Gait & posture最新文献

英文中文

WITHDRAWN: Multidisciplinary biomechanical evaluation of orthopedic foot surgery in cerebral palsy: A clinical case study. 撤回：脑瘫患者足部矫形手术的多学科生物力学评估：临床案例研究。

Gait & posture

Pub Date : 2024-09-01 Epub Date: 2024-09-03 DOI: 10.1016/j.gaitpost.2024.07.247

Gaia van den Heuvel, Wouter Schallig, Babette Mooijekind, Ruud Wellenberg, Melinda Witbreuk, Mario Maas, Marjolein van der Krogt, Annemieke Buizer

引用次数: 0

WITHDRAWN: Using machine learning for clustering the IMU data of patients with sagittal imbalance of the spine. 撤回：使用机器学习对脊柱矢状不平衡患者的 IMU 数据进行聚类。

Gait & posture

Pub Date : 2024-09-01 Epub Date: 2024-09-03 DOI: 10.1016/j.gaitpost.2024.07.207

Sadegh Madadi, Mostafa Rostami, Hadi Farahni, Farshad Nikouee, Ram Haddas, Mohammad Samadian

引用次数: 0

WITHDRAWN: L5-S1 arthrodesis impact on spino-pelvic parameters, gait, and quality-of-life in a patient with chronic low back pain with spondylolisthesis. 撤稿：L5-S1关节置换术对脊柱骨盆参数、步态和生活质量的影响--一名患有脊柱滑脱症的慢性腰痛患者。

Gait & posture

Pub Date : 2024-09-01 Epub Date: 2024-09-03 DOI: 10.1016/j.gaitpost.2024.07.195

Gilles Prince, Rami Rachkidi, Abir Massaad, Ibrahim Hamati, Moustapha Rteil, Joe Azar, Guy Awad, Nadim Freiha, Mohamad Karam, Ayman Assi

引用次数: 0

WITHDRAWN: Lumbar Spine Muscle Force analysis in different Arm Swing States during gait. 撤回：步态中不同手臂摆动状态下的腰椎肌肉力量分析。

Gait & posture

Pub Date : 2024-09-01 Epub Date: 2024-09-03 DOI: 10.1016/j.gaitpost.2024.07.209

Zahrasadat Mousavi, Mostafa Rostami, Sadegh Madadi

引用次数: 0

WITHDRAWN: Predicting outcomes of sagittal imbalance of the spine surgery using IMU data and unsupervised models. 撤回：利用 IMU 数据和无监督模型预测脊柱矢状不平衡手术的结果。

Gait & posture

Pub Date : 2024-09-01 Epub Date: 2024-09-03 DOI: 10.1016/j.gaitpost.2024.07.208

Sadegh Madadi, Mostafa Rostami, Hadi Farahani, Farshad Nikouee, Ram Haddas, Mohammad Samadian

引用次数: 0

WITHDRAWN: Personalized clinical decision-making by evaluating the effects of a selective nerve block on cycling and gait: A clinical case study. 撤稿：通过评估选择性神经阻滞对骑车和步态的影响实现个性化临床决策：临床案例研究。

Gait & posture

Pub Date : 2024-09-01 Epub Date: 2024-09-03 DOI: 10.1016/j.gaitpost.2024.07.251

Hanneke Van Duijnhoven, Lotte Van De Venis, Maarten Nijkrake, Allan Pieterse, Alexander Geurts, Jorik Nonnekes

引用次数: 0

WITHDRAWN: Predicting botulinum toxin-a injection effects on gait in a child with hemiparetic cerebral palsy: A case study. 撤回：预测注射肉毒杆菌毒素对偏瘫脑瘫儿童步态的影响：病例研究。

Gait & posture

Pub Date : 2024-09-01 Epub Date: 2024-09-03 DOI: 10.1016/j.gaitpost.2024.07.186

Kubra Onerge, Nazif Ekin Akalan, Rukiye Sert, Fuat Bilgili

引用次数: 0

How reliable are femoropelvic kinematics during deep squats? The influence of subject-specific skeletal modelling on measurement variability. 深蹲时的股骨盆运动学有多可靠？受试者特定骨骼模型对测量变异性的影响。

Gait & posture

Pub Date : 2024-07-01 Epub Date: 2024-05-08 DOI: 10.1016/j.gaitpost.2024.05.006

Dalia Al Otti, Stijn Ghijselings, Filip Staes, Lennart Scheys

Background: Biplanar radiography displays promising results in the production of subject-specific (S.specific) biomechanical models. However, the focus has predominantly centred on methodological investigations in gait analysis. Exploring the influence of such models on the analysis of high range of motion tasks linked to hip pathologies is warranted. The aim of this study is to investigate the effect of S.Specific modelling techniques on the reliability of deep squats kinematics in comparison to generic modelling.

Methods: 8 able-bodied male participants attended 5 motion capture sessions conducted by 3 observers and performed 5 deep squats in each. Prior to each session a biplanar scan was acquired with the reflective-markers attached. Inverse kinematics of pelvis and thigh segments were calculated based on S.specific and Generic model definition. Agreement between the two models femoropelvic orientation in standing was assessed with Bland-Altman plots and paired t- tests. Inter-trial, inter-session, inter-observer variability and observer/trial difference and ratio were calculated for squat kinematic data derived from the two modelling approaches.

Results: Compared to the Generic model, the S.Specific model produced a calibration trial that is significantly offset into more posterior pelvis tilt (-2.8±2.7), hip extension (-2.2±3.8), hip abduction (-1.2±3.6) and external rotation (-13.8±11.4). The S.specific model produced significantly different squat kinematics in the sagittal plane of the pelvis (entire squat cycle) and hip (between 40 % and 60 % of the squat cycle). Variability analysis indicated that the error magnitude between the two models was comparable (difference<2°). The S.specific model exhibited a lower variability in the observer/trial ratio in the sagittal pelvis and hip, the frontal hip, but showed a higher variability in the transverse hip.

Significance: S.specific modelling appears to introduce a calibration offset that primarily translates into an effect in the sagittal plane kinematics. However, the clinical added value of S.specific modelling in terms of reducing experimental sources of kinematic variability was limited.

背景：双平面放射摄影在制作特定对象（S.specific）生物力学模型方面取得了可喜的成果。然而，重点主要集中在步态分析的方法研究上。有必要探索此类模型对分析与髋关节病变有关的高运动范围任务的影响。方法：8 名健全男性参与者参加了由 3 名观察者进行的 5 次运动捕捉训练，每次进行 5 个深蹲。在每次训练之前，都要进行一次附有反射标记的双平面扫描。根据S.specific和Generic模型定义计算骨盆和大腿节段的逆运动学。通过布兰德-阿尔特曼图和配对 t 检验来评估两种模型在站立时股骨盆方向的一致性。对两种建模方法得出的下蹲运动学数据计算了试验间、时段间、观察者间的变异性以及观察者/试验间的差异和比率：与通用模型相比，S.specific 模型产生的校准试验明显偏向于更多的骨盆后倾（-2.8±2.7）、髋关节伸展（-2.2±3.8）、髋关节外展（-1.2±3.6）和外旋（-13.8±11.4）。S.specific模型在骨盆矢状面（整个深蹲周期）和髋关节矢状面（深蹲周期的40%到60%之间）产生了明显不同的深蹲运动学。变异性分析表明，两个模型之间的误差幅度相当（差异显著性：0.5%）：S.specific建模似乎引入了校准偏移，主要转化为对矢状面运动学的影响。然而，S.specific 建模在减少运动学变异性实验来源方面的临床附加值有限。

{"title":"How reliable are femoropelvic kinematics during deep squats? The influence of subject-specific skeletal modelling on measurement variability.","authors":"Dalia Al Otti, Stijn Ghijselings, Filip Staes, Lennart Scheys","doi":"10.1016/j.gaitpost.2024.05.006","DOIUrl":"10.1016/j.gaitpost.2024.05.006","url":null,"abstract":"Background: Biplanar radiography displays promising results in the production of subject-specific (S.specific) biomechanical models. However, the focus has predominantly centred on methodological investigations in gait analysis. Exploring the influence of such models on the analysis of high range of motion tasks linked to hip pathologies is warranted. The aim of this study is to investigate the effect of S.Specific modelling techniques on the reliability of deep squats kinematics in comparison to generic modelling.Methods: 8 able-bodied male participants attended 5 motion capture sessions conducted by 3 observers and performed 5 deep squats in each. Prior to each session a biplanar scan was acquired with the reflective-markers attached. Inverse kinematics of pelvis and thigh segments were calculated based on S.specific and Generic model definition. Agreement between the two models femoropelvic orientation in standing was assessed with Bland-Altman plots and paired t- tests. Inter-trial, inter-session, inter-observer variability and observer/trial difference and ratio were calculated for squat kinematic data derived from the two modelling approaches.Results: Compared to the Generic model, the S.Specific model produced a calibration trial that is significantly offset into more posterior pelvis tilt (-2.8±2.7), hip extension (-2.2±3.8), hip abduction (-1.2±3.6) and external rotation (-13.8±11.4). The S.specific model produced significantly different squat kinematics in the sagittal plane of the pelvis (entire squat cycle) and hip (between 40 % and 60 % of the squat cycle). Variability analysis indicated that the error magnitude between the two models was comparable (difference<2°). The S.specific model exhibited a lower variability in the observer/trial ratio in the sagittal pelvis and hip, the frontal hip, but showed a higher variability in the transverse hip.Significance: S.specific modelling appears to introduce a calibration offset that primarily translates into an effect in the sagittal plane kinematics. However, the clinical added value of S.specific modelling in terms of reducing experimental sources of kinematic variability was limited.","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"112 ","pages":"120-127"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sleep analysis via wearable sensors in people with Parkinson’s disease 通过穿戴式传感器对帕金森病患者进行睡眠分析

Gait & posture

Pub Date : 2023-09-01 DOI: 10.1016/j.gaitpost.2023.07.244

Salvatore Tedesco, Colum Crowe, Marco Sica, Lorna Kenny, Brendan O'Flynn, David Scott Mueller, Suzanne Timmons, John Barton

Parkinson disease (PD), a well-known illness of motor dysfunction, is characterized by a high prevalence of sleep problems due to degenerative brain changes or comorbid conditions [1]. Wearable devices, in the form of actigraphy, have been shown to also be appropriate for monitoring sleep variables in PD patients [2,3] despite reports that current actigraphy algorithms may misinterpret dysfunctional motor activity, such as tremors, bradykinesia, dyskinesia, and limited arm movement while walking, as well as drug-induced hypermotility, thus making their use problematic in people with PD (PwPD) [4]. The ActiGraph GT3X (Pensacola, FL, USA) accelerometer is capable of recording accelerometry measurements for multiple days at 100 Hz, and has been adopted for massive population-level data collections [5]. In the last few years, Van Hees et al. have developed and made freely available open-source software to estimate sleep variables using data collected from similar off-the-shelf wearable inertial sensors [6]. The goal of this study is to investigate if the ActiGraph data, in combination with Van Hees et al.’s heuristic algorithm Distribution of Change in Z-Angle (HDCZA), can correctly estimate sleep variables in PD patients. To the best of the authors’ knowledge, it is the first study that adopts ActiGraph sensors and this methodology for sleep analysis in PwPD. For further comparison, a custom hardware prototype device named WESAA (Wearable Enabled Symptom Assessment Algorithms) developed at the Tyndall National Institute [8] and with the same capabilities as an ActiGraph device was adopted for additional analysis. Nineteen PD subjects took part in a data collection where participants wore the ActiGraph on their most affected wrist for a minimum of 24 hours and simultaneously filled out a sleep diary. Accelerometer data was collected at 100 Hz. Additionally, six subjects repeated the same data collection protocol while wearing the WESAA system. The heuristic algorithm described in [7] was implemented to detect periods of sleep and compared against the participant diaries. Results are shown in Table I and Figure I in the picture below. Accuracy reported on the subjects using the Actigraph was appropriate with an average 77.8±13.6%, even though results were quite variable across patients (between 31.6% and 91.2%). Less variability is shown with the WESAA device, even though only 6 subjects have carried out this data collection, with an average accuracy of 81.9±6.2% (71.8%-90.2%).Download : Download high-res image (157KB)Download : Download full-size image The present investigation shows that ActiGraph accelerometry data collected over 24 hours, in conjunction with the heuristic algorithm HDCZA for the detection of sleep periods, is an appropriate approach to estimate sleep duration even in PwPD. The same algorithm adopted on the WESAA hardware device shows even more promising results but further investigations with a larger sample size are required to c

帕金森病(PD)是一种众所周知的运动功能障碍疾病，其特点是由于大脑退行性改变或合并症导致睡眠问题的高发[1]。活动记录仪形式的可穿戴设备也被证明适合监测PD患者的睡眠变量[2,3]，尽管有报道称，目前的活动记录仪算法可能会误解功能失调的运动活动，如震颤、运动迟缓、运动障碍、行走时手臂运动受限以及药物引起的运动亢进，从而使其在PD患者(PwPD)中的使用存在问题[4]。ActiGraph GT3X (Pensacola, FL, USA)加速度计能够在100 Hz下记录多天的加速度测量结果，并已被用于大规模的人口数据收集[5]。在过去的几年中，Van Hees等人开发并免费提供了开源软件，利用从类似的现成可穿戴惯性传感器收集的数据来估计睡眠变量[6]。本研究的目的是探讨ActiGraph数据结合Van Hees等人的启发式算法Distribution of Change in Z-Angle (HDCZA)是否能正确估计PD患者的睡眠变量。据作者所知，这是第一个采用ActiGraph传感器和这种方法对PwPD进行睡眠分析的研究。为了进一步比较，我们采用Tyndall National Institute[8]开发的自定义硬件原型设备WESAA (Wearable Enabled Symptom Assessment Algorithms，可穿戴症状评估算法)进行附加分析，该设备与ActiGraph设备具有相同的功能。19名PD受试者参加了一项数据收集，参与者在他们受影响最严重的手腕上佩戴ActiGraph至少24小时，同时填写睡眠日记。加速度计数据以100 Hz的频率收集。此外，六名受试者在佩戴WESAA系统时重复相同的数据收集方案。采用[7]中描述的启发式算法检测睡眠时间，并与参与者日记进行比较。结果如下图表1和图1所示。使用Actigraph的受试者报告的准确率是合适的，平均为77.8±13.6%，尽管不同患者的结果差异很大(31.6%至91.2%)。WESAA装置的变异性较小，尽管只有6名受试者进行了这项数据收集，平均准确率为81.9±6.2%(71.8%-90.2%)。目前的研究表明，在24小时内收集的ActiGraph加速度测量数据，结合启发式算法HDCZA来检测睡眠时间，即使在PwPD中也是一种估计睡眠时间的合适方法。WESAA硬件设备上采用的相同算法显示出更有希望的结果，但需要更大样本量的进一步调查来证实这一点。资金:这项工作由爱尔兰企业(EI)和艾伯维公司根据协议IP 2017 0625部分支持;部分由爱尔兰科学基金会资助，由欧洲区域发展基金在12/RC/2289-P2-INSIGHT下共同资助。

{"title":"Sleep analysis via wearable sensors in people with Parkinson’s disease","authors":"Salvatore Tedesco, Colum Crowe, Marco Sica, Lorna Kenny, Brendan O'Flynn, David Scott Mueller, Suzanne Timmons, John Barton","doi":"10.1016/j.gaitpost.2023.07.244","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.244","url":null,"abstract":"Parkinson disease (PD), a well-known illness of motor dysfunction, is characterized by a high prevalence of sleep problems due to degenerative brain changes or comorbid conditions [1]. Wearable devices, in the form of actigraphy, have been shown to also be appropriate for monitoring sleep variables in PD patients [2,3] despite reports that current actigraphy algorithms may misinterpret dysfunctional motor activity, such as tremors, bradykinesia, dyskinesia, and limited arm movement while walking, as well as drug-induced hypermotility, thus making their use problematic in people with PD (PwPD) [4]. The ActiGraph GT3X (Pensacola, FL, USA) accelerometer is capable of recording accelerometry measurements for multiple days at 100 Hz, and has been adopted for massive population-level data collections [5]. In the last few years, Van Hees et al. have developed and made freely available open-source software to estimate sleep variables using data collected from similar off-the-shelf wearable inertial sensors [6]. The goal of this study is to investigate if the ActiGraph data, in combination with Van Hees et al.’s heuristic algorithm Distribution of Change in Z-Angle (HDCZA), can correctly estimate sleep variables in PD patients. To the best of the authors’ knowledge, it is the first study that adopts ActiGraph sensors and this methodology for sleep analysis in PwPD. For further comparison, a custom hardware prototype device named WESAA (Wearable Enabled Symptom Assessment Algorithms) developed at the Tyndall National Institute [8] and with the same capabilities as an ActiGraph device was adopted for additional analysis. Nineteen PD subjects took part in a data collection where participants wore the ActiGraph on their most affected wrist for a minimum of 24 hours and simultaneously filled out a sleep diary. Accelerometer data was collected at 100 Hz. Additionally, six subjects repeated the same data collection protocol while wearing the WESAA system. The heuristic algorithm described in [7] was implemented to detect periods of sleep and compared against the participant diaries. Results are shown in Table I and Figure I in the picture below. Accuracy reported on the subjects using the Actigraph was appropriate with an average 77.8±13.6%, even though results were quite variable across patients (between 31.6% and 91.2%). Less variability is shown with the WESAA device, even though only 6 subjects have carried out this data collection, with an average accuracy of 81.9±6.2% (71.8%-90.2%).Download : Download high-res image (157KB)Download : Download full-size image The present investigation shows that ActiGraph accelerometry data collected over 24 hours, in conjunction with the heuristic algorithm HDCZA for the detection of sleep periods, is an appropriate approach to estimate sleep duration even in PwPD. The same algorithm adopted on the WESAA hardware device shows even more promising results but further investigations with a larger sample size are required to c","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135297884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The importance of the functional base-of-support for clinical biomechanical balance analysis 功能支撑基础对临床生物力学平衡分析的重要性

Gait & posture

Pub Date : 2023-09-01 DOI: 10.1016/j.gaitpost.2023.07.233

Lizeth Sloot, Elza van Duijnhoven, Merel A. Brehm, Tamaya Van Criekinge, Matthew Millard

The occurrence of falls and balance problems are common in persons of higher age or with neuromuscular disorders. While clinical balance scales are unable to accurately identify balance, biomechanical balance models (such as the extrapolated center-of-mass) need missing information on the base-of-support formed by the feet [1]. People can balance their body mass above this area formed by the feet without taking a compensatory step. Common impairments such as muscle degeneration likely decrease this support area. Therefore, we evaluated changes in the functional base-of-support (fBOS) resulting from ageing and neuromuscular disorders and the impact on gait balance analysis. We assessed the fBOS in 20 young persons (28±7 yrs), 7 with lower leg muscle weakness due to slowly progressive neuromuscular disorders (63±5 yrs; caption Fig. 1), 7 age-matched middle-aged (62±8 yrs) and 7 old persons (80±3 yrs). Ground forces and foot markers were recorded while participants slowly moved their center-of-pressure in as large circles as possible without moving their feet. The fBOS is modeled was the convex hull enclosing this circled area normalized to marker-based foot dimensions [2]. The effect of ageing of the fBOS on dynamic balance outcomes during walking at heel strike (anterior-posterior direction) was assessed in a dataset of 138 persons across the lifespan [3,4]. The fBOS was only 24% of the foot outline formed by markers for young persons (Fig. 1A) and is 84% smaller in patients with neuromuscular disorders (pttest<0.001). The fBOS decreased with age (pANOVA=0.003), with similar values in mid-age (-24%, pttest=0.11) and a 52% decrease in old age (pttest=0.002) compared to young (Fig. 1A). When taken the fBOS into account, dynamic balance shifts from inside to outside the support area. Extrapolating the age-reduction in fBOS, balance changes from increasing to decreasing with age. Fig. 1: Functional Base of Support (fBOS) for the different participant groups.Download : Download high-res image (333KB)Download : Download full-size image Studies overlook the base-of-support as part of dynamic balance analysis [1]. This study shows the importance of using an accurate model of the fBOS, as a single reference marker does not capture 1) the shape of the effective fBOS; 2) the effects of age and disorder; and 3) changes over the gait cycle. Use of the fBOS revealed reductions in balance in older persons, compared to safer margins without the fBOS. The large group variances indicate that individual fBOS measurements are needed for precise balance assessment. We provide the fBOS model per group and code to apply this to measured markers, so researchers can establish clinical meaningful differences in dynamic balance outcomes. As such, this study strives towards the integration of accurate biomechanical balance analysis in clinical gait analysis.

跌倒和平衡问题的发生在老年人或神经肌肉疾病患者中很常见。虽然临床平衡量表无法准确识别平衡，但生物力学平衡模型(如外推质心)需要缺少足部支撑基础的信息[1]。人们可以在这个由脚形成的区域上平衡他们的体重，而不需要采取补充步骤。常见的损伤如肌肉退化可能会减少这一支撑区域。因此，我们评估了衰老和神经肌肉疾病导致的功能支持基础(fBOS)的变化以及对步态平衡分析的影响。我们评估了20名年轻人(28±7岁)的fBOS, 7名因缓慢进行性神经肌肉疾病而下肢肌肉无力(63±5岁;图1)，年龄匹配的中年人(62±8岁)7人，老年人(80±3岁)7人。地面力量和脚部标记被记录下来，同时参与者在不移动脚的情况下尽可能大范围地缓慢移动他们的压力中心。fBOS被建模为包围该圆圈区域的凸壳，归一化为基于标记的脚尺寸[2]。在138人的数据集中评估了fBOS老化对足跟撞击(前后方向)行走时动态平衡结果的影响[3,4]。fBOS仅占年轻人标记物形成的足部轮廓的24%(图1A)，神经肌肉疾病患者的fBOS小84% (pttest<0.001)。fBOS随着年龄的增长而下降(pANOVA=0.003)，与年轻人相比，中年人的fBOS值相似(-24%，pttest=0.11)，老年人的fBOS值下降52% (pttest=0.002)(图1A)。当考虑到fBOS时，动态平衡从内部转移到外部支持区域。推断fBOS的年龄减少，平衡随着年龄的增长从增加到减少。图1:不同参与者群体的功能支持基础(fBOS)。下载:下载高分辨率图片(333KB)下载:下载全尺寸图片作为动平衡分析的一部分，研究忽略了支撑基础[1]。这项研究显示了使用精确的fBOS模型的重要性，因为单一的参考标记不能捕获1)有效fBOS的形状;2)年龄和紊乱的影响;3)步态周期的变化。与不使用fBOS的安全边缘相比，使用fBOS显示老年人平衡能力下降。较大的组方差表明，需要单独的fBOS测量来进行精确的平衡评估。我们提供了每个组的fBOS模型和代码，将其应用于测量的标记物，因此研究人员可以建立动态平衡结果的临床有意义的差异。因此，本研究致力于将准确的生物力学平衡分析整合到临床步态分析中。

{"title":"The importance of the functional base-of-support for clinical biomechanical balance analysis","authors":"Lizeth Sloot, Elza van Duijnhoven, Merel A. Brehm, Tamaya Van Criekinge, Matthew Millard","doi":"10.1016/j.gaitpost.2023.07.233","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.233","url":null,"abstract":"The occurrence of falls and balance problems are common in persons of higher age or with neuromuscular disorders. While clinical balance scales are unable to accurately identify balance, biomechanical balance models (such as the extrapolated center-of-mass) need missing information on the base-of-support formed by the feet [1]. People can balance their body mass above this area formed by the feet without taking a compensatory step. Common impairments such as muscle degeneration likely decrease this support area. Therefore, we evaluated changes in the functional base-of-support (fBOS) resulting from ageing and neuromuscular disorders and the impact on gait balance analysis. We assessed the fBOS in 20 young persons (28±7 yrs), 7 with lower leg muscle weakness due to slowly progressive neuromuscular disorders (63±5 yrs; caption Fig. 1), 7 age-matched middle-aged (62±8 yrs) and 7 old persons (80±3 yrs). Ground forces and foot markers were recorded while participants slowly moved their center-of-pressure in as large circles as possible without moving their feet. The fBOS is modeled was the convex hull enclosing this circled area normalized to marker-based foot dimensions [2]. The effect of ageing of the fBOS on dynamic balance outcomes during walking at heel strike (anterior-posterior direction) was assessed in a dataset of 138 persons across the lifespan [3,4]. The fBOS was only 24% of the foot outline formed by markers for young persons (Fig. 1A) and is 84% smaller in patients with neuromuscular disorders (pttest<0.001). The fBOS decreased with age (pANOVA=0.003), with similar values in mid-age (-24%, pttest=0.11) and a 52% decrease in old age (pttest=0.002) compared to young (Fig. 1A). When taken the fBOS into account, dynamic balance shifts from inside to outside the support area. Extrapolating the age-reduction in fBOS, balance changes from increasing to decreasing with age. Fig. 1: Functional Base of Support (fBOS) for the different participant groups.Download : Download high-res image (333KB)Download : Download full-size image Studies overlook the base-of-support as part of dynamic balance analysis [1]. This study shows the importance of using an accurate model of the fBOS, as a single reference marker does not capture 1) the shape of the effective fBOS; 2) the effects of age and disorder; and 3) changes over the gait cycle. Use of the fBOS revealed reductions in balance in older persons, compared to safer margins without the fBOS. The large group variances indicate that individual fBOS measurements are needed for precise balance assessment. We provide the fBOS model per group and code to apply this to measured markers, so researchers can establish clinical meaningful differences in dynamic balance outcomes. As such, this study strives towards the integration of accurate biomechanical balance analysis in clinical gait analysis.","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Gait & posture

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀