The Biomechanical Influence of Step Width on Typical Locomotor Activities: A Systematic Review.

BACKGROUND: Step width is a spatial variable in the frontal plane, defined as the mediolateral distance between the heel (forefoot during sprinting) of bilateral feet at initial contact. Variations in step width may impact the lower limb biomechanics. This systematic review aimed to synthesize the published findings to determine the influence of acute changes in step width on locomotion biomechanics and provide implications for injury prevention and enhanced sports performance. METHODS: Literature was identified, selected, and appraised in accordance with the methods of a systematic review. Four electronic databases (Web of Science, MEDLINE via PubMed, Scopus, and ScienceDirect) were searched up until May 2023 with the development of inclusion criteria based on the PICO model. Study quality was assessed using the Downs and Black checklist and the measured parameters were summarized. RESULTS: Twenty-three articles and 399 participants were included in the systematic review. The average quality score of the 23 studies included was 9.39 (out of 14). Step width changed the kinematics and kinetics in the sagittal, frontal, and transverse planes of the lower limb, such as peak rearfoot eversion angle and moment, peak hip adduction angle and moment, knee flexion moment, peak knee internal rotation angle, as well as knee external rotation moment. Alteration of step width has the potential to change the stability and posture during locomotion, and evidence exists for the immediate biomechanical effects of variations in step width to alter proximal kinematics and cues to impact loading variables. CONCLUSION: Short-term changes in step width during walking, running, and sprinting influenced multiple lower extremity biomechanics. Narrower step width may result in poor balance and higher impact loading on the lower extremities during walking and running and may limit an athlete's sprint performance. Increasing step width may be beneficial for injury rehabilitation, i.e., for patients with patellofemoral pain syndrome, iliotibial band syndrome or tibial bone stress injury. Wider steps increase the supporting base and typically enhance balance control, which in turn could reduce the risks of falling during daily activities. Altering the step width is thus proposed as a simple and non-invasive treatment method in clinical practice.

Learning effects in over-ground running gait retraining: A six-month follow-up of a quasi-randomized controlled trial.

This study evaluated learning and recall effects following a feedback-based retraining program. A 6-month follow-up of a quasi-randomized controlled trial was performed with and without recall. Twenty runners were assigned to experimental or control groups and completed a 3-week running program. A body-worn system collected axial tibial acceleration and provided real-time feedback on peak tibial acceleration for six running sessions in an athletic training facility. The experimental group received music-based biofeedback in a faded feedback scheme. The controls received tempo-synchronized music as a placebo for blinding purposes. The peak tibial acceleration and vertical loading rate of the ground reaction force were determined in a lab at baseline and six months following the intervention to assess retention and recall. The impacts of the experimental group substantially decreased at follow-up following a simple verbal recall (i.e., run as at the end of the program): peak tibial acceleration:-32%, p = 0.018; vertical loading rate:-34%, p = 0.006. No statistically significant changes were found regarding the retention of the impact variables. The impact magnitudes did not change over time in the control group. The biofeedback-based intervention did not induce clear learning at follow-up, however, a substantial impact reduction was recallable through simple cueing in the absence of biofeedback.

Biomechanical adaptations following a music-based biofeedback gait retraining program to reduce peak tibial accelerations.

PURPOSE: The present study aimed to determine whether runners can reduce impact measures after a six-session in-the-field gait retraining program with real-time musical biofeedback on axial peak tibial acceleration (PTAa ) and identify the associated biomechanical adaptations. METHODS: Twenty trained high-impact runners were assigned to either the biofeedback or the music-only condition. The biofeedback group received real-time feedback on the PTAa during the gait retraining program, whereas the music-only condition received a sham treatment. Three-dimensional gait analysis was conducted in the laboratory before (PRE) and within one week after completing the gait retraining program (POST). Subjects were instructed to replicate the running style from the last gait retraining session without receiving feedback while running overground at a constant speed of 2.9 m∙s-1 . RESULTS: Only the biofeedback group showed significant reductions in both PTAa (∆x̅ = -26.9%, p = 0.006) and vertical instantaneous loading rate (∆x̅ = -29.2%, p = 0.003) from PRE to POST. In terms of biomechanical adaptations, two strategies were identified. Two subjects transitioned toward a more forefoot strike. The remaining eight subjects used a pronounced rearfoot strike and posteriorly inclined shank at initial contact combined with less knee extension at toe-off while reducing vertical excursion of the center of mass. CONCLUSIONS: After completing a music-based biofeedback gait retraining program, runners can reduce impact while running overground in a laboratory. We identified two distinct self-selected strategies used by the participants to achieve reductions in impact.

Foot strike determines the center of pressure behavior and affects impact severity in heel-toe running.

This study assessed the centre of pressure (COP) behaviour and the relationship with impact severity during heel-toe running in conventional athletic footwear. We hypothesized that the COP behaviour depends on its location at foot strike, which would be associated with the vertical loading rate and peak tibial accelerations in heel-toe running. Ground reaction force and tibial acceleration were measured in 104 distance runners running level at ~3.2 m/s. High-speed plantar pressure captured at high temporal resolution (500 Hz) and spatial resolution (7.62 · 5.08 mm/sensor) allowed for localization of the COP directly in the footprint during running in self-selected athletic footwear. More lateral X-coordinates of the COP at first foot contact had, in general, more anterior Y-coordinates (adj.R2:0.609). In heel-toe running, a more anterior foot strike had a greater refined strike index, which was associated with a quicker roll-over in the rearfoot zone. This strike index contributed to greater maximum vertical loading rates (R2:0.121), and greater axial (R2:0.047) and resultant (R2:0.247) peak tibial accelerations. These findings indicate that (1) the COP progression is dependend on the COP location at foot strike; (2) more anterior rearfoot strikes are more likely to have greater impact severity than posterior rearfoot strikes.

Reducing the peak tibial acceleration of running by music-based biofeedback: A quasi-randomized controlled trial.

BACKGROUND: Running retraining with the use of biofeedback on an impact measure has been executed or evaluated in the biomechanics laboratory. Here, the execution and evaluation of feedback-driven retraining are taken out of the laboratory. PURPOSE: To determine whether biofeedback can reduce the peak tibial acceleration with or without affecting the running cadence in a 3-week retraining protocol. STUDY DESIGN: Quasi-randomized controlled trial. METHODS: Twenty runners with high peak tibial acceleration were allocated to either the retraining (n = 10, 32.1 ± 7.8 years, 10.9 ± 2.8 g) or control (n = 10, 39.1 ± 10.4 years, 13.0 ± 3.9 g) groups. They performed six running sessions in an athletic training environment. A body-worn system collected axial tibial acceleration and provided real-time feedback. The retraining group received music-based biofeedback in a faded feedback scheme. Pink noise was superimposed on tempo-synchronized music when the peak tibial acceleration was ≥70% of the runner's baseline. The control group received tempo-synchronized music, which acted as a placebo for blinding purposes. Speed feedback was provided to obtain a stable running speed of ~2.9 m·s-1 . Peak tibial acceleration and running cadence were evaluated. RESULTS: A significant group-by-feedback interaction effect was detected for peak tibial acceleration. The experimental group had a decrease in peak tibial acceleration by 25.5% (mean: 10.9 ± 2.8 g versus 8.1 ± 3.9 g, p = 0.008, d = 1.08, mean difference = 2.77 [0.94, 4.61]) without changing the running cadence. The control group had no statistically significant change in peak tibial acceleration nor in running cadence. CONCLUSION: The retraining protocol was effective at reducing the peak tibial acceleration in high-impact runners by reacting to music-based biofeedback that was provided in real time per wearable technology in a training environment. This reduction magnitude may have meaningful influences on injury risk.

One hundred marathons in 100 days: Unique biomechanical signature and the evolution of force characteristics and bone density.

BACKGROUND: An extraordinary long-term running performance may benefit from low dynamic loads and a high load-bearing tolerance. An extraordinary runner (age = 55 years, height = 1.81 m, mass = 92 kg) scheduled a marathon a day for 100 consecutive days. His running biomechanics and bone density were investigated to better understand successful long-term running in the master athlete. METHODS: Overground running gait analysis and bone densitometry were conducted before the marathon-a-day challenge and near its completion. The case's running biomechanics were compared pre-challenge to 31 runners who were matched by a similar foot strike pattern. RESULTS: The case's peak vertical loading rate (Δx̄ = -61.9 body weight (BW)/s or -57%), peak vertical ground reaction force (Δx̄ = -0.38 BW or -15%), and peak braking force (Δx̄ = -0.118 BW or -31%) were remarkably lower (p < 0.05) than the control group at ∼3.3 m/s. The relatively low loading-related magnitudes were attributed to a remarkably high duty factor (0.41) at the evaluated speed. The foot strike angle of the marathoner (29.5°) was greater than that of the control group, affecting the peak vertical loading rate. Muscle powers in the lower extremity were also remarkably low in the case vs. controls: peak power of knee absorption (Δx̄ = -9.16 watt/kg or -48%) and ankle generation (Δx̄ = -3.17 watt/kg or -30%). The bone mineral density increased to 1.245 g/cm² (+2.98%) near completion of the challenge, whereas the force characteristics showed no statistically significant change. CONCLUSION: The remarkable pattern of the high-mileage runner may be useful in developing or evaluating load-shifting strategies in distance running.

Music-based biofeedback to reduce tibial shock in over-ground running: a proof-of-concept study.

Methods to reduce impact in distance runners have been proposed based on real-time auditory feedback of tibial acceleration. These methods were developed using treadmill running. In this study, we extend these methods to a more natural environment with a proof-of-concept. We selected ten runners with high tibial shock. They used a music-based biofeedback system with headphones in a running session on an athletic track. The feedback consisted of music superimposed with noise coupled to tibial shock. The music was automatically synchronized to the running cadence. The level of noise could be reduced by reducing the momentary level of tibial shock, thereby providing a more pleasant listening experience. The running speed was controlled between the condition without biofeedback and the condition of biofeedback. The results show that tibial shock decreased by 27% or 2.96 g without guided instructions on gait modification in the biofeedback condition. The reduction in tibial shock did not result in a clear increase in the running cadence. The results indicate that a wearable biofeedback system aids in shock reduction during over-ground running. This paves the way to evaluate and retrain runners in over-ground running programs that target running with less impact through instantaneous auditory feedback on tibial shock.

Predicting gait events from tibial acceleration in rearfoot running: A structured machine learning approach.

BACKGROUND: Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles, which may offer complications when dealing with larger data sets and inherent biological variability. RESEARCH QUESTION: Can a structured machine learning approach achieve a more accurate prediction of running gait event timings from tibial accelerometry, compared to the previously utilised heuristic approaches? METHODS: Force-based event detection acted as the criterion measure in order to assess the accuracy, repeatability and sensitivity of the predicted gait events. 3D tibial acceleration and ground reaction force data from 93 rearfoot runners were captured. A heuristic method and two structured machine learning methods were employed to derive initial contact, toe off and stance time from tibial acceleration signals. RESULTS: Both a structured perceptron model (median absolute error of stance time estimation: 10.00 ±â€¯8.73 ms) and a structured recurrent neural network model (median absolute error of stance time estimation: 6.50 ±â€¯5.74 ms) significantly outperformed the existing heuristic approach (median absolute error of stance time estimation: 11.25 ±â€¯9.52 ms). Thus, results indicate that a structured recurrent neural network machine learning model offers the most accurate and consistent estimation of the gait events and its derived stance time during level overground running. SIGNIFICANCE: The machine learning methods seem less affected by intra- and inter-subject variation within the data, allowing for accurate and efficient automated data output during rearfoot overground running. Furthermore offering possibilities for real-time monitoring and biofeedback during prolonged measurements, even outside the laboratory.

Change-Point Detection of Peak Tibial Acceleration in Overground Running Retraining.

A method is presented for detecting changes in the axial peak tibial acceleration while adapting to self-discovered lower-impact running. Ten runners with high peak tibial acceleration were equipped with a wearable auditory biofeedback system. They ran on an athletic track without and with real-time auditory biofeedback at the instructed speed of 3.2 m·s-1. Because inter-subject variation may underline the importance of individualized retraining, a change-point analysis was used for each subject. The tuned change-point application detected major and subtle changes in the time series. No changes were found in the no-biofeedback condition. In the biofeedback condition, a first change in the axial peak tibial acceleration occurred on average after 309 running gait cycles (3'40"). The major change was a mean reduction of 2.45 g which occurred after 699 running gait cycles (8'04") in this group. The time needed to achieve the major reduction varied considerably between subjects. Because of the individualized approach to gait retraining and its relatively quick response due to a strong sensorimotor coupling, we want to highlight the potential of a stand-alone biofeedback system that provides real-time, continuous, and auditory feedback in response to the axial peak tibial acceleration for lower-impact running.

Tibial Acceleration-Based Prediction of Maximal Vertical Loading Rate During Overground Running: A Machine Learning Approach.

Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 ± 2.04 BW⋅s-1, mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 ± 7.90 BW⋅s-1 (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA.

Validity and reliability of peak tibial accelerations as real-time measure of impact loading during over-ground rearfoot running at different speeds.

Studies seeking to determine the effects of gait retraining through biofeedback on peak tibial acceleration (PTA) assume that this biometric trait is a valid measure of impact loading that is reliable both within and between sessions. However, reliability and validity data were lacking for axial and resultant PTAs along the speed range of over-ground endurance running. A wearable system was developed to continuously measure 3D tibial acceleration and to detect PTAs in real-time. Thirteen rearfoot runners ran at 2.55, 3.20 and 5.10 m·s-1 over an instrumented runway in two sessions with re-attachment of the system. Intraclass correlation coefficients (ICCs) were used to determine within-session reliability. Repeatability was evaluated by paired T-tests and ICCs. Concerning validity, axial and resultant PTAs were correlated to the peak vertical impact loading rate (LR) of the ground reaction force. Additionally, speed should affect impact loading magnitude. Hence, magnitudes were compared across speeds by RM-ANOVA. Within a session, ICCs were over 0.90 and reasonable for clinical measurements. Between sessions, the magnitudes remained statistically similar with ICCs ranging from 0.50 to 0.59 for axial PTA and from 0.53 to 0.81 for resultant PTA. Peak accelerations of the lower leg segment correlated to LR with larger coefficients for axial PTA (r range: 0.64-0.84) than for the resultant PTA per speed condition. The magnitude of each impact measure increased with speed. These data suggest that PTAs registered per stand-alone system can be useful during level, over-ground, rearfoot running to evaluate impact loading in the time domain when force platforms are unavailable in studies with repeated measurements.

Exoskeleton assistance symmetry matters: unilateral assistance reduces metabolic cost, but relatively less than bilateral assistance.

BACKGROUND: Many gait impairments are characterized by asymmetry and result in reduced mobility. Exoskeletons could be useful for restoring gait symmetry by assisting only one leg. However, we still have limited understanding of the effects of unilateral exoskeleton assistance. Our aim was to compare the effects of unilateral and bilateral assistance using a within-subject study design. METHODS: Eleven participants walked in different exoskeleton conditions. In the Unilateral conditions, only one leg was assisted. In Bilateral Matched Total Work, half of the assistance from the Unilateral conditions was applied to both legs such that the bilateral sum was equal to that of the Unilateral conditions. In Bilateral Matched Work Per Leg, the same assistance as in the Unilateral conditions was provided to both legs such that the bilateral sum was the double of that of the Unilateral conditions. In the Powered-Off condition, no assistance was provided. We measured metabolic energy consumption, exoskeleton mechanics and kinematics. RESULTS: On average, the Unilateral, Bilateral Matched Total Work and Bilateral Matched Work Per Leg conditions reduced the metabolic rate by 7, 11 and 15%, respectively, compared with the Powered-Off condition. A possible explanation for why the Unilateral conditions effectively reduced the metabolic rate could be that they caused only very little asymmetry in gait biomechanics, except at the ankle and in the horizontal center-of-mass velocity. We found the highest ratio of metabolic rate reduction versus positive work assistance with bilateral assistance and low work per leg (Bilateral Matched Total Work). Statistical analysis indicated that assistance symmetry and assistance per leg are more important than the bilateral summed assistance for reducing the metabolic rate of walking. CONCLUSIONS: These data bridge the gap between conclusions from studies with unilateral and bilateral exoskeletons and inform how unilateral assistance can be used to influence gait parameters, such as center-of-mass velocity.