity of heel pressure ulcers?

Chronic and recurring pressure ulcers (PUs) create an unmet need for predictive biomarkers. In this work, we examine the panniculus carnosus, a thin cutaneous muscle, traditionally considered vestigial in humans, and ask whether the panniculus may play a role in the chronicity and reinjury of heel PUs. To determine whether humans have a panniculus muscle layer at the heel, we dissected eight cadavers. To assess the influence of the panniculus layer on PU, we performed computational simulations of supine weight bearing. Finally, we assessed panniculus regeneration in fluorescent mice. Results show a panniculus layer present in all cadavers examined. Simulations show a thin layer of panniculus muscle causes a dramatic decrease in the volume of soft tissue experiencing high strain and stress, compared to a heel without a panniculus. Importantly, in the mouse model, the panniculus fails to regenerate after PU, even when other cutaneous layers had fully regenerated. Our work shows that the panniculus is able to redistribute load around the heel bone, which might allow it to prevent PUs. Moreover, it is highly susceptible to incomplete regeneration after PU. Poor panniculus regeneration after PU might be a predictive anatomical biomarker for recurrence, and this biomarker should be evaluated prospectively in future clinical trials.

A Musculoskeletal Multibody Algorithm Based On a Novel Rheonomic Constraints Definition Applied to the Lower Limb

In this paper a multibody model was developed in the framework of biotribology of lower limb artificial joints. The presented algorithm performs the inverse dynamics of musculoskeletal systems with the aim to achieve a tool for the calculation of the joint reaction forces. The revolute joint, the cam joint, the spherical joint and the free joint were considered in the analysed lower limb system by introducing a novel analytical formulation of the rheonomic constraint equations based on the quaternions theory. Within the kinematical analysis the curved muscle paths were modelled by simulating their geodesic wrapping over bony surfaces and the muscle actuation states were formulated through the Hill muscle model. The developed analytical model written in Matlab environment allowed to follow the classical musculoskeletal analysis pipeline (kinematical analysis, inverse dynamics and static optimization) applied to the lower limb during the gait kinematics. The objective was to compare the calculated hip joint reactions with the ones obtained in vivo by Bergmann which are established as a reference input for total hip replacements lubrication models, in order to assume the developed algorithm as a fully open and controllable synovial joint tribological configuration generator tool, useful to be coupled with numerical lubrication/contact models in the framework of the artificial joints in silico wear analysis and prediction.

Effect of Suboptimal Neuromuscular Control on the Risk of Massive Wear in Total Knee Replacement

The optimal neuromuscular control (muscle activation strategy that minimises the consumption of metabolic energy) during level walking is very close to that which minimises the force transmitted through the joints of the lower limbs. Thus, any suboptimal control involves an overloading of the joints. Some total knee replacement patients adopt suboptimal control strategies during level walking; this is particularly true for patients with co-morbidities that cause neuromotor control degeneration, such as Parkinson’s Disease (PD). The increase of joint loading increases the risk of implant failure, as reported in one study in PD patients (5.44% of failures at 9 years follow-up). One failure mode that is directly affected by joint loading is massive wear of the prosthetic articular surface. In this study we used a validated patient-specific biomechanical model to estimate how a severely suboptimal control could increase the wear rate of total knee replacements. Whereas autopsy-retrieved implants from non-PD patients typically show average polyethylene wear of 17 mm³ per year, our simulations suggested that a severely suboptimal control could cause a wear rate as high as of 69 mm³ per year. Assuming the risk of implant failure due to massive wear increase linearly with the wear rate, a severely suboptimal control could increase the risk associated to that failure mode from 0.1% to 0.5%. Based on these results, such increase would not be not sufficient to justify alone the higher incidence rate of revision in patients affected by Parkinson’s Disease, suggesting that other failure modes may be involved.

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.