Effects of Foot-Strike Pattern on Neuromuscular Function During a Prolonged Graded Run.

PURPOSE: To study whether, during typical-level running, non-rear-foot strikers (non-RFS) or rear-foot strikers (RFS) presented a similar or different extent of neuromuscular fatigue after a prolonged graded run. METHODS: Sixteen experienced male trail runners (8 non-RFS and 8 RFS) performed a 2.5-hour treadmill graded running exercise. Before and after exercise, neuromuscular tests were performed to assess neuromuscular fatigue of the plantar flexors. Biomechanical gait parameters were acquired with an instrumented treadmill, and electromyographic activity of the lower-limb muscles was collected as an index of muscle activation. RESULTS: There were no significant time × foot strike interactions for neuromuscular (all P ≥ .742), muscle activation (all P ≥ .157), or biomechanical (all P ≥ .096) variables. CONCLUSIONS: A dominant level running foot-strike pattern did not directly affect the extent of neuromuscular fatigue during a prolonged graded run. This suggests that no ideal running foot-strike pattern exists to minimize neuromuscular fatigue during prolonged-duration races wherein cumulative uphill and downhill segments are high, such as in trail running.

Tibial acceleration alone is not a valid surrogate measure of tibial load in response to stride length manipulation.

PURPOSE: This study aimed to evaluate the relationship between peak tibial acceleration and peak ankle joint contact forces in response to stride length manipulation during level-ground running. METHODS: Twenty-seven physically active participants ran 10 trials at preferred speed in each of 5 stride length conditions: preferred, ±5 %, and ±10 % of preferred stride length. Motion capture, force platform, and tibial acceleration data were directly measured, and ankle joint contact forces were estimated using an inverse-dynamics-based static optimization routine. RESULTS: In general, peak axial tibial accelerations (p < 0.001) as well as axial (p < 0.001) and resultant (p < 0.001) ankle joint contact forces increased with stride length. When averaged within the 10 strides of each stride condition, moderate positive correlations were observed between peak axial acceleration and joint contact force (r = 0.49) as well as peak resultant acceleration and joint contact force (r = 0.51). However, 37% of participants illustrated either no relationship or negative correlations. Only weak correlations across participants existed between peak axial acceleration and joint contact force (r = 0.12) as well as peak resultant acceleration and ankle joint contact force (r = 0.18) when examined on a step-by-step basis. CONCLUSION: These results suggest that tibial acceleration should not be used as a surrogate for ankle joint contact force on a step-by-step basis in response to stride length manipulations during level-ground running. A 10-step averaged tibial acceleration metric may be useful for some runners, but an initial laboratory assessment would be required to identify these individuals.

Monthly treatment with romosozumab for 1 year increases bone mineral at the hip, but not the knee, in women with chronic spinal cord injury.

Bone loss below the level of neurological lesion is a well-known complication of spinal cord injury (SCI). To date, most research has focused on pharmaceutical intervention using antiresorptives to prevent bone loss during the acute phase of SCI; however, limited research has investigated treatments for established osteoporosis during chronic SCI. Romosozumab, a monoclonal antibody with both antiresorptive and anabolic effects, has demonstrated significant increases in BMD for women with established PMO. Therefore, the purpose of this study was to examine the efficacy of monthly treatment with romosozumab to improve DXA-derived areal BMD at the hip, and CT-derived BMC and strength at the hip and knee in women with chronic SCI and an inability to ambulate. Twelve female participants with chronic SCI were recruited to receive 1 yr of monthly subcutaneous injections of romosozumab (210 mg). DXA and CT scans were taken at baseline, and months 3, 6, and 12 to quantify bone mineral, and finite element (FE) analysis was used to predict bone strength. Longitudinal mixed effects models were employed to determine the impact of treatment on bone properties. After 12 mo of treatment, areal BMD at the lumbar spine and total hip were significantly increased with median changes of 10.2% (IQR: 8.3-15.2%, p<.001) and 4.2% (IQR: 3.4-7.7%, p = .009), respectively. Improvements at the hip were primarily due to increases in trabecular, not cortical, bone and effects were sufficient to significantly increase FE-predicted strength by 20.3% (IQR: 9.5-37.0%, p = .004). Treatment with romosozumab did not lead to any significant improvement in bone mineral at the distal femur or proximal tibia. These findings provide promising results for romosozumab treatment to improve bone mineral and reduce fracture risk at the hip, but not the knee, in women with chronic SCI.

Adapting the Intensity Gradient for Use with Count-Based Accelerometry Data in Children and Adolescents.

The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7-22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab and at-home protocols. We found strong agreement between GT1M and GT9X counts during the combined in-lab activities (mean bias = 2 counts) and between minutes per day with different intensities of activity (e.g., sedentary, light, moderate, and vigorous) classified using cutpoints (mean bias < 5 min/d at all intensities). We generated bin sizes that could be used to generate IGs from the count data (mean bias = -0.15; 95% LOA [-0.65, 0.34]) compared with the original IG. Therefore, the intensity gradient could be used to analyze count data. The count-based intensity gradient metric will be valuable for re-analyzing historical datasets collected using older accelerometer models, such as the GT1M.

Tibial strains are sensitive to speed perturbations, but not grade perturbations, during running.

A fatigue-failure process is hypothesized to govern the development of tibial stress fractures, where bone damage is highly dependent on the peak strain magnitude. To date, much of the work examining tibial strain during running has ignored uphill and downhill running despite the prevalence of this terrain. This study examined the sensitivity of tibial strain to changes in running grade and speed using a combined musculoskeletal-finite element modelling routine. Seventeen participants ran on a treadmill at ±10, ±5 and 0 deg; at each grade, participants ran at 3.33 m s-1 and at a grade-adjusted speed of 2.50 and 4.17 m s-1 for uphill and downhill grades, respectively. Force and motion data were recorded in each grade and speed combination. Muscle and joint contact forces were estimated using inverse-dynamics-based static optimization. These forces were applied to a participant-adjusted finite element model of the tibia. None of the strain variables (50th and 95th percentile strain and strained volume ≥4000 µÎµ) differed as a function of running grade; however, all strain variables were sensitive to running speed (F1≥9.59, P≤0.03). In particular, a 1 m s-1 increase in speed resulted in a 9% (∼260 µÎµ) and 155% (∼600 mm3) increase in peak strain and strained volume, respectively. Overall, these findings suggest that faster running speeds, but not changes in running grade, may be more deleterious to the tibia.

Predicting Tibia-Fibula Geometry and Density From Anatomical Landmarks Via Statistical Appearance Model: Influence of Errors on Finite Element-Calculated Bone Strain.

State-of-the-art participant-specific finite element models require advanced medical imaging to quantify bone geometry and density distribution; access to and cost of imaging is prohibitive to the use of this approach. Statistical appearance models may enable estimation of participants' geometry and density in the absence of medical imaging. The purpose of this study was to: (1) quantify errors associated with predicting tibia-fibula geometry and density distribution from skin-mounted landmarks using a statistical appearance model and (2) quantify how those errors propagate to finite element-calculated bone strain. Participant-informed models of the tibia and fibula were generated for thirty participants from height and sex and from twelve skin-mounted landmarks using a statistical appearance model. Participant-specific running loads, calculated using gait data and a musculoskeletal model, were applied to participant-informed and CT-based models to predict bone strain using the finite element method. Participant-informed meshes illustrated median geometry and density distribution errors of 4.39-5.17 mm and 0.116-0.142 g/cm3, respectively, resulting in large errors in strain distribution (median RMSE = 476-492 µÎµ), peak strain (limits of agreement =±27-34%), and strained volume (limits of agreement =±104-202%). These findings indicate that neither skin-mounted landmark nor height and sex-based predictions could adequately approximate CT-derived participant-specific geometry, density distribution, or finite element-predicted bone strain and therefore should not be used for analyses comparing between groups or individuals.

Adaptive P-Splines for challenging filtering problems in biomechanics.

Suppression of noise from recorded signals is a critically important data processing step for biomechanical analyses. While a wide variety of filtering or smoothing spline methods are available, the majority of these are not well suited for the analysis of signals with rapidly changing derivatives such as the processing of motion data for impact-like events. This is because commonly used low-pass filtering approaches or smoothing splines typically assume a single fixed cut-off frequency or regularization penalty which fails to describe rapid changes in the underlying function. To overcome these limitations we examine a class of adaptive penalized splines (APS) that extend commonly used penalized spline smoothers by inferring temporal adaptations in regularization penalty from observed data. Three variations of APS are examined in which temporal variation of spline penalization is described via either a series of independent random variables, an autoregressive process or a smooth cubic spline. Comparing the performance of APS on simulated datasets is promising with APS reducing RMSE by 48%-183% compared to a widely used Butterworth filtering approach. When inferring acceleration from noisy measurements describing the position of a pendulum impacting a barrier we observe between a 13% (independent variables) to 28% (spline) reduction in RMSE when compared to a 4th order Butterworth filter with optimally selected cut-off frequency. In addition to considerable improvement in RMSE, APS can provide estimates of uncertainty for fitted curves and generated quantities such as peak accelerations or durations of stationary periods. As a result, we suggest that researchers should consider the use of APS if features such as impact peaks, rates of loading, or periods of negligible acceleration are of interest.

Dirt Track Surface Preparation and Associated Differences in Speed, Stride Length, and Stride Frequency in Galloping Horses.

In racehorses, the risk of musculoskeletal injury is linked to a decrease in speed and stride length (SL) over consecutive races prior to injury. Surface characteristics influence stride parameters. We hypothesized that large changes in stride parameters are found during galloping in response to dirt racetrack preparation. Harrowing of the back stretch of a half-mile dirt racetrack was altered in three individual lanes with decreasing depth from the inside to the outside. Track underlay compaction and water content were changed between days. Twelve horses (six on day 2) were sequentially galloped at a target speed of 16 ms-1 across the three lanes. Speed, stride frequency (SF), and SL were quantified with a GPS/GNSS logger. Mixed linear models with speed as covariate analyzed SF and SL, with track hardness and moisture content as fixed factors (p < 0.05). At the average speed of 16.48 ms-1, hardness (both p < 0.001) and moisture content (both p < 0.001) had significant effects on SF and SL. The largest difference in SL of 0.186 m between hardness and moisture conditions exceeded the 0.10 m longitudinal decrease over consecutive race starts previously identified as injury predictor. This suggests that detailed measurements of track conditions might be useful for refining injury prediction models.

Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage.

Simulation studies, such as finite element (FE) modeling, provide insight into knee joint mechanics without patient involvement. Generic FE models mimic the biomechanical behavior of the tissue, but overlook variations in geometry, loading, and material properties of a population. Conversely, subject-specific models include these factors, resulting in enhanced predictive precision, but are laborious and time intensive. The present study aimed to enhance subject-specific knee joint FE modeling by incorporating a semi-automated segmentation algorithm using a 3D Swin UNETR for an initial segmentation of the femur and tibia, followed by a statistical shape model (SSM) adjustment to improve surface roughness and continuity. For comparison, a manual FE model was developed through manual segmentation (i.e., the de-facto standard approach). Both FE models were subjected to gait loading and the predicted mechanical response was compared. The semi-automated segmentation achieved a Dice similarity coefficient (DSC) of over 98% for both the femur and tibia. Hausdorff distance (mm) between the semi-automated and manual segmentation was 1.4 mm. The mechanical results (max principal stress and strain, fluid pressure, fibril strain, and contact area) showed no significant differences between the manual and semi-automated FE models, indicating the effectiveness of the proposed semi-automated segmentation in creating accurate knee joint FE models. We have made our semi-automated models publicly accessible to support and facilitate biomechanical modeling and medical image segmentation efforts ( https://data.mendeley.com/datasets/k5hdc9cz7w/1 ).

Influence of microarchitecture on stressed volume and mechanical fatigue behaviour of equine subchondral bone.

Fractures of the equine metacarpophalangeal (MCP) joint are among the most common and fatal injuries experienced by racehorses. These bone injuries are a direct result of repetitive, high intensity loading of the skeleton during racing and training and there is consensus that they represent a mechanical fatigue phenomenon. Existing work has found the fatigue life of bone to be strongly determined by bone microarchitecture and the resulting stressed volume (i.e., the volume of bone stressed above assumed yield). The purpose of this study was to quantify the influence of bone microarchitecture on the mechanical fatigue behaviour of equine subchondral bone from the MCP joint across a wide variety of sample types. Forty-eight subchondral bone samples were prepared from the third metacarpal (MC3) and proximal phalanx (P1) of 8 horses and subsequently imaged using high resolution micro-computed tomography (µCT) to quantify microarchitectural features of interest, including bone volume fraction, tissue mineral density, pore size, pore spacing, and pore number. Samples were cyclically loaded in compression to a stress of 70 MPa, and fatigue life was defined as the number of cycles until failure. Finite element models were created from the µCT images and used to quantify stressed volume. Based on the expected log point-wise predictive density, stressed volume was a strong predictor of fatigue life in both the MC3 and P1. A regional analysis indicated fatigue life was more strongly associated with bone volume fraction in the superficial (r2 = 0.32, p < 0.001) and middle (r2 = 0.70, p < 0.001) regions of the subchondral bone, indicating the prominent role that the cortical plate played in the fatigue resistance of equine subchondral bone. By improving our understanding of the variance in fatigue life measurements, this research helps clarify the underlying mechanisms of the mechanical fatigue process and provides a basic understanding of subchondral bone injuries in the equine fetlock joint.