Evidence map›Paper›PMID 28956457›Full record

ReviewExpert review of medical devices2017

Motion sensors in multiple sclerosis: Narrative review and update of applications.

Jeffer Eidi Sasaki, Brian Sandroff, Marcas Bamman, Robert W Motl

Abstract readReview
In one paragraph

Review in Expert review of medical devices, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
3.7field-weighted citation impact, top 7% 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

32 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Gait Kinematics Assessed by ViconSensors (Basel, Switzerland) · 2025
    Observational
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Wearable Sensors Applied in Movement Analysis.Sensors (Basel, Switzerland) · 2022
    Article
  13. Article
  14. Pedometers and Accelerometers in Multiple Sclerosis: Current and New Applications.International journal of environmental research and public health · 2022
    Review
  15. Article
  16. Review
  17. Article
  18. 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

4 authors at 1 institution in 1 country.

Jeffer Eidi Sasakia Department of Physical Therapy , University of Alabama at Birmingham , Birmingham , USA.
Brian Sandroffa Department of Physical Therapy , University of Alabama at Birmingham , Birmingham , USA.
Marcas Bammanb UAB Center for Exercise Medicine , University of Alabama at Birmingham , Birmingham , USA.
Robert W Motla Department of Physical Therapy , University of Alabama at Birmingham , Birmingham , USA.
University of Alabama at Birmingham · US

Funding

Rehabilitation Research Resource to Enhance Clinical Trials (ReACT)P2CHD086851 · NICHD · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI BAMMAN, MARCAS M · 2015 to 2019
$7.1M
NICHD NIH HHS P2C HD086851
6 · The paper itself

Abstract

introductionThe use of motion sensors for measuring physical activity in multiple sclerosis (MS) has evolved with increasing research particularly during the past decade. Areas covered: This manuscript reviews the literature regarding the application of motion sensors for measuring physical activity in MS. We first describe 'what is known' about their use in MS by examining the evidence generated between 1997 and 2012, including the psychometric properties of motion sensors in MS and the development of MS-specific accelerometer cut-points. We then evaluate 'what is new' based on research conducted between 2013 and 2017. This includes newer research on psychometric properties of motion sensors in MS, development of new MS-specific accelerometer and step-rate cut-points, sedentary behavior assessment, and research on fitness trackers and multisensors in MS. The final part presents a picture of 'what is next' for the applications of motion sensors in MS, especially pertaining new opportunities for testing and using fitness trackers in MS, and tracking disease and disability progression based on motion sensor output. Expert commentary: The use of motion sensors in MS has grown substantially over the years; however, a lot more can be done to explore the full potential and utility of these devices in MS.

Indexed as

Fitness TrackersAccelerometryExerciseHumansMultiple SclerosisSedentary Behaviorassessmentmultiple sclerosisPhysical activitysedentary behaviorwearable technology

Identifiers

PMID28956457
PMCPMC6291837
OpenAlexW2756570429

What OpenQuestion holds

Textmetadata
LicenceTDM
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.