Autoregressive Modeling as Diagnostic Tool to Identify Postanterior Cruciate Ligament Reconstruction Limb Asymmetry

A Metric for Identifying Stress Fractures in Runners

PURPOSE

Stress fractures are common overuse running injuries. Individuals with stress fractures exhibit running biomechanics characterized by elevated impact peak and loading rate. While elevated impact peak and loading rate are associated with stress fractures, there are few established metrics used to identify the presence of stress fractures in individuals. Here this study aims to exploit the linear relationship between the impact peak and loading rate to establish a metric to help identify individuals with stress fractures. We hypothesize that the ratio between the impact peak and loading rate will serve as a metric to delineate between healthy controls and those with stress fractures.

METHODS

Fifteen healthy controls and 11 individuals with stress fractures performed a running protocol. A linear regression model fit to the stress fracture impact peak and loading rate data produced a lower 95% confidence limit boundary that served as the demarcation line between the two groups.

RESULTS

Individuals with stress fractures tended to reside above the line with the line accurately classifying 82% of the individuals with stress fractures.

CONCLUSION

The analysis supported the hypothesis and demonstrated how the relationship between impact peak and loading rate can help identify the presence of stress fractures in individuals.Clinical Relevance- The relationship between impact peak and loading rate has the potential to serve as clinically useful metric to identify stress fractures during running.

Kinetic measurement system use in individuals following anterior cruciate ligament reconstruction: a scoping review of methodological approaches

Purpose

Our primary objectives were to (1) describe current approaches for kinetic measurements in individuals following anterior cruciate ligament reconstruction (ACLR) and (2) suggest considerations for methodological reporting. Secondarily, we explored the relationship between kinetic measurement system findings and patient-reported outcome measures (PROMs).

Methods

We followed the PRISMA extension for scoping reviews and Arksey and O’Malley’s 6-stage framework. Seven electronic databases were systematically searched from inception to June 2020. Original research papers reporting parameters measured by kinetic measurement systems in individuals at least 6-months post primary ACLR were included.

Results

In 158 included studies, 7 kinetic measurement systems (force plates, balance platforms, pressure mats, force-measuring treadmills, Wii balance boards, contact mats connected to jump systems, and single-sensor insoles) were identified 4 main movement categories (landing/jumping, standing balance, gait, and other functional tasks). Substantial heterogeneity was noted in the methods used and outcomes assessed; this review highlighted common methodological reporting gaps for essential items related to movement tasks, kinetic system features, justification and operationalization of selected outcome parameters, participant preparation, and testing protocol details. Accordingly, we suggest considerations for methodological reporting in future research. Only 6 studies included PROMs with inconsistency in the reported parameters and/or PROMs.

Conclusion

Clear and accurate reporting is vital to facilitate cross-study comparisons and improve the clinical application of kinetic measurement systems after ACLR. Based on the current evidence, we suggest methodological considerations to guide reporting in future research. Future studies are needed to examine potential correlations between kinetic parameters and PROMs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40634-021-00397-0.

Characterizing gait pattern dynamics during symmetric and asymmetric walking using autoregressive modeling

Gait asymmetry is often observed in populations with varying degrees of neuromuscular control. While changes in vertical ground reaction force (vGRF) peak magnitude are associated with altered limb loading that can be observed during asymmetric gait, the challenge is identifying techniques with the sensitivity to detect these altered movement patterns. Autoregressive (AR) modeling has successfully delineated between healthy and pathological gait during running; but has been little explored in walking. Thus, AR modeling was implemented to assess differences in vGRF pattern dynamics during symmetric and asymmetric walking. We hypothesized that the AR model coefficients would better detect differences amongst the symmetric and asymmetric walking conditions than the vGRF peak magnitude mean. Seventeen healthy individuals performed a protocol that involved walking on a split-belt instrumented treadmill at different symmetric (0.75m/s, 1.0 m/s, 1.5 m/s) and asymmetric (Side 1: 0.75m/s-Side 2:1.0 m/s; Side 1:1.0m/s-Side 2:1.5 m/s) gait conditions. Vertical ground reaction force peaks extracted during the weight-acceptance and propulsive phase of each step were used to construct a vGRF peak time series. Then, a second order AR model was fit to the vGRF peak waveform data to determine the AR model coefficients. The resulting AR coefficients were plotted on a stationarity triangle and their distance from the triangle centroid was computed. Significant differences in vGRF patterns were detected amongst the symmetric and asymmetric conditions using the AR modeling coefficients (p = 0.01); however, no differences were found when comparing vGRF peak magnitude means. These findings suggest that AR modeling has the sensitivity to identify differences in gait asymmetry that could aid in monitoring rehabilitation progression.

Classification of Post-Anterior Cruciate Ligament Reconstruction Running Dynamics using Non-Traditional Features

Despite extensive rehabilitation, nearly half of all post-anterior cruciate ligament reconstruction (ACLR) individuals are unable to perform dynamic tasks at the level they did prior to their injury. This inability can be attributed to unresolved neuromuscular deficits that manifest as altered limb dynamics. While traditional discrete metrics; such as peak vertical ground reaction force (vGRF) and peak knee flexion angle, have been used to successfully differentiate between healthy and pathological running dynamics, recent studies have shown that non-traditional metrics derived from autoregressive (AR) modeling and Smoothed Pseudo Wigner-Ville (SPWV) analysis techniques can also successfully delineate between healthy and pathological populations and could potentially possess greater sensitivity than the traditional metrics. Thus, the objective of this study was to compare the performance of classification models generated from traditional and nontraditional metrics collected from healthy controls and post-ACLR individuals during a running protocol. We hypothesized that the non-traditional metric-based classification model would outperform the traditional metric based model. Thirty-one controls and 31 post-ACLR individuals performed a running protocol from which the traditional metrics - peak vGRF, linear vGRF loading rate and peak knee flexion angle - and nontraditional metrics - dynamic vGRF ratio, AR model coefficients, and a SPWV derived low frequency-high frequency ratio - were extracted from vGRF and knee flexion running waveforms. The results indicated that a fine Gaussian SVM classification model derived from the non-traditional metrics had an accuracy of 87%, specificity of 83% and sensitivity of 61% and it outperformed the classification model derived from traditional metrics. These findings indicate that additional, valuable information can be ascertained from non-traditional metrics that evaluate waveform dynamics. Additionally, it suggests that this or similar models can be used to track the restoration of healthy running dynamics in post-ACLR individuals during rehabilitation.

Autoregressive modeling to assess stride time pattern stability in individuals with Huntington's disease

Background

Huntington’s disease (HD) is a progressive, neurological disorder that results in both cognitive and physical impairments. These impairments affect an individual’s gait and, as the disease progresses, it significantly alters one’s stability. Previous research found that changes in stride time patterns can help delineate between healthy and pathological gait. Autoregressive (AR) modeling is a statistical technique that models the underlying temporal patterns in data. Here the AR models assessed differences in gait stride time pattern stability between the controls and individuals with HD. Differences in stride time pattern stability were determined based on the AR model coefficients and their placement on a stationarity triangle that provides a visual representation of how the patterns mean, variance and autocorrelation change with time. Thus, individuals who exhibit similar stride time pattern stability will reside in the same region of the stationarity triangle. It was hypothesized that individuals with HD would exhibit a more altered stride time pattern stability than the controls based on the AR model coefficients and their location in the stationarity triangle.

Methods

Sixteen control and twenty individuals with HD performed a five-minute walking protocol. Time series’ were constructed from consecutive stride times extracted during the protocol and a second order AR model was fit to the stride time series data. A two-sample t-test was performed on the stride time pattern data to identify differences between the control and HD groups.

Results

The individuals with HD exhibited significantly altered stride time pattern stability than the controls based on their AR model coefficients (AR1 p < 0.001; AR2 p < 0.001).

Conclusions

The AR coefficients successfully delineated between the controls and individuals with HD. Individuals with HD resided closer to and within the oscillatory region of the stationarity triangle, which could be reflective of the oscillatory neuronal activity commonly observed in this population. The ability to quantitatively and visually detect differences in stride time behavior highlights the potential of this approach for identifying gait impairment in individuals with HD.