Gait & posture最新文献

{"title":"Recovery from perturbation is highly individual and similar between different perturbation types and metrics: A dynamical systems perspective of flexibility in walking gait","authors":"J.R. Lawrence Kwek ,&nbsp;John Komar ,&nbsp;Genevieve K.R. Williams ,&nbsp;Jia Yi Chow","doi":"10.1016/j.gaitpost.2026.110099","DOIUrl":"10.1016/j.gaitpost.2026.110099","url":null,"abstract":"<div><h3>Background</h3><div>Flexibility refers to how individuals can adopt alternative solutions to regain or maintain regular walking patterns when constraints are modified. Flexibility is an important yet still emerging characteristic of motor coordination.</div></div><div><h3>Research questions</h3><div>In walking gait, what are the characteristics of flexibility?</div></div><div><h3>Methods</h3><div>Twenty male participants (age 21–30) walked on a split-belt treadmill. Sudden perturbations in the form of belt accelerations or decelerations (direction) during mid-stance phase were applied at pseudo-random intervals (20–30 stride cycles). Kinematic data of the lower body were collected using Vicon motion capture.</div><div>To represent flexibility, Continuous Relative Phase (CRP) variability was calculated for ankle-knee and knee-hip and pelvis-trunk couplings. The relaxation time of CRP variability after perturbation was used to evaluate the main effects of participant, successive perturbations, CRP coupling and perturbation direction. The relaxation time was considered as a component of how flexibility was channelled after perturbation.</div><div>Repeated measures ANOVA and a univariate analysis was conducted to determine main effects on relaxation time.</div></div><div><h3>Results</h3><div>Significant main effects of participant and the effect of successive perturbations were observed, while CRP coupling and perturbation direction did not have an effect on relaxation time.</div><div>Significance: During perturbed walking on a treadmill, the response to perturbation was individual specific, a practice effect to the perturbation was present, and relaxation time did not differ by coupling or perturbation direction. This study provides a foundation for future studies to examine aspects of flexibility during walking, which can be applied to different participant populations.</div></div>","PeriodicalId":12496,"journal":{"name":"Gait & posture","volume":"125 ","pages":"Article 110099"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in lower limb joint moment prediction during typically developed gait: A systematic review and multilevel random-effects meta-analysis","authors":"Jianqi Pan ,&nbsp;Zixiang Gao ,&nbsp;Zhanyi Zhou ,&nbsp;Diwei Chen ,&nbsp;Fengping Li ,&nbsp;Julien S. Baker ,&nbsp;Yaodong Gu","doi":"10.1016/j.gaitpost.2025.110083","DOIUrl":"10.1016/j.gaitpost.2025.110083","url":null,"abstract":"<div><h3>Objective</h3><div>Artificial intelligence (AI) methods have been widely applied in gait analysis, yet quantitative comparisons across models and their input–output specifications remain limited. This study aims to systematically review and synthesize the existing literature to evaluate the effectiveness of AI methods in predicting lower limb joint moments during typically developed (TD) gait.</div></div><div><h3>Methods</h3><div>Relevant studies published before July 1, 2025, were retrieved from five databases (PubMed, Scopus, IEEE Xplore, ScienceDirect, and Web of Science) using Boolean logic operations and were screened according to predefined criteria. Risk of bias and applicability were assessed with PROBAST. Meta-analyses were performed in R using a multilevel random-effects model to examine differences in predictive performance across AI model group, signal input type, and output joints.</div></div><div><h3>Results</h3><div>Eleven studies involving 371 TD participants met the inclusion criteria. Deep neural networks (DNN) showed the best performance for R² (0.88, 95 %CI 0.52–1.24), while traditional machine learning (ML) models demonstrated relative superiority for nRMSE (0.11, 95 %CI 0.06–0.29). Among input types, surface EMG (sEMG) achieved the highest R² (0.96, 95 %CI 0.04–1.89), whereas all inputs except “kinematic and speed and anthropometrics” performed well in the nRMSE analysis. For output joints, the ankle was significantly superior to both the knee (<em>p</em> &lt; 0.001) and the hip (<em>p</em> &lt; 0.001) in terms of R² and nRMSE.</div></div><div><h3>Conclusion</h3><div>AI methods can effectively predict lower limb joint moments during TD gait, but differences exist across model group, input type, and output joints. DNN show advantages in fitting complex data, while traditional ML demonstrates greater robustness in small-sample settings. The sEMG, as a process-related input, exhibits high potential, and predictions for the ankle joint are generally superior. Future studies should expand sample size, explore multimodal inputs and advanced modeling strategies, and further validate the applicability of AI methods in pathological gait.</div></div>","PeriodicalId":12496,"journal":{"name":"Gait & posture","volume":"125 ","pages":"Article 110083"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-modal gait assessment in Parkinson's Disease: A pilot study examining the impact of terrain and environment","authors":"Y. Celik ,&nbsp;L.T. Pearson-Noseworthy ,&nbsp;J. Das ,&nbsp;S. Stuart ,&nbsp;W.L. Woo ,&nbsp;A. Godfrey","doi":"10.1016/j.gaitpost.2026.110094","DOIUrl":"10.1016/j.gaitpost.2026.110094","url":null,"abstract":"<div><h3>Background</h3><div>Impaired gait is common in people with Parkinson’s disease (PwPD), increasing fall risk which is amplified when on complex/uneven terrains. Most studies focus on inertial-based spatiotemporal characteristics alone stratified to domains (e.g., pace) within indoor settings. Less is known about environmental challenges impacting joint kinematics and muscle activation, critical to understanding adaptive gait and informing personalised interventions. This pilot adopts a multimodal approach to examine the effects of different terrains and environments on gait in PwPD.</div></div><div><h3>Methods</h3><div>Ten PwPD completed indoor and outdoor walks across soft and hard terrains, including tiled rubber, rocky cobblestones, asphalt, grass, cobble pavement, and carpet. Spatiotemporal, knee joint kinematics, and muscle activation characteristics were collected via wearables. Multimodal characteristics were extracted by a previous validated data fusion methodology. Wilcoxon signed-rank test was used to investigate the impact of terrain and environment on gait.</div></div><div><h3>Results</h3><div>Pace characteristics were significantly higher on hard terrain where their variability increased during outdoor walks. No significant differences were found in rhythm or asymmetry domains across terrain types or environmental contexts. Knee flexion-extension asymmetry was lower and peak flexion occurred earlier on hard surfaces. Muscle activation levels were significantly lower outdoors, while soft terrains produced higher activation in distal muscles during swing phases, although frequency measures tended to be higher on hard terrain. Terrain related differences were more limited compared to environmental differences in muscle activity.</div></div><div><h3>Conclusions</h3><div>Multimodal gait assessment using wearables can be conducted on PwPD in different settings. Findings demonstrate terrain and environmental demands influence spatial parameters, knee joint kinematics and muscle activity in PwPD which may be more informative for fall risk assessment and personalised interventions.</div></div>","PeriodicalId":12496,"journal":{"name":"Gait & posture","volume":"125 ","pages":"Article 110094"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of multiple levels of visual occlusion on static postural control","authors":"Serkan Uzlasir ,&nbsp;Joshua S. Mohess ,&nbsp;Erik A. Wikstrom","doi":"10.1016/j.gaitpost.2026.110098","DOIUrl":"10.1016/j.gaitpost.2026.110098","url":null,"abstract":"<div><h3>Background</h3><div>Stroboscopic vision (SV) is a novel modality that modulates visual input and can impair postural control due to the resulting visual occlusion. SV tools have multiple levels of visual occlusion (i.e. difficulty) but our understanding of how both instrumented and clinical measures of postural control are impacted by different levels of visual occlusion remain extremely limited. A systematic evaluation across multiple levels within the same cohort is necessary to understand the dose response that could be deployed in applied scenarios.</div></div><div><h3>Objective</h3><div>This study aimed to investigate the effects of different levels of SV on instrumented and clinical measures of static postural control.</div></div><div><h3>Methods</h3><div>Thirty healthy individuals performed a single limb static balance task under six visual conditions: eyes open (EO), SV 2, SV 4, SV 6, and SV 8, and eyes closed (EC). Postural control was assessed via the 95 % confidence ellipse, center of pressure velocity, and a count of movement errors.</div></div><div><h3>Results</h3><div>All levels of SV resulted in significantly worse objective static postural control relative to the EO condition (p &lt; 0.001). SV levels resulted in 95 % confidence ellipse but not center of pressure velocity values that were comparable to EC balance. No differences were noted among levels of SV (p &gt; 0.05) for instrumented postural control. Only SV 6 resulted in worse clinically measured balance relative to EO (p &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>All levels of visual occlusion impair postural control but impairment magnitude is dependent on the outcome measure. Instrumented outcomes are more sensitive to SV effects than clinical outcomes.</div></div>","PeriodicalId":12496,"journal":{"name":"Gait & posture","volume":"125 ","pages":"Article 110098"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145967647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the distance between the center of mass and center of pressure during walking in the young and elder population based on ground reaction forces using artificial neural network","authors":"Chu-Fen Chang ,&nbsp;Anne Dixie M. Lim ,&nbsp;Shih-Wun Hong ,&nbsp;Tung-Wu Lu ,&nbsp;Kelly I.-Rong Lee","doi":"10.1016/j.gaitpost.2025.110077","DOIUrl":"10.1016/j.gaitpost.2025.110077","url":null,"abstract":"<div><h3>Introduction</h3><div>Assessing the interaction between the body’s center of mass (COM) and center of pressure (COP) during gait provides valuable insights into dynamic balance control. However, traditional motion capture methods are costly and impractical in clinical settings, highlighting the need for accessible alternatives. This study utilizes force plate data to explore the use of ANNs in predicting the motion of the COM relative to the COP during walking.</div></div><div><h3>Materials and Methods</h3><div>Eighty-four healthy adults (50 young, 34 older) performed gait trials on a walkway with motion capture and force plate data collection. Ground reaction forces (GRFs) and COM-COP distances (dCOM) were computed for the stance phase. Multi-layer perceptron (MLP) artificial neural network (ANN) models were developed to predict dCOM in the anteroposterior (AP) and mediolateral (ML) directions as well as COM height based on GRFs and anthropometric inputs. Model performance was evaluated using root mean squared error (RMSE) and coefficient of determination (R²).</div></div><div><h3>Results</h3><div>ANN models achieved high predictive accuracy with optimal hidden layer sizes of 5 (AP, COM height) and 9 (ML). RMSE values ranged from 0.89 % to 7.77 %LL, with R² values between 0.74 and 0.92. Significant age-related differences were observed in ML direction and COM height (p &lt; 0.05), but not in AP.</div></div><div><h3>Conclusion</h3><div>This study highlights ANN models as accurate, low-cost tools for dynamic balance assessment, reliably predicting dCOM across directions. The consistency between calculated and predicted group differences supports their application in fall risk evaluation and personalized intervention planning for older adult populations.</div></div>","PeriodicalId":12496,"journal":{"name":"Gait & posture","volume":"125 ","pages":"Article 110077"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpreting common standing postural sway measures","authors":"Mark S. Redfern","doi":"10.1016/j.gaitpost.2025.110084","DOIUrl":"10.1016/j.gaitpost.2025.110084","url":null,"abstract":"<div><h3>Background</h3><div>There are numerous measures of standing balance using force plates presented in the literature. Two of the most common measures are the root-mean-square (RMS) and mean velocity (MV) of the Center of Pressure (CoP). The purpose of this short communication is to promote a greater understanding of the implications of these two common metrics.</div></div><div><h3>Research question</h3><div>What aspects of postural control do the RMS and MV of the CoP measure?</div></div><div><h3>Methods</h3><div>CoP time series measured with a force plate and CoM calculated from motion capture during quiet standing in the AP and ML directions were analyzed. The RMS and MV of the CoP, Center of Mass (CoM), and the difference between the CoP and the CoM (CoP-CoM) were calculated. The relationships among these measures are presented.</div></div><div><h3>Results</h3><div>The <em>CoP</em>_<em>RMS</em> was highly correlated with the <em>CoM</em>_<em>RMS</em> (r &gt; .96), indicating that <em>CoP</em>_<em>RMS</em> measures the amount of sway. The <em>CoP</em>_<em>MV</em> was highly correlated with (<em>CoP-CoM)</em>_<em>RMS</em> (r &gt; .90). The (<em>CoP-CoM)</em>_<em>RMS</em> is related to the torque generation used to maintain stability; therefore <em>CoP</em>_<em>MV</em> is related to the control effort used. The AP and ML measures do have some different characteristics due to the mechanism by which stability is maintained.</div></div><div><h3>Significance</h3><div>The RMS and MV of CoP are effective at capturing two different fundamental aspects of stability: the amount of sway and control effort. This highlights the importance of reporting both <em>CoP</em>_<em>RMS</em> and <em>CoP</em>_<em>MV</em> to allow for a better understanding of standing balance.</div></div>","PeriodicalId":12496,"journal":{"name":"Gait & posture","volume":"125 ","pages":"Article 110084"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Context-dependent coupling of posture, gait, and functional mobility under cognitive and sensory demands in Parkinson’s disease","authors":"Elvira Molinero-Martín,&nbsp;Eduardo Villamil-Cabello,&nbsp;Antonio Luque-Casado,&nbsp;Miguel Fernandez-del-Olmo","doi":"10.1016/j.gaitpost.2025.110087","DOIUrl":"10.1016/j.gaitpost.2025.110087","url":null,"abstract":"<div><h3>Background</h3><div>Postural instability, gait disturbances, and impaired functional mobility are key contributors to falls in Parkinson’s disease (PD). Although their interactions have been explored, the influence of cognitive load and visual information on these relationships remains poorly understood.</div></div><div><h3>Objectives</h3><div>To examine how static postural control, gait, and functional mobility interact in PD, and to determine how these associations are modulated by cognitive demands and the availability of visual feedback.</div></div><div><h3>Methods</h3><div>Thirty individuals with PD (Hoehn &amp; Yahr I–III) were assessed in the ON-medication state. Participants completed gait tasks under self-selected, maximal, and dual-task conditions; the Timed Up and Go (TUG) test; and static posturography under four conditions (single/dual task × eyes open/closed). Correlation analyses, complementary linear mixed-effects models (LMM) and exploratory factor analysis (EFA) were conducted.</div></div><div><h3>Results</h3><div>Significant interactions emerged between dual-task step length and mediolateral sway metrics (path length, velocity and range) and the direction of these associations was context-dependent. TUG performance was associated with postural sway only in low-demand conditions. EFA identified three partially independent factors: anterior–posterior postural control, medial-lateral postural control, and gait.</div></div><div><h3>Conclusions</h3><div>These preliminary findings suggests that gait and posture rely on partially overlapping but flexibly interacting control networks, which become more interdependent as automaticity declines, underscoring the need for rehabilitation strategies that explicitly consider sensory and cognitive influences on motor control.</div></div>","PeriodicalId":12496,"journal":{"name":"Gait & posture","volume":"125 ","pages":"Article 110087"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of marker set configurations in the measurement of hip axial rotation in ballet turnout","authors":"Maria Isabel V. Orselli ,&nbsp;Luciana C. Manfrim ,&nbsp;Bianca Meneses Portela ,&nbsp;Matheus Moutinho ,&nbsp;Flora Moreira Pitta ,&nbsp;Paolo Caravaggi ,&nbsp;Isabel C.N. Sacco","doi":"10.1016/j.gaitpost.2026.110101","DOIUrl":"10.1016/j.gaitpost.2026.110101","url":null,"abstract":"<div><h3>Background</h3><div>The hip is the main contributor to turnout, a key element in classical ballet involving large external rotations of the lower limb. Measuring hip axial rotation during turnout using skin-markers is challenging due to substantial soft tissue artifacts in the thigh. These measurements depend on the chosen thigh marker set, and no consensus exists on the most suitable configuration for large hip motions. We hypothesized that soft tissue artifacts would significantly affect hip rotation measurements obtained by different marker-set protocols.</div></div><div><h3>Methods</h3><div>This study compared three marker set protocols for assessing hip axial rotation during the <em>Assemblé Dessus</em> jump in 17 experienced ballet dancers. A rectangular rigid-cluster (CC) positioned in the thigh mid-lateral portion and a custom-designed wedge-shaped cluster (WC) positioned on the distal portion of the iliotibial band were compared to the Vicon’s Plug-in-Gait (PIG) markers’ protocol. Data was tracked via an 8-camera motion capture system, and Visual 3D was used to calculate hip rotation from the three marker sets. Statistical parametric mapping and one-way ANOVA were used to assess the effect of the marker set on hip rotations.</div></div><div><h3>Results</h3><div>The choice of marker set significantly affected hip axial rotation measurements. PIG provided less reliable estimates than CC and WC, and an offset between WC and CC measurements was observed which may be due to skin twisting caused by a constant torsional load on the thigh.</div></div><div><h3>Conclusions</h3><div>Based on these findings, a rigid cluster protocol may be more suitable to assess larger hip axial rotations, such as in the ballet turnout. However, careful consideration should be given to the cluster proximal-distal location when comparing inter-session data.</div></div>","PeriodicalId":12496,"journal":{"name":"Gait & posture","volume":"125 ","pages":"Article 110101"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recognizing intentions with body segmental cues of gait cycles before direction change during continuous walking","authors":"Yu-Lin Yen,&nbsp;Yun-Ju Lee","doi":"10.1016/j.gaitpost.2025.110069","DOIUrl":"10.1016/j.gaitpost.2025.110069","url":null,"abstract":"<div><h3>Background</h3><div>Gait intention is typically detected using electroencephalogram (EEG) and primarily focuses on recognizing the initiation of walking. Recently, wearable sensors have been extensively used to classify different walking patterns.</div></div><div><h3>Research questions</h3><div>The study was aimed to investigate the effectiveness of Inertial Measurement Units (IMUs) in recognizing directional change intentions during gait, hypothesizing that distinct patterns exist that indicate the initiation of turning before actual turning movements occur.</div></div><div><h3>Methods</h3><div>Twenty healthy participants performed one to two gait cycles before directional changes during straight walking, a 45-degree right turn, and a 90-degree right turn. Seven IMUs were attached to various body segments, and a matching network combined with sliding window bidirectional gated recurrent units was used to recognize turning intentions. The EEG data analysis was used by EEGLAB to determine the event-related desynchronization in the alpha (8–13 Hz) and beta (14–30 Hz) frequency bands.</div></div><div><h3>Results</h3><div>The results showed that the model using different combinations of IMU sensors achieved an optimal accuracy of 96.80 %, with several combinations exceeding 95 %. Subtle movements in different body segments were sufficient to predict upcoming turning intentions. The accuracy dropped to 83.79 % in the model that excluded data from the head segment, while the other models that included the head segment achieved over 94 % accuracy. Furthermore, angular acceleration data was notably more accurate, at 93.46 %, compared to 88.43 % for acceleration data alone.</div></div><div><h3>Significance</h3><div>Successfully highlighted the crucial role of sensing contralateral body and head segments, providing insights into the model's interpretability. The use of IMUs for gait turning intention recognition shows promise in replacing devices that rely on sensing brain waves. Furthermore, this approach opens possibilities for applications such as controlling exoskeletons, including assisting walking robots with strategically placed sensors.</div></div>","PeriodicalId":12496,"journal":{"name":"Gait & posture","volume":"125 ","pages":"Article 110069"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Links between central visual field loss and movement processing during walking","authors":"Natela M. Shanidze ,&nbsp;Catherine P. Agathos ,&nbsp;Toby J. Ellmers ,&nbsp;William R. Young","doi":"10.1016/j.gaitpost.2026.110095","DOIUrl":"10.1016/j.gaitpost.2026.110095","url":null,"abstract":"<div><h3>Background</h3><div>Central visual field loss (CFL) is the most common irreversible visual impairment in aging and is associated with higher fall risk and concerns about falling. This study explored the links between CFL severity, functional balance, and walking-related attentional processing implicated in reduced gait performance.</div></div><div><h3>Methods</h3><div>In Study 1, 29 individuals with CFL and 29 age-matched controls completed the Timed Up and Go (TUG) test. In Study 2, 10 CFL participants and 10 controls performed the TUG while acceleration data were collected from head and trunk IMUs. For both studies, we assessed visual impairment severity (contrast sensitivity) and participants’ attentional processing during walking (Gait-Specific Attentional Profile, G-SAP).</div></div><div><h3>Results</h3><div>Both groups showed positive correlations between TUG duration and G-SAP subscales. G-SAP scores were lower in CFL participants with worse contrast sensitivity indicating reduced cognitive processing during walking. Worse contrast sensitivity was also associated with greater head and trunk acceleration and acceleration variability during walking, suggesting reduced gait stability. Higher rumination and conscious movement processing scores also correlated with improved segmental control in CFL.</div></div><div><h3>Significance</h3><div>Increased cognitive processing of gait is associated with impaired functional balance. This association appears to be reversed in CFL, with severe visual deficit diverting cognitive resources from movement control. This altered strategy may prioritise the acquisition and processing of visuospatial information in CFL. The observed postural instability with increasing CFL severity and a lack of excessive cognitive involvement in movement control suggest heightened gait-specific attention could be leveraged for balance and gait training in CFL.</div></div>","PeriodicalId":12496,"journal":{"name":"Gait & posture","volume":"125 ","pages":"Article 110095"},"PeriodicalIF":2.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}