Evidence map›Paper›PMID 35677343›Full record

ArticleFrontiers in neurology2022

Making Every Step Count: Minute-by-Minute Characterization of Step Counts Augments Remote Activity Monitoring in People With Multiple Sclerosis.

Valerie J Block, Matthew Waliman, Zhendong Xie, Amit Akula, Riley Bove, Mark J Pletcher, Gregory M Marcus, Jeffrey E Olgin, Bruce A C Cree, Jeffrey M Gelfand and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Actionable Wearables Data for the Neurology Clinic: A Proof-of-Concept Tool.Annals of clinical and translational neurology · 2025
    Article
  6. Review
  7. Article
  8. 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

11 authors.

Valerie J BlockDepartment of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.
Matthew WalimanDepartment of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.
Zhendong XieDepartment of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.
Amit AkulaDepartment of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.
Riley BoveDepartment of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.
Mark J PletcherDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States.
Gregory M MarcusDepartment of Medicine, University of California, San Francisco, San Francisco, CA, United States.
Jeffrey E OlginDepartment of Medicine, University of California, San Francisco, San Francisco, CA, United States.
Bruce A C CreeDepartment of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.
Jeffrey M GelfandDepartment of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.
Roland G HenryDepartment of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.

Funding

The Role of B cells in the Origin and Progression of Multiple SclerosisR35NS111644 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI STEPHEN L HAUSER · 2019 to 2026
$9.1M
NINDS NIH HHS R35 NS111644
6 · The paper itself

Abstract

Background: Ambulatory disability is common in people with multiple sclerosis (MS). Remote monitoring using average daily step count (STEPS) can assess physical activity (activity) and disability in MS. STEPS correlates with conventional metrics such as the expanded disability status scale (Expanded Disability Status Scale; EDSS), Timed-25 Foot walk (T25FW) and timed up and go (TUG). However, while STEPS as a summative measure characterizes the number of steps taken over a day, it does not reflect variability and intensity of activity. Objectives: Novel analytical methods were developed to describe how individuals spends time in various activity levels (e.g., continuous low versus short bouts of high) and the proportion of time spent at each activity level. Methods: 94 people with MS spanning the range of ambulatory impairment (unaffected to requiring bilateral assistance) were recruited into FITriMS study and asked to wear a Fitbit continuously for 1-year. Parametric distributions were fit to minute-by-minute step data. Adjusted R Results: Distributional fits determined that the best statistically-valid model across all subjects was a 3-compartment Gaussian Mixture Model (GMM) that characterizes the step behavior within 3 levels of activity: high, moderate and low. The correlation of GMM parameters for baseline step count measures with clinical assessments was improved when compared with STEPS (adjusted R Conclusions: Individuals' step distributions follow a 3-compartment GMM that better correlates with clinic-based performance measures compared with STEPS. These data support the existence of high-moderate-low levels of activity. GMM provides an interpretable framework to better understand the association between different levels of activity and clinical metrics and allows further analysis of walking behavior that takes step distribution and proportion of time at three levels of intensity into account.

Indexed as

accelerometryactivity levelFitbitminute-by-minute stepsmultiple sclerosisremote monitoring

Identifiers

PMID35677343
PMCPMC9167929

What OpenQuestion holds

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Registered trials

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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.