Dataset Information

The effect of a knee brace in dynamic motion-An instrumented gait analysis.

ABSTRACT: BACKGROUND:Osteoarthritis (OA) is a common problem in the older population. To reduce pain and stress in the affected knee joint compartment, a functional knee brace is often prescribed by physicians to protect it from high loads. OBJECTIVES:An instrumented gait analysis should evaluate how the 4-point knee orthosis for varus or valgus load relief (M.4s OA) changes the kinematics of the knee, especially in the frontal plane. METHODS:17 healthy participants took part and were analyzed with an inertial sensor system (MyoMotion) giving continuous, objective information on the anatomical angles. The measurements were made both without wearing a knee brace and with the brace in different settings. RESULTS:The results show a significant reduction in the maximum knee abduction and raised knee adduction. The knee brace, with a strong adjustment in varus or valgus orientation, caused a shift of maximum ab-/adduction in the proposed direction in 69% and 75% of the dynamic tests, respectively. The knee motion in the frontal plane shows individual movement patterns. CONCLUSION:The use of the brace leads to significant changes in the knee's movement. Patient-specific movement patterns may explain different effects of functional knee braces on individual persons. Inertial sensors have been shown to be a low-cost, easy-to-use option for individual movement analysis and further personalized therapy.

SUBMITTER: Siebers HL

PROVIDER: S-EPMC7482934 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The effect of a knee brace in dynamic motion-An instrumented gait analysis.

Siebers Hannah Lena HL Eschweiler Jörg J Pinz Jan J Tingart Markus M Rath Björn B

PloS one 20200910 9

<h4>Background</h4>Osteoarthritis (OA) is a common problem in the older population. To reduce pain and stress in the affected knee joint compartment, a functional knee brace is often prescribed by physicians to protect it from high loads.<h4>Objectives</h4>An instrumented gait analysis should evaluate how the 4-point knee orthosis for varus or valgus load relief (M.4s OA) changes the kinematics of the knee, especially in the frontal plane.<h4>Methods</h4>17 healthy participants took part and wer ...[more]

PMID: 32911488

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Project description:BackgroundFalls are common following stroke and are frequently related to deficits in balance and mobility. This study aimed to investigate the predictive strength of gait and balance variables for evaluating post-stroke falls risk over 12?months following rehabilitation discharge.MethodsA prospective cohort study was undertaken in inpatient rehabilitation centres based in Australia and Singapore. A consecutive sample of 81 individuals (mean age 63?years; median 24?days post stroke) were assessed within one week prior to discharge. In addition to comfortable gait speed over six metres (6mWT), a depth-sensing camera (Kinect) was used to obtain fast-paced gait speed, stride length, cadence, step width, step length asymmetry, gait speed variability, and mediolateral and vertical pelvic displacement. Balance variables were the step test, timed up and go (TUG), dual-task TUG, and Wii Balance Board-derived centre of pressure velocity during static standing. Falls data were collected using monthly calendars.ResultsOver 12?months, 28% of individuals fell at least once. The faller group had increased TUG time and reduced stride length, gait speed variability, mediolateral and vertical pelvic displacement, and step test scores (P?<?0.001-0.048). Significant predictors, when adjusted for country, prior falls and assistance (i.e., physical assistance and/or gait aid use) were stride length, step length asymmetry, mediolateral pelvic displacement, step test and TUG scores (P?<?0.040; IQR-odds ratio(OR)?=?1.37-7.85). With comfortable gait speed as an additional covariate, to determine the additive benefit over standard clinical assessment, only mediolateral pelvic displacement, TUG and step test scores remained significant (P?=?0.001-0.018; IQR-OR?=?5.28-10.29).ConclusionsReduced displacement of the pelvis in the mediolateral direction during walking was the strongest predictor of post-stroke falls compared with other gait variables. Dynamic balance measures, such as the TUG and step test, may better predict falls than gait speed or static balance measures.

Project description:ImportanceRegaining the ability to walk safely is a high priority for adults with vestibular loss. Thus, practitioners need comprehensive knowledge of vestibulopathic gait to design, provide, and/or interpret outcomes of interventions. To date, few studies have characterized the effects of vestibular loss on gait.ObjectivesTo investigate the use of an instrumented 2-minute walk test in adults with vestibular loss, to further characterize vestibulopathic gait, and to assess whether those with chronic vestibular loss have enduring gait deficits.Design, setting, and participantsThis cross-sectional study, conducted between April 3, 2018, and June 27, 2019, recruited adults 20 to 79 years of age from an academic, tertiary, hospital-based, ambulatory care setting who were healthy or had confirmed unilateral or bilateral vestibular hypofunction. Of the 43 adults who were screened from convenience and referred samples, 2 declined, and 7 were excluded because of health conditions.ExposuresThe main exposure was the instrumented 2-minute walk test, which was conducted with participants using wearable inertial measurement units while they walked a 10-m path at their self-selected speed and turned 180° in their self-selected direction at either end.Main outcomes and measuresThe primary measures were spatiotemporal gait metrics (eg, stride length [SL] and peak whole-body turning velocity). Multivariate analysis of variance was used to assess between-group differences. Validity was assessed using the area under the curve from receiver operator characteristic analyses.ResultsData from 17 healthy adults (mean [SD] age, 39.27 [11.20] years; 13 [76%] female) and 13 adults with vestibular loss (mean [SD] age, 60.50 [10.81] years; 6 [46%] female) were analyzed. Very large between-group differences were found for SL (left) (estimated marginal mean [SE] for healthy vs vestibular groups, 1.47 [0.04] m vs 1.31 [0.04] m; Cohen d, 1.35; 95% CI, 0.18-2.52), SL (right) (estimated marginal mean [SE] for healthy vs vestibular groups, 1.46 [0.04] m vs 1.29 [0.04] m; Cohen d, 1.44; 95% CI, 0.25-2.62), and peak turn velocity (estimated marginal mean [SE] for healthy vs vestibular groups, 240.17 [12.78]°/s vs 189.74 [14.70]°/s; Cohen d, 1.23; 95% CI, 0.07-2.40). The area under the curve was 0.79 (95% CI, 0.62-0.95) for SL (left), 0.81 (95% CI, 0.64-0.97) for SL (right), and 0.86 (95% CI, 0.72-0.99) for peak turn velocity.Conclusions and relevanceIn this cross-sectional study, instrumented gait analysis had good discriminative validity and revealed persistent deficits in gait stability in those with chronic vestibular loss. The findings of this study suggest that these clinically and functionally meaningful deficits could be targets for vestibular rehabilitation.

Project description:BackgroundInstrumented assessment of motor symptoms has emerged as a promising extension to the clinical assessment of several movement disorders. The use of mobile and inexpensive technologies such as some markerless motion capture technologies is especially promising for large-scale application but has not transitioned into clinical routine to date. A crucial step on this path is to implement standardized, clinically applicable tools that identify and control for quality concerns.ObjectiveThe main goal of this study comprises the development of a systematic quality control (QC) procedure for data collected with markerless motion capture technology and its experimental implementation to identify specific quality concerns and thereby rate the usability of recordings.MethodsWe developed a post hoc QC pipeline that was evaluated using a large set of short motor task recordings of healthy controls (2010 recordings from 162 subjects) and people with multiple sclerosis (2682 recordings from 187 subjects). For each of these recordings, 2 raters independently applied the pipeline. They provided overall usability decisions and identified technical and performance-related quality concerns, which yielded respective proportions of their occurrence as a main result.ResultsThe approach developed here has proven user-friendly and applicable on a large scale. Raters' decisions on recording usability were concordant in 71.5%-92.3% of cases, depending on the motor task. Furthermore, 39.6%-85.1% of recordings were concordantly rated as being of satisfactory quality whereas in 5.0%-26.3%, both raters agreed to discard the recording.ConclusionsWe present a QC pipeline that seems feasible and useful for instant quality screening in the clinical setting. Results confirm the need of QC despite using standard test setups, testing protocols, and operator training for the employed system and by extension, for other task-based motor assessment technologies. Results of the QC process can be used to clean existing data sets, optimize quality assurance measures, as well as foster the development of automated QC approaches and therefore improve the overall reliability of kinematic data sets.

Project description:BackgroundInstrumented gait analysis (IGA) has been around for a long time but has never been shown to be useful for improving patient outcomes. In this study we demonstrate the potential utility of IGA by showing that machine learning models are better able to estimate treatment outcomes when they include both IGA and clinical (CLI) features compared to when they include CLI features alone.DesignWe carried out a retrospective analysis of data from ambulatory children diagnosed with cerebral palsy who were seen at least twice at our gait analysis center. Individuals underwent a variety of treatments (including no treatment) between sequential gait analyses. We fit Bayesian Additive Regression Tree (BART) models that estimated outcomes for mean stance foot progression to demonstrate the approach. We built two models: one using CLI features only, and one using CLI and IGA features. We then compared the models' performance in detail. We performed similar, but less detailed, analyses for a number of other outcomes. All results were based on independent test data from a 70%/30% training/testing split.ResultsThe IGA model was more accurate than the CLI model for mean stance-phase foot progression outcomes (RMSEIGA = 11∘, RMSECLI = 13∘) and explained more than 1.5 × as much of the variance (R2IGA = .45, R2CLI = .28). The IGA model outperformed the CLI model for every level of treatment complexity, as measured by number of simultaneous surgeries. The IGA model also exhibited superior performance for estimating outcomes of mean stance-phase knee flexion, mean stance-phase ankle dorsiflexion, maximum swing-phase knee flexion, gait deviation index (GDI), and dimensionless speed.InterpretationThe results show that IGA has the potential to be useful in the treatment planning process for ambulatory children diagnosed with cerebral palsy. We propose that the results of machine learning outcome estimators-including estimates of uncertainty-become the primary IGA tool utilized in the clinical process, complementing the standard medical practice of conducting a through patient history and physical exam, eliciting patient goals, reviewing relevant imaging data, and so on.

Dataset Information

The effect of a knee brace in dynamic motion-An instrumented gait analysis.

Publications

The effect of a knee brace in dynamic motion-An instrumented gait analysis.

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