Evidence map›Paper›PMID 33211403›Full record

ArticleAnnals of clinical and translational neurology2021

Biosensor vital sign detects multiple sclerosis progression.

Kristen M Krysko, Alireza Akhbardeh, Jennifer Arjona, Bardia Nourbakhsh, Emmanuelle Waubant, Pierre Antoine Gourraud, Jennifer S Graves

Open access · goldAbstract read
In one paragraph

Article in Annals of clinical and translational neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 26% 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

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Sensors in Multiple Sclerosis.Current neurology and neuroscience reports · 2025
    Review
  2. Review
  3. Article
  4. Article
  5. Assessing fall risk in multiple sclerosis using patient-reported outcomes and wearable gait metrics.Multiple sclerosis journal - experimental, translational and clinical
    Article
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

7 authors at 3 institutions in 2 countries.

Kristen M KryskoUCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0003-0090-597X
Alireza AkhbardehDepartment of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA.
Jennifer ArjonaUCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Bardia NourbakhshDepartment of Neurology, Johns Hopkins University, Baltimore, MD, USA.
Emmanuelle WaubantUCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0001-5188-0157
Pierre Antoine GourraudNantes Université, CHU, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ATIP-Avenir, Nantes, France.
Jennifer S GravesUCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0003-1539-1940
University of California, San Francisco · USJohns Hopkins University · USInserm · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo determine whether a small, wearable multisensor device can discriminate between progressive versus relapsing multiple sclerosis (MS) and capture limb progression over a short interval, using finger and foot tap data.

methodsPatients with MS were followed prospectively during routine clinic visits approximately every 6 months. At each visit, participants performed finger and foot taps wearing the MYO-band, which includes accelerometer, gyroscope, and surface electromyogram sensors. Metrics of within-patient limb progression were created by combining the change in signal waveform features over time. The resulting upper (UE) and lower (LE) extremity metrics' discrimination of progressive versus relapsing MS were evaluated with calculation of AUROC. Comparisons with Expanded Disability Status Scale (EDSS) scores were made with Pearson correlation.

resultsParticipants included 53 relapsing and 15 progressive MS (72% female, baseline mean age 48 years, median disease duration 11 years, median EDSS 2.5, median 10 months follow-up). The final summary metrics differentiated relapsing from secondary progressive MS with AUROC UE 0.93 and LE 0.96. The metrics were associated with baseline EDSS (UE P = 0.0003, LE P = 0.0007). While most had no change in EDSS during the short follow-up, several had evidence of progression by the multisensor metrics.

interpretationWithin a short follow-up interval, this novel multisensor algorithm distinguished progressive from relapsing MS and captured changes in limb function. Inexpensive, noninvasive and easy to use, this novel outcome is readily adaptable to clinical practice and trials as a MS vital sign. This approach also holds promise to monitor limb dysfunction in other neurological diseases.

Indexed as

AlgorithmsSignal Processing, Computer-AssistedWearable Electronic DevicesAdultCohort StudiesDisability EvaluationFemaleHumansLongitudinal StudiesMaleMiddle AgedMultiple Sclerosis, Chronic ProgressiveMultiple Sclerosis, Relapsing-RemittingVital Signs

Identifiers

PMID33211403
PMCPMC7818086
OpenAlexW3106128057

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.