Gait Characteristics Research Articles

Personalized Auditory Rhythmic Cues to Optimize Gait in Older Adults and People With Parkinson Disease.

Gait dysfunction in people with Parkinson disease (PwPD) is common and debilitating. Internal cues (ie, mental singing) may provide a useful alternative to external cueing (ie, listening to music) to improve walking for PwPD. In this study, we individually tailored cues to each participant by allowing them to choose their own cue song and by selecting the cue rate that elicited the longest strides. Fifty-six participants (28 controls and 28 PwPD) performed walking trials in a single-session, cross-sectional study. Initially, uncued trials were compared with cued conditions in which participants walked to music or while they mentally sang at 4 randomized cue rates (90%, 100%, 110%, and 120% of uncued walking cadence). Participants chose their songs from a curated catalog. Cues were optimized according to longest stride lengths as measured by APDM wearable sensors. Repeated measures Analysis of Variance assessed differences between gait characteristics (velocity, cadence, and stride length) and gait variabilities (coefficients of variations) across conditions and groups. Both cue types improved velocity, cadence, and stride length as compared to uncued walking. Internal cues were most beneficial to gait variability. The majority of participants exhibited the longest strides and fastest velocities at the highest cue rate of 120%, though a cue rate of 110% was most beneficial to gait variability. This study shows how internal and external cues personalized by song choice and cue rate may be tailored to individuals. The information gained may help tailor cues to optimize gait performance for older adults and PwPD. for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A518).

Examination of Gait Characteristics Related to Sarcopenia in Community-Dwelling Older Adults: A Study Focusing on Plantar Pressure.

Sarcopenia is a condition characterized by a decrease in skeletal muscle mass and strength with age, which results in a lower gait speed. Decreased gait speed in older individuals with sarcopenia can lead to adverse events such as falls and mortality. It is a major health issue; several studies have investigated gait speed in sarcopenia. However, plantar pressure has not been sufficiently evaluated. Plantar pressure facilitates gait analysis, including gait speed, and plays an important role in preventing adverse events such as falls and mortality. Therefore, the current study aimed to validate gait characteristics, including plantar pressure in community-dwelling older adults with sarcopenia. The current study included community-dwelling Japanese adults aged ≥60 years who participated in health checkups between 2013 and 2018. Sarcopenia was diagnosed by measuring muscle mass and strength based on clinical definition (nonsarcopenia: n = 7662; probable sarcopenia: n = 1208; and sarcopenia: n = 477). Gait parameters (including gait speed, relative plantar pressure, cadence, stride length, step length, step width and foot angle) were measured at a comfortable speed using a computerized electronic walkway. Gait parameters between groups were compared via an analysis of covariance adjusted for age and BMI. In addition, post hoc analyses were performed with Bonferroni correction. The sarcopenia and probable sarcopenia groups had a significantly lower gait speed than the nonsarcopenia group (p < 0.01). Further, the sarcopenia and probable sarcopenia groups had a significantly lower forefoot plantar pressure, stride length and cadence than the nonsarcopenia group (all p < 0.01). When comparing, the sarcopenia group had a greater medial plantar pressure, step length, and foot angle and a lower lateral plantar pressure, cadence, and step width than the probable sarcopenia group (all p < 0.01). The sarcopenia, probable sarcopenia and nonsarcopenia groups differed concerning gait characteristics, including plantar pressure. It is thought that exercise instruction that takes into account walking characteristics is important for probable sarcopenia and sarcopenia.

Differential gait features across Parkinson's disease clinical subtypes.

Clinical subtypes in Parkinson's disease including non-motor manifestations may be more beneficial than subtypes based upon motor manifestations alone. Inclusion of gait metrics may help identity targets for rehabilitation and potentially predict development of non-motor symptoms for individuals with Parkinson's disease. This study aims to characterize gait differences across established multi-domain subtypes. "Motor Only", "Psychiatric & Motor" and "Cognitive & Motor" clinical subtypes were established through motor, cognitive, and psychiatric assessment. Walking was assessed in the "OFF" medication state. Multivariate analysis of variance identified differences in gait domains across clinical subtypes. The "Motor Only" subtype exhibited the fastest velocity, longest step length, and least timing variability (swing, step, stance), compared to "Psychiatric & Motor" and "Cognitive & Motor" subtypes. Stance time differed across subtypes; "Psychiatric & Motor" subtype had the longest stance time, followed by "Cognitive & Motor", then "Motor only". The "Psychiatric & Motor" group had different asymmetry from the "Cognitive & Motor" subtype, as "Psychiatric & Motor" walked with longer steps on their less-affected side while the "Cognitive & Motor" subtype displayed the opposite pattern. No differences were observed for swing time, step velocity variability, step length variability, width measures, or other asymmetry measures. Cognitive and Psychiatric subtypes displayed worse gait performance than the "Motor only" group. Stance time and step length asymmetry were different between Psychiatric and Cognitive subtypes, indicating gait deficits may be related to distinct aspects of non-motor manifestations. Gait signatures may help clinicians distinguish between non-motor subtypes, guiding personalized treatment.

The Relationship Between Body Mass Index and Gait Characteristics and Gait Performance of Children With Duchenne Muscular Dystrophy

Background and Purpose: This study aimed to investigate the relation between body mass index (BMI) and gait characteristics and performance in a cohort with DMD. Methods: Weight and height were measured to calculate BMI. BMI z-scores were used to categorise weight. Height, weight, and BMI were standardised to sex- and age-corrected z-scores based on the Turkish population. The gait characteristics were evaluated with Gait Assessment Scale for Duchenne Muscular Dystrophy (DMD-GAS) and Quality and Independence of Gait Classification Scale for Duchenne Muscular Dystrophy (QIGS-DMD). The gait performance was assessed by the 6-minute walk test and the 10-meter walk/run test. The correlation between body composition and gait parameters was analysed. Results: Eighty-six children with DMD (age:109.9026.53 months, BMI:18.803.13 kg/m2) were included in the study. Among the participants, 40.7% had a BMI above the normal range. There were moderate correlations between DMD-GAS and height, weight, and BMI (r=-0.36, r=-0.44, r=-0.36, respectively) and moderate correlations between QIGS-DMD and height, weight, and BMI (r:0.43, r:0.48, r:0.35, respectively) (p

Plantar Pressure-Based Gait Recognition with and Without Carried Object by Convolutional Neural Network-Autoencoder Architecture

Convolutional neural networks (CNNs) have been widely and successfully demonstrated for closed set recognition in gait identification, but they still lack robustness in open set recognition for unknown classes. To improve the disadvantage, we proposed a convolutional neural network autoencoder (CNN-AE) architecture for user classification based on plantar pressure gait recognition. The model extracted gait features using pressure-sensitive mats, focusing on foot pressure distribution and foot size during walking. Preprocessing techniques, including region of interest (ROI) selection, feature image extraction, and data horizontal flipping, were utilized to establish a CNN model that assessed gait recognition accuracy under two conditions: without carried items and carrying a 500 g object. To extend the application of the CNN to open set recognition for unauthorized personnel, the proposed convolutional neural network-autoencoder (CNN-AE) architecture compressed the average foot pressure map into a 64-dimensional feature vector and facilitated identity determination based on the distances between these vectors. Among 60 participants, 48 were classified as authorized individuals and 12 as unauthorized. Under the condition of not carrying an object, an accuracy of 91.218%, precision of 93.676%, recall of 90.369%, and an F1-Score of 91.993% were achieved, indicating that the model successfully identified most actual positives. However, when carrying a 500 g object, the accuracy was 85.648%, precision was 94.459%, recall was 84.423%, and the F1-Score was 89.603%.

Diagnostic Challenge of Phenotypic Variability in COL2A1-Related Disorders: Four Novel Variants and Expanding the Clinical Spectrum.

Heterozygous COL2A1 gene mutations are associated with type 2 collagenopathies, characterized by a wide, diverse, and overlapping clinical spectrum in related diseases. Our goal is to describe the clinical, radiological, and molecular findings of patients with COL2A1-related dysplasia and investigate the phenotype-genotype correlation. We also aim to emphasize the challenge of categorizing COL2A1-related diseases with similar clinical and radiological phenotypes. Six patients from five unrelated families presented with short-trunk dwarfism, delayed motor milestones, waddling gait, normal intelligence, and similar radiological features, including delayed epiphyseal ossification, epimetaphyseal changes, scoliosis, lordosis, and platyspondyly, underwent whole exome sequencing. Demographic, clinical, laboratory, and radiological data were retrospectively obtained from hospital records. Segregation analysis was conducted using Sanger sequencing in all patients. Based on clinical, radiological, and molecular results, six patients were categorized into Kniest dysplasia, Spondyloepiphyseal dysplasia congenita, and Spondyloepimetaphyseal dysplasia Strudwick type. Four novel variants (c.1023+2T>C, p.Gly465Asp, p.Gly855Asp, p.Gly669Ala) were identified in the COL2A1 gene. Accurate classification of type 2 collagenopathies is essential for providing genetic counseling. Predicting extraskeletal manifestations and reducing morbidity through early diagnosis and treatment will significantly improve the quality of life for patients.

User-Centered Design Framework for Personalized Ankle–Foot Orthoses

Background/Objectives: Integrated design and simulation solutions enable the manufacturing of advanced personalized orthotics that improve patients gait characteristics and balance. The success of such a rehabilitation approach is highly dependent on compliance, i.e., users wearing the orthosis consistently. Specifically, for most young children, functionality is secondary to appearance and peer perception. However, the starting point of the traditional design approach is to address functionality and then try to make the appearance more palatable to the wearer. As a result, compliance is a common issue, resulting in slow and uneven rehabilitation progress. Methods: This work proposes a method that inverts this traditional approach and devises an attractive light design that can be adapted to ensure structural soundness. Results/Conclusions: The broader framework is called the user-centered design process. The main advantage is in the flexibility of the added manufacturing approach, allowing for a personalized design that is attractive to the user, promoting higher compliance.

Gait Video-Based Prediction of Severity of Cerebellar Ataxia Using Deep Neural Networks.

Pose estimation algorithms applied to two-dimensional videos evaluate gait disturbances; however, a few studies have used this method to evaluate ataxic gait. The aim was to assess whether a pose estimation algorithm can predict the severity of cerebellar ataxia by applying it to gait videos. We video-recorded 66 patients with degenerative cerebellar diseases performing the timed up-and-go test. Key points from the gait videos extracted by a pose estimation algorithm were input into a deep learning model to predict the Scale for the Assessment and Rating of Ataxia (SARA) score. We also evaluated video segments that the model focused on to predict ataxia severity. The model achieved a root-mean-square error of 2.30 and a coefficient of determination of 0.79 in predicting the SARA score. It primarily focused on standing, turning, and body sway to assess severity. This study demonstrated that the model may capture gait characteristics from key-point data and has the potential to predict SARA scores. © 2025 International Parkinson and Movement Disorder Society.

Initial Contact with Forefoot or Rearfoot in Spastic Patients After Stroke-Three-Dimensional Gait Analysis.

Post-stroke hemiparetic gait often presents with asymmetric patterns to compensate for stability deficits. This study examines gait differences in chronic stroke patients with spastic hemiparesis based on initial foot contact type-forefoot versus rearfoot. Thirty-four independently walking spastic hemiparetic patients were retrospectively analyzed. Using 3D gait analysis, patients were categorized by initial contact type. Spatiotemporal descriptors, joint kinematics, kinetics, and EMG patterns were compared across groups. Patients with rearfoot initial contact (G1) showed higher cadence, longer single-limb support time and shorter stride times than those with forefoot contact (G0). G1 patients also demonstrated greater knee flexion during stance, enhancing stability. Additionally, G1 patients with abnormal lateral gastrocnemius activation in the swing phase showed increased ankle power at the end of the stance phase. In post-stroke spastic hemiparetic patients, the type of initial foot contact-forefoot or rearfoot-appears to influence gait characteristics, with rearfoot contact associated with a trend toward improved gait parameters, such as increased cadence and longer single-limb support.

Predictive potential of circular walking in prodromal Parkinson's disease

Background Development of objective, reliable and easy-to-use methods to detect the onset of motor changes in Parkinson's disease (PD) is required to identify the temporal window in which neuromodulatory therapies could be implemented. Turning impairments are present at early stages of PD. However, it is unclear, to date, whether circular walking is also altered in prodromal PD. Objective Explore the predictive potential of circular walking in prodromal PD. Methods We included 102 subjects from a nine-year prospective cohort study (with 712 participants) in the current nested case-control analysis: 16 diagnosed with PD during follow-up (incident PD) and 96 healthy controls, matched in gender, age, and education with a 1:6 ratio. Forty-one gait features were extracted from baseline assessments with accelerometers under single and dual-tasking conditions. A Cox proportional hazards regression analysis was used to test the temporal association of non-correlated gait features to the probability of being diagnosed with PD. Results We identified associations between time from baseline measurement to PD diagnosis for eleven gait features, mostly based on harmonic ratios, step and stride variability, and index of harmonicity, partially in combination with gait speed. Most significant associations indicated that low gait symmetry and low rhythmicity were associated with larger hazard of being diagnosed with PD. Area under the curve ranged 0.63-0.69. Conclusions Despite low sensitivity and specificity, the findings potentially reflect prodromal motor impairments of PD manifested during circular walking, assessed quantitatively with a low-cost and wearable instrument. This will contribute to the characterization of pre-diagnostic PD motor symptoms.

Mapping Trajectories of Gait Recovery in Clinical Stroke Rehabilitation.

How gait changes during the early stages of stoke rehabilitation, and which patient characteristics are associated with these changes is still largely unknown. he first objective was to describe the changes in gait during stroke rehabilitation. Secondly, we determined how various patient characteristics were associated with the rate of change of gait over time. Participants were measured every 3 weeks during stroke rehabilitation. The assessment consisted of an inertial measurement unit (IMU) based 2-minute walk test (2MWT), 3 IMU-based balance tests, and standard clinical tests. In the 2MWT, participants were equipped with 3 IMUs, from which speed, variability, asymmetry, and smoothness were calculated. The changes in gait were examined from admission to discharge at an individual level. The effect of patient characteristics on the rate of change of the gait features over time was assessed with growth models. A total of 81 Trajectories from 72 participants were analyzed. On an individual basis, speed increased in 32 trajectories. Only a few trajectories exhibited significant changes in variability, asymmetry, and smoothness over the clinical rehabilitation period. The growth models revealed a significant increase in speed and decrease in variability and smoothness. Only the Berg Balance Scale and gait speed at onset were (negatively) associated with the rates of change of speed and smoothness, respectively. We found a substantial variability in the gait-feature outcomes and their progression in individuals after stroke during clinical rehabilitation. The patient characteristics studied had limited associations with the rate of change of gait features over time.

Application of machine learning for detecting high fall risk in middle aged workers using video-based analysis of the first three steps.

Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of individualized fall prevention programs. The aim of this study was to develop and validate a high fall risk prediction model using machine learning (ML) and video-based first three steps in middle-aged workers. Train data (n=190, age 54.5±7.7 years, 48.9% male) and validation data (n=28, age 52.3±6.0 years, 53.6% male) were enrolled in this study. Pose estimation was performed using a marker-free deep pose estimation method called MediaPipe Pose. The first three steps, including the movements of the arms, legs, trunk, and pelvis, were recorded using an RGB camera, and the gait features were identified. Using these gait features and fall histories, a stratified K-fold was used to ensure balanced training and test data, and the area under the curve (AUC) and 95% confidence interval (CI) were calculated. Of 77 gait features in the first three steps, we found 3 gait features in men with an AUC of 0.909 (95% CI, 0.879-0.939) for fall risk, indicating an 'Excellent' (0.9-1.0) classification, while we determined 5 gait features in women with an AUC of 0.670 (95% CI, 0.621-0.719), indicating a 'sufficient' (0.6-0.7) classification. These findings suggest that fall risk prediction can be developed based on ML and the first three steps in men; however, the accuracy was only sufficient in men. Further development of the formula is required for women to improve its accuracy in the middle-aged working population.

Three-Dimensional Gait Analysis of School-Age Children With Angelman Syndrome: A Case-Control Study.

Gait disturbance is a common motor symptom in Angelman syndrome (AS), but its characteristics have been poorly studied quantitatively. This study aimed to analyze gait characteristics in school-age children with AS using three-dimensional gait analysis (3DGA). Patients with clinically and genetically confirmed AS and healthy children aged 6-15 years were included. For gait assessments, 3DGA was performed using an eight-camera motion analysis system and eight force plates. Gait metrics, including gait speed, step length, step width, gait variability, gait deviation index, and kinematic and kinetic data of lower extremity joints were compared between the groups. Eight children with AS and 24 healthy controls were evaluated. Seven children with AS had flat feet. While step length and gait speed were similar between groups, children with AS showed greater variability in these parameters and larger step widths. Their average gait deviation index was 74.5, indicating significant gait disturbance, and characteristic features included anterior pelvic tilt, insufficient hip extension, excessive knee flexion during early stance, and reduced ankle joint power. School-age children with AS exhibit unstable, prancing gait characterized by knee flexion in the early stance phase, quantifiable using 3DGA. These findings provide foundation for evaluating therapeutic interventions.

Improvement in Gait Abnormality Following Minimally Invasive Posterior Sacroiliac Joint Fusion.

Background Sacroiliac joint (SIJ) dysfunction is a common cause of low back pain and associated gait disturbances that result from aberrant muscle activity and symmetry. This study evaluated the magnitude of improvement in gait characteristics in patients with chronic SIJ pain followed for six months after minimally invasive posterior SIJ fusion. Methods This was a single-arm, prospective, pilot study at two private practice orthopedic pain clinics. Gait characteristics were quantitated using a wireless wearable sensor. Ten patients (mean age: 63 ± 12 years) with abnormal SIJ-associated gait impairment were enrolled and underwent posterior SIJ fusion. Results Average gait velocity improved significantly from 69 ± 28.5 cm/sec at baseline to 99.9 ± 31.5 cm/sec at six months, reflecting an overall average improvement of 30.93 cm/sec or 55.4% (p=0.003). Gait speed, variability, and symmetry impairment parameters also improved with corresponding mean percentage improvements at six months of 27.8% (p=0.02), 19.7% (p=0.17), and 11% (p=0.27). A significant decrease in fall risk and increased timed-up-and-go assessments were noted, with improvements of 32.3% and 24.7%, respectively (p=0.004 for both comparisons). Conclusion These pilot findings demonstrate the first objective assessment of gait characteristics in patients with SIJ dysfunction undergoing minimally invasive posterior SIJ fusion.

Parkinson’s disease screening using a fusion of gait point cloud and silhouette features

Parkinson’s Disease (PD) is a neurodegenerative disorder that is often accompanied by slowness of movement (bradykinesia) or gradual reduction in the frequency and amplitude of repetitive movement (hypokinesia). There is currently no cure for PD, but early detection and treatment can slow down its progression and lead to better treatment outcomes. Vision-based approaches have been proposed for the early detection of PD using gait. Gait can be captured using appearance-based or model-based approaches. Although appearance-based gait contains comprehensive features, it is easily affected by factors such as dressing. On the other hand, model-based gait is robust against changes in dressing and external contours, but it is often too sparse to contain sufficient information. Therefore, we propose a fusion of appearance-based and model-based gait features for PD prediction. First, we extracted keypoint coordinates from gait captured in videos and modeled these keypoints as a point cloud. The silhouette images are also segmented from the videos to obtain an overall appearance representation of the subject. We then perform a binary classification of gait as normal or Parkinsonian using a novel fusion of the gait point cloud and silhouette features, obtaining AUC up to 0.87 and F1-Scores up to 0.82 (precision: 0.85, recall: 0.80).

Machine learning for early detection and severity classification in people with Parkinson’s disease

Early detection of Parkinson’s disease (PD) and accurate assessment of disease progression are critical for optimizing treatment and rehabilitation. However, there is no consensus on how to effectively detect early-stage PD and classify motor symptom severity using gait analysis. This study evaluated the accuracy of machine learning models in classifying early and moderate-stages of PD based on spatiotemporal gait features at different walking speeds. A total of 178 participants were recruited, including 103 individuals with PD (61 early-stage, 42 moderate-stage) and 75 healthy controls. Participants performed a walking test on a 24-m walkway at three speeds: preferred walking speed (PWS), 20% faster (HWS), and 20% slower (LWS). Key features—walking speed at PWS, stride length at HWS, and the coefficient of variation (CV) of the stride length at LWS—achieved a classification accuracy of 78.1% using the random forest algorithm. For early PD detection, the stride length at HWS and CV of the stride length at LWS provided an accuracy of 67.3% with Naïve Bayes. Walking at PWS was the most critical feature for distinguishing early from moderate PD, with an accuracy of 69.8%. These findings suggest that assessing gait over consecutive steps under different speed conditions may improve the early detection and severity assessment of individuals with PD.

The Association Between Lower Limb Joint Position Sense and Different Aspects of Gait Pattern in Individuals With Bilateral Knee Osteoarthritis

Knee osteoarthritis (KOA) can have more pronounced effects on joint position sense (JPS) accuracy and gait characteristics. The aim of this study is to investigate the association between lower limb JPS and different aspects of gait pattern including gait asymmetry and variability and spatiotemporal coordination in individuals with bilateral KOA. In this cross-sectional study, lower limb JPS of 43 individuals with bilateral KOA (mild and moderate) were measured. Participants’ gait patterns during treadmill walking with self-selected comfortable speed were assessed. The correlations between JPS errors and gait parameters of limb with moderate KOA were analyzed. Positive relationships were found between stance time symmetry index and JPS errors of hip abduction (r = .46, P = .003), ankle plantar flexion (r = .33, P = .03), and ankle dorsiflexion (r = .33, P = .03). Positive relationship was found between single limb support time symmetry index and hip abduction JPS error (r = .41, P = .008). Significant negative associations were found between coefficient of variation of step length and JPS errors of knee extension (r = .47, P = .002) and ankle plantar flexion (r = .33, P = .003). Results did not show any significant relationship between lower limb JPS errors and walk ratio. It is likely that lower limb JPS deficits are partially responsible for some changes in gait patterns observed in individuals with bilateral KOA.

Prediction and identification of multiple sclerosis patients from gait features using machine learning algorithms and ensemble methods in embedded sensors environment

Prediction and identification of multiple sclerosis patients from gait features using machine learning algorithms and ensemble methods in embedded sensors environment

Detection and severity classification of ataxia using gait features and a hybrid model

Ataxia, a neurological disorder characterized by impaired coordination and unsteady movements, presents significant challenges for accurate diagnosis and classification. traditional machine-learning (ML) and deep-learning (DL) models often struggle to achieve high accuracy in predicting and classifying this complex condition. This study addresses these limitations by introducing a novel hybrid model, XGBoost-multi-layer-perceptron (XGB-MLP), specifically designed to enhance the accuracy of ataxia prediction and classification. The objective of this research is to develop a more reliable and precise diagnostic tool that outperforms existing ML and DL approaches. The methodology involved integrating the strengths of XGBoost, known for its powerful gradient boosting, with the multi-layer perceptron (MLP) neural network, creating a robust hybrid model. The proposed XGB-MLP model was rigorously tested against conventional models like random forest (RF), logistic regression (LR), support vector machine (SVM), MLP, and standalone XGBoost. The findings reveal that the XGB-MLP model achieves outstanding accuracy rates of 98.91% for ataxia prediction and 97.91% for classification, significantly surpassing the performance of the traditional models.

Possible influence of sex on the relationship between dual-task gait costs and cognitive decline in older adults.

The impact of cognitive decline in older adults can be evaluated with dual-task gait (DTG) testing in which a cognitive task is performed during walking, leading to increased costs of gait. Previous research demonstrated that higher DTG costs correlate with increasing cognitive deficits and with age. The present study was conducted to explore whether the relationship between the DTG costs and cognitive abilities in older individuals is influenced by sex differences. To address this objective, we conducted a study with 216 elderly participants (age range: 60 to 75 years, 127 females). These underwent Cognitive Functional Dementia (CFD) testing to determine their cognitive abilities and DTG testing to evaluate their gait parameters (gait speed, cadence, stride length, stride variance, and stance phase duration) while performing a backward serial number counting task. We carried out a correlation analysis between the CFD scores and the DTG costs. The DTG costs were calculated as the percentage difference between the gait parameters in single- and in dual-task testing, and the effects were compared considering the factors of sex and age. A significant negative correlation between the CFD scores and the dual-task costs of gait parameters was found only in males. The DTG costs did not differ between the sexes, while women obtained superior scores in the CFD test. The higher DTG costs significantly correlated with older age in men. In summary, our study provides evidence that, unlike in women, the DTG costs during the backward serial number counting task significantly increase in older men, correlating with declines in cognitive performance and increasing age. These findings suggest that the assessment of DT gait characteristics in relation to cognitive decline in older adults may manifest differently between sexes.