For incurable diseases, such as multiple sclerosis (MS), the prevention of progression and the preservation of quality of life play a crucial role over the entire therapy period. In MS, patients tend to become ill at a younger age and are so variable in terms of their disease course that there is no standard therapy. Therefore, it is necessary to enable a therapy that is as personalized as possible and to respond promptly to any changes, whether with noticeable symptoms or symptomless. Here, measurable parameters of biological processes can be used, which provide good information with regard to prognostic and diagnostic aspects, disease activity and response to therapy, so-called biomarkers Increasing digitalization and the availability of easy-to-use devices and technology also enable healthcare professionals to use a new class of digital biomarkers—digital health technologies—to explain, influence and/or predict health-related outcomes. The technology and devices from which these digital biomarkers stem are quite broad, and range from wearables that collect patients’ activity during digitalized functional tests (e.g., the Multiple Sclerosis Performance Test, dual-tasking performance and speech) to digitalized diagnostic procedures (e.g., optical coherence tomography) and software-supported magnetic resonance imaging evaluation. These technologies offer a timesaving way to collect valuable data on a regular basis over a long period of time, not only once or twice a year during patients’ routine visit at the clinic. Therefore, they lead to real-life data acquisition, closer patient monitoring and thus a patient dataset useful for precision medicine. Despite the great benefit of such increasing digitalization, for now, the path to implementing digital biomarkers is widely unknown or inconsistent. Challenges around validation, infrastructure, evidence generation, consistent data collection and analysis still persist. In this narrative review, we explore existing and future opportunities to capture clinical digital biomarkers in the care of people with MS, which may lead to a digital twin of the patient. To do this, we searched published papers for existing opportunities to capture clinical digital biomarkers for different functional systems in the context of MS, and also gathered perspectives on digital biomarkers under development or already existing as a research approach.

Background. Balance problems can severely limit quality of life of people with Multiple Sclerosis (pwMS) already in early stages of the disease. PwMS are usually assessed with the Expanded Disability Status Scale (EDSS), which includes a Romberg Test for assessing balance. As the EDSS assessments are subjective to the examining neurologist, the postural stability of pwMS could be objectively quantified by the implementation of static posturography to detect balance problems and address preventive medical care. Methods. In this cross-sectional study, we added static posturography to the neurological EDSS examination in pwMS and healthy subjects to determine how this technique could supplement information to the evaluation of the cerebellar functional system of the neurostatus EDSS as clinical outcome already in early disease stages. Static posturography was performed with subjects standing on a force platform. As outcome variable, delineated area, average speed and average sway were obtained. Unpaired t-test as well as (Welch’s) Analysis of variance (ANOVA) with pairwise post-hoc comparisons according to Games-Howell were used. Spearman rank correlations were implemented to study associations of balance outcomes with EDSS-associated outcomes. Results. 99 pwMS (mean age: 35.01 years; EDSS median: 2.0, 68.69% females) and 30 healthy subjects (mean age: 34.03 years; 70% females) were enrolled. pwMS had worse performance of the three evaluated balance parameters than the healthy group (all p<0.001). Even patients without postural instability as documented in the Romberg test score of the EDSS assessment showed significantly worse outcome regarding the delineated area (+1.97 cm2, 95%-CI (0.61-3.34); p=0.002) versus healthy controls. Similar results were observed for the comparison between pwMS with normal cerebellar function EDSS-system and healthy subjects. There were significant correlations with the EDSS, cerebellar function score and Romberg test for the delineated area and average speed (r’s ranging from 0.330 to 0.537, p<0.001). Conclusions. Static posturography can complement neurological assessment of EDSS as objective and quantitative test especially for MS patients in early stages of the disease.

In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (κ = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (κ = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.

Background: Walking assessment (WA) enables meaningful patient mobility assessment. In this context, patient satisfaction with WA can influence assessment compliance and indirectly affect outcomes. One opportunity to assess patient satisfaction is patient-reported and expert-reported experience measures (PREM). Research on PREMs and WA in daily clinical multiple sclerosis (MS) practice does not exist yet. Methods: We surveyed people with MS about their experience and assessed healthcare professionals’ experience via an interview after patients completed WA. Results: Gait parameters were related to perceived difficulty and strain during performance. Less impaired patients perceived the WA to be less difficult and exhausting but were less likely to use WA results for themselves. Men and patients with higher impairment would perform WA more frequently. A good workflow, a fully performed WA with standardized testing, fully functional measurement systems, support and safeguarding by staff in case of falls, direct feedback after the testing, and patients’ motivation are identified by the experts as necessary factors for a successful WA. Conclusions: As patients’ experience has an impact on patients’ outcomes, long-term monitoring of PREMs should become an integral part of the healthcare service to identify and avoid problems early.

One of the common causes of falls in people with Multiple Sclerosis (pwMS) is walking impairment. Therefore, assessment of gait is of importance in MS. Gait analysis and fall detection can take place in the clinical context using a wide variety of available methods. However, combining these methods while using machine learning algorithms for detecting falls has not been performed. Our objective was to determine the most relevant method for determining fall risk by analyzing eleven different gait data sets with machine learning algorithms. In addition, we examined the most important features of fall detection. A new feature selection ensemble (FS-Ensemble) and four classification models (Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, Support Vector Machine) were used. The FS-Ensemble consisted of four filter methods: Chi-square test, information gain, Minimum Redundancy Maximum Relevance and RelieF. Various thresholds (50%, 25% and 10%) and combination methods (Union, Union 2, Union 3 and Intersection) were examined. Patient-reported outcomes using specialized walking questionnaires such as the 12-item Multiple Sclerosis Walking Scale (MSWS-12) and the Early Mobility Impairment Questionnaire (EMIQ) achieved the best performances with an F1 score of 0.54 for detecting falls. A combination of selected features of MSWS-12 and EMIQ, including the estimation of walking, running and stair climbing ability, the subjective effort as well as necessary concentration and walking fluency during walking, the frequency of stumbling and the indication of avoidance of social activity achieved the best recall of 75%. The Gaussian Naive Bayes was the best classification model for detecting falls with almost all data sets. FS-Ensemble improved the classification models and is an appropriate technique for reducing data sets with a large number of features. Future research on other risk factors, such as fear of falling, could provide further insights.