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.

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.

The online delivery of exercise oncology classes supported with health coaching: a parallel pilot randomized controlled trial.

PURPOSE: The primary objective was to investigate the feasibility of a synchronous, online-delivered, group-based, supervised, exercise oncology maintenance program supported with health coaching. METHODS: Participants had previously completed a 12-week group-based exercise program. All participants received synchronous online delivered exercise maintenance classes, and half were block randomized to receive additional weekly health coaching calls. A class attendance rate of ≥ 70%, a health coaching completion rate of ≥ 80%, and an assessment completion rate of ≥ 70% were set as markers of feasibility. Additionally, recruitment rate, safety, and fidelity of the classes and health coaching calls were reported. Post-intervention interviews were performed to further understand the quantitative feasibility data. Two waves were conducted - as a result of initial COVID-19 delays, the first wave was 8 weeks long, and the second wave was 12 weeks long, as intended. RESULTS: Forty participants (n8WK = 25; n12WK = 15) enrolled in the study with 19 randomized to the health coaching group and 21 to the exercise only group. The recruitment rate (42.6%), attrition (2.5%), safety (no adverse events), and feasibility were confirmed for health coaching attendance (97%), health coaching fidelity (96.7%), class attendance (91.2%), class fidelity (92.6%), and assessment completion (questionnaire = 98.8%; physical functioning = 97.5%; Garmin wear-time = 83.4%). Interviews highlighted that convenience contributed to participant attendance, while the diminished ability to connect with other participants was voiced as a drawback compared to in-person delivery. CONCLUSION: The synchronous online delivery and assessment of an exercise oncology maintenance class with health coaching support was feasible for individuals living with and beyond cancer. Providing feasible, safe, and effective exercise online to individuals living with cancer may support increased accessibility. For example, online may provide an accessible alternative for those living in rural/remote locations as well as for those who may be immunocompromised and cannot attend in-person classes. Health coaching may additionally support individuals' behavior change to a healthier lifestyle. TRIAL REGISTRATION: The trial was retrospectively registered (NCT04751305) due to the rapidly evolving COVID-19 situation that precipitated the rapid switch to online programming.

Blood flow restriction during high-intensity interval cycling exacerbates psychophysiological responses to a greater extent in females than males.

This study aimed to characterize neuromuscular, perceptual, and cardiorespiratory responses to high-intensity interval training (HIIT) with superimposed blood flow restriction in males and females. Twenty-four, healthy individuals (n = 12 females) completed two cycling HIIT protocols to task failure (1-min work phases at 90% of peak power output interspersed by 1-min rest phases). The blood flow restriction (BFR) and control (CON) protocols were identical except for the presence and absence of BFR during rest phases, respectively. The interpolated twitch technique, including maximal voluntary isometric knee extension (MVC) and femoral nerve electrical stimuli, was performed at baseline, every six intervals, and task failure. Perceptual and cardiorespiratory responses were recorded every three intervals and continuously during exercise, respectively. Bayesian inference was used to obtain the joint posterior distribution for all parameters and evidence of an effect was determined via the marginal posterior probability (PP). The BFR shortened task duration by 57.3% compared with CON (PP > 0.99), without a sex difference. The application of BFR exacerbated the rate of decline in neuromuscular measures (MVC and twitch force output), increase of perceptual responses (perceived effort, pain, dyspnea, fatigue), and development of cardiorespiratory parameters (minute ventilation and heart rate), compared with CON (PP > 0.95). In addition, BFR exacerbated the neuromuscular, perceptual, and cardiorespiratory responses to a greater extent in females than males (PP > 0.99). Our results suggest that superimposition of blood flow restriction exacerbates psychophysiological responses to a HIIT protocol to a greater extent in females than males.NEW & NOTEWORTHY To our knowledge, no study has explored sex differences in the neuromuscular, perceptual, and cardiorespiratory indices characterizing exercise tolerance during high-intensity interval training (HIIT) with blood flow restriction (BFR) applied only during rest periods. Our results suggest that BFR elicited a decline in exercise performance that could be attributed to integration of psychophysiological responses. However, this integration was sex-dependent where females demonstrated an exacerbated rate of change in these responses compared with males.

Differences in kinetic variables between injured and uninjured rearfoot runners: A hierarchical cluster analysis.

Running is a popular form of physical activity with a high incidence of running-related injuries. However, the etiology of running-related injuries remains elusive, possibly due to the heterogeneity of movement patterns. The purpose of this study was to investigate whether different clusters existed within a large group of injured and uninjured runners based on their kinetic gait patterns. A sample of 134 injured and uninjured runners were acquired from an existing database and 12 discrete kinetic and spatiotemporal variables which are commonly associated with running injuries were extracted from the ground reaction force waveforms. A principal components analysis followed by an unsupervised hierarchical cluster analysis was performed. The results revealed two distinct clusters of runners which were not associated with injury status (OR = 1.14 [0.57, 2.30], χ2  = 0.143, p = 0.706) or sex (OR = 1.72 [0.85, 3.49], χ2  = 2.3258, p = 0.127). These results suggest that while there appeared to be evidence for two distinct clusters within a large sample of injured and uninjured runners, there is no association between the kinetic variables and running related injuries.

The probability of whole-bone fatigue fracture can be accurately predicted using specimen-specific finite element analysis incorporating a stochastic failure model.

A better understanding of the mechanisms of mechanical fatigue in bone could help improve understanding of the etiology of stress fractures. Investigations of small material samples of bone have identified a nonlinear relationship between strain magnitude, strained volume, and fatigue life, but it is non-trivial to extend these principles to predict the fatigue-life of whole bones which experience complex loading and non-uniform strain distribution. The purpose of this investigation was to experimentally validate a specimen-specific finite element (FE) model that predicts whole-bone fatigue failure using a stochastic model based on strain magnitude and volume. Thirty-four rabbit tibiae were previously tested to failure under cyclic compression, torsion, or both. Strain distribution during the test was estimated from computed-tomography based specimen-specific FE models, and a stochastic failure model based on strain magnitude and volume was used to predict the probability of failure as a function of loading cycles. Model predicted fracture risk matched experimental observations. Respectively, for the 25%, 50%, 75%, and 95% probabilistic predictions, we observed experimental failure ≤ model predicted values in 41%, 53%, 76%, and 80% of the tested specimens. A Brier scoring rule further demonstrated that this model, using strain magnitude and volume, more accurately predicted failure probability compared to two reference models that considered strain magnitude only. In conclusion, the stochastic model may be a powerful tool in future studies to assess mechanical factors that influence stress fracture risk.

A generalised smoothing approach for continuous, planar, inverse kinematics problems.

Bayesian methods have recently been proposed to solve inverse kinematics problems for marker based motion capture. The objective is to find the posterior distribution, a probabilistic summary of our knowledge and corresponding uncertainty about the model parameters such as joint angles, segment angles, segment translations, and marker positions. To date, Bayesian inverse kinematics models have focused on a frame by frame solution, which if repeatedly applied gives estimates that are discontinuous in time. We propose to overcome this limitation for continuous, planar inverse kinematics problems via the use of finite basis representations to model latent kinematic quantities as smooth, continuous functions. Our generalised smoothing approach is able to accurately approximate the solution to planar inverse kinematics problems defined by simple systems of ordinary differential equations in addition to considerably more complex systems such as a planar analysis of human gait. Improvements in accuracy are considerable with a decrease in average RMSE of 0.025 rad observed when estimating ankle joint angle for a randomly selected running stride with the proposed generalised smoothing approach compared to previous time-independent approaches. In addition, the generalised smoothing approach is able to effectively estimate kinematic parameters in the presence of missing data along with derivatives of kinematic quantities without the need for prior filtering or gap-filling of data.

Comparing the performance of Bayesian and least-squares approaches for inverse kinematics problems.

Bayesian inference has recently been identified as an approach for estimating a subjects' pose from noisy marker position data. Previous research suggests that Bayesian inference markedly reduces error for inverse kinematic problems relative to traditional least-squares approaches with estimators having reduced variance despite both least-squares and Bayesian estimators being unbiased. This result is surprising as Bayesian estimators are typically similar to least-squares approaches unless highly informative prior distributions are used. As a result the purpose of this work was to examine the sensitivity of Bayesian inverse kinematics solutions to the prior distribution. Our results highlight that Bayesian solutions to inverse kinematics are sensitive to the choice of prior and that the previously reported superior performance of Bayesian inference is likely due to an overly informative prior distribution which unrealistically uses knowledge of the true kinematic pose. When more realistic, 'weakly-informative' priors, which do not use the known kinematic pose are used then any improvements in estimator accuracy are minimal when compared to the traditional least squares approach. However, with appropriate priors, Bayesian inference can propagate uncertainties related to marker position to uncertainty in joint angles, a valuable contribution for kinematic analyses. When using Bayesian methods, we recommend researchers use weakly-informative priors and conduct a sensitivity analysis to highlight the effects of prior choice on analysis outcomes.

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns.

Previous studies have suggested that runners can be subgrouped based on homogeneous gait patterns; however, no previous study has assessed the presence of such subgroups in a population of individuals across a wide variety of injuries. Therefore, the purpose of this study was to assess whether distinct subgroups with homogeneous running patterns can be identified among a large group of injured and healthy runners and whether identified subgroups are associated with specific injury location. Three-dimensional kinematic data from 291 injured and healthy runners, representing both sexes and a wide range of ages (10-66 years), were clustered using hierarchical cluster analysis. Cluster analysis revealed five distinct subgroups from the data. Kinematic differences between the subgroups were compared using one-way analysis of variance (ANOVA). Against our hypothesis, runners with the same injury types did not cluster together, but the distribution of different injuries within subgroups was similar across the entire sample. These results suggest that homogeneous gait patterns exist independent of injury location and that it is important to consider these underlying patterns when planning injury prevention or rehabilitation strategies.

Fatigue-Related Changes in Running Gait Patterns Persist in the Days Following a Marathon Race.

CONTEXT: The risk of experiencing an overuse running-related injury can increase with atypical running biomechanics associated with neuromuscular fatigue and/or training errors. While it is important to understand the changes in running biomechanics within a fatigue-inducing run, it may be more clinically relevant to assess gait patterns in the days following a marathon to better evaluate the effects of inadequate recovery on injury. OBJECTIVE: To use center of mass (CoM) acceleration patterns to investigate changes in running patterns prior to (PRE) and at 2 (POST2) and 7 (POST7) days following a marathon race. DESIGN: Pre-post intervention study. SETTING: A 200-m oval track surface. PARTICIPANTS: Seventeen recreational marathon runners (10 females, age = 34.2 [5.67] y; 7 males, age = 47.41 [15.32] y). INTERVENTION: Marathon race. MAIN OUTCOME MEASURES: An inertial measurement unit was placed near the CoM to collect triaxial acceleration data during overground running for PRE, POST2, and POST7 sessions. Twenty-two features were extracted from the acceleration waveforms to characterize different aspects of running gait. Lower-limb musculoskeletal pain was also recorded at each session with a visual analog scale. RESULTS: At POST2, runners reported higher self-reported pain and exhibited elevated peak mediolateral acceleration with an increased mediolateral ratio of acceleration root mean square compared with PRE. At POST7, pain was reduced and more similar to PRE, with runners demonstrating increased stride regularity in the vertical direction and decreased peak resultant acceleration. CONCLUSIONS: The observed changes in CoM motion at POST2 may be associated with atypical running biomechanics that can translate to greater mediolateral impulses, potentially increasing the risk of injury. This study demonstrates the use of an accelerometer as an effective tool to detect atypical CoM motion for runners due to fatigue, recovery, and musculoskeletal pain in real-world environments.

New Considerations for Collecting Biomechanical Data Using Wearable Sensors: How Does Inclination Influence the Number of Runs Needed to Determine a Stable Running Gait Pattern?

As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a 'typical' or 'stable' gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics of running gait, this study sought to determine the number of runs needed to establish a stable running pattern during level, downhill, and uphill conditions for both univariate and multivariate analyses of running biomechanical data collected using a single wearable IMU device. Pelvic drop, ground contact time, braking, vertical oscillation, pelvic rotation, and cadence, were recorded from thirty-five recreational runners running in three elevation conditions: level, downhill, and uphill. Univariate and multivariate normal distributions were estimated from differing numbers of runs and stability was defined when the addition of a new run resulted in less than a 5% change in the 2.5 and 97.5 quantiles of the 95% probability density function for each individual runner. This stability point was determined separately for each runner and each IMU variable (univariate and multivariate). The results showed that 2-4 runs were needed to define a stable running pattern for univariate, and 4-5 days were necessary for multivariate analysis across all inclination conditions. Pearson's correlation coefficients were calculated to cross-validate differing elevation conditions and showed excellent correlations (r = 0.98 to 1.0) comparing the training and testing data within the same elevation condition and good to very good correlations (r = 0.63-0.88) when comparing training and testing data from differing elevation conditions. These results suggest that future research involving wearable technology should collect multiple days of data in order to build reliable and accurate representations of an individual's stable gait pattern.