Evidence map›Paper›PMID 34827518›Full record

ReviewBrain sciences2021

Digital Biomarkers in Multiple Sclerosis.

Anja Dillenseger, Marie Luise Weidemann, Katrin Trentzsch, Hernan Inojosa, Rocco Haase, Dirk Schriefer, Isabel Voigt, Maria Scholz, Katja Akgün, Tjalf Ziemssen

Open access · goldAbstract readReview
In one paragraph

Review in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 1 pooled it
10.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

65 citing papers in PubMed, 1 synthesis or guideline pooled it, 110 citations in OpenAlex.

  1. Mobile health interventions in multiple sclerosis: A systematic review.Multiple sclerosis (Houndmills, Basingstoke, England) · 2023
    Pooled it
  2. Review
  3. Detecting progressing events in multiple sclerosis clinics in real-time is possible: No.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026
    Article
  4. AI-enabled Living Labs: Accelerating innovation in multiple sclerosis care and research.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026
    Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Review
  11. Sensors in Multiple Sclerosis.Current neurology and neuroscience reports · 2025
    Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Review
  20. Article

5 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Anja DillensegerMultiple Sclerosis Center Dresden, Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl-Gustav Carus, Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany.
Marie Luise WeidemannMultiple Sclerosis Center Dresden, Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl-Gustav Carus, Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany.
Katrin TrentzschMultiple Sclerosis Center Dresden, Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl-Gustav Carus, Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany.ORCID 0000-0003-2832-4640
Hernan InojosaMultiple Sclerosis Center Dresden, Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl-Gustav Carus, Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany.ORCID 0000-0002-1377-836X
Rocco HaaseMultiple Sclerosis Center Dresden, Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl-Gustav Carus, Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany.ORCID 0000-0003-2465-4909
Dirk SchrieferMultiple Sclerosis Center Dresden, Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl-Gustav Carus, Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany.ORCID 0000-0002-7524-7628
Isabel VoigtMultiple Sclerosis Center Dresden, Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl-Gustav Carus, Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany.ORCID 0000-0003-0097-8589
Maria ScholzMultiple Sclerosis Center Dresden, Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl-Gustav Carus, Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany.ORCID 0000-0002-3400-5921
Katja AkgünMultiple Sclerosis Center Dresden, Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl-Gustav Carus, Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany.
Tjalf ZiemssenMultiple Sclerosis Center Dresden, Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl-Gustav Carus, Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany.ORCID 0000-0001-8799-8202
University Hospital Carl Gustav Carus · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

big datadigital biomarkersdigital health technologydigital twineHealthmultiple sclerosispersonalized therapyprecision medicine

Identifiers

PMID34827518
PMCPMC8615428
OpenAlexW3213283589

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.