Identification of functional parameters for the classification of older female fallers and prediction of 'first-time' fallers

Changes in Navigation Controls and Field-of-View Modes Affect Cybersickness Severity and Spatiotemporal Gait Patterns After Exposure to Virtual Environments

OBJECTIVE

To examine the effects of navigation controls and field-of-view modes on cybersickness severity and gait dynamics after cessation of exposure to a virtual environment (VE).

BACKGROUND

The applications of virtual reality are increasing in various fields; however, whether changes in interaction techniques and visual contents could mitigate the potential gait disturbance following VE exposure remains unclear.

METHOD

Thirty healthy adults wore a head-mounted display to complete six sessions of 12-min run-and-gun tasks using different navigation controls (gamepad, head, natural) and field-of-view modes (full, restricted). Forward and backward walking tasks were performed before and after VE exposure. The degrees of cybersickness and presence were evaluated using questionnaires, along with the in-session task performance. Spatiotemporal gait measures and their variabilities were calculated for each walking task.

RESULTS

The participants experienced less cybersickness with the head and natural controls than with the gamepad. Natural control, based on matching body movements, was associated with the highest degree of presence and best performance. VE navigation using the gamepad showed reduced cadences and increased stride times during postexposure forward-walking tasks. When the VE was presented via the restricted field-of-view mode, increased gait variabilities were observed from backward-walking tasks after VE exposure.

CONCLUSION

Body movement-based navigation controls may alleviate cybersickness. We observed gait adaptation during both ambulation tasks, which was influenced by the navigation control method and field-of-view mode.

APPLICATION

This study provides the first evidence for gait adaptation during balance-demanding tasks after VE exposure, which is valuable for designing guidelines for virtual reality interactions.

Can transcranial direct current stimulation combined with interactive computerized cognitive training boost cognition and gait performance in older adults with mild cognitive impairment? a randomized controlled trial

BACKGROUND

Older adults with Mild Cognitive Impairment (MCI) are often subject to cognitive and gait deficits. Interactive Computerized Cognitive Training (ICCT) may improve cognitive function; however, the effect of such training on gait performance is limited. Transcranial Direct Current Stimulation (tDCS) improves cognition and gait performance. It remains unclear whether combining tDCS with ICCT produces an enhanced synergistic effect on cognition and complex gait performance relative to ICCT alone. This study aimed to compare the effects of tDCS combined with ICCT on cognition and gait performance in older adults with MCI.

METHOD

Twenty-one older adults with MCI were randomly assigned to groups receiving either anodal tDCS and ICCT ( tDCS + ICCT ) or sham tDCS and ICCT ( sham + ICCT ). Participants played Nintendo Switch cognitive games for 40 min per session, simultaneously receiving either anodal or sham tDCS over the left dorsolateral prefrontal cortex for the first 20 min. Cognitive and gait assessments were performed before and after 15 training sessions.

RESULTS

The global cognition, executive function, and working-memory scores improved in both groups, but there were no significant interaction effects on cognitive outcomes. Additionally, the group × time interactions indicated that tDCS + ICCT significantly enhanced dual-task gait performance in terms of gait speed (p = 0.045), variability (p = 0.016), and dual-task cost (p = 0.039) compared to sham + ICCT.

CONCLUSION

The combined effect of tDCS and ICCT on cognition was not superior to that of ICCT alone; however, it had a significant impact on dual-task gait performance. Administering tDCS as an adjunct to ICCT may thus provide additional benefits for older adults with MCI.

TRIAL REGISTRATION

This trial was registered at http://www.

CLINICALTRIALS

in.th/ (TCTR 20,220,328,009).

A machine learning approach to identify important variables for distinguishing between fallers and non-fallers in older women

Falls are a significant ongoing public health concern for older adults. At present, few studies have concurrently explored the influence of multiple measures when seeking to determine which variables are most predictive of fall risks. As such, this cross-sectional study aimed to identify those functional variables (i.e. balance, gait and clinical measures) and physical characteristics (i.e. strength and body composition) that could best distinguish between older female fallers and non-fallers, using a machine learning approach. Overall, 60 community-dwelling older women (≥65 years), retrospectively classified as fallers (n = 21) or non-fallers (n = 39), attended three data collection sessions. Data (281 variables) collected from tests in five separate domains (balance, gait, clinical measures, strength and body composition) were analysed using random forest (RF) and leave-one-variable-out partial least squares correlation analysis (LOVO PLSCA) to assess variable importance. The strongest discriminators from each domain were then aggregated into a multi-domain dataset, and RF, LOVO PLSCA, and logistic regression models were constructed to identify the important variables in distinguishing between fallers and non-fallers. These models were used to classify participants as either fallers or non-fallers, with their performance evaluated using receiver operating characteristic (ROC) analysis. The study found that it is possible to classify fallers and non-fallers with a high degree of accuracy (e.g. logistic regression: sensitivity = 90%; specificity = 87%; AUC = 0.92; leave-one-out cross-validation accuracy = 63%) using a combination of 18 variables from four domains, with the gait and strength domains being particularly informative for screening programmes aimed at assessing falls risk.

Left and Right Cortical Activity Arising from Preferred Walking Speed in Older Adults

Cortical activity and walking speed are known to decline with age and can lead to an increased risk of falls in the elderly. Despite age being a known contributor to this decline, individuals age at different rates. This study aimed to analyse left and right cortical activity changes in elderly adults regarding their walking speed. Cortical activation and gait data were obtained from 50 healthy older individuals. Participants were then grouped into a cluster based on their preferred walking speed (slow or fast). Analyses on the differences of cortical activation and gait parameters between groups were carried out. Within-subject analyses on left and right-hemispheric activation were also performed. Results showed that individuals with a slower preferred walking speed required a higher increase in cortical activity. Individuals in the fast cluster presented greater changes in cortical activation in the right hemisphere. This work demonstrates that categorizing older adults by age is not necessarily the most relevant method, and that cortical activity can be a good indicator of performance with respect to walking speed (linked to fall risk and frailty in the elderly). Future work may wish to explore how physical activity training influences cortical activation over time in the elderly.

Predicting risk of falls in elderly using a single Inertial Measurement Unit on the lower-back by estimating spatio-temporal gait parameters

One of the consequences of aging is the increased risk of falls, especially when someone walks in unknown or uncontrolled environments. Usually, gait is evaluated through observation and clinical assessment scales to identify the state and deterioration of the patient's postural control. Lately, technological systems for bio-mechanical analysis have been used to determine abnormal gait states being expensive, difficult to use, and impossible to apply in real conditions. In this article, we explore the ability of a system based on a single inertial measurement unit located in the lower back to estimate spatio-temporal gait parameters by analyzing the signals available in the Physionet repository "Long Term Movement Monitoring Database" which, together with automatic classification algorithms, allow predicting the risk of falls in the elderly population. Different classification algorithms were trained and evaluated, being the Support Vector Machine classifier with a third-degree polynomial kernel, cost function C = 2 with the best performance, with an Accuracy = 59%, Recall = 91%, and F1- score = 71%, providing promising results regarding a proposal for the quantitative, online and realistic evaluation of gait during activities of daily living, which is where falls actually occur in the target population. Clinical Relevance - This work proposes an early risk of falls detection tool, essential to start preventive treatment strategies to maintain the independence of the elderly through a non-invasive, simple, and low-cost alternative.

A Novel Privacy Preservation and Quantification Methodology for Implementing Home-Care-Oriented Movement Analysis Systems

Human movement is generally evaluated through both observations and clinical assessment scales to identify the state and deterioration of a patient's motor control. Lately, technological systems for human motion analysis have been used in clinics to identify abnormal movement states, while they generally suffer from privacy challenges and concerns especially at home or in remote places. This paper presents a novel privacy preservation and quantification methodology that imitates the forgetting process of human memory to protect privacy in patient-centric healthcare. The privacy preservation principle of this methodology is to change the traditional data analytic routines into a distributed and disposable form (i.e., DnD) so as to naturally minimise the disclosure of patients' health data. To help judge the efficacy of DnD-based privacy preservation, the researchers further developed a risk-driven privacy quantification framework to supplement the existing privacy quantification techniques. To facilitate validating the methodology, this research also involves a home-care-oriented movement analysis system that comprises a single inertial measurement sensor and a mobile application. The system can acquire personal information, raw data of movements and indexes to evaluate the risk of falls and gait at homes. Moreover, the researchers conducted a technological appreciation survey of 16 health professionals to help understand the perception of this research. The survey obtains positive feedback regarding the movement analysis system and the proposed methodology as suitable for home-care scenarios.

Self-paced treadmills do not allow for valid observation of linear and nonlinear gait variability outcomes in patients with Parkinson's disease

BACKGROUND

Due to the imposed constant belt speed, motorized treadmills are known to affect linear and nonlinear gait variability outcomes. This is particularly true of patients with Parkinson's Disease where the treadmill can act as an external pacemaker. Self-paced treadmills update the belt speed in response to the subject's walking speed and might, therefore, be a useful tool for measurement of gait variability in this patient population. This study aimed to compare gait variability during walking at self-paced and constant treadmill speeds with overground walking in individuals with PD and individuals with unimpaired gait.

METHODS

Thirteen patients with Parkinson's Disease and thirteen healthy controls walked under three conditions: overground, on a treadmill at a constant speed, and using three self-paced treadmill modes. Gait variability was assessed with coefficient of variation (CV), sample entropy (SampEn), and detrended fluctuation analysis (DFA) of stride time and length. Systematic and random error between the conditions was quantified.

RESULTS

For individuals with PD, error in variability measurement was less during self-paced modes compared with constant treadmill speed for stride time but not for stride length. However, there was substantial error for stride time and length variability for all treadmill conditions. For healthy controls the error in measurement associated with treadmill walking was substantially less.

SIGNIFICANCE

The large systematic and random errors between overground and treadmill walking prohibit meaningful gait variability observations in patients with Parkinson's Disease using self-paced or constant-speed treadmills.

Finite element derived femoral strength is a better predictor of hip fracture risk than aBMD in the AGES Reykjavik study cohort

Hip fractures associated with a high economic burden, loss of independence, and a high rate of post-fracture mortality, are a major health concern for modern societies. Areal bone mineral density is the current clinical metric of choice when assessing an individual's future risk of fracture. However, this metric has been shown to lack sensitivity and specificity in the targeted selection of individuals for preventive interventions. Although femoral strength derived from computed tomography based finite element models has been proposed as an alternative based on its superior femoral strength prediction ex vivo, such predictions have only shown marginal or no improvement for assessing hip fracture risk. This study compares finite element derived femoral strength to aBMD as a metric for hip fracture risk assessment in subjects (N = 601) from the AGES Reykjavik Study cohort and analyses the dependence of femoral strength predictions and classification accuracy on the material model and femoral loading alignment. We found hip fracture classification based on finite element derived femoral strength to be significantly improved compared to aBMD. Finite element models with non-linear material models performed better at classifying hip fractures compared to finite element models with linear material models and loading alignments with low internal rotation and adduction, which do not correspond to weak femur alignments, were found to be most suitable for hip fracture classification.

Finite Element Assessment of Bone Fragility from Clinical Images

PURPOSE OF REVIEW

We re-evaluated clinical applications of image-to-FE models to understand if clinical advantages are already evident, which proposals are promising, and which questions are still open.

RECENT FINDINGS

CT-to-FE is useful in longitudinal treatment evaluation and groups discrimination. In metastatic lesions, CT-to-FE strength alone accurately predicts impending femoral fractures. In osteoporosis, strength from CT-to-FE or DXA-to-FE predicts incident fractures similarly to DXA-aBMD. Coupling loads and strength (possibly in dynamic models) may improve prediction. One promising MRI-to-FE workflow may now be tested on clinical data. Evidence of artificial intelligence usefulness is appearing. CT-to-FE is already clinical in opportunistic CT screening for osteoporosis, and risk of metastasis-related impending fractures. Short-term keys to improve image-to-FE in osteoporosis may be coupling FE with fall risk estimates, pool FE results with other parameters through robust artificial intelligence approaches, and increase reproducibility and cross-validation of models. Modeling bone modifications over time and bone fracture mechanics are still open issues.

A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open-access code

Postural control is often quantified by recording the trajectory of the center of pressure (COP)-also called stabilogram-during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevance increases with the aging of the population. Due to the complexity of the quantification process, the analyses of sway patterns have been performed empirically using a number of variables, such as ellipse confidence area or mean velocity. This study reviews and compares a wide range of state-of-the-art variables that are used to assess the risk of fall in elderly from a stabilogram. When appropriate, we discuss the hypothesis and mathematical assumptions that underlie these variables, and we propose a reproducible method to compute each of them. Additionally, we provide a statistical description of their behavior on two datasets recorded in two elderly populations and with different protocols, to hint at typical values of these variables. First, the balance of 133 elderly individuals, including 32 fallers, was measured on a relatively inexpensive, portable force platform (Wii Balance Board, Nintendo) with a 25-s open-eyes protocol. Second, the recordings of 76 elderly individuals, from an open access database commonly used to test static balance analyses, were used to compute the values of the variables on 60-s eyes-open recordings with a research laboratory standard force platform.

Quantification of the Link between Timed Up-and-Go Test Subtasks and Contractile Muscle Properties

Frailty and falls are a major public health problem in older adults. Muscle weakness of the lower and upper extremities are risk factors for any, as well as recurrent falls including injuries and fractures. While the Timed Up-and-Go (TUG) test is often used to identify frail members and fallers, tensiomyography (TMG) can be used as a non-invasive tool to assess the function of skeletal muscles. In a clinical study, we evaluated the correlation between the TMG parameters of the skeletal muscle contraction of 23 elderly participants (22 f, age 86.74 ± 7.88) and distance-based TUG test subtask times. TUG tests were recorded with an ultrasonic-based device. The sit-up and walking phases were significantly correlated to the contraction and delay time of the muscle vastus medialis (ρ = 0.55-0.80, p < 0.01). In addition, the delay time of the muscles vastus medialis (ρ = 0.45, p = 0.03) and gastrocnemius medialis (ρ = -0.44, p = 0.04) correlated to the sit-down phase. The maximal radial displacements of the biceps femoris showed significant correlations with the walk-forward times (ρ = -0.47, p = 0.021) and back (ρ = -0.43, p = 0.04). The association of TUG subtasks to muscle contractile parameters, therefore, could be utilized as a measure to improve the monitoring of elderly people's physical ability in general and during rehabilitation after a fall in particular. TUG test subtask measurements may be used as a proxy to monitor muscle properties in rehabilitation after long hospital stays and injuries or for fall prevention.

Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data

Falls are a multifactorial cause of injuries for older people. Subjects with osteoporosis are particularly vulnerable to falls. We study the performance of different computational methods to identify people with osteoporosis who experience a fall by analysing balance parameters. Balance parameters, from eyes open and closed posturographic studies, and prospective registration of falls were obtained from a sample of 126 community-dwelling older women with osteoporosis (age 74.3 ± 6.3) using World Health Organization Questionnaire for the study of falls during a follow-up of 2.5 years. We analyzed model performance to determine falls of every developed model and to validate the relevance of the selected parameter sets. The principal findings of this research were (1) models built using oversampling methods with either IBk (KNN) or Random Forest classifier can be considered good options for a predictive clinical test and (2) feature selection for minority class (FSMC) method selected previously unnoticed balance parameters, which implies that intelligent computing methods can extract useful information with attributes which otherwise are disregarded by experts. Finally, the results obtained suggest that Random Forest classifier using the oversampling method to balance the data independent of the set of variables used got the best overall performance in measures of sensitivity (>0.71), specificity (>0.18), positive predictive value (PPV >0.74), and negative predictive value (NPV >0.66) independent of the set of variables used. Although the IBk classifier was built with oversampling data considering information from both eyes opened and closed, using all variables got the best performance (sensitivity >0.81, specificity >0.19, PPV = 0.97, and NPV = 0.66).

Rate of Torque Development and Torque Steadiness of the Lower Limb and Future Falls Among Community-Dwelling Older Adults Without Previous Falls: A Longitudinal 1-Year Study

The objective was to investigate the association between rate of torque development (RTD) and torque steadiness (TS) of the lower limb and the occurrence of prospective falls in community-dwelling older adults without falls in the previous year. One hundred older adults performed the tests to obtain the RTD and TS of the hip, knee, and ankle. New episodes of falls were monitored through telephone contact for a prospective period of 12 months. The association of RTD and TS with the occurrence of prospective falls was verified by multiple logistic regression adjusted for confounding variables. There was no association between RTD of hip, knee, and ankle and prospective falls. Only the TS at 50% of the peak torque of the hip flexors was associated with the occurrence of future falls (p = .023). Identifying modifiable risk factors for the first fall in older adults is essential for the development of adequate prevention programs.

Lower-Extremity Intra-Joint Coordination and Its Variability between Fallers and Non-Fallers during Gait

Falling is one of the most common causes of hip fracture and death in older adults. A comparison of the biomechanics of the gait in fallers and non-fallers older adults, especially joint coordination and coordination variability, enables the understanding of mechanisms that underpin falling. Therefore, we compared lower-extremity intra-joint coordination and its variability between fallers and non-fallers older adults during gait. A total of 26 older adults, comprising 13 fallers, took part in this study. The participants walked barefoot at a self-selected speed on a 10-m walkway. Gait kinematics in the dominant leg during 10 cycles were captured with 10 motion tracking cameras at a sampling rate of 100 Hz. Spatiotemporal gait parameters, namely, cadence, walking speed, double support time, stride time, width, and length, as well as intra-joint coordination and coordination variability in the sagittal plane were compared between the two groups. Results showed that fallers walked with significant lower cadence, walking speed, and stride length but greater double support and stride time than non-fallers. Significant differences in the ankle-to-knee, knee-to-hip, and ankle-to-hip coordination patterns between fallers and non-fallers and less coordination variability in fallers compared to non-fallers in some instants of the gait cycles were observed. The differences in spatiotemporal gait parameters in fallers compared to non-fallers may indicate an adaptation resulting from decreased efficiency to decrease the risk of falling. Moreover, the differences in segment coordination and its variability may indicate an inconsistency in neuromuscular control. It may also indicate reduced ability to control the motion of the leg in preparation for foot contact with the ground and the knee and ankle motions during loading response. Finally, such differences may show less control in generating power during the push-off phase in fallers.

Idosos caidores e não caidores: Associação com características sociais, fatores econômicos, aspectos clínicos, nível de atividade física e percepção do risco de quedas: um estudo transversal

RESUMO As quedas em idosos são capazes de impactar negativamente sobre diversos aspectos biopsicossociais. O objetivo deste estudo foi verificar a associação de quedas com características sociais, fatores econômicos, aspectos clínicos, nível de atividade física e percepção do risco de quedas em idosos caidores e não caidores. A amostra foi composta por 520 idosos (71,7±7,4 anos) de ambos os sexos (83,7% feminino) que foram entrevistados em relação às características sociais, fatores econômicos, aspectos clínicos, histórico de quedas, nível de atividade física (Minnesota) e percepção do risco de quedas (falls risk awareness questionnaire, PRQ). Para comparação foram utilizados os testes de Mann-Whitney e qui-quadrado e para associação os testes de regressão logística binária e multivariada. Após a análise de dados, identificamos que idosos caidores apresentaram menor escolaridade, classe econômica e PRQ. A escolaridade (OR: 1,35 a 1,28), classe econômica (OR: 1,62) e PRQ (OR: 1,46) apresentaram associação com as quedas, semelhante foi observado em idosos com duas ou mais quedas (escolaridade, OR: 2,44 a 1,92; PRQ, OR: 0,84). A análise de idosos com apenas uma queda evidenciou associação apenas com a PRQ (OR: 0,87). Conclui-se que idosos caidores apresentaram menor percepção do risco de quedas, nível de escolaridade e classe econômica. A PRQ demonstrou-se uma ferramenta importante para programas de prevenção de quedas, pois foi capaz de identificar fatores perceptuais sobre o risco de quedas que podem ser utilizados para avaliação e acompanhamento, bem como direcionar estes programas em idosos da comunidade.

Test-Retest Reliability and the Effects of Walking Speed on Stride Time Variability During Continuous, Overground Walking in Healthy Young Adults

Studies have investigated the reliability and effect of walking speed on stride time variability during walking trials performed on a treadmill. The objective of this study was to investigate the reliability of stride time variability and the effect of walking speed on stride time variability, during continuous, overground walking in healthy young adults. Participants completed: (1) 2 walking trials at their preferred walking speed on 1 day and another trial 2 to 4 days later and (2) 1 trial at their preferred walking speed, 1 trial approximately 20% to 25% faster than their preferred walking speed, and 1 trial approximately 20% to 25% slower than their preferred walking speed on a separate day. Data from a waist-mounted accelerometer were used to determine the consecutive stride times for each trial. The reliability of stride time variability outcomes was generally poor (intraclass correlations: .167-.487). Although some significant differences in stride time variability were found between the preferred walking speed, fast, and slow trials, individual between-trial differences were generally below the estimated minimum difference considered to be a real difference. The development of a protocol to improve the reliability of stride time variability outcomes during continuous, overground walking would be beneficial to improve their application in research and clinical settings.

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

Fall-related functional impairments in patients with neurological gait disorder

Falls are common in patients with neurological disorders and are a primary cause of injuries. Nonetheless, fall-associated gait characteristics are poorly understood in these patients. Objective, quantitative gait analysis is an important tool to identify the principal fall-related motor characteristics and to advance fall prevention in patients with neurological disorders. Fall incidence was assessed in 60 subjects with different neurological disorders. Patients underwent a comprehensive set of functional assessments including instrumented gait analysis, computerized postural assessments and clinical walking tests. Determinants of falls were assessed by binary logistic regression analysis and receiver operator characteristics (ROC). The best single determinant of fallers was a step length reduction at slow walking speed reaching an accuracy of 67.2% (ROC AUC: 0.669; p = 0.027). The combination of 4 spatio-temporal gait parameters including step length and parameters of variability and asymmetry were able to classify fallers and non-fallers with an accuracy of 81.0% (ROC AUC: 0.882; p < 0.001). These findings suggest significant differences in specific spatio-temporal gait parameters between fallers and non-fallers among neurological patients. Fall-related impairments were mainly identified for spatio-temporal gait characteristics, suggesting that instrumented, objective gait analysis is an important tool to estimate patients' fall risk. Our results highlight pivotal fall-related walking deficits that might be targeted by future rehabilitative interventions that aim at attenuating falls.

Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device

Neurological patients can have severe gait impairments that contribute to fall risks. Predicting falls from gait abnormalities could aid clinicians and patients mitigate fall risk. The aim of this study was to predict fall status from spatial-temporal gait characteristics measured by a wearable device in a heterogeneous population of neurological patients. Participants (n = 384, age 49–80 s) were recruited from a neurology ward of a University hospital. They walked 20 m at a comfortable speed (single task: ST) and while performing a dual task with a motor component (DT1) and a dual task with a cognitive component (DT2). Twenty-seven spatial-temporal gait variables were measured with wearable sensors placed at the lower back and both ankles. Partial least square discriminant analysis (PLS-DA) was then applied to classify fallers and non-fallers. The PLS-DA classification model performed well for all three gait tasks (ST, DT1, and DT2) with an evaluation of classification performance Area under the receiver operating characteristic Curve (AUC) of 0.7, 0.6 and 0.7, respectively. Fallers differed from non-fallers in their specific gait patterns. Results from this study improve our understanding of how falls risk-related gait impairments in neurological patients could aid the design of tailored fall-prevention interventions.

Risk factors for future falls among community-dwelling older adults without a fall in the previous year: A prospective one-year longitudinal study

INTRODUCTION

One of the main risk factor for falls is a history of falls itself. Henceforth, preventing a fall is an important strategy for the prevention of new ones. The objective of the present study was to determine whether personal self-perception questions and functional tests might represent risk factors for a fall during the year following a year without any falls among independent community-dwelling older adults, considering a period of 12 prospective months.

METHODS

A total of 101 community-dwelling older adults without a fall in the previous year underwent an initial evaluation (sample characterization, self-perception questionnaire and functional tests) and monthly monitoring of prospective fall episodes by telephone contact. We determined the association between the occurrence of prospective falls (dependent variable) and personal questions and functional tests (independent variables) with multiple binary logistic regression adjusted for confounding variables.

RESULTS

Only age (p = 0.005) and self-perception of general health (p = 0.019) showed association with the occurrence of prospective falls.

CONCLUSION

Our results showed that the only factors bearing an association with the occurrence of prospective falls were age and general health self-perception, which demonstrates the importance of administrating self-perception measures in clinical practice or in epidemiological studies for the prevention of a fall during the year following a year without any falls in older adults.

Revealing the optimal thresholds for movement performance: A systematic review and meta-analysis to benchmark pathological walking behaviour

In order to address whether increased levels of movement output variability indicate pathological performance, we systematically reviewed and synthesized meta-analysis data on healthy and pathological motor behavior. After screening up to 24'000 reports from four databases, 85 studies were included containing 2409 patients and 2523 healthy asymptomatic controls. The optimal thresholds of variability with uncertainty boundaries (in % Coefficient of Variation ± Standard Error) were estimated in 7 parameters: stride time (2.34 ± 0.21), stride length (2.99 ± 0.37), step length (3.34 ± 0.84), swing time (2.94 ± 0.60), step time (3.35 ± 0.23), step width (15.87 ± 1.86), and dual-limb support time (6.08 ± 2.83). All spatio-temporal parameters exhibited a positive effect size (pathology led to increased variability) except step width variability (Effect Size = -0.21). By objectively benchmarking thresholds for pathological motor variability also presented through a case-study, this review provides access to movement signatures to understand neurological changes in an individual that are apparent in movement variability. The comprehensive evidence presented now qualifies stride time variability as a movement biomarker, endorsing its applicability as a viable outcome measure in clinical trials.

A method to concatenate multiple short time series for evaluating dynamic behaviour during walking

Gait variability is a sensitive metric for assessing functional deficits in individuals with mobility impairments. To correctly represent the temporal evolution of gait kinematics, nonlinear measures require extended and uninterrupted time series. In this study, we present and validate a novel algorithm for concatenating multiple time-series in order to allow the nonlinear analysis of gait data from standard and unrestricted overground walking protocols. The full-body gait patterns of twenty healthy subjects were captured during five walking trials (at least 5 minutes) on a treadmill under different weight perturbation conditions. The collected time series were cut into multiple shorter time series of varying lengths and subsequently concatenated using a novel algorithm that identifies similar poses in successive time series in order to determine an optimal concatenation time point. After alignment of the datasets, the approach then concatenated the data to provide a smooth transition. Nonlinear measures to assess stability (Largest Lyapunov Exponent, LyE) and regularity (Sample Entropy, SE) were calculated in order to quantify the efficacy of the concatenation approach using intra-class correlation coefficients, standard error of measurement and paired effect sizes. Our results indicate overall good agreement between the full uninterrupted and the concatenated time series for LyE. However, SE was more sensitive to the proposed concatenation algorithm and might lead to false interpretation of physiological gait signals. This approach opens perspectives for analysis of dynamic stability of gait data from physiological overground walking protocols, but also the re-processing and estimation of nonlinear metrics from previously collected datasets.

Does variability of footfall kinematics correlate with dynamic stability of the centre of mass during walking?

A stable walking pattern is presumably essential to avoid falls. Stability of walking is most accurately determined by the short-term local dynamic stability (maximum Lyapunov exponent) of the body centre of mass. In many studies related to fall risk, however, variability of step width is considered to be indicative of the stability of the centre of mass during walking. However, other footfall parameters, in particular variability of stride time, have also been associated with increased risk for falling. Therefore, the aim of this study was to investigate the association between short-term local dynamic stability of the body centre of mass and different measures of footfall variability. Twenty subjects performed unperturbed walking trials on a treadmill and under increased (addition of 40% body weight) and decreased (harness system) demands to stabilise the body centre of mass. Association between stability of the centre of mass and footfall parameters was established using a structural equation model. Walking with additional body weight lead to greater instability of the centre of mass and increased stride time variability, however had no effect on step width variability. Supported walking in the harness system did not increase centre of mass stability further, however, led to a significant decrease of step width and increase in stride time variability. A structural equation model could only predict 8% of the variance of the centre of mass stability after variability of step width, stride time and stride length were included. A model which included only step width variability as exogenous variable, failed to predict centre of mass stability. Because of the failure to predict centre of mass stability in this study, it appears, that the stability of the centre of mass is controlled by more complex interaction of sagittal and frontal plane temporal and spatial footfall parameters, than those observed by standard variability measures. Anyway, this study does not support the application of step width variability as indicator for medio-lateral stability of the centre of mass during walking.

Less Is More - Estimation of the Number of Strides Required to Assess Gait Variability in Spatially Confined Settings

Background: Gait variability is an established marker of gait function that can be assessed using sensor-based approaches. In clinical settings, spatial constraints and patient condition impede the execution of longer distance walks for the recording of gait parameters. Turning paradigms are often used to overcome these constraints and commercial gait analysis systems algorithmically exclude turns for gait parameters calculations. We investigated the effect of turns in sensor-based assessment of gait variability.

Methods: Continuous recordings from 31 patients with movement disorders (ataxia, essential tremor and Parkinson’s disease) and 162 healthy elderly (HE) performing level walks including 180° turns were obtained using an inertial sensor system. Accuracy of the manufacturer’s algorithm of turn-detection was verified by plotting stride time series. Strides before and after turn events were extracted and compared to respective average of all strides. Coefficient of variation (CoV) of stride length and stride time was calculated for entire set of strides, segments between turns and as cumulative values. Their variance and congruency was used to estimate the number of strides required to reliably assess the magnitude of stride variability.

Results: Non-detection of turns in 5.8% of HE lead to falsely increased CoV for these individuals. Even after exclusion of these, strides before/after turns tended to be spatially shorter and temporally longer in all groups, contributing to an increase of CoV at group level and widening of confidence margins with increasing numbers of strides. This could be attenuated by a more generous turn excision as an alternative approach. Correlation analyses revealed excellent consistency for CoVs after at most 20 strides in all groups. Respective stride counts were even lower in patients using a more generous turn excision.

Conclusion: Including turns to increase continuous walking distance in spatially confined settings does not necessarily improve the validity and reliability of gait variability measures. Specifically with gait pathology, perturbations of stride characteristics before/after algorithmically excised turns were observed that may increase gait variability with this paradigm. We conclude that shorter distance walks of around 15 strides suffice for reliable and valid recordings of gait variability in the groups studied here.

Difference scores between single-task and dual-task gait measures are better than clinical measures for detection of fall-risk in community-dwelling older adults

BACKGROUND

As the proportion of older adults in the population increases, so does the associated prevalence of falls, making falls the leading cause of fatal and nonfatal injuries among adults aged ≥65 years. In response, researchers and clinicians seek to develop a clinical tool that accurately predicts fall risk. These Investigations have included measures of clinical mobility and balance tests, strength, physiologically based tests, postural sway, and mean and variability of gait measures. To date, no study has concurrently explored all these measures to determine which measures, alone or in combination, emerge as the most predictive of fall risk. While there is evidence that dual-task gait conditions are sensitive indicators of fall risk, difference scores between dual-task and single-task gait conditions (DS) have not been explored.

RESEARCH QUESTION

This study included outcome measures representing diverse domains (clinical mobility and balance, strength, physiological, postural sway, and mean and variability of difference scores between dual- and single-task gait conditions) to determine the combination of measures that were the most sensitive for retrospectively classifying fallers from non-fallers.

METHODS

Forty-two (mean: 75.8 yrs ± 3.3) community-dwelling older adults completed a comprehensive battery of 76 measures and classified into two groups based on self-report of having one or more falls in the previous year.

RESULTS

Results suggest that 11 measures captured the salient characteristics of the total cohort (fallers (N = 27) and non-fallers (N = 15) and that five gait measures were sufficient for correctly classifying fallers and non-fallers with 92.3% sensitivity and 66.7% specificity with a total model classification of 82.9%.

SIGNIFICANCE

The five variables comprise mean DS of stride timing, stride width, and stride length and DS in variability for stride width and stride velocity demonstrating that difference in performance between dual-task and single-task gait trials was essential for discriminating fallers and superior to other measures.

Cortical Contribution to Linear, Non-linear and Frequency Components of Motor Variability Control during Standing

Motor variability is an inherent feature of all human movements and reflects the quality of functional task performance. Depending on the requirements of the motor task, the human sensory-motor system is thought to be able to flexibly govern the appropriate level of variability. However, it remains unclear which neurophysiological structures are responsible for the control of motor variability. In this study, we tested the contribution of cortical cognitive resources on the control of motor variability (in this case postural sway) using a dual-task paradigm and furthermore observed potential changes in control strategy by evaluating Ia-afferent integration (H-reflex). Twenty healthy subjects were instructed to stand relaxed on a force plate with eyes open and closed, as well as while trying to minimize sway magnitude and performing a "subtracting-sevens" cognitive task. In total 25 linear and non-linear parameters were used to evaluate postural sway, which were combined using a Principal Components procedure. Neurophysiological response of Ia-afferent reflex loop was quantified using the Hoffman reflex. In order to assess the contribution of the H-reflex on the sway outcome in the different standing conditions multiple mixed-model ANCOVAs were performed. The results suggest that subjects were unable to further minimize their sway, despite actively focusing to do so. The dual-task had a destabilizing effect on PS, which could partly (by 4%) be counter-balanced by increasing reliance on Ia-afferent information. The effect of the dual-task was larger than the protective mechanism of increasing Ia-afferent information. We, therefore, conclude that cortical structures, as compared to peripheral reflex loops, play a dominant role in the control of motor variability.

Motor-cognitive dual-tasking under hypoxia

Hypoxic conditions diminish motor performance and cognitive functions, especially when the motor task and the cognitive task are conducted simultaneously. Hypoxia does further increase prefrontal cortex activity which provokes a reduced capability of efficient resource utilisation. This, again, might evoke that the capacity of the limited mental resources of a dual task will be reached at an earlier stage of task complexity. The purpose of our study was to examine whether a cognitive task would increase gait variability to a higher extent under hypoxic as compared to normoxic conditions. 18 young subjects walked on a treadmill with and without performing a cognitive task under normoxic vs. normobar hypoxic conditions. The variability of stride times was calculated and a two-way ANOVA with repeated measurements was performed to compare single-task walking with a dual task under both environmental conditions. Furthermore, we compared the cognitive performance while walking in different conditions with Wilcoxon tests. An interaction effect (F _1,34 = 6.178; p = 0.018; η _p²  = 0.154) was observed indicating that in the dual-task condition, there was a greater increase in gait variability in hypoxic conditions as compared to normoxic conditions. We further observed that under hypoxic conditions, each participant performed worse in the cognitive task while walking (p < 0.001). Hypoxia might decrease the ability of performing a motor-cognitive dual task. We speculate that performing a dual task under hypoxia requires a shift of resources away from prefrontal regions. However, future research should verify this assumption examining prefrontal cortex activity while dual-task walking under normoxic conditions to analyse the haemodynamic responses of the brain.

What Is the Contribution of Ia-Afference for Regulating Motor Output Variability during Standing?

Motor variability is an inherent feature of all human movements, and describes the system's stability and rigidity during the performance of functional motor tasks such as balancing. In order to ensure successful task execution, the nervous system is thought to be able to flexibly select the appropriate level of variability. However, it remains unknown which neurophysiological pathways are utilized for the control of motor output variability. In responding to natural variability (in this example sway), it is plausible that the neuro-physiological response to muscular elongation contributes to restoring a balanced upright posture. In this study, the postural sway of 18 healthy subjects was observed while their visual and mechano-sensory system was perturbed. Simultaneously, the contribution of Ia-afferent information for controlling the motor task was assessed by means of H-reflex. There was no association between postural sway and Ia-afference in the eyes open condition, however up to 4% of the effects of eye closure on the magnitude of sway can be compensated by increased reliance on Ia-afference. Increasing the biomechanical demands by adding up to 40% bodyweight around the trunk induced a specific sway response, such that the magnitude of sway remained unchanged but its dynamic structure became more regular and stable (by up to 18%). Such regular sway patterns have been associated with enhanced cognitive involvement in controlling motor tasks. It therefore appears that the nervous system applies different control strategies in response to the perturbations: The loss of visual information is compensated by increased reliance on other receptors; while the specific regular sway pattern associated with additional weight-bearing was independent of Ia-afferent information, suggesting the fundamental involvement of supraspinal centers for the control of motor output variability.

Variability of spatial temporal gait parameters and center of pressure displacements during gait in elderly fallers and nonfallers: A 6-month prospective study

Considering that most of the falls in elderly population arise during walking, tests derived from walking performance would be desirable for comprehensive fall risk assessment. The analysis of spatial temporal parameters and the center of pressure displacement, which represents the interaction between the human body and the ground, would be beneficial. The aim of this study was to compare spatial temporal gait parameters and their variability and the variability of the center of pressure displacement between elderly fallers and nonfallers during gait at self-selected, defined and fast speeds. A prospective study design was used. At the baseline, measurements of ground reaction force during gait at self-selected, defined and fast walking speeds by two force plates were performed. In addition, the Tinetti balance assessment tool, the Falls Efficacy Scale-International and the Activities-Specific Balance Confidence Scale were used. Mean and coefficient of variation of spatial temporal gait parameters and standard deviations of center of pressure displacement during loading response, midstance, terminal stance and preswing phases were calculated. Comparison of the fallers and nonfallers exhibited no significant difference in clinical tool, scales or spatial temporal parameters. Compared to nonfallers’ increased variability of walking speed at self-selected and defined speed, step width at fast walking speed and center of pressure displacement during preswing phase in medial-lateral directions at defined walking speed was found in fallers. However, application of the Holm-Bonferroni procedure for multiple comparisons exhibited no significant effect of group in any of the gait parameters. In general, our study did not observe an effect of group (fallers vs. nonfallers) on variability of spatial temporal parameters and center of pressure movement during gait. However, walking speed, step width as well as standard deviation of COP displacement in the medial-lateral direction during preswing exhibited a certain potential for distinguishing between elderly fallers and nonfallers.

Standing Balance and Spatiotemporal Aspects of Gait Are Impaired Upon Nocturnal Awakening in Healthy Late Middle-Aged and Older Adults

STUDY OBJECTIVES

Nocturnal awakenings may constitute a unique risk for falls among older adults. We describe differences in gait and balance between presleep and midsleep testing, and whether changes in the lighting environment during the midsleep testing further affect gait and balance.

METHODS

Twenty-one healthy, late middle-aged and older (64.7 ± 8.0 y) adults participated in this repeated-measures design consisting of four overnight laboratory stays. Each night, participants completed baseline visual acuity, gait, and balance testing. After a 2-h sleep opportunity, they were awakened for 13 min into one of four lighting conditions: very dim white light (< 0.5 lux); dim white light (∼28.0 lux); dim orange light (∼28.0 lux); and white room-level light (∼200 lux). During this awakening, participants completed the same sequence of testing as at baseline.

RESULTS

Low-contrast visual acuity significantly decreased with decreasing illuminance conditions (F(3,45) = 98.26, p < 0.001). Our a priori hypothesis was confirmed in that variation in stride velocity and center of pressure path length were significantly worse during the mid-sleep awakening compared to presleep baseline. Lighting conditions during the awakening, however, did not influence these parameters. In exploratory analyses, we found that over one-third of the tested gait and balance parameters were significantly worse at the midsleep awakening as compared to baseline (p < 0.05), and nearly one-quarter had medium to large effect sizes (Cohen d ≥ 0.5; r ≥ 0.3).

CONCLUSIONS

Balance and gait are impaired during midsleep awakenings among healthy, late middle-aged and older adults. This impairment is not ameliorated by exposure to room lighting, when compared to dim lights.

Can Gait Signatures Provide Quantitative Measures for Aiding Clinical Decision-Making? A Systematic Meta-Analysis of Gait Variability Behavior in Patients with Parkinson's Disease

A disturbed, inconsistent walking pattern is a common feature of patients with Parkinson's disease (PwPD). Such extreme variability in both temporal and spatial parameters of gait has been associated with unstable walking and an elevated prevalence of falls. However, despite their ability to discretise healthy from pathological function, normative variability values for key gait parameters are still missing. Furthermore, an understanding of each parameter's response to pathology, as well as the inter-parameter relationships, has received little attention. The aim of this systematic literature review and meta-analysis was therefore to define threshold levels for pathological gait variability as well as to investigate whether all gait parameters are equally perturbed in PwPD. Based on a broader systematic literature search that included 13′195 titles, 34 studies addressed Parkinson's disease, presenting 800 PwPD and 854 healthy subjects. Eight gait parameters were compared, of which six showed increased levels of variability during walking in PwPD. The most commonly reported parameter, coefficient of variation of stride time, revealed an upper threshold of 2.4% to discriminate the two groups. Variability of step width, however, was consistently lower in PwPD compared to healthy subjects, and therefore suggests an explicit sensory motor system control mechanism to prioritize balance during walking. The results provide a clear functional threshold for monitoring treatment efficacy in patients with Parkinson's disease. More importantly, however, quantification of specific functional deficits could well provide a basis for locating the source and extent of the neurological damage, and therefore aid clinical decision-making for individualizing therapies.

Effect of walking speed on gait sub phase durations

Gait phase durations are important spatiotemporal parameters in different contexts such as discrimination between healthy and pathological gait and monitoring of treatment outcomes after interventions. Although gait phases strongly depend on walking speed, the influence of different speeds has rarely been investigated in literature. In this work, we examined the durations of the stance sub phases and the swing phase for 12 different walking speeds ranging from 0.6 to 1.7 m/s in 21 healthy subjects using infrared cinematography and an instrumented treadmill. We separated the stance phase into loading response, mid stance, terminal stance and pre-swing phase and we performed regression modeling of all phase durations with speed to determine general trends. With an increasing speed of 0.1m/s, stance duration decreased while swing duration increased by 0.3%. All distinct stance sub phases changed significantly with speed. These findings suggest the importance of including all distinct gait sub phases in spatiotemporal analyses, especially when different walking speeds are involved.