Evidence map›Paper›PMID 31485896›Full record

ReviewCurrent neurology and neuroscience reports2019

Next Steps in Wearable Technology and Community Ambulation in Multiple Sclerosis.

Mikaela L Frechette, Brett M Meyer, Lindsey J Tulipani, Reed D Gurchiek, Ryan S McGinnis, Jacob J Sosnoff

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current neurology and neuroscience reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 2 pooled it
5.2field-weighted citation impact, top 4% 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

36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Pedometers and Accelerometers in Multiple Sclerosis: Current and New Applications.International journal of environmental research and public health · 2022
    Review
  16. Article
  17. Article
  18. Advancing Digital Medicine with Wearables in the Wild.Sensors (Basel, Switzerland) · 2022
    Article
  19. Article
  20. 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

6 authors at 2 institutions in 1 country.

Mikaela L FrechetteMotor Control Research Lab, Department of Kinesiology and Community Health, University of Illinois Urbana-Champaign, 301 Freer Hall 906 S Goodwin Ave, Urbana, IL, 61801, USA.
Brett M MeyerM-Sense Research Group, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, 05405, USA.
Lindsey J TulipaniM-Sense Research Group, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, 05405, USA.
Reed D GurchiekM-Sense Research Group, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, 05405, USA.
Ryan S McGinnisM-Sense Research Group, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, 05405, USA.
Jacob J SosnoffMotor Control Research Lab, Department of Kinesiology and Community Health, University of Illinois Urbana-Champaign, 301 Freer Hall 906 S Goodwin Ave, Urbana, IL, 61801, USA. jsosnoff@illinois.edu.
University of Vermont · USUniversity of Illinois Urbana-Champaign · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewWalking impairments are highly prevalent in persons with multiple sclerosis (PwMS) and are associated with reduced quality of life. Walking is traditionally quantified with various measures, including patient self-reports, clinical rating scales, performance measures, and advanced lab-based movement analysis techniques. Yet, the majority of these measures do not fully characterize walking (i.e., gait quality) nor adequately reflect walking in the real world (i.e., community ambulation) and have limited timescale (only measure walking at a single point in time). We discuss the potential of wearable sensors to provide sensitive, objective, and easy-to-use assessment of community ambulation in PwMS. RECENT

findingsWearable technology has the ability to measure all aspects of gait in PwMS yet is under-studied in comparison with other populations (e.g., older adults). Within the studies focusing on PwMS, half that measure pace collected free-living data, while only one study explored gait variability in free-living conditions. No studies explore gait asymmetry or complexity in free-living conditions. Wearable technology has the ability to provide objective, comprehensive, and sensitive measures of gait in PwMS. Future research should investigate this technology's ability to accurately assess free-living measures of gait quality, specifically gait asymmetry and complexity.

Indexed as

Wearable Electronic DevicesGaitHumansMultiple SclerosisWalkingCommunity ambulationGaitGait qualityMultiple sclerosisWearable technology

Identifiers

PMID31485896
OpenAlexW2972021237

What OpenQuestion holds

Textmetadata
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.