Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis

Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors

BACKGROUND AND OBJECTIVES

Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored.

METHODS

We analyzed the gait pattern related to high CSVD burden from 720 participants (aged 55-65 years, 42.5 % male) free of neurological disease in the Taizhou Imaging Study. All participants underwent detailed quantitative gait assessments (obtained from an insole-like wearable gait tracking device) and brain magnetic resonance imaging examinations. Thirty-three gait parameters were summarized into five gait domains. Sparse sliced inverse regression was developed to extract the gait pattern related to high CSVD burden.

RESULTS

The specific gait pattern derived from several gait domains (i.e., angles, phases, variability, and spatio-temporal) was significantly associated with the CSVD burden (OR=1.250, 95 % CI: 1.011-1.546). The gait pattern indicates that people with a high CSVD burden were prone to have smaller gait angles, more stance time, more double support time, larger gait variability, and slower gait velocity. Furthermore, people with this gait pattern had a 25 % higher risk of a high CSVD burden.

CONCLUSIONS

We established a more stable and disease-specific quantitative gait pattern related to high CSVD burden, which is prone to facilitate the identification of individuals with high CSVD burden among the community residents or the general population.

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.

Effects of static exercises on hip muscle fatigue and knee wobble assessed by surface electromyography and inertial measurement unit data

Hip muscle weakness can be a precursor to or a result of lower limb injuries. Assessment of hip muscle strength and muscle motor fatigue in the clinic is important for diagnosing and treating hip-related impairments. Muscle motor fatigue can be assessed with surface electromyography (sEMG), however sEMG requires specialized equipment and training. Inertial measurement units (IMUs) are wearable devices used to measure human motion, yet it remains unclear if they can be used as a low-cost alternative method to measure hip muscle fatigue. The goals of this work were to (1) identify which of five pre-selected exercises most consistently and effectively elicited muscle fatigue in the gluteus maximus, gluteus medius, and rectus femoris muscles and (2) determine the relationship between muscle fatigue using sEMG sensors and knee wobble using an IMU device. This work suggests that a wall sit and single leg knee raise activity fatigue the gluteus medius, gluteus maximus, and rectus femoris muscles most reliably (p < 0.05) and that the gluteus medius and gluteus maximus muscles were fatigued to a greater extent than the rectus femoris (p = 0.031 and p = 0.0023, respectively). Additionally, while acceleration data from a single IMU placed on the knee suggested that more knee wobble may be an indicator of muscle fatigue, this single IMU is not capable of reliably assessing fatigue level. These results suggest the wall sit activity could be used as simple, static exercise to elicit hip muscle fatigue in the clinic, and that assessment of knee wobble in addition to other IMU measures could potentially be used to infer muscle fatigue under controlled conditions. Future work examining the relationship between IMU data, muscle fatigue, and multi-limb dynamics should be explored to develop an accessible, low-cost, fast and standardized method to measure fatiguability of the hip muscles in the clinic.

Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review

This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.

Test-retest reliability of the six-minute walking distance measurements using FeetMe insoles by completely unassisted healthy adults in their homes

Wearable technology provides an opportunity for new ways of monitoring patient gait remotely, through at-home self-administered six-minute walk tests (6MWTs). The purpose of this study was to evaluate the test-retest reliability of FeetMe insoles, a wearable gait assessment device, for measuring the six-minute walking distance (6MWD) during tests conducted with a one-week interval by completely unassisted healthy adults in their homes. Participants (n = 21) performed two 6MWTs at home while wearing the FeetMe insoles, and two 6MWTs at hospital while wearing FeetMe insoles and being assessed by a rater. All assessments were performed with a one-week interval between tests, no assistance was provided to the participants at home. The agreement between the 6MWD measurements made at baseline and at Week 1 was good for all test configurations and was highest for the at-home FeetMe measurements, with an intraclass correlation coefficient (ICC) of 0.95, standard error of the measurement (SEM) of 15.02 m and coefficient of variation (CV) of 3.33%, compared to ICCs of 0.79 and 0.78, SEMs of 25.65 and 26.65 and CVs of 6.24% and 6.10% for the rater and FeetMe measurements at hospital, respectively. Our work demonstrates that the FeetMe system could provide a reliable solution allowing individuals to self-administer 6MWTs independently at home.

Innovative multidimensional gait evaluation using IMU in multiple sclerosis: introducing the semiogram

Background

Quantifying gait using inertial measurement units has gained increasing interest in recent years. Highly degraded gaits, especially in neurological impaired patients, challenge gait detection algorithms and require specific segmentation and analysis tools. Thus, the outcomes of these devices must be rigorously tested for both robustness and relevancy in order to recommend their routine use. In this study, we propose a multidimensional score to quantify and visualize gait, which can be used in neurological routine follow-up. We assessed the reliability and clinical coherence of this method in a group of severely disabled patients with progressive multiple sclerosis (pMS), who display highly degraded gait patterns, as well as in an age-matched healthy subjects (HS) group.

Methods

Twenty-two participants with pMS and nineteen HS were included in this 18-month longitudinal follow-up study. During the follow-up period, all participants completed a 10-meter walk test with a U-turn and back, twice at M0, M6, M12, and M18. Average speed and seven clinical criteria (sturdiness, springiness, steadiness, stability, smoothness, synchronization, and symmetry) were evaluated using 17 gait parameters selected from the literature. The variation of these parameters from HS values was combined to generate a multidimensional visual tool, referred to as a semiogram.

Results

For both cohorts, all criteria showed moderate to very high test-retest reliability for intra-session measurements. Inter-session quantification was also moderate to highly reliable for all criteria except smoothness, which was not reliable for HS participants. All partial scores, except for the stability score, differed between the two populations. All partial scores were correlated with an objective but not subjective quantification of gait severity in the pMS population. A deficit in the pyramidal tract was associated with altered scores in all criteria, whereas deficits in cerebellar, sensitive, bulbar, and cognitive deficits were associated with decreased scores in only a subset of gait criteria.

Conclusions

The proposed multidimensional gait quantification represents an innovative approach to monitoring gait disorders. It provides a reliable and informative biomarker for assessing the severity of gait impairments in individuals with pMS. Additionally, it holds the potential for discriminating between various underlying causes of gait alterations in pMS.

DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units

In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in unfatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments.

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.

Interoceptive and metacognitive facets of fatigue in multiple sclerosis

Numerous disorders are characterised by fatigue as a highly disabling symptom. Fatigue plays a particularly important clinical role in multiple sclerosis (MS) where it exerts a profound impact on quality of life. Recent concepts of fatigue grounded in computational theories of brain-body interactions emphasise the role of interoception and metacognition in the pathogenesis of fatigue. So far, however, for MS, empirical data on interoception and metacognition are scarce. This study examined interoception and (exteroceptive) metacognition in a sample of 71 persons with a diagnosis of MS. Interoception was assessed by prespecified subscales of a standard questionnaire (Multidimensional Assessment of Interoceptive Awareness [MAIA]), while metacognition was investigated with computational models of choice and confidence data from a visual discrimination paradigm. Additionally, autonomic function was examined by several physiological measurements. Several hypotheses were tested based on a preregistered analysis plan. In brief, we found the predicted association of interoceptive awareness with fatigue (but not with exteroceptive metacognition) and an association of autonomic function with exteroceptive metacognition (but not with fatigue). Furthermore, machine learning (elastic net regression) showed that individual fatigue scores could be predicted out-of-sample from our measurements, with questionnaire-based measures of interoceptive awareness and sleep quality as key predictors. Our results support theoretical concepts of interoception as an important factor for fatigue and demonstrate the general feasibility of predicting individual levels of fatigue from simple questionnaire-based measures of interoception and sleep.

Predicting the fatigue in Parkinson's disease using inertial sensor gait data and clinical characteristics

Objectives

The study aimed to analyze the clinical features and gait characteristics of patients with Parkinson's disease (PD) who also suffer from fatigue and to develop a model that can help identify fatigue states in the early stages of PD.

Methodology

A total of 81 PD patients have been enrolled for the Parkinson's Fatigue Scale (PFS-16) assessment and divided into two groups: patients with or without fatigue. Neuropsychological assessments of the two groups, including motor and non-motor symptoms, were collected. The patient's gait characteristics were collected using a wearable inertial sensor device.

Results

PD patients who experienced fatigue had a more significant impairment of motor symptoms than those who did not, and the experience of fatigue became more pronounced as the disease progressed. Patients with fatigue had more significant mood disorders and sleep disturbances, which can lead to a poorer quality of life. PD patients with fatigue had shorter step lengths, lower velocity, and stride length and increased stride length variability. As for kinematic parameters, PD patients with fatigue had lower shank-forward swing max, trunk-max sagittal angular velocity, and lumbar-max coronal angular velocity than PD patients without fatigue. The binary logistic analysis found that Movement Disorder Society-Unified Parkinson's Disease Rating Scale-I (MDS-UPDRS-I) scores, Hamilton Depression Scale (HAMD) scores, and stride length variability independently predicted fatigue in PD patients. The area under the curve (AUC) of these selected factors in the receiver operating characteristic (ROC) analysis was 0.900. Moreover, HAMD might completely mediate the association between Hamilton Anxiety Scale (HAMA) scores and fatigue (indirect effect: β = 0.032, 95% confidence interval: 0.001-0.062), with a percentage of mediation of 55.46%.

Conclusion

Combining clinical characteristics and gait cycle parameters, including MDS-UPDRS-I scores, HAMD scores, and stride length variability, can identify PD patients with a high fatigue risk.

Androgens and NGF Mediate the Neurite-Outgrowth through Inactivation of RhoA

Steroid hormones and growth factors control neuritogenesis through their cognate receptors under physiological and pathological conditions. We have already shown that nerve growth factor and androgens induce neurite outgrowth of PC12 cells through a reciprocal crosstalk between the NGF receptor, TrkA and the androgen receptor. Here, we report that androgens or NGF induce neuritogenesis in PC12 cells through inactivation of RhoA. Ectopic expression of the dominant negative RhoA N19 promotes, indeed, the neurite-elongation of unchallenged and androgen- or NGF-challenged PC12 cells and the increase in the expression levels of βIII tubulin, a specific neuronal marker. Pharmacological inhibition of the Ser/Thr kinase ROCK, an RhoA effector, induces neuritogenesis in unchallenged PC12 cells, and potentiates the effect of androgens and NGF, confirming the role of RhoA/ROCK axis in the neuritogenesis induced by androgen and NGF, through the phosphorylation of Akt. These findings suggest that therapies based on new selective androgen receptor modulators and/or RhoA/ROCK inhibitors might exert beneficial effects in the treatment of neuro-disorders, neurological diseases and ageing-related processes.

Multimodal Agility-Based Exercise Training for Persons With Multiple Sclerosis: A New Framework

INTRODUCTION

Multimodal agility-based exercise training (MAT) has been described as a framework for fall prevention in the elderly but might also be a valuable concept for exercise training in persons with Multiple Sclerosis (pwMS).

THE PROBLEM

Current recommendations advise pwMS to perform a multitude of different exercise training activities, as each of these has its separate evidence. However, pwMS struggle even more than the general population to be physically active. Additionally, Multiple Sclerosis often leads to co-occurring mobility and cognitive dysfunctions, for which simultaneous, time-efficient, and engaging training approaches are still limited in clinical practice and healthcare.

THE SOLUTION

The MAT framework has been developed to integratively improve cardiovascular, neuromuscular, and cognitive function by combining aspects of perception and orientation, change of direction, as well as stop-and-go patterns (ie, agility), in a group-training format. For pwMS, the MAT framework is conceptualized to include 3 Components: standing balance, dynamic balance (including functional leg strength), and agility-based exercises. Within these Components sensory, cognitive, and cardiovascular challenges can be adapted to individual needs.

RECOMMENDATIONS

We recommend investigating multimodal exercise interventions that go beyond easily standardized, unimodal types of exercise (eg, aerobic or resistance exercise), which could allow for time-efficient training, targeting multiple frequent symptoms of persons with mild disability at once. MAT should be compared to unimodal approaches, regarding sensor-based gait outcomes, fatigue-related outcomes, cognition, as well as neuroprotective, and (supportive) disease-modifying effects.

Inertial Gait Sensors to Measure Mobility and Functioning in Hereditary Spastic Paraplegia: A Cross-sectional Multicenter Clinical Study

BACKGROUND AND OBJECTIVES

Hereditary spastic paraplegia (HSP) causes progressive spasticity and weakness of the lower limbs. As neurologic examination and the clinical Spastic Paraplegia Rating Scale (SPRS) are subject to potential patient-dependent and clinician-dependent bias, instrumented gait analysis bears the potential to objectively quantify impaired gait. The aim of this study was to investigate gait cyclicity parameters by application of a mobile gait analysis system in a cross-sectional cohort of patients with HSP and a longitudinal fast progressing subcohort.

METHODS

Using wearable sensors attached to the shoes, patients with HSP and controls performed a 4 × 10 m walking test during regular visits in 3 outpatient centers. Patients were also rated according to the SPRS, and in a subset, questionnaires on quality of life and fear of falling were obtained. An unsupervised segmentation algorithm was used to extract stride parameters and respective coefficients of variation.

RESULTS

Mobile gait analysis was performed in a total of 112 ambulatory patients with HSP and 112 age-matched and sex-matched controls. Although swing time was unchanged compared with controls, there were significant increases in the duration of the total stride phase and the duration of the stance phase, both regarding absolute values and coefficients of variation values. Although stride parameters did not correlate with age, weight, or height of the patients, there were significant associations of absolute stride parameters with single SPRS items reflecting impaired mobility (|r| > 0.50), with patients' quality of life (|r| > 0.44), and notably with disease duration (|r| > 0.27). Sensor-derived coefficients of variation, on the other hand, were associated with patient-reported fear of falling (|r| > 0.41) and cognitive impairment (|r| > 0.40). In a small 1-year follow-up analysis of patients with complicated HSP and fast progression, the absolute values of mobile gait parameters had significantly worsened compared with baseline.

DISCUSSION

The presented wearable sensor system provides parameters of stride characteristics which seem clinically valid to reflect gait impairment in HSP. Owing to the feasibility regarding time, space, and costs, this study forms the basis for larger scale longitudinal and interventional studies in HSP.

Development and deployment of cyclical focal muscle vibration system to improve walking performance in multiple sclerosis

Vibration, a potent mechanical stimulus for activating muscle spindle primary afferents, may improve gait performance in persons with multiple sclerosis (MS), but has yet to be developed and deployed for multiple leg muscles with application during walking training. This study explored the development of a cyclic focal muscle vibration (FMV) system, and the deployment feasibility to correct MS walking swing phase deficits in order to determine whether this intervention warrants comprehensive study. The system was deployed during twelve, two-hour sessions of walking with cyclic FMV over six weeks. Participants served as their own control. Blood pressure, heart rate, walking speed, kinematics (peak hip, knee and ankle angles during swing), toe clearance, and step length were measured before and after deployment with blood pressure and heart rate monitored during deployment. During system deployment, there were no untoward sensations and physiological changes in blood pressure and heart rate, and volitional improvements were found in walking speed, improved swing phase kinematics, toe clearance and step length. This FMV training system was developed and deployed to improve joint flexion during walking in those with MS, and it demonstrated feasibility and benefits. Further study will determine the most effective vibration frequency and dose, carryover effects, and those most likely to benefit from this intervention.

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

The Role of Remote Monitoring in Evaluating Fatigue in Multiple Sclerosis: A Review

Fatigue is one of the most common multiple sclerosis (MS) symptoms. Despite this, monitoring and measuring fatigue (subjective lack of energy)- and fatigability (objectively measurable and quantifiable performance decline)- in people with MS have remained challenging. Traditionally, administration of self-report questionnaires during in-person visits has been used to measure fatigue. However, remote measurement and monitoring of fatigue and fatigability have become feasible in the past decade. Traditional questionnaires can be administered through the web in any setting. The ubiquitous availability of smartphones allows for momentary and frequent measurement of MS fatigue in the ecological home-setting. This approach reduces the recall bias inherent in many traditional questionnaires and demonstrates the fluctuation of fatigue that cannot be captured by standard measures. Wearable devices can assess patients' fatigability and activity levels, often influenced by the severity of subjective fatigue. Remote monitoring of fatigue, fatigability, and activity in real-world situations can facilitate quantifying symptom-severity in clinical and research settings. Combining remote measures of fatigue as well as objective fatigability in a single construct, composite score, may provide a more comprehensive outcome. The more granular data obtained through remote monitoring techniques may also help with the development of interventions aimed at improving fatigue and lowering the burden of this disabling symptom.

Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.

Immersive Virtual Reality and Vestibular Rehabilitation in Multiple Sclerosis: Case Report

Background

Dizziness and imbalance are common and disabling symptoms in patients with multiple sclerosis (MS) and are caused by a central, peripheral, or mixed vestibulopathy. Central vestibular disorder is the most frequently reported vestibular problem in the MS population due to demyelination. Vestibular rehabilitation ameliorates these symptoms and their repercussions and improves quality of life. Immersive virtual reality (VRi) is an emerging tool in this field; however, no previous research has been performed studying its effects in MS.

Objective

The aim of this study was to apply a VRi vestibular training protocol to a patient with MS and assess the effects induced by the experimental intervention.

Methods

This case study included a 54-year-old woman with relapsing-remitting MS. We developed a standardized VRi exercise protocol for vestibular rehabilitation based on the gold-standard Cawthorne-Cooksey vestibular training protocol. The 20-session intervention was made up of 10 initial sessions and 10 advanced sessions. Each 50-minute session was performed two to three times per week for 7 weeks. Four evaluations were carried out over the study period: at baseline (T0), between initial and advances phases (T1), postintervention (T2), and 1 month after the experimental procedure (T3). The research outcomes were dizziness, balance, gait, impact of fatigue, quality of life, repercussions in muscular tone, and usability of the head-mounted display device.

Results

After implementing the VRi vestibular protocol, improvements were seen in the following patient parameters: Dizziness Handicap Inventory score (62 points at T0; 4 points at T2); Berg Balance Scale score (47 points at T0; 54 points at T2); instrumented Timed Up and Go time (8.35 seconds at T0; 5.57 seconds at T2); muscular tone of the erector spinae, rectus femoris, and soleus; Modified Fatigue Impact Scale score (61 points at T0; 37 points at T2); and Multiple Sclerosis Quality of Life-54 values (67.16% in the physical health area at T2; 33.56% in the mental health area at T2). The patient rated the usability of the system as 90%, based on the System Usability Scale, and gave the system a grade of A.

Conclusions

Although further research is needed, this study provided initial evidence that the first VRi vestibular protocol for the MS population can improve dizziness, balance, gait, impact of fatigue, quality of life, and muscular tone through an exergame intervention. This study may help establish a standardized VRi protocol for vestibular rehabilitation.

Outcome measures used in trials on gait rehabilitation in multiple sclerosis: A systematic literature review

Background

Multiple Sclerosis (MS) is associated with impaired gait and a growing number of clinical trials have investigated efficacy of various interventions. Choice of outcome measures is crucial in determining efficiency of interventions. However, it remains unclear whether there is consensus on which outcome measures to use in gait intervention studies in MS.

Objective

We aimed to identify the commonly selected outcome measures in randomized controlled trials (RCTs) on gait rehabilitation interventions in people with MS. Additional aims were to identify which of the domains of the International Classification of Functioning, Disability and Health (ICF) are the most studied and to characterize how outcome measures are combined and adapted to MS severity.

Methods

Pubmed, Cochrane Central, Embase and Scopus databases were searched for RCT studies on gait interventions in people living with MS according to PRISMA guidelines.

Results

In 46 RCTs, we identified 69 different outcome measures. The most used outcome measures were 6-minute walking test and the Timed Up and Go test, used in 37% of the analyzed studies. They were followed by gait spatiotemporal parameters (35%) most often used to inform on gait speed, cadence, and step length. Fatigue was measured in 39% of studies. Participation was assessed in 50% of studies, albeit with a wide variety of scales. Only 39% of studies included measures covering all ICF levels, and Participation measures were rarely combined with gait spatiotemporal parameters (only two studies).

Conclusions

Selection of outcome measures remains heterogenous in RCTs on gait rehabilitation interventions in MS. However, there is a growing consensus on the need for quantitative gait spatiotemporal parameter measures combined with clinical assessments of gait, balance, and mobility in RCTs on gait interventions in MS. Future RCTs should incorporate measures of fatigue and measures from Participation domain of ICF to provide comprehensive evaluation of trial efficacy across all levels of functioning.

Ergonomic Analysis of Functional Endoscopic Sinus Surgery Using Novel Inertial Sensors

OBJECTIVES/HYPOTHESIS

Suboptimal ergonomics during endoscopic sinus surgery can lead to considerable physical discomfort and fatigue for the surgeon. The purpose of this pilot study is to objectively evaluate the ergonomic positions of trainee and attending surgeons while performing functional endoscopic sinus surgery (FESS).

STUDY DESIGN

Pilot prospective trial.

METHODS

Six surgeons (two attendings and four trainees) performed FESS while wearing 11 inertial measurement units (IMUs) affixed to either side of each major joint. Screen placement was standardized to be 1 m directly in front of the surgeon and on the patient's left, 0-15° declined from the surgeons' eyes. Bed height was standardized such that the workspace was 0 to 10 cm below the elbows. IMU data were analyzed to calculate joint angles. Ideal joint angles (i.e., <10° for neck and trunk) were determined by the validated Rapid Entire Body Assessment tool. Subjects subsequently completed a modified National Aeronautics and Space Administration Task Load Index to assess cognitive and physical burden and pain. Student's t-test was employed to detect differences between groups.

RESULTS

Trainees adopted positions involving significantly greater neck flexion (9.90° vs. -6.48°, P = .03) and reported significantly higher frustration levels (3.04 vs. 1.33, P = .02) while operating than attendings. For both cohorts, increased operative time was significantly correlated with greater back flexion (r = 0.90, P = .02; r = 0.55, P = .04, respectively).

CONCLUSIONS

Our data suggest that trainees operate with higher risk neck postures than do attendings. These data indicate high-risk operative postures may be borne of inexperience and present an opportunity for postural interventions at an early stage of training.

LEVEL OF EVIDENCE

N/A Laryngoscope, 2021.