The Role of Wearable Devices in Multiple Sclerosis

The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review

Wearable health devices are becoming vital in chronic disease management because they offer real-time monitoring and personalized care. This review explores their effectiveness and challenges across medical fields, including cardiology, respiratory health, neurology, endocrinology, orthopedics, oncology, and mental health. A thorough literature search identified studies focusing on wearable devices' impact on patient outcomes. In cardiology, wearables have proven effective for monitoring hypertension, detecting arrhythmias, and aiding cardiac rehabilitation. In respiratory health, these devices enhance asthma management and continuous monitoring of critical parameters. Neurological applications include seizure detection and Parkinson's disease management, with wearables showing promising results in improving patient outcomes. In endocrinology, wearable technology advances thyroid dysfunction monitoring, fertility tracking, and diabetes management. Orthopedic applications include improved postsurgical recovery and rehabilitation, while wearables help in early complication detection in oncology. Mental health benefits include anxiety detection, post-traumatic stress disorder management, and stress reduction through wearable biofeedback. In conclusion, wearable health devices offer transformative potential for managing chronic illnesses by enhancing real-time monitoring and patient engagement. Despite significant improvements in adherence and outcomes, challenges with data accuracy and privacy persist. However, with ongoing innovation and collaboration, we can all be part of the solution to maximize the benefits of wearable technologies in healthcare.

Exploratory study to evaluate the acceptability of a wearable accelerometer to assess motor progression in motor neuron disease

Motor neuron disease (MND) is a rapidly progressive condition traditionally assessed using a questionnaire to evaluate physical function, the revised amyotrophic lateral sclerosis functional rating scale (ALSFRS-R). Its use can be associated with poor sensitivity in detecting subtle changes over time and there is an urgent need for more sensitive and specific outcome measures. The ActiGraph GT9X is a wearable device containing multiple sensors that can be used to provide metrics that represent physical activity. The primary aim of this study was to investigate the initial suitability and acceptability of limb-worn wearable devices to group of people with MND in Scotland. A secondary aim was to explore the preliminary associations between the accelerometer sensor data within the ActiGraph GT9X and established measures of physical function. 10 participants with MND completed a 12-week schedule of assessments including fortnightly study visits, both in-person and over videoconferencing software. Participants wore the device on their right wrist and right ankle for a series of movements, during a 6-min walking test and for a period of 24-h wear, including overnight. Participants also completed an ALSFRS-R and questionnaires on their experience with the devices. 80% of the participants found wearing these devices to be a positive experience and no one reported interference with daily living or added burden. However, 30% of the participants experienced technical issues with their devices. Data from the wearable devices correlated with established measures of physical function.

Neuropathic Pain Secondary to Multiple Sclerosis: A Narrative Review

Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system (CNS). Neuropathic pain in MS is a debilitating symptom that significantly impairs the quality of life for a substantial proportion of MS patients. Neuropathic pain in MS stems primarily from demyelination, axonal loss, CNS inflammation, and direct damage to the myelin sheath, leading to pain manifestations such as ongoing extremity pain, Lhermitte's phenomenon, and trigeminal neuralgia (TN). The pathophysiological mechanisms behind MS-related neuropathic pain are explored in this review, highlighting central sensitization, neural dysfunction, spinal thalamic tract dysfunction, and inflammatory processes that exacerbate neuronal damage. Neuropathic pain in MS necessitates comprehensive assessment tools and neurophysiological tests to differentiate neuropathic pain from other MS symptoms accurately. Treatment strategies for MS-related neuropathic pain encompass pharmacological interventions, including anticonvulsants and antidepressants, and emerging therapies targeting specific inflammatory processes. The review advocates for a holistic approach to management, incorporating innovative treatments and multidisciplinary strategies to address both the physical symptoms and psychosocial aspects of this disorder. This comprehensive overview underscores the importance of ongoing research into targeted therapies to improve patient outcomes and enhance the quality of life for those affected by MS.

No Evidence of Disease Activity (NEDA) as a Clinical Assessment Tool for Multiple Sclerosis: Clinician and Patient Perspectives [Narrative Review]

The emergence of high-efficacy therapies for multiple sclerosis (MS), which target inflammation more effectively than traditional disease-modifying therapies, has led to a shift in MS management towards achieving the outcome assessment known as no evidence of disease activity (NEDA). The most common NEDA definition, termed NEDA-3, is a composite of three related measures of disease activity: no clinical relapses, no disability progression, and no radiological activity. NEDA has been frequently used as a composite endpoint in clinical trials, but there is growing interest in its use as an assessment tool to help patients and healthcare professionals navigate treatment decisions in the clinic. Raising awareness about NEDA may therefore help patients and clinicians make more informed decisions around MS management and improve overall MS care. This review aims to explore the potential utility of NEDA as a clinical decision-making tool and treatment target by summarizing the literature on its current use in the context of the expanding treatment landscape. We identify current challenges to the use of NEDA in clinical practice and detail the proposed amendments, such as the inclusion of alternative outcomes and biomarkers, to broaden the clinical information captured by NEDA. These themes are further illustrated with the real-life perspectives and experiences of our two patient authors with MS. This review is intended to be an educational resource to support discussions between clinicians and patients on this evolving approach to MS-specialized care.

Management of multiple sclerosis fatigue in the digital age: from assessment to treatment

Fatigue is one of the most disabling symptoms of Multiple Sclerosis (MS), affecting more than 80% of patients over the disease course. Nevertheless, it has a multi-faceted and complex nature, making its diagnosis, evaluation, and treatment extremely challenging in clinical practice. In the last years, digital supporting tools have emerged to support the care of people with MS. These include not only smartphone or table-based apps, but also wearable devices or novel techniques such as virtual reality. Furthermore, an additional effective and cost-efficient tool for the therapeutic management of people with fatigue is becoming increasingly available. Virtual reality and e-Health are viable and modern tools to both assess and treat fatigue, with a variety of applications and adaptability to patient needs and disability levels. Most importantly, they can be employed in the patient's home setting and can not only bridge clinic visits but also be complementary to the monitoring and treatment means for those MS patients who live far away from healthcare structures. In this narrative review, we discuss the current knowledge and future perspectives in the digital management of fatigue in MS. These may also serve as sources for research of novel digital biomarkers in the identification of disease activity and progression.

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.

Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis

Impairment in persons with multiple sclerosis (PwMS) can often be attributed to symptoms of motor instability and fatigue. Symptom monitoring and queued interventions often target these symptoms. Clinical metrics are currently limited to objective physician assessments or subjective patient reported measures. Recent research has turned to wearables for improving the objectivity and temporal resolution of assessment. Our group has previously observed wearable assessment of supervised and unsupervised standing transitions to be predictive of fall-risk in PwMS. Here we extend the application of standing transition quantification to longitudinal home monitoring of symptoms. Subjects (N=23) with varying degrees of MS impairment were recruited and monitored with accelerometry for a total of  ∼  6 weeks each. These data were processed using a preexisting framework, applying a deep learning activity classifier to isolate periods of standing transition from which descriptive features were extracted for analysis. Participants completed daily and biweekly assessments describing their symptoms. From these data, Canonical Correlation Analysis was used to derive digital phenotypes of MS instability and fatigue. We find these phenotypes capable of distinguishing fallers from non-fallers, and further that they demonstrate a capacity to characterize symptoms at both daily and sub-daily resolutions. These results represent promising support for future applications of wearables, which may soon augment or replace current metrics in longitudinal monitoring of PwMS.

Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones

Goal: Smartphone and wearable devices may act as powerful tools to remotely monitor physical function in people with neurodegenerative and autoimmune diseases from out-of-clinic environments. Detection of progression onset or worsening of symptoms is especially important in people living with multiple sclerosis (PwMS) in order to enable optimally adapted therapeutic strategies. MS symptoms typically follow subtle and fluctuating disease courses, patient-to-patient, and over time. Current in-clinic assessments are often too infrequently administered to reflect longitudinal changes in MS impairment that impact daily life. This work, therefore, explores how smartphones can administer daily two-minute walking assessments to monitor PwMS physical function at home. Methods: Remotely collected smartphone inertial sensor data was transformed through state-of-the-art Deep Convolutional Neural Networks, to estimate a participant's daily ambulatory-related disease severity, longitudinally over a 24-week study. Results: This study demonstrated that smartphone-based ambulatory severity outcomes could accurately estimate MS level of disability, as measured by the EDSS score ([Formula: see text]: 0.56,[Formula: see text]0.001). Furthermore, longitudinal severity outcomes were shown to accurately reflect individual participants' level of disability over the study duration. Conclusion: Smartphone-based assessments, that can be performed by patients from their home environments, could greatly augment standard in-clinic outcomes for neurodegenerative diseases. The ability to understand the impact of disease on daily-life between clinical visits, through objective digital outcomes, paves the way forward to better measure and identify signs of disease progression that may be occurring out-of-clinic, to monitor how different patients respond to various treatments, and to ultimately enable the development of better, and more personalised care.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Modelling the mass adoption potential of wearable medical devices

Digital technologies empower users to manage their health and reduce the burden on the public health system. The mass adoption of wearable medical devices (WMDs) promotes the ageing population’s confidence besides facilitating users. Thus, the current study aims to empirically evaluate the formation of perceived product value (PPV) with the WMDs’ computability, usefulness, cost, and accuracy, the intention to use WMDs influenced by health consciousness (HCS), health anxiety (HAY), product value, and perceived critical mass (PCM), and later the adoption of WMDs among Chinese adults. The study examined the mediating effect of PPV on the relationship between the intention to use WMDs and perceived compatibility (PCT), perceived cost (PCO), perceived usefulness (PUS), and perceived technology accuracy (PTA). This study adopted a cross-sectional approach and used an online survey to collect quantitative data from 1,160 Chinese adults. Data analysis was performed using the partial least squares structural equation modelling (PLS-SEM). Results showed that PCT, PUS, and PTA significant positive effect on PPV. Meanwhile, HCS, PCM, and PPV has a significant positive effect on intention to use WMDs, and the intention to use WMDs and PCM influenced the adoption of WMDs. Consequently, the analysis confirmed that PPV mediated the relationships between the intention to use WMD and PCT, PUS, and PTA. The WMD cost must be reduced to enhance the value of WMDs. Finally, the study’s implications, limitations, and suggestions for future studies are discussed.

Recent advances in the potential applications of luminescence-based, SPR-based, and carbon-based biosensors

Abstract

The need for biosensors has evolved in the detection of molecules, diseases, and pollution from various sources. This requirement has headed to the development of accurate and powerful equipment for analysis using biological sensing component as a biosensor. Biosensors have the advantage of rapid detection that can beat the conventional methods for the detection of the same molecules. Bio-chemiluminescence-based sensors are very sensitive during use in biological immune assay systems. Optical biosensors are emerging with time as they have the advantage that they act with a change in the refractive index. Carbon nanotube-based sensors are another area that has an important role in the biosensor field. Bioluminescence gives much higher quantum yields than classical chemiluminescence. Electro-generated bioluminescence has the advantage of miniature size and can produce a high signal-to-noise ratio and the controlled emission. Recent advances in biological techniques and instrumentation involving fluorescence tag to nanomaterials have increased the sensitivity limit of biosensors. Integrated approaches provided a better perspective for developing specific and sensitive biosensors with high regenerative potentials. This paper mainly focuses on sensors that are important for the detection of multiple molecules related to clinical and environmental applications.

Key points

• The review focusses on the applications of luminescence-based, surface plasmon resonance-based, carbon nanotube-based, and graphene-based biosensors

• Potential clinical, environmental, agricultural, and food industry applications/uses of biosensors have been critically reviewed

• The current limitations in this field are discussed, as well as the prospects for future advancement

Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities

The increasingly ageing population and the tendency to live alone have led science and engineering researchers to search for health care solutions. In the COVID 19 pandemic, the elderly have been seriously affected in addition to suffering from isolation and its associated and psychological consequences. This paper provides an overview of the RobWell (Robotic-based Well-Being Monitoring and Coaching System for the Elderly in their Daily Activities) system. It is a system focused on the field of artificial intelligence for mood prediction and coaching. This paper presents a general overview of the initially proposed system as well as the preliminary results related to the home automation subsystem, autonomous robot navigation and mood estimation through machine learning prior to the final system integration, which will be discussed in future works. The main goal is to improve their mental well-being during their daily household activities. The system is composed of ambient intelligence with intelligent sensors, actuators and a robotic platform that interacts with the user. A test smart home system was set up in which the sensors, actuators and robotic platform were integrated and tested. For artificial intelligence applied to mood prediction, we used machine learning to classify several physiological signals into different moods. In robotics, it was concluded that the ROS autonomous navigation stack and its autodocking algorithm were not reliable enough for this task, while the robot’s autonomy was sufficient. Semantic navigation, artificial intelligence and computer vision alternatives are being sought.

Wearable inertial sensors for human movement analysis: a five-year update

INTRODUCTION

The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings.

AREAS COVERED

Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings.

EXPERT OPINION

IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.

Disability assessment using Google Maps

Objectives

To evaluate the concordance between Google Maps® application (GM®) and clinical practice measurements of ambulatory function (e.g., Ambulation Score (AS) and respective Expanded Disability Status Scale (EDSS)) in people with multiple sclerosis (pwMS).

Materials and methods

This is a cross-sectional multicenter study. AS and EDSS were calculated using GM® and routine clinical methods; the correspondence between the two methods was assessed. A multinomial logistic model is investigated which demographic (age, sex) and clinical features (e.g., disease subtype, fatigue, depression) might have influenced discrepancies between the two methods.

Results

Two hundred forty-three pwMS were included; discrepancies in AS and in EDDS assessments between GM® and routine clinical methods were found in 81/243 (33.3%) and 74/243 (30.4%) pwMS, respectively. Progressive phenotype (odds ratio [OR] = 2.8; 95% confidence interval [CI] 1.1–7.11, p = 0.03), worse fatigue (OR = 1.03; 95% CI 1.01–1.06, p = 0.01), and more severe depression (OR = 1.1; 95% CI 1.04–1.17, p = 0.002) were associated with discrepancies between GM® and routine clinical scoring.

Conclusion

GM® could easily be used in a real-life clinical setting to calculate the AS and the related EDSS scores. GM® should be considered for validation in further clinical studies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10072-021-05389-7.

Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis

Falls are a significant problem for persons with multiple sclerosis (PwMS). Yet fall prevention interventions are not often prescribed until after a fall has been reported to a healthcare provider. While still nascent, objective fall risk assessments could help in prescribing preventative interventions. To this end, retrospective fall status classification commonly serves as an intermediate step in developing prospective fall risk assessments. Previous research has identified measures of gait biomechanics that differ between PwMS who have fallen and those who have not, but these biomechanical indices have not yet been leveraged to detect PwMS who have fallen. Moreover, they require the use of laboratory-based measurement technologies, which prevent clinical deployment. Here we demonstrate that a bidirectional long short-term (BiLSTM) memory deep neural network was able to identify PwMS who have recently fallen with good performance (AUC of 0.88) based on accelerometer data recorded from two wearable sensors during a one-minute walking task. These results provide substantial improvements over machine learning models trained on spatiotemporal gait parameters (21% improvement in AUC), statistical features from the wearable sensor data (16%), and patient-reported (19%) and neurologist-administered (24%) measures in this sample. The success and simplicity (two wearable sensors, only one-minute of walking) of this approach indicates the promise of inexpensive wearable sensors for capturing fall risk in PwMS.

Measuring Outdoor Walking Capacities Using Global Positioning System in People with Multiple Sclerosis: Clinical and Methodological Insights from an Exploratory Study

We aimed at showing how Global Positioning System (GPS) along with a previously validated speed processing methodology could be used to measure outdoor walking capacities in people with multiple sclerosis (MS). We also deal with methodological issues that may occur when conducting such measurements, and explore to what extent GPS-measured outdoor walking capacities (maximal walking distance [MWD_GPS] and usual walking speed) could be related to traditional functional outcomes (6-min total walking distance) in people with MS. Eighteen people with MS, with an Expanded Disability Status Scale score ≤6, completed a 6-min walking test and an outdoor walking session (60 min maximum) at usual pace during which participants were wearing a DG100 GPS receiver and could perform several walking bouts. Among the 12 participants with valid data (i.e., who correctly completed the outdoor session with no spurious GPS signals that could prevent the detection of the occurrence of a walking/stopping bout), the median (90% confidence interval, CI) outdoor walking speed was 2.52 km/h (2.17; 2.93). Ten participants (83% (56; 97)) had ≥1 stop during the session. Among these participants, the median of MWD_GPS was 410 m (226; 1350), and 40% (15; 70) did not reach their MWD_GPS during the first walking bout. Spearman correlations of MWD_GPS and walking speed with 6-min total walking distance were, respectively, 0.19 (-0.41; 0.95) and 0.66 (0.30; 1.00). Further work is required to provide guidance about GPS assessment in people with MS.

Usefulness of Mobile Devices in the Diagnosis and Rehabilitation of Patients with Dizziness and Balance Disorders: A State of the Art Review

Objective

The gold standard for objective body posture examination is posturography. Body movements are detected through the use of force platforms that assess static and dynamic balance (conventional posturography). In recent years, new technologies like wearable sensors (mobile posturography) have been applied during complex dynamic activities to diagnose and rehabilitate balance disorders. They are used in healthy people, especially in the aging population, for detecting falls in the older adults, in the rehabilitation of different neurological, osteoarticular, and muscular system diseases, and in vestibular disorders. Mobile devices are portable, lightweight, and less expensive than conventional posturography. The vibrotactile system can consist of an accelerometer (linear acceleration measurement), gyroscopes (angular acceleration measurement), and magnetometers (heading measurement, relative to the Earth’s magnetic field). The sensors may be mounted to the trunk (most often in the lumbar region of the spine, and the pelvis), wrists, arms, sternum, feet, or shins. Some static and dynamic clinical tests have been performed with the use of wearable sensors. Smartphones are widely used as a mobile computing platform and to evaluate the results or monitor the patient during the movement and rehabilitation. There are various mobile applications for smartphone-based balance systems. Future research should focus on validating the sensitivity and reliability of mobile device measurements compared to conventional posturography.

Conclusion

Smartphone based mobile devices are limited to one sensor lumbar level posturography and offer basic clinical evaluation. Single or multi sensor mobile posturography is available from different manufacturers and offers single to multi-level measurements, providing more data and in some instances even performing sophisticated clinical balance tests.

The Use of Social Media and Digital Devices Among Italian Neurologists

Background: Digital devices and online social networks are changing clinical practice. In this study, we explored attitudes, awareness, opinions, and experiences of neurologists toward social media and digital devices.

Methods: Each member of the Italian Society of Neurology (SIN) participated in an online survey (January to May 2018) to collect information on their attitude toward digital health.

Results: Four hundred and five neurologists participated in the study. At work, 95% of responders use the personal computer, 87% the smartphone, and 43.5% the tablet. These devices are used to obtain health information (91%), maintain contact with colleagues (71%), provide clinical information (59%), and receive updates (67%). Most participants (56%) use social media to communicate with patients, although 65% are against a friendship with them on social media. Most participants interact with patients on social media outside working hours (65.2%) and think that social media have improved (38.0%) or greatly improved (25.4%) the relationship with patients. Most responders (66.7%) have no wearable devices available in clinical practice.

Conclusion: Italian neurologists have different practices and views regarding the doctor–patient relationship in social media. The availability of digital devices in daily practice is limited. The use of social networks and digital devices will increasingly permeate into everyday life, bringing a new dimension to health care. The danger is that advancement will not go hand in hand with a legal and cultural adaptation, thus creating ambiguity and risks for clinicians and patients. Neurologists will need to be able to face the opportunities and challenges of this new scenario.

Can We Improve the Monitoring of People With Multiple Sclerosis Using Simple Tools, Data Sharing, and Patient Engagement?

Technological innovation is transforming traditional clinical practice, enabling people with multiple sclerosis (pwMS) to contribute health care outcome data remotely between clinic visits. In both relapsing and progressive forms of multiple sclerosis (MS), patients may experience variable disability accrual and symptoms throughout their disease course. The potential impact on the quality of life (QoL) in pwMS and their families and carers is profound. The introduction of treatment targets, such as NEDA (no evidence of disease activity) and NEPAD (no evidence of progression or active disease), that guide clinical decision-making, highlight the importance of utilizing sensitive instruments to measure and track disease activity and progression. However, the gold standard neurological disability tool—expanded disability severity scale (EDSS)—has universally recognized limitations. With strides made in our understanding of MS pathophysiology and DMT responsiveness, maintaining the status quo of measuring disability progression is no longer the recommended option. Outside the clinical trial setting, a comprehensive monitoring system has not been robustly established for pwMS. A 21st-century approach is required to integrate clinical, paraclinical, and patient-reported outcome (PRO) data from electronic health records, local databases, and patient registries. Patient and public involvement (PPI) is critical in the design and implementation of this workflow. To take full advantage of the potential of digital technology in the monitoring and care and QoL of pwMS will require iterative feedback between pwMS, health care professionals (HCPs), scientists, and digital experts.

Sempere Á, Vera-Garcia FJ, Barbado D. Understanding the Deterioration of Gait, Postural Control, Lower Limb Strength and Perceived Fatigue Across the Disability Spectrum of People with Multiple Sclerosis

Disability progression is a prominent feature of multiple sclerosis (MS). However, little is known about the extent to which physical condition parameters and perceived fatigue evolve during the disease. We analyzed how strength, balance, core stability and perceived fatigue differ among different cohorts of people with MS (PwMS) with different disability degrees and how these contribute to patients' gait speed and functional mobility. Sixty-three PwMS divided into three groups according to the "Expanded Disability Status Scale" (MS1: EDSS ≤ 1.5; MS2: 2 ≤ EDSS ≤ 3.5; MS3: 4 ≤ EDSS ≤ 6) and 22 healthy controls (HC) participated in this study. MS1 showed lower balance and hip strength compared to HC. MS2 showed lower balance, core stability, gait speed, and functional mobility than MS1. MS3 showed lower gait speed, functional mobility, balance, and knee flexion strength than MS2. No between-group differences were observed in perceived fatigue. Relative weight analysis showed that strength, balance and core stability explained 60%-70% of the variance in gait speed and functional mobility. The decline of each parameter did not evolve at the same rate across the different stages of the disease, being knee flexion strength and balance the most influential factors in the disability progression. Overall, these results provide useful information to guide exercise prescription at different stages of MS.

A wearable sensor identifies alterations in community ambulation in multiple sclerosis: contributors to real-world gait quality and physical activity

People with multiple sclerosis (pwMS) often suffer from gait impairments. These changes in gait have been well studied in laboratory and clinical settings. A thorough investigation of gait alterations during community ambulation and their contributing factors, however, is lacking. The aim of the present study was to evaluate community ambulation and physical activity in pwMS and healthy controls and to compare in-lab gait to community ambulation. To this end, 104 subjects were studied: 44 pwMS and 60 healthy controls (whose age was similar to the controls). The subjects wore a tri-axial, lower back accelerometer during usual-walking and dual-task walking in the lab and during community ambulation (1 week) to evaluate the amount, type, and quality of activity. The results showed that during community ambulation, pwMS took fewer steps and walked more slowly, with greater asymmetry, and larger stride-to-stride variability, compared to the healthy controls (p < 0.001). Gait speed during most of community ambulation was significantly lower than the in-lab usual-walking value and similar to the in-lab dual-tasking value. Significant group (pwMS /controls)-by-walking condition (in-lab/community ambulation) interactions were observed (e.g., gait speed). Greater disability was associated with fewer steps and reduced gait speed during community ambulation. In contrast, physical fatigue was correlated with sedentary activity, but was not related to any of the measures of community ambulation gait quality including gait speed. This disparity suggests that more than one mechanism contributes to community ambulation and physical activity in pwMS. Together, these findings demonstrate that during community ambulation, pwMS have marked gait alterations in multiple gait features, reminiscent of dual-task walking measured in the laboratory. Disease-related factors associated with these changes might be targets of rehabilitation.

Calibration and validation of accelerometry to measure physical activity in adult clinical groups: A systematic review

A growing body of research calibrating and validating accelerometers to classify physical activity intensities has led to a range of cut-points. However, the applicability of current calibration protocols to clinical populations remains to be addressed. The aim of this review was to evaluate the accuracy of the methods for calibrating and validating of accelerometers to estimate physical activity intensity thresholds for clinical populations. Six databases were searched between March and July to 2017 using text words and subject headings. Studies developing moderate-to-vigorous intensity physical activity cut-points for adult clinical populations were included. The risk of bias was assessed using the health measurement instruments and a specific checklist for calibration studies. A total of 543,741 titles were found and 323 articles were selected for full-text assessment, with 11 meeting the inclusion criteria. Twenty-three different methods for calibration were identified using different models of ActiGraph and Actical accelerometers. Disease-specific cut-points ranged from 591 to 2717 counts·min-1 and were identified for two main groups of clinical conditions: neuromusculoskeletal disorders and metabolic diseases. The heterogeneity in the available clinical protocols hinders the applicability and comparison of the developed cut-points. As such, a mixed protocol containing a controlled laboratory exercise test and activities of daily-life is suggested. It is recommended that this be combined with a statistical approach that allows for adjustments according to disease severity or the use of machine learning models. Finally, this review highlights the generalisation of cut-points developed on healthy populations to clinical populations is inappropriate.

Open-Source Remote Gait Analysis: A Post-Surgery Patient Monitoring Application

Critical to digital medicine is the promise of improved patient monitoring to allow assessment and personalized intervention to occur in real-time. Wearable sensor-enabled observation of physiological data in free-living conditions is integral to this vision. However, few open-source algorithms have been developed for analyzing and interpreting these data which slows development and the realization of digital medicine. There is clear need for open-source tools that analyze free-living wearable sensor data and particularly for gait analysis, which provides important biomarkers in multiple clinical populations. We present an open-source analytical platform for automated free-living gait analysis and use it to investigate a novel, multi-domain (accelerometer and electromyography) asymmetry measure for quantifying rehabilitation progress in patients recovering from surgical reconstruction of the anterior cruciate ligament (ACL). Asymmetry indices extracted from 41,893 strides were more strongly correlated (r = −0.87, p < 0.01) with recovery time than standard step counts (r = 0.25, p = 0.52) and significantly differed between patients 2- and 17-weeks post-op (p < 0.01, effect size: 2.20–2.96), and controls (p < 0.01, effect size: 1.74–4.20). Results point toward future use of this open-source platform for capturing rehabilitation progress and, more broadly, for free-living gait analysis.

Exploring Wellness Interventions in Progressive Multiple Sclerosis: an Evidence-Based Review

PURPOSE OF REVIEW

There has been recent interest in the role of lifestyle and wellness-based approaches in the treatment and management of multiple sclerosis (MS). These approaches may be particularly relevant for patients with progressive MS, considering limited therapeutic options currently available. The purpose of this review is to examine the role of wellness-based interventions including exercise training, emotional well-being therapies, and dietary modification in patients with progressive MS.

RECENT FINDINGS

We conducted a literature search on the efficacy of wellness-based interventions in patients with progressive MS published between 1985 and July 2017. The level of evidence for each trial was evaluated using the American Academy of Neurology criteria. Overall, 21 articles reporting on 16 wellness-based interventions were identified: ten trials involved exercise training, three involved emotional wellness therapies, two involved dietary modification, and one was a combined wellness intervention. There is level C evidence (possibly effective; one class II study) for the efficacy of aerobic exercise training on cardiorespiratory fitness in patients with progressive MS. There is level B evidence (probably effective; one class I study) for the efficacy of mindfulness training on psychological distress, depression, anxiety, pain, and quality of life in patients with progressive MS. There is inadequate evidence (level U) for efficacy of dietary modification (one class III study and one class IV study) and combined wellness interventions involving exercise training, meditation, and dietary modification (one class IV study). High-quality research is needed to provide evidence-based recommendations for wellness behaviors and lifestyle change in patients with progressive MS.

Diet, Gut Microbiota, and Vitamins D + A in Multiple Sclerosis

Central to the understanding of the relationships between diet, gut microbiota, and vitamins D and A in multiple sclerosis is low-grade inflammation, which is involved in all chronic inflammatory diseases and is influenced by each of the above effectors. We show that food components have either proinflammatory or anti-inflammatory effects and influence both the human metabolism (the "metabolome") and the composition of gut microbiota. Hypercaloric, high-animal-fat Western diets favor anabolism and change gut microbiota composition towards dysbiosis. Subsequent intestinal inflammation leads to leakage of the gut barrier, disruption of the blood-brain barrier, and neuroinflammation. Conversely, a vegetarian diet, rich in fiber, is coherent with gut eubiosis and a healthy condition. Vitamin D levels, mainly insufficient in a persistent low-grade inflammatory status, can be restored to optimal values only by administration of high amounts of cholecalciferol. At its optimal values (>30 ng/ml), vitamin D requires vitamin A for the binding to the vitamin D receptor and exert its anti-inflammatory action. Both vitamins must be supplied to the subjects lacking vitamin D. We conclude that nutrients, including the nondigestible dietary fibers, have a leading role in tackling the low-grade inflammation associated with chronic inflammatory diseases. Their action is mediated by gut microbiota and any microbial change induced by diet modifies host-microbe interactions in a consequent way, to improve the disease or worsen it.