Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring

Biomechanics beyond the lab: Remote technology for osteoarthritis patient data-A scoping review

The objective of this project is to produce a review of available and validated technologies suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditionally fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (Scopus, Ovid MEDLINE, and PEDro), and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full-text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type, parameters measured, level of remoteness, and a separate table of commercially available systems. The results concluded that from the growing number of available and emerging technologies, there is a well-established range in use and further in development. Of particular note are the wide-ranging available inertial measurement unit systems and the breadth of technology available to record basic gait spatiotemporal measures with highly beneficial and informative functional outputs. With the majority of technologies categorised as suitable for part-remote use, the number of technologies that are usable and fully remote is rare and they usually employ smartphone software to enable this. With many systems being developed for camera-based technology, such technology is likely to increase in usability and availability as computational models are being developed with increased sensitivities to recognise patterns of movement, enabling data collection in the wider environment and reducing costs and creating a better understanding of OA patient biomechanical and functional movement data.

Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders

The aim of this study was to objectively assess and compare gait capacity and gait performance in rehabilitation inpatients with stroke or incomplete spinal cord injury (iSCI) using inertial measurement units (IMUs). We investigated how gait capacity (what someone can do) is related to gait performance (what someone does). Twenty-two inpatients (11 strokes, 11 iSCI) wore ankle positioned IMUs during the daytime to assess gait. Participants completed two circuits to assess gait capacity. These were videotaped to certify the validity of the IMU algorithm. Regression analyses were used to investigate if gait capacity was associated with gait performance (i.e., walking activity and spontaneous gait characteristics beyond therapy time). The ankle positioned IMUs validly assessed the number of steps, walking time, gait speed, and stride length (r ≥ 0.81). The walking activity was strongly (r ≥ 0.76) related to capacity-based gait speed. Maximum spontaneous gait speed and stride length were similar to gait capacity. However, the average spontaneous gait speed was half the capacity-based gait speed. Gait capacity can validly be assessed using IMUs and is strongly related to gait performance in rehabilitation inpatients with neurological disorders. Measuring gait performance with IMUs provides valuable additional information about walking activity and spontaneous gait characteristics to inform about functional recovery.

Cognitive dual-task cost depends on the complexity of the cognitive task, but not on age and disease

Introduction

Dual-tasking (DT) while walking is common in daily life and can affect both gait and cognitive performance depending on age, attention prioritization, task complexity and medical condition. The aim of the present study was to investigate the effects of DT on cognitive DT cost (DTC) (i) in a dataset including participants of different age groups, with different neurological disorders and chronic low-back pain (cLBP) (ii) at different levels of cognitive task complexity, and (iii) in the context of a setting relevant to daily life, such as combined straight walking and turning.

Materials and methods

Ninety-one participants including healthy younger and older participants and patients with Parkinson's disease, Multiple Sclerosis, Stroke and cLBP performed a simple reaction time (SRT) task and three numerical Stroop tasks under the conditions congruent (StC), neutral (StN) and incongruent (StI). The tasks were performed both standing (single task, ST) and walking (DT), and DTC was calculated. Mixed ANOVAs were used to determine the effect of group and task complexity on cognitive DTC.

Results

A longer response time in DT than in ST was observed during SRT. However, the response time was shorter in DT during StI. DTC decreased with increasing complexity of the cognitive task. There was no significant effect of age and group on cognitive DTC.

Conclusion

Our results suggest that regardless of age and disease group, simple cognitive tasks show the largest and most stable cognitive effects during DT. This may be relevant to the design of future observational studies, clinical trials and for clinical routine.

Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care

Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.

Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease

Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision−recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor.

Original article: Validity and reliability of gait metrics derived from researcher-placed and self-placed wearable inertial sensors

To compare the inter-session placement reliability for researcher-placed and self-placed sensors, and to evaluate the validity and reliability of waveforms and discrete variables from researcher-placed and self-placed sensors following a previously described alignment correction algorithm. Fourteen healthy, pain-free participants underwent gait analysis over two data collection sessions. Participants self-placed an inertial sensor on their left tibia and a researcher placed one on their right tibia, before completing 10 overground walking trials. Following an axis correction from a principal component analysis-based algorithm, validity and reliability were assessed within and between days for each sensor placement type through Euclidean distances, waveforms, and discrete outcomes. The placement location of researcher-placed sensors exhibited good inter-session reliability (ICC = 0.85) in comparison to self-placed sensors (ICC = 0.55). Similarly, waveforms from researcher-placed sensors exhibited excellent validity across all variables (CMC ≥ 0.90), while self-placed sensors saw high validity for most axes with reductions in validity for mediolateral acceleration and frontal plane angular velocity. Discrete outcomes saw good to excellent reliability across both sensor placement types. A simple alignment correction algorithm for inertial sensor gait data demonstrated good to excellent validity and reliability in self-placed sensors with no additional data or measures. This method can be used to align sensors easily and effectively despite sensor placement errors during straight, level walking to improve 3D gait data outcomes in data collected with self-placed sensors.

Parkinson's disease multimodal complex treatment improves gait performance: an exploratory wearable digital device-supported study

BACKGROUND

Wearable device-based parameters (DBP) objectively describe gait and balance impairment in Parkinson's disease (PD). We sought to investigate correlations between DBP of gait and balance and clinical scores, their respective changes throughout the inpatient multidisciplinary Parkinson's Disease Multimodal Complex Treatment (PD-MCT), and correlations between their changes.

METHODS

This exploratory observational study assessed 10 DBP and clinical scores at the start (T1) and end (T2) of a two-week PD-MCT of 25 PD in patients (mean age: 66.9 years, median HY stage: 2.5). Subjects performed four straight walking tasks under single- and dual-task conditions, and four balance tasks.

RESULTS

At T1, reduced gait velocity and larger sway area correlated with motor severity. Shorter strides during motor-motor dual-tasking correlated with motor complications. From T1 to T2, gait velocity improved, especially under dual-task conditions, stride length increased for motor-motor dual-tasking, and clinical scores measuring motor severity, balance, dexterity, executive functions, and motor complications changed favorably. Other gait parameters did not change significantly. Changes in motor complications, motor severity, and fear of falling correlated with changes in stride length, sway area, and measures of gait stability, respectively.

CONCLUSION

DBP of gait and balance reflect clinical scores, e.g., those of motor severity. PD-MCT significantly improves gait velocity and stride length and favorably affects additional DBP. Motor complications and fear of falling are factors that may influence the response to PD-MCT. A DBP-based assessment on admission to PD inpatient treatment could allow for more individualized therapy that can improve outcomes.

TRIAL REGISTRATION NUMBER AND DATE

DRKS00020948 number, 30-Mar-2020, retrospectively registered.

Does Executive Function Influence Walking in Acutely Hospitalized Patients With Advanced Parkinson's Disease: A Quantitative Analysis

Introduction

It is well-known that, in Parkinson's disease (PD), executive function (EF) and motor deficits lead to reduced walking performance. As previous studies investigated mainly patients during the compensated phases of the disease, the aim of this study was to investigate the above associations in acutely hospitalized patients with PD.

Methods

A total of seventy-four acutely hospitalized patients with PD were assessed with the delta Trail Making Test (ΔTMT, TMT-B minus TMT-A) and the Movement Disorder Society-revised version of the motor part of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS III). Walking performance was assessed with wearable sensors under single (ST; fast and normal pace) and dual-task (DT; walking and checking boxes as the motor secondary task and walking and subtracting seven consecutively from a given three-digit number as the cognitive secondary task) conditions over 20 m. Multiple linear regression and Bayes factor BF10 were performed for each walking parameter and their dual-task costs while walking (DTC) as dependent variables and also included ΔTMT, MDS-UPDRS III, age, and gender.

Results

Under ST, significant negative effects of the use of a walking aid and MDS-UPDRS III on gait speed and at a fast pace on the number of steps were observed. Moreover, depending on the pace, the use of a walking aid, age, and gender affected step time variability. Under walking-cognitive DT, a resolved variance of 23% was observed in the overall model for step time variability DTC, driven mainly by age (β = 0.26, p = 0.09). Under DT, no other significant effects could be observed. ΔTMT showed no significant associations with any of the walking conditions.

Discussion

The results of this study suggest that, in acutely hospitalized patients with PD, reduced walking performance is mainly explained by the use of a walking aid, motor symptoms, age, and gender, and EF deficits surprisingly do not seem to play a significant role. However, these patients with PD should avoid walking-cognitive DT situations, as under this condition, especially step time variability, a parameter associated with the risk of falling in PD worsens.

Temporal Variation Quantification During Cognitive Dual-Task Gait Using Two IMU Sensors

Multiple tasks are simultaneously performed during walking in our daily life. Distracted walk by smartphone usage is recently getting a social problem. The term dual-task gait refers to the secondary task added to the walking. Attention demanding tasks may influence how a person walks. Since in-lab measurement may not accurately reflect the daily living gait, wearable sensors approach have been proposed for gait analysis in an out-of-lab setting. This study addresses the potential of using only two inertial measurement units (IMUs) attached to the shoes for the assessment of cognitive dual-task gait and how it differs from single-task gait. We found that the proposed system is sensitive to recognizing a tiny change in gait features such as on the double support time and gait indices when subject performing dual-task gait compared to the single-task gait experiment.

Analysing Gait Patterns in Degenerative Lumbar Spine Disease Using Inertial Wearable Sensors: An Observational Study

OBJECTIVE

Using a chest-based inertial wearable sensor, we examined the quantitative gait patterns associated with lumbar disc herniation (LDH), lumbar spinal stenosis (LSS), and chronic mechanical low back pain (CMLBP). 'Pathological gait signatures' were reported as statistically significant group difference (%) from the 'normative' gait values of an age-matched control population.

METHODS

A sample of patients presenting to the Prince of Wales Private Hospital (Sydney, Australia) with primary diagnoses of LDH, LSS, or CMLBP were recruited. Spatial, temporal, asymmetry, and variability metrics were compared with age-matched (±2 years) control participants recruited from the community. Participants were fitted at the sternal angle with an inertial measurement unit, MetaMotionC, and walked unobserved (at a self-selected pace) for 120 m along an obstacle-free, carpeted hospital corridor.

RESULTS

LDH, CMLBP, and LSS groups had unique pathological signatures of gait impairment. The LDH group (n = 33) had marked asymmetry in terms of step length, step time, stance, and single-support asymmetry. The LDH group also involved gait variability with increased step length variation. However, distinguishing the CMLBP group (n = 33) was gait variability in terms increased single-support time variation. The gait of participants with LSS (n = 22) was both asymmetric and variable in step length.

CONCLUSIONS

Wearable sensor-based accelerometry was found to be capable of detecting the gait abnormalities present in patients with LDH, LSS, and CMLBP, when compared to age-matched controls. Objective and quantitative patterns of gait deterioration uniquely varied between these subtypes of lumbar spine disease. With further testing and validation, gait signatures may aid clinical identification of gait-altering pathologies.

Predictive Accuracy of Digital Biomarker Technologies for Detection of Mild Cognitive Impairment and Pre-Frailty Amongst Older Adults: A Systematic Review and Meta-Analysis

Digital biomarker technologies coupled with predictive models are increasingly applied for early detection of age-related potentially reversible conditions including mild cognitive impairment (MCI) and pre-frailty (PF). We aimed to determine the predictive accuracy of digital biomarker technologies to detect MCI and PF with systematic review and meta-analysis. A computer-assisted search on major academic research databases including IEEE-Xplore was conducted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were adopted reporting in this study. Summary receiver operating characteristic curve based on random-effect bivariate model was used to evaluate overall sensitivity and specificity for detection of the respective age-related conditions. A total of 43 studies were selected for final systematic review and meta-analysis. 26 studies reported on detection of MCI with sensitivity and specificity of 0.48-1.00 and 0.55-1.00, respectively. On the other hand, there were 17 studies that reported on the detection of PF with reported sensitivity of 0.53-1.00 and specificity of 0.61-1.00. Meta-analysis further revealed pooled sensitivities of 0.84 (95% CI: 0.79-0.88) and 0.82 (95% CI: 0.74-0.88) for in-home detection of MCI and PF, respectively, while pooled specificities were 0.85 (95% CI: 0.80-0.89) and 0.82 (95% CI: 0.75-0.88), respectively. Besides MCI, and PF, in this work during systematic review, we also found one study which reported a sensitivity of 0.93 and a specificity of 0.57 for detection of cognitive frailty (CF). The meta-analytic result, for the first time, quantifies the predictive efficacy of digital biomarker technologies for detection of MCI and PF. Additionally, we found the number of studies for detection of CF to be notably lower, indicating possible research gaps to explore predictive models on digital biomarker technology for detection of CF.

Agreement, Reliability, and Concurrent Validity of an Outdoor, Wearable-Based Walk Ratio Assessment in Healthy Adults and Chronic Stroke Survivors

Purpose: The walk ratio (WR)—the step-length/cadence relation—is a promising measure for gait control. GPS-running watches deliver clinically relevant outcomes including the WR. The aim of this study was to determine test-retest agreement, reliability and concurrent validity of an outdoor WR assessment using a GPS-running watch.

Methods: Healthy adults and moderate—high functioning stroke survivors (≥6 months), performed the 1 km-outdoor walk twice using a GPS-running watch (Garmin Forerunner 35, GFR35) and a Step Activity Monitor (SAM 3). Global cognition was assessed using the Montreal Cognitive Assessment. Test-retest agreement and reliability were assessed using Bland-Altman plots, standard error of measurement (SEM), intraclass correlation coefficients (ICCs) and smallest detectable changes (SDCs). Concurrent validity was determined by the mean difference (MD), standard error (SE), mean absolute percentage errors (MAPEs) and Spearman’s Rho between GFR35 and SAM3. WR values of the two groups were compared by a Welch’s test. A hierarchical multiple regression was performed with the WR as dependent variable and possible predictors as independent variables.

Results: Fifty-one healthy adults [median: 60.0 (47.0, 67.0) years) and 20 stroke survivors [mean: 63.1 (12.4) years, median: 76 (30, 146) months post-stroke] were included. Test-retest agreement and reliability were excellent (SEM% ≤ 2.2, ICCs > 0.9, SDC% ≤ 6.1) and concurrent validity was high (MAPE < 5, ρ > 0.7) for those walking ≥ 1 m/s. Walking < 1 m/s impaired accurate step counting and reduced agreement, reliability, and validity. The WR differed between healthy adults and stroke survivors (t = −2.126, p = 0.045). The hierarchical regression model including stroke and global cognition (Montreal Cognitive Assessment, 0—30) explained 25% of the WR variance (ΔR² = 0.246, p < 0.001). Stroke had no effect (β = −0.05, p = 0.682), but global cognition was a predictor for an altered WR (β = 0.44, p = 0.001).

Discussion: The outdoor WR assessment using the GFR35 showed excellent test-retest agreement, reliability and concurrent validity in healthy adults and chronic stroke survivors walking at least 1 m/s. As the WR seems relevant in chronic stroke, future studies should further investigate this parameter.

Brain health correlates of mobility-related confidence

BACKGROUND

Mobility is important for independence in older age. While brain health correlates of objectively measured mobility-related features like gait and balance have been reported, we aimed to test neuroimaging and cognitive correlates of subjective measures of mobility-related confidence.

METHODS

We carried out a cross-sectional observational study comprised of N = 29 cognitively unimpaired older adult participants, mean age 75.8 ± 5.8, 52% female, 24% non-white. We measured cognition, hippocampal volume, white matter hyperintensities, cerebral amyloid-β (Aβ), and gait and balance confidence. We tested associations using unadjusted Spearman correlations and correlations partialling out covariates of interest one at a time.

RESULTS

Greater gait confidence was associated with better attention (unadjusted ρ = 0.37, p = 0.05; partially attenuated by adjustment for age, APOE4, anxiety, motivation, gait speed, or Aβ); executive performance (unadjusted ρ = 0.35, p = 0.06; partially attenuated by adjustment for age, APOE4, gait speed, or Aβ); and lower Aβ levels (unadjusted ρ = -0.40, p = 0.04; partially attenuated by adjustment for age, depressive symptoms, motivation, or gait speed). Greater balance confidence was associated with better global cognition (unadjusted ρ = 0.41, p = 0.03; partially attenuated by adjustment for APOE4, gait speed, or Aβ); attention (unadjusted ρ = 0.46, p = 0.01; robust to adjustment); and lower Aβ levels (unadjusted ρ = -0.35, p = 0.07; partially attenuated by adjustment for age, education, APOE4, depressive symptoms, anxiety, motivation, or gait speed).

CONCLUSIONS

Self-reported mobility-related confidence is associated with neuroimaging and cognitive measures and would be easy for providers to use in clinical evaluations. These associations should be further evaluated in larger samples, and longitudinal studies can help determine temporality of declines.

The reliability of gait parameters captured via instrumented walkways: a systematic review and meta-analysis

INTRODUCTION

Electronic pressure-sensitive walkways are commonly available solutions to quantitatively assess gait parameters for clinical and research purposes. Many studies have evaluated their measurement properties in different conditions with variable findings. In order to be informed about the current evidence of their reliability for optimal clinical and scientific decision making, this systematic review provided a quantitative synthesis of the test-retest reliability and minimal detectable change of the captured gait parameters across different test conditions (single and cognitive dual-task conditions) and population groups.

EVIDENCE ACQUISITION

A literature search was conducted in PubMed, Embase, and Scopus until November 2021 to identify articles that examined the test-retest reliability properties of the gait parameters captured by pressure-sensitive walkways (gait speed, cadence, stride length and time, double support time, base of support) in adult healthy individuals or patients. The methodological quality was rated using the Consensus-Based Standards for the Selection of Health Measurement Instruments Checklist. Data were meta-analyzed on intraclass correlation coefficient to examine the test-retest relative reliability. Quantitative synthesis was performed for absolute reliability, examined by the weighted average of minimal detectable change values.

EVIDENCE SYNTHESIS

A total of 44 studies were included in this systematic review. The methodological quality was adequate in half of the included studies. The main finding was that pressure-sensitive walkways are reliable tools for objective assessment of spatial and temporal gait parameters both in single-and cognitive dual-task conditions. Despite few exceptions, the review identified intraclass correlation coefficient higher than 0.75 and minimal detectable change lower than 30%, demonstrating satisfactory relative and absolute reliability in all examined populations (healthy adults, elderly, patients with cognitive impairment, spinocerebellar ataxia type 14, Huntington's disease, multiple sclerosis, Parkinson's disease, rheumatoid arthritis, spinal cord injury, stroke or vestibular dysfunction).

CONCLUSIONS

Current evidence suggested that, despite different populations and testing protocols used in the included studies, the test-retest reliability of the examined gait parameters was acceptable under single and cognitive dual-task conditions. Further high-quality studies with powered sample sizes are needed to examine the reliability findings of the currently understudied and unexplored pathologies and test conditions.

A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.

Dual-Task Abilities During Activities Representative of Daily Life in Community-Dwelling Stroke Survivors: A Pilot Study

Background

In addition to several physical skills, being able to walk in the community, walking independently and safely in the community requires the ability to divide attention between walking and other tasks performed simultaneously. The aims of the present pilot study were to measure cognitive-locomotor dual-task (DT) abilities during activities representative of daily living in stroke survivors and to compare them with age- and gender-matched healthy individuals.

Methods

To assess DT abilities, all participants walked along a virtual shopping mall corridor and memorized a 5-item shopping list. Two levels of task complexity were used for the walking task (with or without virtual agents to avoid) and the cognitive task to recall a list of items (with or without a modification at mid-course). The assessment was conducted using an omnidirectional platform and a virtual reality (VR) headset. Locomotor and cognitive DT costs (DTC) were calculated as the percent change from single-task (ST) performance. Walking speed and minimal distance between the participant and the virtual agents were used to characterize locomotor performance. Cognitive performance was assessed by the number of correctly recalled items. One-sample Wilcoxon tests were used to determine the presence of DTCs and Mann-Whitney tests were performed to compare DTCs between the 2 groups.

Results

Twelve community-dwelling stroke survivors [60.50 years old (25-75^th percentiles: 53.50–65.75); 5 women; 13.41 months post-stroke (5.34–48.90)] and 12 age- and gender- matched healthy individuals were recruited. Significant cognitive or mutual (cognitive and locomotor) interferences were observed in participants with stroke in all DT conditions, except the simplest (no virtual agents, no modifications to the list). For the control group, significant mutual interferences were only observed during the most complex DT condition. A group difference was detected in cognitive DTCs during the most complex DT condition (virtual agents and list modifications; p = 0.02). Stroke survivors had greater cognitive DTCs than the control group.

Conclusions

Using an ecological perspective contributes to understanding behavior of stroke survivors in daily activities. Virtual scenarios appear to be an interesting avenue for a more comprehensive understanding of DT abilities during activities representative of daily living in stroke survivors. The usability and feasibility of such an approach will have to be studied before considering implementation in rehabilitation settings.

Investigating the Impact of Environment and Data Aggregation by Walking Bout Duration on Parkinson's Disease Classification Using Machine Learning

Parkinson's disease (PD) is a common neurodegenerative disease. PD misdiagnosis can occur in early stages. Gait impairment in PD is typical and is linked with an increased fall risk and poorer quality of life. Applying machine learning (ML) models to real-world gait has the potential to be more sensitive to classify PD compared to laboratory data. Real-world gait yields multiple walking bouts (WBs), and selecting the optimal method to aggregate the data (e.g., different WB durations) is essential as this may influence classification performance. The objective of this study was to investigate the impact of environment (laboratory vs. real world) and data aggregation on ML performance for optimizing sensitivity of PD classification. Gait assessment was performed on 47 people with PD (age: 68 ± 9 years) and 52 controls [Healthy controls (HCs), age: 70 ± 7 years]. In the laboratory, participants walked at their normal pace for 2 min, while in the real world, participants were assessed over 7 days. In both environments, 14 gait characteristics were evaluated from one tri-axial accelerometer attached to the lower back. The ability of individual gait characteristics to differentiate PD from HC was evaluated using the Area Under the Curve (AUC). ML models (i.e., support vector machine, random forest, and ensemble models) applied to real-world gait showed better classification performance compared to laboratory data. Real-world gait characteristics aggregated over longer WBs (WB 30-60 s, WB > 60 s, WB > 120 s) resulted in superior discriminative performance (PD vs. HC) compared to laboratory gait characteristics (0.51 ≤ AUC ≤ 0.77). Real-world gait speed showed the highest AUC of 0.77. Overall, random forest trained on 14 gait characteristics aggregated over WBs > 60 s gave better performance (F1 score = 77.20 ± 5.51%) as compared to laboratory results (F1 Score = 68.75 ± 12.80%). Findings from this study suggest that the choice of environment and data aggregation are important to achieve maximum discrimination performance and have direct impact on ML performance for PD classification. This study highlights the importance of a harmonized approach to data analysis in order to drive future implementation and clinical use.

Clinical Trial Registration

[09/H0906/82].

Differential Relationships Between Brain Structure and Dual Task Walking in Young and Older Adults

Almost 25% of all older adults experience difficulty walking. Mobility difficulties for older adults are more pronounced when they perform a simultaneous cognitive task while walking (i.e., dual task walking). Although it is known that aging results in widespread brain atrophy, few studies have integrated across more than one neuroimaging modality to comprehensively examine the structural neural correlates that may underlie dual task walking in older age. We collected spatiotemporal gait data during single and dual task walking for 37 young (18–34 years) and 23 older adults (66–86 years). We also collected T₁-weighted and diffusion-weighted MRI scans to determine how brain structure differs in older age and relates to dual task walking. We addressed two aims: (1) to characterize age differences in brain structure across a range of metrics including volumetric, surface, and white matter microstructure; and (2) to test for age group differences in the relationship between brain structure and the dual task cost (DTcost) of gait speed and variability. Key findings included widespread brain atrophy for the older adults, with the most pronounced age differences in brain regions related to sensorimotor processing. We also found multiple associations between regional brain atrophy and greater DTcost of gait speed and variability for the older adults. The older adults showed a relationship of both thinner temporal cortex and shallower sulcal depth in the frontal, sensorimotor, and parietal cortices with greater DTcost of gait. Additionally, the older adults showed a relationship of ventricular volume and superior longitudinal fasciculus free-water corrected axial and radial diffusivity with greater DTcost of gait. These relationships were not present for the young adults. Stepwise multiple regression found sulcal depth in the left precentral gyrus, axial diffusivity in the superior longitudinal fasciculus, and sex to best predict DTcost of gait speed, and cortical thickness in the superior temporal gyrus to best predict DTcost of gait variability for older adults. These results contribute to scientific understanding of how individual variations in brain structure are associated with mobility function in aging. This has implications for uncovering mechanisms of brain aging and for identifying target regions for mobility interventions for aging populations.

Analysing gait patterns in degenerative lumbar spine diseases: a literature review

OBJECTIVES

To collate the current state of knowledge and explore differences in the spatiotemporal gait patterns of degenerative lumbar spine diseases: lumbar spinal stenosis (LSS), lumbar disc herniation (LDH) and low back pain (LBP).

BACKGROUND

LBP is common presenting complaint with degenerative lumbar spine disease being a common cause. In particular, the gait patterns of LSS, LDH and mechanical-type (facetogenic and discogenic) LBP is not established.

METHODS

A search of the literature was conducted to determine the changes in spatial and temporal gait metrics involved with each type of degenerative lumbar spine disease. A search of databases including Medline, Embase and PubMed from their date of inception to April 18th, 2021 was performed to screen, review and identify relevant studies for qualitative synthesis. Seventeen relevant studies were identified for inclusion in the present review. Of these, 5 studies investigated gait patterns in LSS, 10 studies investigated LBP and 2 studies investigated LDH. Of these, 4 studies employed wearable accelerometry in LSS (2 studies) and LBP (2 studies).

CONCLUSIONS

Previous studies suggest degenerative diseases of the lumbar spine have unique patterns of gait deterioration. LSS is characterised by asymmetry and variability. Spatiotemporal gait deterioration in gait velocity, cadence with increased double-support duration and gait variability are distinguishing features in LDH. LBP involves marginal abnormalities in temporal and spatial gait metrics. Previous studies suggest degenerative diseases of the lumbar spine have unique patterns of gait deterioration. Gait asymmetry and variability, may be relevant metrics for distinguishing between the gait profiles of lumbar spine diseases.

Mobility endpoints in marketing authorisation of drugs: what gets the European medicines agency moving?

Background

Mobility is defined as the ability to independently move around the environment and is a key contributor to quality of life, especially in older age. The aim of this study was to evaluate the use of mobility as a decisive outcome for the marketing authorisation of drugs by the European Medicines Agency (EMA).

Methods

Fifteen therapeutic areas which commonly lead to relevant mobility impairments and alter the quantity and/or the quality of walking were selected: two systemic neurological diseases, four conditions primarily affecting exercise capacity, seven musculoskeletal diseases and two conditions representing sensory impairments. European Public Assessment Reports (EPARs) published by the EMA until September 2020 were examined for mobility endpoints included in their ‘main studies’. Clinical study registries and primary scientific publications for these studies were also reviewed.

Results

Four hundred and eighty-four EPARs yielded 186 relevant documents with 402 ‘main studies’. The EPARs reported 153 primary and 584 secondary endpoints which considered mobility; 70 different assessment tools (38 patient-reported outcomes, 13 clinician-reported outcomes, 8 performance outcomes and 13 composite endpoints) were used. Only 15.7% of those tools distinctly informed on patients’ mobility status. Out of 402, 105 (26.1%) of the ‘main studies’ did not have any mobility assessment. Furthermore, none of these studies included a digital mobility outcome.

Conclusions

For conditions with a high impact on mobility, mobility assessment was given little consideration in the marketing authorisation of drugs by the EMA. Where mobility impairment was considered to be a relevant outcome, questionnaires or composite scores susceptible to reporting biases were predominantly used.

The 180° Turn Phase of the Timed Up and Go Test Better Predicts History of Falls in the Oldest-Old When Compared With the Full Test: A Case-Control Study

The 180° turn phase of the test may better differentiate the oldest-old regarding their history of falls. This is a case-control study designed to detect the ability of the 180° turn timed up and go (TUG) phase to detect a history of falls in the oldest-old. Sixty people aged 85 years and older were assessed in their homes. The single-task and dual-task TUG tests were performed using an inertial sensor (G-Walk). Sociodemographic data, physical activity levels, mental status, depressive symptoms, concern for falls occurrence, number of medicines in use, self-perception of balance, and the functional reach test were also assessed. The logistic regressions revealed the 180° turn phase of both the single-task and dual-task TUG was almost three times better than the full TUG test to detect a history of falls, thus providing insights that can be used to better assess functional mobility in the oldest-old.

Quantification of Daily-Living Gait Quantity and Quality Using a Wrist-Worn Accelerometer in Huntington's Disease

Background: Huntington's disease (HD) leads to altered gait patterns and reduced daily-living physical activity. Accurate measurement of daily-living walking that takes into account involuntary movements (e.g. chorea) is needed. Objective: To evaluate daily-living gait quantity and quality in HD, taking into account irregular movements. Methods: Forty-two individuals with HD and fourteen age-matched non-HD peers completed clinic-based assessments and a standardized laboratory-based circuit of functional activities, wearing inertial measurement units on the wrists, legs, and trunk. These activities were used to train and test an algorithm for the automated detection of walking. Subsequently, 29 HD participants and 22 age-matched non-HD peers wore a tri-axial accelerometer on their non-dominant wrist for 7 days. Measures included gait quantity (e.g., steps per day), gait quality (e.g., regularity) metrics, and percentage of walking bouts with irregular movements. Results: Measures of daily-living gait quantity including step counts, walking time and bouts per day were similar in HD participants and non-HD peers (p > 0.05). HD participants with higher clinician-rated upper body chorea had a greater percentage of walking bouts with irregular movements compared to those with lower chorea (p = 0.060) and non-HD peers (p < 0.001). Even after accounting for irregular movements, within-bout walking consistency was lower in HD participants compared to non-HD peers (p < 0.001), while across-bout variability of these measures was higher (p < 0.001). Many of the daily-living measures were associated with disease-specific measures of motor function. Conclusions: Results suggest that a wrist-worn accelerometer can be used to evaluate the quantity and quality of daily-living gait in people with HD, while accounting for the influence of irregular (choreic-like) movements, and that gait features related to within- and across-bout consistency markedly differ in individuals with HD and non-HD peers.

Outdoor walking exhibits peak ankle and knee flexion differences compared to fixed and adaptive-speed treadmills in older adults

Background

Walking mechanics recorded with a traditional treadmill may not be the same as the mechanics exhibited during activities of daily living due to constrained walking speeds. Adaptive-speed treadmills allow for unconstrained walking speeds similar to outdoor walking. The aim of this study was to determine differences in kinematic walking parameters of older adults between adaptive-speed treadmill (AST), fixed-speed treadmill (FST) and outdoor walking. We hypothesized that self-selected walking speed (SSWS) during AST walking and outdoor walking would increase compared to FST walking. Furthermore, we hypothesized that AST walking and outdoor walking would increase peak knee flexion, hip flexion, and ankle plantarflexion angles compared to FST walking independent of walking speed changes.

Methods

Fourteen older adult participants were asked to complete 3 min of FST and AST walking on a split-belt treadmill. Participants were also asked to complete 6 min of outdoor walking following a circular route in a neighboring park. A wireless inertial measurement unit-based motion capture system was used to record lower extremity kinematics during all walking conditions.

Results

The outdoor walking condition produces significantly higher SSWS compared to FST (p < 0.001) and AST (p = 0.02) conditions. A significantly faster SSWS was exhibited during the AST condition compared to the FST condition (p = 0.026). Significantly higher peak ankle plantarflexion angles are exhibited during the outdoor walking condition compared to the AST (p < 0.001, g = 1.14) and FST (p < 0.001, g = 1.13) conditions after accounting for walking speed. There was a significantly lowered difference between the outdoor walking condition and both AST (p = 0.029, g = 0.49) and FST (p = 0.013, g = 0.63) conditions in peak knee flexion angles after accounting for SSWS. There are no significant differences between outdoor, AST, and FST conditions on peak hip flexion angles. Older adults exhibit changes in peak ankle plantarflexion and peak knee flexion angles during outdoor walking compared to treadmill walking but not between treadmill controller types. We found no differences in the kinematics exhibited by older adults between both AST and FST walking.

Conclusions

Incorporating unconstrained walking speed with the AST while maintaining similar FST sagittal plane kinematics may allow for more translatable conditional balance and walking rehabilitation.

Arm swing responsiveness to dopaminergic medication in Parkinson's disease depends on task complexity

The evidence of the responsiveness of dopaminergic medication on gait in patients with Parkinson’s disease is contradicting. This could be due to differences in complexity of the context gait was in performed. This study analysed the effect of dopaminergic medication on arm swing, an important movement during walking, in different contexts. Forty-five patients with Parkinson’s disease were measured when walking at preferred speed, fast speed, and dual-tasking conditions in both OFF and ON medication states. At preferred, and even more at fast speed, arm swing improved with medication. However, during dual-tasking, there were only small or even negative effects of medication on arm swing. Assuming that dual-task walking most closely reflects real-life situations, the results suggest that the effect of dopaminergic medication on mobility-relevant movements, such as arm swing, might be small in everyday conditions. This should motivate further studies to look at medication effects on mobility in Parkinson’s disease, as it could have highly relevant implications for Parkinson’s disease treatment and counselling.

Evaluation of movement and brain activity

Clinical neurophysiology studies can contribute important information about the physiology of human movement and the pathophysiology and diagnosis of different movement disorders. Some techniques can be accomplished in a routine clinical neurophysiology laboratory and others require some special equipment. This review, initiating a series of articles on this topic, focuses on the methods and techniques. The methods reviewed include EMG, EEG, MEG, evoked potentials, coherence, accelerometry, posturography (balance), gait, and sleep studies. Functional MRI (fMRI) is also reviewed as a physiological method that can be used independently or together with other methods. A few applications to patients with movement disorders are discussed as examples, but the detailed applications will be the subject of other articles.

Walking indoors, outdoors, and on a treadmill: Gait differences in healthy young and older adults

BACKGROUND

Although human gait is typically studied in a laboratory environment, the findings of laboratory-based gait assessments are often applied to daily life scenarios. Assessing gait in varied conditions may offer a better understanding of the influence of environment on gait performance.

RESEARCH QUESTIONS

How do spatiotemporal gait measures differ between indoor overground walking, outdoor walking, and treadmill walking in healthy adults? Do different walking environments exaggerate age-related alterations in gait performance in older compared to young adults?

METHODS

30 young (18-30yrs) and 28 older adults (60-80yrs) completed four randomized conditions at their typical, comfortable walking pace: 1) 8 m of indoor walking, 2) continuous indoor walking, 3) treadmill walking, and 4) outdoor walking on a sidewalk. Wearable inertial sensors recorded gait data and the magnitudes and variability (in standard deviations) of the following gait measures were computed: cadence, percent double support, stride length (with sample entropy), and gait velocity.

RESULTS

Despite the lack of significant univariate interactions between group and walking condition, significant main effects for condition and group were observed in both the magnitude and variability analyses. Treadmill walking resulted in a slower gait with shorter, less variable strides (p < .001), while walking outdoors resulted in faster gait with longer strides (p < .001) compared to other walking conditions. Stride length regularity was reduced when walking outdoors compared to treadmill walking (p = .019).

SIGNIFICANCE

The results showed that the effects of walking condition on gait measures were more dramatic than participant age, and gait performance differs between walking environments in both older and younger adults. Since daily life gait encompasses both tightly controlled and unconstrained, free-living walking, researchers and clinicians should use caution when generalizing gait performance across walking conditions. Measures of gait performance typically used in laboratory gait analyses may not adequately characterize daily life gait in indoor and outdoor environments.

The Effects of Different Types of Dual Tasking on Balance in Healthy Older Adults

Numerous of our daily activities are performed within multitask or dual task conditions. These conditions involve the interaction of perceptual and motor processes involved in postural control. Age-related changes may negatively impact cognition and balance control. Studies identifying changes related to dual-task actions in older people are need. This study aimed to determine the effects of different types of dual-tasking on the balance control of healthy older adults. The sample included 36 community-living older adults, performing two tests—a sway test and a timed up-and-go test—in three conditions: (a) single motor task; (b) dual motor task; and (c) dual motor task with cognitive demands. Cognitive processes (dual-task and cognition) affected static balance, increasing amplitude (p < 0.001) and frequency (p < 0.001) of the center of mass displacements. Dynamic balance revealed significant differences between the single motor condition and the other two conditions during gait phases (p < 0.001). The effect of dual-tasking in older adults suggests that cognitive processes are a main cause of increased variability in balance and gait when under an automatic control. During sit-to-stand, turning, and turn-to-sit movements under dual-tasking, the perceptive information becomes the most important focus of attention, while any cognitive task becomes secondary.

Consensus based framework for digital mobility monitoring

Digital mobility assessment using wearable sensor systems has the potential to capture walking performance in a patient's natural environment. It enables monitoring of health status and disease progression and evaluation of interventions in real-world situations. In contrast to laboratory settings, real-world walking occurs in non-conventional environments and under unconstrained and uncontrolled conditions. Despite the general understanding, there is a lack of agreed definitions about what constitutes real-world walking, impeding the comparison and interpretation of the acquired data across systems and studies. The goal of this study was to obtain expert-based consensus on specific aspects of real-world walking and to provide respective definitions in a common terminological framework. An adapted Delphi method was used to obtain agreed definitions related to real-world walking. In an online survey, 162 participants from a panel of academic, clinical and industrial experts with experience in the field of gait analysis were asked for agreement on previously specified definitions. Descriptive statistics was used to evaluate whether consent (> 75% agreement as defined a priori) was reached. Of 162 experts invited to participate, 51 completed all rounds (31.5% response rate). We obtained consensus on all definitions ("Walking" > 90%, "Purposeful" > 75%, "Real-world" > 90%, "Walking bout" > 80%, "Walking speed" > 75%, "Turning" > 90% agreement) after two rounds. The identification of a consented set of real-world walking definitions has important implications for the development of assessment and analysis protocols, as well as for the reporting and comparison of digital mobility outcomes across studies and systems. The definitions will serve as a common framework for implementing digital and mobile technologies for gait assessment and are an important link for the transition from supervised to unsupervised gait assessment.

Predicting Multiple Sclerosis From Gait Dynamics Using an Instrumented Treadmill: A Machine Learning Approach

OBJECTIVE

Multiple Sclerosis (MS) is a neurological condition which widely affects people 50-60 years of age. While clinical presentations of MS are highly heterogeneous, mobility limitations are one of the most frequent symptoms. This study examines a machine learning (ML) framework for identifying MS through spatiotemporal and kinetic gait features.

METHODS

In this study, gait data during self-paced walking on an instrumented treadmill from 20 persons with MS and 20 age, weight, height, and gender-matched healthy older adults (HOA) were obtained. We explored two strategies to normalize data and minimize dependence on subject demographics; size-normalization (standard body size-based normalization) and regress-normalization (regression-based normalization using scaling factors derived by regressing gait features on multiple subject demographics); and proposed an ML based methodology to classify individual strides of older persons with MS (PwMS) from healthy controls. We generalized both across different walking tasks and subjects.

RESULTS

We observed that regress-normalization improved the accuracy of identifying pathological gait using ML when compared to size-normalization. When generalizing from comfortable walking to walking while talking, gradient boosting machine achieved the optimal subject classification accuracy and AUC of 94.3 and 1.0, respectively and for subject generalization, a multilayer perceptron resulted in the best accuracy and AUC of 80% and 0.86, respectively, both with regression-normalized data.

CONCLUSION

The integration of gait data and ML may provide a viable patient-centric approach to aid clinicians in monitoring MS.

SIGNIFICANCE

The results of this study have future implications for the way regression normalized gait features may be clinically used to design ML-based disease prediction strategies and monitor disease progression in PwMS.

Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units

Loss-of-balance (LOB) events, such as trips and slips, are frequent among community-dwelling older adults and are an indicator of increased fall risk. In a preliminary study, eight community-dwelling older adults with a history of falls were asked to perform everyday tasks in the real world while donning a set of three inertial measurement sensors (IMUs) and report LOB events via a voice-recording device. Over 290 h of real-world kinematic data were collected and used to build and evaluate classification models to detect the occurrence of LOB events. Spatiotemporal gait metrics were calculated, and time stamps for when LOB events occurred were identified. Using these data and machine learning approaches, we built classifiers to detect LOB events. Through a leave-one-participant-out validation scheme, performance was assessed in terms of the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR). The best model achieved an AUROC ≥0.87 for every held-out participant and an AUPR 4-20 times the incidence rate of LOB events. Such models could be used to filter large datasets prior to manual classification by a trained healthcare provider. In this context, the models filtered out at least 65.7% of the data, while detecting ≥87.0% of events on average. Based on the demonstrated discriminative ability to separate LOBs and normal walking segments, such models could be applied retrospectively to track the occurrence of LOBs over an extended period of time.

Catechol-O-methyltransferase (COMT) polymorphism predicts rapid gait speed changes in healthy older adults

IMPORTANCE

Adapting one's gait speed to external circumstances is critical for safe ambulation. Dopamine (DA), critical for adapting to increased task demands, predicts usual gait speed and may exert a greater role in complex tasks like rapid gait speed.

OBJECTIVE

We hypothesized that a genotypic proxy indicator of greater prefrontal DA signaling would predict significantly faster rapid gait.

DESIGN

Longitudinal cohort study over 8 years.

SETTING

Community-dwelling adults with no baseline mobility disability.

PARTICIPANTS

N = 2353 participants from the Health ABC Study.

MEASUREMENTS

Repeated measures of walking speed (meters/sec) were obtained in response to: "walk as fast as possible… (rapid gait) or "walk at your usual pace (usual gait)." Catechol-O-methyltransferase (COMT) val158met polymorphism indicated DA signaling (val/val = higher metabolism, lower DA signaling; met/met = lower metabolism, higher DA signaling).

RESULTS

Participants declined in rapid gait from 1.55 (SD = 0.33) to 1.35 m/s (SD = 0.34). Across the full follow-up period, the met/met genotype was associated with significantly greater rapid gait slowing. In mixed effect models, between-group differences were independent of covariates, and remained similar after adjustment for sensorimotor function, cognition, depressive symptoms, and energy. Follow-up analyses indicated the met/met genotype had a significantly faster rapid gait speed compared to the val/val genotype for the first 3 years (p < 0.01) but not years 4-8 (p > 0.05).

CONCLUSION

Greater prefrontal DA measured with COMT polymorphism may facilitate short-term adaptation to rapid walking demands that are lost over time. Studies should examine whether these effects are long-term and the underlying mechanistic pathways.

Targeted tDCS Mitigates Dual-Task Costs to Gait and Balance in Older Adults

OBJECTIVE

Among older adults, the ability to stand or walk while performing cognitive tasks (ie, dual-tasking) requires coordinated activation of several brain networks. In this multicenter, double-blinded, randomized, and sham-controlled study, we examined the effects of modulating the excitability of the left dorsolateral prefrontal cortex (L-DLPFC) and the primary sensorimotor cortex (SM1) on dual-task performance "costs" to standing and walking.

METHODS

Fifty-seven older adults without overt illness or disease completed 4 separate study visits during which they received 20 minutes of transcranial direct current stimulation (tDCS) optimized to facilitate the excitability of the L-DLPFC and SM1 simultaneously, or each region separately, or neither region (sham). Before and immediately after stimulation, participants completed a dual-task paradigm in which they were asked to stand and walk with and without concurrent performance of a serial-subtraction task.

RESULTS

tDCS simultaneously targeting the L-DLPFC and SM1, as well as tDCS targeting the L-DLPFC alone, mitigated dual-task costs to standing and walking to a greater extent than tDCS targeting SM1 alone or sham (p < 0.02). Blinding efficacy was excellent and participant subjective belief in the type of stimulation received (real or sham) did not contribute to the observed functional benefits of tDCS.

INTERPRETATION

These results demonstrate that in older adults, dual-task decrements may be amenable to change and implicate L-DPFC excitability as a modifiable component of the control system that enables dual-task standing and walking. tDCS may be used to improve resilience and the ability of older results to walk and stand under challenging conditions, potentially enhancing everyday functioning and reducing fall risks. ANN NEUROL 2021.

Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson's Disease

Accurate assessment of Parkinson's disease (PD) ON and OFF states in the usual environment is essential for tailoring optimal treatments. Wearables facilitate measurements of gait in novel and unsupervised environments; however, differences between unsupervised and in-laboratory measures have been reported in PD. We aimed to investigate whether unsupervised gait speed discriminates medication states and which supervised tests most accurately represent home performance. In-lab gait speeds from different gait tasks were compared to home speeds of 27 PD patients at ON and OFF states using inertial sensors. Daily gait speed distribution was expressed in percentiles and walking bout (WB) length. Gait speeds differentiated ON and OFF states in the lab and the home. When comparing lab with home performance, ON assessments in the lab showed moderate-to-high correlations with faster gait speeds in unsupervised environment (r = 0.69; p < 0.001), associated with long WB. OFF gait assessments in the lab showed moderate correlation values with slow gait speeds during OFF state at home (r = 0.56; p = 0.004), associated with short WB. In-lab and daily assessments of gait speed with wearables capture additional integrative aspects of PD, reflecting different aspects of mobility. Unsupervised assessment using wearables adds complementary information to the clinical assessment of motor fluctuations in PD.

Spatio-temporal gait parameters obtained from foot-worn inertial sensors are reliable in healthy adults in single- and dual-task conditions

Inertial measurement units (IMUs) are increasingly popular and may be usable in clinical routine to assess gait. However, assessing their intra-session reliability is crucial and has not been tested with foot-worn sensors in healthy participants. The aim of this study was to assess the intra-session reliability of foot-worn IMUs for measuring gait parameters in healthy adults. Twenty healthy participants were enrolled in the study and performed the 10-m walk test in single- and dual-task ('carrying a full cup of water') conditions, three trials per condition. IMUs were used to assess spatiotemporal gait parameters, gait symmetry parameters (symmetry index (SI) and symmetry ratio (SR)), and dual task effects parameters. The relative and the absolute reliability were calculated for each gait parameter. Results showed that spatiotemporal gait parameters measured with foot-worn inertial sensors were reliable; symmetry gait parameters relative reliability was low, and SR showed better absolute reliability than SI; dual task effects were poorly reliable, and taking the mean of the second and the third trials was the most reliable. Foot-worn IMUs are reliable to assess spatiotemporal and symmetry ratio gait parameters but symmetry index and DTE gait parameters reliabilities were low and need to be interpreted with cautious by clinicians and researchers.

Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning

BACKGROUND

It is not clear how specific gait measures reflect disease severity across the disease spectrum in Parkinson's disease (PD).

OBJECTIVE

To identify the gait and mobility measures that are most sensitive and reflective of PD motor stages and determine the optimal sensor location in each disease stage.

METHODS

Cross-sectional wearable-sensor records were collected in 332 patients with PD (Hoehn and Yahr scale I-III) and 100 age-matched healthy controls. Sensors were adhered to the participant's lower back, bilateral ankles, and wrists. Study participants walked in a ~15-meter corridor for 1 minute under two walking conditions: (1) preferred, usual walking speed and (2) walking while engaging in a cognitive task (dual-task). A subgroup (n = 303, 67% PD) also performed the Timed Up and Go test. Multiple machine-learning feature selection and classification algorithms were applied to discriminate between controls and PD and between the different PD severity stages.

RESULTS

High discriminatory values were found between motor disease stages with mean sensitivity in the range 72%-83%, specificity 69%-80%, and area under the curve (AUC) 0.76-0.90. Measures from upper-limb sensors best discriminated controls from early PD, turning measures obtained from the trunk sensor were prominent in mid-stage PD, and stride timing and regularity were discriminative in more advanced stages.

CONCLUSIONS

Applying machine-learning to multiple, wearable-derived features reveals that different measures of gait and mobility are associated with and discriminate distinct stages of PD. These disparate feature sets can augment the objective monitoring of disease progression and may be useful for cohort selection and power analyses in clinical trials of PD. © 2021 International Parkinson and Movement Disorder Society.

Dual-tasking impacts gait, cognitive performance, and gaze behavior during walking in a real-world environment in older adult fallers and non-fallers

INTRODUCTION

Everyday walking often involves simultaneous performance of a cognitive task in environments with competing auditory and visual stimuli. Previous research has not evaluated task performance in these situations, where older adults are known to fall, limiting our understanding of how older adults adjust their gait, visual scanning (gaze), and cognitive processing to avoid falls (or not). The purpose of this study was to examine the effect of dual-task walking in a high-distraction real-world environment on cognitive performance, gait performance, and gaze behavior in older adult fallers relative to non-fallers.

METHODS

Fourteen community-dwelling, older adult fallers (76.6 ± 9.1 years, 11 females) and 15 community-dwelling, older adult non-fallers (77.4 ± 7.6 years, 11 females) participated. Participants performed single-task walking, single-task cognitive (seated category naming), and dual-task walking (category naming + walking) trials for 1 min each in a real-world environment (busy hospital lobby). Gait speed, stride length variability, stride duration variability, gaze fixation duration on 6 areas of interest (AOIs), and percentage of time fixating on 6 AOIs were recorded during single- and dual-task walking trials. Number of correct responses, time to first response, and mean subsequent response time (measure of rate of decline of response retrieval throughout trial) were determined for single-task cognitive and dual-task walking trials. Two-way MANCOVAs and MANOVAs were used to compare the effects of fall status and task condition on gait and cognitive variables. Hierarchical linear regression models were used to assess predictors of gaze behavior variables.

RESULTS

Compared to single-task, during dual-task trials, participants walked 0.21 m/s slower, had 1.5 fewer verbal responses, and a 2823 ms shorter mean subsequent response time, indicating a faster declining rate of retrieval during the cognitive task. Additionally, during dual-task walking, participants fixated their gaze on Far People (AOI) for a significantly smaller percentage of time and on the Near Walking Path (AOI) for a significantly greater percentage of time than during single-task walking. During all trials, being a non-faller predicted a longer average fixation duration on the Far Environment (AOI) than for fallers. Environmental busyness, baseline gait speed, and baseline executive function impacted gaze behavior.

CONCLUSION

All participants exhibited dual-task decrements in gait and cognitive performance and changes in gaze behavior from single- to dual-task walking. Perhaps of more importance, non-fallers appear to have had more freedom to divert their gaze to less relevant environmental stimuli while walking, and two measures of fall risk impacted patterns of gaze behavior differently. Thus, overt visual attention during walking in real-world environments should be further explored in relation to fall risk.

Gait Performance as an Indicator of Cognitive Deficit in Older People

Background: The purpose of this study was to analyze which gait variables are the best for detecting cognitive impairment and to determine if age and gender can influence gait variations in older people. Methods: 65 participants took part in this study (22 men and 43 women; age: 73.88 ± 9.56 years). We use the Montreal Cognitive Assessment (MoCA) to assess mild cognitive impairment (MCI). Gait speed (GS) and the complex gait test (CGT) were analyzed with photocells Witty (Microgate, Italia). The OptoGait system (Microgate, Italia) was used to analyze step length (SL) and step coefficient of variation (CV sl). Results: There was a significant association between MoCA and SL (r = 0.420; p = 0.002), CV sl (r = −0.591; p < 0.001), and CGT (r = −0.406; p = 0.001). Instrumental activities of daily living showed significant association with SL (r = 0.563; p < 0.001); CV sl (r = −0.762; p < 0.001), CGT (r = −0.622; p < 0.001), and GS (r = 0.418; p < 0.001). CV sl showed the best results with MoCA when linear regression analysis was applied (R² = 0.560; p = 0.007; Y = 23.669 − 0.320x). Participants older than 79 years showed lower MoCA scores and poorer gait parameters than people younger than 79 years. Conclusions: CV sl, SL, CGT, and GS make it possible to detect MCI in older people, especially when these variables are evaluated as a whole.

Distinct cortical thickness patterns link disparate cerebral cortex regions to select mobility domains

The cortical control of gait and mobility involves multiple brain regions. Therefore, one could speculate that the association between specific spatial patterns of cortical thickness may be differentially associated with different mobility domains. To test this possibility, 115 healthy participants aged 27–82 (mean 60.5 ± 13.8) underwent a mobility assessment (usual-walk, dual-task walk, Timed Up and Go) and MRI scan. Ten mobility domains of relatively simple (e.g., usual-walking) and complex tasks (i.e., dual task walking, turns, transitions) and cortical thickness of 68 ROIs were extracted. All associations between mobility and cortical thickness were controlled for age and gender. Scaled Subprofile Modelling (SSM), a PCA-regression, identified thickness patterns that were correlated with the individual mobility domains, controlling for multiple comparisons. We found that lower mean global cortical thickness was correlated with worse general mobility (r = − 0.296, p = 0.003), as measured by the time to complete the Timed Up and Go test. Three distinct patterns of cortical thickness were associated with three different gait domains during simple, usual-walking: pace, rhythm, and symmetry. In contrast, cortical thickness patterns were not related to the more complex mobility domains. These findings demonstrate that robust and topographically distinct cortical thickness patterns are linked to select mobility domains during relatively simple walking, but not to more complex aspects of mobility. Functional connectivity may play a larger role in the more complex aspects of mobility.

Robustness of In-Laboratory and Daily-Life Gait Speed Measures over One Year in High Functioning 61- to 70-Year-Old Adults

INTRODUCTION

Gait speed is a simple and safe measure with strong predictive value for negative health outcomes in clinical practice, yet in-laboratory gait speed seems not representative for daily-life gait speed. This study aimed to investigate the interrelation between and robustness of in-laboratory and daily-life gait speed measures over 12 months in 61- to 70-year-old adults.

METHODS

Gait speed was assessed in laboratory through standardized stopwatch tests and in daily life by 7 days of trunk accelerometry in the PreventIT cohort, at baseline, and after 6 and 12 months. The interrelation was investigated using Pearson's correlations between gait speed measures at each time point. For robustness, changes over time and variance components were assessed by ANOVA and measurement agreement over time by Bland-Altman analyses.

RESULTS

Included were 189 participants (median age 67 years [interquartile range: 64-68], 52.2% females). In-laboratory and daily-life gait speed measures showed low correlations (Pearson's r = 0.045-0.455) at each time point. Moreover, both in-laboratory and daily-life gait speed measures appeared robust over time, with comparable and smaller within-subject than between-subject variance (range 0.001-0.095 m/s and 0.032-0.397 m/s, respectively) and minimal differences between measurements over time (Bland-Altman) with wide limits of agreement (standard deviation of mean difference range: 0.12-0.34 m/s).

DISCUSSION/CONCLUSION

In-laboratory and daily-life gait speed measures show robust assessments of gait speed over 12 months and are distinct constructs in this population of high-functioning adults. This suggests that (a combination of) both measures may have added value in predicting health outcomes.

Gait speed in clinical and daily living assessments in Parkinson's disease patients: performance versus capacity

Gait speed often referred as the sixth vital sign is the most powerful biomarker of mobility. While a clinical setting allows the estimation of gait speed under controlled conditions that present functional capacity, gait speed in real-life conditions provides the actual performance of the patient. The goal of this study was to investigate objectively under what conditions during daily activities, patients perform as well as or better than in the clinic. To this end, we recruited 27 Parkinson's disease (PD) patients and measured their gait speed by inertial measurement units through several walking tests in the clinic as well as their daily activities at home. By fitting a bimodal Gaussian model to their gait speed distribution, we found that on average, patients had similar modes in the clinic and during daily activities. Furthermore, we observed that the number of medication doses taken throughout the day had a moderate correlation with the difference between clinic and home. Performing a cycle-by-cycle analysis on gait speed during the home assessment, overall only about 3% of the strides had equal or greater gait speeds than the patients' capacity in the clinic. These strides were during long walking bouts (>1 min) and happened before noon, around 26 min after medication intake, reaching their maximum occurrence probability 3 h after Levodopa intake. These results open the possibility of better control of medication intake in PD by considering both functional capacity and continuous monitoring of gait speed during real-life conditions.

Fall risk assessment in the wild: A critical examination of wearable sensor use in free-living conditions

BACKGROUND

Despite advances in laboratory-based supervised fall risk assessment methods (FRAs), falls still remain a major public health problem. This can be due to the alteration of behavior in laboratory due to the awareness of being observed (i.e., Hawthorne effect), the multifactorial complex etiology of falls, and our limited understanding of human behaviour in natural environments, or in the' wild'. To address these imitations, a growing body of literature has focused on free-living wearable-sensor-based FRAs. The objective of this narrative literature review is to discuss papers investigating natural data collected by wearable sensors for a duration of at least 24 h to identify fall-prone older adults.

METHODS

Databases (Scopus, PubMed and Google Scholar) were searched for studies based on a rigorous search strategy.

RESULTS

Twenty-four journal papers were selected, in which inertial sensors were the only wearable system employed for FRA in the wild. Gait was the most-investigated activity; but sitting, standing, lying, transitions and gait events, such as turns and missteps, were also explored. A multitude of free-living fall predictors (FLFPs), e.g., the quantity of daily steps, were extracted from activity bouts and events. FLFPs were further categorized into discrete domains (e.g., pace, complexity) defined by conceptual or data-driven models. Heterogeneity was found within the reviewed studies, which includes variance in: terminology (e.g., quantity vs macro), hyperparameters to define/estimate FLFPs, models and domains, and data processing approaches (e.g., the cut-off thresholds to define an ambulatory bout). These inconsistencies led to different results for similar FLFPs, limiting the ability to interpret and compare the evidence.

CONCLUSION

Free-living FRA is a promising avenue for fall prevention. Achieving a harmonized model is necessary to systematically address the inconsistencies in the field and identify FLFPs with the highest predictive values for falls to eventually address intervention programs and fall prevention.

Harnessing digital health to objectively assess cancer-related fatigue: The impact of fatigue on mobility performance

Objective

Cancer-related fatigue (CRF) is highly prevalent among cancer survivors, which may have long-term effects on physical activity and quality of life. CRF is assessed by self-report or clinical observation, which may limit timely diagnosis and management. In this study, we examined the effect of CRF on mobility performance measured by a wearable pendant sensor.

Methods

This is a secondary analysis of a clinical trial evaluating the benefit of exercise in cancer survivors with chemotherapy-induced peripheral neuropathy (CIPN). CRF status was classified based on a Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) score ≤ 33. Among 28 patients (age = 65.7±9.8 years old, BMI = 26.9±4.1kg/m², sex = 32.9%female) with database variables of interest, twenty-one subjects (75.9%) were classified as non-CRF. Mobility performance, including behavior (sedentary, light, and moderate to vigorous activity (MtV)), postures (sitting, standing, lying, and walking), and locomotion (e.g., steps, postural transitions) were measured using a validated pendant-sensor over 24-hours. Baseline psychosocial, Functional Assessment of Cancer Therapy–General (FACT-G), Falls Efficacy Scale–International (FES-I), and motor-capacity assessments including gait (habitual speed, fast speed, and dual-task speed) and static balance were also performed.

Results

Both groups had similar baseline clinical and psychosocial characteristics, except for body-mass index (BMI), FACT-G, FACIT-F, and FES-I (p<0.050). The groups did not differ on motor-capacity. However, the majority of mobility performance parameters were different between groups with large to very large effect size, Cohen’s d ranging from 0.91 to 1.59. Among assessed mobility performance, the largest effect sizes were observed for sedentary-behavior (d = 1.59, p = 0.006), light-activity (d = 1.48, p = 0.009), and duration of sitting+lying (d = 1.46, p = 0.016). The largest correlations between mobility performance and FACIT-F were observed for sitting+lying (rho = -0.67, p<0.001) and the number of steps per day (rho = 0.60, p = 0.001).

Conclusion

The results of this study suggest that sensor-based mobility performance monitoring could be considered as a potential digital biomarker for CRF assessment. Future studies warrant evaluating utilization of mobility performance to track changes in CRF over time, response to CRF-related interventions, and earlier detection of CRF.

Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson's Disease patients

Background

Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson’s disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions.

Methods

Participants (older adults, people with Parkinson’s disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories.

Results

The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document}= 100%, precision \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document}= 100%, F1 score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document}= 100%; FC: recall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document}= 100%, precision \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document}= 100%, F1 score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document}= 100%), slalom walking (IC: recall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document}= 100%, precision \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥ 99%, F1 score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document}=100%; FC: recall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document}= 100%, precision \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥ 99%, F1 score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document}=100%), and turning (IC: recall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥ 85%, precision \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥ 95%, F1 score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥91%; FC: recall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥ 84%, precision \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥ 95%, F1 score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥89%).

Conclusions

Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.

Regularity of kinematic data between single and dual-task treadmill walking in people with Parkinson's disease

BACKGROUND

Regularity, quantified by sample entropy (SampEn), has been extensively used as a gait stability measure. Yet, there is no consensus on the calculation process and variant approaches, e.g. single-scale SampEn with and without incorporating a time delay greater than one, multiscale SampEn, and complexity index, have been used to calculate the regularity of kinematic or kinetic signals. The aim of the present study was to test the discriminatory performance of the abovementioned approaches during single and dual-task walking in people with Parkinson's disease (PD).

METHODS

Seventeen individuals with PD were included in this study. Participants completed two walking trials that included single and dual-task conditions. The secondary task was word searching with twelve words randomly appearing in the participants' visual field. Trunk linear acceleration at sternum level, linear acceleration of the center of gravity, and angular velocity of feet, shanks, and thighs, each in three planes of motion were collected. The regularity of signals was computed using approaches mentioned above for single and dual-task conditions.

RESULTS

Incorporating a time delay greater than one and considering multiple scales helped better distinguish between single and dual-task walking. For all signals, the complexity index, defined as the summary of multiscale SampEn analysis, was the most efficient discriminatory index between single-task walking and dual-tasking in people with Parkinson's disease. Specifically, the complexity index of the trunk linear acceleration of the center of gravity distinguished between the two walking conditions in all three planes of motion.

CONCLUSIONS

The significant results observed across the 24 signals studied in this study are illustrative examples of the complexity index's potential as a gait feature for classifying different walking conditions.

Combining transcranial direct current stimulation with a motor-cognitive task: the impact on dual-task walking costs in older adults

Background

The performance of a secondary task while walking increases motor-cognitive interference and exacerbates fall risk in older adults. Previous studies have demonstrated that transcranial direct current stimulation (tDCS) may improve certain types of dual-task performance, and, that tDCS delivered during the performance of a task may augment the benefits of stimulation, potentially reducing motor-cognitive interference. However, it is not yet known if combining multi-target tDCS with the simultaneous performance of a task related to the tDCS targets reduces or increases dual-task walking costs among older adults. The objectives of the present work were (1) To examine whether tDCS applied during the performance of a task that putatively utilizes the brain networks targeted by the neuro-stimulation reduces dual-task costs, and (2) to compare the immediate after-effects of tDCS applied during walking, during seated-rest, and during sham stimulation while walking, on dual-task walking costs in older adults. We also explored the impact on postural sway and other measures of cognitive function.

Methods

A double-blind, ‘within-subject’ cross-over pilot study evaluated the effects of 20 min of anodal tDCS targeting both the primary motor cortex (M1) and the left dorsolateral prefrontal cortex (lDLPFC) in 25 healthy older adults (73.9 ± 5.2 years). Three stimulation conditions were assessed in three separate sessions: (1) tDCS while walking in a complex environment (tDCS + walking), (2) tDCS while seated (tDCS + seated), and (3) walking in a complex environment with sham tDCS (sham + walking). The complex walking condition utilized virtual reality to tax motor and cognitive abilities. During each session, usual-walking, dual-task walking, quiet standing sway, and cognitive function (e.g., Stroop test) were assessed before and immediately after stimulation. Dual-task costs to gait speed and other measures were computed.

Results

The dual-task cost to gait speed was reduced after tDCS + walking (p = 0.004) as compared to baseline values. Neither tDCS + seated (p = 0.173) nor sham + walking (p = 0.826) influenced this outcome. Similar results were seen for other gait measures and for Stroop performance. Sway was not affected by tDCS.

Conclusions

tDCS delivered during the performance of challenging walking decreased the dual-task cost to walking in older adults when they were tested just after stimulation. These results support the existence of a state-dependent impact of neuro-modulation that may set the stage for a more optimal neuro-rehabilitation.

Trial registration: Clinical Trials Gov Registrations Number: NCT02954328.

Does gait bout definition influence the ability to discriminate gait quality between people with and without multiple sclerosis during daily life?

BACKGROUND

There is currently no consensus about standardized gait bout definitions when passively monitoring walking during normal daily life activities. It is also not known how different definitions of a gait bout in daily life monitoring affects the ability to distinguish pathological gait quality. Specifically, how many seconds of a pause with no walking indicates an end to one gait bout and the start of another bout? In this study, we investigated the effect of 3 gait bout definitions on the discriminative ability to distinguish quality of walking in people with multiple sclerosis (MS) from healthy control subjects (HC) during a week of daily living.

METHODS

15 subjects with MS and 16 HC wore instrumented socks on each foot and one Opal sensor over the lower lumbar area for a week of daily activities for at least 8 h/day. Three gait bout definitions were based on the length of the pause between the end of one gait bout and start of another bout (1.25 s, 2.50 s, and 5.0 s pause). Area under the curve (AUC) was used to compare gait quality measures in MS versus HC.

RESULTS

Total number of gait bouts over the week were statistically significantly different across bout definitions, as expected. However, AUCs of gait quality measures (such as gait speed, stride length, stride time) discriminating people with MS from HC were not different despite the 3 bout definitions.

SIGNIFICANCE

Quality of gait measures that discriminate MS from HC during daily life are not influenced by the length of a gait bout, despite large differences in quantity of gait across bout definitions. Thus, gait quality measures in people with MS versus controls can be compared across studies using different gait bout definitions with pause lengths ≤5 s.

Prevalence and determinants of falls in persons with multiple sclerosis without a clinical disability

BACKGROUND

Falls are common in persons with Multiple Sclerosis (pwMS) and lead to destructive results, specifically with increasing disability. However, there is only scarce data investigating prevalence and determinants of falls in pwMS without a clinical disability. Therefore, this study aimed to investigate proportion of fallers and related factors in pwMS without a clinical disability.

METHODS

One hundred and four pwMS with no clinical disability (EDSS≤1.5) were recruited in this cross-sectional study. The outcome measures comprised of the Timed 25-Foot Walk (T25FW), Six Minute Walk Test (6MWT), Timed Up and Go Test (TUG), Multiple Sclerosis Walking Scale (MSWS-12), Single Leg Stance Test (SLS), Activities-Specific Balance Confidence Scale (ABC), Symbol Digit Modalities Test (SDMT), Modified Fatigue Impact Scale (MFIS), and Beck Depression Inventory-II (BDI-II). The number of falls during the last three months was recorded.

RESULTS

Twenty-five percent of the pwMS reported at least one fall in the last three months. The TUG and MSWS-12 scores were significantly greater in the fallers compared to non-fallers (p<0.05). Whereas the fallers had significantly less ABC scores (p<0.05). Increasing TUG and MSWS-12 score and decreasing ABC score was related with increased risk of being classified as a faller adjusting for EDSS score.

CONCLUSION

The present findings highlight that falls are frequent problem for pwMS, even if they do not have a clinical disability. Therefore, falls prevention strategies are also required in the early stages of the disease in clinical practice. The ABC scale, MSWS-12, and TUG test can be used by the clinicians and researchers to predict potential fallers of the pwMS without a clinical disability.

Will We Do If We Can? Habitual Qualitative and Quantitative Physical Activity in Multi-Morbid, Older Persons with Cognitive Impairment

This study aimed to identify determinants of quantitative dimensions of physical activity (PA; duration, frequency, and intensity) in community-dwelling, multi-morbid, older persons with cognitive impairment (CI). In addition, qualitative and quantitative aspects of habitual PA have been described. Quantitative PA and qualitative gait characteristics while walking straight and while walking turns were documented by a validated, sensor-based activity monitor. Univariate and multiple linear regression analyses were performed to delineate associations of quantitative PA dimensions with qualitative characteristics of gait performance and further potential influencing factors (motor capacity measures, demographic, and health-related parameters). In 94 multi-morbid, older adults (82.3 ± 5.9 years) with CI (Mini-Mental State Examination score: 23.3 ± 2.4), analyses of quantitative and qualitative PA documented highly inactive behavior (89.6% inactivity) and a high incidence of gait deficits, respectively. The multiple regression models (adjusted R² = 0.395–0.679, all p < 0.001) identified specific qualitative gait characteristics as independent determinants for all quantitative PA dimensions, whereas motor capacity was an independent determinant only for the PA dimension duration. Demographic and health-related parameters were not identified as independent determinants. High associations between innovative, qualitative, and established, quantitative PA performances may suggest gait quality as a potential target to increase quantity of PA in multi-morbid, older persons.