**1. Introduction**

eHealth interventions play a growing role in shaping the future healthcare system. The integration of eHealth interventions can enhance the efficiency and quality of patient management and optimize the course of treatment for chronically ill patients [1] by alleviating pressure on health care systems when productivity of labor is restricted [2]. In this

**Citation:** Cloosterman, S.; Wijnands, I.; Huygens, S.; Wester, V.; Lam, K.-H.; Strijbis, E.; den Teuling, B.; Versteegh, M. The Potential Impact of Digital Biomarkers in Multiple Sclerosis in the Netherlands: An Early Health Technology Assessment of MS Sherpa. *Brain Sci.* **2021**, *11*, 1305. https:// doi.org/10.3390/brainsci11101305

Academic Editors: Tjalf Ziemssen, Rocco Haase and Moussa Antoine Chalah

Received: 30 July 2021 Accepted: 26 September 2021 Published: 30 September 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

paper, we investigate the benefits of adding a digital biomarker–based eHealth intervention to the standard of care of multiple sclerosis (MS).

MS is the most prevalent chronic neurological disorder among young adults [3]. The severity and nature of symptoms and disability in MS depend on the location and extent of inflammatory demyelination and axonal loss in the central nervous system due to inflammation. Therefore, MS shows a highly individualized trajectory and large day-to-day variation [4]. Fatigue, decline in cognitive functions, impaired vision, motor and sensory deficits are the most common symptoms in persons with MS (pwMS) [4,5]. There is no cure for MS, but treatment is aimed at reducing neuroinflammation (and indirectly neurodegeneration) to prevent relapses and slow down disability progression. These disease modifying therapies (DMTs) are costly and the choice is plentiful. Current consensus recommends no evidence of disease activity (NEDA) as the treatment goal [6,7].

In the Netherlands, pwMS are under treatment by a neurologist, preferably complemented by a specialized MS nurse [8,9]. They usually have around one or two visits a year. Using MRI of the brain (and if necessary, the spinal cord), the presence of inflammatory disease activity is assessed, generally presenting as new or enlarged T2 lesions. The functioning of pwMS may be monitored by a variety of patient-reported (PRO) or performance-based outcome measures, such as test batteries to assess cognitive function and walking tests for ambulatory function for instance. The Expanded Disability Status Scale (EDSS) is the standard measure for how a person is affected by their MS. This combination of assessments of functioning, degree of disability and treatment effects, enable the determination of whether a pwMS is experiencing disease progression, a relapse or whether NEDA is maintained [6,7].

Typically, pwMS only remember certain days or periods that stand out, and the time in between is not recalled and the physician will not hear all information [10]. eHealth interventions and specifically those with objective measures, like digital biomarkers, in addition to PROs, can help monitor disease and symptom progression, and potentially disease activity [1,10].

Especially for persons with relapse-remitting MS, there are several treatment options and pwMS react differently to the different available drugs [6]. It is often a trade-off between the effectiveness of the drug and occurrence and severity of side effects, i.e., possibly overtreating or undertreating the patient. It is currently not possible to determine which treatment is the most appropriate for an individual pwMS. The disease course is highly heterogeneous and although we are able to assess the effectivity of a treatment according to NEDA [6], it is not yet possible to predict if and when pwMS will reach severe disability or secondary-progressive MS right at the moment after diagnosis. Additionally, subtle changes in functioning or symptoms and day-to-day variation are difficult to capture with the low frequent hospital visits that are currently the standard of care [6,7].

Because eHealth interventions can be applied in the home situation and this enables monitoring on a more frequent basis, the monitoring extends to the period between consultations and shows the individual course of symptoms. Therefore, the results can be used to detect disease activity early and find the optimal disease management for the individual patient.

#### *1.1. eHealth Interventions in MS*

Several eHealth interventions are currently developed and under investigation in MS [1]. These interventions support different aspects of the MS care path, like social, single use case, integrated and complex support, but with the common intention to improve the care path of pwMS leading to better outcomes. Social eHealth interventions (e.g., My Support Plus [11–13]) are usually meant for pwMS to get connected to other pwMS, to obtain information or to get in contact with their neurologist. Single use case solutions focus more on the disease and usually contain one or more measurement methods, which may be digital biomarkers or biomarker components. Scholz et al. [1] distinguish these interventions from the more integrated eHealth interventions, such as Floodlight (Genentech,

Inc., Basel, Switzerland), MSCopilot (Ad Scientiam, Paris, France), MSPT (Cleveland Clinic Foundation & Biogen, Cleveland, OH, USA), etc. These digital biomarker–based eHealth interventions aim at enhancing MS monitoring and to better detect disease activity and progression so that better therapy can be applied. cept is given in Figure 1. The current MS sherpa digital biomarkers are validated to reliably measure cognitive processing speed and walking function [14–16]. These digital biomarkers represent relevant MS symptoms that are selected based on their relevance in MS and relation with

digital biomarkers or biomarker components. Scholz et al. [1] distinguish these interventions from the more integrated eHealth interventions, such as Floodlight (Genentech, Inc., Basel, Switzerland), MSCopilot (Ad Scientiam, Paris, France), MSPT (Cleveland Clinic Foundation & Biogen, Cleveland, OH, USA), etc. These digital biomarker–based eHealth interventions aim at enhancing MS monitoring and to better detect disease activity and

Another example of an integrated eHealth intervention containing digital biomarkers for MS, is MS sherpa (Orikami Digital Health Products, Nijmegen, The Netherlands). MS sherpa is a CE-certified eHealth intervention (medical device) intended to support the monitoring of persons with MS with the help of digital biomarkers, in order to give pwMS and their health care professionals personalized insight into the presence and progress of MS-related symptoms. The digital biomarkers are embedded in a smartphone application for pwMS and consist of tests that pwMS can perform regularly. The results are directly available for their neurologist via a web-based portal for caregivers, integrating MS sherpa into the MS care path. The Orikami Digital Biomarker platform on which the app and portal are built consists of several components to combine the sensors of the smartphone and user input with proprietary algorithms into digital biomarkers and of supporting modules such as a customer support, subscription and consent management and modules for regulatory compliance and authentication. A graphical presentation of MS sherpa con-

*Brain Sci.* **2021**, *11*, x FOR PEER REVIEW 3 of 14

progression so that better therapy can be applied.

Another example of an integrated eHealth intervention containing digital biomarkers for MS, is MS sherpa (Orikami Digital Health Products, Nijmegen, The Netherlands). MS sherpa is a CE-certified eHealth intervention (medical device) intended to support the monitoring of persons with MS with the help of digital biomarkers, in order to give pwMS and their health care professionals personalized insight into the presence and progress of MS-related symptoms. The digital biomarkers are embedded in a smartphone application for pwMS and consist of tests that pwMS can perform regularly. The results are directly available for their neurologist via a web-based portal for caregivers, integrating MS sherpa into the MS care path. The Orikami Digital Biomarker platform on which the app and portal are built consists of several components to combine the sensors of the smartphone and user input with proprietary algorithms into digital biomarkers and of supporting modules such as a customer support, subscription and consent management and modules for regulatory compliance and authentication. A graphical presentation of MS sherpa concept is given in Figure 1. disease activity and relapses. eHealth interventions require the willingness of the users to adhere to the intervention and to include the insights in disease management, therefore it is important to tailor the designs of eHealth interventions to the needs of the different users and to involve them in the development [17–19]. During the development of MS sherpa, input of different users, both pwMS and neurologists, were included via co-creation. Additionally, the designs have been tested via usability testing methods [18]. Adherence to eHealth interventions with digital biomarkers show promising results. MS Sherpa has shown in a one-month study that there was >90% adherence to the scheduled tasks [20]. This is in line with high adherence figures of other digital biomarker–based e-health interventions like Floodlight, which shows 70% adherence in a 24-week study [21], and an acceptability study with MSCopilot that shows that 85% of questioned pwMS are willing to use the intervention more than once a month and that 68% prefer the digital biomarkers over the MSFC [22], supporting the believe that adoption of and adherence to such interventions can be reasonably expected.

**Figure 1.** Graphical representation of the MS sherpa concept.

The current MS sherpa digital biomarkers are validated to reliably measure cognitive processing speed and walking function [14–16]. These digital biomarkers represent relevant MS symptoms that are selected based on their relevance in MS and relation with disease activity and relapses. eHealth interventions require the willingness of the users to adhere to the intervention and to include the insights in disease management, therefore it is important to tailor the designs of eHealth interventions to the needs of the different users and to involve them in the development [17–19]. During the development of MS sherpa, input of different users, both pwMS and neurologists, were included via co-creation. Additionally, the designs have been tested via usability testing methods [18]. Adherence to eHealth interventions with digital biomarkers show promising results. MS Sherpa has shown in a one-month study that there was >90% adherence to the scheduled tasks [20]. This is in line with high adherence figures of other digital biomarker–based e-health interventions like Floodlight, which shows 70% adherence in a 24-week study [21], and an acceptability study with MSCopilot that shows that 85% of questioned pwMS are willing to use the intervention more than once a month and that 68% prefer the digital biomarkers over the MSFC [22], supporting the believe that adoption of and adherence to such interventions can be reasonably expected.

As eHealth interventions, and digital biomarkers more specifically, are a very nascent field, there are currently no RCTs that show their impact to personalized treatment. However, the potential impact of digital biomarker–based integrated eHealth interventions like MS sherpa in the MS care path is more and more being investigated in clinical trials. MS sherpa has multiple clinical trials in preparation or under investigation.

The Dutch Ministry of Health, Welfare and Sport and the iMTA institute aimed to show the potential impact of AI in healthcare, and MS sherpa was selected as a suitable eHealth intervention for Health Technology Assessment by both organizations, because of its already accumulated evidence and the availability of a model for MS to map the impact of an intervention on the care path.

#### *1.2. (Early) Health Technology Assessment ((e)HTA)*

To estimate the impact of health technologies, in terms of costs and benefits that fall upon the health care system and wider society, Health Technology Assessments (HTA) are conducted [23]. Cost-effectiveness analysis (CEA) is a central component of HTA. When a CEA is conducted before all effectiveness estimates have been collected in studies, the analysis is referred to as 'early HTA'.

The result of an early HTA (eHTA) is an estimate of incremental costs and benefits with and without a new technology. The ratio between these increments gives the incremental cost-effectiveness ratio (ICER), which is compared with some reference value reflecting if the technology should be adopted in the basic benefit package [23]. Benefits are expressed in quality adjusted life years (QALYs). In the Netherlands, the National Health Care Institute determines the reference value for cost-effectiveness, based on the disease burden of the health care problem under study [24]. For MS, this value is set at EUR 50,000 per QALY [25].

In this article, we describe an eHTA analysis for the potential impact of MS sherpa, both from the societal and health care perspective using a recently published decision analytic model for MS treatments [25]. The analysis focused on the impact of MS sherpa on treatment decisions, and more specifically on switches of MS medication based on disease insights achieved with MS sherpa. The impact of digital biomarkers on treatment decisions is one of the important concepts to be tested for the MS field.

#### **2. Materials and Methods**

#### *2.1. Early Health Technology Assesment (HTA), Concept and Analyses*

This eHTA was performed according to the Dutch guidelines for economic evaluations [26]. The MS model was used to estimate the costs and benefits of MS standard care with and without the use of MS sherpa and expressed in an ICER.
