Age-related changes in gait biomechanics and their impact on the metabolic cost of walking: Report from a National Institute on Aging workshop.

Changes in old age that contribute to the complex issue of an increased metabolic cost of walking (mass-specific energy cost per unit distance traveled) in older adults appear to center at least in part on changes in gait biomechanics. However, age-related changes in energy metabolism, neuromuscular function and connective tissue properties also likely contribute to this problem, of which the consequences are poor mobility and increased risk of inactivity-related disease and disability. The U.S. National Institute on Aging convened a workshop in September 2021 with an interdisciplinary group of scientists to address the gaps in research related to the mechanisms and consequences of changes in mobility in old age. The goal of the workshop was to identify promising ways to move the field forward toward improving gait performance, decreasing energy cost, and enhancing mobility for older adults. This report summarizes the workshop and brings multidisciplinary insight into the known and potential causes and consequences of age-related changes in gait biomechanics. We highlight how gait mechanics and energy cost change with aging, the potential neuromuscular mechanisms and role of connective tissue in these changes, and cutting-edge interventions and technologies that may be used to measure and improve gait and mobility in older adults. Key gaps in the literature that warrant targeted research in the future are identified and discussed.

Patterns of asymmetry and energy cost generated from predictive simulations of hemiparetic gait.

Hemiparesis, defined as unilateral muscle weakness, often occurs in people post-stroke or people with cerebral palsy, however it is difficult to understand how this hemiparesis affects movement patterns as it often presents alongside a variety of other neuromuscular impairments. Predictive musculoskeletal modeling presents an opportunity to investigate how impairments affect gait performance assuming a particular cost function. Here, we use predictive simulation to quantify the spatiotemporal asymmetries and changes to metabolic cost that emerge when muscle strength is unilaterally reduced and how reducing spatiotemporal symmetry affects metabolic cost. We modified a 2-D musculoskeletal model by uniformly reducing the peak isometric muscle force unilaterally. We then solved optimal control simulations of walking across a range of speeds by minimizing the sum of the cubed muscle excitations. Lastly, we ran additional optimizations to test if reducing spatiotemporal asymmetry would result in an increase in metabolic cost. Our results showed that the magnitude and direction of effort-optimal spatiotemporal asymmetries depends on both the gait speed and level of weakness. Also, the optimal speed was 1.25 m/s for the symmetrical and 20% weakness models but slower (1.00 m/s) for the 40% and 60% weakness models, suggesting that hemiparesis can account for a portion of the slower gait speed seen in people with hemiparesis. Modifying the cost function to minimize spatiotemporal asymmetry resulted in small increases (~4%) in metabolic cost. Overall, our results indicate that spatiotemporal asymmetry may be optimal for people with hemiparesis. Additionally, the effect of speed and the level of weakness on spatiotemporal asymmetry may help explain the well-known heterogenous distribution of spatiotemporal asymmetries observed in the clinic. Future work could extend our results by testing the effects of other neuromuscular impairments on optimal gait strategies, and therefore build a more comprehensive understanding of the gait patterns observed in clinical populations.

Using Bayesian inference to estimate plausible muscle forces in musculoskeletal models.

BACKGROUND: Musculoskeletal modeling is currently a preferred method for estimating the muscle forces that underlie observed movements. However, these estimates are sensitive to a variety of assumptions and uncertainties, which creates difficulty when trying to interpret the muscle forces from musculoskeletal simulations. Here, we describe an approach that uses Bayesian inference to identify plausible ranges of muscle forces for a simple motion while representing uncertainty in the measurement of the motion and the objective function used to solve the muscle redundancy problem. METHODS: We generated a reference elbow flexion-extension motion and computed a set of reference forces that would produce the motion while minimizing muscle excitations cubed via OpenSim Moco. We then used a Markov Chain Monte Carlo (MCMC) algorithm to sample from a posterior probability distribution of muscle excitations that would result in the reference elbow motion. We constructed a prior over the excitation parameters which down-weighted regions of the parameter space with greater muscle excitations. We used muscle excitations to find the corresponding kinematics using OpenSim, where the error in position and velocity trajectories (likelihood function) was combined with the sum of the cubed muscle excitations integrated over time (prior function) to compute the posterior probability density. RESULTS: We evaluated the muscle forces that resulted from the set of excitations that were visited in the MCMC chain (seven parallel chains, 500,000 iterations per chain). The estimated muscle forces compared favorably with the reference forces generated with OpenSim Moco, while the elbow angle and velocity from MCMC matched closely with the reference (average RMSE for elbow angle = 2°; and angular velocity = 32°/s). However, our rank plot analyses and potential scale reduction statistics, which we used to evaluate convergence of the algorithm, indicated that the chains did not fully mix. CONCLUSIONS: While the results from this process are a promising step towards characterizing uncertainty in muscle force estimation, the computational time required to search the solution space with, and the lack of MCMC convergence indicates that further developments in MCMC algorithms are necessary for this process to become feasible for larger-scale models.

Microphysiological systems in early stage drug development: Perspectives on current applications and future impact.

Microphysiological systems (MPS) are making advances to provide more standardized and predictive physiologically relevant responses to test articles in living tissues and organ systems. The excitement surrounding the potential of MPS to better predict human responses to medicines and improving clinical translation is overshadowed by their relatively slow adoption by the pharmaceutical industry and regulators. Collaboration between multiorganizational consortia and regulators is necessary to build an understanding of the strengths and limitations of MPS models and closing the current gaps. Here, we review some of the advances in MPS research, focusing on liver, intestine, vascular system, kidney and lung and present examples highlighting the context of use for these systems. For MPS to gain a foothold in drug development, they must have added value over existing approaches. Ideally, the application of MPS will augment in vivo studies and reduce the use of animals via tiered screening with less reliance on exploratory toxicology studies to screen compounds. Because MPS support multiple cell types (e.g. primary or stem-cell derived cells) and organ systems, identifying when MPS are more appropriate than simple 2D in vitro models for understanding physiological responses to test articles is necessary. Once identified, MPS models require qualification for that specific context of use and must be reproducible to allow future validation. Ultimately, the challenges of balancing complexity with reproducibility will inform the promise of advancing the MPS field and are critical for realization of the goal to reduce, refine and replace (3Rs) the use of animals in nonclinical research.

Taking advantage of external mechanical work to reduce metabolic cost: the mechanics and energetics of split-belt treadmill walking.

KEY POINTS: The neuromotor system generates flexible motor patterns that can adapt to changes in our bodies or environment and also take advantage of assistance provided by the environment. We ask how energy minimization influences adaptive learning during human locomotion to improve economy when walking on a split-belt treadmill. We use a model-based approach to predict how people should adjust their walking pattern to take advantage of the assistance provided by the treadmill, and we validate these predictions empirically. We show that adaptation to a split-belt treadmill can be explained as a process by which people reduce step length asymmetry to take advantage of the work performed by the treadmill to reduce metabolic cost. Our results also have implications for the evaluation of devices designed to reduce effort during walking, as locomotor adaptation may serve as a model approach to understand how people learn to take advantage of external assistance. ABSTRACT: In everyday tasks such as walking and running, we often exploit the work performed by external sources to reduce effort. Recent research has focused on designing assistive devices capable of performing mechanical work to reduce the work performed by muscles and improve walking function. The success of these devices relies on the user learning to take advantage of this external assistance. Although adaptation is central to this process, the study of adaptation is often done using approaches that seem to have little in common with the use of external assistance. We show in 16 young, healthy participants that a common approach for studying adaptation, split-belt treadmill walking, can be understood from a perspective in which people learn to take advantage of mechanical work performed by the treadmill. Initially, during split-belt walking, people step further forward on the slow belt than the fast belt which we measure as a negative step length asymmetry, but this asymmetry is reduced with practice. We demonstrate that reductions in asymmetry allow people to extract positive work from the treadmill, reduce the positive work performed by the legs, and reduce metabolic cost. We also show that walking with positive step length asymmetries, defined by longer steps on the fast belt, minimizes metabolic cost, and people choose this pattern after guided experience of a wide range of asymmetries. Our results suggest that split-belt adaptation can be interpreted as a process by which people learn to take advantage of mechanical work performed by an external device to improve economy.

Individual Differences in Locomotor Function Predict the Capacity to Reduce Asymmetry and Modify the Energetic Cost of Walking Poststroke.

Changes in the control of the lower extremities poststroke lead to persistent biomechanical asymmetries during walking. These asymmetries are associated with an increase in energetic cost, leading to the possibility that reducing asymmetry can improve walking economy. However, the influence of asymmetry on economy may depend on the direction and cause of asymmetry. For example, impairments with paretic limb advancement may result in shorter paretic steps, whereas deficits in paretic support or propulsion result in shorter nonparetic steps. Given differences in the underlying impairments responsible for step length asymmetry, the capacity to reduce asymmetry and the associated changes in energetic cost may not be consistent across this population. Here, we identified factors explaining individual differences in the capacity to voluntarily reduce step length asymmetry and modify energetic cost during walking. A total of 24 individuals poststroke walked on a treadmill, with visual feedback of their step lengths to aid explicit modification of asymmetry. We found that individuals who took longer paretic steps had a greater capacity to reduce asymmetry and were better able to transfer the effects of practice to overground walking than individuals who took shorter paretic steps. In addition, changes in metabolic cost depended on the direction of asymmetry, baseline cost of transport, and reductions in specific features of spatiotemporal asymmetry. These results demonstrate that many stroke survivors retain the residual capacity to voluntarily walk more symmetrically on a treadmill and overground. However, whether reductions in asymmetry reduce metabolic cost depends on individual differences in impairments affecting locomotor function.

Evidence of Energetic Optimization during Adaptation Differs for Metabolic, Mechanical, and Perceptual Estimates of Energetic Cost.

The theory that the sensorimotor system minimizes energetic cost during locomotion has long been supported by both computational models and empirical studies. However, it has yet to be determined if the behavior to which people converge when exposed to a novel perturbation during locomotion is also energetically optimal. We address this issue in the context of adaptation to walking on a split-belt treadmill, which can impose a left-right asymmetry in step lengths. In response to this asymmetry, participants gradually adjust their foot placement to adopt steps of equal length. Here, we characterized metabolic, mechanical, and perceptual estimates of energetic cost associated with a range of asymmetries to determine whether symmetry is the energetically optimal strategy for walking on a split-belt treadmill. We found that taking steps of equal length did not minimize metabolic cost or mechanical cost. In addition, perceptual estimates of cost were not sensitive to changes in asymmetry. However, symmetry was identified as the optimal strategy when energetic cost was estimated from a composite metric that combined both metabolic and mechanical costs. These results suggest that adaptation may arise from optimization of a composite estimate of effort derived from feedback about the interaction between the body and environment.

Associations Between Foot Placement Asymmetries and Metabolic Cost of Transport in Hemiparetic Gait.

Stroke survivors often have a slow, asymmetric walking pattern. They also walk with a higher metabolic cost than healthy, age-matched controls. It is often assumed that spatial-temporal asymmetries contribute to the increased metabolic cost of walking poststroke. However, elucidating this relationship is made challenging because of the interdependence between spatial-temporal asymmetries, walking speed, and metabolic cost. Here, we address these potential confounds by measuring speed-dependent changes in metabolic cost and implementing a recently developed approach to dissociate spatial versus temporal contributions to asymmetry in a sample of stroke survivors. We used expired gas analysis to compute the metabolic cost of transport (CoT) for each participant at 4 different walking speeds: self-selected speed, 80% and 120% of their self-selected speed, and their fastest comfortable speed. We also computed CoT for a sample of age- and gender-matched control participants who walked at the same speeds as their matched stroke survivor. Kinematic data were used to compute the magnitude of a number of variables characterizing spatial-temporal asymmetries. Across all speeds, stroke survivors had a higher CoT than controls. We also found that our sample of stroke survivors did not choose a self-selected speed that minimized CoT, contrary to typical observations in healthy controls. Multiple regression analyses revealed negative associations between speed and CoT and a positive association between asymmetries in foot placement relative to the trunk and CoT. These findings suggest that interventions designed to increase self-selected walking speed and reduce foot-placement asymmetries may be ideal for improving walking economy poststroke.

Learning to be economical: the energy cost of walking tracks motor adaptation.

Many theories of motor control suggest that we select our movements to reduce energy use. However, it is unclear whether this process underlies short-term motor adaptation to novel environments. Here we asked whether adaptation to walking on a split-belt treadmill leads to a more economical walking pattern. We hypothesized that adaptation would be accompanied by a reduction in metabolic power and muscle activity and that these reductions would be temporally correlated. Eleven individuals performed a split-belt adaptation task where the belt speeds were set at 0.5 and 1.5 m s(-1). Adaptation was characterized by step length symmetry, which is the normalized difference in step length between the legs. Metabolic power was calculated based on expired gas analysis, and surface EMG was used to record the activity of four bilateral leg muscles (tibialis anterior, lateral gastrocnemius, vastus lateralis and biceps femoris). All participants initially walked with unequal step lengths when the belts moved at different speeds, but gradually adapted to take steps of equal length. Additionally, net metabolic power was reduced from early adaptation to late adaptation (early, 3.78 ± 1.05 W kg(-1); and late, 3.05 ± 0.79 W kg(-1); P < 0.001). This reduction in power was also accompanied by a bilateral reduction in EMG throughout the gait cycle. Furthermore, the reductions in metabolic power occurred over the same time scale as the improvements in step length symmetry, and the magnitude of these improvements predicted the size of the reduction in metabolic power. Our results suggest that increasing economy may be a key criterion driving locomotor adaptation.