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.

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.

Fatigue-life measurements of bone exhibit a significant amount of scatter, which may be characterized probabilistically using a Weibull analysis. Despite an abundance of fatigue testing literature, a standard recommendation for the number of samples required to adequately characterize the probability of fatigue failure in bone does not exist. The primary objective of this work was to determine the minimum sample size required to fit a Weibull distribution to fatigue-life measurements of cyclically loaded bone. Two existing experimental datasets comprising cortical and subchondral bone samples were used in this work. Weibull parameters were estimated using both the maximum likelihood and rank regression methods. A Monte Carlo simulation was used to estimate Weibull parameters for different sample sizes and a convergence analysis was used to determine the minimum required sample size. A simulated dataset with known population parameters was also used to assess the accuracy of the estimated Weibull parameters and to compare the two estimation methods. Our findings suggest that as many as n = 11 samples may be required to adequately quantify Weibull parameters from fatigue tests of bone. At the converged sample size, Weibull parameters differed from true population-level parameters by 3 %-25 %, depending on the estimation method. The maximum likelihood method provided the most accurate and precise estimates of Weibull parameters. These findings provide a framework for future studies aimed at reliably quantifying the probability of fatigue failure in bone.

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: All participants had previously completed a 12-week group-based exercise study. In the current study, participants were randomized to a 12-week exercise oncology maintenance class with or without health coaching. The primary outcome was feasibility, assessed as intervention attendance, safety and fidelity, study recruitment, attrition and outcome assessment completion. Additionally, semi-structured interviews at the end of the intervention provided participants’ perspectives on intervention feasibility. Results: Forty participants (n _8WK =25; n _12WK =15) enrolled in the study. Feasibility was confirmed for recruitment rate (42.6%), attrition rate (2.5%), safety (no adverse events), 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%). Based on the qualitative feedback, feasibility was facilitated by the convenience, while the diminished ability to connect with other participants online was a drawback compared to in-person delivery. Conclusion: The synchronous online delivery of an exercise oncology maintenance class, the additional health coaching support, and the tools used to measure the intervention effectiveness were feasible for individuals living with and beyond cancer.