Quantity and quality of gait and turning in people with multiple sclerosis, Parkinson's disease and matched controls during daily living

Daily life mobility detects frailty, falls, and functioning in older prostate cancer survivors treated with androgen deprivation therapy

INTRODUCTION

Androgen deprivation therapy (ADT) increases the risk of frailty, falls, and poor physical functioning in older adults with prostate cancer. Detection of frailty is limited to self-report instruments and performance measures, so unbiased tools are needed. We investigated relationships between an unbiased measure - daily life mobility - and ADT history, frailty, fall history, and functioning in older prostate cancer survivors treated with ADT.

MATERIALS AND METHODS

This cross-sectional study recruited prostate cancer survivors with a history of ADT from an exercise clinical trial, an academic medical center, and the community. Participants completed performance measures and surveys to assess frailty, fall history, and physical functioning, then wore instrumented socks for up to seven days to continuously monitor daily life mobility. We performed a principal component analysis on daily life mobility metrics and used regression analyses to investigate relationships between domains of daily life mobility and frailty, fall history, and physical functioning.

RESULTS

Participants (N = 99) were aged 73.0 +/- 7.3 years, most were pre-frail or frail (75 %), and 35 % had fallen at least once in the last year. Daily life mobility metrics clustered into four domains: Gait Pace, Rhythm, Activity, and Balance. Worse scores on Rhythm and Activity were associated with increased odds of frailty (odds ratio [OR] 1.59, 95 % confidence interval [CI]: 1.04, 2.49 and OR 1.81, 95 % CI: 1.19, 2.83, respectively). A worse score on Rhythm was associated with increased odds of ≥1 falls in the previous year (OR 1.60, 95 % CI: 1.05, 2.47). Worse scores on Gait Pace, Rhythm, and Activity were associated with worse physical functioning. Mobility metrics were similar between current and past users of ADT.

DISCUSSION

Continuous passive monitoring of daily life mobility may identify prostate cancer survivors who have developed frailty, falls, and declines in physical functioning.

Gait alteration during turning while walking in older adults with benign paroxysmal positioning vertigo

BACKGROUND

Older adults with Benign Paroxysmal Positioning Vertigo (BPPV) may present with unsteadiness that affects gait patterns.

OBJECTIVE

This study investigated the spatiotemporal gait parameters and indicators of turning difficulty during the Timed Up and Go (TUG) test in older adults with BPPV.

METHODS

This case-controlled study collected data from older adults aged 65 and above with BPPV, young adults with BPPV and older adults without BPPV. Postural stability and self-perception of stability were measured using the Functional Gait Analysis and the Malay version of the Dizziness Handicap Inventory, respectively. The spatiotemporal gait parameters were recorded using a camera. The one-way ANOVA test was used for statistical analysis.

RESULTS

Older adults with BPPV presented with alteration in gait parameters (time and number of steps) compared to older adults without BPPV and adults with BPPV during the TUG test (p < 0.05). During the straight walking tasks of the TUG test, there were significant differences in stride length and velocity between the three groups (p < 0.05). Older adults with BPPV presented with less pivoting and took longer time and more steps to complete the turning phase of the TUG.

CONCLUSIONS

The findings suggest that gait performance was compromised in older adults with BPPV.

Manifestations of walking fatigability in people with multiple sclerosis based on gait quality and distance walked during the six minutes walking test

BACKGROUND

Distance walking fatigability (DWF) in people with multiple sclerosis (pwMS) is defined as a decrease in the distance walking over time. However, declines in gait quality (i.e., gait quality fatigability- GQF) may occur independently or alongside DWF.

OBJECTIVE

i) to investigate how walking fatigability manifests and its prevalence in pwMS; ii) to describe the temporal pattern of the changes of specific gait characteristics during the 6-minute walking test (6MWT) METHODS: Eighty-eight pwMS (EDSS 4[0-6.5], 49[21-70] years) and 47 healthy controls (HC- 46[25-60] years) performed the 6MWT wearing inertial measurement units. Gait characteristics (stride length, sensor-based gait speed, cadence, double support, step duration, stance phase, step duration asymmetry, step duration variability, foot-strike, toe-off, and leg circumduction) and walking distance were recorded in 1-minute intervals. A fatigability index was calculated by comparing the last and first minute of the 6MWT to identify abnormal worsening based on cutoff scores. The manifestation of walking fatigability was counted. The temporal pattern of worsening of gait characteristics during the 6MWT was examined in pwMS exceeding the cutoff values, compared to pwMS without abnormal changes and HC, using a two-way ANOVA (group vs. minutes) RESULTS: Thirty-five pwMS presented both DWF and GQF, 2 presented isolated DWF, 27 presented isolated GQF, and 24 presented non-walking fatigability. PwMS having GQF presented worsening in gait characteristics (cadence, step duration, step duration variability, or toe-off angle) from minute 2 onwards of the 6MWT, while HCs and pwMS without abnormal changes stabilized gait from minute 2 towards the end of the 6MWT.

CONCLUSION

Walking fatigability in pwMS manifests not only as a decrease in walking distance but also as changes in gait quality. Understanding changes in gait characteristics during walking can help tailor rehabilitation interventions.

Evaluation of a finite state machine algorithm to measure stepping with ankle accelerometry: Performance across a range of gait speeds, tasks, and individual walking ability

Wearable sensors, including accelerometers, are a widely accepted tool to assess gait in clinical and free-living environments. Methods to identify phases and subphases of the gait cycle are necessary for comprehensive assessment of pathological gait. The current study evaluated the accuracy of a finite state machine (FSM) algorithm to detect strides by identifying gait cycle subphases from ankle-worn accelerometry. Algorithm performance was challenged across a range of speeds (0.4-2.6 m/s), task conditions (e.g., single- and dual-task walking), and individual characteristics. Specifically, the study included a range of treadmill speeds in young adults and overground walking conditions in older adults with neurological disease. Manually counted and algorithm-derived stride detection from acceleration data were evaluated using error analysis and Bland-Altman plots for visualization. Overall, the algorithm successfully detected strides (>96 % accuracy) across gait speed ranges and tasks, for young and older adults. The accuracy of an FSM algorithm combined with ankle-worn accelerometers, provides an analytical approach with affordable and portable tools that permits comprehensive assessment of gait unbounded by setting and proves to perform well in in walking tasks characterized by variable walking. These algorithm capabilities and advancements are critical for identifying phase dependent gait impairments in clinical and free-living assessment.

Virtual Obstacle Avoidance Strategy: Navigating through a Complex Environment While Interacting with Virtual and Physical Elements

Robotic walking devices can be used for intensive exercises to enhance gait rehabilitation therapies. Mixed Reality (MR) techniques may improve engagement through immersive and interactive environments. This article introduces an MR-based multimodal human-robot interaction strategy designed to enable shared control with a Smart Walker. The MR system integrates virtual and physical sensors to (i) enhance safe navigation and (ii) facilitate intuitive mobility training in personalized virtual scenarios by using an interface with three elements: an arrow to indicate where to go, laser lines to indicate nearby obstacles, and an ellipse to show the activation zone. The multimodal interaction is context-based; the presence of nearby individuals and obstacles modulates the robot's behavior during navigation to simplify collision avoidance while allowing for proper social navigation. An experiment was conducted to evaluate the proposed strategy and the self-explanatory nature of the interface. The volunteers were divided into four groups, with each navigating under different conditions. Three evaluation methods were employed: task performance, self-assessment, and observational measurement. Analysis revealed that participants enjoyed the MR system and understood most of the interface elements without prior explanation. Regarding the interface, volunteers who did not receive any introductory explanation about the interface elements were mostly able to guess their purpose. Volunteers that interacted with the interface in the first session provided more correct answers. In future research, virtual elements will be integrated with the physical environment to enhance user safety during navigation, and the control strategy will be improved to consider both physical and virtual obstacles.

Quantitative Gait and Balance Outcomes for Ataxia Trials: Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers

With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid, finely granulated, digital health measures are highly warranted to augment clinical and patient-reported outcome measures. Gait and balance disturbances most often present as the first signs of degenerative cerebellar ataxia and are the most reported disabling features in disease progression. Thus, digital gait and balance measures constitute promising and relevant performance outcomes for clinical trials.This narrative review with embedded consensus will describe evidence for the sensitivity of digital gait and balance measures for evaluating ataxia severity and progression, propose a consensus protocol for establishing gait and balance metrics in natural history studies and clinical trials, and discuss relevant issues for their use as performance outcomes.

The Use of Novel Instrumented Socks to Detect Changes in Daily Life Mobility During an Exercise Intervention in Prostate Cancer Survivors Treated with Androgen Deprivation Therapy

OBJECTIVES

To describe changes in daily life mobility in prostate cancer survivors treated with androgen deprivation therapy (ADT) after a 6-month exercise intervention using novel instrumented socks and to identify characteristics of participants who exhibited changes in daily life mobility.

METHODS

A subset of participants in a fall prevention exercise trial completed objective tests and patient-reported surveys of physical functioning, and wore instrumented socks for up to 7 days to measure daily life mobility. Changes in cadence, double support proportion, and pitch angle of the foot at toe-off were selected as measures of daily life mobility previously found to be different in men exposed to ADT for prostate cancer versus controls. Daily life mobility was compared from baseline to 6 months using paired t-tests. Characteristics of responders who improved their daily life mobility were compared to nonresponders using two-sample t-tests, Chi-squared proportion tests, or Fisher's Exact Tests.

RESULTS

Our sample included 35 prostate cancer survivors (mean age 71.6 ± 7.8 years). Mean cadence, double support proportion, and pitch angle at toe-off did not change significantly over 6 months of exercise, but 14 participants (40%) improved in at least two of three daily life mobility measures ("responders"). Responders were characterized by lower physical functioning, lower cadence in daily life, fewer comorbidities, and better social and mental/emotional functioning.

CONCLUSIONS

Certain daily life mobility measures potentially impacted by ADT could be measured with instrumented socks and improved by exercise. Men who start with lower physical functioning and better social and mental/emotional functioning appear most likely to benefit, possibly because they have more to gain from exercise and are able to engage in a 6-month intervention.

IMPLICATIONS FOR NURSING PRACTICE

Technology-based approaches could provide nurses with an objective measure of daily life mobility for patients with chronic illness and detect who is responding to rehabilitation.

Imbalance and Falls in Patients with Parkinson's Disease: Causes and Recent Developments in Training and Sensor-Based Assessment

Imbalance and falls in patients with Parkinson's disease (PD) do not only reduce their quality of life but also their life expectancy. Aging-related symptoms as well as disease-specific motor and non-motor symptoms contribute to these conditions and should be treated when appropriate. In addition to an active lifestyle, advanced exercise training is useful and effective, especially for less medically responsive symptoms such as freezing of gait and postural instability at advanced stages. As treadmill training in non-immersive virtual reality, including dual tasks, significantly reduced the number of falls in PD patients, the mechanism(s) explaining this effect should be further investigated. Such research could help to select the most suitable patients and develop the most effective training protocols based on this novel technology. Real-life digital surrogate markers of mobility, such as those describing aspects of endurance, performance, and the complexity of specific movements, can further improve the quality of mobility assessment using wearables.

Information processing speed and disease severity predict real-world ambulation in persons with multiple sclerosis

BACKGROUND

Impairments in real-world gait quality and quantity are multifaceted for individuals with multiple sclerosis (MS), encompassing mobility, cognition, and fear of falling. However, these factors are often examined independently, limiting insights into the combined contributions they make to real-world ambulation.

RESEARCH QUESTION

How do mobility, cognition, and fear of falling contribute to real-world gait quality and quantity in individuals with MS?

METHODS

Twenty individuals with MS underwent a series of cognitive assessments, including the Paced Auditory Serial Addition Test (PASAT), Symbol Digits Modalities Test (SDMT), Stroop Test, and the Selective Reminding Test (SRT). Participants also completed the Falls Efficacy Scale - International (FES-I) and walking impairment using the Patient Determined Disease Steps (PDDS). Following the in-lab session, participants wore an inertial sensor on their lower back and asked to go about their typical daily routines for three days. Metrics of gait speed, stride regularity, time spent walking, and total bouts were extracted from the real-world data.

RESULTS

Significant correlations were found between both real-world gait speed and stride regularity and the SDMT, FES-I, and PDDS. Backward linear regression analysis was conducted for gait speed and stride regularity, with PDDS and SDMT included in the final model for both metrics. These variables explained 63% of the variance in gait speed and 69% of the variance in stride regularity. Results were not significant for gait quantity after adjusting for age and sex.

SIGNIFICANCE

The study's results provide insight regarding the roles of cognition, walking impairment, and fear of falling on real-world ambulation. Deeper understanding of these contributions can inform the development of targeted interventions that aim to improve walking. Additionally, the absence of significant correlations between gait metrics, cognition, and fear of falling with gait quantity underscores the need for further research to identify factors that increased walking in this population.

Daily life mobility detects frailty, falls, and functioning in ADT-treated prostate cancer survivors

Background

Androgen deprivation therapy (ADT) increases the risk of frailty, falls, and, poor physical functioning in prostate cancer survivors. Detection of frailty is limited to self-report instruments and performance measures, so unbiased tools are needed. We investigated relationships between an unbiased measure - daily life mobility - and ADT history, frailty, falls, and functioning in ADT-treated prostate cancer survivors.

Methods

ADT-treated prostate cancer survivors (N=99) were recruited from an exercise clinical trial, an academic medical center, and the community. Participants completed performance measures and surveys to assess frailty, fall history, and physical functioning, then wore instrumented socks to continuously monitor daily life mobility. We performed a principal component analysis on daily life mobility metrics and used regression analyses to investigate relationships between domains of daily life mobility and frailty, fall history, and physical functioning.

Results

Daily life mobility metrics clustered into four domains: Gait Pace, Rhythm, Activity, and Balance. Worse scores on Rhythm and Activity were associated with increased odds of frailty (OR 1.59, 95% CI: 1.04, 2.49 and OR 1.81, 95% CI: 1.19, 2.83, respectively). A worse score on Rhythm was associated with increased odds of ≥1 falls in the previous year (OR 1.60, 95% CI: 1.05, 2.47). Worse scores on Gait Pace, Rhythm, and Activity were associated with worse physical functioning. Mobility metrics were similar between current and past users of ADT.

Conclusions

Continuous passive monitoring of daily life mobility may identify prostate cancer survivors who have or are developing risk for frailty, falls, and declines in physical functioning.

Basal ganglia-spinal cord pathway that commands locomotor gait asymmetries in mice

The basal ganglia are essential for executing motor actions. How the basal ganglia engage spinal motor networks has remained elusive. Medullary Chx10 gigantocellular (Gi) neurons are required for turning gait programs, suggesting that turning gaits organized by the basal ganglia are executed via this descending pathway. Performing deep brainstem recordings of Chx10 Gi Ca2+ activity in adult mice, we show that striatal projection neurons initiate turning gaits via a dominant crossed pathway to Chx10 Gi neurons on the contralateral side. Using intersectional viral tracing and cell-type-specific modulation, we uncover the principal basal ganglia-spinal cord pathway for locomotor asymmetries in mice: basal ganglia → pontine reticular nucleus, oral part (PnO) → Chx10 Gi → spinal cord. Modulating the restricted PnO → Chx10 Gi pathway restores turning competence upon striatal damage, suggesting that dysfunction of this pathway may contribute to debilitating turning deficits observed in Parkinson's disease. Our results reveal the stratified circuit architecture underlying a critical motor program.

Identification of motor progression in Parkinson's disease using wearable sensors and machine learning

Wearable devices offer the potential to track motor symptoms in neurological disorders. Kinematic data used together with machine learning algorithms can accurately identify people living with movement disorders and the severity of their motor symptoms. In this study we aimed to establish whether a combination of wearable sensor data and machine learning algorithms with automatic feature selection can estimate the clinical rating scale and whether it is possible to monitor the motor symptom progression longitudinally, for people with Parkinson's Disease. Seventy-four patients visited the lab seven times at 3-month intervals. Their walking (2-minutes) and postural sway (30-seconds,eyes-closed) were recorded using six Inertial Measurement Unit sensors. Simple linear regression and Random Forest algorithms were utilised together with different routines of automatic feature selection or factorisation, resulting in seven different machine learning algorithms to estimate the clinical rating scale (Movement Disorder Society- Unified Parkinson's Disease Rating Scale part III; MDS-UPDRS-III). Twenty-nine features were found to significantly progress with time at group level. The Random Forest model revealed the most accurate estimation of the MDS-UPDRS-III among the seven models. The model estimations detected a statistically significant progression of the motor symptoms within 15 months when compared to the first visit, whereas the MDS-UPDRS-III did not capture any change. Wearable sensors and machine learning can track the motor symptom progression in people with PD better than the conventionally used clinical rating scales. The methods described in this study can be utilised complimentary to the clinical rating scales to improve the diagnostic and prognostic accuracy.

Links between Neuroanatomy and Neurophysiology with Turning Performance in People with Multiple Sclerosis

Multiple sclerosis is accompanied by decreased mobility and various adaptations affecting neural structure and function. Therefore, the purpose of this project was to understand how motor cortex thickness and corticospinal excitation and inhibition contribute to turning performance in healthy controls and people with multiple sclerosis. In total, 49 participants (23 controls, 26 multiple sclerosis) were included in the final analysis of this study. All participants were instructed to complete a series of turns while wearing wireless inertial sensors. Motor cortex gray matter thickness was measured via magnetic resonance imaging. Corticospinal excitation and inhibition were assessed via transcranial magnetic stimulation and electromyography place on the tibialis anterior muscles bilaterally. People with multiple sclerosis demonstrated reduced turning performance for a variety of turning variables. Further, we observed significant cortical thinning of the motor cortex in the multiple sclerosis group. People with multiple sclerosis demonstrated no significant reductions in excitatory neurotransmission, whereas a reduction in inhibitory activity was observed. Significant correlations were primarily observed in the multiple sclerosis group, demonstrating lateralization to the left hemisphere. The results showed that both cortical thickness and inhibitory activity were associated with turning performance in people with multiple sclerosis and may indicate that people with multiple sclerosis rely on different neural resources to perform dynamic movements typically associated with fall risk.

Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective

BACKGROUND

Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains.

OBJECTIVE

This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades.

METHODS

We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms "multiple sclerosis" and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines.

RESULTS

Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good.

CONCLUSIONS

Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS.

TRIAL REGISTRATION

PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249.

Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson's Disease

Although the multifactorial nature of falls in Parkinson's disease (PD) is well described, optimal assessment for the identification of fallers remains unclear. Thus, we aimed to identify clinical and objective gait measures that best discriminate fallers from non-fallers in PD, with suggestions of optimal cutoff scores.

METHODS

Individuals with mild-to-moderate PD were classified as fallers (n = 31) or non-fallers (n = 96) based on the previous 12 months' falls. Clinical measures (demographic, motor, cognitive and patient-reported outcomes) were assessed with standard scales/tests, and gait parameters were derived from wearable inertial sensors (Mobility Lab v2); participants walked overground, at a self-selected speed, for 2 min under single and dual-task walking conditions (maximum forward digit span). Receiver operating characteristic curve analysis identified measures (separately and in combination) that best discriminate fallers from non-fallers; we calculated the area under the curve (AUC) and identified optimal cutoff scores (i.e., point closest-to-(0,1) corner).

RESULTS

Single gait and clinical measures that best classified fallers were foot strike angle (AUC = 0.728; cutoff = 14.07°) and the Falls Efficacy Scale International (FES-I; AUC = 0.716, cutoff = 25.5), respectively. Combinations of clinical + gait measures had higher AUCs than combinations of clinical-only or gait-only measures. The best performing combination included the FES-I score, New Freezing of Gait Questionnaire score, foot strike angle and trunk transverse range of motion (AUC = 0.85).

CONCLUSION

Multiple clinical and gait aspects must be considered for the classification of fallers and non-fallers in PD.

Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease

Objectives

To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone.

Methods

We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a "best subsets selection strategy" was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed via email every 2 weeks over the year after the study for self-reported falls.

Results

Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10).

Conclusions

These findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD.

Should we use both clinical and mobility measures to identify fallers in Parkinson's disease?

BACKGROUND

Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear.

OBJECTIVE

To identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fallers from non-fallers in PD.

METHODS

People with mild-to-moderate idiopathic PD were classified as fallers (at least one fall; n = 54) or non-fallers (n = 90) based on previous six months falls. Clinical characteristics included demographic, motor and cognitive status and patient-reported outcomes. Mobility (balance and gait) characteristics were derived from body-worn, inertial sensors while performing walking and standing tasks. To investigate the combinations of (up to four) measures that best discriminate fallers from non-fallers in each scenario (i.e., clinical-only, mobility-only and combined clinical + mobility models), we applied logistic regression employing a 'best subsets selection strategy' with a 5-fold cross validation, and calculated the area under the curve (AUC).

RESULTS

The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. The most consistently selected measures in the top-10 ranked models were freezing of gait status (8x), the root mean square of anterior-posterior trunk acceleration while standing on a foam with eyes open (5x), gait double support duration (4x) and the postural instability and gait disorders score from the MDS UPDRS (4x).

CONCLUSIONS

Findings highlight the importance of considering multiple aspects of clinical as well as objective balance and gait characteristics for the classification of fallers and non-fallers in PD.

Outcome Measures for Disease-Modifying Trials in Parkinson's Disease: Consensus Paper by the EJS ACT-PD Multi-Arm Multi-Stage Trial Initiative

BACKGROUND

Multi-arm, multi-stage (MAMS) platform trials can accelerate the identification of disease-modifying treatments for Parkinson's disease (PD) but there is no current consensus on the optimal outcome measures (OM) for this approach.

OBJECTIVE

To provide an up-to-date inventory of OM for disease-modifying PD trials, and a framework for future selection of OM for such trials.

METHODS

As part of the Edmond J Safra Accelerating Clinical Trials in Parkinson Disease (EJS ACT-PD) initiative, an expert group with Patient and Public Involvement and Engagement (PPIE) representatives' input reviewed and evaluated available evidence on OM for potential use in trials to delay progression of PD. Each OM was ranked based on aspects such as validity, sensitivity to change, participant burden and practicality for a multi-site trial. Review of evidence and expert opinion led to the present inventory.

RESULTS

An extensive inventory of OM was created, divided into: general, motor and non-motor scales, diaries and fluctuation questionnaires, cognitive, disability and health-related quality of life, capability, quantitative motor, wearable and digital, combined, resource use, imaging and wet biomarkers, and milestone-based. A framework for evaluation of OM is presented to update the inventory in the future. PPIE input highlighted the need for OM which reflect their experience of disease progression and are applicable to diverse populations and disease stages.

CONCLUSION

We present a range of OM, classified according to a transparent framework, to aid selection of OM for disease-modifying PD trials, whilst allowing for inclusion or re-classification of relevant OM as new evidence emerges.

Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset

INTRODUCTION

Turning in gait digital parameters may be useful in measuring disease progression in Parkinson's disease (PD), however challenges remain over algorithm validation in real-world settings. The influence of clinician observation on turning outcomes is poorly understood. Our objective is to describe a unique in-home video dataset and explore the use of turning parameters as biomarkers in PD.

METHODS

11 participants with PD, 11 control participants stayed in a home-like setting living freely for 5 days (with two sessions of clinical assessment), during which high-resolution video was captured. Clinicians watched the videos, identified turns and documented turning parameters.

RESULTS

From 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between "ON" and "OFF" medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p < 0.001), and b) duration of turn (rho = 0.744, p = 0.009) "OFF" medications. A positive correlation was seen "ON" medications between number of turning steps and clinical rating scale score (rho = 0.618, p = 0.048). Both cohorts took more steps and shorter durations of turn during observed clinical assessments than when free-living.

CONCLUSION

This study shows proof of concept that real-world free-living turn duration and number of turning steps recorded can distinguish between PD medication states and correlate with gold-standard clinical rating scale scores. It illustrates a methodology for ecological validation of real-world digital outcomes.

Suitability of a Low-Cost Wearable Sensor to Assess Turning in Healthy Adults

Background: Turning is a complex measure of gait that accounts for over 50% of daily steps. Traditionally, turning has been measured in a research grade laboratory setting, however, there is demand for a low-cost and portable solution to measure turning using wearable technology. This study aimed to determine the suitability of a low-cost inertial sensor-based device (AX6, Axivity) to assess turning, by simultaneously capturing and comparing to a turn algorithm output from a previously validated reference inertial sensor-based device (Opal), in healthy young adults. Methodology: Thirty participants (aged 23.9 ± 4.89 years) completed the following turning protocol wearing the AX6 and reference device: a turn course, a two-minute walk (including 180° turns) and turning in place, alternating 360° turn right and left. Both devices were attached at the lumbar spine, one Opal via a belt, and the AX6 via double sided tape attached directly to the skin. Turning measures included number of turns, average turn duration, angle, velocity, and jerk. Results: Agreement between the outcomes from the AX6 and reference device was good to excellent for all turn characteristics (all ICCs > 0.850) during the turning 360° task. There was good agreement for all turn characteristics (all ICCs > 0.800) during the two-minute walk task, except for moderate agreement for turn angle (ICC 0.683). Agreement for turn outcomes was moderate to good during the turns course (ICCs range; 0.580 to 0.870). Conclusions: A low-cost wearable sensor, AX6, can be a suitable and fit-for-purpose device when used with validated algorithms for assessment of turning outcomes, particularly during continuous turning tasks. Future work needs to determine the suitability and validity of turning in aging and clinical cohorts within low-resource settings.

Cerebellar Contributions to the Basal Ganglia Influence Motor Coordination, Reward Processing, and Movement Vigor

Both the cerebellum and the basal ganglia are known for their roles in motor control and motivated behavior. These two systems have been classically considered as independent structures that coordinate their contributions to behavior via separate cortico-thalamic loops. However, recent evidence demonstrates the presence of a rich set of direct connections between these two regions. Although there is strong evidence for connections in both directions, for brevity we limit our discussion to the better-characterized connections from the cerebellum to the basal ganglia. We review two sets of such connections: disynaptic projections through the thalamus and direct monosynaptic projections to the midbrain dopaminergic nuclei, the VTA and the SNc. In each case, we review the evidence for these pathways from anatomic tracing and physiological recordings, and discuss their potential functional roles. We present evidence that the disynaptic pathway through the thalamus is involved in motor coordination, and that its dysfunction contributes to motor deficits, such as dystonia. We then discuss how cerebellar projections to the VTA and SNc influence dopamine release in the respective targets of these nuclei: the NAc and the dorsal striatum. We argue that the cerebellar projections to the VTA may play a role in reward-based learning and therefore contribute to addictive behavior, whereas the projection to the SNc may contribute to movement vigor. Finally, we speculate how these projections may explain many of the observations that indicate a role for the cerebellum in mental disorders, such as schizophrenia.

Fall Prediction Based on Instrumented Measures of Gait and Turning in Daily Life in People with Multiple Sclerosis

This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk.

An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks

There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient (ICC2,1)] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (&lt;10 ms). Overall, temporal inaccuracies minimally impacted stride duration, length, and speed (median absolute errors ≤1%). Similar algorithm performances were obtained for all the other five cohorts in GE detection and propagation to the stride parameters, where an excellent absolute agreement with the pressure insoles was also found (ICC2,1=0.817− 0.999). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.

Acute effects of axial loading on postural control during walking and turning in people with multiple sclerosis: A pilot study

BACKGROUND

Impaired sensory integration is heavily involved in gait control and accentuates fall risk in individuals with multiple sclerosis (MS). While axial loading has been found beneficial, little is known about the effect of non-specific axial loads on gait parameters and mobility tasks in those with MS.

RESEARCH QUESTION

What are the effects of non-specific axial loading via weighted vests on walking and turning in those with MS.

METHODS

Twelve participants with MS and eleven age- and gender-matched healthy controls participated in a cross-sectional study. All participants completed five trials of continuous walking with turns wearing weighted vests at 0%, 2%, 4%, 5%, and then 0% of their body weight. Gait parameters were measured using wireless inertial sensors. A 2 (group) x 5 (vest weight) multivariate analysis of variance (MANOVA) was performed to determine any significant differences between groups and across weighted vests for each gait variable. Post-hoc analysis and paired t-tests with corresponding effect sizes were also conducted.

RESULTS

A significant between groups main effect was found for group (F (6100) = 14.74, p = .000) in multiple gait parameters (p < 0.05), although no significant main effect was found for weighted vest. Within group analyses indicated significantly increased cadence and gait speed across varying weighted vests for both MS and control groups (p < 0 >05). Increased vest weight from 0%PRE to 2% also had large effect on shortening double support time and increasing stride length in the MS group.

SIGNIFICANCE

This study provided preliminary evidence that non-specific axial loads of varying weights appear to improve certain gait parameters. As such, this modality may offer mobility benefit and serve as an accessible home-based intervention alternative aimed at improving walking in individuals with MS.

Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis

BACKGROUND

One in two people with multiple sclerosis (PwMS) will fall in a three-month period. Predicting which patients will fall remains a challenge for clinicians. Standardized functional assessments provide insight into balance deficits and fall risk but their use has been limited to supervised visits.

RESEARCH QUESTION

The study aim was to characterize unsupervised 30-second chair stand test (30CST) performance using accelerometer-derived metrics and assess its ability to classify fall status in PwMS compared to supervised 30CST.

METHODS

Thirty-seven PwMS (21 fallers) performed instrumented supervised and unsupervised 30CSTs with a single wearable sensor on the thigh. In unsupervised conditions, participants performed bi-hourly 30CSTs and rated their balance confidence and fatigue over 48-hours. ROC analysis was used to classify fall status for 30CST performance.

RESULTS

Non-fallers (p = 0.02) but not fallers (p = 0.23) differed in their average unsupervised 30CST performance (repetitions) compared to their supervised performance. The unsupervised maximum number of 30CST repetitions performed optimized ROC classification AUC (0.79), accuracy (78.4%) and specificity (90.0%) for fall status with an optimal cutoff of 17 repetitions.

SIGNIFICANCE

Brief durations of instrumented unsupervised monitoring as an adjunct to routine clinical assessments could improve the ability for predicting fall risk and fluctuations in functional mobility in PwMS.

Inertial Sensor Algorithm to Estimate Walk Distance

The “total distance walked” obtained during a standardized walking test is an integral component of physical fitness and health status tracking in a range of consumer and clinical applications. Wearable inertial sensors offer the advantages of providing accurate, objective, and reliable measures of gait while streamlining walk test administration. The aim of this study was to develop an inertial sensor-based algorithm to estimate the total distance walked using older subjects with impaired fasting glucose (Study I), and to test the generalizability of the proposed algorithm in patients with Multiple Sclerosis (Study II). All subjects wore two inertial sensors (Opals by Clario-APDM Wearable Technologies) on their feet. The walking distance algorithm was developed based on 108 older adults in Study I performing a 400 m walk test along a 20 m straight walkway. The validity of the algorithm was tested using a 6-minute walk test (6MWT) in two sub-studies of Study II with different lengths of a walkway, 15 m (Study II-A, n = 24) and 20 m (Study II-B, n = 22), respectively. The start and turn around points were marked with lines on the floor while smaller horizontal lines placed every 1 m served to calculate the manual distance walked (ground truth). The proposed algorithm calculates the forward distance traveled during each step as the change in the horizontal position from each foot-flat period to the subsequent foot-flat period. The total distance walked is then computed as the sum of walk distances for each stride, including turns. The proposed algorithm achieved an average absolute error rate of 1.92% with respect to a fixed 400 m distance for Study I. The same algorithm achieved an absolute error rate of 4.17% and 3.21% with respect to an averaged manual distance for 6MWT in Study II-A and Study II-B, respectively. These results demonstrate the potential of an inertial sensor-based algorithm to estimate a total distance walked with good accuracy with respect to the manual, clinical standard. Further work is needed to test the generalizability of the proposed algorithm with different administrators and populations, as well as larger diverse cohorts.

Bridging the callosal gap in gait: corpus callosum white matter integrity's role in lower limb coordination

Bilateral coordination of the lower extremities is an essential component of mobility. The corpus callosum bridges the two hemispheres of the brain and is integral for the coordination of such complex movements. The aim of this project was to assess structural integrity of the transcallosal sensorimotor fiber tracts and identify their associations with gait coordination using novel methods of ecologically valid mobility assessments in persons with multiple sclerosis and age-/gender-matched neurotypical adults. Neurotypical adults (n = 29) and persons with multiple sclerosis (n = 27) underwent gait and diffusion tensor imaging assessments; the lower limb coordination via Phase Coordination Index, and radial diffusivity, an indirect marker of myelination, were applied as the primary outcome measures. Persons with multiple sclerosis possessed poorer transcallosal white matter microstructural integrity of sensorimotor fiber tracts compared to the neurotypical adults. Further, persons with multiple sclerosis demonstrated significantly poorer bilateral coordination of the lower limbs during over-ground walking in comparison to an age and gender-matched neurotypical cohort. Finally, bilateral coordination of the lower limbs was significantly associated with white matter microstructural integrity of the dorsal premotor and primary motor fiber bundles in persons with multiple sclerosis, but not in neurotypical adults. This analysis revealed that persons with multiple sclerosis exhibit poorer transcallosal microstructural integrity than neurotypical peers. Furthermore, these structural deficits were correlated to poorer consistency and accuracy of gait in those with multiple sclerosis. Together, these results, emphasize the importance of transcallosal communication for gait coordination in those with multiple sclerosis.

Real-Life Turning Movements Capture Subtle Longitudinal and Preataxic Changes in Cerebellar Ataxia

BACKGROUND

Clinical and regulatory acceptance of upcoming molecular treatments in degenerative ataxias might greatly benefit from ecologically valid endpoints that capture change in ataxia severity in patients' real life.

OBJECTIVES

This longitudinal study aimed to unravel quantitative motor biomarkers in degenerative ataxias in real-life turning movements that are sensitive for changes both longitudinally and at the preataxic stage.

METHODS

Combined cross-sectional (n = 30) and longitudinal (n = 14, 1-year interval) observational study in degenerative cerebellar disease (including eight preataxic mutation carriers) compared to 23 healthy controls. Turning movements were assessed by three body-worn inertial sensors in three conditions: (1) instructed laboratory assessment, (2) supervised free walking, and (3) unsupervised real-life movements.

RESULTS

Measures that quantified dynamic balance during turning-lateral velocity change (LVC) and outward acceleration-but not general turning measures such as speed, allowed differentiating ataxic against healthy subjects in real life (effect size δ = 0.68), with LVC also differentiating preataxic against healthy subjects (δ = 0.53). LVC was highly correlated with clinical ataxia severity (scale for the assessment and rating of ataxia [SARA] score, effect size ρ = 0.79) and patient reported balance confidence (activity-specific balance confidence scale [ABC] score, ρ = 0.66). Moreover, LVC in real life-but not general turning measures or the SARA score-allowed detecting significant longitudinal change in 1-year follow-up with high effect size (r_prb  = 0.66).

CONCLUSIONS

Measures of turning allow capturing specific changes of dynamic balance in degenerative ataxia in real life, with high sensitivity to longitudinal differences in ataxia severity and to the preataxic stage. They thus present promising ecologically valid motor biomarkers, even in the highly treatment-relevant early stages of degenerative cerebellar disease. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Surrogates for rigidity and PIGD MDS-UPDRS subscores using wearable sensors

BACKGROUND

Telemedicine has the advantage of expanding access to care for patients with Parkinson's Disease (PD). However, rigidity and postural instability in PD are difficult to measure remotely, and are important measures of functional impairment and fall risk.

RESEARCH QUESTION

Can measures from wearable sensors be used as future surrogates for the MDS-UPDRS rigidity and Postural Instability and Gait Difficulty (PIGD) subscores?

METHODS

Thirty-one individuals with mild to moderate PD wore 3 inertial sensors at home for one week to measure quantity and quality of gait and turning in daily life. Separately, we performed a clinical assessment and balance characterization of postural sway with the same wearable sensors in the laboratory (On medication). We then first performed a traditional correlation analysis between clinical scores and objective measures of gait and balance followed by multivariable linear regression employing a best subset selection strategy.

RESULTS

The number of walking bouts and turns correlated significantly with the rigidity subscore, while the number of turns, foot pitch angle, and sway area while standing correlated significantly with the PIGD subscore (p < 0.05). The multivariable linear regression showed that rigidity subscore was best predicted by the number of walking bouts while the PIGD subscore was best predicted by a combination of number of walking bouts, gait speed, and postural sway.

SIGNIFICANCE

The correlation between objective sensor data and MDS-UPDRS rigidity and PIGD scores paves the way for future larger studies that evaluate use of objective sensor data to supplement remote MDS-UPDRS assessment.

Discriminative Mobility Characteristics between Neurotypical Young, Middle-Aged, and Older Adults Using Wireless Inertial Sensors

Age-related mobility research often highlights significant mobility differences comparing neurotypical young and older adults, while neglecting to report mobility outcomes for middle-aged adults. Moreover, these analyses regularly do not determine which measures of mobility can discriminate groups into their age brackets. Thus, the current study aimed to provide a comprehensive analysis for commonly performed aspects of mobility (walking, turning, sit-to-stand, and balance) to determine which variables were significantly different and furthermore, able to discriminate between neurotypical young adults (YAs), middle-aged adults (MAAs), and older adults (OAs). This study recruited 20 YAs, 20 MAAs, and 20 OAs. Participants came into the laboratory and completed mobility testing while wearing wireless inertial sensors. Mobility tests assessed included three distinct two-minute walks, 360° turns, five times sit-to-stands, and a clinical balance test, capturing 99 distinct mobility metrics. Of the various mobility tests assessed, only 360° turning measures demonstrated significance between YAs and MAAs, although the capacity to discriminate between groups was achieved for gait and turning measures. A variety of mobility measures demonstrated significance between MAAs and OAs, and furthermore discrimination was achieved for each mobility test. These results indicate greater mobility differences between MAAs and OAs, although discrimination is achievable for both group comparisons.

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.

A Preliminary Investigation of the Effects of Obstacle Negotiation and Turning on Gait Variability in Adults with Multiple Sclerosis

Many falls in persons with multiple sclerosis (PwMS) occur during daily activities such as negotiating obstacles or changing direction. While increased gait variability is a robust biomarker of fall risk in PwMS, gait variability in more ecologically related tasks is unclear. Here, the effects of turning and negotiating an obstacle on gait variability in PwMS were investigated. PwMS and matched healthy controls were instrumented with inertial measurement units on the feet, lumbar, and torso. Subjects completed a walk and turn (WT) with and without an obstacle crossing (OW). Each task was partitioned into pre-turn, post-turn, pre-obstacle, and post-obstacle phases for analysis. Spatial and temporal gait measures and measures of trunk rotation were captured for each phase of each task. In the WT condition, PwMS demonstrated significantly more variability in lumbar and trunk yaw range of motion and rate, lateral foot deviation, cadence, and step time after turning than before. In the OW condition, PwMS demonstrated significantly more variability in both spatial and temporal gait parameters in obstacle approach after turning compared to before turning. No significant differences in gait variability were observed after negotiating an obstacle, regardless of turning or not. Results suggest that the context of gait variability measurement is important. The increased number of variables impacted from turning and the influence of turning on obstacle negotiation suggest that varying tasks must be considered together rather than in isolation to obtain an informed understanding of gait variability that more closely resembles everyday walking.

Normative data on spontaneous stride velocity, stride length, and walking activity in a non-controlled environment

Background

Normative data are necessary for validation of new outcome measures. Recently, the 95th centile of stride speed was qualified by the European Medicines Agency as a valid secondary outcome for clinical trials in subjects with Duchenne muscular dystrophy. This study aims to obtain normative data on spontaneous stride velocity and length in a non-controlled environment and their evolution after 12 months.

Method

Ninety-one healthy volunteers (50 females, 41 males), with a mean age of 16 years and 2 months, were recruited and assessed at baseline and 12 months later. The 4-stair climb, 6-min walk test, 10-m walk test and rise from floor assessments were performed. Stride length, stride velocity, and the distance walked per hour were studied in an everyday setting for one month after each evaluation.

Results

Of the 91 subjects assessed, 82 provided more than 50 h of recordings at baseline; and 73 subjects provided the same at the end of the year. We observed significant positive correlations of the stride length with age and height of participants, and a significant increase of the median stride length in children after the period. In this group, the 95th centile stride velocity was not correlated with age and was stable after one year. All measures but the 10MWT were stable in adults after a one-year period.

Conclusion

This study provides with data on the influence of age, height, and gender on stride velocity and length as well as accounting for natural changes after one year in controls.

Dual-Task Costs of Quantitative Gait Parameters While Walking and Turning in People with Parkinson's Disease: Beyond Gait Speed

BACKGROUND

There is a lack of recommendations for selecting the most appropriate gait measures of Parkinson's disease (PD)-specific dual-task costs to use in clinical practice and research.

OBJECTIVE

We aimed to identify measures of dual-task costs of gait and turning that best discriminate performance in people with PD from healthy individuals. We also investigated the relationship between the most discriminative measures of dual-task costs of gait and turning with disease severity and disease duration.

METHODS

People with mild-to-moderate PD (n = 144) and age-matched healthy individuals (n = 79) wore 8 inertial sensors while walking under single and dual-task (reciting every other letter of the alphabet) conditions. Outcome measures included 26 objective measures within four gait domains (upper/lower body, turning and variability). The area under the curve (AUC) from the receiver-operator characteristic plot was calculated to compare discriminative ability of dual-task costs on gait across outcome measures.

RESULTS

PD-specific, dual-task interference was identified for arm range of motion, foot strike angle, turn velocity and turn duration. Arm range of motion (AUC = 0.73) and foot strike angle (AUC = 0.68) had the largest AUCs across dual-task costs measures and they were associated with disease severity and/or disease duration. In contrast, the most commonly used dual-task gait measure, gait speed, showed an AUC of only 0.54.

CONCLUSION

Findings suggest that people with PD rely more than healthy individuals on executive-attentional resources to control arm swing, foot strike, and turning, but not gait speed. The dual-task costs of arm range of motion best discriminated people with PD from healthy individuals.

Middle-age people with multiple sclerosis demonstrate similar mobility characteristics to neurotypical older adults

BACKGROUND

Clinical trials often report significant mobility differences between neurotypical and atypical groups, however, these analyses often do not determine which measures are capable of discriminating between groups. Additionally, indirect evidence supports the notion that some mobility impaired populations demonstrate similar mobility deficits. Thus, the current study aimed to provide a comprehensive analysis of three distinct aspects of mobility (walking, turning, and balance) to determine which variables were significantly different and were also able to discriminate between neurotypical older adults (OA) and middle-aged people with multiple sclerosis (PwMS), and between middle-aged neurotypical adults and PwMS.

METHODS

This study recruited 21 neurotypical OA, 19 middle-aged neurotypical adults, and 30 people with relapsing remitting MS. Participants came into the laboratory on two separate occasions to complete mobility testing while wearing wireless inertial sensors. Testing included a self-selected pace two-minute walk, a series of 180˚ and 360˚ turns, and a clinical balance test capturing a total of 99 distinct mobility characteristics. We determined significant differences for gait and turning measures through univariate analyses and a series of repeated measures analysis of variance in determining significance for balance conditions and measures. In determining discrimination between groups, the Area Under the Curve (AUC) was calculated for all individual mobility measures with a threshold of 0.80, denoting excellent discrimination. Additionally, a stepwise regression of the top five AUC producing variables was performed to determine whether a combination of variables could enhance discrimination while accounting for multicollinearity.

RESULTS

The results between neurotypical OA and middle-aged PwMS demonstrated significant differences for three gait and one turning variable, with no variable or combination of variables able to provide excellent discrimination between groups. Between middle-age neurotypical adults and PwMS a variety of mean and variability gait measures demonstrated significant differences between groups; however, no variable or combination of variables met discriminatory threshold. For turning, five 360˚ turn variables demonstrated significant differences and furthermore, the combination of 360˚ mean turn duration and variability of peak turn velocity were able to discriminate between groups. Finally, the majority of postural sway measures demonstrated significant group differences and the ability to discriminate between groups, particularly during more challenging balance conditions where participants stood on a compliant surface.

CONCLUSION

These results offer a comprehensive analysis of mobility differences and measures capable of discriminating between middle-age neurotypical adults and PwMS. Additionally, these results provide evidence that OA and middle-age PwMS display similar movement characteristics and thus a potential indicator of advanced aging from a mobility perspective.

What gait features influence the amount and intensity of physical activity in people with multiple sclerosis?

Although the mutual relationship between ambulation and physical activity (PA) in people with multiple sclerosis (pwMS) has been described in several studies, there is still a lack of detailed information about the way in which specific aspects of the gait cycle are associated with amount and intensity of PA. This study aimed to verify the existence of possible relationships among PA parameters and the spatio-temporal parameters of gait when both are instrumentally assessed.Thirty-one pwMS (17F, 14 M, mean age 52.5, mean Expanded Disability Status Scale (EDSS) score 3.1) were requested to wear a tri-axial accelerometer 24 hours/day for 7 consecutive days and underwent an instrumental gait analysis, performed using an inertial sensor located on the low back, immediately before the PA assessment period. Main spatio-temporal parameters of gait (i.e., gait speed, stride length, cadence and duration of stance, swing, and double support phase) were extracted by processing trunk accelerations. PA was quantified using average number of daily steps and percentage of time spent at different PA intensity, the latter calculated using cut-point sets previously validated for MS. The existence of possible relationships between PA and gait parameters was assessed using Spearman rank correlation coefficient rho.Gait speed and stride length were the parameters with the highest number of significant correlations with PA features. In particular, they were found moderately to largely correlated with number of daily steps (rho 0.62, P< .001), percentage of sedentary activity (rho = -0.44, P < .001) and percentage of moderate-to-vigorous activity (rho = 0.48, P < .001). Small to moderate significant correlations were observed between PA intensity and duration of stance, swing and double support phases.The data obtained suggest that the most relevant determinants associated with higher and more intense levels of PA in free-living conditions are gait speed and stride length. The simultaneous quantitative assessment of gait parameters and PA levels might represent a useful support for physical therapists in tailoring optimized rehabilitative and training interventions.

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.

Measuring freezing of gait during daily-life: an open-source, wearable sensors approach

Background

Although a growing number of studies focus on the measurement and detection of freezing of gait (FoG) in laboratory settings, only a few studies have attempted to measure FoG during daily life with body-worn sensors. Here, we presented a novel algorithm to detect FoG in a group of people with Parkinson’s disease (PD) in the laboratory (Study I) and extended the algorithm in a second cohort of people with PD at home during daily life (Study II).

Methods

In Study I, we described of our novel FoG detection algorithm based on five inertial sensors attached to the feet, shins and lumbar region while walking in 40 participants with PD. We compared the performance of the algorithm with two expert clinical raters who scored the number of FoG episodes from video recordings of walking and turning based on duration of the episodes: very short (< 1 s), short (2–5 s), and long (> 5 s). In Study II, a different cohort of 48 people with PD (with and without FoG) wore 3 wearable sensors on their feet and lumbar region for 7 days. Our primary outcome measures for freezing were the % time spent freezing and its variability.

Results

We showed moderate to good agreement in the number of FoG episodes detected in the laboratory (Study I) between clinical raters and the algorithm (if wearable sensors were placed on the feet) for short and long FoG episodes, but not for very short FoG episodes. When extending this methodology to unsupervised home monitoring (Study II), we found that percent time spent freezing and the variability of time spent freezing differentiated between people with and without FoG (p < 0.05), and that short FoG episodes account for 69% of the total FoG episodes.

Conclusion

Our findings showed that objective measures of freezing in PD using inertial sensors on the feet in the laboratory are matching well with clinical scores. Although results found during daily life are promising, they need to be validated. Objective measures of FoG with wearable technology during community-living would be useful for managing this distressing feature of mobility disability in PD.

Body-Worn Sensors for Remote Monitoring of Parkinson's Disease Motor Symptoms: Vision, State of the Art, and Challenges Ahead

The increasing prevalence of neurodegenerative conditions such as Parkinson's disease (PD) and related mobility issues places a serious burden on healthcare systems. The COVID-19 pandemic has reinforced the urgent need for better tools to manage chronic conditions remotely, as regular access to clinics may be problematic. Digital health technology in the form of remote monitoring with body-worn sensors offers significant opportunities for transforming research and revolutionizing the clinical management of PD. Significant efforts are being invested in the development and validation of digital outcomes to support diagnosis and track motor and mobility impairments "off-line". Imagine being able to remotely assess your patient, understand how well they are functioning, evaluate the impact of any recent medication/intervention, and identify the need for urgent follow-up before overt, irreparable change takes place? This could offer new pragmatic solutions for personalized care and clinical research. So the question remains: how close are we to achieving this? Here, we describe the state-of-the-art based on representative papers published between 2017 and 2020. We focus on remote (i.e., real-world, daily-living) monitoring of PD using body-worn sensors (e.g., accelerometers, inertial measurement units) for assessing motor symptoms and their complications. Despite the tremendous potential, existing challenges exist (e.g., validity, regulatory) that are preventing the widespread clinical adoption of body-worn sensors as a digital outcome. We propose a roadmap with clear recommendations for addressing these challenges and future directions to bring us closer to the implementation and widespread adoption of this important way of improving the clinical care, evaluation, and monitoring of PD.

Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson's disease, and matched controls

BACKGROUND AND PURPOSE

Recent findings suggest that a gait assessment at a discrete moment in a clinic or laboratory setting may not reflect functional, everyday mobility. As a step towards better understanding gait during daily life in neurological populations, we compared gait measures that best discriminated people with multiple sclerosis (MS) and people with Parkinson's Disease (PD) from their respective, age-matched, healthy control subjects (MS-Ctl, PD-Ctl) in laboratory tests versus a week of daily life monitoring.

METHODS

We recruited 15 people with MS (age mean ± SD: 49 ± 10 years), 16 MS-Ctl (45 ± 11 years), 16 people with idiopathic PD (71 ± 5 years), and 15 PD-Ctl (69 ± 7 years). Subjects wore 3 inertial sensors (one each foot and lower back) in the laboratory followed by 7 days during daily life. Mann-Whitney U test and area under the curve (AUC) compared differences between PD and PD-Ctl, and between MS and MS-Ctl in the laboratory and in daily life.

RESULTS

Participants wore sensors for 60-68 h in daily life. Measures that best discriminated gait characteristics in people with MS and PD from their respective control groups were different between the laboratory gait test and a week of daily life. Specifically, the toe-off angle best discriminated MS versus MS-Ctl in the laboratory (AUC [95% CI] = 0.80 [0.63-0.96]) whereas gait speed in daily life (AUC = 0.84 [0.69-1.00]). In contrast, the lumbar coronal range of motion best discriminated PD versus PD-Ctl in the laboratory (AUC = 0.78 [0.59-0.96]) whereas foot-strike angle in daily life (AUC = 0.84 [0.70-0.98]). AUCs were larger in daily life compared to the laboratory.

CONCLUSIONS

Larger AUC for daily life gait measures compared to the laboratory gait measures suggest that daily life monitoring may be more sensitive to impairments from neurological disease, but each neurological disease may require different gait outcome measures.

Aging, Vestibular Function, and Balance: Proceedings of a National Institute on Aging/National Institute on Deafness and Other Communication Disorders Workshop

Balance impairment and falls are among the most prevalent and morbid conditions affecting older adults. A critical contributor to balance and gait function is the vestibular system; however, there remain substantial knowledge gaps regarding age-related vestibular loss and its contribution to balance impairment and falls in older adults. Given these knowledge gaps, the National Institute on Aging and the National Institute on Deafness and Other Communication Disorders convened a multidisciplinary workshop in April 2019 that brought together experts from a wide array of disciplines, such as vestibular physiology, neuroscience, movement science, rehabilitation, and geriatrics. The goal of the workshop was to identify key knowledge gaps on vestibular function and balance control in older adults and develop a research agenda to make substantial advancements in the field. This article provides a report of the proceedings of this workshop. Three key questions emerged from the workshop, specifically: (i) How does aging impact vestibular function?; (ii) How do we know what is the contribution of age-related vestibular impairment to an older adult's balance problem?; and more broadly, (iii) Can we develop a nosology of balance impairments in older adults that can guide clinical practice? For each of these key questions, the current knowledge is reviewed, and the critical knowledge gaps and research strategies to address them are discussed. This document outlines an ambitious 5- to 10-year research agenda for increasing knowledge related to vestibular impairment and balance control in older adults, with the ultimate goal of linking this knowledge to more effective treatment.

Inertial Sensor Algorithms to Characterize Turning in Neurological Patients With Turn Hesitations

BACKGROUND

One difficulty in turning algorithm design for inertial sensors is detecting two discrete turns in the same direction, close in time. A second difficulty is under-estimation of turn angle due to short-duration hesitations by people with neurological disorders. We aimed to validate and determine the generalizability of a: I. Discrete Turn Algorithm for variable and sequential turns close in time and II: Merged Turn Algorithm for a single turn angle in the presence of hesitations.

METHODS

We validated the Discrete Turn Algorithm with motion capture in healthy controls (HC, n = 10) performing a spectrum of turn angles. Subsequently, the generalizability of the Discrete Turn Algorithm and associated, Merged Turn Algorithm were tested in people with Parkinson's disease (PD, n = 124), spinocerebellar ataxia (SCA, n = 51), and HC (n = 125).

RESULTS

The Discrete Turn Algorithm shows improved agreement with optical motion capture and with known turn angles, compared to our previous algorithm by El-Gohary et al. The Merged Turn algorithm that merges consecutive turns in the same direction with short hesitations resulted in turn angle estimates closer to a fixed 180-degree turn angle in the PD, SCA, and HC subjects compared to our previous turn algorithm. Additional metrics were proposed to capture turn hesitations in PD and SCA.

CONCLUSION

The Discrete Turn Algorithm may be particularly useful to characterize turns when the turn angle is unknown, i.e., during free-living conditions. The Merged Turn algorithm is recommended for clinical tasks in which the single-turn angle is known, especially for patients who hesitate while turning.

Effect of Bout Length on Gait Measures in People with and without Parkinson's Disease during Daily Life

Although the use of wearable technology to characterize gait disorders in daily life is increasing, there is no consensus on which specific gait bout length should be used to characterize gait. Clinical trialists using daily life gait quality as study outcomes need to understand how gait bout length affects the sensitivity and specificity of measures to discriminate pathological gait as well as the reliability of gait measures across gait bout lengths. We investigated whether Parkinson's disease (PD) affects how gait characteristics change as bout length changes, and how gait bout length affects the reliability and discriminative ability of gait measures to identify gait impairments in people with PD compared to neurotypical Old Adults (OA). We recruited 29 people with PD and 20 neurotypical OA of similar age for this study. Subjects wore 3 inertial sensors, one on each foot and one over the lumbar spine all day, for 7 days. To investigate which gait bout lengths should be included to extract gait measures, we determined the range of gait bout lengths available across all subjects. To investigate if the effect of bout length on each gait measure is similar or not between subjects with PD and OA, we used a growth curve analysis. For reliability and discriminative ability of each gait measure as a function of gait bout length, we used the intraclass correlation coefficient (ICC) and area under the curve (AUC), respectively. Ninety percent of subjects walked with a bout length of less than 53 strides during the week, and the majority (>50%) of gait bouts consisted of less than 12 strides. Although bout length affected all gait measures, the effects depended on the specific measure and sometimes differed for PD versus OA. Specifically, people with PD did not increase/decrease cadence and swing duration with bout length in the same way as OA. ICC and AUC characteristics tended to be larger for shorter than longer gait bouts. Our findings suggest that PD interferes with the scaling of cadence and swing duration with gait bout length. Whereas control subjects gradually increased cadence and decreased swing duration as bout length increased, participants with PD started with higher than normal cadence and shorter than normal stride duration for the smallest bouts, and cadence and stride duration changed little as bout length increased, so differences between PD and OA disappeared for the longer bout lengths. Gait measures extracted from shorter bouts are more common, more reliable, and more discriminative, suggesting that shorter gait bouts should be used to extract potential digital biomarkers for people with PD.

Age and environment-related differences in gait in healthy adults using wearables

Technological advances in multimodal wearable and connected devices have enabled the measurement of human movement and physiology in naturalistic settings. The ability to collect continuous activity monitoring data with digital devices in real-world environments has opened unprecedented opportunity to establish clinical digital phenotypes across diseases. Many traditional assessments of physical function utilized in clinical trials are limited because they are episodic, therefore, cannot capture the day-to-day temporal fluctuations and longitudinal changes in activity that individuals experience. In order to understand the sensitivity of gait speed as a potential endpoint for clinical trials, we investigated the use of digital devices during traditional clinical assessments and in real-world environments in a group of healthy younger (n = 33, 18-40 years) and older (n = 32, 65-85 years) adults. We observed good agreement between gait speed estimated using a lumbar-mounted accelerometer and gold standard system during the performance of traditional gait assessment task in-lab, and saw discrepancies between in-lab and at-home gait speed. We found that gait speed estimated in-lab, with or without digital devices, failed to differentiate between the age groups, whereas gait speed derived during at-home monitoring was able to distinguish the age groups. Furthermore, we found that only three days of at-home monitoring was sufficient to reliably estimate gait speed in our population, and still capture age-related group differences. Our results suggest that gait speed derived from activities during daily life using data from wearable devices may have the potential to transform clinical trials by non-invasively and unobtrusively providing a more objective and naturalistic measure of functional ability.

Clinical Relevance of Standardized Mobile Gait Tests. Reliability Analysis Between Gait Recordings at Hospital and Home in Parkinson's Disease: A Pilot Study

BACKGROUND

Gait impairments in Parkinson's disease (PD) are quantified using inertial sensors under standardized test settings in the hospital. Recent studies focused on the assessment of free-living gait in PD. However, the clinical relevance of standardized gait tests recorded at the patient's home is unclear.

OBJECTIVE

To evaluate the reliability of supervised, standardized sensor-based gait outcomes at home compared to the hospital.

METHODS

Patients with PD (n = 20) were rated by a trained investigator using the Unified Parkinson Disease Rating Scale (UPDRS-III). Gait tests included a standardized 4×10 m walk test and the Timed Up and Go Test (TUG). Tests were performed in the hospital (HOSPITAL) and at patients' home (HOME), and controlled for investigator, time of the day, and medication. Statistics included reliability analysis using Intra-Class correlations and Bland-Altman plots.

RESULTS

UPDRS-III and TUG were comparable between HOSPITAL and HOME. PD patients' gait at HOME was slower (gait velocity Δ= -0.07±0.11 m/s, -6.1%), strides were shorter (stride length Δ= -9.2±9.4 cm; -7.3%), and shuffling of gait was more present (maximum toe-clearance Δ= -0.7±2.5 cm; -8.8%). Particularly, narrow walkways (<85 cm) resulted in a significant reduction of gait velocity at home. Reliability analysis (HOSPITAL vs. HOME) revealed excellent ICC coefficients for UPDRS-III (0.950, p < 0.000) and gait parameters (e.g., stride length: 0.898, p < 0.000; gait velocity: 0.914, p < 0.000; stance time: 0.922, p < 0.000; stride time: 0.907, p < 0.000).

CONCLUSION

This pilot study underlined the clinical relevance of gait parameters by showing excellent reliability for supervised, standardized gait tests at HOSPITAL and HOME, even though gait parameters were different between test conditions.