Classifying the variability in impact and active peak vertical ground reaction forces during running using DFA and ARFIMA models

The "impacts cause injury" hypothesis: Running in circles or making new strides?

Some of the earliest biomechanics research focused on running and the ground reaction forces generated with each step. Research in running gait accelerated in the 1970's as the growing popularity in running increased attention to the musculoskeletal injuries sustained by runners. Despite decades of high-quality research, running remains the most common cause of exercise-related musculoskeletal injuries and rates of overuse running-related injuries (RRI) have not appreciably declined since the research began. One leading area of running gait research focuses on discrete variables derived from the vertical ground reaction force, such as the vertical loading rate. Across sub-disciplines of running gait research, vertical loading rate is often discussed as the primary and undisputed variable associated with RRI despite only low to moderate evidence that retrospectively or prospectively injured runners generate greater vertical loading rates than uninjured counterparts. The central thesis of this review is that relying on vertical loading rate is insufficient to establish causal mechanisms for RRI etiology. To present this argument, this review examines the history of the 'impacts cause injury' hypothesis, including a historical look at ground reaction forces in human running and the research from which this hypothesis was generated. Additionally, a synthesis of studies that have tested the hypothesis is provided and recommendations for future research are discussed. Although it is premature to reject or support the 'impacts cause injury' hypothesis, new knowledge of biomechanical risk factors for RRI will remain concealed until research departs from the current path or adopts new approaches to previous paradigms.

A new metric for characterizing limb loading dynamics in post anterior cruciate ligament reconstruction individuals

BACKGROUND

Unresolved neuromuscular deficits often persist in post-anterior cruciate ligament reconstruction (ACLR) individuals manifesting as altered impact and active peak force production during running that can contribute to detrimental limb loading. Elevated impact and active peaks are common in pathological populations indicating a stiffer limb loading strategy. Although impact and active peaks are sensitive to changes in limb loading, to our knowledge, there are no established, standardized measures or cutoff criteria to differentiate between healthy and pathological limb loading. However, prior studies have demonstrated that the ratio between traditional biomechanical measures can be used to successfully establish quantifiable and graphical ranges to delineate between healthy and pathological movement.

RESEARCH QUESTION

Therefore, this study sought to exploit the impact-to-active peak ratio to generate a new, standardized metric to quantify and characterize limb loading dynamics in healthy controls and post-ACLR individuals during running.

METHODS

Twenty-eight post-ACLR individuals and 18 healthy controls performed a running protocol. Impact peak and active peak data were extracted from their strides as they ran at a self-selected speed. A linear regression model was fit to the healthy control data and the models 95 % prediction intervals were used to define a boundary region of healthy limb loading dynamics.

RESULTS

The post-ACLR individuals produced a higher impact-to-active peak ratio than the healthy controls indicating that they adopted a stiffer limb loading strategy. The boundary regions derived from the impact and active peak model successfully classified the healthy controls and post-ACLR individual's limb loading dynamics with an accuracy, sensitivity, and specificity of 89 %, 100 %, and 75 %, respectively.

SIGNIFICANCE

The ability to effectively evaluate limb loading dynamics using impact and active peaks can provide clinicians with a new, non-invasive metric to quantify and characterize healthy and pathological movement in a clinical setting.

Running gait produces long range correlations: A systematic review

BACKGROUND

Walking and running are common forms of locomotion, both of which exhibit variability over many gait cycles. Many studies have investigated the patterns generated from that ebb and flow, and a large proportion suggests human gait exhibits Long Range Correlations (LRCs). LRCs refer to the observation that healthy gait characteristic, like stride times, are positively correlated to themselves over time. Literature on LRCs in walking gait is well known but less attention has been given to LRCs in running gait.

RESEARCH QUESTION

What is the state of the art concerning LRCs in running gait?

METHODS

We conducted a systematic review to identify the typical LRC patterns present in human running gait, in addition to disease, injury, and running surface effects on LRCs. Inclusion criteria were human subjects, running related experiments, computed LRCs, and experimental design. Exclusion criteria were studies on animals, non-humans, walking only, non-running, non-LRC analysis, and non-experiments.

RESULTS

The initial search returned 536 articles. After review and deliberation, our review included 26 articles. Almost every article produced strong evidence for LRCs apparent in running gait and in all running surfaces. Additionally, LRCs tended to decrease due to fatigue, past injury, increased load carriage and seem to be lowest at preferred running speed on a treadmill. No studies investigated disease effects on LRCs in running gait.

SIGNIFICANCE

LRCs seem to increase with deviations away from preferred running speed. Previously injured runners produced decreased LRCs compared to non-injured runners. LRCs also tended to decrease due to an increase in fatigue rate, which has been associated with increased injury rate. Lastly, there is a need for research on the typical LRCs in an overground environment, for which the typical LRCs found in a treadmill environment may or may not transfer.

Changes in acceleration load as measured by inertial measurement units manifest in the upper body after an extended running task

The purpose of this study was to investigate the behaviour of physiological load measures as well as ground reaction forces (GRF) and acceleration load during a prolonged running task that simulated the running demands of an intermittent team sport. Nineteen males completed a maximal aerobic fitness test and an extended running protocol across two sessions. Participants wore a portable metabolic system, and four inertial measurement units (IMU), one on each foot, the lower back and upper back. GRF were measured via an instrumented treadmill. Change in metabolic, IMU and GRF variables across five blocks during the running protocol were assessed using a one-way repeated measures ANOVA. The running protocol elicited large increases in heart rate and oxygen consumption over time. No statistically significant changes in any peak impact accelerations were observed. Resultant acceleration area under the curve (AUC) increased at the lower and upper back locations but was unchanged at the foot. GRF active peak but not impact peak increased during the prolonged run. The results of this study indicate that the effect of an extended running task on IMU measures of external mechanical load is manifested in the upper body, and is effectively measured by AUC.

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.

Bioinspired material architectures from bighorn sheep horncore velar bone for impact loading applications

Rocky Mountain bighorn sheep rams (Ovis canadensis canadensis) routinely conduct intraspecific combat where high energy cranial impacts are experienced. Previous studies have estimated cranial impact forces to be up to 3400 N during ramming, and prior finite element modeling studies showed the bony horncore stores 3 × more strain energy than the horn during impact. In the current study, the architecture of the porous bone within the horncore was quantified, mimicked, analyzed by finite element modeling, fabricated via additive manufacturing, and mechanically tested to determine the suitability of the novel bioinspired material architecture for use in running shoe midsoles. The iterative biomimicking design approach was able to tailor the mechanical behavior of the porous bone mimics. The approach produced 3D printed mimics that performed similarly to ethylene–vinyl acetate shoe materials in quasi-static loading. Furthermore, a quadratic relationship was discovered between impact force and stiffness in the porous bone mimics, which indicates a range of stiffness values that prevents impact force from becoming excessively high. These findings have implications for the design of novel bioinspired material architectures for minimizing impact force.

Using Time-Frequency Analysis to Characterize Altered Knee Dynamics in Post ACL Reconstruction Individuals

More than 250,000 individuals suffer an anterior cruciate ligament (ACL) injury in the United States each year requiring surgery and rehabilitation. However, despite exhaustive rehabilitation individuals are often plagued by neuromuscular deficits that lead to detrimental knee loading and knee osteoarthritis. Traditionally, time domain-based metrics like peak sagittal plane knee angle are used to quantify differences in knee mechanics; however, additional information can potentially be elucidated from time-frequency analyses. Here Smoothed Pseudo Wigner-Ville (SPWV), a time-frequency analysis technique, was used to investigate differences in knee loading dynamics between healthy controls and post ACL reconstruction individuals during running. The results indicated that post ACL reconstruction individuals adopt significantly different loading strategies in their injured limb than their non-injured limb. Individuals adopt a stiffer, more restrictive movement strategy delineated by a stronger low frequency to high frequency (LF/HF) ratio while the non-injured limb exhibit a more oscillatory motion (p<; 0.001). The time domain metrics were unable to identify differences between the ACL injured and non-injured limbs. The ability of SPWV to provide both quantitative and visual means to detect these differences supports its use as a clinical tool to track and monitor joint health.

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

Between-limb deficits in vertical ground reaction force (vGRF) production continue to remain years after anterior cruciate ligament rehabilitation, resulting in altered dynamic stability. However, the challenge is in identifying ways to assess this between-limb stability. This study implemented second-order autoregressive [AR(2)] modeling and its stationarity triangle to both quantitatively and visually delineate differences in dynamic stability from peak vGRF data in controls and post-anterior cruciate ligament reconstruction (ACLR) individuals during running. It was hypothesized that post-ACLR individuals would exhibit less dynamic stability than the controls, and that they would reside in a different location on the stationarity triangle, thus denoting differences in stability. The results presented supported the hypothesis that post-ACLR individuals exhibited significantly less dynamic stability than their control counterparts based on their model coefficients (AR1 P < .01; AR2 P = .02). These findings suggested that the post-ACLR individuals adopted a similar running pattern, possibly due to muscle weakness asymmetry, which was less dynamically stable and potentially places them at greater risk for injury. The ability of this approach to both quantitatively and visually delineate differences between these 2 groups indicates its potential as a return-to-sport decision tool.

Years of running experience influences stride-to-stride fluctuations and adaptive response during step frequency perturbations in healthy distance runners

RESEARCH QUESTION

The current study investigated stride-to-stride fluctuations of step rate and contact time in response to enforced step frequency perturbations as well as adaptation and de-adaptation behavior.

METHODS

Forty distance runners ran at a self-selected speed and were asked to match five different enforced step frequencies (150, 160, 170, 180, and 190 beats per min). The influence of experience was explored, because running is a skill that presumably gets better with practice, and increased years of running experience is protective against injury. Detrended fluctuation analysis was used to determine the strength of long-range correlations in gait fluctuations at baseline, during the perturbation, and post-perturbation. Adaptive response was measured by the ability to match, rate of matching, and aftereffect of step frequency perturbations.

RESULTS

The structure of stride-to-stride fluctuations for step rate and contact time did not change during the perturbation or post-perturbation compared to baseline. However, fluctuations in step rate were affected by the level of perturbation. Runners with the most experience had a less persistent structural gait pattern for both step rate and contact time at baseline. Highly experienced runners also demonstrated the best adaptive response. They better matched the enforced step frequency, reached the enforced step frequency sooner, and returned to preferred step frequency more quickly following removal of the perturbation.

SIGNIFICANCE

These findings indicate baseline locomotor flexibility may be beneficial to achieve task demands and return to a stable state once the task is complete. Increased locomotor flexibility may also be a contributing factor for reduced injury risk in experienced runners.