Gait classification for growing children with Duchenne muscular dystrophy.

Classifying gait patterns into homogeneous groups could enhance communication among healthcare providers, clinical decision making and clinical trial designs in boys with Duchenne muscular dystrophy (DMD). Sutherland's classification has been developed 40 years ago. Ever since, the state-of-the-art medical care has improved and boys with DMD are now longer ambulatory. Therefore, the gait classification requires an update. The overall aim was to develop an up-to-date, valid DMD gait classification. A total of 137 three-dimensional gait analysis sessions were collected in 30 boys with DMD, aged 4.6-17 years. Three classes were distinguished, which only partly aligned with increasing severity of gait deviations. Apart from the mildly affected pattern, two more severely affected gait patterns were found, namely the tiptoeing pattern and the flexion pattern with distinct anterior pelvic tilt and posterior trunk leaning, which showed most severe deviations at the ankle or at the proximal segments/joints, respectively. The agreement between Sutherland's and the current classification was low, suggesting that gait pathology with the current state-of-the-art medical care has changed. However, overlap between classes, especially between the two more affected classes, highlights the complexity of the continuous gait changes. Therefore, caution is required when classifying individual boys with DMD into classes.

Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study.

Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3-90.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of <1%). Individual gait patterns during real-world running, as characterized by consumer-grade wearable sensors, are not well-represented by the same runner's in-lab data. Researchers and clinicians should consider "borrowing" information from a pool of many runners to predict individual gait behavior when using biomechanical data to make clinical or sports performance decisions.

Gait Pattern Analysis: Integration of a Highly Sensitive Flexible Pressure Sensor on a Wireless Instrumented Insole.

Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual's gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric layer placed between screen-printed silver electrodes. The sensor demonstrated inherent stretchability and durability, even when the electrode was bent at a 45-degree angle, it maintained an electrode resistance of approximately 3 Ω. This feature is particularly advantageous for gait monitoring applications. Furthermore, the fabricated flexible capacitive pressure sensor exhibited higher sensitivity and linearity at both low and high pressure and displayed very good stability. Notably, the sensors demonstrated rapid response and recovery times for both under low and high pressure. To further explore the capabilities of these new sensors, they were successfully tested as insole-type pressure sensors for real-time gait signal monitoring. The sensors displayed a well-balanced combination of sensitivity and response time, making them well-suited for gait analysis. Beyond gait analysis, the proposed sensor holds the potential for a wide range of applications within biomedical, sports, and commercial systems where soft and conformable sensors are preferred.

Ambient floor vibration sensing advances the accessibility of functional gait assessments for children with muscular dystrophies.

Muscular dystrophies (MD) are a group of genetic neuromuscular disorders that cause progressive weakness and loss of muscles over time, influencing 1 in 3500-5000 children worldwide. New and exciting treatment options have led to a critical need for a clinical post-marketing surveillance tool to confirm the efficacy and safety of these treatments after individuals receive them in a commercial setting. For MDs, functional gait assessment is a common approach to evaluate the efficacy of the treatments because muscle weakness is reflected in individuals' walking patterns. However, there is little incentive for the family to continue to travel for such assessments due to the lack of access to specialty centers. While various existing sensing devices, such as cameras, force plates, and wearables can assess gait at home, they are limited by privacy concerns, area of coverage, and discomfort in carrying devices, which is not practical for long-term, continuous monitoring in daily settings. In this study, we introduce a novel functional gait assessment system using ambient floor vibrations, which is non-invasive and scalable, requiring only low-cost and sparsely deployed geophone sensors attached to the floor surface, suitable for in-home usage. Our system captures floor vibrations generated by footsteps from patients while they walk around and analyzes such vibrations to extract essential gait health information. To enhance interpretability and reliability under various sensing scenarios, we translate the signal patterns of floor vibration to pathological gait patterns related to MD, and develop a hierarchical learning algorithm that aggregates insights from individual footsteps to estimate a person's overall gait performance. When evaluated through real-world experiments with 36 subjects (including 15 patients with MD), our floor vibration sensing system achieves a 94.8% accuracy in predicting functional gait stages for patients with MD. Our approach enables accurate, accessible, and scalable functional gait assessment, bringing MD progressive tracking into real life.

The Analytical Validity of Stride Detection and Gait Parameters Reconstruction Using the Ankle-Mounted Inertial Measurement Unit Syde®.

The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in comparison to a reference motion capture system. Twelve healthy subjects (age: 23.17 ± 2.04, height: 174.17 ± 6.46 cm) participated in a controlled environment data collection and performed a series of gait tasks with both systems attached to each ankle. A total of 2820 strides were analyzed. The results show a median absolute stride length error of 1.86 cm between the IMU-based wearable device reconstruction and the motion capture ground truth, with the 75th percentile at 3.24 cm. The median absolute stride horizontal velocity error was 1.56 cm/s, with the 75th percentile at 2.63 cm/s. With a measurement error to the reference system of less than 3 cm, we conclude that there is a valid physical recovery of stride length and horizontal velocity from data collected with the IMU-based wearable Syde® device.

Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations.

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

Validity Verification of Human Pose-Tracking Algorithms for Gait Analysis Capability.

Two-dimensional (2D) clinical gait analysis systems are more affordable and portable than contemporary three-dimensional (3D) clinical models. Using the Vicon 3D motion capture system as the standard, we evaluated the internal statistics of the Imasen and open-source OpenPose gait measurement systems, both designed for 2D input, to validate their output based on the similarity of results and the legitimacy of their inner statistical processes. We measured time factors, distance factors, and joint angles of the hip and knee joints in the sagittal plane while varying speeds and gaits during level walking in three in-person walking experiments under normal, maximum-speed, and tandem scenarios. The intraclass correlation coefficients of the 2D models were greater than 0.769 for all gait parameters compared with those of Vicon, except for some knee joint angles. The relative agreement was excellent for the time-distance gait parameter and moderate-to-excellent for each gait motion contraction range, except for hip joint angles. The time-distance gait parameter was high for Cronbach's alpha coefficients of 0.899-0.993 but low for 0.298-0.971. Correlation coefficients were greater than 0.571 for time-distance gait parameters but lower for joint angle parameters, particularly hip joint angles. Our study elucidates areas in which to improve 2D models for their widespread clinical application.

Effects on gait kinematics, pedobarography, functional and subjective results after isolated chopart injury.

BACKGROUND: This study analysed changes in gait and pedobarography and subjective and functional outcomes after isolated Chopart joint injury. METHODS: The results of 14 patients were reviewed. Kinematic 3D gait analysis, comparative bilateral electromyography (EMG) and pedobarography were performed. RESULTS: On the injured side, the 3D gait analysis showed a significantly increased internal rotation and decreased external rotation of the hip and significantly decreased adduction and decreased range of motion (ROM) for the ankle. On the healthy side, the pedobarography revealed a significantly increased mean force in the forefoot, an increased peak maximum force and an increased maximum pressure in the metatarsal. When standing, significantly more weight was placed on the healthy side. The EMG measurements showed no significant differences between the healthy and injured legs. CONCLUSIONS: After isolated Chopart injuries, significant changes in gait and pedobarography can be seen over the long term.

Markerless motion capture provides repeatable gait outcomes in patients with knee osteoarthritis.

Motion analysis has seen minimal adoption for orthopaedic clinical assessments. Markerless motion capture solutions, namely Theia3D, address limitations of previous methods and provide gait outcomes that are robust to clothing choice and repeatable in healthy adults. Repeatability in orthopaedic populations has not been investigated and is important for clinical utility and adoption. The purpose of this study was to evaluate the repeatability of Theia3D for gait analysis in a knee osteoarthritis population. Ten orthopaedic patients with knee osteoarthritis underwent gait analysis on three visits, with an average of 8 days between. Participants were recorded during one-minute overground walking trials at self-selected typical and fast speeds by 8 synchronized video cameras. Video data were processed using Theia3D. Intraclass correlations were used to examine the repeatability of temporal distance metrics as well as segment lengths of the underlying kinematic model. Inter-trial and inter-session variability of lower extremity joint angles were estimated for each point of the gait cycle. Intraclass correlations were greater than 0.98 for all temporal distance metrics for both speeds. Lower body segment lengths had intraclass correlations above 0.90. Participant average joint angle waveforms displayed consistent patterns between visits. The average inter-trial and inter-session variability in joint angles across speeds were 1.17 and 1.45 degrees, respectively. The variability in joint angles between visits was less than typically reported for marker-based methods. Gait outcomes measured with Theia3D were highly repeatable in patients with knee osteoarthritis providing further validation for its use in clinical assessment and longitudinal studies.

Creating an autoencoder single summary metric to assess gait quality to compare surgical outcomes in children with cerebral palsy: The Shriners Gait Index (SGI).

Gait for individuals with movement disorders varies widely and the variability makes it difficult to assess outcomes of surgical and therapeutic interventions. Although specific joints can be assessed by fewer individual measures, gait depends on multiple parameters making an overall assessment metric difficult to determine. A holistic, summary measure can permit a standard comparison of progress throughout treatments and interventions, and permit more straightforward comparison across varied subjects. We propose a single summary metric (the Shriners Gait Index (SGI)) to represent the quality of gait using a deep learning autoencoder model, which helps to capture the nonlinear statistical relationships among a number of disparate gait metrics. We utilized gait data of 412 individuals under the age of 18 collected from the Motion Analysis Center (MAC) at the Shriners Children's - Chicago. The gait data includes a total of 114 features: temporo-spatial parameters (7), lower extremity kinematics (64), and lower extremity kinetics (43) which were min-max normalized. The developed SGI score captured more than 89% variance of all 144 features using subject-wise cross-validation. Such summary metrics holistically quantify an individual's gait which can then be used to assess the impact of therapeutic interventions. The machine learning approach utilized can be leveraged to create such metrics in a variety of contexts depending on the data available. We also utilized the SGI to compare overall changes to gait after surgery with the goal of improving mobility for individuals with gait disabilities such as Cerebral Palsy.

Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors.

BACKGROUND AND OBJECTIVES: Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored. METHODS: We analyzed the gait pattern related to high CSVD burden from 720 participants (aged 55-65 years, 42.5 % male) free of neurological disease in the Taizhou Imaging Study. All participants underwent detailed quantitative gait assessments (obtained from an insole-like wearable gait tracking device) and brain magnetic resonance imaging examinations. Thirty-three gait parameters were summarized into five gait domains. Sparse sliced inverse regression was developed to extract the gait pattern related to high CSVD burden. RESULTS: The specific gait pattern derived from several gait domains (i.e., angles, phases, variability, and spatio-temporal) was significantly associated with the CSVD burden (OR=1.250, 95 % CI: 1.011-1.546). The gait pattern indicates that people with a high CSVD burden were prone to have smaller gait angles, more stance time, more double support time, larger gait variability, and slower gait velocity. Furthermore, people with this gait pattern had a 25 % higher risk of a high CSVD burden. CONCLUSIONS: We established a more stable and disease-specific quantitative gait pattern related to high CSVD burden, which is prone to facilitate the identification of individuals with high CSVD burden among the community residents or the general population.

[A study of cognitive impairment quantitative assessment method based on gait characteristics].

Alzheimer's disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject's MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD in the future.

BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment.

Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait. This study introduces a novel enhancement to the low-powered BlazePose algorithm by incorporating a Seq2seq autoencoder deep learning model. To ensure data accuracy and reliability, synchronized motion capture involving an RGB camera and ten Vicon cameras were employed across three distinct self-selected walking speeds. This investigation presents a groundbreaking avenue for remote gait assessment, harnessing the potential of Seq2seq architectures inspired by natural language processing (NLP) to enhance pose estimation accuracy. When comparing BlazePose alone to the combination of BlazePose and 1D convolution Long Short-term Memory Network (1D-LSTM), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the average mean absolute errors decreased from 13.4° to 5.3° for fast gait, from 16.3° to 7.5° for normal gait, and from 15.5° to 7.5° for slow gait at the left ankle joint angle respectively. The strategic utilization of synchronized data and rigorous testing methodologies further bolsters the robustness and credibility of these findings.

The impact of anxiety on gait impairments in Parkinson's disease: insights from sensor-based gait analysis.

BACKGROUND: Sensor-based gait analysis provides a robust quantitative tool for assessing gait impairments and their associated factors in Parkinson's disease (PD). Anxiety is observed to interfere with gait clinically, but this has been poorly investigated. Our purpose is to utilize gait analysis to uncover the effect of anxiety on gait in patients with PD. METHODS: We enrolled 38 and 106 PD patients with and without anxiety, respectively. Gait parameters were quantitively examined and compared between two groups both in single-task (ST) and dual-task (DT) walking tests. Multiple linear regression was applied to evaluate whether anxiety independently contributed to gait impairments. RESULTS: During ST, PD patients with anxiety presented significantly shorter stride length, lower gait velocity, longer stride time and stance time, longer stance phase, smaller toe-off (TO) and heel-strike (HS) angles than those without anxiety. While under DT status, the differences were diminished. Multiple linear regression analysis demonstrated that anxiety was an independent factor to a serials of gait parameters, particularly ST-TO (B = -2.599, (-4.82, -0.38)), ST-HS (B = -2.532, (-4.71, -0.35)), ST-TO-CV (B = 4.627, (1.71, 7.64)), ST-HS-CV(B = 4.597, (1.66, 7.53)), ST stance phase (B = 1.4, (0.22, 2.58)), and DT stance phase (B = 1.749, (0.56, 2.94)). CONCLUSION: Our study discovered that anxiety has a significant impact on gait impairments in PD patients, especially exacerbating shuffling steps and prolonging stance phase. These findings highlight the importance of addressing anxiety in PD precision therapy to achieve better treatment outcomes.

Markerless gait analysis through a single camera and computer vision.

The assessment of gait performance using quantitative measures can yield crucial insights into an individual's health status. Recently, computer vision-based human pose estimation has emerged as a promising solution for markerless gait analysis, as it allows for the direct extraction of gait parameters from videos. This study aimed to compare the lower extremity kinematics and spatiotemporal gait parameters obtained from a single-camera-based markerless method with those acquired from a marker-based motion tracking system across a healthy population. Additionally, we investigated the impact of camera viewing angles and distances on the accuracy of the markerless method. Our findings demonstrated a robust correlation and agreement (Rxy > 0.75, Rc > 0.7) between the markerless and marker-based methods for most spatiotemporal gait parameters. We also observed strong correlations (Rxy > 0.8) between the two methods for hip flexion/extension, knee flexion/extension, hip abduction/adduction, and hip internal/external rotation. Statistical tests revealed significant effects of viewing angles and distances on the accuracy of the identified gait parameters. While the markerless method offers an alternative for general gait analysis, particularly when marker use is impractical, its accuracy for clinical applications remains insufficient and requires substantial improvement. Future investigations should explore the potential of the markerless system to measure gait parameters in pathological gaits.

Force-sensing treadmill gait analysis system can detect gait abnormalities in haemophilia patients without arthropathy.

BACKGROUND: Joint damage in patients with haemophilia (PwH) is commonly assessed by imaging, but few reports have described how structural changes in joints, for example, haemophilic arthropathy (HA)-affect gait ability. OBJECTIVES: We evaluated gait function among PwH with HA, PwH without HA, and people without haemophilia (non-PwH) using a Zebris FDM-T treadmill (FDM-T), an easy-to-use gait assessment instrument with a force sensor matrix. METHODS: The following gait parameters were collected: centre of pressure trajectory intersection (COPi) anterior/posterior variability, COPi lateral variability, COPi anterior/posterior symmetry, COPi lateral symmetry, single-limb support line (SLSL) length, and SLSL variability. Participants walked at their typical gait speed. The physical function of the PwH was assessed by the Hemophilia Joint Health Score (HJHS). Parameters were compared among the three groups. RESULTS: Twelve PwH with HA, 28 PwH without HA, and 12 non-PwH were enrolled. Gait speed significantly differed between groups (non-PwH, 3.1 ± 0.7; PwH without HA, 2.0 ± 0.7; PwH with HA; 1.5 ± 0.4). The COPi anterior/posterior variability, COPi lateral variability, SLSL length, and SLSL variability were greater in the PwH groups than in the non-PwH group. The COPi lateral symmetry differed between PwH with HA and the other groups. The HJHS was not correlated with gait parameters among PwH with HA. CONCLUSIONS: Gait parameters and speed were abnormal in both PwH with HA and PwH without HA. The FDM-T can be used to identify early stages of physical dysfunction that cannot be detected by conventional functional assessments such as the HJHS.

Population longitudinal analysis of Gait Profile Score and North Star Ambulatory Assessment in children with Duchenne muscular dystrophy.

Duchenne muscular dystrophy (DMD) is a rare X-linked recessive disorder characterized by loss-of-function mutations in the gene encoding dystrophin. These mutations lead to progressive functional deterioration including muscle weakness, respiratory insufficiency, and musculoskeletal deformities. Three-dimensional gait analysis (3DGA) has been used as a tool to analyze gait pathology through the quantification of altered joint kinematics, kinetics, and muscle activity patterns. Among 3DGA indices, the Gait Profile Score (GPS), has been used as a sensitive overall measure to detect clinically relevant changes in gait patterns in children with DMD. To enhance our understanding of the clinical translation of 3DGA, we report here the development of a population nonlinear mixed-effect model that jointly describes the disease progression of the 3DGA index, GPS, and the functional endpoint, North Star Ambulatory Assessment (NSAA). The final model consists of a quadratic structure for GPS progression and a linear structure for GPS-NSAA correlation. Our model was able to capture the improvement in function in GPS and NSAA in younger subjects, as well as the decline of function in older subjects. Furthermore, the model predicted NSAA (CFB) at 1 year reasonably well for DMD subjects ≤7 years old at baseline. The model tended to slightly underpredict the decline in NSAA after 1 year for those >7 years old at baseline, but the prediction summary statistics were well maintained within the standard deviation of observed data. Quantitative models such as this may help answer clinically relevant questions to facilitate the development of novel therapies in DMD.

Update on the reliability of gait analysis interpretation in cerebral palsy: Inter-institution agreement.

BACKGROUND: Studies have shown good reliability for gait analysis interpretation among surgeons from the same institution. However, reliability among surgeons from different institutions remains to be determined. RESEARCH QUESTION: Is gait analysis interpretation by surgeons from different institutions as reliable as it is for surgeons from the same institution? METHODS: Gait analysis data for 67 patients with cerebral palsy (CP) were reviewed prospectively by two orthopedic surgeons from different institutions in the same state, each with > 10 years' experience interpreting gait analysis data. The surgeons identified gait problems and made treatment recommendations for each patient using a rating form. Percent agreement between raters was calculated for each problem and treatment, and compared to expected agreement based on chance using Cohen's kappa. RESULTS: For problem identification, the greatest agreement was seen for equinus (85% agreement), calcaneus (88%), in-toeing (89%), and out-toeing (90%). Agreement for the remaining problems ranged between 66-78%. Percent agreement was significantly higher than expected due to chance for all issues (p ≤ 0.01) with modest kappa values ranging from 0.12 to 0.51. Agreement between surgeons for treatment recommendations was highest for triceps surae lengthening (89% agreement), tibial derotation osteotomy (90%), and foot osteotomy (87%). Agreement for the remaining treatments ranged between 72-78%. Percent agreement for all treatments was significantly higher than the expected values (p ≤ 0.002) with modest kappa values ranging from 0.22 to 0.52. SIGNIFICANCE: Previous research established that computerized gait analysis data interpretation is reliable for surgeons within a single institution. The current study demonstrates that gait analysis interpretation can also be reliable among surgeons from different institutions. Future research should examine reliability among physicians from more institutions to confirm these results.

Development and External Validation of a Gait Test Based Diagnostic Model for Detecting Mild Cognitive Impairment.

OBJECTIVE: To address the lack of large-scale screening tools for mild cognitive impairment (MCI), this study aimed to assess the discriminatory ability of several gait tests for MCI and develop a screening tool based on gait test for MCI. DESIGN: A diagnostic case-control test. SETTING: The general community. PARTICIPANTS: We recruited 134 older adults (≥65 years) for the derivation sample, comprising -69 individuals in the cognitively normal group and -65 in the MCI group (N=134). An additional 70 participants were enrolled for the validation sample. INTERVENTIONS: All participants completed gait tests consisting of a single task (ST) and 3 dual tasks (DTs): counting backwards, serial subtractions 7, and naming animals. MAIN OUTCOME MEASURES: Binary logistic regression analyses were used to develop models, and the efficacy of each model was assessed using receiver operating characteristic (ROC) curve and area under the curve (AUC). The best effective model was the final diagnostic model and validated using ROC curve and calibration curve. RESULTS: The DT gait test incorporating serial subtractions 7 as the cognitive task demonstrated the highest efficacy with the AUC of 0.906 and the accuracy of 0.831 in detecting MCI with "years of education" being adjusted. Furthermore, the model exhibited consistent performance across different age and sex groups. In external validation, the model displayed robust discrimination (AUC=0.913) and calibration (calibrated intercept=-0.062, slope=1.039). CONCLUSIONS: The DT gait test incorporating serial subtractions 7 as the cognitive task demonstrated robust discriminate ability for MCI. This test holds the potential to serve as a large-scale screening tool for MCI, aids in the early detection and intervention of cognitive impairment in older adults.

The effects of modified hardinge approach on hip muscle strength in patients with primary hip arthroplasty: a patient evaluation with isokinetic strength test and gait analyses.

OBJECTIVE: Numerous factors affect abductor strength after Total hip arthroplasty (THA), including surgical technique, prosthesis type, postoperative rehabilitation program, and preoperative patient condition. We prospectively investigated the effects of the modified Hardinge approach on hip muscle strength, which was evaluated using the isokinetic test, functional results, and gait function of patients who underwent primary THA. METHODS: The hip muscles strength were measured using an isokinetic dynamometer. The primary outcomes of the present study were measurement of isokinetic strength of hip abductor muscle strengths using an isokinetic evaluator and gait analyses preoperatively and at 6 months postoperatively in 27 patients. RESULTS: Isokinetic muscle strength test, abductor and other hip circumference groups achieved the preoperative muscle strength at 3 months postoperatively, and the postoperative sixth month values showed a statistically significant improvement compared with the preoperative and third month values. In gait analyze, our temporospatial data showed a slight regression at postoperative 3 months but reached the same values at 6 months postoperatively. Kinematic data showed a significant regression, but the data were not compared with those in the preoperative period. CONCLUSIONS: Adequate muscle strength and physiological gait pattern, similar to the preoperative status, can be achieved at 6 months postoperatively. LEVEL OF EVIDENCE: Level III.