Step Strain Research Articles

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.

Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images. The difficulty-aware (Da) method operates by dynamically modifying the cross-entropy loss function applied to learning tasks. This methodology has the ability to intelligently down-weight simpler tasks, while simultaneously prioritizing more challenging tasks. These adjustments occur automatically and aim to optimize the learning process. Additionally, the task-augmentation (Ta) method is used to enhance the meta-training process by augmenting the number of tasks through image rotation and improving the feature-extraction capability. To implement the expansion of the meta-training tasks, various task instances can be sampled during the meta-training stage. Ultimately, the proposed Ta method was introduced to optimize the initialization parameters and enhance the meta-generalization performance of the model. The DaTa-ML model showed promising results by effectively addressing the challenges associated with few-shot DR classification. The Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 blindness detection data set was used to evaluate the DaTa-ML model. The results showed that with only 1% of the training data (5-way, 20-shot) and a single update step (training time reduced by 90%), the DaTa-ML model had an accuracy rate of 89.6% on the test data, which is a 1.7% improvement over the transfer-learning method [i.e., residual neural network (ResNet)50 pre-trained on ImageNet], and a 16.8% improvement over scratch-built models (i.e., ResNet50 without pre-trained weights), despite having fewer trainable parameters (the parameters used by the DaTa-ML model are only 0.47% of the ResNet50 parameters). The DaTa-ML model provides a more efficient DR classification solution with little annotated data and has significant advantages over state-of-the-art methods. Thus, it could be used to guide and assist ophthalmologists to determine the severity of DR.

Rheological properties of porcine organs: measurements and fractional viscoelastic model.

The rheological properties of porcine heart, kidney, liver and brain were measured using dynamic oscillatory shear tests over a range of frequencies and shear strains. Frequency sweep tests were performed from 0.1Hz to a maximum of 9.5Hz at a shear strain of 0.1%, and strain sweep tests were carried out from 0.01% to 10% at 1Hz. The effect of pre-compression of samples up to 10% axial strain was considered. The experimental measurements were fit to a Semi-Fractional Kelvin Voight (S-FKV) model. The model was then used to predict the stress relaxation in response to a step strain of 0.1%. The prediction was compared to experimental relaxation data for the porcine organ samples, and the results agreed to within 30%. In conclusion, this study measured the rheological properties of porcine organs and used a fractional viscoelastic model to describe the response in frequency and time domain.

Evaluation of the GeSRU-Steps educational video concept (German Society of Residents in Urology e. V.)

Surgical educational videos represent acontemporary, multimedia supplement to surgical education and training. The German Society of Residents in Urology e. V. (GeSRU) developed an educational video platform (steps.GeSRU.de) with free, quality-assured educational videos for urologists, especially for residents. The purpose of this study was to evaluate the GeSRU Steps teaching videos. Prospectively, 29GeSRU Steps training videos were made available (03/2019-05/2023) via amboss.com, and an online questionnaire was inserted following the videos. This comprised 12items on medical, technical, and didactic quality, usefulness for own knowledge acquisition, and sociodemographic data of respondents. Aspects of video quality were assessed with the Acceptability E‑scale and the Global Quality Score. During the survey period, the GeSRU Steps videos implemented on the amboss.com website were viewed 49,698 times. Atotal of 474 questionnaires were answered (rate 0.25%). The collective of respondents consisted of 419 (88%) students, 47 (10%) physicians in training, and 5 (1%) specialists; 351 (74%) were female, 107 (23%) were male, and 4 (1%) were diverse. Each educational video was rated amedian of 10times (range 5-65). The six questions of the Acceptability E‑scale and the Global Quality Score were rated good and very good (81.6-95.8%), respectively. GeSRU teaching videos achieved avery good rating with high user satisfaction. By specific promotion of these teaching videos, which are quality-assured through supervision, the portfolio of surgical videos available at alow threshold can be expanded and can serve as acontemporary education tool.

Navigating materials chemical space to discover new battery electrodes using machine learning

Investigating the role of electrodes' physiochemical properties on their output voltage can be beneficial in developing high-performance batteries. To this end, this study uses a two-step machine learning (ML) approach to predict new electrodes and analyze the effects of their physiochemical properties on the voltage. The first step utilizes an ML model to curate an informative feature space that elucidates the relationship between physiochemical properties and voltage output. The second step trains an active learning model on the informative feature space using Bayesian optimization to screen potential battery electrodes from a dataset of 3656 materials. This strategy successfully identified 41 electrode materials that exhibit good electronic conductivity and host highly electronegative anions. This work provides an efficient strategy to discover novel electrode materials while integrating domain knowledge of chemistry and material science with ML in materials research.

The Effect of Perturbation-Based Balance Training vs Step Training on Reaction Time in Older Persons: A Review.

As the global population ages, understanding the impact of aging on cognitive abilities becomes increasingly crucial. One such cognitive function that exhibits significant changes with advancing age is reaction time (RT). This article presents a comprehensive analysis of RT in older adults, examining its underlying mechanisms, age-related changes, and implications for cognitive functioning. Through a review of existing literature, this study explores various factors influencing RT in older individuals, including sensory, motor, and cognitive processes. It highlights how age-related changes in the central nervous system, such as declines in processing speed and neural efficiency, play a significant role in prolonging RTs. Additionally, the role of cumulative life experiences and environmental factors in shaping reaction time in older adults is discussed.

Rheological response of ferrofluids undergoing unsteady shear flows in the presence of a magnetic field

The rheological response of two commercial ferrofluids to transient shearing flows using a parallel disk rheometer device equipped with a magnetic cell is investigated. The basic difference between the ferrofluids is their volume fraction of magnetic particles. The first transient shear flow examined is a step-strain under the influence of a magnetic field, from which the stress relaxation functions for both magnetic fluids studied are obtained in terms of the magnetic field strength and the intensity of the step strain. The main relaxation times of both fluids are determined and shown to increase with the applied magnetic field parameter after some critical value. We also observed that the shear stress relaxes to a residual stress, which is strongly dependent on both magnetic field and strain strengths. This remarkable residual stress increases as the intensity of the magnetic field rises. In terms of the strain strength, this residual stress is found to have two interesting behaviors. First, for small values of strain, the residual stress increases linearly until a maximum is reached. Further increases in the strain strength lead to a nonlinear decrease in the residual stress. We conjecture that the linear regime is associated with a predominance of elastic deformation of the fluid microstructure while the nonlinear one to its plastic deformation or even to the structure breakup. The second experimental investigation of the magnetic fluids is carried out under the condition of oscillatory shear in a linear viscoelastic regime and in the presence of an applied magnetic field. The main viscoelastic moduli of the ferrofluids as functions of the non-dimensional frequency and the magnetic field intensity are presented. In addition, it is also shown, for both ferrofluids, that viscous and elastic characteristics are severely increased when the applied magnetic field intensity is enhanced. We also determine the shear elastic modulus for both magnetic fluids in the limit of low Deborah number as a function of the magnetic parameter. Compatibility checks between the viscous modulus and the apparent shear viscosity under conditions of the same frequency and shear rate are performed, and the first normal stress difference is calculated.

Effects of protective step training on proactive and reactive motor adaptations in Parkinson's disease patients.

The aim of this study was to investigate to what extent PD affects the ability to walk, respond to balance perturbations in a single training session, and produce acute short-term effects to improve compensatory reactions and control of unperturbed walking stability. Understanding the mechanism of compensation and neuroplasticity to unexpected step perturbation training during walking and static stance can inform treatment of PD by helping to design effective training regimens that remediate fall risk. Current rehabilitation therapies are inadequate at reducing falls in people with Parkinson's disease (PD). While pharmacologic and surgical treatments have proved largely ineffective in treating postural instability and gait dysfunction in people with PD, studies have demonstrated that therapy specifically focusing on posture, gait, and balance may significantly improve these factors and reduce falls. The primary goal of this study was to assess the effectiveness of a novel and promising intervention therapy (protective step training - i.e., PST) to improve balance and reduce falls in people with PD. A secondary goal was to understand the effects of PST on proactive and reactive feedback responses during stance and gait tasks. Multiple-baseline, repeated measures analyses were performed on the multitude of proactive and reactive performance measures to assess the effects of PST on gait and postural stability parameters. In general, the results indicate that participants with PD were able to use experiences with perturbation training to integrate and adapt feedforward and feedback behaviors to reduce falls. The ability of the participants with PD to adapt to changes in task demands suggests that individuals with PD could benefit from the protective step training to facilitate balance control during rehabilitation.

Cognitive Predictors of Responsiveness to Reactive Step Training in People with Parkinson’s Disease at Fall-Risk

Reactive stepping can be improved in people with Parkinson’s Disease (PwPD). However, there is variability in the responsiveness to such training. This study examined if cognition could predict the responsiveness of PwPD to a two-week reactive step training intervention. 25 PwPD (70.52 years ± 7.15; Hoehn & Yahr range 1–3) at risk for falls completed a multiple baseline, open-label, uncontrolled pre-post intervention study. Reactive stepping was trained through a two-week (six-session) intervention with repeated support surface translations. Stepping performance was measured at two baseline assessments (B1 and B2), immediately after the intervention (P1), and two months after training (P2). Primary stepping outcomes were anterior-posterior margin of stability (MOS), step length, and step latency during backward steps. The primary aim assessed whether global cognition (Scales for Outcomes in Parkinson's Disease-Cognition - SCOPA-COG, & Montreal Cognitive Assessment - MoCA) was related to two-month retention of improvements in reactive stepping after practice. The secondary aim explored whether specific cognitive domains predicted retained stepping improvements, including attention/working memory, executive function, language, memory, and visuospatial function. Greater baseline global cognition was related to better two-month retention of step length improvements (SCOPA-COG: p = 0.002, f2 = 0.31; MoCA: p = 0.002, f2 = 0.38). However, only SCOPA-COG retained statistical significance after p-value adjustment for multiple comparisons (p = 0.04). Optimal cut-point analysis revealed that a SCOPA-COG threshold of 31 or higher was optimal for identifying individuals likely to retain improvement. Specific cognitive domains did not predict changes in reactive stepping outcomes. Participants with greater baseline global cognition, particularly as measured by SCOPA-COG, demonstrated greater retention of improvements in reactive stepping. In this cohort, a SCOPA-COG threshold of 31 could predict individuals likely to benefit from the intervention. These findings highlight the potential of cognitive screening to identify people more or less likely to benefit from reactive balance training.

Combined Reactive and Volitional Step Training Improves Balance Recovery and Stepping Reaction Time in People With Parkinson’s Disease: A Randomised Controlled Trial

Background Falls are frequent and devastating events for people with Parkinson’s disease (PD). Here, we investigated whether laboratory-based reactive step training combined with home-based volitional step training was effective in improving balance recovery and stepping ability in people with PD. Methods Forty-four people with idiopathic PD were randomized into intervention or control groups. Intervention participants performed unsupervised volitional step training using home-based exergames (80+ minutes/week) for 12 weeks and attended reactive step training sessions in which they were exposed to slip and trip perturbations at 4 and 8 weeks. Control participants continued their usual activities. Primary outcomes were balance recovery following an induced-trip/slip and choice stepping reaction time (CSRT) at the 12-week reassessment. Secondary outcomes comprised sensorimotor, balance, cognitive, psychological, complex stepping (inhibitory CSRT and Stroop Stepping Test [SST]), gait measures, and falls experienced in everyday life. Results At reassessment, the intervention group had significantly fewer total laboratory-induced falls and faster CSRT compared to the control group (P < .05). The intervention group also had significantly faster inhibitory CSRT and SST movement times and made fewer mistakes in the SST (P < .05). There were no significant differences in the rate of every day falls or other secondary outcome measures between the groups. Conclusion Combined volitional and reactive step training improved balance recovery from an induced-perturbation, voluntary stepping time, and stepping accuracy in cognitively challenging tests in people with PD. Further research is required to determine whether such combined step training can prevent daily-life falls in this population.

An automated chest X-ray analysis for COVID-19, tuberculosis, and pneumonia employing ensemble learning approach

The COVID-19 pandemic has created a demand for rapid and accurate testing to detect the coronavirus, tuberculosis (TB), and pneumonia. Chest X-ray (CXR) images have been used as a source of testing, but the manual analysis is time-consuming and prone to error. To address this issue, an ensemble-based machine-learning approach has been developed to automate the classification of COVID-19, TB, and pneumonia using CXR images. The proposed system is divided into three steps (1) pre-processing, (ii) feature extraction, and (iii) training and classification. In the pre-processing step, the proposed system converts the CXR images into grayscale and then resizes them to the targeted input shape. The histogram of oriented gradients (HOG) and local binary pattern (LBP) are used as feature descriptors to extract the salient features from the CXR image dataset. Lastly, these essential features are learned by different classifiers ,i.e., Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Decision tree (DT), Naïve Bayes (NB), Adaptive Boosting (AdaBoost), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Ensemble Model (EM), for classification of COVID-19, TB, and pneumonia. Experimental results show that the proposed strategy performed well over contemporary methods using the CXR image dataset and achieved a classification accuracy of 98%. In this study, the authors observed that integrating the outperforming Machine Learning (ML) algorithms can yield significantly better results than using them individually.

Bounding the localized to ductile transition in porous rocks: implications for geo-reservoirs

SUMMARY Porous rocks have long been the focus of intense research driven by their importance in our society as host to our most essential resources (oil, gas, water, geothermal energy, etc), yet their rheology remains poorly understood. With increasing depth, porous rocks transition from being brittle (dilational deformation leading to localized failure) to being ductile (homogeneous compactive flow, no failure). The transition between these two regimes is crucial for reservoir engineering. In fact, brittle, localized deformation of porous rocks is generally accompanied by permeability enhancement but also induced seismicity, while ductile deformation leads to aseismic permeability reduction. Decades of experimental work has shown that this transition is not sharp but rather spans a wide P, T domain, but to this day, no clear boundaries have been established. Here, we subjected pre-faulted samples of Volvic trachyandesite to increasing confining pressure, deforming the samples each pressure step and recording strain partitioning between off-fault bulk deformation and on-fault slip. For the first time, we show that the localized–ductile transition (LDT) in porous rocks is bound by the stress criterion σy &amp;lt; σf &amp;lt; σflow. Additionally we show that, in this regime, once both fault sliding and bulk flow are active, the partitioning of strain between the two can be described by the empirical ratio: $(\sigma _\mathrm{f}-\sigma _\mathrm{y})/(\sigma _\mathrm{flow}-\sigma _\mathrm{y}).$ Finally, we propose a critical stress representation that takes into account the existence of the LDT in porous rocks.

Effect Of 25-square Step Training On Functional Mobility In People With Parkinson’s Disease

Effect Of 25-square Step Training On Functional Mobility In People With Parkinson’s Disease

Artificial neural networks and guided gene expression programming to predict wall pressure spectra beneath turbulent boundary layers

This study evaluates the efficacy of two machine learning (ML) techniques, namely, artificial neural networks (ANNs) and gene expression programing (GEP), that use data-driven modeling to predict wall pressure spectra (WPS) underneath turbulent boundary layers. Different datasets of WPS from experiments and high-fidelity numerical simulations covering a wide range of pressure gradients and Reynolds numbers are considered. For both ML methods, an optimal hyperparameter environment is identified that yields accurate predictions. Despite a higher memory consumption, ANN models are faster to train and are much more accurate than the GEP models, yielding an order of magnitude lower logarithmic Mean Squared Error (lMSE) than GEP. Novel training schemes are devised to address the shortcomings of GEP. These include (a) ANN-assisted GEP to reduce the noise in the training data, (b) exploiting the low- and high-frequency trends to guide the GEP search, and (c) a stepped training strategy where the chromosomes are first trained on the canonical datasets, followed by the datasets with complex features. When compared to the baseline scheme, these training strategies accelerated convergence and resulted in models with superior accuracy (≈30% reduction in the median lMSE) and higher reliability (≈75% reduction in the spread of lMSE in the interquartile range). The final GEP models captured the complex trends of WPS across varying flow conditions and pressure gradients, surpassing the accuracy of Goody's model.

Application Experience of Eyesi Operation Simulation Training System in the Teaching of Cataract Surgery for Ophthalmology Masters

Objective: To explore the application effect of the Eyesi surgical simulator in the teaching of cataract surgery for professional ophthalmology postgraduate students. Methods: The professional postgraduate students who were trained in the third year of ophthalmology at the First Affiliated Hospital of Xi’an Medical University were selected as the research objects. After passing the theoretical examination, they were randomly divided into the pig eyeball group, Eyesi group, and pig eye + Eyesi group, with 5 students in each group. The pig eyeball, Eyesi surgery simulator, and pig eye + Eyesi surgery simulator were used for microscopic technique operation and cataract surgery steps training, respectively. After the training, the overall training effects of the three groups of postgraduates were scored, and questionnaires were used to objectively evaluate the three training methods. Results: The scores of the students in the pig eye + Eyesi group were better than those in the Eyesi group, and the students in the Eyesi group were better than those in the pig eyeball group. Conclusion: The Eyesi surgical simulation training system can evaluate the microsurgical skills of professional masters and improve their surgical skills. This system is of great significance for the training of the cataract surgery skills of professional masters.

Osteogenic Effects of Aerobic Both Sides Utilized Ball Versus Aerobic Step Training in Premenopausal Women

Study purpose. Exercise training plays an important role in increasing bone mass. Nevertheless, the osteogenic effects of exercise training using various bench surfaces are unknown. Therefore, this study aimed to compare the osteogenic effects of exercise with a soft surface bench and an aerobic both sides utilized (BOSU) ball with those of aerobic step exercise training (STEP). Materials and methods. Fifty-two sedentary female participants aged 30–45 years were recruited and randomly divided into three groups. Seventeen participants were in the STEP, 17 in the BOSU, and 18 in the control group. The exercise programs of the STEP and BOSU groups were designed to have the same intensity and heart rate range during each stage of the program. During training, music was used to set the tempo for workouts. Results. After week 24, both training groups showed significant improvements in physical fitness, body composition, and body stability (p&lt;0.05). Increased levels of procollagen type I N-terminal propeptide (P1NP), an osteogenesis marker, were observed in both STEP and BOSU groups. Increased bone mineral density was only seen in the BOSU group (p&lt;0.05). Conclusions. Both STEP and BOSU programs effectively improved P1NP levels, muscle strength, and postural control, but only the aerobic BOSU ball training improved bone mineral density in premenopausal women.

Stability Changes in Fall-Prone Individuals With Parkinson Disease Following Reactive Step Training.

Poor reactive steps may lead to falls in people with Parkinson disease (PwPD). However, whether reactive steps can be improved in PwPD at risk for falls or whether step training reduces falls remains unclear. This study aimed to determine whether 2 weeks of reactive step training result in (1) immediate and retained improvements in stepping and (2) fewer prospective falls in PwPD at fall risk. Twenty-five PwPD (70.52 years ± 7.15; Hoehn & Yahr range 1-3) at risk for falls completed a multiple baseline, open-label, uncontrolled pre-/postintervention study. Stepping performance was assessed at 2 baseline assessments (B1 and B2) followed by a 2-week, 6-session training protocol. Stepping was assessed immediately (P1) and 2 months after training (P2). Primary outcomes were anterior-posterior margin of stability (MOS), step length, and step latency during backward stepping. Fall frequency was measured for 2 months before and after training. MOS during backward steps was significantly larger (better) after training ( P < 0.001, d = 0.83), and improvements were retained for 2 months ( P = 0.04, d = 0.66). Step length was not statistically significant different after training ( P = 0.13, d = 0.46) or at follow-up ( P = 0.08, d = 0.62), although effect sizes were medium and large, respectively. Step latency improved after initial exposure ( P = 0.01, d = 0.60) but not following training ( P = 0.43, d = 0.35). Twelve participants experienced fewer falls after training than before (10 = no change, 5 = increase; P = 0.12). Greater improvements in MOS were related to fewer falls ( P = 0.04). Two weeks of reactive step training resulted in immediate and retained improvements in some reactive stepping outcomes in PwPD at risk for falls and may reduce fall risk. Reactive step training may be a viable approach to reduce falls in PwPD.

Physicochemical and in vitro biological evaluation of an injectable self-healing quaternized chitosan/oxidized pectin hydrogel for potential use as a wound dressing material

Injectable self-healing hydrogels are attractive materials for use as wound dressings. To prepare such hydrogels, the current study used quaternized chitosan (QCS) to improve the solubility and antibacterial activity and oxidized pectin (OPEC) to introduce aldehyde groups for Schiff's base reaction with the amine groups from QCS. Self-healing hydrogels were made by co-injection of polymer solutions at specific polymer concentrations and reagent ratios that optimized both Schiff's base reactions and ionic interactions. The optimal hydrogel displayed self-healing 30 min after cutting and continuous self-healing during continuous step strain analysis, rapid gelation (< 1 min), a storage modulus of 394 Pa, and hardness of 700 mN, and compressibility of 162 mN s. The adhesiveness of this hydrogel (133 Pa) was within a suitable range for application as a wound dressing. The extraction media from the hydrogel displayed no cytotoxicity to NCTC clone 929 cells and higher cell migration than the control. While the extraction media from the hydrogel was found not to have antibacterial properties, QCS was verified as having MIC50 of 0.04 mg/mL against both E. coli and S. aureus. Therefore, this injectable self-healing QCS/OPEC hydrogel has the potential use as a biocompatible hydrogel material for wound management.

A Path Planning Algorithm for Mobile Robot Based on MR-QL in the Unknown Scene

The Q-Learning (QL) algorithm is widely used for path planning. As the scene in which the mobile robot is located becomes complex, the algorithm suffers from the limitations of low convergence speed and long exploration paths. Therefore, a Max Reward-Q-learning (MR-QL) path planning algorithm based on maximum reward is proposed for complex unknown scenarios. The original algorithm’s discrete reward function and action selection strategy are improved, and a new reward function is designed to dynamically adjust the reward mechanism to heuristically guide the robot motion. The action selection strategy is also optimized to avoid invalid exploration and improve the algorithm’s convergence. Finally, three experimental environments with different complexity are constructed to demonstrate the feasibility of the proposed algorithm. The simulation results show that the MR-QL algorithm is about 50% of the original algorithm in terms of exploration step length and training time, and the convergence speed of the algorithm is better than the original algorithm.

The Effectiveness of Reactive Step Training in People with Multiple Sclerosis

Background and Purpose Falls are poorly controlled in people with Multiple Sclerosis (PwMS). Reactive stepping after a loss of balance has been associated with falls and is altered in PwMS. However, there is limited data regarding whether reactive step training improves steps in PwMS. We aimed to determine whether reactive stepping improved following two weeks of reactive step training in PwMS. Methods To date, 16 PwMS participated in an 18-week multiple baseline study. Participants attended 2 baseline assessments (B1 and B2) before training, a 2-week, 6-session training protocol, and 2 post-training assessments (P1 and P2) to assess acute (P1) and retained (P2- 8 weeks post-training) effects. Each assessment consisted of 3 backward reactive step trials. Training consisted of 32 stepping trials in the forward, backward, left, and right directions. Outcomes included the anterior-posterior margin of stability (MOS), step length, and step latency during backward stepping. Repeated measures ANOVAs were assessed training (B2 - P1; n = 16) and retention (B2 – P2; n = 8) effects. Performance change and effect sizes (η2- partial eta-squared, 0.01 small, 0.06 medium, 0.14 large) were interpreted as data collection is ongoing. Results In this interim analysis, immediately after training, participants demonstrated quicker (-45 ms, η2= 0.15) and larger steps (+2.5 cm, η2= 0.09), with a larger MOS (+3 cm, η2= 0.12) than before training. Despite only 8 participants completing the 8-week follow-up thus far, improvements in MOS (+ 3cm, η2= 0.11), step length (+7 cm, η2= 0.20), and step latency (-10 ms, η2= 0.03) were retained 2 months after training compared to B2. Discussion These interim findings suggest that reactive step training may be an effective approach to improve reactive balance in PwMS. Given the importance of reactive stepping for fall prevention, this work may impact rehabilitative care and reduce fall-risk for PwMS. Falls are poorly controlled in people with Multiple Sclerosis (PwMS). Reactive stepping after a loss of balance has been associated with falls and is altered in PwMS. However, there is limited data regarding whether reactive step training improves steps in PwMS. We aimed to determine whether reactive stepping improved following two weeks of reactive step training in PwMS. To date, 16 PwMS participated in an 18-week multiple baseline study. Participants attended 2 baseline assessments (B1 and B2) before training, a 2-week, 6-session training protocol, and 2 post-training assessments (P1 and P2) to assess acute (P1) and retained (P2- 8 weeks post-training) effects. Each assessment consisted of 3 backward reactive step trials. Training consisted of 32 stepping trials in the forward, backward, left, and right directions. Outcomes included the anterior-posterior margin of stability (MOS), step length, and step latency during backward stepping. Repeated measures ANOVAs were assessed training (B2 - P1; n = 16) and retention (B2 – P2; n = 8) effects. Performance change and effect sizes (η2- partial eta-squared, 0.01 small, 0.06 medium, 0.14 large) were interpreted as data collection is ongoing. In this interim analysis, immediately after training, participants demonstrated quicker (-45 ms, η2= 0.15) and larger steps (+2.5 cm, η2= 0.09), with a larger MOS (+3 cm, η2= 0.12) than before training. Despite only 8 participants completing the 8-week follow-up thus far, improvements in MOS (+ 3cm, η2= 0.11), step length (+7 cm, η2= 0.20), and step latency (-10 ms, η2= 0.03) were retained 2 months after training compared to B2. These interim findings suggest that reactive step training may be an effective approach to improve reactive balance in PwMS. Given the importance of reactive stepping for fall prevention, this work may impact rehabilitative care and reduce fall-risk for PwMS.

White Matter Correlates of Reactive Stepping in People with Multiple Sclerosis

Background and Purpose Reactive steps are rapid protective responses after balance challenges. People with MS (PwMS) demonstrate impaired stepping, which can increase fall-risk, and these deficits may be caused by white matter (WM) loss. However, the specific neural mechanisms contributing to reactive stepping in PwMS are poorly understood. We aimed to determine WM correlates of reactive stepping and responsiveness to step training. Methods 14 PwMS participated in an 18-week multiple-baseline study. Participants attended 2 baseline assessments (B1 and B2) before training, a 2-week, 6-session training protocol, and a post-training assessment (P1). Each assessment consisted of 3 backward reactive step trials. Training consisted of 32 stepping trials. Outcomes included the anterior-posterior margin of stability (MOS), step length, and step latency. Tract-Based Spatial Statistics (TBSS) was performed to investigate the WM microstructure by correlating fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) with baseline reactive step performance (B1) and immediate responsiveness to training (P1). Results The FA of the right superior longitudinal fasciculus (R-SLF) was associated with baseline step length (p=0.048) and MOS (p=0.048). Corpus Callosum (p=0.047) and right anterior corona radiata (R-ACR) (p=0.047) FA were also related to baseline MOS during backward stepping. FA of the corticospinal tract was also associated with baseline step length (p=0.048). The FA (p=0.047) and the MD (trending- p=0.082) of the L-ACR were also related to step length. No significant associations were found between WM and training responsiveness, but a trend showed an association between the right uncinate fasciculus and a change in step latency (p=0.072). Discussion We identified several WM loci associated with baseline stepping involved in the rapid integration of sensory information and the execution of motor responses. A better understanding of the neural control of reactive stepping may lead to developing more effective rehabilitation strategies and ultimately reducing fall-risk in PwMS. Reactive steps are rapid protective responses after balance challenges. People with MS (PwMS) demonstrate impaired stepping, which can increase fall-risk, and these deficits may be caused by white matter (WM) loss. However, the specific neural mechanisms contributing to reactive stepping in PwMS are poorly understood. We aimed to determine WM correlates of reactive stepping and responsiveness to step training. 14 PwMS participated in an 18-week multiple-baseline study. Participants attended 2 baseline assessments (B1 and B2) before training, a 2-week, 6-session training protocol, and a post-training assessment (P1). Each assessment consisted of 3 backward reactive step trials. Training consisted of 32 stepping trials. Outcomes included the anterior-posterior margin of stability (MOS), step length, and step latency. Tract-Based Spatial Statistics (TBSS) was performed to investigate the WM microstructure by correlating fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) with baseline reactive step performance (B1) and immediate responsiveness to training (P1). The FA of the right superior longitudinal fasciculus (R-SLF) was associated with baseline step length (p=0.048) and MOS (p=0.048). Corpus Callosum (p=0.047) and right anterior corona radiata (R-ACR) (p=0.047) FA were also related to baseline MOS during backward stepping. FA of the corticospinal tract was also associated with baseline step length (p=0.048). The FA (p=0.047) and the MD (trending- p=0.082) of the L-ACR were also related to step length. No significant associations were found between WM and training responsiveness, but a trend showed an association between the right uncinate fasciculus and a change in step latency (p=0.072). We identified several WM loci associated with baseline stepping involved in the rapid integration of sensory information and the execution of motor responses. A better understanding of the neural control of reactive stepping may lead to developing more effective rehabilitation strategies and ultimately reducing fall-risk in PwMS.