Gait Characteristics Research Articles

Influence of texting while walking on lower extremity gait function in young adults

Texting while walking (TWW) is a dual-task activity that young adults perform in their everyday lives. TWW has been reported to affect gait characteristics such as gait speed, stride length, and cadence. However, the influence of TWW on lower extremity gait function has not been investigated. Therefore, the purpose of this study was to quantify gait function by examining gait symmetry and using a time series analysis. Twenty-eight young adults (14 males, 14 females) walked at their preferred speed for 10 m as a baseline condition and a 10 m TWW task. Three-dimensional segment tracking was achieved utilizing a lower extremity and trunk marker set and the Model Statistic was used to test for statistical differences between the hip, knee, and ankle angular joint positions. The hip yielded the most asymmetries (25 out of 101 points) throughout the gait cycle, while asymmetries for the knee and ankle joints yielded 16 out of 101 points and 11 out of 101 points, respectively. The outcomes of this study suggest there are differences between baseline and TWW gait symmetry, however, the percentage of the gait cycle affected was less than 25 % - indicating gait function is not strongly influenced by texting while walking in young adults.

Fall risk prediction using temporal gait features and machine learning approaches.

Falls have been acknowledged as a major public health issue around the world. Early detection of fall risk is pivotal for preventive measures. Traditional clinical assessments, although reliable, are resource-intensive and may not always be feasible. This study explores the efficacy of artificial intelligence (AI) in predicting fall risk, leveraging gait analysis through computer vision and machine learning techniques. Data was collected using the Timed Up and Go (TUG) test and JHFRAT assessment from MMU collaborators and augmented with a public dataset from Mendeley involving older adults. The study introduces a robust approach for extracting and analyzing gait features, such as stride time, step time, cadence, and stance time, to distinguish between fallers and non-fallers. Two experimental setups were investigated: one considering separate gait features for each foot and another analyzing averaged features for both feet. Ultimately, the proposed solutions produce promising outcomes, greatly enhancing the model's ability to achieve high levels of accuracy. In particular, the LightGBM demonstrates a superior accuracy of 96% in the prediction task. The findings demonstrate that simple machine learning models can successfully identify individuals at higher fall risk based on gait characteristics, with promising results that could potentially streamline fall risk assessment processes. However, several limitations were discovered throughout the experiment, including an insufficient dataset and data variation, limiting the model's generalizability. These issues are raised for future work consideration. Overall, this research contributes to the growing body of knowledge on fall risk prediction and underscores the potential of AI in enhancing public health strategies through the early identification of at-risk individuals.

Mitigating Crouch Gait with an Autonomous Pediatric Knee Exoskeleton in the Neurologically Impaired.

Crouch gait is one of the most common compensatory walking patterns found in individuals with neurological disorders due to their limited physical capacity. Notable kinematic characteristics of crouch gait are excessive knee flexion during stance and reduced range of motion during swing. Knee exoskeletons have the potential to improve crouch gait by providing precisely controlled torque assistance directly to the knee joint. In this study, we implemented a finite-state machine-based impedance controller for a powered knee exoskeleton to provide assistance during both stance and swing for five children/young adults who exhibit chronic crouch gait. The assistance provided a strong orthotic effect, increasing stance phase knee extension by an average of 12°. The knee range of motion during swing was increased by an average of 15°. Changes to spatiotemporal outcomes, such as preferred walking speed and percent stance phase, were inconsistent across subjects and indicative of the underlying intricacies of user response to assistance. This study demonstrates the potential of knee exoskeletons operating in impedance control to mitigate the negative kinematic characteristics of crouch gait during both stance and swing phases of gait.

A step length estimation method based on frequency domain feature analysis and gait recognition for pedestrian dead reckoning

Purpose Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait. Design/methodology/approach The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds. Findings Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning. Originality/value A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

A Study of Assisted Screening for Alzheimer's Disease Based on Handwriting and Gait Analysis.

Alzheimer's disease (AD) is a progressive neurodegenerative disease that is not easily detected in the early stage. Handwriting and walking have been shown to be potential indicators of cognitive decline and are often affected by AD. This study proposes an assisted screening framework for AD based on multimodal analysis of handwriting and gait and explores whether using a combination of multiple modalities can improve the accuracy of single modality classification. We recruited 90 participants (38 AD patients and 52 healthy controls). The handwriting data was collected under four handwriting tasks using dot-matrix digital pens, and the gait data was collected using an electronic trail. The two kinds of features were fused as inputs for several different machine learning models (Logistic Regression, SVM, XGBoost, Adaboost, LightGBM), and the model performance was compared. The accuracy of each model ranged from 71.95% to 96.17%. Among them, the model constructed by LightGBM had the best performance, with an accuracy of 96.17%, sensitivity of 95.32%, specificity of 96.78%, PPV of 95.94%, NPV of 96.74%, and AUC of 0.991. However, the highest accuracy of a single modality was 93.53%, which was achieved by XGBoost in gait features. The research results show that the combination of handwriting features and gait features can achieve better classification results than a single modality. In addition, the assisted screening model proposed in this study can achieve effective classification of AD, which has development and application prospects.

MP-Gait: Fine-grained Parkinson's Disease Gait Impairment Assessment with Robust Feature Analysis

Patients with Parkinson's disease (PD) often show gait impairments including shuffling gait, festination, and lack of arm and leg coordination. Quantitative gait analysis can provide valuable insights for PD diagnosis and monitoring. Prior work has utilized 3D motion capture, foot pressure sensors, IMUs, etc. to assess the severity of gait impairment in PD patients These sensors, despite their high precision, are often expensive and cumbersome to wear which makes them not the best option for long-term monitoring and naturalistic deployment settings. In this paper, we introduce mP-Gait, a millimeter-wave (mmWave) radar-based system designed to detect the gait features in PD patients and predict the severity of their gait impairment. Leveraging the high frequency and wide bandwidth of mmWave radar signals, mP-Gait is able to capture high-resolution reflected signals from different body parts during walking. We develop a pipeline to detect walking, extract gait features using signal analysis methods, and predict patients' UPDRS-III gait scores with a machine learning model. As gait features from PD patients with gait impairment are correctly and robustly extracted, mP-Gait is able to observe the fine-grained gait impairment severity fluctuation caused by medication response. To evaluate mP-Gait, we collected gait features from 144 participants (with UPDRS-III gait scores between 0 and 2) containing over 4000 gait cycles. Our results show that mP-Gait can achieve a mean absolute error of 0.379 points in predicting UPDRS-III gait scores.

Abnormal lower limb posture recognition based on spatial gait feature dynamic threshold detection

Lower limb rehabilitation training often involves the use of assistive standing devices. However, elderly individuals frequently experience reduced exercise effectiveness or suffer muscle injuries when utilizing these devices. The ability to recognize abnormal lower limb postures can significantly enhance training efficiency and minimize the risk of injury. To address this, we propose a model based on dynamic threshold detection of spatial gait features to identify such abnormal postures. A human-assisted standing rehabilitation device platform was developed to build a lower limb gait depth dataset. RGB data is employed for keypoint detection, enabling the establishment of a 3D lower limb posture recognition model that extracts gait, time, spatial features, and keypoints. The predicted joint angles, stride length, and step frequency demonstrate errors of 4 %, 8 %, and 1.3 %, respectively, with an average confidence of 0.95 for 3D key points. We employed the WOA-BP neural network to develop a dynamic threshold algorithm based on gait features and propose a model for recognizing abnormal postures. Compared to other models, our model achieves a 96 % accuracy rate in recognizing abnormal postures, with a recall rate of 83 % and an F1 score of 90 %. ROC curve analysis and AUC values reveal that the WOA-BP algorithm performs farthest from the pure chance line, with the highest AUC value of 0.89, indicating its superior performance over other models. Experimental results demonstrate that this model possesses a strong capability in recognizing abnormal lower limb postures, encouraging patients to correct these postures, thereby reducing muscle injuries and improving exercise effectiveness.

Changes in rotator movement during early gait acquisition in after total hip arthroplasty

BackgroundIn walking in healthy adults, rotation of the hip joint affects stride length and shifts the center of gravity, but these are not seen in hip osteoarthritis which affects gait. In gait of total hip arthroplasty, there are few reports on changes in the horizontal plane. This study clarified the preoperative and early postoperative gait characteristics of patients undergoing total hip arthroplasty. MethodsThe analysis included 12 females who underwent initial total hip arthroplasty using a posterolateral approach. Gait was measured pre and postoperatively using a three-dimensional motion analysis device. Statistics were compared pre and postoperative range of motion, muscle strength, walking speed, stride length, gravity movement distance, trunk angle, hip joint angle, and joint moment. FindingsThe maximum hip abduction moment and trunk flexion angle to the surgical side were lower than in healthy subjects. The angle of internal rotation during the stance phase was significantly higher in the postoperative period. The distance of the center of gravity shift in the left and right directions was significantly decreased postoperatively. InterpretationGait disturbance was seen preoperatively and remained after surgery. Walking after total hip arthroplasty provides the hip joint rotates inward which is closer to normal walking. However, no change was observed in the external rotation moment of the hip joint during walking. We suspect that the invasiveness of the posterolateral approach of total hip arthroplasty affects the muscles for external rotation of the hip joint. This can also cause in gait disturbances.

GAIT-CKD (Gait Analysis using Artificial Intelligence for digital Therapeutics of patients with Chronic Kidney Disease): design and methods.

Digital therapeutics are emerging as treatments for diseases and disabilities. In chronic kidney disease (CKD), gait is a potential biomarker for health status and intervention effectiveness. This study aims to analyze gait characteristics in CKD patients, providing baseline data for digital therapeutics development. At baseline and after an 8-week intervention, we performed bioimpedance analysis measurements, the Timed Up and Go, Tinetti, and grip strength tests, and gait analysis in 217 healthy individuals and 276 patients with CKD. Demographic and clinical information was collected, including underlying diseases and medications, laboratory tests, and quality of life satisfaction surveys. Gait analysis was performed using skeleton data, which involved acquiring three-dimensional skeleton data of a walker using a single Kinect sensor. The performance of an artificial intelligence-based classification model in distinguishing between healthy individuals and those with CKD was then investigated. Simultaneously, inertia measurement unit analysis was conducted using measurements taken from the wrist and waist. Most subjects received a health intervention via an app, and their gait was assessed for improvements after an 8-week period. Incidents such as falls, fractures, hospitalizations, and deaths will be investigated in years 1 and 3. This study confirmed that the gaits of healthy individuals and CKD patients were different, and the effect of the 8-week app-based health intervention will be analyzed. The study will yield important baseline data for creating digital therapeutics for CKD patients' diet/exercise in the future.

GAIT CHANGES FOLLOWING CERAMIC-ON-CERAMIC HIP RESURFACING ARTHROPLASTY: A PROSPECTIVE STUDY

A ceramic-on-ceramic hip resurfacing implant (cHRA) was developed and introduced in an MHRA-approved clinical investigation to provide a non metallic alternative hip resurfacing product. This study aimed to examine function and physical activity levels of patients with a cHRA implant using subjective and objective measures both before and 12 months following surgery in comparison with age and gender matched healthy controls.Eighty-two unilateral cHRA patients consented to this study as part of a larger prospective, non-randomised, clinical investigation. In addition to their patient reported outcome measures (PROMs), self- reported measures of physical activity levels and gait analysis were undertaken both pre- operatively (1.5 weeks) and post operatively (52 weeks). This data was then compared to data from a group of 43 age gender and BMI matched group of healthy controls. Kinetics and kinematics were recorded using an instrumented treadmill and 3D Motion Capture. Statistical parametric mapping was used for analysis.cHRA improved the median Harris Hip Score from 63 to 100, Oxford Hip score from 27 to 48 and the MET from 5.7 to 10.3. cHRA improved top walking speed (5.75km vs 7.27km/hr), achieved a more symmetrical ground reaction force profile, (Symmetry Index value: 10.6% vs 0.9%) and increased hip range of motion (ROM) (31.7° vs 45.9°). Postoperative data was not statistically distinguishable from the healthy controls in any domain.This gait study sought to document the function of a novel ceramic hip resurfacing, using those features of gait commonly used to describe the shortcomings of hip arthroplasty. These features were captured before and 12 months following surgery. Preoperatively the gait patterns were typical for OA patients, while at 1 year postoperatively, this selected group of patients had gait patterns that were hard to distinguish from healthy controls despite an extended posterior approach. Applications for regulatory approval have been submitted.

Comparing the effectiveness of robotic plantarflexion resistance and biofeedback between overground and treadmill walking

Individuals with diminished walking performance caused by neuromuscular impairments often lack plantar flexion muscle activity. Robotic devices have been developed to address these issues and increase walking performance. While these devices have shown promise in their ability to increase musculature engagement of the lower limbs when used on a treadmill, most have not been developed or validated for overground walking and community use. Overground walking may limit the effectiveness of robotic devices due to differences in gait characteristics between walking terrains and reduced user engagement. The purpose of this study was to validate our multimodal robotic gait training system for overground walking in individuals with neuromuscular gait impairments. This untethered wearable robotic device can provide an ankle resistive torque proportional to the users’ biological ankle torque. The device can also provide audio biofeedback based on users’ plantar pressure intending to increase ankle power and muscle activity of the plantar flexors. Seven individuals with cerebral palsy participated. Participants walked overground and on a treadmill with our robotic gait training system in a single testing session. Results showed all seven participants to increase peak plantar flexor muscle activity, 10.3% on average, when walking with the gait trainer overground compared to treadmill. When compared to typical baseline overground walking, overground gait trainer use caused individuals to have slightly less knee joint excursion (3°) and moderately more ankle joint excursion (7°). This work supports our vision of using the wearable robotic device as a gait aid and rehabilitation tool in the home and community settings.

Frontoparietal-Striatal Network and Nucleus Basalis Modulation in Patients With Parkinson Disease and Gait Disturbance.

Neural computations underlying gait disorders in Parkinson disease (PD) are multifactorial and involve impaired expression of stereotactic locomotor patterns and compensatory recruitment of cognitive functions. This study aimed to clarify the network mechanisms of cognitive contribution to gait control and its breakdown in patients with PD. Patients with PD were instructed to walk at a comfortable pace on a mat with pressure sensors. The characterization of cognitive-motor interplay was enhanced by using a gait with a secondary cognitive task (dual-task condition) and a gait without additional tasks (single-task condition). Participants were scanned using 3-T MRI and 123I-ioflupane SPECT. According to gait characteristics, cluster analysis assisted by a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding, categorized 56 patients with PD into 3 subpopulations. The preserved gait (PG) subgroup (n = 23) showed preserved speed and variability during gait, both with and without additional cognitive load. Compared with the PG subgroup, the mildly impaired gait (MIG) subgroup (n = 16) demonstrated deteriorated gait variability with additional cognitive load and impaired speed and gait variability without additional cognitive load. The severely impaired gait (SIG) subgroup (n = 17) revealed the slowest speed and highest gait variability. In addition, group differences were found in attention/working memory and executive function domains, with the lowest performance in the SIG subgroup than in the PG and MIG subgroups. Using resting-state functional MRI, the SIG subgroup demonstrated lower functional connectivity of the left and right frontoparietal network (FPN) with the caudate than the PG subgroup did (left FPN, d = 1.21, p < 0.001; right FPN, d = 1.05, p = 0.004). Cortical thickness in the FPN and 123I-ioflupane uptake in the striatum did not differ among the 3 subgroups. By contrast, the severity of Ch4 density loss was significantly correlated with the level of functional connectivity degradation of the FPN and caudate (left FPN-caudate, r = 0.27, p = 0.04). These findings suggest that the functional connectivity of the FPN with the caudate, as mediated by the cholinergic Ch4 projection system, underlies the compensatory recruitment of attention and executive function for damaged automaticity in gait in patients with PD.

Absolute and Relative Reliability of Spatiotemporal Gait Characteristics Extracted from an Inertial Measurement Unit among Senior Adults Using a Passive Hip Exoskeleton: A Test-Retest Study.

Seniors wearing a passive hip exoskeleton (Exo) show increased walking speed and step length but reduced cadence. We assessed the test-retest reliability of seniors' gait characteristics with Exo. Twenty seniors walked with and without Exo (noExo) on a 10 m indoor track over two sessions separated by one week. Speed, step length, cadence and step time variability were extracted from one inertial measurement unit (IMU) placed over the L5 vertebra. Relative and absolute reliability were assessed using the intraclass correlation coefficient (ICC), standard error of measurement (SEM) and minimal detectable change (MDC). The relative reliability of speed, step length, cadence and step time variability ranged from "almost perfect to substantial" for Exo and noExo with ICC values between 0.75 and 0.87 and 0.60 and 0.92, respectively. The SEM and MDC values for speed, step length cadence and step time variability during Exo and noExo were <0.002 and <0.006 m/s, <0.002 and <0.005 m, <0.30 and <0.83 steps/min and <0.38 s and <1.06 s, respectively. The high test-retest reliability of speed, step length and cadence estimated from IMU suggest a robust extraction of spatiotemporal gait characteristics during exoskeleton use. These findings indicate that IMUs can be used to assess the effects of wearing an exoskeleton on seniors, thus offering the possibility of conducting longitudinal studies.

In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification

This study provides an in-depth analysis of GAF-Net, a novel model for video-based person re-identification (Re-ID) that matches individuals across different video sequences. GAF-Net combines appearance-based features with gait-based features derived from skeletal data, offering a new approach that diverges from traditional silhouette-based methods. We thoroughly examine each module of GAF-Net and explore various fusion methods at the both score and feature levels, extending beyond initial simple concatenation. Comprehensive evaluations on the iLIDS-VID and MARS datasets demonstrate GAF-Net’s effectiveness across scenarios. GAF-Net achieves state-of-the-art 93.2% rank-1 accuracy on iLIDS-VID’s long sequences, while MARS results (86.09% mAP, 89.78% rank-1) reveal challenges with shorter, variable sequences in complex real-world settings. We demonstrate that integrating skeleton-based gait features consistently improves Re-ID performance, particularly with long, more informative sequences. This research provides crucial insights into multi-modal feature integration in Re-ID tasks, laying a foundation for the advancement of multi-modal biometric systems for diverse computer vision applications.

Self-Powered Wearable Pressure Sensor System for Smart Healthcare Applications

Wearable sensors mark a groundbreaking advancement in the realm of health monitoring technology. However, traditional wearable devices lack comprehensive solutions for tracking sports exercise behaviors and providing feedback on rehabilitation for those undergoing home-based recovery. To bridge this gap, a self-powered triboelectric nanogenerator (TENG)-based wearable sensing system has been developed with in-house monitoring capabilities. The wearable sensor system is composed of triboelectric pressure sensors with an electro-conductive silicone sheet and patterned Ecoflex film as the contact layers. Highly sensitive TENG sensors have been successfully embedded into a bicycle saddle and human shoe insole for real-time pressure monitoring. Experimental results demonstrate that biomechanical energy, originating from both the pelvic-saddle interactions on bicycle and human gait movements, has been successfully converted into electrical energy. The generated sensor voltage remains stable under frequency variations and external environmental conditions, such as temperature and humidity variations. Utilizing distinctive information contained in individual body mass imbalances and gait characteristics, a smart healthcare monitoring system is envisioned to extract essential biomechanical insights via artificial intelligence, facilitating a pivotal role in in-house monitoring and tracking of sports rehabilitation exercises. The as-designed low-cost, highly scalable self-powered wearable sensor system offers an accessible, user-friendly point-of-care solution for gait detection and rehabilitation monitoring for individuals with various physical impairments, with considerable potential for commercialization. Figure 1

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.

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 18months for unilateral or bilateral hip developmental dysplasia (DDH). 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. Stance time was significantly shorter in both the unilateral (median [IQR]; 590ms, [560.0-612.5] and bilateral (575ms, [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). 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.

The correlation of gait and muscle activation characteristics with locomotion dysfunction grade in elderly individuals.

To investigate the differences and regularity of gait and muscle activation characteristics parameters in the Locomotion Dysfunction Grade (LDG) scale assessment in elderly individuals, and analyse the correlation between objective parameters and scale grading. Thus, to propose a novel detection mode for elderly individuals, which combined the LDG scale with objective detection. It can not only provide quantitative data for intelligent evaluation and rehabilitation, but also provided more accurate reference for the classification of care levels in elderly care policies. Elderly individuals (n = 159) who underwent gait analysis and sEMG at the Chinese Rehabilitation Research Center from January 2019 to September 2023 were included. According to the LDG scale, the elderly individuals were divided into four groups, namely, the LDG4, LDG5, LDG6 groups and the healthy control group. Four indicators, namely, spatiotemporal, kinematic, dynamic gait parameters and muscle activation characteristics data, were collected. Changes in these characteristics of elderly individuals with lower extremity motor dysfunction were evaluated and analysed statistically. The spatiotemporal gait parameters were significantly lower in the LDG4, LDG5, LDG6 groups than in the healthy control group. The double support phase was positively correlated with the LDG, while the swing phase, step length and velocity were negatively correlated (P < 0.05). The movement angles of both hips, knees and ankles were significantly limited and negatively correlated with the LDG (P < 0.05). Compared with those in the healthy control group, the centre of pressure (COP) path length were greater, and the average COP velocity was significantly lower (P < 0.05) in the LDG4, LDG5, LDG6 groups. The regularity of muscle activation clearly changed. The root mean square of the gastrocnemius medialis was positively correlated with LDG (P < 0.05), while the tibialis anterior showed no regularity. As the LDG increased, the differences in spatiotemporal, kinematic and dynamic gait parameters between elderly individuals with motor dysfunction and the healthy individuals gradually increased. The muscle activation characteristics parameters showed an abnormal activation pattern. These parameters were correlated with the LDG, providing a more comprehensive and objective assessment of lower extremity motor function in elderly individuals, improve assessment accuracy, and help accurate rehabilitation.

Development of an aid to detect adults acetabular hip dysplasia (the ALPHA alert): A mixed methods study

ObjectivesTo identify the signs and symptoms that people living with acetabular hip dysplasia (AHD) describe and to provide an aid for translating the findings into practice. MethodsA three-phased mixed methods study. Phase 1 employed an open-question online survey that enabled people with AHD (aged ≥16 years) to describe features associated with their condition. Responses were thematically analysed. A Phase 2 survey used these themes to establish how common those features were. Phase 3 created a mnemonic that prompts clinicians to suspect AHD. ResultsNinety-eight respondents completed Phase 1 and sixty-two completed Phase 2. From the responses, five themes were identified: Demographic and Diagnostic Profile; Characteristics of Posture and Gait; Pain; Childhood Hip and Family History; and Hip Joint Characteristics. Within these themes, 19 common signs and symptoms were reported, represented by the ALPHA mnemonic. ALPHA describes a young age at onset of problems (Age), a limp (Limp), progressing pain (Pain), a history of childhood and family hip anomalies (History) as well as hip joint hypermobility and instability (Articulation). ConclusionThe findings extend current understanding of AHD indicators. ALPHA alerts clinicians to suspect a diagnosis of AHD. ALPHA may facilitate timelier referral of patients for diagnostic X-Ray and appropriate treatment. Future studies should evaluate its clinical utility.

A Pragmatic Review on Spinal Cord Stimulation Therapy for Parkinson's Disease Gait Related Disorders: Gaps and Controversies.

Parkinson's Disease (PD) is a progressive neurological disorder that results in potentially debilitating mobility deficits. Recently, spinal cord stimulation (SCS) has been proposed as a novel therapy for PD gait disorders. The highest levels of evidence remain limited for SCS. In this systematic review and narrative synthesis, the literature was searched using combinations of key phrases indicating spinal cord stimulation and PD. We included pre-clinical studies and all published clinical trials, case reports, conference abstracts as well as protocols for ongoing clinical trials. Additionally, we included trials of SCS applied to atypical parkinsonism. A total of 45 human studies and trials met the inclusion criteria. Based on the narrative synthesis, a number of knowledge gaps and future avenues of potential research were identified. This review demonstrated that evidence for SCS is currently not sufficient to recommend it as an evidence-based therapy for PD related gait disorders. There remain challenges and significant barriers to widespread implementation, including issues regarding patient selection, effective outcome selection, stimulation location and mode, and in programming parameter optimization. Results of early randomized controlled trials are currently pending. SCS is prone to placebo, lessebo and nocebo as well as blinding effects which may impact interpretation of outcomes, particularly when studies are underpowered. Therapies such as SCS may build on current evidence and be shown to improve specific gait features in PD. Early negative trials should be interpreted with caution, as more evidence will be required to develop effective methodologies in order to drive clinical outcomes.

The Time Course of Changes in Prefrontal Cortex Activity During Walking in People With Parkinson’s Disease

Background Walking abnormalities in people with Parkinson’s disease (PD) are characterized by a shift in locomotor control from healthy automaticity to compensatory, executive control, mainly located in the prefrontal cortex (PFC). Although PFC activity during walking increases in people with PD, the time course of PFC activity during walking and its relationship to clinical or gait characteristics is unknown. Objective To identify the time course of PFC activity during walking in people with PD. To investigate whether clinical or gait variables would explain the PFC activity changes. Methods Thirty-eight people with PD tested OFF medication wore a portable, functional near-infrared spectroscopy (fNIRS) system to record relative PFC activity while walking. Wearable inertial sensors recorded spatiotemporal gait characteristics. Based on the PFC activity (fNIRS) in the late phase of the walking task (final 40 seconds), compared to the early phase (initial 40 seconds), participants were separated into 2 groups: reduced or sustained PFC activity. Results People with PD who reduced PFC activity during walking had less impaired gait (eg, faster gait speed) than those who had a sustained increase in PFC activity (P < .05). Cognitive set-shifting ability explained 18% of the PFC activation in the group with a sustained increase in PFC activity (P = .033). Conclusions The time course of reduction in PFC activity corresponds to less impaired gait performance in people with PD, while a sustained increase in PFC activity is related to worse cognitive flexibility. Reduction in PFC activity while walking may indicate a less impaired, automatic control of walking.