Functionality and jump performance in patellar tendinopathy with the application of three different treatments.

OBJECTIVES: The aim is to assess performance characteristics in jumps and functionality in participants with patellar tendinopathy and compare changes with various tendinopathy treatments in the short and medium term. As a secondary objective, the study aims to verify the relationship between changes in knee functionality assessed by the VISA-P and jump capacity in the different treatment groups. DESIGN: A double-blinded randomized controlled trial. METHODS: Recruitment was conducted at sport clubs, with 48 participants with patellar tendinopathy included in the study. Participants were randomized into groups: dry needling (DN), percutaneous electrolysis (PNE), and sham needling as the control group (CG), all combined with eccentric exercise (EE). Functionality and performance during jumps, including squat jump (SJ) and counter movement jump (CMJ), were assessed. RESULTS: Significant differences were found in functionality between the pre-test and post-test evaluations, as well as between the pre-test and follow-up evaluations, in all three groups (p < 0.001). The DN group experienced an improvement in eccentric power (p = 0.021). A moderate correlation was found between the pre-test and post-test changes in functionality and SJ maximum concentric force (r = 0.63, p < 0.01, CI: 0.1; 0.8), CMJ maximum concentric force (r = 0.52, p = 0.05, CI: -0.01; 0.8), and CMJ eccentric power in the DN group (r = 0.63, p = 0.01, CI: 0.1; 0.8). CONCLUSIONS: Eccentric exercise could be effective in improving functionality in patellar tendinopathy and DN could improve eccentric power in jumps performance. Moreover, the DN group experienced an increase in functionality that correlated with the improvements found in jump performance in eccentric power and concentric strength.

Predicting Knee Joint Contact Force Peaks During Gait Using a Video Camera or Wearable Sensors.

PURPOSE: Estimating loading of the knee joint may be helpful in managing degenerative joint diseases. Contemporary methods to estimate loading involve calculating knee joint contact forces using musculoskeletal modeling and simulation from motion capture (MOCAP) data, which must be collected in a specialized environment and analyzed by a trained expert. To make the estimation of knee joint loading more accessible, simple input predictors should be used for predicting knee joint loading using artificial neural networks. METHODS: We trained feedforward artificial neural networks (ANNs) to predict knee joint loading peaks from the mass, height, age, sex, walking speed, and knee flexion angle (KFA) of subjects using their existing MOCAP data. We also collected an independent MOCAP dataset while recording walking with a video camera (VC) and inertial measurement units (IMUs). We quantified the prediction accuracy of the ANNs using walking speed and KFA estimates from (1) MOCAP data, (2) VC data, and (3) IMU data separately (i.e., we quantified three sets of prediction accuracy metrics). RESULTS: Using portable modalities, we achieved prediction accuracies between 0.13 and 0.37 root mean square error normalized to the mean of the musculoskeletal analysis-based reference values. The correlation between the predicted and reference loading peaks varied between 0.65 and 0.91. This was comparable to the prediction accuracies obtained when obtaining predictors from motion capture data. DISCUSSION: The prediction results show that both VCs and IMUs can be used to estimate predictors that can be used in estimating knee joint loading outside the motion laboratory. Future studies should investigate the usability of the methods in an out-of-laboratory setting.

Accuracy of Kinovea-based kinematic gait analysis compared to a three-dimensional motion analysis system in healthy dogs.

OBJECTIVE: The goal of this study was to compare the accuracy of kinematic measurements obtained using the 2-D video-based kinematic motion analysis (KMA) software Kinovea (version 0.9.5; http://www.kinovea.org) with 3-D KMA in healthy dogs. METHODS: In this prospective study, 3-D marker-based KMA (VICON-Nexus, version 2.12.1, and Procalc, version 1.6; VICON Motion Systems Ltd) was performed on healthy dogs (body weight ≥ 20 kg; height at withers > 50 cm) walking on a treadmill (study period: November 2022). Simultaneously, dogs were video recorded by 1 smartphone (iPhone SE; Apple Inc) at a 1.50-m distance perpendicular to the shoulder (60 frames per second; 1,920 X 1,080 pixels) for KMA using Kinovea. Joint angle and joint angle velocity of the shoulder, elbow, carpus, hip, stifle, and tarsus were calculated for 6 synchronized gait cycles. Each gait cycle was divided into 10 increments. The difference between 3-D KMA and Kinovea was assessed for each parameter using robust linear mixed-effects models. RESULTS: 34 dogs were included. The estimated joint angle difference between 3-D KMA and Kinovea was less than 2° for all shoulder and elbow gait cycle increments. For the carpus, hip, stifle, and tarsus, the difference was less than 2° in 9, 5, 4, and 4 out of 10 gait cycle increments, respectively. CONCLUSIONS: Kinovea provides accurate kinematic data for the shoulder and elbow of healthy dogs. Carpal, hip, stifle, and tarsal kinematics were less accurate. CLINICAL RELEVANCE: The use of Kinovea for clinical and research purposes remains limited. Future Kinovea-based studies are needed to investigate the accuracy of carpal, hip, stifle, and tarsal kinematics.

Synthetic 3D full-body skeletal motion from 2D paths using RNN with LSTM cells and linear networks.

Gait analysis has proven to be a key process in the functional assessment of people involving many fields, such as diagnosis of diseases or rehabilitation, and has increased in relevance lately. Gait analysis often requires gathering data, although this can be very expensive and time consuming. One of the main solutions applied in fields when data acquisition is a problem is augmentation of datasets with artificial data. There are two main approaches for doing that: simulation and synthetic data generation. In this article, we propose a parametrizable generative system of synthetic walking simplified human skeletons. For achieving that, a data gathering experiment with up to 26 individuals was conducted. The system consists of two artificial neural networks: a recurrent neural network for the generation of the movement and a multilayer perceptron for determining the size of the segments of the skeletons. The system has been evaluated through four processes: (i) an observational appraisal by researchers in gait analysis, (ii) a visual representation of the distribution of the generated data, (iii) a numerical analysis using the normalized cross-correlation coefficient, and (iv) an angular evaluation to check the kinematic validity of the data. The evaluation concluded that the system is able to generate realistic and accurate gait data. These results reveal a promising path for this research field, which can be further improved through increasing the variety of movements and the user sample.

Gait phase normalization resolves the problem of different phases being compared in gait cycle normalization.

For time-continuous analysis of gait, the problem of variations in cycle durations is resolved by normalizing to the gait cycle, but results depend on the definition of the cycle start. Gait cycle normalization ignores variations in gait phase durations, which results in averaging and comparing data across different phases. We propose gait phase normalization as part of a comprehensive method for independently analyzing magnitude and timing differences. First, gait phases are identified and differences in absolute and/or relative timing of phase durations or any point of interest between conditions or groups are analyzed using standard statistics. Next, time-continuous gait data is normalized to gait phases, and statistical parametric mapping (SPM) is used to assess magnitude differences in gait data. This approach is demonstrated on data recorded from ten young healthy adults walking on a treadmill at five different speeds. Sagittal knee angle was normalized to gait cycle or gait phase using five different gait cycle start events. Walking at different speeds resulted in significant changes in gait phase durations, highlighting a problem ignored by gait cycle normalization. SPM results for knee angle normalized to gait cycle varied from normalization to gait phases. Gait phase normalized SPM results were robust to the definition of the cycle start, in contrast to gait cycle normalized data. The approach of analyzing phase durations and normalizing data to gait phases overcomes previous limitations and enables a comprehensive analysis of magnitude and timing differences in time-continuous gait data and could be readily adapted to other tasks.

Residual gait deviations in children treated by medial open reduction for developmental dysplasia of the hip at long-term follow-up: a comparison with healthy controls.

PURPOSE: This study aimed to analyze and compare gait patterns and deviations at long-term follow-up in children who received medial open reduction (MOR) before 18 months for unilateral or bilateral hip developmental dysplasia (DDH). METHODS: A retrospective chart review was conducted on children who underwent MOR. The study population was divided into two groups: the unilateral group, including unilateral (five children with unilateral) and bilateral (five children with bilateral DDH). Ten healthy children were recruited for the control group. Spatiotemporal, kinematic, stiff-knee gait (SKG), and kinetic gait characteristics were analyzed. RESULTS: Stance time was significantly shorter in both the unilateral (median [IQR]; 590 ms, [560.0-612.5] and bilateral (575 ms, [550-637.5]) groups than in the control group (650, [602.5-677.5]) (p < 0.001), whereas swing time did not differ substantially (p = 0.065) There was no considerable difference in the mean knee flexion at swing between the unilateral (31.6°, [30-36]) and control (30.11°, [27.8-33.6] groups (p > 0.05), but the bilateral group (28.5°, [24.9-32.1]) showed the lower values than the other groups (p < 0.001 for bilateral vs unilateral group; p = 0.008 bilateral vs unilateral group). All the SKG parameters significantly differed among the groups in multi-group comparisons (p < 0.001 for each parameter). Three children had borderline SKG, and two had not-stiff limbs in the unilateral group. In the bilateral group, four children had stiff limbs, and one had borderline SKG. Most kinetic gait parameters were not statistically different between groups (p > 0.05). CONCLUSION: This study has revealed notable deviations in gait patterns of children with DDH treated by MOR at long-term follow-up compared to healthy children's gait. MOR could negatively affect pelvic motion during gait due to impaired functions of the iliopsoas and adductor muscles, and SKG can be encountered secondary to iliopsoas weakness.

Unraveling stroke gait deviations with movement analytics, more than meets the eye: a case control study.

Background: This study aimed to identify and quantify the kinematic and kinetic gait deviations in post-stroke hemiplegic patients with matched healthy controls using Statistical Parametric Mapping (SPM). Methods: Fifteen chronic stroke patients [4 females, 11 males; age 53.7 (standard deviation 12.2) years; body mass 65.4 (10.4) kg; standing height 168.5 (9.6) cm] and 15 matched healthy controls [4 females, 11 males; age 52.9 (11.7) years; body weight 66.5 (10.7) years; standing height 168.3 (8.8) cm] were recruited. In a 10-m walking task, joint angles, ground reaction forces (GRF), and joint moments were collected, analyzed, and compared using SPM for an entire gait cycle. Results: Generally, when comparing the stroke patients' affected (hemiplegic) and less-affected (contralateral) limbs with the control group, SPM identified significant differences in the late stance phase and early swing phase in the joint angles and moments in bilateral limbs (all p < 0.005). In addition, the vertical and anteroposterior components of GRF were significantly different in various periods of the stance phase (all p < 0.005), while the mediolateral component showed no differences between the two groups. Conclusion: SPM was able to detect abnormal gait patterns in both the affected and less-affected limbs of stroke patients with significant differences when compared with matched controls. The findings draw attention to significant quantifiable gait deviations in the less-affected post-stroke limb with the potential impact to inform gait retraining strategies for clinicians and physiotherapists.

Gait analysis using three-dimensional motion and ground reaction force systems in patients with hemiplegia treated with botulinum toxin type A in ankle plantar flexors.

Objective: The efficacy of botulinum toxin type A (BoNT-A) injection on spasticity has usually been measured using the range of motion (ROM) of joints and Modified Ashworth Scale (MAS); however, they only evaluate muscle tone at rest. We objectively analyzed the gait of three patients with hemiplegia using three-dimensional motion analysis and ground reaction force (GRF) systems to evaluate muscle tone during gait. Materials and Methods: We measured passive ankle dorsiflexion ROM with knee extension and the MAS score for clinical evaluation, and gait speed, stride length, single-leg support phase during the gait cycle, joint angle, joint moment, and GRFs for kinematic evaluation before and one month after BoNT-A injection. Results: All patients showed an increase in ankle dorsiflexion ROM, improvement in MAS score, and increase in stride length. Case 1 showed an increase in gait speed, prolongation of the single-leg support phase, increase in hip extension angle and moment, and improvement in the vertical and anterior-posterior components of the GRFs. Case 2 showed an increase in gait speed, improvement in double knee action, increase in ankle plantar flexion moment, and improvement in propulsion in the progressive component of the GRFs. Case 3 exhibited a laterally directed force in the GRFs. Conclusion: We evaluated the effects of BoNT-A injections in three patients with hemiplegia using three-dimensional motion analysis and GRFs. The results of the gait analysis clarified the improvements and problems in hemiplegic gait and enabled objective explanations for patients.

Gait analysis of patients undergoing knee endoprosthesis: functional differences between tibia and femur resections.

With the improvement in survival of patients undergoing knee reconstructive surgeries, the functional parameter became widely studied heading optimize and minimize motor sequelae. In patients undergoing knee endoprosthesis, proximal tibial or distal femoral resections affect the functioning of the knee extensor mechanism, with possible repercussions on gait. Seventeen patients were selected, divided into two groups, undergoing distal femoral or proximal tibial resection, and gait analysis examination was performed. Changes in gait velocity, cadence, step length, and alterations in the support and balance phase were observed. No major statistically significant differences were found in the kinetic and kinematic parameters between the operated groups. The study corroborates that although tibial resections have a higher theoretical risk of compromising the extensor mechanism, such data were not observed in the analyzed sample.

Development of an IMU based 2-segment foot model for an applicable medical gait analysis.

BACKGROUND: The two most commonly instrumented gait analysis tools used are Optical Motion Capture systems (OMC) and Inertial Measurement Units (IMU). To date, OMC based gait analysis is considered the gold-standard. Still, it is space-, cost-, and time-intense. On the other hand IMU systems are more cost- and time effective but simulate the whole foot as a single segment. To get a more detailed model of the foot and ankle, a new 2-segment foot model using IMU was developed, comparable to the multi-segment foot models assessed by OMC. RESEARCH QUESTION: Can an IMU based 2-segment foot model be developed to provide a more detailed representation of the foot and ankle kinematics? METHODS: To establish a 2-segment foot model, in addition to the previous 1-segment foot model an IMU sensor was added to the calcaneus. This allowed the differentiation between the hindfoot and forefoot kinematics. 30 healthy individuals (mean age 27 ± 7 years) were recruited to create a norm data set of a healthy cohort. Moreover, the kinematic data of the 2-segment foot model were compared to those of the traditional 1-segment foot model using statistical parametric mapping. RESULTS: The 2-segment foot model proved to be applicable. Furthermore, it allowed for a more detailed representation of the foot and ankle joints, similar to other multi-segment foot model. The healthy cohort's norm data set showed a homogeneous motion pattern for gait. CONCLUSION: The 2-segment foot model allows for an extension of IMU-based gait analysis. Futures studies must prove the reliability and validity of the 2-segment foot model in healthy and pathologic situations. LEVEL OF EVIDENCE: Level II.

Validation of IMU against optical reference and development of open-source pipeline: proof of concept case report in a participant with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant.

BACKGROUND: Systems that capture motion under laboratory conditions limit validity in real-world environments. Mobile motion capture solutions such as Inertial Measurement Units (IMUs) can progress our understanding of "real" human movement. IMU data must be validated in each application to interpret with clinical applicability; this is particularly true for diverse populations. Our IMU analysis method builds on the OpenSim IMU Inverse Kinematics toolkit integrating the Versatile Quaternion-based Filter and incorporates realistic constraints to the underlying biomechanical model. We validate our processing method against the reference standard optical motion capture in a case report with participants with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant (POI) and without amputation walking over level ground. We hypothesis that by using this novel pipeline, we can validate IMU motion capture data, to a clinically acceptable degree. RESULTS: Average RMSE (across all joints) between the two systems from the participant with a unilateral transfemoral amputation (TFA) on the amputated and the intact sides were 2.35° (IQR = 1.45°) and 3.59° (IQR = 2.00°) respectively. Equivalent results in the non-amputated participant were 2.26° (IQR = 1.08°). Joint level average RMSE between the two systems from the TFA ranged from 1.66° to 3.82° and from 1.21° to 5.46° in the non-amputated participant. In plane average RMSE between the two systems from the TFA ranged from 2.17° (coronal) to 3.91° (sagittal) and from 1.96° (transverse) to 2.32° (sagittal) in the non-amputated participant. Coefficients of Multiple Correlation (CMC) results between the two systems in the TFA ranged from 0.74 to > 0.99 and from 0.72 to > 0.99 in the non-amputated participant and resulted in 'excellent' similarity in each data set average, in every plane and at all joint levels. Normalized RMSE between the two systems from the TFA ranged from 3.40% (knee level) to 54.54% (pelvis level) and from 2.18% to 36.01% in the non-amputated participant. CONCLUSIONS: We offer a modular processing pipeline that enables the addition of extra layers, facilitates changes to the underlying biomechanical model, and can accept raw IMU data from any vendor. We successfully validate the pipeline using data, for the first time, from a TFA participant using a POI and have proved our hypothesis.

Insights into Flexible Bioinspired Fins for Unmanned Underwater Vehicle Systems through Deep Learning.

The last few decades have led to the rise of research focused on propulsion and control systems for bio-inspired unmanned underwater vehicles (UUVs), which provide more maneuverable alternatives to traditional UUVs in underwater missions. Recent work has explored the use of time-series neural network surrogate models to predict thrust and power from vehicle design and fin kinematics. We expand upon this work, creating new forward neural network models that encapsulate the effects of the material stiffness of the fin on its kinematic performance, thrust, and power, and are able to interpolate to the full spectrum of kinematic gaits for each material. Notably, we demonstrate through testing of holdout data that our developed forward models capture the thrust and power associated with each set of parameters with high resolution, enabling highly accurate predictions of previously unseen gaits and thrust and FOM gains through proper materials and kinematics selection. As propulsive efficiency is of utmost importance for flapping-fin UUVs in order to extend their range and endurance for essential operations, a non-dimensional figure of merit (FOM), derived from measures of propulsive efficiency, is used to evaluate different fin designs and kinematics and allow for comparison with other bio-inspired platforms. We use the developed FOM to analyze optimal gaits and compare the performance between different fin materials. The forward model demonstrates the ability to capture the highest thrust and FOM with good precision, which enables us to improve thrust generation by 83.89% and efficiency by 137.58% with proper fin stiffness and kinematics selection, allowing us to improve material selection for bio-inspired fin design.

Data-Driven Identification of Stroke through Machine Learning Applied to Complexity Metrics in Multimodal Electromyography and Kinematics.

A stroke represents a significant medical condition characterized by the sudden interruption of blood flow to the brain, leading to cellular damage or death. The impact of stroke on individuals can vary from mild impairments to severe disability. Treatment for stroke often focuses on gait rehabilitation. Notably, assessing muscle activation and kinematics patterns using electromyography (EMG) and stereophotogrammetry, respectively, during walking can provide information regarding pathological gait conditions. The concurrent measurement of EMG and kinematics can help in understanding disfunction in the contribution of specific muscles to different phases of gait. To this aim, complexity metrics (e.g., sample entropy; approximate entropy; spectral entropy) applied to EMG and kinematics have been demonstrated to be effective in identifying abnormal conditions. Moreover, the conditional entropy between EMG and kinematics can identify the relationship between gait data and muscle activation patterns. This study aims to utilize several machine learning classifiers to distinguish individuals with stroke from healthy controls based on kinematics and EMG complexity measures. The cubic support vector machine applied to EMG metrics delivered the best classification results reaching 99.85% of accuracy. This method could assist clinicians in monitoring the recovery of motor impairments for stroke patients.

Explainable Deep-Learning-Based Gait Analysis of Hip-Knee Cyclogram for the Prediction of Adolescent Idiopathic Scoliosis Progression.

Accurate prediction of scoliotic curve progression is crucial for guiding treatment decisions in adolescent idiopathic scoliosis (AIS). Traditional methods of assessing the likelihood of AIS progression are limited by variability and rely on static measurements. This study developed and validated machine learning models for classifying progressive and non-progressive scoliotic curves based on gait analysis using wearable inertial sensors. Gait data from 38 AIS patients were collected using seven inertial measurement unit (IMU) sensors, and hip-knee (HK) cyclograms representing inter-joint coordination were generated. Various machine learning algorithms, including support vector machine (SVM), random forest (RF), and novel deep convolutional neural network (DCNN) models utilizing multi-plane HK cyclograms, were developed and evaluated using 10-fold cross-validation. The DCNN model incorporating multi-plane HK cyclograms and clinical factors achieved an accuracy of 92% in predicting curve progression, outperforming SVM (55% accuracy) and RF (52% accuracy) models using handcrafted gait features. Gradient-based class activation mapping revealed that the DCNN model focused on the swing phase of the gait cycle to make predictions. This study demonstrates the potential of deep learning techniques, and DCNNs in particular, in accurately classifying scoliotic curve progression using gait data from wearable IMU sensors.

Effects of metronome walking on long-term attractor divergence and correlation structure of gait: a validation study in older people.

This study investigates the effects of metronome walking on gait dynamics in older adults, focusing on long-range correlation structures and long-range attractor divergence (assessed by maximum Lyapunov exponents). Sixty older adults participated in indoor walking tests with and without metronome cues. Gait parameters were recorded using two triaxial accelerometers attached to the lumbar region and to the foot. We analyzed logarithmic divergence of lumbar acceleration using Rosenstein's algorithm and scaling exponents for stride intervals from foot accelerometers using detrended fluctuation analysis (DFA). Results indicated a concomitant reduction in long-term divergence exponents and scaling exponents during metronome walking, while short-term divergence remained largely unchanged. Furthermore, long-term divergence exponents and scaling exponents were significantly correlated. Reliability analysis revealed moderate intrasession consistency for long-term divergence exponents, but poor reliability for scaling exponents. Our results suggest that long-term divergence exponents could effectively replace scaling exponents for unsupervised gait quality assessment in older adults. This approach may improve the assessment of attentional involvement in gait control and enhance fall risk assessment.

"The influence of auditory stimulation on whole body variability in healthy older adults during gait".

Acoustic stimulation appears to be a promising strategy in reducing the risk of falling in older adults, demonstrating effectiveness in improving stability. However, its impact on movement variability, another crucial indicator of fall risk, seems to be limited. This study aims to assess movement variability during walking in a cohort of healthy older adults exposed to three different frequencies of acoustic stimulation (90%, 100% and 110% of each subject's average cadence). Using a systemic approach based on network theory, which considers the intricate relationships between all body segments, we constructed connectivity matrices composed of nodes, represented by bony landmarks, and edges, consisting of the standardised covariance of accelerations between each pair of nodes. By introducing a new metric called Similarity Score (S-score), we quantified the ability of each individual to repeat the same motor pattern at each gait cycle under different experimental conditions. The study revealed that rhythmic auditory stimulation (RAS) at 100% and 90% of the mean cadence significantly increased the S-scores compared to the baseline. These results highlight the effects of RAS in increasing gait repeatability in healthy older adults, with a focus on global kinematics.

SAGA: Stability-Aware Gait Analysis in constraint-free environments.

BACKGROUND: Gait abnormality detection is a challenging task in clinical practice. The majority of the current frameworks for gait abnormality detection involve the individual processes of segmentation, feature estimation, feature learning, and similarity assessment. Since each component of these modules is fixed and they are mutually independent, their performance under difficult circumstances is not ideal. We combine those processes into a single framework, a gait abnormality detection system with an end-to-end network. METHODS: It is made up of convolutional neural networks and Deep-Q-learning methods: one for coordinate estimation and the other for classification. In a single joint learning technique that may be trained together, the two networks are modeled. This method is significantly more efficient for use in real life since it drastically simplifies the conventional step-by-step approach. RESULTS: The proposed model is experimented on MATLAB R2020a. While considering into consideration the stability factor, our proposed model attained an average case accuracy of 95.3%, a sensitivity of 96.4%, and a specificity of 94.1%. SIGNIFICANCE: Our paradigm for quantifying gait analysis using commodity equipment will improve access to quantitative gait analysis in medical facilities and rehabilitation centers while also allowing academics to conduct large-scale investigations for gait-related disorders. Numerous experimental findings demonstrate the effectiveness of the proposed strategy and its ability to provide cutting-edge outcomes.

Comprehensive computerized gait analysis: Barriers to access for children and adolescents.

BACKGROUND: Comprehensive computerized gait analysis (CGA) alters orthopedic surgical plans and improves outcomes. Despite these documented benefits, CGA is not widely available to all patients who could be helped by it. RESEARCH QUESTION: Do social determinants of health impact access to CGA? METHODS: Retrospective review of patients seen for CGA from 2021 to 2022. Dates of referral, insurance approval and completion of CGA, demographics and insurance type were extracted from patient records. Zip codes were used to determine the neighborhood socioeconomic status (SES). Data were analyzed using non-parametric statistics. RESULTS: Insurance type affected time to authorization (private insurance/self-pay: median 9 days; HMO insurance: median 51.5 days; public insurance: median 27 days; p=0.0004). Once authorized, insurance type did not affect time to schedule and complete CGA (p=0.76). Lower neighborhood SES was associated with longer time to authorization but shorter time to complete CGA once authorized. Rescheduling was associated with longer time to complete CGA once authorized (median 29.5 vs. 16 days, p<0.0001). White, non-Hispanic families tended to reschedule more often than non-white or Hispanic families (35 % vs. 18 %, p=0.07). SIGNIFICANCE: Knowledge of barriers to CGA is necessary in order to design and implement effective strategies to widen its availability to all whom it could benefit. Social determinants of health and insurance type are associated with delays in authorization for CGA. Families with public insurance and HMO coverage experience delays in obtaining insurance authorization compared to PPO/self-pay patients, whose tests did not require prior authorization. However, there can also be delays in scheduling and completing CGA once authorized. This is a multi-faceted issue that requires further research.

Wearable accelerometers reveal objective assessment of walking symmetry and regularity in idiopathic scoliosis patients.

Background: Scoliosis is a multifaceted three-dimensional deformity that significantly affects patients' balance function and walking process. While existing research primarily focuses on spatial and temporal parameters of walking and trunk/pelvic kinematics asymmetry, there remains controversy regarding the symmetry and regularity of bilateral lower limb gait. This study aims to investigate the symmetry and regularity of bilateral lower limb gait and examine the balance control strategy of the head during walking in patients with idiopathic scoliosis. Methods: The study involved 17 patients with idiopathic scoliosis of Lenke 1 and Lenke 5 classifications, along with 17 healthy subjects for comparison. Three-dimensional accelerometers were attached to the head and L5 spinous process of each participant, and three-dimensional motion acceleration signals were collected during a 10-meter walking test. Analysis of the collected acceleration signals involved calculating five variables related to the symmetry and regularity of walking: root mean square (RMS) of the acceleration signal, harmonic ratio (HR), step regularity, stride regularity, and gait symmetry. Results: Our analysis reveals that, during the walking process, the three-dimensional motion acceleration signals acquired from the lumbar region of patients diagnosed with idiopathic scoliosis exhibit noteworthy disparities in the RMS of the vertical axis (RMS-VT) and the HR of the vertical axis (HR-VT) when compared to the corresponding values in the healthy control (RMS-VT: 1.6 ± 0.41 vs. 3 ± 0.47, P < 0.05; HR-VT: 3 ± 0.72 vs. 3.9 ± 0.71, P < 0.05). Additionally, the motion acceleration signals of the head in three-dimensional space, including the RMS in the anterior-posterior and vertical axis, the HR-VT, and the values of step regularity in both anterior-posterior and vertical axis, as well as the values of stride regularity in all three axes, are all significantly lower than those in the healthy control group (P < 0.05). Conclusion: The findings of the analysis suggest that the application of three-dimensional accelerometer sensors proves efficacious and convenient for scrutinizing the symmetry and regularity of walking in individuals with idiopathic scoliosis. Distinctive irregularities in gait symmetry and regularity manifest in patients with idiopathic scoliosis, particularly within the antero-posterior and vertical direction. Moreover, the dynamic balance control strategy of the head in three-dimensional space among patients with idiopathic scoliosis exhibits a relatively conservative nature when compared to healthy individuals.

Cortical intermittent theta burst stimulation on gait pathomechanics and urinary tract dysfunction in incomplete spinal cord injury patients: Protocol for a randomized controlled trial.

Gait impairment and neurogenic bladder are co-existing common findings in incomplete spinal cord injury (iSCI). Repetitive transcranial magnetic stimulation (rTMS), evident to be a promising strategy adjunct to physical rehabilitation to regain normal ambulation in SCI. However, there is a need to evaluate the role of Intermittent theta burst stimulation (iTBS), a type of patterned rTMS in restoring gait and neurogenic bladder in SCI patients. The aim of the present study is to quantify the effect of iTBS on spatiotemporal, kinetic, and kinematic parameters of gait and neurogenic bladder dyssynergia in iSCI. After maturing all exclusion and inclusion criteria, thirty iSCI patients will be randomly divided into three groups: Group-A (sham), Group-B (active rTMS) and Group-C (active iTBS). Each group will receive stimulation adjunct to physical rehabilitation for 2 weeks. All patients will undergo gait analysis, as well assessment of bladder, electrophysiological, neurological, functional, and psychosocial parameters. All parameters will be assessed at baseline and 6th week (1st follow-up). Parameters except urodynamics and gait analysis will also be assessed after the end of the 2 weeks of the intervention (post-intervention) and at 12th week (2nd follow-up). Appropriate statistical analysis will be done using various parametric and non-parametric tests based on results.