Evidence map›Paper›PMID 38693308›Full record

Observational studyJournal of neurology2024

Gait and heart rate: do they measure trait or state physical fatigue in people with multiple sclerosis?

Irina Galperin, David Buzaglo, Eran Gazit, Nathaniel Shimoni, Raz Tamir, Keren Regev, Arnon Karni, Jeffrey M Hausdorff

Open access · hybridAbstract readObservational Study
In one paragraph

Observational study in Journal of neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 4 institutions in 2 countries.

Irina GalperinCenter for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.ORCID http://orcid.org/0000-0002-8373-6915
David BuzagloCenter for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Eran GazitCenter for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Nathaniel ShimoniOwlytics Healthcare Ltd., Ramat-Gan, Israel.
Raz TamirOwlytics Healthcare Ltd., Ramat-Gan, Israel.
Keren RegevNeuroimmunology and Multiple Sclerosis Unit of the Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Arnon KarniNeuroimmunology and Multiple Sclerosis Unit of the Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Jeffrey M HausdorffCenter for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. jhausdor@tlvmc.gov.il.ORCID http://orcid.org/0000-0001-7544-5846
Tel Aviv University · ILBen-Gurion University of the Negev · ILRush University Medical Center · USTel Aviv Sourasky Medical Center · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTrait and state physical fatigue (trait-PF and state-PF) negatively impact many people with multiple sclerosis (pwMS) but are challenging symptoms to measure. In this observational study, we explored the role of specific gait and autonomic nervous system (ANS) measures (i.e., heart rate, HR, r-r interval, R-R, HR variability, HRV) in trait-PF and state-PF.

methodsForty-eight pwMS [42 ± 1.9 years, 65% female, EDSS 2 (IQR: 0-5.5)] completed the Timed Up and Go test (simple and with dual task, TUG-DT) and the 6-min walk test (6MWT). ANS measures were measured via a POLAR H10 strap. Gait was measured using inertial-measurement units (OPALs, APDM Inc). Trait-PF was evaluated via the Modified Fatigue Impact Scale (MFIS) motor component. State-PF was evaluated via a Visual Analog Scale (VAS) scale before and after the completion of the 6MWT. Multiple linear regression models identified trait-PF and state-PF predictors.

resultsBoth HR and gait metrics were associated with trait-PF and state-PF. HRV at rest was associated only with state-PF. In models based on the first 3 min of the 6MWT, double support (%) and cadence explained 47% of the trait-PF variance; % change in R-R explained 43% of the state-PF variance. Models based on resting R-R and TUG-DT explained 39% of the state-PF. DISCUSSION: These findings demonstrate that specific gait measures better capture trait-PF, while ANS metrics better capture state-PF. To capture both physical fatigue aspects, the first 3 min of the 6MWT are sufficient. Alternatively, TUG-DT and ANS rest metrics can be used for state-PF prediction in pwMS when the 6MWT is not feasible.

Indexed as

FatigueGaitHeart RateMultiple SclerosisAdultAutonomic Nervous SystemFemaleHumansMaleMiddle AgedWalk TestAutonomic nervous systemGaitHeart rateMultiple sclerosisPhysical fatigueState fatigue

Identifiers

PMID38693308
PMCPMC11233359
OpenAlexW4396589269

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.