Digital Biomarkers in Multiple Sclerosis

Sensitive Identification of Asymmetries and Neuromuscular Deficits in Lower Limb Function in Early Multiple Sclerosis

BACKGROUND

In the early stages of multiple sclerosis (MS), there are no objective sensitive functional assessments to identify and quantify early subclinical neuromuscular deficits and lower limb strength asymmetries during complex movements. Single-countermovement jumps (SLCMJ), a maximum single leg vertical jump, on a force plate allow functional evaluation of unilateral lower limb performance in performance diagnostics and could therefore provide early results on asymmetries in MS.

OBJECTIVE

Objective evaluation of early lower limb neuromuscular deficits and asymmetries in people with multiple sclerosis (pwMS) using SLCMJ on a force plate.

METHODS

A study was conducted with pwMS (N = 126) and healthy controls (N = 97). All participants performed 3 maximal SLCMJs on a force plate. Temporal, kinetic, and power jump parameters were collected. The Expanded Disability Status Scale (EDSS) was performed on all participants. A repeated measures analysis of covariance (ANCOVA) with age, Body-Mass-Index, and gender as covariates was used.

RESULTS

PwMS with normal muscle strength according to the manual muscle tests showed significantly reduced SLCMJ performance compared to HC. In both groups, jumping performance differed significantly between the dominant and non-dominant leg, with higher effect size for pwMS. A significant interaction effect between leg dominance and group was found for propulsive time, where the pwMS showed an even higher difference between the dominant and non-dominant leg compared to HC. Furthermore, there was a significant small correlation between leg asymmetries and EDSS in pwMS.

CONCLUSION

The study shows that the SLCMJ on a force plate is suitable for the early detection of subclinical lower limb neuromuscular deficits and strength asymmetries in MS.

Influence of virtual reality and task complexity on digital health metrics assessing upper limb function

BACKGROUND

Technology-based assessments using 2D virtual reality (VR) environments and goal-directed instrumented tasks can deliver digital health metrics describing upper limb sensorimotor function that are expected to provide sensitive endpoints for clinical studies. Open questions remain about the influence of the VR environment and task complexity on such metrics and their clinimetric properties.

METHODS

We aim to investigate the influence of VR and task complexity on the clinimetric properties of digital health metrics describing upper limb function. We relied on the Virtual Peg Insertion Test (VPIT), a haptic VR-based assessment with a virtual manipulation task. To evaluate the influence of VR and task complexity, we designed two novel tasks derived from the VPIT, the VPIT-2H (VR environment with reduced task complexity) and the PPIT (physical task with reduced task complexity). These were administered in an observational longitudinal study with 27 able-bodied participants and 31 participants with multiple sclerosis (pwMS, VPIT and PPIT only) and the value of kinematic and kinetic metrics, their clinimetric properties, and the usability of the assessment tasks were compared.

RESULTS

Intra-participant variability strongly increased with increasing task complexity (coefficient of variation + 56%) and was higher in the VR compared to the physical environment (+ 27%). Surprisingly, this did not translate into significant differences in the metrics' measurement error and test-retest reliability across task conditions (p > 0.05). Responsiveness to longitudinal changes in pwMS was even significantly higher (effect size + 0.35, p < 0.05) for the VR task with high task complexity compared to the physical instrumented task with low task complexity. Increased inter-participant variability might have compensated for the increased intra-participant variability to maintain good clinimetric properties. No significant influence of task condition on concurrent validity was present in pwMS. Lastly, pwMS rated the PPIT with higher usability than the VPIT (System Usability Scale + 7.5, p < 0.05).

CONCLUSION

The metrics of both the VR haptic- and physical task-based instrumented assessments showed adequate clinimetric properties. The VR haptic-based assessment may be superior when longitudinally assessing pwMS due to its increased responsiveness. The physical instrumented task may be advantageous for regular clinical use due to its higher usability. These findings highlight that both assessments should be further validated for their ideal use-cases.

Digital outcome measures are associated with brain atrophy in patients with multiple sclerosis

BACKGROUND

Digital monitoring of people with multiple sclerosis (PwMS) using smartphone-based monitoring tools is a promising method to assess disease activity and progression.

OBJECTIVE

To study cross-sectional and longitudinal associations between active and passive digital monitoring parameters and MRI volume measures in PwMS.

METHODS

In this prospective study, 92 PwMS were included. Clinical tests [Expanded Disability Status Scale (EDSS), Timed 25 Foot Walk test (T25FW), 9-Hole Peg Test (NHPT), and Symbol Digit Modalities Test (SDMT)] and structural MRI scans were performed at baseline (M0) and 12-month follow-up (M12). Active monitoring included the smartphone-based Symbol Digit Modalities Test (sSDMT) and 2 Minute Walk Test (s2MWT), while passive monitoring was based on smartphone keystroke dynamics (KD). Linear regression analyses were used to determine cross-sectional and longitudinal relations between digital and clinical outcomes and brain volumes, with age, disease duration and sex as covariates.

RESULTS

In PwMS, both sSDMT and SDMT were associated with thalamic volumes and lesion volumes. KD were related to brain, ventricular, thalamic and lesion volumes. No relations were found between s2MWT and MRI volumes. NHPT scores were associated with lesion volumes only, while EDSS and T25FW were not related to MRI. No longitudinal associations were found for any of the outcome measures between M0 and M12.

CONCLUSION

Our results show clear cross-sectional correlations between digital biomarkers and brain volumes in PwMS, which were not all present for conventional clinical outcomes, supporting the potential added value of digital monitoring tools.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

A scoping review assessing the usability of digital health technologies targeting people with multiple sclerosis

Digital health technologies (DHTs) have become progressively more integrated into the healthcare of people with multiple sclerosis (MS). To ensure that DHTs meet end-users' needs, it is essential to assess their usability. The objective of this study was to determine how DHTs targeting people with MS incorporate usability characteristics into their design and/or evaluation. We conducted a scoping review of DHT studies in MS published from 2010 to the present using PubMed, Web of Science, OVID Medline, CINAHL, Embase, and medRxiv. Covidence was used to facilitate the review. We included articles that focused on people with MS and/or their caregivers, studied DHTs (including mhealth, telehealth, and wearables), and employed quantitative, qualitative, or mixed methods designs. Thirty-two studies that assessed usability were included, which represents a minority of studies (26%) that assessed DHTs in MS. The most common DHT was mobile applications (n = 23, 70%). Overall, studies were highly heterogeneous with respect to what usability principles were considered and how usability was assessed. These findings suggest that there is a major gap in the application of standardized usability assessments to DHTs in MS. Improvements in the standardization of usability assessments will have implications for the future of digital health care for people with MS.

Digital symbol-digit modalities test with modified flexible protocols in patients with CNS demyelinating diseases

Cognitive impairment (CI) is prevalent in central nervous system demyelinating diseases, such as multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD). We developed a novel tablet-based modified digital Symbol Digit Modalities Test (MD-SDMT) with adjustable protocols that feature alternating symbol-digit combinations in each trial, lasting one or two minutes. We assessed 144 patients (99 with MS and 45 with NMOSD) using both MD-SDMT protocols and the traditional paper-based SDMT. We also gathered participants' feedback through a questionnaire regarding their preferences and perceived reliability. The results showed strong correlations between MD-SDMT and paper-based SDMT scores (Pearsons correlation: 0.88 for 2 min; 0.85 for 1 min, both p < 0.001). Among the 120 respondents, the majority preferred the digitalized SDMT (55% for the 2 min, 39% for the 1 min) over the paper-based version (6%), with the 2 min MD-SDMT reported as the most reliable test. Notably, patients with NMOSD and older individuals exhibited a preference for the paper-based test, as compared to those with MS and younger patients. In summary, even with short test durations, the digitalized SDMT effectively evaluates cognitive function in MS and NMOSD patients, and is generally preferred over the paper-based method, although preferences may vary with patient characteristics.

From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal

Wearable sensor technologies are becoming increasingly relevant in health research, particularly in the context of chronic disease management. They generate real-time health data that can be translated into digital biomarkers, which can provide insights into our health and well-being. Scientific methods to collect, interpret, analyze, and translate health data from wearables to digital biomarkers vary, and systematic approaches to guide these processes are currently lacking. This paper is based on an observational, longitudinal cohort study, BarKA-MS, which collected wearable sensor data on the physical rehabilitation of people living with multiple sclerosis (MS). Based on our experience with BarKA-MS, we provide and discuss ten lessons we learned in relation to digital biomarker development across key study phases. We then summarize these lessons into a guiding framework (DACIA) that aims to informs the use of wearable sensor data for digital biomarker development and chronic disease management for future research and teaching.

Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data

BACKGROUND AND PURPOSE

Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system, with numerous therapeutic options, but a lack of biomarkers to support a mechanistic approach to precision medicine. A computational approach to precision medicine could proceed from clinical decision support systems (CDSSs). They are digital tools aiming to empower physicians through the clinical applications of information technology and massive data. However, the process of their clinical development is still maturing; we aimed to review it in the field of MS.

METHODS

For this scoping review, we screened systematically the PubMed database. We identified 24 articles reporting 14 CDSS projects and compared their technical and software development aspects.

RESULTS

The projects position themselves in various contexts of usage with various algorithmic approaches: expert systems, CDSSs based on similar patients' data visualization, and model-based CDSSs implementing mathematical predictive models. So far, no project has completed its clinical development up to certification for clinical use with global release. Some CDSSs have been replaced at subsequent project iterations. The most advanced projects did not necessarily report every step of clinical development in a dedicated article (proof of concept, offline validation, refined prototype, live clinical evaluation, comparative prospective evaluation). They seek different software distribution options to integrate into health care: internal usage, "peer-to-peer," and marketing distribution.

CONCLUSIONS

This review illustrates the potential of clinical applications of information technology and massive data to support MS management and helps clarify the roadmap for future projects as a multidisciplinary and multistep process.

The new era of artificial intelligence in neuroradiology: current research and promising tools

Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

From progression to progress: The future of multiple sclerosis

Significant advances have been made in the diagnosis and treatment of multiple sclerosis in recent years yet challenges remain. The current classification of MS phenotypes according to disease activity and progression, for example, does not adequately reflect the underlying pathophysiological mechanisms that may be acting in an individual with MS at different time points. Thus, there is a need for clinicians to transition to a management approach based on the underlying pathophysiological mechanisms that drive disability in MS. A Canadian expert panel convened in January 2023 to discuss priorities for clinical discovery and scientific exploration that would help advance the field. Five key areas of focus included: identifying a mechanism-based disease classification system; developing biomarkers (imaging, fluid, digital) to identify pathologic processes; implementing a data-driven approach to integrate genetic/environmental risk factors, clinical findings, imaging and biomarker data, and patient-reported outcomes to better characterize the many factors associated with disability progression; utilizing precision-based treatment strategies to target different disease processes; and potentially preventing disease through Epstein-Barr virus (EBV) vaccination, counselling about environmental risk factors (e.g. obesity, exercise, vitamin D/sun exposure, smoking) and other measures. Many of the tools needed to meet these needs are currently available. Further work is required to validate emerging biomarkers and tailor treatment strategies to the needs of individual patients. The hope is that a more complete view of the individual's pathobiology will enable clinicians to usher in an era of truly personalized medicine, in which more informed treatment decisions throughout the disease course achieve better long-term outcomes.

Consensus quality indicators for monitoring multiple sclerosis

Multiple sclerosis (MS) as a chronic, degenerative autoimmune disease of the central nervous system has a longitudinal and heterogeneous course with increasing treatment options and risk profiles requiring constant monitoring of a growing number of parameters. Despite treatment guidelines, there is a lack of strategic and individualised monitoring pathways, including respective quality indicators (QIs). To address this, we systematically developed transparent, traceable, and measurable QIs for MS monitoring. Through literature review, expert discussions, and consensus-building, existing QIs were identified and refined. In a two-stage online Delphi process involving MS specialists (on average 53 years old and with 25 years of professional experience), the QIs were evaluated for content, clarity, and intelligibility, resulting in a set of 24 QIs and checklists to assess the quality of care. The final QIs provide a structured approach to document, monitor, and enhance the quality of care for people with MS across their treatment journey.

Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis

Multiple sclerosis (MS) is one of the chronic and neurodegenerative diseases of the central nervous system (CNS). It generally affects motor, sensory, cerebellar, cognitive, and language functions. It is thought that identifying MS speech disorders using quantitative methods will make a significant contribution to physicians in the diagnosis and follow-up of MS patients. In this study, it was aimed to investigate the speech disorders of MS via objective speech analysis techniques. The study was conducted on 20 patients diagnosed with MS according to McDonald's 2017 criteria and 20 healthy volunteers without any speech or voice pathology. Speech data obtained from patients and healthy individuals were analyzed with the PRAAT speech analysis program, and classification algorithms were tested to determine the most effective classifier in separating specific speech features of MS disease. As a result of the study, the K-nearest neighbor algorithm (K-NN) was found to be the most successful classifier (95%) in distinguishing pathological sounds which were seen in MS patients from those in healthy individuals. The findings obtained in our study can be considered as preliminary data to determine the voice characteristics of MS patients.

Data on Ocrelizumab Treatment Collected by MS Patients in Germany Using Brisa App

BACKGROUND

With a rising number of multiple sclerosis (MS) cases and increasing pressure on health systems, digital companion apps like Brisa, designed specifically for people with MS, can play an important role in the patient journey. These apps enable the collection of real-time longitudinal data that are critical to our understanding of the pathophysiology and progression of MS.

METHODS

This retrospective, descriptive analysis consists of data from Brisa users who registered between 6 August 2021 and 8 September 2022. Of the unique users, 37.7% (n = 1593) fulfilled the inclusion criteria including information about medication and demographics and tracked one or more symptoms and/or patient-reported outcomes. Users were classified as moderate-efficacy treatment users, high-efficacy treatment users and ocrelizumab users, and the reporting frequency and scores of symptoms and patient-reported outcomes were analyzed.

RESULTS

The largest cohort of Brisa users (405) reported treatment with ocrelizumab and were mostly diagnosed 2-5 years before the survey. The most reported MS symptoms were similar between OUs (ocrelizumab users), HETUs (high-efficacy treatment users) and METUs (moderate-efficacy treatment users). OUs on average reported symptoms and answered questionnaires more frequently. Baseline scores between HETUs and OUs were similar, whereas baseline scores of METUs were slightly lower in comparison. In a further analysis of OUs, disability scores increased with age; users aged 26-45 years had higher pain scores than 18-25-year-olds. No significant differences were found in quality of life, bowel control and vision between age groups.

CONCLUSION

These findings show that the characteristics of the Brisa cohort are similar to the results of other studies and registries and can provide a representative overview of everyday disease management. Thereby, these results can bridge the gap between clinical research and real patient experience, but they also raise new questions, such as how often the hard-and-early therapy approach is already used and whether baseline characteristics and reasons for choosing a particular treatment contribute to the different outcomes over time. Answering these questions requires further research and analysis.

Definitions of digital biomarkers: a systematic mapping of the biomedical literature

BACKGROUND

Technological devices such as smartphones, wearables and virtual assistants enable health data collection, serving as digital alternatives to conventional biomarkers. We aimed to provide a systematic overview of emerging literature on 'digital biomarkers,' covering definitions, features and citations in biomedical research.

METHODS

We analysed all articles in PubMed that used 'digital biomarker(s)' in title or abstract, considering any study involving humans and any review, editorial, perspective or opinion-based articles up to 8 March 2023. We systematically extracted characteristics of publications and research studies, and any definitions and features of 'digital biomarkers' mentioned. We described the most influential literature on digital biomarkers and their definitions using thematic categorisations of definitions considering the Food and Drug Administration Biomarkers, EndpointS and other Tools framework (ie, data type, data collection method, purpose of biomarker), analysing structural similarity of definitions by performing text and citation analyses.

RESULTS

We identified 415 articles using 'digital biomarker' between 2014 and 2023 (median 2021). The majority (283 articles; 68%) were primary research. Notably, 287 articles (69%) did not provide a definition of digital biomarkers. Among the 128 articles with definitions, there were 127 different ones. Of these, 78 considered data collection, 56 data type, 50 purpose and 23 included all three components. Those 128 articles with a definition had a median of 6 citations, with the top 10 each presenting distinct definitions.

CONCLUSIONS

The definitions of digital biomarkers vary significantly, indicating a lack of consensus in this emerging field. Our overview highlights key defining characteristics, which could guide the development of a more harmonised accepted definition.

Shifting from the treat-to-target to the early highly effective treatment approach in patients with multiple sclerosis - real-world evidence from Germany

Background

While evidence highlights the effectiveness of initiating disease-modifying therapy with a high-efficacy medication for multiple sclerosis (MS) patients with poor prognostic factors, it remains unclear whether this approach has been adopted by a broad range of MS providers in Germany yet.

Objective

To assess the adoption of the early highly effective treatment (EHT) compared to the treat-to-target treatment approach with the option of escalating treatment efficacy over time in Germany based on real-world evidence data.

Design

Patient-level pharmacy dispensing data from the Permea platform were analysed from 2020 to 2022.

Methods

In total, 29,529 therapy beginners (>18 years) were included to analyse shifts in treatment approaches over time and switching behaviour. Medication classification adhered to the German Society of Neurology guidelines and designated fumarates, glatiramer acetate, teriflunomide and interferons as low-efficacy category 1 medications; cladribine and S1P-modulators as medium-efficacy category 2 medications; and alemtuzumab, natalizumab, ocrelizumab, ofatumumab and rituximab (off-label) as high-efficacy category 3 medications.

Results

Our results show that 70.0% of patients redeemed their first prescription for category 1 medication, 16.3% for category 2 and 13.7% for category 3 medications. The proportion of prescriptions filled shifted from 2020 to 2022 with a decrease of 14.7% for category 1 drugs and an increase of 12.5% for category 3 drugs. 93.2% of patients stayed on their initially prescribed medication category. 3.2% of category 1 and 3.7% of category 2 therapy beginners escalated to category 3 medication. 3.4% of category 3 medication users de-escalated their treatment to category 1 or category 2.

Conclusion

While most individuals started their treatment according to the treat-to-target approach and remained on their initially prescribed medication category, there has been a steadily increasing shift towards the EHT approach since 2020. These insights demonstrate that, while not officially recommended by German guidelines, MS providers increasingly adopt the EHT approach.

[Digital medicine in neurological research-Between hype and evidence]

BACKGROUND

The rapid advancement of digital medicine and health technologies in neurology offers both significant potential and challenges. This article outlines fundamental aspects of digital medicine related to neurological research and highlights application examples of digital technologies in neurological research.

AIM

To provide a comprehensive overview of current digital developments in neurology and their impact on neurological research.

MATERIAL AND METHODS

In this narrative review articles from various sources and references related to digital medicine and health technologies in neurology were compiled and analyzed.

RESULTS AND DISCUSSION

The data presented indicate that digital health technologies and digital therapeutics have the potential to decisively shape neurological care and research; however, it is emphasized that a critical evaluation and evidence-based approach to these technologies are essential to determine their actual value in neurology.

Cortical Thin Patch Fraction Reflects Disease Burden in MS: The Mosaic Approach

BACKGROUND AND PURPOSE

GM pathology plays an essential role in MS disability progression, emphasizing the importance of neuroradiologic biomarkers to capture the heterogeneity of cortical disease burden. This study aimed to assess the validity of a patch-wise, individual interpretation of cortical thickness data to identify GM pathology, the "mosaic approach," which was previously suggested as a biomarker for assessing and localizing atrophy.

MATERIALS AND METHODS

We investigated the mosaic approach in a cohort of 501 patients with MS with respect to 89 internal and 651 external controls. The resulting metric of the mosaic approach is the so-called thin patch fraction, which is an estimate of overall cortical disease burden per patient. We evaluated the mosaic approach with respect to the following: 1) discrimination between patients with MS and controls, 2) classification between different MS phenotypes, and 3) association with established biomarkers reflecting MS disease burden, using general linear modeling.

RESULTS

The thin patch fraction varied significantly between patients with MS and healthy controls and discriminated among MS phenotypes. Furthermore, the thin patch fraction was associated with disease burden, including the Expanded Disability Status Scale, cognitive and fatigue scores, and lesion volume.

CONCLUSIONS

This study demonstrates the validity of the mosaic approach as a neuroradiologic biomarker in MS. The output of the mosaic approach, namely the thin patch fraction, is a candidate biomarker for assessing and localizing cortical GM pathology. The mosaic approach can furthermore enhance the development of a personalized cortical MS biomarker, given that the thin patch fraction provides a feature on which artificial intelligence methods can be trained. Most important, we showed the validity of the mosaic approach when referencing data with respect to external control MR imaging repositories.

Mobile health interventions in multiple sclerosis: A systematic review

BACKGROUND

Persons with multiple sclerosis (pwMS) might be particularly well suited to benefit from digital health applications because they are, on average, younger and less severely disabled than patients with many other chronic diseases. Many digital health applications for pwMS have been developed.

OBJECTIVES

Analysis of the evidence of digital health applications to improve health outcomes from a patient perspective.

METHODS

A systematic review was performed on all randomized controlled trials (RCTs) that have studied mobile health interventions for pwMS, that is, which can be applied with a smartphone, tablet, or laptop to improve patient-reported outcomes.

RESULTS

Of the 1127 articles identified in the literature search, 13 RCTs fit the inclusion criteria. Two trials studied messaging systems, two depression interventions, one addressed MS fatigue, five cognition, and three mobility issues, of which two focused on spasticity management. One trial aimed to enhance physical activity. Most were pilot studies that cannot yield definitive conclusions regarding efficacy. One depression intervention and one fatigue intervention showed significant results across several outcomes.

CONCLUSION

Several mobile self-guided digital health applications for pwMS have been tested in RCTs, and two interventions targeting depression and fatigue have demonstrated significant effects. Challenges remain regarding implementation into routine care.

An Exploratory Study on the Utility of Patient-Generated Health Data as a Tool for Health Care Professionals in Multiple Sclerosis Care

BACKGROUND

Patient-generated health data (PGHD) are data collected through technologies such as mobile devices and health apps. The integration of PGHD into health care workflows can support the care of chronic conditions such as multiple sclerosis (MS). Patients are often willing to share data with health care professionals (HCPs) in their care team; however, the benefits of PGHD can be limited if HCPs do not find it useful, leading patients to discontinue data tracking and sharing eventually. Therefore, understanding the usefulness of mobile health (mHealth) solutions, which provide PGHD and serve as enablers of the HCPs' involvement in participatory care, could motivate them to continue using these technologies.

OBJECTIVE

The objective of this study is to explore the perceived utility of different types of PGHD from mHealth solutions which could serve as tools for HCPs to support participatory care in MS.

METHOD

A mixed-methods approach was used, combining qualitative research and participatory design. This study includes three sequential phases: data collection, assessment of PGHD utility, and design of data visualizations. In the first phase, 16 HCPs were interviewed. The second and third phases were carried out through participatory workshops, where PGHD types were conceptualized in terms of utility.

RESULTS

The study found that HCPs are optimistic about PGHD in MS care. The most useful types of PGHD for HCPs in MS care are patients' habits, lifestyles, and fatigue-inducing activities. Although these subjective data seem more useful for HCPs, it is more challenging to visualize them in a useful and actionable way.

CONCLUSION

HCPs are optimistic about mHealth and PGHD as tools to further understand their patients' needs and support care in MS. HCPs from different disciplines have different perceptions of what types of PGHD are useful; however, subjective types of PGHD seem potentially more useful for MS care.

Reshaping neuroimmunology: diagnosis and treatment in the era of precision medicine

Precision medicine has revolutionized the field of neuroimmunology, with innovative approaches that characterize diseases based on their biology, deeper understanding of the factors leading to heterogeneity within the same disease, development of targeted therapies, and strategies to tailor therapies to each patient. This review explores the impact of precision medicine on various neuroimmunological conditions, including multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), optic neuritis, autoimmune encephalitis, and immune-mediated neuropathies. We discuss advances in disease subtyping, recognition of novel entities, promising biomarkers, and the development of more selective monoclonal antibodies and cutting-edge synthetic cell-based immunotherapies in neuroimmunological disorders. In addition, we analyze the challenges related to affordability and equity in the implementation of these emerging technologies, especially in situations with limited resources.

Optical coherence tomography (OCT) measurements and disability in multiple sclerosis (MS): A systematic review and meta-analysis

BACKGROUND

Studies have demonstrated that people with multiple sclerosis (pwMS) experience visual impairments and neurodegenerative retinal processes. The disability progression in pwMS may be associated with retinal changes assessed with optical coherence tomography (OCT). This meta-analysis aims at synthesizing the correlations between OCT measurements of disability in pwMS.

METHODS

We systematically searched four databases (PubMed/MEDLINE, Embase, Scopus, and Web of Science) from inception to November 2022, then conducted a meta-analysis using a random effects model to determine the pooled correlation coefficient(r) between OCT measurements and disability scales by R version 4.2.3 with the meta version 6.2-1 package.

RESULTS

From 3129 studies, 100 studies were included. Among 9051 pwMS, the female-to-male ratio was 3.15:1, with a mean age of 39.57 ± 6.07 years. The mean disease duration and Expanded Disability Status Scale (EDSS) were 8.5 ± 3.7 and 2.7 ± 1.1, respectively. Among the pooled subgroup analyses, macular ganglion cell inner plexiform layer (mGCIPL) in patients with relapsing-remitting (pwRRMS) and peripapillary retinal nerve fiber layer (pRNFL) in patients with progressive MS (pwPMS) had strong correlations with EDSS, r = -0.33 (95% CI: -0.45 to -0.20, I2 = 45%, z-score = -4.86, p < 0.001) and r = -0.20 (95% CI:-0.58 to 0.26, I2 = 76%, z-score = -0.85, p = 0.395), respectively. According to subgroup analysis on pwMS without optic neuritis (ON) history, the largest correlation was seen between EDSS and macular ganglion cell complex (mGCC): r = -0.39 (95% CI: -0.70 to 0.04, I2 = 79%, z-score = -1.79, p = 0.073).

CONCLUSION

OCT measurements are correlated with disability in pwMS, and they can complement the comprehensive neurological visit as an additional paraclinical test.

The role of miRNAs in multiple sclerosis pathogenesis, diagnosis, and therapeutic resistance

In recent years, microRNAs (miRNAs) have gained increased attention from researchers around the globe. Although it is twenty nucleotides long, it can modulate several gene targets simultaneously. Their mal expression is a signature of various pathologies, and they provide the foundation to elucidate the molecular mechanisms of each pathology. Among the debilitating central nervous system (CNS) disorders with a growing prevalence globally is the multiple sclerosis (MS). Moreover, the diagnosis of MS is challenging due to the lack of disease-specific biomarkers, and the diagnosis mainly depends on ruling out other disabilities. MS could adversely affect patients' lives through its progression, and only symptomatic treatments are available as therapeutic options, but an exact cure is yet unavailable. Consequently, this review hopes to further the study of the biological features of miRNAs in MS and explore their potential as a therapeutic target.

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.

Literature review of digital twin in healthcare

This article aims to make a bibliometric literature review using systematic scientific mapping and content analysis of digital twins in healthcare to know the evolution, domain, keywords, content type, and kind and purpose of digital twin's implementation in healthcare, so a consolidation and future improvement of existing knowledge can be made and gaps for new studies can be identified. The increase in publications of digital twins in healthcare is quite recent and it is still concentrated in the domain of technology sources. The subject is majorly concentrated in patient's digital twin group and in precision medicine and aspects, issues and/or policies subgroups, although the publications keywords mirror it only at the group side. Digital twins in healthcare are probably stepping out of the infancy phase. On the other hand, digital twins in hospital group and the device and facilities management subgroups are more mature with all knowledge gathered from the manufacturing sector. There is an absence of some publication's types in general, device and care subgroup and no whole body or hospital digital twin was reported. Based on the presented arguments, guidelines for future research were presented: advance in the creation of general frameworks, in subgroups not as much explored, and in groups and subgroups already explored, but that need more advancement to achieve the main goals of a whole human or hospital digital twin with the main issues resolved.

Building a monitoring matrix for the management of multiple sclerosis

Multiple sclerosis (MS) has a longitudinal and heterogeneous course, with an increasing number of therapy options and associated risk profiles, leading to a constant increase in the number of parameters to be monitored. Even though important clinical and subclinical data are being generated, treating neurologists may not always be able to use them adequately for MS management. In contrast to the monitoring of other diseases in different medical fields, no target-based approach for a standardized monitoring in MS has been established yet. Therefore, there is an urgent need for a standardized and structured monitoring as part of MS management that is adaptive, individualized, agile, and multimodal-integrative. We discuss the development of an MS monitoring matrix which can help facilitate data collection over time from different dimensions and perspectives to optimize the treatment of people with MS (pwMS). In doing so, we show how different measurement tools can combined to enhance MS treatment. We propose to apply the concept of patient pathways to disease and intervention monitoring, not losing track of their interrelation. We also discuss the use of artificial intelligence (AI) to improve the quality of processes, outcomes, and patient safety, as well as personalized and patient-centered care. Patient pathways allow us to track the patient's journey over time and can always change (e.g., when there is a switch in therapy). They therefore may assist us in the continuous improvement of monitoring in an iterative process. Improving the monitoring process means improving the care of pwMS.

Precision Medicine 2.0: How Digital Health and AI Are Changing the Game

In the era of rapid IT developments, the health domain is undergoing a considerable transformation [...].

Digital Transformation in Healthcare: Technology Acceptance and Its Applications

Technological innovation has become an integral aspect of our daily life, such as wearable and information technology, virtual reality and the Internet of Things which have contributed to transforming healthcare business and operations. Patients will now have a broader range and more mindful healthcare choices and experience a new era of healthcare with a patient-centric culture. Digital transformation determines personal and institutional health care. This paper aims to analyse the changes taking place in the field of healthcare due to digital transformation. For this purpose, a systematic bibliographic review is performed, utilising Scopus, Science Direct and PubMed databases from 2008 to 2021. Our methodology is based on the approach by Wester and Watson, which classify the related articles based on a concept-centric method and an ad hoc classification system which identify the categories used to describe areas of literature. The search was made during August 2022 and identified 5847 papers, of which 321 fulfilled the inclusion criteria for further process. Finally, by removing and adding additional studies, we ended with 287 articles grouped into five themes: information technology in health, the educational impact of e-health, the acceptance of e-health, telemedicine and security issues.

The influence of the COVID-19 pandemic on the prescription of multiple sclerosis medication in Germany

BACKGROUND

Real-word evidence from diverse data sources is increasingly important in terms of generating rapid insights to effectively manage patient populations, especially during major public health disruptions such as the ongoing COVID-19 pandemic. Patients with chronic and inflammatory diseases - such as multiple sclerosis (MS) - were reported to experience potentially negative effects due to the use of immunosuppressive drugs in combination with a COVID-19 infection. In this research, we explored the impact of the COVID-19 pandemic on medication use in patients with MS in Germany.

METHODS

Patient-level pharmacy dispensing data from the Permea platform - covering approximately 44% of all community pharmacy dispensing in Germany - were analysed from 2019 - 2021. Longitudinal use patterns of MS medication and antidepressants and patient demographics were assessed. Daily variation in MS medication use was specifically studied around the dates of the first and second lockdowns in Germany.

RESULTS

We included data from 539,400 prescriptions which included at least 1 MS drug. The medication data showed a stable level of monthly prescriptions for MS medication at 2.02 ± 0.03 prescriptions per pharmacy during the study period. Although there was a sharp increase in daily prescriptions before the first lockdown (from an average 660.08 ± 137.59 daily prescriptions in the observed period to a maximum dispensing number of 998 daily prescriptions), the overall number of prescriptions remained at pre-pandemic levels (603 ± 90.31 daily prescriptions in 2019). Similar trends were observed for monthly co-prescribed antidepressant use per pharmacy (0.10 ± 0.01 in 2019-0.11 ± 0.02 in 2020).

CONCLUSION

Throughout the COVID-19 pandemic, the use of MS medications and co-prescribed antidepressants was stable. These insights from real-world data demonstrate the value of evidence-based insights for managing patient care.

Driving time-based identification of gaps in specialised care coverage: An example of neuroinflammatory diseases in Germany

Objective

Due to the growing complexity in monitoring and treatment of many disorders, disease-specific care and research networks offer patients certified healthcare. However, the networks' ability to provide health services close to patients' homes usually remains vague. Digital Health Technologies (DHTs) help to provide better care, especially if implemented in a targeted manner in regions undersupplied by specialised networks. Therefore, we used a car travel time-based isochrone approach to identify care gaps using the example of the neuroinflammation-focused German healthcare and research networks for multiple sclerosis (MS), myasthenia gravis (MG), myositis and immune-mediated neuropathy.

Methods

Excellence centres were mapped, and isochrones for 30, 60, 90 and 120 minutes were calculated. The resulting geometric figures were aggregated and used to mask the global human settlement population grid 2019 to estimate German inhabitants that can reach centres within the given periods.

Results

While 96.48% of Germans can drive to an MS-focused centre within one hour, coverage is lower for the rare disease networks for MG (48.3%), myositis (43.1%) and immune-mediated neuropathy (56.7%). Within 120 minutes, more than 80% of Germans can reach a centre of any network. Besides the generally worse covered rural regions such as North-Eastern Germany, the rare disease networks also show network-specific regional underrepresentation.

Conclusion

An isochrone-based approach helps identify regions where specialised care is hard to reach, which might be especially troublesome in the case of an often disabled patient collective. Patient care could be improved by focusing deployments of disease-specific DHTs on these areas.

Current Technology Developments Can Improve the Quality of Research and Level of Evidence for Rehabilitation Interventions: A Narrative Review

The current important limitations to the implementation of Evidence-Based Practice (EBP) in the rehabilitation field are related to the validation process of interventions. Indeed, most of the strict guidelines that have been developed for the validation of new drugs (i.e., double or triple blinded, strict control of the doses and intensity) cannot-or can only partially-be applied in rehabilitation. Well-powered, high-quality randomized controlled trials are more difficult to organize in rehabilitation (e.g., longer duration of the intervention in rehabilitation, more difficult to standardize the intervention compared to drug validation studies, limited funding since not sponsored by big pharma companies), which reduces the possibility of conducting systematic reviews and meta-analyses, as currently high levels of evidence are sparse. The current limitations of EBP in rehabilitation are presented in this narrative review, and innovative solutions are suggested, such as technology-supported rehabilitation systems, continuous assessment, pragmatic trials, rehabilitation treatment specification systems, and advanced statistical methods, to tackle the current limitations. The development and implementation of new technologies can increase the quality of research and the level of evidence supporting rehabilitation, provided some adaptations are made to our research methodology.

Large-scale cross-sectional online survey on patient-neurologist communication, burden of disease assessment and disease monitoring in people with multiple sclerosis

Background

Management of multiple sclerosis (MS) requires a high level of communication between health care professionals (HCPs) and people with MS (pwMS) including profound investigation and discussion of symptoms to identify therapeutic needs. For treatment decisions, monitoring of disease activity is important, in this respect self-monitoring devices and apps, as well as magnetic resonance imaging are important tools.

Methods

MS Perspectives is a cross-sectional online survey conducted in Germany which was designed to collect data, among others, on the communication between pwMS and HCPs regarding treatment goals, symptom assessment, usage of devices and apps to self-monitor health functions, as well as to identify patients' attitude toward the role of magnetic resonance imaging (MRI). Between December 2021 and February 2022, 4,555 pwMS completed the survey.

Results

In total, 63.7% of participants reported that treatment goals have been discussed with their HCPs. Symptoms worsening in the past 12 months independent of relapses was more often reported by pwMS than inquired by HCPs, according to patients' report. Devices or apps for health monitoring were used by less than half of participants. Frequency of MRI controls was much lower in participants with longer compared to shorter disease duration (47.5 vs. 86.3%). The proportion of patients with annual or semiannual scans was highest among pwMS receiving infusion therapy (93.5%), followed by oral medication (82.5%) and injectables (73.4%), and lowest for pwMS without immunotherapy (58.2%).

Conclusion

MS Perspectives identified a rather low patient involvement regarding treatment goals and symptom assessment in clinical practice. Regarding this and our findings for health self-monitoring and MRI usage, strategies for improving patient-HCP communication and disease monitoring may be considered.

Correlation and differences of patient-reported outcomes vs. Likert-Rating of MS symptoms in a real-world cohort using a digital patient app

Background

Multiple Sclerosis (MS) is a chronic and progressive neurological autoimmune disease currently affecting 250,000 individuals in Germany. Patients suffering from the disease can be severely impaired in their day-to-day activities. BRISA is a digital app specifically designed to help MS patients monitor their disease by regularly tracking symptoms. Lengthy and time-consuming questionnaires for patient-reported outcomes (PRO) are the standard method to assess the patients' current condition. Here, we examine whether simplified versions of these questionnaires can provide comparable information regarding individual symptom presentations in BRISA users.

Methods

828 users were included in the analysis. Patients who provided onboarding information and answered at least one questionnaire and the corresponding simplified smiley symptoms assessment were included. Correlation of questionnaire and symptom scores was calculated using Pearson's correlation.

Results

Our analysis cohort predominantly consisted of female, 26-55-year-olds. Relapsing-remitting MS (RRMS) was the most common MS type recorded. Most patients were diagnosed 2-5 years ago. Questionnaires regarding fatigue and vision impairment were among the most answered, those regarding bowel movement and sexual satisfaction received fewest responses. Overall, the scores from questionnaires and symptoms correlated positively. Scoring correlation could also be shown across the subgroups divided by gender, age groups, type of MS, and time since diagnosis of the disease.

Conclusion

Scores recorded from traditional PRO questionnaires can be reflected more easily as a trend in a simplified scale using smileys. Nevertheless, traditional questionnaires are needed to also maintain a more objective assessment. In conclusion, the patient will benefit most from an adaptive combination of regular traditional PRO questionnaire assessments and simplified symptom recording.

Multiple sclerosis progression: time for a new mechanism-driven framework

Traditionally, multiple sclerosis has been categorised by distinct clinical descriptors-relapsing-remitting, secondary progressive, and primary progressive-for patient care, research, and regulatory approval of medications. Accumulating evidence suggests that the clinical course of multiple sclerosis is better considered as a continuum, with contributions from concurrent pathophysiological processes that vary across individuals and over time. The apparent evolution to a progressive course reflects a partial shift from predominantly localised acute injury to widespread inflammation and neurodegeneration, coupled with failure of compensatory mechanisms, such as neuroplasticity and remyelination. Ageing increases neural susceptibility to injury and decreases resilience. These observations encourage a new consideration of the course of multiple sclerosis as a spectrum defined by the relative contributions of overlapping pathological and reparative or compensatory processes. New understanding of key mechanisms underlying progression and measures to quantify progressive pathology will potentially have important and beneficial implications for clinical care, treatment targets, and regulatory decision-making.

Using Biomarkers for Cognitive Enhancement and Evaluation in Mobile Applications

Clinicians are increasingly using biomarkers to diagnose and monitor cognitive conditions such as mild cognitive impairment, Alzheimer's disease, and dementia. Biomarkers are classified into two main categories based on their clinical goal: disease-associated biomarkers and drug-related biomarkers. In the case of disease-associated biomarkers, neuroimaging biomarkers are used to predict and validate Alzheimer's disease at any of its stages including mild cognitive impairment. The use of mobile and wearable devices to collect data about a person's daily activities and behaviors has led to the emergence of a new type of biomarker known as digital biomarkers. This type of data provides a digital reflection of a person's function in the context of everyday life and can be used to monitor and track changes in an individual's health and behaviors over time. The use of biomarkers in mobile applications for cognitive enhancement and evaluation can provide valuable insights into an individual's cognitive health and can help to optimize treatment and prevention strategies.

Integrating Rehabilomics into the Multi-Omics Approach in the Management of Multiple Sclerosis: The Way for Precision Medicine?

Over recent years, significant improvements have been made in the understanding of (epi)genetics and neuropathophysiological mechanisms driving the different forms of multiple sclerosis (MS). For example, the role and importance of the bidirectional communications between the brain and the gut-also referred to as the gut-brain axis-in the pathogenesis of MS is receiving increasing interest in recent years and is probably one of the most promising areas of research for the management of people with MS. However, despite these important advances, it must be noted that these data are not-yet-used in rehabilitation. Neurorehabilitation is a cornerstone of MS patient management, and there are many techniques available to clinicians and patients, including technology-supported rehabilitation. In this paper, we will discuss how new findings on the gut microbiome could help us to better understand how rehabilitation can improve motor and cognitive functions. We will also see how the data gathered during the rehabilitation can help to get a better diagnosis of the patients. Finally, we will discuss how these new techniques can better guide rehabilitation to lead to precision rehabilitation and ultimately increase the quality of patient care.

Digital Twins for Managing Health Care Systems: Rapid Literature Review

BACKGROUND

Although most digital twin (DT) applications for health care have emerged in precision medicine, DTs can potentially support the overall health care process. DTs (twinned systems, processes, and products) can be used to optimize flows, improve performance, improve health outcomes, and improve the experiences of patients, doctors, and other stakeholders with minimal risk.

OBJECTIVE

This paper aims to review applications of DT systems, products, and processes as well as analyze the potential of these applications for improving health care management and the challenges associated with this emerging technology.

METHODS

We performed a rapid review of the literature and reported available studies on DTs and their applications in health care management. We searched 5 databases for studies published between January 2002 and January 2022 and included peer-reviewed studies written in English. We excluded studies reporting DT usage to support health care practice (organ transplant, precision medicine, etc). Studies were analyzed based on their contribution toward DT technology to improve user experience in health care from human factors and systems engineering perspectives, accounting for the type of impact (product, process, or performance/system level). Challenges related to the adoption of DTs were also summarized.

RESULTS

The DT-related studies aimed at managing health care systems have been growing over time from 0 studies in 2002 to 17 in 2022, with 7 published in 2021 (N=17 studies). The findings reported on applications categorized by DT type (system: n=8; process: n=5; product: n=4) and their contributions or functions. We identified 4 main functions of DTs in health care management including safety management (n=3), information management (n=2), health management and well-being promotion (n=3), and operational control (n=9). DTs used in health care systems management have the potential to avoid unintended or unexpected harm to people during the provision of health care processes. They also can help identify crisis-related threats to a system and control the impacts. In addition, DTs ensure privacy, security, and real-time information access to all stakeholders. Furthermore, they are beneficial in empowering self-care abilities by enabling health management practices and providing high system efficiency levels by ensuring that health care facilities run smoothly and offer high-quality care to every patient.

CONCLUSIONS

The use of DTs for health care systems management is an emerging topic. This can be seen in the limited literature supporting this technology. However, DTs are increasingly being used to ensure patient safety and well-being in an organized system. Thus, further studies aiming to address the challenges of health care systems challenges and improve their performance should investigate the potential of DT technology. In addition, such technologies should embed human factors and ergonomics principles to ensure better design and more successful impact on patient and doctor experiences.

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person’s activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.

Personal Digital Twin: A Close Look into the Present and a Step towards the Future of Personalised Healthcare Industry

Digital twins (DTs) play a vital role in revolutionising the healthcare industry, leading to more personalised, intelligent, and proactive healthcare. With the evolution of personalised healthcare, there is a significant need to represent a virtual replica for individuals to provide the right type of care in the right way and at the right time. Therefore, in this paper, we surveyed the concept of a personal digital twin (PDT) as an enhanced version of the DT with actionable insight capabilities. In particular, PDT can bring value to patients by enabling more accurate decision making and proper treatment selection and optimisation. Then, we explored the progression of PDT as a revolutionary technology in healthcare research and industry. However, although several research works have been performed for smart healthcare using DT, PDT is still at an early stage. Consequently, we believe that this work can be a step towards smart personalised healthcare industry by guiding the design of industrial personalised healthcare systems. Accordingly, we introduced a reference framework that empowers smart personalised healthcare using PDTs by bringing together existing advanced technologies (i.e., DT, blockchain, and AI). Then, we described some selected use cases, including the mitigation of COVID-19 contagion, COVID-19 survivor follow-up care, personalised COVID-19 medicine, personalised osteoporosis prevention, personalised cancer survivor follow-up care, and personalised nutrition. Finally, we identified further challenges to pave the PDT paradigm toward the smart personalised healthcare industry.

Demographic Patterns of MS Patients Using BRISA: An MS-Specific App in Germany

Background: Multiple sclerosis (MS) is a chronic, progressive neurological autoimmune disease impacting quality of life. BRISA is an app designed to help MS patients in Germany track their disease course by symptom-monitoring. This study aimed to understand demographic and health-related characteristics of BRISA users. Methods: Demographic data provided by 2095 users were analyzed to describe characteristics such as sex, age, type of MS, and medication. The distribution of tracked symptoms based on age and time since diagnosis were studied. Furthermore, the covariance of specific symptom pairs was analyzed. Results: BRISA users are predominantly female and between 26 and 55 years old. Relapsing–remitting MS was the most prevalent form of MS. First-line category 1 drugs were most frequently used, followed by high-efficacy category 3 drugs (e.g., monoclonal antibodies). The relative frequencies of use of category 1 and category 2 drugs (e.g., spingosine-1-phosphate-receptor modulators) significantly altered with time since diagnosis. Fatigue, concentration disorders, tingling, forgetfulness, and pain were the top five symptoms affecting users. Conclusion: The results highlight the diversity among MS patients and the need for extensive cohort characterization in the real-world scenario. In-depth analysis could help in identifying novel insights that could aid in disease management.

An Update on the Measurement of Motor Cerebellar Dysfunction in Multiple Sclerosis

Multiple sclerosis (MS) is a progressive disease that often affects the cerebellum. It is characterised by demyelination, inflammation, and neurodegeneration within the central nervous system. Damage to the cerebellum in MS is associated with increased disability and decreased quality of life. Symptoms include gait and balance problems, motor speech disorder, upper limb dysfunction, and oculomotor difficulties. Monitoring symptoms is crucial for effective management of MS. A combination of clinical, neuroimaging, and task-based measures is generally used to diagnose and monitor MS. This paper reviews the present and new tools used by clinicians and researchers to assess cerebellar impairment in people with MS (pwMS). It also describes recent advances in digital and home-based monitoring for people with MS.

The need for a strategic therapeutic approach: multiple sclerosis in check

Multiple sclerosis (MS) is the most common chronic autoimmune neurological disease. Its therapeutic management has drastically evolved in the recent years with the development of specific disease-modifying therapies (DMTs). Together with the established injectables, oral and intravenous alternatives are now available for MS patients with significant benefits to modulate the disease course. Certain drugs present with a higher efficacy than the others, profiles and frequencies of adverse events differentiate as well. Thus due to the several and different treatment alternatives, the therapeutic approach adopted by neurologists requires a tactical focus for a targeted, timed, and meaningful treatment decision. An integration of rational and emotional control with proper communication skills is necessary for shared decision-making with patients. In this perspective paper, we reinforce the necessary concept of strategic MS treatment approach using all available therapies based on scientific evidence and current experience. We apply a didactic analogy to the strategic game chess. The opening with oriented attack (i.e. already in early disease stages as clinical isolated syndrome), a correct choice of chess pieces to move (i.e. among the several DMTs), a re-assessment reaction to different scenarios (e.g. sustained disease activity, adverse events, and family planning) and the advantage of real-world data are discussed to try the best approach to ultimately successfully approach the best personalized MS treatment.