The relationship between trunk and foot acceleration variability during walking shows minor changes in persons with multiple sclerosis

Postural behaviour in people with multiple sclerosis: A complexity paradox

BACKGROUND

Balance deficits are a major concern for people with multiple sclerosis (pwMS). Measuring complexity of motor behaviour can offer an insight into MS-related changes in adaptability of the balance control system when dealing with increasingly complex tasks.

QUESTION

Does postural behaviour complexity differ between pwMS at early stages of the disease and healthy controls (HC)? Does postural behaviour complexity change across increasingly complex tasks?

METHODS

Forty-eight pwMS and 24 HC performed four increasingly complex postural tasks with eyes open (EO), eyes closed (EC), on firm (FS) and compliant surface (CS). Lumbar and sternum sensors recorded 3D acceleration, from which complexity index (CI) was calculated using multiscale sample entropy (MSE) in the frontal and sagittal planes.

RESULTS

We found that only the complexity index in both planes during the eyes closed on compliant surface (EC-CS) task was significantly lower in pwMS compared to HC. We also found that complexity in pwMS was significantly lower during EC-CS compared to the other three tasks when using both lumbar and sternum sensors.

SIGNIFICANCE

Increasing the complexity of postural tasks reduces the complexity of postural behaviour in pwMS. This paradox may reflect reduced adaptability of the sensorimotor integration processes at early stages of MS. CI can provide a different perspective on balance deficits and could potentially be a more sensitive biomarker of MS progression and an early indicator of balance deficit.

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.

Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis

Falls are frequent and associated with morbidity in persons with multiple sclerosis (PwMS). Symptoms of MS fluctuate, and standard biannual clinical visits cannot capture these fluctuations. Remote monitoring techniques that leverage wearable sensors have recently emerged as an approach sensitive to disease variability. Previous research has shown that fall risk can be identified from walking data collected by wearable sensors in controlled laboratory conditions however this data may not be generalizable to variable home environments. To investigate fall risk and daily activity performance from remote data, we introduce a new open-source dataset featuring data collected from 38 PwMS, 21 of whom are identified as fallers and 17 as non-fallers based on their six-month fall history. This dataset contains inertial-measurement-unit data from eleven body locations collected in the laboratory, patient-reported surveys and neurological assessments, and two days of free-living sensor data from the chest and right thigh. Six-month (n = 28) and one-year repeat assessment (n = 15) data are also available for some patients. To demonstrate the utility of these data, we explore the use of free-living walking bouts for characterizing fall risk in PwMS, compare these data to those collected in controlled environments, and examine the impact of bout duration on gait parameters and fall risk estimates. Both gait parameters and fall risk classification performance were found to change with bout duration. Deep learning models outperformed feature-based models using home data; the best performance was observed with all bouts for deep-learning and short bouts for feature-based models when evaluating performance on individual bouts. Overall, short duration free-living walking bouts were found to be the least similar to laboratory walking, longer duration free-living walking bouts provided more significant differences between fallers and non-fallers, and an aggregation of all free-living walking bouts yields the best performance in fall risk classification.

How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway

Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose.

Changes in trunk and head acceleration during the 6-minute walk test and its relation to falls risk for adults with multiple sclerosis

For persons with multiple sclerosis (MS), the general decline in neuromuscular function underlies diminished balance, impaired gait and consequently, increased risk of falling. During gait, optimal control of head motion is an important feature which is achieved partly through control of the trunk-neck region to dampen gait-related oscillations. The primary aim of this study was to examine the effect performing a 6-minute walk test (6MWT) has on head, neck and trunk accelerations in individuals with MS. This was addressed using a repeated measures generalized linear model. We were also interested in assessing whether the 6MWT has an impact on a person's falls risk and specific physiological measures related to falls. Finally the relation between the amplitude (i.e., mean RMS) of head and trunk accelerations and falls risk was examined using linear regression. The main results were that over the course of the 6MWT, individuals progressively slowed down coupled with a concurrent increase in gait-related upper body accelerations (p's > 0.05). Despite the increased acceleration, no significant changes in attenuation from the trunk to the head were observed, indicating that persons were able to maintain an optimal level of control over these oscillations. Performing the 6MWT also had a negative impact on posture, with falls risk significantly increasing following this test (p > 0.05). Interestingly, the overall falls risk values were strongly linked with vertical accelerations about the trunk and head, but not average walking speed during the 6MWT. Overall, performing the 6MWT leads to changes in walking speed, upper body acceleration patterns and increases in overall falls risk.

Trunk and foot acceleration variability during walking relates to fall history and clinical disability in persons with multiple sclerosis

BACKGROUND

Persons with multiple sclerosis are often at higher risk for falling, but clinical disability scales and fall risk questionnaires are subjective and don't provide specific feedback about why an individual is unstable. The purpose of this study was to determine how relationships between trunk and foot acceleration variability relate to physiological impairments, clinical disability scales, and mobility questionnaires in persons with multiple sclerosis.

METHODS

15 fallers and 25 non-fallers with multiple sclerosis walked on a treadmill at normal walking speed while trunk and foot accelerations were recorded with wireless accelerometers and variability measures were extracted and used to calculate the gait stability index metrics as a ratio of trunk acceleration variability divided foot acceleration variability. Subjects' sensorimotor delays and lower extremity vibration sensitivity were tested. Subjects also completed clinical disability scales (Guy's Neurological Disability Scale and Patient Reported Expanded Disability Status Scale) and mobility questionnaires (Falls Efficacy Scale, Activities Balance Confidence Scale, 12 Item Multiple Sclerosis Walk Scale).

FINDINGS

Multiple gait stability index metrics were significantly correlated with clinical measures of disability and mobility in multiple sclerosis subjects (r = 0.354-0.528), but no correlations were found for sensorimotor delays or lower extremity sensation. Multiple gait stability indices performed at least as well as clinical questionnaires for separating fallers from non-fallers.

INTERPRETATION

The gait stability indices can potentially be used outside of a laboratory setting to measure walking characteristics related to fall history and disability level in people with multiple sclerosis.

Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies and provide limitations and possible future directions in the field of gait assessment. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial and EMG based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature.

Wearable sensors can reliably quantify gait alterations associated with disability in people with progressive multiple sclerosis in a clinical setting

Gait disability in people with progressive multiple sclerosis (MS) is difficult to quantify using existing clinical tools. This study aims to identify reliable and objective gait-based biomarkers to monitor progressive multiple sclerosis (MS) in clinical settings. During routine clinical visits, 57 people with secondary progressive MS and 24 healthy controls walked for 6 minutes wearing three inertial motion sensors. Fifteen gait measures were computed from the sensor data and tested for between-session reliability, for differences between controls and people with moderate and severe MS disability, and for correlation with Expanded Disability Status Scale (EDSS) scores. The majority of gait measures showed good to excellent between-session reliability when assessed in a subgroup of 23 healthy controls and 25 people with MS. These measures showed that people with MS walked with significantly longer step and stride durations, reduced step and stride regularity, and experienced difficulties in controlling and maintaining a stable walk when compared to controls. These abnormalities significantly increased in people with a higher level of disability and correlated with their EDSS scores. Reliable and objective gait-based biomarkers using wearable sensors have been identified. These biomarkers may allow clinicians to quantify clinically relevant alterations in gait in people with progressive MS within the context of regular clinical visits.

Trunk and pelvis movement compensation in people with multiple sclerosis: Relationships to muscle function and gait performance outcomes

BACKGROUND

Problems with gait are common in people with multiple sclerosis (MS), but little is known about pelvis and trunk kinematics, especially in the frontal plane.

RESEARCH QUESTION

Are pelvis and trunk kinematics in people with MS related to muscle function, spatiotemporal parameters, and gait performance?

METHODS

In this cross-sectional study, 20 people with MS (Expanded Disability Status Scale 1.5-5.5) and 10 people with comparable age and sex (CTL) underwent threedimensional gait analysis, muscle function assessments (hip and trunk strength and endurance), and gait performance measures (Timed 25-Foot Walk - T25FW, 2-Minute Walk Test - 2MWT). Frontal and sagittal plane pelvis and trunk excursion during the stance period of walking were compared between groups; and in the MS group, associations were determined between kinematic variables, muscle function, spatiotemporal parameters, and gait performance.

RESULTS

Compared to the CTL group, the MS group had significantly greater sagittal plane trunk and pelvis excursion for both the stronger (p = 0.031) and weaker (p = 0.042) sides; less frontal plane trunk and pelvis excursion for both the stronger (p = 0.008) and weaker (p = 0.024) sides; and more sagittal plane trunk excursion for the stronger side (p = 0.047) during stance phase. There were low-to-moderate correlations in the MS group for sagittal plane pelvis excursion with muscle function (p = 0.019 to 0.030), spatiotemporal parameters (p < 0.001 to 0.005), and gait performance (p = < 0.001 to 0.001). Using linear regression, frontal and sagittal plane pelvis excursion were significant predictors of both T25FW and 2MWT, explaining 34 % and 46 % of the variance of each gait performance measure, respectively.

SIGNIFICANCE

Rehabilitation interventions may consider addressing pelvis movement compensations in order to improve spatiotemporal parameters and gait performance in people with MS.

Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis

Inertial measurement units (IMUs) allow accurate quantification of gait impairment of people with multiple sclerosis (pwMS). Nonetheless, it is not clear how IMU-based metrics might be influenced by pragmatic aspects associated with clinical translation of this approach, such as data collection settings and gait protocols. In this study, we hypothesised that these aspects do not significantly alter those characteristics of gait that are more related to quality and energetic efficiency and are quantifiable via acceleration related metrics, such as intensity, smoothness, stability, symmetry, and regularity. To test this hypothesis, we compared 33 IMU-based metrics extracted from data, retrospectively collected by two independent centres on two matched cohorts of pwMS. As a worst-case scenario, a walking test was performed in the two centres at a different speed along corridors of different lengths, using different IMU systems, which were also positioned differently. The results showed that the majority of the temporal metrics (9 out of 12) exhibited significant between-centre differences. Conversely, the between-centre differences in the gait quality metrics were small and comparable to those associated with a test-retest analysis under equivalent conditions. Therefore, the gait quality metrics are promising candidates for reliable multi-centric studies aiming at assessing rehabilitation interventions within a routine clinical context.

Coordination of trunk and foot acceleration during gait is affected by walking velocity and fall history in elderly adults

BACKGROUND

Falling is a significant concern for many elderly adults but identifying individuals at risk of falling is difficult, and it is not clear how elderly adults adapt to challenging walking.

AIMS

The aim of the current study was to determine the effects of walking at non-preferred speeds on the coordination between foot and trunk acceleration variability in healthy elderly adults with and without fall history compared to healthy young adults.

METHODS

Subjects walked on a treadmill at 80%-120% of their preferred walking speed while trunk and foot accelerations were recorded with wireless inertial sensors. Variability of accelerations was measured by root mean square, range, sample entropy, and Lyapunov exponent. The gait stability index was calculated using each variability metric in the frontal and sagittal plane by taking the ratio of trunk acceleration variability divided by foot acceleration variability.

RESULTS

Healthy young adults demonstrated larger trunk accelerations relative to foot accelerations at faster walking speeds compared to elderly adults, but both young and elderly adults show similar adaption to their acceleration regularity. Between group differences showed that elderly adult fallers coordinate acceleration variability between the trunk and feet differently compared to elderly non-fallers and young adults.

DISCUSSION

The current results indicate that during gait, elderly fallers demonstrate more constrained, less adaptable trunk movement relative to their foot movement and this pattern is different compared to elderly non-fallers and healthy young.

CONCLUSIONS

Coordination between trunk and foot acceleration variability plays an important role in maintaining stability during gait.

Altered visual and somatosensory feedback affects gait stability in persons with multiple sclerosis

Persons with multiple sclerosis (PwMS) often report problems due to sensory loss and have an inability to appropriately reweight sensory information. Both of these issues can affect individual's ability to maintain stability when walking under challenging conditions. The purpose of the current study was to determine how gait stability is adapted when walking under challenging sensory conditions where vision and somatosensation at the feet is manipulated. 25 healthy adults and 40 PwMS (15 fallers, 25 non-fallers) walked on a treadmill at their preferred normal walking speed under 3 conditions: normal walking, altered vision using goggles that shifted visual field laterally, and altered somatosensation using shoes with compliant foam soles. Inertial measurement united recorded acceleration at the lumbar and right ankle, and acceleration variability measures were calculated including root mean square (RMS), range, sample entropy (SaEn), and Lyapunov exponents (LyE). A gait stability index (GSI) was calculated using each of the four variability measures as the ratio of lumbar acceleration variability divided by foot acceleration variability in the frontal and sagittal planes. The sagittal and frontal GSI_RMS were larger in the somatosensory condition compared to the normal and visual conditions (p < 0.001). The frontal GSI_SaEn was greater in the visual condition compared to the somatosensory condition (p = 0.021). The frontal and sagittal GSI_LyE was greater in the somatosensory condition compared to the normal and visual conditions (p < 0.002). The current study showed that HC, MS non-fallers and MS fallers largely adapted to altered sensory feedback during walking in a similar manner. However, MS faller subjects may be more reliant on visual feedback compared to MS non-fallers and HC subjects.

Novel technology for mobility and balance tracking in patients with multiple sclerosis: a systematic review

INTRODUCTION

Mobility and balance impairments in patients with multiple sclerosis (MS) are major factors for decreased quality of life. Novel sensing technologies have great potential to efficiently capture subtle changes in mobility and balance performance, and thus improve current practices by providing an easy-to-implement, objective, and continuous functional tracking in MS population. Areas covered: This review details the collective findings of novel technology utilization in mobility and balance tracking in patients with MS. Thirty-three were systematically identified and included in this review. Pertinent methodological features (participant demographics, sensing technology, study aims, functional assessment protocols, and outcome measures) were extracted from each article. The construct validity, reliability, clinical relevance, and discriminative ability of sensor-based assessment in the MS population were summarized. Expert commentary: Sensor-based balance and mobility assessment are valid in comparison with reference standard techniques and are reliable to measure performance in the MS population. Sensor-based measures are also associated with validated clinical outcomes and are sensitive to functional deficits in individuals with MS. Such technologies may greatly improve the likelihood of detecting mobility and balance dysfunctions in real-world environments, thus allowing healthcare professionals to monitor interventions and manage disease progression precisely and efficiently Abbreviations: PwMS: Patients with Multiple Sclerosis; BBS: Berg Balance Scale; DGI: Dynamic Gait Index; ABC: Activity-specific Balance Confidence; T25FW: Timed 25 Foot Walk; 6MWT: 6 minute walk test; TUG: Timed Up and Go test; EO: Eyes Open; EC: Eyes Closed; ICC: Intraclass Correlation Coefficient; EDSS: Expanded Disability Status Scale; MFIS: Modified Fatigue Impact Scale; MSWS: Multiple Sclerosis Walking Scale; MSIS: Mutliple Sclerosis Impact Scale; PPA: Physiological Profile Assessment; HC: Healthy Control; AP: Anterior-posterior direction; ML: Mediolateral direction.